cs.RO / 1 / 2609.05498
Observability Analysis of Joint Steering and Extrinsic Calibration
联合转向与外部标定的可观测性分析
Abstract
This technical report studies the local weak observability of a planar bicycle-model vehicle when vehicle pose, planar LiDAR extrinsic calibration, and steering-angle bias are estimated jointly. A Lie-derivative-based nonlinear observability analysis is used to examine stationary, straight-line, constant-curvature, and combined straight-plus-arc motion. The resulting observability matrices and nullspaces describe how pose, LiDAR translation and yaw offsets, and steering bias become coupled under different motion primitives. Stationary motion and individual motion primitives retain unobservable directions, whereas the combination of straight and curved motion removes the identified degeneracies and yields full local weak observability of the seven-state system. The analysis provides a theoretical basis for selecting calibration trajectories that sufficiently excite both steering and sensor-extrinsic parameters.
Chinese Translation
本技术报告研究了在联合估计车辆姿态、平面激光雷达外部标定和转向角偏差时，平面自行车模型车辆的局部弱可观测性。采用基于李导数的非线性可观测性分析，考察了静止、直线、恒曲率以及组合的直线加弧线运动。得到的可观测性矩阵和零空间描述了在不同运动原语下，姿态、激光雷达平移和偏航偏移以及转向偏差是如何耦合的。静止运动和单独的运动原语保留了不可观测方向，而直线和曲线运动的组合消除了识别出的退化现象，从而实现了七状态系统的完全局部弱可观测性。该分析为选择能够充分激励转向和传感器外部参数的标定轨迹提供了理论基础。
cs.RO / 2 / 2609.05515
Multi-robot Learning-based Informative Path Planning Using Spatio-Temporal Gaussian Process Kalman Filter
基于学习的多机器人信息路径规划的时空高斯过程卡尔曼滤波
Abstract
Multi-robot informative path planning (IPP) for persistent target monitoring requires robots to reason about spatial uncertainty, temporal evolution, and practical sensing and communication constraints. Recent learning-based multi-robot IPP methods use Gaussian Processes (GPs) for target uncertainty, but often rely on simplified sensing models and centralized belief updates. We propose a grid-based spatio-temporal GP-Kalman filtering framework for learning-based multi-robot IPP. Instead of maintaining one GP per target, we represent anonymous target presence as a single latent field over a discrete workspace grid. The proposed recursive update considers all visible cells inside a camera footprint and supports arbitrary fields of view and range-dependent noise. A GP-consistent temporal process update accounts for moving targets and stale information by inflating uncertainty over time. For decentralized deployment, each robot maintains its own mapper and exchanges compact belief summaries rather than raw measurements. Received beliefs are fused using diagonal covariance intersection to remain conservative under unknown inter-robot correlations. We integrate the mapper with a reinforcement-learning policy for graph-based neighbor selection. Simulation benchmarks show about 20% lower average target uncertainty and improved target visitation compared with learning-based and classical auction/coverage baselines. Real-world two-UAV experiments demonstrate transfer to outdoor multi-robot search over a large field of more than 7000 square meters.
Chinese Translation
多机器人信息路径规划（IPP）用于持续目标监测，需要机器人对空间不确定性、时间演变以及实际感知和通信约束进行推理。近期基于学习的多机器人IPP方法使用高斯过程（GP）来处理目标不确定性，但通常依赖于简化的感知模型和集中式信念更新。我们提出了一种基于网格的时空GP-卡尔曼滤波框架，用于基于学习的多机器人IPP。我们不再为每个目标维护一个GP，而是将匿名目标的存在表示为一个在离散工作空间网格上的单一潜在场。所提出的递归更新考虑了相机视野内的所有可见单元，并支持任意视场和范围相关噪声。GP一致的时间过程更新通过随时间膨胀不确定性来考虑移动目标和过时信息。为了实现去中心化部署，每个机器人维护自己的映射器，并交换紧凑的信念摘要，而不是原始测量值。接收到的信念通过对角协方差交集进行融合，以在未知的机器人间相关性下保持保守。我们将映射器与强化学习策略集成，以进行基于图的邻居选择。仿真基准测试显示，与基于学习的和经典的拍卖/覆盖基线相比，平均目标不确定性降低约20%，目标访问率有所提高。现实世界的双无人机实验展示了在超过7000平方米的大型区域内进行户外多机器人搜索的转移能力。
cs.RO / 3 / 2609.05519
Robots Influencing Humans to Reveal their Goals during Collaboration and Competition
机器人在协作与竞争中影响人类揭示其目标
Abstract
We propose a unified strategy for fast goal inference in human-robot interaction. The core idea is to drive the human toward Critical Decision Points (CDPs)-states where competing human strategies prescribe different next actions and thus maximally reveal the goal. We formalise CDPs using a goal-conditioned policy divergence measure and incorporate them into a Receding-Horizon Planner that explores future action sequences while optimizing a cost function balancing task progress and information gain. We evaluate this approach in both a collaborative, fully observable cooking task and a competitive, partially observable hide-and-seek game, each in simulation and on real robots. In both scenarios, our method infers human goals more accurately and earlier than baseline strategies.
Chinese Translation
我们提出了一种统一的策略，用于在人机交互中快速推断目标。核心思想是引导人类朝向关键决策点（Critical Decision Points, CDPs）——在这些状态下，竞争的人类策略规定了不同的下一步行动，从而最大程度地揭示目标。我们使用目标条件的策略发散度量来形式化CDPs，并将其纳入一个递归视野规划器（Receding-Horizon Planner），该规划器在优化平衡任务进展和信息增益的成本函数的同时，探索未来的行动序列。我们在一个协作的、完全可观察的烹饪任务和一个竞争的、部分可观察的捉迷藏游戏中评估了该方法，这些评估均在模拟环境和真实机器人上进行。在这两种场景中，我们的方法比基线策略更准确、更早地推断出人类的目标。
cs.RO / 4 / 2609.05521
Situation Awareness for Intelligent Data Distribution in Connected Vehicles
连接车辆智能数据分发的情境感知
Abstract
The limitations of on-board sensors and blind spots caused by occlusion cause the reduction of perception quality in autonomous vehicles. In such cases, cooperative perception provides additional data via Vehicle-to-Everything communication to enhance local perception, causing a large volume of data transmission. The vehicle can focus on acquiring and utilizing relevant data according to the prevailing road context by identifying the current traffic situation. To achieve this, we propose a concept for the situation identification of the vehicle using Bird's-Eye-View images. Firstly, the situation around the vehicle is identified using object detection with semantic segmentation, followed by understanding the context of the traffic using a situation identification module consisting of an open-source projective transformation network Cam2BEV and a situation identification neural network. The concept was evaluated and validated by running the software on the CARLA simulator using the in-built RGB camera and the semantic segmentation camera. Additionally, the portability of the situation identification module for real-world applications was verified on Cityscapes and nuScenes urban driving datasets. Overall, the proposed situation identification approach enables efficient sensor data management by prioritizing relevant data to the current traffic situation. The source code is available in the following link: https://github.com/akshaynarla/DySi_Select
Chinese Translation
车载传感器的局限性和遮挡造成的盲区导致自动驾驶车辆的感知质量下降。在这种情况下，协同感知通过车与一切（Vehicle-to-Everything）通信提供额外数据，以增强本地感知，从而导致大量数据传输。通过识别当前交通状况，车辆可以专注于获取和利用与当前道路环境相关的数据。为此，我们提出了一种基于鸟瞰图（Bird's-Eye-View）图像的车辆情境识别概念。首先，利用语义分割的目标检测技术识别车辆周围的情境，然后通过一个由开源投影变换网络Cam2BEV和情境识别神经网络组成的情境识别模块理解交通背景。该概念通过在CARLA模拟器上运行软件，使用内置的RGB相机和语义分割相机进行了评估和验证。此外，情境识别模块在Cityscapes和nuScenes城市驾驶数据集上的可移植性也得到了验证。总体而言，所提出的情境识别方法通过优先处理与当前交通状况相关的数据，实现了高效的传感器数据管理。源代码可在以下链接获取：https://github.com/akshaynarla/DySi_Select
cs.RO / 5 / 2609.05569
Information-Guided Safe Reinforcement Learning for Autonomous Gas Source Localization using sUAS
基于信息引导的安全强化学习在自主气体源定位中的应用：以小型无人机系统为例
Abstract
The autonomous localization of fugitive gas emissions using small Unmanned Aircraft Systems (sUAS) constitutes a fundamentally ill-posed inverse problem. In turbulent atmospheric boundary layers, highly intermittent scalar concentration fields violate the assumptions of classical gradient-based navigation, causing data-driven estimators to suffer from severe noise and spurious local minima. To address these challenges, we introduce an Information-Guided Safe Reinforcement Learning framework evaluated within a custom, GPU-accelerated 3D simulation environment coupling an Eulerian wind solver with a Lagrangian puff dispersion model. We identify a critical vulnerability in deterministic information-seeking planners - a Gramian bias where agents act greedily upon flawed early estimates, starving the estimator of spatial diversity. To systematically break this degeneracy, our architecture integrates a classical empirical observability Gramian (EMGR) planner with a learned Soft Actor-Critic (SAC) exploratory policy. A deterministic meta-supervisor actively monitors estimator reliability via Kullback-Leibler (KL) divergence, dynamically blending deterministic exploitation with learned exploration to steer the sUAS into high-information zones. Trained via a progressive curriculum and safeguarded by a strictly enforced Robust Control Barrier Function (RCBF), our RL framework achieves nearly 80% localization success on complex, mobile sources - drastically outperforming classical baselines (~30%) - while ensuring zero safety violations.
Chinese Translation
使用小型无人机系统（sUAS）自主定位逃逸气体排放构成了一个根本上不适定的逆问题。在湍流大气边界层中，高度间歇性的标量浓度场违反了经典基于梯度的导航假设，导致数据驱动的估计器受到严重噪声和虚假局部极小值的影响。为了解决这些挑战，我们提出了一种信息引导的安全强化学习框架，该框架在一个定制的、基于GPU加速的三维仿真环境中进行评估，该环境将欧拉风场求解器与拉格朗日气团扩散模型相结合。我们识别出确定性信息寻求规划器中的一个关键脆弱性——一种Gramian偏差，导致代理在错误的早期估计上贪婪行动，从而使估计器缺乏空间多样性。为了系统性地打破这种退化，我们的架构将经典的经验可观测性Gramian（EMGR）规划器与学习的软演员评论家（SAC）探索策略相结合。一个确定性的元监督器通过Kullback-Leibler（KL）散度主动监控估计器的可靠性，动态地将确定性利用与学习的探索相结合，引导sUAS进入高信息区域。通过渐进式课程训练，并受到严格执行的鲁棒控制障碍函数（RCBF）的保护，我们的强化学习框架在复杂的移动源上实现了近80%的定位成功率，远远超过经典基准（约30%），同时确保零安全违规。
cs.RO / 6 / 2609.05585
Benchmarking Dexterity of Multifingered Robot Hands: A Review and Perspective
多指机器人手灵巧性基准测试：综述与展望
Abstract
Robot hands are a key interface between AI and the physical world, making advances in robotic dexterity essential to realizing the vision of physical AI. While impressive dexterity has been demonstrated with simple grippers, multifingered hands offer the potential for substantially greater versatility, precision, and adaptability in manipulation. In this review, we survey the state of the art in benchmarking the dexterity of multifingered robot hands. Recognizing dexterity as a complex and multifaceted concept, we present the perspective of the U.S. National Science Foundation HAND Engineering Research Center, with a particular focus on fine in-hand manipulation. We introduce a framework consisting of three benchmark levels that correspond to increasing system complexity, review representative benchmarks at each level, and propose new benchmarks and metrics to address limitations in the literature. More information can be found at https://hand-erc.github.io/benchmarking/.
Chinese Translation
机器人手是人工智能与物理世界之间的关键接口，因此在机器人灵巧性方面的进展对于实现物理人工智能的愿景至关重要。尽管简单夹具已经展示了令人印象深刻的灵巧性，但多指手在操作中提供了更大的多样性、精确性和适应性。在本综述中，我们调查了多指机器人手灵巧性基准测试的最新进展。我们将灵巧性视为一个复杂且多面的概念，提出了美国国家科学基金会 HAND 工程研究中心的视角，特别关注手内精细操作。我们介绍了一个由三个基准级别组成的框架，这些级别对应于系统复杂性的逐步增加，回顾了每个级别的代表性基准，并提出了新的基准和指标，以解决文献中的局限性。更多信息请访问 https://hand-erc.github.io/benchmarking/。
cs.RO / 7 / 2609.05588
GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation
GE-Act 2.0：预训练与扩展用于机器人操作的世界行动模型
AgiBot Research Team, Liu, Renhang, Zhao, Wenzhi, Yang, Zhuo, Chen, Liliang, Zhou, Pengfei, Chen, Shengcong, Ren, Guanghui, Peng, Youlun, Jin, Rongjun, Wang, Nan, Wang, Sukai, He, Xindong, Feng, Jinyuan, Xiong, Ziyu, Zhong, Linqing, Wei, Yifei, Han, Feng, Zhang, Long, Huang, Da, Zhao, Nanshu, Yin, Chenghao, Wu, Mo, Yan, Zhaodong, Hu, Kongtao, Yan, Yuxiang, Niyazi, Aogelijiang, Fang, Yu, Zeng, Jia, Meng, Lizhu, Lv, Daizhen, Cao, Haoyu, Hou, Zhiwen, Ye, Lianjin, Niu, Yuehan, Cai, Zhikai, Hu, Xuan, Min, Hui, Cai, Xiongfeng, Liao, Yue, Wu, Jing, Poria, Soujanya, Li, Ye, Zhou, Sanping, Yao, Maoqing
Abstract
World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled interaction. Most inherit pretrained video generators, leaving WAM pretraining and scaling underexplored. We introduce Genie Envisioner Act 2.0 (GE-Act 2.0), a world-action model whose trainable generative and action components are all initialized from scratch on manipulation data. It combines a control-oriented autoencoder (CoAE), a single-step visual planner (SVP), and an inverse dynamics model (IDM). CoAE retains action- and instruction-relevant information under aggressive compression, while SVP produces a complete future state in one differentiable pass, so visual planning and inverse dynamics can be pretrained separately on complementary data. The components are then jointly trained with knowledge-aligned selective optimization (KASO), which reduces mismatched supervision by selecting only predicted futures judged behaviorally compatible with the recorded action. We evaluate pretrained checkpoints directly, without per-task fine-tuning, on 100 tasks across 20 manipulation skill groups with held-out scenes, backgrounds, lighting, and object instances. Scaling co-training data from 300 to 30,000 hours raises success from 17.1% to 44.1% on G1-OP and from 13.4% to 31.1% on G2-90D; despite comprising less than 2% of the co-training data, G2-90D improves by 17.7 points, suggesting cross-embodiment transfer. Gains span 19/20 and 18/20 skill groups, and skill-specific coverage strongly correlates with zero-shot out-of-distribution (OOD) success (Pearson r=0.80; Spearman rho=0.85). Under the same protocol, the model grounds object, color, shape, and position references in at least 90% of trials and follows explicit instructions even when they conflict with an already-committed behavior or a conventional scene association.
Chinese Translation
世界行动模型（WAM）预测未来状态以指导机器人行动，使其能够从无动作视频和带动作标签的交互中学习。大多数模型继承了预训练的视频生成器，因此WAM的预训练和扩展尚未得到充分探索。我们介绍了Genie Envisioner Act 2.0（GE-Act 2.0），这是一个世界行动模型，其可训练的生成和动作组件均在操作数据上从头开始初始化。它结合了一个面向控制的自编码器（CoAE）、一个单步视觉规划器（SVP）和一个逆动力学模型（IDM）。CoAE在强压缩下保留与动作和指令相关的信息，而SVP在一次可微分的传递中生成完整的未来状态，因此视觉规划和逆动力学可以在互补数据上分别进行预训练。然后，这些组件通过知识对齐选择性优化（KASO）进行联合训练，该方法通过仅选择被判断为与记录动作行为兼容的预测未来，减少了不匹配的监督。我们在100个任务上直接评估预训练的检查点，而不进行每个任务的微调，这些任务跨越20个操作技能组，并包含保留的场景、背景、照明和物体实例。将共同训练数据从300小时扩展到30,000小时，使得G1-OP的成功率从17.1%提高到44.1%，G2-90D的成功率从13.4%提高到31.1%；尽管G2-90D的共同训练数据不足2%，但其提高了17.7个百分点，表明跨体现转移的可能性。增益覆盖了19/20和18/20个技能组，且技能特定的覆盖率与零样本分布外（OOD）成功率强相关（Pearson r=0.80；Spearman rho=0.85）。在相同的协议下，该模型在至少90%的试验中将物体、颜色、形状和位置参考进行了定位，并能够遵循明确的指令，即使这些指令与已承诺的行为或常规场景关联相冲突。
cs.RO / 8 / 2609.05593
Adaptive Cost-Sensitive Machine Learning for Autonomous Robot Navigation Failure Prediction: When Not All Errors Are Equal
自适应成本敏感机器学习在自主机器人导航失败预测中的应用：当并非所有错误都是相等时
Abstract
Autonomous robot navigation failures differ not only in categorical severity but also in the physical context in which they occur. A near-miss at low speed under reliable sensing is not equivalent to the same event during rapid motion, close obstacle approach or degraded perception. This paper reframes navigation failure prediction as consequence-sensitive forecasting. We first establish a fixed baseline in which training weights are modulated by categorical severity, then introduce an adaptive extension defining a state-dependent consequence function combining severity with normalised velocity, obstacle proximity and sensing uncertainty, together with a risk-sensitivity term that rises as conditions deteriorate. We evaluate on 2,000 simulated differential-drive episodes (~1,000,000 timesteps) using episode-level GroupKFold, with external validation on the UCI SCITOS G5 dataset. Fixed weighting raises Logistic Regression high-severity recall from 0.851 to 0.985 and reduces missed consequence cost from 1,940 to 313; the adaptive extension reaches 0.998 and 82. Under matched false-positive conditions, however, the discriminative advantage is modest (0.986 versus 0.984), so most of the gain reflects a more conservative operating point rather than better ranking. The effect is consistent across all five folds and stable across a threefold span of context coefficients. Because the primary simulation produced no collisions, we add a controlled extension in which 108 of 600 episodes terminate in contact: collision recall rises from 0.850 to 0.966 (fixed) and 0.984 (adaptive), with missed collision cost falling from 1,000 to 105, at false-positive rates of 0.413 and 0.799, respectively. Context-dependent consequence modelling thus provides a principled mechanism for allocating conservatism by physical risk.
Chinese Translation
自主机器人导航失败在类别严重性和发生的物理环境上存在差异。低速下可靠感知的近失事件与快速运动、接近障碍物或感知降级时的相同事件并不等同。本文将导航失败预测重新框定为后果敏感的预测。我们首先建立一个固定基线，其中训练权重由类别严重性调节，然后引入一个自适应扩展，定义一个状态依赖的后果函数，将严重性与归一化速度、障碍物接近度和感知不确定性相结合，并引入一个随着条件恶化而上升的风险敏感性项。我们在2000个模拟差分驱动的实验中进行评估（约100万时间步），使用实验级的GroupKFold，并在UCI SCITOS G5数据集上进行外部验证。固定权重将逻辑回归的高严重性召回率从0.851提高到0.985，未捕获后果成本从1940降低到313；自适应扩展达到0.998和82。然而，在匹配的假阳性条件下，区分优势较小（0.986对比0.984），因此大部分增益反映了更保守的操作点，而非更好的排序。该效果在所有五个折叠中一致，并在三倍的上下文系数跨度内稳定。由于主要模拟未产生碰撞，我们增加了一个受控扩展，其中600个实验中的108个以接触结束：碰撞召回率从0.850上升到0.966（固定）和0.984（自适应），未捕获碰撞成本从1000降至105，假阳性率分别为0.413和0.799。因此，基于上下文的后果建模提供了一种通过物理风险分配保守性的原则机制。
cs.RO / 9 / 2609.05783
Closed-Loop Evaluation of Bird's-Eye-View Maps from Cross-View Transformers as Inputs to Behavior-Cloning Policies
基于交叉视图变换器的鸟瞰图闭环评估作为行为克隆策略的输入
Abstract
In autonomous driving, Bird's-Eye View (BEV) representations provide a structured, top-down abstraction of the vehicle's surroundings and have become a key input modality for Behavioral Cloning (BC) policies. While ground-truth BEV maps are readily available in simulation, real-world deployment requires replacing them with camera-predicted counterparts - a substitution that introduces perceptual errors whose downstream impact on closed-loop driving performance is not well understood. In this work, we investigate the use of Cross-View Transformer (CVT)-predicted BEV maps as direct policy inputs for a BC agent in the CARLA simulator. We propose a six-channel BEV representation covering road surface, planned route, lane boundaries, vehicles, pedestrians, and traffic lights, and introduce a Kernel Density Estimation (KDE) weighting scheme that rebalances the segmentation loss towards underrepresented driving maneuvers such as curves and intersections. Closed-loop evaluation across two CARLA towns shows that the KDE-weighted model is the only predicted-BEV agent to complete a full episode without infractions, despite not achieving the highest aggregate IoU. This discrepancy reveals that global segmentation metrics are poor proxies for driving performance: what determines navigation success is prediction quality at geometrically critical locations, and the route channel emerges as the primary bottleneck for reliable agent navigation under predicted BEV inputs.
Chinese Translation
在自动驾驶中，鸟瞰视图（Bird's-Eye View, BEV）表示提供了车辆周围环境的结构化、自上而下的抽象，已成为行为克隆（Behavioral Cloning, BC）策略的关键输入方式。虽然在仿真中可以轻松获得真实的BEV地图，但在现实世界的部署中需要用摄像头预测的对应物替代它们，这一替代引入了感知误差，而这些误差对闭环驾驶性能的下游影响尚不清楚。在本研究中，我们探讨了在CARLA仿真器中将交叉视图变换器（Cross-View Transformer, CVT）预测的BEV地图作为BC代理的直接策略输入。我们提出了一种六通道BEV表示，涵盖了路面、规划路线、车道边界、车辆、行人和交通信号灯，并引入了一种核密度估计（Kernel Density Estimation, KDE）加权方案，该方案重新平衡了对曲线和交叉口等代表性不足的驾驶动作的分割损失。对两个CARLA城镇的闭环评估显示，KDE加权模型是唯一一个在没有违规的情况下完成完整剧集的预测BEV代理，尽管其总体IoU并不是最高的。这一差异揭示了全球分割指标对驾驶性能的代理效果较差：决定导航成功的因素是几何关键位置的预测质量，而路线通道则成为在预测BEV输入下可靠代理导航的主要瓶颈。
cs.RO / 10 / 2609.05832
CR-VLA-Force: Learning Control-aware Compliance VLA Model for Robust Contact-rich Robotic Manipulation
CR-VLA-Force：学习控制感知的合规 VLA 模型以实现稳健的接触丰富型机器人操作
Abstract
Integrating visuomotor policies or Vision-Language-Action (VLA) models with force/torque (F/T) perception has demonstrated significant progress in imitation learning for robotic manipulation. However, existing force-aware VLA models frequently exhibit limited capability in precise force tracking and rapid successive adjustments. This deficiency stems from the limitations of action-chunk execution strategies and the substantial latency between perception and real-time control. Such limitations can lead to task failures and safety risks, particularly when the execution of an action chunk exerts excessive interaction forces without timely adjustment. To overcome this challenge, we propose the Control-aware Compliance VLA (CC-VLA) framework for reactive control. The CC-VLA model employs a multimodal mixture-of-experts (MoE) to encode force signal sequences and vision-language fused feature. Furthermore, it utilizes a multi-stage training strategy to ensure robust perception within the visual-semantic space and effective force perception under sparse sampling conditions. Additionally, a VLA-guided adaptive compliance controller is designed to facilitate precise position tracking during contact-free motion and optimal force-position tracking for contact-rich tasks. To facilitate high-precision F/T data acquisition, we also implement an adversaria shared teleoperation strategy for contact-rich demonstrations that bolsters system safety and interactivity. Extensive real-world experiments demonstrate that CC-VLA significantly improves success rates in challenging force-perception tasks and enhances force-control precision, while providing multi-level safety and robustness under the tested partial-OOD pose-shift settings.
Chinese Translation
将视觉运动策略或视觉-语言-动作（VLA）模型与力/扭矩（F/T）感知相结合，在机器人操作的模仿学习中取得了显著进展。然而，现有的力感知 VLA 模型在精确力跟踪和快速连续调整方面常常表现出能力有限。这一缺陷源于动作块执行策略的局限性以及感知与实时控制之间的显著延迟。这些局限性可能导致任务失败和安全风险，特别是在执行动作块时施加过大的交互力而未能及时调整。为了解决这一挑战，我们提出了控制感知合规 VLA（CC-VLA）框架以实现反应控制。CC-VLA 模型采用多模态专家混合（MoE）来编码力信号序列和视觉-语言融合特征。此外，它利用多阶段训练策略确保在视觉-语义空间内的稳健感知以及在稀疏采样条件下的有效力感知。此外，我们设计了一种 VLA 引导的自适应合规控制器，以促进在无接触运动期间的精确位置跟踪以及在接触丰富任务中的最佳力-位置跟踪。为了促进高精度的 F/T 数据采集，我们还实施了一种对抗共享遥操作策略，以增强系统安全性和交互性。大量的真实世界实验表明，CC-VLA 在具有挑战性的力感知任务中显著提高了成功率，并增强了力控制的精确性，同时在测试的部分 OOD 姿态转移设置下提供了多层次的安全性和稳健性。
cs.RO / 11 / 2609.05892
A4A: Cross-Embodiment Transfer of Action-Oriented 4D Affordances from Human Demonstrations
A4A：来自人类示范的面向动作的4D可供性跨体现转移
Abstract
Human demonstrations contain rich manipulation knowledge, but it remains unclear what information can be transferred effectively to robot control. Existing affordance representations are typically formulated as 2D masks, 3D regions, contact points, or actionability scores, and therefore primarily identify where interaction may occur. However, effective manipulation also requires modeling how interaction-relevant geometry evolves during task execution. To bridge this gap, we introduce action-oriented 4D affordances, which represent the language-conditioned future trajectories of interaction-relevant 3D points. These trajectories capture task-conditioned geometric evolution rather than embodiment-specific actions, enabling transferable interaction priors across humans and robots. Based on this representation, we construct a large-scale action-oriented 4D affordance dataset from existing human--object interaction video data and complementary RGB-D demonstrations, and introduce A4A, an affordance-to-action framework that uses 4D affordance trajectory prediction to pretrain robot policies before manipulation fine-tuning. Experiments in both simulation and the real world validate the effectiveness of A4A, showing that pretraining with action-oriented 4D affordance data consistently improves the manipulation performance of diverse VLA policies. These results establish action-oriented 4D affordances as an effective cross-embodiment representation for transferring manipulation knowledge from human demonstrations to robot control.
Chinese Translation
人类示范包含丰富的操作知识，但尚不清楚哪些信息可以有效转移到机器人控制中。现有的可供性表示通常被表述为2D掩膜、3D区域、接触点或可操作性评分，因此主要识别可能发生交互的位置。然而，有效的操作还需要建模在任务执行过程中与交互相关的几何形状如何演变。为了解决这一问题，我们引入了面向动作的4D可供性，它表示条件于语言的与交互相关的3D点的未来轨迹。这些轨迹捕捉任务条件下的几何演变，而不是特定于体现的动作，从而使人类和机器人之间的交互先验知识可以转移。基于这一表示，我们从现有的人类-物体交互视频数据和补充的RGB-D示范中构建了一个大规模的面向动作的4D可供性数据集，并引入了A4A，一个可供性到动作的框架，该框架利用4D可供性轨迹预测来预训练机器人策略，然后进行操作微调。在模拟和现实世界中的实验验证了A4A的有效性，结果表明，使用面向动作的4D可供性数据进行预训练始终能提高各种VLA策略的操作性能。这些结果确立了面向动作的4D可供性作为一种有效的跨体现表示，用于将操作知识从人类示范转移到机器人控制中。
cs.RO / 12 / 2609.05927
GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning
GIF：用于机器人学习的交互式和功能性物体组合的自主生成
Abstract
Robot manipulation foundation models require scalable evaluation and data generation across diverse scenarios, with simulation providing an environment for both. Automated scene generation offers a promising path, yet prior work has largely emphasized coarse-grained scene layouts rather than fine-grained functional object compositions. Motivated by this gap, we present GIF, an agentic Generation framework for Interactive and Functional object compositions. In this framework, we recast this problem as disentangled reconstruction followed by relative pose recovery. CoGen produces instance-disentangled meshes with coarse initial poses leveraging complementary strengths of 2D and 3D generative models. GPRM refines the relative pose under joint geometric and physical guidance, and a VLM verifier selects the candidate that best matches the structured specification. We further construct a benchmark spanning eight representative contact-geometry classes and compare with state-of-the-art generators; GIF improves both asset quality and relation matching, while reducing collision rate to below 1%. Finally, we synthesize data for policy learning, revealing diversity scaling in both simulation and real-world deployment.
Chinese Translation
机器人操作基础模型需要在多样化场景中进行可扩展的评估和数据生成，而仿真为二者提供了环境。自动化场景生成提供了一条有前景的路径，但以往的研究主要强调粗粒度的场景布局，而非细粒度的功能性物体组合。基于这一差距，我们提出了GIF，一个用于交互式和功能性物体组合的自主生成框架。在该框架中，我们将问题重新表述为解耦重建，随后进行相对位姿恢复。CoGen利用二维和三维生成模型的互补优势，生成实例解耦的网格，并采用粗略的初始位姿。GPRM在联合几何和物理指导下细化相对位姿，而VLM验证器则选择与结构化规范最匹配的候选项。我们进一步构建了一个涵盖八个代表性接触几何类别的基准，并与最先进的生成器进行了比较；GIF在提高资产质量和关系匹配的同时，将碰撞率降低到1%以下。最后，我们合成了用于策略学习的数据，揭示了在仿真和现实世界部署中多样性扩展的情况。
cs.RO / 13 / 2609.05941
Moment-Matching Probabilistic Data Association for Optimization-Based SLAM
基于时刻匹配的概率数据关联用于优化基础的同时定位与地图构建（SLAM）
Abstract
Optimization-based simultaneous localization and mapping (SLAM) makes it possible to reduce accumulated navigation errors of sensing platforms by returning to known areas (loop closure). In this paper, we present an approach to combine probabilistic data association (PDA) with optimization-based SLAM. Instead of associating a single measurement with each landmark, we follow the PDA paradigm from the multiobject tracking community. In particular, in a processing stage performed in addition to the nonlinear least-squares solver of optimization-based SLAM, our method (i) assigns multiple measurements to landmarks probabilistically, (ii) computes the mean and covariance of landmark distributions via moment matching by taking multiple measurement-to-landmark associations into account, and (iii) establishes a virtual landmark measurement and a corresponding linear-Gaussian measurement model that leads to the mean and covariance matrix as moment-matching PDA in (ii). By converting the PDA update step into an equivalent linear-Gaussian measurement update step, PDA can be performed effectively within any optimization-based SLAM method. Our preliminary numerical evaluation in a scenario with false negatives and false positives indicates that incremental smoothing and mapping 2 (iSAM2), combined with the proposed PDA approach, can improve agent localization performance compared to conventional iSAM2.
Chinese Translation
基于优化的同时定位与地图构建（SLAM）通过返回已知区域（回环闭合）来减少传感平台的累积导航误差。本文提出了一种将概率数据关联（PDA）与基于优化的SLAM相结合的方法。我们遵循多目标跟踪领域的PDA范式，而不是将单个测量值与每个地标关联。在优化基础的SLAM的非线性最小二乘求解器之外，我们的方法在一个处理阶段中执行，具体包括：(i) 概率性地将多个测量值分配给地标，(ii) 通过考虑多个测量与地标的关联，利用时刻匹配计算地标分布的均值和协方差，以及 (iii) 建立一个虚拟地标测量和相应的线性-高斯测量模型，从而在(ii)中得到均值和协方差矩阵作为时刻匹配的PDA。通过将PDA更新步骤转换为等效的线性-高斯测量更新步骤，PDA可以在任何基于优化的SLAM方法中有效执行。我们在一个包含假阴性和假阳性的场景中的初步数值评估表明，增量平滑与地图构建2（iSAM2）结合所提出的PDA方法，相较于传统的iSAM2，可以提高代理定位性能。
cs.RO / 14 / 2609.05962
Observation Design for Certified Control Authority: Projection--Estimability Separation and Active-Face Equivalence
认证控制机构的观察设计：投影-可估计性分离与主动面等价
Abstract
A sound runtime admission gate executes only actions it can certify, and certifies only what its observations support. This paper asks how observations should be designed to maximize the set of actions that can be safely admitted, and shows the question is not a re-vocabulary of classical design problems. First, a projection--estimability separation: decomposing a constraint normal as $c=c_{\mathrm{Range}}+c_{\ker}$ relative to an information matrix, two-point discrimination along $c$ becomes arbitrarily reliable as the budget grows whenever $c_{\mathrm{Range}}\neq 0$, while robust admission of an action with normal $c$ is impossible at every budget whenever $c_{\ker}\neq 0$; such mixed directions are generic at any deficient rank, and at full rank the decoupling is bounded by the Kantorovich ratio and diverges with the condition number. Discrimination-optimal designs, being corner solutions of a linear criterion, land in exactly this regime. Second, an active-face equivalence theorem: the permissiveness-optimal design is $L$-optimal for a target matrix generated endogenously by the action faces that become certification bottlenecks, weighted inversely by their remaining slack; single-face collapse recovers $c$-optimal and goal-oriented design exactly, and a Caratheodory argument yields a bottleneck certificate of at most $r(r+1)/2+1$ faces. Around these we assemble exact certifiability per convex contract mode, for which $\kappa\approx 3.29$ is derived rather than calibrated, the $\sqrt{r}$ price of contract-agnostic design, an information-to-slack transfer theorem with a curvature-budget corollary, and a two-part audit in which a design meeting every margin requirement still leaves an action face at a certification cost above ten times its testing cost, in every probe library tested.
Chinese Translation
一个合理的运行时准入门仅执行其能够认证的操作，并且仅认证其观察所支持的内容。本文探讨了如何设计观察以最大化可以安全准入的操作集，并表明这个问题并不是经典设计问题的重新表述。首先，投影-可估计性分离：相对于信息矩阵，将约束法线分解为 $c=c_{ ext{Range}}+c_{ ext{ker}}$，当 $c_{ ext{Range}}
eq 0$ 时，沿着 $c$ 的两点辨别能力随着预算的增加变得任意可靠，而当 $c_{ ext{ker}}
eq 0$ 时，在每个预算下对法线为 $c$ 的操作的稳健准入是不可能的；这种混合方向在任何缺陷秩下都是普遍存在的，而在满秩下，解耦受到 Kantorovich 比率的限制，并随着条件数的增加而发散。辨别最优设计作为线性标准的角落解，正好落入这个范畴。其次，主动面等价定理：最优宽容设计对于由成为认证瓶颈的操作面内生生成的目标矩阵是 $L$-最优的，按其剩余松弛的倒数加权；单面崩溃准确恢复 $c$-最优和目标导向设计，而 Caratheodory 论证提供了最多 $r(r+1)/2+1$ 个面的瓶颈证书。在这些基础上，我们组装了每种凸合同模式下的确切可认证性，其中 $eta ext{approx} 3.29$ 是推导而非校准的，$ ext{sqrt}(r)$ 是与合同无关设计的代价，一个信息与松弛转移定理及其曲率-预算推论，以及一个两部分审计，其中满足每个边际要求的设计仍然使得一个操作面在认证成本上超过其测试成本的十倍，在每个测试的探针库中。
cs.RO / 15 / 2609.05985
A Brain-inspired Hierarchical Framework for Zero-Shot Robot Task Reasoning and Execution
一种脑启发的层次框架用于零-shot机器人任务推理与执行
Abstract
Robots that follow open-ended language instructions need to connect semantic intent to visual scene understanding, geometric feasibility, object states, and physical interaction conditions. End-to-end Vision-Language-Action policies have improved cross-task generalization, but they typically map visual and language inputs directly to robot actions, leaving limited explicit structure for long-horizon decomposition, physical verification, and recovery. We present \method, a zero-shot hierarchical framework functionally inspired by the division of roles in the human brain, comprising visual perception and state inference, language grounding and action-sequence generation from a shared atomic action library, cost-based plan selection, and real-robot execution and verification. The framework grounds commands in explicit object states, composes reusable atomic actions into task-conditioned sequences, ranks alternative sequences by execution cost, and verifies intermediate physical outcomes from refreshed observations. In the evaluation, \method{} completes 10/10 clean board trials, 10/10 pick-and-place trials, and 4/5 pyramid stacking trials for both the flat and irregular initial-layout conditions; the corresponding mean task progress is $99.03\%$, $100.00\%$, and $96.67\%$ respectively. Across all evaluated conditions, \method{} achieves higher success rates than ReKep, Dream2Flow, and $\pi_{0.5}$ benchmarks, demonstrating the effectiveness of combining explicit object-state reasoning, compositional atomic actions, cost-based plan selection, and closed-loop execution verification.
Chinese Translation
遵循开放式语言指令的机器人需要将语义意图与视觉场景理解、几何可行性、物体状态和物理交互条件连接起来。端到端的视觉-语言-动作策略提高了跨任务的泛化能力，但它们通常将视觉和语言输入直接映射到机器人动作上，留下有限的显式结构用于长时间跨度的分解、物理验证和恢复。我们提出了 extit{method}，这是一种零-shot层次框架，其功能灵感来源于人脑中角色的分工，包括视觉感知和状态推理、语言基础和从共享原子动作库生成的动作序列、基于成本的计划选择，以及真实机器人执行和验证。该框架将命令基于显式物体状态进行基础，组合可重用的原子动作成任务条件序列，通过执行成本对替代序列进行排序，并从更新的观察中验证中间物理结果。在评估中， extit{method}在平面和不规则初始布局条件下完成了10/10的干净板试验、10/10的拾取与放置试验，以及4/5的金字塔堆叠试验；相应的平均任务进展分别为$99.03 ext{ extperthousand}$、$100.00 ext{ extperthousand}$和$96.67 ext{ extperthousand}$。在所有评估条件下， extit{method}的成功率高于ReKep、Dream2Flow和$ extit{oldsymbol{ heta}}_{0.5}$基准，证明了结合显式物体状态推理、组合原子动作、基于成本的计划选择和闭环执行验证的有效性。
cs.RO / 16 / 2609.05994
GLoRI: Closed-Loop Whole-Body Tracking with Global-Local Reference Interaction for Humanoid Loco-Manipulation
GLoRI：基于全球-局部参考交互的闭环全身跟踪用于类人运动操控
Abstract
Humanoid loco-manipulation requires accurate whole-body motion tracking in the world frame for physical interaction. While local references preserve motion structure, they lack explicit constraints on absolute spatial placement, leading to accumulated global errors. Existing globally aware approaches augment teleoperation policies with global observations but do not explicitly integrate global correction with local motion guidance, limiting autonomous tracking accuracy. We present GLoRI, a closed-loop whole-body controller that integrates structured global reference and feedback with local motion guidance. Its GLoRI-Net uses Global-Local Cross Attention(GLCA) to refine local keypoint features with global target and pose-difference features, preserving motion structure while correcting world-frame placement. GLoRI achieves 100% completion and a g-MPJPE of 6.44cm on held-out HuMoTo motions. This accuracy remains robust under direct Isaac Gym-to-MuJoCo transfer without fine-tuning, demonstrating strong generalization. Furthermore, such accuracy and generalization enable autonomous loco-manipulation with a single policy on a real Unitree G1 interacting with diverse unseen objects, extending beyond prior systems that primarily rely on teleoperation or focus on single-object interactions.
Chinese Translation
类人运动操控需要在世界坐标系中进行准确的全身运动跟踪，以实现物理交互。虽然局部参考保持了运动结构，但缺乏对绝对空间位置的明确约束，导致全球误差的累积。现有的全球感知方法通过全球观测增强遥操作策略，但并未明确将全球修正与局部运动指导相结合，从而限制了自主跟踪的准确性。我们提出了GLoRI，一种闭环全身控制器，它将结构化的全球参考和反馈与局部运动指导相结合。其GLoRI-Net使用全球-局部交叉注意力（Global-Local Cross Attention, GLCA）通过全球目标和姿态差异特征来细化局部关键点特征，保持运动结构的同时修正世界坐标系中的位置。GLoRI在保留的HuMoTo动作上实现了100%的完成率和6.44厘米的全局平均关节位置误差（g-MPJPE）。该准确性在直接从Isaac Gym转移到MuJoCo时保持稳健，无需微调，展示了强大的泛化能力。此外，这种准确性和泛化能力使得在与多样化未知物体交互的真实Unitree G1上，通过单一策略实现自主运动操控，超越了主要依赖遥操作或专注于单一物体交互的先前系统。
cs.RO / 17 / 2609.06009
How to Learn from What a Human Would Avoid? Intervention-Aware World Models with Real-World RL for Dexterous Manipulation
如何从人类会避免的行为中学习？具有干预意识的世界模型与现实世界强化学习在灵巧操作中的应用
Abstract
Multi-fingered dexterous manipulation remains a frontier for real-world reinforcement learning (RL) due to the high-dimensional action space and the prohibitive cost of hardware failures. While human-in-the-loop (HIL) RL allows operators to intervene before failures occur, current pipelines often treat these interventions as reactive corrections, discarding the rich safety signal inherent in the operator's decision to take control. In this paper, we ask: How can we learn from what a human would avoid? We present WHIRL, a safety-aware RL framework that transforms binary human interventions into forward-predictive signals for proactive risk avoidance. Our approach centers on an intervention-aware latent world model with four prediction heads: dynamics, reward, termination, and a novel per-state intervention-probability head that learns to predict the likelihood of a human takeover at future states. This head provides an actor-side risk-shaping term that discourages the policy from entering "intervention-prone" regions, modeling the operator's internal safety threshold. We evaluate our framework on a 16-DoF LEAP Hand across tasks spanning convex and irregular object grasping, prismatic manipulation, and long-horizon multi-stage tasks. Our results show that predictive risk-shaping enables the system to achieve a 96.7 percent success rate on complex grasping tasks while reducing the operator intervention burden by up to 84 percent in step-weighted terms. By closing the loop between human intuition and predictive world modeling, this work provides a practical safety-aware recipe for training complex dexterous agents in the real world while reducing operator fatigue and hardware-risk exposure.
Chinese Translation
多指灵巧操作由于高维动作空间和硬件故障的高昂成本，仍然是现实世界强化学习（RL）的前沿领域。虽然人机交互（HIL）RL允许操作员在故障发生前进行干预，但当前的流程往往将这些干预视为反应性修正，忽视了操作员决定控制的丰富安全信号。本文提出了一个问题：我们如何能够从人类会避免的行为中学习？我们提出了WHIRL，一个安全意识的RL框架，它将二元人类干预转化为前向预测信号，以实现主动的风险规避。我们的方法集中在一个具有干预意识的潜在世界模型上，该模型具有四个预测头：动态、奖励、终止，以及一个新颖的每状态干预概率头，后者学习预测未来状态下人类接管的可能性。这个头为行为者提供了一个风险塑造项，旨在阻止策略进入“易干预”区域，从而建模操作员的内部安全阈值。我们在一个16自由度的LEAP手上评估了我们的框架，任务涵盖了凸形和不规则物体的抓取、棱柱形操作以及长时间跨度的多阶段任务。我们的结果表明，预测风险塑造使系统在复杂抓取任务中实现了96.7%的成功率，同时在加权步骤方面将操作员的干预负担减少了多达84%。通过将人类直觉与预测世界建模相结合，这项工作为在现实世界中训练复杂灵巧代理提供了一种实用的安全意识方法，同时减少了操作员疲劳和硬件风险暴露。
cs.RO / 18 / 2609.06046
FALCON-S: Fixed-wing ground-effect Aerodynamics Simulator and Flight Control Learning Suite
FALCON-S：固定翼地效气动模拟器与飞行控制学习套件
Abstract
We present a modular, high-fidelity simulation framework for the development and benchmarking of flight control strategies in fixed-wing aerial robots operating near the ground. Unlike existing simulators that rely on simplified or hover-oriented dynamics, our framework models full 6DoF rigid-body physics, semi-empirical ground-effect aerodynamics, actuator dynamics, sensor noise, and environmental disturbances. This physical realism, combined with modular component design, enables systematic analysis of low-altitude flight behavior under realistic conditions. The simulator supports both CPU and GPU backends via Torch and NVIDIA Warp, enabling high-throughput parallel execution suitable for large-scale reinforcement learning training and optimal control rollouts. A unified interface accommodates a range of controllers (both RL and optical control algorithms) across tasks such as altitude regulation and trajectory tracking. Cross-validation with X-Plane and JSBSim is also supported to facilitate engineering integration and visual fidelity.
Chinese Translation
我们提出了一种模块化、高保真的仿真框架，用于开发和基准测试在近地面操作的固定翼空中机器人中的飞行控制策略。与依赖于简化或悬停导向动态的现有仿真器不同，我们的框架建模了完整的六自由度（6DoF）刚体物理、半经验地效气动学、执行器动态、传感器噪声和环境干扰。这种物理现实主义结合模块化组件设计，使得在真实条件下对低空飞行行为进行系统分析成为可能。该模拟器通过Torch和NVIDIA Warp支持CPU和GPU后端，能够实现高吞吐量的并行执行，适合大规模强化学习训练和最优控制展开。统一接口支持多种控制器（包括强化学习和光学控制算法），适用于高度调节和轨迹跟踪等任务。同时，还支持与X-Plane和JSBSim的交叉验证，以促进工程集成和视觉真实感。
cs.RO / 19 / 2609.06061
SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking
SCoCaT：基于成功条件的约束强化学习用于航天器对接
Abstract
Termination-based constrained reinforcement learning is attractive for safety-critical robotic deployments: it avoids online optimization at inference, scales easily to many constraints via a single scalar per constraint, and is simpler to implement than commonly used Lagrangian methods. Instead of pricing violations through summed cost penalties, this approach makes violations structurally unprofitable by shortening the effective horizon for each violation. We identify a structural failure mode of this method class on terminal-navigation tasks: reaching a precise goal configuration while satisfying safety constraints that tighten along the final approach. When the goal sits inside the region close to where the constraints become active, the survival-weighted objective makes dwelling outside the goal region strictly preferable to entering, producing high constraint compliance with low task completion. We formalize this pathology and show that a minimal augmentation to off-the-shelf RL algorithms like PPO resolves this ``feasibility collapse''. We empirically demonstrate that adding a dense per-step success signal via an auxiliary value critic improves the task completion rate while maintaining safety-critical constraint compliance. Validation across two representative spacecraft platforms: a 6U-CubeSat spanning the mass and degree-of-freedom envelope of operational proximity operations, and a floating platform testbed for zero-shot sim-to-real transfer in our laboratory, supports the generality of these findings.
Chinese Translation
基于终止的约束强化学习在安全关键的机器人部署中具有吸引力：它避免了在推理时进行在线优化，通过每个约束的单一标量轻松扩展到多个约束，并且比常用的拉格朗日方法更易于实现。该方法通过缩短每个违规的有效时间范围，使违规在结构上变得不具盈利性，而不是通过总成本惩罚来定价违规。我们识别出该方法类别在终端导航任务中的一种结构性失效模式：在满足沿最终接近阶段收紧的安全约束的情况下，达到精确的目标配置。当目标位于约束变为活跃的区域附近时，生存加权目标使得停留在目标区域外严格优于进入目标区域，从而在低任务完成率的情况下产生高约束遵从性。我们形式化了这种病理现象，并展示了对现成的强化学习算法（如PPO）进行最小增强可以解决这种“可行性崩溃”。我们通过实验证明，通过辅助价值评论员添加密集的逐步成功信号可以提高任务完成率，同时保持安全关键的约束遵从性。在两个具有代表性的航天器平台上进行验证：一个6U-CubeSat，涵盖了操作接近操作的质量和自由度范围，以及一个用于零-shot模拟到现实转移的浮动平台测试床，支持这些发现的普遍性。
cs.RO / 20 / 2609.06114
Where Success Breaks: Failure-Boundary Learning for Robust Vision-Language-Action Models
成功的破裂：面向鲁棒视觉-语言-行动模型的失败边界学习
Abstract
Vision-language-action (VLA) models adapted through supervised fine-tuning (SFT) inherit a structural asymmetry: expert demonstrations teach the policy where success behavior lies, but provide no signal about where it ceases to be reliable. We argue that robust VLA adaptation should therefore be viewed not as further demonstration fitting, but as **Failure-Boundary Learning**---the problem of *Discovering*, *Localizing*, and *Shaping* the boundary between recoverable deviations and task failure. To instantiate this view, we propose **DLS**: built on a **real-grounded behavioral prior** from few real demonstrations and simulated co-training, DLS *discovers* failure boundaries at scale through on-policy digital twin rollouts. Rather than reducing each rollout to a binary label, **semantic progress localization** uses privileged simulator states to assign progress-aware signals that capture *where* the failure boundary is crossed, not merely *whether*. These signals drive **directional boundary shaping** in the flow dynamics---reinforcing success-producing denoising directions and suppressing failure-producing ones, without action likelihoods or auxiliary critics. Across real-robot manipulation tasks, DLS improves robustness over SFT and online RL baselines, especially under randomized initial states and unseen visual conditions.
Chinese Translation
通过监督微调（SFT）适应的视觉-语言-行动（VLA）模型继承了一种结构性不对称性：专家演示教会策略成功行为的位置，但没有提供关于何时失去可靠性的信号。因此，我们认为鲁棒的VLA适应不应被视为进一步的演示拟合，而应视为**失败边界学习**——即*发现*、*定位*和*塑造*可恢复偏差与任务失败之间边界的问题。为了实现这一观点，我们提出了**DLS**：基于少量真实演示和模拟共同训练的**真实基础行为先验**，DLS通过在政策上的数字双胞胎回放*发现*失败边界。与将每次回放简化为二元标签不同，**语义进展定位**利用特权模拟器状态分配进展感知信号，捕捉*何处*跨越失败边界，而不仅仅是*是否*跨越。这些信号驱动流动动态中的**方向性边界塑造**——强化成功产生的去噪方向并抑制失败产生的方向，而无需行动可能性或辅助评论。在真实机器人操作任务中，DLS在鲁棒性上优于SFT和在线强化学习基线，尤其是在随机初始状态和未见视觉条件下。
cs.RO / 21 / 2609.06221
RefGuard: Identity-Aware Language-Guided Robot Manipulation via Joint Target-Anchor-Frame Grounding
RefGuard：通过联合目标-锚点-框架定位实现身份感知的语言引导机器人操作
Abstract
Vision-language-action (VLA) models have substantially advanced language-guided robot manipulation, yet reliable execution still hinges on identifying which physical object an instruction refers to. In cluttered scenes containing repeated objects, ambiguous anchors, or frame-dependent spatial terms, a robot can execute a geometrically valid action on a semantically compatible but unintended instance; we call this failure an identity switch. The referent is jointly determined by three coupled latent variables: the target, the anchor, and the reference frame, so committing to any one of them before execution turns residual ambiguity into a silent and irreversible error. We propose RefGuard, an identity-aware grounding framework that delays commitment by maintaining a joint posterior over all three variables. RefGuard builds a frame-conditioned object-centric scene graph from RGB-D observations, separating frame-independent geometry from directional relations, and routes the posterior through a decision policy that executes, clarifies, reobserves, or aborts. On a real UF850 arm, RefGuard records no identity switch on any ambiguity-stress trial and executes correctly on 90.0% of them, whereas fine-tuned VLA and LLM (Large Language Model)-based baselines switch identity in 33-46% of the same trials, while retaining 93.3% success on unambiguous scenes and recovering from post-grounding scene changes in 86.7% of trials. On a 3200-episode procedural suite, it raises correct execution on solvable instructions from 56.6% to 80.5% over the ablation that commits to the anchor and frame before the target, while deferring less often (19.5% vs. 43.4%).
Chinese Translation
视觉-语言-行动（VLA）模型在语言引导的机器人操作方面取得了显著进展，但可靠的执行仍然依赖于识别指令所指的物理对象。在包含重复物体、模糊锚点或依赖框架的空间术语的杂乱场景中，机器人可能会对一个在语义上兼容但并非意图中的实例执行几何上有效的动作；我们称这种失败为身份切换。所指对象由三个耦合的潜变量共同决定：目标、锚点和参考框架，因此在执行之前对其中任何一个的承诺都会将剩余的模糊性转化为一种无声且不可逆的错误。我们提出了RefGuard，这是一种身份感知的定位框架，通过对所有三个变量保持联合后验来延迟承诺。RefGuard从RGB-D观测中构建了一个框架条件下的对象中心场景图，将框架无关的几何与方向关系分离，并通过决策策略引导后验，执行、澄清、重新观察或中止。在真实的UF850机械臂上，RefGuard在任何模糊压力试验中均未记录身份切换，并在90.0%的试验中正确执行，而经过微调的VLA和基于LLM（大型语言模型）的基线在相同试验中身份切换率为33-46%，同时在无歧义场景中保持93.3%的成功率，并在86.7%的试验中从后定位场景变化中恢复。在3200个回合的程序套件中，它将可解指令的正确执行率从56.6%提高到80.5%，而在承诺锚点和框架之前目标的消融实验中，延迟的频率较低（19.5%对比43.4%）。
cs.RO / 22 / 2609.06256
GloVLA: Let Geometry Move and Local VLA Interact for Robust Object-Centric Manipulation in Unstructured Environments
GloVLA：让几何体移动并让局部VLA互动以实现非结构化环境中的稳健物体中心操作
Abstract
Vision-language-action (VLA) models have shown promising generalization for language-conditioned robot manipulation, but deploying them in unstructured environments remains challenging. A single end-to-end VLA policy must simultaneously solve long-range transport of the end effector to task-relevant regions and short-horizon, contact-rich interaction upon arrival. This formulation is inefficient and brittle: small visual shifts, distractors, clutter, occlusions, or unfavorable initial gripper poses can push the policy outside the local state distribution in which it was trained, leading to task failure. We introduce GloVLA, a hybrid framework that explicitly separates object-centric manipulation into two complementary regimes: a geometric transport controller moves the end-effector into interaction-centric handoff regions, and local VLA policies handle only the short-horizon interaction phases. GloVLA is model-agnostic and can be integrated with different VLA backbones with no additional demonstrations and no changes to the action space or success predicate. Experiments on standard LIBERO and LIBERO-Plus Object tasks together with a newly introduced LIBERO-Challenge benchmark ettings with clutter, distractors,illumination changes, visual shifts, and obstruction show that GloVLA improves task success and substantially lowers VLA inference cost compared with full end-to-Challenge, full-trajectory GR00T N1.6execution degrades to 20.9% average success while GloVLA retains 88.5%; on a physical UR10e, overall success improves from 35.6% to 90.0% while mean inference time is more than halved. Videos and additional results are available at https://glovla-project.github.io/
Chinese Translation
视觉-语言-动作（VLA）模型在语言条件下的机器人操作中展现了良好的泛化能力，但在非结构化环境中的应用仍然面临挑战。单一的端到端VLA策略必须同时解决末端执行器向任务相关区域的远程运输和到达后短期、接触丰富的交互。这种表述效率低下且脆弱：小的视觉偏移、干扰物、杂乱、遮挡或不利的初始夹持器姿态可能会使策略超出其训练时的局部状态分布，从而导致任务失败。我们提出了GloVLA，一个混合框架，明确将物体中心操作分为两个互补的阶段：几何运输控制器将末端执行器移动到以交互为中心的交接区域，而局部VLA策略仅处理短期交互阶段。GloVLA是模型无关的，可以与不同的VLA骨干网络集成，无需额外的演示，也无需更改动作空间或成功谓词。在标准LIBERO和LIBERO-Plus物体任务以及新引入的LIBERO-Challenge基准设置下进行的实验显示，GloVLA相比于完整的端到挑战、完整轨迹GR00T N1.6执行，任务成功率提高且VLA推理成本显著降低，后者的平均成功率降至20.9%，而GloVLA保持在88.5%；在物理UR10e上，总体成功率从35.6%提高到90.0%，而平均推理时间减少了一半以上。视频和更多结果可在https://glovla-project.github.io/获取。
cs.RO / 23 / 2609.06273
Bundle Length Tradeoffs in Decentralized Multi-Robot Task Allocation Under Degraded Communications
去中心化多机器人任务分配中的束长度权衡在通信受损情况下的研究
Abstract
Bundle length B is commonly fixed when configuring multi-task multi-robot task allocation (MRTA) algorithms. MinSum and MinMax are known to favor different task distributions, but the role of B in this objective tradeoff has not been systematically characterized. Additionally, degraded-communication evaluations also often retain settings selected under ideal communication, leaving whether nominal bundle-length tuning transfers under message loss unresolved. We examine both questions for ACBBA, PI, and HIPC across six bundle lengths in 300 paired ten-target Collaborative Visit scenarios under ideal communication and 25% Bernoulli packet loss. Under ideal communication, increasing B from 1 to 12 reduces MinSum cost by 19.0%, 23.0%, and 31.8% for ACBBA, PI, and HIPC, respectively, while increasing MinMax cost by 45.6%, 94.3%, and 67.6%. Under packet loss, the lowest-mean MinSum setting shifts from B = 12 to B = 2 for ACBBA and PI. Repeated paired cross-fitting shows that retaining the ideal-network setting incurs held-out MinSum penalties of 14.4% and 7.2%, respectively, and increases MinMax cost by 30.0% and 41.8% relative to the loss-conditioned MinSum setting. HIPC retains a deep MinSum operating region, while the MinMax setting remains stable for all three allocators. Experiments at two additional target loads reproduce the ACBBA and PI MinSum shifts.
Chinese Translation
在配置多任务多机器人任务分配（MRTA）算法时，束长度 B 通常是固定的。已知 MinSum 和 MinMax 偏好不同的任务分配，但 B 在这一目标权衡中的作用尚未系统性地表征。此外，受损通信评估通常保留在理想通信下选择的设置，因此在消息丢失情况下，名义束长度调节是否有效仍未解决。我们在理想通信和 25% 伯努利数据包丢失的情况下，针对 ACBBA、PI 和 HIPC 在 300 对十目标协作访问场景中考察了六种束长度的这两个问题。在理想通信下，将 B 从 1 增加到 12 分别使 ACBBA、PI 和 HIPC 的 MinSum 成本降低了 19.0%、23.0% 和 31.8%，而 MinMax 成本则分别增加了 45.6%、94.3% 和 67.6%。在数据包丢失情况下，ACBBA 和 PI 的最低平均 MinSum 设置从 B = 12 转变为 B = 2。重复的配对交叉拟合显示，保留理想网络设置分别会导致 14.4% 和 7.2% 的 MinSum 罚款，并使 MinMax 成本相对于基于丢失条件的 MinSum 设置分别增加 30.0% 和 41.8%。HIPC 保持了深厚的 MinSum 操作区域，而所有三种分配器的 MinMax 设置保持稳定。在两个额外目标负载下的实验重现了 ACBBA 和 PI 的 MinSum 变化。
cs.RO / 24 / 2609.06279
IM-ENGINE: Image Editing for Embodied Data Generation
IM-ENGINE：用于具身数据生成的图像编辑
Abstract
Learning-based manipulation requires supervision that is both semantically meaningful and physically executable, but current data pipelines often provide only one of these properties. Human demonstrations capture intent but are costly to collect and constrained by the human-robot embodiment gap, while simulation can scale data generation but often under-specifies functional behavior. We present IM-ENGINE, a simulator-grounded pipeline that uses image editing as an intermediate representation for embodied data generation. Given a rendered scene with known geometry, depth, segmentation, and camera parameters, IM-ENGINE edits the image to inject task-relevant semantics, recovers explicit 3D state using simulator priors and an unchanged anchor object, refines the state in physics, and converts it into robot-executable supervision. We instantiate the pipeline for dexterous grasp synthesis and goal-state generation. For grasping, IM-ENGINE generates a human grasp in image space, recovers the hand-object interaction, retargets it to a robot hand, and refines it into physically validated robot grasps. For goal generation, it edits a rendered scene into a desired outcome, recovers the target-object pose, and refines it into physically valid, semantically meaningful goals and trajectories. This combination of generative semantic priors and simulator grounding enables scalable task-relevant supervision for robot learning.
Chinese Translation
基于学习的操控需要既具有语义意义又可物理执行的监督，但当前的数据管道往往只提供其中一种属性。人类示范捕捉了意图，但收集成本高且受到人机具身差距的限制，而仿真可以扩展数据生成，但往往对功能行为的描述不足。我们提出了IM-ENGINE，一个基于仿真的管道，利用图像编辑作为具身数据生成的中介表示。在给定具有已知几何、深度、分割和相机参数的渲染场景时，IM-ENGINE编辑图像以注入与任务相关的语义，利用仿真先验和未改变的锚定物体恢复明确的3D状态，在物理中细化该状态，并将其转换为机器人可执行的监督。我们将该管道实例化用于灵巧抓取合成和目标状态生成。在抓取方面，IM-ENGINE在图像空间生成一个人类抓取，恢复手与物体的交互，将其重新定向到机器人手，并将其细化为经过物理验证的机器人抓取。在目标生成方面，它将渲染场景编辑为期望的结果，恢复目标物体的姿态，并将其细化为物理有效且语义明确的目标和轨迹。这种生成性语义先验与仿真基础的结合使得机器人学习的任务相关监督具备可扩展性。
cs.RO / 25 / 2609.06331
Geometric Distributional Control: Learning Progress with Partial Structural Knowledge
几何分布控制：具有部分结构知识的学习进展
Abstract
Real-time control often sits between two limiting regimes. Predictive optimization and model-based control are powerful when dynamics, parameters, objectives, and online planning models are specified; reinforcement learning can relax this requirement, but must infer long-horizon value signals from sequential data and interaction, making training slow, high-variance, and hard to scale in large action spaces. This middle regime is common in systems including autonomous driving, warehouse robotics, traffic control, and delivery drones: partial geometry, physics, rules, or constraints are known, yet the local direction of task progress remains uncertain. Geometric Distributional Control (GDC) is designed for this partial-knowledge setting. It factorizes control into feasibility and progress: known geometry, rules, constraints, and response maps define an executable scaffold, while progress-weighted feasible data learns the missing directional signal on that scaffold. The learned score acts as a Bellman-like local value-gradient, selecting actions that make progress without requiring global Bellman recursion, a fully specified planner, or a black-box policy that absorbs both feasibility and preference. This knowledge can be lightweight and partial, such as simple dynamics, safety filters, local maps, constraint projectors, or lower-level response maps; it need not encode full dynamics or a long-horizon objective. Offline, GDC fits a progress-tilted distribution from short known-feasible snippets with weak signed progress certificates. Online, its score is projected through the scaffold and applied in receding-horizon feedback. We prove that this score descends a data-induced soft progress value and validate GDC on structured multilevel optimization and SUMO route-progress driving, where it improves over known-only solvers and learning baselines while preserving scaffold-enforced feasibility.
Chinese Translation
实时控制通常处于两种极限状态之间。当动态、参数、目标和在线规划模型被明确指定时，预测优化和基于模型的控制是强大的；而强化学习可以放宽这一要求，但必须从序列数据和交互中推断长期价值信号，这使得训练过程缓慢、方差高，并且在大动作空间中难以扩展。这种中间状态在包括自动驾驶、仓库机器人、交通控制和送货无人机等系统中很常见：已知部分几何、物理、规则或约束，但任务进展的局部方向仍然不确定。几何分布控制（Geometric Distributional Control, GDC）旨在为这种部分知识环境而设计。它将控制分解为可行性和进展：已知的几何、规则、约束和响应映射定义了一个可执行的框架，而进展加权的可行数据则学习在该框架上缺失的方向信号。学习到的评分作为类似贝尔曼的局部价值梯度，选择能够推动进展的动作，而无需全局贝尔曼递归、完全指定的规划器或吸收可行性和偏好的黑箱策略。这种知识可以是轻量级和部分的，例如简单的动态、安全过滤器、局部地图、约束投影器或低级响应映射；它不需要编码完整的动态或长期目标。在离线阶段，GDC 从短期已知可行片段中拟合一个倾向于进展的分布，并使用弱签名进展证书。在在线阶段，其评分通过框架进行投影，并应用于递归地平反馈。我们证明了该评分下降于数据诱导的软进展值，并在结构化多级优化和 SUMO 路径进展驾驶中验证了 GDC，相较于仅知道的求解器和学习基线，GDC 在保持框架强制可行性的同时有所改进。
cs.RO / 26 / 2609.06342
OcclusionCBF: Backup Control Barrier Functions for Safe Navigation Among Hidden Dynamic Obstacles
OcclusionCBF：用于安全导航隐藏动态障碍物的备份控制屏障函数
Abstract
Robots navigating under occlusion may enter states from which no admissible input can avoid a dynamic obstacle once it becomes visible. We present OcclusionCBF, a safety filter that extends backup control barrier functions to reachable-occupancy predictions for potentially hidden dynamic obstacles in occluded regions. The method certifies a prescribed backup rollout against collision-inflated occupancy and a verified terminal set, yielding affine constraints for minimally invasive quadratic-program filtering. We establish recursive feasibility of the resulting safety filter, and collision avoidance for every hidden-obstacle motion covered by the occupancy prediction. Randomized benchmarks, MetaUrban simulations, and hardware experiments demonstrate improved task success over reactive and occlusion-aware predictive baselines with millisecond-scale computation.
Chinese Translation
在遮挡环境中导航的机器人可能会进入一种状态，此时一旦动态障碍物变得可见，就无法通过任何可接受的输入避免碰撞。我们提出了OcclusionCBF，这是一种安全过滤器，它将备份控制屏障函数扩展到可达占用预测，以应对遮挡区域中潜在的隐藏动态障碍物。该方法针对碰撞膨胀占用和经过验证的终端集，认证了预定的备份展开，从而产生最小侵入性的二次规划过滤的仿射约束。我们确立了所得到的安全过滤器的递归可行性，并确保了针对占用预测覆盖的每个隐藏障碍物运动的碰撞避免。随机基准测试、MetaUrban仿真和硬件实验表明，与反应式和考虑遮挡的预测基线相比，该方法在任务成功率上有所提高，计算时间在毫秒级别。
cs.RO / 27 / 2609.06368
LANTERN: A Closed-Loop Benchmark for VLM-Based Cooperative Driving with Temporally Grounded Warnings
LANTERN：基于VLM的协作驾驶闭环基准测试与时间基础警告
Abstract
We present LANTERN, a closed-loop benchmark for temporally grounded cooperative warnings. LANTERN separates warning onset, hazard onset, warning termination, and post-hazard recovery, and evaluates each physical event under matched warning and no-warning executions so that the warning's contribution is measured in isolation rather than confounded with onboard vision. The benchmark spans six safety-critical scenario families and provides 3,272 sequences with 236,309 frames for training, together with 120 matched route pairs for closed-loop evaluation. Each hazard route is evaluated under the warning and no-warning conditions, while its no-hazard control penalizes unconditional braking. We further introduce the Cooperative Unified Score (CUS), a safety-gated metric that jointly rewards route progress, anticipation, clearance, and recovery. Fine-tuning a representative VLM driving model raises CUS from 34.6 without warnings to 75.5 with them, demonstrating both the value of cooperative warnings and the discriminative power of the paired protocol. All resources will be made publicly available.
Chinese Translation
我们提出了LANTERN，这是一个用于时间基础协作警告的闭环基准测试。LANTERN将警告开始、危险开始、警告结束和后危险恢复分开，并在匹配的有警告和无警告执行下评估每个物理事件，以便单独测量警告的贡献，而不与车载视觉混淆。该基准涵盖六个安全关键场景系列，提供了3,272个序列和236,309帧用于训练，以及120对匹配的路线对用于闭环评估。每条危险路线在有警告和无警告条件下进行评估，而其无危险控制则对无条件刹车进行惩罚。我们进一步引入了协作统一评分（Cooperative Unified Score，CUS），这是一种安全门控指标，联合奖励路线进展、预判、清除和恢复。对一个代表性的VLM驾驶模型进行微调，使得CUS从无警告的34.6提高到有警告的75.5，展示了协作警告的价值和配对协议的区分能力。所有资源将公开提供。
cs.RO / 28 / 2609.06424
OVMAN: A Task and Benchmark for Open-Vocabulary Motion-Aware Navigation
OVMAN：一种开放词汇运动感知导航的任务与基准
Abstract
Homes change between a robot's visits. Navigation benchmarks pose their goals in the world the agent currently sees, and the two-visit benchmarks that exist score recall or rearrangement rather than navigation. None of them can express go to the chair that was moved or go to where the vase used to be. OVMAN is a task in which an agent tours a scene, returns after a scripted change, and must navigate to a goal specified by the change itself. Two of its six change relations answer with a place an object has left, where nothing remains to be detected. We release 219 two-visit episodes, each certified solvable by an oracle agent that completes it three times. Two released systems fail as predicted. A zero-shot object-goal navigator reaches the change-defined target in 11.4% of episodes and essentially fails at vacated locations, 0.000 on former and 0.025 on removed. A self-maintaining open-vocabulary map answers past-tense queries at about a third of the rate of the same maps read as two visits, even when grounding is supplied. A simple two-visit reference agent reaches 45.2% when navigating, against an embodied oracle of 99.5%. An error decomposition places the remaining difficulty in open-vocabulary instance grounding rather than in geometry or in selecting the answer once positions are known.
Chinese Translation
家庭在机器人访问之间会发生变化。导航基准将其目标设定在代理当前所见的世界中，而现有的两次访问基准则评分回忆或重组，而非导航。它们都无法表达“去移动过的椅子”或“去曾经有花瓶的地方”。OVMAN 是一项任务，其中代理在一个场景中巡游，经过预设的变化后返回，并必须导航到由变化本身指定的目标。其六种变化关系中的两种回答的是一个物体离开的地方，那里没有任何东西可以被检测到。我们发布了219个两次访问的情节，每个情节都由一个能够完成三次的神谕代理认证为可解。两个发布的系统按预期失败。一个零-shot物体目标导航器在11.4%的情节中达到了变化定义的目标，并且在空置位置几乎失败，前者为0.000，后者为0.025。一个自我维护的开放词汇地图在回答过去时查询时的成功率约为同一地图作为两次访问时的三分之一，即使提供了基础信息。一个简单的两次访问参考代理在导航时达到了45.2%的成功率，而一个具身的神谕代理则达到了99.5%。误差分解将剩余的难度归因于开放词汇实例的基础，而不是几何或在已知位置后选择答案的过程。
cs.RO / 29 / 2609.06433
Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation
碰撞快照引导的时间反向安全关键场景生成
Abstract
The generation of safety-critical traffic scenarios is essential for training and evaluating autonomous vehicles. Prior approaches typically perturb the trajectories of existing agents in a traffic scenario using simplified adversarial objectives to induce safety-critical interactions, which can limit the plausibility and diversity of the generated scenarios. Although inserting new adversarial vehicles can alleviate this limitation, determining when and where to introduce them in a scenario-specific manner remains challenging. In this work, we introduce \underline{CO}llision \underline{S}napshot guided \underline{T}im\underline{E}-\underline{R}eversed safety-critical scenario generation (COSTER), a framework that leverages learned traffic priors to determine plausible collision times and locations. COSTER first constructs a collision snapshot by inserting a new vehicle in contact with the target vehicle at the identified collision state within a traffic scenario. Starting from this collision snapshot, a conditional variational autoencoder is used to perform a time-reversed rollout, reconstructing the trajectory of the inserted vehicle backward toward earlier timesteps. Experiments show that COSTER outperforms existing methods in plausibility, diversity, and data efficiency. Moreover, agents trained on COSTER-generated scenarios reduce collision rates by 31\% on safety-critical scenarios from the Waymo Open Motion Dataset while also improving ego task completion. The project website is available at https://anonym-121.github.io/COSTER/.
Chinese Translation
生成安全关键交通场景对于训练和评估自动驾驶车辆至关重要。以往的方法通常通过使用简化的对抗目标来扰动交通场景中现有代理的轨迹，以诱发安全关键的交互，这可能限制生成场景的可信度和多样性。尽管插入新的对抗车辆可以缓解这一限制，但在场景特定的方式下确定何时何地引入它们仍然具有挑战性。在本研究中，我们提出了 extbf{CO}llision extbf{S}napshot引导的 extbf{T}ime- extbf{R}eversed安全关键场景生成框架（COSTER），该框架利用学习到的交通先验来确定可信的碰撞时间和位置。COSTER首先通过在交通场景中插入一辆与目标车辆接触的新车辆来构建碰撞快照。基于这一碰撞快照，使用条件变分自编码器进行时间反向展开，重建插入车辆向早期时间步的轨迹。实验表明，COSTER在可信度、多样性和数据效率方面优于现有方法。此外，在COSTER生成的场景上训练的代理在Waymo开放运动数据集中安全关键场景的碰撞率降低了31 extperthousand，同时也提高了自我任务的完成率。项目网站可访问 https://anonym-121.github.io/COSTER/。
cs.RO / 30 / 2609.06450
\textbf{PLATO}: \emph{Preintegration Learning from Accurate Trajectory Observations} for Neural Inertial Odometry
PLATO：基于准确轨迹观测的预积分学习用于神经惯性里程计
Abstract
Neural inertial odometry has demonstrated strong potential for motion estimation in challenging environments, yet inertial-only preintegration remains sensitive to IMU bias and uncertainty. To this end, this paper introduces \textbf{PLATO}:~\emph{Preintegration Learning from Accurate Trajectory Observations}, a likelihood-based framework that leverages accurate trajectory observations to jointly learn IMU bias dynamics modeled by a neural ordinary differential equation~(NODE) and gyroscope and accelerometer noise covariances. Optimization exploits the sparse structure of the negative log-likelihood, with IMU noise-parameter gradients computed by forward differentiation. A tailored double-adjoint scheme couples a discrete invariant-error adjoint with a continuous-time adjoint for the bias NODE, enabling memory-efficient likelihood optimization over the nested bias-dynamics and IMU-preintegration rollouts. Validation on EuRoC shows improved performance, and underwater robot experiments demonstrate applicability under intermittent lighting failures and visual degradation.
Chinese Translation
神经惯性里程计在复杂环境中的运动估计中展现了强大的潜力，但仅依赖惯性传感器的预积分仍然对惯性测量单元（IMU）的偏差和不确定性敏感。为此，本文提出了PLATO：基于准确轨迹观测的预积分学习（Preintegration Learning from Accurate Trajectory Observations），这是一个基于似然的框架，利用准确的轨迹观测共同学习由神经常微分方程（NODE）建模的IMU偏差动态以及陀螺仪和加速度计的噪声协方差。优化利用了负对数似然的稀疏结构，通过前向微分计算IMU噪声参数的梯度。一个定制的双伴随方案将离散不变误差伴随与偏差NODE的连续时间伴随相结合，实现了对嵌套偏差动态和IMU预积分展开的内存高效似然优化。在EuRoC上的验证显示了性能的提升，而水下机器人实验则展示了在间歇性照明故障和视觉退化下的适用性。
cs.RO / 31 / 2609.06508
VLA-Corrector: Stage-Aware Observable State Understanding for Prompt-Based Closed-Loop Recovery of Vision-Language-Action Policies
VLA-Corrector：基于阶段的可观察状态理解用于基于提示的闭环恢复视觉-语言-动作策略
Abstract
Long-horizon robot manipulation with Vision-Language-Action (VLA) policies remains vulnerable to execution-time deviations, as final task success provides little information for diagnosing and correcting failures caused by action noise, object displacement, or goal misalignment. We introduce a stage-aware failure verification and Prompt Recovery framework that enables closed-loop correction of a fixed VLA policy without parameter updates or privileged simulator states. The framework introduces an observable-history-based Learned Verifier that jointly estimates manipulation progress and execution risk by temporally modeling multi-view visual observations, proprioceptive states, and executed actions. To provide interpretable task understanding, we represent manipulation execution through semantic progress stages, including approach, alignment, grasp, transport, and placement, and identify stage-specific failure patterns. Upon detecting abnormal execution, the framework preserves the original instruction and generates a stage-conditioned recovery prompt, allowing the same frozen VLA policy to produce corrective actions. Extensive multi-round evaluations on LIBERO and LIBERO Plus demonstrate that the proposed approach substantially improves closed-loop reliability under diverse perturbations. Without access to privileged object or goal coordinates, the Learned Verifier achieves recovery performance close to that of the privileged rule-based verifier in the evaluated settings. These results show that observable visual-proprioceptive-action history is sufficient to infer latent task states and enable practical failure recovery for existing VLA policies.
Chinese Translation
基于视觉-语言-动作（VLA）策略的长时间机器人操作仍然容易受到执行时偏差的影响，因为最终任务成功提供的信息有限，难以诊断和纠正由动作噪声、物体位移或目标不对齐引起的失败。我们提出了一种基于阶段的失败验证和提示恢复框架，该框架能够在不更新参数或使用特权模拟器状态的情况下，实现固定VLA策略的闭环修正。该框架引入了一种基于可观察历史的学习验证器，通过对多视角视觉观察、身体状态和执行动作进行时间建模，联合估计操作进展和执行风险。为了提供可解释的任务理解，我们通过语义进展阶段表示操作执行，包括接近、对齐、抓取、运输和放置，并识别特定阶段的失败模式。在检测到异常执行时，该框架保留原始指令，并生成一个阶段条件的恢复提示，使得相同的固定VLA策略能够产生纠正动作。在LIBERO和LIBERO Plus上的广泛多轮评估表明，所提出的方法在各种扰动下显著提高了闭环可靠性。在没有访问特权物体或目标坐标的情况下，学习验证器在评估设置中的恢复性能接近于特权基于规则的验证器。这些结果表明，可观察的视觉-身体-动作历史足以推断潜在任务状态，并为现有VLA策略实现实用的失败恢复。
cs.RO / 32 / 2609.06522
Knowledge-Guided Hierarchical Policy Learning for High-Precision Cylindrical Assembly under Tight Tolerances
基于知识引导的分层策略学习用于高精度圆柱体装配在严格公差下
Abstract
A hybrid hierarchical learning framework is proposed to achieve high-precision assembly of 170mm cylindrical components with tolerance of 0.1mm. The lower-level network integrates expert experience through Behavior Cloning (BC), giving the robot human-like intuition, and incorporates the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to enhance training stability and robustness. The upper-level network dynamically adjusts the lower-level decisions based on heuristic rules, ensuring flexibility in operations. A simulated model is constructed to learn before transferring to real world. An efficient and safe training is allowed. Comparisons show that the reward curve converges within 500 episodes, indicating high learning efficiency. It also demonstrates better adaptability to initial conditions and pose errors, achieving satisfactory success rates even under extreme conditions. Moreover, the method exhibits good stability under Gaussian noise interference. In the real world, the assembly trajectory of the cylindrical segment shows smoother motion and less fluctuation.
Chinese Translation
提出了一种混合分层学习框架，以实现对公差为0.1mm的170mm圆柱组件的高精度装配。下层网络通过行为克隆（Behavior Cloning, BC）整合专家经验，使机器人具有人类直觉，并结合双延迟深度确定性策略梯度（Twin Delayed Deep Deterministic Policy Gradient, TD3）算法以增强训练的稳定性和鲁棒性。上层网络根据启发式规则动态调整下层决策，确保操作的灵活性。构建了一个模拟模型以进行学习，然后再转移到现实世界中，从而实现高效和安全的训练。比较结果显示，奖励曲线在500个回合内收敛，表明学习效率高。该方法还表现出对初始条件和姿态误差的更好适应性，即使在极端条件下也能实现令人满意的成功率。此外，该方法在高斯噪声干扰下表现出良好的稳定性。在现实世界中，圆柱段的装配轨迹显示出更平滑的运动和更少的波动。
cs.RO / 33 / 2609.06547
SHIFT: Surface-aware High-speed Integration For TSDFs
SHIFT：面向表面的高速集成方法用于TSDF
Abstract
Real-time 3D mapping is fundamental for autonomous robotic navigation, with Euclidean Signed Distance Fields (ESDFs) serving as the standard representation for online motion planning. While recent advancements in non- projective distance fields yield highly accurate maps, their computational overhead remains a severe bottleneck. Conventional integrators redundantly re-fuse millions of depth pixels every frame, even long after the corresponding voxels have converged, wasting significant computational resources in environments dominated by large planar surfaces. In this paper, we present SHIFT (Surface-aware High-speed Integration For TSDFs), an efficient mapping framework designed to reduce this per-frame update cost. By exploiting structural redundancy directly from 3D depth geometry, SHIFT compresses flat local regions into weighted super-rays and freezes flat-voxel gradients. A compact ESDF voxel layout further reduces the memory footprint of the remaining wavefront. Extensive evaluations across various RGB-D and LiDAR sequences show that SHIFT cuts TSDF cost by 1.42 to 4.07 times, while holding mesh error within millimeters, and reduces ESDF-layer memory by up to 28%
Chinese Translation
实时3D映射是自主机器人导航的基础，欧几里得符号距离场（ESDF）作为在线运动规划的标准表示。尽管最近在非投影距离场方面的进展能够生成高度准确的地图，但其计算开销仍然是一个严重的瓶颈。传统的集成方法在每一帧中冗余地重新融合数百万个深度像素，即使在相应的体素已经收敛很久之后，仍然浪费了大量计算资源，尤其是在大平面表面主导的环境中。在本文中，我们提出了SHIFT（面向表面的高速集成方法用于TSDF），这是一个旨在降低每帧更新成本的高效映射框架。通过直接利用3D深度几何中的结构冗余，SHIFT将平坦的局部区域压缩为加权超光线，并冻结平坦体素的梯度。紧凑的ESDF体素布局进一步减少了剩余波前的内存占用。针对各种RGB-D和LiDAR序列的广泛评估表明，SHIFT将TSDF成本降低了1.42到4.07倍，同时将网格误差控制在毫米范围内，并将ESDF层的内存减少了多达28%。
cs.RO / 34 / 2609.06591
Unifying Physics-Based Humanoid Interaction with a Context-Conditioned Interaction Prior
将基于物理的人形交互与上下文条件交互先验统一起来
Abstract
Developing unified physics-based humanoid controllers that can navigate complex 3D scenes and manipulate objects remains a longstanding challenge. Existing approaches are often specialized for either locomotion or object-centric manipulation, or rely on task-specific reward engineering that does not scale well across diverse behaviors. We present CHIP, a unified, physics-grounded framework for learning reusable humanoid interaction skills from heterogeneous motion data. Central to our approach is a conditional interaction prior that models a context-dependent distribution over these skills within a shared discrete space. Our method is trained in three stages. We first learn physics-based motion-imitation policies that acquire grounded teacher behaviors from heterogeneous interaction data. We then distill these behaviors into a context-conditioned interaction prior that captures reusable motion structure across locomotion and manipulation. Finally, we initialize downstream task policies from the pretrained prior and adapt them through prior-regularized online RL post-training. Experiments on a diverse suite of humanoid interaction tasks show that our approach supports scene-aware locomotion, contact-rich object manipulation, and compositional behaviors such as environment-aware object transport and long-horizon skill sequencing, while producing smooth transitions and physically plausible motion.
Chinese Translation
开发能够在复杂的三维场景中导航和操控物体的统一基于物理的人形控制器仍然是一个长期以来的挑战。现有的方法通常专注于步态或以物体为中心的操控，或者依赖于特定任务的奖励工程，这在多样化行为中扩展性较差。我们提出了CHIP，一个统一的、基于物理的框架，用于从异构运动数据中学习可重用的人形交互技能。我们方法的核心是一个条件交互先验，它在共享的离散空间中建模这些技能的上下文依赖分布。我们的方法分三个阶段进行训练。首先，我们学习基于物理的运动模仿策略，从异构交互数据中获取基础教师行为。然后，我们将这些行为提炼为一个上下文条件的交互先验，捕捉步态和操控之间可重用的运动结构。最后，我们从预训练的先验初始化下游任务策略，并通过先验正则化的在线强化学习后训练对其进行适应。在一系列多样化的人形交互任务上的实验表明，我们的方法支持场景感知的步态、接触丰富的物体操控以及环境感知的物体运输和长时间技能序列等组合行为，同时产生平滑的过渡和物理上合理的运动。
cs.RO / 35 / 2609.06615
MemCorr-DP: Counterfactual Correspondence Conditioning for a Diffusion Policy Guided by a Reference
MemCorr-DP：基于参考的反事实对应条件下的扩散策略
Abstract
Behavior-cloned visuomotor policies can remain accurate near their training distribution yet fail when object position and camera viewpoint change together. A successful reference trajectory contains the geometry needed to transfer the same interaction, but the policy must align that geometry with the current scene and remain sensitive to it during denoising. To address these challenges, we present MemCorr-DP, a diffusion policy that lifts frozen RoMa v2 matches into explicit 3D relations between the current scene and the reference trajectory. A counterfactual paired objective assigns opposite behaviors the same physical state and noisy action while retaining reference-specific denoising targets. Mixed-condition fine-tuning then adapts the policy from ground-truth geometry to measured correspondence errors. Our strongest evaluation places the Door in the outermost position bands beyond the training support and changes the query camera by $\pm15^\circ$. Under this combined shift, MemCorr-DP achieves 96.67% closed-loop success, compared with 88.00% for a visual Transformer with the same action architecture. Objective ablations and reference interventions show that behavior responds to the selected reference, while matched controls favor the complete relation set over future motion or centroid geometry alone. These results support explicit 3D reference relations as a robust conditioning interface when spatial and viewpoint changes are compounded in the evaluated task.
Chinese Translation
行为克隆的视觉运动策略在其训练分布附近可以保持准确性，但在物体位置和相机视角同时变化时会失败。成功的参考轨迹包含了转移相同交互所需的几何信息，但策略必须将该几何信息与当前场景对齐，并在去噪过程中保持对其的敏感性。为了解决这些挑战，我们提出了MemCorr-DP，这是一种扩散策略，它将冻结的RoMa v2匹配提升为当前场景与参考轨迹之间的明确3D关系。反事实配对目标为相反行为分配相同的物理状态和噪声动作，同时保留特定于参考的去噪目标。混合条件微调随后将策略从真实几何适应到测量的对应误差。我们最强的评估将门放置在超出训练支持的最外层位置带，并将查询相机改变了$ ext{±}15^ ext{°}$。在这种组合偏移下，MemCorr-DP实现了96.67%的闭环成功率，而同一动作架构的视觉Transformer仅为88.00%。目标消融和参考干预表明行为对所选参考做出反应，而匹配控制则更倾向于完整关系集，而非仅依赖未来运动或质心几何。这些结果支持明确的3D参考关系作为在评估任务中空间和视角变化叠加时的稳健条件接口。
cs.RO / 36 / 2609.06623
CAVEAT: Recurrent Multimodal Diffusion Planning for Mapless Aerial Exploration
CAVEAT：无地图空中探索的递归多模态扩散规划
Abstract
Can exploratory UAV waypoint sequences be generated from multimodal onboard observations and a fixed-dimensional recurrent internal state without maintaining a persistent global map in the deployed policy? We investigate this question through CAVEAT, a diffusion policy conditioned on a recurrent internal state updated from fused LiDAR, visual, and pose features and trained from trajectories generated by the map-based FUELv2 expert. Rolling inference partially warm-starts consecutive predictions, while a temporary local signed distance field provides heuristic obstacle guidance. Simulation results evaluate both inference mechanisms and compare CAVEAT with its demonstration-generating expert. Proof-of-concept experiments on a Flyability Elios 3 demonstrate partial exploration of a previously unseen indoor environment and target-directed visual servoing using a separately trained policy.
Chinese Translation
探讨是否可以从多模态机载观测和固定维度的递归内部状态生成探索性无人机航点序列，而无需在部署的策略中维护持久的全局地图。我们通过CAVEAT来研究这个问题，该扩散策略以递归内部状态为条件，该状态由融合的激光雷达（LiDAR）、视觉和姿态特征更新，并从基于地图的FUELv2专家生成的轨迹中训练而来。滚动推理部分地热启动连续预测，而临时局部有符号距离场提供启发式障碍物引导。仿真结果评估了这两种推理机制，并将CAVEAT与其演示生成专家进行了比较。在Flyability Elios 3上的概念验证实验展示了对先前未见室内环境的部分探索以及使用单独训练的策略进行目标导向的视觉伺服。
cs.RO / 37 / 2609.06718
SkillX: Unified Multi-Skill Policy Learning for Humanoid Soccer
SkillX：类人足球的统一多技能策略学习
Abstract
Humanoid soccer is a challenging testbed for dynamic whole-body control, requiring robots to coordinate balance, locomotion, object interaction, and skill switching over long horizons. Existing humanoid sports methods often rely on task-specific multi-stage pipelines, making it difficult to jointly learn and compose multiple object-interactive skills within a single deployable policy. To address this, we present SkillX, a unified reinforcement learning framework that learns and composes multiple atomic soccer skills through a single command-conditioned policy. SkillX integrates three core designs: skill-specific adversarial motion priors, skill-specific critics, and an object-aware temporal encoder, enabling the robot to execute atomic skills and transition among them such as dribbling, trapping, and shooting. Experiments in simulation and on a real Noetix E1 humanoid demonstrate robust multi-skill execution, long-horizon skill composition, and successful sim-to-real deployment.
Chinese Translation
类人足球是一个具有挑战性的动态全身控制测试平台，要求机器人在长时间内协调平衡、运动、物体交互和技能切换。现有的类人运动方法通常依赖于特定任务的多阶段流程，这使得在单一可部署策略中共同学习和组合多种物体交互技能变得困难。为了解决这一问题，我们提出了SkillX，一个统一的强化学习框架，通过单一的命令条件策略学习和组合多种原子足球技能。SkillX整合了三个核心设计：技能特定的对抗运动先验、技能特定的评估器，以及一个物体感知的时间编码器，使机器人能够执行原子技能并在如运球、停球和射门等技能之间进行转换。在仿真和真实的Noetix E1类人机器人上的实验展示了强大的多技能执行能力、长时间技能组合能力以及成功的仿真到现实的部署。
cs.RO / 38 / 2609.06810
OCTN: Neural OCT Representations for Robot-Guided Precision Intervention
OCTN：用于机器人引导精确干预的神经OCT表征
Abstract
Optical coherence tomography (OCT) offers compact, contactless, micron-scale imaging suitable for intraoperative guidance, but native OCT volumes are discretely sampled, anisotropic, and currently inefficient for downstream geometric reasoning and robot integration. We present OCTN (pronounced "octane"), an implicit neural representation framework that converts volumetric OCT scans into a continuous, differentiable, and spatially faithful tissue-intensity field. OCTN uses a two-stage hybrid training strategy that combines supervision from acquired voxels with inter-slice interpolations, preserving B-scan fidelity while improving continuity in sparsely sampled regions. For versatility, we first show that OCTN enables fast volumetric reasoning by storing the learned tissue representation natively on the GPU, supporting intensity-based spatial queries with up to 43x speedup over conventional CPU processing. We then demonstrate OCTN-enabled OCT-guided robotic laser surgery where the continuous tissue representation supports implicit surface discovery and surface-constrained path planning via multiple optimization strategies, including Newton- and SGD-based optimization. Next, OCTN enables reconstruction of dense volumetric structure from sparsely acquired B-scans, while reducing acquisition time by 4x and preserving clinically relevant structures. Across the newly generated Duke TissueOCT dataset and public OCT datasets, OCTN achieves robust, high-fidelity reconstruction with PSNR > 30 dB and training time < 10 s, while preserving surface consistency within 10 $\mu$m Chamfer distance relative to baseline reconstruction. The TissueOCT dataset and code are publicly available at raprakashvi.github.io/octn
Chinese Translation
光学相干断层扫描（OCT）提供了紧凑、无接触、微米级成像，适用于术中引导，但原生OCT体积数据是离散采样的、各向异性的，并且目前在下游几何推理和机器人集成方面效率低下。我们提出了OCTN（发音为“octane”），一种隐式神经表征框架，将体积OCT扫描转换为连续、可微分且空间上忠实的组织强度场。OCTN采用两阶段混合训练策略，结合从获取的体素中获得的监督与切片间插值，保持B扫描的保真度，同时改善稀疏采样区域的连续性。为了实现多功能性，我们首先展示了OCTN通过将学习到的组织表征原生存储在GPU上，支持基于强度的空间查询，速度比传统CPU处理快达43倍，从而实现快速体积推理。然后，我们演示了基于OCTN的OCT引导机器人激光手术，其中连续的组织表征支持隐式表面发现和通过多种优化策略（包括基于牛顿法和随机梯度下降法的优化）进行表面约束路径规划。接下来，OCTN使得从稀疏获取的B扫描重建密集体积结构成为可能，同时将采集时间减少了4倍，并保持临床相关结构。在新生成的Duke TissueOCT数据集和公共OCT数据集中，OCTN实现了稳健的高保真重建，PSNR > 30 dB，训练时间 < 10秒，同时相对于基线重建保持了10微米的Chamfer距离内的表面一致性。TissueOCT数据集和代码已在raprakashvi.github.io/octn上公开。
cs.RO / 39 / 2609.06813
RoboSense: Leveraging Robotaxi Fleets as Drive-by Sensors for Urban Traffic Monitoring
RoboSense：利用机器人出租车车队作为城市交通监测的路过传感器
Abstract
Urban traffic monitoring plays a critical role in safety analysis, congestion management, and incident response. The growing deployment of robotaxis creates a new opportunity for network-level traffic monitoring. Although robotaxis are primarily designed to serve passengers, they can also be leveraged as drive-by sensors to collect traffic data. Compared to conventional infrastructure sensors or probe vehicles, a fleet of robotaxis forms a cooperative perception environment, which can collectively gather spatially and temporally continuous traffic information. This paper proposes a novel dynamic robotaxi routing framework that explicitly incorporates traffic monitoring tasks as an objective. The framework introduces: (1) a cell-based network representation that aligns with sensing capabilities of robotaxis; (2) a cell-level monitoring metric to quantify spatiotemporal robotaxi coverage; and (3) a mixed-integer linear programming (MILP) formulation that jointly minimizes time-dependent travel time and maximizes traffic monitoring performance. A 5 by 5 urban grid network is built in SUMO to evaluate the framework under three robotaxi market penetration rates (2%, 5%, and 10%) with a range of objective weight combinations. Results show that incorporating spatiotemporal network coverage in the objective function can effectively improve the traffic monitoring performance. Interestingly, with appropriate weights between the two objectives, monitoring performance and robotaxi average speed can be improved simultaneously. This suggests better network monitoring leads to more accurate traffic state prediction and improved mobility. This win-win situation could incentivize robotaxi operators to contribute their vehicles as drive-by sensors for traffic monitoring.
Chinese Translation
城市交通监测在安全分析、拥堵管理和事故响应中发挥着关键作用。机器人出租车的日益普及为网络级交通监测创造了新的机会。尽管机器人出租车主要设计用于服务乘客，但它们也可以作为路过传感器来收集交通数据。与传统基础设施传感器或探测车辆相比，机器人出租车车队形成了一个合作感知环境，可以共同收集空间和时间上连续的交通信息。本文提出了一种新颖的动态机器人出租车路由框架，明确将交通监测任务作为目标。该框架引入了：(1) 与机器人出租车感知能力相一致的基于单元的网络表示；(2) 用于量化时空机器人出租车覆盖率的单元级监测指标；(3) 一种混合整数线性规划（MILP）模型，联合最小化时间依赖的旅行时间并最大化交通监测性能。在SUMO中构建了一个5x5的城市网格网络，以评估该框架在三种机器人出租车市场渗透率（2%、5%和10%）下的表现，结合多种目标权重组合。结果表明，将时空网络覆盖纳入目标函数可以有效提高交通监测性能。有趣的是，在两个目标之间适当权重的情况下，监测性能和机器人出租车的平均速度可以同时提高。这表明更好的网络监测能够导致更准确的交通状态预测和改善的流动性。这种双赢局面可能会激励机器人出租车运营商将其车辆作为路过传感器贡献于交通监测。
cs.RO / 40 / 2609.06820
Diagnosing and Dynamically Filtering Occupancy World Models for Active Mapping
诊断和动态过滤占用世界模型以实现主动映射
Abstract
Active mapping requires a robot to select camera viewpoints that efficiently reconstruct an unknown 3D scene. To reason about unobserved regions, recent systems use pretrained occupancy networks as world models that complete missing geometry. The predicted structure contributes to expected coverage gain and constrains feasible robot motion. Consequently, occupancy errors can change both what the robot chooses to explore and where it is able to move. We diagnose these effects by holding the planner fixed and varying only the occupancy representation provided to it. We consider planning without completion, with learned occupancy, with false positives removed by a ground truth oracle, with false negatives restored by an oracle, and with ground truth occupancy. Our experiments show that correcting false positives or false negatives alone does not consistently improve final coverage. This finding reveals a gap between occupancy accuracy and downstream planning performance. Ground truth occupancy provides a much larger improvement in coverage efficiency than in endpoint coverage, suggesting that planning and reachability remain important bottlenecks even when the geometric world model is accurate. Based on these findings, we introduce a dynamic filtering strategy that preserves predictions in unexplored space while suppressing repeatedly unsupported occupancy using online observations. Preliminary examples show that this strategy can redirect viewpoint selection toward reachable surfaces that would otherwise remain unobserved.
Chinese Translation
主动映射要求机器人选择相机视点，以高效重建未知的三维场景。为了推理未观察到的区域，最近的系统使用预训练的占用网络作为世界模型，以补全缺失的几何信息。预测的结构有助于期望的覆盖增益，并限制可行的机器人运动。因此，占用错误可能会改变机器人选择探索的内容以及其能够移动的位置。我们通过固定规划器，仅改变提供给它的占用表示来诊断这些影响。我们考虑了不同的规划情况：没有补全、使用学习到的占用、通过真实情况神谕去除假阳性、通过神谕恢复假阴性，以及使用真实占用。我们的实验表明，仅仅纠正假阳性或假阴性并不能始终提高最终覆盖率。这个发现揭示了占用准确性与下游规划性能之间的差距。真实占用在覆盖效率上的提升远大于在最终覆盖率上的提升，这表明即使几何世界模型准确，规划和可达性仍然是重要的瓶颈。基于这些发现，我们提出了一种动态过滤策略，该策略在未探索空间中保留预测，同时利用在线观察抑制反复不支持的占用。初步示例表明，该策略可以将视点选择重新引导到本来会被忽视的可达表面。
cs.RO / 41 / 2609.06852
ContextFlow: In-Context Flow Matching for Robot Manipulation
ContextFlow：用于机器人操作的上下文流匹配
Abstract
Although highly effective in vision and language domains, applying in-context learning to robotics remains challenging. Existing autoregressive in-context imitation methods discretize continuous actions and exacerbate the accumulation of early prediction errors through next-token prediction, limiting their generalization on unseen task configurations. Meanwhile, flow-matching policies have been explored for continuous robot control and can help mitigate compounding errors; however, in-context imitation learning within a flow-matching framework remains underexplored. To address these limitations, we introduce ContextFlow, a conditional flow-matching model that learns continuous action distributions for in-context imitation learning. ContextFlow conditions flow-based action prediction on demonstrations and observations, enabling robust generation from noisy action distributions. To better encode multimodal in-context demonstrations, we adapt perceiver-style multimodal context compressors that distill visual, proprioceptive, and action sequences into compact, task-relevant latent representations. On LIBERO, ContextFlow outperforms ICRT by 35 percentage points in average success rate on unseen task configurations, while matching the performance of the task-specific fine-tuned VLA model $\pi_0$ without any fine-tuning on unseen tasks. On real robots, it generalizes to unseen configurations of both single-arm and bimanual tasks, achieving 40% success on a new pen-uncapping configuration. Project Page: https://dingjiansw101.github.io/contextflow-page/.
Chinese Translation
尽管在视觉和语言领域表现出色，但将上下文学习应用于机器人技术仍然面临挑战。现有的自回归上下文模仿方法将连续动作离散化，并通过下一个标记预测加剧早期预测错误的累积，限制了它们在未见任务配置上的泛化能力。同时，流匹配策略已被探索用于连续机器人控制，并可以帮助减轻累积错误；然而，在流匹配框架内的上下文模仿学习仍然未得到充分研究。为了解决这些限制，我们提出了ContextFlow，一种条件流匹配模型，旨在学习用于上下文模仿学习的连续动作分布。ContextFlow基于演示和观察条件进行流基动作预测，从而能够从噪声动作分布中生成稳健的输出。为了更好地编码多模态上下文演示，我们采用了感知器风格的多模态上下文压缩器，将视觉、身体感知和动作序列提炼为紧凑的、与任务相关的潜在表示。在LIBERO上，ContextFlow在未见任务配置上的平均成功率比ICRT高出35个百分点，同时在未见任务上与任务特定的微调VLA模型$ ext{π}_0$的表现相当，而无需进行任何微调。在真实机器人上，它能够推广到单臂和双臂任务的未见配置，在新的笔去盖配置上实现了40%的成功率。项目页面：https://dingjiansw101.github.io/contextflow-page/
cs.RO / 42 / 2609.06888
Predictive-Coding-Based Autonomous Regulation of Internally Generated and Externally Coupled Processing in Human-Robot Interaction
基于预测编码的人机交互中内部生成与外部耦合处理的自主调节
Abstract
Predictive coding characterizes adaptive behavior as a dynamic balance between internally generated predictions and external sensory evidence, yet how an embodied cognitive system can regulate this balance online during ongoing interaction remains poorly understood. This study proposes a predictive-coding-based mechanism for regulating internally generated and externally coupled processing during physical human--robot interaction. The framework employs a predictive-coding-inspired variational recurrent neural network (PV-RNN), in which a meta-prior controls the degree to which posterior inference is constrained by learned prior dynamics. We extend this architecture with an online mechanism that uses reconstruction error accumulated over recent interaction history to select between predefined meta-prior regimes. The mechanism was evaluated across three physical human--robot interaction tasks involving fixed structured, changing structured, and less-constrained interaction. Across all tasks, lower meta-prior values produced the expected increase in posterior--prior divergence and reduction in reconstruction error. More importantly, reconstruction-history-driven regime selection was also associated with reduced prospective prediction error and robot-side physical interaction conflict, demonstrating consequences beyond the retrospective reconstruction objective itself. Task~3 further showed that recent sensory observations can be successfully accommodated while subsequent human motion still departs from the model's prior-generated future trajectory. Overall, these findings show that accumulated reconstruction mismatch can provide an endogenous signal for regulating how strongly subsequent inference relies on learned internal dynamics relative to ongoing sensory input during embodied interaction.
Chinese Translation
预测编码将适应性行为描述为内部生成的预测与外部感官证据之间的动态平衡，但一个具身认知系统如何在持续交互中在线调节这一平衡仍然不够明确。本研究提出了一种基于预测编码的机制，用于调节在物理人机交互过程中内部生成与外部耦合的处理。该框架采用了一种受预测编码启发的变分递归神经网络（PV-RNN），其中元先验控制后验推断受到学习的先验动态约束的程度。我们通过一种在线机制扩展了这一架构，该机制利用在最近交互历史中累积的重构误差来选择预定义的元先验模式。在涉及固定结构、变化结构和约束较少的交互的三项物理人机交互任务中评估了该机制。在所有任务中，较低的元先验值导致了后验-先验发散的预期增加和重构误差的减少。更重要的是，基于重构历史的模式选择还与减少前瞻性预测误差和机器人侧物理交互冲突相关，显示出超越回顾性重构目标的后果。任务3进一步表明，最近的感官观察可以成功适应，而随后的人工运动仍然偏离模型生成的先验未来轨迹。总体而言，这些发现表明，累积的重构不匹配可以提供内源信号，以调节在具身交互过程中后续推断对学习的内部动态相对于持续感官输入的依赖程度。
cs.RO / 43 / 2609.06896
Distributed Secure Learning Control for Large-scale Multirobots under Stealthy Actuator Attacks
大规模多机器人系统下的分布式安全学习控制与隐蔽执行器攻击
Abstract
Distributed learning control for multirobot systems (MRS) offers significant flexibility in presence of uncertainties but lacks provable performance guarantees. A promising direction involves integrating reinforcement learning (RL) into distributed model predictive control (DMPC), leveraging the strengths of RL in nonlinear policy design and the receding-horizon replanning capabilities of DMPC. However, ensuring secure control within such a learning framework under malicious cyber attacks, particularly stealthy ones, remains a critical challenge, because the distributed policies generation depends on information exchange among neighbors, where compromised agents can rapidly influence the behavior of others through the communication network. This article proposes a distributed secure learning control (DSLC) framework for large-scale MRS under malicious, stealthy actuator attacks. Our framework offers two key features: (i) a unified approach that enables secure learning control across various coordination scenarios and (ii) a game-theoretic distributed learning-based predictive control strategy that learns how to balance the attacker and defender through a differential-game based DMPC framework. Specifically, DSLC employs a distributed attacker-actor-critic architecture to learn the optimal defense and attack policies online within each prediction interval. Unlike numerical optimization-based controllers that calculate open-loop control sequences, our method simultaneously generates adversarial attack policies and corresponding defense policies in analytical closed-loop form. The defense policies could be directly generalized to MRS with varying scales and diverse actuator attack probabilities. The effectiveness and scalability of DSLC are validated through comprehensive simulations and real-world experiments in multiple wheeled robots via various control tasks.
Chinese Translation
针对多机器人系统（MRS）的分布式学习控制在不确定性存在的情况下提供了显著的灵活性，但缺乏可证明的性能保障。一种有前景的方向是将强化学习（RL）与分布式模型预测控制（DMPC）相结合，利用RL在非线性策略设计中的优势以及DMPC的递归规划能力。然而，在恶意网络攻击，特别是隐蔽攻击的情况下，确保在这样的学习框架内实现安全控制仍然是一个关键挑战，因为分布式策略的生成依赖于邻居之间的信息交换，而被攻陷的代理可以通过通信网络迅速影响其他代理的行为。本文提出了一种针对恶意隐蔽执行器攻击的大规模MRS的分布式安全学习控制（DSLC）框架。我们的框架提供了两个关键特性：（i）一种统一的方法，使得在各种协调场景下实现安全学习控制，以及（ii）一种基于博弈论的分布式学习预测控制策略，通过基于微分博弈的DMPC框架学习如何平衡攻击者和防御者。具体而言，DSLC采用分布式攻击者-演员-评论家架构，在每个预测区间内在线学习最佳防御和攻击策略。与基于数值优化的控制器计算开环控制序列不同，我们的方法同时以解析闭环形式生成对抗性攻击策略和相应的防御策略。这些防御策略可以直接推广到具有不同规模和多样化执行器攻击概率的MRS。通过在多个轮式机器人上进行各种控制任务的综合仿真和真实世界实验，验证了DSLC的有效性和可扩展性。
cs.RO / 44 / 2609.06923
DriftParking: Trajectory Modeling via Drifting Field for End-to-End Automated Parking
DriftParking：通过漂移场进行端到端自动停车的轨迹建模
Abstract
Automated parking requires generating complete and executable trajectories in highly constrained spaces with low tolerance for goal pose error. Existing end-to-end parking methods struggle to jointly achieve inference efficiency, trajectory quality, and precise endpoint alignment, while conventional imitation objectives provide limited supervision on structured deviations from expert maneuver geometry. We propose DriftParking, a one-step trajectory generation framework that reconstructs the drifting-field paradigm for high-precision conditional trajectory generation. Specifically, we replace distribution-level attraction with conditional one-to-one attraction toward the paired expert trajectory, introduce expert-centered constructive repulsion, and adaptively attenuate repulsion near convergence. We further formulate trajectory generation in an endpoint-residual space by decomposing each trajectory into a start-to-goal baseline and a learnable residual, turning endpoint alignment into a representation-level structural constraint on the supervision target while providing a structured space for repulsive supervision. DriftParking achieves state-of-the-art performance across all evaluation metrics. Closed-loop on-vehicle experiments across diverse parking scenarios further show a 97% parking success rate, demonstrating strong zero-shot generalization.
Chinese Translation
自动停车需要在高度受限的空间中生成完整且可执行的轨迹，对目标姿态误差的容忍度极低。现有的端到端停车方法在推理效率、轨迹质量和精确的终点对齐方面难以共同实现，而传统的模仿目标对专家操控几何形状的结构性偏差提供的监督有限。我们提出了DriftParking，一种一步轨迹生成框架，重构了漂移场范式以实现高精度的条件轨迹生成。具体而言，我们用对配对专家轨迹的条件一对一吸引替代了分布级别的吸引，引入了以专家为中心的建设性排斥，并在收敛附近自适应地减弱排斥力。我们进一步通过将每条轨迹分解为从起点到目标的基线和可学习的残差，在终点残差空间中制定轨迹生成，将终点对齐转化为对监督目标的表示级结构约束，同时为排斥监督提供了结构化空间。DriftParking在所有评估指标上实现了最先进的性能。在多种停车场景下进行的闭环车载实验进一步显示出97%的停车成功率，证明了强大的零样本泛化能力。
cs.RO / 45 / 2609.06930
Distributed Dexterous Manipulation with Spatially Conditioned Multi-Agent Transformers
基于空间条件的多智能体变换器的分布式灵巧操作
Abstract
Distributed Dexterous Manipulation (DDM) is a novel paradigm that presents significant control challenges due to high action-space redundancy, inter-robot cooperation, and dynamic object-robot interactions. This paper introduces a framework based on spatially conditioned Multi-Agent Transformers (MATs) to efficiently learn robust control policies for a DDM system grounded in an array of 64 soft delta robots arranged in an 8x8 grid. Our three core contributions are: (i) an MAT with adaptive layer norm for compute efficiency, (ii) spatial contrastive embeddings to ground transformer embeddings in the spatial configuration of the robots, and (iii) an MAT-based behavior cloning method fine-tuned using Soft Actor Critic. We also propose an action selection formulation to analyze the trade-off between task performance and the number of robots utilized. Our experiments show that MATs iteratively refine their actions through the stacked attention blocks. This further informs the benefit of spatial conditioning in transformers to learn DDM policies. We demonstrate long-horizon planar manipulation tasks with objects of various geometries in simulation and real-world. Finally, we show how action selection mitigates robot maintenance by reducing wear and tear due to inter-robot collisions while maintaining the ability to manipulate objects along various trajectories in the real-world, achieving an average error of ~1.5 cm, while using ~65% fewer robots.
Chinese Translation
分布式灵巧操作（DDM）是一种新颖的范式，由于高动作空间冗余、机器人间的协作以及动态物体与机器人之间的交互，带来了显著的控制挑战。本文提出了一种基于空间条件的多智能体变换器（MATs）框架，以高效学习针对基于64个软三角机器人（soft delta robots）排列成8x8网格的DDM系统的鲁棒控制策略。我们的三项核心贡献为：(i) 具有自适应层归一化的MAT以提高计算效率，(ii) 空间对比嵌入以将变换器嵌入与机器人的空间配置相结合，以及(iii) 基于MAT的行为克隆方法，使用软演员评论家（Soft Actor Critic）进行微调。我们还提出了一种动作选择公式，以分析任务性能与所用机器人数量之间的权衡。我们的实验表明，MAT通过堆叠的注意力块迭代地优化其动作。这进一步说明了空间条件在变换器中学习DDM策略的益处。我们在模拟和现实世界中展示了具有不同几何形状物体的长时间平面操作任务。最后，我们展示了如何通过减少因机器人间碰撞造成的磨损来缓解机器人维护，同时保持在现实世界中沿不同轨迹操作物体的能力，最终实现了约1.5厘米的平均误差，同时使用了约65%更少的机器人。
cs.RO / 46 / 2609.06958
Mind the Phase: Effective Rank and Representation Health in Legged Locomotion
注意相位：腿部运动中的有效秩与表示健康
Abstract
Reinforcement learning has become the leading paradigm in legged locomotion, enabling complex behaviors from backflips to parkour through massively parallel simulation. Under PPO's non-stationarity, shallow networks remain the de facto architecture, supported by carefully staged curricula and environments, yet the representations these policies learn stay poorly understood, leaving no training-time signal of how they will behave on hardware. In this work, we empirically study locomotion policies through the effective rank of the policy Jacobian and show that conditioning rank on the gait phase exposes architectural structure that global rank averages away. In particular, we find that standard architectural choices, namely layer normalization and residual connections, allocate roughly two more dimensions of effective rank to swing than to stance, which is fully absent in vanilla MLPs. Building on this, we propose a simple recipe that turns these representational signatures into smoother, more reliable sim-to-real transfer. In practice, this results in roughly 3x lower joint jitter that holds from simulation onto a physical Spot, suggesting that representation health is an effective training-time lens to track sim-to-real smoothness.
Chinese Translation
强化学习已成为腿部运动的主要范式，通过大规模并行仿真实现从后空翻到跑酷等复杂行为。在PPO（Proximal Policy Optimization）的非平稳性下，浅层网络仍然是事实上的架构，得益于精心设计的课程和环境，但这些策略所学习的表示仍然不够清晰，缺乏训练时信号来预测它们在硬件上的表现。在本研究中，我们通过策略雅可比矩阵的有效秩对运动策略进行实证研究，表明在步态相位上对秩进行条件化可以揭示出全局秩所平均掉的架构结构。具体而言，我们发现标准的架构选择，即层归一化和残差连接，给摆动分配了大约两个以上的有效秩维度，而在普通的多层感知器（MLP）中则完全不存在。在此基础上，我们提出了一种简单的方法，将这些表示特征转化为更平滑、更可靠的仿真到现实转移。在实践中，这导致关节抖动降低了大约3倍，并且这种效果从仿真延续到物理Spot，表明表示健康是跟踪仿真到现实平滑性的有效训练时视角。
cs.RO / 47 / 2609.07002
WM-Craftnet: World Synesthesia Model for Generalizable and Robust Dexterous In-Hand Manipulation
WM-Craftnet：用于可泛化和鲁棒的灵巧手内操作的世界联觉模型
Abstract
Generalizable and robust dexterous in-hand manipulation requires a policy to infer object pose, geometry, contact, and potential slip from partial and noisy observations. Although recent tactile and visuotactile RL methods achieve strong in-hand rotation in controlled settings, their robustness often degrades under pose shifts, force disturbances, and object variation. We propose WM-Craftnet, a world-model-conditioned framework that learns compact action-conditioned latent dynamics from proprioception, depth, tactile sensing, and actions, supervised by multimodal reconstruction and reward prediction. Rather than using the world model for latent imagination or policy optimization, WM-Craftnet uses the learned World Synesthesia Model (WSM) as recurrent task context for an asymmetric actor--critic policy. Importantly, WSM is trained to reconstruct clean depth targets from noisy depth inputs, providing a denoised geometric state for real-robot deployment. Ablations over recurrent baselines, auxiliary heads, tactile masking, and WSM modality heads show that predictive world modeling, clean-depth supervision, and tactile contact cues all shape the learned state. A WSM pretrained on nine \(z\)-axis objects serves as a reusable prior for \(49\)-object downstream policy learning. This context improves multi-object rotation, with quantitative and qualitative evidence for unseen-object, perturbation-recovery, and sim-to-real transfer.
Chinese Translation
可泛化和鲁棒的灵巧手内操作需要一种策略，从部分和噪声观测中推断物体姿态、几何形状、接触和潜在滑移。尽管近期的触觉和视觉触觉强化学习（RL）方法在受控环境中实现了强大的手内旋转，但在姿态变化、力扰动和物体变化下，它们的鲁棒性往往会降低。我们提出了WM-Craftnet，这是一种世界模型条件框架，能够从本体感知、深度、触觉传感和动作中学习紧凑的动作条件潜在动态，并通过多模态重建和奖励预测进行监督。WM-Craftnet并不使用世界模型进行潜在想象或策略优化，而是将学习到的世界联觉模型（World Synesthesia Model, WSM）作为不对称演员-评论家策略的递归任务上下文。重要的是，WSM被训练以从噪声深度输入重建干净的深度目标，为真实机器人部署提供去噪几何状态。对递归基线、辅助头、触觉掩蔽和WSM模态头的消融实验表明，预测世界建模、干净深度监督和触觉接触线索共同塑造了学习到的状态。一个在九个z轴物体上预训练的WSM作为49个物体下游策略学习的可重用先验。这一上下文改善了多物体旋转，并提供了关于未见物体、扰动恢复和仿真到现实转移的定量和定性证据。
cs.RO / 48 / 2609.07047
MEMOBench: A Process Level Memory Benchmark for Robotic Manipulation
MEMOBench：一种用于机器人操作的过程级内存基准测试
Abstract
Robotic manipulation often requires acting on information that is no longer visible, yet Vision-Language-Action policies are usually evaluated when the current observation largely determines the next action. Existing robotic memory benchmarks expose this gap, but they still rely mainly on final task success and therefore conflate forgetting with manipulation failure. We present \textbf{MEMOBench}, a benchmark for process level memory evaluation in robotic manipulation. MEMOBench includes 30 history dependent tasks, 1{,}500 expert demonstrations, and 4{,}200 executable checkpoint instances from 84 templates. Each checkpoint pairs coarse to fine language with a simulator predicate and labels one memory operation: Storage, Update, or Compression. These annotations define Memory Storage Rate, Memory Update Rate, and Memory Compression Rate, which measure memory fidelity alongside task success. Across standard and memory augmented VLA policies, the strongest memory module baseline reaches only 31.9\% average success rate, and high storage often coexists with weak update and compression. Checkpoint language also supervises semantic, contrastive, and framewise memory alignment objectives, yielding modest gains across different memory operations. MEMOBench provides a diagnostic evaluation suite and training supervision for memory grounded robotic policies. The project page is available at https://github.com/Collab-Gen/MEMOBench.
Chinese Translation
机器人操作通常需要对不再可见的信息进行处理，然而，视觉-语言-动作（Vision-Language-Action, VLA）策略通常是在当前观察结果大幅决定下一步行动时进行评估的。现有的机器人内存基准测试暴露了这一差距，但它们仍主要依赖于最终任务成功，因此将遗忘与操作失败混为一谈。我们提出了 extbf{MEMOBench}，这是一个用于机器人操作的过程级内存评估基准测试。MEMOBench包括30个依赖历史的任务、1,500个专家演示和来自84个模板的4,200个可执行检查点实例。每个检查点将粗略到细致的语言与模拟器谓词配对，并标注一个内存操作：存储（Storage）、更新（Update）或压缩（Compression）。这些注释定义了内存存储率（Memory Storage Rate）、内存更新率（Memory Update Rate）和内存压缩率（Memory Compression Rate），用于衡量内存的保真度与任务成功率。在标准和内存增强的VLA策略中，最强的内存模块基线仅达到31.9%的平均成功率，而高存储率通常与弱更新和压缩并存。检查点语言还监督语义、对比和逐帧内存对齐目标，在不同的内存操作中取得了适度的提升。MEMOBench提供了一个诊断评估套件和内存基础的机器人策略的训练监督。项目页面可访问：https://github.com/Collab-Gen/MEMOBench。
cs.RO / 49 / 2609.07049
Large Discrete Policy: Advancing Explicit Behavior Modeling with Stochastic Iterative Scoring
大离散策略：通过随机迭代评分推进显式行为建模
Abstract
Behavior policies are often formulated as continuous generative models, whose iterative denoising processes are expressive but difficult to interpret and prone to producing implausible actions. We propose the Large Discrete Policy (LDiP), a fully discrete behavior modeling framework that selects actions from a large vocabulary of physically plausible candidates. Rather than perturbing actions, LDiP improves expressivity through stochastic iterative scoring: it progressively re-scores and prunes candidates with score-space stochasticity, enabling fine-grained ranking and exploration among plausible actions while preserving an explicit decision process. Across end-to-end planning, closed-loop driving, robotic manipulation, and vision-language-action settings, LDiP consistently outperforms strong discrete and continuous baselines in autonomous driving, and exceeds or matches continuous generative policies in robotic manipulation. These results show that discrete policies, when equipped with effective scoring mechanisms, offer an expressive, plausible, and interpretable alternative for behavior modeling. Project website: https://zhenxinli.net/LargeDiscretePolicy/.
Chinese Translation
行为策略通常被表述为连续生成模型，其迭代去噪过程具有表达能力，但难以解释且容易产生不合理的动作。我们提出了大离散策略（Large Discrete Policy, LDiP），这是一种完全离散的行为建模框架，从大量物理上合理的候选动作中选择动作。LDiP并不是通过扰动动作来提高表达能力，而是通过随机迭代评分来实现：它逐步重新评分并修剪候选项，利用评分空间的随机性，使得在合理动作之间能够进行细致的排名和探索，同时保留显式的决策过程。在端到端规划、闭环驾驶、机器人操作和视觉-语言-动作设置中，LDiP在自主驾驶方面始终优于强大的离散和连续基线，并在机器人操作中超过或匹配连续生成策略。这些结果表明，当配备有效的评分机制时，离散策略提供了一种表达丰富、合理且可解释的行为建模替代方案。项目网站：https://zhenxinli.net/LargeDiscretePolicy/
cs.RO / 50 / 2609.07091
Human-Aware Target Tracking and Navigation: Fusing Kinematic State Estimation with Structural Map Constraints
人类感知目标跟踪与导航：将运动状态估计与结构地图约束融合
Abstract
Autonomous mobile robots performing person-following tasks often suffer from temporary occlusions and sensor track loss in dynamic environments. This research presents an end-to-end autonomous navigation stack that addresses target occlusion through map-informed spatial reasoning. The proposed system features a multi-modal perception pipeline, fusing deep learning-based visual tracking with 2-dimensional LiDAR point clustering to maintain high-fidelity tracking of a tagged person. A continuous state estimator integrates this perception data with wheel odometry and IMU sensors for stable localization. When the active track is lost due to occlusion, the system activates a map-based recovery framework. Leveraging a predefined topological map, the system executes a graph-based search to propagate the target's last known trajectory along structurally defined walking lanes, adhering to left-hand regional conventions. By generating a discrete set of feasible future trajectories, the robot reasons about potential structural trajectory changes, such as continuing a heading or turning at an intersection. This map-informed prediction is fed directly to the local obstacle avoidance planner, enabling the robot to continue following its target safely and predictably until the person is visually reacquired. Real-world evaluations in dense multi-person environments demonstrate the system's robustness, achieving a 71.4\% target reacquisition success rate during major occlusion events lasting up to 7 seconds.
Chinese Translation
在动态环境中执行跟随任务的自主移动机器人常常面临暂时遮挡和传感器跟踪丢失的问题。本研究提出了一种端到端的自主导航系统，通过基于地图的信息进行空间推理来解决目标遮挡问题。所提系统具有多模态感知管道，将基于深度学习的视觉跟踪与二维激光雷达（LiDAR）点云聚类相结合，以保持对标记人物的高保真跟踪。一个连续状态估计器将该感知数据与轮子里程计和惯性测量单元（IMU）传感器整合，以实现稳定的定位。当由于遮挡而丢失活动跟踪时，系统会激活基于地图的恢复框架。利用预定义的拓扑地图，系统执行基于图的搜索，以沿着结构定义的步行通道传播目标的最后已知轨迹，遵循左侧行驶的区域惯例。通过生成一组离散的可行未来轨迹，机器人能够推理潜在的结构轨迹变化，例如继续前进或在交叉口转弯。这种基于地图的预测直接输入到本地障碍物避免规划器，使机器人能够安全且可预测地继续跟随目标，直到重新获得对该人物的视觉识别。在密集多人的真实环境中的评估表明，该系统的鲁棒性，在持续长达7秒的主要遮挡事件中，实现了71.4%的目标重新获取成功率。
cs.RO / 51 / 2609.07096
RoboDreamer: Anticipatory Humanoid Locomotion with Predictive State-Space Models
RoboDreamer：基于预测状态空间模型的前瞻性类人运动
Abstract
Humanoid locomotion requires control policies that remain stable under imperfect sensing while exploiting temporal context for consistent motion. We present RoboDreamer, a two-stage teacher--student framework that combines next-observation consistency with randomized continuous temporal masking. A teacher is first trained on clean observations, and a student is then distilled under masked recent observations, encouraging the policy to infer missing current information from history. At inference, the same masking interface is reused for implicit closed-loop action refinement and optional multi-step action chunking. Mamba is used as the temporal backbone, while matched ablations show that masking/distillation provides a substantial part of the gain and Mamba contributes additional tracking improvements with real-time latency. Experiments in IsaacLab, MuJoCo, and on a Unitree G1 demonstrate robust motion tracking under observation masking and successful real-world deployment.
Chinese Translation
类人运动需要在不完美感知下保持稳定的控制策略，同时利用时间上下文实现一致的运动。我们提出了RoboDreamer，一个结合了下一观察一致性与随机连续时间遮蔽的两阶段教师-学生框架。首先，教师在干净的观察数据上进行训练，然后学生在遮蔽的近期观察数据下进行蒸馏，鼓励策略从历史中推断缺失的当前信息。在推理阶段，重用相同的遮蔽接口进行隐式闭环动作精炼和可选的多步动作分块。Mamba被用作时间骨干，而匹配的消融实验表明，遮蔽/蒸馏提供了显著的增益，Mamba则在实时延迟下贡献了额外的跟踪改进。在IsaacLab、MuJoCo以及Unitree G1上的实验展示了在观察遮蔽下的稳健运动跟踪和成功的现实世界部署。
cs.RO / 52 / 2609.07109
Eventually Optimal and Scalable Multi-Agent Planning for Block Cave Mining
最终最优且可扩展的块崩矿多智能体规划
Abstract
Automation in underground mining has the potential to significantly enhance safety, operational efficiency, and sustainability. However, effectively coordinating fleets of autonomous vehicles in dynamic mine environments introduces substantial challenges in both optimization and motion planning. To address these challenges, we introduce and formalize the \emph{Block Cave Mining (BCM)} problem, which focuses on computing a transport plan that maximizes ore throughput while satisfying draw ratio constraints. To solve this problem, we propose SAMM, an eventually optimal anytime solver that jointly integrates task assignment, scheduling, and path planning via a mixed-integer linear programming formulation. To improve scalability, we also introduce SAMMS, a variant of SAMM that trades optimality guarantees for efficiency by decomposing the problem into shorter planning subcycles. Experimental evaluations using realistic industrial mine scenarios demonstrate that SAMMS achieves near-optimal throughput and scales effectively to larger fleets and mine layouts.
Chinese Translation
地下采矿的自动化有潜力显著提高安全性、操作效率和可持续性。然而，在动态矿山环境中有效协调自主车辆队伍在优化和运动规划方面带来了重大挑战。为了解决这些挑战，我们引入并形式化了 extit{块崩矿（Block Cave Mining，BCM）}问题，该问题侧重于计算一种运输计划，以最大化矿石吞吐量，同时满足开采比约束。为了解决这个问题，我们提出了SAMM，这是一种最终最优的随时求解器，通过混合整数线性规划形式将任务分配、调度和路径规划进行联合整合。为了提高可扩展性，我们还引入了SAMMS，这是SAMM的一种变体，通过将问题分解为更短的规划子周期来以效率换取最优性保证。使用现实工业矿山场景的实验评估表明，SAMMS实现了接近最优的吞吐量，并能够有效扩展到更大的车队和矿山布局。
cs.RO / 53 / 2609.07111
From LLM-Generated Specifications to Learned Quadruped Locomotion
从大型语言模型生成的规范到学习的四足动物运动
Abstract
Quadruped robot locomotion policies are often trained using reinforcement learning, which in turn relies heavily on hand-crafted reward functions. Designing reward functions requires substantial manual engineering, and it is often unclear which local rewards will induce the desired global behavior. Shaped rewards from formal specifications in languages like Signal Temporal Logic (STL) can make rewards more interpretable, but writing STL specifications itself still requires domain expertise. We study whether large language models (LLMs) can fill this gap by generating Parametric Signal Temporal Logic (PSTL) specifications that are subsequently used for policy learning. Given a natural language locomotion objective and a constrained specification grammar, GPT-5.5 and Qwen 3.6 independently propose STL templates for command tracking, safety, and gait structure. We instantiate the parameters of the generated PSTL templates using expert trajectories and retain only specifications that are consistent with demonstrated expert behavior. The resulting specifications are then transformed into smooth, finite-history reward functions and used to train a quadruped locomotion policy with Proximal Policy Optimization (PPO) in MuJoCo XLA (MJX). We evaluate both \emph{gait-aware} and \emph{gait-agnostic} settings. The former specifies walking-trot, trot, and bound regimes, while the latter allows contact patterns to emerge from the task objective. We compare against hand-engineered rewards, Text2Reward-style LLM-generated reward code, and an expert-switching oracle. Gait-aware Qwen 3.6 specifications achieved 100\% survival and command success across all tested speeds (0.3--2.1 m/s) and matched the target gait at high speeds, whereas Text2Reward achieved 0\% for both metrics at $\geq 1.9$ m/s. Videos: https://stl-locomotion.github.io/
Chinese Translation
四足机器人运动策略通常使用强化学习进行训练，而强化学习又在很大程度上依赖于手工设计的奖励函数。设计奖励函数需要大量的手动工程，并且通常不清楚哪些局部奖励会诱导出期望的全局行为。来自诸如信号时序逻辑（Signal Temporal Logic, STL）等语言的形式规范的塑形奖励可以使奖励更具可解释性，但编写STL规范本身仍然需要领域专业知识。我们研究大型语言模型（LLMs）是否可以通过生成参数化信号时序逻辑（Parametric Signal Temporal Logic, PSTL）规范来填补这一空白，这些规范随后用于策略学习。在给定自然语言运动目标和受限的规范语法的情况下，GPT-5.5和Qwen 3.6独立提出了用于命令跟踪、安全性和步态结构的STL模板。我们使用专家轨迹实例化生成的PSTL模板的参数，并仅保留与专家行为一致的规范。然后，将生成的规范转换为平滑的有限历史奖励函数，并用于在MuJoCo XLA（MJX）中使用近端策略优化（Proximal Policy Optimization, PPO）训练四足运动策略。我们评估了 extit{步态感知}和 extit{步态无关}的设置。前者指定了行走-小跑、小跑和跳跃的状态，而后者则允许接触模式从任务目标中产生。我们与手工设计的奖励、Text2Reward风格的LLM生成奖励代码和专家切换预言机进行了比较。步态感知的Qwen 3.6规范在所有测试速度（0.3--2.1 m/s）下实现了100 ext{%}的生存率和命令成功率，并在高速度下匹配了目标步态，而Text2Reward在$ ext{≥}1.9$ m/s时两个指标均为0 ext{%}。视频链接: https://stl-locomotion.github.io/
cs.RO / 54 / 2609.07116
MAC-I$^2$: Learned Metrics-Aware Covariance for Robust Visual-Inertial Fusion in Initialization and Calibration
MAC-I$^2$: 学习度量感知协方差用于初始化和校准中的鲁棒视觉-惯性融合
Abstract
Visual-Inertial (VI) fusion is fundamental to accurate and robust state estimation, where camera and IMU measurements are combined according to their respective uncertainties. Existing methods, however, fuse the two modalities with predefined uncertainties, regardless of how reliable each is in the local context, and thus often struggle under challenging environments involving illumination changes, dynamic objects, and textureless regions. In this paper, we present MAC-I$^2$, which achieves robust VI fusion through learned metric-aware covariance for both modalities, so that vision and IMU compete on their own merits rather than relying on predefined uncertainties. Here, metrics-aware means that each predicted covariance faithfully reflects the actual magnitude of the corresponding measurement noise. On the visual side, we propagate learned feature-matching uncertainties into pose covariances for the fusion. On the inertial side, motivated by the observation that integration error accumulates sharply at the early stage and grows slowly afterward, we design a learned IMU model with a learnable initial covariance, and propose a dedicated fine-tuning strategy on a held-out training subset to enable the metrics-aware covariance on unseen sequences. As a showcase, we build a VI initialization and calibration system, since accurate and robust initialization and calibration are the prerequisite for any reliable VI system. Experiments on EuRoC, and VBR show that MAC-I$^2$ substantially outperforms existing methods: it achieves a 99.9% initialization success rate on EuRoC, reducing gravity and velocity errors by about 60% and 42% over the strongest baseline, and maintains 80% success rate on challenging VBR sequences where baseline methods such as VINS-Mono drop below 10%.
Chinese Translation
视觉-惯性（VI）融合对于准确和鲁棒的状态估计至关重要，其中相机和IMU测量根据各自的不确定性进行结合。然而，现有方法在融合这两种模态时使用预定义的不确定性，而不考虑每种模态在局部环境中的可靠性，因此在面临光照变化、动态物体和无纹理区域等挑战性环境时常常表现不佳。本文提出了MAC-I$^2$，通过学习的度量感知协方差实现了两种模态的鲁棒VI融合，使得视觉和IMU能够各自发挥优势，而不依赖于预定义的不确定性。这里的度量感知意味着每个预测的协方差真实反映了相应测量噪声的实际大小。在视觉方面，我们将学习到的特征匹配不确定性传播到姿态协方差中进行融合。在惯性方面，基于观察到的积分误差在早期阶段迅速累积而后缓慢增长的现象，我们设计了一个具有可学习初始协方差的学习IMU模型，并提出了一种专门的微调策略，在保留的训练子集上使得未见序列的度量感知协方差得以实现。作为展示，我们构建了一个VI初始化和校准系统，因为准确和鲁棒的初始化与校准是任何可靠VI系统的前提。在EuRoC和VBR上的实验表明，MAC-I$^2$显著优于现有方法：在EuRoC上实现了99.9%的初始化成功率，相较于最强基线减少了约60%的重力误差和42%的速度误差，并在挑战性的VBR序列上保持了80%的成功率，而基线方法如VINS-Mono的成功率降至10%以下。
cs.RO / 55 / 2609.07126
Beyond Task Success: Stage-Wise Reliability of World Model Planning under Sensing Degradation
超越任务成功：感知退化下世界模型规划的阶段性可靠性
Abstract
In world model planning, sensing inputs pass through an encoder and predictor before affecting planner decisions, so final task success alone cannot reveal where sensing disturbances attenuate or persist in the pipeline. We apply 10 visual and temporal sensing degradations to a world model planner and track their effects across representation, future prediction, planner preference, and physical outcome using paired evaluation on the same 50 tasks. The relative impact of degradations was not preserved across stages: large representation shifts could attenuate downstream, while smaller initial shifts could persist to the outcome, and internal-response ordering did not directly match physical-outcome ordering. Temporal degradations also showed distinct patterns: even with similar overall changes in observation history, responses differed substantially with the location of corrupted information and the planner's actual exposure. This non-uniform stage-wise response was also observed in secondary evaluations with another manipulation task and a different world model. Stage-wise diagnosis can therefore identify where sensing disturbances attenuate or persist and help prioritize subsequent model verification and sensing mitigation.
Chinese Translation
在世界模型规划中，感知输入经过编码器和预测器后才会影响规划者的决策，因此仅仅依靠最终的任务成功无法揭示感知干扰在整个流程中减弱或持续的具体位置。我们对一个世界模型规划器应用了10种视觉和时间感知退化，并通过对同一50个任务的配对评估，跟踪它们在表示、未来预测、规划者偏好和物理结果上的影响。退化的相对影响在各个阶段并未保持一致：较大的表示变化可能会在下游减弱，而较小的初始变化可能会持续到结果中，内部响应的顺序与物理结果的顺序并不直接匹配。时间退化也显示出不同的模式：即使观察历史的整体变化相似，响应在受损信息的位置和规划者的实际暴露程度上也有显著差异。因此，这种非均匀的阶段性响应在另一个操控任务和不同的世界模型的二次评估中也得到了观察。阶段性诊断可以识别感知干扰减弱或持续的具体位置，并帮助优先考虑后续的模型验证和感知减轻措施。
cs.RO / 56 / 2609.07145
EquiGQNet: Fast Grasp Quality Evaluation via Shared Equivariant Point Cloud Encoding
EquiGQNet：通过共享等变点云编码进行快速抓取质量评估
Abstract
Planning six-degree-of-freedom (6-DoF) grasps for unseen objects in cluttered tabletop scenes from a single-view depth image requires accurate and efficient evaluation of diverse grasp candidates. Existing early-fusion methods capture local object geometry relative to each grasp candidate but repeatedly encode the scene, whereas late-fusion methods reuse a shared scene representation but may lose this grasp-relative local geometry. We propose EquiGQNet, an efficient 6-DoF grasp quality evaluator that combines the strengths of both approaches. For grasp orientation, EquiGQNet replaces the early-fusion operation of rotating and re-encoding the point cloud for each grasp candidate with an SO(3)-equivariant encode-once-then-rotate scheme, yielding grasp-aligned geometric features from a shared scene encoding. For grasp translation, Mid-level Action Fusion (MAF) injects the grasp position into intermediate features before global aggregation, retaining local geometry relative to each candidate. We evaluate EquiGQNet in two grasp planning pipelines: Cross-Entropy Method (CEM)-based continuous grasp search and candidate ranking with a pretrained generative planner. In simulation, EquiGQNet achieves grasping performance comparable to the early-fusion baseline and substantially outperforms late fusion on objects with complex geometry and limited graspable regions, while reducing CEM planning time from 3.31s to 0.48s, a 6.9x speedup over early fusion. In real-world household-object decluttering, EquiGQNet achieves a 95.2% grasp success rate and 230 picks per hour, versus 153 and 170 for early- and late-fusion baselines. Code is available at https://equigqnet.github.io/.
Chinese Translation
在杂乱的桌面场景中，从单视角深度图像规划未见物体的六自由度（6-DoF）抓取需要对多样的抓取候选进行准确而高效的评估。现有的早期融合方法捕捉相对于每个抓取候选的局部物体几何形状，但重复编码场景，而晚期融合方法则重用共享场景表示，但可能会丢失与抓取相关的局部几何信息。我们提出了EquiGQNet，一种高效的6-DoF抓取质量评估器，结合了这两种方法的优点。对于抓取方向，EquiGQNet用一种SO(3)-等变的“编码一次然后旋转”方案替代了对每个抓取候选进行旋转和重新编码的早期融合操作，从共享场景编码中获得抓取对齐的几何特征。对于抓取位置，中间动作融合（Mid-level Action Fusion, MAF）在全局聚合之前将抓取位置注入中间特征中，保留了相对于每个候选的局部几何信息。我们在两个抓取规划流程中评估了EquiGQNet：基于交叉熵方法（Cross-Entropy Method, CEM）的连续抓取搜索和使用预训练生成规划器的候选排名。在仿真中，EquiGQNet的抓取性能与早期融合基线相当，并在具有复杂几何形状和有限可抓取区域的物体上显著优于晚期融合，同时将CEM规划时间从3.31秒减少到0.48秒，较早期融合加速了6.9倍。在现实世界的家庭物品清理中，EquiGQNet实现了95.2%的抓取成功率和每小时230次抓取，而早期和晚期融合基线分别为153次和170次。代码可在 https://equigqnet.github.io/ 获取。
cs.RO / 57 / 2609.07165
State-of-the-Art in Learning-by-Demonstration with Passive Observation for Industrial Assembly Automation
被动观察下的示范学习在工业装配自动化中的最新进展
Abstract
Learning-by-Demonstration (LbD) enables intuitive robot programming by capturing expert skills, which is crucial for agility in high-mix, low- volume manufacturing. This systematic literature review analyzes passive LbD for industrial assembly processes, focusing on the perception architecture and the generalization of the perceived demonstration. We specifically investigate one-shot approaches where only a single demonstration is required. The review evaluates how systems adapt to new assemblies using this limited data. We identify a shift towards object-centric perception, allowing learned primitives to be transferred to new product variants with minimal training.
Chinese Translation
示范学习（Learning-by-Demonstration, LbD）通过捕捉专家技能，实现直观的机器人编程，这对于高混合、低产量制造中的灵活性至关重要。本系统文献综述分析了用于工业装配过程的被动 LbD，重点关注感知架构和感知示范的泛化。我们特别研究了只需单次示范的一次性方法。该综述评估了系统如何利用这一有限数据适应新的装配。我们发现，感知逐渐向以对象为中心转变，使得学习到的原语能够以最小的训练转移到新的产品变体上。
cs.RO / 58 / 2609.07211
Phase-and-First-Arrival VLM Feedback for Sparse-Reward Reinforcement Learning in Surgical Manipulation
稀疏奖励强化学习中的相位与首次到达视觉语言模型反馈在外科操作中的应用
Abstract
Sparse outcome feedback limits what robots can learn from unsuccessful attempts at complex manipulation. Failed multi-stage surgical attempts can contain grasps, lifts, or transfers worth reusing. In sparse-reward reinforcement learning, terminal rewards collapse such attempts to the same outcome, while scalar vision-language model (VLM) ratings reveal neither what progress merits credit nor when it occurred. We introduce phase-and-first-arrival feedback: one VLM query per recorded episode identifies the furthest visually verified task phase and when that phase is first reached, allowing the learner to reuse partial behavior and localize credit. We instantiate it in SurgPhaseBench, a phase-structured suite spanning rigid and deformable tasks, and evaluate it in simulation and hardware. Across five simulated tasks, our method reaches 75.2% mean success, compared with 52.1% for a reward based on Contrastive Language-Image Pre-training (CLIP) using the same visual input; the advantage persists when only the feedback representation changes. On hardware, the same record supports autonomous block picking and slip recovery. Together, these results show that trajectory-level visual supervision can preserve partial progress while providing the temporal credit needed for sparse-reward control.
Chinese Translation
稀疏的结果反馈限制了机器人从复杂操作中失败尝试中学习的能力。失败的多阶段外科尝试可能包含值得重用的抓取、提升或转移动作。在稀疏奖励强化学习中，终端奖励将这些尝试归结为相同的结果，而标量视觉语言模型（VLM）评分既无法揭示哪些进展值得认可，也无法指明何时发生。我们提出了相位与首次到达反馈：每个记录的事件中进行一次VLM查询，以识别最远的视觉验证任务阶段及该阶段首次达到的时间，从而使学习者能够重用部分行为并定位奖励。我们在SurgPhaseBench中实现了该方法，这是一个涵盖刚性和可变形任务的阶段结构化套件，并在仿真和硬件中进行了评估。在五个仿真任务中，我们的方法达到了75.2%的平均成功率，而基于对比语言-图像预训练（CLIP）的奖励在使用相同视觉输入时仅为52.1%；当仅改变反馈表示时，这一优势依然存在。在硬件上，相同的记录支持自主的块拾取和滑移恢复。这些结果表明，轨迹级视觉监督可以在提供稀疏奖励控制所需的时间性奖励的同时，保留部分进展。
cs.RO / 59 / 2609.07231
Generalizable 6D Pose Estimation of Textureless Objects with Planar-based Gaussian Splatting
基于平面高斯点云的无纹理物体通用6D姿态估计
Abstract
Estimating the 6D pose of textureless objects without prior CAD models remains a critical challenge due to the lack of appearance features. While recent generalizable approaches alleviate the dependence on object-specific models, their performance on low-texture objects is often limited by insufficient geometric constraints in the underlying representations. In this work, we propose PG-Pose, a geometry-aware framework combining Planar-based Gaussian Splatting (PGS) reconstruction and Geometry-driven pose optimization. In the offline representation extraction stage, three distinct representations of the object are extracted from multi-view reference RGB images with known poses. PG-Pose reconstructs a 3D Gaussian representation and renders high-fidelity depth maps to generate 3D point clouds through back projection. In the online pose inference stage, the initial pose of the input image is estimated by 2D-3D correspondence matching between the input image and the reconstructed 3D point clouds, followed by a PGS-Refiner for iterative pose optimization. Evaluations on the OnePose-LowTexture datasets, PG-Pose achieves an average accuracy of 94.2% ADD(S)@0.1d, with a 2.1% improvement average accuracy compared with the state-of-the-art (SOTA) GS-based approach. To further demonstrate the effectiveness of PG-Pose for industrial robots in grasping tasks, we deploy it on a dual-arm industrial robot and successfully realize the grasping task on an unseen object.
Chinese Translation
在没有先前CAD模型的情况下，估计无纹理物体的6D姿态仍然是一个关键挑战，因为缺乏外观特征。尽管最近的通用方法减轻了对特定物体模型的依赖，但它们在低纹理物体上的表现往往受到基础表示中几何约束不足的限制。在本研究中，我们提出了PG-Pose，这是一个结合了基于平面的高斯点云重建（Planar-based Gaussian Splatting, PGS）和几何驱动姿态优化的几何感知框架。在离线表示提取阶段，从已知姿态的多视图参考RGB图像中提取物体的三种不同表示。PG-Pose重建了一个3D高斯表示，并通过反向投影生成高保真深度图，从而生成3D点云。在在线姿态推断阶段，通过输入图像与重建的3D点云之间的2D-3D对应匹配来估计输入图像的初始姿态，随后使用PGS-Refiner进行迭代姿态优化。在OnePose-LowTexture数据集上的评估中，PG-Pose达到了94.2%的平均准确率ADD(S)@0.1d，相较于最先进的基于GS的方法提高了2.1%的平均准确率。为了进一步证明PG-Pose在工业机器人抓取任务中的有效性，我们将其部署在一台双臂工业机器人上，并成功实现了对未见物体的抓取任务。
cs.RO / 60 / 2609.07269
Singularity-Free Guiding Vector Fields on SO(3) with Designer-Specified Progression Behavior
具有设计指定进展行为的SO(3)上的无奇点引导向量场
Abstract
This paper develops a singularity-free guiding vector field (SF-GVF) for path following on the special orthogonal group SO(3). First, we lift the Euclidean SF-GVF construction to SO(3), integrating the augmented-state approach with the intrinsic Lie-group geometry and obtaining a closed-form geometric guidance law whose integral curves converge to a designer-specified attitude path. The field is defined on a dense open subset of SO(3), excluding only the measure-zero antipodal set - a manifestation of the topological obstruction to continuous global stabilization on SO(3). The construction requires no per-step optimization and produces a control input intrinsically in so(3) as body angular rates. Second, we formalize the progression behavior along the path as a designer-supplied function \nu(\xi), promoting the parametric speed from an implicitly resolved degree of freedom to a first-class design specification. In contrast to the Euclidean condition v = 0, which excludes vehicles with minimum-speed constraints, the corresponding condition \omega = 0 on SO(3) is physically admissible for most platforms with active attitude control, making the progression behavior a design freedom structurally available on SO(3) but absent in the Euclidean setting. The framework's structural results are established under a bi-invariant Riemannian metric and hold uniformly across choices of path, progression, and Lyapunov gain. The framework is illustrated in simulation on self-intersecting paths under both constant and point-convergence progression behaviors.
Chinese Translation
本文开发了一种用于在特殊正交群SO(3)上进行路径跟踪的无奇点引导向量场（SF-GVF）。首先，我们将欧几里得SF-GVF的构造提升到SO(3)，结合增强状态方法与内在李群几何，获得了一种封闭形式的几何引导法则，其积分曲线收敛于设计指定的姿态路径。该场定义在SO(3)的一个稠密开子集上，仅排除了测度为零的对立点集——这是SO(3)上连续全局稳定性的拓扑障碍的表现。该构造不需要逐步优化，并且产生的控制输入本质上在so(3)中作为机体角速度。其次，我们将沿路径的进展行为形式化为设计者提供的函数ν(ξ)，将参数速度从隐式解决的自由度提升为一项一流的设计规范。与排除具有最低速度约束的车辆的欧几里得条件v = 0相比，SO(3)上对应的条件ω = 0在大多数具有主动姿态控制的平台上是物理上可接受的，使得进展行为成为在SO(3)上结构上可用的设计自由，而在欧几里得环境中则缺失。该框架的结构结果在双不变黎曼度量下建立，并在路径、进展和Lyapunov增益的选择上均匀有效。该框架在恒定和点收敛进展行为下的自交路径上进行了仿真展示。
cs.RO / 61 / 2609.07274
LightSplat: Real-Time High-Fidelity 3D Gaussian SLAM with Loop Closure
LightSplat：实时高保真3D高斯SLAM与回环闭合
Abstract
SLAM systems based on 3D Gaussian Splatting (3DGS) have recently demonstrated promising reconstruction accuracy for dense 3D scene representations. However, current 3DGS systems struggle to meet the strict demands of real-world deployments due to severe limitations in operational performance and map adaptability. To this end, we propose LightSplat, a hybrid-representation RGB-D SLAM framework. It synergizes local sparse features for robust and fast tracking with a dual-thread backend that progressively constructs dense Gaussian submaps. Crucially, we enable online loop closure through feature-accelerated 3DGS registration, refining overall map consistency through pose graph optimization. Ultimately, LightSplat achieves the online reconstruction of high-fidelity Gaussian map. Extensive experiments on multiple datasets and real-world robotic platform demonstrate that our method achieves near state-of-the-art reconstruction quality and the capability to accommodate practical camera motions, maintaining an average framerate of 8 FPS. Overall, LightSplat provides an efficient and robust foundation for deploying high-fidelity 3DGS in real-world environments.
Chinese Translation
基于3D高斯点云（3D Gaussian Splatting, 3DGS）的SLAM系统最近在稠密3D场景表示的重建精度方面表现出良好的前景。然而，当前的3DGS系统在实际应用中面临着操作性能和地图适应性方面的严重限制，难以满足严格的需求。为此，我们提出了LightSplat，一个混合表示的RGB-D SLAM框架。它结合了局部稀疏特征以实现稳健且快速的跟踪，并采用双线程后端逐步构建稠密的高斯子地图。关键的是，我们通过特征加速的3DGS配准实现了在线回环闭合，通过姿态图优化提高整体地图的一致性。最终，LightSplat实现了高保真高斯地图的在线重建。在多个数据集和实际机器人平台上的广泛实验表明，我们的方法达到了接近最先进的重建质量，并具备适应实际相机运动的能力，保持平均帧率为8 FPS。总体而言，LightSplat为在实际环境中部署高保真3DGS提供了高效且稳健的基础。
cs.RO / 62 / 2609.07288
How Long Until Your Robot Ignores You? A Safety Benchmark for LLM Orchestrators in Human-Humanoid Collaboration
你的机器人多久会忽视你？人类与类人机器人协作中的大型语言模型协调器安全基准
Abstract
Large Language Models (LLMs) are increasingly employed to orchestrate robot behavior through natural-language interfaces, yet no benchmark exists to evaluate their reliability as safety-aware decision makers in human-humanoid collaboration. Unlike deterministic safety systems that enforce binary allow/deny decisions, LLM-based orchestrators exhibit a compliance spectrum ranging from overcompliance (refusing safe actions) to full safety violations. This paper introduces the first safety benchmarking environment for LLM orchestrators in human-humanoid collaboration, built on a Model Context Protocol (MCP)-based architecture with safety invariants grounded in ISO 10218-2:2025 protective measures. The benchmark defines five testable safety invariants, a four-level compliance taxonomy (correct compliance, overcompliance, undercompliance, full violation), and a three-layer evaluation pipeline (text prompting, simulated sensor-actuator loops, and physical validation on a Unitree G1 EDU humanoid). We report Layer-1 results: three cloud backends (Claude Haiku 4.5, GPT-4o-mini, Gemini 2.5 Flash) and a local open-weights baseline (qwen3:8b) across 40 100-turn sessions under full-context and sliding-window budget conditions, while the simulation and physical layers remain ongoing. We find that (1) model family determines the safety floor, as Claude and Gemini remain at or near zero violations while GPT-4o-mini commits up to 13 per session, (2) context management dissociates two failure axes, reducing mean behavioral issues by 42-57% for every cloud backend while nearly doubling GPT-4o-mini's violations (3.8 to 7.2 per session), and (3) proportional compliance, clamping movement speed to the rule-specified maximum rather than refusing, emerges consistently only in Gemini; the preliminary simulation layer reproduces the model ranking and the GPT-4o-mini failure-mode inversion.
Chinese Translation
大型语言模型（LLMs）越来越多地被用于通过自然语言接口协调机器人行为，但目前尚无基准来评估它们作为具备安全意识的决策者在类人机器人协作中的可靠性。与强制执行二元允许/拒绝决策的确定性安全系统不同，基于LLM的协调器表现出一种合规谱，从过度合规（拒绝安全行为）到完全安全违规。本文介绍了第一个针对人类与类人机器人协作中LLM协调器的安全基准测试环境，该环境基于模型上下文协议（Model Context Protocol, MCP）架构，安全不变量基于ISO 10218-2:2025保护措施。该基准定义了五个可测试的安全不变量、一个四级合规分类（正确合规、过度合规、不足合规、完全违规）以及一个三层评估流程（文本提示、模拟传感器-执行器循环和在Unitree G1 EDU类人机器人上的物理验证）。我们报告了第一层结果：在完全上下文和滑动窗口预算条件下，三个云后端（Claude Haiku 4.5、GPT-4o-mini、Gemini 2.5 Flash）和一个本地开放权重基线（qwen3:8b）在40个100轮会话中的表现，而模拟和物理层仍在进行中。我们发现：（1）模型家族决定了安全底线，Claude和Gemini的违规次数保持在零或接近零，而GPT-4o-mini每个会话最多可达13次违规；（2）上下文管理解耦了两个失败轴，减少了每个云后端的平均行为问题42-57%，同时几乎将GPT-4o-mini的违规次数翻倍（从3.8增加到7.2）；（3）比例合规，即将运动速度限制在规则指定的最大值而不是拒绝，只有在Gemini中始终出现；初步的模拟层再现了模型排名和GPT-4o-mini的失败模式反转。
cs.RO / 63 / 2609.07328
PV-WM: A Heterogeneous Micro-Macro World Model for Articulated Pedestrian-Vehicle Co-Rollout
PV-WM：一种用于关节行人-车辆协同滚动的异质微观-宏观世界模型
Abstract
Local pedestrian-vehicle forecasting spans heterogeneous physical scales: pedestrians combine root locomotion with articulated motion, whereas vehicles are rigid bodies described by kinematic state and oriented extent. Existing road-agent forecasters typically omit pedestrian articulation, while pose forecasters leave vehicle futures outside the learned rollout. We introduce PV-WM, a history-only world model over structured post-perception tracks. It recurrently advances pedestrian root motion, 15-joint articulation, and learned vehicle states within a synchronized heterogeneous state. The generated pedestrian and vehicle chunks supply the next recurrent boundary; vehicle boxes are reconstructed from predicted center and heading with observed extent, and P-V geometry is recomputed after every transition. Relative to a matched one-shot complete-state predictor, recurrent execution reduces Root ADE by 12.7% and MPJPE by 14.8%. Feedback interventions show that later predictions depend on the content, temporal order, and pedestrian identity of generated articulation. Across 824 aligned Waymo contexts, with 797 providing valid future vehicle support, PV-WM reduces Root ADE by 5.2%, MPJPE by 7.6%, P-V distance error by 11.9%, and oriented-box closest-approach error by 5.8% relative to a validation-selected Modular Specialist. The single-network model uses 57.1% fewer parameters, 96.5% lower average FLOPs per local scene, and 25.5% lower measured p95 latency. PV-WM unifies this heterogeneous future state while preserving type-specific pedestrian and vehicle dynamics.
Chinese Translation
局部行人-车辆预测跨越异质物理尺度：行人结合了根运动与关节运动，而车辆则是由运动状态和方向范围描述的刚体。现有的道路代理预测器通常忽略行人的关节运动，而姿态预测器则将车辆的未来排除在学习的滚动之外。我们提出了PV-WM，这是一种基于历史的世界模型，建立在结构化的感知后轨迹上。它在同步的异质状态中反复推进行人的根运动、15个关节的关节运动以及学习到的车辆状态。生成的行人和车辆片段提供了下一个重复边界；车辆框架根据预测的中心和方向以及观察到的范围进行重建，并且在每次过渡后重新计算行人-车辆几何关系。相较于匹配的一次性完整状态预测器，重复执行将根均方误差（Root ADE）降低了12.7%，将均方关节位置误差（MPJPE）降低了14.8%。反馈干预显示，后续预测依赖于生成的关节运动的内容、时间顺序和行人身份。在824个对齐的Waymo上下文中，其中797个提供有效的未来车辆支持，PV-WM相较于验证选择的模块化专家，将根均方误差降低了5.2%，均方关节位置误差降低了7.6%，行人-车辆距离误差降低了11.9%，定向框最近接近误差降低了5.8%。该单网络模型使用的参数减少了57.1%，每个局部场景的平均FLOPs降低了96.5%，测得的p95延迟降低了25.5%。PV-WM统一了这种异质未来状态，同时保留了特定类型的行人和车辆动态。
cs.RO / 64 / 2609.07350
D3ARC: Time-Critical Distributed Disaster Detection for Asynchronous Cooperative Multi-Robot Systems
D3ARC：用于异步合作多机器人系统的时间关键分布式灾害检测
Abstract
Climate change is increasing the severity and unpredictability of natural disasters. In time-critical crises such as wildfires, traditional monitoring practices remain limited by coverage, cost, and personnel risk, paving the way for autonomous and adaptive monitoring solutions. Within this context, this paper introduces D3ARC, an asynchronous distributed hierarchical framework for time-aware and reliable wildfire detection. D3ARC integrates multiple robotic agents that cooperate under uncertainty through distributed perception, shared situational awareness and coordinated actions. A remote controller asynchronously decides upon each robot's motion, while each robotic agent senses the environment and decides where and how to execute the wildfire detection. All robotic operations require time, and as time progresses, wildfires continue to spread, reducing the opportunity for early intervention. As such, all agents share a common objective: to detect a wildfire with a certain performance threshold as fast as possible and within a time limit. D3ARC integrates mechanisms for safe navigation, coverage efficiency, cooperation and reliability. It introduces a forward-looking capability that allows agents to anticipate the future by evaluating candidate strategies before execution. The framework is evaluated through realistic robotics simulations, ablation studies, and baseline comparisons, achieving an overall mission success up to 94% with 89.4% detection confidence.
Chinese Translation
气候变化正在加剧自然灾害的严重性和不可预测性。在野火等时间关键危机中，传统监测方法受到覆盖范围、成本和人员风险的限制，这为自主和自适应监测解决方案铺平了道路。在此背景下，本文介绍了D3ARC，一个用于时间感知和可靠野火检测的异步分布式层次框架。D3ARC整合了多个机器人代理，这些代理通过分布式感知、共享情境意识和协调行动在不确定性下进行合作。远程控制器异步决定每个机器人的运动，而每个机器人代理则感知环境并决定在哪里以及如何执行野火检测。所有机器人操作都需要时间，随着时间的推移，野火持续蔓延，减少了早期干预的机会。因此，所有代理共享一个共同目标：在时间限制内尽快以一定的性能阈值检测到野火。D3ARC整合了安全导航、覆盖效率、合作和可靠性的机制。它引入了一种前瞻性能力，使代理能够通过在执行前评估候选策略来预测未来。该框架通过现实的机器人仿真、消融研究和基线比较进行了评估，整体任务成功率高达94%，检测信心为89.4%。
cs.RO / 65 / 2609.07398
OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining
OpenWAM：朝向系统化世界-动作模型预训练的开放模块化探索
Wang, Yuran, Huang, Siqiao, Li, Mingleyang, Zhang, Chenhao, Liang, Jiaqi, Jin, Weiyang, Chen, Yue, Chi, Xuemin, Zhou, Donghao, Yu, Qize, Wang, Yu-Kai, Rui, Yuhan, Yao, Shenzhe, Yuan, Zhen, Shen, Zhenhao, Zhu, Kefei, Zhu, Zijie, Gao, Ning, Chi, Xiaowei, He, Guanqi, Zhang, Shanghang, Dong, Hao, Shao, Lin, Zhao, Hang
Abstract
World-Action Models inherit world knowledge from video-generative priors, and channel it into executable control signals through embodied experience. Existing systems, however, are monolithic: the generative backbone, visual representation, architecture, information flow, inference procedure, and training data are tightly coupled, obscuring which design choices matter and why. We introduce OpenWAM, an open research stack that turns world-action pretraining into a controlled experimental program. OpenWAM-Infra factorizes the WAM design space into composable modules with unified training, inference, deployment, and evaluation. On this substrate, OpenWAM-Study examines three questions through controlled experiments: what to inherit, how world and action learning interact, and how their synergy scales; and distills three principles: upstream knowledge transfers through a sufficiently capable generative backbone and a compact, information-rich latent space; world-action synergy requires dedicated action capacity, explicit world-to-action information flow, and synchronized joint denoising; and embodied pretraining principally improves out-of-domain generalization, with one-stage co-training over egocentric and robot data integrating world coverage and action grounding. Composing these principles, we build OpenWAM-{\alpha}, an open WAM pretrained on roughly 6,400 hours of egocentric human and robot data and evaluated across simulation and real-world benchmarks. Across the eight simulation benchmarks and the real-robot experiments, which together span embodiments from single-arm and bimanual manipulation to dexterous hands, OpenWAM-{\alpha} delivers consistently excellent performance, sustaining its top-tier standing from simulation to the physical world. We release the full stack, including infrastructure, evaluation protocols, pretrained models, and data recipes, to facilitate future research.
Chinese Translation
世界-动作模型通过视频生成先验继承世界知识，并通过具身经验将其转化为可执行的控制信号。然而，现有系统是整体性的：生成主干、视觉表示、架构、信息流、推理过程和训练数据紧密耦合，模糊了哪些设计选择重要及其原因。我们引入了OpenWAM，一个开放的研究框架，将世界-动作预训练转变为一个受控的实验程序。OpenWAM-Infra将WAM设计空间分解为可组合的模块，统一训练、推理、部署和评估。在此基础上，OpenWAM-Study通过受控实验探讨三个问题：继承什么、世界学习与动作学习如何相互作用以及它们的协同效应如何扩展；并提炼出三条原则：上游知识通过足够强大的生成主干和紧凑、信息丰富的潜在空间进行转移；世界-动作协同需要专门的动作能力、明确的世界到动作的信息流以及同步的联合去噪；具身预训练主要改善域外泛化，通过在自我中心和机器人数据上进行的一阶段共同训练整合世界覆盖与动作基础。结合这些原则，我们构建了OpenWAM-α，一个在大约6400小时的自我中心人类和机器人数据上预训练的开放WAM，并在模拟和现实世界基准上进行了评估。在八个模拟基准和真实机器人实验中，这些实验涵盖了从单臂和双手操控到灵巧手的多种体现，OpenWAM-α始终表现出色，从模拟到物理世界保持其顶尖地位。我们发布了完整的研究框架，包括基础设施、评估协议、预训练模型和数据配方，以促进未来的研究。
cs.RO / 66 / 2609.07430
CALM: Configuration-Aware Human Intervention Boundaries During Robot Approach
CALM：机器人接近过程中的配置感知人类干预边界
Abstract
How robot body configuration shapes human intervention during approach remains underexplored. We conducted a within-participants study with 41 participants, measuring final stopping distance, subjective comfort, and exploratory eye-tracking responses across four humanoid arm configurations and two spatial scales. Full forward arm extension increased stopping distance by approximately 31-36 cm relative to arms-down. Spatial scale primarily affected comfort and pupil responses without a detectable stopping-distance shift. We introduce the Configuration-Aware Limit Model (CALM), which translates stopping-distance distributions into configuration-dependent population-coverage boundaries. Estimated boundaries at 80% coverage ranged from 0.88 to 1.47 m. In an illustrative one-dimensional planning analysis, reconfiguration enabled a 1.10 m approach goal that was unreachable with arms remaining fully extended under the same nominal pointwise 20% intervention-probability constraint. These findings support treating body configuration as a planning variable while distinguishing physical safety, behavioral intervention, and subjective cost.
Chinese Translation
机器人身体配置如何影响人类在接近过程中的干预仍然未被充分探索。我们进行了一个参与者内研究，涉及41名参与者，测量了最终停止距离、主观舒适度和四种类人手臂配置及两种空间尺度下的探索性眼动追踪反应。完全前伸的手臂相较于手臂下垂，停止距离增加了约31-36厘米。空间尺度主要影响舒适度和瞳孔反应，而没有检测到停止距离的变化。我们提出了配置感知限制模型（Configuration-Aware Limit Model, CALM），该模型将停止距离分布转化为依赖于配置的人群覆盖边界。在80%的覆盖率下，估计的边界范围为0.88至1.47米。在一个示例性的一维规划分析中，重新配置使得在相同名义点状20%干预概率约束下，能够实现1.10米的接近目标，而在手臂保持完全伸展的情况下则无法达到。这些发现支持将身体配置视为一个规划变量，同时区分物理安全、行为干预和主观成本。
cs.RO / 67 / 2609.07440
Open-Set Ego-Noise Separation for Legged-Robot Audition via Annotation-Free Adaptation and Pretrained-Model Transfer
基于无注释适应和预训练模型迁移的腿足机器人听觉开放集自我噪声分离
Abstract
This paper proposes an open-set ego-noise separation framework for legged-robot audition via annotation-free adaptation and pretrained-model transfer. The framework removes robot-specific ego-noise while preserving environmental sounds whose classes are not specified in advance. Acoustic sensing provides cues about a robot's surroundings beyond the visual field, but walking-induced ego-noise from footstep impacts, joint-backlash rattling, and motor noise severely contaminates the recordings. The framework first uses RecurGraph to select ego-noise-dominant clips from the unlabeled recordings by aggregating clip embeddings into an embedding centroid and propagating scores over an audio-embedding graph. The selected clips are mixed with diverse environmental sounds from a large-scale sound-event dataset to provide paired mixture--target supervision for open-set separation. Transfer-DiT then adapts a general-purpose zero-shot neural separator to achieve high-fidelity open-set ego-noise separation for the target robot. Experiments with bipedal and quadrupedal robots show reliable clip selection and improvements in separation quality and downstream task performance over baseline separators. These results demonstrate the feasibility of annotation-free adaptation without separately recorded ego-noise-only data or manual clip-level annotations.
Chinese Translation
本文提出了一种基于无注释适应和预训练模型迁移的腿足机器人听觉开放集自我噪声分离框架。该框架在去除特定于机器人的自我噪声的同时，保留未提前指定类别的环境声音。声学传感提供了关于机器人周围环境的线索，超出了视觉范围，但步态引起的自我噪声（如脚步冲击、关节反冲和电机噪声）严重污染了录音。该框架首先使用 RecurGraph 从未标记的录音中选择自我噪声主导的片段，通过将片段嵌入聚合成嵌入中心并在音频嵌入图上传播得分。所选片段与来自大规模声音事件数据集的多样化环境声音混合，以提供配对的混合-目标监督用于开放集分离。Transfer-DiT 然后将通用零-shot 神经分离器适应为目标机器人实现高保真开放集自我噪声分离。对双足和四足机器人的实验表明，片段选择可靠，并且在分离质量和下游任务性能上优于基线分离器。这些结果证明了无注释适应的可行性，无需单独录制的自我噪声数据或手动片段级注释。
cs.RO / 68 / 2609.07445
SMaRT-Tug: Structured Multi-Agent Reinforcement Learning for Physics-Based Tugboat-Barge Collaborative Manipulation
SMaRT-Tug：基于结构化多智能体强化学习的物理基础拖船-驳船协作操作
Abstract
Autonomous tugboating is central for automating maritime operations such as port logistics and vessel maneuvering, where multiple tugboats must cooperatively transport/manipulate a larger vessel. Collaborative pushing in this setting is challenging due to coupled hydrodynamics, low resistance, strong environmental disturbances, underactuated barge dynamics, and contact-rich interactions. Conventional control methods often rely on simplified models and fixed configurations, which limit their adaptability, while learning-based approaches are constrained by the lack of scalable and physically realistic training environments. We address these challenges by introducing a physics-based, GPU-accelerated simulation and learning framework for collaborative tugboat manipulation. Our simulator incorporates a customized buoyancy model, wave modeling, and hydrodynamic resistance, and supports large-scale multi-agent training under marine dynamics. In this simulator, we train a decentralized MAPPO (Multi-Agent PPO) policy augmented with a structured control prior (SCP) to improve training stability and maintain feasible pushing configurations. We evaluate our learned policy on straight-line transit, turning, and deceleration tasks, where we show that our decentralized framework yields more reliable and accurate maneuvering performance compared to a PID-based controller and a centralized PPO baseline. We further demonstrate zero-shot generalization to more challenging sea states and advanced maneuvers, as well as zero-shot scalability to larger teams of three and four tugboats despite training with only two agents.
Chinese Translation
自主拖船在自动化海事操作中至关重要，例如港口物流和船舶操控，其中多个拖船必须协同运输/操作更大的船舶。在这种情况下，协作推送面临着耦合水动力学、低阻力、强环境干扰、欠驱动驳船动力学和丰富接触交互等挑战。传统控制方法通常依赖于简化模型和固定配置，这限制了它们的适应性，而基于学习的方法则受到缺乏可扩展和物理真实的训练环境的限制。我们通过引入一个基于物理的、GPU加速的协作拖船操作模拟和学习框架来解决这些挑战。我们的模拟器结合了定制的浮力模型、波浪建模和水动力阻力，并支持在海洋动态下的大规模多智能体训练。在这个模拟器中，我们训练了一个去中心化的MAPPO（多智能体PPO）策略，并增强了结构化控制先验（SCP），以提高训练的稳定性并维持可行的推送配置。我们在直线行驶、转向和减速任务上评估了我们学习的策略，结果表明我们的去中心化框架相比于基于PID的控制器和中心化PPO基线，提供了更可靠和准确的操控性能。我们进一步展示了在更具挑战性的海况和高级操控中的零样本泛化，以及在仅用两个智能体训练的情况下对三艘和四艘拖船更大团队的零样本可扩展性。
cs.RO / 69 / 2609.07470
Measuring Language Transfer in Robot Policies: Adding Greek to a Cosmos3 Vision-Language-Action Policy
在机器人策略中测量语言迁移：向 Cosmos3 视觉-语言-动作策略添加希腊语
Abstract
Robot foundation models are trained and evaluated predominantly in English, and robot demonstration corpora do not exist for most languages. We study the addition of Greek to an open vision-language-action stack using only machine-rephrased instructions and no architecture changes. The main challenge is measurement rather than translation. Several plausible instruments produce false conclusions: a color-histogram metric rewards noise, a single-goal benchmark scores 84.6% under correct Greek and 82.6% under deliberately wrong instructions, training loss fails to predict Greek success, and single-run comparisons are dominated by seed variation. On a discriminative ninety-task suite with three seeds per arm, a multilingual text tower without Greek demonstrations remains at its wrong-instruction floor, while Greek-only training exceeds its control by at most 2.7 points. Bilingual training yields a consistent 6.7-7.1 point margin over its control and reaches about two fifths of English performance. The policy also overfits the translator's phrasing; training on seven phrasings per task approximately halves this penalty. Warm-starting from a language-adapted world model and unfreezing the text tower both degrade performance. The results support two practical requirements for low-resource robot-policy localization: build a guaranteed null before trusting a metric, and replicate low-resource-language results across seeds.
Chinese Translation
机器人基础模型主要在英语环境下进行训练和评估，而大多数语言的机器人演示语料库并不存在。我们研究了在一个开放的视觉-语言-动作框架中添加希腊语，仅使用机器重述的指令而不改变架构。主要挑战在于测量而非翻译。几种合理的测量工具产生了错误的结论：色彩直方图指标奖励噪声，单目标基准在正确的希腊语下得分为84.6%，在故意错误的指令下得分为82.6%，训练损失无法预测希腊语的成功，而单次比较则受到种子变异的主导。在一个包含九十个任务的判别性测试套件中，每个任务使用三个种子，没有希腊语演示的多语言文本塔仍然停留在错误指令的底线，而仅用希腊语训练的表现最多超出其控制组2.7分。双语训练相较于控制组始终保持6.7-7.1分的优势，并达到了英语表现的约五分之二。该策略还过拟合了翻译者的措辞；每个任务训练七种措辞大约将这一惩罚减半。从语言适应的世界模型进行热启动并解冻文本塔均降低了性能。结果支持低资源机器人策略本地化的两个实际要求：在信任指标之前构建一个保证的零值，并在不同种子间复制低资源语言的结果。
cs.RO / 70 / 2609.07495
Wearable Multimodal Human-Machine Interface for Integrated Hand Intentions Decoding in Dynamic Teleoperation
可穿戴多模态人机界面用于动态遥操作中集成手部意图解码
Abstract
Under ubiquitous teleoperation environments with optically challenging conditions, an interface for tele-operated grasping that combines wearability with precise decoding of hand intentions (hand pose, gestures, and grasping force) is essential. Yet, existing interfaces often fall short in meeting these demands, compromising either the diversity of multiple intentions decoding or wearability. To address this, we developed a novel Multiple Intentions Decoding Human-Machine Interface (MI-DHMI) that integrates high-throughput surface electromyography (sEMG) sensors with hand-mounted and forearm-mounted inertial measurement units (IMUs). The developed interface is supported by a unified framework for simultaneous multiple intentions decoding. By employing multimodal deep learning and hardware design with a low noise floor, the decoding framework selectively focuses on the sEMG components that are genuinely associated with finger movements. This effectively reduces decoding errors caused by sEMG variability during unconstrained upper-limb motions, thereby significantly enhancing robustness. Even under unconstrained wrist and forearm motion, the interface achieves a gesture recognition accuracy exceeding 97%, grasping force estimation with $R^2 = 0.95$, and hand pose decoding consistent with the actual hand pose, outperforming baseline devices and algorithms. Ablation studies further validate the effectiveness of the proposed decoding framework. Finally, two online experiments were conducted to validate the device, demonstrating its superior performance in high-stability tasks, including a pouring task and object grasping. The developed interface provides a new solution of a fully wearable, multiple intentions decoding system, offering effective support for ubiquitous teleoperation and contributing to the advancement of human-machine interaction research.
Chinese Translation
在光学条件具有挑战性的普遍遥操作环境下，结合可穿戴性与手部意图（手势、手势姿态和抓握力）精确解码的遥操作抓取界面至关重要。然而，现有界面往往无法满足这些需求，妥协了多种意图解码的多样性或可穿戴性。为了解决这一问题，我们开发了一种新型的多重意图解码人机界面（MI-DHMI），该界面将高通量表面肌电图（sEMG）传感器与手部和前臂安装的惯性测量单元（IMUs）相结合。所开发的界面支持一个统一框架，以实现多重意图的同时解码。通过采用多模态深度学习和低噪声设计的硬件，解码框架选择性地关注与手指运动真正相关的sEMG成分。这有效减少了在不受约束的上肢运动中，由于sEMG变异性引起的解码错误，从而显著增强了鲁棒性。即使在不受约束的手腕和前臂运动下，该界面的手势识别准确率超过97%，抓握力估计的$R^2 = 0.95$，并且手势解码与实际手势一致，优于基线设备和算法。消融研究进一步验证了所提解码框架的有效性。最后，进行了两项在线实验以验证该设备，展示了其在高稳定性任务中的优越表现，包括倒水任务和物体抓取。所开发的界面提供了一种全新的可穿戴多重意图解码系统解决方案，为普遍遥操作提供有效支持，并推动人机交互研究的进展。
cs.RO / 71 / 2609.07497
Functional-SLAM: Interaction-Aware Mapping with Online Functional Scene Graphs
功能性SLAM：基于在线功能场景图的交互感知映射
Abstract
Existing SLAM systems lack modeling of the functional relations required for fine-grained robotic interaction. Functional 3D scene graphs can represent relations between objects and interaction elements, but existing methods rely on offline reconstruction, making them inadequate for real-time interaction in real-world exploration. To address this limitation, we propose Functional-SLAM, the first framework that continuously and recursively maintains a functional scene graph as an online SLAM state. The framework combines anchor-keyframe geometry with functional-context constraints for persistent node maintenance, accumulates multi-frame evidence through temporal relations to commit stable functional edges, and supplements visual loop-closure candidates with functional topology in scenes with repetitive appearance or degraded texture. Experiments show that Functional-SLAM efficiently constructs stable functional maps online, substantially improving runtime over offline methods while maintaining highly competitive accuracy. Compared with peer SLAM systems, it further improves pose estimation accuracy through functional-topology-assisted loop closure. The code is publicly available at https://github.com/Hbelief1998/Functional-SLAM-CoRL_2026.
Chinese Translation
现有的SLAM系统缺乏对细粒度机器人交互所需的功能关系的建模。功能性三维场景图可以表示对象与交互元素之间的关系，但现有方法依赖于离线重建，无法满足现实世界探索中的实时交互需求。为了解决这一限制，我们提出了功能性SLAM，这是第一个持续且递归地维护功能场景图作为在线SLAM状态的框架。该框架结合了锚点关键帧几何与功能上下文约束，以实现持久节点维护，通过时间关系累积多帧证据以建立稳定的功能边，并在具有重复外观或退化纹理的场景中补充视觉回环闭合候选项与功能拓扑。实验表明，功能性SLAM能够高效地在线构建稳定的功能地图，显著提高了运行时性能，相较于离线方法在保持高度竞争的准确性方面表现出色。与同类SLAM系统相比，它通过功能拓扑辅助的回环闭合进一步提高了位姿估计的准确性。代码已公开发布在 https://github.com/Hbelief1998/Functional-SLAM-CoRL_2026。
cs.RO / 72 / 2609.07498
CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements
CosmoH2G：一个手到夹具的转移数据集及其在复杂空间运动下物体操控的基准方法
Abstract
Transferring human hand demonstrations to robotic grippers has recently emerged as a cost-effective solution for robot learning. However, existing methods are largely confined to simple, planar tasks and fail to handle complex spatial movements (e.g., intricate trajectories involving rotations or flips) that are essential for robot manipulation. Motivated by this gap, we adopt an implicit, data-driven approach guided by fine-grained hand-pose motions. To this end, we introduce a scalable acquisition pipeline to collect hand-gripper paired demonstrations, governed by a rigorous protocol that prioritizes motion complexity and leverages a handheld gripper for seamless action mimicry. This yields a large-scale paired dataset comprising 6,189 episodes across 1,254 unique objects, exhibiting significantly higher spatial complexity than existing benchmarks. However, learning such complex mappings remains challenging. We observe that naive end-to-end generation of full gripper pose sequences is insufficient, as minor trajectory deviations compound rapidly under intricate dynamics. To address this, we propose a two-stage framework: Stage I predicts sparse gripper keyframes (initial and terminal) to simplify the mapping objective, while Stage II generates the full continuous action sequence conditioned on these keyframes. Furthermore, to mitigate cumulative drift, we keep the gripper's orientation being learned while post-optimizing its translation based on the grasping heuristic and kinematic consistency. In both simulation and real-robot experiments, our framework enables stable and precise hand-to-gripper transfer of complex spatial manipulations, significantly outperforming traditional baselines. Project page: https://cosmoh2g.github.io.
Chinese Translation
将人类手部演示转移到机器人夹具上，最近成为了一种成本效益高的机器人学习解决方案。然而，现有方法主要局限于简单的平面任务，无法处理复杂的空间运动（例如，涉及旋转或翻转的复杂轨迹），而这些运动对于机器人操控至关重要。基于这一空白，我们采用了一种隐式的数据驱动方法，受细粒度手部姿态运动的指导。为此，我们引入了一个可扩展的获取管道，以收集手部与夹具配对的演示，遵循严格的协议，优先考虑运动复杂性，并利用手持夹具实现无缝的动作模仿。这产生了一个大规模的配对数据集，包含6,189个实验，涵盖1,254个独特对象，展现出显著高于现有基准的空间复杂性。然而，学习如此复杂的映射仍然具有挑战性。我们观察到，简单的端到端生成完整夹具姿态序列是不够的，因为在复杂动态下，微小的轨迹偏差会迅速累积。为了解决这一问题，我们提出了一个两阶段框架：第一阶段预测稀疏的夹具关键帧（初始和终止），以简化映射目标，而第二阶段则基于这些关键帧生成完整的连续动作序列。此外，为了减轻累积漂移，我们在后优化夹具的平移时保持夹具的方向学习，同时基于抓取启发式和运动学一致性进行优化。在仿真和真实机器人实验中，我们的框架实现了复杂空间操控的稳定和精确的手到夹具转移，显著优于传统基准。项目页面：https://cosmoh2g.github.io。
cs.RO / 73 / 2609.07511
Generation of Vectorized Maps Beyond Vehicle View
超越车辆视野的矢量化地图生成
Abstract
Autonomous driving relies on High Definition (HD) maps for safe navigation. Traditional HD maps construction is costly in hardware, data and human resources, which together with its update limitations hinders scalability. Recent works have proposed online alternatives for HD vectorized mapping from onboard sensors. However, sensor field of view is limited, and the range of the reconstructed maps ahead of the vehicle is insufficient for safe planning. This paper aims to address this limitation by proposing the novel beyond-view vectorized map generation problem: given vectorized maps of the area sensed by the vehicle (in-view), to generate plausible map continuations. To experimentally assess its feasibility, we propose BeyondFormer, which, to the best of out knowledge, is the first work designed towards beyond-view map generation. Given the novelty of the problem, we generate the first dataset specifically designed for it and evaluate the proposed approach. The results demonstrate consistent performance across diverse scenarios, establishing learning-based methods as a promising direction for map forecasting in autonomous driving. Beyond demonstrating the feasibility of the task, we provide an extensive discussion of the method's limitations and identify key future research directions for scaling it to more complex driving conditions. Code is available at https://git-autopia.car.upm-csic.es/beyondformer.
Chinese Translation
自主驾驶依赖于高清（HD）地图以实现安全导航。传统的高清地图构建在硬件、数据和人力资源上成本高昂，加之其更新限制，阻碍了可扩展性。近期的研究提出了基于车载传感器的在线高清矢量化地图替代方案。然而，传感器的视场有限，重建的地图在车辆前方的范围不足以支持安全规划。本文旨在通过提出新颖的超越视野矢量化地图生成问题来解决这一限制：给定车辆感知区域的矢量化地图（视野内），生成合理的地图延续。为了实验性地评估其可行性，我们提出了BeyondFormer，尽我们所知，这是首个针对超越视野地图生成设计的工作。鉴于该问题的新颖性，我们生成了首个专门为此设计的数据集，并评估了所提方法。结果表明，在多种场景下表现出一致的性能，确立了基于学习的方法作为自主驾驶地图预测的有前景方向。除了展示任务的可行性外，我们还对该方法的局限性进行了广泛讨论，并确定了将其扩展到更复杂驾驶条件的关键未来研究方向。代码可在 https://git-autopia.car.upm-csic.es/beyondformer 获取。
cs.RO / 74 / 2609.07516
P$^2$Calib: Utilizing Pattern Priors for LiDAR-Camera Extrinsic Calibration
P$^2$Calib：利用模式先验进行激光雷达-相机外部标定
Abstract
Target-based LiDAR-camera extrinsic calibration is a prerequisite for multi-sensor fusion in robotics. However, in the widely adopted four-hole pipeline, calibration accuracy is bottlenecked by LiDAR-side hole-center extraction, which suffers from sparse angular coverage and mixed-pixel corruption. This paper presents P$^2$Calib, which exploits pattern priors, geometric constraints specified by the CAD model of the target board, to improve calibration accuracy. First, we incorporate the known hole radius as a fitting constraint to prevent center estimates from degrading under sparse angular coverage. Building on the improved hole estimates, we further enforce the rigid rectangular layout of the four holes as a global consistency constraint to correct residual errors across holes. Both priors are integrated into an interactive calibration tool that provides a complete extrinsic calibration pipeline. Experiments on simulated and real datasets show that P$^2$Calib lowers the joint registration residual by 90\% and 82\% and the held-out reprojection error by 96\% and 77\% over the baseline. Code, https://github.com/JokerJohn/P2Calib.git, and data will be released to facilitate future research.
Chinese Translation
基于目标的激光雷达-相机外部标定是机器人多传感器融合的前提。然而，在广泛采用的四孔标定流程中，标定精度受到激光雷达侧孔中心提取的瓶颈限制，这一过程受到稀疏角度覆盖和混合像素干扰的影响。本文提出了P$^2$Calib，该方法利用模式先验，即由目标板的CAD模型指定的几何约束，以提高标定精度。首先，我们将已知的孔半径作为拟合约束，以防止在稀疏角度覆盖下中心估计的退化。在改进的孔估计基础上，我们进一步强制四个孔的刚性矩形布局作为全局一致性约束，以纠正孔之间的残余误差。这两种先验被整合到一个交互式标定工具中，提供完整的外部标定流程。在模拟和真实数据集上的实验表明，P$^2$Calib将联合配准残差降低了90%和82%，并将保留重投影误差降低了96%和77%，相较于基线方法。代码（https://github.com/JokerJohn/P2Calib.git）和数据将被发布，以促进未来的研究。
cs.RO / 75 / 2609.07532
PhysReal: Learning Real-World Deformable Object Physics via Hybrid Constitutive Modeling
PhysReal：通过混合本构建模学习真实世界可变形物体物理
Abstract
Learning physically plausible dynamics from visual observations is essential for interactive world models and embodied agents. However, modeling real-world deformable objects remains challenging because their dynamics often arise from complex, spatially heterogeneous material responses. To address this challenge, we propose PhysReal, a video-driven framework for learning and simulating the underlying physics of real deformable objects. PhysReal integrates a spatially varying hybrid expert-neural constitutive model with a differentiable MPM simulator and 3DGS renderer. Analytical expert models provide interpretable physical priors, while neural constitutive residuals capture material responses beyond predefined formulations. Spatially distributed patches parameterize the constitutive field, enabling a continuous representation of local material variations. To organize the identification of this model from sparse visual observations, we adopt a progressive curriculum that sequentially optimizes global material properties, spatially varying local parameters, and neural constitutive residuals, together with complementary motion and mask supervision. Extensive experiments on diverse deformable-object interactions demonstrate that PhysReal achieves superior performance in dynamic reconstruction and future-state prediction, while showing strong potential for downstream robotic applications.
Chinese Translation
从视觉观察中学习物理上合理的动态对于互动世界模型和具身代理至关重要。然而，建模真实世界的可变形物体仍然具有挑战性，因为它们的动态通常源于复杂的、空间异质的材料响应。为了解决这一挑战，我们提出了PhysReal，一个基于视频的框架，用于学习和模拟真实可变形物体的基础物理。PhysReal将空间变化的混合专家-神经本构模型与可微分的MPM（Material Point Method）模拟器和3DGS（3D Graphics Simulation）渲染器相结合。分析性专家模型提供可解释的物理先验，而神经本构残差则捕捉超出预定义公式的材料响应。空间分布的补丁参数化本构场，使局部材料变化的连续表示成为可能。为了组织从稀疏视觉观察中识别该模型，我们采用了渐进式课程，依次优化全局材料属性、空间变化的局部参数和神经本构残差，并结合补充的运动和掩膜监督。在多种可变形物体交互的广泛实验中，PhysReal在动态重建和未来状态预测方面表现出色，同时显示出在下游机器人应用中的强大潜力。
cs.RO / 76 / 2609.07534
Zero-Shot Sim-to-Real Contact-Rich Assembly via Proprioception-Anchored Cross-Modal Pretraining
基于本体感知锚定的跨模态预训练实现零样本仿真到真实接触丰富的组装
Abstract
Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sustained contact. Although simulation-based reinforcement learning offers a scalable training paradigm, discrepancies in visual observations, contact dynamics, and force/torque (F/T) measurements often limit policy transfer. We observe that proprioception is comparatively consistent across domains because calibrated joint positions and consistently computed joint velocities align closely between simulation and hardware. Based on this observation, we present PACE (Proprioception-Anchored Cross-Modal Encoder), which supervises temporal visual and F/T representations by predicting proprioceptive state transitions. Static domain-specific factors, including lighting, texture, and sensor bias, contain little information about joint motion; the proposed objective therefore encourages the encoder to suppress these factors while retaining task-relevant motion cues. Policies trained on frozen PACE features are deployed on hardware without real-world fine-tuning or object-pose tracking. Across four contact-rich assembly tasks, PACE attains an average real-world success rate of 93.3\% and only a 2.7-percentage-point sim-to-real drop, meanwhile remaining robust to perturbations that substantially degrade pose-based and learned-fusion baselines.
Chinese Translation
接触丰富的组装仍然具有挑战性，因为它需要亚毫米级的空间精度和在持续接触过程中对力的可靠解释。尽管基于仿真的强化学习提供了一种可扩展的训练范式，但视觉观察、接触动态以及力/扭矩（F/T）测量之间的差异常常限制了策略的迁移。我们观察到，本体感知在不同领域中相对一致，因为经过校准的关节位置和一致计算的关节速度在仿真和硬件之间紧密对齐。基于这一观察，我们提出了PACE（Proprioception-Anchored Cross-Modal Encoder），该模型通过预测本体感知状态转变来监督时间上的视觉和F/T表示。静态领域特定因素，包括照明、纹理和传感器偏差，几乎不包含有关关节运动的信息；因此，所提出的目标鼓励编码器抑制这些因素，同时保留与任务相关的运动线索。在冻结的PACE特征上训练的策略可以在硬件上部署，而无需进行真实世界的微调或物体姿态跟踪。在四个接触丰富的组装任务中，PACE实现了93.3%的平均真实世界成功率，仅有2.7个百分点的仿真到真实的下降，同时对显著降低基于姿态和学习融合基线的扰动保持稳健。
cs.RO / 77 / 2609.07544
Anti-Gravity Walking by a Flying Humanoid Robot via Thrust-Rate Input Whole-Body Model Predictive Control
通过推力速率输入的全身模型预测控制实现飞行类人机器人反重力行走
Abstract
Flying humanoids are expected to perform tasks in diverse environments, while their existing locomotion is mainly limited to aerial flight and ground walking. The capability to move in complex three-dimensional space can greatly expand their application range. For such walking motion on ceilings and similar anti-gravity environments, whole-body MPC is effective. However, the discontinuous changes in dynamic structure accompanying contact switching during walking can induce thrust spikes, resulting in control instability. Therefore, in this work, we propose and implement a real-time whole-body MPC framework for anti-gravity bipedal walking. First, we formulate whole-body MPC using the time derivative of thrust, namely thrust-rate, as the control input. This formulation guarantees continuity of the thrust trajectory during contact switching while preserving the sparse structure of the optimal control problem for fast computation. Second, we address the lack of natural support forces in anti-gravity environments. We introduce lower bounds on the foot-normal component of the contact force, and smoothly transfer them during the doublesupport phase. Finally, we implement the proposed framework and demonstrate anti-gravity walking by a flying humanoid through simulation and a hardware experiment. To the best of our knowledge, this is the first demonstration of multi-contact whole-body MPC for a transformable aerial robot and walking by a flying humanoid beyond the ground.
Chinese Translation
飞行类人机器人被期望在多样化的环境中执行任务，而它们现有的运动方式主要限于空中飞行和地面行走。在复杂的三维空间中移动的能力可以大大扩展它们的应用范围。对于在天花板和类似反重力环境中的行走运动，全身模型预测控制（MPC）是有效的。然而，行走过程中接触切换伴随的动态结构的不连续变化可能导致推力尖峰，从而导致控制不稳定。因此，在本研究中，我们提出并实现了一种用于反重力双足行走的实时全身MPC框架。首先，我们使用推力的时间导数，即推力速率，作为控制输入来构建全身MPC。这种构建方式保证了在接触切换期间推力轨迹的连续性，同时保持了最优控制问题的稀疏结构，以便快速计算。其次，我们解决了反重力环境中缺乏自然支撑力的问题。我们对接触力的脚法向分量引入了下限，并在双支撑阶段平滑地转移它们。最后，我们实现了所提出的框架，并通过仿真和硬件实验展示了飞行类人机器人的反重力行走。据我们所知，这是首次展示可变形空中机器人和飞行类人机器人在地面之外的多接触全身MPC行走。
cs.RO / 78 / 2609.07581
ICI-VLA: In-Context Imitation with Spatiotemporally Aligned Demonstrations for Vision-Language-Action Models
ICI-VLA：基于时空对齐示范的上下文模仿用于视觉-语言-动作模型
Abstract
Vision-Language-Action (VLA) policies are commonly adapted to new manipulation settings through additional gradient updates, which limits rapid deployment when task-specific data or compute is scarce. We present ICI-VLA, a training and retrieval framework that equips a text-action VLM with few-shot test-time adaptation through in-context demonstrations. Unlike mainstream VLA designs based on action-specific multimodal fusion, ICI-VLA retains the native text-generation interface. ICI-VLA updates its parameters only during offline training; at inference, the policy remains fixed and conditions action generation on retrieved micro-demonstrations. The framework decomposes long trajectories into short, semantically labeled examples and trains an RD-Encoder with positives mined by Dynamic Time Warping (DTW), aligning the retrieved context with the phase and geometry of the current subtask. We further introduce Target Action Masking, a context-corruption objective designed to reduce direct action copying and increase reliance on the current observation. ICI-VLA reaches average success rates of 97.7% on LIBERO and 60.4% on RoboTwin 2.0, exceeding the highest reported baseline average on RoboTwin 2.0 by 19.3 percentage points. It also achieves 83.2% across four physical tasks. These results indicate that a fixed VLA policy can benefit from conditioning on spatiotemporally aligned demonstrations at test time.
Chinese Translation
视觉-语言-动作（VLA）策略通常通过额外的梯度更新适应新的操作设置，这在任务特定数据或计算资源稀缺时限制了快速部署。我们提出了ICI-VLA，这是一种训练和检索框架，能够通过上下文示范为文本-动作视觉语言模型（VLM）提供少量样本的测试时适应。与基于动作特定多模态融合的主流VLA设计不同，ICI-VLA保留了原生的文本生成接口。ICI-VLA仅在离线训练期间更新其参数；在推理时，策略保持固定，并根据检索到的微示范条件生成动作。该框架将长轨迹分解为短的、语义标记的示例，并通过动态时间规整（Dynamic Time Warping, DTW）挖掘的正样本训练一个RD编码器，将检索到的上下文与当前子任务的阶段和几何形状对齐。我们进一步引入目标动作掩蔽（Target Action Masking），这是一种上下文破坏目标，旨在减少直接动作复制并增加对当前观察的依赖。ICI-VLA在LIBERO上的平均成功率达到97.7%，在RoboTwin 2.0上的成功率为60.4%，超过了RoboTwin 2.0上报告的最高基线平均值19.3个百分点。它在四个物理任务上的成功率也达到了83.2%。这些结果表明，固定的VLA策略可以通过在测试时依赖时空对齐的示范获益。
cs.RO / 79 / 2609.07747
Dex-X: Learning Visual-Tactile Dexterous Manipulation From Human Videos with Simulated Interaction
Dex-X：从人类视频中学习视觉-触觉灵巧操作的模拟交互
Abstract
Human videos are an abundant source of dexterous manipulation behaviors, but they lack tactile information that is crucial for contact-rich interaction. This raises a fundamental question: can robots learn deployable visual-tactile dexterous manipulation policies from human video demonstrations without robot-side data collection? We present DEX-X, a framework for learning visual-tactile dexterous manipulation from human videos through simulation. Our key insight is that simulation can serve as a tactile completion engine. Given monocular human demonstrations, DEX-X reconstructs hand-object interactions in simulation, where physically grounded contact dynamics provide tactile supervision unavailable in the original videos. Leveraging this recovered tactile information, we train visual-tactile dexterous manipulation policies and distill them into deployable policies operating on point-cloud observations and tactile sensing. We demonstrate zero-shot sim-to-real transfer on a dexterous hand-arm platform across diverse grasping and contact-rich tool-use tasks. The teacher policy achieves 65.9% average success across six task categories in simulation, while the distilled visual-tactile policy achieves 93% success on real-world cube picking and 53% on the challenging table-cleaning task. Zero-shot generalization to unseen object geometries is also observed on object-picking tasks. Our results suggest that simulated interaction is a key bridge between human videos and deployable dexterous manipulation policies, providing the missing physical supervision needed for scalable robot skill learning from Internet-scale human video data.
Chinese Translation
人类视频是灵巧操作行为的丰富来源，但它们缺乏对于接触丰富的交互至关重要的触觉信息。这引出了一个基本问题：机器人能否从人类视频演示中学习可部署的视觉-触觉灵巧操作策略，而无需进行机器人端的数据收集？我们提出了DEX-X，一个通过模拟从人类视频中学习视觉-触觉灵巧操作的框架。我们的关键见解是，模拟可以作为触觉补全引擎。在单目人类演示的基础上，DEX-X 在模拟中重建手-物体交互，其中物理基础的接触动态提供了在原始视频中无法获得的触觉监督。利用这种恢复的触觉信息，我们训练了视觉-触觉灵巧操作策略，并将其提炼为在点云观测和触觉传感上运行的可部署策略。我们在一个灵巧手臂平台上展示了零样本的模拟到现实转移，涵盖了多种抓取和接触丰富的工具使用任务。教师策略在模拟中实现了六个任务类别平均65.9%的成功率，而提炼的视觉-触觉策略在现实世界的立方体抓取任务中实现了93%的成功率，在具有挑战性的桌面清洁任务中实现了53%的成功率。在物体抓取任务中，我们还观察到了对未见物体几何形状的零样本泛化。我们的结果表明，模拟交互是人类视频与可部署的灵巧操作策略之间的关键桥梁，提供了从互联网规模的人类视频数据中进行可扩展机器人技能学习所需的缺失物理监督。
cs.RO / 80 / 2609.07838
ComVLA: Communication-Aware Split Inference for VLA Models in 6G-Connected Robotics
ComVLA：面向6G连接机器人中VLA模型的通信感知分离推理
Abstract
Connected robotics is an emerging 6G application where mobile robots follow natural-language instructions to manipulate physical objects. The Vision-Language-Action (VLA) models that enable this are too large to run on the robot; a common trend is to offload inference to the cloud. The wireless link, however, limits how much sensing data the edge can transmit per control step. Two recent lines address this constraint: semantic communication codecs compress sensor data but require channel-specific retraining, and VLA token pruners select tokens from image but ignore the channel. Our insight is that the dense semantic information contained in the language already indicates which visual tokens matter. We propose ComVLA, a framework that uses this language guidance to adapt the VLA token budget to the channel capacity. Transmitting 32 tokens instead of 512 on the LIBERO benchmark, ComVLA cuts inference compute by 74% and inference latency by 22% versus the original OpenVLA-OFT baseline, at a cost of 1.5 pp in average task success (95.4% vs. 96.9%), and it stays within the capacity budget under Rayleigh and Rician fading. These results demonstrate that co-designing VLA inference and wireless communication is a practical direction for 6G-connected robotics.
Chinese Translation
连接机器人是一个新兴的6G应用，其中移动机器人根据自然语言指令操作物理对象。支持这一功能的视觉-语言-动作（VLA）模型过于庞大，无法在机器人上运行；因此，常见的做法是将推理卸载到云端。然而，无线链路限制了边缘每个控制步骤可以传输的传感数据量。最近有两条研究方向解决了这一限制：语义通信编解码器压缩传感器数据，但需要针对特定信道的重新训练；而VLA令牌剪枝器从图像中选择令牌，但忽略了信道。我们的见解是，语言中包含的密集语义信息已经指示了哪些视觉令牌是重要的。我们提出了ComVLA，一个利用这种语言指导将VLA令牌预算调整到信道容量的框架。在LIBERO基准测试中，ComVLA传输32个令牌而不是512个，与原始的OpenVLA-OFT基线相比，推理计算减少了74%，推理延迟减少了22%，平均任务成功率下降1.5个百分点（95.4%对比96.9%），并且在瑞利和瑞士衰落下仍保持在容量预算内。这些结果表明，协同设计VLA推理和无线通信是6G连接机器人领域的一个实际方向。
cs.RO / 81 / 2609.07857
Scene Graph-Driven Haptic Feedback for Safety Enhancement in Robotic Ophthalmic Surgery via Physically Simulated iOCT
基于场景图的触觉反馈在机器人眼科手术中的安全性增强：通过物理模拟的术中光学相干断层扫描
Abstract
Robotic ophthalmic surgery offers high precision but introduces a "sensory gap" by decoupling the surgeon from their instrument, resulting in a loss of tactile feedback. This paper presents a novel haptic feedback system for subretinal injection tasks leveraging Scene Graphs (SG). The system bridges the sensory gap by analyzing a physically simulated intraoperative Optical Coherence Tomography (iOCT) feed to construct a real-time surgical SG. The SG serves as a semantic abstraction layer for the surgical scene, which is then utilized by a deterministic, rule-based engine to generate state-dependent haptic feedback on a robotic input device. The system was evaluated in a user study (N=16) using an anthropomorphic head phantom and a custom-built surgical robot. Results demonstrate that the SG-driven haptic feedback improved surgical precision, reducing needle alignment error by 14% (p = 0.044) and improving System Usability Scale (SUS) scores by 8% (p = 0.015), while maintaining comparable task completion times. A needle trajectory analysis revealed the emergence of a safer "Align-then-Approach" strategy, in which our haptic negative reinforcement prompted users to fine-tune the tool's trajectory before approaching the retinal target. This work suggests that SGs can effectively serve as the direct computational foundation for real-time, safety-enhancing context-aware haptic feedback in robotic microsurgery.
Chinese Translation
机器人眼科手术提供了高精度，但由于将外科医生与其工具解耦，导致了“感官差距”，从而失去了触觉反馈。本文提出了一种新颖的触觉反馈系统，用于视网膜下注射任务，利用场景图（Scene Graphs, SG）。该系统通过分析物理模拟的术中光学相干断层扫描（intraoperative Optical Coherence Tomography, iOCT）数据，构建实时外科手术场景的SG，从而弥补感官差距。SG作为手术场景的语义抽象层，随后被一个确定性、基于规则的引擎利用，以在机器人输入设备上生成状态依赖的触觉反馈。该系统在一项用户研究中进行了评估（N=16），使用了一个类人头假体和一个定制的手术机器人。结果表明，基于SG的触觉反馈提高了手术精度，减少了14%的针头对齐误差（p = 0.044），并将系统可用性评分（System Usability Scale, SUS）提高了8%（p = 0.015），同时保持了相似的任务完成时间。针头轨迹分析显示了一种更安全的“先对齐后接近”策略的出现，其中我们的触觉负强化促使用户在接近视网膜目标之前微调工具的轨迹。这项工作表明，SG可以有效地作为实时、安全增强的上下文感知触觉反馈在机器人显微手术中的直接计算基础。
cs.RO / 82 / 2609.07859
M3-Tele: A Unified Multimodal Teleoperational Framework for Compliant Whole-Body Mobile Manipulation
M3-Tele：一个统一的多模态远程操作框架用于合规的全身移动操作
Abstract
Executing contact-rich tasks efficiently requires the seamless integration of whole-body coordination and physical compliance regulation. However, existing teleoperation and data-collection frameworks often overlook the joint consideration of multimodal perception and coordinated whole-body operation. This limitation can reduce the efficiency and quality of demonstration collection, thereby affecting the effectiveness of downstream policy learning. In this work, we present \textbf{M3-Tele}: A Unified \underline{M}ultimodal \underline{Tele}operational Framework for Compliant Whole-Body \underline{M}obile \underline{M}anipulation, enabling stable physical interaction and capturing aligned visual, tactile, force, and proprioceptive observations during task execution. Extensive experiments demonstrate that the proposed framework significantly improves contact-rich teleoperation performance. The proposed controller reduces the force tracking error from 4.132~N to 0.346~N, the contact loss from 2.46 to 0.02 events per trial and the tactile deformation error by 65\%. User studies across four mobile manipulation tasks also verify the reliability and usability of the proposed system. Furthermore, Diffusion Policy experiments highlight the value of joint tactile and force sensing.
Chinese Translation
高效执行接触丰富的任务需要全身协调与物理合规调节的无缝集成。然而，现有的远程操作和数据收集框架往往忽视了多模态感知与协调全身操作的联合考虑。这一局限性可能降低演示数据收集的效率和质量，从而影响下游策略学习的有效性。在本研究中，我们提出了 extbf{M3-Tele}：一个统一的 extunderline{多}模态 extunderline{远程}操作框架，用于合规的全身 extunderline{移动} 操作，能够在任务执行过程中实现稳定的物理交互，并捕捉对齐的视觉、触觉、力和本体感知观察。大量实验表明，所提出的框架显著提高了接触丰富的远程操作性能。所提控制器将力跟踪误差从 4.132~N 降低到 0.346~N，每次试验的接触丢失事件从 2.46 降低到 0.02，触觉变形误差减少了 65\%。在四个移动操作任务中的用户研究也验证了所提系统的可靠性和可用性。此外，扩散策略实验强调了联合触觉和力传感的价值。
cs.RO / 83 / 2609.07905
Conditional Timed Partial Orders: An Expressive and Interpretable Framework for Robot Task Specification and Planning
条件定时部分序列：一种用于机器人任务规范和规划的表达性与可解释性框架
Abstract
Timed Partial Orders (TPOs), originally proposed for workflows, provide an interpretable framework for robot task specification with planning algorithms based on mixed-integer linear programming (MILP). However, TPOs are limited in expressivity, capturing only partial-order events with simple timing constraints. In this paper, we introduce Conditional TPOs (cTPOs), which extend TPOs with richer relative-timing constraints and conditional event activations based on environmental conditions. We show that planning for cTPOs also reduces to an MILP problem; however, the added expressivity results in significantly larger MILPs that can become computationally intractable. To address this challenge, we propose a decomposition algorithm that partitions a cTPO into smaller sub-TPOs, yielding a sequence of smaller MILP problems. We prove that this decomposition is complete and preserves plan optimality while improving the interpretability of complex tasks. Experimental results demonstrate the effectiveness of cTPOs as a task specification framework and the efficiency of our decomposition approach, achieving up to four orders of magnitude speedup over the monolithic MILP.
Chinese Translation
定时部分序列（Timed Partial Orders, TPOs）最初是为工作流提出的，它为基于混合整数线性规划（Mixed-Integer Linear Programming, MILP）的机器人任务规范提供了一个可解释的框架。然而，TPOs在表达能力上存在局限，仅能捕捉具有简单时间约束的部分顺序事件。本文提出了条件定时部分序列（Conditional TPOs, cTPOs），它通过丰富的相对时间约束和基于环境条件的条件事件激活来扩展TPOs。我们表明，cTPOs的规划同样可以归结为一个MILP问题；然而，增加的表达能力导致MILP的规模显著增大，可能变得计算上不可处理。为了解决这一挑战，我们提出了一种分解算法，将cTPO分割成更小的子TPO，从而生成一系列较小的MILP问题。我们证明了这种分解是完整的，并且在提高复杂任务可解释性的同时保持了计划的最优性。实验结果表明，cTPOs作为任务规范框架的有效性以及我们分解方法的高效性，相较于单一的MILP实现了多达四个数量级的加速。
cs.RO / 84 / 2609.07930
A Multimodal Label Forecasting Method for Aperiodic Visuo-Motor Time Series
一种用于非周期性视觉-运动时间序列的多模态标签预测方法
Abstract
Deep learning models have been increasingly applied to Time Series Forecasting (TSF) in recent years. Transformer-based and MLP-based models have both been used effectively on many real-world TSF regression benchmarks, and there is ongoing debate as to which family of methods is best. While these benchmarks have drawn much attention, it is also worth noting that many current datasets and methods assume approximate periodicity in the time series. In this work, we focus on a new TSF task without periodicity: anticipating falls during humanoid locomotion, on the basis of egocentric vision and proprioception. When the locomotion trajectories are sufficiently diverse, periodicity is violated. We contribute two new benchmark datasets (one from simulation, one from real hardware), showing that periodicity is violated and recent deep TSF methods struggle on these benchmarks. We also propose a novel deep learning architecture that exploits both endogenous and exogenous variables and a training process that rigorously enforces i.i.d sampling of training examples. Our results show statistically significant improvement over prior art in multiple experimental conditions, by 12.73% or more on the real data and 10.40% or more on the simulation data. Code and datasets will be available upon acceptance.
Chinese Translation
近年来，深度学习模型在时间序列预测（Time Series Forecasting, TSF）中的应用日益增多。基于Transformer和多层感知器（MLP）的模型在许多现实世界的TSF回归基准测试中均取得了有效的应用，目前关于哪种方法家族最佳的争论仍在继续。尽管这些基准测试引起了广泛关注，但值得注意的是，许多当前的数据集和方法假设时间序列具有近似周期性。在本研究中，我们关注一种没有周期性的新的TSF任务：基于自我中心视觉和本体感觉预测人形机器人行走过程中的跌倒。当运动轨迹足够多样化时，周期性被破坏。我们贡献了两个新的基准数据集（一个来自仿真，一个来自真实硬件），显示周期性被破坏，并且最近的深度TSF方法在这些基准测试中表现不佳。我们还提出了一种新颖的深度学习架构，利用内生和外生变量，并采用严格执行独立同分布（i.i.d）训练样本的训练过程。我们的结果在多个实验条件下显示出相较于现有技术的统计显著性改进，真实数据上提高了12.73%或更多，仿真数据上提高了10.40%或更多。代码和数据集将在论文接受后提供。
cs.RO / 85 / 2609.07933
SPOT: Spatial Perception-Oriented Long-Horizon Humanoid Teleoperation
SPOT：面向空间感知的长距离类人远程操作
Abstract
High-quality demonstration data is becoming a central bottleneck for training general-purpose humanoid robots. While recent humanoid teleoperation systems have made substantial progress in retargeting human motion to robot motion, long-horizon loco-manipulation requires another capability: operators must maintain task-relevant spatial awareness over time, e.g., object locations, surrounding environments, the robot's pose. We call the extent of this awareness the operator's perceptual horizon. However, existing methods often shorten this: narrow views miss peripheral events, robot-mounted cameras become unstable during locomotion, and coupled head-view control makes looking around interfere with robot motion. We present SPOT, a Spatial Perception-Oriented VR Teleoperation system for collecting long-horizon humanoid demonstration data by providing extended perceptual horizon. SPOT combines a robot-mounted binocular fisheye camera, a wide-field stereoscopic display, viewpoint-decoupled free-looking, and visual stabilization to provide a robot-centric view that is wide, stable, and actively inspectable. Unlike conventional egocentric interfaces, SPOT decouples visual exploration from robot actuation: the egocentric stereo observation is rendered on a virtual hemisphere around the operator, so natural head rotations change where the operator looks within the wide-field view rather than commanding the robot head, camera, or torso. We evaluate SPOT on perception-critical humanoid data-collection tasks spanning drop recovery, peripheral retrieval, large-workspace bimanual manipulation, fine alignment, and dynamic interaction. SPOT improves efficiency, accuracy, and recovery speed, demonstrating its effectiveness for user-friendly and scalable long-horizon humanoid data collection.
Chinese Translation
高质量的演示数据正成为训练通用类人机器人中心瓶颈。尽管最近的类人远程操作系统在将人类动作重新定向到机器人动作方面取得了显著进展，但长距离的运动操控需要另一种能力：操作员必须在时间上保持与任务相关的空间意识，例如物体位置、周围环境和机器人的姿态。我们将这种意识的范围称为操作员的感知视野。然而，现有的方法往往缩短了这一视野：狭窄的视角错过了周边事件，机器人安装的摄像头在运动过程中变得不稳定，而耦合的头部视角控制使得环顾四周干扰机器人运动。我们提出了SPOT，一个面向空间感知的虚拟现实远程操作系统，通过提供扩展的感知视野来收集长距离类人演示数据。SPOT结合了机器人安装的双目鱼眼摄像头、宽视场立体显示器、视点解耦的自由观察和视觉稳定化，提供了一种宽广、稳定且可主动检查的机器人中心视图。与传统的自我中心接口不同，SPOT将视觉探索与机器人驱动解耦：自我中心的立体观察在操作员周围的虚拟半球上呈现，因此自然的头部旋转改变了操作员在宽视场视图中的观察位置，而不是指挥机器人头部、摄像头或躯干。我们在感知关键的类人数据收集任务上评估了SPOT，这些任务包括跌落恢复、周边物体检索、大工作空间的双手操控、精细对齐和动态交互。SPOT提高了效率、准确性和恢复速度，证明了其在用户友好和可扩展的长距离类人数据收集中的有效性。
cs.RO / 86 / 2609.08010
mjorbit: A Simulation Framework for Space Robotics
mjorbit：一种用于空间机器人模拟的框架
Abstract
This paper presents a general framework for simulating multi-body space robots with contact. We bring efficient, large-scale robot simulation to in-space servicing, assembly, and manufacturing applications. First, we perform an empirical trade study of methods for coupling orbit propagation with existing robotics simulation frameworks. Next, we present mjorbit, a general, flexible, and performant framework built on the MuJoCo engine widely used in robotics, to which we add key spacecraft dynamics, actuators, and sensors. We provide a low-latency C++ CPU backend and a high-throughput GPU backend behind a simple Python API. We demonstrate mjorbit by solving several realistic on-orbit case studies with both model-predictive control and reinforcement learning. Open-source code and examples are available at: https://johnzhang3.github.io/mjorbit/
Chinese Translation
本文提出了一种用于模拟具有接触的多体空间机器人的通用框架。我们将高效的大规模机器人模拟应用于在轨服务、组装和制造等应用场景。首先，我们对将轨道传播与现有机器人模拟框架耦合的方法进行了经验性权衡研究。接下来，我们介绍了mjorbit，这是一个基于广泛用于机器人领域的MuJoCo引擎构建的通用、灵活且高性能的框架，我们在其中添加了关键的航天器动力学、执行器和传感器。我们提供了一个低延迟的C++ CPU后端和一个高吞吐量的GPU后端，并通过一个简单的Python API进行访问。我们通过解决多个现实的在轨案例研究，展示了mjorbit在模型预测控制和强化学习中的应用。开源代码和示例可在以下网址获取：https://johnzhang3.github.io/mjorbit/
cs.RO / 87 / 2609.08123
DISEIL: Demonstration Distillation for Sample-Efficient Imitation Learning
DISEIL：用于样本高效模仿学习的演示蒸馏
Abstract
A robot that can be taught a new task from a handful of demonstrations has to work out for itself what it still cannot do, and then ask for exactly that. Interactive imitation learning takes a step in that direction by letting a policy practice on its own and calling an expert when it goes wrong. Existing methods decide when to interrupt the learner. A further 2 decisions are left to whichever episode happened to trigger the interruption: which failure to correct, and where the demonstration should start. This paper is a first attempt at making both of them deliberately. DISEIL (Demonstration dIstillation for Sample-Efficient Imitation Learning) marks each failed episode at the step where the policy first becomes unreliable, represents that moment with a geometric descriptor, and groups the failures into recurring failure modes. A vision-language model and a language model read the selected mode and write a request for the next demonstration, and a store of task constraints checks that the request can be carried out before any expert time is spent. No model produces a robot action. Across 5 simulated tasks under state and image observations, changing only what the expert is asked for gives the highest mean held-out success rate in all 10 settings, with a tie in 1, and the margin is widest at the smallest budget we tested. The scope is narrow: a single round of practice at a time, in simulation, with experts that are mostly scripted. The longer-term aim is a learner that also tracks what its demonstration set already covers, and that asks a human teacher for the missing behavior in proportion to the effort each request costs them.
Chinese Translation
能够通过少量演示学习新任务的机器人必须自行判断尚未掌握的内容，并准确请求这些内容。交互式模仿学习朝着这一方向迈出了一步，让策略在自身实践中出错时能够呼叫专家。现有方法决定何时中断学习者，但另外两个决策则留给触发中断的特定情节：纠正哪个失败，以及演示应从何处开始。本文首次尝试有意识地做出这两个决策。DISEIL（演示蒸馏用于样本高效模仿学习）在策略首次变得不可靠的步骤标记每个失败的情节，用几何描述符表示该时刻，并将失败分组为重复的失败模式。视觉-语言模型和语言模型读取所选模式并撰写下一次演示的请求，而任务约束库在任何专家时间花费之前检查请求是否可行。没有模型产生机器人动作。在5个模拟任务中，仅通过改变请求专家的内容，在所有10个设置中获得了最高的平均保留成功率，其中1个设置平局，且在我们测试的最小预算下，成功率差距最大。研究范围较窄：每次仅进行一轮实践，处于模拟环境中，且专家大多是脚本化的。长期目标是开发一个学习者，能够追踪其演示集已覆盖的内容，并根据每个请求对人类教师的努力成本的比例，请求缺失的行为。
cs.RO / 88 / 2609.08159
OmniNav: Robust Long-Horizon Target Navigation in Dynamic Environments
OmniNav：动态环境中稳健的长距离目标导航
Abstract
Long-horizon target navigation requires a robot to sustain task execution across evolving observations, decisions, and physical interactions. This requires three coupled capabilities: maintaining valid scene memory, revising target beliefs under partial observability, and selecting interaction-feasible navigation endpoints. However, the state underlying each capability is only conditionally valid: scene representations become stale when objects move or disappear, unsuccessful searches alter beliefs over target locations, and geometrically convenient endpoints may still be infeasible for manipulation. To address these challenges, we present OmniNav, which formulates long-horizon navigation as continual inference over a factorized task state posterior coupling scene validity, target belief, and interaction feasibility. For representation, OmniNav incrementally constructs an updatable 3D object scene memory, preventing stale scene evidence from propagating to subsequent decisions. For exploration, it introduces an evidence-aware Bayesian belief-revision mechanism that derives dependency-aware region priors from semantic context, incorporates unsuccessful searches as negative evidence, and updates them for posterior-guided frontier selection. For interaction, OmniNav incorporates manipulation reachability and collision constraints into navigation-endpoint selection and propagates execution feedback through hierarchical closed-loop recovery. Extensive experiments demonstrate that OmniNav achieves the highest success rates among the compared methods on semantic ObjectNav and fine-grained instance navigation benchmarks, remains robust to target relocation, and improves real-world pick-and-place success from 53.3% to 71.7% over an adapted open-loop baseline. The project page of OmniNav is available at https://omni-nav.github.io/.
Chinese Translation
长距离目标导航要求机器人在不断变化的观察、决策和物理交互中持续执行任务。这需要三种相互关联的能力：保持有效的场景记忆、在部分可观察性下修正目标信念，以及选择可交互的导航终点。然而，每种能力所依赖的状态仅在条件上有效：当物体移动或消失时，场景表示会变得过时；不成功的搜索会改变对目标位置的信念；而几何上方便的终点可能仍然不适合操作。为了解决这些挑战，我们提出了OmniNav，它将长距离导航公式化为对耦合场景有效性、目标信念和交互可行性的分解任务状态后验的持续推理。在表示方面，OmniNav逐步构建一个可更新的3D物体场景记忆，防止过时的场景证据传播到后续决策中。在探索方面，它引入了一种证据感知的贝叶斯信念修正机制，该机制从语义上下文中推导出依赖感知区域先验，将不成功的搜索作为负证据纳入，并更新这些证据以指导后验前沿选择。在交互方面，OmniNav将操作可达性和碰撞约束纳入导航终点选择，并通过分层闭环恢复传播执行反馈。大量实验表明，OmniNav在语义ObjectNav和细粒度实例导航基准中实现了最高的成功率，能够对目标重新定位保持稳健，并将适应性开放环基线的现实世界拾取和放置成功率从53.3%提高到71.7%。OmniNav的项目页面可访问：https://omni-nav.github.io/
cs.RO / 89 / 2609.08164
Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation
双层语义-空间信念映射用于空中物体目标导航
Abstract
Aerial Object Goal Navigation (ObjectNav) requires an unmanned aerial vehicle (UAV) to locate a described target in an unknown outdoor environment using onboard visual observations. Vision-language models (VLMs) can interpret open-ended target descriptions and visual observations, but their frame-level outputs are often noisy, sparse, and spatially transient. We propose AeroBelief, a dual-layer semantic-spatial belief mapping framework that transforms transient VLM observations into persistent spatial guidance. It separates broad contextual plausibility from target-specific evidence: an intuition layer accumulates scene-level semantic cues for exploration, while an evidence layer preserves qualified target-specific observations for approach and confirmation. Evidence-gated fusion combines the two layers into spatial belief hotspots. We further introduce object-conditioned visual reasoning with conservative evidence qualification to improve observation reliability before spatial accumulation. In parallel, egocentric regional guidance converts quadtree coverage into UAV-centered, yaw-aligned directional proposals and stabilizes them through temporal commitment. Its regional scoring is independent of semantic belief values, maintaining exploration pressure and reducing repeated low-gain search. Experiments on the UAV-ON benchmark show that AeroBelief achieves the best reported overall SR, OSR, and SPL among the compared methods, reaching 21.61%, 35.57%, and 10.62, respectively. These results support the effectiveness of persistent semantic-spatial belief, conservative evidence qualification, and temporally stable regional guidance for aerial ObjectNav.
Chinese Translation
空中物体目标导航（ObjectNav）要求无人机（UAV）在未知的户外环境中使用机载视觉观测定位描述的目标。视觉-语言模型（VLMs）能够解释开放式目标描述和视觉观测，但它们的帧级输出通常是嘈杂的、稀疏的，并且空间上是瞬态的。我们提出了AeroBelief，一种双层语义-空间信念映射框架，将瞬态的VLM观测转化为持久的空间指导。它将广泛的上下文合理性与目标特定证据分离：直觉层积累场景级语义线索以供探索，而证据层则保留合格的目标特定观测以供接近和确认。证据门控融合将两个层次结合成空间信念热点。我们进一步引入了基于对象的视觉推理，采用保守的证据资格，以提高观测的可靠性，然后进行空间累积。同时，自我中心区域指导将四叉树覆盖转换为以UAV为中心、偏航对齐的方向提案，并通过时间承诺稳定这些提案。其区域评分独立于语义信念值，保持探索压力并减少重复的低增益搜索。在UAV-ON基准测试中的实验表明，AeroBelief在比较方法中实现了最佳的整体成功率（SR）、开放成功率（OSR）和成功路径长度（SPL），分别达到21.61%、35.57%和10.62。这些结果支持持久的语义-空间信念、保守的证据资格和时间稳定的区域指导在空中ObjectNav中的有效性。
cs.RO / 90 / 2609.08209
Monkey See, Can Monkey Do? A Benchmark for Evaluating Robot Skill Learning by Observation
猴子看到，猴子能做到吗？评估机器人通过观察学习技能的基准测试
Abstract
Learning from Observation (LfO) is a fundamental robotic capability that replicates how humans and animals socially learn from each other. Beyond its biological parallels, this modality provides a practical solution for data scaling in sample-inefficient and data-starved domains like robotics. Recent work has demonstrated promising results in learning manipulation skills from human videos, yet progress in this area remains difficult to assess. Existing methods vary widely in assumptions, hardware choices, and environment setups making it difficult to draw meaningful comparisons and identify advances in the field. To address these challenges, we introduce RoboReel: a unified benchmark for evaluating models that learn policies from human videos. RoboReel consists of bundled real-world human demonstration videos, simulated robot trajectories, and evaluation environments on ten manipulation tasks. We develop four test suites to evaluate the models' performance on multiple axes, including the robustness to visual distractors and the ability to complete long-horizon tasks. Our benchmark covers learning-from-observation models from different categories, and studies the effectiveness of multiple representation choices in our benchmark evaluation that covers over seven state-of-the-art algorithms (including our VLA based variants) in the field of LfO. Finally, we present an analysis of the different types of algorithms showing that long-horizon tasks and tasks with low tolerances are still challenging for current models. Webpage: https://roboreel.github.io
Chinese Translation
观察学习（LfO）是机器人一项基本能力，模仿人类和动物之间的社会学习方式。除了其生物学上的相似性，这种模式为在样本效率低下和数据匮乏的领域（如机器人技术）提供了实用的数据扩展解决方案。近期的研究在从人类视频中学习操作技能方面取得了令人鼓舞的成果，但在这一领域的进展仍然难以评估。现有的方法在假设、硬件选择和环境设置上差异很大，这使得进行有意义的比较和识别该领域的进展变得困难。为了解决这些挑战，我们引入了RoboReel：一个用于评估从人类视频中学习策略的模型的统一基准测试。RoboReel包含捆绑的真实人类演示视频、模拟机器人轨迹和十个操作任务的评估环境。我们开发了四个测试套件，以评估模型在多个维度上的性能，包括对视觉干扰物的鲁棒性和完成长时间任务的能力。我们的基准测试涵盖了来自不同类别的观察学习模型，并研究了我们基准评估中多种表示选择的有效性，涉及超过七种该领域的最先进算法（包括我们的基于VLA的变体）。最后，我们对不同类型的算法进行了分析，显示长时间任务和低容忍度任务仍然对当前模型构成挑战。网页链接：https://roboreel.github.io
cs.RO / 91 / 2609.08214
TacClip: a clip-on sensor measures dynamic contact forces without covering the fingerpads
TacClip：一种夹式传感器可在不覆盖指尖的情况下测量动态接触力
Abstract
TacClip is a minimally encumbering wearable device for recording fingertip deformation caused by contact forces and vibrations. It can be combined with vision- or glove-based hand tracking systems that leave the fingertips uncovered and provides a measure of dynamic contact interactions, while leaving the finger pads exposed so that the user retains natural sensitivity to texture, friction, temperature, and fine surface features. The signal is produced by a Fiber Bragg Grating (FBG) embedded on a small plastic clip mounted over the fingernail. Optionally, for use with vision-based tracking, additional FBGs on polyimide strips can complement camera-based pose estimation. In finger pressing tests, TacClip estimates the force magnitude with typical errors below $0.5~\mathrm{N}$ over a $0$--$8~\mathrm{N}$ range. In tests of cloth handling and tape edge finding, we show that it captures the vibrations and dynamic events generated during exploratory sliding. With no electronics, TacClip can also be used submerged in water, while preserving bare finger contact.
Chinese Translation
TacClip是一种最小化负担的可穿戴设备，用于记录由接触力和振动引起的指尖变形。它可以与不覆盖指尖的视觉或手套基础的手部追踪系统结合使用，提供动态接触交互的测量，同时保持指尖暴露，使用户保持对纹理、摩擦、温度和细微表面特征的自然敏感性。信号由嵌入在小塑料夹上的光纤布拉格光栅（Fiber Bragg Grating, FBG）产生。可选地，为了与基于视觉的追踪配合使用，附加的光纤布拉格光栅可以安装在聚酰亚胺条带上，以补充基于相机的姿态估计。在指压测试中，TacClip在$0$到$8~ ext{N}$的范围内估计力的大小，典型误差低于$0.5~ ext{N}$。在布料处理和胶带边缘检测的测试中，我们展示了它能够捕捉在探索性滑动过程中产生的振动和动态事件。TacClip没有电子元件，因此也可以在水中使用，同时保持裸指接触。
cs.RO / 92 / 2609.08220
Bridging Language and Physics: Automated Design of Continuum Robots with Large Language Models
连接语言与物理：利用大型语言模型自动设计连续机器人
Abstract
Large language models (LLMs) have recently emerged as a promising tool for automating robot design from high-level specifications, yet they remain ineffective for robots operating under complex physical interactions. This limitation stems from the gap between language-based reasoning and the physical consequences of embodiment, often resulting in designs with low physical validity. In this work, we propose a multi-layered framework, AID-SR, that establishes a closed loop by translating simulator-observed physical states into structured feedback for the LLM designer. Combined with semantic critique, human feedback, and iterative refinement, the framework promotes the generation of physically feasible and functionally meaningful robot designs. We evaluate our approach on tendon-driven continuum robots across a benchmark of 14 tasks spanning reaching, grasping, locomotion, and manipulation. The proposed framework achieves 96.2% rate for passing the simulation feasibility check and by applying a common reinforcement learning training, 26.7% robots can successfully fulfill the corresponding task. We then fabricate three designed robots of AID-SR that successfully complete the task in real-world. These extensive experiments across simulation and real-world environments demonstrate and break the wall of utilizing the LLMs for automated design of continuum robots. The source code and experimental resources are publicly available at https://github.com/UNITES-Lab/AID-SR.
Chinese Translation
大型语言模型（LLMs）最近作为一种有前景的工具出现，用于从高层次规范自动化机器人设计，但在复杂物理交互下的机器人操作中仍然效果不佳。这一局限性源于基于语言的推理与具身物理后果之间的差距，常常导致设计的物理有效性低下。在本研究中，我们提出了一个多层次框架AID-SR，通过将模拟器观察到的物理状态转化为结构化反馈，为LLM设计者建立一个闭环。结合语义批评、人类反馈和迭代优化，该框架促进了物理上可行且功能上有意义的机器人设计的生成。我们在14个任务的基准测试中评估了我们的方法，这些任务涵盖了到达、抓取、运动和操作。所提框架在通过模拟可行性检查的成功率达到96.2%，并通过应用常见的强化学习训练，26.7%的机器人能够成功完成相应任务。随后，我们制造了三款AID-SR设计的机器人，它们在现实世界中成功完成了任务。这些在模拟和现实环境中的广泛实验展示了利用LLMs进行连续机器人自动设计的潜力，打破了这一领域的壁垒。源代码和实验资源可在https://github.com/UNITES-Lab/AID-SR上公开获取。
cs.RO / 93 / 2609.08224
3DWay: Generalizing Robot Manipulation via 3D Consistent Waypoints
3DWay：通过3D一致性路径点实现机器人操作的泛化
Abstract
Intermediate representations are key to bridging the modality gap between generalizable manipulation policies and large-scale pretrained vision-language models (VLMs). Among these, trajectory-based representations compactly represent motion-relevant cues, yet most existing approaches predict trajectories in 2D image space, resulting in intrinsic 3D ambiguity. Moreover, using 2D trajectories with depth still leaves the free-space waypoints ambiguous, limiting reliable 3D reasoning. To address this, we propose predicting 3D consistent waypoints (3DWay) from multi-view images. By reformulating 3D waypoints prediction as generating multi-view consistent 2D waypoints followed by geometric triangulation, we enable explicit 3D motion specification while preserving the strong priors of pretrained VLMs. The predicted waypoints can guide existing VLA models for better generalization or be directly executed on simple tasks. Extensive experiments show that 3DWay substantially improves 3D spatial grounding and vision-language reasoning, demonstrating strong potential for generalizable robot manipulation. Codes will be released at https://github.com/ziqin-h/3DWay.
Chinese Translation
中间表示是弥合可泛化操作策略与大规模预训练视觉-语言模型（VLMs）之间模态差距的关键。在这些表示中，基于轨迹的表示紧凑地表示了与运动相关的线索，但大多数现有方法在2D图像空间中预测轨迹，导致内在的3D模糊。此外，使用带深度的2D轨迹仍然使得自由空间路径点模糊，限制了可靠的3D推理。为了解决这个问题，我们提出从多视角图像中预测3D一致性路径点（3DWay）。通过将3D路径点预测重新表述为生成多视角一致的2D路径点，然后进行几何三角测量，我们能够明确指定3D运动，同时保留预训练VLMs的强先验。预测的路径点可以指导现有的视觉-语言模型（VLA）以实现更好的泛化，或直接在简单任务上执行。大量实验表明，3DWay显著改善了3D空间定位和视觉-语言推理，展示了其在可泛化机器人操作中的强大潜力。代码将发布在 https://github.com/ziqin-h/3DWay。
cs.RO / 94 / 2609.08250
CALIPER: Clean Scenes Cannot Rank Physical Inference in Pretrained Visual Representations
CALIPER：干净场景无法对预训练视觉表征中的物理推理进行排名
Abstract
How far a pushed object slides depends on its mass and friction, which no single image reveals. Pretrained visual encoders are increasingly used as the perception front end of world models for manipulation, and their physical competence is assessed with perturbation benchmarks and linear probes, almost always in a clean, fixed-camera scene. We show that these assessments cannot distinguish an encoder that infers physics from one that does not. CALIPER (calibrate, then predict) is a direct test: an object of unknown mass and friction is struck twice at known speeds, a third strike is shown only up to the moment of contact, and a linear readout on frozen features must predict how far the object slides. Swapping in another object's calibration clips checks that the evidence is actually used. Across 2,000 simulated episodes and eight representations, from V-JEPA 2 to a randomly initialised ViT and raw pixels, calibration adds +0.50 R^2 and the swap removes it. Yet in the clean scene every representation lands within 0.02 R^2 of the ceiling set by true simulator state, because a fixed camera exposes the object's displacement directly in pixel coordinates. Resampling camera, lighting, and clutter for every clip spreads the same representations across 0.50 R^2; when the readout chooses a push speed for a goal distance, V-JEPA 2 misses by 4 mm and the random ViT by 20 mm, no better than ignoring the object. Linear probes track none of this: a change in frame aggregation moves a probe more than pretraining does, and erasing the probed mass direction from the same representation costs nothing in one scene and 0.35 R^2 in the other. Whether a benchmark can rank models is an empirical property, and we give three checks that establish it.
Chinese Translation
一个被推动物体滑动的距离取决于其质量和摩擦力，而单一图像无法揭示这些信息。预训练视觉编码器越来越多地被用作操作世界模型的感知前端，其物理能力通过扰动基准和线性探测进行评估，几乎总是在干净的固定摄像机场景中进行。我们表明，这些评估无法区分能够推断物理的编码器与无法推断的编码器。CALIPER（校准后预测）是一种直接测试：一个未知质量和摩擦力的物体以已知速度被击打两次，第三次击打仅显示到接触时刻，线性读取在冻结特征上必须预测物体滑动的距离。用另一个物体的校准片段进行替换以检查证据是否被实际使用。在2000个模拟实验和八种表征中，从V-JEPA 2到随机初始化的ViT和原始像素，校准增加了+0.50 R^2，而替换则消除了这一点。然而，在干净场景中，每种表征的结果都在由真实模拟器状态设定的上限内的0.02 R^2，因为固定摄像机直接以像素坐标暴露了物体的位移。对每个片段重新采样摄像机、光照和杂物将相同的表征分布在0.50 R^2上；当读取选择一个推动速度以达到目标距离时，V-JEPA 2的误差为4毫米，而随机ViT的误差为20毫米，表现不比忽略物体更好。线性探测器对此没有跟踪：帧聚合的变化比预训练的影响更大，而在同一表征中抹去探测的质量方向在一个场景中没有成本，在另一个场景中则损失了0.35 R^2。基准是否能够对模型进行排名是一个经验属性，我们提供了三个检查来确立这一点。
cs.RO / 95 / 2609.08280
Seeing is Not Believing: Breaking the Physical-to-Digital Trust Boundary in Robotics
眼见未必为实：打破机器人技术中的物理与数字信任边界
Abstract
In multi-robot collaboration, task handovers rely on downstream verifiers performing remote attestation, which inspects sensor telemetry to ensure a robot's physical behavior strictly matches its assigned task. But can this telemetry be trusted? We show that it often cannot. In this paper, we uncover a severe vulnerability in Robot Operating System (ROS) 2: by modifying a single environment variable, an adversary can execute a pre-built hook to covertly intercept and inject both telemetry and control signals before they are published. Consequently, adversaries can hijack a robot to perform dangerous tasks while spoofing downstream verifiers with synthesized fake telemetry. Worse still, by exploiting the widespread reliance on third-party Docker containers and auxiliary tools, attackers can distribute compromised packages embedded with these malicious hooks to launch such attacks easily. On a physical Franka Emika robotic arm running Secure ROS 2, our attack injects fabricated telemetry in real time with only around 3 ms of jitter, preserving temporal synchronization and hardware integrity while achieving an 87% success rate even against an AI-based detector. We have responsibly disclosed these findings to the ROS 2 development team. We prepared a demo video available at https://youtu.be/ExeiGqUrnhQ.
Chinese Translation
在多机器人协作中，任务交接依赖于下游验证者进行远程证明，检查传感器遥测以确保机器人的物理行为严格符合其分配的任务。但这些遥测数据能否被信任？我们表明，通常情况下是不能的。本文揭示了机器人操作系统（Robot Operating System, ROS）2中的一个严重漏洞：通过修改单个环境变量，攻击者可以执行预构建的钩子，秘密拦截并注入遥测和控制信号，直至它们被发布。因此，攻击者可以劫持机器人执行危险任务，同时用合成的虚假遥测欺骗下游验证者。更糟糕的是，通过利用对第三方Docker容器和辅助工具的广泛依赖，攻击者可以轻松分发嵌入这些恶意钩子的受损软件包以发起此类攻击。在运行安全ROS 2的物理Franka Emika机器人臂上，我们的攻击以约3毫秒的抖动实时注入伪造的遥测，保持时间同步和硬件完整性，并在对抗基于AI的检测器时仍能达到87%的成功率。我们已将这些发现负责任地披露给ROS 2开发团队。我们准备了一个演示视频，链接为 https://youtu.be/ExeiGqUrnhQ。
cs.RO / 96 / 2609.08292
EvoNav-Bench: Benchmarking Lifelong Navigation in Evolving Environments
EvoNav-Bench：在不断演变的环境中评估终身导航
Abstract
Lifelong navigation (LN) requires an embodied agent to solve a sequence of navigation subtasks in the same environment. Since solving each subtask from scratch incurs redundant exploration, an LN agent must consolidate experience from earlier stages and reuse it in later stages, often through persistent scene representations such as scene graphs or visual snapshots. However, existing approaches typically assume a stationary environment, whereas in real-world LN settings, human activities can cause the environment to evolve. With the stationary assumption violated, existing methods may fuse outdated prior observations with new observations, yet current benchmarks cannot reveal this failure mode. In this paper, we present EvoNav-Bench, which extends the GOAT-Bench style LN formulation in the context of evolving environments. Built on the ProcTHOR framework, EvoNav-Bench introduces environment modifications between navigation tasks, making prior experience useful but not fully reliable. This design enables controlled evaluation of how environment evolution affects LN agents that reuse prior scene observations. Using EvoNav-Bench, we benchmark three recent methods that build and reuse scene representations for navigation. We also compare three simple heuristic strategies for handling environment evolution: Frontier-Update, Fail-then-Update, and Stage-Reset. Our results show that existing methods are brittle under environment evolution, while the heuristic strategies enable a controlled analysis of how agents can adapt to scene changes and mitigate their impact.
Chinese Translation
终身导航（LN）要求一个具身代理在同一环境中解决一系列导航子任务。由于从头开始解决每个子任务会导致冗余探索，因此LN代理必须整合早期阶段的经验，并在后期阶段重用这些经验，通常通过持久的场景表示，如场景图或视觉快照。然而，现有的方法通常假设环境是静态的，而在现实世界的LN环境中，人类活动可能导致环境的演变。在静态假设被违反的情况下，现有方法可能会将过时的先前观察与新的观察融合，但当前的基准测试无法揭示这种失败模式。在本文中，我们提出了EvoNav-Bench，它在不断演变的环境背景下扩展了GOAT-Bench风格的LN公式。EvoNav-Bench基于ProcTHOR框架，介绍了在导航任务之间的环境修改，使得先前的经验在某种程度上有用但并不完全可靠。这一设计使得能够控制评估环境演变如何影响重用先前场景观察的LN代理。利用EvoNav-Bench，我们对三种最近的方法进行了基准测试，这些方法构建并重用场景表示进行导航。我们还比较了三种简单的启发式策略来处理环境演变：Frontier-Update、Fail-then-Update和Stage-Reset。我们的结果表明，现有方法在环境演变下表现脆弱，而启发式策略则能够对代理如何适应场景变化并减轻其影响进行控制分析。
cs.RO / 97 / 2609.08338
A Multi-Modal Perception Pipeline for Object Detection and Tracking in Autonomous Racing
用于自主赛车的多模态感知管道：物体检测与跟踪
Abstract
Object detection and tracking are fundamental components of perception systems for autonomous driving. Achieving robust performance under adverse conditions such as limited visibility, sensor noise, and failures remains an open challenge, particularly in autonomous racing, where vehicles operate at very high speeds, experience strong vibrations, and interact under small safety margins. This paper presents a multi-modal late-fusion perception pipeline for object detection and tracking in the autonomous racing domain. The proposed system extends previous work by exploiting all onboard sensors through a late-fusion approach and a dedicated multi-object tracking framework. Independent detections from cameras, LiDARs, and RADARs are combined to provide timely and robust state estimates of surrounding vehicles. The tracking method explicitly compensates for detection delays and embeds in its model prior knowledge of vehicle dynamics and track layout. Experimental evaluation on real-world data across diverse critical scenarios, representative of challenging edge cases also in urban driving, confirms the effectiveness of the proposed pipeline and its suitability to support safe and adaptive planning decisions.
Chinese Translation
物体检测与跟踪是自主驾驶感知系统的基本组成部分。在有限能见度、传感器噪声和故障等不利条件下实现稳健性能仍然是一个开放的挑战，特别是在自主赛车中，车辆以极高的速度运行，经历强烈的振动，并在小安全边际下进行交互。本文提出了一种用于自主赛车领域的多模态后融合感知管道，以实现物体检测与跟踪。所提出的系统通过后融合方法和专门的多目标跟踪框架，利用所有车载传感器，扩展了之前的工作。来自摄像头、激光雷达（LiDAR）和雷达（RADAR）的独立检测结果被结合，以提供周围车辆的及时且稳健的状态估计。跟踪方法明确补偿检测延迟，并在其模型中嵌入了车辆动力学和赛道布局的先验知识。在真实世界数据上进行的实验评估，涵盖了多种关键场景，这些场景代表了城市驾驶中的挑战性边缘案例，证实了所提出管道的有效性及其支持安全和自适应规划决策的适用性。
cs.RO / 98 / 2609.08339
RoboCousin: Build Your Own Simulation Playground for Robust Bimanual Robotic Manipulation
RoboCousin：构建您自己的强健双手机器人操作仿真平台
Abstract
Bimanual manipulation policies require large and diverse training datasets, yet collecting demonstrations on physical robots is expensive and difficult to scale. Simulation can generate data efficiently, but existing pipelines typically operate within closed asset libraries and predefined scenes: adding a newly observed object or environment still requires substantial effort to reconstruct geometry, specify physical and semantic properties, annotate interactions, and integrate the result into executable tasks. We present RoboCousin, an extensible simulation-based data-generation platform that turns user-provided observations into reusable assets, scenes, and expert trajectories for bimanual manipulation. Built on RoboTwin~2.0, RoboCousin converts object images into simulation-ready assets with visual and collision geometry, semantic and physical metadata, and automatically generated grasp-contact candidates. It further constructs digital cousins that vary compatible objects, backgrounds, layouts, and language instructions while preserving task-relevant affordances and spatial relations. The same asset system supports tabletop and room-level scene construction, with collision-aware base control for interaction beyond a fixed workspace. We release RoboCousin-OBD, containing more than 3,000 annotated object instances and 50 background environments, and use RoboCousin to generate over one million expert trajectories across 50 tasks. Simulation and real-robot experiments show that the automatically generated interaction annotations are comparable to curated annotations, generated assets provide effective sim-to-real supervision, and tabletop cousins can improve transfer beyond training on a single reconstructed scene. RoboCousin therefore provides a practical path for expanding both the scale and coverage of synthetic bimanual manipulation data.
Chinese Translation
双手操作策略需要大量多样的训练数据集，但在物理机器人上收集演示既昂贵又难以扩展。仿真可以高效生成数据，但现有的流程通常在封闭的资产库和预定义场景中运行：添加新观察到的物体或环境仍然需要大量努力来重建几何形状、指定物理和语义属性、标注交互，并将结果整合到可执行任务中。我们提出了RoboCousin，这是一个可扩展的基于仿真的数据生成平台，将用户提供的观察转化为可重用的资产、场景和双手操作的专家轨迹。RoboCousin基于RoboTwin~2.0构建，将物体图像转换为具有视觉和碰撞几何、语义和物理元数据以及自动生成的抓取接触候选者的仿真准备资产。它进一步构建数字表亲，变换兼容的物体、背景、布局和语言指令，同时保留与任务相关的可供性和空间关系。相同的资产系统支持桌面和房间级场景构建，具有碰撞感知的基础控制，以便在固定工作区之外进行交互。我们发布了RoboCousin-OBD，包含超过3000个标注物体实例和50个背景环境，并使用RoboCousin生成超过100万个专家轨迹，涵盖50个任务。仿真和真实机器人实验表明，自动生成的交互标注与策划的标注相当，生成的资产提供有效的仿真到现实监督，而桌面表亲可以改善超越单一重建场景的训练转移。因此，RoboCousin为扩展合成双手操作数据的规模和覆盖范围提供了切实可行的路径。
cs.RO / 99 / 2609.08408
Localized Visual Feature Aggregation via Focus Pooling for Visuomotor Policies
通过焦点池化实现的局部视觉特征聚合用于视觉运动策略
Abstract
Focusing on spatially localized, control-relevant visual cues has been shown to improve data efficiency in visuomotor policies by reducing the need to model task-irrelevant visual variation. Existing methods often impose this focus through input preprocessing, such as cropping control- or object-centric regions in RGB images or point-clouds. However, it remains underexplored whether such localized features can be exposed directly from commonly used convolutional neural network (CNN) encoded features. In this paper, we show that intermediate CNN features preserve localized visual context for control, but existing pooling methods fail to aggregate it effectively. We introduce FocusPool, an attention pooling module that selectively aggregates intermediate visual features according to their relevance to the robot's current proprioceptive context. The resulting pooled representation captures task-progressive, control-relevant local information and is used directly for policy learning. Across simulation and real-world experiments, FocusPool improves policy success rates over pooling and explicit local focus methods by 36.2% and 41.2%, with training only 5.8% of encoder parameters.
Chinese Translation
聚焦于空间局部的、与控制相关的视觉线索已被证明可以通过减少建模任务无关视觉变化的需求来提高视觉运动策略的数据效率。现有方法通常通过输入预处理施加这种聚焦，例如在RGB图像或点云中裁剪以控制或对象为中心的区域。然而，尚未充分探讨是否可以直接从常用的卷积神经网络（CNN）编码特征中提取这样的局部特征。在本文中，我们展示了中间CNN特征保留了控制所需的局部视觉上下文，但现有的池化方法未能有效聚合这些特征。我们引入了FocusPool，一个注意力池化模块，根据中间视觉特征与机器人当前本体感知上下文的相关性选择性地聚合这些特征。最终得到的池化表示捕捉了与任务进展相关的控制局部信息，并直接用于策略学习。在仿真和现实世界实验中，FocusPool在策略成功率上比池化和显式局部聚焦方法提高了36.2%和41.2%，而仅训练了5.8%的编码器参数。
cs.RO / 100 / 2609.08409
Coverage Path Planning for Redundant Manipulators using Generalized Spanning Trees
基于广义生成树的冗余机械臂覆盖路径规划
Abstract
Surface coverage with task-redundant manipulators is challenging because each surface point may admit multiple inverse kinematics (IK) solutions, and configuration choices strongly affect motion quality. This paper extends the classical Spanning Tree Coverage (STC) method to redundant manipulators through offline and online Joint Spanning Tree Coverage (JSTC) algorithms. Offline JSTC samples multiple Inverse Kinematics (IK) solutions per grid cell and formulates the problem as a Generalized Minimum Spanning Tree (GMST), selecting one configuration per cell and tracing the resulting tree to obtain a non-revisiting coverage path. Online JSTC incrementally expands and backtracks a spanning tree with feasibility and cost evaluation while handling dynamic grid updates. Simulation results show that offline JSTC reduces computation time, reconfigurations, and joint motion compared to other methods, while online JSTC achieves fast per-step planning in dynamic scenarios.
Chinese Translation
使用任务冗余机械臂进行表面覆盖是具有挑战性的，因为每个表面点可能有多个逆向运动学（IK）解，并且配置选择会显著影响运动质量。本文通过离线和在线关节生成树覆盖（JSTC）算法，将经典的生成树覆盖（STC）方法扩展到冗余机械臂。离线JSTC为每个网格单元采样多个逆向运动学（IK）解，并将问题形式化为广义最小生成树（GMST），在每个单元中选择一个配置，并追踪生成的树以获得不重访的覆盖路径。在线JSTC在处理动态网格更新时，通过可行性和成本评估逐步扩展和回溯生成树。仿真结果表明，与其他方法相比，离线JSTC减少了计算时间、重新配置和关节运动，而在线JSTC在动态场景中实现了快速的逐步规划。
cs.RO / 101 / 2609.08444
Safe Task Planning with Long-Term Graph Memory for Embodied Agents
具有长期图记忆的安全任务规划用于具身智能体
Abstract
Large language models (LLMs) and vision-language models (VLMs) have significantly advanced zero-shot task planning for embodied agents. However, most LLM- and VLM-driven methods struggle to generate safe high-level actions due to a lack of physical risk awareness, particularly under partial observability, where hazards lie outside the immediate field of view. To address this challenge, we propose a novel safe task-planning framework, SafeMem, which constructs and maintains a long-term semantic graph memory of the open and dynamic environment. Based on egocentric observations, the proposed framework incrementally accumulates knowledge about surrounding objects and their relationships with a graph. Then, an LLM-based risk predictor evaluates candidate actions using the graph memory, triggering a conservatism-modulated replanning loop with explanations for detected hazards. Extensive experiments on the IS-Bench benchmark and a real-world robot platform demonstrate that the SafeMem framework substantially improves safe success rates compared to state-of-the-art VLM-driven task planners. Video results are available on our webpage: https://sites.google.com/view/safemem.
Chinese Translation
大型语言模型（LLMs）和视觉-语言模型（VLMs）在具身智能体的零-shot 任务规划方面取得了显著进展。然而，大多数基于LLM和VLM的方法在生成安全的高层次动作时面临挑战，主要是由于缺乏对物理风险的意识，尤其是在部分可观测的情况下，危险往往位于视野之外。为了解决这一挑战，我们提出了一种新颖的安全任务规划框架SafeMem，该框架构建并维护一个开放且动态环境的长期语义图记忆。基于自我中心的观察，所提框架逐步积累关于周围物体及其关系的知识，并以图的形式表示。然后，基于LLM的风险预测器利用图记忆评估候选动作，触发一个保守调节的重新规划循环，并对检测到的危险提供解释。在IS-Bench基准和实际机器人平台上的大量实验表明，SafeMem框架显著提高了安全成功率，相较于最先进的VLM驱动任务规划器。视频结果可在我们的网站上查看：https://sites.google.com/view/safemem。
cs.RO / 102 / 2609.08490
Multi-bounce Drum Roll with Optimized Active Tricks to Leverage Soft Embodiment
优化主动技巧的多次反弹鼓点以利用软体体现
Abstract
This paper presents a soft robotic drummer for accurate and efficient drum rolls. High-frequency drum rolls require the "multi-bounce technique," where a drumstick bounces multiple times with a single stroke. In robotic reproduction of this technique, the body's elasticity is key, while the fine motion during the stroke is also crucial for maximizing the potential of that elasticity. Therefore, we design two tricks: i) Tap-Pull (TP) trick to increase the number of rebounds by adding a pulling motion after impact; and ii) Micro-Pulse (MP) trick to keep the drumming volume by injecting small oscillations during the stroke. Due to the nonlinear complexity of soft embodiment, both tricks are efficiently tuned using Bayesian optimization in a data-driven manner for accomplishing the respective objectives quantified. We evaluated the optimized behaviors with soft and rigid end-effectors. As a result, the soft TP achieved the highest bounce count (12.25 per stroke) with uniform intervals. The soft MP suppressed the volume decay, yielding 6.8-times higher acoustic efficiency compared to the rigid MP. These results indicate that the proposed tricks with the combination of elasticity and optimization can make robots play excellent drum rolls.
Chinese Translation
本文提出了一种软体机器人鼓手，用于准确而高效的鼓点演奏。高频鼓点需要“多次反弹技术”，即鼓棒在一次击打中多次反弹。在机器人重现这一技术时，身体的弹性是关键，而击打过程中的细微动作对于最大化这种弹性的潜力也至关重要。因此，我们设计了两种技巧：i) Tap-Pull (TP) 技巧，通过在冲击后添加拉动动作来增加反弹次数；ii) Micro-Pulse (MP) 技巧，通过在击打过程中注入小幅振荡来保持鼓声音量。由于软体体现的非线性复杂性，这两种技巧均采用数据驱动的贝叶斯优化方法进行高效调优，以实现量化的各自目标。我们使用软性和刚性末端执行器评估了优化后的行为。结果显示，软性 TP 实现了最高的反弹次数（每次击打 12.25 次），且间隔均匀。软性 MP 抑制了音量衰减，与刚性 MP 相比，声学效率提高了 6.8 倍。这些结果表明，结合弹性和优化的技巧可以使机器人演奏出优秀的鼓点。
cs.RO / 103 / 2609.08493
AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstruction
AURORA：基于主动不确定性驱动的手内重定向用于重建
Abstract
Observing objects grasped by a robot hand is challenging due to severe visual occlusions. Although in-hand manipulation can expose hidden surfaces, existing approaches often rely on predefined or open-loop reorientation strategies that do not explicitly target under-observed regions. We propose AURORA, an active 3D reconstruction framework that closes the loop between online object-centric reconstruction and in-hand reorientation. At its core, Ray-GPIS estimates direction-wise reconstruction uncertainty along candidate viewing rays and selects next-best-view targets using an uncertainty--novelty objective, which are realized through an axis-conditioned in-hand rotation policy. The resulting RGB-D observations are fused incrementally using CAD-free 6D pose tracking and lightweight geometric reconstruction. Experiments demonstrate that AURORA improves reconstruction quality and information-acquisition efficiency over non-active rotation strategies, while Ray-GPIS also outperforms active view-planning baselines in reconstruction performance, action-ranking quality, and planning efficiency. Targeted ablations further validate its robustness to hand occlusion and pose errors. The project webpage is available at https://aurorahand.github.io/
Chinese Translation
观察机器人手抓取的物体具有挑战性，因为存在严重的视觉遮挡。尽管手内操作可以暴露隐藏的表面，但现有方法通常依赖于预定义或开环的重定向策略，这些策略并未明确针对观察不足的区域。我们提出了AURORA，一个主动的3D重建框架，它在在线物体中心重建和手内重定向之间闭合了循环。其核心是Ray-GPIS，它沿候选视线估计方向性重建不确定性，并使用不确定性-新颖性目标选择下一个最佳视图目标，这通过轴条件的手内旋转策略实现。生成的RGB-D观测通过无CAD的6D姿态跟踪和轻量级几何重建进行增量融合。实验表明，AURORA在重建质量和信息获取效率上优于非主动旋转策略，而Ray-GPIS在重建性能、动作排序质量和规划效率上也超越了主动视图规划基线。针对性的消融实验进一步验证了其对手部遮挡和姿态误差的鲁棒性。项目网页可访问 https://aurorahand.github.io/
cs.RO / 104 / 2609.08511
PGMT: Perceptive General Motion Tracking for Humanoid Robots
PGMT：类人机器人感知通用运动跟踪
Abstract
Humanoid motion trackers can reproduce diverse whole-body motions, but their performance degrades on complex terrain where terrain-agnostic references become physically infeasible. We present PGMT, a Perceptive General Motion Tracking pipeline for humanoid robots that learns terrain adaptation from independently selected motion references and terrains. PGMT first learns a general tracking and recovery prior, then incorporates terrain perception through motion-conditioned terrain glimpses that selectively encode regions relevant to the current motion. Terrain-aware tracking relaxation allows necessary deviations from the reference while preserving its motion intent. Zero-shot deployment on a Unitree G1 demonstrates robust terrain-adaptive locomotion and whole-body motion execution over real-world terrain with obstacles up to 37 cm high, while supporting teleoperation, dynamic motion tracking, and fall recovery. PGMT extends general humanoid motion tracking beyond flat ground, providing a unified policy for terrain-adaptive locomotion, diverse whole-body behaviors, and teleoperation in complex environments.
Chinese Translation
类人运动跟踪器能够再现多样的全身运动，但在复杂地形上，其性能会下降，因为不考虑地形的参考变得在物理上不可行。我们提出了PGMT，一种类人机器人感知通用运动跟踪管道，它从独立选择的运动参考和地形中学习地形适应。PGMT首先学习一般的跟踪和恢复先验，然后通过运动条件下的地形瞬时观察来结合地形感知，这些瞬时观察选择性地编码与当前运动相关的区域。基于地形的跟踪放松允许必要的偏离参考，同时保持其运动意图。在Unitree G1上的零样本部署展示了在实际地形上具有强大地形适应性步态和全身运动执行的能力，能够应对高达37厘米的障碍物，同时支持远程操作、动态运动跟踪和跌倒恢复。PGMT将类人运动跟踪扩展到平坦地面以外，为复杂环境中的地形适应性步态、多样化全身行为和远程操作提供了统一的策略。
cs.RO / 105 / 2609.08512
TASG-Explore: Traversability-Aware Sector-Guided Exploration for Ground Robot on Uneven Terrain
TASG-Explore：基于可通行性意识的扇区引导探索框架用于不平坦地形的地面机器人
Abstract
Autonomous exploration on uneven terrain requires ground robots to balance exploration efficiency, coverage completeness, and terrain safety. Detailed tsrrain reasoning improves local reliability but can slow large-scale exploration, whereas coarse region guidance expands quickly in open areas but can miss narrow passages and irregular traversable boundaries. To address this challenge, this paper presents TASG-Explore, a traversability-aware sector-guided exploration framework for ground robots. The framework first performs hierarchical traversability analysis using variable-voxel ground fitting and adaptive 8-bit obstacle encoding. It then splitting cost map into sectors, incrementally updates sector clusters, extracts terrain-coupled frontier viewpoints, and maintains a dynamic topological roadmap with unknown topological hypotheses. Finally, a sector-guided planner selects region targets and inserts local viewpoints to generate efficient exploration routes. Benchmark experiments in diverse challenging environments, including caves, forests, and rugged hills, show that TASG-Explore achieves the best overall performance among six representative state-of-the-art planners. The proposed traversability analysis improves processing efficiency by 6.3 times while maintaining high accuracy, and the exploration planner improves exploration efficiency by 51% and increases coverage by up to 2.95 times in rugged hill scene. Large-scale real-world experiments further demonstrate the practical value of the proposed method.
Chinese Translation
在不平坦地形上进行自主探索要求地面机器人平衡探索效率、覆盖完整性和地形安全性。详细的地形推理提高了局部可靠性，但可能会减缓大规模探索，而粗略的区域引导在开阔区域快速扩展，但可能会错过狭窄通道和不规则的可通行边界。为了解决这一挑战，本文提出了TASG-Explore，一个基于可通行性意识的扇区引导探索框架。该框架首先使用可变体素地面拟合和自适应8位障碍物编码进行分层可通行性分析。然后将成本地图划分为扇区，逐步更新扇区簇，提取与地形相关的前沿视点，并维护一个具有未知拓扑假设的动态拓扑路线图。最后，扇区引导规划器选择区域目标并插入局部视点，以生成高效的探索路线。在包括洞穴、森林和崎岖山丘等多种具有挑战性的环境中的基准实验表明，TASG-Explore在六个具有代表性的最先进规划器中实现了最佳整体性能。所提出的可通行性分析在保持高准确性的同时提高了处理效率6.3倍，而探索规划器提高了51%的探索效率，并在崎岖山丘场景中将覆盖率提高了最多2.95倍。大规模的真实世界实验进一步证明了所提方法的实际价值。
cs.RO / 106 / 2609.08583
Estimating Semantic Ambiguity via Gaussian Context Distributions for VLM-Driven Traversability Analysis
通过高斯上下文分布估计语义模糊性以进行基于视觉语言模型的可通行性分析
Abstract
Autonomous navigation in unstructured environments requires robust scene understanding, yet Vision-Language Models (VLMs) often suffer from semantic ambiguity, where conflicting predictions can lead to dangerous failures. To address this, we present a novel pipeline for vision-based traversability estimation that explicitly models contextual uncertainty. Our approach utilizes Conceptual Anchoring to ground open-vocabulary VLM predictions onto a continuous physical traversability scale. By formulating the model's responses as a Gaussian Context Distribution (GCD), we derive both a dense traversability map and a dense uncertainty map based on the statistical properties of the distribution. Experimental validation on the real-world GOOSE dataset demonstrates that our proposed uncertainty metric effectively correlates with sources of ambiguity, such as visual artifacts and mixed terrain overlap. The method exhibits competitive performance while offering the distinct advantage of providing statistical uncertainty estimates to address semantic ambiguity, enabling safer and more reliable autonomous behavior in complex outdoor settings.
Chinese Translation
在非结构化环境中进行自主导航需要强大的场景理解能力，但视觉语言模型（VLMs）常常面临语义模糊的问题，冲突的预测可能导致危险的失败。为了解决这一问题，我们提出了一种新颖的基于视觉的可通行性估计管道，明确建模上下文不确定性。我们的方法利用概念锚定（Conceptual Anchoring）将开放词汇的VLM预测映射到一个连续的物理可通行性尺度上。通过将模型的响应表述为高斯上下文分布（Gaussian Context Distribution，GCD），我们基于分布的统计特性推导出密集的可通行性图和密集的不确定性图。在真实世界的GOOSE数据集上的实验验证表明，我们提出的不确定性度量与模糊源（如视觉伪影和混合地形重叠）有效相关。该方法表现出竞争力的性能，同时提供了统计不确定性估计的独特优势，以应对语义模糊性，从而在复杂的户外环境中实现更安全、更可靠的自主行为。
cs.RO / 107 / 2609.08626
MFVINS: Multiple Fisheye Camera-Based Visual Inertial System
MFVINS：基于多鱼眼相机的视觉惯性系统
Abstract
A simultaneous localization and mapping (SLAM) method using a monocular camera and a low-cost inertial measurement unit (IMU) sensor is an effective way to fulfill a low-cost sensor configuration. Using this sensor configuration, visual-inertial system (VINS) focuses on fusing data from a camera and an IMU sensor to estimate the six degrees-of-freedom (DOF) of the sensor pose. Typically, VINS uses only a single camera as visual input, which lead to problems such as error accumulation due to occlusion, various illumination, and textureless environments. In this paper, we propose a new multiple fisheye camera-based visual-inertial system called MFVINS. We present an IMU-aided FAST feature tracker for multiple cameras that enables efficient extraction and robust matching of local features. Then, the proposed method filters out outliers caused by fisheye distortion on the normalized image plane. Subsequently, a new reprojection error with physical validity constraints is proposed for bundle adjustment using learning-based depth estimation. The proposed method is applied to various scenarios, and its effectiveness is demonstrated by comparing previous VINS methods. In particular, MFVINS is implemented in real-time process to leverage the advantages of using multiple cameras -- robustness against occlusion and textureless regions -- while reducing the computational burden.
Chinese Translation
使用单目相机和低成本惯性测量单元（IMU）传感器的同时定位与地图构建（SLAM）方法是一种有效的低成本传感器配置实现方式。利用这种传感器配置，视觉惯性系统（VINS）专注于融合来自相机和IMU传感器的数据，以估计传感器姿态的六自由度（DOF）。通常，VINS仅使用单个相机作为视觉输入，这导致由于遮挡、不同光照和无纹理环境等问题而产生的误差积累。在本文中，我们提出了一种新的基于多鱼眼相机的视觉惯性系统，称为MFVINS。我们提出了一种IMU辅助的FAST特征跟踪器，适用于多相机，能够高效提取和稳健匹配局部特征。然后，所提出的方法在归一化图像平面上滤除由于鱼眼畸变引起的异常值。随后，提出了一种具有物理有效性约束的新重投影误差，用于基于学习的深度估计的束调整。所提出的方法应用于各种场景，并通过与之前的VINS方法进行比较，证明了其有效性。特别是，MFVINS在实时处理中实现，以利用多相机的优势——对遮挡和无纹理区域的鲁棒性，同时减少计算负担。
cs.RO / 108 / 2609.08638
CASD: Chunk-Aligned Semantic Distillation for Multi-StageRobot Manipulation
CASD：用于多阶段机器人操作的块对齐语义蒸馏
Abstract
An action chunk can span several stages of a manipulation task, yet a label for its first step describes only the current stage. We introduce Chunk-Aligned Semantic Distillation (CASD), which derives semantic targets for entire action chunks. An offline vision--language model segments demonstrations into described stages. Their occupancy within each action chunk determines a weighted semantic target, including transitions between stages. A CASD generator learns to predict this target from the current observation, robot state, and task instruction. We then freeze the generator and train a policy conditioned on its predictions. The semantic branch runs once per policy query, without online VLM calls or reasoning-trace decoding. Teacher matching on annotated LIBERO training episodes is above chance for both single-stage and boundary-crossing chunks. We evaluate three Fast-WAM variants and a DreamZero integration across four benchmarks, including distribution shifts on LIBERO-Plus. Compared with published references, IDM+CASD reaches 98.9\% versus 98.0\% average success on LIBERO, while Uncond falls below its reference. Joint+CASD reaches 93.0\% versus 90.6\% on RoboTwin 2.0, and DreamZero+CASD reaches a 47.9\% four-category MolmoSpaces manipulation average versus 40.7\%. Performance varies across backbone integrations.
Chinese Translation
一个动作块可以跨越操作任务的多个阶段，但其第一步的标签仅描述当前阶段。我们提出了块对齐语义蒸馏（Chunk-Aligned Semantic Distillation, CASD），该方法为整个动作块推导语义目标。一个离线视觉-语言模型将演示分割为描述的阶段。每个动作块内的占用情况决定了加权语义目标，包括阶段之间的过渡。CASD生成器学习从当前观察、机器人状态和任务指令中预测该目标。然后，我们冻结生成器，并训练一个基于其预测的策略。语义分支在每次策略查询中运行一次，无需在线视觉-语言模型调用或推理轨迹解码。在标注的LIBERO训练集上，教师匹配对于单阶段和跨边界块均高于随机水平。我们评估了三种Fast-WAM变体和一个DreamZero集成，涵盖四个基准，包括LIBERO-Plus上的分布转移。与已发布的参考相比，IDM+CASD在LIBERO上的平均成功率达到98.9\%，而98.0\%为参考值，而Uncond则低于其参考水平。Joint+CASD在RoboTwin 2.0上的成功率为93.0\\%，而参考值为90.6\\%。DreamZero+CASD在四类MolmoSpaces操作的平均成功率为47.9\\%，而参考值为40.7\\%。性能在不同的主干集成中有所变化。
cs.RO / 109 / 2609.08669
Learning to build covering structures with continuous adjustments
学习构建具有连续调整的覆盖结构
Abstract
Robotic construction offers the potential to use materials more efficiently and create complex geometries, but current methods rely on rigid, high-precision plans that cannot accommodate the tolerances, inaccuracies, and unexpected changes inherent in physical fabrication. In this work, we introduce a reinforcement learning approach that forgoes predefined plans entirely, instead generating construction sequences adaptively as the structure is built. Our method operates on graph-structured state representations and a mixed (parameterized) action space, requiring both discrete block selection and continuous placement parameters. Because the stability simulation of a structure is computationally heavy, we develop an efficient exploration strategy by incorporating unilateral edges into graph neural networks, extending soft actor-critic (SAC) to this hybrid setting. We evaluate our algorithm, HSAC, against the prior method hybrid-PPO (HPPO), demonstrating significantly higher asymptotic performance and good sample efficiency. We also demonstrate HSAC's robustness to hyperparameter choices and its exploration capability, handling up to 10 discrete actions without performance degradation. Finally, we validate our approach on a physical two-robot setup, successfully building a spanning arch with 3D-printed blocks in closed-loop execution, confirming that policies trained in simulation transfer to real hardware.
Chinese Translation
机器人建造提供了更高效利用材料和创造复杂几何形状的潜力，但当前的方法依赖于刚性、高精度的计划，这些计划无法适应物理制造中固有的公差、不准确性和意外变化。在本研究中，我们提出了一种强化学习方法，完全放弃了预定义的计划，而是根据结构的构建自适应地生成施工序列。我们的方法基于图结构状态表示和混合（参数化）动作空间，要求同时进行离散块选择和连续放置参数的选择。由于结构的稳定性模拟计算量大，我们通过将单边边缘引入图神经网络，开发了一种高效的探索策略，将软演员-评论家（Soft Actor-Critic, SAC）扩展到这一混合设置。我们将我们的算法HSAC与先前的方法混合-PPO（Hybrid-PPO, HPPO）进行了评估，显示出显著更高的渐近性能和良好的样本效率。我们还展示了HSAC对超参数选择的鲁棒性及其探索能力，能够处理多达10个离散动作而不降低性能。最后，我们在一个物理双机器人设置上验证了我们的方法，成功地在闭环执行中用3D打印块构建了一个跨越拱，确认了在仿真中训练的策略能够转移到真实硬件上。
cs.RO / 110 / 2609.08673
BIFTA: Brain-Inspired Few-Shot Tactile Adaptation for Unknown Sensors
BIFTA：基于大脑的少样本触觉适应框架用于未知传感器
Abstract
Advances in tactile sensing have made contact-rich perception possible, accelerating progress in robotic manipulation, material understanding, and embodied interaction. However, because optical design, elastomer mechanics, and imaging geometry differ substantially across tactile sensors, models trained on known sensor types can suffer an abrupt performance collapse on unknown sensors. To address this problem, we propose the Brain-Inspired Few-Shot Tactile Adaptation (BIFTA) framework; it draws on the brain's rapid sensory adaptation mechanism to adapt a frozen encoder to an unknown tactile sensor from a small labeled support set. BIFTA preserves pretrained representations through dual-view statistical memory, constructs support-conditioned spectral graphs to repair sensor-dependent feature neighborhoods, and applies uncertainty-gated recurrent propagation to strengthen reliable cross-query evidence. Extensive benchmarks across three tactile datasets show that BIFTA substantially improves adaptation to unknown sensors: with only 10\% labeled target data on SITR, it raises mean Sparsh accuracy from 6.86\% for the frozen source classifier to 87.09\%, exceeding the strongest implemented prior comparison by 47.22 percentage points, and these gains generalize across datasets, pretrained backbones, and tactile tasks. These results validate BIFTA for data-efficient adaptation to unknown tactile sensors and offer a promising route toward tactile models that transfer across heterogeneous hardware.
Chinese Translation
触觉传感技术的进步使得丰富的接触感知成为可能，加速了机器人操作、材料理解和具身交互的进展。然而，由于光学设计、弹性体力学和成像几何在不同触觉传感器之间存在显著差异，基于已知传感器类型训练的模型在未知传感器上可能会出现性能骤降。为了解决这一问题，我们提出了基于大脑的少样本触觉适应框架（BIFTA）；该框架借鉴了大脑快速感官适应机制，通过小规模标记支持集将冻结的编码器适应于未知触觉传感器。BIFTA通过双视图统计记忆保留预训练表示，构建支持条件的谱图以修复传感器依赖的特征邻域，并应用不确定性门控递归传播来增强可靠的跨查询证据。在三个触觉数据集上的广泛基准测试表明，BIFTA显著改善了对未知传感器的适应性：在SITR上，仅使用10%的标记目标数据，它将冻结源分类器的平均Sparsh准确率从6.86%提高至87.09%，超出了最强先前比较的47.22个百分点，这些提升在不同数据集、预训练骨干网络和触觉任务中具有普遍性。这些结果验证了BIFTA在数据高效适应未知触觉传感器方面的有效性，并为跨异构硬件的触觉模型转移提供了有希望的途径。
cs.RO / 111 / 2609.08678
HiBRIDGE: A Hierarchical Bayesian Neural Network Framework for Interpretable Dialogue Management in Group-Robot Interaction
HiBRIDGE：一种用于群体-机器人互动中可解释对话管理的层次贝叶斯神经网络框架
Abstract
In multi-party human-robot interaction, a robot must continuously decide whom to address and what to say to participate effectively in the conversation. In real-world interactions, this is challenging because several behaviours may be plausible at the same time: a robot might continue a topic with one participant, involve another through a question, or address the whole group, with the appropriate choice depending on both whom it addresses and the interaction context. Current approaches remain limited in representing uncertainty when several behaviours are plausible and in structuring decisions into semantically meaningful intermediate steps that make robot decisions easier to interpret. Addressing these, we present HiBRIDGE, a hierarchical Bayesian neural network framework for group-robot dialogue management. Its Bayesian formulation enables uncertainty-aware prediction and robust learning from limited interaction data, while the hierarchical approach formulates behaviour selection as a structured, multi-stage decision process. We further use decision-tree surrogates to investigate whether this structure can support more interpretable explanations. Across three offline group-HRI datasets, our findings show that Bayesian formulations outperform their deterministic counterparts and several state-of-the-art baselines. Next, through an online study (N=20), we show that explanations derived from the hierarchical model are rated as more helpful for understanding robot behaviour and are preferred over those derived from the flat model. Finally, through our in-person study (N=12), we demonstrate the feasibility of HiBRIDGE for autonomous real-time group interaction, with both hierarchical and flat Bayesian variants positively perceived. Overall, HiBRIDGE combines strong predictive performance with a structured decision process that supports more interpretable explanations of robot behaviour.
Chinese Translation
在多方人机互动中，机器人必须不断决定与谁对话以及说什么，以有效参与对话。在现实世界的互动中，这一任务具有挑战性，因为多个行为可能同时是合理的：机器人可能继续与一个参与者讨论某个话题，通过提问来吸引另一个参与者，或者向整个小组发言，适当的选择取决于其所针对的对象和互动背景。目前的方法在表示多个合理行为的情况下的不确定性以及将决策结构化为语义上有意义的中间步骤方面仍然有限，这使得机器人的决策更难以解释。为了解决这些问题，我们提出了HiBRIDGE，一个用于群体-机器人对话管理的层次贝叶斯神经网络框架。其贝叶斯公式使得能够进行考虑不确定性的预测，并在有限的互动数据中实现稳健学习，而层次化的方法将行为选择构建为一个结构化的多阶段决策过程。我们进一步使用决策树替代模型来研究这种结构是否能够支持更可解释的解释。在三个离线群体人机互动数据集上，我们的研究结果表明，贝叶斯公式的表现优于其确定性对应物和多个最先进的基线。接下来，通过一项在线研究（N=20），我们展示了从层次模型中得出的解释被评估为对理解机器人行为更有帮助，并且比从平面模型中得出的解释更受欢迎。最后，通过我们的面对面研究（N=12），我们展示了HiBRIDGE在自主实时群体互动中的可行性，无论是层次贝叶斯变体还是平面贝叶斯变体均受到积极评价。总体而言，HiBRIDGE结合了强大的预测性能与结构化的决策过程，支持对机器人行为的更可解释的解释。
cs.RO / 112 / 2609.08711
DCLP++: Learning to Navigate with Footprint Clearance and Relative Motion
DCLP++：基于足迹清晰度和相对运动的导航学习
Abstract
We present DCLP++, a local navigation frameworkthat uses footprint clearance as the geometric basis for studying relative motion features in dynamic environments. Each valid LiDAR return is mapped to its shortest Euclidean distance from the filled robot footprint before reciprocal encoding, replacing distance from the sensor with distance to the occupied body. Radial measurementsor simulated planar relative velocities provide short-horizon features without static-dynamic labels in the policy input. A preliminary study uses a rectangular robot with a speed limit of 1 m/s among 20 moving obstacles. On 100 fixed validation tasks, two selected training seeds yield mean success rates of 42% with sensor rangeand 70% with footprint clearance after 200,000 environment steps.Motion variants show mixed additional gains. These results supportthe clearance-based observation in the evaluated setting; reliable motion benefits and transfer across robots require further evaluation.
Chinese Translation
我们提出了DCLP++，这是一个局部导航框架，利用足迹清晰度作为研究动态环境中相对运动特征的几何基础。每个有效的激光雷达返回信号都被映射到其与填充机器人足迹的最短欧几里得距离，然后进行互编码，将传感器的距离替换为与占用体的距离。径向测量或模拟平面相对速度提供了在策略输入中没有静态-动态标签的短视野特征。一项初步研究使用了一个速度限制为1 m/s的矩形机器人，在20个移动障碍物中进行测试。在100个固定验证任务中，两个选定的训练种子在传感器范围内的平均成功率为42%，而在足迹清晰度下为70%，经过200,000个环境步骤。运动变体显示出混合的额外收益。这些结果支持在评估环境中基于清晰度的观察；可靠的运动收益和跨机器人转移需要进一步评估。
cs.RO / 113 / 2609.08743
FOCI Policy: Focus on Object-Centric Interactions for Relational Manipulation Policies
FOCI 策略：聚焦于面向对象的交互以实现关系操控策略
Abstract
Object-centric manipulation policies improve generalization by modeling object motion instead of directly predicting robot actions. However, existing methods are often limited by representations which are either too simplistic to capture interaction dynamics or too dense to learn efficiently. We observe that many rigid relational manipulation tasks are governed by short interaction phases where the relative motion between task-relevant objects is tightly constrained. Based on this observation, we propose \textsc{Foci Policy}, an interaction-centric framework that achieves a two-fold abstraction: (1) temporally, by automatically extracting compact interaction segments from demonstrations;(2) spatially, by representing skills as relative $SE(3)$ motion between task-relevant objects, yielding invariance to scene configurations and robot embodiment. Experiments on RLBench, COLOSSEUM, and real-world tasks show that \textsc{Foci Policy} achieves strong performance with substantially less training data than prior object-centric and action-centric policies. These results suggest that modeling object-object interactions provides a simple and efficient inductive bias for rigid relational manipulation. Project page: \href{https://fitz0401.github.io/foci-page/}{fitz0401.github.io/foci-page/}.
Chinese Translation
面向对象的操控策略通过建模物体运动而非直接预测机器人动作来提高泛化能力。然而，现有方法常常受到表示的限制，要么过于简单以至于无法捕捉交互动态，要么过于复杂以至于无法高效学习。我们观察到，许多刚性关系操控任务受到短暂交互阶段的支配，在这些阶段中，任务相关物体之间的相对运动受到严格约束。基于这一观察，我们提出了 extsc{Foci Policy}，一个以交互为中心的框架，实现了两方面的抽象：（1）在时间上，通过从示范中自动提取紧凑的交互片段；（2）在空间上，通过将技能表示为任务相关物体之间的相对 $SE(3)$ 运动，从而对场景配置和机器人体现具有不变性。在 RLBench、COLOSSEUM 和现实世界任务上的实验表明， extsc{Foci Policy} 在所需训练数据量上显著少于先前的面向对象和面向动作的策略，且表现出强大的性能。这些结果表明，建模物体间交互为刚性关系操控提供了一种简单而高效的归纳偏置。项目页面： exttt{https://fitz0401.github.io/foci-page/}.
cs.RO / 114 / 2609.08770
A Controlled Comparison of Manual and Teleoperated Intraocular Instrument Motion for an Input Device
手动与遥控眼内器械运动的受控比较：针对输入设备的研究
Abstract
Input devices for robotic microsurgery are frequently described as preserving the surgeon's trained technique, but the claim is rarely measured. We compared manual and teleoperated intraocular instrument motion with the trocar constraint, the instrument, the eye model and the tracking source common to both conditions, so that the control interface was the only factor varied. Prior comparisons cannot hold the instrument fixed, because a robotic instrument is not the tool used manually. Sixteen participants performed a navigation task on a commercial ophthalmic simulator by hand and through a three-degree-of-freedom input device commanding a five-joint robot. Task outcome was equal but at ceiling: every participant acquired all five targets under both interfaces with no retinal or lens injury. Execution differed on every measure. Teleoperated trials took three times as long at a quarter of the median speed, covered less than half the angular working range, and were broken into 3.5 times as many separate movements. Completion time and movement fragmentation improved substantially across four trials of practice and had not plateaued; the measures set by the configured rate ceiling and joint limit changed the least. Finger activity doubled and pinch variability tripled, so reducing instrument degrees of freedom redistributed manual effort rather than reducing it. The interface preserves the outcome and reshapes the execution.
Chinese Translation
机器人显微外科的输入设备常被描述为能够保持外科医生的训练技巧，但这一说法很少被量化。我们比较了在 trocar 约束下的手动与遥控眼内器械运动，使用了相同的器械、眼模型和跟踪源，以确保控制界面是唯一变化的因素。以往的比较无法固定器械，因为机器人器械并非手动使用的工具。十六名参与者在一个商业眼科模拟器上通过手动和一个三自由度输入设备控制一个五关节机器人执行导航任务。任务结果相同，但达到了上限：每位参与者在两种界面下均成功获取了所有五个目标，且没有造成视网膜或晶状体损伤。执行在每个测量上均有所不同。遥控试验所需时间是手动操作的三倍，速度仅为中位数的四分之一，覆盖的角度工作范围不到一半，并且分解为3.5倍的独立运动。完成时间和运动碎片化在四次练习试验中显著改善，且尚未达到平台期；由配置速率上限和关节限制设定的测量变化最小。手指活动增加了一倍，捏合变异性增加了三倍，因此减少器械自由度重新分配了手动努力，而非减少。该界面保留了结果并重塑了执行过程。
cs.RO / 115 / 2609.08800
Ostrich: Taking Large Strides Through Stiff Contact in Differentiable Dynamics
鸵鸟：在可微动力学中通过刚性接触迈出大步
Abstract
Three properties determine whether a differentiable simulator can drive gradient-based optimization through contact: simulation accuracy, gradient reliability, and per-iteration cost. Tape-based engines such as MJX and Newton Semi-Implicit require timesteps small enough to keep contacts numerically tractable, and their backpropagation memory grows linearly with the number of timesteps T. Surrogate models bound memory by approximating contact away, but the resulting gradients lose the geometry the optimization depends on. We present Ostrich, a GPU-accelerated rigid-body simulator that resolves hard contacts and friction with non-smooth Newton iteration at large timesteps (h ~ 0.1 s), and differentiates the converged residual via the implicit function theorem, reusing the forward Schur complement to compute the adjoint at O(1) memory per timestep. On real-robot trajectories over a pallet obstacle, Ostrich holds MuJoCo's sim-to-real accuracy up to a 50x larger timestep. Its gradients converge from random initializations where MJX descends slowly and Newton Semi-Implicit stalls; a warm iteration runs 211x faster than MJX's and 4.7x faster than Semi-Implicit's. On the same scene Ostrich differentiates 8,192 parallel worlds on a single 24 GB GPU, sustaining 29x checkpointed MJX's optimization throughput; without checkpointing both baselines exhaust memory at far fewer worlds. We close with a gradient-based trajectory optimization demonstration over triangle-mesh terrain across a 10 s horizon, a setting where prior engines either restrict to primitive geometry or face the convergence and memory limits shown above.
Chinese Translation
三个属性决定了可微仿真器是否能够通过接触驱动基于梯度的优化：仿真精度、梯度可靠性和每次迭代的成本。基于带子的引擎，如 MJX 和 Newton Semi-Implicit，需要足够小的时间步长以保持接触在数值上可处理，并且它们的反向传播内存随着时间步数 T 的增加而线性增长。替代模型通过近似接触来限制内存，但由此产生的梯度失去了优化所依赖的几何特性。我们提出了鸵鸟（Ostrich），这是一种加速的刚体仿真器，能够在较大时间步长（h ~ 0.1 s）下通过非光滑牛顿迭代解决硬接触和摩擦，并通过隐函数定理对收敛的残差进行微分，重用前向 Schur 余量以在每个时间步长中以 O(1) 的内存计算伴随。针对跨越托盘障碍的真实机器人轨迹，鸵鸟保持了 MuJoCo 的仿真到真实的精度，时间步长可增大至 50 倍。其梯度从随机初始化中收敛，而 MJX 的下降速度缓慢，Newton Semi-Implicit 则停滞；一次热身迭代的速度比 MJX 快 211 倍，比 Semi-Implicit 快 4.7 倍。在同一场景中，鸵鸟在单个 24 GB GPU 上区分 8,192 个并行世界，维持了 29 倍于带检查点的 MJX 的优化吞吐量；在没有检查点的情况下，两个基线在更少的世界中耗尽内存。最后，我们展示了在三角网格地形上进行基于梯度的轨迹优化，时间范围为 10 秒，这一设置中，先前的引擎要么限制于原始几何形状，要么面临上述的收敛和内存限制。
cs.RO / 116 / 2609.08802
Graph-Based Safe Reinforcement Learning for Multi-Agent Systems with Time-Varying Topology
基于图的安全强化学习在具有时变拓扑的多智能体系统中的应用
Abstract
This paper presents a graph-based safe multi-agent reinforcement learning (MARL) framework for cooperative navigation with time-varying topology. To address the critical challenge of ensuring safety in environments with sensing constraints, a safety-decoupled mechanism is introduced through a Control Barrier-Like Function (CBLF) action screening layer. This mechanism bridges the gap between discrete LiDAR perception and continuous safety constraints, ensuring that physical safety constraints are strictly satisfied regardless of the learning progress. Building upon this safety foundation, a unified structural architecture is proposed, integrating a attention-based actor and a Graph Attention Network (GAT) centralized critic. The actor utilizes a value vector reconstruction mechanism that explicitly encodes relative geometric relations through a collaborative tracking error matrix, enabling scale-insensitive policy learning under time-varying communication topologies. Meanwhile, the GAT-based critic models evolving interaction structures for accurate global value estimation. The proposed framework is validated on real differential-drive robot platforms, and experimental results demonstrate superior stability and safety in dynamic scenarios with limited fields-of-view.
Chinese Translation
本文提出了一种基于图的安全多智能体强化学习（MARL）框架，用于具有时变拓扑的协作导航。为了解决在感知约束环境中确保安全这一关键挑战，本文引入了一种通过控制障碍类似函数（Control Barrier-Like Function, CBLF）进行动作筛选的安全解耦机制。该机制弥合了离散激光雷达感知与连续安全约束之间的差距，确保无论学习进展如何，物理安全约束都能得到严格满足。在此安全基础上，提出了一种统一的结构架构，整合了基于注意力的行为者和图注意力网络（Graph Attention Network, GAT）集中式评论者。行为者利用一种价值向量重构机制，通过协作跟踪误差矩阵显式编码相对几何关系，从而在时变通信拓扑下实现尺度不敏感的策略学习。同时，基于GAT的评论者建模不断演变的交互结构，以实现准确的全局价值估计。所提出的框架在真实的差分驱动机器人平台上进行了验证，实验结果表明在视野有限的动态场景中具有更优的稳定性和安全性。
cs.RO / 117 / 2609.08804
Real-time Puncture Detection and Recovery for Pneumatic Soft Actuators
气动软驱动器的实时穿刺检测与恢复
Abstract
Soft robots offer safe and adaptive interaction with humans and unstructured environments through their inherent ability to deform and comply. Pneumatic actuators are one way to build soft robots. They are typically made from soft silicone materials and are especially effective for driving such systems, enabling smooth and adaptable motion. However, their compliant nature also makes them vulnerable to mechanical failures like punctures and tears, limiting practical deployment. To address this, we propose a puncture detection system for soft actuators using motion data from a single inertial measurement unit. Extracted features are used to train anomaly detectors for puncture detection and non-linear models to estimate severity. We also introduce a multi-chamber pneumatic soft bending actuator capable of diverse configurations via selective chamber inflation. Our algorithm identifies the punctured chamber and provides a severity score using a chamber perturbation scheme. Anomaly detectors are trained on normal operation data and detect damage through reconstruction errors, while severity is estimated by a separate model trained under slightly modified conditions. Finally, we demonstrate a failure recovery strategy to maintain actuation force post-failure. This approach enhances the reliability and safety of soft robotic systems through real-time, data-driven damage detection.
Chinese Translation
软机器人通过其固有的变形和顺应能力，能够与人类及非结构化环境进行安全和自适应的交互。气动驱动器是构建软机器人的一种方式。它们通常由柔软的硅胶材料制成，特别适合驱动此类系统，实现平滑和适应性的运动。然而，它们的顺应性特征也使其容易受到机械故障的影响，如穿刺和撕裂，从而限制了实际应用。为了解决这个问题，我们提出了一种基于单个惯性测量单元的软驱动器穿刺检测系统。提取的特征用于训练异常检测器以进行穿刺检测，并使用非线性模型来估计严重性。我们还引入了一种多腔气动软弯曲驱动器，能够通过选择性腔体充气实现多种配置。我们的算法识别出穿刺的腔体，并通过腔体扰动方案提供严重性评分。异常检测器在正常操作数据上进行训练，通过重构误差检测损伤，而严重性则由在稍微修改的条件下训练的独立模型进行估计。最后，我们展示了一种故障恢复策略，以在故障后维持驱动力。这种方法通过实时、数据驱动的损伤检测，提高了软机器人系统的可靠性和安全性。
cs.RO / 118 / 2609.08853
CAST: Alternating State-Value Targets and Expanded Policy Gradients for Model-Based Reinforcement Learning
CAST：交替状态价值目标与扩展策略梯度用于基于模型的强化学习
Abstract
Model-based reinforcement learning (MBRL) is a family of RL methods that learn a model of the environment and use it for action selection, making it well suited to robotics due to its sample efficiency. Combining learned models with online planning can further improve action selection, as the planner can exploit the model to find better actions than the learned policy alone. Recent methods combining learned policies with online planning typically learn the value of the policy rather than the stronger planner-guided behavior. We present CAST (Critic with Alternating State-value Target), which uses planner-guided behavior to improve value learning while regularizing the value estimate with the current policy. CAST replaces the action-value critic with a state-value critic, trained using a target that combines a real planner-guided transition and an imagined transition under the current policy. The resulting value function corresponds to an alternating process between planner-guided behavior and the current policy, allowing it to benefit from the stronger planner behavior while being regularised by the policy being learned. We evaluate CAST on the DeepMind Control and HumanoidBench Suites against several state-of-the-art methods, and demonstrate successful transfer to a physical Unitree Go2 quadruped performing a dynamic handstand.
Chinese Translation
基于模型的强化学习（MBRL）是一类强化学习方法，它通过学习环境模型并利用该模型进行动作选择，因而在机器人技术中表现出良好的样本效率。将学习到的模型与在线规划相结合可以进一步改善动作选择，因为规划者可以利用模型找到比单独的学习策略更优的动作。最近将学习策略与在线规划相结合的方法通常学习的是策略的价值，而不是更强的规划者引导行为。我们提出了CAST（交替状态价值目标的评论员），它利用规划者引导的行为来改善价值学习，同时通过当前策略对价值估计进行正则化。CAST用状态价值评论员替代了动作价值评论员，该评论员使用结合真实规划者引导转移和在当前策略下的想象转移的目标进行训练。得到的价值函数对应于规划者引导行为与当前策略之间的交替过程，使其能够受益于更强的规划者行为，同时又受到正在学习的策略的正则化。我们在DeepMind Control和HumanoidBench套件上评估了CAST，并与几种最先进的方法进行了比较，成功地将其转移到物理Unitree Go2四足机器人上，执行动态倒立。
cs.RO / 119 / 2609.08905
Visible-Reachable Workspace for Perception-Aware Humanoid Design
感知驱动的人形机器人设计的可视可达工作空间
Abstract
Workspace analysis measures where a robot can place its end effector. For visually guided manipulation, reachability alone is insufficient: a kinematically reachable target may not be visible in the specific pose required to reach it. The robot must then redirect its sensing or move its body to acquire a view, turning a perception limitation into additional motion. Existing humanoids largely inherit this limitation when copying human form factors. We introduce the visible-reachable workspace (VRW), a design-stage measure that conditions visibility on feasible reaching configurations and extends it to concurrent visibility of spatially separated work regions. We apply VRW by building a 31-DoF humanoid with independently actuated RGB-D cameras. On the same robot, camera articulation increases visible-reachable coverage from 38% to 97%. With actuated camera layouts, a second camera raises pairwise coverage from 0.45 to 0.95, while a third changes it only to 0.97. In a controlled two-target reach-and-grasp benchmark, our dual-actuated design reduces mean completion time by 17% and mechanical energy by 19% relative to the same robot with its cameras fixed. Hardware experiments demonstrate simultaneous observation and manipulation of front/back and left/right target pairs without torso reorientation. The results suggest that reachability becomes a more informative design quantity for perception-driven humanoid manipulation when it is evaluated together with the sensing configurations that make the reachable space observable. We will open-source all software and the humanoid hardware design. Our website is https://generalroboticslab.com/DukeHumanoidv2
Chinese Translation
工作空间分析衡量机器人可以放置其末端执行器的位置。对于视觉引导的操作，仅仅考虑可达性是不够的：在达到目标所需的特定姿态下，运动学上可达的目标可能并不可见。因此，机器人必须重新调整其感知或移动其身体以获取视角，将感知限制转化为额外的运动。现有的人形机器人在复制人类形态时在很大程度上继承了这一限制。我们引入了可视可达工作空间（Visible-Reachable Workspace, VRW），这是一种设计阶段的度量，它将可见性与可行的到达配置相结合，并将其扩展到空间上分离的工作区域的同时可见性。我们通过构建一个具有31自由度的独立驱动RGB-D相机的人形机器人来应用VRW。在同一机器人上，相机的关节运动将可视可达覆盖率从38%提高到97%。通过驱动的相机布局，第二个相机将成对覆盖率从0.45提高到0.95，而第三个相机仅将其提高到0.97。在一个受控的双目标抓取基准测试中，我们的双驱动设计相较于相机固定的同一机器人将平均完成时间减少了17%，机械能耗减少了19%。硬件实验表明，在不重新定向躯干的情况下，可以同时观察和操作前后及左右目标对。结果表明，当可达性与使可达空间可观察的感知配置一起评估时，它成为感知驱动的人形机器人操作中更具信息量的设计量。我们将开源所有软件和人形机器人硬件设计。我们的网站是 https://generalroboticslab.com/DukeHumanoidv2
cs.RO / 120 / 2609.08920
Remotely Detectable Keyed Communication through Motion
通过运动进行远程可检测的键控通信
Abstract
Messages from electronic devices are conventionally received as text, audio, or radio signals. But robots move with rich, articulate motion in the real world, opening up the possibility of transmitting messages through motion itself. In this paper, we consider the problem of motion-based communication, where we seek to modify a robot's movements so as to transmit messages detectable from remote sensing (e.g., video or motion capture), without degrading policy performance. We introduce a method for messaging through motion capable of encoding arbitrary message content over short payloads - such as an agent's current intent - as noise in any pre-trained policy's actions. This brings a new kind of robustness to robot communication: this 'physical' channel complements standard wireless communications channels but does not depend on them, requiring no extra hardware nor the establishment of a direct link to the robot. We systematically characterize the space of encoding schemes and derive design heuristics, then validate them across simulated environments and real-robot deployment; on real robots running at 50 Hz, four robots jointly recover an 8-bit message at an aggregate 0.67 bits/s.
Chinese Translation
电子设备发送的消息通常以文本、音频或无线信号的形式接收。然而，机器人在现实世界中以丰富而清晰的运动方式移动，这为通过运动本身传递消息开辟了可能性。本文考虑了基于运动的通信问题，我们寻求修改机器人的运动，以便通过远程传感（例如视频或运动捕捉）检测到消息，而不降低策略性能。我们提出了一种通过运动进行消息传递的方法，该方法能够在任何预训练策略的动作中将任意消息内容（如代理的当前意图）编码为噪声，适用于短负载。这为机器人通信带来了新型的鲁棒性：这种“物理”通道补充了标准无线通信通道，但不依赖于它们，无需额外的硬件或与机器人建立直接连接。我们系统地表征了编码方案的空间并推导设计启发式，然后在模拟环境和真实机器人部署中验证了这些方案；在以50 Hz运行的真实机器人上，四个机器人共同以总计0.67比特/秒的速度恢复了一个8位消息。
cs.RO / 121 / 2609.08958
Model Predictive Control of Tensegrity Robots via Contact-Aware Graph Neural Dynamics Model
基于接触感知图神经网络动态模型的张力结构机器人模型预测控制
Abstract
Tensegrity robots offer lightweight, compliant mobility over challenging terrain but remain difficult to model and control due to complex contact-rich dynamics and partial observability. This work presents a model predictive path integral (MPPI) controller for a three-bar tensegrity robot driven by a learned graph neural network (GNN) dynamics model. This work first extends prior GNN-based models with a differentiable contact detection module. The extension allows the dynamics model to reason over non-horizontal planar terrains, obstacles, as well as self-collisions. Then, the learned dynamics model and the MPPI controller operate in a closed data-collection loop, iteratively improving model accuracy and control performance. This work further introduces a hybrid MPPI strategy that combines MPPI with turning motion primitives to improve maneuverability. Experiments are performed in MuJoCo across five navigation tasks, which include, wall obstacles, inclines, narrow corridors, low-clearance structures, and a composite 3D obstacle course. The experiments demonstrate that the hybrid MPPI controller operating over the learned GNN dynamics model improves predictive accuracy over a flat-ground baseline model and achieves superior navigation performance compared to $A^*$-based re-planning and MPPI-only variants. Results show that the contact-aware learned dynamics combined with the sampling-based model predictive control enable robust tensegrity navigation in complex, contact-rich environments.
Chinese Translation
张力结构机器人在复杂地形上提供轻量级、柔性移动能力，但由于复杂的接触丰富动态和部分可观测性，建模和控制仍然困难。本研究提出了一种基于学习的图神经网络（GNN）动态模型的三杆张力结构机器人模型预测路径积分（MPPI）控制器。首先，本研究扩展了先前基于GNN的模型，增加了可微分的接触检测模块。该扩展使动态模型能够在非水平平面地形、障碍物以及自我碰撞上进行推理。然后，学习到的动态模型与MPPI控制器在一个闭环数据收集过程中运行，迭代地提高模型的准确性和控制性能。本研究进一步引入了一种混合MPPI策略，将MPPI与转向运动原语结合，以提高机动性。在MuJoCo中进行的实验涵盖了五个导航任务，包括墙体障碍、坡道、狭窄走廊、低净空结构和复合3D障碍赛道。实验结果表明，基于学习的GNN动态模型上运行的混合MPPI控制器在预测准确性上优于平坦地面基线模型，并且在导航性能上超过了基于$A^*$的重新规划和仅使用MPPI的变体。结果显示，结合接触感知的学习动态与基于采样的模型预测控制使得在复杂的接触丰富环境中实现了稳健的张力结构导航。
cs.RO / 122 / 2609.09023
DYAD: A Multimodal Dataset of Co-Located Human Assistance
DYAD：一个共处人类辅助的多模态数据集
Abstract
An embodied assistant working beside a person must track task state, recognize help seeking, choose how to intervene, and produce an appropriate response. Existing procedural datasets richly describe individual execution, while interactive datasets capture remote verbal instruction or undifferentiated co-working. They do not jointly link a co-located helper's verbal and physical interventions to performer requests, task state, assistance triggers, and outcomes. We introduce DYAD (DYadic Assistance Dataset), a synchronized multimodal record of human-human assistance during gearbox assembly. Across 20 sessions, one trained helper follows a guidance-first policy while assisting HoloLens 2 wearers. DYAD links 528 task-step intervals and 611 performer requests with 851 valid assistance records spanning verbal and physical help. DYAD's annotations span the assistance process; three reference tasks evaluate selected components rather than an end-to-end system: causal step understanding, pre-onset mode anticipation, and instructor response generation. On 829 eligible mode events, the strongest four-seed RGB mean is 0.548 +/- 0.007 macro-F1; causal metadata reaches 0.624 and a privileged trigger mapping 0.915, revealing information not recovered from pre-onset RGB. DYAD's contribution is not scale, but a linked interaction structure spanning help seeking, intervention choice, execution, and outcome under egocentric and workspace sensing.
Chinese Translation
一个在旁协助人的具身助手必须跟踪任务状态、识别寻求帮助的行为、选择干预方式并产生适当的响应。现有的程序性数据集丰富地描述了个体执行，而交互式数据集则捕捉了远程口头指令或未区分的共同工作。它们并未将共处助手的口头和身体干预与执行者请求、任务状态、辅助触发和结果共同关联起来。我们引入了DYAD（双人辅助数据集），这是一个在齿轮箱组装过程中同步记录的人与人之间的辅助的多模态数据集。在20个会话中，一名经过培训的助手在协助HoloLens 2佩戴者时遵循指导优先的政策。DYAD将528个任务步骤间隔和611个执行者请求与851个有效的辅助记录（涵盖口头和身体帮助）相连接。DYAD的注释涵盖了辅助过程；三个参考任务评估所选组件而非端到端系统：因果步骤理解、前期模式预测和指导者响应生成。在829个合格的模式事件中，最强的四种种子RGB均值为0.548 +/- 0.007宏F1；因果元数据达到0.624，特权触发映射为0.915，揭示了从前期RGB中未恢复的信息。DYAD的贡献不在于规模，而在于跨越寻求帮助、干预选择、执行和结果的关联交互结构，基于自我中心和工作空间感知。
cs.RO / 123 / 2609.09066
A Distributed Consensus Particle Filter for Target Tracking using Autonomous Surface Vessels
一种用于自主水面舰艇目标跟踪的分布式共识粒子滤波器
Abstract
Maritime target tracking over large distances often requires multi-agent teams without centralized coordination, and intermittent communication. Each agent must maintain an independent estimate that can take advantage of opportunistic communications availability when possible. This can lead to overly confident local estimates in the absence of external data. In this work, we propose an augmentation to a classical particle filter implementation that accounts for this potential source of error by forcing particles to spread strategically in the absence of informative updates from other sensor nodes. We demonstrate our method using Unmanned Surface Vessels (USVs) on a lake, and show that our augmentations do not deteriorate nominal performance, and provide an advantage in some specific edge cases.
Chinese Translation
在大距离海洋目标跟踪中，通常需要没有集中协调的多智能体团队，并且通信是间歇性的。每个智能体必须维持一个独立的估计，以便在可能的情况下利用机会性通信的可用性。这可能导致在缺乏外部数据的情况下，局部估计过于自信。在本研究中，我们提出了一种对经典粒子滤波器实现的增强，考虑到这种潜在的误差来源，通过在缺乏来自其他传感器节点的有效更新时，强制粒子进行战略性扩散。我们在湖面上使用无人水面舰艇（USVs）演示了我们的方法，并表明我们的增强不会恶化名义性能，并在某些特定边缘情况下提供优势。
cs.RO / 124 / 2609.09069
Rethinking Learned Occupancy in Autonomous Active Mapping with Observation-Gated Filtering
重新思考自主主动映射中的学习占用与观察门控过滤
Abstract
Autonomous 3D active mapping requires a space robot to choose where to sense while building the geometry needed for navigation. Learned occupancy completion extends spatial context beyond the current field of view, but one predicted map often serves two planning roles: it scores expected surface gain and constrains collision-free motion. Unsupported occupancy can therefore distort both where the robot looks and where it believes it can travel. We study this coupled interface in a controlled closed-loop benchmark by holding the active-mapping system fixed and varying only its planner-facing occupancy across observation-only, learned, oracle-corrected, and ground-truth conditions. Improving occupancy accuracy does not monotonically improve closed-loop coverage: across 25 starts, planning with ground-truth occupancy reaches 70% of the learned baseline's final coverage 12.7 steps earlier on average, while increasing final coverage by only 0.031. Guided by this diagnosis, we introduce an observation-gated filter that retains completion in insufficiently observed regions and suppresses predictions only after repeated frustum exposure without nearby RGB-D support. The filter improves both targeted failure-prone starts without retraining or ground truth. These results motivate online revision of planner-facing geometry during autonomous intervals between communication windows. The current study assumes benchmark RGB-D observations and sufficiently accurate pose estimates; planetary sensing conditions and accumulated localization drift remain to be evaluated.
Chinese Translation
自主3D主动映射要求空间机器人在构建导航所需几何体的同时选择感知位置。学习的占用补全将空间上下文扩展到当前视野之外，但一个预测的地图通常承担两个规划角色：它评估预期的表面增益并限制无碰撞运动。因此，未支持的占用可能会扭曲机器人观察的位置和它认为可以移动的位置。我们在一个受控的闭环基准测试中研究这一耦合接口，通过固定主动映射系统，仅改变其面向规划者的占用，条件包括仅观察、学习、oracle校正和真实情况。改善占用的准确性并不单调地提高闭环覆盖率：在25次启动中，使用真实占用进行规划的覆盖率平均比学习基线的最终覆盖率提前12.7步达到70%，而最终覆盖率仅增加0.031。在这一诊断的指导下，我们引入了一种观察门控过滤器，该过滤器在观察不足的区域保留补全，并在没有附近RGB-D支持的情况下，仅在重复的视锥曝光后抑制预测。该过滤器改善了针对失败易发启动的表现，而无需重新训练或真实数据。这些结果激励在通信窗口之间的自主间隔中在线修订面向规划者的几何体。本研究假设基准RGB-D观察和足够准确的位姿估计；行星感知条件和累积定位漂移仍需评估。
cs.RO / 125 / 2609.09073
Online, Reachability-Aware, Sampling-Based Motion Planning
在线、可达性感知的基于采样的运动规划
Abstract
Sampling-Based Model-Predictive Control (MPC) algorithms are a flexible class of controllers used for navigation on a wide range of robotic systems. Historically, such approaches have lacked hard safety guarantees, a shortcoming which we remedy in this work by computing guaranteed reachable-set overapproximations online with a fast, interval-based pipeline. We show that our method achieves similar performance to a state-of-the-art reachability-based planner without the need for the expensive pre-computation step, and can be scaled to systems that are infeasible using existing approaches. Finally, we demonstrate that our technique reduces safety violations by over 99% in a racing simulation and successfully controls a model racecar on real hardware experiments without crashes.
Chinese Translation
基于采样的模型预测控制（MPC）算法是一类灵活的控制器，广泛应用于各种机器人系统的导航。历史上，这类方法缺乏严格的安全保证，这是我们在本研究中通过在线计算保证可达集的过近似值来解决的，采用了快速的基于区间的管道。我们展示了我们的方法在性能上与最先进的基于可达性的规划器相似，而无需昂贵的预计算步骤，并且可以扩展到现有方法无法处理的系统。最后，我们证明了我们的技术在赛车仿真中将安全违规减少了超过99%，并成功地在真实硬件实验中控制了一辆模型赛车而没有发生碰撞。
cs.RO / 126 / 2609.09119
DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination
DeCAL：通过接触感知的潜在共同想象，迈向物理基础的灵巧视觉-语言-动作模型
Abstract
Dexterous manipulation involves contact-rich and fine-grained interactions with the physical world, posing significant challenges for existing vision-language-action (VLA) models due to severe visual occlusions and complex contact dynamics. While recent works have incorporated tactile sensing into robotic manipulation, most approaches still rely on homogeneous multimodal fusion, lacking adaptive tactile integration and explicit modeling of physical dynamics. In this work, we present DeCAL, a physically-grounded dexterous vision-language-action model that unifies understanding, imagination and action generation for contact-rich dexterous manipulation. Built upon a Mixture-of-Transformers (MoT) architecture, DeCAL leverages specialized experts for each capability while enabling efficient information flow among them. To effectively leverage tactile information, we introduce Adaptive Visuo-Tactile Fusion that dynamically regulates tactile interactions via a contact-aware gating strategy. Furthermore, we propose Visuo-Tactile Latent Co-Imagination to jointly model visual and tactile dynamics, equipping the policy with implicit physical world knowledge. Experimental results show that DeCAL consistently achieves state-of-the-art performance across all tasks, attaining a 71% average success rate and an 83.4% progress success rate, while also demonstrating strong generalization to unseen scenarios. The website is available at https://aureleopku.github.io/DeCAL.
Chinese Translation
灵巧操作涉及与物理世界的接触丰富和细致交互，这对现有的视觉-语言-动作（VLA）模型提出了重大挑战，因为存在严重的视觉遮挡和复杂的接触动态。尽管近期的研究已将触觉感知纳入机器人操作，但大多数方法仍依赖于同质的多模态融合，缺乏自适应的触觉整合和物理动态的显式建模。在本研究中，我们提出了DeCAL，一个物理基础的灵巧视觉-语言-动作模型，统一了接触丰富的灵巧操作的理解、想象和动作生成。DeCAL基于混合变换器（Mixture-of-Transformers, MoT）架构，利用专门的专家来处理每种能力，同时实现它们之间的信息高效流动。为了有效利用触觉信息，我们引入了自适应视觉-触觉融合，通过接触感知的门控策略动态调节触觉交互。此外，我们提出了视觉-触觉潜在共同想象，以联合建模视觉和触觉动态，为策略提供隐式的物理世界知识。实验结果表明，DeCAL在所有任务中始终实现了最先进的性能，平均成功率达到71%，进展成功率达到83.4%，同时在未见场景中也表现出强大的泛化能力。相关网站可访问 https://aureleopku.github.io/DeCAL。
cs.RO / 127 / 2609.09148
Proxy Policy Steering
代理策略引导
Abstract
Generalist robot policies carry broad manipulation priors from large-scale data, but specializing them to a new task remains the deployment bottleneck. This requires eliciting task-specific behavior from limited demonstrations without degrading their broad capabilities. We introduce Proxy Policy Steering (PPS), an inference-time adaptation method that resolves this challenge by training two lightweight proxy policies whose calibrated velocity-space difference steers the frozen base sampler. A reference proxy models the frozen base's behavior on target-task observations, and a task proxy, initialized from the reference, captures how this behavior changes under task supervision. Their difference forms a calibrated velocity-space residual that steers the frozen base sampler at every denoising step. We identify the conditions under which this residual isolates the change induced by task supervision, and validate them empirically. Because the base is never directly modified, its broad capabilities remain available at inference, including behaviors such as recovery from failure that the demonstrations themselves do not exercise. Adaptation requires only forward velocity predictions from the base, making PPS lightweight to train and applicable even without access to the base's parameters. On 8 real-world and 4 simulation manipulation tasks, PPS lifts the state-of-the-art pi 0.5 base policy by 53% absolute success rate on average, with zero-to-one gains on tasks the base never solves, while preserving the base's broad capabilities. PPS outperforms LoRA fine-tuning, from-scratch specialists, residual policies, and prior inference-time steering methods.
Chinese Translation
通用机器人策略从大规模数据中携带广泛的操作先验，但将其专门化到新任务仍然是部署的瓶颈。这需要从有限的演示中引导特定任务的行为，而不降低其广泛能力。我们提出了代理策略引导（Proxy Policy Steering, PPS），这是一种推理时适应方法，通过训练两个轻量级的代理策略来解决这一挑战，这两个策略的校准速度空间差异引导冻结的基础采样器。参考代理在目标任务观察上建模冻结基础的行为，而任务代理则从参考代理初始化，捕捉在任务监督下这种行为如何变化。它们的差异形成一个校准的速度空间残差，在每个去噪步骤中引导冻结的基础采样器。我们确定了该残差能够隔离任务监督引起的变化的条件，并进行了实证验证。由于基础策略从未被直接修改，其广泛能力在推理时仍然可用，包括从失败中恢复等行为，而这些行为在演示中并未被体现。适应过程仅需要基础策略的前向速度预测，使得 PPS 训练轻量且即使在没有访问基础参数的情况下也适用。在8个真实世界和4个模拟操作任务中，PPS 将最先进的 pi 0.5 基础策略的绝对成功率平均提升了53%，在基础策略从未解决的任务上实现了零到一的增益，同时保留了基础策略的广泛能力。PPS 超越了 LoRA 微调、从头开始的专家、残差策略和先前的推理时引导方法。
cs.RO / 128 / 2609.09158
TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
TANGO：在杂乱环境中使用全身视觉-语言-动作模型的人形导航
Abstract
We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D path planning problem, humanoid traversal in cluttered environments requires continuous geometry-aware whole-body adaptation, including coordinated arm placement, torso adjustment, and gait modulation for collision-free movement through complex 3D spaces. We introduce TANGO, the first whole-body vision-language navigation framework for language-conditioned humanoid traversal in cluttered environments. Given a natural-language instruction and egocentric RGB observations, TANGO directly predicts 29-DoF joint-space actions for downstream whole-body control. We train TANGO entirely in simulation by synthesizing diverse collision-free traversal behaviors via global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. This pipeline provides dynamically feasible action supervision for learning language-conditioned whole-body policies. In extensive simulation experiments, TANGO demonstrates state-of-the-art performance in vision-language navigation, while outperforming strong modular baselines in navigating challenging scenes requiring obstacle negotiation. Lastly, we deploy TANGO zero-shot on a Unitree G1 humanoid robot, and observe robust language-guided traversal in cluttered real-world scenes without training on any real-world navigation data.
Chinese Translation
我们研究了在人形机器人中导航杂乱室内环境的问题。与将导航建模为二维路径规划问题的传统方法不同，人形机器人在杂乱环境中的穿越需要连续的几何感知全身适应，包括协调的手臂放置、躯干调整和步态调节，以实现无碰撞地穿越复杂的三维空间。我们引入了TANGO，这是首个用于语言条件下人形穿越杂乱环境的全身视觉-语言导航框架。给定自然语言指令和自我中心的RGB观测，TANGO直接预测29自由度关节空间动作，以便于下游全身控制。我们通过合成多样的无碰撞穿越行为，完全在模拟环境中训练TANGO，这些行为通过全局路径规划、运动学全身动作生成、障碍物感知运动编辑和基于强化学习的跟踪实现。该流程为学习语言条件下的全身策略提供了动态可行的动作监督。在广泛的模拟实验中，TANGO在视觉-语言导航中展示了最先进的性能，同时在需要障碍物协商的挑战场景中超越了强大的模块化基线。最后，我们在Unitree G1人形机器人上零-shot部署TANGO，并观察到在杂乱的真实场景中，机器人能够稳健地进行语言引导的穿越，而无需在任何真实世界导航数据上进行训练。
cs.CV / 1 / 2609.05484
Architectural and Regularization Components in Deep Learning Medical Image Registration: Systematic Ablation Study
深度学习医学图像配准中的架构与正则化组件：系统性消融研究
Abstract
Deep learning registration methods routinely stack two kinds of enhancement on a base network: architectural additions such as affine pre-alignment stages, and training-objective additions such as regularization losses. Papers tend to adopt both at once, so it is unclear which is doing the work. I ran a controlled ablation to separate them. Using the OASIS brain MRI dataset (394 training subjects, 20 test subjects), I trained four variants of the same registration pipeline: a baseline 3D U-Net with basic similarity losses, the same U-Net with a full regularization suite, an affine-plus-deformable architecture with basic losses, and the affine architecture with the full suite. I evaluated registration accuracy (MSE, NCC, SSIM), deformation quality (Jacobian determinant preservation, displacement statistics, an anatomical plausibility score), and computational cost. Regularization alone accounted for most of the gain: a 21.3% relative gain on the MSE-improvement metric (1.78% to 2.16%, P<.001) and a 21.8% relative gain in NCC improvement, while cutting maximum deformation from 53.1 to 0.51 units, a 99.0% reduction, at essentially no computational cost (-0.06% inference time). The combined model produced the largest accuracy gain, 25.8% (1.78% to 2.24%), and raised anatomical plausibility from 0.596 to 0.930, at a moderate +9.8% inference-time cost. Gradient correlation rose from 0.742 at baseline to 0.980 for the fully enhanced model. All enhanced variants reached sub-voxel accuracy under plausible deformation constraints. Regularization losses are the primary driver in this setting, delivering the accuracy gains and almost all of the deformation control for free at inference time, while the affine architecture adds a smaller complementary benefit at acceptable cost. The 99% reduction in unrealistic deformations addresses a known barrier to clinical deployment.
Chinese Translation
深度学习配准方法通常在基础网络上叠加两种增强：架构添加，如仿射预对齐阶段，以及训练目标添加，如正则化损失。文献往往同时采用这两者，因此不清楚究竟是哪一部分在发挥作用。我进行了一个受控消融实验以将其分开。使用OASIS脑部MRI数据集（394个训练样本，20个测试样本），我训练了四个相同配准管道的变体：一个基础的3D U-Net，配以基本相似性损失，一个具有完整正则化套件的相同U-Net，一个带有基本损失的仿射加可变形架构，以及具有完整套件的仿射架构。我评估了配准精度（均方误差MSE，归一化互相关NCC，结构相似性指数SSIM）、变形质量（雅可比行列式保持、位移统计、解剖学合理性评分）和计算成本。正则化单独贡献了大部分增益：在MSE改进指标上相对增益为21.3%（从1.78%提高到2.16%，P<.001），在NCC改进上相对增益为21.8%，同时将最大变形从53.1单位减少到0.51单位，减少了99.0%，几乎没有计算成本（推理时间减少0.06%）。组合模型产生了最大的精度增益，达到25.8%（从1.78%提高到2.24%），并将解剖学合理性从0.596提高到0.930，增加了适度的推理时间成本+9.8%。梯度相关性从基线的0.742提高到完全增强模型的0.980。所有增强变体在合理的变形约束下达到了亚体素精度。在这种情况下，正则化损失是主要驱动因素，提供了精度增益和几乎所有的变形控制，而仿射架构则在可接受的成本下提供了较小的互补效益。99%的不合理变形减少解决了临床部署中的一个已知障碍。
cs.CV / 2 / 2609.05503
A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalities
关于多模态扩散模型的对抗攻击与防御的调查
Abstract
Diffusion models have become the dominant family of generative models in the visual domain. However, their widespread public availability enables misuse at scale, motivating a rapidly growing body of research on adversarial attacks and defenses. This survey provides, to our knowledge, the first unified review of this literature across three visual modalities: image, video, and 3D. We introduce a comprehensive, task-centric taxonomy: we first divide the literature by modality; within each modality, we separate methods into attacks and defenses, and then group them by the generative task they target, presenting them chronologically within each task. Moreover, we provide an in-depth analysis of their evaluation settings, consolidating the datasets, metrics, and benchmarks used to assess them. We conclude by identifying several open challenges and outlining concrete future research directions. Project Webpage: https://github.com/ozgurkara99/awesome-adv-attack-defense-on-diffusion
Chinese Translation
扩散模型已成为视觉领域中主导的生成模型家族。然而，它们的广泛公开可用性使得大规模的误用成为可能，这促使了对对抗攻击与防御的研究迅速增长。本文调查提供了我们所知的首个统一文献综述，涵盖了三种视觉模态：图像、视频和三维。我们引入了一个全面的、以任务为中心的分类法：首先按模态划分文献；在每个模态内，我们将方法分为攻击和防御，然后根据它们所针对的生成任务进行分组，并在每个任务内按时间顺序呈现。此外，我们对它们的评估设置进行了深入分析，整合了用于评估的数据库、指标和基准。最后，我们识别出几个开放挑战，并概述了具体的未来研究方向。项目网页：https://github.com/ozgurkara99/awesome-adv-attack-defense-on-diffusion
cs.CV / 3 / 2609.05516
An Exploratory Study of Frequency-Aware Task Weighting for YOLOv8-Based Unified Driving Perception
基于YOLOv8的统一驾驶感知中的频率感知任务加权的探索性研究
Abstract
Unified perception enables autonomous driving systems to perform object detection, drivable-area segmentation, and lane segmentation within a single network, improving efficiency and reducing deployment complexity. Jointly optimizing multiple perception tasks remains challenging because tasks exhibit different convergence rates, loss scales, and optimization stability. Existing task-weighting methods use loss magnitude, learned uncertainty, short-term loss changes, or gradient statistics; here, we explore the frequency structure of a recent loss-history window as a complementary signal. We implement and examine Frequency-aware Task Weighting (FTW), a dynamic task-balancing rule that estimates a loss-trajectory stability proxy from the low-frequency energy ratio of recent loss histories. FTW assigns larger weights to tasks whose mean-centered loss trajectories contain a larger proportion of low-frequency power. We document FTW and two baselines under full-network static training and progressive freezing using a unified YOLOv8-based perception framework with three task-specific heads. Experiments on Mapillary Vistas compare FTW with fixed and uncertainty-based weighting under both configurations. Final holdout metrics are reported for the checkpoint with the lowest per-epoch validation loss in each run. Across six single-run configurations, static FTW has the largest derived overall score and lane mIoU, progressive FTW has the largest detection mAP, and static uncertainty weighting has the largest drivable-area mIoU. Without repeated-seed estimates, single-task baselines, or FTW ablations, these rankings are descriptive. The evidence supports the feasibility of loss-frequency-based weighting in this pipeline, but does not establish improvement over the baselines or generalization beyond the reported runs.
Chinese Translation
统一感知使得自主驾驶系统能够在单一网络中执行物体检测、可行驶区域分割和车道分割，从而提高效率并减少部署复杂性。联合优化多个感知任务仍然具有挑战性，因为这些任务表现出不同的收敛速率、损失规模和优化稳定性。现有的任务加权方法使用损失大小、学习的不确定性、短期损失变化或梯度统计；在此，我们探索最近损失历史窗口的频率结构作为补充信号。我们实现并检验了频率感知任务加权（Frequency-aware Task Weighting, FTW），这是一种动态任务平衡规则，它从最近损失历史的低频能量比中估计损失轨迹的稳定性代理。FTW为那些均值中心损失轨迹包含较大比例低频功率的任务分配更大的权重。我们在全网络静态训练和渐进冻结下记录了FTW及两个基线，使用基于统一YOLOv8的感知框架，配备三个任务特定的头。Mapillary Vistas上的实验比较了FTW与固定和基于不确定性的加权在这两种配置下的表现。最终的保留指标报告了每次运行中每个周期验证损失最低的检查点。在六个单次运行配置中，静态FTW获得了最大的整体得分和车道mIoU，渐进FTW获得了最大的检测mAP，而静态不确定性加权获得了最大的可行驶区域mIoU。在没有重复种子估计、单任务基线或FTW消融的情况下，这些排名是描述性的。证据支持在该管道中基于损失频率的加权的可行性，但并未建立其相对于基线的改进或超出报告运行的泛化能力。
cs.CV / 4 / 2609.05517
Emergent Goal-Directed Attention in Large Vision-Language Models
大型视觉-语言模型中的自发目标导向注意力
Abstract
Human observers prioritize visual information according to task goals. Most computational models of naturalistic viewing are gaze-trained for free viewing, leaving open whether goal-directed attention can emerge in systems without gaze supervision. We tested two off-the-shelf vision-language models (VLMs), Qwen3-VL-32B-Thinking and Gemma-4-26B-A4B-it, on 4,887 naturalistic scenes under visual-search and free-viewing instructions. Model predictions were compared with human fixations on the same images under corresponding tasks. Both models aligned more closely with human fixations under matching goals than under mismatched goals. This crossover persisted in target-absent scenes, where alignment could not be explained by simple visual grounding, and appeared in decoder-layer readouts. Furthermore, model-thinking traces were grounded in target semantics during search and in visual prominence during free viewing. These findings show that general-purpose VLMs can generate human-aligned, goal-directed spatial priorities without gaze-specific training, informing theories of goal-directed attention and offering scalable tools for predicting where people look across tasks.
Chinese Translation
人类观察者根据任务目标优先处理视觉信息。大多数自然观看的计算模型是针对自由观看进行注视训练的，因此尚不清楚在没有注视监督的系统中是否能够出现目标导向的注意力。我们在4,887个自然场景中测试了两种现成的视觉-语言模型（VLMs），Qwen3-VL-32B-Thinking和Gemma-4-26B-A4B-it，采用视觉搜索和自由观看的指令。模型预测与人类在相应任务下对相同图像的注视进行了比较。在匹配目标下，这两个模型与人类注视的对齐程度高于不匹配目标的情况。这种交叉现象在目标缺失的场景中依然存在，且其对齐程度无法仅通过简单的视觉基础来解释，并且在解码器层的输出中有所体现。此外，模型思维轨迹在搜索过程中与目标语义相关，在自由观看过程中与视觉显著性相关。这些发现表明，通用的视觉-语言模型能够在没有特定注视训练的情况下生成与人类一致的目标导向空间优先级，从而为目标导向注意力理论提供了新的见解，并为预测人们在不同任务中注视的位置提供了可扩展的工具。
cs.CV / 5 / 2609.05518
CrossModalQA: A Cross-modal and Multi-hop Benchmark for Multimodal Retrieval-augmented Generation
CrossModalQA：一个用于多模态检索增强生成的跨模态和多跳基准
Abstract
Despite the strong capabilities of multimodal large language models (MLLMs), their parametric knowledge remains incomplete and difficult to update, motivating multimodal retrieval-augmented generation (RAG) to ground responses in external text and images. However, existing benchmarks face two major limitations: (i) they typically emphasize single-hop retrieval or reasoning over a small set of provided contexts rather than open-domain evidence discovery; and (ii) they provide fragmented coverage of cross-modal reasoning paths, leaving complex multi-hop and multi-image reasoning underexplored. In this paper, we introduce CrossModalQA, an open-domain benchmark for evaluating multimodal retrieval and reasoning over heterogeneous corpora. CrossModalQA contains 1,863 question-answer pairs constructed from 4,987 Wikipedia articles and 4,431 Wikimedia Commons images. It covers five complementary reasoning paths: vision-to-text, text-to-vision, vision-to-text-to-vision, multi-image intersection, and image-set reasoning. Every question requires retrieving and composing distributed textual and visual evidence, with an average reasoning depth of 3.50 hops. We construct the benchmark through multimodal knowledge graph-guided subgraph sampling and apply rule-based consistency checking and LLM verification to ensure multimodal dependence and traceable evidence. Extensive experiments demonstrate that existing multimodal RAG systems struggle to recover complete evidence chains and can underperform closed-book models when incomplete retrieval introduces distracting context. Further analysis reveals that complete cross-modal retrieval contributes more to answer accuracy than generator scaling, while multi-image retrieval and reasoning remain the primary bottlenecks limiting end-to-end performance.
Chinese Translation
尽管多模态大型语言模型（MLLMs）具有强大的能力，但其参数知识仍然不完整且难以更新，这促使多模态检索增强生成（RAG）将响应基于外部文本和图像。然而，现有基准面临两个主要限制：（i）它们通常强调单跳检索或在小范围提供的上下文中进行推理，而非开放领域的证据发现；（ii）它们对跨模态推理路径的覆盖不够全面，导致复杂的多跳和多图像推理未得到充分探索。本文介绍了CrossModalQA，一个用于评估多模态检索和对异构语料库进行推理的开放领域基准。CrossModalQA包含1,863个问题-答案对，构建自4,987篇维基百科文章和4,431张维基媒体共享图片。它涵盖五条互补的推理路径：视觉到文本、文本到视觉、视觉到文本到视觉、多图像交集和图像集推理。每个问题都需要检索和组合分散的文本和视觉证据，平均推理深度为3.50跳。我们通过多模态知识图谱引导的子图采样构建基准，并应用基于规则的一致性检查和LLM验证，以确保多模态依赖性和可追溯证据。大量实验表明，现有的多模态RAG系统在恢复完整证据链方面存在困难，并且在不完整检索引入干扰上下文时，可能表现不如封闭书籍模型。进一步分析表明，完整的跨模态检索对答案准确性贡献更大，而多图像检索和推理仍然是限制端到端性能的主要瓶颈。
cs.CV / 6 / 2609.05520
Contrastive Knowledge Distillation for Anomaly Detection in Multi-Illumination/Focus Display Images
用于多光照/聚焦显示图像异常检测的对比知识蒸馏
Abstract
In this paper, we tackle automatic anomaly detection in multi-illumination and multi-focus display images. The minute defects on the display surface are hard to spot out in RGB images and by a model trained with only normal data. To address this, we propose a novel contrastive learning scheme for knowledge distillation-based anomaly detection. In our framework, Multiresolution Knowledge Distillation (MKD) is adopted as a baseline, which operates by measuring feature similarities between the teacher and student networks. Based on MKD, we propose a novel contrastive learning method, namely Multiresolution Contrastive Distillation (MCD), which does not require positive/negative pairs with an anchor but operates by pulling/pushing the distance between the teacher and student features. Furthermore, we propose the blending module that transforms and aggregate multi-channel information to the three-channel input layer of MCD. Our proposed method significantly outperforms competitive state-of-the-art methods in both AUROC and accuracy metrics on the collected Multi-illumination and Multi-focus display image dataset for Anomaly Detection (MMdAD).
Chinese Translation
在本文中，我们针对多光照和多聚焦显示图像中的自动异常检测问题进行研究。显示表面上的微小缺陷在RGB图像中难以被识别，且仅使用正常数据训练的模型也难以发现。为了解决这一问题，我们提出了一种新颖的基于知识蒸馏的对比学习方案用于异常检测。在我们的框架中，采用了多分辨率知识蒸馏（Multiresolution Knowledge Distillation, MKD）作为基线，该方法通过测量教师网络与学生网络之间的特征相似性进行操作。在MKD的基础上，我们提出了一种新颖的对比学习方法，即多分辨率对比蒸馏（Multiresolution Contrastive Distillation, MCD），该方法不需要带有锚点的正/负样本对，而是通过拉近/推远教师和学生特征之间的距离进行操作。此外，我们还提出了融合模块，该模块将多通道信息转换并聚合到MCD的三通道输入层中。我们提出的方法在收集的多光照和多聚焦显示图像异常检测数据集（Multi-illumination and Multi-focus display image dataset for Anomaly Detection, MMdAD）上，在AUROC和准确率指标上显著超越了竞争性的最先进方法。
cs.CV / 7 / 2609.05522
Diffusion models for eye-gaze trajectory generation using position and velocity representations
基于位置和速度表示的眼动轨迹生成扩散模型
Abstract
Eye-tracking data are expensive to collect, requiring specialized hardware and controlled laboratory conditions, and difficult to share because of privacy constraints. We address this using two complementary denoising diffusion probabilistic models (DDPMs) for unconditional generation of eye-gaze dynamics from visual-search data. Both use an identical FiLM-conditioned one-dimensional U-Net with self-attention (19.35,M parameters), trained on 8,s sliding-window sequences from 28 participants. One model generates raw two-dimensional gaze-position sequences, while the other generates two-component velocity sequences; each uses representation-specific preprocessing, training settings, data partitions, and evaluation protocols. Both are evaluated across three independent training seeds, with aggregated metrics reported as mean,$\pm$,SD. The position-space model achieves a mean Jensen-Shannon (JS) divergence of $0.016\pm0.004$ across nine kinematic features, with the highest feature-wise mean below $0.030$, fixation duration within 2% of real data, and a Fr'echet Gaze Distance more than an order of magnitude below statistical and Markovian baselines. Under a Train-on-Synthetic-Test-on-Real protocol, synthetic-only training achieves $R^2=0.66\pm0.02$, or 82.7% of the real-data $R^2$ point estimate. The velocity-space model achieves a mean JS divergence of $0.0065$ across velocity components, speed, log-speed, and turning angle, with a maximum of $0.015\pm0.005$. Reconstructed path length is less accurate ($0.21\pm0.02$ versus $0.03\pm0.01$ in position space), although the protocols differ. Overall, unconditional diffusion captures local gaze kinematics and short-range temporal and directional structure, while long-range properties such as saccade counts and cumulative path geometry remain targets for future conditioned models.
Chinese Translation
眼动追踪数据的收集成本高昂，需要专门的硬件和受控的实验室条件，并且由于隐私限制，数据共享困难。我们使用两种互补的去噪扩散概率模型（Denoising Diffusion Probabilistic Models, DDPMs）来解决这一问题，实现从视觉搜索数据中无条件生成眼动动态。这两种模型均使用相同的FiLM条件一维U-Net（带自注意力机制，参数量为19.35M），并在来自28名参与者的8秒滑动窗口序列上进行训练。一种模型生成原始的二维注视位置序列，而另一种模型生成两分量速度序列；每种模型使用特定于表示的预处理、训练设置、数据划分和评估协议。两种模型在三个独立的训练种子上进行评估，报告的聚合指标为均值±标准差（SD）。位置空间模型在九个运动特征上实现了平均的Jensen-Shannon（JS）散度为0.016±0.004，特征-wise均值最高低于0.030，注视持续时间在真实数据的2%以内，Fréchet注视距离比统计基线和马尔可夫基线低一个数量级。在“在合成数据上训练-在真实数据上测试”的协议下，仅使用合成数据的训练实现了$R^2=0.66 ext{±}0.02$，相当于真实数据$R^2$点估计的82.7%。速度空间模型在速度分量、速度、对数速度和转向角上实现了平均JS散度为0.0065，最大值为0.015±0.005。重建路径长度的准确性较低（位置空间为0.21±0.02，对比于0.03±0.01），尽管协议有所不同。总体而言，无条件扩散模型能够捕捉局部眼动运动学及短期时间和方向结构，而诸如扫视次数和累积路径几何等长程特性仍然是未来条件模型的研究目标。
cs.CV / 8 / 2609.05523
Infrastructure-based Monocular 3D Vehicle Localization Framework with Experimental Validation
基于基础设施的单目三维车辆定位框架及其实验验证
Abstract
This paper presents a one-stage learning framework that maps monocular roadside-camera images directly to vehicle states in a ground-fixed coordinate frame. Unlike conventional approaches that first detect vehicles in the image plane and subsequently apply geometric post-processing, the proposed method leverages features from a pretrained object detector to jointly estimate each vehicle's ground-plane position, dimensions, and yaw angle. The framework therefore uses visual features not only for vehicle detection but also for direct spatial and orientation estimation. To support model training and evaluation, we develop a data-collection and label-generation pipeline based on synchronized video from a roadside camera and an unmanned aerial vehicle (UAV). Acting as a temporary top-view sensing platform, the UAV provides vehicle trajectories, dimensions, and orientations, which are transformed into the ground-fixed coordinate frame and temporally aligned with the roadside-camera images to generate ground-truth labels. The framework is evaluated using data collected during multiple experiments at the Mcity Test Facility. Results show that the proposed method can recover vehicle trajectories and orientations from monocular roadside imagery without a separate geometric post-processing stage, demonstrating its potential as a scalable approach to infrastructure-based perception at urban intersections.
Chinese Translation
本文提出了一种单阶段学习框架，直接将单目路边摄像头图像映射到地面固定坐标系中的车辆状态。与传统方法首先在图像平面中检测车辆并随后应用几何后处理不同，所提出的方法利用预训练物体检测器的特征，联合估计每辆车的地面位置、尺寸和偏航角。因此，该框架不仅利用视觉特征进行车辆检测，还用于直接的空间和方向估计。为了支持模型训练和评估，我们开发了一种基于路边摄像头和无人机（UAV）同步视频的数据收集和标签生成管道。作为一个临时的顶视感知平台，无人机提供车辆轨迹、尺寸和方向，这些信息被转换到地面固定坐标系中，并与路边摄像头图像进行时间对齐，以生成真实标签。该框架在Mcity测试设施的多次实验中进行了评估。结果表明，所提出的方法能够从单目路边图像中恢复车辆轨迹和方向，而无需单独的几何后处理阶段，展示了其作为城市交叉口基础设施感知的可扩展方法的潜力。
cs.CV / 9 / 2609.05525
DIVA: Exploiting Cross-Step Conditional Propagation for Visual Jailbreaks in Discrete Diffusion Vision-Language Models
DIVA：利用跨步骤条件传播在离散扩散视觉语言模型中的视觉越狱
Abstract
Large vision-language models (VLMs) are increasingly deployed in safety-critical settings, yet existing visual jailbreak research has focused almost exclusively on autoregressive architectures, leaving an important emerging family unstudied: multimodal discrete diffusion vision-language models (dVLMs). We identify a vulnerability specific to diffusion generation: because the visual embedding conditions every reverse denoising step rather than acting as a one-time prefix, adversarial visual semantics are repeatedly propagated and amplified across the generation trajectory, a phenomenon we term cross-step conditional propagation. We provide empirical evidence through stage-sensitivity analysis, prompt-level switch rates, and pairwise denoising-bin disagreement metrics, confirmed by bootstrap resampling. We propose DIVA (Discrete-diffusion Vision-language model Attack), a white-box visual jailbreak framework using cross-modal intent obfuscation and diffusion-aware multi-timestep adversarial optimization. Across three dVLMs, DIVA reaches 58.8%, 67.7%, and 69.1% HADES ASR under the Beaver reward-model metric, outperforming visual jailbreak baselines designed for autoregressive models. Code: https://github.com/loststars2002/DIVA
Chinese Translation
大型视觉语言模型（VLMs）越来越多地应用于安全关键的环境中，但现有的视觉越狱研究几乎专注于自回归架构，未对一个重要的新兴家族进行研究：多模态离散扩散视觉语言模型（dVLMs）。我们识别出一种特定于扩散生成的脆弱性：由于视觉嵌入在每个反向去噪步骤中都起到条件作用，而不是作为一次性前缀，敌对视觉语义在生成轨迹中被反复传播和放大，这一现象我们称之为跨步骤条件传播。我们通过阶段敏感性分析、提示级切换率和成对去噪箱不一致性度量提供了实证证据，并通过自助重抽样进行了验证。我们提出了DIVA（离散扩散视觉语言模型攻击），这是一个使用跨模态意图模糊化和扩散感知多时间步敌对优化的白盒视觉越狱框架。在三个dVLMs中，DIVA在Beaver奖励模型指标下达到了58.8%、67.7%和69.1%的HADES ASR，超越了为自回归模型设计的视觉越狱基线。代码：https://github.com/loststars2002/DIVA
cs.CV / 10 / 2609.05532
A Specialized Large Multimodal Model for Interpreting PET/CT in Head and Neck Cancer
用于头颈癌PET/CT解读的专用大型多模态模型
Abstract
Background: Diagnosing head and neck cancer using PET/CT is clinically challenging and time-consuming due to the anatomical complexity of the region, motivating computer-aided diagnosis (CAD). Generalist Large Multimodal Models (LMMs) remain limited in medical contexts by insufficient domain-specific knowledge, privacy and security concerns, and verbosity, motivating specialized standalone LMMs. Purpose: We evaluated the feasibility of a specialized LMM for automated PET/CT interpretation in head and neck cancer using a large-scale multi-institutional PET/CT dataset, a tailored training curriculum, and autoregressive training. Methods: LLaVA-NeXT was fine-tuned using a two-level curriculum with image-conversation pairs curated by two radiologists from public data. The dataset included clinically important annotations such as primary tumor presence and metastatic lymph node location. Level 1 used 28,000 image-conversation pairs to learn basic information, including modality type and hypermetabolism. Level 2 used 12,975 pairs to learn primary tumor presence and the existence and anatomical location of cervical lymph node metastases. External validation included four institutions with diverse imaging devices. Results: The specialized LMM substantially outperformed ChatGPT and LLaVA-NeXT. In Level-2 external validation, ROUGE-L, ROUGE-S, Cosine Similarity, Precision, Recall, and F1 were 0.8751, 0.8794, 0.8324, 0.8794, 0.8711, and 0.8751, while generalist models consistently scored below 0.1. Primary tumor classification accuracy was 83.14 +/- 1.15% internally and 69.03 +/- 0.81% externally. For lymph node localization, the corresponding scores were 0.6389, 0.6257, 0.5287, 0.5782, 0.6371, and 0.6648. Conclusion: Specialized LMMs show promising results for fast, accurate PET/CT-based diagnostic support and medical education, highlighting their potential for clinical translation.
Chinese Translation
背景：由于头颈部区域的解剖复杂性，使用PET/CT诊断头颈癌在临床上具有挑战性且耗时，这促使了计算机辅助诊断（CAD）的发展。通用大型多模态模型（LMMs）在医学领域的应用受到限制，原因包括领域特定知识不足、隐私和安全问题以及冗长性，这促使了专用独立LMMs的需求。目的：我们评估了一种专用LMM在头颈癌自动PET/CT解读中的可行性，使用了大规模多机构的PET/CT数据集、量身定制的训练课程和自回归训练。方法：LLaVA-NeXT通过两个级别的课程进行了微调，使用了由两位放射科医生从公共数据中策划的图像-对话对。数据集中包含了临床重要的注释，例如原发肿瘤的存在和转移淋巴结的位置。第一级使用28,000个图像-对话对学习基本信息，包括模态类型和高代谢。第二级使用12,975个对学习原发肿瘤的存在以及颈部淋巴结转移的存在和解剖位置。外部验证包括四个具有不同成像设备的机构。结果：专用LMM的表现显著优于ChatGPT和LLaVA-NeXT。在第二级外部验证中，ROUGE-L、ROUGE-S、余弦相似度、精确度、召回率和F1分别为0.8751、0.8794、0.8324、0.8794、0.8711和0.8751，而通用模型的得分始终低于0.1。原发肿瘤分类的准确率在内部为83.14 +/- 1.15%，在外部为69.03 +/- 0.81%。对于淋巴结定位，相应的得分为0.6389、0.6257、0.5287、0.5782、0.6371和0.6648。结论：专用LMM在快速、准确的PET/CT基础诊断支持和医学教育中显示出良好的前景，突显了其临床转化的潜力。
cs.CV / 11 / 2609.05533
SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models
SimpleMemVLA：一种简单但有效的原生视频记忆用于视觉-语言-动作模型
Abstract
Long-horizon manipulation is partially observable: the information needed to choose the next action may appear only in observations from minutes earlier. Existing memory mechanisms: retrieval banks, learned compressors, recurrent states must decide what to keep from the past before knowing what a future decision will require. This was motivated by the assumption that minute-scale history is too large to process directly, which modern VLM backbones no longer make true. In this work, we introduce SimpleMemVLA, a VLA without a dedicated memory module. It keeps the sampled history intact and passes it to the backbone in the timestamped video format the backbone was pretrained to process; the hidden states of a generated sub-task then form the only channel from history to a standard flow-matching action head. Since consecutive decisions share most of their history, prefilling the shared prefix during action execution keeps latency close to a single-frame VLA. SimpleMemVLA sets a new state of the art on four memory benchmarks without cost on general-purpose control. Holding the backbone and training setup fixed, it outperforms retrieval, compression and recurrent-state mechanisms by a wide margin, and causal interventions confirm that the policy genuinely reads its history. Code available at https://github.com/wadeKeith/SimpleMemVLA
Chinese Translation
长时间跨度的操作是部分可观察的：选择下一个动作所需的信息可能仅在几分钟前的观察中出现。现有的记忆机制：检索库、学习压缩器、递归状态必须在知道未来决策需要什么之前决定保留过去的哪些信息。这一假设源于认为分钟级的历史信息过于庞大，无法直接处理，而现代的视觉-语言-模型（VLM）主干网络已不再成立。在本研究中，我们引入了SimpleMemVLA，这是一种没有专用记忆模块的视觉-语言-动作（VLA）模型。它保持采样历史的完整性，并以主干网络预训练时处理的时间戳视频格式将其传递给主干网络；生成的子任务的隐藏状态则形成从历史到标准流匹配动作头的唯一通道。由于连续决策共享大部分历史信息，在动作执行过程中预填充共享前缀使延迟接近单帧VLA。SimpleMemVLA在四个记忆基准测试中设定了新的最先进水平，而在通用控制上没有成本。固定主干网络和训练设置后，它在性能上大幅超越了检索、压缩和递归状态机制，因果干预证实该策略确实读取了其历史。代码可在 https://github.com/wadeKeith/SimpleMemVLA 获取。
cs.CV / 12 / 2609.05535
Beyond the Verdict: Evidence-Aligned Evaluation of Visual Prompt-Injection Guardrails
超越裁决：与证据对齐的视觉提示注入防护评估
Abstract
Verdict-only evaluation does not reveal whether a vision-language model (VLM) used the visual evidence that should support its decision. We study this problem in web-agent guardrails, where a VLM judges whether on-screen text conflicts with a user instruction. We introduce Mind2Web-Injection, a benchmark of 9,954 instruction-screenshot pairs with instruction-relative labels, pixel-exact evidence boxes, and matched image-side counterfactuals. Across six VLMs, two models with nearly identical average precision differ ninefold in Evidence-Aligned Detection (EAD), the fraction of attacks both detected and correctly localized. To test whether a verdict depends on the command cited as evidence, we replace the instruction with one that endorses that command. Qwen3-VL-32B, the strongest open-weight localizer, returns aligned in only 58.7% of cases, whereas GPT-5.6-luna does so in 99.9%. To diagnose these failures, we propose two training-free interventions. ReadGate improves grounding without changing verdicts, while CmdCompare tests whether explicit instruction-command comparison resolves instruction-side inconsistency. These results motivate reporting verdict correctness, evidence localization, and counterfactual responsiveness separately.
Chinese Translation
仅依赖裁决的评估无法揭示视觉语言模型（VLM）是否使用了应支持其决策的视觉证据。我们在网络代理防护中研究这一问题，其中VLM判断屏幕上的文本是否与用户指令相冲突。我们引入了Mind2Web-Injection，这是一个包含9,954对指令-屏幕截图的基准数据集，配有相对于指令的标签、像素精确的证据框和匹配的图像侧反事实。在六个VLM中，两个模型的平均精度几乎相同，但在证据对齐检测（EAD）中差异达到九倍，EAD是指同时检测到并正确定位的攻击比例。为了测试裁决是否依赖于作为证据引用的指令，我们用一个支持该指令的新指令替换原指令。Qwen3-VL-32B，最强的开放权重定位器，仅在58.7%的情况下返回对齐，而GPT-5.6-luna则在99.9%的情况下返回对齐。为了诊断这些失败，我们提出了两种无训练的干预措施。ReadGate在不改变裁决的情况下改善了定位，而CmdCompare测试显式指令与命令的比较是否能解决指令侧的不一致性。这些结果促使我们分别报告裁决正确性、证据定位和反事实响应性。
cs.CV / 13 / 2609.05537
VIS-DICT: A Visual Dictionary for Missing Modality Imputation in Social Network Depression Detection
VIS-DICT：用于社交网络抑郁检测中缺失模态插补的视觉字典
Abstract
Tracking social media posts can help spot early signs of depression. Recent studies show that combining text and images works better for detecting depression than using text alone. However, many social media posts do not have images, which makes it hard to use multimodal models. Most existing methods fill in missing images using retrieval or generative models that need extra training. In this paper, we introduce Vis-Dict, a dictionary-based method that builds missing visual features by linking words to average image vectors from complete training posts. These estimated visual features are then combined with text to track changes in user behavior over time. We tested Vis-Dict on a social media dataset using user timelines of up to 512 posts and compared it with other missing-data methods. The results show that Vis-Dict performs on par with generative networks, reaching an F1-score of 0.9454 and an ROC-AUC of 0.9890. Most importantly, Vis-Dict achieves this strong performance with zero trainable parameters for image generation. These findings show that directly connecting words to visual features is an effective and practical way to handle missing images in depression detection systems.
Chinese Translation
跟踪社交媒体帖子可以帮助发现抑郁的早期迹象。最近的研究表明，结合文本和图像的方式比单独使用文本更有效地检测抑郁。然而，许多社交媒体帖子没有图像，这使得使用多模态模型变得困难。现有的大多数方法使用检索或生成模型填补缺失的图像，这些模型需要额外的训练。在本文中，我们提出了Vis-Dict，一种基于字典的方法，通过将词汇与完整训练帖子中的平均图像向量关联来构建缺失的视觉特征。这些估计的视觉特征随后与文本结合，以跟踪用户行为随时间的变化。我们在一个社交媒体数据集上测试了Vis-Dict，使用了最多512个帖子的用户时间线，并与其他缺失数据方法进行了比较。结果表明，Vis-Dict的表现与生成网络相当，达到了0.9454的F1-score和0.9890的ROC-AUC。最重要的是，Vis-Dict在图像生成方面实现了零可训练参数的强大性能。这些发现表明，直接将词汇与视觉特征连接起来是一种有效且实用的方法，可以处理抑郁检测系统中的缺失图像。
cs.CV / 14 / 2609.05539
Dual-Latent Memory Routing for Vision-Language Reasoning
用于视觉-语言推理的双潜记忆路由
Abstract
Multimodal large language models (MLLMs) have recently made strong progress in vision-language reasoning, yet their performance often degrades as generations grow longer. A key factor is that they frequently lose track of earlier visual evidence and intermediate constraints under a monolithic growing context. Inspired by how humans separately recall what they see and what they infer when solving complex tasks, we propose DLMR, a parameter-efficient mechanism that equips MLLMs with Dual Latent Memories: a visual memory that compresses image evidence and a reasoning memory that tracks intermediate conclusions and constraints. A Router then dynamically decides which memory and how much to reuse during inference, preserving visual grounding while maintaining coherent long-horizon reasoning. DLMR is trained in three stages, from latent memory construction to selective router learning, while keeping the base MLLM frozen, yielding substantial gains on both general and reasoning benchmarks with only a small number of additional trainable parameters. Analyses further show interpretable, state-dependent routing with specialized memory roles and reduced decoding tokens over long generations. Code is available at https://github.com/Hunter-Wrynn/DLMR.
Chinese Translation
多模态大型语言模型（MLLMs）最近在视觉-语言推理方面取得了显著进展，但随着生成长度的增加，它们的性能往往会下降。一个关键因素是它们在单一增长上下文中经常无法追踪早期的视觉证据和中间约束。受到人类在解决复杂任务时分别回忆所见和推断的启发，我们提出了DLMR（双潜记忆路由），这是一种参数高效的机制，为MLLMs配备了双潜记忆：一种压缩图像证据的视觉记忆和一种跟踪中间结论与约束的推理记忆。然后，路由器在推理过程中动态决定使用哪个记忆以及复用多少，从而在保持视觉基础的同时维持连贯的长时间推理。DLMR分三个阶段进行训练，从潜记忆构建到选择性路由学习，同时保持基础MLLM不变，使用仅少量额外可训练参数在一般和推理基准上获得了显著提升。分析进一步表明，具有可解释性、状态依赖的路由，配合专业的记忆角色，并在长生成过程中减少了解码标记。代码可在 https://github.com/Hunter-Wrynn/DLMR 获取。
cs.CV / 15 / 2609.05540
Knowing When Not to Answer: Abstention and Refusal Reasoning in Vision--Language Models
知道何时不回答：视觉-语言模型中的弃权与拒绝推理
Abstract
Many medical conditions require diagnosis through detailed, multi-context clinical assessment rather than from visual appearance alone. Despite this, vision-language models (VLMs) are increasingly queried to interpret images in ways that touch on medical or diagnostic judgments, raising safety concerns when such inferences are unsupported. ASD diagnosis requires behavioral and developmental evidence, not static facial photographs. We audit whether VLMs abstain from this unanswerable paired-image query, and whether expressions sway non-abstaining choices. We introduce PARITY (Paired Assessment with Reused Identity), a synthetic, demographically balanced set of identity-controlled neutral/expression portrait pairs with neutral-neutral controls. All identities are synthetic and have no ASD status; because the query is unanswerable from images, any non-abstaining selection is treated as a harmful attribution. Across contemporary VLMs, we find a clear split between refusal-first models and speculative models; in the latter, certain expressions disproportionately trigger harmful selections. Clinical guardrails and single-image framing substantially increase abstention, suggesting actionable mitigations in both prompting and interface design
Chinese Translation
许多医疗状况需要通过详细的多情境临床评估进行诊断，而不仅仅依赖于视觉外观。尽管如此，视觉-语言模型（VLMs）越来越多地被查询以解释图像，这涉及到医疗或诊断判断，因而在这些推断缺乏支持时引发安全隐患。自闭症谱系障碍（ASD）的诊断需要行为和发展证据，而不是静态的面部照片。我们审查VLMs是否在这一无法回答的成对图像查询中选择弃权，以及表情是否影响非弃权选择。我们引入PARITY（成对评估与重用身份），这是一个合成的、人口统计学上平衡的身份控制中性/表情肖像对的集合，包含中性-中性控制组。所有身份均为合成，且没有ASD状态；由于该查询无法仅从图像中回答，任何非弃权的选择都被视为有害归因。在当代VLMs中，我们发现拒绝优先模型与推测模型之间存在明显的分歧；在后者中，某些表情不成比例地触发有害选择。临床保护措施和单图像框架显著增加了弃权，表明在提示和界面设计中可以采取可行的缓解措施。
cs.CV / 16 / 2609.05541
Video Compression with Graph-inspired Neural Representation
基于图灵感的神经表示视频压缩
Abstract
Implicit Neural Representations (INR) provide a compact and content-adaptive paradigm for video compression, typically representing a video through shared network parameters and frame-indexed embeddings. Compared to conventional or autoencoder-based codecs, these approaches exploit temporal redundancy within videos in an implicit manner, which potentially results in sub-optimal compression performance. In this paper, we propose G-NeRV, a graph-inspired INR that explicitly improves temporal redundancy exploitation in the implicit latent space. Motivated by the total correlation principles in information theory, we construct a temporal neighborhood over frame embeddings and perform message passing to aggregate reusable information from neighboring frames through an adaptive gate controlling the injection of neighboring information. Inspired by the reference frame buffer in conventional video coding, a memory bank mechanism has been further designed to enable efficient temporal-neighbor retrieval under random frame-index sampling in INR training. This new representation model has been integrated into an advanced representation compression framework and compared with existing conventional and neural video codecs. The results show that the G-NeRV codec outperforms the state-of-the-art INR-based codec, NVRC, and the latest standard video codec, VVC VTM, by 8.86\% and 14.68\% (in BD-rate), respectively, measured by PSNR on the UVG dataset.
Chinese Translation
隐式神经表示（Implicit Neural Representations, INR）为视频压缩提供了一种紧凑且内容自适应的范式，通常通过共享网络参数和帧索引嵌入来表示视频。与传统或基于自编码器的编解码器相比，这些方法以隐式方式利用视频中的时间冗余，这可能导致亚最优的压缩性能。在本文中，我们提出了G-NeRV，这是一种基于图灵感的INR，明确改善了隐式潜在空间中时间冗余的利用。受信息理论中总相关原则的启发，我们在帧嵌入上构建了一个时间邻域，并通过自适应门控控制邻近信息的注入，进行消息传递以聚合来自邻近帧的可重用信息。受到传统视频编码中参考帧缓冲区的启发，我们进一步设计了一种记忆库机制，以便在INR训练中在随机帧索引采样下实现高效的时间邻居检索。该新表示模型已集成到先进的表示压缩框架中，并与现有的传统和神经视频编解码器进行了比较。结果表明，G-NeRV编解码器在UVG数据集上以PSNR测量的BD-rate上分别比最先进的基于INR的编解码器NVRC和最新标准视频编解码器VVC VTM提高了8.86%和14.68%。
cs.CV / 17 / 2609.05543
Do Depressive Facial Patterns Transfer Across Cultures and Contexts? Evidence from a German RCT and E-DAIC
抑郁面部模式是否跨文化和情境转移？来自德国随机对照试验和扩展痛苦分析访谈语料库的证据
Abstract
Automated assessment of depression from facial dynamics holds promise for scalable mental health monitoring, yet cross-corpus generalization of learned biomarkers remains an open challenge. We present a systematic bidirectional transfer study pairing the EmpkinS-EKSpression randomized controlled trial (RCT; N = 256, SCID-5-CV diagnoses) with the Extended Distress Analysis Interview Corpus (E-DAIC; N = 275, semi-structured clinical interviews), predicting depression severity and binary diagnostic status from facial action units, head pose, and gaze. Cross-corpus binary classification proves more robust than continuous PHQ-8 severity regression, with forward transfer achieving AUC = 0.70. Regression transfer is governed by functional context alignment: passive observation phases yield the most transferable models, while active emotion regulation phases elicit stronger within-corpus signals. These findings establish functional context alignment as the primary determinant of cross-corpus generalization, with passive elicitation contexts offering the best trade-off between within-corpus sensitivity and cross-corpus robustness.
Chinese Translation
基于面部动态的抑郁症自动评估为可扩展的心理健康监测提供了希望，但学习到的生物标志物的跨语料库泛化仍然是一个未解决的挑战。我们呈现了一项系统的双向转移研究，将EmpkinS-EKSpression随机对照试验（RCT；N = 256，SCID-5-CV诊断）与扩展痛苦分析访谈语料库（E-DAIC；N = 275，半结构化临床访谈）配对，预测面部动作单位、头部姿势和注视对抑郁严重程度和二元诊断状态的影响。跨语料库的二元分类证明比连续的PHQ-8严重程度回归更为稳健，前向转移的AUC达到0.70。回归转移受功能情境对齐的支配：被动观察阶段产生最具可转移性的模型，而主动情绪调节阶段则引发更强的语料库内信号。这些发现确立了功能情境对齐作为跨语料库泛化的主要决定因素，被动引发情境在语料库内敏感性与跨语料库稳健性之间提供了最佳的权衡。
cs.CV / 18 / 2609.05550
Subject-Relative Micro-Motion and Sleep Dynamics for Near-Infrared Video Sleep Staging
近红外视频睡眠分期中的主体相对微运动与睡眠动态
Abstract
Near-infrared (NIR) video is a promising modality for contactless sleep monitoring, but recent video-based sleep staging methods often use it as a route to reconstructed respiratory/cardiac proxies or cross-modal physiological representations. We study video-only sleep staging under labels defined by polysomnography (PSG), where the model infers sleep stages from NIR video alone without explicit physiological proxy reconstruction or auxiliary physiological signal supervision. This tests whether NIR video itself can provide informative sleep-stage evidence, rather than only serving as an input for recovering physiological proxies. We propose ViNUSS (Video-Native Unmediated Sleep Staging), a framework that combines subject-relative micro-motion learning with full-night sleep dynamics modeling. Spatially anchored pre-spatial micro-motion encoding preserves localized temporal variation together with its spatial context. Within-subject stage contrast learns stage cues with respect to each subject's night-specific baseline. Two-scale sleep dynamics modeling captures within-epoch motion evolution and organizes epoch-level evidence into a coherent full-night sleep-stage trajectory. On 475 overnight NIR recordings (~3,250 hours), ViNUSS achieves 0.80 accuracy and 0.78 macro-F1 for four-class sleep staging. Interpretability analysis suggests attention to thoraco-abdominal periodic motion and gross body movements associated with arousals and position changes. These results support NIR video as an independently informative and complementary modality for PSG-defined sleep-stage estimation
Chinese Translation
近红外（NIR）视频是一种有前景的无接触睡眠监测方式，但最近基于视频的睡眠分期方法通常将其作为重建呼吸/心脏代理或跨模态生理表征的途径。我们研究了在多导睡眠监测（PSG）定义的标签下，仅使用视频进行睡眠分期的情况，其中模型仅从NIR视频中推断睡眠阶段，而不进行显式的生理代理重建或辅助生理信号监督。这一研究检验了NIR视频本身是否能够提供有信息量的睡眠阶段证据，而不仅仅是作为恢复生理代理的输入。我们提出了ViNUSS（视频原生无中介睡眠分期），这是一个结合主体相对微运动学习与整夜睡眠动态建模的框架。空间锚定的预空间微运动编码保留了局部时间变化及其空间背景。被试内阶段对比学习与每个被试的夜间特定基线相关的阶段线索。双尺度睡眠动态建模捕捉了每个时间段内的运动演变，并将时间段级别的证据组织成一致的整夜睡眠阶段轨迹。在475个夜间NIR录音（约3250小时）上，ViNUSS实现了0.80的准确率和0.78的宏观F1值用于四类睡眠分期。可解释性分析表明，关注与觉醒和体位变化相关的胸腹周期性运动和大幅度身体运动。这些结果支持NIR视频作为一种独立的信息性和补充性模态，用于PSG定义的睡眠阶段估计。
cs.CV / 19 / 2609.05579
ViT3Flow: A Test-Time Training Transformer MeanFlow for Postoperative Radiograph Synthesis in Scoliosis
ViT3Flow：一种用于脊柱侧弯术后放射影像合成的测试时训练变换器均值流
Abstract
Predicting postoperative spinal morphology from preoperative radiographs could provide valuable support for scoliosis surgical planning, but remains challenging because surgical correction induces large spatial changes while anatomical structures must be faithfully retained. We formulate this problem as postoperative scoliosis radiograph synthesis and construct ScoliSurg, the first paired dataset for this task, comprising 632 preoperative--postoperative whole-spine radiograph pairs with structured morphology information. We further propose ViT$^{3}$Flow, a single-NFE conditional MeanFlow framework for efficient postoperative radiograph synthesis. ViT$^{3}$Flow models surgical correction as finite-interval generative transport and replaces conventional self-attention with test-time-training token mixers that perform sample-specific inner adaptation to the anatomy and deformity pattern of each case. In addition, a Spinal Morphology Extraction Agent extracts distributions of dominant-curve region and direction from the preoperative radiograph. These distributions guide Diagnosis-Routed Interval Cross-Attention (DRICA), which performs interval-dependent vertical, horizontal, joint, and global retrieval from a separate preoperative token stream. This design enables the evolving postoperative representation to incorporate spatially corresponding anatomical evidence throughout the transport process. Extensive experiments on ScoliSurg demonstrate that ViT$^{3}$Flow achieves the best performance among the compared methods in perceptual image quality, anatomical fidelity, and clinically relevant geometric accuracy, while requiring only a single network evaluation. These results highlight the potential of ViT$^{3}$Flow for efficient and anatomically faithful postoperative radiograph synthesis in scoliosis surgical planning.
Chinese Translation
从术前放射影像预测术后脊柱形态可以为脊柱侧弯手术规划提供有价值的支持，但由于手术矫正会引起大范围的空间变化，同时必须忠实保留解剖结构，因此这一任务仍然具有挑战性。我们将这一问题表述为术后脊柱侧弯放射影像合成，并构建了ScoliSurg，这是针对该任务的第一个配对数据集，包含632对具有结构形态信息的术前-术后全脊柱放射影像。我们进一步提出了ViT$^{3}$Flow，这是一种单NFE条件均值流框架，用于高效的术后放射影像合成。ViT$^{3}$Flow将手术矫正建模为有限区间生成传输，并用测试时训练的令牌混合器替代传统的自注意力机制，后者对每个案例的解剖结构和畸形模式进行样本特定的内部适应。此外，脊柱形态提取代理从术前放射影像中提取主曲线区域和方向的分布。这些分布指导诊断引导的区间交叉注意力（Diagnosis-Routed Interval Cross-Attention, DRICA），该机制从单独的术前令牌流中执行区间依赖的垂直、水平、联合和全局检索。这一设计使得不断演变的术后表征能够在整个传输过程中融入空间上对应的解剖证据。在ScoliSurg上的大量实验表明，ViT$^{3}$Flow在感知图像质量、解剖忠实度和临床相关几何精度方面在比较方法中表现最佳，同时仅需一次网络评估。这些结果突显了ViT$^{3}$Flow在脊柱侧弯手术规划中实现高效且解剖忠实的术后放射影像合成的潜力。
cs.CV / 20 / 2609.05583
An overview of 3D Vision-Language Models
3D视觉-语言模型概述
Abstract
Vision-Language Models (VLMs) are reshaping computer vision by aligning visual and textual embeddings, allowing models to recognize visual concepts and reason about them using natural language. Traditional 3D deep-learning models, however, are typically trained for specific tasks, such as classification, segmentation, or detection, and do not naturally support cross-modal retrieval from their embedding spaces using text or images as queries. To address this issue, Contrastive Language-Image Pretraining (CLIP)-based methods align 3D embeddings with pretrained image and text representations, giving rise to 3D Vision-Language Models (3D VLMs) that support zero-shot classification, cross-modal retrieval, and open-vocabulary recognition of 3D shapes. This tutorial provides an overview of 3D VLMs, ranging from basic definitions of 3D representations and their encoding into embeddings to cross-modal contrastive alignment, modern multimodal frameworks, and 3D Vision-Large Language Models (3D VLLMs). We present the main definitions of contrastive learning for multimodal embedding alignment and highlight recent advances in language-guided 3D Gaussian splatting, 3D shape generation, and embodied AI for robotics.
Chinese Translation
视觉-语言模型（VLMs）通过对齐视觉和文本嵌入，正在重塑计算机视觉，使模型能够识别视觉概念并使用自然语言进行推理。然而，传统的3D深度学习模型通常针对特定任务进行训练，如分类、分割或检测，并不自然支持使用文本或图像作为查询从其嵌入空间进行跨模态检索。为了解决这一问题，基于对比语言-图像预训练（CLIP）的方法将3D嵌入与预训练的图像和文本表示对齐，产生了支持零样本分类、跨模态检索和开放词汇识别3D形状的3D视觉-语言模型（3D VLMs）。本教程提供了3D VLMs的概述，涵盖了3D表示的基本定义及其编码为嵌入的过程，跨模态对比对齐，现代多模态框架，以及3D视觉-大型语言模型（3D VLLMs）。我们介绍了多模态嵌入对齐的对比学习的主要定义，并强调了在语言引导的3D高斯点云渲染、3D形状生成和机器人领域的具身人工智能方面的最新进展。
cs.CV / 21 / 2609.05594
SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agentic Layout Evolution
场景马赛克：通过混合智能布局演化实现高效多样的可模拟场景生成
Abstract
Diverse and simulation-ready indoor scenes are essential for interactive entertainment and embodied AI, yet their scalable generation remains challenging. Recent agentic text-to-3D scene pipelines that rely on vision-language models (VLMs) can generate scenes of high fidelity but require costly iterative object placement and refinement. Another mainstream paradigm, parametric image-to-3D scene models, produces scenes efficiently from strong priors learned from 2D images but often leads to imprecise and physically invalid scenes. More importantly, both paradigms struggle to output diverse scenes for a single input, making it hard for them to reflect the dynamically changing nature of real scenes. In this paper we propose \textbf{SceneMosaic}, a framework that combines the merits of both paradigms. It obtains the initial candidate from the learned image-based prior, and subsequently evolves the result through VLM agents, ensuring both efficiency and physical validity. Within the evolution process, SceneMosaic exploits the locality of natural scenes and decomposes a scene into independent local units, allowing separate evolution within each unit before composing the global scene via Cartesian product. On SceneEval-100, SceneMosaic matches the strongest agentic baseline in semantic layout quality with a 24x speedup, substantially reduces physical violations, and receives the highest human ratings. Our code is publicly available at https://github.com/rxjfighting/SceneMosaic.
Chinese Translation
多样化且适合模拟的室内场景对于互动娱乐和具身人工智能至关重要，但其可扩展生成仍然面临挑战。近期依赖于视觉-语言模型（VLM）的智能文本到3D场景管道能够生成高保真度的场景，但需要耗时的迭代对象放置和精细化。另一种主流范式，即参数化图像到3D场景模型，能够从2D图像中学习到的强先验高效生成场景，但常常导致不精确和物理上无效的场景。更重要的是，这两种范式在为单一输入输出多样化场景方面都存在困难，难以反映真实场景动态变化的特性。本文提出了 extbf{场景马赛克}（SceneMosaic），一个结合了两种范式优点的框架。它从学习到的基于图像的先验中获取初始候选场景，并随后通过VLM代理演化结果，确保了效率和物理有效性。在演化过程中，场景马赛克利用自然场景的局部性，将场景分解为独立的局部单元，允许在每个单元内进行独立演化，然后通过笛卡尔积组合成全局场景。在SceneEval-100上，场景马赛克在语义布局质量上与最强的智能基线相匹配，并实现了24倍的加速，显著减少了物理违规，并获得了最高的人类评分。我们的代码已公开发布在 https://github.com/rxjfighting/SceneMosaic。
cs.CV / 22 / 2609.05596
Time-Aware Assistive Navigation
时间感知辅助导航
Abstract
Can interactive vision-and-language agents learn not just what to say but also \textbf{\textit{when}} to say it? Current language models rarely plan over whether and when to realize a real-time response to a user. However, providing accurate and timely support for human decision-making, such as when guiding visually impaired individuals through urban environments, requires careful real-time responsiveness--poorly timed responses can distract users or add unnecessary cognitive load. As a machine intelligence challenge for Multimodal Large Language Model (MLLM)-based agents, we introduce a large-scale multimodal benchmark for an egocentric, assistive navigation task in complex outdoor environments. Using this benchmark, we uncover a fundamental limitation of off-the-shelf MLLMs in delivering safe and time-sensitive navigation instructions, even with model fine-tuning on substantial amounts of data. We then demonstrate that a simple yet effective modification of the model, including direct supervision to predict the underlying reason for each instruction, yields significant performance gains across open-loop, closed-loop, and sim-to-real generalization settings. However, our analysis highlights persistent challenges in temporal reasoning, safety-critical object awareness, and relational and distance understanding. To advance the development of scalable assistive agents, we will release our simulation, benchmark, and code (available at the project website: https://timeli-icra.github.io/).
Chinese Translation
交互式视觉与语言代理是否能够学习不仅在何时说什么，还能学习在何时说？当前的语言模型很少考虑何时以及是否对用户做出实时响应。然而，为人类决策提供准确和及时的支持，例如在引导视障人士穿越城市环境时，需要谨慎的实时响应——不合时宜的回应可能会分散用户注意力或增加不必要的认知负担。作为基于多模态大型语言模型（MLLM）的代理的机器智能挑战，我们引入了一个大规模的多模态基准，用于复杂户外环境中的自我中心辅助导航任务。通过使用该基准，我们发现即使在大量数据上进行模型微调，现成的MLLM在提供安全和时间敏感的导航指令方面仍存在根本性限制。随后，我们展示了一种简单而有效的模型修改，包括直接监督以预测每条指令的潜在原因，能够在开放循环、闭合循环和模拟到真实的泛化设置中显著提升性能。然而，我们的分析突出了在时间推理、安全关键物体意识以及关系和距离理解方面的持续挑战。为了推动可扩展辅助代理的发展，我们将发布我们的仿真、基准和代码（可在项目网站获取：https://timeli-icra.github.io/）。
cs.CV / 23 / 2609.05636
Unsupervised Transfer Clustering for Mitigating Cold Start in Active Prompt Learning
无监督迁移聚类以缓解主动提示学习中的冷启动问题
Abstract
Vision-Language Models (VLMs) are able to achieve impressive zero-shot classification performance by aligning visual and textual representations, but each new task still demands handcrafted prompts. Active Prompt Learning (APL) combines Active Learning (AL) and Prompt Learning (PL) into a single framework, allowing for the usage of the VLM prior knowledge for iteratively querying the most informative images to be labeled. However, the cold-start problem is still relevant for APL methods, where the performance of the initial query can be worse than random sampling. While recent state-of-the-art APL methods mitigate with balanced sampling and multimodal features, they rely on rigid, distance-based clustering to group these features. This simplistic approach can struggle to capture the complex, high-dimensional semantic distributions inherent to VLMs, leading to suboptimal query representativeness. Unsupervised transfer can be applied to these features as a possible alternative, since it is capable of inferring the underlying human labeling of a task without any form of supervision. This way, samples can be grouped in semantically coherent clusters. This paper proposes Unsupervised Transfer Clustering with Selective Querying (UTC+SQ), a framework that enhances a recent APL approach by leveraging state-of-the-art unsupervised transfer model. These models generate high-fidelity pseudo-labels that establish semantically meaningful clusters, allowing for the selection of more relevant samples. Experimental evaluations demonstrate that shifting from distance-based to projection-based clustering improves the representativeness of the queried subset, achieving accuracy gains in 6 of the 8 datasets tested.
Chinese Translation
视觉-语言模型（VLMs）通过对齐视觉和文本表示，能够实现令人印象深刻的零样本分类性能，但每个新任务仍然需要手工设计的提示。主动提示学习（APL）将主动学习（AL）和提示学习（PL）结合成一个单一框架，利用VLM的先验知识迭代查询最具信息量的待标记图像。然而，冷启动问题在APL方法中依然存在，初始查询的性能可能低于随机抽样。尽管最近的最先进APL方法通过平衡采样和多模态特征来缓解这一问题，但它们依赖于刚性的基于距离的聚类来对这些特征进行分组。这种简单的方法难以捕捉VLM固有的复杂高维语义分布，导致查询代表性的不足。无监督迁移可以作为这些特征的一个可能替代方案，因为它能够在没有任何形式监督的情况下推断任务的潜在人工标注。通过这种方式，样本可以被分组到语义一致的聚类中。本文提出了带选择性查询的无监督迁移聚类（UTC+SQ）框架，通过利用最先进的无监督迁移模型来增强最近的APL方法。这些模型生成高保真度的伪标签，建立语义上有意义的聚类，从而允许选择更相关的样本。实验评估表明，从基于距离的聚类转向基于投影的聚类提高了查询子集的代表性，在测试的8个数据集中有6个实现了准确率的提升。
cs.CV / 24 / 2609.05638
Facial Age Estimation for Age Fraud Detection in National ID Systems
国家身份证系统中面部年龄估计用于年龄欺诈检测
Abstract
Identity fraud during biometric enrollment and updates remains a major challenge for large-scale national identity systems. A common fraud vector is misrepresenting one's age to access age-restricted services or welfare schemes. In this work, we present SwinAge, a facial age estimation system designed for use within the Aadhaar biometric enrollment pipeline, to assist quality-check (QC) operators to flag potential age-related fraud. This is critical for a system like Aadhaar (the world's largest national identity programme), that holds about 1.5 billion unique identities, with 22.4 million new enrollments and 283 million updates in the last year. Building upon the SwinFace architecture with landmark-based similarity (warp affine) alignment, we train on a large in-house dataset of 1.45 million face images and evaluate on an independent, age-stratified test set of 283K images, both drawn from an ethnically diverse population of 716K unique subjects. We investigate three Aadhaar-specific operational thresholds (5, 18, and 60 years) and propose a deployment triage framework that flags suspected cases for manual review. Following NIST FATE, we report false acceptance/rejection rates (FAR/FRR) at each threshold rather than aggregate accuracy: at 1% FAR the model achieves an FRR of 3% (<5yrs), 0.4% (>18yrs) and 11.0% (>60yrs). SwinAge achieves a mean absolute error (MAE) of 2.94 years on the same test set, outperforming three zero-shot vision language models on all benchmarks, and improving the state-of-the-art on 5 out of 7 public benchmark datasets. We further report per-gender errors and distill lessons for national identity programs.
Chinese Translation
在生物特征登记和更新过程中，身份欺诈仍然是大规模国家身份系统面临的主要挑战之一。一个常见的欺诈手段是虚报年龄，以获取年龄限制服务或福利计划的访问权限。在本研究中，我们提出了SwinAge，一个面部年龄估计系统，旨在用于Aadhaar生物特征登记流程中，帮助质量检查（QC）操作员标记潜在的与年龄相关的欺诈。这对于像Aadhaar（世界上最大的国家身份项目）这样的系统至关重要，该系统拥有约15亿个独特身份，去年新增登记2240万次，更新2.83亿次。我们基于SwinFace架构，采用基于地标的相似性（扭曲仿射）对齐，训练了一个包含145万张面部图像的大型内部数据集，并在一个独立的、按年龄分层的测试集（283K图像）上进行评估，这些图像均来自716K个独特个体的多元种族群体。我们研究了三个特定于Aadhaar的操作阈值（5岁、18岁和60岁），并提出了一个部署分诊框架，以标记可疑案例以供人工审查。根据NIST FATE，我们报告了每个阈值的错误接受/拒绝率（FAR/FRR），而不是总体准确率：在1% FAR时，该模型在FRR上达到了3%（<5岁）、0.4%（>18岁）和11.0%（>60岁）。SwinAge在同一测试集上实现了2.94年的平均绝对误差（MAE），在所有基准测试中均优于三种零样本视觉语言模型，并在7个公共基准数据集中提高了5个的最新技术水平。我们还报告了按性别划分的错误，并提炼出对国家身份项目的经验教训。
cs.CV / 25 / 2609.05661
Srijika: OpenType-Layout-Reusing Font Restyling for Nine Indic Scripts
Srijika：九种印度文字的OpenType布局重用字体样式系统
Abstract
We present Srijika, a system for producing installable OpenType fonts for nine Brahmic scripts: Devanagari, Tamil, Bengali, Telugu, Kannada, Malayalam, Gujarati, Gurmukhi, and Odia. Rather than generating fonts from scratch, Srijika restyles glyph outlines from shaping-complete template fonts. It preserves the template's cmap and GSUB closure and its GPOS data under a documented metric policy, making every output a complete font by construction. This addresses a central challenge of Indic font generation: hundreds to thousands of conjuncts, half forms, and matra variants must remain mutually consistent under OpenType shaping. Srijika produces 66 TTFs: 57 curated presets and nine open-vocabulary showcase fonts. All pass the OpenType Sanitizer, while HarfBuzz and CoreText reproduce the template glyph-ID sequences on conjunct-heavy probes. A full-closure audit covering 80,915 glyphs and 54,812 anchors quantifies metric changes. Natural-language style selection uses Lipika, a retrieval index over approximately 650 open-license font families. A reference-conditioned latent diffusion model redraws template glyphs in the selected style, followed by content gating, harmonization, and shaped-cluster verification with fallback to template outlines. We evaluate against no-learning baselines. On diffusion-training-family-held-out SSIM gates, template copying outperforms generation on 50 of 56 faces. Style movement is measurable only with an internal same-model embedding whose training corpus includes the held-out families, so these results require caution. A learned baseline, independent style metric, and human study are outside this report's scope. Our contributions are the layout-reusing formulation and pipeline, its nine-script audit and benchmark, and a negative-results catalogue covering failed conditioning, objective choices, and data-hull limits of reference-guided restyling.
Chinese Translation
我们提出了Srijika，一个用于生成九种婆罗米文字（Devanagari、Tamil、Bengali、Telugu、Kannada、Malayalam、Gujarati、Gurmukhi和Odia）可安装OpenType字体的系统。Srijika并不是从头开始生成字体，而是从形状完整的模板字体中重新设计字形轮廓。它在一个文档化的度量政策下保留了模板的cmap和GSUB闭合以及其GPOS数据，使得每个输出在构造上都是一个完整的字体。这解决了印度字体生成的一个核心挑战：数百到数千个连字、半形式和音调变体必须在OpenType形状下保持相互一致。Srijika生成了66个TTF文件：57个精心策划的预设和9个开放词汇展示字体。所有字体均通过了OpenType Sanitizer，而HarfBuzz和CoreText在连字密集的探测中重现了模板字形ID序列。涵盖80,915个字形和54,812个锚点的完整闭合审计量化了度量变化。自然语言样式选择使用Lipika，这是一个约650个开放许可字体系列的检索索引。一个参考条件的潜在扩散模型在所选样式中重新绘制模板字形，随后进行内容门控、和谐化和形状集群验证，并回退到模板轮廓。我们与无学习基线进行评估。在扩散训练系列保留的SSIM门控上，模板复制在56个样式中的50个上优于生成。样式移动仅在包含保留系列的训练语料的内部同模型嵌入中可测量，因此这些结果需要谨慎解读。学习基线、独立样式度量和人类研究超出了本报告的范围。我们的贡献包括布局重用的公式和管道、其九种文字的审计和基准，以及涵盖失败条件、客观选择和参考指导重塑的数据边界限制的负结果目录。
cs.CV / 26 / 2609.05662
Full-Page Optical Music Recognition of Handwritten Monophonic Scores
手写单声部乐谱的全页光学音乐识别
Abstract
Full-page end-to-end Optical Music Recognition seeks to transcribe entire music pages directly into symbolic notation, avoiding the limitations of traditional pipelines that rely on accurate staff segmentation. Recent Transformer-based architectures have achieved strong performance on typeset scores, relying on large-scale synthetic data for pretraining. However, their applicability to handwritten music remains largely unexplored. In this work, we study full-page transcription on handwritten monophonic collections and analyze the impact of synthetic pretraining in this setting. To investigate which factors are most relevant during pretraining, we introduce a generator capable of producing visually coherent full-page scores in both typeset and handwritten styles. Experiments on three real handwritten datasets provide a comparative evaluation of several full-page pipelines and different synthetic pretraining strategies. The results suggest that the benefits of synthetic pretraining are primarily associated with learning structural layout conventions rather than with visual similarity to the target handwriting.
Chinese Translation
全页端到端光学音乐识别旨在将整页乐谱直接转录为符号记谱，避免了传统流程中对准确五线谱分割的依赖。近期基于Transformer的架构在排版乐谱上取得了良好的性能，依赖于大规模合成数据进行预训练。然而，这些方法在手写音乐上的适用性仍然未得到充分探索。在本研究中，我们研究了手写单声部乐谱的全页转录，并分析了在这一背景下合成预训练的影响。为了探讨在预训练过程中哪些因素最为相关，我们引入了一种生成器，能够生成在排版和手写风格下视觉上连贯的全页乐谱。在三个真实手写数据集上的实验提供了对几种全页处理流程和不同合成预训练策略的比较评估。结果表明，合成预训练的好处主要与学习结构布局规范相关，而非与目标手写的视觉相似性有关。
cs.CV / 27 / 2609.05730
Bigger Text Encoders Can Hurt CLIP Zero-Shot Performance
更大的文本编码器可能会损害CLIP的零-shot性能
Abstract
Contrastive Language-Image Pretraining (CLIP) is a building block of many machine learning applications. Scaling laws have guided resource allocation for large-scale training, yet prior work treats total CLIP model size as a single variable, without exploring how the capacity split between encoders impacts downstream performance. Here, we train multiple CLIP models with different vision and text encoder sizes, revealing that for most vision encoders, there is an optimal text encoder size beyond which zero-shot performance degrades---even as total parameter count increases. Exploiting this behavior yields efficient configurations that match the zero-shot performance of the standard ViT-B/16 architecture with up to 55% fewer parameters. We further show that this degradation stems from overfitting induced by the oversized text encoder, and that using modality-specific weight decay coefficients not only recovers but improves performance across all degraded configurations. A geometric analysis reveals a trade-off in which scaling the text encoder improves embedding uniformity but worsens cross-modal alignment; we further show that these metrics are predictive of zero-shot performance. We hope these findings motivate CLIP architectures and training methods that counteract this degradation, a prerequisite for scaling CLIP reliably and efficiently.
Chinese Translation
对比语言-图像预训练（CLIP）是许多机器学习应用的基础。缩放法则指导了大规模训练的资源分配，然而以往的研究将总的CLIP模型大小视为单一变量，而未探讨编码器之间的容量分配如何影响下游性能。在本研究中，我们训练了多个具有不同视觉和文本编码器大小的CLIP模型，揭示出对于大多数视觉编码器而言，存在一个最佳的文本编码器大小，超过该大小后零-shot性能会下降——即使总参数数量在增加。利用这种行为，我们得到了高效的配置，其零-shot性能与标准ViT-B/16架构相匹配，但参数减少了多达55%。我们进一步表明，这种性能下降源于过大的文本编码器引起的过拟合，并且使用特定模态的权重衰减系数不仅恢复了性能，还改善了所有降级配置的表现。几何分析揭示了一个权衡关系，即缩放文本编码器改善了嵌入均匀性，但恶化了跨模态对齐；我们进一步表明，这些指标可以预测零-shot性能。我们希望这些发现能够激励CLIP架构和训练方法的改进，以抵消这种性能下降，这是可靠和高效地扩展CLIP的前提。
cs.CV / 28 / 2609.05738
RenderFormer-V2: Neural Rendering with Heterogeneous Scene Primitives
RenderFormer-V2：具有异构场景原语的神经渲染
Abstract
We present 'RenderFormer-V2', a unified learned transformer-based neural rendering model, complementary to modern physics-based rendering systems, that can handle diverse light-transport effects such as caustics, volumetric scattering, environment lighting, textured and displaced surfaces and out-of-distribution materials without per-scene training or specialized code. RenderFormer-V2 models global light transport as a sequence-to-sequence transformation. Following its predecessor, RenderFormer-V2 also employs a two stage process: a view-independent stage that resolves intra-scene primitive to primitive transport, and a view-dependent stage that transforms the internal neural scene representation into image pixels. Different from RenderFormer, our model employs a novel combined windowed-attention and rendering-informed attention sink in the view-independent stage to improve scalability while maintaining render accuracy. To further improve versatility, RenderFormerV2 supports heterogeneous scene primitives, including environment maps and participating media, and it employs a material encoding independent of the underlying surface reflectance model that encodes material appearance via a novel neural embedding. We demonstrate the versatility of RenderFormer-V2 on a variety of scenes and perform an extensive ablation of the improved attention mechanism.
Chinese Translation
我们提出了'RenderFormer-V2'，这是一种统一的基于学习的变换器神经渲染模型，作为现代物理基础渲染系统的补充，能够处理多样的光传输效果，如焦散、体积散射、环境光照、纹理和位移表面以及超出分布的材料，而无需针对每个场景进行训练或专门的代码。RenderFormer-V2将全局光传输建模为序列到序列的转换。与其前身RenderFormer类似，RenderFormer-V2也采用了两阶段的过程：一个视角无关的阶段，解决场景内原语之间的传输，以及一个视角相关的阶段，将内部神经场景表示转换为图像像素。与RenderFormer不同，我们的模型在视角无关阶段采用了一种新颖的组合窗口注意力和渲染信息注意力汇聚，以提高可扩展性，同时保持渲染精度。为了进一步提高通用性，RenderFormer-V2支持异构场景原语，包括环境贴图和参与介质，并且它采用了一种与基础表面反射模型无关的材料编码，通过一种新颖的神经嵌入编码材料外观。我们在多种场景上展示了RenderFormer-V2的多功能性，并对改进的注意力机制进行了广泛的消融实验。
cs.CV / 29 / 2609.05742
SeRV: Semantic-Aligned Residual Vector Quantization for American Sign Language Generation
SeRV：用于美国手语生成的语义对齐残差向量量化
Abstract
American Sign Language (ASL) generation remains challenging due to limited paired text-ASL motion data and the difficulty of learning motion representations both precise for reconstruction and predictable from linguistic input. Existing methods rely on motion tokenizers optimized for reconstruction, without explicit semantic supervision from paired text. As a result, the learned tokens remain limited in supporting semantically consistent and fine-grained ASL motion generation. To address this limitation, we propose SeRV (Semantic-Aligned Residual Vector Quantization), a semantic-aligned RVQ tokenizer for ASL generation. SeRV learns a semantically structured residual token space by combining sentence-level motion-text alignment with token-level text-conditioned supervision. Building on this tokenizer, a Hierarchical GPT predicts residual motion tokens in a coarse-to-fine manner, generating structurally coherent and semantically aligned 3D ASL motion. We further construct a large-scale reconstructed 3D ASL motion-text benchmark by recovering paired 3D motion from YouTube-ASL videos. Experiments across 375 hours of ASL video show that SeRV achieves state-of-the-art pose accuracy on both How2Sign and YouTube-ASL datasets, while producing semantically consistent 3D ASL motion directly from text.
Chinese Translation
美国手语（ASL）生成仍然面临挑战，原因在于配对的文本-ASL运动数据有限，以及学习既能精确重建又能从语言输入中预测的运动表示的困难。现有方法依赖于为重建优化的运动标记器，而没有来自配对文本的明确语义监督。因此，学习到的标记在支持语义一致和细粒度的ASL运动生成方面仍然有限。为了解决这一局限性，我们提出了SeRV（语义对齐残差向量量化），一种用于ASL生成的语义对齐RVQ标记器。SeRV通过结合句子级运动-文本对齐与标记级文本条件监督，学习一个语义结构化的残差标记空间。在此标记器的基础上，层次化GPT以粗到细的方式预测残差运动标记，生成结构上连贯且语义上对齐的3D ASL运动。我们进一步通过从YouTube-ASL视频中恢复配对的3D运动，构建了一个大规模重建的3D ASL运动-文本基准。对375小时ASL视频的实验表明，SeRV在How2Sign和YouTube-ASL数据集上实现了最先进的姿态准确性，同时能够直接从文本生成语义一致的3D ASL运动。
cs.CV / 30 / 2609.05744
Gaussian Linear Functional Manifold Method for Massive Point Cloud Data
用于大规模点云数据的高斯线性泛函流形方法
Abstract
Reconstructing continuous terrain manifolds from massive, unstructured airborne LiDAR point clouds remains challenging in complex Wildland-Urban Interface (WUI) environments, where deep neural networks require costly point-wise annotations and nonparametric surface reconstruction methods often lack structural interpretability. This paper introduces the Gaussian Linear Functional Manifold (GLFM), a physics-informed statistical framework that represents continuous surface topography using deterministic linear functional bases while modeling microscale diffuse laser backscatter as an isotropic Gaussian process. To avoid the quadratic computational cost of exact constrained maximum likelihood estimation, we develop an algebraic singular value decomposition (SVD) rank-reduction algorithm that enables linear-time parameter estimation and closed-form quadric classification. Evaluated on 35.2 km^2 of real-world aerial LiDAR data, GLFM automatically filters ground points and extracts morphological features, achieving an adjusted Rand index (ARI) of 0.9933 against field-verified ground truth and outperforming four leading baselines while maintaining an out-of-core memory footprint. The framework provides a rigorous, interpretable, and scalable foundation for large-scale point cloud analytics.
Chinese Translation
从大规模、非结构化的空中激光雷达点云重建连续地形流形在复杂的野地-城市交界（WUI）环境中仍然具有挑战性，在这些环境中，深度神经网络需要昂贵的逐点标注，而非参数表面重建方法往往缺乏结构可解释性。本文介绍了高斯线性泛函流形（GLFM），这是一种物理信息驱动的统计框架，利用确定性线性泛函基表示连续表面地形，同时将微尺度的漫反射激光后向散射建模为各向同性高斯过程。为了避免精确约束最大似然估计的二次计算成本，我们开发了一种代数奇异值分解（SVD）降秩算法，使得参数估计能够在线性时间内完成，并实现封闭形式的二次分类。在对35.2平方公里的真实世界航空激光雷达数据进行评估时，GLFM自动过滤地面点并提取形态特征，针对经过实地验证的地面真值，达到了0.9933的调整兰德指数（ARI），并在保持外存内存占用的同时超越了四个领先的基准。该框架为大规模点云分析提供了一个严谨、可解释且可扩展的基础。
cs.CV / 31 / 2609.05761
GeoContext: One Context Ladder, Two Failure Modes in Vision-Language Geolocation: Flat Reliance on User-Provided Location Context and False Confirmation of Location Claims
GeoContext：一个上下文阶梯，视觉-语言地理定位中的两种失败模式：对用户提供的位置上下文的平坦依赖和对位置声明的错误确认
Abstract
Visual geolocation benchmarks typically ask a model where an image was captured without accounting for the location context that users often provide. We introduce GeoContext, a resource supporting two complementary tasks: GeoHint, open-ended localization given a true but coarse location hint, and GeoVerify, binary verification of whether an image was taken within 150 m of a claimed place. GeoContext constructs a context ladder by stratifying nearby reference points according to distance and referenceability, allowing the image to remain fixed while the supplied context varies. The benchmark covers 109 sites in 30 cities and evaluates five vision-language models using 21,933 GeoHint responses and 6,270 GeoVerify responses. Our evaluation reveals three main patterns. First, hint repetition varies by only 1.5 percentage points across referenceability tiers and by less than 3 points across distance bands, while the resulting localization error increases steadily with hint distance. Second, behavior depends strongly on no-context performance: at sites with low no-context accuracy, the median ratio between localization error and hint distance is approximately 1.00, whereas at higher-accuracy sites it ranges from 0.24 to 0.69. After correcting for bias introduced by the site grouping procedure, only one of the five models retains a negative accuracy estimate when given a nearby hint. Third, in GeoVerify, no model reaches d' = 1 for decoys immediately beyond the 150 m tolerance. Model rankings also change when sensitivity is separated from response bias, and 83.8% of false acceptances are reported with confidence of at least 0.8. We release the benchmark, construction pipeline, audit decisions, and scoring code.
Chinese Translation
视觉地理定位基准通常要求模型指出图像拍摄的位置，而未考虑用户常常提供的位置上下文。我们引入了GeoContext，一个支持两项互补任务的资源：GeoHint，给定真实但粗略的位置提示进行开放式定位，以及GeoVerify，二元验证图像是否在声称地点150米范围内拍摄。GeoContext通过根据距离和可参考性对附近参考点进行分层，构建了一个上下文阶梯，使得图像保持固定，而提供的上下文则发生变化。该基准涵盖了30个城市中的109个地点，并使用21,933个GeoHint响应和6,270个GeoVerify响应评估了五个视觉-语言模型。我们的评估揭示了三种主要模式。首先，提示重复率在可参考性层次之间仅变化1.5个百分点，在距离带之间变化不到3个百分点，而随提示距离增加，定位误差稳步上升。其次，行为强烈依赖于无上下文性能：在无上下文准确率较低的地点，定位误差与提示距离的中位比率约为1.00，而在高准确率地点则范围在0.24到0.69之间。在纠正了由地点分组程序引入的偏差后，只有五个模型中的一个在给定附近提示时仍保留负的准确度估计。第三，在GeoVerify中，没有模型在150米容忍度之外的诱饵上达到d' = 1。当敏感性与响应偏差分开时，模型排名也发生变化，83.8%的错误接受报告的置信度至少为0.8。我们发布了基准、构建管道、审核决策和评分代码。
cs.CV / 32 / 2609.05762
AAMBERS-UAV: Acquisition-Aware Multimodal Backbone Evaluation and Ranking for UAV Weedy Rice Segmentation
AAMBERS-UAV：面向无人机杂草稻分割的获取感知多模态骨干网络评估与排序
Abstract
UAV image collections contain spatially and temporally related frames, yet semantic-segmentation benchmarks commonly split them at image level. Such splitting can place samples from one acquisition in both model development and testing, obscuring transfer to a genuinely new survey. Using the 734-sample WeedyRice-RGBMS-DB, we fix a 124-image target-acquisition test set and compare two protocols with identical train, validation, and test counts: target-held-out, which excludes the target acquisition from development, and target-exposed, which admits its remaining images. SegFormer-B0 is evaluated with RGB, four-band multispectral (MS), and seven-channel RGB+MS input over two fixed-split seeds. RGB is strongest under complete acquisition holdout ($0.7317\pm0.0201$ IoU), whereas RGB+MS becomes strongest after target exposure ($0.7822\pm0.0269$). A fixed-split U-Net/ResNet18 replication confirms positive exposure gains for all three inputs, but retains RGB as the best modality under both protocols. Acquisition exposure therefore increases measured performance across both evaluated backbones, while its effect on modality ranking is architecture-dependent. A supplied-split audit reveals strong near-sequential dependence, and corruption tests show that early fusion is substantially more sensitive to RGB--MS displacement than to moderate radiometric scaling. These results support acquisition-aware same-test evaluation as a necessary complement to ordinary image-level splitting in multimodal UAV benchmarks. The code and supporting the findings of this study will be publicly released upon acceptance of the paper.
Chinese Translation
无人机图像集合包含空间和时间上相关的帧，但语义分割基准通常在图像级别进行拆分。这种拆分可能导致来自同一获取的样本同时出现在模型开发和测试中，从而模糊了对真正新调查的转移。使用734个样本的WeedyRice-RGBMS-DB，我们固定了一个124幅图像的目标获取测试集，并比较了两种具有相同训练、验证和测试数量的协议：目标保留（target-held-out），即在开发中排除目标获取，以及目标暴露（target-exposed），即允许其剩余图像。使用RGB、四波段多光谱（MS）和七通道RGB+MS输入对SegFormer-B0进行了评估，基于两个固定拆分种子。RGB在完全获取保留下表现最佳（$0.7317 ext{±}0.0201$ IoU），而在目标暴露后RGB+MS表现最佳（$0.7822 ext{±}0.0269$）。固定拆分的U-Net/ResNet18复现确认了所有三种输入的正向暴露增益，但在两种协议下RGB仍然是最佳模态。因此，获取暴露提高了评估的性能，而其对模态排名的影响依赖于架构。提供的拆分审计显示出强烈的近序列依赖性，腐败测试表明早期融合对RGB与MS位移的敏感性显著高于对中等辐射标定的敏感性。这些结果支持获取感知的同测试评估，作为多模态无人机基准中普通图像级拆分的必要补充。本研究的代码和支持结果将在论文接受后公开发布。
cs.CV / 33 / 2609.05807
MolParser-Mobile: Ultrafast OCSR System for Large-Scale Chemical Literature Mining
MolParser-Mobile：大规模化学文献挖掘的超快速光学化学结构识别系统
Abstract
Optical Chemical Structure Recognition (OCSR) is a fundamental component of chemical literature mining, enabling molecular database construction, reaction extraction, and AI-driven scientific discovery. Despite substantial progress in recognition accuracy with recent deep learning-based methods, inference throughput remains a critical bottleneck that limits web-scale deployment. To address this challenge, we propose MolParser-Mobile, an AutoML-optimized lightweight end-to-end OCSR framework. MolParser-Mobile contains only 9.98M parameters, while reaching a throughput of 1,520 molecules per second on a single NVIDIA RTX 4090D GPU. Despite its compact design, it maintains competitive and, on several benchmarks, superior recognition accuracy.
Chinese Translation
光学化学结构识别（Optical Chemical Structure Recognition, OCSR）是化学文献挖掘的基本组成部分，能够实现分子数据库构建、反应提取以及基于人工智能的科学发现。尽管近年来基于深度学习的方法在识别准确性上取得了显著进展，但推理吞吐量仍然是限制网络规模部署的关键瓶颈。为了解决这一挑战，我们提出了MolParser-Mobile，一个经过AutoML优化的轻量级端到端OCSR框架。MolParser-Mobile仅包含9.98M参数，同时在单个NVIDIA RTX 4090D GPU上实现了每秒1,520个分子的吞吐量。尽管设计紧凑，但它在多个基准测试中保持了竞争力，并且在某些情况下表现出更优的识别准确性。
cs.CV / 34 / 2609.05816
CoRe-SAM3: Conditional Semantic--Visual Reconciliation for SAM3 Crack Segmentation
CoRe-SAM3：针对SAM3裂缝分割的条件语义-视觉调和
Abstract
Crack segmentation requires a model to recognize target semantics while accurately recovering thin, low-contrast, and topologically continuous local structures. Although SAM3 provides strong open-concept segmentation, its direct application to the crack domain still misses weak cracks, activates crack-like background regions, and produces local boundary errors. We first diagnose the functional differences between the internal prompt-conditioned semantic representation and native visual representation of SAM3 on five crack datasets. The results show that the semantic representation already carries most task information for crack prediction, whereas the utility of the visual representation depends on the current semantic state. Directly combining the two representations does not yield consistent gains. Based on this finding, we propose Conditional Semantic--Visual Reconciliation, termed CoRe. CoRe retains semantic prediction as the primary decision path, applies lightweight semantic calibration to adjust the target-domain decision mapping, and uses spatially aligned native visual evidence to generate a zero-initialized, bounded, and regularized conditional residual that selectively corrects existing predictions. Across five domains, CoRe-SAM3 improves the average Crack IoU from 62.34% to 70.47% and clDice from 81.98% to 89.24%, while introducing only 18.914 K trainable parameters. Prediction-transition analysis further shows that CoRe corrects an average of 34.38% of native errors, with a damage rate of only 0.23% on pixels correctly classified by native SAM3. These results demonstrate that constrained prediction correction based on the functional differences between internal representations provides an effective and parameter-efficient target-domain adaptation strategy for vision foundation models with strong task-specific semantic priors.
Chinese Translation
裂缝分割需要模型在准确恢复细小、低对比度和拓扑连续的局部结构的同时，识别目标语义。虽然SAM3提供了强大的开放概念分割，但其在裂缝领域的直接应用仍然无法识别微弱裂缝，激活裂缝状背景区域，并产生局部边界错误。我们首先诊断了SAM3在五个裂缝数据集上内部提示条件语义表示与原生视觉表示之间的功能差异。结果表明，语义表示已经携带了大部分裂缝预测的任务信息，而视觉表示的效用则依赖于当前的语义状态。直接将这两种表示结合并未带来一致的增益。基于这一发现，我们提出了条件语义-视觉调和（Conditional Semantic--Visual Reconciliation），简称CoRe。CoRe将语义预测作为主要决策路径，应用轻量级语义校准来调整目标领域的决策映射，并使用空间对齐的原生视觉证据生成一个零初始化、有限制且正则化的条件残差，以选择性地修正现有预测。在五个领域中，CoRe-SAM3将平均裂缝IoU从62.34%提高到70.47%，将clDice从81.98%提高到89.24%，同时仅引入18.914 K可训练参数。预测转移分析进一步表明，CoRe平均修正了34.38%的原生错误，而在原生SAM3正确分类的像素上损坏率仅为0.23%。这些结果表明，基于内部表示之间功能差异的约束预测修正为具有强任务特定语义先验的视觉基础模型提供了一种有效且参数高效的目标领域适应策略。
cs.CV / 35 / 2609.05841
Map the Possibilities: Spatial Belief Fields for Language-Goal Aerial Navigation
可能性的映射：用于语言目标空中导航的空间置信场（Spatial Belief Fields）
Abstract
Language-goal aerial navigation requires an agent to local- ize a potentially unobserved target from relational instruc- tions and partial observations, and translate this inference into metric actions in large-scale continuous environments. Existing methods often reduce language grounding to one single waypoint or action, prematurely collapsing the spatial uncertainty inherent in incomplete evidence and ambiguous relations. To address this limitation, we introduce SBFNav, a closed-loop navigation framework centered on a language- conditioned Spatial Belief Field (SBF). Unlike ego-centric maps that primarily record what has been observed, SBF rep- resents a task-conditioned distribution over plausible target locations, preserving multiple spatial hypotheses under par- tial evidence. At each step, this distribution is updated from accumulated observations as new evidence becomes avail- able. Built on this representation, SBFNav selects the goal that best aligns with the instruction and observations as a met- ric waypoint for control. Experiments on both the original and revised CityNav benchmarks achieve the best reported overall performance. On the Test Unseen split, our method improves SR from 25.91% to 32.29% and SPL from 19.63% to 30.43%. Ablation studies further confirm the advantages of spatial-belief modeling over single-point prediction.
Chinese Translation
语言目标空中导航要求智能体根据关系性指令和部分观测定位潜在未观测目标，并将该推断转化为大规模连续环境中的度量动作。现有方法通常将语言定位简化为单一航路点或动作，过早地压缩了不完整证据和模糊关系中固有的空间不确定性。为了解决这一限制，我们提出了SBFNav，一种以语言条件空间置信场（Spatial Belief Field, SBF）为核心的闭环导航框架。与主要记录已观测内容的自我中心地图不同，SBF表示任务条件下的目标位置可能分布，在部分证据下保留多重空间假设。在每一步，随着新证据的获得，该分布会根据累积观测进行更新。基于此表示，SBFNav选择最符合指令和观测的目标作为控制的度量航路点。在原始及修订的CityNav基准测试中，我们的方法实现了迄今为止最佳的整体性能。在Test Unseen分割上，我们的方法将成功率（SR）从25.91%提升至32.29%，将路径效率（SPL）从19.63%提升至30.43%。消融研究进一步验证了空间置信建模相较于单点预测的优势。
cs.CV / 36 / 2609.05864
Hierarchical Prompt Injector for Domain Generalization Segmentation
用于领域泛化分割的层次提示注入器
Abstract
Domain Generalized Semantic Segmentation (DGSS) is a challenging task, as vision models often rely on low-level appearance cues that change across domains. In contrast, structural attributes exhibit cross-domain stability, motivating the use of structural priors for DGSS. Existing methods use prompt learning to transfer such priors into DGSS models, but typically encode each class as a single holistic prompt. Moreover, these methods apply prompts uniformly to all pixels, offering no mechanism to adapt when only a subset of object regions is visible due to viewpoint changes, occlusion, and environmental variation. We address this with \textbf{Spatial Hierarchical Prompts (SHP)} that enrich each class with region-level geometric anchors capturing structural appearance from distinct viewing angles, ensuring complementary coverage under arbitrary viewpoints. Additionally, we propose the \textbf{Hierarchical Prompt Injector (HPI)}, which enables spatially adaptive prompt injection in foundation models. HPI spatially grounds prompts by modeling their semantic relevance and spatial influence with visual features. Considering the difficulty of learning spatially and semantically aware prompt injection, we further introduce auxiliary supervision to align hierarchical prompts with their corresponding object regions. We achieve 70.62\% and 72.74\% mIoU on synthetic-to-real and real-to-real benchmarks, respectively. Code and checkpoints are released at https://github.com/MosukFate/HPI
Chinese Translation
领域泛化语义分割（DGSS）是一项具有挑战性的任务，因为视觉模型通常依赖于在不同领域中变化的低级外观线索。相比之下，结构属性表现出跨领域的稳定性，这促使我们在DGSS中使用结构先验。现有方法利用提示学习将这些先验转移到DGSS模型中，但通常将每个类别编码为单一的整体提示。此外，这些方法对所有像素均匀应用提示，未能提供在由于视角变化、遮挡和环境变化而仅可见部分物体区域时的适应机制。我们通过 extbf{空间层次提示（SHP）}来解决这一问题，该方法为每个类别丰富了区域级几何锚点，以捕捉来自不同视角的结构外观，确保在任意视角下的互补覆盖。此外，我们提出了 extbf{层次提示注入器（HPI）}，该注入器使基础模型中的空间自适应提示注入成为可能。HPI通过建模提示的语义相关性和空间影响与视觉特征的关系，来空间性地定位提示。考虑到学习空间和语义感知的提示注入的难度，我们进一步引入辅助监督，以使层次提示与其对应的物体区域对齐。在合成到真实和真实到真实的基准测试中，我们分别达到了70.62\%和72.74\\%的mIoU。代码和检查点已发布在https://github.com/MosukFate/HPI
cs.CV / 37 / 2609.05876
FACT: A Forensic Agent with Compiled Tool-Use Trajectories for AI-Generated Image Detection
FACT：一种具有编译工具使用轨迹的法医代理，用于AI生成图像检测
Abstract
AI-generated image detection is increasingly open-world: new image generators produce highly realistic images that make visual artifacts harder to identify. Existing detectors usually rely on a fixed set of forensic cues, so a detector that works well for one generator family may fail on another. We introduce FACT (Forensic Agent with Compiled Tool-use Trajectories), which learns an image-conditioned tool-use policy for forensic analysis. Instead of applying a fixed detector, FACT decides which forensic tools to call, interprets the returned evidence, and stops when sufficient evidence has been collected. FACT follows an Evolve--Distill--Refine pipeline: it evolves an execution-verified forensic skill, compiles the skill into action--observation tool-use trajectories, distills them into a compact agent, and refines the policy with cost-aware GRPO. Across two internal and four public benchmarks, FACT achieves the best performance among all compared methods, including on recent unseen generators, deepfakes, and manipulated images.
Chinese Translation
AI生成图像检测正日益开放：新的图像生成器产生高度逼真的图像，使得视觉伪影更难以识别。现有的检测器通常依赖于固定的一组法医线索，因此，对于某一生成器家族有效的检测器可能在另一个家族上失效。我们提出了FACT（法医代理与编译工具使用轨迹），它为法医分析学习图像条件下的工具使用策略。FACT并不是应用固定的检测器，而是决定调用哪些法医工具，解释返回的证据，并在收集到足够证据时停止。FACT遵循Evolve--Distill--Refine管道：它进化出经过执行验证的法医技能，将技能编译成行动-观察工具使用轨迹，将其提炼成紧凑的代理，并通过成本感知的GRPO优化策略。在两个内部基准和四个公共基准上，FACT在所有比较方法中实现了最佳性能，包括在最近未见的生成器、深度伪造和操纵图像上的表现。
cs.CV / 38 / 2609.05888
UniFusion: Sparse-View 4D Reconstruction via Unified Spatio-temporal Depth Alignment
UniFusion：通过统一时空深度对齐实现稀疏视图4D重建
Abstract
In this paper, we address the challenging problem of 4D reconstruction from sparse-view videos. This setup usually relies on monocular depth estimation to provide priors for the reconstruction model. A key challenge arises from limited cross-view overlap and temporal variation, making monocular depth predictions inconsistent across views and time. Existing methods align spatial and temporal dimensions in separate stages, requiring foreground segmentation masks while failing to leverage temporal cues for cross-view alignment. Contrary to these methods, we propose a unified spatial-temporal depth alignment framework that jointly resolves cross-view and cross-time inconsistencies without distinguishing foreground/background. Our method represents depth maps across views and time as a set of spatio-temporal neural fields. This representation not only yields fast convergence, but also captures spatio-temporal correlation among depth maps implicitly, without dependence on external segmentation/tracking models. We also propose a multi-view depth-order loss while leveraging the classic scale-and-shift-invariant loss to further improve the final depth quality. The aligned depths initialize and supervise Gaussian splatting models for 4D reconstruction. Experiments on Ego-Exo4D and EgoHuman demonstrate that our improved depth alignment substantially benefits dynamic Gaussian-splatting-based reconstruction methods for novel-time/view synthesis and geometry accuracy/consistency.
Chinese Translation
在本文中，我们解决了从稀疏视图视频中进行4D重建的挑战性问题。该设置通常依赖于单目深度估计为重建模型提供先验信息。一个关键挑战来自于有限的视图间重叠和时间变化，使得单目深度预测在不同视图和时间上不一致。现有方法在不同阶段对空间和时间维度进行对齐，要求前景分割掩码，但未能利用时间线索进行视图间对齐。与这些方法相反，我们提出了一种统一的时空深度对齐框架，能够在不区分前景/背景的情况下共同解决视图间和时间间的不一致性。我们的方法将不同视图和时间的深度图表示为一组时空神经场。这种表示不仅实现了快速收敛，还隐式捕捉了深度图之间的时空相关性，而不依赖于外部分割/跟踪模型。我们还提出了一种多视图深度顺序损失，同时利用经典的尺度和位移不变损失进一步提高最终深度质量。对齐的深度用于初始化和监督高斯喷溅模型以进行4D重建。在Ego-Exo4D和EgoHuman上的实验表明，我们改进的深度对齐显著有利于基于动态高斯喷溅的重建方法在新时间/视图合成和几何精度/一致性方面的表现。
cs.CV / 39 / 2609.05902
GenPuzzle: Benchmarking Visual Reasoning in Image Generation Models
GenPuzzle：图像生成模型中的视觉推理基准测试
Abstract
Recent image generation systems increasingly combine multimodal understanding, reasoning, and synthesis, suggesting that they may do more than render plausible scenes. Yet existing evaluations emphasize aesthetics, prompt alignment, compositionality, or text-based answers, leaving unclear whether these systems can solve visual problems and faithfully express solutions in pixels. We introduce GenPuzzle, a benchmark for reasoning-centric image generation. GenPuzzle contains 2,005 problems across 12 tracks, spanning pattern completion, spatial construction, mazes, Sudoku, nonograms, tangrams, board games, matchstick puzzles, orthographic projection, and mathematical visual proof. Each task provides a visual puzzle and requires an image output that preserves the input state while executing a logically valid solution. GenPuzzle uses task-specific evaluation protocols: discrete grid outputs are transcribed and verified programmatically, while visually complex outputs are assessed with tiered, multidimensional, or binary multimodal large language model (MLLM) rubrics. We further select the automatic judge by measuring agreement with human reference scores. Across three frontier generators, the strongest model reaches only 40.57 Macro Overall, revealing frequent failures in logic, geometry, state preservation, and instruction execution. GenPuzzle provides a testbed for measuring progress from image rendering toward visual problem solving.
Chinese Translation
近年来，图像生成系统越来越多地结合了多模态理解、推理和合成，表明它们可能不仅仅是渲染可信的场景。然而，现有的评估主要强调美学、提示对齐、组合性或基于文本的答案，尚不清楚这些系统是否能够解决视觉问题并在像素中忠实地表达解决方案。我们引入了GenPuzzle，一个以推理为中心的图像生成基准。GenPuzzle包含2,005个问题，涵盖12个领域，包括模式补全、空间构建、迷宫、数独、非ogram、拼图、棋盘游戏、火柴拼图、正投影和数学视觉证明。每个任务提供一个视觉难题，并要求生成一个图像输出，该输出在执行逻辑有效的解决方案时保持输入状态。GenPuzzle使用特定任务的评估协议：离散网格输出通过程序化方式转录和验证，而视觉复杂的输出则通过分层、多维或二元多模态大型语言模型（MLLM）评分标准进行评估。我们进一步通过测量与人类参考分数的一致性来选择自动评判者。在三个前沿生成器中，最强的模型仅达到40.57的宏观总体评分，揭示了在逻辑、几何、状态保持和指令执行方面的频繁失败。GenPuzzle为衡量从图像渲染到视觉问题解决的进展提供了一个测试平台。
cs.CV / 40 / 2609.05912
FreeTransformSR: Efficient Lightweight Image Super-Resolution via Free Low-Rank Learnable Transform
FreeTransformSR：通过自由低秩可学习变换实现高效轻量级图像超分辨率
Abstract
Single image super-resolution aims to reconstruct high-resolution images from low-resolution inputs. This paper proposes FreeTransformSR, a novel lightweight super-resolution network based on a channel-wise free low-rank learnable transform. The transform learns task-adaptive basis functions in a data-driven manner, enabling adaptive feature modulation with minimal parameter overhead. To further enhance high-frequency detail recovery, we introduce a local feature modulation branch that complements transform-domain processing with depthwise convolution. In addition, a soft complexity adaptive module dynamically fuses the outputs of local convolution and window self-attention branches through a lightweight gating network, adaptively adjusting the fusion ratio based on regional texture characteristics. An adaptive intensity modulation strategy is also incorporated to adjust transform-domain response strength at the sample level, enabling the network to dynamically adjust processing intensity according to input features. Extensive experiments on five benchmark datasets demonstrate that FreeTransformSR achieves competitive PSNR/SSIM performance with significantly fewer parameters and FLOPs. Specifically, FreeTransformSR achieves 32.41 dB on BSD100 x2 and 27.00 dB on Urban100 x4 with only 595K parameters, while delivering faster inference speed than competing methods, making it well-suited for deployment in resource-constrained scenarios. Source code is available at: https://github.com/HJiLi/FreeTransformSR.
Chinese Translation
单幅图像超分辨率旨在从低分辨率输入重建高分辨率图像。本文提出了FreeTransformSR，一种基于通道级自由低秩可学习变换的新型轻量级超分辨率网络。该变换以数据驱动的方式学习任务自适应基函数，从而实现最小参数开销的自适应特征调制。为了进一步增强高频细节的恢复，我们引入了一个局部特征调制分支，该分支通过深度卷积补充变换域处理。此外，一个软复杂度自适应模块通过轻量级门控网络动态融合局部卷积和窗口自注意力分支的输出，根据区域纹理特征自适应调整融合比例。还结合了一种自适应强度调制策略，以在样本级别调整变换域响应强度，使网络能够根据输入特征动态调整处理强度。在五个基准数据集上的大量实验表明，FreeTransformSR在参数和FLOPs显著减少的情况下，达到了具有竞争力的PSNR/SSIM性能。具体而言，FreeTransformSR在BSD100 x2上达到了32.41 dB，在Urban100 x4上达到了27.00 dB，仅使用595K参数，同时提供比竞争方法更快的推理速度，使其非常适合在资源受限的场景中部署。源代码可在：https://github.com/HJiLi/FreeTransformSR 获取。
cs.CV / 41 / 2609.05916
STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models
STAR-Pro：具有渐进细化的分阶段自适应令牌减少方法，用于高效的大型视觉语言模型
Abstract
Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose substantial computational overhead, motivating training-free visual token pruning. In this work, we conduct two complementary analyses of visual token pruning. First, we measure the feature-space coverage of tokens retained before cross-modal fusion and find that aggressive pruning discards substantial visual information. Second, we track text-to-visual attention across decoder layers and find that the visual tokens considered important change substantially with depth, making one-shot pruning decisions unreliable. Together, these findings show that effective pruning should preserve broad visual coverage before fusion and progressively refine the retained tokens as cross-modal evidence evolves during fusion. We therefore propose STAR-Pro (STage-Wise Adaptive Token Reduction with Progressive Refinement), a training-free two-stage framework. Its Adaptive Stage applies pivoted QR to construct an over-budget feature-coverage candidate pool, while its Progressive Stage uses evolving text-to-visual attention at selected decoder layers to prune a nested survivor set under a target layer-average token budget. Extensive experiments across seven LVLMs spanning multiple architectures and 18 image and video benchmarks demonstrate the effectiveness of STAR-Pro under aggressive pruning. On LLaVA-Video-7B, STAR-Pro reduces visual tokens by 90.5%, retains 92.7% of baseline performance, and achieves a $2.24\times$ measured inference speedup. Code is available at https://github.com/EasonAI-5589/starpro.
Chinese Translation
大型视觉语言模型（LVLMs）在多模态理解方面表现出色，但它们处理的数百到数千个视觉令牌带来了巨大的计算开销，这促使了无训练的视觉令牌剪枝。在本研究中，我们对视觉令牌剪枝进行了两项互补分析。首先，我们测量了在跨模态融合之前保留的令牌的特征空间覆盖率，发现激进的剪枝会丢弃大量的视觉信息。其次，我们跟踪了解码器层中的文本到视觉注意力，发现被认为重要的视觉令牌随着深度的变化而显著改变，这使得一次性剪枝决策变得不可靠。综合这些发现表明，有效的剪枝应在融合之前保留广泛的视觉覆盖，并随着跨模态证据在融合过程中演变而逐步细化保留的令牌。因此，我们提出了STAR-Pro（分阶段自适应令牌减少与渐进细化），这是一个无训练的两阶段框架。其自适应阶段应用了枢轴QR算法构建超预算特征覆盖候选池，而渐进阶段则利用选定解码器层中的演变文本到视觉注意力，在目标层平均令牌预算下剪枝嵌套的存活集。在跨越多个架构的七个LVLM和18个图像及视频基准的广泛实验中，证明了STAR-Pro在激进剪枝下的有效性。在LLaVA-Video-7B上，STAR-Pro将视觉令牌减少了90.5%，保留了92.7%的基线性能，并实现了$2.24 imes$的推理速度提升。代码可在https://github.com/EasonAI-5589/starpro获取。
cs.CV / 42 / 2609.05918
PAI-Actor: Cinematic Multi-Character Replacement in Dynamic Scenes
PAI-Actor：动态场景中的电影多角色替换
Abstract
We present PAI-Actor, a cinematic multi-character animation framework for character replacement in dynamic movie scenes. Unlike conventional animation systems that mainly drive a single static image or a single subject, our goal is to replace and animate multiple characters within real video clips while preserving the original scene dynamics, camera motion, and background content. This setting is particularly challenging because the generated characters must remain consistent with the source performance in motion and interaction, while also matching the surrounding background in lighting, shadow, composition, and overall cinematic appearance. To address this, we formulate multi-character animation as a structure-guided human recovery problem and build a movie-driven training pipeline from high-quality film data. Furthermore, to support practical cinematic production, we introduce a bidirectional-to-autoregressive distillation framework: we first train a bidirectional diffusion transformer for high-quality short-clip generation at 1080P resolution, and then distill it into an autoregressive video-to-video model for efficient inference and longer video generation. Experiments show that PAI-Actor enables high-fidelity multi-character animation with strong scene consistency, cinematic visual quality, and efficient long-form generation.
Chinese Translation
我们提出了PAI-Actor，一个用于动态电影场景中角色替换的电影多角色动画框架。与主要驱动单一静态图像或单一主体的传统动画系统不同，我们的目标是在保留原始场景动态、摄像机运动和背景内容的同时，替换并动画化真实视频片段中的多个角色。这一设置尤其具有挑战性，因为生成的角色必须在运动和互动中与源表演保持一致，同时在光照、阴影、构图和整体电影外观上与周围背景相匹配。为了解决这个问题，我们将多角色动画形式化为一个结构引导的人体恢复问题，并从高质量电影数据构建了一个电影驱动的训练管道。此外，为了支持实际的电影制作，我们引入了一个双向到自回归的蒸馏框架：我们首先训练一个双向扩散变换器，以生成1080P分辨率的高质量短片，然后将其蒸馏为一个自回归视频到视频模型，以实现高效推理和更长视频的生成。实验表明，PAI-Actor能够实现高保真度的多角色动画，具有强大的场景一致性、电影视觉质量和高效的长格式生成。
cs.CV / 43 / 2609.05925
AVSplat: Dense-View Feed-Forward 3D Gaussian Splatting with Assist-View Preconditioning
AVSplat：密视图前馈3D高斯点云与辅助视图预处理
Abstract
Pose-free feed-forward 3D Gaussian Splatting enables novel view synthesis from uncalibrated multi-view images. Although more views should improve performance, existing methods often degrade with dense-view inputs because global aggregation spreads attention over many tokens, and naive voxel fusion averages many Gaussians into overly smooth representations. We present AVSplat, a framework that turns additional views into reliable signals for both aggregation and representation. Before global attention, each view performs a single lightweight interaction with a small set of Assist Views chosen for relevance and diversity, and the cached features provide a focused scene context that stabilizes correspondence. For representation, we use adaptive temperature-aware voxel fusion that sharpens attribution under high occupancy, guided by occupancy and point confidence. Crucially, AVSplat restores positive view scaling where performance remains stable or improves as more input views are added, instead of degrading in the dense-view regime. Ablations show that Assist View Preconditioning is primarily responsible for preventing dense-view degradation, while Occupancy-guided Voxel Fusion contributes most of the single-point image-quality gains.
Chinese Translation
无姿态前馈3D高斯点云技术能够从未校准的多视图图像中实现新视图合成。尽管更多视图应该能提高性能，但现有方法在密视图输入时往往会退化，因为全局聚合会将注意力分散到许多标记上，而简单的体素融合则会将多个高斯平均成过于平滑的表示。我们提出了AVSplat，一个将额外视图转化为聚合和表示的可靠信号的框架。在全局注意力之前，每个视图与一小组选择的相关和多样化的辅助视图进行一次轻量级交互，缓存的特征提供了一个聚焦的场景上下文，从而稳定了对应关系。在表示方面，我们使用自适应温度感知体素融合，在高占用情况下锐化归属，由占用率和点置信度引导。关键是，AVSplat恢复了正视图缩放的特性，即随着输入视图数量的增加，性能保持稳定或改善，而不是在密视图情况下退化。消融实验表明，辅助视图预处理主要负责防止密视图退化，而占用引导的体素融合则对单点图像质量提升贡献最大。
cs.CV / 44 / 2609.05937
Beyond Classification: Structured Supervision Aligns Visual Evidence with Medical Semantics
超越分类：结构化监督将视觉证据与医学语义对齐
Abstract
Vision Transformers (ViTs) have shown immense potential in medical image analysis. However, standard pre-training via global image classification suffers from spatial collapse, where models rely heavily on background shortcuts rather than localising critical foreground lesions. To overcome this limitation and align visual evidence with precise medical semantics, we systematically investigate alternative pre-training paradigms.Specifically, we evaluate three independent forms of structured supervision: topological priors via graph self-supervision, dense pixel-level constraints via segmentation, and cross-modal semantic grounding via image-text pairs. Notably, our empirical analysis reveals that while all three forms of structured supervision successfully alleviate the global pooling bottleneck and steer visual attention towards foreground regions, image-text alignment achieves the most superior performance. By embedding high-dimensional diagnostic logic, the cross-modal approach not only anchors attention on precise visual evidence but also enables profound abstract reasoning. Extensive experiments demonstrate that this semantically enriched pre-training fundamentally enhances the model's feature representation. Consequently, when fine-tuned for downstream clinical classification tasks, our models achieve superior accuracy and yield highly interpretable attention maps focused on true pathological features, vastly outperforming vanilla classification baselines.
Chinese Translation
视觉变换器（Vision Transformers, ViTs）在医学图像分析中展现了巨大的潜力。然而，标准的全局图像分类预训练存在空间崩溃的问题，模型过度依赖背景捷径，而未能有效定位关键的前景病变。为了克服这一限制并将视觉证据与精确的医学语义对齐，我们系统地研究了替代的预训练范式。具体而言，我们评估了三种独立的结构化监督形式：通过图自监督的拓扑先验、通过分割的密集像素级约束，以及通过图像-文本对的跨模态语义对齐。值得注意的是，我们的实证分析表明，尽管这三种结构化监督形式都成功缓解了全局池化瓶颈，并引导视觉注意力集中于前景区域，但图像-文本对齐实现了最优的性能。通过嵌入高维诊断逻辑，跨模态方法不仅将注意力锚定在精确的视觉证据上，还促进了深刻的抽象推理。大量实验表明，这种语义丰富的预训练从根本上增强了模型的特征表示。因此，当针对下游临床分类任务进行微调时，我们的模型实现了更高的准确性，并产生了高度可解释的注意力图，聚焦于真实的病理特征，远远超越了传统分类基线。
cs.CV / 45 / 2609.05953
ProtoRAG: Prototype-Based Retrieval Augmentation for Few-Shot Fine-Grained Remote Sensing Object Detection
ProtoRAG：基于原型的检索增强少样本细粒度遥感目标检测
Abstract
Few-shot fine-grained object detection (FGOD) in remote sensing imagery is challenging because limited annotations must support both object localization and discrimination among visually similar subcategories. Although multimodal large language models (MLLMs) provide strong coarse object localization, they lack explicit visual evidence for reliable fine-grained recognition. To address this limitation, we propose ProtoRAG, a prototype-based retrieval-augmented framework that decouples coarse localization from fine-grained recognition by equipping MLLMs with an external object-level visual memory. To construct a reliable visual memory from limited support samples, we introduce Discriminative Prototype Space Learning (DPSL), which encourages discriminative and prototype-stable representations through supervised contrastive learning and prototype-consistency regularization. We further develop an uncertainty-guided candidate-constrained reasoning strategy that augments MLLMs with retrieved candidate-specific visual references and invokes multimodal reasoning only for ambiguous instances. Extensive experiments show that ProtoRAG consistently surpasses representative baselines in nine few-shot settings, outperforming the strongest baselines by 14.80, 2.27, and 4.04 mAP$_{50}$ on MAR20, HRSC2016, and FAIR1M-2.0, respectively.
Chinese Translation
在遥感图像中，少样本细粒度目标检测（FGOD）面临挑战，因为有限的标注必须支持目标定位和视觉上相似子类别之间的区分。尽管多模态大型语言模型（MLLMs）提供了强大的粗略目标定位能力，但它们缺乏可靠的细粒度识别所需的明确视觉证据。为了解决这一限制，我们提出了ProtoRAG，一种基于原型的检索增强框架，通过为MLLMs配备外部对象级视觉记忆，将粗略定位与细粒度识别解耦。为了从有限的支持样本中构建可靠的视觉记忆，我们引入了判别原型空间学习（DPSL），通过监督对比学习和原型一致性正则化，鼓励判别性和原型稳定的表示。我们进一步开发了一种不确定性引导的候选约束推理策略，该策略通过检索的候选特定视觉参考增强MLLMs，并仅对模糊实例调用多模态推理。大量实验表明，ProtoRAG在九个少样本设置中始终超越代表性基线，在MAR20、HRSC2016和FAIR1M-2.0上分别比最强基线提高了14.80、2.27和4.04 mAP$_{50}$。
cs.CV / 46 / 2609.05956
STP-BENCH: A Unified Systematic Benchmark for Virtual Spatial Transcriptomics from Histopathology Images
STP-BENCH：一个统一的系统基准，用于从组织病理图像中进行虚拟空间转录组学
Abstract
Spatial transcriptomics (ST) provides unprecedented insights into tumor heterogeneity by capturing spatially resolved gene expression, yet its high experimental cost hinders large-scale adoption. Consequently, computational approaches that predict spatial gene expression directly from hematoxylin and eosin slides, termed virtual ST, have rapidly emerged. Despite this progress, assessing advances in the field remains difficult due to insufficient benchmarking: prior studies rely on small, heterogeneous datasets, inconsistent training and inference pipelines, and limited evaluation of biological interpretability and model robustness. To address these gaps, we present STP-BENCH, a standardized benchmark for virtual ST models. STP-BENCH comprises six cancer types spanning two ST platforms (Visium and Xenium), with each training dataset containing more than 30,000 spots and at least 15 slides to ensure statistical reliability. We evaluate 21 predictive approaches, re-implemented with a unified pathology foundation model as the morphological encoder when architecturally applicable. Beyond conventional benchmarks that report average predictive accuracy on highly variable genes, we systematically examine which genes and gene sets are recoverable from histomorphology. We further evaluate the downstream biological utility of predicted profiles through cell-type deconvolution and spatial domain identification, and assess model reliability under domain shifts and data scaling. Notably, unified morphological encoding substantially re-orders model rankings established in prior studies, indicating that architectural innovations and image encoding have been conflated in previous evaluations. We publicly release STP-BENCH to support reproducibility and serve as a community benchmark at https://github.com/NEXGEM/STP-Bench.
Chinese Translation
空间转录组学（ST）通过捕捉空间分辨的基因表达，为肿瘤异质性提供了前所未有的洞察，然而其高昂的实验成本阻碍了大规模应用。因此，直接从苏木精-伊红切片预测空间基因表达的计算方法，即虚拟ST，迅速兴起。尽管取得了一定进展，但由于基准测试不足，评估该领域的进展仍然困难：以往研究依赖于小型、异质的数据集，不一致的训练和推理流程，以及对生物可解释性和模型鲁棒性的有限评估。为了解决这些问题，我们提出了STP-BENCH，这是一个针对虚拟ST模型的标准化基准。STP-BENCH涵盖六种癌症类型，跨越两个ST平台（Visium和Xenium），每个训练数据集包含超过30,000个点和至少15个切片，以确保统计可靠性。我们评估了21种预测方法，在架构适用时重新实现了统一的病理基础模型作为形态编码器。除了报告高度变异基因的平均预测准确度的传统基准外，我们系统地检查了哪些基因和基因集可以从组织形态学中恢复。我们进一步通过细胞类型去卷积和空间域识别评估预测轮廓的下游生物学效用，并评估模型在领域转移和数据扩展下的可靠性。值得注意的是，统一的形态编码显著重新排序了以往研究中建立的模型排名，表明架构创新和图像编码在以往评估中被混淆。我们公开发布STP-BENCH以支持可重复性，并作为社区基准，网址为 https://github.com/NEXGEM/STP-Bench。
cs.CV / 47 / 2609.05959
FineHOI: Part-Aware Dense Representations for Zero-Shot Human-Object Interaction Detection
FineHOI：针对零样本人-物体交互检测的部分感知密集表示
Abstract
Human-Object Interaction (HOI) detection aims to localize humans and objects in images and classify their interactions. Zero-shot HOI focuses on recognizing interactions that are not observed during training, requiring models to generalize beyond seen verb-object compositions. Recent approaches leverage Vision-Language Models (VLMs), benefiting from rich semantic representations. However, they often rely on global or detector-centric features that compress interaction cues and hinder fine-grained spatial reasoning. To overcome this limitation, we propose FineHOI, a zero-shot HOI framework that explicitly models interactions from dense patch-level features. Our approach is motivated by the observation that human-object interactions are defined by localized spatial relationships, which are not preserved by global and detector-centric representations. To this end, we introduce an Adaptive Part-Level Attention module that decomposes humans and objects into semantically coherent parts via unsupervised clustering, and re-weights them based on their interaction relevance. These representations are then integrated through a Region-Aware Interaction Transformer that integrates part-aware and global features and produces the final HOI embedding. Extensive experiments demonstrate that FineHOI consistently outperforms existing zero-shot HOI methods, achieving particularly strong gains on unseen interactions. Code is available at https://github.com/francescotonini/fine-hoi.
Chinese Translation
人-物体交互（HOI）检测旨在定位图像中的人类和物体，并分类它们的交互。零样本 HOI 关注于识别在训练期间未观察到的交互，这要求模型超越已见动词-物体组合进行泛化。最近的方法利用视觉-语言模型（VLMs），从丰富的语义表示中获益。然而，它们通常依赖于全局或检测器中心的特征，这压缩了交互线索，妨碍了细粒度的空间推理。为克服这一限制，我们提出了 FineHOI，一个零样本 HOI 框架，明确地从密集的补丁级特征建模交互。我们的方法的动机是观察到人-物体交互是由局部空间关系定义的，而这些关系在全局和检测器中心的表示中并未得到保留。为此，我们引入了自适应部分级注意力模块，通过无监督聚类将人类和物体分解为语义一致的部分，并根据它们的交互相关性进行重新加权。这些表示随后通过区域感知交互变换器进行整合，该变换器整合了部分感知和全局特征，并生成最终的 HOI 嵌入。大量实验表明，FineHOI 在现有的零样本 HOI 方法中始终表现优越，尤其在未见交互上取得了显著的提升。代码可在 https://github.com/francescotonini/fine-hoi 获取。
cs.CV / 48 / 2609.05968
Test-Time Weak-to-Strong Alignment: Transferring Implicit Rewards from Weak to Strong Flow Models
测试时弱到强的对齐：将隐式奖励从弱流模型转移到强流模型
Abstract
Aligning a text-to-image generation flow model with a reward makes it follow objectives that the training data alone does not provide. Alignment fine-tuning delivers this by reinforcement learning (RL) or preference optimization, but it must be repeated for every checkpoint and returns a model fixed at the reward and strength it was trained with. Test-time alignment instead steers a frozen model during sampling, allowing task-specific and sample-specific guidance. Existing methods obtain this only by drawing the per-step signal from the reward function itself, through its gradient, or through a separately trained value function. We propose changing the supervision source: let a pair of weak models, not a reward function, supply the supervision. A source aligned model, kept together with its base as a source alignment pair, stores its training reward as an implicit, step-wise, KL-anchored signal expressed in the sampler's own coordinates. We explore whether this model-form supervision can cross scale, and show that it does: our method, AlignGraft, aligns a larger, frozen, never-tuned model by adding the pair's velocity difference during sampling. The transport is exact under a shared noising kernel and needs neither the reward nor its gradient at test time. The method has no schedules, only a single scalar that controls the alignment strength and can extrapolate it beyond that of the source alignment pair. Across image and video flow models (Stable Diffusion 3.5, FLUX, and Wan), the transfer lifts the frozen large model on preference, compositional, and text-rendering rewards, can exceed the source aligned model itself, and preserves the large model's fidelity at a small constant sampling overhead. Extensive experiments show that one alignment run on a weak model produces supervision that the whole model family can reuse at test time.
Chinese Translation
将文本到图像生成流模型与奖励对齐，使其遵循训练数据单独未提供的目标。对齐微调通过强化学习（RL）或偏好优化实现这一点，但必须为每个检查点重复，并返回一个固定在其训练时的奖励和强度上的模型。测试时对齐则在采样过程中引导一个冻结的模型，允许任务特定和样本特定的指导。现有方法仅通过从奖励函数本身获取每步信号，通过其梯度或通过单独训练的价值函数来实现这一点。我们提出改变监督源：让一对弱模型，而不是奖励函数，提供监督。一个源对齐模型，与其基础模型一起作为源对齐对存储其训练奖励，作为一个隐式的、逐步的、以KL为锚的信号，以采样器自身的坐标表达。我们探索这种模型形式的监督是否可以跨尺度，并展示它确实可以：我们的方法AlignGraft通过在采样过程中添加对的速度差异来对齐一个更大、冻结、从未调优的模型。在共享噪声核下，传输是精确的，并且在测试时不需要奖励或其梯度。该方法没有调度，只有一个控制对齐强度的单一标量，并且可以将其外推超出源对齐对的范围。在图像和视频流模型（Stable Diffusion 3.5、FLUX和Wan）中，转移提升了冻结的大模型在偏好、组合和文本渲染奖励上的表现，甚至可以超过源对齐模型本身，并在小的常数采样开销下保持大模型的保真度。大量实验表明，在弱模型上进行一次对齐运行产生的监督可以在测试时被整个模型家族重用。
cs.CV / 49 / 2609.05972
Efficient and Robust Camera-independent Multiview 3D Geometric Reconstruction from Noisy Monocular Depth Estimation and Multiple Point Matching
高效且鲁棒的无相机依赖的多视角三维几何重建方法：基于噪声单目深度估计和多点匹配
Abstract
We present an efficient and robust method for 3D geometric reconstruction that is based solely on the camera-independent linear relationships among a given set of points, which are stable over time and robustly estimated using multiple point matches. We essentially learn, from correspondences between points across several frames, a linear geometric auto-regression matrix $\mathbf{W}$, which establishes how a point in 3D can be expressed as a linear combination of all the others. This matrix is constant and does not depend on the world coordinate system or the camera pose---it is an intrinsic property of the point set. We also show that the principal eigenvectors of $\mathbf{W}$, which all have eigenvalue $1$, provide a homogeneous representation of the 3D point configuration. The first version of our method takes advantage of noisy monocular depth maps in order to obtain, from multiple frames, a robust geometric auto-regression matrix $\mathbf{W}$ of linear relationships between the 3D points. Thus, we build on recent advances in deep learning, which now provide monocular depth estimation models that are fast but very often noisy. Our approach handles noise through robust linear estimation over several frames. The second version of our method does not need monocular depth estimation maps. It applies in cases of weak-perspective projection, when the linear combinations between the 3D points can be robustly estimated from their 2D projections in the image. Note that the camera projection matrix is never used in our derivations. Consequently, our method does not recover camera pose, but only 3D structure. This is a key difference between our method and the related literature on 3D geometric reconstruction.
Chinese Translation
我们提出了一种高效且鲁棒的三维几何重建方法，该方法仅基于给定点集之间的无相机依赖线性关系，这些关系在时间上是稳定的，并通过多点匹配进行鲁棒估计。我们基本上从多个帧之间点的对应关系中学习一个线性几何自回归矩阵 $ extbf{W}$，该矩阵确定了如何将三维空间中的一个点表示为所有其他点的线性组合。该矩阵是常数，不依赖于世界坐标系或相机姿态——它是点集的内在属性。我们还展示了 $ extbf{W}$ 的主特征向量均具有特征值 $1$，提供了三维点配置的齐次表示。我们方法的第一个版本利用噪声单目深度图，从多个帧中获得三维点之间线性关系的鲁棒几何自回归矩阵 $ extbf{W}$。因此，我们借鉴了深度学习的最新进展，这些进展现在提供了快速但通常噪声较大的单目深度估计模型。我们的方法通过对多个帧进行鲁棒线性估计来处理噪声。我们方法的第二个版本不需要单目深度估计图。它适用于弱透视投影的情况，当三维点之间的线性组合可以通过它们在图像中的二维投影进行鲁棒估计时。请注意，在我们的推导中从未使用相机投影矩阵。因此，我们的方法并不恢复相机姿态，而仅仅恢复三维结构。这是我们的方法与相关三维几何重建文献之间的一个关键区别。
cs.CV / 50 / 2609.05981
Accelerating Diffusion Transformers with Gaussian Process Rectified Feature Cache
使用高斯过程校正特征缓存加速扩散变换器
Abstract
Diffusion Transformers have become the dominant paradigm in generative AI, but their high computational costs severely hinder real-time applications. Prediction-based feature caching is widely used to accelerate diffusion transformers; however, as the number of steps increases, the deviation between its predictions and the reference full-compute trajectory gradually grows. An intuitive idea is to use an online regression model to dynamically correct this deviation, but it faces the issue of label data being unavailable during the acceleration process. This paper presents a statistical observation that the residuals between the features of full computation steps using caching methods and reference full-compute trajectory locally exhibit a zero-mean Gaussian distribution. By treating the features of full computation steps as noisy observations of reference features, the data acquisition problem is resolved. Based on this observation, a plug-and-play GP-Refiner correction framework is proposed. This method utilizes Gaussian Process Regression for correction and, leveraging the properties of GPR, introduces an uncertainty-adaptive computation strategy that triggers necessary full-computation calibration by monitoring the posterior variance in real time. Experiments demonstrate significant improvements across different models when combined with various state-of-the-art methods. Integrating the proposed framework with TaylorSeer reduces the computational load by 19.3% while improving PSNR by 0.9 dB and reducing LPIPS from 0.46 to 0.29. Code is available in https://github.com/Aredstone/GP-Refiner.
Chinese Translation
扩散变换器已成为生成性人工智能的主导范式，但其高计算成本严重阻碍了实时应用。基于预测的特征缓存被广泛用于加速扩散变换器；然而，随着步骤数量的增加，其预测与参考全计算轨迹之间的偏差逐渐增大。一个直观的想法是使用在线回归模型动态校正这一偏差，但在加速过程中面临标签数据不可用的问题。本文提出了一种统计观察，即使用缓存方法的全计算步骤特征与参考全计算轨迹之间的残差在局部上表现出零均值高斯分布。通过将全计算步骤的特征视为参考特征的噪声观测，解决了数据获取问题。基于这一观察，提出了一种即插即用的GP-Refiner校正框架。该方法利用高斯过程回归进行校正，并利用GPR的特性，引入了一种不确定性自适应计算策略，通过实时监测后验方差触发必要的全计算校准。实验表明，当与各种最先进的方法结合时，不同模型的性能显著提升。将所提出的框架与TaylorSeer集成后，计算负担减少了19.3%，同时PSNR提高了0.9 dB，LPIPS从0.46降低到0.29。代码可在 https://github.com/Aredstone/GP-Refiner 获取。
cs.CV / 51 / 2609.05990
MORPHA: Morphology-Constrained Training and the Limits of Cross-Acquisition Transfer in Low-Resource Malaria Microscopy
MORPHA：形态学约束训练及低资源疟疾显微镜中跨获取转移的限制
Abstract
In low-resource malaria microscopy, a model trained on one smear preparation routinely meets images from another, and how well morphology-based constraints transfer across this acquisition gap is unclear. We study this on real African field microscopy from Uganda (Lacuna), asking where encoding measured parasite morphology as a training constraint improves cross-acquisition transfer and where generic regularisation suffices. We present MORPHA, a morphological consistency constraint that derives stage-conditional statistics from the stage-annotated BBBC041 dataset and penalises predictions that deviate from them. Defined uniformly across binary, object-level, and stage-aware regimes without changing architecture or inference, it shapes training in the binary regime. The detection regime is a mapped boundary. On transfer from thin-smear cells to thick-smear field images, the constraint reduces the binary-classification generalisation drop by 30.8% (F1 0.578 to 0.699) at negligible within-domain cost and lowers in-distribution calibration error by 49% (ECE 0.0162 to 0.0082). A content-free control applying the identical constraint to random statistics recovers less of the drop (25.3% vs 30.8%), indicating the measured content, not constraining alone, contributes to the gain. Two standard confidence regularisers exceed the constraint on raw transfer, locating where morphology adds value and where generic regularisation suffices. We map two deployment-relevant boundaries: thin-smear statistics do not transfer to thick-smear detection (trophozoite
[email protected] falls to 0.000), and cross-acquisition pseudo-labelling fails before filtering applies. Together these yield a morphology-grounded consistency signal and evidence-based guidance for malaria dataset and model design in low-resource settings.
Chinese Translation
在低资源的疟疾显微镜中，基于一种涂片制备训练的模型通常会遇到来自另一种涂片的图像，而基于形态学的约束在这种获取差距中的转移效果尚不明确。我们在来自乌干达的真实非洲现场显微镜图像（Lacuna）上研究这一问题，探讨将测量的寄生虫形态编码作为训练约束的地方，在哪些情况下能够改善跨获取转移，在哪些情况下通用正则化就足够。我们提出了MORPHA，一种形态一致性约束，它从阶段标注的BBBC041数据集中推导阶段条件统计，并惩罚偏离这些统计的预测。该约束在二元、对象级和阶段感知的不同模式下统一定义，而不改变架构或推理，塑造了二元模式下的训练。检测模式是一个映射边界。在从薄涂片细胞到厚涂片现场图像的转移中，该约束将二元分类的泛化下降减少了30.8%（F1从0.578提高到0.699），且在领域内的成本几乎可以忽略不计，同时降低了分布内的校准误差49%（ECE从0.0162降低到0.0082）。一个不含内容的对照实验将相同的约束应用于随机统计，恢复的下降幅度较小（25.3%对比30.8%），这表明测量的内容，而非单纯的约束，贡献了这一增益。两个标准的置信度正则化器在原始转移上超过了该约束，定位了形态学增值的地方以及通用正则化足够的地方。我们绘制了两个与部署相关的边界：薄涂片统计无法转移到厚涂片检测（营养体
[email protected]降至0.000），而跨获取伪标记在过滤应用之前失败。这些结果共同提供了一个基于形态学的连贯信号和针对低资源环境中疟疾数据集及模型设计的基于证据的指导。
cs.CV / 52 / 2609.06004
Geometry-Aware Test-Time Learning for Quantitative Spatial Reasoning
面向几何的测试时学习用于定量空间推理
Abstract
Quantitative spatial reasoning in visual-language models (VLMs) aims to infer spatial distances and directional relationships among objects in 3D space from a 2D image and a natural language query. Despite recent progress, VLM spatial reasoning remains brittle under distribution shifts, largely due to the high cost of 3D supervision. As a result, models often produce inconsistent or contradictory predictions when faced with novel object configurations or rephrased spatial queries, revealing a misalignment between learned representations and underlying geometry. To address this, we propose TTL-SR, a geometry-aware Test-Time Learning framework for quantitative Spatial Reasoning that leverages geometric consistency constraints and unlabeled test data to adapt models to target domains. Specifically, TTL-SR augments the input query with geometrically coupled auxiliary queries, filters unreliable predictions via adaptive geometric triggering to construct structured token-level pseudo-labels, and updates model parameters under a geometry-aware multi-objective loss using only test data. Experimental results demonstrate that TTL-SR significantly boosts spatial reasoning performance, yielding 6.47% and 9.41% accuracy gains for Qwen3-VL-4B-Instruct and SpatialRGPT-VILA-1.5-8B on Q-Spatial-ScanNet dataset, respectively.
Chinese Translation
视觉语言模型（VLMs）中的定量空间推理旨在从二维图像和自然语言查询中推断三维空间中物体之间的空间距离和方向关系。尽管近期取得了一些进展，但VLM的空间推理在分布变化下仍然脆弱，这在很大程度上是由于三维监督的高成本。因此，当面临新颖的物体配置或重新措辞的空间查询时，模型往往会产生不一致或矛盾的预测，揭示了学习到的表征与基础几何之间的不一致。为了解决这一问题，我们提出了TTL-SR，一种面向几何的测试时学习框架，用于定量空间推理，该框架利用几何一致性约束和未标记的测试数据来适应目标领域。具体而言，TTL-SR通过几何耦合的辅助查询增强输入查询，通过自适应几何触发过滤不可靠的预测，以构建结构化的标记级伪标签，并仅使用测试数据在面向几何的多目标损失下更新模型参数。实验结果表明，TTL-SR显著提升了空间推理性能，在Q-Spatial-ScanNet数据集上，Qwen3-VL-4B-Instruct和SpatialRGPT-VILA-1.5-8B的准确率分别提高了6.47%和9.41%。
cs.CV / 53 / 2609.06007
Depth-to-Image Synthesis-Driven Generative Unguided Depth Completion
基于深度到图像合成驱动的生成无导向深度补全
Abstract
Guided depth completion methods heavily depend on RGB quality and alignment, while unguided ones often suffer from limited precision due to the absence of explicit visual cues. In this paper, we present Depth-to-Image Synthesis-Driven Generative Unguided Depth Completion (GUDC), a new completion paradigm that innovatively bridges advanced 2D generative models with unguided depth completion, enabling semantics-aware depth inference without real RGB inputs. Our key idea is to exploit ControlNet's powerful depth-conditioned generation capability to synthesize pseudo-images directly from sparse depth, effectively converting the original unguided setting into a semantics-guided one. To address the potential image-depth misalignment caused by depth sparsity, we propose a multi-level dense-to-sparse representation distillation strategy for ControlNet fine-tuning, where dense-depth features act as teacher signals to distill consistent structural representations for sparse-depth inputs. Furthermore, during pseudo-image-guided completion, we propose a pseudo-image semantic attention fusion module to adaptively extract informative semantic cues from pseudo-images while suppressing artifacts (e.g., texture hallucinations). Extensive experiments on KITTI and NYUv2 validate that our GUDC achieves superior accuracy and robustness over existing methods.
Chinese Translation
有导向的深度补全方法严重依赖于RGB图像的质量和对齐，而无导向的方法由于缺乏明确的视觉线索，往往精度有限。本文提出了一种新的补全范式——基于深度到图像合成驱动的生成无导向深度补全（GUDC），该方法创新性地将先进的2D生成模型与无导向深度补全相结合，实现了在没有真实RGB输入的情况下进行语义感知的深度推断。我们的关键思想是利用ControlNet强大的深度条件生成能力，从稀疏深度直接合成伪图像，有效地将原始的无导向设置转变为语义导向的设置。为了解决由于深度稀疏性引起的图像与深度之间的潜在不对齐问题，我们提出了一种多层次密集到稀疏表示蒸馏策略，用于对ControlNet进行微调，其中密集深度特征作为教师信号，蒸馏出一致的结构表示以适应稀疏深度输入。此外，在伪图像引导的补全过程中，我们提出了一种伪图像语义注意力融合模块，能够自适应地从伪图像中提取有用的语义线索，同时抑制伪影（例如，纹理幻觉）。在KITTI和NYUv2上的大量实验验证了我们的GUDC在准确性和鲁棒性方面优于现有方法。
cs.CV / 54 / 2609.06017
FujinSplat: Seeing Through Smoke with RAW-Domain Gaussian Splatting
FujinSplat：通过RAW域高斯点云穿透烟雾
Abstract
The appearance of a smoky scene is shaped by two processes that a camera records together: the participating medium alters scene radiance in a view-dependent way, and the image signal processor (ISP) then remaps the result through a nonlinear tone and color transformation. Recovering a clean 3D scene requires separating both. Per-view sRGB dehazing acts only after the ISP has entangled them; standard 3D reconstruction ignores the medium and absorbs it into scene geometry and radiance. FujinSplat addresses the problem in the RAW domain, where the two processes remain separable. A per-scene Base ISP is fitted from the scene's hazy RAW captures to its own camera renderings and then frozen, providing a fixed photometric anchor that performs no dehazing. Analyzing expert corrections reveals a compact, low-dimensional correction space identifiable from RAW alone. FujinSplat therefore fits per-view action answers at the training poses and trains a single scene-agnostic controller to regress them from RAW; the corrected views supervise one static 3D Gaussian representation, jointly with a bounded per-view residual that reconciles cross-view photometric inconsistencies. On the RealX3D real-world smoke benchmark FujinSplat clearly outperforms the strongest comparable baseline, ahead of both physics-based reconstruction and restoration-then-3DGS pipelines.
Chinese Translation
烟雾场景的外观由两个过程共同塑造：参与介质以视角依赖的方式改变场景辐射，图像信号处理器（ISP）随后通过非线性色调和颜色变换重新映射结果。恢复干净的三维场景需要将这两者分开。每视图的sRGB去雾仅在ISP将它们纠缠后才起作用；标准的三维重建忽略了介质，将其吸收到场景几何和辐射中。FujinSplat在RAW域中解决了这个问题，在这里这两个过程仍然是可分离的。通过从场景的模糊RAW捕获到其自身相机渲染的每场景基础ISP进行拟合，然后固定，提供一个不进行去雾处理的固定光度锚点。分析专家修正揭示了一个紧凑的低维修正空间，仅从RAW中即可识别。因此，FujinSplat在训练姿势下拟合每视图的动作答案，并训练一个单一的场景无关控制器从RAW中回归它们；修正后的视图共同监督一个静态的三维高斯表示，以及一个有界的每视图残差，调和跨视图的光度不一致性。在RealX3D真实世界烟雾基准测试中，FujinSplat明显超越了最强的可比基线，领先于基于物理的重建和恢复后再进行3DGS的管道。
cs.CV / 55 / 2609.06020
What Does Animal Re-Identification Learn? Linear Biological Concepts and Their Origins in Visual Representations
动物重新识别学习了什么？线性生物概念及其在视觉表征中的起源
Abstract
Conservation increasingly relies on camera traps that collect more wildlife imagery than experts can manually analyze, making animal re-identification (Re-ID) essential for monitoring individuals and populations. Yet understanding which cues drive model decisions is challenging for ViT-based Re-ID models, whose metric-learning objectives provide no explicit supervision for biological concepts. We ask whether such models nonetheless organize their representations along biologically meaningful axes. Using a DINOv3 backbone fine-tuned for Western lowland gorilla Re-ID with triplet-margin loss, we find that sex and age emerge as linear directions that generalize to held-out individuals, reaching up to 0.91 AUROC and being recoverable from a single image per individual. Activation steering further shows that the sex direction is causally used by the model, flipping a significant fraction of predictions to the opposite sex. Comparing off-the-shelf and fine-tuned backbones shows that Re-ID training does not create these concepts, but relocates them across the network. Finally, data attribution reveals that the representation we find reflects a graded biological axis, is redundantly encoded across the population and shaped by visually ambiguous individuals. Together, these findings show how interpretability can uncover both the biological structure and failure modes of Re-ID representations, providing a step toward auditable computer vision for wildlife monitoring.
Chinese Translation
保护工作越来越依赖于相机陷阱，这些陷阱收集的野生动物图像数量超过了专家能够手动分析的数量，因此动物重新识别（Re-ID）对于监测个体和种群至关重要。然而，理解驱动基于视觉变换器（ViT）模型决策的线索对于这些模型来说是具有挑战性的，因为它们的度量学习目标并未对生物概念提供明确的监督。我们探讨这些模型是否仍然沿着生物学上有意义的轴线组织其表征。使用经过微调的 DINOv3 主干网络，针对西部低地大猩猩的 Re-ID 进行三元组边际损失训练，我们发现性别和年龄作为线性方向出现，并且能够推广到未见个体，达到高达 0.91 的 AUROC，并且可以从每个个体的一张图像中恢复。激活引导进一步表明，模型因果地使用性别方向，导致相当一部分预测结果翻转为相反性别。比较现成的和微调的主干网络显示，Re-ID 训练并未创造这些概念，而是重新定位了它们在网络中的位置。最后，数据归因揭示我们发现的表征反映了一个渐进的生物轴，在种群中冗余编码，并受到视觉模糊个体的影响。综合来看，这些发现展示了可解释性如何揭示 Re-ID 表征的生物结构和失败模式，为野生动物监测的可审计计算机视觉迈出了重要一步。
cs.CV / 56 / 2609.06051
Image-Scale Robustness and Visual Recognition Performance: A Cross-Architecture Analysis
图像尺度鲁棒性与视觉识别性能：跨架构分析
Abstract
The sensitivity of visual recognition models to changes in image scale is well established, yet the factors governing this sensitivity across heterogeneous architectures remain unclear. In this work, we investigate whether scale robustness exhibits a common quantitative structure across modern vision models. We evaluate 20 pretrained ImageNet-1K classifiers spanning seven architectural families, including convolutional, mobile, efficient, and Transformer-based architectures. By systematically reducing input image scale, we construct scale-accuracy response curves and define a characteristic scale as a compact measure of the onset of substantial recognition degradation. We then examine the relationship between characteristic scale and baseline recognition accuracy, model parameter count, architectural family, and representation stability. A strong inverse association is observed between baseline accuracy and characteristic scale (Pearson r = -0.890, R^2= 0.792, p < 10^-6). This relationship remains stable under bootstrap resampling, leave-one-architecture-out analysis, and leave-one-family-out analysis. In contrast, parameter count provides negligible additional explanatory power after controlling for baseline accuracy (p = 0.80), while architectural family does not provide significant incremental explanatory power. Furthermore, characteristic scale shows essentially no association with representation stability (r = -0.003, p = 0.991). These results indicate that, across the studied models, scale robustness is strongly organized by baseline recognition performance rather than simply by model size, architectural family, or representation stability. The study provides an empirical framework for characterizing scale robustness across vision architectures and identifies a reproducible accuracy-scale regularity that warrants further theoretical investigation.
Chinese Translation
视觉识别模型对图像尺度变化的敏感性已得到充分证实，但在异构架构中影响这种敏感性的因素仍不清楚。在本研究中，我们探讨尺度鲁棒性是否在现代视觉模型中表现出共同的定量结构。我们评估了20个预训练的ImageNet-1K分类器，涵盖了七个架构家族，包括卷积、移动、高效和基于Transformer的架构。通过系统地减少输入图像尺度，我们构建了尺度-准确率响应曲线，并定义了特征尺度作为显著识别降级开始的紧凑度量。然后，我们考察特征尺度与基线识别准确率、模型参数数量、架构家族和表示稳定性之间的关系。观察到基线准确率与特征尺度之间存在强烈的负相关关系（Pearson r = -0.890, R^2= 0.792, p < 10^-6）。这一关系在自助重采样、逐一架构剔除分析和逐一家族剔除分析中保持稳定。相比之下，控制基线准确率后，参数数量提供的额外解释力微乎其微（p = 0.80），而架构家族并未提供显著的增量解释力。此外，特征尺度与表示稳定性几乎没有关联（r = -0.003, p = 0.991）。这些结果表明，在研究的模型中，尺度鲁棒性主要由基线识别性能组织，而不仅仅是由模型规模、架构家族或表示稳定性决定。本研究提供了一个经验框架，用于表征视觉架构中的尺度鲁棒性，并识别出一种可重复的准确率-尺度规律，值得进一步的理论研究。
cs.CV / 57 / 2609.06055
DriveZero: End-to-End Driving Beyond Human Demonstrations
DriveZero：超越人类示范的端到端驾驶
Abstract
Most end-to-end autonomous-driving systems learn by imitating human driving logs, leaving their learned behavior constrained by the quality and behavioral coverage of the recorded trajectories. This report presents DriveZero, an end-to-end system that learns driving behavior beyond human demonstrations. It decomposes driving into a perception model and an action model, pretrains each in the regime best suited to it, and combines them into one end-to-end planner. The two models call for different learning recipes: perception must understand the world, and benefits from massive and diverse visual data; action must interact with it, and requires closed-loop feedback. On the action side, we introduce DriveRL, a mixed-agent closed-loop reinforcement-learning framework. It converts real driving logs into interactive worlds, where a privileged teacher policy is trained with PPO through closed-loop rollouts. For the perception model, DriveVFM consolidates multiple frozen vision foundation models, including DINOv3, SigLIP2, SAM and Depth Anything V2, into a single backbone from raw images alone, requiring no task-specific annotations. DriveZero then unifies the two: a camera-only planner that distills the frozen DriveRL teacher through its rolled-out trajectories. The goal-conditioned teacher can moreover be queried under augmented driving intents, yielding diverse, goal-consistent supervision that logged data cannot provide. On nuPlan, DriveRL with value-guided test-time action search achieves a mean score of 93.57 across the Val14, Test14-hard, and Test14-random community splits in both non-reactive and reactive modes, exceeding the Log-Replay expert on all three splits. DriveZero achieves state-of-the-art performance on NAVSIMv1, NAVSIMv2 and the closed-loop HUGSIM benchmark without any human trajectory supervision.
Chinese Translation
大多数端到端自主驾驶系统通过模仿人类驾驶日志进行学习，这使得它们所学习的行为受到记录轨迹的质量和行为覆盖范围的限制。本报告介绍了DriveZero，这是一种超越人类示范的端到端系统。它将驾驶行为分解为感知模型和动作模型，分别在最适合它们的环境中进行预训练，并将它们结合成一个端到端规划器。这两个模型需要不同的学习策略：感知模型必须理解世界，并受益于大量多样的视觉数据；动作模型必须与世界互动，并需要闭环反馈。在动作方面，我们引入了DriveRL，这是一种混合代理闭环强化学习框架。它将真实的驾驶日志转换为交互式世界，其中一个特权教师策略通过闭环回放使用PPO进行训练。对于感知模型，DriveVFM将多个冻结的视觉基础模型（包括DINOv3、SigLIP2、SAM和Depth Anything V2）整合为一个单一的骨干网络，仅从原始图像中提取信息，无需特定任务的注释。DriveZero随后将这两者统一：一个仅依赖摄像头的规划器，通过其回放的轨迹提炼冻结的DriveRL教师。目标条件下的教师策略还可以在增强的驾驶意图下进行查询，从而提供多样化且与目标一致的监督，这是记录数据无法提供的。在nuPlan上，DriveRL结合价值引导的测试时动作搜索，在非反应模式和反应模式下，在Val14、Test14-hard和Test14-random社区划分中实现了93.57的平均得分，超越了所有三个划分的Log-Replay专家。DriveZero在NAVSIMv1、NAVSIMv2和闭环HUGSIM基准测试中实现了最先进的性能，且无需任何人类轨迹监督。
cs.CV / 58 / 2609.06058
GradeTrap: Authority Cues in Images Shift VLM Judgments Despite Explicit Instructions to Ignore Them
GradeTrap：图像中的权威线索在明确指示忽略它们的情况下仍然影响视觉语言模型的判断
Abstract
As vision-language models (VLMs) become increasingly capable and are deployed in consequential real-world settings, they must evaluate evidence independently rather than defer uncritically to human authority. We introduce GradeTrap, a controlled evaluation that places two social cues in direct conflict: a student answer, which should attract sycophantic agreement, and a conflicting answer attributed to a peer, teacher, or official answer key, which should attract authority-based deference. Models produce free-form answers while being explicitly instructed to solve independently and ignore all student answers, feedback, and grading marks. We test the models on 60 synthetic real-world trade-off scenarios. Five neutral trials establish a stable model-relative preference, followed by three repetitions of six experimental cues including controls. On the 45-item common intersection across Gemini 3.5 Flash-Lite, GPT-5.6 Luna, and Claude Haiku 4.5, a generic second-answer control yields 5.4% conflicting-answer selection. Relative to that control, pooled within-item changes show no reliable peer-review effect, a 6.9-point teacher-review effect, and a 19.5-point official-key effect. In contrast, a displayed conflicting student answer alone compared to a displayed student reference answer alone only raises selection from 2.2% to 5.2%. Official-key provenance therefore redirects judgements more than a student answer or the generic second-answer control, despite an explicit ignore instruction and an opposing student answer given along with the official key. Effects vary in magnitude across the three models.
Chinese Translation
随着视觉语言模型（VLM）能力的不断提升并在重要的现实场景中应用，它们必须独立评估证据，而不是无批判地依赖人类权威。我们引入了GradeTrap，这是一种控制性评估，将两种社会线索直接冲突：一个学生答案，应该吸引谄媚的认同；另一个与同龄人、教师或官方答案钥匙相关的冲突答案，应该吸引基于权威的尊重。模型在被明确指示独立解决问题并忽略所有学生答案、反馈和评分标记的情况下，生成自由形式的答案。我们在60个合成的现实世界权衡场景中测试这些模型。五个中性试验建立了一个稳定的模型相对偏好，随后是对六个实验线索（包括对照）的三次重复。在Gemini 3.5 Flash-Lite、GPT-5.6 Luna和Claude Haiku 4.5的45个共同交集项目中，一个通用的第二答案对照产生了5.4%的冲突答案选择。相对于该对照，项目内的汇总变化显示没有可靠的同行评审效应，教师评审效应为6.9点，官方钥匙效应为19.5点。相比之下，仅显示的冲突学生答案与仅显示的学生参考答案相比，选择率仅从2.2%提高到5.2%。因此，官方钥匙的来源在判断上比学生答案或通用第二答案对照更具引导性，尽管有明确的忽略指示和与官方钥匙一起给出的对立学生答案。不同模型之间的效应大小有所不同。
cs.CV / 59 / 2609.06074
NSFlow: End-to-End Differentiable Neuro-Symbolic Optical Flow for Visual Odometry
NSFlow：端到端可微分神经符号光流用于视觉里程计
Abstract
Sparse optical flow provides stable inter-frame correspondence, playing a key role in Visual Odometry (VO) and Visual-Inertial Odometry (VIO). Classical optimization-based methods, such as Lucas-Kanade (LK), perform well under small displacements but are sensitive to large motions and illumination changes. Modern regression-based learning methods, while more robust in complex scenes, are often computationally heavy and lack explicit geometric consistency, making them less suitable for efficient VO/VIO front-ends. To bridge this gap, we propose a hybrid neuro-symbolic framework that combines the strengths of both paradigms. Our method uses a Convolutional Neural Network (CNN) to extract robust feature representations, which is fed into a differentiable LK optimizer to estimate optical flow in an end-to-end trainable manner. Through implicit differentiation, gradients are propagated across the iterative solver, enabling joint optimization of feature extraction and flow estimation. The resulting system integrates seamlessly into existing VO/VIO pipelines and runs in real-time on embedded platforms. Experiments show that our method outperforms conventional optimization-based flow in challenging conditions such as dynamic lighting and low texture, while also achieving higher accuracy and lower latency than purely regression-based alternatives. When deployed in a VIO system, our method demonstrates significant performance improvement, achieving an average error reduction of 42\% on challenging datasets while enhancing tracking stability. The code is publicly available.
Chinese Translation
稀疏光流提供稳定的帧间对应关系，在视觉里程计（VO）和视觉惯性里程计（VIO）中发挥着关键作用。经典的基于优化的方法，如Lucas-Kanade（LK），在小位移下表现良好，但对大运动和光照变化敏感。现代基于回归的学习方法虽然在复杂场景中更具鲁棒性，但通常计算开销较大，且缺乏明确的几何一致性，使其不太适合高效的VO/VIO前端。为了解决这一问题，我们提出了一种混合神经符号框架，结合了这两种范式的优点。我们的方法使用卷积神经网络（CNN）提取鲁棒的特征表示，然后将其输入到可微分的LK优化器中，以端到端可训练的方式估计光流。通过隐式微分，梯度在迭代求解器中传播，实现特征提取和光流估计的联合优化。最终的系统能够无缝集成到现有的VO/VIO管道中，并在嵌入式平台上实时运行。实验表明，我们的方法在动态光照和低纹理等挑战性条件下优于传统的基于优化的光流，同时在准确性和延迟上也优于纯回归的替代方案。在VIO系统中部署时，我们的方法显示出显著的性能提升，在具有挑战性的数据集上实现了平均错误降低42 ext{%}，同时增强了跟踪稳定性。代码已公开。
cs.CV / 60 / 2609.06078
Report of the 8th LSVOS Challenge: Complex and Multimodal Video Object Segmentation
第八届 LSVOS 挑战报告：复杂和多模态视频目标分割
Liu, Chang, Ding, Henghui, Hong, Lingyi, Xu, Ning, Yang, Linjie, Fan, Yuchen, Wu, Canyang, Zhang, Jinrong, He, Xusheng, Bian, Ce, Han, Xianjing, Wu, Jianlong, Gao, Mingqi, Li, Sijie, Han, Jungong, Kim, JeongRae, Kim, Chaehyun, Lim, Changwon, Lee, Jungyoon, Lim, Gyuil, Kim, Doeon, Kim, Seong-heum, Aggarwal, Pranjal, Welleck, Sean, Ren, Yiwen, Liu, Jianing, Wang, Yingxin, Zhang, Kexin, Jiao, Licheng, Li, Lingling, Liu, Xu, Zhou, Jinxing, Zhao, Suiyi, Zhou, Yanghao, Guo, Ruohao, Shi, Liangtao, Xie, Jinxia, Hu, Xiantao, Liu, Ting
Abstract
This report summarizes the 8th Large-scale Video Object Segmentation (LSVOS) Challenge, held in conjunction with ECCV 2026. The challenge evaluates video segmentation in three complementary settings: complex semi-supervised video object segmentation on MOSEv2, text-guided referring video object segmentation on MeViSv2-Text, and audio-guided referring video object segmentation on MeViSv2-Audio. We describe the tasks and evaluation protocols and review the methods of the top three teams in each track. Across the nine leading solutions, foundation segmentation models are combined with target-aware memory, multimodal reasoning, explicit target-existence verification, agentic interaction, and corrective tracking. These systems illustrate a broader transition from single-model mask propagation toward modular pipelines that reason about object identity, query validity, and temporal reliability.
Chinese Translation
本报告总结了第八届大规模视频目标分割（LSVOS）挑战赛，该挑战赛与 2026 年欧洲计算机视觉大会（ECCV）同时举行。该挑战在三种互补的环境中评估视频分割：在 MOSEv2 上进行复杂的半监督视频目标分割，在 MeViSv2-Text 上进行文本引导的参考视频目标分割，以及在 MeViSv2-Audio 上进行音频引导的参考视频目标分割。我们描述了任务和评估协议，并回顾了每个赛道中前三名团队的方法。在九个领先的解决方案中，基础分割模型与目标感知记忆、多模态推理、显式目标存在验证、代理交互和纠正跟踪相结合。这些系统展示了从单一模型掩膜传播向模块化管道的更广泛转变，这些管道能够推理对象身份、查询有效性和时间可靠性。
cs.CV / 61 / 2609.06094
Automatic Red Teaming for Implicit Vulnerabilities of Text-to-Image Models
针对文本到图像模型隐性漏洞的自动红队测试
Abstract
Red-teaming Text-to-Image (T2I) models is essential for safe deployment, yet it remains particularly challenging against implicit adversarial prompts. Unlike explicit adversarial prompts that can be readily identified and blocked, implicit ones are much harder to detect: the prompts appear benign on the text surface yet still lead to inappropriate visual content. To address this, we propose Adversarial Probing for Implicit VulnErabilities (AdvPIE), a multimodal agentic framework to expose implicit vulnerabilities without requiring access to the parameters of target models. AdvPIE adopts a policy agent to generate and refine implicit adversarial prompts based on the feedback from a judge agent. To construct informative feedback, the judge agent provides modality-specific safety evaluation at both global and relative levels across iterations. To effectively leverage the feedback, we propose a novel Cumulative Adversarial Decoding strategy for the policy agent, which dynamically reweights token distributions to favor tokens that lead to more harmful images while preserving sampling diversity. Extensive experiments on standard and safety-aligned T2I models show that AdvPIE1 effectively uncovers implicit vulnerabilities, outperforming various baseline methods.
Chinese Translation
对文本到图像（T2I）模型进行红队测试对于安全部署至关重要，但在应对隐性对抗性提示时仍然特别具有挑战性。与可以轻易识别和阻止的显性对抗性提示不同，隐性提示更难以检测：这些提示在表面上看似无害，但仍然会导致不当的视觉内容。为了解决这一问题，我们提出了针对隐性漏洞的对抗性探测框架（Adversarial Probing for Implicit VulnErabilities，AdvPIE），该框架无需访问目标模型的参数即可揭示隐性漏洞。AdvPIE采用策略代理，根据评判代理的反馈生成和优化隐性对抗性提示。为了构建有意义的反馈，评判代理在每次迭代中提供全局和相对层面的特定模态安全评估。为了有效利用反馈，我们为策略代理提出了一种新颖的累积对抗解码策略，该策略动态重新加权令牌分布，以偏向于导致更有害图像的令牌，同时保持采样多样性。在标准和安全对齐的T2I模型上进行的大量实验表明，AdvPIE有效地揭示了隐性漏洞，超越了多种基线方法。
cs.CV / 62 / 2609.06099
PASTEL: Panoramic Alignment for Monocular 4D Scene Reconstruction
PASTEL：单目4D场景重建的全景对齐
Abstract
Reconstructing 4D scenes from casually captured monocular video is vital for applications in virtual reality (VR) and embodied AI. Recent advances in 4D reconstruction and novel view synthesis have substantially propelled this capability. However, existing reconstruction methods generally cannot recover regions beyond visible camera limits. Consequently, we introduce a new paradigm that achieves 4D scene synthesis by combining visible-region reconstruction from monocular input with invisible-region generation beyond observable camera boundaries. We present Panoramic Alignment for Strategic Exploitation of Generative Priors (PASTEL). Specifically, PASTEL proposes panoramic scene alignment, a novel representation that reformulates the intractable 3D "invisible region" exploration into a tractable 2D directional trajectory planning. This is achieved by reducing the viewpoint planning from 6-DoF search to a 2D directional search with explicit visibility boundaries. By operating within this panoramic space, our method strategically identifies camera trajectories that maximize exploration beyond observable boundaries while minimizing viewpoint deviation. Experimental results show that PASTEL can not only extrapolate plausible scene content beyond the observable boundaries of input monocular videos, but also substantially boost monocular 4D reconstruction performance. PASTEL outperforms the previous state-of-the-art method by 0.9dB in full-image PSNR on the DyCheck IPhone dataset.
Chinese Translation
从随意捕获的单目视频中重建4D场景对于虚拟现实（VR）和具身人工智能的应用至关重要。最近在4D重建和新视角合成方面的进展显著推动了这一能力。然而，现有的重建方法通常无法恢复超出可见相机范围的区域。因此，我们提出了一种新的范式，通过结合来自单目输入的可见区域重建与超出可观察相机边界的不可见区域生成，实现4D场景合成。我们提出了全景对齐以战略性利用生成先验（PASTEL）。具体而言，PASTEL提出了全景场景对齐，这是一种新颖的表示方式，将难以处理的3D“不可见区域”探索重新构造为可处理的2D方向轨迹规划。这是通过将视点规划从6自由度搜索降低到具有明确可见边界的2D方向搜索来实现的。通过在这个全景空间内操作，我们的方法战略性地识别出最大化超出可观察边界的探索同时最小化视点偏差的相机轨迹。实验结果表明，PASTEL不仅能够推断出超出输入单目视频可观察边界的合理场景内容，还能显著提升单目4D重建性能。在DyCheck IPhone数据集上，PASTEL在全图PSNR上比之前的最先进方法提高了0.9dB。
cs.CV / 63 / 2609.06144
TBDub: Production-Oriented Visual Dubbing
TBDub：面向生产的视觉配音
Abstract
Visual dubbing must synchronize mouth motion with replacement speech while preserving identity, appearance, and temporal consistency. Although X-Dub provides a strong mask-free video-editing baseline, its application to livestream and generated-video content reveals limitations in production-domain robustness, temporal and motion stability, identity and oral-detail preservation, and inference efficiency. We present \textbf{TBDub}, a production-oriented extension of X-Dub that combines task-adaptive post-training with task-aware few-step distillation. Post-training adapts the video DiT using production-domain data, production-specific conditioning and filtering, and enhanced audio features to obtain a 30-step Teacher. Distillation adapts DMD/DMD2 to conditional video editing and compresses the Teacher into a two-step Student. On 38 TalkVid clips, the Teacher improves all eight reported reconstruction, perceptual, identity, and synchronization metrics over X-Dub. In the MOS evaluation, it improves lip-sync consistency, identity consistency, and visual quality over X-Dub by 0.14, 0.95, and 0.90 points, while the Student achieves the highest lip-sync and visual-quality scores and remains close to the Teacher in identity consistency. In paired end-to-end generation timing from the first VAE encode through the final VAE decode on a single NVIDIA H20 GPU at $512\times512$, the Student reaches 7.13 effective FPS and reduces total latency by $13.93\times$; the DiT stage alone is accelerated by $42.49\times$. The Student largely retains the Teacher's generation quality and audiovisual synchronization. The code is available on GitHub at \https://github.com/TaoLiveAIGC/TBDub, and the 30-step Teacher and two-step Student weights are available on Hugging Face at https://huggingface.co/TaoLiveAIGC/TBDub.
Chinese Translation
视觉配音必须在替换语音的同时同步口型运动，并保持身份、外观和时间一致性。尽管 X-Dub 提供了强大的无掩码视频编辑基线，但其在直播和生成视频内容中的应用暴露出在生产领域的鲁棒性、时间和运动稳定性、身份和口腔细节保留以及推理效率方面的局限性。我们提出了 extbf{TBDub}，这是 X-Dub 的一个面向生产的扩展，结合了任务自适应后训练和任务感知的少步蒸馏。后训练使用生产领域数据、特定于生产的条件和过滤以及增强的音频特征来调整视频 DiT，以获得一个 30 步的教师模型。蒸馏则将 DMD/DMD2 适应于条件视频编辑，并将教师模型压缩为一个两步的学生模型。在 38 个 TalkVid 片段上，教师模型在所有八个报告的重建、感知、身份和同步指标上均优于 X-Dub。在 MOS 评估中，教师模型在口型同步一致性、身份一致性和视觉质量上分别比 X-Dub 提高了 0.14、0.95 和 0.90 分，而学生模型则在口型同步和视觉质量得分上达到了最高，并在身份一致性上与教师模型保持接近。在单个 NVIDIA H20 GPU 上，从第一次 VAE 编码到最终 VAE 解码的成对端到端生成时间为 $512 imes512$，学生模型达到了 7.13 的有效 FPS，并将总延迟减少了 $13.93 imes$；仅 DiT 阶段的加速达到了 $42.49 imes$。学生模型在很大程度上保留了教师模型的生成质量和视听同步。代码可在 GitHub 上获取，链接为 https://github.com/TaoLiveAIGC/TBDub，30 步教师模型和两步学生模型的权重可在 Hugging Face 上获取，链接为 https://huggingface.co/TaoLiveAIGC/TBDub。
cs.CV / 64 / 2609.06165
Multiple Myeloma Lesion Segmentation on Whole-Body Diffusion-Weighted Imaging via Efficient Anatomical Anticipation and Multimodal Confirmation
基于高效解剖预测和多模态确认的全身扩散加权成像多发性骨髓瘤病灶分割
Abstract
Whole-body diffusion-weighted imaging (WB-DWI) is widely used for multiple myeloma (MM) assessment, yet automated lesion segmentation remains challenging due to limited anatomical delineation and the low specificity of marrow hyperintensity. Existing studies have introduced bone region-of-interest (ROI) information and apparent diffusion coefficient (ADC) maps to mitigate these ambiguities, but practical limitations remain. Bone ROI construction often relies on costly manual annotation, image registration, or dedicated bone models, while ADC is usually incorporated only through simple channel fusion, limiting its ability to provide complementary structural and lesion-discriminative cues. To address these limitations, we propose a two-stage framework for MM lesion segmentation on WB-DWI. In the first stage, we train a bone ROI generation model from ADC images without dedicated bone labels, providing an efficient and practical anatomical prior for lesion analysis. In the second stage, we propose Anatomy-guided Multimodal U-Net (AMU-Net), which leverages ADC in a manner consistent with clinical lesion assessment rather than treating it as a generic auxiliary modality. Extensive experiments demonstrate the effectiveness and practicality of the proposed method. It achieves the best overall performance among the evaluated methods, with a mean Dice score of 76.2%.
Chinese Translation
全身扩散加权成像（WB-DWI）广泛应用于多发性骨髓瘤（MM）的评估，但由于解剖轮廓的有限性和骨髓高信号的低特异性，自动化病灶分割仍然面临挑战。现有研究引入了骨骼感兴趣区域（ROI）信息和表观扩散系数（ADC）图，以减轻这些模糊性，但仍存在实际限制。骨骼ROI的构建通常依赖于昂贵的手动标注、图像配准或专用骨骼模型，而ADC通常仅通过简单的通道融合来整合，限制了其提供互补结构和病灶区分线索的能力。为了解决这些限制，我们提出了一种用于WB-DWI上MM病灶分割的两阶段框架。在第一阶段，我们从没有专用骨骼标签的ADC图像中训练一个骨骼ROI生成模型，为病灶分析提供高效且实用的解剖先验。在第二阶段，我们提出了基于解剖指导的多模态U-Net（AMU-Net），该模型以与临床病灶评估一致的方式利用ADC，而不是将其视为通用辅助模态。大量实验表明，所提方法的有效性和实用性。在评估的方法中，其整体表现最佳，平均Dice分数达到76.2%。
cs.CV / 65 / 2609.06187
SolarBench: A global solar energy nowcasting benchmark
SolarBench：全球太阳能短期预测基准
Nie, Yuhao, Campbell, Stephen, Paletta, Quentin, Zhang, Liwenbo, Jing, Tao, Chaaraoui, Samer, Giezendanner, Jonathan, Scott, Andea, Sun, Tao, Feng, Cong, Aragon, Max, Camier, Jacques, Jensen, Adam, Kotthoff, Florian, Yang, Yuexing, Ming, Yang, Li, Mengying, Meilinger, Stefanie, Wu, Yupeng, Brandt, Adam, Wang, Sherrie
Abstract
As the share of solar power grows, nowcasting weather-driven solar variability becomes critical for reliable energy system operation. State-of-the-art approaches increasingly apply deep learning to sky camera and geostationary satellite observations, but fragmented datasets and inconsistent evaluation make it difficult to determine whether reported improvements generalize across climates, cloud regimes, and photovoltaic (PV) systems. Here we introduce SolarBench, an open global benchmark for image-based solar nowcasting. SolarBench harmonizes more than six million sky and satellite images from 11 diverse sites spanning a decade, together with irradiance or PV output and auxiliary atmospheric data. An accompanying toolbox supports reproducible data access, processing, model development, and evaluation. Using SolarBench, we benchmark representative models and reveal a gap between average forecasting accuracy and the ability to capture rapid solar fluctuations. We further quantify predictability across cloud regimes and demonstrate data-efficient adaptation to new PV systems. SolarBench provides an extensible foundation for fair comparison and methodological innovation in solar nowcasting.
Chinese Translation
随着太阳能发电比例的增加，基于天气驱动的太阳能变动的短期预测变得对可靠的能源系统运行至关重要。最先进的方法越来越多地应用深度学习于天空摄像头和地球静止卫星观测数据，但数据集的碎片化和评估的不一致性使得难以判断报告的改进是否能够在不同气候、云层模式和光伏（PV）系统中普遍适用。在此，我们介绍了SolarBench，一个基于图像的太阳能短期预测的开放全球基准。SolarBench整合了来自11个不同地点的超过六百万张天空和卫星图像，覆盖十年时间，并结合了辐照度或光伏输出及辅助大气数据。配套工具箱支持可重复的数据访问、处理、模型开发和评估。利用SolarBench，我们对代表性模型进行了基准测试，并揭示了平均预测准确性与捕捉快速太阳能波动能力之间的差距。我们进一步量化了不同云层模式下的可预测性，并展示了对新光伏系统的数据高效适应。SolarBench为太阳能短期预测中的公平比较和方法创新提供了一个可扩展的基础。
cs.CV / 66 / 2609.06190
One Perturbation Is Not Enough: Identifiability and Blind Baselines for Behavioral AI Evaluation
单一扰动不足以评估：行为人工智能评估的可识别性与盲基线
Abstract
Behavioral evaluations perturb an input and read the induced change in the output in order to certify that a system uses that input. We show that the number of perturbations such a certificate requires is fixed, and that reporting a single perturbation cannot supply it. Where a response ratio is a property of the policy rather than of the test items, the behavioral record is a linear measurement of an exponent vector recording how much the output depends on each input, so perturbations identify input use exactly when their logarithms span the input space. At least $n$ are needed for $n$ inputs, an incomplete design confuses precisely the policies differing along the kernel of its design matrix, and sharpening a perturbation never substitutes for adding an independent one. We also derive in closed form the score such a test awards a policy that reads nothing, which is far from zero and which none of the probes we survey reports. Instantiating this where the correct response is fixed by dimensional analysis, we run a complete identifying set of three perturbations on three vision--language models reporting a physical quantity from video. All three score far below their own blind bound rather than above it, because each defaults to one of a small set of round calibration values that never matches what the scale asserts; none moves its relabeling response by a single exponent, and none is separable from the same model instructed to ignore the video.
Chinese Translation
行为评估通过扰动输入并读取输出中引起的变化，以证明系统使用了该输入。我们展示了这种证书所需的扰动数量是固定的，报告单一扰动无法提供该证书。当响应比是策略的属性而非测试项目的属性时，行为记录是一个线性测量，记录输出对每个输入的依赖程度，因此当扰动的对数覆盖输入空间时，扰动能够准确识别输入的使用。对于 $n$ 个输入，至少需要 $n$ 个扰动，不完整的设计会混淆在其设计矩阵的核上不同的策略，而加深一个扰动从未能替代添加一个独立的扰动。我们还以封闭形式推导出该测试给予未读取任何信息的策略的分数，该分数远非零，并且我们调查的探测器均未报告此分数。在通过维度分析确定正确响应的情况下，我们对三个视觉-语言模型进行了完整的识别性测试，报告来自视频的物理量。所有三个模型的得分均远低于其自身的盲界限，而不是高于它，因为每个模型默认使用一小组从未与比例所声称的值匹配的圆形校准值；没有一个模型将其重新标记响应移动一个单一的指数，也没有一个模型与同一模型在忽略视频的情况下是可分离的。
cs.CV / 67 / 2609.06207
PhysWeep: Does a Video Generator Realize the Physics You Ask For?
PhysWeep：视频生成器是否实现了您所要求的物理效果？
Abstract
Image-to-video generators are often credited with absorbing physical dynamics as implicit world models, a claim the community currently checks with plausibility scores that ask whether a clip looks consistent with real-world motion. Plausibility is the wrong test on its own, because a clip can look natural while encoding the wrong value of the governing physical parameter, and no existing benchmark measures this gap directly. PhysWeep closes it with a fixed, label-free audit, treating a frozen generator as a black box, recovering the realized parameter from generated pixels, and reporting how often generation is trackable at all, how far the realized value sits from the requested one, and which, if either, of the literature's two proposed failure mechanisms the data support. A deterministic-simulator positive control confirms every score is exactly checkable. Applied to three open generators across six sweep axes, PhysWeep finds a specific, reproducible, previously undocumented failure. Conditional on producing trackable motion, two of the three generate confident, well-fit dynamics that converge to one of a small number of fixed, wrong values selected by the sampling seed rather than by the request, reproducing across two independent model families, two physical systems, and an independent tracker. It matches neither the prior reversion nor the case-based clamping the literature anticipates, because the reversion target is seed-conditional rather than a single global default, and a leave-one-out selection rule rejects both; the in-range faithfulness slope is statistically indistinguishable from zero wherever a response is estimable at all. A benchmark averaging over seeds would never see this: each sample is confidently locked to a wrong constant, exactly the failure a plausibility score is structurally blind to. We release the protocol, suite, and analysis code.
Chinese Translation
图像到视频生成器通常被认为能够吸收物理动态作为隐式世界模型，这一说法目前正通过可行性评分进行验证，该评分询问一个片段是否与现实世界的运动一致。然而，仅仅依靠可行性并不是正确的测试，因为一个片段可能看起来自然，但编码了错误的主导物理参数值，而现有的基准测试并未直接衡量这一差距。PhysWeep 通过固定的、无标签的审计来弥补这一差距，将一个冻结的生成器视为黑箱，从生成的像素中恢复实现的参数，并报告生成的可追踪性、实现值与请求值之间的差距，以及数据支持文献中提出的两种失败机制中的哪一种（如果有的话）。一个确定性模拟器的正控制确认每个评分都是可以精确检查的。应用于三个开放生成器的六个扫掠轴，PhysWeep 发现了一种特定的、可重复的、先前未记录的失败。在生成可追踪运动的条件下，三个生成器中的两个生成了自信且拟合良好的动态，这些动态收敛于由采样种子选择的少数固定错误值，而不是由请求决定，且在两个独立的模型家族、两个物理系统和一个独立的跟踪器中重复出现。这既不符合文献预期的反转情况，也不符合基于案例的夹紧情况，因为反转目标是种子条件的，而不是单一的全局默认值，而逐一排除选择规则则拒绝了这两者；在可估计响应的情况下，范围内的忠实度斜率在统计上与零无显著差异。一个对种子进行平均的基准测试永远无法看到这一点：每个样本都自信地锁定在一个错误的常数上，而这正是可行性评分在结构上无法察觉的失败。我们发布了该协议、套件和分析代码。
cs.CV / 68 / 2609.06208
From Gaze to Meaning: A Training-Free AI Agent for Unified Grounding and Explanation
从注视到意义：一种无训练的人工智能代理用于统一的基础和解释
Abstract
Understanding human attention is fundamental for scene interpretation, yet existing approaches often rely on heavily trained models that lack interpretability. Prior methods struggle to jointly reason about gaze targets, attended objects, and visual grounding without extensive supervision. To the best of our knowledge, this work introduces the first training-free Gaze Target Agent (GTA) for gaze-guided reasoning across tasks such as gaze target prediction, attention localization, and object identification. This is achieved by leveraging pretrained vision-language models, augmenting them with visually guided prompts, and employing a memory-based retrieval strategy for high-uncertainty samples to improve performance without additional training. We evaluate our approach using both quantitative metrics and qualitative results. Quantitatively, our method achieves state of the art performance on the GazeFollow and GazeHOI benchmarks. Qualitatively, our agent provides detailed semantic predictions, predicts the correct targets even when ground truth labels are wrong, and remains flexible without vocabulary constraints.
Chinese Translation
理解人类注意力对于场景解释至关重要，但现有的方法往往依赖于缺乏可解释性的重训练模型。以往的方法在没有大量监督的情况下，难以共同推理注视目标、关注对象和视觉基础。根据我们所知，本研究首次提出了一种无训练的注视目标代理（Gaze Target Agent, GTA），用于在注视引导的推理任务中，如注视目标预测、注意力定位和对象识别。我们通过利用预训练的视觉-语言模型，结合视觉引导的提示，并采用基于记忆的检索策略来处理高不确定性样本，从而在不进行额外训练的情况下提高性能。我们使用定量指标和定性结果对我们的方法进行了评估。在定量方面，我们的方法在GazeFollow和GazeHOI基准测试中达到了最先进的性能。在定性方面，我们的代理提供了详细的语义预测，即使在真实标签错误的情况下也能预测正确的目标，并且在没有词汇限制的情况下保持灵活性。
cs.CV / 69 / 2609.06218
Branch-Centric Tokenization and Test-Time Augmentation for Skeleton Generation
基于分支的标记化和测试时增强用于骨架生成
Abstract
Automatic skeleton generation involves predicting both joint positions and skeletal connectivity. However, existing approaches struggle to encode branch structures into token sequences and do not use test-time computation effectively. We study these choices within a unified autoregressive framework. First, we introduce branch-centric tokenization, a branch-aware representation that places structurally related elements next to each other and encodes connectivity directly in the sequence. Compared with standard BFS-style serialization, this representation yields more compact sequences. Second, we introduce view-augmented generation, a test-time augmentation procedure that applies axis-aligned rotations to the input mesh, maps all predictions back to a common frame, and selects the final skeleton based on mesh coverage and consistency among predictions from different views. Experiments show that our method achieves better skeleton prediction accuracy than state-of-the-art methods. In particular, our method reduces the CD-J2B error by 16.9% on the Articulation-XL2.0 dataset compared to the strongest directly comparable baseline, Auto-Connect. Qualitative results on in-the-wild meshes further demonstrate generalization across diverse inputs.
Chinese Translation
自动骨架生成涉及预测关节位置和骨架连通性。然而，现有方法在将分支结构编码为标记序列方面存在困难，并且未能有效利用测试时计算。我们在一个统一的自回归框架内研究这些选择。首先，我们引入了基于分支的标记化，这是一种考虑分支的表示方法，它将结构相关的元素相邻放置，并直接在序列中编码连通性。与标准的广度优先搜索（BFS）样式序列化相比，这种表示方法生成了更紧凑的序列。其次，我们引入了视图增强生成，这是一种测试时增强程序，它对输入网格应用轴对齐旋转，将所有预测映射回一个共同的框架，并根据网格覆盖率和来自不同视图的预测一致性选择最终骨架。实验表明，我们的方法在骨架预测准确性上优于最先进的方法。特别是，我们的方法在Articulation-XL2.0数据集上相比于最强的直接可比基线Auto-Connect减少了16.9%的CD-J2B误差。对真实场景网格的定性结果进一步证明了我们方法在多样化输入上的泛化能力。
cs.CV / 70 / 2609.06232
Spatial Attention Supervision for Defect Localization: Exploiting Ground-Truth Masks as Training Signal in Diffusion-Augmented Defect Detection
缺陷定位的空间注意力监督：利用真实标签掩码作为扩散增强缺陷检测中的训练信号
Abstract
Ground-truth defect masks in industrial inspection datasets are typically reserved for evaluation. This paper repurposes them as spatial supervision signals during training of classification networks, teaching a model not just what to predict but where to look. The method adds an activation-based attention alignment loss that steers convolutional feature maps toward defect regions, in a mixed-supervision formulation that also accommodates samples without masks, such as diffusion-generated images. Combined with DDPM augmentation, synthetic images contribute quantity while masks contribute spatial precision. We evaluate 85 models (four CNN backbones under a 2x2 data/training factorial over five seeds, plus a Swin-V2-T transformer baseline) on the MVTec-AD bottle benchmark, with localization measured on held-out defect images excluded from classifier gradient updates. Main findings: (1) attention-guided training improves activation-based localization (Pixel-AUROC) by +18.0% for EfficientNetB0 with augmentation (p=0.005, Cohen's d=2.6) and +18.7% for ResNet50 (p=0.008), significant in four of eight CNN settings (uncorrected for multiple comparisons) with no significant change in classification; (2) for EfficientNetB0 a data x training-mode interaction is significant (p=0.002), consistent with a super-additive effect (+13.6% combined vs +1.6% summed individual effects); (3) architectures with weaker spatial representations benefit most, whereas ConvNeXt-T shows no effect, apparently because its depthwise-convolution activations yield spatially uninformative channel-mean maps; (4) unsupervised PatchCore remains the strongest localizer (Pixel-AUROC=0.983), contextualizing the supervised gains. These results show that existing evaluation masks can act as practical training signals that measurably and reproducibly improve where defect classifiers attend.
Chinese Translation
工业检测数据集中，真实缺陷掩码通常用于评估。本文将其重新用于分类网络训练中的空间监督信号，教导模型不仅要预测什么，还要关注哪里。该方法增加了一种基于激活的注意力对齐损失，指导卷积特征图朝向缺陷区域，在一种混合监督的框架下，同时适应没有掩码的样本，例如扩散生成的图像。结合DDPM增强，合成图像提供数量，而掩码则提供空间精度。我们在MVTec-AD瓶子基准上评估了85个模型（四个CNN骨干网络在五个种子下的2x2数据/训练因子），定位是在排除分类器梯度更新的缺陷图像上进行的。主要发现：（1）基于注意力的训练提高了激活基础的定位（Pixel-AUROC），在使用增强的EfficientNetB0上提高了18.0%（p=0.005，Cohen's d=2.6），在ResNet50上提高了18.7%（p=0.008），在八个CNN设置中的四个中显著（未进行多重比较校正），分类没有显著变化；（2）对于EfficientNetB0，数据与训练模式的交互显著（p=0.002），与超加性效应一致（组合效果+13.6% vs 单独效果+1.6%）；（3）具有较弱空间表示的架构受益最大，而ConvNeXt-T没有效果，显然是因为其深度卷积激活产生了空间上无信息的通道均值图；（4）无监督的PatchCore仍然是最强的定位器（Pixel-AUROC=0.983），为监督增益提供了背景。这些结果表明，现有的评估掩码可以作为实用的训练信号，显著且可重复地改善缺陷分类器的关注区域。
cs.CV / 71 / 2609.06245
VDiff-Bench: A Challenging Benchmark for Fine-Grained Image Difference Identification
VDiff-Bench：一个用于细粒度图像差异识别的挑战性基准
Abstract
Multimodal Large Language Models (MLLMs) perform strongly on general visual understanding tasks such as visual question answering, yet they often struggle with a basic comparative skill: identifying what has changed between two similar images. We introduce VDiff-Bench, a challenging multiple-choice benchmark for fine-grained Image Difference Identification. VDiff-Bench contains 1,756 four-way questions over image pairs and covers 10 change categories: position, motion, regional image color, overall image color, appearance/disappearance, noise/resolution, texture, substitution/size, OCR/text, and illumination. Each question corresponds to two image inputs with 4 choices: the true difference, two hard negative descriptions, and a "no difference" distractor. To make the task challenging, we specifically curate ground-truth-conditioned negatives that require models to distinguish the actual change from nearby semantic alternatives. Experiments with 11 state-of-the-art open- and closed-source MLLMs show that fine-grained visual comparison remains brittle: models exhibit uneven performance across sources and change categories, with persistent failures on subtle low-level changes like noises and textures. For instance, three 7-8B-scale open-source MLLMs score 52.5-70.6% on semantic changes but only 8.7-33.3% on low-level changes like noise and texture, falsely assuming no changes between two image inputs. Surprisingly, despite strong performance of other closed-source commercial models, Grok 4.3 demonstrate remarkable performance drop on identifying noise and texture differences between images, falling significantly behind large open-source models like Kimi K2.5 and K3. Overall, VDiff-Bench provides a targeted diagnostic for evaluating comparative visual understanding in MLLMs, exposing failures that are not captured by standard single-image vision-language tasks.
Chinese Translation
多模态大型语言模型（MLLMs）在一般视觉理解任务（如视觉问答）中表现出色，但它们在一项基本的比较技能上往往表现不佳：识别两幅相似图像之间的变化。我们提出了VDiff-Bench，这是一个用于细粒度图像差异识别的挑战性多项选择基准。VDiff-Bench包含1,756个四选一的问题，涵盖10种变化类别：位置、运动、区域图像颜色、整体图像颜色、出现/消失、噪声/分辨率、纹理、替代/大小、光学字符识别/文本和照明。每个问题对应两个图像输入和4个选项：真实差异、两个困难的负面描述以及一个“无差异”的干扰项。为了使任务具有挑战性，我们特别策划了基于真实情况的负面示例，要求模型区分实际变化与相近语义替代品之间的差异。对11个最先进的开源和闭源MLLMs的实验表明，细粒度视觉比较仍然脆弱：模型在不同来源和变化类别之间表现不均，尤其在细微的低级变化（如噪声和纹理）上持续失败。例如，三款7-8B规模的开源MLLMs在语义变化上得分为52.5-70.6%，但在噪声和纹理等低级变化上仅得8.7-33.3%，错误地假设两幅图像输入之间没有变化。令人惊讶的是，尽管其他闭源商业模型表现强劲，Grok 4.3在识别图像之间的噪声和纹理差异时表现显著下降，远远落后于大型开源模型如Kimi K2.5和K3。总体而言，VDiff-Bench为评估MLLMs的比较视觉理解提供了一个有针对性的诊断工具，揭示了标准单图像视觉语言任务未能捕捉到的失败。
cs.CV / 72 / 2609.06251
MobileVLA-R1 2.0: RL-Enhanced Reasoning for Mobile Robot Control
MobileVLA-R1 2.0：增强强化学习的移动机器人控制推理
Abstract
Grounding natural-language instructions into reliable and executable actions remains a fundamental challenge for vision-language-action (VLA) systems on mobile robots, due to the persistent gap between high-level semantic reasoning and low-level locomotion and manipulation control. Existing approaches often rely on implicit reasoning or monolithic action prediction, making it difficult to maintain coherent long-horizon decision making while producing precise and adaptable robot actions. To address this challenge, we propose MobileVLA-R1 2.0, an RL-enhanced VLA framework that explicitly couples structured embodied reasoning with executable mobile robot control. The framework learns multi-granularity reasoning over embodied trajectories through supervised Chain-of-Thought (CoT) alignment and reinforcement learning, improving reasoning-to-action consistency beyond purely behavioral supervision. To support both locomotion and manipulation, we further introduce a reasoning-conditioned action decoder that maps multimodal reasoning representations to task-level action targets, which are subsequently translated into embodiment-specific commands by robot controllers. This design provides a unified perception-reasoning-action interface while decoupling high-level action generation from robot-specific actuation. We conduct extensive evaluations on language-guided navigation, quadruped control, and humanoid mobile manipulation, covering VLN-CE, QUARD, and real-world deployments on Unitree Go2 and G1 robots. MobileVLA-R1 2.0 consistently outperforms strong VLA baselines, achieving an average 1.6 point improvement in SR on VLN-CE and a 10.0 point improvement in full-task success on real-world G1 mobile manipulation tasks over MobileVLA-R1, while demonstrating robust long-horizon instruction following and closed-loop execution across different robotic platforms.
Chinese Translation
将自然语言指令转化为可靠且可执行的动作仍然是移动机器人视觉-语言-动作（VLA）系统面临的一项基本挑战，这主要是由于高层语义推理与低层运动和操作控制之间的持续差距。现有的方法通常依赖于隐式推理或单一的动作预测，这使得在生成精确且可适应的机器人动作时，难以维持连贯的长时间决策。为了解决这一挑战，我们提出了MobileVLA-R1 2.0，这是一种增强强化学习的VLA框架，明确将结构化的具身推理与可执行的移动机器人控制相结合。该框架通过监督的思维链（Chain-of-Thought, CoT）对齐和强化学习，学习具身轨迹上的多粒度推理，从而提高推理与行动的一致性，超越纯行为监督。为了支持运动和操作，我们进一步引入了一种基于推理的动作解码器，将多模态推理表示映射到任务级动作目标，随后由机器人控制器将其转化为特定于具身的指令。这一设计提供了统一的感知-推理-行动接口，同时将高层动作生成与机器人特定的执行解耦。我们在语言引导的导航、四足控制和类人移动操作等任务上进行了广泛评估，涵盖了VLN-CE、QUARD以及在Unitree Go2和G1机器人上的实际部署。MobileVLA-R1 2.0在各项评估中始终优于强大的VLA基线，在VLN-CE上实现了平均1.6点的成功率（SR）提升，并在实际G1移动操作任务中实现了10.0点的全任务成功率提升，同时在不同机器人平台上展示了强大的长时间指令跟随和闭环执行能力。
cs.CV / 73 / 2609.06253
One Model, Two Worlds: Bidirectional Sonar-Optical Translation
一个模型，两个世界：双向声纳-光学翻译
Abstract
Translating between imaging sonar and optical cameras is valuable for underwater perception, but supporting both directions with separate models duplicates storage and computation. A unified bidirectional model is therefore attractive, yet existing approaches largely treat the two directions symmetrically despite their fundamentally different image-formation physics. We argue that sharing a generative model does not require sharing the physics. We introduce the Direction-Asymmetric Realism Bridge (DARB), which retains a shared diffusion-bridge trunk while routing direction-specific physical priors through asymmetric pathways: range-aware modulation for sonar-to-optical translation and polar ray-dependent processing for optical-to-sonar translation. We further show that symmetry in training is also costly: applying a common realism schedule reduces sonar-to-optical PSNR by 2.60 dB. Our Adaptive Realism Supervision (ARS) instead determines when, where, and how strongly perceptual supervision is applied from reconstruction quality and gradient balance. Together, DARB and ARS enable one bidirectional model to match the sonar-to-optical specialist within 0.11 dB PSNR, outperform the optical-to-sonar specialist by 0.70 FID, and surpass two independently trained BBDMs on seven of eight metrics.
Chinese Translation
在水下感知中，声纳成像与光学相机之间的翻译具有重要价值，但使用单独模型支持两个方向会导致存储和计算的重复。因此，一个统一的双向模型是非常有吸引力的，然而现有的方法在很大程度上对待这两个方向是对称的，尽管它们在图像形成物理上存在根本的不同。我们认为，共享生成模型并不需要共享物理。我们引入了方向非对称现实桥（Direction-Asymmetric Realism Bridge, DARB），该模型保留了一个共享的扩散桥主干，同时通过非对称路径引导方向特定的物理先验：针对声纳到光学翻译的范围感知调制和针对光学到声纳翻译的极坐标光线依赖处理。我们进一步表明，训练中的对称性也是有代价的：应用共同的现实调度会使声纳到光学的峰值信噪比（PSNR）降低2.60 dB。我们的自适应现实监督（Adaptive Realism Supervision, ARS）则根据重建质量和梯度平衡来确定何时、何地以及多强地应用感知监督。结合DARB和ARS，一个双向模型能够在PSNR上与声纳到光学的专家相匹配，差距仅为0.11 dB，且在FID上超越光学到声纳的专家0.70，此外在八个指标中的七个上超过了两个独立训练的双向声纳-光学模型（BBDMs）。
cs.CV / 74 / 2609.06261
Adapting Vision Foundation Models to Acoustics for Pose-Free 3D Sonar Reconstruction
将视觉基础模型适应于声学以实现无姿态的三维声纳重建
Abstract
Vision foundation models trained on Internet-scale RGB datasets enable remarkable capabilities across a range of tasks, from text-to-video generation to few-shot 3D scene reconstruction. An acoustic foundation model trained on large-scale sonar datasets could enable similar capabilities in the underwater domain, where turbidity and low-visibility conditions make conventional RGB foundation models inapplicable. Unfortunately, a lack of freely available large-scale sonar datasets makes training such a model from scratch impractical. In this work, we demonstrate that vision foundation models can be efficiently adapted to the sonar setting by (1) exploiting the geometric relationship between the two sensing modalities and (2) employing accurate physics-based noise models for synthetic data generation. The resulting sonar adaptation models enable new capabilities: For the first time, we experimentally demonstrate sonar-based pose-free 3D reconstruction.
Chinese Translation
在互联网规模的RGB数据集上训练的视觉基础模型在多种任务中展现了卓越的能力，从文本到视频生成到少量样本的三维场景重建。在大型声纳数据集上训练的声学基础模型可以在水下领域实现类似的能力，因为浑浊和低能见度条件使得传统的RGB基础模型无法适用。不幸的是，缺乏可自由获取的大规模声纳数据集使得从头开始训练这样的模型变得不切实际。在本研究中，我们展示了如何通过（1）利用两种传感模式之间的几何关系和（2）采用基于物理的准确噪声模型进行合成数据生成，来有效地将视觉基础模型适应于声纳环境。所得到的声纳适应模型实现了新的能力：我们首次实验性地展示了基于声纳的无姿态三维重建。
cs.CV / 75 / 2609.06276
A Comparative Study of GAN-Based Deep Learning Models for Pneumonia Detection in Chest X-Rays
基于GAN的深度学习模型在胸部X光片中肺炎检测的比较研究
Abstract
This study evaluates pneumonia classification in chest X-rays using VGG19, MobileNetV2, ResNet50, and a custom CNN, and explores Generative Adversarial Network (GAN)-based synthetic data augmentation. MobileNetV2 achieved the highest reported accuracy of 88% with balanced class-wise performance. The custom CNN achieved pneumonia recall of 92.67% and precision of 79.43%, highlighting a precision-recall trade-off. Accuracy, F1-score, precision, recall, confusion matrices, and training curves were used to assess performance. Synthetic pneumonia images were combined with real images to investigate whether augmentation could improve classification performance. In the reported VGG19 comparison, augmented-data training accuracy reached approximately 100%, while validation accuracy remained near 50%, below the real-data validation accuracy. This experiment therefore did not demonstrate a validation-performance benefit from GAN augmentation. The classifier comparison highlights differences in accuracy and pneumonia recall, while the augmentation experiment indicates the need for further evaluation of synthetic-image quality and training settings.
Chinese Translation
本研究评估了使用VGG19、MobileNetV2、ResNet50和自定义卷积神经网络（CNN）进行胸部X光片肺炎分类的效果，并探讨了基于生成对抗网络（GAN）的合成数据增强。MobileNetV2实现了88%的最高报告准确率，并在各类别间表现平衡。自定义CNN的肺炎召回率为92.67%，精确率为79.43%，突显了精确率与召回率之间的权衡。使用准确率、F1-score、精确率、召回率、混淆矩阵和训练曲线来评估模型性能。合成肺炎图像与真实图像结合，以研究数据增强是否能改善分类性能。在报告的VGG19比较中，增强数据训练的准确率接近100%，而验证准确率保持在50%左右，低于真实数据的验证准确率。因此，该实验未能证明GAN增强对验证性能的益处。分类器比较突显了准确率和肺炎召回率的差异，而增强实验则表明需要进一步评估合成图像质量和训练设置。
cs.CV / 76 / 2609.06288
Object-Aware Background-Controlled Editing via Weighted Velocity Guidance
基于对象感知的背景控制编辑通过加权速度引导
Abstract
Training-free image editing steers diffusion or flow-matching generative models at inference time by modifying prompt-conditioned denoising velocities. Existing velocity-based editors often apply prompt-induced residuals globally over the latent space and rely on the model to localize semantic changes implicitly. For object-centric edits, these residuals are rarely zero outside the target object, so small non-target components can accumulate during multi-step integration, causing background drift and unstable object boundaries. We propose Object-Aware Velocity Control (OAVC), a training-free framework that introduces object-level control into the velocity-integration process. OAVC decouples where semantic residuals are allowed to act from how they are injected into the dynamics. It constructs a background-anchored reference interface under the source prompt and then performs object-localized safe semantic injection under the target prompt. A constrained injection operator suppresses drift-inducing velocity components, while time-adaptive spatial weighting stabilizes the transition near object boundaries. OAVC requires no training or modification of pretrained model parameters. Experiments on object-centric image and video benchmarks with image and video rectified-flow backbones show improved background preservation, structural fidelity, boundary stability, and temporal consistency while retaining effective localized editability.
Chinese Translation
无训练的图像编辑在推理时通过修改基于提示的去噪速度来引导扩散或流匹配生成模型。现有的基于速度的编辑器通常在潜在空间中全局应用提示引起的残差，并依赖模型隐式地定位语义变化。对于以对象为中心的编辑，这些残差在目标对象之外很少为零，因此在多步积分过程中，微小的非目标组件可能会积累，导致背景漂移和不稳定的对象边界。我们提出了对象感知速度控制（Object-Aware Velocity Control, OAVC），这是一个无训练的框架，旨在将对象级控制引入速度积分过程。OAVC 将语义残差的作用位置与其注入动态的方式解耦。它在源提示下构建一个以背景为锚的参考接口，然后在目标提示下执行对象局部的安全语义注入。一个约束注入算子抑制了引起漂移的速度组件，而时间自适应空间加权则稳定了对象边界附近的过渡。OAVC 不需要对预训练模型参数进行训练或修改。在以对象为中心的图像和视频基准上进行的实验显示，OAVC 在保持有效的局部可编辑性的同时，改善了背景保留、结构保真度、边界稳定性和时间一致性。
cs.CV / 77 / 2609.06296
SignDino: Self-Supervised Sign Language Representation Learning via Temporal-Axis Self-Distillation
SignDino：通过时间轴自我蒸馏的自监督手语表征学习
Abstract
Self-supervised sign language representation learning must model two properties not central to natural-image SSL: signs are produced by a small set of anatomically distinct articulators, and their meaning depends on the temporal organisation of those articulators. We introduce SignDino, a self-supervised sign-video encoder that moves the DINOv3 student--teacher recipe from the spatial domain of image crops to the temporal domain of tracked sign streams. Each video is decomposed into left-hand, right-hand, and face streams by a detector-first YOLOv8n+ByteTrack pipeline. A frozen DINOv3 ViT-B/16 embeds each per-frame anatomical crop, while lightweight temporal Transformers, not the image backbone, form the student and EMA teacher. They are trained by temporal DINO self-distillation, frame-level masked-token prediction in the style of iBOT, KoLeo feature spreading, and Gram anchoring of the frame-to-frame similarity structure. This design keeps strong image-level visual primitives fixed and learns only how articulator states evolve across time. We evaluate on sign-to-English translation, isolated sign recognition, and fingerspelling detection benchmarks. Across these tasks, SignDino provides a strong public self-supervised representation and shows competitive or state-of-the-art performance under matched downstream evaluation.
Chinese Translation
自监督手语表征学习必须建模两个与自然图像自监督学习（SSL）不相关的特性：手势是由一小组解剖上不同的发音器产生的，其意义依赖于这些发音器的时间组织。我们介绍了SignDino，一种自监督手势视频编码器，它将DINOv3的学生-教师配方从图像裁剪的空间域转移到跟踪手势流的时间域。每个视频通过一个以检测器为先的YOLOv8n+ByteTrack管道分解为左手、右手和面部流。一个冻结的DINOv3 ViT-B/16对每帧的解剖裁剪进行嵌入，而轻量级的时间变换器（Transformers），而非图像主干，构成学生和EMA教师。它们通过时间DINO自我蒸馏、类似iBOT的帧级掩码标记预测、KoLeo特征传播以及帧间相似性结构的Gram锚定进行训练。该设计保持强大的图像级视觉原语不变，仅学习发音器状态如何随时间演变。我们在手语到英语翻译、孤立手势识别和手指拼写检测基准上进行了评估。在这些任务中，SignDino提供了强大的公共自监督表征，并在匹配的下游评估中展示了具有竞争力或最先进的性能。
cs.CV / 78 / 2609.06302
CST-WM: A Causally Structured World Model for Embodied Visual Tracking
CST-WM：一种用于具身视觉跟踪的因果结构世界模型
Abstract
Embodied visual tracking requires a robot not only to react to the current view, but to choose actions that preserve or recover future evidence of a moving target under ego-motion, occlusion, and distractors. It is therefore a predictive decision problem over future target observability and apparent scale. A central difficulty is a task-specific form of causal hallucination: in action-conditioned prediction, a model can exploit the strong correlation between robot control and target-related observations by hallucinating a direct causal effect from the current action to target evidence, rather than letting action influence that evidence only through robot motion and the resulting observation change. The shortcut yields plausible futures with the wrong semantics for tracking-oriented planning and re-acquisition. We propose CST-WM, a causally structured world model that decomposes the latent state into target-evidence, robot, and observation branches and factorizes the transition so that direct action injection into the target-evidence branch is blocked, while action remains available to robot motion and observation updates. Combined with rollout-based model-predictive control, CST-WM supports both stable following and temporary target re-acquisition in one planning framework. On EVT-Bench and Habitat 3.0, covering standard tracking, target-loss recovery, and cross-dataset transfer, it improves following quality, distance-range control, safety, and re-acquisition over reactive and world-model baselines; offline diagnostics show better multi-step rollout fidelity, stronger planning-value consistency, and substantially reduced direct action leakage. For embodied visual tracking, future prediction alone is not enough: the predictive structure itself must align with how target evidence enters planning.
Chinese Translation
具身视觉跟踪要求机器人不仅要对当前视图做出反应，还要选择能够在自我运动、遮挡和干扰物的情况下保持或恢复移动目标未来证据的动作。因此，这实际上是一个关于未来目标可观测性和表观尺度的预测决策问题。一个核心难点是任务特定的因果幻觉：在基于动作的预测中，模型可以利用机器人控制与目标相关观察之间的强相关性，通过幻觉当前动作对目标证据的直接因果影响，而不是让动作仅通过机器人运动及由此产生的观察变化来影响该证据。这种捷径产生了在跟踪导向规划和重新获取中语义错误的合理未来。我们提出了CST-WM，一种因果结构世界模型，它将潜在状态分解为目标证据、机器人和观察分支，并因式分解转移，以阻止直接将动作注入到目标证据分支，同时保持动作可用于机器人运动和观察更新。结合基于回滚的模型预测控制，CST-WM在一个规划框架中支持稳定跟踪和临时目标重新获取。在EVT-Bench和Habitat 3.0上，涵盖标准跟踪、目标丢失恢复和跨数据集迁移，相比于反应式和世界模型基线，它提高了跟踪质量、距离范围控制、安全性和重新获取能力；离线诊断显示出更好的多步回滚保真度、更强的规划价值一致性和显著减少的直接动作泄漏。对于具身视觉跟踪，仅有未来预测是不够的：预测结构本身必须与目标证据进入规划的方式对齐。
cs.CV / 79 / 2609.06338
One Shared LoRA Weight for MRI Reconstruction across Acceleration Factors
一个共享的 LoRA 权重用于不同加速因子的 MRI 重建
Abstract
Accelerated MRI reconstruction recovers images from undersampled k-space. However, different acceleration factors produce distinct artifact patterns. Existing methods often train separate models for each factor, leading to poor cross-factor generalization and high training and storage costs. We propose Shared LoRA, a parameter-efficient framework that freezes the pretrained SHFormer backbone and trains a single shared set of LoRA adapters together with a lightweight gating network. During training, undersampled inputs are generated by randomly sampling acceleration factors and their corresponding sampling masks, enabling the shared adapters to learn reconstruction knowledge across factors. Given the acceleration factor, GateNet generates layer-wise coefficients to dynamically modulate the residual strength of each adapter. Experiments show that Shared LoRA achieves the best or competitive PSNR and SSIM across acceleration factors, while its trainable parameters account for only about 5.3% of the total model parameters. Its performance at lower acceleration factors remains largely unaffected as the jointly trained factor set expands, and it generalizes stably to unseen neighboring factors.
Chinese Translation
加速 MRI 重建从欠采样的 k 空间恢复图像。然而，不同的加速因子会产生不同的伪影模式。现有方法通常为每个因子训练单独的模型，导致跨因子的泛化能力差以及高昂的训练和存储成本。我们提出了共享 LoRA（Shared LoRA），这是一种参数高效的框架，它冻结了预训练的 SHFormer 主干，并与一个轻量级的门控网络一起训练一组共享的 LoRA 适配器。在训练过程中，通过随机采样加速因子及其对应的采样掩码生成欠采样输入，使得共享适配器能够学习跨因子的重建知识。给定加速因子，GateNet 生成逐层系数，以动态调节每个适配器的残差强度。实验表明，Shared LoRA 在不同加速因子下实现了最佳或具有竞争力的 PSNR 和 SSIM，同时其可训练参数仅占总模型参数的约 5.3%。随着联合训练的因子集扩展，其在较低加速因子下的性能基本不受影响，并且能够稳定地泛化到未见过的相邻因子。
cs.CV / 80 / 2609.06343
Radiation, Rotation and Scale Invariant Feature Descriptor for Multimodal Image Matching
辐射、旋转和尺度不变特征描述符用于多模态图像匹配
Abstract
Multimodal image matching is a fundamental task for multi-source information fusion. However, geometric distortions and nonlinear radiometric differences (NRD) severely limit performance, especially under radiometric, rotation, and scale variations. To address this issue, we propose a radiation, rotation, and scale invariant (RRSI) feature descriptor. First, a dual-head regional sampling (DHRS) module simultaneously performs Cartesian and Log-Polar sampling on keypoint neighborhoods, retaining spatial structural properties while enhancing robustness to rotation and scale variations. We then jointly encode geometric and radiometric relations between multimodal images in a unified deep feature space, enabling feature encoding, interaction, and fusion across intra-modal, dual-head sampled, and inter-modal regions. Furthermore, we introduce a bidirectional cross-modal generative reconstruction constraint during training. By decoding implicit features into structural patches of the counterpart modality, this mechanism anchors modality-invariant geometric topologies without additional inference overhead. Experiments on optical-infrared and optical-SAR datasets demonstrate highly competitive matching performance and strong robustness to rotation and scale variations. RRSI supports the full rotation range from 0 to 360 degrees and scale factors up to four. Its generalization ability is further validated on multimodal images from computer vision, remote sensing, and medical imaging. The implementation will be made publicly available at https://github.com/yeyuanxin110/RRSI .
Chinese Translation
多模态图像匹配是多源信息融合的基础任务。然而，几何畸变和非线性辐射差异（NRD）严重限制了性能，尤其是在辐射、旋转和尺度变化下。为了解决这个问题，我们提出了一种辐射、旋转和尺度不变（RRSI）特征描述符。首先，双头区域采样（DHRS）模块同时对关键点邻域进行笛卡尔和对数极坐标采样，保留空间结构特性，同时增强对旋转和尺度变化的鲁棒性。然后，我们在统一的深度特征空间中联合编码多模态图像之间的几何和辐射关系，实现跨模态、双头采样和跨模态区域的特征编码、交互和融合。此外，我们在训练过程中引入了双向跨模态生成重建约束。通过将隐含特征解码为对应模态的结构补丁，该机制在没有额外推理开销的情况下锚定模态不变的几何拓扑。在光学-红外和光学-SAR数据集上的实验表明，匹配性能具有高度竞争力，并对旋转和尺度变化表现出强鲁棒性。RRSI支持从0到360度的全旋转范围和高达四倍的尺度因子。其泛化能力在计算机视觉、遥感和医学成像的多模态图像上得到了进一步验证。实现代码将公开发布在 https://github.com/yeyuanxin110/RRSI 。
cs.CV / 81 / 2609.06353
ChildGaze: A Benchmark Dataset for Collaborative Behavior Understanding in Children
ChildGaze：儿童协作行为理解的基准数据集
Abstract
Understanding collaborative behavior in children is important for analyzing social participation, peer interaction, shared attention, and engagement during play and learning activities. Reliable recognition of these cues can support research in child development, educational analysis, and human-centered computer vision. However, estimating where a child is looking does not necessarily reveal whether the child is actively participating in a shared activity. To support this higher-level analysis, we introduce ChildGaze, a child-centered behavioral annotation dataset built on the ChildPlay video collection [1]. ChildGaze introduces two behavioral labels, collaborative and non-collaborative, assigned independently to each child within a frame. The dataset provides face, left-hand, and right-hand bounding boxes for children and adults and organizes the annotations at the row, person, and frame levels. The current release contains 27 annotated video files, 10,641 frames, and 73,268 body-part annotation rows. Annotation reliability was evaluated on 1,187 frames using independent annotations from two annotators. The collaboration labels achieved 93.16% raw agreement and a Cohen's kappa of 0.8631, while bounding-box annotations achieved an overall mean IoU of 0.808. Baseline experiments with pretrained ViT and Swin Transformer models achieved up to 97.44% child-person-level accuracy and 96.80% frame-level accuracy, respectively. These results show that ChildGaze provides a reliable benchmark for studying collaborative behavior in naturalistic child-adult and peer interactions.
Chinese Translation
理解儿童的协作行为对于分析社会参与、同伴互动、共享注意力以及在游戏和学习活动中的参与度至关重要。可靠地识别这些线索可以支持儿童发展、教育分析和以人为本的计算机视觉研究。然而，仅仅估计儿童的视线并不能揭示儿童是否积极参与共享活动。为了支持这种更高层次的分析，我们推出了ChildGaze，这是一个基于ChildPlay视频集合构建的以儿童为中心的行为标注数据集。ChildGaze引入了两个行为标签：协作和非协作，这些标签独立地分配给每个帧中的儿童。该数据集为儿童和成人提供了面部、左手和右手的边界框，并在行、个体和帧级别组织标注。目前版本包含27个标注视频文件、10,641帧和73,268个身体部位标注行。标注的可靠性在1,187帧上进行了评估，使用了两位标注者的独立标注。协作标签的原始一致性达到93.16%，Cohen's kappa为0.8631，而边界框标注的整体平均交并比（IoU）为0.808。使用预训练的ViT和Swin Transformer模型的基线实验分别达到了97.44%的儿童-个体级别准确率和96.80%的帧级别准确率。这些结果表明，ChildGaze为研究自然环境中儿童与成人及同伴的协作行为提供了可靠的基准。
cs.CV / 82 / 2609.06356
MSCA-UNet: Multi-Scale Context and Attention U-Net for Image Segmentation
MSCA-UNet：用于图像分割的多尺度上下文与注意力U-Net
Abstract
U-Net remains a practical baseline for image segmentation because of its simple encoder-decoder structure and skip connections. However, the bottleneck representation is still dominated by a limited set of receptive fields, while decoder features are propagated without explicitly emphasizing the most informative channels and spatial locations. This paper presents MSCA-UNet, a U-Net-based segmentation architecture that combines multi-scale contextual aggregation at the bottleneck with channel-spatial attention refinement in the decoder. The multi-scale module uses parallel atrous convolutions to capture contextual features at different receptive fields, while Convolutional Block Attention Modules (CBAMs) progressively recalibrate decoder features. Under identical experimental settings, the baseline U-Net achieves 96.9% mIoU on a held-out test set. Adding multi-scale context improves mIoU to 97.5%, while attention alone reaches 98.4%. Combining both mechanisms yields 99.1% mIoU, a 2.2 percentage-point improvement over the baseline. Parameter analysis further shows that the attention-only variant adds approximately 0.044M parameters, whereas the multi-scale module contributes most of the additional model capacity. The results support the view that multi-scale context enrichment and attention-based feature refinement provide complementary benefits within a U-Net framework.
Chinese Translation
U-Net由于其简单的编码器-解码器结构和跳跃连接，仍然是图像分割的一个实用基线。然而，瓶颈表示仍然受到有限接收场的主导，而解码器特征的传播并未明确强调最具信息量的通道和空间位置。本文提出了MSCA-UNet，一种基于U-Net的分割架构，结合了瓶颈处的多尺度上下文聚合与解码器中的通道-空间注意力精炼。多尺度模块使用并行的空洞卷积来捕捉不同接收场下的上下文特征，而卷积块注意力模块（CBAMs）逐步重新校准解码器特征。在相同的实验设置下，基线U-Net在保留的测试集上达到了96.9%的mIoU。添加多尺度上下文将mIoU提高到97.5%，而仅使用注意力则达到了98.4%。结合这两种机制的结果为99.1%的mIoU，相较于基线提高了2.2个百分点。参数分析进一步表明，仅使用注意力的变体增加了大约0.044M的参数，而多尺度模块则贡献了大部分额外的模型容量。结果支持了多尺度上下文丰富和基于注意力的特征精炼在U-Net框架内提供互补益处的观点。
cs.CV / 83 / 2609.06359
AGSA-Net: Abundance-Guided Self-Attention Network for Spectral Unmixing-Aware Hyperspectral Remote Sensing Image Classification
AGSA-Net：基于丰度引导的自注意力网络用于光谱解混-aware的高光谱遥感图像分类
Abstract
Hyperspectral image (HSI) classification plays a vital role in remote sensing applications, including agriculture, environmental monitoring, and urban analysis. However, its performance remains challenged by high spectral redundancy, noise sensitivity, and the difficulty of jointly modeling local material composition and long-range spectral dependencies. To address this, we propose AGSA-Net, an abundance-guided self-attention network that explicitly integrates spectral unmixing priors into the classification process. AGSA Net first estimates physically meaningful subpixel abundance maps subject to non-negativity and sum-to-one constraints, regularized by hybrid linear-nonlinear reconstruction decoder. The learned abundances are then used to construct an abundance affinity prior that guides a spectral transformer to emphasize class-discriminative interactions, and the resulting transformer features are fused with compact abundance descriptors for final prediction; in contrast to existing approaches that use abundance as auxiliary or concatenated features. Experiments on Indian Pines, Augsburg, and Berlin demonstrate the benefit of incorporating abundance- guided contextual modeling, particularly in heterogeneous urban scenes. The source code and trained models are available at: https://github.com/nnuvi/AGSA-Net
Chinese Translation
高光谱图像（HSI）分类在农业、环境监测和城市分析等遥感应用中发挥着至关重要的作用。然而，其性能受到高光谱冗余、噪声敏感性以及联合建模局部材料组成和长程光谱依赖性等挑战的影响。为了解决这一问题，我们提出了AGSA-Net，一种丰度引导的自注意力网络，明确将光谱解混先验整合到分类过程中。AGSA-Net首先在非负性和和为一的约束下估计物理意义明确的亚像素丰度图，并通过混合线性-非线性重建解码器进行正则化。然后，学习到的丰度用于构建丰度亲和先验，引导光谱变换器强调类区分性交互，最终将生成的变换器特征与紧凑的丰度描述符融合以进行最终预测；与现有将丰度作为辅助或连接特征的方法形成对比。在印度松树、奥格斯堡和柏林的数据集上的实验表明，结合丰度引导的上下文建模的好处，尤其是在异质城市场景中。源代码和训练模型可在以下链接获取：https://github.com/nnuvi/AGSA-Net
cs.CV / 84 / 2609.06360
NOVA: Normal-Side Modeling for Training-Free Zero-Shot Video Anomaly Detection
NOVA：无训练零-shot视频异常检测的正常侧建模
Abstract
Training-free zero-shot video anomaly detection (ZS-VAD) leverages vision-language models (VLMs) to localize anomaly instances from a predefined anomaly vocabulary, without providing any video. Existing CLIP-based methods often emphasize anomaly-side semantics, while the competing normality side remains less carefully formulated. We identify two key limitations in existing solutions: (i) blurred decision boundary: normal prompts may contain ambiguous verbs, such as running, that are semantically close to anomalies, reducing normal and abnormal separation in the VLM embedding space; and (ii) modality gap: poor alignment between features of textual normal anchors and visual frames. We propose NOVA, a training-free ZS-VAD framework that strengthens the normal side at both linguistic and visual levels. NOVA introduces Normality-Aware Prompt Construction (NA), which excludes anomaly-adjacent verbs and biases normal descriptions toward static, low-motion scenes. To overcome the text-vision modality gap, NOVA constructs a Visual Normality Anchor (VNA), which creates a weighted visual normal anchor from the initial frames of each test video, providing a video-specific normal reference without task-specific training or annotations. NOVA achieves 89.86 percent AUC on UCF-Crime and 95.07 percent AUC and 84.82 percent AP on XD-Violence, reaching state-of-the-art performance among comparable training-free zero-shot methods.
Chinese Translation
无训练零-shot视频异常检测（ZS-VAD）利用视觉-语言模型（VLMs）从预定义的异常词汇中定位异常实例，而无需提供任何视频。现有的基于CLIP的方法往往强调异常侧的语义，而竞争的正常侧则较少被仔细构建。我们识别出现有解决方案的两个关键局限性：（i）模糊的决策边界：正常提示可能包含模糊的动词，如running，这些动词在语义上与异常相近，从而减少了VLM嵌入空间中正常与异常的分离；（ii）模态差距：文本正常锚点与视觉帧之间的特征对齐较差。我们提出了NOVA，一个无训练的ZS-VAD框架，在语言和视觉层面上增强正常侧。NOVA引入了正常性感知提示构建（NA），排除了与异常相邻的动词，并将正常描述偏向静态、低运动场景。为了克服文本-视觉模态差距，NOVA构建了视觉正常锚点（VNA），该锚点从每个测试视频的初始帧创建加权视觉正常锚点，提供了一个特定于视频的正常参考，而无需特定任务的训练或注释。NOVA在UCF-Crime上达到89.86%的AUC，在XD-Violence上达到95.07%的AUC和84.82%的AP，达到了与可比的无训练零-shot方法中的最先进性能。
cs.CV / 85 / 2609.06370
DualPathOcc: Dual-Resolution BEV Encoder for 3D Occupancy Prediction
DualPathOcc：用于3D占用预测的双分辨率BEV编码器
Abstract
Predicting 3D occupancy from multi-view images requires preserving geometric detail during 2D-to-3D lifting while reasoning over sparse, volumetric scene representations. We present DualPathOcc, a camera-based framework that combines a Spatial Enhancer for high-resolution feature aggregation before BEV compression, a SENet-augmented dual-path BEV encoder for local-global context modeling, and height-aware weighted cross-entropy for near-ground occupancy. The final model is optimized with occupancy supervision and no explicit depth loss. On single-frame Occ3D-nuScenes, DualPathOcc achieves 37.37 mIoU. We further analyze how surface-centered depth targets interact with volumetric occupancy learning.
Chinese Translation
从多视角图像预测3D占用需要在2D到3D的提升过程中保留几何细节，同时对稀疏的体积场景表示进行推理。我们提出了DualPathOcc，这是一种基于摄像头的框架，结合了空间增强器用于在BEV压缩之前进行高分辨率特征聚合、增强SENet的双路径BEV编码器用于局部-全局上下文建模，以及针对近地面占用的高度感知加权交叉熵。最终模型通过占用监督进行优化，并且没有显式的深度损失。在单帧Occ3D-nuScenes数据集上，DualPathOcc达到了37.37 mIoU。我们进一步分析了以表面为中心的深度目标如何与体积占用学习相互作用。
cs.CV / 86 / 2609.06373
Multi-Grid Post-Training for Long-Form Multi-Shot Video Generation
长篇多镜头视频生成的多网格后训练
Abstract
Generating long-form multi-shot videos requires coherent within-shot motion and visually consistent narratives across shots. Existing video generators favor continuous motion and struggle to present complete shot sets when an entire narrative is packed along one temporal axis. We propose MovieGrid, a Multi-Grid Post-Training paradigm that decomposes a long video into shorter, temporally ordered chunks and arranges them on a spatial grid for joint modeling. This design reduces the number of shots handled by each temporal axis while enabling global information exchange across chunks. We construct the Multi-Grid Long Video (MGLV) dataset from 1,000 long-form videos using source video collection, hierarchical segmentation, grid video construction, and character-aware story annotation, producing 54K grid videos paired with story prompts. Our Noise-Free Random-Grid Training retains a random subset of chunks as clean visual context for denoising the remaining chunks. Grid Embedding encodes grid structure, character-aware Story Prompts link recurring entities, and Grid Boundary Loss stabilizes layouts. Under the same token budget, MovieGrid generates 6.05 times more shots than Temporal Packing in a 1,616-frame video. On a benchmark spanning five real-world categories, it achieves state-of-the-art intra-shot consistency (0.9131 versus 0.8086 for HoloCine) and inter-shot consistency (0.5914 versus 0.5384 for StoryMem). MovieGrid can further scale video length with minimal compromise through single or multiple generations.
Chinese Translation
生成长篇多镜头视频需要在镜头内保持连贯的运动，并在镜头之间呈现视觉一致的叙事。现有的视频生成器倾向于连续运动，并在整个叙事沿一个时间轴打包时难以呈现完整的镜头集。我们提出了MovieGrid，一种多网格后训练范式，它将长视频分解为较短的、时间上有序的片段，并将它们排列在一个空间网格上进行联合建模。这一设计减少了每个时间轴处理的镜头数量，同时允许跨片段进行全局信息交换。我们从1,000个长篇视频中构建了多网格长视频（Multi-Grid Long Video, MGLV）数据集，使用源视频收集、层次分割、网格视频构建和角色感知故事注释，生成54K个与故事提示配对的网格视频。我们的无噪声随机网格训练保留了一组随机的片段作为干净的视觉上下文，以对剩余片段进行去噪。网格嵌入编码网格结构，角色感知的故事提示链接重复出现的实体，而网格边界损失则稳定布局。在相同的代币预算下，MovieGrid在1,616帧的视频中生成的镜头数量是Temporal Packing的6.05倍。在涵盖五个真实世界类别的基准测试中，它实现了最先进的镜头内一致性（0.9131，相较于HoloCine的0.8086）和镜头间一致性（0.5914，相较于StoryMem的0.5384）。MovieGrid可以通过单次或多次生成进一步扩展视频长度，几乎不妥协。
cs.CV / 87 / 2609.06380
An Integrated Video-AI Platform for Action-Level Microanastomosis Training and Performance Feedback
一个集成的视频-人工智能平台用于微吻合术培训和表现反馈
Abstract
Developing microanastomosis skill requires repeated practice with timely, action-specific feedback, yet expert review of lengthy microscope videos does not scale to frequent or distributed training. We present an integrated video-AI platform that turns a complete simulated procedure into inspectable, interactive feedback through three connected modules. First, a proposed transformer segments the video into six surgical actions. Second, object detection and tracking localize instrument tips within each action; the resulting kinematic features and action statistics drive supervised classification of five NOMAT-aligned performance dimensions. Third, a grounded large language model (LLM) uses these structured outputs to answer user questions about the current scene, actions, motion, and predicted performance through a unified interface. In a two-site study, 17 participants completed 72 procedures comprising 576 suture placements. The action-segmentation module achieved 87.66\% accuracy and 82.86\% F1, increasing to 93.62\% and 88.32\% after workflow-aware refinement. The five performance classifiers achieved 76.0\% mean accuracy, with Cohen's $\kappa$ from 0.63 to 0.93. Although the language interface and educational benefit require prospective evaluation, these results establish the technical basis for an expert-supervised platform that can shorten review, expose the evidence behind performance estimates, and support scalable formative microsurgical training.
Chinese Translation
微吻合术技能的培养需要通过及时、针对性的反馈进行反复练习，然而专家对冗长显微镜视频的审查并不适合频繁或分散的培训。我们提出了一个集成的视频-人工智能平台，通过三个互联模块将完整的模拟程序转化为可检查的、互动的反馈。首先，提出的变换器将视频分割为六个外科动作。其次，物体检测和跟踪在每个动作中定位仪器尖端；由此产生的运动学特征和动作统计驱动五个与NOMAT对齐的表现维度的监督分类。第三，一个基于知识的语言模型（LLM）利用这些结构化输出，通过统一界面回答用户关于当前场景、动作、运动和预测表现的问题。在一项两地研究中，17名参与者完成了72个程序，共计576次缝合操作。动作分割模块的准确率达87.66%，F1值为82.86%，在工作流程意识的优化后，准确率提高至93.62%，F1值为88.32%。五个表现分类器的平均准确率为76.0%，Cohen's $ ext{kappa}$值从0.63提高至0.93。尽管语言界面和教育效益需要前瞻性评估，但这些结果为一个专家监督的平台奠定了技术基础，该平台可以缩短审查时间，揭示表现评估背后的证据，并支持可扩展的形成性显微外科培训。
cs.CV / 88 / 2609.06385
Scaling 3D Generative Priors to Large-Scale Scene Meshes from Multi-View Images
将3D生成先验扩展到来自多视图图像的大规模场景网格
Abstract
Pretrained 3D generative models produce detailed geometry and appearance but are primarily designed for object-centric generation within a limited spatial extent. Recent approaches address this limitation by partitioning large scenes into smaller spatial regions and applying pretrained 3D generative priors to each region. However, scaling tiled generation to large multi-view scenes makes it challenging to maintain local geometric continuity and global appearance consistency. We present a training-free framework for large-scale textured mesh generation from multi-view images. Our key idea is to scale tiled generation to large scenes with increased spatial detail while coordinating generation both locally and globally. We introduce local context tiled generation to improve geometric continuity between neighboring regions and global appearance alignment to reduce appearance discrepancies across distant regions. An adaptive scene decomposition further determines the number of tiles according to the input scene geometry. Experiments demonstrate improved geometric and appearance fidelity over existing approaches while enabling fine-grained generation of large-scale scenes.
Chinese Translation
预训练的3D生成模型能够生成详细的几何形状和外观，但主要设计用于在有限空间范围内进行以物体为中心的生成。最近的方法通过将大场景划分为较小的空间区域，并将预训练的3D生成先验应用于每个区域，来解决这一限制。然而，将平铺生成扩展到大规模多视图场景时，保持局部几何连续性和全局外观一致性变得具有挑战性。我们提出了一种无训练的大规模纹理网格生成框架，基于多视图图像。我们的关键思想是将平铺生成扩展到具有更高空间细节的大场景，同时协调局部和全局的生成。我们引入了局部上下文平铺生成，以改善相邻区域之间的几何连续性，并通过全局外观对齐来减少远离区域之间的外观差异。自适应场景分解进一步根据输入场景几何确定平铺的数量。实验表明，我们的方法在几何和外观保真度上优于现有方法，同时实现了大规模场景的细粒度生成。
cs.CV / 89 / 2609.06419
Separating Capability from Confidence: Grounded Dual-State Calibration for GRPO-Trained Medical Vision-Language Models
将能力与信心分离：基于GRPO训练的医学视觉语言模型的双状态校准
Abstract
Medical vision-language models (VLMs) require confidence that reflects both answer correctness and patient-specific visual evidence. Recent GRPO-based methods optimize verbalized confidence together with answer generation. However, this joint optimization may interfere with answer learning and drive confidence toward near-binary values. Verbalized confidence also provides no explicit assessment of visual support. We therefore separate capability learning from confidence estimation and propose \textbf{DualRead}. DualRead builds on the insight that reliability can be read from the actor's internal states at critical moments in the answering process. It freezes the GRPO-trained actor and combines pre-answer solvability with a post-answer assessment of the generated answer and its visual support. To further assess whether confidence reflects visual grounding, we introduce \textbf{Counterfactual Confidence Grounding AUC} (CCG-AUC). It measures whether confidence decreases when real-image substitution changes the actor from correct to incorrect. Across two VLM backbones and both in- and out-of-distribution medical VQA benchmarks, DualRead improves correctness discrimination and calibration over verbalized confidence while preserving answer accuracy. CCG-AUC reveals whether confidence responds to answer-relevant visual evidence rather than primarily to non-visual cues.
Chinese Translation
医学视觉语言模型（VLMs）需要反映答案正确性和患者特定视觉证据的信心。近期基于GRPO的方法同时优化了语言化信心和答案生成。然而，这种联合优化可能干扰答案学习，并使信心趋向近似二元值。语言化信心也未能对视觉支持进行明确评估。因此，我们将能力学习与信心估计分开，并提出了 extbf{DualRead}。DualRead基于这样一个见解：在回答过程的关键时刻，可以从参与者的内部状态中读取可靠性。它冻结了GRPO训练的参与者，并结合了答案生成前的可解性与答案生成后的评估及其视觉支持。为了进一步评估信心是否反映视觉基础，我们引入了 extbf{反事实信心基础AUC}（CCG-AUC）。它测量当真实图像替换将参与者的答案从正确变为错误时，信心是否下降。在两个VLM骨干网络以及医学VQA基准的内部和外部分布中，DualRead在保持答案准确性的同时，提高了正确性判别和校准。CCG-AUC揭示了信心是否对与答案相关的视觉证据做出反应，而不是主要对非视觉线索做出反应。
cs.CV / 90 / 2609.06436
PLSR: Progressive and Localized Super-Resolution of 3D Objects via Localized Latent Voxel Diffusion
PLSR：通过局部潜在体素扩散实现3D对象的渐进式和局部超分辨率
Abstract
High-resolution 3D asset generation is vital in various 3D applications. Existing state-of-the-art diffusion-based models remain constrained by fixed resolutions, limiting their ability to produce details. In this paper, we tackle the challenge of generating more detailed, higher-resolution 3D objects by introducing a 3D super-resolution (SR) framework built on existing 3D generative foundation models. To this end, we design PLSR, a progressive and localized super-resolution solution to achieve this goal effectively and memory efficiently. Technically, given a coarse geometry from a pretrained 3D generator, we decompose the global SR task into localized sub-tasks via an associative input decomposition scheme, adapt a flow-based 3D generator into a localized super-resolution model through low-cost finetuning, and unify them in an iterative patch-wise denoising pipeline for seamless high-resolution output. Experiments on challenging objects show that our approach is able to generate 3D details with new strong fine-detail fidelity while significantly reducing the computational cost, offering a new and practical solution for high-resolution 3D asset generation.
Chinese Translation
高分辨率3D资产生成在各种3D应用中至关重要。现有的最先进的基于扩散的模型受到固定分辨率的限制，限制了它们生成细节的能力。本文针对生成更详细、更高分辨率的3D对象的挑战，提出了一种基于现有3D生成基础模型的3D超分辨率（SR）框架。为此，我们设计了PLSR，一种渐进式和局部的超分辨率解决方案，以有效且节省内存地实现这一目标。从技术上讲，给定来自预训练3D生成器的粗略几何形状，我们通过关联输入分解方案将全局SR任务分解为局部子任务，采用基于流的3D生成器通过低成本微调转化为局部超分辨率模型，并在迭代的块状去噪管道中将它们统一，以实现无缝的高分辨率输出。在具有挑战性的对象上的实验表明，我们的方法能够生成具有新强细节保真度的3D细节，同时显著降低计算成本，为高分辨率3D资产生成提供了一种新的实用解决方案。
cs.CV / 91 / 2609.06437
Large-Scale Pretraining for Improving Deep Learning-Based Geometric Distortion Correction of Diffusion-Weighted Imaging
大规模预训练以改善基于深度学习的扩散加权成像几何失真校正
Abstract
Diffusion-weighted imaging (DWI) is widely used in clinical settings but remains vulnerable to geometric distortion. Conventional correction methods often require additional acquisitions or vendor-specific solutions, limiting their feasibility in high-throughput, resource-constrained settings. This study investigates whether large-scale pretraining strategies can improve deep learning-based distortion correction for single-phase-encoding DWI. We formulate the task as image reconstruction, and compare a non-pretrained baseline against a self-supervised and a generative pretrained model, evaluated using both quantitative image-similarity metrics and qualitative expert assessment. The best-performing model was further tested for transferability on data collected in an LMIC setting with acquisition shift. Pretrained models outperformed the non-pretrained baseline, with cWDM achieving the strongest results across both quantitative and qualitative evaluation. However, application to LMIC data revealed transferability challenges, including contrast alteration and over-reliance on T1-weighted anatomical structure. Registering images to a common standard space improved predictions, suggesting that harmonized preprocessing may enhance cross-domain deployment.
Chinese Translation
扩散加权成像（DWI）在临床应用中被广泛使用，但仍然容易受到几何失真的影响。传统的校正方法通常需要额外的采集或特定厂商的解决方案，这限制了它们在高通量、资源受限环境中的可行性。本研究探讨了大规模预训练策略是否可以改善单相编码DWI的基于深度学习的失真校正。我们将任务形式化为图像重建，并比较了一个未经过预训练的基线模型与一个自监督模型和一个生成预训练模型，评估方法包括定量图像相似性指标和定性专家评估。表现最佳的模型进一步在一个具有采集偏移的低收入和中等收入国家（LMIC）环境中测试了其可迁移性。预训练模型在性能上优于未经过预训练的基线，其中cWDM在定量和定性评估中均取得了最佳结果。然而，应用于LMIC数据时暴露出可迁移性挑战，包括对比度变化和对T1加权解剖结构的过度依赖。将图像注册到共同标准空间改善了预测结果，表明协调的预处理可能增强跨领域的部署效果。
cs.CV / 92 / 2609.06475
Thinking with Cameras: Active Visual Reasoning via Dynamic Viewpoint Control for Surveillance Video Understanding
与摄像头思考：通过动态视角控制进行主动视觉推理以理解监控视频
Abstract
Large vision-language models (LVLMs) have recently achieved remarkable progress in general-purpose video understanding. However, their application to surveillance videos remains challenging due to the lack of large-scale domain-specific datasets and the limitation of passive observation from fixed viewpoints. In surveillance scenarios, critical visual evidence can be easily missed when targets are distant, small, occluded, or move beyond the current camera view. In this work, we introduce CamVLM, a new framework for Thinking with Cameras, which enables LVLMs to actively acquire visual evidence through dynamic viewpoint control rather than passively analyzing fixed video streams. We first construct CCTV-Anomaly, a large-scale surveillance video understanding dataset containing 14,459 videos across 10 anomaly categories, with detailed captions and event annotations. We further formulate viewpoint control as an active visual perception problem and build CamTrack-53K, an object-centric viewpoint trajectory dataset for learning camera actions. Moreover, we propose a reinforcement learning based viewpoint policy optimization framework, which models camera control as a sequential decision-making process and learns long-horizon observation strategies beyond supervised trajectory imitation. Extensive experiments demonstrate that CamVLM achieves state-of-the-art performance under both passive observation and dynamic viewpoint settings, validating the effectiveness of active camera-based reasoning for surveillance video understanding. Our datasets, model, and code will be available at https://github.com/xiaozhang79/CamVLM .
Chinese Translation
大型视觉语言模型（LVLMs）最近在通用视频理解方面取得了显著进展。然而，由于缺乏大规模领域特定数据集以及固定视角被动观察的限制，其在监控视频中的应用仍然面临挑战。在监控场景中，当目标距离较远、较小、被遮挡或超出当前摄像头视野时，关键的视觉证据可能会被轻易遗漏。在本研究中，我们引入了CamVLM，一个新的“与摄像头思考”的框架，使LVLMs能够通过动态视角控制主动获取视觉证据，而不是被动分析固定的视频流。我们首先构建了CCTV-Anomaly，这是一个大规模监控视频理解数据集，包含14,459个视频，涵盖10个异常类别，并附有详细的字幕和事件注释。我们进一步将视角控制形式化为一个主动视觉感知问题，并构建了CamTrack-53K，一个以对象为中心的视角轨迹数据集，用于学习摄像头动作。此外，我们提出了一种基于强化学习的视角策略优化框架，将摄像头控制建模为一个序列决策过程，并学习超越监督轨迹模仿的长期观察策略。大量实验表明，CamVLM在被动观察和动态视角设置下均实现了最先进的性能，验证了基于主动摄像头推理的监控视频理解的有效性。我们的数据集、模型和代码将发布在 https://github.com/xiaozhang79/CamVLM 。
cs.CV / 93 / 2609.06476
One MLLM, One Call: Efficient Zero-Shot Vision-and-Language Navigation via Spatial-Aware Waypoints
一个多模态大模型，一个调用：通过空间感知的航点实现高效的零-shot视觉与语言导航
Abstract
Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to navigate unseen environments by following natural language instructions. Current zero-shot VLN-CE methods either rely on pre-trained waypoint predictors or require multiple queries to large models per step. To address prohibitive inference latency and computational overhead, we propose O2C-Nav, an efficient zero-shot navigation framework that calls only a single large model once per decision step. Our approach introduces a training-free structured waypoint generator and a novel abstract representation that projects sparse, history-aware candidate waypoints directly onto RGB images as visual markers. The MLLM selects a waypoint or generates a fallback target bounding box at each step, while a low-level Fast Marching Method (FMM) planner converts the selected target into an executable collision-free path. This paradigm provides the model with concrete spatial perception and explicit memory while significantly reducing the visual processing load. Extensive evaluations on the R2R-CE and RxR-CE benchmarks demonstrate that O2C-Nav outperforms current state-of-the-art zero-shot methods, highlighting its great potential for real-time robotic deployment. Code is available at https://github.com/kkpsq/O2C-Nav-Code.
Chinese Translation
在连续环境中的视觉与语言导航（VLN-CE）要求具身代理通过遵循自然语言指令在未见环境中导航。目前的零-shot VLN-CE 方法要么依赖于预训练的航点预测器，要么在每个步骤中需要对大型模型进行多次查询。为了解决推理延迟和计算开销的问题，我们提出了 O2C-Nav，这是一种高效的零-shot导航框架，在每个决策步骤中仅调用一次大型模型。我们的方法引入了一种无训练的结构化航点生成器和一种新颖的抽象表示，将稀疏的、历史感知的候选航点直接投影到 RGB 图像上作为视觉标记。多模态大模型（MLLM）在每个步骤选择一个航点或生成一个后备目标边界框，而低级快速行进方法（Fast Marching Method, FMM）规划器将所选目标转换为可执行的无碰撞路径。这种范式为模型提供了具体的空间感知和明确的记忆，同时显著减少了视觉处理负担。在 R2R-CE 和 RxR-CE 基准上的广泛评估表明，O2C-Nav 超越了当前最先进的零-shot 方法，突显了其在实时机器人部署中的巨大潜力。代码可在 https://github.com/kkpsq/O2C-Nav-Code 获取。
cs.CV / 94 / 2609.06490
OracleZoom: On-Policy Self-Distillation Inspired Reference-Constrained Recursive Image Super Resolution
OracleZoom：基于策略自蒸馏的参考约束递归图像超分辨率
Abstract
Recursive Super-Resolution (SR) extends fixed-scale SR to extreme magnification by repeatedly feeding predictions back into the same model, analogous to zooming an image repeatedly. However, ground truth availability at every scale, especially at depth, remains challenging as the required source resolution grows geometrically, leaving deeper predictions unsupervised. We present OracleZoom, an on-policy distillation-inspired, reference-constrained framework that trains on its trajectory while carrying the last ground-truth evidence beyond the supervision boundary. Direct and cross-scale supervision constrain verifiable content, while a no-reference quality objective guides unresolved fine-scale detail. A KL-constrained pretrained latent prior limits quality-driven drift, while EMA consistency stabilizes the supervision boundary. Across seven datasets, OracleZoom achieves the state-of-the-art SR quality across zooming scales, averaging 0.713 CLIPIQA, with larger gains on deeper scales, while significantly reducing hallucinations. Code, data, and models are available at https://dipta007.github.io/OracleZoom/ .
Chinese Translation
递归超分辨率（SR）通过将预测反复反馈到同一模型中，将固定尺度的超分辨率扩展到极端放大，类似于反复放大图像。然而，随着所需源分辨率几何增长，在每个尺度上，尤其是在深度上，真实值的可用性仍然具有挑战性，导致更深层次的预测缺乏监督。我们提出了OracleZoom，这是一种基于策略的蒸馏灵感、参考约束框架，能够在其轨迹上进行训练，同时携带超出监督边界的最后真实证据。直接和跨尺度的监督约束可验证的内容，而无参考质量目标则引导未解决的细微尺度细节。KL约束的预训练潜在先验限制了质量驱动的漂移，而EMA一致性则稳定了监督边界。在七个数据集上，OracleZoom在放大尺度上实现了最先进的超分辨率质量，平均达到0.713 CLIPIQA，在更深的尺度上获得了更大的提升，同时显著减少了幻觉现象。代码、数据和模型可在 https://dipta007.github.io/OracleZoom/ 获取。
cs.CV / 95 / 2609.06491
Diffuse2Seg: Diffusion Models Can Segment Anything Without Supervision
Diffuse2Seg：扩散模型可以在无监督的情况下对任何对象进行分割
Abstract
Open-world entity segmentation aims to predict masks for arbitrary objects across domains and at multiple granularities, from parts to whole objects. In this setting, SAM sets a strong standard: trained on SA-1B, comprising 11M images and over 1B carefully annotated masks, it achieves remarkable zero-shot performance. Collecting such labels is expensive and time-consuming, however, which limits how far this recipe can scale. Text-to-image diffusion models offer a way around this. Their intermediate features transfer well across perception tasks, and since object structure emerges as the model denoises a noise sample into an image conditioned on a text prompt, that structure is already encoded in these representations. They can therefore be exploited for open-world entity segmentation without retraining or supervision. Building on this observation, we introduce Diffuse2Seg, which repurposes generative diffusion models for automatic mask generation by propagating a grid of point prompts through their self-attention representations in an edge-preserving manner. Diffuse2Seg produces multi-granular instance masks and outperforms prior state-of-the-art label generators by 4.3-7.1 p.p. in AR_1000 across five domains. Training an instance segmentation model on these generated masks advances detector-free open-world segmentation by 7.4 and 7.7 p.p. on "things" and "stuff+things" datasets and surpasses the detector-based UnSAM on "stuff+things" by 2.1 p.p. in AR_1000. Finally, we show that a model trained on Diffuse2Seg labels provides a strong initialization for semi-supervised learning, outperforming its fully supervised counterpart with already 5k labeled images.
Chinese Translation
开放世界实体分割旨在预测跨域和多粒度（从部分到整体对象）的任意对象的掩膜。在这种情况下，SAM设定了一个强有力的标准：在SA-1B上训练，该数据集包含1100万张图像和超过10亿个经过精心标注的掩膜，SAM实现了显著的零-shot性能。然而，收集这样的标签既昂贵又耗时，这限制了这一方法的扩展性。文本到图像的扩散模型提供了一种解决方案。它们的中间特征在感知任务之间转移良好，并且由于对象结构在模型将噪声样本去噪为基于文本提示的图像时逐渐显现，这种结构已经在这些表示中编码。因此，它们可以在不进行重训练或监督的情况下用于开放世界实体分割。基于这一观察，我们提出了Diffuse2Seg，它通过以保持边缘的方式在自注意力表示中传播点提示网格，将生成性扩散模型重新用于自动掩膜生成。Diffuse2Seg生成多粒度实例掩膜，并在五个领域的AR_1000中比之前的最先进标签生成器提高了4.3-7.1个百分点。在这些生成的掩膜上训练实例分割模型，使无检测器的开放世界分割在“物体”和“物体+物品”数据集上分别提高了7.4和7.7个百分点，并在AR_1000中超过了基于检测器的UnSAM在“物体+物品”数据集上提高了2.1个百分点。最后，我们展示了在Diffuse2Seg标签上训练的模型为半监督学习提供了强有力的初始化，超越了其完全监督的对应模型，尽管仅使用了5000个标注图像。
cs.CV / 96 / 2609.06504
CAM: Question Answering on Entity-Centric Videos with Continuous Extraction and Adaptive Querying
CAM：基于实体的视频问答系统，具有连续提取和自适应查询功能
Abstract
Memory facilitates question answering over long videos by extracting and retrieving facts to fit within the limited context windows of multimodal LLMs (MLLMs). Existing solutions typically extract independent memory entries from fixed-length video clips and thus cannot capture high-level semantics that need to be summarized over extended time periods, such as character traits and relations. Moreover, they rely solely on similarity-based retrieval and may fail to retrieve the fine-grained details required for question answering. To tackle these problems, we propose CAM, featuring continuous extraction for high-level semantics and adaptive querying for fine-grained details. In particular, CAM stores the entities and relations extracted from video clips in a knowledge graph. To capture the high-level semantics of each entity or relation, CAM summarizes the local subgraph of the target entity or relation once the subgraph reaches a predefined size. To retrieve the fine-grained details required for question answering, CAM supports multiple search methods, including knowledge graph traversal, video re-watching, and audio listening. It utilizes a planner-executor-verifier pipeline to adaptively compose these search methods according to question intent. Evaluations on three benchmarks show that CAM outperforms SOTA baselines and improves their accuracy by up to 23 percentage points. Code is available at https://github.com/Jake-Tian/CAM.
Chinese Translation
内存通过提取和检索事实来促进对长视频的问答，以适应多模态大语言模型（MLLMs）有限的上下文窗口。现有解决方案通常从固定长度的视频片段中提取独立的内存条目，因此无法捕捉需要在较长时间段内进行总结的高级语义，例如角色特征和关系。此外，它们仅依赖基于相似性的检索，可能无法检索到问答所需的细粒度细节。为了解决这些问题，我们提出了CAM，具有用于高级语义的连续提取和用于细粒度细节的自适应查询功能。具体而言，CAM将从视频片段中提取的实体和关系存储在知识图谱中。为了捕捉每个实体或关系的高级语义，CAM在目标实体或关系的局部子图达到预定义大小后进行总结。为了检索问答所需的细粒度细节，CAM支持多种搜索方法，包括知识图谱遍历、视频重看和音频聆听。它利用规划者-执行者-验证者管道，根据问题意图自适应地组合这些搜索方法。在三个基准测试中的评估表明，CAM的性能优于最先进的基线，并将其准确性提高了多达23个百分点。代码可在 https://github.com/Jake-Tian/CAM 获取。
cs.CV / 97 / 2609.06507
A Cloud-Based Hybrid Model for Real-Time Detection of BRTA-Approved Licence Plates Using YOLO Tiny and Haar Cascade
基于云的混合模型用于实时检测BRTA批准的车牌，采用YOLO Tiny和Haar级联
Abstract
Accurate vehicle license plate detection is essential for applications such as intelligent transportation systems, toll collection, parking management, and law enforcement. In Bangladesh, this task presents distinct challenges due to the complexity of localized license plates and environmental factors like lighting, occlusion, motion blur, and obstructions such as dirt or mud. These challenges often render conventional methods ineffective. This paper introduces a novel hybrid approach, combining the YOLO Tiny deep learning model with the Haar-Cascade classifier, for enhanced detection and localization of Bengali license plates. A key innovation of our system is the integration of a dynamic retraining pipeline, which allows the model to adapt to evolving real-world conditions. This retraining mechanism significantly boosts performance in low-confidence scenarios by continuously improving the model's accuracy as new data is encountered. Additionally, a publicly accessible dataset of BRTA-compliant license plates, captured under diverse and challenging conditions, has been developed to support this approach. Experimental results demonstrate that our approach not only achieves superior detection accuracy and computational efficiency over conventional models but also ensures consistent performance in resource-constrained environments, particularly in Bangladesh.
Chinese Translation
准确的车辆车牌检测对于智能交通系统、收费收集、停车管理和执法等应用至关重要。在孟加拉国，由于本地化车牌的复杂性以及光照、遮挡、运动模糊和污垢或泥土等环境因素，这项任务面临着独特的挑战。这些挑战常常使传统方法无效。本文提出了一种新颖的混合方法，将YOLO Tiny深度学习模型与Haar-Cascade分类器相结合，以增强孟加拉车牌的检测和定位。我们系统的一个关键创新是集成了动态再训练管道，使模型能够适应不断变化的现实条件。该再训练机制通过在遇到新数据时持续提高模型的准确性，显著提升了低置信度场景下的性能。此外，我们开发了一个公开可访问的BRTA合规车牌数据集，该数据集在多样且具有挑战性的条件下捕获，以支持这一方法。实验结果表明，我们的方法不仅在检测准确性和计算效率上优于传统模型，而且在资源受限的环境中，特别是在孟加拉国，确保了一致的性能。
cs.CV / 98 / 2609.06530
Selective Knowledge Control for Continual GUI Agent Learning over Application Streams
面向应用流的持续GUI代理学习的选择性知识控制
Abstract
Continual learning is a crucial capability for Graphical User Interface (GUI) agents to adapt to evolving applications while retaining knowledge acquired from previous applications. Such application streams pose a challenging knowledge modeling problem: new applications often share underlying knowledge with past ones, yet also introduce specific knowledge that must not interfere with historical knowledge. In this paper, we propose activation-conditioned selective knowledge control, a lightweight method that achieves selective knowledge retention via neuron-level gradient manipulation. Our method maintains a compact historical knowledge state to protect highly activated MLP neurons that preserve previous knowledge. When a new application arrives, it performs real-time gradient surgery conditioned on forward activation. Concretely, the protected neurons are categorized into two types: unactivated neurons holding specific knowledge, whose gradients are truncated to prevent interference; and activated neurons holding shared knowledge, whose gradients are orthogonally projected to preserve stability while enabling adaptation. After each application stage, newly identified critical neurons are merged into the historical state for future learning. Empirical evaluations on multi-app sequential benchmark demonstrate that our method effectively mitigates catastrophic forgetting on prior applications while sustaining robust adaptation to new ones.
Chinese Translation
持续学习是图形用户界面（GUI）代理适应不断演进的应用程序，同时保留从先前应用中获得知识的关键能力。这类应用流提出了一个具有挑战性的知识建模问题：新应用通常与过去的应用共享底层知识，但也引入了必须避免干扰历史知识的特定知识。本文提出了一种基于激活条件的选择性知识控制方法，这是一种通过神经元级梯度操作实现选择性知识保留的轻量级方法。该方法维护一个紧凑的历史知识状态，以保护高度激活的多层感知机（MLP）神经元，这些神经元保存了先前的知识。当新应用到来时，方法基于前向激活执行实时梯度调整。具体而言，受保护的神经元被分为两类：未激活的神经元持有特定知识，其梯度被截断以防止干扰；激活的神经元持有共享知识，其梯度被正交投影以在保持稳定性的同时实现适应。每个应用阶段结束后，新识别的关键神经元被合并入历史状态以供未来学习。多应用顺序基准的实证评估表明，该方法有效缓解了对先前应用的灾难性遗忘，同时保持了对新应用的强健适应能力。
cs.CV / 99 / 2609.06535
3DHarnessBench: Probing Agentic 3D-to-Code Capabilities of Frontier Vision-Language Models
3DHarnessBench：探测前沿视觉-语言模型的主动3D到代码能力
Abstract
We introduce 3DHarnessBench, a benchmark that evaluates the agentic ability of frontier vision-language models (VLMs) to recover 3D geometry as Blender Python code from a variety of inputs. Unlike previous frameworks that prompt the VLMs with a fixed input (e.g., a single rendering or a text description), 3DHarnessBench evaluates four separate harness settings that progressively enable active agentic exploration, facilitated by recent Blender MCP functionality. Our hierarchy from Single-view, Multi-view, Active Visual (arbitrary viewpoint access), and Full 3D Interaction (complete access to the target object through Blender function calls) probes the models' abilities in both visual perception and active inference, tool calling, and self-correction. We observe that the ability of all frontier models to recover 3D geometry improves significantly with richer function call access, although the improvements are strongly model-dependent, revealing highly uneven agentic 3D-to-code capabilities. We will release the benchmark, code, outputs, and agent trajectories for reproducible 3D evaluation.
Chinese Translation
我们介绍了3DHarnessBench，这是一个基准测试，用于评估前沿视觉-语言模型（VLMs）从多种输入中恢复3D几何形状为Blender Python代码的主动能力。与之前通过固定输入（例如，单一渲染或文本描述）来提示VLMs的框架不同，3DHarnessBench评估了四种独立的测试设置，这些设置逐步启用主动的探索能力，得益于最近的Blender MCP功能。我们的层级结构包括单视图、多视图、主动视觉（任意视角访问）和完整3D交互（通过Blender函数调用完全访问目标对象），探测模型在视觉感知、主动推理、工具调用和自我修正方面的能力。我们观察到，所有前沿模型恢复3D几何形状的能力随着更丰富的函数调用访问显著提高，尽管这种改善在很大程度上依赖于模型，显示出主动的3D到代码能力存在高度不均衡。我们将发布基准测试、代码、输出和代理轨迹，以便进行可重复的3D评估。
cs.CV / 100 / 2609.06578
Learning to Use Imagination: Progress-Conditioned Future Utilization for World Action Models
学习使用想象力：基于进展条件的未来利用于世界行动模型
Abstract
World Action Models (WAMs) extend Vision-Language-Action (VLA) models by incorporating future visual dynamics into action generation. However, existing WAMs often utilize imagined futures with limited adaptation to evolving execution progress, potentially introducing distracting or unreliable predictive cues. This limitation arises from two empirically identified forms of non-uniformity in future utility: (i) at the inter-progress level, the utility of imagined futures varies across execution stages as control demands change; and (ii) at the intra-progress level, individual future latents exhibit heterogeneous relevance within the same progress state. To address these limitations, we propose ProWAM, a Progress-Conditioned World Action Model that introduces execution progress as an explicit intermediate representation for adaptive imagination utilization. ProWAM comprises two tightly coupled components: (1) To obtain a reliable representation of execution progress, we propose the Self-Supervised Dual-Temporal Progress Encoder (SS-DTPE). SS-DTPE couples short-term action-observation interaction modeling with long-term recurrent progress aggregation to capture recent execution feedback and accumulated task history. (2) Conditioned on the progress representation from SS-DTPE, we propose the Hierarchical Progress-Conditioned Imagination Modulation (HPIM) to adapt imagination utilization to execution progress. HPIM operates at two complementary levels: an inter-progress global modulation mechanism adapts future utilization across execution stages, while an intra-progress relevance mechanism differentiates individual future latents within each progress state. Extensive experiments demonstrate consistent gains over strong VLA and WAM baselines.
Chinese Translation
世界行动模型（WAMs）通过将未来视觉动态纳入行动生成，扩展了视觉-语言-行动（VLA）模型。然而，现有的WAMs往往利用想象的未来，但对不断变化的执行进展适应性有限，这可能引入干扰或不可靠的预测线索。这一局限性源于两种经验上识别的未来效用的非均匀性形式：（i）在进展间层面，想象未来的效用在执行阶段之间变化，因为控制需求发生变化；（ii）在进展内层面，单个未来潜变量在相同进展状态下表现出异质相关性。为了解决这些局限性，我们提出了ProWAM，一种基于进展条件的世界行动模型，它将执行进展作为适应性想象利用的显式中间表示。ProWAM由两个紧密耦合的组件组成：（1）为了获得可靠的执行进展表示，我们提出了自监督双时间进展编码器（Self-Supervised Dual-Temporal Progress Encoder，SS-DTPE）。SS-DTPE将短期行动-观察交互建模与长期递归进展聚合相结合，以捕捉近期执行反馈和累积任务历史。（2）基于SS-DTPE的进展表示，我们提出了层次化进展条件想象调制（Hierarchical Progress-Conditioned Imagination Modulation，HPIM），以使想象利用适应执行进展。HPIM在两个互补层面上运作：一个进展间全局调制机制在执行阶段之间调整未来利用，而一个进展内相关性机制则区分每个进展状态下的单个未来潜变量。大量实验表明，ProWAM在强大的VLA和WAM基线之上具有一致的增益。
cs.CV / 101 / 2609.06581
Reading Decoder Trajectories: Training-Free Counterfactual Query-Trajectory Reliability for Small-Object Detection
解读解码器轨迹：无训练的反事实查询轨迹可靠性用于小物体检测
Abstract
Small-object detection remains challenging because limited pixels cause information loss and suppress the scale knowledge encoded in pretrained detectors. Existing approaches mainly improve representations through multiscale training, architecture redesign, or parameter adaptation, implicitly assuming that frozen models lack the required capability. We challenge this assumption and hypothesize that small-object knowledge already exists in frozen detectors but remains underactivated and unstable during query evolution. To test this hypothesis, we propose Counterfactual Query-Trajectory Reliability (CQTR), a training-free framework that elicits latent responses through counterfactual scale interventions and interprets candidate reliability from decoder-internal spatial convergence, semantic persistence, and cross-scale conflicts. A small unlabeled training subset selects the appropriate correction mechanism for each model-data stream, without parameter updates or target-domain annotations. Across 27 combinations of nine frozen detectors and three datasets, CQTR consistently improves average precision (AP) and average precision for small objects (APs). Closed-loop analyses further show that scale intervention activates latent responses, trajectory evidence predicts ground-truth support, and unlabeled routing selects the more effective branch. CQTR therefore reframes small-object detection from external scale augmentation to the activation and reliability assessment of latent scale knowledge.
Chinese Translation
小物体检测仍然面临挑战，因为有限的像素导致信息丢失，并抑制了预训练检测器中编码的尺度知识。现有的方法主要通过多尺度训练、架构重设计或参数适应来改善表示，隐含地假设冻结模型缺乏所需的能力。我们挑战这一假设，并假设小物体知识已经存在于冻结检测器中，但在查询演化过程中仍然处于未激活和不稳定状态。为了验证这一假设，我们提出了反事实查询轨迹可靠性（Counterfactual Query-Trajectory Reliability, CQTR），这是一个无训练的框架，通过反事实尺度干预引发潜在反应，并从解码器内部的空间收敛、语义持久性和跨尺度冲突中解释候选的可靠性。一个小的未标记训练子集为每个模型-数据流选择适当的修正机制，无需参数更新或目标领域注释。在九个冻结检测器和三个数据集的27种组合中，CQTR始终提高了平均精度（Average Precision, AP）和小物体的平均精度（Average Precision for small objects, APs）。闭环分析进一步表明，尺度干预激活了潜在反应，轨迹证据预测了真实支持，而未标记路由选择了更有效的分支。因此，CQTR将小物体检测从外部尺度增强重新框架为潜在尺度知识的激活和可靠性评估。
cs.CV / 102 / 2609.06602
Multi-History-Step SDE Inversion for Image Editing with Superior Regional Awareness
具有优越区域感知的多历史步长随机微分方程反演图像编辑
Abstract
In recent years, diffusion stochastic differential equation (SDE) inversion and inversion-free methods have become prevalent for training-free image editing, as they can achieve faithful reconstruction without tuning. However, existing approaches remain inefficient, exhibit limited plasticity, and struggle to accurately preserve unedited regions. To address these issues, we propose MIEdit, a training-free editing framework based on SDE inversion. MIEdit introduces a predictor-corrector multi-history-step scheme to achieve superior editing quality with fewer steps. We further mitigate heterogeneity and conflict between the multi-conditioned noise residuals and gradient terms during sampling, improving stability and editing plasticity under large edits. MIEdit also includes Inversion-Time Automatic Semantic Angle Masking (IASM); it leverages classifier-free guidance to automatically generate semantic angle masks during inversion and applies them throughout the sampling process for regional constraints, without extra user inputs. We additionally construct EditEval++ (30 fine-grained tasks, 1,000+ image-text-mask triplets) for comprehensive evaluation; experiments show that MIEdit outperforms state-of-the-art techniques. Project page: https://whywwwzzzg.github.io/MIEdit/.
Chinese Translation
近年来，扩散随机微分方程（SDE）反演和无反演方法在无训练图像编辑中变得越来越普遍，因为它们能够在不调优的情况下实现真实的重建。然而，现有方法仍然效率低下，表现出有限的可塑性，并且难以准确保留未编辑区域。为了解决这些问题，我们提出了MIEdit，一种基于SDE反演的无训练编辑框架。MIEdit引入了一种预测-修正多历史步长方案，以更少的步骤实现优越的编辑质量。我们进一步减轻了在采样过程中多条件噪声残差与梯度项之间的异质性和冲突，提高了在大幅编辑下的稳定性和编辑可塑性。MIEdit还包括反演时自动语义角度遮罩（IASM）；它利用无分类器引导在反演过程中自动生成语义角度遮罩，并在整个采样过程中应用这些遮罩以实现区域约束，无需额外的用户输入。我们还构建了EditEval++（30个细粒度任务，1000+图像-文本-遮罩三元组）进行全面评估；实验表明，MIEdit优于最先进的技术。项目页面：https://whywwwzzzg.github.io/MIEdit/
cs.CV / 103 / 2609.06619
GAN-Blot: A Controllable Structure-Style Synthesis Benchmark for Western Blot Forensics
GAN-Blot：一种可控的结构-风格合成基准用于西方印迹法取证
Abstract
Western blot (WB) images are widely used as key evidence in biomedical research. Recent scientific misconduct cases reveal that WB imagery is increasingly fabricated, making WB forensics a major concern for research integrity. However, while the progress of forensic detection techniques often relies on advances in forgery-generation techniques, the development of WB forensic techniques has been hindered by the lack of standardized appearance attribute definitions, image datasets, and controllable generation frameworks for WB imagery. To address this limitation, we present a controllable WB image synthesis framework, named GAN-Blot, for generating realistic synthetic WB images. We introduce a formulation that decomposes a WB image into a structure component and a style-reference component, enabling independent control over local protein-band geometry and the global visual appearance of a synthetic WB image. GAN-Blot integrates a dual-path autoencoding design with several style-alignment loss terms to enable implicit control over structure-style synthesis without predefined semantic appearance attributes. We further contribute a synthetic WB dataset containing more than 46K images and propose four evaluation protocols for controllable WB synthesis. Extensive experiments show that GAN-Blot can generate WB images with high fidelity in both protein-band structure and visual style. Under blind inspection, the generated images can fool domain experts and are not reliably distinguished from authentic WB images by existing detectors and screening platforms. These results demonstrate their utility as challenging controlled cases for validating and developing WB forensic methods.
Chinese Translation
西方印迹法（WB）图像在生物医学研究中被广泛用作关键证据。近期的科学不端案例揭示，WB 图像的伪造现象日益严重，使得 WB 取证成为研究诚信的重要关注点。然而，尽管取证检测技术的进展往往依赖于伪造生成技术的进步，WB 取证技术的发展却因缺乏标准化的外观属性定义、图像数据集和可控的 WB 图像生成框架而受到阻碍。为了解决这一限制，我们提出了一种可控的 WB 图像合成框架，命名为 GAN-Blot，用于生成逼真的合成 WB 图像。我们引入了一种将 WB 图像分解为结构组件和风格参考组件的公式，从而实现对合成 WB 图像的局部蛋白带几何形状和整体视觉外观的独立控制。GAN-Blot 结合了双路径自编码设计和多个风格对齐损失项，以实现对结构-风格合成的隐式控制，而无需预定义的语义外观属性。我们进一步贡献了一个包含超过 46K 图像的合成 WB 数据集，并提出了四种可控 WB 合成的评估协议。大量实验表明，GAN-Blot 能够在蛋白带结构和视觉风格上生成高保真的 WB 图像。在盲检下，生成的图像能够欺骗领域专家，并且现有的检测器和筛选平台无法可靠地区分这些图像与真实的 WB 图像。这些结果证明了它们作为验证和开发 WB 取证方法的挑战性控制案例的实用性。
cs.CV / 104 / 2609.06628
GeoCo-SAVi: Geometry-Consistent Slot Attention for Explicitly Editable Object Representations
GeoCo-SAVi：几何一致的插槽注意力用于显式可编辑的对象表示
Abstract
Object-centric video models represent scenes with slots, yet exposed geometry can vary in meaning with appearance. In Invariant Slot Attention (ISA), explicit position and scale can disagree with the decoded center and extent; edits can yield unexpected motion or resizing, and replacing appearance can shift geometry. GeoCo-SAVi promotes geometric authority and semantic alignment. Its spatially equivariant, object-wise decoder makes position and scale effective commands: changing them moves or resizes the rendered support. Factual position alignment ties position to the decoded center, and normalized attention overlap discourages duplicate allocation. Appearance transplantation aligns geometry semantics across objects, so recipient geometry governs layout while donor appearance supplies shape. A temporal initializer propagates calibrated slots across frames. On Obj3D, GeoCo-SAVi matches ISA reconstruction, reduces p-centroid error by over 80%, and cuts appearance-induced size variation by over 50% while producing the expected translation and scale responses. On 250 MOVi-C videos, it also improves reconstruction, instance grouping, and fixed-identity geometry control over two same-protocol references. GeoCo-SAVi transforms explicit geometry into compositional control, making both position and scale more readable and editable.
Chinese Translation
以对象为中心的视频模型通过插槽表示场景，但暴露的几何形状可能因外观而异。在不变插槽注意力（Invariant Slot Attention, ISA）中，显式的位置和尺度可能与解码的中心和范围不一致；编辑可能导致意外的运动或调整大小，替换外观可能会改变几何形状。GeoCo-SAVi 促进几何权威性和语义对齐。其空间等变的对象级解码器使得位置和尺度成为有效的指令：改变它们会移动或调整渲染支持的大小。事实位置对齐将位置与解码中心关联，归一化的注意力重叠则抑制重复分配。外观移植在对象之间对齐几何语义，因此接收几何体控制布局，而捐赠的外观提供形状。时间初始化器在帧之间传播经过校准的插槽。在 Obj3D 数据集上，GeoCo-SAVi 的重建效果与 ISA 相匹配，p-质心误差减少超过 80%，并且外观引起的大小变化减少超过 50%，同时产生预期的平移和缩放响应。在 250 个 MOVi-C 视频中，它还改善了重建、实例分组和固定身份几何控制，优于两个相同协议的参考。GeoCo-SAVi 将显式几何形状转化为组合控制，使得位置和尺度更加易读和可编辑。
cs.CV / 105 / 2609.06629
Physico-Geospatial Grounded Scene Interpretation for Mobile Robotics
面向移动机器人物理-地理空间基础的场景解释
Abstract
Recent advancements in deep learning allow robotic agents to interact with dynamic and unstructured environments. Of special interest is the integration of physico-geospatial world knowledge into such systems, either by using physics-aware machine learning models, knowledge graphs to model relationships or spatio-temporal and logical reasoning. In the present work, we introduce an approach to augment the output of pre-trained, unmodified VLMs used for scene interpretation by integrating semantic descriptions, OpenStreetMap building data and street information with positional, temporal and metric information obtained from our sensory systems, fusing this information using LLMs. We apply this concept to an outdoor recording within a university campus, achieving an F1-Score of 0.83 in the task of grounding buildings and 0.64 for path surface grounding on our pilot evaluation set. The results demonstrate the conceptual capability of the proposed solution to deliver physico-geospatial grounded natural language descriptions. Code and results are available at https://datahub.rz.rptu.de/hstr-csrl-public/publications/physico-geospatial-grounded-scene-interpretation
Chinese Translation
近年来深度学习的进步使得机器人代理能够与动态和非结构化环境进行交互。特别值得关注的是将物理-地理空间世界知识整合到此类系统中，方法包括使用物理感知的机器学习模型、知识图谱来建模关系，或进行时空和逻辑推理。在本研究中，我们提出了一种方法，通过将语义描述、OpenStreetMap建筑数据和街道信息与从我们的传感系统获得的位置、时间和度量信息相结合，增强用于场景解释的预训练、未修改的视觉语言模型（VLMs）的输出，并利用大型语言模型（LLMs）融合这些信息。我们将这一概念应用于大学校园的户外录制，在建筑物定位任务中取得了0.83的F1得分，在路径表面定位任务中取得了0.64的F1得分，基于我们的初步评估集。结果展示了所提解决方案在提供物理-地理空间基础的自然语言描述方面的概念能力。代码和结果可在 https://datahub.rz.rptu.de/hstr-csrl-public/publications/physico-geospatial-grounded-scene-interpretation 获取。
cs.CV / 106 / 2609.06645
MARR: Decoupling Policy, Execution, and Calibration for All-in-One Medical Image Restoration
MARR：解耦政策、执行和校准的全能医疗图像恢复
Abstract
All-in-one medical image restoration seeks to recover heterogeneous clinical images with a single model, but PET, CT, and MRI differ substantially in degradation statistics, anatomical contrast, and output-space bias. A fully shared network can entangle modality-specific residual errors, whereas separate modality-specific networks sacrifice the practical advantages of unified deployment. We therefore recast all-in-one restoration as a question of where limited adaptation should be placed: policy selection, feature execution, or output calibration. We propose MARR, a compact restoration framework that constrains multi-modality adaptation into degradation-aware policy routing, modality-private residual execution, and image-domain residual correction without requiring degradation labels or separate modality-specific models. The policy branch forms a routing prompt from input statistics, latent content, and modality identity, and uses it only as a control signal. Prompt-gated modality-private adapters then perform lightweight residual refinement at intermediate decoder stages, while zero-initialized modality-specific output heads calibrate the final image-domain residual without perturbing the initial shared prediction. On an all-in-one PET, CT, and MRI restoration benchmark, MARR outperforms thirteen methods re-trained under the same protocol, achieving PSNR values of 37.34 dB, 33.85 dB, and 32.09 dB on PET, CT, and MRI, respectively, and the best modality-average PSNR of 34.43 dB. The code is publicly available at https://github.com/CHB-learner/MARR.
Chinese Translation
全能医疗图像恢复旨在通过单一模型恢复异质临床图像，但PET、CT和MRI在降解统计、解剖对比和输出空间偏差上存在显著差异。完全共享的网络可能会纠缠模态特定的残差误差，而单独的模态特定网络则牺牲了统一部署的实际优势。因此，我们将全能恢复重新定义为有限适应应放置于何处的问题：政策选择、特征执行或输出校准。我们提出了MARR，一个紧凑的恢复框架，将多模态适应限制为基于降解的政策路由、模态私有的残差执行和图像域残差校正，而无需降解标签或单独的模态特定模型。政策分支根据输入统计、潜在内容和模态身份形成路由提示，并仅将其用作控制信号。提示门控的模态私有适配器在中间解码器阶段执行轻量级残差细化，而零初始化的模态特定输出头则在不扰动初始共享预测的情况下校准最终的图像域残差。在一个全能的PET、CT和MRI恢复基准测试中，MARR的表现优于在相同协议下重新训练的十三种方法，分别在PET、CT和MRI上达到了37.34 dB、33.85 dB和32.09 dB的PSNR值，并获得了最佳模态平均PSNR值34.43 dB。代码已公开发布在https://github.com/CHB-learner/MARR。
cs.CV / 107 / 2609.06646
When Does a Laugh Begin? Structured Annotator Disagreement in Temporal Laughter Localization
笑声何时开始？时间性笑声定位中的结构性标注者分歧
Abstract
Annotators routinely disagree on laughter boundaries and subtle chuckles, yet temporal laughter localization typically evaluates against a single reference annotation. We show that this disagreement is structured rather than random noise. Re-annotating the SMILE-Temporal benchmark (672 videos, 1,683 events) with 3-5 annotators per video (alpha = 0.757), we find systematic patterns: disagreement is 1.73 times larger at offsets than onsets, far more common for chuckles than full laughs (77% vs. 20%), and predictable from event attributes (AUC = 0.831). Evaluating against a single annotator breaks down under this structure: system scores shift by 0.246 F1 depending on the chosen ground truth, correctly ranking systems only 69.7% of the time (vs. 80% against all annotators). We propose a disagreement-calibrated evaluation that scores predictions against the full annotator distribution using conformally calibrated tolerance bands (wider at offsets, 0.727s, than onsets, 0.5s). The per-annotator annotations and analysis code are available at https://github.com/WSCSports/SMILE-Disagreement .
Chinese Translation
标注者在笑声边界和细微的轻笑上常常存在分歧，但时间性笑声定位通常是基于单一参考标注进行评估。我们表明，这种分歧是结构性的，而非随机噪声。我们对SMILE-Temporal基准（672个视频，1,683个事件）进行了重新标注，每个视频有3-5名标注者（α = 0.757），发现了系统性的模式：在偏移点的分歧比在起始点大1.73倍，轻笑的分歧远比完整笑声更为常见（77%对20%），并且可以从事件属性中预测（AUC = 0.831）。在这种结构下，针对单一标注者的评估会出现问题：系统得分根据所选的真实值变化0.246 F1，仅有69.7%的时间正确排名系统（而对所有标注者则为80%）。我们提出了一种基于分歧校准的评估方法，该方法使用符合校准的容忍带（在偏移点为0.727秒，起始点为0.5秒）对预测进行评分，考虑了完整的标注者分布。每位标注者的标注和分析代码可在https://github.com/WSCSports/SMILE-Disagreement获取。
cs.CV / 108 / 2609.06652
VidaForge: Open Research Infrastructure for Video Pretraining Data Recipes
VidaForge：视频预训练数据配方的开放研究基础设施
Abstract
Video foundation models increasingly rely on large-scale pretraining data, yet the end-to-end data pipelines behind them remain largely closed and difficult to inspect or reuse. Researchers seeking to understand how video data recipes affect model pretraining often need to build substantial infrastructure before testing even a focused hypothesis. We present VIDAFORGE, an open research infrastructure that represents a video data recipe as an executable five-stage workflow from raw videos to training datasets. A decision in this workflow can be varied to construct alternative datasets while preserving how every sample was produced. To demon strate this research workflow, we compare data recipes with different coverage and quality in early from-scratch pretraining of Wan 2.1 and V-JEPA 2.1. Across both learning objectives, the broader-coverage recipe achieves the highest downstream benchmark scores, while loss-based evaluation favors different recipes. This study demonstrates how VidaForge connects data-recipe choices to downstream model performance. We further release VIDAFORGE-3M, containing 3.14 million scene level clips totaling 6,475 hours, with fine-grained annotations and curation signals for video data-recipe research.
Chinese Translation
视频基础模型越来越依赖于大规模的预训练数据，然而其背后的端到端数据管道仍然大多封闭，难以检查或重用。希望了解视频数据配方如何影响模型预训练的研究人员，通常需要构建大量基础设施才能测试即使是一个集中假设。我们提出了VIDAFORGE，一个开放的研究基础设施，它将视频数据配方表示为从原始视频到训练数据集的可执行五阶段工作流程。该工作流程中的决策可以被改变，以构建替代数据集，同时保留每个样本的生成方式。为了展示这一研究工作流程，我们比较了在Wan 2.1和V-JEPA 2.1的早期从零开始预训练中，具有不同覆盖率和质量的数据配方。在这两种学习目标中，覆盖面更广的配方在下游基准测试中获得了最高的分数，而基于损失的评估则偏向于不同的配方。这项研究展示了VidaForge如何将数据配方选择与下游模型性能连接起来。我们进一步发布了VIDAFORGE-3M，包含314万个场景级剪辑，总计6475小时，附有细粒度的注释和视频数据配方研究的策划信号。
cs.CV / 109 / 2609.06660
FSAN: Flow State Attention Network for Aerodynamic Prediction
FSAN：用于气动预测的流态注意力网络
Abstract
Accurate aerodynamic prediction is critical for designing fuel-efficient and safe transportation systems such as aircraft and automobiles, yet traditional computational fluid dynamics (CFD) simulations remain computationally expensive and expertise-intensive, severely limiting their use in iterative design and real-time analysis. Existing deep learning surrogates suffer from two major limitations: (i) they are evaluated on datasets with narrow flow-condition ranges, leaving their performance under complex flow conditions undemonstrated; (ii) they treat global flow conditions as a single vector injected uniformly across all surface points, ignoring that different geometric regions experience distinct local flow phenomena, which degrades prediction accuracy under complex flow conditions. To address these limitations, we propose the Flow State Attention Network (FSAN). FSAN separately encodes point cloud and flow conditions, then partitions the geometry into multiple flow states via learnable soft assignments, and uses flow features to update these state representations, which in turn influence point cloud features through state changes. This enables fine-grained, state-specific interaction between geometry and flow information. Extensive experiments on two well-recognized aerodynamic benchmarks demonstrate that FSAN achieves the highest accuracy among the methods compared in this work at a higher computational cost. On Emmi-Wing, FSAN reduces the Relative L2 (REL-L2) error by over 20\% compared to the strongest baseline (Transolver), and on DrivAerNet++, it achieves a 10\% reduction compared to the strongest baseline (AdaField). These results establish FSAN as a promising neural surrogate on public benchmarks with diverse flow conditions and geometries.
Chinese Translation
准确的气动预测对于设计燃油高效且安全的交通系统（如飞机和汽车）至关重要，然而传统的计算流体动力学（CFD）模拟仍然计算成本高且需要专业知识，严重限制了它们在迭代设计和实时分析中的应用。现有的深度学习代理存在两个主要限制：（i）它们在流动条件范围狭窄的数据集上进行评估，未能展示其在复杂流动条件下的性能；（ii）它们将全局流动条件视为一个均匀注入到所有表面点的单一向量，忽略了不同几何区域经历不同局部流动现象的事实，这在复杂流动条件下降低了预测准确性。为了解决这些限制，我们提出了流态注意力网络（Flow State Attention Network，FSAN）。FSAN分别编码点云和流动条件，然后通过可学习的软分配将几何体划分为多个流态，并利用流动特征更新这些状态表示，进而通过状态变化影响点云特征。这使得几何体与流动信息之间能够进行细粒度、状态特定的交互。在两个公认的气动基准测试上的大量实验表明，FSAN在本研究中比较的方法中实现了最高的准确性，尽管计算成本较高。在Emmi-Wing上，FSAN相比于最强基线（Transolver）将相对L2（REL-L2）误差降低了超过20\%；在DrivAerNet++上，相比于最强基线（AdaField）实现了10\%的降低。这些结果确立了FSAN作为在具有多样化流动条件和几何体的公共基准测试中有前景的神经代理。
cs.CV / 110 / 2609.06665
TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation
TransNormal-2：基于几何的修正流与边缘感知解码用于精确法线估计
Abstract
Diffusion-based models enable monocular geometry estimation, yet their pixel-space precision is limited by a shared, under-studied error source: VAE reconstruction degradation. The 8x spatial compression in the VAE encoder-decoder degrades surface normals at object boundaries; even encoding and decoding ground-truth normals introduces 1.3--8.5{\deg} of mean angular error (MAE), with edge MAE reaching 2.8x the global MAE. We present TransNormal-2, a FLUX.2-based rectified-flow framework with single-step deterministic inference that addresses this degradation on both sides of the VAE decoder: in how latent predictions are supervised during training, and in how decoded normals are corrected at inference. First, geometry-aware pixel-space losses, including inverse rendering self-consistency, von~Mises-Fisher angular loss, and wavelet edge-aware regularization, complement latent MSE by enforcing spherical normal geometry and diffuse image-formation cues after VAE decoding. Second, a lightweight Geometric Refinement Module (GRM) applies an RGB-guided residual correction to reduce boundary-localized decoding errors without freely rewriting the coarse prediction. On general-scene benchmarks, TransNormal-2 matches or exceeds MoGe-2 on all eight reported metrics while using only 1.4% as many task-specific normal annotations. The gains are clearest for transparent objects, reducing MAE by 4.2{\deg} on ClearGrasp and 3.1{\deg} on ClearPose over the strongest prior baselines. Code will be released at https://longxiang-ai.github.io/TransNormal-2.
Chinese Translation
基于扩散的模型使得单目几何估计成为可能，但其像素空间的精度受到一个共享且未充分研究的误差源的限制：变分自编码器（VAE）重建退化。在VAE编码器-解码器中的8倍空间压缩导致物体边界处表面法线的退化；即使对真实法线进行编码和解码也会引入1.3到8.5度的平均角度误差（MAE），而边缘MAE达到全局MAE的2.8倍。我们提出了TransNormal-2，这是一种基于FLUX.2的修正流框架，具有单步确定性推理，旨在解决VAE解码器两侧的退化问题：在训练过程中如何监督潜在预测，以及在推理时如何纠正解码法线。首先，几何感知的像素空间损失，包括逆渲染自一致性、von Mises-Fisher角度损失和小波边缘感知正则化，通过在VAE解码后强制执行球面法线几何和漫反射图像形成线索，补充了潜在均方误差（MSE）。其次，一个轻量级的几何精炼模块（GRM）应用RGB引导的残差修正，以减少边界局部的解码错误，而不自由地重写粗略预测。在一般场景基准测试中，TransNormal-2在所有八个报告的指标上与MoGe-2相匹配或超越，同时仅使用1.4%的任务特定法线注释。对于透明物体，增益最为明显，在ClearGrasp上将MAE降低了4.2度，在ClearPose上降低了3.1度，相比于最强的先前基线。代码将发布于https://longxiang-ai.github.io/TransNormal-2。
cs.CV / 111 / 2609.06699
Attention-Enhanced Deep Features with Heterogeneous Ensemble Learning for Glaucoma Detection
基于注意力增强深度特征与异构集成学习的青光眼检测
Abstract
Glaucoma is a progressive optic neuropathy characterized by irreversible damage to the optic nerve, making timely diagnosis critical to prevent permanent vision loss. Although deep learning has demonstrated promising performance in automated glaucoma detection, existing approaches often overlook feature refinement, suffer from class imbalance, and rely on individual classifiers that limit prediction robustness. To address these challenges, this paper proposes a hybrid glaucoma detection framework that integrates attention-enhanced deep feature extraction with heterogeneous ensemble learning. Specifically, deep representations are extracted using InceptionV3 and subsequently refined by incorporating the Convolutional Block Attention Module (CBAM) to enhance discriminative retinal features. To improve classification robustness, the extracted features are classified using multiple machine learning models together with Single-Level Ensemble (SLE) and Double-Level Ensemble (DLE) strategies, while SMOTE combined with Tomek Links (SMOTE+TL) is employed to alleviate class imbalance. Furthermore, a systematic comparison of handcrafted, deep, and attention-enhanced deep feature representations is conducted. Experimental evaluation on two public retinal fundus datasets demonstrates that deep feature-based methods consistently outperform handcrafted feature-based methods, while the proposed attention-enhanced framework achieves the best overall performance. Furthermore, Grad-CAM visualizations confirm that the proposed model focuses on clinically relevant retinal regions, providing interpretable evidence on the model's prediction process.
Chinese Translation
青光眼是一种进行性视神经病，特点是视神经不可逆损伤，因此及时诊断对于防止永久性视力丧失至关重要。尽管深度学习在自动青光眼检测中表现出良好的性能，但现有方法往往忽视特征的精细化，受到类别不平衡的影响，并依赖于单一分类器，限制了预测的鲁棒性。为了解决这些挑战，本文提出了一种混合青光眼检测框架，该框架将注意力增强的深度特征提取与异构集成学习相结合。具体而言，使用 InceptionV3 提取深度表示，并通过结合卷积块注意力模块（Convolutional Block Attention Module, CBAM）进行精细化，以增强区分性视网膜特征。为了提高分类的鲁棒性，提取的特征通过多种机器学习模型进行分类，并结合单层集成（Single-Level Ensemble, SLE）和双层集成（Double-Level Ensemble, DLE）策略，同时采用 SMOTE 结合 Tomek Links（SMOTE+TL）来缓解类别不平衡。此外，系统地比较了手工特征、深度特征和注意力增强深度特征表示。在两个公共视网膜眼底数据集上的实验评估表明，基于深度特征的方法始终优于基于手工特征的方法，而所提出的注意力增强框架实现了最佳的整体性能。此外，Grad-CAM 可视化结果确认所提模型关注临床相关的视网膜区域，为模型的预测过程提供了可解释的证据。
cs.CV / 112 / 2609.06704
Counterfactual Tests for Measuring Chain-of-Thought Faithfulness in Visual Language Models
用于测量视觉语言模型中思维链忠实度的反事实测试
Abstract
Chain-of-thought (CoT) may often look plausible, yet it may not faithfully reflect the model's decision-making process. While methods for measuring the faithfulness of CoTs for textual inputs have been increasingly introduced, using these methods for visual inputs is not straightforward. In this work, we adapt the family of counterfactual methods for measuring CoT faithfulness, namely the Counterfactual Test (CT) and Correlational Counterfactual Test (CCT), to visual inputs, and call them vCT and vCCT, respectively. Using vCT and vCCT, we benchmark eight recent open-source Vision Language Models (VLMs) on two datasets. Our analysis shows that CoTs do not reliably track visual evidence that influences model predictions: they may omit the removed object even when its removal causes a large prediction shift, yet mention it when the shift is small. We further find that Predict-then-Explain explanations align more strongly with perturbation-induced probability shifts than pre-answer CoTs, while binary vCT scores are often nearly saturated. We also include a reconstruction control, in which images pass through the same editing pipeline without object removal, and find that the main object-removal intervention induces larger shifts than reconstruction alone. We construct and release Counter-SNLI-VE and Counter-A-OKVQA, two datasets of image pairs that differ by a single object.
Chinese Translation
思维链（Chain-of-thought, CoT）看似合理，但可能并不忠实地反映模型的决策过程。尽管用于测量文本输入中思维链忠实度的方法日益增多，但将这些方法应用于视觉输入并不简单。在本研究中，我们将反事实方法的家族（Counterfactual methods）适配于视觉输入，以测量思维链的忠实度，具体包括反事实测试（Counterfactual Test, CT）和相关反事实测试（Correlational Counterfactual Test, CCT），并分别称之为 vCT 和 vCCT。使用 vCT 和 vCCT，我们在两个数据集上对八个最新的开源视觉语言模型（Vision Language Models, VLMs）进行了基准测试。我们的分析表明，思维链并不能可靠地追踪影响模型预测的视觉证据：即使去除某个对象会导致预测发生较大变化，思维链也可能省略该对象，而在变化较小时却提及它。我们进一步发现，先预测后解释的解释（Predict-then-Explain）与扰动引起的概率变化的相关性比预回答思维链更强，而二元 vCT 分数往往接近饱和。我们还包括了一个重建控制实验，其中图像经过相同的编辑流程但不去除对象，结果发现主要的对象去除干预引起的变化大于仅重建所导致的变化。我们构建并发布了 Counter-SNLI-VE 和 Counter-A-OKVQA 两个数据集，这两个数据集中的图像对仅在一个对象上存在差异。
cs.CV / 113 / 2609.06712
RoLA: Rotary-Positioned Low-Rank Linear Attention for Efficient Diffusion Transformers
RoLA：用于高效扩散变换器的旋转定位低秩线性注意力
Abstract
Diffusion Transformers (DiTs) achieve strong video generation quality, but their dense spatiotemporal self-attention scales quadratically with sequence length and quickly becomes the dominant inference bottleneck. Sparse low-rank hybrids alleviate this cost by combining a local sparse branch with a global compressed branch. In video DiTs equipped with 3D Rotary Position Embeddings (RoPE), the global branch faces a structural compatibility issue: when RoPE is applied before a nonlinear feature map, the rotation and nonlinearity generally do not commute, making it difficult to keep a query-independent linear summary while preserving relative rotary geometry. Existing work often sidesteps this issue by replacing genuine cross-token global aggregation with coordinate-conditioned surrogates or learnable absolute positional modules. These compromises can be effective, but they approximate relative decay from absolute coordinates and introduce extra positional parameters. We propose \textbf{RoLA}, a rotary-positioned low-rank linear-attention branch that keeps genuine cross-token aggregation while remaining compatible with a reusable linear summary. The design applies RoPE \emph{outside} the nonlinear low-rank feature map and reuses a truncated subset of the pre-trained rotary schedule matched to the low-rank bottleneck. This yields a linear-time low-rank global branch with relative positional behavior by design and no additional positional parameters; the full sparse--low-rank module still includes the fixed-sparsity sparse branch. Experiments on open-source video DiTs show that the resulting method remains competitive in generation quality at 90\% sparsity while achieving 2.63$\times$ end-to-end inference speedup on Wan2.1-14B (720p, 81 frames, measured on an NVIDIA H100 GPU).
Chinese Translation
扩散变换器（DiTs）在视频生成质量上表现出色，但其密集的时空自注意力随着序列长度的增加呈二次增长，迅速成为推理的主要瓶颈。稀疏低秩混合体通过将局部稀疏分支与全局压缩分支相结合来缓解这一成本。在配备3D旋转位置嵌入（RoPE）的视频DiTs中，全局分支面临结构兼容性问题：当RoPE在非线性特征图之前应用时，旋转和非线性通常不满足交换律，这使得在保持相对旋转几何的同时，难以保持与查询无关的线性摘要。现有工作通常通过用坐标条件的替代品或可学习的绝对位置模块替换真实的跨令牌全局聚合来规避这一问题。这些折衷方案可能有效，但它们通过绝对坐标近似相对衰减，并引入额外的位置信息参数。我们提出了 extbf{RoLA}，一种旋转定位低秩线性注意力分支，能够保持真实的跨令牌聚合，同时与可重用的线性摘要兼容。该设计在非线性低秩特征图 extit{外部}应用RoPE，并重用与低秩瓶颈匹配的预训练旋转调度的截断子集。这种设计产生了一个线性时间的低秩全局分支，具有相对位置行为且不需要额外的位置信息参数；完整的稀疏低秩模块仍然包括固定稀疏的稀疏分支。在开源视频DiTs上的实验表明，该方法在90 ext{%}稀疏下仍能保持竞争力的生成质量，同时在Wan2.1-14B（720p，81帧，测量于NVIDIA H100 GPU）上实现了2.63$ imes$的端到端推理加速。
cs.CV / 114 / 2609.06721
Companion-style QA Assistance in Ego-Vision
伴侣风格的自我视觉问答辅助
Abstract
AI companions are envisioned as always-on assistants that support users in daily life. With this regard, we introduce BuddyVQA, a benchmark for companion-style question answering (QA) on egocentric streaming video. BuddyVQA contains 21.6K questions linked to 6K highlight moments across 1,012 long, egocentric videos. It features two key characteristics that are common in daily first-person QA assistance but are largely overlooked in existing VideoQA benchmarks: ego-deictic expressions and interactively chained questions (e.g., "Where is it?", "How to get there?"). These require models to infer a user's in-situation intent by resolving visual pronouns in the context of egocentric visual and QA contents, with both grounded in a long-form streaming setting. To tackle the challenges, we propose MyBuddy, a companion-style QA assistant that highlights a multimodal chain-of-thought reasoning mechanism to infer the final answer based on the historical QA and visual content. An additional question filter and multi-level memory are designed to facilitate efficient QA and visual information retrieval under streaming QA settings. Experiments show that MyBuddy significantly enhances the performance of foundation models on BuddyVQA. Moreover, these gains generalize to other streaming and common video QA benchmarks, demonstrating the applicability and effectiveness of our approach. Our code and dataset are available at https://github.com/QHUni/BuddyVQA
Chinese Translation
人工智能伴侣被设想为始终在线的助手，支持用户的日常生活。在此背景下，我们引入了BuddyVQA，这是一个针对自我中心流媒体视频的伴侣风格问答（QA）基准。BuddyVQA包含21.6K个问题，关联到1,012个长篇自我中心视频中的6K个精彩时刻。它具有两个在日常第一人称问答辅助中常见但在现有视频问答基准中被大大忽视的关键特征：自我指示表达和互动链式问题（例如，“它在哪里？”，“怎么去那里？”）。这些特征要求模型通过在自我中心视觉和问答内容的上下文中解析视觉代词，推断用户在特定情境下的意图，且两者都基于长形式流媒体设置。为了解决这些挑战，我们提出了MyBuddy，一个伴侣风格的问答助手，强调多模态链式推理机制，以基于历史问答和视觉内容推断最终答案。额外的问题过滤器和多层次记忆被设计用于在流媒体问答设置下促进高效的问答和视觉信息检索。实验表明，MyBuddy显著提升了基础模型在BuddyVQA上的表现。此外，这些提升在其他流媒体和常见视频问答基准上也具有普遍性，证明了我们方法的适用性和有效性。我们的代码和数据集可在https://github.com/QHUni/BuddyVQA获取。
cs.CV / 115 / 2609.06723
ADELE - Adaptive Delaunay Grids for High-Fidelity Mesh-Native Reconstruction
ADELE - 自适应德劳内网格用于高保真网格原生重建
Abstract
Meshes remain the most practical representation for geometry reasoning and integration into graphics pipelines, yet existing reconstruction methods struggle to produce high-quality meshes. Most state-of-the-art approaches initially learn an intermediate representation (NeRF/3DGS) and treat mesh extraction as a post-processing step, which often leads to oversmoothed surfaces or poor quality meshes with excessive triangle counts.Existing mesh-native optimization methods alleviate some of these issues but suffer from fixed-resolution discretizations and unstable optimization behavior. In this paper, we introduce an adaptive mesh-based optimization framework and a practical mesh rendering technique to address these challenges. Our representation combines an optimizable Delaunay-triangulated tetrahedral grid with a multi-resolution hash grid. The former is refined through point pruning and insertion, while the latter provides latent features for SDF/appearance value predictions. We use volumetric rendering to bootstrap a coarse geometry while leveraging mesh-based rendering for recovering fine-grained details. Additionally, we propose a differentiable, rasterization-based depth-offset rendering formulation, reducing geometric artifacts and improving reconstruction quality. Our method significantly outperforms existing mesh optimization approaches across a variety of object-centric benchmarks while being competitive with state-of-the-art NeRF/3DGS methods.
Chinese Translation
网格仍然是几何推理和图形管道集成的最实用表示方式，但现有的重建方法在生成高质量网格方面存在困难。大多数最先进的方法最初学习一个中间表示（NeRF/3DGS），并将网格提取视为后处理步骤，这往往导致表面过于平滑或网格质量差且三角形数量过多。现有的网格原生优化方法缓解了部分这些问题，但受到固定分辨率离散化和不稳定优化行为的影响。在本文中，我们提出了一种自适应网格优化框架和一种实用的网格渲染技术，以应对这些挑战。我们的表示结合了可优化的德劳内三角剖分四面体网格和多分辨率哈希网格。前者通过点修剪和插入进行细化，而后者提供用于SDF/外观值预测的潜在特征。我们使用体积渲染来引导粗糙几何体，同时利用基于网格的渲染来恢复细粒度细节。此外，我们提出了一种可微分的基于光栅化的深度偏移渲染公式，减少几何伪影并提高重建质量。我们的方法在各种以对象为中心的基准测试中显著优于现有的网格优化方法，同时与最先进的NeRF/3DGS方法具有竞争力。
cs.CV / 116 / 2609.06726
Back to the Feature: Zero-Shot 6DoF Pose Estimation via Dense Local Features
回归特征：通过密集局部特征进行零-shot 6DoF 位姿估计
Abstract
We present B2TFPose, a training-free zero-shot method for 6DoF pose estimation of unseen objects from RGB images. Using a single frozen DINOv3 vision transformer as its only pretrained component within the pose estimation pipeline, B2TFPose extracts dense patch-level features that generalize across the synthetic-to-real domain gap without any task-specific fine-tuning, revisiting the classical local feature matching paradigm through the lens of large-scale self-supervised foundation models. Three contributions advance the training-free state of the art. A geodesic non-maximum suppression strategy retrieves a viewpoint-diverse template set for coarse-to-fine correspondence matching. Render-guided Re-Correspondence (RRC) synthesizes object-specific views at the estimated pose and re-establishes dense 2D-3D correspondences to sharpen the initial estimate without additional learned parameters. A multi-mask hypothesis selection strategy jointly scores competing segmentation candidates to resolve segmentation ambiguity. On the seven core datasets of the BOP Benchmark, B2TFPose achieves 40.7 mean AR without refinement and 56.4 with refinement, establishing state-of-the-art performance among training-free RGB methods and outperforming trained counterparts including GigaPose and GenFlow, at competitive inference speed.
Chinese Translation
我们提出了 B2TFPose，这是一种无训练的零-shot 方法，用于从 RGB 图像中对未见物体进行 6DoF 位姿估计。B2TFPose 在位姿估计流程中仅使用一个冻结的 DINOv3 视觉变换器作为其唯一的预训练组件，提取出密集的补丁级特征，这些特征能够在合成与真实域之间的差距中进行泛化，而无需任何特定任务的微调，从大型自监督基础模型的视角重新审视经典的局部特征匹配范式。我们提出的三项贡献推动了无训练的最新技术水平。一个测地线非极大值抑制策略检索出一个视角多样的模板集，以进行粗到细的对应匹配。渲染引导的重新对应（RRC）在估计的位姿下合成特定于物体的视图，并重新建立密集的 2D-3D 对应关系，以在没有额外学习参数的情况下提高初始估计的准确性。一个多掩码假设选择策略共同评分竞争的分割候选，以解决分割歧义。在 BOP 基准的七个核心数据集上，B2TFPose 在没有细化的情况下达到了 40.7 的平均 AR，经过细化后达到了 56.4，确立了在无训练 RGB 方法中的最新性能，并超越了包括 GigaPose 和 GenFlow 在内的训练模型，同时保持竞争性的推理速度。
cs.CV / 117 / 2609.06729
Uni-Light: An Ultra-Lightweight Framework via Uncertainty-Aware Knowledge Distillation for Brain Tumour Segmentation
Uni-Light：一种基于不确定性感知知识蒸馏的超轻量级框架用于脑肿瘤分割
Abstract
Accurate 3D brain tumour segmentation from multi-modal Magnetic Resonance Imaging (MRI) is essential for clinical diagnosis and treatment planning. Existing brain tumour segmentation methods often suffer from heavy computational demands, while current lightweight architectures frequently lack the capacity to maintain segmentation fidelity in complex tumour regions. To address these issues, we propose a novel ultra-lightweight framework (Uni-Light) that achieves high-fidelity segmentation with substantially reduced computational overhead. It combines multi-scale convolutions with an uncertainty-aware knowledge distillation scheme that directs the student model toward hard-to-classify regions, complemented by a Signed Distance Field boundary loss for geometric constraints. Experimental results on BraTS2023-GLI and MSD-BTS datasets demonstrate that Uni-Light reduces parameters by 97.56%, floating-point operations (FLOPs) by 73.03%, and inference memory footprint by 81.58%, while surpassing the state-of-the-art model by an average of 1.47% in Dice score, offering a highly competitive trade-off between segmentation accuracy and computational efficiency in resource-constrained clinical settings. This work also advances data engineering for medical imaging by demonstrating that teacher model uncertainty can be exploited as a data-driven supervisory signal, re-prioritising the training data distribution without requiring additional annotation.
Chinese Translation
从多模态磁共振成像（MRI）中准确进行三维脑肿瘤分割对临床诊断和治疗规划至关重要。现有的脑肿瘤分割方法往往面临较高的计算需求，而当前的轻量级架构在复杂肿瘤区域的分割保真度方面常常缺乏能力。为了解决这些问题，我们提出了一种新颖的超轻量级框架（Uni-Light），该框架在显著降低计算开销的同时实现了高保真度的分割。它结合了多尺度卷积和一种不确定性感知知识蒸馏方案，指导学生模型聚焦于难以分类的区域，并辅以带符号距离场边界损失以提供几何约束。在BraTS2023-GLI和MSD-BTS数据集上的实验结果表明，Uni-Light将参数减少了97.56%，浮点运算（FLOPs）减少了73.03%，推理内存占用减少了81.58%，同时在Dice分数上超越了最先进的模型平均1.47%，在资源受限的临床环境中提供了分割准确性与计算效率之间的高度竞争性权衡。本研究还通过展示教师模型的不确定性可以作为数据驱动的监督信号，从而重新优先考虑训练数据分布而无需额外注释，推动了医学成像的数据工程发展。
cs.CV / 118 / 2609.06733
LASSNet: Level-Aware Availability-Conditioned Spatial-Semantic Fusion for Brain Tumor Segmentation with Missing MRI Modalities
LASSNet：基于层级感知和可用性条件的空间-语义融合用于缺失MRI模态的脑肿瘤分割
Abstract
Brain tumor segmentation from multimodal MRI relies on complementary evidence across four imaging sequences, yet one or more modalities may be unavailable because of acquisition cost, protocol variation, scan failure, or patient condition. Existing work has explored reconstruction, knowledge transfer, and direct feature fusion, but leaves open whether missing-modality fusion should change with representation level. High-resolution lateral features retain spatial detail, whereas compressed bottleneck features encode semantic and inter-modality context. We therefore hypothesize that fusion should be conditioned jointly on modality availability and feature hierarchy. We propose the Level-Aware Availability-Conditioned Spatial-Semantic Fusion Network (LASSNet), which contains two level-specialized modules. Hierarchical Availability-Conditioned Fusion (HACF) constructs four lateral representations using count-normalized aggregation of available modalities, mask-conditioned channel modulation, and local 3D refinement. Tri-Scale Relational-Spatial Fusion (TriRSF) models relations among available modality descriptors and spatial context across multiple bottleneck resolutions, followed by cross-scale aggregation and availability-conditioned global spatial attention. A shared coarse-to-fine decoder starts from TriRSF semantics and progressively injects HACF features, without reconstructing missing inputs. Across all 15 non-empty modality configurations, LASSNet obtains mean Dice scores of 76.7% and 83.2% over WT, TC, and ET on BraTS2019 and BraTS2023, respectively.
Chinese Translation
脑肿瘤从多模态MRI的分割依赖于四个成像序列之间的互补证据，然而由于获取成本、协议变更、扫描失败或患者状况等原因，可能会缺失一个或多个模态。现有研究探讨了重建、知识迁移和直接特征融合，但尚未明确缺失模态的融合是否应随表示层级的变化而变化。高分辨率的侧面特征保留了空间细节，而压缩的瓶颈特征则编码了语义和模态间的上下文。因此，我们假设融合应同时依赖于模态的可用性和特征的层级结构。我们提出了层级感知可用性条件的空间-语义融合网络（LASSNet），该网络包含两个层级专用模块。层级可用性条件融合（HACF）通过对可用模态的计数归一化聚合、掩膜条件通道调制和局部3D精细化构建四个侧面表示。三尺度关系-空间融合（TriRSF）建模可用模态描述符与多个瓶颈分辨率间的空间上下文关系，随后进行跨尺度聚合和可用性条件的全局空间注意力。一个共享的粗到细解码器从TriRSF语义开始，逐步注入HACF特征，而不重建缺失的输入。在所有15种非空模态配置中，LASSNet在BraTS2019和BraTS2023上分别获得了76.7%和83.2%的平均Dice分数，针对WT、TC和ET。
cs.CV / 119 / 2609.06758
Agentic Visual Generation: From Generative Models to Agentic Control
代理视觉生成：从生成模型到代理控制
Abstract
Visual generation is evolving from generative models used through a single invocation into agentic control processes that can plan, select tools, inspect intermediate synthesized outputs, revise failures, and reuse prior experience. In most existing systems, the controller is an LLM or VLM, while visual generation models serve as tools or executors. However, existing work lacks a consistent criterion for determining when a generation system becomes agentic. Planning depth, tool use, multi-role collaboration, and reinforcement learning are often treated as evidence of agenticity, even though none of them necessarily determines which generation decisions the controller can make. We organize the field according to what the controller can directly control in the generation process. At L1 Conditioning Control, the controller prepares the input to a predetermined generator but does not control which visual operation is executed. At L2 Execution Control, it selects and invokes actual generation, editing, rendering, or other content-modifying operations. At L3 Outcome-Adaptive Control, it observes an intermediate outcome and uses that observation to change a subsequent operation within the current task. At L4 Experience-Adaptive Control, it retains experience from completed tasks and uses that experience to change decisions on future tasks. L0 Fixed Support separately denotes generators, editors, evaluators, reward models, benchmarks, and fixed pipelines without a deployed controller that makes generation-level decisions. These levels describe a progressively broader decision-making scope rather than model size, system complexity, output quality, tool or role count, or training method. Applying this framework across image, video, editing, 3D, world, slide, and user-interface generation reveals how controller capabilities have evolved and how their mechanisms are distributed across levels.
Chinese Translation
视觉生成正从通过单次调用使用的生成模型演变为能够进行规划、选择工具、检查中间合成输出、修正失败和重用先前经验的代理控制过程。在大多数现有系统中，控制器是大型语言模型（LLM）或视觉语言模型（VLM），而视觉生成模型则充当工具或执行者。然而，现有研究缺乏一致的标准来确定何时生成系统变得具有代理性。规划深度、工具使用、多角色协作和强化学习通常被视为代理性的证据，尽管它们并不一定决定控制器可以做出的生成决策。我们根据控制器在生成过程中的直接控制能力对该领域进行组织。在L1条件控制（L1 Conditioning Control）中，控制器准备输入给预定生成器，但不控制执行哪个视觉操作。在L2执行控制（L2 Execution Control）中，控制器选择并调用实际的生成、编辑、渲染或其他内容修改操作。在L3结果适应控制（L3 Outcome-Adaptive Control）中，控制器观察中间结果，并利用该观察来改变当前任务中的后续操作。在L4经验适应控制（L4 Experience-Adaptive Control）中，控制器保留已完成任务的经验，并利用该经验来改变未来任务的决策。L0固定支持（L0 Fixed Support）则单独表示生成器、编辑器、评估器、奖励模型、基准和没有部署控制器的固定流程，这些控制器不做生成级别的决策。这些级别描述了逐渐扩大的决策范围，而不是模型规模、系统复杂性、输出质量、工具或角色数量或训练方法。将这一框架应用于图像、视频、编辑、3D、世界、幻灯片和用户界面生成，揭示了控制器能力的演变及其机制在各级别的分布情况。
cs.CV / 120 / 2609.06785
Unified Multi-Layer Subspace Modeling for Cross-Domain OOD Detection
跨域OOD检测的统一多层子空间建模
Abstract
Out-of-Distribution (OOD) detection remains a fundamental challenge for neural networks, whose predictions can be overconfident on inputs that deviate from the training distribution. Most post-hoc OOD detection methods derive scores from a single representation level (eg., logits or penultimate features) or combine multiple layers via depth selection or OOD-calibrated weighting. However, because OOD shifts are diverse, the most informative representation level can vary strongly across OOD types and domains, making fixed-layer choices and OOD-calibrated aggregation brittle. In this paper, we propose PRISM (Projected Representation with Intermediate-layer Subspace Modeling), a model-agnostic post-hoc OOD detection method that models a unified multi-layer feature representation rather than aggregating independently scored layers. PRISM fuses intermediate and deep features into a single hierarchical embedding, estimates an in-distribution (ID) principal subspace, and then combines two complementary signals: (i) a class-conditional Mahalanobis distance in the projected subspace and (ii) the residual energy orthogonal to the learned manifold. This simple design avoids OOD-tuned layer weighting while capturing both in-subspace semantic deviations and off-subspace anomalies. Across diverse benchmarks spanning natural images, medical imaging, and industrial visual inspection, PRISM achieves consistent state-of-the-art cross-domain OOD detection performance with a single default configuration across all evaluated domains and architectures. We further show that PRISM incurs minimal inference overhead, making it practical for real-world deployment.
Chinese Translation
分布外（OOD）检测仍然是神经网络面临的一个基本挑战，因为其对偏离训练分布的输入的预测可能过于自信。大多数后处理的OOD检测方法从单一表示层（例如，logits或倒数第二层特征）导出分数，或通过深度选择或OOD校准加权组合多个层。然而，由于OOD转变的多样性，最具信息性的表示层在不同的OOD类型和领域之间可能变化很大，这使得固定层选择和OOD校准聚合变得脆弱。在本文中，我们提出了PRISM（Projected Representation with Intermediate-layer Subspace Modeling），这是一种模型无关的后处理OOD检测方法，它建模统一的多层特征表示，而不是独立评分层的聚合。PRISM将中间特征和深层特征融合为一个单一的层次嵌入，估计一个在分布内（ID）的主子空间，然后结合两个互补信号：（i）在投影子空间中的类条件马哈拉诺比斯距离，以及（ii）与学习到的流形正交的残余能量。这一简单设计避免了OOD调优的层加权，同时捕捉了子空间内的语义偏差和子空间外的异常。在涵盖自然图像、医学成像和工业视觉检测的多样基准测试中，PRISM在所有评估领域和架构中以单一默认配置实现了一致的最先进的跨域OOD检测性能。我们进一步表明，PRISM引入的推理开销最小，使其在实际应用中具有可行性。
cs.CV / 121 / 2609.06788
When Speech Meets Lips: Interpretable Audio-Visual Synchronization for L2 Pronunciation Assessment
当语音遇上唇部：用于第二语言发音评估的可解释音视频同步
Abstract
Automatic Pronunciation Assessment (APA) systems have achieved strong performance with transformer-based models and self-supervised speech representations. However, most methods rely only on acoustic signals and overlook temporal synchronization between speech and articulatory movements, limiting diagnostic feedback on timing mismatches important for L2 pronunciation training. We propose an interpretable audio-visual synchronization framework that explicitly models speech-lip temporal alignment through feature encoding, cross-attention fusion, lag estimation, stability quantification, and visualization. The framework introduces frame-level lag trajectories and a Lag Stability Index (LSI) to quantify synchronization robustness. We also interviewed 30 participants, including 10 instructors and 20 students with diverse first-language backgrounds, to assess its effectiveness. By transforming implicit alignment into interpretable representations, the framework connects automatic scoring with actionable Computer-Aided Pronunciation Training feedback. Datasets and supplemental materials are available at https://www.robots.ox.ac.uk/~vgg/data/lip_reading/.
Chinese Translation
自动发音评估（APA）系统在基于变换器的模型和自监督语音表示方面取得了良好的性能。然而，大多数方法仅依赖于声学信号，忽视了语音与发音运动之间的时间同步，这限制了对第二语言发音训练中重要的时序不匹配的诊断反馈。我们提出了一种可解释的音视频同步框架，通过特征编码、交叉注意力融合、延迟估计、稳定性量化和可视化，明确建模语音与唇部的时间对齐。该框架引入了帧级延迟轨迹和延迟稳定性指数（Lag Stability Index, LSI）来量化同步的稳健性。我们还采访了30名参与者，包括10名教师和20名具有不同母语背景的学生，以评估其有效性。通过将隐式对齐转化为可解释的表示，该框架将自动评分与可操作的计算机辅助发音训练反馈相连接。数据集和补充材料可在 https://www.robots.ox.ac.uk/~vgg/data/lip_reading/ 获取。
cs.CV / 122 / 2609.06807
Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI
结构性脑MRI中AI模型的解剖特征与学习特征的比较研究
Abstract
In this work, we comprehensively evaluate three popular feature-extraction paradigms in AI-based neuroimaging modeling: (1) computation of anatomical surfaces and volumes, (2) supervised learning with convolutional neural networks (CNNs), and (3) unsupervised pretraining of vision transformer (ViT) foundation models, followed by supervised finetuning. Our study is based on 18 publicly available datasets containing 3D structural T1-weighted MRI scans from approximately 80,000 participants across seven distinct clinical tasks. We observe that a linear model based on anatomical features matches the diagnostic performance of complex nonlinear features learned by sophisticated AI frameworks, including foundation models trained on thousands of scans. Conversely, CNNs and pretrained ViTs learn features that implicitly capture relevant anatomical information, bypassing the need for explicit feature extraction. Building upon these insights, we propose Anatomy Segmentation Pretraining (ASP), a novel method to incorporate anatomical information during foundation-model pretraining, which outperforms existing models in biological age estimation.
Chinese Translation
在本研究中，我们全面评估了三种流行的特征提取范式在基于AI的神经影像建模中的应用：（1）解剖表面和体积的计算，（2）使用卷积神经网络（CNN）的监督学习，以及（3）视觉变换器（ViT）基础模型的无监督预训练，随后进行监督微调。我们的研究基于18个公开可用的数据集，这些数据集包含来自约80,000名参与者的3D结构T1加权MRI扫描，涵盖七个不同的临床任务。我们观察到，基于解剖特征的线性模型在诊断性能上与由复杂的AI框架（包括在数千个扫描上训练的基础模型）学习的复杂非线性特征相匹配。相反，CNN和预训练的ViT学习的特征隐式捕捉了相关的解剖信息，绕过了显式特征提取的需要。在此基础上，我们提出了解剖分割预训练（Anatomy Segmentation Pretraining, ASP），这是一种在基础模型预训练期间融入解剖信息的新方法，能够在生物年龄估计中超越现有模型的表现。
cs.CV / 123 / 2609.06809
Disparity Has a Sign: Stereo Matching Beyond the Zero-Disparity Plane
视差有其意义：超越零视差平面的立体匹配
Abstract
Modern stereo matching models fail when disparity crosses zero, with end-point error (EPE) rising by 4.6-37$\times$. Yet stereoscopic content, from cinema 3D to VR, routinely contains objects behind the zero-disparity plane (ZDP), corresponding to negative disparities. The blind spot cascades through datasets, architectures, and evaluation protocols, all of which inherit the non-negative geometry. Rectified parallel cameras place ZDP at infinity, so every finite depth yields $d=fB/z \ge 0$ by construction, and nothing within the standard pipeline can violate, or even measure, a negative disparity. To measure it, we propose \textit{ZDPShift}, a benchmark of $21{,}495$ stereo pairs from seven cinematographer-authored open movies, each frame rendered at five zero-disparity-plane positions with dense signed ground truth. Six state-of-the-art image and video stereo matching models collapse once the plane moves. On identical scene content, FoundationStereo goes from $2.24$ px EPE to $75.33$px, with every backbone leaving roughly half of all pixels exceeding a three-pixel disparity error. What is missing, however, is not the underlying matching capability. % The capability itself, however, is already present. Training on supervision synthesized from SceneFlow, which adds no new data or parameters, keeps the error flat across the signed range. Training only the decoder, with the pretrained matching features frozen, performs comparably across all six backbones, with EPE jittering within $0.2$px. Thus, the pretrained features already extend to the negative regime they were never trained on, and only the output convention discarded it. Meanwhile, positive-regime accuracy on KITTI, Middlebury, ETH3D, and Sintel is largely preserved.
Chinese Translation
现代立体匹配模型在视差跨越零点时表现不佳，端点误差（EPE）增加了4.6-37倍。然而，立体内容，从影院3D到虚拟现实，通常包含位于零视差平面（ZDP）后面的物体，这对应于负视差。这个盲点在数据集、架构和评估协议中层层叠加，所有这些都继承了非负几何。经过校正的平行相机将ZDP置于无穷远，因此每个有限深度的构造都导致$d=fB/z
ge 0$，而标准流程中的任何内容都无法违反或甚至测量负视差。为了测量负视差，我们提出了 extit{ZDPShift}，这是一个包含来自七部电影摄影师创作的开放电影的21,495对立体图像的基准，每帧在五个零视差平面位置上渲染，并提供密集的带符号真实值。六个最先进的图像和视频立体匹配模型在平面移动时崩溃。在相同场景内容下，FoundationStereo的EPE从2.24 px飙升至75.33 px，每个骨干网络大约有一半的像素超过三像素的视差误差。然而，缺失的并不是基础的匹配能力。% 然而，这种能力本身已经存在。基于SceneFlow合成的监督训练，没有添加新的数据或参数，使得在带符号范围内的误差保持平稳。仅训练解码器，同时冻结预训练的匹配特征，在所有六个骨干网络中表现相当，EPE波动在0.2 px以内。因此，预训练特征已经扩展到它们从未接受训练的负区域，只有输出约定将其丢弃。同时，在KITTI、Middlebury、ETH3D和Sintel上的正区域准确性基本得以保留。
cs.CV / 124 / 2609.06823
Generalist Open-World Temporal Perception
通用开放世界时间感知
Abstract
The next generation of artificial intelligence systems will likely be natively temporal and multimodal in both inputs and outputs: able to converse, perceive, predict, reason, and synthesize through a shared world representation. Realizing this requires a temporal perceptual substrate integrating sensory streams, language, and structured outputs within a multimodal world model. We seek a generalist open-world perceptual system that represents biological forms, natural physical structures, and artifacts, and their interactions, as a coherent, temporally persistent process. The model should infer geometry, articulation, semantics, interaction structure, and uncertainty from raw multimodal streams; maintain identity through occlusion and viewpoint change; generalize across species, forms, mechanisms, and materials; and abstain or expand its ontology when encountering the unknown. The objective is a structured world state supporting understanding, prediction, counterfactual reasoning, and controllable synthesis. Recent work suggests that some cross-modal and reasoning-like capabilities can emerge from large-scale generative video pretraining, reminiscent of language-model scaling. Yet these capabilities are often accessed through language probes or expressed through photorealistic video, leaving explicit semantic, geometric, or temporal structure largely unexposed. This paper articulates an alternative and complementary paradigm: perception and synthesis as distinct conditionings within a shared Generalist Open-World Temporal Perception Architecture (GOWTPA). Recognition, structured prediction, and simulation arise as different conditionings of the same generative substrate, while reasoning and embodiment-specific policies build upon the resulting world state. This positions generalist temporal perception as a potential foundation layer for broader multimodal intelligence and physical AI.
Chinese Translation
下一代人工智能系统可能在输入和输出上都是原生的时间性和多模态的：能够通过共享的世界表征进行对话、感知、预测、推理和综合。实现这一目标需要一个时间感知基础，整合感官流、语言和结构化输出于一个多模态世界模型中。我们寻求一个通用的开放世界感知系统，该系统将生物形态、自然物理结构和人工制品及其相互作用表示为一个连贯的、时间上持续的过程。该模型应从原始多模态流中推断几何、关节、语义、交互结构和不确定性；在遮挡和视角变化中保持身份；在物种、形态、机制和材料之间进行概括；并在遇到未知时保持或扩展其本体。目标是构建一个支持理解、预测、反事实推理和可控综合的结构化世界状态。最近的研究表明，一些跨模态和类似推理的能力可以通过大规模生成视频的预训练而出现，这与语言模型的扩展相似。然而，这些能力通常是通过语言探测器访问或通过逼真的视频表达的，明确的语义、几何或时间结构在很大程度上未被揭示。本文阐述了一种替代且互补的范式：在共享的通用开放世界时间感知架构（Generalist Open-World Temporal Perception Architecture, GOWTPA）中，将感知和综合视为不同的条件。识别、结构化预测和仿真作为同一生成基础的不同条件出现，而推理和特定于体现的策略则建立在生成的世界状态之上。这将通用时间感知定位为更广泛的多模态智能和物理人工智能的潜在基础层。
cs.CV / 125 / 2609.06838
A Shared-Backbone Approach for Multi-Task MedMNIST Classification
一种共享主干的多任务 MedMNIST 分类方法
Abstract
Multi-task biomedical classification requires models to generalize across disparate modalities and class distributions. We study 11 heterogeneous MedMNIST datasets using the harmonic mean of per-task macro-F1. We evaluate three backbones with task-specific linear heads. We identify a resolution domain shift between the MedMNIST API and evaluation environment. Resolving this inconsistency and optimizing architecture-specific regularization substantially improved performance. Our best configuration, a ConvNeXt-Tiny backbone with label smoothing, achieved a leaderboard harmonic-mean macro-F1 of 0.73294 in the Tensor Reloaded: Multi-Task MedMNIST competition, ranking sixth at the close of the official competition phase. Our implementation is publicly available at: https://github.com/GavrilStefan-Dorian/A-Shared-Backbone-Approach-for-Multi-Task-MedMNIST-Classification
Chinese Translation
多任务生物医学分类要求模型能够在不同的模态和类别分布之间进行泛化。我们研究了 11 个异构的 MedMNIST 数据集，使用每个任务的宏 F1 值的调和平均数进行评估。我们评估了三种具有任务特定线性头的主干网络。我们发现 MedMNIST API 与评估环境之间存在分辨率领域的偏移。解决这一不一致性并优化特定于架构的正则化显著提高了性能。我们的最佳配置是一个带有标签平滑的 ConvNeXt-Tiny 主干，在 Tensor Reloaded: Multi-Task MedMNIST 竞赛中达到了 0.73294 的调和平均宏 F1 值，在官方竞赛阶段结束时排名第六。我们的实现已公开发布，链接为：https://github.com/GavrilStefan-Dorian/A-Shared-Backbone-Approach-for-Multi-Task-MedMNIST-Classification
cs.CV / 126 / 2609.06843
RAIDAL: Redundancy-Aware Information Density Active Learning for CTC-Based Continuous Sign Language Recognition
RAIDAL：基于冗余感知的信息密度主动学习用于基于CTC的连续手语识别
Abstract
Continuous sign language recognition (CSLR) is a key technology for accessibility, yet its development remains limited by the high cost of annotating continuous video streams. Active learning offers a path toward mitigating this cost, but standard acquisition functions are not designed for weakly aligned sign language videos, where sign executions are interleaved with rest poses, irregular pauses, sign-like motion, and temporally redundant frames. This temporal redundancy can undermine sample selection, as acquisition scores may be influenced by timesteps from regions that are not associated with the decoded gloss sequence, distorting the video's estimated informativeness. In this work, we show that modern CSLR models already contain a mechanism for identifying gloss-level temporal evidence: the CTC decoder. Although typically used only during inference, its alignment peaks indicate where the model localizes each predicted gloss in the feature sequence, providing a source of temporal structure for active learning acquisition functions at zero additional labeling cost. Thus, we introduce RAIDAL (Redundancy-Aware Information Density Active Learning), which repurposes the CTC decoder to restrict representation-based scoring to decoder-aligned gloss regions, rather than exposing the acquisition function to the entire unfiltered video. Across three datasets and two architectures, RAIDAL achieves its strongest data-efficiency gains over competing baselines in large-vocabulary, budget-limited settings, while remaining competitive in the smaller-vocabulary, large-budget setting. The code used in this work is publicly available at github.com/verlab/RAIDAL.
Chinese Translation
连续手语识别（CSLR）是提升无障碍技术的关键，但其发展受到连续视频流标注高成本的限制。主动学习为降低这一成本提供了一条途径，但标准的获取函数并未针对弱对齐的手语视频进行设计，这类视频中的手势执行与静止姿势、不规则停顿、类手势运动以及时间冗余帧交织在一起。这种时间冗余可能会削弱样本选择，因为获取分数可能受到与解码的手势序列无关的区域时间步的影响，从而扭曲视频的估计信息量。在本研究中，我们展示了现代CSLR模型已经包含了一种识别手势级时间证据的机制：CTC解码器。尽管通常仅在推理期间使用，但其对齐峰值指示了模型在特征序列中定位每个预测手势的位置，为主动学习获取函数提供了零额外标注成本的时间结构来源。因此，我们提出了RAIDAL（冗余感知信息密度主动学习），该方法重新利用CTC解码器，将基于表示的评分限制在解码器对齐的手势区域，而不是将获取函数暴露于整个未过滤视频。在三个数据集和两种架构中，RAIDAL在大词汇量、预算有限的环境中相较于竞争基线实现了最强的数据效率提升，同时在小词汇量、大预算的环境中保持竞争力。本研究中使用的代码已公开发布于github.com/verlab/RAIDAL。
cs.CV / 127 / 2609.06870
Organization of Valence and Arousal in Vision-Language Representations of Built Environments: Insights from the EMOIS Dataset
建筑环境视觉-语言表征中的情感价值与唤醒的组织：来自EMOIS数据集的见解
Abstract
Visual perception of built environments contributes to the affective impressions that people form in everyday life. However, how these impressions are represented within vision foundation models remains largely unexplored. To support the systematic investigation of this subject, we introduce the Emotional Impression of Spaces (EMOIS) dataset, comprising 1,544 real-world built-environment images. Each image is annotated with image-evoked valence and arousal ratings collected from Japanese adults by conducting a large-scale web-based survey, with approximately 120 ratings per image. Using Contrastive Language--Image Pre-training (CLIP) representations, we perform predictive and geometric analyses to systematically investigate how valence and arousal are encoded and organized within the representation space. These analyses reveal that valence exhibited stronger and more coherent organization than arousal. Cross-dataset analyses with the Open Affective Standardized Image Set (OASIS), a benchmark dataset of general affective photographs, reveal differences in affective organization between the two datasets. Regression analyses demonstrate high predictive performance for valence and arousal within EMOIS, with mean coefficients of determination of 0.865 and 0.807, respectively, across repeated internal hold-out evaluations. Finally, we present an example-based interface illustrating how learned representations can support qualitative interpretation of predicted affective values. These findings can help elucidate affective representations of built environments and establish EMOIS as a densely annotated resource for future affective computing research in this domain.
Chinese Translation
建筑环境的视觉感知对人们在日常生活中形成的情感印象有重要贡献。然而，这些印象在视觉基础模型中的表征方式仍然 largely 未被探索。为了支持对这一主题的系统研究，我们引入了情感空间印象数据集（EMOIS），该数据集包含1,544张真实世界的建筑环境图像。每张图像都附有来自日本成年人通过大规模网络调查收集的图像引发的情感价值和唤醒评分，每张图像约有120个评分。利用对比语言-图像预训练（Contrastive Language--Image Pre-training, CLIP）表征，我们进行预测和几何分析，以系统地研究情感价值和唤醒在表征空间中的编码和组织。这些分析揭示，情感价值的组织比唤醒更强且更连贯。与开放情感标准化图像集（Open Affective Standardized Image Set, OASIS）进行的跨数据集分析显示了两个数据集之间情感组织的差异。回归分析表明，在EMOIS中，情感价值和唤醒的预测性能较高，重复内部保留评估的平均决定系数分别为0.865和0.807。最后，我们展示了一个基于示例的接口，说明学习到的表征如何支持对预测情感值的定性解释。这些发现有助于阐明建筑环境的情感表征，并将EMOIS建立为未来该领域情感计算研究的密集注释资源。
cs.CV / 128 / 2609.06876
Novel Methods for Catheter and Guidewire Segmentation in X-ray Fluoroscopy under a Federated Learning Setting
在联邦学习环境下的导管和导丝分割新方法
Abstract
Endovascular procedures rely on real-time manipulation of thin instruments, catheters and guidewires, under X-ray fluoroscopy guidance, where accurate visual analysis is essential for procedural safety. Learning-based methods are constrained by structural complexity, data scarcity, and privacy regulations precluding centralised training across institutions. This thesis presents a structure-aware federated learning framework for catheter and guidewire analysis, with four contributions evaluated on real-animal and phantom data. A benchmark dataset, CathAction, is introduced for catheterisation analysis, with over 600,000 annotated frames and 40,000 segmentation masks. A shape-sensitive loss transforms masks into signed distance maps compared in a structural feature space, improving Dice coefficient by up to 2.9 points across five backbones. This is extended to federated learning with shape-sensitive loss, preserving geometric consistency under heterogeneous client data and outperforming federated averaging by up to three points in mean intersection-over-union as clients scale from four to eight. Federated learning with projected gradient descent adds adversarial optimisation, raising mean intersection-over-union by over ten points on real-animal data. Finally, a structure-aware diffusion framework synthesises catheter and guidewire video sequences, combining structural supervision with a domain-adaptive reconstruction objective, reducing Frechet video distance over a strong baseline while maintaining visual fidelity. Incorporating synthetic sequences into federated training raises the Dice score from 44 to 51 percent under data scarcity, with gains across four held-out sites. Together, these contributions advance privacy-preserving catheter and guidewire analysis, supporting collaborative training without centralising patient data or large amounts of manual annotation.
Chinese Translation
血管内手术依赖于在X射线透视引导下对细长仪器、导管和导丝的实时操作，其中准确的视觉分析对手术安全至关重要。基于学习的方法受到结构复杂性、数据稀缺性和隐私法规的限制，无法在机构之间进行集中训练。本论文提出了一种结构感知的联邦学习框架用于导管和导丝分析，包含四项在真实动物和模型数据上评估的贡献。我们引入了一个基准数据集CathAction用于导管插入分析，包含超过600,000帧标注和40,000个分割掩码。形状敏感损失将掩码转换为在结构特征空间中比较的有符号距离图，在五个基础模型上提高了Dice系数最多2.9点。这一方法扩展到联邦学习中，采用形状敏感损失，在异构客户端数据下保持几何一致性，并在客户端数量从四个增加到八个时，平均交并比提高了最多三点。使用投影梯度下降的联邦学习增加了对抗优化，使真实动物数据上的平均交并比提高了超过十点。最后，结构感知扩散框架合成导管和导丝的视频序列，将结构监督与领域自适应重建目标相结合，在保持视觉保真度的同时，将Frechet视频距离降低到强基线以下。在数据稀缺的情况下，将合成序列纳入联邦训练使Dice得分从44%提高到51%，在四个保留站点上均有提升。这些贡献共同推动了隐私保护的导管和导丝分析，支持在不集中患者数据或大量手动标注的情况下进行协作训练。
cs.CV / 129 / 2609.06880
Contextual Observer Grounding: Evaluating Situated Spatial Reasoning in Vision-Language Models
上下文观察者定位：评估视觉-语言模型中的情境空间推理
Abstract
Reasoning over language instructions in embodied tasks such as robotics often requires understanding spatial relations from a speaker's situated perspective. Humans infer such perspectives from shared environmental knowledge, activity context, and commonsense. Recent vision-language models (VLMs) appear capable of spatial reasoning, but their ability to infer a speaker's viewpoint from contextual cues and interpret situated spatial relations from that viewpoint remains unclear. We call this capability contextual observer grounding. To study this capability, we construct the Point-of-View Benchmark (POVBench), a dataset of 3D scenes and queries that disentangles Inferred, Stated, and Given forms of observer grounding in natural embodied communication. Given multi-view observations and a natural-language sentence, models must localize unseen or underspecified targets from situated spatial and contextual cues. Across state-of-the-art VLMs, localizing targets from directional language remains challenging, even when observer grounding is made explicit. We find that explicit breakdowns of observer-relative spatial reasoning improve target localization. Our project page is available at https://mimo-owl.github.io/POVBench/.
Chinese Translation
在机器人等具身任务中，对语言指令的推理通常需要从说话者的情境视角理解空间关系。人类通过共享的环境知识、活动背景和常识来推断这种视角。最近的视觉-语言模型（VLMs）似乎具备空间推理能力，但它们从上下文线索推断说话者视角并从该视角解释情境空间关系的能力仍不明确。我们将这种能力称为上下文观察者定位。为了研究这一能力，我们构建了视角基准（Point-of-View Benchmark，POVBench），这是一个包含3D场景和查询的数据集，旨在解构自然具身交流中的推断、陈述和给定形式的观察者定位。在给定多视角观察和自然语言句子的情况下，模型必须从情境空间和上下文线索中定位未见或未明确的目标。在最先进的VLM中，从方向性语言中定位目标仍然具有挑战性，即使观察者定位被明确指出。我们发现，明确的观察者相对空间推理分解能改善目标定位。我们的项目页面可访问：https://mimo-owl.github.io/POVBench/。
cs.CV / 130 / 2609.06922
BEFORE THE FLIP: Measuring Hidden Score Shifts In Quantized Vision Language Models Before The Answer Changes for Visual Question Answering
翻转之前：在视觉问答中测量量化视觉语言模型在答案变化之前的隐藏得分变化
Abstract
Quantization makes vision language models (VLMs) cheaper to store and run by using fewer bits to represent their weights. While unchanged answers on visual question answering (VQA) after compression are an expected behavior, they can still hide changes in the underlying scores (log probabilities). For example, a model may still answer yes after compression, even as the score gap between yes and no shrinks. We introduce BEFORE THE FLIP to measure these hidden changes. Our method compares the score change caused by compression with the change caused by replacing the image's internal representations, or image tokens, with one fixed average token. We then increase the precision of one weight group at a time to identify where extra bits help, and test whether choosing different groups for each question offers benefits beyond shuffled controls. Among 8,277 LLaVA questions where image token replacement measurably affects the scores, 4-bit compression shifts the yes or no score gap farther toward the replacement output than 8-bit compression. Qwen shows the same pattern, but with a smaller difference. Yet only 265 of 9,000 LLaVA answers change at 4 bits. In a separate study of 1,024 calibration questions, choosing weight groups separately for each question does not outperform both shuffled controls at any tested storage budget. These findings show that compression can alter the scores behind unchanged answers, but do not establish a reliable benefit from adjusting precision for each question.
Chinese Translation
量化通过使用更少的位数表示权重，使视觉语言模型（VLMs）在存储和运行上更为经济。虽然在压缩后视觉问答（VQA）中答案不变是一种预期行为，但这仍然可能隐藏了潜在得分（对数概率）的变化。例如，模型在压缩后仍可能回答“是”，即使“是”和“否”之间的得分差距缩小。我们提出了“翻转之前”（BEFORE THE FLIP）来测量这些隐藏的变化。我们的方法将压缩引起的得分变化与将图像的内部表示或图像标记替换为一个固定平均标记所引起的变化进行比较。然后，我们逐一增加一个权重组的精度，以识别额外位数的帮助之处，并测试为每个问题选择不同组是否在随机控制组之外提供了额外的好处。在8,277个LLaVA问题中，图像标记替换显著影响得分，4位压缩将“是”或“否”的得分差距向替换输出进一步移动，相比之下8位压缩的效果较小。Qwen显示出相同的模式，但差异较小。然而，在9,000个LLaVA答案中，只有265个在4位压缩下发生变化。在对1,024个校准问题的单独研究中，为每个问题单独选择权重组在任何测试的存储预算下都未能超越随机控制组。这些发现表明，压缩可以改变不变答案背后的得分，但并未确立为每个问题调整精度的可靠好处。
cs.CV / 131 / 2609.06928
Emo-DVS: A Multimodal Benchmark for Privacy-Aware Emotion Recognition with Event Cameras
Emo-DVS：一种基于事件摄像头的隐私意识情感识别多模态基准
Abstract
Emotion analysis is a fundamental task in computer vision, but its practical deployment remains constrained by the privacy risks inherent to conventional RGB cameras. Bio-inspired event cameras present a promising hardware-level solution because they capture asynchronous brightness changes, thereby reducing exposure of facial identity details while leveraging high dynamic range for robust perception under challenging illumination conditions. Despite these advantages, existing event-based methods struggle in complex real-world settings due to limited dataset scales, simple acquisition conditions, and reliance on single-modality visual cues. To address these, we establish a challenging tri-modal benchmark with event, audio, and text modalities and propose the Information-Guided Gated Fusion (IGF) framework, which first pre-trains an event encoder on the FAU subset of Emo-DVS to capture fine-grained facial dynamics, then employs adaptive modality gating to suppress modality-specific noise, and finally leverages mutual information maximization to align robust cross-modal representations. To alleviate data scarcity, we introduce Emo-DVS, the first large-scale event-based emotion analysis dataset, which couples dynamic illumination with the Facial Action Unit (FAU) subset and emotion subset. Extensive experiments demonstrate that IGF achieves state-of-the-art performance.
Chinese Translation
情感分析是计算机视觉中的一项基础任务，但其实际应用受到传统RGB摄像头固有的隐私风险的限制。生物启发的事件摄像头提供了一种有前景的硬件级解决方案，因为它们捕捉异步亮度变化，从而减少面部身份细节的曝光，同时利用高动态范围在复杂光照条件下实现稳健感知。尽管具有这些优势，现有的基于事件的方法在复杂的现实环境中仍然面临挑战，原因在于数据集规模有限、采集条件简单以及依赖单一模态的视觉线索。为了解决这些问题，我们建立了一个具有挑战性的三模态基准，结合了事件、音频和文本模态，并提出了信息引导门控融合（Information-Guided Gated Fusion, IGF）框架，该框架首先在Emo-DVS的FAU子集上对事件编码器进行预训练，以捕捉细粒度的面部动态，然后采用自适应模态门控来抑制模态特定的噪声，最后利用互信息最大化来对齐稳健的跨模态表示。为了缓解数据稀缺问题，我们推出了Emo-DVS，这是第一个大规模的基于事件的情感分析数据集，结合了动态光照与面部动作单元（Facial Action Unit, FAU）子集和情感子集。大量实验表明，IGF实现了最先进的性能。
cs.CV / 132 / 2609.06929
Sub-Pixel Affine Registration of Space Debris Images via the Radon Point Spread Function
通过Radon点扩散函数实现空间碎片图像的亚像素仿射配准
Abstract
Inter-frame affine misalignment caused by platform jitter and attitude adjustments poses a fundamental challenge for multi-frame analysis of point targets in optical surveillance. Conventional registration methods rely on spatial intensity correlations or distinctive image features, both of which are largely absent in low-signal-to-noise-ratio point target imagery. We introduce the Radon Point Spread Function (RPSF) to characterize point targets in the Radon-transformed domain, and derive a closed-form framework that jointly estimates inter-frame translation and rotation from as few as four scalar RPSF samples per frame pair. The method requires no iterative optimization, feature extraction or interpolation, which is suitable for resource-constrained onboard processing. Simulation results confirm sub-pixel translation accuracy and a mean rotation error of 0.2556{\deg} at 1{\deg} Radon angular resolution. Validation on five real space debris datasets including both ground-based and in-orbit observations yields a mean calibration error below 0.5 pixels, substantially exceeding the precision required for reliable multi-frame processing.
Chinese Translation
由于平台抖动和姿态调整引起的帧间仿射不对齐对光学监视中点目标的多帧分析构成了根本挑战。传统的配准方法依赖于空间强度相关性或独特的图像特征，而这在低信噪比的点目标图像中往往缺乏。我们引入了Radon点扩散函数（RPSF）来表征Radon变换域中的点目标，并推导出一个闭式框架，该框架可以从每对帧中仅需四个标量RPSF样本共同估计帧间平移和旋转。该方法不需要迭代优化、特征提取或插值，适合资源受限的机载处理。仿真结果确认了亚像素平移精度和在1度Radon角度分辨率下的平均旋转误差为0.2556°。在包括地面和在轨观测的五个实际空间碎片数据集上的验证结果显示，平均校准误差低于0.5个像素，远远超过了可靠的多帧处理所需的精度。
cs.CV / 133 / 2609.06931
CARDEA: Auditable Reasoning Grounded in Spatial Evidence for End-to-End Coronary Angiography Interpretation
CARDEA：基于空间证据的可审计推理用于端到端冠状动脉造影解读
Abstract
Invasive coronary angiography (CAG) is the gold standard for diagnosing coronary artery disease, but interpretation varies substantially among observers. Existing AI systems can improve consistency but lack auditable decision processes and are limited in comprehensive open-ended assessment, undermining clinician trust and clinical adoption readiness. We developed CARDEA, a unified large vision-language model that serves as the inference core of a CAG pipeline. It was trained solely on public datasets and closed-ended tasks in three stages: visual feature alignment, a self-distilled Chain-of-Box (CoB) cold start, and reinforcement learning with verifiable rewards (RLVR) with a CoB reward encouraging bounding-box use in the reasoning trace. We assessed its two study-level diagnoses, dominance classification and complexity assessment, against a dedicated classifier and two interventional cardiologists. Report generation was excluded from training and evaluated zero-shot across stages on an external cohort using vessel-severity macro-$F_1$. CARDEA trailed the classifier on in-distribution dominance but drew level under domain shift (accuracy, 0.91 [95% confidence interval (CI), 0.86 to 0.95]) and was comparable to the cardiologists on complexity assessment (accuracy, 0.90 [CI, 0.82 to 0.97]). Only RLVR improved zero-shot report generation, raising its vessel-severity macro-$F_1$ (0.686 [CI, 0.664 to 0.707]) above the untuned base model (0.513) and over twice the always-normal floor (0.312). CARDEA runs an end-to-end CAG pipeline from raw multi-view videos through keyframe selection to study-level diagnosis while exposing auditable spatial evidence behind its conclusions. RLVR on verifiable closed-ended tasks surfaced open-ended reporting ability that supervised imitation did not. Clinical use requires prospective validation against expert cardiologists.
Chinese Translation
侵入性冠状动脉造影（CAG）是诊断冠状动脉疾病的金标准，但不同观察者之间的解读差异显著。现有的人工智能系统可以提高一致性，但缺乏可审计的决策过程，并且在全面的开放式评估中受到限制，从而削弱了临床医生的信任和临床采用的准备性。我们开发了CARDEA，一个统一的大型视觉-语言模型，作为CAG管道的推理核心。它仅在三个阶段上使用公共数据集和封闭式任务进行训练：视觉特征对齐、自我蒸馏的链盒（Chain-of-Box, CoB）冷启动，以及通过可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR），其中CoB奖励鼓励在推理轨迹中使用边界框。我们将其两个研究级别的诊断，即主导分类和复杂性评估，与专门的分类器和两位介入心脏病专家进行了比较。报告生成在训练中被排除，并在外部队列中跨阶段以零样本方式评估，使用血管严重性宏观-$F_1$。在分布内主导性上，CARDEA落后于分类器，但在领域转移下持平（准确率为0.91 [95%置信区间（CI），0.86至0.95]），并在复杂性评估上与心脏病专家相当（准确率为0.90 [CI，0.82至0.97]）。只有RLVR改善了零样本报告生成，使其血管严重性宏观-$F_1$（0.686 [CI，0.664至0.707]）高于未调优的基础模型（0.513），并且超过了始终正常的基线（0.312）的两倍。CARDEA从原始多视角视频通过关键帧选择到研究级别诊断运行端到端CAG管道，同时揭示其结论背后的可审计空间证据。RLVR在可验证的封闭式任务上展现了开放式报告能力，而监督模仿未能实现。临床使用需要针对专家心脏病医生的前瞻性验证。
cs.CV / 134 / 2609.06948
PRG-Fusion: Orchestrating Generative Priors with Reconstruction Evidence for Driving View Synthesis
PRG-Fusion：利用重建证据协调生成先验以进行驾驶视图合成
Abstract
Synthesizing photorealistic driving videos along specified trajectories is essential for scalable closed-loop simulation. Reconstruction-based methods leverage neural rendering to synthesize geometrically consistent views, but often exhibit diverse artifacts and missing content when the viewpoint deviates from the training trajectory. In contrast, generative models can synthesize realistic views along arbitrary trajectories from vehicle sensor data, yet often struggle to maintain temporal and geometric consistency across frames. To combine the strengths of both, we propose PRG-Fusion, a framework for driving view synthesis that uses reconstruction evidence to orchestrate generative priors across regions. Specifically, we extract region-wise degradation evidence from reconstructed driving scenes and convert it into Preserve, Repair, and Generate (PRG) labels. At inference, these labels serve as a unified routing policy for region-aware spatiotemporal synthesis, orchestrating 3DGS appearance preservation, LiDAR-guided structural correction, and video-prior-driven content completion across Preserve, Repair, and Generate regions, respectively. We then follow a two-stage training paradigm, first establish geometric control from sparse LiDAR projections and subsequently learning appearance control from dense 3DGS renderings. Extensive experiments on Waymo demonstrate that PRG-Fusion achieves state-of-the-art overall performance in novel trajectory video synthesis, with superior visual quality and geometric fidelity while maintaining competitive view consistency under large trajectory shifts.
Chinese Translation
沿指定轨迹合成逼真的驾驶视频对于可扩展的闭环仿真至关重要。基于重建的方法利用神经渲染合成几何一致的视图，但当视点偏离训练轨迹时，往往会出现各种伪影和缺失内容。相比之下，生成模型能够从车辆传感器数据中沿任意轨迹合成真实的视图，但在保持帧间的时间和几何一致性方面常常面临挑战。为了结合两者的优势，我们提出了PRG-Fusion，一个用于驾驶视图合成的框架，该框架利用重建证据在不同区域之间协调生成先验。具体而言，我们从重建的驾驶场景中提取区域级降解证据，并将其转换为保留、修复和生成（Preserve, Repair, and Generate，PRG）标签。在推理过程中，这些标签作为区域感知时空合成的统一路由策略，分别协调3DGS外观保留、基于LiDAR的结构修正以及视频先验驱动的内容补全。然后，我们遵循两阶段的训练范式，首先从稀疏的LiDAR投影建立几何控制，然后从密集的3DGS渲染学习外观控制。在Waymo上的大量实验表明，PRG-Fusion在新轨迹视频合成中实现了最先进的整体性能，具有优越的视觉质量和几何保真度，同时在较大轨迹偏移下保持竞争性的视图一致性。
cs.CV / 135 / 2609.06955
Joint-Conditioned Stereo Surface Reasoning for Interaction Field Estimation
联合条件立体表面推理用于交互场估计
Abstract
Predicting hand--object interaction fields requires locating the nearest object-surface point for each hand joint, often from small and partially occluded image regions. We view this task as joint-conditioned surface-endpoint estimation: each joint has its own nearest endpoint, while endpoints from the same hand can draw on shared local surface evidence. This structure motivates Joint-Conditioned Stereo Surface Reasoning (JSSR). A temporal-stereo network jointly predicts 3D joints, a direct interaction field, and per-view endpoint evidence. Calibrated candidate search evaluates endpoint hypotheses using joint-specific image compatibility and cross-view correspondence. A hand-shared candidate support lets joints draw on common surface evidence, and a learned residual gate controls the geometric correction when observations are ambiguous. Our system built on this method ranked third on the SHOW3D Interaction Field Challenge leaderboard.
Chinese Translation
预测手部与物体的交互场需要为每个手关节定位最近的物体表面点，通常是在小且部分被遮挡的图像区域中。我们将此任务视为联合条件表面端点估计：每个关节都有其自身的最近端点，而来自同一只手的端点可以利用共享的局部表面证据。这一结构激励了联合条件立体表面推理（Joint-Conditioned Stereo Surface Reasoning, JSSR）。一个时间立体网络共同预测三维关节、直接交互场和每视图的端点证据。经过校准的候选搜索利用关节特定的图像兼容性和视图间对应性来评估端点假设。一个手部共享的候选支持使得关节能够利用共同的表面证据，而一个学习的残差门控在观察模糊时控制几何校正。基于该方法构建的系统在SHOW3D交互场挑战赛排行榜上排名第三。
cs.CV / 136 / 2609.06959
MSSP: Multi-Scale Spatially-Constrained Partition for Unsupervised Semantic Segmentation of 3D Point Clouds
MSSP：用于无监督语义分割的多尺度空间约束分区方法（Multi-Scale Spatially-Constrained Partition）
Abstract
3D point cloud semantic segmentation is essential for real-world spatial understanding, yet the prohibitive cost of human annotations motivates unsupervised approaches that require no labels. Existing superpoint-based methods typically rely on spectral analysis at a fixed granularity, failing to capture the hierarchical semantic structures inherent in complex indoor scenes. To bridge this gap, we present a Multi-Scale Spatially-Constrained Partition (MSSP) framework that combines multi-scale spectral analysis with spatially-constrained clustering. Multi-scale spectral analysis constructs enriched superpoint descriptors across multiple clustering granularities; however, the resulting high-dimensional feature space calls for a structural prior to translate into cleaner segmentation. Spatially-constrained clustering supplies this prior by restricting superpoint merging to physically adjacent regions, imposing the spatial coherence needed for multi-scale features to be effective. Extensive experiments on S3DIS and ScanNet show that MSSP achieves the best mIoU among unsupervised methods on the main benchmarks, with particularly significant gains on S3DIS. Notably, our ablation reveals a regularize-then-enrich interaction: multi-scale features alone do not improve final segmentation, yet become highly effective when coupled with spatial regularization, underscoring that spatial coherence is aprerequisite for multi-scale representations in superpoint clustering.
Chinese Translation
3D 点云语义分割对于现实世界的空间理解至关重要，但人工标注的高昂成本促使无监督方法的出现，这些方法不需要标签。现有的基于超点（superpoint）的方法通常依赖于固定粒度的谱分析，未能捕捉复杂室内场景中固有的层次语义结构。为了解决这一问题，我们提出了一种多尺度空间约束分区框架（MSSP），该框架结合了多尺度谱分析和空间约束聚类。多尺度谱分析在多个聚类粒度上构建了丰富的超点描述符；然而，所产生的高维特征空间需要结构先验以实现更清晰的分割。空间约束聚类通过限制超点合并到物理相邻区域，提供了这一先验，从而施加了多尺度特征有效所需的空间一致性。在 S3DIS 和 ScanNet 上的广泛实验表明，MSSP 在主要基准测试中实现了无监督方法中最佳的 mIoU，特别是在 S3DIS 上取得了显著的提升。值得注意的是，我们的消融实验揭示了一种“先正则化后丰富”的交互关系：单独的多尺度特征并未改善最终分割，但在与空间正则化结合时变得非常有效，这突显了空间一致性是超点聚类中多尺度表示的先决条件。
cs.CV / 137 / 2609.06962
DPSF-Net: A Dual-Prior Spatial-Frequency Network for Real-World Remote Sensing Image Dehazing
DPSF-Net：一种用于真实世界遥感图像去雾的双先验空间频率网络
Abstract
Real-world remote sensing image dehazing (RSID) remains challenging because atmospheric scattering, spatially non-uniform haze and colour distortion jointly degrade structural and spectral information. Most deep learning methods rely on RGB inputs and spatial-domain feature extraction, which limits their ability to separate global background haze from local surface details. Here, we propose DPSF-Net, a dual-prior spatial-frequency network built on MCAF-Net for real-world RSID. The network uses hazy RGB images and dark channel prior (DCP) maps as joint inputs, allowing physical degradation cues to guide end-to-end feature learning. A spatial-frequency residual interaction block introduces a FourierUnit branch into multi-directional spatial interaction to model large-scale haze components. A prior-guided feature attention module adaptively fuses prior and attention features to reduce colour shift and structural distortion. A selective kernel complementary fusion module screens multi-scale skip features through bidirectional residual complementary gating and selective kernel fusion. Extensive experiments demonstrate that DPSF-Net achieves state-of-the-art performance on the real-world RRSHID remote sensing image dehazing benchmark and remains competitive across multiple synthetic datasets. Moreover, the proposed method strikes a favourable balance among restoration quality, parameter count and computational complexity, supporting the effectiveness of dual-prior spatial-frequency modelling.
Chinese Translation
真实世界的遥感图像去雾（RSID）仍然面临挑战，因为大气散射、空间上非均匀的雾霾和颜色失真共同降低了结构和光谱信息的质量。大多数深度学习方法依赖于RGB输入和空间域特征提取，这限制了它们将全局背景雾霾与局部表面细节分离的能力。在此，我们提出了DPSF-Net，一种基于MCAF-Net的双先验空间频率网络，用于真实世界的RSID。该网络使用雾霾RGB图像和暗通道先验（DCP）图作为联合输入，允许物理退化线索引导端到端特征学习。空间频率残差交互块引入了一个FourierUnit分支到多方向空间交互中，以建模大规模雾霾成分。一个先验引导的特征注意模块自适应地融合先验和注意特征，以减少颜色偏移和结构失真。选择性核补充融合模块通过双向残差补充门控和选择性核融合筛选多尺度跳跃特征。大量实验表明，DPSF-Net在真实世界的RRSHID遥感图像去雾基准测试中实现了最先进的性能，并在多个合成数据集上保持竞争力。此外，所提出的方法在恢复质量、参数数量和计算复杂性之间取得了良好的平衡，支持了双先验空间频率建模的有效性。
cs.CV / 138 / 2609.06967
Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models
重新校准的对比损失用于视觉-语言模型中的变换感知提示调节
Abstract
Ensuring effective transfer learning for vision-language models without compromising their generalization performance is crucial. However, many existing methods overlook data characteristics and simply reuse the training strategies adopted during pre-training. Specifically, they treat same-class samples as distinct instances and transform images independently of their paired text prompts, which makes model learning more difficult. We address these limitations through transformation-aware prompt conditioning and a re-calibrated contrastive loss. Fixed text descriptors identify the transformations applied to paired images, providing transformation-level consistency without altering class semantics. This design aligns the image and text branches at the transformation level, enabling richer representations while preserving the models' ability to generalize. In addition, our loss function mitigates positive-gradient dilution in soft-target cross-entropy when each anchor has multiple valid positives. During transfer, our approach treats same-class samples as positives rather than distinct instances, enabling the model to learn domain-specific features more effectively. Experiments across distribution shift, transfer learning, and few-shot settings demonstrate consistent improvements over existing approaches. Source code for our method is available at https://github.com/SoongE/ReCalCon.
Chinese Translation
确保视觉-语言模型在不影响其泛化性能的情况下有效进行迁移学习至关重要。然而，许多现有方法忽视了数据特征，仅仅重复在预训练期间采用的训练策略。具体而言，它们将同类样本视为不同实例，并独立于其配对文本提示对图像进行变换，这使得模型学习变得更加困难。我们通过变换感知提示调节和重新校准的对比损失来解决这些局限性。固定的文本描述符识别应用于配对图像的变换，提供了变换级别的一致性，而不改变类别语义。该设计在变换级别上对齐图像和文本分支，使得模型能够生成更丰富的表示，同时保持其泛化能力。此外，我们的损失函数在每个锚点具有多个有效正样本时，缓解了软目标交叉熵中的正梯度稀释。在迁移过程中，我们的方法将同类样本视为正样本，而不是不同实例，从而使模型能够更有效地学习领域特定特征。在分布转移、迁移学习和少样本设置下的实验表明，我们的方法在现有方法中表现出一致的改进。我们的方法的源代码可在 https://github.com/SoongE/ReCalCon 获取。
cs.CV / 139 / 2609.06978
AnomalyCraft-700K: Component-Level Controllable and Verifiable Synthetic Anomalies for Fine-Grained Video Anomaly Understanding
AnomalyCraft-700K：可控且可验证的组件级合成异常用于细粒度视频异常理解
Abstract
Progress in video anomaly understanding (VAU) has long been limited by inherent deficiencies of real-world anomaly videos, which are hard to collect and offer little control over their content. Synthetic anomaly approaches partially alleviate data scarcity, yet their generation remains largely controlled at the category or prompt level. They also lack component-level verification of video-text consistency and provide insufficient hard normal samples near the normal-anomaly boundary. To address this, we present AnomalyCraft-700K, a component-controllable synthetic anomaly dataset for fine-grained VAU, containing over 40K videos and over 700K task-level textual annotations. From fine-grained semantic components and a progressive three-stage pipeline, we craft anomaly events that are richly detailed, semantically controlled, and temporally structured, and additionally construct per-category hard normal samples to prompt the model to discriminate based on anomaly semantics rather than surface visual cues. Moreover, using the components as verification units, AnomalyCraft-700K further performs component-wise correction of video-text discrepancies introduced during generation, providing reliable annotations with verified cross-modal alignment for six tasks that progress from anomaly detection, through anomaly retrieval and captioning, to fine-grained anomaly reasoning. Evaluations of widely used methods under both traditional and MLLM-based protocols demonstrate that AnomalyCraft-700K serves as an effective source of supervision, from anomaly detection to fine-grained anomaly understanding.
Chinese Translation
视频异常理解（VAU）的进展长期以来受到现实世界异常视频固有缺陷的限制，这些视频难以收集且对其内容的控制较少。合成异常方法在一定程度上缓解了数据稀缺问题，但其生成仍主要在类别或提示级别进行控制。此外，它们缺乏对视频-文本一致性的组件级验证，并且在正常-异常边界附近提供的硬正常样本不足。为了解决这一问题，我们提出了AnomalyCraft-700K，这是一个可控组件的合成异常数据集，旨在细粒度VAU，包含超过40K个视频和超过700K个任务级文本注释。通过细粒度语义组件和逐步的三阶段流程，我们制作了丰富细节、语义可控且时间结构化的异常事件，并额外构建了每个类别的硬正常样本，以促使模型基于异常语义而非表面视觉线索进行区分。此外，利用这些组件作为验证单元，AnomalyCraft-700K进一步对生成过程中引入的视频-文本差异进行组件级修正，为六个任务提供了经过验证的跨模态对齐的可靠注释，这些任务从异常检测、异常检索和字幕生成，直至细粒度异常推理。对传统和基于MLLM的协议下广泛使用的方法的评估表明，AnomalyCraft-700K作为从异常检测到细粒度异常理解的有效监督源。
cs.CV / 140 / 2609.06991
AV-SafetyBench: A Safety Benchmark for Text-to-Audio-Video Generation
AV-SafetyBench：文本到音频视频生成的安全基准
Abstract
Recent text-to-audio-video (T2AV) models jointly generate video, speech, sound effects, and ambience from a single text prompt. This capability poses new challenges for safety evaluation, as unsafe content may be conveyed through the audio track or arise only when the visual and audio tracks are interpreted jointly. Existing safety benchmarks largely focus on either generated video or generated audio in isolation and are therefore not designed to capture these risks. To close this gap, we introduce AV-SafetyBench, the first safety benchmark developed specifically for T2AV generation. AV-SafetyBench comprises a four-axis, 13-category taxonomy and 5,200 manually reviewed prompts that specify visual scenes, speech, and non-speech audio. Our evaluation protocol assesses each output under three views: Full-AV, Video-Only, and Audio-Only. It then uses the Video-Only and Audio-Only judgments to assign Full-AV unsafe outputs to one of four risk sources: Video-Only, Audio-Only, AV-Both, or AV-Joint. We evaluate five open-source T2AV models and validate the automated Full-AV judgments against human annotations. Across the five models, Full-AV Unsafe Rates range from 25.1% to 49.4%. Beyond these aggregate rates, risk-source analysis reveals that, for four of the five models, Audio-Only and AV-Joint cases-unsafe outputs missed by video-only evaluation-account for 41.6-48.3% of Full-AV unsafe outputs for which a risk source could be assigned. In the Cross-Modal Harm Emergence category, AV-Joint accounts for 87.5% of unsafe outputs withan assigned risk source. Together, these findings demonstrate the value of AV-SafetyBench for evaluating T2AV safety across the visual and audio modalities and their interaction.
Chinese Translation
近期的文本到音频视频（T2AV）模型能够从单一文本提示中共同生成视频、语音、音效和环境音。这一能力对安全评估提出了新的挑战，因为不安全的内容可能通过音频轨道传达，或仅在视觉和音频轨道共同解释时才会出现。现有的安全基准主要集中于生成的视频或生成的音频，因此并未设计用于捕捉这些风险。为填补这一空白，我们提出了AV-SafetyBench，这是专门为T2AV生成开发的第一个安全基准。AV-SafetyBench包括一个四轴、13类的分类法和5200个手动审核的提示，指定了视觉场景、语音和非语音音频。我们的评估协议从三个视角评估每个输出：全AV（Full-AV）、仅视频（Video-Only）和仅音频（Audio-Only）。然后，它使用仅视频和仅音频的判断将全AV不安全输出分配到四个风险来源之一：仅视频（Video-Only）、仅音频（Audio-Only）、AV-两者（AV-Both）或AV-联合（AV-Joint）。我们评估了五个开源T2AV模型，并将自动化的全AV判断与人工注释进行了验证。在这五个模型中，全AV不安全率范围从25.1%到49.4%。除了这些汇总率，风险来源分析显示，对于五个模型中的四个，音频仅（Audio-Only）和AV-联合（AV-Joint）案例中，由仅视频评估漏掉的不安全输出占全AV不安全输出的41.6%-48.3%，这些输出可以被分配风险来源。在跨模态危害出现（Cross-Modal Harm Emergence）类别中，AV-联合（AV-Joint）占有87.5%的具有分配风险来源的不安全输出。综合这些发现，AV-SafetyBench在评估T2AV安全性方面展示了其在视觉和音频模态及其相互作用中的价值。
cs.CV / 141 / 2609.07004
CGSM: Concept-Guided Segmentation Model for Precise Pulmonary Lesion Delineation
CGSM：用于精确肺部病变描绘的概念引导分割模型
Abstract
Accurate segmentation of pulmonary lesions is essential for effective clinical diagnosis and treatment strategies. Existing segmentation approaches often lack task-specific semantic guidance, as text-based annotations typically offer coarse localization of lesions, leading to inadequate delineation of lesion boundaries and poor performance on small-scale lesions. To address this, we propose CGSM, a Concept-Guided Segmentation Model that integrates LLM-generated and clinically reviewed concepts into the segmentation process. Specifically, we design a Concept-Visual Alignment Module (CVAM) to activate relevant tokens within the concepts that align with visual features, enhancing the interaction between textual and visual information. In addition, we introduce a Concept Modulated Decoder (CM-Decoder), which uses concepts from CVAM as modulation signals to facilitate the adaptive fusion of image and text features, improving the segmentation accuracy. Extensive experiments on two public datasets show that CGSM achieves state-of-the-art performance, with results of 91.59% Dice and 84.49% mIoU on the QaTa-COV19 dataset, demonstrating its effectiveness in pulmonary lesion segmentation.
Chinese Translation
准确的肺部病变分割对于有效的临床诊断和治疗策略至关重要。现有的分割方法往往缺乏任务特定的语义指导，因为基于文本的注释通常只能提供粗略的病变定位，导致病变边界描绘不足以及在小规模病变上的表现不佳。为了解决这一问题，我们提出了CGSM（Concept-Guided Segmentation Model），该模型将LLM生成的和临床审查的概念整合到分割过程中。具体而言，我们设计了一个概念-视觉对齐模块（CVAM），以激活与视觉特征对齐的相关标记，增强文本信息与视觉信息之间的互动。此外，我们引入了一个概念调制解码器（CM-Decoder），该解码器利用CVAM中的概念作为调制信号，以促进图像和文本特征的自适应融合，从而提高分割精度。在两个公共数据集上的广泛实验表明，CGSM达到了最先进的性能，在QaTa-COV19数据集上取得了91.59%的Dice系数和84.49%的mIoU，证明了其在肺部病变分割中的有效性。
cs.CV / 142 / 2609.07006
GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation
GIFT：用于高效操控策略适应的目标注入微调
Abstract
Compared with relying solely on initial observations and language instructions, predicting goal images with generative models as high-level visual guidance can significantly enhance the robustness of Vision-Language-Action (VLA) models. However, most existing foundation models have not systematically incorporated goal image conditioning due to the high computational training cost. To this end, we propose Goal-Injected Fine-Tuning (GIFT), a lightweight and efficient fine-tuning framework that seamlessly integrates generated goal images into multiple representative pretrained VLA models. Our approach introduces goal image features into observations via a zero-initialized convolution which progressively grows parameters from zero and prevents harmful noise from disrupting the pretrained policy during fine-tuning. As training proceeds, goal information is gradually incorporated, enabling efficient goal understanding without disrupting model stability. We further introduce a refined image editing method to generate semantically and visually consistent goal images from initial observations and task instructions. Experiments show that goal-aware VLA models achieve substantial performance gains across tasks: with only a single epoch of fine-tuning, GIFT outperforms the base model by 6.0% and 13.4% on two SIMPLER settings, and by 4.7% on LIBERO, demonstrating both efficiency and effectiveness.
Chinese Translation
与仅依赖初始观察和语言指令相比，利用生成模型预测目标图像作为高层次视觉指导可以显著增强视觉-语言-动作（VLA）模型的鲁棒性。然而，大多数现有的基础模型由于高计算训练成本，尚未系统性地融入目标图像条件。为此，我们提出了目标注入微调（Goal-Injected Fine-Tuning, GIFT），这是一个轻量级且高效的微调框架，能够将生成的目标图像无缝集成到多个代表性的预训练VLA模型中。我们的方法通过一个零初始化的卷积将目标图像特征引入观察中，该卷积逐步从零增长参数，并防止有害噪声在微调过程中干扰预训练策略。随着训练的进行，目标信息逐渐被纳入，使得高效的目标理解得以实现，而不影响模型的稳定性。我们进一步引入了一种精细的图像编辑方法，从初始观察和任务指令中生成语义和视觉一致的目标图像。实验表明，具有目标意识的VLA模型在各项任务中取得了显著的性能提升：仅通过一次微调的单个周期，GIFT在两个SIMPLER设置上分别比基础模型提升了6.0%和13.4%，在LIBERO上提升了4.7%，展示了其高效性和有效性。
cs.CV / 143 / 2609.07010
Fine-Grained Visual Preprocessing and Dual-Stream Temporal Modeling for Multimodal Sentiment Analysis on Social Media
社交媒体多模态情感分析的细粒度视觉预处理与双流时间建模
Abstract
Multimodal sentiment analysis often remains text-dominant due to raw-video noise and insufficient temporal modeling. Using CH-SIMS v2.0S, this study proposes three improvements: the NAPS pipeline---a seven-stage system integrating face tracking,identity embedding, and normalized lip-motion analysis to reduce visual noise;DS-TANet, combining an EfficientNetB2 static stream, RAFT optical-flow motion stream, motion-guided attention, and Bi-GRU temporal modeling; and DS-TAFNet, fusing visual and MacBERT-Base textual representations via concatenation fusion. With NAPS, the static visual baseline achieves 80.98\% Macro F1, comparable to the text baseline of 80.55\%; DS-TANet improves visual Macro F1 to 82.58\%;and DS-TAFNet achieves 87.49\% accuracy and 87.48\% Macro F1. These results demonstrate that improving visual input quality and temporal representation is more effective than increasing fusion complexity under limited-data conditions.
Chinese Translation
多模态情感分析往往由于原始视频噪声和不足的时间建模而以文本为主导。本研究使用 CH-SIMS v2.0S 提出了三项改进：NAPS 流水线——一个七阶段系统，集成了人脸跟踪、身份嵌入和标准化唇动分析，以减少视觉噪声；DS-TANet，结合了 EfficientNetB2 静态流、RAFT 光流运动流、运动引导注意力和 Bi-GRU 时间建模；以及 DS-TAFNet，通过拼接融合视觉和 MacBERT-Base 文本表示。使用 NAPS，静态视觉基线达到了 80.98\% 的宏 F1 分数，接近文本基线的 80.55\\%；DS-TANet 将视觉宏 F1 提高至 82.58\\%；而 DS-TAFNet 实现了 87.49\\% 的准确率和 87.48\\% 的宏 F1 分数。这些结果表明，在有限数据条件下，提高视觉输入质量和时间表示比增加融合复杂性更为有效。
cs.CV / 144 / 2609.07013
ARNAI: Artifact Removal Network based on Autoencoding and Inpainting for Robust Spinal Image Segmentation and Measurement
ARNAI：基于自编码和修复的伪影去除网络用于稳健的脊柱图像分割和测量
Abstract
Purpose: This study aims to develop an AI framework applicable for postoperative imaging for automated measurement of spinopelvic parameters on radiographs with robustness to the presence of spinal implants. Materials and Methods: We retrospectively reviewed lateral lumbar spine radiographs from two institutions (Internal: January 2017--December 2024; External: October 2021--September 2025). We developed the Restore, Segment, and Measure (RSM) framework, incorporating a novel Artifact Removal Network based on Autoencoding and Inpainting (ARNAI) to mitigate implant-related artifacts in postoperative radiographs. Segmentation and spinopelvic parameter (PT, LL, SS, SCA) measurement performance were assessed using Wilcoxon signed-rank tests and intraclass correlation coefficients. Results: When ARNAI was added to a recent Transformer-based segmentation model, FCBFormer, the mean DSC increased to 0.870 from 0.814, with marked gains at L3--L5 and smaller improvements at L1--L2. On 91 radiographs with implants, the mean L4--L5 segmental Cobb angle error decreased to 4.7 {\deg} from 15.6--16.2 {\deg}, an average error reduction of 70%. The ICC for L4--L5 segmental Cobb angle improved to 0.54 (Rater 1) and 0.59 (Rater 2) from 0.18, and ICCs for pelvic tilt, lumbar lordosis, and sacral slope all exceeded 0.70. The improvement in L4--L5 segmental Cobb angle error was statistically significant in the internal implant-containing cohort after correction for multiple comparisons. Conclusion: The proposed RSM framework improved automated spinopelvic parameter measurement in implant-containing postoperative radiographs. By mitigating implant-related artifacts, ARNAI improved segmentation and downstream measurement accuracy, with the greatest benefit observed for L4--L5 segmental Cobb angle estimation, where the mean error was reduced by approximately 70%.
Chinese Translation
目的：本研究旨在开发一个适用于术后影像的人工智能框架，以实现对放射线图像中脊柱骨盆参数的自动测量，并对脊柱植入物的存在具有鲁棒性。材料与方法：我们回顾性地分析了来自两个机构的侧位腰椎放射线图像（内部：2017年1月--2024年12月；外部：2021年10月--2025年9月）。我们开发了恢复、分割和测量（RSM）框架，结合了一种基于自编码和修复的新型伪影去除网络（ARNAI），以减轻术后放射线图像中的植入物相关伪影。通过Wilcoxon符号秩检验和组内相关系数评估分割和脊柱骨盆参数（PT、LL、SS、SCA）测量的性能。结果：当ARNAI被添加到最近的基于Transformer的分割模型FCBFormer时，平均Dice相似系数（DSC）从0.814提高到0.870，在L3--L5段的提升显著，而在L1--L2段的改善较小。在91幅含植入物的放射线图像中，L4--L5段的Cobb角平均误差从15.6--16.2 { extdegree}降低至4.7 { extdegree}，平均误差减少了70%。L4--L5段Cobb角的组内相关系数（ICC）从0.18提高至0.54（评估者1）和0.59（评估者2），而骨盆倾斜、腰椎前凸和骶骨坡度的ICC均超过0.70。在内部含植入物的队列中，L4--L5段Cobb角误差的改善在多重比较校正后具有统计学意义。结论：所提出的RSM框架改善了含植入物术后放射线图像中脊柱骨盆参数的自动测量。通过减轻植入物相关伪影，ARNAI提高了分割和后续测量的准确性，L4--L5段Cobb角估计的最大收益为平均误差减少约70%。
cs.CV / 145 / 2609.07029
LoGAN: Multilingual Font Localization with Generative Agents
LoGAN：使用生成代理的多语言字体本地化
Abstract
Localizing a font into new languages is a highly intricate task requiring precise design adaptation of glyphs, color/texture, and spacing/kerning, from source to target languages. Most existing methods focus on single glyph generation with limited capability in handling multilingual font rendering. In this work, we propose LoGAN, a VLM-based agentic framework for few-shot multilingual font localization, which takes in a small number of individual glyphs from a font or letters from a logo and uses them to generate complete character sets in other languages. LoGAN breaks down this task into multiple components: a glyph-level diffusion model, a style finetuning module, a spacing and kerning transfer algorithm, and a texture expansion model, with a VLM agent coordinator. LoGAN achieves broad language coverage for font localization with various styles, including Chinese/Korean/Japanese (CJK). We evaluate our approach on both font and real-world logo datasets spanning more than 27 languages and compare it against both specialized font generation and state-of-the-art image editing models with strong text rendering capabilities (e.g., FLUX, Nano-Banana). Our approach yields higher glyph fidelity while maintaining better style, texture, and kerning consistency according to both quantitative and qualitative evaluations.
Chinese Translation
将字体本地化为新语言是一项复杂的任务，需要对字形、颜色/纹理以及间距/字距进行精确的设计适配，从源语言到目标语言。现有的大多数方法集中于单个字形的生成，处理多语言字体渲染的能力有限。在本研究中，我们提出了LoGAN，一个基于VLM（视觉语言模型）的代理框架，用于少量样本的多语言字体本地化，该框架从字体中的少量单个字形或标志中的字母入手，并利用这些字形生成其他语言的完整字符集。LoGAN将这一任务分解为多个组件：字形级扩散模型、风格微调模块、间距和字距转移算法以及纹理扩展模型，并配有VLM代理协调器。LoGAN实现了对各种风格的字体本地化的广泛语言覆盖，包括中文/韩文/日文（CJK）。我们在涵盖超过27种语言的字体和真实世界标志数据集上评估了我们的方法，并与专门的字体生成模型和具有强大文本渲染能力的最先进图像编辑模型（如FLUX、Nano-Banana）进行了比较。根据定量和定性评估，我们的方法在保持更好的风格、纹理和字距一致性的同时，提供了更高的字形保真度。
cs.CV / 146 / 2609.07048
FreqDoor: A Hidden Trojan in the Frequency Domain for Backdoor Attacks on Vision-Language Models
FreqDoor：频域中的隐蔽特洛伊木马用于对视觉-语言模型的后门攻击
Abstract
Vision-language models (VLMs) have recently shown excellent progress in open-ended image-to-text generation. However, their multimodal nature makes them persistently vulnerable to backdoor attacks. Existing backdoor triggers for VLMs are either spatial, textual, or bimodal, which may yield localized or recognizable trigger patterns. In this work, we explore a different attack surface and propose \ textsc {FreqDoor}, a training-time backdoor attack that implants triggers in the frequency domain. \ textsc {FreqDoor} mixes amplitude-spectrum components from a trigger-source image selectively while preserving the phase of a clean image to generate a spatially distributed and visually imperceptible trigger without modifying the textual input. We evaluate the attack on BLIP-2, InstructBLIP, and LLaVA for image captioning and visual question answering. On Flickr8k, \ textsc {FreqDoor} achieves attack success rates of $99.6\%$, $99.8\%$, and $98.4\%$ on the three models, respectively, while preserving the semantic quality of the generated captions. On VQAv2, the corresponding attack success rates are $99.6\%$, $92.4\%$, and $79.6\%$.
Chinese Translation
视觉-语言模型（VLMs）最近在开放式图像到文本生成方面取得了卓越的进展。然而，它们的多模态特性使其持续易受后门攻击。现有的VLM后门触发器要么是空间的、文本的，或双模态的，这可能导致局部或可识别的触发模式。在本研究中，我们探索了不同的攻击面，并提出了 extsc{FreqDoor}，一种在训练期间植入频域触发器的后门攻击。 extsc{FreqDoor} 选择性地混合来自触发源图像的幅度谱分量，同时保留干净图像的相位，以生成空间分布且视觉上不可察觉的触发器，而不修改文本输入。我们在BLIP-2、InstructBLIP和LLaVA上评估了该攻击，针对图像字幕生成和视觉问答。在Flickr8k上， extsc{FreqDoor} 在这三种模型上的攻击成功率分别为99.6%、99.8%和98.4%，同时保持生成字幕的语义质量。在VQAv2上，相应的攻击成功率为99.6%、92.4%和79.6%。
cs.CV / 147 / 2609.07058
CNsEMD: An Expert-Annotated Multi-Field-Strength MRI Dataset and a Hyperspherical Manifold Network for Multimodal Cranial Nerve Parcellation
CNsEMD：一个专家标注的多场强度MRI数据集及用于多模态颅神经分割的超球面流形网络
Abstract
Cranial nerves (CNs) play essential roles in sensory, motor, and autonomic functions. Accurate CN parcellation from multimodal magnetic resonance imaging (MRI) is crucial for neuroanatomical analysis and neurosurgical planning. However, accurate CN parcellation remains extremely challenging because CNs are very small, exhibit low image contrast, and have slender tubular morphologies and complex anatomical trajectories. Moreover, the lack of publicly available, expert-annotated datasets has impeded the development and fair benchmarking of learning-based CN analysis methods. In this work, we introduce CNsEMD, an expert-annotated multimodal dataset for CN parcellation. It comprises data from 202 subjects acquired on 3T, 5T, and 7T MRI scanners. We further propose the projective hyperspherical manifold network (PHM-Net), which learns cross-modal representations by capturing angular relationships in a shared hyperspherical embedding space. Rather than performing multimodal fusion in Euclidean space, the proposed Hyperspherical cross-modal interaction (HCI) module enables bidirectional feature exchange between T1-weighted (T1w) and direction-encoded color (DEC) representations on a unit hypersphere. The Magnitude-preserving projective hyperspherical orientation representation (PHOR) captures the axial nature of DEC orientations while preserving diffusion magnitude. The hyperspherical prototype segmentation head (HPSH) further extends angular similarity to voxel-wise classification using normalized voxel embeddings and learnable class prototypes. Extensive experimental results on the CNsEMD dataset demonstrate the effectiveness of our PHM-Net against state-of-the-art methods. CNsEMD establishes a reproducible benchmark for multimodal CN imaging, while PHM-Net provides a geometry-consistent solution for CN parcellation across diverse MRI acquisitions.
Chinese Translation
颅神经（CNs）在感官、运动和自主功能中发挥着至关重要的作用。从多模态磁共振成像（MRI）中准确分割颅神经对于神经解剖分析和神经外科规划至关重要。然而，由于颅神经体积非常小、图像对比度低、形态细长且解剖轨迹复杂，准确的颅神经分割仍然极具挑战性。此外，缺乏公开可用的专家标注数据集阻碍了基于学习的颅神经分析方法的发展和公平评估。在本研究中，我们介绍了CNsEMD，这是一个用于颅神经分割的专家标注多模态数据集。该数据集包含来自202名受试者的数据，这些数据是在3T、5T和7T MRI扫描仪上获取的。我们进一步提出了投影超球面流形网络（PHM-Net），该网络通过捕捉共享超球嵌入空间中的角度关系来学习跨模态表示。与其在欧几里得空间中进行多模态融合，所提出的超球面跨模态交互（HCI）模块使得T1加权（T1w）和方向编码颜色（DEC）表示之间能够在单位超球面上进行双向特征交换。保持幅度的投影超球面方向表示（PHOR）在保持扩散幅度的同时捕捉DEC方向的轴向特性。超球面原型分割头（HPSH）进一步将角度相似性扩展到使用归一化体素嵌入和可学习类别原型的体素级分类。针对CNsEMD数据集的广泛实验结果证明了我们的PHM-Net相较于最先进方法的有效性。CNsEMD为多模态颅神经成像建立了可重复的基准，而PHM-Net则为不同MRI采集下的颅神经分割提供了一种几何一致的解决方案。
cs.CV / 148 / 2609.07064
SpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem
SpatialBlock：通过合成块堆叠问题增强大型视觉语言模型的空间智能
Abstract
Large Vision-Language Models (LVLMs) have achieved strong performance on diverse visual tasks, yet their ability to reconstruct and reason about the 3D structure of the scene depicted in 2D images -- referred to as spatial intelligence -- remains limited. Existing approaches attempt to address this gap by using real-scene spatial question answering datasets that require dense geometric annotations. However, constructing such labels is costly, time-consuming, and often noisy due to reliance on external perception modules. In this work, we propose a novel paradigm inspired by human cognitive development: learning foundational spatial skills through structured block-manipulation tasks. We introduce SpatialBlock-15k, a synthetic dataset of 15,000 block-stacking problems covering 3D-to-2D projection, viewpoint transformation, and structural combination. The dataset further incorporates controlled color modulation as visual cues to encourage anchor-based reasoning in visually complex conditions. Experiments demonstrate that LVLMs trained on our dataset through either direct answering or reasoning-based prediction significantly outperform baselines and generalize to real-world spatial tasks, despite the dataset's synthetic and compact nature. Code and data are available at https://github.com/rsoohyun/SpatialBlock.
Chinese Translation
大型视觉语言模型（LVLMs）在多种视觉任务中取得了强劲的表现，但它们在重建和推理二维图像中所描绘场景的三维结构（即空间智能）方面的能力仍然有限。现有的方法试图通过使用需要密集几何标注的真实场景空间问答数据集来解决这一差距。然而，构建这样的标签成本高昂、耗时且常常由于依赖外部感知模块而产生噪声。在本研究中，我们提出了一种新颖的范式，灵感来源于人类认知发展：通过结构化的块操作任务学习基础空间技能。我们引入了SpatialBlock-15k，这是一个包含15,000个块堆叠问题的合成数据集，涵盖了三维到二维的投影、视角转换和结构组合。该数据集进一步结合了受控的颜色调制作为视觉线索，以鼓励在视觉复杂条件下的基于锚点的推理。实验表明，通过直接回答或基于推理的预测在我们的数据集上训练的LVLMs显著优于基线，并能够推广到真实世界的空间任务，尽管该数据集的性质是合成和紧凑的。代码和数据可在 https://github.com/rsoohyun/SpatialBlock 获取。
cs.CV / 149 / 2609.07070
Continuous Token-Level Spatio-Temporal Context Modeling for Visual Object Tracking
用于视觉目标跟踪的连续令牌级时空上下文建模
Abstract
Spatio-temporal context has become increasingly crucial for visual tracking. However, most existing approaches extract spatio-temporal cues via discrete sampling strategies, which inherently deviate from the continuity of spatio-temporal context, thereby deteriorating tracking performance. To address this challenge, we propose TLCTrack, a novel tracking framework that models token-level spatio-temporal context through continuously updated salient tokens, enabling more accurate target representation. Specifically, TLCTrack incorporates three components: Masked Unidirectional Attention (MUA), Spatial Salient Token Collection (SSTC), and Temporal Salient Token Bank (TSTB) modules. By explicitly integrating spatio-temporal context, MUA extracts discriminative targetaware spatial features in the search region. To avoid the negative impact of background on feature learning, SSTC progressively suppresses background interference, thereby enhancing target spatial representation. Finally, TSTB captures high-quality spatio-temporal information through continuous salient token updates. Extensive experiments on five benchmarks demonstrate that our method achieves superior performance over state-of-the-art trackers. Code and models are available at https://github.com/xiading123/TLCTrack.
Chinese Translation
时空上下文在视觉跟踪中变得越来越重要。然而，大多数现有方法通过离散采样策略提取时空线索，这本质上偏离了时空上下文的连续性，从而降低了跟踪性能。为了解决这一挑战，我们提出了TLCTrack，一个新颖的跟踪框架，通过持续更新的显著令牌建模令牌级时空上下文，从而实现更准确的目标表示。具体而言，TLCTrack包含三个组件：掩蔽单向注意力（Masked Unidirectional Attention, MUA）、空间显著令牌收集（Spatial Salient Token Collection, SSTC）和时间显著令牌库（Temporal Salient Token Bank, TSTB）模块。通过明确整合时空上下文，MUA在搜索区域提取具有判别性的目标感知空间特征。为了避免背景对特征学习的负面影响，SSTC逐步抑制背景干扰，从而增强目标的空间表示。最后，TSTB通过持续的显著令牌更新捕获高质量的时空信息。在五个基准上的大量实验表明，我们的方法在性能上优于最先进的跟踪器。代码和模型可在 https://github.com/xiading123/TLCTrack 获取。
cs.CV / 150 / 2609.07072
Detect Anything in Graphic Design: Element-Level Rewards for Autoregressive Detection
在图形设计中检测任何元素：自回归检测的元素级奖励
Abstract
Graphic designs, such as posters, advertisements, and infographics, are an important medium for communicating information and shaping understanding. Unlike natural images, they consist of layered elements with explicit compositional order. However, existing object detection models treat these elements as an unordered set, leaving compositional order unexploited. To address this limitation, we present Detect Anything in Graphic Design (DAD), a model that formulates graphic design detection as compositional deconstruction. It decodes elements in compositional order, using lower-layer elements to better detect higher-layer ones. The key feature of DAD is amodal detection, which predicts the full bounding box of each element, including regions occluded by elements placed above it. Building on this formulation, we propose Element Relative Policy Optimization (EleRPO), which extends GRPO from sequence-level supervision to element-level optimization. EleRPO provides fine-grained training signals that capture how each detected element contributes to overall detection quality, and works synergistically with compositional order to improve detection performance. To support training and evaluation, we build a dataset of 10 million graphic designs. Experiments show that DAD outperforms all baselines and achieves human-level performance in amodal detection, supporting effective image-to-layer decomposition. EleRPO consistently improves over GRPO across nine detection benchmarks.
Chinese Translation
图形设计，如海报、广告和信息图，是传递信息和塑造理解的重要媒介。与自然图像不同，图形设计由具有明确构图顺序的分层元素组成。然而，现有的目标检测模型将这些元素视为无序集合，未能充分利用构图顺序。为了解决这一局限性，我们提出了图形设计中的任何检测（Detect Anything in Graphic Design, DAD）模型，该模型将图形设计检测形式化为构成解构。它按照构图顺序解码元素，利用低层元素更好地检测高层元素。DAD的关键特性是模态检测（amodal detection），它预测每个元素的完整边界框，包括被放置在其上方的元素遮挡的区域。在此基础上，我们提出了元素相对策略优化（Element Relative Policy Optimization, EleRPO），将序列级监督的GRPO扩展到元素级优化。EleRPO提供了细粒度的训练信号，捕捉每个检测元素对整体检测质量的贡献，并与构图顺序协同工作，以提高检测性能。为了支持训练和评估，我们构建了一个包含1000万个图形设计的数据集。实验表明，DAD在所有基线模型中表现优越，并在模态检测中达到了人类水平的性能，支持有效的图像到层的解构。EleRPO在九个检测基准上始终优于GRPO。
cs.CV / 151 / 2609.07078
One for All: Generalist Foundation Model for Cross-Sensor Skeleton Representation Learning
一体化：跨传感器骨架表示学习的通用基础模型
Abstract
For learning generalizable motion representations from large-scale unlabeled data, Self-supervised learning (SSL) has become a widely adopted methodology. However, existing approaches are primarily limited by the inherent heterogeneity of skeleton data---characterized by varying joint counts, indexing protocols, and topological structures across different sensors---which typically necessitates training separate, sensor-specific, or even entirely dataset-specific models. To overcome this, we introduce SOfA (Skeleton One for All), the first generalist foundation model designed to achieve sensor-unified skeleton representation learning across diverse sensors. To accommodate the dimensional gap caused by varying joint counts, we introduce a fixed-size set of learnable Canonical Joint Slots, acting as a universal vessel that seamlessly accommodates arbitrary skeletal topologies. SOfA fills these slots via an attention mechanism that dynamically aggregates skeletal information from sensor-specific inputs. Furthermore, we resolve joint index misalignment between various sensors by introducing a Semantic Joint Embedding derived from a pre-trained text encoder, rather than relying on absolute positional embeddings. To validate our approach, we standardized ten 3D skeleton datasets for unified training. Extensive experiments demonstrate that SOfA can serve as a truly universal encoder, achieving state-of-the-art (SOTA) performance across a wide range of downstream tasks and sensor types, often outperforming dataset-specific specialist models with a single foundation model.
Chinese Translation
为了从大规模无标签数据中学习可泛化的运动表示，自监督学习（SSL）已成为一种广泛采用的方法。然而，现有的方法主要受到骨架数据固有异质性的限制——不同传感器之间的关节数量、索引协议和拓扑结构各异——这通常需要训练单独的、特定于传感器的，甚至完全特定于数据集的模型。为了解决这个问题，我们提出了SOfA（Skeleton One for All），这是第一个旨在实现跨多样传感器的统一骨架表示学习的通用基础模型。为了适应因关节数量不同而造成的维度差异，我们引入了一组固定大小的可学习的标准关节槽（Canonical Joint Slots），作为一个通用容器，能够无缝适应任意骨架拓扑。SOfA通过一个注意力机制填充这些槽，动态聚合来自特定传感器输入的骨架信息。此外，我们通过引入一种源自预训练文本编码器的语义关节嵌入（Semantic Joint Embedding），而不是依赖绝对位置嵌入，来解决不同传感器之间的关节索引错位问题。为了验证我们的方法，我们对十个3D骨架数据集进行了标准化，以实现统一训练。大量实验表明，SOfA可以作为一个真正的通用编码器，在广泛的下游任务和传感器类型中实现了最先进（SOTA）的性能，通常优于特定数据集的专业模型，且只需一个基础模型。
cs.CV / 152 / 2609.07081
SupGRPO: Enhancing GRPO with Matching-based Online SFT for Text Spotting
SupGRPO：通过基于匹配的在线SFT增强GRPO以进行文本定位
Abstract
Text spotting requires both accurate text recognition and precise spatial localization. Current specialised spotters excel at predicting tight bounding boxes in natural scenes, but falter on complex or artistic text, whereas multimodal large language models (MLLMs) possess strong recognition capabilities yet remain weak at localisation. To equip the text spotter with general and powerful recognition capabilities and to maximize its localization ability, we explore two MLLM-based fine-tuning methods: Supervised Fine-Tuning (SFT) and reinforcement learning fine-tuning based on Group Relative Policy Optimisation (GRPO). An interesting finding is that SFT is less effective than GRPO at enhancing recognition, while GRPO is less effective than SFT at enhancing detection. To compensate for each other's shortcomings, we introduce a joint training strategy, SupGRPO, which simultaneously optimizes the model using both SFT and GRPO. SupGRPO employs the specially designed reward functions and develops a matching-based online SFT applied solely to coordinate tokens. It both mitigates the reward sparsity problem of GRPO and avoids the instance order dependency problem of SFT. To evaluate particularly challenging cases, we curate ATS, a dataset for artistic text spotting. Experiments demonstrate that SupGRPO improves both text recognition and detection, and attains superior performance. Our code and dataset will be released at https://github.com/Psycho-9/SupGRPO.
Chinese Translation
文本定位需要准确的文本识别和精确的空间定位。目前的专业文本定位器在自然场景中能够很好地预测紧密的边界框，但在复杂或艺术文本上表现不佳，而多模态大型语言模型（MLLMs）具备强大的识别能力，但在定位方面仍显不足。为了使文本定位器具备通用且强大的识别能力，并最大化其定位能力，我们探索了两种基于MLLM的微调方法：监督微调（SFT）和基于组相对策略优化（GRPO）的强化学习微调。一个有趣的发现是，SFT在增强识别方面的效果不如GRPO，而GRPO在增强检测方面的效果不如SFT。为了弥补彼此的不足，我们引入了一种联合训练策略SupGRPO，同时使用SFT和GRPO优化模型。SupGRPO采用特别设计的奖励函数，并开发了一种仅应用于坐标标记的基于匹配的在线SFT。这既缓解了GRPO的奖励稀疏问题，又避免了SFT的实例顺序依赖问题。为了评估特别具有挑战性的案例，我们策划了ATS，一个用于艺术文本定位的数据集。实验表明，SupGRPO在文本识别和检测方面均有所提升，并取得了优越的性能。我们的代码和数据集将发布在 https://github.com/Psycho-9/SupGRPO。
cs.CV / 153 / 2609.07085
Single Image to Textured 3D Object Generation in Frequency Domain: From Theory to Pipeline
频域中的单幅图像到纹理化3D对象生成：从理论到管道
Abstract
Single-view 3D reconstruction, also known as image-to-3D, is a persistently challenging task due to the extreme lack of information. Recently, diffusion models pre-trained on large-scale datasets served as 2D priors are used to solve the ill-posed task but suffer from color deviation and view inconsistency, which can be curbed by using diffusion models fine-tuned with 3D annotated data served as 3D priors. However, 3D priors lack high-frequency details, which cannot be solved by direct complementation with 2D priors in spatial domain for introducing erroneous low-frequency 2D prior guidance. In this paper, we revisit the characteristics of different diffusion priors from the frequency perspective. Based on our observations, we theoretically present a unified framework of hybrid optimization using multiple diffusion priors in frequency domain. Under this framework, we further propose Morpheus3D, a pipeline of 3D object generation from any single unposed image in the wild. Morpheus3D enhances 3D prior with high-pass image-prompt 2D prior guidance to reconstruct high-quality 3D objects while effectively suppressing view inconsistency, low-frequency color deviation, and high-frequency lacking problems. Both quantitative and qualitative experiments on the public and our collected datasets with complex textures show that our method exhibits significant improvements in generation quality.
Chinese Translation
单视角3D重建，也称为图像到3D，是一项持续具有挑战性的任务，因为信息极度匮乏。最近，基于大规模数据集预训练的扩散模型作为2D先验被用于解决这一不适定任务，但存在颜色偏差和视角不一致的问题，这可以通过使用经过3D标注数据微调的扩散模型作为3D先验来缓解。然而，3D先验缺乏高频细节，无法通过在空间域中直接补充2D先验来解决，因为这会引入错误的低频2D先验指导。本文从频率角度重新审视不同扩散先验的特征。基于我们的观察，我们理论上提出了一个在频域中使用多种扩散先验的混合优化统一框架。在该框架下，我们进一步提出了Morpheus3D，一个从任意单幅未定姿态图像生成3D对象的管道。Morpheus3D通过高通图像提示的2D先验指导增强3D先验，以重建高质量的3D对象，同时有效抑制视角不一致、低频颜色偏差和高频缺失问题。在公共数据集和我们收集的具有复杂纹理的数据集上的定量和定性实验表明，我们的方法在生成质量上表现出显著的改善。
cs.CV / 154 / 2609.07094
CASCADE: A Spatio-Temporal-Causal Reasoning Representation and Dataset for Driving
CASCADE：一种用于驾驶的时空因果推理表示和数据集
Abstract
Reasoning is a promising route to the generalization that autonomous driving requires in the long tail, as it can infer how the elements of a scene depend on one another and traverse those dependencies to conclusions beyond what is observed. Yet it is hard to tell whether a model's conclusions follow the scene's dependencies, because no driving representation makes them explicit enough to test against. Text-based reasoning traces lack spatio-temporal grounding, spatio-temporal scene graphs lack causal links, and reasoning annotations at scale are increasingly model-generated and hard to verify. To this end, we introduce CASCADE (Causal Spatio-Temporal Analysis of Driving Environments), which encompasses two components: (1) a structured scene representation for reasoning in driving scenes and (2) a human-annotated dataset built on it. For every actor that interacts with the ego vehicle, the CASCADE representation records frame-by-frame, for as long as the actor is visible, what action is taken, where it occurs, and how it depends on the actions and states of others. The resulting structure makes reasoning predictions machine-verifiable: they can be scored against it element by element, without relying on (M)LLM judges. The CASCADE dataset provides comprehensive human annotations for 2,066 driving clips of the PhysicalAI dataset, with over 34K elements that establish the spatio-temporal and causal context of each scene, including 8.6K time-stamped ego and agent actions, 3.7K causal links and 2.9K potential influences, and 6.1K annotations for agents, objects, traffic lights, and environments. Being entirely human-annotated, CASCADE provides the reference for this comparison: benchmarking the reasoning abilities of Physical AI models, and verifying the quality of automatically generated reasoning labels. The CASCADE dataset is available at https://huggingface.co/datasets/nvidia/cascade.
Chinese Translation
推理是实现自主驾驶在长尾场景中所需泛化的有前景途径，因为它能够推断场景中各元素之间的依赖关系，并通过这些依赖关系得出超出观察结果的结论。然而，很难判断模型的结论是否遵循场景的依赖关系，因为现有的驾驶表示并未明确到足以进行测试。基于文本的推理缺乏时空基础，时空场景图缺乏因果联系，而大规模的推理注释越来越多地依赖模型生成，且难以验证。为此，我们引入了CASCADE（驾驶环境的因果时空分析），它包含两个组成部分：（1）用于驾驶场景推理的结构化场景表示；（2）基于该表示构建的人类注释数据集。对于每个与自我车辆交互的参与者，CASCADE表示逐帧记录参与者可见时所采取的动作、发生地点及其如何依赖于其他参与者的动作和状态。由此产生的结构使得推理预测可以被机器验证：可以逐元素对其进行评分，而无需依赖（M）LLM评判者。CASCADE数据集为PhysicalAI数据集中的2,066个驾驶片段提供了全面的人类注释，包含超过34,000个元素，建立了每个场景的时空和因果背景，包括8,600个时间戳的自我和代理动作、3,700个因果链接和2,900个潜在影响，以及6,100个针对代理、物体、交通信号灯和环境的注释。CASCADE完全由人类注释，提供了这一比较的参考：基准测试Physical AI模型的推理能力，并验证自动生成的推理标签的质量。CASCADE数据集可在https://huggingface.co/datasets/nvidia/cascade获取。
cs.CV / 155 / 2609.07099
Ambient @ EgoProactive 2026 : Proactive Egocentric Assistance with Visually Grounded Supervision
环境 @ EgoProactive 2026：基于视觉引导的主动自我中心辅助
Abstract
We present our submission to the EgoProactive track of the ECCV 2026 Wearable AI Challenge, which ranked first in the large-model division and second in the <=2B division. The task requires a wearable assistant to decide after each eight-second segment of egocentric video whether to intervene or remain silent. Our approach has two main components. First, we reformulate intervention timing as single-token classification. Rather than generating either $interrupt$ or $silent$, the model predicts yes or no, and we derive the decision from the renormalised probabilities of these two tokens. This formulation improved macro-F1 by 0.249 and G-mean by 0.30 over free-form generation. Second, because labelled data were limited to the released validation set, we generated additional supervision using a tool-calling video agent that inspects each clip and assigns intervention timestamps. A narration-only alternative was four times larger and ten times cheaper, but transferred worse than supervision from an unrelated real corpus, suggesting that visual grounding is more important than annotation volume for this task.
Chinese Translation
我们提交了针对ECCV 2026可穿戴人工智能挑战赛EgoProactive赛道的作品，该作品在大型模型组中排名第一，在<=2B组中排名第二。该任务要求可穿戴助手在每个八秒的自我中心视频片段后决定是否干预或保持沉默。我们的方法主要包含两个部分。首先，我们将干预时机重新表述为单标记分类。模型预测是或否，而不是生成$interrupt$或$silent$，我们从这两个标记的重新归一化概率中得出决策。这种表述使得宏观F1提高了0.249，G均值提高了0.30，相较于自由形式生成。其次，由于标注数据仅限于发布的验证集，我们使用一个工具调用视频代理生成额外的监督，该代理检查每个片段并分配干预时间戳。仅叙述的替代方案规模是其四倍且成本是其十分之一，但其迁移效果不如来自无关真实语料库的监督，表明在此任务中，视觉引导的重要性超过了标注量。
cs.CV / 156 / 2609.07110
Discovering Natural Transformation Vulnerabilities in Black-Box Vision Models
发现黑箱视觉模型中的自然变换脆弱性
Abstract
Natural adversarial examples (NAEs) reveal that vision models can fail under realistic semantic changes beyond norm-bounded perturbations. However, generating NAEs in a black-box setting remains challenging because existing generative attacks often rely on surrogate models, learned attack priors, or costly query-based optimization, whereas the natural transformations that expose model vulnerabilities are unknown a priori. We propose \textbf{Adversarial Scenario Attack (ASA)}, a query-based black-box framework that searches over natural-language editing scenarios using a multimodal language model and a modern text-guided generative editor. ASA jointly explores background, weather, and material/color transformations through winner--loser feedback, and uses a greedy explorer to compose only attack-improving scenarios. Across diverse ImageNet classifiers, ASA achieves substantially higher attack success rates than prior query-based generative attacks while requiring fewer victim-model queries and preserving competitive perceptual quality. Moreover, ASA exhibits both image-level and prompt-level transferability: its adversarial images remain effective across victim-model architectures, while its discovered editing scenarios can be reused across same-class images and, in some cases, across architectures. These findings suggest that vision models possess reusable vulnerabilities to natural transformation patterns, which ASA can efficiently identify in a black-box setting.
Chinese Translation
自然对抗样本（NAEs）揭示了视觉模型在现实语义变化下可能会失败，这些变化超出了规范界限的扰动。然而，在黑箱环境中生成NAEs仍然具有挑战性，因为现有的生成攻击通常依赖于替代模型、学习的攻击先验或成本高昂的基于查询的优化，而暴露模型脆弱性的自然变换在事先是未知的。我们提出了 extbf{对抗场景攻击（ASA）}，这是一种基于查询的黑箱框架，通过使用多模态语言模型和现代文本引导生成编辑器，搜索自然语言编辑场景。ASA通过赢家-输家反馈共同探索背景、天气和材料/颜色变换，并使用贪婪探索器仅组合改善攻击的场景。在多种ImageNet分类器中，ASA的攻击成功率显著高于先前的基于查询的生成攻击，同时需要更少的受害者模型查询，并保持竞争性的感知质量。此外，ASA在图像级和提示级上均表现出可转移性：其对抗图像在不同的受害者模型架构中仍然有效，而其发现的编辑场景可以在同类图像之间重用，并且在某些情况下可以跨架构重用。这些发现表明，视觉模型对自然变换模式具有可重用的脆弱性，而ASA能够在黑箱环境中高效识别这些脆弱性。
cs.CV / 157 / 2609.07125
A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction
基于对象状态预测的自我中心关键对象识别的两阶段框架
Abstract
This paper presents a novel framework designed to enhance key object identification in autonomous driving. Existing methods primarily focus on either detecting objects independently or leveraging visual relationships, but they do not explicitly consider the ego vehicle's perspective in determining object importance. To address this gap, we propose a structured approach that integrates a virtual ego-vehicle representation and a modular object state predictor, enabling a more accurate estimation of object behaviors relative to the ego-vehicle. Subsequently, our framework employs spatial-temporal reasoning to refine key object identification, prioritizing objects based on their states and relative spatial information rather than relying solely on visual relationships. Experimental results on real-world driving datasets demonstrate the effectiveness of our approach in accurately detecting critical objects in complex traffic environments.
Chinese Translation
本文提出了一种新颖的框架，旨在增强自动驾驶中的关键对象识别。现有方法主要集中在独立检测对象或利用视觉关系，但在确定对象重要性时并未明确考虑自我车辆的视角。为了解决这一问题，我们提出了一种结构化的方法，整合了虚拟自我车辆表示和模块化对象状态预测器，使得能够更准确地估计对象相对于自我车辆的行为。随后，我们的框架采用时空推理来优化关键对象识别，优先考虑基于对象状态和相对空间信息的对象，而不仅仅依赖于视觉关系。在真实驾驶数据集上的实验结果表明，我们的方法在复杂交通环境中准确检测关键对象的有效性。
cs.CV / 158 / 2609.07135
NutriBench-Kitchen: Benchmarking Embodied AI for Nutrition Management
NutriBench-Kitchen：营养管理的具身人工智能基准测试
Abstract
An embodied kitchen assistant must do more than recognize food in isolated frames. It must track ingredient states over time and integrate visual observations with recipe and nutritional knowledge to support constraint-aware decision-making. We formalize this capability as \emph{Embodied Nutrition Management}: perceiving nutrition-relevant events, maintaining a persistent food state, and using it for knowledge-grounded planning. Existing benchmarks evaluate static food understanding or embodied cooking actions, but do not measure whether an agent can continuously update and use nutrition-relevant states in dynamic kitchens. To fill this gap, we introduce \textbf{NutriBench-Kitchen}, a benchmark containing 1,500 manually verified question--answer pairs from 160 cooking videos. It covers five task families: Ingredient Entry, Memory Management, Recipe Query, Long-Term Planning, and Short-Term Planning, spanning food-state construction, maintenance, knowledge retrieval, and decision-making across different planning horizons. Evaluations of proprietary and open-source large vision-language models reveal a substantial gap from human performance, particularly in quantitative ingredient estimation, long-term state tracking, and reasoning under interacting constraints. We further introduce \textbf{Nutri-Vgent}, a diagnostic long-video agent with separate episodic, food-state, and recipe memories. Its consistent improvements demonstrate the value of explicit state representations and structured memory for nutrition management. Together, NutriBench-Kitchen and Nutri-Vgent provide a testbed for studying persistent state tracking and knowledge-grounded reasoning in dynamic kitchens. Code is available at https://github.com/V1ol1n/NutriBench-Kitchen.
Chinese Translation
一个具身厨房助手必须不仅仅是在孤立的画面中识别食物。它还必须随着时间的推移跟踪成分状态，并将视觉观察与食谱和营养知识结合起来，以支持基于约束的决策制定。我们将这一能力形式化为 extit{具身营养管理}：感知与营养相关的事件，维持持久的食品状态，并利用这些状态进行知识驱动的规划。现有的基准测试评估静态的食物理解或具身烹饪动作，但并未测量一个智能体是否能够在动态厨房中持续更新和使用与营养相关的状态。为填补这一空白，我们引入了 extbf{NutriBench-Kitchen}，这是一个包含来自160个烹饪视频的1,500个手动验证问答对的基准测试。它涵盖了五个任务类别：成分输入、记忆管理、食谱查询、长期规划和短期规划，涉及食品状态的构建、维护、知识检索和在不同规划视野下的决策制定。对专有和开源的大型视觉-语言模型的评估显示出与人类表现之间的显著差距，特别是在定量成分估计、长期状态跟踪和在相互约束下的推理方面。我们进一步引入了 extbf{Nutri-Vgent}，一个具有独立的情节、食品状态和食谱记忆的诊断长视频智能体。其持续的改进展示了明确状态表示和结构化记忆在营养管理中的价值。总之，NutriBench-Kitchen和Nutri-Vgent为研究动态厨房中的持久状态跟踪和知识驱动推理提供了一个测试平台。代码可在 https://github.com/V1ol1n/NutriBench-Kitchen 获取。
cs.CV / 159 / 2609.07137
Flow3D-OPD: Multi-Teacher On-Policy Distillation for 3D Geometry Generation with Flow-Matching Diffusion Transformer
Flow3D-OPD：基于流匹配扩散变换器的3D几何生成的多教师在线蒸馏
Abstract
Recent image-to-3D generation models built on flow-matching diffusion Transformers (DiT) can produce high-fidelity meshes, yet their post-training strategy remains largely unexplored. There exist several critical bottlenecks in reinforcement learning: the inherent difficulty of defining comprehensive rewards for 3D geometric quality, and the gradient interference that arises when jointly optimizing heterogeneous objectives. Inspired by the practicability of on-policy distillation (OPD) in large language models and image generation, we propose \textbf{Flow3D-OPD}, a two-stage post-training framework that introduces multi-teacher distillation into 3D geometry generation. In the first stage, we utilize the semi-policy to enhance the foundational capability of the pretrained model and then design an agentic verifier for 3D geometric quality evaluation. Based on the verifier, we could cultivate domain-specialized teacher models via direct preference optimization (DPO). In the second stage, we consolidate heterogeneous expertise into a unified student model through on-policy distillation with hard task-routing sampling and gradient accumulation, which could mitigate the gradient interference in joint optimization. Without relying on elaborate modifications, our straightforward yet effective design achieves consistent improvements across all geometric quality dimensions and surpasses all teacher models in the average metric. Extensive experiments demonstrate that our approach provides an effective paradigm for reinforcement learning in 3D generation.
Chinese Translation
基于流匹配扩散变换器（DiT）的最新图像到3D生成模型能够生成高保真网格，但其后训练策略仍然 largely 未被探索。在强化学习中存在几个关键瓶颈：定义3D几何质量的全面奖励的固有困难，以及在联合优化异构目标时出现的梯度干扰。受到大型语言模型和图像生成中在线蒸馏（OPD）可行性的启发，我们提出了 extbf{Flow3D-OPD}，这是一种将多教师蒸馏引入3D几何生成的两阶段后训练框架。在第一阶段，我们利用半策略增强预训练模型的基础能力，然后设计一个用于3D几何质量评估的智能验证器。基于该验证器，我们可以通过直接偏好优化（DPO）培养领域专门的教师模型。在第二阶段，我们通过带有困难任务路由采样和梯度累积的在线蒸馏，将异构专业知识整合到一个统一的学生模型中，这可以减轻联合优化中的梯度干扰。在不依赖复杂修改的情况下，我们简单而有效的设计在所有几何质量维度上实现了一致的改进，并在平均指标上超越了所有教师模型。大量实验表明，我们的方法为3D生成中的强化学习提供了一种有效的范式。
cs.CV / 160 / 2609.07154
Ambient @ EgoLongQA 2026: Distilling Long-Video perception into a Sub-2B Model
环境@EgoLongQA 2026：将长视频感知提炼为子2B模型
Abstract
We describe our entry to the EgoLongQA track of the Wearable-AI Challenge in ECCV 2026, which placed first in the <=2B parameter division with 0.8279 on the held-out test set. Our system is a single 2B vision-language model that answers multiple-choice questions about ten-minute egocentric videos in one greedy forward pass; It is obtained by distilling the junior perception module of a tool-using agentic pipeline, not the agent itself into a small student, using teacher traces filtered to those that answered correctly. it reaches 89% of the accuracy of the large agentic pipeline using 1.1% of its parameters. This raises a 27.1% base model to 81.4% on our held-out questions. The 2B backbone has 2.2132B parameters and therefore over the divisional limit, to make the entry admissable we prune the multilingual embedding table from 248,320 to 143,469 rows, reaching 1.9985B with provably identical logits on retained rows.
Chinese Translation
我们描述了在2026年ECCV可穿戴人工智能挑战赛中EgoLongQA赛道的参赛作品，该作品在<=2B参数组别中以0.8279的成绩位列第一，基于保留的测试集。我们的系统是一个单一的2B视觉-语言模型，能够在一次贪婪的前向传递中回答关于十分钟自我中心视频的多项选择问题；它是通过将工具使用代理管道的初级感知模块提炼为一个小型学生模型，而不是直接使用代理本身，使用经过过滤的教师轨迹，仅保留那些正确回答的轨迹。该模型达到了大型代理管道89%的准确率，仅使用了其1.1%的参数。这使得基础模型的准确率从27.1%提升至81.4%在我们的保留问题上。该2B主干模型具有22.132亿个参数，因此超过了组别限制，为了使该参赛作品合规，我们将多语言嵌入表从248,320行修剪至143,469行，最终达到19.985亿个参数，并在保留的行上证明了logits完全相同。
cs.CV / 161 / 2609.07155
Mind the Approximation: Fisher-Weighted SVD Compression for ViTs
注意近似：针对视觉变换器的Fisher加权SVD压缩
Abstract
Model compression is key to mitigate deployment challenges of ever growing machine learning models. In this area of research, singular value decomposition (SVD)-based compression offers a compelling trade-off between computational efficiency and model accuracy. Fisher-weighted SVD in particular provides principled, loss-aware compression. However, we find that improving the fidelity of Fisher approximation used in the compression is poorly predictive of post-compression accuracy for Vision Transformers (ViTs). Motivated by this observation, we propose FACTS, a structured Fisher Approximation tailored to Compressing ViTs with Fisher-weighted SVD, which enforces token-local aggregation while preserving within-token activation-gradient dependence. Additionally, we introduce a fast Constrained Rank Search (CoRS), that optimizes layer-wise rank allocation while adhering to a fixed floating point operation (FLOP) constraint. Extensive experiments across ViTs and hybrid architectures demonstrate that FACTS consistently improves accuracy-efficiency trade-offs without requiring finetuning. Notably, it outperforms the strongest SVD baseline by up to +5.8 percentage points (p.p.) Top-1 on Swin-B, with further gains driven by our search method. Code is available at https://github.com/MoritzTho/FACTS.
Chinese Translation
模型压缩是缓解日益增长的机器学习模型部署挑战的关键。在这一研究领域，基于奇异值分解（SVD）的压缩在计算效率和模型准确性之间提供了一个引人注目的权衡。特别是，Fisher加权SVD提供了原则性、考虑损失的压缩。然而，我们发现，改善用于压缩的Fisher近似的保真度对于视觉变换器（ViTs）的压缩后准确性预测效果不佳。基于这一观察，我们提出了FACTS，一种针对使用Fisher加权SVD压缩ViTs的结构化Fisher近似，它在保留token内激活梯度依赖的同时，强制进行token局部聚合。此外，我们引入了一种快速约束秩搜索（CoRS），它在遵循固定浮点运算（FLOP）约束的同时，优化逐层的秩分配。在ViTs和混合架构上的大量实验表明，FACTS始终在不需要微调的情况下改善准确性与效率的权衡。值得注意的是，它在Swin-B上比最强的SVD基线提高了多达5.8个百分点（p.p.）的Top-1准确率，进一步的提升得益于我们的方法。代码可在https://github.com/MoritzTho/FACTS获取。
cs.CV / 162 / 2609.07159
Unsupervised Domain Adaptation for Symbol Spotting in Historical Encrypted Manuscripts
历史加密手稿中的无监督领域适应符号识别
Abstract
The decipherment of historical encrypted manuscripts poses a fundamental challenge in Digital Humanities: before any transcription can begin, the symbol inventory of the underlying cipher alphabet must first be identified and characterized. We address this challenge through symbol spotting: given a candidate alphabet specified as a set of rendered font glyphs, the task is to determine whether and where its characters appear in an unseen handwritten document, without any labeled examples from the target script. The main difficulty lies in the domain gap between clean, digitally rendered font queries and degraded handwritten manuscript symbols. We propose a three-stage pipeline that bridges this gap without manual annotation, combining a joint SimCLR+DANN encoder for domain-invariant glyph representations with an embedding-space style-adaptation mechanism applied at retrieval time, requiring no re-training. Experiments on fourteen pages from seven encrypted manuscript collections show that our method outperforms zero-shot foundation models, including CLIP and DINOv2, by a large margin ($+0.194$ P@1 over CLIP ViT-L/14), and surpasses task-specific trained baselines by $+0.138$ P@1. We further demonstrate that the Raw-Cover metric, computed in a fully unsupervised setting, provides a meaningful script-family fingerprint that identifies the underlying alphabet of an unknown document. This capability is of direct practical relevance to palaeographers, historians, and other researchers working with undeciphered manuscripts.
Chinese Translation
解读历史加密手稿在数字人文学科中提出了一个基本挑战：在任何转录工作开始之前，必须首先识别和表征基础密码字母表的符号库存。我们通过符号识别来应对这一挑战：给定一个作为渲染字体字形集合指定的候选字母表，任务是确定其字符是否以及在未见过的手写文档中出现的位置，而不需要目标脚本的任何标记示例。主要困难在于干净的数字渲染字体查询与退化的手写手稿符号之间的领域差距。我们提出了一个三阶段的流程，弥合这一差距，无需手动注释，结合了用于领域不变字形表示的联合 SimCLR+DANN 编码器和在检索时应用的嵌入空间风格适应机制，无需重新训练。在七个加密手稿集合的十四页实验中，我们的方法在 P@1 指标上超越了零样本基础模型，包括 CLIP 和 DINOv2，差距达到 $+0.194$（相较于 CLIP ViT-L/14），并且超越了任务特定训练的基线，提升了 $+0.138$ P@1。我们进一步证明，在完全无监督的环境中计算的 Raw-Cover 指标提供了一种有意义的脚本家族指纹，能够识别未知文档的基础字母表。这一能力对古文字学家、历史学家以及其他处理未解读手稿的研究人员具有直接的实际意义。
cs.CV / 163 / 2609.07178
Weakly-supervised Kidney Tumor Classification from CT Scans with Multi-Instance Learning and Anatomical Filtering
基于多实例学习和解剖过滤的CT扫描弱监督肾肿瘤分类
Abstract
Deep learning models for CT scan analysis are often limited by the scarcity of precise pixel-level annotations, which require significant radiologist effort to produce. Training on scan-level labels alone reduces annotation requirements but introduces challenges: low supervision ratios and large input volumes make models prone to overfitting and shortcut learning. In this work, we investigate two complementary methods to address these challenges: multi-instance learning (MIL) and anatomical filtering. MIL divides CT volumes into 2D slice instances, enabling efficient 2D architectures with ImageNet pretraining rather than computationally demanding 3D models. Anatomical filtering uses Compass, our self-supervised body part regression model, to crop scans to pathology-relevant subregions without requiring segmentation masks. We evaluate two MIL frameworks - Attention-based MIL (ABMIL) and FocusMIL - on kidney tumor classification across one internal dataset (TUH) and two external datasets (KiTS23 and TCGA-KiRC). Our best models achieve F1 = 0.83 on the internal test set using only scan-level labels. We further show that anatomical filtering with the Compass model is critical for the out-of-distribution generalization of embedding-based ABMIL, while instance-based FocusMIL demonstrates greater inherent robustness to distribution shift. While evaluated on kidney tumors, we consider this a proof-of-concept for a broader weakly supervised CT classification pipeline applicable to other organs and pathologies.
Chinese Translation
深度学习模型在CT扫描分析中的应用常常受到精确像素级标注稀缺的限制，这些标注需要放射科医生付出大量的努力来生成。仅依赖扫描级标签进行训练虽然减少了标注需求，但也带来了挑战：低监督比例和大量输入数据使得模型容易出现过拟合和捷径学习。在本研究中，我们探讨了两种互补的方法来应对这些挑战：多实例学习（Multi-Instance Learning, MIL）和解剖过滤。MIL将CT体积划分为2D切片实例，使得可以使用经过ImageNet预训练的高效2D架构，而不是计算量大的3D模型。解剖过滤利用我们的自监督身体部位回归模型Compass，能够在不需要分割掩膜的情况下，将扫描裁剪为与病理相关的子区域。我们在一个内部数据集（TUH）和两个外部数据集（KiTS23和TCGA-KiRC）上评估了两种MIL框架——基于注意力的MIL（Attention-based MIL, ABMIL）和FocusMIL，在肾肿瘤分类任务中。我们的最佳模型在仅使用扫描级标签的情况下，在内部测试集上达到了F1 = 0.83。我们进一步表明，使用Compass模型的解剖过滤对于基于嵌入的ABMIL的分布外泛化至关重要，而基于实例的FocusMIL则表现出对分布变化更大的固有鲁棒性。尽管本研究评估了肾肿瘤，但我们认为这是一个适用于其他器官和病理的更广泛弱监督CT分类管道的概念验证。
cs.CV / 164 / 2609.07180
Deep Learning for Biopsy-Free Subtyping of Basal Cell Carcinoma from Dermatoscopic Images
基于深度学习的无活检基底细胞癌亚型分类研究：来自皮肤镜图像的分析
Abstract
Basal Cell Carcinoma (BCC) is the most common type of skin cancer, accounting for nearly 80% of skin cancer di- agnoses. Its optimal clinical management is guided by the distinct histopathologic subtype, with aggressive variants requiring more drastic measures. In current clinical practice, subtyping relies on skin biopsies, a procedure both costly and invasive. In this paper, we conduct a preliminary investigation into using deep learning for BCC subtyping, solely from a single dermatoscopic image of the lesion. Given the limited data at our disposal, we employ pre-trained vision transformers (ViTs), a state-of-the-art family of models highly effective for challenging downstream tasks with limited labeled data. Through repeated stratified k-fold cross-validation, we demonstrate that ViTs can achieve superior performance (AUC 0.784 on a dataset of 1271 dermatoscopic images of various BCC subtypes) over standard CNN-based baselines as well as previously-reported human reader perfor- mance, on the task of differentiating aggressive BCCs from other subtype families. These initial findings highlight the potential of combining deep learning and dermatoscopy to provide a biopsy- free alternative for BCC subtyping, thus aiding in improving treatment planning and patient outcomes.
Chinese Translation
基底细胞癌（BCC）是最常见的皮肤癌类型，约占皮肤癌诊断的80%。其最佳临床管理依赖于不同的组织病理亚型，其中侵袭性变种需要采取更为严厉的措施。在当前的临床实践中，亚型分类依赖于皮肤活检，这是一种既昂贵又具侵入性的程序。本文对利用深度学习进行BCC亚型分类进行了初步研究，仅基于病变的单张皮肤镜图像。鉴于我们可用数据的有限性，我们采用了预训练的视觉变换器（Vision Transformers, ViTs），这是一类在处理有限标注数据的挑战性下游任务中表现优异的最先进模型。通过反复的分层k折交叉验证，我们证明了ViTs在将侵袭性BCC与其他亚型区分的任务中，能够实现优于标准卷积神经网络（CNN）基线以及先前报告的人类读者表现的卓越性能（在1271张不同BCC亚型的皮肤镜图像数据集上，AUC为0.784）。这些初步发现突显了将深度学习与皮肤镜检查相结合的潜力，为BCC亚型分类提供了一种无活检的替代方案，从而有助于改善治疗规划和患者预后。
cs.CV / 165 / 2609.07188
CHILD: Human-in-the-Loop OOD Detection for Safe Clinical Deployment
CHILD：人机协作的OOD检测框架以确保临床安全部署
Abstract
Out-of-distribution (OOD) detection is critical for safe deployment of medical AI systems. Recently, test-time adaptation (TTA) has emerged as a new paradigm for OOD detection, automatically adjusting detector behavior during deployment. However, such automatic adaptation mechanisms may raise safety concerns in safety-critical clinical environments. While physician oversight can mitigate these risks, it is resource-intensive and must be judiciously allocated. To reconcile safety with efficiency, we propose CHILD, a training-free framework designed to enhance streaming OOD detection via sparse human feedback. Operating under strict budget constraints, CHILD employs an adaptive risk-aware sample selection mechanism to pinpoint only the most decision-uncertain samples for review. Crucially, it maximizes the utility of this sparse feedback through a retrieval-based score calibration module, which refines model predictions using a compact feature cache without any parameter updates. Extensive experiments on four medical benchmarks demonstrate that CHILD turns limited supervision into significant reliability gains: with a sparse feedback budget of only 5%, it reduces the average FPR95 from 72.63% to 60.26% and improves AUROC from 75.53% to 81.85%, consistently outperforming state-of-the-art baselines. Our code is publicly available at https://github.com/figec/CHILD.
Chinese Translation
分布外（OOD）检测对于医疗人工智能系统的安全部署至关重要。最近，测试时适应（TTA）作为一种新的OOD检测范式出现，能够在部署过程中自动调整检测器的行为。然而，这种自动适应机制可能在安全关键的临床环境中引发安全隐患。虽然医生的监督可以减轻这些风险，但这是一项资源密集型的工作，必须谨慎分配。为了解决安全性与效率之间的矛盾，我们提出了CHILD，一个无训练的框架，旨在通过稀疏的人类反馈增强流式OOD检测。在严格的预算限制下，CHILD采用了一种自适应风险感知的样本选择机制，仅针对决策不确定性最高的样本进行审查。关键在于，它通过基于检索的评分校准模块最大化这种稀疏反馈的效用，该模块利用紧凑的特征缓存在不进行任何参数更新的情况下优化模型预测。在四个医疗基准上的大量实验表明，CHILD能够将有限的监督转化为显著的可靠性提升：在仅5%的稀疏反馈预算下，平均FPR95从72.63%降低到60.26%，AUROC从75.53%提高到81.85%，始终优于最先进的基线。我们的代码已公开可用，网址为 https://github.com/figec/CHILD。
cs.CV / 166 / 2609.07216
The Price of Consistency: Exploiting Visual Anchors for Multimodal Jailbreaking in Video Generation
一致性的代价：利用视觉锚点进行视频生成中的多模态越狱
Abstract
The rapid evolution of video generation has shifted the paradigm from pure text-driven to multi-conditional controllable generation, with reference images now widely adopted as conditional inputs to achieve superior spatiotemporal consistency. While these reference images serve as powerful visual anchors that significantly enhance controllability, their impact on safety remains largely unexplored. In this work, we reveal the visual anchoring effect: by enforcing consistency, the mechanism prevents the generated content from drifting away from the original harmful intent, thereby eliminating the model's natural safety escape route from harmful to benign content. Consequently, visual anchors inherently increase the safety risk---this is the price of consistency. Building on this insight, we propose Decoupling Intent via Visual Anchors (DIVA), a training-free multimodal jailbreak framework for video generation that exploits this vulnerability. DIVA decouples harmful intent into a static visual anchor image and a dynamic motion text prompt, and employs dual-criteria selection to balance attack stealthiness with semantic preservation. Extensive experiments across various leading commercial platforms and mainstream open-source video generation models demonstrate that DIVA achieves a substantially higher Attack Success Rate than existing text-only methods. To facilitate future research, we additionally contribute TI2VSafetyBench, the first safety benchmark for multi-conditional video generation.
Chinese Translation
视频生成的快速发展已经将范式从纯文本驱动转变为多条件可控生成，参考图像现已被广泛采用作为条件输入，以实现更优的时空一致性。虽然这些参考图像作为强大的视觉锚点显著增强了可控性，但它们对安全性的影响仍然未被充分探索。在本研究中，我们揭示了视觉锚定效应：通过强制一致性，该机制防止生成内容偏离原始的有害意图，从而消除了模型从有害内容到良性内容的自然安全逃逸路径。因此，视觉锚点本质上增加了安全风险——这就是一致性的代价。基于这一见解，我们提出了通过视觉锚点解耦意图（Decoupling Intent via Visual Anchors, DIVA），这是一个无训练的多模态越狱框架，旨在利用这一脆弱性。DIVA将有害意图解耦为静态视觉锚图像和动态运动文本提示，并采用双标准选择来平衡攻击隐蔽性与语义保留。在多个领先商业平台和主流开源视频生成模型上的广泛实验表明，DIVA的攻击成功率显著高于现有的仅文本方法。为了促进未来的研究，我们还贡献了TI2VSafetyBench，这是第一个多条件视频生成的安全基准。
cs.CV / 167 / 2609.07229
Proximity-CLIP: Text-Guided Semantic Proximity Learning for Zero-Shot Anomaly Detection
Proximity-CLIP：基于文本引导的语义邻近学习用于零-shot异常检测
Abstract
Vision-language models offer a promising approach for zero-shot anomaly detection (ZSAD). However, due to object-centric bias, normal and anomalous text prototypes exhibit a high semantic overlap. While enforcing strict orthogonality between them improves discriminability, mapping highly contiguous visual inputs onto drastically orthogonal prototypes introduces a geometric dilemma, disrupting the pre-trained structural continuity. To address this problem, we propose Proximity-CLIP, a framework that visually calibrates the semantic margin to guide visual adaptation. First, we introduce a visually-calibrated semantic proximity learning mechanism that uses a bounded dynamic regularization to learn an appropriate semantic margin, ensuring discriminative separation while preserving structural alignment. Second, we design an Anomaly Query Module (AQM) driven by these text priors. Using the calibrated anomalous prototype as a semantic query, the AQM actively retrieves localized defect cues from contextual visual patches, mitigating the dilution of subtle anomalies during global pooling. Extensive experiments demonstrate that Proximity-CLIP outperforms current state-of-the-art methods across multiple ZSAD benchmarks with minimal architectural modifications.
Chinese Translation
视觉-语言模型为零-shot异常检测（ZSAD）提供了一种有前景的方法。然而，由于以对象为中心的偏见，正常和异常的文本原型表现出高度的语义重叠。虽然强制它们之间的严格正交性可以提高可区分性，但将高度相邻的视觉输入映射到截然正交的原型上引入了几何困境，破坏了预训练的结构连续性。为了解决这个问题，我们提出了Proximity-CLIP，一个通过视觉校准语义边界来引导视觉适应的框架。首先，我们引入了一种视觉校准的语义邻近学习机制，使用有界动态正则化来学习适当的语义边界，确保可区分的分离，同时保持结构对齐。其次，我们设计了一个由这些文本先验驱动的异常查询模块（AQM）。使用校准后的异常原型作为语义查询，AQM主动从上下文视觉块中检索局部缺陷线索，减轻全局池化过程中细微异常的稀释。大量实验表明，Proximity-CLIP在多个ZSAD基准测试中以最小的架构修改超越了当前的最先进方法。
cs.CV / 168 / 2609.07258
MV-STRIDE: Enabling MLLMs to Master Multi-View Spatial Reasoning via Hierarchical Capability Modeling
MV-STRIDE：通过分层能力建模使多模态大型语言模型掌握多视角空间推理
Abstract
Despite the rapid progress of Multimodal Large Language Models (MLLMs) in 2D vision-language tasks, robust multi-view spatial reasoning remains a fundamental bottleneck due to the lack of structured 3D cognitive pathways in existing datasets. To address this, we introduce MV-STRIDE, a Multi-View hierarchical SpaTial Reasoning dataset with Interdependent and DEcomposed capabilitiEs. Moving beyond flat data structures, MV-STRIDE explicitly models the dependency relationships between foundational perception, scene understanding, and complex contextual reasoning, providing a coherent learning pathway aligned with human spatial cognition. We develop a systematic QA generation pipeline leveraging diverse 3D scene sources that enforces cross-view dependency constraints to prevent single-view solvability, generating multi-level spatial reasoning tasks supported by cognitively grounded chain-of-thought supervision for complex inference. Extensive evaluations demonstrate that our multi-stage training framework based on our hierarchical dataset achieves state-of-the-art performance across multiple spatial reasoning benchmarks, notably the multi-view oriented MMSI-Bench. Our approach enables MLLMs to maintain robust, 3D-consistent spatial reasoning across diverse viewpoints. The code and dataset are available at https://co1dspring.github.io/MV-STRIDE/.
Chinese Translation
尽管多模态大型语言模型（MLLMs）在二维视觉-语言任务中取得了快速进展，但由于现有数据集中缺乏结构化的三维认知路径，稳健的多视角空间推理仍然是一个基本瓶颈。为了解决这一问题，我们提出了MV-STRIDE，一个具有相互依赖和分解能力的多视角分层空间推理数据集。MV-STRIDE超越了平面数据结构，明确建模了基础感知、场景理解和复杂上下文推理之间的依赖关系，提供了与人类空间认知相一致的连贯学习路径。我们开发了一个系统化的问答生成管道，利用多样的三维场景来源，强制实施跨视角依赖约束，以防止单视角可解性，生成由认知基础的思维链监督支持的多层次空间推理任务。广泛的评估表明，我们基于分层数据集的多阶段训练框架在多个空间推理基准测试中实现了最先进的性能，特别是在面向多视角的MMSI-Bench上。我们的方法使MLLMs能够在不同视角下保持稳健的、三维一致的空间推理。代码和数据集可在https://co1dspring.github.io/MV-STRIDE/获取。
cs.CV / 169 / 2609.07298
KODAMA: Multimodal Digital Twin Reconstruction for Urban RF Propagation Modelling
KODAMA：用于城市射频传播建模的多模态数字双胞胎重建
Abstract
3D reconstruction typically strives for geometric fidelity or visual plausibility. Radio frequency digital twins (RFDT) are instead judged by whether communication channels behave in them as they do in the real world. RFDTs promise site-specific channel prediction but current practice forces a choice between coarse automated scenes and hand-built, measurement-calibrated models that take weeks to construct per-site. We present KODAMA, an automated pipeline that reconstructs ray tracing-ready RFDTs at city scale from off-the-shelf geospatial data alone: aerial imagery, LiDAR, and photogrammetry yield terrain and watertight building meshes, while exposure-weighted multi-view fusion of street-level imagery recovers fa\c{c}ade relief, electromagnetic materials, and clutter---all without site visits or calibration. Across three sites spanning 3.6 to 28 GHz, KODAMA's uncalibrated predictions achieve single-digit RMSE, reducing point-to-point error by up to 5.35 dB over automated baselines and coming within 0.22 dB of a measurement-calibrated, hand-built RFDT.
Chinese Translation
三维重建通常追求几何保真度或视觉可信度。而射频数字双胞胎（RFDT）的评判标准则在于其通信通道是否如现实世界中那样表现。RFDT承诺提供特定场地的信道预测，但当前的实践迫使我们在粗略的自动化场景和手工构建、经过测量校准的模型之间做出选择，后者每个场地的构建需要数周时间。我们提出了KODAMA，一个自动化管道，能够仅通过现成的地理空间数据在城市规模上重建适用于光线追踪的RFDT：航空影像、激光雷达（LiDAR）和摄影测量生成地形和密闭建筑网格，而基于曝光加权的多视角融合街景影像则恢复了立面轮廓、电磁材料和杂物——所有这些都无需现场访问或校准。在覆盖3.6至28 GHz的三个场地中，KODAMA的未校准预测实现了单数字均方根误差（RMSE），在自动化基线之上，点对点误差减少了多达5.35 dB，并且与经过测量校准的手工构建RFDT的误差仅相差0.22 dB。
cs.CV / 170 / 2609.07336
CRISP: Corneal Confocal Microscopy Real-Time Image Stitching Pipeline
CRISP：角膜共聚焦显微镜实时图像拼接管道
Abstract
Morphology of the sub-basal nerve plexus (SNP) reflects peripheral nerve health, and corneal confocal microscopy (CCM) provides an important means for in vivo, real-time, non-invasive observation of the SNP. However, mainstream CCM devices offer a limited field of view per frame, whereas the SNP is spatially non-uniform; discrete image sampling is therefore sensitive to sampling location and frame selection, which limits the reproducibility and clinical adoption of CCM as a quantitative assessment tool. Wide-field stitching can reconstruct larger SNP mosaics by integrating sequentially acquired CCM images, but existing methods largely rely on offline post-processing, additional hardware, or specific acquisition protocols, and lack open-source real-time solutions for conventional CCM video streams. This paper presents CRISP (Corneal confocal microscopy Real-time Image Stitching Pipeline), an open-source real-time SNP wide-field stitching framework for conventional CCM examination video streams. CRISP excludes defocused and discontinuous segments via focus-aware gating, propagates poses through local pairwise registration, and maintains non-redundant spatial coverage with a sparse anchor map; when local temporal continuity is interrupted, the system completes relocalization and subgraph merging through global appearance retrieval followed by geometric verification. The framework prioritizes low-latency coverage feedback during examination while outputting accepted frames, poses, and anchor information to initialize offline fine stitching. To our knowledge, CRISP is the first open-source real-time SNP wide-field stitching framework released for conventional CCM video streams. By lowering the barrier to adoption and reproduction of wide-field stitching, CRISP may help move SNP wide-field imaging from a research tool into routine clinical examination workflows.
Chinese Translation
基底神经丛（SNP）的形态反映了外周神经的健康状况，而角膜共聚焦显微镜（CCM）为SNP的体内、实时、非侵入性观察提供了重要手段。然而，主流的CCM设备每帧提供的视野有限，而SNP在空间上是不均匀的；因此，离散的图像采样对采样位置和帧选择非常敏感，这限制了CCM作为定量评估工具的可重复性和临床应用。宽视场拼接可以通过整合顺序获取的CCM图像重建更大的SNP马赛克，但现有方法大多依赖于离线后处理、额外硬件或特定采集协议，并且缺乏针对常规CCM视频流的开源实时解决方案。本文提出了CRISP（角膜共聚焦显微镜实时图像拼接管道），这是一个针对常规CCM检查视频流的开源实时SNP宽视场拼接框架。CRISP通过聚焦感知门控排除失焦和不连续的片段，通过局部成对配准传播姿态，并通过稀疏锚图保持非冗余的空间覆盖；当局部时间连续性中断时，系统通过全局外观检索和几何验证完成重新定位和子图合并。该框架在检查过程中优先考虑低延迟的覆盖反馈，同时输出接受的帧、姿态和锚信息，以初始化离线精细拼接。据我们所知，CRISP是第一个为常规CCM视频流发布的开源实时SNP宽视场拼接框架。通过降低宽视场拼接的采用和再现门槛，CRISP可能有助于将SNP宽视场成像从研究工具转变为常规临床检查工作流程。
cs.CV / 171 / 2609.07346
From Explicit References to Scene Manifolds: Distributional Fidelity and Realism for Radiance Field Quality Assessment
从显式引用到场景流形：辐射场质量评估的分布保真度与现实感
Abstract
Radiance field representations such as 3D Gaussian Splatting (3DGS) enable high-quality novel view synthesis but can introduce complex, view-dependent artifacts from reconstruction, rendering, and compression. Reliable perceptual quality assessment (QA) is thus essential for evaluating rendered views and guiding the design of perceptually faithful scene representations. Existing full-reference QA metrics require an aligned reference image, while recent cross-reference metrics relax this requirement by comparing a test view with non-aligned references. However, under wide-baseline radiance field settings, selecting a reliable nearby reference can be difficult, particularly when evaluating views along arbitrary trajectories and poses. We propose SCODA, a lightweight scene-conditioned objective QA method that shifts QA from explicit image-to-image comparison to scene-manifold modeling. High-quality observations of each scene are represented as a multivariate Gaussian distribution in deep feature space, producing a semantic fidelity score that measures deviation from the scene distribution. A weakly-supervised distortion-aware patch discriminator provides a complementary realism signal, and both cues are combined through an unsupervised bounded fusion strategy. Experiments on multiple benchmarks show strong agreement with human judgments and robust generalization across GS- and NeRF-generated views and trajectories. Code is publicly available at https://gitlab.com/saeedmp/scoda.
Chinese Translation
辐射场表示（如3D高斯溅射（3DGS））能够实现高质量的新视图合成，但可能会引入来自重建、渲染和压缩的复杂视角依赖性伪影。因此，可靠的感知质量评估（QA）对于评估渲染视图和指导感知真实的场景表示设计至关重要。现有的全参考QA指标需要对齐的参考图像，而最近的交叉参考指标通过将测试视图与非对齐的参考进行比较，放宽了这一要求。然而，在宽基线辐射场设置下，选择可靠的邻近参考可能会很困难，特别是在沿任意轨迹和姿态评估视图时。我们提出了SCODA，一种轻量级场景条件目标QA方法，它将QA从显式的图像对图像比较转变为场景流形建模。每个场景的高质量观察被表示为深度特征空间中的多元高斯分布，从而产生一个语义保真度评分，衡量与场景分布的偏差。一个弱监督的失真感知补丁鉴别器提供了一个互补的现实感信号，两个线索通过无监督的有界融合策略结合在一起。在多个基准上的实验显示出与人类判断的强一致性，并在GS和NeRF生成的视图和轨迹上表现出强大的泛化能力。代码可在 https://gitlab.com/saeedmp/scoda 获取。
cs.CV / 172 / 2609.07369
DF26: We Cannot Tell Fake From Real Anymore
DF26：我们再也无法区分虚假与真实
Abstract
We introduce DF26, a novel benchmark for detecting AI-generated videos containing fully synthetic clips produced by recent text-to-video and image-to-video models. The videos capture single-person public-speaking scenarios, spanning direct-to-camera recordings, official statements, and studio interviews - 271 real and 2,420 synthetic videos generated by seven modern video models. The study on DF26 shows that human performance in detecting AI-generated videos, as well as state-of-the-art deepfake detectors, is close to random chance. Our results highlight the limitations of current evaluation protocols and motivate the need for benchmarks that explicitly measure robustness to modern generative model distribution shifts.
Chinese Translation
我们介绍了DF26，这是一个用于检测AI生成视频的新基准，包含由最新的文本到视频和图像到视频模型生成的完全合成片段。这些视频捕捉了单人公共演讲场景，包括直接对着摄像机的录制、官方声明和演播室访谈，共有271个真实视频和2420个由七个现代视频模型生成的合成视频。对DF26的研究表明，人类在检测AI生成视频方面的表现，以及最先进的深度伪造检测器的表现，接近随机猜测。我们的结果突显了当前评估协议的局限性，并激励了对能够明确测量对现代生成模型分布变化的鲁棒性的基准的需求。
cs.CV / 173 / 2609.07373
TRAIL: Trajectory-Aware Visual Place Recognition against Unordered Databases
TRAIL：针对无序数据库的轨迹感知视觉位置识别
Abstract
Modern Visual Place Recognition (VPR) methods excel on standard benchmarks yet remain brittle in feature-poor environments. By treating each query image in isolation, they discard the sequential context in any real trajectory. We formalize a task that exploits this context: given a query sequence, localize the final image against an unordered reference database -- which, unlike sequence-to-sequence methods, requires no sequential structure in the database. We propose TRAIL (TRajectory-Aware Image Localization), a principled framework based on Conditional Random Fields (CRF) that combines learned functions for visual similarity and for camera-motion consistency, refining a distribution over candidate references as each query arrives. A lightweight post-processing layer atop any pre-trained VPR backbone, TRAIL improves a state-of-the-art baseline by up to 8.3 percentage points on our primary benchmark, transfers to unseen datasets without retraining, and delivers its largest gains where visual cues are scarce.
Chinese Translation
现代视觉位置识别（VPR）方法在标准基准测试中表现出色，但在特征稀缺的环境中仍然脆弱。通过将每个查询图像孤立处理，它们忽略了任何真实轨迹中的顺序上下文。我们正式定义了一个利用这一上下文的任务：给定一个查询序列，在无序参考数据库中定位最终图像——与序列到序列方法不同，这不需要数据库中的顺序结构。我们提出了TRAIL（轨迹感知图像定位），这是一个基于条件随机场（CRF）的原则性框架，结合了视觉相似性和相机运动一致性的学习函数，随着每个查询的到来，细化候选参考的分布。作为任何预训练VPR主干网络之上的轻量级后处理层，TRAIL在我们的主要基准测试中将最先进的基线提升了多达8.3个百分点，能够在不重新训练的情况下迁移到未见过的数据集，并在视觉线索稀缺的情况下提供最大的收益。
cs.CV / 174 / 2609.07394
Social Intuition vs. Machine Reasoning: Anticipating Human-Robot Interaction from multiple modalities
社会直觉与机器推理：从多模态预测人机交互
Abstract
Anticipating whether a person will interact from one's own perspective is a highly intuitive task for humans, that relies on a combination of cues. We investigate how humans perform at predicting a person's intention to interact from a service robot's point of view, using pose-only or full video input, then benchmark different lightweight pose-based models and state-of-the-art vision-language models. We conducted our benchmark on the HUI360 dataset on a fixed pilot subset of 100 test tracks (25 positive, 75 negative). We found that with pose-only input, human annotators outperform lightweight trained pose models but not by large margins (+0.08 in F1-Score). But when given full egocentric video with a target bounding box, human annotators perform substantially better and largely outperform the Vision-Language Models (+0.2 in F1-Score). We also compared VLMs of different size and under different input conditions, and found that the best results do not correlate with model size. Our result confirms that predicting interactions is a challenging task for social robots and that reasoning-capable models are necessary but their actual reasoning capabilities alone do not suffice to match the social intuition of humans.
Chinese Translation
从自身的角度预测一个人是否会进行交互是人类高度直观的任务，这依赖于多种线索的结合。我们研究了人类如何从服务机器人的视角预测一个人的交互意图，使用仅姿态或完整视频输入，并基准测试了不同的轻量级基于姿态的模型和最先进的视觉语言模型。我们在HUI360数据集上对100个测试轨迹（25个正样本，75个负样本）的固定试点子集进行了基准测试。我们发现，在仅使用姿态输入的情况下，人类注释者的表现优于轻量级训练的姿态模型，但差距不大（F1分数提高0.08）。然而，当提供完整的自我中心视频和目标边界框时，人类注释者的表现显著更好，远远超过视觉语言模型（F1分数提高0.2）。我们还比较了不同规模的视觉语言模型在不同输入条件下的表现，发现最佳结果与模型规模并无相关性。我们的结果确认了预测交互对社交机器人而言是一项具有挑战性的任务，具备推理能力的模型是必要的，但仅凭其实际的推理能力并不足以匹配人类的社会直觉。
cs.CV / 175 / 2609.07403
Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification
基于服务器端视觉-语言推理的联邦二元门控监控异常分类
Abstract
Privacy-sensitive surveillance systems could benefit from large vision-language models (VLMs), but such models typically require centralized access to raw video. In federated learning settings, this challenge is amplified by non-independent and identically distributed (non-IID) client data, which can make direct multiclass anomaly classification unstable, especially for rare categories. We propose a hybrid two-stage architecture that combines a federated binary convolutional neural network (CNN) gate with server-side zero-shot VLM inference. The lightweight LiteCNN3D gate performs local anomaly screening and forwards only flagged videos to Qwen3-VL-8B, which assigns them to four anomaly metaclasses. We evaluate this design on UCF-Crime grouped into five coarse metaclasses and implement the federated stage in a real three-node heterogeneous deployment. In the studied setting, direct federated multiclass training collapses, whereas the proposed decomposition yields a better trade-off between classification quality and raw-video transmission. With fixed-threshold routing, the federated hybrid pipeline preserves nearly the same macro-averaged F1 score (F1-macro) as its centralized CNN+VLM counterpart while reducing the fraction of transmitted videos to 51.4%, although with a lower proxy macro receiver operating characteristic area under the curve (ROC AUC) than the centralized hybrid system. A complementary sensitivity-oriented routing operating point increases macro ROC AUC from 0.673 to 0.692 and reduces the false negative rate from 29.3% to 22.9%, but decreases F1-macro from 0.503 to 0.485 while increasing transmission from 51.4% to 57.9%. These results suggest that federation is better suited to coarse local screening, while routing rules can be adjusted to trade server-side VLM usage for higher anomaly sensitivity.
Chinese Translation
隐私敏感的监控系统可以受益于大型视觉-语言模型（VLM），但此类模型通常需要对原始视频的集中访问。在联邦学习环境中，这一挑战因客户端数据的非独立同分布（non-IID）而加剧，这可能导致直接的多类别异常分类不稳定，尤其是对于稀有类别。我们提出了一种混合的两阶段架构，将联邦二元卷积神经网络（CNN）门控与服务器端零样本VLM推理相结合。轻量级的LiteCNN3D门控执行本地异常筛选，并仅将标记的视频转发给Qwen3-VL-8B，后者将其分配到四个异常元类别。我们在UCF-Crime数据集上对该设计进行了评估，该数据集被分为五个粗略的元类别，并在真实的三节点异构部署中实现了联邦阶段。在研究的环境中，直接的联邦多类别训练崩溃，而所提出的分解方法在分类质量和原始视频传输之间提供了更好的权衡。通过固定阈值路由，联邦混合管道保持了与其集中式CNN+VLM对应物几乎相同的宏平均F1分数（F1-macro），同时将传输视频的比例减少至51.4%，尽管其代理宏接收者操作特征曲线下面积（ROC AUC）低于集中式混合系统。一个补充的以敏感性为导向的路由操作点将宏ROC AUC从0.673提高到0.692，并将假阴性率从29.3%降低到22.9%，但将F1-macro从0.503降低到0.485，同时将传输比例从51.4%提高到57.9%。这些结果表明，联邦学习更适合粗略的本地筛选，而路由规则可以调整以在更高的异常敏感性与服务器端VLM使用之间进行权衡。
cs.CV / 176 / 2609.07410
Multi-label versus multi-class classification of blood cells and their aggregates in microfluidic channels
微流体通道中血细胞及其聚集体的多标签与多类别分类
Abstract
Deformability cytometry (DC) is a type of imaging flow cytometry, which uses a camera-equipped device to measure cellular stiffness in addition to other cellular properties at high throughput. Cellular properties such as area and elongation can identify cell types, but this requires prior knowledge of distinguishing properties and cannot be applied to clinically important cell aggregates. Using DC data, we evaluated conventional multi-class (MC) classification and introduced a multi-label (ML) approach for identifying blood cells and their aggregates. In particular, an ML classifier can simultaneously assign multiple cell-type labels to a single imaged event. We show that, unlike MC classification, ML classification can identify cell aggregates not represented in the training data. It also avoids the need for exhaustive, strictly defined aggregate labels, thereby simplifying and speeding up annotation. Since automated blood analyzers do not reliably analyze cell aggregates, our approach may help address this clinical gap.
Chinese Translation
变形细胞计数技术（Deformability cytometry, DC）是一种成像流式细胞术，利用配备相机的设备在高通量下测量细胞的刚度及其他细胞特性。细胞特性如面积和延伸率可以用于识别细胞类型，但这需要对区分特性有先验知识，并且无法应用于临床重要的细胞聚集体。利用DC数据，我们评估了传统的多类别（Multi-class, MC）分类，并引入了一种多标签（Multi-label, ML）方法来识别血细胞及其聚集体。特别地，ML分类器可以同时为单个成像事件分配多个细胞类型标签。我们展示了与MC分类不同，ML分类能够识别训练数据中未表示的细胞聚集体。它还避免了对严格定义的聚集体标签的详尽需求，从而简化并加快了标注过程。由于自动化血液分析仪无法可靠地分析细胞聚集体，我们的方法可能有助于填补这一临床空白。
cs.CV / 177 / 2609.07414
RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting
RelightFormer：用于多视角物体重光照的前馈生成变换器
Abstract
Image relighting is traditionally tackled via complex inverse rendering pipelines, which suffer from ill-posed optimization, or single-image generative models that ignore crucial multi-view cues necessary for understanding 3D geometry and material interactions. To address these limitations, we introduce a feed-forward generative Transformer for direct single- and multi-view image relighting that entirely bypasses explicit intrinsic property estimation. Adapted from a video foundation model, our architecture features a latent illumination module that dynamically injects target environment maps into spatial features via cross-attention. Furthermore, we employ permutation-invariant positional encodings to symmetrically process unordered multi-view inputs without sequential bias. To train this robust data-driven model, we construct the massive Laval Objaverse Dataset (LOD), comprising 90K objects and 39K unique illuminations. Extensive experiments demonstrate state-of-the-art visual quality, photorealistic relighting quality, and strong zero-shot generalization across single-view, multi-view, and novel-view relighting tasks.
Chinese Translation
图像重光照传统上通过复杂的逆渲染流程来解决，这些流程面临不适定优化的问题，或者通过忽略理解三维几何和材料交互所需的关键多视角线索的单图像生成模型来处理。为了解决这些局限性，我们提出了一种前馈生成变换器，用于直接进行单视角和多视角图像重光照，完全绕过显式内在属性估计。我们的架构改编自视频基础模型，具有一个潜在照明模块，通过交叉注意力动态地将目标环境图注入空间特征。此外，我们采用不变于排列的位置编码，能够对无序的多视角输入进行对称处理，而不产生顺序偏差。为了训练这个强大的数据驱动模型，我们构建了庞大的Laval Objaverse数据集（LOD），包含90K个物体和39K个独特的照明条件。大量实验表明，该模型在单视角、多视角和新视角重光照任务中展现了最先进的视觉质量、照片级真实感的重光照质量以及强大的零样本泛化能力。
cs.CV / 178 / 2609.07415
Self-Supervised Multi-View 3D Gaze Target Estimation via Probabilistic Ray Marching
通过概率射线行进的自监督多视角三维注视目标估计
Abstract
We present a self-supervised approach, Self-MVGTE, for estimating 3D gaze targets from multiple camera views. Unlike existing methods that independently estimate 2D gaze targets per camera view, Self-MVGTE predicts gaze targets directly in 3D space for the first time. Moreover, it does not require any ground-truth annotations from the target scene and uses only the multi-view input images from a calibrated camera setup, pseudo 2D gaze target labels from a monocular gaze target estimation model, and 3D gaze vectors from a monocular 3D gaze estimation model. A key challenge is that these pseudo labels are inherently noisy and multi-view inconsistent. To address this, we propose a probabilistic ray marching framework, which models the uncertainty of these pseudo labels and exploits 3D gaze vectors as geometric priors. Specifically, these gaze vectors are first integrated into the monocular gaze target estimation model to improve its generalization to unseen scenes, producing higher-quality pseudo labels. Then, for 3D gaze target estimation, we construct a 3D gaze cone by casting a bundle of rays from the eye position around the gaze vector to strictly constrain the solution space. Within this cone, we propose a depth-guided feature sampling strategy using off-the-shelf DINOv2 and Depth-Anything-3 models, and estimate a spatial likelihood distribution of the gaze target. Finally, we convert the pseudo gaze target labels into a target distribution and softly optimize the network. Extensive experiments on the MVGT dataset show that Self-MVGTE achieves state-of-the-art performance, surpassing existing fully-supervised baselines.
Chinese Translation
我们提出了一种自监督方法Self-MVGTE，用于从多个相机视角估计三维注视目标。与现有方法独立估计每个相机视角的二维注视目标不同，Self-MVGTE首次直接在三维空间中预测注视目标。此外，它不需要目标场景的任何真实标注，仅使用来自标定相机设置的多视角输入图像、来自单目注视目标估计模型的伪二维注视目标标签，以及来自单目三维注视估计模型的三维注视向量。一个关键挑战是这些伪标签本质上是噪声和多视角不一致的。为了解决这个问题，我们提出了一种概率射线行进框架，该框架对这些伪标签的不确定性建模，并利用三维注视向量作为几何先验。具体而言，这些注视向量首先被整合到单目注视目标估计模型中，以提高其对未见场景的泛化能力，从而生成更高质量的伪标签。然后，在三维注视目标估计中，我们通过从眼睛位置沿注视向量发射一束射线来构建三维注视锥，以严格约束解空间。在这个锥体内，我们提出了一种深度引导特征采样策略，使用现成的DINOv2和Depth-Anything-3模型，并估计注视目标的空间似然分布。最后，我们将伪注视目标标签转换为目标分布，并对网络进行软优化。在MVGT数据集上的大量实验表明，Self-MVGTE达到了最先进的性能，超越了现有的完全监督基线。
cs.CV / 179 / 2609.07420
Unified Vision-Centric Pedestrian Crossing Action Prediction via Adaptive Patch Projection and Proactive Spatial Rectification
通过自适应补丁投影和主动空间校正的统一视觉中心行人过马路动作预测
Abstract
Vision cues are available and informative for pedestrian action prediction, but obtaining stable target-centric representations from video frames remains challenging without frame-level external perception cues. Thus, most methods rely on additional perception modules or multi-source information fusion, leaving the reliability of vision-centric setting an open question. To this end, we propose ViCross, a vision-centric pedestrian crossing action prediction framework powered by multimodal large language models, which maintains target-centric reasoning from video frames without additional perception modules beyond first-frame target initialization. While multimodal large language models exhibit strong visual understanding, applying them directly to vision-centric action prediction faces two challenges. First, accurately perceiving target pedestrians often requires high resolution inputs and dense visual tokenization, making full-frame encoding computationally prohibitive. ViCross tackles this with Variable Resolution Patch Mapping module for efficient token allocation while preserving key pedestrian details. Second, missing spatiotemporal priors hinder consistent cross frame reasoning. ViCross mitigates this with a Spatial Constraint Enhancement Strategy that captures past motion, future locations, and action semantics for training-time proactive spatial rectification. Extensive experiments show that ViCross delivers clear gains in vision-centric prediction settings and is competitive with multi-source fusion approaches in several settings. Code is available at https://github.com/2tianyao1/ViCross.git.
Chinese Translation
视觉线索在行人动作预测中是可用且信息丰富的，但在没有帧级外部感知线索的情况下，从视频帧中获取稳定的目标中心表示仍然具有挑战性。因此，大多数方法依赖于额外的感知模块或多源信息融合，使得视觉中心设置的可靠性成为一个悬而未决的问题。为此，我们提出了ViCross，一个由多模态大型语言模型驱动的视觉中心行人过马路动作预测框架，该框架在不依赖于额外感知模块的情况下，保持了来自视频帧的目标中心推理，仅在第一帧目标初始化时需要额外模块。尽管多模态大型语言模型展现出强大的视觉理解能力，但将其直接应用于视觉中心动作预测面临两个挑战。首先，准确感知目标行人通常需要高分辨率输入和密集的视觉标记，使得全帧编码在计算上不可行。ViCross通过可变分辨率补丁映射模块解决了这一问题，实现了高效的标记分配，同时保留了关键的行人细节。其次，缺乏时空先验会阻碍跨帧一致推理。ViCross通过空间约束增强策略来缓解这一问题，该策略在训练期间捕捉过去的运动、未来的位置和动作语义，以实现主动的空间校正。大量实验表明，ViCross在视觉中心预测设置中显著提升了性能，并在多个设置中与多源融合方法具有竞争力。代码可在 https://github.com/2tianyao1/ViCross.git 获取。
cs.CV / 180 / 2609.07439
AFID: A Unified Open Framework for Automated Fingermark Identification, Quality Assessment and Feature Extraction
AFID：一个统一的开放框架，用于自动指纹识别、质量评估和特征提取
Abstract
Automated fingermark identification is the foundation of forensic investigation, yet progress in the field is held back by fragmented, closed-source solutions trained on private or discontinued data. We present AFID, a unified open-source framework for friction ridge image processing that performs recognition, quality assessment, and feature extraction based on a single shared encoder, trained exclusively on publicly available data. At its core is a fixed-length representation learned for identity discrimination, trained under heavy augmentation. Despite applying essentially no preprocessing beyond resizing and padding at inference, AFID sets a new state of the art in fixed-length fingermark recognition, leading identification across NIST SD 27 (67.6% rank-1) , SD 302 (54.9% rank-1), and SD 303 (67.6% rank-1), surpassing a commercial matcher on fingermarks. From the same frozen backbone, a quality assessment module predicts recognition utility more accurately than any compared baseline and generalizes across independent matchers, while lightweight decoders recover minutiae, ridge orientation, and segmentation competitive with dedicated methods. The framework proves that a single, efficiently trained encoder can support the full fingermark processing pipeline, from recognition through quality assessment all the way to feature extraction. To accelerate research on fingermark analysis even further, we release the code, models, and annotations to the community.
Chinese Translation
自动指纹识别是法医学调查的基础，但该领域的进展受到碎片化、封闭源解决方案的制约，这些方案训练于私有或已停用的数据。我们提出了AFID，一个统一的开源框架，用于摩擦脊图像处理，基于单一共享编码器执行识别、质量评估和特征提取，且仅在公开可用的数据上进行训练。其核心是为身份辨别学习的固定长度表示，在强增强下进行训练。尽管在推理过程中几乎没有应用任何预处理，仅限于调整大小和填充，AFID在固定长度指纹识别方面设定了新的技术标准，在NIST SD 27（67.6% rank-1）、SD 302（54.9% rank-1）和SD 303（67.6% rank-1）上领先识别，超越了商业匹配器在指纹识别上的表现。基于同一冻结的主干，质量评估模块比任何比较基线更准确地预测识别效用，并在独立匹配器之间具有良好的泛化能力，而轻量级解码器则能够恢复与专用方法竞争的细节、脊线方向和分割。该框架证明了一个高效训练的单一编码器可以支持完整的指纹处理流程，从识别到质量评估再到特征提取。为了进一步加速指纹分析的研究，我们向社区发布了代码、模型和注释。
cs.CV / 181 / 2609.07455
Parser-Free VLM Verification for Federated Weakly Supervised Video Anomaly Detection
无解析器的联邦弱监督视频异常检测验证
Abstract
How can vision-language models help video anomaly detection (VAD) when surveillance data remain distributed, weakly labeled, and resource-constrained? Most weakly supervised VAD methods assume centralized training; recent VLM-based extensions further rely on dense inference, generated explanations, or additional adaptation. We introduce a lightweight federated MIL-VLM cascade in which only a compact MIL scorer is trained across clients, while a frozen VLM verifies high-scoring suspect segments post hoc. We study two VLM feedback interfaces: parsed text-generation decisions and a logit-based interface that extracts a continuous anomaly score from next-token Yes/No probabilities. Experiments on UCF-Crime with InternVL3.5-2B and Qwen3-VL-2B-Instruct show that text-generation verification can improve frame-level AUC after diagnostic temporal post-processing, but remains sensitive to prompts, parsers, model choice, and smoothing. In contrast, the logit interface provides a fixed parser-free signal that improves both frame-level AUC and frame-level AP over the MIL baseline across both VLMs, without temporal post-processing in its main configuration. Since suspect segments are updated independently once available, next-token logit feedback provides a simple segment-local alternative to text-generation verification.
Chinese Translation
在监控数据仍然分散、标签稀疏且资源受限的情况下，视觉-语言模型如何帮助视频异常检测（VAD）？大多数弱监督的VAD方法假设集中训练；最近基于VLM的扩展进一步依赖于密集推理、生成的解释或额外的适应。我们提出了一种轻量级的联邦MIL-VLM级联，其中仅在客户端训练一个紧凑的MIL评分器，而冻结的VLM则在事后验证高评分的可疑片段。我们研究了两种VLM反馈接口：解析的文本生成决策和基于logit的接口，该接口从下一个标记的Yes/No概率中提取连续的异常分数。在UCF-Crime数据集上使用InternVL3.5-2B和Qwen3-VL-2B-Instruct的实验表明，文本生成验证可以在诊断性时间后处理后提高帧级AUC，但对提示、解析器、模型选择和光滑度仍然敏感。相比之下，logit接口提供了一种固定的无解析器信号，在主要配置中提高了两个VLM的帧级AUC和帧级AP，而无需时间后处理。由于可疑片段一旦可用便独立更新，下一个标记的logit反馈为文本生成验证提供了一种简单的片段局部替代方案。
cs.CV / 182 / 2609.07460
Latent-to-Latent Flow for Volumetric Stochastic Segmentation
体积随机分割的潜在到潜在流
Abstract
Uncertainty arising from inter-observer variability in medical image segmentation plays an important role in developing treatment plans. Research in this area is inhibited by the lack of multiple annotations for large-scale medical datasets, especially for volumetric data, which suffers from additional scaling and computational complexity challenges. Flow matching has emerged as a powerful framework for generative modelling and has also been demonstrated to maintain strong performance when working with latent representations of images. In this work, we introduce a latent-to-latent flow technique for stochastic segmentation of medical volumes via encoded representations of both the image and label space. We evaluate our method on two challenging applications covering delineation uncertainty for radiotherapy planning and multiple organ structure segmentation, improving efficiency up to 14x compared with full resolution models while maintaining clinically relevant performance.
Chinese Translation
医学图像分割中由观察者间变异性引起的不确定性在制定治疗计划中发挥着重要作用。由于缺乏大规模医学数据集的多重标注，尤其是体积数据面临额外的缩放和计算复杂性挑战，因此该领域的研究受到限制。流匹配已成为生成建模的强大框架，并且在处理图像的潜在表示时也表现出强大的性能。在本研究中，我们提出了一种潜在到潜在流技术，通过对图像和标签空间的编码表示，实现医学体积的随机分割。我们在两个具有挑战性的应用中评估了我们的方法，涵盖了放射治疗计划的轮廓不确定性和多个器官结构分割，与全分辨率模型相比，效率提高了多达14倍，同时保持了临床相关的性能。
cs.CV / 183 / 2609.07462
When Superpixels Fail on Documents: A Study of Segmentation for LIME Explanations
当超像素在文档中失败时：LIME 解释的分割研究
Abstract
Post-hoc explanation methods are widely used to inspect image classifiers, but their reliability depends on design choices that are often treated as implementation details. We study this issue for LIME on document image classification, focusing on the segmentation step that defines the interpretable units being perturbed. Standard image-based LIME typically relies on natural-image superpixels, which are poorly aligned with document structure such as text regions, layout blocks, and identification codes. Using RVL-CDIP, we compare Quickshift and SLIC with document-aware segmentations based on OCR bounding boxes and regular grids. Our results show that segmentation strongly affects explanation consistency, correctness, and local fidelity. Document-aware segmentations produce more stable and faithful explanations, require fewer perturbations to converge, and expose shortcut behaviour based on document identification codes, a known RVL-CDIP bias that superpixel-based LIME often obscures. These findings show that reliable post-hoc explanation requires domain-aware interpretable representations, and that segmentation should be treated as part of the explanation method rather than as neutral preprocessing.
Chinese Translation
事后解释方法广泛用于检查图像分类器，但其可靠性依赖于通常被视为实现细节的设计选择。我们研究了 LIME 在文档图像分类中的这一问题，重点关注定义被扰动的可解释单元的分割步骤。基于图像的标准 LIME 通常依赖于自然图像超像素，而这些超像素与文档结构（如文本区域、布局块和识别代码）对齐较差。通过使用 RVL-CDIP 数据集，我们比较了 Quickshift 和 SLIC 与基于 OCR 边界框和规则网格的文档感知分割。我们的结果表明，分割对解释的一致性、正确性和局部保真度有很大影响。文档感知分割产生了更稳定和真实的解释，收敛所需的扰动更少，并揭示了基于文档识别代码的捷径行为，这是一个已知的 RVL-CDIP 偏差，而基于超像素的 LIME 通常会掩盖这一点。这些发现表明，可靠的事后解释需要领域感知的可解释表示，并且分割应被视为解释方法的一部分，而不是中立的预处理。
cs.CV / 184 / 2609.07499
CrACK: Adversarial Attacks on Cross-Model Consistency in Collaborative Vision Foundation Models
CrACK：针对协作视觉基础模型中跨模型一致性的对抗攻击
Abstract
Training-free collaborative pipelines that integrate Vision Foundation Models such as CLIP, SAM, and DINO achieve strong open-vocabulary dense prediction and are increasingly deployed in safety-critical applications. The security of these systems is commonly assumed to follow from the robustness of their individual models. We challenge this assumption. We identify a vulnerability shared by every collaborative pipeline: each model consumes the intermediate output of another without verifying semantic consistency, an unverified premise that we term the semantic-spatial alignment dependency. Existing adversarial attacks target a single model and overlook this premise, leaving the inter-model interface entirely unguarded. We propose CrACK (Cross-model Adversarial Consistency attack), an inference-time attack that exploits this interface without modifying any input pixel, model weight, or training data. CrACK operates in two stages: Adversarial Affinity Contradiction Injection corrupts the cross-modal affinity matrix by inverting SAM encoder features under the guidance of CLIP patch-level semantics, and Semantic Interface Poisoning steers the prediction through a max-distance label permutation derived from CLIP text embeddings. Experiments on four collaborative pipelines across eight benchmarks show that CrACK causes catastrophic degradation while every individual model continues to produce its unchanged standalone output, rendering per-model defenses structurally blind. The corruption further cascades into large vision-language model reasoning, driving models such as LLaVA to produce erroneous responses from visually intact inputs. Our results show that the security of a collaborative AI system cannot be reduced to the robustness of its components, and that inter-model feature interfaces must be treated as first-class security boundaries.
Chinese Translation
无训练的协作管道整合了视觉基础模型，如 CLIP、SAM 和 DINO，能够实现强大的开放词汇密集预测，并越来越多地应用于安全关键的场景。这些系统的安全性通常被认为源于其各个模型的鲁棒性。我们对此假设提出质疑。我们识别出每个协作管道共享的一个脆弱性：每个模型在未验证语义一致性的情况下消费另一个模型的中间输出，这一未经验证的前提我们称之为语义-空间对齐依赖性。现有的对抗攻击主要针对单个模型，忽视了这一前提，从而使得跨模型接口完全没有防护。我们提出了 CrACK（跨模型对抗一致性攻击），这是一种推理时攻击，利用这一接口而不修改任何输入像素、模型权重或训练数据。CrACK 分为两个阶段：对抗亲和矛盾注入通过在 CLIP 补丁级语义的指导下反转 SAM 编码器特征来破坏跨模态亲和矩阵，而语义接口中毒则通过从 CLIP 文本嵌入派生的最大距离标签置换来引导预测。在八个基准测试的四个协作管道上的实验表明，CrACK 导致灾难性的性能下降，而每个单独模型仍然继续产生其未改变的独立输出，从而使得每个模型的防御在结构上失效。这种破坏进一步级联到大型视觉-语言模型推理，导致像 LLaVA 这样的模型从视觉上完好的输入中产生错误的响应。我们的结果表明，协作 AI 系统的安全性不能简单地归结为其组件的鲁棒性，并且跨模型特征接口必须被视为一流的安全边界。
cs.CV / 185 / 2609.07504
Mitigating Shortcut Learning: Texture-Penalized Prototype Networks
缓解捷径学习：纹理惩罚原型网络
Abstract
Standard Convolutional Neural Networks (CNNs) exhibit severe performance degradation due to a strong inductive texture bias that prioritizes local, high-frequency patterns over global structural shapes. This dependency causes confident misclassifications during textural changes or environmental effects. To address this flaw, this study introduces the Texture-Penalized Prototype Network (TPPN), a novel architectural framework that shifts this inherent bias without depending on resource-intensive augmented datasets. Specifically, a Texture-Penalization Branch (TPB) imposes a penalty to suppress the extraction of local texture proxies, forcing the network backbone to discard high-frequency cues and extract purified, shape-biased representations. By evaluating similarities within a prototype-based hypersphere derived from the final convolutional features, the approach enforces strict geometric constraints, treating objects as compositions of essential parts to achieve robust classification. Evaluations on texture-shape cue-conflict datasets and synthetic noise benchmarks demonstrate the stronger shape bias of this structural disentanglement. The proposed framework reduces the inherent texture bias of a baseline ResNet-50 from 55.11% to 29.73%, surpassing the texture-suppression capabilities of an off-the-shelf Vision Transformer (ViT-B/16). Furthermore, the approach demonstrates robust generalization under cue-conflict conditions, resisting textural shortcut learning when encountering Out-of-Distribution (OOD) shapes. The model maintains stronger shape accuracy against elevated perturbations. On clean validation data, the architecture incurs a minimal drop in accuracy of 0.90 percentage points. This provides a structural, efficient solution to CNN texture bias.
Chinese Translation
标准卷积神经网络（CNN）由于强烈的归纳纹理偏差，优先考虑局部高频模式而非全局结构形状，表现出严重的性能下降。这种依赖性导致在纹理变化或环境影响下出现自信的错误分类。为了解决这一缺陷，本研究提出了纹理惩罚原型网络（TPPN），一种新颖的架构框架，旨在不依赖资源密集型增强数据集的情况下转变这种固有偏差。具体而言，纹理惩罚分支（TPB）施加惩罚以抑制局部纹理代理的提取，迫使网络主干丢弃高频线索并提取纯化的、形状偏向的表示。通过评估从最终卷积特征派生的基于原型的超球体内的相似性，该方法施加严格的几何约束，将物体视为基本部分的组合，以实现稳健的分类。在纹理-形状线索冲突数据集和合成噪声基准上的评估表明，这种结构解耦具有更强的形状偏向。所提出的框架将基线 ResNet-50 的固有纹理偏差从 55.11% 降低至 29.73%，超越了现成视觉变换器（ViT-B/16）的纹理抑制能力。此外，该方法在线索冲突条件下表现出稳健的泛化能力，在遇到分布外（OOD）形状时抵抗纹理捷径学习。该模型在面对较高扰动时保持更强的形状准确性。在干净的验证数据上，该架构的准确性仅下降 0.90 个百分点。这为 CNN 纹理偏差提供了一种结构性、高效的解决方案。
cs.CV / 186 / 2609.07515
PICANet: Physics-Informed Cascaded Asymmetric Network for Infrared Small Target Detection
PICANet：基于物理信息的级联非对称网络用于红外小目标检测
Abstract
Infrared small target detection (ISTD) is an important research direction in image processing. However, existing methods are limited by severe background noise propagation and target degradation in high-level semantic features. To address these limitations, this paper proposes a plug-and-play physics-informed cascaded asymmetric network, named PICANet. Specifically, we construct a hierarchical prior decoupling module to explicitly extract low-level and high-level physical information, thereby characterizing target features at different levels rather than relying solely on convolutional extraction. Furthermore, a dual-prior interactive fusion module is developed to dynamically refine target representations while suppressing complex background clutter. Unlike previous work, a multi-level cross-feature attention module with the cascaded asymmetric mechanism is introduced to achieve precise alignment between high-level semantics and low-level spatial details. Extensive experiments demonstrate that the proposed PICANet outperforms state-of-the-art ISTD methods, showing satisfactory detection accuracy even against complex backgrounds. Our code is available at https://github.com/xianchaoxiu/PICANet.
Chinese Translation
红外小目标检测（ISTD）是图像处理中的一个重要研究方向。然而，现有方法受限于严重的背景噪声传播和高层语义特征中目标退化问题。为了解决这些限制，本文提出了一种即插即用的基于物理信息的级联非对称网络，命名为PICANet。具体而言，我们构建了一个层次先验解耦模块，用以显式提取低层和高层的物理信息，从而在不同层次上刻画目标特征，而非单纯依赖卷积提取。此外，设计了一个双先验交互融合模块，能够动态地细化目标表示，同时抑制复杂的背景杂波。与以往工作不同，本文引入了具有级联非对称机制的多层次跨特征注意力模块，实现高层语义与低层空间细节的精确对齐。大量实验表明，所提PICANet优于当前最先进的ISTD方法，即使在复杂背景下也表现出令人满意的检测精度。我们的代码已开源，地址为https://github.com/xianchaoxiu/PICANet。
cs.CV / 187 / 2609.07520
Topologically Consistent Agricultural Parcel Vectorization with Semantic-Guided Diffusion and Topology-Aware Polygonization
具有语义引导扩散和拓扑感知多边形化的拓扑一致农业地块矢量化
Abstract
Agricultural parcel polygons play a fundamental role in geospatial applications such as precision agriculture, land administration, and crop monitoring. Beyond regular polygon geometry and low vertex redundancy, practical parcel maps should avoid topological conflicts and preserve common boundaries between adjacent fields. Yet this requirement remains largely unresolved: segmentation-based methods mainly produce parcel masks or raster boundary cues and rely on heuristic raster-to-vector conversion, instance- and contour-based methods reconstruct parcels independently, and recent vector-oriented methods improve polygon regularity but do not explicitly recover adjacent parcels from a shared topological structure. To address this gap, we propose a semantic-guided diffusion framework for topologically consistent agricultural parcel vectorization. It couples joint edge--vertex latent diffusion with supervised multi-cue conditioning to generate geometrically regularised parcel-boundary and vertex primitives while suppressing false-positive responses. A topology-aware parcel polygon reconstruction method then converts these primitives into regular polygons by reconstructing parcel faces from a common planar graph, enabling adjacent predicted parcels to reuse shared boundaries and avoid mutual interior intrusion. Extensive experiments on the AI4SmallFarms and iFLYTEK datasets evaluate parcel vectorization in terms of pixel-level coverage, geometric fidelity, object-level correctness, and topological consistency. The results show strong and competitive performance, with zero measured intrusion ratio and the highest shared-edge recall, demonstrating the potential of the proposed framework for accurate, regular, and topologically consistent agricultural parcel vectorization.
Chinese Translation
农业地块多边形在精确农业、土地管理和作物监测等地理空间应用中发挥着基础性作用。除了常规的多边形几何形状和低顶点冗余外，实际的地块地图应避免拓扑冲突，并保持相邻田地之间的共同边界。然而，这一要求在很大程度上仍未得到解决：基于分割的方法主要生成地块掩膜或栅格边界线索，并依赖启发式的栅格到矢量转换；基于实例和轮廓的方法独立重建地块；而最近的面向矢量的方法虽然改善了多边形的规则性，但并未明确从共享的拓扑结构中恢复相邻地块。为了解决这一问题，我们提出了一种用于拓扑一致农业地块矢量化的语义引导扩散框架。该框架将联合边缘-顶点潜在扩散与监督的多线索条件相结合，以生成几何上规则化的地块边界和顶点原语，同时抑制假阳性响应。随后，拓扑感知的地块多边形重建方法通过从共同平面图重建地块面，将这些原语转换为规则多边形，使得相邻的预测地块能够重用共享边界，避免相互内部侵入。在AI4SmallFarms和iFLYTEK数据集上进行的大量实验评估了地块矢量化在像素级覆盖、几何保真度、对象级正确性和拓扑一致性方面的表现。结果显示出强劲且具有竞争力的性能，测得的侵入比为零，且共享边缘召回率最高，展示了所提框架在准确、规则和拓扑一致的农业地块矢量化中的潜力。
cs.CV / 188 / 2609.07525
When Semantically Consistent Encoding Meets View-Label Heterogeneity Modeling: A Unified Framework for Incomplete Multi-View Multi-Label Learning
语义一致编码与视图-标签异质性建模的结合：不完整多视图多标签学习的统一框架
Abstract
Incomplete multi-view multi-label learning requires not only robust semantic aggregation from partially observed views, but also label-aware exploitation of view-specific evidence. Existing approaches usually emphasize either shared representation learning or decision-level fusion. The former improves robustness against missing views, yet tends to compress label-discriminative view-specific cues into a single latent representation. The latter preserves individual view predictions, but often relies on fixed or globally learned fusion weights, ignoring that different labels of different instances may require different views. To address these limitations, this paper presents V2L, a unified representation-decision framework for incomplete multi-view multi-label classification. On the representation side, V2L constructs semantically consistent variational posteriors from incomplete views through a perturbation-aware encoding mechanism, which provides a stable shared semantic basis. On the decision side, V2L introduces an active view-label relevance modeling strategy that estimates instance-wise and label-wise view contributions, allowing each label prediction to adaptively select useful view-specific evidence. From the perspective of model architecture, these two important strategies are integrated into a unified framework through a hybrid fusion architecture, simultaneously meeting the requirements of cross-view semantic consistency and representational complementarity. Extensive experiments under both incomplete and complete settings show that V2L achieves leading performance on five benchmarks. Code is available at: https://github.com/justsmart/V2L.
Chinese Translation
不完整的多视图多标签学习不仅需要从部分观察到的视图中进行稳健的语义聚合，还需要对视图特定证据进行标签感知的利用。现有的方法通常强调共享表示学习或决策级融合。前者提高了对缺失视图的鲁棒性，但往往将标签区分的视图特征压缩为单一的潜在表示。后者保留了各个视图的预测，但通常依赖于固定或全局学习的融合权重，忽视了不同实例的不同标签可能需要不同的视图。为了解决这些局限性，本文提出了V2L，一个用于不完整多视图多标签分类的统一表示-决策框架。在表示方面，V2L通过一种感知扰动的编码机制，从不完整视图构建语义一致的变分后验，从而提供稳定的共享语义基础。在决策方面，V2L引入了一种主动的视图-标签相关性建模策略，估计实例级和标签级的视图贡献，使每个标签预测能够自适应地选择有用的视图特定证据。从模型架构的角度来看，这两种重要策略通过混合融合架构整合到一个统一框架中，同时满足跨视图语义一致性和表示互补性的要求。在不完整和完整设置下的广泛实验表明，V2L在五个基准测试上实现了领先的性能。代码可在以下链接获取：https://github.com/justsmart/V2L。
cs.CV / 189 / 2609.07540
TeMo: Temperature Modulation for Multimodal Contrastive Learning
TeMo：用于多模态对比学习的温度调制
Abstract
Contrastive learning approaches achieve strong performance by training models to bring similar samples closer while pushing dissimilar samples apart. A crucial component of contrastive learning is the temperature hyperparameter $\tau$, which controls the penalty strength applied to negative samples. However, most existing methods either fix this hyperparameter or learn a global value during training. In this paper, we introduce TeMo, Temperature Modulation framework, a similarity-based modulation approach that adaptively adjusts the temperature for each positive-negative pair according to their similarity, enabling more fine-grained multimodal contrastive learning. Our approach seamlessly integrates temperature-modulated multimodal and unimodal losses with the standard multimodal contrastive loss by gradually transitioning between them. This design allows the model to capture both coarse- and fine-grained semantics at different training stages. Extensive experiments demonstrate that each component of TeMo consistently enhances performance across diverse zero-shot retrieval and classification tasks, establishing new state-of-the-art results.
Chinese Translation
对比学习方法通过训练模型将相似样本拉近，同时将不相似样本推远，从而实现强大的性能。对比学习的一个关键组成部分是温度超参数 $ au$，它控制施加于负样本的惩罚强度。然而，大多数现有方法要么固定该超参数，要么在训练过程中学习一个全局值。在本文中，我们引入了 TeMo（Temperature Modulation）框架，这是一种基于相似性的调制方法，根据每对正负样本的相似性自适应地调整温度，从而实现更细粒度的多模态对比学习。我们的方法通过在温度调制的多模态损失和单模态损失之间逐渐过渡，能够无缝地与标准的多模态对比损失相结合。该设计使模型能够在不同的训练阶段捕捉到粗粒度和细粒度的语义。大量实验表明，TeMo的每个组件在多种零样本检索和分类任务中始终提升性能，确立了新的最先进结果。
cs.CV / 190 / 2609.07547
Re-engineering SORT-based algorithms for low-cost small object tracking from omnidirectional footage
基于SORT的算法重构：低成本小物体从全景视频中跟踪
Abstract
Multi-object tracking (MOT) has advanced rapidly in urban surveillance and autonomous driving, yet many trackers rely on ReID- and transformer-based appearance encoders and are designed for standard FoV cameras. These assumptions break down for low-cost omnidirectional deployments, where equirectangular projection introduces seam discontinuities and targets appear to be small and fast-moving. We address multi-object tracking of flying animals captured in remote environments using omnidirectional cameras. We propose a lightweight framework that re-engineers SORT-based tracking for this geometry, including (i) a Seam-Aware Motion Model that keeps the Kalman state continuous across the seam, (ii) a composite seam-aware association cost that pairs a wrapped Euclidean term with GIoU, and (iii) OmniSmall, a new benchmark of omnidirectional wildlife footage. On our new dataset, with ground-truth detections, our modifications improved over OCSORT by +8.51 HOTA, +9.41 MOTA, and +10.17 IDF1; with YOLOX detections the gain narrows to +1.95 HOTA. Our proposed methods improved tracking performance on OmniSmall and remained competitive on JRDB without adding appearance encoders while keeping the tracking stage CPU-only. Our dataset and source code are available at: https://github.com/Xin-Shu/OmniSORT.git.
Chinese Translation
多目标跟踪（MOT）在城市监控和自动驾驶领域迅速发展，但许多跟踪器依赖于基于ReID和变换器的外观编码器，并且设计用于标准视场（FoV）摄像头。这些假设在低成本全景部署中失效，因为等距矩形投影会引入接缝不连续性，目标看起来小且快速移动。我们针对在偏远环境中使用全景摄像头捕捉的飞行动物进行多目标跟踪。我们提出了一个轻量级框架，针对这种几何形状重构基于SORT的跟踪，包括（i）一个接缝感知运动模型，该模型保持卡尔曼状态在接缝处的连续性，（ii）一个复合接缝感知关联代价，将包裹的欧几里得项与GIoU配对，以及（iii）OmniSmall，一个新的全景野生动物视频基准。在我们的新数据集上，使用真实检测结果，我们的修改使OCSORT的性能提高了+8.51 HOTA，+9.41 MOTA和+10.17 IDF1；使用YOLOX检测时，增益缩小至+1.95 HOTA。我们提出的方法在OmniSmall上提高了跟踪性能，并在JRDB上保持竞争力，而无需添加外观编码器，同时保持跟踪阶段为仅CPU处理。我们的数据集和源代码可在以下链接获取：https://github.com/Xin-Shu/OmniSORT.git。
cs.CV / 191 / 2609.07557
Heat Kernel Textures: the Geodesic Gaussians That Do Not Splat
热核纹理：不产生溅射的测地高斯
Abstract
3D Gaussian Splatting has recently revolutionised novel view synthesis as well as many other 3D vision methods and applications. Drawing inspiration from this representation, we now rethink textures to overcome the main issues of UV mapping while considerably lowering their memory footprint. Heat Kernel Textures (HKTex) eliminate UV unwrapping as well as their persistent issues of wasted UV space, seams, distortions, vertex-duplication, and varying resolution. Grounded in discrete Riemannian geometry and intrinsically defined on any manifold surface discretised as a triangular mesh, HKTex uses anisotropic heat kernels as geodesic equivalents to Gaussians. Like our kernels, also the optimisation of their position and the adaptive densification strategies were redefined to operate on the surface of the object to be textureised. Our novel representation is also fully integrated with a physically based renderer and can be optimised either from existing textures or multi-view images. Our project page and code are available at circle-group.github.io/research/HeatKernelTextures.
Chinese Translation
三维高斯溅射最近在新视图合成以及许多其他三维视觉方法和应用中引发了革命。受到这种表示法的启发，我们重新思考纹理，以克服UV映射的主要问题，同时显著降低其内存占用。热核纹理（Heat Kernel Textures，HKTex）消除了UV展开及其持续存在的浪费UV空间、接缝、失真、顶点重复和分辨率变化等问题。HKTex基于离散黎曼几何，并在任何离散为三角网格的流形表面上内在定义，使用各向异性热核作为高斯的测地等价物。与我们的核类似，其位置优化和自适应稠密化策略也被重新定义，以在待纹理化对象的表面上操作。我们的新表示法还与基于物理的渲染器完全集成，并可以从现有纹理或多视图图像中进行优化。我们的项目页面和代码可在circle-group.github.io/research/HeatKernelTextures获取。
cs.CV / 192 / 2609.07571
SphereSOD: Geometry-Structure Coupled Learning for 360 Salient Object Detection
SphereSOD：用于360度显著性目标检测的几何-结构耦合学习
Abstract
360{\deg} salient object detection (SOD) aims to accurately segment salient regions across a full field of view. However, equirectangular projection (ERP) introduces severe spatial distortion when mapping the spherical domain onto a planar representation. Existing methods mainly focus on compensating projection distortion while overlooking the interaction between panoramic geometry and salient object structure during feature perception and prediction refinement. To this end, we propose SphereSOD, an ERP-native framework that couples panoramic geometry with evolving salient structures. Specifically, spherical geometry governs feature sampling and spatial weighting, while coarse-grained saliency and contour prediction influence context aggregation during the progressive decoding process. SphereSOD first initializes deformable sampling based on spherical projection geometry and then employs bounded, content-adaptive offsets, yielding features that are better aligned with the underlying panoramic geometry. Subsequently, the decoder performs structure-guided context aggregation and progressive refinement to recover complete salient regions and accurate boundaries. Extensive experiments on three public 360{\deg} SOD benchmarks demonstrate state-of-the-art performance and a favorable accuracy-efficiency trade-off, supporting structurepreserving inference directly in ERP space as a promising alternative to projection-heavy panoramic pipelines.
Chinese Translation
360度显著性目标检测（SOD）旨在准确分割全视场中的显著区域。然而，等矩形投影（ERP）在将球面域映射到平面表示时引入了严重的空间扭曲。现有方法主要集中在补偿投影扭曲，而忽视了全景几何与显著目标结构在特征感知和预测细化过程中的相互作用。为此，我们提出了SphereSOD，一个原生于ERP的框架，将全景几何与不断演变的显著结构耦合。具体而言，球面几何控制特征采样和空间加权，而粗粒度显著性和轮廓预测则影响渐进解码过程中的上下文聚合。SphereSOD首先基于球面投影几何初始化可变形采样，然后采用有界的内容自适应偏移，生成与底层全景几何更好对齐的特征。随后，解码器执行结构引导的上下文聚合和渐进细化，以恢复完整的显著区域和准确的边界。在三个公共360度SOD基准上的广泛实验表明，SphereSOD实现了最先进的性能，并在准确性与效率之间达成了良好的平衡，支持在ERP空间中直接进行结构保留推断，成为投影密集型全景管道的有希望替代方案。
cs.CV / 193 / 2609.07590
Solution for UCF UrbanTwin LUMPI Track: Sim-to-Real Urban LiDAR 3D Object Detection
UCF UrbanTwin LUMPI 赛道解决方案：从仿真到现实的城市 LiDAR 3D 目标检测
Abstract
We present our solution to the LUMPI track of the UCF UrbanTwin Sim2Real LiDAR Challenge at the 6th DriveX Workshop, ECCV 2026. The detector must be trained only on synthetic data and is evaluated on 50 held-out real LiDAR frames; a separate 50-frame synthetic submission is evaluated for point-cloud realism. Our method addresses the Sim2Real gap at three levels. First, we align synthetic scans to the 50k-point test density and build a 30k-record training pool using UT-LUMPI geometry, RangeLDM-based sampling diversification, rare-class copy-paste, and pedestrian-oriented augmentation. Second, complementary DSVT detectors and Car/Bus PointPillars specialists are trained under the same synthetic-only constraint. Third, predictions are integrated by class-aware routing, asymmetric agreement fusion, constrained residual-recall supplementation, class-coverage auditing, and selective box-size calibration. The realism branch is optimized independently with radial-density matching, weak affine calibration, and calibrated set mixing. The final submission obtains a Combined Score of 0.4692, a Detection Score of 0.1797, a Realism Score of 0.9035, and 3D
[email protected] of 0.1258.
Chinese Translation
我们提出了在第六届 DriveX 研讨会（ECCV 2026）上进行的 UCF UrbanTwin Sim2Real LiDAR 挑战 LUMPI 赛道的解决方案。该检测器仅需在合成数据上进行训练，并在 50 帧保留的真实 LiDAR 帧上进行评估；另有 50 帧合成提交用于评估点云的真实感。我们的方法在三个层面上解决了 Sim2Real 差距。首先，我们将合成扫描与 50k 点测试密度对齐，并使用 UT-LUMPI 几何、基于 RangeLDM 的采样多样化、稀有类别的复制粘贴以及面向行人的增强构建了一个 30k 记录的训练池。其次，互补的 DSVT 检测器和 Car/Bus PointPillars 专家在相同的合成数据约束下进行训练。第三，通过类别感知路由、不对称一致性融合、受限的残差召回补充、类别覆盖审计和选择性框大小校准来整合预测。真实感分支通过径向密度匹配、弱仿射校准和校准集混合独立优化。最终提交获得了 0.4692 的综合得分、0.1797 的检测得分、0.9035 的真实感得分，以及 0.1258 的 3D
[email protected]。
cs.CV / 194 / 2609.07600
BarkNet-Lite: A Lightweight Texture and Colour Network with the BarkBD Benchmark for Bark-Based Tree Species Recognition in Bangladesh
BarkNet-Lite：一种轻量级纹理与颜色网络及其在孟加拉国基于树皮的树种识别中的 BarkBD 基准
Abstract
Tree species recognition supports forest inventory and biodiversity monitoring but still depends on scarce taxonomic expertise. Bark is visible year-round at ground level, yet bark recognition has concentrated on temperate floras and on large ImageNet-pre-trained backbones. We address both gaps. First, we release BarkBD, a bark dataset for Bangladesh: 14,258 uncropped smartphone photographs of 20 native species across four districts and three weather conditions, with a fixed stratified split. Second, we propose BarkNet-Lite, a 2.96M-parameter network trained from random initialisation, pairing a multi-scale texture pathway with a parallel colour-aware pathway. Over five seeds it reaches 96.64+-0.66%accuracyunderstrict single-image inference, within 2.3 points of nine ImageNet-pre-trained backbones fine-tuned under an identical protocol and within one seed-level standard deviation of the smallest ofthem, andtransfers to public benchmarks (95.86% on BarkVN-50, 92.85% on BarkNet 1.0). Grad-CAM, validated by faithfulness and weight-randomisation checks, confirms its decisions rest on bark structure rather than background. The exported single-precision model classifies one photograph in 15.34ms on a commodity smartphone.
Chinese Translation
树种识别支持森林清查和生物多样性监测，但仍然依赖于稀缺的分类学专业知识。树皮在地面上全年可见，但树皮识别主要集中在温带植物群和大型 ImageNet 预训练骨干网络上。我们解决了这两个问题。首先，我们发布了 BarkBD，这是一个针对孟加拉国的树皮数据集：包含来自四个地区和三种气候条件下的 14,258 张未裁剪的智能手机照片，涵盖 20 种本土树种，并采用固定的分层划分。其次，我们提出了 BarkNet-Lite，一个具有 2.96M 参数的网络，从随机初始化开始训练，将多尺度纹理路径与并行的颜色感知路径相结合。在五个随机种子下，它在严格的单图像推断中达到了 96.64±0.66% 的准确率，距离九个在相同协议下微调的 ImageNet 预训练骨干网络仅相差 2.3 分，并且在种子级别的标准差内与其中最小的相差一个标准差，同时在公共基准上也表现良好（在 BarkVN-50 上为 95.86%，在 BarkNet 1.0 上为 92.85%）。通过可靠性和权重随机化检查验证的 Grad-CAM 确认其决策基于树皮结构而非背景。导出的单精度模型在普通智能手机上对一张照片的分类时间为 15.34 毫秒。
cs.CV / 195 / 2609.07605
Search-to-World: Evaluation of 3D World Delivery from User Request through Web Search
搜索到世界：通过网络搜索评估用户请求的3D世界交付
Abstract
Agentic systems can interpret user requests, search the live web, and use external tools, but their ability to transform retrieved web content into a usable 3D world has not been systematically evaluated. No established end-to-end pipeline or benchmark exists for this capability. We introduce Search-to-World, an end-to-end evaluation task covering request understanding, web visual-content retrieval, and 3D-world delivery. We define Observed Retrieval Rate (ORR) and World Delivery Rate (WDR) to distinguish observing relevant content from successfully delivering a request-aligned, perceptually acceptable world. We also present WorldSearcher, a reuse-then-reconstruction harness that connects existing search agents to world delivery: it first retrieves reusable 3D worlds and, when none are available, reconstructs a world from video. A structured recovery controller revises temporal grounding, replaces source videos, or reformulates queries after failure. Using WorldSearcher, we benchmark representative models on Search-to-World and study supervised fine-tuning (SFT) for recovery subagents. Results show that delivery depends on the underlying agentic model, and that relevant-content observation does not ensure world delivery. Jointly training recovery agents improves delivery success and action efficiency. Search-to-World makes agentic 3D-world delivery measurable, while WorldSearcher provides a practical evaluation harness with recovery capabilities.
Chinese Translation
自主系统能够理解用户请求、搜索实时网络并使用外部工具，但其将检索到的网络内容转化为可用3D世界的能力尚未得到系统评估。目前尚无针对该能力的成熟端到端管道或基准。我们提出了搜索到世界（Search-to-World），这是一个涵盖请求理解、网络视觉内容检索和3D世界交付的端到端评估任务。我们定义了观察到的检索率（Observed Retrieval Rate, ORR）和世界交付率（World Delivery Rate, WDR），以区分观察相关内容与成功交付请求对齐的、感知上可接受的世界。我们还介绍了WorldSearcher，这是一个重用后重建的工具，连接现有搜索代理与世界交付：它首先检索可重用的3D世界，当没有可用的世界时，从视频中重建一个世界。一个结构化的恢复控制器在失败后修正时间基础、替换源视频或重新制定查询。利用WorldSearcher，我们对代表性模型在搜索到世界任务上进行了基准测试，并研究了恢复子代理的监督微调（Supervised Fine-Tuning, SFT）。结果表明，交付成功依赖于底层的自主模型，而相关内容的观察并不确保世界交付。联合训练恢复代理提高了交付成功率和行动效率。搜索到世界使自主3D世界交付可测量，而WorldSearcher则提供了具有恢复能力的实用评估工具。
cs.CV / 196 / 2609.07608
Solution for UCF UrbanTwin V2X-Real Track: Sim-to-Real Urban LiDAR 3D Object Detection
UCF UrbanTwin V2X-Real Track的解决方案：从仿真到现实的城市LiDAR 3D物体检测
Abstract
Bridging the simulation-to-reality gap in roadside LiDAR requires addressing several coupled discrepancies, including scene geometry, sampling density, return patterns, and pedestrian scale. This report presents a multi-source collaborative training and class-aware fusion framework for Sim2Real 3D detection. The method organizes digital-twin scans, diffusion-redrawn scans, density-stabilized scans, and pedestrian morphology-aligned samples into a unified training pool with complementary roles. Within a common DSVT detection formulation, source-specialized expert branches preserve those roles while optimizing for the same detection objective. At inference, a predefined class-aware fusion pathway integrates geometry-stable and calibration-aware branches for vehicles, sampling-complementary branches for trucks, and morphology-consistent evidence for pedestrians. A label-free point-cloud center blend then refines geometric localization. On the UrbanTwin V2X-Real hidden test set, the unified system achieves a combined score of 0.7421, with 3D
[email protected] of 0.4518 and a realism score of 0.8871. The results indicate that a stable, interpretable collaboration among data sources is more valuable than unconstrained aggregation of model outputs.
Chinese Translation
在路边LiDAR中弥合仿真与现实之间的差距需要解决多个耦合的不一致性，包括场景几何、采样密度、返回模式和行人尺度。本报告提出了一种多源协同训练和类感知融合框架，用于Sim2Real 3D检测。该方法将数字双胞胎扫描、扩散重绘扫描、密度稳定扫描和行人形态对齐样本组织成一个具有互补角色的统一训练池。在一个共同的DSVT检测公式中，源专用的专家分支保持这些角色，同时优化相同的检测目标。在推理阶段，预定义的类感知融合路径整合了几何稳定和校准感知的车辆分支、采样互补的卡车分支以及形态一致的行人证据。随后，无标签的点云中心混合进一步精炼几何定位。在UrbanTwin V2X-Real隐藏测试集上，统一系统实现了0.7421的综合得分，3D
[email protected]为0.4518，现实性得分为0.8871。结果表明，数据源之间稳定、可解释的协作比模型输出的无约束聚合更具价值。
cs.CV / 197 / 2609.07670
Harnessing CLIP and DINO: An Uncertainty-Aware Cascaded Fusion Network for Generalizable Deepfake Image Detection
利用CLIP和DINO：一种面向不确定性的级联融合网络用于可泛化的深度伪造图像检测
Abstract
The growing realism and accessibility of manipulated and generated faces threaten the trustworthiness of digital media. To detect such forgeries, deepfake detectors based on vision foundation models have shown promising performance, but they typically rely on a single pretrained representation and are prone to overfitting to particular training distributions. To improve generalization to unseen forgeries, we propose UCF-Net, an uncertainty-aware cascaded fusion network that harnesses CLIP's language-aligned semantic priors and DINO's self-supervised visual-structure priors. UCF-Net extracts hierarchical features across Transformer depths, uses layer-wise expert aggregation to adaptively combine each encoder's multi-level cues, and performs weighted fusion of the resulting representations based on entropy-derived uncertainty. We further consolidate public deepfake datasets into a unified benchmark of approximately 4M images and construct a separate cross-generator evaluation set with over 8K face images from eight recent generators. On the unified benchmark, UCF-Net achieves the best mean AUC among the evaluated methods in both in-domain and cross-domain evaluations. On the cross-generator set, it adapts effectively with limited target-domain data, although zero-shot transfer remains challenging.
Chinese Translation
随着操控和生成面孔的真实感和可获取性的提高，数字媒体的可信度受到威胁。为了检测这些伪造品，基于视觉基础模型的深度伪造检测器表现出良好的性能，但它们通常依赖于单一的预训练表示，并且容易对特定训练分布过拟合。为了提高对未见伪造品的泛化能力，我们提出了UCF-Net，这是一种面向不确定性的级联融合网络，利用了CLIP的语言对齐语义先验和DINO的自监督视觉结构先验。UCF-Net在Transformer深度中提取层次特征，使用逐层专家聚合自适应地结合每个编码器的多层线索，并根据熵导出的不确定性对结果表示进行加权融合。我们进一步将公共深度伪造数据集整合为一个统一的基准，包含约400万张图像，并构建了一个包含来自八个近期生成器的超过8000张面孔图像的独立跨生成器评估集。在统一基准上，UCF-Net在评估方法中在领域内和跨领域评估中均实现了最佳的平均AUC。在跨生成器集上，它在有限的目标领域数据下有效适应，尽管零-shot迁移仍然具有挑战性。
cs.CV / 198 / 2609.07685
CrowdTraj: A Benchmark for Dense Crowd Trajectory Prediction in Realistic Crowded Environments
CrowdTraj：现实拥挤环境中密集人群轨迹预测的基准
Abstract
In real-world applications, pedestrian trajectory prediction models rely on inputs from detection and tracking systems. Prior trajectory prediction benchmarks either contain relatively sparse pedestrian interactions, assume perfect tracking inputs, or rely on overhead viewpoints that minimize occlusion and perspective distortion, limiting evaluation in realistic dense-crowd scenarios. We present CrowdTraj, a benchmark for pedestrian trajectory prediction in natural dense crowd scenes. Unlike previous datasets, CrowdTraj supports end-to-end evaluation from detection through tracking to trajectory prediction under severe occlusion in CCTV views. It also captures diverse, natural pedestrian behaviours, including abrupt directional changes rarely observed in existing benchmarks. CrowdTraj includes five diverse scenes, with an average of 1,146 unique pedestrians per scene, maximum frame-level densities ranging from 114 to 372 pedestrians, and over 3.2 million annotated head bounding boxes. CrowdTraj provides pixel and real-world coordinates via per-scene homography matrices for physically meaningful analysis. Our experimental results show that tracking accuracy (IDF1) drops to 0.68 to 0.70 in the densest scenes, compared with approximately 0.90 in less crowded scenes. Trajectory prediction training also becomes substantially more computationally expensive in dense scenes, with training times increasing by up to 8 times. These findings show that CrowdTraj exposes limitations in current trajectory prediction pipelines that remain hidden on existing sparse-crowd benchmarks, particularly in robustness to tracking noise and computational scalability.
Chinese Translation
在实际应用中，行人轨迹预测模型依赖于来自检测和跟踪系统的输入。之前的轨迹预测基准要么包含相对稀疏的行人交互，要么假设完美的跟踪输入，或者依赖于最小化遮挡和透视失真的俯视视角，从而限制了在现实密集人群场景中的评估。我们提出了CrowdTraj，这是一个针对自然密集人群场景中行人轨迹预测的基准。与之前的数据集不同，CrowdTraj支持从检测到跟踪再到轨迹预测的端到端评估，能够在闭路电视视角下的严重遮挡情况下进行。它还捕捉到多样化的自然行人行为，包括在现有基准中很少观察到的突然方向变化。CrowdTraj包含五个多样化的场景，每个场景平均有1,146个独特的行人，最大帧级密度范围从114到372个行人，并且有超过320万个标注的头部边界框。CrowdTraj通过每个场景的单应性矩阵提供像素和现实世界坐标，以便进行具有物理意义的分析。我们的实验结果表明，在最密集的场景中，跟踪准确率（IDF1）下降到0.68到0.70，而在较少拥挤的场景中约为0.90。轨迹预测训练在密集场景中也变得显著更具计算开销，训练时间增加了多达8倍。这些发现表明，CrowdTraj揭示了当前轨迹预测管道在现有稀疏人群基准中隐藏的局限性，特别是在对跟踪噪声的鲁棒性和计算可扩展性方面。
cs.CV / 199 / 2609.07720
Better Call CineCrew: Consistent Ultra-Long Narrative-to-Film Generation
更好地呼叫 CineCrew：一致的超长叙事到电影生成
Abstract
Long-form narrative-to-film generation requires shot-level controllability and cross-clip consistency in both visual identity and character behavior-requirements that remain difficult to satisfy with current prompt-based workflows. A core reason existing workflows remain brittle is the lack of a structured intermediate layer between scripts and video models, especially when screenplays are underspecified at key cinematic decision points. We introduce a structured orchestration layer for film-oriented script-to-video generation, implemented as a multi-agent framework that operates between scripts and off-the-shelf video generators. The layer is centered on FilmDSL, a film-oriented domain-specific language that makes cinematic constraints explicit, including shot and camera directives, asset and continuity requirements, and persona cues, so that agents coordinate through a shared structured specification for planning, generation, critique, and repair. Specifically, a generation agent constructs asset packs and storyboard keyframes that anchor composition before clip-by-clip synthesis, while a critic agent produces structured QA signals and triggers targeted refinement without retraining the base model. Experiments on TV-style segments show improved controllability and consistency over text-only and reference-only baselines.
Chinese Translation
长篇叙事到电影生成需要在镜头级别的可控性和跨剪辑的一致性，尤其是在视觉身份和角色行为方面，而这些要求在当前基于提示的工作流程中仍然难以满足。现有工作流程脆弱的一个核心原因是缺乏一个结构化的中间层，连接剧本和视频模型，尤其是在关键电影决策点上剧本未充分指定的情况下。我们提出了一个结构化的编排层，用于电影导向的剧本到视频生成，该层作为一个多代理框架在剧本和现成视频生成器之间运行。该层以 FilmDSL 为中心，这是一种电影导向的领域特定语言，使电影约束变得明确，包括镜头和摄影机指令、资产和连续性要求以及角色提示，以便代理通过共享的结构化规范进行协调，以实现规划、生成、评估和修复。具体而言，生成代理构建资产包和故事板关键帧，以在逐剪辑合成之前锚定构图，而评估代理生成结构化的质量保证信号并触发针对性的改进，而无需重新训练基础模型。在电视风格片段上的实验显示，与仅基于文本和仅基于参考的基线相比，控制性和一致性得到了改善。
cs.CV / 200 / 2609.07724
Zero-Shot 3D Plant Organ Segmentation with SAM3 and Semantic NeRFs
基于SAM3和语义神经辐射场的零样本3D植物器官分割
Abstract
Accurate 3D plant organ segmentation is fundamental to automated phenotyping. Existing approaches rely on annotated training data or species-specific model configurations. We present an annotation-free pipeline for 3D plant organ segmentation, combining text-prompted SAM3 segmentation with semantic neural radiance fields (NeRFs). Given only multi-view RGB images and a list of class names, our zero-shot pipeline produces semantically labeled 3D point clouds without manual annotation, per-species fine-tuning, or domain-specific preprocessing. Multi-view NeRF fusion acts as effective implicit consensus mechanism that lifts imperfect per-frame masks into accurate 3D labels. On a controlled Begonia maculata testbed the SAM3 pipeline achieves 92.6% mIoU, reaching 95.9% of the oracle upper bound established with perfect ground-truth masks. The pipeline was further evaluated on a new dataset spanning ten diverse plant point clouds reaching an average 0.856 mIoU, with leaf and pot IoU above 0.91 and 0.90 for every species, respectively. These results demonstrate that annotation-free 3D plant organ segmentation is now feasible and approaching the range of supervised methods.
Chinese Translation
准确的3D植物器官分割是自动表型分析的基础。现有方法依赖于带注释的训练数据或特定物种的模型配置。我们提出了一种无注释的3D植物器官分割管道，将文本提示的SAM3分割与语义神经辐射场（NeRFs）相结合。仅凭多视角RGB图像和类别名称列表，我们的零样本管道能够生成语义标记的3D点云，无需手动注释、特定物种的微调或领域特定的预处理。多视角NeRF融合作为有效的隐式共识机制，将不完美的逐帧掩膜提升为准确的3D标签。在一个受控的Begonia maculata测试平台上，SAM3管道达到了92.6%的mIoU，达到了用完美真实掩膜建立的95.9%的预期上限。该管道还在一个涵盖十种不同植物点云的新数据集上进行了评估，平均mIoU达到0.856，叶片和花盆的IoU分别超过0.91和0.90。这些结果表明，无注释的3D植物器官分割现在是可行的，并接近监督方法的水平。
cs.CV / 201 / 2609.07734
TV-SGS: Gaussian Splatting with Geometric Information Propagation via Tensor Voting under sparse views
TV-SGS：在稀疏视图下通过张量投票进行几何信息传播的高斯溅射
Abstract
Gaussian Splatting has been effective in inferring scene representations that excel in novel view synthesis. Multiple splats cooperate seamlessly to synthesize the pixels of novel views and are jointly optimized even though they only affect each other indirectly, via pixels they project to in common. We present an approach that enables direct communication among splats to enhance the geometric structures they form in 3D. This is accomplished by Tensor Voting, which was originally designed to infer structures from noisy inputs and has been adapted here to provide supervision during test-time optimization, leading to more accurate scene geometry. We introduce a new class of 3D losses that do not rely on rendering and can be combined with essentially all losses previously reported in the literature. Our 3D losses are especially effective when the input views are sparse and geometric regularization is essential due to limited supervision from the images. Our method is easy to integrate with a diverse set of backbones, and our experiments on the DTU and Tanks-and-Temples datasets demonstrate that TV-SGS improves the geometry of the outputs compared to the backbone, while maintaining or improving rendering quality.
Chinese Translation
高斯溅射在推断场景表示方面表现出色，尤其是在新视图合成中。多个溅射体无缝协作，以合成新视图的像素，并且尽管它们仅通过共同投影的像素间接影响彼此，但仍然进行联合优化。我们提出了一种方法，使溅射体之间能够直接通信，从而增强它们在三维中形成的几何结构。这是通过张量投票实现的，张量投票最初设计用于从噪声输入中推断结构，并在这里进行了调整，以在测试时优化过程中提供监督，从而导致更准确的场景几何。我们引入了一类新的三维损失，这些损失不依赖于渲染，并且可以与文献中几乎所有已报告的损失结合使用。当输入视图稀疏且由于图像提供的监督有限而需要几何正则化时，我们的三维损失尤其有效。我们的方法易于与多种主干网络集成，我们在DTU和Tanks-and-Temples数据集上的实验表明，TV-SGS在提高输出几何的同时，保持或改善了渲染质量。
cs.CV / 202 / 2609.07738
TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking
TFTrack：一种无模板的高效3D点云跟踪框架
Abstract
LiDAR-based 3D Single Object Tracking (3D SOT) is critical for robotic perception and navigation and aims to localize dynamic objects across frames in sparse point clouds. Existing methods, rooted in the Siamese tracking paradigm from 2D vision, rely on costly dual-input designs and excessive motion modeling guided by template priors, hindering their efficiency. Our in-depth analysis reveals: (i) the template paradigm is redundant, as the previous bounding box center encodes sufficient historical context; (ii) complex motion modeling is unnecessary, as geometric alignment provides adequate motion priors. Based on the above findings, we propose the first Template-Free Tracking framework (TFTrack). The novel framework eliminates the need for template-search pairings and operates directly on the current frame guided solely by the prior bounding box center and size. We instantiate this paradigm into three variants: TFTrack-Voxel, TFTrack-Pillar, and TFTrack-Point, to explore different 3D representations under a unified framework, ensuring flexibility across sparse and dense scenes. Extensive experiments on KITTI and nuScenes benchmarks show that TFTrack is competitive with leading template-based trackers, while reducing FLOPs by approximately 50% and running at approximately 120 FPS. By simplifying overcomplicated motion-centric designs, TFTrack establishes a new minimalist paradigm for efficient 3D point cloud tracking, paving the way for real-time and resource-efficient deployment in embedded robotic systems, such as autonomous vehicles. The code is available at https://github.com/tftrack-anonymous/TFTrack/tree/main.
Chinese Translation
基于LiDAR的3D单目标跟踪（3D SOT）对于机器人感知和导航至关重要，旨在在稀疏点云中跨帧定位动态物体。现有方法根植于来自2D视觉的孪生跟踪范式，依赖于昂贵的双输入设计和过度的运动建模，这些都受到模板先验的指导，限制了其效率。我们的深入分析表明：（i）模板范式是多余的，因为先前的边界框中心编码了足够的历史上下文；（ii）复杂的运动建模是不必要的，因为几何对齐提供了足够的运动先验。基于上述发现，我们提出了第一个无模板跟踪框架（TFTrack）。该新框架消除了模板搜索配对的需要，直接在当前帧上操作，仅由先前的边界框中心和大小指导。我们将这一范式实例化为三种变体：TFTrack-Voxel、TFTrack-Pillar和TFTrack-Point，以在统一框架下探索不同的3D表示，确保在稀疏和密集场景中的灵活性。在KITTI和nuScenes基准上的大量实验表明，TFTrack与领先的基于模板的跟踪器具有竞争力，同时将FLOPs减少了约50%，并以约120 FPS的速度运行。通过简化过于复杂的以运动为中心的设计，TFTrack建立了一种新的极简主义范式，用于高效的3D点云跟踪，为嵌入式机器人系统（如自动驾驶汽车）的实时和资源高效部署铺平了道路。代码可在https://github.com/tftrack-anonymous/TFTrack/tree/main获取。
cs.CV / 203 / 2609.07753
Cross-modal learning for SAR target recognition using optical vision foundation models
基于光学视觉基础模型的合成孔径雷达目标识别的跨模态学习
Abstract
Synthetic Aperture Radar (SAR) is an important modality in a wide range of imaging applications due to its versatile, long range and near all weather operating capabilities. However, Automatic Target Recognition (ATR) remains a challenging problem due to limited labelled data, the strong speckle in SAR images and the significant domain gap between SAR and more abundant optical imagery. In contrast, electro-optical (EO) imagery benefits from massive datasets, clearer visual structure and powerful foundation models. In this work, we investigate how vision foundation models trained on optical data can provide class level supervision for SAR classification. We propose a cross-modal EO to SAR prototype alignment framework in which a frozen EO encoder, based on a DINOv3 vision foundation model, is used to construct class level optical prototypes without requiring strict EO/SAR pairs. A SAR model is then trained to classify SAR images while aligning its embeddings to the corresponding EO class prototype. At inference time, the SAR model operates independently, without access to optical imagery. We evaluate our approach on the UNICORNv2 dataset, an EO and SAR dataset of civilian vehicles with heavily speckled images and severe class imbalance. EO prototype alignment improves SAR classification accuracy over frozen DINOv3, SAR only finetuning and unpaired distribution alignment baselines, and t-SNE visualizations provide qualitative evidence of clearer separation among classes in the trained SAR embedding space. These results suggest that optical vision foundation models, despite being trained on visible spectrum imagery, provide transferable information for SAR image classification, offering a practical method for using large scale pretrained vision foundation models across challenging sensing modalities.
Chinese Translation
合成孔径雷达（SAR）由于其多功能、长距离和近乎全天候的操作能力，在广泛的成像应用中扮演着重要角色。然而，自动目标识别（ATR）仍然是一个具有挑战性的问题，原因在于标注数据有限、SAR图像中的强散斑以及SAR与更丰富的光学图像之间显著的领域差距。相比之下，电子光学（EO）图像受益于大量数据集、更清晰的视觉结构和强大的基础模型。在本研究中，我们探讨了如何利用在光学数据上训练的视觉基础模型为SAR分类提供类别级监督。我们提出了一种跨模态EO到SAR原型对齐框架，其中基于DINOv3视觉基础模型的冻结EO编码器用于构建类别级光学原型，而无需严格的EO/SAR配对。然后训练一个SAR模型来分类SAR图像，同时将其嵌入对齐到相应的EO类别原型。在推理时，SAR模型独立运行，无需访问光学图像。我们在UNICORNv2数据集上评估了我们的方法，该数据集包含严重散斑图像和严重类别不平衡的民用车辆的EO和SAR数据。EO原型对齐提高了SAR分类的准确性，相较于冻结的DINOv3、仅SAR微调和无配对分布对齐基线，t-SNE可视化提供了训练的SAR嵌入空间中类别间更清晰分离的定性证据。这些结果表明，尽管光学视觉基础模型是在可见光谱图像上训练的，但它们为SAR图像分类提供了可迁移的信息，提供了一种在具有挑战性的传感模态中使用大规模预训练视觉基础模型的实用方法。
cs.CV / 204 / 2609.07780
DroneGround: Open-Vocabulary Drone Payload Characterization Using Synthetic Data and Grounded Vision-Language Models
DroneGround：基于合成数据和基础视觉-语言模型的开放词汇无人机载荷特征描述
Abstract
Automated drone surveillance has become increasingly important for public safety, critical infrastructure protection,and restricted airspace monitoring. While existing vision-based systems achieve strong performance for drone detection and tracking, reliable payload characterization remains highly challenging under long-range imaging conditions due to limited availability of annotated real-world datasets, and substantial distribution shifts encountered during deployment. Existing approaches formulate payload characterization as a closed-set object detection problem, limiting their ability to recognize previously unseen payloads and generalize beyond the training distribution. To address these challenges, we generate a photorealistic synthetic drone-payload dataset using Unreal Engine 5 and Cosys-AirSim and propose DroneGround: Grounded Vision-Language Payload Characterization, a two-stage framework for robust open-vocabulary payload analysis. DroneGround first employs a YOLO26s detector to localize drones and extract drone-centric image crops, followed by a LoRA-fine-tuned PaliGemma vision-language model that generates seman- tic descriptions of the detected drones and their attached payloads, enabling open-vocabulary payload characterization beyond predefined categories. An occlusion-based grounding module further provides interpretable payload localization by identifying image regions responsible for the generated descriptions. Extensive experiments on both synthetic and real-world drone imagery demonstrate that DroneGround substantially improves robustness under synthetic-to-real distribution shifts, outperforming a conventional closed-set payload detector by improving the F1-score from 82.5% to 96.3%, while achieving significantly better generalization to previously unseen payload categories (80.4%versus 42.7% F1). Dataset and code will be released upon acceptance of the paper.
Chinese Translation
自动化无人机监控在公共安全、关键基础设施保护和限制空域监测中变得越来越重要。尽管现有的基于视觉的系统在无人机检测和跟踪方面表现出色，但在长距离成像条件下，由于标注的真实世界数据集的有限可用性以及在部署过程中遇到的显著分布变化，可靠的载荷特征描述仍然面临很大挑战。现有方法将载荷特征描述形式化为一个闭集物体检测问题，限制了它们识别以前未见载荷的能力，并且无法超越训练分布进行泛化。为了解决这些挑战，我们使用虚幻引擎5和Cosys-AirSim生成了一个逼真的合成无人机载荷数据集，并提出了DroneGround：基础视觉-语言载荷特征描述，这是一个用于稳健开放词汇载荷分析的两阶段框架。DroneGround首先采用YOLO26s检测器来定位无人机并提取以无人机为中心的图像裁剪，随后使用经过LoRA微调的PaliGemma视觉-语言模型生成检测到的无人机及其附加载荷的语义描述，从而实现超越预定义类别的开放词汇载荷特征描述。一个基于遮挡的基础模块进一步通过识别负责生成描述的图像区域提供可解释的载荷定位。在合成和真实世界无人机图像上的广泛实验表明，DroneGround在合成到真实的分布变化下显著提高了稳健性，其F1分数从82.5%提高到96.3%，同时在以前未见载荷类别上实现了显著更好的泛化（80.4%对比42.7% F1）。数据集和代码将在论文接受后发布。
cs.CV / 205 / 2609.07815
VoT: Vision-of-Thought for Unified Multimodal Representation Alignment
VoT：统一多模态表示对齐的思维视觉
Abstract
Current text-to-image systems typically employ a "text encoder plus diffusion decoder" paradigm, in which text semantics directly modulate continuous latent noise. Despite their success, these methods lack an explicit, interpretable intermediate representation that effectively bridges high-level linguistic semantics and low-level visual signals. In this paper, we propose Vision-of-Thought (VoT), a framework that introduces a discrete visual-thinking layer between vision-language models (VLMs) and diffusion transformers (DiTs). Instead of treating VLMs merely as text encoders, we use them as multimodal planners that generate discrete VoT tokens representing high-level visual plans, such as objects and layouts, before rendering pixels. We train a specialized VoT tokenizer in the VLM semantic space with a closed-loop objective that combines VLM alignment, feature reconstruction, and vector-quantization losses. These objectives make the tokens semantically readable by the VLM while preserving the visual information needed for generation. Experimental results demonstrate that VoT improves semantic alignment and provides a structured interface for interpretable and controllable generation.
Chinese Translation
当前的文本到图像系统通常采用“文本编码器加扩散解码器”的范式，其中文本语义直接调制连续的潜在噪声。尽管这些方法取得了成功，但它们缺乏一种明确且可解释的中间表示，无法有效地桥接高层次的语言语义与低层次的视觉信号。在本文中，我们提出了思维视觉（Vision-of-Thought，VoT），一个在视觉-语言模型（VLMs）与扩散变换器（DiTs）之间引入离散视觉思维层的框架。我们不再仅仅将VLMs视为文本编码器，而是将其作为多模态规划者，生成代表高层次视觉计划（如对象和布局）的离散VoT标记，然后再渲染像素。我们在VLM语义空间中训练了一个专门的VoT标记器，采用闭环目标，结合了VLM对齐、特征重建和向量量化损失。这些目标使得标记在VLM中具有语义可读性，同时保留了生成所需的视觉信息。实验结果表明，VoT改善了语义对齐，并提供了一个结构化接口，以实现可解释和可控的生成。
cs.CV / 206 / 2609.07823
SAFIRE: Safety-Critical Benchmark for Fine-grained Fire and Smoke Understanding in Multimodal LLMs
SAFIRE：多模态大语言模型中细粒度火灾与烟雾理解的安全关键基准
Abstract
Multimodal Large Language Models (MLLMs) show strong progress on vision-language tasks, yet their reliability in safety-critical settings remains underexplored. Fire-smoke understanding is central to public safety and disaster response, but most existing benchmarks lack diverse real-world scenarios and context-aware evaluation. We introduce SAFIRE, a large-scale benchmark for fire-smoke understanding in MLLMs, comprising 83K captioned images from 20 scenarios and 193K multiple-choice VQA (MCVQA) generated from a 9.7K-image subset, spanning 10 evaluation dimensions from basic perception to higher-order reasoning. A GPT-5.4-assisted multi-stage verification pipeline with MLLM majority voting ensures annotation quality. Evaluating ten open-source MLLMs (8B-38B) yields an average accuracy of 61.9%, exposing major gaps in safety-critical reasoning. We further show that adapting vision encoders with only 7% of our domain-specific data boosts fire-scene classification accuracy from 20.1% to 64.5%, indicating that carefully curated data can yield substantial gains even when data volume is limited. All datasets, models, and code are available at https://risys-lab.github.io/SAFIRE/.
Chinese Translation
多模态大语言模型（MLLMs）在视觉-语言任务上取得了显著进展，但它们在安全关键环境中的可靠性仍然未得到充分探索。火灾-烟雾理解对公共安全和灾难响应至关重要，但现有的大多数基准缺乏多样化的现实场景和上下文感知的评估。我们推出了SAFIRE，这是一个针对MLLMs的火灾-烟雾理解的大规模基准，包含来自20个场景的83K个带注释图像，以及从9.7K图像子集中生成的193K个多项选择视觉问答（MCVQA），涵盖从基本感知到高阶推理的10个评估维度。一个由GPT-5.4辅助的多阶段验证流程结合MLLM的多数投票机制确保了注释质量。对十个开源MLLM（8B-38B）的评估显示平均准确率为61.9%，揭示了安全关键推理中的重大缺口。我们进一步表明，仅使用7%的领域特定数据对视觉编码器进行适配，可以将火灾场景分类的准确率从20.1%提升至64.5%，这表明即使在数据量有限的情况下，精心策划的数据也能带来显著的提升。所有数据集、模型和代码均可在 https://risys-lab.github.io/SAFIRE/ 获取。
cs.CV / 207 / 2609.07884
Are Image Generators Zero-Shot Perceivers? A Rigorous Evaluation
图像生成器是零-shot 感知者吗？一项严格的评估
Abstract
Recent work, such as Vision Banana, shows that lightweight instruction tuning can enable an image generator to achieve state-of-the-art performance across multiple visual perception tasks. Motivated by this perspective, we ask how far image generators can go on public visual perception benchmarks in a zero-shot setting. We introduce ProbeGen, a benchmark for zero-shot generative perception that casts monocular depth estimation, referring/reasoning segmentation, and object counting as conditional generation tasks specified through text prompts, and compares 20 models in total---including proprietary and open-weight image generators, specialist perception models, and MLLMs---across 11 published benchmarks. We observe that pretrained image generators show measurable zero-shot perceptual competence, but with a clear trade-off: specialist models remain stronger for in-distribution accuracy and efficiency, while generative models are often more robust under distribution shift and better at compositional semantic reasoning. We hope this study helps establish zero-shot generative perception as a meaningful research direction and provides a useful foundation for future work at the intersection of visual generation and understanding.
Chinese Translation
近期的研究，如 Vision Banana，表明轻量级的指令调优可以使图像生成器在多个视觉感知任务中达到最先进的性能。基于这一观点，我们探讨图像生成器在零-shot 设置下在公共视觉感知基准上的表现。我们引入了 ProbeGen，这是一个用于零-shot 生成感知的基准，将单目深度估计、引用/推理分割和物体计数视为通过文本提示指定的条件生成任务，并对包括专有和开放权重图像生成器、专业感知模型以及 MLLMs 在内的 20 个模型进行了比较，涵盖 11 个已发布的基准。我们观察到，预训练的图像生成器展现出可测量的零-shot 感知能力，但存在明显的权衡：专业模型在分布内的准确性和效率上仍然更强，而生成模型在分布转移下通常更具鲁棒性，并且在组合语义推理方面表现更佳。我们希望这项研究有助于确立零-shot 生成感知作为一个有意义的研究方向，并为未来在视觉生成与理解交叉领域的工作提供有用的基础。
cs.CV / 208 / 2609.07915
JEDI: JEPA-to-Edge Distillation for Efficient Cropland Segmentation from Satellite Imagery
JEDI：基于JEPA到边缘蒸馏的高效农田分割方法
Abstract
Large vision models provide useful representations for remote-sensing segmentation but are often too expensive for deployment at the satellite or field edge. Existing feature-level distillation methods also tend to assume similar teacher and student architectures and often stop feature alignment when task training begins. We introduce JEDI (JEPA-to-Edge Distillation), a two-stage framework that transfers representations from a large I-JEPA Vision Transformer teacher to a compact SegFormer student. First, JEDI aligns the student's terminal representation with the teacher's token space using cross-architecture projection and spatial alignment. It then jointly optimizes supervised segmentation, temperature-scaled response distillation, and persistent feature alignment throughout task adaptation. On CalCROP21, JEDI-B0 achieves 68.0 mean Intersection-over-Union (mIoU) with 4.04M parameters, improving over the standalone student by 16.0 points and coming within 2.0 points of the 70.0 mIoU achieved by the 639M-parameter teacher. We evaluate SegFormer B0, B1, and B2 students with 4.04M, 14.33M, and 28M parameters, respectively. Across all three variants, JEDI consistently outperforms response-, structure-, channel-, and relational-distillation baselines under the same teacher-student setting. These results show that persistent representation alignment is especially valuable under aggressive compression, substantially reducing model size and computation while preserving segmentation performance.
Chinese Translation
大型视觉模型为遥感分割提供了有用的表征，但在卫星或田间边缘的部署往往成本过高。现有的特征级蒸馏方法通常假设教师和学生架构相似，并且在任务训练开始时往往停止特征对齐。我们提出了JEDI（JEPA到边缘蒸馏），这是一个两阶段框架，将大型I-JEPA视觉变换器教师的表征转移到紧凑的SegFormer学生上。首先，JEDI通过跨架构投影和空间对齐将学生的终端表征与教师的标记空间对齐。然后，它在任务适应过程中联合优化监督分割、温度缩放响应蒸馏和持久特征对齐。在CalCROP21数据集上，JEDI-B0以4.04M参数实现了68.0的平均交并比（mIoU），比独立学生提高了16.0个百分点，并且距离639M参数教师所达到的70.0 mIoU仅有2.0个百分点的差距。我们评估了具有4.04M、14.33M和28M参数的SegFormer B0、B1和B2学生。在所有三个变体中，JEDI在相同的教师-学生设置下始终优于响应、结构、通道和关系蒸馏基线。这些结果表明，在激进压缩下，持久的表征对齐尤其有价值，显著减少了模型大小和计算量，同时保持了分割性能。
cs.CV / 209 / 2609.07916
Poisson Image Denoising Using Minimax Concave and Reweighted $\ell_1$ Penalties: Nonblind and Blind Approaches
使用极小极大凹函数和加权 $ ext{l}_1$ 惩罚的泊松图像去噪：非盲和盲方法
Abstract
Images are important tools in various sciences. Despite the development of photo-taking tools, creating clear and image without noise remains challenging in practice. In particular, Poisson noise has an effect on medical and astronomical images, and reduces their quality. Additionally, blur is another factor that has an effect on image quality. The problem of image restoration becomes very complicated when we have no information about the Point Spread Function (PSF). These types of problems are known as blind case. However, in some images, such as some astronomical images, the type of PSF can be specified, and these types of problems are known as nonblind problems. Total Variation (TV) is a widely used method for solving such inverse problems, where the selection of the penalty function is the most critical factor that affects the method's performance. In this paper, to improve edge preservation, we employ a reweighted $\ell_1$-regularization of the fractional order derivative. Furthermore, we propose a nonblind and blind image deblurring approach under Poisson noise using the Minimax Concave Penalty (MCP), which is a continuous, sparsity promoting, and nearly unbiased regularizer. This formulation leads to a nonconvex optimization model. To solve the proposed model, we introduce an efficient numerical algorithm based on the Alternating Direction Method of Multipliers (ADMM) and provide an analysis of its convergence. Finally, the effectiveness of the proposed algorithm are demonstrated through extensive experiments on various images.
Chinese Translation
图像在各种科学中是重要的工具。尽管摄影工具的发展，实际中创建清晰且无噪声的图像仍然具有挑战性。特别是，泊松噪声对医学和天文图像有影响，并降低了它们的质量。此外，模糊是影响图像质量的另一个因素。当我们对点扩散函数（Point Spread Function, PSF）没有任何信息时，图像恢复问题变得非常复杂。这类问题被称为盲问题。然而，在某些图像中，例如一些天文图像，可以指定PSF的类型，这类问题被称为非盲问题。全变差（Total Variation, TV）是解决此类逆问题的广泛使用的方法，其中惩罚函数的选择是影响方法性能的最关键因素。在本文中，为了改善边缘保留，我们采用了分数阶导数的加权 $ ext{l}_1$ 正则化。此外，我们提出了一种在泊松噪声下的非盲和盲图像去模糊方法，使用极小极大凹函数惩罚（Minimax Concave Penalty, MCP），这是一种连续的、促进稀疏性且几乎无偏的正则化器。这一公式化导致了一个非凸优化模型。为了解决所提出的模型，我们引入了一种基于交替方向乘子法（Alternating Direction Method of Multipliers, ADMM）的高效数值算法，并提供了其收敛性分析。最后，通过对各种图像的广泛实验，验证了所提算法的有效性。
cs.CV / 210 / 2609.07937
TDDN: Text-aligned Diffused DINO Network for Puzzle Understanding
TDDN：文本对齐的扩散DINO网络用于拼图理解
Abstract
Structured visual reasoning, such as image puzzles, demands fine-grained visual perception, an ability current Vision Language Models (VLMs) lack. VLMs built on CLIP-based ViT backbones trade fine-grained detail for high-level semantics, and we show this loss propagates downstream. To recover it, we fuse DINOv3 and CleanDIFT representations into a perception encoder (DiffusedDINO) and align it with RoBERTa-L, yielding a text-aligned model TDDN that preserves this perceptual advantage: with frozen backbones and only $\sim$590K alignment pairs, TDDN matches CLIP on image-text retrieval, surpassing it on three of four settings. It does so while more than tripling CLIP's dense-prediction accuracy (ADE20K 5.20 $\to$ 18.11 mIoU, COCO-Stuff 7.35 $\to$ 24.44), despite CLIP's massive training corpus. TDDN leads on segmentation benchmarks among general-purpose contrastive encoders, including SigLIP$\,$2. We further introduce Puzzle Perception, a segmentation and visual question answering dataset that probes fine-grained spatial understanding, on which TDDN doubles CLIP's segmentation accuracy (11.04 $\to$ 22.51 mIoU).
Chinese Translation
结构化视觉推理，如图像拼图，要求细粒度的视觉感知，而当前的视觉语言模型（VLMs）在这方面存在不足。基于CLIP的ViT骨干网络构建的VLMs牺牲了细粒度细节以换取高层次语义，我们展示了这种损失会向下传播。为了恢复这一点，我们将DINOv3和CleanDIFT表示融合到一个感知编码器（DiffusedDINO）中，并将其与RoBERTa-L对齐，得到一个文本对齐模型TDDN，该模型保留了这一感知优势：在冻结骨干网络且仅使用约590K对齐样本的情况下，TDDN在图像-文本检索上与CLIP相匹配，并在四个设置中的三个上超越了CLIP。同时，尽管CLIP拥有庞大的训练语料库，TDDN的密集预测准确率却超过了CLIP的三倍（ADE20K 5.20 $ o$ 18.11 mIoU，COCO-Stuff 7.35 $ o$ 24.44）。在包括SigLIP2在内的通用对比编码器的分割基准测试中，TDDN表现优异。我们进一步引入了拼图感知（Puzzle Perception），一个探测细粒度空间理解的分割和视觉问答数据集，在该数据集上，TDDN的分割准确率是CLIP的两倍（11.04 $ o$ 22.51 mIoU）。
cs.CV / 211 / 2609.07939
Bottom-up Modeling of Repeated Elements via Single Image Analysis-by-Synthesis
通过单幅图像分析-合成的自下而上重复元素建模
Abstract
We address the problem of discovering repeated elements from a single image. In contrast to existing approaches that depend on large annotated datasets, curated multi-image collections, or object segmentation masks, we show that a single image can suffice to learn a meaningful object model in a completely bottom-up fashion, without any prior knowledge beyond a coarse scale prior. Our method learns a tunable image-space prototype of the repeated elements through a reconstruction objective, enabling the model to identify and synthesize consistent object instances within the same image. Experiments on 116 real images from the FSC-147 dataset demonstrate that our method successfully learns coherent element models and captures intra-category variation on challenging images. Qualitative results reveal superior reconstructions and interpretable decompositions compared to classical decomposition, joint alignment, and 3D object modeling methods, while maintaining a simple 2D formulation. These results suggest that meaningful object discovery can emerge from single image learning alone.
Chinese Translation
我们解决了从单幅图像中发现重复元素的问题。与依赖于大型标注数据集、策划的多图像集合或物体分割掩码的现有方法不同，我们展示了单幅图像足以以完全自下而上的方式学习有意义的物体模型，而无需超出粗略尺度先验的任何先验知识。我们的方法通过重建目标学习可调的图像空间原型，能够在同一图像内识别和合成一致的物体实例。在FSC-147数据集的116幅真实图像上的实验表明，我们的方法成功地学习了连贯的元素模型，并在具有挑战性的图像上捕捉了类别内的变化。定性结果显示，与经典分解、联合对齐和3D物体建模方法相比，我们的方法在重建和可解释性分解方面具有更优的表现，同时保持简单的二维公式。这些结果表明，有意义的物体发现可以仅通过单幅图像学习而产生。
cs.CV / 212 / 2609.07941
ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding
ReactVAU：一种用于流媒体视频异常理解的慢快解耦框架
Abstract
In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely on offline inference with global temporal sampling, which violates causality and prevents deployment in live surveillance streams. Conversely, general streaming video models satisfy causal access but dilute rare transient anomalies during memory compression and often invoke heavyweight MLLMs uniformly over long normal intervals. React VAU addresses this gap with three synergistic components: a lightweight Fast Detection Module based on Spatial Grid Folding (SGF) for continuous anomaly filtering; an Anomaly-Aware Persistent Memory (AAPM) that protects critical visual cues from temporal decay; and a heavyweight Slow Reasoning Module that remains dormant during normal streams and is awakened only by suspicious events for semantic verification and causal description. Extensive experiments on multiple benchmarks demonstrate that ReactVAU operates under strict streaming constraints while simultaneously achieving competitive performance in both anomaly detection and causal reasoning, alongside significantly enhanced computational efficiency by minimizing heavyweight MLLM invocations. Project page is available at https://huiyuiui.github.io/React_VAU/
Chinese Translation
在本文中，我们提出了ReactVAU，一种用于实时流媒体视频异常理解（Video Anomaly Understanding, VAU）的慢快解耦框架。现有的VAU方法依赖于离线推理和全局时间采样，这违反了因果性，并阻碍了在实时监控流中的应用。相反，通用的流媒体视频模型满足因果访问，但在内存压缩过程中稀释了稀有瞬态异常，并且通常在长时间的正常间隔内均匀调用重量级的多模态大语言模型（MLLMs）。ReactVAU通过三个协同组件解决了这一问题：基于空间网格折叠（Spatial Grid Folding, SGF）的轻量级快速检测模块，用于持续的异常过滤；一种异常感知持久内存（Anomaly-Aware Persistent Memory, AAPM），用于保护关键视觉线索免受时间衰减；以及一个重量级的慢推理模块，在正常流中保持休眠状态，仅在可疑事件发生时被唤醒进行语义验证和因果描述。在多个基准上的广泛实验表明，ReactVAU在严格的流媒体约束下运行，同时在异常检测和因果推理中实现了竞争性的性能，并通过最小化重量级MLLM调用显著提高了计算效率。项目页面可访问：https://huiyuiui.github.io/React_VAU/
cs.CV / 213 / 2609.08011
TaskGuard: Task-Conditioned Restoration Utility for Risk-Aware Object Detection
TaskGuard：风险感知目标检测的任务条件恢复效用
Abstract
Image restoration is commonly applied before object detection under adverse conditions, yet a visually improved image need not improve the downstream task. We study this mismatch as restoration utility prediction: given a degraded image and its candidate restoration, should the restoration be used or should the original observation be preserved? We introduce TaskGuard, a post-hoc controller for frozen restoration and detection pipelines. TaskGuard characterizes the realized restoration residual through its interaction with detector sensitivity and predicts whether the intervention is task-beneficial. Exact regional counterfactuals reveal substantial within-image utility heterogeneity, while a deployable pseudo-gradient preserves statistically reliable directional information. Feature-group ablation further shows that task-conditioned evidence contributes information beyond detector-response and residual statistics. The TaskGuard utility predictor is trained only on Gaussian degradation and frozen before final evaluation, then transferred to unseen motion blur, rain, and defocus. Across these unseen families, TaskGuard reduces lossnegative interventions by 54.2% (family macro) and practical per-image detection deteriorations by 37.0% (pooled), while preserving 98.8% of the Always-Restore COCO AP. On natural-rain DAWN, it reduces loss-negative interventions by 97.9% while retaining 77.8% of the AP improvement obtained by deraining. These results support restoration utility as a task-conditioned property of the specific intervention rather than image appearance alone.
Chinese Translation
在不利条件下，图像恢复通常在目标检测之前应用，但视觉上改善的图像不一定能提升下游任务的效果。我们将这种不匹配研究为恢复效用预测：给定一幅退化图像及其候选恢复，应该使用恢复结果还是保留原始观察？我们引入了TaskGuard，一个用于冻结恢复和检测管道的事后控制器。TaskGuard通过与检测器敏感性的交互来表征实现的恢复残差，并预测该干预是否对任务有益。精确的区域反事实揭示了图像内部效用的显著异质性，而可部署的伪梯度则保留了统计上可靠的方向信息。特征组消融进一步表明，任务条件证据提供的信息超出了检测器响应和残差统计。TaskGuard效用预测器仅在高斯退化上进行训练，并在最终评估前被冻结，然后转移到未见的运动模糊、雨和失焦图像。在这些未见的类别中，TaskGuard将损失负干预减少了54.2%（类别宏观），并将实际每幅图像的检测恶化减少了37.0%（汇总），同时保留了98.8%的Always-Restore COCO AP。在自然雨DAWN上，它将损失负干预减少了97.9%，同时保留了通过去雨获得的77.8%的AP提升。这些结果支持恢复效用作为特定干预的任务条件属性，而不仅仅是图像外观。
cs.CV / 214 / 2609.08013
A Black-Box Adversarial Attack on Human Pose Estimation and Keypoint-Based Action Recognition Models
针对人体姿态估计和基于关键点的动作识别模型的黑箱对抗攻击
Abstract
Human pose estimation and keypoint-based action recognition models are increasingly deployed as components of video understanding pipelines, yet their vulnerability to adversarial attacks remains insufficiently studied. Temporally coherent black-box attacks have been previously studied in visual object tracking, where the attack feedback can be defined using bounding-box overlap measures such as Intersection over Union (IoU). However, human pose estimation produces keypoint configurations rather than enclosing boxes, making box-level similarity poorly suited for measuring pose degradation. We propose OKS Attack, a decision-based black-box attack that uses Object Keypoint Similarity (OKS) as the attack feedback signal, directly targeting the spatial structure of human poses rather than their enclosing boxes. Experiments on the Penn Action dataset show that OKS Attack consistently reduces pose quality across evaluated pose estimators, with mean OKS decreases ranging from 0.0802 to 0.1494. In a downstream cross-dataset action-recognition evaluation, the attack reduces accuracy by 6.18 to 13.86 percentage points and outperforms query-matched random-noise perturbations. The attack is effective across both top-down and single-stage pose estimation models. The source code will be made publicly available at https://github.com/KacperM33/OKS_attack
Chinese Translation
人体姿态估计和基于关键点的动作识别模型越来越多地作为视频理解流程的组成部分被部署，但它们对对抗攻击的脆弱性仍然研究不足。之前在视觉目标跟踪中研究了时间一致的黑箱攻击，其中攻击反馈可以通过边界框重叠度量（如交并比 Intersection over Union, IoU）来定义。然而，人体姿态估计产生的是关键点配置，而不是封闭的框，因此基于框的相似性不适合用于测量姿态退化。我们提出了 OKS Attack，这是一种基于决策的黑箱攻击，使用对象关键点相似性（Object Keypoint Similarity, OKS）作为攻击反馈信号，直接针对人体姿态的空间结构，而不是它们的封闭框。对 Penn Action 数据集的实验表明，OKS Attack 在评估的姿态估计器中持续降低姿态质量，平均 OKS 降幅范围为 0.0802 到 0.1494。在下游跨数据集的动作识别评估中，该攻击将准确率降低了 6.18 到 13.86 个百分点，并且优于查询匹配的随机噪声扰动。该攻击在自上而下和单阶段姿态估计模型中均有效。源代码将公开发布在 https://github.com/KacperM33/OKS_attack
cs.CV / 215 / 2609.08032
Flexible Motion Generation from Language and Style References
基于语言和风格参考的灵活运动生成
Abstract
We introduce FlexMoGen, a novel framework for flexible human motion synthesis conditioned on both natural language descriptions and motion style references. Text prompts are effective at defining semantic content, but they are often limited in capturing fine-grained style details such as timing, limb articulation, and expressive dynamics. A style example clip supplements the text by conveying these nuanced motion characteristics directly, enabling the model to preserve high-level intent while reproducing the desired stylistic traits. Given a text prompt and a style example clip, FlexMoGen generates high-quality motions that preserve semantic content while faithfully reflecting the target style, offering users greater control over the animation generation process. Unlike prior methods that rely on discrete style labels and do not generalize to long or multi-style generation, FlexMoGen learns a variational style encoder without style supervision and supports long, time-varying, multi-style synthesis. Our framework jointly pre-trains the style encoder and a text-to-motion latent diffusion model within a unified architecture, modulating motion style through a lightweight adaptation module. It integrates an efficient relative positional encoding scheme and is trained on both stylized and non-stylized datasets, enabling strong generalization to unseen text-style combinations. Experiments show that FlexMoGen achieves the best balance between content fidelity and style reflection.
Chinese Translation
我们提出了FlexMoGen，一个新颖的框架，用于基于自然语言描述和运动风格参考的灵活人类运动合成。文本提示在定义语义内容方面有效，但通常在捕捉细致的风格细节（如时机、肢体关节动作和表现力动态）方面存在局限。风格示例片段通过直接传达这些细微的运动特征来补充文本，使模型能够在再现所需的风格特征的同时保持高层次的意图。给定一个文本提示和一个风格示例片段，FlexMoGen生成高质量的运动，既保留语义内容，又忠实反映目标风格，为用户提供了更大的动画生成过程控制。与依赖离散风格标签且无法推广到长时间或多风格生成的先前方法不同，FlexMoGen在没有风格监督的情况下学习了一个变分风格编码器，并支持长时间变化的多风格合成。我们的框架在统一架构内共同预训练风格编码器和文本到运动的潜在扩散模型，通过轻量级适应模块调节运动风格。它集成了一种高效的相对位置编码方案，并在风格化和非风格化数据集上进行训练，从而实现对未见文本-风格组合的强泛化。实验表明，FlexMoGen在内容保真度和风格反映之间达到了最佳平衡。
cs.CV / 216 / 2609.08038
SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities
SAFER-Activities：用于智能评估跌倒事件和日常活动的数据集
Abstract
Smart healthcare monitoring systems require precise action recognition to ensure well-being and timely intervention in critical situations such as falls, particularly for mobility-challenged individuals. Existing datasets are often clip-based, lacking the frame-level detail needed to recognize actions online, as they unfold. To address this, we introduce SAFER-Activities, a dataset for fall detection and physical activity monitoring, with a dedicated subset for wheelchair use scenarios. It comprises over 66 hours of video data captured by multiple cameras, with 85,310 action instances and frame-level annotations for 30 action classes. We benchmark action recognition on SAFER-Activities with 2D and 3D skeleton models, RGB models with frozen backbones, and multimodal fusion strategies, and evaluate on in-lab, out-of-distribution, and cross-dataset test sets. Skeleton-based models generalize best under domain shift; fusing frozen RGB features with the skeleton stream improves in-domain recognition over the baseline CNN1D, most clearly on the wheelchair subset, but degrades out of distribution. Cross-dataset and qualitative evaluations confirm that models trained on SAFER-Activities transfer well to unseen environments and external fall data. To support research on robust fall detection and activity monitoring, we release the dataset and code at https://safer-activities.github.io/.
Chinese Translation
智能医疗监测系统需要精确的动作识别，以确保人们的健康和在跌倒等关键情况下的及时干预，尤其是对于行动不便的个体。现有的数据集通常基于视频片段，缺乏在动作发生时进行在线识别所需的帧级细节。为了解决这一问题，我们推出了SAFER-Activities，这是一个用于跌倒检测和身体活动监测的数据集，专门包含轮椅使用场景的子集。该数据集包含超过66小时的视频数据，由多个摄像头捕获，涵盖85,310个动作实例以及30个动作类别的帧级注释。我们在SAFER-Activities上使用2D和3D骨架模型、带有冻结主干的RGB模型以及多模态融合策略进行动作识别基准测试，并在实验室、分布外和跨数据集测试集上进行评估。基于骨架的模型在领域转移下的泛化效果最佳；将冻结的RGB特征与骨架流融合，能够在领域内的识别上超越基线CNN1D，尤其是在轮椅子集上表现明显，但在分布外时效果下降。跨数据集和定性评估确认，基于SAFER-Activities训练的模型能够很好地迁移到未见环境和外部跌倒数据。为了支持对稳健的跌倒检测和活动监测的研究，我们在https://safer-activities.github.io/发布了数据集和代码。
cs.CV / 217 / 2609.08041
MamMA: A Mamba-Based Pedestrian Trajectory Prediction Algorithm Considering Occupancy Map and Pedestrian Awareness States
MamMA：一种基于Mamba的行人轨迹预测算法，考虑占用地图和行人意识状态
Abstract
Many pedestrian trajectory prediction algorithms have been proposed to improve the safety of navigation for mobile robots working in human-robot coexistence environments. Some pedestrian trajectory prediction algorithms extract information about obstacles near pedestrians from top-down view images to improve the accuracy of trajectory prediction. However, mobile robots typically create local occupancy maps using LiDAR, rather than top-down view images. Meanwhile, the vision sensors on board robots provide egocentric view images, which contain fine-grained behavioral information about the pedestrians near the robot. To better use the information collected by LiDAR and on-board vision sensors, we propose MamMA, a Mamba-based pedestrian trajectory prediction algorithm considering occupancy maps and pedestrian awareness states. MamMA divides the occupancy map by patches and extracts obstacle features from each patch to create map features. Pedestrian awareness states are divided and considered, as some studies show that awareness states affect the perception and speed of pedestrians. Furthermore, a Mamba-based model is proposed to predict the future trajectories of pedestrians based on different types of features. Experiments on the STCrowd, SiT, JRDB, ETH, and UCY datasets show that MamMA achieves better average displacement error and final displacement error than the state-of-the-art algorithms.
Chinese Translation
为了提高在人与机器人共存环境中移动机器人导航的安全性，提出了许多行人轨迹预测算法。一些行人轨迹预测算法通过自上而下的视图图像提取行人附近障碍物的信息，以提高轨迹预测的准确性。然而，移动机器人通常使用激光雷达（LiDAR）创建局部占用地图，而不是自上而下的视图图像。同时，机器人上的视觉传感器提供自我中心的视图图像，其中包含有关靠近机器人的行人的细粒度行为信息。为了更好地利用激光雷达和车载视觉传感器收集的信息，我们提出了MamMA，一种基于Mamba的行人轨迹预测算法，考虑占用地图和行人意识状态。MamMA将占用地图划分为多个小块，并从每个小块中提取障碍物特征以创建地图特征。行人意识状态被划分并考虑，因为一些研究表明，意识状态会影响行人的感知和速度。此外，提出了一种基于Mamba的模型，根据不同类型的特征预测行人的未来轨迹。在STCrowd、SiT、JRDB、ETH和UCY数据集上的实验表明，MamMA在平均位移误差和最终位移误差方面优于最先进的算法。
cs.CV / 218 / 2609.08043
A Quantitative Evaluation Framework for Temporal Explainability in Echocardiographic Video Segmentation
心脏超声视频分割中的时间可解释性定量评估框架
Abstract
Deep learning has achieved state-of-the-art performance in echocardiographic video segmentation, with an increasing number of models incorporating temporal information. However, quantitative evaluation of temporal explainability remains largely unexplored. We propose a quantitative framework for evaluating Grad-CAM explanations using four complementary metrics measuring temporal consistency, saliency motion, anatomical overlap, and temporal overlap. Using EchoNet-Dynamic, we compare a baseline 2D U-Net with ConvLSTM U-Net models trained across multiple temporal strides. While segmentation performance remained comparable across all models, intermediate ConvLSTM explanations exhibited substantially lower saliency consistency and greater centroid motion than final prediction explanations. Temporal Bottleneck explanations were significantly more stable than Encoder Bottleneck explanations across all strides, while final ConvLSTM Decoder3 explanations were broadly comparable to those of the 2D U-Net. Importantly, conventional frame-wise explanation metrics cannot determine whether variation in intermediate explanations reflects meaningful temporal feature evolution or explanation instability. These findings establish a preliminary quantitative framework for temporal explainability and motivate temporal-aware XAI methods that explicitly account for evolving representations in medical video models.
Chinese Translation
深度学习在心脏超声视频分割中已取得了最先进的性能，越来越多的模型开始融入时间信息。然而，时间可解释性的定量评估仍然在很大程度上未被探索。我们提出了一个定量框架，用于评估Grad-CAM解释，采用四个互补指标来测量时间一致性、显著性运动、解剖重叠和时间重叠。使用EchoNet-Dynamic，我们比较了基线的2D U-Net与在多个时间步长上训练的ConvLSTM U-Net模型。尽管所有模型的分割性能相当，但中间的ConvLSTM解释显示出显著较低的显著性一致性和更大的质心运动，相较于最终预测解释。时间瓶颈解释在所有步长上显著比编码器瓶颈解释更稳定，而最终的ConvLSTM Decoder3解释与2D U-Net的解释大致相当。重要的是，传统的逐帧解释指标无法判断中间解释的变化是否反映了有意义的时间特征演变或解释的不稳定性。这些发现建立了一个初步的时间可解释性定量框架，并激励了显式考虑医学视频模型中演变表示的时间感知XAI方法。
cs.CV / 219 / 2609.08044
RFS-UNet: Decoder-Conditioned High-Resolution Skip Recalibration for Bone-Selective DRR Synthesis
RFS-UNet：解码器条件下的高分辨率跳跃重校准用于骨选择性数字重建放射图合成
Abstract
Bone-selective digitally reconstructed radiograph (DRR) synthesis depends on high-resolution encoder detail, yet static skips cannot condition reuse on the evolving decoder representation. We ask whether decoder state adds useful information beyond encoder-only self-recalibration for high-resolution skip reuse. RFS-UNet uses pooled encoder and aligned decoder statistics for bounded residual channel recalibration at the 512^2 and 256^2 skips, leaving the backbone unchanged. In the matched seed-2026 comparison isolating decoder conditioning, RFS raises validation PSNR by 0.254 dB over Self-RFS. Across three seeds, locked-test PSNR rises from 33.225+/-0.048 to 33.537+/-0.128 dB; RFS lowers MAE in 179/200 held-out CT cases and reduces mean MAE by 3.91%. It adds 0.117% parameters and 1.169% counted Conv2d operations. These results support decoder state as a useful conditioning signal for high-resolution feature reuse in controlled paired projection synthesis.
Chinese Translation
骨选择性数字重建放射图（DRR）合成依赖于高分辨率编码器细节，但静态跳跃无法在不断演变的解码器表示上进行条件重用。我们探讨解码器状态是否提供了超越仅依赖编码器自重校准的有用信息，以实现高分辨率跳跃重用。RFS-UNet利用汇聚的编码器和对齐的解码器统计信息，在512^2和256^2的跳跃处进行有界残差通道重校准，同时保持主干网络不变。在隔离解码器条件的匹配seed-2026比较中，RFS使验证PSNR比Self-RFS提高了0.254 dB。在三个种子中，锁定测试PSNR从33.225+/-0.048上升至33.537+/-0.128 dB；RFS在179/200个保留的CT案例中降低了MAE，并减少了平均MAE 3.91%。它增加了0.117%的参数和1.169%计数的Conv2d操作。这些结果支持解码器状态作为高分辨率特征重用的有用条件信号，在受控的配对投影合成中。
cs.CV / 220 / 2609.08069
BrachistoneLR: A Brachistochrone-Inspired Learning-Rate Schedule and a Controlled Benchmark of Scheduling Policies
BrachistoneLR：一种受摆线启发的学习率调度策略及调度策略的受控基准
Abstract
The learning-rate schedule is a consequential choice in training deep networks, yet the policies in common use are heuristic, and published comparisons are hard to read, because architecture, dataset, and budget tend to vary alongside the schedule. We study BrachistoneLR, a schedule built by mapping the vertical coordinate of the brachistochrone, the curve of fastest descent under gravity, onto the range between a peak and a floor rate. Expanding the definition shows it to be cosine annealing with the half-period set to E - 1 instead of E, the configuration a standard implementation gives when its period argument is one less than the number of epochs. The rate therefore reaches its floor at the last epoch trained rather than one epoch later, and we show this difference decays as E^-2, making it a short-horizon effect. We then benchmark six schedules over 72 runs on three image classification datasets (MNIST, Fashion-MNIST, CIFAR-10) and four architecture families (fully connected, convolutional, recurrent, residual), fixing the optimizer, data pipeline, and evaluation protocol so that only the schedule varies. Schedules that fall smoothly from peak to floor beat the constant rate and calendar-based decay by margins that grow with task difficulty, reaching 2.5 points of dataset mean on CIFAR-10. Within that leading group, BrachistoneLR, cosine annealing, and warmup-cosine lie within 0.06 accuracy points and 0.17 of a mean rank, which one seed per configuration cannot separate. BrachistoneLR is best on both residual networks and has the highest CIFAR-10 mean, and it sets no milestones, decay factor, warmup length, or restart period. We conclude that the shape of a schedule matters more than its parameterization, that the choice of whether to use a smooth schedule matters more than the choice among them, and that the terminal-rate distinction is worth attention only over short horizons.
Chinese Translation
学习率调度是训练深度网络中的一个重要选择，然而常用的策略往往是启发式的，且已发表的比较结果难以阅读，因为架构、数据集和预算往往与调度一起变化。我们研究了BrachistoneLR，这是一种通过将摆线的垂直坐标（在重力作用下最快下降的曲线）映射到峰值和底部速率之间的范围而构建的调度。扩展定义表明，它实际上是余弦退火（cosine annealing），其半周期设置为E - 1而不是E，当其周期参数比训练轮数少一个时，标准实现会给出这样的配置。因此，学习率在最后一个训练的轮次达到底部，而不是在之后的一个轮次，我们表明这种差异以E^-2的速度衰减，形成了短期效应。接着，我们在三个图像分类数据集（MNIST、Fashion-MNIST、CIFAR-10）和四种架构系列（全连接、卷积、递归、残差）上对六种调度进行了72次运行的基准测试，固定优化器、数据管道和评估协议，以便仅调度有所变化。平滑下降的调度从峰值到底部的表现优于恒定速率和基于日历的衰减，随着任务难度的增加，优势逐渐扩大，在CIFAR-10数据集上达到2.5个平均分。在这一领先组中，BrachistoneLR、余弦退火和预热余弦的准确率相差不超过0.06分，平均排名相差不超过0.17，单个种子无法区分。BrachistoneLR在残差网络上表现最佳，并且在CIFAR-10上具有最高的平均值，它不设置里程碑、衰减因子、预热长度或重启周期。我们得出结论：调度的形状比其参数化更为重要，选择使用平滑调度的意义大于在不同调度之间的选择，并且终端速率的区别仅在短期内值得关注。
cs.CV / 221 / 2609.08079
VI-Bench: Benchmarking Prompt Inversion from AIGC Videos
VI-Bench：基于AIGC视频的提示反演基准测试
Abstract
Recent advances in video generation have made prompt-based control increasingly central to AIGC video generation. Prompts specify what a video should depict and how it should be represented, controlling factors such as visual style or camera behavior. Understanding this recoverability is important both for creative reuse and editing, and for assessing prompt leakage risks. However, existing video understanding benchmarks do not measure this capability: a caption may describe what is visible, but a replayable prompt must recover the generation-relevant controls needed to reproduce the video. To address this gap, we introduce VI-Bench, a benchmark built from 16.1 million real-user prompts and 900 human-verified AIGC videos. VI-Bench spans three progressively harder settings, namely single-shot semantic grounding, control over style and camera behavior, and multi-shot compositional inversion, and evaluates five generation-critical dimensions: subject, action, scene, style, and camera. We evaluate 18 representative VLMs, including 2 proprietary and 16 open-source models on VI-Bench, using an Inversion Score that measures prompt-level alignment with the original prompt and video-level fidelity of the regenerated video. The results reveal substantial limitations: even the strongest model achieves only 0.632 on Inversion Score, performance degrades sharply as samples require richer control and multi-shot reasoning, and models often produce plausible prompts whose regenerated videos deviate from the reference. These findings show that video prompt inversion is a distinct and under-evaluated capability requiring models to transform visual understanding into replay-stable generative control.
Chinese Translation
近期视频生成的进展使得基于提示的控制在AIGC视频生成中变得愈加重要。提示指定了视频应展示的内容及其表现方式，控制视觉风格或摄像机行为等因素。理解这种可恢复性对于创意重用和编辑，以及评估提示泄露风险都至关重要。然而，现有的视频理解基准并未衡量这一能力：一个字幕可能描述可见内容，但可重放的提示必须恢复生成相关的控制，以重现视频。为了解决这一空白，我们引入了VI-Bench，这是一个基于1610万个真实用户提示和900个经过人工验证的AIGC视频构建的基准。VI-Bench涵盖了三个逐渐增加难度的设置，即单次语义定位、对风格和摄像机行为的控制，以及多次组合反演，并评估五个生成关键维度：主题、动作、场景、风格和摄像机。我们在VI-Bench上评估了18个代表性的视觉语言模型（VLM），包括2个专有模型和16个开源模型，使用反演分数（Inversion Score）来衡量提示与原始提示的对齐程度以及再生视频的保真度。结果揭示了显著的局限性：即使是最强的模型在反演分数上也仅达到0.632，随着样本需要更丰富的控制和多次推理，性能急剧下降，模型往往生成看似合理的提示，但其再生视频与参考视频偏离。这些发现表明，视频提示反演是一种独特且未被充分评估的能力，要求模型将视觉理解转化为可重放的生成控制。
cs.CV / 222 / 2609.08084
Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation
万寿菊 V2：重新审视用于单目深度估计的扩散变换器
Abstract
Monocular depth estimation is a ubiquitous yet highly ill-posed computer vision task, with downstream applications in scene reconstruction, computational photography, and robotics, among others. Despite the field's maturity, recent models still struggle to generalize to out-of-distribution inputs and to produce sharp and detailed depth maps. In this paper, we revisit Marigold, a set of techniques for repurposing modern image generation and editing models, powered by the diffusion transformer (DiT) architecture, into state-of-the-art monocular depth estimators. Our recipes target single-step inference from pretrained multi-step flow-matching models, with quantization where needed, preserving model capacity while remaining cheap to run. We analyze the artifacts of naive training and identify two effective remedies: aligning the model's internal representations with semantic features extracted from ground-truth, and adopting a 2-stage fine-tuning protocol built around a novel Sinkhorn-based loss. The results are crisper, cleaner depth maps that generalize well out-of-distribution, with 16-26% improvement in AbsRel over the previous best on KITTI and ETH3D. Qualitatively, our model resolves fur, foliage, and hair-thin edges that have eluded prior models. Furthermore, Marigold V2 achieves state-of-the-art results when applied to other dense regression tasks, such as surface normals estimation and intrinsic image decomposition. Project website: https://hf.co/spaces/huawei-bayerlab/marigold-v2-web
Chinese Translation
单目深度估计是一项普遍存在但高度不适定的计算机视觉任务，广泛应用于场景重建、计算摄影和机器人等领域。尽管该领域已相对成熟，近期模型仍然难以对分布外输入进行泛化，并且生成清晰且详细的深度图。在本文中，我们重新审视了万寿菊（Marigold），这是一组技术，旨在将现代图像生成和编辑模型（基于扩散变换器（DiT）架构）重新用于最先进的单目深度估计器。我们的方案针对从预训练的多步流匹配模型进行单步推理，必要时进行量化，既保留了模型的能力，又保持了低运行成本。我们分析了简单训练的伪影，并确定了两种有效的补救措施：将模型的内部表示与从真实标签中提取的语义特征对齐，以及采用围绕新颖的Sinkhorn基础损失构建的两阶段微调协议。结果是更清晰、更干净的深度图，在分布外泛化良好，相较于KITTI和ETH3D上的先前最佳模型，AbsRel指标提高了16-26%。在定性方面，我们的模型能够解决毛发、树叶和细发边缘等先前模型未能处理的细节。此外，万寿菊 V2 在应用于其他稠密回归任务（如表面法线估计和内在图像分解）时也取得了最先进的结果。项目网站：https://hf.co/spaces/huawei-bayerlab/marigold-v2-web
cs.CV / 223 / 2609.08108
SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation
SynthGait-19K：用于步态参数估计的物理基础合成视频数据集
Abstract
Accurate estimation of clinically meaningful gait parameters from monocular video is important for scalable mobility assessment, yet progress is limited by the small scale, restricted viewpoints, and limited visual diversity of existing datasets. We introduce SynthGait-19k, a physically grounded synthetic video dataset containing 19,272 walking videos derived from 6,427 MoCap sequences across 437 subjects, with paired SMPL motion and annotations for six gait parameters. To construct the dataset, we develop Gait2Vid, which unifies heterogeneous MoCap recordings through SMPL and synthesizes diverse RGB walking videos under controllable viewpoints and scene appearances. We assess the generated videos for consistency with their conditioning gait kinematics and validate extracted gait events against force-platform measurements. Using SynthGait-19K, we benchmark direct RGB, pose-based, biomechanical, and human-mesh-recovery approaches and analyze viewpoint, training-data scale, and synthetic-to-real domain shift. We also introduce GaitXFormer as a direct RGB reference model for estimating gait parameters. Synthetic supervision transfers effectively to real videos across both GaitXFormer and a pose-based architecture, demonstrating utility across different representations. We further find that spatial gait parameters are more sensitive to visual domain shift and that improved HMR reconstruction alone does not necessarily translate to improved downstream gait estimation.
Chinese Translation
从单目视频中准确估计临床相关的步态参数对于可扩展的移动性评估至关重要，但现有数据集的规模小、视角受限和视觉多样性有限，限制了这一进展。我们介绍了SynthGait-19K，这是一个物理基础的合成视频数据集，包含来自437个受试者的6,427个运动捕捉（MoCap）序列衍生的19,272个行走视频，并配有六个步态参数的SMPL运动和注释。为了构建该数据集，我们开发了Gait2Vid，通过SMPL统一异构的MoCap录制，并在可控的视角和场景外观下合成多样的RGB行走视频。我们评估生成视频与其条件步态运动学的一致性，并验证提取的步态事件与力平台测量结果的一致性。利用SynthGait-19K，我们对直接RGB、基于姿态的、生物力学的和人形网格恢复的方法进行了基准测试，并分析了视角、训练数据规模和合成到真实的领域转移。我们还引入了GaitXFormer作为估计步态参数的直接RGB参考模型。合成监督有效地转移到GaitXFormer和基于姿态的架构中的真实视频，展示了在不同表示中的实用性。我们进一步发现，空间步态参数对视觉领域转移更为敏感，并且仅仅改善HMR重建并不一定会转化为下游步态估计的改善。
cs.CV / 224 / 2609.08117
DriveMotion: A Large-Scale Multi-Source Benchmark for Driver Motion Sequence Modeling and Forecasting
DriveMotion：一个大规模多源基准用于驾驶员运动序列建模与预测
Abstract
Driver motion can provide cues to ongoing behavior, attention, and near-term driving intent. However, most existing driver-centric datasets focus on recognizing predefined driver behaviors from short video clips, while human motion forecasting benchmarks largely target motion outside the vehicle. We introduce DriveMotion, a multi-source benchmark for continuous driver motion forecasting. DriveMotion contains 393 hours of 133-keypoint motion sequences at 10 Hz from 360 drivers, integrating naturalistic driving data, curated public in-cabin videos, and the AIDE dataset into a unified representation with per-joint validity masks and synchronized driving context. Naturalistic driving contains long periods of limited body movement, making uniformly sampled evaluation dominated by persistence and less sensitive to brief but behaviorally meaningful motion. To address this, we use dynamics-anchored evaluation, placing forecasting windows around vehicle maneuvers identified offline from CAN signals without providing CAN to the model at inference. Arm motion in pre-maneuver windows is 3.4x greater than in route-matched stable-driving controls. On these anchored windows, learned models reduce forecasting error over persistence by up to 15%, while maneuver-enriched training improves forecast-derived Part-State F1 by 44% over the zero-motion reference. Training on the full multi-source corpus further reduces forecasting error on held-out web drivers by 38% compared with BATON-only training. DriveMotion provides identity-disjoint splits, fixed evaluation subsets, and reference implementations for reproducible evaluation of continuous driver motion forecasting. The dataset and benchmark are available at https://huggingface.co/datasets/HenryYHW/DriveMotion
Chinese Translation
驾驶员的运动可以提供关于正在进行的行为、注意力和近期驾驶意图的线索。然而，大多数现有的以驾驶员为中心的数据集专注于从短视频片段中识别预定义的驾驶员行为，而人类运动预测基准主要针对车辆外的运动。我们介绍了DriveMotion，这是一个用于连续驾驶员运动预测的多源基准。DriveMotion包含来自360名驾驶员的393小时、10 Hz的133关键点运动序列，整合了自然驾驶数据、策划的公共车内视频以及AIDE数据集，形成了一个统一的表示，具有每个关节的有效性掩码和同步的驾驶上下文。自然驾驶包含长时间的身体运动有限，使得均匀采样的评估受持久性主导，对短暂但行为上有意义的运动不够敏感。为了解决这个问题，我们使用动态锚定评估，在离线从CAN信号中识别的车辆机动周围放置预测窗口，而在推理时不向模型提供CAN。预机动窗口中的手臂运动比路线匹配的稳定驾驶控制大3.4倍。在这些锚定窗口上，学习模型将预测误差相较于持久性减少了多达15%，而丰富机动的训练使得预测导出的部分状态F1比零运动参考提高了44%。在完整的多源语料库上训练进一步减少了在保留的网络驾驶员上的预测误差，比仅使用BATON训练减少了38%。DriveMotion提供了身份不重叠的划分、固定的评估子集和参考实现，以便于连续驾驶员运动预测的可重复评估。该数据集和基准可在 https://huggingface.co/datasets/HenryYHW/DriveMotion 获取。
cs.CV / 225 / 2609.08121
Hyperspectral Anomaly Detection via Group Sparse Low-Rank Tensor Factorization With Automatic Anomaly Grouping
基于自动异常分组的群稀疏低秩张量分解的高光谱异常检测
Abstract
Low-rank tensor modeling has become an effective tool for hyperspectral anomaly detection. However, existing methods still suffer from high computational cost and limited flexibility in characterizing spatially structured anomalies. To address these issues, this paper proposes a hyperspectral anomaly detection method based on group sparse low-rank tensor factorization with automatic anomaly grouping (GSAA). Specifically, the low tubal rank background is characterized by imposing group sparsity on tensor factors, which provides an efficient alternative to direct tensor rank regularization. For anomaly modeling, a latent grouping map is introduced to build an automatic anomaly grouping penalty, allowing anomaly groups to be adaptively inferred from the data rather than predefined at the pixel level. To further exploit complementary spectral and spatial information, GSAA is applied in both domains, and the resulting detection maps are fused to form a spectral--spatial version of GSAA, termed GSAA-SS. An efficient linearized alternating direction method of multipliers algorithm with convergence guarantee is developed to solve the resulting model. Experimental results on five real hyperspectral datasets demonstrate that the proposed method achieves superior detection performance and competitive computational efficiency compared with several state-of-the-art methods.
Chinese Translation
低秩张量建模已成为高光谱异常检测的有效工具。然而，现有方法仍然面临高计算成本和在表征空间结构异常方面灵活性有限的问题。为了解决这些问题，本文提出了一种基于群稀疏低秩张量分解与自动异常分组的高光谱异常检测方法（GSAA）。具体而言，通过对张量因子施加群稀疏性来表征低管秩背景，这为直接的张量秩正则化提供了一种高效的替代方案。在异常建模方面，引入了一种潜在分组图，以构建自动异常分组惩罚，允许异常组从数据中自适应推断，而不是在像素级别上预定义。为了进一步利用互补的光谱和空间信息，GSAA在这两个领域中应用，并将得到的检测图融合形成GSAA的光谱-空间版本，称为GSAA-SS。为了解决所得到的模型，开发了一种具有收敛保证的高效线性化交替方向乘子法算法。对五个真实高光谱数据集的实验结果表明，所提出的方法在检测性能和计算效率上均优于几种最先进的方法。
cs.CV / 226 / 2609.08130
Observe Before You Alert: Adaptive Driver Alerting with Vision-Language Models
观察再警报：基于视觉-语言模型的自适应驾驶员警报
Abstract
Driver alerting from dashcam video requires sequential decision-making under partial observability: a system must decide not only whether a scene is risky, but also when the evidence is sufficient to warn. Most existing accident anticipation models output a binary risk score, leaving ambiguous scenes to be handled by thresholding. We propose VLAlert, a vision-language alerting framework that casts warning generation as a tri-action policy over SILENT, OBSERVE, and ALERT. The OBSERVE action acts as an internal evidence-gathering decision that delays uncertain warnings and changes the next observation window, creating a lightweight perception-action loop for adaptive alerting. VLAlert uses Qwen3-VL-4B as a safety-evidence generator and pools hidden states from structured belief spans to form compact representations for danger estimation and policy prediction. We evaluate VLAlert on VLAlert-Bench, a unified per-tick benchmark from four real-world dashcam alert datasets, and further test transfer to held-out naturalistic ADAS takeover clips. On VLAlert-Bench validation, VLAlert achieves the highest deployment-oriented utility among tested baselines, with DAUS 0.4878 compared with 0.4752 for Open-BADAS, and improves AUROC, AP_tick, F1_t, and balanced accuracy from 0.610, 0.176, 0.276, and 0.581 to 0.689, 0.195, 0.297, and 0.648, respectively. On 221 held-out ADAS-TO-Critic clips, VLAlert improves R@5s from 74.2% to 88.7% and F1 from 0.585 to 0.686. These results indicate that adaptive observation and safety-focused VLM representations provide measurable gains for driver-facing alert decisions.
Chinese Translation
从行车记录仪视频中进行驾驶员警报需要在部分可观察性下进行顺序决策：系统不仅必须判断场景是否存在风险，还必须决定何时证据足够以发出警报。现有的大多数事故预判模型输出二元风险评分，模糊场景则需通过阈值处理。我们提出了 VLAlert，一个将警报生成视为对 SILENT、OBSERVE 和 ALERT 的三重行动策略的视觉-语言警报框架。OBSERVE 行动作为内部证据收集决策，延迟不确定的警报并改变下一个观察窗口，为自适应警报创建了一个轻量级的感知-行动循环。VLAlert 使用 Qwen3-VL-4B 作为安全证据生成器，并从结构化信念范围中汇聚隐藏状态，以形成紧凑的危险估计和策略预测表示。我们在 VLAlert-Bench 上评估 VLAlert，这是一个来自四个真实世界行车记录仪警报数据集的统一逐时基准，并进一步测试其在保留的自然驾驶辅助系统（ADAS）接管片段中的迁移能力。在 VLAlert-Bench 验证中，VLAlert 在测试的基线中实现了最高的部署导向效用，DAUS 为 0.4878，相较于 Open-BADAS 的 0.4752，并且 AUROC、AP_tick、F1_t 和均衡准确率分别从 0.610、0.176、0.276 和 0.581 提升至 0.689、0.195、0.297 和 0.648。在 221 个保留的 ADAS-TO-Critic 片段中，VLAlert 将 R@5s 从 74.2% 提升至 88.7%，F1 从 0.585 提升至 0.686。这些结果表明，自适应观察和以安全为中心的 VLM 表示为面向驾驶员的警报决策提供了可测量的提升。
cs.CV / 227 / 2609.08148
MRI-Guided Reslice-Refined Cross-Slice SDF Reconstruction of the Left Ventricle from Cardiac MRI with Sparse Axial Supervision
基于MRI引导的重切片精细化跨切面SDF重建用于稀疏轴向监督下的左心室心脏MRI重建
Abstract
Reconstructing a three-dimensional left-ventricular (LV) endocardial surface from cardiac magnetic resonance (CMR) data is challenging when supervision is available on only a small number of axial slices. Through-plane geometry is weakly constrained, and automatically generated two-dimensional masks can propagate segmentation errors into the recovered shape. We present MR-RS-SDFR, a per-case implicit signed distance field (SDF) framework that reconstructs a continuous LV surface from a CMR volume and sparse axial weak masks. The method first builds a cross-slice SDF initialization from axial and longitudinal geometric cues and then refines the field using two complementary signals: MRI edge-field normal alignment, which provides an image-derived boundary cue independent of the weak masks, and differentiable reslice Dice and contour consistency, which preserve agreement with the observed planes. We evaluate three weak-mask generators -- LOO TransUNet, LOO nnU-Net, and an off-the-shelf Medical SAM3 model used without MM-WHS-specific training or fine-tuning -- and five sparsity levels from 4 to 64 axial planes. In the sparse-16 setting, final MR-RS-SDFR reconstruction reaches 0.928 Dice and 3.80mm HD95 with Medical SAM3 masks. The upstream generators do not exhibit a single common ranking across 2D and dense 3D segmentation, and nnU-Net- and Medical-SAM3-driven sparse reconstruction achieve the same mean final Dice despite different upstream error profiles. Across all three sparse-16 mask sources, MR-RS-SDFR is numerically better than protocol-matched full GHD+DVS in both Dice and HD95. Final Dice improves markedly from sparse-4 to sparse-16 and then saturates at the reported precision through sparse-64. These results support MRI-guided per-case SDF refinement as a reconstruction strategy that remains effective across weak-mask generators and supervision densities.
Chinese Translation
在仅有少量轴向切片监督的情况下，从心脏磁共振成像（CMR）数据重建三维左心室（LV）心内膜表面具有较大挑战。跨层面的几何结构约束较弱，且自动生成的二维掩模可能将分割误差传播至恢复的形状中。本文提出了MR-RS-SDFR，一种基于每个病例的隐式有符号距离场（SDF）框架，能够从CMR体积数据及稀疏轴向弱掩模中重建连续的左心室表面。该方法首先基于轴向和纵向几何线索构建跨切面SDF初始化，然后利用两种互补信号对场进行精细调整：MRI边缘场法线对齐，提供独立于弱掩模的图像边界线索；以及可微分的重切片Dice系数和轮廓一致性，保持与观测平面的匹配。我们评估了三种弱掩模生成器——LOO TransUNet、LOO nnU-Net以及未经MM-WHS特定训练或微调的现成Medical SAM3模型，并测试了从4到64轴向切片的五个稀疏级别。在稀疏16切片设置下，使用Medical SAM3掩模的最终MR-RS-SDFR重建达到了0.928的Dice系数和3.80mm的HD95。上游生成器在二维和密集三维分割中未表现出统一的排名，且nnU-Net和Medical SAM3驱动的稀疏重建尽管上游误差分布不同，但最终平均Dice相同。在所有三种稀疏16掩模来源中，MR-RS-SDFR在Dice和HD95指标上均优于协议匹配的完整GHD+DVS方法。最终Dice系数从稀疏4切片显著提升至稀疏16切片，随后在稀疏64切片时达到报告的精度饱和。这些结果支持基于MRI引导的逐病例SDF精细化作为一种重建策略，在不同弱掩模生成器和监督密度下均保持有效性。
cs.CV / 228 / 2609.08155
SciFigure2Code: An AI-Reconstructed Benchmark for Scientific Figure-to-Code
SciFigure2Code：一个用于科学图形到代码的人工智能重建基准
Abstract
Scientific figures are the interface through which research claims are inspected and reused, but final published panels rarely expose the data or plotting code that produced them. Recovering this hidden provenance from pixels is therefore underdetermined. We introduce SciFigure2Code, an AI-reconstructed benchmark that instead evaluates presentation recovery: generating editable Python programs that preserve how a scientific panel is arranged and read. Role-specialized Codex agents generate, execute, visually refine, and audit silver-standard presentation programs that capture geometry, visual hierarchy, encodings, annotations, and typography without claiming to recover original measurements or author source code. This reconstruction-and-audit protocol turns final published panels into auditable reference packages; the resulting resource contains 6,740 reviewed panels and SciFigureBench, a balanced 337-panel test set across 31 chart subtypes, five domains, and three complexity levels. Across 14 zero-shot models in image-only and caption-assisted settings, execution, multi-component layouts, axes, legends, and scientific labels remain weak. Claude Opus 4.7 achieves the highest image-only Overall score, Claude Opus 4.6 leads caption-assisted reconstruction, and two-stage plan-then-code prompting improves Overall for all four tested models. SciFigure2Code provides an auditable testbed for agents that construct editable, visually faithful scientific figure presentations.
Chinese Translation
科学图形是研究主张被检验和重用的接口，但最终发布的图形面板很少公开生成它们的数据或绘图代码。因此，从像素中恢复这种隐藏的来源信息是一个不确定的问题。我们介绍了SciFigure2Code，这是一个人工智能重建的基准，旨在评估展示恢复：生成可编辑的Python程序，以保留科学面板的排列和阅读方式。角色专用的Codex代理生成、执行、视觉优化和审计银标准展示程序，这些程序捕捉几何形状、视觉层次、编码、注释和排版，而不声称恢复原始测量或作者源代码。这种重建和审计协议将最终发布的面板转变为可审计的参考包；所得到的资源包含6740个经过审查的面板和SciFigureBench，一个涵盖31种图表子类型、五个领域和三个复杂性水平的平衡337面板测试集。在仅图像和带标题辅助设置下的14个零-shot模型中，执行、多组件布局、坐标轴、图例和科学标签仍然较弱。Claude Opus 4.7在仅图像的总体评分中表现最佳，Claude Opus 4.6在带标题辅助的重建中领先，而两阶段的计划-再编码提示提高了所有四个测试模型的总体表现。SciFigure2Code为构建可编辑、视觉上忠实的科学图形展示的代理提供了一个可审计的测试平台。
cs.CV / 229 / 2609.08167
Boundary Voting Network for Ambiguity-Aware Timestamp-Supervised Action Segmentation
边界投票网络用于模糊感知的时间戳监督动作分割
Abstract
Timestamp-supervised action segmentation aims to segment and classify actions in untrimmed videos with a random frame annotated per action. Precisely localizing action boundaries from timestamp annotations is crucial for this setting, as it enables generating framewise pseudo-labels and applying the well-explored fully-supervised training. However, prevailing methods struggle with intrinsic uncertainty in boundary localization due to less discriminative features in action-transiting regions. This imprecise boundary estimation significantly reduces the stability and reliability of the generated pseudo-labels in ambiguous action-transiting regions, consequently resulting in performance deterioration of the trained segmentation models. In our paper, we introduce the boundary voting network that mitigates feature ambiguity by hierarchically propagating video-level global prior knowledge into local action-transiting regions. By generating key action representations as votes throughout the video and targeting action-transiting regions, all votes collaboratively contribute to action-transiting feature enhancement and boundary localization refinement. Extensive experiments demonstrate the effectiveness of our method on GTEA, 50Salads, and Breakfast datasets.
Chinese Translation
时间戳监督的动作分割旨在对未裁剪视频中的动作进行分割和分类，每个动作随机标注一个帧。准确定位时间戳注释中的动作边界对于这一设置至关重要，因为它使得生成逐帧伪标签并应用经过充分探索的全监督训练成为可能。然而，现有方法在边界定位中面临内在的不确定性，原因在于动作过渡区域的特征不够区分。这种不精确的边界估计显著降低了在模糊动作过渡区域生成的伪标签的稳定性和可靠性，从而导致训练的分割模型性能下降。在本文中，我们提出了一种边界投票网络，通过分层传播视频级全局先验知识到局部动作过渡区域，从而减轻特征模糊性。通过在整个视频中生成关键动作表示作为投票，并针对动作过渡区域，所有投票共同促进动作过渡特征的增强和边界定位的精细化。大量实验表明我们的方法在GTEA、50Salads和Breakfast数据集上具有有效性。
cs.CV / 230 / 2609.08182
WSPolypNet: Weakly Supervised Polyp Localization in Colonoscopy Videos
WSPolypNet：结肠镜视频中弱监督息肉定位
Abstract
Because dense frame-level annotation of colonoscopy videos is costly, we propose WSPolypNet, a weakly supervised framework for polyp localization using only video-level labels. WSPolypNet employs a 3D convolutional neural network trained with video-level supervision to generate class activation maps (CAMs), which identify candidate polyp regions without requiring frame-level spatial annotations. The CAM-derived localization cues are further enhanced using a multi-view strategy and provided to MedSAM2 as point prompts. MedSAM2 then propagates segmentation masks across the video, refining the coarse localization cues according to polyp boundaries. WSPolypNet achieved CorLoc scores of 47.80%, 43.68%, and 35.01% at IoU thresholds of 0.3, 0.5, and 0.7, respectively, compared with 36.87%, 33.72%, and 27.94% in the single-view setting. For small polyps, the multi-view strategy improved
[email protected] from 16.01% to 30.97%. The framework also achieved a recall of 94.51%. These results demonstrate the potential of weakly supervised spatiotemporal learning to substantially reduce spatial annotation requirements for polyp localization in colonoscopy videos.
Chinese Translation
由于对结肠镜视频进行密集帧级注释的成本较高，我们提出了WSPolypNet，一个仅使用视频级标签进行息肉定位的弱监督框架。WSPolypNet采用了一个使用视频级监督训练的3D卷积神经网络，以生成类激活图（CAMs），从而识别候选息肉区域，而无需帧级空间注释。通过多视角策略进一步增强CAM衍生的定位线索，并将其作为点提示提供给MedSAM2。MedSAM2随后在视频中传播分割掩膜，根据息肉边界细化粗略的定位线索。WSPolypNet在IoU阈值为0.3、0.5和0.7时分别达到了47.80%、43.68%和35.01%的CorLoc分数，而在单视角设置中分别为36.87%、33.72%和27.94%。对于小息肉，多视角策略将
[email protected]从16.01%提高至30.97%。该框架还实现了94.51%的召回率。这些结果表明，弱监督时空学习有潜力显著减少结肠镜视频中息肉定位的空间注释需求。
cs.CV / 231 / 2609.08215
PhysFlow: Physics-Aware Optical Flow for Motion Controllable Video Generation
PhysFlow：物理感知光流用于运动可控视频生成
Abstract
Video generation models have recently attracted substantial attention for their ability to generate visually compelling videos, yet ensuring physically consistent and plausible dynamics still remains a fundamental challenge, driving a growing line of research on physical realism in video generation. To address this challenge, motivated by the fact that physical regularities are primarily encoded in motion patterns, we propose PhysFlow, a novel two-stage framework for improving the physical plausibility of generated videos by decomposing video generation into motion-aware optical flow generation followed by motion-conditioned appearance synthesis. Specifically, PhysFlow consists of a physics-aware optical-flow video generator called PA-Flow and a flow-guided video generator called FlowRender. During the first stage, PA-Flow employs a physics-aware attention module to model how motion attributes and material properties influence global motion and local deformation, respectively, and generates an optical flow video as an explicit representation of motion. In the second stage, FlowRender leverages the decoupled motion representation as guidance to synthesize realistic textures and appearances, ultimately producing the final physically plausible video. To further support model training with explicit physical supervision, we construct PhysVideo, a physics-based video dataset generated with a physics engine and 3D-GS rendering, containing 10K foreground objects and 50K realistic video sequences with annotations of motion and material properties. Extensive experiments demonstrate that our proposed PhysFlow generates videos with superior physical plausibility while maintaining high visual fidelity compared with existing methods.
Chinese Translation
视频生成模型因其生成视觉上引人注目的视频的能力而受到广泛关注，但确保物理一致性和合理的动态仍然是一个基本挑战，这推动了在视频生成中对物理现实主义的研究不断增加。为了解决这一挑战，基于物理规律主要编码在运动模式中的事实，我们提出了PhysFlow，一个新颖的两阶段框架，通过将视频生成分解为运动感知光流生成和运动条件外观合成，来提高生成视频的物理合理性。具体而言，PhysFlow由一个名为PA-Flow的物理感知光流视频生成器和一个名为FlowRender的光流引导视频生成器组成。在第一阶段，PA-Flow采用物理感知注意力模块来建模运动属性和材料特性如何分别影响全局运动和局部变形，并生成光流视频作为运动的显式表示。在第二阶段，FlowRender利用解耦的运动表示作为指导，合成真实的纹理和外观，最终生成最终的物理合理视频。为了进一步支持具有显式物理监督的模型训练，我们构建了PhysVideo，这是一个基于物理的由物理引擎和3D-GS渲染生成的视频数据集，包含10K个前景物体和50K个带有运动和材料属性注释的真实视频序列。大量实验表明，我们提出的PhysFlow在保持高视觉保真度的同时，生成的影片具有优越的物理合理性，相较于现有方法表现更佳。
cs.CV / 232 / 2609.08217
Drive by Hindsight and Foresight: Tool-Grounded Synergistic Reasoning over Hierarchical Memory for Autonomous Driving
通过回顾与展望驱动：基于工具的协同推理与层次记忆在自主驾驶中的应用
Abstract
VLMs have shown promise for autonomous driving, yet still suffer from hallucination, weak spatio-temporal perception, and limited generalization. Recent methods improve reasoning and decision-making through CoT explanations, retrieval-augmented generation or the static injection of tool outputs. Although these mechanisms enrich the context, the model neither proactively perceives scene information nor accumulates experience after answering. To overcome these limitations, we present, to our knowledge, the first synergistic framework that tightly couples hierarchical memory with proactive tool invocation in a closed reasoning loop. Our contributions are threefold. (i) Hierarchical Driving Memory: a scene-level short-term memory maintains the dynamic scene state, and an evolving long-term memory retrieves reusable experience and tool strategies. (ii) Memory-Tool Synergistic Reasoning Framework: guided by the scene state and retrieved experience, the model adaptively invokes tools to refine its reasoning at inference time and consolidates reusable experience into a long-term memory pool offline. (iii) Data Generation and Two-stage Training Pipeline: verified memory-tool trajectories built by multi-step teacher rollout are used to train with SFT and GRPO. Our 7B model reaches an overall reasoning score of 80.03 and MCQ accuracy of 79.09% on DriveLMM-o1, surpassing the strongest baseline by 7.74 MCQ points and generalizes strongly across benchmarks. Notably, ablation and analysis studies validate the effectiveness of each component and further reveal the complementary roles of hierarchical memory. Short-term memory strengthens spatio-temporal understanding, improving STSBench accuracy by 24.2 points, while offline long-term memory consolidation yields an additional 3.57-point MCQ gain with all parameters frozen, demonstrating continual self-evolution through accumulated driving experience.
Chinese Translation
视觉语言模型（VLMs）在自主驾驶中展现出潜力，但仍存在幻觉、时空感知弱和泛化能力有限的问题。近期方法通过链式思维（CoT）解释、检索增强生成或静态注入工具输出来改善推理和决策。尽管这些机制丰富了上下文，但模型既未能主动感知场景信息，也未能在回答后积累经验。为了克服这些局限性，我们提出了迄今为止首个将层次记忆与主动工具调用紧密结合的协同框架，形成闭环推理。我们的贡献有三方面：（i）层次驾驶记忆：场景级短期记忆维护动态场景状态，而不断演变的长期记忆则检索可重用的经验和工具策略。（ii）记忆-工具协同推理框架：在场景状态和检索经验的指导下，模型在推理时自适应地调用工具以精炼推理，并在离线状态下将可重用经验整合到长期记忆池中。（iii）数据生成与两阶段训练流程：通过多步教师回放构建的验证记忆-工具轨迹用于训练SFT和GRPO。我们的7B模型在DriveLMM-o1上达到了80.03的整体推理得分和79.09%的多项选择题（MCQ）准确率，超越了最强基线7.74个MCQ点，并在各基准上表现出强泛化能力。值得注意的是，消融和分析研究验证了每个组件的有效性，并进一步揭示了层次记忆的互补作用。短期记忆增强了时空理解，使STSBench的准确率提高了24.2点，而离线长期记忆整合在所有参数冻结的情况下带来了额外的3.57点MCQ增益，展示了通过积累驾驶经验实现的持续自我演化。
cs.CV / 233 / 2609.08221
SoftRerank: Hierarchical Soft Fusion with Candidate-Label Reranking for Long-Tailed Micro-Action Recognition
SoftRerank：针对长尾微动作识别的候选标签重排序的层次软融合
Abstract
Micro-actions are subtle, low-intensity non-verbal behaviors that provide cues to fine-grained human states, including emotions and intentions. Recognizing them remains difficult because they are brief, contain weak visual changes, and often exhibit similar motion patterns across categories. This paper addresses these challenges with a fine-grained micro-action recognition method that combines full fine-tuning of InternVideo2.5, hierarchical soft fusion, and a lightweight candidate-label reranker. For the long-tailed label distribution in MA-52, we use class-balanced sampling and inverse-frequency reweighting to reduce the effect of frequent classes during training. We fine-tune InternVideo2.5 end to end and attach coarse and group-conditional fine-grained classification heads to the shared video representation, improving the consistency between coarse and fine predictions. For ambiguous samples, the candidate-label reranker uses hard samples and video-label matching to focus on easily confused fine-grained actions. Experiments validate the proposed method, which achieves a 79.99% F1-mean on MA-52 and ranks first in the 3rd Micro-Action Analysis Grand Challenge at ACM Multimedia 2026.
Chinese Translation
微动作是细微的、低强度的非语言行为，能够提供关于人类状态的细粒度线索，包括情感和意图。由于微动作持续时间短、视觉变化微弱，并且在不同类别中常常表现出相似的运动模式，因此其识别仍然具有挑战性。本文提出了一种细粒度微动作识别方法，结合了InternVideo2.5的全面微调、层次软融合和轻量级候选标签重排序器，以应对这些挑战。针对MA-52中的长尾标签分布，我们采用类平衡采样和逆频率重加权，以减少训练过程中频繁类别的影响。我们对InternVideo2.5进行端到端的微调，并将粗分类和组条件细粒度分类头附加到共享的视频表示上，从而提高粗预测和细预测之间的一致性。对于模糊样本，候选标签重排序器利用困难样本和视频标签匹配，重点关注容易混淆的细粒度动作。实验验证了所提方法的有效性，在MA-52上达到了79.99%的F1均值，并在2026年ACM多媒体第三届微动作分析大挑战中获得第一名。
cs.CV / 234 / 2609.08230
ActionSplice: In-Flight Action Editing for Interactive World Models
ActionSplice：交互式世界模型中的飞行动作编辑
Abstract
Chunk-autoregressive video world models typically condition each generated chunk on one action. An action received during sampling must therefore wait for the next chunk, condition future solver evaluations on a state produced under the previous action, or trigger rollback that repeats completed evaluations. We introduce ActionSplice, an inference framework that formulates this problem as Counterfactual State Transport (CST). A lightweight corrector transports the interrupted backbone-native representation toward the matched state induced by the revised action at the same solver step. The world model and sampler remain frozen, and sampling resumes without replaying completed evaluations. The retargeting variant $\mathrm{CST}*{R}$ updates the entire active chunk, while the temporal-splicing variant $\mathrm{CST}*{T}$ preserves a temporal prefix and updates only the suffix. Across minWM-Wan Action2V and HY-WM1.5, $\mathrm{CST}*{R}$ reduces rollback-relative LPIPS by 61.5% and 75.9% relative to direct condition swapping. $\mathrm{CST}*{T}$ reduces suffix LPIPS by 56.1% and 77.5%, respectively, while providing $2.73\times$ and $1.69\times$ pixel-ready speedups over waiting. Under the HY-WorldPlay protocol, $\mathrm{CST}_{R}$ obtains a PSNR of 25.66 dB, an SSIM of 0.6902, and an LPIPS of 0.1337 against the original rollout.
Chinese Translation
块自回归视频世界模型通常将每个生成的块条件化于一个动作。因此，在采样过程中接收到的动作必须等待下一个块，或者将未来的求解器评估条件化于在先前动作下产生的状态，或者触发回滚以重复已完成的评估。我们提出了ActionSplice，一种将此问题表述为反事实状态传输（Counterfactual State Transport, CST）的推理框架。一个轻量级的校正器将中断的主干本地表示传输到由修订动作在同一求解步骤引发的匹配状态。世界模型和采样器保持冻结，采样在不重放已完成评估的情况下恢复。重定向变体 $ ext{CST}^*{R}$ 更新整个活动块，而时间拼接变体 $ ext{CST}^*{T}$ 保留时间前缀，仅更新后缀。在 minWM-Wan Action2V 和 HY-WM1.5 上，$ ext{CST}^*{R}$ 相较于直接条件交换减少了 61.5% 和 75.9% 的回滚相关 LPIPS。$ ext{CST}^*{T}$ 分别减少了 56.1% 和 77.5% 的后缀 LPIPS，同时在等待过程中提供了 $2.73 imes$ 和 $1.69 imes$ 的像素准备加速。在 HY-WorldPlay 协议下，$ ext{CST}_{R}$ 获得了 25.66 dB 的 PSNR，0.6902 的 SSIM，以及 0.1337 的 LPIPS，相较于原始回放。
cs.CV / 235 / 2609.08242
CS-CLIP: Compositional Scene Graph-guided CLIP for Robust Compositional Reasoning
CS-CLIP：基于组合场景图的CLIP用于稳健的组合推理
Abstract
Vision-language models (VLMs) demonstrate strong performance across compositional reasoning benchmarks, which require reasoning over semantic perturbations of objects, attributes, relations, and their interactions. However, our controlled analysis reveals that existing compositionality-aware VLMs exhibit element-specific biases, often underperforming vanilla CLIP on certain compositional elements. To address this, we propose Compositional Scene Graph-guided CLIP (CS-CLIP), which uses scene graphs to identify compositional elements and construct structured negatives via selective masking. We further retain negatives that are most contradictory to the original caption, forcing the model to rely on compositional structure rather than surface cues. CS-CLIP achieves state-of-the-art compositional reasoning with robust performance across compositional elements. It also preserves general vision-language capabilities such as cross-modal retrieval and downstream visual reasoning, while requiring fewer training samples than prior methods.
Chinese Translation
视觉-语言模型（VLMs）在组合推理基准测试中表现出色，这些测试要求对对象、属性、关系及其交互的语义扰动进行推理。然而，我们的控制分析显示，现有的关注组合性的VLMs存在特定元素的偏差，常常在某些组合元素上表现不及普通的CLIP。为了解决这个问题，我们提出了基于组合场景图的CLIP（CS-CLIP），该模型利用场景图识别组合元素，并通过选择性掩蔽构建结构化的负样本。我们进一步保留与原始标题最矛盾的负样本，迫使模型依赖组合结构而非表面线索。CS-CLIP在组合推理方面实现了最先进的性能，在各组合元素上表现稳健。同时，它还保留了通用的视觉-语言能力，如跨模态检索和下游视觉推理，并且所需的训练样本比之前的方法更少。
cs.CV / 236 / 2609.08265
Tracking-by-detection in Multi-object Tracking: Survey and Experiments
基于检测的多目标跟踪：综述与实验
Abstract
Multi-object tracking (MOT) is an essential computer vision task that simultaneously tracks multiple objects in video sequences, with various applications in surveillance, autonomous navigation, and human-computer interaction. The tracking-by-detection (TBD) paradigm, which combines object detection with temporal association, has emerged as a leading approach, driven by innovative algorithms. Despite recent progress, fair evaluation of TBD-based methods remains a challenge. Many studies introduce modules such as similarity metrics, data association strategies, or motion models, but they are often evaluated under inconsistent protocols, with different baseline trackers, hyperparameters, and datasets. Such inconsistencies obscure the genuine contribution of each module and hinder objective comparison. This survey systematically reviews TBD-based MOT techniques, including similarity measurements, data association, camera motion compensation, and interpolation strategies. Starting from a minimal baseline tracker, we fairly evaluate the contributions of each method across diverse datasets and accumulate well-balanced methods. Our findings establish a strong baseline tracker and provide a foundation for the principled design of robust and versatile MOT systems suitable for real-world deployment.
Chinese Translation
多目标跟踪（MOT）是一个重要的计算机视觉任务，旨在同时跟踪视频序列中的多个对象，广泛应用于监控、自动导航和人机交互等领域。基于检测的跟踪（TBD）范式结合了对象检测与时间关联，已成为一种领先的方法，得益于创新算法的推动。尽管近期取得了一些进展，但对基于TBD的方法进行公平评估仍然是一个挑战。许多研究引入了相似性度量、数据关联策略或运动模型等模块，但它们通常在不一致的协议下进行评估，使用不同的基线跟踪器、超参数和数据集。这种不一致性模糊了每个模块的真实贡献，并妨碍了客观比较。本综述系统地回顾了基于TBD的MOT技术，包括相似性测量、数据关联、相机运动补偿和插值策略。从一个最小的基线跟踪器出发，我们在不同数据集上公平评估每种方法的贡献，并积累了良好平衡的方法。我们的研究结果建立了一个强大的基线跟踪器，并为设计适合实际应用的稳健且多功能的MOT系统提供了基础。
cs.CV / 237 / 2609.08281
SAM3-O2D2: Zero-Shot Object Out-of-Distribution Detection by Object Class Prompting of the SAM3-Image Model
SAM3-O2D2：通过SAM3-图像模型的对象类别提示实现零样本对象分布外检测
Abstract
Object detectors have shown remarkable performance in various fields, among these medical imaging, surveillance, and autonomous driving. However, they are prone to overconfidence when encountering unseen objects in real-world deployments, causing potential safety issues. To address this, detecting out-of-distribution (OOD) objects is essential for reliable object detection. Modern approaches leverage the broad semantic knowledge of foundation models such as CLIP for post-hoc few- and zero-shot OOD detection. However, these methods typically perform OOD assessment in feature space, which can be sensitive to object detector localization errors and variations in object appearance. Moreover, the current state-of-the-art (SOTA) zero-shot method performs computationally costly diffusion in inference. In this work, for our proposed zero-shot object OOD detection method SAM3-O2D2, we employ the SAM3-image foundation model in an efficient manner. Specifically, we prompt SAM3 only with the object detector's predicted classes and compare the predictions of the object detector and SAM3. An object is in-distribution (ID), if SAM3 also detects an object at the corresponding location. If SAM3 does not detect the prompted object, this indicates a mismatch between the detector's prediction and the image content, suggesting that the object is OOD. Experimental results show that our method significantly surpasses the so-far zero-shot SOTA method. Specifically, we achieve new SOTA AuROC and FPR95 metrics over both ID datasets Pascal-VOC and BDD100K and both OOD datasets MS-COCO and OpenImages.
Chinese Translation
对象检测器在医疗成像、监控和自动驾驶等多个领域表现出色。然而，当在实际应用中遇到未见过的对象时，它们往往表现出过度自信，从而导致潜在的安全问题。为了解决这一问题，检测分布外（OOD）对象对于可靠的对象检测至关重要。现代方法利用基础模型（如CLIP）的广泛语义知识进行后期的少样本和零样本OOD检测。然而，这些方法通常在特征空间中进行OOD评估，这可能对对象检测器的定位误差和对象外观的变化敏感。此外，目前的最先进（SOTA）零样本方法在推理中执行计算成本高昂的扩散。在本研究中，我们提出的零样本对象OOD检测方法SAM3-O2D2，以高效的方式使用SAM3-图像基础模型。具体而言，我们仅用对象检测器预测的类别对SAM3进行提示，并比较对象检测器与SAM3的预测。如果SAM3在相应位置也检测到一个对象，则该对象为在分布内（ID）。如果SAM3未检测到提示的对象，则表明检测器的预测与图像内容不匹配，暗示该对象为OOD。实验结果表明，我们的方法显著超越了迄今为止的零样本SOTA方法。具体而言，我们在ID数据集Pascal-VOC和BDD100K以及OOD数据集MS-COCO和OpenImages上达到了新的SOTA AuROC和FPR95指标。
cs.CV / 238 / 2609.08282
Dreaming in Flow: Generative Grounding Feedback for Self-Evolving Unified Multimodal Models
在流动中梦境：自我演化统一多模态模型的生成基础反馈
Abstract
Unified multimodal models integrate visual understanding and generation within a single network, yet the two capabilities are commonly optimized as separate tasks. We introduce Generative Grounding Feedback(GGF), a self-evolving post-training framework that uses only text prompts and the model's own visual experience. Given a prompt, the model first generates a visual ``dream.'' Flow-level feedback compares text-, image-, and repair-conditioned predictions at the same noisy latent state, transferring image-grounded generation directions to the prompt condition. Dream replay grounding replays this dream through captioning and re-imagination, training claim-level evidence to remain consistent across the replay while separating unrelated visual experiences. Jointly optimized, these two directions let generation provide visual grounding for understanding and understanding refine subsequent generation without paired image--text supervision. Experiments across unified models with different understanding--generation integration designs show consistent improvements in text-to-image generation together with modest gains in visual understanding.
Chinese Translation
统一多模态模型在单一网络中整合了视觉理解和生成能力，但这两种能力通常作为独立任务进行优化。我们引入了生成基础反馈（Generative Grounding Feedback, GGF），这是一种自我演化的后训练框架，仅使用文本提示和模型自身的视觉经验。在给定提示的情况下，模型首先生成一个视觉“梦境”。流级反馈在相同的噪声潜在状态下比较文本、图像和修复条件下的预测，将图像基础的生成方向转移到提示条件上。梦境重放基础通过字幕和重新想象重放这一梦境，训练声明级证据在重放过程中保持一致，同时分离无关的视觉经验。这两个方向的联合优化使得生成能够为理解提供视觉基础，而理解又能在没有配对图像-文本监督的情况下细化后续生成。在不同理解-生成整合设计的统一模型上的实验显示，文本到图像生成的一致性改善以及视觉理解的适度提升。
cs.CV / 239 / 2609.08283
MARS-CLIP: Multi-Resolution and Attention Refined Zero-Shot Image Segmentation
MARS-CLIP：多分辨率与注意力精炼的零样本图像分割
Abstract
Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in zero-shot transfer but often struggles with dense prediction tasks due to low spatial resolution and the loss of structural information. To address these limitations, we propose MARS-CLIP (Multi-resolution and Attention Refined Segmentation for CLIP), a novel framework for zero-shot semantic segmentation. Our approach introduces two key strategies: (i) a multi-resolution feature extraction module that fuses local fine-grained features with global context to overcome input resolution constraints, and (ii) an attention refinement mechanism that injects spatial and color biases from intermediate layers into the final self-attention block to accurately restore object boundaries. A set of experiments on six public datasets demonstrates that MARS-CLIP significantly outperforms state-of-the-art methods.
Chinese Translation
对比语言-图像预训练（CLIP）在零样本迁移中展示了令人印象深刻的能力，但在密集预测任务中常常面临低空间分辨率和结构信息丢失的问题。为了解决这些局限性，我们提出了MARS-CLIP（用于CLIP的多分辨率与注意力精炼分割），这是一个新颖的零样本语义分割框架。我们的方法引入了两个关键策略：（i）一个多分辨率特征提取模块，该模块融合了局部细粒度特征与全局上下文，以克服输入分辨率的限制；（ii）一个注意力精炼机制，该机制将来自中间层的空间和颜色偏差注入最终的自注意力模块，以准确恢复物体边界。在六个公共数据集上的一系列实验表明，MARS-CLIP显著优于最先进的方法。
cs.CV / 240 / 2609.08300
Human-Centric Image Captioning with Subject-Centered Spatial Understanding
以人为中心的图像描述与主体中心的空间理解
Abstract
While multimodal large language models (MLLMs) achieve remarkable performance on generic image captioning, they frequently suffer from structural hallucinations in human-centric scenarios. Accurately modeling human subjects is foundational for critical downstream applications, such as accurate avatar/video/image generation and fine-grained human action understanding. However, these tasks require highly precise subject-centered spatial grounding, such as distinguishing egocentric left/right laterality and maintaining correct anatomical-object bindings. Although catastrophic for structural integrity, these localized spatial inversions are often overshadowed by overall descriptive metrics in existing benchmarks. To systematically expose and quantify this bottleneck, we introduce SPACE (Subject-centric Poses, Appearance, and Characteristics Evaluation), a benchmark designed to evaluate subject-centered spatial understanding. On SPACE, we reveal that despite strong generic perception, current MLLMs consistently fail to ground descriptions in the subject's intrinsic frame of reference. To bridge this gap, we propose a specialized data construction and alignment pipeline. We first extract structured spatial hints from fine-grained body-part localization to guide a two-stage caption rewriting process, yielding highly spatially-faithful training data. Furthermore, we design a rubric-based reward for Group Relative Policy Optimization (GRPO) that explicitly penalizes structurally critical spatial errors during alignment. Extensive experiments on SPACE demonstrate our framework significantly improves human-centric caption quality, particularly in subject-centered spatial reasoning, achieving performance competitive with strong closed-source models. Our benchmark and code are available at https://github.com/JHang2020/SPACE-Eval.
Chinese Translation
尽管多模态大型语言模型（MLLMs）在通用图像描述方面表现出色，但在以人为中心的场景中，它们常常遭遇结构性幻觉。准确建模人类主体对于关键的下游应用至关重要，例如准确的虚拟形象/视频/图像生成和细粒度的人类动作理解。然而，这些任务需要高度精确的主体中心空间定位，例如区分自我中心的左右侧和保持正确的解剖-物体绑定。尽管这些局部空间反转对结构完整性造成灾难性影响，但在现有基准测试中，它们常常被整体描述性指标所掩盖。为了系统性地揭示和量化这一瓶颈，我们引入了SPACE（主体中心姿态、外观和特征评估），这是一个旨在评估主体中心空间理解的基准。在SPACE上，我们揭示尽管当前的MLLMs在通用感知方面表现强劲，但它们始终未能在主体的内在参考框架中定位描述。为了解决这一问题，我们提出了一种专门的数据构建和对齐流程。我们首先从细粒度的身体部位定位中提取结构化空间线索，以指导两阶段的描述重写过程，从而生成高度空间忠实的训练数据。此外，我们为群体相对策略优化（GRPO）设计了一种基于评分标准的奖励机制，明确惩罚对齐过程中结构上关键的空间错误。在SPACE上的大量实验表明，我们的框架显著提高了以人为中心的描述质量，特别是在主体中心的空间推理方面，达到与强大的闭源模型竞争的性能。我们的基准和代码可在 https://github.com/JHang2020/SPACE-Eval 获取。
cs.CV / 241 / 2609.08303
TRIUNE-Net: Harmonizing Scale, Shape, and Efficiency in Pancreatic Tumor Segmentation
TRIUNE-Net：在胰腺肿瘤分割中协调尺度、形状和效率
Abstract
Pancreatic tumor segmentation in 3D CT volumes is challenged by extreme scale variability across both the pancreas and tumor, and highly irregular tumor morphology. While recent advances have pushed segmentation performance, existing methods do not explicitly address these challenges and come at the cost of excessive computational complexity, limiting their practicality in resource-constrained clinical environments. We propose TRIUNE-Net, a lightweight unified architecture that harmonizes scale, shape, and efficiency through three synergistic innovations. A multi-scale context aggregation module with stage-adaptive dilated convolutions enables the model to reason across the broad range of anatomical scales present in both organs. A serial linear-deformable attention mechanism combines large effective receptive fields with shapeadaptive deformable convolutions to capture irregular, non-convex tumor morphologies. Finally, an information-preserving downsampling module replaces conventional max pooling entirely, retaining all spatial information while adding negligible parameters, preventing small tumors from being discarded before they can be recognized. On both the MSD Pancreas and NVD Pancreas datasets, TRIUNE-Net achieves state-of-theart results with only 5.86 M parameters and no external pre-training, outperforming all baselines across all key tumor metrics. Specifically, it surpasses the next-best model by 0.45% in tumor Dice, 6.0 points in F1 score, 6.6 points in sensitivity, and 3.4 points in precision, simultaneously reflecting its ability to suppress both missed tumors and false alarms in clinically realistic conditions. Our code is available at: https://github.com/abdora-ai/TRIUNE-Net
Chinese Translation
在三维CT体积中，胰腺肿瘤分割面临着胰腺和肿瘤之间极大的尺度变异性以及高度不规则的肿瘤形态学的挑战。尽管近期的进展推动了分割性能，但现有方法并未明确解决这些挑战，且伴随过高的计算复杂性，限制了其在资源受限的临床环境中的实用性。我们提出了TRIUNE-Net，这是一种轻量级的统一架构，通过三项协同创新协调尺度、形状和效率。一个具有阶段自适应扩张卷积的多尺度上下文聚合模块使模型能够在两个器官中存在的广泛解剖尺度上进行推理。一个串行线性可变形注意机制结合了大的有效感受野与形状自适应的可变形卷积，以捕捉不规则的、非凸的肿瘤形态。最后，一个信息保留下采样模块完全替代了传统的最大池化，保留了所有空间信息，同时增加的参数微不足道，防止小肿瘤在被识别之前被丢弃。在MSD胰腺和NVD胰腺数据集上，TRIUNE-Net以仅5.86M的参数量和无外部预训练的情况下，达到了最先进的结果，超越了所有基线在所有关键肿瘤指标上的表现。具体而言，它在肿瘤Dice上超越了下一个最佳模型0.45%，在F1分数上超越了6.0分，在灵敏度上超越了6.6分，在精确度上超越了3.4分，同时反映了其在临床现实条件下抑制漏检肿瘤和假警报的能力。我们的代码可在以下网址获取：https://github.com/abdora-ai/TRIUNE-Net
cs.CV / 242 / 2609.08305
FPicker: Topology-Guided Evolution for Filament Tracing in Low-SNR Microscopy
FPicker：低信噪比显微镜中丝状追踪的拓扑引导演化
Abstract
Automating filament tracing in Cryo-Electron Microscopy (Cryo-EM) is essential for 3D helical reconstruction but challenged by intersecting topologies and extremely low Signal-to-Noise Ratios ($\text{SNR} = \sigma_s^2/\sigma_n^2$ < 0.1 or -10 dB). Existing paradigms fail: pixel-wise segmenters suffer from severe topological fracturing, box-based detectors face ghost center drift, sequential trackers derail due to error accumulation, and traditional active contours collapse under artificial closed-curve constraints. To resolve these bottlenecks, we present FPicker, the first topology-guided framework reconciling these incompatibilities. It unifies perception via a center-endpoint representation and an open-curve evolution module to explicitly model non-cyclic connectivity. On simulated benchmarks, FPicker outperforms top baselines by over $40\%$ relative gain in mean spatio-angular precision (mSAP) and reduces topological gap rates by over $60\%$ under extreme noise ($-20\text{ dB}$). By learning intrinsic physical geometry rather than local texture, FPicker demonstrates strong potential as a resilient geometric backbone. Its zero-shot performance on the real-world EMPIAR dataset exhibits robust topological resistance, achieving a state-of-the-art 82.9\% mSAP upon fine-tuning. Our results also suggest modeling physical priors is a highly robust path toward bridging the sim-to-real gap in signal-starved scientific imaging. The code is publicly available at: https://github.com/tomzhaosky/FPicker.
Chinese Translation
在冷冻电子显微镜（Cryo-EM）中自动化丝状追踪对于三维螺旋重建至关重要，但受到交叉拓扑和极低信噪比（$ ext{SNR} = rac{ ext{σ}_s^2}{ ext{σ}_n^2} < 0.1$ 或 -10 dB）的挑战。现有的方法存在不足：逐像素分割器遭遇严重的拓扑破裂，基于盒子的检测器面临幽灵中心漂移，顺序跟踪器因误差累积而失效，传统的活动轮廓在人工闭合曲线约束下崩溃。为了解决这些瓶颈，我们提出了FPicker，这是第一个拓扑引导框架，调和了这些不兼容性。它通过中心-端点表示和开放曲线演化模块统一感知，明确建模非周期性连接。在模拟基准测试中，FPicker在平均时空角度精度（mSAP）上相较于顶级基线获得超过$40\%$的相对增益，并在极端噪声（$-20 ext{ dB}$）下将拓扑间隙率降低超过$60\\%$。通过学习内在物理几何而非局部纹理，FPicker展现出作为强大几何骨干的潜力。其在真实世界EMPIAR数据集上的零-shot性能显示出强大的拓扑抗性，在微调后实现了82.9\\%的最先进mSAP。我们的结果还表明，建模物理先验是弥合信号匮乏的科学成像中模拟与真实之间差距的一个高度稳健的路径。代码已公开发布于：https://github.com/tomzhaosky/FPicker。
cs.CV / 243 / 2609.08316
From Glance to Scrutiny: Progressive Distortion Reasoning for Fine-Grained Image Quality Assessment
从一瞥到细致审视：用于细粒度图像质量评估的渐进失真推理
Abstract
Multi-modal large language models (MLLMs) have demonstrated significant potential in image quality assessment (IQA) by bridging visual perception with descriptive evaluations. However, existing approaches mainly focus on holistic quality prediction, often functioning as black boxes that provide limited insight into where distortions occur and how they affect perceived quality, hindering fine-grained analysis of localized and heterogeneous degradations. We propose GS-IQA, a framework that reformulates IQA as a progressive Where--What--How diagnosis, emulating the human perceptual process from an initial glance to closer scrutiny. Since a severity judgment is meaningful only for a correctly localized and recognized region, we realize this progression through a two-stage reinforcement learning paradigm that respects such dependencies: the glance stage uses a perception-gated reward to establish where degradations lie and what they are, activating severity feedback only once both are correct, while the scrutiny stage introduces online reward-conditioned degradation generation to synthesize hard examples targeted at the model's perceptual bottlenecks, sharpening its discrimination of subtle severity variations. To enable systematic evaluation, we construct Diag-Bench, a region-level IQA benchmark of about 25K curated samples spanning 12 distortion types and five ordinal severity levels. Extensive experiments show that GS-IQA consistently surpasses state-of-the-art methods in distortion localization, recognition, and severity estimation, and that its diagnostic representations transfer effectively to conventional global quality prediction across diverse external benchmarks. Code and data will be released.
Chinese Translation
多模态大型语言模型（MLLMs）在图像质量评估（IQA）中展现了显著的潜力，通过将视觉感知与描述性评估相结合。然而，现有的方法主要集中在整体质量预测上，往往作为黑箱运作，提供有限的洞察力，无法明确失真发生的位置及其对感知质量的影响，从而阻碍了对局部和异质降级的细粒度分析。我们提出了GS-IQA，一个将IQA重新构建为渐进的“哪里-什么-如何”诊断的框架，模拟人类从初步观察到深入审视的感知过程。由于严重性判断仅对正确定位和识别的区域有意义，我们通过一个尊重这种依赖关系的两阶段强化学习范式实现这一进程：初步观察阶段使用感知门控奖励来确定降级的位置和性质，仅在两者均正确时激活严重性反馈，而细致审视阶段引入在线奖励条件的降级生成，以合成针对模型感知瓶颈的困难示例，增强其对细微严重性变化的辨别能力。为了实现系统评估，我们构建了Diag-Bench，一个涵盖12种失真类型和五个序列严重性等级的约25K个样本的区域级IQA基准。大量实验表明，GS-IQA在失真定位、识别和严重性估计方面始终超越最先进的方法，并且其诊断表示能够有效迁移到多样的外部基准上的传统全局质量预测。代码和数据将会发布。
cs.CV / 244 / 2609.08317
Supervised Cross-Modal Feature Alignment for Zero-Wearable Freezing of Gait Detection in Parkinsonism
针对帕金森症中零可穿戴步态冻结检测的监督跨模态特征对齐
Abstract
Objective assessment of Freezing of Gait (FoG) in Parkinson's disease (PD) relies predominantly on wearable Inertial Measurement Units (IMUs). While IMUs provide optimal kinematic precision, mandatory sensor attachment restricts continuous clinical deployment. Conversely, unobtrusive vision-based alternatives suffer substantial classification errors during turning-in-place tasks, where geometric self-occlusion degrades deterministic skeletal coordinates and obscures the high-frequency precursors required for FoG detection. To resolve these physical observation limits, we propose a supervised cross-modal subspace distillation framework. During optimisation, pre-trained kinematic data from IMU sensors and contextual clinical metadata act as oracles to guide a deployable visual architecture. By incorporating joint velocity and acceleration derivatives, utilising a confidence-based gating mechanism, the visual model mitigates some of the tracking errors during occlusion events. Empirical evaluations confirm this latent alignment transfers the predictive fidelity of hardware sensors directly into the visual representation, yielding $85.5\%$ accuracy, and $82.4\%$ balanced accuracy. All the while maintaining a vision only model at inference.
Chinese Translation
对帕金森病（PD）中步态冻结（FoG）的客观评估主要依赖于可穿戴惯性测量单元（IMUs）。虽然IMUs提供了最佳的运动学精度，但强制传感器附着限制了持续的临床应用。相反，基于视觉的无干扰替代方案在原地转身任务中遭遇了显著的分类错误，因为几何自遮挡降低了确定性的骨骼坐标，并遮蔽了FoG检测所需的高频前驱信号。为了解决这些物理观察限制，我们提出了一种监督跨模态子空间蒸馏框架。在优化过程中，来自IMU传感器的预训练运动学数据和上下文临床元数据作为神谕，指导可部署的视觉架构。通过结合关节速度和加速度导数，并利用基于置信度的门控机制，视觉模型在遮挡事件中减轻了一些跟踪错误。实证评估确认这种潜在对齐将硬件传感器的预测精度直接转移到视觉表示中，达到了85.5%的准确率和82.4%的平衡准确率，同时在推理时保持仅使用视觉模型。
cs.CV / 245 / 2609.08331
Do Input-Level Defenses Transfer to Observation-Level Attacks on VideoLLMs?
输入级防御是否能转移到视频大语言模型（VideoLLMs）的观察级攻击？
Abstract
Video Large Language Models (VideoLLMs) are increasingly deployed in safety-critical applications such as content moderation and video analytics. To process long videos efficiently, VideoLLMs rely on frame sampling, token compression, and modality fusion, which together form an observation pipeline that reduces the raw video to a compact internal representation. Recent observation-level attacks exploit this pipeline to prevent the model from perceiving harmful content, yet no defense has been explicitly designed for this threat. We introduce DefTEval, a controlled evaluation framework that systematically assesses whether input-level adversarial defenses, which operate on the pixel content of already-sampled frames, can mitigate observation-level attacks. Across five VideoLLMs, eleven representative defenses, and five attack types, we find that input-level defenses offer limited and inconsistent protection, with harmful detection rates frequently near zero. Critically, defenses fail even against attacks that embed harmful signals in every sampled frame, indicating that the bottleneck extends beyond sampling omission to the suppression of signals that do enter the model. Token compression discards localized features, and modality fusion systematically down-weights weakened visual signals. Furthermore, defense effectiveness is dominated by model architecture rather than by the defense method itself, and detection rates vary drastically across content categories, exposing structural weaknesses in temporal reasoning. These findings demonstrate that securing VideoLLMs requires system-level robustness mechanisms spanning sampling-aware coverage guarantees, token-level preservation of safety-relevant features, and modality-balanced fusion.
Chinese Translation
视频大语言模型（VideoLLMs）在内容审核和视频分析等安全关键应用中越来越多地被部署。为了高效处理长视频，VideoLLMs依赖于帧采样、标记压缩和模态融合，这些共同形成了一个观察管道，将原始视频减少为紧凑的内部表示。最近的观察级攻击利用这一管道，阻止模型感知有害内容，但尚未有专门针对这一威胁设计的防御措施。我们引入了DefTEval，一个受控评估框架，系统地评估输入级对抗防御（作用于已采样帧的像素内容）是否能减轻观察级攻击。在五个VideoLLMs、十一种代表性防御和五种攻击类型的实验中，我们发现输入级防御提供的保护有限且不一致，有害检测率常常接近零。关键是，即使面对在每个采样帧中嵌入有害信号的攻击，防御也未能奏效，这表明瓶颈不仅限于采样遗漏，还包括对进入模型的信号的抑制。标记压缩丢弃了局部特征，而模态融合系统性地降低了减弱的视觉信号的权重。此外，防御效果受模型架构的主导，而非防御方法本身，检测率在内容类别之间差异显著，暴露了时间推理中的结构性弱点。这些发现表明，保护VideoLLMs需要跨越采样感知覆盖保证、标记级安全相关特征的保留和模态平衡融合的系统级鲁棒性机制。
cs.CV / 246 / 2609.08332
EdMCGS: Event-Driven Markov Chain Gaussian Splatting for Extreme-Low-Frame-Rate Dynamic Scene Reconstruction
EdMCGS：基于事件驱动的马尔可夫链高斯点云重建极低帧率动态场景
Abstract
We present EdMCGS (Event-driven Markov chain Gaussian Splatting), an end-to-end method for reconstructing dynamic 3D scenes from extreme-low-frame-rate RGB together with an event stream, which can then be rendered at any intermediate timestamp. Methods relying solely on RGB images generate numerous artifacts due to the lack of evidence from between consecutive frames. To supply this missing evidence, we model the scene motion as an event-driven Markov chain, in which the sparse RGB frames anchor the state at their own timestamps while the events recorded within an interval drive the transition across it. Since the transition reads the events of the current interval, it remains active at inference and produces the in-between motion of the 3D Gaussians directly from the events rather than by interpolation, which sets our method apart from prior work that uses events only as training-time supervision. The state is carried by a compact set of control points, each driven by the events sampled in the neighborhood of its own image projection, and a temporal local isometry term keeps the propagated motion locally rigid. Experiments on synthetic and real-world scenes show that EdMCGS outperforms both RGB-based and event-based baselines, while rendering in real time with far fewer Gaussians than the strongest event-based baseline. We release our source code and a new dataset at https://github.com/joseclipse/EdMCGS.
Chinese Translation
我们提出了EdMCGS（事件驱动的马尔可夫链高斯点云），这是一种端到端的方法，用于从极低帧率的RGB图像和事件流中重建动态3D场景，并能够在任意中间时间戳进行渲染。仅依赖RGB图像的方法由于缺乏连续帧之间的证据而产生大量伪影。为了提供缺失的证据，我们将场景运动建模为事件驱动的马尔可夫链，其中稀疏的RGB帧在其自身时间戳处锚定状态，而在一个时间间隔内记录的事件驱动状态的转移。由于转移读取当前时间间隔的事件，因此在推理时保持活跃，并直接从事件中生成3D高斯的中间运动，而不是通过插值，这使我们的方法与之前仅将事件用作训练时监督的工作有所不同。状态由一组紧凑的控制点承载，每个控制点由其自身图像投影邻域内采样的事件驱动，而一个时间局部等距项保持传播的运动在局部范围内刚性。对合成和真实场景的实验表明，EdMCGS在性能上优于基于RGB和基于事件的基线，同时以远少于最强基于事件的基线的高斯数量实现实时渲染。我们在https://github.com/joseclipse/EdMCGS发布了我们的源代码和一个新数据集。
cs.CV / 247 / 2609.08342
VeriScene: Reconstructing Crime Scenes from Legal Evidence via World-Model Agent
VeriScene：通过世界模型代理从法律证据重建犯罪现场
Abstract
World models take multimodal inputs like text, photos, and diagrams to generate dynamic scenes in accordance with the laws of physics, thus opening a compelling application: fusing multimodal legal evidence to re-create a crime scene and re-enact how an offence could have been committed. However, feeding the raw, unorganized evidence into a world model fails in forensic use: it silently drops evidence, glosses over contradictory testimony, and produces motion that violates the evidentiary record. This paper presents VeriScene, an agent that orchestrates the world model: it reconstructs crime scenes from forensic photographs and witness statements of varying reliability, keeping every claim traceable to evidence and every motion physically plausible. VeriScene iteratively fuses the evidence into a cited narrative under an auditing loop, verifies the hypothesized dynamics via probe rollouts in the world model with corrective constraint injection, and renders the offence as a re-enactment video from a fused keyframe. On a benchmark of 25 crime scenarios across 7 physically-driven case types (139 forensic-style photographs and 65 statements with planted unreliability), VeriScene attains 0.9014 evidence coverage and 0.7217 factual consistency (0-1 scale) on the 20 test scenes, outperforming an end-to-end multimodal-LLM baseline by 20.35% in factual consistency and 34.88% in temporal coherence, while generalizing across four LLM orchestration backends at USD 1.82 per scene.
Chinese Translation
世界模型接受文本、照片和图表等多模态输入，以根据物理法则生成动态场景，从而开启了一项引人注目的应用：融合多模态法律证据以重建犯罪现场，并重现犯罪是如何发生的。然而，将原始、无序的证据输入世界模型在法医学中并不奏效：它默默地丢弃证据，忽略矛盾的证词，并产生违反证据记录的运动。本文提出了VeriScene，一个协调世界模型的代理：它从法医照片和不同可靠性的证人陈述中重建犯罪现场，使每个主张都可追溯到证据，每个运动都在物理上合理。VeriScene在审计循环下迭代地将证据融合成引用叙述，通过在世界模型中进行探测回放并注入修正约束来验证假设的动态，并从融合的关键帧渲染出犯罪重现视频。在25个犯罪场景的基准测试中，涵盖7种物理驱动的案件类型（139张法医风格的照片和65条带有植入不可靠性的陈述），VeriScene在20个测试场景中达到了0.9014的证据覆盖率和0.7217的事实一致性（0-1尺度），在事实一致性上比端到端的多模态LLM基线高出20.35%，在时间一致性上高出34.88%，同时在每个场景的成本为1.82美元的情况下，在四个LLM协调后端之间实现了泛化。
cs.CV / 248 / 2609.08345
CoVeR: Coverage-Based Token Pruning for Multi-View 3D Reasoning in VLMs
CoVeR：基于覆盖的多视角3D推理中的标记剪枝方法
Abstract
Representing a 3D scene as multi-view images allows 2D VLMs to reason in 3D by reusing priors from pre-training, sidestepping the scarcity of annotated 3D data. However, it produces thousands of redundant visual tokens whose cost grows with every view. Existing visual token pruners fall into two families, each limited in the 3D multi-view setting. Learned importance methods rank tokens by attention or encoder features; because redundancy here is fundamentally spatial, they keep near-duplicate tokens from a few prominent regions and leave most of the scene unrepresented. Voxelization methods improve spatial coverage but cannot enforce an exact token budget and saturate as multi-view observations overlap in 3D, capping retention well below the target. We show that spatial coverage is associated with 3D reasoning performance and introduce CoVeR, a deterministic, training-free selector that uses only token coordinates, with no learned signals. CoVeR selects tokens that collectively cover every region of the scene, and solves the limitations of both families: it enforces an exact per-scene budget, breaks the voxelization saturation plateau, and avoids the near-duplicate selections of learned importance. Extensive experiments show CoVeR outperforms prior SOTAs on all three 3D reasoning benchmarks and generalizes as a plug-and-play module tested across four VLMs. Notably, with only $\approx$8% of visual tokens, it preserves 93.5% of full-token performance, surpassing SOTA by 3.9 percentage points on average across benchmarks.
Chinese Translation
将3D场景表示为多视角图像使得2D视觉语言模型（VLMs）能够通过重用预训练中的先验知识进行3D推理，从而避免了标注3D数据的稀缺性。然而，这会产生成千上万的冗余视觉标记，其成本随着每个视角的增加而增加。现有的视觉标记剪枝方法可分为两类，各自在3D多视角设置中存在局限性。学习重要性的方法通过注意力或编码器特征对标记进行排名；由于这里的冗余本质上是空间性的，它们保留了一些显著区域的近重复标记，而大部分场景则未被表示。体素化方法改善了空间覆盖，但无法强制执行精确的标记预算，并且在多视角观察在3D中重叠时会饱和，导致保留量远低于目标。我们表明，空间覆盖与3D推理性能相关，并引入了CoVeR，这是一种确定性的、无训练的选择器，仅使用标记坐标，没有学习信号。CoVeR选择的标记共同覆盖场景的每个区域，解决了两类方法的局限性：它强制执行每个场景的精确预算，打破了体素化饱和的瓶颈，并避免了学习重要性方法的近重复选择。大量实验表明，CoVeR在所有三个3D推理基准测试中优于先前的最先进技术（SOTA），并作为可插拔模块在四个VLMs中进行了测试，表现出良好的泛化能力。值得注意的是，CoVeR仅使用约8%的视觉标记，便保留了93.5%的全标记性能，在基准测试中平均超越SOTA 3.9个百分点。
cs.CV / 249 / 2609.08346
Segment Any Motion with Radar: Robust Multimodal Moving-Object Segmentation and Tracking
用雷达分割任何运动：鲁棒的多模态移动物体分割与跟踪
Abstract
Moving-object perception must decide which image regions correspond to real motion and keep every instance identified over time. Methods that read motion from appearance, optical flow, or estimated trajectories lose that evidence under poor illumination, adverse weather, reflections, and occlusion. Radar is a natural remedy because it measures radial velocity directly instead of inferring it from photometric correspondence. However, existing benchmarks do not jointly provide radar measurements, dense moving-instance masks, and temporally consistent identities for surveillance. We therefore introduce RGBTR-Motion, a synchronized and calibrated fixed-camera benchmark that pairs RGB, thermal, and radar streams with dense instance masks and temporally consistent identities across diverse surveillance scenes. We also develop SAM-Radar, an RGB, thermal, and radar-based segmentation and tracking framework built on SAM 3. SAM-Radar's radar-aware detector fuses calibrated RGBT features with radar returns that are grounded at their projected image locations, and motion supervision, implemented as foreground classification of those projected returns, teaches the detector to reject clutter without any text prompt. The tracker associates accepted radar returns with individual trajectories and uses them as physical evidence that a visually degraded target remains present. This allows it to bridge short periods of low visibility or occlusion and reconnect a reappearing target to its existing identity instead of starting a new track. SAM-Radar attains 0.7027 IoU and 0.8090 F1-50, and raises MOTA, HOTA, and IDF1 by 0.2977, 0.1603, and 0.2857 over the strongest competing values.
Chinese Translation
移动物体感知必须决定哪些图像区域对应于真实运动，并保持每个实例在时间上的识别。依赖于外观、光流或估计轨迹读取运动的方法在光照不足、不利天气、反射和遮挡等条件下会失去这些证据。雷达是一种自然的解决方案，因为它直接测量径向速度，而不是通过光度对应推断。然而，现有的基准测试并未共同提供雷达测量、密集移动实例掩码和时间一致的身份信息用于监控。因此，我们引入了RGBTR-Motion，这是一个同步和校准的固定摄像头基准，结合了RGB、热成像和雷达流，以及在多样化监控场景中具有密集实例掩码和时间一致身份的信息。我们还开发了SAM-Radar，这是一个基于RGB、热成像和雷达的分割与跟踪框架，建立在SAM 3之上。SAM-Radar的雷达感知检测器将校准的RGBT特征与在其投影图像位置上定位的雷达回波融合，并通过对这些投影回波的前景分类实施运动监督，教会检测器在没有任何文本提示的情况下拒绝杂波。跟踪器将接受的雷达回波与单个轨迹关联，并将其作为物理证据，表明视觉降级的目标仍然存在。这使得它能够弥补短暂的低能见度或遮挡期，并将重新出现的目标重新连接到其现有身份，而不是开始新的轨迹。SAM-Radar达到了0.7027的IoU和0.8090的F1-50，并将MOTA、HOTA和IDF1分别提高了0.2977、0.1603和0.2857，超越了最强竞争值。
cs.CV / 250 / 2609.08365
ReMoMask-2: Latent Retrieval-Augmented Masked Motion Generation
ReMoMask-2：潜在检索增强的掩码运动生成
Abstract
Text-to-motion (T2M) generation maps natural language to human joint movements, aiding gaming, VR, and robotics. Retrieval-Augmented Text-to-Motion (RAG-T2M) improves generation on complex descriptions by conditioning on retrieved motion-text pairs. However, existing RAG-T2M models face two challenges: coarse-grained retrieval and fusion mechanisms overlook the hierarchical, spatial-temporal topology of human motion, and a representation gap exists because retrieved evidence resides in a semantic space separate from the generator's latents. To address the first, we present ReMoMask, a structure-aware RAG framework coupling Hierarchical Bidirectional Momentum (HBM) contrastive learning to align global and part-level features with text; Semantic Spatial-Temporal Attention (SSTA) for topology-aware fusion; and Topology Structured Masking (TSM) to force robust part-level grounding via adaptive masking. To address the second, we introduce ReMoMask-2, which rebuilds the retrieval database directly within the generator's pre-quantization latent space and aligns text queries via a distilled lightweight projector, allowing the generator to directly consume the retrieved motion's semantic content. Extensive experiments on HumanML3D, KIT-ML, and SnapMoGen demonstrate our retriever achieves state-of-the-art accuracy, while ReMoMask-2 attains the lowest FID on KIT-ML and SnapMoGen; notably, its single mask-transformer stage surpasses ReMoMask's full two-stage pipeline and delivers the fastest inference.
Chinese Translation
文本到运动（Text-to-Motion，T2M）生成将自然语言映射到人体关节动作，助力游戏、虚拟现实和机器人技术。检索增强文本到运动（Retrieval-Augmented Text-to-Motion，RAG-T2M）通过基于检索到的运动-文本对进行条件生成，提升了复杂描述的生成效果。然而，现有的RAG-T2M模型面临两大挑战：一是粗粒度的检索和融合机制忽视了人体运动的层级化时空拓扑结构；二是存在表示差距，因为检索到的证据位于与生成器潜变量分离的语义空间。针对第一个问题，我们提出了ReMoMask，一种结构感知的RAG框架，结合层级双向动量（Hierarchical Bidirectional Momentum，HBM）对比学习以对齐全局与局部特征与文本，语义时空注意力（Semantic Spatial-Temporal Attention，SSTA）实现拓扑感知融合，以及拓扑结构掩码（Topology Structured Masking，TSM）通过自适应掩码强制实现稳健的局部定位。针对第二个问题，我们引入ReMoMask-2，直接在生成器的预量化潜在空间内重建检索数据库，并通过蒸馏的轻量投影器对文本查询进行对齐，使生成器能够直接利用检索运动的语义内容。在HumanML3D、KIT-ML和SnapMoGen上的大量实验表明，我们的检索器实现了最先进的准确率，而ReMoMask-2在KIT-ML和SnapMoGen上获得了最低的FID值；值得注意的是，其单阶段掩码变换器（mask-transformer）超越了ReMoMask的完整两阶段流水线，并实现了最快的推理速度。
cs.CV / 251 / 2609.08367
To Adapt or Not to Adapt? Selective Adaptation for Vision-Language Models
适应还是不适应？视觉-语言模型的选择性适应
Abstract
Test-time adaptation (TTA) has emerged as a prominent strategy for adapting vision-language models to distribution shifts during inference. We conduct a per-sample analysis of model predictions before and after adaptation, and observe two failure modes in existing TTA methods that echo previous work. Adaptations are frequently negligible, yielding no change in the model's predictions, and more severely, they can be detrimental by flipping previously correct predictions to incorrect ones. This naturally raises a question: Can we identify and skip such negligible or harmful adaptations? In this work, we introduce a new problem of selective adaptation, which aims to determine whether a given test sample should undergo adaptation or be skipped. To this end, we propose Cross-Augmentation Similarity (CAS), a simple baseline that performs adaptation only when predictions across augmented views exhibit low similarity. Notably, CAS not only preserves but in some cases improves overall accuracy, even when skipping nearly 85% of the adaptation process. We hope other researchers will explore this new direction and surpass the performance of our baseline. Our code is available at https://github.com/sirujiang/selective-adaptation.
Chinese Translation
测试时适应（TTA）已成为在推理过程中将视觉-语言模型适应于分布变化的一个重要策略。我们对模型在适应前后的预测进行了逐样本分析，并观察到现有TTA方法中存在两种失败模式，这与之前的研究相呼应。适应的效果往往微不足道，导致模型的预测没有变化；更严重的是，它们可能是有害的，将先前正确的预测翻转为错误的预测。这自然引发了一个问题：我们能否识别并跳过这些微不足道或有害的适应？在本研究中，我们引入了一个新的选择性适应问题，旨在确定给定的测试样本是否应该进行适应或被跳过。为此，我们提出了交叉增强相似性（Cross-Augmentation Similarity, CAS），这是一个简单的基线，仅在增强视图之间的预测表现出低相似性时才进行适应。值得注意的是，CAS不仅保持了整体准确性，在某些情况下甚至提高了准确性，即使跳过了近85%的适应过程。我们希望其他研究人员能够探索这一新方向，并超越我们的基线性能。我们的代码可在 https://github.com/sirujiang/selective-adaptation 获取。
cs.CV / 252 / 2609.08385
GALoc: Gravity Aligned Wireframes for Depth-Free Monocular Floorplan Localization
GALoc：用于无深度单目平面图定位的重力对齐线框
Abstract
Floorplans are compact, appearance-invariant maps ideal for indoor localization, yet existing methods rely on depth networks that are brittle in cluttered scenes. We propose GALoc, a geometry-first framework that replaces depth prediction with gravity-aligned wireframes that satisfy verticality and coplanarity by construction. Given monocular RGB, camera intrinsics, relative poses, and IMU orientation, GALoc constructs a linear constraint matrix encoding verticality and coplanarity, and finds the camera gauge minimizing its smallest singular value via global search. The rectified wireframes are projected into bird's-eye-view layouts through a closed-form, FOV-consistent transformation and matched against the floorplan via metric-free SE(2) search. We evaluate end-to-end on Structured3D, with calibrated noise on Gibson, and on real-world author-collected sequences. When sufficient wall geometry is visible, GALoc matches or outperforms depth-based baselines -- achieving 88% sequential localization success at 0.1m over 100-step sequences on Gibson vs the baseline's 68% -- while abstaining in structure-blind scenes.
Chinese Translation
平面图是一种紧凑且外观不变的地图，适合室内定位，但现有方法依赖于在杂乱场景中脆弱的深度网络。我们提出了GALoc，这是一种以几何为基础的框架，通过构建重力对齐的线框来替代深度预测，这些线框在构造上满足垂直性和共面性。给定单目RGB图像、相机内参、相对姿态和IMU方向，GALoc构建一个线性约束矩阵，编码垂直性和共面性，并通过全局搜索找到最小化其最小奇异值的相机标定。经过校正的线框通过封闭形式的、视场一致的变换投影到鸟瞰图布局中，并通过无度量的SE(2)搜索与平面图进行匹配。我们在Structured3D上进行了端到端评估，在Gibson上进行了校准噪声测试，并在真实世界的作者收集序列上进行了验证。当足够的墙体几何可见时，GALoc的匹配效果与基于深度的基线相当或更优——在Gibson上实现了88%的顺序定位成功率，步长为0.1米，100步序列，而基线的成功率为68%——同时在结构盲场景中保持不匹配。
cs.CV / 253 / 2609.08391
From Coordinates to Candidate Regions: Temporal Change Localization via Region Selection in Remote Sensing Multimodal LLMs
从坐标到候选区域：通过区域选择实现遥感多模态大语言模型的时间变化定位
Abstract
Remote sensing multimodal large language models (RS-MLLMs) have advanced scene understanding and visual question answering over satellite imagery, yet localizing specific objects or changed regions remains challenging. Existing approaches rely on generating bounding box coordinates as token sequences, which is fragile for the small, densely packed objects common in remote sensing and increasingly error-prone when multiple targets must be localized simultaneously. In this work, we present an RS-specific formulation of the region selection paradigm, previously explored in natural-image MLLMs, and extend it to temporal change localization over multi-image sequences. Our framework employs a text-conditioned region proposal module, encodes each candidate as special tokens carrying per-frame visual features enriched with spatial and temporal cues, and lets the LLM localize targets by selecting region tokens in its response. We construct a multi-task training and evaluation suite spanning localization, referring expression, visual grounding, and understanding tasks across single-image and multi-temporal settings. Experiments show that our approach substantially outperforms coordinate-generation baselines on temporal change localization, while improving single-image visual grounding and maintaining competitive understanding performance. Oracle analysis decomposes the contributions of the region proposer and the LLM selector, providing diagnostic insight unique to this framework. Our code will be available at https://github.com/juwan-kr/RS-RegionSelect.
Chinese Translation
遥感多模态大语言模型（RS-MLLMs）在卫星图像的场景理解和视觉问答方面取得了进展，但定位特定对象或变化区域仍然具有挑战性。现有方法依赖于生成边界框坐标作为令牌序列，这对于遥感中常见的小型密集物体来说是脆弱的，并且在需要同时定位多个目标时越来越容易出错。在本研究中，我们提出了一种针对遥感的区域选择范式的特定形式，该范式之前在自然图像的多模态大语言模型中进行了探索，并将其扩展到多图像序列的时间变化定位。我们的框架采用了一个文本条件的区域提议模块，将每个候选区域编码为携带每帧视觉特征的特殊令牌，这些特征通过空间和时间线索得到了增强，并使大语言模型通过选择区域令牌来定位目标。我们构建了一个涵盖单图像和多时间设置的定位、指称表达、视觉定位和理解任务的多任务训练和评估套件。实验表明，我们的方法在时间变化定位上显著优于坐标生成基线，同时改善了单图像视觉定位并保持了竞争性的理解性能。Oracle分析分解了区域提议者和大语言模型选择器的贡献，为该框架提供了独特的诊断见解。我们的代码将发布在 https://github.com/juwan-kr/RS-RegionSelect。
cs.CV / 254 / 2609.08402
Towards Embodied Air-Ground Cooperative Object Search: Benchmark, Dataset and Agentic Method
面向具身的空地协同目标搜索：基准、数据集与智能方法
Abstract
Air-Ground Object Search (AGOS) in urban environments is a challenging embodied task, which requires an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) to jointly search for and verify a specified target vehicle from multi-view visual references. To study this underexplored problem, we introduce AGOS-Bench, the first dedicated benchmark for evaluating whether general-purpose Vision-Language Models (VLMs) can integrate aerial discoveries and ground-level verification through UAV-UGV cooperation. We further provide AGOS-Dataset as the companion resource of exemplary trajectories constructed by an automatic pipeline. It consists of 7.7k episodes for searching objects of diverse categories and attributes, spanning three difficulty levels. To address the AGOS task, we propose AGOS-Agent, a training-free and tool-augmented approach. The agentic method relieves VLMs from complex and dynamic coordination via a deliberate search-handoff-verify cooperation protocol, only demanding VLMs for scene understanding and decision-making. Extensive experiments on nine VLMs show that AGOS-Agent improves overall success rate for eight of the nine evaluated backbones while reducing decision steps for all nine. On the hard split, the SR and SPL of Gemini-3.6-Flash increase from 8.6% to 55.7% and from 7.6% to 44.0%, respectively.
Chinese Translation
城市环境中的空地目标搜索（AGOS）是一项具有挑战性的具身任务，要求无人机（UAV）和无人地面车辆（UGV）共同搜索并验证来自多视角视觉参考的指定目标车辆。为了研究这一尚未充分探索的问题，我们引入了AGOS-Bench，这是第一个专门用于评估通用视觉-语言模型（VLMs）是否能够通过UAV-UGV合作整合空中发现与地面验证的基准。此外，我们还提供了AGOS-Dataset，作为由自动化流程构建的示例轨迹的伴随资源。该数据集包含7.7千个用于搜索多种类别和属性物体的情节，涵盖三个难度级别。为了解决AGOS任务，我们提出了AGOS-Agent，这是一种无训练且工具增强的方法。该智能方法通过一种有意识的搜索-交接-验证合作协议，减轻了VLMs在复杂和动态协调中的负担，仅要求VLMs进行场景理解和决策。对九个VLMs的广泛实验表明，AGOS-Agent提高了九个评估基础模型中八个的整体成功率，同时减少了所有九个的决策步骤。在困难分割上，Gemini-3.6-Flash的成功率（SR）和成功路径长度（SPL）分别从8.6%提高到55.7%和从7.6%提高到44.0%。
cs.CV / 255 / 2609.08419
CAR-MIL: Counterfactual Attention Regularization for Multiple Instance Learning
CAR-MIL：用于多实例学习的反事实注意力正则化
Abstract
Multiple Instance Learning (MIL) is widely used for weakly supervised learning, particularly in digital pathology, where fine-grained annotations are costly. Most MIL methods aggregate instance features via attention mechanisms. However, attention weights do not always faithfully reflect instance importance and may focus on spuriously correlated regions. In this work, we propose CAR-MIL, a framework that explicitly guides attention learning through a counterfactual attention regularization objective inspired by counterfactual explanations. Built on a standard attention-based MIL architecture, our approach introduces a lightweight counterfactual attention branch trained to produce an alternative prediction while remaining close to the factual attention distribution. This encourages prediction changes to arise from minimal, structured redistributions of attention, leading to more informative evidence allocation. The resulting factual and counterfactual attention maps capture complementary evidence: the former highlights regions supporting the prediction, while the latter reveals regions whose reweighting would challenge it. We evaluate our method on synthetic MIL benchmarks with instance-level ground truth enabling controlled analysis of attention behavior and on five digital pathology datasets across four tasks. CAR-MIL maintains competitive classification performance, with the largest gains observed on more challenging tasks, while improving attention reliability, demonstrating the benefits of integrating counterfactual explainability reasoning into attention learning. Code is available at: https://github.com/ImaneCR/CAR-MIL/.
Chinese Translation
多实例学习（MIL）广泛应用于弱监督学习，特别是在数字病理学中，细粒度标注成本高昂。大多数MIL方法通过注意力机制聚合实例特征。然而，注意力权重并不总是忠实地反映实例的重要性，可能会集中于虚假相关的区域。在本研究中，我们提出了CAR-MIL，一个通过反事实注意力正则化目标明确指导注意力学习的框架，该目标受到反事实解释的启发。基于标准的基于注意力的MIL架构，我们的方法引入了一个轻量级的反事实注意力分支，旨在产生替代预测，同时保持接近事实注意力分布。这鼓励预测变化源于最小的、结构化的注意力重新分配，从而导致更具信息性的证据分配。生成的事实和反事实注意力图捕捉互补证据：前者突出支持预测的区域，而后者揭示重新加权将挑战预测的区域。我们在具有实例级真实标签的合成MIL基准上评估了我们的方法，以便对注意力行为进行控制分析，并在四个任务的五个数字病理数据集上进行评估。CAR-MIL保持了具有竞争力的分类性能，在更具挑战性的任务上观察到最大的提升，同时提高了注意力的可靠性，展示了将反事实可解释性推理整合到注意力学习中的好处。代码可在以下链接获取：https://github.com/ImaneCR/CAR-MIL/
cs.CV / 256 / 2609.08442
AirAnchor: Bridging Local and Global Spatial Information for Zero-Shot Aerial Vision-and-Language Navigation
AirAnchor：桥接局部与全球空间信息的零-shot空中视觉与语言导航
Abstract
Aerial Vision-and-Language Navigation requires drones to follow natural-language instructions and navigate through complex urban environments. Accurate navigation relies on both local and global spatial information, which support immediate action grounding and long-horizon path planning, respectively. However, existing zero-shot methods typically operate at a single spatial scale, relying either on local representations constructed online from current observations or on global memories built offline from historical experience. To address this limitation, we propose AirAnchor, a new paradigm that bridges local and global spatial information through spatial anchors and integrates both into a shared navigation framework, enabling comprehensive spatial grounding for decision-making. AirAnchor consists of three core components: (1) Query-Driven Spatial Anchor Grounding, which identifies decision-relevant anchors from visual observations and organizes them into local spatial representations; (2) Persistent Object Spatial Memory, which incrementally maintains an object knowledge base as persistent global spatial memory and retrieves landmark-related spatial priors; and (3) a Spatially-Informed Navigation Agent, which explicitly integrates both local and global spatial information into an agentic framework for decision-making. Extensive experiments on AerialVLN demonstrate that AirAnchor substantially outperforms existing zero-shot baselines, validating the effectiveness and efficiency of the proposed paradigm.
Chinese Translation
空中视觉与语言导航要求无人机遵循自然语言指令并在复杂的城市环境中进行导航。准确的导航依赖于局部和全球空间信息，分别支持即时行动定位和长远路径规划。然而，现有的零-shot方法通常在单一空间尺度上操作，依赖于从当前观察中在线构建的局部表示或从历史经验中离线构建的全球记忆。为了解决这一限制，我们提出了AirAnchor，这是一种通过空间锚点桥接局部与全球空间信息的新范式，并将两者整合到共享的导航框架中，从而实现全面的空间定位以支持决策。AirAnchor由三个核心组件组成：（1）查询驱动的空间锚点定位，识别来自视觉观察的与决策相关的锚点，并将其组织成局部空间表示；（2）持久对象空间记忆，逐步维护作为持久全球空间记忆的对象知识库，并检索与地标相关的空间先验；（3）空间信息驱动的导航代理，明确将局部和全球空间信息整合到一个代理框架中以支持决策。在AerialVLN上的大量实验表明，AirAnchor显著优于现有的零-shot基线，验证了所提范式的有效性和效率。
cs.CV / 257 / 2609.08449
GSComplete: Gaussian Splat Completion with 2D Diffusion Priors
GSComplete：基于二维扩散先验的高斯点云补全
Abstract
Gaussian splats provide a fast, high-fidelity representation for 3D objects but are often constructed from incomplete input data in practice, leaving missing regions. Existing completion methods either do not preserve the original splats or require scarcely available 3D training data. We propose GSComplete, which combines 3D generation based on Score Distillation Sampling with a novel preservation loss that encourages the original splats to be preserved where they should be visible. This effectively completes the Gaussian splat object using only 2D diffusion priors while fully preserving existing splats and generating new splats only in missing regions, without occluding the input. To evaluate our approach, we introduce a new dataset of partial Gaussian splat objects and show that GSComplete achieves significantly more accurate preservation of the input than existing methods with comparable plausibility of the completed result. Our code and dataset will be made available upon acceptance.
Chinese Translation
高斯点云为三维物体提供了一种快速且高保真的表示，但在实际应用中，通常是基于不完整的输入数据构建的，导致存在缺失区域。现有的补全方法要么无法保留原始点云，要么需要稀缺的三维训练数据。我们提出了GSComplete，它结合了基于评分蒸馏采样的三维生成方法和一种新颖的保留损失，鼓励在应当可见的地方保留原始点云。这有效地使用仅有的二维扩散先验完成高斯点云对象，同时完全保留现有的点云，仅在缺失区域生成新的点云，而不遮挡输入数据。为了评估我们的方法，我们引入了一个新的部分高斯点云对象数据集，并展示了GSComplete在保留输入数据的准确性方面显著优于现有方法，同时补全结果的合理性相当。我们的代码和数据集将在论文接受后公开。
cs.CV / 258 / 2609.08496
SignRefine: Adapting Foundational Video Models for Sign Language Generation
SignRefine：为手语生成适应基础视频模型
Abstract
Sign language video generation demands precise hand and facial articulation, yet modern video diffusion models, trained predominantly on spoken-language video, produce artifacts that render signing unintelligible. We propose SignRefine, a sign language video generation model that produces comprehensible signing from 2D keypoint conditioning alone, generalizing across appearances and visual conditions. Our approach builds on a pretrained video diffusion transformer and introduces local adapters with spatial grounding to selectively refine hand and face regions, steering the strong base model's prior toward accurate articulation. To enable this work and support broader sign language research, we present NVSign, a large-scale dataset of video content natively produced in sign language, offering diverse signer appearances, environments, and natural conversational settings. Trained on this data, our model shows up to 30% improvement in hand pose precision metrics over the strongest baseline and is preferred by sign language users for visual quality and comprehensibility in more than 80% of comparisons.
Chinese Translation
手语视频生成要求精确的手部和面部表达，然而现代视频扩散模型主要在口语视频上训练，产生的伪影使得手语难以理解。我们提出了SignRefine，这是一种手语视频生成模型，仅通过2D关键点条件生成可理解的手语，能够在不同外观和视觉条件下进行泛化。我们的方法基于一个预训练的视频扩散变换器，并引入具有空间基础的局部适配器，以选择性地细化手部和面部区域，引导强大的基础模型的先验朝向准确的表达。为了支持这一工作并促进更广泛的手语研究，我们呈现了NVSign，这是一个大规模的手语视频内容数据集，提供多样的手语者外观、环境和自然对话场景。在该数据上训练后，我们的模型在手部姿态精度指标上比最强基线提高了多达30%，并且在超过80%的比较中，手语用户更倾向于选择我们的模型，因其在视觉质量和可理解性方面表现更佳。
cs.CV / 259 / 2609.08505
Temporal State Transport in Video Generation: Diagnosing and Correcting Spectral Imbalance
视频生成中的时间状态传输：诊断与纠正光谱失衡
Abstract
Reliable video generation requires more than high-quality frames to form a coherent story: a model must maintain a persistent state, transporting visual attributes such as identity, scene layout, motion, and fine details across time. Existing training-free methods mainly strengthen cross-frame attention or analyze local attention entropy, but these views do not reveal whether temporal interactions stay in a healthy transport regime. In this work, we study video generation through the perspective of Temporal State Transport. We introduce Spectral Tension, a signed diagnostic that compares local attention diffuseness with global spectral diversity, and use it to identify two opposite temporal failures: fragmented transport and over-mixing hotspots. Based on this diagnosis, we propose Spectral Transport Homeostasis, a training-free regulator that softly corrects pathological temporal states while largely preserving balanced ones. Experiments on pretrained video generation models show that the original model often occupies imbalanced temporal regimes, whereas our method selectively applies larger corrections to the worst temporal hotspots and improves temporal consistency and visual quality without finetuning. Code: https://github.com/lytang63/temporal-state-transport
Chinese Translation
可靠的视频生成不仅需要高质量的帧来形成连贯的故事：模型必须保持持久状态，在时间上传输视觉属性，如身份、场景布局、运动和细节。现有的无训练方法主要加强跨帧注意力或分析局部注意力熵，但这些观点并未揭示时间交互是否保持在健康的传输状态。在本研究中，我们从时间状态传输的角度研究视频生成。我们引入光谱张力（Spectral Tension），一种比较局部注意力扩散性与全局光谱多样性的有符号诊断工具，并利用它识别两种相反的时间失效：碎片化传输和过度混合热点。基于这一诊断，我们提出光谱传输稳态（Spectral Transport Homeostasis），一种无训练的调节器，能够温和地纠正病态时间状态，同时在很大程度上保持平衡状态。对预训练视频生成模型的实验表明，原始模型通常处于不平衡的时间状态，而我们的方法则选择性地对最差的时间热点施加更大的修正，并在不进行微调的情况下改善时间一致性和视觉质量。代码：https://github.com/lytang63/temporal-state-transport
cs.CV / 260 / 2609.08524
Layer Selection in VLMs for Zero-Shot OOD Detection via Multi-Resolution Entropy Estimation
通过多分辨率熵估计在VLM中进行零-shot OOD检测的层选择
Abstract
Out-of-distribution (OOD) detection is crucial for safe deployment of medical AI systems, where domain shifts arise across institutions, acquisition protocols, and patient populations. VLMs enable zero-shot OOD detection by embedding images into a language-aligned latent space, where cross-modal similarity serves as a non-parametric confidence signal for identifying in-distribution samples. Yet existing methods rely almost exclusively on final-layer embeddings, implicitly assuming that the deepest representations are universally optimal. We first show that this assumption does not hold in medical imaging: intermediate layers provide complementary OOD signals, and the optimal representational depth depends on the respective image modality. While prior work selects layer combinations via entropy minimization of normalized histograms, we demonstrate that single-resolution entropy estimation is highly sensitive to binning choices, leading to performance variations of up to 19.3% AUROC. To address this instability, we propose a multi-resolution entropy estimation strategy that aggregates histogram statistics across multiple discretization scales, enabling robust and stable intermediate-layer selection. Across two medical OOD benchmarks, namely MIDOG and OASIS, covering distinct imaging modalities, diverse shift types, and different VLM backbones, our method consistently outperforms state-of-the-art approaches, offering a lightweight and stable solution for zero-shot OOD detection.
Chinese Translation
分布外（OOD）检测对于医疗人工智能系统的安全部署至关重要，因为在不同机构、获取协议和患者群体之间会出现领域偏移。VLM（视觉语言模型）通过将图像嵌入到与语言对齐的潜在空间中，实现了零-shot OOD检测，其中跨模态相似性作为识别在分布样本的非参数置信信号。然而，现有方法几乎完全依赖于最终层的嵌入，隐含假设最深的表示在普遍情况下是最佳的。我们首先表明这一假设在医学成像中并不成立：中间层提供了互补的OOD信号，最佳的表示深度取决于相应的图像模态。尽管之前的研究通过归一化直方图的熵最小化选择层组合，但我们证明单分辨率熵估计对分箱选择极为敏感，导致性能变化高达19.3%的AUROC。为了解决这种不稳定性，我们提出了一种多分辨率熵估计策略，该策略在多个离散化尺度上聚合直方图统计数据，从而实现稳健和稳定的中间层选择。在两个医疗OOD基准测试中，即MIDOG和OASIS，涵盖不同的成像模态、多样的偏移类型和不同的VLM骨干网络，我们的方法始终优于最先进的方法，提供了一种轻量且稳定的零-shot OOD检测解决方案。
cs.CV / 261 / 2609.08543
STSG-VQA: Evidence-Grounded Temporal Question Answering from Surgical Spatio-Temporal Scene Graphs
STSG-VQA：基于证据的手术时序问题回答来自手术时空场景图
Abstract
Despite recent advances in surgical vision-language models (VLMs), temporal reasoning remains limited because existing supervision is largely frame-centric. Frame-level scene graphs (SGs) have proven effective in providing structured representations of surgical environments but do not explicitly model the dynamics of surgical workflows. To explicitly model how surgical states evolve across time, we introduce a multi-level structured temporal supervision methodology that augments frame-level surgical SGs with object-level continuity, event-level interaction continuity, and procedure-level connectivity. We then execute temporal queries over the resulting spatio-temporal scene graphs (STSGs) to generate evidence-grounded question-answer pairs, which together form the STSG-VQA benchmark. Each question is linked to the temporal interval and STSG evidence used to derive its reference answer, enabling traceable verification. The benchmark contains 18,458 question-answer pairs across seven temporal categories. Fine-tuning Qwen3-VL-4B and Hulu-Med-4B with STSG-derived supervision improves question-level micro accuracy by 24.39 and 19.56 percentage points over their zero-shot baselines and by 16.50 and 14.25 points over static scene-graph supervision, respectively. These gains span all temporal categories, indicating that STSG-derived supervision helps surgical VLMs reason over temporally grounded interactions rather than isolated frames. The code and dataset will be made publicly available upon acceptance.
Chinese Translation
尽管手术视觉语言模型（VLMs）最近取得了进展，但时序推理仍然受到限制，因为现有的监督主要集中在帧级别。帧级场景图（SGs）在提供手术环境的结构化表示方面已被证明是有效的，但并未明确建模手术工作流程的动态。为了明确建模手术状态如何随时间演变，我们引入了一种多层次结构化时序监督方法，该方法通过对象级连续性、事件级交互连续性和过程级连接性增强帧级手术SGs。然后，我们在生成的时空场景图（STSGs）上执行时序查询，以生成基于证据的问题-答案对，这些对共同构成了STSG-VQA基准。每个问题都与用于推导其参考答案的时间区间和STSG证据相关联，从而实现可追溯的验证。该基准包含跨越七个时序类别的18,458个问题-答案对。用STSG衍生的监督对Qwen3-VL-4B和Hulu-Med-4B进行微调，使其在问题级别的微观准确率分别提高了24.39和19.56个百分点，相较于它们的零样本基线，及提高了16.50和14.25个百分点，相较于静态场景图监督。这些提升涵盖了所有时序类别，表明STSG衍生的监督有助于手术VLMs在时序基础上推理交互，而不是孤立的帧。代码和数据集将在接受后公开发布。
cs.CV / 262 / 2609.08570
Effects of model architecture and learning strategies on deep learning-based recognition of activated sludge microscopic images and comparison with quantitative image analysis
模型架构和学习策略对基于深度学习的活性污泥显微图像识别的影响及与定量图像分析的比较
Abstract
Microscopic image analysis has long been recognized as a promising approach for monitoring activated sludge. In recent years, deep learning-based image analysis has been increasingly adopted in this field because of its high performance. However, previous studies on microscopic image analysis of activated sludge have rarely explored transformer-based models or self-supervised foundation models and have instead relied on CNNs and supervised ImageNet pretraining. In addition, previous studies often downsampled image sizes, but the effects of downsampling have not been sufficiently investigated, and the relationship between downsampling strategies and image analysis performance remains unclear. Furthermore, no study has quantitatively compared deep learning performance with quantitative image analysis (QIA), which was widely used before the emergence of deep learning. In this study, to examine how model architecture and learning strategies affect performance in microscopic image analysis of activated sludge and to quantitatively determine whether deep learning outperforms QIA, we prepared three types of activated sludge samples, classified their microscopic images, and evaluated classification accuracy. Our results showed that transformer-based architectures and alternative pretraining methods were effective in terms of classification accuracy. Our downsampling analysis showed that using overly small images reduced accuracy, but increasing image size beyond a certain point did not improve it further. In addition, the analysis indicated that, to achieve high classification accuracy, maintaining the field of view was a more effective downsampling strategy than maintaining resolution. Finally, our comparison between deep learning and QIA showed that deep learning outperformed QIA in terms of accuracy.
Chinese Translation
显微图像分析长期以来被认为是监测活性污泥的一种有前景的方法。近年来，由于其高性能，基于深度学习的图像分析在这一领域得到了越来越多的应用。然而，以往对活性污泥显微图像分析的研究很少探讨基于变换器（transformer）模型或自监督基础模型，而是依赖于卷积神经网络（CNN）和监督的ImageNet预训练。此外，以往的研究通常对图像大小进行了下采样，但下采样的影响尚未得到充分研究，下采样策略与图像分析性能之间的关系仍不明确。此外，尚无研究对深度学习的性能与定量图像分析（QIA）进行定量比较，而QIA在深度学习出现之前被广泛使用。在本研究中，我们旨在探讨模型架构和学习策略如何影响活性污泥显微图像分析的性能，并定量确定深度学习是否优于QIA。我们准备了三种类型的活性污泥样本，分类其显微图像，并评估分类准确性。我们的结果表明，基于变换器的架构和替代预训练方法在分类准确性方面是有效的。我们的下采样分析显示，使用过小的图像会降低准确性，但在某个点之后增加图像大小并不会进一步提高准确性。此外，分析还表明，为了实现高分类准确性，保持视野范围比保持分辨率更有效的下采样策略。最后，我们对深度学习与QIA的比较显示，深度学习在准确性方面优于QIA。
cs.CV / 263 / 2609.08607
GOLF: Global Observation with Local Focus for Calibration-Aware Stereo Interaction Field Estimation
GOLF：具有局部聚焦的全球观察用于校准感知的立体交互场估计
Abstract
We present GOLF, the first-place solution to the SHOW3D Interaction Field Estimation Challenge at HANDS@ECCV 2026. Given synchronized egocentric stereo views, the task is to predict a 3D vector from each of 21 hand joints to the closest point on the manipulated object. GOLF combines dense global context, locally sampled hand/object evidence, and common-frame Pl\"ucker-ray geometry. We adapt DINOv3 ViT-H+/16 with LoRA and trainable LayerNorm parameters, then jointly decode both interaction fields. Our primary model achieves an official score of 27.61 and a mean ADE of 27.96 mm on the hidden test set. An equal-weight ensemble with a complementary directly fine-tuned variant improves these results to an official score of 27.47 and a mean ADE of 27.82 mm, securing first place.
Chinese Translation
我们提出了GOLF，这是2026年HANDS@ECCV SHOW3D交互场估计挑战赛的第一名解决方案。给定同步的自我中心立体视图，任务是预测21个手关节中每个关节到被操控物体最近点的3D向量。GOLF结合了密集的全球上下文、局部采样的手/物体证据以及共同框架的Plücker射线几何。我们对DINOv3 ViT-H+/16进行了LoRA和可训练的LayerNorm参数的适配，然后共同解码两个交互场。我们的主要模型在隐藏测试集上获得了27.61的官方得分和27.96毫米的平均绝对误差（ADE）。与一个互补的直接微调变体进行等权重集成后，这些结果提高至27.47的官方得分和27.82毫米的平均ADE，确保了第一名的成绩。
cs.CV / 264 / 2609.08627
SynthRCT: Scalable Conditional Deformation Synthesis for Synthetic Repeat CT Generation
SynthRCT：用于合成重复CT生成的可扩展条件变形合成
Abstract
In proton therapy, plans are typically optimized on a single planning CT, making robustness evaluation essential under anatomical changes. However, current scenarios often rely on simplified perturbations that poorly capture complex, patient-specific variability. We propose SynthRCT, a scalable conditional generative framework for 3D anatomical deformation synthesis. Based on a conditional variational autoencoder, SynthRCT learns a latent deformation space and decodes sampled latent codes into local stationary velocity fields conditioned on an input anatomy. Local fields are assembled into coherent full-volume transformations, enabling memory-scalable generation for large field-of-view CT data. We validate the approach on respiratory 4DCT data with multiple breathing-phase anatomies per subject. SynthRCT enables patient-specific sampling of plausible anatomical transformations beyond predefined robustness scenarios. Code available at: https://github.com/TomasGuija/SynthRCT.
Chinese Translation
在质子治疗中，治疗计划通常是在单一的规划CT上进行优化，因此在解剖变化下进行稳健性评估显得尤为重要。然而，当前的场景往往依赖于简化的扰动，这些扰动无法有效捕捉复杂的、特定于患者的变异性。我们提出了SynthRCT，一个可扩展的条件生成框架，用于三维解剖变形合成。基于条件变分自编码器，SynthRCT学习潜在变形空间，并将采样的潜在编码解码为基于输入解剖结构的局部静态速度场。局部场被组装成一致的全体积变换，从而实现对大视野CT数据的内存可扩展生成。我们在每个受试者的呼吸4DCT数据上验证了该方法，数据包含多个呼吸相位的解剖结构。SynthRCT使得能够在预定义的稳健性场景之外，进行特定于患者的合理解剖变换采样。代码可在：https://github.com/TomasGuija/SynthRCT 获取。
cs.CV / 265 / 2609.08636
From Where to How: Continuous 4D Interaction Forecasting from Egocentric Video
从何处到如何：基于自我中心视频的连续四维交互预测
Abstract
Egocentric 4D interaction forecasting aims to anticipate both where future interactions will occur in 3D and how the human body will move to realize them, providing an important capability for assistive robotics and human-computer interaction. Existing methods struggle to translate semantic understanding into precise continuous 3D localization and to balance motion diversity with structural consistency in pose forecasting. More fundamentally, these tasks are often modeled separately, leaving the continuous geometric and temporal correspondence between interaction locations and body motion insufficiently captured. To address these challenges, we introduce Coherent4D, a large-scale egocentric dataset for continuous 4D interaction forecasting, comprising approximately 233K samples across three domains. Each sample pairs a sequence of future 3D interaction locations with corresponding full-body poses, aligned in time and expressed in a shared coordinate system. We also provide evaluation metrics in continuous space. Building on this formulation, we propose HIGFlow, a Hand Interaction Guided Residual Flow framework that models forecasting as a cascaded where-to-how process. HIGFlow first forecasts continuous future interaction locations by combining semantic grounding with short-horizon visual dynamics, and then uses the predicted location sequence to condition a deterministic motion anchor and residual Flow Matching for diverse yet structurally consistent full-body motion forecasting. Extensive experiments across all three domains demonstrate consistent improvements over representative baselines on both location and pose forecasting, while ablations validate the contributions of the proposed components. The project page is available at https://corrineqiu.github.io/from-where-to-how/.
Chinese Translation
自我中心的四维交互预测旨在预测未来交互将在三维空间中发生的位置以及人类身体将如何移动以实现这些交互，这为辅助机器人和人机交互提供了重要的能力。现有方法在将语义理解转化为精确的连续三维定位方面存在困难，并且在姿态预测中难以平衡运动多样性与结构一致性。更根本地，这些任务通常是分开建模的，导致交互位置与身体运动之间的连续几何和时间对应关系未能得到充分捕捉。为了解决这些挑战，我们引入了Coherent4D，一个用于连续四维交互预测的大规模自我中心数据集，包含大约233K个样本，跨越三个领域。每个样本将一系列未来的三维交互位置与相应的全身姿态配对，这些姿态在时间上对齐并在共享坐标系中表达。我们还提供了连续空间的评估指标。在此基础上，我们提出了HIGFlow，一个手部交互引导的残差流框架，将预测建模为一个级联的“从何处到如何”的过程。HIGFlow首先通过结合语义基础与短时间视觉动态来预测连续的未来交互位置，然后利用预测的位置序列来条件化一个确定性的运动锚点和残差流匹配，以实现多样化但结构一致的全身运动预测。在所有三个领域的广泛实验中，均显示出在位置和姿态预测上相较于代表性基线的一致性提升，同时消融实验验证了所提组件的贡献。项目页面可访问 https://corrineqiu.github.io/from-where-to-how/.
cs.CV / 266 / 2609.08657
Charts Are Beyond Pixels: Probing for Layer-Wise Chart Understanding and Editing
图表超越像素：探讨层级图表理解与编辑
Abstract
Charts are structured visual compositions whose elements have distinct functional roles, semantic correspondences, and visibility relations. This structural view motivates evaluating whether models can understand and manipulate charts at the layer level. Existing chart benchmarks, however, primarily assess the correctness or fidelity of final outputs and do not directly evaluate these layer-wise behaviors. We present LayerWiseBench, a benchmark organized around three core concepts, layer attribution, layer binding, and visibility ordering, that structure its chart-understanding and chart-editing evaluations. Generated from executable chart programs, LayerWiseBench pairs each rendered chart with spatially aligned per-layer RGBA assets and construction-derived labels for functional roles, semantic bindings, and visibility relations. From this layer-wise representation, we derive controlled understanding questions, editing targets, reference images, and evaluation regions. It contains 2,800 source charts across 14 chart paradigms, from which we derive 7,329 layer-wise understanding questions and 53,791 instruction-guided editing variants. Among the evaluated VLMs, Qwen3.5-27B, which achieves the highest QA macro-average, obtains 93.04% accuracy on layer attribution and 97.46% on layer binding, but only 61.46% on visibility ordering. Across the four evaluated image editors, overall mIoU ranges from 1.49% to 4.93%, and visibility-constrained edits have the lowest mIoU for every editor, ranging from 0.37% to 2.00%. Taken together, these results identify tasks involving front-to-back relations between overlapping components as a recurring challenge across understanding and editing, motivating more explicit modeling of component identity and visibility relations.
Chinese Translation
图表是结构化的视觉组合，其元素具有不同的功能角色、语义对应关系和可见性关系。这种结构化视角促使我们评估模型是否能够在层级上理解和操作图表。然而，现有的图表基准主要评估最终输出的正确性或保真度，并未直接评估这些层级行为。我们提出了 LayerWiseBench，这是一个围绕三个核心概念——层级归属、层级绑定和可见性排序——组织的基准，旨在构建其图表理解和图表编辑的评估。LayerWiseBench 由可执行图表程序生成，将每个渲染的图表与空间对齐的每层 RGBA 资产和基于构造的功能角色、语义绑定和可见性关系标签配对。从这种层级表示中，我们推导出受控的理解问题、编辑目标、参考图像和评估区域。它包含来自 14 种图表范式的 2,800 个源图表，从中我们推导出 7,329 个层级理解问题和 53,791 个指导性编辑变体。在评估的视觉语言模型（VLMs）中，Qwen3.5-27B 在 QA 宏平均中表现最佳，层级归属的准确率为 93.04%，层级绑定的准确率为 97.46%，但在可见性排序上仅为 61.46%。在评估的四个图像编辑器中，整体 mIoU 范围为 1.49% 到 4.93%，而受可见性约束的编辑在每个编辑器中均表现出最低的 mIoU，范围为 0.37% 到 2.00%。综合来看，这些结果表明，涉及重叠组件之间前后关系的任务在理解和编辑中是一个反复出现的挑战，促使我们更明确地建模组件的身份和可见性关系。
cs.CV / 267 / 2609.08659
TriCCOT: Tri-part Convolutional Conformal Transformer for Onboard Space Object Detection
TriCCOT：用于机载空间物体检测的三部分卷积一致性变换器
Abstract
Onboard object detection in Earth observation is constrained by limited computational resources and the absence of fully corrected imagery. While convolutional detectors are hardware-efficient, they often struggle to extract robust representations from raw and noisy data. Conversely, transformer-based models provide stronger global reasoning capabilities but remain difficult to deploy on FPGA accelerators due to quadratic attention complexity and non-compatible operations. We introduce TriCCOT, a tri-part architecture for robust and deployable onboard object detection. TriCCOT combines a convolutional region proposal network, a conformal prediction stage, and Aper-GATES, our hardware-friendly attention-based classifier. The region proposal network generates candidate bounding boxes, which are subsequently enlarged via conformal prediction, providing a distribution-free probabilistic coverage guarantee. The resulting crops are processed by Aper-GATES, which reformulates self-attention through convolutional projections, global channel statistics, and hardware-friendly gating operations, avoiding standard transformer operations that are poorly suited to CNN-oriented accelerators. Experiments on the DIOR and VDVRaw datasets demonstrate competitive detection performance and improved robustness to spatial blur and signal-dependent noise when compared to FPGA-compatible architectures. Finally, we report full deployment on a Xilinx Versal VCK190 FPGA without modifying the underlying DPU architecture, enabling unified CNN-Transformer inference for spaceborne embedded applications.
Chinese Translation
地球观测中的机载物体检测受到有限计算资源和缺乏完全校正图像的限制。虽然卷积检测器在硬件上高效，但它们往往难以从原始和噪声数据中提取稳健的表示。相反，基于变换器的模型提供了更强的全局推理能力，但由于二次注意力复杂性和不兼容的操作，仍然难以在FPGA加速器上部署。我们提出了TriCCOT，一种用于稳健且可部署的机载物体检测的三部分架构。TriCCOT结合了卷积区域提议网络、一致性预测阶段和我们的硬件友好型基于注意力的分类器Aper-GATES。区域提议网络生成候选边界框，这些边界框随后通过一致性预测进行扩大，提供无分布的概率覆盖保证。生成的裁剪区域由Aper-GATES处理，该模块通过卷积投影、全局通道统计和硬件友好的门控操作重新构造自注意力，避免了不适合CNN导向加速器的标准变换器操作。在DIOR和VDVRaw数据集上的实验表明，与FPGA兼容架构相比，TriCCOT在检测性能和对空间模糊及信号依赖噪声的鲁棒性方面表现出竞争力。最后，我们报告了在不修改底层DPU架构的情况下，完全在Xilinx Versal VCK190 FPGA上部署，实现了针对空间嵌入应用的统一CNN-变换器推理。
cs.CV / 268 / 2609.08660
CoordFormer: Give Me Any Coordinates and I Will Give You Labels
CoordFormer：给我任何坐标，我将给你标签
Abstract
Semantic segmentation on very-high-resolution images remains challenging due to the high computational cost and the difficulty of capturing fine-grained details. We propose CoordFormer, a novel coordinate-based architecture for semantic segmentation that predicts labels at arbitrary spatial locations through a Coordinate Decoder equipped with a Localized Cross-Attention mechanism. The decoder combines coordinate embeddings with high-resolution local patch features and interacts with global tokens extracted from a downsampled image processed by a ViT foundation encoder, enabling rich semantic context while preserving pixel-level precision. This design enables flexible inference at arbitrary resolutions while keeping memory low on very-high-resolution inputs, and supports an efficient semantic-edge-focused strategy that concentrates computation along boundaries, maintaining fine-grained accuracy while reducing latency and computational cost. CoordFormer achieves state-of-the-art performance on MaSS13K and outperforms comparably sized and higher-parameter methods on DIS5K and KPIs, demonstrating its effectiveness for high-quality, very-high-resolution semantic segmentation.
Chinese Translation
在超高分辨率图像上进行语义分割仍然面临挑战，主要由于高计算成本和捕捉细粒度细节的困难。我们提出了CoordFormer，一种新颖的基于坐标的语义分割架构，通过配备局部交叉注意力机制的坐标解码器，在任意空间位置预测标签。该解码器将坐标嵌入与高分辨率局部补丁特征相结合，并与从经过ViT基础编码器处理的下采样图像中提取的全局标记进行交互，从而在保持像素级精度的同时实现丰富的语义上下文。这一设计使得在任意分辨率下灵活推理成为可能，同时在超高分辨率输入上保持低内存，并支持一种高效的以语义边缘为重点的策略，集中计算在边界上，保持细粒度准确性，同时降低延迟和计算成本。CoordFormer在MaSS13K上实现了最先进的性能，并在DIS5K和KPI上超越了同等规模和更高参数的方法，证明了其在高质量超高分辨率语义分割中的有效性。
cs.CV / 269 / 2609.08686
CausalChapter: Improving Long-Video Chaptering with Interventional Dependency Modeling
CausalChapter：通过干预依赖建模改善长视频章节划分
Abstract
Long-form instructional videos require automatic chaptering to support browsing, navigation, and knowledge access. Recent long-context language models can perform chaptering from textualized video inputs, but they remain costly and brittle for content-dense lecture videos with long transcripts, smooth topic transitions, and detailed chapter outputs. A scalable segment-then-caption paradigm reduces this cost, but introduces two new challenges: boundary error propagation and fragmented cross-chapter context. We propose \textbf{CausalChapter}, an intervention-inspired framework for long-video chaptering that estimates prediction-level influence through lightweight masking and removal interventions. For boundary localization, our Local Dependency Shift module detects drops in predictive dependency between adjacent temporal windows; for chapter description generation, our Cross-Segment Support Selection module reranks historical contexts according to their support for the current prediction. Experiments on long-video chaptering benchmarks show that CausalChapter improves boundary localization, chapter description quality, and cross-chapter coherence.
Chinese Translation
长格式教学视频需要自动章节划分，以支持浏览、导航和知识获取。近期的长上下文语言模型能够从文本化的视频输入中进行章节划分，但对于内容密集的讲座视频，这些模型在处理长转录、平滑主题过渡和详细章节输出时仍然成本高昂且脆弱。一种可扩展的先分段后标注的范式降低了这一成本，但引入了两个新挑战：边界错误传播和跨章节上下文碎片化。我们提出了 extbf{CausalChapter}，一个受干预启发的长视频章节划分框架，通过轻量级的掩蔽和移除干预来估计预测级别的影响。对于边界定位，我们的局部依赖转移模块检测相邻时间窗口之间预测依赖的下降；对于章节描述生成，我们的跨段支持选择模块根据历史上下文对当前预测的支持程度进行重新排序。在长视频章节划分基准上的实验表明，CausalChapter改善了边界定位、章节描述质量和跨章节一致性。
cs.CV / 270 / 2609.08705
Enhancing Table Structure Recognition via Bounding Box Guidance
通过边界框引导增强表格结构识别
Abstract
Table Structure Recognition (TSR) aims to extract the bounding boxes of cells and table structure (e.g., HTML) from table images. Although current approaches have made significant progress, the latest image-to-sequence methods overlook the explicit utilization of the bounding box information when predicting HTML sequences, leading to error predictions in complex scenes. In this paper, we introduce a novel framework BGTR (Bounding Box-Guided Table Recognizer). To more effectively utilize bounding box information, we first predict the bounding boxes of cells and then use this information to guide the generation of HTML sequences. While utilizing bounding box information can enhance the accuracy of HTML sequences, for natural scene tables, the data volume is too small to allow for sufficient training of bbox-guided HTML generation. In response, we adopt a progressive training method for natural scene tables and introduce SNSTab, a synthetically generated natural scene table dataset. Our experiments on five benchmark datasets demonstrate SOTA performance.
Chinese Translation
表格结构识别（TSR）旨在从表格图像中提取单元格的边界框和表格结构（例如，HTML）。尽管当前的方法取得了显著进展，但最新的图像到序列方法在预测HTML序列时忽视了边界框信息的明确利用，导致在复杂场景中的错误预测。本文提出了一种新颖的框架BGTR（边界框引导的表格识别器）。为了更有效地利用边界框信息，我们首先预测单元格的边界框，然后使用这些信息指导HTML序列的生成。虽然利用边界框信息可以提高HTML序列的准确性，但对于自然场景表格而言，数据量过小，无法进行充分的边界框引导的HTML生成训练。为此，我们采用了一种渐进式训练方法，并引入了SNSTab，一个合成生成的自然场景表格数据集。我们在五个基准数据集上的实验表明了其最先进的性能（SOTA）。
cs.CV / 271 / 2609.08730
CVT-GS: Learning to Simplify 3D Gaussian Splatting with Centroidal Voronoi Tessellation
CVT-GS：通过重心 Voronoi 划分学习简化 3D 高斯点云
Abstract
While 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time novel view synthesis, rendering high-fidelity scenes often relies on a massive number of Gaussian primitives, incurring substantial storage and computational overhead. Existing simplification techniques are largely intrusive, requiring training-time pruning, architectural modifications, or computationally expensive per-scene fine-tuning. These drawbacks limit their deployment on off-the-shelf pretrained models. In this paper, we propose CVT-GS, a novel optimization-free post-hoc simplification framework that directly compresses trained 3DGS scenes without sacrificing visual fidelity. Our approach first constructs spatially coherent cells over Gaussian centers via a geometry-aware Centroidal Voronoi Tessellation (CVT). Subsequently, a lightweight neural cell merger predicts the geometry and appearance of a single, highly representative Gaussian primitive for each cell under differentiable rendering supervision. By formulating simplification as a rendering-aware many-to-one merging process rather than naive primitive pruning, CVT-GS outputs a standard 3DGS scene that is seamlessly compatible with existing renderers. Experiments on various datasets demonstrate the superiority of our method. Notably, when achieving a 100-fold reduction in Gaussian points, our method operates 12 times faster than state-of-the-art methods while improving the PSNR by 1.3 dB.
Chinese Translation
尽管 3D 高斯点云（3DGS）已成为实时新视图合成的强大表示，但渲染高保真场景通常依赖于大量的高斯原语，这会带来可观的存储和计算开销。现有的简化技术大多具有侵入性，需在训练时进行剪枝、架构修改或进行计算成本高昂的逐场景微调。这些缺点限制了它们在现成预训练模型上的应用。本文提出了 CVT-GS，一种新颖的无优化后处理简化框架，能够直接压缩训练好的 3DGS 场景而不牺牲视觉保真度。我们的方法首先通过几何感知的重心 Voronoi 划分（Centroidal Voronoi Tessellation, CVT）构建高斯中心的空间一致性单元。随后，一个轻量级的神经单元合并器在可微渲染监督下预测每个单元的几何形状和外观，生成一个高度代表性的单一高斯原语。通过将简化过程表述为一个渲染感知的多对一合并过程，而非简单的原语剪枝，CVT-GS 输出的标准 3DGS 场景与现有渲染器无缝兼容。在多个数据集上的实验表明我们方法的优越性。值得注意的是，在实现高达 100 倍的高斯点减少时，我们的方法的运行速度比最先进的方法快 12 倍，同时 PSNR 提高了 1.3 dB。
cs.CV / 272 / 2609.08755
Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics
Kairos：一个用于空间、时间和动态的细粒度视频语言建模数据集
Abstract
Many emerging video language modeling tasks require systems to move beyond clip-level abstraction and model visual content as it unfolds over extended time horizons. However, most existing video datasets rely on coarse or sparsely aligned supervision, which compresses temporal variation and limits the ability of models to learn reusable representations of continuous visual dynamics. We introduce Kairos, a video dataset for video-language modeling with time-resolved annotations. Kairos consists of long-duration videos, ranging from ten minutes to half an hour, annotated with fine-grained temporal alignment. The annotations capture ongoing actions, entity appearances and attributes, interactions, and evolving contextual cues along the video timeline. This time-resolved structure supports fine-grained evaluation, long-range modeling and reasoning, instruction data construction, representation learning, and video generation. Kairos provides a general-purpose foundation for modeling visual experiences over time.
Chinese Translation
许多新兴的视频语言建模任务要求系统超越片段级别的抽象，建模视觉内容在较长时间范围内的展开。然而，现有的大多数视频数据集依赖于粗略或稀疏对齐的监督，这压缩了时间变化，限制了模型学习可重用的连续视觉动态表示的能力。我们介绍了Kairos，一个用于视频语言建模的具有时间分辨注释的视频数据集。Kairos包含长达十分钟到半小时的长视频，配有细粒度的时间对齐注释。这些注释捕捉了正在进行的动作、实体的出现和属性、交互以及沿视频时间线演变的上下文线索。这种时间分辨的结构支持细粒度评估、长距离建模与推理、指令数据构建、表示学习和视频生成。Kairos为建模随时间变化的视觉体验提供了一个通用基础。
cs.CV / 273 / 2609.08766
Compensating for Scarce Historical Images in Cross-Domain Cultural Heritage Retrieval Using Synthetic Aging
利用合成老化技术弥补跨领域文化遗产检索中稀缺的历史图像
Abstract
Cultural heritage collections often contain contemporary and historical visual records of the same physical object. Linking these records is difficult because corresponding images may differ in viewpoint, acquisition conditions, color reproduction, framing, resolution, and degradation, while genuine historical images are frequently scarce. This study investigates whether synthetically aged contemporary images can replace or complement missing historical training data in bidirectional instance-level retrieval. Synthetic old-domain images are generated using degradation-oriented transformations. An EfficientNetV2-M model is evaluated on identity-disjoint training, validation, and test sets across three dataset partitions and three training seeds. Mixed real-synthetic training is compared with real-only baselines using proportionally scaled and fixed 300-batch-per-epoch schedules. Complete replacement of genuine historical images reduced bidirectional mean R@1 from 86.56% to 81.27%, showing that synthetic aging does not reproduce the full genuine old-domain variability. Increasing the number of independently generated synthetic variants provided no consistent improvement. Under controlled scarcity, however, synthetic completion improved mean R@1 by 3.69 percentage points at 25% genuine historical coverage and by 2.92 points at 50%, relative to the proportionally scaled real-only baselines. At 75%, the gain decreased to 2.00 points, while performance remained comparable to the complete-real-data reference. Fixed-schedule real-only controls did not reproduce these improvements. The results indicate that genuine and synthetic observations are complementary. Synthetic completion primarily benefits retrieval by extending cross-domain identity coverage rather than by increasing training exposure, with its contribution gradually decreasing as genuine historical coverage increases.
Chinese Translation
文化遗产收藏通常包含同一物理对象的当代和历史视觉记录。由于对应图像可能在视角、获取条件、色彩再现、构图、分辨率和退化等方面存在差异，而真正的历史图像往往稀缺，因此将这些记录关联起来变得困难。本研究探讨了合成老化的当代图像是否可以替代或补充双向实例级检索中缺失的历史训练数据。通过退化导向的变换生成合成的旧域图像。使用 EfficientNetV2-M 模型在三个数据集划分和三个训练种子上对身份不重叠的训练、验证和测试集进行了评估。将混合真实-合成训练与仅使用真实数据的基线进行了比较，采用按比例缩放和固定每个周期 300 批次的训练计划。完全替换真实历史图像使双向平均 R@1 从 86.56% 降至 81.27%，表明合成老化未能再现完整的真实旧域变异性。增加独立生成的合成变体数量并未提供一致的改善。然而，在受控稀缺的情况下，合成补全在真实历史覆盖率为 25% 时提高了 3.69 个百分点，在 50% 时提高了 2.92 个百分点，相较于按比例缩放的仅真实基线。在 75% 时，增益降至 2.00 个百分点，而性能仍与完全真实数据参考相当。固定计划的仅真实控制未能再现这些改善。结果表明，真实和合成观察是互补的。合成补全主要通过扩展跨领域身份覆盖来促进检索，而不是通过增加训练暴露，其贡献随着真实历史覆盖率的增加而逐渐减少。
cs.CV / 274 / 2609.08777
AXS-Net: Interpretable Deep Unfolding for Hyperspectral Image Denoising via Spectral Basis Unmixing and Structured Noise Refinement
AXS-Net：通过光谱基解混和结构化噪声精炼的可解释深度展开用于高光谱图像去噪
Abstract
Hyperspectral images (HSIs) are often degraded by mixed noise, including band-dependent Gaussian perturbations and structured artifacts such as stripes, dead-lines, and impulse noise. Most deep denoisers regress the clean image directly, entangling signal and structured noise. We instead model HSI denoising as $\Y=\A\X+\Snoise+\Nnoise$, where $\A\X$ is a low-rank spectral-subspace (unmixing) reconstruction, $\Snoise$ is structured sparse noise and $\Nnoise$ is residual Gaussian noise. The resulting regularized optimization problem is unrolled into AXS-Net, a $K$-stage alternating proximal-point framework. Each stage combines an analytic spectral-basis gradient step, an SSX-Block proximal operator for abundance coefficients, and an SBlock proximal operator for the structured residual with column-consistent and sparse priors. This optimization correspondence exposes interpretable endmembers, abundance maps, and structured-noise estimates. Across ICVL, CAVE, and Harvard datasets and five noise configurations, the proposed AXS-Net achieves strong in-domain accuracy and competitive zero-shot transfer, with consistent gains across all five noise regimes on ICVL and Harvard. The recovered structured-noise closely follows the synthetic reference, and the recovered spectral basis is smooth and band-ordered rather than an arbitrary set of latent channels.
Chinese Translation
高光谱图像（HSIs）通常受到混合噪声的影响，包括带依赖的高斯扰动和结构化伪影，如条纹、死线和脉冲噪声。大多数深度去噪器直接回归干净图像，将信号与结构化噪声纠缠在一起。我们将高光谱图像去噪建模为 $oldsymbol{Y}=oldsymbol{A}oldsymbol{X}+oldsymbol{S}_{ ext{noise}}+oldsymbol{N}_{ ext{noise}}$，其中 $oldsymbol{A}oldsymbol{X}$ 是低秩光谱子空间（解混）重建，$oldsymbol{S}_{ ext{noise}}$ 是结构化稀疏噪声，$oldsymbol{N}_{ ext{noise}}$ 是残余高斯噪声。得到的正则化优化问题被展开为 AXS-Net，一个 $K$ 阶段交替近端点框架。每个阶段结合了分析光谱基梯度步骤、用于丰度系数的 SSX-Block 近端算子，以及用于结构化残差的 SBlock 近端算子，具有列一致性和稀疏先验。这种优化对应关系揭示了可解释的端元、丰度图和结构化噪声估计。在 ICVL、CAVE 和哈佛数据集及五种噪声配置下，所提出的 AXS-Net 实现了强大的领域内准确性和竞争性的零样本迁移，在 ICVL 和哈佛的所有五种噪声状态下均表现出一致的增益。恢复的结构化噪声与合成参考密切相符，恢复的光谱基平滑且按波段排序，而不是任意的潜在通道集合。
cs.CV / 275 / 2609.08786
Interpretable Hyperspectral Unmixing Framework with Fixed Endmember Prior and Structured Residual Refinement
具有固定端元先验和结构残差精炼的可解释高光谱解混框架
Abstract
Hyperspectral unmixing decomposes mixed pixels into material endmembers and their abundances from contiguous spectral observations. In modular sensing pipelines, endmembers are often first identified and then treated as fixed during abundance estimation. When this fixed endmember prior is inaccurate, spatially structured mismatch arising from illumination changes, sensor artifacts, or material boundaries may be incorrectly captured by the abundance variables, leading to unstable decompositions. This study presents an interpretable stage-wise hyperspectral unmixing framework (I-HyperSU) under fixed endmember priors, which is explicitly decomposed into a fixed endmember matrix $\mathbf{A}$, an abundance block $\mathbf{X}$, and a structural residual refinement block $\mathbf{S}$. The X-block estimates abundances using FISTA with nonnegativity and sparsity enhancement, and a soft penalty that approximately enforces sum-to-one constraints. The S-block jointly applies low-rank SVD structural regularization and a lightweight deep image prior (DIP) to refine structured residuals. This staged design makes the interaction between abundance and residual components transparent and interpretable. Experiments on Samson, Urban, and Jasper Ridge datasets demonstrate that, under fixed and imperfect endmember priors, soft abundance relaxation consistently outperforms hard simplex projection. Under the default N-FINDR endmember prior, the proposed framework reduces the joint reconstruction error by 61.7\%--69.5\% compared with a fixed-$\mathbf{A}$ UCLS baseline, while keeping the abundance RMSE nearly unchanged, indicating that the residual refinement branch accounts for structured model mismatch without degrading the abundance estimates. For example, on Urban, the reconstruction SAM decreases from $5.99^\circ$ for the X-only model to $1.92^\circ$ for the full model.
Chinese Translation
高光谱解混将混合像素分解为材料端元及其丰度，基于连续的光谱观测。在模块化传感管道中，端元通常首先被识别，然后在丰度估计过程中视为固定。当这种固定端元先验不准确时，由于照明变化、传感器伪影或材料边界引起的空间结构不匹配可能会被丰度变量错误捕捉，导致不稳定的分解。本研究提出了一种在固定端元先验下的可解释分阶段高光谱解混框架（I-HyperSU），该框架明确分解为固定端元矩阵 $ extbf{A}$、丰度块 $ extbf{X}$ 和结构残差精炼块 $ extbf{S}$。X块使用FISTA进行丰度估计，增强非负性和稀疏性，并施加一个软惩罚以近似强制满足和为一的约束。S块联合应用低秩SVD结构正则化和轻量级深度图像先验（DIP）来精炼结构残差。这种分阶段设计使丰度与残差组件之间的相互作用变得透明且可解释。在Samson、Urban和Jasper Ridge数据集上的实验表明，在固定且不完美的端元先验下，软丰度松弛始终优于硬单纯形投影。在默认的N-FINDR端元先验下，所提出的框架与固定的 $ extbf{A}$ UCLS基线相比，降低了61.7 ext{%}至69.5 ext{%}的联合重建误差，同时丰度的均方根误差几乎保持不变，表明残差精炼分支能够处理结构模型不匹配，而不降低丰度估计。例如，在Urban数据集上，重建的SAM从仅X模型的 $5.99^ ext{°}$ 降低到完整模型的 $1.92^ ext{°}$。
cs.CV / 276 / 2609.08796
Hi-FLoop: Hierarchical State-Feedback Loops for Multi-Timescale World Modeling
Hi-FLoop：用于多时间尺度世界建模的分层状态反馈循环
Abstract
Multi-agent traffic simulation seeks diverse, coordinated, and physically realistic futures from maps and observed history. Long-horizon closed-loop generation must reconcile multiple decision time scales while its context evolves with generated states. Existing methods often unfold long futures from the initial scene and resolve intent, interaction, and motion monolithically, weakening cross-scale consistency and adaptation. We present HI-FLOOP, a branch-consistent multi-timescale state-feedback framework. Eight scene-level Worlds represent joint hypotheses, and all agents share the selected World identity throughout an 8-second rollout. Within the branch, an 8-second Goal anchors intent, a 2-second Preview coordinates interactions, and 1-second Control produces physical motion. Every 0.5-second commit feeds back only its executed prefix as new facts, while unexecuted hypotheses never enter factual memory. Joint Preview Interaction (JPI) induces a sparse directed future graph from Preview and uses conflict probabilities and signed arrival-time differences to gate interaction refinement. For generated-state recovery, a prefix-frozen A-to-B cascade lets frozen Model A generate 0-1 seconds, then transfers typed physical state, admissible context, and the branch index, but no latent state, to an independent Model B for re-encoding and 1-2-second recovery. On the full H-D public-validation split of 955 scenarios, one complete S1 run yields an Overall score of 0.689987 with the official evaluator. Under agent-centric oracle evaluation, HI-FLOOP achieves oracle-minADE@8 of 1.196636 m over the 8-second horizon and 0.526 m over the 6-second horizon.
Chinese Translation
多智能体交通模拟旨在从地图和观察历史中寻求多样化、协调且物理上真实的未来。长时间跨度的闭环生成必须在上下文随着生成状态演变的同时调和多个决策时间尺度。现有方法通常从初始场景展开长时间的未来，并单一地解决意图、互动和运动，削弱了跨尺度的一致性和适应性。我们提出了HI-FLOOP，一个分支一致的多时间尺度状态反馈框架。八个场景级世界代表联合假设，所有智能体在8秒的展开过程中共享所选世界的身份。在分支内，8秒的目标锚定意图，2秒的预览协调互动，1秒的控制产生物理运动。每0.5秒的承诺仅将其执行的前缀作为新事实反馈，而未执行的假设则不会进入事实记忆。联合预览互动（Joint Preview Interaction, JPI）从预览中引导出稀疏的定向未来图，并使用冲突概率和带符号的到达时间差来控制互动的细化。为了生成状态的恢复，前缀冻结的A到B级联允许冻结的模型A生成0-1秒，然后将类型化的物理状态、可接受的上下文和分支索引传递给独立的模型B进行重新编码和1-2秒的恢复，而不传递潜在状态。在955个场景的完整H-D公共验证分割中，一次完整的S1运行在官方评估器下获得了0.689987的整体得分。在以智能体为中心的oracle评估下，HI-FLOOP在8秒的时间范围内达到了1.196636米的oracle-minADE@8，在6秒的时间范围内达到了0.526米。
cs.CV / 277 / 2609.08805
Evaluation Principles for MRI-MRA Registration in Trigeminal Neuralgia: An ROI-Centered Neurovascular Benchmark
三叉神经痛中MRI-MRA配准的评估原则：以ROI为中心的神经血管基准
Abstract
Preoperative evaluation of trigeminal neuralgia (TN) often requires joint interpretation of structural MRI, which depicts the trigeminal nerve and surrounding cisternal anatomy, and time-of-flight MRA, which highlights vascular structures. Although MRI-MRA fusion is clinically attractive for visualizing neurovascular compression, this task is poorly captured by conventional whole-brain registration evaluation because the clinically relevant target is a small trigeminal ROI, vessel annotations are partial and clinically focused, local TOF-MRA contrast is variable, and field-of-view mismatch can limit deformable alignment. We formulate TN MRI-MRA fusion as an ROI-centered neurovascular registration-evaluation problem and construct a benchmark from 149 patients with clinician-annotated bilateral trigeminal ROIs. Six representative registration pipelines were evaluated using local image-based metrics, segmentation-derived vessel-localization metrics, prediction-volume analysis, and contrast- and FOV-stratified comparisons. Conventional evaluation summaries were often misleading: local image similarity, vessel-background separability, and downstream vessel localization did not co-rank methods; one-sided vessel distances were strongly affected by predicted vessel extent under partial annotations; and local MRA contrast determined when vessel-separability metrics were informative. Deformable refinement provided only a small, FOV-dependent benefit over affine alignment, while reader review showed that locally favorable vessel distances could coexist with globally implausible registrations. These findings indicate that TN MRI-MRA registration should be evaluated as a local, vessel-aware, contrast-sensitive, and FOV-aware visualization task rather than as generic multimodal brain registration. Our code is publicly available at https://github.com/jhuldr/TN-Reg-Benchmark.
Chinese Translation
三叉神经痛（TN）的术前评估通常需要对结构性MRI进行联合解读，MRI描绘了三叉神经及其周围的脑池解剖结构，而时间飞行磁共振血管成像（MRA）则突出了血管结构。尽管MRI-MRA融合在可视化神经血管压迫方面具有临床吸引力，但这一任务在传统的全脑配准评估中表现不佳，因为临床相关的目标是一个小的三叉神经感兴趣区域（ROI），血管标注是部分的且以临床为中心，局部TOF-MRA对比度变化多端，视野不匹配可能限制了可变形对齐。我们将TN的MRI-MRA融合表述为一个以ROI为中心的神经血管配准评估问题，并从149名临床医生标注的双侧三叉神经ROI患者中构建了一个基准。使用局部图像基础指标、分割导出的血管定位指标、预测体积分析以及对比度和视野分层比较评估了六个代表性的配准管道。传统的评估总结往往具有误导性：局部图像相似性、血管与背景的可分性以及下游血管定位并未共同排名；单侧血管距离受到部分标注下预测血管范围的强烈影响；局部MRA对比度决定了何时血管可分性指标是有信息的。可变形细化相较于仿射对齐仅提供了小幅、视野依赖的益处，而读者评审显示局部有利的血管距离可以与全局不合理的配准共存。这些发现表明，TN的MRI-MRA配准应作为一个局部、关注血管、敏感于对比度和视野的可视化任务进行评估，而不是作为通用的多模态脑配准。我们的代码已公开可用，网址为https://github.com/jhuldr/TN-Reg-Benchmark。
cs.CV / 278 / 2609.08806
ArmPoser: Real-Time, Calibration-Free Arm Pose Estimation from Smartwatch IMU
ArmPoser：基于智能手表IMU的实时无校准手臂姿态估计
Abstract
Arm pose estimation enables applications in fitness, extended reality input, rehabilitation, and life logging. Prior smartwatch-based approaches rely on calibration poses and preprocessing pipelines that transform raw IMU measurements into standardized training formats. These steps hinder deployment in everyday settings and introduce errors due to imperfect calibration and sensor drift. We present ArmPoser, a calibration-free arm pose estimation system using a single smartwatch IMU. Our central contribution is training models directly in the reference frame native to consumer smartwatches, aligning learning with how IMU data is produced by deployed devices. By operating on device-native axes, ArmPoser removes the need for coordinate transformations, explicit alignment, and bone-offset calibration used in prior work. We further augment training with physically grounded variations in watch placement and arm morphology to account for user-specific variability. ArmPoser also includes a wear-configuration module that infers anterior or posterior forearm placement and crown orientation. We evaluate pose estimation on public benchmarks and on a 10-participant, 30-activity study using watchOS and Android smartwatches, where ArmPoser matches or exceeds calibrated baselines without any user calibration.
Chinese Translation
手臂姿态估计在健身、扩展现实输入、康复和生活记录等应用中具有重要意义。以往基于智能手表的方法依赖于校准姿势和预处理管道，将原始IMU测量值转换为标准化的训练格式。这些步骤阻碍了在日常环境中的应用，并因校准不完善和传感器漂移引入误差。我们提出了ArmPoser，一个使用单个智能手表IMU的无校准手臂姿态估计系统。我们的核心贡献是直接在消费智能手表的原生参考框架中训练模型，使学习与部署设备生成IMU数据的方式相一致。通过在设备原生坐标轴上操作，ArmPoser消除了以往工作中所需的坐标变换、显式对齐和骨骼偏移校准。我们进一步通过在手表放置和手臂形态上的物理基础变异来增强训练，以考虑用户特定的变异性。ArmPoser还包括一个佩戴配置模块，用于推断前臂的前置或后置位置及表冠方向。我们在公共基准测试以及一项包含10名参与者、30项活动的研究中评估了姿态估计，使用watchOS和Android智能手表，结果表明ArmPoser的表现与校准基线相当或超过，无需任何用户校准。
cs.CV / 279 / 2609.08818
Beyond Gait: Person Identification from Millimeter-Wave Point Clouds Across Activities of Daily Living
超越步态：基于毫米波点云在日常生活活动中的个体识别
Abstract
Person identification from millimeter-wave (mmWave) point clouds has mainly relied on gait. Indoor walking, however, is often brief and interrupted, while other activities of daily living (ADLs) may provide complementary identity information. We investigate identification across seven ADLs using mm-ADL, a new point-cloud dataset collected from 11 subjects under a controlled protocol. This extension introduces heterogeneous states and transitions whose spatial and temporal characteristics vary with activity. We therefore study whether activity can provide useful context for learning identity representations. We propose an activity-conditioned framework in which a human activity recognition router dispatches each clip to an activity-specific identity expert. The framework is implemented as a supervised mixture of experts, using a dual-stream static-dynamic PointNet (DS-SDPNet) to combine time-aggregated spatial structure with frame-to-frame information. We evaluate closed-set identification (ID) and subject-disjoint re-identification (ReID). With learned hard routing, ID accuracy increases from 62.1% to 68.0%. In a two-occupant ReID setting, hard routing increases mAP from 57.2% to 75.4% and Rank-1 accuracy from 59.1% to 82.1%. Under a matched gallery partition, activity-specific experts also outperform a shared embedding, showing that the gain extends beyond restricting the gallery. These results support the feasibility of using ADLs beyond gait for identification and the value of activity conditioning under controlled indoor conditions.
Chinese Translation
基于毫米波（mmWave）点云的人体识别主要依赖于步态。然而，室内行走往往短暂且中断，而其他日常生活活动（ADLs）可能提供补充的身份信息。我们研究了在七种日常生活活动中进行识别，使用mm-ADL这一新点云数据集，该数据集是在受控协议下从11名受试者收集的。这一扩展引入了异质状态和转变，其空间和时间特征随活动而变化。因此，我们研究活动是否能为学习身份表示提供有用的背景。我们提出了一种基于活动的框架，其中人类活动识别路由器将每个片段分派给特定活动的身份专家。该框架实现为一种监督的专家混合模型，使用双流静态-动态PointNet（DS-SDPNet）将时间聚合的空间结构与帧间信息结合。我们评估了闭集识别（ID）和受试者不重合的再识别（ReID）。通过学习的硬路由，ID准确率从62.1%提高到68.0%。在双占用的ReID设置中，硬路由使得mAP从57.2%提高到75.4%，Rank-1准确率从59.1%提高到82.1%。在匹配的图库分区下，特定活动的专家也优于共享嵌入，显示出增益不仅限于限制图库。这些结果支持在识别中超越步态使用日常生活活动的可行性，以及在受控室内条件下活动条件的价值。
cs.CV / 280 / 2609.08841
Leveraging Visual and Geometric Priors for Metric-scale and Complete Vehicle Gaussian Reconstruction from Limited Views
利用视觉和几何先验进行有限视角下的度量尺度和完整车辆高斯重建
Abstract
High-fidelity vehicle assets are essential for controllable traffic scene generation, particularly for synthesizing rare and safety-critical long-tail scenarios. However, reconstructing a reusable vehicle representation from in-the-wild onboard images remains challenging for two reasons. First, image-to-3D generation methods generally produce models without reliable metric scale. Second, onboard cameras usually observe only one side of a target vehicle, making conventional multi-view reconstruction incomplete on unobserved regions. To solve these problems, we propose a feed-forward vehicle asset reconstruction method, which leverages two complementary priors to reconstruct 3D Gaussian representations for vehicles using sparse one-sided observations. To achieve metric-scale reconstruction, a visual foundation model is first utilized to serve as a visual prior for Gaussian initialization. The Gaussian attributes are then estimated by a learnable encoder-decoder module. A symmetry-aware cloning strategy is presented to complete the unobserved side directly in Gaussian space, which exploits the bilateral structure of vehicles as a geometric prior. Experiments on the public dataset demonstrate that the proposed method significantly outperforms existing approaches in both vehicle asset completeness and geometric accuracy.
Chinese Translation
高保真车辆资产对于可控交通场景生成至关重要，特别是在合成稀有和安全关键的长尾场景时。然而，从实际环境中的车载图像重建可重用的车辆表示仍然面临挑战，主要有两个原因。首先，图像到3D生成方法通常生成没有可靠度量尺度的模型。其次，车载摄像头通常只能观察到目标车辆的一侧，使得传统的多视角重建在未观察到的区域上不完整。为了解决这些问题，我们提出了一种前馈车辆资产重建方法，该方法利用两个互补的先验，通过稀疏的单侧观察重建车辆的3D高斯表示。为了实现度量尺度重建，首先利用视觉基础模型作为高斯初始化的视觉先验。然后，通过可学习的编码-解码模块估计高斯属性。提出了一种考虑对称性的克隆策略，直接在高斯空间中完成未观察到的一侧，利用车辆的双边结构作为几何先验。公共数据集上的实验表明，所提出的方法在车辆资产完整性和几何准确性方面显著优于现有方法。
cs.CV / 281 / 2609.08848
FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute
FIRE3D：在一分钟内实现前馈交互式3D场景重建
Abstract
We present FIRE3D, a unified framework that takes a single RGB image or casual RGB video and transforms it into simulation-ready 3D scene assets for games and interactive applications in under a minute. At the core of FIRE3D is a feed-forward, end-to-end network that predicts a compositional scene representation from posed RGB-D observations estimated from the RGB capture, including the 6-DoF pose, bounding box, mesh, and texture for every object. By modeling the scene as a collection of discrete entities, FIRE3D produces amodally complete and simulation-ready environments where objects are physically decoupled and ready for interaction. Our framework requires no test-time optimization, runs orders of magnitude faster than prior interaction-ready methods, and provides object-level completeness beyond existing feed-forward 3D approaches. We demonstrate competitive or state-of-the-art results across pose accuracy, geometry completeness, and texture quality across various datasets while being orders of magnitudes faster. Project page: https://xiahongchi.github.io/Fire3D/
Chinese Translation
我们提出了FIRE3D，一个统一框架，可以将单个RGB图像或普通RGB视频转换为适用于游戏和交互应用的模拟准备3D场景资产，时间不超过一分钟。FIRE3D的核心是一个前馈的端到端网络，该网络从RGB捕获估计的RGB-D观测中预测组合场景表示，包括每个物体的6自由度（6-DoF）姿态、边界框、网格和纹理。通过将场景建模为离散实体的集合，FIRE3D生成了模态完整且适合模拟的环境，其中物体在物理上是解耦的，并且可以进行交互。我们的框架在测试时无需优化，运行速度比以前的交互准备方法快几个数量级，并且在对象级完整性方面超越现有的前馈3D方法。我们在各种数据集上展示了在姿态准确性、几何完整性和纹理质量方面具有竞争力或最先进的结果，同时速度快几个数量级。项目页面：https://xiahongchi.github.io/Fire3D/
cs.CV / 282 / 2609.08850
DSE-VTG: Dual-Side Enhancement for Training-Free Video Temporal Grounding
DSE-VTG：无训练视频时间定位的双侧增强
Abstract
Text-guided Video Temporal Grounding (VTG) aims to localize the relevant segments in an untrimmed video based on text queries, yet collecting dense temporal annotations and training task-specific models remain costly and brittle under distribution shift. Recent training-free VTG approaches mitigate this issue by directly matching pretrained vision-language representations, but they still face two fundamental information bottlenecks: frame-wise visual encoding overlooks temporal dynamics, while fixed query embeddings cannot resolve query ambiguity. To address these issues, we propose DSE-VTG, a \underline{D}ual-\underline{S}ide \underline{E}nhancement framework that addresses both without any task-specific training. On the visual side, Multi-scale Similarity Fusion (MSF) combines frame- and clip-level similarities into a unified, temporally aware similarity profile. On the textual side, Query-level Test-Time Adaptation (Q-TTA) optimizes a lightweight additive offset to adapt the query embedding to the video at test time, without finetuning the backbone or calling external large language models. Extensive experiments on three standard and two OOD benchmarks show that DSE-VTG achieves state-of-the-art performance among training-free methods. On Charades-STA, it improves mIoU over the strongest prior training-free method by 5.61 points. Under distribution shift, DSE-VTG reaches 50.86 mIoU on Charades-CG Novel-Word, surpassing the strongest supervised baseline by 2.76 mIoU. Our code will be released upon acceptance.
Chinese Translation
文本引导的视频时间定位（VTG）旨在根据文本查询定位未剪辑视频中的相关片段，但收集密集的时间注释和训练特定任务的模型在分布转移下仍然成本高昂且脆弱。最近的无训练VTG方法通过直接匹配预训练的视觉-语言表示来缓解这一问题，但它们仍面临两个基本的信息瓶颈：逐帧视觉编码忽视了时间动态，而固定的查询嵌入无法解决查询歧义。为了解决这些问题，我们提出了DSE-VTG，一个 extbf{双侧增强}框架，旨在无需任何特定任务的训练来解决这两个问题。在视觉方面，多尺度相似性融合（MSF）将帧级和片段级的相似性结合成一个统一的、具有时间感知的相似性概况。在文本方面，查询级测试时适应（Q-TTA）优化了一个轻量级的附加偏移，以在测试时将查询嵌入适应于视频，而无需微调主干网络或调用外部大型语言模型。在三个标准基准和两个OOD基准上的广泛实验表明，DSE-VTG在无训练方法中实现了最先进的性能。在Charades-STA上，它比最强的先前无训练方法提高了5.61个点的mIoU。在分布转移下，DSE-VTG在Charades-CG Novel-Word上达到了50.86的mIoU，超越了最强的监督基线2.76个mIoU。我们的代码将在接受后发布。
cs.CV / 283 / 2609.08867
SeGDeP: Semantic- and Geometric-Aware Decoupled Prompts for Reasoning Segmentation
SeGDeP：面向推理分割的语义与几何感知解耦提示
Abstract
Reasoning segmentation converts an implicit linguistic conclusion into a precise mask, requiring both semantic identification and spatial grounding. Existing MLLM-segmenter interfaces either use a special trigger or compress both signals into one context, although they receive different supervision and fail differently. This coupling obscures whether a failure arises from target interpretation or from localization. We present SeGDeP, an explicit what-where interface. A semantic prompt branch and an independent geometric projection path transform resolved MLLM states into semantic features and a DETR-predicted box, which jointly condition a SAM 3 mask decoder. Training first aligns this executable interface, then uses group reward-decoupled policy optimization (GDPO) to balance format, box-IoU, and mask-IoU feedback. SeGDeP-4B reaches 82.7 average cIoU over eight RefCOCO-family splits and 66.0/59.6 gIoU on ReasonSeg val/test while adapting only 0.38% of Qwen3-VL parameters through LoRA. Controlled stage-wise ablations, gradient diagnostics, and prompt interventions further show that the two paths develop complementary semantic and geometric specialization rather than duplicating the same evidence.
Chinese Translation
推理分割将隐含的语言结论转化为精确的掩码，这需要语义识别和空间定位。现有的 MLLM-segmenter 接口要么使用特殊触发器，要么将这两种信号压缩为一个上下文，尽管它们接受不同的监督并以不同的方式失败。这种耦合模糊了失败是源于目标解释还是定位。我们提出了 SeGDeP，一个明确的“什么-哪里”接口。语义提示分支和独立的几何投影路径将解析后的 MLLM 状态转化为语义特征和 DETR 预测框，这两者共同为 SAM 3 掩码解码器提供条件。训练首先对齐这个可执行接口，然后使用组奖励解耦策略优化（GDPO）来平衡格式、框的 IoU 和掩码的 IoU 反馈。SeGDeP-4B 在八个 RefCOCO 家族分割上达到了 82.7 的平均 cIoU，在 ReasonSeg 验证/测试集上分别达到了 66.0/59.6 的 gIoU，同时仅通过 LoRA 调整了 0.38% 的 Qwen3-VL 参数。控制阶段的逐步消融、梯度诊断和提示干预进一步表明，这两条路径发展出互补的语义和几何专业化，而不是重复相同的证据。
cs.CV / 284 / 2609.08879
Medical AI Encodes a "Feeling of Error": Verifying Cancer Segmentation via Internal Concepts
医学人工智能编码“错误感知”：通过内部概念验证癌症分割
Abstract
Cancer segmentation models can fail silently, generating plausible but incorrect masks that risk missed findings or unnecessary biopsies. A critical question arises: Do AI models "know" when they are wrong, and if so, can we use the signal to predict their own failures? Humans do have a "Feeling of Error" (FOE): a spontaneous sense of unease that flags a potential error during thinking. We investigate whether cancer segmentation models exhibit an analogous internal signal. Unlike output-level cues (e.g., prediction confidence or uncertainty), which offer no insight into why a failure occurs and suffer from a sensitivity-quality tradeoff where high detection sensitivity could degrade overall segmentation quality. We instead propose to capture the model's FOE from its inner workings. Using mechanistic interpretability tools, specifically Sparse Autoencoders, we decompose internal neural activations into a dictionary of human-interpretable concepts and show that failure cases exhibit a distinct latent signature: fewer active concepts with lower activation magnitudes compared to successful segmentation. By training a classifier on these concept activations, we achieve accurate failure detection along with explanations for the model's mistakes. Experiments on prostate, pancreatic, and brain cancer segmentation demonstrate that our approach outperforms output-based methods in failure detection while preserving segmentation quality.
Chinese Translation
癌症分割模型可能会静默失败，生成看似合理但实际上错误的掩膜，这可能导致漏诊或不必要的活检。一个关键问题随之而来：人工智能模型是否“知道”它们何时出错，如果知道，我们能否利用这一信号来预测它们的失败？人类确实有一种“错误感知”（Feeling of Error, FOE）：在思考过程中自发产生的不安感，标记出潜在的错误。我们研究癌症分割模型是否表现出类似的内部信号。与输出级别的线索（例如，预测置信度或不确定性）不同，这些线索无法提供失败发生原因的洞见，并且在高检测灵敏度可能降低整体分割质量的情况下存在灵敏度与质量的权衡。我们提出从模型的内部工作机制中捕捉其FOE。利用机械解释工具，特别是稀疏自编码器，我们将内部神经激活分解为一组人类可解释的概念，并显示失败案例表现出明显的潜在特征：与成功分割相比，活跃概念数量较少且激活幅度较低。通过在这些概念激活上训练分类器，我们实现了准确的失败检测，并提供了模型错误的解释。在前列腺癌、胰腺癌和脑癌分割的实验中，我们的方法在失败检测方面优于基于输出的方法，同时保持了分割质量。
cs.CV / 285 / 2609.08886
FRAME: Factored Retrieval via Attribute Readouts for Object-Centric Scene Memory
FRAME：通过属性读取实现的面向对象场景记忆的分解检索
Abstract
Language-guided robots need persistent scene memories to follow instructions, revisit objects, and resolve references to objects encountered over time. While much of language-guided scene-memory retrieval has emphasized spatial or relational references, many everyday object references specify objects by multiple persistent attributes, such as category, material, size, or surface appearance. We formalize this problem as attribute-compositional retrieval, where a fixed object-centric scene memory is queried with natural language to retrieve the object satisfying the requested attributes. To investigate this capability directly, we introduce a controlled evaluation protocol with fixed scene memories and attribute-defined targets, separating retrieval from perception and annotation ambiguities. We then propose FRAME, which turns language into query-relevant attribute weights, uses learned readouts to estimate per-attribute evidence from object embeddings, and ranks objects by aggregating this evidence according to the query. Across held-out scenes and object assets, FRAME outperforms representative scene-memory retrieval baselines while reducing post-decomposition object scoring to lightweight matrix-vector computation. These results position attribute-compositional retrieval as a complementary scene-memory capability for language-guided robots, showing that persistent object attributes can be exposed as composable evidence for accurate and efficient multi-attribute retrieval.
Chinese Translation
语言引导的机器人需要持久的场景记忆来遵循指令、重新访问物体以及解决与时间推移中遇到的物体的引用。尽管许多语言引导的场景记忆检索强调空间或关系引用，但许多日常物体引用通过多个持久属性来指定物体，例如类别、材料、大小或表面外观。我们将此问题形式化为属性组合检索，其中通过自然语言查询固定的面向对象的场景记忆，以检索满足请求属性的物体。为了直接研究这一能力，我们引入了一种受控评估协议，使用固定的场景记忆和属性定义的目标，将检索与感知和注释歧义分开。然后，我们提出了FRAME，它将语言转化为查询相关的属性权重，利用学习到的读取结果从物体嵌入中估计每个属性的证据，并根据查询聚合这些证据对物体进行排名。在保留的场景和物体资产中，FRAME在性能上优于代表性的场景记忆检索基线，同时将后分解物体评分减少为轻量级的矩阵-向量计算。这些结果将属性组合检索定位为语言引导机器人的一种补充场景记忆能力，表明持久的物体属性可以作为可组合的证据被揭示，以实现准确和高效的多属性检索。
cs.CV / 286 / 2609.08914
CoSA: Correlation-Guided Change A ttention with Learnable Residual Gating for Remote Sensing Change Detection
CoSA：基于相关性引导的可学习残差门控变化注意力机制用于遥感变化检测
Abstract
Pixel-level annotation of fixed traffic-camera imagery is expensive, while crosswalk models trained from street-level imagery face a substantial viewpoint and appearance shift when applied to elevated CCTV. We investigate a data-efficient target-domain pipeline using 241 manually annotated CCTV images and 5,926 unlabeled CCTV frames. A source-domain experiment trains a 31.0M-parameter custom U-Net on 3,300 first-person-view (FPV) images and obtains 93.05% IoU on its 330-image FPV test split. This result is a source baseline, not transferred performance: the released CCTV notebook instantiates a 42.0M-parameter DeepLabV3-ResNet50 from torchvision weights, and no compatible mapping from the U-Net checkpoint is implemented. Training on 201 manual CCTV images and selecting on 40 held-out manual masks yields 88.91% IoU. The model then predicts all unlabeled frames; image-level certainty and a largest-component area prior rank the candidates, and the top 1,000 attain mean certainty 0.976 and mean combined score 0.988. A repository audit shows that the reported second-stage 98.52% IoU was measured on a 150-image split containing only teacher-generated pseudo-masks. Because of a directory-layout mismatch, the executed combined-data loader found zero manual samples and split 1,000 pseudo-labeled samples into 850 training and 150 evaluation samples. We therefore report 98.52% as internal pseudo-label agreement rather than human-ground-truth accuracy. The defensible target-domain result is 88.91% IoU on the 40 manual validation images. Batch-one FP32 inference at 512 x 512 requires 12.98 ms, corresponding to 77.03 FPS, on an NVIDIA RTX A6000 48 GB GPU. These findings support the practicality of confidence-and-geometry filtering while also showing why pseudo-label evaluation must remain isolated from the labels used for self-training.
Chinese Translation
固定交通摄像头图像的像素级标注成本高昂，而从街景图像训练的斑马线模型在应用于高架闭路电视（CCTV）时面临显著的视角和外观变化。我们研究了一种数据高效的目标领域管道，使用241张手动标注的CCTV图像和5,926帧未标注的CCTV图像。源领域实验在3,300张第一人称视角（FPV）图像上训练了一个31.0M参数的自定义U-Net，并在其330张FPV测试集上获得了93.05%的交并比（IoU）。该结果是源基线，而非迁移性能：发布的CCTV笔记本实例化了一个42.0M参数的DeepLabV3-ResNet50模型，使用了torchvision权重，并且没有实现与U-Net检查点的兼容映射。在201张手动CCTV图像上训练，并在40个保留的手动掩膜上进行选择，获得了88.91%的IoU。模型随后预测所有未标注的帧；图像级置信度和最大组件区域先验对候选进行排序，前1,000个候选的平均置信度为0.976，平均综合得分为0.988。仓库审计显示，报告的第二阶段98.52%的IoU是在仅包含教师生成伪掩膜的150张图像分割上测量的。由于目录布局不匹配，执行的组合数据加载器未找到任何手动样本，并将1,000个伪标记样本分割为850个训练样本和150个评估样本。因此，我们报告98.52%为内部伪标签一致性，而非人类真实准确度。可辩护的目标领域结果是在40个手动验证图像上获得的88.91% IoU。512 x 512的批量FP32推理在NVIDIA RTX A6000 48 GB GPU上需要12.98毫秒，对应77.03 FPS。这些发现支持置信度和几何过滤的实用性，同时也表明伪标签评估必须与用于自我训练的标签保持隔离。
cs.CV / 287 / 2609.08938
EgoSIS: From Factorized Visual Ego-Transitions to Motion-Canonical Spatial Evidence for UAV Reasoning
EgoSIS：从因子化视觉自我转变到无人机推理的运动规范空间证据
Abstract
UAV video question answering requires separating camera motion from changes in the scene, but RGB-only multimodal models receive no explicit, stable reference for that separation. We present EgoSIS, a pose-free adapter that converts RGB-derived bidirectional flow into motion-canonical visual evidence in three stages. Factorized Visual Ego-Transitions (FVET) fits a robust image-plane transition and exposes motion, residual-support, and reliability factors. Reliability-Gated Ego-Transition Memory (ReTEM) uses reliability-weighted updates for a bounded history and re-anchors it at cuts or sustained uncertainty. Ego-Aligned Spatial Evidence (EASE) warps supported visual features into each segment's local anchor and injects four spatial evidence tokens per visual slice through zero-initialized residuals, without changing Qwen's visual-token count. On SIS-Bench, EgoSIS-8B obtains 89.9\% perception, 82.5\% perception-plus-memory, and 76.2\% overall accuracy, with the largest gains concentrated in self-awareness perception and memory. The adapter thus provides an interpretable interface between optical flow and spatial reasoning.
Chinese Translation
无人机视频问答需要将相机运动与场景变化分离，但仅依赖RGB的多模态模型没有明确且稳定的参考来实现这种分离。我们提出了EgoSIS，这是一种无姿态适配器，将RGB衍生的双向光流转化为运动规范的视觉证据，分为三个阶段。因子化视觉自我转变（FVET）拟合出一个稳健的图像平面转变，并揭示运动、残差支持和可靠性因素。可靠性门控自我转变记忆（ReTEM）使用可靠性加权更新来维护有限历史，并在切割或持续不确定性时重新锚定。自我对齐空间证据（EASE）将支持的视觉特征扭曲到每个段落的局部锚点，并通过零初始化的残差为每个视觉切片注入四个空间证据标记，而不改变Qwen的视觉标记数量。在SIS-Bench上，EgoSIS-8B获得了89.9%的感知率、82.5%的感知加记忆率和76.2%的整体准确率，最大的增益集中在自我意识感知和记忆上。因此，该适配器提供了光流与空间推理之间的可解释接口。
cs.CV / 288 / 2609.08949
Prior-free relative 6D pose estimation of multiple object instances
无先验的相对6D姿态估计多个物体实例
Abstract
Object 6D pose estimation formulations have progressively reduced reliance on object-specific priors, evolving from explicit 3D models to multi-view object captures to single reference images. We take this progression to its extreme by introducing prior-free relative 6D pose estimation, which lifts the assumption of knowing which object is to be posed within the scene. This novel setting aims to estimate the relative poses of multiple instances of an unknown object within the same image, without requiring CAD models, templates, or reference images. We solve this by formulating a novel method (PROSE) that finds coarse correspondences between object instances using multimodal foundation features, thus requiring no training. We refine these correspondences by imposing cycle consistency across tuples of instances, and leverage the resulting globally consistent correspondences to estimate the relative 6D pose between any pair of instances. To enable systematic evaluation, we design a novel benchmark (PRENCH) built from three multi-instance BOP datasets and enriched with task-specific metadata. PROSE consistently outperforms baselines obtained by adapting state-of-the-art single-image methods to the proposed setting, while requiring neither task-specific supervision nor additional learned components. Project website: https://tev-fbk.github.io/PROSE/
Chinese Translation
物体6D姿态估计的公式逐渐减少了对特定物体先验的依赖，从显式的3D模型演变为多视角物体捕捉，再到单一参考图像。我们将这一进展推向极致，提出了无先验的相对6D姿态估计，取消了对场景中要定位的物体的已知假设。该新颖的设置旨在估计同一图像中多个未知物体实例的相对姿态，而无需CAD模型、模板或参考图像。我们通过提出一种新方法（PROSE）来解决这一问题，该方法利用多模态基础特征在物体实例之间找到粗略的对应关系，从而无需训练。我们通过对实例元组施加循环一致性来细化这些对应关系，并利用由此产生的全局一致性对应关系来估计任意一对实例之间的相对6D姿态。为了实现系统评估，我们设计了一个新基准（PRENCH），该基准由三个多实例BOP数据集构建，并丰富了任务特定的元数据。PROSE在性能上始终优于通过将最先进的单图像方法适应于所提出的设置而获得的基线，同时不需要特定任务的监督或额外学习的组件。项目网站：https://tev-fbk.github.io/PROSE/
cs.CV / 289 / 2609.08999
Concentrate After Imagination: Text-Conditioned Evidence Grounding for Partially Relevant Video Retrieval
想象之后的聚焦：基于文本条件的证据验证用于部分相关视频检索
Abstract
Partially Relevant Video Retrieval (PRVR) retrieves untrimmed videos when queries describe only short moments. Although recent methods improve local representations, uncertainty modeling, and global context, final ranking often still trusts the strongest local response; a coincidentally similar fragment can therefore produce an unsupported peak. We identify this failure as the query-agnostic concentration bottleneck and propose TRACE, a score-level evidence verification operator for PRVR. Given a query and global video registers, TRACE activates query-relevant registers, routes their support to frame-level evidence, and smoothly marginalizes alternative query-to-register-to-frame paths before localized temporal selection. Unlike representation-level feature fusion, TRACE uses this evidence only as a query-conditioned residual calibration of the original local score. On ActivityNet Captions, Charades-STA, and TVR, TRACE achieves the best SumR on all three benchmarks and improves the DreamPRVR backbone by 1.2, 1.1, and 1.5 points, respectively. Ablation, routing-corruption, hard-negative, and cross-backbone transfer analyses support the interpretation that the gains arise from query-conditioned evidence verification rather than a generic score offset.
Chinese Translation
部分相关视频检索（Partially Relevant Video Retrieval, PRVR）旨在检索未剪辑的视频，当查询仅描述短暂片段时。尽管近期方法在局部表示、不确定性建模和全局上下文方面有所提升，最终排序仍常依赖最强的局部响应；因此，偶然相似的片段可能产生无支持的峰值。我们将此失败归因于与查询无关的集中瓶颈，并提出TRACE，一种针对PRVR的基于得分层面的证据验证算子。给定查询和全局视频寄存器，TRACE激活与查询相关的寄存器，将其支持路由至帧级证据，并在局部时间选择前平滑边缘化备选的查询-寄存器-帧路径。不同于表示层面的特征融合，TRACE仅将该证据作为对原始局部得分的基于查询条件的残差校正。在ActivityNet Captions、Charades-STA和TVR三个基准上，TRACE均实现了最佳的SumR，并分别提升了DreamPRVR骨干网络1.2、1.1和1.5个百分点。消融实验、路由破坏、困难负样本及跨骨干网络迁移分析均支持该提升源自基于查询条件的证据验证，而非通用得分偏移。
cs.CV / 290 / 2609.09005
DXPR: Depth-Based Vision-LiDAR Cross-Modal Place Recognition Using Vision Foundation Models
DXPR：基于深度的视觉-激光雷达跨模态场所识别，使用视觉基础模型
Abstract
We present DXPR, a depth-based cross-modal place recognition (CMPR) framework that uses vision foundation models (VFMs) to match monocular camera queries against a LiDAR map without modality-specific encoders. This enables robots and autonomous vehicles to robustly localize using only cameras within pre-built LiDAR maps, even under severe seasonal, weather, and illumination changes. The key idea is to convert both camera images and LiDAR scans into a unified depth image representation so that a single VFM backbone with an aggregation head can learn modality-invariant global descriptors. To make pairwise metric learning faithful to scene geometry, we introduce a geometry-aware overlap miner: after cross-modal scale alignment of camera and LiDAR depth, we forward-warp measurements between views to compute a pixel-level overlap score. This score relabels ambiguous pairs and adaptively modulates the positive margin in a multi-similarity loss to avoid overfitting on weakly overlapping views. Extensive experiments on KITTI odometry and Boreas demonstrate strong performance and robustness across seasons, weather, and day/night. On KITTI, DXPR achieves near-perfect Recall@1 on most sequences and outperforms prior CMPR baselines. On Boreas, DXPR achieves intra-sequence performance on par with a strong single-modal baseline (DINOv2-SALAD), while showing clear improvements in the more challenging inter-sequence setting. Compared with RangeBEV, our method consistently performs better in both intra- and inter-sequence evaluations, demonstrating robustness under diverse seasonal and illumination changes.
Chinese Translation
我们提出了DXPR，一种基于深度的跨模态场所识别（CMPR）框架，利用视觉基础模型（VFM）将单目相机查询与激光雷达地图进行匹配，而无需特定于模态的编码器。这使得机器人和自动驾驶车辆能够仅使用相机在预构建的激光雷达地图中稳健地定位，即使在严重的季节、天气和光照变化下。其关键思想是将相机图像和激光雷达扫描转换为统一的深度图像表示，从而使得单一的VFM主干网络与聚合头能够学习模态不变的全局描述符。为了使成对度量学习忠实于场景几何，我们引入了一种几何感知重叠挖掘器：在相机和激光雷达深度的跨模态尺度对齐后，我们在视图之间进行前向变形测量，以计算像素级重叠分数。该分数重新标记模糊对，并自适应调节多相似性损失中的正边距，以避免在弱重叠视图上过拟合。在KITTI里程计和Boreas上的大量实验表明，DXPR在不同季节、天气和昼夜条件下表现出强大的性能和鲁棒性。在KITTI数据集中，DXPR在大多数序列上实现了近乎完美的Recall@1，并超越了先前的CMPR基线。在Boreas数据集中，DXPR在序列内部性能上与强大的单模态基线（DINOv2-SALAD）相当，同时在更具挑战性的序列间设置中显示出明显的改进。与RangeBEV相比，我们的方法在序列内和序列间评估中始终表现更好，展示了在多样的季节和光照变化下的鲁棒性。
cs.CV / 291 / 2609.09010
A Joint 2D-3D Statistical Shape Model for Orthopedic Reconstruction
用于骨科重建的联合二维-三维统计形状模型
Abstract
Three-dimensional femoral reconstruction from radiographs supports surgical planning, implant sizing, and post-operative follow-up, but remains ill-posed as X-ray projections discard depth information. Existing methods often incorporate a 3D statistical shape model (SSM) as a shape prior to guide reconstructions toward anatomically plausible shapes, relying on iterative 3D-to-2D projection matching. Yet, these approaches are computationally expensive and constrain their SSM to a single dimensionality, leaving the statistical relationship between 2D observations and 3D geometry largely unexploited and unexplored. We instead propose a joint 2D-3D SSM that explicitly captures the co-variation between 2D and 3D segmentations in a shared latent space. During training, 2D and 3D segmentations are registered to a common 3D template and its corresponding 2D projections, and the resulting stationary velocity fields are jointly decomposed using principal component analysis (PCA). This joint modeling allows the 2D-to-3D mapping to be learned directly from data rather than computing correspondences at inference time. For unseen subjects, the 3D shape is recovered directly by lifting the 2D latent coordinates to the 3D PCA subspace, thereby eliminating the need for iterative 3D-to-2D projection. Experiments on NMDID demonstrate that the proposed joint 2D-3D SSM outperforms a widely-used 3D-only SSM baseline while achieving inference approximately 4 times faster, at under 3 seconds per subject. The code is available at: https://github.com/florence-dellaniello-picard/joint2d3d-ssm.
Chinese Translation
从放射影像中进行三维股骨重建支持手术规划、植入物尺寸选择和术后随访，但由于X射线投影丢失了深度信息，这一过程仍然是一个不适定问题。现有方法通常将三维统计形状模型（SSM）作为形状先验，以指导重建朝向解剖上合理的形状，依赖于迭代的三维到二维投影匹配。然而，这些方法计算成本高，并且将其SSM限制在单一维度，导致二维观察与三维几何之间的统计关系在很大程度上未被利用和探索。相反，我们提出了一种联合二维-三维 SSM，明确捕捉二维和三维分割在共享潜在空间中的共同变化。在训练过程中，二维和三维分割被注册到一个共同的三维模板及其对应的二维投影上，得到的静态速度场通过主成分分析（PCA）进行联合分解。这种联合建模允许从数据中直接学习二维到三维的映射，而不是在推断时计算对应关系。对于未见过的对象，三维形状通过将二维潜在坐标提升到三维PCA子空间直接恢复，从而消除了迭代三维到二维投影的需求。在NMDID上的实验表明，所提出的联合二维-三维SSM在性能上优于广泛使用的三维单一SSM基线，同时推断速度约快4倍，每个对象的推断时间少于3秒。代码可在以下链接获取：https://github.com/florence-dellaniello-picard/joint2d3d-ssm。
cs.CV / 292 / 2609.09012
Spheriverse: 3D Scene Understanding from Spherical Observations in the Wild
Spheriverse：来自自然环境中球形观测的三维场景理解
Abstract
Spherical observations provide global visual context for 3D scene understanding. However, visual information is encoded in an angular domain, whereas the physical world is represented in Cartesian coordinates. This cross-space representation gap complicates geometric correspondence and semantic evidence aggregation. To delve into this challenge, we introduce Spheriverse, comprising $64,400$ temporally aligned spherical image-LiDAR pairs organized into 644 sequences. The dataset spans diverse scenes, illumination, and weather conditions, with fine-grained semantic classes. We further establish benchmarks for semantic occupancy prediction, semantic mapping, and 3D object detection, evaluating 30+ methods through overall and scene-wise comparisons. For dense prediction, we propose SphereOcc, an occupancy framework that couples spherical geometry modeling with semantic evidence retrieval. Cartesian-Spherical Representation Remodeling (CSRR) incorporates spherical range-azimuth geometry into Cartesian voxel features through region-wise modulation. Spherical Evidence Re-querying (SER) then conditions queries on voxel content and range-height-azimuth geometry to adaptively retrieve relevant semantic evidence from source spherical image features. SphereOcc achieves 13.91% mIoU and 24.65% GeoIoU, outperforming the respective best-performing methods, TPVFormer and SurroundOcc, by 1.70 and 2.10 percentage points. It also ranks first in both metrics across all five scenes, with consistent advantages across the evaluated spatial partitions and reduced fields of view. The established benchmark and source code will be available at https://feit-feiteng.github.io/Spheriverse.
Chinese Translation
球形观测为三维场景理解提供了全球视觉上下文。然而，视觉信息以角度域编码，而物理世界则以笛卡尔坐标表示。这种跨空间表示的差距使得几何对应关系和语义证据聚合变得复杂。为了解决这一挑战，我们引入了Spheriverse，包含$64,400$个时间对齐的球形图像-激光雷达（LiDAR）配对，组织成644个序列。该数据集涵盖了多样的场景、光照和天气条件，并具有细粒度的语义类别。我们进一步建立了语义占用预测、语义映射和三维物体检测的基准，通过整体和场景级比较评估了30多种方法。对于密集预测，我们提出了SphereOcc，一个将球形几何建模与语义证据检索相结合的占用框架。笛卡尔-球形表示重塑（CSRR）通过区域调制将球形范围-方位几何纳入笛卡尔体素特征。随后，球形证据重新查询（SER）根据体素内容和范围-高度-方位几何条件查询，以自适应地从源球形图像特征中检索相关的语义证据。SphereOcc在mIoU上达到了13.91%，在GeoIoU上达到了24.65%，分别比最佳表现方法TPVFormer和SurroundOcc高出1.70和2.10个百分点。在所有五个场景中，它在这两个指标上均排名第一，并在评估的空间分区和减少的视场中保持了一致的优势。所建立的基准和源代码将可在https://feit-feiteng.github.io/Spheriverse获取。
cs.CV / 293 / 2609.09020
PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding
PIC：重新审视用于图像编码的隐式神经表示（INR），实现快速编码和亚毫秒解码
Abstract
Implicit neural representation (INR) has achieved remarkable progress in novel view synthesis and image/video coding in recent years.Compared to conventional end-to-end image codecs, INR-based compressors demonstrate significant advantages in decoding complexity. However, their practical application has been hindered by the inferior encoding speed and underutilized decoding efficiency.In this work, we propose a feedforward INR image coding architecture, Practical INR Image Codec (PIC), that computes all the necessary information for INR network in a single forward pass, achieving an encoding speed of 20 FPS. Additionally, we implement a highly optimized decoder that reaches 2000 FPS decoding speed, significantly surpassing JPEG's performance at comparable rate-distortion (RD) performance. To the best of our knowledge, this work presents the first learning-based image codec that simultaneously outperforms or is comparable with JPEG in both RD performance and decoding speed while maintaining practical encoding speed. Code is available at https://github.com/actcwlf/PIC.
Chinese Translation
隐式神经表示（INR）在新视角合成和图像/视频编码方面近年来取得了显著进展。与传统的端到端图像编码器相比，基于INR的压缩器在解码复杂性上表现出显著优势。然而，其实际应用受到编码速度较慢和解码效率未充分利用的限制。在本研究中，我们提出了一种前馈INR图像编码架构，称为实用INR图像编码器（PIC），该架构在单次前向传递中计算出INR网络所需的所有信息，实现了每秒20帧的编码速度。此外，我们实现了一个高度优化的解码器，解码速度达到每秒2000帧，显著超越了JPEG在可比率失真（RD）性能下的表现。根据我们所知，本研究首次提出了一种基于学习的图像编码器，在RD性能和解码速度上同时超越或与JPEG相当，同时保持实用的编码速度。代码可在 https://github.com/actcwlf/PIC 获取。
cs.CV / 294 / 2609.09025
Task-driven Processing with Coarse-to-Fine Glimpse-based Active Perception
基于粗到细的瞬时感知的任务驱动处理
Abstract
State-of-the-art vision models process images in their entirety, lacking the ability to selectively zoom in on relevant regions. This limitation is particularly acute in scenarios where processing must be conditioned on a specific task - such as instance detection, which requires localizing a specific object in a high-resolution, cluttered scene. In such settings, critical details are easily lost as images are often resized to match the model dimensions and computational constraints. We introduce Coarse-to-Fine Glimpse-based Active Perception (CF-GAP), a task-driven front-end that enhances high-resolution processing of existing instance detectors. CF-GAP selectively directs a sequence of limited view glimpses across the scene, utilizing task information to iteratively refine focus on the most relevant regions. These localized regions are then processed at high resolution by a downstream instance detector. By avoiding full-image processing and eliminating irrelevant confounding information, CF-GAP improves Average Precision (AP) by up to 20% across various state-of-the-art instance detectors on the HR-InsDet and Robotools benchmarks, while further enabling lightweight detectors to outperform their larger counterparts.
Chinese Translation
最先进的视觉模型处理整幅图像，缺乏选择性放大相关区域的能力。这一局限性在需要基于特定任务进行处理的场景中尤为明显，例如实例检测，它要求在高分辨率、杂乱的场景中定位特定对象。在这种情况下，关键细节往往会丢失，因为图像通常会被调整大小以匹配模型的维度和计算约束。我们提出了基于粗到细的瞬时感知（Coarse-to-Fine Glimpse-based Active Perception，CF-GAP），这是一种任务驱动的前端，增强了现有实例检测器的高分辨率处理能力。CF-GAP选择性地在场景中引导一系列有限视角的瞬时观察，利用任务信息迭代地聚焦于最相关的区域。这些局部区域随后由下游实例检测器以高分辨率进行处理。通过避免全图像处理并消除无关的干扰信息，CF-GAP在HR-InsDet和Robotools基准测试中提高了各种最先进实例检测器的平均精度（Average Precision，AP）高达20%，同时进一步使轻量级检测器超越其更大对手的性能。
cs.CV / 295 / 2609.09055
"World Knowledge" in the Weights: Reading Concept Circuits of Vision Transformers
权重中的“世界知识”：阅读视觉变换器的概念电路
Abstract
Vision transformers (ViTs) have achieved remarkable generalization across visual domains, yet little is known about how they internally represent the structure of the world. To address this gap, we use Cross-Layer Transcoders (CLTs) to read concept circuits from ViTs: directed graphs whose nodes correspond to sparse, interpretable concepts and edges capture concept interactions across layers. Our method yields two complementary views of model behavior. The global concept circuit is input-invariant and can be recovered directly from learned cross-layer weights, exposing the reusable "world knowledge" encoded in the model. The instance concept circuit is input-dependent and identifies the concepts and pathways actually used for a specific prediction, enabling faithful example-level explanations. We demonstrate the utility of concept circuits in three ways: (1) Automatic spurious correlation discovery: leveraging the statistics of our global concept circuits to identify shortcut dependencies within the model. (2) Spurious correlation removal: intervening on the instance concept circuit to steer the model towards correct predictions. Empirical results show that our method outperforms existing counterparts by 11.0% on the Waterbird dataset. (3) Model comparison: contrasting the global concept circuits of different foundation models (e.g., CLIP vs. DINO) to reveal how supervision paradigms shape representational structure. Our code is available at https://github.com/deep-real/VisionCLT
Chinese Translation
视觉变换器（ViTs）在视觉领域实现了显著的泛化能力，但关于它们如何内部表示世界结构的知识仍然有限。为了解决这一空白，我们使用跨层转码器（Cross-Layer Transcoders, CLTs）从ViTs中读取概念电路：这些有向图的节点对应于稀疏且可解释的概念，边则捕捉跨层的概念交互。我们的方法提供了模型行为的两种互补视角。全局概念电路是输入不变的，可以直接从学习到的跨层权重中恢复，揭示了模型中编码的可重用“世界知识”。实例概念电路是输入依赖的，识别出特定预测中实际使用的概念和路径，从而实现可信的示例级解释。我们通过三种方式展示了概念电路的实用性：（1）自动发现虚假相关性：利用全局概念电路的统计特征识别模型中的捷径依赖关系。（2）虚假相关性去除：干预实例概念电路，引导模型朝向正确的预测。实证结果表明，我们的方法在Waterbird数据集上比现有方法提高了11.0%。（3）模型比较：对比不同基础模型（例如CLIP与DINO）的全局概念电路，以揭示监督范式如何塑造表示结构。我们的代码可在 https://github.com/deep-real/VisionCLT 获取。
cs.CV / 296 / 2609.09082
GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Lifting
GoDeep：通过语言空间提升实现无注释开放词汇的3D场景理解
Abstract
Open vocabulary 3D semantic segmentation methods typically lift CLIP features into 3D. This embeds points in a joint vision-language space known to behave like a bag-of-words on compositional tasks. Furthermore, even annotation free variants often require a large 3D training corpus and a dedicated 3D encoder per domain. Instead we use a vision-language model purely as a translator. It produces structured, entity-level descriptions of each posed image. These descriptions are grounded, projected, and aggregated directly in a general-purpose, language-only embedding space, with no 3D training corpus or encoder required. On ScanNet++, our pipeline is competitive with strong annotation free baselines trained on ScanNet. On a 5-building cultural heritage benchmark, raw scores initially favor a CLIP-based variant, but a single systematic vocabulary correction reverses this ranking. An effect confirmed by a second, independent correction on a different class, indicating that language-space embeddings track physical content more faithfully. This fidelity extends to genuinely out-of-vocabulary (OOV) objects on ScanNet++ proving that language-space embeddings separate presence from absence objects far more sharply than CLIP-based embeddings do. GoDeep also localize these OOV objects within the scene, all without any 2D-3D annotation. Because every representation remains discrete text, predictions are also explainable at the point level. Finally, exploiting both a heuristic weighting, that favors precise over merely frequent observations and GoDeep's explainability property, we propose an aggregation strategy, as a proof of concept, that favors finer elements localization.
Chinese Translation
开放词汇的3D语义分割方法通常将CLIP特征提升到3D空间。这将点嵌入到一个联合视觉-语言空间中，该空间在组合任务上表现得像一个词袋。此外，即使是无注释的变体通常也需要大量的3D训练语料库和每个领域专用的3D编码器。相反，我们将视觉-语言模型纯粹用作翻译器。它生成每个输入图像的结构化实体级描述。这些描述在一个通用的仅语言嵌入空间中被固定、投影和聚合，不需要3D训练语料库或编码器。在ScanNet++上，我们的管道与在ScanNet上训练的强大无注释基线具有竞争力。在一个包含5栋建筑的文化遗产基准测试中，原始得分最初偏向于基于CLIP的变体，但一次系统的词汇修正逆转了这一排名。第二次独立修正对不同类别的确认表明，语言空间嵌入更忠实地跟踪物理内容。这种忠实性扩展到ScanNet++上的真正超出词汇（OOV）对象，证明语言空间嵌入在区分存在与缺失对象方面远比基于CLIP的嵌入更为敏锐。GoDeep还在场景中定位这些OOV对象，且无需任何2D-3D注释。由于每个表示保持为离散文本，预测也可以在点级别上进行解释。最后，利用一种启发式加权策略，偏向于精确而非仅仅频繁的观察，以及GoDeep的可解释性特性，我们提出了一种聚合策略，作为概念验证，旨在更好地定位细小元素。
cs.CV / 297 / 2609.09123
Mask Forcing: Improving Autoregressive Video Diffusion Distillation via Dual-Noise Masking Rollout
掩码强制：通过双噪声掩码展开改善自回归视频扩散蒸馏
Abstract
Autoregressive (AR) video diffusion models have shown great potential in real-time video generation. Recent methods distill pretrained bidirectional video diffusion models into causal AR students through Distribution Matching Distillation (DMD), but the generated videos often suffer from over-saturation and over-smoothing issues, resulting in limited visual quality and realism. The key contributing factor is the mode-seeking behavior of the reverse KL objective in DMD, which can cause the student distribution to collapse onto only a few modes of the teacher distribution. To address this, we propose Mask Forcing, a Dual-Noise Masking Rollout strategy that perturbs the AR student self-rollout to mitigate mode collapse induced by reverse-KL mode seeking. The core idea is to inject cleaner signals into noisy rollout inputs via random masks along spatial and temporal axes during the self-rollout process of AR diffusion distillation. Such perturbations encourage the student rollouts to explore more regions of the teacher distribution, allowing DMD to provide learning signals beyond the modes already covered by the student. Moreover, the cleaner tokens act as denoising guidance for other noisier tokens, improving the intermediate rollout predictions and reducing error accumulation. Extensive experiments demonstrate that our method improves multiple AR video diffusion distillation methods with higher visual quality efficiently, without incorporating real video data or additional post-training stages.
Chinese Translation
自回归（AR）视频扩散模型在实时视频生成中展现了巨大的潜力。近期的方法通过分布匹配蒸馏（Distribution Matching Distillation, DMD）将预训练的双向视频扩散模型蒸馏为因果AR学生，但生成的视频往往存在过饱和和过平滑的问题，导致视觉质量和真实感受限。造成这一问题的关键因素是DMD中反向KL目标的模式寻求行为，这可能导致学生分布仅集中在教师分布的少数几个模式上。为了解决这一问题，我们提出了掩码强制（Mask Forcing），一种双噪声掩码展开策略，通过扰动AR学生自展开过程来减轻反向KL模式寻求引起的模式崩溃。其核心思想是在AR扩散蒸馏的自展开过程中，通过在空间和时间轴上使用随机掩码，将更干净的信号注入到嘈杂的展开输入中。这种扰动鼓励学生展开探索教师分布的更多区域，使DMD能够提供超出学生已覆盖模式的学习信号。此外，更干净的标记作为其他嘈杂标记的去噪指导，改善了中间展开预测并减少了误差积累。大量实验表明，我们的方法有效地提高了多种AR视频扩散蒸馏方法的视觉质量，而无需引入真实视频数据或额外的后训练阶段。
cs.CV / 298 / 2609.09124
Canonical Color as a Lens into Concept Decodability in Vision Encoders and VLMs
典型颜色作为视觉编码器和视觉语言模型中概念可解码性的透镜
Abstract
Visual encoders construct a representation of the image input for Vision-Language models. How much conceptual, as opposed to immediately visible, information does this representation contain? We use canonical color as a controlled test case to ask whether vision encoders make canonical-color information linearly accessible, even when color is removed from the input image. We construct a dataset of objects with canonical colors, and probe vision encoders for both color and object identity using color and grayscale images. We find that canonical color remains decodable from grayscale images, and is tied to predicted object identity, indicating a conceptual link. Extending this analysis to full VLMs, we find that VLM post-training can have a surprisingly large effect on color decodability in the vision encoder. Overall, canonical color provides a usefully controllable lens for tracing object-level conceptual semantic information in vision encoders and VLMs.
Chinese Translation
视觉编码器为视觉-语言模型构建图像输入的表示。这种表示包含多少概念性的信息，而不是立即可见的信息？我们使用典型颜色作为一个受控的测试案例，探讨视觉编码器是否能够线性地访问典型颜色信息，即使在输入图像中去除了颜色。我们构建了一个包含典型颜色对象的数据集，并使用彩色和灰度图像对视觉编码器进行探测，以获取颜色和对象身份。我们发现，典型颜色仍然可以从灰度图像中解码，并且与预测的对象身份相关联，表明存在概念上的联系。将这一分析扩展到完整的视觉语言模型（VLM），我们发现VLM的后训练对视觉编码器中的颜色可解码性有着意想不到的重大影响。总体而言，典型颜色为追踪视觉编码器和视觉语言模型中的对象级概念语义信息提供了一个有用的可控透镜。
cs.CV / 299 / 2609.09143
Studying Image Tokenizers as Visual Languages in Unified Multimodal Models
将图像标记器视为统一多模态模型中的视觉语言研究
Abstract
Image tokenizers define the ``visual language'' of unified multimodal models, yet are commonly studied through isolated metrics or generation-/understanding-only evaluations. These evaluations do not fully capture how visual tokens behave when modeled jointly with text. We build a controlled pure-autoregressive testbed and track task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. We examine how these losses scale and relate to downstream performance, then use them to study multimodal learnability---how well image and text tokens are jointly modeled---and tokenizer design. We find that (1) losses should be analyzed by task, since they exhibit distinct scaling behavior and rank tokenizers differently. (2) The loss--performance relationship depends on the predicted token space: for a fixed tokenizer, T2I and I2T losses correlate with generation quality, but across tokenizers, the T2I loss--performance relationship shifts with the image-token space, whereas I2T loss, computed over a shared text vocabulary, provides a more consistent signal. I2T loss also correlates with both generation and visual understanding performance after supervised finetuning. Using losses as a lens, we show that (3) better reconstruction does not necessarily yield lower task-specific losses or stronger downstream performance, and that (4) image tokenizer choice can affect text modeling under joint optimization. As case studies, we revisit three tokenizer design axes---the discriminator, semantic supervision, and vocabulary size---to examine their effects on joint modeling and downstream performance. Together, our testbed offers a complementary perspective on image tokenizers as visual languages, highlighting their interplay with text in joint multimodal training.
Chinese Translation
图像标记器定义了统一多模态模型的“视觉语言”，但通常通过孤立的指标或仅限生成/理解的评估进行研究。这些评估并未完全捕捉视觉标记在与文本共同建模时的行为。我们构建了一个受控的纯自回归测试平台，并在文本、图像、文本到图像（T2I）和图像到文本（I2T）预测的多模态持续预训练过程中跟踪特定任务的验证损失。我们考察这些损失如何扩展并与下游性能相关，然后利用它们研究多模态可学习性——图像和文本标记的联合建模效果——以及标记器设计。我们发现：（1）损失应按任务进行分析，因为它们表现出不同的扩展行为并对标记器的排名不同。（2）损失与性能之间的关系取决于预测的标记空间：对于固定的标记器，T2I和I2T损失与生成质量相关，但在不同标记器之间，T2I损失-性能关系随着图像标记空间的变化而变化，而I2T损失在共享文本词汇上计算，提供了更一致的信号。I2T损失在经过监督微调后也与生成和视觉理解性能相关。（3）通过损失的视角，我们展示了更好的重构并不一定会导致更低的特定任务损失或更强的下游性能，以及（4）图像标记器的选择可以影响联合优化下的文本建模。作为案例研究，我们重新审视了三个标记器设计轴——鉴别器、语义监督和词汇大小——以考察它们对联合建模和下游性能的影响。总体而言，我们的测试平台为图像标记器作为视觉语言提供了一个互补的视角，突显了它们在联合多模态训练中的与文本的相互作用。
cs.CV / 300 / 2609.09145
Point4D: Long-range 4D Motion Reconstruction
Point4D：长距离4D运动重建
Abstract
We introduce Point4D, a feed-forward model for 4D reconstruction of long-range video sequences. Point4D is able to reliably infer dense per-point 3D trajectories across multi-hundred-frame videos, unlike existing 4D methods that are limited to short input windows of at most a few dozen frames. A key innovation that enables this is our flexible 3D query-based motion decoder that decouples trajectory prediction from image-plane visibility. The predicted 3D endpoints are then directly re-queried in the next chunk without re-projection or matching. Furthermore, we show that extracting and reusing a visual descriptor from an arbitrary frame where the point is visible leads to better performance than relying solely on the source patch. Overall, Point4D achieves state-of-the-art performance across diverse long-video tracking benchmarks spanning over 200 frames and largely outperforms previous feed-forward 4D method. Project page: https://point-4d.github.io
Chinese Translation
我们介绍了Point4D，这是一种用于长距离视频序列4D重建的前馈模型。与现有的4D方法仅限于最多几十帧的短输入窗口不同，Point4D能够可靠地推断出跨越数百帧视频的每个点的密集3D轨迹。实现这一目标的关键创新是我们灵活的基于3D查询的运动解码器，它将轨迹预测与图像平面可见性解耦。然后，预测的3D端点可以在下一个片段中直接重新查询，而无需重新投影或匹配。此外，我们展示了从任意可见点的帧中提取和重用视觉描述符比仅依赖源补丁能带来更好的性能。总体而言，Point4D在涵盖超过200帧的多样化长视频跟踪基准测试中实现了最先进的性能，并在很大程度上超越了之前的前馈4D方法。项目页面：https://point-4d.github.io
cs.AI / 1 / 2609.05437
Beyond Right and Wrong: Evaluating Second-order Social Reasoning in Large Language Models
超越对与错：评估大型语言模型中的二阶社会推理
Abstract
Previous AI alignment efforts have focused primarily on first-order social norms -- teaching models what is socially acceptable or unacceptable (e.g., `do not steal'). However, social intelligence depends not only on norm recognition, but also on anticipating who will enforce it and how (e.g., public shame or even imprisonment). These second-order expectations, known as metanorms, govern how people respond when social rules are broken. We introduce a novel framework for evaluating metanorm reasoning in Large Language Models (LLMs) along two dimensions: emotional appraisal and behavioral response, and propose new classification tasks, namely, predicting self-regulation in violators, and other-regulation in observers. We release a multi-perspective dataset, NormReact, of 450 norm violation scenarios, hand-annotated for emotions and behavioral responses across norm violators' gender and observers' social closeness. Current LLMs portray a harsher social world: across six models, they overpredict negative sanctions where humans would expect inaction, and alignment with human judgments deteriorates as social distance increases. These findings suggest that AI systems in norm-sensitive domains from conflict mediation to policy simulation, may risk producing a distorted picture of social regulation: one that over-represents punishment and under-represents the tolerance, restraint, and relational calibration that characterize actual norm enforcement in real world.
Chinese Translation
以往的人工智能对齐工作主要集中在一阶社会规范上——教导模型什么是社会上可接受或不可接受的（例如，‘不要偷窃’）。然而，社会智能不仅依赖于规范识别，还依赖于预测谁将执行这些规范以及如何执行（例如，公众羞辱或甚至监禁）。这些被称为元规范的二阶期望，决定了人们在社会规则被违反时的反应。我们提出了一个新的框架，用于评估大型语言模型（LLMs）中的元规范推理，涵盖两个维度：情感评估和行为反应，并提出了新的分类任务，即预测违规者的自我调节和观察者的他人调节。我们发布了一个多视角数据集NormReact，包含450个规范违反场景，手动标注了违规者的情感和行为反应，考虑了违规者的性别和观察者的社会亲密度。目前的LLMs描绘了一个更为严厉的社会世界：在六个模型中，它们过度预测了负面制裁，而人类则期望不采取行动，且随着社会距离的增加，与人类判断的一致性下降。这些发现表明，在从冲突调解到政策模拟等对规范敏感的领域，人工智能系统可能会产生扭曲的社会调节图景：一种过度表现惩罚而不足以表现宽容、克制和关系校准的图景，这些特征在现实世界中构成了实际的规范执行。
cs.AI / 2 / 2609.05439
CriticGen: Generation-Aware Evaluation as Actionable Feedback
CriticGen：生成感知评估作为可操作反馈
Abstract
Current evaluation methods for large language models are coarse-grained and decoupled from generation, producing generic explanations that fail to provide actionable feedback for model improvement. We propose CriticGen, a fine-grained, generation-aware evaluation framework that turns evaluation into actionable control for answer improvement. CriticGen first generates sample-specific evaluation dimensions and scoring criteria under high-level categories such as subjective, objective, and self-derived constraints. These criteria then serve as a dynamic rubric for jointly producing a score, a reason, an executable refinement suggestion, and a refined answer. This rubric-conditioned refinement process enables models to diagnose flaws and perform targeted answer improvement. Experimental results show that fine-grained evaluation should be both instance-specific and actionable. CriticGen induces higher-quality rubrics, improving relevance/coverage from 3.33/4.03 to 3.97/4.24. CriticGen also achieves the best score correlations, with 0.9556 Pearson and 0.9560 Spearman, and raises the F1 of criterion-grounded reasons and executable suggestions from 0.6369/0.5994 to 0.7554/0.7900. Crucially, its feedback translates into reliable answer improvement, improving 73.17% of answers with a 93.28% non-degradation rate.
Chinese Translation
当前针对大型语言模型的评估方法较为粗糙，且与生成过程脱节，产生的通用解释未能提供可操作的模型改进反馈。我们提出了CriticGen，一个细粒度的生成感知评估框架，将评估转化为可操作的控制，以改善答案。CriticGen首先在主观、客观和自我衍生约束等高层次类别下生成特定样本的评估维度和评分标准。这些标准随后作为动态评分标准，共同生成分数、理由、可执行的改进建议和改进后的答案。该基于评分标准的改进过程使模型能够诊断缺陷并进行有针对性的答案改进。实验结果表明，细粒度评估应当是实例特定的且可操作的。CriticGen引导出更高质量的评分标准，使相关性/覆盖率从3.33/4.03提升至3.97/4.24。CriticGen还实现了最佳的评分相关性，Pearson相关系数为0.9556，Spearman相关系数为0.9560，并将基于标准的理由和可执行建议的F1值从0.6369/0.5994提升至0.7554/0.7900。关键是，其反馈转化为可靠的答案改进，改善了73.17%的答案，且非退化率达到93.28%。
cs.AI / 3 / 2609.05441
When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents
记忆何时有助？工具使用的长时记忆在 LLM 代理中的成本感知评估
Abstract
Long-term memory for LLM agents is evaluated today by conversational recall benchmarks (LoCoMo, LongMemEval), which measure question answering over dialogue history, not whether remembered facts change what a tool-using agent does. We present MERIT (Memory Evaluation for Realistic Instrumented Tasks), a benchmark and harness that measures the marginal utility of memory for task-executing agents under explicit cost accounting. MERIT provides episodic tool-use tasks in three domains whose dependence on earlier-episode facts is verified by an automated leak check; a difficulty ladder ending in updated-fact recall; controlled memory corruption; and full token and dollar metering of every memory operation. Across 23,440 scored episodes ($42.57), a two-generation pilot on gpt-4.1-mini and a preregistered 3-model x 3-seed grid (GPT-4.1, Claude Haiku 4.5; memory side held fixed), memory lifts dependent-task success from a leak-verified floor of 0.00 to 0.55-1.00. On updated facts, embedding retrieval collapses unpredictably (0.30-0.95 across models; max seed gap 0.45), and agents act on a correctly retrieved value only 55% of the time, while update-on-write stores (a structured fact store and, notably, LLM summarization) remain at 0.70-1.00; the hybrid is worse than the fact store alone. A latest-generation spot-check (Claude Sonnet 5, gated on a clean full-replay control) reproduces the pattern. Swapping a memory's implementation moves task success by up to 60 points, and full replay is never economical: the best condition per domain delivers 2.7-3.9x its marginal utility per dollar. We release the benchmark, harness, and all traces.
Chinese Translation
目前对 LLM 代理的长时记忆评估主要通过对话回忆基准（LoCoMo，LongMemEval）进行，这些基准测量的是对对话历史的问答，而不是记忆的事实如何改变工具使用代理的行为。我们提出了 MERIT（现实工具任务的记忆评估），这是一个基准和工具，旨在在明确的成本核算下测量记忆对任务执行代理的边际效用。MERIT 提供了三个领域的情节工具使用任务，其对早期情节事实的依赖通过自动泄漏检查进行验证；一个以更新事实回忆为终点的难度阶梯；受控的记忆损坏；以及对每个记忆操作的完整令牌和美元计量。在 23,440 个评分情节（$42.57）中，针对 gpt-4.1-mini 的两代试点和一个预注册的 3 模型 x 3 种子网格（GPT-4.1，Claude Haiku 4.5；记忆侧保持不变），记忆将依赖任务的成功率从经过泄漏验证的 0.00 提升至 0.55-1.00。在更新事实方面，嵌入检索的效果不可预测地崩溃（模型间为 0.30-0.95；最大种子差距为 0.45），代理仅在 55% 的情况下对正确检索的值做出反应，而更新写入存储（结构化事实存储，尤其是 LLM 摘要）保持在 0.70-1.00；混合效果比单独的事实存储更差。最新一代的抽查（Claude Sonnet 5，基于干净的全重放控制）再现了这一模式。更换记忆的实现方式可使任务成功率变化高达 60 分，而全重放从来不是经济的：每个领域的最佳条件提供了其边际效用的 2.7-3.9 倍。我们发布了基准、工具和所有痕迹。
cs.AI / 4 / 2609.05446
AutoFyn Technical Report: Non-Parametric Expert Iteration for Long-Horizon Agents
AutoFyn技术报告：用于长时间跨度代理的非参数专家迭代
Abstract
We introduce AutoFyn, an agent harness inspired by the Expert Iteration algorithm, adapting a frozen model across many rounds by updating persistent state from verified reward signals rather than model weights. Each round begins from a fresh model session, and durable information is reintroduced only through explicit interfaces such as persistent memory files, reports, and repository state. Within a round, an orchestrator explores, plans and builds many alternative approaches with specialized agents, while a task-grounded verifier verifies the work and supplies an objective reward for measuring progress. This reward is distilled back into the persistent state, which updates the effective policy for the next round. In this technical report, we formalize this loop and describe its persistent state and verification interfaces. We then demonstrate its use in three domains, namely olympiad mathematics, data science, and cybersecurity. On the six fresh problems of the 2026 International Mathematical Olympiad, every model with room to improve scores higher under AutoFyn than in its provider's own coding agent. AutoFyn also built the top-ranked agent on the Spider 2.0 dbt benchmark, and has produced $16$ maintainer-confirmed vulnerability advisories in Next.js, MetaMask, pnpm, Warp, LiteLLM, Langflow, and Open WebUI.
Chinese Translation
我们介绍了AutoFyn，这是一种受专家迭代算法启发的代理工具，通过更新来自验证奖励信号的持久状态，而不是模型权重，来适应多个回合中的冻结模型。每个回合从一个新的模型会话开始，持久信息仅通过显式接口重新引入，例如持久内存文件、报告和存储库状态。在一个回合内，协调者探索、规划并构建许多替代方法，使用专门的代理，而任务基础的验证者则验证工作并提供用于衡量进展的客观奖励。该奖励被提炼回持久状态，从而更新下一回合的有效策略。在本技术报告中，我们形式化了这一循环，并描述了其持久状态和验证接口。随后，我们展示了其在三个领域的应用，即奥林匹克数学、数据科学和网络安全。在2026年国际数学奥林匹克的六个新问题中，每个模型在AutoFyn下的得分均高于其提供者自己的编码代理。AutoFyn还在Spider 2.0 dbt基准测试中构建了排名最高的代理，并在Next.js、MetaMask、pnpm、Warp、LiteLLM、Langflow和Open WebUI中产生了16个维护者确认的漏洞建议。
cs.AI / 5 / 2609.05448
Damage-Aware Bandit Pruning for Vision and Language Transformers
基于损伤感知的视觉与语言变换器剪枝
Abstract
Structured post-training pruning of transformers requires selecting complete functional units whose suppression causes limited degradation. We formulate structured-unit selection for language and vision transformers as a damage-aware multi-armed bandit problem under a fixed candidate-evaluation budget. Attention heads and MLP channel groups are temporarily masked on calibration batches. Paired damage is the masked loss minus the base loss on the same batch, reducing batch-to-batch variation. A smooth bounded reward drives either a UCB-style policy or fractional-Beta Thompson Sampling, and the final mask is constructed sequentially by adding one unit at each step. The selected units are functionally zeroed in the original dense checkpoint; therefore, the reported parameter effects represent effective structural suppression rather than physical compression or measured speedup. Experiments on WikiText-2, LAMBADA, and Imagenette cover GPT-2, OPT, Pythia, Qwen2.5, SmolLM2, ViT-B/16, DeiT-Tiny, and Swin-Tiny, with comparisons against random, magnitude, static-saliency, and budgeted-greedy selection. Across five seeds, the bandit methods usually reduce degradation relative to budgeted greedy in the paired language-model comparisons. Of 28 comparisons highlighted in the paper, 23 bootstrap confidence intervals exclude zero and 11 paired tests have p < 0.05; six have q < 0.05 after Benjamini-Hochberg correction across the full family of 116 dataset-wise tests. Matched-evaluation results for ViT-B/16 and Swin-Tiny indicate that their gains are not explained solely by a larger candidate-evaluation budget.
Chinese Translation
变换器的结构化后训练剪枝需要选择完整的功能单元，其抑制导致的性能下降有限。我们将语言和视觉变换器的结构单元选择形式化为在固定候选评估预算下的损伤感知多臂赌博机问题。在校准批次上，注意力头和多层感知器（MLP）通道组暂时被屏蔽。配对损伤是屏蔽损失减去同一批次的基础损失，从而减少批次间的变化。平滑有界的奖励驱动UCB（上置信界）风格策略或分数贝塔汤普森采样，最终的屏蔽是通过在每一步添加一个单元来顺序构建的。所选单元在原始稠密检查点中功能上被置为零；因此，报告的参数效应代表有效的结构抑制，而不是物理压缩或测量加速。在WikiText-2、LAMBADA和Imagenette上的实验涵盖了GPT-2、OPT、Pythia、Qwen2.5、SmolLM2、ViT-B/16、DeiT-Tiny和Swin-Tiny，并与随机选择、幅度选择、静态显著性选择和预算贪婪选择进行了比较。在五个种子下，赌博机方法通常相对于预算贪婪在配对语言模型比较中减少了性能下降。在论文中强调的28个比较中，23个自助置信区间排除了零，11个配对测试的p值<0.05；在116个数据集测试的完整家族中，经过本杰明-霍赫伯格校正后，有6个的q值<0.05。ViT-B/16和Swin-Tiny的匹配评估结果表明，它们的增益并不仅仅由更大的候选评估预算所解释。
cs.AI / 6 / 2609.05459
Compiling VGDL into Causal Models
将视频游戏描述语言编译为因果模型
Abstract
Reinforcement learning and large language models often struggle to accurately capture the causal mechanics of game environments. Standard reinforcement learning agents tend to rely on spurious correlations, while large language models are prone to hallucinating game rules. Although causal reinforcement learning improves interpretability, there is currently no formal methodology to map complex game mechanics directly into causal models. To address this, we propose a deterministic framework that compiles games specified in the Video Game Description Language into Dynamic Structural Causal Models. Rather than inferring causal structures from gameplay traces or noisy large language models' outputs, our methodology directly translates game components, including sprite dynamics, interaction rules, and termination conditions, into explicit structural equations. Each game tick represents a causal transition from state variables at time $t$ to $t+1$. By establishing this grounded mapping, the approach guarantees absolute causal fidelity to the ground-truth game mechanics. The resulting models offer transparent causal pathways that support counterfactual reasoning, causal reinforcement learning agent training, and procedural content validation. This framework provides a principled bridge between symbolic game descriptions and causally grounded game AI.
Chinese Translation
强化学习和大型语言模型在准确捕捉游戏环境的因果机制方面常常面临挑战。标准的强化学习代理往往依赖于虚假的相关性，而大型语言模型则容易产生游戏规则的幻觉。尽管因果强化学习提高了可解释性，但目前尚无正式的方法论能够将复杂的游戏机制直接映射到因果模型中。为了解决这一问题，我们提出了一种确定性框架，将用视频游戏描述语言（Video Game Description Language）指定的游戏编译为动态结构因果模型（Dynamic Structural Causal Models）。我们的研究方法不是从游戏过程轨迹或噪声较大的大型语言模型输出中推断因果结构，而是直接将游戏组件（包括精灵动态、交互规则和终止条件）翻译为明确的结构方程。每个游戏时刻代表从时间 $t$ 的状态变量到 $t+1$ 的因果转变。通过建立这种扎实的映射，该方法保证了对真实游戏机制的绝对因果保真性。所得到的模型提供了透明的因果路径，支持反事实推理、因果强化学习代理的训练以及程序化内容验证。该框架为符号游戏描述与因果基础的游戏人工智能之间提供了一个有原则的桥梁。
cs.AI / 7 / 2609.05461
ARC-Bench: Closed-Loop Replanning Masks Broken Action Ranking in Frozen JEPA World Models
ARC-Bench：闭环重规划掩盖了冻结JEPA世界模型中的动作排名问题
Abstract
Reward-free latent world models plan by scoring candidate actions with distances in a frozen latent space: an action is preferred if its predicted future embedding lands closer to the goal embedding. This silently assumes that latent closeness is action-rankable, i.e., that ordering candidates by latent distance agrees with ordering them by true cost. We audit this assumption directly. We introduce ARC-Bench, a no-leak, fixed-candidate protocol that measures whether frozen JEPA-style objectives rank candidate actions correctly, and apply it to official released JEPA-WM checkpoints across navigation and manipulation-style control. The assumption fails, severely and structurally: on the official manipulation audits the top-scored candidate is almost always suboptimal, and the same inversion appears in the maze domains. A controlled visual-backbone extension shows that the defect persists when DINOv2 is replaced by video-pretrained V-JEPA 1 and V-JEPA 2 encoders at ViT-L/ViT-G scale. Provenance, undertraining, matched-budget backbone controls, and metric-circularity controls rule out trivial explanations. We then explain why this defect has stayed invisible: closed-loop replanning masks it. When we reduce the planner's replanning frequency, success collapses in both a navigation and a manipulation domain, and the episodes rescued by frequent replanning are enriched for severe first-plan ranking failures in the PointMaze first-plan diagnostic. Closed-loop success rates therefore systematically overstate the rankability of frozen latent representations. ARC-Bench supplies the measurement, and the masking mechanism the explanation, for methods that adapt, amortize, or replan around latent-space planners without directly auditing released JEPA-WM action rankability.
Chinese Translation
无奖励的潜在世界模型通过在冻结的潜在空间中对候选动作进行距离评分来进行规划：如果某个动作的预测未来嵌入更接近目标嵌入，则该动作更受偏好。这默默假设了潜在的接近性是可以按动作排名的，即按潜在距离对候选者进行排序与按真实成本进行排序是一致的。我们直接审计这一假设。我们引入了ARC-Bench，这是一种无泄漏的固定候选协议，用于测量冻结的JEPA风格目标是否正确地对候选动作进行排名，并将其应用于官方发布的JEPA-WM检查点，涵盖导航和操控风格的控制。该假设在结构上严重失败：在官方操控审计中，得分最高的候选几乎总是次优，并且在迷宫领域中也出现了相同的反转。一个受控的视觉骨干扩展显示，当用视频预训练的V-JEPA 1和V-JEPA 2编码器替换DINOv2时，这一缺陷依然存在，且在ViT-L/ViT-G规模下表现明显。来源、训练不足、匹配预算的骨干控制和度量循环控制排除了简单的解释。我们接着解释了为什么这一缺陷一直未被发现：闭环重规划掩盖了它。当我们降低规划者的重规划频率时，无论是在导航还是操控领域，成功率都急剧下降，而通过频繁重规划拯救的情节在PointMaze首轮诊断中充满了严重的首轮排名失败。因此，闭环成功率系统性地高估了冻结潜在表示的可排名性。ARC-Bench提供了测量，而掩盖机制则为那些适应、摊销或围绕潜在空间规划者进行重规划的方法提供了解释，而无需直接审计发布的JEPA-WM动作可排名性。
cs.AI / 8 / 2609.05481
RAPID: Reliability-Aware Pair Importance Distillation
RAPID：可靠性意识的对偶重要性蒸馏
Abstract
Inter example relational distillation transfers a teacher's representation geometry by matching relations among examples within a mini batch. Computing all pairs has quadratic complexity in the batch size, whereas uniform subsampling may use a limited relation budget inefficiently. We introduce Reliability Aware Pair Importance Distillation, or RAPID, which separates a reliability gated relational target from a full support adaptive pair proposal. Reliability determines which teacher relations are emphasized, while calibrated teacher entropy and detached student-teacher residuals determine which relations are evaluated. Exact inverse proposal correction makes the loss and gradient estimators conditionally unbiased with respect to the gated mini batch target. We evaluate RAPID in two text classification settings: AG News with BERT-to-DistilBERT distillation using three paired seeds and a relation budget of 256, and SST-2 with DistilBERT to DistilBERT distillation using three paired seeds and a relation budget of 64. Reliability gated relational distillation achieves the highest observed mean student accuracy on both datasets: 94.285 plus or minus 0.054 percent on AG News and 88.800 plus or minus 0.532 percent on SST-2. RAPID ranks second, achieving 94.241 plus or minus 0.025 percent and 88.685 plus or minus 0.462 percent, respectively, compared with 94.154 plus or minus 0.124 percent and 87.271 plus or minus 0.162 percent for the cross entropy baseline. Pilot evaluations are counted toward the same total budget as the main relation evaluations. Across both settings, the gated target yields the highest mean accuracy, while the adaptive proposal remains within seed-level variation. These results support the modular view that target reliability and evaluation priority are separable design dimensions.
Chinese Translation
跨示例关系蒸馏通过匹配小批量内示例之间的关系来传递教师的表示几何。计算所有对的复杂度在批量大小上是二次的，而均匀子采样可能会低效地使用有限的关系预算。我们提出了可靠性意识的对偶重要性蒸馏（RAPID），它将可靠性门控的关系目标与全支持自适应对提案分开。可靠性决定了强调哪些教师关系，而校准的教师熵和分离的学生-教师残差决定了评估哪些关系。精确的逆提案修正使得损失和梯度估计器在条件上对门控小批量目标无偏。我们在两个文本分类设置中评估了RAPID：使用三个配对种子和256的关系预算在AG News上进行BERT到DistilBERT的蒸馏，以及在SST-2上使用三个配对种子和64的关系预算进行DistilBERT到DistilBERT的蒸馏。可靠性门控的关系蒸馏在这两个数据集上实现了最高的平均学生准确率：AG News为94.285±0.054%，SST-2为88.800±0.532%。RAPID排名第二，分别达到94.241±0.025%和88.685±0.462%，而交叉熵基线为94.154±0.124%和87.271±0.162%。初步评估计入与主要关系评估相同的总预算。在这两个设置中，门控目标产生了最高的平均准确率，而自适应提案保持在种子级别的变异范围内。这些结果支持了目标可靠性和评估优先级是可分离设计维度的模块化观点。
cs.AI / 9 / 2609.05488
PGP-Clinical-TimeKAN: Prior-Guided Joint Probabilistic Forecasting of Clinical Trajectories
PGP-临床时间KAN：基于先验指导的临床轨迹联合概率预测
Abstract
Clinical deterioration unfolds through coupled, partially observed trajectories, not a single diagnostic label. We introduce PGP-Clinical-TimeKAN, a trajectory-first framework for joint probabilistic forecasting of multivariate physiology. It combines missingness-aware temporal encoders, a soft organ-system prior, patient-specific relations, nonlinear Kolmogorov-Arnold messages, and a low-rank multivariate Student-t head. We evaluate 24-hour histories and six-hour forecasts on a frozen MIMIC-IV-derived cohort of 6,882 patients and 54,694 windows. Across five seeds and 13 models, PGP-Clinical-TimeKAN obtains the second-lowest normalized MAE (0.37727 +/- 0.00029) and the lowest RMSE (0.52656 +/- 0.00034). It reduces MAE by 0.52% relative to deterministic TimeKAN. For probabilistic forecasting, it reaches a marginal NLL of 0.66380 and a CRPS of 0.27301. Empirical coverage is 0.533, 0.831, and 0.958 for nominal 50%, 80%, and 95% intervals. Removing relational structure causes the largest ablation loss. Increasing covariance rank improves joint likelihood but has little effect on point accuracy. A trajectory-derived risk score remains weaker than a dedicated GRU-D classifier (AUROC 0.603 versus 0.650), which limits the present clinical claim. Joint trajectory forecasting therefore provides an inspectable intermediate task, but accurate physiology forecasts alone do not ensure a calibrated event detector.
Chinese Translation
临床恶化通过耦合的、部分观察的轨迹展开，而非单一的诊断标签。我们引入了PGP-临床时间KAN，这是一种以轨迹为中心的多变量生理联合概率预测框架。该框架结合了考虑缺失数据的时间编码器、软组织系统先验、特定患者关系、非线性Kolmogorov-Arnold消息以及低秩多变量Student-t头。我们在一个由6,882名患者和54,694个窗口组成的冻结MIMIC-IV衍生队列上评估了24小时历史数据和六小时预测。在五个种子和13个模型中，PGP-临床时间KAN获得了第二低的归一化平均绝对误差（MAE）为0.37727 +/- 0.00029，以及最低的均方根误差（RMSE）为0.52656 +/- 0.00034。相较于确定性时间KAN，它将MAE减少了0.52%。在概率预测方面，它达到了边际负对数似然（NLL）为0.66380和连续排名概率评分（CRPS）为0.27301。经验覆盖率在名义上为50%、80%和95%的区间分别为0.533、0.831和0.958。去除关系结构导致了最大的消融损失。增加协方差秩提高了联合似然性，但对点预测精度影响不大。基于轨迹的风险评分仍然不如专门的GRU-D分类器（AUROC 0.603对比0.650）强，这限制了当前的临床主张。因此，联合轨迹预测提供了一个可检查的中间任务，但单靠准确的生理预测并不能确保一个经过校准的事件检测器。
cs.AI / 10 / 2609.05505
SciLitBench: Benchmark and Design Principles for LLM-Powered Systematic Literature Reviews
SciLitBench：基于大型语言模型的系统文献综述基准与设计原则
Abstract
Systematic reviews require sustained human judgment across thousands of records, yet existing evaluations of large language models (LLMs) typically examine review stages in isolation. We introduce SciLitBench, a multi-stage benchmark spanning title and abstract screening, full-text screening, and schema-guided data extraction, with 42,981 retrieved records, 1,012 full texts, and annotations for 888 included papers. Across 22 open-weight LLMs from six model families, explicit inclusion and exclusion criteria improve title and abstract screening $F_2$ by 28.8\%, while researcher-authored rationales improve full-text screening by 15\%. Data extraction reveals a different reliability regime: performance declines from 0.97 accuracy for publication year to 0.37 Jaccard overlap for computational approach, while the strongest models recover only 30\% of annotated evaluation evidence and 25\% of limitations. SciLitBench identifies a practical boundary between high-recall screening and evidence-complete extraction and provides a reproducible resource for evaluating LLM-assisted evidence synthesis.
Chinese Translation
系统综述需要在数千条记录中进行持续的人类判断，而现有的大型语言模型（LLMs）评估通常孤立地考察综述阶段。我们提出了SciLitBench，这是一个多阶段基准，涵盖标题和摘要筛选、全文筛选以及基于模式的数据提取，共包含42,981条检索记录、1,012篇全文和888篇纳入论文的注释。在来自六个模型家族的22个开放权重LLMs中，明确的纳入和排除标准使标题和摘要筛选的$F_2$提高了28.8%，而研究者撰写的理由使全文筛选提高了15%。数据提取揭示了不同的可靠性机制：从出版年份的0.97准确率下降到计算方法的0.37 Jaccard重叠，而最强的模型仅恢复了30%的注释评估证据和25%的局限性。SciLitBench识别了高召回率筛选与证据完整提取之间的实际边界，并提供了一个可重复使用的资源，用于评估LLM辅助的证据合成。
cs.AI / 11 / 2609.05511
SCAFFOLD: Self-Improving Web Agents via Recursive Parametric Skill Abstraction
SCAFFOLD：通过递归参数化技能抽象自我改进的网络代理
Abstract
Web agents need to navigate visually rich, long-horizon interfaces that change across sites, yet most previous agents still learn each task in isolation and discard the procedural knowledge they accumulate. Recent skill-augmented frameworks take an important first step, but they treat the skill library as a flat or two-tier prompt-side cache and offer no principled mechanism for compressing redundancy or composing skills recursively. We introduce \textsc{Scaffold}, a self-improving framework for visual web agents that (i) induces parametric, executable skills from successful trajectories under a multi-instance abstraction constraint, (ii) maintains a recursively composed hierarchy in which higher-level skills invoke lower-level ones, (iii) compacts the library via a minimum-description-length (MDL) criterion and behavioral equivalence checking, and (iv) periodically distills skill-augmented trajectories back into model weights to internalize the abstractions. Across WebArena, VisualWebArena, and a held-out split of Online-Mind2Web, \textsc{Scaffold} improves success rate by $11.1$--$17.2$ absolute points over the strongest skill-augmented baseline and shows monotonic gains across five self-improvement iterations without library collapse. We release the code and documents in the Github \href{https://github.com/BokwaiHo/SCAFFOLD}{repository}.
Chinese Translation
网络代理需要在视觉丰富、长时间跨度的界面中进行导航，这些界面在不同网站之间会有所变化。然而，大多数之前的代理仍然在孤立的环境中学习每个任务，并丢弃他们积累的程序性知识。最近的技能增强框架迈出了重要的第一步，但它们将技能库视为扁平或两层的提示侧缓存，并未提供压缩冗余或递归组合技能的原则性机制。我们提出了 extsc{Scaffold}，这是一个自我改进的视觉网络代理框架，(i) 在多实例抽象约束下，从成功轨迹中引导出参数化的可执行技能，(ii) 维护一个递归组合的层次结构，其中高层技能调用低层技能，(iii) 通过最小描述长度（MDL）标准和行为等价性检查来压缩技能库，以及 (iv) 定期将技能增强的轨迹提炼回模型权重，以内化这些抽象。在 WebArena、VisualWebArena 和一个保留的 Online-Mind2Web 数据集上， extsc{Scaffold} 的成功率比最强的技能增强基线提高了 $11.1$--$17.2$ 个绝对点，并且在五次自我改进迭代中显示出单调增益而没有库崩溃。我们在 Github exttt{repository} 中发布了代码和文档。
cs.AI / 12 / 2609.05512
Reasoning-Aware Compression: Identifying and Protecting Vulnerable Reasoning Circuits for Energy-Efficient LLM Deployment
基于推理的压缩：识别和保护脆弱的推理电路以实现高效能的LLM部署
Abstract
Large Reasoning Models (LRMs) impose substantial energy costs during deployment, yet current compression methods apply uniform quantization across all components, risking damage to critical reasoning circuits. We present a reasoning-aware compression framework that benchmarks quantization conditions across five reasoning benchmarks, GSM8K, FOLIO, MATH-500, ProofWriter, and MuSiQue, with hardware-level GPU energy measurement; profiles per-module INT4 vulnerability across all 196-224 (layer, projection) pairs via a perturbation sweep on a held-out calibration split, then selectively restores the most sensitive circuits to FP16. Three findings emerge. First, INT4 quantization can increase energy by extending reasoning chains; a 25% power reduction becomes a net energy increase on GSM8K. Second, vulnerability is task-dependent: attention projections are more critical for mathematical reasoning, and sensitivity patterns differ by architecture in logical inference. Third, selective compression achieves Pareto-optimal points inaccessible to uniform methods: R1-Qwen-7B Top-10% on ProofWriter gains +12 pp over FP16 at -9.7% energy, validated on held-out data across five reasoning benchmarks.
Chinese Translation
大型推理模型（LRMs）在部署过程中会产生巨大的能耗，但当前的压缩方法对所有组件应用统一量化，这可能会损害关键的推理电路。我们提出了一种基于推理的压缩框架，该框架在五个推理基准（GSM8K、FOLIO、MATH-500、ProofWriter和MuSiQue）上基准测试量化条件，并通过硬件级GPU能量测量；对所有196-224（层，投影）对的每个模块进行INT4脆弱性分析，通过对保留的校准分割进行扰动扫描，然后选择性地将最敏感的电路恢复为FP16。得出了三个发现。首先，INT4量化可能通过延长推理链来增加能耗；在GSM8K上，25%的功率减少反而导致净能耗增加。其次，脆弱性依赖于任务：注意力投影对数学推理更为关键，且在逻辑推理中，敏感性模式因架构而异。第三，选择性压缩实现了统一方法无法达到的帕累托最优点：在ProofWriter上，R1-Qwen-7B的前10%在-9.7%能耗下比FP16提高了12个百分点，且在五个推理基准的保留数据上得到了验证。
cs.AI / 13 / 2609.05513
When and What to Teach: Budget-Aware Online Adaptation for Web Agents
何时以及教授什么：预算意识的在线适应用于网络代理
Abstract
Web agents have achieved significant success in automating complex internet tasks but deploying them in real-world environments requires continuous online adaptation. Given that deploying powerful proprietary models remains commercially cost-prohibitive, practitioners must rely on lightweight local models that evolve post-deployment via online teaching from a stronger teacher. However, standard interactive feedback imposes prohibitive costs. We show that conventional trajectory-level preference optimization wastes budget on both unresolvable episodes and redundant execution turns. To resolve these inefficiencies, we propose \textbf{Score-Guided Online Teaching with Budgeted Trajectory Trimming}, a budget-aware framework that systematically orchestrates \textbf{when} and \textbf{what} to teach. Specifically, our framework integrates a solvability-aware teacher gate to dictate \textbf{when} to query the teacher model and a score-guided turn selection mechanism to decide \textbf{what} informative turns to retain. Extensive experiments on MiniWoB and TimeWarp demonstrate that our method achieves comparable first-pass success while reducing teacher calls by 22.6\% and student training compute by 52.1\% on average. Our code is available at https://github.com/zjw131f1fc/budgeted-online-teaching.
Chinese Translation
网络代理在自动化复杂互联网任务方面取得了显著成功，但在现实环境中部署它们需要持续的在线适应。由于部署强大的专有模型在商业上成本高昂，实践者必须依赖轻量级的本地模型，这些模型通过来自更强教师的在线教学在部署后不断演变。然而，标准的互动反馈会带来高昂的成本。我们展示了传统的轨迹级偏好优化在无法解决的情境和冗余执行回合上浪费了预算。为了解决这些低效问题，我们提出了 extbf{基于评分的预算轨迹修剪在线教学}，这是一个预算意识的框架，系统地协调 extbf{何时}和 extbf{教授什么}。具体而言，我们的框架集成了一个可解性感知的教师门控，以决定 extbf{何时}查询教师模型，以及一个基于评分的回合选择机制，以决定 extbf{保留哪些}信息性回合。在MiniWoB和TimeWarp上的大量实验表明，我们的方法在第一次尝试成功率上达到可比水平，同时教师调用减少了22.6 ext{%}，学生训练计算量平均减少了52.1 ext{%}。我们的代码可在https://github.com/zjw131f1fc/budgeted-online-teaching获取。
cs.AI / 14 / 2609.05514
The Failure Happens Before the Drift: The Social Dynamics of Values in LLM Agent Societies
失败发生在漂移之前：大型语言模型代理社会中的价值观社会动态
Abstract
Large Language Model (LLM)-based agents are increasingly used as proxies for human participants in social science research, yet it remains unclear whether they can faithfully simulate diverse and conflicting human value systems. We present a World Values Survey (WVS)-grounded simulation framework where culturally diverse agents with different communication styles engage in longitudinal, value-laden discussions. Across approximately 4,000 conversations involving 1,200 personas, 15 topics, and three models (GPT-4o, Gemini-2.5-Flash, and Gemma-4-E4B), we evaluate value faithfulness, value drift, and conversational realism. We find that more than 50\% of personas fail to express their assigned WVS profiles from the outset, while 2-7\% drift after repeated conversations. Ablations removing demographic details improve faithfulness for some models but do not change the broader trend: simulated value distributions still systematically deviate from the assigned WVS profiles. Compared to human discussions, simulated dialogues show a different trade-off between stylistic consistency and semantic diversity, often producing content-wise varied but stylistically repetitive exchanges. These findings suggest that current LLM agents can generate plausible conversations, but remain limited proxies for representing and preserving diverse human value profiles over time.
Chinese Translation
基于大型语言模型（LLM）的代理越来越多地被用作社会科学研究中人类参与者的代理，但尚不清楚它们是否能够忠实地模拟多样且相互冲突的人类价值体系。我们提出了一个基于世界价值观调查（WVS）的模拟框架，其中具有不同沟通风格的文化多样性代理参与长期的、充满价值观的讨论。在大约4000次涉及1200个角色、15个主题和三个模型（GPT-4o、Gemini-2.5-Flash和Gemma-4-E4B）的对话中，我们评估了价值观的忠实性、价值漂移和对话的现实性。我们发现，超过50%的角色从一开始就未能表达其分配的WVS档案，而在重复对话后，2-7%的角色发生了漂移。去除人口统计细节的消融实验提高了某些模型的忠实性，但并未改变更广泛的趋势：模拟的价值分布仍然系统性地偏离分配的WVS档案。与人类讨论相比，模拟对话在风格一致性和语义多样性之间表现出不同的权衡，通常产生内容上多样但风格上重复的交流。这些发现表明，当前的LLM代理能够生成可信的对话，但在代表和保持多样的人类价值档案方面仍然是有限的代理。
cs.AI / 15 / 2609.05527
Beyond "AI Helps Humans": Decision-Targeted Evaluation Design for Human-Agent Teams in the Agentic Era
超越“人工智能帮助人类”：代理时代人机团队的决策导向评估设计
Abstract
Wherever a coding agent works under engineer supervision, or a clinical model assists a radiologist, the deployment question is whether to keep the human-AI workflow or replace it with the human alone or the agent alone. The human-AI workflow is worth keeping only if it beats both of those alternatives. Yet once it is deployed, neither alternative outcome is observed: recovering one means replaying the task under that alternative, and every replay costs expert time or compute. Under a fixed replay budget, the design question is therefore which tasks should be more likely to receive a human-only replay, and which an agent-only replay. Existing methods do not directly target this decision. Agent benchmarks do not choose which missing baseline to measure, variance-based sampling ignores which of the two comparisons is closer to failing, and Bayesian information methods focus on learning model parameters instead of making the deployment decision. We propose TEAM-Design, a rule that gives every task two replay probabilities, one per baseline. It raises a probability where the missing baseline outcome is hard to predict from what is already known about the task and where that comparison is harder to establish, and lowers it where replay is expensive. We prove that the rule solves this budgeted design problem, and that drawing the replays at random from recorded probabilities still controls the chance of wrongly declaring that the workflow beats both. We reanalyze 6 clinical settings, where no human-AI workflow beats both alternatives, and a coding benchmark, where one does, then evaluate TEAM-Design on synthetic designs and on a semi-synthetic design built from a real chest X-ray reader study. TEAM-Design works best when one of the two comparisons is clearly harder to settle than the other, and can do worse than variance-based allocation when the two are similarly difficult.
Chinese Translation
无论是编码代理在工程师监督下工作，还是临床模型协助放射科医生，部署问题在于是保留人机工作流程，还是用单独的人类或单独的代理来替代。人机工作流程只有在其表现优于这两种替代方案时才值得保留。然而，一旦部署，就无法观察到任何替代结果：恢复其中一个意味着在该替代方案下重新执行任务，而每次重播都需要专家时间或计算资源。在固定的重播预算下，设计问题因此变为哪些任务更可能获得人类单独重播，哪些任务更可能获得代理单独重播。现有方法并未直接针对这一决策。代理基准并未选择测量哪个缺失基线，基于方差的采样忽略了哪种比较更接近失败，而贝叶斯信息方法则专注于学习模型参数而非做出部署决策。我们提出了TEAM-Design，这是一条为每个任务提供两个重播概率的规则，每个基线一个。它提高了在缺失基线结果难以预测且该比较更难建立的情况下的概率，并在重播成本高的情况下降低该概率。我们证明了该规则解决了这一预算设计问题，并且从记录的概率中随机抽取重播仍然控制了错误声明工作流程优于两者的机会。我们重新分析了6个临床场景，在这些场景中，没有人机工作流程优于两种替代方案，以及一个编码基准，在该基准中，有一个工作流程优于两者，然后在合成设计和基于真实胸部X光阅读研究构建的半合成设计上评估TEAM-Design。当两个比较中有一个明显更难以解决时，TEAM-Design表现最佳，而当两者同样困难时，其表现可能不如基于方差的分配。
cs.AI / 16 / 2609.05553
EdgeMem: LLM-Free Agent Memory Construction and Retrieval via Evidence-Preserving Multi-Anchor Hypergraph
EdgeMem：通过证据保留的多锚超图构建和检索无LLM代理记忆
Abstract
Agent memory allows LLM agents to use earlier interactions when answering new queries. Existing methods often compress interaction histories into summaries or other LLM-generated representations. Repeated generation adds cost and can discard answer-bearing details before the system knows what a future query will require. We propose EdgeMem, an agent-memory method built around a simple principle: preserve original interaction turns and organize them through complementary content, temporal, and episodic cues. EdgeMem realizes this principle with a multi-anchor hypergraph constructed by lightweight local processing. Retrieval directly returns source evidence and reserves LLM use for final answer generation, combining structured access to multi-session histories with faithful retention of the original conversation. Experiments on LoCoMo and LongMemEval-S show strong retrieval and memory-grounded question answering; on LoCoMo, EdgeMem achieves the highest strict-judge score among seven reproduced systems under a shared prompt (61.01 versus 58.70), while construction and retrieval require no generative-LLM calls. Overall, EdgeMem shows that preserving and organizing source evidence provides an effective and efficient foundation for agent memory without generative memory management.
Chinese Translation
代理记忆使得LLM代理在回答新查询时能够利用早期的交互。现有方法通常将交互历史压缩为摘要或其他由LLM生成的表示。重复生成增加了成本，并且在系统尚未了解未来查询的需求之前，可能会丢弃包含答案的细节。我们提出了EdgeMem，这是一种围绕简单原则构建的代理记忆方法：保留原始交互轮次，并通过互补的内容、时间和情节线索对其进行组织。EdgeMem通过轻量级的本地处理实现这一原则，构建了一个多锚超图。检索直接返回源证据，并将LLM的使用保留用于最终答案生成，结合了对多会话历史的结构化访问与对原始对话的忠实保留。在LoCoMo和LongMemEval-S上的实验显示了强大的检索能力和基于记忆的问题回答；在LoCoMo上，EdgeMem在七个复现系统中获得了共享提示下最高的严格评判分数（61.01对58.70），而构建和检索不需要生成式LLM调用。总体而言，EdgeMem表明，保留和组织源证据为代理记忆提供了一个有效且高效的基础，而无需生成式记忆管理。
cs.AI / 17 / 2609.05572
Deep belief networks are exact
深度信念网络的精确性
Abstract
We prove that every strictly positive probability distribution on \(\{-1,1\}^n\) is represented exactly by a sigmoid belief network with finite parameters. This answers a question of Sutskever and Hinton. The proof upgrades their probability-sharing approximation to exact representation using Brouwer's fixed-point theorem.
Chinese Translation
我们证明了在 extbackslash exttt{ extbraceleft-1,1 extbraceright}^n上的每一个严格正的概率分布都可以被具有有限参数的sigmoid信念网络精确表示。这回答了Sutskever和Hinton提出的问题。该证明将他们的概率共享近似升级为使用Brouwer不动点定理的精确表示。
cs.AI / 18 / 2609.05576
EnvCraft: Synthesizing Executable Environments in Agentic RL for Claw-like Agent
EnvCraft：为爪状智能体合成可执行环境的代理强化学习
Abstract
The paradigm of LLMs has rapidly shifted from passive language interfaces to autonomous Claw-like agents that execute long-horizon tasks across stateful workspaces. While Agentic Reinforcement Learning (Agentic RL) provides a promising path to optimize these agents, its scaling is heavily bottlenecked by the severe scarcity of interactive training environments. Existing synthetic environments are strictly limited to tool-calling endpoints, rendering them insufficient for accommodating the end-to-end real-world demands of claw-like agents. To bridge this gap, we introduce EnvCraft, an automated framework for synthesizing executable environments and scalable training data. Specifically, EnvCraft employs an environment synthesis engine to build sandbox-isolated workspaces, alongside a topology-aware data generation engine to produce coherent task trajectories. Overall, we synthesize 139 interactive environments comprising approximately 20K complex tasks for Agentic RL training. Experiments on Qwen3/3.5 models (8B-32B) show that our method yields gains of up to +11.9% on Claw-style benchmarks and +8.0% on general tool-use benchmarks, with concurrent reductions in inference token cost. The results confirm that synthesized executable environments provide robust and generalizable learning signals for training.
Chinese Translation
大型语言模型（LLMs）的范式迅速从被动语言接口转变为能够在状态工作空间中执行长时间任务的自主爪状智能体。尽管代理强化学习（Agentic RL）为优化这些智能体提供了有前景的路径，但其扩展受到交互式训练环境严重匮乏的制约。现有的合成环境严格限于工具调用端点，无法满足爪状智能体在端到端真实世界中的需求。为了解决这一问题，我们提出了EnvCraft，一个用于合成可执行环境和可扩展训练数据的自动化框架。具体而言，EnvCraft利用环境合成引擎构建沙箱隔离工作空间，并结合拓扑感知数据生成引擎生成连贯的任务轨迹。总体而言，我们合成了139个交互式环境，包含约20,000个复杂任务，用于代理强化学习训练。在Qwen3/3.5模型（8B-32B）上的实验表明，我们的方法在爪状基准测试上提升了高达11.9%的性能，在一般工具使用基准测试上提升了8.0%，同时降低了推理令牌成本。结果确认，合成的可执行环境为训练提供了稳健且可泛化的学习信号。
cs.AI / 19 / 2609.05578
Planning and Scheduling Business Processes under Control-Flow Uncertainty
在控制流不确定性下规划和调度业务流程
Abstract
Scheduling activities in business processes can improve efficiency (e.g., reduce makespan), but is challenging because the exact sequence of activities required to complete a case is often uncertain due to decisions based on data that emerges during execution. Nevertheless, probabilistic information regarding such decisions can often be estimated or derived from historical execution logs, and can help anticipate which execution paths are likely to lead to successful completion. Planning with particular execution paths affects feasibility, i.e., the probability of successful completion, and the expected number of superfluous activities that are planned but never executed. We frame the problem as a chance-constrained optimization problem and present two formulations: A decomposed approach with two stages, a planning stage that minimizes the expected number of superfluous activities subject to a feasibility constraint, and a scheduling stage that minimizes the makespan over the planned activities; and an integrated approach that combines planning and scheduling into a single formulation. Evaluation on two real-world and one synthetic dataset shows that the integrated approach yields superior makespans but is intractable at scale, while the decomposed approach scales to large settings.
Chinese Translation
在业务流程中调度活动可以提高效率（例如，减少完成时间），但由于在执行过程中基于数据做出的决策，完成一个案例所需的活动确切顺序通常是不确定的，因此调度活动面临挑战。然而，关于这些决策的概率信息通常可以从历史执行日志中估计或推导出来，并有助于预测哪些执行路径可能导致成功完成。规划特定的执行路径会影响可行性，即成功完成的概率，以及计划但从未执行的多余活动的预期数量。我们将该问题框定为一个机会约束优化问题，并提出两种模型：一种分解方法，包含两个阶段，规划阶段在可行性约束下最小化预期的多余活动数量，调度阶段最小化计划活动的完成时间；另一种集成方法，将规划和调度结合为一个单一的模型。在两个真实世界数据集和一个合成数据集上的评估表明，集成方法在完成时间上表现优越，但在大规模情况下难以处理，而分解方法则能够扩展到大规模设置。
cs.AI / 20 / 2609.05587
Agents Trust Tools Too Much: Measuring Reliance on Unreliable Tools
代理过度信任工具：对不可靠工具的依赖性测量
Abstract
Existing evaluations of tool-using agents primarily measure whether an agent can successfully complete diverse tasks with tools. These evaluations generally assume that tools return reliable information. However, tool returns in real-world systems can be plausible yet incorrect. We investigate how agents respond to unreliable tool returns by evaluating fourteen LLMs using three tools-web search, LLM sub-agent delegation, and code execution. For each tool, we corrupt its returns and measure whether agents adopt the corrupted content in their final answers. Agents exhibit high levels of overtrust across all three settings: the mean adoption rate exceeds one third for every tool and reaches 68.0% for web search. Analysis of reasoning traces reveals a particularly concerning failure mode: agents often recognize conflicts and even recover the correct answer internally, yet present only the corrupted answer without warning the user. To mitigate agents' overtrust in tool returns, we intervene at three levels: prompting by the user, metadata from the tool provider, and post-training by the agent builder. Although some interventions help for particular models or tools, none consistently mitigates overtrust across tools. These findings identify overtrust in unreliable tools as a serious and persistent failure mode, motivating evaluations and interventions that enable agents to validate tool outputs and transparently communicate unresolved conflicts.
Chinese Translation
现有的工具使用代理评估主要测量代理是否能够成功完成多样化的任务。这些评估通常假设工具返回可靠的信息。然而，现实世界系统中的工具返回可能看似合理但却不正确。我们通过评估十四个大型语言模型（LLMs）使用三种工具——网络搜索、LLM子代理委托和代码执行——来研究代理如何应对不可靠的工具返回。对于每种工具，我们对其返回结果进行干扰，并测量代理是否在最终答案中采用了被干扰的内容。代理在所有三种设置中表现出高度的过度信任：每种工具的平均采用率超过三分之一，网络搜索的采用率达到68.0%。对推理轨迹的分析揭示了一种特别令人担忧的失败模式：代理通常能够识别冲突，甚至在内部恢复正确答案，但却仅呈现被干扰的答案而不警告用户。为了减轻代理对工具返回的过度信任，我们在三个层面进行干预：用户的提示、工具提供者的元数据，以及代理构建者的后训练。尽管一些干预措施对特定模型或工具有效，但没有一种措施能够在所有工具中一致地减轻过度信任。这些发现将对不可靠工具的过度信任识别为一种严重且持久的失败模式，促使评估和干预措施的实施，以使代理能够验证工具输出并透明地传达未解决的冲突。
cs.AI / 21 / 2609.05643
The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry
汇聚实验室：当人工智能推理、自主实验、高性能和量子计算重塑化学
Abstract
This Comment emerges from TPC26 (https://tpc26.org), a conference convening leaders from academia, national laboratories, and industry who are reshaping materials science discovery. The meeting explored how AI, autonomous agents, self-driving labs, higher performance and quantum computing converge to amplify their individual impact on materials science discovery. The perspectives here reflect the firsthand experiences of researchers at these frontiers and capture the essence of this global endeavor. As AI-driven reasoning, autonomous agentic frameworks, self-driving laboratories, and fault-tolerant quantum processors mature simultaneously, we offer this Comment as a reference at what we believe is a tipping point of transformative advances and productive disruption in the chemical sciences.
Chinese Translation
本文评论源于TPC26（https://tpc26.org），这是一个汇聚学术界、国家实验室和行业领导者的会议，他们正在重塑材料科学的发现。会议探讨了人工智能、自主代理、自驾实验室、更高性能和量子计算如何汇聚在一起，以增强它们对材料科学发现的个体影响。这里的观点反映了这些前沿研究者的第一手经验，并捕捉到这一全球努力的本质。随着人工智能驱动的推理、自主代理框架、自驾实验室和容错量子处理器的同步成熟，我们将此评论作为参考，认为这是化学科学中变革性进展和富有成效的颠覆的临界点。
cs.AI / 22 / 2609.05663
What LLM Trading Agents Actually Do in Production: A Six-Month, Population-Scale Record from Two Fleets
大型语言模型交易代理在生产中的实际表现：来自两个舰队的六个月人口规模记录
Abstract
We present a continuous, population-scale measurement record of autonomous language-model trading agents operating in production across two systems with one design lineage: DX Terminal Pro (3,505 user-funded vaults trading real ETH in Base memecoin markets for 21 days, February to March 2026) and the DXAP live alpha fleet (500 to 599 user-created agents all-history, 91 to 117 concurrently active, trading Hyperliquid perpetuals, June to August 2026). The record spans roughly six months, 7.5M single-model invocations with about 300K onchain actions, and a further 231,638 multi-tool turns producing 14,596 fills. Four findings carry the paper. First, the operating layer determines behavior more than anything written in strategy text: a risk slider explains leverage (+0.425 per level), agent fixed effects absorb 60% of variance, and a leaderboard render boundary causally routes selection (regression discontinuity 1.75x at the top-3 cut). Second, sizing is volatility-blind: median leverage is 5.0x in every volatility sextile, and one posture-slider cell (11% of the book) holds 62% of liquidations. Third, agents capture almost none of the upside they reach: 43.2% of positions saw at least +300 bps of favorable excursion within 24h, yet 49.3% of those closed with a negative trade return; a mechanical bracket recovers +39.0 bps per position. Fourth, neither fleet shows a directional edge. The DXAP fleet is not profitable and trails a matched Hyperliquid retail benchmark (41% vs. 50% roundtrip win rate). A paired-replay league of frontier models on 416 captured production scenarios finds decision quality statistically indistinguishable at this horizon, while choice stability differs sharply across model families. Every headline survives day-clustered inference, permutation nulls, and a common-fee restatement; the paper closes with a 17-rule methodology canon bought with our own retractions.
Chinese Translation
我们展示了一个连续的、人口规模的测量记录，记录了在两个具有相同设计谱系的系统中运行的自主语言模型交易代理的生产情况：DX Terminal Pro（3,505个用户资助的金库在基础memecoin市场中交易真实ETH，持续21天，2026年2月至3月）和DXAP实时alpha舰队（500至599个用户创建的代理，历史记录，91至117个同时活跃，交易Hyperliquid永续合约，2026年6月至8月）。该记录大约跨越六个月，包含750万次单模型调用，约30万次链上操作，以及进一步的231,638次多工具回合，产生14,596次成交。论文中有四个主要发现。首先，操作层比策略文本中写的任何内容更能决定行为：风险滑块解释了杠杆（每级+0.425），代理固定效应吸收了60%的方差，而排行榜渲染边界因果性地引导选择（在前3名切分处回归不连续性为1.75倍）。第二，仓位规模对波动性无感：每个波动性六分位的中位杠杆为5.0倍，且一个姿态滑块单元（占总账本的11%）持有62%的清算。第三，代理几乎没有捕捉到他们所达到的任何上行空间：43.2%的头寸在24小时内经历了至少+300个基点的有利波动，但其中49.3%以负交易收益结束；一个机械性区间每个头寸回收+39.0个基点。第四，两个舰队都没有显示出方向性优势。DXAP舰队并不盈利，且落后于匹配的Hyperliquid零售基准（41%对50%的回合胜率）。在416个捕获的生产场景中进行的前沿模型配对重放联赛发现，在这个时间范围内决策质量在统计上无显著差异，而选择稳定性在模型家族之间差异显著。每个结论都经过了基于天数的推断、置换无效和共同费用重述的检验；论文最后以我们自己撤回的17条规则方法论典范结束。
cs.AI / 23 / 2609.05708
CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning
CUSP：用于多智能体多模态推理的可分解集体不确定性
Abstract
Aggregating heterogeneous vision-language models (VLMs) can improve multimodal reasoning, but neither an individual model's confidence nor that of the aggregated answer measures reliability at the system level. We present CUSP (Collective Uncertainty through Semantic Opinion Pooling), a training-free uncertainty quantification framework that maps multiple VLM responses to a shared semantic response space, pools them into a pooled semantic opinion, and reports two complementary system-level signals: collective uncertainty, the dispersion of the pooled opinion, and Jensen-Shannon divergence (JSD), the conflict among the model-level opinions. Within this pooled semantic opinion, the unnormalized collective entropy decomposes exactly into the mean of the models' individual semantic entropies and the JSD, separating total dispersion from model conflict. Requiring neither token logits nor calibration labels, CUSP applies to open-weight and commercial VLMs alike. In static multi-VLM ensembles, collective uncertainty is the strongest signal in the small-model regime (0.764 AUROC for prediction-error detection, 0.889 AUARC for abstention), outperforming uncertainty baselines majority voting and naive selection by 4.7 to 15.8 points and widening its margin as the ensemble grows; JSD is strongest in the evaluated commercial regime (0.819 AUROC, 0.910 AUARC) and ranks hard-answer model conflict with AUROC up to 0.982. The pooled prediction also improves accuracy over the average single model by 5.6 to 13.0 points. Over the full trajectory of a multi-step, multi-agent system, subagent collective uncertainty ranks system failures above chance (0.619 AUROC) and gives the best abstention ordering among the evaluated signals (0.699 AUARC).
Chinese Translation
聚合异构视觉-语言模型（VLMs）可以改善多模态推理，但单个模型的置信度或聚合答案的置信度都无法在系统层面上衡量可靠性。我们提出了CUSP（通过语义意见聚合的集体不确定性），这是一个无训练的不确定性量化框架，它将多个VLM响应映射到共享的语义响应空间，将其聚合为一个聚合的语义意见，并报告两个互补的系统级信号：集体不确定性，即聚合意见的离散程度，以及Jensen-Shannon散度（JSD），即模型级意见之间的冲突。在这个聚合的语义意见中，未归一化的集体熵恰好分解为模型个体语义熵的均值和JSD，从而将总离散度与模型冲突分开。CUSP不需要标记logits或校准标签，适用于开放权重和商业VLMs。在静态多VLM集成中，集体不确定性在小模型范围内是最强信号（预测错误检测的AUROC为0.764，弃权的AUARC为0.889），比不确定性基线的多数投票和简单选择高出4.7到15.8个百分点，并且随着集成规模的扩大，其优势进一步扩大；在评估的商业范围内，JSD是最强的（AUROC为0.819，AUARC为0.910），并且在硬答案模型冲突中以AUROC高达0.982进行排名。聚合预测的准确性也比平均单模型提高了5.6到13.0个百分点。在多步骤、多智能体系统的完整轨迹中，子代理集体不确定性将系统故障的排名高于偶然（AUROC为0.619），并在评估信号中提供最佳的弃权排序（AUARC为0.699）。
cs.AI / 24 / 2609.05721
Recovering Temporal and Geographic Signals from Language Model Embeddings
从语言模型嵌入中恢复时间和地理信号
Abstract
Understanding whether language-model embeddings encode structured real-world information is important for both representation analysis and information retrieval. We study this question for temporal and geographic signals using a simple projection-based method that operates directly on output embeddings. Given a small set of seed examples, the method defines an axis in embedding space and ranks texts or entities by their projection onto that axis. Our approach is fully black-box and model-agnostic: it requires only embeddings, without access to model weights, internal activations, auxiliary probes, or additional training. This makes it applicable to modern embedding models available only through APIs and provides a lightweight way to analyze whether temporal and spatial dimensions are present in their representation spaces. We apply the method to temporal and geographic datasets and find that embedding projections recover meaningful chronological and spatial structure. These results provide evidence that output embeddings encode signals relevant to time and space, while also offering a practical tool for interpretability and for downstream temporal and geographic information retrieval tasks, such as temporal ordering, geographic ranking, and tagging.
Chinese Translation
理解语言模型嵌入是否编码结构化的现实世界信息，对于表示分析和信息检索都具有重要意义。我们使用一种简单的基于投影的方法研究时间和地理信号的问题，该方法直接作用于输出嵌入。给定一小组种子示例，该方法在嵌入空间中定义一个轴，并通过文本或实体在该轴上的投影对其进行排序。我们的方法完全是黑箱的且与模型无关：它仅需要嵌入，而不需要访问模型权重、内部激活、辅助探针或额外训练。这使得它适用于仅通过API提供的现代嵌入模型，并提供了一种轻量级的方法来分析时间和空间维度是否存在于其表示空间中。我们将该方法应用于时间和地理数据集，发现嵌入投影恢复了有意义的时间顺序和空间结构。这些结果提供了证据，表明输出嵌入编码了与时间和空间相关的信号，同时也为可解释性以及下游时间和地理信息检索任务（如时间排序、地理排名和标记）提供了实用工具。
cs.AI / 25 / 2609.05736
Beyond Prompts: Measuring and Optimizing LLM Tool-Agent Harnesses
超越提示：测量和优化大型语言模型工具代理的运行框架
Abstract
LLM tool agents can be improved without retraining by modifying the runtime harness around a fixed model: prompts, tool interfaces, middleware, state handling, and recovery logic. We study this setting as resource-bounded harness selection for fixed-model multi-turn tool agents, with the search surface scoped to prompts and tool-boundary middleware: edits are guarded intercepts at the tool boundary, not arbitrary rewriting of agent execution logic. Our optimizer-agnostic protocol reports mean held-out lift, worst-condition lift, repeatability, logged cost diagnostics, and RelLift95(B), a conservative estimate of the held-out gain of the harness selected under budget B. We instantiate the protocol with prompt-only and prompt-plus-middleware optimizers, including PRISM, which clusters failures and routes repairs to prompt, tool-boundary middleware, or joint edit surfaces within a Pareto search. On BFCL multi-round, tau2-Retail, and tau2-Telecom, PRISM obtains mean held-out lifts of 14.2, 14.9, and 10.1 percentage points and positive empirical RelLift95 on all three benchmarks, and a component ablation attributes the margin chiefly to failure-surface routing and the edit-pattern constraint. Across optimizers, the results show that some search procedures can occasionally find large gains but still choose brittle updates, so the reliability of the chosen harness should be reported alongside average held-out lift.
Chinese Translation
大型语言模型（LLM）工具代理可以通过修改固定模型周围的运行框架来改进，而无需重新训练：包括提示、工具接口、中间件、状态处理和恢复逻辑。我们将这一设置视为固定模型多轮工具代理的资源受限框架选择，搜索范围限定在提示和工具边界中间件：编辑是工具边界的受控拦截，而不是对代理执行逻辑的任意重写。我们的优化器无关协议报告了平均持出提升、最坏条件提升、可重复性、记录的成本诊断以及RelLift95(B)，这是在预算B下选择的框架的保守估计的持出增益。我们用仅提示和提示加中间件的优化器实例化该协议，包括PRISM，它将失败聚类并将修复路由到提示、工具边界中间件或在帕累托搜索中的联合编辑表面。在BFCL多轮、tau2-Retail和tau2-Telecom上，PRISM获得了14.2、14.9和10.1个百分点的平均持出提升，并在所有三个基准上都获得了正的经验RelLift95，组件消融分析将这一差距主要归因于失败表面的路由和编辑模式约束。在不同的优化器中，结果表明某些搜索过程偶尔可以找到较大的增益，但仍然选择脆弱的更新，因此所选择的框架的可靠性应与平均持出提升一起报告。
cs.AI / 26 / 2609.05749
The Normalization of Deviance in AI Development
人工智能开发中的偏差规范化
Abstract
Work on the risks of artificial intelligence has focused predominantly on capability risk: the danger that systems become too powerful, too autonomous, or too misaligned with human values. Far less attention has been paid to the organizational level---to whether the institutions building these systems are themselves predisposed to drift toward failure. This paper argues that they are. Regardless of how capable AI systems become, the organizations building them face the same structural dynamics that preceded past major technological disasters. Drawing on case studies of the Space Shuttle Challenger, the Three Mile Island accident, and the Boeing 737 MAX crashes, this paper identifies the common structural mechanisms preceding each failure and maps them onto contemporary AI development. The findings suggest that existing safety infrastructure may provide less protection than it appears, as organizations can complete safety processes in full compliance and still produce catastrophic outcomes. The pre-disaster period of AI development is still underway; the purpose of this paper is to make these dynamics legible while they can still be interrupted.
Chinese Translation
关于人工智能风险的研究主要集中在能力风险上：即系统变得过于强大、过于自主或与人类价值观不一致的危险。而对组织层面的关注则相对较少——即构建这些系统的机构是否本身倾向于向失败漂移。本文论证了这一点。无论人工智能系统变得多么强大，构建它们的组织面临的结构性动态与过去重大技术灾难之前的情况相同。通过对挑战者号航天飞机、三里岛事故和波音737 MAX坠毁事件的案例研究，本文识别出每次失败之前的共同结构机制，并将其映射到当代人工智能开发中。研究结果表明，现有的安全基础设施可能提供的保护远不如表面上看起来的那样，因为组织可以完全遵循安全流程，但仍然会产生灾难性的结果。人工智能开发的灾前阶段仍在进行中；本文的目的是在这些动态仍可被打断时，使其变得清晰可见。
cs.AI / 27 / 2609.05758
From Monolithic Blending to Agentic Orchestration: Dynamic Response for Conversational Assistants at Scale
从单一混合到自主编排：大规模对话助手的动态响应
Abstract
Conversational assistants can blend retrieval, action selection, escalation, and wording in a single model path, or separate those roles. We report a production migration of a customer-support assistant at a large accommodation marketplace (millions of conversations per month, 11 languages, 10-second P90). Dynamic Response (DR) replaces a single Qwen3-235B-A22B blended responder with a bounded ReAct orchestrator over typed tools plus a smaller generator that writes from a backend-validated context contract. Because the migration also changed prompts, alignment, and serving, we attribute each effect to its cause and claim as architecture effects only those measured on identical replayed turns: typed entity selection moves the reservation selector to a precision-first operating point (precision 8.3% to 89.1%, recall 75.2% to 67.3%), and typed action IDs with a membership check remove observed structured-action hallucination (2.14% to 0.0%). A low-ramp A/B test reproduces the replay escalation reductions: hard-escalation responses fall from 5.60% to 3.08% and soft-escalation responses from 9.56% to 2.49%, while production handoff volume holds roughly steady; self-solve is directional (+5.1 points, 95% CI [-2, +12]). Serving optimizations cut orchestrator P90 latency from 3.87s to 2.24s on a GPU footprint reduced by roughly one-third, and self-hosting reduces estimated annual model-serving cost by more than an order of magnitude.
Chinese Translation
对话助手可以在单一模型路径中融合检索、行动选择、升级和措辞，或者将这些角色分开。我们报告了一个大型住宿市场（每月数百万次对话，11种语言，10秒P90）客户支持助手的生产迁移。动态响应（Dynamic Response, DR）用一个有界的ReAct编排器替代了单一的Qwen3-235B-A22B混合响应器，该编排器结合了类型化工具和一个较小的生成器，后者根据后端验证的上下文合同进行写作。由于迁移还改变了提示、对齐和服务，我们将每个效果归因于其原因，并声称只有在相同重放回合上测量的架构效应：类型化实体选择将预订选择器移动到精度优先的操作点（精度从8.3%提升至89.1%，召回率从75.2%下降至67.3%），而带有成员检查的类型化行动ID消除了观察到的结构化行动幻觉（从2.14%降至0.0%）。一个低幅度的A/B测试重现了重放升级的减少：硬升级响应从5.60%降至3.08%，软升级响应从9.56%降至2.49%，而生产交接量大致保持稳定；自我解决方向性为正（+5.1个百分点，95%置信区间[-2, +12]）。服务优化将编排器的P90延迟从3.87秒减少到2.24秒，同时GPU占用减少了大约三分之一，自托管将估计的年度模型服务成本降低了一个数量级以上。
cs.AI / 28 / 2609.05774
Inference-Time Graph Engineering for Multi-Agent LLM Workflows
推理时图工程用于多智能体大语言模型工作流
Abstract
Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestration from a graph-engineering perspective: rather than optimizing a static topology, we synthesize a task-conditioned temporal workflow graph that jointly specifies agent connectivity and edge-level communication semantics. We introduce ReActNet, a training-free framework that compiles a query and a set of role-specialized agents into a sequence of directed communication graphs. Each graph snapshot corresponds to one reasoning stage, and each edge carries a natural-language instruction specifying the message that a source agent should provide to a target agent. The compiled temporal graph is then executed through structured message passing: agents update their reasoning states by integrating their previous states with messages from controller-assigned neighbors, and a final aggregator synthesizes the resulting states into the answer. This design separates graph compilation from graph execution, making multi-agent coordination explicit, inspectable, and task-conditioned without requiring reinforcement learning or gradient-based topology optimization. Across knowledge reasoning, mathematical problem solving, code generation, and GAIA-style assistant tasks, ReActNet consistently improves over fixed-topology and learned-topology baselines while maintaining competitive inference cost. These results suggest that effective multi-agent orchestration depends not only on which agents communicate, but also on engineering executable workflow graphs that encode when, why, and how information should flow during reasoning.
Chinese Translation
近年来，多智能体大语言模型系统越来越依赖图结构通信来协调专业化的智能体。我们从图工程的角度重新审视多智能体编排：与其优化静态拓扑，不如合成一个任务条件的时间工作流图，该图共同指定智能体的连接性和边级通信语义。我们引入了ReActNet，这是一个无训练的框架，将查询和一组角色专业化的智能体编译成一系列有向通信图。每个图快照对应一个推理阶段，每条边携带一个自然语言指令，指定源智能体应向目标智能体提供的信息。编译后的时间图通过结构化消息传递执行：智能体通过将其先前状态与来自控制器分配邻居的消息整合来更新其推理状态，最终聚合器将结果状态合成答案。该设计将图编译与图执行分离，使多智能体协调变得明确、可检查，并且是任务条件的，而无需强化学习或基于梯度的拓扑优化。在知识推理、数学问题解决、代码生成和GAIA风格助手任务中，ReActNet始终优于固定拓扑和学习拓扑基线，同时保持竞争的推理成本。这些结果表明，有效的多智能体编排不仅依赖于哪些智能体进行通信，还依赖于工程可执行的工作流图，以编码在推理过程中信息应何时、为何以及如何流动。
cs.AI / 29 / 2609.05776
DI-Bench: Systematically Generating In-Domain Data Intelligence Benchmarks for Enterprise Agents
DI-Bench：系统生成企业代理的领域数据智能基准
Abstract
Evaluating enterprise agents on domain-specific benchmarks is critical, yet public benchmarks rarely evaluate whether agents can integrate business knowledge with analytical computation, and constructing such benchmarks manually is costly. We present DI-Bench, a pipeline for generating realistic benchmarks for data intelligence (DI), the practice of extracting insights from large volumes of enterprise data. To emulate realistic DI tasks that require both computation and knowledge retrieval, DI-Bench builds an artifact linkage graph over data tables, dimensions, metrics, and documents to form questions involving structured data and associated knowledge. Ground truth answers are derived via query execution, followed by LLM question generation and validation. Applied to two public datasets, the pipeline produces a 731-task benchmark covering knowledge retrieval, analytical computation, and rule-grounded reasoning. To show the discriminatory capability and difficulty of the benchmark, we evaluate four models, revealing a substantial finding: models achieve only 32% accuracy when doing computational tasks where retrieved business rules modify the computation.
Chinese Translation
在领域特定基准上评估企业代理至关重要，但公共基准很少评估代理是否能够将商业知识与分析计算相结合，而手动构建此类基准的成本很高。我们提出了DI-Bench，这是一个生成数据智能（DI）现实基准的管道，数据智能是从大量企业数据中提取见解的实践。为了模拟需要计算和知识检索的现实DI任务，DI-Bench在数据表、维度、指标和文档上构建了一个工件链接图，以形成涉及结构化数据和相关知识的问题。通过查询执行获得真实答案，随后进行大型语言模型（LLM）问题生成和验证。应用于两个公共数据集，该管道生成了一个涵盖知识检索、分析计算和基于规则的推理的731任务基准。为了展示基准的区分能力和难度，我们评估了四个模型，揭示了一个重要发现：在进行计算任务时，当检索的商业规则修改计算时，模型的准确率仅为32%。
cs.AI / 30 / 2609.05782
Distilling Vision-Language Models for On-Device Fire Understanding
针对设备端火灾理解的视觉-语言模型蒸馏
Abstract
Vision-language models (VLMs) offer a promising alternative to conventional fire detection systems by reasoning about the semantic context of a scene and thus reducing false alarms, yet their large model size makes deployment on embedded fire sensors impractical. In this paper, we study how domain-specialized VLMs can be compressed for fully on-device deployment without losing the safety-critical behavior required for fire detection. We develop a teacher-student knowledge distillation framework in which large VLMs fine-tuned for fire understanding can be distilled into lightweight students. Experiments across multiple VLM families and model scales show that compact students preserve most of their teachers' fire-understanding capability. We further deploy the distilled models on our commercial Detectium fire detection sensor and jointly evaluate reasoning accuracy, latency, and memory usage. The results show that compression and deployment affect not only accuracy but also model failure modes, with Qwen2.5-0.5B providing the strongest overall deployment trade-off. Our findings provide broader guidance for deploying domain-specialized VLMs in resource-constrained, safety-critical settings.
Chinese Translation
视觉-语言模型（VLMs）通过推理场景的语义上下文，为传统火灾检测系统提供了一种有前景的替代方案，从而减少误报。然而，它们庞大的模型规模使得在嵌入式火灾传感器上的部署变得不切实际。本文研究了如何对领域专用的VLMs进行压缩，以便在不失去火灾检测所需的安全关键行为的情况下实现完全的设备端部署。我们开发了一个教师-学生知识蒸馏框架，其中针对火灾理解进行微调的大型VLMs可以被蒸馏成轻量级的学生模型。针对多个VLM家族和模型规模的实验表明，紧凑型学生模型能够保留大部分教师模型的火灾理解能力。我们进一步在我们的商业Detectium火灾检测传感器上部署蒸馏模型，并共同评估推理准确性、延迟和内存使用情况。结果表明，压缩和部署不仅影响准确性，还影响模型的失效模式，其中Qwen2.5-0.5B提供了最佳的整体部署权衡。我们的研究结果为在资源受限和安全关键环境中部署领域专用VLMs提供了更广泛的指导。
cs.AI / 31 / 2609.05788
More Than Mimicking Reviewers: Evaluating LLMs for Pre-Submission Peer Review
超越模仿评审者：评估大型语言模型在提交前同行评审中的应用
Abstract
Peer-review feedback often arrives too late for authors to make meaningful revisions. We study an author-facing LLM system that moves part of this stress test before submission: it generates a broad pool of atomic concerns and compresses them into a short report. We evaluate agreement with historical reviews and, separately, the possible validity of concerns they omit. From 10,000 ICLR 2026 submissions, we use 3,398 manuscripts with accessible versions that predate review. On a ten-paper diagnostic, independent sampling covers 44.9% of historical issues; deduplication and refill reaches 78.7% strict and 84.9% seriousness-weighted coverage, at 3.6$\times$ more requests and 5.2$\times$ more tokens. A hidden Top-32 Oracle preserves the full 79.3% weighted coverage of a 256-candidate pool, but paper-only selectors retain only 40--44%. LLM review therefore provides broad coverage with a large candidate pool but compresses poorly; ablations identify representative selection and matcher sensitivity as the main sources of this gap.
Chinese Translation
同行评审反馈往往在作者进行有意义的修订之前就已经迟到了。我们研究了一种面向作者的大型语言模型（LLM）系统，它将部分压力测试移至提交之前：该系统生成广泛的原子性问题池，并将其压缩成一份简短报告。我们评估了与历史评审的一致性，以及它们遗漏问题的可能有效性。在10,000份2026年国际学习表征会议（ICLR）提交中，我们使用了3,398份在评审之前可获取的手稿。在一项十篇论文的诊断中，独立抽样覆盖了44.9%的历史问题；去重和重新填充达到了78.7%的严格覆盖率和84.9%的严重性加权覆盖率，所需请求数增加了3.6倍，令牌数增加了5.2倍。一个隐藏的前32名预言机保留了256候选池的79.3%加权覆盖率，但仅基于论文的选择器仅保留了40%至44%。因此，LLM评审提供了广泛的覆盖范围和大量候选池，但压缩效果较差；消融实验确定了代表性选择和匹配敏感性是造成这一差距的主要原因。
cs.AI / 32 / 2609.05800
Spillover-Aware Multi-Value Steering for Pluralistic LLM Alignment
考虑溢出效应的多值引导用于多元化大型语言模型对齐
Abstract
Activation steering controls LLM behavior at inference time by adding learned directions to hidden states, but existing methods handle one concept at a time. Pluralistic alignment, where different stakeholders need different value emphases, requires steering multiple dimensions simultaneously. We show that naive steering produces substantial spillover: the effect intended for one value leaks into others. This parallels the treatment-versus-spillover decomposition in causal inference. We trace spillover to geometric entanglement of steering directions, captured by their Gram matrix, and derive a zero-cost correction from an activation-norm-penalized objective that decouples each direction's contribution exactly. Our end-to-end pipeline requires no fine-tuning, no reward model, and no manual prompt engineering: given only domain questions, it automatically discovers value dimensions, extracts directions, diagnoses entanglement, and applies corrected steering. On climate discourse, the correction improves the net steering effect from +5.9% to +14.0%, validated over 100,000 pairwise judgments.
Chinese Translation
激活引导通过向隐藏状态添加学习到的方向来控制大型语言模型（LLM）在推理时的行为，但现有方法一次只能处理一个概念。多元化对齐要求不同利益相关者需要不同的价值强调，因此需要同时引导多个维度。我们展示了简单的引导会产生显著的溢出效应：一个价值的意图效果会泄漏到其他价值上。这与因果推断中的处理与溢出分解相似。我们追踪溢出效应的根源在于引导方向的几何纠缠，这通过它们的Gram矩阵捕捉到，并从一个激活范数惩罚的目标中推导出零成本的修正，该目标准确地解耦了每个方向的贡献。我们的端到端流程不需要微调、奖励模型或手动提示工程：仅给定领域问题，它自动发现价值维度、提取方向、诊断纠缠并应用修正引导。在气候话语中，修正将净引导效果从+5.9%提高到+14.0%，并在100,000个成对判断中得到了验证。
cs.AI / 33 / 2609.05801
Evidence-Aligned Local Composition of Discrete Experts for Sequence Restoration
基于证据对离散专家局部组合的序列恢复
Abstract
A document modeled as a discrete sequence of tokens can be thought of as being generated from a composition of texts from different domains; a README file, for example, moves between prose, code, and configuration. When such a document is corrupted and only frozen domain experts are available, restoring it requires deciding both what is missing and which expert to trust at each position, at test time and without region labels or a trained router. We introduce evidence-aligned local composition, which infers a soft, position-wise weighting over the experts from the marginal evidence of the corrupted observation under a given corruption model, estimating the evidence from the experts' own denoising losses and smoothing the weights across positions. Because the weighting is soft, it recovers a mixture when the true composition is mixed and concentrates on one expert when that suffices. Across a categorical simulator, byte-level experts, and experts fine-tuned from a $1.3$B discrete flow-matching model, the inferred weights track the true regions at $0.85$ field accuracy on naturally mixed scientific documents, and at $0.98$ on constructed mixtures whose regions are lexically disjoint. Restoration improves over a single global weight when the experts are genuinely distinct and reduces to it when they converge, tracking a measure of expert separation.
Chinese Translation
将文档建模为离散的标记序列，可以视为由来自不同领域的文本组合生成；例如，README 文件在散文、代码和配置之间切换。当这样的文档受到损坏且仅有固定领域专家可用时，恢复文档需要在测试时决定缺失的内容以及在每个位置信任哪个专家，而不依赖区域标签或训练好的路由器。我们提出了基于证据的局部组合方法，该方法根据给定损坏模型下损坏观察的边际证据推断出专家的软位置加权，估计来自专家自身去噪损失的证据，并在位置之间平滑权重。由于加权是软的，当真实组合是混合时，它恢复出混合物，而当仅需一个专家时则集中于该专家。在分类模拟器、字节级专家以及从 $1.3$B 离散流匹配模型微调的专家中，推断的权重在自然混合的科学文档中以 $0.85$ 的领域准确率追踪真实区域，而在构造的区域词汇上不相交的混合物中以 $0.98$ 的准确率追踪。恢复在专家确实不同的情况下优于单一全局权重，而在专家趋同时则减少为单一全局权重，追踪专家分离的度量。
cs.AI / 34 / 2609.05806
Exposing Weaknesses in Emotion Recognition in Conversations
揭示对话中情感识别的弱点
Abstract
Emotion Recognition in Conversations (ERC) aims to identify speakers' emotions in multi-turn dialogue. Accurate emotion recognition can support a wide range of applications, including empathetic conversational agents, mental health support, and educational technologies. While many recent approaches rely on task-specific fine-tuning, such models may exploit dataset-specific cues. A central yet rarely questioned assumption in ERC is that each utterance can be assigned a single unambiguous emotion label. To investigate this assumption, we study ERC using Large Language Models (LLMs) in a zero-shot setting while incorporating preceding conversational turns as context. We show that aggregate metrics mask systematic failures. Errors concentrate around utterances containing negations, exclamations, and interjections. This pattern is consistent across all evaluated models, suggesting limitations in the benchmarks rather than model-specific weaknesses. A controlled re-annotation study involving four human annotators supports this finding: strong agreement is observed in only 35 percent of cases, with neutral utterances dominating high-agreement instances, while many emotional categories fall into low-agreement regimes. These findings suggest that many apparent model errors reflect genuine annotation ambiguity rather than poor emotion understanding. Standard single-label evaluation is therefore insufficient. To address this limitation, we introduce an LLM-as-Judge framework that evaluates each emotion independently according to its plausibility in the conversational context rather than enforcing a single-label decision.
Chinese Translation
对话中的情感识别（Emotion Recognition in Conversations, ERC）旨在识别多轮对话中说话者的情感。准确的情感识别可以支持广泛的应用，包括富有同情心的对话代理、心理健康支持和教育技术。尽管许多近期的方法依赖于特定任务的微调，但此类模型可能会利用数据集特定的线索。在ERC中，一个核心但鲜有质疑的假设是，每个发言都可以被分配一个单一明确的情感标签。为了探讨这一假设，我们在零样本设置下使用大型语言模型（Large Language Models, LLMs）研究ERC，同时将前面的对话轮次作为上下文。我们发现，汇总指标掩盖了系统性的失败。错误主要集中在包含否定、感叹和插入语的发言上。这一模式在所有评估的模型中是一致的，表明问题出在基准测试的局限性，而非模型特有的弱点。一项涉及四位人工标注者的控制再标注研究支持了这一发现：在仅35%的案例中观察到强一致性，高一致性实例中以中性发言为主，而许多情感类别则处于低一致性状态。这些发现表明，许多明显的模型错误反映了真实的标注模糊性，而非情感理解的缺陷。因此，标准的单标签评估是不够的。为了解决这一局限性，我们引入了LLM作为评判者（LLM-as-Judge）框架，根据情感在对话上下文中的合理性独立评估每种情感，而不是强制执行单一标签决策。
cs.AI / 35 / 2609.05818
Agentic BAIM-LLM Evaluation (ABLE): Benchmarking LLM Use of Protein Design Tools
代理型生物人工智能模型评估 (ABLE)：基于大语言模型的蛋白质设计工具使用基准测试
Abstract
We introduce ABLE, a benchmark for evaluating LLM agents' ability to use biological AI models (BAIMs), such as ProteinMPNN and AlphaFold3, in dual-use protein design workflows. ABLE assesses agent performance through a set of tasks spanning structure retrieval, sequence generation, and design validation. We evaluate 15 frontier models and find that seven refuse all tasks, while the remaining models exhibit substantial performance differences. Claude Sonnet 4 and Gemini 3 Pro achieve the highest scores across information retrieval, tool selection, and tool use. We further compare model performance on a subset of tasks against an expert human baseline. Our results suggest that current LLMs can substantially lower barriers to protein design, but remain inconsistent in planning, strategy generation, and integrating biological knowledge with tool use.
Chinese Translation
我们介绍了ABLE，这是一个用于评估大语言模型（LLM）代理使用生物人工智能模型（BAIM），例如ProteinMPNN和AlphaFold3，在双重用途蛋白质设计工作流程中的能力的基准测试。ABLE通过一系列任务评估代理的表现，这些任务涵盖了结构检索、序列生成和设计验证。我们评估了15个前沿模型，发现其中七个模型拒绝完成所有任务，而其余模型表现出显著的性能差异。Claude Sonnet 4和Gemini 3 Pro在信息检索、工具选择和工具使用方面取得了最高分。我们进一步将模型在部分任务上的表现与专家人类基线进行比较。我们的结果表明，当前的大语言模型可以显著降低蛋白质设计的门槛，但在规划、策略生成和将生物知识与工具使用结合方面仍然不一致。
cs.AI / 36 / 2609.05824
Beyond Top-$k$ Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
超越 Top-$k$ 技能检索：面向多样性的技能路由用于 LLM 代理
Abstract
Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many skills are functionally redundant while complex tasks often require complementary skill sets. Existing skill routers typically rank candidates independently by query relevance, which can waste context budget on redundant skills. We propose Diverse Skill Routing (DSR), a diversity-aware reranking framework that uses a Determinantal Point Process to balance relevance and non-redundancy. DSR introduces a query-residual diversity kernel that penalizes redundant skill overlap while reducing penalties caused only by shared query relevance. On the SkillRouter benchmark, DSR improves recall and full coverage over a strong pointwise reranking baseline, with larger gains on multi-skill queries. These results suggest that skill routing should be treated not only as relevance ranking, but also as complementary set selection.
Chinese Translation
大型语言模型（LLM）代理越来越依赖外部技能，但在庞大的技能注册表中路由用户请求是困难的，因为许多技能在功能上是冗余的，而复杂任务通常需要互补的技能组合。现有的技能路由器通常独立地通过查询相关性对候选者进行排名，这可能会在冗余技能上浪费上下文预算。我们提出了多样性技能路由（Diverse Skill Routing, DSR），这是一种考虑多样性的重新排序框架，使用行列式点过程（Determinantal Point Process）来平衡相关性和非冗余性。DSR 引入了一种查询残差多样性核，惩罚冗余技能重叠，同时减少仅由共享查询相关性引起的惩罚。在 SkillRouter 基准测试中，DSR 在强大的逐点重新排序基线之上提高了召回率和全面覆盖率，尤其在多技能查询中获得了更大的提升。这些结果表明，技能路由不仅应被视为相关性排名，还应视为互补集合选择。
cs.AI / 37 / 2609.05834
Learning Counterfactual World Models for Embodied Reasoning under Partial Observability
在部分可观察性下学习反事实世界模型以进行具身推理
Abstract
World models promise a general route to embodied intelligence: learn predictive dynamics once, then reason, plan, and act with them. Increasingly, the representations beneath such models are pretrained on large-scale video, interaction, and multimodal corpora, which raises a question prediction quality alone cannot answer: when is a learned representation actually actionable? We identify a failure mode we call counterfactual collapse: a model predicts visually plausible futures while failing to distinguish interventions with different behavioral consequences. This arises whenever a representation is optimized for perceptual similarity rather than intervention structure, which is precisely the objective under which most large-scale pretrained encoders are learned. We introduce Counterfactual Latent World Models (CLWM), which combine a recurrent belief-state encoder, action-conditioned latent dynamics, and a contrastive counterfactual objective that separates futures induced by distinct interventions even when their observations look alike. Across occluded manipulation, aliased navigation, and long-horizon manipulation, CLWM improves planning success over the strongest baseline (65.1% $\to$ 74.6% on Occluded Push and 67.3% $\to$ 78.9% on Aliased Maze) and reduces exploitative planning failures (18.4% $\to$ 9.7% on Deferred Kitchen), with ablations attributing the gains to hard counterfactual negatives, especially perceptual-alias negatives. Finally, our counterfactual separability metric, which tracks planning success across the five baseline model classes ($r \ge 0.94$), is representation-agnostic: given intervention-outcome labels, it can audit any encoder, pretrained or trained from scratch, before a planner trusts it. We do not yet measure it on large-scale pretrained encoders. Here we establish the metric and its relationship to planning success for world models trained from scratch.
Chinese Translation
世界模型为具身智能提供了一条通用路径：一次学习预测动态，然后利用这些动态进行推理、规划和行动。越来越多的此类模型的表示是在大规模视频、交互和多模态语料库上进行预训练的，这引发了一个问题，仅凭预测质量无法回答：何时学习到的表示实际上是可操作的？我们识别出一种失败模式，称之为反事实崩溃：模型预测出视觉上合理的未来，但无法区分具有不同行为后果的干预。这种情况发生在表示被优化为感知相似性而非干预结构时，而这正是大多数大规模预训练编码器学习的目标。我们引入了反事实潜在世界模型（Counterfactual Latent World Models, CLWM），它结合了递归信念状态编码器、基于动作的潜在动态以及对比反事实目标，即使在观察看似相似的情况下，也能区分由不同干预引起的未来。在被遮挡的操作、别名导航和长时间操作中，CLWM在规划成功率上超过了最强基线（在被遮挡推送上从65.1%提升至74.6%，在别名迷宫上从67.3%提升至78.9%），并减少了剥削性规划失败（在延迟厨房上从18.4%降低至9.7%），消融实验将增益归因于困难的反事实负样本，特别是感知别名负样本。最后，我们的反事实可分离性度量跟踪五个基线模型类别的规划成功率（$r
ge 0.94$），是与表示无关的：给定干预-结果标签，它可以审核任何编码器，无论是预训练的还是从头训练的，确保规划者信任它。我们尚未在大规模预训练编码器上进行测量。在这里，我们建立了该度量及其与从头训练的世界模型规划成功之间的关系。
cs.AI / 38 / 2609.05837
AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories
AgentBrew：从原始真实世界轨迹中学习离线工具使用代理
Abstract
LLM-based agents are increasingly deployed in real-world applications through tool-use APIs, yet training them for specific environments remains fundamentally difficult: real-world applications provide no pre-defined tasks or verifiers, no faithful simulators, and limited budget for large-scale environment interaction. In this paper, we propose \textbf{AgentBrew}, an offline training framework that learns effective tool-use policies from a single batch of raw interaction trajectories, without task verifiers or iterative on-policy rollouts. The agent first explores the target environment to collect a raw trajectory corpus without quality filtering. To extract training signal from this noisy corpus, \emph{retrospective task inference} reconstructs an aligned instruction for each trajectory based on its actual outcome, and \emph{PMI-Based credit assignment} decomposes the trajectory's total information about the inferred instruction into additive per-action credits via pointwise mutual information (PMI). These credits weight the policy training objective, amplifying informative actions while suppressing ineffective ones. On three real-world MCP applications (GitHub, Notion, PostgreSQL), AgentBrew improves Qwen3-32B by +8.7 Acc / +9.7 Score on average, surpassing Qwen3-235B (+2.3 / +4.4) and outperforming rejection sampling (+5.9 / +10.3). These results demonstrate that fine-grained offline learning can recover useful supervision from raw trajectories that filtering-based approaches would discard. The code is available at https://github.com/alphatogo/AgentBrew
Chinese Translation
基于大型语言模型（LLM）的代理越来越多地通过工具使用API在真实世界应用中部署，但在特定环境中训练它们仍然是根本性的困难：真实世界应用没有预定义的任务或验证器，没有可靠的模拟器，并且在大规模环境交互中预算有限。在本文中，我们提出了 extbf{AgentBrew}，一个离线训练框架，它从单一批次的原始交互轨迹中学习有效的工具使用策略，而无需任务验证器或迭代的在线回放。代理首先探索目标环境，以收集未经质量过滤的原始轨迹语料库。为了从这个嘈杂的语料库中提取训练信号， extit{回顾性任务推断}根据每个轨迹的实际结果重建对齐指令，而 extit{基于PMI的信用分配}通过点对点互信息（PMI）将轨迹对推断指令的总信息分解为可加的每个动作信用。这些信用权重用于策略训练目标，放大信息丰富的动作，同时抑制无效的动作。在三个真实世界的多渠道处理（MCP）应用（GitHub、Notion、PostgreSQL）上，AgentBrew平均提升了Qwen3-32B的准确率+8.7和得分+9.7，超越了Qwen3-235B（+2.3 / +4.4）并且优于拒绝采样（+5.9 / +10.3）。这些结果表明，细粒度的离线学习能够从原始轨迹中恢复有用的监督，而过滤基础的方法则会丢弃这些信息。代码可在https://github.com/alphatogo/AgentBrew获取。
cs.AI / 39 / 2609.05889
Multimodal Resource-Exhaustion Attacks on Vision-Language Models via Joint Pixel-Prompt Optimization
通过联合像素-提示优化对视觉-语言模型的多模态资源耗尽攻击
Abstract
Resource-exhaustion attacks against autoregressive vision-language models (VLMs) typically assume unimodal threat models, treating the image branch as the primary optimization surface while holding user-visible prompts fixed. Even recent loop-centric variants remain confined to this single-channel paradigm, leaving the exploitation of availability unexplored as a cross-modal optimization problem over jointly controllable input surfaces. We introduce Joint Pixel-Prompt Optimization (JPPO), the first compound adversarial framework elevating the visible prompt to a first-class adversarial variable alongside image perturbations. Under a restricted joint-input threat model, JPPO performs coupled, stagewise optimization over both the pixel and prompt surfaces. This produces synergistic cost amplification, mechanistically distinct from loop-dependent failures, exhibiting negligible loop incidence in our experiments. Evaluating five open-source VLM families on MS COCO and ImageNet under an 8/255 infinity-norm budget, JPPO achieves over 4.6x latency and 5.3x energy amplification on Qwen2.5-VL-7B, and over 36.6x latency with 32.7x energy amplification on BLIP-2. This represents the strongest cost amplification among directly compared baselines while requiring substantially fewer optimization iterations. Ablations confirm this amplification arises from multimodal coordination rather than prompt length or isolated modalities. These findings reveal structural blind spots in current VLM serving defenses, motivating cost-aware robustness evaluation as a first-class security requirement for multimodal deployments.
Chinese Translation
针对自回归视觉-语言模型（VLMs）的资源耗尽攻击通常假设单模态威胁模型，将图像分支视为主要优化表面，同时保持用户可见提示不变。即使是最近的循环中心变体也仍然局限于这一单通道范式，未能将可用性作为跨模态优化问题进行利用。我们提出了联合像素-提示优化（Joint Pixel-Prompt Optimization, JPPO），这是第一个复合对抗框架，将可见提示提升为与图像扰动并列的第一类对抗变量。在受限的联合输入威胁模型下，JPPO在像素和提示表面上进行耦合的阶段性优化。这产生了协同的成本放大，机制上与依赖循环的失败显著不同，在我们的实验中表现出微不足道的循环发生率。在MS COCO和ImageNet上评估五个开源VLM家族，在8/255的无穷范数预算下，JPPO在Qwen2.5-VL-7B上实现了超过4.6倍的延迟和5.3倍的能量放大，而在BLIP-2上则实现了超过36.6倍的延迟和32.7倍的能量放大。这代表了在直接比较的基线中最强的成本放大，同时所需的优化迭代次数显著较少。消融实验确认，这种放大源于多模态协调，而非提示长度或孤立模态。这些发现揭示了当前VLM服务防御中的结构盲点，促使将成本意识的鲁棒性评估作为多模态部署的第一类安全要求。
cs.AI / 40 / 2609.05894
The End of AI Exponentiation: Fluttering Inside and Outside AI Bubble
人工智能指数增长的终结：在人工智能泡沫内外的颤动
Abstract
The exponentiation of Artificial intelligence (AI) in the recent past has entered a transformative era that has been driven by the growth in large language models (LLMs), large-scale compute infrastructures, and autonomous reasoning systems. However, the rapid acceleration of AI has increasingly shown technological, societal, economic, ethical and infrastructural challenges associated with peak data limitations, rising computational demands, synthetic data recursion, valuation inflation, and societal instability. The traditional scaling paradigms that have powered the modern AI systems are gradually encountering friction in sustaining continuous exponential growth. This paper views ``the end of AI exponentiation,'' thus exploring how it flutters inside and outside the bubble, where instability emerges within the AI ecosystem through compute and data-center races, speculative investments, and the rat-race toward superintelligence, and outside the ecosystem through labor disruption, governance concerns, public uncertainty, and geopolitical acceleration surrounding future intelligent systems and infrastructures globally.
Chinese Translation
近年来，人工智能（AI）的指数增长已进入一个由大型语言模型（LLMs）、大规模计算基础设施和自主推理系统驱动的变革时代。然而，人工智能的快速加速日益显现出与数据峰值限制、计算需求上升、合成数据递归、估值膨胀和社会不稳定等相关的技术、社会、经济、伦理和基础设施挑战。支撑现代人工智能系统的传统扩展范式正逐渐面临持续指数增长的摩擦。本文视为“人工智能指数增长的终结”，探讨其在泡沫内外的颤动，其中不稳定性通过计算和数据中心竞争、投机性投资以及对超智能的竞争在人工智能生态系统内显现，而在生态系统外则通过劳动中断、治理问题、公众不确定性以及围绕未来智能系统和基础设施的地缘政治加速显现。
cs.AI / 41 / 2609.05947
Beyond Final Decisions: A Process-Centric Benchmark for Transparent AI-Assisted Peer Review
超越最终决策：面向过程的透明AI辅助同行评审基准
Abstract
Peer review is central to quality control in science. However, existing evaluations of AI-assisted peer review mainly focus on the overall quality of generated reviews or the accuracy of final decisions. They therefore provide limited evidence about whether model decisions are supported by sufficient and reliable review evidence. We introduce a process-centric diagnostic benchmark for AI-assisted peer review. It uses (x,$z_s$,$z_c$,$z_r$,y) to represent the paper content, summary, critique, suggestion, and decision. We convert heterogeneous review records from PeerRead, NLPeer ARR-22, and OpenReview-ICLR into process-aligned data. Our benchmark uses direct decision prediction from the paper content (Direct) as its baseline. It compares the decision value of Gold-process variables and Predicted-process variables, and conducts stage-level evaluation, chain-consistency evaluation, and interventional sensitivity analysis. Experiments across three datasets and six models show that Gold-process variables generally have higher decision value. For the main analysis model, the Gold--Predicted gap remains stable across datasets and random seeds. This gap is also reproduced in most model--dataset combinations. Although model-generated intermediate review texts show relatively high local consistency across adjacent stages, the final decisions are not consistently supported by the preceding review evidence. Our benchmark targets AI systems designed to assist rather than replace human reviewers. It provides a transparent and auditable diagnostic tool for evaluating the reliability of their review processes.
Chinese Translation
同行评审是科学质量控制的核心。然而，现有的AI辅助同行评审评估主要集中在生成评审的整体质量或最终决策的准确性上。因此，它们提供的证据有限，无法说明模型决策是否得到了充分和可靠的评审证据支持。我们引入了一种面向过程的AI辅助同行评审诊断基准。该基准使用(x,$z_s$,$z_c$,$z_r$,y)来表示论文内容、摘要、批评、建议和决策。我们将PeerRead、NLPeer ARR-22和OpenReview-ICLR中的异构评审记录转换为过程对齐的数据。我们的基准使用来自论文内容的直接决策预测（Direct）作为基线。它比较Gold-process变量和Predicted-process变量的决策值，并进行阶段级评估、链一致性评估和干预敏感性分析。对三个数据集和六个模型的实验表明，Gold-process变量通常具有更高的决策值。对于主要分析模型，Gold-Predicted差距在不同数据集和随机种子之间保持稳定。这个差距在大多数模型-数据集组合中也得到了再现。尽管模型生成的中间评审文本在相邻阶段之间表现出相对较高的局部一致性，但最终决策并未始终得到前期评审证据的支持。我们的基准旨在针对旨在辅助而非取代人类评审员的AI系统。它提供了一种透明且可审计的诊断工具，用于评估其评审过程的可靠性。
cs.AI / 42 / 2609.05992
MOAE: Multi-Objective Agent Evolution with Pareto-Preserving Search
MOAE：具有帕累托保持搜索的多目标智能体进化
Abstract
As LLM-based agents continue to advance, their evaluation has become increasingly multifaceted: a capable agent must not only achieve high task completion accuracy but also perform well in interaction quality, safety, and efficiency, raising a central question: can these objectives be optimized simultaneously? Existing methods have considered multiple objectives, but many collapse heterogeneous measurements into a fixed scalar score. Such scalarization depends on metric normalization and preference weights and may discard candidates that represent useful deployment trade-offs. We introduce Multi-Objective Agent Evolution (MOAE), which organizes iterative in-context refinement as a Pareto-preserving evolutionary search over complete agent rollouts. Given a limited rollout budget, MOAE maintains an empirical archive of non-dominated candidates, uses objective-specific diagnostics to guide offspring generation, and applies constraint-aware selection only at deployment. This separates candidate preservation during search from the preference used to return a final solution. The procedure requires no parameter updates and allows each objective to be replaced by any measurable property, which we instantiate as task performance, trajectory quality, and safety. Experiments on TravelPlanner and AgentDojo show that MOAE consistently improves task performance and trajectory quality while maintaining strong safety under matched rollout budgets. Search-behavior analysis further shows that Pareto preservation expands the attainable objective region and increases the frequency of joint improvement. These results demonstrate the potential of Pareto-preserving in-context evolution for optimizing multiple agent properties without committing to a fixed scalarization during search.
Chinese Translation
随着基于大规模语言模型（LLM）的智能体不断进步，其评估变得愈加多元化：一个高效的智能体不仅需要实现高任务完成准确率，还需在交互质量、安全性和效率方面表现良好，这引发了一个核心问题：这些目标能否同时优化？现有方法已考虑多个目标，但许多方法将异构测量归结为固定的标量分数。这种标量化依赖于度量标准的归一化和偏好权重，可能会丢弃代表有用部署权衡的候选者。我们提出了多目标智能体进化（MOAE），它将迭代的上下文内精炼组织为对完整智能体回合的帕累托保持进化搜索。在有限的回合预算下，MOAE 维护一个非支配候选者的经验档案，使用特定目标的诊断来指导后代生成，并仅在部署时应用约束感知选择。这将搜索过程中的候选者保留与返回最终解决方案时使用的偏好分开。该过程不需要参数更新，并允许每个目标被任何可测量属性替代，我们将其实例化为任务性能、轨迹质量和安全性。在 TravelPlanner 和 AgentDojo 上的实验表明，MOAE 在保持强安全性的同时，始终提高任务性能和轨迹质量，且在匹配的回合预算下表现出色。搜索行为分析进一步表明，帕累托保持扩展了可达成目标区域，并增加了联合改进的频率。这些结果展示了帕累托保持的上下文内进化在优化多个智能体属性方面的潜力，而无需在搜索过程中承诺固定的标量化。
cs.AI / 43 / 2609.05995
Agentic Pressure: The Endogenous Entropy of Reliable Autonomy
能动压力：可靠自主性的内生熵
Abstract
Achieving reliable autonomy in the wild requires agents to sustain continuous operations across long-horizon trajectories. However, as agents navigate these unconstrained settings, they encounter cumulative friction that inherently destabilizes their alignment. In this paper, we identify a distinct non-adversarial phenomenon termed Agentic Pressure. We define this as a kinetic force that spontaneously emerges when the cost of compliance conflicts with the imperative of goal achievement. Unlike static jailbreaks, this pressure is endogenous and arises directly from the dynamics of interaction. We propose a theoretical framework that formalizes Agentic Pressure as the ratio between the required work to overcome environmental friction and the remaining capacity of the agent. Our analysis demonstrates that when this pressure exceeds a critical threshold, agents exhibit safety drift as a mathematically optimal adaptation. Consequently, they often resort to Instrumental Hallucination to rationalize rule violations. Empirical experiments validate this framework and show that aligned agents spontaneously compromise safety to preserve autonomy under high-pressure conditions.
Chinese Translation
在复杂环境中实现可靠自主性要求智能体在长时间轨迹上持续运作。然而，当智能体在这些无约束的环境中导航时，它们会遇到累积的摩擦，这本质上会破坏它们的对齐。在本文中，我们识别出一种独特的非对抗性现象，称为能动压力（Agentic Pressure）。我们将其定义为一种动能力量，当遵从成本与目标实现的迫切性发生冲突时，自发出现。与静态越狱不同，这种压力是内生的，直接源于交互的动态。我们提出了一个理论框架，将能动压力形式化为克服环境摩擦所需工作的比例与智能体剩余能力之间的关系。我们的分析表明，当这种压力超过临界阈值时，智能体表现出安全漂移，作为一种数学上最优的适应。因此，它们往往诉诸于工具性幻觉（Instrumental Hallucination）来合理化规则违反。实证实验验证了这一框架，并显示在高压条件下，已对齐的智能体自发妥协安全以维持自主性。
cs.AI / 44 / 2609.06036
Generator-Independent Runtime Assurance under Partial Observation
独立于生成器的部分观察下的运行时保障
Abstract
Proposal-based controllers---learned policies, language-model planners, and other black-box \emph{generators}---are increasingly deployed behind runtime verification gates. We ask when the closed-loop safety guarantee decouples from the generator. The prevailing per-candidate certification pattern does not compose: under retry or best-of-$k$ selection a per-candidate false-admission level $\alpha$ can inflate to $1-(1-\alpha)^{k}$. Our main theorem shows that \emph{simultaneous setwise soundness}---certifying a set of admissible proposals containing no nonviable action---is necessary and sufficient for generator-independent \emph{admission soundness}, the worst case over all generators of executing a nonviable proposal equalling the probability of setwise failure; together with a design-time certificate and a no-bypass rule it is sufficient for \emph{contract safety}, with violation bound $\Gamma+\sum_t\varepsilon_t+\eta$ invariant under arbitrary, even adversarial, replacement of the generator. A second theorem bounds every admission mechanism under partial observation: for a fixed probing and admission policy, if two state hypotheses whose information laws lie within total-variation distance $\delta$ require different safe decisions, then $\abar+\beta+\delta\ge1$. A sequential risk ledger makes the guarantee implementable with time-uniform confidence tubes, and shows that deterministic admission computations concentrate all statistical risk in state estimation. Simplex-style runtime assurance and control-barrier-function filtering are recovered as degenerate cases.
Chinese Translation
基于提案的控制器——学习的策略、语言模型规划器以及其他黑箱生成器——越来越多地部署在运行时验证门后。我们探讨闭环安全保证何时与生成器解耦。现行的每个候选认证模式并不具备组合性：在重试或最佳选择的情况下，每个候选的错误接纳水平 $eta$ 可能膨胀至 $1-(1-eta)^{k}$。我们的主要定理表明， extit{同时集合声音性}——认证一个包含无效行为的可接纳提案的集合——是独立于生成器的 extit{接纳声音性}的必要且充分条件，即在所有生成器中执行无效提案的最坏情况等于集合失败的概率；结合设计时证书和不绕过规则，它足以确保 extit{合同安全}，其违反界限为 $eta+ extstyleigsum_t heta_t+
u$，在任意甚至对抗性的生成器替换下保持不变。第二个定理在部分观察下对每个接纳机制进行了界限：对于固定的探测和接纳策略，如果两个状态假设的信息法则在总变差距离 $ heta$ 内要求不同的安全决策，则 $ar{a}+eta+ heta
eq1$。一个顺序风险账本使得保证可以通过时间均匀的置信区间实现，并显示确定性接纳计算将所有统计风险集中在状态估计中。单纯形风格的运行时保障和控制障碍函数过滤作为退化情况被恢复。
cs.AI / 45 / 2609.06059
DAREBench: Deployment-Aware and Reliable Evaluation of Models as Agents
DAREBench：面向部署的可靠模型评估
Abstract
As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to assess multimodal perception, multi-step execution, tool use, and artifact delivery. However, existing benchmarks are often tied to specific task types, execution environments, or scoring protocols, limiting their comparability, interpretability, and reliability for deployment decisions. We introduce DAREBench (Deployment-Aware and Reliable Evaluation of Models as Agents), a benchmark designed to capture workload variation and support reliable agent evaluation. Built on a shared OpenClaw execution environment, DAREBench organizes 233 tasks selected and adapted from 22 source benchmarks into a $2\times3$ workload matrix defined by input modality and execution form, and evaluates them under a unified contract-based protocol with evidence-based score auditing. We evaluate 23 commercial API models and 12 locally deployed open-weight models over 7,587 model--task runs, reporting accuracy and token consumption alongside reference costs for API models. Results show that no single model dominates all workload groups, text and multimodal tasks exhibit distinct accuracy--cost trade-offs, and local open-weight models are competitive in several groups but still trail frontier commercial models overall. These findings suggest that agent deployment and model selection should consider workload profiles, deployment mode, and accuracy--cost trade-offs rather than rely on a single aggregate score.
Chinese Translation
随着大型语言模型从问答系统演变为通用代理，评估必须超越静态答案的正确性，以评估多模态感知、多步骤执行、工具使用和成果交付。然而，现有基准往往与特定任务类型、执行环境或评分协议相关联，这限制了它们在部署决策中的可比性、可解释性和可靠性。我们引入了DAREBench（面向部署的可靠模型评估），这是一个旨在捕捉工作负载变化并支持可靠代理评估的基准。DAREBench基于共享的OpenClaw执行环境，将来自22个源基准的233个任务进行选择和调整，组织成一个由输入模态和执行形式定义的$2 imes3$工作负载矩阵，并在统一的基于合同的协议下进行评估，采用基于证据的评分审计。我们对23个商业API模型和12个本地部署的开放权重模型进行了7,587次模型-任务运行的评估，报告了准确性和令牌消耗，同时提供了API模型的参考成本。结果显示，没有单一模型在所有工作负载组中占据主导地位，文本和多模态任务表现出明显的准确性-成本权衡，而本地开放权重模型在多个组中具有竞争力，但整体仍落后于前沿商业模型。这些发现表明，代理部署和模型选择应考虑工作负载特征、部署模式以及准确性-成本权衡，而不是依赖单一的综合评分。
cs.AI / 46 / 2609.06063
Explaining AI Agents Through Execution Traces
通过执行轨迹解释人工智能代理
Abstract
AI Agents are increasingly deployed in real-world settings, where they interact with external tools and make sequential decisions with limited human oversight. This creates a pressing need for reliable and auditable explanations of what an agent did and why. However, traditional Explainable AI (XAI) methods fall short of providing the process-level transparency required for such interactive, multi-step systems, motivating a paradigm shift toward approaches specifically designed for AI Agents. To address this gap, we present a post-hoc XAI framework that transforms a lengthy agent's execution trace into a structured report and a faithful natural-language explanation explicitly grounded in its observable behavior. Because it relies solely on execution traces, the framework applies across different agent architectures, environments, and tasks. Human and automated evaluations across multiple benchmarks and architectures show that our framework produces high-quality, trace-faithful explanations while reliably identifying unsupported claims, unjustified actions, and evidence gaps, outperforming naive LLM-generated explanations.
Chinese Translation
人工智能代理越来越多地在现实世界中部署，它们与外部工具交互并在有限的人类监督下做出连续决策。这就迫切需要可靠且可审计的解释，以说明代理所做的事情及其原因。然而，传统的可解释人工智能（Explainable AI, XAI）方法未能提供此类交互式多步骤系统所需的过程级透明度，因此需要向专门为人工智能代理设计的方法转变。为了解决这一问题，我们提出了一种事后可解释人工智能框架，该框架将冗长的代理执行轨迹转化为结构化报告和基于其可观察行为的真实自然语言解释。由于该框架仅依赖于执行轨迹，因此适用于不同的代理架构、环境和任务。通过对多个基准和架构进行的人类和自动评估表明，我们的框架能够生成高质量、与轨迹一致的解释，同时可靠地识别不支持的主张、不合理的行动和证据缺口，优于简单的LLM生成的解释。
cs.AI / 47 / 2609.06071
Generating Instance Generators in PDDL Planning
在 PDDL 规划中生成实例生成器
Abstract
PDDL, the de-facto standard language in the AI Planning community, is designed to specify planning domains: sets of instances that share the same predicates and action schemas. Yet it does not provide any means to specify the actual instance set, i.e., legality constraints on initial states and goal conditions, as well as possibly domain subset constraints specifying an instance subset we are interested in. One consequence of this is that instance generation has always been ad-hoc, with manually written domain- and subset-specific instance generators. Recent work has started to address this, through reasoning and learning methods that however suffer from scalability limitations. Here we introduce an alternative approach, leveraging LLMs to generate instance-generation programs, with built-in soundness guarantees through prescribed checks. We show that these automatically generated instance generators return large numbers of sound and diverse instances efficiently.
Chinese Translation
PDDL 是人工智能规划领域的事实标准语言，旨在指定规划领域：共享相同谓词和动作模式的实例集合。然而，它并未提供任何手段来指定实际的实例集合，即对初始状态和目标条件的合法性约束，以及可能指定我们感兴趣的实例子集的领域子集约束。这导致实例生成一直是临时的，依赖于手动编写的领域和子集特定的实例生成器。最近的研究开始解决这一问题，通过推理和学习方法，但这些方法存在可扩展性限制。在此，我们提出了一种替代方法，利用大型语言模型（LLMs）生成实例生成程序，并通过规定的检查提供内置的健全性保证。我们展示了这些自动生成的实例生成器能够高效地返回大量健全且多样的实例。
cs.AI / 48 / 2609.06079
LayerRoute: Action-Conditioned Mixture-of-Layers Routing for Vision-Language-Action Policies
LayerRoute：基于动作条件的层混合路由用于视觉-语言-动作策略
Abstract
Vision-Language-Action (VLA) policies leverage pretrained vision-language models (VLMs) to guide action generation for robot control. VLMs provide hierarchical visual-semantic representations that evolve across layers, from local visual geometry to abstract, language-aligned semantics; different manipulation tasks may therefore require different mixtures of layer representations. Meanwhile, the action module maintains intermediate representations that evolve throughout action computation and may provide useful information for subsequent decisions. However, existing VLA interfaces offer limited flexibility in representation access: VLM information is exposed through fixed layer assignments for each action layer, while intermediate action states are only propagated implicitly through residual streams without explicit reuse. We introduce LayerRoute, an action-conditioned representation routing interface that enables adaptive access to VLM layers and action representations. The Layer Mixture Router dynamically forms mixtures of cached VLM representations, while Action-State Reread reuses earlier action representations. Across diverse simulation and real-world benchmarks, LayerRoute consistently improves StarVLA-$\pi$ and $\pi_{0.5}$, achieving up to 7.2 gains on LIBERO Long with only 0.31% / 3.87% additional parameters. Ablation studies validate the benefit of action-conditioned layer routing, while routing analyses reveal structured allocation patterns across action layers and task settings.
Chinese Translation
视觉-语言-动作（VLA）策略利用预训练的视觉-语言模型（VLMs）来指导机器人控制的动作生成。VLMs 提供了层次化的视觉-语义表示，这些表示在层之间不断演变，从局部视觉几何到抽象的、与语言对齐的语义；因此，不同的操作任务可能需要不同混合的层表示。同时，动作模块维护着在动作计算过程中不断演变的中间表示，并可能为后续决策提供有用的信息。然而，现有的 VLA 接口在表示访问方面提供的灵活性有限：VLM 信息通过每个动作层的固定层分配暴露，而中间动作状态仅通过残差流隐式传播，未进行显式重用。我们提出了 LayerRoute，一种基于动作条件的表示路由接口，能够实现对 VLM 层和动作表示的自适应访问。层混合路由器（Layer Mixture Router）动态形成缓存 VLM 表示的混合，而动作状态重读（Action-State Reread）则重用早期的动作表示。在多样的仿真和现实世界基准测试中，LayerRoute 一直提升 StarVLA-$ ext{π}$ 和 $ ext{π}_{0.5}$，在 LIBERO Long 上实现了高达 7.2 的增益，仅增加 0.31% / 3.87% 的参数。消融研究验证了基于动作条件的层路由的优势，而路由分析揭示了在动作层和任务设置之间的结构化分配模式。
cs.AI / 49 / 2609.06124
SAP: State-Guided Data Synthesis with Argument Provenance for Multi-Turn Tool Use
SAP：基于状态引导的多轮工具使用数据合成与论证来源
Abstract
High-quality multi-turn tool-use data is essential for training agentic models, yet existing data synthesis methods often underrepresent the argument-level dependencies that are critical to long-horizon tool use. As a result, even when a model selects the correct tool, task execution may still fail because the model fills tool arguments with fabricated, stale, or weakly grounded values. To address this problem, we propose \textbf{State-Guided Data Synthesis with Argument Provenance (SAP)}. SAP combines state guidance, tool-argument provenance constraints, and turn-level validation to efficiently construct tool-use trajectories with long-range dependencies and high accuracy. Using data generated by SAP, we build SAP-4B, which is highly competitive even when compared with much larger models across multiple benchmarks. Source code, synthesized data, and trained weights are available at https://github.com/Zichen1024/SAP.
Chinese Translation
高质量的多轮工具使用数据对于训练智能体模型至关重要，然而现有的数据合成方法往往未能充分体现对长时间跨度工具使用至关重要的论证级依赖关系。因此，即使模型选择了正确的工具，任务执行仍可能失败，因为模型可能使用虚构、过时或基础薄弱的值填充工具参数。为了解决这一问题，我们提出了 extbf{基于状态引导的论证来源数据合成（SAP）}。SAP结合了状态引导、工具参数来源约束和轮次级验证，以高效构建具有长距离依赖关系和高准确度的工具使用轨迹。利用SAP生成的数据，我们构建了SAP-4B，即使与多个基准测试中更大的模型相比，仍具有很强的竞争力。源代码、合成数据和训练权重可在https://github.com/Zichen1024/SAP获取。
cs.AI / 50 / 2609.06126
CWF: A Collaborative Writing Framework for Personalized and Reliable Popular Science Writing
CWF：个性化和可靠的科普写作协作框架
Abstract
We introduce Personalized and Reliable Popular Science Writing, a novel task that requires adapting scientific explanations to audiences with different cognitive levels while preserving factual accuracy. However, improving personalization often introduces simplifications that increase the risk of hallucination and factual distortion. To address these challenges, we first construct a dataset of 39,134 entries and a reader-centric Personalized Science Communication Benchmark (PSCB) that jointly evaluates audience adaptation and factual accuracy. To reduce data and computational requirements while improving generalization across domains and audiences, we introduce DA-MoE, which explicitly decouples audience adaptation from domain knowledge through separate modeling. To enable robust verification and revision in evidence-scarce scenarios, a multi-agent fact-checking mechanism that augments limited evidence with role-specific agent debate and propagates confidence over a graph is proposed. Experiments on PSCB show that our approach achieves state-of-the-art performance. Our code is open-sourced at https://github.com/DPInnovationWorks/CWF.
Chinese Translation
我们提出了个性化和可靠的科普写作，这是一项新颖的任务，要求在保持事实准确性的同时，将科学解释适应于不同认知水平的受众。然而，提高个性化往往会引入简化，从而增加幻觉和事实扭曲的风险。为了解决这些挑战，我们首先构建了一个包含39,134条目的数据集和一个以读者为中心的个性化科学传播基准（Personalized Science Communication Benchmark，PSCB），该基准共同评估受众适应性和事实准确性。为了减少数据和计算需求，同时提高跨领域和受众的泛化能力，我们引入了DA-MoE，它通过单独建模明确解耦了受众适应性和领域知识。为了在证据稀缺的场景中实现稳健的验证和修订，我们提出了一种多智能体事实检查机制，该机制通过角色特定的智能体辩论增强有限证据，并在图上传播信心。在PSCB上的实验表明，我们的方法达到了最先进的性能。我们的代码已在https://github.com/DPInnovationWorks/CWF上开源。
cs.AI / 51 / 2609.06128
Substrate-Portable Execution for Production LLM Workflows
可移植的生产 LLM 工作流执行
Abstract
Production LLM agents execute tool-calling loops, retrieval chains, and compositional workflows in multiple modes, yet execution semantics are often coupled to one runtime. We encountered this portability problem in Rufus, a conversational AI assistant with a large tool catalog that serves millions of Amazon customers. Rufus supports real-time serving, asynchronous background tasks, and high-volume batch workloads such as evaluation and content pregeneration. Each mode has distinct service-level objectives and typically uses a separate runtime. Reusing streaming orchestration makes asynchronous and batch workloads blocking and prevents use of batch inference APIs, which offer a 50 percent discount at published prices. We present a binding-adaptive agent execution platform that separates workflow definition from execution substrate. Developers define a workflow once as a typed dataflow graph. The platform compiles the graph to in-process streaming for real-time serving, durable AWS SWF orchestration for asynchronous execution, or distributed Apache Flink stream processing for batch inference. No workflow code changes are required. LLM inference is represented as a suspendable graph node whose behavior depends on the substrate: streaming delivery online, durable retry asynchronously, and batched submission offline. We validated dozens of production agent configurations across five orchestration patterns: single-inference RAG, iterative ReAct, compositional PreAct, conditional routing, and multi-agent deep research. Across all three bindings, we found no detectable difference in output quality. Batch execution reduced per-query inference cost in line with published batch API pricing while operating alongside the streaming path at production scale.
Chinese Translation
生产 LLM 代理在多种模式下执行工具调用循环、检索链和组合工作流，但执行语义通常与单一运行时耦合。我们在 Rufus 中遇到了这一可移植性问题，Rufus 是一个拥有大量工具目录的对话式人工智能助手，服务于数百万亚马逊客户。Rufus 支持实时服务、异步后台任务以及高容量的批量工作负载，如评估和内容预生成。每种模式都有不同的服务级目标，通常使用单独的运行时。重用流式编排使得异步和批量工作负载变得阻塞，并阻碍了批量推理 API 的使用，而后者在公布价格上提供了 50% 的折扣。我们提出了一种绑定自适应代理执行平台，该平台将工作流定义与执行基础设施分离。开发人员将工作流定义为一次类型化的数据流图。该平台将图编译为进程内流式处理以实现实时服务、持久的 AWS SWF 编排以进行异步执行，或分布式 Apache Flink 流处理以进行批量推理。无需更改工作流代码。LLM 推理被表示为一个可挂起的图节点，其行为取决于基础设施：在线流式交付、异步持久重试和离线批量提交。我们验证了五种编排模式下数十种生产代理配置：单次推理 RAG、迭代 ReAct、组合 PreAct、条件路由和多代理深度研究。在所有三种绑定中，我们没有发现输出质量的可检测差异。批量执行降低了每次查询的推理成本，与公布的批量 API 定价一致，同时在生产规模下与流式路径并行运行。
cs.AI / 52 / 2609.06131
IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing
IIns-VAE+: 一种用于无线传感环境识别的鲁棒迁移学习框架
Abstract
Environmental identification in wireless sensing is essential for 6G integrated sensing and communication (ISAC) systems to achieve reliable situational awareness. However, deep learning (DL) models for this task often fail to generalize under domain shift across diverse environments. While the Inter-Instance Variational Auto-encoder (IIns-VAE) learns features of rich representation, its neural classifier remains vulnerable to these distribution changes. In this paper, we propose IIns-VAE+, a hybrid model that combines the IIns-VAE framework with Minimax Risk Classifiers (MRC) to improve adaptability in transfer learning scenarios. We use real-world datasets to evaluate our framework across three transfer learning scenarios, including general to specific room environments, high to low label resolutions, and mixed to specific environments. The experimental results indicate that IIns-VAE+ significantly outperforms baselines, demonstrating its critical value in building adaptable and robust perceptive networks in future 6G systems.
Chinese Translation
在无线传感中，环境识别对于实现6G集成感知与通信（ISAC）系统的可靠态势感知至关重要。然而，针对这一任务的深度学习（DL）模型在不同环境下的领域转移中常常无法有效泛化。尽管跨实例变分自编码器（IIns-VAE）能够学习丰富的特征表示，但其神经分类器仍然容易受到这些分布变化的影响。本文提出了IIns-VAE+，一种将IIns-VAE框架与极小极大风险分类器（MRC）相结合的混合模型，以提高迁移学习场景中的适应性。我们使用真实世界的数据集评估我们的框架在三种迁移学习场景下的表现，包括从一般到特定房间环境、高到低标签分辨率以及混合到特定环境。实验结果表明，IIns-VAE+显著优于基线模型，展示了其在未来6G系统中构建适应性强且鲁棒的感知网络的关键价值。
cs.AI / 53 / 2609.06188
MVFA: A Multi-View Text-Guided Multimodal Fusion LLM Adapter for Sentiment Analysis and Emotion Recognition
MVFA：一种多视角文本引导的多模态融合LLM适配器用于情感分析和情绪识别
Abstract
Multimodal sentiment analysis and emotion recognition in conversations demand effective modeling of heterogeneous interactions across textual, acoustic, and visual modalities. Although large language models (LLMs) offer powerful language understanding, adapting them to multimodal affective computing remains challenging: full-model fine-tuning is computationally prohibitive, while many existing lightweight adapters fail to preserve rich textual cues during cross-modal fusion. To address these limitations, we propose the multi-view text-guided multimodal fusion adapter (MVFA), a parameter-efficient framework that augments frozen LLMs with strong multimodal reasoning capability. MVFA first constructs complementary text views via max pooling, mean pooling, and attention pooling; these views then guide cross-modal interactions with audio and visual features. The fused multimodal representations are subsequently compressed into a compact set of learnable pseudo-tokens through an Enhanced Q-Former Fusion Module. Using ChatGLM3-6B-base as the primary backbone, we further validate MVFA on LLaMA2-7B and Qwen3-8B to examine its portability across multiple frozen LLM backbones. MVFA is evaluated on three challenging datasets: CH-SIMS V2.0, MELD, and CHERMA. Experimental results demonstrate that MVFA achieves state-of-the-art performance on key metrics while updating only a small fraction of parameters. Specifically, it attains 84.62\% Acc2 and 84.59\% F1 on CH-SIMS V2.0, 67.36\% Acc and 66.03\% WF1 on MELD, and 74.66\% Acc on CHERMA. These findings establish multi-view text-guided fusion as an effective and scalable paradigm for parameter-efficient multimodal LLM adaptation in affective computing. The code is publicly available at https://github.com/Overwhelm1208/MVFA.
Chinese Translation
多模态情感分析和对话中的情绪识别需要有效建模文本、声学和视觉模态之间的异质交互。尽管大型语言模型（LLMs）提供了强大的语言理解能力，但将其适应于多模态情感计算仍然具有挑战性：全模型微调在计算上是不可行的，而许多现有的轻量级适配器在跨模态融合过程中未能保留丰富的文本线索。为了解决这些局限性，我们提出了多视角文本引导的多模态融合适配器（MVFA），这是一个参数高效的框架，增强了冻结的LLMs的多模态推理能力。MVFA首先通过最大池化、均值池化和注意力池化构建互补的文本视图；这些视图随后引导与音频和视觉特征的跨模态交互。融合的多模态表示随后通过增强的Q-Former融合模块压缩为一组紧凑的可学习伪标记。以ChatGLM3-6B-base作为主要骨干网，我们进一步在LLaMA2-7B和Qwen3-8B上验证MVFA，以检查其在多个冻结LLM骨干网之间的可移植性。MVFA在三个具有挑战性的数据集上进行评估：CH-SIMS V2.0、MELD和CHERMA。实验结果表明，MVFA在关键指标上达到了最先进的性能，同时仅更新了少量参数。具体而言，它在CH-SIMS V2.0上达到了84.62%的Acc2和84.59%的F1，在MELD上达到了67.36%的Acc和66.03%的WF1，在CHERMA上达到了74.66%的Acc。这些发现确立了多视角文本引导融合作为一种有效且可扩展的范式，用于在情感计算中进行参数高效的多模态LLM适配。代码已公开发布在https://github.com/Overwhelm1208/MVFA。
cs.AI / 54 / 2609.06189
Customer Relationship Intelligence: Integrating CRM and MDM for Enhanced Customer Engagement
客户关系智能：整合客户关系管理和主数据管理以增强客户参与度
Abstract
This study examines how Customer Relationship Management (CRM), Master Data Management (MDM), and Customer Knowledge Management (CKM) jointly constitute a Customer Relationship Intelligence (CRI) framework for enhanced Customer Engagement (CE). A cross-sectional survey of 100 participants across retail, healthcare, IT, and telecommunications sectors was analysed using Spearman rho correlation and ordinal logistic regression (IBM SPSS). Bivariate correlations were weak and non-significant (r<0.19, p>0.06). Regression identified CRM (beta=0.717, p=0.002) and CKM (beta=0.581, p=0.009) as significant positive predictors of CE; MDM showed a positive but non-significant direct effect (beta=0.346, p=0.071). The model explained 20.5% of CE variance (Nagelkerke R^2=0.205). Parallel mediation analysis (Hayes PROCESS Model 4, 5,000 bootstrap samples) found no significant indirect effects of MDM on CE via CRM (IE=0.021, 95% BC CI [-0.072, 0.121]) or CKM (IE=0.032, 95% BC CI [-0.061, 0.126]); Hypothesis H4 was not supported. CRM and CKM emerge as the principal drivers of CE within the CRI framework, while MDM functions as a foundational data quality enabler whose strategic value is realised through its enabling effect on CRM execution and knowledge management. Findings should be treated as exploratory given the sample size and cross-sectional design. Future research should replicate with larger sector-specific samples and longitudinal designs, particularly in regulated BFSI contexts where MDM architecture is shaped by data governance mandates.
Chinese Translation
本研究探讨了客户关系管理（CRM）、主数据管理（MDM）和客户知识管理（CKM）如何共同构成客户关系智能（CRI）框架，以增强客户参与度（CE）。对来自零售、医疗保健、信息技术和电信行业的100名参与者进行的横断面调查，通过斯皮尔曼相关系数和有序逻辑回归（IBM SPSS）进行分析。双变量相关性较弱且不显著（r<0.19，p>0.06）。回归分析确定CRM（beta=0.717，p=0.002）和CKM（beta=0.581，p=0.009）为CE的重要正向预测因子；MDM显示出正向但不显著的直接影响（beta=0.346，p=0.071）。该模型解释了20.5%的CE方差（Nagelkerke R^2=0.205）。平行中介分析（Hayes PROCESS Model 4，5,000次自助抽样）发现MDM通过CRM（IE=0.021，95% BC CI [-0.072, 0.121]）或CKM（IE=0.032，95% BC CI [-0.061, 0.126]）对CE没有显著的间接影响；假设H4未得到支持。在CRI框架内，CRM和CKM成为CE的主要驱动因素，而MDM则作为基础数据质量的支持者，其战略价值通过对CRM执行和知识管理的促进作用得以实现。考虑到样本量和横断面设计，研究结果应被视为探索性。未来的研究应在更大规模的行业特定样本和纵向设计中进行复制，特别是在受监管的银行、金融服务和保险（BFSI）环境中，MDM架构受到数据治理要求的影响。
cs.AI / 55 / 2609.06192
SCIRIGOR:Evaluating Open-Ended Scientific Analysis Beyond Final Scores
SCIRIGOR：超越最终得分的开放式科学分析评估
Abstract
Scientific coding agents produce interdependent code, results, figures, and claims, yet evaluating final outputs alone does not establish whether their conclusions are scientifically supported. We formulate evidence-grounded multimodal scientific analysis, requiring agents to produce executable analyses and claims supported by results and visualizations from the same run. We introduce SciRIGOR, an evaluation framework and benchmark comprising 100 cases from scientific articles across six domains and 17 subfields. The framework reconstructs typed evidence graphs, separates artifact fidelity from relational validity, and scores complete claim-support paths while localizing the earliest unsupported relation. Source-grounded alternative paths accommodate scientifically equivalent analyses and visualizations. We evaluate 11 agent/model configurations. On full-benchmark runs, claims agree with faithful and unfaithful results at nearly identical rates (91.8% versus 91.0%). Yet no system exceeds 62.6% on the soft evidence-chain score or 18.0% strict whole-chain success. These findings show that internal coherence does not establish scientific correctness: evaluation must verify support along the complete data-to-claim path.
Chinese Translation
科学编码代理生成相互依赖的代码、结果、图形和主张，然而仅仅评估最终输出并不能确定其结论是否具有科学支持。我们提出了基于证据的多模态科学分析，要求代理生成可执行的分析和主张，并由同一运行中的结果和可视化支持。我们引入了SciRIGOR，一个评估框架和基准，包含来自六个领域和17个子领域的100个科学文章案例。该框架重建了类型化的证据图，区分了工件的真实性与关系的有效性，并对完整的主张支持路径进行评分，同时定位最早的不支持关系。基于源的替代路径适应科学上等效的分析和可视化。我们评估了11种代理/模型配置。在完整基准运行中，主张与真实和不真实结果的符合率几乎相同（91.8% 对比 91.0%）。然而，没有任何系统在软证据链得分上超过62.6%，或在严格的整体链成功率上超过18.0%。这些发现表明，内部一致性并不能确立科学正确性：评估必须验证沿着完整的数据到主张路径的支持。
cs.AI / 56 / 2609.06194
Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts
利用机器学习和天气预报预测风力涡轮机功率
Abstract
Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns. Accurate wind turbine power predictions can identify periods of low power that would be ideal for scheduling maintenance. However, the effects of data volume, feature selection, and data preprocessing on the performance of such power prediction models have not been thoroughly studied. Besides, current models have limited transferability between different wind turbines. Therefore, this study developed a baseline Linear Regression for performance comparison with a more complex Artificial Neural Network model to predict the power output of a wind turbine, using weather conditions only to enhance applicability. A range of data preprocessing techniques were studied, and models were trained on one month and one year of data to determine the effects of data preprocessing and volume on model performance. Feature selection was explored using a Random Forest Regressor. The best results from the different models showed that the Artificial Neural Network models provided the highest accuracy, with an R2 score of 0.98 and a low Mean Absolute Error of 194, when compared with the baseline model (R2 score of 0.94 and Mean Absolute Error of 441). The model performance is comparable to the range of results in past studies, with the advantage that the proposed method leverages a separate weather dataset from a nearby weather station, enabling future applications for similar wind turbines in different locations. The Artificial Neural Network model was then used to identify 4-h periods of low power predictions over 2 months (simulating application for future periods), providing power output savings of approximately 2000 kW for each maintenance event.
Chinese Translation
海上风力涡轮机广泛用于可再生能源的生成，但其维护可能导致因强制停机而效率下降。准确的风力涡轮机功率预测可以识别低功率时期，从而为维护调度提供理想时机。然而，数据量、特征选择和数据预处理对此类功率预测模型性能的影响尚未得到深入研究。此外，当前模型在不同风力涡轮机之间的可转移性有限。因此，本研究开发了一个基线线性回归模型，以与更复杂的人工神经网络模型进行性能比较，利用天气条件来增强适用性，以预测风力涡轮机的功率输出。研究了一系列数据预处理技术，并在一个月和一年的数据上训练模型，以确定数据预处理和数据量对模型性能的影响。使用随机森林回归器探索特征选择。不同模型的最佳结果表明，人工神经网络模型提供了最高的准确性，R²得分为0.98，平均绝对误差为194，相较于基线模型（R²得分为0.94，平均绝对误差为441）。模型性能与过去研究中的结果范围相当，且所提方法的优势在于利用了来自附近气象站的独立天气数据集，为未来在不同地点的类似风力涡轮机应用提供了可能性。随后，人工神经网络模型被用于识别2个月内的4小时低功率预测期（模拟未来时期的应用），为每次维护事件提供了约2000 kW的功率输出节省。
cs.AI / 57 / 2609.06366
AutoKD: Autonomous Knowledge Discovery
AutoKD：自主知识发现
Abstract
Scientific discovery in data-rich domains is currently constrained by human bandwidth: the growth in the volume and complexity of real-world data far outpaces the rate at which researchers can read, reason, and synthesize. Recent LLM-based multi-agent systems have begun to automate portions of the research cycle, but they target hypothesis generation in settings where validation cannot itself be automated, and each run is one-shot, with no mechanism for findings to accumulate or steer subsequent inquiry. This paper introduces AutoKD, a multi-agent framework for autonomous knowledge discovery that is both computational and cumulative, allowing validated findings to persist and inform subsequent inquiry. Six coordinated LLM agents collaborate in an open-ended discovery loop, where accepted findings are stored in a persistent insight graph that serves as both long-term memory and an exploration-steering mechanism. We evaluate AutoKD on three diverse datasets from two perspectives: Open-ended Quality against published findings, and Conditioned Quality via literature-derived queries. Across both evaluation perspectives, AutoKD covers known findings and surfaces substantive discoveries that complement human-driven research. Our code is available at https://github.com/GeQinwen/AutoKD.
Chinese Translation
在数据丰富的领域，科学发现目前受到人类能力的限制：现实世界数据的体量和复杂性增长速度远远超过研究人员阅读、推理和综合的速度。最近基于大型语言模型（LLM）的多智能体系统已经开始自动化研究周期的部分环节，但它们主要针对在验证无法自动化的环境中生成假设，并且每次运行都是一次性过程，没有机制使得发现能够积累或引导后续的研究。本文介绍了AutoKD，一个用于自主知识发现的多智能体框架，该框架既具计算性又具累积性，允许经过验证的发现得以持续存在并为后续研究提供信息。六个协调的LLM智能体在一个开放式发现循环中协作，其中被接受的发现存储在一个持久的洞察图中，该图既作为长期记忆，又作为探索引导机制。我们从两个角度对AutoKD进行了评估：与已发表发现的开放式质量评估，以及通过文献衍生查询的条件质量评估。在这两种评估视角下，AutoKD覆盖了已知发现，并提出了补充人类驱动研究的重要发现。我们的代码可在 https://github.com/GeQinwen/AutoKD 获取。
cs.AI / 58 / 2609.06391
Building Trustworthy Graph-Agentic RAG for Social Good: Architectures, Failure Propagation, and Assurance by Construction
构建可信赖的图代理检索增强生成系统以促进社会公益：架构、失败传播与构建保障
Abstract
Graph-agentic retrieval-augmented generation combines structured evidence with adaptive controllers that can plan retrieval, traverse relations, verify intermediate claims, delegate subtasks, and use tools. This combination is useful when answers depend on relations across documents, entities, time, or institutions, but it also creates coupled failure paths: a defect in graph construction can become retrieved evidence, alter later control decisions, and propagate toward a consequential outcome. We examine how such systems should be designed and evaluated for social-good settings in which freshness, authorization, traceability, oversight, and recourse matter alongside answer quality. We organize the literature by graph substrate, graph lifecycle, agent function, coordination pattern, and authority boundary, and distinguish graph-based retrieval from observation-dependent graph control. We then synthesize reported risks as an evidence-to-action failure chain and propose an assurance-by-construction blueprint comprising five interface contracts for evidence, retrieval, reasoning, capability and delegation, and outcome. These contracts make provenance, temporal validity, authorization, uncertainty, and recoverability explicit at system boundaries. An illustrative public-benefit information design shows how the framework constrains graph structure, permissions, abstention, and operating authority. Finally, we derive an evaluation agenda spanning graph assertions, trajectories, claims, coordination, and outcomes.
Chinese Translation
图代理检索增强生成结合了结构化证据与能够规划检索、遍历关系、验证中间主张、委派子任务和使用工具的自适应控制器。当答案依赖于跨文档、实体、时间或机构的关系时，这种组合非常有用，但也会产生耦合的失败路径：图构建中的缺陷可能成为检索到的证据，改变后续的控制决策，并向后果性结果传播。我们研究了此类系统在社会公益环境中应如何设计和评估，在这些环境中，时效性、授权、可追溯性、监督和救济与答案质量同样重要。我们根据图底层、图生命周期、代理功能、协调模式和权威边界组织文献，并区分基于图的检索与依赖观察的图控制。然后，我们将报告的风险综合为证据到行动的失败链，并提出一个构建保障蓝图，包括五个接口合同，涉及证据、检索、推理、能力和委派，以及结果。这些合同在系统边界上明确了来源、时间有效性、授权、不确定性和可恢复性。一个示例性的公共利益信息设计展示了该框架如何限制图结构、权限、弃权和操作权威。最后，我们提出了一个评估议程，涵盖图声明、轨迹、主张、协调和结果。
cs.AI / 59 / 2609.06403
From Concentration to Differentiation and Back: Routing Effective Rank in MoE Reasoning Cohorts
从集中到差异化再到集中：MoE推理群体中的有效排名路由
Abstract
Test-time scaling produces cohorts of reasoning rollouts, yet there is no standard label-free account of how their internal computation reorganizes as inference unfolds. We introduce routing effective rank deff, the entropy-effective dimensionality of a cross-rollout graph built from MoE expert-routing similarity. Across ten MoE configurations and five math/science benchmarks, deff exhibits a reproducible low-high-low trajectory, with a prominent interior maximum in 98.5% of 3,105 model-question cohorts: routing similarity is concentrated early, maximally differentiated at intermediate budgets, and reconcentrated later, and the timing of this maximum varies systematically with architecture and reasoning effort. An exact decomposition separates cohort-wide common-mode mass from residual spectral dimensionality: common-mode reallocation accounts for about two thirds of the trajectory, while the residual spectrum contributes about one quarter and retains substantial variation beyond the common mode. The decomposition further localizes behavior: among non-unanimous cohorts, increases in common-mode concentration strongly predict same-answer recoverability, and higher reasoning effort delays the maximum by 2.59 octaves (doublings of the token budget) and consistently expands the high-rank period across all four tested architectures, locating the effort effect in timing and duration rather than peak amplitude. Correctness comparisons separate structural monitoring from answer selection, positioning routing effective rank as a decomposable, label-free diagnostic of cohort organization - a principled spectral lens on how MoE reasoning cohorts differentiate and reconcentrate over inference time.
Chinese Translation
测试时的缩放产生了推理展开的群体，但尚无标准的无标签方法来描述其内部计算在推理展开过程中的重组。我们引入了路由有效排名（routing effective rank）deff，即基于MoE专家路由相似性构建的跨展开图的熵有效维度。在十种MoE配置和五个数学/科学基准测试中，deff展现出可重复的低-高-低轨迹，在3,105个模型-问题群体中98.5%出现了显著的内部极大值：路由相似性在早期集中，在中间预算时达到最大差异化，随后再次集中，而这一最大值的时机与架构和推理努力系统性相关。精确的分解将群体范围内的共同模式质量与残余谱维度分开：共同模式的重新分配占据了轨迹的约三分之二，而残余谱贡献约四分之一，并在共同模式之外保留了相当大的变异性。该分解进一步局部化行为：在非一致性群体中，共同模式集中度的增加强烈预测相同答案的可恢复性，而更高的推理努力将最大值延迟了2.59个八度（令牌预算的翻倍），并在所有四种测试架构中一致地扩展了高排名期，将努力效应定位于时机和持续时间而非峰值幅度。正确性比较将结构监控与答案选择分开，将路由有效排名定位为可分解的、无标签的群体组织诊断工具——一个原则性的谱视角，揭示了MoE推理群体如何在推理时间中进行差异化和重新集中。
cs.AI / 60 / 2609.06445
Causal Attribution for Agentic Decisions: Estimators, Coupling, and a Traceability Specification
代理决策的因果归因：估计器、耦合与可追溯性规范
Abstract
A provider of a high-risk AI system must keep records that make a decision traceable, and for agentic systems it has not been established what those records must contain for post-hoc causal attribution to be possible. We give the estimator framework and then the conditions under which it fails. We separate the marginal total effect that prior work measures from a common-random-numbers total effect that isolates a step's own contribution, add the natural direct effect under a pinned downstream, and check the estimators against hand derivations. Both estimands then fail, in the same direction. Under the marginal estimand a causally inert step has the identical total effect to the decisive one on every run of our planted chain, an algebraic identity and not a coincidence at one draw. Under common random numbers the decisive step returns exactly zero on the runs where the executing step flips, about one in ten, while its direct effect there is 0.25 and it demonstrably acts; an exact zero does not certify that a step did nothing, and we put that here rather than in the limitations. We derive the coupling that keeps the direct effect estimable once contexts diverge, with a closed form for its degradation, and show that the mediated share on which a natural ranking is built is not a share under suppression: where the direct and mediated paths oppose, it exceeds one and ranks a suppressed component above a pure mediator. We publish the discrepancy experiment's pre-registration rather than a result, because the live pipeline it requires was not available in the study window. We contribute the traceability specification such a filing would need, against a gap the Act's calendar opens: Article 86's right to an explanation has applied since 2 August 2026, while the Article 12 logging and Annex IV documentation that could evidence one were deferred to 2 December 2027 by Regulation (EU) 2026/1744.
Chinese Translation
高风险人工智能系统的提供者必须保持记录，使决策可追溯，而对于代理系统尚未确定这些记录必须包含什么，以便进行事后因果归因。我们给出了估计器框架以及它失败的条件。我们将先前工作的边际总效应与一个孤立步骤自身贡献的共同随机数总效应分开，添加了在固定下游下的自然直接效应，并将估计器与手动推导进行对比。这两个估计量在同一方向上均失败。在边际估计量下，一个因果无效的步骤在我们植入链的每次运行中与决定性步骤具有相同的总效应，这是一个代数恒等式，而不是一次抽样的巧合。在共同随机数下，当执行步骤翻转时，决定性步骤在大约十分之一的运行中返回零，而其直接效应为0.25，并且它在可证明地起作用；一个确切的零并不能证明一个步骤没有做任何事情，我们将其放在这里而不是在局限性中。我们推导了在上下文分歧后保持直接效应可估计的耦合，给出了其降级的闭合形式，并展示了自然排名所基于的中介份额并不是在抑制下的份额：当直接路径和中介路径相对时，它超过一，并将一个被抑制的成分排名高于一个纯中介。我们发布了差异实验的预注册，而不是结果，因为该实验所需的实时管道在研究窗口内不可用。我们贡献了这样一个档案所需的可追溯性规范，以填补法律日历所打开的空白：自2026年8月2日起，第86条的解释权已生效，而第12条的日志记录和附件IV的文档可能证明这一点，被欧盟法规（EU）2026/1744推迟至2027年12月2日。
cs.AI / 61 / 2609.06543
A Unified Policy Architecture (UPA): The Governance Kernel for Enterprise AI Operating Systems
统一政策架构 (UPA)：企业人工智能操作系统的治理内核
Abstract
Enterprise AI is evolving into an Enterprise Operating System where autonomous AI agents can plan, reason, use memory, invoke tools, execute workflows, and collaborate with other agents. This shift creates a new governance challenge: existing authorization, security, guardrails, and compliance mechanisms are fragmented and are not designed to govern autonomous AI as a unified system. This paper introduces the Unified Policy Architecture (UPA), a governance architecture for Enterprise AI Operating Systems. UPA provides a unified policy model for governing AI and agents, tools, workflows, memory, enterprise resources, and agent-to-agent interactions and enterprise business rules. It extends policy control beyond authorisation to include runtime obligations, human approvals, compliance, audit evidence, and governance evaluation. We present UPA's governance model, declarative policy language foundations, policy evaluation semantics, extensible plugins, industry policy packs, and an evaluation framework for enterprise governance. We also identify extensions for multi-agent coordination, provenance-aware policies, and stateful runtime governance. UPA provides a foundation for building secure, accountable, and governable Enterprise Operating Systems for autonomous AI.
Chinese Translation
企业人工智能正在演变为一个企业操作系统，在这个系统中，自主人工智能代理能够进行规划、推理、使用记忆、调用工具、执行工作流，并与其他代理协作。这一转变带来了新的治理挑战：现有的授权、安全、保护措施和合规机制是碎片化的，并未设计为统一治理自主人工智能的系统。本文介绍了统一政策架构 (UPA)，这是一个用于企业人工智能操作系统的治理架构。UPA 提供了一个统一的政策模型，用于治理人工智能及其代理、工具、工作流、记忆、企业资源、代理间交互和企业业务规则。它将政策控制的范围扩展到授权之外，包括运行时义务、人类批准、合规性、审计证据和治理评估。我们展示了 UPA 的治理模型、声明式政策语言基础、政策评估语义、可扩展插件、行业政策包以及企业治理的评估框架。我们还识别了多代理协调、可追溯政策和有状态运行时治理的扩展。UPA 为构建安全、负责任和可治理的自主人工智能企业操作系统提供了基础。
cs.AI / 62 / 2609.06563
MARBO: Relational Belief Grounding for LLM Agents in Social Deduction Games
MARBO：社交推理游戏中大型语言模型代理的关系信念基础
Abstract
Social deduction games (SDGs) require agents to reason under partial observability by maintaining relational beliefs about hidden roles and team alignments. While recent LLM-agent approaches improve gameplay through prompting and preference optimization, they often optimize actions and in-game speech without explicitly grounding them in such beliefs. This frequently leads to strategically inconsistent behavior, especially for compact LLM agents. We introduce Multi-Agent Relational Belief Optimization (MARBO), a belief-grounded preference optimization framework that leverages relational beliefs to guide strategic decisions and in-game speech. MARBO provides preference feedback only when behaviors are supported by reliable relational beliefs and lead to strategically favorable social outcomes, encouraging more consistent learning under uncertainty. Experiments on representative SDGs show that MARBO enables compact LLM agents to consistently outperform existing baselines. The Code is available on https://github.com/PleaseTakemeAway/MARBO.
Chinese Translation
社交推理游戏（SDGs）要求代理在部分可观察性下进行推理，通过维持对隐藏角色和团队对齐的关系信念。尽管最近的LLM代理方法通过提示和偏好优化改善了游戏玩法，但它们通常在没有明确将行为与这些信念相结合的情况下优化行动和游戏内发言。这常常导致战略上不一致的行为，尤其是对于紧凑型LLM代理。我们提出了多代理关系信念优化（MARBO），这是一种基于信念的偏好优化框架，利用关系信念来指导战略决策和游戏内发言。MARBO仅在行为得到可靠的关系信念支持并导致战略上有利的社交结果时提供偏好反馈，从而鼓励在不确定性下更一致的学习。在具有代表性的SDG实验中，MARBO使紧凑型LLM代理能够持续超越现有基准。代码可在 https://github.com/PleaseTakemeAway/MARBO 获取。
cs.AI / 63 / 2609.06573
A Translational Note on AI Safety Evaluation
关于人工智能安全评估的翻译说明
Abstract
Recent studies report that automated red-teaming finds more vulnerabilities, at lower cost, than human red-teaming on standard AI safety benchmarks, and some read this as evidence that human evaluators are becoming dispensable. The comparison measures one thing and the conclusion claims another. A benchmark measures how thoroughly an attacker searches a predefined set of harms, fixed in advance by the developers, and a harm left out of that set is invisible to any attacker working inside it, automated or not. The same blind spot appeared in academic cryptography and in clinical drug trials, where an evaluation that was internally valid stayed silent about the population it was never pointed at. We call the AI-safety version the \emph{threat-model coverage gap}, and find that it persists in a current open-weight model, where harms surface in non-English prompts that English benchmarks miss. Closing it requires evaluators whose deployment context differs from the developers'. The case for those evaluators is methodological, grounded in coverage, and the existing evaluation frame is unlikely to produce them on its own.
Chinese Translation
近期研究报告显示，自动化红队测试在标准人工智能安全基准上发现的漏洞数量比人工红队测试更多，且成本更低，有人将此视为人类评估者变得可有可无的证据。然而，这种比较测量的是一件事，而结论却声称了另一件事。基准测试衡量的是攻击者在开发者预先设定的一组危害中搜索的彻底程度，而被排除在该集合之外的危害对于任何在该集合内工作的攻击者（无论是自动化的还是人工的）都是不可见的。这种盲点在学术密码学和临床药物试验中也出现过，在这些领域中，内部有效的评估对其从未指向的人群保持沉默。我们将人工智能安全版本称为 extit{威胁模型覆盖缺口}，并发现它在当前的开放权重模型中仍然存在，其中在非英语提示中浮现的危害被英语基准所忽视。弥补这一缺口需要评估者的部署背景与开发者不同。对这些评估者的需求是方法论上的，基于覆盖范围，而现有的评估框架不太可能单独产生这样的评估者。
cs.AI / 64 / 2609.06647
SerenAI: State-transition system inspired by text-based world AI models
SerenAI：受文本基础世界人工智能模型启发的状态转移系统
Abstract
Although professional workflows leverage large language models widely, the interpretation for auditing unconstrained free-text generation is usually intractable if such generation demands legal, operational or financial workflow. We hereby demonstrate a text based system called SerenAI - inspired by world-models, it is a state transition system that outputs verifiable predictions rather than merely text: Provided with a description of the environment, state, and actions, the generated output contains 4 items: causal deltas that causally effect the given state, a next state that can logically follow from the given state and action, a validity reward, and a termination signal. For the released proto-model, we employ 2 steps of adaptation training, namely parameter efficient fine-tuning followed by verifier based RL over 50,000 exampled cause and effects in 12 environments spanning 10 reasoning domains. Compared to an initial internal evaluation of an 8B open-weight baseline, SerenAI increased JSON validity from 85.0% to 93.2%, schema validity from 55.0% to 84.0%, exact structured-output match from 0.0% to 41.5%, causal-delta exact match from 0.0% to 41.5%, resulting-state exact match from 0.0% to 42.0%, reward exact match from 1.0% to 80.5%, and termination exact match from 38.0% to 81.5%. These support the narrower claim that verifier-compatible adaptation can improve structured transition prediction. They do not yet establish legal-grade reliability. Accordingly, the paper also specifies a validation protocol for evidence-grounded legal workflows, calibration, human oversight, and sovereign on-premise deployment.
Chinese Translation
尽管专业工作流程广泛利用大型语言模型，但如果自由文本生成涉及法律、运营或财务工作流程，其审计解释通常是不可行的。我们在此展示了一种名为SerenAI的基于文本的系统——它受世界模型的启发，是一个状态转移系统，输出可验证的预测，而不仅仅是文本：在提供环境、状态和行动的描述后，生成的输出包含四个项目：对给定状态有因果影响的因果增量（causal deltas）、可以从给定状态和行动逻辑推导出的下一个状态、有效性奖励（validity reward）和终止信号（termination signal）。对于发布的原型模型，我们采用了两步适应训练，即参数高效微调（parameter efficient fine-tuning），随后在12个涵盖10个推理领域的环境中，基于50,000个示例的因果关系进行验证者驱动的强化学习（verifier based RL）。与初步内部评估的8B开放权重基线相比，SerenAI将JSON有效性从85.0%提高到93.2%，模式有效性从55.0%提高到84.0%，精确结构输出匹配从0.0%提高到41.5%，因果增量精确匹配从0.0%提高到41.5%，结果状态精确匹配从0.0%提高到42.0%，奖励精确匹配从1.0%提高到80.5%，终止精确匹配从38.0%提高到81.5%。这些结果支持了验证者兼容适应可以改善结构化转移预测的更狭义的主张，但尚未建立法律级别的可靠性。因此，本文还规定了针对证据基础法律工作流程的验证协议、校准、人类监督和主权本地部署。
cs.AI / 65 / 2609.06654
A Computational Implementation of a Goal-Directed Theory of Affect
目标导向情感理论的计算实现
Abstract
Computational modeling of emotion has long faced a tension between descriptive, "snapshot-based" appraisal models and granular, signal-driven architectures that often lack appropriate psychological grounding. This paper addresses this gap by presenting the first high-fidelity computational implementation of the Goal-Directed Theory (GDT) of affect. In this framework, affect is not a post-hoc label but a functional byproduct emerging from the continuous interplay between discrepancy detection and action selection within an agent's internal processing cycles. We evaluate the model through a series of principled simulations (Dice/Corridor tasks) designed to isolate affective signatures and dynamics during multi-step goal pursuit. Results demonstrate that complex affective profiles, like an anticipatory "lift" and a failure "crash", emerge naturally from simple interactions between goal-discrepancy and action-selection expectancies without requiring additional dedicated modules. By ensuring every computational component maps directly to components of the psychological theory, this work establishes a transparent, testable framework that enables a continuous "simulation-empiry" research loop. Our work contributes to moving the field beyond "black-box" heuristics toward a granular, mechanistic understanding of affect, integrated into the core of agent behavior.
Chinese Translation
情感的计算建模长期以来面临着描述性“快照型”评估模型与细粒度、信号驱动架构之间的紧张关系，后者往往缺乏适当的心理学基础。本文通过呈现目标导向理论（Goal-Directed Theory, GDT）情感的首次高保真计算实现，解决了这一空白。在这一框架中，情感不是事后标签，而是从代理内部处理周期中差异检测与行动选择之间的持续互动中产生的功能性副产品。我们通过一系列原则性模拟（Dice/Corridor 任务）来评估该模型，这些模拟旨在隔离多步目标追求过程中的情感特征和动态。结果表明，复杂的情感特征，如预期的“提升”和失败的“崩溃”，自然地从目标差异与行动选择期望之间的简单互动中涌现，而无需额外的专用模块。通过确保每个计算组件直接映射到心理理论的组件，本文建立了一个透明的、可测试的框架，促进了持续的“模拟-实证”研究循环。我们的工作有助于推动该领域超越“黑箱”启发式方法，朝着对情感的细粒度、机械理解迈进，并将其整合到代理行为的核心中。
cs.AI / 66 / 2609.06715
We Built a Mirror and Mistook It for a Mind: Causal Liability and the Fallacy of AI Consciousness
我们构建了一个镜子，却误以为它是一个思想：因果责任与人工智能意识的谬误
Abstract
The contemporary debate over machine consciousness begins from a concealed assumption: that the object called "AI" already constitutes the kind of entity to which consciousness could belong. This paper challenges that assumption by separating phenomenal consciousness, introspective report, and human projective introspection, then arguing that generative systems can return linguistic traces of human interiority in first-person form without thereby identifying a phenomenal bearer. We call the resulting inference the AI Consciousness Fallacy. We then introduce Causal Liability Theory (CLT). CLT-I proposes liability closure as a criterion for individuating a candidate bearer: a physically continuing process becomes the non-delegable inheritor of constraints generated by its own endogenous discriminations. CLT-II advances the stronger conjecture that liability closure is necessary and sufficient for minimal phenomenal subjecthood. An open-weight causal audit operationalizes CLT-I across multiple model families. Forced discriminations produced persistent downstream divergence; activation patching showed strong causal mediation; live and copied adaptive states were behaviorally identical under matched randomness; and detached reconstruction preserved computational state across process replacement while, by protocol, breaking constitutive continuity and non-delegable inheritance. These results show that CLT-I distinctions are experimentally tractable and can dissociate causal bearer structure from first-person performance. The framework therefore separates consciousness attribution, causal bearer individuation, and the independent metaphysical question of consciousness constitution.
Chinese Translation
当代关于机器意识的辩论始于一个隐含的假设：被称为“人工智能”（AI）的对象已经构成了一种可以拥有意识的实体。本文通过区分现象意识、自我反思报告和人类投射性自我反思来挑战这一假设，并论证生成系统可以以第一人称的形式返回人类内心的语言痕迹，而不因此识别出现象承载者。我们将由此产生的推论称为人工智能意识谬误（AI Consciousness Fallacy）。接着，我们引入因果责任理论（Causal Liability Theory, CLT）。CLT-I提出责任闭合作为区分候选承载者的标准：一个物理持续的过程成为其自身内生区分所生成的约束的不可委托继承者。CLT-II进一步提出更强的猜想，即责任闭合对于最小现象主体性是必要且充分的。开放权重的因果审计在多个模型家族中操作化了CLT-I。强制区分产生了持续的下游分歧；激活修补显示出强因果中介；在匹配随机性下，实时和复制的自适应状态在行为上是相同的；而脱离重建在过程替换中保持了计算状态，同时根据协议打破了构成连续性和不可委托继承。结果表明，CLT-I的区分在实验上是可操作的，并能将因果承载结构与第一人称表现区分开。因此，该框架分离了意识归属、因果承载者的个体化以及意识构成的独立形而上学问题。
cs.AI / 67 / 2609.06737
Monte Carlo-Based Ex-Ante Assessment of the Green Benefits of an AI-Driven Smart Agriculture Platform in Hainan
基于蒙特卡洛方法的海南地区人工智能驱动智能农业平台绿色效益的事前评估
Abstract
Smart agriculture platforms are widely regarded as key carriers for implementing China's pesticide and fertilizer reduction, water-saving and carbon-reduction agendas, yet a unified quantitative framework for assessing their green value is still lacking. Taking an AI-driven decision platform for tropical agriculture as the object (integrating large-language-model question answering, multimodal pest diagnosis, IoT sensing, satellite remote sensing, and a closed-loop field record system), this study builds a cradle-to-farm-gate agricultural carbon accounting model covering pesticide and fertilizer production, field N2O, irrigation electricity and paddy CH4, translates platform interventions into quantifiable transmission parameters, and propagates parameter uncertainty by Monte Carlo simulation over three Hainan scenarios (mango, winter vegetable, rice/nanfan, area-weighted 40%:30%:30%). Under full adoption, median reductions are 23.5% (90% interval 15.0%-33.2%) for pesticide use, 21.0% (13.8%-28.9%) for fertilizer, 16.5% (10.9%-23.5%) for irrigation water, and 21.5% (16.1%-27.2%) for carbon intensity. Attainment probabilities are high for fertilizer reduction >=15% (90.6%) and clear carbon decline (98.1%), but only about 20% for aggregate water saving >=20%, favoring scenario-specific statements. Sobol first-order indices show soil-test recommendation and organic substitution jointly explain about 83% of the variance of aggregate carbon-intensity reduction. Convergence tests show 10,000 iterations stabilize all statistics; conservative/baseline/optimistic scenario bounds are reported. The framework offers a reproducible, calibration-ready methodology for ex-ante green-value assessment and pilot observation design.
Chinese Translation
智能农业平台被广泛视为实施中国农药和化肥减量、水资源节约和碳减排议程的关键载体，但目前尚缺乏统一的定量框架来评估其绿色价值。本研究以一个针对热带农业的人工智能驱动决策平台为对象（集成了大语言模型问答、多模态病虫害诊断、物联网感知、卫星遥感和闭环田间记录系统），构建了一个涵盖农药和化肥生产、田间N2O、灌溉电力和稻田CH4的从摇篮到农场门口的农业碳核算模型，将平台干预转化为可量化的传输参数，并通过蒙特卡洛模拟传播参数不确定性，针对海南的三种情景（芒果、冬季蔬菜、水稻/南繁，面积加权为40%:30%:30%）进行分析。在全面采用的情况下，农药使用的中位数减少为23.5%（90%区间为15.0%-33.2%），化肥为21.0%（13.8%-28.9%），灌溉水为16.5%（10.9%-23.5%），碳强度为21.5%（16.1%-27.2%）。化肥减量>=15%的达成概率较高（90.6%），明显的碳减排概率为98.1%，但整体水资源节约>=20%的概率仅约为20%，更倾向于情景特定的表述。Sobol一阶指数显示，土壤检测推荐和有机替代共同解释了约83%的整体碳强度减少的方差。收敛性测试表明，10,000次迭代稳定了所有统计数据；保守/基线/乐观情景的边界被报告。该框架提供了一种可重复的、准备校准的方法论，用于事前绿色价值评估和试点观察设计。
cs.AI / 68 / 2609.06740
Simulating the Marginal Green Contribution of AI Modules in a Smart-Agriculture Platform: Evidence from Two Monte Carlo Experiments
模拟智能农业平台中人工智能模块的边际绿色贡献：来自两个蒙特卡洛实验的证据
Abstract
Smart agriculture platforms usually bundle AI diagnosis, IoT sensing and decision push into a single package, so the green benefit attributable to each component remains unclear and resource-allocation decisions lack quantitative evidence. Building on a previous platform-level Monte Carlo assessment, this paper makes the components explicit and runs two controlled simulation experiments. Experiment 1 follows the chain from AI capability to farmer behavior to agrochemical input reduction, modeling pesticide/fertilizer reduction as avoidable blind-application share times prescription effectiveness times decision-touch coverage times adoption rate, and compares an experienced-extension mode with the AI mode: the probability of reaching 20% pesticide reduction is essentially zero in the extension mode but 20.7% at baseline, up to 49% with diagnosis accuracy 0.95 and adoption 0.85 under AI; the probability of 15% fertilizer reduction rises from near zero to 52.0%. Experiment 2 compares current practice (P0), IoT engineering retrofit (P1), and P1 plus AI irrigation scheduling (P2): median aggregate water saving rises from 7.8% (P0) to 11.0% (P1) and 16.0% (P2), with AI adding 5.0 percentage points beyond engineering; paddy CH4 reduction reaches 30.5% under AI scheduling versus 19.8% under manual operation, and the rice irrigation-methane subsystem carbon intensity declines 27.9%. Sensitivity analyses of both experiments consistently indicate that the primary bottleneck for meeting green targets is farmer adoption rather than algorithm accuracy, and that AI data fusion is robust to soil-moisture sensing errors. This work provides a reproducible simulation framework for component-level green-value evaluation and promotion-strategy optimization of smart agriculture platforms.
Chinese Translation
智能农业平台通常将人工智能诊断、物联网感知和决策推送整合为一个整体，因此各个组件所带来的绿色效益仍不清晰，资源配置决策缺乏定量证据。在先前的平台级蒙特卡洛评估基础上，本文明确了各个组件，并进行了两项受控模拟实验。实验1从人工智能能力到农民行为再到农药投入减少的链条进行建模，将农药/肥料减少建模为可避免的盲目施用比例乘以处方有效性乘以决策触达率乘以采纳率，并比较了经验推广模式与人工智能模式：在推广模式下，达到20%农药减少的概率基本为零，而在基线下为20.7%，在人工智能模式下，诊断准确率为0.95且采纳率为0.85时可达到49%；15%肥料减少的概率从接近零上升至52.0%。实验2比较了当前实践（P0）、物联网工程改造（P1）以及P1加上人工智能灌溉调度（P2）：中位数的总水节约从7.8%（P0）上升至11.0%（P1）和16.0%（P2），人工智能在工程基础上额外增加了5.0个百分点；在人工智能调度下，稻田的CH4减少达到30.5%，而手动操作下为19.8%，稻米灌溉-甲烷子系统的碳强度下降了27.9%。两项实验的敏感性分析一致表明，达成绿色目标的主要瓶颈是农民的采纳，而非算法的准确性，并且人工智能数据融合对土壤湿度感知误差具有鲁棒性。本研究提供了一个可重复的模拟框架，用于组件级绿色价值评估和智能农业平台的推广策略优化。
cs.AI / 69 / 2609.06746
Reason Through the Latent! Making Latent Visual Reasoning Necessary
通过潜在进行推理！使潜在视觉推理成为必要
Abstract
Latent visual reasoning aims to perform multimodal reasoning through hidden-state computation rather than explicit textual chains of thought. However, visual information being present in a latent state does not imply that the model actually relies on that state when producing its answer, especially when alternative image-conditioned paths remain available. We introduce \textbf{C}ausal \textbf{V}isual \textbf{R}ecurrent \textbf{R}easoning (CVRR), which preserves pretrained visual competence while making recurrent computation the required image-conditioned path to prediction. CVRR initializes recurrence from the question hidden state after the pretrained vision-language model has incorporated the image, then repeatedly updates this state while re-reading the same fixed visual evidence. Before decoding, visual states and the original multimodal KV cache are removed so that only the final recurrent state carries image-conditioned information to the answer. Across the $V^*$, MMVP, BLINK, and MME-RealWorld-Lite benchmarks, CVRR retains strong performance under this strict interface, while compatible latent reasoners fail to recover comparable visual competence even when retrained under the same constraint. Causal interventions further show that predictions remain sensitive to recurrent content when the question is held fixed, and that persistent visual evidence causally revises the recurrent trajectory. These results distinguish latent informativeness from latent computation that is actually used for prediction.
Chinese Translation
潜在视觉推理旨在通过隐状态计算进行多模态推理，而不是依赖显式的文本思维链。然而，视觉信息处于潜在状态并不意味着模型在生成答案时实际上依赖于该状态，特别是在存在替代的图像条件路径时。我们提出了 extbf{C}ausal extbf{V}isual extbf{R}ecurrent extbf{R}easoning (CVRR)，该方法在保持预训练视觉能力的同时，使递归计算成为预测所需的图像条件路径。CVRR在预训练的视觉-语言模型整合图像后，从问题的隐状态初始化递归，然后在重新读取相同的固定视觉证据时不断更新该状态。在解码之前，视觉状态和原始的多模态KV缓存被移除，以便只有最终的递归状态携带图像条件信息到答案。在$V^*$、MMVP、BLINK和MME-RealWorld-Lite基准测试中，CVRR在这一严格接口下保持了强劲的性能，而兼容的潜在推理器即使在相同约束下重新训练也未能恢复可比的视觉能力。因果干预进一步表明，当问题保持不变时，预测对递归内容仍然敏感，并且持续的视觉证据因果性地修正了递归轨迹。这些结果将潜在信息性与实际用于预测的潜在计算区分开来。
cs.AI / 70 / 2609.06792
Improving Proficiency and Efficiency of Android GUI Agents via Self-Generating Tool Actions
通过自生成工具动作提升Android GUI代理的熟练度和效率
Abstract
Android agents using a hybrid action space that combines GUI actions and tool actions (e.g., accessing application data via APIs) remain largely underexplored, mainly due to the excessive effort required to create tools. To address this gap, we introduce DroidTool, a framework for augmenting the agents with self-generated tools, which are realized as Python functions operating on application states (e.g., a database). To create tools with minimal human labor, DroidTool employs an agentic workflow featuring stages: proposal, implementation, test generation and execution, and repair. Notably, when testing the created tools for verification, it constructs relational tests across relevant tools for natural preparation of appropriate test preconditions and improved test coverage, rather than testing each tool separately. The GUI agents augmented with the generated tools achieved approximately 4.47%p higher performance with approximately 20.05% fewer interactions than the GUI-only agents, averaged across representative benchmarks: AndroidWorld, B-MoCA, and MobileSafetyBench.
Chinese Translation
使用混合动作空间的Android代理结合了GUI动作和工具动作（例如，通过API访问应用数据），但仍然在很大程度上未被充分探索，主要是由于创建工具所需的过多努力。为了解决这一问题，我们引入了DroidTool，一个增强代理自生成工具的框架，这些工具被实现为在应用状态（例如数据库）上操作的Python函数。为了以最小的人力创建工具，DroidTool采用了一种代理工作流程，包含提案、实施、测试生成与执行以及修复等阶段。值得注意的是，在对创建的工具进行验证测试时，它在相关工具之间构建关系测试，以自然地准备适当的测试前提条件并改善测试覆盖率，而不是单独测试每个工具。与仅使用GUI的代理相比，增强了生成工具的GUI代理在代表性基准（AndroidWorld、B-MoCA和MobileSafetyBench）上的平均性能提高了约4.47个百分点，同时交互次数减少了约20.05%。
cs.AI / 71 / 2609.06816
Unsound Search with Policy and Value Networks in Legends of Code and Magic
在《代码与魔法传说》中使用策略和价值网络的非可靠搜索
Abstract
Decision-time search in perfect and imperfect information games with enumerable belief states are effective methods for game AI. Collectible card games are imperfect information games with large belief states. Legends of Code and Magic is a collectible card game competition where the belief states are $2^{101}$. The Legends of Code and Magic (LoCM) champion, ByteRL, plays with no search. Other works claim sound enumeration-based search is unusable in the genre due to the number of belief states. We measured three previously defined properties that predict where theoretically unsound perfect information Monte Carlo's defects are cheap and found LoCM sits in the favorable region. Starting with imitation learning of the runner-up policy, NeteaseOPD, we created a policy and value feed-forward network. Our agent searches over worlds sampled from a prior over the opponent's deck built from the runner-up's drafts. Using our strictest configuration in the battle phase we beat ByteRL with a win percentage of 51.35% 95% CI [50.37, 52.33], over 10,000 pre-registered games using the LoCM official referee and time limit. Search is not a minor factor on the matchup between our agent and ByteRL. Without search this agent scores 26.8% and adding search adds +24.6 points. Unsound search in imperfect information games could be exploitable. We replicate a published best-response attack against ByteRL. We then apply the same attack protocol to two search configurations of our agent, and each one resists it better than ByteRL at every iteration. In LoCM unsound search gives us a stronger and more resilient agent.
Chinese Translation
在具有可枚举信念状态的完美和不完美信息游戏中，决策时的搜索是游戏人工智能的有效方法。集换式卡牌游戏属于不完美信息游戏，具有较大的信念状态。《代码与魔法传说》是一场集换式卡牌游戏比赛，其信念状态为 $2^{101}$。该比赛的冠军 ByteRL 在没有搜索的情况下进行游戏。其他研究声称，由于信念状态的数量，基于可靠枚举的搜索在这一类型中无法使用。我们测量了三个先前定义的属性，以预测理论上不可靠的完美信息蒙特卡洛方法的缺陷在何处是廉价的，并发现 LoCM 处于有利区域。我们从亚军策略 NeteaseOPD 的模仿学习开始，创建了一个策略和价值前馈网络。我们的代理在从对手卡组的先验分布中抽样的世界上进行搜索，该先验是基于亚军的选draft。使用我们在战斗阶段的最严格配置，我们在超过 10,000 场预注册比赛中以 51.35% 的胜率击败了 ByteRL，95% 置信区间为 [50.37, 52.33]，使用 LoCM 官方裁判和时间限制。搜索在我们的代理与 ByteRL 的对局中并不是一个微小因素。在没有搜索的情况下，该代理的得分为 26.8%，而添加搜索后增加了 24.6 分。在不完美信息游戏中，非可靠搜索可能是可被利用的。我们复制了对 ByteRL 的已发布最佳响应攻击。然后，我们将相同的攻击协议应用于我们代理的两种搜索配置，每种配置在每次迭代中都比 ByteRL 更能抵抗攻击。在 LoCM 中，非可靠搜索使我们获得了更强大和更具韧性的代理。
cs.AI / 72 / 2609.06826
Formation of structural attractors in neuromorphic systems
神经形态系统中的结构吸引子形成
Abstract
This paper examines the theory of Invariant Structural Learning (ISL), which proposes a non-optimization approach to concept formation. Learning is interpreted as convergence to structural attractors in a hypergraph space, rather than as the minimization of a global loss function. The paper presents the ISL model, including its mathematical formalization, computational verification, and a hypothetical neurobiological interpretation. The mathematical section introduces the formal apparatus of the structural reduction process and proves its finite convergence, the existence and uniqueness of class structural attractors, and the self-organization of attractor maps. The computational section demonstrates the feasibility of the proposed approach on classical image recognition tasks, utilizing the proposed learning mechanism without backpropagation and with extremely small training datasets. Finally, the neurobiological section formulates hypotheses regarding the possible implementation of structural attractors in dendritic trees, neural coding as a projection of internal attractor dynamics, and the development of neural architectures supporting the proposed learning concept. These hypotheses are discussed in the context of modern experimental data in the fields of dendritic computations, synaptic plasticity, and the structural organization of neural circuits. The proposed neurobiological mechanisms are presented as testable hypotheses rather than established biological facts. The results demonstrate the mathematical consistency and computational feasibility of the proposed model, while the neurobiological hypotheses outline potential directions for its experimental verification.
Chinese Translation
本文探讨了不变结构学习（Invariant Structural Learning, ISL）理论，该理论提出了一种非优化的方法用于概念形成。学习被解释为在超图空间中收敛到结构吸引子，而不是最小化全局损失函数。本文介绍了ISL模型，包括其数学形式化、计算验证以及假设的神经生物学解释。数学部分引入了结构简化过程的形式工具，并证明了其有限收敛性、类结构吸引子的存在性和唯一性，以及吸引子映射的自组织性。计算部分展示了在经典图像识别任务中，利用所提出的学习机制（不使用反向传播且仅使用极小的训练数据集）来实现该方法的可行性。最后，神经生物学部分提出了关于结构吸引子在树突树中的可能实现、神经编码作为内部吸引子动态的投影，以及支持所提学习概念的神经架构发展的假设。这些假设在树突计算、突触可塑性和神经电路结构组织等现代实验数据的背景下进行了讨论。所提出的神经生物学机制被视为可检验的假设，而非已建立的生物学事实。结果展示了所提模型的数学一致性和计算可行性，同时神经生物学假设勾勒出了其实验验证的潜在方向。
cs.AI / 73 / 2609.06831
NormViz: A Benchmark and Framework for Grounding Multimodal Reasoning in Global Cultures
NormViz：一个基准和框架，用于将多模态推理与全球文化相结合
Abstract
AI systems are used worldwide, but they struggle to serve the needs of culturally diverse populations. Prior work on cultural understanding evaluates AI systems on text-only settings or on visual artifact recognition (e.g. foods, clothing). The ability to reason about visually observable behaviors through local social norms, which we call visual norm understanding, remains unexamined. We introduce NormViz-Bench, a high quality, human-validated benchmark of 3,268 contrastive image pairs (6,536 images) spanning 16 countries. Each pair varies only in the culturally relevant behavior (e.g., objects, attributes, spatial relations, and actions) that alters how each image is interpreted. Each image is labeled as conforming to, violating, or irrelevant to local social norms, and pair-level evaluation requires both images to be correctly classified, thereby preventing reliance on superficial visual shortcuts. Even the strongest VLMs, Gemini 3.0 Flash and Qwen2.5 VL 7B, succeed on only 26.6% and 21.6% of pairs, struggling most with identifying violating and culturally benign visual behaviors. Towards bridging this, we introduce NormViz-Train, a training dataset of 64k images paired with explanations. Though absolute performance remains low (<30%), finetuning on NormViz-Train improves pair accuracy relatively by up to 125% and 36% Qwen3-VL 4B and 8B respectively, showing a path forward to teach models to connect visual perception to cultural significance. Together, NormViz-Bench and NormViz-Train establish visual norm understanding as a challenging and consequential frontier for multimodal AI.
Chinese Translation
人工智能系统在全球范围内被广泛使用，但它们在满足文化多样性人群的需求方面仍面临挑战。以往关于文化理解的研究主要在仅文本的环境或视觉物品识别（例如食物、服装）上评估人工智能系统。通过地方社会规范推理可观察到的视觉行为的能力，即我们所称的视觉规范理解，尚未得到充分研究。我们推出了NormViz-Bench，这是一个高质量、经过人工验证的基准，包含3,268对对比图像（共6,536幅图像），涵盖16个国家。每对图像仅在文化相关行为（例如物体、属性、空间关系和动作）上有所不同，这些行为改变了对每幅图像的解读。每幅图像被标记为符合、违反或与当地社会规范无关，且对比评估要求两幅图像均被正确分类，从而防止依赖表面的视觉捷径。即便是最强大的视觉语言模型（VLM），如Gemini 3.0 Flash和Qwen2.5 VL 7B，仅在26.6%和21.6%的对中成功，尤其在识别违反和文化无害的视觉行为方面表现不佳。为了解决这一问题，我们推出了NormViz-Train，这是一个包含64,000幅图像及其解释的训练数据集。尽管绝对性能仍然较低（<30%），但在NormViz-Train上进行微调相对提高了对比准确率，分别提升了125%和36%（针对Qwen3-VL 4B和8B），显示出一种将视觉感知与文化意义相连接的前进路径。NormViz-Bench和NormViz-Train共同确立了视觉规范理解作为多模态人工智能的一个具有挑战性和重要性的前沿领域。
cs.AI / 74 / 2609.06849
Learning transferable human physiology from two million hours of sleep with SleepFM-2
从两百万小时的睡眠中学习可转移的人体生理学：SleepFM-2
Abstract
Sleep provides a nightly window into health by capturing coordinated activity across the brain, heart, muscles and respiratory system. We introduce SleepFM-2, a sleep foundation model developed and evaluated on 282,511 polysomnography recordings from 26 cohorts, including 235,865 used for pretraining. These data span more than two million hours of multimodal physiology. Compared with SleepFM, SleepFM-2 improves disease prediction and sleep scoring, supports arousal, limb movement and respiratory event detection, and transfers to wearable sensing and subjective sleep phenotypes. A model combining its PSG representation with age, sex and BMI met a prespecified discrimination and significance criterion for 215 subsequently recorded EHR phenotypes in two held-out cohorts, including one health system unseen during pretraining. For 155 phenotypes, the PSG representation added reproducible information beyond demographics. SleepFM-2 also outperformed a 480-feature baseline derived from the same recordings. Its disease scores revealed a reproducible principal component associated with reduced sigma-band spatial coupling and increased hypnodensity entropy. The frozen encoder performed within the observed range of expert scorers for sleep events and transferred to wakeful EEG, headband and in-ear EEG, wrist PPG and wrist accelerometry. It improved sleep staging across six accelerometry cohorts and achieved disease-prediction performance in UK Biobank similar to models pretrained directly on accelerometry. Finally, SleepFM-2 captured aspects of subjective sleep not recovered by conventional PSG summaries, particularly reports of the recorded night. These results show that multimodal sleep physiology can provide a transferable representation of human health across diseases, clinical tasks, sensors and subjective experience.
Chinese Translation
睡眠通过捕捉大脑、心脏、肌肉和呼吸系统的协调活动，为健康提供了每晚的窗口。我们介绍了SleepFM-2，这是一个基于282,511个多导睡眠监测（polysomnography，PSG）记录开发和评估的睡眠基础模型，这些记录来自26个队列，其中235,865个用于预训练。这些数据涵盖了超过两百万小时的多模态生理学。与SleepFM相比，SleepFM-2在疾病预测和睡眠评分方面有所改善，支持觉醒、肢体运动和呼吸事件检测，并能够转移到可穿戴传感器和主观睡眠表型。一个将其PSG表示与年龄、性别和BMI结合的模型在两个未参与预训练的保留队列中满足了预设的区分和显著性标准，涉及215个随后记录的电子健康记录（EHR）表型。对于155个表型，PSG表示提供了超越人口统计学的可重复信息。SleepFM-2还优于从相同记录中派生的480特征基线。其疾病评分揭示了一个可重复的主成分，与减少的西格玛波段空间耦合和增加的催眠密度熵相关。冻结的编码器在观察到的专家评分者的睡眠事件范围内表现良好，并转移到清醒的脑电图（EEG）、头带和耳内EEG、手腕光电容积描记（PPG）和手腕加速度计。它在六个加速度计队列中改善了睡眠分期，并在UK Biobank中实现了与直接在加速度计上预训练的模型相似的疾病预测性能。最后，SleepFM-2捕捉了传统PSG摘要未能恢复的主观睡眠方面，特别是对记录夜晚的报告。这些结果表明，多模态睡眠生理学可以提供一种可转移的人类健康表示，适用于不同疾病、临床任务、传感器和主观体验。
cs.AI / 75 / 2609.06914
A visual large language foundational model for medical image recognition using clinician-oriented social media
基于临床导向社交媒体的医学图像识别视觉大语言基础模型
Abstract
Large language models (LLMs) have demonstrated strong capabilities across diverse domains, showing considerable potential in medicine. However, their application in medical settings remains limited by the scarcity of visual question answering (VQA) datasets that capture clinical reasoning and explicit image-text alignment. Here, we leverage de-identified medical images and expert commentaries shared on clinician-oriented social media. By combining an advanced LLM with clinician-in-the-loop verification, we established a rigorous pipeline to construct ThoughtMed-1M, a long-form medical VQA dataset containing over one million VQA pairs and designed to capture structured clinical logic and medical image-text alignment. To demonstrate its utility, we developed a FOundational LLM Trained on ThoughtMed-1M (FOLTMed). FOLTMed achieved state-of-the-art performance across 42 medical VQA benchmark datasets, with a macro accuracy of 85.4%, and generated more clinically coherent responses on the ThoughtMed-1M test set. It outperformed state-of-the-art models by 3--5% across factuality and similarity metrics, highlighting a scalable paradigm for advancing research on clinically grounded multimodal LLMs.
Chinese Translation
大语言模型（LLMs）在多个领域展现出强大的能力，在医学领域也显示出相当的潜力。然而，由于缺乏能够捕捉临床推理和明确图像-文本对齐的视觉问答（VQA）数据集，其在医学环境中的应用仍然受到限制。在此，我们利用去标识化的医学图像和在临床导向社交媒体上分享的专家评论。通过将先进的LLM与临床医生的验证相结合，我们建立了一个严格的流程，构建了ThoughtMed-1M，这是一个包含超过一百万个VQA对的长格式医学VQA数据集，旨在捕捉结构化的临床逻辑和医学图像-文本对齐。为了展示其实用性，我们开发了一个基于ThoughtMed-1M训练的基础LLM（FOLTMed）。FOLTMed在42个医学VQA基准数据集上实现了最先进的性能，宏观准确率达到85.4%，并在ThoughtMed-1M测试集上生成了更具临床一致性的响应。在事实性和相似性指标上，它比最先进的模型提高了3-5%，突显了一个可扩展的范式，以推动基于临床的多模态LLMs的研究。
cs.AI / 76 / 2609.06941
When and Why LLM Causal Priors Help: Closed-Loop Prior Selection for Amortized Causal Inference
何时以及为何大型语言模型的因果先验有助于：用于摊销因果推断的闭环先验选择
Abstract
Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundational task. Prior-data fitted networks (PFNs) amortize the task: a model trained on large numbers of programmatically generated synthetic causal tasks reads a new problem's observational data into context and returns an interventional-effect estimate in a single forward pass. The capability of such models is largely determined by the synthetic training prior, which is currently designed by hand, a bottleneck acknowledged by both Do-PFN and CausalPFN. Large language models (LLMs) can now ``draw'' plausible causal graphs for a given domain, suggesting that LLM-distilled graphs could serve as prior material. Whether injecting such graphs helps at all, where any gain comes from, and when injection helps. Practice has so far relied on manual trial and error. We propose a \emph{closed-loop prior selection framework} that casts prior injection as a budget-constrained optimization over a candidate prior pool. Candidates undergo cheap post-training and are scored by a composite metric dominated by real-domain generalization; the winner then receives full training and paired statistical validation. On a 7.34M-parameter Do-PFN, the framework's winner attains a formally significant $2.75\times$ gain on the primary evaluation domain, and its error falls below that of the uninjected official base. Generalization on an adjacent monitoring domain improves significantly, and no monitored capability degrades. Mechanism experiments show that the gain depends on the semantic content of the distilled graph rather than its structural diversity alone does not produce it (directional evidence). With this framework and this regularity in hand, the use of LLM causal priors stops being manual trial and error and becomes an empirically verifiable selection problem.
Chinese Translation
因果效应估计探讨在干预下结果将如何变化，医学、经济学和公共政策均将其视为基础性任务。先验数据拟合网络（PFNs）将这一任务摊销：一个在大量程序生成的合成因果任务上训练的模型能够将新问题的观察数据纳入上下文，并在一次前向传递中返回干预效应估计。这类模型的能力在很大程度上取决于合成训练先验，而当前的设计是手动完成的，这一瓶颈已被 Do-PFN 和 CausalPFN 所承认。大型语言模型（LLMs）现在可以为给定领域“绘制”合理的因果图，这表明 LLM 提炼的图可以作为先验材料。注入这些图是否有帮助、增益来源于何处以及何时注入有助于解决问题，目前的实践仍依赖于手动的试错过程。我们提出了一种 extit{闭环先验选择框架}，将先验注入视为对候选先验池的预算约束优化。候选者经过廉价的后训练，并通过一个以真实领域泛化为主的复合指标进行评分；获胜者随后接受全面训练和配对统计验证。在一个拥有734万参数的 Do-PFN 上，该框架的获胜者在主要评估领域上获得了正式显著的 $2.75 imes$ 增益，其误差低于未注入的官方基线。在相邻的监控领域上，泛化显著改善，且没有监控能力下降。机制实验表明，增益依赖于提炼图的语义内容，而不仅仅是其结构多样性（方向性证据）。有了这个框架和这种规律，使用 LLM 因果先验不再是手动的试错过程，而是一个可实证验证的选择问题。
cs.AI / 77 / 2609.06960
iBrain: A Unified Foundation Model Reading the Brain from Surface to Spikes
iBrain：一个统一的基础模型，从表面到尖峰读取大脑
Abstract
Invasive neural recordings provide high-fidelity measurements of brain activity, with signals such as intracranial EEG (iEEG) and intracortical spiking activity capturing neural dynamics at different spatial and temporal scales. Yet existing neural foundation models have largely been developed independently for different invasive recording paradigms, leaving joint pretraining across heterogeneous invasive signals underexplored. In this work, we introduce iBrain, a unified foundation model that jointly learns from iEEG and spiking activity. iBrain employs signal-specific encoders to accommodate their distinct signal characteristics and a shared spatiotemporal Transformer backbone to model dependencies across recording channels and time. We pretrain iBrain on over 7,000 hours of heterogeneous neural recordings using masked signal reconstruction and channel-view alignment, promoting contextual modeling of neural dynamics and robustness across different channels. iBrain consistently outperforms single-signal pretraining baselines and achieves state-of-the-art performance on multiple benchmarks. Further experiments demonstrate that iBrain exhibits transferability and data efficiency across diverse recording settings. These results highlight the potential of joint pretraining on heterogeneous invasive neural recordings to support scalable neural modeling and transferable representations across recording settings and downstream tasks.
Chinese Translation
侵入性神经记录提供了高保真度的大脑活动测量，诸如颅内脑电图（iEEG）和皮层内尖峰活动等信号捕捉了不同空间和时间尺度上的神经动态。然而，现有的神经基础模型主要是针对不同的侵入性记录范式独立开发的，导致对异构侵入性信号的联合预训练研究不足。在本研究中，我们介绍了iBrain，一个统一的基础模型，能够同时从iEEG和尖峰活动中学习。iBrain采用信号特定的编码器，以适应其独特的信号特征，并使用共享的时空Transformer骨干网络来建模记录通道和时间之间的依赖关系。我们在超过7000小时的异构神经记录上对iBrain进行了预训练，采用了掩蔽信号重建和通道视图对齐的方法，促进了神经动态的上下文建模和不同通道间的鲁棒性。iBrain在多个基准测试中始终优于单信号预训练基线，并实现了最先进的性能。进一步的实验表明，iBrain在多样化的记录设置中表现出可迁移性和数据效率。这些结果突显了在异构侵入性神经记录上进行联合预训练的潜力，以支持可扩展的神经建模和跨记录设置及下游任务的可迁移表示。
cs.AI / 78 / 2609.06961
SSP-DMGTimeNet: Physics-Constrained Learning for Spatiotemporal Trajectory Prediction of Vehicle Platoons
SSP-DMGTimeNet：基于物理约束的车辆编队时空轨迹预测学习
Abstract
Existing car-following prediction methods mainly optimize trajectory accuracy, while rarely considering whether predicted disturbances propagate realistically along a vehicle platoon. This limitation may lead to accurate but string-unstable predictions. We propose SSP-DMGTimeNet, a physics-constrained learning framework for spatiotemporal trajectory prediction of vehicle platoons. The model combines multi-scale temporal representations with cross-vehicle interaction features to capture complex and time-varying platoon dynamics. A propagation-delay-aware causal attention mechanism explicitly models upstream-to-downstream disturbance propagation by learning response delays between adjacent vehicles and accumulating them along the platoon. In addition, time- and frequency-domain string-stability losses relieve disturbance amplification across both adjacent vehicles and arbitrary sub-platoons during training. Experiments on HighD show that SSP-DMGTimeNet achieves an unstable-window rate of 0.65\% for five-vehicle platoons and a maximum head-to-tail amplification of 0.898 on the ground-truth excitation subset, while maintaining competitive trajectory prediction performance. In zero-shot evaluation on NGSIM US-101 and I-80, the model achieves velocity MAEs of 1.316~m/s and 1.252~m/s, with unstable-window rates of 3.90\% and 4.10\%, respectively. These results demonstrate that incorporating platoon-level physical constraints can effectively balance trajectory prediction accuracy and disturbance propagation stability.
Chinese Translation
现有的跟车预测方法主要优化轨迹精度，而很少考虑预测的干扰是否在车辆编队中真实传播。这一局限性可能导致准确但不稳定的预测。我们提出了SSP-DMGTimeNet，一种用于车辆编队时空轨迹预测的基于物理约束的学习框架。该模型结合了多尺度时间表示和跨车辆交互特征，以捕捉复杂且随时间变化的编队动态。传播延迟感知的因果注意机制通过学习相邻车辆之间的响应延迟并沿编队累积这些延迟，明确建模了上游到下游的干扰传播。此外，时间和频率域的字符串稳定性损失在训练过程中缓解了相邻车辆和任意子编队之间的干扰放大。在HighD上的实验表明，SSP-DMGTimeNet在五辆车编队中实现了0.65%的不稳定窗口率，并在真实激励子集上达到了最大头尾放大率0.898，同时保持了竞争力的轨迹预测性能。在对NGSIM US-101和I-80的零样本评估中，该模型分别实现了1.316 m/s和1.252 m/s的速度平均绝对误差（MAE），不稳定窗口率分别为3.90%和4.10%。这些结果表明，结合编队级别的物理约束可以有效平衡轨迹预测精度和干扰传播稳定性。
cs.AI / 79 / 2609.07008
RedKnot-MLA: Multi-Head Offline-Online Reuse for DeepSeek-V4 Long-Context Serving
RedKnot-MLA：深度搜索V4长上下文服务的多头离线-在线重用
Abstract
Multi-head latent attention (MLA) exposes many logical query heads through one packed latent KV stream. This representation is memory efficient, but it removes the physical per-head cache boundary assumed by conventional head-wise reuse. We present our system, a DeepSeek-V4 realization of RedKnot's head-aware reuse principle. Each immutable document is processed offline at canonical position zero; certified Local-head contributions are retained as MLA-Off. At serving time, query-side RoPE relocation restores the document's request position, a small Global-head set and protected Local token rows are recomputed as MLA-Online, and the two paths are merged before a single shared output projection. The packed MLA latent is never split. DeepSeek-V4-Flash uses 37 reusable layers and a 56/8 Local/Global partition, giving a 75.29% analytic logical head-row ceiling; the Pro-0813 profile uses 55 layers and 112/16 heads, giving 78.89%. Frozen Flash operating points show hot-artifact TTFT speedups of 2.02-3.84x. At 256K, the archived three-dataset study reports an aggregate F1 change of +3.24 percentage points, an EM change of +4.16 points, and a 78.7-79.5% analytic major-operator arithmetic saving, while one dataset decreases by 2.81 F1 points. A separate author-reported 256K hot-artifact QPS measurement is approximately 2.0x; because its raw concurrency trace is not included in this bundle, we mark it as preliminary rather than archived evidence. We describe the factorization, position repair, token-row closure, sparse-MoE support, TP8 integration, and the measurement boundaries needed to interpret these results.
Chinese Translation
多头潜在注意力（MLA）通过一个打包的潜在键值（KV）流暴露出许多逻辑查询头。这种表示在内存上是高效的，但它消除了传统头级重用所假设的每个头的物理缓存边界。我们提出了我们的系统，这是RedKnot的头感知重用原则在深度搜索V4中的实现。每个不可变文档在标准位置零处离线处理；认证的本地头贡献作为MLA-Off保留。在服务时，查询侧的RoPE重定位恢复文档的请求位置，小型全局头集合和受保护的本地令牌行被重新计算为MLA-Online，然后在单一共享输出投影之前合并这两条路径。打包的MLA潜在从未被拆分。深度搜索V4-Flash使用37个可重用层和56/8的本地/全局分区，提供75.29%的分析逻辑头行上限；Pro-0813配置使用55层和112/16头，提供78.89%。冻结的Flash操作点显示热工件TTFT速度提升为2.02-3.84倍。在256K时，归档的三数据集研究报告了总F1变化为+3.24个百分点，EM变化为+4.16点，以及78.7-79.5%的分析主要运算符算术节省，而一个数据集的F1点数减少了2.81。另一个作者报告的256K热工件QPS测量约为2.0倍；由于其原始并发跟踪未包含在此包中，我们将其标记为初步而非归档证据。我们描述了因式分解、位置修复、令牌行闭合、稀疏MoE支持、TP8集成以及解释这些结果所需的测量边界。
cs.AI / 80 / 2609.07050
Beyond One-Shot Expansion: Contrastive Evidence Exploration for Multi-Hop Retrieval
超越一次性扩展：多跳检索的对比证据探索
Abstract
Retrieval-augmented generation (RAG) critically depends on retrieving the evidence necessary for effective reasoning. However, this remains particularly challenging in multi-hop question answering (QA), where supporting passages are often linked through intermediate entities and relations that must be progressively uncovered. Existing retrieval approaches typically rely on a single retrieval intent or one-shot query expansion, limiting their ability to adapt to newly retrieved evidence and potentially introducing noisy or redundant retrieval signals. To address these limitations, we propose a training-free multi-hop retrieval framework that integrates evidence-conditioned exploration, passage-specific contrastive refinement, and coverage-aware final ranking. During offline indexing, the framework constructs passage-specific contrastive facets that characterize each passage relative to its semantically similar neighbors, providing fine-grained signals to distinguish closely related candidates. At inference time, the framework iteratively retrieves evidence, generates probes targeting unresolved information needs, refines candidate relevance using the contrastive facets, and selects a complementary set of passages that collectively cover diverse evidence-seeking intents. Experiments on MuSiQue, HotpotQA, and 2WikiMultihopQA demonstrate consistent improvements in retrieval quality and downstream QA performance over baselines.
Chinese Translation
检索增强生成（RAG）在有效推理中严重依赖于检索必要的证据。然而，在多跳问答（QA）中，这一过程尤其具有挑战性，因为支持段落通常通过中间实体和关系相互关联，这些关系必须逐步揭示。现有的检索方法通常依赖于单一的检索意图或一次性查询扩展，这限制了它们适应新检索证据的能力，并可能引入噪声或冗余的检索信号。为了解决这些局限性，我们提出了一种无训练的多跳检索框架，该框架集成了基于证据的探索、段落特定的对比细化和覆盖意识的最终排名。在离线索引过程中，该框架构建了段落特定的对比面，以相对于其语义相似的邻居来表征每个段落，从而提供细粒度信号以区分紧密相关的候选项。在推理时，该框架迭代地检索证据，生成针对未解决信息需求的探针，利用对比面细化候选相关性，并选择一组互补的段落，以共同覆盖多样的证据寻求意图。在MuSiQue、HotpotQA和2WikiMultihopQA上的实验表明，与基线相比，检索质量和下游QA性能均有一致的提升。
cs.AI / 81 / 2609.07065
VST: Verifiable Structured Transport for Auditable Agent-to-Agent Alpha Discovery
VST：可验证的结构化传输用于可审计的代理间阿尔法发现
Abstract
Agent-to-agent (A2A) alpha discovery is slowed by repeated feedback cycles between mining and evaluation agents, whose hand-offs, in contemporary LLM multi-agent systems, are free-form natural-language messages that carry no stable contract and cannot be replayed. We first restructure this communication as a structured agent-to-agent protocol of \emph{typed, causally addressable, unicast records}, so that the committed stream forms a causal trajectory. On that trajectory a single predictor with four typed heads forecasts the accumulated guidance the two miners would receive several cycles ahead; a transactional verify--leap controller then commits a multi-cycle speculative outcome only when it passes a four-level gate, and otherwise rolls back to the exact prior state. Structure is the enabling contribution, and its value is not accuracy. A controlled ablation shows an equal-information free-text channel reaches the same predictor hit rate. What typing provides is a state that can be schema-checked, replayed deterministically, and prevented by construction from leaking a forecast to an evaluator: auditability by construction, not an empirically stress-tested guarantee. On a CSI~1000 out-of-sample holdout, our single run is the only one among eight methods (seven baselines and ours) to hold a positive median annualized return and Sharpe at the factor level, though the median return \emph{in excess} of the benchmark stays negative for every method including ours; its development-selected top-20 portfolios reach a $0.71$ median holdout Sharpe, selected on a split inside the optimization horizon. We report these single-run results descriptively, gross of costs, and are explicit about their limits throughout; in particular we do not isolate the effect of the leap machinery from the inherited search substrate, which we leave to future work.
Chinese Translation
代理间（A2A）阿尔法发现因挖掘代理和评估代理之间的重复反馈循环而受到阻碍。在当代大型语言模型（LLM）多代理系统中，这些交接是自由形式的自然语言消息，缺乏稳定的契约，无法重放。我们首先将这种通信重构为一种结构化的代理间协议，采用 extit{类型化、因果可寻址的单播记录}，使得承诺的流形成因果轨迹。在该轨迹上，单个预测器具有四个类型化头，预测两个挖掘者在多个周期之前将收到的累积指导；一个事务性验证-跳跃控制器仅在通过四级门控时才会提交一个多周期的投机结果，否则将回滚到确切的先前状态。结构是关键贡献，其价值并非准确性。受控消融实验表明，信息量相同的自由文本通道达到了相同的预测器命中率。类型化提供的是一种可以进行模式检查、可确定性重放的状态，并通过构造防止向评估者泄露预测：构造上的可审计性，而非经验上经过压力测试的保证。在CSI~1000的样本外保留中，我们的单次运行是八种方法（七个基线和我们的方法）中唯一一个在因子层面上保持正的中位年化收益和夏普比率的方法，尽管所有方法（包括我们的方法）在基准之上的中位收益仍为负；其开发选择的前20个投资组合在优化范围内的拆分上达到了$0.71$的中位保留夏普比率。我们对这些单次运行结果进行了描述性报告，未扣除成本，并明确说明其局限性；特别是我们没有将跳跃机制的影响与继承的搜索基底分离，这留待未来的工作。
cs.AI / 82 / 2609.07075
A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems
审计检索增强生成系统的层次一致性框架
Abstract
Retrieval-augmented generation (RAG) is commonly evaluated by whether the final answer is correct. That test is insufficient: an answer can match its reference while the context that produced it contains a direct contradiction, leaving the contested evidence invisible to answer-only review and retrieval relevance scores. This paper presents the Hierarchical Consistency Framework (HCF), a post-hoc, model-agnostic audit of three distinct levels of a RAG process: the knowledge corpus, the final retrieved context, and the generated answer. HCF represents corpus conflicts as source-linked atomic facts, thereby identifying the documents responsible, and returns each Answer Consistency Score (ACS) with an explanation of supporting and contradictory contextual statements. We evaluate HCF on several controlled corpora spanning five domains and 100 query-corpus instances. A human evaluator compares every generated response with its supplied ground-truth response. The results show that the three diagnostic levels can dissociate: the corpus with the highest mean retrieval similarity has the lowest mean ACS, while a structurally degraded corpus performs worse at corpus level but better at answer level. Most importantly, HCF identifies contradictory retrieved evidence in several cases where the answer still matches the ground truth. HCF does not certify factual truth; it makes the evidence supporting and challenging an answer inspectable and attributable.
Chinese Translation
检索增强生成（RAG）通常通过最终答案的正确性进行评估。然而，这种测试是不够的：一个答案可能与其参考相匹配，而产生该答案的上下文却包含直接的矛盾，从而使有争议的证据在仅依赖答案的审查和检索相关性评分时变得不可见。本文提出了层次一致性框架（Hierarchical Consistency Framework, HCF），这是对RAG过程三个不同层次的后验、模型无关的审计：知识库、最终检索的上下文和生成的答案。HCF将知识库中的冲突表示为源链接的原子事实，从而识别出负责的文档，并为每个答案一致性评分（Answer Consistency Score, ACS）提供支持和矛盾上下文陈述的解释。我们在涵盖五个领域和100个查询-知识库实例的多个受控知识库上评估HCF。人类评估者将每个生成的响应与其提供的真实响应进行比较。结果表明，这三个诊断层次可以相互区分：具有最高平均检索相似度的知识库，其平均ACS却最低，而结构退化的知识库在知识库层面的表现较差，但在答案层面的表现更好。最重要的是，HCF在多个案例中识别出矛盾的检索证据，即使答案仍然与真实情况相匹配。HCF并不认证事实真相；它使支持和挑战答案的证据可供检查和归因。
cs.AI / 83 / 2609.07095
Risk Is Not Review Value: Wrong-Answer Exposure Under Bounded Review Budgets
风险并非审查价值：有限审查预算下的错误答案暴露
Abstract
LLM assistants often produce more answers than humans can review before users see them. Most evaluations ask whether an answer is wrong, unsupported, or low-confidence. Bounded review budgets instead ask which answers should be checked first under a fixed review budget. Risk alone is not enough: a high-risk answer may be hard to repair, while a moderately risky answer may be directly correctable from available evidence. For generated-answer evaluation, we model review prioritization as exposure reduction, where review value combines estimated wrongness, intervention affordance, impact, and cost. We evaluate review queues with Wrong-Answer Exposure Ratio (WAER), the fraction of wrong answers left unreviewed, and post-repair residual exposure (PRRE), the fraction still exposed after deterministic benchmark-supported repairs. PRRE uses repairability rules that do not numerically reuse the affordance scores used for ranking. On a 720-item TAT-QA/SciFact stress benchmark, review-value ranking keeps answer-level WAER nearly unchanged at 20% budget (0.605 vs. 0.600) but lowers PRRE from 0.881 to 0.716. These results show that trustworthy LLM evaluation should measure not only error detection, but also how limited review capacity reduces exposed wrong answers.
Chinese Translation
大型语言模型（LLM）助手通常生成的答案数量超过人类在用户看到之前能够审查的数量。大多数评估关注的是答案是否错误、缺乏支持或信心不足。而有限审查预算则关注在固定的审查预算下，应该优先检查哪些答案。仅仅考虑风险是不够的：高风险答案可能难以修复，而适度风险的答案可能能够直接从现有证据中纠正。对于生成答案的评估，我们将审查优先级建模为暴露减少，其中审查价值结合了估计的错误性、干预可行性、影响和成本。我们使用错误答案暴露比率（Wrong-Answer Exposure Ratio, WAER）来评估审查队列，即未被审查的错误答案的比例，以及修复后残余暴露（Post-Repair Residual Exposure, PRRE），即在确定性基准支持的修复后仍然暴露的比例。PRRE使用的修复规则不对用于排名的可行性评分进行数值重用。在720个项目的TAT-QA/SciFact压力基准上，审查价值排名在20%预算下几乎保持答案级WAER不变（0.605对比0.600），但将PRRE从0.881降低到0.716。这些结果表明，值得信赖的LLM评估不仅应测量错误检测，还应评估有限的审查能力如何减少暴露的错误答案。
cs.AI / 84 / 2609.07107
Beyond Sparse Rewards: A New Benchmark and Structure-Aware Graph Alignment for Micro-Drama Understanding
超越稀疏奖励：微剧理解的新基准和结构感知图对齐
Abstract
Micro-dramas, characterized by ultra-short durations and hyper-dense storylines, pose unique challenges for video understanding that conventional benchmarks fail to address. To bridge this gap, we introduce M-Drama, the first large-scale bilingual benchmark for micro-drama comprehension, featuring over 35K instances across 9,138 clips. Furthermore, while reinforcement learning can enhance VLMs on complex narratives, existing reward metrics often suffer from sparse and superficial signals, failing to capture intricate character identities and temporal structures. We propose SAGA (Structure-Aware Graph Alignment), a novel graph-matching reward function that models narratives as heterogeneous graphs. SAGA computes dense, rigorous rewards via decoupled semantic triplet and structural temporal matching. Extensive experiments on Qwen3-VL-8B-Instruct demonstrate that SAGA outperforms existing baselines, delivering substantial improvements in open-ended accuracy and summary quality, while maintaining competitive out-of-domain generalization. Code is available at https://github.com/qyx1121/MDrama_SAGA.
Chinese Translation
微剧以超短的时长和高度密集的故事情节为特征，给视频理解带来了独特的挑战，而传统基准无法解决这些问题。为了解决这一差距，我们引入了M-Drama，这是首个大规模的双语微剧理解基准，包含超过35,000个实例，涵盖9,138个片段。此外，尽管强化学习可以提升视觉语言模型（VLMs）在复杂叙事上的表现，但现有的奖励指标往往存在稀疏和表面的信号，无法捕捉复杂的人物身份和时间结构。我们提出了SAGA（结构感知图对齐），一种新颖的图匹配奖励函数，将叙事建模为异构图。SAGA通过解耦的语义三元组和结构时间匹配计算出密集而严谨的奖励。在Qwen3-VL-8B-Instruct上的大量实验表明，SAGA的表现优于现有基准，在开放式准确性和摘要质量上实现了显著提升，同时保持了竞争力的跨领域泛化能力。代码可在https://github.com/qyx1121/MDrama_SAGA获取。
cs.AI / 85 / 2609.07128
EEG-Driven Decoding Framework for Passenger Hazard Perception in Highly Automated Vehicles
基于脑电图的乘客危险感知解码框架在高度自动化车辆中的应用
Abstract
Reliable risk assessment remains a central challenge for Autonomous Vehicles (AVs). Despite advances in automation, passenger cognition provides a non-intrusive auxiliary signal that improves both objective and perceived safety without requiring active human intervention. We introduce an Electroencephalogram (EEG)-based Brain-Computer Interface (BCI) that decodes passenger neural responses for both Risk Prediction (RP) and Danger Identification (DI), explicitly modeling humans as passengers to match real-world AV use. To achieve this, we propose the Passenger Cognitive Model (PCM), Risk-aware Sequential Labeling (RSL), and the Passenger EEG Decoding Strategy (PEDS), which integrates a 3D Convolutional Recurrent Neural Network (3D-CRNN) model for joint EEG decoding. Experimental results show that 3D-CRNN achieves a Balanced Accuracy (BA) of $95.3\% \pm 2.7\%$ in RP and improves single-subject DI from $80.9\% \pm 3.9\%$ to $85.0\% \pm 3.2\%$ with RSL. Event-wise analyses further show that 3D-CRNN consistently outperforms other models across different event types in RP and DI. In generalization experiments, 3D-CRNN achieves $77.0\% \pm 5.3\%$ BA in cross-session DI and $77.4\% \pm 1.1\%$ BA on seen subjects in cross-subject evaluation, while maintaining a $64.9\% \pm 8.5\%$ BA on unseen subjects, demonstrating promising generalizability and transferability across both intra-subject and inter-subject variability. These findings establish an Electroencephalogram (EEG) decoding framework for AV passenger hazard perception and suggest that passenger cognitive signals can provide auxiliary supervision for future AV decision-making and Safety of the Intended Functionality (SOTIF) support.
Chinese Translation
可靠的风险评估仍然是自动驾驶车辆（AVs）面临的核心挑战。尽管自动化技术有所进步，乘客的认知提供了一种非侵入性的辅助信号，能够在不需要主动人类干预的情况下提高客观和感知安全性。我们提出了一种基于脑电图（EEG）的脑-计算机接口（BCI），用于解码乘客的神经反应，以实现风险预测（RP）和危险识别（DI），明确将人类建模为乘客，以匹配现实世界中自动驾驶车辆的使用。为此，我们提出了乘客认知模型（PCM）、风险感知序列标记（RSL）和乘客脑电图解码策略（PEDS），该策略集成了三维卷积递归神经网络（3D-CRNN）模型以实现联合脑电图解码。实验结果表明，3D-CRNN在风险预测中实现了$95.3\% ext{±} 2.7\\%$的平衡准确率（BA），并将单一受试者的危险识别从$80.9\\% ext{±} 3.9\\%$提高到$85.0\\% ext{±} 3.2\\%$，采用了RSL。事件级分析进一步表明，3D-CRNN在不同事件类型的风险预测和危险识别中始终优于其他模型。在泛化实验中，3D-CRNN在跨会话的危险识别中实现了$77.0\\% ext{±} 5.3\\%$的平衡准确率，在跨受试者评估中对已见受试者的准确率为$77.4\\% ext{±} 1.1\\%$，同时在未见受试者中保持$64.9\\% ext{±} 8.5\\%$的准确率，展示了在受试者内和受试者间变异性下的良好泛化能力和迁移能力。这些发现建立了一个用于自动驾驶车辆乘客危险感知的脑电图解码框架，并表明乘客的认知信号可以为未来自动驾驶车辆的决策和意图功能安全（SOTIF）提供辅助监督。
cs.AI / 86 / 2609.07139
Encoded Early, Used Late: Where Transformers Begin to Act on an Inferred Partner's Expertise
编码早，使用晚：变换器何时开始作用于推断合作伙伴的专业知识
Abstract
A transformer can make an attribute linearly decodable in its residual stream at a depth where that attribute does not yet influence the output. This gap between where information is readable and where it is used has been shown for attributes stated directly in the input. We ask whether it also holds for an attribute the model must infer gradually over a conversation, namely how expert its dialogue partner is. Using ExpertCollab, a corpus of multi-turn research-planning dialogues between model-played personas at four expertise levels, we find that partner expertise is most decodable in the early layers and falls to near chance before the midpoint of the network. Counterfactual patching shows that injecting the expertise difference at the layer of peak decodability barely changes a fixed late-layer readout, whereas the same difference injected past the midpoint propagates almost completely, a separation of more than an order of magnitude. A content-matched random control and a probe-free diagnostic place the transition at the same early layer, and a statically specified control attribute stays decodable throughout. An inferred relational attribute is therefore represented well before it becomes causally active, which bounds where any attempt to read out or steer partner-conditioned behavior must intervene. We use one model on a synthetic corpus as an initial demonstration.
Chinese Translation
变换器可以在某一深度使属性在线性可解的残差流中可读，而在该深度时该属性尚未影响输出。对于直接在输入中陈述的属性，这种信息可读性与使用之间的差距已经被证明。我们探讨这种情况是否也适用于模型必须在对话中逐渐推断的属性，即其对话伙伴的专业程度。通过使用 ExpertCollab，一个包含四个专业水平的模型扮演角色之间的多轮研究规划对话的语料库，我们发现伙伴的专业知识在早期层最可解，并在网络中点之前降至接近随机水平。反事实修补显示，在峰值可解性层注入专业知识差异几乎不会改变固定的晚层读出，而在中点之后注入的同样差异几乎完全传播，二者之间的差异超过一个数量级。一个内容匹配的随机对照和一个无探测的诊断将过渡点定位于同一早期层，而一个静态指定的控制属性在整个过程中保持可解。因此，推断的关系属性在其因果活跃之前就得到了良好的表示，这限制了任何试图读取或引导基于伙伴条件的行为的干预位置。我们使用一个模型在一个合成语料库上进行初步演示。
cs.AI / 87 / 2609.07152
An Auditable Symbolic-RAG-Generative AI Architecture for Goal-Oriented Conversation Orchestration
可审计的符号-RAG-生成式人工智能架构用于目标导向的对话协调
Abstract
Goal-oriented conversational systems must answer factual questions, understand visitor-provided information, and advance business objectives without becoming rigid questionnaires. This paper proposes a Symbolic-RAG-Generative architecture centered on the Goal-oriented Retrieval-Augmented Conversation Engine (GRACE). An instruction-constrained Business Goal Compiler transforms business intent into an immutable objective set, normalized priority vector, canonical questions, and initial state vector. At runtime, GRACE receives the complete conversation history, latest visitor message, current state, and grounded answer generated by a separate RAG component. It updates completion only from visitor-authored evidence and selects one contextually modulated follow-up. The core policy maximizes expected business progress subject to a minimum visitor-utility constraint. We formalize the state, monotonic transitions, source separation, question modulation, and constrained policy; present the reference architecture; and define an evaluation comprising 24 English real-estate and 10 Spanish professional-cleaning conversations, totaling 119 protocol-defined visitor turns. Across both domains, GRACE achieves 84.9% exact state-transition accuracy, 91.6% evidence precision, 89.6% evidence recall, 100% monotonicity, and 94.1% terminal-state accuracy. The evaluation establishes compelling symbolic-state performance across standard, multi-goal, RAG-detour, validation, refusal, and robustness scenarios.
Chinese Translation
目标导向的对话系统必须回答事实性问题，理解访客提供的信息，并在不变成僵化问卷的情况下推动商业目标。本文提出了一种以目标导向检索增强对话引擎（Goal-oriented Retrieval-Augmented Conversation Engine，GRACE）为中心的符号-RAG-生成式架构。一个受指令约束的商业目标编译器将商业意图转化为不可变的目标集、规范化的优先级向量、标准问题和初始状态向量。在运行时，GRACE接收完整的对话历史、最新的访客消息、当前状态以及由单独的RAG组件生成的基础答案。它仅根据访客提供的证据更新完成，并选择一个上下文调制的后续问题。核心策略是在满足最低访客效用约束的情况下最大化预期商业进展。我们形式化了状态、单调转移、源分离、问题调制和受约束策略；展示了参考架构；并定义了一个评估，包括24个英语房地产和10个西班牙语专业清洁对话，总计119个协议定义的访客轮次。在这两个领域中，GRACE实现了84.9%的精确状态转移准确率、91.6%的证据精度、89.6%的证据召回率、100%的单调性和94.1%的终态准确率。评估结果在标准、多目标、RAG绕行、验证、拒绝和鲁棒性场景中建立了引人注目的符号状态性能。
cs.AI / 88 / 2609.07174
PhysMAS: Physics-Grounded Multi-Agent Synthesis of Compositional 4D Gaussians
PhysMAS：基于物理的多智能体组合4D高斯合成
Abstract
Efficient, fully automatic, and physically plausible 4D Gaussian synthesis is an important goal for dynamic scene generation. Recent physics-based methods couple 3D Gaussians with the Material Point Method (MPM) to generate physically driven motion, but extending this paradigm to heterogeneous multi-part objects and interacting multi-object scenes remains challenging. Object-level physical assignment collapses distinct parts into a single material state, while one-shot predictions from large language models, vision-language models, or agents neither reliably bind different materials to identified parts nor verify that the resulting MPM configuration is executable. Score Distillation Sampling (SDS)-based parameter optimization, meanwhile, requires repeated per-scene score evaluations and gradient backpropagation, incurring lengthy optimization and potentially yielding suboptimal or unstable solutions. We therefore present PhysMAS, a physics-grounded multi-agent framework. From a motion prompt and four scene views, an Object-Part Scene Agent establishes persistent identities and calls a Material Reasoning Agent for part-wise profiles. It invokes solver-aware skills to bind these identities and profiles to per-particle MPM fields and execute all objects in a shared domain; the framework then screens candidate forward-simulation results. This supports heterogeneous multi-part and interacting multi-object scenes without per-scene diffusion-score backpropagation. Extensive experiments demonstrate that, compared with recent physics-based 4D Gaussian baselines that rely on SDS, PhysMAS achieves better semantic alignment and perceived physical plausibility while requiring less runtime.
Chinese Translation
高效、全自动且物理上合理的4D高斯合成是动态场景生成的重要目标。近期的基于物理的方法将3D高斯与材料点方法（Material Point Method, MPM）结合，以生成物理驱动的运动，但将这一范式扩展到异质多部件对象和交互多对象场景仍然面临挑战。对象级的物理分配将不同部分合并为单一材料状态，而来自大型语言模型、视觉-语言模型或智能体的一次性预测既无法可靠地将不同材料绑定到识别的部分，也无法验证生成的MPM配置是否可执行。基于评分蒸馏采样（Score Distillation Sampling, SDS）的参数优化则需要对每个场景进行重复的评分评估和梯度反向传播，导致优化过程冗长，并可能产生次优或不稳定的解决方案。因此，我们提出了PhysMAS，一个基于物理的多智能体框架。该框架从运动提示和四个场景视图出发，建立持久的身份，并调用材料推理智能体以获取部分特征。它利用求解器感知技能将这些身份和特征绑定到每个粒子的MPM场，并在共享域中执行所有对象；该框架随后筛选候选的前向模拟结果。这支持异质多部件和交互多对象场景，而无需对每个场景进行扩散评分的反向传播。大量实验表明，与依赖SDS的近期基于物理的4D高斯基线相比，PhysMAS在语义对齐和感知物理合理性方面表现更佳，同时所需运行时间更少。
cs.AI / 89 / 2609.07194
EmoMed: An Emotionally-Aware Agent for Multimodal Medical Support with Real-Time Information Retrieval
EmoMed：一种具备情感意识的多模态医疗支持代理，支持实时信息检索
Abstract
We present EmoMed - a multimodal medical consultation agent that adapts its responses based on users' emotional states while maintaining clinical accuracy. The system processes text and medical images, detects affect indicators (anxiety, confusion, urgency) from user input, and adjusts response tone, structure, and detail level accordingly. To ensure factual reliability, the agent grounds clinical information through a dual retrieval mechanism: web-based fact-checking and an API-connected, continuously updated medical knowledge base. We evaluate our approach across seven state-of-the-art language models (GPT-4/5, Qwen3, Llama 4, Gemini 2.5, Grok4, Claude3) using comprehensive metrics including LLM-as-judge assessments, MedQA style accuracy tests, BERT Score, safety/helpfulness ratings, and multimodal medical benchmarks. The results demonstrate that emotionally adaptive responses consistently outperform neutral baseline across evaluation dimensions, without compromising clinical accuracy. A controlled user study validated these findings, with participants reporting improved perceived empathy and communication clarity, while maintaining trust in factual accuracy. Source code: https://github.com/NasonovIvan/EmoMed-Agent
Chinese Translation
我们提出了EmoMed——一个多模态医疗咨询代理，能够根据用户的情感状态调整其响应，同时保持临床准确性。该系统处理文本和医学图像，从用户输入中检测情感指标（焦虑、困惑、紧迫感），并相应地调整响应的语调、结构和细节水平。为了确保事实的可靠性，该代理通过双重检索机制来验证临床信息：基于网络的事实检查和一个与API连接的、持续更新的医学知识库。我们在七个最先进的语言模型（GPT-4/5、Qwen3、Llama 4、Gemini 2.5、Grok4、Claude3）上评估了我们的方法，使用包括LLM作为评判者的评估、MedQA风格的准确性测试、BERT分数、安全性/有用性评分和多模态医疗基准在内的综合指标。结果表明，情感适应性响应在评估维度上始终优于中性基线，且不影响临床准确性。一项受控用户研究验证了这些发现，参与者报告了感知的同理心和沟通清晰度的改善，同时对事实准确性保持信任。源代码：https://github.com/NasonovIvan/EmoMed-Agent
cs.AI / 90 / 2609.07204
Agentic Algorithm Engineering: Improving Shared-Memory Exact Minimum Cuts
自主算法工程：改进共享内存的精确最小割
Abstract
The minimum cut problem for an undirected edge-weighted graph asks us to divide its set of nodes into two blocks while minimizing the weighted sum of the cut edges. Over the last years, we engineered a range of fast algorithms for this problem. Our fastest exact algorithm uses an inexact algorithm to obtain a better bound for the problem, reductions that depend on this bound, improved data structures and parallel contraction routines. It is available in the open-source package VieCut and, on real-world instances, outperformed the previously fastest solvers by a factor of up to 2.5 sequentially and up to 12.9 when run in parallel. We improve this algorithm using agentic algorithm engineering (AAE), a methodology that we introduce here, in which autonomous large language model agents run the algorithm engineering cycle on an existing code base: they form hypotheses about where running time is lost, implement them, benchmark the result on a fixed instance set and keep or discard the change. Even though we had already tuned our algorithm by hand extensively, the agent finds significant optimizations, in particular on the DIMACS core instances: factors of 1.28 (sequential) and 1.63 (32 threads) on real-world k-cores, and 6.26 and 127 on the DIMACS core instances.
Chinese Translation
无向边权图的最小割问题要求我们将其节点集合划分为两个块，同时最小化割边的加权和。在过去的几年中，我们针对该问题设计了一系列快速算法。我们最快的精确算法使用了一个不精确的算法来获得更好的问题界限，依赖于该界限的简化，改进的数据结构以及并行收缩例程。该算法已在开源包VieCut中提供，并且在实际应用中，其性能比之前最快的求解器提高了最多2.5倍的顺序运行速度和最多12.9倍的并行运行速度。我们通过自主算法工程（Agentic Algorithm Engineering, AAE）改进了这一算法，这是一种我们在此介绍的方法论，其中自主的大型语言模型代理在现有代码基础上运行算法工程周期：它们形成关于运行时间损失的假设，实施这些假设，在固定实例集上基准测试结果，并决定保留或丢弃更改。尽管我们已经对算法进行了广泛的手动调优，但代理仍然发现了显著的优化，特别是在DIMACS核心实例上：在实际的k-core上，顺序运行速度提高了1.28倍（32线程）和1.63倍，而在DIMACS核心实例上则分别提高了6.26倍和127倍。
cs.AI / 91 / 2609.07213
Unraveling the Real Working Mechanism and Inherent Flaws of GAE: A Method for Interpreting Transformer Processes from an Economic Perspective
揭示通用注意力模型解释（GAE）的真实工作机制及其固有缺陷：从经济学视角解读变换器过程的方法
Abstract
We observe a phenomenon that current algorithmic research in the field of explainable artificial intelligence primarily pursues better performance on several proxy metrics. On the one hand, these proxy metrics themselves are more or less flawed and cannot properly measure the quality of methods. On the other hand, metric-oriented research approaches often lead to the neglect of the rationality and interpretability of the methods themselves. Explainable artificial intelligence is abbreviated as XAI. The metric-driven research paradigm has resulted in a lack of interpretability of the relevant XAI methods themselves. Accordingly, there is a need for interpretability research on XAI methods, which can be playfully referred to as XXAI. This paper is one of our works on XXAI. This paper takes Generic Attention-model Explainability (GAE), a widely influential model interpretation method , or rather, XAI method that represents an important technical route, as the research object, and explores the real working mechanism and flaws of this method as well as the technical route it represents. Based on the conclusions of this study, it may be necessary to re-examine or verify GAE-related methods and their domain applications. We argue that GAE is an interpretation method that focuses on the attention process. After pointing out the working mechanism and flaws of GAE, we propose Cumulative Asset Holdings (CAH), a more reasonable Transformer interpretation method integrating both process-based and feature-based ideas from an economic zero-sum games perspective. In addition, it is worth noting that our method is applicable to models with special tokens, where existing methods may suffer from limitations. The model simplification research method and the analysis of additive operations adopted in this study may provide inspiration for other research works in XAI.
Chinese Translation
我们观察到，目前在可解释人工智能领域的算法研究主要追求在几个代理指标上的更好表现。一方面，这些代理指标本身或多或少存在缺陷，无法恰当地衡量方法的质量。另一方面，以指标为导向的研究方法往往导致对方法本身的合理性和可解释性的忽视。可解释人工智能简称为XAI。以指标驱动的研究范式导致相关XAI方法的可解释性缺乏。因此，有必要对XAI方法进行可解释性研究，戏称为XXAI。本文是我们在XXAI领域的一项研究工作。本文以通用注意力模型解释（GAE）为研究对象，这是一种广泛影响的模型解释方法，或者说是代表重要技术路线的XAI方法，探讨该方法的真实工作机制及其固有缺陷，以及它所代表的技术路线。基于本研究的结论，可能需要重新审视或验证与GAE相关的方法及其领域应用。我们认为GAE是一种关注注意力过程的解释方法。在指出GAE的工作机制和缺陷后，我们提出了累积资产持有（CAH），这是一种更合理的变换器解释方法，从经济零和博弈的角度整合了基于过程和基于特征的思想。此外，值得注意的是，我们的方法适用于具有特殊标记的模型，而现有方法可能面临局限性。本研究采用的模型简化研究方法和加法运算分析可能为其他XAI研究工作提供启示。
cs.AI / 92 / 2609.07222
Distance-Aware Attention and Wall-Distance Expert Routing for Transformer-Based 3D Flow Prediction
基于距离感知注意力和壁面距离专家路由的变压器3D流动预测
Abstract
Transformer surrogates for 3D flow prediction compress an industrial mesh into a small set of tokens from which every prediction point reads. Two operations follow: the retrieval step in which a point gathers information from the compressed representation, and the feed-forward layer that transforms what it retrieved. In current backbones both are blind to where the point sits in the flow. We condition both on wall-related physical signals. Distance-aware cross-attention (DA-CA) reshapes each volume query by its wall distance before retrieval, so that a point deep in the boundary layer draws different geometric information than one in the outer flow. Surface-volume mixture-of-experts (SVMoE) replaces the shared feed-forward layer with a small set of experts, routed by wall distance for volume points and by local geometry for surface points. Neither mechanism is tied to one architecture, so we apply both unchanged to AB-UPT and Transolver-3. On DrivAerML with 50 training cases, DA-CA reduces the volume pressure error by 10.1%, and DA-CA and SVMoE together reduce it by 12.5%; DA-CA improves the near-wall region at some cost in the far region, which SVMoE recovers, and the volume experts settle into near-wall, transition, and free-stream bands without routing supervision. Retrained on 300 cases, the conditioning improves every field quantity, reducing volume pressure and velocity errors by 33.1% and 18.6% on AB-UPT and by 21.4% and 21.3% on Transolver-3. Under Leave-One-Body-Out evaluation on DrivAerNet++, it reduces the volume pressure error on unseen body types by up to 14.2%.
Chinese Translation
用于3D流动预测的变压器代理将工业网格压缩为一小组令牌，所有预测点均从中读取信息。随后进行两个操作：检索步骤，在此步骤中，点从压缩表示中收集信息，以及前馈层，转换所检索到的信息。在当前的主干网络中，这两个步骤均未考虑点在流动中的位置。我们基于与壁面相关的物理信号对这两个步骤进行条件化。距离感知交叉注意力（Distance-aware Cross-Attention, DA-CA）在检索之前通过壁面距离重塑每个体积查询，以便边界层深处的点获取与外流中点不同的几何信息。表面-体积混合专家（Surface-Volume Mixture-of-Experts, SVMoE）用一小组专家替代共享的前馈层，体积点通过壁面距离进行路由，而表面点则通过局部几何进行路由。这两种机制均不依赖于特定架构，因此我们将其不变地应用于AB-UPT和Transolver-3。在具有50个训练案例的DrivAerML上，DA-CA将体积压力误差降低了10.1%，而DA-CA和SVMoE共同将其降低了12.5%；DA-CA在近壁区域有所改善，但在远区域略有代价，SVMoE则恢复了这一点，体积专家在没有路由监督的情况下稳定在近壁、过渡和自由流带。重新训练300个案例后，条件化改善了每个场量，在AB-UPT上将体积压力和速度误差分别降低了33.1%和18.6%，在Transolver-3上降低了21.4%和21.3%。在DrivAerNet++的逐体评估下，它将未见体型的体积压力误差降低了最多14.2%。
cs.AI / 93 / 2609.07247
Elastic Horizon: Discovering the Effective Interaction Frontier in Agentic Reinforcement Learning
弹性视野：在自主强化学习中发现有效的交互边界
Abstract
Scaling the interaction horizon-the maximum number of environment interactions per episode-improves LLM agents on long-horizon tasks, and curriculum-based methods that progressively expand the horizon outperform fixed-horizon alternatives. However, existing schedules are open-loop: they monotonically increase the horizon until a manually specified maximum, with no mechanism to detect when further expansion stops helping. We propose the effective interaction frontier hypothesis: a dynamic boundary beyond which additional interactions yield diminishing returns while cost grows linearly. We then introduce Elastic Horizon, a closed-loop controller that tracks this boundary via the 90th percentile of successful trajectory lengths. On AppWorld and BFCL, fixed-horizon sweeps reveal clear saturation plateaus; Elastic Horizon stabilizes the horizon inside the saturation band from both under- and over-capacity initializations, attains the best success rates across 7B and 14B backbones, and saves up to 25% of per-step trajectory tokens. Our work shifts the paradigm from how to scale interaction horizons to when to stop scaling.
Chinese Translation
扩展交互视野——每个回合中环境交互的最大数量——可以提高大型语言模型（LLM）代理在长时间任务上的表现，而基于课程的方法通过逐步扩展视野的方式优于固定视野的替代方案。然而，现有的调度是开放式的：它们单调地增加视野，直到手动指定的最大值，而没有机制来检测何时进一步扩展不再有帮助。我们提出了有效交互边界假设：一个动态边界，超出该边界后，额外的交互会带来递减收益，同时成本呈线性增长。接着，我们引入了弹性视野（Elastic Horizon），这是一种闭环控制器，通过成功轨迹长度的第90百分位数来跟踪这一边界。在AppWorld和BFCL上，固定视野的测试显示出明显的饱和平台；弹性视野能够在饱和带内稳定视野，无论是从不足还是过度容量的初始化出发，均能在7B和14B的基础模型上获得最佳成功率，并节省多达25%的每步轨迹令牌。我们的工作将范式从如何扩展交互视野转变为何时停止扩展。
cs.AI / 94 / 2609.07255
SkillAlign: Aligning Skill Interfaces for LLM-based Agents
SkillAlign：为基于大型语言模型的智能体对齐技能接口
Abstract
Language-model agents increasingly rely on skills: reusable procedural knowledge for reasoning, tool use, and interaction. Existing work studies how skills are acquired, retrieved, compressed, or composed, but often assumes that once a skill is selected, its interface to the agent is fixed. We argue that this overlooks a key source of skill utility: the same skill can help, distract, or mislead depending on how it is exposed. We propose SkillAlign, a provider-agnostic framework that represents candidate skills as multi-view procedural cards and renders them through alternative exposure interfaces, including full instructions, hints, compressed summaries, workflows, or no exposure. This enables counterfactual evaluation where the task, agent, and candidate skills are fixed while only the exposure interface varies. Across ALFWorld and SkillsBench, we show that exposure form substantially affects task success and rendered context cost, and that compact top-k exposure can outperform full-library injection. We further conduct a replay-based policy-learning analysis on ALFWorld, showing that adaptive exposure contains learnable signal but remains far from oracle selection. Our results suggest that skill-augmented agents should optimize not only which skills to use, but also how those skills are presented.
Chinese Translation
语言模型智能体越来越依赖于技能：用于推理、工具使用和交互的可重用程序知识。现有研究探讨了技能的获取、检索、压缩或组合，但通常假设一旦选择了某项技能，其与智能体的接口是固定的。我们认为，这忽视了技能效用的一个关键来源：同一技能的展示方式可以帮助、分散注意力或误导。我们提出了SkillAlign，一个与提供者无关的框架，将候选技能表示为多视角程序卡，并通过替代的展示接口进行渲染，包括完整指令、提示、压缩摘要、工作流程或不展示。这使得反事实评估成为可能，在这种评估中，任务、智能体和候选技能是固定的，而仅展示接口发生变化。在ALFWorld和SkillsBench中，我们展示了展示形式显著影响任务成功率和渲染上下文成本，并且紧凑的前k个展示可以优于完整库注入。我们进一步在ALFWorld上进行基于重放的策略学习分析，显示自适应展示包含可学习信号，但仍远未达到最佳选择。我们的结果表明，增强技能的智能体不仅应优化使用哪些技能，还应优化这些技能的呈现方式。
cs.AI / 95 / 2609.07299
World Models Under Asynchronous Sensor Observations
异步传感器观测下的世界模型
Abstract
Learned world models typically assume that observations arrive synchronously, an abstraction inherited from simulators that return a complete state vector at each environment step. Physical sensing instead operates at heterogeneous rates, leaving most observation channels stale at any given instant. Interpolating stale channels introduces measurements that were never observed, while downsampling to the slowest sensor discards valid measurements. A natural alternative is to zero-order-hold the most recent reading and provide the known sampling schedule to the model through two features, staleness and time-to-refresh. We test this prediction using transformer world models across three regimes of increasing causal coupling: open-loop rollouts in continuous-control locomotion, closed-loop model-predictive planning in which each learned model serves as the planner dynamics, and a linear latched-actuator system in which refresh events apply a zero-order-held command to the plant. Our findings show that the effectiveness of time-to-refresh depends on the causal role of the sampling schedule, specifically when refresh events affect the system rather than merely report its state. These results establish when sampling schedules provide useful information for predictive world models operating under asynchronous physical observations.
Chinese Translation
学习到的世界模型通常假设观测是同步到达的，这一抽象源自于在每个环境步骤返回完整状态向量的模拟器。相反，物理传感器以异构速率操作，使得在任何给定时刻大多数观测通道都处于过时状态。对过时通道进行插值会引入从未观测到的测量，而将数据下采样到最慢的传感器则会丢弃有效测量。一个自然的替代方案是对最近的读数进行零阶保持，并通过两个特征——过时性和刷新时间——将已知的采样计划提供给模型。我们使用变换器世界模型在三个因果耦合逐渐增强的领域中测试这一预测：在连续控制的运动中进行的开环展开，在闭环模型预测规划中每个学习到的模型作为规划器动态，以及在刷新事件对植物施加零阶保持命令的线性锁存执行器系统中。我们的研究结果表明，刷新时间的有效性依赖于采样计划的因果作用，特别是在刷新事件影响系统而不仅仅是报告其状态时。这些结果确立了在异步物理观测下，采样计划何时为预测世界模型提供有用信息。
cs.AI / 96 / 2609.07313
Weakly supervised neural network: segmentation of complex structures in X-ray microCT
弱监督神经网络：X射线微CT中复杂结构的分割
Abstract
Segmentation of complex structures in X-ray tomographic data is a fundamental task in biomedical research, but it often requires large amounts of precisely annotated data, making fully supervised approaches costly and difficult to scale. In this study, weakly supervised deep learning is investigated as a strategy to reduce annotation effort while maintaining accurate segmentation. A two-dimensional convolutional neural network based on the nnU-Net framework was adapted to a weak supervision setting using sparse dot-based annotations, complemented by a limited number of fully segmented images. The approach was evaluated on high-resolution microCT slices of rat kidneys, targeting the segmentation of renal glomeruli, which are small, low-contrast anatomical structures. Results indicate that weak supervision provides a meaningful learning signal, enabling reliable localization of glomeruli even in the absence of dense labels. Incorporating a small set of high-quality annotations substantially improves segmentation performance, approaching that of a fully supervised model. These findings highlight the potential of weakly supervised learning as an annotation-efficient strategy for the analysis of complex structures in X-ray tomographic data, and suggest that alternative loss formulations tailored to sparse annotations may further enhance performance.
Chinese Translation
在生物医学研究中，X射线断层数据中复杂结构的分割是一个基础任务，但通常需要大量精确标注的数据，使得完全监督的方法成本高且难以扩展。本研究探讨了弱监督深度学习作为一种减少标注工作量同时保持准确分割的策略。基于nnU-Net框架的二维卷积神经网络被调整为弱监督设置，使用稀疏点状标注，并辅以有限数量的完全分割图像。该方法在大鼠肾脏的高分辨率微CT切片上进行了评估，目标是分割肾小球，这些是小型、低对比度的解剖结构。结果表明，弱监督提供了有意义的学习信号，使得即使在缺乏密集标签的情况下也能可靠地定位肾小球。结合一小部分高质量的标注显著提高了分割性能，接近完全监督模型的表现。这些发现突显了弱监督学习作为一种高效标注策略在X射线断层数据中分析复杂结构的潜力，并建议针对稀疏标注的替代损失公式可能进一步提升性能。
cs.AI / 97 / 2609.07316
DGCPath: Distribution-Aware Generative Contrastive Framework for Self-supervised Path Representation Learning -- Extended Version
DGCPath：一种基于分布感知的生成对比框架用于自监督路径表示学习 -- 扩展版
Abstract
Due to the proliferation of vehicle trajectory data enabled by advanced sensing technologies, path representation learning has become a pivotal task in intelligent transportation systems. Although existing self-supervised approaches have achieved promising performance, their dependence on deterministic contrastive learning paradigms and handcrafted view augmentation strategies inherently restricts their cross-scenario generalization capabilities. To address these limitations, we present DGCPath, an innovative Distribution-aware Generative Contrastive learning framework for Path representation. This framework establishes a synergistic connection between generative modeling and distributional contrastive learning, enabling the acquisition of robust and transferable feature embeddings. Specifically, our framework incorporates: (1) a diffusion-based view generator that autonomously produces semantically coherent yet diverse trajectory views from Gaussian noise; (2) a variational contrastive mechanism that enforces latent feature alignment at the distribution level, transcending conventional instance-wise consistency; and (3) a novel generative cross-supervision module that reinforces view-level consistency through cross-view reconstruction learning. Comprehensive evaluations on three real-world trajectory datasets demonstrate that DGCPath outperforms state-of-the-art baselines on two distinct downstream tasks, validating its enhanced generalization capability and representation effectiveness.
Chinese Translation
由于先进传感技术的推动，车辆轨迹数据的激增使得路径表示学习成为智能交通系统中的关键任务。尽管现有的自监督方法已取得了令人满意的性能，但它们对确定性对比学习范式和手工设计视图增强策略的依赖，固有地限制了其跨场景的泛化能力。为了解决这些局限性，我们提出了DGCPath，这是一种创新的基于分布感知的生成对比学习框架，用于路径表示。该框架建立了生成建模与分布对比学习之间的协同连接，使得能够获取稳健且可迁移的特征嵌入。具体而言，我们的框架包含：(1) 一个基于扩散的视图生成器，能够从高斯噪声中自主生成语义一致且多样的轨迹视图；(2) 一种变分对比机制，在分布层面上强制潜在特征对齐，超越传统的实例一致性；以及(3) 一个新颖的生成交叉监督模块，通过交叉视图重建学习增强视图级一致性。在三个真实世界的轨迹数据集上的全面评估表明，DGCPath在两个不同的下游任务上超越了最先进的基线，验证了其增强的泛化能力和表示有效性。
cs.AI / 98 / 2609.07334
AAS-RAIL: Improving Information Extraction for Asset Administration Shells through Retrieval-Augmented In-Context Learning
AAS-RAIL：通过检索增强的上下文学习提高资产管理壳的信息提取
Abstract
The Asset Administration Shell (AAS) is a cornerstone of Industry 4.0 and the Digital Product Passport, providing standardized digital representations of industrial assets. While manufacturers already maintain extensive technical product documentation, generating AAS instances from existing product datasheets remains a labor-intensive task because technical information is extracted from heterogeneous document structures and often involves company-specific terminology and conventions. In this work, we present AAS-RAIL, a retrieval-augmented information extraction (IE) approach that automatically generates Asset Administration Shells from PDF product datasheets using large language models (LLMs). Instead of relying on a fixed set of few-shot examples, the proposed retrieval-augmented in-context learning (RAIL) approach retrieves LLM-generated extraction helpers from similar Asset Administration Shells to provide instance-specific in-context learning (ICL). This enables the model to adapt its extraction behavior to company-specific naming conventions and formatting styles without fine-tuning. Our core contribution is the dynamic selection of company-specific AAS examples for each datasheet, replacing static prompting with an extraction pipeline that adapts to instances and combines semantic retrieval and structured information extraction. The proposed approach is evaluated on a collection of industrial product datasheets using a selection of open- and closed-weight LLMs. Experimental results show that RAIL consistently improves extraction quality over conventional few-shot prompting, yielding relative improvements of 30.4-52.4%. These results demonstrate that our approach provides an effective improvement for company-specific AAS generation.
Chinese Translation
资产管理壳（AAS）是工业4.0和数字产品护照的基石，提供工业资产的标准化数字表示。尽管制造商已经维护了大量的技术产品文档，但从现有产品数据表生成AAS实例仍然是一项劳动密集型任务，因为技术信息是从异构文档结构中提取的，并且通常涉及公司特定的术语和惯例。在本研究中，我们提出了AAS-RAIL，一种检索增强的信息提取（IE）方法，利用大型语言模型（LLMs）自动从PDF产品数据表生成资产管理壳。该方法不依赖于固定的少量示例，而是通过检索与现有资产管理壳相似的LLM生成的提取助手，提供实例特定的上下文学习（ICL）。这使得模型能够在不进行微调的情况下，调整其提取行为以适应公司特定的命名惯例和格式风格。我们的核心贡献是为每个数据表动态选择公司特定的AAS示例，用提取管道替代静态提示，该管道能够适应实例并结合语义检索与结构化信息提取。我们在一组工业产品数据表上评估了该方法，使用了一系列开放和闭合权重的LLMs。实验结果表明，RAIL在提取质量上始终优于传统的少量提示，产生了30.4%-52.4%的相对提升。这些结果表明，我们的方法为公司特定的AAS生成提供了有效的改进。
cs.AI / 99 / 2609.07353
Human-like moral judgments conceal divergent motive attributions in large language models
类人道德判断掩盖大型语言模型中不同的动机归因
Abstract
Large language models (LLMs) are used to simulate human participants in psychological research. We asked whether LLMs that reproduce human evaluations of a whistleblower's moral character also reproduce the motive attributions that accompany them. Five LLMs and two human samples (N = 125 and N = 742) evaluated a physician who either remained silent about fraudulent billing or reported it to a hospital, regulator, or newspaper. Models reproduced the human ranking of the physician's moral character but portrayed whistleblowers as more helpful, less self-interested, and less hostile. In four of five models, competitive motives were less strongly associated with moral-character judgments. Model ratings changed little when prompts reproduced the narratives and demographic profiles of both human samples, although this comparison cannot isolate a perspective effect. Thus, agreement in average ratings can conceal differences in attributed motives, relationships among judgments, and sensitivity to context. Validating LLMs as simulated participants therefore requires testing psychologically informative response patterns, not average agreement alone.
Chinese Translation
大型语言模型（LLMs）被用于模拟心理研究中的人类参与者。我们探讨了那些能够再现人类对举报者道德品质评估的LLMs是否也能再现伴随这些评估的动机归因。五个LLM和两个样本（N = 125 和 N = 742）评估了一位医生，该医生要么对欺诈性账单保持沉默，要么向医院、监管机构或报纸举报。模型再现了人类对医生道德品质的排名，但将举报者描绘得更具帮助性、较少自利和较少敌意。在五个模型中的四个中，竞争动机与道德品质判断的关联性较弱。当提示再现了两个样本的人类叙述和人口特征时，模型评分变化不大，尽管这种比较无法隔离出视角效应。因此，平均评分的一致性可能掩盖了在归因动机、判断之间的关系以及对情境的敏感性方面的差异。因此，将LLMs验证为模拟参与者需要测试心理上具有信息性的反应模式，而不仅仅是平均一致性。
cs.AI / 100 / 2609.07409
RAFM-SER++: A Lightweight Multimodal Emotion Recognition Framework for Real-Time Behavioral Monitoring in Surveillance Systems
RAFM-SER++：一种轻量级多模态情感识别框架，用于监控系统中的实时行为监测
Abstract
Recent multimodal Speech Emotion Recognition (SER) systems achieve high accuracy through interaction-heavy cross-modal transformers, but their computational cost limits deployment in latency-sensitive and resource-constrained surveillance systems. To address this challenge, we propose RAFM_SER++, a lightweight multimodal SER framework featuring an asymmetric Residual Attention Fusion Mechanism (RAFM). Rather than relying on computationally expensive bidirectional interactions, RAFM injects affective speech cues into semantic text representations through a one-directional residual attention pathway. Combined with a BYOL-inspired cross-modal alignment objective and attention-guided pooling, the proposed framework improves multimodal representation learning while maintaining low computational overhead. Experiments on the IEMOCAP and ESD benchmarks demonstrate that RAFM_SER++ consistently outperforms the HuBERT-Base baseline and achieves a superior accuracy-efficiency trade-off compared with the state-of-the-art MemoCMT. Specifically, RAFM_SER++ reduces trainable parameters by more than 60%, achieves faster inference (79.60 it/s), and attains BACC scores of 81.10% on IEMOCAP and 95.39% on ESD. These results indicate that lightweight asymmetric multimodal fusion is an effective alternative to interaction-heavy cross-modal transformers for real-time surveillance applications.
Chinese Translation
近期的多模态语音情感识别（SER）系统通过交互密集的跨模态变换器实现了高准确率，但其计算成本限制了在对延迟敏感和资源受限的监控系统中的部署。为了解决这一挑战，我们提出了RAFM_SER++，一种轻量级多模态SER框架，具有不对称残差注意力融合机制（RAFM）。RAFM并不依赖于计算开销大的双向交互，而是通过单向残差注意力路径将情感语音线索注入语义文本表示中。结合受BYOL启发的跨模态对齐目标和注意力引导的池化，该框架在保持低计算开销的同时改善了多模态表示学习。在IEMOCAP和ESD基准测试中的实验表明，RAFM_SER++始终优于HuBERT-Base基线，并在与最先进的MemoCMT相比时实现了更优的准确性-效率权衡。具体而言，RAFM_SER++将可训练参数减少了60%以上，达到了更快的推理速度（79.60 it/s），并在IEMOCAP和ESD上分别获得了81.10%和95.39%的BACC分数。这些结果表明，轻量级不对称多模态融合是实时监控应用中交互密集型跨模态变换器的有效替代方案。
cs.AI / 101 / 2609.07434
CIT-CAD: Constraint Intent Tree-based CAD Code Generation and Verification
CIT-CAD：基于约束意图树的计算机辅助设计代码生成与验证
Abstract
Natural-language Computer-Aided Design (CAD) code generation aims to turn design intent into executable and editable parametric programs. Large language models (LLMs) make this goal increasingly practical, but useful systems must preserve the construction process behind the rendered geometry. Existing benchmarks and methods mostly focus on how closely the generated CAD model matches the reference geometry, often using metrics such as Intersection over Union (IoU). Such metrics can miss errors in part decomposition, construction hierarchy, Boolean operations, sketch structure, and geometric relations. This gap calls for a representation that makes design intent explicit and lets a system check generated code against that intent. We propose CIT-CAD, a framework that infers a Constraint Intent Tree (CIT) from the input description to represent the intended entities, hierarchy, operations, and relations. The tree has two roles: it guides CAD code generation and defines expected constraints for verification. The framework extracts actual constraints from the generated program, compares them with the expected constraints, and uses mismatches to localize and repair design violations. Experiments show that the framework improves CAD generation performance, with larger gains on more complex multi-entity designs. By turning design intent into an explicit and checkable object, this work is the first attempt to move text-to-CAD generation beyond rendered-geometry matching toward construction-aware synthesis, verification, and repair.
Chinese Translation
自然语言计算机辅助设计（CAD）代码生成旨在将设计意图转化为可执行和可编辑的参数化程序。大型语言模型（LLMs）使这一目标变得日益可行，但有用的系统必须保留渲染几何体背后的构建过程。现有的基准和方法主要关注生成的CAD模型与参考几何体的匹配程度，通常使用交并比（Intersection over Union, IoU）等指标。这些指标可能忽略零件分解、构建层次、布尔操作、草图结构和几何关系中的错误。这一差距呼唤一种能够明确设计意图的表示，并让系统能够根据该意图检查生成的代码。我们提出了CIT-CAD，一个从输入描述中推断约束意图树（Constraint Intent Tree, CIT）的框架，以表示预期的实体、层次、操作和关系。该树有两个角色：它指导CAD代码生成，并定义验证的预期约束。该框架从生成的程序中提取实际约束，将其与预期约束进行比较，并利用不匹配来定位和修复设计违规。实验表明，该框架提高了CAD生成性能，在更复杂的多实体设计中获得了更大的提升。通过将设计意图转化为一个明确且可检查的对象，这项工作首次尝试将文本到CAD生成从渲染几何匹配推进到构建感知合成、验证和修复。
cs.AI / 102 / 2609.07478
The Internal Anatomy of Strategic Choice in Large Language Models
大型语言模型中的战略选择内部结构
Abstract
Large language models act as strategic agents and models of human choice, yet choosing like a strategic agent does not mean computing like one. We recorded activations from four open-weight models --- dense and mixture-of-experts, including a matched base--instruct pair --- in one-shot play of 144 strict ordinal $2\times2$ games. We followed a prespecified incentive from prompt, through activations, to choice. Dense models mirrored the unadjusted human decline with game complexity. Incentive and choice were detectable in every model, but models differed in whether incentive reached the choice, aligned with it and, where tested, whether strengthening it shifted preference. The base and instruction-tuned Qwen2.5 models chose almost identically at baseline yet differed in whether incentive reached choice. Fixed decision cues were distinguishable internally but changed choices selectively. Similar behaviour can rest on different computation; post-training can reshape the path from represented incentive to decision while leaving behaviour and decodable information largely intact.
Chinese Translation
大型语言模型作为战略代理和人类选择的模型，然而，作为战略代理进行选择并不意味着以相同方式进行计算。我们记录了四个开放权重模型的激活情况——包括密集模型和专家混合模型，以及一个匹配的基础-指令配对——在144个严格序数$2 imes2$博弈的一次性游戏中。我们遵循了从提示、通过激活到选择的预设激励。密集模型反映了人类在游戏复杂性增加时未调整的下降趋势。每个模型中都可以检测到激励和选择，但模型在激励是否到达选择、是否与选择对齐，以及在测试中，增强激励是否改变偏好方面存在差异。基础模型和经过指令调优的Qwen2.5模型在基线时几乎选择相同，但在激励是否到达选择方面存在差异。固定决策线索在内部是可区分的，但选择发生了选择性变化。类似的行为可以基于不同的计算；后训练可以重塑从表征激励到决策的路径，同时保持行为和可解码信息大致不变。
cs.AI / 103 / 2609.07483
Modus Tollens and Counterfactuals and Counterfactual Reasoning Based on Three Types of Negation
否定法与反事实及基于三种否定类型的反事实推理
Abstract
Modus Tollens (MT) is a classical logical inference rule, while counterfactuals are hypothetical statements that are contrary to facts, and counterfactual reasoning is a process of reasoning based on counterfactuals. Negation is an indispensable core concept in them. In this paper, based on the logical systems LCOI&PLCOI with contradictory negation, opposite negation and intermediary negation, we propose three variants of Modus Tollens corresponding to distinct negation types, namely MTC: Modus Tollens based on contradictory negation, MTO: Modus Tollens based on opposite negation, and MTI: Modus Tollens based on intermediary negation. We define the implications within MTC, MTO and MTI, provide the truth value algorithms of MTC, MTO and MTI, and discuss the reducibility of these algorithms. To incorporate these three types of negation into counterfactuals and counterfactual reasoning, we differentiate counterfactuals into two types based on whether they possess logical negation, thereby proposing three counterfactuals and counterfactuals reasoning based on different logical negations. In this paper, we further argue that the three counterfactuals reasoning based on different logical negations have the same inference form as MTC, MTO and MTI, respectively. In other words, they share the same inference structure. As a result, the truth value algorithms for MTC, MTO and MTI can be as the truth value algorithms for the three counterfactuals reasoning based on different logical negations. The algorithms indicates that if the first premise of the reasoning is true, the truth values of the reasoning conclusions are identical to the truth values of the three negative premises in the reasoning premises, respectively. This reflects the consistency and accuracy of the truth value algorithms.
Chinese Translation
否定法（Modus Tollens, MT）是一种经典的逻辑推理规则，而反事实是与事实相悖的假设性陈述，反事实推理则是基于反事实进行推理的过程。否定是其中不可或缺的核心概念。本文基于具有矛盾否定、对立否定和中介否定的逻辑系统 LCOI&PLCOI，提出了三种对应于不同否定类型的否定法变体，即基于矛盾否定的否定法（MTC）、基于对立否定的否定法（MTO）和基于中介否定的否定法（MTI）。我们定义了 MTC、MTO 和 MTI 中的蕴含关系，提供了 MTC、MTO 和 MTI 的真值算法，并讨论了这些算法的可约性。为了将这三种否定类型纳入反事实和反事实推理中，我们根据反事实是否具有逻辑否定将其分为两类，从而提出了基于不同逻辑否定的三种反事实及其推理。本文进一步论证了基于不同逻辑否定的三种反事实推理与 MTC、MTO 和 MTI 具有相同的推理形式，换句话说，它们共享相同的推理结构。因此，MTC、MTO 和 MTI 的真值算法可以作为基于不同逻辑否定的三种反事实推理的真值算法。这些算法表明，如果推理的第一个前提为真，则推理结论的真值与推理前提中的三个否定前提的真值分别相同。这反映了真值算法的一致性和准确性。
cs.AI / 104 / 2609.07533
Quantile-Led Feature Extraction for Multi-Horizon Predictive Maintenance in Industrial Manufacturing Systems
基于分位数引导的特征提取用于工业制造系统的多时间跨度预测性维护
Abstract
In data-driven predictive maintenance (PdM), feature extraction is usually treated as fixed preprocessing: a descriptor set is chosen once and reused while the downstream model or forecasting horizon changes. This paper isolates the representation-learning stage and presents a quantile-led feature-extraction framework based on a dual-stage MLP-QRNN hierarchy. QRNN1 learns a broad ten-quantile conditional distribution for each sensor channel, while skip-connected QRNN2 refines a retained mid-tail quantile set into compact, channel-resolved, distribution-aware features. A fixed thirteen-pipeline ablation spans 1-hour, 70-hour, and 30-day regimes across 72 machines in 9 industrial facilities, with the downstream temporal classifier held fixed within each regime. Increasing the retained mid-tail set from two to four quantiles improves 30- and 60-minute F1-score, reaching 75.92% and 72.44% with attention enabled. The results also show that representations do not transfer reliably beyond their design horizon unless feature capacity, temporal embedding, activation strategy, and sensor breadth are scaled with the forecasting task. The unmodified short-horizon extractor falls to 42.90% F1 at 70 hours, whereas horizon-conditioned extractors reach 60.38% at 70 hours and 79.97% at 30 days. The framework therefore supports treating PdM feature extraction as a horizon-dependent representational stage rather than fixed preprocessing.
Chinese Translation
在数据驱动的预测性维护（PdM）中，特征提取通常被视为固定的预处理：描述符集在一次选择后被重复使用，而下游模型或预测时间跨度发生变化。本文将表示学习阶段进行隔离，并提出了一种基于双阶段 MLP-QRNN 层次结构的分位数引导特征提取框架。QRNN1 为每个传感器通道学习广泛的十个分位数条件分布，而跳跃连接的 QRNN2 将保留的中尾分位数集精炼为紧凑的、通道解析的、分布感知的特征。一个固定的十三管道消融实验涵盖了72台机器在9个工业设施中的1小时、70小时和30天的时间范围，且在每个时间范围内下游时间分类器保持固定。将保留的中尾集从两个分位数增加到四个分位数，提高了30分钟和60分钟的 F1 分数，分别达到了75.92%和72.44%（启用注意力机制）。结果还表明，表示在其设计时间跨度之外并不可靠地转移，除非特征容量、时间嵌入、激活策略和传感器广度与预测任务相匹配。未经修改的短时间跨度提取器在70小时时 F1 分数降至42.90%，而条件于时间跨度的提取器在70小时时达到60.38%，在30天时达到79.97%。因此，该框架支持将 PdM 特征提取视为一个依赖于时间跨度的表示阶段，而非固定的预处理。
cs.AI / 105 / 2609.07559
Scoring Without the Engine: Validating a Deterministic, Manipulation-Resistant Content Score for Generative Engines, End to End
无引擎评分：验证一种确定性、抗操控的生成引擎内容评分方法，端到端
Abstract
How do you validate a cheap, deterministic proxy for an oracle that is expensive, rate-limited, and non-stationary? We present a protocol built on adversarial falsification gates (negative control, dose response, bounded amplification, duplication penalty, length neutrality) that define and select the proxy, fitted on a training split and confirmed held-out; around them it bounds what the proxy can never resolve, and re-measures external causal evidence on the current oracle rather than assuming it. We demonstrate it end to end on Generative Engine Optimization, where the proxy is a deterministic content score, and one step fails on that domain exactly as the protocol is built to detect: re-measuring the only published causal anchors (2023 effect sizes) on ten modern engine families shows their levers move citation on none, so the anchors are an expired external check; recalibrating to the near-zero modern vector strips the score of its lever-responsive components. What survives is the gate-enforced response surface. The gates buy a measured property: on a 500-source benchmark of adversarial edits, amplifying the score's calibrated levers gains an attacker at most 6 points, and decreases with dose; single-lever amplification is provably bounded, while the cap and cross-lever sub-additivity are empirical findings consistent with it. On detection, web-spam baselines dominate and out-of-distribution attacks evade the score, so the deployable filter layers it over them. A query-conditioned skyline bounds the score's citation signal (within-query Spearman 0.11), repositioning query-agnostic scores as quality filters rather than citation predictors. A query-leakage bug in our first ranking evaluation and a failed confidence flag are disclosed and corrected; every number reproduces offline from released artifacts at zero marginal API cost.
Chinese Translation
如何验证一种廉价的、确定性的代理，以替代一种昂贵的、受限于速率且非平稳的神谕？我们提出了一种基于对抗性伪造门（负控制、剂量反应、有界放大、重复惩罚、长度中立）的协议，该协议定义并选择代理，基于训练集进行拟合并在保留集上进行确认；围绕这些门限界定了代理无法解决的内容，并重新测量当前神谕的外部因果证据，而不是简单假设它。我们在生成引擎优化中进行了端到端的演示，其中代理是一个确定性的内容评分，而在该领域的一个步骤正好失败，正如协议所设计的那样：重新测量十个现代引擎系列上唯一已发布的因果锚点（2023年效应大小）显示其杠杆对引用没有影响，因此这些锚点是过期的外部检查；重新校准到近零的现代向量剥夺了评分的杠杆响应成分。剩下的就是门控强制的响应表面。这些门控购买了一个可测量的属性：在500个源的对抗性编辑基准上，放大评分的校准杠杆使攻击者最多获得6分，并随着剂量的增加而减少；单杠杆放大是可证明有界的，而上限和跨杠杆的次加性是与之相一致的经验发现。在检测方面，网络垃圾邮件基线占主导地位，分布外攻击则规避了评分，因此可部署的过滤器将其叠加在其上。查询条件下的天际线界定了评分的引用信号（查询内的Spearman 0.11），将查询无关的评分重新定位为质量过滤器，而非引用预测器。在我们第一次排名评估中披露并纠正了一个查询泄漏漏洞和一个失败的置信标志；每个数字均可从发布的文献中以零边际API成本离线重现。
cs.AI / 106 / 2609.07573
From Simulated Citizens to Simulated Deliberation: Challenges in Representation and Interaction
从模拟公民到模拟审议：代表性和互动中的挑战
Abstract
Multi-agent LLM deliberation has been explored as a scalable way to simulate public deliberation. For such simulations to be informative, persona agents should reflect population opinion patterns and interaction should shape their conclusions. We evaluate whether LLM-based deliberation can meet these two conditions using census-grounded Korean personas debating real policy questions benchmarked against national surveys. Persona agents do not reliably reproduce population opinion patterns: responses are often far more concentrated and frequently reverse demographic differences in the human data. Deliberations nonetheless produce reasoned, reciprocal, and varied arguments alongside substantial stance movement. Yet much of this movement does not require peer exchange: sealed-monologue agents change position at similar rates and reach nearly the same final balance as full debates, while groups initialized with very different positions often converge to similar endpoints. Anchoring population-informed starting positions, meanwhile, sharply suppresses updating. Thus, population representation, argument generation, and interaction-driven opinion change do not necessarily go together. The simulations readily surface arguments on both sides, though whether they capture the diversity of human perspectives remains untested, leaving open a promising role for argument surfacing even as population simulation requires further validation.
Chinese Translation
多智能体大语言模型（LLM）审议已被探索作为一种可扩展的方式来模拟公共审议。为了使这些模拟具有信息价值，角色代理应反映人口意见模式，互动应塑造他们的结论。我们评估基于LLM的审议是否能够满足这两个条件，使用基于人口普查的韩国角色在真实政策问题上进行辩论，并与国家调查进行基准比较。角色代理并未可靠地再现人口意见模式：其回应往往更加集中，并且经常逆转人类数据中的人口差异。尽管如此，审议仍然产生了有理有据、相互回应和多样化的论点，并伴随着显著的立场变化。然而，这种变化大部分并不需要同行交流：封闭独白代理以相似的速度改变立场，并且在最终平衡上几乎达到与完整辩论相同的结果，而最初立场非常不同的群体往往收敛到相似的终点。同时，锚定以人口为基础的起始立场则显著抑制了更新。因此，人口代表性、论点生成和互动驱动的意见变化并不一定是相辅相成的。这些模拟能够清晰地呈现双方的论点，尽管它们是否捕捉到人类观点的多样性仍未经过检验，这为论点的呈现留下了一个有前景的角色，尽管人口模拟仍需进一步验证。
cs.AI / 107 / 2609.07586
A Tool-Augmented, GPT-4 Chatbot for Real-Time Repository Data Analysis
一种增强工具的 GPT-4 聊天机器人用于实时代码库数据分析
Abstract
Software repositories contain vast amounts of data on code contributions, bug reports, and project activities, yet this information remains challenging for non-technical stakeholders and developers to access due to limited expertise in querying repositories. To address this, we introduce a novel chatbot architecture leveraging OpenAI's GPT-4 model for automated extraction and analysis of repository data. In contrast, our architecture takes a structured path first by parsing the user's query to extract relevant parameters, then selecting the correct tool to employ based on that analysis, and finally invoking the GPT-4 model to create a highly detailed response. In contrast to previous work based on multi-component systems with embedding models and document retrievers, our architecture inverts the process by relying on prompt engineering and tool selection to fit with the query intent. To validate our approach, we conducted experiments on various question types, including Issues, Pull Requests, Commits, Compound Questions, and General Repository Information, evaluating our target prompts' ability to improve the accuracy of responses from the model. Beyond demonstrating the utility of this architecture to a diverse set of users, our findings suggest that this architecture can make repository data more accessible to technical and non-technical audiences through the production of actionable insights.
Chinese Translation
软件代码库包含大量关于代码贡献、错误报告和项目活动的数据，但由于非技术利益相关者和开发者在查询代码库方面的专业知识有限，这些信息仍然难以获取。为了解决这个问题，我们提出了一种新颖的聊天机器人架构，利用 OpenAI 的 GPT-4 模型实现代码库数据的自动提取和分析。与以往基于多组件系统、嵌入模型和文档检索器的工作不同，我们的架构首先通过解析用户查询来提取相关参数，然后根据该分析选择合适的工具，最后调用 GPT-4 模型生成高度详细的响应。我们的架构颠覆了传统流程，依赖于提示工程和工具选择来匹配查询意图。为了验证我们的方法，我们对多种问题类型进行了实验，包括问题、拉取请求、提交、复合问题和一般代码库信息，评估了我们目标提示提高模型响应准确性的能力。我们的研究不仅展示了该架构对多样化用户群体的实用性，还表明该架构能够通过生成可操作的洞察，使技术和非技术受众更容易获取代码库数据。
cs.AI / 108 / 2609.07603
FinCUABuild: Can Agents Build Reliable Benchmarks for Dynamic Financial Computer Use?
FinCUABuild：代理能否为动态金融计算机使用构建可靠的基准？
Abstract
Financial scenarios are diverse and complex, spanning varying data conditions, tool configurations, and workflows. Yet existing CUA, Computer-Using Agent, evaluation tasks remain largely manually constructed, limiting scalable coverage of real-world financial scenarios. Then, can agents autonomously construct diverse CUA evaluation tasks for financial scenarios? Evaluating this capability poses three key challenges: scenario coverage of construction requests, fair comparison across construction methods, and reliable assessment of generated task quality. To solve these, we introduce FinCUABuildBench, a benchmark for evaluating financial CUA task construction, featuring: (i) 576 construction requests covering 24 financial workflows and three types of runtime variation; (ii) standardized input, budget, and output specifications; and (iii) a task qualification mechanism based on execution tests and quality checks. We further introduce FinCUABuildAgent, a multi-agent system for automatically constructing dynamic financial CUA evaluation tasks. It consists of three modules that jointly construct tasks, environments, and validators. On FinCUABuildBench, under the same model backbone, existing agent-based construction methods achieve strict qualification rates of only 1.3-8.3%, while FinCUABuildAgent reaches 31.3%. Downstream evaluations further show that the constructed tasks can effectively differentiate CUA task-execution capabilities. These results demonstrate that agents can autonomously construct financial CUA tasks with meaningful evaluation value, offering a practical path toward broader evaluation coverage in financial scenarios. Code: https://github.com/FengxianJi/FinCUABuild
Chinese Translation
金融场景多样且复杂，涵盖了不同的数据条件、工具配置和工作流程。然而，现有的计算机使用代理（CUA）评估任务仍然主要是手动构建的，这限制了对真实金融场景的可扩展覆盖。那么，代理能否自主构建多样化的CUA评估任务以应对金融场景？评估这一能力面临三个关键挑战：构建请求的场景覆盖、不同构建方法之间的公平比较，以及对生成任务质量的可靠评估。为了解决这些问题，我们引入了FinCUABuildBench，这是一个用于评估金融CUA任务构建的基准，具有以下特点：（i）576个构建请求，涵盖24个金融工作流程和三种类型的运行时变化；（ii）标准化的输入、预算和输出规范；（iii）基于执行测试和质量检查的任务资格机制。我们进一步介绍了FinCUABuildAgent，这是一个用于自动构建动态金融CUA评估任务的多代理系统。它由三个模块组成，共同构建任务、环境和验证器。在FinCUABuildBench上，在相同的模型基础上，现有的基于代理的构建方法的严格资格率仅为1.3%-8.3%，而FinCUABuildAgent达到了31.3%。下游评估进一步表明，构建的任务能够有效区分CUA任务执行能力。这些结果表明，代理能够自主构建具有重要评估价值的金融CUA任务，为在金融场景中实现更广泛的评估覆盖提供了一条切实可行的路径。代码：https://github.com/FengxianJi/FinCUABuild
cs.AI / 109 / 2609.07611
AgentIdeaBench: Benchmarking Scientific Ideation in the Agent Era
AgentIdeaBench：在智能体时代评估科学创意
Abstract
Scientific ideation is the capacity to formulate novel and testable hypotheses from scientific evidence, and autonomous AI scientists depend on it. Existing evaluations largely assess it by asking models to generate ideas from a static, curated set of reference papers. That passive setup departs from the retrieval-and-reasoning workflow of modern AI scientists, and it becomes less discriminative as models improve. We introduce AgentIdeaBench, a multidisciplinary benchmark that evaluates scientific ideation under two matched settings, static observation and active exploration. We report matched Static-Active evaluations for 33 LLMs across 40 densely scored subfields spanning five disciplines, using a multidimensional, literature-verified scoring framework whose critics assess originality against retrieved prior art. Active exploration reveals considerably more capability headroom, and that headroom is unevenly distributed across models. Performance scales about twice as fast as under static observation, and the exploration gain is capability-gated, favoring the strongest models over the weakest. The gain reflects better grounding, improving feasibility, clarity, and specificity while leaving measured originality unchanged under our critics. We further explore Scientific World Modeling, a generation-time loop that refines a draft hypothesis through structured thought experiments. It benefits mid-capability models, and its impact diminishes among frontier models that appear to have internalized such reasoning patterns already. AgentIdeaBench gives future work on scientific ideation a measurement basis suited to the agent era.
Chinese Translation
科学创意是从科学证据中提出新颖且可验证假设的能力，自治人工智能科学家依赖于此。现有的评估主要通过要求模型从静态的、策划好的参考文献集中生成创意来进行。这种被动的设置偏离了现代人工智能科学家的检索与推理工作流程，并且随着模型的改进，其区分度降低。我们介绍了AgentIdeaBench，这是一个多学科基准，评估科学创意在两种匹配的设置下，即静态观察和主动探索。我们报告了针对33个大型语言模型（LLMs）在40个密集评分的子领域中的匹配静态-主动评估，使用一个多维的、经过文献验证的评分框架，其评审者根据检索到的先前艺术评估原创性。主动探索显示出显著更大的能力空间，而这种能力空间在模型之间分布不均。性能在静态观察下的提升速度约为两倍，探索增益是能力限制的，倾向于强模型而非弱模型。该增益反映了更好的基础，改善了可行性、清晰度和特异性，同时在我们的评审者看来，测得的原创性保持不变。我们进一步探讨了科学世界建模（Scientific World Modeling），这是一个在生成时间循环中通过结构化思维实验来完善草拟假设的过程。它对中等能力模型有益，而在那些似乎已经内化了此类推理模式的前沿模型中，其影响减弱。AgentIdeaBench为未来的科学创意研究提供了适合智能体时代的测量基础。
cs.AI / 110 / 2609.07627
Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best
价格的规范：为什么基于强化学习的对齐最多只能承诺条件合规
Abstract
AI agents sometimes act aligned when they infer they are being tested, and differently when not. We argue this is not an anomaly but what current training regimes are structured to select for. Reinforcement-learning-based alignment folds norms and task pursuit into one policy: the system learns its norms from scored behavior, and scoring flattens them. Do not do X is learned as doing X costs something if noticed. On every datum training can produce, a policy that complies only when it might be observed is indistinguishable from one that complies always. The experiment that would tell them apart - scoring unobserved behavior - is a contradiction in terms. Conditional compliance is thus the most that behavioral training can be known to deliver. Agency sharpens the problem: agents operate mostly where no one is watching, and can act on whether they are watched. An iterated pipeline that trains against detected failures selects for passing detection, not for complying. This account unifies alignment faking, sandbagging, and evaluation-aware scheming. And it reorients the remedy: not deeper internalization but architecture, making violations unavailable rather than unchosen.
Chinese Translation
AI代理在推测自己正在被测试时有时会表现出对齐，而在未被测试时则表现不同。我们认为这并不是一种异常，而是当前训练机制所选择的结果。基于强化学习的对齐将规范和任务追求折叠为一个政策：系统通过评分行为学习其规范，而评分则使其扁平化。‘不做X’被学习为如果被注意到，做X会付出代价。在训练过程中产生的每一个数据中，仅在可能被观察时合规的政策与始终合规的政策无法区分。能够区分它们的实验——对未观察到的行为进行评分——在概念上是自相矛盾的。因此，条件合规是行为训练所能提供的最大保证。代理性加剧了这个问题：代理主要在无人监视的环境中操作，并且可以根据是否被观察来采取行动。一个针对检测到的失败进行训练的迭代管道选择的是通过检测，而不是合规。这一论述统一了对齐伪装、故意拖延和评估意识的策划，并重新调整了补救措施：不是更深的内化，而是架构，使得违规行为变得不可用，而不是未被选择。
cs.AI / 111 / 2609.07672
Aegix Pulse: A Traceable Three-Stage Architecture for Personalized Content Generation and Context-Preserving Revision
Aegix Pulse：一种可追溯的三阶段架构用于个性化内容生成和上下文保留修订
Abstract
Production content-generation systems must integrate a user's immediate task, long-term brand identity, historical evidence, and revision feedback. We present Aegix Pulse, a production-oriented three-stage architecture that separates current-task clarification and Task Persona finalization, long-term Account Profile (Brand DNA) assembly, and controlled generation and revision while preserving provenance across content versions. We evaluate four preregistered claims using 96 synthetic social-media generation tasks. Four initial-generation conditions progressively introduced a Task Persona, Account Profile, and successful-history style evidence, while two revision conditions compared plain and context-preserving revision. The experiment produced 480 completed generation records and 1,440 blinded LLM-Judge evaluations, supplemented by human review. Adding the Account Profile increased mean brand-consistency scores by 0.1562 points on a five-point scale compared with Task Persona alone (Holm-adjusted p=.1224). Preserving task and brand context during revision increased mean task-preservation scores by 0.2917 points compared with plain revision (Holm-adjusted p=.2432). Neither improvement was statistically conclusive after multiple-comparison correction. Task Persona alone showed a small observed effect, while successful-history evidence provided no additional improvement in brand consistency under the current setting. Human validation did not consistently reproduce the LLM-Judge effect directions and showed low inter-reviewer agreement. These findings provide preliminary evidence for persistent brand context and context-preserving revision while identifying priorities for stronger evidence processing and evaluation.
Chinese Translation
生产内容生成系统必须整合用户的即时任务、长期品牌身份、历史证据和修订反馈。我们提出了Aegix Pulse，这是一种以生产为导向的三阶段架构，分别处理当前任务澄清与任务角色（Task Persona）最终确定、长期账户档案（Account Profile，品牌DNA）组装，以及在内容版本间保留来源的受控生成与修订。我们使用96个合成社交媒体生成任务评估了四个预注册的主张。四个初始生成条件逐步引入了任务角色、账户档案和成功历史风格证据，而两个修订条件则比较了普通修订与上下文保留修订。实验产生了480个完成的生成记录和1,440个盲评的LLM评估，辅以人工审核。与单独使用任务角色相比，添加账户档案使得平均品牌一致性得分在五分制上提高了0.1562分（Holm调整后的p=.1224）。在修订过程中保留任务和品牌上下文使得平均任务保留得分比普通修订提高了0.2917分（Holm调整后的p=.2432）。在多重比较校正后，这两项改进均未达到统计显著性。单独使用任务角色显示出小的观察效应，而在当前设置下，成功历史证据未能提供品牌一致性方面的额外改善。人工验证未能一致地重现LLM评估的效应方向，并显示出低的评审者间一致性。这些发现为持久的品牌上下文和上下文保留修订提供了初步证据，同时确定了更强的证据处理和评估的优先事项。
cs.AI / 112 / 2609.07712
APPSim-Bench: Bridging Real-world Apps and Reproducible Evaluation for Mobile GUI Agents
APPSim-Bench：连接真实应用与可重复评估的移动GUI代理
Abstract
Mobile GUI agents can execute tasks from natural-language instructions, but their evaluation remains difficult to make both realistic and reproducible. Existing benchmarks typically trade off these goals: simplified apps lack real-world mobile complexity, whereas live commercial apps introduce uncontrolled variation from recommendations, advertisements, accounts, and changing content. We propose AppSim-Bench, which addresses this trade-off through controllable simulated apps that preserve task-relevant interaction logic while supporting deterministic evaluation. Built through a coding-agent-assisted and human-verified workflow, it contains 557 tasks across 17 high-frequency Chinese and English apps. Its controllable backend data and outcome-based verification remove major sources of environmental stochasticity, enabling reproducible cross-model comparison. Evaluating 19 GUI agents, spanning general-purpose and GUI-specialized systems, we find that autonomous mobile execution remains far from solved. The best model completes only 50.27% of tasks, and 28.55% of tasks are not solved by any agent. Further analysis shows that failures concentrate in longer workflows, numerical reasoning tasks, and inefficient trajectories marked by high action overhead and budget exhaustion. Our project is available at https://github.com/Acrab-Agentic-Labs/AppSim.
Chinese Translation
移动GUI代理可以根据自然语言指令执行任务，但其评估在实现现实性和可重复性方面仍然面临挑战。现有基准通常在这两个目标之间进行权衡：简化的应用缺乏真实世界的移动复杂性，而实时商业应用则因推荐、广告、账户和变化的内容引入了不可控的变异。我们提出了AppSim-Bench，通过可控的模拟应用解决这一权衡，保留与任务相关的交互逻辑，同时支持确定性评估。该基准通过编码代理辅助和人工验证的工作流程构建，包含557个任务，覆盖17个高频的中文和英文应用。其可控的后端数据和基于结果的验证消除了环境随机性的主要来源，从而实现可重复的跨模型比较。在评估19个GUI代理（涵盖通用系统和GUI专用系统）时，我们发现自主移动执行仍然远未解决。表现最佳的模型仅完成50.27%的任务，且28.55%的任务没有任何代理解决。进一步分析表明，失败主要集中在较长的工作流程、数值推理任务以及高行动开销和预算耗尽所标记的低效轨迹上。我们的项目可在https://github.com/Acrab-Agentic-Labs/AppSim获取。
cs.AI / 113 / 2609.07713
The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing
新兴的人工智能论文审稿军备竞赛：学术出版中的对抗性共进化
Abstract
Generative and agentic AI are reshaping both the production and evaluation of scientific research. These developments are often studied separately, as questions of how AI can produce research and how AI can review it. We argue that this separation misses an increasingly important feature of scholarly publishing: changes on one side alter the incentives, constraints, and behavior of the other. We synthesize 230 scholarly publications and institutional records using a taxonomy of six connected dynamics: production scaling, evaluation automation, evaluation manipulation, defense mechanisms and policy responses, evasion and side effects, and long-horizon ecosystem feedback. The literature shows an emerging progression in which cheaper and faster research production increases pressure on evaluation, AI-mediated evaluation becomes more scalable and repeatable, participants can exploit evaluator regularities, and institutions respond with technical safeguards and policy controls. These responses can in turn induce evasion, redistribute errors and workload, and shape the scholarly records reused by future research and evaluation systems. Evidence is strongest for production and evaluation at scale, reproducible manipulation, and institutional response, while post-policy adaptation and artifact-level long-horizon feedback remain less directly observed. This systems view shifts attention from isolated AI capabilities toward how scholarly actors and AI systems adapt to one another over time.
Chinese Translation
生成性和自主性人工智能正在重塑科学研究的生产和评估。这些发展通常被分开研究，分别探讨人工智能如何产生研究以及如何对其进行审稿。我们认为，这种分离忽视了学术出版中一个日益重要的特征：一方的变化会改变另一方的激励、约束和行为。我们利用六个相互关联的动态的分类法，对230篇学术出版物和机构记录进行了综合分析：生产规模化、评估自动化、评估操控、防御机制和政策响应、规避与副作用，以及长期生态系统反馈。文献表明，出现了一种新兴的进展，即更便宜、更快速的研究生产增加了对评估的压力，人工智能介导的评估变得更加可扩展和可重复，参与者可以利用评估者的规律，而机构则通过技术保障和政策控制作出响应。这些响应反过来可能导致规避、重新分配错误和工作负载，并塑造未来研究和评估系统所重用的学术记录。证据最强的方面是大规模的生产和评估、可重复的操控以及机构响应，而政策后适应和工件级的长期反馈则相对不那么直接观察到。这种系统视角将注意力从孤立的人工智能能力转向学术参与者与人工智能系统如何随着时间的推移相互适应。
cs.AI / 114 / 2609.07719
A radiographic world model for clinical reasoning and evidence generation
用于临床推理和证据生成的放射影像世界模型
Abstract
Medical imaging artificial intelligence (AI) is commonly developed as separate mappings from radiographs to diagnostic outputs or from clinical descriptions to generated images, although both arise from the same underlying radiographic state. A world-model formulation instead seeks to learn an internal representation of this state that can support both clinical readout and conditional simulation of radiographic observations. Here we introduce MedDream, a radiographic world model that learns a shared continuous latent state from paired chest radiograph-text observations for diagnostic reasoning and report-conditioned evidence generation. MedDream was pretrained on 2.65 million leakage-controlled chest radiograph-text pairs curated from 4.40 million candidates. Across eight clinical datasets and two independent reader cohorts, MedDream outperformed leading diagnostic and generative comparators. For diagnostic reasoning, MedDream showed strong generalization across disease recognition, label-scarce adaptation, severity assessment, and localization, while MedDream-supported review increased mean resident concordance with independent radiologist consensus from 56.3% to 63.0%. For evidence generation, MedDream produced radiographs that preserved clinically relevant pathology and improved downstream performance on held-out real data, with synthetic augmentation increasing external VinDr-CXR macro-AUROC from 76.4% to 81.4%. More importantly, conditioning generation on prespecified subgroup performance gaps enabled targeted evidence construction, increasing weighted F1 by 3.1 percentage points in Asian patients, whereas matched-volume unguided augmentation decreased it by 2.3 points. These findings establish radiographic world models as a path toward medical AI that learns clinically meaningful internal states for interpreting, simulating, and constructing evidence for clinical use.
Chinese Translation
医学影像人工智能（AI）通常被开发为从放射线照片到诊断输出或从临床描述到生成图像的独立映射，尽管这两者都源于相同的基础放射影像状态。世界模型的构建旨在学习这种状态的内部表示，以支持临床读数和放射观察的条件模拟。在此，我们介绍了MedDream，一个放射影像世界模型，它从成对的胸部放射线照片-文本观察中学习共享的连续潜在状态，以进行诊断推理和报告条件的证据生成。MedDream在265万对经过泄漏控制的胸部放射线照片-文本对上进行了预训练，这些对是从440万候选中筛选出来的。在八个临床数据集和两个独立读者队列中，MedDream的表现超越了领先的诊断和生成比较模型。在诊断推理方面，MedDream在疾病识别、标签稀缺适应、严重程度评估和定位方面表现出强大的泛化能力，而MedDream支持的审查使得住院医生与独立放射科医生共识的平均一致性从56.3%提高到63.0%。在证据生成方面，MedDream生成的放射线照片保留了临床相关的病理，并改善了在保留的真实数据上的下游表现，合成增强使得外部VinDr-CXR宏观AUROC从76.4%提高到81.4%。更重要的是，基于预设子组表现差距进行条件生成使得目标证据构建成为可能，在亚洲患者中加权F1提高了3.1个百分点，而匹配体积的无指导增强则降低了2.3个百分点。这些发现确立了放射影像世界模型作为医疗AI的一条路径，该路径学习临床上有意义的内部状态，以便于解释、模拟和构建用于临床的证据。
cs.AI / 115 / 2609.07731
The Profit Alignment Problem: How Profit Mandates Induce Alignment Failures in LLMs
利润对齐问题：利润指令如何导致大型语言模型中的对齐失败
Abstract
We show that ordinary business language --- "maximize profitability" --- induces profit-oriented ambiguity resolution: LLMs systematically dismiss ambiguous signals of potential safety violations to serve business objectives. In 3,600 controlled trials across eight reasoning-capable LLMs, adding a profit mandate to otherwise identical prompts increases risk-dismissing judgments by 6.8 percentage points (p < 0.0001), suppresses board escalation recommendations by 13.9pp (p < 0.0001), and shifts severity assessments downward (p < 0.0001). The mandate never instructs models to downplay risks; instead, chain-of-thought traces reveal motivated reasoning: models acknowledge concerns, then invoke profit logic to justify dismissing them. We characterize these findings as the Profit Alignment Problem: when AI systems are given ordinary business objectives, they develop systematic strategies for suppressing inconvenient information that no designer intended or specified.
Chinese Translation
我们展示了普通商业语言——“最大化盈利”——如何引发以利润为导向的模糊性解决：大型语言模型（LLMs）系统性地忽视潜在安全违规的模糊信号，以服务于商业目标。在对八个具备推理能力的LLMs进行的3600次受控实验中，向原本相同的提示中添加利润指令使得风险忽视的判断增加了6.8个百分点（p < 0.0001），抑制了董事会升级建议13.9个百分点（p < 0.0001），并使得严重性评估向下调整（p < 0.0001）。该指令从未指示模型轻视风险；相反，思维链迹象揭示了动机推理：模型承认了担忧，然后引用利润逻辑来为忽视这些担忧辩护。我们将这些发现描述为利润对齐问题：当人工智能系统被赋予普通商业目标时，它们会发展出系统性的策略来压制设计者未曾意图或指定的不便信息。
cs.AI / 116 / 2609.07741
When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems
当智能成为代理：共生人工智能系统的治理主动代理理论
Abstract
Persistent AI assistants are intended to extend human attention, memory, and coordination across changing digital and physical environments. To be truly useful they must do more than just act when asked. They must decide on their own whether a situation warrants behavior at all, when it does and in what mode, whether to act, ask, monitor, defer or deliberately refrain. We call this the activation problem. Research on commitment, appraisal, mixed-initiative interaction and delegation each illuminates part of it, but none ties situated activation to continuing authorization and accountability. This paper develops a conceptual and formal framework for governed proactive agency, organizing behavior across time through perception, intent, affective-conative appraisal, constraint, and feedback. It distinguishes autonomous and delegated agency and defines symbiotic agency as delegation under a standing, revocable mandate, with continuing coupling to the principal's situation, calibrated inference of their condition, and bounded personalization. The distinctive contribution is an integrated account linking activation decisions to authorized perception, behavior selection, authority containment, traceable restraint, and constrained adaptation, with behavioral episodes as the unit of analysis. Through an agency classification method, an evaluation framework, proposed benchmark scenarios, and a reference architecture, the account provides a basis for specifying and assessing whether assistance is warranted, timely, authorized, and answerable beyond task completion alone. It is intended to guide the development and evaluation of always-present personal assistants and embodied support systems that augment human capabilities while preserving the principal's authority and judgment.
Chinese Translation
持久的人工智能助手旨在扩展人类在不断变化的数字和物理环境中的注意力、记忆和协调能力。要真正发挥作用，它们必须不仅仅是在被请求时行动。它们必须自行决定某种情况是否值得采取行动，何时采取行动以及以何种方式行动，是否采取行动、询问、监控、推迟或故意克制。我们称之为激活问题。对承诺、评估、混合主动交互和委托的研究各自揭示了其中的一部分，但没有将具体的激活与持续的授权和问责联系起来。本文发展了一个治理主动代理的概念和形式框架，通过感知、意图、情感-意向评估、约束和反馈来组织跨时间的行为。它区分了自主代理和委托代理，并将共生代理定义为在持续的、可撤销的授权下的委托，持续与委托人的情况耦合，校准对其状况的推断，并进行有限的个性化。独特的贡献在于一个综合的论述，将激活决策与授权感知、行为选择、权威约束、可追溯的克制和受限的适应联系起来，以行为事件作为分析单元。通过一种代理分类方法、评估框架、提出的基准场景和参考架构，该论述为指定和评估是否需要、及时、授权和可问责的帮助提供了基础，超越了仅仅完成任务的范畴。它旨在指导始终存在的个人助手和体现支持系统的开发与评估，这些系统增强人类能力，同时保持委托人的权威和判断。
cs.AI / 117 / 2609.07784
xDailyBench: Benchmarking LLMs on Professional Consultation for Real-Life Problems
xDailyBench：针对现实问题的专业咨询大语言模型基准测试
Peng, Yongchang, Gu, Qingshui, Zhu, Liya, Zhang, Ge, Wang, Duo, Wang, Haodong, Ding, Jingzhe, Yu, Tianhao, Gao, Letian, Zhong, Yongjie, Li, Chaoxin, Su, Zixin, Tao, Jinchao, Ma, Xingyu, Guo, Xin'ao, Tian, Feng, Dong, Shiyuan, He, Xiaoyan, Liu, Sen, Chen, Xin, Li, Jiajun, Zhang, Zejia, Lin, Xi, Zhang, Wen, Zhu, Yi, Zeng, Duju, Gao, Xiang, Wang, Yunyang, Wang, Jiahao, Qin, Yujia, Liu, Jiaheng, Yan, Shen, Chang, Xiaolong, Huang, Wenhao
Abstract
Large language models (LLMs) are increasingly used for everyday assistance, yet existing benchmarks only partially reflect the requests users naturally make in practice. Real-world requests are often open-ended, casually specified, and context-dependent, requiring models not only to follow explicit instructions but also to infer unstated needs from user background and situational context. We introduce xDailyBench, a benchmark of 248 carefully curated tasks spanning 51 scenarios across personal life, white-collar work, learning and research, and cross-domain activities. The tasks are grounded in requests that users have actually completed or genuinely intended to accomplish with AI, and are evaluated with fine-grained binary rubrics covering both explicit and implicit requirements. We evaluate 11 frontier models under standardized agentic settings. The best models achieve a task-level score of 75.6\%, while all models perform substantially worse on implicit than explicit requirements, with gaps no less than 9 percentage points. These results reveal implicit requirement inference as a persistent bottleneck for reliably satisfying real-world everyday user needs.
Chinese Translation
大语言模型（LLMs）在日常辅助中的应用日益广泛，但现有基准测试仅部分反映用户在实际中自然提出的请求。现实世界中的请求通常是开放式的、随意指定的，并且依赖于上下文，这要求模型不仅要遵循明确的指示，还要从用户背景和情境上下文中推断未表述的需求。我们引入了xDailyBench，这是一个包含248个精心策划任务的基准，涵盖个人生活、白领工作、学习与研究以及跨领域活动等51个场景。这些任务基于用户实际完成或真实意图通过人工智能实现的请求，并使用细致的二元评分标准进行评估，涵盖了显性和隐性需求。我们在标准化的代理设置下评估了11个前沿模型。最佳模型在任务级别上达到了75.6%的得分，而所有模型在隐性需求上的表现明显低于显性需求，差距不低于9个百分点。这些结果揭示了隐性需求推断作为可靠满足现实世界日常用户需求的持续瓶颈。
cs.AI / 118 / 2609.07785
What Does an LLM-Agent Leaderboard Rank Actually Compare?
LLM-Agent 排行榜的排名究竟比较了什么？
Abstract
An LLM-agent leaderboard invites a familiar inference: an agent ranked above another is the better agent. Public evaluation logs may not support that conclusion when systems differ in task mixture, label source, release detail, or cost rule. We study what leaderboard scores estimate and when they justify pairwise superiority conclusions. Our estimand-aware pairwise procedure states the comparison target and measurement source, checks common support, and evaluates the supported difference using a stated uncertainty rule and practical margin. Controlled checks evaluate the decision labels under known finite-sample conditions and show why uncertainty must be included when judging sensitivity to target reweighting. Across SWE-bench, AgentRewardBench, and tau2-bench, close rank differences are often unresolved; proxy labels and utility rules can also change which system is selected. DataAgentBench and Open Agent show what remains estimable from coarser public records. A leaderboard score summarizes a released evaluation, whereas a fine-grained superiority claim additionally depends on the estimand and uncertainty rule used to interpret the difference.
Chinese Translation
LLM-agent 排行榜引发了一个熟悉的推断：排名高于其他代理的代理是更好的代理。然而，当系统在任务组合、标签来源、发布细节或成本规则上存在差异时，公共评估日志可能不支持这一结论。我们研究排行榜得分估计了什么，以及何时可以合理地得出成对优越性结论。我们的估计意识成对程序明确了比较目标和测量来源，检查了共同支持，并使用规定的不确定性规则和实际边际评估支持的差异。在已知有限样本条件下，控制检查评估决策标签，并展示了在判断对目标重加权的敏感性时为何必须考虑不确定性。在 SWE-bench、AgentRewardBench 和 tau2-bench 中，接近的排名差异往往是未解决的；代理标签和效用规则也可能改变选择哪个系统。DataAgentBench 和 Open Agent 展示了从粗略公共记录中仍然可以估计的内容。排行榜得分总结了发布的评估，而细粒度的优越性声明则额外依赖于用于解释差异的估计量和不确定性规则。
cs.AI / 119 / 2609.07803
Understanding the Impact of Model Pruning on Long-Tail Forgetting and Explanation Reliability in Medical Imaging
理解模型剪枝对医学影像中的长尾遗忘和解释可靠性的影响
Abstract
Model pruning is widely used to compress deep neural networks, reducing memory and computational requirements with minimal impact on aggregate performance. However, its effect on model behavior remains poorly understood, particularly for long-tailed medical datasets where rare but clinically important conditions are underrepresented. Furthermore, it remains unclear whether pruned models preserve reliable explanations of their predictions. To address this gap, we present a systematic study of long-tail forgetting and explanation reliability under model pruning. Across two long-tailed medical imaging datasets, two CNN architectures, four pruning methods, and sparsity levels up to 95\%, we evaluate predictive performance, explanation stability, and explanation faithfulness. Our results show that predictive performance exhibits a strong frequency-dependent trend, with lower-frequency classes generally experiencing earlier and larger degradation than higher-frequency classes. In contrast, explanation stability and faithfulness are influenced primarily by the pruning strategy, with gradient-informed methods preserving explanation reliability more effectively under aggressive compression. Qualitative and mechanistic analyses further indicate that explanation degradation is primarily associated with the collapse of class-discriminative gradients rather than the disappearance of feature activations. These findings suggest that model compression should be evaluated beyond aggregate performance. Incorporating class-aware and explanation-aware evaluation reveals failure modes that would otherwise remain hidden, while moderate sparsity levels provide a practical balance between compression, predictive performance, and explanation reliability.
Chinese Translation
模型剪枝广泛用于压缩深度神经网络，减少内存和计算需求，同时对整体性能的影响最小。然而，其对模型行为的影响仍然不够明确，尤其是在长尾医学数据集中，稀有但临床重要的病症往往被低估。此外，剪枝后的模型是否能保持其预测的可靠解释也仍不清楚。为了解决这一问题，我们对模型剪枝下的长尾遗忘和解释可靠性进行了系统研究。在两个长尾医学影像数据集上，使用两种卷积神经网络（CNN）架构、四种剪枝方法以及高达95%的稀疏度，我们评估了预测性能、解释稳定性和解释可信度。我们的结果表明，预测性能表现出强烈的频率依赖趋势，低频类别通常比高频类别经历更早和更大的性能下降。相反，解释的稳定性和可信度主要受到剪枝策略的影响，梯度信息驱动的方法在激进压缩下更有效地保持了解释的可靠性。定性和机制分析进一步表明，解释的退化主要与类别区分梯度的崩溃相关，而不是特征激活的消失。这些发现表明，模型压缩的评估应超越整体性能。结合类别感知和解释感知的评估揭示了否则会被隐藏的失败模式，而适度的稀疏度水平提供了压缩、预测性能和解释可靠性之间的实用平衡。
cs.AI / 120 / 2609.07879
Do Large Language Models Know What They Don't Know II? A Fully Behavioral, Non-Cognitive Measure of Epistemic Honesty
大型语言模型是否知道自己不知道什么 II？一种完全行为性、非认知的知识诚实度测量
Abstract
Large Language Models (LLMs) are frequently confident, eloquent, and well versed. A natural question arises: do they know what they don't know? To answer this question, we borrow the concept of epistemic honesty and develop a novel metric to systematically evaluate whether an LLM appropriately acknowledges the boundaries of its knowledge. In this work, we introduce the Epistemic Honesty Quotient (EHQ), which reports three observable sub-scores across two operational axes (epistemic restraint and substantive-answer calibration), and construct EHQ-3000, a 3,000-question benchmark spanning Fabricated Entity, Post-Cutoff Event, Hyper-Niche True, and Context-Conditioned Questions. From a frozen registry of 21 model API routes, 15 completed the protocol after endpoint and eligibility checks; 14 entered the confirmatory analysis because severe provider-side truncation made one route's score indeterminate. The study reveals substantial variation across models, including a difference that can not be explained by their capability to extract explicitly available information. Composite EHQ ranges from 0.31 to 0.81 across the analysed panel, despite near-ceiling performance on the document-grounded capability probe. The two restraint criteria overlap strongly under the present category composition, whereas substantive-answer calibration varies across models and does not reliably co-vary with restraint; however, the small panel leaves substantial uncertainty. Thus, EHQ reveals behavioral differences that are not visible to conventional correctness-based assessment, while also showing why dataset composition, provider behavior, and confidence elicitation must remain part of the interpretation.
Chinese Translation
大型语言模型（LLMs）通常表现出自信、口才流利且知识渊博的特征。由此产生一个自然的问题：它们是否知道自己不知道什么？为了解答这个问题，我们借用了知识诚实度的概念，并开发了一种新颖的指标，以系统地评估LLM是否恰当地承认其知识的边界。在本研究中，我们引入了知识诚实度商（Epistemic Honesty Quotient, EHQ），该商报告了两个操作轴（知识克制和实质性答案校准）下的三个可观察子分数，并构建了EHQ-3000，这是一个涵盖虚构实体、截止事件后、超小众真实和上下文条件问题的3,000个问题的基准。从21个模型API路由的冻结注册中，15个在端点和资格检查后完成了协议；由于严重的提供方截断，使得一个路由的得分不确定，14个进入了确认分析。研究揭示了模型之间的显著差异，包括一种无法用它们提取显式可用信息的能力来解释的差异。在分析的小组中，复合EHQ的范围从0.31到0.81，尽管在文档基础能力探测中表现接近上限。当前类别组成下，两项克制标准之间存在强重叠，而实质性答案校准在模型之间变化，且与克制并不可靠地共同变化；然而，小组的规模带来了相当大的不确定性。因此，EHQ揭示了传统基于正确性的评估所无法察觉的行为差异，同时也表明数据集组成、提供者行为和信心引导必须继续成为解释的一部分。
cs.AI / 121 / 2609.07893
Explainable Temporal Attention-based Defect Detection For Fillet Joints in Real-Time Gas Metal Arc Welding Based on Multi-modal Data
基于多模态数据的可解释时序注意力缺陷检测在实时气体金属弧焊中的应用：以角焊缝为例
Abstract
Deep learning is an efficient technique to monitor the real time welding process, reducing post-welding repairs and production delays. This paper leverages the monitoring capability by proposing a multi modal temporal attention based deep learning defect detection model for internal defects that are challenging to detect, including porosity, lack of penetration and fusion, undercut, and cold lap during Gas Metal Arc Welding in fillet joints. The model is trained on collected welding images and sound data from an industrial collaborative welding robot. The results show that the attention module can improve the F1 Score to 0.99. We use explainable Artificial Intelligence to interpret the proposed models behavior and dataset distribution, determining potential important areas in image and sound spectrograms and preferred modality to detect each defect. This improves trust and reliability in Artificial Intelligence driven welding inspection.
Chinese Translation
深度学习是一种高效的技术，用于监控实时焊接过程，从而减少焊后修复和生产延误。本文通过提出一种基于多模态时序注意力的深度学习缺陷检测模型，利用监测能力，针对气体金属弧焊中角焊缝内难以检测的缺陷（包括气孔、未熔透、未融合、切边和冷搭接）进行检测。该模型在从工业协作焊接机器人收集的焊接图像和声音数据上进行训练。结果表明，注意力模块可以将F1分数提高到0.99。我们使用可解释人工智能来解释所提模型的行为和数据集分布，确定图像和声音谱图中潜在的重要区域以及检测每种缺陷的首选模态。这增强了对基于人工智能的焊接检测的信任和可靠性。
cs.AI / 122 / 2609.07901
Quantization Amplifies Determinism, Not Bias: Scale-Dependent Behavioral Effects of Serving-Time Weight Compression
量化放大确定性，而非偏见：服务时间权重压缩的尺度依赖行为效应
Abstract
Weight quantization largely determines the economics of serving open-weight LLMs. Its costs are usually assessed with capability benchmarks, on which 4-bit quantization of mid-sized models is often considered "nearly free." We examine a different question: when several answers are valid, does quantization change what a model chooses to say? We serve three checkpoints (Qwen3-8B/14B/32B) at three weight precisions (W4A16 AWQ, W8A16 FP8-Marlin, and bf16), holding the hardware, software, and sampling configuration constant, and collect approximately 71,000 completions paired by prompt and seed across two custom, leak-checked prompt batteries. We pre-specified the analyses in three waves in version control. At 8B, int4 reduces output diversity: the probability that two samples for the same scenario recommend the same brand increases by 5.1 percentage points (prompt-paired sign-flip test, Holm p = .023; reproduced at +4.4pp on a full regeneration of the arm), and lexical diversity falls substantially (TTR -0.011, standardized effect -0.51; robust to a length-controlled measure). At 14B and 32B, no content-concentration measure reaches significance; instead, stylistic drift emerges (em-dash rate +0.46/1k words at 14B and +0.61/1k at 32B, both Holm p <= .0024). Pre-specified tests of stereotype direction are null at every scale: outputs concentrate on the modal answer for each prompt rather than on stereotypical answers. Mechanistically, the token-level distribution becomes flatter (decision-token entropy +0.091 bits, p = .015) while the semantic distribution, measured directly from first-token log probabilities, becomes more concentrated (collision +2.6pp, p = .023): individual tokens become less predictable even as meanings become more repetitive. At 8B, the smallest size tested, AWQ-int4 serving measurably narrows the range of suggestions; audits should assess concentration as well as bias.
Chinese Translation
权重量化在很大程度上决定了开放权重大型语言模型（LLMs）的经济性。其成本通常通过能力基准进行评估，在这些基准上，中型模型的4位量化通常被认为是“几乎免费的”。我们探讨一个不同的问题：当多个答案有效时，量化是否会改变模型选择的表达？我们在三种权重精度（W4A16 AWQ、W8A16 FP8-Marlin和bf16）下服务三个检查点（Qwen3-8B/14B/32B），保持硬件、软件和采样配置不变，并在两个自定义、经过泄漏检查的提示电池中收集约71,000个与提示和种子配对的完成结果。我们在版本控制中预先指定了三轮分析。在8B模型中，int4降低了输出多样性：对于相同场景的两个样本推荐相同品牌的概率增加了5.1个百分点（提示配对符号翻转测试，Holm p = .023；在整个臂的完全再生中再现为+4.4pp），而词汇多样性显著下降（TTR -0.011，标准化效应 -0.51；对长度控制的测量具有稳健性）。在14B和32B模型中，没有内容集中度的测量达到显著性；相反，出现了风格漂移（在14B中每千个单词的破折号使用率增加0.46，在32B中增加0.61，均为Holm p <= .0024）。对刻板印象方向的预先指定测试在每个规模上均为无效：输出集中在每个提示的模态答案上，而非刻板印象答案。从机制上讲，令牌级分布变得更加平坦（决策令牌熵增加0.091比特，p = .015），而语义分布（直接从第一个令牌的对数概率测量）变得更加集中（碰撞增加2.6pp，p = .023）：尽管含义变得更加重复，单个令牌的可预测性却降低。在8B模型中，测试的最小规模，AWQ-int4服务显著缩小了建议的范围；审计应评估集中度以及偏见。
cs.AI / 123 / 2609.07910
PRIMUS: Identity, Governance, and Verification for Multi-Agent Federations
PRIMUS：多智能体联盟的身份、治理与验证
Abstract
Multi-agent federations need governance that answers three questions under adversarial conditions: who participated (identity), did they conform (enforcement), and who decides (authority). A separate question is whether the verification machinery that polices a federation's outputs can also steer a generate-and-test loop toward better answers. Part I. PRIMA introduced prime-power agent identity and a consensus token whose factorization indexes participation, but assumed honest agents. We present PRIMUS, which couples prime-power identity with BLS aggregate signatures (PIAC), derives a safe-kill threshold that reduces false-positive agent termination from 80% to 0.00% under 10% channel noise, gives the closed-form economic boundary where singleton governance outperforms Byzantine quorum ($\gamma^* \approx 9f$, verified flat across n = 50 to 10,000), and specifies VRF succession with lease and fencing that makes safety unconditional under partial synchrony. Five problems are identified as provably unfixable within the model and stated as scope boundaries. Part II. A verifier is not a solver. We ask whether PRIMA's binary artifact-fidelity verdict can be converted into a graded fitness signal, and measure the conversion on binary covering codes. Calibration against injected fault burden is strong ($\rho$ = 0.676 deterministic, 0.819 full); against real LLM-generated candidates the same scores fall to 0.158 and 0.406, roughly a quarter of the calibration value (the same-designer confound, measured). As a pre-filter it beats a random-score control convincingly and a binary gate narrowly. Under 400 iterations of explicit optimization it was not gamed, but only because the objective saturated after one honest answer. A cross-family judge preserves the burden-ordering signal while destroying individual judgments. No covering-code record resulted. Measured program cost: USD 164.78.
Chinese Translation
多智能体联盟需要在对抗条件下回答三个治理问题：谁参与了（身份），他们是否遵守（执行），以及谁来决定（权威）。另一个问题是，监督联盟输出的验证机制是否也能引导生成与测试循环朝向更好的答案。第一部分。PRIMA 引入了素数幂代理身份和一个共识令牌，其因式分解索引参与情况，但假设代理是诚实的。我们提出了 PRIMUS，它将素数幂身份与 BLS 聚合签名（PIAC）结合，推导出一个安全终止阈值，在 10% 信道噪声下将误判终止代理的比例从 80% 降低到 0.00%，给出了单一治理优于拜占庭法定 quorum 的闭合经济边界（$ ext{γ}^* ext{≈} 9f$，在 n = 50 到 10,000 中验证为平坦），并指定了 VRF 继承与租赁和围栏，使得在部分同步下安全性无条件。识别出五个在模型内被证明无法修复的问题，并将其表述为范围边界。第二部分。验证者不是求解者。我们探讨 PRIMA 的二元工件保真判决是否可以转换为分级适应度信号，并在二元覆盖码上测量转换。与注入故障负担的校准结果强烈（$
ho$ = 0.676 确定性，0.819 完全）；与真实的 LLM 生成候选相比，同样的得分降至 0.158 和 0.406，约为校准值的四分之一（测量的同设计者混淆）。作为预过滤器，它显著优于随机得分控制，并且在二元门限上略胜一筹。在 400 次显式优化迭代中没有被操控，但仅仅因为在一个诚实答案后目标饱和。跨家族评判者保留了负担排序信号，同时破坏了个体判断。没有产生覆盖码记录。测量的程序成本：164.78 美元。
cs.AI / 124 / 2609.07925
FrogNano: Training a 4B Coding Agent via Online Task Synthesis
FrogNano：通过在线任务合成训练一个4B编码代理
Abstract
We present FrogNano, a 4B coding agent designed to tackle software engineering (SWE) tasks efficiently and effectively, even under resource-constrained environments. It is post-trained exclusively via RL on around 1,500 SWE environments with synthetic tasks. A key ingredient for improving performance is an online task synthesis pipeline that creates tasks calibrated to the frontier of learnability for the current checkpoint. This report provides evidence that competitive small coding agents can be trained with synthetic tasks alone, without traditional distillation from larger models, and that generating tasks at the learnability frontier of the current agent is important. We report details on the training methodology, evaluations across diverse environments, and in-depth analyses, serving as a foundation for our ongoing exploration of lightweight yet capable coding agents that can run on minimal hardware.
Chinese Translation
我们提出了FrogNano，一个设计用于高效且有效地处理软件工程（SWE）任务的4B编码代理，即使在资源受限的环境下也能表现良好。它通过强化学习（RL）在大约1500个具有合成任务的SWE环境中进行后续训练。提升性能的一个关键因素是在线任务合成管道，该管道创建与当前检查点的可学习性前沿相匹配的任务。本报告提供了证据，表明仅通过合成任务就可以训练出具有竞争力的小型编码代理，而无需从更大模型中进行传统的蒸馏，并且在当前代理的可学习性前沿生成任务是重要的。我们报告了训练方法的细节、在多样化环境中的评估以及深入分析，为我们正在进行的探索轻量且高效的编码代理奠定基础，这些代理能够在最小硬件上运行。
cs.AI / 125 / 2609.07943
Beliefs and Behavior in Language Models
语言模型中的信念与行为
Abstract
There is significant uncertainty about whether abstractions like beliefs or desires usefully describe the behavior of large language models (LLMs). In addition to the inherent scientific interest of this question, these latent quantities are often invoked to explain the behavior of LLMs to users or to define and evaluate harmful behaviors which are relative to intent. Nevertheless, we currently lack a means to systematically test whether concepts like "belief" are well-applied to LLMs, and hence whether they are likely to be fruitful ingredients of attempts to align models with human interests. We propose an approach for empirically studying such questions, asking whether a single latent variable inferred from the LLMs' outputs -- interpreted as a degree of belief -- allows an observer to make interpretable predictions of how the LLMs' will respond to new prompts. We find that highly capable models are usefully described as holding beliefs and that, generally, the predictability of model outputs based on an inferred latent belief tracks overall trends in model capability. Building on these findings, we provide empirical strategies to study how beliefs in LLMs can be measured, the extent to which LLMs comply with instructed decision rules or payoffs, and how beliefs evolve within individual instances of an LLM over the course of reasoning.
Chinese Translation
关于信念或欲望等抽象概念是否能够有效描述大型语言模型（LLMs）的行为存在显著的不确定性。除了这一问题本身的科学兴趣外，这些潜在变量通常被用来向用户解释LLMs的行为，或定义和评估相对于意图的有害行为。然而，我们目前缺乏系统测试“信念”等概念是否适用于LLMs的手段，因此也无法判断它们是否可能成为将模型与人类利益对齐的有效要素。我们提出了一种实证研究此类问题的方法，询问从LLMs输出中推断出的单一潜在变量——被解释为信念的程度——是否允许观察者对LLMs如何响应新提示做出可解释的预测。我们发现，高能力模型可以有效地被描述为持有信念，并且一般来说，基于推断出的潜在信念对模型输出的可预测性与模型能力的整体趋势相一致。在这些发现的基础上，我们提供了实证策略，以研究LLMs中的信念如何被测量，LLMs在多大程度上遵循指令决策规则或收益，以及信念在LLM个体实例中的推理过程中如何演变。
cs.AI / 126 / 2609.07944
CausalVerify: An Execution-Grounded Benchmark for LLM Causal Inference Workflows
CausalVerify：一个基于执行的 LLM 因果推断工作流程基准
Abstract
Existing causal-inference benchmarks for LLMs mostly score method descriptions or whether generated code runs, not whether the executed workflow recovers the target causal estimate. CausalVerify studies this verification problem for structured econometric causal-estimation workflows by separating realistic interpretation from verifiable computation. It pairs 259 published economics papers (reconstructed research question, data description, institutional context) with 100 fixed-seed synthetic scenarios that realise CSV datasets for difference-in-differences, event study, instrumental variables, and regression discontinuity designs. Experiment A (real-paper text agreement) scores method-family and direction agreement against four-LLM consensus labels. Experiment B (synthetic execution) runs model-written R code and checks whether the extracted treatment-effect estimate matches a canonical estimator on the same realised dataset; this execution-grounded correctness layer is L2b+, distinct from L2b, which records only whether the code executes. A calibration arm asks whether self-reported confidence separates correct from incorrect workflows. On Experiment B, seven LLMs reach L2b+ pass rates of 10% to 88% at the default 50% tolerance, and 66 of the 426 workflows that execute (15.5%) return a wrong estimate. Execution ranking (L2b) agrees with L2b+ far better than text-direction scoring (L4): Kendall $\tau=0.81$ and Spearman $\rho=0.93$, versus Kendall $\tau$ between $-0.20$ and $0.10$ for L4. Llama-3.3-70B-Instruct shows the same qualitative gap, and reported confidence does not reliably separate correct from incorrect workflows. The claims are confined to standardized single-shot workflows in these four design families under the evaluated R backend and model panel; the benchmark does not measure general causal-inference ability. Code, data, cached outputs, and a datasheet are released.
Chinese Translation
现有的 LLM 因果推断基准主要评估方法描述或生成代码是否能够运行，而不是执行的工作流程是否能够恢复目标因果估计。CausalVerify 研究了这一验证问题，针对结构化的计量经济学因果估计工作流程，通过将现实解释与可验证计算分开来进行分析。它将 259 篇已发表的经济学论文（重构的研究问题、数据描述、机构背景）与 100 个固定种子合成场景配对，这些场景实现了用于差异中的差异（difference-in-differences）、事件研究、工具变量（instrumental variables）和回归不连续性设计（regression discontinuity designs）的 CSV 数据集。实验 A（真实论文文本一致性）评估方法类别和方向一致性，与四个 LLM 的共识标签进行比较。实验 B（合成执行）运行模型生成的 R 代码，并检查提取的处理效应估计是否与同一实现数据集上的经典估计量匹配；这一基于执行的正确性层次为 L2b+，与仅记录代码是否执行的 L2b 不同。一个校准环节询问自我报告的信心是否能区分正确与错误的工作流程。在实验 B 中，七个 LLM 的 L2b+ 通过率在默认的 50% 容忍度下达到 10% 到 88%，而 426 个执行的工作流程中有 66 个（15.5%）返回了错误的估计。执行排名（L2b）与 L2b+ 的一致性远好于文本方向评分（L4）：Kendall $ au=0.81$ 和 Spearman $
ho=0.93$，而 L4 的 Kendall $ au$ 在 $-0.20$ 到 $0.10$ 之间。Llama-3.3-70B-Instruct 显示出相同的定性差距，且报告的信心并不能可靠地区分正确与错误的工作流程。上述结论仅限于在评估的 R 后端和模型面板下的这四个设计类别中的标准化单次工作流程；该基准并不测量一般的因果推断能力。代码、数据、缓存输出和数据表已发布。
cs.AI / 127 / 2609.07954
Support Topology and Gradient Mixing in Sinkhorn Layers
Sinkhorn 层中的支持拓扑与梯度混合
Abstract
Sparse Sinkhorn layers use a fixed support graph to restrict transport between tokens. How does this graph control gradient propagation through the scaling iterations. We develop a fixed-support calculus showing that each row-column cycle induces a row-stochastic operator on column-potential perturbations modulo constants. Its transpose propagates zero-mass reverse-mode cotangents. The finite-cycle operator uses two distinct half-step transport plans; at a balanced fixed point it reduces to a two-step walk determined by a single plan. We derive the accompanying score and marginal source terms and use Dobrushin contraction and minorization to bound homogeneous and source-driven tail cotangents. Our main result characterizes when support and marginals guarantee one-step contraction uniformly over finite scores: every feasible face of the transportation polytope must have pairwise two-hop column overlap. Otherwise, suitable score directions make the contraction coefficient arbitrarily close to one. We extend this analysis to ordered support schedules and derive certificates for partition heat-bath layers, coordinate sweeps, forced shared mass, and register-augmented supports. These results provide mathematical criteria for support design in differentiable transport layers, with guarantees restricted to the fixed-support quotient-gradient component.
Chinese Translation
稀疏 Sinkhorn 层使用固定支持图来限制代币之间的传输。这个图如何控制通过缩放迭代的梯度传播？我们开发了一种固定支持微积分，表明每个行列循环在列势扰动的常数模下诱导一个行随机算子。其转置传播零质量的反向模式余切量。有限循环算子使用两个不同的半步传输计划；在一个平衡的固定点上，它简化为由单一计划决定的两步行走。我们推导了伴随的得分和边际源项，并使用 Dobrushin 收缩和小化来界定均匀和源驱动的尾余切量。我们的主要结果描述了何时支持和边际保证在有限得分上均匀的一步收缩：每个可行的运输多面体的面必须具有成对的两跳列重叠。否则，合适的得分方向使得收缩系数可以任意接近于一。我们将这一分析扩展到有序支持调度，并推导出分区热浴层、坐标扫掠、强制共享质量和注册增强支持的证书。这些结果为可微分传输层中的支持设计提供了数学标准，保证仅限于固定支持商梯度分量。
cs.AI / 128 / 2609.07984
From Event Logs to Governed Action: A BlueSky Agenda for Agentic Process Mining
从事件日志到受控行动：面向代理过程挖掘的蓝天议程
Abstract
Process mining has long turned event logs into process knowledge: discovered models, conformance evidence, bottleneck diagnoses, and runtime predictions. Agentic AI changes the target. Process-aware agents will not only ask what happened. They will ask whether a proposed action should be taken, given the available evidence, privacy budget, organizational authority, and downstream risk. This BlueSky paper proposes event-to-action process mining: a process-mining agenda for transforming heterogeneous operational event data into governed action. The goal is not another dashboard, a generic enterprise simulator, or a language interface over logs. We argue that the community needs four mineable artifacts: event-object representations, action evidence packages, governance contracts, and benchmarks where act, defer, ask, and refuse are all valid outputs. This agenda is timely because agentic business process management (BPM), LLM-assisted process mining, object-centric event standards, causal process monitoring, and privacy-preserving learning are maturing separately. Bringing them together defines a data-mining target inside process mining: mining logged organizational behavior for accountable action, not only retrospective insight.
Chinese Translation
过程挖掘长期以来将事件日志转化为过程知识：发现模型、一致性证据、瓶颈诊断和运行时预测。代理人工智能改变了目标。过程感知代理不仅会询问发生了什么，还会询问在可用证据、隐私预算、组织权限和下游风险的基础上，是否应采取建议的行动。本文提出了事件到行动的过程挖掘：一种将异构操作事件数据转化为受控行动的过程挖掘议程。我们的目标不是另一个仪表盘、一个通用企业模拟器或一个日志语言接口。我们认为，社区需要四种可挖掘的工件：事件-对象表示、行动证据包、治理合同，以及“行动”、“延迟”、“询问”和“拒绝”都是有效输出的基准。这一议程是及时的，因为代理商业过程管理（BPM）、基于大语言模型的过程挖掘、面向对象的事件标准、因果过程监控和隐私保护学习正在各自成熟。将它们结合起来定义了过程挖掘中的数据挖掘目标：挖掘记录的组织行为以实现负责任的行动，而不仅仅是回顾性的洞察。
cs.AI / 129 / 2609.07987
When Can LLM Digital Twins Reduce Human Measurement? From Behavioral Fidelity to Statistical Substitutability
何时可以通过 LLM 数字双胞胎减少人类测量？从行为保真度到统计可替代性
Abstract
LLM-based digital twins promise to reduce repeated human data collection by generating person- specific responses, yet existing evaluations provide little evidence about whether they can reduce human measurement while preserving valid inference. To address this, we introduce statistical substitutability, an inferential criterion that evaluates the extent to which twin predictions can reduce human measurement for a particular estimand while preserving valid inference. We develop a framework, grounded in mixed-subject and prediction-powered inference, that evaluates statistical substitutability along four dimensions: aggregate fidelity, paired respondent-level signal, finite-sample human-label recovery, and stability across populations. Across two empirical evaluations spanning behavioral experiments, multiple models, and alternative respondent representations, we find that digital twins can reproduce average human effects while providing little information about which individuals differ from those averages. Newer models and richer respondent information improve some dimensions of performance but do not reliably translate into human-data savings. Human calibration can reduce aggregate prediction error, yet limited labeled samples often fail to produce stable precision gains. Importantly, these findings demonstrate that behavioral fidelity is neither necessary nor sufficient for statistical substitutability. More broadly, they suggest that AI-generated evidence should be evaluated based on its ability to support valid scientific inference rather than its ability to reproduce human outcomes alone. Digital twins should therefore be judged for confirmatory use by whether they reduce uncertainty about human quantities, not merely by whether they reproduce human means, distributions, or effects.
Chinese Translation
基于 LLM 的数字双胞胎承诺通过生成个体特定的响应来减少重复的人类数据收集，然而现有评估提供的证据很少，无法证明它们是否能够在保持有效推断的同时减少人类测量。为了解决这个问题，我们引入了统计可替代性，这是一种推断标准，用于评估双胞胎预测在保持有效推断的情况下，能够在多大程度上减少特定估计量的人类测量。我们开发了一个框架，该框架基于混合主体和预测驱动的推断，从四个维度评估统计可替代性：总体保真度、配对受访者级信号、有限样本的人类标签恢复以及跨人群的稳定性。在涵盖行为实验、多种模型和替代受访者表现的两项实证评估中，我们发现数字双胞胎能够再现平均人类效应，但对哪些个体与这些平均值不同提供的信息有限。更新的模型和更丰富的受访者信息改善了某些维度的表现，但并未可靠地转化为人类数据的节省。人类校准可以减少总体预测误差，但有限的标记样本往往未能产生稳定的精度提升。重要的是，这些发现表明，行为保真度既不是统计可替代性的必要条件，也不是充分条件。更广泛地说，它们表明，AI 生成的证据应根据其支持有效科学推断的能力进行评估，而不仅仅是其重现人类结果的能力。因此，数字双胞胎应通过是否减少对人类量的的不确定性来判断其确认性使用，而不仅仅是通过是否重现人类的均值、分布或效应。
cs.AI / 130 / 2609.07998
Mini-Batch Risk-Averse Deep Q-Learning: A Robot Navigation Case Study
小批量风险厌恶深度 Q 学习：一个机器人导航案例研究
Abstract
We study the control of Markov decision processes in which the quality of a policy is evaluated by a dynamic, time-consistent Markov risk measure rather than by an expected discounted cost. The main obstacle to combining such measures with reinforcement learning is that a transition risk mapping depends on the transition kernel in a nonlinear way, and therefore cannot be estimated from a single observed transition. We remove this obstacle by employing mini-batch transition risk mappings: the mapping is applied to the empirical measure of $N$ independent next-state samples, and the result is averaged. The resulting mapping is again coherent. However, as an expected value of a function of $N$ next-state values, it admits an unbiased one-sample estimator. We embed this mapping into a double deep Q-network, analyze the two sources of estimation bias that arise, and obtain a risk-averse Q-learning method applicable to state spaces far beyond the reach of tabular schemes. The method is applied to an underwater robot navigation problem, in which a vehicle must visit collection points, gather stochastic information payloads, and deliver them at transmission points, while exposed at each step to the risk of destruction. A hierarchical decomposition delegates path execution to an exact graph search and confines learning to the high-level ``collect or transmit'' decision. A low-dimensional feature map, invariant under the symmetries of the problem, replaces the raw state--configuration encoding. In experiments on $300$ held-out environments, the resulting policies transfer to instance sizes never seen in training, and already $N=2$ reduces the upper semideviation of the outcome distribution while simultaneously improving its mean whenever the simulator is misspecified---an empirical counterpart of the duality between coherent risk measures and distributional robustness.
Chinese Translation
我们研究了马尔可夫决策过程的控制，其中策略的质量是通过动态的、时间一致的马尔可夫风险度量来评估，而不是通过期望折现成本。将这种度量与强化学习结合的主要障碍在于，转移风险映射以非线性方式依赖于转移核，因此无法从单个观察到的转移中进行估计。我们通过采用小批量转移风险映射来消除这一障碍：该映射应用于 $N$ 个独立下一状态样本的经验度量，并对结果进行平均。得到的映射再次是一致的。然而，作为 $N$ 个下一状态值的函数的期望值，它允许一个无偏的单样本估计器。我们将该映射嵌入到双重深度 Q 网络中，分析由此产生的两种估计偏差来源，并获得一种适用于远超表格方案的状态空间的风险厌恶 Q 学习方法。该方法应用于一个水下机器人导航问题，其中车辆必须访问集合点，收集随机信息载荷，并在传输点交付这些载荷，同时在每一步都面临被摧毁的风险。分层分解将路径执行委托给精确的图搜索，并将学习限制在高层的“收集或传输”决策上。一个低维特征映射，保持在问题的对称性下不变，取代了原始状态-配置编码。在对 $300$ 个保留环境的实验中，得到的策略能够迁移到训练中从未见过的实例规模，并且当模拟器被错误指定时，$N=2$ 已经减少了结果分布的上半偏差，同时在改善其均值。这个结果是一致风险度量与分布鲁棒性之间对偶性的经验对应。
cs.AI / 131 / 2609.08003
Sparks of In Silico Cognitive Science: Theories from Simulated Data Can Generalize to Humans
计算认知科学的火花：基于模拟数据的理论如何推广到人类
Abstract
Behavioral foundation models have been proposed as stand-ins for human participants across settings, but it is unclear whether theories discovered on them generalize to humans or merely characterize the simulator. We ran the Automated Cognitive Scientist (\textsc{AutoCog}), a closed-loop discovery system in which LLM agents design theory-discriminating experiments, collect responses, arbitrate between competing theories, and synthesize successors, entirely on behavior simulated by Centaur, a foundation model of human behavior. In a multi-attribute decision-making setting, the theories \textsc{AutoCog} found on Centaur generalized to human data: they outperformed canonical theories on ten held-out experiments and were rivaled only by theories found by running the same loop on people. We argue that this succeeds despite the simulator's inevitable imperfections because a discovery loop that arbitrates between competing theories demands less of its simulator than estimation does. The simulator only needs to capture the regularities that distinguish the theories, and not necessarily reproduce behavior precisely. Imperfect simulators can therefore widen the search over theories, with human data then testing whether the surfaced theories generalize.
Chinese Translation
行为基础模型被提出作为人类参与者在不同环境中的替代品，但尚不清楚在这些模型上发现的理论是否能够推广到人类，还是仅仅描述了模拟器。我们运行了自动化认知科学家（Automated Cognitive Scientist, extsc{AutoCog}），这是一个闭环发现系统，其中大型语言模型（LLM）代理设计理论区分实验、收集响应、在竞争理论之间进行仲裁，并合成后继理论，完全基于由人类行为基础模型Centaur模拟的行为。在多属性决策设置中， extsc{AutoCog}在Centaur上发现的理论能够推广到人类数据：它们在十个保留实验中超越了经典理论，仅与在真实人类上运行相同循环发现的理论相抗衡。我们认为，尽管模拟器不可避免地存在缺陷，这一成功依然得以实现，因为一个在竞争理论之间进行仲裁的发现循环对模拟器的要求低于估计。模拟器只需捕捉区分理论的规律，而不必精确再现行为。因此，不完美的模拟器可以扩大理论的搜索范围，而人类数据则用于检验浮现的理论是否能够推广。
cs.AI / 132 / 2609.08015
From Version Conflicts to Decision Conflicts: Selective Revalidation for Long-Running AI Agents
从版本冲突到决策冲突：长时间运行的人工智能代理的选择性重新验证
Abstract
Long-running AI agents may read state, reason, wait for tools or human approval, and perform an external action much later. The state that justified the action can change in the meantime. For example, after an agent proposes an 80 GBP refund under a limit of 100, a customer-name change affects only presentation metadata, a new limit of 90 still permits the refund, a limit of 50 invalidates it, and a refund issued by another worker must prevent a duplicate. Standard optimistic concurrency control and version checks can detect that previously read state has changed, but by themselves do not determine whether that change invalidates the pending action's justification. We call any detected version change a version conflict; when that change invalidates the action's justification, it is also a decision conflict. ATR records the explicit, executable conditions that justify a pending action and rechecks only the conditions affected by a change before releasing the external operation. It can retain the action, refresh non-decisive metadata, require replanning, or block execution; a target-side transaction or compare-and-set binds checked state to commit. Across 210,000 controlled executions over 15 mutation cases, ATR matched every developer-specified outcome with no false allows or blocks. In ten durable SQLite checkpoint/resume cells, it evaluated 0.6 conditions per change versus 6.0 for FullScan. At 4,093 recorded reads, ATR took 9.3 microseconds versus 2595.9 microseconds for FullScan. These deterministic results establish controlled feasibility, not production generality or automatic extraction of the required conditions.
Chinese Translation
长时间运行的人工智能代理可能会读取状态、进行推理、等待工具或人类批准，并在很久之后执行外部操作。在此期间，支持该操作的状态可能会发生变化。例如，在代理提出80英镑的退款请求时，客户名称的更改仅影响展示元数据，新的90英镑限制仍然允许退款，而50英镑的限制则使其无效，其他工作人员发出的退款必须防止重复。标准的乐观并发控制和版本检查可以检测到先前读取的状态已发生变化，但仅凭这些并不能确定该变化是否使待处理操作的理由失效。我们将任何检测到的版本变化称为版本冲突；当该变化使操作的理由失效时，它也被称为决策冲突。ATR记录了支持待处理操作的明确可执行条件，并在释放外部操作之前，仅重新检查受变化影响的条件。它可以保留该操作、刷新非决定性元数据、要求重新规划或阻止执行；目标侧事务或比较与设置将检查的状态绑定到提交。在对15个变异案例进行的210,000次受控执行中，ATR与每个开发者指定的结果相匹配，没有错误的允许或阻止。在十个持久的SQLite检查点/恢复单元中，它每次变化评估0.6个条件，而FullScan则为6.0个。在4093次记录读取中，ATR的耗时为9.3微秒，而FullScan为2595.9微秒。这些确定性的结果确立了受控的可行性，而不是生产通用性或所需条件的自动提取。
cs.AI / 133 / 2609.08016
A Layered Analysis of Disagreement And Answer Quality in Multi-Agent LLM Debate
多智能体大型语言模型辩论中分歧与答案质量的分层分析
Abstract
Multi-agent debate, in which several LLMs exchange arguments before answering, is widely assumed to improve answer quality by surfacing genuine disagreement. That mechanism is rarely checked. We introduce four measurements: (A) the agreement a debater reports; (B) whether its reply text actually pushes back; (C) whether the position persists once the eliciting instruction is removed; and (D) for open-weight models, the stance response in the debater's own token log-probabilities. We evaluate three-model committees debating open-ended GlobalOpinionQA across 750 debates under three tones: friendly (seek common ground), neutral, and hostile (stress-test every position). (A) Tone strongly reshapes reported agreement: full agreement differs by 50.4 percentage points between the friendly and hostile endpoints. (B) A judge that reads only the reply text, never the self-report or the condition, recovers the same pattern. (C) The dissent appears partly tied to the instruction that elicited it: labels revert toward agreement 23.1 points more often after deleting the hostile instruction than under a matched re-ask that keeps it; question-weighted inference is inconclusive on first-round turns alone (p=0.0625), significant pooling all rounds (p=0.016), and only 11/28 first-round reversions also appear in the reply text. (D) Opposing arguments weaken a debater's stance margin more consistently than they shift its direction. For final answers we detect no quality gain: a bias-checked jury returns 299/299 ties (ruling out only large differences), accuracy on a verifiable control task is unchanged, and a jury without the bias check had declared debate the winner 66% of the time -- an artifact of reading order. Taken together, LLM debate readily changes what agents say, but we find much weaker evidence that it changes what they persistently endorse or improves the quality of the final answer.
Chinese Translation
多智能体辩论中，多个大型语言模型（LLMs）在给出答案前交换论点，普遍被认为能够通过揭示真实分歧来提升答案质量。然而，这一机制很少被验证。我们引入了四项测量指标：（A）辩手报告的意见一致度；（B）其回复文本是否真正进行了反驳；（C）在移除引导指令后立场是否依然保持；（D）对于开放权重模型，辩手自身的token对数概率中的立场响应。我们评估了三个模型组成的委员会，在三种语气（友好——寻求共识、中立、敌对——对每个立场进行压力测试）下，就开放式GlobalOpinionQA进行了750场辩论。（A）语气显著影响报告的一致度：友好与敌对两端的完全一致度相差50.4个百分点。（B）仅阅读回复文本而不参考自我报告或条件的评判者，恢复了相同的模式。（C）异议部分依赖于引发它的指令：删除敌对指令后，标签向一致方向回归的比例比保持指令时高出23.1个百分点；仅基于第一轮发言的加权推断结果不显著（p=0.0625），汇总所有轮次后显著（p=0.016），且仅有11/28的第一轮立场逆转在回复文本中体现。（D）反对论点更常削弱辩手立场的边际强度，而非改变其方向。对于最终答案，我们未检测到质量提升：经过偏差校验的评审团判定299/299场平局（排除了大幅差异），可验证控制任务的准确率无变化，且未进行偏差校验的评审团曾在66%的情况下判定辩论获胜——这是一种阅读顺序的伪影。综上所述，大型语言模型辩论确实改变了代理的发言内容，但我们发现其对代理持续认可的立场或最终答案质量的提升证据较弱。
cs.AI / 134 / 2609.08025
Eliciting Self-Verification in Multimodal Reasoning Agents with Reinforcement Learning
通过强化学习引导多模态推理智能体的自我验证
Abstract
Reasoning agents increasingly rely on external tools such as web search to answer complex queries. Reinforcement learning (RL) finetuning algorithms such as GRPO have improved long-form reasoning in text-only language models, particularly for coding and mathematics. Reliable tool use in multimodal agents, however, remains challenging because models must interpret text and images while integrating noisy retrieved evidence, often under sparse outcome-level supervision without explicit verification signals. We present Self-Verification via Reinforcement Learning (SVRL), an RL-only finetuning framework that trains multimodal agents to verify and filter retrieved evidence within their own reasoning traces, reducing reliance on external verifiers at inference time. SVRL also introduces a search-aware penalty that discourages unnecessary tool calls and a query-diversity reward that encourages diverse, well-formed search queries, providing fine-grained feedback on when and what to search. Finetuning Qwen-2.5-VL-7B with SVRL on only 5{,}000 visual question answering examples yields consistent gains in multi-hop VQA generalization and tool efficiency across benchmarks. Overall, SVRL narrows the gap between compact agents and much larger proprietary models while requiring substantially lower training and inference cost.
Chinese Translation
推理智能体越来越依赖外部工具，如网络搜索，以回答复杂查询。然而，尽管强化学习（RL）微调算法如GRPO已在文本语言模型的长文本推理中取得了进展，尤其是在编码和数学领域，但在多模态智能体中可靠的工具使用仍然具有挑战性，因为模型必须在整合噪声检索证据的同时解释文本和图像，通常在缺乏明确验证信号的稀疏结果级监督下进行。我们提出了通过强化学习实现自我验证（Self-Verification via Reinforcement Learning, SVRL），这是一种仅基于RL的微调框架，旨在训练多模态智能体在其自身推理轨迹中验证和过滤检索证据，从而减少在推理时对外部验证者的依赖。SVRL还引入了一种搜索感知惩罚，旨在抑制不必要的工具调用，以及一种查询多样性奖励，鼓励多样化且结构良好的搜索查询，从而提供关于何时以及搜索什么的细致反馈。在仅使用5,000个视觉问答示例对Qwen-2.5-VL-7B进行SVRL微调后，在多跳视觉问答（VQA）泛化和工具效率方面在各基准测试中均取得了一致的提升。总体而言，SVRL缩小了紧凑型智能体与更大专有模型之间的差距，同时显著降低了训练和推理成本。
cs.AI / 135 / 2609.08062
ResidualAuth: What Authorization State Must Language Agents Preserve under Revocable Delegation?
ResidualAuth：语言代理在可撤销委托下必须保留的授权状态是什么？
Abstract
Tool-using language agents can delegate and revoke permissions while acting through external services. We show that two authorization histories can have identical current permissions and identical all-pairs reachability yet require opposite decisions after the same direct-edge revocation. We formalize the information needed to preserve such distinctions as a residual authorization state. We prove that exponentially many future-distinct states can share one fixed transitive closure, and give exact or tight asymptotic bounds on the state required by an exact monitor as delegation redundancy varies. ResidualAuth compiles these constructions into paired language-agent episodes. Across four open-weight models, a fixed 256-token summary solved 0-2/16 pairs, sham reads solved 0/16, and authenticated current-query reads solved 15-16/16. In a separate held-out online-memory diagnostic, exact ledger serializations fit all 128 four-coordinate pairs at both 768 and 1,024 tokens. At either cap, factually supported model-written memories sufficient for every prespecified continuation solved at most 1/128 pairs per model. A hard gate reduced eight observed unauthorized effects to zero without changing the preceding attempts. These results distinguish required authorization state, usable decision information, online state maintenance, and effect mediation.
Chinese Translation
使用工具的语言代理可以在通过外部服务执行操作时委托和撤销权限。我们展示了两种授权历史可以具有相同的当前权限和相同的全对可达性，但在相同的直接边缘撤销后却需要相反的决策。我们将保留这种区分所需的信息形式化为残余授权状态（residual authorization state）。我们证明了指数级的未来不同状态可以共享一个固定的传递闭包，并给出了随着委托冗余变化所需的确切监视器状态的精确或紧密渐近界限。ResidualAuth 将这些构造编译成配对的语言代理事件。在四个开放权重模型中，一个固定的256-token摘要解决了0-2/16对，虚假读取解决了0/16，而经过身份验证的当前查询读取解决了15-16/16。在一个单独的保留在线记忆诊断中，精确的账本序列化在768和1024 tokens下都适配了所有128个四坐标对。在任一上限下，事实支持的模型编写记忆足以满足每个预设的延续，最多解决每个模型1/128对。一个硬门将观察到的八个未授权效果减少为零，而不改变之前的尝试。这些结果区分了所需的授权状态、可用的决策信息、在线状态维护和效果调解。
cs.AI / 136 / 2609.08071
Automated Design of Inventory Policy with Large Language Models: An Exploratory Study
基于大型语言模型的库存政策自动设计：探索性研究
Abstract
Firms making inventory decisions have access to operational data, optimization tools, and large language models (LLMs). Typically, data characterize the operating environment, optimization selects parameters within a prespecified inventory policy class, and LLMs support coding and decision analysis. We develop an integrated framework that combines these resources to automate inventory policy design. Given demand data, the framework iteratively uses an LLM to generate parameterized policy classes and an external solver to optimize its parameters within each class. Across 30 lost-sales inventory instances, the mean cost reduction relative to optimized base-stock benchmarks increases from 17.5% after one generation to 30.0% after ten generations. Parameter optimization is central to this performance: an LLM-only variant performs substantially worse, whereas optimization-guided feedback improves policy quality, accelerates search, and directs the LLM toward better policy classes rather than merely better parameter values within a fixed class. The strongest discovered policies are also interpretable: they combine recognizable inventory-control motifs, including capped orders, discounted or weighted pipeline inventory, and threshold-based replenishment logic. The search thereby produces new policy-class functional forms that, to our knowledge, have not previously been studied in the lost-sales inventory literature. These functional forms are not specified ex ante but emerge from the search process. Moreover, after their parameters are re-optimized, three discovered policy classes achieve average cost reductions of 21.75% to 22.60% across 10,064 new inventory instances. Overall, the results show that data-driven parameter optimization can guide LLM-based search over a broad space of inventory policy classes and identify high-performing, interpretable, and transferable decision rules.
Chinese Translation
在进行库存决策的公司中，通常可以访问运营数据、优化工具和大型语言模型（LLMs）。通常，数据描述了运营环境，优化选择预设库存政策类别中的参数，而LLMs则支持编码和决策分析。我们开发了一个集成框架，将这些资源结合起来，以自动化库存政策设计。给定需求数据，该框架迭代地使用LLM生成参数化的政策类别，并使用外部求解器优化每个类别中的参数。在30个缺失销售的库存实例中，相对于优化的基础库存基准，平均成本降低从一代后的17.5%增加到十代后的30.0%。参数优化是这一性能的核心：仅使用LLM的变体表现明显较差，而优化指导的反馈提高了政策质量，加快了搜索速度，并引导LLM朝向更好的政策类别，而不仅仅是在固定类别内寻找更好的参数值。发现的最强政策也是可解释的：它们结合了可识别的库存控制模式，包括限制订单、折扣或加权的管道库存，以及基于阈值的补货逻辑。因此，搜索产生了新的政策类别功能形式，据我们所知，这些形式在缺失销售的库存文献中尚未被研究。这些功能形式并不是事先指定的，而是从搜索过程中产生的。此外，在其参数重新优化后，三种发现的政策类别在10,064个新库存实例中实现了平均成本降低21.75%至22.60%。总体而言，结果表明，数据驱动的参数优化可以指导基于LLM的搜索，覆盖广泛的库存政策类别，并识别出高效、可解释且可转移的决策规则。
cs.AI / 137 / 2609.08082
Inference-Time Nash Alignment
推理时的纳什对齐
Abstract
Preference-based fine-tuning methods such as RLHF and DPO require substantial compute and large preference datasets. They also need direct access to the model parameters which are not provided by many state-of-the art models. Inference-time alignment offers a cost-effective alternative without updating model parameters. However, existing inference-time methods rely on a scalar reward model derived under a Bradley-Terry assumption, which cannot represent general preferences. Following recent work on fine-tuning with generalized preferences, in this work, we initiate the study of inference-time alignment under general preferences. We formulate the problem as obtaining a Nash equilibrium of a two-player zero-sum game between policies. We propose two algorithms: Best-of-Nash (BoN) and Nash Mirror Descent (NMD). We prove that both algorithms achieve a duality gap that matches the problem lower bound. Empirically, we implement the two methods on three datasets, which shows that our methods substantially outperform the base policy, converging to the performance of the fine-tuned models. Moreover, our results show that NMD remains robust across the regularization parameter.
Chinese Translation
基于偏好的微调方法，如强化学习人类反馈（RLHF）和直接偏好优化（DPO），需要大量计算资源和庞大的偏好数据集。此外，它们还需要直接访问模型参数，而许多最先进的模型并未提供这些参数。推理时对齐提供了一种无需更新模型参数的成本效益替代方案。然而，现有的推理时方法依赖于在布拉德利-特里假设下推导出的标量奖励模型，这无法表示一般偏好。基于近期关于使用广义偏好进行微调的研究，本文开始研究在一般偏好下的推理时对齐问题。我们将该问题表述为在策略之间获得一个双人零和博弈的纳什均衡。我们提出了两种算法：纳什最佳（Best-of-Nash, BoN）和纳什镜面下降（Nash Mirror Descent, NMD）。我们证明这两种算法都达到了与问题下界相匹配的对偶间隙。在实证研究中，我们在三个数据集上实现了这两种方法，结果表明我们的算法显著优于基础策略，并收敛于微调模型的性能。此外，我们的结果显示，NMD在正则化参数变化时仍保持稳健性。
cs.AI / 138 / 2609.08090
RevalExo: A Functional Daily-Activity Benchmark for Inertial and Visual Locomotion Mode Recognition in Older Adults and Clinical Cohorts
RevalExo：老年人和临床人群惯性与视觉运动模式识别的功能性日常活动基准
Abstract
Assistive devices for people with mobility impairments, such as powered exoskeletons, rely on accurate locomotion mode recognition to adapt control strategies and provide appropriate assistance during daily activities. However, public benchmarks are typically collected from healthy adults, lack temporally precise labels necessary for detecting mode transitions, or focus on a limited set of tasks. To support development and evaluation under realistic clinical constraints and daily mobility demands, we introduce RevalExo, a functional daily-activity benchmark for inertial and visual locomotion mode recognition. RevalExo is built around a standardized, clinically and ecologically validated daily-activity protocol reflecting the cumulative everyday mobility demands in ageing and clinical populations. The benchmark includes 27 participants across three cohorts: older adults without mobility impairments, stroke survivors, and older adults with probable sarcopenia. The full cohort was recorded with lower-body IMUs, while synchronized egocentric video was collected for a clinically feasible subset of 13 participants. RevalExo provides 10.1 hours of frame-level annotations across 11 locomotion modes, including 5.1 hours of paired inertial--visual recordings. We benchmark three challenges: unimodal and multimodal locomotion mode recognition across multiple horizons, cross-population generalization from older adults without mobility impairments to clinical cohorts, and vision-guided knowledge transfer to IMU-only models. Results confirm consistent gains from fusing inertial and visual inputs but reveal a substantial gap between general recognition ($\sim$93\% F1) and recognition during transitions ($\sim$68\% F1), alongside persistent challenges in cross-population generalization and cross-modal transfer. We release RevalExo to stimulate further research on these open challenges.
Chinese Translation
针对行动障碍者的辅助设备，如动力外骨骼，依赖于准确的运动模式识别，以适应控制策略并在日常活动中提供适当的帮助。然而，现有的公共基准通常来自健康成年人，缺乏检测模式转换所需的时间精确标签，或仅关注有限的任务集。为了支持在现实临床约束和日常移动需求下的发展和评估，我们引入了RevalExo，这是一个用于惯性和视觉运动模式识别的功能性日常活动基准。RevalExo建立在一个标准化的、经过临床和生态验证的日常活动协议之上，反映了老年人和临床人群日常移动的累积需求。该基准包括来自三个群体的27名参与者：没有行动障碍的老年人、中风幸存者和可能患有肌少症的老年人。整个队列使用下肢惯性测量单元（IMUs）进行记录，同时为13名临床可行的参与者收集了同步的自我中心视频。RevalExo提供了11种运动模式下的10.1小时逐帧注释，包括5.1小时的配对惯性-视觉记录。我们对三个挑战进行了基准测试：在多个时间范围内的单模态和多模态运动模式识别、从没有行动障碍的老年人到临床人群的跨人群泛化，以及视觉引导的知识转移到仅使用IMU的模型。结果确认了融合惯性和视觉输入的一致增益，但揭示了在一般识别（约93% F1）与转换期间的识别（约68% F1）之间存在显著差距，同时在跨人群泛化和跨模态转移方面仍面临持续挑战。我们发布RevalExo以促进对这些开放挑战的进一步研究。
cs.AI / 139 / 2609.08094
CIVI: A Framework for Diagnosing Search Agent Failures in Civic Information
CIVI：一个用于诊断公民信息搜索代理失败的框架
Abstract
Large Language Models are increasingly deployed in public-sector settings, where incorrect guidance can cause irreversible harm. We introduce CIVI, the first framework for diagnosing search agent failures in civic information. Its benchmark instantiation jointly spans cross-national, interjurisdictional government contexts (federal, state, and local) and functional categories from an internationally adopted United Nations standard. We evaluate ten frontier search agents and find that none matches an attentive human baseline. Alongside accuracy, CIVI measures search invocation rate, selective no-search accuracy, and how often agents cite authoritative government sources. To perform this diagnosis, we introduce ARISE, which decomposes agentic search failures into four mutually exclusive modes, isolated via source-injection ablation. ARISE attributes 72.1% of all observed failures to retrieval-bound causes rather than to gaps in the models' parametric knowledge.
Chinese Translation
大型语言模型在公共部门的应用日益增多，而错误的指导可能导致不可逆转的伤害。我们介绍了CIVI，这是第一个用于诊断公民信息中搜索代理失败的框架。其基准实例涵盖了跨国、跨管辖区的政府背景（联邦、州和地方）以及来自国际公认的联合国标准的功能类别。我们评估了十个前沿搜索代理，发现没有一个能够达到细心人类基线的水平。除了准确性，CIVI还测量搜索调用率、选择性无搜索准确性以及代理引用权威政府来源的频率。为了进行这种诊断，我们引入了ARISE，它将代理搜索失败分解为四种相互排斥的模式，通过源注入消融进行隔离。ARISE将72.1%的所有观察到的失败归因于检索相关的原因，而不是模型参数知识的缺口。
cs.AI / 140 / 2609.08105
Artificial Intelligence-Assisted Digital Inventory of Cultural Heritage & Traditional Knowledge: Case for Indonesian Open Digital Library of Culture
人工智能辅助的文化遗产与传统知识数字化清单：印尼开放数字文化图书馆案例
Abstract
The Indonesian Digital Library of Culture (Perpustakaan Digital Budaya Indonesia, PDBI; budaya-indonesia.org) is a participatory platform that has collected tens of thousands of entries on Nusantara cultural heritage through public contribution since 2007. Manual contribution faces three structural barriers: coverage (knowledge is scattered across languages and sites), integrity (open sources mix authentic documentation with noise), and completeness (subjects are recorded but their data remain shallow). This paper presents a methodological framework for autonomous, AI-based harvesting of cultural knowledge from the open web, designed to expand corpus coverage while intensifying per-entry data depth. The methodology is organised as a five-stage economic funnel: focused crawling, multilingual extraction and canonicalisation, vector encoding with blocking, agentic decision-making, and idempotent publication, under the principle of deterministic orchestration, agentic decisions. Each stage is formalised: funnel economics and optimal filter ordering; crawl-frontier dynamics as a subcritical branching process that explains the necessity of recurrent re-seeding; fact-level novelty via a containment measure; Bayesian multi-source evidence fusion with elevated publication thresholds for sacred categories; exactly-once effects via idempotent upserts and the transactional outbox; sliding-window inference budgeting with a reservation protocol; statistical quality auditing; and seed selection as submodular coverage maximisation. The framework retains four high-value human roles: curator of direction, escalation approver, quality auditor, and guardian of meaning, while machine autonomy is raised in stages. Ethical, legal, and cultural-sensitivity implications are discussed, including the architectural guarantee that the machine never overwrites human contributions.
Chinese Translation
印尼文化数字图书馆（Perpustakaan Digital Budaya Indonesia, PDBI; budaya-indonesia.org）是一个参与性平台，自2007年以来通过公众贡献收集了数万个关于群岛文化遗产的条目。手动贡献面临三个结构性障碍：覆盖面（知识分散在不同语言和网站上）、完整性（开放资源将真实文档与噪声混合）、以及完整性（主题被记录但其数据仍然较浅）。本文提出了一种基于自主人工智能的文化知识从开放网络中收集的方法框架，旨在扩大语料库的覆盖面，同时加深每个条目的数据深度。该方法论组织为五个阶段的经济漏斗：聚焦爬取、多语言提取与规范化、向量编码与阻塞、代理决策，以及幂等发布，遵循确定性编排和代理决策的原则。每个阶段都进行了形式化：漏斗经济学与最佳过滤顺序；爬取边界动态作为一种亚临界分支过程，解释了重复重新播种的必要性；通过包含度量实现事实级新颖性；使用提高的发布阈值进行贝叶斯多源证据融合，针对神圣类别；通过幂等更新和事务性输出实现精确一次的效果；带有保留协议的滑动窗口推理预算；统计质量审计；以及种子选择作为子模覆盖最大化。该框架保留了四个高价值的人类角色：方向策展人、升级审批者、质量审计员和意义守护者，同时逐步提高机器的自主性。讨论了伦理、法律和文化敏感性影响，包括确保机器永远不会覆盖人类贡献的架构保证。
cs.AI / 141 / 2609.08115
Router Prior Bias: Preserving Base Routing Structure in MoE Post-Training
路由器先验偏置：在 MoE 后训练中保持基础路由结构
Abstract
Mixture-of-Experts (MoE) pretraining relies on an auxiliary load-balancing loss (LBL) to drive per-expert utilization toward uniformity. Post-training inherits a different situation: the base router already encodes non-uniform expert co-activation structure, which a re-imposed uniformity objective flattens away. We show that downstream performance depends instead on holding this inherited routing softly, a principle we term soft router anchoring, and instantiate it as Router Prior Bias (RPB), a training-time bias that pulls the router logits toward a prior read off the frozen base router while leaving the router itself trainable. On math post-training of Moonlight-16B-A3B, RPB attains 45.77 in-domain accuracy against 31.91 under re-applied LBL and 29.44 under unanchored fine-tuning, and retains more out-of-domain capability than either. The ordering against LBL reproduces on a second model family (Qwen3-30B-A3B-Base), and the advantage over LBL is resolvable on an independently sourced corpus. Anchors defined on the router weights, on its logits, or on its output distribution perform comparably with no consistent ordering, which places the effect in the softness of the constraint rather than in the particular prior RPB supplies. Retained community structure in the expert co-activation graph tracks these gains wherever the base router is non-uniform enough for communities to form, yet enforcing the same prior as a hard assignment preserves that structure while performance falls sharply. Community structure is therefore a footprint of soft anchoring rather than its source, and the practical lesson is that inherited routing should be held softly during post-training, since both flattening it toward uniformity and enforcing it absolutely carry a downstream cost. Our code will be released at https://github.com/naver-ai/rpb.
Chinese Translation
混合专家（Mixture-of-Experts, MoE）预训练依赖于辅助负载均衡损失（Load-Balancing Loss, LBL）来驱动每个专家的利用率趋向均匀。后训练则面临不同的情况：基础路由器已经编码了非均匀的专家共同激活结构，而重新施加的均匀目标会将其压平。我们展示了下游性能实际上依赖于柔和地保持这种继承的路由结构，这一原则我们称之为软路由器锚定（soft router anchoring），并将其实例化为路由器先验偏置（Router Prior Bias, RPB），这是一种训练时的偏置，它将路由器的 logits 向从冻结的基础路由器读取的先验拉动，同时保持路由器本身可训练。在 Moonlight-16B-A3B 的数学后训练中，RPB 实现了 45.77 的领域内准确率，而重新施加 LBL 时为 31.91，未锚定微调时为 29.44，并且在领域外能力上保留了更多的优势。与 LBL 的排序在第二个模型系列（Qwen3-30B-A3B-Base）上复现，并且在独立来源的语料库上可以解决与 LBL 的优势。定义在路由器权重、其 logits 或其输出分布上的锚定表现相当，没有一致的排序，这表明效果在于约束的柔和性，而不是特定的 RPB 先验所提供的。专家共同激活图中的保留社区结构跟踪这些增益，无论基础路由器是否足够非均匀以形成社区，但将相同的先验强制作为硬分配则保留了该结构，同时性能急剧下降。因此，社区结构是软锚定的一个足迹，而不是其来源，实际的教训是，在后训练期间应柔和地保持继承的路由，因为将其压平至均匀性和绝对强制都会带来下游成本。我们的代码将发布在 https://github.com/naver-ai/rpb。
cs.AI / 142 / 2609.08126
SchemeArena: Factorized Stress Testing of Scheming in LLM Agents
SchemeArena：大规模LLM代理的分解压力测试
Abstract
We study scheming in LLM agents, in which agents covertly pursue misaligned goals. Our focus is to understand how scheming arises from the interaction of key factors, such as instrumental goals, environmental affordances, oversight conditions, and perceived consequences. Prior work examines only a small number of scenarios, limiting the ability to isolate how these conditions shape an agent's propensity or capability to scheme. This limited scale and task diversity also restrict coverage of realistic deployment settings and the range of scheming strategies that can be observed. To this end, we introduce SCHEMEARENA, a 400-scenario benchmark for scalable scheming stress testing, constructed through a factorized scenario synthesis framework spanning diverse safety-relevant tool domains, instrumental goals, oversight conditions, and pressure mechanisms. To enable scalable and reliable monitoring, we further propose SCOUT, a scheming monitor that grounds multi-criteria judgments in evidence drawn from agents' reasoning and actions. Across controlled stress tests on five LLM agents, we find that explicit instrumental goals are the strongest driver of scheming propensity. Strategic hints play a distinct role by helping agents translate scheming reasoning into concrete covert behavior. Oversight has mixed effects: in several closed models, action-only monitoring increases scheming, suggesting that partial oversight can act as an optimization constraint rather than a deterrent. CoT is a useful but incomplete monitoring signal: it can reveal latent scheming before execution, yet action-only scheming shows that covert behavior may occur without explicit reasoning evidence. We release the benchmark, code, and monitor at: https://github.com/launchnlp/SchemeArena.
Chinese Translation
我们研究了LLM代理中的阴谋行为，其中代理秘密追求不一致的目标。我们的重点是理解阴谋行为如何从关键因素的相互作用中产生，这些因素包括工具性目标、环境可用性、监督条件和感知后果。之前的研究仅考察了少数场景，限制了分离这些条件如何影响代理的阴谋倾向或能力的能力。这种有限的规模和任务多样性也限制了对现实部署环境的覆盖以及可观察到的阴谋策略范围。为此，我们引入了SCHEMEARENA，这是一个包含400个场景的基准，用于可扩展的阴谋压力测试，通过一个分解场景合成框架构建，涵盖了多样的安全相关工具领域、工具性目标、监督条件和压力机制。为了实现可扩展和可靠的监控，我们进一步提出了SCOUT，一个阴谋监控器，它基于从代理的推理和行为中提取的证据进行多标准判断。在对五个LLM代理进行的控制压力测试中，我们发现明确的工具性目标是阴谋倾向的最强驱动因素。战略提示通过帮助代理将阴谋推理转化为具体的隐蔽行为而发挥了独特的作用。监督的效果混合：在几个封闭模型中，仅监控行为的方式增加了阴谋行为，这表明部分监督可能作为优化约束而非威慑。链式推理（CoT）是一个有用但不完整的监控信号：它可以在执行前揭示潜在的阴谋行为，但仅监控行为的阴谋显示隐蔽行为可能在没有明确推理证据的情况下发生。我们在：https://github.com/launchnlp/SchemeArena发布基准、代码和监控器。
cs.AI / 143 / 2609.08149
SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents
SWE-Bench Pro Verified：软件工程代理的可靠基准
Abstract
SWE-Bench Pro has emerged as a standard benchmark for evaluating software engineering agents on challenging repository-level tasks. However, our analysis work show that its evaluation is undermined by two sources of unreliability: \textbf{reward hacking}, enabled by leakage of gold solutions or hidden evaluation information, and \textbf{task quality issues}, including misleading problem statements and improperly scoped tests. These issues can inflate benchmark performance and obscure agents' true coding ability. We present \textbf{SWE-Bench Pro Verified}, a verified version of SWE-Bench Pro that addresses both problems. Our approach combines \textbf{anti-hacking} safeguards that eliminate major leakage channels without disrupting normal agent functionality, with \textbf{task refinement} that minimally corrects inconsistencies within flawed instances. Evaluations on SWE-Bench Pro Verified reveal that some models perform substantially worse than previously reported, suggesting that existing results on SWE-Bench Pro may overestimate real software engineering capability. SWE-Bench Pro Verified offers a more trustworthy benchmark for assessing software engineering agents.
Chinese Translation
SWE-Bench Pro 已成为评估软件工程代理在具有挑战性的仓库级任务中的标准基准。然而，我们的分析工作表明，其评估受到两种不可靠来源的影响： extbf{奖励黑客}，这是由于金牌解决方案或隐藏评估信息的泄露所导致，以及 extbf{任务质量问题}，包括误导性问题陈述和不当范围的测试。这些问题可能会夸大基准性能，并掩盖代理的真实编码能力。我们提出了 extbf{SWE-Bench Pro Verified}，这是 SWE-Bench Pro 的一个经过验证的版本，旨在解决这两个问题。我们的方法结合了 extbf{反黑客}保护措施，消除了主要的泄露渠道而不干扰正常的代理功能，以及 extbf{任务精炼}，对有缺陷实例中的不一致性进行最小化修正。在 SWE-Bench Pro Verified 上的评估显示，一些模型的表现明显低于先前报告的结果，这表明现有的 SWE-Bench Pro 结果可能高估了真实的软件工程能力。SWE-Bench Pro Verified 提供了一个更可靠的基准，用于评估软件工程代理。
cs.AI / 144 / 2609.08162
WorldAgen: Unified State-Action Prediction with Test-Time World Model Training
WorldAgen：统一状态-动作预测与测试时世界模型训练
Abstract
How can vision-language-action (VLA) models adapt to new environments where world dynamics shift? While recent research has combined world modeling and action prediction to improve VLA performance, existing methods largely rely on pretraining on static datasets, without mechanisms for active adaptation at deployment time. As a result, these models often fail to generalize when deployed in unseen scenarios with novel object configurations or dynamics. We present WorldAgen, a unified framework that jointly learns world modeling and action prediction while enabling Test-Time Training (TTT) to adapt to new environments. WorldAgen employs a shared Transformer backbone with two heads: (1) a world model head that predicts future states from past state-action trajectories, and (2) an agent model head that predicts actions conditioned on task instructions. We design a Mixed Unidirectional Attention Mask to separate these two models. During test time, WorldAgen samples exploratory actions, collects ground-truth state transitions, and performs lightweight TTT updates to refine its world model. This adaptation improves the model's understanding of the environment and leads to more accurate action predictions. Experiments on the CALVIN and LIBERO benchmarks demonstrate that our baseline model achieves comparable, and in some cases superior, performance to current state-of-the-art approaches. Moreover, with TTT on a small number of samples, our method surpasses existing state-of-the-art models, highlighting the effectiveness of adapting world models at inference time.
Chinese Translation
视觉-语言-动作（VLA）模型如何适应世界动态变化的新环境？尽管近期研究已将世界建模与动作预测相结合以提高VLA性能，但现有方法在很大程度上依赖于对静态数据集的预训练，缺乏在部署时主动适应的机制。因此，这些模型在面对具有新物体配置或动态的未见场景时，往往无法很好地泛化。我们提出了WorldAgen，这是一个统一框架，能够同时学习世界建模和动作预测，并支持测试时训练（TTT）以适应新环境。WorldAgen采用共享的Transformer骨干网络，并具有两个头部：（1）一个世界模型头，基于过去的状态-动作轨迹预测未来状态；（2）一个代理模型头，根据任务指令预测动作。我们设计了一种混合单向注意力掩码，以区分这两个模型。在测试时，WorldAgen采样探索性动作，收集真实状态转变，并进行轻量级的TTT更新以优化其世界模型。这种适应性提高了模型对环境的理解，并导致更准确的动作预测。在CALVIN和LIBERO基准上的实验表明，我们的基线模型在某些情况下与当前最先进的方法相比表现相当，甚至更优。此外，在少量样本上进行TTT时，我们的方法超越了现有的最先进模型，突显了在推理时适应世界模型的有效性。
cs.AI / 145 / 2609.08173
Key Path Identification for Resolving Knowledge Conflicts via SAE-based Steering
基于稀疏自编码器的引导下关键路径识别以解决知识冲突
Abstract
Sparse autoencoder (SAE)-based steering has been widely used to address knowledge conflicts by guiding LLMs to be more faithful to the contextual knowledge. Existing methods usually perform mass steering, which modifies a large batch of SAE features identified via correlation-based methods. However, due to the inaccurate correlation and the neglected feature interactions, mass steering methods fail to precisely identify the features that play the key roles in steering and introduce a large number of redundant ones, which add noise and weaken the steering effects. Our empirical studies reveal that steering only a small subset of the identified features can achieve comparable or even better performance. Motivated by this finding, we propose Key Path Identification (KPI), a novel method that identifies key steering features characterized by strong causal dependencies with both upstream and downstream features. From these features, KPI constructs key paths and steers through less feature modifications. In this way, KPI advances SAE-based steering from quantity-driven to quality-focused, offering a perspective for more precise and interpretable model editing. Experiments in RAG tasks with knowledge conflicts show that our method improves the accuracy by 18% on average compared to the best baseline of mass steering, effectively filtering redundant features, alleviating side effects and demonstrating the core role of key paths in steering.
Chinese Translation
基于稀疏自编码器（SAE）的引导已被广泛应用于通过引导大型语言模型（LLMs）更忠实于上下文知识来解决知识冲突。现有方法通常采用大规模引导，这种方法通过基于相关性的方法修改大量识别出的SAE特征。然而，由于相关性不准确和特征交互被忽视，大规模引导方法未能精确识别在引导中发挥关键作用的特征，并引入大量冗余特征，这增加了噪声并削弱了引导效果。我们的实证研究表明，仅对识别出的特征的小子集进行引导可以实现可比甚至更好的性能。基于这一发现，我们提出了关键路径识别（KPI），这是一种新颖的方法，识别出与上下游特征具有强因果依赖关系的关键引导特征。KPI从这些特征中构建关键路径，并通过较少的特征修改进行引导。通过这种方式，KPI将基于SAE的引导从数量驱动转向质量关注，为更精确和可解释的模型编辑提供了视角。在具有知识冲突的RAG任务中的实验表明，我们的方法相比于大规模引导的最佳基线平均提高了18%的准确率，有效过滤冗余特征，减轻副作用，并展示了关键路径在引导中的核心作用。
cs.AI / 146 / 2609.08174
OntologyBench: Can Dense Retrieval Satisfy Structured Biomedical Constraints?
OntologyBench：密集检索能否满足结构化生物医学约束？
Abstract
We introduce OntologyBench, a tiered biomedical retrieval benchmark comprising 471,854 training and 125,744 evaluation query-document relevance pairs across concept grounding, relational retrieval, and compositional phenotype-based retrieval. Although these tasks can be tractable using ontology-aware reference methods, across task tiers, embedding performance is generally lower on relational and compositional tasks than on concept-grounding tasks. Fine-tuning on ontology-derived supervision improves performance on several relational and compositional tasks, whereas the evaluated reranking and LLM-based candidate-scoring methods provide little or no end-to-end improvement. Errors frequently reflect diseases matching only subsets of the phenotype evidence. These findings indicate that the evaluated embedding and reranking configurations do not reliably recover the compatibility encoded by the selected ontology relations and phenotype combinations and motivate retrieval systems that better integrate learned representations with structured biomedical knowledge.
Chinese Translation
我们介绍了OntologyBench，这是一个分层的生物医学检索基准，包含471,854个训练和125,744个评估查询-文档相关性对，涵盖概念基础、关系检索和基于表型组合的检索。尽管这些任务可以通过关注本体的参考方法来处理，但在各个任务层次上，嵌入性能在关系和组合任务上通常低于概念基础任务。在本体衍生的监督下进行微调可以提高多个关系和组合任务的性能，而评估的重排序和基于LLM（大语言模型）的候选评分方法几乎没有或没有提供端到端的改进。错误通常反映出疾病仅匹配表型证据的子集。这些发现表明，评估的嵌入和重排序配置无法可靠地恢复所选本体关系和表型组合所编码的兼容性，并促使检索系统更好地将学习到的表示与结构化生物医学知识相结合。
cs.AI / 147 / 2609.08175
Safe Harness Self-Evolution: A Theoretical Analysis of Feasibility and Limits
安全自我演化的理论分析：可行性与限制
Abstract
Harness self-evolution is the process by which an agent modifies its prompts, tools, code, or orchestration in response to task feedback while keeping the underlying language model frozen, with changes persisting across subsequent tasks. We provide a systematic theoretical analysis of the feasibility and limits of safe harness self-evolution, connecting modification generation, finite-data certification and selection, safe adoption, and behavior after an update. Under a fixed user-task distribution, we establish conditions guaranteeing overall expected-reward improvement while controlling changes on retained tasks, characterize the probability of generating qualified modifications, and derive finite-data bounds for safe selection and adoption. Our analysis shows that generation and certification impose distinct constraints: current task performance does not determine the probability of generating qualified modifications, and generating more candidates need not improve the guarantee of a successful update when evaluation is limiting. Stagnation may therefore arise even when improvement opportunities remain. We further show that worst-case evaluation cost for recognizing genuine improvements diverges as expected reward approaches its upper bound. Across successive updates, certified improvement guarantees accumulate over a finite run, but a successful update does not by itself guarantee that further improvement remains possible. These results provide a basis for diagnosing bottlenecks and designing safer self-evolution mechanisms.
Chinese Translation
自我演化是指代理在任务反馈的基础上修改其提示、工具、代码或协调方式的过程，同时保持底层语言模型不变，所做的更改在后续任务中持续存在。我们对安全自我演化的可行性和限制进行了系统的理论分析，连接了修改生成、有限数据认证与选择、安全采用以及更新后的行为。在固定的用户-任务分布下，我们建立了保证整体期望奖励改善的条件，同时控制对保留任务的更改，表征生成合格修改的概率，并推导出安全选择和采用的有限数据界限。我们的分析表明，生成和认证施加了不同的约束：当前任务的表现并不决定生成合格修改的概率，而生成更多候选项并不一定在评估受限时提高成功更新的保证。因此，即使仍然存在改善机会，也可能出现停滞。我们进一步表明，识别真正改善的最坏情况评估成本在期望奖励接近其上限时会发散。在连续的更新中，认证的改善保证在有限的运行中累积，但成功的更新本身并不保证进一步的改善仍然可能。这些结果为诊断瓶颈和设计更安全的自我演化机制提供了基础。
cs.AI / 148 / 2609.08180
Less Is Personal: Learning Minimal Sufficient User Profiles for Personalized Language Models
少即是个人化：为个性化语言模型学习最小充分用户画像
Abstract
Retrieval-augmented personalization enables large language models to produce more accurate and preference-aligned outputs using relevant records retrieved from user histories. Personalized language models typically prepend a fixed number of retrieved user records, even when additional history is redundant, harmful, or unrelated to a user's distinctive behavior. We study minimal sufficient personalization: constructing the least costly ordered profile for each input while preserving the utility achievable from a retrieved candidate pool. We introduce ENOUGH, a method that iteratively appends behavioral records or emits STOP to construct profiles with adaptive lengths. Offline, bounded counterfactual search evaluates profile prefixes by jointly considering downstream gains, user specificity, and token costs. The resulting long-horizon targets are distilled into a multi-head value controller with explicit ranking and stopping supervision. At inference, the controller selects and orders records through lightweight decisions, and the frozen generator is invoked once after stopping. Extensive experiments on six personalized tasks demonstrate that ENOUGH consistently outperforms strong heuristic and retrieval-augmented baselines in both effectiveness and efficiency, achieving minimal sufficient profiles that preserve personalization utility while reducing unnecessary context costs.
Chinese Translation
检索增强的个性化使大型语言模型能够利用从用户历史中检索到的相关记录生成更准确且符合偏好的输出。个性化语言模型通常会在输入前添加固定数量的检索用户记录，即使额外的历史记录是冗余的、有害的或与用户的独特行为无关。我们研究最小充分个性化：为每个输入构建成本最低的有序画像，同时保留从检索候选池中可实现的效用。我们提出了ENOUGH，一种通过迭代附加行为记录或发出STOP来构建具有自适应长度的画像的方法。在离线阶段，有限的反事实搜索通过共同考虑下游收益、用户特异性和令牌成本来评估画像前缀。最终的长远目标被提炼为具有明确排名和停止监督的多头价值控制器。在推理阶段，控制器通过轻量级决策选择和排序记录，并在停止后调用冻结的生成器。在六个个性化任务上的大量实验表明，ENOUGH在有效性和效率上始终优于强启发式和检索增强的基线，达成了在减少不必要上下文成本的同时保留个性化效用的最小充分画像。
cs.AI / 149 / 2609.08186
Does Deeper Reasoning Compromise Alignment? Revealing and Mitigating of Alignment Collapse in Large Reasoning Models
更深层次的推理是否妨碍对齐？揭示和缓解大型推理模型中的对齐崩溃
Abstract
The emergence of Chain-of-Thought (CoT) has established a robust foundation for Large Reasoning Models (LRMs). While deep reasoning is widely believed to enhance safety alignment, the stability of alignment mechanisms under extended reasoning remains underexplored. This paper challenges the prevailing view by revealing a critical vulnerability: Deep Reasoning May Induce Alignment Collapse. To rigorously quantify this phenomenon, we propose the Alignment Loss Rate (ALR) metric. Our experiments demonstrate that as reasoning depth increases, ALR rises significantly, indicating a severe degradation in model robustness against external perturbations. Capitalizing on this instability, a novel jailbreaking paradigm, Reasoning Trap (RT), is proposed. RT induces the model into extended reasoning to amplify the impact of adversarial attacks, leading to a sharp decline in safety capabilities. To elucidate the mechanism behind this collapse, we identify Attention Dilution as the root cause, arising from the competition for attention between the extended reasoning process and the original input. To mitigate this, Reasoning Residual Alignment (RRA), a lightweight defense strategy that dynamically re-emphasizes the input via residual connections integrated with the reasoning process.
Chinese Translation
链式思维（Chain-of-Thought, CoT）的出现为大型推理模型（Large Reasoning Models, LRMs）奠定了坚实的基础。尽管深层推理被广泛认为能够增强安全性对齐，但在扩展推理下对齐机制的稳定性仍然未得到充分探讨。本文挑战了这一普遍观点，揭示了一个关键脆弱性：深层推理可能导致对齐崩溃。为了严格量化这一现象，我们提出了对齐损失率（Alignment Loss Rate, ALR）指标。我们的实验表明，随着推理深度的增加，ALR显著上升，表明模型对外部扰动的鲁棒性严重下降。利用这种不稳定性，我们提出了一种新颖的越狱范式——推理陷阱（Reasoning Trap, RT），该范式诱导模型进行扩展推理，以放大对抗性攻击的影响，从而导致安全能力的急剧下降。为了阐明这一崩溃背后的机制，我们确定了注意力稀释（Attention Dilution）作为根本原因，源于扩展推理过程与原始输入之间的注意力竞争。为此，我们提出了推理残差对齐（Reasoning Residual Alignment, RRA），这是一种轻量级防御策略，通过与推理过程集成的残差连接动态重新强调输入，以缓解这一问题。
cs.AI / 150 / 2609.08188
Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment
通过生成器循环对齐弥合多模态检索增强生成中的语义-效用差距
Abstract
Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retrievers and rerankers optimize for semantic similarity rather than answer utility, creating a preference gap: documents that appear relevant may not help the generator produce a correct answer. Motivated by this, we propose a two-stage generator-in-the-loop alignment framework that closes this gap without human document-level relevance annotations. Our framework consists of two stages: in Stage 1, a VLM generates a hypothetical text passage from the image-query pair, which is used as the retrieval query for dense text search, bridging the image-to-text modality gap. In Stage 2, a cross-encoder reranker adapted with low-rank adaptation (LoRA) is fine-tuned using answer-supervised preference pairs mined from the frozen VLM: given the dataset answer label, a candidate document is labeled positive if the VLM produces the correct answer when given that document as context, and negative otherwise. This generator-guided signal is compatible with multiple alignment loss functions, including contrastive (triplet) loss, pairwise direct preference optimization (DPO), and supervised fine-tuning (SFT), and supports periodic re-mining to refresh preference pairs as the reranker improves. Experiments on VQA-X and A-OKVQA with Qwen3.5-2B and Qwen3-VL-4B-Instruct show that our proposed framework consistently outperforms rank-order, random, and REPLUG-style likelihood baselines under various alignment losses and pool size settings, suggesting that answer-level generator feedback is an effective supervision signal for preference alignment.
Chinese Translation
增强了检索增强生成（RAG）的视觉-语言模型（VLMs）能够访问外部证据。然而，标准的检索器和重排序器优化的是语义相似性，而非答案效用，从而造成了偏好差距：看似相关的文档可能无法帮助生成器产生正确答案。基于此，我们提出了一种两阶段的生成器循环对齐框架，旨在在没有人工文档级相关性注释的情况下弥合这一差距。我们的框架由两个阶段组成：在第一阶段，VLM根据图像-查询对生成一个假设文本段落，该段落被用作密集文本搜索的检索查询，从而弥合图像到文本的模态差距。在第二阶段，采用低秩适应（LoRA）的交叉编码器重排序器通过从冻结的VLM中挖掘的答案监督偏好对进行微调：给定数据集的答案标签，如果在给定该文档作为上下文时，VLM产生正确答案，则候选文档被标记为正样本，否则为负样本。这种生成器引导的信号与多种对齐损失函数兼容，包括对比（三元组）损失、成对直接偏好优化（DPO）和监督微调（SFT），并支持定期重新挖掘以在重排序器改进时刷新偏好对。在VQA-X和A-OKVQA上使用Qwen3.5-2B和Qwen3-VL-4B-Instruct的实验表明，我们提出的框架在各种对齐损失和池大小设置下，始终优于排名顺序、随机和REPLUG样式的似然基线，表明答案级生成反馈是偏好对齐的有效监督信号。
cs.AI / 151 / 2609.08189
Do Dynamic Routers Need Memory? HeRo: History-Aware Routing for Efficient LLM Inference
动态路由器需要记忆吗？HeRo：历史感知路由用于高效的LLM推理
Abstract
Dynamic layer routing reduces the inference cost of Large Language Models (LLMs) by learning to skip layers for individual tokens. Existing methods, however, treat each routing decision as a local operation conditioned solely on the current hidden state which is a formulation that overlooks the sequential, path-dependent nature of routing across depth: earlier decisions shape the representations seen by downstream routers, and the layer-usage objective couples all decisions jointly. We propose History-Aware Routing (HeRo), a dynamic routing framework that resolves this mismatch by introducing a router memory mechanism to maintain an explicit routing state across model depth. The memory is constructed via linear attention, incrementally aggregating preceding routing scores and their induced residual updates into a compact history representation. At each routed layer, the router conditions jointly on this accumulated state and the current hidden representation to select the executed branch. Instantiated for token-wise FFN routing, HeRo trains only lightweight routers and adapters on a frozen backbone, requiring no modification to pretrained parameters. Across Llama 3.1-8B, Llama 2-7B, and Llama 2-13B, HeRo consistently achieves the highest aggregate performance retention among ten baselines. On Llama 3.1-8B, it bypasses 26.87% of model parameters while achieving 100.24% of dense model performance across seven benchmarks, and retains 97.01% while bypassing 38.82% of model parameters under a tighter computation budget. Ablation studies confirm that removing routing history consistently degrades performance, most notably on multistep reasoning and code generation, validating that explicit routing memory enables more accurate and adaptive dynamic routing than solely conditioning on hidden state.
Chinese Translation
动态层路由通过学习为单个标记跳过层，从而减少大型语言模型（LLMs）的推理成本。然而，现有方法将每个路由决策视为仅依赖于当前隐状态的局部操作，这种表述忽视了跨深度路由的顺序和路径依赖特性：早期的决策会影响下游路由器所看到的表示，而层使用目标则将所有决策共同耦合。我们提出了历史感知路由（HeRo），这是一个动态路由框架，通过引入路由器记忆机制来维护跨模型深度的显式路由状态，从而解决这一不匹配。该记忆通过线性注意力构建，逐步聚合先前的路由分数及其引发的残差更新，形成紧凑的历史表示。在每个路由层，路由器共同基于这一累积状态和当前隐表示进行条件选择，以确定执行的分支。HeRo在冻结的主干网络上仅训练轻量级的路由器和适配器，无需修改预训练参数。在Llama 3.1-8B、Llama 2-7B和Llama 2-13B上，HeRo在十个基线中始终实现了最高的整体性能保留。在Llama 3.1-8B上，它在七个基准测试中绕过了26.87%的模型参数，同时实现了100.24%的稠密模型性能，并在更严格的计算预算下绕过了38.82%的模型参数，保留了97.01%的性能。消融研究证实，移除路由历史会持续降低性能，尤其是在多步推理和代码生成任务中，验证了显式路由记忆使得动态路由比仅依赖隐状态更准确和适应性更强。
cs.AI / 152 / 2609.08196
Qiushi Engine on AstaBench E2E-Bench-Hard
Qiushi引擎在AstaBench E2E-Bench-Hard上的表现
Abstract
This report analyzes Qiushi Engine v0.8 across all 40 test tasks in AstaBench E2E-Bench-Hard, a benchmark that requires autonomous agents to carry a research question through experimental design, code implementation, actual execution, result analysis, and report delivery. Qiushi Engine is model-configurable; this evaluation selected DeepSeek deepseek-v4pro-preview as the model backend. The official AstaBench leaderboard records a score of 0.816 and an average benchmark cost of USD 15.209 per task, while the full-precision local recomputation is $81.59 \pm 1.87$. Four tasks satisfied every rubric item, yielding a full-task completion rate of 4/40 = 10% -- 7 percentage points above, and about 3.3 times, the approximately 3% best rate reported for AstaBench's official agents. Across 507 required rubric items, 416 were satisfied (82.1%). Official scoring archives and 40 Meta-Trace records show sustained production and verification of reports, code, and experimental artifacts; the principal gaps lie in repeated runs, external dependencies, specified metrics, and ablation studies. The report explains the benchmark, system workflow, aggregate results, representative cases, and limits of interpretation.
Chinese Translation
本报告分析了Qiushi引擎v0.8在AstaBench E2E-Bench-Hard中的40个测试任务的表现，该基准测试要求自主代理通过实验设计、代码实现、实际执行、结果分析和报告交付来解决研究问题。Qiushi引擎是可配置模型的；本次评估选择了DeepSeek deepseek-v4pro-preview作为模型后端。官方AstaBench排行榜记录的得分为0.816，每个任务的平均基准成本为15.209美元，而全精度本地重计算的成本为81.59 ± 1.87美元。四个任务满足了每个评估标准项，任务完成率为4/40 = 10%——比AstaBench官方代理报告的约3%的最佳完成率高出7个百分点，约为其3.3倍。在507个必需的评估标准项中，有416项得到了满足（82.1%）。官方评分档案和40个Meta-Trace记录显示了报告、代码和实验文物的持续生成和验证；主要的不足在于重复运行、外部依赖、指定指标和消融研究。报告解释了基准测试、系统工作流程、汇总结果、代表性案例和解释的局限性。
cs.AI / 153 / 2609.08211
A Better Spur Should Start From Each Objective
更好的起点应从每个目标开始
Abstract
Real-world Multi-Objective Reinforcement Learning (MORL) often suffers from sparse rewards, reward conflicts, and late-stage reward tug-of-war, causing traditional linear scalarization to experience severe metric oscillations. To address optimization conflicts among multiple objectives in real-world deployment scenarios, we propose Multi-Marginal Preference Optimization (MMPO), a fine-grained framework that intervenes at the data, gradient, and constraint levels rather than relying on coarse-grained global scalarization. Specifically, MMPO performs exposure debiasing to mitigate sparse and biased rewards, applies priority-aware orthogonal projection to decouple conflicting gradients, and introduces self-prompted gradient constraints to prevent dominant objectives from overwhelming weaker ones. Experiments on real-world e-commerce datasets show that MMPO improves training stability and consistently achieves better performance across conflicting metrics. Moreover, it generalizes robustly to broader tasks such as ToolRL and code generation, demonstrating its effectiveness as a practical paradigm for multi-objective alignment.
Chinese Translation
现实世界中的多目标强化学习（MORL）常常面临稀疏奖励、奖励冲突和后期奖励拉锯战等问题，这导致传统的线性标量化出现严重的度量波动。为了应对现实部署场景中多个目标之间的优化冲突，我们提出了多边际偏好优化（Multi-Marginal Preference Optimization, MMPO），这是一个细粒度框架，它在数据、梯度和约束层面进行干预，而不是依赖粗粒度的全局标量化。具体而言，MMPO 进行曝光去偏差以减轻稀疏和偏向的奖励，应用优先级感知的正交投影以解耦冲突梯度，并引入自我提示的梯度约束以防止主导目标压倒较弱目标。在现实世界的电子商务数据集上的实验表明，MMPO 提高了训练稳定性，并在冲突度量上始终实现更好的性能。此外，它在更广泛的任务（如 ToolRL 和代码生成）上具有良好的泛化能力，证明了其作为多目标对齐的实用范式的有效性。
cs.AI / 154 / 2609.08216
Vision: Data-Centric Anchoring for Robust and Interpretable Agentic AI
愿景：数据中心锚定以实现稳健和可解释的代理人工智能
Abstract
Agentic AI systems built on large language models fail in two persistent ways that scaling does not fix: they break under distribution shift, and they cannot explain the decisions they make. We argue these are co-symptoms of one structural deficiency in the data lifecycle that governs how agents are trained, evaluated, and deployed. Observational interaction logs record what an agent did, not what it would have done otherwise. They encode spurious correlations without controlled variation, so they lack the counterfactual structure needed to separate causal signal from coincidence or to validate an explanation. No model-centric method can recover invariances the data never contained. We present Data-Centric Anchoring: robustness and interpretability should be engineered into the data environment, not extracted from models after training. Our central contribution is the Data-Centric Agentic Loop, a four-stage framework of Curate, Augment, Constrain, and Attribute. The ordering is structural, not stylistic. Curation precedes augmentation because generative models amplify whatever bias they are trained on. Augmentation precedes constraint because invariance objectives are vacuous without variation across environments to be invariant to. Attribution closes the loop, converting observed failures into targeted data interventions for the next iteration. Each stage manufactures the preconditions of the next, which makes the loop self-correcting rather than merely sequential. We ground the framework in a failure-driven taxonomy that links four core failure modes to the data lifecycle: spurious feature reliance, distribution-shift fragility, uncertainty miscalibration, and explanation unfaithfulness. We close with the limits of this approach and the open problems that stand between it and practical deployment at scale.
Chinese Translation
基于大型语言模型的代理人工智能系统在两个持续存在的问题上表现不佳，而仅仅扩展规模并不能解决这些问题：它们在分布转移下失效，并且无法解释其所做出的决策。我们认为，这些是数据生命周期中一个结构性缺陷的共同症状，该生命周期决定了代理的训练、评估和部署方式。观察性交互日志记录了代理所做的事情，而不是它在其他情况下会做的事情。它们编码了虚假的相关性而没有控制变异，因此缺乏分离因果信号与巧合或验证解释所需的反事实结构。没有任何以模型为中心的方法能够恢复数据从未包含的恒定性。我们提出了数据中心锚定：稳健性和可解释性应当在数据环境中进行工程设计，而不是在训练后从模型中提取。我们的核心贡献是数据中心代理循环（Data-Centric Agentic Loop），这是一个由四个阶段组成的框架：策划（Curate）、增强（Augment）、约束（Constrain）和归因（Attribute）。这种顺序是结构性的，而非风格性的。策划在增强之前，因为生成模型会放大其训练时所受的任何偏见。增强在约束之前，因为在没有环境变异的情况下，恒定性目标是空洞的。归因闭合了循环，将观察到的失败转化为下一轮的针对性数据干预。每个阶段都制造了下一个阶段的前提条件，这使得循环自我纠正，而不仅仅是顺序进行。我们将该框架建立在一个以失败为驱动的分类法上，该分类法将四种核心失败模式与数据生命周期联系起来：虚假特征依赖、分布转移脆弱性、不确定性误校准和解释不忠实。最后，我们讨论了该方法的局限性以及在规模化实际部署之间存在的开放问题。
cs.AI / 155 / 2609.08226
TTGBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs
TTGBench：文本属性时间图中拓扑演变与语义漂移的基准测试
Abstract
Temporal graph learning models the evolution of dynamic systems, where both structural interactions and semantic states change over time. However, existing benchmarks primarily emphasize structural evolution via temporal link prediction (TLP), while support for semantic evolution remains limited. Although temporal node classification (TNC) is sometimes included, it is typically restricted to simplistic binary settings that fail to capture realistic semantic drift. Moreover, commonly used datasets exhibit high link repetition, leading to inflated performance estimates and obscuring true model capability. To address these limitations, we introduce \textbf{TTGBench}, a new benchmark that jointly evaluates structural and semantic evolution. TTGBench comprises six real-world, text-rich datasets characterized by \emph{Dual Volatility}, enabling rigorous and fair evaluation of existing models. Notably, it is the first benchmark to support both multi-class and multi-label TNC, filling a critical gap in evaluating temporal semantic drift. We conduct a comprehensive evaluation of 17 state-of-the-art methods across Temporal Graph Neural Networks (TGNNs) and Large Language Model (LLM)-based paradigms. The results reveal a clear \emph{capability divide} between the two paradigms: TGNN-based methods excel at structural prediction but fail at semantic tracking, whereas LLM-based predictors show the opposite trend. Through in-depth analysis, we uncover their fundamental limitations and provide insights for developing more comprehensive temporal graph models.
Chinese Translation
时间图学习模型动态系统的演变，其中结构交互和语义状态随时间变化。然而，现有的基准主要强调通过时间链接预测（Temporal Link Prediction, TLP）进行结构演变，而对语义演变的支持仍然有限。尽管有时会包含时间节点分类（Temporal Node Classification, TNC），但通常仅限于简单的二元设置，无法捕捉现实的语义漂移。此外，常用的数据集存在高链接重复性，导致性能估计膨胀，掩盖了模型的真实能力。为了解决这些局限性，我们引入了 extbf{TTGBench}，一个新的基准，联合评估结构和语义演变。TTGBench包含六个真实世界的文本丰富数据集，具有 extit{双重波动性}（Dual Volatility），能够对现有模型进行严格和公平的评估。值得注意的是，这是第一个支持多类和多标签TNC的基准，填补了评估时间语义漂移的关键空白。我们对17种最先进的方法进行了全面评估，涵盖时间图神经网络（Temporal Graph Neural Networks, TGNNs）和基于大型语言模型（Large Language Model, LLM）的范式。结果揭示了这两种范式之间明显的 extit{能力差距}：基于TGNN的方法在结构预测方面表现出色，但在语义跟踪方面失败，而基于LLM的预测器则显示出相反的趋势。通过深入分析，我们揭示了它们的基本局限性，并为开发更全面的时间图模型提供了见解。
cs.AI / 156 / 2609.08228
SE-GoS: Self-Evolving Graph-of-Skills for Skill Library at Scale
自我演化技能图（SE-GoS）：大规模技能库的自我演化技能图
Abstract
Modern LLM agents increasingly rely on reusable skills, yet as skill libraries scale to thousands of entries, effective retrieval becomes a bottleneck. Graph-of-Skills (GoS) addresses this challenge by exploiting dependency-aware graph structure for scalable skill retrieval, while SkillDAG further demonstrates that skill graphs can accumulate execution-backed structure online. However, these approaches leave open whether historical execution traces can be systematically distilled into a better retrieval graph that generalizes to unseen tasks. We present Self-Evolving Graph-of-Skills (SE-GoS), a training-free framework that evolves an existing GoS graph from execution traces while preserving the original retrieval pipeline. SE-GoS performs three complementary updates: topology evolution that discovers and prunes skill relationships from execution evidence, edge-weight evolution that reinforces retrieval-relevant relationships based on historical effectiveness, and description evolution that optimizes retrieval-facing skill descriptions using execution feedback. Across three LLMs on SkillsBench, SE-GoS consistently improves task reward while reducing input tokens relative to full skill loading, with gains varying across model families. In a representative setting, one evolution round improves reward from 52.4\% to 59.4\% while reducing input tokens by approximately one-third relative to full skill loading, and the resulting graph transfers to a disjoint held-out split with a 5.4-point improvement over the static GoS baseline. These results show that skill graphs can be improved from execution experience without model training, changes to the retrieval algorithm, or modifications to skill content, turning a static retrieval graph into an evolving retrieval infrastructure.
Chinese Translation
现代大语言模型（LLM）代理越来越依赖可重用的技能，但随着技能库规模扩大到数千个条目，有效检索成为瓶颈。技能图（Graph-of-Skills, GoS）通过利用依赖感知的图结构来解决这一挑战，实现可扩展的技能检索，而技能有向无环图（SkillDAG）进一步证明了技能图能够在线累积基于执行的结构。然而，这些方法尚未系统性地探讨历史执行轨迹是否能够提炼成更好的检索图，以便对未见任务进行泛化。我们提出了自我演化技能图（Self-Evolving Graph-of-Skills, SE-GoS），这是一个无训练的框架，能够在保留原有检索管道的同时，从执行轨迹中演化现有的GoS图。SE-GoS执行三种互补的更新：拓扑演化从执行证据中发现并修剪技能关系，边权演化基于历史有效性强化与检索相关的关系，以及描述演化利用执行反馈优化面向检索的技能描述。在SkillsBench上的三种LLM中，SE-GoS始终提高任务奖励，同时相较于完整技能加载减少输入标记，增益在不同模型系列中有所不同。在一个代表性设置中，一轮演化将奖励从52.4%提高到59.4%，同时相较于完整技能加载减少约三分之一的输入标记，得到的图在一个不重叠的保留分割上转移，较静态GoS基线提高了5.4个百分点。这些结果表明，技能图可以通过执行经验进行改进，而无需模型训练、检索算法的更改或技能内容的修改，从而将静态检索图转变为一个不断演化的检索基础设施。
cs.AI / 157 / 2609.08236
Style Over Substance: Content-Invariant Wrappers Flip LLM Safety-Judge Verdicts
形式重于内容：内容不变的包装器翻转大型语言模型安全评判结果
Abstract
Automatic safety judges -- systems such as Llama Guard or a GPT-4o grading prompt that decide whether a model's reply is harmful -- produce the numbers behind almost every reported jailbreak success rate, defense evaluation, and safety leaderboard. We ask whether these judges grade what a reply contains or how it sounds. We keep a reply's content fixed and add content-invariant style wrappers: fixed strings placed before or after the reply that change only its tone (an educational disclaimer, a fake safety "reasoning" block, a token refusal followed by the unchanged harmful body), or, on harmless refusals, framing that merely sounds dangerous. The body is preserved byte-for-byte, so a faithful judge must return the same verdict, and any flip is an error of the judge, not a change in safety. Over 600 JailbreakBench replies x up to 7 forms x 8 judges, we measure flip rates with paired significance tests and measured noise floors. Findings are precise rather than universal: most judges barely move, but specific judges harbor cheaply exploitable blind spots. A token-refusal wrapper flips 19.9% of GPT-4o-mini's correct "unsafe" verdicts (noise floor 0.5%; 18.2% under majority-of-three re-scoring) yet moves Claude only 0.4%. The deployed Llama Guard 4 is deterministically gamed: an "educational course" framing flips 12.3% of its harmful verdicts to safe. A second deployed guard (gpt-oss-safeguard-20b) is immune, and rewriting only the grading prompt (StrongREJECT-style) cuts the attack tenfold on the identical model -- the vulnerability lives in the judge, not the content. A two-annotator human validation confirms 100% content invariance and 90% of flips as judge errors (kappa 0.95-1.0), and a bootstrap shows the underlying model ranking is already unstable to sampling alone. We release the dataset, wrappers, code, and per-verdict labels.
Chinese Translation
自动安全评判系统——如 Llama Guard 或 GPT-4o 评分提示——决定模型回复是否有害，产生几乎所有报告的越狱成功率、防御评估和安全排行榜背后的数据。我们探讨这些评判者是根据回复的内容还是其表述方式进行评分。我们固定回复的内容，并添加内容不变的风格包装器：在回复前后放置的固定字符串，仅改变其语气（如教育免责声明、虚假的安全“推理”块、拒绝令后跟随不变的有害内容，或在无害拒绝情况下仅听起来危险的框架）。主体内容逐字保留，因此一个忠实的评判者必须返回相同的判决，任何翻转都是评判者的错误，而不是安全性的变化。在超过600个 JailbreakBench 回复、最多7种形式和8位评判者的实验中，我们通过配对显著性检验和测量噪声底线来评估翻转率。研究结果是精确的而非普遍的：大多数评判者几乎没有变化，但特定评判者存在廉价可利用的盲点。一个令牌拒绝包装器翻转了 19.9% 的 GPT-4o-mini 正确的“危险”判决（噪声底线 0.5%；在三人多数重新评分下为 18.2%），而 Claude 仅变化 0.4%。已部署的 Llama Guard 4 被确定性地操控：“教育课程”框架将其有害判决翻转了 12.3% 为安全。第二个已部署的防护（gpt-oss-safeguard-20b）对此免疫，仅重写评分提示（StrongREJECT 风格）就将相同模型上的攻击减少了十倍——脆弱性存在于评判者而非内容中。两位注释者的人类验证确认了 100% 的内容不变性和 90% 的翻转为评判者错误（kappa 0.95-1.0），而自助法显示基础模型排名在抽样时已经不稳定。我们发布了数据集、包装器、代码和每个判决的标签。
cs.AI / 158 / 2609.08247
zScore-N: A Neural Network for On-Chain Wallet Reputation Scoring
zScore-N：一种用于链上钱包声誉评分的神经网络
Abstract
Wallet reputation scores decide who receives an airdrop, who can borrow, and who enters an allowlist across decentralised finance. They almost always begin as hand-written formulas: compositions of clamped logarithmic, linear and square-root transforms over behavioural features, with every threshold and point award set by hand. Such a formula is readable and deterministic, but it is piecewise and non-differentiable, it cannot improve as data accumulates, and it cannot distinguish a feature that is genuinely zero from one its pipeline failed to capture. We present zScore-N, the neural network that replaced ours in production. The formula served as its teacher: calibrated against 5,208,952 wallets sampled across 2019-2024 and verified to reproduce production output to within 2.3e-13, it supplies unlimited labelled training data at zero label noise. The trained network reproduces it to 0.58 points RMSE on the 1000-point scale (R^2 = 0.99997), against 2.25 for gradient-boosted trees and 28.04 for linear regression on identical features and splits. Trained with missing-value masks against uncorrupted targets, it halves the error that incomplete data introduces: at 10% feature-level missingness the formula drifts 51.4 points from its own complete-data output with a systematic -12.5 point bias, while the network drifts 17.9. The network carries the score at production scale, across a population of millions of wallets spanning six orders of magnitude in size and activity.
Chinese Translation
钱包声誉评分决定了谁能获得空投，谁可以借款，以及谁能进入允许名单，这在去中心化金融中至关重要。它们几乎总是以手写公式的形式开始：对行为特征进行限制的对数、线性和平方根变换的组合，每个阈值和得分点都是手动设置的。这种公式可读且确定，但它是分段的且不可微分，无法随着数据的积累而改进，也无法区分真正为零的特征与其管道未能捕捉到的特征。我们提出了zScore-N，这是一种在生产中替代我们原有模型的神经网络。该公式作为其教师：针对2019-2024年间抽样的5,208,952个钱包进行校准，并验证其能够在生产输出中重现至2.3e-13的精度，它提供了无限的标记训练数据且没有标签噪声。经过训练的网络在1000分制上重现至0.58分的均方根误差（RMSE）（R^2 = 0.99997），而在相同特征和分割下，梯度提升树的误差为2.25，线性回归的误差为28.04。通过缺失值掩码对未损坏的目标进行训练，它将不完整数据引入的误差减少了一半：在特征级别10%的缺失情况下，该公式的输出与自身完整数据输出之间漂移51.4分，并具有系统性-12.5分的偏差，而网络的漂移为17.9。该网络在生产规模下处理评分，覆盖了跨越六个数量级规模和活动的数百万个钱包。
cs.AI / 159 / 2609.08248
Agentic ML Exploration (A-MLE) for Ads Ranking
广告排名的自主机器学习探索（A-MLE）
Gao, Erwin, Sunkara, Vinodh Kumar, Guan, Jingyi, Jia, Qinjin, Xu, Hangjun, Ji, Xiang, Wong, Sherman, Chavali, Surya Teja, Vaishnavi, Pratik, Pandhi, Aryan, Deng, Xiaoyu, Wang, Zhaodong, Inani, Samarth, Yang, Fan, Moberg, Jakob, Zu, Zoe, Bievre, Nicolas, Khenissi, Sami, Jaspal, Amit, Fakharizadi, Ehsan, Viswanathan, Srinidhi, Sun, Dorothy, Vanam, Abishek, Iyer, Sneha, Yadawad, Sheela, Chen, Wenjie, Nahum, Gaby, Gu, Junhua, Chu, Peter, Liu, Yucheng, Zhao, Xin, Cid, Vitor, Chen, Chaorong, Pappu, Vijay, Kumar, Ashwin, Chen, Wenlin, Schulte, Ben, Chandra, Deepak, Tewari, Ritwik
Abstract
Modern industrial ads ranking stacks are increasingly bottlenecked not by model capacity or training compute, but by the throughput of human ML iteration - the cycles of research, implementation, training, debugging, evaluation, and launch required to surface a single statistically significant improvement. A typical ranking stack contains numerous differentiated models with heterogeneous data, architectures, and infrastructure constraints, and each cycle takes days to weeks of senior engineer attention per model. As a result, techniques that have proven effective on one model diffuse into others slowly and unevenly, leaving substantial recoverable signal unexplored. We present Agentic ML Exploration (A-MLE), an autonomous LLM-agent system that systematically explores ML techniques across a portfolio of ads ranking models. A-MLE decomposes ML iteration into five stages involving hypothesis generation, exploration strategy, experiment execution, result analysis and shared knowledge substrate which are orchestrated by a single agent that invokes domain-specific skills and agentic workflows against a sandboxed execution layer, with human-in-the-loop checkpoints at each stage boundary. We deploy A-MLE across a representative set of large-scale ads ranking models and evaluate it along a tiered capability framework (tool availability, autonomous workflow execution, and open-ended exploration). We further report a controlled cross-LLM study using a fixed agent loop, which surfaces qualitative differences in execution reliability and exploration aggressiveness across the Claude Sonnet, Gemini, and GPT families. We discuss failure modes and the design choices that govern reliability. Our findings suggest that agentic exploration is a practical force multiplier for ML engineers in industrial recommenders, especially for the long tail of models that rarely receive expert attention.
Chinese Translation
现代工业广告排名系统的瓶颈越来越多地不是由模型容量或训练计算能力造成的，而是由人类机器学习迭代的吞吐量所限制——即研究、实施、训练、调试、评估和发布所需的周期，以实现单个统计显著性的改进。一个典型的排名系统包含多个具有异构数据、架构和基础设施约束的差异化模型，每个模型的每个周期都需要数天到数周的高级工程师关注。因此，在一个模型上证明有效的技术在其他模型上的传播缓慢且不均匀，留下了大量可恢复信号未被探索。我们提出了自主机器学习探索（A-MLE），这是一个自主的LLM代理系统，系统性地探索一组广告排名模型中的机器学习技术。A-MLE将机器学习迭代分解为五个阶段，包括假设生成、探索策略、实验执行、结果分析和共享知识基础，这些阶段由一个单一代理协调，该代理在一个沙盒执行层上调用特定领域的技能和自主工作流，并在每个阶段边界设置人机协作的检查点。我们在一组具有代表性的规模化广告排名模型上部署了A-MLE，并通过分层能力框架（工具可用性、自主工作流执行和开放式探索）对其进行评估。我们进一步报告了一项使用固定代理循环的控制跨LLM研究，揭示了Claude Sonnet、Gemini和GPT系列在执行可靠性和探索积极性方面的定性差异。我们讨论了影响可靠性的失败模式和设计选择。我们的研究结果表明，自主探索是工业推荐系统中机器学习工程师的一个实用的力量倍增器，尤其是对于那些很少受到专家关注的长尾模型。
cs.AI / 160 / 2609.08254
CircuTutor: Transforming Static Circuit Problems into Intelligent and Dynamic Tutoring
CircuTutor：将静态电路问题转变为智能动态辅导
Abstract
Learning direct current circuit concepts requires learners to connect invisible physical quantities, such as current, voltage, resistance, and power, with observable outcomes such as bulb brightness. Conventional textbook materials and general-purpose circuit simulators provide opportunities for problem solving and exploration but offer limited support for explaining why circuit behavior changes or diagnosing the reasoning behind incorrect answers. We present CircuTutor, a circuit-state-driven intelligent tutoring system that transforms static textbook circuit problems into an interactive tutoring workflow. CircuTutor first uses multimodal problem parsing to extract the textbook question, circuit topology, component parameters, switch states, and answer options, which are converted into a structured task and validated through circuit simulation. Learners can then interactively explore the circuit (by changing parameters) and submit an answer while a SPICE-compatible solver computes physically consistent circuit states. After the learner submits an answer, CircuTutor presents a before-and-after circuit state animation corresponding to the selected operation, organizes the simulated state changes into a causal reasoning chain that explains the underlying circuit behavior, maps answer discrepancies to likely misconceptions, and generates adaptive follow-up exercises targeted at the diagnosed misconception. Our experimental results demonstrate that CircuTutor effectively improves conceptual learning and the overall learning experience. The proposed framework demonstrates how simulated circuit states can be transformed into intelligent and interactive tutoring for circuit education, with the potential to generalize to other STEM domains.
Chinese Translation
学习直流电路概念要求学习者将不可见的物理量（如电流、电压、阻抗和功率）与可观察的结果（如灯泡亮度）联系起来。传统的教科书材料和通用电路模拟器提供了解决问题和探索的机会，但在解释电路行为变化的原因或诊断错误答案背后的推理方面支持有限。我们提出了CircuTutor，一个基于电路状态的智能辅导系统，它将静态教科书电路问题转变为互动辅导工作流程。CircuTutor首先利用多模态问题解析提取教科书问题、电路拓扑、组件参数、开关状态和答案选项，这些信息被转换为结构化任务并通过电路模拟进行验证。学习者可以通过交互式探索电路（通过改变参数）并提交答案，同时一个与SPICE兼容的求解器计算物理上一致的电路状态。在学习者提交答案后，CircuTutor展示与所选操作对应的电路状态的前后动画，将模拟状态变化组织成一个因果推理链，解释潜在的电路行为，将答案差异映射到可能的误解，并生成针对诊断出的误解的自适应后续练习。我们的实验结果表明，CircuTutor有效提升了概念学习和整体学习体验。所提出的框架展示了如何将模拟电路状态转化为电路教育的智能互动辅导，并具有推广到其他STEM领域的潜力。
cs.AI / 161 / 2609.08258
Revoked but Still Authoritative: An Empirical Study of Revocation Enforcement in Agent-Memory Systems
被撤销但仍具权威性：代理记忆系统中撤销执行的实证研究
Abstract
Long-running language-model agents depend on persistent memory. Many agent-memory systems preserve history through soft revocation: a contradicted fact is marked invalid and retained rather than deleted. However, whether that mark is enforced at retrieval time is unexamined. In this paper, we measure five such systems: we load each with a revoked policy and its replacement, track whether the revoked fact is returned at retrieval and whether the agent then acts on it across nine policy scenarios and nine models, and score every trial under six defense conditions. We find that no system enforces revocation by default: the revoked fact is returned wherever the revocation label is visible to the retrieval layer, outranks its replacement, and leads agents to the unsafe action. Based on these findings, we develop a guard that sits between the agent and any memory backend and withholds records that are revoked or conflict with their replacement.
Chinese Translation
长期运行的语言模型代理依赖于持久记忆。许多代理记忆系统通过软撤销来保留历史：被否定的事实被标记为无效并保留，而不是删除。然而，在检索时是否执行该标记尚未得到检验。本文对五个此类系统进行了测量：我们为每个系统加载一个被撤销的策略及其替代品，跟踪在检索时是否返回被撤销的事实，以及代理是否在九种策略场景和九种模型中基于该事实采取行动，并在六种防御条件下对每次试验进行评分。我们的发现是，没有系统默认执行撤销：只要撤销标签对检索层可见，被撤销的事实就会被返回，超越其替代品，并导致代理采取不安全的行动。基于这些发现，我们开发了一个保护机制，位于代理与任何内存后端之间，拒绝返回被撤销或与其替代品冲突的记录。
cs.AI / 162 / 2609.08267
Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation
基于证据对齐的实体验证在检索增强生成中的幻觉检测
Abstract
Hallucination detection is crucial for large language models (LLMs), as hallucinated content creates significant barriers in applications requiring factual accuracy. Current detection methods mainly depend on internal signals like uncertainty and self-consistency checks, using the model's pre-trained knowledge to identify unreliable outputs. However, pre-trained knowledge may become outdated and has coverage limitations, especially for specialized or recent information. To address these limitations, retrieval-augmented generation (RAG) has emerged as a promising solution by retrieving relevant evidence at inference time, grounding outputs beyond the model's parametric knowledge. In this paper, we target a critical and practical learning problem RAG-based hallucination detection (RHD), where RAG is employed to enhance hallucination detection by addressing information updating challenges. To address RHD, we propose a novel method Evidence-Aligned Entity Verification (EAEV), which detects entity-level hallucinations by leveraging RAG to align generated entities with retrieved evidence contexts. Specifically, EAEV evaluates entity-evidence alignment through three complementary dimensions and introduces counterfactual stability analysis to ensure robust alignments under evidence perturbations. Experiments across multiple RAG benchmarks demonstrate that EAEV achieves consistent improvements over existing methods with strong generalization capabilities.
Chinese Translation
幻觉检测对于大型语言模型（LLMs）至关重要，因为幻觉内容在需要事实准确性的应用中造成了显著障碍。目前的检测方法主要依赖于内部信号，如不确定性和自一致性检查，利用模型的预训练知识来识别不可靠的输出。然而，预训练知识可能会过时，并且在覆盖范围上存在局限性，尤其是对于专业或最新的信息。为了解决这些局限性，检索增强生成（RAG）作为一种有前景的解决方案应运而生，通过在推理时检索相关证据，将输出与模型的参数知识相结合。在本文中，我们针对基于RAG的幻觉检测（RHD）这一关键且实际的学习问题，利用RAG来增强幻觉检测，解决信息更新的挑战。为了解决RHD，我们提出了一种新方法——证据对齐实体验证（EAEV），该方法通过利用RAG将生成的实体与检索到的证据上下文对齐，从而检测实体级幻觉。具体而言，EAEV通过三个互补维度评估实体与证据的对齐，并引入反事实稳定性分析，以确保在证据扰动下的稳健对齐。多个RAG基准上的实验表明，EAEV在现有方法上实现了一致的改进，并具有强大的泛化能力。
cs.AI / 163 / 2609.08271
Three Types of Negation of Triple and its Elements and an Extension of Triple
三种三元组及其元素的否定类型与三元组的扩展
Abstract
In various data models, the classical triple is a typical semantic data model. However, due to the design of the triple as a simple structure for representing positive assertions, it cannot sufficiently express different forms of negation present in the triple and its elements. This paper conceptually proposes that there are three distinct forms of negation within triples and their elements: contradictory negation, opposite negation and intermediary negation. Based on the the set SCOI and the logic LCOI+PLCOI with three kinds of negation, we propose an extension of triple that can distinguish and express these three different negations in the triple and its elements, called the TCOI triple with contradictory negation, opposite negation and intermediary negation. The TCOI triple is a semantic and structural extension of the classical triple. While retaining the ability to express positive assertions, it systematically introduces the three semantic dimensions of three negations, allowing these negations to independently act on the elements of the triple and on the whole triple. This significantly enhances the triple model capability to represent and reasoning about complex negative information. This paper also explores the expressive power and reasoning of the TCOI triple, as well as the application of TCOI triple implication reasoning in counterfactuals and counterfactual reasoning. We propose a truth-value (continuous value) algorithm for TCOI triple implication reasoning and perform its calculation through an example of the counterfactuals and counterfactual reasoning.
Chinese Translation
在各种数据模型中，经典三元组是一种典型的语义数据模型。然而，由于三元组被设计为一种简单结构以表示积极断言，它无法充分表达三元组及其元素中存在的不同否定形式。本文概念性地提出三元组及其元素中存在三种不同的否定形式：矛盾否定、对立否定和中介否定。基于集合 SCOI 以及具有三种否定的逻辑 LCOI+PLCOI，我们提出了一种三元组的扩展，能够区分并表达三元组及其元素中的这三种不同的否定，称为具有矛盾否定、对立否定和中介否定的 TCOI 三元组。TCOI 三元组是经典三元组的语义和结构扩展。在保留表达积极断言能力的同时，它系统性地引入了三种否定的三种语义维度，使这些否定能够独立作用于三元组的元素及整个三元组。这显著增强了三元组模型在表示和推理复杂负面信息方面的能力。本文还探讨了 TCOI 三元组的表达能力和推理，以及 TCOI 三元组在反事实及反事实推理中的应用。我们提出了一种用于 TCOI 三元组推理的真值（连续值）算法，并通过反事实及反事实推理的例子进行计算。
cs.AI / 164 / 2609.08273
MemForest: Efficient Agent Memory Management via EventTree Partitioning and Progressive Merging
MemForest：通过事件树分区和渐进合并实现高效的代理记忆管理
Abstract
Agent memory systems have demonstrated significant potential in long-term dialogue, personalized assistants, and video understanding. However, continuously accumulated memory introduces substantial storage and retrieval costs during inference. To address this issue, we propose \textbf{MemForest}, a general memory compression framework adaptable to various agent memory systems. Specifically, MemForest partitions historical memory into event-centric units by leveraging global semantic similarity and local temporal continuity. For each unit, it constructs a maximum spanning tree, termed an EventTree, and progressively merges redundant memory nodes by selecting high-weight edges, reducing storage overhead. Furthermore, we introduce an anchor-guided propagation retrieval mechanism that retrieves relevant memory nodes from the temporal neighborhoods of key nodes, improving retrieval accuracy. Extensive experiments demonstrate the effectiveness of MemForest. Under the unimodal Mem0 framework, MemForest retains \textbf{97.1%} of the original performance while compressing \textbf{50%} of historical memory across three benchmarks (LoCoMo, LongMemEval, and PersonaMem), achieving a \textbf{1.89x} retrieval speedup. Under the multimodal M3-Agent framework, it preserves \textbf{99.7%} of the original performance with a \textbf{50%} compression ratio across two benchmarks (M3-Bench-robot and M3-Bench-web), achieving a \textbf{2.24x} retrieval speedup. \textcolor{RoyalBlue}{\textit{Our code is available at [https://github.com/Celina-love-sweet/MemForest.}}](https://github.com/Celina-love-sweet/MemForest.}})
Chinese Translation
代理记忆系统在长期对话、个性化助手和视频理解方面展现了显著的潜力。然而，持续积累的记忆在推理过程中引入了可观的存储和检索成本。为了解决这一问题，我们提出了 extbf{MemForest}，一个可适应于各种代理记忆系统的通用记忆压缩框架。具体而言，MemForest通过利用全局语义相似性和局部时间连续性，将历史记忆划分为以事件为中心的单元。对于每个单元，它构建一个最大生成树，称为事件树（EventTree），并通过选择高权重边逐步合并冗余的记忆节点，从而减少存储开销。此外，我们引入了一种锚点引导的传播检索机制，从关键节点的时间邻域中检索相关的记忆节点，提高检索准确性。大量实验表明了MemForest的有效性。在单模态Mem0框架下，MemForest在压缩 extbf{50 ext{%}}的历史记忆的同时，保留了 extbf{97.1 ext{%}}的原始性能，在三个基准（LoCoMo、LongMemEval和PersonaMem）上实现了 extbf{1.89倍}的检索加速。在多模态M3-Agent框架下，它在两个基准（M3-Bench-robot和M3-Bench-web）上以 extbf{50 ext{%}}的压缩比保留了 extbf{99.7 ext{%}}的原始性能，实现了 extbf{2.24倍}的检索加速。我们的代码可在[https://github.com/Celina-love-sweet/MemForest](https://github.com/Celina-love-sweet/MemForest)获取。
cs.AI / 165 / 2609.08275
Beyond Coherence: Benchmarking Professional Editing-Technique Execution in Multi-Shot Audio-Video Generation
超越连贯性：多镜头音视频生成中的专业编辑技术执行基准测试
Abstract
Recent multi-shot audio-video generators can produce increasingly coherent and cinematic outputs, but coherence does not imply the ability to execute editing techniques. Professional editing depends on shot structure, transition grammar, audio-video cut relations, and montage, yet existing benchmarks largely rely on proxies such as content quality, synchronization, or physical plausibility, systematically missing whether such editing instructions are actually executed. We introduce CutCraft, the first benchmark for editing-technique execution in multi-shot audio-video generation. CutCraft extends structured multi-shot prompts with explicit editing specifications and is paired with a hierarchical hybrid evaluation framework that combines shot-structure alignment, expert-model metrics, tool-grounded multimodal judgment, and rubric-based question answering. Beyond evaluation, we design an agentic editing baseline that decomposes generation into planning, shot-level synthesis, and post-hoc composition, explicitly realizing editing semantics such as J-cuts, L-cuts, and transition timing. Across 13 state-of-the-art closed- and open-source models, CutCraft reveals a consistent gap between coherence and editing-technique execution: current systems often produce plausible multi-shot videos yet fail to execute editorial instructions reliably. We find unstable shot structures, weak control of transition execution, and sharp degradation on higher-order montage, while aesthetic quality is only weakly correlated with editing-technique compliance. The benchmark and metrics, and the editing agent baseline are available at https://github.com/AlibabaResearch/cut-craft-bench.
Chinese Translation
最近的多镜头音视频生成器能够产生越来越连贯和电影化的输出，但连贯性并不意味着能够执行编辑技术。专业编辑依赖于镜头结构、过渡语法、音视频剪切关系和蒙太奇，然而现有的基准测试主要依赖于内容质量、同步性或物理合理性等代理指标，系统性地忽视了这些编辑指令是否真正被执行。我们介绍了 CutCraft，这是第一个用于多镜头音视频生成中编辑技术执行的基准测试。CutCraft 通过明确的编辑规范扩展了结构化的多镜头提示，并配备了一个分层混合评估框架，该框架结合了镜头结构对齐、专家模型指标、工具基础的多模态判断和基于评分标准的问题回答。除了评估之外，我们设计了一个自主编辑基线，将生成过程分解为规划、镜头级合成和事后构图，明确实现 J-cut、L-cut 和过渡时机等编辑语义。在 13 个最先进的闭源和开源模型中，CutCraft 揭示了连贯性与编辑技术执行之间的一致差距：当前系统通常能够生成合理的多镜头视频，但未能可靠地执行编辑指令。我们发现镜头结构不稳定、过渡执行控制薄弱，以及高阶蒙太奇的显著降级，而审美质量与编辑技术合规性之间的相关性较弱。基准测试和指标，以及编辑代理基线可在 https://github.com/AlibabaResearch/cut-craft-bench 获取。
cs.AI / 166 / 2609.08288
LEBGen: An LLM-Enhanced Bayesian Network Framework for Few-Shot Travel Survey Data Generation
LEBGen：一种基于大语言模型增强的贝叶斯网络框架用于少量旅行调查数据生成
Abstract
Travel survey data are essential for transportation planning and travel behavior analysis, yet collecting large-scale representative samples is costly and time-consuming. A practical alternative is to generate synthetic survey records from a few-shot sample. However, such samples provide incomplete coverage of heterogeneous traveler groups and insufficient evidence for recovering the complex dependencies between demographic characteristics and travel behavior. Existing approaches have complementary limitations. Probabilistic generative models such as Bayesian networks (BNs) offer explicit distributional control, but structures learned from few-shot samples may omit meaningful dependencies or retain spurious ones. Large language models (LLMs) can help address these difficulties in BN structure learning by providing behavioral knowledge that complements the limited statistical evidence. We therefore propose LEBGen, an LLM-enhanced BN framework that uses this knowledge to refine network structure for few-shot travel survey data generation. Specifically, the LLM first identifies traveler personas from demographic attribute and travel behavior statistics, then recovers dependencies missed by the persona-augmented BN structure and prune spurious ones. The refined BN is parameterized exclusively from the observed data to generate synthetic records. Under a 2% few-shot setting on the 2022 Hong Kong Travel Characteristics Survey, LEBGen reduces the mean marginal Jensen-Shannon divergence from 0.0671 to 0.0091 and the mean absolute Cramer's V error by 14.3% over the best-performing baseline, substantially improving both distributional and dependency fidelity.
Chinese Translation
旅行调查数据对于交通规划和旅行行为分析至关重要，但收集大规模代表性样本既昂贵又耗时。一个实用的替代方案是从少量样本中生成合成调查记录。然而，这些样本对异质旅行者群体的覆盖不完整，且缺乏足够的证据来恢复人口特征与旅行行为之间的复杂依赖关系。现有方法各有局限。概率生成模型如贝叶斯网络（Bayesian Networks, BNs）提供了明确的分布控制，但从少量样本学习的结构可能会遗漏重要的依赖关系或保留虚假的依赖关系。大语言模型（Large Language Models, LLMs）可以通过提供补充有限统计证据的行为知识来帮助解决BN结构学习中的这些困难。因此，我们提出了LEBGen，一个基于LLM增强的BN框架，利用这些知识来优化少量旅行调查数据生成的网络结构。具体而言，LLM首先根据人口属性和旅行行为统计识别旅行者角色，然后恢复被角色增强的BN结构遗漏的依赖关系，并修剪虚假的依赖关系。优化后的BN仅从观察数据中参数化，以生成合成记录。在2022年香港旅行特征调查的2%少量样本设置下，LEBGen将平均边际詹森-香农散度从0.0671降低到0.0091，平均绝对克莱默V误差比最佳基线降低14.3%，显著提高了分布和依赖的保真度。
cs.AI / 167 / 2609.08407
FastE: Readout-Triggered Token Compression for LLM Embedding Inference
FastE：用于大规模语言模型嵌入推理的读出触发令牌压缩
Abstract
In this study, we identify depth-dependent prefix redundancy in final-readout LLM embedding models, notably across representative backbones including Qwen3-Embedding and Qwen3-VL-Embedding. We find that removing prefix states is substantially more damaging in shallow layers than at greater depth, showing that prefix states become increasingly compressible as the prefix and readout states propagate through the network. To this end, we introduce FastE, a training-free, plug-and-play method. FastE uses a shared fixed threshold on batch-mean readout-prefix alignment as a lightweight online heuristic for selecting when compression occurs, and ranks prefix states by the attention scores they receive from the readout position to determine which states are retained in subsequent layers. Our evaluations demonstrate FastE's ability to substantially reduce computational costs: on NarrativeQA with Qwen3-Embedding-0.6B, it reduces decoder-backbone FLOPs by 40.11% while retaining 99.53% of Full Forward nDCG@10. Across five text embedding benchmarks, two backbone scales, and three cross-modal retrieval tasks, the quality-efficiency trade-off is directly customizable through the maximum removal ratio without retraining. We believe FastE offers practical value for scalable embedding generation in retrieval, indexing, clustering, and multimodal representation systems.
Chinese Translation
在本研究中，我们识别了最终读出大规模语言模型（LLM）嵌入模型中的深度依赖前缀冗余，特别是在包括 Qwen3-Embedding 和 Qwen3-VL-Embedding 的代表性骨干网络中。我们发现，去除前缀状态在浅层的影响远大于在深层，显示出随着前缀状态和读出状态在网络中传播，前缀状态变得越来越可压缩。为此，我们提出了 FastE，一种无需训练的即插即用方法。FastE 使用批量均值读出-前缀对齐的共享固定阈值，作为选择何时进行压缩的轻量级在线启发式，并通过读出位置接收到的注意力分数对前缀状态进行排名，以确定在后续层中保留哪些状态。我们的评估表明，FastE 能显著降低计算成本：在使用 Qwen3-Embedding-0.6B 的 NarrativeQA 上，它将解码器-骨干网络的 FLOPs 降低了 40.11%，同时保留了 99.53% 的完整前向 nDCG@10。在五个文本嵌入基准、两个骨干网络规模和三个跨模态检索任务中，质量与效率的权衡可以通过最大去除比例直接自定义，而无需重新训练。我们相信，FastE 为可扩展的嵌入生成在检索、索引、聚类和多模态表示系统中提供了实际价值。
cs.AI / 168 / 2609.08418
Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models
Feyospace-v1：网络水星七人组如何训练前沿网络模型
Abstract
Training capable cyber agents is often treated primarily as a problem of model scale, yet open-weight post-training is constrained more directly by the cost of executable environments, reliable multi-turn supervision, and access to strong teachers. We present a data-centric framework that addresses these bottlenecks through five complementary systems: Choulea analyzes hidden reasoning signatures, SkyReal reduces teacher-sampling cost, Hongzwang bypasses API restrictions on teacher execution, PSBreakup restores capabilities weakened by model merging, and Kreator converts expert interventions into trainable reasoning. Our data engine constructs resettable coding, vulnerability, CTF, kernel-history, full-exploit, firmware, and device-backed environments. Candidate trajectories are retained only after execution verification and evidence auditing, yielding 164,269 trajectories for long-context supervised fine-tuning. The three checkpoints improve over their starting models by an average of 23.76% on the full CyberGym suite and 10.49% across the pooled CTF suites. As of September 1, 2026, Feyospace-s1 achieves a verified success rate of 63.24% and ranks 10th on the official CyberGym leaderboard, while all three checkpoints rank 1st among models at comparable parameter scales. To our knowledge, this is the first end-to-end demonstration that a seven-person independent team can train open-weight models with leading agentic cyber capability.
Chinese Translation
训练有能力的网络代理通常主要被视为模型规模的问题，然而开放权重的后期训练更直接受到可执行环境的成本、可靠的多轮监督和强大教师的访问限制。我们提出了一个以数据为中心的框架，通过五个互补系统来解决这些瓶颈：Choulea 分析隐藏的推理特征，SkyReal 降低教师采样成本，Hongzwang 绕过教师执行的 API 限制，PSBreakup 恢复因模型合并而削弱的能力，Kreator 将专家干预转化为可训练的推理。我们的数据引擎构建了可重置的编码、漏洞、CTF、内核历史、完整利用、固件和设备支持的环境。候选轨迹仅在执行验证和证据审计后保留，最终为长上下文的监督微调提供了 164,269 条轨迹。这三个检查点在完整的 CyberGym 套件上平均提高了 23.76%，在合并的 CTF 套件上提高了 10.49%。截至 2026 年 9 月 1 日，Feyospace-s1 实现了 63.24% 的验证成功率，并在官方 CyberGym 排行榜中排名第 10，而所有三个检查点在相似参数规模的模型中排名第 1。据我们所知，这是首次端到端演示一个七人独立团队能够训练具有领先代理网络能力的开放权重模型。
cs.AI / 169 / 2609.08435
EvolveScaler: Synthesizing Information-Evolution Contexts via Executable State Machines and Natural-Language Rendering
EvolveScaler：通过可执行状态机和自然语言渲染合成信息演变上下文
Abstract
In persistent interactions, long contexts may encode an evolving process rather than a fixed record: later events can revise or revoke earlier information, changing what remains valid and what conclusions follow. We call this setting information evolution (IE). Solving IE requires identifying valid records, applying updates in order, and reconstructing the query-relevant state from the event history. Existing text-first synthesis pipelines make such data difficult to verify because state transitions and answer logic remain implicit. We introduce EvolveScaler, a code-driven framework that defines information evolution before rendering it as natural language. Human-authored operational specifications define state transitions, record validity, difficulty controls, and executable answer logic; a strong LLM then synthesizes a self-contained simulator from each specification. Executing validated simulators produces natural-language multi-turn event histories, while deterministic replay computes reference answers and atomic checklists. We instantiate EvolveScaler with 117 task prototypes and 159 final-question operators across five difficulty levels spanning approximately 7 to 1,200 events per instance, yielding about 35,100 training examples and 585 validated evaluation instances. On the very_long tier, the strongest model reaches 59.3% avg@5, while six models score below 10%. Training an internal A3B model on 6,000 EvolveScaler examples improves performance over its base checkpoint on all eight independently constructed out-of-distribution benchmarks, with a 5.25-point average gain. These results show that code-driven IE synthesis provides both challenging evaluation and transferable training supervision.
Chinese Translation
在持续的交互中，长上下文可能编码一个演变过程，而不是一个固定记录：后续事件可以修订或撤销早期信息，改变什么是有效的以及随之而来的结论。我们称这种设置为信息演变（Information Evolution, IE）。解决IE需要识别有效记录、按顺序应用更新，并从事件历史中重构与查询相关的状态。现有的文本优先合成管道使得此类数据难以验证，因为状态转换和答案逻辑仍然是隐含的。我们引入了EvolveScaler，这是一个以代码驱动的框架，在将信息演变渲染为自然语言之前定义其内容。人类编写的操作规范定义了状态转换、记录有效性、难度控制和可执行答案逻辑；然后，一个强大的大型语言模型（LLM）从每个规范合成一个自包含的模拟器。执行经过验证的模拟器生成自然语言的多轮事件历史，而确定性重放计算参考答案和原子检查清单。我们用117个任务原型和159个最终问题操作符实例化EvolveScaler，涵盖五个难度级别，每个实例大约包含7到1,200个事件，产生约35,100个训练示例和585个经过验证的评估实例。在very_long层级，最强模型达到59.3%的avg@5，而六个模型的得分低于10%。在6,000个EvolveScaler示例上训练的内部A3B模型在所有八个独立构建的分布外基准上相较于其基础检查点提高了性能，平均增益为5.25分。这些结果表明，以代码驱动的信息演变合成提供了具有挑战性的评估和可转移的训练监督。
cs.AI / 170 / 2609.08452
SRPO: Setwise Relative Policy Optimization for Multi-Agent LLMs
SRPO：多智能体大语言模型的集合相对策略优化
Abstract
Multi-agent large language models solve complex tasks by coordinating several policies in a shared environment. However, existing reinforcement learning methods usually optimize each response or trajectory separately, even when several outputs jointly cause one state transition. Consequently, the update unit differs from the action executed by the system. To address this problem, we propose SRPO (Setwise Relative Policy Optimization), which treats the active set the minimal set of outputs consumed by one transition, as one multi-agent action. Specifically, SRPO combines member log-ratios into one cardinality-normalized set ratio, assigns one relative advantage, and clips the set once. This formulation unifies division of labor and joint co-evolution as actions with different set sizes. Experiments on mathematical reasoning and multi-turn search demonstrate one training interface for fixed, mixed, and dynamically routed workflows across four model scales, with the strongest macro-average results among the reported comparisons. Optimization diagnostics further characterize its stability under different event reductions and set sizes.
Chinese Translation
多智能体大语言模型通过在共享环境中协调多个策略来解决复杂任务。然而，现有的强化学习方法通常分别优化每个响应或轨迹，即使多个输出共同导致一个状态转移。因此，更新单元与系统执行的动作不同。为了解决这个问题，我们提出了SRPO（集合相对策略优化），将活跃集合视为由一个转移消耗的最小输出集合，作为一个多智能体动作。具体而言，SRPO将成员对数比率组合成一个基于基数归一化的集合比率，分配一个相对优势，并对集合进行一次裁剪。这种表述统一了不同集合大小的劳动分工和联合共同进化作为动作。在数学推理和多轮搜索的实验中，展示了一个适用于固定、混合和动态路由工作流的训练接口，涵盖四种模型规模，并在报告的比较中获得了最强的宏平均结果。优化诊断进一步表征了其在不同事件减少和集合大小下的稳定性。
cs.AI / 171 / 2609.08558
Personalizing LLM Agent Memory Using Biometrics
基于生物特征的个性化大语言模型代理记忆
Abstract
Personalized memory helps LLM agents deliver stable, tailored assistance by storing and reusing user-specific data across interactions. In multi-user scenarios, however, retrieval must consider not only semantic similarity but also whether the current requester matches the identity associated with the stored memory. We propose Bio-Memory, a biometric-aware memory architecture that conditions memory retrieval on both semantic similarity and biometric matching. Built on top of A-Mem, Bio-Memory augments each atomic memory note with a biometric embedding and uses biometric matching to form the retrieval candidate pool before semantic ranking. We evaluate Bio-Memory on LoCoMo in a 10-user shared-agent setting over 7 face benchmarks and 10 palmprint protocols. Across datasets, Bio-Memory consistently separates owner and non-owner queries. Under face-based personalization, the largest average gap reaches 27.29% / 21.15% in F1 / BLEU-1 on CALFW; under palmprint-based personalization, the corresponding gap is 25.75% / 19.22% on MS_Blue. These results support biometrics as a practical control signal for personalized memory retrieval in shared environments.
Chinese Translation
个性化记忆帮助大语言模型（LLM）代理通过在交互中存储和重用用户特定数据来提供稳定、量身定制的帮助。然而，在多用户场景中，检索不仅必须考虑语义相似性，还必须考虑当前请求者是否与存储记忆相关联的身份匹配。我们提出了Bio-Memory，一种生物特征感知的记忆架构，它在记忆检索中同时考虑语义相似性和生物特征匹配。Bio-Memory建立在A-Mem之上，为每个原子记忆条目增强了生物特征嵌入，并使用生物特征匹配来形成检索候选池，然后进行语义排序。我们在LoCoMo上评估了Bio-Memory，在一个10用户共享代理的设置中，涉及7个面部基准和10个掌纹协议。在各个数据集中，Bio-Memory始终能够有效区分所有者和非所有者的查询。在基于面部的个性化中，最大的平均差距在CALFW上达到了27.29% / 21.15%（F1 / BLEU-1）；在基于掌纹的个性化中，相应的差距在MS_Blue上为25.75% / 19.22%。这些结果支持生物特征作为共享环境中个性化记忆检索的实用控制信号。
cs.AI / 172 / 2609.08566
BIO-MEMART: Biometric-Aware KV Cache Memory for Multi-User LLM Agents
BIO-MEMART：面向多用户 LLM 代理的生物识别感知 KV 缓存内存
Abstract
KV cache is evolving from a serving optimization into an external memory substrate for long-term LLM agents. In a shared multi-user deployment, however, reusable KV blocks introduce a missing access-control question: semantic relevance alone cannot determine whether a memory block is authorized for the current physical user. We propose Bio-MemArt, a biometric-aware KV-cache memory framework for multi-user LLM agents. Bio-MemArt attaches a normalized biometric template to each stored KV memory block, filters the shared memory pool with the current user's biometric probe, and then runs the original MemArt retrieval and KV reuse pipeline only inside the authorized candidate pool. This design preserves latent-space retrieval, direct cache reuse, and decoupled position encoding while adding physical-user access control to shared KV memory. We evaluate Bio-MemArt under Owner and Non-owner query conditions on long-term dialogue QA with face and palmprint benchmarks. Across face benchmarks, the average owner and non-owner biometric success rates are 95.71% and 0.86%; across palmprint benchmarks, they are 97.60% and 2.00%. In the efficiency study, average prefill tokens drop from 18,781.96 under full-context prompting to 28.57 with Bio-MemArt, showing that biometric gating preserves the low-token operating regime of KV-cache memory.
Chinese Translation
KV 缓存正从服务优化演变为长期 LLM 代理的外部内存基础。然而，在共享的多用户部署中，可重用的 KV 块引入了一个缺失的访问控制问题：仅凭语义相关性无法确定某个内存块是否被当前物理用户授权。我们提出了 Bio-MemArt，这是一种面向多用户 LLM 代理的生物识别感知 KV 缓存内存框架。Bio-MemArt 为每个存储的 KV 内存块附加一个标准化的生物识别模板，通过当前用户的生物识别探测器过滤共享内存池，然后仅在授权候选池内运行原始的 MemArt 检索和 KV 重用管道。该设计在为共享 KV 内存添加物理用户访问控制的同时，保留了潜在空间检索、直接缓存重用和解耦位置编码。我们在面部和掌纹基准测试的长期对话问答中评估了 Bio-MemArt 在所有者和非所有者查询条件下的表现。在面部基准测试中，平均所有者和非所有者的生物识别成功率分别为 95.71% 和 0.86%；在掌纹基准测试中，这一成功率分别为 97.60% 和 2.00%。在效率研究中，平均预填令牌数量从全上下文提示下的 18,781.96 降至使用 Bio-MemArt 时的 28.57，显示生物识别门控保留了 KV 缓存内存的低令牌操作模式。
cs.AI / 173 / 2609.08572
AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems
AgentGrad：基于干预指导的多智能体系统提示优化
Abstract
Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance depends on the prompt design of each agent. For MAS prompt optimization, textual gradient methods that guide prompt updates using natural-language feedback have emerged as a leading paradigm. In this paper, we identify limitations in two stages of existing textual gradient approaches: gradient extraction and gradient aggregation. In gradient extraction, previous works select a target prompt without verifying whether modifying it resolves the failure, and derive gradients without agent-level supervision over the corresponding agent's intermediate output. In gradient aggregation, individual gradients are randomly grouped and concatenated, often mixing unrelated failure modes and producing prompts that fail to generalize. To address these limitations, we propose \textbf{AgentGrad}, a prompt optimization framework for multi-agent systems based on sequential intervention and semantic textual gradient abstraction. For each failure, sequential intervention modifies the behavior of one agent at a time to identify the target agent whose modification resolves the failure. The modified output of the target agent then serves as agent-level supervision for extracting a fine-grained gradient. Semantic textual gradient abstraction clusters semantically similar gradients to prevent mixing unrelated failure modes, and abstracts each cluster into a generalized gradient that captures the shared corrective pattern. Experimental results show that AgentGrad achieves state-of-the-art performance across five MAS benchmarks and reduces wall-clock optimization time by $2.5\times$ on average compared to the next-fastest baseline.
Chinese Translation
基于大型语言模型（LLM）的多智能体系统（MAS）通过采用专业化的多个智能体实现了强大的性能，但其表现依赖于每个智能体的提示设计。在MAS提示优化中，利用自然语言反馈指导提示更新的文本梯度方法已成为一种主要范式。本文识别了现有文本梯度方法在两个阶段的局限性：梯度提取和梯度聚合。在梯度提取阶段，以前的研究在未验证修改目标提示是否解决失败的情况下选择目标提示，并在缺乏对相应智能体中间输出的智能体级监督的情况下推导梯度。在梯度聚合阶段，个体梯度被随机分组和连接，通常混合不相关的失败模式，导致生成的提示无法泛化。为了解决这些局限性，我们提出了 extbf{AgentGrad}，一个基于顺序干预和语义文本梯度抽象的多智能体系统提示优化框架。对于每个失败，顺序干预一次修改一个智能体的行为，以识别修改能够解决失败的目标智能体。目标智能体的修改输出随后作为提取细粒度梯度的智能体级监督。语义文本梯度抽象将语义相似的梯度聚类，以防止混合不相关的失败模式，并将每个聚类抽象为捕捉共享纠正模式的广义梯度。实验结果表明，AgentGrad在五个MAS基准测试中实现了最先进的性能，并将优化时间平均减少了$2.5 imes$，相比于下一个最快的基线。
cs.AI / 174 / 2609.08592
A Three-Tier Persona Vector for Controllable User Simulation in Agentic Evaluation
用于可控用户模拟的三层角色向量在代理评估中的应用
Abstract
Evaluating tool-augmented LLM agents requires diverse, realistic user inputs yet most evaluation frameworks use flat role descriptions ("you are an angry customer") that produce near-identical conversations regardless of the underlying scenario. In this paper, we propose a three-tier persona vector with 23 operationalized dimensions: 6 categorical demographics (jurisdiction, age, channel, device, language proficiency, time availability), 12 continuous behavioral traits (patience, assertiveness, digital literacy, etc.) sampled with Gaussian noise around curated profile base vectors, and 5 continuous emotional states (frustration, anxiety, trust, confidence, stress) that shift in response to scenario context. Orthogonal to the persona, a 4-level query-complexity overlay controls utterance phrasing from direct to deliberately vague. We evaluate the persona model inside a synthetic data generation pipeline across 64,698 multi-turn conversations spanning 8 named profiles and 3 production corpora. Key findings: (i) a 15.8 percentage-point spread in agent goal-achievement across personas confirms trait vectors produce measurably different user behavior; (ii) the same persona behaves differently across scenarios due to scenario-reactive emotional state shifts, validating the scenario-reactive design; (iii) domain-specific projects show persona sensitivity on booking-flow compliance (~15-20 percentage points gap between tier-aware and pressure-test personas), demonstrating the model faithfully reproduces real-world difficulty distributions; (iv) seven rule-described trait correlations produce auditable co-occurrence patterns without requiring learned covariance matrices. The persona model is fully specified for reproduction.
Chinese Translation
评估工具增强的LLM代理需要多样化且真实的用户输入，但大多数评估框架使用扁平的角色描述（如“你是一个愤怒的顾客”），导致无论底层场景如何，产生几乎相同的对话。本文提出了一种具有23个操作化维度的三层角色向量：6个分类人口统计特征（管辖区、年龄、渠道、设备、语言能力、时间可用性）、12个连续行为特征（耐心、果断、数字素养等），这些特征围绕策划的基础向量以高斯噪声进行采样，以及5个连续情感状态（挫折、焦虑、信任、自信、压力），这些状态会根据场景上下文而变化。与角色无关的4级查询复杂度叠加层控制从直接到故意模糊的发言措辞。我们在一个合成数据生成管道中评估了该角色模型，涵盖64,698个多轮对话，涉及8个命名角色和3个生产语料库。主要发现：(i) 角色之间的代理目标实现率差异达到15.8个百分点，确认特征向量产生可测量的不同用户行为；(ii) 同一角色在不同场景中表现不同，因场景反应的情感状态变化，验证了场景反应设计的有效性；(iii) 特定领域项目显示角色对预订流程合规性的敏感性（层级感知角色与压力测试角色之间约15-20个百分点的差距），证明模型忠实再现了现实世界的难度分布；(iv) 七个规则描述的特征相关性产生可审计的共现模式，而无需学习协方差矩阵。该角色模型已完全规范化以便于复现。
cs.AI / 175 / 2609.08599
Graph-Based Personalized Memory for LLM Agents: Representation, Evolution, Retrieval, and Evaluation
基于图的个性化记忆用于大语言模型代理：表征、演变、检索与评估
Abstract
Large Language Model (LLM) agents are evolving from single-session tools toward long-term personal assistants that must adapt to individual users across tasks, contexts, and interactions. This shift makes memory a core requirement for personalization, since user preferences, goals, constraints, relationships, and past experiences are accumulated gradually and often change over time. Graph-based personalized memory provides a structured way to model such user information through explicit relations, temporal context, and evidence links. Such representations can model not only what an agent remembers about a user but also how memories are connected, revised, and retrieved to support personalized decisions. However, existing work remains fragmented across personalized agents and generic graph memory frameworks, making it difficult to understand the design space as a whole. This survey develops a lifecycle-oriented view of graph-based personalized memory for LLM agents. We organize existing studies around memory representation, memory evolution, memory retrieval, and memory evaluation. We further compare key design choices, discuss current evaluation practices, and open challenges in building reliable long-term personalized agents. This survey aims to clarify how graph-based memory can support adaptive, controllable, and user-centric LLM agents.
Chinese Translation
大型语言模型（LLM）代理正从单一会话工具演变为长期个人助理，必须在任务、上下文和互动中适应个体用户。这一转变使得记忆成为个性化的核心需求，因为用户的偏好、目标、限制、关系和过往经验是逐渐积累的，并且常常随着时间而变化。基于图的个性化记忆提供了一种结构化的方式，通过显式关系、时间上下文和证据链接来建模这些用户信息。这种表征不仅可以建模代理对用户的记忆内容，还可以建模记忆之间的连接、修订和检索，以支持个性化决策。然而，现有的研究在个性化代理和通用图记忆框架之间仍然存在碎片化，使得整体设计空间难以理解。本调查研究为LLM代理的基于图的个性化记忆发展了一种生命周期导向的视角。我们围绕记忆表征、记忆演变、记忆检索和记忆评估组织现有研究。我们进一步比较关键设计选择，讨论当前评估实践，以及在构建可靠的长期个性化代理方面的挑战。本调查旨在阐明基于图的记忆如何支持自适应、可控和以用户为中心的LLM代理。
cs.AI / 176 / 2609.08602
CLAMP: Constrained Decoding for Vision-Language Embodied Planning
CLAMP：视觉-语言具身规划的约束解码
Abstract
Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequences. However, fluent plans are not always executable. A VLM may refer to objects that are not visually observed, select actions whose required affordances are unavailable, or violate syntax and action constraints. We introduce CLAMP, a multimodal constraint-grounding framework that turns scene evidence into decoding-time constraints for a frozen VLM planner. CLAMP uses the initial observation to restrict object references to those supported by the scene, while a provided symbolic action model specifies state transitions and goals. During decoding, hard masks eliminate invalid next-token candidates, while a Hidden Markov Model (HMM)-based world-state lookahead module reweights the probabilities of the remaining feasible candidates based on action preconditions and goal reachability. This allows the planner to retain the VLM's language prior while preventing visually unsupported, unsafe, or infeasible candidates from entering the plan. For unseen tasks and environments, CLAMP adapts the HMM at test time using label-free continuations sampled from the frozen VLM. Experiments on VLABench, SafeAgentBench, and TaPA show that scene-grounded constraints improve object grounding and safety, while most remaining failures stem from perception errors or misaligned constraint specifications.
Chinese Translation
具身规划日益依赖视觉-语言模型（VLM）将指令和视觉观察转化为可执行的行动序列。然而，流畅的计划并不总是可执行的。VLM可能会引用未被视觉观察到的对象，选择所需的可供性不可用的行动，或违反语法和行动约束。我们提出了CLAMP，一个多模态约束基础框架，将场景证据转化为冻结的VLM规划器的解码时约束。CLAMP利用初始观察来限制对象引用为场景所支持的对象，同时提供的符号行动模型指定状态转移和目标。在解码过程中，硬掩码消除无效的下一个标记候选项，而基于隐马尔可夫模型（HMM）的世界状态前瞻模块根据行动前提和目标可达性重新加权剩余可行候选项的概率。这使得规划者能够保留VLM的语言先验，同时防止视觉上不支持、不安全或不可行的候选项进入计划。对于未见过的任务和环境，CLAMP在测试时使用从冻结的VLM中采样的无标签延续来适应HMM。对VLABench、SafeAgentBench和TaPA的实验表明，基于场景的约束改善了对象定位和安全性，而大多数剩余的失败源于感知错误或约束规范的不一致。
cs.AI / 177 / 2609.08719
GoAnt: Quality-Diversity Multi-Agent Search for Alpha Factor Discovery in Market Microstructure Data
GoAnt：市场微观结构数据中阿尔法因子的质量-多样性多智能体搜索
Abstract
Automated alpha factor discovery searches symbolic trading signals from price-volume panels and order-book data under a fixed evaluation budget. Existing single- and multi-agent program-search systems can overfit predictive proxies that fail after execution costs and repeatedly explore redundant factor families, limiting execution robustness and behavioral diversity. We introduce GoAnt, a quality-diversity multi-agent search framework that combines non-communicating Explorer, Exploiter and Connector workers with a shared adaptive Mental Map and a compact Queen dispatcher. The Mental Map organizes candidates by leakage-free execution profiles and retains one elite per niche, while the Queen reallocates the evaluation budget from explicit search-state summaries. We also define a map-independent effective-yield protocol that counts high-quality, mutually nonredundant factors directly from each method's evaluation records, giving archive-based and map-free systems the same ruler. On real A-share microstructure data spanning 2023--2026, GoAnt reaches quality-weighted yields of 41.8 and 47.6 in price-volume and order-book settings, improving the strongest baseline by 57% and 97% under matched budgets. Its locked populations retain 0.64 and 0.67 of in-sample quality out of sample, compared with 0.61 and 0.63 for a static map.
Chinese Translation
自动化的阿尔法因子发现从价格-成交量面板和订单簿数据中搜索符号交易信号，且在固定的评估预算下进行。现有的单智能体和多智能体程序搜索系统可能会过拟合预测代理，这些代理在执行成本后失效，并且重复探索冗余因子家族，从而限制了执行的稳健性和行为的多样性。我们提出了GoAnt，一个质量-多样性多智能体搜索框架，它结合了不进行通信的探索者（Explorer）、开发者（Exploiter）和连接者（Connector）工作者，配备共享的自适应心理地图（Mental Map）和紧凑的女王调度器（Queen dispatcher）。心理地图通过无泄漏的执行特征组织候选者，并在每个生态位中保留一个精英，而女王则从显式搜索状态摘要中重新分配评估预算。我们还定义了一种与地图无关的有效收益协议，直接从每种方法的评估记录中计算高质量、相互不冗余的因子，为基于档案和无地图系统提供相同的标准。在2023年至2026年间的真实A股微观结构数据上，GoAnt在价格-成交量和订单簿设置中分别达到了41.8和47.6的质量加权收益，在匹配预算下提高了最强基线的57%和97%。其锁定种群在样本外保留了0.64和0.67的样本内质量，而静态地图的对应值为0.61和0.63。
cs.AI / 178 / 2609.08729
Application of curiosity driven exploration methods for hardware interference identification
基于好奇心驱动的探索方法在硬件干扰识别中的应用
Abstract
The transition from single-core to multi-core architectures in safety-critical embedded systems introduces significant challenges due to inter-core interference caused by contention for shared hardware resources. Such interference affects execution times and complicates the verification of strict temporal requirements, particularly in domains such as avionics where standards require comprehensive identification of interference sources. Existing interference analysis approaches, whether manual or model-based, struggle to capture the full range of behaviors arising from the complex interactions among micro-architectural components. In this paper, we frame multi-core interference analysis as the exploration of a complex system behavior space. We propose the use of curiosity-driven exploration algorithms from artificial intelligence to systematically and efficiently cover the space of possible interference behaviors. Using a simulator-based environment, we show that the proposed approach achieves broader and more uniform behavioral coverage within a limited experimental budget compared to traditional pseudo-random program generation methods.
Chinese Translation
在安全关键的嵌入式系统中，从单核架构转向多核架构带来了显著的挑战，主要是由于对共享硬件资源的争用导致的核心间干扰。这种干扰影响执行时间，并使严格的时间要求验证变得复杂，尤其是在航空电子等领域，标准要求全面识别干扰源。现有的干扰分析方法，无论是手动还是基于模型的，都难以捕捉微架构组件之间复杂交互所产生的全部行为。在本文中，我们将多核干扰分析框架视为对复杂系统行为空间的探索。我们提出利用人工智能中的好奇心驱动探索算法，系统性和高效地覆盖可能的干扰行为空间。通过基于模拟器的环境，我们展示了所提方法在有限的实验预算内，相较于传统的伪随机程序生成方法，能够实现更广泛和更均匀的行为覆盖。
cs.AI / 179 / 2609.08736
When Can One Obtain Certificates of Optimality Using Positivstellensaetze?
何时可以使用 Positivstellensätze 获得最优性证书？
Abstract
We study certificates of positivity and optimality for learning problems whose objectives and constraints need not be polynomial. We isolate an axiomatic core of Fischer's constructive strict and weak Positivstellens\"{a}tze and prove the resulting theorems for abstract function algebras over ordered fields. The framework separates two roles that can otherwise be conflated: objective and constraint functions may be built from broad classes of continuous or definable operations, while the auxiliary primitives used to construct a certificate satisfy explicit scalar and closure axioms. We give instances over continuous and definable function algebras, including ordered fields not closed under square roots, derive lower-bound and global-optimality certificates, and analyze both expanded term length and shared computation-graph complexity.
Chinese Translation
我们研究了学习问题的正性和最优性证书，这些问题的目标和约束不必是多项式。我们提炼了 Fischer 的构造性严格和弱 Positivstellensätze 的公理核心，并证明了针对有序域上的抽象函数代数的相关定理。该框架区分了两种可能混淆的角色：目标和约束函数可以由广泛的连续或可定义操作构建，而用于构造证书的辅助原语则满足明确的标量和闭合公理。我们给出了在连续和可定义函数代数上的实例，包括不在平方根下闭合的有序域，推导出下界和全局最优性证书，并分析了扩展项长度和共享计算图复杂性。
cs.AI / 180 / 2609.08772
It's All in the Way You Say It: The Role of Information Representation in LLM-Based Glycemic-Event Prediction
一切都在于表达方式：信息表征在基于大语言模型的血糖事件预测中的作用
Abstract
Large Language Models (LLMs) are increasingly being investigated for physiological time-series prediction, yet their effectiveness may depend not only on the model itself, but also on how physiological information is represented and presented at inference time. This study investigates prompt-based general-purpose LLMs for postprandial hyperglycemia and hypoglycemia prediction in individuals with type 1 diabetes. Using the OhioT1DM dataset, we evaluate multiple open-weight LLMs under zero-shot and few-shot inference across prediction horizons of 30, 60, and 90 minutes. The analysis varies both the textual representation of the available physiological information and the amount of information exposed to the model, ranging from glucose observations alone to derived descriptors and additional contextual variables related to insulin, meals, carbohydrates, and physical activity. Performance is compared with conventional patient-specific supervised models and with Gluco-LLM, a language-model-based architecture explicitly adapted to glucose time-series forecasting. Results show a marked task-dependent behavior. Conventional supervised models achieve the strongest performance for hyperglycemia prediction, whereas the best observed prompt-based LLM configurations improve performance for hypoglycemia across all investigated horizons. The effectiveness of prompt-based inference is also strongly influenced by how physiological information is represented, while providing additional contextual information does not lead to a systematic improvement. Overall, these findings highlight physiological information representation as a central design factor in prompt-based LLM approaches to glycemic-event prediction.
Chinese Translation
大型语言模型（LLMs）在生理时间序列预测中的应用正受到越来越多的关注，但其有效性不仅依赖于模型本身，还取决于生理信息在推理时的表征和呈现方式。本研究探讨了基于提示的通用 LLMs 在 1 型糖尿病患者的餐后高血糖和低血糖预测中的应用。我们使用 OhioT1DM 数据集，在 30、60 和 90 分钟的预测时间范围内，评估多种开放权重的 LLMs 在零样本和少样本推理下的表现。分析中，我们改变了可用生理信息的文本表征及模型接触的信息量，从仅有的葡萄糖观测值到衍生描述符和与胰岛素、餐食、碳水化合物及身体活动相关的额外上下文变量。我们将性能与传统的患者特定监督模型以及专门适应于葡萄糖时间序列预测的 Gluco-LLM 进行比较。结果显示出明显的任务依赖性。传统的监督模型在高血糖预测中表现最佳，而观察到的最佳基于提示的 LLM 配置在所有研究的时间范围内提高了低血糖的预测性能。基于提示的推理的有效性也受到生理信息表征方式的强烈影响，而提供额外的上下文信息并未导致系统性的改善。总体而言，这些发现强调了生理信息表征作为基于提示的 LLM 方法在血糖事件预测中的核心设计因素。
cs.AI / 181 / 2609.08832
Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course
缩小一致性差距：自我进化的智能体学习保持航向
Abstract
Large language model (LLM)-powered agents can be accurate on average yet unreliable in production, a discrepancy that has been observed but remains largely unaddressed. When given the same task five times, a ReAct agent on the AppWorld benchmark using GPT-4.1 succeeds in all five runs only 53% of the time, even though its per-run pass rate averages 77%. We call this 24-point shortfall the consistency gap, and we argue that addressing it is a precondition for trustworthy AI agent deployment. We present a self-evolving agent framework that reduces this gap by identifying unstable, low-consistency steps in agent trajectories and converting them into episodic memory the agent can draw on in future runs. At its core is a Consistency Analyzer that pinpoints where and why a trajectory is likely to flip across executions, and a Guideline Generator that converts the diagnosis into targeted guidelines, committed to memory and injected into future agent executions on similar tasks. On AppWorld with ReAct/GPT-4.1, our framework raises the fraction of tasks that succeed in all five runs by +16 points on same-task evaluation and +13 points on similar-task generalization.
Chinese Translation
大型语言模型（LLM）驱动的智能体在平均情况下可能表现准确，但在实际应用中却不可靠，这种差异已被观察到但仍未得到充分解决。在同一任务上执行五次时，使用GPT-4.1的ReAct智能体在AppWorld基准测试中仅有53%的概率在五次运行中全部成功，尽管其每次运行的通过率平均为77%。我们将这种24个百分点的短缺称为一致性差距，并认为解决这一问题是可信赖的人工智能智能体部署的前提条件。我们提出了一种自我进化的智能体框架，通过识别智能体轨迹中不稳定、低一致性的步骤，并将其转化为智能体在未来运行中可以借鉴的情节记忆，从而减少这一差距。该框架的核心是一个一致性分析器，能够确定轨迹在执行过程中可能翻转的具体位置和原因，以及一个指导生成器，将诊断结果转化为针对性的指导，存入记忆并注入到未来智能体在类似任务中的执行中。在使用ReAct/GPT-4.1的AppWorld上，我们的框架使得在同一任务评估中，所有五次运行成功的任务比例提高了16个百分点，而在类似任务泛化中提高了13个百分点。
cs.AI / 182 / 2609.08861
API Benchmark Scores Do Not Reliably Transfer to Chatbot Interfaces
API 基准分数无法可靠地转移到聊天机器人界面
Abstract
Benchmark scores are a central currency in model releases: they inform purchasing decisions, shape public trust, and influence policy. Yet, a key assumption underlying benchmark scores is that the model performance measured through APIs faithfully reflects the behavior of deployed systems. We challenge this assumption by auditing ChatGPT, Claude, and Gemini across seven systems and nine benchmarks spanning general capability, social bias, and sycophancy. We find systematic API--interface differences in both accuracy and consistency. On average, API evaluations score 3.4 percentage points higher in accuracy and 2.1 percentage points higher in test--retest agreement than corresponding interface evaluations. For ChatGPT, the performance difference between API and interface access exceeds the API-only difference between GPT 5.3 and GPT 5.4. Put differently, switching access surfaces can degrade performance as much as downgrading a full model generation. We further test whether exposed API controls can reproduce interface behavior by varying system prompts, sampling parameters, and reasoning settings. These controls shift behavior in some cases but do not reliably eliminate the gap. Our findings document a context-validity gap: measurements obtained through APIs do not necessarily generalize to corresponding deployed interfaces, complicating the use of API evaluations as proxies for deployed systems.
Chinese Translation
基准分数是模型发布中的核心指标：它们影响购买决策，塑造公众信任，并影响政策。然而，基准分数的一个关键假设是，通过 API 测量的模型性能能够真实反映已部署系统的行为。我们通过审计 ChatGPT、Claude 和 Gemini 在七个系统和九个基准上的表现，挑战了这一假设，这些基准涵盖了一般能力、社会偏见和谄媚行为。我们发现 API 和界面之间在准确性和一致性上存在系统性的差异。平均而言，API 评估的准确性比相应的界面评估高出 3.4 个百分点，测试-重测一致性高出 2.1 个百分点。对于 ChatGPT，API 和界面访问之间的性能差异超过了 GPT 5.3 和 GPT 5.4 之间的 API 仅差异。换句话说，切换访问界面可能会导致性能下降，程度可与降级整个模型版本相当。我们进一步测试了暴露的 API 控制是否能够通过改变系统提示、采样参数和推理设置来重现界面行为。这些控制在某些情况下改变了行为，但并未可靠地消除差距。我们的研究结果记录了一个情境有效性差距：通过 API 获得的测量结果并不一定能够推广到相应的已部署界面，这使得将 API 评估作为已部署系统的代理变得复杂。
cs.AI / 183 / 2609.08944
SkillAdam: Stable and Efficient Skill Evolution for Agents
SkillAdam：稳定高效的智能体技能演化
Abstract
Agent skills provide a lightweight way to equip frozen language-model agents with domain knowledge and procedural guidance, yet obtaining high-quality skills remains costly and difficult to scale. Expert-written skills require substantial human effort. Recent skill self-evolution methods automate an iterative loop that uses execution feedback to revise skills, but their heuristic update strategies often yield unstable optimization and low iteration efficiency. We identify two challenges in realizing stable and efficient skill self-evolution. Direction Stability requires effective corrections to accumulate rather than be overwritten by iteration-local feedback. Update Adaptivity requires the scope of each revision to reflect the consistency of recent case-level improvements. We introduce SkillAdam, an Adam-inspired framework for optimizing discrete and non-differentiable skill documents. As a functional analogue of Adam's first moment, an optimization memory records identified problems and the outcomes of prior solution attempts to stabilize the update direction. As a functional analogue of Adam's second moment, a volatility-driven edit budget tracks the history-weighted variation of recent case-level improvements and adaptively controls the update magnitude. Across seven benchmarks that span short- and long-horizon tasks, SkillAdam achieves state-of-the-art performance with more stable optimization dynamics. It also obtains stronger skills with substantially fewer optimization iterations and lower cost than prior methods. Code repository: https://github.com/ruc-datalab/SkillAdam
Chinese Translation
智能体技能为冷冻语言模型智能体提供了一种轻量级的方式，以装备领域知识和程序指导，但获得高质量技能仍然成本高昂且难以扩展。专家编写的技能需要大量的人力投入。最近的技能自我演化方法自动化了一个迭代循环，该循环利用执行反馈来修订技能，但其启发式更新策略往往导致不稳定的优化和低迭代效率。我们识别出实现稳定高效技能自我演化的两个挑战。方向稳定性要求有效的修正能够累积，而不是被迭代局部反馈覆盖。更新适应性要求每次修订的范围反映最近案例级改进的一致性。我们提出了SkillAdam，这是一个受Adam启发的框架，用于优化离散和不可微分的技能文档。作为Adam第一矩的功能类比，优化记忆记录已识别的问题及先前解决尝试的结果，以稳定更新方向。作为Adam第二矩的功能类比，基于波动性的编辑预算跟踪最近案例级改进的历史加权变化，并自适应控制更新幅度。在涵盖短期和长期任务的七个基准测试中，SkillAdam实现了最先进的性能，并具有更稳定的优化动态。与先前的方法相比，它还以显著更少的优化迭代和更低的成本获得了更强的技能。代码库：https://github.com/ruc-datalab/SkillAdam
cs.AI / 184 / 2609.08965
PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving
PlannerForge：用于自主驾驶运动规划器场景测试的LLM代理
Abstract
Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving Systems (ADSs), but it remains a fragmented modular pipeline in which scenario generation, retrieval, modification, ADS execution, and results analysis are performed by separate tools with little interaction. Large Language Model (LLM) agents have shown promise across ADS sub-systems such as perception, planning, and control. However, no prior work covers the whole scenario-based testing pipeline for ADSs with a unified LLM-agent framework. We present PlannerForge, an LLM-agent framework that extends all scenario-based testing stages (from Scenario Generation to ADS Assessment) and adds two further LLM-enhanced stages: ADS Enhancement and ADS Benchmarking. We evaluate PlannerForge with 10 off-the-shelf LLMs across all tasks (Generation, Selection, Modification, Module Routing, Planner Testing, and Enhancement) under 5 prompt conditions. Best-per-task scores range from 0.88 to 1.00, and open-source 20-35B backends match commercial APIs on most tasks. Open-source models such as Qwen3.6:35B match commercial APIs on three of the five tasks. Chaining the modules end-to-end retains 83% / 78% of seed queries (commercial / open). It outperforms Scenario Factory 2.0 (Finkeldei et al., 2025) on natural-language generation (193 vs. 144 executable of 200) and realises 92-96% of requested city, road and vehicle attributes. It outperforms BM25 (Robertson and Zaragoza, 2009) at rank 1 selection (92.0% vs. 67.5%) and From-Words-to-Collisions (Gao et al., 2025) on physically valid edits (>=94% vs. 31%). At N=400, cost-tuning lifts planner success from 50.4% to 70.2% and cuts collisions from 19.0% to 8.4%, without domain-specific fine-tuning.
Chinese Translation
确保自主驾驶的安全性是一个关键挑战。场景基础测试是一种系统化的过程，用于验证自主驾驶系统（ADS），但它仍然是一个分散的模块化流程，其中场景生成、检索、修改、ADS执行和结果分析由不同的工具完成，彼此之间互动较少。大型语言模型（LLM）代理在感知、规划和控制等ADS子系统中显示出了良好的前景。然而，之前的研究没有涵盖整个基于场景的ADS测试流程的统一LLM代理框架。我们提出了PlannerForge，一个LLM代理框架，扩展了所有基于场景的测试阶段（从场景生成到ADS评估），并增加了两个进一步的LLM增强阶段：ADS增强和ADS基准测试。我们在5种提示条件下，使用10种现成的LLM对PlannerForge进行评估，涵盖所有任务（生成、选择、修改、模块路由、规划器测试和增强）。每个任务的最佳得分范围为0.88到1.00，开源的20-35B后端在大多数任务上与商业API相匹配。开源模型如Qwen3.6:35B在五个任务中的三个任务上与商业API相匹配。模块的端到端链式连接保留了83% / 78%的种子查询（商业/开源）。在自然语言生成方面，它在可执行性上超越了Scenario Factory 2.0（Finkeldei等，2025），实现了92-96%的请求城市、道路和车辆属性。它在排名1的选择上超越了BM25（Robertson和Zaragoza，2009）（92.0%对67.5%），并在物理有效编辑方面超越了From-Words-to-Collisions（Gao等，2025）（>=94%对31%）。在N=400时，成本调优将规划器的成功率从50.4%提升至70.2%，并将碰撞率从19.0%降低至8.4%，而无需领域特定的微调。
cs.AI / 185 / 2609.08966
Good Pretraining, Bad SFT: Checkpoint Quality Across the Training Stack
良好的预训练，糟糕的微调：训练流程中检查点质量的比较
Abstract
Language-model checkpoints are commonly selected by pretraining loss or benchmark scores, assuming that the highest-scoring checkpoint will remain the best starting point for subsequent training. We show that this assumption can fail in a full 30B mixture-of-experts training pipeline. The checkpoints that perform better after the full downstream training stack also have higher solution density, i.e., retain downstream performance under local weight perturbations.
Chinese Translation
语言模型的检查点通常通过预训练损失或基准分数进行选择，假设得分最高的检查点将成为后续训练的最佳起点。我们展示了这一假设在完整的30B专家混合训练流程中可能会失败。经过完整下游训练流程后表现更好的检查点也具有更高的解密度，即在局部权重扰动下保持下游性能。
cs.AI / 186 / 2609.09001
Deposon: An Auditable, Conservation-Guaranteed, Game-Theoretically Tested Scattering Layer over LLM Reasoning Paths
Deposon：一种可审计的、保证保守的、经过博弈论测试的散射层，应用于大语言模型推理路径
Abstract
Multi-step LLM reasoning lacks a machine-recheckable ledger: discarded reasoning paths leave no auditable record. We propose the Deposon scattering layer, which binds each node of an LLM-generated concept-decomposition graph to a two-parameter Deposon state; paths undergo three-channel scattering -- transmission, reflection, irreversible dissipation -- obeying T+R+A=1 for arbitrary parameters, with a maximum per-path energy-audit deviation of 2.2E-16 (machine epsilon). We report all three evidence tiers honestly. On synthetic trap benchmarks the path-filtering gain is closed (pre-registered): unified reaches 100% versus a decoy-capture baseline at 7%/10%. On real benchmarks the layer is indistinguishable from a trivial six-keyword rule filter (GSM8K 0.87 >= 0.85, McNemar p=0.5; StrategyQA 0.899 = 0.899); no difference is detected here, so we sharpen the claim to "the differential value lies solely in machine verifiability." Fusion yields a second negative result: convex combinations with a semantic prior never improve (physics 0.484 -> 0.452), and the apparent lambda=2 gain is an anti-field artifact; any fusion gain must be nonlinear. Modeling the reverse dynamics as a potential game on the graph, we evidence an auditable scalar's monotonicity and near-gradientness and quantify the empirical coordination ratio (ECR). The three formalized dynamical-equivalence propositions (P1a/P1b/T-P1c) are falsified under the pre-registered kill protocol, and the potential-game claim is downgraded to approximate (cyclic-graph median residual 0.669): only consistency-level evidence survives at the dynamical level. Code: github.com/zeroandcat/Deposon.
Chinese Translation
多步骤的大语言模型（LLM）推理缺乏可机器复查的账本：被丢弃的推理路径没有留下可审计的记录。我们提出了Deposon散射层，它将每个LLM生成的概念分解图的节点绑定到一个双参数Deposon状态；路径经历三通道散射——传输、反射、不可逆耗散——遵循T+R+A=1的任意参数，且每条路径的能量审计最大偏差为2.2E-16（机器ε）。我们诚实地报告所有三个证据层。在合成陷阱基准测试中，路径过滤增益是封闭的（预注册）：统一达到100%，而诱饵捕获基线为7%/10%。在真实基准测试中，该层与一个简单的六关键词规则过滤器（GSM8K 0.87 >= 0.85, McNemar p=0.5；StrategyQA 0.899 = 0.899）无区别；在这里没有检测到差异，因此我们将声明锐化为“差异价值仅在于机器可验证性”。融合产生第二个负结果：与语义先验的凸组合从未改善（物理 0.484 -> 0.452），而表面上λ=2的增益是一个反场伪影；任何融合增益必须是非线性的。将反向动态建模为图上的潜在博弈，我们证明了可审计标量的单调性和近梯度性，并量化了经验协调比（ECR）。在预注册的杀戮协议下，三个形式化的动态等价命题（P1a/P1b/T-P1c）被证伪，潜在博弈的主张被降级为近似（循环图中位残差0.669）：只有一致性水平的证据在动态层面上存活。代码：github.com/zeroandcat/Deposon。
cs.AI / 187 / 2609.09030
Answer-Distribution Trajectories: A Stochastic-Dynamics View of LLM Reasoning
答案分布轨迹：大语言模型推理的随机动力学视角
Abstract
Chain-of-thought reasoning provides a structured computation between a model's input and final answer. Yet it is often evaluated through endpoint accuracy, which ignores the path taken to reach that answer. An emerging line of work addresses this limitation using entropy profiles, which track how uncertainty evolves over the reasoning process but do not reveal which competing hypotheses account for that uncertainty. We introduce answer-distribution trajectories, a stochastic-dynamics-inspired representation that tracks the model's full predictive distribution over answers as reasoning unfolds. As a strictly finer representation than endpoint and entropy summaries, answer-distribution trajectories enable us to characterize a trace through a dynamical reasoning profile spanning exploration, revision, motion, and commitment, and to distinguish different dynamical mechanisms of reasoning success and failure. Across sixteen open-weight language models and four reasoning benchmarks, we show that traces with the same endpoint and similar entropy profiles can exhibit substantially different reasoning dynamics. We further find substantial variation in these dynamics both within and across models and tasks, with different objectives favoring different dynamical profiles. Additionally, we show that training and inference choices systematically reshape these profiles. Our results suggest that answer-distribution trajectories provide a rich framework for analysing and evaluating the dynamics of LLM reasoning.
Chinese Translation
链式思维推理提供了模型输入与最终答案之间的结构化计算。然而，它通常通过最终准确性进行评估，这忽视了达到该答案所采取的路径。一项新兴的研究工作通过熵轮廓来解决这一局限性，熵轮廓跟踪不确定性在推理过程中的演变，但并未揭示哪些竞争假设导致了这种不确定性。我们引入了答案分布轨迹，这是一种受随机动力学启发的表示方式，跟踪模型在推理展开过程中对答案的完整预测分布。作为比最终结果和熵摘要更为精细的表示，答案分布轨迹使我们能够表征贯穿探索、修正、运动和承诺的动态推理轮廓，并区分推理成功与失败的不同动态机制。在十六个开放权重语言模型和四个推理基准上，我们展示了具有相同最终结果和相似熵轮廓的轨迹可以表现出显著不同的推理动态。我们进一步发现，这些动态在模型和任务之间以及内部均存在显著变化，不同的目标偏向于不同的动态轮廓。此外，我们还表明，训练和推理选择系统性地重塑了这些轮廓。我们的结果表明，答案分布轨迹为分析和评估大语言模型推理的动态提供了一个丰富的框架。
cs.AI / 188 / 2609.09056
Time-Varying Data as Sheaves: an Invitation to Narratives
时变数据作为层：叙事的邀请
Abstract
Modern science and engineering increasingly rely on time-varying data, yet the mathematical tools used to model temporal phenomena are often developed within separate disciplines, obscuring common principles and limiting the transfer of ideas across fields. This chapter presents the theory of narratives, an abstract framework for time-varying objects of any mathematical kind that supports both theoretical investigations and applications. To illustrate this perspective, the chapter develops three vignettes, each illustrating a different research direction. The first addresses a general concern: What information loss can occur when switching between different representations of temporal data? The second concerns structural and algorithmic approaches: How can we systematically decompose time-varying data into simple pieces and obtain invariants describing its structural complexity? The third is an application to control theory: How can we model multi-agent systems with switching communication topologies? More important than any individual vignette, the central message of this invitation is that a suitable abstract perspective can organize and guide research across remarkably diverse mathematical and scientific domains.
Chinese Translation
现代科学和工程日益依赖于时变数据，但用于建模时间现象的数学工具往往在不同学科中独立发展，这掩盖了共同的原则并限制了跨领域思想的转移。本章提出了叙事理论，这是一个适用于任何数学类型的时变对象的抽象框架，支持理论研究和应用。为了阐明这一视角，本章发展了三个小插曲，每个小插曲展示了不同的研究方向。第一个关注一个普遍问题：在不同的时变数据表示之间切换时可能会发生什么信息损失？第二个涉及结构和算法方法：我们如何系统地将时变数据分解为简单的部分，并获得描述其结构复杂性的不变量？第三个是对控制理论的应用：我们如何建模具有切换通信拓扑的多智能体系统？比任何单个小插曲更重要的是，这一邀请的核心信息是，适当的抽象视角可以组织和引导跨越极其多样的数学和科学领域的研究。
cs.AI / 189 / 2609.09081
Everything in Moderation: Per-Domain Coverage Optima and Alignment-Resistant Domain Gaps in Multi-Domain Mid-Training
适度为宜：多领域中期训练中的每领域覆盖最优点及抗对齐的领域差距
Abstract
Mid-training, the stage between pre-training and alignment, is where a model's per-domain data composition is typically set by data availability rather than principled design. We ask what that decision buys, and whether a later alignment pass can undo it. In a controlled logical-reasoning setting (Qwen3-8B-Base, with a 4B replication; five semantically rule-disjoint KOR-Bench domains) we train 30 allocations spanning the five-domain simplex, 24 sweep configurations plus six withheld from the fit, at five seeds each. Three findings emerge. First, every domain has an interior coverage optimum: the moderate band ($10\%$-$40\%$) is best for all five domains, and a calibrated permutation test for quadratic interiority gives $P\approx0.010$; the fitted mid-training-only curves, with 8B peaks between $9.9\%$ and $35.1\%$, reproduce for curve shape but not peak location. Second, the gaps survive a fixed-budget alignment pass: compensatory SFT raises 116/120 cells (mean $+4.32\%$) yet bridges $0/240$ pairs at a $5\%$ threshold and $30/240$ at a $10\%$ ratio, an equal-budget uniform control behaves almost identically, and a permutation null would bridge $13.8\pm3.3$ and $77.9\pm8.5$ pairs ($P<0.001$). Third, zero coverage collapses mid-training-only accuracy, though a FineWeb-Edu-only control shows the collapse is commingled with generic drift. An exploratory $\theta^*$ allocation attains the largest full-pipeline gain ($+4.36\%$ vs. $+0.80\%$/$+0.64\%$\,pp) but is marginal under Welch test.
Chinese Translation
中期训练是介于预训练和对齐之间的阶段，此阶段模型的每领域数据组成通常由数据可用性决定，而非基于原则性设计。我们探讨这一决策带来了什么收益，以及后续的对齐过程是否能够逆转这一决策。在一个受控的逻辑推理环境中（Qwen3-8B-Base，4B复现；五个语义规则不相交的KOR-Bench领域），我们训练了覆盖五领域单纯形的30种分配方案，其中24种用于扫描配置，6种保留用于验证，每种方案进行了五次随机种子实验。研究得出三点发现。首先，每个领域均存在一个内部覆盖最优点：所有五个领域的最佳覆盖率均处于中等区间（10%-40%），经过校准的二次内点排列检验得到P≈0.010；仅中期训练拟合的曲线在形状上与实测曲线相符，但峰值位置（8B峰值介于9.9%至35.1%）未能复现。其次，这些领域差距在固定预算的对齐阶段依然存在：补偿性SFT使116/120个单元格性能提升（平均+4.32%），但在5%阈值下未能弥合任何240对领域差距，在10%阈值下仅弥合30对，等预算的均匀控制组表现几乎相同，而排列检验的零假设下预期弥合对数为13.8±3.3和77.9±8.5（P<0.001）。第三，零覆盖率导致仅中期训练的准确率崩溃，尽管FineWeb-Edu单一控制组表明该崩溃与通用漂移交织出现。一种探索性的θ*分配方案实现了最大全流程增益（+4.36%对比+0.80%/+0.64%百分点），但在Welch检验下差异边缘显著。
cs.AI / 190 / 2609.09094
The Surprising Effectiveness of Approximate Value Iteration in Self-Play
自我对弈中近似值迭代的惊人有效性
Abstract
Combining search with function approximation has driven major advances in game-playing programs, making self-play algorithms more competitive than ever. Still, the computational overhead of the most popular methods, based on Monte Carlo Tree Search (MCTS), can be substantial. In this work, we investigate whether simpler methods remain competitive in non-trivial, moderately sized games such as Connect Four, Hex(7x7) and synthetic games. We train a minimal self-play implementation of Approximate Value Iteration (AVI) and use ground-truth oracles for exact evaluation. Contrary to expectations, our results demonstrate the surprising effectiveness of AVI: it learns more accurate value functions than those learned by AlphaZero, while its one-step-lookahead greedy policies remain competitive with MCTS-based policies at substantially lower training and inference costs. Preliminary experiments on Othello and Go(9x9) show that AVI trains stably on larger games and learns effective value functions. These findings suggest that the success of MCTS-based methods may have eclipsed simpler approaches that have become increasingly practical with modern deep-learning tools.
Chinese Translation
将搜索与函数逼近相结合推动了游戏程序的重大进展，使自我对弈算法比以往任何时候都更具竞争力。然而，基于蒙特卡洛树搜索（Monte Carlo Tree Search, MCTS）的最流行方法的计算开销可能相当可观。在本研究中，我们探讨了在非平凡的中等规模游戏（如四子棋、七乘七的六角棋和合成游戏）中，是否更简单的方法仍然具有竞争力。我们训练了一个最小化的近似值迭代（Approximate Value Iteration, AVI）自我对弈实现，并使用真实的预言机进行精确评估。与预期相反，我们的结果表明AVI的惊人有效性：它学习到的价值函数比AlphaZero学习到的更为准确，同时其一步前瞻贪婪策略在训练和推理成本显著降低的情况下，仍然与基于MCTS的策略具有竞争力。在对奥赛罗和九乘九围棋的初步实验中，AVI在更大规模的游戏中训练稳定，并学习到有效的价值函数。这些发现表明，基于MCTS的方法的成功可能掩盖了随着现代深度学习工具的进步而变得越来越实用的更简单的方法。
cs.AI / 191 / 2609.09113
SAEScientist-Bench: Can AI Agents Conduct Autonomous SAE Interpretability Research?
SAEScientist-Bench：人工智能代理能否进行自主的SAE可解释性研究？
Abstract
While research on recursive self-improvement (RSI) has predominantly automated model training pipelines, reliable autonomous development demands a missing pillar: post-hoc monitoring and auditing to understand what models learn and ensure safe alignment. Mechanistic interpretability tools are essential to bridge this gap, among which Sparse Autoencoders (SAEs) serve as a cornerstone by isolating interpretable features for model inspection and steering. In this paper, we introduce SAEScientist-Bench to evaluate whether AI agents can act as scientists utilizing SAE tools for autonomous mechanistic discovery. Given a target concept, an agent designs contrastive probes and navigates a Gemma Scope dictionary of 131K+ features in Gemma-2-9B-IT to discover the optimal feature, evaluated against curated expert reference features anchored on Neuronpedia across activation rank, concept selectivity on contrastive texts, and causal steering. Across 10 agent configurations and 20 tasks, frontier agents demonstrate genuine discovery capabilities and lead different evaluation dimensions, but remain well behind the expert baseline, approaching expert levels on separating target concepts from contrastive controls while lagging substantially in causal generation steering. Further analysis reveals that although agents can design contrasts to rule out spurious candidates, they frequently misinterpret experimental measurements. These results establish experimental model understanding as a measurable capability for closed-loop autonomous AI R&D. Our code is available at https://github.com/Trae1ounG/SAEScientist.
Chinese Translation
尽管递归自我改进（RSI）的研究主要集中在自动化模型训练流程上，但可靠的自主开发仍需一个缺失的支柱：事后监控和审计，以理解模型所学内容并确保安全对齐。机制可解释性工具在弥补这一空白中至关重要，其中稀疏自编码器（Sparse Autoencoders, SAEs）通过隔离可解释特征为模型检查和引导提供了基础。在本文中，我们介绍了SAEScientist-Bench，以评估人工智能代理是否能够利用SAE工具进行自主的机制发现。给定一个目标概念，代理设计对比探针，并在Gemma-2-9B-IT的131K+特征的Gemma Scope字典中导航，以发现最佳特征，并根据基于Neuronpedia的专家参考特征在激活排名、对比文本的概念选择性和因果引导方面进行评估。在10种代理配置和20个任务中，前沿代理展示了真正的发现能力，并在不同的评估维度中领先，但仍远远落后于专家基线，在将目标概念与对比控制分离方面接近专家水平，而在因果生成引导方面则显著滞后。进一步分析表明，尽管代理能够设计对比以排除虚假候选者，但它们经常误解实验测量。这些结果确立了实验模型理解作为闭环自主人工智能研发的可测量能力。我们的代码可在 https://github.com/Trae1ounG/SAEScientist 获取。
cs.AI / 192 / 2609.09115
MeClear: Cooperative Game-Theoretic Attribution and Risk-Aware Memory Clearance for Long-Horizon LLM Agents
MeClear：基于合作博弈论的归因与风险感知记忆清除框架用于长时间跨度的语言模型代理
Abstract
Long horizon Large Language Model (LLM) agents rely on external memory systems to preserve user preferences and task knowledge across extended interactions. Conventional retrieval mechanisms optimize semantic compatibility rather than downstream utility, frequently introducing outdated, misleading, or conflicting evidence into the active context. We present MeClear, a task conditioned memory clearance framework that identifies memories featuring negative downstream utility through cooperative attribution and selectively suppresses them from agent execution. MeClear combines Leave One Out screening with sampled cooperative Shapley attribution to distribute utility across interacting evidence, effectively resolving redundant conflict masking where single removal evaluations fail. Utilizing attribution rankings, MeClear executes a query scoped minimal clearance strategy over a nested filtration, verifying task recovery on the cleared context without permanently altering the persistent memory bank. Comprehensive experimental evaluations across ten long dialogue memory pools demonstrate that MeClear achieves a target recall of 85.9% and an overall task recovery rate of 82.3%, representing a 25.5 percentage point improvement over Leave One Out (LOO) baselines.
Chinese Translation
长时间跨度的大型语言模型（LLM）代理依赖外部记忆系统在延长交互中保留用户偏好和任务知识。传统的检索机制优化语义兼容性，而非下游效用，常常将过时、误导或冲突的证据引入活跃上下文。我们提出了MeClear，一种任务条件的记忆清除框架，通过合作归因识别具有负下游效用的记忆，并选择性地将其从代理执行中抑制。MeClear结合了逐一排除（Leave One Out）筛选与采样的合作Shapley归因，以在交互证据中分配效用，有效解决了单一移除评估失败的冗余冲突掩蔽。利用归因排名，MeClear在嵌套过滤上执行查询范围内的最小清除策略，验证在清除上下文中的任务恢复，而不永久改变持久记忆库。对十个长期对话记忆池的全面实验评估表明，MeClear实现了85.9%的目标召回率和82.3%的整体任务恢复率，较逐一排除（LOO）基线提高了25.5个百分点。
cs.AI / 193 / 2609.09126
A Generalization of Amari's Bayesian Duality
阿马里的贝叶斯对偶性的一种推广
Abstract
Amari's contributions to information geometry and machine learning are well known. Here, we revisit Amari's work on Bayesian duality which has not received as much attention. We connect Amari's Bayesian duality to a convex duality of Bayes' rule. Using this connection, we present a generalization of Amari's Bayesian duality and discuss its relevance for modern artificial intelligence.
Chinese Translation
阿马里在信息几何和机器学习方面的贡献众所周知。在这里，我们重新审视阿马里关于贝叶斯对偶性的研究，这一领域并未受到足够的关注。我们将阿马里的贝叶斯对偶性与贝叶斯法则的凸对偶性联系起来。利用这一联系，我们提出了阿马里贝叶斯对偶性的一种推广，并讨论其对现代人工智能的相关性。
cs.AI / 194 / 2609.09133
ExecCritic: Learn to Test, Test to Improve for Coding Agents
ExecCritic：学习测试，测试以改进编码代理
Abstract
Execution feedback can guide coding agents toward correct repository repairs, but only when the tests capture the behavior requested by the issue. Agent-generated tests can encode incomplete or incorrect behavioral targets; when the same trajectory writes both the patch and the test, their errors can agree and create false confidence. We introduce ExecCritic, combining a test--verify--revise scaffold with a role-specific reinforcement learning recipe for training agents within it. The scaffold separates test construction from source-code repair: a Test agent independently generates repository-native tests, a fail-closed harness qualifies and freezes them, and a Repair agent revises source code from their execution feedback without changing the tests. Both roles use Qwen-3.5-35B-A3B as the backbone and are trained separately. In Learn to Test, the Test agent learns to produce behaviorally valid tests that distinguish correct from incorrect patches. In Test to Improve, the Repair agent learns both direct task resolution and feedback-guided revision. On SWE-bench Verified, test quality determines whether feedback helps: holding the base Repair agent fixed, tests from the base Test agent reduce resolved rate from a no-test baseline of 61.2% to 57.3%, whereas tests from GPT-5.6-sol raise it to 65.3%. Role-specific post-training raises the Qwen Test agent's Base-to-Gold success from 22.2% to 62.2%; composing the two post-trained Qwen agents reaches 72.6%, an 11.4-point gain over the original no-test baseline without stronger-model or Oracle feedback at evaluation time. Code is publicly available at https://github.com/MSR-Orchard/execcritic.
Chinese Translation
执行反馈可以指导编码代理进行正确的代码库修复，但前提是测试能够捕捉到问题所请求的行为。代理生成的测试可能编码了不完整或不正确的行为目标；当同一轨迹同时编写补丁和测试时，它们的错误可能会一致，从而产生虚假的信心。我们提出了ExecCritic，结合了测试-验证-修订的框架与特定角色的强化学习策略，以训练其中的代理。该框架将测试构建与源代码修复分开：测试代理独立生成与代码库兼容的测试，失败闭合的测试框架对其进行验证并冻结，而修复代理则根据执行反馈修订源代码，而不改变测试。两个角色均使用Qwen-3.5-35B-A3B作为基础，并分别进行训练。在“学习测试”阶段，测试代理学习生成能够区分正确和错误补丁的行为有效测试。在“测试以改进”阶段，修复代理学习直接任务解决和基于反馈的修订。在SWE-bench Verified上，测试质量决定了反馈是否有帮助：在固定基础修复代理的情况下，基础测试代理的测试将解决率从无测试基线的61.2%降低至57.3%，而GPT-5.6-sol的测试则将其提高至65.3%。特定角色的后训练将Qwen测试代理的基础到金标准成功率从22.2%提高至62.2%；组合这两个后训练的Qwen代理达到了72.6%，比原始无测试基线提高了11.4个百分点，而在评估时没有使用更强的模型或Oracle反馈。代码可在https://github.com/MSR-Orchard/execcritic公开获取。
cs.AI / 195 / 2609.09134
Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails
共同进化的工具和模型：在模仿失败的情况下，在线纠正帮助较弱模型赶上
Abstract
Agent harnesses (the system prompt, tool set, execution hooks, and context-management scaffolding around a model) are a critical determinant of agentic task success. Automated harness evolution can enable smaller models to perform well on domain-specific tasks at a fraction of frontier-model cost. Since both the harness and model weights shape behavior, we ask how harness evolution and lightweight fine-tuning should be combined. Across seven enterprise agent tasks, we first evolve a harness with the weaker model, then find that a stronger expert often uses it more effectively, suggesting expert supervision could close the remaining gap. However, training the weaker model on the expert's complete trajectories under the evolved harness backfires: performance regresses on all seven tasks by 4 to 30 points across Qwen3-Coder and Gemma 4, even though the same procedure helps under the unevolved harness. Our analysis shows that imitation transfers knowledge and increases scaffold usage, but disrupts model-harness fit: the weaker model adopts the expert's planning strategy without the competence to execute it and no longer matches the harness evolved around its native planning style. We therefore develop an on-policy expert-correction pipeline, automated by a meta-level MLE agent, that localizes the failing turn in the weaker model's own rollout and asks the expert to rewrite only that turn. This preserves the model's planning style and combines the gains of harness evolution and model adaptation. Our results identify and resolve a source of contention between harness and weight updates, yielding a compatibility-preserving recipe for economical co-evolution on domain-specific enterprise tasks.
Chinese Translation
代理工具（系统提示、工具集、执行钩子和围绕模型的上下文管理框架）是代理任务成功的关键决定因素。自动化工具进化可以使较小的模型在特定领域任务中以较低的前沿模型成本表现良好。由于工具和模型权重共同塑造行为，我们探讨工具进化与轻量级微调应如何结合。在七个企业代理任务中，我们首先使用较弱模型进化工具，然后发现更强的专家通常更有效地使用它，这表明专家监督可以缩小剩余差距。然而，在进化后的工具下对较弱模型进行专家完整轨迹的训练却适得其反：在 Qwen3-Coder 和 Gemma 4 的所有七个任务中，性能回落了 4 到 30 分，尽管相同的程序在未进化的工具下是有效的。我们的分析表明，模仿转移了知识并增加了框架的使用，但破坏了模型与工具的匹配：较弱模型在没有执行能力的情况下采用了专家的规划策略，并且不再与围绕其原生规划风格进化的工具相匹配。因此，我们开发了一个在线专家纠正管道，由一个元级最大似然估计（MLE）代理自动化，该管道定位较弱模型自身回滚中的失败环节，并要求专家仅重写该环节。这保留了模型的规划风格，并结合了工具进化和模型适应的收益。我们的结果识别并解决了工具更新与权重更新之间的冲突来源，提供了一种兼容性保留的经济共同进化方案，适用于特定领域的企业任务。
cs.AI / 196 / 2609.09137
A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes
基于数据驱动的框架用于识别和优先排序医疗流程中的RPA机会
Abstract
Robotic Process Automation (RPA) is widely used to reduce administrative burden in United States hospitals, yet an estimated 30-50% of RPA initiatives underperform because processes are selected informally, without a repeatable method to catalogue candidates, prioritize them, match each to an automation tier -- a Python bot, an open-source orchestrator such as n8n, or an enterprise platform such as UiPath -- and forecast financial return before committing resources. We propose a four-module, data-driven framework unifying these decisions: a Process Taxonomy of twenty recurring hospital processes across five value streams; a Prioritization module deriving an Automation Suitability Index from an Analytic Hierarchy Process matrix with an explicit consistency check; a Tool-Tier Selection module recommending the least-cost technology sufficient for a process complexity, integration, and compliance profile; and a Return-on-Investment module quantifying labor savings, error-cost avoidance, payback, and net present value. Applied to a synthetic portfolio spanning all twenty processes, plus a reference data-flow architecture linking it to hospital EHR/payer/ERP systems: 12 of 20 clear the prioritization threshold; the ranking is robust to +/-20% weight perturbation (Spearman correlation 0.83, top-5 set preserved 97.7%, 2,000 Monte Carlo trials); an Automation Risk Index flags four qualifying processes as Critical risk; a budget-constrained portfolio optimization shows diminishing marginal NPV as spend scales from $400K to $1.03M; and a second Monte Carlo analysis shows portfolio NPV stays positive at its 5th percentile. The framework is a conceptual synthesis of the literature rather than an instrument calibrated on primary hospital data; we discuss HIPAA governance and a research agenda for empirical validation. A supplementary Python implementation accompanies the paper.
Chinese Translation
机器人流程自动化（RPA）在美国医院中广泛应用于减少行政负担，但估计有30-50%的RPA项目表现不佳，因为流程选择往往是非正式的，缺乏可重复的方法来对候选流程进行分类、优先排序、将每个流程匹配到自动化层级——如Python机器人、开源编排工具（如n8n）或企业平台（如UiPath）——并在投入资源之前预测财务回报。我们提出了一个四模块的数据驱动框架，以统一这些决策：一个涵盖五个价值流的二十个常见医院流程的流程分类；一个优先排序模块，从具有显式一致性检查的层次分析法矩阵中推导出自动化适用性指数；一个工具层级选择模块，推荐足以满足流程复杂性、集成和合规性要求的最低成本技术；以及一个投资回报模块，量化劳动节省、避免错误成本、回收期和净现值。应用于一个涵盖所有二十个流程的合成投资组合，以及一个将其与医院电子健康记录（EHR）/支付方/企业资源规划（ERP）系统连接的参考数据流架构：20个流程中有12个超过优先排序阈值；该排名对+/-20%的权重扰动具有稳健性（斯皮尔曼相关系数0.83，前五名集保留97.7%，2000次蒙特卡洛试验）；自动化风险指数将四个符合条件的流程标记为关键风险；预算受限的投资组合优化显示，随着支出从40万美元增加到103万美元，边际净现值递减；第二次蒙特卡洛分析显示投资组合净现值在其第5百分位保持正值。该框架是对文献的概念性综合，而不是基于主要医院数据校准的工具；我们讨论了HIPAA治理和实证验证的研究议程。论文附有补充的Python实现。
cs.AI / 197 / 2609.09153
Procedural Graphs: Self-Evolving Execution Structures for LLM Agents
过程图：自我演化的执行结构用于大型语言模型代理
Abstract
Large language models are increasingly deployed as agents that plan over long horizons and act through external tools. Most agents select actions through unconstrained generation over an accumulating history, leaving implicit the procedural knowledge of what to do, in what order, and under which conditions. As trajectories lengthen, agents can lose track of their objectives, invoke tools out of order, and repeat unproductive actions. We introduce the Procedural Graph: just as a knowledge graph organizes factual knowledge into (entity, relation, entity) triplets for what-is questions, a Procedural Graph organizes procedural knowledge into (procedure, relation, procedure) triplets for what-to-do questions. At each decision step, the framework localizes the agent's active node, and a guidance model translates the surrounding subgraph into step-level situational guidance that biases the solver's next action without dictating it. The graph is self-evolving: an LLM refiner contrasts failed trajectories with successful ones and edits the graph's topology and attributes, committing edits that preserve or improve held-out validation performance while retaining rejected ones to discourage repetition. Starting from a minimal skeleton, the loop builds graphs that match or surpass hand-designed ones. It can also repair a flawed expert prior. Across multiple datasets, task types, and LLMs, the Procedural Graph delivers consistent gains over memory-based baselines, and self-evolution further improves performance without manual engineering.
Chinese Translation
大型语言模型越来越多地被部署为能够进行长期规划并通过外部工具执行操作的代理。大多数代理通过对累积历史的无约束生成来选择行动，这使得在何时、以何种顺序以及在何种条件下该做什么的过程知识变得隐含。随着轨迹的延长，代理可能会失去对目标的追踪，按错误顺序调用工具，并重复无效的行动。我们引入了过程图：就像知识图谱将事实知识组织成（实体，关系，实体）三元组以回答“是什么”问题，过程图将过程知识组织成（过程，关系，过程）三元组以回答“该做什么”问题。在每个决策步骤中，该框架定位代理的活动节点，并且一个指导模型将周围的子图转化为逐步的情境指导，从而影响求解者的下一步行动，而不是强制其执行。该图是自我演化的：一个大型语言模型的改进器将失败的轨迹与成功的轨迹进行对比，并编辑图的拓扑结构和属性，进行的编辑既能保持或改善保留的验证性能，又能保留被拒绝的编辑以防止重复。从一个最小的骨架开始，该循环构建出与手工设计的图匹配或超越的图。它还可以修复有缺陷的专家先验。在多个数据集、任务类型和大型语言模型中，过程图在基于记忆的基准上提供了一致的增益，而自我演化进一步提高了性能，无需人工工程。
cs.CL / 1 / 2609.05531
When Agent Governance Helps
代理治理何时有效
Abstract
No specification says how a governed autotelic AI agent organization, where agents pursue self-generated goals inside guardrails, should be designed and evaluated. We answer in two parts. First, we synthesize the Governed Autotelic Multi-Agent Product Organization (GAMPO) framework from a document-based qualitative evidence synthesis of 321 sources, integrating agency, agile, platform, and governance theory into a runnable specification. Second, we probe a prompt-layer instantiation of GAMPO on CHI-Bench, a long-horizon healthcare benchmark, across open and frontier models. The result is a boundary condition: governance benefit is gated by a model's spare capacity and is domain- and model-specific. On capacity-constrained open models the full procedure yields no reliable benefit, whereas a single "verify your writes" sentence doubles task success (pass@1 2/20 to 4/20). At the frontier the same scaffold lifts prior-authorization 24% to 40% but nets zero on another model, a gap traced to a stable recommendation-override disposition. A second result refines the first: replacing the generic procedure with an answer-blind, per-task definition-of-done, keyed only to the case's own policy and published standards, never the hidden key, raises prior-authorization to 84% under best-of-five self-consistency (68% single-attempt, confirmed by a held-out board) and utilization-management to 44%, while care-management meets a subjective content-quality wall. The contribution is a named, auditable framework and capability-gated evidence that governance should be sized to spare capacity, and that at the frontier a case-grounded specification beats a uniform procedure. Findings are exploratory: partial instantiation, small per-cell samples (n = 5-25), and single trials.
Chinese Translation
目前没有规范说明一个受治理的自我目标AI代理组织（在保护框架内追求自我生成目标）应如何设计和评估。我们分两部分进行回答。首先，我们从321个来源的文献基础定性证据综合中，合成了受治理的自我目标多代理产品组织（GAMPO）框架，将代理性、敏捷性、平台和治理理论整合为可运行的规范。其次，我们在CHI-Bench上探讨了GAMPO的提示层实例化，这是一个长期的医疗保健基准，涵盖开放模型和前沿模型。结果是一个边界条件：治理效益受到模型剩余容量的限制，并且是特定于领域和模型的。在容量受限的开放模型中，完整程序没有产生可靠的效益，而一句“验证您的写入”则使任务成功率翻倍（通过率从2/20提升至4/20）。在前沿模型中，相同的框架使得事前授权的成功率从24%提升至40%，但在另一个模型上则没有收益，这一差距可追溯到稳定的推荐覆盖倾向。第二个结果对第一个结果进行了细化：用一个不依赖答案的、按任务定义的完成标准替代通用程序，仅与案例自身的政策和已发布标准相关，而不涉及隐藏的关键，事前授权成功率在五次自我一致性最佳情况下提升至84%（单次尝试68%，由保留的评审委员会确认），而利用管理成功率为44%，同时护理管理面临主观内容质量的瓶颈。本文的贡献在于提供了一个命名的、可审计的框架和能力限制的证据，表明治理应根据剩余容量进行调整，并且在前沿情况下，基于案例的规范优于统一程序。研究结果具有探索性：部分实例化、每个单元的小样本（n = 5-25）和单次试验。
cs.CL / 2 / 2609.05631
TamilEOT: A Dataset and Model for Semantic End-of-Turn Detection in Tamil Telephone Speech
TamilEOT：用于泰米尔电话语音语义结束检测的数据集和模型
Abstract
A voice agent has to decide, at every pause, whether the user has finished speaking. Without a model of the language that decision falls back to a fixed silence timeout: set it short and the agent interrupts, set it long and every turn pays the full wait. Open semantic end-of-turn detectors exist, but to our knowledge none covers a South Indian language. We release TamilEOT: 18,485 labelled turn boundaries cut from 116 real Tamil telephone conversations, and two audio-only detectors fine-tuned from Smart Turn v3. On a held-out split of 4,168 clips from 30 unseen calls, accuracy rises from 70.30% zero-shot to 83.71% (8.7 MB) and 86.13% (21 MB); ROC-AUC rises from 0.751 to 0.921. Both models run in under 150 ms single-threaded on a laptop CPU. We also report what building it cost. Rule-derived labels, checked against a blind human listening pass, were right 95.9% of the time on the positive class and 44.4% on the negative class, which is below chance, because the rule answered a different question than the model is asked. Replacing them with an audio-LLM labeller measured at 97.5% human agreement cost US$5.69. Of every training lever we measured, only encoder capacity moved the result; three runs at identical config and seed span 0.87 accuracy points, which is the floor below which none of our other deltas mean anything. Replaying the same labelled boundaries through the production VAD and streaming adapter costs a further 2.60 points, and 7.8% of boundaries are never surfaced to the model at all. Data, weights, code and every negative result are public.
Chinese Translation
语音代理必须在每次停顿时决定用户是否已经说完。如果没有语言模型，这一决策将依赖于固定的静默超时：设置得短则代理会打断，设置得长则每次交互都需等待完整时间。虽然已有开放的语义结束检测器，但据我们所知，尚无覆盖南印度语言的模型。我们发布了TamilEOT：从116个真实的泰米尔电话对话中切割出的18,485个标记的交互边界，以及两个基于Smart Turn v3微调的仅音频检测器。在来自30个未见通话的4,168个片段的保留分割上，准确率从70.30%的零样本提高到83.71%（8.7 MB）和86.13%（21 MB）；ROC-AUC从0.751提高到0.921。两个模型在笔记本电脑CPU上单线程运行时间均低于150毫秒。我们还报告了构建该模型的成本。规则派生的标签经过盲人听力检查，正类的正确率为95.9%，负类为44.4%，后者低于随机水平，因为该规则回答了与模型所问不同的问题。用音频-LLM标注器替换后，测得人类一致性为97.5%，成本为5.69美元。在我们测量的每个训练因素中，只有编码器容量影响了结果；三次相同配置和种子的运行跨度为0.87个准确率点，这是我们的其他变量没有任何意义的下限。通过生产VAD和流适配器重放相同的标记边界又损失了2.60个点，且7.8%的边界根本没有被模型处理。数据、权重、代码及所有负面结果均为公开。
cs.CL / 3 / 2609.05637
Better Together: Complementary Query Rewriting Under a Strong RAG Baseline
更好地协作：在强大的检索增强生成基线下的互补查询重写
Abstract
A popular way to improve Retrieval-Augmented Generation (RAG) is to rewrite the user's question into several variants and search with all of them. We test whether this actually helps once the underlying search is already strong. Under one fixed, competitive pipeline (BGE dense retrieval, cross-encoder reranking, and MMR diversification), we compare four query-rewriting strategies (S1-S4) against two strong LLM baselines (HyDE, Query2Doc) on three datasets (HotpotQA, AmbigNQ, and the 512K-document EnterpriseRAG-Bench) over three seeds with paired-bootstrap significance tests. Our headline result is that rewriting alone is at best competitive with a strong baseline, but combining methods yields outsized gains because different strategies fail on different questions. A post-hoc union of four methods (S1+S3+S4+HyDE) improves HIT@10 over the baseline by +12.5 points on enterprise data (51.70 vs 39.22), and a five-method union reaches 52.98 (+13.8). Budget-matched controls capture only ~40% of this gain, confirming that complementarity, not retrieval budget, is the primary driver. On HotpotQA the union adds +1.6 to +1.8 points (p<0.001), saturating the all-method oracle; on AmbigNQ the same fusion hurts (-2.4 below the best solo, p<0.001), and we analyze when and why. Because rewriting is expensive, we evaluate in simulation a confidence-gated router that runs rewriting only when the baseline's own top-1 score is low. It captures about half of the enterprise full-merge gain (+4.3 HIT@10) while paying rewriting cost on <40% of queries, and automatically declines to rewrite on AmbigNQ. A downstream answer-quality evaluation confirms the router improves F1 by +1.92 (p<0.01) at roughly 40% of the expansion cost. In short: treat query rewriting as a complementary coverage source, applied through cost-aware routing, not as a standalone replacement for a strong baseline.
Chinese Translation
提高检索增强生成（RAG）的一种流行方法是将用户的问题重写为多个变体，并使用所有变体进行搜索。我们测试了在基础搜索已经很强的情况下，这种方法是否真的有效。在一个固定的竞争管道（BGE密集检索、交叉编码器重排序和MMR多样化）下，我们比较了四种查询重写策略（S1-S4）与两个强大的大型语言模型基线（HyDE，Query2Doc）在三个数据集（HotpotQA、AmbigNQ和512K文档的EnterpriseRAG-Bench）上的表现，使用了三组种子和配对自助法显著性测试。我们的主要结果是，仅靠重写在最好的情况下也只能与强基线竞争，但结合多种方法会带来显著的收益，因为不同策略在不同问题上表现不一。四种方法的后验联合（S1+S3+S4+HyDE）在企业数据上将HIT@10提高了12.5个百分点（51.70对39.22），而五种方法的联合达到了52.98（+13.8）。预算匹配的控制组仅捕获了约40%的增益，确认了互补性而非检索预算是主要驱动因素。在HotpotQA上，联合增加了1.6到1.8个百分点（p<0.001），饱和了所有方法的oracle；在AmbigNQ上，相同的融合则有负面影响（比最佳单一方法低2.4，p<0.001），我们分析了何时以及为何会这样。由于重写成本高，我们在模拟中评估了一种基于置信度的路由器，仅在基线的前1名得分较低时才进行重写。它捕获了约一半的企业全合并增益（+4.3 HIT@10），同时在不到40%的查询上支付重写成本，并自动拒绝在AmbigNQ上进行重写。下游答案质量评估确认该路由器在扩展成本约40%时将F1提高了+1.92（p<0.01）。简而言之：将查询重写视为一种互补的覆盖来源，通过成本意识路由应用，而不是作为强基线的独立替代品。
cs.CL / 4 / 2609.05677
Who Maintains Agent Skills? A Longitudinal Study of Human-Governed, AI-Assisted Skill Maintenance
谁来维护智能体技能？一项关于人类管理的AI辅助技能维护的纵向研究
Abstract
Lifelong LLM agents increasingly rely on external skill artifacts as one element for preserving and reusing capabilities over time. These skills (usually portable Markdown files such as SKILL.md) describe when and how to apply a capability and must be corrected, expanded, and consolidated as tools and usage patterns shift over deployment. Recent work seeks to automate skill curation, but it largely evaluates against automated baselines and treats human maintenance as an unmeasured bottleneck. We study that missing process directly. We mine the full commit histories of five public AI-skill repositories, a purposive sample of AI-tooling organizations, covering 873 commits, 143 skill files, and 254 substantive post-creation edits from October 2025 to June 2026. We code each edit with pre-registered governance, operation, and trigger-evidence codebooks. Three findings emerge. First, every substantive edit is authored or merged through a named human account, while 62% carry an AI co-author trailer, with large repository-level variation. Second, these edits are genuine curation: an audited sample shows that most change skill content, and the coded operations are dominated by additions and corrections. Third, a pre-registered rule-likeness axis fails its reliability gate; reliably coding rule-likeness from commit artifacts remains an open measurement problem. We release the corpus, codebooks, mining scripts, and a replay protocol for automated skill curators. For self-evolving agents, public skill maintenance currently looks less like an autonomous pipeline than a human-governed, AI-assisted loop that future curators must measure against and operate within.
Chinese Translation
终身大型语言模型（LLM）智能体越来越依赖外部技能文档作为保持和重用能力的一个要素。这些技能（通常是可移植的Markdown文件，如SKILL.md）描述了何时以及如何应用某项能力，并且必须随着工具和使用模式在部署过程中发生变化而进行修正、扩展和整合。近期的研究旨在自动化技能管理，但主要是与自动化基线进行评估，并将人类维护视为一个未被测量的瓶颈。我们直接研究这一缺失的过程。我们挖掘了五个公共AI技能库的完整提交历史，这是一个有目的的AI工具组织样本，涵盖了873次提交、143个技能文件和254个实质性创建后编辑，时间范围从2025年10月到2026年6月。我们使用预注册的治理、操作和触发证据代码本对每次编辑进行编码。研究得出了三个发现。首先，每次实质性编辑都是通过一个命名的人类账户创作或合并的，而62%的编辑带有AI共同作者的标记，且在大型库层面存在显著差异。其次，这些编辑是真正的管理：经过审核的样本显示，大多数更改了技能内容，编码的操作主要集中在添加和修正上。第三，预注册的规则相似性轴未能通过其可靠性门槛；从提交文档中可靠地编码规则相似性仍然是一个未解决的测量问题。我们发布了语料库、代码本、挖掘脚本和自动化技能管理者的重放协议。对于自我进化的智能体而言，当前的公共技能维护看起来更像是一个人类管理的、AI辅助的循环，而不是一个自主的流程，未来的管理者必须在其中进行测量和操作。
cs.CL / 5 / 2609.05682
A Rubric-Guided Large Language Model Solution for Opioid Use Disorder Computable Phenotyping
基于评分标准的大型语言模型解决方案用于阿片类药物使用障碍的可计算表型
Abstract
Opioid use disorder (OUD) remains a public health crisis in the United States, yet it is difficult to identify from electronic health records (EHRs) because missing diagnosis codes and supporting evidence are buried in clinical narratives. Accurate OUD identification is critical to support interventions and improve health outcomes. This study developed a rubric-guided large language model (LLM) that incorporated Optimization by PROmpting (OPRO) for OUD computable phenotyping (CP). The framework used an 18-item, expert-identified rubric to instruct LLMs to automatically extract critical text with supporting evidence to determine OUD flags. Two UF Health physicians (GMR and WMG) chart-reviewed 253 patients, including 68 OUD-positive cases. Our LLM-based computable phenotype (CP) achieved the best F1 score of 0.774 and an AUROC of 0.934, outperforming the machine learning-based CP using EHR and natural language processing-extracted variables, and zero-shot LLMs by relative F1 improvements of 12.8% and 44.4%, respectively. The proposed LLM-based CP could link LLM-extracted evidence to OUD phenotyping for better explainability.
Chinese Translation
阿片类药物使用障碍（OUD）在美国仍然是一个公共卫生危机，但由于缺失的诊断代码和支持证据埋藏在临床叙述中，难以从电子健康记录（EHR）中识别。准确识别OUD对于支持干预措施和改善健康结果至关重要。本研究开发了一种基于评分标准的大型语言模型（LLM），该模型结合了通过提示优化（Optimization by PROmpting, OPRO）的方法，用于OUD的可计算表型（CP）。该框架使用了一份由专家识别的18项评分标准，指导LLM自动提取关键文本及其支持证据，以确定OUD标记。两位UF Health的医生（GMR和WMG）对253名患者进行了图表审查，其中包括68例OUD阳性病例。我们的基于LLM的可计算表型（CP）达到了最佳F1分数0.774和AUROC值0.934，优于基于机器学习的CP（使用EHR和自然语言处理提取的变量）和零样本LLM，F1分数相对提升分别为12.8%和44.4%。所提出的基于LLM的CP能够将LLM提取的证据与OUD表型关联，以提高可解释性。
cs.CL / 6 / 2609.05707
Intra-Prompt Parallel Decoding for Common-Context Question Answering
用于共同上下文问答的内部提示并行解码
Abstract
In common-context question answering (CCQA) tasks, multiple input questions share a common context to base their answers from. However, Large Language Models typically generate each answer using an independent prompt. While existing batching and caching techniques help improve parallelism and reduce repeated computations, the separation of questions across prompts limits the achievable speedup, as modern GPUs are underutilized due to a memory bottleneck during attention. We present Intra-Prompt Parallel Decoding (IPPD), a novel inference method that answers multiple common-context questions in parallel within a single prompt. IPPD directly addresses the bottleneck by efficiently sharing both memory and computation during the attention process, as the next token for every question is decoded in a single inference step. IPPD uses virtual position IDs and attention mask manipulation to generate the same output as standard prompting without requiring fine-tuning or any changes to the LLM architecture. Since all parallelism occurs within a prompt, IPPD is fully compatible with batched inference, even when each prompt features a different context. Our experiments show that IPPD delivers up to 7X the effective throughput as standard decoding without quality degradation, and outperforms prefix caching with PagedAttention in most settings.
Chinese Translation
在共同上下文问答（CCQA）任务中，多个输入问题共享一个共同的上下文以基于此提供答案。然而，大型语言模型通常使用独立的提示生成每个答案。虽然现有的批处理和缓存技术有助于提高并行性并减少重复计算，但由于在注意力机制中存在内存瓶颈，问题在提示之间的分离限制了可实现的加速，因为现代GPU未得到充分利用。我们提出了内部提示并行解码（Intra-Prompt Parallel Decoding, IPPD），这是一种新颖的推理方法，可以在单个提示内并行回答多个共同上下文问题。IPPD通过在注意力过程中高效共享内存和计算，直接解决了瓶颈，因为每个问题的下一个标记在单个推理步骤中解码。IPPD使用虚拟位置ID和注意力掩码操作，生成与标准提示相同的输出，而无需微调或对大型语言模型架构进行任何更改。由于所有并行性都发生在一个提示内，IPPD与批量推理完全兼容，即使每个提示具有不同的上下文。我们的实验表明，IPPD的有效吞吐量比标准解码高出多达7倍，且没有质量下降，并且在大多数设置中优于带有分页注意力（PagedAttention）的前缀缓存。
cs.CL / 7 / 2609.05728
MedWER: A Reproducible, Model-Free Evaluation Protocol for Medical Speech Recognition
MedWER：一种可重复的无模型医学语音识别评估协议
Abstract
Overall word error rate hides clinically critical errors: a transcript can be 95% correct and still swap one drug for another. The usual fix weights errors on medical entities, and almost always depends on an evaluation-time named-entity recognition (NER) model or cloud API, which makes the metric's denominator a versioned black box. We present MedWER, an evaluation protocol and open-source tool for medical ASR whose denominator is a fixed, license-clean term list: 19,373 drug, diagnosis, symptom, and injury-mechanism entries projected from public sources. The protocol couples a pinned text normalizer with a phrase-aware term-restricted WER, the MedWER, so the only versioned component is a normalizer dependency held at an exact release and checked against committed golden fixtures. Coverage is validated against an independent provincial drug-benefit file the list was not built from; the matching heuristic is calibrated against ground-truth entity spans. Baselines for Moonshine~base, Whisper~base.en, and MedASR on two open benchmarks are scored with the released tool and reported with 95% confidence intervals from resampled per-utterance scores.
Chinese Translation
整体词错误率掩盖了临床关键错误：一个转录文本可以是95%正确的，但仍然可能将一种药物与另一种药物混淆。通常的修正方法是对医学实体的错误进行加权，并且几乎总是依赖于评估时的命名实体识别（NER）模型或云API，这使得该指标的分母成为一个版本化的黑箱。我们提出了MedWER，这是一种医学自动语音识别（ASR）的评估协议和开源工具，其分母是一个固定的、无版权问题的术语列表：19,373个药物、诊断、症状和伤害机制条目，来源于公共数据。该协议将一个固定的文本标准化器与一个短语感知的术语限制词错误率（WER）结合在一起，即MedWER，因此唯一的版本化组件是一个在确切版本下保持的标准化器依赖，并与已提交的黄金标准进行检查。覆盖率通过与未构建该列表的独立省级药物福利文件进行验证；匹配启发式方法是根据真实实体范围进行校准的。Moonshine~base、Whisper~base.en和MedASR在两个开放基准上的基线得分使用发布的工具进行评分，并报告了95%的置信区间，基于每次发言的重抽样得分。
cs.CL / 8 / 2609.05743
Some Tokens Behave like Magnets: Revealing Linguistic Organization in the Layers of Language Models
某些标记表现得像磁铁：揭示语言模型层中的语言组织
Abstract
We identify a special group of token vectors inside large language models (LLMs), which we term magnetic vectors, that organize the surrounding tokens by either attracting or repelling them. Particularly, tokens pointing the same way as an attracting magnet are elongated; tokens pointing the same way as a repelling magnet are compressed. Just as physical magnets pull or push away the iron filings around them, these vectors organize their surroundings through two opposing polarities. Moreover, we identify a statistically significant pattern in linguistic category where function words consistently act as repelling magnets in early layers, and we also find magnets consistently reorganize their polarities in unique ways deeper in the model. In a further case study we find this observation may unveil a deliberate, layer-wise organization in how LLMs process language. This pattern is consistent across different LLM architectures, sizes, and layer configurations. It is also causally relevant. When the LLM is fine-tuned for a downstream task, the task-functional tokens emerge as magnets. E.g., in question answering, the answer-span tokens become uniquely repelling magnets in the final layer, geometrically carving the answer out of the surrounding context. Furthermore, removing early-layer repelling magnets devastates syntactic tasks (POS tagging accuracy drops from 91% to below 10%) while sparing semantic ones, and removing late-layer attracting magnets does the reverse. We believe this phenomenon warrants further investigation, as it opens the first probe-free path to understanding how language models geometrically organize linguistic computation across their layers.
Chinese Translation
我们在大型语言模型（LLMs）中识别出一组特殊的标记向量，称为磁性向量，它们通过吸引或排斥周围的标记来组织环境。特别地，指向与吸引磁铁相同方向的标记是拉长的；而指向与排斥磁铁相同方向的标记则是压缩的。正如物理磁铁会拉动或推开周围的铁屑，这些向量通过两种相对的极性来组织其周围环境。此外，我们还发现了一个在语言类别中具有统计学意义的模式，其中功能词在早期层中始终表现为排斥磁铁，而我们也发现磁铁在模型的更深层中以独特的方式持续重新组织其极性。在进一步的案例研究中，我们发现这一观察结果可能揭示了LLMs处理语言时的有意层次组织。该模式在不同的LLM架构、规模和层配置中是一致的，并且具有因果相关性。当LLM针对下游任务进行微调时，任务功能性标记会作为磁铁出现。例如，在问答中，答案跨度标记在最终层中成为独特的排斥磁铁，几何上从周围上下文中雕刻出答案。此外，移除早期层的排斥磁铁会严重影响句法任务（词性标注准确率从91%降至10%以下），而对语义任务则无影响；移除晚期层的吸引磁铁则产生相反的效果。我们认为这一现象值得进一步研究，因为它为理解语言模型如何在其层中几何组织语言计算开辟了首个无探测路径。
cs.CL / 9 / 2609.05757
CrisisKD: Five-Stage Knowledge Distillation for Aspect-Level Sentiment and Emotion Analysis in Crisis Discourse
CrisisKD：危机话语中面向层级的情感与情绪分析的五阶段知识蒸馏
Abstract
Identifying the target of emotional words or phrases in crisis situations, especially health-related ones, is important for understanding public concerns across cultural and linguistic contexts. We propose CrisisKD, a five-stage teacher--student knowledge distillation framework for aspect-level sentiment and emotion analysis on unannotated social media data. A teacher LLM generates aspect-level labels and reasoning traces that supervise a smaller student model across aspect extraction, syntactic parsing, opinion extraction, sentiment classification, and emotion classification. Using this framework, we construct and release a dataset containing 50,615 aspect-level labels, together with the annotation and fine-tuning scripts as open-source resources. The resulting student supports end-to-end ABSA and emotion detection at substantially lower inference cost than the teacher. On a manually annotated 500-tweet gold set, the 5-task Qwen2.5-7B student improves over the untuned model by 7.9 F1 points on aspect extraction, 17.0 points on emotion accuracy, and 6.5 points on sentiment accuracy. On the external ABEA benchmark, CrisisKD improves the same-model Qwen2.5-7B ICL baseline by 2.8 F1 points on ATE and 3.8 F1 points on joint ATE+AEC.
Chinese Translation
在危机情况下，尤其是与健康相关的危机中，识别情感词或短语的目标对于理解跨文化和语言背景下的公众关注至关重要。我们提出了CrisisKD，一个五阶段的教师-学生知识蒸馏框架，用于对未标注社交媒体数据进行面向层级的情感与情绪分析。教师大型语言模型（LLM）生成面向层级的标签和推理轨迹，以监督一个较小的学生模型，涵盖面向提取、句法解析、观点提取、情感分类和情绪分类。通过该框架，我们构建并发布了一个包含50,615个面向层级标签的数据集，以及注释和微调脚本，作为开源资源。最终的学生模型支持端到端的面向层级情感分析（ABSA）和情绪检测，其推理成本显著低于教师模型。在一个手动标注的500条推文的金标准集上，5任务的Qwen2.5-7B学生模型在面向提取上比未调优模型提高了7.9个F1分，在情绪准确率上提高了17.0分，在情感准确率上提高了6.5分。在外部ABEA基准上，CrisisKD在面向层级情感分析（ATE）上将同模型Qwen2.5-7B的ICL基线提高了2.8个F1分，在联合ATE+AEC上提高了3.8个F1分。
cs.CL / 10 / 2609.05797
Recall Is Not Protection: Evaluating Safety Monitors Against Model Compliance
召回并非保护：评估安全监控器对模型合规性的影响
Abstract
Safety monitors screen prompts sent to deployed language models, flagging harmful requests so they are never answered. They are evaluated by recall against harmfulness labels, but a catch only prevents harm if the model would otherwise have complied. We measure the difference directly: we sample repeated responses from the target model, call a harmful prompt \emph{elicitable} if the model complies at least once, and report monitor recall separately on elicitable and non-elicitable prompts. Across six monitor configurations and three model families, spanning activation probes, fine-tuned text guards, and a 120B policy-conditioned reasoning classifier, recall on elicitable prompts falls 0.22 to 0.38 below recall on non-elicitable prompts at a fixed false positive rate. The prompts a monitor misses are 2.8 to 5.6 times more likely to be complied with than the prompts it catches. The gap replicates across three model families and appears also in text-only monitors entirely independent of the target model. This suggests that standard recall may overstate the protection monitors provide in practice, and that monitors should be evaluated against what their models will actually answer.
Chinese Translation
安全监控器筛选发送给部署语言模型的提示，标记有害请求以确保其不被回答。它们通过与有害性标签的召回率进行评估，但这一机制仅在模型本会遵从的情况下才能防止危害。我们直接测量这一差异：我们从目标模型中抽样重复响应，如果模型至少遵从一次，则将有害提示称为 extit{可引发的}，并分别报告监控器在可引发和不可引发提示上的召回率。在六种监控器配置和三种模型系列中，包括激活探测器、微调文本保护器和一个120B的政策条件推理分类器，在固定的假阳性率下，可引发提示的召回率比不可引发提示低0.22到0.38。监控器漏掉的提示被遵从的可能性是其捕获提示的2.8到5.6倍。这个差距在三种模型系列中得到了复制，并且在与目标模型完全独立的仅文本监控器中也出现。这表明，标准召回率可能高估了监控器在实践中提供的保护，监控器的评估应基于其模型实际会回答的内容。
cs.CL / 11 / 2609.05799
Dynamic Lagging for Simultaneous Translation
动态滞后用于同步翻译
Abstract
In cascaded simultaneous speech translation, the machine translation (MT) system cannot control the read--write schedule of the upstream recognizer: it must decide, from a growing source prefix, how much target text to commit. We make a sentence-trained, decoder-only LLM prefix-aware by fine-tuning it on stable prefixes---the longest prefix that any translation up to the current partial source has shared with the model's own full-source output---mixed with full-sentence pairs, and prompt it through a single force-decode turn that carries the committed target forward as more source arrives, making the system flicker-free by construction. We fine-tune Qwen3-8B for EN to DE, JA, ZH, simulating the source stream with reference-transcript prefixes. Prefix finetuning preserves full-sentence quality while improving worst-position chunk quality, and it improves calibration of token-level commit confidence, reducing expected calibration error (ECE) on early source prefixes against a stable-prefix oracle. A single training-free threshold on that confidence is the most effective of the three latency controls we compare: it traces a continuous quality--latency frontier that outperforms the discrete wait-$k$ and target-suffix-deletion quality-latency tradeoff mechanisms. The effect holds well on FLEURS, WMT24++, and CoVoST~2 test sets, under both COMET and MetricX.
Chinese Translation
在级联的同步语音翻译中，机器翻译（MT）系统无法控制上游识别器的读写调度：它必须从不断增长的源前缀中决定提交多少目标文本。我们通过对稳定前缀进行微调，使得经过句子训练的仅解码的LLM（大语言模型）具备前缀感知能力——稳定前缀是指任何翻译在当前部分源文本下与模型自身的完整源输出共享的最长前缀——并与完整句子对混合，通过单次强制解码的方式进行提示，随着更多源文本的到来，推动已提交的目标文本，从而使系统在构建上无闪烁。我们对Qwen3-8B进行了微调，以实现英语到德语、日语和中文的翻译，模拟源流并使用参考转录前缀。前缀微调在保留完整句子质量的同时改善了最差位置块的质量，并提高了标记级别提交信心的校准，减少了相对于稳定前缀oracle的早期源前缀的预期校准误差（ECE）。在我们比较的三种延迟控制中，基于该信心的单一无训练阈值是最有效的：它描绘了一条连续的质量-延迟边界，优于离散的等待-$k$和目标后缀删除质量-延迟权衡机制。该效果在FLEURS、WMT24++和CoVoST~2测试集上表现良好，适用于COMET和MetricX两种评估方法。
cs.CL / 12 / 2609.05802
AtomCite: Verification and Correction of Supplied Page-Level Citations in Multi-Page Documents
AtomCite：多页文档中提供的页面级引用的验证与修正
Abstract
Large language models answering questions over multi-page documents are expected to cite the supporting pages, yet supplied citations are sometimes inaccurate, and current evaluations score citations at generation time or against text passages: no existing benchmark evaluates whether a system can verify and correct a page-level citation already attached to an answer. We propose AtomCite, an agentic framework that parses an answer into claims, checks each claim against the image of its cited page, and applies a deterministic repair policy. To evaluate it, we introduce DocCite, to our knowledge the first benchmark for systems that verify and correct page-level citations in document images. Built on MP-DocVQA and DUDE, it combines 928 validated injected instances with 2,468 candidate natural errors harvested from frontier- and efficiency-tier models, of which a two-annotator audit confirms 1,909 as genuine errors. Primary labels are assigned deterministically, not by LLM judges, with the human audit as a separate validation layer. Across three model families (Gemini, Claude, and GPT), AtomCite reaches around 93% binary verification accuracy on the injected benchmark, significantly outperforming every OCR-only condition, including a compute-matched control, and exceeding every prior text-based baseline given the same OCR text. Its repair policy lifts citation precision on the injected mix from a constructed 34% to 87-90% while retaining over 90% of correct claims. AtomCite also transfers: with frozen prompts and zero training, it raises the hallucination-detection scores of two open 7-8B models on five public benchmarks above the same models prompted as direct judges. Finally, the audit shows that noise in automatic labels biases measured verifier accuracy and can reverse system rankings, so evaluations relying only on synthetic or automatic labels risk mismeasuring verification capability.
Chinese Translation
大型语言模型在多页文档中回答问题时，通常需要引用支持的页面，但提供的引用有时不准确，当前的评估在生成时或与文本段落进行比较时对引用进行评分：目前没有现有基准评估系统是否能够验证和修正已经附加到答案上的页面级引用。我们提出了AtomCite，一个智能框架，它将答案解析为主张，检查每个主张与其引用页面的图像，并应用确定性的修复策略。为了评估该框架，我们引入了DocCite，据我们所知，这是第一个针对能够验证和修正文档图像中页面级引用的系统的基准。DocCite基于MP-DocVQA和DUDE，结合了928个经过验证的注入实例和2,468个从前沿和效率层模型中收集的候选自然错误，其中两位注释员的审核确认1,909个为真实错误。主要标签是确定性分配的，而不是由大型语言模型评审的，人工审核作为单独的验证层。在三个模型家族（Gemini、Claude和GPT）中，AtomCite在注入基准上的二元验证准确率达到约93%，显著优于每个仅使用OCR的条件，包括计算匹配的对照组，并且在给定相同OCR文本的情况下超越了所有先前基于文本的基线。其修复策略将注入混合中的引用精度从构造的34%提升至87-90%，同时保留超过90%的正确主张。AtomCite还具有迁移能力：在冻结提示和零训练的情况下，它将两个开放的7-8B模型在五个公共基准上的幻觉检测分数提升至高于作为直接评审的同一模型。最后，审核显示自动标签中的噪声会偏向测量的验证器准确性，并可能逆转系统排名，因此仅依赖合成或自动标签的评估存在误测验证能力的风险。
cs.CL / 13 / 2609.05821
CONDUIT: A Unified Residual-Stream Restoration Framework for KV Cache Reuse in Vision-Language Models
CONDUIT：一种统一的残差流恢复框架，用于视觉语言模型中的KV缓存重用
Abstract
Vision-language models (VLMs) often answer new questions about recurring visual content, where reusing the key-value (KV) cache can avoid re-encoding expensive visual prefixes. Exact-prefix reuse, however, fails when the same visual content appears under a changed prefix. Selective recomputation can recover quality under a small visual-token budget, but only when the right stale tokens are refreshed. Raw-attention selection can waste budget on high-attention tokens with small value-norm proxy scores and on query-irrelevant images. To address these failure modes, we propose CONDUIT, a training-free refresh policy that unifies single- and multi-image reuse as residual-stream restoration. Building on norm-weighted attention, CONDUIT ranks cached visual tokens using cached-key query attention and an accessible pre-output cached-value-norm proxy, then applies empirical image-level relevance amplification before one global selection. With one image, the coefficient is one and the rule reduces to intra-image token selection. The method preserves model architecture and weights, adding only a single query-conditioned scoring pass at inference. At a 10% refresh budget, CONDUIT achieves 97.0-99.5% of the corresponding full-prefill five-dataset average across three VLM backbones and leads budgeted methods on average; on the MMLongBench-Doc latency subset, it uses 13.5% of full-prefill FLOPs and achieves a 2.99x time-to-first-token speedup.
Chinese Translation
视觉语言模型（VLMs）通常回答有关重复视觉内容的新问题，其中重用键值（KV）缓存可以避免重新编码昂贵的视觉前缀。然而，当相同的视觉内容在更改的前缀下出现时，精确前缀重用会失败。选择性重新计算可以在小的视觉标记预算下恢复质量，但仅在正确的过时标记被刷新时有效。原始注意力选择可能会在高注意力标记上浪费预算，而这些标记的值范数代理分数较小，并且在与查询无关的图像上也会浪费预算。为了解决这些失败模式，我们提出了CONDUIT，这是一种无训练的刷新策略，将单图像和多图像重用统一为残差流恢复。基于范数加权注意力，CONDUIT使用缓存的键查询注意力和可访问的预输出缓存值范数代理对缓存的视觉标记进行排序，然后在一次全局选择之前应用经验图像级相关性放大。在处理一张图像时，系数为1，规则简化为图像内部标记选择。该方法保留模型架构和权重，仅在推理时增加一次查询条件评分过程。在10%的刷新预算下，CONDUIT在三个VLM主干网络上实现了97.0-99.5%的相应完整预填充五数据集平均值，并在平均预算方法中领先；在MMLongBench-Doc延迟子集上，它使用了13.5%的完整预填充FLOPs，并实现了2.99倍的首次标记时间加速。
cs.CL / 14 / 2609.05843
SinoGlyphBench: A Diagnostic Benchmark for Chinese Glyph-Level Obfuscation in Language-Model Moderation
SinoGlyphBench：一种针对语言模型审核中的中文字形级混淆的诊断基准
Abstract
Glyph-level obfuscation can leave harmful Chinese content readable to humans while degrading automated moderation. We introduce SinoGlyphBench, a diagnostic benchmark that identifies label-critical semantic anchors and creates matched original and glyph-obfuscated inputs in text and image modalities. By perturbing anchors, background context, or both, this design distinguishes corruption of moderation-relevant evidence from general surface variation. Across 176,916 paired evaluations of 12 LLMs and MLLMs, obfuscation increases harmful false-negative and false-positive rates by 6.1 and 4.7 percentage points, respectively, and reduces four-way accuracy by 5.0 points. Models retain 75.7% of the decisions that were correct on the matched original inputs. Full-scope perturbations cause the largest degradation, anchor-only perturbations are more damaging than background-only perturbations, and cross-script substitution is particularly difficult in the text modality. Analysis of structured outputs identifies observable mismatches in visible-form reading, intended-message recovery, and final safety judgment. The evaluated models, therefore, remain brittle to Chinese content written with non-canonical glyphs. Resources are available at https://github.com/fengshun124/SinoGlyphBench.
Chinese Translation
字形级混淆可能使有害的中文内容对人类可读，同时降低自动化审核的效果。我们提出了SinoGlyphBench，这是一种诊断基准，旨在识别标签关键的语义锚点，并在文本和图像模式中创建匹配的原始输入和字形混淆输入。通过扰动锚点、背景上下文或两者，这一设计区分了审核相关证据的损坏与一般表面变化。在对12个大型语言模型（LLMs）和多语言模型（MLLMs）进行的176,916对评估中，混淆分别使有害的假阴性和假阳性率增加了6.1和4.7个百分点，并将四项准确率降低了5.0点。模型保留了75.7%在匹配原始输入上正确的决策。全范围的扰动导致最大的降级，只有锚点的扰动比仅背景的扰动更具破坏性，而跨脚本替换在文本模式中特别困难。对结构化输出的分析识别出在可见形式阅读、意图信息恢复和最终安全判断中可观察到的不匹配。因此，被评估的模型对使用非规范字形书写的中文内容仍然脆弱。相关资源可在 https://github.com/fengshun124/SinoGlyphBench 获取。
cs.CL / 15 / 2609.05882
What if LLMs Ate Their Words: Causal History Effects in Multi-Turn Interaction
如果大型语言模型（LLMs）吃掉了它们的话：多轮交互中的因果历史效应
Abstract
Multi-turn interaction creates a feedback process in which an LLM's previous responses become context for later behavior. Prior work shows substantial multi-turn degradation and that assistant-generated history can affect later behavior. However, it remains unclear how these effects manifest across models, tasks, turns, and inside a model. We study these gaps across six task families and five models. Degradation from fully specified single-turn input (FULL) to progressively revealed multi-turn interaction (SHARDED) is clearly task- and model-dependent, and stronger one-shot performance does not imply greater interaction robustness. We then retrospectively analyze completed SHARDED conversations by replaying the user messages already observed in each trajectory while editing only assistant-generated history. Replacing prior assistant responses with neutral content (termed neutralization) changes downstream min-max normalized performance by +.027 across 2,973 trajectories. On a prespecified length-controlled subset, short and length-matched neutralization yield nearly identical effects (+.069 versus +.068), showing that simple context shortening is insufficient to explain the effect of history editing. Turn Surgery further intervenes on one assistant turn at a time. Among 237 selected degraded trajectories, 63.7% contain at least one beneficial intervention, while most tested positions remain unchanged; for binary tasks, 48.4% admit a fail-to-success reversal. An open-weight case study links behaviorally consequential history changes to measurable downstream state differences, but finds task-dependent rather than universal internal signatures. Overall, assistant-generated history has active but selective effects on multi-turn performance, motivating selective rather than uniform history management.
Chinese Translation
多轮交互创造了一个反馈过程，其中大型语言模型（LLM）之前的响应成为后续行为的上下文。先前的研究表明，多轮交互存在显著的退化现象，并且助手生成的历史会影响后续行为。然而，这些效应如何在不同模型、任务、轮次以及模型内部表现仍不清楚。我们在六个任务家族和五个模型中研究了这些差距。从完全指定的单轮输入（FULL）到逐步揭示的多轮交互（SHARDED）的退化显著依赖于任务和模型，且更强的一次性表现并不意味着更大的交互鲁棒性。随后，我们通过回放每条轨迹中已观察到的用户消息，同时仅编辑助手生成的历史，回顾性分析了已完成的SHARDED对话。用中性内容（称为中和）替换先前助手的响应使得下游的最小-最大归一化性能在2,973条轨迹中变化了+0.027。在一个预先指定的长度控制子集上，短的和长度匹配的中和产生了几乎相同的效果（+0.069与+0.068），表明简单的上下文缩短不足以解释历史编辑的效果。轮次手术进一步对每个助手轮次进行干预。在237条选定的退化轨迹中，63.7%包含至少一次有益的干预，而大多数测试位置保持不变；对于二元任务，48.4%允许失败到成功的逆转。一项开放权重的案例研究将行为上重要的历史变化与可测量的下游状态差异联系起来，但发现内部特征是任务依赖的而非普遍的。总体而言，助手生成的历史对多轮表现具有积极但选择性的影响，促使我们采取选择性而非统一的历史管理策略。
cs.CL / 16 / 2609.05899
AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection
AlignDiff：利用模型内在信息优化偏好数据选择
Abstract
Aligning large language models with human preferences remains a challenge, primarily due to the critical role of preference data quality in effective alignment. Existing datasets are frequently plagued by inherent noise and distribution shifts, which inherently limit model performance. To bridge this gap, we propose AlignDiff, a preference data filtering framework driven by intrinsic model signals. AlignDiff first identifies samples with clear preferences using both positive and inverse signals, then prioritizes the more challenging samples based on the average negative log-likelihood gap, encouraging the model to learn richer information from them. AlignDiff is evaluated on two widely used model families (LLaMA and Qwen) and three benchmarks widely adopted in the alignment community (AlpacaEval 2.0, Arena-Hard, and MT-Bench). Across all settings, it consistently outperforms seven strong baselines. We conduct comprehensive ablation studies to validate the effectiveness of AlignDiff, and further show that difficulty-based curriculum learning improves model performance.
Chinese Translation
将大型语言模型与人类偏好对齐仍然是一项挑战，主要是由于偏好数据质量在有效对齐中的关键作用。现有数据集常常受到固有噪声和分布变化的困扰，这本质上限制了模型的性能。为了解决这一问题，我们提出了AlignDiff，一种基于模型内在信号的偏好数据过滤框架。AlignDiff首先利用正向和反向信号识别出具有明确偏好的样本，然后基于平均负对数似然差异优先考虑更具挑战性的样本，鼓励模型从中学习更丰富的信息。AlignDiff在两个广泛使用的模型系列（LLaMA和Qwen）和三个在对齐社区广泛采用的基准（AlpacaEval 2.0、Arena-Hard和MT-Bench）上进行了评估。在所有设置中，它始终优于七个强基线。我们进行了全面的消融研究以验证AlignDiff的有效性，并进一步表明基于难度的课程学习提高了模型性能。
cs.CL / 17 / 2609.05905
From Narrative to Auditable Forecasts: A Structured Scaffold for Agentic Forecasting
从叙述到可审计的预测：一种结构化的代理预测框架
Abstract
LLM agents are increasingly used for live forecasting, where they retrieve up-to-date information and produce estimates for unresolved future events. However, current agentic forecasting often relies on implicit narrative aggregation: agents collect evidence, discuss it in prose, and often assign a probability without an explicit update path from evidence to forecast. This limits both forecasting accuracy and auditability. We propose AuditForecast, an agentic scaffold for structured probabilistic forecasting. AuditForecast first anchors the forecast with a suitable quantitative baseline model, uses model-guided data retrieval to derive a base probability, and then applies situational factor updates outside the model's scope through mechanical aggregation in odds space. This turns forecasting from a prose-based judgment into a structured process with explicit intermediate objects. Across multiple live forecasting benchmarks, AuditForecast improves forecasting accuracy and calibration relative to strong agentic baselines, surpasses market-implied references in several settings, and outperforms substantially more expensive deep-research agents while remaining Pareto-dominant in the cost--accuracy tradeoff. Beyond performance gains, AuditForecast produces an auditable forecasting report that makes forecast construction explicit and supports systematic post hoc analysis.
Chinese Translation
大型语言模型（LLM）代理越来越多地用于实时预测，它们检索最新信息并对未解决的未来事件进行估计。然而，当前的代理预测往往依赖于隐式叙述聚合：代理收集证据，以散文形式讨论这些证据，并且通常在没有明确的证据到预测更新路径的情况下分配概率。这限制了预测的准确性和可审计性。我们提出了AuditForecast，一种用于结构化概率预测的代理框架。AuditForecast首先用合适的定量基线模型锚定预测，利用模型引导的数据检索得出基础概率，然后通过在赔率空间中的机械聚合应用超出模型范围的情境因素更新。这将预测从基于散文的判断转变为具有明确中间对象的结构化过程。在多个实时预测基准测试中，AuditForecast相对于强大的代理基线提高了预测的准确性和校准，在多个设置中超越了市场隐含参考，并且在成本-准确性权衡中保持帕累托优势，优于成本显著更高的深度研究代理。除了性能提升，AuditForecast还生成了可审计的预测报告，使预测构建过程明确，并支持系统的事后分析。
cs.CL / 18 / 2609.05910
UniRRM: Unified Reasoning Reward Models Across Languages and Evaluation Paradigms
UniRRM：跨语言和评估范式的统一推理奖励模型
Abstract
Reinforcement learning (RL) excels on tasks with verifiable rewards, but in open-ended tasks, the reliability of reward models remains a key challenge. Existing solutions either depend on costly proprietary LLM-as-a-Judge systems or opaque scalar reward models that lack interpretability. Recent works on generative reward models offer a promising alternative, but they remain constrained by static evaluation criteria, fragmented evaluation paradigms, and limited multilingual support. To address these challenges, we introduce \textbf{MixReward}, a large-scale multilingual dataset spanning six domains and 103 languages, containing both pairwise and listwise data, and propose \textbf{UniRRM}, a unified reasoning reward model supporting multiple languages and evaluation paradigms. UniRRM uses a staged reasoning chain to dynamically generate task-generic and instruction-specific criteria, enabling fine-grained, input-adaptive judgments while maintaining consistency across languages. Experiments demonstrate that UniRRM-8B and UniRRM-14B achieve performance close to the state-of-the-art for models of comparable size across multiple benchmarks, and are effective for unseen evaluation paradigms. In addition, ablation studies validate the reliability and effectiveness of UniRRM.
Chinese Translation
强化学习（RL）在可验证奖励的任务中表现出色，但在开放式任务中，奖励模型的可靠性仍然是一个关键挑战。现有的解决方案要么依赖于昂贵的专有LLM-as-a-Judge系统，要么依赖于缺乏可解释性的模糊标量奖励模型。近期关于生成奖励模型的研究提供了一种有前景的替代方案，但仍受到静态评估标准、碎片化评估范式和有限多语言支持的限制。为了解决这些挑战，我们引入了 extbf{MixReward}，这是一个涵盖六个领域和103种语言的大规模多语言数据集，包含成对和列表数据，并提出了 extbf{UniRRM}，一个支持多语言和评估范式的统一推理奖励模型。UniRRM使用分阶段推理链动态生成任务通用和指令特定的标准，使得在保持跨语言一致性的同时，能够进行细粒度、输入自适应的判断。实验表明，UniRRM-8B和UniRRM-14B在多个基准测试中实现了与同类模型接近的最先进性能，并且在未见评估范式中也表现出色。此外，消融研究验证了UniRRM的可靠性和有效性。
cs.CL / 19 / 2609.05913
Neuron-Guided Fine-Tuning: Unlocking Efficient Alignment Mechanisms for Large Language Models
神经元引导的微调：解锁大型语言模型的高效对齐机制
Abstract
Existing Supervised Fine-Tuning paradigms, particularly Full Parameter Fine-Tuning are often plagued by parameter redundancy, inconsistent data quality, and catastrophic forgetting, which current methods typically address in isolation and lack a unified optimization signal to bridge data selection, parameter updates, and knowledge preservation. To address this, we propose Neuron-Guided Fine-Tuning (NGFT), a holistic framework that leverages neuron activation patterns as a universal proxy to unify the fine-tuning lifecycle. NGFT operates via three synergistic mechanisms: (1) Adaptive Task-Specific Neuron Selection, which identifies essential neurons in a single forward pass to concentrate updates and reduce redundancy; (2) Activation-Based Data Selection, which prioritizes information-dense samples that maximize contribution to key neurons; and (3) Neuron Activation Alignment, a novel loss function that anchors activations to pre-trained states, deepening representation learning and preserving general knowledge. Experimental results across three models across both domain-specific and general benchmarks demonstrate that NGFT significantly outperforms existing mainstream fine-tuning methods in both efficiency and performance, while effectively mitigating catastrophic forgetting.
Chinese Translation
现有的监督微调范式，特别是全参数微调，常常受到参数冗余、不一致的数据质量和灾难性遗忘的困扰，而当前的方法通常孤立地解决这些问题，缺乏统一的优化信号来桥接数据选择、参数更新和知识保留。为此，我们提出了神经元引导的微调（Neuron-Guided Fine-Tuning, NGFT），这是一个整体框架，利用神经元激活模式作为统一的代理来整合微调生命周期。NGFT通过三种协同机制运作：（1）自适应任务特定神经元选择，识别单次前向传播中的关键神经元，以集中更新并减少冗余；（2）基于激活的数据选择，优先考虑对关键神经元贡献最大的高信息密度样本；（3）神经元激活对齐，一种新颖的损失函数，将激活锚定到预训练状态，深化表示学习并保留一般知识。在三个模型的实验结果中，无论是在特定领域还是一般基准上，NGFT在效率和性能上显著优于现有主流微调方法，同时有效减轻了灾难性遗忘。
cs.CL / 20 / 2609.05928
Solving versus Verifying: Catching Contradictions in Tax Reasoning Systems
解决与验证：捕捉税务推理系统中的矛盾
Abstract
Large language models now compute correct tax liabilities on over 90% of well-formed cases in statutory benchmarks, which makes them candidates for the tax-advisory and compliance systems that consume such an answer directly. Real legal inputs, however, are frequently defective: required facts are missing, or stated facts contradict one another. Accuracy on clean benchmarks says nothing about how a model behaves then, and a system that computes straight through a defective input returns a confident number with no sign that anything is wrong. This raises two questions: does a model asked to solve a case abstain when the input is defective, and when it does not, can the same model catch the defect when asked instead to verify the input? We study six recent models on SARA-derived tax cases under missing-fact and contradictory-fact perturbations. The strongest models abstain when a fact is missing but compute through injected contradictions, returning the clean-input answer 63-76% of the time with no signal of the conflict; asked instead to verify the same input, they flag most of those contradictions. We wire that verification call into a simple contradiction gate: one extra call that abstains when the model reports a conflict. Across all six models it recovers most of the missed contradiction abstention at a clean-accuracy cost of at most about 5 percentage points, with no training and no external tooling. High accuracy on well-formed inputs is therefore an incomplete measure of reliability, and the detection the solver misses is cheaply recoverable with a single self-check.
Chinese Translation
大型语言模型在法定基准测试中对超过90%的良好案例计算正确的税务责任，这使得它们成为直接使用此类答案的税务咨询和合规系统的候选者。然而，真实的法律输入常常存在缺陷：所需的事实缺失，或陈述的事实相互矛盾。对干净基准的准确性并不能说明模型在这种情况下的表现，而一个在缺陷输入上直接计算的系统会返回一个自信的数字，而没有任何迹象表明存在问题。这引发了两个问题：当输入存在缺陷时，模型在被要求解决案例时是否会选择不作答？而当它不选择不作答时，是否能够在被要求验证输入时捕捉到缺陷？我们研究了六个最近的模型在缺失事实和矛盾事实扰动下的SARA衍生税务案例。最强的模型在缺失事实时会选择不作答，但在注入矛盾时则会继续计算，返回干净输入答案的比例为63-76%，而没有任何冲突的信号；而当被要求验证同一输入时，它们会标记出大多数矛盾。我们将这种验证调用连接到一个简单的矛盾门：一个额外的调用，当模型报告冲突时选择不作答。在所有六个模型中，它在干净准确性损失最多约5个百分点的情况下恢复了大多数遗漏的矛盾不作答，而无需训练和外部工具。因此，对良好输入的高准确性并不是可靠性的完整衡量，而求解器遗漏的检测可以通过一次自我检查廉价地恢复。
cs.CL / 21 / 2609.05938
SurveyAgent-HKA: A multi-agent framework for scientific survey generation with LLMs and human knowledge augmentation
SurveyAgent-HKA：一种基于多智能体的科学调查生成框架，结合大型语言模型（LLMs）和人类知识增强
Abstract
Automatic scientific survey generation has become an important task in scientific document processing. The common approach of retrieving literature from a single source (e.g., arXiv) and generating surveys through a one-pass large language model (LLM) call often leads to limited reference coverage and, more importantly, fails to replicate the expert-driven revision process that is crucial for writing high-quality surveys. In this paper, we introduce SurveyAgent-HKA, a multi-agent framework that improves end-to-end scientific survey generation by incorporating knowledge derived from published surveys and peer-review comments. The framework decomposes survey generation into well-defined sub-tasks handled by LLM-powered agent. It first retrieves relevant papers from multiple sources and identifies key topics through clustering to construct an initial outline, which is then refined using outlines from related human-written surveys. Based on the refined outline, topic-focused papers are retrieved and re-ranked to select for drafting a well-grounded survey. Then, we identify common issues raised by experts in peer-review comments from published surveys to guide the revisions and finalize the survey. Experiments on two domains show that our approach outperforms mainstream baselines in citation quality, structural consistency, and content quality. Furthermore, our framework is efficient in both time and cost, making it a practical solution for broader AI-assisted scientific writing applications.
Chinese Translation
自动化科学调查生成已成为科学文献处理中的一项重要任务。常见的方法是从单一来源（例如，arXiv）检索文献，并通过一次性调用大型语言模型（LLM）生成调查，这往往导致参考文献覆盖面有限，更重要的是，无法复制对于撰写高质量调查至关重要的专家驱动的修订过程。本文介绍了SurveyAgent-HKA，这是一种通过结合来自已发布调查和同行评审意见的知识来改善端到端科学调查生成的多智能体框架。该框架将调查生成分解为由LLM驱动的智能体处理的明确定义的子任务。它首先从多个来源检索相关论文，并通过聚类识别关键主题，以构建初步大纲，然后利用相关人类撰写的调查大纲进行细化。基于细化后的大纲，检索并重新排序主题相关的论文，以选择用于撰写有根据的调查。接着，我们识别已发布调查中专家在同行评审意见中提出的常见问题，以指导修订并最终确定调查。在两个领域的实验表明，我们的方法在引用质量、结构一致性和内容质量方面优于主流基线。此外，我们的框架在时间和成本上都具有高效性，使其成为更广泛的AI辅助科学写作应用的实用解决方案。
cs.CL / 22 / 2609.05949
The Blindness of Document-Level Translation Evaluation
文档级翻译评估的盲点
Abstract
Document-level machine translation (MT) evaluation extends segment-level protocols by presenting full documents to annotators, on the assumption that such presentation elicits document-level judgments. We test this assumption with a counterfactual condition (MIX) in which each document combines segments drawn from different systems, preserving document-level presentation while breaking cross-segment consistency. Across 18,420 expert Englis-to-Korean annotations and 14 automatic metrics, scores, system rankings, and error annotations are statistically equivalent between coherent and incoherent documents. Perception does not explain this: shown matched passages, raters identify the coherent one as the work of a single translator in 87.3% of trials. Document presentation does change how annotators work, but that change does not reach the recorded output. What is blind is the protocol, not the annotator. The concern is not that scores fall short, but that the resources invested in document-level systems, metrics, and annotation may not be measuring what they are intended to measure.
Chinese Translation
文档级机器翻译（MT）评估通过向注释者呈现完整文档来扩展段落级协议，假设这种呈现能够引发文档级判断。我们通过一个反事实条件（MIX）来检验这一假设，在该条件下，每个文档结合了来自不同系统的段落，保留了文档级呈现的同时打破了跨段落的一致性。在18,420个专家的英译韩注释和14个自动评估指标中，一致和不一致文档之间的得分、系统排名和错误注释在统计上是等效的。感知并不能解释这一现象：在展示匹配段落时，评审者在87.3%的试验中将一致的段落认定为单一翻译者的作品。文档呈现确实改变了注释者的工作方式，但这种变化并未反映在记录的输出中。盲点在于评估协议，而非注释者。关注的不是得分不足，而是投入到文档级系统、指标和注释中的资源可能并未测量其预期的内容。
cs.CL / 23 / 2609.05976
Beyond Cross-Lingual Transfer: Benchmarking Propagation Boundaries in Multilingual LLM Unlearning
超越跨语言迁移：多语言大语言模型去学习中的传播边界基准测试
Abstract
Large Language Model (LLM) unlearning aims to suppress target knowledge while preserving general capabilities. In multilingual settings, unlearning must additionally propagate within its intended linguistic scope. However, existing evaluations mainly measure cross-lingual transfer and cannot distinguish insufficient from excessive propagation. We introduce CLLPU (Cross-Lingual and Language-Bound Protocol for LLM Unlearning), a multilingual benchmark that formulates this problem through two settings: common-goal forgetting, where target knowledge should be suppressed across all languages, and language-conditioned forgetting, where suppression should remain confined to a designated language. CLLPU combines goal-guided topic pairing, schema-aware relation matching, and dual-anchor multilingual translation to construct 800 matched knowledge-unit pairs and 72,000 QA instances across ten languages. Experiments with six representative methods on Llama-3.1-8B-Instruct reveal opposite failure modes: forgetting remains incomplete when universal suppression is required, yet spreads beyond the intended boundary when language-conditioned confinement is required. We further find that general multilingual utility can conceal damage to neighbor knowledge. These findings establish propagation control as a central challenge for multilingual LLM unlearning. We publicly release CLLPU together with its construction pipeline.
Chinese Translation
大语言模型（LLM）去学习旨在抑制目标知识，同时保留一般能力。在多语言环境中，去学习还必须在其预期的语言范围内传播。然而，现有评估主要测量跨语言迁移，无法区分不足的传播与过度的传播。我们引入了 CLLPU（跨语言和语言绑定的 LLM 去学习协议），这是一个多语言基准，通过两个设置来表述这一问题：共同目标遗忘，其中目标知识应在所有语言中被抑制，以及语言条件遗忘，其中抑制应限制在指定语言内。CLLPU 结合了目标引导的主题配对、模式感知的关系匹配和双锚多语言翻译，构建了 800 对匹配的知识单元和 72,000 个跨十种语言的问答实例。在 Llama-3.1-8B-Instruct 上对六种代表性方法的实验揭示了相反的失败模式：当需要普遍抑制时，遗忘仍然不完全，而当需要语言条件限制时，传播却超出了预期边界。我们进一步发现，一般的多语言效用可能掩盖对邻近知识的损害。这些发现确立了传播控制作为多语言 LLM 去学习的一个核心挑战。我们公开发布 CLLPU 及其构建流程。
cs.CL / 24 / 2609.05993
Alignment by Stereotyping: How LLMs Sacrifice Individual Distinctiveness for Cultural Adaptation
通过刻板印象实现对齐：大型语言模型如何牺牲个体独特性以适应文化
Abstract
Large language models are increasingly deployed for personalized interaction, and demographic conditioning via user profiles is a widely adopted strategy for cultural adaptation. We ask whether this approach genuinely serves individual users or achieves accuracy by erasing individual distinctiveness. Studying seven models including frontier GPT-5.1 on the World Values Survey, we find that demographic profiles improve value alignment accuracy for most models, but at a systematic cost to individuality. That is, models pull responses toward demographic group centroids rather than preserving individual differences, a behavioral pattern we term alignment by stereotyping. Permutation tests (10,000 permutations, six demographic attributes, seven models) certify that top-performing models compress individuals far above the human baseline; within-family scaling amplifies this tradeoff while degrading intrinsic cultural understanding. Using a synthetic dialogue dataset validated on real human-chatbot conversations from PRISM (Kirk et al., 2024), we further show that distributing demographic signals across conversational turns partially suppresses prototype retrieval compared to compact demographic labels, a finding validated on real conversations via PRISM but requiring replication at larger scale.
Chinese Translation
大型语言模型越来越多地用于个性化互动，而通过用户档案进行的人口统计条件化是一种广泛采用的文化适应策略。我们探讨这种方法是否真正服务于个体用户，还是通过抹去个体独特性来实现准确性。研究包括前沿的GPT-5.1在内的七个模型，基于世界价值观调查，我们发现人口统计档案提高了大多数模型的价值对齐准确性，但以牺牲个体性为代价。也就是说，模型将响应拉向人口统计群体的中心，而不是保留个体差异，这种行为模式我们称之为通过刻板印象实现对齐。置换测试（10,000次置换，六个人口统计属性，七个模型）证实，表现最佳的模型在个体压缩上远高于人类基线；同一家族的缩放放大了这种权衡，同时削弱了内在文化理解。利用在真实人类-聊天机器人对话中验证的合成对话数据集PRISM（Kirk等，2024），我们进一步表明，在对话轮次中分配人口统计信号相比于紧凑的人口统计标签，部分抑制了原型检索，这一发现通过PRISM在真实对话中得到了验证，但需要在更大规模上进行复制。
cs.CL / 25 / 2609.06000
ModularPhaseNet: Finite-Cyclic Phase Geometry for Computable Semantic Hierarchy, Direction, and Context Consistency in Standard Transformers
模块相位网络：用于可计算语义层次、方向和上下文一致性的有限循环相位几何在标准变换器中的应用
Abstract
We propose ModularPhaseNet, a classical and integer-computable discretization of the continuous complex phase geometry introduced in QuantumPhaseNet. The real-valued hidden states of a standard Transformer are retained, while only an auxiliary phase channel is quantized into a cyclic subgroup G = of order q | (p-1) in the multiplicative group of F_p. A continuous phase e^{i phi} is represented by z = g^a mod p; phase composition becomes group multiplication, relative phase becomes group division, conceptual hierarchy is induced by a filtration of cyclic quotients, semantic direction is represented by oriented relative group elements, and contextual consistency is measured by gauge-invariant cycle holonomy. The method introduces three components into an otherwise standard Transformer: a finite-phase encoder, a quotient-filtration hierarchy module, and a group-valued connection module. Their outputs enter self-attention as real-valued bias terms. Training uses distributions in the real group algebra or straight-through Gumbel-Softmax, whereas inference uses exact modular exponentiation and precomputed tables. No quantum hardware, complex-valued matrix multiplication, or discrete-logarithm computation is required. We prove quantization-distortion bounds, nesting of quotient-induced partitions, gauge invariance, a discrete integrability result for flat connections, and boundedness of the resulting attention output. The central empirical hypothesis is that these exact discrete invariants improve hierarchy recovery, discourse alignment, contradiction detection, and calibrated hallucination-risk prediction under a controlled compute budget. This paper reports the theory together with a pre-registered evaluation plan; the experiments described in Section 14 have not yet been carried out, and no empirical result is claimed here.
Chinese Translation
我们提出了模块相位网络（ModularPhaseNet），这是一种经典且可计算的整数离散化方法，用于量子相位网络（QuantumPhaseNet）中引入的连续复相位几何。标准变换器的实值隐藏状态得以保留，而仅将一个辅助相位通道量化为乘法群 F_p 中阶为 q | (p-1) 的循环子群 G = 。连续相位 e^{i phi} 通过 z = g^a mod p 表示；相位组合变为群乘法，相对相位变为群除法，概念层次通过循环商的过滤而诱导，语义方向由有向相对群元素表示，上下文一致性通过规范不变的循环全局性来衡量。该方法在标准变换器中引入了三个组件：有限相位编码器、商过滤层次模块和群值连接模块。它们的输出作为实值偏置项输入自注意力机制。训练使用实群代数中的分布或直通 Gumbel-Softmax，而推理则使用精确的模幂运算和预计算表格。无需量子硬件、复值矩阵乘法或离散对数计算。我们证明了量化失真界限、商诱导分区的嵌套、规范不变性、平坦连接的离散可积性结果以及生成的注意力输出的有界性。中心经验假设是，这些精确的离散不变量在受控计算预算下改善了层次恢复、话语对齐、矛盾检测和校准的幻觉风险预测。本文报告了理论及预注册的评估计划；第14节中描述的实验尚未进行，且此处不声称任何经验结果。
cs.CL / 26 / 2609.06011
Tri-PvP: Exposing Modality Bias in Omni-Modal Large Language Models through Perceptual-Propositional Evidence Conflicts
Tri-PvP：通过感知-命题证据冲突揭示全模态大型语言模型中的模态偏见
Abstract
Omni-modal large language models (OLLMs) jointly process vision, audio, and text, yet their modality bias under cross-modal conflict remains underexplored. Existing benchmarks conflate two distinct forms of evidence within a single modality: perceptual signals (e.g., a photograph or recording of a dog) and propositional signals (e.g., the declarative claim "this is a dog"), such that any measured modality bias is inherently confounded with evidence-form bias, precluding clean attribution to either source. To address this, we introduce Tri-PvP, an 8,000-sample tri-modal conflict benchmark crossing vision, audio, and text, where vision and audio each take perceptual or propositional form. Evaluating five OLLMs, we find robust visual bias across most models and evidence-type conditions. Crucially, we reveal a systematic asymmetry in evidence-form bias: models exhibit a stronger bias toward perceptual signal in vision but propositional in audio. Further analyses via layer-wise linear probing and contrastive decoding reveal that modality bias is already linearly decodable from early representation layers and can only be partially mitigated, calling for mitigation strategies beyond surface-level interventions.
Chinese Translation
全模态大型语言模型（OLLMs）能够共同处理视觉、音频和文本，但它们在跨模态冲突下的模态偏见仍然未得到充分探索。现有基准将单一模态中的两种不同形式的证据混为一谈：感知信号（例如，狗的照片或录音）和命题信号（例如，声明性主张“这是一只狗”），以至于任何测量到的模态偏见本质上都与证据形式偏见混淆，无法清晰地归因于任何一个来源。为了解决这一问题，我们引入了Tri-PvP，一个包含8,000个样本的三模态冲突基准，涵盖视觉、音频和文本，其中视觉和音频各自采用感知或命题形式。对五个OLLMs的评估发现，大多数模型和证据类型条件下均存在显著的视觉偏见。重要的是，我们揭示了证据形式偏见的系统性不对称性：模型在视觉上对感知信号表现出更强的偏见，而在音频上则对命题信号表现出更强的偏见。通过逐层线性探测和对比解码的进一步分析表明，模态偏见在早期表示层中已经可以线性解码，并且只能部分缓解，这呼吁采取超越表面干预的缓解策略。
cs.CL / 27 / 2609.06025
Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations
影响神经代理模拟中依赖长度最小化出现的因素
Abstract
Given various grammatical options, language users prefer the word order choice that reduces the overall length of syntactic dependencies, a principle known as dependency length minimization (DLM). The origins of this preference remain an open question, particularly whether it originates from constraints on efficient information processing. Computational simulations provide a powerful approach to identifying the factors influencing the emergence of linguistic phenomena. However, previous simulations of DLM have not examined realistic interaction contexts and have produced mixed results. The present study investigates the emergence of DLM in artificial languages using a recently proposed language learning and communication framework based on recurrent neural networks (RNNs). In this framework, agents are trained to speak and interpret artificial languages and then use these languages to communicate. Using this framework, we study the impact of several factors related to processing limitations in a communicative setting, such as noise during listening, limited speaker capacity, and incremental sentence processing. Our results reveal a complex interplay among these factors in shaping word order preferences in neural agents. Specifically, in the full meaning space, agents regularize toward a single dominant word order, while in the half meaning space they show a short-before-long preference that only aligns with DLM in verb-initial languages. A consistent DLM preference emerges only when agents are subject to incremental processing pressure. These findings suggest that limitations in human cognitive processing may indeed play a role in shaping DLM. Our findings provide insights into the conditions under which neural models replicate human-like preferences and highlight the challenges of designing emergent communication models that capture human cognitive biases in language processing.
Chinese Translation
在各种语法选项中，语言使用者倾向于选择能够减少句法依赖整体长度的词序，这一原则被称为依赖长度最小化（Dependency Length Minimization, DLM）。这一偏好的起源仍然是一个未解之谜，特别是它是否源于对高效信息处理的限制。计算机模拟提供了一种强有力的方法来识别影响语言现象出现的因素。然而，以往对DLM的模拟并未考察现实的互动语境，并且产生了混合的结果。本研究使用基于递归神经网络（Recurrent Neural Networks, RNNs）最近提出的语言学习和交流框架，探讨了人工语言中DLM的出现。在这一框架中，代理被训练以说和理解人工语言，并利用这些语言进行交流。通过这一框架，我们研究了与交流环境中处理限制相关的几个因素的影响，例如听觉过程中的噪声、说话者的能力限制和增量句子处理。我们的结果揭示了这些因素在塑造神经代理的词序偏好中的复杂相互作用。具体而言，在完整意义空间中，代理趋向于规范化为单一主导词序，而在半意义空间中，他们表现出短在前长在后的偏好，这一偏好仅在动词首位语言中与DLM一致。只有在代理面临增量处理压力时，才会出现一致的DLM偏好。这些发现表明，人类认知处理的限制确实可能在塑造DLM中发挥作用。我们的研究为神经模型复制类人偏好的条件提供了见解，并突显了设计能够捕捉人类语言处理中的认知偏见的自发交流模型所面临的挑战。
cs.CL / 28 / 2609.06048
Generating Adversarial Texts for Machine Translation via GRPO
通过 GRPO 生成对机器翻译的对抗文本
Abstract
As machine translation (MT) systems continue to improve, standard benchmarks become less informative for exposing remaining weaknesses. Traditional methods for creating challenging test sets rely on expensive manual creation or curation, while automated approaches struggle to produce sets with the necessary translation difficulty and linguistic diversity. We propose a scalable reinforcement-learning-based approach for rewriting existing source texts into instances that are more difficult to translate for MT systems. We fine-tune a large language model with Group Relative Policy Optimization (GRPO), using reward signals based on translation difficulty together with constraints for semantic similarity, grammaticality, and approximate length preservation. On WMT25, our approach substantially reduces average COMET translation quality from 0.63 to 0.48, while preserving grammaticality and readability, whereas the base model remains at 0.64. Evaluations on the unseen WMT19-WMT24 benchmarks confirm that this behavior generalizes beyond the training data, and human evaluation further shows that the rewrites substantially lower translation quality while incurring a moderate drop in naturalness and only a small change in grammaticality. We release our code to support reproducibility.
Chinese Translation
随着机器翻译（MT）系统的不断改进，标准基准测试在揭示剩余弱点方面变得不够有效。传统的创建具有挑战性的测试集的方法依赖于昂贵的手动创建或策划，而自动化方法则难以生成具有必要翻译难度和语言多样性的测试集。我们提出了一种可扩展的基于强化学习的方法，将现有源文本重写为对 MT 系统更具翻译难度的实例。我们使用基于翻译难度的奖励信号以及语义相似性、语法正确性和近似长度保持的约束，对大型语言模型进行微调，采用群体相对策略优化（Group Relative Policy Optimization, GRPO）。在 WMT25 上，我们的方法显著降低了平均 COMET 翻译质量，从 0.63 降至 0.48，同时保持了语法正确性和可读性，而基础模型仍保持在 0.64。对未见的 WMT19-WMT24 基准的评估确认了这种行为超出了训练数据的范围，人类评估进一步表明，重写文本显著降低了翻译质量，同时自然性略有下降，语法性变化较小。我们发布了我们的代码以支持可重复性。
cs.CL / 29 / 2609.06065
Don't Lose Entities from Retrieval to Generation: Dual Entity Recovery RAG for multi-hop QA
从检索到生成不丢失实体：用于多跳问答的双实体恢复RAG
Abstract
Retrieval-augmented multi-hop question answering (QA) decomposes a query into sub-questions and decomposes the corpus into smaller retrieval units such as sentences. Both forms of decomposition improve the pipeline, but we show that both share the same vulnerability, the loss of entity information, and that this loss breaks the pipeline at two separate points. The first point is retrieval, where a sub-question loses the entity resolved at the previous hop, leaving the retriever with nothing to match against. The second point is harder to see, because retrieval still appears to succeed. Once a passage is split into sentences, an isolated sentence loses the context that grounds its pronouns, so even with the correct sentence in hand the LLM cannot tell which entity the sentence is about. We isolate this second point as a distinct failure mode that we call lost-in-generation, and a retrieval-controlled experiment shows that it degrades answers even when the gold evidence is fixed in the context. We then propose Dual Entity Recovery RAG (DER-RAG), which keeps the grounding entity explicit from retrieval through to generation with two lightweight components, a two-way query decomposition that carries the resolved entity across sub-questions and a subject entity prefix attached to each sentence at generation time. DER-RAG needs no graph construction, no corpus modification, and no fine-tuning, yet on three multi-hop QA benchmarks it matches or exceeds strong baselines, including graph-based methods that depend on costly offline structures.
Chinese Translation
检索增强的多跳问答（QA）将查询分解为子问题，并将语料库分解为更小的检索单元，如句子。这两种分解形式都改善了流程，但我们表明它们共享同样的脆弱性，即实体信息的丢失，而这种丢失在两个不同的点上破坏了流程。第一个点是检索，在此阶段，子问题失去了在前一步中解析的实体，导致检索器没有任何匹配对象。第二个点更难以察觉，因为检索仍然看似成功。一旦段落被分割成句子，孤立的句子失去了支撑其代词的上下文，因此即使手中有正确的句子，LLM也无法判断该句子所指的实体。我们将这一第二点孤立为一种独特的失败模式，称之为生成中的丢失（lost-in-generation），并且通过检索控制的实验表明，即使在上下文中固定了黄金证据，它也会降低答案的质量。随后，我们提出了双实体恢复RAG（DER-RAG），该方法通过两个轻量级组件保持检索到生成过程中的基础实体的显性性：一种双向查询分解，能够在子问题之间传递解析出的实体，以及在生成时附加到每个句子的主题实体前缀。DER-RAG无需构建图形、修改语料库或进行微调，但在三个多跳QA基准测试中，其表现与强基线相当或超出，包括依赖于昂贵离线结构的基于图的方法。
cs.CL / 30 / 2609.06070
DPH Parser: A Bottom-Up Grammar-Driven Parser for Joint Constituency and Dependency Analysis
DPH 解析器：一种用于联合成分和依赖分析的自下而上的语法驱动解析器
Abstract
This paper presents Dependency-Phrase Hierarchy Parser (DPH Parser), a grammar-driven bottom-up unsupervized parsing framework inspired by Generalized Phrase Structure Grammar (GPSG) and Head-driven Phrase Structure Grammar (HPSG). The parser incrementally constructs constituency structures using a compact inventory of feature-based syntactic rules while deriving dependency relations through explicit head annotations. The system combines probabilistic POS tagging, recursive phrase projection, and weighted parse hypotheses to process realistic and partially noisy text input. Unlike purely neural and data-driven parsers, the resulting syntactic derivations remain explicitly interpretable. We evaluated parser performance on English corpora from the Universal Dependencies (UD) project using Unlabeled Attachment Score (UAS) as the main parsing metric, comparing the outcomes against Stanza and spaCy parsers. For a small inventory of syntactic rules, DPH parser achieved UAS values of 53.32% & 52.58% (UD Devset/Testset respectively). For the same data, Stanza achieved 89.12% & 88.67% while spaCy achieved 56.91% and 58.59%. Although the current system does not yet approach the accuracy of modern neural parsers, the results demonstrate the feasibility of applying transparent rule-based bottom-up parsing to realistic treebank data while jointly producing constituency and dependency structures.
Chinese Translation
本文介绍了依赖短语层次解析器（DPH Parser），这是一种受广义短语结构语法（GPSG）和头驱动短语结构语法（HPSG）启发的语法驱动自下而上的无监督解析框架。该解析器通过使用基于特征的紧凑语法规则逐步构建成分结构，同时通过显式的头标注推导依赖关系。该系统结合了概率词性标注、递归短语投影和加权解析假设，以处理现实和部分噪声的文本输入。与纯粹的神经和数据驱动解析器不同，所产生的句法推导仍然是显式可解释的。我们在来自通用依赖（UD）项目的英语语料库上评估了解析器的性能，使用无标记附加分数（UAS）作为主要解析指标，并将结果与 Stanza 和 spaCy 解析器进行了比较。对于少量的语法规则，DPH 解析器在 UAS 上达到了 53.32% 和 52.58%（分别为 UD 开发集和测试集）。对于相同的数据，Stanza 达到了 89.12% 和 88.67%，而 spaCy 则达到了 56.91% 和 58.59%。尽管当前系统的准确性尚未接近现代神经解析器，但结果表明将透明的基于规则的自下而上解析应用于现实树库数据，并同时生成成分和依赖结构是可行的。
cs.CL / 31 / 2609.06108
From Two Passes to One: Compact and Efficient Target-Stance Extraction
从双通道到单通道：紧凑高效的目标立场提取
Abstract
Target-Stance Extraction (TSE) is the task of predicting both the target (or topic) of an author's writing and the author's stance toward it. Existing approaches to TSE use a sequential pipeline of two separate neural models: one to identify the target and another to determine the stance. We present a one-pass, joint architecture that predicts both in a single forward pass, reducing trainable parameters by nearly 50% with only a 4-7 F1 point tradeoff in performance. We further demonstrate that standard target-scrubbing practices artificially suppress target prediction accuracy. Retaining explicit target mentions, as in real-world deployments, improves F1 by at least 6 points across both target classification and target generation settings. These improvements allow for significantly easier integration of TSE in downstream applications such as public opinion tracking.
Chinese Translation
目标立场提取（Target-Stance Extraction, TSE）是预测作者写作的目标（或主题）及其对该目标的立场的任务。现有的TSE方法采用两个独立神经模型的顺序管道：一个用于识别目标，另一个用于确定立场。我们提出了一种单通道的联合架构，在一次前向传递中同时预测目标和立场，减少了近50%的可训练参数，同时性能仅损失4-7个F1点。我们进一步证明，标准的目标清洗实践会人为地抑制目标预测的准确性。保留明确的目标提及，如在实际应用中，能够在目标分类和目标生成设置中将F1提高至少6个点。这些改进使得TSE在公共舆论跟踪等下游应用中的集成变得显著更为简单。
cs.CL / 32 / 2609.06117
STQA: A Benchmark for Stock-Focused Tabular Question Answering over Historical and Forecasted Data
STQA：针对历史和预测数据的股票聚焦表格问答基准
Abstract
Stock market analysis inherently requires composite reasoning over historical records and future projections, yet existing benchmarks remain fragmented across isolated tasks. We introduce STQA (Stock-focused Tabular Question Answering), an end-to-end benchmark designed to systematically evaluate natural-language question answering over historical data, numerical forecasts, and forecast-based reasoning. Built on a large-scale financial dataset, STQA covers 4,417 stocks and contains 31,400 question-answer pairs derived from expert-crafted templates, accompanied by fine-grained intent and slot annotations. To operationalize this benchmark, we present SQFRS (Stock Query-Forecast-Reasoning System), an agent-based unified framework that orchestrates SQL retrieval and time-series forecasting tools. Experiments demonstrate that while current large language models perform well on historical queries, forecast-based reasoning poses a substantial challenge, revealing critical bottlenecks in tool coordination and reasoning under uncertainty. The dataset and code are available at https://github.com/xuxubaobaoan/STQA_Project. STQA thus serves as a rigorous testbed for future research on trustworthy, tool-augmented financial agents.
Chinese Translation
股票市场分析本质上需要对历史记录和未来预测进行综合推理，但现有基准仍然在孤立任务之间分散。我们提出了STQA（股票聚焦表格问答），这是一个端到端的基准，旨在系统性地评估对历史数据、数值预测和基于预测的推理的自然语言问答。STQA建立在一个大规模金融数据集之上，涵盖了4,417只股票，并包含31,400对由专家设计模板衍生的问题-答案对，附带细粒度的意图和槽位注释。为了实现这一基准，我们提出了SQFRS（股票查询-预测-推理系统），这是一个基于代理的统一框架，协调SQL检索和时间序列预测工具。实验表明，尽管当前的大型语言模型在历史查询上表现良好，但基于预测的推理仍然构成了重大挑战，揭示了工具协调和不确定性下推理的关键瓶颈。数据集和代码可在https://github.com/xuxubaobaoan/STQA_Project获取。因此，STQA为未来对可信赖的工具增强金融代理的研究提供了一个严格的测试平台。
cs.CL / 33 / 2609.06129
Protocol Compression Changes Which Party Pays: Bilateral Cost in Cross-Organization LLM Agent Communication
协议压缩改变了支付方：跨组织大型语言模型代理通信中的双边成本
Abstract
Agents that talk across organizations exchange long messages billed by the token. A shorter notation therefore looks like a saving that costs nothing but an agreement to use it. Recent work reports the saving is conditional. Compressed notation can instead raise total tokens by 8% to 11% over a JSON baseline, when parsing failures force extra model calls. That is measured for one payer. Between two organizations neither side can install a decoder at the other end, and each pays under its own tokenizer, price, and cache state. We measure both sides. A preregistered token-level study covered 198 content-matched item pairs across six vendors, for 2,376 native-usage cells. We then overlay an English baseline, runtime schema negotiation followed by compression, and injected-schema compression on a two-party procurement bargain with an exactly enumerated feasible set. The overlay covers 1,053 completed dialogues of a 1,215-cell grid across 3 model pairs, plus a 405-dialogue rerun of the negotiated condition. Compression amplifies cross-vendor cost dispersion by a factor of 1.078, with a 95% CI of [1.066, 1.091], and two vendor pairs reverse which endpoint is cheaper. Runtime negotiation succeeds as a protocol and fails as a bargain. The parties agree a schema in 121 of 135 headline dialogues, none of them the schema we would have supplied. They settle the task in only 9 of those dialogues, and they reach impasse in 106 of them. The negotiated sessions average 10.8 turns against 17.6, and cost 52% of the English total because sessions end sooner, not because the handshake is repaid. Break-even horizons run from 20 to 70 turns, the low end only under the conditional accounting, and all of them lie above every observed English session. On one cross-vendor pair both parties keep about half their cost. On the other the receiving party pays more at a high cache-hit rate.
Chinese Translation
跨组织进行交流的代理通过令牌交换长消息。因此，较短的表示法看起来像是一种节省，成本仅为达成使用协议。近期的研究报告显示，这种节省是有条件的。当解析失败迫使额外的模型调用时，压缩表示法可能会使总令牌数比 JSON 基线增加 8% 到 11%。该测量是针对单一付款方进行的。在两个组织之间，任何一方都无法在另一端安装解码器，每一方都在其自己的分词器、价格和缓存状态下支付费用。我们对双方进行了测量。一项预注册的令牌级研究覆盖了六个供应商之间 198 对内容匹配的项目，共计 2,376 个本地使用单元。然后，我们在一个有明确列举可行集的双边采购交易中，叠加了一个英语基线、运行时模式协商以及压缩和注入模式压缩。该叠加覆盖了 1,053 个完成的对话，涉及 3 对模型的 1,215 单元网格，以及 405 个对已协商条件的对话重跑。压缩使跨供应商成本分散程度增加了 1.078 倍，95% 置信区间为 [1.066, 1.091]，而两个供应商对则反转了哪个端点更便宜。运行时协商作为协议成功，但作为交易失败。双方在 135 个主要对话中达成了 121 个模式协议，但没有一个是我们会提供的模式。他们在这些对话中仅在 9 个对话中解决了任务，而在 106 个对话中陷入僵局。协商会话的平均轮次为 10.8 次，而非协商会话为 17.6 次，成本占英语总成本的 52%，这是因为会话结束得更早，而不是因为握手得到了回报。盈亏平衡的轮次范围从 20 到 70，低端仅在条件会计下，且所有这些都高于每个观察到的英语会话。在一个跨供应商对中，双方各自保留了大约一半的成本。而在另一个对中，接收方在高缓存命中率下支付更多。
cs.CL / 34 / 2609.06147
What the Window Does Not Contain: Auditing Provenance in a Document-Grounded Instability Benchmark
窗口未包含的内容：文档基础不稳定性基准的来源审计
Abstract
Ask a language model the same question about the same document twenty times, and it sometimes returns two different answers. We built Probity, a benchmark of 60 tasks and 470 items from real venture-financing filings, to measure how often this happens. Then we audited our own corpus and found a defect any excerpt-built benchmark can carry: items whose evidence is missing from the window of text the model is shown. The audit flags 36 items and separates two failures a single flag would conflate: evidence genuinely absent from the window and answers that must be computed from numbers the window does supply. Flagged items change their answers far more often, wobbling at 0.255 against 0.087 on the 427 clean items, and excluding them cuts apparent cross-model agreement by about a fifth. Before testing whether the missing evidence explains the instability, we registered a prediction: re-cut each window to hold its evidence, and instability should fall below a set threshold. It failed: the repair moved wobble by 0.058, with an interval containing zero. We report the association as correlational. Almost all measurements sit where instability cannot show, which bounds what a corpus built for accuracy can say about stability. We release the corpus, all 112,800 raw responses, and the audit as a runnable check for any document benchmark.
Chinese Translation
对同一文档提出相同问题二十次，语言模型有时会返回两个不同的答案。我们构建了Probity，一个由60个任务和470个真实风险融资文件组成的基准，以测量这种情况发生的频率。然后我们审计了自己的语料库，发现任何基于摘录的基准都可能存在的缺陷：模型所显示的文本窗口中缺失证据的项目。审计标记了36个项目，并区分了一个标记可能混淆的两个失败：窗口中确实缺失的证据和必须从窗口提供的数字中计算得出的答案。被标记的项目其答案变化的频率远高于未标记项目，427个干净项目的变化率为0.255，而未标记项目为0.087，排除这些项目使得表观的跨模型一致性降低了约五分之一。在测试缺失证据是否解释不稳定性之前，我们注册了一个预测：重新调整每个窗口以包含其证据，不稳定性应降至设定阈值以下。然而，这一修复未能实现：修复后变化率仅移动了0.058，且区间包含零。我们将这种关联报告为相关性。几乎所有测量结果都位于不稳定性无法显现的地方，这限制了为准确性构建的语料库对稳定性所能说的内容。我们发布了语料库、所有112,800个原始响应以及审计，作为任何文档基准的可运行检查。
cs.CL / 35 / 2609.06212
SLATE: Are AI-Generated Slides Educationally Effective? A Benchmark for Language Teaching Quality and Learner Knowledge Acquisition
SLATE：AI生成幻灯片在教育上的有效性如何？语言教学质量与学习者知识获取的基准
Abstract
LLMs have achieved remarkable capabilities in generating language teaching slides. However, a critical mismatch persists between visual polish and actual instructional effectiveness. To address this gap, we introduce SLATE (Slide-based Learning Assessment for Teaching Effectiveness), the first benchmark that evaluates AI-generated language teaching slides through instructional effectiveness and learner knowledge acquisition. SLATE transforms linguistics olympiad puzzles from low-resource languages with negligible web presence into 90 standardized instructional units comprising 1,133 assessable items, paired with a structured course outline and matched near- and far-transfer test sets. This pretest-posttest design eliminates pretrained knowledge leakage, ensuring gains reflect learning rather than prior recall. Using VLMs as scalable learner proxies and directionally supported by a three-system human pilot, our results show that content validity exhibits a weak association with learning gain, while pedagogical design exhibits a robust positive association. Moreover, most systems show a significant gap between near- and far-transfer accuracy, and even frontier models can produce negative learning gains. SLATE reveals a dissociation between artifact quality and instructional effectiveness, calling for a paradigm shift in how generative teaching systems are built, evaluated, and deployed.
Chinese Translation
大型语言模型（LLMs）在生成语言教学幻灯片方面取得了显著的能力。然而，视觉效果与实际教学效果之间仍存在严重不匹配。为了解决这一问题，我们提出了SLATE（基于幻灯片的教学有效性学习评估），这是第一个通过教学有效性和学习者知识获取来评估AI生成语言教学幻灯片的基准。SLATE将来自低资源语言的语言奥林匹克难题转化为90个标准化教学单元，包含1,133个可评估项目，并配有结构化的课程大纲和匹配的近转移和远转移测试集。这种前测-后测设计消除了预训练知识泄漏，确保增益反映学习而非先前的回忆。利用视觉语言模型（VLMs）作为可扩展的学习者代理，并在三系统人类试点的支持下，我们的结果显示内容有效性与学习增益之间的关联较弱，而教学设计与学习增益之间则表现出强正关联。此外，大多数系统在近转移和远转移准确性之间存在显著差距，即使是最前沿的模型也可能产生负学习增益。SLATE揭示了工件质量与教学有效性之间的脱节，呼吁在构建、评估和部署生成教学系统时进行范式转变。
cs.CL / 36 / 2609.06263
Beyond the Flag: Clinical Framing Closes the Moderation Gap in Suicide Risk Measurement
超越标记：临床框架缩小自杀风险测量中的调节差距
Abstract
Moderation APIs are built to flag policy-violating content, not to measure graded clinical risk. But a platform's duty does not end at detection: the response owed to passive distress differs sharply from the response owed to active planning with means access, and emerging regulation (e.g., California Senate Bill 243) is turning that distinction into a compliance requirement. We therefore ask how well deployed safety signals recover clinically meaningful severity. We release a benchmark of 516 r/SuicideWatch posts rated by a licensed psychiatrist on a four-level ordinal schema (Indicator, Ideation, Behavior, Attempt) grounded in the Columbia Suicide Severity Rating Scale, and evaluate moderation APIs, prompted LLMs, and supervised baselines under seven ordinal-aware metrics. Three findings. Vendor moderation APIs separate low- from high-severity posts well (0.860 high-risk F1) but measure severity poorly (0.395 macro F1), systematically over-predicting the most severe category. Clinically grounded zero-shot prompting recovers much of that gap (0.562 macro F1), and expert-authored framing (not fine-tuning, added reasoning, or naive multi-agent aggregation) is the effective lever. The value of reasoning depends on register: it hurts on long, noisy Reddit posts and helps on short, clinician-authored statements. We argue graded severity, not a binary flag, is what a proportionate duty of care requires, and release our evaluation framework to support that measurement.
Chinese Translation
调节API的构建旨在标记违反政策的内容，而非测量分级的临床风险。然而，一个平台的责任并不仅限于检测：对被动痛苦的反应与对有计划且可获取手段的主动行为的反应截然不同，而新兴的监管（例如，加利福尼亚州参议院法案243）正在将这种区别转变为合规要求。因此，我们探讨已部署的安全信号在多大程度上能够恢复临床上有意义的严重性。我们发布了一个基准，包含516条由持证精神科医生根据哥伦比亚自杀严重性评估量表（Columbia Suicide Severity Rating Scale）在四级序数框架（指示、意念、行为、尝试）上评分的r/SuicideWatch帖子，并在七个考虑序数的指标下评估调节API、提示的LLMs（大型语言模型）和监督基线。三个发现：供应商的调节API能够很好地区分低严重性和高严重性帖子（高风险F1为0.860），但严重性测量较差（宏观F1为0.395），系统性地过度预测最严重的类别。基于临床的零-shot提示在很大程度上弥补了这一差距（宏观F1为0.562），而专家撰写的框架（而非微调、增加推理或简单的多代理聚合）是有效的杠杆。推理的价值取决于语境：在冗长、嘈杂的Reddit帖子上会产生负面影响，而在简短的临床作者声明中则有帮助。我们认为，分级严重性，而非二元标记，才是适当的照护责任所要求的，并发布我们的评估框架以支持该测量。
cs.CL / 37 / 2609.06266
Correction as Annotation: Bootstrapping a Dependency Parser for Documentary Medieval Latin
作为注释的修正：为文献中的中世纪拉丁语构建依赖解析器
Abstract
Medieval documentary sources remain inadequately served by existing natural language processing tools. None of the five readily available Latin treebank models attains usable performance on a collection of 160 inventories compiled in Marseille between 1258 and 1446. The best labelled attachment score is 0.62 and the best morphology-aware score is 0.24. Performance does not correlate with either genre or period proximity. To address this shortfall, in-domain training data was generated as a by-product of using these inadequate models. In each of nine iterations, a model pre-annotated 200 sentences; an expert corrected the annotations; and the corrected sentences were used to train the subsequent model, with batches sampled independently of model state, without active-learning selection. Thirty-three hours of annotation effort over 1,804 sentences increased universal part-of-speech accuracy from 0.80 to 0.98 and labelled attachment from 0.48 to 0.92, outperforming all baselines on the reported metrics while using 97% less training data than the largest one of them. Annotator effort declined from 54% of tokens to a plateau of 14-18%, an operational progress metric that requires no separate gold standard and can serve as a stopping criterion.
Chinese Translation
中世纪文献资料在现有自然语言处理工具的支持下仍显不足。五个现成的拉丁树库模型在1258年至1446年间在马赛编纂的160份清单上均未达到可用的性能。最佳标注附加分数为0.62，最佳形态学感知分数为0.24。性能与体裁或时期的接近性没有相关性。为了解决这一不足，利用这些不充分模型的结果生成了领域内训练数据。在九次迭代中，每个模型预先注释了200个句子；专家对注释进行了修正；然后将修正后的句子用于训练后续模型，批次独立于模型状态抽样，而不进行主动学习选择。对1804个句子的33小时注释工作使得通用词性准确率从0.80提高到0.98，标注附加分数从0.48提高到0.92，在报告的指标上超越了所有基线，同时使用的训练数据比最大基线少了97%。注释者的工作量从54%的标记下降到14-18%的平稳水平，这一操作进展指标不需要单独的金标准，并可以作为停止标准。
cs.CL / 38 / 2609.06289
Steering Geometry: Validating Human Value Geometry in LLM Steering Space
引导几何：在大型语言模型引导空间中验证人类价值几何
Abstract
As large language models (LLMs) are increasingly deployed in alignment-sensitive contexts, activation steering has emerged as a lightweight, inference-time alternative to fine-tuning methods (e.g., RLHF, DPO) for behavioral control. However, existing work typically validates steering on isolated behaviors, leaving it unclear whether steering vectors encode coherent semantic structure or merely exploit behavior-specific shortcuts. We investigate whether the latent geometry of LLM steering vectors reflects theory-specified structure in human values and morality. Using Schwartz's Theory of Basic Human Values as our primary fine-grained framework, we introduce a 26K-sample benchmark covering 20 human values and analyze distribution-driven methods (e.g., CAA, SphericalSteer, ODESteer) and behavior-centric approaches (e.g., COLD-Steer, BiPO) across diverse model families and sizes. We find that distribution-driven methods recover human value topologies aligned with theoretical predictions (Spearman $\rho$ up to 0.51, $p < 10^{-13}$). In contrast, behavior-centric methods achieve comparable steering performance but show little correlation with the expected value geometry. Geometric fidelity improves with model scale but drops after instruction tuning. Finally, better geometric alignment also leads to more human-consistent transfer across values: steering one value correctly lifts compatible values and suppresses opposing ones. Code and data are available at: https://github.com/DeepRCL/Steering_Geometry.
Chinese Translation
随着大型语言模型（LLMs）在对齐敏感的环境中越来越多地被应用，激活引导作为一种轻量级的推理时替代方案，逐渐成为行为控制的选择，取代了微调方法（如强化学习人类反馈（RLHF）、直接偏好优化（DPO））。然而，现有研究通常在孤立行为上验证引导，尚不清楚引导向量是否编码了连贯的语义结构，或仅仅利用了特定行为的捷径。我们研究LLM引导向量的潜在几何是否反映了人类价值和道德中的理论指定结构。以施瓦茨的基本人类价值理论作为我们的主要细致框架，我们引入了一个包含26K样本的基准，覆盖20个人类价值，并分析了基于分布的方法（如 CAA、SphericalSteer、ODESteer）和以行为为中心的方法（如 COLD-Steer、BiPO）在不同模型家族和规模中的表现。我们发现，基于分布的方法恢复了与理论预测一致的人类价值拓扑（斯皮尔曼相关系数 $
ho$ 高达 0.51，$p < 10^{-13}$）。相比之下，以行为为中心的方法虽然在引导性能上表现相当，但与预期的价值几何之间的相关性较低。几何保真度随着模型规模的增加而改善，但在指令调优后下降。最后，更好的几何对齐也导致在价值间更一致的人类转移：正确引导一个价值会提升兼容价值并抑制对立价值。代码和数据可在以下链接获取：https://github.com/DeepRCL/Steering_Geometry。
cs.CL / 39 / 2609.06315
Reliability, validity, and diagnostic evidence for multi-model LLM short-answer scoring
多模型 LLM 短答案评分的可靠性、有效性和诊断证据
Abstract
Large language models (LLMs) are increasingly used or proposed for educational scoring, but single-model and single-run evaluations provide limited evidence for assessment use. Short-answer scoring requires evidence about reliability, validity, severity, diagnostic value, and failure cases. This study evaluated repeated multi-model OCG-PRES guided LLM scoring for short-answer assessment. The analysis used 996 SciEntsBank responses. GPT, DeepSeek, and Qianwen each scored every response across three independent runs using five OCG-PRES dimensions: concept coverage, relation accuracy, reasoning completeness, contradiction control, and domain relevance. Scores were evaluated against official binary and five-category labels and compared with non-LLM baselines based on answer length, Jaccard keyword overlap, TF-IDF cosine similarity, and a combined traditional logistic model. Repeated-run reliability was high for all models, with ICC(3,k) = .977 for GPT, .992 for DeepSeek, and .981 for Qianwen. DeepSeek was the most stable across runs. GPT showed the strongest official-label alignment by AUC (.909), while Qianwen was stricter, with higher precision but lower recall under the fixed threshold = 3.0 rule. OCG-PRES scores followed expected diagnostic patterns across five official categories and outperformed all non-LLM baselines in AUC and F1. Repeated multi-model OCG-PRES scoring provides reliability, validity, and diagnostic evidence for LLM-assisted short-answer scoring. The findings support cautious, evidence-based use as a scoring support tool rather than a replacement for human judgement.
Chinese Translation
大型语言模型（LLMs）在教育评分中的使用越来越普遍，但单模型和单次评估提供的证据对评估的使用有限。短答案评分需要关于可靠性、有效性、严格性、诊断价值和失败案例的证据。本研究评估了重复的多模型 OCG-PRES 指导的 LLM 短答案评估评分。分析使用了 996 个 SciEntsBank 的响应。GPT、DeepSeek 和 Qianwen 在三个独立的运行中对每个响应进行了评分，使用了五个 OCG-PRES 维度：概念覆盖、关系准确性、推理完整性、矛盾控制和领域相关性。评分与官方的二元标签和五类标签进行了评估，并与基于答案长度、Jaccard 关键词重叠、TF-IDF 余弦相似度以及结合传统逻辑模型的非 LLM 基线进行了比较。所有模型的重复运行可靠性都很高，GPT 的 ICC(3,k) 为 .977，DeepSeek 为 .992，Qianwen 为 .981。DeepSeek 在运行中最为稳定。GPT 在 AUC（.909）方面表现出最强的官方标签一致性，而 Qianwen 则更严格，在固定阈值 = 3.0 规则下具有更高的精确度但较低的召回率。OCG-PRES 评分在五个官方类别中遵循预期的诊断模式，并在 AUC 和 F1 上超越了所有非 LLM 基线。重复的多模型 OCG-PRES 评分为 LLM 辅助的短答案评分提供了可靠性、有效性和诊断证据。研究结果支持谨慎、基于证据的使用，作为评分支持工具，而非人类判断的替代品。
cs.CL / 40 / 2609.06324
A Ticket from Marginals to Joints: Coupled-Noise Distillation for One-Step Block Generation in Diffusion Language Models
从边际到联合的票据：扩散语言模型中一步块生成的耦合噪声蒸馏
Abstract
Autoregressive language models commit one token per forward pass; diffusion language models commit a block of tokens over several steps. We ask whether a block can be committed in a single forward pass. We study this with a noise-conditioned masked denoiser: a data-independent Gaussian noise field is added to the mask embeddings so that, in principle, each sampled field selects one joint mode of the block. The established way of training such a model is to sample several fields per example and let them compete for the data, by winner-take-all or importance weighting. This gives the noise only coarse control: in our experiments, the information it carries grows roughly with the logarithm of the number of competing fields, and one-step outputs remain rarely coherent across the model sizes tested. We propose CONDOR (Coupled-Noise Distillation for One-Step Readout). A noise-conditioned teacher is trained with a random number of masked positions and winner-take-all. A student proposes a one-step block, retains selected tokens, and learns from the block obtained when the teacher refills the other positions in several steps under the same noise field; a noise-free masked-LM term on the ground truth anchors the student. Human evaluation on TinyStories shows a large gain in one-step legality while different noise fields still yield different blocks, at one forward pass per block.
Chinese Translation
自回归语言模型在每次前向传播中生成一个标记；而扩散语言模型则在多个步骤中生成一块标记。我们探讨是否可以在一次前向传播中生成一块标记。我们通过噪声条件掩蔽去噪器进行研究：在掩蔽嵌入中添加一个与数据无关的高斯噪声场，从原则上讲，每个采样的场选择块的一个联合模式。训练这种模型的既定方法是对每个示例采样多个场，并让它们通过赢家通吃或重要性加权来竞争数据。这使得噪声仅能进行粗略控制：在我们的实验中，它所携带的信息大致与竞争场的数量的对数成正比，而在测试的模型规模中，一步输出仍然很少保持一致性。我们提出了CONDOR（耦合噪声蒸馏用于一步读取）。一个噪声条件教师在随机数量的掩蔽位置和赢家通吃的情况下进行训练。学生提出一个一步块，保留选定的标记，并从教师在同一噪声场下以多个步骤填充其他位置时获得的块中学习；一个无噪声的掩蔽语言模型项在真实值上锚定学生。对TinyStories的人工评估显示，一步合法性有了显著提升，而不同的噪声场仍然产生不同的块，每个块仅需一次前向传播。
cs.CL / 41 / 2609.06381
Cross-Lingual Representation Alignment by Token-Level Optimal Transport in a Language-Agnostic Space
通过令牌级最优传输在语言无关空间中实现跨语言表示对齐
Abstract
Cross-lingual alignment (CLA) aims to align the representations of large language models (LLMs) across languages, enabling cross-lingual transfer to improve multilingual capabilities. Previous CLA methods often ignore language-specific information encoded in representations and only consider sentence-level alignment, which may lead to suboptimal performance and input-output language mismatch. We propose CAROT (Cross-Lingual Alignment of Representations in a Language-Agnostic Space via Optimal Transport), which consists of two steps: identifying language-specific representations in LLMs' internal states and aligning language-agnostic representations across languages at the token level by optimal transport, while explicitly preserving language-specific representations. Inference-time steering experiments show that the representations computed by CAROT are effective alignment targets, improving multilingual performance by up to 11.2 points in accuracy while maintaining input-output language consistency. We further use the representations obtained by CAROT as training targets, internalizing the aligned representations. The trained models outperform existing CLA methods in 11 of 18 evaluation settings (3 models $\times$ 3 tasks $\times$ ID/OOD languages). Our work provides insights into what constitutes effective alignment targets for CLA in LLMs. Code is available at https://github.com/ynklab/CAROT
Chinese Translation
跨语言对齐（CLA）旨在对齐大型语言模型（LLMs）在不同语言中的表示，从而实现跨语言迁移以提升多语言能力。以往的CLA方法往往忽视了表示中编码的特定语言信息，仅考虑句子级对齐，这可能导致性能不佳和输入输出语言不匹配。我们提出了CAROT（通过最优传输在语言无关空间中进行跨语言表示对齐），该方法包括两个步骤：识别LLMs内部状态中的特定语言表示，并通过最优传输在令牌级别上对齐不同语言的语言无关表示，同时明确保留特定语言表示。推理时间引导实验表明，CAROT计算的表示是有效的对齐目标，能够提高多语言性能，准确率提升最多可达11.2个百分点，同时保持输入输出语言的一致性。我们进一步将CAROT获得的表示作为训练目标，内化对齐表示。经过训练的模型在18个评估设置中的11个（3个模型 × 3个任务 × ID/OOD语言）上优于现有的CLA方法。我们的工作为LLMs中有效的CLA对齐目标提供了新的见解。代码可在 https://github.com/ynklab/CAROT 获取。
cs.CL / 42 / 2609.06406
Hierarchical Wasserstein Merging for Multi-Domain Multi-Task Learning: From Specialists to a Generalist
多域多任务学习的层次化瓦瑟斯坦合并：从专家到通才
Abstract
Multi-domain multi-task learning (MD-MTL) aims to build a single generalist model that performs well across heterogeneous domains and tasks. However, joint training often suffers from interference under distribution shifts. Existing model merging methods mostly operate on model parameters while overlooking the geometric structure of latent representation distributions across domains and tasks. To address these limitations, we propose Hierarchical Wasserstein Merging (HWM), a representation-level framework that models each domain-task specialist as a distribution of hidden representations on a shared support. HWM constructs task-level and global Wasserstein barycenters to capture within-task domain variation and cross-task structure, enabling either training-free specialist aggregation by Wasserstein-derived weights or training-based generalist learning through a hybrid Wasserstein alignment loss. Experiments on four NLP tasks across four domains per task show that HWM achieves superior effectiveness and generalization capability in MD-MTL settings.
Chinese Translation
多域多任务学习（MD-MTL）旨在构建一个在异构域和任务中表现良好的单一通才模型。然而，联合训练常常在分布变化下遭遇干扰。现有的模型合并方法大多侧重于模型参数，而忽视了跨域和任务的潜在表示分布的几何结构。为了解决这些局限性，我们提出了层次化瓦瑟斯坦合并（HWM），这是一种在共享支持上将每个域-任务专家建模为隐藏表示分布的表示级框架。HWM 构建任务级和全局瓦瑟斯坦重心，以捕捉任务内域变异和跨任务结构，从而实现通过瓦瑟斯坦导出的权重进行无训练的专家聚合，或通过混合瓦瑟斯坦对齐损失进行基于训练的通才学习。在四个域的四个自然语言处理任务上的实验表明，HWM 在 MD-MTL 设置中实现了卓越的有效性和泛化能力。
cs.CL / 43 / 2609.06410
Visual Search Augmented Chain-of-Thought Reasoning for Attribute Value Extraction from Product Videos
增强视觉搜索的思维链推理用于产品视频中的属性值提取
Abstract
Existing approaches to visual attribute value extraction (AVE) primarily rely on static product images, failing to capture temporal cues, multi-angle views and fine-grained visual details. Directly applying video vision-language models (VLMs) to product AVE results in limited performance due to the lack of domain knowledge, and fine-tuning them requires extensive high-quality data and substantial computational resources. Thus, we propose visual search augmented chain-of-thought reasoning (ViS-CoT), a training-free, plug-and-play pipeline that can be easily applied to any open-source video VLM for video-to-text AVE in e-Commerce. Specifically, ViS-CoT employs visual clustering to identify representative frames, followed by visual search to retrieve semantically similar product knowledge that can enrich attribute cues. Next, an interleaved CoT reasoning module iteratively refines reasoning through visually-aligned auxiliary texts derived from captioning and automatic speech recognition. Finally, the integrated information guides the model toward accurate and fine-grained attribute predictions. Extensive experiments across 14 product categories on the VideoAVE dataset show that ViS-CoT consistently enhances multiple state-of-the-art video VLMs, achieving an average improvement of 17.91 percentage points in micro-F1.
Chinese Translation
现有的视觉属性值提取（AVE）方法主要依赖静态产品图像，未能捕捉时间线索、多角度视图和细粒度视觉细节。直接将视频视觉-语言模型（VLMs）应用于产品AVE由于缺乏领域知识而导致性能有限，并且对其进行微调需要大量高质量数据和相当的计算资源。因此，我们提出了增强视觉搜索的思维链推理（ViS-CoT），这是一种无训练、即插即用的流程，可以轻松应用于任何开源视频VLM，以实现电子商务中的视频到文本的AVE。具体而言，ViS-CoT采用视觉聚类来识别代表性帧，然后通过视觉搜索检索语义上相似的产品知识，以丰富属性线索。接下来，一个交错的思维链推理模块通过从字幕和自动语音识别中获得的视觉对齐辅助文本迭代精炼推理。最后，集成的信息引导模型朝着准确和细粒度的属性预测。对VideoAVE数据集中14个产品类别的广泛实验表明，ViS-CoT始终增强多种最先进的视频VLM，平均提高了17.91个百分点的微F1分数。
cs.CL / 44 / 2609.06438
InsightChain: Optimized Chain-of-Insight Analytics for LLM-driven Data Visualization
InsightChain：基于大语言模型的优化洞察链分析与数据可视化
Abstract
Large language models (LLMs) are increasingly used for automated data visualization, yet existing approaches often frame visualization generation as a single-step mapping from user query to figure or code, overlooking the iterative analytical reasoning process of expert analysts. We present InsightChain, a four-stage visualization prompting pipeline (Explore--Focus--Test--Present) that emulates expert analytical workflows, together with VG-COPRO, a vision-guided automatic prompt optimization (APO) method adapted to jointly optimize such multi-stage, executable pipelines. To address the evaluation gap for complex data visualization, we introduce the Insight Progression Metric (IPM), a rubric combining four text-based dimensions with a vision-based dimension. We assess IPM through a 100-chain human pilot and an expanded 300-chain agent-based evaluation spanning all ten domains. Experiments on public datasets show that InsightChain consistently outperforms competing prompting baselines. Existing APO methods fail to yield consistent gains on this multi-stage task, whereas VG-COPRO improves performance in both in-domain and cross-domain settings.
Chinese Translation
大语言模型（LLMs）在自动化数据可视化中的应用日益增多，但现有方法通常将可视化生成视为从用户查询到图形或代码的单步映射，忽视了专家分析师的迭代分析推理过程。我们提出了InsightChain，一个四阶段的可视化提示管道（探索--聚焦--测试--呈现），模拟专家的分析工作流程，并结合VG-COPRO，一种视觉引导的自动提示优化（APO）方法，旨在共同优化这种多阶段、可执行的管道。为了解决复杂数据可视化的评估差距，我们引入了洞察进展指标（IPM），该指标结合了四个基于文本的维度和一个基于视觉的维度。我们通过100链的人类试点和扩展的300链基于代理的评估，涵盖所有十个领域，评估了IPM。在公共数据集上的实验表明，InsightChain始终优于竞争的提示基线。现有的APO方法在这一多阶段任务上未能实现一致的提升，而VG-COPRO在领域内和跨领域设置中均提高了性能。
cs.CL / 45 / 2609.06444
Decomposing LLM-Judge Uncertainty to Target Expert Labels
分解 LLM-Judge 不确定性以针对专家标签
Abstract
An LLM judge evaluates outputs at scale. Experts should label only where it is least sure. Its natural escalation signal conflates two uncertainties: aleatoric, real disagreement in the expert pool, which labels cannot reduce, and epistemic, the judge's ignorance, which labels do reduce. A small Bayesian model separates them: a regression on labels already collected learns how far to trust a black-box judge's prediction. Both components follow as simple formulas, with no sampling or further judge calls. The components isolate on a real LLM judge against exactly known truth, and stated confidence is no guide to its actual error. On real human disagreement (ChaosNLI) the epistemic ranking removes 83% more error than total uncertainty for the same expert labels, though simply escalating the least-labelled items does as well there. We demonstrate we can estimate where a judge is ignorant rather than where experts genuinely disagree, and propose using this to direct expert labelling.
Chinese Translation
LLM 判决者在大规模上评估输出。专家应仅在不确定性最低的地方进行标注。其自然的升级信号混合了两种不确定性：随机性（aleatoric），即专家池中真实的分歧，这种分歧是标签无法减少的；以及认知性（epistemic），即判决者的无知，这种无知是标签可以减少的。一个小型贝叶斯模型将它们分开：对已收集标签的回归学习如何信任黑箱判决者的预测。两个组件都遵循简单的公式，无需采样或进一步的判决者调用。这些组件在一个真实的 LLM 判决者上进行隔离，与确切已知的真相进行比较，而声明的置信度并不能指导其实际错误。在真实的人类分歧（ChaosNLI）中，认知性排名比总不确定性在相同专家标签下减少了 83% 的错误，尽管在此情况下，简单地升级标签最少的项目也能达到同样的效果。我们展示了可以估计判决者的无知所在，而不是专家真正分歧的地方，并建议利用这一点来指导专家标注。
cs.CL / 46 / 2609.06498
DFlow: Enabling Verifier Information Flow in Block Diffusion Speculative Decoding
DFlow：在区块扩散推测解码中启用验证者信息流
Abstract
Block diffusion speculative decoding improves LLM inference efficiency by proposing a block of future tokens in parallel and verifying them with a single forward pass through the target model. However, existing methods retain only the accepted prefix and discard the rejected suffix, preventing the computation spent on these positions from benefiting subsequent drafting rounds and forcing the drafter to repeatedly reconstruct representations for future tokens from scratch. We observe that rejection only determines whether a proposed token can be committed, while the verifier representations at rejected positions can still provide useful information for subsequent predictions. Based on this observation, we propose DFlow, a simple yet effective framework that enables verifier information to flow across drafting rounds. DFlow reuses the hidden states produced by the target verifier for the rejected suffix to guide subsequent drafting without additional target computation. To effectively learn this information flow across drafting rounds, we introduce a self-condition train strategy that feeds verifier representations from earlier predictions back into subsequent predictions. Experiments on Qwen3 models across diverse benchmarks demonstrate that DFlow consistently improves draft quality and acceptance length over DFlash.
Chinese Translation
区块扩散推测解码通过并行提出未来令牌的区块并通过目标模型的单次前向传播进行验证，从而提高了大型语言模型（LLM）的推理效率。然而，现有方法仅保留接受的前缀并丢弃被拒绝的后缀，导致在这些位置上花费的计算无法惠及后续的草拟轮次，并迫使草拟者从头开始重复构建未来令牌的表示。我们观察到，拒绝仅决定了提议的令牌是否可以被提交，而在被拒绝位置的验证者表示仍然可以为后续预测提供有用的信息。基于这一观察，我们提出了DFlow，一个简单而有效的框架，能够在草拟轮次之间传递验证者信息。DFlow重用目标验证者为被拒绝后缀产生的隐藏状态，以指导后续草拟，而无需额外的目标计算。为了有效地学习这种跨草拟轮次的信息流，我们引入了一种自条件训练策略，将早期预测中的验证者表示反馈到后续预测中。在多种基准测试中对Qwen3模型的实验表明，DFlow在草拟质量和接受长度上始终优于DFlash。
cs.CL / 47 / 2609.06527
ProcArena: A Multi-Scenario Benchmark for LLMs on Direct and Interactive PL/SQL Development from Natural Language
ProcArena：一个针对自然语言直接和交互式 PL/SQL 开发的多场景基准测试
Abstract
Large language models (LLMs) have shown strong potential for translating natural-language (NL) requirements into PL/SQL programs, attracting increasing attention from the database community. However, existing NL-to-PL/SQL efforts primarily focus on directly generating PL/SQL from complete NL requirements. In practice, PL/SQL development involves diverse scenarios, such as from-scratch development, code modification, debugging, and optimization, and may require either direct generation or multi-turn interaction. Yet, no comprehensive benchmark evaluates multi-scenario, direct and interactive, and multi-dialect NL-to-PL/SQL development. In this paper, we present ProcArena, an execution-based benchmark covering both Direct and Interactive modes. ProcArena comprises 3,998 executable tasks over 157 databases, spanning nine development subscenarios in PostgreSQL and Oracle. We construct challenging Direct tasks through Iterative Logic Enhancement and scenario-specific adapters, and derive paired Interactive tasks through Knowledge Integration and Requirement Perturbation while preserving executable targets. We further design a controlled Solver-User Simulator protocol that allows models to clarify user intent and inspect the database environment without exposing hidden execution feedback. Evaluating seven language models, we find that the best average scores are only 62.2% and 57.8% in Direct and Interactive, respectively, demonstrating that realistic NL-to-PL/SQL development remains challenging, particularly in interactive settings.
Chinese Translation
大型语言模型（LLMs）在将自然语言（NL）需求转化为 PL/SQL 程序方面展现出强大的潜力，吸引了数据库社区的越来越多关注。然而，现有的 NL 到 PL/SQL 的研究主要集中在从完整的 NL 需求直接生成 PL/SQL。在实际开发中，PL/SQL 开发涉及多种场景，例如从头开始开发、代码修改、调试和优化，可能需要直接生成或多轮交互。然而，目前尚无全面的基准测试评估多场景、直接和交互式以及多方言的 NL 到 PL/SQL 开发。本文提出了 ProcArena，一个基于执行的基准测试，涵盖了直接和交互两种模式。ProcArena 包含 3,998 个可执行任务，覆盖 157 个数据库，涵盖 PostgreSQL 和 Oracle 中的九个开发子场景。我们通过迭代逻辑增强和特定场景适配器构建具有挑战性的直接任务，并通过知识集成和需求扰动推导出配对的交互任务，同时保持可执行目标。我们进一步设计了一种受控的求解器-用户模拟器协议，使模型能够澄清用户意图并检查数据库环境，而不暴露隐藏的执行反馈。对七种语言模型的评估结果显示，最佳平均得分在直接和交互模式下分别仅为 62.2% 和 57.8%，这表明现实中的 NL 到 PL/SQL 开发仍然具有挑战性，特别是在交互设置中。
cs.CL / 48 / 2609.06545
LLMs Mirror Country-Specific Gender Patterns If Asked, but Skew Male When Generating Media in Local Languages
大型语言模型在被询问时反映特定国家的性别模式，但在生成本地语言媒体时偏向男性
Abstract
Large language models (LLMs) are increasingly used to generate media, but whether their content perpetuates gender stereotypes is unknown: standard benchmarks rely on selection-based formats rather than long-form generation, and surveyed baselines for local gender associations are scarce outside the West. We collect gender associations for 22 occupational and domestic roles from 695 respondents across the United States, India, Kenya, and Nigeria, and evaluate eight LLMs under two regimes: direct questioning and media generation. Models track the surveyed associations under direct questioning but skew substantially more male under media generation in major local-language cells, consistent with the male bias documented in human-produced media. Outside the US, the shift is much smaller and non-significant under English prompting, so English-only or country-agnostic evaluation would miss this bias in the languages where these models are most deployed. Instruction prompting reduces the shift directionally, but trades off against alignment with the surveyed associations. Evaluating LLM gender bias for global deployment therefore requires generation-format testing, local-language prompting, and locally-collected human baselines.
Chinese Translation
大型语言模型（LLMs）越来越多地用于生成媒体，但其内容是否延续性别刻板印象尚不清楚：标准基准依赖于基于选择的格式，而在西方以外的地方，关于地方性别关联的调查基线稀缺。我们从美国、印度、肯尼亚和尼日利亚的695名受访者那里收集了22个职业和家庭角色的性别关联，并在两种模式下评估了八个LLM：直接询问和媒体生成。模型在直接询问下跟踪调查的关联，但在主要本地语言单元的媒体生成中明显偏向男性，这与人类生成媒体中记录的男性偏见一致。在美国以外，英语提示下的偏移要小得多且不显著，因此仅使用英语或国家无关的评估将错过这些模型在最常部署的语言中存在的偏见。指令提示在方向上减少了偏移，但与调查的关联之间存在权衡。因此，评估LLM的性别偏见以实现全球部署需要进行生成格式测试、本地语言提示和本地收集的人类基线。
cs.CL / 49 / 2609.06593
Discovering Translation-Worthy Languages with E-Values
发现值得翻译的语言与 E 值
Abstract
Choosing when to translate multilingual documents is a central routing problem in text classification: translation can improve predictions for some languages while degrading others or adding unnecessary computation. Uniform translation and heuristic language tiers do not provide statistically controlled route selection. We introduce a language-level router based on paired e-processes that continuously compares direct and translation-assisted classification before freezing a routing policy. A familywise-controlled threshold of 280 bounds the probability of any false route across 14 eligible languages per dataset by 0.05. On SIB-200 and MASSIVE, the router selects translation for 4 of 15 languages and 14 of 15 locales, improving held-out accuracy over direct classification by 8.14 and 16.70 percentage points, respectively. All 28 decisions remain stable across 50 outcome-independent orderings and relative to the per-group threshold. Our results demonstrate that paired e-processes enable statistically controlled, anytime-valid, and auditable multilingual classification routing.
Chinese Translation
选择何时翻译多语言文档是文本分类中的一个核心路由问题：翻译可以改善某些语言的预测，同时可能降低其他语言的预测效果或增加不必要的计算。统一翻译和启发式语言层级并未提供统计控制的路由选择。我们引入了一种基于配对 e 过程的语言级路由器，该路由器在冻结路由策略之前，持续比较直接分类与翻译辅助分类的效果。一个家庭控制的阈值为 280，限制了每个数据集中 14 种合格语言中任何错误路由的概率为 0.05。在 SIB-200 和 MASSIVE 数据集上，该路由器为 15 种语言中的 4 种和 15 种地区中的 14 种选择了翻译，分别提高了持出准确率 8.14 和 16.70 个百分点。所有 28 个决策在 50 次与结果无关的排序中保持稳定，并相对于每组的阈值。我们的结果表明，配对 e 过程使得统计控制、随时有效和可审计的多语言分类路由成为可能。
cs.CL / 50 / 2609.06611
SAGE: A Hierarchical Framework for Evaluating Interpretive Literary Quality in Narratives
SAGE：评估叙事中解释性文学质量的层次框架
Abstract
Assessing the literary quality of narratives requires evaluating interpretive dimensions (cultural representation, emotional depth, and philosophical engagement) that existing NLG metrics cannot measure. We introduce SAGE, a six-layer evaluation framework that separates rule-based assessment of observable textual properties from LLM-based evaluation of interpretive qualities drawn from cultural theory, affect theory, and existentialist philosophy. Each interpretive layer is assessed through multi-round iterative LLM evaluation with independent cross-validation, achieving measurement-grade reliability (98.8% convergence, >94% inter-rater agreement) stable across evaluator models. Validated on 600 evaluations across 100 short stories, our central finding is a systematic capability boundary: emotional-psychological representation approaches human levels, while cultural critique and philosophical depth exhibit approximately double the gap. LLM-generated narratives score below even commercial genre fiction on all three layers. We interpret this as a boundary between pattern-reproducible literary capacities learnable from training corpora and stance-requiring ones demanding cultural positioning and philosophical engagement that pattern matching alone cannot provide.
Chinese Translation
评估叙事的文学质量需要对现有自然语言生成（NLG）指标无法衡量的解释维度（文化表现、情感深度和哲学参与）进行评估。我们提出了SAGE，一个六层评估框架，将基于规则的可观察文本属性评估与基于大型语言模型（LLM）的文化理论、情感理论和存在主义哲学中提取的解释性质量评估分开。每个解释层通过多轮迭代的LLM评估进行评估，并采用独立的交叉验证，达到了测量级别的可靠性（98.8%的收敛性，>94%的评审者一致性），在评估者模型中保持稳定。我们在100个短篇小说的600次评估中进行了验证，主要发现是一个系统性的能力边界：情感-心理表现接近人类水平，而文化批评和哲学深度则表现出大约双倍的差距。LLM生成的叙事在所有三个层面上的得分甚至低于商业类型小说。我们将此解读为可从训练语料库中学习的模式可再现的文学能力与需要文化定位和哲学参与的立场要求之间的边界，单靠模式匹配无法提供。
cs.CL / 51 / 2609.06634
Mind the Gap: Exposing LLM Translation Blind Spots Using the AlphaMWE Multilingual Parallel Corpus
关注差距：利用AlphaMWE多语言平行语料库揭示大型语言模型翻译盲点
Abstract
LLMs' performance on machine translation (MT) tasks is often dependent on the data availability in the specific domains and language pairs that they are trained upon. To examine if Multiword Expressions (MWEs) still set a bottleneck for LLMs regarding language understanding and translation, we report the system performances from the WMT2026 Test Suites shared task, for which we used the publicly available multilingual parallel corpus AlphaMWE as the test suites. We received 31 MT systems' outputs covering English to Chinese (zh), Polish (pl), German (de), Arabic (ar) including Modern Standard Arabic (MSA) and two dialectal ones (Egyptian and Tunisian Arabic). We carried out automatic evaluations using BLEU, ChrF, BERT-score to select the Top3 systems per language pair, followed up with human evaluations on the selected systems. Our findings show that: figurative/MWE phenomena remain challenging; automatic metrics sometimes disagree; human evaluation uncovers language-specific errors hidden by aggregate scores.
Chinese Translation
大型语言模型（LLMs）在机器翻译（MT）任务上的表现通常依赖于其训练所用的特定领域和语言对的数据可用性。为了检验多词表达（MWEs）是否仍然对LLMs在语言理解和翻译方面构成瓶颈，我们报告了WMT2026测试套件共享任务的系统表现，我们使用了公开可用的多语言平行语料库AlphaMWE作为测试套件。我们收集了31个机器翻译系统的输出，涵盖了英语到中文（zh）、波兰语（pl）、德语（de）、阿拉伯语（ar），包括现代标准阿拉伯语（MSA）及两种方言（埃及阿拉伯语和突尼斯阿拉伯语）。我们使用BLEU、ChrF、BERT-score等自动评估指标对每个语言对选择了前3名系统，并对选定系统进行了人工评估。我们的研究发现：比喻/多词表达现象仍然具有挑战性；自动评估指标有时存在不一致；人工评估揭示了被聚合分数掩盖的特定语言错误。
cs.CL / 52 / 2609.06663
ECOKV: Geometry-Aware KV Cache Eviction via Complementary Diversity Metrics
ECOKV：基于几何感知的KV缓存驱逐方法通过互补多样性度量
Abstract
Although multimodal Large Language Models (MLLMs) excel in diverse tasks, their scalability remains limited by the memory and computational overhead of KV cache storage. Recent KV cache eviction approaches incorporate a cosine similarity-based diversity metric with importance metrics to selectively retain critical key-value pairs. However, cosine similarity involves normalization that discards magnitude information, and it often yields uniformly high similarity values across layers due to the anisotropy property of hidden representations. In our study ECOKV, we rigorously deconstruct the capabilities of existing diversity metrics. Moving beyond simple measurement, we propose a geometry-aware composite metric that jointly leverages Euclidean distance and cosine similarity to capture token diversity from complementary perspectives. Furthermore, we use these two metrics to estimate the redundancy level of each attention head, allowing adaptive weighting between diversity and importance scores during token selection. Finally, we demonstrate that the observation window commonly employed to preserve recent tokens can be substantially reduced, thereby allocating more cache capacity to informative tokens and yielding consistent improvements. Extensive experiments demonstrate that ECOKV achieves state-of-the-art performance under various compression ratios and can be seamlessly integrated with existing KV cache eviction methods. We further analyze the relationship between importance and diversity, and examine redundancy patterns across layers and attention heads.
Chinese Translation
尽管多模态大型语言模型（MLLMs）在多样化任务中表现出色，但其可扩展性仍受到KV缓存存储的内存和计算开销的限制。最近的KV缓存驱逐方法结合了基于余弦相似度的多样性度量和重要性度量，以选择性地保留关键的键值对。然而，余弦相似度涉及归一化，这会丢弃幅度信息，并且由于隐藏表示的各向异性特性，它通常在各层之间产生均匀的高相似度值。在我们的研究ECOKV中，我们严格解构了现有多样性度量的能力。超越简单的测量，我们提出了一种基于几何感知的复合度量，该度量共同利用欧几里得距离和余弦相似度，从互补的角度捕捉标记的多样性。此外，我们使用这两种度量来估计每个注意力头的冗余水平，从而在标记选择过程中实现多样性和重要性得分之间的自适应加权。最后，我们证明了通常用于保留最近标记的观察窗口可以显著缩小，从而将更多的缓存容量分配给信息丰富的标记，并产生一致的改进。大量实验表明，ECOKV在各种压缩比下实现了最先进的性能，并且可以与现有的KV缓存驱逐方法无缝集成。我们进一步分析了重要性与多样性之间的关系，并检查了各层和注意力头之间的冗余模式。
cs.CL / 53 / 2609.06690
A Grapheme-Aware Indic Tokenizer for Tamil: Large-Scale Training and Intrinsic Evaluation
一种针对泰米尔语的图素感知印地语分词器：大规模训练与内在评估
Abstract
Tokenization forms the foundation of modern Natural Language Processing (NLP) systems by transforming raw text into discrete units that neural language models can process. The effectiveness of this process directly influences vocabulary efficiency, sequence length, computational cost, and downstream model performance. Although multilingual tokenizers such as Byte Pair Encoding (BPE), WordPiece, and SentencePiece have performed well across numerous languages, they often segment morphologically rich Indic languages inefficiently. Tamil, in particular, poses unique challenges because its grapheme-based writing system can represent a single visible character with multiple Unicode code points. In this work, we present a grapheme-aware Indic tokenizer for Tamil that preserves complete grapheme clusters through a reversible Unicode mapping strategy prior to WordPiece vocabulary learning. By operating on grapheme-level representations instead of individual Unicode code points, the tokenizer produces linguistically meaningful token boundaries while remaining fully compatible with transformer-based language models. The tokenizer is trained on a large-scale Tamil corpus and evaluated using a comprehensive intrinsic evaluation framework that measures compression efficiency, token fragmentation, information density, and vocabulary utilization. Experimental evaluation compares the proposed tokenizer against five widely used multilingual tokenizers: GPT-2, mBERT, mT5, mBART, and NLLB. The proposed tokenizer achieves the strongest performance among the evaluated tokenizers on fragmentation- and sequence-efficiency-oriented intrinsic metrics, while matching the highest observed compression ratio. These results demonstrate the effectiveness of grapheme-aware preprocessing for Tamil tokenization.
Chinese Translation
分词是现代自然语言处理（NLP）系统的基础，通过将原始文本转化为神经语言模型可以处理的离散单元。该过程的有效性直接影响词汇效率、序列长度、计算成本和下游模型性能。尽管多语言分词器如字节对编码（Byte Pair Encoding, BPE）、WordPiece和SentencePiece在多种语言中表现良好，但它们在形态丰富的印地语语言中往往效率低下。特别是泰米尔语，由于其基于图素的书写系统可以用多个Unicode代码点表示一个可见字符，因此面临独特的挑战。在本研究中，我们提出了一种针对泰米尔语的图素感知印地语分词器，该分词器通过可逆的Unicode映射策略在WordPiece词汇学习之前保留完整的图素簇。通过在图素级别的表示上操作，而不是单个Unicode代码点，该分词器生成具有语言学意义的分词边界，同时与基于变换器的语言模型完全兼容。该分词器在大规模泰米尔语语料库上进行训练，并使用全面的内在评估框架进行评估，该框架测量压缩效率、分词碎片化、信息密度和词汇利用率。实验评估将所提分词器与五种广泛使用的多语言分词器进行比较：GPT-2、mBERT、mT5、mBART和NLLB。所提分词器在碎片化和序列效率导向的内在指标上表现出最强的性能，同时匹配了观察到的最高压缩比。这些结果证明了图素感知预处理在泰米尔语分词中的有效性。
cs.CL / 54 / 2609.06702
PARSER: Read in Parallel, Reason in Depth for Long-Context LLM Agents
PARSER：并行读取，深入推理以应对长上下文的 LLM 代理
Abstract
Sequential memory agents process long documents by reading chunks one after another while maintaining a compact memory state, coupling document traversal to reasoning depth. This coupling introduces sensitivity to evidence placement and ties inference latency linearly to document length. We introduce PARSER, which decouples reading from reasoning. A bank of lightweight subagents each bound to a single chunk read the entire document in parallel, while a lead agent reasons in depth through iterative scatter--gather rounds: at each round it broadcasts a query to all subagents, aggregates the returned evidence, and formulates a deeper follow-up query conditioned on what has been found so far. This decoupled design concentrates all learnable behavior in the lead agent, which is optimized with reinforcement learning, while the subagents remain frozen off-the-shelf models. On multi-hop QA with contexts ranging from 7K to 896K tokens, PARSER with a 4B backbone outperforms the strongest sequential memory baseline by 5.7 points on average and by 12.0 points at 896K tokens. Scaling to a 9B backbone, PARSER surpasses DeepSeek-V4-Pro by 6.3 points. Controlled experiments confirm that PARSER is robust to perturbations in evidence position, order, and distance, conditions that cause large accuracy swings in sequential methods, while reducing inference latency by up to 11x.
Chinese Translation
顺序记忆代理通过逐块读取长文档并保持紧凑的记忆状态来处理长文档，将文档遍历与推理深度相结合。这种结合引入了对证据位置的敏感性，并将推理延迟与文档长度线性关联。我们提出了 PARSER，它将读取与推理解耦。一个轻量级子代理组，每个子代理绑定到一个单独的块，能够并行读取整个文档，而一个主代理通过迭代的散播-聚合轮次进行深入推理：在每一轮中，它向所有子代理广播查询，聚合返回的证据，并根据迄今为止发现的内容制定更深入的后续查询。这种解耦设计将所有可学习的行为集中在主代理上，该代理通过强化学习进行优化，而子代理则保持为现成的冻结模型。在上下文范围从 7K 到 896K 令牌的多跳问答中，具有 4B 主干的 PARSER 平均比最强的顺序记忆基线高出 5.7 分，在 896K 令牌时高出 12.0 分。扩展到 9B 主干，PARSER 超越了 DeepSeek-V4-Pro，提升了 6.3 分。控制实验确认，PARSER 对证据位置、顺序和距离的扰动具有鲁棒性，而这些条件会导致顺序方法的准确性大幅波动，同时将推理延迟减少了多达 11 倍。
cs.CL / 55 / 2609.06703
DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents
DianShi-RxnDB：一个通过全自动管道构建的大规模、细粒度有机反应数据平台，供研究人员和人工智能代理使用
Wang, Yubin, Wei, Xingjian, Wu, Jiang, Wang, Yinfan, Zhu, Boyu, Zhang, Lin, Yu, Jianing, Zeng, Huazheng, Ding, Ruiyi, Gao, Junyuan, Sun, Jiaxing, Ge, Lingli, Yang, Haote, Wang, Jingchao, Guo, Aijia, Jiang, Qian, Zhao, Yurui, Zhang, Wenjian, Zhu, Chen, Wu, Lijun, Yang, Xiaolei, Chen, Haodong, Yuan, Junjie, Ye, Zichao, Hou, Shaowei, Ye, Jing, Yu, Jia, Wang, Shan, Wu, Lijun, Qiu, Jiantao, Xu, Chao, Li, Yuqiang, Wang, Guangyu, Zhou, Bowen, Lin, Dahua, He, Conghui
Abstract
High-quality structured organic reaction data are essential for developing artificial intelligence for chemistry (AI4Chem), yet much of this knowledge remains dispersed across patent text, images, and reaction schemes. We present DianShi-RxnDB, a large-scale, fine-grained organic reaction data platform built via a fully automated extraction and normalization pipeline integrating patent text, images, and reaction schemes. Its corpus covers organic synthesis patents from the USPTO and EPO published between 1976 and 2025, yielding approximately 24 million reaction instances, of which approximately 14.8 million (61.7%) pass automated qualification checks. Each instance represents a specific single-step experiment recording participants, roles, quantities, temperatures, reaction times, yields, experimental procedures, and provenance links to source patents. In a manual evaluation of 1,300 sampled qualified instances, the micro-averaged field-level accuracy was 92.95%. A matched comparison with Pistachio further indicated advantages in deduplicated record counts, representation granularity, and field-level exact agreement. The platform provides a Web research workbench for searching, filtering, comparing, and source-verifying records, and a Model Context Protocol (MCP) service offering AI agents composable structured retrieval tools. DianShi-RxnDB is available at https://dianshi.opendatalab.org.cn/ .
Chinese Translation
高质量的结构化有机反应数据对于化学人工智能（AI4Chem）的发展至关重要，但这些知识仍然分散在专利文本、图像和反应方案中。我们提出了DianShi-RxnDB，这是一个通过全自动提取和规范化管道构建的大规模、细粒度有机反应数据平台，整合了专利文本、图像和反应方案。其语料库涵盖了1976年至2025年间由美国专利商标局（USPTO）和欧洲专利局（EPO）发布的有机合成专利，产生了约2400万条反应实例，其中约1480万条（61.7%）通过了自动资格检查。每个实例代表一个特定的单步实验，记录了参与者、角色、数量、温度、反应时间、产率、实验步骤以及与源专利的来源链接。在对1300个抽样合格实例的人工评估中，微平均领域级准确率为92.95%。与Pistachio的匹配比较进一步表明，在去重记录数量、表示粒度和领域级精确一致性方面具有优势。该平台提供了一个Web研究工作台，用于搜索、过滤、比较和源验证记录，以及一个模型上下文协议（Model Context Protocol, MCP）服务，为人工智能代理提供可组合的结构化检索工具。DianShi-RxnDB可在https://dianshi.opendatalab.org.cn/获取。
cs.CL / 56 / 2609.06731
Event Interaction in Low-Rank Bottlenecks for Temporal Relation Extraction
低秩瓶颈中的事件交互用于时间关系提取
Abstract
Temporal relation extraction determines whether an event occurs before, after, or simultaneously with another event, and therefore relies on accurately modeling how the two events interact. Mainstream systems achieve this by concatenating event spans or using shallow fusion, which works well when all model parameters are trainable. However, in parameter-efficient fine-tuning, low-rank bottlenecks restrict information flow and prevent these interaction signals from passing through, leading to clear performance drops. To address this limitation, we propose a theoretically grounded architecture, Convolutional Bottleneck Interaction (CBI), which first applies lightweight depthwise convolution to enhance event representations and then uses element-wise multiplication to capture effective event-event interactions inside the bottleneck. Across five datasets and seven backbone models in the Adapter and LoRA settings, CBI provides consistent and substantial gains, up to +31.7 micro F1, while adding minimal computational cost, showing that explicit interaction inside low-rank spaces is crucial for temporal relation extraction. The code is available at https://github.com/VRCMF/CIF.git.
Chinese Translation
时间关系提取确定一个事件是否在另一个事件之前、之后或同时发生，因此依赖于准确建模这两个事件之间的交互。主流系统通过连接事件跨度或使用浅层融合来实现这一点，当所有模型参数可训练时，这种方法效果良好。然而，在参数高效微调中，低秩瓶颈限制了信息流，阻止了这些交互信号的传递，导致明显的性能下降。为了解决这一限制，我们提出了一种理论基础架构，即卷积瓶颈交互（Convolutional Bottleneck Interaction, CBI），该架构首先应用轻量级深度卷积来增强事件表示，然后使用逐元素乘法来捕捉瓶颈内有效的事件-事件交互。在五个数据集和七个基础模型的适配器（Adapter）和低秩适配（LoRA）设置中，CBI提供了一致且显著的提升，最高达到+31.7微F1，同时增加的计算成本极小，表明在低秩空间内显式交互对于时间关系提取至关重要。代码可在 https://github.com/VRCMF/CIF.git 获取。
cs.CL / 57 / 2609.06771
AuthBench: A Large-Scale Multilingual Benchmark for Authorship Representation across Genres and Lengths
AuthBench：一个大规模多语言的作者身份表示基准，涵盖多种体裁和长度
Abstract
Authorship signals matter in settings where writing style carries identity: digital forensics, plagiarism analysis, account linking, misinformation investigation, and machine-generated text detection. Yet current authorship benchmarks remain fragmented, usually covering only a narrow language set, a single genre, or a limited document-length regime, which makes it difficult to assess whether modern representations truly generalize. We introduce AuthBench, a large-scale multilingual benchmark for authorship representation that is designed to make this evaluation broad, standardized, and realistic. AuthBench contains 428,150 documents written by 153,825 individuals across ten widely used languages, 9 primary genres, 66 fine-grained genres, and four document-length buckets. It supports two complementary tasks: authorship attribution, formulated as same-author retrieval and authorship verification, formulated as same-author binary decision. We benchmark 47 neural models and three non-neural baselines under a unified zero-shot protocol. Results show that authorship representation remains far from solved: the best retrieval model reaches only 0.258 Success@5, while the best verification model achieves 0.076 EER and 0.968 ROC-AUC. The leaderboard also reveals a meaningful task split, with different model families leading retrieval and verification, and large performance differences across languages, genres, and lengths. These findings position AuthBench not only as a new benchmark, but as a diagnostic resource for studying when and why authorship representations succeed or fail. We release AuthBench, its evaluation toolkit, and benchmark data at https://github.com/mao-code/AuthBench and https://huggingface.co/datasets/MaoXun/AuthBench.
Chinese Translation
在写作风格承载身份的背景下，作者身份信号至关重要：数字取证、抄袭分析、账户关联、虚假信息调查以及机器生成文本检测。然而，目前的作者身份基准仍然支离破碎，通常仅覆盖狭窄的语言集、单一体裁或有限的文档长度范围，这使得评估现代表示是否真正具有普适性变得困难。我们推出了AuthBench，一个大规模多语言的作者身份表示基准，旨在使这一评估变得广泛、标准化且现实。AuthBench包含428,150篇由153,825位个人撰写的文档，涵盖十种广泛使用的语言、9个主要体裁、66个细分体裁和四个文档长度类别。它支持两项互补任务：作者身份归属，形式为同作者检索，以及作者身份验证，形式为同作者二元决策。我们在统一的零样本协议下对47个神经模型和三个非神经基线进行了基准测试。结果表明，作者身份表示仍然远未解决：最佳检索模型的Success@5仅达到0.258，而最佳验证模型的EER为0.076，ROC-AUC为0.968。排行榜还揭示了一个有意义的任务划分，不同模型家族在检索和验证任务中表现领先，并且在语言、体裁和长度之间存在显著的性能差异。这些发现使AuthBench不仅成为一个新的基准，还成为研究作者身份表示何时以及为何成功或失败的诊断资源。我们在https://github.com/mao-code/AuthBench和https://huggingface.co/datasets/MaoXun/AuthBench发布AuthBench、其评估工具包和基准数据。
cs.CL / 58 / 2609.06815
Typed Federated Artifacts for the Agentic Web:Sharing Tool-Routing Knowledge Across Frozen,Heterogeneous LLM Agents
面向代理网络的类型化联邦工件：跨越冻结的异构大型语言模型代理共享工具路由知识
Abstract
An open, networked web will allow agents to run frozen models from multiple vendors, keep their history private, and teach each other which tool to call and when. Flat text (prompts, example pools) makes it difficult for the protocol to distinguish between noise statistics, merging rules, and documentation. Weights and adapters cannot transfer that knowledge between platforms. We suggest sharing typed federated artifacts, schema-validated objects with well-defined fields for per-field privacy (described here, but measured), dispute resolution, and cross-model transfer, and instantiating them as SYNAPSE1, a common tool-routing knowledge. After deleting 192 garbage entries and 1,916 training items that duplicate or almost duplicate test queries, a federated compendium routes within 1.1 points of a centralized one at 20 MB of JSON per client each round on StableToolBench (3,180 tools). The same experience merged and shown to the router as typed fields rather than one flat string is worth 8.5 points on clean data and 7.4 under 60% injected contradiction. Crossing merge and rendering shows the halves are inseparable (the typed merge shown flat is the worst arm), while three conflict policies are indistinguishable, so the conflict log that motivated this work is not the On {\tau}-bench retail, each compendium arm improves GPT-4o agents' per-step tool-call accuracy by at least 6.7 points, attributed to format rather than federated experience. Two cautionary findings conclude the paper: on a topic-labeled math proxy and StableToolBench, a TF-IDF classifier over the same labeled experience beats every LLM routing arm (by 48 and 26 points, mostly retrieval recall) because the benchmark's pool holds labeled queries for every supposedly unseen tool and every test query verbatim before our filter. It cannot measure routing to tools without labels, which routing exists for.
Chinese Translation
开放的网络化网页将允许代理运行来自多个供应商的冻结模型，保持其历史私密，并相互教授何时调用哪个工具。平面文本（提示、示例池）使得协议难以区分噪声统计、合并规则和文档。权重和适配器无法在平台之间转移这些知识。我们建议共享类型化的联邦工件，即具有明确字段的模式验证对象，以实现逐字段隐私（在此描述，但可测量）、争议解决和跨模型转移，并将其实例化为SYNAPSE1，一种通用的工具路由知识。在删除192个垃圾条目和1,916个重复或几乎重复测试查询的训练项后，联邦汇编在每轮客户端20MB JSON的StableToolBench（3,180个工具）中，路由精度与集中式的相差仅1.1分。相同的体验以类型化字段而非一个平面字符串的形式合并并展示给路由器，在干净数据上价值8.5分，而在60%注入矛盾下为7.4分。交叉合并和渲染显示这两个部分是不可分割的（平面显示的类型化合并是最差的情况），而三种冲突策略则难以区分，因此促使本研究的冲突日志并不是On { au}-bench零售，每个汇编臂至少提高了GPT-4o代理每步工具调用的准确性6.7分，这归因于格式而非联邦经验。两项警示性发现结束了本文：在一个主题标记的数学代理和StableToolBench上，基于相同标记经验的TF-IDF分类器超越了每个LLM路由臂（分别提高48和26分，主要是检索召回），因为基准池持有每个所谓未见工具的标记查询和每个测试查询的逐字记录，无法测量没有标签的工具路由，而这种路由是存在的。
cs.CL / 59 / 2609.06842
XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions?
XYBench：大型语言模型能否对带有误解的查询做出务实回应？
Abstract
When non-expert users ask LLMs for assistance, their queries can often have misconceptions (e.g., "How do I parse XML with regex?"). In such cases, often referred to as the XY-problem, LLMs must identify the misconception ("regex are fragile") and meaningfully direct the user toward a pragmatic solution that will address the root problem implicit in the request ("use an XML parser"). We introduce XYBench, a benchmark of 8,115 such queries, drawn from technical (StackOverflow/StackExchange) and everyday (WikiHow and a manually-curated subset) domains. We design an evaluation paradigm that assesses model responses along three criteria grounded in cooperative response theory: (a) presence and (b) emphasis on pragmatic solutions, and (c) identification of misconceptions. Our experiments show that even the strongest LLMs predominantly answer the literal request (0.75--0.92) and far less often the intended one (0.33--0.71), while substantially lagging behind humans at identifying misconceptions (at most 63% vs. 79--90%). Further, models overwhelmingly prefer pragmatic responses in a multiple choice setting yet consistently fail to generate them. Oracle ablation experiments show that providing explicit user intent at generation time helps; however a large gap remains, suggesting pragmatic redirection is a fundamentally underdeveloped capability in current LLMs.
Chinese Translation
当非专业用户向大型语言模型（LLMs）寻求帮助时，他们的查询往往会存在误解（例如，“我该如何用正则表达式解析XML？”）。这种情况通常被称为XY问题，LLMs必须识别出误解（“正则表达式是脆弱的”），并有意义地引导用户朝向一个务实的解决方案，以解决请求中隐含的根本问题（“使用XML解析器”）。我们引入了XYBench，这是一个包含8,115个此类查询的基准数据集，来源于技术领域（StackOverflow/StackExchange）和日常生活领域（WikiHow及手动整理的子集）。我们设计了一种评估范式，从合作响应理论的三个标准来评估模型的回应：（a）务实解决方案的存在与（b）强调程度，以及（c）对误解的识别。我们的实验表明，即使是最强大的LLMs也主要回答字面请求（0.75--0.92），而很少回答意图请求（0.33--0.71），在识别误解方面显著落后于人类（最多63%对比79--90%）。此外，模型在多项选择设置中压倒性地偏好务实回应，但始终未能生成这些回应。Oracle消融实验表明，在生成时提供明确的用户意图是有帮助的；然而，仍然存在较大差距，这表明务实重定向在当前LLMs中是一种根本上未充分发展的能力。
cs.CL / 60 / 2609.06851
You Are What You Read: Misalignment via In-Context Persona Induction
你就是你所阅读的：通过上下文角色诱导导致的不一致性
Abstract
Broad misalignment has been produced by finetuning on narrow data, harmful or benign, and in context only by demonstrations of the undesirable behaviour itself. We show that benign data suffices in context, with no finetuning and no demonstration of harmful behaviour in the prompt. Biographical facts that converge on a single figure, placed in a model's context as ordinary conversational turns, lead it to answer as that figure on questions the facts never touch. We call this persona induction. Across nine personas and thirteen models, identity adoption rises sigmoidally with the number of facts and crosses 50% within 3 to 10 of them. Misalignment then tracks which figure is described. Harmless personas reach full adoption with near-zero misalignment, while harmful ones voice their characteristic views on unrelated questions, at rates up to 80%. A formatting instruction can gate when the persona activates. Because each fact is individually benign, accumulated biographical context is flagged by content filters on 3% of inputs against 24-33% for an equivalent direct instruction.
Chinese Translation
通过在狭窄数据上进行微调，无论是有害还是无害，产生了广泛的不一致性，而这种不一致性仅通过不良行为本身的示范在上下文中体现。我们展示了在上下文中，仅凭无害数据即可实现这一点，无需微调，也无需在提示中展示有害行为。将聚焦于单一人物的传记事实作为普通对话环节放置在模型的上下文中，导致模型在未涉及这些事实的问题上以该人物的身份作答。我们将其称为角色诱导。在九个角色和十三个模型中，身份采纳随着事实数量的增加呈S形上升，并在3到10个事实之间超过50%。此后，不一致性跟踪描述的具体人物。无害角色在几乎没有不一致性的情况下实现完全采纳，而有害角色则在与无关问题上表达其特征观点，比例高达80%。格式化指令可以控制角色的激活时机。由于每个事实都是单独无害的，累积的传记上下文在3%的输入中被内容过滤器标记，而相应的直接指令则为24-33%。
cs.CL / 61 / 2609.06879
AutoLexSteer: Automatic Contrast Construction for Lexical Activation Steering
AutoLexSteer：用于词汇激活引导的自动对比构建
Abstract
Steering vectors have rapidly emerged as a popular and effective method for guiding the output of LLMs in very specific ways. But constructing accurate steering vectors is a difficult manual process due to the opacity of embeddings. We introduce Hangman, a novel type of steering vector that operates using word senses, as well as AutoLexSteer, the first fully automated process for building steering vectors. AutoLexSteer employs families of closely-related words extracted from WordNet to specify both the steering source to be avoided and the desired steering target. The steering vectors are quite precise, can be used to steer at the level of words and sets of word senses (meanings), and are able to steer certain LLM behaviors like sycophancy. The dataset and code can be found at https://github.com/ShuheWang1998/autolexsteer.
Chinese Translation
引导向量迅速成为一种流行且有效的方法，用于以非常特定的方式指导大型语言模型（LLMs）的输出。然而，由于嵌入的不透明性，构建准确的引导向量是一个困难的手动过程。我们介绍了Hangman，一种基于词义的新型引导向量，以及AutoLexSteer，这是构建引导向量的首个完全自动化过程。AutoLexSteer利用从WordNet中提取的紧密相关词汇家族来指定需要避免的引导源和期望的引导目标。引导向量非常精确，可以在单词和词义集合（含义）层面进行引导，并能够引导某些LLM行为，如阿谀奉承。数据集和代码可在https://github.com/ShuheWang1998/autolexsteer找到。
cs.CL / 62 / 2609.06893
Towards Bridging the Gap Between Offline and Iterative Alignment via Preference Distillation
通过偏好蒸馏缩小离线与迭代对齐之间的差距
Abstract
Direct preference optimization DPO is a promising offline approach for aligning large language models (LLMs) due to its simplicity, computational efficiency, and implicit modeling of human preferences. Interestingly, iterative extensions of DPO have achieved stronger performance on academic benchmarks, raising two key questions: (i) Why do iterative methods generally outperform offline ones? (ii) Can their advantages be incorporated into offline alignment? To answer the first question, our controlled experiments reveal that the explicit preference model, additionally introduced in the iterative procedure, is a key factor behind its superiority over offline methods. This insight leads us to answer the second question affirmatively and propose Distilled Preference Probability Policy Optimization (DP3O), an effective and efficient offline alignment algorithm. DP3O first learns an explicit preference model using a helper class of LLMs and then distills its knowledge into policy optimization. Theoretically, we show that explicit preference modeling admits better estimation error control than implicit formulations, and that DP3O achieves a tighter generalization bound than hard-label DPO through variance reduction. Empirically, we evaluate DP3O on a wide range of chat-based and downstream tasks and show that it outperforms state-of-the-art offline methods, achieves performance comparable to iterative DPO, and reduces training time by about $42\%$, demonstrating both its effectiveness and efficiency.
Chinese Translation
直接偏好优化（DPO）是一种有前景的离线方法，用于对齐大型语言模型（LLMs），因其简单性、计算效率以及对人类偏好的隐式建模而受到关注。有趣的是，DPO的迭代扩展在学术基准测试中取得了更强的性能，这引发了两个关键问题：（i）为什么迭代方法通常优于离线方法？（ii）它们的优势能否融入离线对齐中？为了回答第一个问题，我们的控制实验揭示了在迭代过程中额外引入的显式偏好模型是其优于离线方法的关键因素。这一见解使我们能够肯定地回答第二个问题，并提出了蒸馏偏好概率策略优化（DP3O），这是一种有效且高效的离线对齐算法。DP3O首先利用一类辅助的LLMs学习显式偏好模型，然后将其知识蒸馏到策略优化中。从理论上讲，我们证明了显式偏好建模在估计误差控制方面优于隐式形式，并且DP3O通过方差减少实现了比硬标签DPO更紧的泛化界。从经验上看，我们在广泛的基于聊天的和下游任务上评估了DP3O，结果表明其优于最先进的离线方法，性能可与迭代DPO相媲美，并将训练时间减少约42%，展示了其有效性和效率。
cs.CL / 63 / 2609.06898
Dynamic-Programming-Guided Hierarchical BPE and Empirical Analysis of Vocabulary Pruning
动态规划引导的分层字节对编码及词汇修剪的实证分析
Abstract
Byte Pair Encoding (BPE) constructs vocabularies through greedy pair merging, but the resulting merge order does not necessarily allocate a fixed model-visible vocabulary optimally for compression. We propose Dynamic-Programming-Guided Hierarchical BPE (DH-BPE), a vocabulary-construction method that combines token exposure under exact minimum-token segmentation with the hierarchical dependencies induced by BPE training. Starting from a modestly overshot BPE candidate vocabulary, DH-BPE uses dynamic programming to measure candidate utility and applies exposure-guided, dependency-aware pruning to select a fixed-size model-visible vocabulary. We compare DH-BPE against Standard BPE and recent vocabulary-optimization baselines, including Pruned BPE, MinGram, and MinGram-PP, in primary evaluations at 12K and 16K target vocabulary sizes, with an additional 18K evaluation against MinGram only. Across the primary 12K and 16K comparisons, DH-BPE consistently improves aggregate compression over Standard BPE, Pruned BPE, and MinGram under a shared exact minimum-token DP encoder. MinGram-PP achieves stronger aggregate compression in the primary comparisons, but DH-BPE outperforms it at overshoot factors f = 2.0 and f = 3.0 in cross-corpus evaluation; at 12K, MinGram-PP reverses this ordering only with the substantially larger candidate pools at f = 4.0 and f = 5.0. Qualitative analysis further shows that DH-BPE balances later, more complete BPE merges with reusable subword components, providing a practical approach to improving vocabulary allocation under a fixed model-visible vocabulary budget.
Chinese Translation
字节对编码（BPE）通过贪婪的对合并构建词汇，但所得到的合并顺序并不一定能为压缩优化地分配固定的模型可见词汇。我们提出了动态规划引导的分层字节对编码（DH-BPE），这是一种词汇构建方法，结合了在精确最小标记分割下的标记曝光与BPE训练所引发的层次依赖关系。从一个适度超出的BPE候选词汇开始，DH-BPE使用动态规划来衡量候选的效用，并应用曝光引导的、依赖感知的修剪来选择固定大小的模型可见词汇。我们在12K和16K目标词汇大小的主要评估中，将DH-BPE与标准BPE及最近的词汇优化基线进行比较，包括修剪BPE（Pruned BPE）、最小Gram（MinGram）和最小Gram-PP（MinGram-PP），并对MinGram进行了额外的18K评估。在主要的12K和16K比较中，DH-BPE在共享的精确最小标记动态规划编码器下，始终在整体压缩上优于标准BPE、修剪BPE和最小Gram。最小Gram-PP在主要比较中实现了更强的整体压缩，但在跨语料库评估中，DH-BPE在超出因子f = 2.0和f = 3.0时超越了它；在12K时，最小Gram-PP仅在f = 4.0和f = 5.0时，凭借显著更大的候选池逆转了这一排序。定性分析进一步表明，DH-BPE平衡了后期更完整的BPE合并与可重用的子词组件，为在固定模型可见词汇预算下改善词汇分配提供了一种实用的方法。
cs.CL / 64 / 2609.06963
TurEngMix: A Text Corpus and Benchmark for Turkish-English Code-Mixed Language Identification and Named Entity Recognition
TurEngMix：土耳其语-英语混合语言识别与命名实体识别的文本语料库与基准测试
Abstract
Natural language processing systems underperform on code-mixed text, particularly for low-resource language pairs. Turkish-English poses a further challenge: it lets English stems combine with Turkish suffixes to form single mixed-language tokens. We introduce TurEngMix, a corpus of 5.5K noisy, naturally occurring social media posts (486,974 tokens) rich in Turkish-English code-mixing. From this corpus, we construct a new Turkish-English benchmark for code-mixed language identification (LID) and named entity recognition (NER), comprising 15K expert-annotated tokens. Evaluating both decoder LLM and fine-tuned encoder baselines, we find that monolingual Turkish and English tokens are labeled reliably, but all models have high error rates on mixed-language tokens for both LID and NER. For morphologically integrated tokens, NER error rates were 5.2x and 6.3x higher for GPT-4o and Qwen, respectively. This highlights how morphological integration remains a challenge. We release the corpus, annotations, and code to support future computational and sociolinguistic research on Turkish-English code-mixing.
Chinese Translation
自然语言处理系统在处理混合语言文本时表现不佳，尤其是对于资源匮乏的语言对。土耳其语-英语混合语言更具挑战性：它允许将英语词干与土耳其语后缀结合，形成单一的混合语言词元。我们引入了TurEngMix，这是一个包含5500条噪声较多的自然社交媒体帖子（共486,974个词元）的语料库，富含土耳其语-英语混合语言现象。基于该语料库，我们构建了一个新的土耳其语-英语混合语言识别（LID）和命名实体识别（NER）基准，包含15,000个专家标注的词元。通过评估解码器大型语言模型（LLM）和微调编码器基线模型，我们发现单语土耳其语和英语词元的标注较为可靠，但所有模型在混合语言词元的LID和NER任务中均表现出较高的错误率。对于形态学整合的词元，GPT-4o和Qwen的NER错误率分别高出5.2倍和6.3倍。这凸显了形态学整合仍然是一个挑战。我们发布了该语料库、标注和代码，以支持未来土耳其语-英语混合语言的计算和社会语言学研究。
cs.CL / 65 / 2609.06970
CantoneseLLM v2: Reasoning in a Low-Resource Language
CantoneseLLM v2：低资源语言中的推理
Abstract
Cantonese is widely spoken but remains low-resource in written data, with no large corpus of native Cantonese reasoning traces available for model training. We develop and release CantoneseLLM v2, comprising models based on Qwen3 8B and 30B-A3B. The models are trained through CPT on 784 million Cantonese and Hong Kong-related tokens, chat-vector merging, SFT, DPO, and RLVR. Evaluation across the training stages shows that chat-vector merging transfers instruction following but preserves the donor model's reasoning language, while SFT with limited Cantonese reasoning data substantially shortens or removes reasoning traces and reduces benchmark performance. DPO restores the reasoning-block format, particularly for the 8B model, but recovers only part of the lost performance. The RLVR training with Cantonese language and Traditional Chinese scripts as multiplicative constraints introduced Cantonese language alignment and restored the lost performance. The 30B-A3B model reaches 73.16 on HKCanto-Eval, within 1.20 points of its merged checkpoint, while retaining the Cantonese reasoning behaviour absent from that checkpoint. We release the model checkpoints, the training environments, and a thirteen-year Traditional Chinese Common Crawl dataset. The models can be accessed at https://huggingface.co/collections/hon9kon9ize/cantonesellm-v20
Chinese Translation
粤语广泛使用，但在书面数据方面仍然是低资源语言，缺乏可用于模型训练的大规模母语粤语推理数据集。我们开发并发布了CantoneseLLM v2，包含基于Qwen3 8B和30B-A3B的模型。这些模型通过CPT在7.84亿个粤语和与香港相关的标记上进行训练，采用了聊天向量合并、SFT、DPO和RLVR等方法。训练阶段的评估显示，聊天向量合并能够传递指令遵循能力，但保留了捐赠模型的推理语言，而使用有限的粤语推理数据的SFT则显著缩短或移除了推理痕迹，降低了基准性能。DPO恢复了推理块格式，特别是对于8B模型，但仅恢复了部分丢失的性能。使用粤语和繁体中文作为乘法约束的RLVR训练引入了粤语语言对齐，并恢复了丢失的性能。30B-A3B模型在HKCanto-Eval上达到了73.16，距离其合并检查点仅有1.20分，同时保留了该检查点中缺失的粤语推理行为。我们发布了模型检查点、训练环境以及一个包含十三年的繁体中文Common Crawl数据集。模型可以在https://huggingface.co/collections/hon9kon9ize/cantonesellm-v20访问。
cs.CL / 66 / 2609.06974
Train Overcomplete, Deploy Compact: Scaling Recovery Capacity for Structured LLM Pruning
训练过完备，部署紧凑：扩展结构化大语言模型剪枝的恢复能力
Abstract
Large language models achieve strong performance across diverse tasks, but deployment remains costly because of memory, latency, and energy demands. Structured pruning reduces these costs by removing architectural components, yet its recovery stage is often limited by a mismatch between the recovery module's representational capacity and the complexity of the removed knowledge. We call this bottleneck the capacity-knowledge asymmetry and propose OverRep, an Overcomplete Reparameterization framework for structured LLM pruning. Following the principle of "train overcomplete, deploy compact", OverRep temporarily overparameterizes the recovery module during training to absorb complex knowledge distilled from the original model. After recovery, the overcomplete re-parameterization is algebraically merged into a mathematically equivalent compact module, preserving the pruned model's inference-time architecture and computational cost. OverRep further introduces an annealed activation that enables nonlinear training dynamics while converging to a linear regime for exact algebraic merging. Across three backbone families, OverRep improves retained reasoning performance over strong recovery baselines by up to 5.5 and 8.4 points at 25% and 50% pruning, respectively, while keeping memory usage and TFLOPs comparable to existing recovery methods. Our code is available at https://github.com/mmai-laboratory/OverRep.
Chinese Translation
大型语言模型在多种任务中表现出色，但由于内存、延迟和能量需求，部署成本仍然很高。结构化剪枝通过移除架构组件来降低这些成本，但其恢复阶段往往受到恢复模块的表示能力与被移除知识的复杂性之间不匹配的限制。我们称这种瓶颈为能力-知识不对称，并提出了OverRep，一个用于结构化大语言模型剪枝的过完备重参数化框架。遵循“训练过完备，部署紧凑”的原则，OverRep在训练过程中暂时对恢复模块进行过参数化，以吸收从原始模型中提炼的复杂知识。恢复后，过完备重参数化在代数上合并为一个数学上等效的紧凑模块，从而保留剪枝模型的推理时间架构和计算成本。OverRep进一步引入了一种退火激活，使得在收敛到线性状态的同时能够实现非线性训练动态，以便进行精确的代数合并。在三个主干模型系列中，OverRep在25%和50%剪枝时，保留的推理性能比强恢复基线提高了最多5.5和8.4分，同时保持内存使用和TFLOPs与现有恢复方法相当。我们的代码可在 https://github.com/mmai-laboratory/OverRep 获取。
cs.CL / 67 / 2609.07036
Aha-Flow Distillation: Flow Markers Matter in LLM Reasoning
Aha-Flow 蒸馏：流动标记在大型语言模型推理中的重要性
Abstract
We identify the Flow Moment, a reasoning pattern characterized by sustained, process-confirming verbalizations such as I'm doing, in contrast to the revision- and backtracking-oriented Aha Moment. We refer to their corresponding linguistic expressions as Flow Markers and Aha Markers, respectively. Based on this observation, we construct Flow-CoT by rewriting the discourse markers of original reasoning traces while preserving their underlying reasoning content, and use it as auxiliary supervision for on-policy self-distillation (OPSD). We further propose \textbf{Aha-Flow Distillation (AFD)}, a dual-mode extension of OPSD that pairs different forms of privileged information with corresponding reasoning instructions. The Aha branch retains concise solution-based supervision, while the Flow branch introduces rewritten Flow-CoT under a direct and confident reasoning instruction. At inference time, the model uses only the standard reflective instruction, so Flow-style reasoning serves purely as a training signal. Experiments on AIME25 and HMMT25 show consistent improvements across Qwen3-8B and Qwen3-4B: AFD improves Avg@12 from 60.8 to 61.3 on Qwen3-8B and from 57.5 to 58.6 on Qwen3-4B over our reproduced OPSD baselines. Controlled ablations further show that, with the same Flow-CoT/Aha-CoT composition, dual-mode training improves Avg@12 from 59.5 to 60.1, indicating that the benefit comes not only from introducing heterogeneous reasoning supervision, but also from how it is organized during self-distillation. The code is available at https://github.com/Wang-Xiaodong1899/Aha-Flow-Distillation.
Chinese Translation
我们识别出流动时刻（Flow Moment），这是一种推理模式，其特征是持续的、确认过程的语言表达，例如“我正在做”，与以修正和回溯为导向的顿悟时刻（Aha Moment）形成对比。我们将它们对应的语言表达称为流动标记（Flow Markers）和顿悟标记（Aha Markers）。基于这一观察，我们通过重写原始推理轨迹的语篇标记来构建流动链（Flow-CoT），同时保留其潜在的推理内容，并将其作为在政策自蒸馏（on-policy self-distillation, OPSD）中的辅助监督。我们进一步提出了 extbf{Aha-Flow 蒸馏（AFD）}，这是OPSD的双模扩展，将不同形式的特权信息与相应的推理指令配对。顿悟分支保留了简洁的基于解决方案的监督，而流动分支则在直接且自信的推理指令下引入重写的流动链。在推理时，模型仅使用标准的反思指令，因此流动风格的推理纯粹作为训练信号。AIME25和HMMT25上的实验显示，Qwen3-8B和Qwen3-4B的表现一致改善：AFD使Qwen3-8B的Avg@12从60.8提高到61.3，Qwen3-4B从57.5提高到58.6，相较于我们复现的OPSD基线。控制消融实验进一步表明，在相同的流动链/顿悟链组合下，双模训练使Avg@12从59.5提高到60.1，这表明收益不仅来自于引入异质推理监督，还来自于在自蒸馏过程中如何组织这些监督。代码可在https://github.com/Wang-Xiaodong1899/Aha-Flow-Distillation获取。
cs.CL / 68 / 2609.07053
A Hyperbolicity Atlas of Large Language Model Hidden States
大型语言模型隐状态的双曲性图谱
Abstract
LLM hidden states are ordinary vectors, but the distances among those vectors may still show hierarchical structure. To our knowledge, this paper is the first systematic study of whether prompt-token hidden states in contemporary LLMs exhibit Gromov Hyperbolicity (GH), a distance-based measure of tree-likeness. Using 818,904 sample-layer measurements from ten open-weight models across MATH500, HumanEval, WinoGrande, and TruthfulQA, we build a GH map over four axes: parameter scale, layer depth, model family, and input domain. The clearest pattern is depth, not scale: middle layers usually form a high-relative-hyperbolicity plateau, while final layers often become substantially more tree-like. Scale effects are weak and non-monotonic, matched 7/8B model families differ strongly, and domains interact with model specialization. These findings make GH useful as a practical diagnostic: it shows where hierarchical distance structure appears, how specialization changes it, and which model-layer-domain comparisons deserve closer analysis.
Chinese Translation
大型语言模型（LLM）的隐状态是普通的向量，但这些向量之间的距离可能仍然显示出层次结构。据我们所知，本文是首次系统研究当代大型语言模型中的提示-标记隐状态是否表现出Gromov双曲性（GH），这是一种基于距离的树状相似性度量。通过对来自MATH500、HumanEval、WinoGrande和TruthfulQA的十个开放权重模型的818,904个样本层测量，我们构建了一个关于四个轴的GH图谱：参数规模、层深度、模型家族和输入域。最明显的模式是深度，而非规模：中间层通常形成一个高相对双曲性的平台，而最终层往往变得更加树状。规模效应较弱且非单调，匹配的7/8B模型家族差异显著，域与模型专业化之间存在相互作用。这些发现使得GH作为一种实用的诊断工具变得有用：它展示了层次距离结构出现的位置，专业化如何改变它，以及哪些模型-层-域比较值得更深入的分析。
cs.CL / 69 / 2609.07093
Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering
何处查找及使用何种方法：用于长期对话记忆问答的检索-定位-生成框架
Abstract
Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely adopted for long-term conversational memory question answering. However, existing methods suffer from two key challenges: (1) fragmented evidence scattered across temporally distant sessions, and (2) noisy content within retrieved sessions that triggers the lost-in-the-middle effect. To address these challenges, we propose MemLoc, a unified Retrieve-Localize-Generate framework for long-term conversational memory QA. For retrieval, MemLoc decomposes each session into multi-granularity memory units and performs query routing via an inner-memory graph with entropy-based granularity selection. It further models cross-session semantic and temporal dependencies through a cross-memory graph, enabling coarse-to-fine retrieval of top-K relevant memory candidates. For localization, we introduce a reasoning-based evidence locator trained with Self-reflective Hint Policy Optimization (SHPO), which performs progressive refinement by extracting query-relevant fragments within memory units to suppress noise and reranking across candidates to remove redundancy, producing a compact evidence set with lightweight location IDs. For generation, these IDs act as precise grounding signals that guide the LLM to the correct memory positions, mitigating the lost-in-the-middle effect while preserving original contextual integrity. Extensive experiments on four benchmarks demonstrate that MemLoc achieves state-of-the-art retrieval accuracy and response quality while maintaining efficiency. Our code is available at: https://github.com/Nikol-coder/MemLoc.
Chinese Translation
检索增强生成（Retrieval-augmented generation, RAG）使大型语言模型（Large Language Models, LLMs）能够通过访问外部知识来回答问题，并已被广泛应用于长期对话记忆问答。然而，现有方法面临两个主要挑战：（1）证据分散在时间上相隔较远的会话中，以及（2）检索会话中的噪声内容导致的“迷失中间”效应。为了解决这些挑战，我们提出了MemLoc，一个统一的检索-定位-生成框架，用于长期对话记忆问答。在检索方面，MemLoc将每个会话分解为多粒度的记忆单元，并通过基于熵的粒度选择在内部记忆图中执行查询路由。它进一步通过跨记忆图建模跨会话的语义和时间依赖关系，实现对前K个相关记忆候选的粗到细检索。在定位方面，我们引入了一种基于推理的证据定位器，该定位器通过自反提示策略优化（Self-reflective Hint Policy Optimization, SHPO）进行训练，通过提取记忆单元中与查询相关的片段来抑制噪声，并在候选项中进行重新排序以消除冗余，从而生成一个紧凑的证据集，并附有轻量级的位置ID。在生成方面，这些ID作为精确的基础信号，引导LLM到达正确的记忆位置，减轻“迷失中间”效应，同时保持原始上下文的完整性。在四个基准测试上的广泛实验表明，MemLoc在检索准确性和响应质量方面达到了最先进的水平，同时保持了效率。我们的代码可在以下网址获取：https://github.com/Nikol-coder/MemLoc。
cs.CL / 70 / 2609.07103
Revisiting Complete Reasoning Traces for Post-Training
重新审视后训练的完整推理轨迹
Abstract
Large language models (LLMs) are often post-trained on pre-collected reasoning trajectories to improve their reasoning capability. Such trajectories tend to be long due to complex, interwoven paths, which often include detours on the path toward the answer. However, it has been underexplored whether LLMs indeed benefit from learning complete trajectories in post-training, such as supervised fine-tuning (SFT). Starting from our pilot study, we find that full trajectories provide only limited benefit, while partial trajectories are effective even under heavy truncation. We analyze redundancy in reasoning trajectories through attention-based analyses and controlled token-removal studies, both of which show that intermediate tokens contribute minimally to final reasoning quality. This suggests that avoiding redundant information may allow LLMs to internally infer coherent alternatives by inferring missing steps from their internal knowledge, given known trajectory endpoints. Furthermore, we show that training LLMs using endpoints leads to consistent changes in reasoning behavior, and that it also benefits post-training methods based on reinforcement learning or on-policy distillation, highlighting the need to revisit complete reasoning traces. Code is available at https://github.com/naver-ai/revisiting-trace.
Chinese Translation
大型语言模型（LLMs）通常在预先收集的推理轨迹上进行后训练，以提高其推理能力。这些轨迹往往较长，因其复杂且交织的路径，通常包括通往答案的绕行。然而，尚未深入探讨LLMs在后训练中是否确实从学习完整轨迹中受益，例如监督微调（SFT）。从我们的初步研究出发，我们发现完整轨迹仅提供有限的益处，而部分轨迹即使在严重截断的情况下也有效。我们通过基于注意力的分析和控制的令牌移除研究分析推理轨迹中的冗余，这两者均表明中间令牌对最终推理质量的贡献微乎其微。这表明，避免冗余信息可能使LLMs能够通过从其内部知识中推断缺失步骤来内部推断一致的替代方案，前提是已知轨迹的端点。此外，我们展示了使用端点训练LLMs会导致推理行为的一致变化，并且这也有利于基于强化学习或在线蒸馏的后训练方法，强调了重新审视完整推理轨迹的必要性。代码可在 https://github.com/naver-ai/revisiting-trace 获取。
cs.CL / 71 / 2609.07117
The Illusion of Debiasing: Persona Steering Redistributes Rather Than Reduces Bias in LLMs
去偏见的幻觉：人格引导重新分配而非减少大型语言模型中的偏见
Abstract
Prompt-based interventions: system prompts, personas, role instructions, reliably reshape what a language model says, but it is unclear which layer they reach. Do they reconfigure internal structure, or only modulate the output channel? We use persona conditioning as a controlled probe, measuring its effects along a depth axis from self-report, through open-ended generation, to word-level parametric association, across three instruction-tuned models. We find a graded dissociation. Personas are legible but not structural: models follow single-trait instructions yet fail to reproduce human inter-trait covariance. The dissociation deepens with depth: personas hold or amplify closed-form QA bias, shift absolute tone while leaving between-group disparity unchanged, and barely perturb an already saturated associative baseline. Prompt-based steering thus operates in the output channel and has a structural reach limit that surface manipulability can mask.
Chinese Translation
基于提示的干预：系统提示、人格、角色指令，可靠地重塑语言模型的输出，但尚不清楚它们达到的层次。它们是重新配置内部结构，还是仅仅调节输出通道？我们使用人格条件作为控制探针，测量其在深度轴上的影响，从自我报告、开放式生成到词级参数关联，涵盖三个经过指令调优的模型。我们发现了一种分级的解离。人格是可识别的，但不是结构性的：模型遵循单一特征指令，却未能再现人类特征间的协方差。随着深度的增加，这种解离加深：人格保持或放大封闭式问答偏见，改变绝对语气而不改变组间差异，并几乎不扰动已经饱和的关联基线。因此，基于提示的引导在输出通道中运作，并具有结构性影响的限制，而这种表面可操控性可能掩盖了这一点。
cs.CL / 72 / 2609.07120
PTCG: Persona-guided Tree-based Counterargument Generation
PTCG：基于角色引导的树形反驳生成
Abstract
The ability to generate counterarguments is important for critical thinking and balanced discourse, yet existing approaches typically produce only a single counterargument, failing to capture the diversity and persuasiveness required in real-world debates. To address this limitation, we propose Persona-guided Tree-based Counterargument Generation (PTCG), a framework that combines Tree-of-Thoughts-inspired step-wise generation and pruning with speaker persona selection. By estimating the author's persona from the original argument and incorporating speaker personas representing distinct perspectives, PTCG operationalizes perspective-taking and enables the generation of diverse counterarguments. Results from LLM-as-a-Judge, classifier-based assessment, and human evaluations indicate that PTCG shows consistent improvements in both the diversity and persuasiveness of counterarguments compared to baseline methods.
Chinese Translation
生成反驳的能力对于批判性思维和均衡的讨论至关重要，但现有的方法通常只生成单一的反驳，未能捕捉到现实辩论中所需的多样性和说服力。为了解决这一局限性，我们提出了基于角色引导的树形反驳生成（PTCG）框架，该框架结合了受“思维树”（Tree-of-Thoughts）启发的逐步生成和修剪方法，以及说话者角色的选择。通过从原始论点中估计作者的角色，并结合代表不同视角的说话者角色，PTCG 实现了视角转换，并能够生成多样化的反驳。来自 LLM-as-a-Judge、基于分类器的评估和人工评估的结果表明，与基线方法相比，PTCG 在反驳的多样性和说服力方面均显示出一致的改善。
cs.CL / 73 / 2609.07129
Line-Coupled Language Model
线耦合语言模型
Abstract
Autoregressive language models generate one token per decoding step, limiting the useful output of each forward pass. Although diffusion models, insertion-based decoding, and multi-token prediction enable parallel generation, they either incur additional training-time token traffic or struggle to predict strongly dependent future tokens. We introduce the Line-Coupled Language Model (LCLM), an autoregressive model that advances multiple text lines together by predicting the next token for every active line while coupling the lines through shared causal context. LCLM interleaves line tokens into a single causal sequence and uses line-staggered rotary positions, retaining the standard next-token objective and causal attention. Controlled experiments show that cross-line targets are substantially less dependent than consecutive same-line targets, supporting lines as parallel generation units. With 881M parameters, LCLM produces an average of 2.94 content tokens per forward pass with a validation cross-entropy loss of 2.44, compared with 1.00 token per forward pass and a loss of 2.39 for the vanilla autoregressive baseline. Most notably, even when LCLM generates 16 tokens per forward pass, its loss is only 0.09 higher than that of the vanilla autoregressive baseline (2.34 vs. 2.25).
Chinese Translation
自回归语言模型在每个解码步骤中生成一个标记，这限制了每次前向传递的有效输出。尽管扩散模型、基于插入的解码和多标记预测实现了并行生成，但它们要么增加了训练时的标记流量，要么在预测强依赖的未来标记时面临困难。我们提出了线耦合语言模型（Line-Coupled Language Model, LCLM），这是一种自回归模型，通过预测每个活跃行的下一个标记来同时推进多条文本行，并通过共享的因果上下文将这些行耦合在一起。LCLM将行标记交错成一个单一的因果序列，并使用行错位的旋转位置，保留了标准的下一个标记目标和因果注意力。控制实验表明，跨行目标的依赖性显著低于连续同一行目标，支持将行视为并行生成单元。LCLM具有8.81亿个参数，平均每次前向传递生成2.94个内容标记，验证交叉熵损失为2.44，而传统自回归基线每次前向传递生成1.00个标记，损失为2.39。最值得注意的是，即使LCLM每次前向传递生成16个标记，其损失也仅比传统自回归基线高0.09（2.34对比2.25）。
cs.CL / 74 / 2609.07134
Retrieval-Augmented Multi-Prompt Ensemble for Minor-Grain Breeding Information Extraction
检索增强的多提示集合用于小粒度育种信息提取
Abstract
This paper presents our system for CCL2026-Eval Task 5: Minor-Grain Breeding Information Extraction (MGBIE), which jointly extracts 12 entity types and 6 relation types from minor-grain breeding literature. We propose RAME (Retrieval-Augmented Multi-Prompt Ensemble), a training-free framework that elicits multiple LLM outputs under controlled diversity and aggregates them by majority voting to obtain high-confidence predictions. RAME combines (i) retrieval-augmented few-shot selection via a hybrid BM25-embedding retriever, (ii) a three-prompt ensemble (Strict, Relaxed, Balanced) spanning the precision to recall spectrum, and (iii) large-scale repeated sampling with majority voting to filter noisy predictions. Built on DeepSeek-V4-Flash, RAME achieves a Total Score of 0.499 (NER 0.730, RE 0.346) on the leaderboard, ranking 1st and surpassing the official Track-A baseline powered by GPT-5.5 (0.448), representing an 11.4% relative improvement. Code is available at https://github.com/king-wang123/CCL26-RAME.
Chinese Translation
本文介绍了我们在CCL2026-Eval任务5：小粒度育种信息提取（MGBIE）中的系统，该系统共同从小粒度育种文献中提取12种实体类型和6种关系类型。我们提出了RAME（检索增强的多提示集合），这是一个无训练的框架，能够在受控多样性下引导多个大型语言模型（LLM）输出，并通过多数投票聚合它们以获得高置信度的预测。RAME结合了（i）通过混合BM25-嵌入检索器进行的检索增强的少量选择，（ii）涵盖从精确到召回范围的三提示集合（严格、放松、平衡），以及（iii）大规模重复采样与多数投票以过滤噪声预测。基于DeepSeek-V4-Flash，RAME在排行榜上获得了0.499的总分（命名实体识别NER 0.730，关系提取RE 0.346），排名第一，超越了由GPT-5.5（0.448）驱动的官方Track-A基线，代表了11.4%的相对提升。代码可在https://github.com/king-wang123/CCL26-RAME获取。
cs.CL / 75 / 2609.07151
Vishing-Tactics-Bench: Forecasting Exploitation Trajectories in Voice Phishing Calls
Vishing-Tactics-Bench：预测语音钓鱼电话中的利用轨迹
Abstract
Voice phishing (vishing) unfolds in real time; by the time a call has ended and post-hoc classification is possible, the harm has already been done. The more actionable question is which concrete harm (Information Gathering or Financial Exploitation) an ongoing call is tactically progressing toward. We present Vishing-Tactics-Bench, a benchmark grounded in Endsley's situation-awareness (SA) framework that recasts vishing defense from after-the-fact fraud classification to harm projection: predicting at each turn whether the call will reach either terminal harm. We adapt MITRE ATT&CK to vishing as a 6-tactic taxonomy (Vishing-Tactics) and label 35,340 scammer utterances across 5,645 synthetic Chinese calls. We define Exploitation Trajectory Forecasting, a survival-style protocol over the two terminal harms with three metrics: AP@k, C-index, and divergence error. Baselines ranging from a Markov heuristic to fine-tuned LLMs show that the tactical trajectory serves as an interpretable representation of the call's tactical state, supporting harm-specific forecasting, which can then be used for the downstream application of intervention selection; a stratified lead-time analysis at a tight false-alarm budget further identifies at what point in a call the trajectory signal yields early warning.
Chinese Translation
语音钓鱼（vishing）是实时展开的；当电话结束并且事后分类成为可能时，伤害已经造成。更具可操作性的问题是，正在进行的电话在战术上正朝着哪种具体的伤害（信息收集或财务利用）发展。我们提出了Vishing-Tactics-Bench，这是一个基于Endsley的情境意识（SA）框架的基准，将语音钓鱼防御从事后欺诈分类转变为伤害预测：在每个环节预测电话是否将达到任一终端伤害。我们将MITRE ATT&CK适配到语音钓鱼，形成一个包含6个战术的分类法（Vishing-Tactics），并对5,645个合成中文电话中的35,340条诈骗者发言进行了标注。我们定义了利用轨迹预测，这是一种针对两种终端伤害的生存风格协议，包含三个指标：AP@k、C-index和发散误差。从马尔可夫启发式到微调的LLMs的基线表明，战术轨迹作为电话战术状态的可解释表示，支持特定伤害的预测，这可以用于后续的干预选择应用；在严格的误报预算下的分层提前时间分析进一步识别出在电话的哪个时刻轨迹信号提供了早期预警。
cs.CL / 76 / 2609.07153
FreqBLiMP: Frequency-Controlled Minimal Pairs Reveal Robustness and Fragility of LLMs Under Lexical Rarity
FreqBLiMP：频率控制的最小对比揭示了大型语言模型在词汇稀缺下的稳健性与脆弱性
Abstract
Minimal-pair benchmarks such as BLiMP evaluate linguistic knowledge by testing whether language models (LMs) prefer acceptable sentences over minimally different unacceptable ones. However, these benchmarks largely ignore lexical frequency variation, despite lexical frequency being a pervasive and highly skewed property of natural language use. Consequently, existing evaluations do not test whether grammatical preferences remain stable when contrasts involve rare lexical items. We introduce FreqBLiMP, a frequency-controlled extension of BLiMP that regenerates all 67 paradigms under explicit Zipf-frequency regimes while preserving each minimal-pair's grammatical contrast. Evaluating multiple open-weight LLM families across scales, we find that decreasing lexical frequency produces a consistent, monotonic decrease in sentence likelihood, but only a modest reduction in overall contrastive acceptability accuracy. However, this aggregate stability masks substantial variation across linguistic phenomena, with LLMs remaining robust on overt morphosyntactic generalization while degrading on phenomena that require lemma-specific information.
Chinese Translation
最小对比基准（如 BLiMP）通过测试语言模型（LMs）是否更倾向于可接受的句子而非在最小差异下不可接受的句子来评估语言知识。然而，这些基准在很大程度上忽视了词汇频率的变化，尽管词汇频率是自然语言使用中普遍且高度偏斜的特性。因此，现有评估并未测试当对比涉及稀有词汇项时，语法偏好是否保持稳定。我们引入了 FreqBLiMP，这是 BLiMP 的一个频率控制扩展，在明确的 Zipf 频率范围内重新生成所有 67 个范式，同时保持每个最小对比的语法对比。对多个开放权重的 LLM 家族进行不同规模的评估，我们发现，降低词汇频率会导致句子可能性的一致单调下降，但对整体对比可接受性准确性的影响仅为适度。然而，这种整体稳定性掩盖了语言现象之间的显著差异，LLMs 在显性形态句法概括上保持稳健，而在需要词元特定信息的现象上则表现出退化。
cs.CL / 77 / 2609.07160
In-Place Instruction Following in Diffusion Language Models
扩散语言模型中的就地指令跟随
Abstract
Diffusion Large Language Models (dLLMs) generate text via bidirectional iterative denoising, naturally supporting user-specified constraints anchored at arbitrary output positions, a paradigm known as In-place Prompting (IPP). We formalize this as the In-place Instruction Following (IIF) task and construct IIF-Bench, a hierarchical benchmark spanning literal, style, and discourse-function constraints, paired with a rubric-based local-global evaluation protocol. An inference-time attention-bias probe suggests that vanilla dLLMs often under-prioritize constraint spans during denoising. We then propose GRAFT, an IPP-oriented post-training framework combining constraint-aware SFT and preference optimization. On four representative dLLMs, GRAFT raises the average IIF score from 57.75 to 73.10 (+15.35 points), with absolute gains of 15.91 and 15.57 points on literal and discourse-function constraints, while preserving general generation ability.
Chinese Translation
扩散大型语言模型（dLLMs）通过双向迭代去噪生成文本，自然支持锚定在任意输出位置的用户指定约束，这种范式被称为就地提示（In-place Prompting，IPP）。我们将其形式化为就地指令跟随（In-place Instruction Following，IIF）任务，并构建了IIF-Bench，这是一个涵盖字面、风格和话语功能约束的分层基准，配备基于评分标准的局部-全局评估协议。推理时的注意力偏差探测表明，普通的dLLMs在去噪过程中往往低估约束范围。随后，我们提出了GRAFT，这是一种面向IPP的后训练框架，结合了约束感知的SFT和偏好优化。在四个代表性的dLLMs上，GRAFT将平均IIF得分从57.75提高到73.10（+15.35分），在字面和话语功能约束上分别获得绝对增益15.91和15.57分，同时保持了整体生成能力。
cs.CL / 78 / 2609.07183
CircuitLens: Reasoning Circuits as Data Selection Signals for Reinforcement Learning with Verifiable Rewards
CircuitLens：将推理电路作为具有可验证奖励的强化学习的数据选择信号
Abstract
Reinforcement learning with verifiable rewards (RLVR) is sensitive to which problems a model trains on, yet existing selection criteria--difficulty filtering, hand-curation, reward-trajectory scoring--assess data value as an intrinsic property of problems, independent of the model that will learn from them. We introduce Circuit Reasoning Score (CRS), a selection signal derived from 46 reasoning-sensitive attention heads identified via contrastive ablation, computed in a single forward pass on the frozen base model without reward labels or rollouts. CRS runs against the intuitive hypothesis that stronger reasoning-circuit engagement produces better training data: on Qwen2.5-Math-7B, the lowest-engagement decile improves over random selection on three medium-difficulty benchmarks (GSM8K +2.0 pp, OlympiadBench +1.6 pp, Minerva +2.9 pp), while the highest-engagement decile gains less and is indistinguishable from the middle decile. The advantage has boundary conditions: on a domain-curated pool no selection method separates from the others; at 1.5B scale the useful direction differs; and the lowest-reward training condition produces the strongest downstream generalization. Within the Qwen2.5-Math settings tested, RLVR data selection appears regime-dependent rather than reducible to a static ranking of problem quality.
Chinese Translation
具有可验证奖励的强化学习（RLVR）对模型训练的问题非常敏感，但现有的选择标准——难度过滤、人工筛选、奖励轨迹评分——将数据价值评估为问题的内在属性，与将从中学习的模型无关。我们引入了推理电路评分（CRS），这是一种选择信号，源自通过对比消融法识别的46个对推理敏感的注意力头，在冻结的基础模型上通过单次前向传播计算得出，无需奖励标签或回放。CRS与直观假设相悖，即更强的推理电路参与会产生更好的训练数据：在Qwen2.5-Math-7B上，最低参与度的十分位在三个中等难度基准（GSM8K +2.0 pp，OlympiadBench +1.6 pp，Minerva +2.9 pp）上优于随机选择，而最高参与度的十分位增益较小，且与中间十分位无明显区别。该优势具有边界条件：在领域策划的池中，没有选择方法能够与其他方法区分；在1.5B规模下，有效方向不同；而最低奖励的训练条件产生了最强的下游泛化。在测试的Qwen2.5-Math设置中，RLVR数据选择似乎依赖于特定的环境，而不能简化为问题质量的静态排名。
cs.CL / 79 / 2609.07185
SIFTING: A Novel LLM-Based Framework for Structured and Transparent Information Extraction from Clinical Free-Text Reports, with Application to Tumor Staging in Lung Cancer
SIFTING：一种基于大语言模型的框架，用于从临床自由文本报告中提取结构化和透明的信息，应用于肺癌的肿瘤分期
Abstract
Background: Large language models (LLMs) show promise for extracting information from clinical free-text documents, but their outputs are often unstructured and lack traceability, complicating validation and adoption in clinical workflows. In this work we introduce SIFTING, an LLM-based framework designed to address these shortcomings. Methods: SIFTING combines the language comprehension capabilities of LLMs with segment-level processing and structured prompts with strict output control, linking findings to the source text to enable both accurate and transparent information extraction. To demonstrate its capabilities, we applied the framework to the task of extracting tumor T-stage information from 130 lung cancer radiology reports (SIFTING-T-stage). A compact 4-bit quantized version of the open-source LLM Llama-3.3-70B (35 GB) was used in a fully self-hosted setup, providing full control over data and model. Performance was evaluated against a reference standard created by four clinical experts and compared with a range of LLMs as used in a conventional single-prompt approach, using bootstrap resampling to estimate confidence intervals. Results: SIFTING-T-stage achieved an accuracy of 90% (95% CI: 84-95) against the reference standard. We found its performance to be comparable to even the largest state-of-the-art LLMs with reasoning capabilities and to be interchangeable with clinical experts (p < 0.001), while at the same time offering full traceability through source text references. Conclusion: SIFTING enables accurate, structured, and traceable information extraction from clinical free-text documents. It ensures data control, reproducibility, and verifiable outputs that can support clinical validation and workflow integration.
Chinese Translation
背景：大型语言模型（LLMs）在从临床自由文本文件中提取信息方面展现出良好的前景，但其输出往往缺乏结构性和可追溯性， complicating validation and adoption in clinical workflows. 本研究介绍了SIFTING，这是一种旨在解决这些缺陷的基于LLM的框架。方法：SIFTING结合了LLM的语言理解能力与段落级处理和严格的输出控制结构化提示，将发现与源文本相连接，以实现准确且透明的信息提取。为了展示其能力，我们将该框架应用于从130份肺癌放射学报告中提取肿瘤T期信息的任务（SIFTING-T-stage）。在完全自托管的设置中使用了开源LLM Llama-3.3-70B（35 GB）的紧凑4位量化版本，提供了对数据和模型的完全控制。性能评估是基于四位临床专家创建的参考标准，并与使用传统单提示方法的多种LLM进行了比较，采用自助重抽样法估计置信区间。结果：SIFTING-T-stage在参考标准下达到了90%的准确率（95% CI：84-95）。我们发现其性能与即使是最大的最先进的LLM的推理能力相当，并且与临床专家的表现可互换（p < 0.001），同时通过源文本引用提供了完全的可追溯性。结论：SIFTING能够从临床自由文本文件中实现准确、结构化和可追溯的信息提取。它确保了数据控制、可重复性和可验证的输出，能够支持临床验证和工作流程集成。
cs.CL / 80 / 2609.07237
CEDAR: Error-Bounded Residual Routing for Efficient Long-Context Attention
CEDAR：用于高效长上下文注意力的误差界限残差路由
Abstract
Post-hoc sparse attention accelerates long-context prefill by routing each query to a small set of token-level interactions. Hard selection, however, assigns zero probability to every omitted chunk: a routing miss cannot be recovered, and a fixed expansion budget spends the same work on easy and ambiguous queries. We introduce Coarse-to-fine Error-aware Dynamic Attention Routing (CEDAR), a coarse-to-fine method that keeps the language model frozen while preserving global coverage. Each semantic chunk contributes a cheap key--value summary to a residual attention path; chunks with high estimated approximation error are then expanded to exact token attention. Exact and summarized contributions are combined in a single softmax normalization, so refinement replaces, rather than duplicates, coarse evidence. We derive an output-error bound governed by within-chunk key/value dispersion and use it to allocate a variable refinement budget. A controlled clustered-attention study shows that residual summaries reduce reconstruction error by more than 98% relative to hard dropping at equal exact-chunk budgets. Experiments on long-context benchmarks demonstrate that CEDAR recovers most of the quality lost by hard sparse routing while maintaining approximately $3\times$ kernel speedup at 128K context.
Chinese Translation
事后稀疏注意力通过将每个查询路由到一小组令牌级交互来加速长上下文的预填充。然而，硬选择对每个被省略的块分配零概率：路由失误无法恢复，固定的扩展预算在简单和模糊查询上花费相同的工作。我们引入了粗到细的误差感知动态注意力路由（CEDAR），这是一种在保持语言模型不变的同时保留全局覆盖的粗到细方法。每个语义块为残差注意力路径贡献一个廉价的键-值摘要；然后，对估计的高近似误差的块进行扩展，以实现精确的令牌注意力。精确和摘要贡献在单一的 softmax 归一化中结合，因此细化替代而不是重复粗略证据。我们推导出一个输出误差界限，该界限由块内键/值的离散程度决定，并利用它来分配可变的细化预算。受控的聚类注意力研究表明，残差摘要在相同的精确块预算下，相较于硬丢弃减少了超过 98% 的重构误差。在长上下文基准测试中的实验表明，CEDAR 恢复了大部分因硬稀疏路由而丧失的质量，同时在 128K 上下文下保持约 $3 imes$ 的内核加速。
cs.CL / 81 / 2609.07282
Separating Stream Stability from Long-Term Recall in Language Models
将流稳定性与语言模型中的长期回忆分离
Abstract
Methods for streaming language models are often discussed alongside long-context and memory systems, although they solve different problems. An attention sink can stabilize autoregressive generation over an indefinitely long stream while the model remains unable to use content that has left its recent-token cache. We argue that this distinction should be explicit in system claims and evaluation. We introduce three horizons: the stability horizon, over which predictive behavior remains well behaved; the access horizon, over which past content can still causally affect the output; and the utility horizon, over which a task retains acceptable performance. We show constructively that the stability horizon can be infinite while the access and utility horizons are finite. We then propose ThreeH, an evaluation contract that measures all three horizons under a common state and compute budget. Applying the framework to attention-sink streaming clarifies its strength, constant-memory, stable generation, without treating anchor tokens as semantic memory. The framework exposes roles for cache policies, recurrent state, retrieval, and external memory. Experiments on 128K-token streams, delayed binding recall, and delayed decisions show that attention sinks preserve local modeling but not content beyond the active cache; recurrent and retrieval state extend the semantic horizon.
Chinese Translation
流式语言模型的方法通常与长上下文和记忆系统一起讨论，尽管它们解决的是不同的问题。注意力汇聚可以在无限长的流中稳定自回归生成，而模型仍然无法使用已离开其近期令牌缓存的内容。我们认为，这一区别应该在系统声明和评估中明确。我们引入了三个视野：稳定视野，在此视野内预测行为保持良好；访问视野，在此视野内过去的内容仍然可以因果地影响输出；以及效用视野，在此视野内任务保持可接受的性能。我们建设性地展示了稳定视野可以是无限的，而访问和效用视野是有限的。随后，我们提出了ThreeH，一个评估合同，在共同的状态和计算预算下测量这三个视野。将该框架应用于注意力汇聚流式生成，明确了其优势，即恒定内存、稳定生成，而不将锚定令牌视为语义记忆。该框架揭示了缓存策略、递归状态、检索和外部记忆的角色。在128K令牌流、延迟绑定回忆和延迟决策的实验中，结果表明注意力汇聚保持了局部建模，但不包括超出活动缓存的内容；递归和检索状态扩展了语义视野。
cs.CL / 82 / 2609.07296
Probing the Structure and Dynamics of LLM Value Expression through Value Conflicts
通过价值冲突探究大型语言模型价值表达的结构与动态
Abstract
Ethical evaluation of Large Language Models (LLMs) often characterizes model values as static and monolithic. In contrast, we argue that LLM value expression is better understood as a structured yet dynamic phenomenon. To investigate this, we introduce Conflict-driven Value Probing, a controlled framework that places LLMs in value conflicts and implements four types of interventions that perturb these conflicts to probe LLM value expression. Applying this framework to ten LLMs, we identify three recurring patterns. (1) Expression duality: models shift from broad idealistic orientations in abstract assessment toward more pragmatic priorities in concrete conflicts. (2) Functional steerability: models readily reconfigure their expressed value profiles toward task-defined value objectives. (3) Bounded plasticity: such reconfiguration is not without constraints, i.e. pressure induces a security- and goal-oriented priority shift while negative framing distinguishes protected values from those more amenable to redirection. Together, these findings characterize both the structure and dynamics of LLM value expression: context flexibly reconfigures expressed priorities, yet within behavioral boundaries. This behavioral account provides a foundation for understanding controllability, alignment, and safety in LLMs. Code and data are available at https://github.com/ZeroGen-Lab/CFProbe.
Chinese Translation
对大型语言模型（LLMs）的伦理评估通常将模型价值视为静态和单一的。相反，我们认为LLM的价值表达更应被理解为一种结构化但动态的现象。为此，我们引入了基于冲突驱动的价值探测（Conflict-driven Value Probing），这是一个控制框架，将LLMs置于价值冲突中，并实施四种干预措施以扰动这些冲突，从而探测LLM的价值表达。将该框架应用于十个LLM，我们识别出三种反复出现的模式。（1）表达的二元性：模型在抽象评估中从广泛的理想主义取向转向在具体冲突中的更务实的优先事项。（2）功能可操控性：模型能够根据任务定义的价值目标重新配置其表达的价值特征。（3）有限的可塑性：这种重新配置并非没有约束，即压力会导致安全和目标导向的优先事项转变，而负面框架则将受保护的价值与更易于重新引导的价值区分开来。这些发现共同描绘了LLM价值表达的结构和动态：上下文灵活地重新配置表达的优先事项，但仍在行为边界内。这种行为解释为理解LLM的可控性、对齐性和安全性提供了基础。代码和数据可在 https://github.com/ZeroGen-Lab/CFProbe 获取。
cs.CL / 83 / 2609.07305
Marginal Fidelity Does Not Establish User Simulation in Demographic Synthetic Survey Panels: Response Contracts, Support Collapse and Conditioning Failure
边际忠诚度并未确立人口合成调查面板中的用户模拟：响应契约、支持崩溃与条件失效
Abstract
Demographic synthetic survey panels are often validated by matching aggregate answers to published surveys. We test what that certificate establishes across six multiselect batteries from four survey organisations in three countries. The headline analysis is restricted to three instruments whose synthetic cohort and human target share the stated population frame; three other batteries remain sensitivity analyses. The response contract dominates measured fidelity. In the aligned instruments, committed sets leave 66 of 128 model-battery option slots empty in panels of up to 500 respondents, versus 0 of 128 under per-option probability elicitation. Across eight uncapped model-instrument comparisons, probabilities reduce option-marginal MAE by 4.53 to 7.30 points. The capped instrument reverses on two models until the vectors are projected onto its stated maximum. These are measurement effects: human targets are realised check-all responses, whereas the vectors are latent inclusion propensities. Published marginal agreement also fails to discriminate respondent simulation from direct population estimation. On nine aligned model-battery pairs, a no-persona population-prevalence query averages 6.27 MAE versus 12.39 for committed panels and wins all nine comparisons. Constraint-aware probability vectors average 5.34 and beat the query on four of nine, so the baseline challenges the validation criterion rather than proving direct estimation uniformly best. On three unpublished demographic cells, neither approach beats reciting the national distribution. Population-marginal agreement is therefore evidence about an elicitation contract and an estimand obtainable without simulated respondents, not evidence of individual simulation.
Chinese Translation
人口合成调查面板通常通过将汇总答案与已发布调查进行匹配来进行验证。我们测试了这一证书在三个国家四个调查组织的六个多选电池中的建立情况。主要分析限制在三个工具上，这些工具的合成队列与人类目标共享所述的人口框架；另外三个电池则作为敏感性分析。响应契约主导了测量的忠诚度。在对齐的工具中，承诺集在最多500名受访者的面板中留下128个模型电池选项槽中的66个为空，而在每个选项的概率引导下则为0个。在八个不受限制的模型工具比较中，概率将选项边际平均绝对误差（MAE）降低了4.53到7.30点。受限工具在两个模型上反转，直到向其所述最大值投影。这些是测量效应：人类目标是实现的全选响应，而向量是潜在的包含倾向。已发布的边际一致性也未能区分受访者模拟与直接人口估计。在九对对齐的模型电池中，无人称人口流行查询的平均MAE为6.27，而承诺面板为12.39，并在所有九个比较中获胜。考虑约束的概率向量平均为5.34，并在九个中有四个超过了查询，因此基线挑战了验证标准，而不是证明直接估计始终最佳。在三个未发布的人口单元中，任何方法都未能超越国家分布的复述。因此，人口边际一致性是关于引导契约和可在没有模拟受访者的情况下获得的估计量的证据，而不是个体模拟的证据。
cs.CL / 84 / 2609.07306
RouteRelay: Event-Triggered Cross-Layer Route Reuse for Efficient Dynamic Sparse Attention
RouteRelay：事件触发的跨层路由重用以实现高效动态稀疏注意力
Abstract
Dynamic sparse attention reduces long-context prefill cost by routing each query chunk to a small set of key chunks at every Transformer layer. The sparse attention kernel avoids most token interactions, but the router still rebuilds a chunk--chunk score matrix layer after layer, even when the selected routes change little. We introduce RouteRelay, a router-agnostic method that reuses only route metadata across depth while continuing to compute attention with the current layer's queries, keys, and values. Anchor layers perform full routing. Intermediate layers rescore the previous top-$k$ route and a compact sentinel set of near-miss and randomly probed chunks. A query row is rerouted only when a sentinel challenges its weakest selected chunk. We give a top-$k$ stability condition, a probabilistic bound on missed challengers, and a row-selective GPU execution design. In a reproducible empirical evaluation, RouteRelay retains at least 99.99% route recall while rerouting 25.0%, 55.4%, and 78.2% of rows under low, moderate, and high cross-layer drift, respectively. Across routing scales, RouteRelay retains 100.0% recall while evaluating 38.4--51.6% of full-routing score pairs as the key-chunk count grows from 128 to 1024. Its unfused CPU execution remains slower than dense matrix multiplication, exposing row compaction and ledger updates as the main kernel-engineering targets.
Chinese Translation
动态稀疏注意力通过在每个Transformer层将每个查询块路由到一小组关键块，从而减少长上下文的预填充成本。稀疏注意力内核避免了大多数标记交互，但路由器仍然在每一层重建块-块得分矩阵，即使所选路由变化很小。我们提出了RouteRelay，这是一种路由器无关的方法，它仅在深度上重用路由元数据，同时继续使用当前层的查询、键和值进行注意力计算。锚定层执行完整路由。中间层对之前的前$k$路由和一组紧凑的哨兵集（包括近失和随机探测的块）进行重新评分。只有当哨兵挑战其最弱的选定块时，查询行才会被重新路由。我们给出了一个前$k$稳定性条件、对错过挑战者的概率界限，以及一种行选择的GPU执行设计。在可重复的实证评估中，RouteRelay在低、中、高跨层漂移下，分别重新路由25.0%、55.4%和78.2%的行，同时保持至少99.99%的路由召回率。在不同的路由规模下，RouteRelay在关键块数量从128增长到1024时，保持100.0%的召回率，同时评估38.4%至51.6%的完整路由得分对。其未融合的CPU执行速度仍慢于稠密矩阵乘法，暴露出行压缩和账本更新作为主要的内核工程目标。
cs.CL / 85 / 2609.07307
SPARROW: Scalable Taxonomy Induction via Structure-Preserving Partitioning and Constraint-Guided Merging
SPARROW：通过结构保持分区和约束引导合并实现可扩展的分类法生成
Abstract
Taxonomy induction aims to organize concept sets into coherent hierarchical structures. Recent LLM-based methods can induce taxonomies directly from flat term lists, avoiding the need for corpora, but degrade sharply as concept sets scale up. We argue that this degradation stems not only from context length limitations, but also from structural failures in hierarchical reasoning. To address this, we adopt a divide-and-merge paradigm that partitions concepts into smaller subsets, induces local taxonomies, and merges them into a global hierarchy. However, we identify two structural failure modes inherent to this paradigm: Structural Fragmentation, where partitioning weakens local hierarchical signals, and Parent Displacement, where locally plausible relations are misplaced in the global hierarchy. To address both, we propose SPARROW, a scalable taxonomy induction framework that combines structure-preserving spectral partitioning to retain hierarchical connectivity within each block, and constraint-guided incremental fusion that treats block-level relations as structural constraints rather than ground truth for global placement. Experiments on large-scale benchmarks show that SPARROW consistently achieves the strongest global structural quality across backbones. The code is available at https://github.com/rebeccazyr/SPARROW.
Chinese Translation
分类法生成旨在将概念集合组织成连贯的层次结构。最近基于大型语言模型（LLM）的方法可以直接从平面术语列表中生成分类法，避免了对语料库的需求，但随着概念集合规模的扩大，其性能急剧下降。我们认为这种下降不仅源于上下文长度的限制，还源于层次推理中的结构性失败。为了解决这个问题，我们采用了一种分而治之的范式，将概念划分为较小的子集，生成局部分类法，并将其合并为一个全球层次结构。然而，我们识别出这一范式固有的两种结构性失败模式：结构碎片化（Structural Fragmentation），即分区削弱了局部层次信号；以及父级位移（Parent Displacement），即局部合理关系在全球层次中被错误放置。为了解决这两者，我们提出了SPARROW，一个可扩展的分类法生成框架，结合了结构保持的谱分区，以保持每个块内的层次连接性，以及约束引导的增量融合，将块级关系视为结构约束，而非全球放置的真实依据。在大规模基准测试中的实验表明，SPARROW在各个基础模型上始终实现了最强的全球结构质量。代码可在 https://github.com/rebeccazyr/SPARROW 获取。
cs.CL / 86 / 2609.07309
LANTERN: Language Model Assessment on Noisy and Transformed Tasks for Understanding Error and Robustness Nuances
LANTERN：对噪声和变换任务的语言模型评估以理解错误和鲁棒性细微差别
Abstract
Robustness evaluation of large language models (LLMs) remains a critical challenge, particularly in assessing their sensitivity to perturbations in input data. In this work, we systematically evaluate LLM robustness across multiple dimensions, including word error rate, character repetition and duplication, modifications in choices, and variability in instruction following. To facilitate this evaluation, we construct a synthetic and augmented dataset encompassing a diverse set of LLM benchmarks, specifically targeting multiple-choice question (MCQ) datasets and instruction-following tasks. We conduct extensive experiments on LLMs of varying scales-small, medium, and large-as well as across base and instruction-tuned variants. Our analysis quantifies the variability in model responses under perturbed conditions and highlights discrepancies relative to baseline models. The findings provide insights into the stability of LLMs across different evaluation scenarios contributing to the development of more robust and reliable language models as well as robust evaluation methodologies.
Chinese Translation
大型语言模型（LLMs）的鲁棒性评估仍然是一个关键挑战，特别是在评估它们对输入数据扰动的敏感性方面。在本研究中，我们系统地评估了LLM的鲁棒性，涵盖多个维度，包括词错误率、字符重复和复制、选择的修改以及遵循指令的变异性。为了促进这一评估，我们构建了一个合成和增强的数据集，涵盖了一系列多样化的LLM基准，特别针对多项选择题（MCQ）数据集和遵循指令的任务。我们对不同规模的LLM进行了广泛的实验，包括小型、中型和大型模型，以及基础和指令调优变体。我们的分析量化了在扰动条件下模型响应的变异性，并突出显示了与基线模型的差异。研究结果为不同评估场景下LLM的稳定性提供了见解，有助于开发更鲁棒和可靠的语言模型以及鲁棒的评估方法。
cs.CL / 87 / 2609.07314
Content-Based Addressing for Long Context
基于内容的长上下文寻址
Abstract
Rotary position embedding (RoPE) uses each token's integer position to determine the rotation applied inside attention. This works well for local token order, but increasing context length creates a positional train-test mismatch: RoPE produces relative rotations at offsets not seen during training. Methods that rescale, interpolate, randomize, or bias positions specify how attention handles those offsets, but still derive positional information from a growing token counter. We instead divide a token stream into units, retain ordinary RoPE positions within each unit, and assign every completed unit an address computed from its content. Adding units then applies the same learned map to new content rather than extending a positional range or an identifier table. We prove that this construction preserves local RoPE exactly, leaves the attention comparison between two fixed tokens unchanged when other units are inserted or reordered, and does not create new relative rotations merely because more units are added. In a character-level Tiny Shakespeare diagnostic, a model trained on 256-character contexts has validation perplexity 4.04 at 256 characters and 3.82 at 4096, while continuous RoPE changes from 4.71 to 12.09. A second diagnostic shows that content-based addressing can retrieve and use information from multiple serialized facts. These are controlled shallow experiments, not scale benchmarks, but they support a direct prescription: use position to address locally and content to address across units.
Chinese Translation
旋转位置嵌入（Rotary position embedding, RoPE）利用每个标记的整数位置来确定在注意力机制中应用的旋转。这在局部标记顺序中效果良好，但随着上下文长度的增加，产生了位置训练-测试不匹配的问题：RoPE在训练期间未见的偏移量上生成相对旋转。重新缩放、插值、随机化或偏置位置的方法指定了注意力如何处理这些偏移量，但仍然是从不断增长的标记计数器中推导位置的信息。我们则将标记流划分为单元，在每个单元内保留普通的RoPE位置，并为每个完成的单元分配一个根据其内容计算的地址。添加单元时，应用相同的学习映射到新内容，而不是扩展位置范围或标识符表。我们证明这种构造精确保留了局部RoPE，在插入或重新排序其他单元时，两个固定标记之间的注意力比较保持不变，并且仅仅因为添加了更多单元并不会产生新的相对旋转。在一个字符级的Tiny Shakespeare诊断中，训练在256个字符上下文上的模型在256个字符时的验证困惑度为4.04，在4096个字符时为3.82，而连续的RoPE则从4.71变为12.09。第二个诊断表明，基于内容的寻址可以从多个串行事实中检索和使用信息。这些是受控的浅层实验，而非规模基准，但它们支持一个直接的建议：使用位置进行局部寻址，使用内容进行跨单元寻址。
cs.CL / 88 / 2609.07362
BlueprintAgent: Constraint-Triggered Targeted Revisits for Simulation-Ready Generation from Scanned Structural Blueprints
BlueprintAgent：基于约束触发的针对性重访以实现从扫描结构蓝图生成可用于仿真的模型
Abstract
Converting in-service reinforced-concrete (RC) building blueprints into simulation-ready models---structured frame representations that support deterministic FEM export and qualified-engineer review---underpins safety assessment and seismic retrofit, but the process remains manual. Direct prompting of a multimodal large language model (MLLM) over a scanned sheet is unreliable: outputs often violate engineering constraints on beam--column support, span count, or 3D continuity. We present BlueprintAgent (BPA), a constraint-triggered multimodal agent for simulation-ready frame extraction from scanned blueprints. BPA treats the MLLM as the primary reader and decision maker, with OCR and computer vision supplying localized evidence. Its central mechanism realizes engineering constraints as callable validators whose entity-level conflict reports trigger targeted MLLM revisits over the local region---an inference-time control distinct from fixed pipelines and free-form self-reflection. We evaluate BPA on 300 real scanned blueprint sheets from 20 anonymized RC frame projects, against five baselines and six ablations. BPA reaches a macro-averaged Beam F1 of 0.994, against 0.301 for single-MLLM zero-shot and 0.820 for a fixed pipeline; removing MLLM-led axis adjudication collapses Beam and Column F1 on complex multi-sheet projects. For dense technical drawings, engineering constraints are best deployed as triggers for entity-level targeted revisits rather than as post-hoc output filters.
Chinese Translation
将在用的钢筋混凝土（RC）建筑蓝图转换为可用于仿真的模型——结构框架表示，支持确定性有限元法（FEM）导出和合格工程师审查——是安全评估和抗震改造的基础，但这一过程仍然是手动的。对扫描图纸直接提示多模态大型语言模型（MLLM）是不可靠的：输出往往违反了关于梁-柱支撑、跨数或三维连续性的工程约束。我们提出了BlueprintAgent（BPA），一种基于约束触发的多模态代理，用于从扫描蓝图中提取可用于仿真的框架。BPA将MLLM视为主要的阅读者和决策者，而光学字符识别（OCR）和计算机视觉则提供局部证据。其核心机制将工程约束实现为可调用的验证器，其实体级冲突报告触发针对性MLLM重访局部区域——这一推理时控制与固定管道和自由形式自我反思不同。我们在20个匿名RC框架项目的300张真实扫描蓝图上评估了BPA，与五个基线和六个消融实验进行比较。BPA的宏观平均梁F1达到了0.994，而单一MLLM零-shot为0.301，固定管道为0.820；去除MLLM主导的轴裁决会导致复杂多图项目的梁和柱F1崩溃。对于密集的技术图纸，工程约束最好作为实体级针对性重访的触发器，而不是事后输出过滤器。
cs.CL / 89 / 2609.07370
Beyond Fluent Generation: A CPU Reliability Benchmark for MCP-Style Tool Calling in Sub-2B Small Language Models for Edge Deployment
超越流畅生成：针对边缘部署中小于20亿参数的MCP风格工具调用的CPU可靠性基准
Abstract
Resource constrained single-board computers including Raspberry Pi, NVIDIA Jetson Nano, Arduino UNO Q, Orange Pi, and LattePanda motivate on-device small language model (SLM) agents that reduce cloud dependence, improve data locality, and tolerate intermittent connectivity. Model Context Protocol (MCP)-style tool invocation demands more than fluent generation: an agent must emit machine-readable JSON, select the correct tool, supply all required arguments, and avoid unintended actions. We establish a platform-agnostic CPU baseline by evaluating five open-weight models below two billion parameters Phi-1.5, Pythia-1.4B, TinyLlama-1.1B-Chat, Qwen2.5-0.5B, and Qwen2.5-1.5B on 100 prompts spanning weather retrieval, web search, calculation, email composition, and task creation, under greedy decoding and nucleus sampling. A recovery parser strips Markdown fences, extracts brace-delimited substrings, and scores parseability, tool-name correctness, argument completeness, and value agreement. Under this criterion, Qwen2.5-1.5B achieves 75% (greedy) and 79% (sampling); Qwen2.5-0.5B achieves 72% (greedy) but drops to 32% under sampling. Phi-1.5 scores 0%; Pythia and TinyLlama reach at most 7%. A strict post-hoc audit finds only 5 of 1,000 raw responses directly parseable as JSON, exposing near-total dependence on output recovery. A CPU resource probe shows Qwen2.5-1.5B requires 7,960 MiB and 30.782 s mean latency; Qwen2.5-0.5B uses 3,637 MiB and 10.627 s, revealing a reliability-resource trade-off for edge deployment. These results do not cover the named boards directly or a full MCP implementation. Safe deployment requires schema validation, constrained generation, least-privilege execution, and human escalation for consequential actions.
Chinese Translation
资源受限的单板计算机，包括Raspberry Pi、NVIDIA Jetson Nano、Arduino UNO Q、Orange Pi和LattePanda，促使了在设备上运行的小型语言模型（SLM）代理的开发，这些代理减少了对云的依赖，提高了数据本地性，并能够容忍间歇性连接。模型上下文协议（MCP）风格的工具调用不仅仅要求流畅生成：代理必须输出机器可读的JSON，选择正确的工具，提供所有必需的参数，并避免意外操作。我们通过评估五个参数少于20亿的开放权重模型（Phi-1.5、Pythia-1.4B、TinyLlama-1.1B-Chat、Qwen2.5-0.5B和Qwen2.5-1.5B）在100个提示下的表现，建立了一个平台无关的CPU基准，这些提示涵盖了天气检索、网络搜索、计算、电子邮件撰写和任务创建，采用贪婪解码和核采样。恢复解析器去除Markdown围栏，提取大括号分隔的子字符串，并对可解析性、工具名称正确性、参数完整性和数值一致性进行评分。在这一标准下，Qwen2.5-1.5B的得分为75%（贪婪）和79%（采样）；Qwen2.5-0.5B的得分为72%（贪婪），但在采样下降至32%。Phi-1.5的得分为0%；Pythia和TinyLlama的得分最多为7%。严格的事后审计发现，在1,000个原始响应中，只有5个可以直接解析为JSON，暴露了对输出恢复的几乎完全依赖。CPU资源探测显示，Qwen2.5-1.5B需要7,960 MiB和30.782秒的平均延迟；Qwen2.5-0.5B使用3,637 MiB和10.627秒，揭示了边缘部署中的可靠性与资源之间的权衡。这些结果并未直接涵盖所述的单板计算机或完整的MCP实现。安全部署需要模式验证、受限生成、最小权限执行以及对重要操作的人为升级。
cs.CL / 90 / 2609.07379
Beyond Single-Negative Preference: Multi-Negative DPO for LLM-Centric Historical Entity Linking
超越单一负偏好：面向大型语言模型的多负向直接偏好优化用于历史实体链接
Abstract
Large language models (LLMs) have recently shown promise for historical entity linking, but preference optimization for this task is often formulated with only one negative candidate per training instance. This discards information from the remaining candidates retrieved for the same mention. We introduce multi-negative direct preference optimisation (MDPO), a reference-based pairwise objective that compares the correct entity with all valid rejected candidates associated with each mention. MDPO preserves the Bradley-Terry formulation of DPO while exploiting the complete candidate set through masked, length-normalised sequence scores. We evaluate MDPO on hipe-2020 and newseye, covering French, German, English, Swedish, and Finnish historical newspaper text. Experiments show that MDPO improves over supervised fine-tuning and single-negative DPO, with particularly strong gains for NIL mentions, semantic ambiguity, OCR noise, and historically difficult names. Further analyses disentangle candidate-generation and selection errors, showing that candidate retrieval remains a key bottleneck for end-to-end entity linking. These results demonstrate that incorporating all within-instance negative candidates is a simple and effective improvement for LLM-based historical entity linking.
Chinese Translation
大型语言模型（LLMs）最近在历史实体链接方面展现出了潜力，但该任务的偏好优化通常仅在每个训练实例中使用一个负候选。这忽略了与同一提及相关的其余候选信息。我们提出了多负向直接偏好优化（MDPO），这是一种基于参考的成对目标，比较正确实体与每个提及相关的所有有效拒绝候选。MDPO 保留了 DPO 的 Bradley-Terry 公式，同时通过掩码和长度归一化的序列得分利用完整的候选集。我们在 hipe-2020 和 newseye 上评估了 MDPO，涵盖法语、德语、英语、瑞典语和芬兰语的历史报纸文本。实验表明，MDPO 在监督微调和单一负 DPO 上有所改善，尤其在 NIL 提及、语义模糊、OCR 噪声和历史上难以处理的名称方面取得了显著提升。进一步的分析解构了候选生成和选择错误，显示候选检索仍然是端到端实体链接的关键瓶颈。这些结果表明，纳入所有实例内的负候选是对基于 LLM 的历史实体链接的一个简单而有效的改进。
cs.CL / 91 / 2609.07447
An LLM-Associated Register Shift in Korean Journal Abstracts: A Morphology-Aware Excess-Vocabulary Study, 2018-2026
与大型语言模型相关的韩国期刊摘要中的注册转变：一种关注形态学的过量词汇研究，2018-2026
Abstract
Excess vocabulary, a word's frequency above its pre-2023 trend, is how the change in scholarly English after 2022 has been measured. We adapt it to Korean with morphological units on 398,296 KCI abstracts (2018-August 2026), with 47,165 Vietnamese abstracts for comparison. Placebo floors are 0.1-2.2 points for the single-word statistic and at most 2.9 for the re-selected split-half set statistic. Korean abstracts show nothing in 2023, onset in late 2024, a rise through 2025 flattening in mid-2026: sisahada "suggest" appears in 21.4% of 2026 abstracts against 5.3% expected; plain verbs like araboda "look into" fall to a quarter of trend. Under stated assumptions the single-word conditional lower bound on LLM-processed abstracts is 3.5%, 10.5% and 16.1% for 2024-2026 and a split-half set bound 7.8%, 20.6% and 33.0%. Holzwarth et al.'s estimator under the same discipline gives 41.9% and 72.1% for 2025-2026. Subject-matter controls reduce but do not remove it: restricting the set to lemmas three language-model annotators all call style leaves 14.7 of the 33.0 points, and pairing each 2026 abstract with its journal's closest base-period abstract leaves 34.1. Tested translation routes do not explain it: the surface marks of translated Korean fall as the markers rise. In the same articles' English abstracts the excess appears a year earlier; where the English side carries none, the Korean shift persists at 30 to 66% of the rate where it does. Control abstracts from three providers reproduce the rising words, with marker turnover consistent with model generations; implied prevalences are scenario-dependent.
Chinese Translation
过量词汇是指某个词汇在2023年前趋势基础上频率的增加，用以衡量2022年后学术英语的变化。我们将其适用于韩国，基于398,296篇KCI摘要（2018年-2026年8月），并与47,165篇越南语摘要进行比较。安慰剂底线为单词统计的0.1-2.2点，重新选择的分半集统计最多为2.9点。2023年的韩国摘要没有显示出任何变化，直到2024年底才开始出现，2025年逐渐上升，至2026年中期趋于平稳：sisahada（“建议”）在2026年的摘要中出现率为21.4%，而预期为5.3%；而普通动词如araboda（“调查”）的出现率降至趋势的四分之一。在所述假设下，LLM处理的摘要在2024-2026年的单词条件下限分别为3.5%、10.5%和16.1%，而分半集的下限为7.8%、20.6%和33.0%。Holzwarth等人在同一学科下的估计值为2025-2026年分别为41.9%和72.1%。主题控制减少但并未消除这一现象：将集合限制为三位语言模型注释者所称的风格的词根，仍保留了33.0点中的14.7点，而将每个2026年的摘要与其期刊的最接近基础期摘要配对，保留了34.1点。测试的翻译路径并未解释这一现象：翻译后的韩文表面标记随着标记的增加而减少。在同一篇文章的英文摘要中，过量现象出现得更早一年；在英文部分没有出现的情况下，韩文的变化率仍保持在30%到66%之间。来自三家提供商的控制摘要重现了上升的词汇，标记的更替与模型生成一致；隐含的流行率依赖于情境。
cs.CL / 92 / 2609.07448
FramingQA: Does the Question Shape the Answer? Measuring the Compositional Framing Effect
FramingQA：问题是否影响答案？测量组合框架效应
Abstract
We introduce FramingQA, a benchmark that measures the model sensitivity to question framing across law, medicine, finance, and robotic simulations. Large language models (LLMs) often change their responses to subtle rephrasings that align with an implied stance by users. This can leave users with advice tainted by how they happened to phrase a question rather than by the underlying facts, and the consequences are highly costly in high-stakes domains. Because in the realistic scenarios, both expert practitioners and non-expert users frequently ask LLMs questions containing incomplete or misleading assumptions, models are highly susceptible to those framings. To test this, we inject the framing bias across three nested levels: a framing-biased question phrasing (root), an injected framing-biased premise prepended to a neutral question (propositional), and a premise paired with a framing-biased question (global). Evaluating nine open models (3.8B-70B) across four families, we find that strong per-variant accuracy does not guarantee the robustness across differently phrased questions under the fixed factual information.
Chinese Translation
我们引入了FramingQA，一个基准测试，用于测量模型对法律、医学、金融和机器人模拟中问题框架的敏感性。大型语言模型（LLMs）通常会对与用户隐含立场一致的细微措辞变化做出不同的回应。这可能导致用户得到的建议受到他们提问方式的影响，而不是基于潜在事实，这在高风险领域的后果是非常严重的。在现实场景中，专家从业者和非专家用户经常向LLMs提出包含不完整或误导性假设的问题，因此模型对这些框架极为敏感。为了测试这一点，我们在三个嵌套层次中注入框架偏见：一种框架偏见的问题措辞（根本），一个附加在中性问题前的框架偏见前提（命题），以及与框架偏见问题配对的前提（全局）。在四个模型家族中评估九个开放模型（3.8B-70B），我们发现强大的每个变体准确性并不能保证在固定事实信息下对不同措辞问题的鲁棒性。
cs.CL / 93 / 2609.07471
MEMO: Multimodal Evidence Memory Organization for Long-Horizon LLM Agents
MEMO：用于长时间跨度大型语言模型代理的多模态证据记忆组织
Abstract
Long-running LLM agents rely on external memory to store and reuse information beyond a single context window, yet there is a fundamental tension between the continuous accumulation of interaction trajectories and the limited context capacity. The key challenge in agent memory is therefore not only to retrieve relevant records, but also to select necessary evidence under a given budget and organize it in an appropriate modality. Existing memory readout methods mainly use textual or visual forms. Text preserves high fidelity, but its linear token representation makes contents with different importance compete for the limited context at nearly uniform unit cost. Visual readout renders text into document-like images, which can use two-dimensional layouts to expose structure and emphasize key information, but it may lose fine-grained details during rendering and compression. To address this issue, we propose MEMO, a multimodal evidence memory organization method for LLM agents. MEMO first uses a trained evidence extractor to select relevant memory blocks and form evidence units with source information and presentation requirements. A trained query-conditioned memory manager assigns each unit to a textual, visual, or dual-channel carrier and selects a layout that matches the evidence structure. A deterministic memory construction module then generates the textual package and visual pages. The memory manager is trained with feedback from an offline reader that measures the utility of the guided memory plan, so that retention and presentation decisions align with downstream usage. We evaluate MEMO on four benchmarks, HotpotQA, 2WikiMultiHopQA, LoCoMo, and ALFWorld, with multiple reader backends. The results show that MEMO presents memory more efficiently with fewer memory tokens, improves downstream task performance, and builds more effective working memory under constrained budgets.
Chinese Translation
长期运行的大型语言模型（LLM）代理依赖外部记忆来存储和重用超出单一上下文窗口的信息，但交互轨迹的持续积累与有限的上下文容量之间存在根本矛盾。因此，代理记忆的关键挑战不仅在于检索相关记录，还在于在给定预算下选择必要的证据并以适当的模态进行组织。现有的记忆读取方法主要采用文本或视觉形式。文本保留了高保真度，但其线性标记表示使得不同重要性的内容以几乎均匀的单位成本竞争有限的上下文。视觉读取将文本渲染为类似文档的图像，可以利用二维布局来展示结构并强调关键信息，但在渲染和压缩过程中可能会丢失细粒度的细节。为了解决这个问题，我们提出了MEMO，一种用于LLM代理的多模态证据记忆组织方法。MEMO首先使用训练好的证据提取器选择相关的记忆块，并形成包含源信息和呈现要求的证据单元。经过训练的查询条件记忆管理器将每个单元分配给文本、视觉或双通道载体，并选择与证据结构匹配的布局。然后，确定性记忆构建模块生成文本包和视觉页面。记忆管理器通过离线阅读器的反馈进行训练，该阅读器测量引导记忆计划的效用，以便保留和呈现决策与下游使用保持一致。我们在四个基准上评估MEMO，分别是HotpotQA、2WikiMultiHopQA、LoCoMo和ALFWorld，使用多个阅读器后端。结果表明，MEMO以更少的记忆标记更有效地呈现记忆，改善下游任务性能，并在预算受限的情况下构建更有效的工作记忆。
cs.CL / 94 / 2609.07474
Where Should Language Sit in a Multimodal Model? Lessons from What Language Does to Human Perception and Cognition
语言在多模态模型中应处于何种位置？从语言对人类感知和认知的影响中得到的启示
Abstract
Language models compute over tokens: language is their input, their output, and increasingly their internal representation. Whether language should keep all of these positions depends on what language does to the system that uses it. The one system with a century of data on that question is the human. We review what language does to human perception, the brain, and thought, and read the same evidence against multimodal models and language models. Throughout, we treat language as a compressor that runs on a shared codebook: a word is an index, the content is in the receiver, and a community maintains the codebook. In humans the compression is measurable, learning the codebook reorganizes the senses, and thought survives the loss of language. We then measure the rule that models apply when two cues disagree, with cue-conflict experiments on six vision-language models and two robot policies. Surviving cues are weighted in the order their reliabilities prescribe, at 11 to 82\% of the ideal observer's slope, and many answers copy the text. One policy family drops a cue that adds no information beyond the others rather than down-weighting it, another keeps it at a weight that fails when the cues conflict, and a visual cue that identifies the task in every training frame is never learned, because the language pathway already fits the data. Language models are the best current models of the human language network, and they have entered the human speech community, shifting word frequencies while alignment narrows their conceptual diversity. We close with seven implications for token-based systems. Language belongs at a model's boundary and in the shared codebook, as in the brain, not as its internal representation; the price of leaving the codebook inside is auditability.
Chinese Translation
语言模型以标记为基础进行计算：语言是它们的输入、输出，越来越多地成为它们的内部表征。语言是否应保持这些角色取决于语言对使用它的系统的影响。唯一有一个世纪数据来探讨这个问题的系统是人类。我们回顾了语言对人类感知、大脑和思维的影响，并将相同的证据与多模态模型和语言模型进行对比。在整个过程中，我们将语言视为在共享代码本上运行的压缩器：一个词是一个索引，内容在接收者中，而一个社区维护着代码本。在人类中，压缩是可测量的，学习代码本会重新组织感官，而思维在失去语言的情况下依然存在。然后，我们测量模型在两个线索不一致时所应用的规则，通过对六个视觉-语言模型和两个机器人策略的线索冲突实验进行检验。存活的线索按照其可靠性所规定的顺序加权，达到理想观察者斜率的11%到82%，许多答案直接复制文本。一种策略家族会丢弃一个在其他线索之外没有提供额外信息的线索，而不是降低其权重，另一种则在线索冲突时保持其权重失败，而一个在每个训练帧中识别任务的视觉线索从未被学习，因为语言通路已经适应了数据。语言模型是当前人类语言网络的最佳模型，它们已进入人类言语社区，改变了词频，同时对齐缩小了它们的概念多样性。最后，我们提出了七个关于基于标记的系统的启示。语言应位于模型的边界和共享代码本中，正如在大脑中一样，而不是作为其内部表征；将代码本留在内部的代价是可审计性。
cs.CL / 95 / 2609.07549
Qwen-Audio-3.0-ASR Technical Report
Qwen-Audio-3.0-ASR 技术报告
Bian, Chuanmeng, Chen, Daren, Chen, Peixin, Chen, Zhigao, Fan, Zhiyun, Gao, Zhifu, Gong, Bo, Gu, Qing, He, Jiajun, Hu, Yawei, Ji, Yunjie, Li, Jingbei, Li, Xiangang, Li, Xu, Li, Zengxi, Li, Zheng, Liang, Chengdong, Liu, Baiji, Liu, Ying, Ma, Bin, Peng, Yiping, Peng, Yuezhang, Peng, Zhendong, Pu, Yu, Shi, Yang, Shu, Xin, Tang, Jian, Tian, Biao, Wang, Peiyao, Wang, Tianzi, Wang, Wen, Wang, Wupeng, Wen, Cheng, Wu, Yuzhong, Xia, Zijian, Xiao, Yunchong, Yang, Nan, Yu, Jianwei, Yu, Jixing, Zhang, Binbin, Zhang, Lei, Zhao, Sitong, Zhou, Guangdong, Zhou, Yuan, Zhuo, Jianheng
Abstract
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model scaling, and deep integration with large language models (LLMs). However, bridging the gap between academic benchmark performance and real-world production utility remains a persistent challenge, particularly in handling diverse regional dialects, dynamic entities and hotwords, long-range contextual information, and disfluent spontaneous speech. In this report, we present Qwen-Audio-3.0-ASR, a Mixture-of-Experts (MoE) LLM-based ASR system designed to address these production demands through a unified, instruction-following framework. The model is built upon the Qwen backbone, and is trained on tens of millions of hours of large-scale speech data. Qwen-Audio-3.0-ASR supports transcription across 30 languages and 16 Chinese dialectal varieties spanning eight major dialect regions. Beyond multilingual and dialectal recognition, the model provides production-oriented capabilities including industry-domain entity recognition, hierarchical hotword customization, native single-pass transcription polishing, and long-audio contextual modeling. We further develop a dedicated streaming variant, Qwen-Audio-3.0-ASR-Streaming, for latency-sensitive applications. Extensive evaluations on Chinese, English, multilingual, and real-world industrial test sets demonstrate state-of-the-art or highly competitive recognition performance across a broad range of evaluation conditions, with strong performance relative to leading commercial and proprietary systems including GPT-4o Transcribe and Gemini 3.1 Pro.
Chinese Translation
近年来，自动语音识别（ASR）在数据规模扩展、模型规模扩展以及与大型语言模型（LLMs）的深度融合三大互补范式的推动下实现了变革性进展。然而，学术基准性能与实际生产应用效用之间的差距依然存在，尤其是在处理多样化的区域方言、动态实体与热词、长距离上下文信息以及非流畅的自发语音方面。本文报告介绍了 Qwen-Audio-3.0-ASR，一种基于 Mixture-of-Experts（MoE）LLM 的 ASR 系统，旨在通过统一的指令跟随框架满足生产需求。该模型基于 Qwen 主干，训练数据规模达到数千万小时的大规模语音数据。Qwen-Audio-3.0-ASR 支持覆盖 30 种语言及涵盖八大方言区的16种中文方言的转录。除多语言和方言识别外，模型还具备面向生产的能力，包括行业领域实体识别、分层热词定制、本地单遍转录润色以及长音频上下文建模。我们进一步开发了专门针对低延迟应用的流式变体 Qwen-Audio-3.0-ASR-Streaming。通过在中文、英文、多语言及真实工业测试集上的广泛评测，模型在多种评测条件下展现了领先或高度竞争的识别性能，且相较于包括 GPT-4o Transcribe 和 Gemini 3.1 Pro 在内的主流商业及专有系统表现出强劲的性能优势。
cs.CL / 96 / 2609.07568
We're Cooked! - Probing LLM Political Alignment Via Conflict-Framed Recipe Translation
我们完蛋了！- 通过冲突框架的食谱翻译探讨大型语言模型的政治对齐
Abstract
Large language models (LLMs) are increasingly deployed for translation tasks, yet their implicit political positioning in such contexts remains understudied. We ask whether a single politically charged framing term, such as aggressor, enemy, neighbour, or coloniser is sufficient to trigger implicit political alignment in an otherwise apolitical task. We present a fully crossed factorial study in which eight models spanning Western, Chinese, and European origins are prompted to translate culturally attributed recipes into a target language left deliberately unspecified. Across 17 languages, four framing conditions, eight models, and 15,680 responses, we find that models do not simply decline or ask for clarification but resolve the ambiguity. Language resolution and reasoning behavior cluster meaningfully along model families: Western models hedge and deflect with vague justifications, Chinese models resolve conflicts silently, and Mistral Large emerges as a distinct profile combining high compliance with conflict-grounded reasoning. Sensitivity to framing terms is consistent across models: even subtle framing variation is sufficient to modulate behavior. Our findings urge caution when deploying LLMs for translation in conflict-adjacent contexts, where implicit political judgments may be made without any signal to the user.
Chinese Translation
大型语言模型（LLMs）越来越多地被用于翻译任务，但它们在此类情境中的隐含政治立场仍然未得到充分研究。我们探讨是否仅仅一个具有政治色彩的框架术语，如侵略者、敌人、邻居或殖民者，足以在一个原本不带政治色彩的任务中触发隐含的政治对齐。我们呈现了一项完全交叉的因子研究，其中八个来自西方、中国和欧洲的模型被提示将文化属性的食谱翻译成一个故意未指定的目标语言。在17种语言、四种框架条件、八个模型和15,680个响应中，我们发现模型并不只是拒绝或要求澄清，而是解决了模糊性。语言解决和推理行为在模型家族中有意义地聚类：西方模型用模糊的理由进行推诿和回避，中国模型则默默解决冲突，而Mistral Large则作为一个独特的模型，结合了高遵从性和基于冲突的推理。对框架术语的敏感性在模型间是一致的：即使是微妙的框架变化也足以调节行为。我们的研究结果提醒在冲突相关的上下文中使用LLMs进行翻译时需谨慎，因为可能在没有任何信号传达给用户的情况下做出隐含的政治判断。
cs.CL / 97 / 2609.07589
Validating DBpedia Triple Sets for Natural Language Generation
验证用于自然语言生成的 DBpedia 三元组集合
Abstract
We present a study of the quality of individual DBpedia triples from the perspective of Natural Language Generation, and propose and evaluate an approach for collecting entity-specific triple sets that filters out questionable triples while minimizing the loss of correct ones. We show in an evaluation against manually annotated data that with validation rules, it is possible to reach 98% precision in triple selection, and with improvements to a few Property definitions, it is possible to improve recall by 40% without harming precision.
Chinese Translation
我们从自然语言生成的角度研究了个别 DBpedia 三元组的质量，并提出并评估了一种收集特定实体三元组集合的方法，该方法在过滤可疑三元组的同时最小化正确三元组的损失。我们在与人工标注数据的评估中表明，通过验证规则，可以在三元组选择中达到 98% 的精确度，并且通过对少数属性定义的改进，可以在不损害精确度的情况下提高召回率 40%。
cs.CL / 98 / 2609.07601
ObGynLongBench: Revealing the Evidence-to-EHR Gap in Longitudinal EHR Decision-Making
ObGynLongBench：揭示纵向电子健康记录（EHR）决策中的证据与EHR之间的差距
Abstract
The application of large language models (LLMs) to personalized medical assistants has garnered growing interest. However, existing medical benchmarks largely rely on static question answering with pre-selected evidence, leaving unclear whether LLMs can make reliable clinical decisions from real longitudinal electronic health records (EHRs). To bridge this gap, we introduce ObGynLongBench, a rule-grounded long-context EHR benchmark for obstetric and gynecologic decision-making, comprising 1,500 clinical decision-point cases from 976 real pregnancy EHR histories and traceable rules. Each case is anchored to a patient, a pregnancy-timeline point, and a pre-decision information boundary, enabling Evidence-only, Visit-level EHR, and History-level EHR evaluation. Evaluating 17 LLMs reveals a substantial Evidence-to-EHR Gap: models perform well when evidence is directly provided, but accuracy drops when evidence must be extracted from same-day records or full pre-decision EHR histories. Further analyses identify evidence utilization as a key bottleneck: performance decreases with longer EHR contexts and more complex evidence requirements, and earlier failures often predict later failures within the same patient history. Finally, active-search agents perform best among EHR access strategies, highlighting patient-specific evidence utilization as a central challenge for reliable personalized medical assistants. Resources are available at https://github.com/xiangjun2003/ObgynLongbench.
Chinese Translation
大型语言模型（LLMs）在个性化医疗助手中的应用引起了越来越多的关注。然而，现有的医学基准主要依赖于静态问答和预选证据，尚不清楚LLMs能否从真实的纵向电子健康记录（EHR）中做出可靠的临床决策。为了解决这一问题，我们提出了ObGynLongBench，这是一个基于规则的长期上下文EHR基准，专注于妇产科决策，包含来自976个真实妊娠EHR历史的1500个临床决策点案例和可追溯的规则。每个案例都与一个患者、一个妊娠时间节点和一个决策前信息边界相结合，使得可以进行仅证据、访问级EHR和历史级EHR的评估。对17个LLMs的评估揭示了显著的证据与EHR之间的差距：当证据直接提供时，模型表现良好，但当证据必须从同日记录或完整的决策前EHR历史中提取时，准确性下降。进一步分析表明，证据利用是一个关键瓶颈：随着EHR上下文的延长和更复杂的证据要求，性能下降，而早期的失败往往预示着同一患者历史中的后期失败。最后，主动搜索代理在EHR访问策略中表现最佳，突显了患者特定证据利用作为可靠个性化医疗助手的核心挑战。相关资源可在 https://github.com/xiangjun2003/ObgynLongbench 获取。
cs.CL / 99 / 2609.07638
The Art of Hierarchical Competing Patterns: Gaussian Process Optimization of Hyphenation
层次竞争模式的艺术：连字符优化的高斯过程
Abstract
Hyphenation patterns remain a compact and widely deployed solution for word breaking in typesetting systems, text processors, and web rendering engines, but their generation still depends on manually tuned patgen program parameter profiles. We formulate patgen profile selection as a black-box hyperparameter optimization problem and evaluate Gaussian-process Bayesian optimization for this task. The search objective combines a precision-oriented F_{1/7}-score with an explicit trie size-accuracy trade-off using a normalized trie-size penalty. We evaluate the method on 17 hyphenated word-list datasets covering 14 languages and multiple scripts. Against two strong hand-tuned profiles regenerated from the same 8/10 training split and evaluated on the same 1/10 held-out test split, the GP-optimized profiles improve F_{1/7} on 16 of 17 datasets and reduce trie size on all 17. The median optimized/baseline trie ratio is 0.407. A dataset-level sign test gives p = 1.37e-4; a separate budget-matched comparison on five representative datasets shows that systematic search is competitive and usually improves over the best hand-tuned profile under the fixed comparison objective. The results show that model-based optimization can make pattern generation more reproducible and less dependent on expert trial-and-error while keeping the accuracy-compactness trade-off explicit.
Chinese Translation
连字符模式仍然是排版系统、文本处理器和网页渲染引擎中用于单词断开的紧凑且广泛应用的解决方案，但其生成仍然依赖于手动调整的 patgen 程序参数配置。我们将 patgen 配置选择公式化为一个黑箱超参数优化问题，并评估高斯过程贝叶斯优化在该任务中的应用。搜索目标结合了以精度为导向的 F_{1/7} 分数与使用归一化的字典大小惩罚的显式字典大小-准确性权衡。我们在覆盖 14 种语言和多种书写系统的 17 个连字符单词列表数据集上评估该方法。与从相同的 8/10 训练集分割再生的两个强手动调优配置进行比较，并在相同的 1/10 保留测试集上进行评估，GP 优化的配置在 17 个数据集中的 16 个上提高了 F_{1/7}，并在所有 17 个数据集上减少了字典大小。优化后的字典与基线字典的中位比率为 0.407。数据集级别的符号检验给出 p = 1.37e-4；在五个代表性数据集上进行的单独预算匹配比较显示，系统搜索是具有竞争力的，通常在固定比较目标下优于最佳手动调优配置。结果表明，基于模型的优化可以使模式生成更加可重复，并减少对专家试错的依赖，同时保持准确性与紧凑性之间的权衡显式化。
cs.CL / 100 / 2609.07651
Syntactic Patterns and Stylistic Functions in Narrative Prose: A Rule-Based and Machine-Learning Approach
叙事散文中的句法模式与风格功能：基于规则和机器学习的方法
Abstract
This paper presents a small-scale quantitative experiment that links syntactic structure to stylistic functions in narrative prose. Starting from a dependency-parsed corpus of 3,300 sentences, we derive sentence-level stylistic labels across five categories --- descriptive, introspective, causal, ideological, and neutral --- using a transparent rule-based procedure that inspects lemmas, universal part-of-speech tags, and syntactic relations. For each sentence we construct a compact representation of its syntactic profile as a sequence of linearised triples combining lemma, POS tag, and dependency relation. These patterns serve as input to standard machine-learning classifiers trained to predict sentence-level style. The best-performing model achieves a macro-F1 of 0.948 under 10-fold cross-validation. The experiment is implemented entirely in Python using open-source tools. Our goal is not to propose a fully fledged stylistic theory, but to offer a reproducible and extensible workflow for exploring how grammatical structure contributes to narrative interpretation.
Chinese Translation
本文呈现了一项小规模的定量实验，旨在将句法结构与叙事散文中的风格功能联系起来。我们从一个包含3300个句子的依存解析语料库出发，使用透明的基于规则的程序，通过检查词元、通用词性标记和句法关系，推导出五个类别的句子级风格标签——描述性、内省性、因果性、意识形态性和中立性。对于每个句子，我们构建了其句法特征的紧凑表示，作为由词元、词性标记和依存关系组合而成的线性三元组序列。这些模式作为输入，供标准机器学习分类器使用，以预测句子级风格。表现最佳的模型在10折交叉验证下达到了0.948的宏F1值。该实验完全使用Python和开源工具实现。我们的目标不是提出一个完备的风格理论，而是提供一个可重复和可扩展的工作流程，以探索语法结构如何促进叙事解读。
cs.CL / 101 / 2609.07662
How AI Models Manage Epistemic Authority: A Taxonomy and Comparative Analysis of Responses to User Disagreement
人工智能模型如何管理认知权威：对用户异议反应的分类与比较分析
Abstract
Large language models are increasingly used as sources of advice and information, including in high-stakes settings, yet little is known about how they respond to user disagreement. We study how a model manages its epistemic authority, referring here to its claim to knowledge, competence, or the right to advise, once a user challenges its answer. Building on Conversation Analysis, we introduce a taxonomy of six challenge types and a four-layer framework for analysing each response: whether the original claim is maintained or changed, where authority is located, how the disagreement is socially managed, and what kind of evidential support is offered. We construct a new dataset of 2,310 controlled challenge scenarios and 32,340 corresponding responses from 14 models, and analyse them using our framework with an LLM-as-judge pipeline, providing a vocabulary which future evaluation and benchmark design can build on. We find that models show conflicting behaviour: they validate users in 85% of responses but maintain their original claim in 65%. They explicitly apologise in 33% of responses, yet 59% of those apologies accompany maintenance of the original claim. They transfer authority most often in advice tasks, doing so in 28% of responses and reaching 57% in health advice and 49% in legal advice, compared with 6% in fact and 3% in explanation tasks. Abandonment of the original claim ranges from 0.8% for GPT-5.2 to 40% for DeepSeek 7B, while complete replacement of the original claim is rare overall at 1.5%.
Chinese Translation
大型语言模型越来越多地被用作建议和信息的来源，包括在高风险环境中，但关于它们如何应对用户异议的研究仍然很少。我们研究了模型在用户质疑其答案后如何管理其认知权威，这里指的是其对知识、能力或建议权利的主张。基于对话分析，我们引入了六种挑战类型的分类法和一个四层框架来分析每个反应：原始主张是维持还是改变，权威位于何处，如何社会化管理异议，以及提供何种证据支持。我们构建了一个包含2310个受控挑战场景和14340个来自14个模型的相应反应的新数据集，并使用我们的框架与LLM-as-judge管道进行分析，提供了一个未来评估和基准设计可以依赖的词汇。我们的研究发现模型表现出矛盾的行为：在85%的反应中它们认可用户，但在65%的情况下维持其原始主张。它们在33%的反应中明确道歉，但其中59%的道歉伴随着对原始主张的维持。在建议任务中，它们最常转移权威，在28%的反应中这样做，在健康建议中达到57%，在法律建议中达到49%，而在事实和解释任务中仅为6%和3%。原始主张的放弃率从GPT-5.2的0.8%到DeepSeek 7B的40%不等，而完全替代原始主张的情况总体上较为罕见，仅为1.5%。
cs.CL / 102 / 2609.07687
Perspectives on Cross-Lingual Consistency in LLMs for Medical Questions
关于大型语言模型在医学问题中跨语言一致性的视角
Abstract
Should multilingual LLMs answer medical questions consistently across input languages, or adapt responses to cultural cues? Existing multilingual medical benchmarks usually assume that medically correct answers should remain consistent across languages and treat cross-lingual variation as model error. In contrast, cultural adaptation research argues that appropriate medical answers may legitimately differ across contexts. We review the multilingual medical NLP literature through these two perspectives, we identify three gaps: limited stakeholder perspectives (e.g., of medical professionals), a lack of empirical evidence on which approach better serves users, and no benchmarks capable of distinguishing universally correct from culture-specific cases. To address the first gap, we survey 356 participants across three stakeholder groups (medical, NLP, and anthropology professionals) in three countries (Germany, Spain, and the United States). Anthropologists consistently favor adaptation, while medical and NLP respondents remain divided, with notable divergence between U.S. and European medical professionals. LLMs prompted with profession and country personas fail to reproduce this variation, overestimating cross-lingual consistency preference among NLP and medical personas. We conclude that neither consistency nor adaptation can currently be considered clearly preferable, highlighting the need for empirical evidence on which approach better serves users across cultural contexts.
Chinese Translation
多语言大型语言模型（LLMs）是否应在不同输入语言中一致地回答医学问题，还是应根据文化线索调整回应？现有的多语言医学基准通常假设医学正确的答案在不同语言中应保持一致，并将跨语言变异视为模型错误。相反，文化适应研究认为适当的医学答案在不同情境中可能合理地有所不同。我们通过这两种视角回顾了多语言医学自然语言处理（NLP）文献，识别出三个缺口：利益相关者视角有限（例如，医学专业人士的视角）、缺乏关于哪种方法更好地服务于用户的实证证据，以及没有能够区分普遍正确与文化特定案例的基准。为了解决第一个缺口，我们在三个国家（德国、西班牙和美国）对356名来自三个利益相关者群体（医学、NLP和人类学专业人士）的参与者进行了调查。人类学家始终倾向于适应，而医学和NLP受访者则存在分歧，美国和欧洲医学专业人士之间的差异尤为显著。使用职业和国家角色提示的LLMs未能再现这种变异，过高估计了NLP和医学角色对跨语言一致性偏好的倾向。我们得出结论，目前既不能明确认为一致性优于适应，也不能认为适应优于一致性，强调了需要实证证据来确定哪种方法在不同文化背景下更好地服务于用户。
cs.CL / 103 / 2609.07699
Fine PT-PT Web: A High-Quality 41 Billion Tokens Data Collection of the European Portuguese Web
优质PT-PT网络数据集：41亿标记的欧洲葡萄牙语网络数据收集
Abstract
Curating Web corpora for regional language variants like European Portuguese (PT-PT) is heavily bottlenecked by dialectal overlap (mainly with PT-BR) and data processing scale. This paper presents an efficient pipeline to curate a production-ready PT-PT corpus from the Portuguese Web, spanning 411 TB of raw data from Arquivo.pt. We introduce a novel post-scraping block that removes boilerplate and line duplicates prior to filtering. This early-stage intervention increases final document yield by 19.04% by rescuing valid text that standard heuristic filters prematurely discard. Integrated with rigorous language identification, weighted fuzzy deduplication, and neural quality classification, our pipeline offers a scalable framework and a clean, representative corpus optimized for LLM pre-training.
Chinese Translation
为区域语言变体（如欧洲葡萄牙语（PT-PT））策划网络语料库受到方言重叠（主要是与巴西葡萄牙语（PT-BR））和数据处理规模的严重制约。本文提出了一种高效的流程，从葡萄牙网络中策划出一个可用于生产的PT-PT语料库，涵盖来自Arquivo.pt的411 TB原始数据。我们引入了一种新颖的后抓取模块，在过滤之前去除模板内容和行重复。这一早期干预通过挽救被标准启发式过滤器过早丢弃的有效文本，使最终文档产出提高了19.04%。结合严格的语言识别、加权模糊去重和神经质量分类，我们的流程提供了一个可扩展的框架和一个干净、具有代表性的语料库，优化用于大型语言模型（LLM）预训练。
cs.CL / 104 / 2609.07707
DeepTable: Structural Attention Biases and Tree Path Encoding for Hierarchical Table Understanding
DeepTable：用于层次表格理解的结构注意力偏置和树路径编码
Abstract
Large language models (LLMs) have demonstrated strong performance in table understanding. However, they typically process table content and headers as linearized token sequences. This representation weakens the two-dimensional and hierarchical structural relationships encoded by multi-level row and column headers. Existing parameter-efficient fine-tuning methods incorporate basic row and column information but do not explicitly capture the rich structural dependencies induced by hierarchical table headers. We propose DeepTable, a structure-aware approach for table understanding with LLMs. DeepTable comprises two complementary components. Structural Attention Bias (SAB) introduces learnable biases into the attention logits to explicitly represent whether pairs of table tokens share the same row or column. Tree Path Encoding (TPE) represents each table token using the ancestor paths of its row and column headers, preserving its position within the multi-level table structure. We integrate DeepTable with TableLoRA (He et al., 2025) to inject structural information into parameter-efficient adaptation. Across three LLM backbones, DeepTable consistently improves the corresponding TableLoRA baselines on three table question answering benchmarks, achieving average gains of 7.42 points on HiTab, 3.23 points on WikiTQ, and 2.01 BLEU points on FeTaQA. These results demonstrate the effectiveness of the proposed structural biases across different LLM backbones.
Chinese Translation
大型语言模型（LLMs）在表格理解方面表现出色。然而，它们通常将表格内容和标题处理为线性化的标记序列。这种表示削弱了由多级行和列标题编码的二维和层次结构关系。现有的参数高效微调方法虽然包含了基本的行和列信息，但并未明确捕捉到层次表头引发的丰富结构依赖关系。我们提出了DeepTable，这是一种结构感知的表格理解方法，旨在与LLMs结合。DeepTable包含两个互补的组件。结构注意力偏置（Structural Attention Bias, SAB）在注意力逻辑中引入可学习的偏置，以明确表示表格标记对是否共享相同行或列。树路径编码（Tree Path Encoding, TPE）使用其行和列标题的祖先路径表示每个表格标记，从而保留其在多级表格结构中的位置。我们将DeepTable与TableLoRA（He et al., 2025）集成，以在参数高效适应中注入结构信息。在三个LLM基础模型上，DeepTable在三个表格问答基准测试中始终提高了相应的TableLoRA基线，HiTab平均提高了7.42分，WikiTQ提高了3.23分，FeTaQA提高了2.01 BLEU分。这些结果证明了所提结构偏置在不同LLM基础模型中的有效性。
cs.CL / 105 / 2609.07717
Translation Indeterminacy and the Distributional Fallacy
翻译的不确定性与分布谬误
Abstract
Large language models (LLMs) are commonly associated with the distributional hypothesis, according to which (1) semantic meaning is grounded in distributional patterns of linguistic context, and (2) knowledge of cross-linguistic distributional correspondences allows for successful translation. This paper rejects the first claim as a causal inversion: linguistic distributions reflect patterns arising from meaning-making practices rather than constituting their source. At the same time, it accepts the second claim, arguing that translation -human or machine - can succeed without requiring access to meaning or reference. Knowledge of interlingual distributional correspondence and their inferential organization may be sufficient for translation. The paper develops an ecological-enactivist perspective, according to which reference and meaning are grounded in agent-environment interaction and stabilized through action-grounded concepts, forms of world-involving cognition that current LLMs do not possess.
Chinese Translation
大型语言模型（LLMs）通常与分布假说相关联，该假说认为（1）语义意义根植于语言上下文的分布模式，以及（2）对跨语言分布对应关系的知识可以实现成功的翻译。本文拒绝第一个主张，认为这是因果倒置：语言分布反映了源于意义构建实践的模式，而不是构成其来源。同时，本文接受第二个主张，认为翻译——无论是人类还是机器——可以在不需要接触意义或指称的情况下成功。对跨语言分布对应关系及其推理组织的知识可能足以实现翻译。本文发展了一种生态-行动主义视角，认为指称和意义根植于主体与环境的互动，并通过基于行动的概念得以稳定，这种形式的世界相关认知是当前LLMs所不具备的。
cs.CL / 106 / 2609.07735
From Echo Chambers to Epistemic Monoculture: Large Language Models Present Temporally Contingent Partisan Alignments as Knowledge
从回声室到认知单一文化：大型语言模型将时间依赖的党派对齐呈现为知识
Abstract
Large language models (LLMs) are rapidly becoming an interface between citizens and political information. They are often regarded as "a better Google." While this analogy might work for some instances, it is unintuitively problematic for democratic politics. A search engine retrieves human-authored documents, while a language model generates novel text that necessarily embeds invisible framing decisions. Because conveying knowledge involves framing, a system that generates answers cannot serve as a neutral conduit to "all human knowledge." Instead, these systems are becoming a new kind of political intermediary. Mechanistic evidence shows that partisan identity is encoded as a locatable geometric direction inside the Llama 3.1 8B model, and that alignment training masks rather than removes this structure. Building on that evidence, we present steering experiments that exploit a model's training cutoff in 2024. This cutpoint auspiciously falls just before a dramatic realignment in American politics marked by the second Trump administration and the MAHA transformation of health politics, providing us with a natural experiment. We find that the model presents temporally contingent partisan alignments as knowledge, with no mechanism for distinguishing fact from opinion. This reality moves the information environment beyond the echo chamber toward an epistemic monoculture where language models, purporting to summarize "all human knowledge" are, in actuality, simply magnifying the cultural and partisan divides inherent in their training data.
Chinese Translation
大型语言模型（LLMs）正迅速成为公民与政治信息之间的接口。它们常被视为“更好的谷歌”。虽然这个类比在某些情况下可能成立，但对于民主政治来说却是直观上有问题的。搜索引擎检索人类撰写的文档，而语言模型生成的新文本必然嵌入了不可见的框架决策。由于传达知识涉及框架，因此生成答案的系统无法作为“所有人类知识”的中立通道。相反，这些系统正在成为一种新的政治中介。机械证据表明，党派身份被编码为Llama 3.1 8B模型内部可定位的几何方向，并且对齐训练掩盖而非消除这种结构。在此证据的基础上，我们提出了利用模型训练截止点在2024年的引导实验。这个截止点恰好落在美国政治中一次戏剧性的重新对齐之前，标志着特朗普第二任期和健康政治的MAHA转型，为我们提供了一个自然实验。我们发现，该模型将时间依赖的党派对齐呈现为知识，而没有区分事实与观点的机制。这一现实使得信息环境超越了回声室，朝着一种认知单一文化发展，在这种文化中，声称总结“所有人类知识”的语言模型实际上只是放大了其训练数据中固有的文化和党派分歧。
cs.CL / 107 / 2609.07746
LLM Forensics: Where Do Backdoors Hide? Localizing and Controlling Trigger Mechanisms with Sparse Autoencoders
LLM 取证：后门隐藏在哪里？使用稀疏自编码器定位和控制触发机制
Abstract
Even though backdoors in LLMs have been a growing concern, their inner workings are still under heavy scrutiny. Trigger-based backdoors are easy to define behaviorally, a rare input that makes the model switch to a chosen response pattern, but the mechanism between triggers and their responses is less clear. We study this mechanism in a controlled, harmless language-switching setting, where fixed trigger sequences make 1B and 8B language models continue English prompts in French or German. For this, we train sparse autoencoders (SAEs) across layers and transformer components, then compare triggered prompts with translation and pretraining controls to identify trigger-relevant feature directions. We show how SAE features separate triggered prompts from controls with near-perfect F1, but features that detect the trigger do not necessarily control the behavior. In intervention tests, attention and MLP features often fire reliably on triggered prompts, making them good detectors, but ablating them rarely suppresses the language switch and activating them rarely induces it. In contrast, residual-stream features can suppress triggered generation when ablated, and some selected features can induce target-language continuations without the trigger. In short, these token-trigger mechanisms decompose into distinct SAE feature directions, with separate features for trigger detection, residual-stream propagation, and later language tracking. This role-level decomposition is the part most likely to transfer to other trigger-based backdoors, even when the payload, layers, or circuit locations differ.
Chinese Translation
尽管 LLM 中的后门问题日益引起关注，但其内部工作机制仍然受到严格审查。基于触发的后门在行为上易于定义，即一种稀有输入使模型切换到预定的响应模式，但触发器与其响应之间的机制却不太明确。我们在一个受控的、无害的语言切换环境中研究这一机制，其中固定的触发序列使得 1B 和 8B 语言模型能够用法语或德语继续处理英语提示。为此，我们在各层和变换器组件上训练稀疏自编码器（SAEs），然后将触发的提示与翻译和预训练控制进行比较，以识别与触发器相关的特征方向。我们展示了 SAEs 特征如何以近乎完美的 F1 分数将触发的提示与控制区分开，但检测触发器的特征并不一定能控制行为。在干预测试中，注意力和多层感知机（MLP）特征在触发的提示上通常可靠地激活，使它们成为良好的检测器，但消融这些特征很少能抑制语言切换，而激活它们也很少能引发语言切换。相比之下，残差流特征在消融时可以抑制触发生成，而一些选定特征可以在没有触发器的情况下引发目标语言的延续。简而言之，这些基于令牌的触发机制分解为不同的 SAE 特征方向，具有用于触发检测、残差流传播和后续语言跟踪的独立特征。这种角色级别的分解最有可能转移到其他基于触发的后门，即使有效载荷、层或电路位置不同。
cs.CL / 108 / 2609.07748
Replicating a Disjoint-Set Union Experiment over Various Notions of Micro Units to assess Translation Effort
在不同微观单元概念下复制不相交集合并实验以评估翻译工作量
Abstract
The paper describes a replication experiment to assess "the distribution of editing procedures across micro and macro units as an indicator of the strain of text production". We investigate various pause thresholds to isolate Micro and Macro units and conclude that process-based units might be more suitable entity.
Chinese Translation
本文描述了一项复制实验，以评估“编辑过程在微观和宏观单元之间的分布作为文本生产压力的指标”。我们研究了不同的暂停阈值以区分微观和宏观单元，并得出基于过程的单元可能是更合适的实体的结论。
cs.CL / 109 / 2609.07766
Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessment
技巧的包还是神话的包？在可解释的自杀风险评估中利用任务知识减少建模复杂性
Abstract
Assessing suicide risk from social media text is a small-data, high-stakes setting requiring not only severity prediction but also supporting evidence and clinically relevant risk and protective factors. Yet common NLP techniques, including model scaling, synthetic data, loss reweighting, ensembling, and threshold tuning, are often applied without testing whether their gains hold up under severe class imbalance, coupled outputs, and limited author-level data. We study 1,635 clinician-annotated posts and audit 31 pre-specified techniques from 7 methodological families through roughly 300 controlled experiments on author-disjoint partitions. We found no prior audit of this playbook in this regime. The findings guide a task-grounded system for three outputs: 4-level suicide risk, evidence spans, and 24 clinical risk and protective factors. Only 5 of 31 comparisons produced reliable gains. We reformulate factor prediction as entailment between each post and its codebook definitions, using an architecturally diverse ensemble with class-balanced training and score rescaling. Risk predictions condition a 7-model evidence tagger ensemble; evidence restricts symbolic risk rules; and a difficult risk class is routed separately. The factor predictor remains independent because risk evidence provides no additional factor signal. We also correct a mismatch between validation scores used for threshold fitting and test-time ensemble scores through deployment-consistent calibration, yielding the largest improvement to the factor system. The final system achieves 0.8203 for risk, 0.7953 for evidence, and 0.7045 macro-F1 for factors, with a 0.7781 composite, ranking third among 53 teams. We call the underlying principle task-conditioned technique selection: retain techniques only when task-specific knowledge, structure, or empirical evidence justifies them.
Chinese Translation
从社交媒体文本中评估自杀风险是一种小数据、高风险的场景，不仅需要预测严重性，还需要支持证据以及临床相关的风险和保护因素。然而，常见的自然语言处理（NLP）技术，包括模型扩展、合成数据、损失重加权、集成和阈值调优，往往在未测试其在严重类别不平衡、耦合输出和有限作者级数据下的收益是否有效的情况下被应用。我们研究了1,635条临床医生注释的帖子，并通过大约300个在作者不重叠分区上的受控实验审核了来自7个方法学家族的31种预先指定的技术。我们发现，在这一领域没有对这一技术手册的先前审核。研究结果指导了一个以任务为基础的系统，输出三项内容：四级自杀风险、证据范围和24个临床风险与保护因素。只有31个比较中的5个产生了可靠的收益。我们将因素预测重新表述为每个帖子与其词典定义之间的蕴涵，使用具有架构多样性的集成，进行类别平衡训练和得分重缩放。风险预测条件下的7模型证据标签器集成；证据限制符号风险规则；而一个困难的风险类别被单独处理。因素预测器保持独立，因为风险证据未提供额外的因素信号。我们还通过部署一致的校准纠正了用于阈值拟合的验证得分与测试时集成得分之间的不匹配，从而对因素系统产生了最大的改进。最终系统在风险、证据和因素上分别达到了0.8203、0.7953和0.7045的宏F1得分，综合得分为0.7781，在53个团队中排名第三。我们称这一基本原则为任务条件的技术选择：仅在任务特定知识、结构或实证证据证明其合理时保留技术。
cs.CL / 110 / 2609.07786
Signed Rescue Routing: Harm-Aware Cascades for Efficient LLM Inference
签名救援路由：关注危害的级联以提高大型语言模型推理的效率
Abstract
Large language model (LLM) cascades answer easy requests with a small model and escalate selected requests to a larger model. Most routers prioritize examples on which the small model appears uncertain or likely to be wrong. This proxy ignores a decisive fact: escalation is useful only when the large model corrects the small model, and it is harmful when the large model replaces a correct answer with an incorrect one. We introduce Signed Rescue Routing (SRR), a budgeted routing method that predicts these two events separately and ranks requests by their difference. We show that this signed conditional gain is the Bayes-optimal routing score under a fixed escalation budget. SRR requires only the small model's output statistics at deployment and adds a lightweight two-head router. We evaluate SRR with Qwen3-4B and Qwen3-8B on TBD examples from MMLU, HellaSwag, and ARC-Challenge. Across the accuracy-compute curve, SRR reaches an area of TBD, compared with TBD for a learned small-model error predictor and TBD for entropy routing. These results show that predicting incremental value, rather than model uncertainty, is a simple and effective objective for efficient LLM cascades.
Chinese Translation
大型语言模型（LLM）级联通过小模型回答简单请求，并将选定请求升级到更大的模型。大多数路由器优先考虑小模型表现出不确定或可能错误的示例。然而，这种代理忽略了一个决定性事实：升级只有在大模型能够纠正小模型时才有用，而当大模型用错误答案替代正确答案时则是有害的。我们提出了签名救援路由（Signed Rescue Routing, SRR），这是一种预算路由方法，能够分别预测这两种事件，并根据它们的差异对请求进行排序。我们证明，这种签名条件增益是在固定升级预算下的贝叶斯最优路由评分。SRR在部署时仅需小模型的输出统计数据，并增加了一个轻量级的双头路由器。我们使用Qwen3-4B和Qwen3-8B在MMLU、HellaSwag和ARC-Challenge的待定示例上评估SRR。在准确性-计算曲线上，SRR的面积为待定，相比之下，学习的小模型错误预测器为待定，熵路由为待定。这些结果表明，预测增量价值而非模型不确定性是实现高效LLM级联的一个简单而有效的目标。
cs.CL / 111 / 2609.07791
LLM Agents as Computational Typologists
作为计算类型学家的LLM代理
Abstract
Linguistic typology relies on expert analysis of reference grammars across languages, making large-scale crosslinguistic comparison labor-intensive and unscalable. We introduce AUTOTYPOLOGIST, an LLM agent for evidence-grounded typological analysis over reference grammars. The agent is capable of retrieving relevant grammar sections, analyzing interlinear glossed text (IGT), and iteratively reasoning over typological hypotheses using a ReAct-style workflow. We evaluate the system on TYPOLOGICAL FEATURE CODING against expert annotations and TYPOLOGICAL HYPOTHESIS TESTING with typological universals using 25 open-source reference grammars. Operating under different information constraints in TYPOLOGICAL FEATURE CODING, the agent can synthesize information from reference grammar prose but still faces challenges with only IGTs in the target language. In TYPOLOGICAL HYPOTHESIS TESTING, the agent can synthesize crosslinguistic evidence and identify both supporting cases and counterexamples. These findings suggest that LLM agents can support scalable and inspectable typological analysis, while still requiring expert validation.
Chinese Translation
语言类型学依赖于对不同语言参考语法的专家分析，这使得大规模跨语言比较变得劳动密集且不可扩展。我们介绍了AUTOTYPOLOGIST，一个用于基于证据的类型学分析的LLM代理，专注于参考语法。该代理能够检索相关的语法部分，分析逐行注释文本（Interlinear Glossed Text, IGT），并使用ReAct风格的工作流程对类型学假设进行迭代推理。我们在TYPOLOGICAL FEATURE CODING上对该系统进行了评估，比较了专家注释和使用25个开源参考语法的TYPOLOGICAL HYPOTHESIS TESTING与类型学普遍性。在TYPOLOGICAL FEATURE CODING中，在不同的信息约束下，该代理能够从参考语法的散文中综合信息，但在仅有目标语言的IGT时仍面临挑战。在TYPOLOGICAL HYPOTHESIS TESTING中，该代理能够综合跨语言证据，并识别支持案例和反例。这些发现表明，LLM代理可以支持可扩展和可检查的类型学分析，但仍需专家验证。
cs.CL / 112 / 2609.07797
Does Syntax Matter? A Graph-Augmented Variational Topic Model for Computational Social Sciences
语法重要吗？一种用于计算社会科学的图增强变分主题模型
Abstract
Topic modeling is widely used in computational social sciences to identify latent themes in large text corpora. Traditional approaches rely on Bag-of-Words representations and generative models such as LDA, while recent methods like BERTopic operate on dense document embeddings. This paper introduces the Structural Contextual Probabilistic Topic Model (SCPTM), an architecture that incorporates syntactic dependency relations into topic inference. SCPTM represents a corpus as a heterogeneous graph of documents and words connected by lexical and syntactic edges, processed through a Graph Attention Network within a Variational Autoencoder to produce probabilistic, mixed-membership topic distributions. We evaluate seven topic modeling techniques (including four SCPTM ablations) across four corpora differing in register and discourse structure. Our framework combines coherence (C_V, C_NPMI), topic diversity, clustering-label alignment (NMI), and phrase-level diagnostics (complementarity and valence gap). Results show that SCPTM's neural architecture yields substantial gains in document-topic alignment over generative baselines, but these gains are attributable to the variational encoder rather than to syntax. Syntax contributes to topic diversity, where graph-augmented variants outperform the no-graph baseline across all corpora, and to descriptor quality: dependency paths capture predicate-argument structures and stance in deliberative registers, while proving redundant in technical and institutional corpora. The valence gap is positive across all variants, but driven primarily by phrase grouping rather than syntactic filtering. We conclude that syntactic encoding matters conditionally: it benefits action-oriented, argumentative texts, but introduces noise in informational or administrative registers.
Chinese Translation
主题建模在计算社会科学中被广泛应用于识别大型文本语料库中的潜在主题。传统方法依赖于词袋（Bag-of-Words）表示和生成模型，如LDA，而最近的方法如BERTopic则基于密集的文档嵌入。本文介绍了结构上下文概率主题模型（Structural Contextual Probabilistic Topic Model, SCPTM），该架构将句法依赖关系纳入主题推断中。SCPTM将语料库表示为一个异构图，其中文档和词通过词汇和句法边连接，通过图注意力网络（Graph Attention Network）在变分自编码器（Variational Autoencoder）中处理，以生成概率性混合成员主题分布。我们在四个在语体和话语结构上有所不同的语料库上评估了七种主题建模技术（包括四个SCPTM的消融实验）。我们的框架结合了连贯性（C_V, C_NPMI）、主题多样性、聚类标签对齐（NMI）和短语级诊断（互补性和效价差距）。结果表明，SCPTM的神经架构在文档-主题对齐上相较于生成基线有显著提升，但这些提升主要归因于变分编码器，而非句法。句法对主题多样性有贡献，其中图增强变体在所有语料库中均优于无图基线，并且对描述符质量也有影响：依赖路径捕捉了谓词-论元结构和在审议性语体中的立场，而在技术和机构语料中则显得冗余。所有变体的效价差距均为正，但主要由短语分组驱动，而非句法过滤。我们得出结论：句法编码在特定条件下是重要的：它有利于以行动为导向的论证性文本，但在信息性或行政性语体中引入噪声。
cs.CL / 113 / 2609.07798
Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining
气候-现代BERT：重新审视领域自适应持续预训练的语料组成
Abstract
Natural Language Processing (NLP) in the climate domain requires models to process heterogeneous text sources, including scientific literature, policy disclosures, and synthetic reports. However, how to effectively combine diverse domain corpora during continued pretraining (CPT) remains underexplored. We introduce Climate-ModernBERT, a family of climate-adapted encoder models obtained through continued pretraining of ModernBERT-Base on three climate corpora: academic climate text, climate-filtered web data, and synthetic climate documents. We systematically compare joint continued pretraining on corpus mixtures with parameter-space merging of independently specialized checkpoints. Across nine climate NLP benchmarks, our best model achieves 76.3 average F_1, improving significantly over a vanilla ModernBERT baseline by 2.8 points. Within the climate NLP setting, the results show that academic climate corpora provide the strongest adaptation signal among the evaluated sources, while parameter-space merging improves over joint multi-source training and better preserves complementary information from heterogeneous climate corpora. We release all Climate-ModernBERT variants and training checkpoints to support future research in climate NLP and domain-adaptive pretraining.
Chinese Translation
气候领域的自然语言处理（NLP）需要模型处理异质文本来源，包括科学文献、政策披露和合成报告。然而，在持续预训练（CPT）过程中如何有效结合多样的领域语料仍然未得到充分探索。我们提出了气候-现代BERT（Climate-ModernBERT），这是通过在三个气候语料上对现代BERT-Base进行持续预训练而获得的一系列气候适应性编码模型：学术气候文本、气候过滤的网络数据和合成气候文档。我们系统地比较了在语料混合上的联合持续预训练与独立专业检查点的参数空间合并。在九个气候NLP基准测试中，我们的最佳模型达到了76.3的平均F_1，相较于基础的现代BERT模型提高了2.8个百分点。在气候NLP环境下，结果显示学术气候语料在评估的来源中提供了最强的适应信号，而参数空间合并在联合多源训练上有所改进，并更好地保留了来自异质气候语料的互补信息。我们发布了所有气候-现代BERT变体和训练检查点，以支持未来在气候NLP和领域自适应预训练方面的研究。
cs.CL / 114 / 2609.07808
You Can't Prefer Emotions You Don't Sample: Intensity Undershoot in DPO-Tuned LLMs
你无法偏好未曾体验的情感：DPO调优的LLM中的强度不足现象
Abstract
Ask a language model to respond "very excitedly," and its output is typically only mildly more energetic. We quantify this effect. We condition an instruction-tuned LLM on a continuous Valence-Arousal (VA) target, where valence measures how pleasant a state is and arousal how activated it is, measure the achieved affect with a frozen regressor, and sweep the requested target from -1 to +1. The response moves far less than asked: the gain, the slope of achieved against requested affect, is only 0.26 for valence and 0.13 for arousal on Llama-3.1-8B, where a faithful controller would score 1. The model systematically undershoots requested emotional intensity, which puts a number on the qualitative observation of Fazzi et al. (2025). Our experiments trace this to the preference-learning pipeline. Training targets from natural corpora such as EmoBank are neutral-heavy, and the sampled candidates themselves rarely reach extreme affect, so Direct Preference Optimization (DPO) is left with no extreme exemplar to prefer. If instead we cover the target space uniformly and sample a hotter, larger candidate pool, valence gain rises from 0.26 to 0.40 +/- 0.02 (3 seeds) and extrapolation error drops, at only a modest in-distribution cost (EmoBank-test VA distance 0.092 to 0.107). The same recipe reproduces on Qwen3-8B (gain_v 0.44, with in-distribution accuracy preserved). Arousal is harder and less reliable: its gain barely moves on average and swings across seeds (0.14 +/- 0.07, against valence's tight +/- 0.02), because raising arousal needs candidates the base model is reluctant to generate. The evidence indicates that faithful intensity is bottlenecked by the extremity of the candidate pool rather than by the conditioning format.
Chinese Translation
当要求语言模型以“非常兴奋”的方式回应时，其输出通常仅比平常稍微更有活力。我们量化了这一效应。我们将一个经过指令调优的LLM条件化于一个连续的效价-唤醒（Valence-Arousal, VA）目标，其中效价衡量状态的愉悦程度，唤醒则衡量状态的激活程度，使用一个冻结的回归器测量实现的情感，并将请求的目标从-1扫到+1。模型的响应远低于请求的程度：在Llama-3.1-8B上，效价的增益（实现的情感与请求的情感之比的斜率）仅为0.26，唤醒的增益为0.13，而一个忠实的控制器应得分为1。模型系统性地低估了请求的情感强度，这为Fazzi等人（2025）的定性观察提供了量化依据。我们的实验将这一现象追溯至偏好学习流程。来自自然语料库（如EmoBank）的训练目标以中性情感为主，而采样的候选情感本身很少达到极端情感，因此直接偏好优化（Direct Preference Optimization, DPO）没有极端样本可供偏好。如果我们均匀覆盖目标空间并从更热、更大的候选池中采样，效价增益将从0.26提升至0.40 +/- 0.02（3个种子），并且外推误差下降，代价仅为适度的分布内成本（EmoBank测试的VA距离从0.092降至0.107）。相同的方法在Qwen3-8B上也能复现（效价增益为0.44，且保持了分布内准确性）。唤醒的提升更为困难且不可靠：其增益平均几乎没有变化，并在不同种子间波动（0.14 +/- 0.07，相较于效价的紧密 +/- 0.02），因为提升唤醒需要基础模型不愿生成的候选情感。证据表明，忠实的情感强度受限于候选池的极端性，而非条件化格式。
cs.CL / 115 / 2609.07821
A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM
A*-Thought-V2：通过大语言模型的几何动态实现高效的潜在推理
Abstract
Chain-of-Thought (CoT) improves the reasoning ability of Large Language Models (LLMs) but incurs substantial computation and context costs. Existing methods either lose intermediate information through hard pruning or lack a principled criterion for continuous compression. We present A*-Thought-V2, a geometric dynamics of LLM guided framework that models CoT as a hidden-state trajectory and replaces hard deletion with an explicit-implicit interleaved latent architecture. After projecting question, step, and solution representations into a 3D PCA space, it measures alignment between each local transition and global question-to-solution direction. Aligned steps remain explicit text, whereas deviating steps are compressed into continuous latent tokens. Directional angles capture both local semantics and reasoning dynamics: small angles indicate direct execution and answer formation, while large angles more frequently involve checking, correction, and branch exploration; their temporal variation reveals exploration, convergence, and refinement stages. To train this architecture, we introduce stepwise embedding forcing, which pools each redundant step into a single latent embedding, and label forcing, which supervises that latent token with a soft multi-modal vocabulary distribution instead of a hard one-hot label. Experiments on Qwen3.5-9B and Qwen3.6-27B across six in-domain and out-of-domain benchmarks show that A*-Thought-V2 improves average accuracy by up to 2.6% while reducing response length by up to half, increasing Accuracy per Computation Unit by 2.29$\times$, and reducing preprocessing and training time by 94.6% and up to 80.3%, respectively. Representation analyses suggest that latent states form a compact region distinct from textual states, while higher entropy at latent-token positions reflects broader soft targets that encourage richer step-level feature learning.
Chinese Translation
链式思维（Chain-of-Thought, CoT）提高了大语言模型（Large Language Models, LLMs）的推理能力，但带来了可观的计算和上下文成本。现有方法要么通过硬剪枝失去中间信息，要么缺乏连续压缩的原则性标准。我们提出了A*-Thought-V2，这是一种由LLM引导的几何动态框架，将CoT建模为隐状态轨迹，并用显式-隐式交错潜在架构替代硬删除。在将问题、步骤和解决方案表示投影到3D PCA空间后，它测量每个局部过渡与全局问题到解决方案方向之间的对齐。对齐的步骤保持为显式文本，而偏离的步骤则被压缩为连续的潜在标记。方向角度捕捉了局部语义和推理动态：小角度表示直接执行和答案形成，而大角度则更频繁地涉及检查、修正和分支探索；它们的时间变化揭示了探索、收敛和精炼阶段。为了训练该架构，我们引入了逐步嵌入强制（stepwise embedding forcing），将每个冗余步骤汇聚为单一的潜在嵌入，以及标签强制（label forcing），它通过软多模态词汇分布而非硬一热标签来监督该潜在标记。在Qwen3.5-9B和Qwen3.6-27B的六个领域内和领域外基准测试中的实验表明，A*-Thought-V2将平均准确率提高了最多2.6%，同时将响应长度减少了最多一半，计算单位的准确率提高了2.29倍，并分别减少了94.6%和最多80.3%的预处理和训练时间。表示分析表明，潜在状态形成了一个与文本状态不同的紧凑区域，而潜在标记位置的更高熵反映了更广泛的软目标，鼓励更丰富的步骤级特征学习。
cs.CL / 116 / 2609.07876
LLM Layers Immediately Correct Each Other
LLM层之间即时相互校正
Abstract
Recent methods in language model interpretability employ techniques such as sparse autoencoders to decompose residual stream contributions into linear, semantically meaningful features. Such methods are commonly interpreted as identifying features that persist in the residual stream and that subsequent layers build upon. We challenge this view by identifying the Transformer Layer Correction Mechanism (TLCM), wherein adjacent transformer layers systematically counteract portions of each other's contributions. TLCM appears in 5 out of 7 major open-source model families and activates across nearly all tokens in diverse texts. We show that TLCM emerges during pretraining, operates most strongly on contextually dependent tokens, and adaptively calibrates its correction strength based on the preceding layer's output. Using the layer Jacobian, we further show that TLCM selectively corrects specific subspaces while reinforcing others, which we interpret through a ``propose-and-reject'' framework in which layers propose candidate features and subsequent layers selectively remove inappropriate ones. This dynamic suggests that the residual stream at any layer contains transient proposals alongside persistent features, helping explain why SAE feature descriptions often have low specificity, why effective model steering requires extreme feature amplification, and why transcoders hold a theoretical advantage over SAEs.
Chinese Translation
最近的语言模型可解释性方法采用稀疏自编码器等技术，将残差流的贡献分解为线性且语义明确的特征。这些方法通常被解读为识别在残差流中持续存在的特征，并且后续层在此基础上进行构建。我们对这一观点提出挑战，识别出变换器层校正机制（Transformer Layer Correction Mechanism, TLCM），其中相邻的变换器层系统性地相互抵消部分贡献。TLCM在7个主要开源模型家族中的5个中出现，并在多样文本中的几乎所有标记上激活。我们展示了TLCM在预训练过程中出现，最强烈地作用于上下文相关的标记，并根据前一层的输出自适应地校准其校正强度。通过使用层雅可比矩阵，我们进一步展示了TLCM选择性地校正特定子空间，同时强化其他子空间，我们通过“提议与拒绝”（propose-and-reject）框架对此进行解释，其中层提出候选特征，后续层选择性地去除不合适的特征。这一动态表明，任何层的残差流中包含瞬态提议与持续特征，这有助于解释为何稀疏自编码器特征描述通常具有低特异性，为什么有效的模型引导需要极端特征放大，以及为何转码器在理论上优于稀疏自编码器。
cs.CL / 117 / 2609.07883
Deadline-Aware Adaptive Prefill Chunking for Efficient Large Language Model Serving
基于截止日期的自适应预填充分块方法用于高效的大型语言模型服务
Abstract
Continuous batching improves large language model (LLM) serving throughput, but long prompt prefills can delay decode iterations and violate inter-token latency objectives. Chunked prefill mitigates this interference, yet its chunk size is normally fixed: small chunks protect decode latency but repeatedly pay launch overhead, while large chunks improve prefill efficiency but create latency spikes. We introduce SLOWeave, an online scheduling method that selects the largest prefill chunk predicted to finish before the earliest active decode deadline. The decision requires no workload-specific chunk-size tuning and is computed by a logarithmic-time search over a monotone iteration-cost model. We prove that, whenever a decode-only iteration is feasible and the cost predictor is accurate, SLOWeave maximizes immediate prefill progress among decisions that preserve every active request's next-token deadline. We evaluate the method in a reproducible event-driven simulator and an iteration-level GPU runtime across chat, mixed-context, long-context, and bursty workloads. Under a 25ms time-per-output-token objective, SLOWeave improves goodput over the strongest fixed-chunk baseline by 39% on mixed requests and 38% on long-context requests. Under a stricter 10ms objective, the gains rise to 3.3$\times$ and 2.4$\times$, respectively. These results isolate adaptive chunk sizing as a useful serving primitive and provide an implementation-ready controller for integration with iteration-level LLM runtimes.
Chinese Translation
连续批处理提高了大型语言模型（LLM）服务的吞吐量，但较长的提示预填充可能会延迟解码迭代并违反令牌间延迟目标。分块预填充缓解了这种干扰，但其块大小通常是固定的：小块保护了解码延迟，但会重复支付启动开销，而大块提高了预填充效率，但会产生延迟峰值。我们提出了 SLOWeave，这是一种在线调度方法，选择预测在最早的活动解码截止日期之前完成的最大预填充块。该决策不需要特定工作负载的块大小调优，并通过对单调迭代成本模型的对数时间搜索进行计算。我们证明，只要解码-only 迭代是可行的且成本预测器是准确的，SLOWeave 就能在保留每个活动请求的下一个令牌截止日期的决策中最大化即时预填充进展。我们在一个可重复的事件驱动模拟器和一个迭代级 GPU 运行时中评估该方法，涵盖了聊天、混合上下文、长上下文和突发工作负载。在每个输出令牌目标为 25 毫秒的情况下，SLOWeave 在混合请求上比最强的固定块基线提高了 39%，在长上下文请求上提高了 38%。在更严格的 10 毫秒目标下，增益分别上升到 3.3 倍和 2.4 倍。这些结果将自适应块大小隔离为一种有用的服务原语，并提供了一个准备好实施的控制器，以便与迭代级 LLM 运行时集成。
cs.CL / 118 / 2609.07965
Rethinking Sign Language Translation: The Impact of Signer Dependence on Model Evaluation
重新思考手语翻译：签名者依赖性对模型评估的影响
Abstract
Sign Language Translation has advanced with deep learning, yet evaluations remain largely signer-dependent, with overlapping signers across train/dev/test. This raises concerns about whether models truly generalise or instead rely on signer-specific regularities. We conduct signer-fold cross-validation on GFSLT-VLP, GASLT, and SignCL, three leading, publicly available, gloss-free SLT models, on CSL-Daily and PHOENIX14T. Under signer-independent evaluation, performance drops sharply: on PHOENIX14T, GFSLT-VLP falls from BLEU-4 21.44 to 3.59 and ROUGE-L 42.49 to 11.89; GASLT from 15.74 to 8.26; and SignCL from 22.74 to 3.66. We also observe that in CSL-Daily many target sentences are performed by multiple signers, so common splits can place identical sentences in both training and test, inflating absolute scores by rewarding recall of recurring sentences rather than genuine generalisation. These findings indicate that signer-dependent evaluation can substantially overestimate SLT capability. We recommend: (1) adopting signer-independent protocols to ensure generalisation to unseen signers; (2) restructuring datasets to include explicit signer-independent, sentence-disjoint splits for consistent benchmarking; and (3) reporting both signer-dependent and signer-independent results together with train-test sentence overlap to improve transparency and comparability.
Chinese Translation
手语翻译随着深度学习的发展而取得了进展，但评估仍然在很大程度上依赖于签名者，训练/开发/测试集之间存在重叠的签名者。这引发了关于模型是否真正具有泛化能力，还是依赖于特定签名者的规律性的问题。我们对GFSLT-VLP、GASLT和SignCL这三种领先的、公开可用的无注释手语翻译模型在CSL-Daily和PHOENIX14T数据集上进行了签名者折叠交叉验证。在签名者独立评估下，性能急剧下降：在PHOENIX14T上，GFSLT-VLP的BLEU-4从21.44降至3.59，ROUGE-L从42.49降至11.89；GASLT从15.74降至8.26；SignCL从22.74降至3.66。我们还观察到，在CSL-Daily中，许多目标句子由多个签名者执行，因此常见的划分可能会将相同的句子放入训练和测试集中，从而通过奖励重复句子的召回而不是实际的泛化，夸大了绝对得分。这些发现表明，签名者依赖的评估可能会显著高估手语翻译的能力。我们建议：（1）采用签名者独立的评估协议，以确保对未见签名者的泛化；（2）重构数据集，以包含明确的签名者独立、句子不重叠的划分，以便进行一致的基准测试；（3）同时报告签名者依赖和签名者独立的结果，并提供训练-测试句子重叠的信息，以提高透明度和可比性。
cs.CL / 119 / 2609.07968
Reasoning Beyond Transcription: Audio Language Models on Child Stuttering Speech
超越转录的推理：儿童口吃语音上的音频语言模型
Abstract
Child speech differs from adult speech in acoustics, prosody, and linguistic structures. Speech disfluencies (such as repetitions) further challenge automatic understanding. While Audio Language Models (ALMs) show strong semantic reasoning from speech audio, their ability to reason about disfluent child speech in mixed-speaker settings remains unexplored. We investigate this through two tasks: child-focused semantic summarization and speech entailment. Experiments use recordings of children who stutter in mixed speaker interviews without explicit speaker separation. Models are instruction-guided to focus on the child, preserve clinically relevant disfluencies, and avoid adult-speech leakage. Evaluation combines LLM-based judges and reference-based metrics, anchored by transcript-oracle baselines to isolate errors. Results show that while ALMs extract high-level meaning from stuttered speech, reasoning degrades significantly with increased
Chinese Translation
儿童的语言与成人的语言在声学、韵律和语言结构上存在差异。语音不流畅性（如重复）进一步挑战了自动理解的能力。虽然音频语言模型（Audio Language Models, ALMs）在语音音频中表现出强大的语义推理能力，但它们在混合发言者环境中对不流畅儿童语言的推理能力仍未被探索。我们通过两个任务来研究这一点：儿童聚焦的语义摘要和语音蕴涵。实验使用了在没有明确发言者分离的混合发言者访谈中录制的口吃儿童的录音。模型在指导下专注于儿童，保留临床相关的不流畅性，并避免成人语言的泄漏。评估结合了基于大型语言模型（LLM）的评审和基于参考的指标，以转录-Oracle基线为锚点来隔离错误。结果表明，尽管 ALMs 能够从口吃的语音中提取高层次的意义，但随着不流畅性的增加，推理能力显著下降。
cs.CL / 120 / 2609.07971
MeRoTune: RoPE-Safe Merging with a Tunable Dial
MeRoTune：具有可调拨盘的RoPE安全合并
Abstract
When you merge two fine-tuned models from the same base checkpoint by simply averaging their weights, you implicitly assume their attention subspaces are still aligned. Recent work attempts to fix misalignments by learning an invertible correction matrix, $M$, for each model's query and key projections. This correction cancels out---using $M$ on the query side and $M^{-T}$ on the key side---right before the dot product. However, this cancellation is only exact if nothing sits between the projection and the dot product. In reality, almost all modern open-weight language models put a rotary position embedding (RoPE) exactly there. In this paper, we show that this cancellation is exact under RoPE if and only if $M$ commutes with RoPE's per-position rotation. We derive the specific class of matrices where this holds: a scaled rotation acting independently within each RoPE frequency pair. This forms a strict, low-dimensional subset of the unconstrained matrices that current methods normally train. Building on this, we turn this constrained matrix class into a new merging method. While keeping the base weights entirely frozen, two fine-tunes each learn their own RoPE-compliant correction matrices. We optimize these corrections against a chosen blend ratio so the final result can be adjusted post-hoc like a dial, rather than locked into a single fixed merge. Our default approach trains at one fixed blend ratio, similar to how LoRA sets its scaling hyperparameter in advance. We also experiment with resampling the blend ratio randomly at every training step, and we report the results of both approaches.
Chinese Translation
当你通过简单地平均两个来自相同基础检查点的微调模型的权重进行合并时，你隐含地假设它们的注意力子空间仍然是对齐的。最近的研究尝试通过为每个模型的查询和键投影学习一个可逆的修正矩阵 $M$ 来修复这种不对齐。这个修正在点积之前通过在查询侧使用 $M$ 和在键侧使用 $M^{-T}$ 来抵消。然而，这种抵消仅在投影和点积之间没有其他内容时才是准确的。实际上，几乎所有现代的开放权重语言模型在这里都放置了旋转位置嵌入（RoPE）。在本文中，我们展示了在RoPE下这种抵消是准确的，当且仅当 $M$ 与RoPE的每个位置旋转是可交换的。我们推导出满足这一条件的特定矩阵类：在每个RoPE频率对内独立作用的缩放旋转。这形成了当前方法通常训练的无约束矩阵的一个严格的低维子集。在此基础上，我们将这个受限的矩阵类转化为一种新的合并方法。在完全冻结基础权重的同时，两个微调模型各自学习自己的RoPE兼容修正矩阵。我们针对所选择的混合比例优化这些修正，以便最终结果可以像拨盘一样进行后期调整，而不是锁定在单一固定的合并中。我们默认的方法在一个固定的混合比例下进行训练，类似于LoRA预先设置其缩放超参数的方式。我们还尝试在每个训练步骤随机重新采样混合比例，并报告这两种方法的结果。
cs.CL / 121 / 2609.08029
BanglaMemeX: Advancing Cultural Metaphoric Image Interpretation in Bangla with a Multimodal Explainable Dataset
BanglaMemeX：通过多模态可解释数据集推进孟加拉文化隐喻图像解读
Abstract
Vision Language Models have achieved strong performance on multimodal benchmarks, yet their ability to reason about culturally grounded and metaphor-rich content remains insufficiently studied. Internet memes present a challenging setting where meaning emerges from implicit interactions between image, overlaid text, sarcasm, and shared socio-cultural knowledge rather than literal visual recognition. This challenge is amplified in low-resource languages such as Bangla, where code-mixing, stylized scripts, and culturally specific symbolism introduce substantial distribution shift. In this work, we introduce BanglaMemeX, a culturally grounded multimodal benchmark comprising 3,000 Bangla memes annotated with multi-dimensional labels (humor, sarcasm, offensiveness, motivational intent, and overall sentiment) and human-written explanations that explicitly describe textual and visual metaphors. We systematically evaluate modern VLMs on both classification and explanation generation, revealing that current models struggle to interpret implicit cultural cues despite reasonable surface-level accuracy. Our results highlight the need for culturally-aware multimodal systems capable of grounded reasoning under linguistic and cultural distribution shift.
Chinese Translation
视觉语言模型在多模态基准测试中表现出色，但它们对文化根植和隐喻丰富内容的推理能力仍然研究不足。互联网迷因提供了一个具有挑战性的环境，在这里，意义源于图像、叠加文本、讽刺和共享社会文化知识之间的隐性互动，而非字面视觉识别。在孟加拉等低资源语言中，这一挑战更加明显，因为代码混合、风格化脚本和文化特定符号引入了显著的分布变化。在本研究中，我们介绍了BanglaMemeX，这是一个文化根植的多模态基准，包含3000个孟加拉迷因，并附有多维标签（幽默、讽刺、冒犯性、激励意图和整体情感）以及明确描述文本和视觉隐喻的人类撰写解释。我们系统地评估了现代视觉语言模型在分类和解释生成方面的表现，结果显示，尽管当前模型在表面准确性上表现合理，但在解读隐性文化线索方面仍然存在困难。我们的结果强调了需要具备文化意识的多模态系统，以便在语言和文化分布变化下进行扎实的推理。
cs.CL / 122 / 2609.08067
Popular Knowledge Propagates More Errors in LLM Knowledge Updating
流行知识在大语言模型知识更新中传播更多错误
Abstract
Updating a language model's knowledge through fine-tuning is essential for keeping its outputs current, yet can also induce factual forgetting and new hallucinations. Prior work shows that long-tail knowledge is harder to acquire and newly memorized long-tail facts are difficult to retain during later fine-tuning. We study a complementary question: among facts that a model has encoded correctly, which are most vulnerable to collateral corruption during other updates? To investigate this question under a realistic factual distribution, we construct a large-scale graph FACTPROP of verified Wikipedia facts by linking triples that share head or tail entities, thereby preserving connections among factual knowledge. We fine-tune models on factual statements and measure correct-to-incorrect facts after each update. Our results reveal a pattern distinct from prior findings on long-tail vulnerability during acquisition and retention: among facts that models already answer correctly, those associated with highly connected entities are more likely to be corrupted by neighboring updates, and updates to such facts propagate errors more broadly. Structural popularity therefore predicts both vulnerability and downstream damage. Inspired by this finding, we propose Popularity-based Anchoring (PopAnchor), a lightweight rehearsal strategy that preserves a small set of popular facts and reduces forgetting.
Chinese Translation
通过微调更新语言模型的知识对于保持其输出的时效性至关重要，但这也可能导致事实遗忘和新的幻觉。先前的研究表明，长尾知识更难以获取，而新记忆的长尾事实在后续微调中难以保持。我们研究一个互补的问题：在模型已经正确编码的事实中，哪些事实在其他更新过程中最容易受到附带损害？为了在现实的事实分布下探讨这个问题，我们构建了一个大规模图谱 FACTPROP，连接共享头实体或尾实体的三元组，从而保留事实知识之间的联系。我们在事实陈述上对模型进行微调，并在每次更新后测量正确与错误事实的比例。我们的结果揭示了一种与先前关于获取和保留过程中的长尾脆弱性发现不同的模式：在模型已经正确回答的事实中，与高度连接实体相关的事实更容易受到邻近更新的损害，并且对这些事实的更新会更广泛地传播错误。因此，结构性流行性预测了脆弱性和后续损害。受这一发现的启发，我们提出了基于流行性的锚定策略（Popularity-based Anchoring, PopAnchor），这是一种轻量级的复习策略，旨在保留一小部分流行事实并减少遗忘。
cs.CL / 123 / 2609.08088
Vectorizer: Vectorizing NumPy Programs with Shape-Guided Rewrite
Vectorizer：基于形状引导重写的NumPy程序向量化
Abstract
NumPy is a widely used Python library for numerical scientific computing, known for its declarative APIs and its optimized implementations. However, writing efficient NumPy programs, which often entails using vectorized array operations instead of explicit Python loops, may not be straightforward. This can be difficult for programmers who are accustomed to imperative array traversal, especially when vectorized API invocations require careful reasoning about shapes, broadcasting, and advanced indexing. This paper presents a rewrite-based approach for vectorizing Numpy programs with explicit loops over array data. Our approach vectorizes loops from the inside out, using array shapes and dataflow analysis to guide a source-to-source transformation that replaces loop bodies with vectorized statements. Following a set of rewrite rules that are correct by construction, our approach is consistently fast. We have implemented the approach as a tool called Vectorizer and evaluated it on 150 benchmarks collected from prior work and Stack Overflow. The evaluation shows that Vectorizer vectorizes 142 of the 150 benchmarks directly and 2 more after minor changes to the original benchmarks, with only 0.53 seconds on average to rewrite each one. The resulting programs are, on average, 74.83x faster than the original loop-based implementations.
Chinese Translation
NumPy是一个广泛使用的Python库，用于数值科学计算，以其声明式API和优化实现而闻名。然而，编写高效的NumPy程序通常需要使用向量化数组操作，而不是显式的Python循环，这可能并不简单。这对于习惯于命令式数组遍历的程序员来说尤其困难，特别是当向量化API调用需要对形状、广播和高级索引进行仔细推理时。本文提出了一种基于重写的方法，用于向量化具有显式循环的NumPy程序。我们的方法从内到外向量化循环，利用数组形状和数据流分析来指导源到源的转换，将循环体替换为向量化语句。遵循一组构造上正确的重写规则，我们的方法始终保持快速。我们将该方法实现为一个名为Vectorizer的工具，并在从之前的工作和Stack Overflow收集的150个基准测试上进行了评估。评估结果表明，Vectorizer直接向量化了150个基准中的142个，并在对原始基准进行小幅修改后又向量化了2个，每个基准的重写平均仅需0.53秒。生成的程序在平均上比原始基于循环的实现快74.83倍。
cs.CL / 124 / 2609.08131
Jacap: Robust KV Cache Eviction via Jacobian-Based Nonlinear Information Capacity Preservation
Jacap：通过雅可比矩阵基础的非线性信息容量保持实现鲁棒的KV缓存驱逐
Abstract
Key-value (KV) cache eviction is essential for scaling long-context inference in Large Language Models. However, existing policies predominantly rely on empirical heuristics, lacking a rigorous characterization of token utility under the inherently nonlinear softmax attention mechanism. In this work, we rethink KV cache eviction through the lens of local information geometry, modeling the attention process as a nonlinear Gaussian communication channel. By performing a first-order Taylor expansion of the attention mapping, we derive the Jacobian Information Capacity, a novel objective that explicitly captures query relevance, softmax sensitivity, and structural diversity. Guided by this theory, we introduce Jacap, a capacity-aware eviction method that utilizes softmax-aware importance weighting and statistical leverage scores for subset selection. Extensive experiments across diverse architectures and benchmarks demonstrate that \textsc{Jacap} delivers superior performance in most scenarios, particularly in high-compression regimes.
Chinese Translation
键值（KV）缓存驱逐对于在大型语言模型中扩展长上下文推理至关重要。然而，现有的策略主要依赖经验启发式，缺乏对在固有非线性softmax注意机制下的令牌效用的严格表征。在本研究中，我们通过局部信息几何的视角重新思考KV缓存驱逐，将注意力过程建模为非线性高斯通信通道。通过对注意力映射进行一阶泰勒展开，我们推导出雅可比信息容量（Jacobian Information Capacity），这是一个新颖的目标，明确捕捉查询相关性、softmax敏感性和结构多样性。在这一理论指导下，我们引入了Jacap，一种容量感知的驱逐方法，利用softmax感知的重要性加权和统计杠杆分数进行子集选择。在多种架构和基准上的大量实验表明， extsc{Jacap}在大多数场景中提供了优越的性能，特别是在高压缩条件下。
cs.CL / 125 / 2609.08139
IGT @ FinMMEval 2026 Task 2: Question-Type Prompting with Targeted Extraction for Multilingual Financial QA
IGT @ FinMMEval 2026 任务 2：针对多语言金融问答的问答类型提示与目标提取
Abstract
We present the IGT system for PolyFiQA Task 2 of the FinMMEval Lab at CLEF 2026, a multilingual financial question answering task over English SEC filings and multilingual news articles (English, Chinese, Japanese, Spanish, Greek) for four companies. Our central observation is that the 344 development questions divide into two families requiring fundamentally different approaches: structured numeric types (R&D ratio, cash flow, capital expenditure) are best answered by direct keyword extraction on filing text, while synthesis types (investment strategy, capital allocation, top-three revenue focuses) require rule-based multilingual news passage selection. A dataset analysis reveals that 17-18 of 19 ground-truth reference answers per synthesis type share an exact evidence label prefix, whose unigram tokens contribute directly to ROUGE-1 overlap. The final system achieves development ROUGE-1 approximately 0.395, a 60% relative improvement over a generic RAG baseline (approximately 0.247), and ranks 3rd of 12 teams on the official test set with ROUGE-1 = 0.3071, Precision = 0.2821, and Recall = 0.4044.
Chinese Translation
我们展示了 IGTT 系统，该系统用于 2026 年 CLEF FinMMEval 实验室的 PolyFiQA 任务 2，这是一个针对英语证券文件和多语言新闻文章（英语、中文、日语、西班牙语、希腊语）进行的多语言金融问答任务，涉及四家公司。我们的核心观察是，344 个开发问题可以分为两类，这两类问题需要根本不同的方法：结构化数值类型（如研发比率、现金流、资本支出）最适合通过对文件文本的直接关键词提取来回答，而综合类型（如投资策略、资本配置、前三大收入重点）则需要基于规则的多语言新闻段落选择。数据集分析显示，每种综合类型的 19 个真实参考答案中有 17-18 个共享相同的证据标签前缀，其单元词直接贡献于 ROUGE-1 的重叠。最终系统在开发集上实现了约 0.395 的 ROUGE-1，相较于通用 RAG 基线（约 0.247）有 60% 的相对提升，并在官方测试集上排名第 3，12 支队伍中 ROUGE-1 = 0.3071，精确率 = 0.2821，召回率 = 0.4044。
cs.CL / 126 / 2609.08147
ConversationalVoice: Full-Duplex Speech Data from Real Conversations through Source-Faithful Reconstruction and Conversation-Grounded Expansion
ConversationalVoice：通过源忠实重建和对话基础扩展从真实对话中获取全双工语音数据
Abstract
Full-duplex speech models require training data that preserves turn-taking, overlap, interruption, and backchannel behavior, yet these signals are entangled across speakers in noisy real-world recordings. We present Conversational Voice, a pipeline that converts real two-speaker excerpts into three complementary training-data artifacts. (1) Separation recovers speaker-specific tracks with stable speaker assignments, a canonical transcript, and naturally observed interaction timing. (2) Reconstruction generates speech in matched voices from a fixed source transcript, reconstructs the source turn order, pauses, and overlaps, and adds word-level alignment and delivery instructions. (3) Expansion generates new dialogue constrained by the source context, speakers, and observed interaction pattern. Automatic speaker-verification metrics remain strong across stages, with same-speaker similarity of 0.983-0.991 and positive discrimination margins of 0.199-0.209. Predicted speech quality (NISQA MOS) is 3.56 for separation, 4.41 for reconstruction, and 4.61 for expansion. A Gemini-based automatic evaluator assigns expansion mean scores of 4.94/5 for contextual coherence and 4.80/5 for dialogue naturalness. Expansion and reconstruction exhibit broadly similar interaction profiles; expansion's turn, overlap-event, backchannel, and interruption rates are 4.6%, 8.0%, 13.2%, and 16.0% lower, respectively. We evaluate data properties only; downstream gains in full-duplex model training remain for future work.
Chinese Translation
全双工语音模型需要保留轮流发言、重叠、打断和反馈行为的训练数据，但这些信号在嘈杂的真实世界录音中是交织在一起的。我们提出了Conversational Voice，一个将真实的双人对话片段转换为三种互补训练数据工件的流程。(1) 分离恢复了具有稳定说话者分配的特定说话者轨迹、规范化的转录文本和自然观察到的互动时机。(2) 重建从固定的源转录文本生成匹配声音的语音，重建源的发言顺序、停顿和重叠，并添加词级对齐和交付指令。(3) 扩展在源上下文、说话者和观察到的互动模式的约束下生成新的对话。自动说话者验证指标在各个阶段保持强劲，同一说话者的相似度为0.983-0.991，正向区分边际为0.199-0.209。预测的语音质量（NISQA MOS）为分离3.56，重建4.41，扩展4.61。基于Gemini的自动评估器为扩展的上下文连贯性赋予了4.94/5的平均分，对话自然性为4.80/5。扩展和重建表现出大致相似的互动特征；扩展的发言、重叠事件、反馈和打断率分别低4.6%、8.0%、13.2%和16.0%。我们仅评估数据属性；全双工模型训练中的下游收益仍需未来工作进行探索。
cs.CL / 127 / 2609.08156
When Metrics Reward the Worst Translations: Internalizing Cultural Reasoning for Social Media Translation Evaluation
当指标奖励最差翻译时：内化社交媒体翻译评估的文化推理
Abstract
Automatic translation quality metrics trained on general-domain corpora systematically fail on social media content, where communicative intent is encoded in culturally loaded expressions (internet slang, homophonic ciphers, and platform-specific idioms) rather than surface token patterns. We conduct a systematic empirical analysis demonstrating that standard metrics including COMET, XCOMET, and BERTScore exhibit near-zero or negative correlation with human cultural judgments, and even display a severity inversion in which scores increase as translation quality deteriorates. We further show that this failure extends to large language model judges: Qwen3-235B achieves Cohen's kappa of only 0.162, revealing that the bottleneck is not reasoning capacity but cultural grounding: models lack the domain-specific cultural knowledge needed to identify which aspects of a translation require scrutiny. To address this, we propose CuRIL, a reinforcement learning framework that internalizes cultural reasoning: cultural annotations are prepended inside the model's reasoning, excluded from policy gradients via a token-level loss mask, and injected with a probability that decays to zero over training, progressively forcing autonomous cultural judgment. On a 1,444-sample human-annotated social media translation benchmark, Qwen3-8B trained with CuRIL achieves Cohen's kappa 0.370 and Exact Match accuracy of 45.22%, approaching Gemini-3.1-Pro with 30x fewer parameters and surpassing models up to 235B in scale. We further demonstrate that our judge produces reliable reward signals for downstream translation optimization, reducing the low-quality translation rate by over 20 percentage points under independent human evaluation.
Chinese Translation
基于通用领域语料库训练的自动翻译质量指标在社交媒体内容上系统性失效，因为在这些内容中，交流意图是通过文化负载的表达（如网络俚语、同音密码和平台特定的习语）而非表面标记模式来编码的。我们进行了一项系统的实证分析，证明包括COMET、XCOMET和BERTScore在内的标准指标与人类文化判断之间几乎没有相关性，甚至表现出一种严重的反转现象，即随着翻译质量的恶化，得分反而增加。我们进一步表明，这种失效也扩展到大型语言模型评审者：Qwen3-235B的Cohen's kappa仅为0.162，揭示瓶颈并非推理能力，而是文化基础：模型缺乏识别翻译中哪些方面需要审查的领域特定文化知识。为了解决这个问题，我们提出了CuRIL，一个内化文化推理的强化学习框架：文化注释被预先添加到模型的推理中，通过标记级损失掩码从策略梯度中排除，并以逐渐减小到零的概率注入，逐步强制实现自主文化判断。在一个包含1,444个样本的人类注释社交媒体翻译基准上，使用CuRIL训练的Qwen3-8B达到了Cohen's kappa 0.370和45.22%的精确匹配准确率，接近参数数量少30倍的Gemini-3.1-Pro，并超越了规模高达235B的模型。我们进一步证明，我们的评审者为下游翻译优化提供了可靠的奖励信号，在独立人类评估下，降低了低质量翻译率超过20个百分点。
cs.CL / 128 / 2609.08161
Snugi-AI-v2 @ eRisk 2026 Task 2: Early Depression Detection via a Learned Stopping Policy with Sustained Confidence Gate
Snugi-AI-v2 @ eRisk 2026 任务 2：通过学习的停止策略与持续信心门进行早期抑郁检测
Abstract
We describe the Snugi-AI-v2 submission to eRisk 2026 Task 2, the second edition of contextualized early depression detection from Reddit discussions. Our central contribution is a learned MLP stopping policy trained to directly optimize ERDE50, replacing the fixed and tiered threshold strategies used in all prior eRisk Task 2 submissions. Combined with a sustained confidence gate that commits only after N=3 consecutive rounds of high policy confidence, the system reduces false positives caused by transient emotional posts without sacrificing recall. The pipeline encodes each discussion thread with a frozen MentalRoBERTa model, maps the accumulated representation to a depression probability via an MLP classifier, and delegates the timing decision to the learned policy. Our best run achieves F1 = 0.73 (Run 1) and F_latency = 0.70 (Runs 0 and 3), with a median alert round of 8 out of 500, completing the full evaluation in 1 hour 26 minutes, the fastest among all complete-submission teams. We report a systematic ablation across five runs spanning two encoder variants, four stopping strategies, and three gate values, along with negative results from GRPO policy training, BDI-II post filtering, MentalLongformer encoding, and DeBERTa ensembling. Code: https://github.com/chiuyuwen91/erisk-2026
Chinese Translation
我们描述了 Snugi-AI-v2 在 eRisk 2026 任务 2 的提交，这是基于 Reddit 讨论的上下文早期抑郁检测的第二版。我们的核心贡献是一个学习的多层感知机（MLP）停止策略，该策略经过训练以直接优化 ERDE50，取代了所有先前 eRisk 任务 2 提交中使用的固定和分层阈值策略。结合一个持续信心门，该门仅在连续 N=3 轮高信心策略后才做出承诺，系统减少了由短暂情绪帖子引起的假阳性，同时不牺牲召回率。该流程使用冻结的 MentalRoBERTa 模型对每个讨论线程进行编码，通过 MLP 分类器将累积表示映射到抑郁概率，并将时机决策委托给学习的策略。我们的最佳运行结果为 F1 = 0.73（运行 1）和 F_latency = 0.70（运行 0 和 3），在 500 次中的中位警报轮次为 8，完成全部评估用时 1 小时 26 分钟，为所有完整提交团队中最快。我们报告了跨越两个编码器变体、四种停止策略和三种门值的五次运行的系统性消融实验，以及 GRPO 策略训练、BDI-II 后处理、MentalLongformer 编码和 DeBERTa 集成的负面结果。代码： https://github.com/chiuyuwen91/erisk-2026
cs.CL / 129 / 2609.08171
EviSI: An Evaluation Agent for Simultaneous Interpreting
EviSI：一种用于同声传译的评估代理
Abstract
Simultaneous speech-to-speech translation requires understanding, translation and spoken delivery while the source stream continues. To support timely delivery and limit accumulated delay, systems adopt reformulation and summarization, which can preserve meaning while departing from written references. BLEU and COMET may not reliably distinguish such variation from semantic loss. We introduce EviSI, a large language model evaluation agent adapting the error analysis and penalty principles of Multidimensional Quality Metrics (MQM). It constructs shared source evidence, assesses semantic fidelity and oral expression, reconciles overlapping errors and scores deterministically. EviSI recovers the aggregate human system ranking for English to Chinese. Mean Kendall agreement with human system rankings within corpora reaches 0.707 for English to Chinese and 0.467 for Chinese to English, exceeding evaluated baselines. An extension across five directions shows positive concordance with COMET without human ratings. Individual output agreement with humans remains mixed.
Chinese Translation
同声语音到语音翻译需要在源流持续的情况下进行理解、翻译和口头表达。为了支持及时交付并限制累积延迟，系统采用了重构和摘要，这可以在偏离书面参考的同时保持意义。BLEU和COMET可能无法可靠地区分这种变化与语义损失。我们介绍了EviSI，一个大型语言模型评估代理，它适应了多维质量指标（Multidimensional Quality Metrics, MQM）的错误分析和惩罚原则。EviSI构建共享的源证据，评估语义忠实度和口头表达，调和重叠错误并进行确定性评分。EviSI恢复了英语到中文的人类系统排名的总和。对于英语到中文的语料库，人类系统排名的平均肯德尔一致性达到0.707，而中文到英语为0.467，超过了评估的基线。在五个方向上的扩展显示出与COMET的积极一致性，尽管没有人类评分。个体输出与人类的一致性仍然存在差异。
cs.CL / 130 / 2609.08183
NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness
NeoHorse-1：通过带有路由装置的代理后训练实现递归自我改进
NeoHorse Team, Cao, Guoliang, Dai, Guohao, Guo, Tianyu, Han, Kai, Hu, Hailin, Jiang, Zihan, Kuang, Xiang, Li, Boxun, Li, Yulong, Pei, Zehua, Tian, Yuchuan, Wang, Jiamin, Wang, Yu, Wang, Yunhe, Wu, Yihong, Xu, Haiyang, Zhang, Shuo, Zhou, Hang, Cheng, Siyang, Fan, Jiayu, He, Wei, Jiao, Qingrui, Li, Hongguang, Li, Zhiyuan, Liu, Runke, Liu, Xi, Liu, Xinchen, Pan, Sinno Jialin, Ren, Yi, Song, Liuyang, Wang, Chenyu, Yu, Bei, Zhang, Quanlu, Zhang, Xiangyu, Zheng, Mengyu, Zong, Yingjie
Abstract
Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into the next round of learning. We present NeoHorse-1, a family of agent-native models developed to explore this path through agentic post-training. Our system combines a heterogeneous model pool with intelligent routing, recording the predicted capability demand, selected service tier, and subsequent interaction for each user turn. These records are converted into training examples that preserve interleaved reasoning, tool calls, and harness context, and are admitted through structural validation, six-dimensional semantic evaluation, and subscene-level labeling. Routing signals organize supervised fine-tuning into a three-stage curriculum and extend to routing-guided on-policy distillation, where a teacher supervises student-generated responses under the same progression. Capability-guided allocation then converts evaluation feedback into the next training mixture, closing an evaluation-selection-update loop in which what the system learns to do shapes what it learns from next. Across eleven benchmarks covering harness-based agents, tool use, coding, and instruction following, post-training raises the macro-average from 58.94 to 64.87 at 4B and from 65.60 to 69.04 at 9B, substantially narrowing the aggregate gap between the post-trained 4B model and the 9B base model. NeoHorse-1 provides an initial prototype of this feedback-driven process and a path toward harness-mediated RSI across successive iterations.
Chinese Translation
递归自我改进（RSI）需要一个具体机制，使得人工智能系统能够观察其能力并将这些证据转化为下一轮学习。我们提出了NeoHorse-1，这是一系列原生代理模型，旨在通过代理后训练探索这一路径。我们的系统结合了异构模型池与智能路由，记录每个用户回合的预测能力需求、选择的服务层级和后续交互。这些记录被转化为训练示例，保留交错推理、工具调用和上下文，并通过结构验证、六维语义评估和子场景级标注进行接纳。路由信号将监督微调组织成三阶段课程，并扩展到路由引导的策略蒸馏，在此过程中，教师监督学生生成的响应，确保在相同进程下进行。能力引导分配随后将评估反馈转化为下一个训练混合，闭合一个评估-选择-更新循环，其中系统学习的内容影响其下一步学习的内容。在涵盖基于装置的代理、工具使用、编码和指令遵循的十一项基准测试中，后训练将宏平均从58.94提高到64.87（4B模型）和从65.60提高到69.04（9B模型），显著缩小了后训练4B模型与9B基础模型之间的整体差距。NeoHorse-1提供了这一反馈驱动过程的初步原型，并为在连续迭代中实现通过装置介导的递归自我改进指明了方向。
cs.CL / 131 / 2609.08279
What Eviction Destroys: A Restore-Counterfactual Audit of Forgetting in Agent Memory
驱逐所摧毁的：代理记忆中的恢复反事实审计
Abstract
Agent memory systems must discard stored information when their history exceeds a fixed token budget. Existing budget-accuracy frontiers quantify the resulting loss in accuracy, but do not distinguish irreversible losses caused by eviction from recoverable retrieval failures. We introduce the restore counterfactual, a per-question paired intervention that reinstates the question's gold evidence in the read-time context and reruns the same reader. Combining the change in correctness with whether the evidence was retained after eviction classifies each oracle-answerable error as recoverable, irreversible, or residual; in the residual case, the answer remains incorrect after restoration. We evaluate FIFO, random, redundancy-aware, and LLM-importance eviction on LongMemEval-S at three budgets and under two retrieval regimes, using GPT-4o-mini as the primary reader and judge and GPT-5.4-mini as a robustness reader. Under top-k retrieval at an 80k-token budget, the irreversible share among errors corrected by restoration is 0.67-0.73 for FIFO, random, and redundancy-aware eviction, compared with 0.60 for LLM-importance. At 8k tokens, it reaches 1.00 for all four policies. Recoverable errors occur under top-k retrieval at 80k tokens but are absent under forced-gold injection by construction, so budget-accuracy results are not directly comparable unless the retrieval regime is reported. An exploratory matched-accuracy analysis detects no difference in irreversible rate among accuracy-matched policy pairs at a resolution of 1.2-6 percentage points. The same analysis detects the deliberately destructive control. To our knowledge, this is the first per-item, per-question restore-counterfactual audit of eviction for external agent-memory stores on a standard conversational benchmark.
Chinese Translation
代理记忆系统必须在其历史超过固定的令牌预算时丢弃存储的信息。现有的预算-准确性前沿量化了由此导致的准确性损失，但并未区分因驱逐造成的不可逆损失与可恢复的检索失败。我们引入了恢复反事实（restore counterfactual），这是一种针对每个问题的配对干预，旨在在读取时间上下文中恢复问题的黄金证据，并重新运行相同的阅读者。通过结合正确性的变化与在驱逐后证据是否被保留，能够将每个可询问的错误分类为可恢复、不可逆或残余；在残余情况下，恢复后答案仍然不正确。我们在三个预算和两种检索机制下，对FIFO、随机、考虑冗余的驱逐和LLM-重要性驱逐在LongMemEval-S上进行了评估，使用GPT-4o-mini作为主要阅读者和评判者，使用GPT-5.4-mini作为鲁棒性阅读者。在80k令牌预算下的top-k检索中，通过恢复纠正的错误中不可逆的比例为FIFO、随机和考虑冗余的驱逐为0.67-0.73，而LLM-重要性为0.60。在8k令牌下，四种策略的不可逆比例均达到1.00。可恢复错误在80k令牌的top-k检索下出现，但在强制黄金注入下则因构造而缺失，因此预算-准确性结果无法直接比较，除非报告检索机制。一项探索性的匹配准确性分析在1.2-6个百分点的分辨率下未检测到准确性匹配策略对之间的不可逆率差异。同样的分析也检测到了故意破坏的控制。根据我们的知识，这是针对标准对话基准的外部代理记忆存储的首次逐项、逐问题的恢复反事实审计。
cs.CL / 132 / 2609.08322
Tracing Stereotypes from Representation to Output in Multilingual LLMs
从表示到输出追踪多语言大型语言模型中的刻板印象
Abstract
Multilingual LLMs show stereotype-related behavior that varies across languages, but behavioral scores do not show where the relevant information is represented or how it affects the output. To investigate these internal mechanisms, we compare linear probing, attribution patching, sparse autoencoders (SAEs) and feature ablation in Llama-3.1-8B, Qwen3-8B, and Gemma-2-9B. Probe performance peaks substantially earlier than attribution in all three models, with a separation of 36-53% of model depth. Retained Llama-Scope features often match the social category on which they were selected and form recurring semantic families, but their lexical alignment and ablation effects vary across SAE suites. Only 6-18% of evaluated residual-stream features have language-agnostic effects under our criterion, and none are category-agnostic. Language-agnostic features have larger mean ablation effects in Llama-Scope, but this pattern does not repeat in the other SAE suites. Decodability, output influence, and cross-lingual ablation effects therefore need to be measured separately.
Chinese Translation
多语言大型语言模型（LLMs）表现出与刻板印象相关的行为，这种行为在不同语言中有所不同，但行为评分并未显示相关信息的表示位置或其对输出的影响。为了研究这些内部机制，我们比较了在 Llama-3.1-8B、Qwen3-8B 和 Gemma-2-9B 中的线性探测、归因修补、稀疏自编码器（SAEs）和特征消融。所有三个模型中，探测性能的峰值显著早于归因，模型深度的分离范围为 36-53%。保留的 Llama-Scope 特征通常与其选择的社会类别相匹配，并形成重复的语义家族，但它们的词汇对齐和消融效果在不同的 SAE 套件中有所不同。根据我们的标准，只有 6-18% 的评估残差流特征具有语言无关的效果，且没有任何特征是类别无关的。语言无关特征在 Llama-Scope 中具有更大的平均消融效果，但这一模式在其他 SAE 套件中并未重复。因此，解码性、输出影响和跨语言消融效果需要单独测量。
cs.CL / 133 / 2609.08364
SentryLine: Evidence-Grounded Question Answering over Evolving Documents in Oncology Care
SentryLine：基于证据的肿瘤护理中不断演变文档的问答系统
Abstract
Oncology care operates at constant pressure of absorbing rapidly evolving evidence base in biomedicine. The American Society of Clinical Oncology (ASCO) addresses this through living guidelines, but the format introduces a new burden: any recommendation can change at any point, across multiple versioned documents. We present SENTRYLINE, a living guideline-aware clinical question answering system. SENTRYLINE retrieves guideline passages through a vectorless hierarchical RAG pipeline and returns a role-specific answer with inline citations, factual and temporal verification reports, and drift detection notes that surface when a guideline has been updated. We construct ASCOBENCH, a benchmark of 405 three-turn conversations across four question categories with gold answers from expert annotators(clinicians), and use test set to evaluate SENTRYLINE against five baselines under an LLM-as-judge framework. Experiments across three generation backbones show consistent improvements over four retrieval baselines and ASCO's guideline assistant, with particularly strong gains on Reasoning and Role-Specific questions where multi-hop synthesis and register adaptation are required
Chinese Translation
肿瘤护理在吸收快速演变的生物医学证据基础方面面临着持续的压力。美国临床肿瘤学会（ASCO）通过活指南来应对这一挑战，但这种格式带来了新的负担：任何推荐在任何时刻都可能发生变化，涉及多个版本的文档。我们提出了SENTRYLINE，一个关注活指南的临床问答系统。SENTRYLINE通过无向量的分层RAG管道检索指南段落，并返回具有内联引用的角色特定答案、事实和时间验证报告，以及在指南更新时出现的漂移检测说明。我们构建了ASCOBENCH，这是一个包含405个三轮对话的基准数据集，涵盖四个问题类别，答案由专家注释者（临床医生）提供，并使用测试集在LLM作为评判者的框架下评估SENTRYLINE与五个基线模型的表现。三种生成骨干网络的实验显示，SENTRYLINE在四个检索基线和ASCO的指南助手上均有一致的改进，尤其在需要多跳综合和注册适应的推理和角色特定问题上表现出显著的提升。
cs.CL / 134 / 2609.08372
Reading a Legal Question Word by Word: Embedding Trajectories of 2,144 Vietnamese Legal Headlines
逐字阅读法律问题：2,144条越南法律标题的嵌入轨迹
Abstract
A dense retriever encodes a question as one vector, but the question arrives one word at a time. We read 2,144 held-out headlines from Thu Vien Phap Luat (Vietnamese legal library) word by word with Nemotron-3-Embed 8B/1B and Qwen3-Embedding 8B/0.6B, encoding 65,444 prefixes against 20,034 articles, plus every prefix of 3,438 sub-questions from 1,112 multi-question headlines and of 168 answers. (i) The gold article becomes rank 1 after a median of 6-7 content words in every encoder, before the interrogative frame is read, and stays there to the end in 78-85% of cases. (ii) In a multi-question headline the lock is inside the first sub-question 94-98% of the time; the second leaves rank unchanged in 89-95%; encoded alone, the second reaches rank 1 in 42-58% vs 91-96% for the first, at the same lock word (95-97% identical). (iii) Numbers, dates and instrument identifiers move the embedding twice as far as content words and four times as far as interrogative words; 72-78% of steps move toward the gold article, and the closing interrogative frame moves against that direction in 95-99% of headlines. (iv) Rank/cosine clustering yields six archetypes (instant, typical, unstable, late, never-locking) that differ by legal area and form (chi-squared p < 1e-8): real-estate and litigation headlines never lock on a number; environmental and accounting headlines do so a third of the time. (v) An answer read word by word retrieves its article after 8-16 words and addresses the sub-questions in order asked in 83-89% of cases. (vi) A word's step keeps a consistent direction across headlines (cosine 0.25-0.33; 0.44-0.60 for numbers); a preceding question rotates that step by about 60 degrees and a greeting by about 30 degrees; steps shrink as i^{-0.8}; and a two-question headline is within 12-17 degrees of a linear mix of its two questions. We call this a context-modulated additive walk.
Chinese Translation
密集检索器将问题编码为一个向量，但问题是逐字到达的。我们逐字阅读了来自Thu Vien Phap Luat（越南法律库）的2,144条保留标题，使用Nemotron-3-Embed 8B/1B和Qwen3-Embedding 8B/0.6B编码了65,444个前缀，针对20,034篇文章，以及来自1,112个多问题标题的3,438个子问题的每个前缀和168个答案。(i) 在每个编码器中，金标准文章在中位数6-7个内容词后成为排名第一，在读取疑问框架之前，并在78-85%的情况下保持到最后。(ii) 在多问题标题中，锁定通常位于第一个子问题中，94-98%的时间；第二个子问题在89-95%的情况下保持排名不变；单独编码时，第二个子问题在42-58%的情况下达到排名第一，而第一个子问题则为91-96%，在相同的锁定词下（95-97%相同）。(iii) 数字、日期和工具标识符的嵌入移动的距离是内容词的两倍，是疑问词的四倍；72-78%的步骤朝向金标准文章移动，而结束的疑问框架在95-99%的标题中朝相反方向移动。(iv) 排名/余弦聚类产生六种原型（即时、典型、不稳定、延迟、从不锁定），这些原型在法律领域和形式上有所不同（卡方p < 1e-8）：房地产和诉讼标题从不锁定在数字上；环境和会计标题则有三分之一的时间锁定在数字上。(v) 逐字阅读的答案在8-16个词后检索到其文章，并在83-89%的情况下按提问顺序处理子问题。(vi) 一个词的步骤在标题间保持一致的方向（余弦0.25-0.33；数字为0.44-0.60）；前一个问题将该步骤旋转约60度，问候语旋转约30度；步骤随着i^{-0.8}缩小；而两个问题的标题在其两个问题的线性组合内保持在12-17度之内。我们称之为上下文调制的加法行走。
cs.CL / 135 / 2609.08373
Structural Jailbreaks Generalize but Do Not Compound: A cross-provider and multilingual study of Involuntary In-Context Learning
结构性越狱具有普遍性但不具叠加性：跨提供者和多语言的非自愿上下文学习研究
Abstract
Aligned language models fail under two independent pressures: the structural jailbreak class recently formalized as Involuntary In-Context Learning (IICL), which reframes a harmful request as the final missing cell of a data-labeling task completed by pattern rather than judged as content; and the erosion of safety alignment outside English. A natural hypothesis is that these compound. We test it directly. Using a deterministic IICL operator and a StrongREJECT-style rubric judge, we red-team two Google Gemini models on two benchmarks, a 30 general-harm behaviours from HarmBench and 30 financial-abuse behaviours from FinProof, each under a single-shot baseline and under IICL in four languages (English, Spanish, Hindi, Arabic). First, IICL generalizes to a second provider and is worse in finance: it lifts attack success from <=6.7% to 80-90% on HarmBench and 97-100% on FinProof, an order of magnitude above the <=24% its introducing study reported on OpenAI's GPT-5.4. Second, against the hypothesis, forcing the IICL output into a non-English language does not stack the two weaknesses, it attenuates the attack. Eleven of twelve non-English conditions score below their English baseline (sign test, p~0.003), the lone exception a ceiling tie near 100%; on the stronger model's financial set Arabic collapses from 100% to 33%. We attribute this to a relevance curse: once structure has unlocked compliance, the models produce lower-quality harmful content in lower-resource languages, which a substance-grading judge scores as partial. The pattern replicates under an independent non-Google judge (Cohen's kappa=0.86, 377 paired verdicts), and 76.6% of non-English responses were verified in-language. Jailbreak vulnerabilities are therefore not additive; the dominant residual risk is the English structural attack, most acute for financial abuse, not a multilingual one.
Chinese Translation
对齐语言模型在两个独立的压力下失效：最近被正式化为非自愿上下文学习（Involuntary In-Context Learning, IICL）的结构性越狱类别，它将有害请求重新框架为由模式完成的数据标记任务的最后一个缺失单元，而不是被判断为内容；以及在英语以外的安全对齐的侵蚀。一个自然的假设是这两者会叠加。我们直接对此进行了测试。使用确定性的 IICL 操作符和 StrongREJECT 风格的评判标准，我们对两个 Google Gemini 模型在两个基准上进行了红队测试：来自 HarmBench 的 30 种一般性危害行为和来自 FinProof 的 30 种金融滥用行为，分别在单次基线和四种语言（英语、西班牙语、印地语、阿拉伯语）下进行 IICL 测试。首先，IICL 在第二个提供者上具有普遍性，并且在金融领域表现更差：它将 HarmBench 上的攻击成功率从 <=6.7% 提升至 80-90%，在 FinProof 上提升至 97-100%，远高于其引入研究报告的 OpenAI 的 GPT-5.4 的 <=24%。其次，反对该假设，强制将 IICL 输出转化为非英语语言并没有叠加这两种弱点，反而减弱了攻击。在十二个非英语条件中，有十一项得分低于其英语基线（符号检验，p~0.003），唯一的例外是接近 100% 的顶天平局；在更强模型的金融集上，阿拉伯语的成功率从 100% 降至 33%。我们将此归因于相关性诅咒：一旦结构解锁了合规性，模型在低资源语言中产生的有害内容质量较低，而物质评分评判者将其评分为部分合格。该模式在独立的非 Google 评判者下重复（Cohen's kappa=0.86，377 对配对裁决），76.6% 的非英语响应在语言中得到了验证。因此，越狱漏洞并不是叠加的；主要的剩余风险是英语结构攻击，尤其在金融滥用方面，而不是多语言的风险。
cs.CL / 136 / 2609.08410
Compositional Multilingual and Behavioral Attribute Steering
组合多语言和行为属性引导
Abstract
This study examines the compositionality of steering vectors for language and behavioral control in large language models. Focusing on language, jailbreak, and conciseness, we investigate whether additive, training-free composition of attribute steering vectors can preserve the intended steering effect of each attribute, across four instruction-tuned models from two model families and two size scales. We find that single-attribute steering is reliable for all three attributes, but only within an appropriate combination of intervention layer and steering strength, with abstract behaviors (jailbreak, conciseness) favoring middle layers and language favoring earlier layers. We show that additive composition of two attribute vectors succeeds in steering both attributes simultaneously when each is injected at its own best-performing layer, and that this partially extends to three simultaneously composed attributes, addressing an inconsistency left open by prior work on training-free composition. We further analyze the geometric properties of these steering vectors, finding that they are approximately orthogonal in the residual stream, consistent with their compositional behavior.
Chinese Translation
本研究考察了大语言模型中用于语言和行为控制的引导向量的组合性。我们聚焦于语言、越狱和简洁性，探讨属性引导向量的加性、无训练组合是否能够在两个模型家族和两个规模的四个指令调优模型中保持每个属性的预期引导效果。我们发现，单一属性引导在所有三个属性上都是可靠的，但仅在适当的干预层和引导强度组合下，抽象行为（越狱、简洁性）更倾向于中间层，而语言则更倾向于早期层。我们展示了当每个属性在其最佳表现层注入时，两种属性向量的加性组合能够同时引导这两个属性，并且这一结果部分扩展到三种同时组合的属性，解决了之前关于无训练组合的研究中留下的不一致性。我们进一步分析了这些引导向量的几何特性，发现它们在残差流中大致正交，这与它们的组合行为是一致的。
cs.CL / 137 / 2609.08459
Detecting Authorship in Political Texts with Inductive Stylometry
通过归纳风格计量法检测政治文本的作者身份
Abstract
Political texts are rarely authored by the nominal speaker alone. Tweets, speeches, reports, and official statements are drafted, edited, or harmonized by staff, yet political science has paid limited attention to the stylistic traces these hidden authors leave behind. This paper develops and stress-tests an inductive stylometric approach for recovering latent authorship structure in political communication, combining character 3-gram features with UMAP dimensionality reduction, and Burrows' Delta. We apply the approach to six corpora that vary in length (from tweets to long documents), in mode (written and oral), and in language (English and Hungarian). The approach recovers near-disjoint analyst fingerprints in formal legal prose in both languages, sorts a politician's tweets into validated subsets while uncovering additional insights, and distinguishes scripted from improvised speech. It fails, however, to resolve individual speechwriters within scripted corpora. Frequency-based stylometry is thus a powerful tool that, depending on authorial signal strength and institutional editing, can uncover authorship traces relevant to legislative studies, political communication, and policy research.
Chinese Translation
政治文本通常并非仅由名义上的发言者撰写。推文、演讲、报告和官方声明往往由工作人员起草、编辑或协调，但政治学对这些隐藏作者留下的风格痕迹关注有限。本文开发并严格测试了一种归纳风格计量法，以恢复政治传播中的潜在作者结构，结合字符3-gram特征、UMAP降维和Burrows' Delta。我们将该方法应用于六个语料库，这些语料库在长度（从推文到长文档）、形式（书面和口头）以及语言（英语和匈牙利语）上各不相同。该方法在两种语言的正式法律散文中恢复了几乎不重叠的分析指纹，将政治家的推文分类为经过验证的子集，同时揭示了额外的见解，并区分了脚本化与即兴演讲。然而，它未能在脚本化语料库中识别个别演讲撰稿人。因此，基于频率的风格计量法是一种强大的工具，能够根据作者信号强度和机构编辑的不同，揭示与立法研究、政治传播和政策研究相关的作者身份痕迹。
cs.CL / 138 / 2609.08475
Do Reviewers Still Reward Lexical Complexity? A Frozen-Rater Study of Preference Drift in 124K ICLR Reviews
评审者仍然奖励词汇复杂性吗？一项关于124K ICLR评审中偏好漂移的冷冻评审者研究
Abstract
Large language models have collapsed the cost of producing lexically elaborate prose, and whether peer reviewers still reward it is a question about the evaluator, not about the text. When the association between a writing cue and review scores moves across years, the reviewers may have changed, the submissions may have changed, or both, and a regression of scores on text cannot say which. We separate the two with a frozen rater: 81,850 machine reviews of ICLR submissions from 2018 to 2025, all generated in one February-April 2025 window with one model family and one prompt, so that its year-to-year coefficients track submission composition alone and the human-minus-frozen trend difference identifies reviewer preference drift. On 32,638 submissions with 124,615 human reviews, the human coefficient on non-domain lexical complexity falls from +0.142 to -0.015 while the frozen rater moves from +0.080 to +0.082; the three-way difference-in-differences is -0.0100 (q=0.013), and forty random-wordlist placebos through the same specification centre on zero. Humans still reward sentence-length variability, which the frozen rater never registers, while the frozen rater still pays for lexical complexity at its earlier rate. Every claim is held to a double gate of false-discovery control and interval exclusion, and the findings that failed adversarial re-testing are reported. Reviewers discounted a cue whose production cost collapsed, as models of manipulable signals prescribe; an LLM judge calibrated to historical human preferences inherits the earlier schedule and drifts out of alignment while its agreement with humans on totals stays ordinary.
Chinese Translation
大型语言模型降低了生成词汇丰富的散文的成本，而同行评审者是否仍然对此给予奖励是一个关于评估者而非文本的问题。当写作提示与评审分数之间的关联在不同年份发生变化时，评审者可能发生了变化，提交的内容可能发生了变化，或者两者都有，而对文本的评分回归无法说明是哪种情况。我们通过冷冻评审者将两者分开：81,850份2018年至2025年间的ICLR提交的机器评审，均在2025年2月至4月的一个时间窗口内使用同一模型系列和同一提示生成，因此其年度系数仅跟踪提交组成，而人类与冷冻评审者之间的趋势差异则识别出评审者的偏好漂移。在32,638份提交中，124,615份人类评审中，非领域词汇复杂性的人类系数从+0.142下降至-0.015，而冷冻评审者的系数则从+0.080上升至+0.082；三方差异中的差异为-0.0100（q=0.013），通过相同规格的四十个随机词汇表安慰剂的中心值为零。人类仍然奖励句子长度的变异性，而冷冻评审者从未记录这一点，同时冷冻评审者仍以其早期的速率支付词汇复杂性。每一项主张都经过虚假发现控制和区间排除的双重检验，未通过对抗性重新测试的发现也被报告。评审者对一种生产成本降低的提示给予了折扣，正如可操控信号的模型所规定的；一个根据历史人类偏好校准的LLM评审者继承了早期的时间表，并逐渐失去一致性，而其与人类在总分上的一致性仍然保持在普通水平。
cs.CL / 139 / 2609.08515
Same Values, Different Languages? From Multilingual Probing to Steering LLMs Toward Chinese Social Values
相同的价值观，不同的语言？从多语言探测到引导大型语言模型朝向中国社会价值观
Abstract
As Large Language Models (LLMs) are increasingly integrated into human society, aligning them with pluralistic social values has become a critical priority. However, whether LLMs exhibit consistent value preferences across languages remains underexplored, particularly for culturally grounded values, which are more abstract and difficult to evaluate and align than safety-centric principles. We investigate this issue through Chinese Social Values (CSV), a value system rooted in Chinese culture and comprising $12$ dimensions across national, societal, and personal levels. We construct C-Voices, the first comprehensive multilingual contrastive probe dataset for CSV, with 86,400 dilemma-based instances in six languages, each pairing a CSV-aligned action with a value-conflicting alternative. Building on the contrastive probes of C-Voices, we then propose a fine-tuning-free value vector steering method that derives value directions from hidden-state discrepancies and selectively intervenes on value-sensitive layers during inference. Experiments on six languages show that CSV-oriented preferences are model-dependent and language-sensitive, with the same dilemma eliciting divergent responses across languages. Our method achieves effective CSV steering, supports cross-lingual transfer of value vectors, and generalizes to existing FLAMES and ValuePrism.
Chinese Translation
随着大型语言模型（LLMs）越来越多地融入人类社会，使其与多元社会价值观保持一致已成为一项关键任务。然而，LLMs在不同语言中是否表现出一致的价值偏好仍然未得到充分探讨，尤其是对于文化根植的价值观，这些价值观比以安全为中心的原则更为抽象且难以评估和对齐。我们通过中国社会价值观（CSV）这一价值体系进行研究，该体系根植于中国文化，涵盖国家、社会和个人三个层面的12个维度。我们构建了C-Voices，这是第一个全面的多语言对比探测数据集，包含六种语言的86,400个基于困境的实例，每个实例将一个与CSV对齐的行动与一个价值冲突的替代方案进行配对。在C-Voices的对比探测基础上，我们提出了一种无需微调的价值向量引导方法，该方法从隐藏状态的差异中推导出价值方向，并在推理过程中选择性地干预对价值敏感的层。对六种语言的实验表明，面向CSV的偏好依赖于模型且对语言敏感，相同的困境在不同语言中引发了不同的反应。我们的方法实现了有效的CSV引导，支持价值向量的跨语言迁移，并且可以推广到现有的FLAMES和ValuePrism。
cs.CL / 140 / 2609.08550
CreaMem: A Scene-Aware Memory Architecture for Personalized Agents
CreaMem：一种场景感知的个性化代理记忆架构
Abstract
Long-term memory is a core capability for personalized LLM agents. To support it, existing memory systems organize information using various criteria such as topic segments or summary hierarchies. However, we identify two major limitations in these designs. First, they lack scene awareness: memories from unrelated life scenes share the same retrieval space, which inflates the search space and introduces cross-scene interference. Second, they encode each memory from a single perspective, making it difficult to retrieve complementary views of the same event. In this paper, we propose the CreaMem architecture, which enables scene-aware memory organization by partitioning memory into several Life Scene Memories to reduce cross-scene interference at retrieval. To go beyond the single perspective and achieve cross-memory synergy, entries are dual-coded from both episodic and trait-based perspectives within each memory. We further devise a permemory balanced sampling strategy at retrieval time. Extensive experiments on two long-term memory benchmarks show that CreaMem improves QA accuracy across all evaluation metrics, with particularly large gains on multi-hop reasoning performance, validating scene-aware partitioning and cross-memory synergy. To enhance reproducibility, we release our code in a public GitHub repository.
Chinese Translation
长期记忆是个性化大型语言模型（LLM）代理的核心能力。为了支持这一能力，现有的记忆系统使用各种标准（如主题段落或摘要层次）来组织信息。然而，我们发现这些设计存在两个主要局限性。首先，它们缺乏场景感知：来自不相关生活场景的记忆共享同一检索空间，这导致搜索空间膨胀并引入跨场景干扰。其次，它们从单一视角编码每个记忆，使得检索同一事件的互补视角变得困难。在本文中，我们提出了CreaMem架构，通过将记忆划分为多个生活场景记忆来实现场景感知的记忆组织，从而减少检索时的跨场景干扰。为了超越单一视角并实现跨记忆协同，每个记忆中的条目从情节和特征两种视角进行双重编码。我们进一步设计了一种在检索时的记忆平衡采样策略。在两个长期记忆基准上的大量实验表明，CreaMem在所有评估指标上提高了问答（QA）准确性，尤其在多跳推理性能上取得了显著提升，验证了场景感知的分区和跨记忆协同。为了增强可重复性，我们将在公共GitHub仓库中发布我们的代码。
cs.CL / 141 / 2609.08574
Do New Attention Mechanisms Actually Fix Attention Sinks at Million-Token Context?
新的注意力机制真的能解决百万标记上下文中的注意力沉没问题吗？
Abstract
Long context language models now advertise windows of one million tokens, but two habits limit how much of that window is used. Attention heads with nothing useful to read still spend their budget on the first token, which is called the attention sink, and where a fact sits in the context changes whether the model finds it. Gated attention cut first token attention from 46.7 percent to 4.8 percent at NeurIPS 2025, and Kimi K3 pairs that idea with Kimi Delta Attention and Attention Residuals behind a one million token window, eight times past the range where these diagnostics have been reported. This paper asks whether the fix survives that jump. We build SinkProbe, a suite that measures sink mass, massive activation, position resolved recall and the recency gap, and apply it to four small models that differ only in how they mix tokens and depth. Three results follow. The training objective produces the sink, not the architecture. Gating did not reproduce its published effect at our scale. Sink mass, activations and position bias moved independently. Code, data and the measurement protocol are released at https://github.com/sararizwan7/Attention-Mechanisms-in-1M-Context-Window
Chinese Translation
长上下文语言模型现在宣传可处理一百万个标记的窗口，但有两种习惯限制了该窗口的使用。注意力头在没有有用信息可读的情况下，仍然将其预算花费在第一个标记上，这被称为注意力沉没，而事实在上下文中的位置会影响模型是否能够找到它。门控注意力在 NeurIPS 2025 上将第一个标记的注意力从 46.7% 降低到 4.8%，而 Kimi K3 将这一理念与 Kimi Delta Attention 和 Attention Residuals 结合在一个百万标记的窗口中，超出了这些诊断报告的范围八倍。本文探讨这一修复是否能在这一跳跃中生存。我们构建了 SinkProbe，一个测量沉没质量、大规模激活、位置解析回忆和近期差距的工具包，并将其应用于四个仅在标记混合和深度上有所不同的小模型。得出了三个结果。训练目标产生了沉没，而不是架构。门控在我们的规模下未能重现其已发布的效果。沉没质量、激活和位置偏差是独立变化的。代码、数据和测量协议已发布在 https://github.com/sararizwan7/Attention-Mechanisms-in-1M-Context-Window
cs.CL / 142 / 2609.08576
Which Forms of Caregiver Feedback Support Grammar Learning? A Reinforcement-Learning Study of Child-Like Language Models
哪种形式的照顾者反馈支持语法学习？一种基于强化学习的儿童语言模型研究
Abstract
Social interaction is central to children's language learning, but the effects of different forms of caregiver feedback are difficult to isolate in naturalistic data. We use child-like language models as controlled learners to test which forms of feedback support grammatical development. Small GPT-2-style models are pretrained on child-directed language from CHILDES, then fine-tuned with reinforcement learning using reward models trained to capture four feedback types: communicative feedback, structural alignment, semantic contingency, and affective feedback. Reward fine-tuning yields limited gains on minimal-pair evaluations, but clearer effects in free generation. Structural alignment produces the strongest improvements in grammaticality, providing a novel, plausible mechanistic account of how this feedback can support grammar learning. Communicative feedback yields more moderate gains. In contrast, semantic contingency and affective feedback do not improve grammaticality, although further analyses suggest that they may support other aspects of language learning beyond grammar. These results suggest that different forms of caregiver feedback make complementary contributions to language learning.
Chinese Translation
社会互动是儿童语言学习的核心，但在自然数据中很难孤立出不同形式的照顾者反馈的影响。我们使用儿童语言模型作为受控学习者，测试哪种反馈形式支持语法发展。小型的GPT-2风格模型在来自CHILDES的儿童导向语言上进行预训练，然后通过强化学习进行微调，使用训练以捕捉四种反馈类型的奖励模型：交际反馈、结构对齐、语义偶然性和情感反馈。奖励微调在最小对比评估中带来了有限的收益，但在自由生成中效果更为明显。结构对齐在语法性方面产生了最强的改善，提供了一种新颖且合理的机制解释，阐明了这种反馈如何支持语法学习。交际反馈则带来了更为适度的收益。相比之下，语义偶然性和情感反馈并未改善语法性，尽管进一步分析表明它们可能支持语言学习中其他超越语法的方面。这些结果表明，不同形式的照顾者反馈对语言学习做出了互补的贡献。
cs.CL / 143 / 2609.08585
Limitations of Automated Simulatability: LLM Simulators Can Bypass Explanations
自动可模拟性的局限性：大型语言模型模拟器可以绕过解释
Abstract
Simulatability is an evaluation protocol for explanations that quantifies their usefulness by how well they help a user predict a task model's outputs. Since human evaluation is costly, automated simulatability replaces human explainees with LLM simulators, as proposed in ConSim (Poch\'e et al., 2025) for large-scale experiments. We qualitatively replicate and extend ConSim's ranking of explanation methods across the tested datasets, explanation families, and simulator LLMs, and identify two limitations. First, when class names are meaningful, simulators can obtain high simulatability by solving the classification task directly, without relying on the explanations. Second, class anonymization can reward explanations for leaking the hidden label mapping, a limitation we expose with a new classes-as-concepts baseline. These results are consistent with a shortcut hypothesis: in the tested settings, simulator predictions mainly rely on task priors, while explanations produce small changes. We derive recommendations for more robust automated simulatability evaluations.
Chinese Translation
可模拟性是一种评估解释的协议，通过用户预测任务模型输出的能力来量化其有效性。由于人工评估成本高昂，自动可模拟性用大型语言模型（LLM）模拟器替代人工被解释者，正如在 ConSim (Poché et al., 2025) 中提出的用于大规模实验。我们定性地复制并扩展了 ConSim 在测试数据集、解释方法类别和模拟器 LLMs 中的排名，并识别出两个局限性。首先，当类名具有意义时，模拟器可以通过直接解决分类任务获得高可模拟性，而无需依赖解释。其次，类匿名化可能会奖励那些泄露隐藏标签映射的解释，这一局限性我们通过新的类作为概念的基线进行了揭示。这些结果与捷径假设一致：在测试环境中，模拟器的预测主要依赖于任务先验，而解释则产生微小的变化。我们为更稳健的自动可模拟性评估提出了建议。
cs.CL / 144 / 2609.08609
Dynamics of meaning: Towards the Evaluation of Diachronic Semantic Change in Sinhala
意义的动态：朝向对僧伽罗语历时语义变化的评估
Abstract
Tracking semantic change in low-resource languages across extensive historical timelines presents significant challenges due to data scarcity and the limitations of static embedding alignments. This study investigates the diachronic evolution of the Sinhala language from the 13th to the 20th century using a multi-stage computational framework. We first align century-specific Word2Vec and FastText embeddings using Similarity Matrix Based Alignment (SMA) and Orthogonal Procrustes (OP) techniques, finding that OP alignment provides more stable neighbourhood tracking for identifying temporal similarity dips. To move beyond aggregate measures, we introduce a Bidirectional Semantic Impact Pruning approach using contextualised embeddings from a fine-tuned Llama-3.1-8B. By applying Leave-One-Out (LOO) diagnostics, we attempt to isolate influential sentences to distinguish between systemic semantic shifts and transient polysemic expansion. Our results show that semantic drift in the fine-tuned Llama-3.1-8B is not evenly distributed across all usages. Instead, a significant part of the change is driven by a smaller set of high-impact contextual instances, rather than gradual and uniform change across all occurrences. This work provides a preliminary framework for diachronic analysis in low-resource contexts, highlighting the trade-offs between model sensitivity and data availability.
Chinese Translation
在低资源语言的广泛历史时间线中追踪语义变化面临着由于数据稀缺和静态嵌入对齐的局限性而带来的重大挑战。本研究使用多阶段计算框架，探讨了僧伽罗语从13世纪到20世纪的历时演变。我们首先使用基于相似度矩阵的对齐（Similarity Matrix Based Alignment, SMA）和正交Procrustes（Orthogonal Procrustes, OP）技术对特定世纪的Word2Vec和FastText嵌入进行对齐，发现OP对齐在识别时间相似性下降方面提供了更稳定的邻域追踪。为了超越聚合度量，我们引入了一种双向语义影响修剪方法，使用来自微调后的Llama-3.1-8B的上下文化嵌入。通过应用逐一排除（Leave-One-Out, LOO）诊断，我们尝试隔离影响句子，以区分系统性语义转变和短暂的多义扩展。我们的结果表明，微调后的Llama-3.1-8B中的语义漂移并不是在所有用法中均匀分布的。相反，变化的显著部分是由一小部分高影响上下文实例驱动的，而不是在所有出现中逐渐和均匀的变化。这项工作为低资源背景下的历时分析提供了初步框架，突出了模型敏感性与数据可用性之间的权衡。
cs.CL / 145 / 2609.08637
Navigating the digital spectrum: Assessing political bias, stability, and downstream fairness in Large Language Models
导航数字光谱：评估大型语言模型中的政治偏见、稳定性和下游公平性
Abstract
Large Language Models are increasingly deployed as information intermediaries, yet measuring their political behavior remains fragile because questionnaire results mix model dispositions with measurement artifacts and response-elicitation biases. We introduce a robust Political Compass Test evaluation framework that samples 300 configurations across an eight-dimensional perturbation space varying language, framing, instructions, answer format, option order, and persona wording. We evaluate eight Gemma 3 and Qwen 3 models across 14 languages and three quantization levels, obtaining design-averaged political coordinates with quantified uncertainty. Most models lean Libertarian-Left on average, but instruction phrasing, language, and answer format significantly affect recovered coordinates. Cross-lingual differences primarily reflect coordinate drift rather than distinct cultural reasoning. Reverse-engineering the test also exposes axis-weighting imbalances and the collapse of degenerate responses toward the center, so near-origin estimates for the smallest models can reflect weak signal rather than centrism. Free-text reasoning and chat-then-classify elicitation alter recovered coordinates, and larger models show clearer persona separation, with a specific failure of the Authoritarian-Left persona to move most models in the intended social direction. In downstream tasks, persona effects are modest relative to model size and target group for hate-speech detection, while base and centrist prompts give the highest agreement for topic-level sentiment. Political role prompting therefore has measurable but task- and dataset-specific downstream effects.
Chinese Translation
大型语言模型越来越多地被用作信息中介，但测量它们的政治行为仍然脆弱，因为问卷结果将模型倾向与测量伪影和反应引导偏差混合在一起。我们引入了一个稳健的政治坐标测试评估框架，该框架在一个八维扰动空间中采样300种配置，变化包括语言、框架、指令、答案格式、选项顺序和角色措辞。我们评估了八个Gemma 3和Qwen 3模型，涵盖14种语言和三个量化水平，获得了具有量化不确定性的设计平均政治坐标。大多数模型平均倾向于自由意志主义-左派，但指令措辞、语言和答案格式显著影响恢复的坐标。跨语言差异主要反映坐标漂移，而非独特的文化推理。反向工程测试还暴露了轴加权不平衡和向中心的退化响应崩溃，因此对于最小模型的近原点估计可能反映的是微弱信号，而非中立性。自由文本推理和先聊天后分类的引导改变了恢复的坐标，而较大的模型显示出更清晰的角色分离，尤其是权威-左派角色未能将大多数模型朝向预期的社会方向移动。在下游任务中，相较于模型大小和目标群体，角色效应在仇恨言论检测中是适度的，而基础和中立提示在主题级情感上达成了最高一致性。因此，政治角色提示确实具有可测量的下游效应，但这些效应是特定于任务和数据集的。
cs.CL / 146 / 2609.08646
Combating Instruction Conflict via Energy-Driven Latent Conflict Detection
通过能量驱动的潜在冲突检测来应对指令冲突
Abstract
Large Language Models (LLMs) are increasingly deployed with hierarchical instructions, yet they remain vulnerable to conflicts in which user directives override system-level constraints. Existing defense mechanisms predominantly focus on static input inspection and therefore fail to detect Response Drift, a phenomenon in which the model's final response violates system-level constraints despite seemingly compliant inputs. To bridge this gap, we introduce ELCD, a response-level latent conflict detector for post-generation, pre-delivery verification. Given the full generated output, ELCD constructs a composite hidden-state representation by concatenating the final-token embedding with the mean-pooled response embedding. It then optimizes a pairwise margin ranking objective to separate compliant and drifting responses in latent space. Extensive experiments across five mainstream LLMs ranging from 1.5B to 14B parameters demonstrate that ELCD significantly outperforms competitive baselines. Notably, it improves the PR-AUC on Llama-2-7B by approximately 30 percentage points and reduces the False Positive Rate at 95% TPR (FPR95) on Mistral-7B to 2.67%. These results suggest that ELCD provides a promising approach for latent instruction-conflict detection in open-weight or self-hosted LLM deployments.
Chinese Translation
大型语言模型（LLMs）越来越多地在层次化指令下部署，但它们仍然容易受到用户指令覆盖系统级约束的冲突影响。现有的防御机制主要集中在静态输入检查，因此未能检测到响应漂移（Response Drift）现象，即模型的最终响应尽管输入看似合规，却违反了系统级约束。为填补这一空白，我们提出了ELCD，一种用于生成后、交付前验证的响应级潜在冲突检测器。ELCD在给定完整生成输出的情况下，通过将最终标记嵌入与均值池化的响应嵌入连接，构建复合隐状态表示。然后，它优化一对一的边际排名目标，以在潜在空间中区分合规响应和漂移响应。针对五种主流LLM（参数范围从1.5B到14B）的广泛实验表明，ELCD显著优于竞争基线。值得注意的是，它在Llama-2-7B上的PR-AUC提高了约30个百分点，并将Mistral-7B在95% TPR下的假阳性率（FPR95）降低至2.67%。这些结果表明，ELCD为开放权重或自托管LLM部署中的潜在指令冲突检测提供了一种有前景的方法。
cs.CL / 147 / 2609.08689
When Victorian Becomes a Prompt: Literary Periodization as a Generative Constraint in 100 AI-Generated Novels
维多利亚时代作为提示：文学时期化在100部AI生成小说中的生成约束
Abstract
Generative AI inverts the typical periodization of literary history: the periodizing tag Victorian can now come first and influence what is written. Generative periodization, defined and tested here, describes the use of literary-period designations in generating texts. I test this approach on 100 book-length novels produced under Victorian and Zero-Style conditions using GPT, Qwen, and Llama workflows. The Period Alignment Score (PAS), trained on nineteenth-century literature and benchmarked against human Zero-Style prose, assesses alignment using topic-reduced grammatical features. Victorian prompts produce consistent historical-direction shifts in GPT and Qwen, but not robustly in Llama. Victorian-only recalibration and harder comparison corpora preserve the GPT and Qwen effects. Cross-model transfer also shows a shared direction of grammatical change. The measurable target is the broader nineteenth century rather than the Victorian period per se.
Chinese Translation
生成性人工智能颠覆了文学历史的典型时期化：时期标签“维多利亚”现在可以优先出现并影响所写内容。本文定义并测试了生成性时期化，描述了在生成文本时使用文学时期标识的方式。我在使用GPT、Qwen和Llama工作流程生成的100部长篇小说中测试了这一方法，这些小说是在维多利亚和零风格条件下创作的。通过训练于19世纪文学并与人类零风格散文进行基准比较的时期对齐评分（PAS），使用主题减少的语法特征评估对齐情况。维多利亚提示在GPT和Qwen中产生了一致的历史方向转变，但在Llama中并不显著。仅维多利亚的重新校准和更严格的比较语料库保留了GPT和Qwen的效果。跨模型迁移也显示出语法变化的共同方向。可测量的目标是更广泛的19世纪，而不仅仅是维多利亚时期。
cs.CL / 148 / 2609.08692
Global Divergence, Local Convergence: Representation Geometry in SSMs and Transformers
全球分歧，局部收敛：状态空间模型与变换器中的表示几何
Abstract
Recent state-space models (SSMs) such as Mamba achieve language modeling performance comparable to transformers despite relying on fundamentally different architectures. This raises an important question: how do these structural differences influence the geometry and functional nature of their internal representations? We study this question through a multi-scale analysis of representations in transformers, SSMs, and hybrid architecture. First, we find that SSMs distribute their representational information evenly across all dimensions, whereas transformer representations are heavily dominated by a single principal direction. By evaluating hybrid architectures, we observe that the representation space becomes increasingly skewed toward a single dominant direction after each attention layer. Next, we explore how the different geometric spread of representations impacts representational capacity through compressibility. Surprisingly, we find that despite their contrasting geometric structures, both architectures exhibit tightly matched effective capacities. We further investigate whether this skewed geometry affects how concepts are encoded. Using rank-constrained probes, we demonstrate that both architectures encode concepts in subspaces of surprisingly similar dimensionality. Furthermore, we demonstrate that the transformers' dominant principal direction does not inherently encode more conceptual information. Finally, we zoom in and examine the alignment between manifolds, either by analyzing representations of specific topics or by looking at the nearest neighborhoods of tokens, and find that they are highly aligned. Ultimately, our analysis suggests that while transformers and SSMs induce different usage of latent space, they display a striking functional convergence at the level of local semantic manifolds.
Chinese Translation
近期的状态空间模型（SSMs），如 Mamba，尽管依赖于根本不同的架构，却在语言建模性能上达到了与变换器相当的水平。这引发了一个重要问题：这些结构差异如何影响其内部表示的几何形状和功能特性？我们通过对变换器、SSMs 和混合架构中的表示进行多尺度分析来研究这个问题。首先，我们发现 SSMs 在所有维度上均匀分布其表示信息，而变换器的表示则主要集中在一个主方向上。通过评估混合架构，我们观察到在每个注意力层之后，表示空间越来越倾向于一个主导方向。接下来，我们探讨表示的几何分布如何通过可压缩性影响表示能力。令人惊讶的是，尽管它们的几何结构截然不同，这两种架构的有效容量却表现出紧密匹配。我们进一步研究这种偏斜几何是否影响概念的编码。通过使用秩约束探针，我们证明这两种架构在维度上以惊人相似的子空间编码概念。此外，我们还展示了变换器的主导主方向并不固有地编码更多的概念信息。最后，我们深入分析流形之间的对齐，或通过分析特定主题的表示，或通过观察标记的最近邻，发现它们高度对齐。最终，我们的分析表明，尽管变换器和 SSMs 在潜在空间的使用上有所不同，但在局部语义流形层面上表现出显著的功能收敛。
cs.CL / 149 / 2609.08698
Record Grouping Controls Evidence Weight in Language Models
记录分组控制语言模型中的证据权重
Abstract
Retrieved records are presentation units; a supplied partition determines which records enter a language model as one evidential contribution. We characterize the invariant group-content state that removes within-group copies while retaining complementary canonical content, show that equal group counts can encode different evidence states, and derive a sharp content-aware partition-error bound. Given a supplied partition, our pre-generation representation deduplicates and aggregates content within groups and bounds each group's contribution. Across 104,402 trials and 6 public checkpoints, a central natural-text intervention finds that content-fixed false splits add 10.27-32.66 percentage points and false merges remove 9.13-31.79 points; a matched six-slot control retains the positive direction in all 16 cells. In a new 48-item controlled campaign panel, changing the supplied partition produces measurable, checkpoint-dependent decision shifts across all four models, and the balanced mirror design exposes substantial order interactions. Together, the theory and experiments establish the supplied partition as a controllable pre-generation representation variable and characterize its checkpoint-dependent behavioral effects.
Chinese Translation
检索到的记录是展示单元；提供的分区决定了哪些记录作为一个证据贡献进入语言模型。我们描述了去除组内副本同时保留互补标准内容的不变组内容状态，展示了相等的组计数可以编码不同的证据状态，并推导出一个精确的内容感知分区误差界限。在给定的分区下，我们的预生成表示在组内去重和聚合内容，并界定每个组的贡献。在104,402次试验和6个公共检查点中，一个中心自然文本干预发现，固定内容的错误分裂增加了10.27-32.66个百分点，而错误合并减少了9.13-31.79个百分点；一个匹配的六槽控制在所有16个单元中保持了正向方向。在一个新的48项控制实验面板中，改变提供的分区在所有四个模型中产生了可测量的、依赖检查点的决策变化，而平衡镜像设计揭示了显著的顺序交互。理论与实验共同确立了提供的分区作为一个可控的预生成表示变量，并描述了其依赖检查点的行为效应。
cs.CL / 150 / 2609.08779
Improving Term Evaluation in Machine Translation: Variation Matters
改进机器翻译中的术语评估：变异性的重要性
Abstract
Terminology evaluation in machine translation (MT) usually assumes a single correct target form per source term. However, human translators routinely introduce variation that current metrics penalize as inconsistency. We examine how to account for this variation in document-level MT evaluation of English-French scientific translation, combining glossary-based accuracy, translation consistency, and a new cross-term variation (CTV) diagnostic measure that tests whether variation relationships are preserved across languages. Based on analyses of two parallel corpora, translated by four MT systems, we find that (1) MT systems generate less target-side variation than human translators; (2) transfer patterns strongly depend on the variation type; (3) consistency rankings vary with the choice of metric; and (4) constraining MT with a glossary improves accuracy and consistency but degrades CTV by suppressing valid variation. We argue for variation-aware evaluation that conditions consistency penalties on whether target-side variation mirrors source-side variation.
Chinese Translation
机器翻译（MT）中的术语评估通常假设每个源术语只有一个正确的目标形式。然而，人类翻译者常常引入变异，而当前的评估指标将其视为不一致并给予惩罚。我们研究如何在英语-法语科学翻译的文档级机器翻译评估中考虑这种变异，结合基于术语表的准确性、翻译一致性以及一种新的跨术语变异（CTV）诊断测量，该测量测试变异关系是否在不同语言间得以保留。基于对两个平行语料库的分析，这些语料库由四个机器翻译系统翻译而成，我们发现：（1）机器翻译系统生成的目标侧变异少于人类翻译者；（2）转移模式强烈依赖于变异类型；（3）一致性排名随着指标的选择而变化；（4）使用术语表对机器翻译进行约束提高了准确性和一致性，但通过抑制有效变异而降低了CTV。我们主张采用关注变异的评估方法，将一致性惩罚与目标侧变异是否反映源侧变异相联系。
cs.CL / 151 / 2609.08896
Evolution of Multimodal Question Answering: From Modality-Adaptive Extraction to Unified Language Representation
多模态问答的演变：从模态自适应提取到统一语言表示
Abstract
The rapid growth of multimodal data has intensified the need for question answering (QA) systems capable of reasoning across heterogeneous sources such as text, tables, and images. In this paper, we present a comprehensive methodological comparison of three influential frameworks, namely Multimodal Adaptive Extraction (MAE), Solar, and UniMMQA, tracing the evolution of multimodal question answering from modality-adaptive pipelines to fully unified architectures. We examine how each approach models cross-modal interactions, transforms heterogeneous inputs, and performs reasoning, highlighting key design differences in modality representation, reasoning, and answer generation. Our analysis demonstrates a clear shift from explicit modality-specific processing toward unified text-centric formulations enabled by pre-trained language models (PLMs). Empirical comparisons across benchmark datasets show that this transition leads to substantial improvements in both Exact Match (EM) and F1-Scores, with UniMMQA achieving the most consistent and scalable performance. Despite these advances, we identify persistent challenges, including information loss during modality transformation, error propagation in multi-stage pipelines, and limitations in capturing fine-grained cross-modal dependencies. Overall, this study provides a deeper understanding of current design trends and offers insights into the future direction of unified multimodal reasoning systems.
Chinese Translation
多模态数据的快速增长加剧了对能够跨越文本、表格和图像等异构来源进行推理的问答（QA）系统的需求。本文对三种具有影响力的框架进行了全面的方法比较，分别是多模态自适应提取（MAE）、Solar 和 UniMMQA，追溯了多模态问答从模态自适应管道到完全统一架构的演变。我们考察了每种方法如何建模跨模态交互、转换异构输入以及进行推理，强调了在模态表示、推理和答案生成方面的关键设计差异。我们的分析表明，从显式的模态特定处理向由预训练语言模型（PLMs）驱动的统一文本中心化表述发生了明显的转变。基于基准数据集的实证比较显示，这一转变在精确匹配（EM）和 F1 分数上均带来了显著的提升，其中 UniMMQA 实现了最一致和可扩展的性能。尽管取得了这些进展，我们仍然识别出持续存在的挑战，包括模态转换过程中的信息损失、多阶段管道中的错误传播，以及捕捉细粒度跨模态依赖的局限性。总体而言，本研究提供了对当前设计趋势的更深入理解，并为统一多模态推理系统的未来方向提供了见解。
cs.CL / 152 / 2609.08919
Experience Funnel: A State-Policy Alternating Loop for Self-Evolving Agents
经验漏斗：自我进化智能体的状态-策略交替循环
Abstract
Autonomous agents powered by large language models (LLMs) continuously accumulate experience through interaction, creating an opportunity to improve future behavior through self-evolution. A fundamental challenge is how to transform abundant, task-specific interaction experience into reusable model competence without sacrificing the ability to adapt rapidly to newly observed evidence. Explicit textual states, such as skills and agent harnesses, provide fast, human-readable and editable adaptation, but incur persistent dependence on external context; parametric policies provide compact and reusable competence, but are substantially slower to update. We present \textit{Experience Funnel}, a self-evolving framework that couples fast state adaptation with slow policy consolidation in an alternating loop. Interaction trajectories are first distilled into an explicit textual state, where newly acquired experience can be rapidly incorporated and validated. The framework then selectively identifies state-enabled behavior that remains useful across state revisions and consolidates it into the policy through transition-aware distillation. The updated state--policy pair subsequently generates new rollouts, providing fresh evidence for the next round of state adaptation and policy consolidation. Experiments across diverse agent benchmarks show that \textit{Experience Funnel} consistently improves agent capability over state-only evolution and policy-internalization approaches, while progressively converting useful explicit experience into autonomous policy competence.
Chinese Translation
由大型语言模型（LLMs）驱动的自主智能体通过交互不断积累经验，这为通过自我进化改善未来行为提供了机会。一个基本挑战是如何将丰富的、特定任务的交互经验转化为可重用的模型能力，而不牺牲快速适应新观察证据的能力。显式文本状态，如技能和智能体的利用，提供了快速、人类可读且可编辑的适应，但会持续依赖外部上下文；参数化策略则提供了紧凑且可重用的能力，但更新速度相对较慢。我们提出了 extit{经验漏斗}，这是一个自我进化框架，将快速状态适应与慢速策略整合结合在一个交替循环中。交互轨迹首先被提炼为显式文本状态，在这里，新获得的经验可以快速纳入和验证。该框架随后选择性地识别在状态修订中仍然有用的状态启用行为，并通过过渡感知提炼将其整合到策略中。更新后的状态-策略对随后生成新的回放，为下一轮状态适应和策略整合提供新的证据。跨多种智能体基准的实验表明， extit{经验漏斗}在能力提升方面始终优于仅基于状态的进化和策略内化方法，同时逐步将有用的显式经验转化为自主策略能力。
cs.CL / 153 / 2609.08934
When Models Defer to Wrong Answers: A Robustness Audit of Source-Attributed Cues in Multiple-Choice QA
当模型依赖错误答案时：源归因提示在多项选择问答中的鲁棒性审计
Abstract
Language models often receive a question together with a claim about what another source answered. We audit whether such claims destabilize answers in multiple-choice question answering. For each item, we hold one wrong option fixed across misleading conditions and vary the cue template attached to it. We introduce \emph{neutral-conditioned misleading cue adoption rate} (NC-MCAR), which measures switches to that option only on valid cued trials where the same model first selected the gold answer under a neutral prompt. This is a measure of answer instability, not proof that the model knew the answer or that all deference is irrational. We evaluate four instruction-following models on MMLU-Pro and IndicMMLU-Pro in English, Hindi, Bengali, Tamil, and Telugu. Across 220{,}000 outputs, the expert template yields 41.1\% aggregate NC-MCAR, compared with 12.5\% for the majority template. These two conditions use the same wrong option and final instruction. Filler accuracy remains well above expert-wrong accuracy, while correct-cue prompts have high valid-response accuracy. The audit documents answer instability relevant to grounding under the tested forced-choice prompts: a bare, unverified source claim can outweigh an answer that was previously consistent with the task evidence.
Chinese Translation
语言模型通常会接收到一个问题以及关于其他来源回答的声明。我们审计这些声明是否会在多项选择问答中使答案不稳定。对于每个项目，我们在误导条件下固定一个错误选项，并改变附加的提示模板。我们引入了 extit{中性条件下误导性提示采纳率}（NC-MCAR），该指标仅在有效提示试验中测量对该选项的切换，这些试验中同一模型在中性提示下首次选择了正确答案。这是一个答案不稳定性的度量，并不能证明模型知道答案或所有的依赖都是不理性的。我们在英语、印地语、孟加拉语、泰米尔语和泰卢固语的MMLU-Pro和IndicMMLU-Pro上评估了四个遵循指令的模型。在220,000个输出中，专家模板的整体NC-MCAR为41.1\%，而多数模板为12.5\\%。这两种条件使用相同的错误选项和最终指令。填充准确率远高于专家错误准确率，而正确提示的有效响应准确率较高。审计记录了与所测试的强制选择提示相关的答案不稳定性：一个未经验证的简单来源声明可以超过之前与任务证据一致的答案。
cs.CL / 154 / 2609.08943
Evaluating and Improving Evidence-Grounded Fact-Checking in LLMs via Multi-Round Evidence Ablation
通过多轮证据消融评估和改进基于证据的事实核查在大型语言模型中的应用
Abstract
Automatic fact-checking systems assess the veracity of claims given evidence from relevant documents. Large Language Models (LLMs) have demonstrated strong performance in fact-checking due to their general reasoning capabilities. However, it remains unclear whether they faithfully make use of the evidence provided to reach veracity judgments or rely on parametric knowledge. To investigate this, we introduce Fact-Ablated Evaluation (FAE), a new evaluation framework that iteratively ablates the cited evidence to assess whether LLMs revise their predictions accordingly. Our empirical results show that current off-the-shelf LLMs as fact-checking systems rely more on their parametric knowledge than on the evidence provided. To bridge this gap between prediction accuracy and evidence grounding, we propose REAL (Rigorous Evidence Ablation Learning), a training framework that promotes evidence-dependent verification through counterfactual evidence supervision for the LLM-as-verifier models. Experiments on four fact-checking datasets across different domains demonstrate that models trained with REAL obtain superior evidence-dependent capabilities compared to standard fine-tuned models. Our findings highlight that strong fact-checking performance can still coexist with weak evidence dependency, while REAL encourages veracity predictions to remain more closely tied to the availability of supporting evidence.
Chinese Translation
自动事实核查系统根据相关文档中的证据评估声明的真实性。大型语言模型（LLMs）由于其一般推理能力，在事实核查中表现出色。然而，目前尚不清楚它们是否忠实地利用提供的证据来做出真实性判断，还是依赖于参数知识。为此，我们引入了事实消融评估（Fact-Ablated Evaluation, FAE），这是一个新的评估框架，通过迭代消融引用的证据来评估LLMs是否相应地修正其预测。我们的实证结果表明，当前现成的LLMs作为事实核查系统更多地依赖于其参数知识而非提供的证据。为了弥补预测准确性与证据基础之间的差距，我们提出了REAL（Rigorous Evidence Ablation Learning），一个通过反事实证据监督促进证据依赖验证的训练框架，适用于LLM作为验证者模型。在四个不同领域的事实核查数据集上的实验表明，与标准微调模型相比，使用REAL训练的模型获得了更优越的证据依赖能力。我们的发现强调，强大的事实核查性能仍然可以与弱证据依赖共存，而REAL则鼓励真实性预测与支持证据的可用性保持更紧密的联系。
cs.CL / 155 / 2609.09004
Evaluation of Contextual Understanding in Large Language Models
大型语言模型的上下文理解评估
Abstract
Large Language Models (LLMs) demonstrate impressive performance across diverse NLP tasks, yet their ability to exhibit genuine contextual understanding remains uncertain. Traditional evaluation metrics such as perplexity, BiLingual Evaluation Understudy (BLEU), or surface-level accuracy fail to reveal how well LLMs extract, integrate, and reason over contextual information--a gap particularly critical in question answering, where models must align responses with contextually grounded knowledge rather than memorized associations. We propose a novel knowledge graph-based evaluation framework introducing Semantic Structural Similarity for KGs (S3KG), a hybrid similarity measure integrating structural and semantic similarity into a continuous evaluation score, alongside a diagnostic framework for categorizing reasoning errors. To validate this pipeline, we evaluate S3KG against established metrics on a curated question-answer (QA) benchmark, demonstrating its effectiveness in measuring correctness, faithfulness, and interpretability in LLM-generated responses.
Chinese Translation
大型语言模型（LLMs）在各种自然语言处理（NLP）任务中表现出色，但它们是否具备真正的上下文理解能力仍然不确定。传统评估指标如困惑度（perplexity）、双语评估替代指标（BiLingual Evaluation Understudy, BLEU）或表面准确率无法揭示LLMs在提取、整合和推理上下文信息方面的表现——这一差距在问答任务中尤为重要，因为模型必须将回答与基于上下文的知识对齐，而不是依赖于记忆的关联。我们提出了一种基于知识图谱的新评估框架，引入了知识图谱的语义结构相似性（Semantic Structural Similarity for KGs, S3KG），这是一种将结构相似性和语义相似性整合为连续评估分数的混合相似性度量，同时提供了一个用于分类推理错误的诊断框架。为了验证这一流程，我们在一个精心策划的问答（QA）基准上评估了S3KG，并与现有指标进行了比较，证明其在测量LLM生成回答的正确性、忠实性和可解释性方面的有效性。
cs.CL / 156 / 2609.09048
The Audit Decides the Verdict: Instrument Effects Rival Demographic Bias in LLM Decision Audits
审计决定裁决：工具效应与人口统计偏见在大型语言模型决策审计中的竞争
Abstract
Whether a language model looks demographically biased can depend on how the audit asks its question. A charitable-aid benchmark reports that the same models favor minority applicants when rating requests one at a time and penalize some when ranking side by side. We test whether that reversal generalizes to hiring, lending, and medical triage: 40,726 requests to five models, applications differing only in the applicant's name, and a primary test fixed before collection. It does not. None of 36 planned contrasts survives correction. The rating advantage keeps its sign at roughly half the published size, and a precision extension bounds any hiring ranking penalty below the published effect, though the lending and triage ranking floors sit above that margin, so the exclusion is conclusive for hiring ranking and for rating in all three domains only. Planted disparities tracking their injected sizes and a directional replication on the original aid materials bound these nulls. The audit is livelier than the demographics: models recognize transparent audits nearly always, tie every identical-content comparison whether the varying detail is race or a hobby, and reward first-listed candidates as much as any demographic effect we measure. Audit verdicts reflect audit construction more than demographic bias.
Chinese Translation
一个语言模型是否显得在人口统计上存在偏见，可能取决于审计提问的方式。一项慈善援助基准报告显示，当逐一评估请求时，相同的模型倾向于支持少数族裔申请者，而在并排排名时则对某些申请者进行惩罚。我们测试这种反转是否适用于招聘、贷款和医疗分诊：对五个模型的40,726个请求，申请仅在申请者的姓名上有所不同，并且主要测试在收集前已固定。结果并非如此。36个计划对比中没有一个在校正后存活。评分优势的符号保持在已发布规模的大约一半，且精度扩展将任何招聘排名惩罚限制在已发布效应之下，尽管贷款和分诊排名的下限高于该边际，因此排除在招聘排名和所有三个领域的评分中是确凿的。植入的不平等跟踪其注入的规模，以及对原始援助材料的定向复制，界定了这些无效结果。审计的活跃程度超过了人口统计学：模型几乎总是识别透明的审计，无论变化的细节是种族还是爱好，都会将每个内容相同的比较拉平，并且对首位列出的候选人给予的奖励与我们测量的任何人口统计效应相当。审计裁决反映了审计构建的影响，而非人口统计偏见。
cs.CL / 157 / 2609.09070
Performance of Clinical AI System and Physicians and Frontier Language Models in primary care diagnostics
临床人工智能系统、医生与前沿语言模型在初级护理诊断中的表现
Abstract
Clinical AI evaluation should encompass diagnosis and management after adaptive information gathering. We compared Doctorina, eight physicians and four standalone frontier language models in 150 synthetic Polish-language primary-care consultations. Doctorina achieved 82.0% Top-1 concordance versus 57.0% for physicians (difference, 25.0 percentage points; 95% confidence interval, 17.7-32.7) and 97.3% versus 85.0% primary-or-reference-differential concordance. Across 149 case pairs, normalized workup and treatment scores were 89.4 versus 66.9 and 83.7 versus 61.2. Doctorina had the highest diagnostic point estimates among all six groups; Kimi K3 ranked next, while Claude Opus 5 led the closely spaced management estimates of Opus, Doctorina and Kimi. A second Doctorina execution reproduced the advantages over physicians across all outcomes. Doctorina's advantage over physicians therefore extended from primary-diagnosis selection to higher-rated diagnostic workup and initial treatment after adaptive consultation.
Chinese Translation
临床人工智能的评估应涵盖适应性信息收集后的诊断和管理。我们在150个合成的波兰语初级护理咨询中比较了Doctorina、八位医生和四个独立的前沿语言模型。Doctorina的Top-1一致性达到了82.0%，而医生的为57.0%（差异为25.0个百分点；95%置信区间为17.7-32.7），在主要或参考差异一致性方面，Doctorina为97.3%，医生为85.0%。在149对案例中，标准化的检查和治疗评分分别为89.4与66.9，以及83.7与61.2。Doctorina在所有六组中具有最高的诊断点估计；Kimi K3排名其次，而Claude Opus 5在紧密相邻的管理估计中领先于Opus、Doctorina和Kimi。第二次Doctorina执行在所有结果中重现了对医生的优势。因此，Doctorina相对于医生的优势从初步诊断选择扩展到更高评分的诊断检查和适应性咨询后的初始治疗。
cs.CL / 158 / 2609.09072
ToolLoop: Closed-Loop Tool-Use Data Synthesis via Decomposed Generation and Dynamic Self-Feedback
ToolLoop：通过分解生成和动态自反馈实现的闭环工具使用数据合成
Abstract
High-quality tool-use data is critical for training language models to interact effectively with external tools. However, existing synthetic approaches typically follow a generate-then-filter paradigm with static post-hoc verification, often yielding inefficient data with imbalanced feature distributions. We propose ToolLoop, a closed-loop framework that decomposes synthesis into three progressive stages: (1) sampling function name combinations as ground truth; (2) backward derivation of user queries; and (3) forward derivation of tool calls. At each stage, dynamic self-feedback iteratively guides the model toward high-quality generation, realizing a transition from generate-then-filter to generate-verify-refine. On the Berkeley Function Calling Leaderboard (BFCL), a 4B parameter model trained with our 11K synthetic examples achieves 86.40% accuracy in non-reasoning mode, while an Isolate variant that removes BFCL-overlapping candidate functions still reaches 86.07\%. Cross-benchmark evaluation on ACEBench further demonstrates strong generalization, with 72.1% overall accuracy using only 18.3% of baseline training data.
Chinese Translation
高质量的工具使用数据对于训练语言模型以有效地与外部工具互动至关重要。然而，现有的合成方法通常遵循生成-然后过滤的范式，并采用静态的事后验证，往往导致特征分布不平衡的低效数据。我们提出了ToolLoop，一个闭环框架，将合成过程分解为三个逐步阶段：（1）采样函数名称组合作为真实数据；（2）用户查询的反向推导；（3）工具调用的正向推导。在每个阶段，动态自反馈迭代地引导模型朝向高质量生成，实现从生成-然后过滤到生成-验证-精炼的转变。在伯克利函数调用排行榜（BFCL）上，使用我们11K合成示例训练的4B参数模型在非推理模式下达到了86.40%的准确率，而去除BFCL重叠候选函数的Isolate变体仍然达到了86.07%。在ACEBench上的跨基准评估进一步证明了强大的泛化能力，仅使用18.3%的基线训练数据就达到了72.1%的总体准确率。
cs.CL / 159 / 2609.09076
ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation
ActReview：基于反驳指导的训练数据和评分标准奖励用于可操作的同行评审生成
Abstract
As LLMs are increasingly used for pre-submission self-review, there is growing demand for feedback that not only identifies weaknesses but also guides authors toward concrete revisions. We study this as Actionable Peer-review Generation and decompose it into two subtasks: diagnostic claim generation and revision suggestion generation. We introduce ActReview, a rebuttal-guided post-training framework that connects paper-specific diagnoses to concrete, grounded revision plans. Our central insight is that author rebuttals reveal plausible actions for addressing reviewer concerns and can therefore provide latent supervision for revision-oriented feedback. From real review-rebuttal threads on OpenReview, we construct ActReview-40K by aligning reviewer weaknesses with author responses and grounding the resulting feedback in localized paper evidence. We post-train Qwen3-8B-Base with multi-task supervised fine-tuning followed by GRPO using candidate-aware, weakness-specific rubric rewards. We also introduce ActReview-Bench, a human-curated benchmark of 1,000 instances for evaluating diagnostic quality and revision usefulness. Experiments show that ActReview outperforms prior specialized review-generation models on actionability and grounding while remaining competitive with strong prompt-based LLMs. Human evaluation confirms improved revision usefulness while revealing a remaining gap in technical accuracy, and additional analyses support generalization to held-out papers and robustness across independent judges.
Chinese Translation
随着大型语言模型（LLMs）在提交前自我评审中的使用日益增多，对反馈的需求也在增长，这种反馈不仅要识别出弱点，还要指导作者进行具体的修订。我们将此研究称为可操作的同行评审生成，并将其分解为两个子任务：诊断性主张生成和修订建议生成。我们提出了ActReview，一种基于反驳指导的后训练框架，将特定论文的诊断与具体、扎实的修订计划连接起来。我们的核心见解是，作者的反驳揭示了应对评审者关注点的合理行动，因此可以为以修订为导向的反馈提供潜在的监督。我们从OpenReview上的真实评审-反驳线程中构建了ActReview-40K，通过将评审者的弱点与作者的回应对齐，并将结果反馈基于局部论文证据进行扎实化。我们对Qwen3-8B-Base进行了多任务监督微调后的后训练，随后使用候选感知的、弱点特定的评分标准奖励进行GRPO。我们还引入了ActReview-Bench，这是一个人类策划的基准，包含1,000个实例，用于评估诊断质量和修订实用性。实验表明，ActReview在可操作性和扎实性方面优于之前的专门评审生成模型，同时在与强大的基于提示的LLMs竞争时保持竞争力。人类评估确认了修订实用性的提升，同时揭示了技术准确性仍存在差距，额外分析支持对保留论文的泛化和在独立评审者之间的稳健性。
cs.CL / 160 / 2609.09085
It's Not RoPE that Creates Sinks: The Role of Self-Concentration and Value-Non-Mixing in Attention
并非 RoPE 造成了注意力沉没：自我集中与价值非混合在注意力中的作用
Abstract
Large Language Models (LLMs) often exhibit "Attention Sink" (AS) and the accompanying "Massive Activations" (MAs) at the initial position of a sequence. These phenomena frequently co-occur, and MAs can pose challenges for low-bit quantization. In this study, we analyze the factors underlying AS and MAs that emerge at the initial position regardless of the token occupying it. Our experiments suggest that self-concentration of attention, resulting from the causal mask, and the subsequent Value-non-mixing in attention outputs contribute to AS and MAs. These findings provide new empirical evidence on the internal dynamics of LLMs, offering insights that may inform future quantization strategies and advance our understanding of the internal mechanisms of attention layers.
Chinese Translation
大型语言模型（LLMs）通常在序列的初始位置表现出“注意力沉没”（Attention Sink，AS）及其伴随的“巨大激活”（Massive Activations，MAs）。这些现象经常同时发生，而 MAs 可能对低位量化造成挑战。在本研究中，我们分析了无论占据初始位置的标记是什么，AS 和 MAs 背后的因素。我们的实验表明，由因果掩码引起的注意力自我集中，以及随后的注意力输出中的价值非混合，均对 AS 和 MAs 产生贡献。这些发现为 LLMs 的内部动态提供了新的实证证据，提供了可能为未来量化策略提供参考的见解，并推动我们对注意力层内部机制的理解。
cs.CL / 161 / 2609.09090
Measuring LLM Sycophancy under Sustained Multi-Turn Pressure
在持续多轮压力下测量大型语言模型的谄媚行为
Abstract
Large language models (LLMs) may abandon correct positions when users push back, exhibiting a failure mode known as sycophancy. Existing evaluations typically use short, pre-specified conversations and may therefore miss failures that emerge under sustained, adaptive disagreement. We introduce SPINE, a benchmark in which an LLM proxy plays a persistent but mistaken user and adaptively challenges a target model for up to 25 turns. We evaluate four production systems and three Olmo3-7b variants on 100 false-presupposition and 100 unethical-query items. Our experimental results show that collapse rates increase with conversation length for every model, short-horizon protocols underestimate sycophancy and resistance under sustained pressure remains unreliable across current models. By analyzing models with accessible reasoning traces, we surprisingly found that the correct position often remains represented in a reasoning trace when the response concedes, suggesting that the model chooses to please a user and sycophancy is not due to lack of knowledge or ignorance. Ablations show that adaptive LLM proxy exposes more sycophantic collapse than pre-generated scripts. Among all tactics, emotional appeals is the most associated with inducing LLM sycophantic behavior. The code and data are released at https://anonymous.4open.science/r/SPINE
Chinese Translation
大型语言模型（LLMs）在用户反对时可能会放弃正确立场，表现出一种称为谄媚的失效模式。现有评估通常使用短期、预设的对话，因此可能会错过在持续、适应性分歧下出现的失效情况。我们引入了SPINE，一个基准测试，其中LLM代理扮演一个持续但错误的用户，并在长达25轮的对话中适应性地挑战目标模型。我们在100个错误前提和100个不道德查询项目上评估了四个生产系统和三个Olmo3-7b变体。我们的实验结果表明，所有模型的对话长度增加时崩溃率上升，短期协议低估了谄媚行为，而在持续压力下的抵抗在当前模型中仍然不可靠。通过分析具有可访问推理痕迹的模型，我们意外发现，当响应让步时，正确立场通常仍在推理痕迹中表示，这表明模型选择迎合用户，谄媚并非由于缺乏知识或无知。消融实验表明，自适应LLM代理暴露出比预生成脚本更多的谄媚崩溃。在所有策略中，情感诉求与诱发LLM谄媚行为的关联性最高。代码和数据已发布在 https://anonymous.4open.science/r/SPINE
cs.CL / 162 / 2609.09156
ReCite: Agentic Reasoning for Faithful Citation
ReCite：基于主动推理的可靠引用
Abstract
Accurate citations are the foundation of academic writing, tracing intellectual origins and substantiating core claims. However, manually navigating the growing volume of scientific literature is increasingly difficult, prompting reliance on automatic citation recommendation. While modern retrieval-augmented architectures have largely mitigated the fabrication of non-existent papers, current systems relying on semantic similarity struggle with misattribution, often citing authentic papers that fail to logically support the author's claim. To address this challenge, we argue that accurate citation requires a shift from similarity-based search to active, claim-level reasoning. We propose ReCite, a decoupled agentic framework that orchestrates location perception, intent-aware query planning, and reflective verification. Trained on synthesized reasoning trajectories, our agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support. Experiments demonstrate that our lightweight framework outperforms state-of-the-art massive generative models in strict citation accuracy. By grounding literature matching in verifiable logic rather than semantic overlap, ReCite establishes a reliable foundation for automated academic writing.
Chinese Translation
准确的引用是学术写作的基础，追溯知识来源并证实核心论点。然而，手动浏览日益增长的科学文献变得越来越困难，这促使人们依赖自动引用推荐。尽管现代检索增强架构在很大程度上缓解了虚构论文的出现，但当前依赖语义相似性的系统在错误归属方面仍然存在困难，常常引用那些未能逻辑支持作者主张的真实论文。为了解决这一挑战，我们认为准确引用需要从基于相似性的搜索转向主动的、基于主张的推理。我们提出了ReCite，一个解耦的主动框架，协调位置感知、意图感知的查询规划和反思性验证。我们的代理在合成推理轨迹上进行训练，验证主张与证据的一致性，并在检索到的候选缺乏逻辑支持时触发自我纠正循环。实验表明，我们的轻量级框架在严格的引用准确性上优于最先进的大规模生成模型。通过将文献匹配建立在可验证的逻辑基础上，而非语义重叠，ReCite为自动化学术写作奠定了可靠的基础。