cs.RO / 1 / 2606.07723
VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation
VoLo：一种用于开放词汇长时间操作的物理协调器
Abstract
Open-vocabulary long-horizon manipulation requires robots to reason over flexible instructions and complex multi-object scenes while adaptively planning, executing, monitoring, and recovering from failures. We address these demands with a closed agent loop in which a VLM orchestrates heterogeneous robot capabilities as interruptible tools. Unlike in virtual AI agents, the timing of decisions, actions and tool calls is important in a physical world that does not pause for reasoning. We refer to this setting as Physical Orchestration, and propose VoLoAgent, a VLM that plans, monitors, and recovers by treating a VLA/WAM as an interruptible tool it steers mid-rollout alongside vision models and action primitives. To evaluate these long-horizon capabilities, we introduce RoboVoLo, a high-fidelity benchmark for open-vocabulary long-horizon manipulation across common sense, memory/state tracking, complex references, and world knowledge, with both task-level success and failure-mode diagnostics. Experiments show VoLoAgent substantially outperforms single VLA/VLM or tool-based systems, with validation on real-robot experiments. Project page: https://chicychen.github.io/VoLo/
Chinese Translation
开放词汇长时间操作要求机器人在灵活指令和复杂多物体场景中进行推理，同时适应性地规划、执行、监控并从失败中恢复。我们通过一个封闭的代理循环来满足这些需求，其中一个视觉语言模型（VLM）协调异构机器人能力作为可中断的工具。与虚拟人工智能代理不同，在物理世界中，决策、行动和工具调用的时机至关重要，因为物理世界不会因推理而暂停。我们将这种设置称为物理协调，并提出VoLoAgent，一个通过将视觉语言代理（VLA）/工作空间管理（WAM）视为可中断工具来进行规划、监控和恢复的VLM，它在与视觉模型和动作原语的联合执行中进行引导。为了评估这些长时间能力，我们引入了RoboVoLo，这是一个高保真基准，针对开放词汇长时间操作，涵盖常识、记忆/状态跟踪、复杂引用和世界知识，并提供任务级成功和失败模式诊断。实验表明，VoLoAgent在真实机器人实验中显著优于单一的VLA/VLM或基于工具的系统。项目页面：https://chicychen.github.io/VoLo/
cs.RO / 2 / 2606.07813
MinNav: Minimalist Navigation Using Optical Flow For Active Tiny Aerial Robots
MinNav：基于光流的极简导航方法用于主动微型空中机器人
Abstract
Navigation using a monocular camera is pivotal for autonomous operation on tiny aerial robots due to their perfect balance of versatility, cost and accuracy. In this paper, we introduce MinNav, a navigation stack based on optical flow and its uncertainty to fly through a scene with static and dynamic obstacles and unknown-shaped gaps without any prior knowledge of the scene components and/or their locations/ordering. We further improve success rate by using the activeness of the robot to move around in an exploratory way to find obstacles and navigate. We successfully evaluate and demonstrate the proposed approach in many real-world experiments in various environments with static and dynamic obstacles and unknown-shaped gaps with an overall success rate of 70%. To the best of our knowledge, this is the first solution to tackle all the aforementioned navigation cases without prior knowledge using a monocular camera. Our approach is on par in performance with depth based methods with factors of magnitude less computation required and can readily run onboard tiny aerial robots. The accompanying video, supplementary material, code and dataset can be found at https://pear.wpi.edu/research/minnav.html
Chinese Translation
使用单目相机进行导航对于微型空中机器人的自主操作至关重要，因为它们在多功能性、成本和精度之间达到了完美的平衡。在本文中，我们介绍了MinNav，一个基于光流及其不确定性的导航系统，能够在没有任何场景组件及其位置/顺序的先验知识的情况下，穿越具有静态和动态障碍物及未知形状间隙的场景。我们进一步通过利用机器人主动探索的特性，提升了成功率，以便发现障碍物并进行导航。我们在多种环境中进行了多次真实世界实验，成功评估并展示了所提出的方法，整体成功率达到了70%。据我们所知，这是第一个在没有先验知识的情况下，使用单目相机解决上述所有导航情况的方案。我们的方法在性能上与基于深度的方法相当，但所需计算量少了几个数量级，并且可以直接在微型空中机器人上运行。相关视频、补充材料、代码和数据集可以在 https://pear.wpi.edu/research/minnav.html 找到。
cs.RO / 3 / 2606.07855
Path Planning Using Deep Deterministic Policy Gradient: A Reinforcement Learning Approach
基于深度确定性策略梯度的路径规划：一种强化学习方法
Abstract
Path-planning for autonomous vehicles in threat-laden environments is a fundamental challenge because the problem is nonlinear and nonconvex even in simplest scenarios. While traditional optimal control methods can be used to find ideal paths, the computational time is often too slow for real-time decision-making. To solve this challenge, we propose a method based on Deep Deterministic Policy Gradient (DDPG) and model the threat as possibly multiple circular 'no-go' zones. A mission is regarded as a failure if the vehicle enters this restricted zone at any time or does not reach a neighborhood of the destination. The DDPG agent is trained through trial and error in a simulated environment, learning a direct mapping from its current state (position and heading) to a series of feasible actions that guide the agent to safely reach its destination. The reword function has three parts: (a) an attractive field centered at the final destination, (b) some repulsive fields centered at the origins of circular obstacles, and (c) a penalty of control energy consumption (the magnitude of heading change) that indirectly in favor for straight path. The DDPG trains the agent using these incentives to find the largest possible set of starting points wherein a safe path to the destination is guaranteed. This provides critical information for mission planning, showing beforehand whether a task is achievable from a given starting point, assisting pre-mission planning activities. The approach is validated in simulation. A comparison between the DDPG method and a traditional optimal control (pseudo-spectral) method is carried out. The results show that the learning-based agent produces effective paths while being significantly faster, making it a better fit for real-time applications.
Chinese Translation
在充满威胁的环境中为自主车辆进行路径规划是一项基本挑战，因为即使在最简单的场景中，该问题也是非线性和非凸的。虽然传统的最优控制方法可以用来寻找理想路径，但计算时间往往过于缓慢，无法满足实时决策的需求。为了解决这一挑战，我们提出了一种基于深度确定性策略梯度（Deep Deterministic Policy Gradient, DDPG）的方法，并将威胁建模为可能存在多个圆形的“禁行”区域。如果车辆在任何时候进入这个限制区域，或者未能到达目的地的邻域，则任务被视为失败。DDPG代理在模拟环境中通过试错学习，学习从其当前状态（位置和朝向）到一系列可行动作的直接映射，以引导代理安全到达目的地。奖励函数由三部分组成：(a) 以最终目的地为中心的吸引场，(b) 以圆形障碍物的原点为中心的一些排斥场，以及(c) 控制能量消耗的惩罚（朝向变化的幅度），这间接有利于直线路径。DDPG利用这些激励来训练代理，寻找最大可能的起始点集合，以确保到达目的地的安全路径。这为任务规划提供了重要信息，提前显示从给定起始点是否可以实现任务，辅助任务前的规划活动。该方法在模拟中得到了验证，并对DDPG方法与传统最优控制（伪谱）方法进行了比较。结果表明，基于学习的代理能够生成有效路径，同时显著加快速度，使其更适合实时应用。
cs.RO / 4 / 2606.07902
End-to-End Control of a Powered Knee-Ankle Prosthesis Towards Unified, Tuning-Free Assistance
面向统一、无调节辅助的动力膝踝假肢的端到端控制
Abstract
Powered prostheses conventionally rely on impedance controllers that require extensive manual tuning and explicit mode classification. In this work, we present real-time deployment of an end-to-end prosthesis controller that estimates continuous actuator signals from onboard sensors, eliminating the need for intent classifiers and subject-specific tuning. Temporal Convolutional Networks were trained on a multi-terrain dataset from 18 individuals with transfemoral amputation and deployed in real time across five locomotion modes. Four participants (three able-bodied, one with transfemoral amputation) ambulated across level ground, ramp ascent and descent, and stair ascent and descent. During level walking, the deployed controller reproduced the training-data scaling of peak ankle torque with walking speed (deployed 0.85 Nm/kg per m/s, p = 0.001; training 0.96 Nm/kg per m/s, 95% CI [0.42, 1.50], p = 0.002), after excluding one outlier traced to atypical prosthesis loading. During ramp ascent, the controller scaled knee pre-flexion with grade (deployed 2.92 deg/deg, p = 0.027; training 3.30 deg/deg, 95% CI [1.83, 4.77], p < 0.001). During ramp descent, the controller increased resistive knee torque relative to level walking (deployed +0.16 Nm/kg, p < 0.001; training +0.16 Nm/kg, p = 0.008). Seamless stair transitions were generated for both intact- and prosthetic-side-leading sequences in ascent and descent, despite the training data containing only one limb-leading sequence. These results provide initial evidence towards end-to-end control that can provide unified, mode-adaptive prosthetic assistance without subject-specific tuning.
Chinese Translation
动力假肢通常依赖于阻抗控制器，这需要广泛的手动调节和明确的模式分类。在本研究中，我们展示了一种端到端假肢控制器的实时部署，该控制器从机载传感器估计连续的执行器信号，消除了对意图分类器和特定个体调节的需求。我们使用来自18名股骨上截肢者的多地形数据集训练了时间卷积网络，并在五种运动模式下实时部署。四名参与者（其中三名为健全人，一名为股骨上截肢者）在平坦地面、坡道上升和下降以及楼梯上升和下降中行走。在平坦行走期间，部署的控制器再现了训练数据中峰值踝关节扭矩与行走速度的比例关系（部署为0.85 Nm/kg每m/s，p = 0.001；训练为0.96 Nm/kg每m/s，95% CI [0.42, 1.50]，p = 0.002），在排除一个因假肢负载异常而导致的离群值后。在坡道上升期间，控制器根据坡度调整膝关节预屈曲（部署为2.92 deg/deg，p = 0.027；训练为3.30 deg/deg，95% CI [1.83, 4.77]，p < 0.001）。在坡道下降期间，控制器相对于平坦行走增加了抵抗性膝关节扭矩（部署为+0.16 Nm/kg，p < 0.001；训练为+0.16 Nm/kg，p = 0.008）。尽管训练数据仅包含一个肢体主导序列，但在上升和下降过程中为完整侧和假肢侧的主导序列生成了无缝的楼梯过渡。这些结果为端到端控制提供了初步证据，表明可以在不需要特定个体调节的情况下提供统一的、模式自适应的假肢辅助。
cs.RO / 5 / 2606.07934
X-OP: Cross-Morphology Whole-Body Teleoperation via MPC Retargeting
X-OP：基于MPC重定向的跨形态全身遥操作
Abstract
Whole-body teleoperation is essential for scalable robot data collection in loco-manipulation tasks, yet existing approaches relying on exoskeleton suits or multi-camera setups impose prohibitive cost, complexity, and environmental constraints. Recent methods using a single extended reality (XR) device with end-to-end reinforcement learning policies partially address these limitations but require robot-specific retraining, suffer from out-of-distribution failures, and rely on motion retargeting that neglects dynamic feasibility. We propose a hierarchical whole-body teleoperation framework driven by a single XR device that generalizes across diverse robot morphologies without retraining robot-specific policies. A Model Predictive Control (MPC)-based motion retargeter jointly optimizes alignment with the operator's intent and the robot's dynamic feasibility, generating optimal commands for existing low-level controllers. To ensure robust online execution, we introduce a state synchronization method that resets the simulator state at each MPC step to handle noisy real-world measurements and contact sensitivity, and integrate SLAM-based global pose feedback to mitigate long-term drift. Simulation results show higher success rates on whole-body control tasks for both a humanoid (over 30% lower completion time and 20% lower power consumption) and a mobile manipulator (zero collisions) compared to baselines. Real-world experiments further validate the effectiveness and flexibility of our method, demonstrating the successful deployment of the proposed retargeter on both platforms for whole-body control tasks and the ease of allowing users to adjust teleoperation behavior based on their preferences. This plug-and-play framework offers a scalable, morphology-agnostic solution for whole-body robot teleoperation, enabling real-time behavioral customization and broad applicability across platforms.
Chinese Translation
全身遥操作对于在局部操作任务中进行可扩展的机器人数据收集至关重要，但现有依赖外骨骼套装或多摄像头设置的方法存在高昂的成本、复杂性和环境限制。最近使用单一扩展现实（XR）设备和端到端强化学习策略的方法在一定程度上解决了这些限制，但需要针对特定机器人的重新训练，容易出现分布外失败，并依赖于忽视动态可行性的运动重定向。我们提出了一种由单一XR设备驱动的分层全身遥操作框架，该框架能够在不同机器人形态之间进行泛化，而无需重新训练特定机器人的策略。基于模型预测控制（MPC）的运动重定向器联合优化操作员意图与机器人动态可行性的对齐，为现有低级控制器生成最优指令。为了确保稳健的在线执行，我们引入了一种状态同步方法，在每个MPC步骤重置模拟器状态，以处理噪声现实世界测量和接触敏感性，并集成基于SLAM的全局位姿反馈以减轻长期漂移。仿真结果显示，在全身控制任务中，类人机器人（完成时间降低超过30%，功耗降低20%）和移动操控器（零碰撞）的成功率均高于基线。现实世界实验进一步验证了我们方法的有效性和灵活性，展示了所提重定向器在两个平台上成功部署于全身控制任务，并且用户能够根据个人偏好轻松调整遥操作行为。该即插即用框架提供了一种可扩展的、形态无关的全身机器人遥操作解决方案，实现了实时行为定制，并在多个平台上具有广泛的适用性。
cs.RO / 6 / 2606.07974
PRISM: PRior-guided Imagination Sampling in world Models
PRISM：基于先验引导的世界模型想象采样
Abstract
A learned world model provides a powerful physical intuition for evaluating future states. But its effectiveness in continuous control also depends critically on how candidate actions are generated for model-based planning. Rather than solely asking how accurately a model can simulate the future, we ask: which candidate actions are worth evaluating in the first place? Existing planners typically search arbitrarily or use expert demonstrations only to initialize a sampling mean, discarding the expert's state-conditioned confidence. Properly guiding this search requires a robust action prior, yet current approaches often rely on independent visual encoders or large-scale VLMs to obtain one. We argue that this architectural bloat is unnecessary: the exact same data - and the learned representations of the world model itself - inherently encode the agent's action intuition. We introduce PRISM, a task-agnostic framework that extracts both from a single dataset while maintaining strict architectural simplicity. Building on a standard JEPA-style latent world model, PRISM attaches a lightweight MLP directly to its frozen encoder to predict a state-conditioned Gaussian prior. At plan time, PRISM fuses this prior into the planner's sampling distribution via a precision-weighted Product-of-Gaussians update. This parameter-free, closed-form integration steers the sampling process, making the prior confident where it is and ceding control where it is not. PRISM improves success rates by 35 percentage points over vanilla world-model-based MPC on Cube and 32 percentage points on PushT, without introducing significant inference overhead.
Chinese Translation
学习的世界模型为评估未来状态提供了强大的物理直觉。然而，其在连续控制中的有效性也在很大程度上依赖于候选动作的生成方式，以便进行基于模型的规划。我们不仅关注模型能多准确地模拟未来，还提出了一个问题：哪些候选动作值得首先进行评估？现有的规划器通常采用任意搜索或仅使用专家演示来初始化采样均值，忽略了专家的状态条件信心。有效引导这一搜索需要一个稳健的动作先验，但当前的方法往往依赖独立的视觉编码器或大规模的视觉语言模型（VLM）来获得先验。我们认为这种架构膨胀是没有必要的：完全相同的数据——以及世界模型本身学习到的表示——本质上编码了智能体的动作直觉。我们提出了PRISM，一个任务无关的框架，它从单一数据集中提取这两者，同时保持严格的架构简洁性。在标准的JEPA风格潜在世界模型的基础上，PRISM将一个轻量级的多层感知器（MLP）直接附加到其冻结的编码器上，以预测状态条件的高斯先验。在规划时，PRISM通过精度加权的高斯乘积更新将该先验融合到规划器的采样分布中。这种无参数的闭式集成引导了采样过程，使得先验在需要的地方保持自信，而在不需要的地方则放弃控制。PRISM在Cube任务上比传统的基于世界模型的模型预测控制（MPC）提高了35个百分点的成功率，在PushT任务上提高了32个百分点，而没有引入显著的推理开销。
cs.RO / 7 / 2606.08015
Q-VGM: Q-Guided Value-Gradient Matching for Flow-Matching VLA Policies
Q-VGM：基于Q引导的价值梯度匹配用于流匹配的视觉-语言-动作（VLA）策略
Abstract
We propose Q-Guided Value-Gradient Matching (Q-VGM), an off-policy reinforcement learning (RL) method that tackles a long-standing challenge in fine-tuning flow-matching vision-language-action (VLA) policies: efficiently improving an expressive flow-matching action expert with respect to a learned Q-function. Effective improvement must exploit the first-order (gradient) information of the critic, but this is difficult for flow policies, because directly back-propagating the value through their multi-step denoising process is numerically unstable at VLA scale, while the tractable action likelihoods required by policy-gradient methods are unavailable under iterative denoising. Existing value-based methods either backpropagate through the full denoising chain, use the critic only at test time without updating the policy, or distill critic-improved actions as terminal labels without supervising the velocity field. Q-VGM sidesteps these issues by leveraging VGG-Flow, a value-gradient view of flow alignment in generative modeling that transforms value gradient into a denoising-time value-gradient field rather than an unstable end-to-end objective. This requires no action likelihoods and no backpropagation through the denoising chain, and operates on a fixed replay buffer. The critic is an action-sensitive Cal-QL ensemble over compact RLT features with per-layer action injection. Q-VGM enables a practical few-shot initialization then learn-from-experience paradigm: starting from a few-shot-SFT pi0.5 VLA, the method leverages self-generated rollout data to substantially improve task performance without additional expert supervision. On LIBERO, Q-VGM raises the average success rate from 75.0% to 92.5%; on RoboTwin 2.0, from 76.4% to 87.2%; and on two real-robot tabletop tasks, from 40.0% to 67.5%, outperforming all same-backbone, same-critic baselines across all three settings.
Chinese Translation
我们提出了Q引导的价值梯度匹配（Q-VGM），这是一种离线强化学习（RL）方法，旨在解决在微调流匹配视觉-语言-动作（VLA）策略时面临的长期挑战：高效地改善与学习的Q函数相关的表达性流匹配动作专家。有效的改进必须利用评论者的一级（梯度）信息，但对于流策略来说，这很困难，因为直接通过其多步去噪过程反向传播价值在VLA规模上数值不稳定，而政策梯度方法所需的可处理动作似然在迭代去噪下不可用。现有的基于价值的方法要么通过完整的去噪链进行反向传播，要么仅在测试时使用评论者而不更新策略，或者将评论者改进的动作提炼为终端标签而不监督速度场。Q-VGM通过利用VGG-Flow，采用生成建模中的流对齐的价值梯度视图，将价值梯度转化为去噪时间的价值梯度场，而不是不稳定的端到端目标，从而规避了这些问题。这不需要动作似然，也不需要通过去噪链进行反向传播，并且在固定的重放缓冲区上运行。评论者是一个对动作敏感的Cal-QL集成，基于紧凑的RLT特征，并在每层进行动作注入。Q-VGM使得一种实用的少样本初始化然后经验学习的范式成为可能：从少样本-SFT pi0.5 VLA开始，该方法利用自生成的回滚数据显著提高任务性能，而无需额外的专家监督。在LIBERO上，Q-VGM将平均成功率从75.0%提高到92.5%；在RoboTwin 2.0上，从76.4%提高到87.2%；在两个真实机器人桌面任务上，从40.0%提高到67.5%，在所有三个设置中超越了所有相同骨干、相同评论者的基线。
cs.RO / 8 / 2606.08029
IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations
IntentNav：从人类示范中学习空间-视觉物体导航
Abstract
Object navigation requires a robot to search for an unobserved target in an unknown environment by deciding where to explore next under partial observability. Effective search resembles human-like exploration: selectively probing visually promising frontiers while relying on spatial memory to avoid redundant revisits. We propose IntentNav, a spatial-visual imitation framework that learns human-like ObjectNav policies from human demonstrations. To infer high-level search intent from low-level human actions, we introduce Frontier-based Human-Intent Labeling, which looks ahead in human demonstrations and labels the frontier that best explains the demonstrator's future search direction. We construct a spatial-visual candidate space, where BEV memory tracks explored regions, unexplored frontiers, and trajectory history, while egocentric visual memory provides semantic cues for each candidate. A VLM policy is trained to select among these grounded candidates, using Intent-Aligned Objective to encourage consistent and human-like exploration. IntentNav achieves state-of-the-art performance on the MP3D, HM3D-v1 and HM3D-v2 ObjectNav benchmarks. The proposed candidate-level navigation interface transfers zero-shot to wheeled, quadruped, and humanoid robots without further VLM fine-tuning. \href{https://anonymous.4open.science/w/IntentNav/}{Project page}.
Chinese Translation
物体导航要求机器人在未知环境中搜索未观察到的目标，需在部分可观测的情况下决定下一步的探索方向。有效的搜索类似于人类的探索：选择性地探测视觉上有前景的边界，同时依赖空间记忆以避免冗余的重访。我们提出了IntentNav，一个空间-视觉模仿框架，从人类示范中学习类人物体导航策略。为了从低级人类动作中推断出高级搜索意图，我们引入了基于边界的人类意图标注（Frontier-based Human-Intent Labeling），该方法在观察人类示范时向前看，并标注出最佳解释示范者未来搜索方向的边界。我们构建了一个空间-视觉候选空间，其中鸟瞰视图（BEV）记忆跟踪已探索区域、未探索边界和轨迹历史，而自我中心视觉记忆为每个候选提供语义线索。我们训练了一个视觉语言模型（VLM）策略，以在这些有基础的候选中进行选择，使用意图对齐目标（Intent-Aligned Objective）来鼓励一致且类人的探索。IntentNav在MP3D、HM3D-v1和HM3D-v2物体导航基准测试中达到了最先进的性能。所提出的候选级导航接口在不进行进一步VLM微调的情况下，能够零-shot迁移到轮式、四足和类人机器人上。
cs.RO / 9 / 2606.08039
MuJoCo-Drones-Gym: A GPU-Accelerated Multi-Drone Simulator for Control and Reinforcement Learning
MuJoCo-Drones-Gym：一种用于控制和强化学习的GPU加速多无人机模拟器
Abstract
Robotic simulators are a cornerstone of modern research in aerial robotics, serving both as a vehicle for the development of new control algorithms and as the data source for training reinforcement learning (RL) policies. Yet, existing quadcopter learning environments often face a trade-off between physical fidelity, multi-agent support, and the throughput required by modern deep RL pipelines. In this paper, we present MuJoCo-Drones-Gym, an open-source Gymnasium-compatible multi-drone environment built on top of the MuJoCo physics engine. MuJoCo-Drones-Gym supports an arbitrary number of Bitcraze Crazyflie 2.x nano-quadcopters and exposes a modular API for selecting (i)~the physics model (rigid-body MuJoCo, explicit Python dynamics, or any subset of ground effect, blade drag, and inter-drone downwash), (ii)~the action interface (per-motor RPMs, collective normalized thrust, velocity setpoints, or PID waypoint commands), and (iii)~the observation space (kinematic state vectors, RGB / depth / segmentation cameras, or neighbourhood adjacency information). A PettingZoo ParallelEnv wrapper enables drop-in multi-agent reinforcement learning, while a suite of seven task environments, hover, velocity tracking, multi-drone hover, waypoint navigation, formation flight, gate racing, and a generic multi-agent template, demonstrates the breadth of the interface. We describe the environment design, the underlying physics and quadcopter dynamics, and illustrate its use through control and learning examples that mirror those of the closely related gym-pybullet-drones project, while taking advantage of MuJoCo's improved contact handling, rendering, and parallelizability.
Chinese Translation
机器人模拟器是现代空中机器人研究的基石，既是新控制算法开发的载体，也是训练强化学习（RL）策略的数据来源。然而，现有的四旋翼学习环境往往在物理逼真性、多智能体支持和现代深度RL管道所需的吞吐量之间面临权衡。本文介绍了MuJoCo-Drones-Gym，一个基于MuJoCo物理引擎构建的开源Gymnasium兼容多无人机环境。MuJoCo-Drones-Gym支持任意数量的Bitcraze Crazyflie 2.x纳米四旋翼，并提供了一个模块化API，用于选择（i）物理模型（刚体MuJoCo、显式Python动力学，或地面效应、桨叶阻力和无人机间下洗的任意子集），（ii）动作接口（每个电机的转速、集体归一化推力、速度设定点或PID航点命令），以及（iii）观察空间（运动状态向量、RGB/深度/分割相机或邻域邻接信息）。PettingZoo ParallelEnv包装器支持即插即用的多智能体强化学习，而七个任务环境的套件，包括悬停、速度跟踪、多无人机悬停、航点导航、编队飞行、门赛和通用多智能体模板，展示了接口的广泛性。我们描述了环境设计、基础物理和四旋翼动力学，并通过控制和学习示例来说明其使用，这些示例与密切相关的gym-pybullet-drones项目相似，同时利用了MuJoCo在接触处理、渲染和并行化方面的改进。
cs.RO / 10 / 2606.08057
EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets
EgoAERO：从单个自我中心视频中学习灵巧操作，无需物体资产
Abstract
Egocentric RGB-D videos offer a natural source of human dexterous manipulation demonstrations, but existing data is difficult to use for robot learning because object pose, geometry, and contact information are often missing or require pre-scanned object assets. We present EgoAERO, the first framework that learns dexterous manipulation from a single egocentric RGB-D human demonstration without object assets. EgoAERO reconstructs contact-consistent hand-object trajectories through asset-free object tracking and reconstruction, ego motion compensation, and adaptive contact optimization, then converts them into robot policies using two-stage residual learning. We further introduce an online quality assessment mechanism and construct EgoDex-R, a large-scale egocentric dataset with 4.3M RGB-D frames for dexterous policy learning. Simulation and real-world experiments show that EgoAERO enables single-demonstration dexterous manipulation and achieves downstream performance close to CAD-based reconstructions on HOI4D.
Chinese Translation
自我中心的RGB-D视频提供了人类灵巧操作演示的自然来源，但现有数据由于物体姿态、几何形状和接触信息常常缺失或需要预先扫描的物体资产，因此难以用于机器人学习。我们提出了EgoAERO，这是第一个从单个自我中心RGB-D人类演示中学习灵巧操作的框架，无需物体资产。EgoAERO通过无资产的物体跟踪和重建、自我运动补偿和自适应接触优化，重建接触一致的手-物体轨迹，然后使用两阶段残差学习将其转换为机器人策略。我们进一步引入了一种在线质量评估机制，并构建了EgoDex-R，这是一个大规模自我中心数据集，包含430万帧RGB-D图像，用于灵巧策略学习。仿真和现实世界实验表明，EgoAERO能够实现单次演示的灵巧操作，并在HOI4D上实现接近基于CAD重建的下游性能。
cs.RO / 11 / 2606.08059
Perceptive Behavior Foundation Model: Adapting Human Motion Priors to Robot-Centric Terrain
感知行为基础模型：将人类运动先验适应于机器人中心的地形
Abstract
Humanoid behavior foundation models aim to acquire reusable whole-body control policies from broad human motion priors, enabling a single controller to produce diverse and expressive behaviors. However, existing motion-centric foundation policies largely assume that the reference motion is already physically compatible with the robot's surroundings. This assumption breaks when the demonstrator, operator, and robot inhabit different environments: a human motion may specify the intended behavior, but not the footholds, clearance, body height, or contact timing required by the robot's local terrain. We introduce \emph{Perceptive Behavior Foundation Model} (Perceptive BFM), a terrain-aware humanoid control framework that grounds human motion priors in robot-centric perception. The model preserves raw kinematic motion references as the behavioral interface, while using local terrain observations to adapt contacts, posture, and timing. To provide scalable terrain supervision, we develop \emph{terrain-conformal reference synthesis} (TCRS), which converts locomotion-oriented human motion clips into terrain-consistent references through contact-aware foothold construction, foot-geometry-aware swing optimization, support-aware root reconstruction, collision repair, and multi-point inverse kinematics. We then train a blind adapted-reference teacher and transfer its terrain-conformal behavior to a deployed raw-reference student through target-frame action alignment. The student is an identity-gated Transformer tracker whose terrain features enter through residual pathways initialized to preserve the motion-tracking prior and trained to produce local corrections only when needed.
Chinese Translation
类人行为基础模型旨在从广泛的人类运动先验中获取可重用的全身控制策略，使单一控制器能够产生多样且富有表现力的行为。然而，现有的以运动为中心的基础政策在很大程度上假设参考运动已经与机器人周围环境物理兼容。当示范者、操作员和机器人处于不同环境时，这一假设会失效：人类运动可能指定了预期的行为，但并未考虑机器人当地形所需的支撑点、间隙、身体高度或接触时机。我们提出了 extit{感知行为基础模型}（Perceptive BFM），这是一个具有地形感知的类人控制框架，将人类运动先验与机器人中心的感知相结合。该模型保留原始的运动学运动参考作为行为接口，同时利用当地形观察来调整接触、姿态和时机。为了提供可扩展的地形监督，我们开发了 extit{地形一致参考合成}（TCRS），通过接触感知的支撑点构建、足部几何感知的摆动优化、支持感知的根部重建、碰撞修复和多点逆运动学，将以运动为导向的人类运动片段转换为地形一致的参考。然后，我们训练一个盲适应参考教师，并通过目标帧动作对齐将其地形一致行为转移到已部署的原始参考学生。该学生是一个身份门控的Transformer跟踪器，其地形特征通过初始化为保留运动跟踪先验的残差路径进入，并仅在需要时训练以产生局部修正。
cs.RO / 12 / 2606.08064
Cooperative Long Rope Skipping via Multi-Agent Reinforcement Learning
基于多智能体强化学习的合作长绳跳绳
Abstract
Humans exhibit remarkable motor agility, enabling a wide range of dynamic skills such as running and jumping, which highlights the great potential of humanoid robots for athletic locomotion. Among athletic sports, long rope skipping requires two rope turners to cooperatively swing the rope while adapting to a player under different jumping rhythms, making it a meaningful yet challenging task for humanoid robots. Although existing methods for humanoid sports have achieved success in single-agent and interaction-free settings, such as running, dancing, and parkour, task scenarios that require precise coordination among multiple participants remain largely unexplored. To this end, we propose Marope, a multi-agent reinforcement learning (MARL) framework for cooperative long rope skipping with multiple humanoid robots. Specifically, Marope adopts a hierarchical reinforcement learning framework for policy training. At the lower level, it learns decentralized rope manipulation policies through MARL, while at the upper level, a centralized scheduling policy is trained to coordinate the execution of the lower-level policies. To improve generalization across different player behavioral styles, Marope further incorporates diverse jumping policies into cooperative game training. We evaluate our approach on Unitree G1 humanoid robots in both simulation and real-world settings. Experimental results demonstrate that Marope outperforms various baselines, achieving more efficient and stable rope manipulation as well as more robust and adaptable cooperation with varied players.
Chinese Translation
人类展现出卓越的运动灵活性，使其能够进行多种动态技能，如跑步和跳跃，这突显了类人机器人在运动移动方面的巨大潜力。在运动项目中，长绳跳绳需要两个转绳者协同摆动绳子，同时适应不同跳跃节奏的运动员，这使其成为类人机器人面临的一项有意义但具有挑战性的任务。尽管现有的类人运动方法在单智能体和无交互设置下取得了成功，例如跑步、舞蹈和跑酷，但需要多个参与者之间精确协调的任务场景仍然未得到充分探索。为此，我们提出了Marope，一个用于多个类人机器人合作长绳跳绳的多智能体强化学习（MARL）框架。具体而言，Marope采用分层强化学习框架进行策略训练。在下层，它通过MARL学习去中心化的绳子操作策略，而在上层，训练一个中心化的调度策略以协调下层策略的执行。为了提高不同运动员行为风格下的泛化能力，Marope进一步将多样化的跳跃策略纳入合作游戏训练中。我们在Unitree G1类人机器人上对我们的方法进行了仿真和现实环境下的评估。实验结果表明，Marope在绳子操作的效率和稳定性以及与不同运动员的合作的鲁棒性和适应性方面均优于各种基线方法。
cs.RO / 13 / 2606.08094
vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models
vla.cpp：一种统一的视觉-语言-动作模型推理运行时
Abstract
Vision-Language-Action (VLA) policies are typically shipped as Python/PyTorch stacks that assume a workstation-class GPU, a mismatch for the hardware on which robots actually run. We present vla.cpp, a portable C++ inference runtime built on llama.cpp. To our knowledge, it is the first ggml-class engine to natively serve the flow-matching and diffusion VLA inference pattern, in which a cached vision-language prefix is consumed by a cross-attending action expert integrated over several solver steps. A single runtime serves seven architectures spanning five backbone and four action-head families behind one request/response protocol, with each model packaged as a self-contained bundle. On LIBERO-Object, the engine matches a state-of-the-art checkpoint to within one episode out of 200, and runs BitVLA at 100% success in 1.3 GiB of memory. The same bundle runs unchanged across three hardware tiers, from a consumer GPU down to an 8 GB embedded module. A cross-hardware roofline analysis shows that batch-1 VLA inference is compute-bound, so utilization rather than bandwidth is the deployment lever; an IMMA ladder GEMM derived from this analysis cuts BitVLA per-step latency by 4.5x. We then frame an on-robot stress test on an ALOHA arm that isolates the latency constraint under which a learned VLA must replan against a moving target on the hardware it was trained for. Code, demo videos, and the reproducible benchmark scaffold are available at https://fai-modelopt-tech.github.io/vla-cpp.github.io/.
Chinese Translation
视觉-语言-动作（VLA）策略通常以Python/PyTorch堆栈的形式发布，这假设了工作站级别的GPU，这与机器人实际运行的硬件不匹配。我们提出了vla.cpp，这是一种基于llama.cpp构建的可移植C++推理运行时。据我们所知，它是第一个原生支持流匹配和扩散VLA推理模式的ggml级引擎，其中缓存的视觉-语言前缀被跨注意力的动作专家在多个求解步骤中整合使用。一个运行时支持七种架构，涵盖五种主干网络和四种动作头系列，所有模型都通过一个请求/响应协议打包为自包含的包。在LIBERO-Object上，该引擎的性能与最先进的检查点在200个回合中相差不超过一个回合，并且在1.3 GiB内存中以100%的成功率运行BitVLA。相同的包在三个硬件层级上保持不变，从消费级GPU到8 GB嵌入模块。跨硬件屋顶线分析表明，批量1的VLA推理是计算密集型的，因此利用率而非带宽是部署的杠杆；基于此分析得出的IMMA阶梯GEMM将BitVLA每步延迟降低了4.5倍。随后，我们在ALOHA机械臂上框定了一个机器人压力测试，隔离了学习到的VLA必须在其训练的硬件上针对移动目标重新规划的延迟约束。代码、演示视频和可重复的基准框架可在https://fai-modelopt-tech.github.io/vla-cpp.github.io/获取。
cs.RO / 14 / 2606.08099
Cybernetic Android Avatar "Yui": System Integration, Field Deployment, and Evaluation
网络控制的安卓化身 "Yui": 系统集成、现场部署与评估
Abstract
Remote communication technologies have become widely used; however, supporting a sense of shared physical space and conveying rich non-verbal cues remain challenging in many social interaction scenarios. This study presents "Yui," a full-body cybernetic android avatar designed to integrate operator-side immersive teleoperation with interlocutor-side human-like social signaling. Yui combines a 55-degrees of freedom full-body mechanism with a previously developed android head, facial expression and gaze control, upper-body and arm motion, hand actuation, and a mobile platform. It can be operated through either the immersive mode using a head mounted display-based interface or desktop mode using a webcam-based interface. We evaluated the system through three real-world deployments: a long-term public exhibition at Expo 2025 in Osaka, Kansai, Japan; a remote educational exchange between elementary school students; and a public interaction study with general participants. During the Expo deployment, two units accumulated approximately 1131 h of operation, demonstrating both operational feasibility and maintenance challenges. In the public study, both operators and interlocutors reported positive impressions of co-presence and willingness to use the system. Interlocutors also rated the avatar positively in terms of human likeness and the transmission of emotions and intentions. The results indicate usability for general operators while suggesting room for improvement in precise controllability. These findings provide field-derived evidence and design implications for socially deployable full-body android avatars.
Chinese Translation
远程通信技术已被广泛应用；然而，在许多社交互动场景中，支持共享物理空间的感觉和传达丰富的非语言线索仍然具有挑战性。本研究提出了 "Yui"，一个全身网络控制的安卓化身，旨在将操作员侧的沉浸式遥控与对话者侧的人类社交信号整合在一起。Yui结合了一个具有55个自由度的全身机制、先前开发的安卓头部、面部表情和视线控制、上半身和手臂运动、手部驱动以及移动平台。它可以通过沉浸式模式（使用头戴显示器接口）或桌面模式（使用网络摄像头接口）进行操作。我们通过三次真实场景的部署对系统进行了评估：在日本关西大阪的2025年世博会上的长期公共展览；小学生之间的远程教育交流；以及与普通参与者的公共互动研究。在世博会部署期间，两个单位累计约1131小时的操作，展示了操作的可行性和维护的挑战。在公共研究中，操作员和对话者均报告了对共在感的积极印象以及使用该系统的意愿。对话者还在人的相似性以及情感和意图的传递方面对该化身给予了积极评价。结果表明，普通操作员的可用性良好，但在精确控制方面仍有改进空间。这些发现为社会可部署的全身安卓化身提供了基于现场的证据和设计启示。
cs.RO / 15 / 2606.08102
Continual Quadruped Robots Coordination via Semantic Skill Discovery
通过语义技能发现实现持续四足机器人协调
Abstract
Multi-quadruped coordination has attracted increasing attention due to its enhanced payload capacity, broader contact coverage, and improved adaptability to challenging tasks. Existing methods for multi-quadruped manipulation typically focus on predefined or closed task families, often relying on multi-agent reinforcement learning (MARL) to train task-specific coordination policies. However, such methods struggle in open-ended continual learning settings, where tasks arrive sequentially and robots are expected to acquire new coordination skills while reusing previously learned ones without catastrophic forgetting. To address this challenge, we propose Conquer, a semantic skill-library framework that formulates continual multi-quadruped coordination as a retrieve-adapt-update process. First, to accommodate varying team sizes across tasks, we design a team-structured Self-Allies-Goal (SAG) backbone that supports variable-cardinality robot teams by explicitly modeling each robot's own state, teammate context, and task goal. For each incoming task, Conquer constructs a task-level semantic descriptor from pre-execution information and retrieves a relevant skill from the library for adaptation. After successful execution, Conquer updates the skill library by extracting trajectory-level semantic descriptors and organizing them according to semantic distance, thereby enabling continual skill accumulation and cross-task knowledge transfer. Simulation experiments show that Conquer achieves a final average success rate of 95.6%, demonstrating strong forward transfer and negligible catastrophic forgetting. Real-world rollouts on Unitree Go2 teams further validate the deployment feasibility of Conquer for practical multi-quadruped coordination. Simulation and real-robot demonstration videos are available at: https://conquer-project.pages.dev/.
Chinese Translation
多四足协调因其增强的载荷能力、更广的接触覆盖范围以及对挑战性任务的适应性而受到越来越多的关注。现有的多四足操作方法通常集中于预定义或封闭的任务家族，往往依赖于多智能体强化学习（MARL）来训练特定任务的协调策略。然而，这些方法在开放式持续学习环境中表现不佳，在这种环境中，任务是顺序到达的，机器人需要在不发生灾难性遗忘的情况下获取新的协调技能，同时重用先前学习的技能。为了解决这一挑战，我们提出了Conquer，一个将持续多四足协调形式化为检索-适应-更新过程的语义技能库框架。首先，为了适应不同任务中的团队规模变化，我们设计了一个团队结构的自我盟友目标（Self-Allies-Goal, SAG）主干，它通过显式建模每个机器人的自身状态、队友上下文和任务目标来支持可变基数的机器人团队。对于每个到来的任务，Conquer从执行前信息中构建任务级语义描述符，并从库中检索相关技能进行适应。成功执行后，Conquer通过提取轨迹级语义描述符并根据语义距离进行组织来更新技能库，从而实现持续的技能积累和跨任务知识转移。仿真实验表明，Conquer的最终平均成功率达到95.6%，显示出强大的前向转移能力和微不足道的灾难性遗忘。对Unitree Go2团队的实际部署进一步验证了Conquer在实际多四足协调中的可行性。仿真和真实机器人演示视频可在以下链接查看：https://conquer-project.pages.dev/
cs.RO / 16 / 2606.08103
Revisiting Articulated Parts Perception in Robot Manipulation
重新审视机器人操作中的关节部件感知
Abstract
We are surrounded by various objects with movable, articulated parts, e.g., box, handle, door. An accurate and generalizable perception of articulated parts is essential to enhance robotic manipulation capabilities. Building on this need, recent efforts in articulated parts perception have followed two main directions: One line of work uses pose-based representation, which requires high manual cost; in parallel, affordance-based methods extract future object motion from point tracking without additional manual efforts, but suffer from low-quality data. In this paper, we propose a new representation of articulated parts, Geometric Primary Structure (GPS), an abstraction of the part geometry structure to balance scalability and quality. For efficient and scalable data collection, GPS is integrated with a portable Virtual Reality (VR) device and requires only one minute to annotate one object sequence. This direct human annotation provides higher quality than the estimated affordance. With this efficient VR-GPS system, we collect 41K frames for 234 objects across six part classes, and train a generalizable GPS model with a single RGB-D object image as input. For object manipulation, we deploy a heuristic policy based on GPS prediction. Without any in-domain fine-tuning, our method achieves an 73% success rate, covering 270 initial states for 9 objects. Our code, data and reusable tool are available at https://enlighten0707.github.io/gps.
Chinese Translation
我们周围有各种具有可动关节部件的物体，例如箱子、手柄和门。对关节部件的准确和可泛化的感知对于增强机器人操作能力至关重要。基于这一需求，最近在关节部件感知方面的研究主要沿着两个方向展开：一方面，基于姿态的表示方法需要高昂的人工成本；另一方面，基于可用性的算法通过点跟踪提取未来物体运动，无需额外的人工努力，但数据质量较低。本文提出了一种新的关节部件表示方法——几何主结构（Geometric Primary Structure, GPS），它是部件几何结构的抽象，旨在平衡可扩展性和质量。为了高效和可扩展的数据收集，GPS与便携式虚拟现实（Virtual Reality, VR）设备集成，只需一分钟即可注释一个物体序列。这种直接的人类注释提供了比估计的可用性更高的质量。通过这一高效的VR-GPS系统，我们收集了234个物体的41K帧数据，涵盖六个部件类别，并以单个RGB-D物体图像作为输入训练了一个可泛化的GPS模型。对于物体操作，我们基于GPS预测部署了一种启发式策略。在没有任何领域内微调的情况下，我们的方法实现了73%的成功率，覆盖了9个物体的270个初始状态。我们的代码、数据和可重用工具可在https://enlighten0707.github.io/gps获取。
cs.RO / 17 / 2606.08104
Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations
线性嵌入空间中的强化学习解锁软机器人配置的可泛化控制
Abstract
Soft-bodied organisms such as octopuses and elephant trunks exhibit remarkable morphological adaptability, dynamically reconfiguring body shape and stiffness, and flexibly adjusting their control strategies to enable versatile behaviors. Inspired by these biological systems, various soft robots have emerged in recent decades, featuring diverse materials, stiffnesses, and morphologies tailored to specific tasks. Despite substantial advances in the materials and structural designs of soft robots, developing a generalizable control framework capable of rapid adaptation across diverse configurations remains a long-standing challenge. Existing controllers are limited to fixed configurations, demanding laborious configuration-specific remodelling and policy redesign for new configurations. Here, we introduce a generalizable control system that enables rapid adaptation across diverse soft robot configurations via reinforcement learning in a shared linear Koopman embedding space. By encoding robot dynamics into this embedding space, our method decouples control policies from specific morphologies, allowing real-time, model-free policy adaptation across diverse configurations without retraining from scratch. We validate our system across 33 distinct robot configurations. Our system achieves a 75 times reduction in transfer samples across configurations, while sustaining robust performance under high-speed motion, heavy payloads, and multiactuator faults, and achieving real-world skills previously unattainable in soft robotics. This work establishes a unified and adaptable control paradigm for diverse soft robot configurations, bridging mechanical reconfigurability with control flexibility, and may offer broader insights for generalizable control in complex physical systems.
Chinese Translation
软体生物如章鱼和大象的鼻子展现出显著的形态适应能力，能够动态重新配置身体形状和刚度，并灵活调整控制策略以实现多样化行为。受到这些生物系统的启发，近年来出现了各种软机器人，这些机器人采用多种材料、刚度和形态，针对特定任务进行了定制。尽管在软机器人材料和结构设计方面取得了显著进展，但开发一种能够在多种配置中快速适应的可泛化控制框架仍然是一个长期挑战。现有控制器仅限于固定配置，要求为新配置进行繁琐的特定配置重建和策略重新设计。在此，我们介绍了一种可泛化控制系统，通过在共享的线性Koopman嵌入空间中进行强化学习，使得在多种软机器人配置中能够快速适应。通过将机器人动态编码到该嵌入空间，我们的方法将控制策略与特定形态解耦，允许在不同配置中实时、无模型地适应策略，而无需从头开始重新训练。我们在33种不同的机器人配置中验证了我们的系统。我们的系统在配置间实现了75倍的转移样本减少，同时在高速运动、重负载和多执行器故障下保持稳健性能，并实现了在软机器人领域之前无法达到的现实世界技能。这项工作建立了一种统一且可适应的控制范式，适用于多样化的软机器人配置，架起了机械可重构性与控制灵活性之间的桥梁，并可能为复杂物理系统中的可泛化控制提供更广泛的见解。
cs.RO / 18 / 2606.08107
Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data
Ego-Pi：针对自我中心人类和机器人数据的变换学习精调
Abstract
Robotics faces a fundamental challenge of data scarcity. Unlike language or vision research, there is no internet-scale dataset for robotic manipulation. A promising path forward is to leverage egocentric human data, which can be collected more easily, with greater breadth, and at a larger scale. Towards this end, we investigate key design choices for learning across human and humanoid embodiments equipped with dexterous five-finger hands, using the $\pi_{0.5}$ model as a foundation. Our results show that human data enables robots to learn new task semantics and compose existing skills into novel behaviors without corresponding robot data. The paper website is here: https://egopipaper.github.io/
Chinese Translation
机器人技术面临数据稀缺的根本挑战。与语言或视觉研究不同，机器人操作没有互联网规模的数据集。一个有前景的解决方案是利用自我中心的人类数据，这类数据更易于收集，具有更广泛的覆盖面，并且可以在更大规模上获取。为此，我们研究了在配备灵巧五指手的类人和人类体现上进行学习的关键设计选择，以$ ext{pi}_{0.5}$模型为基础。我们的结果表明，人类数据使机器人能够学习新的任务语义，并将现有技能组合成新颖的行为，而无需相应的机器人数据。论文网站链接为：https://egopipaper.github.io/
cs.RO / 19 / 2606.08136
Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning
基于深度库普曼算子的学习预测控制用于自主车辆运动规划
Abstract
Model Predictive Control (MPC) is widely used for autonomous-vehicle (AV) motion planning, but its real-time applicability is often limited by the need for accurate models and online solution of nonlinear, nonconvex optimization problems in dynamic road environments. Actor-critic reinforcement learning offers a promising alternative for online policy generation, yet its policy-learning process often lacks explicit control-theoretic structure. This article proposes a learning predictive control (LPC) framework with deep Koopman operators for efficient real-time motion planning under nonconvex constraints. To address nonlinear and uncertain vehicle dynamics, a deep-Koopman-based predictor is used to lift the system into an interpretable linear observable space in a data-driven manner. Unlike traditional MPC, which computes open-loop control sequences, the proposed LPC framework yields a closed-loop state-feedback policy within each prediction interval through receding-horizon actor-critic learning. To ensure safety under nonconvex environmental constraints, LPC constructs convex local surrogate representations of obstacles and defines corresponding potential-field functions. These functions and their gradients are directly embedded into the actor-critic structure, enabling efficient, safety-aware policy learning. Extensive simulations and real-world experiments on the HongQi-EHS3 platform demonstrate favorable performance in diverse obstacle-avoidance scenarios in terms of safety, computational efficiency, and driving comfort, compared with benchmark methods such as CBF-MPC and LMPCC.
Chinese Translation
模型预测控制（MPC）广泛应用于自主车辆（AV）运动规划，但其实时适用性常常受到对准确模型和动态路况环境中非线性、非凸优化问题在线求解需求的限制。演员-评论家强化学习为在线策略生成提供了一种有前景的替代方案，然而其策略学习过程往往缺乏明确的控制理论结构。本文提出了一种基于深度库普曼算子的学习预测控制（LPC）框架，以实现非凸约束下的高效实时运动规划。为了解决非线性和不确定的车辆动力学问题，采用深度库普曼预测器以数据驱动的方式将系统提升到可解释的线性可观察空间。与传统的MPC计算开环控制序列不同，所提出的LPC框架通过递归视野的演员-评论家学习在每个预测区间内生成闭环状态反馈策略。为了确保在非凸环境约束下的安全性，LPC构建了障碍物的凸局部代理表示，并定义了相应的势场函数。这些函数及其梯度被直接嵌入到演员-评论家结构中，从而实现高效的、安全意识的策略学习。在HongQi-EHS3平台上进行的广泛仿真和真实世界实验表明，与CBF-MPC和LMPCC等基准方法相比，在安全性、计算效率和驾驶舒适性等多样化的避障场景中表现出良好的性能。
cs.RO / 20 / 2606.08152
Vision-Guided Dual-Arm Humanoid Robotic Disassembly of End-of-Life 18650 Lithium-ion Battery Packs
视觉引导的双臂类人机器人对废弃18650锂离子电池包的拆解
Abstract
The growing volume of retired lithium-ion battery packs from electric vehicles and portable electronics calls for automated disassembly that is safe, flexible, and selective down to the individual cell. Existing robotic systems, however, mostly assume known pack poses, external fixtures, or specialised tooling, leaving fixture-free cell-level disassembly under pose uncertainty largely unsolved. This paper presents a vision-guided dual-arm pipeline that disassembles a 21-cell 18650 pack from an arbitrary initial pose using only general-purpose parallel-jaw grippers, RGB-D sensing, and a pre-trained grasp detector. Pose uncertainty is absorbed by a learn-and-filter perception stack with discrete look-and-move wrist-camera corrections, while a mid-task support transfer between the two arms extends the effective workspace without any external clamp. The pipeline achieves an 8/10 end-to-end success rate, a cell-localisation root-mean-square error of $2.4$\,mm, and a mean cycle time of 6.0\,minutes per pack, providing a practical, fixture-free building block for industrial battery recycling.
Chinese Translation
随着电动车和便携电子设备中退役锂离子电池包数量的增加，迫切需要一种安全、灵活且能够选择性拆解到单个电池单元的自动化拆解方法。然而，现有的机器人系统大多假设电池包的姿态已知、外部夹具存在或使用专用工具，这使得在姿态不确定的情况下进行无夹具的电池单元级拆解仍然未得到有效解决。本文提出了一种视觉引导的双臂拆解流程，该流程能够在任意初始姿态下，仅使用通用的平行夹爪、RGB-D传感器和预训练的抓取检测器，拆解一个21单元的18650电池包。姿态不确定性通过一个学习与过滤的感知堆栈来吸收，该堆栈结合了离散的观察与移动腕部相机校正，而两个手臂之间的中途支持转移则在没有任何外部夹具的情况下扩展了有效工作空间。该流程实现了8/10的端到端成功率，电池单元定位的均方根误差为2.4毫米，每个电池包的平均循环时间为6.0分钟，为工业电池回收提供了一个实用的无夹具构建模块。
cs.RO / 21 / 2606.08154
SynthICL: Scalable In-context Imitation Learning with Synthetic Data
SynthICL：基于合成数据的可扩展上下文模仿学习
Abstract
In-context imitation learning (ICIL) enables robots to learn new tasks from a small number of demonstrations by conditioning a pre-trained policy on task-specific examples, without retraining at test time. Despite this promise, training generalizable and scalable in-context imitation policies remains an open challenge. We present SynthICL, a scalable framework that trains ICIL policies entirely from RGB-only synthetic data. Specifically, we build a data generation pipeline to produce high-fidelity ICIL data and train a flow-matching transformer policy on the resulting dataset. SynthICL avoids the need for depth sensing, precise camera calibration, and real-world training data in prior approaches, offering a simpler and more scalable alternative. We further incorporate subgoal prediction by training the model to predict the next subgoal images, enabling more precise and visually grounded control. Evaluated on 16 unseen real-world manipulation tasks, SynthICL achieves an average success rate of 79% with only one demonstration provided at test time and outperforms prior methods. Project page: https://synth-icl.github.io
Chinese Translation
上下文模仿学习（ICIL）使机器人能够通过在任务特定示例上对预训练策略进行条件化，从少量演示中学习新任务，而无需在测试时进行重新训练。尽管这一前景令人期待，训练可泛化和可扩展的上下文模仿策略仍然是一个未解决的挑战。我们提出了SynthICL，这是一个完全基于仅RGB合成数据训练ICIL策略的可扩展框架。具体而言，我们构建了一个数据生成管道，以生成高保真度的ICIL数据，并在生成的数据集上训练流匹配变换器策略。SynthICL避免了先前方法中对深度传感、精确相机标定和真实世界训练数据的需求，提供了一种更简单和可扩展的替代方案。我们进一步通过训练模型预测下一个子目标图像来结合子目标预测，从而实现更精确和视觉基础的控制。在16个未见过的真实世界操作任务上进行评估，SynthICL在仅提供一个演示的测试条件下实现了79%的平均成功率，并超越了先前的方法。项目页面：https://synth-icl.github.io
cs.RO / 22 / 2606.08169
CLASP: Language-Driven Robot Skill Selection and Composition using Task-Parameterized Learning
CLASP：基于语言驱动的机器人技能选择与组合的任务参数化学习
Abstract
Enabling robots to understand and execute tasks from natural language commands while maintaining data efficiency remains challenging. Foundation models such as vision-language-action (VLA) and vision-language models (VLMs) provide intuitive interaction channels but require extensive data; task-parameterized imitation learning achieves data efficiency but lacks natural language grounding. This work bridges this gap through a modular architecture combining task-parameterized kernelized movement primitives (TP-KMPs) with pretrained VLMs. During learning, skills are acquired from 2 to 5 kinesthetic demonstrations, and the VLM generates skill schemas describing each skill's parameters and preconditions. During execution, the VLM interprets commands to select skills, reason about parameter bindings, and create novel behaviors through covariance-weighted composition. When no skill or composition suffices, the system identifies capability gaps and requests targeted demonstrations, all without fine-tuning. Validation on a 7-DoF manipulator shows success rates of 73.3%-100% in scenarios requiring skill selection, composition, and active learning.
Chinese Translation
使机器人能够理解和执行来自自然语言指令的任务，同时保持数据效率仍然具有挑战性。基础模型如视觉-语言-动作（VLA）和视觉-语言模型（VLMs）提供了直观的交互渠道，但需要大量数据；而任务参数化模仿学习实现了数据效率，但缺乏自然语言的基础。本研究通过一种模块化架构将任务参数化核化运动原语（TP-KMPs）与预训练的VLMs结合起来，弥补了这一空白。在学习过程中，技能通过2到5次动觉演示获得，VLM生成描述每项技能参数和前提条件的技能模式。在执行过程中，VLM解析指令以选择技能，推理参数绑定，并通过协方差加权组合创造新行为。当没有技能或组合足够时，系统识别能力差距并请求针对性的演示，所有这些都无需微调。在一个7自由度的操纵器上的验证显示，在需要技能选择、组合和主动学习的场景中成功率达到73.3%-100%。
cs.RO / 23 / 2606.08170
Learning from Human Driving: A Human-in-the-Loop Online Behavior Cloning Framework for Autonomous Driving
从人类驾驶中学习：一种用于自动驾驶的人机协同在线行为克隆框架
Abstract
With the evolution of large foundation models (LFMs), data-driven autonomous driving has made significant strides. However, existing paradigms still face severe challenges in complex interaction and long-tail scenarios due to distribution shift and causal confusion. These limitations often result in a lack of human-level decision-making flexibility and safety in extreme conditions. To overcome this limitation, this paper proposes a Human-in-the-Loop Online Behavior Cloning frame work (HiL-OBC) for autonomous driving, which aims to deeply integrate the cross-modal perceptual capabilities of LFMs with the high-level driving intelligence of human experts. Specifically, HiL-OBC deployment is executed through three critical phases: policy initialization with human intervention, latent behavioral modeling with Bayesian policy adaptation, and online deploy ment and updates. Furthermore, we design a Multi-modal Online Behavior Cloning (MOBC) model, which optimizes the base driving policy online through a lightweight network architecture, a takeover trigger mechanism, and a multi-variant loss function, thereby enhancing the system's decision-making robustness in complex environments. We evaluated the HiL-OBC on the LangAuto-Human CARLA benchmark. Experimental results demonstrate that the driving policies optimized via the human-in-the-loop mechanism achieve substantial performance gains: the DS of StructNav, LFG, and LMDrive increased by 47.25%, 31.59%, and 32.12%, respectively, with a simultaneous of various experimental settings and key components highlights the advantages of human-in-the-loop learning in improving decision-making robustness and overall driving performance.
Chinese Translation
随着大型基础模型（LFMs）的发展，数据驱动的自动驾驶取得了显著进展。然而，现有范式在复杂交互和长尾场景中仍面临严重挑战，这主要是由于分布转移和因果混淆。这些限制往往导致在极端条件下缺乏人类水平的决策灵活性和安全性。为了解决这一限制，本文提出了一种用于自动驾驶的人机协同在线行为克隆框架（HiL-OBC），旨在将LFMs的跨模态感知能力与人类专家的高级驾驶智能深度融合。具体而言，HiL-OBC的部署通过三个关键阶段执行：人类干预下的策略初始化、基于贝叶斯策略适应的潜在行为建模，以及在线部署和更新。此外，我们设计了一种多模态在线行为克隆（MOBC）模型，通过轻量级网络架构、接管触发机制和多变损失函数在线优化基础驾驶策略，从而增强系统在复杂环境中的决策鲁棒性。我们在LangAuto-Human CARLA基准上评估了HiL-OBC。实验结果表明，通过人机协同机制优化的驾驶策略实现了显著的性能提升：StructNav、LFG和LMDrive的DS分别增加了47.25%、31.59%和32.12%，同时各种实验设置和关键组件的比较突显了人机协同学习在提高决策鲁棒性和整体驾驶性能方面的优势。
cs.RO / 24 / 2606.08186
Propeller-Assisted Robust 3D Hopping Robot with Hierarchical Force Allocation
带有分层力分配的螺旋桨辅助稳健3D跳跃机器人
Abstract
Monopedal hopping robots are conceptually simple but highly dynamic and inherently unstable. Achieving robust 3D hopping is still difficult because ground reaction forces are available only during the short stance phase, while the robot is underactuated in flight. A key unresolved issue is how to improve flight-phase control authority. Propeller assistance provides a promising solution, but it requires careful coordination of leg-generated contact forces and propeller thrusts across stance and flight. This paper presents Pro-OMEGA2, a propeller-assisted 3D monopedal hopping robot with an active 3-RSR parallel leg and a trunk-mounted tri-rotor for auxiliary attitude regulation. To address the force coordination challenge, we propose a Hierarchical Force Allocation (HFA) framework based on a single rigid body (SRB) model. The leg generates the main stance contact wrench, while the tri-rotor provides auxiliary attitude regulation, compensating the residual attitude moment in stance and maintaining attitude during flight. Real-robot experiments in indoor and outdoor scenarios demonstrate sustained 3D hopping, including terrain transitions and impulsive push recovery, validating robustness under unmodeled contact and external disturbances.
Chinese Translation
单足跳跃机器人在概念上简单，但具有高度动态性和固有的不稳定性。实现稳健的3D跳跃仍然困难，因为地面反作用力仅在短暂的支撑阶段可用，而机器人在飞行中处于欠驱动状态。一个关键的未解决问题是如何提高飞行阶段的控制能力。螺旋桨辅助提供了一种有前景的解决方案，但它需要在支撑和飞行阶段之间仔细协调腿部产生的接触力和螺旋桨推力。本文介绍了Pro-OMEGA2，这是一种带有主动3-RSR并联腿和机身安装三旋翼的螺旋桨辅助3D单足跳跃机器人，用于辅助姿态调节。为了解决力协调的挑战，我们提出了一种基于单刚体（SRB）模型的分层力分配（HFA）框架。腿部产生主要的支撑接触扭矩，而三旋翼提供辅助姿态调节，补偿支撑阶段的残余姿态力矩并在飞行期间维持姿态。在室内和室外场景中的真实机器人实验展示了持续的3D跳跃，包括地形过渡和冲击推力恢复，验证了在未建模接触和外部干扰下的稳健性。
cs.RO / 25 / 2606.08214
Agentic Neuro-Symbolic Planning and Commissioning for Human-in-the-Loop Industrial Robotics with Digital Twins
面向人机协作的工业机器人代理神经符号规划与委托，结合数字双胞胎技术
Abstract
Flexible robotic automation requires systems that interpret operator intent, verify physical feasibility, and recover from execution failures across both the planning and execution stages. This paper proposes an agentic neuro-symbolic framework for human-in-the-loop industrial robotics, in which LLMs are used for tasks that require language understanding or contextual reasoning, while all verification, sequencing, and execution remain deterministic. The framework adapts the Planner-Generator-Evaluator (PGE) harness pattern from software engineering into a Specifier-Designer-Inspector (SDI) architecture for industrial robotics, combined with LangGraph-based dynamic routing for failure recovery. A two-tier recovery mechanism addresses structure-level replanning through context-aware orchestration and execution-level geometric failures through deterministic recovery skills. A Unity3D digital twin supports human inspection, modification, and re-verification prior to physical execution. Evaluated on natural-language commands across multiple difficulty levels against ten baselines, the proposed method achieves the highest task success. Ablation results confirm that structured command expansion, symbolic verification, selective LLM routing, and recovery skills are each individually necessary.
Chinese Translation
灵活的机器人自动化需要能够解释操作员意图、验证物理可行性并在规划和执行阶段恢复执行失败的系统。本文提出了一种面向人机协作的工业机器人代理神经符号框架，其中使用大型语言模型（LLMs）处理需要语言理解或上下文推理的任务，而所有的验证、排序和执行保持确定性。该框架将软件工程中的规划者-生成器-评估器（Planner-Generator-Evaluator, PGE）模式改编为工业机器人领域的规范者-设计者-检查者（Specifier-Designer-Inspector, SDI）架构，并结合基于LangGraph的动态路由用于故障恢复。双层恢复机制通过上下文感知的编排处理结构级重新规划，通过确定性恢复技能处理执行级几何故障。Unity3D数字双胞胎支持在物理执行之前进行人工检查、修改和重新验证。在针对十个基线模型的多难度自然语言命令评估中，所提出的方法实现了最高的任务成功率。消融实验结果确认，结构化命令扩展、符号验证、选择性LLM路由和恢复技能各自都是必要的。
cs.RO / 26 / 2606.08249
Disturbance-Aware Aerial Robotics for Ethical Wildlife Monitoring
关注干扰的空中机器人在伦理野生动物监测中的应用
Abstract
Reliable wildlife monitoring is essential for ecology and conservation, yet many existing methods, such as tagging, capture, and close-range observation, can alter the very behaviors they aim to measure. Aerial robots offer a scalable alternative, which has shown promising performance in multiple studies. Nonetheless, existing approaches typically lack behavioral awareness, rely on fixed heuristics, or require real-world training data that are costly, impractical, and ethically difficult to obtain. As a result, there remains no general framework for adaptive drone-based monitoring that can both preserve ecological validity and scale across species, behaviors, and robotic platforms. In this study, we introduce a disturbance-aware reinforcement-learning-based framework for heterogeneous aerial robotic fleets that enables autonomous wildlife tracking while explicitly minimizing behavioral disruption. We couple a zoologically grounded simulation environment with fitted animal movement models derived from real trajectory statistics, and train control policies using a reward formulation that captures the trade-off between observation quality and disturbance risk. Across three species (pigeon, jackal, and spur-winged lapwing) with distinct ecologies and motion patterns and four increasingly strategic behavior models common in nature, the learned policies consistently surpassed currently used rule-based baselines and generalized across monitoring tasks, animal dynamics, and drone types. These results establish disturbance-aware learning as a viable foundation for non-invasive autonomous wildlife observation, opening a path towards scalable, ethically responsible, and scientifically reliable robotic monitoring in ecology and conservation.
Chinese Translation
可靠的野生动物监测对于生态学和保护至关重要，但许多现有方法，如标记、捕捉和近距离观察，可能会改变它们旨在测量的行为。空中机器人提供了一种可扩展的替代方案，在多项研究中显示出良好的性能。然而，现有方法通常缺乏行为意识，依赖固定的启发式规则，或需要昂贵、不可行且伦理上难以获取的真实世界训练数据。因此，目前尚不存在一种能够在保持生态有效性的同时，适用于不同物种、行为和机器人平台的自适应无人机监测的通用框架。在本研究中，我们提出了一种基于强化学习的关注干扰的框架，适用于异构空中机器人群体，使其能够在明确最小化行为干扰的同时实现自主的野生动物追踪。我们将基于动物学的模拟环境与从真实轨迹统计中推导出的动物运动模型相结合，并使用一种奖励公式训练控制策略，以捕捉观察质量与干扰风险之间的权衡。在三种具有不同生态和运动模式的物种（鸽子、豺和翅尖鹤）以及四种在自然界中常见的日益复杂的行为模型中，学习到的策略始终超越了当前使用的基于规则的基线，并在监测任务、动物动态和无人机类型之间实现了泛化。这些结果确立了关注干扰的学习作为非侵入性自主野生动物观察的可行基础，为生态学和保护领域的可扩展、伦理负责和科学可靠的机器人监测开辟了道路。
cs.RO / 27 / 2606.08253
Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
注意你的步伐：一种通用学习框架用于精确的人形机器人足迹跟踪
Abstract
Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately. While reinforcement learning with velocity-commanded policies has achieved remarkable robustness in humanoid locomotion, this approach lacks explicit control of the foothold placement, leading to unsafe behavior, such as stepping onto human feet, or imprecise navigation, hindering the following manipulation task. Conversely, explicit foothold-tracking policies offer a promising alternative by directly being commanded with target foot poses. However, existing approaches are often limited by unrealistic state assumptions, compromising real-world deployment, or they are part of staged pipelines, making them tied to specific downstream tasks. In this work, we introduce a novel, lightweight framework for training general-purpose 3D foothold-tracking policies. By dynamically providing footstep support through a goal sampler, this method enables the learned policy to be agnostic to specific terrains. Our new target representation effectively mitigates challenges arising in the real world, such as noisy and inaccurate pose estimation and foot contact estimation. Designed for direct real-world transfer, our policy acts as a standalone low-level controller that can be seamlessly paired with various high-level foothold generators. We demonstrate the effectiveness of our framework through extensive experiments in simulation and in the real world. By coupling our policy with different upstream planners, we achieve natural and accurate locomotion in challenging settings, paving the way for loco-manipulation tasks in complex environments.
Chinese Translation
使人形机器人能够在复杂动态环境中操作仍然是一个关键挑战，根本上受到稳健、安全和准确导航能力的限制。尽管使用速度指令策略的强化学习在人形机器人运动中取得了显著的稳健性，但这种方法缺乏对足迹放置的明确控制，导致不安全的行为，例如踩到人类的脚，或不精确的导航，从而妨碍后续的操作任务。相反，明确的足迹跟踪策略通过直接指令目标足部姿态提供了一个有前景的替代方案。然而，现有的方法往往受到不切实际的状态假设的限制，妨碍了其在现实世界中的部署，或者它们是分阶段管道的一部分，使其与特定的下游任务紧密相关。在本研究中，我们提出了一种新颖的轻量级框架，用于训练通用的3D足迹跟踪策略。通过动态提供足迹支持，该方法使得学习到的策略对特定地形具有无关性。我们新的目标表示有效缓解了现实世界中出现的挑战，例如噪声和不准确的姿态估计以及足部接触估计。我们的策略旨在实现直接的现实世界转移，作为一个独立的低级控制器，可以与各种高级足迹生成器无缝配对。我们通过在模拟和现实世界中的广泛实验展示了我们框架的有效性。通过将我们的策略与不同的上游规划器结合，我们在具有挑战性的环境中实现了自然和准确的运动，为复杂环境中的运动操作任务铺平了道路。
cs.RO / 28 / 2606.08278
SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation
SIMPLE：基于仿真的类人机器人运动操控策略学习与评估
Abstract
Humanoid foundation models are advancing faster than we can evaluate them. While real-world testing is expensive and difficult to reproduce, existing simulation benchmarks focus primarily on table-top or wheeled robots. A scalable and reproducible benchmark for whole-body humanoid loco-manipulation remains an open problem. To this end, we present SIMPLE, a unified simulation testbed for humanoid policy learning and evaluation. SIMPLE couples the accurate contact-rich dynamics of MuJoCo with the photorealistic rendering of IsaacSim. It provides a large-scale environment comprising 60 diverse whole-body tasks, 50 indoor scenes, and over 1,000 object assets. To facilitate scalable data collection, the framework integrates two data generation pipelines: automated trajectory generation via motion planning and a low-latency VR teleoperation interface. We further integrate and benchmark mainstream humanoid policies at scale in SIMPLE, including lightweight imitation networks, large vision-language-action (VLA) models, and recent world action models (WAMs). Our experiments reveal a strong correlation between policy performance in simulation and the real world. Furthermore, we demonstrate that policies trained on data collected in SIMPLE can be transferred zero-shot to physical humanoid robots under similar settings, providing a robust and reproducible foundation for humanoid robotics research.
Chinese Translation
类人基础模型的发展速度超过了我们对其评估的能力。虽然现实世界的测试成本高昂且难以重复，但现有的仿真基准主要集中在桌面或轮式机器人上。针对整体类人机器人运动操控的可扩展和可重复的基准仍然是一个未解决的问题。为此，我们提出了SIMPLE，一个统一的类人机器人策略学习与评估的仿真测试平台。SIMPLE将MuJoCo的精确接触丰富动力学与IsaacSim的照片级真实感渲染相结合。它提供了一个大规模环境，包括60个多样化的全身任务、50个室内场景和超过1000个物体资产。为了促进可扩展的数据收集，该框架集成了两个数据生成管道：通过运动规划的自动轨迹生成和低延迟的虚拟现实远程操作接口。我们进一步在SIMPLE中集成并基准测试主流的类人机器人策略，包括轻量级模仿网络、大型视觉-语言-动作（VLA）模型和最新的世界动作模型（WAMs）。我们的实验揭示了仿真中策略性能与现实世界之间的强相关性。此外，我们展示了在SIMPLE中收集的数据训练的策略可以在类似设置下零-shot 转移到物理类人机器人，为类人机器人研究提供了一个稳健且可重复的基础。
cs.RO / 29 / 2606.08281
Impedance MPC for Physical Human-Robot Interaction: Predictive Disturbance Rejection with Joint-Limit Safety
用于物理人机交互的阻抗模型预测控制：具有关节极限安全性的预测干扰抑制
Abstract
Physical human-robot interaction (pHRI) demands simultaneous trajectory accuracy and compliant safety under unplanned contact. Classical impedance control incurs a nonzero steady-state position error under sustained human force -- the applied force divided by the task stiffness -- which integral action reduces only within a narrow stable-gain budget. We present a two-layer Impedance MPC that resolves this tension. Layer~1 analytically cancels gravity, Coriolis, and task-space inertia, reducing the residual plant to a configuration-independent double integrator with a constant state-transition matrix. Layer~2 solves a 30-variable convex QP at 100\,Hz, exploiting this constant structure so the free-response matrix is precomputed once; an augmented Kalman filter estimates the persistent disturbance state, giving a formal zero-steady-state-error guarantee. A null-space inverse-barrier potential and a task-space workspace projection enforce joint-limit safety across the tested workspace. On a 7-DOF Franka FR3, Impedance MPC with Kalman augmentation attains sub-0.05\,mm steady-state error versus 44.8\,mm for classical impedance (a $>$800-fold reduction) under a sustained 15\,N force, sub-millimeter tracking on four 3-D circles, and graceful robustness to measurement noise and inertial mismatch up to 30\%.
Chinese Translation
物理人机交互（pHRI）要求在非计划接触下实现轨迹精度与安全性的兼容。经典的阻抗控制在持续的人力作用下会产生非零的稳态位置误差——施加的力除以任务刚度——而积分作用仅在狭窄的稳定增益预算内得以减少。我们提出了一种两层阻抗模型预测控制（Impedance MPC），解决了这一矛盾。第一层通过解析方式消除了重力、科里奥利力和任务空间惯性，将残余植物简化为一个与配置无关的双重积分器，并具有恒定的状态转移矩阵。第二层以100 Hz的频率求解一个30变量的凸二次规划（QP），利用这一恒定结构，使得自由响应矩阵只需预先计算一次；增强型卡尔曼滤波器估计持续的干扰状态，提供了正式的零稳态误差保证。零空间逆障碍势能和任务空间工作空间投影在测试的工作空间内强制执行关节极限安全。在一个7自由度的Franka FR3机器人上，具有卡尔曼增强的阻抗MPC在持续15 N的力下达到了小于0.05 mm的稳态误差，而经典阻抗控制则为44.8 mm（减少了超过800倍），在四个三维圆上的跟踪误差小于1 mm，并且在测量噪声和惯性不匹配高达30%的情况下表现出良好的鲁棒性。
cs.RO / 30 / 2606.08288
MotionVLA: Injecting Geometric Motion into Vision-Language-Action Model
MotionVLA：将几何运动注入视觉-语言-动作模型
Abstract
Vision-language-action (VLA) models increasingly condition robot policies on history, depth, or 4D features to resolve ambiguity in long-horizon manipulation. However, more spatiotemporal evidence is not necessarily better: when the injected evidence is not motion-consistent, it can introduce geometric drift, fragmented temporal cues, and unstable action generation. This raises a simple question: should a VLA remember past frames, or remember the motion that connects them? We introduce MotionVLA, a motion-history interface that converts a short past-only video window into compact, time-continuous trajectory-field tokens. Instead of treating history as a sparse set of ndependently lifted frames, MotionVLA represents recent observations as physically coherent motion evidence. Current visual tokens query this history to retrieve task-relevant motion information, which is then recoupled into the VLA stream under trajectory-grounded supervision. Experiments across simulation benchmarks and preliminary real-robot rollouts show that MotionVLA improves long-horizon manipulation while producing smoother and more direct executions. These results suggest that effective VLA memory is not just about providing more 4D context, but about exposing motion-consistent evidence that is usable for control.
Chinese Translation
视觉-语言-动作（VLA）模型越来越多地依赖历史、深度或四维特征来解决长时间操作中的模糊性。然而，更多的时空证据并不一定更好：当注入的证据不符合运动一致性时，它可能会引入几何漂移、碎片化的时间线索和不稳定的动作生成。这引发了一个简单的问题：VLA应该记住过去的帧，还是记住连接它们的运动？我们提出了MotionVLA，一种运动历史接口，将短期的仅包含过去的视频窗口转换为紧凑的、时间连续的轨迹场标记。MotionVLA并不是将历史视为一组稀疏的独立提升帧，而是将最近的观察表示为物理上连贯的运动证据。当前的视觉标记查询这一历史，以检索与任务相关的运动信息，然后在轨迹基础的监督下重新结合到VLA流中。通过模拟基准和初步的真实机器人实验，结果表明MotionVLA在改善长时间操作的同时，产生了更平滑和更直接的执行。这些结果表明，有效的VLA记忆不仅仅是提供更多的四维上下文，而是暴露出可用于控制的运动一致性证据。
cs.RO / 31 / 2606.08341
Uncertainty-Aware Intention Prediction for Human-to-Robot Assembly Teleoperation
面向人机协作装配遥操作的基于不确定性感知的意图预测
Abstract
In assisted teleoperation for human-robot collaboration, accurate intention prediction is critical for enabling timely and reliable robotic assistance during long-horizon manipulation and assembly tasks. These systems require continuous understanding of user behavior to recognize actions, anticipate intentions, and detect mistakes in real time. However, robot teleoperation demonstrations are costly and hardware-limited, whereas human demonstrations are easier to collect and provide rich temporal structure. To address this challenge, we propose an uncertainty-aware human-to-robot intention prediction framework that combines: (1) hierarchical transfer learning, where MS-TCN++ is pretrained on human hand demonstrations and fine-tuned on limited robot teleoperation data to capture low-level actions and high-level task intentions; (2) a conformal prediction module that provides frame-level prediction sets with statistical coverage guarantees for reliable uncertainty quantification and early intention estimation; and (3) VLM-guided segment correction, which selectively reviews low-confidence or temporally uncertain segments using visual and temporal context. The framework supports action recognition, temporal segmentation, intention anticipation, and mistake detection for assisted teleoperation. Experiments on robot assembly demonstrations with 22 action classes show that human-to-robot fine-tuning improves the robot test-set Edit score from 70.50 to 80.70 using only 16 robot demonstrations. Edit-safe VLM correction further improves frame accuracy from 45.21% to 46.42% and increases F1@25 and F1@50 while preserving the Edit score. These results show that human demonstrations provide scalable pretraining data for robust, uncertainty-aware robot action segmentation. Code and data: project website.
Chinese Translation
在用于人机协作的辅助遥操作中，准确的意图预测对于在长时间的操作和装配任务中实现及时和可靠的机器人辅助至关重要。这些系统需要持续理解用户行为，以实时识别动作、预测意图并检测错误。然而，机器人遥操作演示的成本高且受硬件限制，而人类演示则更易于收集并提供丰富的时间结构。为了解决这一挑战，我们提出了一种基于不确定性感知的人机意图预测框架，该框架结合了：(1) 层次转移学习，其中 MS-TCN++ 在人类手部演示上进行预训练，并在有限的机器人遥操作数据上进行微调，以捕捉低级动作和高级任务意图；(2) 一种符合预测模块，提供具有统计覆盖保证的帧级预测集，以实现可靠的不确定性量化和早期意图估计；(3) VLM引导的段落修正，使用视觉和时间上下文选择性地审查低置信度或时间上不确定的段落。该框架支持辅助遥操作中的动作识别、时间分割、意图预测和错误检测。在包含22个动作类别的机器人装配演示实验中，基于人类的微调使机器人测试集的编辑分数从70.50提高到80.70，仅使用了16个机器人演示。编辑安全的VLM修正进一步将帧精度从45.21%提高到46.42%，并在保持编辑分数的同时提高了F1@25和F1@50。这些结果表明，人类演示为稳健的、不确定性感知的机器人动作分割提供了可扩展的预训练数据。代码和数据：项目网站。
cs.RO / 32 / 2606.08414
PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation
PACT：自我演化的物理安全对齐用于具身操作中的扩散策略
Abstract
Diffusion policies have achieved remarkable success in robotic manipulation, yet they often fail to satisfy strict physical constraints required for safe deployment. Existing approaches impose safety either prematurely during training or reactively via external guardrails at test time, limiting policy expressivity and overall scalability. We propose Physical safety Alignment for Constrained Trajectories (PACT), a self-evolving post-training framework that projects pretrained diffusion policies onto constraint-feasible regions without accessing demonstration data or task rewards. PACT distills constraint gradients into the diffusion model through a reverse-KL objective with dense supervision across timesteps. It incorporates a curriculum that progressively tightens constraints while maintaining theoretically bounded policy shift and monotone improvement, mitigating the safety-performance trade-off from catastrophic forgetting. On simulated and real-world embodied manipulation benchmarks, PACT significantly reduces safety violations by 31.0% on average while improving task success by 30.7%.
Chinese Translation
扩散策略在机器人操作中取得了显著成功，但它们往往无法满足安全部署所需的严格物理约束。现有方法要么在训练过程中过早地施加安全约束，要么在测试时通过外部保护措施进行反应性处理，这限制了策略的表达能力和整体可扩展性。我们提出了物理安全对齐约束轨迹（PACT），这是一个自我演化的后训练框架，能够在不访问示范数据或任务奖励的情况下，将预训练的扩散策略投影到符合约束的区域。PACT通过在时间步长上进行密集监督的反向KL目标，将约束梯度提炼到扩散模型中。它引入了一种逐步收紧约束的课程，同时保持理论上有界的策略变动和单调改进，从而减轻了由于灾难性遗忘造成的安全与性能之间的权衡。在模拟和真实世界的具身操作基准测试中，PACT平均将安全违规率降低了31.0%，同时任务成功率提高了30.7%。
cs.RO / 33 / 2606.08440
GraspFoM: Towards Reconstruction-Driven Robotic Grasping with 3D Foundation Priors
GraspFoM：基于重建驱动的机器人抓取与3D基础先验的结合
Abstract
Robotic grasping is a fundamental capability in robotic manipulation. Yet grasping remains challenging under partial observations. Reliable grasping depends on both local contact cues and object-level 3D structure. Existing geometry-aware grasping methods recognize the value of reconstruction, but they typically treat geometry as an intermediate prediction rather than a reusable object prior for grasping. In this paper, we present GraspFoM, a unified framework that leverages 3D foundation priors (SAM3D) to build a shared 3D object latent for both reconstruction and grasp pose prediction. Built on this shared object latent, we introduce an anchor-initialized truncated pose-reasoning diffuser that predicts continuous and multimodal grasp poses without directly relying on discrete grasp candidates. We further investigate the interaction between reconstruction and grasping through a reconstruction-aware scorer and a residual latent updater. Reconstruction provides grounded geometric cues, while grasp supervision refines the shared object latent toward grasp-relevant affordances. GraspFoM jointly predicts grasp poses and reconstructs high-fidelity 3D assets in mesh and 3DGS forms. Comprehensive experiments demonstrate that GraspFoM achieves state-of-the-art results on both reconstruction and grasping. Notably, these improvements require only a small number of additional trainable parameters. Component-wise ablation studies also demonstrate the contribution of each component.
Chinese Translation
机器人抓取是机器人操作中的一项基本能力。然而，在部分观察下，抓取仍然具有挑战性。可靠的抓取依赖于局部接触线索和物体级的3D结构。现有的几何感知抓取方法认识到重建的重要性，但通常将几何视为中间预测，而不是可重用的抓取对象先验。在本文中，我们提出了GraspFoM，一个统一框架，利用3D基础先验（SAM3D）构建一个共享的3D物体潜在表示，用于重建和抓取姿态预测。在这个共享物体潜在表示的基础上，我们引入了一种锚点初始化的截断姿态推理扩散器，能够在不直接依赖离散抓取候选的情况下预测连续和多模态的抓取姿态。我们进一步通过一个重建感知评分器和一个残差潜在更新器研究重建与抓取之间的相互作用。重建提供了扎实的几何线索，而抓取监督则将共享物体潜在表示细化为与抓取相关的可用性。GraspFoM联合预测抓取姿态，并重建高保真度的3D资产，形式包括网格和3DGS。全面的实验表明，GraspFoM在重建和抓取方面均达到了最先进的结果。值得注意的是，这些改进仅需少量额外的可训练参数。组件级消融研究也证明了每个组件的贡献。
cs.RO / 34 / 2606.08458
Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning
基于不确定性引导的LLM推理的个性化和稳健的主动机器人辅助
Abstract
Proactive robot assistance in household environments requires accurate prediction of human activities and object usage under dynamic and noisy conditions. Existing approaches often rely on complex spatio-temporal models, which can be computationally expensive and sensitive to environmental variability. In this paper, we propose GLOBE, a lightweight framework that combines n-gram Markov models for capturing temporal behavioral patterns with uncertainty-guided large language model (LLM) reasoning. The framework performs sequential prediction efficiently while selectively invoking LLM reasoning only when the model confidence is low. To evaluate performance under realistic conditions, we introduce HOMER-Noise, a noisy extension of the HOMER+ dataset that simulates structured disturbances such as object movements caused by humans, pets, and toddlers. Experimental results show that GLOBE achieves competitive performance with state-of-the-art methods while improving robustness and computational efficiency across both clean and noisy settings. The framework is further validated through a proof-of-concept integration with a Stretch 3 mobile manipulator, demonstrating its potential application in real-world human-robot interaction scenarios.
Chinese Translation
在家庭环境中，主动机器人辅助需要在动态和噪声条件下准确预测人类活动和物体使用。现有的方法通常依赖于复杂的时空模型，这可能导致计算开销大且对环境变化敏感。本文提出了GLOBE，一个轻量级框架，结合了n-gram马尔可夫模型以捕捉时间行为模式，并利用不确定性引导的大型语言模型（LLM）推理。该框架高效地执行顺序预测，同时仅在模型信心较低时选择性地调用LLM推理。为了在现实条件下评估性能，我们引入了HOMER-Noise，这是HOMER+数据集的一个噪声扩展，模拟了由人类、宠物和幼儿引起的物体移动等结构性干扰。实验结果表明，GLOBE在清洁和噪声环境中均能与最先进的方法达到竞争性能，同时提高了稳健性和计算效率。该框架还通过与Stretch 3移动操控器的概念验证集成得到了进一步验证，展示了其在现实人机交互场景中的潜在应用。
cs.RO / 35 / 2606.08470
LUNA-AD: Lightweight Uncertainty-Aware Language Model with Lifelong Learning for Autonomous Driving
LUNA-AD：用于自动驾驶的轻量级不确定性感知语言模型与终身学习
Abstract
While large language models (LLMs) offer promising reasoning capabilities, their integration into safety-critical driving systems is hindered by limited reasoning diversity, high computational overhead, and static learning paradigms. To address these challenges, we propose LUNA-AD, a lightweight uncertainty-aware language model with lifelong learning for autonomous driving (AD). LUNA-AD features a tri-system architecture that reconciles complex multimodal behavioral reasoning, efficient deployment, and continual refinement. We design a multi-agent analytical system to generate uncertainty-aware decision-making demonstrations through diverse hypothesis exploration. A dual-head lightweight heuristic model is distilled to unify the inference of decision distributions and textual explanations while enabling efficient deployment. Furthermore, a reflection-driven lifelong learning mechanism operates on multimodal decision outputs and preserves strategic diversity, allowing for the refinement of candidate decisions and rationales via closed-loop feedback to enhance driving robustness. Extensive experiments on nuPlan benchmarks demonstrate that LUNA-AD achieves state-of-the-art success rates under both non-reactive and reactive modes, with drastically reduced inference latency compared to existing knowledge-driven AD frameworks.
Chinese Translation
尽管大型语言模型（LLMs）展现出良好的推理能力，但由于推理多样性有限、计算开销高和静态学习范式的限制，它们在安全关键的驾驶系统中的应用受到阻碍。为了解决这些挑战，我们提出了LUNA-AD，一种用于自动驾驶（AD）的轻量级不确定性感知语言模型，具备终身学习能力。LUNA-AD采用三系统架构，调和复杂的多模态行为推理、高效部署和持续优化。我们设计了一个多智能体分析系统，通过多样化的假设探索生成不确定性感知的决策演示。一个双头轻量级启发式模型被提炼出来，以统一决策分布和文本解释的推理，同时实现高效部署。此外，一个基于反思驱动的终身学习机制在多模态决策输出上运行，保持战略多样性，允许通过闭环反馈优化候选决策和理由，从而增强驾驶的鲁棒性。在nuPlan基准上的大量实验表明，LUNA-AD在非反应模式和反应模式下均实现了最先进的成功率，同时与现有的知识驱动自动驾驶框架相比，推理延迟显著降低。
cs.RO / 36 / 2606.08495
EgoPriMo: Egocentric Motion Generation for Interactive Humanoid Control
EgoPriMo：用于交互式类人控制的自我中心运动生成
Abstract
Humanoid robots require whole-body motions that adapt to scene context, task requirements, and user intent. Motion tracking reproduces specified trajectories, and humanoid vision-language-action systems provide semantic interfaces, but neither offers a scalable and interactive prior for broad full-body behavior. We introduce EgoPriMo (Egocentric Motion Prior for Humanoid Robots), a unified framework that learns such priors from egocentric human demonstrations. Given egocentric observations and a text prompt, EgoPriMo reconstructs, generates, and forecasts SMPL-based full-body motion. Language is used as a high-level control signal rather than a complete motion specification. At the core of EgoPriMo is a Triple-stream DiT that jointly models body dynamics, egocentric visual context, and text; task-conditioning masks route different tasks and missing-modality data through the same checkpoint. Experiments on Nymeria and EgoExo4D show that one checkpoint improves egocentric motion generation over UniEgoMotion while supporting reconstruction and forecasting; the generated SMPL motions can also be executed by a Unitree humanoid controller. These results indicate a practical path from scalable egocentric observations to generalizable and interactive humanoid motion priors.
Chinese Translation
类人机器人需要能够适应场景上下文、任务要求和用户意图的全身运动。运动跟踪重现指定的轨迹，而类人视觉-语言-动作系统提供语义接口，但两者都未能提供可扩展和交互式的广泛全身行为先验。我们提出了EgoPriMo（自我中心运动先验用于类人机器人），这是一个统一框架，能够从自我中心的人类示范中学习这些先验。给定自我中心的观察和文本提示，EgoPriMo重建、生成和预测基于SMPL的全身运动。语言被用作高层控制信号，而不是完整的运动规范。EgoPriMo的核心是一个三流DiT（Triple-stream DiT），它联合建模身体动态、自我中心视觉上下文和文本；任务条件掩码通过同一检查点引导不同任务和缺失模态数据。对Nymeria和EgoExo4D的实验表明，一个检查点在支持重建和预测的同时，改善了自我中心运动生成，相较于UniEgoMotion；生成的SMPL运动也可以由Unitree类人控制器执行。这些结果表明，从可扩展的自我中心观察到可推广和交互式的类人运动先验的实用路径。
cs.RO / 37 / 2606.08508
ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies
ActProbe：用于生成机器人策略早期故障检测的动作空间探测器
Abstract
Generative robot policies fail unpredictably at deployment: they hesitate at critical moments, drift off-task, or commit to unrecoverable actions. Existing online failure detectors either require white-box access to policy internals or add runtime overhead through resampling and observation-side signals. Our empirical analysis shows that emitted action chunks themselves already carry strong predictive signal for impending failures in generative robot policies. Motivated by this observation, we introduce ActProbe, a lightweight, pure action-space detector that uses two compact signals available from a single forward pass: Temporal Consistency Error (TCE) between consecutive action chunks and Action Chunk Magnitude (ACM) of the current chunk. ActProbe maps these signals to per-step failure probabilities with a task-conditioned LSTM-MLP architecture. Across a diverse suite of generative robot policies and benchmarks, ActProbe raises alerts before failures become visually recognizable, improving the accuracy (F1)-timeliness Pareto frontier of failure detection by an average hypervolume gain of +12.7% over both internal- and external-feature baselines, with a +9.0% early-detection ROC-AUC lead on unseen tasks. ActProbe further transfers to deployment, predicting failures on unseen real-robot pick tasks and accelerating RL fine-tuning (PPO) with 2.9x fewer environment interactions.
Chinese Translation
生成机器人策略在部署时会出现不可预测的故障：它们在关键时刻犹豫不决、偏离任务或做出不可恢复的动作。现有的在线故障检测器要么需要对策略内部进行白盒访问，要么通过重新采样和观察侧信号增加运行时开销。我们的实证分析表明，发出的动作块本身已经携带了强烈的预测信号，能够预示生成机器人策略即将发生的故障。基于这一观察，我们提出了ActProbe，这是一种轻量级的纯动作空间检测器，利用从单次前向传播中获得的两个紧凑信号：连续动作块之间的时间一致性误差（Temporal Consistency Error, TCE）和当前块的动作块幅度（Action Chunk Magnitude, ACM）。ActProbe将这些信号映射到每一步的故障概率，采用任务条件的LSTM-MLP架构。在多样化的生成机器人策略和基准测试中，ActProbe在故障变得可视化之前就发出警报，将故障检测的准确性（F1）与时效性帕累托前沿提高了平均超体积增益12.7%，相较于内部和外部特征基线，且在未见任务上提前检测的ROC-AUC领先9.0%。ActProbe还成功转移到部署阶段，能够在未见的真实机器人抓取任务中预测故障，并以2.9倍更少的环境交互加速强化学习微调（PPO）。
cs.RO / 38 / 2606.08513
Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
基于强化学习的自主水下航行器端到端运动规划与执行研究
Abstract
Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control. This paper explores the feasibility of an end-to-end Deep Reinforcement Learning (DRL) approach that maps raw sensor data directly to thruster commands, reducing manual engineering. We propose a hierarchical reinforcement learning (HRL) architecture splitting the problem into two Markov Decision Processes. A High-Level (HL) policy operating at 2Hz processes raw $84 \times 84$ pixel monocular camera frames, stacked $100 \times 100$ pixel forward-looking imaging sonar, and proprioceptive data to generate spatial subgoals. Simultaneously, a Low-Level (LL) policy operating at 10Hz converts these subgoals into thruster commands. The HL policy is trained using Reinforcement Learning from Prior Demonstrations (RLPD) within a modified Sample-Efficient Robotic Reinforcement Learning (SERL) framework, while the LL policy utilizes Soft Actor-Critic (SAC) combined with Hindsight Experience Replay (HER). Evaluated in the high-fidelity HoloOcean simulator, our method demonstrates successful obstacle avoidance, achieving trajectory lengths closely approximating (within 4% to 6% of) an $\text{RRT}^*$ planning baseline. Furthermore, the learned policy exhibits strong robustness to simulated sensor noise and decreased visibility. While the system navigates familiar geometries effectively, experiments reveal generalization limitations when encountering unvisited areas with novel obstacle shapes. Ultimately, this work demonstrates the promise of sample-efficient, end-to-end DRL for underwater navigation using minimal computational hardware.
Chinese Translation
自主水下航行器（AUVs）传统上依赖于复杂且高度工程化的管道来进行感知、路径规划和运动控制。本文探讨了一种端到端深度强化学习（Deep Reinforcement Learning, DRL）方法的可行性，该方法将原始传感器数据直接映射到推进器指令，从而减少手动工程的需求。我们提出了一种层次化强化学习（Hierarchical Reinforcement Learning, HRL）架构，将问题分解为两个马尔可夫决策过程。高层（High-Level, HL）策略以2Hz的频率处理原始$84 imes 84$像素的单目相机帧、堆叠的$100 imes 100$像素前视成像声纳和本体感知数据，以生成空间子目标。同时，低层（Low-Level, LL）策略以10Hz的频率将这些子目标转换为推进器指令。HL策略在修改后的样本高效机器人强化学习（Sample-Efficient Robotic Reinforcement Learning, SERL）框架内，使用来自先前演示的强化学习（Reinforcement Learning from Prior Demonstrations, RLPD）进行训练，而LL策略则结合了软演员-评论家（Soft Actor-Critic, SAC）和事后经验重放（Hindsight Experience Replay, HER）。在高保真度的HoloOcean模拟器中评估，我们的方法展示了成功的障碍物规避，轨迹长度与$ ext{RRT}^*$规划基线的接近（在4%到6%之内）。此外，所学习的策略对模拟传感器噪声和可见度降低表现出强大的鲁棒性。尽管该系统能够有效导航熟悉的几何形状，但实验显示在遇到未访问区域和新型障碍物形状时存在泛化限制。最终，这项工作展示了在使用最小计算硬件的情况下，样本高效的端到端DRL在水下导航中的潜力。
cs.RO / 39 / 2606.08520
Two Bridges, One Pathway: From VLMs to Generalizable VLAs with Embodied Trajectory-Coupled Data
两座桥梁，一条路径：从视觉语言模型（VLMs）到可泛化的视觉语言行动（VLAs）与具身轨迹耦合数据
Abstract
Vision-language models (VLMs) are powerful general-purpose reasoners, yet converting them into robot control policies (VLAs) is surprisingly difficult. The root cause is a two-fold gap: VLMs are trained on internet-scale images with language-understanding objectives, while VLAs must perceive robot scenes and predict motor actions. Fine-tuning a VLM directly on robot action data forces the model to cross both gaps at once -- the learning curve is steep and the rich generalizations learned during pretraining tend to degrade rather than transfer. We argue that this gap can be bridged gradually with the right intermediate data. We introduce \emph{embodied trajectory-coupled (ETC) data} -- vision-language supervision derived from the same robot scenes and trajectories used for action learning. Because ETC data shares the visual context of robot operation while retaining familiar language-understanding objectives, it provides a natural stepping stone between VLM pretraining and VLA fine-tuning. Building on this, we design a three-stage training recipe. Distribution Bridging first adapts the VLM to embodied visual-language semantics. Objective Bridging then gradually shifts the model toward action prediction while preserving the acquired representations. Retentive Adaptation finally specializes the policy to the target deployment domain. We further show that mixing task-relevant out-of-distribution ETC data with a small amount of action data enables the model to generalize to novel visual-language conditions without requiring additional robot demonstrations. Simulation and real-robot experiments confirm that this gradual bridging strategy is the key to transferring VLM generalization into robust, deployable robot policies.
Chinese Translation
视觉语言模型（VLMs）是强大的通用推理工具，但将其转换为机器人控制策略（VLAs）却出乎意料地困难。根本原因在于存在双重差距：VLMs是在具有语言理解目标的互联网规模图像上训练的，而VLAs必须感知机器人场景并预测运动行为。直接在机器人动作数据上微调VLM迫使模型同时跨越这两个差距——学习曲线陡峭，预训练期间学到的丰富泛化往往会退化而非转移。我们认为，这一差距可以通过合适的中间数据逐步弥合。我们引入了具身轨迹耦合（ETC）数据——从用于动作学习的相同机器人场景和轨迹中衍生的视觉语言监督。由于ETC数据共享机器人操作的视觉上下文，同时保留熟悉的语言理解目标，它为VLM预训练和VLA微调之间提供了一个自然的过渡。基于此，我们设计了一个三阶段的训练方案。分布桥接首先将VLM适应于具身视觉语言语义。目标桥接随后逐步将模型转向动作预测，同时保留所获得的表征。保持适应最终将策略专门化到目标部署领域。我们进一步表明，将与任务相关的分布外ETC数据与少量动作数据混合，使模型能够在无需额外机器人演示的情况下，泛化到新的视觉语言条件。仿真和真实机器人实验确认，这种逐步桥接策略是将VLM泛化转移到稳健、可部署的机器人策略的关键。
cs.RO / 40 / 2606.08530
GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation
GEAR-VLA：学习几何感知的动作表示以实现可泛化的机器人操作
Abstract
Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot embodiments. We argue that this stems from the lack of a unified geometry-aware manipulation representation, leaving existing VLAs vulnerable to low-level trajectory supervision, misaligned 3D features, and embodiment differences. To address this, we propose GEAR-VLA, a VLA framework for learning unified geometry-aware action representations for generalizable robotic manipulation. GEAR-VLA adopts coarse-to-fine action learning, where multi-source embodied pretraining equips the VLM with embodied reasoning and discrete action understanding before latent action tokens connect action semantics to a gradient-decoupled DiT continuous action expert. It further performs semantic-aligned 3D integration by aligning a trainable 3D spatial backbone with the VLA representation while freezing the original VLM-aligned visual pathway. To share this representation across robots, GEAR-VLA uses embodiment canonicalization, where embodiment-aware states and embodiment-invariant actions confine robot differences to the low-level interface. Extensive simulation and real-world experiments demonstrate strong generalization: GEAR-VLA achieves state-of-the-art performance on LIBERO, zero-shot LIBERO-Plus, and RoboTwin 2.0, reaches 85.9% success on AgileX and 81.0% on the pretraining-unseen LDT-01 embodiment, and obtains 90.1% success on a 6,360-trial universal grasping benchmark with 212 unseen objects. Code and models will be released at https://github.com/babynabeauty/GEAR-VLA.
Chinese Translation
视觉-语言-动作（VLA）模型在基准测试中表现出色，但在面对未见物体、背景变化和不同机器人形态的实际部署中仍然面临挑战。我们认为这源于缺乏统一的几何感知操作表示，使得现有的VLA模型容易受到低级轨迹监督、3D特征不对齐和形态差异的影响。为了解决这个问题，我们提出了GEAR-VLA，一个用于学习统一几何感知动作表示以实现可泛化机器人操作的VLA框架。GEAR-VLA采用粗到细的动作学习，其中多源具身预训练使得视觉语言模型（VLM）具备具身推理和离散动作理解能力，然后潜在动作标记将动作语义与梯度解耦的DiT连续动作专家连接。它进一步通过将可训练的3D空间主干与VLA表示对齐，同时冻结原始VLM对齐的视觉通路，执行语义对齐的3D集成。为了在机器人之间共享这一表示，GEAR-VLA使用形态标准化，其中具身感知状态和形态不变动作将机器人差异限制在低级接口。大量的仿真和现实世界实验表明了强大的泛化能力：GEAR-VLA在LIBERO、零-shot LIBERO-Plus和RoboTwin 2.0上达到了最先进的性能，在AgileX上成功率达到85.9%，在预训练未见的LDT-01形态上达到81.0%，并在一个包含212个未见物体的6,360次试验的通用抓取基准上获得了90.1%的成功率。代码和模型将发布在https://github.com/babynabeauty/GEAR-VLA。
cs.RO / 41 / 2606.08542
When Video Misreads: Closed-Loop Distillation of Reading Heuristics for Exploratory Manipulation Trace QA
当视频误读时：探索性操作轨迹问答的闭环蒸馏阅读启发式
Abstract
Exploratory manipulation often turns an apparent failed attempt into the key evidence for what to do next. For example, a robot pulls a locked cabinet drawer, fails, and only succeeds after opening the lock. The failed pull reveals a latent precondition (the drawer is locked) that determines the minimal-success action chain (the fewest actions that complete the task), here [lock-open, drawer-pull]. Correctly reading this trace is therefore the prerequisite for recovering that chain. We formalize this setting as Exploratory Manipulation Trace QA (EMT-QA): given synchronized video and proprioception from an exploratory trace, predict the minimal-success action chain under the latent precondition revealed by the probe. However, even state-of-the-art VLMs and embodied multimodal LLMs misread this evidence: they do not reliably recover the chain from raw video, raw proprioception, or their combination. We introduce Closed-Loop Trace Distillation, a pipeline that uses a per-task coding agent to inspect labeled training traces and distill a one-line natural-language prompt over the trace, which we call the Distilled Reading Heuristic (DRH). At inference, no agent is invoked and no model weights are updated; a frozen VLM receives the raw trace plus the DRH as a prompt entry. Across three simulator and two real-robot tasks, the DRH improves chain accuracy by +0.38 to +0.47 over the best raw-modality baseline. The same DRH also serves as the sole specification for one-shot programmatic classifiers that match the prompted VLM.
Chinese Translation
探索性操作常常将明显的失败尝试转变为下一步行动的关键证据。例如，一个机器人拉动一个上锁的柜子抽屉，失败后只有在打开锁后才能成功。这次失败的拉动揭示了一个潜在的前提条件（抽屉被锁住），决定了最小成功行动链（完成任务所需的最少动作），在这里为[锁-开，抽屉-拉动]。因此，正确解读这一轨迹是恢复该链的前提。我们将这一设置形式化为探索性操作轨迹问答（Exploratory Manipulation Trace QA, EMT-QA）：给定来自探索轨迹的同步视频和本体感知，预测在探测揭示的潜在前提条件下的最小成功行动链。然而，即使是最先进的视觉语言模型（VLMs）和具身多模态大语言模型（LLMs）也会误读这一证据：它们无法可靠地从原始视频、原始本体感知或它们的组合中恢复该链。我们引入了闭环轨迹蒸馏（Closed-Loop Trace Distillation），这是一个使用每个任务编码代理检查标记训练轨迹并蒸馏出一行自然语言提示的管道，我们称之为蒸馏阅读启发式（Distilled Reading Heuristic, DRH）。在推理阶段，不调用任何代理，也不更新任何模型权重；一个冻结的VLM接收原始轨迹加上DRH作为提示输入。在三个模拟器和两个真实机器人任务中，DRH相较于最佳原始模态基线提高了链的准确性0.38到0.47。相同的DRH也作为单次程序分类器的唯一规范，与提示的VLM相匹配。
cs.RO / 42 / 2606.08548
OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation
OASIS：从仿真数据收集到现实世界的人形机器人运动操控
Abstract
Recent progress in robot manipulation has been largely driven by learning from large-scale demonstrations. For humanoid robot loco-manipulation tasks, however, existing data sources force an unsatisfying tradeoff between trajectory quality and scalability. Real-world teleoperation provides the highest-quality trajectories but requires dedicated physical space and time-consuming scene resets. Simulation offers an alternative way out of this dilemma: it can produce clean, embodiment-aligned data at scale without any physical hardware. In this paper, we propose OASIS, a simulation-data-driven framework for humanoid loco-manipulation. OASIS automatically reconstructs realistic object assets from real-world images using a 3D generative model. Based on these assets, trajectories are first collected through teleoperation in simulation, and then augmented under diverse domain randomizations in a post-processing stage. With the resulting simulation data, we further design a hierarchical visuomotor policy for humanoid loco-manipulation. Extensive experiments on the real humanoid robot show that, under zero-shot deployment, the policy trained on our simulation data achieves higher success rates on most tasks than that trained on real-robot teleoperation data, owing largely to the broad lighting and environmental variations covered by our simulation rendering, which real-robot data fails to capture. The project page is available at https://oasis-humanoid.github.io/.
Chinese Translation
近年来，机器人操控的进展在很大程度上得益于从大规模演示中学习。然而，对于人形机器人运动操控任务，现有的数据源在轨迹质量和可扩展性之间存在不令人满意的权衡。现实世界的远程操作提供了最高质量的轨迹，但需要专门的物理空间和耗时的场景重置。仿真提供了一种解决这一困境的替代方案：它可以在没有任何物理硬件的情况下，以规模化的方式生成干净且符合体现的数据信息。在本文中，我们提出了OASIS，一个基于仿真数据的人形机器人运动操控框架。OASIS利用3D生成模型自动从现实世界图像中重建逼真的物体资产。基于这些资产，轨迹首先通过仿真中的远程操作进行收集，然后在后处理阶段通过多样的领域随机化进行增强。利用生成的仿真数据，我们进一步设计了一个用于人形机器人运动操控的分层视觉运动策略。在真实人形机器人上的大量实验表明，在零样本部署下，基于我们的仿真数据训练的策略在大多数任务上的成功率高于基于真实机器人远程操作数据训练的策略，这主要得益于我们的仿真渲染覆盖了广泛的光照和环境变化，而真实机器人数据未能捕捉到这些变化。项目页面可访问 https://oasis-humanoid.github.io/。
cs.RO / 43 / 2606.08555
FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation
FAWAM：面向力的世界动作模型用于闭环接触丰富的操作
Abstract
Force signals provide critical interaction cues for contact-rich robotic manipulation. However, existing methods mostly use force as an additional observation modality, without fully exploiting its role in modeling future interaction dynamics or guiding execution-time feedback correction. In this paper, we propose FAWAM, a force-aware world action model that incorporates force information at three levels: perception, prediction, and closed-loop execution. FAWAM first encodes historical 6-axis force/torque signals to modulate action generation, then jointly predicts future actions and end-effector wrenches to explicitly model contact evolution. It further introduces a residual correction module that uses the predicted wrench trajectory as an execution-time reference to refine actions online based on real-time force feedback. Real-world experiments across multiple contact-rich tasks show that FAWAM improves the average success rate by 36.25% over vision-only baselines and 21.25% over existing force-aware baselines, demonstrating the effectiveness of our force-aware framework for robust contact-rich manipulation.
Chinese Translation
力信号为接触丰富的机器人操作提供了关键的交互线索。然而，现有的方法大多将力作为一种额外的观察模态，而未能充分利用其在建模未来交互动态或指导执行时反馈修正中的作用。本文提出了FAWAM，一种面向力的世界动作模型，结合了三个层次的力信息：感知、预测和闭环执行。FAWAM首先编码历史的6轴力/扭矩信号以调节动作生成，然后共同预测未来的动作和末端执行器的扭矩，以显式建模接触演变。它进一步引入了一个残差修正模块，该模块使用预测的扭矩轨迹作为执行时参考，以根据实时力反馈在线优化动作。在多个接触丰富任务上的真实世界实验表明，FAWAM的平均成功率比仅使用视觉的基线提高了36.25%，比现有的面向力的基线提高了21.25%，证明了我们面向力的框架在稳健的接触丰富操作中的有效性。
cs.RO / 44 / 2606.08564
Real-IKEA: Physical Fidelity is the Prerequisite for Robust Manipulation
真实IKEA：物理逼真性是稳健操控的前提
Abstract
Robotic manipulation robustness often founders on the physics gap between simplified simulations and the resistance-laden real world. In this work, we emphasize that physical realism in articulated interaction is an important ingredient for robust policy learning. We present Real-IKEA, a dataset and simulation framework designed with physical accuracy as a first-class goal. Real-IKEA provides 1,079 articulated asset configurations, derived from 83 authentic IKEA handles and knobs processed through a meticulous six-step physical workflow. For contact-geometry accuracy, we introduce a bidirectional surface-deviation metric to quantify collision meshes. For dynamics realism, we establish resistance-calibrated configurations that vary damping and friction. Crucially, we demonstrate through a Reinforcement Learning (RL) policy that high-fidelity assets enable the discovery of robust "hooking" and "levering" strategies that prioritize mechanical advantage over fragile friction-pulling. Together, these results position Real-IKEA as a critical benchmark for developing manipulation policies capable of human-level robustness in articulated object tasks.
Chinese Translation
机器人操控的稳健性常常受到简化仿真与充满阻力的现实世界之间物理差距的影响。在本研究中，我们强调关节交互中的物理真实感是稳健策略学习的重要组成部分。我们提出了Real-IKEA，一个以物理准确性为首要目标设计的数据集和仿真框架。Real-IKEA提供了1,079种关节资产配置，这些配置源自83个真实的IKEA把手和旋钮，经过细致的六步物理工作流程处理。为了确保接触几何的准确性，我们引入了一种双向表面偏差度量来量化碰撞网格。为了实现动力学的真实感，我们建立了经过阻力校准的配置，以变化阻尼和摩擦。关键是，我们通过强化学习（Reinforcement Learning, RL）策略展示了高保真资产能够发现稳健的“钩住”（hooking）和“杠杆”（levering）策略，这些策略优先考虑机械优势而非脆弱的摩擦拉动。综合这些结果，使得Real-IKEA成为开发能够在关节物体任务中实现人类水平稳健性的操控策略的关键基准。
cs.RO / 45 / 2606.08610
HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning
HARBOR：一种用于自主机器人强化学习的框架
Abstract
Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains limited by the engineering pipeline surrounding the algorithms. Building tasks, shaping rewards, and tuning hyperparameters require substantial expert effort, making RL workflows costly and difficult to scale. We introduce HARBOR, an agentic framework that frames robot RL automation as a harness-engineering problem: given a simulator codebase and a task specification, it automates the workflow from environment setup to policy training in simulation. HARBOR decomposes such high-level objectives into bounded stages executed by specialized agents through standardized commands, persistent artifacts, executable gates, and reusable knowledge, and scales iteration via decentralized parallel trials and experience learning across runs. We evaluate HARBOR across 6 benchmarks and 16 tasks in total, spanning manipulation, locomotion, and bimanual dexterous control. We demonstrate that HARBOR automates the simulation RL workflow end-to-end, designs rewards, tunes algorithms to match or improve over default configurations, and reduces engineering effort at practical token and wall-clock cost; the resulting policies can also be transferred to real robots.
Chinese Translation
强化学习（RL）已成为机器人学习的强大范式，特别是在模拟到现实的设置中，但其更广泛的应用仍受到围绕算法的工程流程的限制。构建任务、塑造奖励和调整超参数需要大量专家的努力，使得强化学习工作流程成本高昂且难以扩展。我们介绍了HARBOR，一个将机器人强化学习自动化框架视为一个工程问题的自主框架：给定一个模拟器代码库和任务规范，它自动化了从环境设置到策略训练的工作流程。HARBOR将这些高层目标分解为由专门代理通过标准化命令、持久化工件、可执行门和可重用知识执行的有限阶段，并通过去中心化的并行试验和跨运行的经验学习来扩展迭代。我们在6个基准和总共16个任务中评估了HARBOR，涵盖了操作、运动和双手灵巧控制。我们证明HARBOR能够端到端地自动化模拟强化学习工作流程，设计奖励，调整算法以匹配或超越默认配置，并在实际代币和时间成本上减少工程工作量；所得到的策略也可以转移到真实机器人上。
cs.RO / 46 / 2606.08655
PhysGraph: A Physics-aware 3D Scene Graph for Perception and Reasoning
PhysGraph：一种物理感知的3D场景图用于感知与推理
Abstract
To perform a wide range of daily tasks, robots need to construct a 3D representation that is semantically rich, physically grounded, and structured enough to support task planning and affordance prediction. However, existing approaches primarily focus on semantic retrieval, often overlooking physical and kinematic factors. Methods that attempt to model physical properties typically rely on narrow training sets or single-object modeling, limiting scalability and generalization across diverse object types. To address these challenges, we present PhysGraph, a framework that unifies symbolic reasoning with structured 3D geometry to model kinematic and physical properties in cluttered scenes. Given RGB-D observations, PhysGraph reconstructs object-centric 3D geometry and associates object instances across views. It then decomposes objects into functional parts and infers materials and articulations through visual reasoning. Evaluated on both synthetic and real-world datasets, PhysGraph achieves state-of-the-art results in semantic segmentation, multi-object mass estimation, and articulation prediction. With its simple yet effective design, PhysGraph produces physically consistent and semantically structured scene graphs, serving as a structured 3D representation for downstream tasks such as constraint-aware 3D affordance prediction and real-to-sim transfer, both of which are demonstrated in our experiments.
Chinese Translation
为了执行广泛的日常任务，机器人需要构建一个语义丰富、物理基础扎实且结构化足够以支持任务规划和可供性预测的3D表示。然而，现有的方法主要集中在语义检索上，往往忽视了物理和运动学因素。试图建模物理属性的方法通常依赖于狭窄的训练集或单一物体建模，这限制了在多样物体类型之间的可扩展性和泛化能力。为了解决这些挑战，我们提出了PhysGraph，一个将符号推理与结构化3D几何相结合的框架，以建模杂乱场景中的运动学和物理属性。给定RGB-D观测，PhysGraph重建以物体为中心的3D几何，并在视图之间关联物体实例。然后，它将物体分解为功能部件，并通过视觉推理推断材料和关节。经过在合成和真实世界数据集上的评估，PhysGraph在语义分割、多物体质量估计和关节预测方面达到了最先进的结果。凭借其简单而有效的设计，PhysGraph生成物理一致且语义结构化的场景图，作为下游任务的结构化3D表示，如约束感知的3D可供性预测和真实到仿真转移，这两者在我们的实验中得到了验证。
cs.RO / 47 / 2606.08657
Latent Diffusion Policy: Shaping Latent Spaces for Diffusion-Based Robotic Manipulation
潜在扩散策略：为基于扩散的机器人操作塑造潜在空间
Abstract
Diffusion-based visuomotor policies operating directly in raw action spaces conflate scene comprehension with trajectory generation within a single denoising process. The resulting velocity field must simultaneously encode scene information and generate precise trajectories, increasing learning complexity and limiting performance on tasks demanding precise temporal coordination across multiple arms. To simplify this joint learning problem, we introduce Latent Diffusion Policy (LDP), a two-stage framework performing flow matching in a deliberately shaped latent space. By absorbing scene understanding into an observation-conditioned CVAE encoder, LDP concentrates the conditional distribution of each observation. Consequently, the flow model avoids implicitly resolving scene-dependent structures; instead, it generates within a pre-concentrated distribution featuring a smoother velocity field, simplifying learning from limited demonstrations. Furthermore, to capture temporal dependencies among latent tokens, LDP trains with per-token diffusion forcing and employs staircase inference sampling to resolve the resulting distributional mismatch. We also propose reconstruction FID (rFID) as a lightweight proxy predicting downstream task success solely from latent space statistics. On coordination-intensive tasks from RoboTwin 2.0, LDP outperforms DP3 by a substantial margin and transfers effectively to real-world bimanual deployments.
Chinese Translation
基于扩散的视觉运动策略直接在原始动作空间中操作，将场景理解与轨迹生成融合在单一的去噪过程中。由此产生的速度场必须同时编码场景信息并生成精确的轨迹，这增加了学习的复杂性，并限制了在需要多臂精确时间协调的任务中的表现。为简化这一联合学习问题，我们提出了潜在扩散策略（Latent Diffusion Policy, LDP），这是一个在故意塑造的潜在空间中执行流匹配的两阶段框架。通过将场景理解吸收到观察条件的条件变分自编码器（CVAE）中，LDP 集中每个观察的条件分布。因此，流模型避免了隐式解决场景依赖结构；相反，它在一个预集中分布中生成，特征是更平滑的速度场，从而简化了从有限示例中学习。此外，为了捕捉潜在标记之间的时间依赖性，LDP 通过每个标记的扩散强制进行训练，并采用阶梯推理采样来解决由此产生的分布不匹配。我们还提出了重建FID（rFID）作为一个轻量级代理，仅根据潜在空间统计预测下游任务的成功。在RoboTwin 2.0的协调密集任务中，LDP显著优于DP3，并有效转移到真实世界的双手部署中。
cs.RO / 48 / 2606.08666
Language as a Sensor: Calibrated Spatial Belief Estimation in 3D Scenes from Natural Language
语言作为传感器：基于自然语言的3D场景校准空间信念估计
Abstract
Robots deployed in human-centric environments routinely receive natural-language descriptions of spatial information ("I left my backpack on the table") that reference parts of the world beyond their perceptual field of view. Traditional metric-semantic mapping ignores this signal, while off-the-shelf multimodal models remain limited in 3D spatial reasoning and are not directly amenable to fusion with other sensor modalities. To convert language observations into a calibrated spatial distribution, we train a Language Sensor Model (LSM) that maps each utterance and its scene-graph context to a multimodal distribution, with mixture weights encoding referential ambiguity (e.g., "which table") and component covariances encoding spatial uncertainty (e.g., where "on the table" the target lies). We then introduce VL-Map (Vision-Language Metric-Semantic Mapping), a probabilistic framework that treats these language predictions as stochastic observations and fuses them with onboard perception within a unified belief map. On the VLA-3D benchmark as well as on a real-world mobile robot, LSM is the only language predictor whose covariance estimates remain within the calibrated regime; fused into VL-Map, it leads to more accurate predictions of the target object location (~70% more probability mass on the true target compared to the strongest foundation-model baseline).
Chinese Translation
部署在以人为中心环境中的机器人常常接收关于空间信息的自然语言描述（例如：“我把背包放在桌子上”），这些描述涉及超出其感知视野的世界部分。传统的度量-语义映射忽视了这一信号，而现成的多模态模型在3D空间推理方面仍然有限，且不易与其他传感器模态融合。为了将语言观察转换为校准的空间分布，我们训练了一个语言传感器模型（Language Sensor Model, LSM），该模型将每个话语及其场景图上下文映射到一个多模态分布，其中混合权重编码了指称歧义（例如：“哪个桌子”），而成分协方差编码了空间不确定性（例如，“在桌子上”目标的位置）。随后，我们引入了VL-Map（视觉-语言度量-语义映射），这是一个概率框架，将这些语言预测视为随机观察，并在统一的信念图中与车载感知融合。在VLA-3D基准测试以及真实世界的移动机器人上，LSM是唯一一个其协方差估计保持在校准范围内的语言预测器；融合到VL-Map中后，它能够更准确地预测目标物体的位置（与最强基础模型基线相比，目标的真实位置的概率质量提高了约70%）。
cs.RO / 49 / 2606.08688
PhysAgent: Automating Physics-Based 4D Synthesis via Trajectory-Grounded Multi-Agent Feedback
PhysAgent：通过轨迹驱动的多智能体反馈自动化物理基础的4D合成
Abstract
Achieving fully automated, physically plausible 3D motion synthesis is a core objective in graphics and generative AI. However, configuring complex environmental force fields still relies entirely on manual expert intervention, creating a severe bottleneck for large-scale simulation data generation. Existing automated methods primarily focus on material optimization and exhibit severe modality gaps and technical flaws when applied to the vastly more complex force field optimization space: naive Large Language Models (LLMs) lack underlying simulation feedback, causing severe physical inaccuracies, while traditional Score Distillation Sampling (SDS) suffers from sluggish gradients, local optima entrapment, and a mathematical inability to dynamically switch discrete force fields. To address this, we propose PhysAgent, the first simulator-in-the-loop multi-agent framework that leverages multimodal inputs for automated, physically grounded 4D synthesis. By decoupling intrinsic materials from extrinsic dynamics, PhysAgent utilizes a Semantic Agent equipped with an externalized Force Field Skill module to master simulation rules and generate valid initializations. Subsequently, the Refine Agents, driven by Trajectory-Grounded Multi-Agent Feedback, leverage vision foundation models to extract dense point trajectories from rendered frames. By converting these explicit motion trajectories into structured textual descriptors, the agent harnesses LLM commonsense reasoning to execute zero-shot macroscopic leaps, effectively escaping local optima and dynamically switching discrete force fields. Extensive experiments demonstrate that PhysAgent rapidly generates stable, diverse physical scenes from arbitrary multimodal prompts, significantly outperforming existing baselines in both generation diversity and physical accuracy.
Chinese Translation
实现完全自动化、物理上合理的3D运动合成是图形学和生成性人工智能的核心目标。然而，复杂环境力场的配置仍然完全依赖人工专家干预，这为大规模仿真数据生成造成了严重瓶颈。现有的自动化方法主要集中在材料优化上，并在应用于更为复杂的力场优化空间时表现出严重的模态差距和技术缺陷：简单的大型语言模型（LLMs）缺乏基础的仿真反馈，导致严重的物理不准确性，而传统的评分蒸馏采样（SDS）则面临梯度缓慢、局部最优困境以及在数学上无法动态切换离散力场的问题。为了解决这一问题，我们提出了PhysAgent，这是第一个将仿真器纳入循环的多智能体框架，利用多模态输入实现自动化、物理基础的4D合成。通过将内在材料与外在动态解耦，PhysAgent利用配备外部力场技能模块的语义智能体掌握仿真规则并生成有效的初始化。随后，受轨迹驱动的多智能体反馈驱动的精炼智能体，利用视觉基础模型从渲染帧中提取稠密的点轨迹。通过将这些显式运动轨迹转换为结构化文本描述，智能体利用LLM的常识推理执行零-shot宏观跃迁，有效逃离局部最优并动态切换离散力场。大量实验表明，PhysAgent能够快速生成稳定、多样的物理场景，来自任意多模态提示，显著超越现有基准在生成多样性和物理准确性方面的表现。
cs.RO / 50 / 2606.08725
Real-Time and Accurate Collision-Free Teleoperation via Differentiable Constraint-Based Trajectory Planning
基于可微约束的轨迹规划实现实时准确的无碰撞遥操作
Abstract
In teleoperation, the human operator typically controls only the end-effector pose, which often leads to self-collisions of the manipulator and collisions with environmental obstacles, since joints and links are not controlled individually. A common strategy to mitigate this issue is to enhance the operator's input using optimal-control-based trajectory planning. As derivative-based solvers require differentiable constraints, existing approaches either approximate robots and obstacles with spheres, reducing geometric accuracy, or approximate derivatives, degrading convergence and increasing computation times. We address these limitations by adapting a recent formulation of differentiable collision-avoidance constraints, based on duality in convex optimization, to the teleoperation setting. The robot is approximated with capsules and the environment with polytopes. We compare the resulting trajectory planning method against state-of-the-art techniques in simulation with varying numbers of obstacles and evaluate it on a UR5e manipulator in a real-world teleoperation test. Results show that our approach achieves lower computation times while enabling more accurate obstacle modeling, leading to smoother and collision-free end-effector teleoperation.
Chinese Translation
在遥操作中，操作者通常只控制末端执行器的姿态，这往往导致操纵器的自碰撞和与环境障碍物的碰撞，因为关节和连杆并未单独控制。缓解这一问题的常见策略是通过基于最优控制的轨迹规划增强操作者的输入。由于基于导数的求解器需要可微约束，现有方法要么用球体近似机器人和障碍物，从而降低几何精度，要么近似导数，导致收敛性下降和计算时间增加。我们通过将基于凸优化对偶性的可微碰撞避免约束的最新公式调整为遥操作环境，来解决这些局限性。机器人用胶囊近似，环境用多面体近似。我们在模拟中将所得到的轨迹规划方法与最先进的技术进行比较，障碍物数量各异，并在实际遥操作测试中对UR5e操纵器进行评估。结果表明，我们的方法在实现更准确的障碍物建模的同时，计算时间更短，从而实现更平滑和无碰撞的末端执行器遥操作。
cs.RO / 51 / 2606.08729
IR-SIM: A Lightweight Skill-Native Simulator for Navigation, Learning, and Benchmarking
IR-SIM：一种轻量级技能原生导航模拟器，用于导航、学习和基准测试
Abstract
Simulation plays a key role in automated robotics research supported by large language models (LLMs). However, existing simulators often require custom code or complex interfaces, creating a barrier to rapid prototyping and automated algorithm development. To this end, we propose the Intelligent Robot Simulator (IR-SIM), a lightweight skill-native navigation simulator designed for rapid scenario construction, benchmarking, and robot learning. In IR-SIM, scenarios are entirely defined by YAML configuration files that specify mobile robot kinematics, geometric collision checking, LiDAR sensing, visualization, and behavior modules. This design makes robotic simulation fully describable and reproducible, allowing scenarios to be generated and modified from text prompts through the proposed IR-SIM agent skills. The resulting scenarios can be used for automated benchmarking of navigation algorithms and for automated generation of training data for learning methods. Furthermore, IR-SIM provides bridges to high fidelity simulators and real world deployment, allowing users to validate their algorithms in more realistic settings after prototyping without extra coding. The experiments showcase the convenience and versatility of IR-SIM in multiple tasks: constructing navigation scenarios from natural language, training a collision avoidance policy, benchmarking social navigation policies, and bridging to high fidelity simulators and real world deployment. The project website is available at https://github.com/hanruihua/ir-sim.
Chinese Translation
仿真在由大型语言模型（LLMs）支持的自动化机器人研究中发挥着关键作用。然而，现有的模拟器通常需要自定义代码或复杂的接口，这为快速原型设计和自动化算法开发设置了障碍。为此，我们提出了智能机器人模拟器（IR-SIM），这是一种轻量级的技能原生导航模拟器，旨在快速构建场景、进行基准测试和机器人学习。在IR-SIM中，场景完全由YAML配置文件定义，这些文件指定了移动机器人运动学、几何碰撞检测、激光雷达（LiDAR）感知、可视化和行为模块。这种设计使得机器人仿真完全可描述和可重复，允许通过所提出的IR-SIM代理技能从文本提示生成和修改场景。生成的场景可用于导航算法的自动化基准测试和学习方法的训练数据自动生成。此外，IR-SIM还提供了与高保真模拟器和现实世界部署的桥梁，使用户能够在原型设计后在更真实的环境中验证其算法，而无需额外编码。实验展示了IR-SIM在多个任务中的便利性和多功能性：从自然语言构建导航场景、训练碰撞避免策略、基准测试社会导航策略，以及与高保真模拟器和现实世界部署的桥接。项目网站可访问：https://github.com/hanruihua/ir-sim。
cs.RO / 52 / 2606.08737
Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
Dream-Tac：一种统一的触觉世界动作模型用于接触丰富的机器人操控
Abstract
World action models inherit the predictive capability of world models, enabling action generation to be guided by anticipated future observations. However, they rely primarily on vision and often fail in contact-rich manipulation, where critical cues arise from physical interaction. In this paper, we propose Dream-Tac, a unified Tactile-World Action Model that jointly models actions, future visual observations, and tactile dynamics. Specifically, Dream-Tac introduces (i) contact-gated visuotactile fusion to selectively integrate tactile signals and (ii) a contact-aware attention bias to better regulate cross-modal interactions during manipulation. To support real-time deployment, we further design a dual-level acceleration strategy, reformulating the contact-aware bias to preserve the fused attention path during training and introducing cache-based diffusion acceleration at inference, achieving up to 2.9$\times$ faster training and 1.8$\times$ faster inference. Across six contact-rich manipulation tasks, Dream-Tac improves action accuracy by 31.7\% on average, demonstrating the effectiveness of unified visuotactile world modeling.Code is available at https://github.com/LYFCLOUDFAN/Dream-Tac.
Chinese Translation
世界动作模型继承了世界模型的预测能力，使得动作生成能够受到预期未来观察的指导。然而，它们主要依赖视觉，往往在接触丰富的操控中失败，因为关键线索来自物理交互。本文提出了Dream-Tac，一种统一的触觉-世界动作模型，联合建模动作、未来视觉观察和触觉动态。具体而言，Dream-Tac引入了（i）接触门控的视触融合，以选择性地整合触觉信号，以及（ii）接触感知的注意力偏置，以更好地调节操控过程中的跨模态交互。为了支持实时部署，我们进一步设计了一种双层加速策略，重新构建接触感知的偏置，以在训练过程中保持融合的注意力路径，并在推理时引入基于缓存的扩散加速，实现了训练速度提高至2.9倍，推理速度提高至1.8倍。在六个接触丰富的操控任务中，Dream-Tac平均提高了31.7%的动作准确性，证明了统一视触世界建模的有效性。代码可在 https://github.com/LYFCLOUDFAN/Dream-Tac 获取。
cs.RO / 53 / 2606.08741
Safe, Fluent and Acceptable Motion Generation and Execution for Human--Robot Interaction in Manufacturing Environments
安全、流畅且可接受的人机交互运动生成与执行在制造环境中的应用
Abstract
Robots operating in human environments must not only ensure physical safety but also exhibit behaviors that are understandable, fluent, and acceptable to human partners. This paper investigates motion generation strategies that combine safety guarantees with interaction quality considerations, such as motion smoothness and human comfort. While the design of robots capable of ensuring safety in shared human-robot environments has enabled closer and more advanced forms of interaction, these new proximity-based tasks require moving beyond purely technical considerations. In particular, robot behavior must also be addressed from psycho-cognitive and social perspectives. In this context, we argue for the relevance of integrating social-aware motion control into robotic systems. First, we identify the motion parameters that influence human perception and operator experience. Then, we implement a Model Predictive Control (MPC) framework that generates four distinct socially-informed robot behaviors. Finally, we conduct a user study to evaluate and validate these behaviors and assess their social impact on non-expert participants. The results demonstrate that variations in robot behavior significantly affect the perceived social acceptability of the system. These findings highlight the importance of incorporating human-centered considerations into motion generation strategies for robots operating in shared environments.
Chinese Translation
在与人类共同工作的环境中，机器人不仅必须确保物理安全，还需表现出易于理解、流畅且被人类伙伴接受的行为。本文探讨了结合安全保障与交互质量（如运动平滑性和人类舒适度）的运动生成策略。尽管能够确保共享人机环境安全的机器人设计促进了更紧密和更高级的交互形式，但这些基于接近的任务要求超越单纯的技术考量。特别是，机器人行为还必须从心理认知和社会角度进行探讨。在此背景下，我们主张将社会意识运动控制整合到机器人系统中的重要性。首先，我们识别出影响人类感知和操作体验的运动参数。然后，我们实现了一个模型预测控制（Model Predictive Control, MPC）框架，生成四种不同的社会信息驱动的机器人行为。最后，我们进行了一项用户研究，以评估和验证这些行为，并评估其对非专家参与者的社会影响。结果表明，机器人行为的变化显著影响了系统的社会可接受性。这些发现强调了在为在共享环境中工作的机器人制定运动生成策略时，纳入以人为中心的考量的重要性。
cs.RO / 54 / 2606.08743
Guided Discovery of New Behaviors using Diffusion Policies
利用扩散策略引导新行为的发现
Abstract
Diffusion models have become a powerful tool for generative modeling in robotics, with diffusion policies excelling at modeling multimodal action-trajectory distributions. However, when demonstrations are limited, standard sampling often reproduces dominant behaviors while neglecting valid but rare modes, limiting the discovery of novel solutions. Existing approaches, such as guidance methods or combining reinforcement learning with diffusion, either push samples into infeasible regions or struggle to escape local minima, failing to systematically uncover diverse behaviors. To address these challenges, we propose a framework that combines Feynman-Kac correctors with a novel guiding potential that systematically guides diffusion policy samples towards promising yet underrepresented samples. These trajectories are refined using sampling-based trajectory optimization and reincorporated into the training set to retrain the diffusion policy. Our method effectively mines and repairs novel trajectories, enabling the systematic discovery of diverse and executable behaviors. We demonstrate the effectiveness of our framework across a range of manipulation environments, consistently discovering new behaviors.
Chinese Translation
扩散模型已成为机器人生成建模的强大工具，扩散策略在建模多模态动作轨迹分布方面表现出色。然而，当演示样本有限时，标准采样往往会重现主导行为，而忽视有效但稀有的模式，从而限制了新解决方案的发现。现有方法，如引导方法或将强化学习与扩散结合，往往将样本推向不可行区域，或难以逃脱局部最小值，未能系统性地揭示多样化的行为。为了解决这些挑战，我们提出了一种框架，将费曼-卡克校正器与一种新颖的引导势能相结合，系统性地引导扩散策略样本朝向有前景但代表性不足的样本。这些轨迹通过基于采样的轨迹优化进行精炼，并重新纳入训练集以重新训练扩散策略。我们的方法有效地挖掘和修复新轨迹，使得多样化和可执行行为的系统发现成为可能。我们在一系列操作环境中展示了我们框架的有效性，持续发现新行为。
cs.RO / 55 / 2606.08765
RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation
RGB-S：用于稳健灵巧操作的图像对齐触觉显著性
Abstract
Effective visuo-tactile integration is critical for robotic dexterous manipulation, especially when visual observations are unreliable or occluded. However, robustly aligning sparse, heterogeneous tactile measurements with dense visual representations remains a fundamental challenge. Most existing approaches require policies to learn cross-modal correspondences implicitly from limited demonstrations, without leveraging geometric priors. As a result, they are often data-inefficient and generalize poorly when visual observations are degraded. To address this limitation, we propose a framework that explicitly grounds physical contacts in the image domain. Using robot forward kinematics and camera calibration, we project tactile sensor locations directly onto the RGB image plane. We then render force-modulated Gaussian saliency maps to model spatial uncertainty arising from kinematic and calibration errors. By integrating these 2D spatial anchors through a zero-initialized conditioning architecture, our method injects physical contact priors into standard visual backbones while preserving pre-trained visual representations. We evaluate our method on six dexterous manipulation tasks in both simulation and the real world under severe visual occlusions. Real-world experiments show that explicit RGB-S grounding in the image domain improves real-world occluded manipulation success rates by $26.7$ percentage points over the strongest implicit visuo-tactile baseline, suggesting its improved spatial reasoning and robustness to occlusion. Project page: touch-as-saliency.github.io
Chinese Translation
有效的视觉-触觉融合对于机器人灵巧操作至关重要，尤其是在视觉观察不可靠或被遮挡的情况下。然而，将稀疏的异质触觉测量与密集的视觉表示稳健地对齐仍然是一个基本挑战。大多数现有方法要求策略从有限的示范中隐式学习跨模态对应关系，而不利用几何先验。因此，当视觉观察受到损害时，它们通常数据效率低下且泛化能力差。为了解决这一限制，我们提出了一个框架，明确将物理接触与图像域相结合。通过使用机器人前向运动学和相机标定，我们将触觉传感器位置直接投影到RGB图像平面上。然后，我们渲染力调制的高斯显著性图，以建模由运动学和标定误差引起的空间不确定性。通过通过零初始化的条件架构整合这些二维空间锚点，我们的方法将物理接触先验注入到标准视觉骨干网络中，同时保留预训练的视觉表示。我们在六个灵巧操作任务上评估了我们的方法，包括在严重视觉遮挡下的仿真和现实世界实验。现实世界实验表明，在图像域中明确的RGB-S接地使得现实世界遮挡操作的成功率比最强的隐式视觉-触觉基线提高了26.7个百分点，这表明其在空间推理和对遮挡的鲁棒性方面有所改善。项目页面：touch-as-saliency.github.io
cs.RO / 56 / 2606.08775
Unifying Object-Centric World Models and Diffusion Policy: A Hierarchical Framework for Multi-Stage Robotic Tasks
统一对象中心世界模型与扩散策略：多阶段机器人任务的分层框架
Abstract
Visual world models have shown great potential in learning complex system dynamics. Recent advancements leverage these models as transition functions within Model Predictive Control (MPC) frameworks to solve various control tasks. When applied to robotics, however, they are limited to single-stage tasks such as reaching or grasping, and struggle with multi-stage ones that demand complex sequential planning. In this work, we introduce WorldDP, a world model framework designed for multi-stage robotic manipulation. Our hierarchical approach utilizes a high-level world model as a transition function to optimize for feasible subgoals during runtime, which are subsequently reached by a low-level Diffusion Policy. To further aid in learning dynamics and planning, we incorporate object-centric representations that decouple environmental entities and enable us to plan sequentially with respect to each. Evaluated across several robotics benchmarks, WorldDP consistently outperforms existing baselines, validating that coupling the world model's physically grounded planning with diffusion policy's efficient execution yields superior multi-stage performance.
Chinese Translation
视觉世界模型在学习复杂系统动态方面展现出了巨大的潜力。最近的进展利用这些模型作为模型预测控制（Model Predictive Control, MPC）框架中的转移函数，以解决各种控制任务。然而，在机器人应用中，它们仅限于单阶段任务，如到达或抓取，而在需要复杂顺序规划的多阶段任务中表现不佳。在本研究中，我们引入了WorldDP，一个旨在多阶段机器人操作的世界模型框架。我们的分层方法利用高层次的世界模型作为转移函数，在运行时优化可行的子目标，这些子目标随后由低层次的扩散策略（Diffusion Policy）实现。为了进一步帮助学习动态和规划，我们结合了对象中心表示，解耦环境实体，并使我们能够针对每个实体进行顺序规划。在多个机器人基准测试中的评估表明，WorldDP始终优于现有基准，验证了将世界模型的物理基础规划与扩散策略的高效执行相结合可以实现卓越的多阶段性能。
cs.RO / 57 / 2606.08828
Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video
Video2Sim2Real：从单个人类视频中获取全栈自主灵巧技能
Abstract
Human manipulation videos are a convenient and intuitive source for robot learning. However, directly transferring human dexterity to robots remains challenging due to perception errors and embodiment gap. To address this, we introduce Video2Sim2Real, a full-stack framework for autonomous skill acquisition from a single human manipulation video. Our framework first uses off-the-shelf foundation models to reconstruct a simulator-ready digital twin and extract robot and object motion priors. Rather than treating the extracted robot motion as a reliable reference throughout execution, our key idea is to recover and leverage the most fundamental sources of supervision from the demonstrated skill: We identify object-centric keyframes to optimize the corresponding robot configurations using object information from the simulator, and use these configurations as anchors that refine the robot motion such that it ultimately has the desired impact on the environment. To bridge the remaining sim-to-real gap, we introduce a sim-to-real strategy that decouples robustness to noisy and incomplete perception from variations in hand-object interaction dynamics. Specifically, we learn to recalibrate robot configurations from noisy real-world point clouds via IL, and leverage residual RL to perform local finger-level adaptations to ensure for robust and effective interactions. Finally, a collision-aware motion planning module enables spatial generalization to novel object configurations. Across several everyday manipulation tasks, Video2Sim2Real improves simulated task success, safety, and trajectory coherence over numerous baselines, and achieves better sim-to-real transfer than existing techniques. These results demonstrate a promising path toward autonomous dexterous skill acquisition from human videos.
Chinese Translation
人类操作视频是机器人学习的便捷且直观的来源。然而，由于感知误差和体现差距，直接将人类的灵巧性转移到机器人上仍然具有挑战性。为了解决这个问题，我们提出了Video2Sim2Real，一个从单个人类操作视频中获取自主技能的全栈框架。我们的框架首先利用现成的基础模型重建一个适合模拟器的数字双胞胎，并提取机器人和物体运动先验。我们并不将提取的机器人运动视为执行过程中的可靠参考，而是我们的关键思想是恢复并利用示范技能中最基本的监督来源：我们识别以物体为中心的关键帧，以使用来自模拟器的物体信息优化相应的机器人配置，并将这些配置作为锚点来细化机器人运动，使其最终对环境产生预期的影响。为了弥合剩余的模拟到现实的差距，我们引入了一种将对噪声和不完整感知的鲁棒性与手-物体交互动态变化解耦的模拟到现实策略。具体而言，我们通过逆向学习（IL）从噪声的现实世界点云中重新校准机器人配置，并利用剩余强化学习（RL）进行局部手指级别的适应，以确保鲁棒和有效的交互。最后，一个考虑碰撞的运动规划模块使得能够在新物体配置上进行空间泛化。在多个日常操作任务中，Video2Sim2Real在模拟任务成功率、安全性和轨迹一致性方面优于众多基线，并且在模拟到现实的转移方面优于现有技术。这些结果展示了从人类视频中自主获取灵巧技能的一个有前景的路径。
cs.RO / 58 / 2606.08881
Benchmarking Vision-Language-Action Models on SO-101: Failure and Recovery Analysis
在 SO-101 上对视觉-语言-动作模型的基准测试：失败与恢复分析
Abstract
Vision-Language-Action (VLA) models have demonstrated strong generalization in robotic manipulation, yet existing evaluations are primarily conducted in simulation or on expensive robotic platforms, leaving their robustness on affordable real-world robots largely unexplored. We present a standardized real-world benchmark for evaluating representative VLA and imitation learning policies on the low-cost SO-101 robotic platform. The benchmark comprises four representative manipulation tasks together with unified evaluation protocols, enabling systematic comparison under embodiment uncertainty. Using real-world teleoperated demonstrations, we fine-tune and evaluate $\pi_{0.5}$, SmolVLA, Wall-X, and ACT directly on the physical platform. Beyond conventional task success rates, the benchmark incorporates a structured failure taxonomy, semantic- and execution-level failure decomposition, and recovery-aware evaluation metrics to characterize policy robustness. Experimental results show that stronger pretrained VLA policies generally outperform the imitation learning baseline, although performance remains highly task-dependent under low-cost robotic deployment conditions. Execution instability emerges as the dominant failure source, while recovery capability varies substantially across architectures. These results highlight the importance of failure and recovery analysis beyond binary task success and establish SO-101 as a practical benchmark for evaluating embodied AI systems under realistic low-cost robotic deployment conditions.
Chinese Translation
视觉-语言-动作（VLA）模型在机器人操作中展现了强大的泛化能力，但现有评估主要在模拟环境或昂贵的机器人平台上进行，导致其在经济实惠的真实机器人上的鲁棒性尚未得到充分探索。我们提出了一种标准化的真实世界基准，用于评估代表性的 VLA 和模仿学习策略在低成本 SO-101 机器人平台上的表现。该基准包含四个代表性的操作任务以及统一的评估协议，使得在体现不确定性下的系统比较成为可能。通过真实世界的遥操作演示，我们对 $ ext{π}_{0.5}$、SmolVLA、Wall-X 和 ACT 在物理平台上进行了微调和评估。除了传统的任务成功率外，该基准还引入了结构化的失败分类法、语义和执行层面的失败分解，以及关注恢复的评估指标，以表征策略的鲁棒性。实验结果表明，较强的预训练 VLA 策略通常优于模仿学习基线，尽管在低成本机器人部署条件下，性能仍然高度依赖于具体任务。执行不稳定性成为主要的失败来源，而恢复能力在不同架构之间差异显著。这些结果强调了超越二元任务成功的失败与恢复分析的重要性，并确立了 SO-101 作为在现实低成本机器人部署条件下评估具身人工智能系统的实用基准。
cs.RO / 59 / 2606.08922
PTDL:Multi-Terrain Fall Recovery via Phase-Terrain Decoupled Learning
PTDL：通过相位-地形解耦学习实现多地形跌倒恢复
Abstract
Humanoid robots can fall on slopes, gravel, and uneven ground in unstructured environments. We target integrated fall recovery and locomotion: rebuilding balance from a fallen state using proprioception alone and resuming velocity-commanded walking at the fall site. Prior methods often stop at quasi-static rise, neglect the post-fall ground-contact phase, or, when trained on mixed terrains without separating recovery and locomotion phases or per-surface constraints, collapse to a single compromise get-up across surfaces. We propose Phase--Terrain Decoupled Learning (PTDL), which decouples training supervision along phase and terrain axes while deploying one proprioceptive policy. On the phase axis, projected-gravity-gated dual motion-prior discriminators and a probe-to-walk transition link post-fall recovery to commanded walking. On the terrain axis, terrain-stratified recovery shaping assigns surface-specific training supervision on flat ground, gravel, and slopes; terrain labels are training-only and withheld from policy observations, enabling implicit post-fall strategy selection at deployment. We validate PTDL on a 29-DoF Unitree G1 across flat ground, gravel, and slopes up to 20 degrees in simulation and on hardware, achieving stable cross-terrain recovery, smooth recovery-to-locomotion transitions, and differentiated post-fall rise behaviors under one deployed policy.
Chinese Translation
类人机器人在非结构化环境中可能会在斜坡、碎石和不平坦的地面上跌倒。我们针对集成的跌倒恢复和运动：仅利用本体感觉从跌倒状态中重建平衡，并在跌倒现场恢复速度指令行走。以往的方法往往停留在准静态的站起阶段，忽视了跌倒后的接地阶段，或者在未将恢复与运动阶段或每种表面约束分开训练的情况下，在混合地形上训练时，导致在不同表面上出现单一妥协的起身方式。我们提出了相位-地形解耦学习（Phase--Terrain Decoupled Learning，PTDL），该方法在相位和地形轴上解耦训练监督，同时部署一种本体感觉策略。在相位轴上，投影重力门控的双运动先验鉴别器和探测-行走过渡将跌倒后的恢复与指令行走连接起来。在地形轴上，地形分层恢复塑形在平坦地面、碎石和斜坡上分配特定于表面的训练监督；地形标签仅用于训练，并在策略观察中被保留，从而在部署时实现隐式的跌倒后策略选择。我们在29自由度的Unitree G1机器人上验证了PTDL，在模拟和硬件上跨平坦地面、碎石和高达20度的斜坡实现了稳定的跨地形恢复、平滑的恢复到运动的过渡，以及在一种部署策略下的差异化跌倒后站起行为。
cs.RO / 60 / 2606.08992
SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning
SpaceVLN：具有在线空间认知记忆和推理的零-shot视觉与语言导航代理
Abstract
Vision-and-Language Navigation in continuous environments requires agents to understand the spatial structure of previously unseen environments in order to follow language instructions. Although foundation models have opened a promising path toward zero-shot navigation without task-specific policy training, many navigators still rely on local visual cues and linear history-based reasoning, overlooking the spatial nature of navigation across explored regions, traversed paths, landmarks, and their spatial relations. In this paper, we propose SpaceVLN, a navigation agent built around Spatial Cognitive Memory and Task-Guided Spatial Reasoning. Specifically, SpaceVLN introduces an efficient stagewise closed-loop framework where planning and execution are organized around verifiable space--landmark stages. During navigation, the agent progressively abstracts explored regions into Spatial Waypoints and dynamically maintains subtask-grounded landmark evidence, forming a hierarchical Spatial Cognitive Memory for progress localization and spatial-relation understanding. Built on this memory, Spatial-CoT integrates task-progress reasoning with spatial perception, analysis, and prediction, enabling Task-Guided Spatial Reasoning for embodied navigation. The unified stage interface enables SpaceVLN to address both Vision-and-Language Navigation and Object-Goal Navigation under a unified zero-shot setting, without task-specific policy training. Across R2R-CE, RxR-CE, GN-Bench, and HM3D-OVON, SpaceVLN achieves state-of-the-art zero-shot performance, and real-robot deployment further validates its applicability. These results highlight Spatial Cognitive Memory and Task-Guided Spatial Reasoning as a practical foundation for stronger embodied navigation agents.
Chinese Translation
在连续环境中进行视觉与语言导航要求代理理解先前未见环境的空间结构，以便遵循语言指令。尽管基础模型为无需特定任务策略训练的零-shot导航开辟了有希望的路径，但许多导航器仍然依赖于局部视觉线索和基于线性历史的推理，忽视了在探索区域、经过路径、地标及其空间关系中的导航空间特性。本文提出了SpaceVLN，这是一种围绕空间认知记忆和任务引导空间推理构建的导航代理。具体而言，SpaceVLN引入了一种高效的阶段性闭环框架，其中规划和执行围绕可验证的空间-地标阶段组织。在导航过程中，代理逐步将探索过的区域抽象为空间航点，并动态维护与子任务相关的地标证据，形成用于进度定位和空间关系理解的层次化空间认知记忆。基于此记忆，Spatial-CoT将任务进度推理与空间感知、分析和预测相结合，实现了用于具身导航的任务引导空间推理。统一的阶段接口使得SpaceVLN能够在统一的零-shot设置下同时处理视觉与语言导航和目标物体导航，而无需特定任务的策略训练。在R2R-CE、RxR-CE、GN-Bench和HM3D-OVON数据集上，SpaceVLN实现了最先进的零-shot性能，真实机器人部署进一步验证了其适用性。这些结果突显了空间认知记忆和任务引导空间推理作为更强大具身导航代理的实用基础。
cs.RO / 61 / 2606.09088
Autonomous FPV Flight with Translational Optical Flow and Uncertainty Mask
基于平移光流和不确定性掩模的自主FPV飞行
Abstract
Autonomous FPV quadrotor flight in complex environments using a monocular RGB camera as the sole exteroceptive sensor remains a fundamental challenge. Recent research has shown that using optical flow as the input of a neural network can achieve end-to-end autonomous flight in cluttered scenes. However, extracting the most relevant information from the flow estimation is the key bottleneck limiting agility and robustness. Existing methods struggle to disentangle obstacle-induced optical flow from the ego-motion background flow and suffer from low signal-to-noise ratios near the focus of expansion (FoE). To address these issues, we decompose the optical flow into translational and rotational components and utilize only the translational flow, which captures scene geometry and depth cues. In addition, we introduce an uncertainty mask derived from inconsistencies between forward and backward flow estimates. This mask highlights obstacle structures, including those within the FoE region. Both cues are fed to a control policy trained in a differentiable simulation framework, which enables efficient first-order optimization across perception and control. We validate our approach through extensive experiments in both simulated and real-world forest environments. The proposed system achieves robust flight at speeds of up to 13.91 m/s in simulation and 11.79 m/s in real-world tests, with a 93.3\% success rate over 30 real-world trials, nearly doubling the previously reported 6 m/s real-world speed of the monocular-RGB optical-flow UAV obstacle avoidance system.
Chinese Translation
在复杂环境中，使用单目RGB相机作为唯一的外部感知传感器进行自主FPV四旋翼飞行仍然是一个基本挑战。最近的研究表明，使用光流作为神经网络的输入可以在杂乱场景中实现端到端的自主飞行。然而，从光流估计中提取最相关的信息是限制灵活性和鲁棒性的关键瓶颈。现有方法难以将障碍物引起的光流与自我运动背景流分离，并且在扩展焦点（FoE）附近信噪比低。为了解决这些问题，我们将光流分解为平移和旋转分量，仅利用平移光流，该光流捕捉场景几何和深度线索。此外，我们引入了一个不确定性掩模，该掩模源于前向和后向光流估计之间的不一致性。该掩模突出显示障碍物结构，包括FoE区域内的结构。这两种线索被输入到一个在可微分仿真框架中训练的控制策略中，从而实现感知与控制之间的高效一阶优化。我们通过在模拟和真实世界森林环境中的广泛实验验证了我们的方法。所提出的系统在模拟中以高达13.91 m/s的速度实现了稳健飞行，在真实世界测试中以11.79 m/s的速度飞行，在30次真实世界试验中成功率达到93.3\%，几乎是之前报告的单目RGB光流无人机障碍物避让系统6 m/s真实世界速度的两倍。
cs.RO / 62 / 2606.09099
LAEI: Layered Autonomous Edge Intelligence Framework for Robust UAV Swarm Operations
LAEI：用于鲁棒无人机群操作的分层自主边缘智能框架
Abstract
Autonomous UAV swarms require scalable coordination mechanisms that maintain mission performance under limited communication, environmental uncertainty, and component failures. Centralized approaches provide global coordination but suffer from communication bottlenecks and single-node vulnerabilities, whereas fully decentralized methods often lack mission-level consistency. This paper presents Layered Autonomous Edge Intelligence (LAEI), a UAV-swarm framework that combines onboard learned policies with lightweight mission-level supervision. Each UAV performs local perception, obstacle avoidance, and action selection onboard, while the supervisory layer provides adaptive goal reassignment, fault-aware recovery, and context-dependent policy guidance without directly controlling low-level actions. LAEI further incorporates recovery strategies, including dynamic reassociation, backup supervisory support, and fallback local autonomy, to maintain mission continuity under representative failure scenarios. We evaluate LAEI in simulated UAV-swarm scenarios using mission completion time, collision rate, and coverage efficiency. The results show that LAEI reduces mission completion time and improves operational efficiency while maintaining collision-aware distributed UAV-level decision-making.
Chinese Translation
自主无人机群需要可扩展的协调机制，以在有限的通信、环境不确定性和组件故障下保持任务性能。集中式方法提供全球协调，但容易遭遇通信瓶颈和单节点脆弱性，而完全去中心化的方法往往缺乏任务级一致性。本文提出了分层自主边缘智能（Layered Autonomous Edge Intelligence, LAEI），这是一个将机载学习策略与轻量级任务级监督相结合的无人机群框架。每架无人机在机载执行本地感知、障碍物规避和动作选择，而监督层则提供自适应目标重新分配、故障感知恢复和上下文相关的策略指导，而不直接控制低层次的动作。LAEI进一步结合了恢复策略，包括动态重新关联、备份监督支持和后备本地自主，以在典型故障场景下保持任务连续性。我们在模拟的无人机群场景中评估了LAEI，使用任务完成时间、碰撞率和覆盖效率作为指标。结果表明，LAEI减少了任务完成时间，提高了操作效率，同时保持了碰撞感知的分布式无人机级决策能力。
cs.RO / 63 / 2606.09108
RAM: Reachability Across Morphologies
RAM：跨形态的可达性
Abstract
Many stages of the robotic lifecycle, from morphology synthesis to operation, rely fundamentally on the reachable workspace. However, current methods for approximating workspaces are slow, imprecise, or tied to a single morphology. We introduce Reachability Across Morphologies (RAM): a morphology-conditioned, implicit neural representation that acts as a fast, differentiable surrogate for pose reachability, generalising to unseen morphologies while inherently accounting for self-collisions. To train RAM, we publish a large-scale dataset of $3\cdot10^{10}$ samples generated solely from forward kinematics. Experiments show that our model achieves an $ F_1$-score of $86\%$ at nanosecond inference, outperforming the baseline by $14\%$ while reducing inference time by three orders of magnitude. We further demonstrate speed-ups of one and two orders of magnitude for gradient-based morphology and trajectory optimisation, respectively. Website: https://timwalter.github.io/ram.
Chinese Translation
机器人生命周期的许多阶段，从形态合成到操作，基本上依赖于可达工作空间。然而，目前近似工作空间的方法速度较慢、不够精确，或仅限于单一形态。我们提出了跨形态可达性（Reachability Across Morphologies, RAM）：一种形态条件的隐式神经表示，作为姿态可达性的快速、可微分的替代方案，能够推广到未见过的形态，同时内在地考虑自碰撞。为了训练RAM，我们发布了一个大规模数据集，包含$3 imes10^{10}$个样本，这些样本完全由正向运动学生成。实验表明，我们的模型在纳秒推理下达到了$F_1$分数的$86 ext{%}$，比基线提高了$14 ext{%}$，同时推理时间减少了三个数量级。我们进一步展示了基于梯度的形态和轨迹优化分别加速了一个和两个数量级。网站：https://timwalter.github.io/ram。
cs.RO / 64 / 2606.09134
From USD Scenes to Knowledge Graphs: Zero-Shot Ontology Grounding with LLMs
从USD场景到知识图谱：基于大型语言模型的零样本本体对接
Abstract
Constructing knowledge graphs from 3D simulation scenes is essential for robot task reasoning, but the key bottleneck, grounding scene objects to formal ontology classes, still relies on manually curated dictionaries that are brittle and do not generalize across assets. We investigate whether large language models (LLMs) can automate this grounding step for Universal Scene Description (USD) scenes as a zero-shot, training-free alternative. On a kitchen scene (125 objects) with SOMA-HOME Ontology, LLMs achieve 90-96% exact-match accuracy with descriptive names and 49-89% with abbreviated names, substantially outperforming dictionary and embedding baselines. Under fully opaque names, context-augmented prompting recovers up to 48%. Feature ablation reveals that LLMs primarily exploit semantic cues in the scene graph (sibling names and parent paths); anonymizing these cues reduces accuracy to 0-6%, while geometry alone yields only 4-17%.
Chinese Translation
从3D仿真场景构建知识图谱对于机器人任务推理至关重要，但关键瓶颈在于将场景对象与正式本体类别对接，这一过程仍然依赖于脆弱且无法跨资产泛化的手动编制字典。我们研究了大型语言模型（LLMs）是否能够作为零样本、无训练的替代方案，自动化这一对接步骤，针对通用场景描述（USD）场景进行实验。在一个包含125个对象的厨房场景中，使用SOMA-HOME本体，LLMs在描述性名称上实现了90-96%的精确匹配准确率，而在缩写名称上则为49-89%，显著优于字典和嵌入基线。在完全不透明的名称下，增强上下文的提示恢复了多达48%的准确率。特征消融实验表明，LLMs主要利用场景图中的语义线索（兄弟名称和父路径）；对这些线索进行匿名化会将准确率降低到0-6%，而仅依赖几何信息的准确率仅为4-17%。
cs.RO / 65 / 2606.09155
Bridged SBI: Correcting Biased Low-Fidelity Posteriors for Cost-Efficient High-Fidelity Inference
桥接式SBI：纠正偏差的低保真后验以实现成本高效的高保真推断
Abstract
Accurate calibration of particle-based simulators is crucial for robotic earthwork simulation, but analytical calibration is challenging due to this task's highly nonlinear particle dynamics and the black-box nature of conventional simulators. Although simulation-based inference (SBI) can estimate posterior distributions over simulation parameters solely from forward simulations, applying SBI directly to high-fidelity (HF) particle simulators is often computationally prohibitive. Low-fidelity (LF) simulators with coarser particles can reduce this cost, but changes in particle size and particle count shift the parameter values needed to reproduce the same observation, producing biased LF posteriors. We propose Bridged SBI, which leverages a biased but informative LF posterior to guide HF inference. This method first uses inexpensive LF simulations to identify a coarse high-density parameter region, and then it learns a local residual bridge to transport LF posterior samples toward HF-consistent regions by correcting the LF--HF discrepancy. We analyze how sequential multi-fidelity SBI (Naive-MF) can suffer from LF-induced posterior miscoverage when it directly relies on the LF posterior without discrepancy correction. We then show that Bridged SBI is designed to alleviate this issue by explicitly modeling the LF--HF discrepancy through residual correction. Experiments on both sim-to-sim particle-parameter calibration and real-to-sim calibration with real soil observation show that Bridged SBI produces more accurate and reliable HF posteriors than HF-only SBI or the Naive-MF baseline, especially under limited HF simulation costs.
Chinese Translation
粒子基础模拟器的准确校准对于机器人土方模拟至关重要，但由于该任务的高度非线性粒子动力学和传统模拟器的黑箱特性，分析校准面临挑战。尽管基于模拟的推断（SBI）可以仅通过前向模拟来估计模拟参数的后验分布，但直接将SBI应用于高保真（HF）粒子模拟器通常计算成本过高。低保真（LF）模拟器通过使用较粗的粒子可以降低这一成本，但粒子大小和粒子数量的变化会导致需要不同的参数值以重现相同的观察，从而产生偏差的LF后验。我们提出了桥接式SBI，它利用偏差但信息丰富的LF后验来指导HF推断。该方法首先使用廉价的LF模拟来识别一个粗略的高密度参数区域，然后学习一个局部残差桥，将LF后验样本通过校正LF与HF之间的差异，传输到HF一致的区域。我们分析了顺序多保真SBI（Naive-MF）在直接依赖LF后验而未进行差异校正时，如何受到LF引起的后验覆盖不当的影响。然后，我们展示了桥接式SBI旨在通过残差校正显式建模LF与HF之间的差异来缓解这一问题。在粒子参数的模拟到模拟校准以及使用真实土壤观测的真实到模拟校准的实验中，桥接式SBI产生的HF后验比仅使用HF的SBI或Naive-MF基线更准确、更可靠，尤其是在HF模拟成本有限的情况下。
cs.RO / 66 / 2606.09183
Autonomous Obstacle Removal for Excavators through Policy Learning with Particle Simulation
通过粒子模拟进行政策学习的挖掘机自主障碍物移除
Abstract
Autonomous obstacle removal from the ground is an important earthwork task, but this is difficult to automate because an excavator must adapt its excavation trajectories over repeated cycles as soil-obstacle conditions change. Learning such state-dependent behavior requires a training environment that reproduces accumulated soil-obstacle interactions, including contact states, terrain deformation, and obstacle visibility. Accordingly, particle-based simulation is suitable for the relevant policy learning. However, particle simulation is computationally expensive, and repeated excavation cycles further increase the learning cost. We observe that the burial condition of an obstacle governs both task difficulty and simulation cost: deeper burial makes obstacle removal harder while also requiring more particles for accurate simulation. This observation motivates a burial-conditioned curriculum learning strategy. We propose a time-efficient sim-to-real policy learning framework in which the policy observes terrain and obstacle information from RGB-D measurements and then outputs a parameterized excavation trajectory; in this process, the simulator reproduces in a real-world excavator the same observation-action interface it uses under controllable burial conditions. The curriculum begins with shallow burial conditions and progressively increases burial depth while adjusting particle count, thus simultaneously controlling task difficulty and simulation cost. Experiments show that the proposed framework successfully learns an effective obstacle-removal policy, whereas baseline methods fail even after a full week of training. The proposed curriculum achieves effective performance within three days and achieves successful transfer to a real 12-ton excavator operating on open ground with various steel obstacles, thus demonstrating robust obstacle removal.
Chinese Translation
从地面自主移除障碍物是一个重要的土方作业任务，但由于挖掘机必须在土壤-障碍物条件变化的情况下调整其挖掘轨迹，因此这一过程难以实现自动化。学习这种状态依赖的行为需要一个能够再现累积土壤-障碍物交互的训练环境，包括接触状态、地形变形和障碍物可见性。因此，基于粒子的模拟适合于相关的政策学习。然而，粒子模拟的计算成本较高，重复的挖掘周期进一步增加了学习成本。我们观察到，障碍物的埋藏条件决定了任务的难度和模拟成本：埋藏越深，障碍物移除的难度越大，同时也需要更多的粒子以实现准确的模拟。这一观察促使我们提出了一种基于埋藏条件的课程学习策略。我们提出了一种时间高效的从模拟到现实的政策学习框架，其中政策通过RGB-D测量观察地形和障碍物信息，然后输出参数化的挖掘轨迹；在此过程中，模拟器在真实的挖掘机上再现其在可控埋藏条件下使用的相同观察-行动接口。课程从浅埋藏条件开始，逐步增加埋藏深度，同时调整粒子数量，从而同时控制任务难度和模拟成本。实验表明，所提出的框架成功学习到有效的障碍物移除政策，而基线方法即使经过一整周的训练也未能成功。所提出的课程在三天内实现了有效的性能，并成功转移到一台在开放地面上操作的12吨挖掘机，能够处理各种钢制障碍物，从而展示了强大的障碍物移除能力。
cs.RO / 67 / 2606.09188
Trajectory Optimization in Single and Dual-UAV Bearing-Only Target Localization
单一及双无人机基于方位角的目标定位轨迹优化
Abstract
Bearing-only target localization is a fundamental problem in optical measurement and finds extensive applications in unmanned aerial vehicle (UAV) technology. Effective trajectory planning establishes favorable observation geometries, thereby enhancing the target localization accuracy of bearing-only UAV systems. This paper proposes an trajectory optimization method for unmanned aerial vehicles (UAVs) in bearing-only target localization scenarios. By leveraging the Fisher Information Matrix (FIM), the proposed approach dynamically integrates the geometric configuration and vehicle maneuverability into the optimization framework. Specifically, we introduce a spectrally-weighted FIM objective function that provides better gradient dynamics near degenerate configurations, enabling the planner to rapidly escape from poor observation conditions. For dual-UAV scenarios, an intersection angle sine term is introduced to optimize triangulation geometry by improving the sight-line intersection angle, thereby preventing trajectory aggregation. Furthermore, we propose an improved Particle Swarm Optimization (PSO) algorithm with motion model constraints and particle normalization to ensure the physical feasibility of the trajectory and enhance the compatibility with the objective functions. Simulation results demonstrate that the proposed method reduces the median localization error by 99.21% compared to conventional FIM-based approaches in single-UAV scenarios, and achieves a 69.70% improvement for dual-UAV configurations, exhibits superior performance in long-duration bearing-only target localization of maneuverability targets at extended ranges.
Chinese Translation
基于方位角的目标定位是光学测量中的一个基本问题，并在无人机（UAV）技术中得到了广泛应用。有效的轨迹规划能够建立有利的观测几何，从而提高基于方位角的无人机系统的目标定位精度。本文提出了一种用于无人机在基于方位角目标定位场景中的轨迹优化方法。通过利用费舍尔信息矩阵（FIM），所提出的方法将几何配置和飞行器机动性动态地整合到优化框架中。具体而言，我们引入了一种谱加权的FIM目标函数，该函数在退化配置附近提供更好的梯度动态，使得规划者能够快速摆脱不良观测条件。在双无人机场景中，引入了交角正弦项，以通过改善视线交角来优化三角测量几何，从而防止轨迹聚集。此外，我们提出了一种改进的粒子群优化（PSO）算法，结合运动模型约束和粒子归一化，以确保轨迹的物理可行性并增强与目标函数的兼容性。仿真结果表明，所提出的方法在单无人机场景中将中位数定位误差降低了99.21%，在双无人机配置中实现了69.70%的改善，在长时间基于方位角的机动目标定位中表现出优越的性能，尤其是在较远的范围内。
cs.RO / 68 / 2606.09203
Deterministic Execution of ROS~2 Applications via Lingua Franca
通过 Lingua Franca 实现 ROS~2 应用的确定性执行
Abstract
The Robot Operating System~2 (ROS 2) is a widely used middleware for robotic systems, characterized by a publish-subscribe (pub-sub) communication mechanism in which computation is structured as callbacks dispatched by ROS 2 executors. Despite its popularity, the pub-sub pattern in ROS 2 is inherently nondeterministic: the order in which these callbacks run is nondeterministic even within a single executor, and distributed deployments add further nondeterminism from the interleaving of messages across nodes and from network latency. Such nondeterminism often leads to concurrency issues and makes it virtually impossible to analyze for safeness and provide guarantees. We present a framework that is able to convert an unmodified ROS 2 application and run it under Lingua Franca (LF), a coordination language for deterministic execution using logical time, so that the same input always produces the same deterministic execution order. We first describe which ROS 2 features can be executed deterministically under logical time. Such features enable the possibility to establish an automatic conversion framework to extract information from a ROS 2 application and directly convert it into an LF program. The rich features of LF, such as logical-time delays, federated execution across processes, and fault handling, can then be applied to make the ROS 2 application be executed in a deterministic and timing-predictable manner without changing the ROS 2 code. We evaluate the framework on a synthetic example and on the Autoware reference system. We show that the order in which callbacks are executed differs in default ROS 2, while also having end-to-end latencies that vary across executions. In contrast, our LF-controlled ROS 2 system produces a deterministic execution order and consistent end-to-end latencies.
Chinese Translation
机器人操作系统~2（ROS 2）是一种广泛使用的机器人系统中间件，其特点是采用发布-订阅（pub-sub）通信机制，计算结构为由 ROS 2 执行器调度的回调函数。尽管其受欢迎，ROS 2 中的发布-订阅模式本质上是非确定性的：即使在单个执行器内，这些回调函数的运行顺序也是非确定性的，而分布式部署则进一步增加了来自节点间消息交错和网络延迟的非确定性。这种非确定性常常导致并发问题，使得分析安全性和提供保证几乎不可能。我们提出了一个框架，能够将未修改的 ROS 2 应用转换并在 Lingua Franca（LF）下运行，LF 是一种用于使用逻辑时间实现确定性执行的协调语言，从而确保相同的输入始终产生相同的确定性执行顺序。我们首先描述哪些 ROS 2 特性可以在逻辑时间下以确定性方式执行。这些特性使得建立一个自动转换框架成为可能，从 ROS 2 应用中提取信息并直接转换为 LF 程序。LF 的丰富特性，如逻辑时间延迟、跨进程的联合执行和故障处理，可以应用于使 ROS 2 应用以确定性和时间可预测的方式执行，而无需更改 ROS 2 代码。我们在一个合成示例和 Autoware 参考系统上评估了该框架。结果表明，在默认的 ROS 2 中，回调函数的执行顺序存在差异，同时各次执行的端到端延迟也有所不同。相比之下，我们的 LF 控制的 ROS 2 系统产生了确定的执行顺序和一致的端到端延迟。
cs.RO / 69 / 2606.09215
MotionWAM: Towards Foundation World Action Models for Real-Time Humanoid Loco-Manipulation
MotionWAM：面向实时类人运动操作的基础世界动作模型
Abstract
World Action Models (WAMs) couple a video dynamics prior to the policy and have shown encouraging results on tabletop manipulation, but iterative denoising over high-dimensional video-action latents leaves them too slow for real-time humanoid loco-manipulation. The problem is compounded by the dominant hierarchical paradigm, in which a high-level manipulation policy controls only the upper body while a low-level controller tracks coarse base commands -- placing upper and lower body in inconsistent action spaces and reducing the legs to balance-preserving locomotion. We present MotionWAM, a real-time WAM that drives autonomous humanoid loco-manipulation from a single egocentric camera by conditioning the policy on the intermediate denoising features of a video world model. MotionWAM replaces the upper-lower split with a unified motion latent and predicts whole-body motion tokens that jointly cover locomotion, torso motion, height regulation, foot interaction, and hand manipulation in a single action space. A three-stage learning framework progressively adapts the video world model to egocentric visual dynamics and to the target humanoid embodiment. On nine real-world Unitree G1 tasks, MotionWAM runs in real time, substantially outperforms Vision-Language-Action (VLA) baselines fine-tuned on the same demonstrations by over 30% in overall success rate, and executes task-driven foot interaction that decoupled upper-lower policies cannot reach. Our results suggest that video-pretrained WAMs can be lifted from tabletop manipulation to coordinated, human-like whole-body humanoid control.
Chinese Translation
世界动作模型（WAMs）将视频动态先验与策略结合，在桌面操作中显示出令人鼓舞的结果，但在高维视频-动作潜变量上进行迭代去噪使其在实时类人运动操作中速度过慢。这个问题因主导的层次范式而加剧，其中高层操作策略仅控制上半身，而低层控制器则跟踪粗略的基础指令——这使得上下半身处于不一致的动作空间中，并将腿部简化为保持平衡的运动。我们提出了MotionWAM，一种实时WAM，通过将策略条件化于视频世界模型的中间去噪特征，从单个自我中心摄像头驱动自主类人运动操作。MotionWAM用统一的运动潜变量替代了上下分离，预测覆盖运动、躯干运动、高度调节、脚部交互和手部操作的全身运动标记，所有这些都在一个单一的动作空间中进行。一个三阶段学习框架逐步将视频世界模型适应于自我中心视觉动态和目标类人化体现。在九个真实世界的Unitree G1任务中，MotionWAM实时运行，整体成功率比在相同演示上微调的视觉-语言-动作（VLA）基线高出30%以上，并执行那些上下政策无法达到的任务驱动脚部交互。我们的结果表明，经过视频预训练的WAM可以从桌面操作提升到协调的类人全身控制。
cs.RO / 70 / 2606.09236
Self-Paced Curriculum Reinforcement Learning for Autonomous Superbike Racing in Simulation
自适应课程强化学习在仿真环境中实现自主超级摩托车赛车
Abstract
Autonomous Racing has seen remarkable progress through deep Reinforcement Learning (RL), primarily for four-wheeled vehicles. However, motorbikes introduce substantially greater complexity due to the need to manage balance and lean angle, in addition to more reactive steering and throttle control, and a smaller weight. In this work, we present a framework for training an autonomous agent to race a superbike in VRider SBK, a physics-accurate Unity-based motorbike simulator. Our approach integrates Soft Actor-Critic (SAC) with Self-Paced curriculum Deep reinforcement Learning (SPDL), which dynamically generates progressively more challenging tasks based on the agent's performance, without requiring manual curriculum design. The agent's state space comprises proprioceptive features extended with lean-angle history, along with global track features via course points. The reward signal is shaped to encourage progress along the track while penalizing instability-inducing behaviors specific to two-wheeled dynamics. Preliminary experimental results demonstrate that SPDL outperforms SAC alone in training efficiency, lap time, and driving stability across multiple tracks and motorbike models, establishing a first baseline for RL-based autonomous motorbike racing.
Chinese Translation
自主赛车在深度强化学习（RL）领域取得了显著进展，主要集中在四轮车辆上。然而，摩托车由于需要管理平衡和倾斜角度，此外还需更为灵敏的转向和油门控制，以及更小的重量，带来了更大的复杂性。在本研究中，我们提出了一个框架，用于训练自主代理在VRider SBK中进行超级摩托车赛车，该模拟器基于物理精确的Unity平台。我们的方法将软演员-评论家（Soft Actor-Critic, SAC）与自适应课程深度强化学习（Self-Paced curriculum Deep reinforcement Learning, SPDL）相结合，动态生成基于代理表现的逐步更具挑战性的任务，而无需手动设计课程。代理的状态空间包括扩展了倾斜角度历史的本体感知特征，以及通过课程点获取的全局轨道特征。奖励信号的设计旨在鼓励沿轨道的进展，同时惩罚特定于两轮动力学的不稳定行为。初步实验结果表明，SPDL在训练效率、圈速和驾驶稳定性方面优于单独使用SAC，建立了基于强化学习的自主摩托车赛车的首个基准。
cs.RO / 71 / 2606.09237
Can we stabilize an inverted pendulum with feedback from a time-of-flight camera?
我们能否通过飞行时间相机的反馈来稳定倒立摆？
Abstract
Time-of-flight cameras are popular in robotics for providing direct depth information while being compact, inexpensive, and robust to lighting conditions, but their low spatial resolution and depth noise are widely believed to preclude precise feedback control. In this paper, we show that an inexpensive, low-resolution time-of-flight camera provides sufficient feedback to reliably and precisely balance an inverted pendulum on a cart--a canonical benchmark for fast, unstable dynamics.
Chinese Translation
飞行时间相机因其提供直接的深度信息，同时体积小、成本低且对光照条件具有良好的适应性而在机器人技术中广受欢迎。然而，它们的低空间分辨率和深度噪声普遍被认为会妨碍精确的反馈控制。本文展示了一种廉价、低分辨率的飞行时间相机能够提供足够的反馈，以可靠且精确地平衡一个放置在小车上的倒立摆——这是一个快速、不稳定动态的经典基准。
cs.RO / 72 / 2606.09255
RPO-PDT: Demonstrating Role-Play-Based Knowledge Adaptation for Student Support Dialogue (Demonstration System)
RPO-PDT：基于角色扮演的知识适应在学生支持对话中的应用（演示系统）
Abstract
We present RPO-PDT: a retrieval-grounded, role-play-based dialogue system for adaptive student support in higher education. RPO-PDT is: (1) able to provide institution-specific Personal Development Tutor (PDT) guidance using structured knowledge sources; (2) constrained by explicit persona, boundary, confidentiality, and safety policies; and (3) designed around a reverse-roleplay loop where unresolved interactions are replayed from the student perspective, enabling alternative tutor strategies to be generated and stored as reusable strategy memory. RPO-PDT supports both text-based and Furhat-based embodied interaction for demonstrating grounded, safe, and adaptive student-support dialogue.
Chinese Translation
我们提出了RPO-PDT：一个基于检索的、基于角色扮演的对话系统，用于高等教育中的适应性学生支持。RPO-PDT具备以下特点：（1）能够利用结构化知识源提供特定于机构的个人发展导师（Personal Development Tutor, PDT）指导；（2）受到明确的人物设定、边界、保密和安全政策的约束；（3）围绕一个反向角色扮演循环设计，其中未解决的互动从学生的视角重新播放，从而生成和存储可重复使用的策略记忆。RPO-PDT支持基于文本和Furhat的具身互动，以演示基于事实、安全和适应性的学生支持对话。
cs.RO / 73 / 2606.09258
Back to the Familiar Future: Failure Recovery for VLA Policies via Pre-Imagined Milestone Selection
回归熟悉的未来：通过预设里程碑选择实现 VLA 策略的故障恢复
Abstract
Vision-language-action (VLA) policies can deviate from nominal trajectories during manipulation, even when tasks remain physically feasible. Recovering from these deviations is challenging, as they push the policy into unfamiliar state spaces where direct re-planning frequently destabilizes action sequences. We propose Back to the Familiar Future (B2FF), a recovery framework for foresight-driven VLAs that leverages future visual conditioning as a recovery interface. Before execution, the VLA generates a milestone bank of familiar future states conditioned on the clean initial observation. At recovery time, a recoverability-aware selector selects a recovery milestone from this bank and enforces it as a fixed visual goal. This enables the VLA to robustly map off-trajectory observations back to a familiar future. On failure-injected LIBERO, under controlled recovery timing aligned with the injected failure, B2FF increases the average success rate of a baseline VLA from 56.3% to 74.0%, demonstrating that pre-imagined milestones can guide recovery without fine-tuning the low-level action generator.
Chinese Translation
视觉-语言-行动 (VLA) 策略在操作过程中可能会偏离正常轨迹，即使任务在物理上仍然可行。从这些偏差中恢复是具有挑战性的，因为它们将策略推入不熟悉的状态空间，在这些状态空间中，直接重新规划往往会导致行动序列的不稳定。我们提出了回归熟悉的未来 (B2FF) 框架，这是一个针对前瞻性驱动的 VLA 的恢复框架，利用未来视觉条件作为恢复接口。在执行之前，VLA 生成一个基于干净初始观察的熟悉未来状态的里程碑库。在恢复时，一个考虑恢复性的选择器从这个库中选择一个恢复里程碑，并将其强制作为固定的视觉目标。这使得 VLA 能够稳健地将偏离轨迹的观察映射回熟悉的未来。在故障注入的 LIBERO 上，在与注入故障对齐的受控恢复时机下，B2FF 将基线 VLA 的平均成功率从 56.3% 提高到 74.0%，证明了预设里程碑可以在不微调低级行动生成器的情况下指导恢复。
cs.RO / 74 / 2606.09268
VGP-Nav: Metric-Aware Visual Geometric Perception for Robot Navigation
VGP-Nav：面向度量的视觉几何感知用于机器人导航
Abstract
Reliable robotic navigation necessitates the seamless integration of accurate global localization and dense, metric-consistent obstacle perception. A common strategy to achieve these capabilities involves integrating diverse sensing modalities: cameras offer rich visual features for localization, while active sensors like LiDAR provide direct metric measurements. However, such multi-sensor configurations necessitate complex spatial-temporal calibration and increase deployment overhead. Although vision-only approaches offer a low-cost and scalable alternative, existing monocular visual systems typically struggle to simultaneously achieve efficient, globally consistent localization and dense, metric-consistent geometric perception. To bridge this gap, we propose \textbf{VGP-Nav}, a unified framework for \textit{Metric-Aware Visual Geometric Perception} that relies solely on monocular RGB input to jointly support metric localization and obstacle perception. Our key insight is to anchor localization-grounded visual geometry to physically meaningful scale constraints derived from ground-plane geometry, thereby providing a reliable metric reference for monocular perception. VGP-Nav resolves monocular scale ambiguity online and produces localization-grounded, metric obstacle representations that are directly applicable to downstream planning. Extensive experiments demonstrate strong generalization across diverse environments and successful deployment on real mobile robots, highlighting the practicality of our approach for scalable, low-cost, and safe autonomous navigation.
Chinese Translation
可靠的机器人导航需要准确的全球定位与密集的、度量一致的障碍物感知的无缝集成。实现这些能力的常见策略是整合多种传感方式：相机提供丰富的视觉特征用于定位，而激光雷达（LiDAR）等主动传感器则提供直接的度量测量。然而，这种多传感器配置需要复杂的时空校准，并增加了部署的开销。尽管仅使用视觉的方法提供了一种低成本且可扩展的替代方案，但现有的单目视觉系统通常难以同时实现高效的、全球一致的定位和密集的、度量一致的几何感知。为了解决这一问题，我们提出了 extbf{VGP-Nav}，一个统一的 extit{面向度量的视觉几何感知}框架，仅依赖单目RGB输入来共同支持度量定位和障碍物感知。我们的关键见解是将基于定位的视觉几何锚定到源自地面平面几何的物理意义明确的尺度约束，从而为单目感知提供可靠的度量参考。VGP-Nav在线解决单目尺度模糊问题，并生成基于定位的、度量障碍物表示，这些表示可以直接应用于后续规划。大量实验表明，该方法在多样化环境中具有强大的泛化能力，并成功部署于真实移动机器人，突显了我们的方法在可扩展、低成本和安全的自主导航中的实用性。
cs.RO / 75 / 2606.09286
VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands
VAIC：基于视觉引导的人形机器人灵活物体交互控制通过解耦指令
Abstract
Humanoid robots hold immense potential for real-world assistance, yet agile interaction with objects in unstructured environments demands tightly coupled whole-body coordination. Despite recent advancements, current controllers face a critical deployment gap. They rely heavily on dense reference trajectories and perfect state observability, which inherently limits physical generalization. We present Vision Guided Agile Interaction Control (VAIC), a unified framework that bridges this gap by operating exclusively on onboard depth, historical proprioception, and a decoupled user command interface. VAIC employs a two-stage distillation paradigm. First, a privileged teacher policy masters diverse interaction skills using precise object kinematics and exact environmental states. Second, a deployable student policy distills these capabilities by replacing full body tracking with velocity targets across multiple axes and an interaction indicator for each frame. The student utilizes a recurrent object adaptation module to implicitly infer unobservable object dynamics from raw depth streams and proprioception. Evaluations and real-world deployments on the humanoid robot demonstrate that a single VAIC policy successfully executes highly diverse dynamic tasks. These tasks include box carrying, cart interaction, and skateboarding, consistently outperforming baselines and advancing autonomous humanoid deployment.
Chinese Translation
人形机器人在现实世界的辅助中具有巨大的潜力，但在非结构化环境中与物体的灵活交互需要紧密耦合的全身协调。尽管近期取得了一些进展，当前的控制器仍面临关键的部署差距。它们在很大程度上依赖于密集的参考轨迹和完美的状态可观测性，这本质上限制了物理上的泛化能力。我们提出了视觉引导灵活交互控制（VAIC），这是一个统一框架，通过仅依赖机载深度、历史本体感知和解耦用户命令接口来弥补这一差距。VAIC采用两阶段蒸馏范式。首先，特权教师策略利用精确的物体运动学和准确的环境状态掌握多样的交互技能。其次，可部署的学生策略通过在多个轴上用速度目标替代全身跟踪，并为每帧提供交互指示，来提炼这些能力。学生利用递归物体适应模块，从原始深度流和本体感知中隐式推断不可观测的物体动态。对人形机器人的评估和实际部署表明，单一的VAIC策略成功执行高度多样的动态任务。这些任务包括箱子搬运、推车交互和滑板运动，始终超越基线，推动自主人形机器人的部署。
cs.RO / 76 / 2606.09292
Dual Quaternion-Based Unscented Kalman Filter with Visual Inertial Odometry for Navigation in GPS-Denied Environments
基于双四元数的无迹卡尔曼滤波器与视觉惯性里程计结合的GPS缺失环境导航
Abstract
Reliable navigation in GPS-denied environments remains a fundamental challenge in robotics, aerospace, and autonomous vehicle applications. This paper presents a Dual Quaternion-Based Unscented Kalman Filter (DQUKF) equipped with a Visual Inertial Odometry (VIO) algorithm for accurate state estimation enabling navigation in GPS denied locations. The proposed framework formulates the DQUKF in an error state manner, where the nominal pose is represented by a unit dual quaternion and the local pose error is represented by a 6-dimensional twistor parameterization used for sigma point generation, covariance propagation, and measurement correction. In parallel, the VIO algorithm tracks features across image frames, synchronizes measurements between the IMU and camera, and provides visual constraints that complement inertial propagation. Simulation results on the EuRoC MAV dataset show that the proposed DQUKF converges under high initialization uncertainty and achieves a position RMSE of 0.2584~m in the difficult flight sequence, outperforming the benchmark filters.
Chinese Translation
在GPS缺失环境中实现可靠导航仍然是机器人技术、航空航天和自动驾驶车辆应用中的一个基本挑战。本文提出了一种基于双四元数的无迹卡尔曼滤波器（DQUKF），该滤波器配备了视觉惯性里程计（VIO）算法，以实现准确的状态估计，从而支持在GPS缺失地点的导航。所提出的框架以误差状态的方式构建DQUKF，其中名义姿态由单位双四元数表示，而局部姿态误差则通过6维扭转参数化表示，用于生成西格玛点、协方差传播和测量校正。同时，VIO算法在图像帧之间跟踪特征，协调IMU与相机之间的测量，并提供补充惯性传播的视觉约束。在EuRoC MAV数据集上的仿真结果表明，所提出的DQUKF在高初始化不确定性下收敛，并在困难的飞行序列中实现了0.2584米的位置均方根误差（RMSE），优于基准滤波器。
cs.RO / 77 / 2606.09314
KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation
KPGrasp：可扩展的关键点流匹配用于灵巧抓取生成
Abstract
Generating high-quality dexterous grasps remains challenging for learning-based methods, which often depend on carefully tuned contact losses or costly contact-based test-time refinement. We present KPGrasp, a flow-matching framework that learns dexterous grasp priors from large-scale data rather than relying on contact losses or contact-based test-time refinement. KPGrasp couples an all-Euclidean 3D hand-keypoint parameterization with a simple yet scalable Transformer flow model. The parameterization avoids the drawbacks of the conventional mixed SE(3) pose and joint-angle output space, expresses grasps in the same frame as the object point cloud, and thus enables native spatial reasoning; the Transformer flow model is trained with only the standard flow-matching loss and scales effectively with data, model capacity, and batch size. Experiments demonstrate state-of-the-art performance on two simulation benchmarks. On the Dexonomy benchmark, it reaches a 76.3% grasp success rate, improving over the strongest directly comparable baseline by 47.4% while reducing penetration depth to 2.4 mm. The same model also achieves the best average performance on the DexGrasp Anything benchmark without fine-tuning. For batched inference, KPGrasp requires only 0.032 s per grasp. Finally, real-world experiments on 20 diverse objects demonstrate that the pipeline can be deployed in a real-world setup.
Chinese Translation
生成高质量的灵巧抓取对于基于学习的方法仍然具有挑战性，这些方法通常依赖于精心调校的接触损失或昂贵的基于接触的测试时优化。我们提出了KPGrasp，一种流匹配框架，它从大规模数据中学习灵巧抓取先验，而不是依赖于接触损失或基于接触的测试时优化。KPGrasp将全欧几里得3D手部关键点参数化与简单而可扩展的Transformer流模型相结合。该参数化避免了传统混合SE(3)姿态和关节角输出空间的缺点，将抓取表示在与物体点云相同的坐标系中，从而实现原生空间推理；Transformer流模型仅使用标准流匹配损失进行训练，并能有效地随着数据、模型容量和批量大小的增加而扩展。实验表明，在两个仿真基准上达到了最先进的性能。在Dexonomy基准上，它的抓取成功率达到76.3%，比最强的直接可比基线提高了47.4%，同时将穿透深度减少到2.4毫米。同一模型在DexGrasp Anything基准上也实现了最佳的平均性能，无需微调。对于批量推理，KPGrasp每个抓取仅需0.032秒。最后，在20种不同物体上的实际实验表明，该流程可以在实际环境中部署。
cs.RO / 78 / 2606.09337
TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation
TORL-VLA：用于接触丰富操作的触觉引导在线强化学习
Abstract
Vision-Language-Action (VLA) models have become a powerful framework for robotic manipulation, and recent studies have introduced tactile or force feedback into VLAs to address contact-rich tasks. However, these models are typically deployed as offline policies. When contact conditions shift from the training distribution, the policy cannot perform online adaptation, leading to problems such as inappropriate contact forces and inefficient retries. Therefore, we propose TORL-VLA, a tactile-guided online reinforcement learning framework that couples tactile feedback with policy refinement for contact-rich manipulation. Our method introduces a tactile-derived wrench-aware VLA to predict reference actions and future wrench sequences, while a lightweight online RL module is used to refine the reference actions. To stabilize learning from mixed exploratory policy-generated and human-intervention data, we introduce an intervention-censored critic that prevents post-intervention success from being wrongly credited to policy-generated actions preceding intervention. Real-robot experiments on long-horizon contact-rich tasks, including latch manipulation, coffee-cup placement, and egg handling, show that TORL-VLA improves success rates at both subtask and full-task levels, as well as time-bounded execution efficiency over strong baselines.
Chinese Translation
视觉-语言-动作（VLA）模型已成为机器人操作的强大框架，最近的研究将触觉或力反馈引入VLA，以解决接触丰富的任务。然而，这些模型通常作为离线策略进行部署。当接触条件偏离训练分布时，策略无法进行在线适应，导致不当的接触力和低效的重试等问题。因此，我们提出了TORL-VLA，一种将触觉反馈与策略优化结合的触觉引导在线强化学习框架，用于接触丰富的操作。我们的方法引入了一种基于触觉的扭矩感知VLA，以预测参考动作和未来的扭矩序列，同时使用轻量级在线强化学习模块来优化参考动作。为了稳定来自混合探索策略生成和人类干预数据的学习，我们引入了一种干预审查批评者，防止干预后的成功错误地归因于干预前的策略生成动作。在长时间接触丰富任务上的真实机器人实验，包括锁扣操作、咖啡杯放置和鸡蛋处理，表明TORL-VLA在子任务和全任务层面上提高了成功率，并在强基线之上提升了时间限制执行效率。
cs.RO / 79 / 2606.09350
Taming Perception Jitter: Uncertainty-Aware LiDAR Object Detection for Reliable Motion Classification
抑制感知抖动：面向可靠运动分类的考虑不确定性的LiDAR物体检测
Abstract
Reliable motion classification is critical for autonomous driving, as false dynamic predictions of static objects can cascade into unnecessary planner interventions. Unstable bounding box predictions can lead to spurious velocity estimates in tracking and falsely predicted trajectories. We present a deployment-friendly mitigation strategy that augments a 3D object detector with aleatoric uncertainty estimates and applies a two-sample z-test over short observation windows to separate true motion from jitter. Integrated into Autoware with minimal changes, the approach reuses existing data association for minimal compute overhead. Empirical results show parity with velocity thresholding on nuScenes, but substantially fewer false dynamic predictions and unnecessary stops in real-world test drives, explained by the presence of an intermediate jitter band in the recorded data that speed-only rules misclassify. This demonstrates that uncertainty-aware detection and lightweight statistical testing can deliver practical performance gains for autonomous driving in noisier real-world settings.
Chinese Translation
可靠的运动分类对于自动驾驶至关重要，因为静态物体的错误动态预测可能导致不必要的规划干预。不稳定的边界框预测可能导致跟踪中的虚假速度估计和错误预测的轨迹。我们提出了一种适合部署的缓解策略，该策略通过引入随机不确定性估计来增强3D物体检测器，并在短时间观察窗口内应用两样本z检验，以区分真实运动和抖动。该方法与Autoware集成，所需更改最小，重用现有的数据关联以减少计算开销。实证结果表明，在nuScenes数据集上，该方法与速度阈值法具有相当的效果，但在实际测试驾驶中显著减少了错误的动态预测和不必要的停车，这可以通过记录数据中存在的中间抖动带来解释，而仅基于速度的规则则会错误分类。这表明，考虑不确定性的检测和轻量级统计测试能够在噪声较大的现实环境中为自动驾驶带来实际的性能提升。
cs.RO / 80 / 2606.09355
MosaicIMU: Composing Carrier Experts for Generalizable Neural Inertial Odometry
MosaicIMU：为可泛化神经惯性里程计构建载体专家
Abstract
Robust inertial odometry is essential for various carriers when external sensing is unreliable. Learning-based methods reduce integration drift by capturing local motion priors, but these methods often remain tied to a particular carrier, limiting generalization across heterogeneous platforms. We present MosaicIMU, a carrier-conditioned Mixture-of-Experts (MoE) pretraining-and-adaptation framework for generalizable neural inertial odometry. MosaicIMU uses a prototype-based router to compose carrier-specific expert features, decodes local velocity and uncertainty constraints, and integrates them with a history-aware EKF. For unseen domain adaptation, it freezes the pretrained base model and learns a new lightweight expert residual branch. For edge-deployment, it further reuses the router to select informative online samples for efficient incremental updates. Experiments show that MosaicIMU consistently outperforms learning-based baselines, reducing average ATE and RTE-10s by 40% and 34%, respectively. These results highlight that MosaicIMU provides a scalable pretraining-to-deployment paradigm for generalizable and adaptive neural inertial odometry.
Chinese Translation
在外部传感不可靠的情况下，稳健的惯性里程计对于各种载体至关重要。基于学习的方法通过捕捉局部运动先验来减少积分漂移，但这些方法往往与特定载体紧密相关，限制了在异构平台上的泛化能力。我们提出了MosaicIMU，一种载体条件下的专家混合（Mixture-of-Experts, MoE）预训练与适应框架，用于可泛化的神经惯性里程计。MosaicIMU使用基于原型的路由器来组合载体特定的专家特征，解码局部速度和不确定性约束，并将其与历史感知的扩展卡尔曼滤波（EKF）相结合。对于未见领域的适应，它冻结预训练的基础模型，并学习一个新的轻量级专家残差分支。为了边缘部署，它进一步重用路由器选择信息丰富的在线样本，以实现高效的增量更新。实验表明，MosaicIMU在各项指标上始终优于基于学习的基线，平均绝对误差（ATE）和10秒内的相对误差（RTE-10s）分别降低了40%和34%。这些结果突显了MosaicIMU为可泛化和自适应的神经惯性里程计提供了一个可扩展的预训练到部署的范式。
cs.RO / 81 / 2606.09381
ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstration
ReGIL：基于检索的单一示范模仿学习
Abstract
Learning robot manipulation policies with deep neural networks from a single demonstration remains highly challenging, as even small deviations from the demonstrated trajectory can quickly compound into failure, while collecting substantial online interaction data is costly. We propose ReGIL, a retrieval-guided imitation learning framework that treats a single demonstration as an external memory. ReGIL repeatedly queries this static memory throughout training to simultaneously guide exploration, generate the regularization buffer, and construct rewards. Specifically, it computes rewards through local temporal alignment between the current trajectory and the retrieved segment, providing step-wise and informative feedback for policy improvement. We evaluate ReGIL on robotic manipulation tasks from the LIBERO and Meta-World benchmarks under the single demonstration setting. ReGIL outperforms prior baselines in both success rate and training efficiency. In real-robot experiments, using only one demonstration and less than one hour of online training, ReGIL achieves over 75% success rate across three manipulation tasks with randomness in both initial robot pose and target position. These results demonstrate that leveraging the single demonstration as reusable memory can provide more than static supervision for efficient robot learning. More details can be found on our website: https://regil2026.github.io/
Chinese Translation
从单一示范中学习机器人操控策略，利用深度神经网络仍然面临极大的挑战，因为即使是对示范轨迹的小偏差也可能迅速导致失败，而收集大量在线交互数据的成本很高。我们提出了ReGIL，一个基于检索的模仿学习框架，将单一示范视为外部记忆。ReGIL在训练过程中反复查询这一静态记忆，以同时指导探索、生成正则化缓冲区并构建奖励。具体而言，它通过当前轨迹与检索到的片段之间的局部时间对齐来计算奖励，为策略改进提供逐步和有针对性的反馈。我们在LIBERO和Meta-World基准下的机器人操控任务中评估了ReGIL，在单一示范设置下，ReGIL在成功率和训练效率上均优于先前的基线。在真实机器人实验中，仅使用一个示范和不到一小时的在线训练，ReGIL在三个操控任务中实现了超过75%的成功率，且初始机器人姿态和目标位置均存在随机性。这些结果表明，将单一示范作为可重用记忆的利用，可以为高效的机器人学习提供超过静态监督的支持。更多细节请访问我们的网站：https://regil2026.github.io/
cs.RO / 82 / 2606.09416
Harness Engineering for Physical AI: Robot Middleware Is the Harness Layer
物理人工智能的工程框架：机器人中间件是框架层
Abstract
Robot middleware faces a new role in the era of Physical AI. Learned policies, planners, and vision-language-action (VLA) models now enter deployed robots as causal participants on the control path, but the layer that integrates them with timing, scheduling, and network has not been named. Recent language-agent work names this layer the harness, the external system that mediates tools, manages state, bounds resources, and records execution. The robotics community has not yet adopted this framing, and we propose that robot middleware is that harness. A Physical AI harness differs from a software harness in where it intervenes. A software harness mediates at tool-call boundaries. A Physical AI harness must mediate at control, computing, and communication simultaneously, because a learned policy's output crosses all three: its commands shift the trajectory, its inference time shifts the schedule, and its payload shifts the bandwidth. Robot middleware is the lowest robot-stack layer with mediating abstractions over all three, so it is best positioned to compose their enforcement. It already provides most of what a harness needs but lacks the enforcement for an AI model. We name this missing enforcement as three functions: Projection gates each output at emission, Isolation bounds the model's execution and transmission slot, and Transfer falls back to a verified baseline when checks fail. Each appears today as hand-built application code in deployed robot systems, built on surfaces robot middleware already provides. Robot middleware should host them not as the best single-axis enforcer but as the layer that composes all three. We sketch this as a ROS 2 Harness Profile, a deployment artifact that carries an AI model's declared output region, inference budget, and operating regime while the middleware enforces them across ROS 2, DDS, and Zenoh.
Chinese Translation
在物理人工智能时代，机器人中间件面临着新的角色。学习到的策略、规划器和视觉-语言-动作（VLA）模型现在作为控制路径中的因果参与者进入部署的机器人，但将它们与时间、调度和网络集成的层尚未被命名。最近的语言-代理研究将这一层称为框架，它是调解工具、管理状态、限制资源和记录执行的外部系统。机器人社区尚未采用这种框架，我们提出机器人中间件就是这个框架。物理人工智能框架与软件框架的不同之处在于其干预的方式。软件框架在工具调用边界进行调解，而物理人工智能框架必须同时在控制、计算和通信上进行调解，因为学习到的策略的输出跨越了这三者：其命令改变了轨迹，其推理时间改变了调度，其有效载荷改变了带宽。机器人中间件是最低的机器人栈层，具有对这三者的调解抽象，因此它最适合于组合它们的执行。它已经提供了框架所需的大部分内容，但缺乏对人工智能模型的执行。我们将这一缺失的执行命名为三个功能：投影（Projection）在发射时限制每个输出，隔离（Isolation）限制模型的执行和传输时隙，转移（Transfer）在检查失败时回退到经过验证的基线。这些功能在当前的部署机器人系统中作为手工构建的应用代码出现，建立在机器人中间件已经提供的表面上。机器人中间件应将它们作为不是最佳单轴执行者的层来托管，而是作为组合这三者的层。我们将其勾勒为一个ROS 2框架配置文件（ROS 2 Harness Profile），这是一个部署工件，携带人工智能模型声明的输出区域、推理预算和操作模式，同时中间件在ROS 2、DDS和Zenoh中强制执行这些内容。
cs.RO / 83 / 2606.09451
Dense Force Estimation with an Event-based Optical Tactile Sensor
基于事件的光学触觉传感器的密集力估计
Abstract
Humans rely on spatially dense, geometry and force-aware tactile feedback at high temporal resolution for dexterous manipulation. While vision-based tactile sensors enable dense force estimation, they are limited by camera frame rates, motion blur, and data bandwidth. Event-based optical tactile sensors offer an attractive alternative with microsecond temporal resolution and low motion blur, but existing methods are restricted to predicting only net forces. We introduce the first framework for dense 3D force field reconstruction using event-based optical tactile sensors. Our approach estimates 3D surface displacements from event data and maps them to forces via the inverse Finite Elements Method (iFEM). Shear displacements are recovered through the proposed event-based marker tracking algorithm, while normal displacements are predicted by a convolutional neural network trained on a collected dataset of synchronized force-displacement-event data. Experiments demonstrate accurate reconstruction of physically grounded forces, achieving a mean absolute error of (0.14 N, 0.10 N, 0.93 N) over force ranges up to (4 N, 4 N, 20 N), while operating at an average of 100 Hz. This work constitutes a first step toward enabling dense force feedback for high-frequency control in robotic grasping and dexterous manipulation.
Chinese Translation
人类在灵巧操作中依赖于空间密集、几何和力感知的触觉反馈，且具有高时间分辨率。虽然基于视觉的触觉传感器能够实现密集力估计，但受到相机帧率、运动模糊和数据带宽的限制。基于事件的光学触觉传感器提供了一个具有微秒级时间分辨率和低运动模糊的有吸引力的替代方案，但现有方法仅限于预测净力。我们提出了第一个使用基于事件的光学触觉传感器进行密集三维力场重建的框架。我们的方法从事件数据中估计三维表面位移，并通过逆有限元法（iFEM）将其映射到力上。通过所提出的基于事件的标记跟踪算法恢复剪切位移，而法向位移则通过在收集的同步力-位移-事件数据集上训练的卷积神经网络进行预测。实验表明，物理基础力的重建准确，力范围达到（4 N, 4 N, 20 N）时的平均绝对误差为（0.14 N, 0.10 N, 0.93 N），且以平均100 Hz的频率运行。这项工作是实现机器人抓取和灵巧操作中高频控制的密集力反馈的第一步。
cs.RO / 84 / 2606.09457
$\omega$-EVA: Envision, Verify, and Act with Latent Interactive World Models
$eta$-EVA：通过潜在交互世界模型进行设想、验证和行动
Abstract
Embodied policies typically map current observations directly to actions, leaving candidate-action consequences implicit. World models provide predictive supervision, representations, or external simulation, but rarely let a policy inspect the imagined consequence of its own proposal before acting. We introduce $\omega$-EVA, a latent interactive world model that realizes an Envision--Verify--Act loop for embodied action generation. Its three-stage framework learns action-conditioned latent dynamics, trains a language-conditioned flow policy on dynamics-aware visual representations, and feeds the policy's proposal back through the world model. A tri-branch refiner jointly reasons over the current state, proposal-conditioned future, and proposed action to produce the final action chunk. Because consequence reasoning remains in latent feature space, $\omega$-EVA avoids generating future videos at inference. Evaluations across diverse single-arm, bimanual, long-horizon, and perturbed simulation settings show that the complete interaction pipeline consistently improves the proposal policy, while latent diagnostics indicate meaningful action-conditioned future structure. With approximately 1.2B parameters and no additional robot-data pretraining, $\omega$-EVA demonstrates a compact and competitive performance--scale--data trade-off, making the world model an active action-feedback module rather than a passive predictor.
Chinese Translation
具身策略通常将当前观察直接映射到动作上，而将候选动作的后果隐含在内。世界模型提供预测性监督、表征或外部仿真，但很少让策略在行动之前检查其自身提议的想象后果。我们引入了$eta$-EVA，一种潜在交互世界模型，实现了具身动作生成的设想-验证-行动循环。其三阶段框架学习基于动作的潜在动态，在动态感知的视觉表征上训练语言条件流策略，并将策略的提议反馈到世界模型中。一个三分支的精炼器共同推理当前状态、基于提议的未来和提议的动作，以生成最终的动作片段。由于后果推理仍然处于潜在特征空间，$eta$-EVA在推理时避免生成未来视频。在多样的单臂、双手、长时间和扰动仿真设置下的评估表明，完整的交互管道始终改善提议策略，而潜在诊断表明有意义的基于动作的未来结构。$eta$-EVA具有约12亿个参数且没有额外的机器人数据预训练，展示了紧凑且具有竞争力的性能-规模-数据权衡，使得世界模型成为一个主动的动作反馈模块，而不是被动的预测器。
cs.RO / 85 / 2606.09476
Goal Sets, Not Goal States: Queryable Robot Goals through Goal-Set Hindsight Relabeling
目标集合，而非目标状态：通过目标集合回溯重标定实现可查询的机器人目标
Abstract
Hindsight relabeling usually turns achieved future states into exact goals, which can overconstrain offline robot learning when task success depends only on a subset of the state. We propose Goal-Set Hindsight Relabeling (GS-HER), a predicate-level generalization of HER in which achieved states certify query-defined goal sets rather than singleton goal states. A binary query specifies which variables define success, making the goal predicate an inference-time input while leaving the underlying offline GCRL algorithm unchanged. Across OGBench tasks and five offline goal-conditioned learners, GS-HER improves performance when full-state goals are bottlenecked by nuisance dimensions and turns hindsight relabeling into a reusable goal interface: one checkpoint can answer multiple robot goal predicates without retraining.
Chinese Translation
回溯重标定通常将已实现的未来状态转化为精确的目标，这在任务成功仅依赖于状态的子集时可能会对离线机器人学习造成过度约束。我们提出了目标集合回溯重标定（Goal-Set Hindsight Relabeling, GS-HER），这是对HER的谓词级别推广，其中已实现的状态认证查询定义的目标集合，而非单一目标状态。一个二元查询指定哪些变量定义成功，使得目标谓词成为推理时的输入，同时保持底层离线GCRL算法不变。在OGBench任务和五个离线目标条件学习者中，当全状态目标受到干扰维度的瓶颈时，GS-HER提高了性能，并将回溯重标定转变为可重用的目标接口：一个检查点可以在不重新训练的情况下回答多个机器人目标谓词。
cs.RO / 86 / 2606.09499
Targeting World Models to Compromise Robot Learning Pipelines
针对世界模型的攻击以破坏机器人学习管道
Abstract
World models have recently seen a rapid growth in both their popularity and capability as more data efficient tools for generating robot training data or simulating real world environments, with many works proposing their integration into the robot learning pipeline. While highly practical, in this work we demonstrate that world models introduce a uniquely stealthy and effective data poisoning entry point into the robot learning supply chain that can result in the deployment of unsafe or otherwise compromised robotic policies despite training on seemingly safe ground truth training data. In contrast to traditional data poisoning techniques which directly implant dangerous trajectories into sold or uploaded datasets, our novel attack methods inject malicious prompts or compromising transition dynamics into visibly safe teleoperated datasets which are only activated once fed through a world model as input. This can result in the generation of synthetic, dangerous robot training trajectories and subsequently unsafe or compromised robot policies. We demonstrate the effectiveness of our attacks against both state of the art action conditioned and text conditioned world models, showing a full end-to-end backdoor on a downstream DRL policy and a proof-of-concept for the VLA setting. Overall these findings necessitate research into more secure world models and reevaluating their position within the robot learning supply chain.
Chinese Translation
近年来，世界模型因其作为生成机器人训练数据或模拟现实环境的数据高效工具而迅速增长，许多研究提议将其整合进机器人学习管道。尽管这种方法非常实用，但在本研究中，我们展示了世界模型引入了一种独特的隐蔽且有效的数据中毒入口，可能导致在训练看似安全的真实训练数据的情况下，部署不安全或其他受损的机器人策略。与传统的数据中毒技术直接将危险轨迹植入销售或上传的数据集不同，我们的新型攻击方法将恶意提示或妨碍过渡动态注入到明显安全的遥控数据集中，这些数据集仅在通过世界模型作为输入时才会被激活。这可能导致生成合成的危险机器人训练轨迹，从而产生不安全或受损的机器人策略。我们展示了我们的攻击对最先进的动作条件和文本条件世界模型的有效性，展示了对下游深度强化学习（DRL）策略的完整端到端后门，以及在变换学习架构（VLA）设置下的概念验证。总体而言，这些发现需要对更安全的世界模型进行研究，并重新评估它们在机器人学习供应链中的位置。
cs.RO / 87 / 2606.09569
Efficient Minimal Solvers for Relative Pose Estimation in Autonomous Driving Applications
用于自主驾驶应用的高效最小求解器在相对姿态估计中的应用
Abstract
With the advancement of visual sensing systems, computer vision is playing an increasingly important role in autonomous driving and robot navigation. Relative pose estimation in multi-camera systems is essential for accurate vehicle localization and environment perception, demanding high real-time performance and robustness. Existing methods, however, often involve high computational costs and rely heavily on abundant feature matches, limiting their applicability in time-sensitive driving scenarios. To address these limitations, this paper introduces a unified framework for efficient relative pose estimation, built upon a novel translation parameterization and first-order rotation approximation. Within this framework, we propose three efficient minimal solvers specifically designed for autonomous vehicles. The first solver integrates the vertical direction prior from Inertial Measurement Units (IMUs), the second utilizes the rotation axis direction prior during steering maneuvers, and the third is designed for planar motion - a realistic assumption for ground vehicles operating on structured roads. By reducing both the minimal number of point correspondences and the algebraic complexity, our methods enable faster hypothesis generation within RANSAC-based pipelines, improving suitability for real-time systems. Extensive experiments on synthetic datasets and the KITTI autonomous driving benchmark demonstrate that the proposed solvers achieve a favorable balance between speed and accuracy compared to existing state-of-the-art algorithms.
Chinese Translation
随着视觉传感系统的进步，计算机视觉在自主驾驶和机器人导航中扮演着越来越重要的角色。在多摄像头系统中，相对姿态估计对于准确的车辆定位和环境感知至关重要，这要求具备高实时性能和鲁棒性。然而，现有方法通常涉及高计算成本，并且在很大程度上依赖于丰富的特征匹配，这限制了它们在时间敏感的驾驶场景中的适用性。为了解决这些限制，本文提出了一种统一的高效相对姿态估计框架，该框架基于一种新颖的平移参数化和一阶旋转近似。在此框架内，我们提出了三种专为自主车辆设计的高效最小求解器。第一个求解器整合了来自惯性测量单元（IMUs）的垂直方向先验，第二个利用了在转向操作期间的旋转轴方向先验，第三个则针对平面运动而设计——这是对在结构化道路上行驶的地面车辆的现实假设。通过减少点对应的最小数量和代数复杂性，我们的方法能够在基于RANSAC的管道中更快地生成假设，提高了实时系统的适用性。在合成数据集和KITTI自主驾驶基准上的大量实验表明，所提出的求解器在速度和准确性之间达到了良好的平衡，相较于现有的最先进算法表现优越。
cs.RO / 88 / 2606.09572
CT-VAM: A Cerebello-Thalamic-Inspired Vision-Action Model for Efficient Visuomotor Control
CT-VAM：一种基于小脑-丘脑的视觉-动作模型，用于高效的视觉运动控制
Abstract
Vision-language-action models have shown strong promise for robot manipulation, yet raw language is primarily needed to specify task intent rather than to be repeatedly processed during high-frequency low-level execution. Motivated by this separation, we propose a cerebello-thalamic-inspired vision-action model (CT-VAM) for efficient task-conditioned visuomotor control. CT-VAM acts as a compact local execution policy that predicts action chunks from dualview visual observations, proprioception, and a lightweight task condition, potentially enabling a practical cloud-edge paradigm in which high-level semantic reasoning can be handled by large models while fast closed-loop control runs on local hardware. To fuse heterogeneous inputs effectively, CT-VAM introduces TARS (Thalamic Action Routing Stream), a stream-separated conditional attention decoder that independently routes action, visual and task streams, preventing dense sensory tokens from overwhelming compact task-relevant conditions. With only 68M parameters, CT-VAM achieves LIBERO success rates competitive with substantially larger VLA models, while reducing inference latency. Together with flow-consistent inpainting for asynchronous chunk execution, CT-VAM supports high-frequency control and demonstrates robust realworld deployment on resource-constrained robotic platforms.
Chinese Translation
视觉-语言-动作模型在机器人操作中展现出了强大的潜力，但原始语言主要用于指定任务意图，而不是在高频低级执行过程中反复处理。基于这一分离，我们提出了一种基于小脑-丘脑的视觉-动作模型（CT-VAM），用于高效的任务条件视觉运动控制。CT-VAM 作为一种紧凑的局部执行策略，从双视角视觉观察、自我感知和轻量级任务条件中预测动作块，潜在地实现了一种实用的云-边缘范式，其中高层次的语义推理可以由大型模型处理，而快速的闭环控制则在本地硬件上运行。为了有效融合异构输入，CT-VAM 引入了 TARS（丘脑动作路由流），这是一种流分离的条件注意解码器，独立路由动作、视觉和任务流，防止密集的感官标记淹没紧凑的任务相关条件。CT-VAM 仅用 6800 万个参数，就达到了与大规模 VLA 模型相竞争的 LIBERO 成功率，同时减少了推理延迟。结合流一致的图像修复技术以支持异步块执行，CT-VAM 支持高频控制，并在资源受限的机器人平台上展示了强大的实际部署能力。
cs.RO / 89 / 2606.09610
Shape Formation for the Cooperative Transportation of Arbitrary Objects Using Multi-Agent Reinforcement Learning
基于多智能体强化学习的任意物体协同运输形状形成
Abstract
Cooperative object transportation is essential in numerous domains, including industrial to domestic services. A popular transportation strategy is to carry objects on top of multi-robot systems. The corresponding task is typically solved by decomposing it into three interconnected subproblems: formation control, cooperative navigation, and collision avoidance. A particular challenge posed by real-world objects is their potentially arbitrary shape and non-uniform mass distribution, necessitating robot formations that securely support the object. In this work, we address the challenge of pattern formation control for transporting such real-world objects by proposing a novel multi-agent reinforcement learning approach. Our approach enables a multi-robot system to autonomously position itself underneath an object to support its weight while avoiding obstacles during the formation process. Our evaluations with diverse environments and varying numbers of robots show that our approach leads to policies that reliably produce balanced formations and generalize to cluttered scenes and objects with complex geometry and non-uniform mass distribution.
Chinese Translation
协同物体运输在多个领域中至关重要，包括工业和家庭服务。一个流行的运输策略是通过多机器人系统在物体上方进行搬运。相应的任务通常通过将其分解为三个相互关联的子问题来解决：形状控制、协同导航和避碰。现实世界物体带来的一个特定挑战是它们可能具有任意形状和不均匀的质量分布，这需要机器人形成能够安全支撑物体的结构。在本研究中，我们通过提出一种新颖的多智能体强化学习方法，解决了运输此类现实世界物体的形状形成控制挑战。我们的方法使多机器人系统能够自主定位在物体下方以支撑其重量，同时在形成过程中避免障碍物。我们在多样化环境和不同数量机器人下的评估表明，我们的方法能够产生可靠的平衡形状，并且能够推广到杂乱场景和具有复杂几何形状及不均匀质量分布的物体。
cs.RO / 90 / 2606.09615
DexPIE: Stable Dexterous Policy Improvement from Real-World Experience
DexPIE：基于真实世界经验的稳定灵巧策略改进
Abstract
Dexterous manipulation presents substantial challenges for imitation learning due to its high-dimensional action space and complex contact-rich dynamics. Policies trained purely from demonstrations often suffer from compounding errors during deployment and require large amounts of expert data to achieve reliable performance. To move beyond the limitations of demonstration data, in this work, we propose DexPIE, a post-training framework for dexterous policy improvement from experience collected through real-world deployment. First, DexPIE enables effective exploration coverage through a dexterous-hand-adapted intervention system and multi-stage DAgger-style data collection across initial and intermediate task stages, providing reliable supervision for accurate policy evaluation. To reduce temporal noise between post-training rollouts and demonstration data, we introduce asynchronous inference in the relative action space, which better aligns rollout data with demonstrated behavior and allows the critic to learn a value function induced by a more consistent underlying policy. Finally, DexPIE improves the policy through conditioning on a continuous optimality indicator, allowing the policy to leverage the quality of data in a more fine-grained manner. Across three challenging real-world dexterous manipulation tasks, DexPIE achieves a 37% improvement in success rate over the demonstration-based reference policy, outperforming all baseline methods and demonstrating stronger robustness. The source code and dataset will be made publicly available.
Chinese Translation
灵巧操作由于其高维动作空间和复杂的接触丰富动态，为模仿学习带来了重大挑战。仅通过示范训练的策略在部署过程中往往会遭遇累积错误，并且需要大量专家数据以实现可靠的性能。为了超越示范数据的局限性，本文提出了DexPIE，一个基于真实世界部署经验的灵巧策略后训练改进框架。首先，DexPIE通过一个适应灵巧手的干预系统和多阶段DAgger风格的数据收集，促进了有效的探索覆盖，提供了可靠的监督以进行准确的策略评估。为了减少后训练回放与示范数据之间的时间噪声，我们在相对动作空间中引入了异步推理，这更好地将回放数据与示范行为对齐，并允许评论者学习由更一致的基础策略诱导的价值函数。最后，DexPIE通过对连续最优性指标的条件化来改进策略，使策略能够以更细致的方式利用数据质量。在三个具有挑战性的真实世界灵巧操作任务中，DexPIE在成功率上比基于示范的参考策略提高了37%，超越了所有基线方法，并展示了更强的鲁棒性。源代码和数据集将公开发布。
cs.RO / 91 / 2606.09620
Motion planning for hundreds of floating robots
数百个漂浮机器人运动规划
Abstract
Planning collision-free motion for large robot fleets is difficult because collision avoidance induces strong inter-agent coupling that grows rapidly with team size. We consider omnidirectional floating robots on water, where choreographies are specified by sparse keyframes and an interactive tool must generate trajectories within seconds, even when transitions span minutes and thousands of time steps. We propose a scalable pipeline that builds a collision graph from an initialization, decomposes the coupled problem into interaction clusters, and solves clusters independently (and in parallel) with robustness mechanisms for common decomposition pathologies. We validate the approach in simulations up to 500 robots. The synthesized trajectories have also been deployed in two real-world demonstrations, on Lake Z\"urich with a fleet of 24 Way of Water crafts and at the Time Space Existence 2025 Venice Biennale.
Chinese Translation
为大型机器人队伍规划无碰撞运动是困难的，因为碰撞避免引发的强烈代理间耦合随着团队规模的增加而迅速增长。我们考虑在水面上的全向漂浮机器人，其中编排由稀疏关键帧指定，并且一个交互工具必须在几秒钟内生成轨迹，即使过渡时间长达数分钟且涉及数千个时间步。我们提出了一种可扩展的流程，从初始化构建碰撞图，将耦合问题分解为交互集群，并独立（并行）解决这些集群，同时针对常见分解病态提供鲁棒性机制。我们在最多500个机器人的仿真中验证了该方法。合成的轨迹还在两个现实世界的演示中得到了应用，一是在苏黎世湖上使用24艘“水之道”船队，二是在2025年威尼斯双年展的“时间空间存在”展览中。
cs.RO / 92 / 2606.09630
ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies
ReCoVLA：基于视觉语言模型的奖励编译用于视觉-语言-动作策略中的故障恢复
Abstract
Vision-language-action (VLA) policies provide strong priors for language-conditioned manipulation, but remain brittle in off-nominal states requiring targeted recovery. We propose ReCoVLA -- a failure-conditioned residual recovery framework that keeps a pretrained VLA policy frozen, uses an external vision-language model (VLM) to infer the failure mode and recovery stage, and compiles a structured reward from task-relevant components. Rather than using the VLM to generate actions or rewards directly, ReCoVLA uses it as a semantic reward selector: it predicts a recovery descriptor and reward mask for in-simulation residual-policy training, followed by zero-shot sim-to-real deployment of the trained recovery policies. This decouples high-level failure understanding from low-level corrective control to support different VLAs. Experiments across short-horizon, long-horizon, and contact-rich manipulation tasks show that ReCoVLA outperforms the tested baselines on average. In simulation, our reward compiler improves average success from 36.7% for the fine-tuned $\pi_{0.5}$ baseline to 66.7%. In physical zero-shot sim-to-real experiments, ReCoVLA achieves the best average performance, with 61.7% success.
Chinese Translation
视觉-语言-动作（VLA）策略为语言条件下的操作提供了强有力的先验知识，但在需要针对性恢复的非正常状态下仍然脆弱。我们提出了ReCoVLA——一种基于故障条件的残差恢复框架，该框架保持预训练的VLA策略不变，利用外部视觉语言模型（VLM）推断故障模式和恢复阶段，并从与任务相关的组件中编制结构化奖励。ReCoVLA并不是直接使用VLM生成动作或奖励，而是将其作为语义奖励选择器：它预测恢复描述符和奖励掩码用于模拟中的残差策略训练，随后进行训练恢复策略的零-shot仿真到现实部署。这将高层次的故障理解与低层次的纠正控制解耦，以支持不同的VLA。在短时间范围、长时间范围和接触丰富的操作任务中的实验表明，ReCoVLA在平均上优于测试的基线。在仿真中，我们的奖励编译器将微调的$ ext{π}_{0.5}$基线的平均成功率从36.7%提高到66.7%。在物理零-shot仿真到现实实验中，ReCoVLA实现了最佳的平均表现，成功率为61.7%。
cs.RO / 93 / 2606.09640
Physics-Aware Sparse Learning and Selective Online Adaptation for Euler-Lagrange Robot Dynamics
物理感知的稀疏学习与选择性在线适应在欧拉-拉格朗日机器人动力学中的应用
Abstract
Accurate dynamics models are essential for model-based robotic control, yet nominal Euler--Lagrange models often become inaccurate in the presence of payload variation, unmodeled coupling, friction, aerodynamic effects, and changing operating conditions. Most learning-based correction methods improve prediction accuracy by introducing a single additive residual, but do not preserve the internal mechanical structure of Euler--Lagrange systems. This leads to models that do not preserve symmetry, positive-definiteness, or the coupling between inertia and velocity-dependent terms, which can result in physically inconsistent predictions and reduced reliability when embedded in model-based controllers. We propose a structure-preserving residual learning framework that decomposes model mismatch into an inertia correction, the corresponding induced Coriolis term, and a generalized-force residual. The mechanical component is learned under physical constraints, while the disturbance-sensitive component is represented through a sparse history-dependent latent interaction model and adapted online using Bayesian linear regression. This separation preserves key mechanical structure while restricting adaptation to the part of the dynamics most affected by changing conditions. Experiments across multiple robotic platforms, including mobile, aerial, and manipulator systems, show that the proposed method improves dynamics prediction and trajectory tracking under coupled and time-varying dynamics. These results highlight the value of combining structured residual modeling, compact latent interaction selection, and selective online adaptation for real-world model-based control.
Chinese Translation
准确的动力学模型对于基于模型的机器人控制至关重要，但在负载变化、未建模耦合、摩擦、气动效应和变化的操作条件下，名义上的欧拉-拉格朗日模型往往变得不准确。大多数基于学习的修正方法通过引入单一的附加残差来提高预测精度，但未能保持欧拉-拉格朗日系统的内部机械结构。这导致模型无法保持对称性、正定性或惯性与速度依赖项之间的耦合，从而可能导致物理上不一致的预测，并在嵌入基于模型的控制器时降低可靠性。我们提出了一种结构保持的残差学习框架，将模型不匹配分解为惯性修正、相应的诱导科里奥利项和广义力残差。机械部分在物理约束下进行学习，而对干扰敏感的部分则通过稀疏历史依赖的潜在交互模型表示，并使用贝叶斯线性回归进行在线适应。这种分离保留了关键的机械结构，同时将适应限制在最受变化条件影响的动力学部分。针对多个机器人平台，包括移动、空中和操纵系统的实验表明，所提出的方法在耦合和时间变化的动力学下改善了动力学预测和轨迹跟踪。这些结果突显了将结构化残差建模、紧凑的潜在交互选择和选择性在线适应结合用于现实世界基于模型控制的价值。
cs.RO / 94 / 2606.09645
Modeling Components and Connections in Cyber-Physical Systems
网络物理系统中的组件与连接建模
Abstract
Text based configuration files for cyber-physical systems show the hierarchy of component modules well but often hide the details of connections and interfaces between modules. A model-based visual approach to these configuration files can better capture this information. The XML structure of Robot Operating System (ROS) launch files can be improved using a modeling approach. This paper presents ROSLaunchVisual, a model-integrated environment built on WebGME for designing, visualizing, and managing ROS launch files. The tool raises the level of abstraction by allowing developers to create and modify launch files using a graphical interface that represents nodes, publishers, subscribers, and arguments as interconnected components. The tool provides a dynamic system analysis that can then be used in the static development and analysis of new and existing launch files. ROSLaunchVisual incorporates features such as metamodel-driven validation, automatic import/export of launch files, and visual communication mapping. Plugins further enhance functionality by updating libraries, checking for semantic errors, and managing remaps. By making launch file creation more intuitive and less error-prone, ROSLaunchVisual improves development efficiency and system understanding, especially in collaborative or large-scale robotics projects.
Chinese Translation
基于文本的网络物理系统配置文件很好地展示了组件模块的层次结构，但往往隐藏了模块之间连接和接口的细节。基于模型的可视化方法可以更好地捕捉这些信息。机器人操作系统（Robot Operating System, ROS）启动文件的XML结构可以通过建模方法进行改进。本文提出了ROSLaunchVisual，这是一个基于WebGME构建的模型集成环境，用于设计、可视化和管理ROS启动文件。该工具通过允许开发者使用图形界面创建和修改启动文件，将节点、发布者、订阅者和参数表示为相互连接的组件，从而提高了抽象层次。该工具提供了动态系统分析，随后可用于新旧启动文件的静态开发和分析。ROSLaunchVisual集成了元模型驱动的验证、启动文件的自动导入/导出以及可视化通信映射等功能。插件进一步增强了功能，通过更新库、检查语义错误和管理重映射来提升工具的实用性。通过使启动文件的创建更加直观且减少错误，ROSLaunchVisual提高了开发效率和系统理解，特别是在协作或大规模机器人项目中。
cs.RO / 95 / 2606.09719
Safe Polytope-in-Polytope Motion Planning and Control with Control Barrier Functions
基于控制屏障函数的安全多面体内多面体运动规划与控制
Abstract
Autonomous mobile robots operating in tight environments require motion planning frameworks that account for the physical footprint of the robot. Simplifying the geometry to a point or a circle is conservative and discards information needed to successfully and safely traverse narrow passages. This work proposes a safe local motion planning and control method that guarantees that a polytopic robot footprint stays inside a continuously updated convex free-space region. The containment condition is formulated as a set of discrete-time control barrier function constraints within a model predictive controller. The number of safety constraints depends on the complexity of the local free-space geometry and the robot shape, instead of the number of obstacles. The proposed free-space formulation does not need any obstacle detection or segmentation. A comparative analysis against a polytope-based obstacle avoidance formulation confirms favorable scaling up to a reduction of 91$\times$ in computation time as the number of obstacles increases. The approach is validated in simulation with an autonomous surface vehicle and on hardware with a non-holonomic mobile robot, using both occupancy grids and LiDAR sensing. The experiments demonstrate safe real-time motion planning and control at 10~Hz on an onboard embedded computer, including reactive avoidance of dynamic obstacles.
Chinese Translation
在狭小环境中运行的自主移动机器人需要考虑机器人物理轮廓的运动规划框架。将几何简化为点或圆形是保守的，并且丢弃了成功和安全穿越狭窄通道所需的信息。本研究提出了一种安全的局部运动规划和控制方法，确保多面体机器人轮廓保持在持续更新的凸自由空间区域内。包含条件被表述为一组离散时间控制屏障函数约束，嵌入模型预测控制器中。安全约束的数量取决于局部自由空间几何的复杂性和机器人形状，而不是障碍物的数量。所提出的自由空间表述不需要任何障碍物检测或分割。与基于多面体的障碍物避免表述的比较分析确认，随着障碍物数量的增加，计算时间最多可减少91倍。该方法在仿真中通过自主水面车辆进行验证，并在硬件上通过非完整移动机器人进行测试，使用占用网格和激光雷达传感器。实验表明，在嵌入式计算机上以10 Hz的频率实现安全的实时运动规划和控制，包括对动态障碍物的反应性避让。
cs.RO / 96 / 2606.09740
ProbeAct: Probe-Guided Training-Free Failure Recovery in Vision-Language-Action Models
ProbeAct：基于探针的无训练视觉-语言-动作模型故障恢复
Abstract
Vision-Language-Action (VLA) models demonstrate strong perfor-1 mance on language-conditioned robotic manipulation within their training dis-2 tribution, yet their generalization capabilities remain fundamentally limited. They3 lack the robustness required to handle perturbations, frequently failing when con-4 fronted with lighting changes, altered camera viewpoints, or small initial-state5 variations. We propose PROBEACT, a training-free runtime intervention frame-6 work that detects and recovers from grasping and placement failures in pre-7 trained VLA policies without modifying their weights or requiring additional8 demonstrations. PROBEACT combines three components: (i) a lightweight multi-9 target hidden-state probe that predicts the 3D positions of task-relevant objects10 from intermediate VLA features, with Hungarian-matched identity tracking for11 multi-object scenes; (ii) an object-agnostic kinematic state machine that detects12 grasp, transport, and placement failures using only gripper-internal signals and13 end-effector kinematics; and (iii) a hierarchical Control Barrier Function (CBF)14 filter that encodes repeated-failure locations as soft safe-set constraints, mini-15 mally correcting VLA actions while preserving baseline behavior. As a plug-and-16 play, training-free intervention loop, PROBEACT is orthogonal to existing train-17 ing pipelines. Evaluated on the LIBERO-plus benchmark, our framework acts as18 a universal safety net, improving the success rate of the OpenVLA-OFT model19 from 69.6% to 74.1%, while demonstrating broad applicability to both base and20 fine-tuned VLA policies.
Chinese Translation
视觉-语言-动作（VLA）模型在其训练分布内的语言条件下的机器人操作中表现出强大的性能，但其泛化能力仍然存在根本性的限制。它们缺乏处理扰动所需的鲁棒性，常常在面对光照变化、相机视角变化或小的初始状态变化时失败。我们提出了PROBEACT，一个无训练的运行时干预框架，能够在不修改预训练VLA策略的权重或不需要额外演示的情况下，检测并恢复抓取和放置失败。PROBEACT结合了三个组件：（i）一个轻量级的多目标隐状态探针，从中间VLA特征中预测与任务相关的物体的3D位置，并在多物体场景中使用匈牙利匹配的身份跟踪；（ii）一个与物体无关的运动状态机，仅使用夹持器内部信号和末端执行器运动学来检测抓取、运输和放置失败；（iii）一个分层控制障碍函数（CBF）过滤器，将重复失败的位置编码为软安全集约束，最小限度地修正VLA动作，同时保持基线行为。作为一个即插即用的无训练干预循环，PROBEACT与现有的训练管道是正交的。在LIBERO-plus基准测试中评估，我们的框架充当了一个通用安全网，将OpenVLA-OFT模型的成功率从69.6%提高到74.1%，同时展示了对基础和微调VLA策略的广泛适用性。
cs.RO / 97 / 2606.09749
Your Model Already Knows: Attention-Guided Safety Filter for Vision-Language-Action Models
您的模型已经知道：基于注意力引导的视觉-语言-动作模型安全过滤器
Abstract
Vision-Language-Action (VLA) models have demonstrated impressive end-to-end performance across a variety of robotic manipulation tasks. However, these policies offer no guarantees against collisions with task-irrelevant objects in the scene. Existing safety filters sidestep this problem by querying a vision-language model (VLM) to identify obstacles and their locations. This, however, is too slow to run in the control loop and can only be invoked at episode initialization, leaving the filter unable to track moving obstacles. We discover that a small number of attention heads within a VLA model reliably localize the object the policy intends to approach. These heads can be exploited within a training-free safety framework that obtains the active target from the attention heads at every step, treats the remainder of the scene as obstacles, and feeds these into a Control Barrier Function (CBF) filter. Together with a lightweight real-time object tracker, this allows for collision avoidance for non-static obstacles. We evaluate our framework on SafeLIBERO, which we extend with moving obstacles. On the original static benchmark, our method performs comparably to an oracle that uses privileged simulator state to identify the target, emulating a VLM-based identification step run once at episode initialization. On the dynamic variant, where the oracle's init-time target assignment becomes stale, our method substantially outperforms it by 43%, on average. Our findings suggest that the perceptual signals needed for real-time safety filtering are already present within VLA policies and can be exploited without additional training or heavy auxiliary models.
Chinese Translation
视觉-语言-动作（VLA）模型在各种机器人操作任务中展现了令人印象深刻的端到端性能。然而，这些策略并未对场景中与任务无关的物体碰撞提供任何保障。现有的安全过滤器通过查询视觉-语言模型（VLM）来识别障碍物及其位置，从而规避了这一问题。然而，这种方法在控制循环中运行过于缓慢，仅能在情节初始化时调用，无法跟踪移动障碍物。我们发现，VLA模型中的少数注意力头能够可靠地定位策略意图接近的物体。这些注意力头可以在无训练的安全框架中加以利用，该框架在每一步从注意力头获取活动目标，将场景的其余部分视为障碍物，并将其输入控制障碍函数（CBF）过滤器。结合轻量级实时物体跟踪器，这使得对非静态障碍物的碰撞避免成为可能。我们在SafeLIBERO上评估了我们的框架，并扩展了移动障碍物。在原始静态基准上，我们的方法与使用特权模拟器状态识别目标的神谕模型表现相当，模拟了一次在情节初始化时运行的基于VLM的识别步骤。在动态变体中，由于神谕模型的初始化时目标分配变得过时，我们的方法平均超越了它43%。我们的研究结果表明，实时安全过滤所需的感知信号已经存在于VLA策略中，并且可以在不进行额外训练或使用重型辅助模型的情况下加以利用。
cs.RO / 98 / 2606.09758
Difference-Aware Retrieval Policies for Imitation Learning
关注差异的模仿学习检索策略
Abstract
Parametric imitation learning via behavior cloning can suffer from poor generalization to out-of-distribution states due to compounding errors during deployment. We show that reusing the training data during inference via a semi-parametric retrieval-based imitation learning approach can alleviate this challenge. We present Difference-Aware Retrieval Policies for Imitation Learning (DARP), a semi-parametric retrieval-based imitation learning approach that addresses this limitation by reparameterizing the imitation learning problem in terms of local neighborhood structure rather than direct state-to-action mappings. Instead of learning a global policy, DARP trains a model to predict actions based on $k$-nearest neighbors from expert demonstrations, their corresponding actions, and the relative distance vectors between neighbor states and query states. DARP requires no additional assumptions beyond those made for standard behavior cloning -- it does not require additional data collection, online expert feedback, or task-specific knowledge. We demonstrate consistent performance improvements of 15-46% over standard behavior cloning across diverse domains, including continuous control and robotic manipulation, and across different representations, including high-dimensional visual features. Code and demos are available at https://weirdlabuw.github.io/darp-site/.
Chinese Translation
通过行为克隆的参数化模仿学习在部署过程中可能因累积误差而导致对分布外状态的泛化能力较差。我们展示了通过一种半参数化的基于检索的模仿学习方法在推理过程中重用训练数据可以缓解这一挑战。我们提出了关注差异的模仿学习检索策略（Difference-Aware Retrieval Policies for Imitation Learning, DARP），这是一种半参数化的基于检索的模仿学习方法，通过将模仿学习问题重新参数化为局部邻域结构，而不是直接的状态到动作映射，从而解决了这一局限性。DARP不是学习一个全局策略，而是训练一个模型，根据来自专家演示的$k$-最近邻、相应的动作以及邻居状态与查询状态之间的相对距离向量来预测动作。DARP不需要超出标准行为克隆所做的额外假设——它不需要额外的数据收集、在线专家反馈或任务特定知识。我们在多个领域（包括连续控制和机器人操作）以及不同的表示（包括高维视觉特征）中展示了DARP在标准行为克隆基础上持续提升15-46%的性能。代码和演示可在 https://weirdlabuw.github.io/darp-site/ 获取。
cs.RO / 99 / 2606.09777
AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning
AetheRock：一种用于力导向视觉-触觉学习的臂戴式机器人教学系统
Abstract
Force and tactile sensing are indispensable in contact-rich manipulation. However, force-aware robot learning faces critical challenges due to the incompatible assembly of tactile and force sensors in handheld or wearable devices. To address these limitations, we first introduce AetheRock for gripper-force, vision, and tactile data collection, which is an arm-worn device featuring a modular and easily manufactured visuo-tactile sensor, GelSlim-MiniFab, at the fingertip, a resistive pressure sensor at the human finger contact region, a customized PCB module, and a wearable kit for comfortable and robust collection. Building on this, we propose ForceVT, a representation learning framework that uses force and vision to guide fidelity-agnostic tactile learning, enabling robust inference in any tactile situation. Real-world experiments show that AetheRock achieves qualified data efficiency and that ForceVT effectively alleviates inefficiencies when visuo-tactile sensors exhibit manufacturing and utilization inconsistencies. Overall, our work mitigates the limitations of gripper-force vision-tactile robot learning through innovative hardware design and algorithms.
Chinese Translation
在接触丰富的操作中，力和触觉感知是不可或缺的。然而，由于手持或可穿戴设备中触觉传感器和力传感器的组装不兼容，力感知机器人学习面临着重大挑战。为了解决这些局限性，我们首先介绍了AetheRock，这是一种用于抓手力、视觉和触觉数据采集的臂戴式设备，配备了一个模块化且易于制造的视觉-触觉传感器GelSlim-MiniFab，位于指尖处，此外在与人手接触的区域设置了一个电阻式压力传感器，配有定制的PCB模块和舒适稳固的可穿戴套件，以便于数据的采集。在此基础上，我们提出了ForceVT，一个利用力和视觉引导无关保真度的触觉学习的表征学习框架，从而在任何触觉情境中实现稳健的推理。实际实验表明，AetheRock实现了合格的数据效率，而ForceVT有效缓解了在视觉-触觉传感器制造和使用不一致时的低效问题。总体而言，我们的工作通过创新的硬件设计和算法减轻了抓手力视觉-触觉机器人学习的局限性。
cs.RO / 100 / 2606.09798
SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps
SynManDex：从合成的人类预抓取中合成类人灵巧抓取
Abstract
Human hand-object interactions encode functional intent, but direct transfer to robotic hands often fails under morphology, contact, and reachability constraints. We present SynManDex, a synthetic pipeline that uses generated human pre-grasps as affordance-aware proposals and resolves the final contacts with robot-native optimization. SynManDex samples object-conditioned digital human pre-grasps, retargets them to dexterous robotic hand poses, optimizes force-closure contacts on the target embodiment, and admits trajectories that pass checks from each step. The resulting keyframes support both grasp-and-lift demonstrations and various prehensile manipulation tasks such as tea pouring, photo taking, and flute playing, designed via VLM agents. As a result, SynManDex combines high grasp quality (86.4\% grasp stability) with 4.67/5 human-likeness (93.4\%). It achieves 80.7\% successes in simulation and 25/30 (83.3\%) real-robot successes when applied to a 36-DOF bimanual dexterous robotic platform.
Chinese Translation
人类手部与物体的交互编码了功能意图，但在形态、接触和可达性约束下，直接转移到机器人手上往往失败。我们提出了SynManDex，这是一种合成管道，利用生成的人类预抓取作为具备可供性意识的提案，并通过机器人原生优化解决最终接触。SynManDex对物体条件下的数字人类预抓取进行采样，将其重新定向到灵巧机器人手的姿态，优化目标实体上的力闭合接触，并允许通过每一步检查的轨迹。生成的关键帧支持抓取与提升演示以及各种抓取操作任务，如倒茶、拍照和吹奏长笛，这些任务是通过VLM代理设计的。因此，SynManDex将高抓取质量（86.4%的抓取稳定性）与4.67/5的人类相似度（93.4%）结合在一起。在模拟中实现了80.7%的成功率，并在应用于一个36自由度的双手灵巧机器人平台时取得了25/30（83.3%）的真实机器人成功率。
cs.RO / 101 / 2606.09811
AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
AHA-WAM：基于观察引导的上下文路由的异步视野自适应世界动作建模
Abstract
World-action models have emerged as a promising paradigm for robot manipulation, jointly modeling visual scene dynamics and actions to inject physical priors into policy learning. However, existing world-action models couple world prediction and action execution at the same temporal resolution, forcing the world branch to model near-term frame variations that are redundant and weakly informative. We posit that strictly binding world prediction and action execution to the same temporal rhythm may underutilize the potential of the video branch for embodied control. Therefore, we propose AHA-WAM, an Asynchronous Horizon-Adaptive World-Action Model built on a dual Diffusion Transformer (DiT) architecture that reorganizes world-action modeling around this temporal asymmetry. AHA-WAM instantiates the video DiT as a low-frequency world planner that maintains rolling key-value memory over past observations and exposes reusable layerwise latent context encoding long-horizon scene evolution, while a high-frequency action DiT executes short action chunks in closed loop by querying this context through layerwise joint attention. To support asynchronous execution, we introduce horizon-adaptive offset training and Observation-Guided Video-Context Routing (OVCR), which together let the action expert exploit long-horizon world context while remaining responsive to real-time execution state without rerunning the video DiT. Experiments on RoboTwin and real-world manipulation tasks show that AHA-WAM achieves state-of-the-art performance without any robot-data pretraining, attaining 92.80% average success on RoboTwin and 78.3% success across 4 real-world tasks, while reaching 24.17 Hz closed-loop control with a 4.59x speedup over Fast-WAM.
Chinese Translation
世界动作模型作为机器人操作的一个有前景的范式，已逐渐受到关注，它将视觉场景动态与动作联合建模，以将物理先验知识注入策略学习。然而，现有的世界动作模型将世界预测和动作执行绑定在相同的时间分辨率上，迫使世界分支建模近端帧变化，这些变化往往是冗余且信息量不足的。我们认为，严格将世界预测和动作执行绑定在相同的时间节奏上可能会低估视频分支在具身控制中的潜力。因此，我们提出了AHA-WAM，一种基于双扩散变换器（Dual Diffusion Transformer, DiT）架构构建的异步视野自适应世界动作模型，该模型围绕这种时间不对称性重新组织世界动作建模。AHA-WAM将视频DiT实例化为低频世界规划器，维护对过去观察的滚动键值记忆，并暴露可重用的逐层潜在上下文编码以支持长时间场景演变，而高频动作DiT通过逐层联合注意力查询该上下文，在闭环中执行短动作片段。为了支持异步执行，我们引入了视野自适应偏移训练和观察引导的视频上下文路由（Observation-Guided Video-Context Routing, OVCR），这两者共同使得动作专家能够利用长视野世界上下文，同时对实时执行状态保持响应，而无需重新运行视频DiT。在RoboTwin和真实世界操作任务上的实验表明，AHA-WAM在没有任何机器人数据预训练的情况下实现了最先进的性能，在RoboTwin上达到92.80%的平均成功率，并在4个真实世界任务中取得78.3%的成功率，同时实现了24.17 Hz的闭环控制，相较于Fast-WAM实现了4.59倍的加速。
cs.RO / 102 / 2606.09813
iMaC: Translating Actions into Motion and Contact Images for Embodied World Models
iMaC：将动作转化为运动和接触图像的具身世界模型
Abstract
Embodied world models have emerged as a pivotal paradigm for visual robotic decision-making and interactive environment simulation. However, conventional embodied frameworks rely on low-dimensional structured action vectors (e.g., joint angles and end-effector poses), which suffer from limited expressive capacity, poor generalization across diverse embodiments, and unnatural dynamic modeling for complex physical interactions. To address these limitations, this paper proposesiMac (Image as Action Control), a novel unified control paradigm that treats raw visual images as native action representations for embodied world models. Departing from traditional explicit kinematic action encoding, iMac formulates continuous visual manipulation as image-based action tokens, which inherently encapsulate spatial motion intentions, interactive geometric constraints and subtle physical dynamics. We construct a dual-branch embodied architecture consisting of an image-action encoder and a dynamic world predictor: the encoder compresses target-driven visual images into compact action embeddings, while the predictor learns environment transition rules conditioned on image actions to achieve high-fidelity future state prediction and closed-loop embodied control. Extensive experiments are conducted on public embodied manipulation benchmarks and real-world robotic scenarios. The results demonstrate that iMac outperforms vector-based action control baselines in prediction accuracy, task success rate and cross-scene generalization ability. Moreover, our image-action design eliminates the reliance on manually defined action spaces, realizing flexible and universal control for heterogeneous embodied agents. This work provides an innovative visual-action perspective for embodied world models, offering a simple yet effective paradigm for scalable robotic perception and manipulation.
Chinese Translation
具身世界模型已成为视觉机器人决策和互动环境模拟的关键范式。然而，传统的具身框架依赖于低维结构化动作向量（例如，关节角度和末端执行器姿态），这些向量在表达能力、跨多样化具身体的泛化能力以及复杂物理交互的动态建模方面存在局限。为了解决这些问题，本文提出了iMac（Image as Action Control），一种新颖的统一控制范式，将原始视觉图像视为具身世界模型的本地动作表示。与传统的显式运动学动作编码不同，iMac将连续视觉操作形式化为基于图像的动作标记，这些标记本质上封装了空间运动意图、交互几何约束和微妙的物理动态。我们构建了一个双分支的具身架构，包括图像-动作编码器和动态世界预测器：编码器将目标驱动的视觉图像压缩为紧凑的动作嵌入，而预测器则学习基于图像动作的环境转移规则，以实现高保真度的未来状态预测和闭环具身控制。在公共具身操作基准和真实世界机器人场景上进行了广泛的实验。结果表明，iMac在预测准确性、任务成功率和跨场景泛化能力方面优于基于向量的动作控制基线。此外，我们的图像-动作设计消除了对手动定义动作空间的依赖，实现了对异构具身体的灵活和通用控制。本研究为具身世界模型提供了一种创新的视觉-动作视角，为可扩展的机器人感知和操作提供了一种简单而有效的范式。
cs.RO / 103 / 2606.09827
MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models
MemoryVLA++：通过记忆和想象在视觉-语言-行动模型中的时间建模
Abstract
Temporal modeling is essential for robotic manipulation, as effective control requires both memory of past interactions and imagination of future states. However, most VLA models rely primarily on the current observation and therefore struggle with long-horizon, temporally dependent tasks. Cognitive science suggests that humans rely on working memory to buffer short-lived context, the hippocampal system to preserve episodic memory of past experience, and internal models to imagine possible future state evolution. Inspired by these mechanisms, we propose MemoryVLA++, a full temporal modeling framework that equips VLA models with memory and imagination for robotic manipulation. A pretrained VLM encodes the current observation into perceptual and cognitive tokens, forming working memory. These tokens query a Perceptual-Cognitive Memory Bank to retrieve relevant historical context. This bank stores low-level details and high-level semantics from past interactions, and is updated through redundancy-aware consolidation. A world model imagines future states in a denoising latent space, and the imagined latents are integrated under memory guidance to form full temporal-aware tokens. The resulting tokens condition a diffusion action expert to predict temporally consistent action sequences. We conduct extensive experiments on 5 simulation benchmarks and 3 categories of real-robot tasks across 3 robots, covering general manipulation, long-horizon temporal tasks, robustness, and generalization. Our method achieves strong performance across Libero, SimplerEnv, Mikasa-Robo, Calvin, Libero-Plus, and diverse real-robot tasks, validating the effectiveness of full temporal modeling with memory and imagination. For example, on real robots, it achieves +9%, +26%, +28% gains on general, memory-dependent, and imagination-dependent tasks. Project Page: https://shihao1895.github.io/MemoryVLA-PP-Web
Chinese Translation
时间建模对于机器人操作至关重要，因为有效的控制需要对过去交互的记忆和对未来状态的想象。然而，大多数视觉-语言-行动（VLA）模型主要依赖于当前观察，因此在长时间跨度和时间依赖的任务中表现不佳。认知科学表明，人类依赖工作记忆来缓冲短暂的上下文，海马系统来保存过去经验的情节记忆，以及内部模型来想象可能的未来状态演变。受到这些机制的启发，我们提出了MemoryVLA++，这是一个完整的时间建模框架，为VLA模型提供记忆和想象以进行机器人操作。预训练的视觉-语言模型（VLM）将当前观察编码为感知和认知标记，形成工作记忆。这些标记查询感知-认知记忆库，以检索相关的历史上下文。该记忆库存储来自过去交互的低级细节和高级语义，并通过冗余感知整合进行更新。世界模型在去噪潜在空间中想象未来状态，想象的潜在变量在记忆指导下整合，形成完整的时间感知标记。生成的标记条件化扩散行动专家，以预测时间一致的行动序列。我们在5个仿真基准和3类真实机器人任务上进行了广泛实验，涵盖一般操作、长时间跨度的时间任务、鲁棒性和泛化。我们的方法在Libero、SimplerEnv、Mikasa-Robo、Calvin、Libero-Plus以及多种真实机器人任务中表现出色，验证了通过记忆和想象实现完整时间建模的有效性。例如，在真实机器人上，它在一般、依赖记忆和依赖想象的任务上分别取得了+9%、+26%、+28%的提升。项目页面：https://shihao1895.github.io/MemoryVLA-PP-Web
cs.CV / 1 / 2606.07558
Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing
针对跨世纪扫描文档档案的页面图像分类器进行微调，以便进一步的内容特定处理
Abstract
Purpose: Digitization projects in the humanities produce vast, heterogeneous archives of historical documents, making manual sorting impractical at scale. This work addresses the need for an automated system to classify scanned page images based on visual content type - text, tables, and graphics - enabling content-specific downstream processing such as Optical Character Recognition (OCR) or structured data extraction. Methods: An image classification system was developed and evaluated on a dataset of over 48,000 annotated historical page images from century-old Czech archaeological archives, refined through four successive annotation stages with domain-expert review. A Random Forest Classifier baseline was established using hand-crafted image features. Subsequently, deep learning architectures were fine-tuned and compared: Convolutional Neural Networks (EfficientNetV2, RegNetY), Vision and Document Image Transformers (ViT, DiT), and multimodal CLIP models. An 11-category label scheme was designed collaboratively with domain experts and evaluated via five-fold cross-validation. Results: The feature-based baseline achieved approximately 75% accuracy. Fine-tuned CNNs and Transformers substantially outperformed it, with RegNetY-16GF achieving 99.16% and ViT-large 99.12% Top-1 accuracy on the held-out test set. CLIP ViT-B/16 reached 99.14% with optimized text descriptions. Conclusion: Image-only models, particularly RegNetY-16GF, deliver near-perfect classification accuracy and produce consistent labels across 649,508 unlabeled archival pages with over 90% inter-model agreement. Fine-tuned CLIP, despite competitive test-set accuracy, showed under 65% agreement with image-only models on unlabeled data, making it less suitable for deployment. The final models, annotated dataset, and software are publicly available under open-source licenses.
Chinese Translation
目的：人文学科的数字化项目产生了大量异质的历史文档档案，使得在大规模下进行手动分类变得不切实际。本研究旨在满足对自动化系统的需求，该系统能够根据视觉内容类型（文本、表格和图形）对扫描页面图像进行分类，从而支持后续的内容特定处理，如光学字符识别（OCR）或结构化数据提取。方法：开发并评估了一种图像分类系统，使用来自百年捷克考古档案的超过48,000个注释历史页面图像的数据集，通过四个连续的注释阶段并经过领域专家审查进行精炼。使用手工制作的图像特征建立了随机森林分类器的基线。随后，对深度学习架构进行了微调和比较：卷积神经网络（EfficientNetV2、RegNetY）、视觉和文档图像变换器（ViT、DiT）以及多模态CLIP模型。与领域专家共同设计了11类标签方案，并通过五折交叉验证进行评估。结果：基于特征的基线达到了约75%的准确率。微调后的CNN和变换器显著超越了基线，其中RegNetY-16GF在保留的测试集上达到了99.16%的Top-1准确率，ViT-large达到了99.12%。CLIP ViT-B/16在优化文本描述的情况下达到了99.14%。结论：仅基于图像的模型，特别是RegNetY-16GF，提供了近乎完美的分类准确率，并在649,508个未标记的档案页面上产生了一致的标签，模型间一致性超过90%。尽管微调后的CLIP在测试集上表现出竞争力的准确率，但在未标记数据上与仅基于图像的模型的协议低于65%，使其不太适合部署。最终模型、注释数据集和软件在开放源代码许可证下公开可用。
cs.CV / 2 / 2606.07585
Multimodal Group Emotion Recognition In-the-Wild Towards a Privacy-Safe Non-Individual Approach
野外多模态群体情感识别：朝向隐私安全的非个体化方法
Abstract
This thesis addresses group emotion recognition (GER) in-the-wild with a focus on privacy preservation. Unlike traditional emotion recognition methods that rely on individual-level cues such as face, gaze, or voice analysis, this work uses collective audio-video signals to infer emotions at the group level, reducing risks of individual monitoring and surveillance. Two complementary frameworks are proposed. The first is a cross-attention multimodal architecture for audio-video fusion, combined with Frames Attention Pooling (FAP) for temporal aggregation. It is supported by synthetic data augmentation and validated through ablation studies, demonstrating robustness in real-world GER conditions. The second framework, Variational Encoder Multi-Decoder (VE-MD), learns a shared latent space for emotion classification and structural representation prediction, including body and face cues. Two decoding strategies, DETR-based and heatmap-based, are explored to analyze the role of structural representations in group and individual settings. The thesis makes three main contributions: it clarifies the role of multimodality and structural cues in group-level affective computing; introduces two architectures for privacy-preserving multimodal GER; and shows that competitive performance can be achieved without using individual features as input data.
Chinese Translation
本论文探讨了野外环境下的群体情感识别（GER），重点关注隐私保护。与传统的情感识别方法依赖于面部、视线或声音分析等个体级线索不同，本研究利用集体音视频信号推断群体层面的情感，从而降低个体监控和监视的风险。提出了两个互补的框架。第一个是用于音视频融合的交叉注意力多模态架构，结合了帧注意力池化（Frames Attention Pooling, FAP）进行时间聚合。该框架得益于合成数据增强，并通过消融研究验证，显示出在真实世界GER条件下的鲁棒性。第二个框架是变分编码器多解码器（Variational Encoder Multi-Decoder, VE-MD），用于学习情感分类和结构表示预测的共享潜在空间，包括身体和面部线索。探索了基于DETR和热图的两种解码策略，以分析结构表示在群体和个体设置中的作用。论文的三大贡献包括：阐明了多模态和结构线索在群体层面情感计算中的作用；引入了两种隐私保护的多模态GER架构；并展示了在不使用个体特征作为输入数据的情况下，仍能实现竞争性的性能。
cs.CV / 3 / 2606.07590
SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions
SlideCheck：通过数据集分布指导病理基础模型的自监督预训练
Abstract
Pathology foundation models are pretrained on large streams of WSI-derived patches, while supervision during data construction is often slide-level, sparse, or heterogeneous. This mismatch makes it difficult to understand and control which biological patterns enter the pretraining data. We propose SlideCheck, a lightweight pretraining data guidance tool built on frozen pathology foundation model patch features. Rather than serving as a standalone patch diagnostic model, SlideCheck provides explicit abnormality and malignancy scores for organizing, filtering, and auditing pathology pretraining data. SlideCheck uses a dual-head MLP to separately model broad abnormal morphology and malignant evidence. A regularized feature-space scorer provides a supervised anchor for patch-level evidence estimation, while score-attention agreement combines patch scores with WSI-level MIL attention to mine high-confidence pseudo labels. The same scores are then used to construct broad-positive ViT pretraining subsets, where a patch is selected if either abnormality or malignancy evidence exceeds a threshold. Experiments show that SlideCheck-defined data distributions influence the downstream behavior of self-supervised ViT pretraining, indicating that biological composition is an important controllable factor in pathology foundation model development. Curated subsets can approach full-data performance, suggesting that explicitly scored patch pools may support more efficient and auditable pretraining data construction. These findings position SlideCheck as a data guidance and auditing layer for transforming large, undifferentiated patch pools into controllable and reusable pretraining datasets.
Chinese Translation
病理基础模型是在大量来源于全切片图像（WSI）的补丁上进行预训练的，而在数据构建过程中，监督通常是以切片为单位的、稀疏的或异质的。这种不匹配使得理解和控制哪些生物模式进入预训练数据变得困难。我们提出了SlideCheck，这是一种基于冻结的病理基础模型补丁特征构建的轻量级预训练数据指导工具。SlideCheck并不是作为一个独立的补丁诊断模型，而是提供明确的异常和恶性评分，以便于组织、过滤和审计病理预训练数据。SlideCheck使用双头多层感知器（MLP）分别建模广泛的异常形态和恶性证据。一个正则化的特征空间评分器为补丁级证据估计提供了监督锚点，而评分注意力一致性则将补丁评分与全切片图像级的多实例学习（MIL）注意力结合，以挖掘高置信度的伪标签。然后使用相同的评分构建广泛正样本的视觉变换器（ViT）预训练子集，当异常或恶性证据超过阈值时，选择该补丁。实验表明，SlideCheck定义的数据分布影响自监督ViT预训练的下游行为，表明生物组成是病理基础模型开发中一个重要的可控因素。经过精心策划的子集可以接近全数据性能，表明明确评分的补丁池可能支持更高效和可审计的预训练数据构建。这些发现将SlideCheck定位为一个数据指导和审计层，旨在将大型、未分化的补丁池转变为可控和可重复使用的预训练数据集。
cs.CV / 4 / 2606.07593
A Mechanistic Analysis of Adversarial Fine-tuning of Vision Transformers
对视觉变换器对抗微调的机制分析
Abstract
The widespread use of image classification models in high-risk, real-world situations necessitates making these models robust to slight disturbances or perturbations, such as blurring or sharpening, in the input images. While vision transformers (ViTs) play an integral role in many modern-day multi-modal models like Vision-Language-Models (VLMs) and Vision-Language-Action (VLA) models, they have received a lack of attention in the setting of robustness. In this work, we analyze the effects of adversarial fine-tuning, a popular method for improving model robustness to image perturbations, on a ViT's performance on perturbed and regular images through a mechanistic lens. We adversarially train a ViT on low-frequency and high-frequency image corruptions, and attempt to explain changes in downstream model performance through an examination of the model's attention mechanisms, internal representations, and knowledge evolution. Overall, our results suggest that, while fine-tuning on inputs with common corruptions improves model performance and certainty on new instances of corrupted data, these improvements do not transfer to other classes of corruptions not seen in the training. Additionally, despite observing changes in visual attention and knowledge evolution across layers, we found that adversarial training did not lead to fundamental changes in the sparse representations learned by ViTs.
Chinese Translation
在高风险的现实场景中，图像分类模型的广泛应用要求这些模型对输入图像中的轻微干扰或扰动（如模糊或锐化）具有鲁棒性。尽管视觉变换器（ViTs）在许多现代多模态模型中（如视觉-语言模型（VLMs）和视觉-语言-动作（VLA）模型）发挥着重要作用，但在鲁棒性方面却受到的关注较少。在本研究中，我们从机制的角度分析了对抗微调这一提高模型对图像扰动鲁棒性的流行方法对ViT在扰动和正常图像上的性能影响。我们对ViT进行了低频和高频图像损坏的对抗训练，并试图通过对模型的注意机制、内部表征和知识演变的分析来解释下游模型性能的变化。总体而言，我们的结果表明，尽管在常见损坏的输入上进行微调可以提高模型在新损坏数据实例上的性能和确定性，但这些改进并未转移到训练中未见过的其他类别的损坏。此外，尽管观察到跨层的视觉注意力和知识演变的变化，我们发现对抗训练并未导致ViTs所学习的稀疏表征发生根本性变化。
cs.CV / 5 / 2606.07595
VisualLeakBench: Reproducible Action-Boundary Propagation Failures in Vision-Language Agents
VisualLeakBench：视觉-语言智能体中的可重复的动作边界传播失败
Abstract
Vision-language agents increasingly consume screenshots, documents, and user interfaces before writing to memory, sending messages, or invoking external tools. We study a concrete failure mode in this setting: action-boundary propagation, where sensitive or unsafe visible text is copied from an image into downstream tool arguments. We present VisualLeakBench, a diversified 500-image benchmark spanning UI, chat, document, form, and dashboard scenes, and evaluate a stratified 100-image agent subset with four production VLM systems under two workflows: note capture and external handoff. At baseline, target strings are propagated into tool arguments in 78.8% of PII cases and 85.5% of rendered unsafe-text cases. Under a defensive system prompt, rendered unsafe-text propagation remains high at 52.6%, while PII tool propagation falls to 2.0%, largely by suppressing tool use rather than preserving utility. Rates are tool-surface dependent: search-like tools suppress PII propagation, but rendered unsafe text still crosses tool boundaries. We measure visual-to-tool propagation rather than downstream instruction execution. We additionally provide a labeled-target oracle upper-bound diagnostic that localizes most failures at the tool boundary while leaving response-side leakage as residual risk.
Chinese Translation
视觉-语言智能体越来越多地在写入内存、发送消息或调用外部工具之前处理截图、文档和用户界面。我们研究了这种情况下的一种具体失败模式：动作边界传播，其中敏感或不安全的可见文本从图像复制到下游工具参数中。我们提出了VisualLeakBench，这是一个涵盖用户界面、聊天、文档、表单和仪表板场景的多样化500张图像基准，并在两种工作流程下对四个生产型视觉-语言模型（VLM）系统进行了分层100张图像的代理子集评估：笔记捕获和外部交接。在基线情况下，目标字符串在78.8%的个人身份信息（PII）案例和85.5%的渲染不安全文本案例中传播到工具参数中。在防御性系统提示下，渲染的不安全文本传播仍然高达52.6%，而PII工具传播降至2.0%，这主要是通过抑制工具使用而非保持效用来实现的。传播率依赖于工具表面：类似搜索的工具抑制PII传播，但渲染的不安全文本仍然跨越工具边界。我们测量的是视觉到工具的传播，而不是下游指令的执行。此外，我们提供了一个标记目标的上界诊断，能够定位大多数失败发生在工具边界，同时将响应侧泄漏视为残余风险。
cs.CV / 6 / 2606.07613
Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence
你能相信你所看到的吗？人类与人工智能对合成法律证据的检测
Abstract
Visual evidence has long been treated as a reliable form of legal proof, but advances in artificial intelligence (AI) are undermining that assumption. This article asks how well humans and frontier multimodal large language models (MLLMs) can distinguish authentic evidentiary photographs from AI-generated counterparts in the object-centric scenarios typical of civil disputes. We built Synthetic Legal Evidence Detection (SLED-1400), a dataset of 200 authentic evidence images paired with 1,200 synthetic counterparts produced by six contemporary text-to-image generators across ten evidence categories. The same stimuli and response format were used in a controlled web experiment with 136 lay participants and in a standardized evaluation of four MLLMs (GPT-5.1, Gemini-3-Pro, Gemini-3-Flash, Qwen3-VL-235B). Human accuracy was 64.8% overall, and 48.5% and 51.0% on the two strongest generators (Gemini-3-Pro-Image and Flux-2-Max), indistinguishable from chance. MLLMs never misclassified an authentic image (100% specificity), but missed most synthetic outputs from the harder generators, with average MLLM detection at 5.9% on Gemini-3-Pro-Image outputs. Human and MLLM errors were largely uncorrelated, while the four MLLMs were strongly correlated with each other. Neither group is a reliable standalone authenticator. We argue that visual evidence in legal proceedings should be treated as inherently contestable, and that a workable procedural response must combine trained human review, MLLM screening, and provenance infrastructure such as C2PA Content Credentials.
Chinese Translation
视觉证据长期以来被视为可靠的法律证明形式，但人工智能（AI）的进步正在削弱这一假设。本文探讨人类和前沿的多模态大型语言模型（MLLMs）在典型民事争议的以物为中心场景中，能够多好地区分真实证据照片与AI生成的对应图像。我们构建了合成法律证据检测（SLED-1400）数据集，该数据集包含200张真实证据图像，配对1200张由六个当代文本生成图像模型生成的合成图像，涵盖十个证据类别。在一个控制的网络实验中，使用相同的刺激和反应格式，招募了136名普通参与者，并对四个MLLMs（GPT-5.1、Gemini-3-Pro、Gemini-3-Flash、Qwen3-VL-235B）进行了标准化评估。人类的整体准确率为64.8%，在两个最强生成器（Gemini-3-Pro-Image和Flux-2-Max）上的准确率分别为48.5%和51.0%，与随机猜测无异。MLLMs从未错误分类真实图像（特异性100%），但错过了大多数来自更难生成器的合成输出，平均MLLM检测率在Gemini-3-Pro-Image输出上为5.9%。人类和MLLM的错误大体上不相关，而四个MLLM之间则高度相关。两组都不是可靠的独立鉴定者。我们认为，在法律程序中，视觉证据应被视为本质上具有争议性的，并且可行的程序响应必须结合经过培训的人类审查、MLLM筛选和如C2PA内容凭证等来源基础设施。
cs.CV / 7 / 2606.07620
SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors
SENTRY：视觉变换器在软错误下的统计可靠性分析
Abstract
With the growth of Vision Transformers in safety-critical domains like autonomous systems and medical imaging, ensuring their reliability against soft errors is paramount. While ViTs offer state-of-the-art accuracy, their massive parameter counts render exhaustive fault injection campaigns infeasible. To bridge this gap, a statistical fault injection framework is presented, leveraging finite-population sampling theory to provide formal reliability guarantees. It is demonstrated that failure rates are bounded within a 1% margin at 99\% confidence using only a few thousand samples, regardless of model scale. This methodology achieves up to a 10,700 times reduction in experimental cost compared to exhaustive approaches, while preserving the ability to localize vulnerabilities across architectural components. Through extensive evaluation of different architectures like ViT-Tiny and ViT-Small, a highly non-uniform reliability landscape is uncovered. It is shown that while only 3% of FP32 bit-flips result in failure, the vast majority of these events lead to catastrophic accuracy collapse. Specific vulnerabilities are localized to normalization layers and critical exponent bits within the IEEE-754 format, providing a mathematical foundation and actionable insights for the design of hardened, edge-deployed ViT architectures.
Chinese Translation
随着视觉变换器在自主系统和医学成像等安全关键领域的应用增长，确保其对软错误的可靠性至关重要。尽管视觉变换器（ViTs）提供了最先进的准确性，但其庞大的参数量使得全面的故障注入实验变得不可行。为了解决这一问题，提出了一种统计故障注入框架，利用有限总体抽样理论提供正式的可靠性保证。研究表明，仅使用几千个样本，故障率在99%的置信度下被限制在1%的范围内，无论模型规模如何。该方法在实验成本上相比全面方法实现了高达10,700倍的降低，同时保留了定位架构组件中脆弱性的能力。通过对不同架构（如ViT-Tiny和ViT-Small）的广泛评估，揭示了高度不均匀的可靠性格局。结果显示，尽管仅有3%的FP32位翻转导致失败，但绝大多数事件会导致灾难性的准确性崩溃。特定的脆弱性被定位于归一化层和IEEE-754格式中的关键指数位，为设计强化的、边缘部署的视觉变换器架构提供了数学基础和可行的见解。
cs.CV / 8 / 2606.07626
Eyes All Around: Design and Analysis of 360-Degree LiDAR Perception Using Equivariant Feature Learning in Unstructured Traffic
全方位观察：使用等变特征学习设计与分析360度LiDAR感知在非结构化交通中的应用
Abstract
Perception in dense, unstructured urban traffic remains a major challenge for autonomous driving because of the wide variety of road users, frequent occlusions, irregular motion patterns, and the lack of standardized road layouts. Although recent LiDAR based 3D object detectors have shown strong performance in structured driving scenarios, most are developed and evaluated for limited field of view settings, and their behavior under full surround 360-degree sensing is still not well understood. This paper studies a 360-degree LiDAR perception pipeline for autonomous driving, with particular attention to panoramic sensing, azimuthal sector wise spatial processing, and transformation equivariant feature extraction in complex urban scenes. The paper presents a practical 360-degree perception framework that combines sector wise panoramic processing with rotation equivariant sparse convolutions and evaluates its behavior on a custom Ouster OS0 LiDAR dataset collected across diverse Indian urban traffic conditions. The results show generally stable detection across several object classes, with the strongest performance for cars at 92.02/90.51, buses at 80.53/76.34, and trucks at 78.59/74.16, while lower scores for pedestrians at 67.45/61.02, cyclists at 73.21/69.54, and motorcyclists at 71.20/68.13 reflect the greater difficulty of detecting smaller and more variable road users in dense urban scenes.
Chinese Translation
在密集的非结构化城市交通中，感知仍然是自动驾驶面临的主要挑战，因为道路使用者种类繁多、频繁遮挡、运动模式不规则，以及缺乏标准化的道路布局。尽管近期基于LiDAR的3D物体检测器在结构化驾驶场景中表现出色，但大多数都是在有限视场设置下开发和评估的，其在全方位360度感知下的表现仍不够明确。本文研究了一种用于自动驾驶的360度LiDAR感知管道，特别关注全景感知、方位角分区空间处理和复杂城市场景中的变换等变特征提取。本文提出了一个实用的360度感知框架，该框架结合了分区全景处理与旋转等变稀疏卷积，并在一个针对多样化印度城市交通条件收集的定制Ouster OS0 LiDAR数据集上评估其表现。结果显示，在多个物体类别中检测结果总体稳定，其中汽车的表现最佳，达到92.02/90.51，公交车为80.53/76.34，卡车为78.59/74.16，而行人67.45/61.02、骑自行车者73.21/69.54和摩托车骑行者71.20/68.13的得分较低，反映出在密集城市场景中检测较小且变化多端的道路使用者的难度更大。
cs.CV / 9 / 2606.07633
AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation
AMN：一种自适应多尺度融合网络，结合边界和不确定性建模用于细胞核分割
Abstract
Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quantification, and prognosis prediction. Existing approaches rely on either convolutional or transformer-based encoders in isolation, limiting their ability to simultaneously capture fine-grained local texture and long-range spatial context. We present AMN (Adaptive Multi-Scale Nuclei Network), a dual-encoder segmentation framework that jointly leverages a Swin Transformer and a ResNet-50 feature pyramid, fused via a learned per-channel gating mechanism that dynamically weighs each encoder's contribution at every scale. AMN is trained with a multi-objective loss combining class-weighted focal loss, boundary-aware loss with positive-pixel emphasis, and a novel uncertainty-modulated classification term that suppresses overconfident erroneous predictions. Evaluated on the CoNIC benchmark across seven nuclei classes, AMN achieves a mean Dice of 0.82 and mean F1 of 0.68, with an F1 of 0.67 on the diagnostically challenging lymphocyte class. AMN outperforms eight baseline models spanning pure-CNN, pure-transformer, and recent hybrid architectures: U-Net, ResU-Net, DeepLabV3+, SegNet, ViT-Small, HmsU-Net, ConvFormer-UNet, and BEFUnet. Cross-dataset evaluation on MoNuSeg demonstrates strong generalization without retraining and validating the domain robustness of the learned representations.
Chinese Translation
在组织病理图像中准确分类细胞核亚型对于肿瘤分级、免疫浸润定量和预后预测等下游任务至关重要。现有方法依赖于单独使用卷积或基于变换器的编码器，这限制了它们同时捕捉细粒度局部纹理和长距离空间上下文的能力。我们提出了AMN（自适应多尺度细胞核网络），这是一种双编码器分割框架，联合利用Swin Transformer和ResNet-50特征金字塔，通过学习的每通道门控机制进行融合，该机制动态地权衡每个编码器在每个尺度的贡献。AMN的训练采用多目标损失，结合了类别加权的焦点损失、带有正像素强调的边界感知损失，以及一种新颖的不确定性调制分类项，以抑制过于自信的错误预测。在七个细胞核类别的CoNIC基准测试中评估，AMN实现了0.82的平均Dice和0.68的平均F1，在诊断上具有挑战性的淋巴细胞类别上F1为0.67。AMN在纯CNN、纯变换器和近期混合架构的八个基线模型中表现优越，包括U-Net、ResU-Net、DeepLabV3+、SegNet、ViT-Small、HmsU-Net、ConvFormer-UNet和BEFUnet。对MoNuSeg的跨数据集评估展示了强大的泛化能力，无需重新训练，验证了学习表示的领域鲁棒性。
cs.CV / 10 / 2606.07635
NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis
NeuroAlign：用于轻度认知障碍分析的动态与结构神经影像的层次多模态融合
Abstract
Multimodal neuroimaging fusion of functional MRI (fMRI) and diffusion tensor imaging (DTI) provides complementary information for cognitive impairment analysis, but remains challenged by heterogeneous feature spaces and misaligned representations. We propose \textit{NeuroAlign}, a hierarchical framework for structured multimodal fusion. It introduces (1) \textit{Dual-Modal Hierarchical Alignment} (DMHA), which models multi-scale dynamic connectivity and aligns dynamic-static and functional-structural embeddings; and (2) \textit{Dual-Domain Hierarchical Interaction} (DDHI), which enables fine-grained modulation and global interaction between connectivity- and region-level features. To support feature-level inspection, we design \textit{Synergistic Activation Mapping} (SAM), a gradient-free, marker-oriented attribution method for DFC, SFC, ALFF, and FA. Evaluated on GUTCM, ADNI, and OASIS under five-fold validation, NeuroAlign achieves competitive MCI/SCD detection and preliminary cross-dataset transferability. Attribution analyses reveal modality-specific and partially consistent brain patterns, providing model-derived evidence for multimodal representation analysis.
Chinese Translation
功能性磁共振成像（fMRI）和扩散张量成像（DTI）的多模态神经影像融合为认知障碍分析提供了互补信息，但仍面临异质特征空间和不对齐表示的挑战。我们提出了 extit{NeuroAlign}，一个用于结构化多模态融合的层次框架。它引入了（1） extit{双模态层次对齐}（DMHA），该方法建模多尺度动态连接，并对齐动态-静态和功能-结构嵌入；（2） extit{双领域层次交互}（DDHI），该方法实现连接层面和区域层面特征之间的细粒度调制和全局交互。为了支持特征级别的检查，我们设计了 extit{协同激活映射}（SAM），这是一种无梯度、以标记为导向的归因方法，适用于DFC、SFC、ALFF和FA。在GUTCM、ADNI和OASIS上进行五折验证评估，NeuroAlign在轻度认知障碍/轻度认知障碍症状检测方面表现出竞争力，并展示了初步的跨数据集迁移能力。归因分析揭示了模态特异性和部分一致的大脑模式，为多模态表示分析提供了模型衍生的证据。
cs.CV / 11 / 2606.07636
Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing
Crayotter：可追溯的多代理长视频编辑工作流
Abstract
Editing a long-form video from heterogeneous footage requires more than selecting clips: an agent must preserve narrative intent across material preparation, timeline construction, post-production, and revision while leaving enough evidence to diagnose failures. We present \textbf{Crayotter}, an open-source multimodal multi-agent system for prompt-driven video editing. Crayotter organizes production into three phases: coverage-aware material preparation, artifact-based editing research, and tool-grounded timeline execution. Each phase externalizes inspectable artifacts, including coverage reports, multimodal analyses, editing blueprints, tool calls, and intermediate renders. These artifacts make an editing run traceable and allow failed segments to be diagnosed and selectively revised instead of requiring a full restart. We evaluate Crayotter on 23 editing themes against CapCut-Mate and CutClaw. Under human evaluation, Crayotter achieves an average score of 3.40/5, compared with 2.44 and 1.70 for the two baselines, with consistent gains in theme alignment, narrative coherence, and editing smoothness. We additionally describe a replayable trajectory schema and verifiable reward design that prepare these workflows for future policy optimization. Code, traces, and examples are publicly available at https://github.com/idwts/Crayotter.
Chinese Translation
从异构素材中编辑长视频不仅仅是选择剪辑：代理必须在素材准备、时间线构建、后期制作和修订过程中保持叙事意图，同时留下足够的证据以诊断失败。我们提出了 extbf{Crayotter}，一个开源的多模态多代理系统，用于基于提示的视频编辑。Crayotter将制作过程组织为三个阶段：关注覆盖率的素材准备、基于文物的编辑研究和工具驱动的时间线执行。每个阶段都外部化可检查的文物，包括覆盖率报告、多模态分析、编辑蓝图、工具调用和中间渲染。这些文物使得编辑过程可追溯，并允许对失败的片段进行诊断和选择性修订，而不需要完全重启。我们在23个编辑主题上对Crayotter进行了评估，与CapCut-Mate和CutClaw进行了比较。在人类评估中，Crayotter的平均得分为3.40/5，而两个基线的得分分别为2.44和1.70，在主题一致性、叙事连贯性和编辑流畅性方面均取得了一致的提升。此外，我们还描述了一个可重放的轨迹模式和可验证的奖励设计，为未来的策略优化准备了这些工作流。代码、追踪和示例可在 https://github.com/idwts/Crayotter 上公开获取。
cs.CV / 12 / 2606.07638
Anchor-Conditioned Compositional Control for Landscape Image Generation
基于锚点条件的风景图像生成组合控制
Abstract
Image generative models, though widely used as creative tools, offer limited support for the kind of compositional control that photographers and visual artists routinely exercise. This paper presents early results on an anchor conditioned finetuning framework for landscape image generation, in which a four dimensional compositional anchor vector is extracted from training images and injected into a diffusion model via a decoupled cross attention mechanism with Fourier encoding and three way classifier free guidance dropout. Quantitative evaluation against a baseline and three ablation variants shows that the proposed architecture achieves the highest horizon detection rate of 0.850 and the highest rule of thirds alignment of 0.817. A category specific ablation further demonstrates that training on compositionally homogeneous scene subsets reduces horizon deviation by up to 40 percent compared to mixed training. This establishes that compositional control precision is category dependent.
Chinese Translation
图像生成模型虽然被广泛用作创意工具，但对摄影师和视觉艺术家常规使用的组合控制支持有限。本文提出了一种用于风景图像生成的锚点条件微调框架的初步结果，其中从训练图像中提取出一个四维组合锚点向量，并通过解耦的交叉注意机制与傅里叶编码和三路无分类器引导丢弃注入到扩散模型中。与基线和三个消融变体的定量评估表明，所提出的架构实现了最高的地平线检测率0.850和最高的三分之一法对齐率0.817。特定类别的消融进一步证明，与混合训练相比，在组合同质场景子集上训练将地平线偏差减少了多达40%。这表明组合控制的精度依赖于类别。
cs.CV / 13 / 2606.07639
MOSS-Video-Preview: Toward Real-Time Video Understanding via Cross-Attention
MOSS-视频预览：通过交叉注意力实现实时视频理解
Wang, Pengyu, Tan, Chenkun, Zhou, Shaojun, Huang, Wei, Zhou, Qirui, Huang, Zhan, Ye, Zhen, Cheng, Jijun, Qian, Xiaomeng, Chen, Yanxin, He, Xingyang, Zeng, Huazheng, Wang, Chenghao, Wang, Pengfei, Wang, Hongkai, Gao, Shanqing, Tian, Yixian, Liu, Chenghao, Wang, Xinghao, Jiang, Botian, Qiu, Xipeng
Abstract
Video understanding is shifting from the offline paradigm -- taking a fully recorded video as input and producing a single answer after it ends -- toward real-time interaction, in which the model perceives new frames while still replying, revises its answer as new evidence appears, and remains silent when there is nothing to say. We present MOSS-Video-Preview to validate this paradigm. Our central claim is that perception must not be blocked by generation; its natural realization is a two-channel architecture. We argue that a cross-attention backbone is better suited to real-time vision-language fusion than the prevailing decoder-only design: visual features enter through a side channel rather than joining the autoregressive sequence, so perception and generation run on separate, non-blocking pathways -- reducing the frequency of visual processing and exposing a clean channel-wise interface for independent compression. We complement this with a data synthesis pipeline that converts dense captions into real-time understanding QA whose answers are revised to match what the model has perceived so far, and we specialize an offline model on these data to elicit real-time behavior. Our model trails the strong Qwen2.5-VL-7B baseline overall -- a gap we attribute primarily to data and scale rather than the architecture -- yet attains competitive offline video and multimodal understanding, remains robust on the spatial and fine-grained temporal reasoning central to real-time use, and acquires behaviors that offline models lack: continuous perception, answer revision, and timely silence. On a single H200 with 256 frames per video, it achieves about a 5x speedup in time to first token and 2.7x higher decoding throughput, with negligible degradation in offline ability. Our study of paradigm, architecture, and data outlines a viable path toward real-time video understanding.
Chinese Translation
视频理解正从离线范式转向实时交互，即模型在回复时感知新帧，并在新证据出现时修正其答案，当没有信息可说时保持沉默。我们提出MOSS-视频预览以验证这一范式。我们的核心论点是，感知不应被生成所阻塞；其自然实现是双通道架构。我们认为，交叉注意力骨干网比现有的仅解码器设计更适合实时视觉-语言融合：视觉特征通过侧通道进入，而不是加入自回归序列，从而使感知和生成在独立的非阻塞路径上运行——减少视觉处理的频率，并为独立压缩提供干净的通道接口。我们还补充了一个数据合成管道，将密集字幕转换为实时理解问答，其答案根据模型迄今为止的感知进行修正，并针对这些数据专门训练一个离线模型以引发实时行为。总体而言，我们的模型在强大的Qwen2.5-VL-7B基线下表现稍逊——我们将这一差距主要归因于数据和规模，而非架构——但在离线视频和多模态理解方面达到了竞争力，且在实时使用中对空间和细粒度时间推理保持稳健，获得了离线模型所缺乏的行为：持续感知、答案修正和及时沉默。在单个H200上，每个视频256帧，其首次令牌的时间实现了约5倍的加速，解码吞吐量提高了2.7倍，离线能力几乎没有下降。我们对范式、架构和数据的研究勾勒出了一条通向实时视频理解的可行路径。
cs.CV / 14 / 2606.07640
No Free Lunch for Synthetic Images under Data Scarcity Conditions
在数据稀缺条件下合成图像的无免费午餐理论
Abstract
This study investigates the trade-offs between fidelity, privacy, and utility in synthetic data generation under conditions of data scarcity and privacy sensitivity. We propose an evaluation framework that jointly assesses these three dimensions and apply it to three widely used generative models, VAE, GAN, and DDPM. The evaluation spans three image datasets, MNIST, OCTMNIST, and OrganAMNIST, encompassing both general-purpose and medical imaging domains. Notable differences arise between the three models in their behaviour when differential privacy mechanisms are introduced during training. GAN and DDPM demonstrate greater robustness, maintaining higher fidelity and downstream utility across a range of noise levels, while VAE degrades more rapidly as privacy constraints increase. This study highlights the importance of a multidimensional evaluation of deep generative models, also noting that their behaviour significantly differs when privacy techniques are applied.
Chinese Translation
本研究探讨了在数据稀缺和隐私敏感条件下，合成数据生成中的保真度、隐私和效用之间的权衡。我们提出了一个评估框架，联合评估这三个维度，并将其应用于三种广泛使用的生成模型：变分自编码器（VAE）、生成对抗网络（GAN）和去噪扩散概率模型（DDPM）。评估涵盖了三个图像数据集：MNIST、OCTMNIST和OrganAMNIST，涉及一般用途和医学成像领域。当在训练过程中引入差分隐私机制时，三种模型的行为出现显著差异。GAN和DDPM表现出更强的鲁棒性，在各种噪声水平下保持较高的保真度和下游效用，而VAE在隐私约束增加时则迅速下降。本研究强调了对深度生成模型进行多维评估的重要性，并指出在应用隐私技术时，它们的行为显著不同。
cs.CV / 15 / 2606.07641
Readable Yet Unpredictable: Rotated-Outcome Prediction in Vision-Language Models
可读但不可预测：视觉-语言模型中的旋转结果预测
Abstract
Can vision-language models predict what a 180{\deg} rotation would reveal from the original image alone? We study this ability through Rotated-Outcome Prediction: given an original image, a model must answer what would be seen or read after a 180{\deg} in-plane rotation, without directly observing the rotated target. To isolate this gap, we introduce RotOutBench, a paired diagnostic benchmark spanning open visual cases and controlled text-image rotations. A sharp pattern emerges: many VLMs can recognize the relevant content when directly given either the original or rotated image, yet fail to infer the rotated result from the original image alone. On controlled text-image rotations, predicted-rotation accuracy collapses to near zero even for models with high direct-reading accuracy. A model-level case study further shows that the prediction state can approach a rotated-image reading state, while the final readout still shifts toward the original string. Current VLMs can recognize a transformed visual state when it is shown, but often fail to predict that state from the original view.
Chinese Translation
视觉-语言模型能否仅通过原始图像预测180°旋转后会揭示什么？我们通过旋转结果预测（Rotated-Outcome Prediction）研究这一能力：给定一幅原始图像，模型必须回答在进行180°平面旋转后会看到或读取到什么，而不直接观察旋转后的目标。为了隔离这一差距，我们引入了RotOutBench，这是一个涵盖开放视觉案例和受控文本-图像旋转的配对诊断基准。一个明显的模式浮现：许多视觉-语言模型（VLMs）在直接给出原始图像或旋转图像时能够识别相关内容，但却无法仅从原始图像推断出旋转结果。在受控的文本-图像旋转中，即使对于具有高直接读取准确率的模型，预测旋转的准确率也几乎降至零。模型级案例研究进一步表明，预测状态可以接近旋转图像的读取状态，而最终的读取结果仍然偏向于原始字符串。目前的视觉-语言模型能够在展示时识别变换的视觉状态，但往往无法从原始视图预测该状态。
cs.CV / 16 / 2606.07642
Do VLMs See What Sensors Feel? A Scalable Expert-Guided Design for Wheelchair Accessibility Assessment from Street View
视觉语言模型能否感知传感器所感受到的内容？一种可扩展的专家引导设计用于街景轮椅无障碍评估
Abstract
Assessing built-environment interaction, such as wheelchair accessibility, is difficult because real-world mobility is shaped by distributed, context-dependent, and temporary barriers that are hard to capture at scale. To support scalable assessment, this paper examines whether vision-language models (VLMs) can identify accessibility barriers from Google Street View (GSV) imagery. We propose an expert-guided retrieval-augmented framework that combines GSV images, ADA-informed guidance, and expert-derived rubrics to evaluate accessibility dimensions. We collect a campus-scale dataset at the University of Florida, linking 407 unique GSV locations with GPS-derived wheelchair dwell behavior as a mobility-friction signal. Results show that VLM ratings are both negatively correlated and distributionally similar with dwell time, indicating partial but consistent alignment with a behavioral proxy for mobility friction. Visual cue analysis shows that certain environmental objects, such as curb ramps and crosswalks, are associated with higher VLM accessibility scores, while alignment remains limited for subtle surface conditions, transient obstructions, and viewpoint-dependent barriers. Overall, our findings show the potential of expert-guided VLMs for scalable accessibility assessment aligning with sensor-derived indicators of real-world wheelchair navigation.
Chinese Translation
评估建筑环境的互动，例如轮椅无障碍性，是一项困难的任务，因为现实世界的移动性受到分布式、依赖于上下文和临时障碍的影响，这些障碍难以大规模捕捉。为了支持可扩展的评估，本文探讨了视觉语言模型（VLMs）是否能够从谷歌街景（GSV）图像中识别无障碍障碍。我们提出了一种专家引导的检索增强框架，该框架结合了GSV图像、符合美国残疾人法案（ADA）的指导和专家制定的评估标准，以评估无障碍维度。我们在佛罗里达大学收集了一个校园规模的数据集，将407个独特的GSV位置与基于GPS的轮椅停留行为关联，作为移动摩擦信号。结果表明，VLM评分与停留时间呈负相关且分布相似，表明与移动摩擦的行为代理存在部分但一致的对齐。视觉线索分析显示，某些环境物体（如人行道坡道和斑马线）与更高的VLM无障碍评分相关，而对于微妙的表面条件、瞬时障碍和视角依赖的障碍，评分的对齐仍然有限。总体而言，我们的研究结果展示了专家引导的VLM在与传感器衍生的现实世界轮椅导航指标一致的可扩展无障碍评估中的潜力。
cs.CV / 17 / 2606.07643
AVI-Bench: Toward Human-like Audio-Visual Intelligence of Omni-MLLMs
AVI-Bench：迈向类人音视频智能的全能多模态大语言模型
Abstract
Recent advances in Omni-Multimodal Large Language Models (Omni-MLLMs) have enabled strong integration of vision, audio, and language. However, their audio-visual intelligence (AVI) remains insufficiently evaluated due to the lack of systematic and comprehensive benchmarks. We introduce AVI-Bench, a cognitively inspired benchmark that evaluates Omni-MLLMs across three stages, perception, understanding, and reasoning, through cross-modal tasks requiring joint audio-visual interpretation. This design enables fine-grained diagnosis of model capabilities and failure modes. To further assess robustness beyond familiar domains, we propose AVI-Bench-PriSe, an extension that probes models' primitive audio-visual sensation using unfamiliar, low-semantic stimuli, testing generalization beyond common training distributions. Extensive experiments on both open-source and closed-source models reveal substantial limitations in current Omni-MLLMs. Based on these findings, we present a four-level AVI taxonomy. Overall, AVI-Bench provides a principled evaluation framework to guide the development of more robust and generalizable AVI. Project website: https://fudancvl.github.io/AVI-Bench/
Chinese Translation
最近，全能多模态大语言模型（Omni-MLLMs）的进展使得视觉、音频和语言的强集成成为可能。然而，由于缺乏系统和全面的基准，其音视频智能（AVI）仍然评估不足。我们引入了AVI-Bench，这是一个受认知启发的基准，通过需要联合音视频解释的跨模态任务，从感知、理解和推理三个阶段评估Omni-MLLMs。这一设计使得对模型能力和失效模式的细致诊断成为可能。为了进一步评估在熟悉领域之外的鲁棒性，我们提出了AVI-Bench-PriSe，这是一个扩展，使用不熟悉的低语义刺激探测模型的原始音视频感知，测试超越常见训练分布的泛化能力。在对开源和闭源模型进行的广泛实验中，揭示了当前Omni-MLLMs的显著局限性。基于这些发现，我们提出了一个四级AVI分类法。总体而言，AVI-Bench提供了一个原则性评估框架，以指导更鲁棒和可泛化的AVI的发展。项目网站：https://fudancvl.github.io/AVI-Bench/
cs.CV / 18 / 2606.07645
FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction
FineGen：基于VLM的多智能体细粒度图像-文本数据集构建框架
Abstract
The scarcity of hard negative samples in current vision-language datasets significantly hinders fine-grained perception. To address this, we propose FineGen, a VLM-based Multi-Agent framework for automated dataset construction. By employing a collaborative Generation-Verification-Correction pipeline with a closed-loop feedback mechanism, FineGen ensures synthesized hard negatives are semantically valid yet strictly contradictory to visual content. Applying this to ImageNet, we construct FineGen-100K, a hierarchical dataset containing over 147,000 attribute-specific hard negatives with a rigorous 1:10 positive-to-negative ratio. Extensive evaluations confirm a 96.7% attribute validity rate. Crucially, downstream validation on the FG-OVD benchmark shows that fine-tuning on FineGen-100K yields a substantial +14.4% accuracy improvement on hard samples, significantly outperforming state-of-the-art methods.
Chinese Translation
当前视觉-语言数据集中稀缺的困难负样本显著阻碍了细粒度感知。为了解决这一问题，我们提出了FineGen，一个基于VLM的多智能体自动化数据集构建框架。通过采用协作生成-验证-修正管道，并结合闭环反馈机制，FineGen确保合成的困难负样本在语义上有效且严格与视觉内容相矛盾。将此方法应用于ImageNet，我们构建了FineGen-100K，一个包含超过147,000个属性特定困难负样本的分层数据集，严格遵循1:10的正负样本比例。广泛的评估确认了96.7%的属性有效性。重要的是，在FG-OVD基准上的下游验证显示，在FineGen-100K上进行微调可使困难样本的准确率显著提高14.4%，远超当前最先进的方法。
cs.CV / 19 / 2606.07646
DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation
DOME：从稀疏监督中学习可转移的领域变量以实现测试时适应
Abstract
Test-time adaptation (TTA) aims to align a model to shifting test domains using only unlabeled streaming data. Most existing methods implicitly infer a single global domain distribution, ignoring the multidimensional and sample-specific nature of real-world domain shifts, leading to fragile adaptation. We propose DOME, an effective domain encoder that explicitly models each sample's domain in a zero-shot manner. DOME leverages vision-language pretraining to extract dense, continuous representations, parameterizes domains as distributional variables, and introduces a momentum-updated sparse domain bank for disentangled supervision. By injecting these explicit domain cues into downstream models, even a basic entropy-minimization TTA strategy achieves state-of-the-art performance across ImageNet-C, ImageNet-R, and ImageNet-Sketch, outperforming complex TTA approaches. Our results demonstrate that robust adaptation stems not from intricate adaptation algorithms, but from explicit, structured domain representation.
Chinese Translation
测试时适应（TTA）旨在仅使用未标记的流数据将模型与不断变化的测试领域对齐。现有大多数方法隐式推断单一的全局领域分布，忽视了现实世界领域变化的多维性和样本特异性，导致适应性脆弱。我们提出了DOME，一种有效的领域编码器，以零样本的方式显式建模每个样本的领域。DOME利用视觉-语言预训练提取稠密的连续表示，将领域参数化为分布变量，并引入动量更新的稀疏领域库以实现解耦监督。通过将这些显式领域线索注入下游模型，即使是基本的熵最小化TTA策略也能在ImageNet-C、ImageNet-R和ImageNet-Sketch上实现最先进的性能，超越复杂的TTA方法。我们的结果表明，稳健的适应性并非源于复杂的适应算法，而是源于显式的、结构化的领域表示。
cs.CV / 20 / 2606.07647
Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation
关注关键点：针对大型视觉语言模型（LVLMs）幻觉缓解的令牌级视觉敏感性引导
Abstract
Large vision language models (LVLMs) have made rapid advancements and are deployed across various applications, yet hallucinations remain a major challenge. Activation steering is appealing due to its minimal training overhead and controllability at inference time. However, we found that during autoregressive decoding, visual conditioning affects token prediction sparsely and locally across decoding steps, and many existing methods that average image-versus-no-image differences over the entire sequence dilute these critical signals, yielding low signal-to-noise ratio steering directions. Additionally, many existing methods apply a fixed steering strength, which misallocates the intervention budget, over-perturbs non-critical tokens, and can cause instability. To address these limitations, we propose Token-Level Visual-Sensitivity Steering (TLVS) for hallucination mitigation. Our approach first extracts token-level steering vectors and refines them, and then applies fine-grained, visual-sensitivity-adaptive steering only where it matters. This lightweight, plug-and-play mechanism requires only minimal training for calibration and can be applied across diverse vision-language models. It modulates the steering strength at each decoding step, selectively suppressing hallucination-prone spans while preserving evidence-grounded content. We evaluate TLVS on several benchmarks, including POPE, AMBER, CHAIR (COCO), MMHal, and HallusionBench, demonstrating consistent improvements over previous steering methods.
Chinese Translation
大型视觉语言模型（LVLMs）迅速发展并广泛应用于各种场景，但幻觉仍然是一个主要挑战。激活引导因其训练开销小和推理时可控性而备受关注。然而，我们发现，在自回归解码过程中，视觉条件对令牌预测的影响是稀疏且局部的，而许多现有方法通过对整个序列的图像与无图像差异进行平均，稀释了这些关键信号，导致信噪比低的引导方向。此外，许多现有方法应用固定的引导强度，这错误地分配了干预预算，过度扰动非关键令牌，并可能导致不稳定性。为了解决这些局限性，我们提出了令牌级视觉敏感性引导（Token-Level Visual-Sensitivity Steering, TLVS）以缓解幻觉。我们的方法首先提取令牌级引导向量并对其进行精炼，然后仅在关键位置应用细粒度的、视觉敏感性自适应的引导。这种轻量级、即插即用的机制仅需最小的校准训练，并可应用于多种视觉语言模型。它在每个解码步骤中调节引导强度，选择性地抑制易幻觉的片段，同时保留基于证据的内容。我们在多个基准测试上评估了TLVS，包括POPE、AMBER、CHAIR（COCO）、MMHal和HallusionBench，显示出相较于之前的引导方法的一致性改进。
cs.CV / 21 / 2606.07648
AQIFormer: A Transformer-Based Multi-View Architecture for Cross-City Air Quality Classification
AQIFormer：一种基于变换器的多视角架构用于跨城市空气质量分类
Abstract
Air pollution represents one of the most critical environmental and public health challenges globally, with traditional sensor-based monitoring systems facing significant scalability and economic constraints. Image-based air quality estimation has emerged as a promising alternative, leveraging the visual characteristics of atmospheric pollutants in traffic scenes. However, existing methods suffer from limited cross-city generalization and inadequate exploitation of multi-view perspectives. We present AQIFormer, a novel transformer-based ensemble architecture that addresses these fundamental limitations through innovative dual-view integration, weather-aware attention mechanisms, and comprehensive multi-task learning. Our approach uniquely combines front and rear traffic imagery with meteorological parameters to achieve robust air quality classification across diverse urban environments. Extensive evaluation on a comprehensive dataset of 26,678 synchronized front-rear image pairs demonstrates good performance with 89.96% accuracy, representing a 14.96% improvement over state-of-the-art methods. Most importantly, our model maintains exceptional cross-city generalization capabilities, achieving 81.67% accuracy on an independent dataset collected in Nagpur, India with only 8.29% performance degradation using few-shot adaptation with minimal training samples.
Chinese Translation
空气污染是全球最严重的环境和公共健康挑战之一，传统的基于传感器的监测系统面临着显著的可扩展性和经济限制。基于图像的空气质量估计作为一种有前景的替代方案应运而生，利用了交通场景中大气污染物的视觉特征。然而，现有方法在跨城市泛化能力和多视角利用方面存在不足。我们提出了AQIFormer，这是一种新颖的基于变换器的集成架构，通过创新的双视角整合、天气感知注意机制和全面的多任务学习来解决这些基本局限。我们的方法独特地结合了前后交通图像与气象参数，以实现跨多样城市环境的稳健空气质量分类。在一个包含26,678对同步前后图像的综合数据集上的广泛评估显示，我们的方法表现良好，准确率达到89.96%，比最先进的方法提高了14.96%。最重要的是，我们的模型保持了卓越的跨城市泛化能力，在印度纳格浦尔收集的独立数据集上实现了81.67%的准确率，使用少量训练样本进行少样本适应时仅有8.29%的性能下降。
cs.CV / 22 / 2606.07649
ViMax: Agentic Video Generation
ViMax：自主视频生成
Abstract
Long-form video generation requires systematic narrative planning and visual consistency that current short-clip methods cannot provide. Existing methods generate isolated sequences without narrative structure and lack mechanisms for maintaining character and environmental consistency across scenes. We present ViMax, an agentic video generation framework that addresses video creation through coordinated multi-agent collaboration where specialized components negotiate narrative decisions, visual continuity, and production quality. Our framework employs a hierarchical narrative engine with retrieval-augmented generation for global story coherence and a dependency-aware visual consistency mechanism that tracks character and environmental states across temporal boundaries, while VLM-guided agents continuously monitor and refine both narrative coherence and visual fidelity. The framework enables coordinated agent collaboration to generate extended narrative content. This maintains both storytelling integrity and visual coherence across multi-scene timelines.
Chinese Translation
长格式视频生成需要系统的叙事规划和视觉一致性，而当前的短片方法无法提供这些。现有方法生成孤立的序列，缺乏叙事结构，并且缺乏在场景之间保持角色和环境一致性的机制。我们提出了ViMax，一个自主视频生成框架，通过协调的多智能体协作来解决视频创作问题，其中专门的组件协商叙事决策、视觉连续性和制作质量。我们的框架采用层次化叙事引擎，结合检索增强生成（retrieval-augmented generation）以实现全球故事的一致性，并使用依赖感知的视觉一致性机制，跟踪跨时间边界的角色和环境状态，同时VLM引导的智能体持续监控和优化叙事一致性和视觉保真度。该框架使得协调的智能体协作能够生成扩展的叙事内容，从而在多场景时间线中保持叙事完整性和视觉一致性。
cs.CV / 23 / 2606.07653
A Dataset for Dynamic Human Preferences for Vision Language Models
动态人类偏好的视觉语言模型数据集
Abstract
Given the increased adoption of Vision Language Models (VLMs) in human-interactive settings, it is important that we evaluate how well these models can adapt to real-time preferences for different users. While an increasing number of vision-language benchmarks have recently been introduced, they focus largely on evaluating static capabilities and generally-held preferences learned from extensive training data. This work introduces a new benchmark for evaluating the ability of VLMs to understand dynamic human-preferences, i.e. preferences that are passed in-context at inference time. We provide an automated pipeline for generating this benchmark with variations on image dependence, a dynamic multi-modal human-preference dataset, and evaluations of state-of-the-art models on the novel benchmark.
Chinese Translation
随着视觉语言模型（VLMs）在与人类互动环境中的应用日益增加，评估这些模型如何适应不同用户的实时偏好变得尤为重要。尽管最近引入了越来越多的视觉语言基准，但它们主要集中在评估静态能力和从大量训练数据中学习到的一般偏好上。本研究引入了一个新的基准，用于评估VLMs理解动态人类偏好的能力，即在推理时上下文中传递的偏好。我们提供了一个自动化的管道来生成这个基准，涵盖了图像依赖性的变化、一个动态多模态人类偏好数据集，以及对最先进模型在这一新基准上的评估。
cs.CV / 24 / 2606.07654
MM-Matryoshka: Towards Budget-Elastic Visual Document Retrieval via a 2D Multimodal Matryoshka Training Framework
MM-Matryoshka：通过二维多模态Matryoshka训练框架实现预算弹性的视觉文档检索
Abstract
Multi-vector visual document retrievers achieve strong fine-grained matching by representing each page with multiple vectors from deep Vision-Language Models (VLMs), but this design makes deployment expensive in both storage and computational overhead. Existing efficiency techniques usually optimize only part of this budget, leaving multimodal retrievers without a unified way to trade accuracy for both vector width and encoder depth. Therefore, we propose MM-Matryoshka, a 2D Matryoshka training framework for budget-elastic Visual Document Retrieval (VDR), enabling ColPali-style multi-vector retrieval elastic along both dimension and layer. At inference time, a single retriever can select a 2D selectable budget without training separate models for different budgets. Through comprehensive experiments across multiple representative backbones, we demonstrate that by retaining significantly higher quality than direct truncation baselines while substantially reducing storage and computational overhead, MM-Matryoshka can offer robust budget elasticity for efficient VDR.
Chinese Translation
多向量视觉文档检索器通过使用来自深度视觉-语言模型（VLMs）的多个向量来实现强大的细粒度匹配，但这种设计使得在存储和计算开销方面的部署成本较高。现有的效率技术通常只优化这一预算的一部分，导致多模态检索器缺乏统一的方式来在向量宽度和编码器深度之间权衡准确性。因此，我们提出了MM-Matryoshka，一个用于预算弹性视觉文档检索（VDR）的二维Matryoshka训练框架，使得ColPali风格的多向量检索在维度和层次上都具备弹性。在推理时，单个检索器可以选择一个二维可选预算，而无需为不同预算训练单独的模型。通过对多个代表性骨干网络进行全面实验，我们证明了MM-Matryoshka在显著降低存储和计算开销的同时，保持比直接截断基线显著更高的质量，从而为高效的VDR提供了强大的预算弹性。
cs.CV / 25 / 2606.07658
What neurosurgeons need to see: synthetic intra-operative MRI from ultrasound for brain-shift compensation in brain tumour surgery
神经外科医生需要看到的：用于脑肿瘤手术中脑位移补偿的超声合成术中MRI
Abstract
Maximal safe resection is the primary objective in glioma surgery. Neuronavigation guidance is progressively degraded by brain shift after dural opening. Intraoperative MRI can compensate but needs dedicated infrastructure and is rarely available, whereas intraoperative ultrasound (ioUS) is inexpensive, repeatable, and compatible with routine workflows. Navigation systems combining ioUS with preoperative MRI usually rely on rigid registration; even deformable multimodal registration is limited by ultrasound speckle contrast, a narrow field of view, and the inability to represent structures absent from the preoperative scan, most critically the resection cavity and residual tumor. We propose an end-to-end pipeline that generates a new whole-brain MRI volume in the preoperative imaging space by merging the preoperative MRI, a synthetic MRI generated from the ioUS, and a deformable registration anchored on that synthetic image. It integrates a 2.5D residual-transformer synthesis backbone (ResViT-2.5D) and a two-stage registration coupling NiftyReg with a synthesis-anchored SynthMorph stage, operating directly on raw scanner inputs. On a post-resection ReMIND cohort, ResViT-2.5D produced synthetic images closely matching the intraoperative T2 across structural, intensity, and perceptual metrics. In 14 subjects with 215 expert landmarks, the synthesis-anchored registration reduced the mean target registration error from 6.27 to 5.86 mm, matching a strong classical NiftyReg baseline (5.85 mm) while yielding a diffeomorphic deformation field in every subject. The contribution is not a gain in registration accuracy but the integrated volume itself, which inside the ultrasound field of view it reflects the intraoperative post-resection state. This provides the surgeon with an MRI-like update of the operative field with potential for integration into surgical-navigation workflows.
Chinese Translation
最大安全切除是胶质瘤手术的主要目标。硬膜打开后，神经导航指导逐渐受到脑位移的影响。术中MRI可以进行补偿，但需要专用基础设施且很少可用，而术中超声（ioUS）则廉价、可重复，并与常规工作流程兼容。结合ioUS和术前MRI的导航系统通常依赖于刚性配准；即使是可变形的多模态配准也受到超声斑点对比度、视野狭窄以及无法表示术前扫描中缺失结构（特别是切除腔和残余肿瘤）的限制。我们提出了一种端到端的管道，通过合并术前MRI、从ioUS生成的合成MRI以及基于该合成图像的可变形配准，生成术前成像空间中的新全脑MRI体积。它集成了一个2.5D残余变换合成主干（ResViT-2.5D）和一个两阶段配准，将NiftyReg与基于合成的SynthMorph阶段耦合，直接在原始扫描仪输入上操作。在一个后切除ReMIND队列中，ResViT-2.5D生成的合成图像在结构、强度和感知指标上与术中T2图像紧密匹配。在14名受试者的215个专家标记中，基于合成的配准将平均目标配准误差从6.27毫米降低到5.86毫米，匹配了强大的经典NiftyReg基线（5.85毫米），同时在每个受试者中产生了一个可微分变形场。该贡献不是配准精度的提升，而是集成体积本身，它在超声视野内反映了术中切除后的状态。这为外科医生提供了类似MRI的手术区域更新，具有整合到手术导航工作流程中的潜力。
cs.CV / 26 / 2606.07659
Real-Time Industrial Defect Detection on Edge Hardware Using Fine-Tuned YOLOv8: A Systematic Benchmark on the NEU Surface Defect Database and MVTec AD with Automotive & Battery Manufacturing Extensions
基于微调YOLOv8的边缘硬件实时工业缺陷检测：在NEU表面缺陷数据库和MVTec AD上的系统基准测试及汽车与电池制造扩展
Abstract
Automated surface defect detection is critical for ensuring rigorous quality control in high-speed manufacturing environments. While deep learning models offer remarkable accuracy, deploying them on resource-constrained edge hardware without introducing significant latency remains a persistent challenge. This paper presents Industrial-YOLO, an edge-optimized framework built upon a fine-tuned YOLOv8 architecture specifically engineered for real-time industrial defect detection. We conduct a systematic benchmark utilizing the NEU surface defect database for steel sheets and the MVTec AD dataset, supplemented with custom automotive manufacturing extensions representing real-world structural anomalies (scratches, pits, and inclusions). To bridge the gap between algorithmic complexity and edge hardware constraints, target-specific optimizations are introduced via TensorRT and OpenVINO acceleration engines. Experimental results demonstrate that Industrial-YOLO achieves a high-velocity inference speed exceeding 120 FPS on the NVIDIA Jetson Orin platform while maintaining an exceptional mean Average Precision (mAP) of 98.5%. The proposed framework showcases highly robust, zero-latency performance when deployed directly onto an active automotive assembly line, offering a scalable blueprint for next-generation automated optical inspection (AOI) systems.
Chinese Translation
自动化表面缺陷检测对于确保高速制造环境中的严格质量控制至关重要。尽管深度学习模型提供了显著的准确性，但在资源受限的边缘硬件上部署这些模型而不引入显著延迟仍然是一个持续的挑战。本文提出了Industrial-YOLO，一个基于微调YOLOv8架构的边缘优化框架，专门用于实时工业缺陷检测。我们利用NEU表面缺陷数据库（针对钢板）和MVTec AD数据集进行系统基准测试，并补充了代表现实世界结构异常（划痕、凹坑和夹杂物）的定制汽车制造扩展。为了解决算法复杂性与边缘硬件约束之间的差距，通过TensorRT和OpenVINO加速引擎引入了特定目标的优化。实验结果表明，Industrial-YOLO在NVIDIA Jetson Orin平台上实现了超过120 FPS的高速度推理，同时保持了98.5%的卓越平均精度（mAP）。所提出的框架在直接部署到活跃的汽车装配线上时展现出高度稳健的零延迟性能，为下一代自动光学检测（AOI）系统提供了可扩展的蓝图。
cs.CV / 27 / 2606.07660
Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation
我们是否需要教基础模型什么是生成图像？通过解析谱适应进行无梯度生成伪影检测
Abstract
Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations. Under optimization on limited samples, models overfit to local domain shortcuts. Fine-tuning massive weights on specialized data introduces erroneous inductive biases, inducing a measurable $\mathcal{L}_2$ norm perturbation in the high-dimensional feature space -- a phenomenon we formalize as anchor drift. Amplified by nonlinear activations, this drift impairs zero-shot forgery detection across unseen domains.We propose a gradient-free methodology reframing detection from binary classification to an out-of-distribution (OOD) anomaly measurement problem. Treating a frozen foundation model as a stable coordinate system, we establish an absolute natural anchor on the real visual manifold by analytically decoupling statistical and semantic deviations, derived from attention-weighted spatial moments and orthogonal projection of perceptual inconsistencies. Evaluated in an extreme zero-shot setting (trained on face forgeries, tested on universal Text-to-Image generations), our method significantly outperforms gradient-optimized paradigms. Backpropagation-free forward passes and linear solvers enable hardware-agnostic, edge-deployable calibration with minimal latency. Furthermore, the Sherman-Morrison formula unlocks instantaneous online learning against novel attacks and enables privacy-preserving federated collaboration via covariance delta transmission.
Chinese Translation
通过基于梯度的更新来调整基础模型以检测生成伪影，会损害其内在表示。在有限样本的优化下，模型会过拟合于局部领域捷径。在专门数据上微调大量权重会引入错误的归纳偏差，从而在高维特征空间中诱发可测量的 $ ext{L}_2$ 范数扰动——我们将这一现象正式化为锚漂移。由于非线性激活的放大，这种漂移损害了在未见领域中的零-shot伪造检测。我们提出了一种无梯度的方法，将检测重新框定为一个分布外（OOD）异常测量问题。将冻结的基础模型视为一个稳定的坐标系统，我们通过解析解耦统计和语义偏差，在真实视觉流形上建立一个绝对自然锚点，该偏差源自于加权空间矩和感知不一致性的正交投影。在极端的零-shot设置下进行评估（在面部伪造上训练，在通用文本到图像生成上测试），我们的方法显著优于基于梯度优化的范式。无反向传播的前向传递和线性求解器使得硬件无关、边缘可部署的校准成为可能，并且延迟极小。此外，Sherman-Morrison公式解锁了针对新攻击的即时在线学习，并通过协方差增量传输实现隐私保护的联邦协作。
cs.CV / 28 / 2606.07661
PereStruct: Multimodal Semantic Assembly for Robust Historical Document Parsing
PereStruct：用于鲁棒历史文档解析的多模态语义组装
Abstract
Parsing historical documents with complex, non-standard layouts remains a fundamental bottleneck in large-scale archival digitization. Unlike modern typography, historical newspapers exhibit severe physical degradation and highly irregular page structures that confound even state-of-the-art vision-language models, presenting severe out-of-distribution challenges. We address this gap with an automated pipeline specifically designed for parsing historical newspapers, documents characterized by particularly intricate multi-column layouts. Our approach combines a fine-tuned YOLO architecture for layout analysis and block detection, trained on 1,426 fully human-annotated scanned pages, with a novel semantic assembly module that reconstructs articles by jointly modeling lexical-semantic similarity via TF-IDF, visual embeddings from our fine-tuned YOLO, and geometric layout constraints. This multi-modal integration yields state-of-the-art performance, achieving an F1 score of 0.904 on block-to-article mapping. Notably, end-to-end evaluation against vision-language models (Qwen3.6-35B-A3B and Qwen3.6-Plus) demonstrates that PereStruct achieves substantially higher fidelity (BLEU approximately 0.96 vs 0.34), validating that modular architectures excel where generic VLMs fail on complex historical layouts. To support reproducibility and advance research in this domain, we release both the training corpus of 599 annotated pages and a curated PereStruct benchmark of 93 pages with expert-verified ground-truth block-to-article mappings. This framework establishes a robust foundation for high-fidelity digitization and semantic reconstruction of complex archival materials.
Chinese Translation
解析具有复杂、非标准布局的历史文档仍然是大规模档案数字化中的一个基本瓶颈。与现代排版不同，历史报纸表现出严重的物理退化和高度不规则的页面结构，这使得即使是最先进的视觉-语言模型也面临严重的分布外挑战。我们通过一个专门设计的自动化管道来解决这一问题，该管道专门用于解析历史报纸，这些文档的特点是特别复杂的多列布局。我们的方法结合了经过微调的YOLO架构用于布局分析和块检测，该架构在1426个完全人工标注的扫描页面上进行训练，以及一个新颖的语义组装模块，该模块通过TF-IDF、来自我们微调的YOLO的视觉嵌入和几何布局约束共同建模词汇-语义相似性来重建文章。这种多模态整合实现了最先进的性能，在块到文章的映射中达到了0.904的F1分数。值得注意的是，与视觉-语言模型（Qwen3.6-35B-A3B和Qwen3.6-Plus）的端到端评估表明，PereStruct在保真度上显著更高（BLEU约为0.96对比0.34），验证了模块化架构在复杂历史布局中优于通用视觉语言模型的表现。为了支持可重复性并推动该领域的研究，我们发布了599个标注页面的训练语料库和93个经过专家验证的块到文章映射的精心策划的PereStruct基准。这一框架为复杂档案材料的高保真数字化和语义重建奠定了坚实的基础。
cs.CV / 29 / 2606.07669
MemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarios
MemoVAD：通过动态语义记忆在边缘计算场景中实现资源高效的视频异常检测
Abstract
Deploying Video Anomaly Detection (VAD) in real-world surveillance faces a fundamental tension between the demand for high-level semantics to ensure effectiveness and the limited computational resources of edge devices. Vision-Language Models (VLMs) provide rich open-vocabulary semantics, but their latency and computational cost preclude on-device deployment. To address the challenge, we propose MemoVAD, an edge-cloud collaborative framework that selectively incorporates VLM semantics into streaming VAD. MemoVAD runs most inference on the edge with a lightweight detector and a causal Temporal Context Encoder (TCE) to model temporal dependencies. Specifically, we introduce an Uncertainty-Aware Gating (UAG) policy grounded in Subjective Logic to model perceived uncertainty and query the cloud-based VLM only for high-uncertainty and semantically novel clips. Besides, a Dynamic Semantic Memory (DSM) is designed to cache VLM-verified prototypes for efficient retrieval, enabling the edge model to progressively incorporate VLM-level semantics via a semantic adapter. Experiments on UCF-Crime and XD-Violence datasets via a real edge device show that MemoVAD substantially reduces communication overhead while surpassing state-of-the-art performance.
Chinese Translation
在现实世界的监控中部署视频异常检测（VAD）面临着确保有效性所需的高层语义与边缘设备有限计算资源之间的根本矛盾。视觉-语言模型（VLMs）提供了丰富的开放词汇语义，但其延迟和计算成本使得在设备上部署变得不可行。为了解决这一挑战，我们提出了MemoVAD，一个边缘-云协同框架，选择性地将VLM语义融入流媒体VAD中。MemoVAD在边缘运行大部分推理，使用轻量级检测器和因果时间上下文编码器（TCE）来建模时间依赖性。具体而言，我们引入了一种基于主观逻辑的感知不确定性建模的意识不确定性门控（UAG）策略，仅针对高不确定性和语义新颖的片段查询云端VLM。此外，设计了一种动态语义记忆（DSM）来缓存VLM验证的原型，以便高效检索，使边缘模型能够通过语义适配器逐步融入VLM级别的语义。在UCF-Crime和XD-Violence数据集上通过真实边缘设备进行的实验表明，MemoVAD显著降低了通信开销，同时超越了现有的最先进性能。
cs.CV / 30 / 2606.07670
Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting
液态神经网络作为动态3D高斯点云的即插即用连续时间变形场
Abstract
Deformable 3D Gaussian Splatting (D-3DGS) re-constructs dynamic scenes from monocular video by deforming a canonical set of 3D Gaussians through a positional-encoded MLP of frame time t. Although fitted to a continuous variable, the MLP couples no two values of t in its architecture and effectively predicts discrete per-frame offsets, leaving temporal smoothness to emerge only as a byproduct of optimisation. We redesign the deformation field as a stack of Closed-form Continuous-time (CfC) cells, a Liquid Neural Network (LNN), that is the closed-form solution of the Liquid Time-constant ODE while preserving every other part of the D-3DGS pipeline. Each cell exposes a sigmoidal time gate that interpolates between two candidate hidden states, baking a learned smooth response to t into the loss landscape without invoking any numerical solver. On the eight D-NeRF and seven NeRF-DS scenes the liquid field matches or exceeds the MLP baseline in aggregate, with its largest gains concentrated on the scenes with the most high-frequency articulated motion. The result is a near-zero-friction architectural design that turns the discrete MLP deformation field into an explicit continuous-time function of t.
Chinese Translation
可变形的3D高斯点云（D-3DGS）通过对一组规范的3D高斯进行变形，利用单目视频重建动态场景，变形过程通过一个位置编码的多层感知器（MLP）与帧时间t相结合。尽管适应于一个连续变量，MLP在其架构中并未将任何两个t值耦合，并有效地预测离散的每帧偏移，使得时间平滑性仅作为优化的副产品出现。我们将变形场重新设计为一组闭式连续时间（CfC）单元的堆叠，即液态神经网络（LNN），它是液态时间常数常微分方程的闭式解，同时保留D-3DGS管道的其他部分。每个单元都暴露出一个S型时间门，在两个候选隐藏状态之间进行插值，将对t的学习平滑响应嵌入损失空间，而无需调用任何数值求解器。在八个D-NeRF和七个NeRF-DS场景中，液态场在整体上与MLP基线相匹配或超越，其最大的增益集中在具有最高频率关节运动的场景上。最终结果是一个近乎零摩擦的架构设计，将离散的MLP变形场转变为t的显式连续时间函数。
cs.CV / 31 / 2606.07674
Simultaneous hyperkinetic movement disorders phenotyping: a cross-cohort pediatric transfer study using routine videos, markerless pose estimation and a tabular foundation model
同时性高动症运动障碍表型研究：基于常规视频、无标记姿态估计和表格基础模型的跨队列儿童转移研究
Abstract
Objective: To develop and externally test a video-based framework for simultaneous detection of hyperkinetic MDs phenomenologies: dystonia, tremor, myoclonus, chorea, athetosis, ballismus, stereotypies, and tics using routine clinical recordings, with explicit testing of external, cross-cohort transfer from adult to pediatric populations. Methods: In this proof-of-concept study, the framework combines markerless pose estimation, kinematic descriptors, and a pretrained fondation model. A shared predictive backbone was developed on 21 adults with confirmed hyperkinetic MDs and 4 healthy controls assessed under a standardized protocol. External validation was performed on an independent external cohort: a real-world pediatric sample (n=12, monogenic combined MDs). For the external dataset, the backbone was deployed without retraining; lightweight calibration adjusted only the final subject-level decision step using a small labeled subset of patients selected by clinicians as representative of the cohort's phenotypic range. Results: After local calibration of the decision layer on the clinician-selected subset, performance improved consistently on the held-out pediatric patients (n=7): Hamming accuracy rose from 0.804 to 0.839 and the Jaccard index from 0.548 to 0.633. This calibrated performance was preserved, and the Jaccard index further improved, when the evaluation was restricted to the phenomenologies with more definite clinician agreement (Hamming accuracy 0.9, Jaccard index 0.786), indicating that the gains did not rest on the least-reliable labels.
Chinese Translation
目的：开发并外部测试一个基于视频的框架，以同时检测高动症运动障碍（MDs）的现象学：肌张力障碍、震颤、肌阵挛、舞蹈症、手足徐动症、球动症、刻板行为和抽动症，使用常规临床记录，并明确测试从成人到儿童人群的外部跨队列转移。方法：在这项概念验证研究中，该框架结合了无标记姿态估计、运动学描述符和一个预训练的基础模型。基于21名确认有高动症运动障碍的成人和4名健康对照者在标准化协议下进行评估，开发了一个共享的预测骨干。外部验证在一个独立的外部队列上进行：一个真实世界的儿童样本（n=12，单基因合并运动障碍）。对于外部数据集，骨干在不重新训练的情况下部署；轻量级校准仅调整了使用临床医生选择的小型标记子集作为代表队列表型范围的最终个体决策步骤。结果：在临床医生选择的子集上对决策层进行局部校准后，持留的儿童患者（n=7）的性能持续改善：汉明准确率从0.804上升到0.839，雅卡尔指数从0.548上升到0.633。当评估限制在临床医生更一致同意的现象学时，这一校准性能得以保持，雅卡尔指数进一步改善（汉明准确率0.9，雅卡尔指数0.786），表明这些提升并不依赖于最不可靠的标签。
cs.CV / 32 / 2606.07687
What Makes Video World Model Latents Action-Relevant: Prediction over Reconstruction
视频世界模型潜变量为何与动作相关：预测优于重建
Abstract
Video world models are increasingly used to provide predictive visual representations, yet it remains unclear which pretraining signals induce action-relevant structure in their latent spaces. We study this question through a unified probe-based evaluation across diverse encoder families, including image-only self-supervision, video pretraining with and without latent prediction, reconstruction-based autoencoders, diffusion models, and shortcut-forcing dynamics models. Using a common inverse-dynamics probing objective, we find that action-relevant structure is driven primarily by temporal video pretraining rather than pixel reconstruction fidelity: models with strong pixel decoding quality can exhibit near-zero action recoverability, while video-pretrained self-supervised encoders consistently achieve the best Pareto trade-off between visual fidelity and action prediction. Comparing V-JEPA and VideoMAE further shows that most gains arise from natural-video temporal context, with feature-level latent prediction providing a smaller additional benefit. These trends transfer across robotic benchmarks, though CALVIN reveals that static-environment tasks can partially mask the importance of temporal structure by allowing strong image priors to suffice. Finally, inverse-dynamics supervision substantially improves robustness to visual corruption, suggesting that action-aware objectives regularize latent geometry beyond clean-setting performance. Our results identify temporal predictive structure -- not reconstruction fidelity -- as the primary ingredient underlying action-relevant video representations.
Chinese Translation
视频世界模型越来越多地被用于提供预测性的视觉表示，但尚不清楚哪些预训练信号会在其潜在空间中诱导与动作相关的结构。我们通过对多种编码器家族进行统一的探测评估来研究这个问题，包括仅图像自监督、带有和不带有潜在预测的视频预训练、基于重建的自编码器、扩散模型和强制动态模型。使用共同的逆动态探测目标，我们发现与动作相关的结构主要由时间视频预训练驱动，而非像素重建的保真度：具有强像素解码质量的模型可能表现出接近零的动作可恢复性，而经过视频预训练的自监督编码器则始终在视觉保真度和动作预测之间实现最佳的帕累托权衡。比较 V-JEPA 和 VideoMAE 进一步表明，大多数收益来自自然视频的时间上下文，而特征级潜在预测提供的额外好处较小。这些趋势在机器人基准测试中也得到了验证，尽管 CALVIN 显示静态环境任务可能部分掩盖时间结构的重要性，因为强图像先验足以满足需求。最后，逆动态监督显著提高了对视觉损坏的鲁棒性，表明与动作相关的目标在干净环境性能之外对潜在几何结构进行了正则化。我们的结果表明，时间预测结构——而非重建保真度——是与动作相关的视频表示的主要成分。
cs.CV / 33 / 2606.07689
Struct-Searcher: Agentic Structural Thinking Advances Multimodal Deep Information Seeking
结构搜索者：主动结构思维推动多模态深度信息获取
Abstract
Deep research agents have attracted increasing attention for their ability to collect large-scale online information to acquire target knowledge, with recent efforts shifting from purely text-based information seeking to multimodal settings. However, existing agentic workflows are largely aligned with evidence accumulation models, which linearly aggregate evidence and lack principled mechanisms for handling contradictory information across heterogeneous modalities. Towards this end, we propose Struct-Searcher, a structural agentic workflow grounded in belief revision theory that explicitly maintains an evolving multimodal structural graph throughout the reasoning process, enabling effective conflict-aware multimodal deep information seeking. Extensive experiments across multiple benchmark datasets and backbone models demonstrate that Struct-Searcher is (1) plug-and-play and model-agnostic, yielding an average relative accuracy improvement of 17.2% on BrowseComp-VL across five different backbones. (2) top-performing, consistently outperforming state-of-the-art vision-language models (VLMs) and deep research agents, with relative accuracy improvements of 3.7% on MM-BrowseComp, 1.5% on HLE-VL, and 0.7% on BrowseComp-VL over the second-best competing approach.
Chinese Translation
深度研究代理因其收集大规模在线信息以获取目标知识的能力而受到越来越多的关注，最近的研究努力已从纯文本信息获取转向多模态环境。然而，现有的主动工作流程主要与证据积累模型相一致，这些模型线性地聚合证据，并缺乏处理异构模态间矛盾信息的原则性机制。为此，我们提出了结构搜索者（Struct-Searcher），这是一种基于信念修正理论的结构化主动工作流程，在推理过程中显式地维护一个不断演变的多模态结构图，从而实现有效的冲突感知多模态深度信息获取。在多个基准数据集和基础模型上的广泛实验表明，结构搜索者具有以下特点：(1) 插拔式和模型无关，在五种不同基础模型的 BrowseComp-VL 上平均相对准确率提高了 17.2%。(2) 性能卓越，持续超越最先进的视觉-语言模型（VLMs）和深度研究代理，在 MM-BrowseComp 上相对准确率提高 3.7%，在 HLE-VL 上提高 1.5%，在 BrowseComp-VL 上提高 0.7%，优于第二名竞争方法。
cs.CV / 34 / 2606.07708
Cross-View Urban Traffic Dataset: Drone-Supervised Ground Truth for Monocular Bird's-Eye View Localization
跨视角城市交通数据集：无人机监督的单目鸟瞰视图定位的真实数据
Abstract
We introduce a dataset and benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone videos recorded at real urban intersections. The benchmark targets two linked tasks: cross-view identity matching between street-view and drone-view object tracks, and ego-to-bird's-eye-view prediction using aerial supervision. In contrast to prior urban driving and V2X datasets, our benchmark provides identity-level alignment across radically different viewpoints together with standardized evaluation, annotation tooling, and baseline implementations. This setting is motivated by intersection-centric traffic analysis, where identity preservation, local interactions, and global spatial structure must be reasoned about jointly across views. We evaluate methods at both the track and frame levels, including cross-view ID precision/recall/IDF1, near--far breakdowns, temporal stability, and consistency metrics. We also provide baseline results for wedge-based cross-view matching and for three BEV prediction baselines: inverse perspective mapping, a MonoLayout-style learned baseline, and a regression baseline. The results show that the benchmark is feasible but challenging: cross-view matching achieves strong recall yet remains limited by over-assignment and temporal inconsistency, while ego-to-BEV prediction benefits from aerial supervision but remains far from saturated under lightweight monocular sensing. We hope that this benchmark will support future research on cross-view perception, urban scene alignment, and ego-to-global traffic understanding.
Chinese Translation
我们介绍了一个跨视角城市交通感知的数据集和基准，该数据集由在真实城市交叉口录制的同步自我中心自行车视频和空中无人机视频构成。该基准针对两个相关任务：街景与无人机视角物体轨迹之间的跨视角身份匹配，以及利用空中监督进行的自我视角到鸟瞰视角的预测。与之前的城市驾驶和V2X数据集相比，我们的基准提供了在截然不同的视角之间的身份级对齐，同时配备了标准化的评估、注释工具和基线实现。此设置的动机源于以交叉口为中心的交通分析，其中身份保留、局部交互和全局空间结构必须在视角之间共同推理。我们在轨迹和帧级别上评估方法，包括跨视角ID精度/召回率/IDF1、近远分类、时间稳定性和一致性指标。我们还提供了基于楔形的跨视角匹配的基线结果，以及三种鸟瞰视图预测基线：逆透视映射、MonoLayout风格的学习基线和回归基线。结果表明，该基准是可行但具有挑战性的：跨视角匹配实现了较强的召回率，但仍受到过度分配和时间不一致的限制，而自我视角到鸟瞰视角的预测受益于空中监督，但在轻量级单目传感下仍远未饱和。我们希望这个基准能够支持未来在跨视角感知、城市场景对齐和自我到全局交通理解方面的研究。
cs.CV / 35 / 2606.07756
DroneDAR: Long-Range Drone Distance Estimation Using Monocular Vision and Bounding-Box Features
DroneDAR：基于单目视觉和边界框特征的长距离无人机距离估计
Abstract
Accurate distance estimation for small drones in long-range imagery is important for tracking and situational awareness, yet remains challenging due to extreme target scale variation, background clutter, and noisy visual cues. This paper studies monocular drone distance estimation using image crops together with bounding-box geometry, a practical setting in which a detector provides a candidate drone region and the model predicts range from appearance and box-derived features. We evaluate a Droneranger-style baseline, and introduce a new DroneDAR (Drone Detection And Ranging) model that combines a convolutional backbone with explicit bounding-box cues through a lightweight gating mechanism. Experiments analyze how backbone capacity, crop resolution, and regression loss functions affect performance across distance regimes. We further examine common failure modes at long distances, including sensitivity to bounding-box noise and reduced texture detail in the crop. The results provide guidance for designing and training range estimators that remain robust under real-world long-range conditions and highlight directions for improving reliability when drones occupy only a few pixels.
Chinese Translation
在长距离图像中对小型无人机进行准确的距离估计对于跟踪和态势感知至关重要，但由于目标尺度变化极大、背景杂乱和视觉线索噪声等因素，这一任务仍然具有挑战性。本文研究了使用图像裁剪和边界框几何信息进行单目无人机距离估计的实用场景，其中检测器提供候选无人机区域，模型根据外观和基于框的特征预测距离。我们评估了Droneranger风格的基线，并引入了一种新的DroneDAR（无人机检测与测距）模型，该模型通过轻量级门控机制将卷积骨干网络与显式边界框线索相结合。实验分析了骨干网络容量、裁剪分辨率和回归损失函数如何影响不同距离范围内的性能。我们进一步考察了在长距离下常见的失败模式，包括对边界框噪声的敏感性和裁剪中纹理细节的减少。结果为设计和训练在现实世界长距离条件下保持稳健的距离估计器提供了指导，并强调了在无人机仅占据少量像素时提高可靠性的方向。
cs.CV / 36 / 2606.07766
Quantum-Enhanced Similarity Measures for Polarimetric Materials Classification
量子增强相似性度量用于极化材料分类
Abstract
We present a quantum--classical hybrid pipeline for polarimetric material classification that casts this as a point-matching problem. Voxel cubes, containing polarized light reflections, are used to train an encoder to produce 32-dimensional embeddings for the voxels of the cubes. At inference, the encoder head is discarded and the embeddings are encoded as probability amplitudes of quantum states. Next, a SWAP-test circuit estimates the fidelity between each of the 32D embeddings from the query cube and a dataset of anchor cubes. The aggregated fidelity serves as materials similarity scores, and the class of the anchor with highest aggregated fidelity is deemed as the class of the queried material. We evaluate our approach on a dataset of 23 materials ($\approx$800 samples each) derived from their Mueller matrices. The point-matching approaches from the proposed quantum SWAP-test and a classical classifier using Optimal Transport are compared. Our results demonstrate the competitive classification accuracy alongside open-set discrimination potential, establishing it as a viable path toward NISQ-based material recognition.
Chinese Translation
我们提出了一种量子-经典混合管道，用于将极化材料分类视为一个点匹配问题。使用包含极化光反射的体素立方体来训练编码器，以生成体素的32维嵌入。在推理阶段，编码器头被丢弃，嵌入被编码为量子态的概率幅度。接下来，SWAP测试电路估计查询立方体的每个32维嵌入与锚立方体数据集之间的保真度。聚合的保真度作为材料相似性分数，具有最高聚合保真度的锚的类别被视为查询材料的类别。我们在一个由23种材料（每种材料约800个样本）构成的数据集上评估了我们的方法，这些材料源自其穆勒矩阵。我们比较了所提出的量子SWAP测试的点匹配方法与使用最优传输的经典分类器。我们的结果表明，该方法在分类准确性和开放集区分潜力方面具有竞争力，确立了其作为基于NISQ的材料识别的可行路径。
cs.CV / 37 / 2606.07775
DALE-CT: Depth-Aware Foundation Models for Computed Tomography
DALE-CT：用于计算机断层扫描的深度感知基础模型
Abstract
Recent breakthroughs in self-supervised learning (SSL), such as the Latent-Euclidean Joint-Embedding Predictive Architecture (LeJEPA), alongside successes in integrating visual encoders with language models, have driven the demand for adaptable, high-capacity vision encoders in Computed Tomography (CT). In this work, we explore 2D slice-based architectures as a flexible alternative to native 3D models for processing volumetric CT data. Using the CT-RATE dataset, we trained DALE-CT (Depth-Aware Latent-Euclidean Computed Tomography), a 2D model family built entirely from scratch using LeJEPA, and compared its performance against a continually pre-trained DINOv2 baseline. To enhance representation quality, we developed a novel 3D depth-aware pre-training strategy anchored by dense auxiliary supervision from both automated anatomical masks and human-annotated abnormalities. Under linear probe evaluation with Multiple Instance Learning (MIL) for multi-abnormality detection, the frozen backbone of this dual-supervised model (DALE-CT-2S) achieves a Macro AUROC of 0.833. This performance demonstrates near-parity with state-of-the-art 3D vision-language models, achieved entirely from scratch with significantly less data and no textual supervision. To ensure reproducibility, all training code, evaluation scripts, and model weights have been made publicly available.
Chinese Translation
近年来，自监督学习（SSL）领域取得了重大突破，例如潜在欧几里得联合嵌入预测架构（Latent-Euclidean Joint-Embedding Predictive Architecture, LeJEPA），以及视觉编码器与语言模型结合的成功，推动了对可适应的高容量视觉编码器在计算机断层扫描（CT）中的需求。在本研究中，我们探索了基于2D切片的架构，作为处理体积CT数据的原生3D模型的灵活替代方案。我们使用CT-RATE数据集训练了DALE-CT（深度感知潜在欧几里得计算机断层扫描），这是一个完全从头开始构建的2D模型系列，基于LeJEPA，并将其性能与持续预训练的DINOv2基线进行了比较。为了提高表示质量，我们开发了一种新颖的3D深度感知预训练策略，该策略由来自自动解剖掩膜和人工标注异常的密集辅助监督所支撑。在使用多实例学习（Multiple Instance Learning, MIL）进行多异常检测的线性探测评估中，该双重监督模型（DALE-CT-2S）的冻结主干网络达到了0.833的宏观AUROC。这一性能与最先进的3D视觉-语言模型几乎相当，且完全是从头开始实现的，所需数据显著更少，并且没有文本监督。为了确保可重复性，所有训练代码、评估脚本和模型权重均已公开发布。
cs.CV / 38 / 2606.07861
The Last Visible Pixel: Probing Fine-Scale Perception in Vision-Language Models
最后一个可见像素：探究视觉-语言模型中的细尺度感知
Abstract
Recent vision-language models (VLMs) excel at multimodal understanding and reasoning, yet their fine-grained visual perception remains underexplored. A natural extension of ``How many r are there in Strawberry?'' asks: how small a visual pattern can a VLM reliably perceive? As such, we introduce FineSightBench, a new benchmark that systematically probes this limit by separating perception tasks (pixel-level recognition of letters, shapes, objects) from reasoning tasks (spatial reasoning, counting, ordering over small targets) across controlled scales of 4--48px. Through comprehensive experiments and detailed failure mode analysis on state-of-the-art models, we reveal a sharp dissociation: perception saturates around 12px, while reasoning remains limited even at larger scales, with persistent numeracy and sequence errors. These findings expose fundamental deficiencies in VLMs' fine-scale visual reasoning that demand more rigorous evaluation.
Chinese Translation
近期的视觉-语言模型（VLMs）在多模态理解和推理方面表现出色，但其细粒度的视觉感知仍然未得到充分探索。一个自然的延伸问题是：“草莓中有多少个 r？”进一步询问：VLM 能可靠地感知多小的视觉模式？因此，我们引入了 FineSightBench，这是一个新的基准，通过将感知任务（字母、形状、物体的像素级识别）与推理任务（空间推理、小目标的计数和排序）分开，系统性地探测这一极限，控制尺度为 4-48 像素。通过对最先进模型进行全面实验和详细的失败模式分析，我们揭示了一个明显的分离现象：感知在约 12 像素处饱和，而推理即使在更大尺度下仍然受限，持续存在数值和序列错误。这些发现揭示了 VLM 在细尺度视觉推理中的基本缺陷，亟需更严格的评估。
cs.CV / 39 / 2606.07872
VisualFLIP: Do Predictions Depend on Task-Critical Visual Evidence in Multimodal Reasoning?
VisualFLIP：预测是否依赖于多模态推理中的任务关键视觉证据？
Abstract
When a multimodal large language model answers a visual reasoning question correctly, is the prediction actually supported by the task-critical visual evidence? Correct answers can coexist with flawed reasoning, making accuracy alone an incomplete test of grounding. We introduce VisualFLIP, a paired benchmark with 1,374 images arranged as same-question perturbation pairs across cardinality, attribute, spatial, and logic tasks. Each pair keeps the question fixed but minimally changes the evidence so the gold answer deterministically flips. We evaluate 24 MLLMs with pair accuracy, which requires solving both sides of a pair, and Collapse Rate (CR), which measures how often a model that solves at least one side repeats the same non-empty answer for both images. Together, these metrics show that paired correctness and evidence dependence are related but distinct: capable models can still fail to update after task-critical visual changes, and collapse becomes more severe for some models when the edited image follows an earlier answer in a sequential setting. Further details are available on our project page: https://didizhu-judy.github.io/VisualFLIP/
Chinese Translation
当一个多模态大型语言模型正确回答视觉推理问题时，该预测是否实际上得到了任务关键视觉证据的支持？正确答案可以与错误推理共存，仅仅依靠准确性来测试基础是不够的。我们引入了VisualFLIP，这是一个配对基准，包含1,374张图像，按照基数、属性、空间和逻辑任务排列为同一问题扰动对。每对保持问题不变，但最小地改变证据，以便金标准答案确定性地翻转。我们评估了24个多模态大型语言模型（MLLMs）的配对准确性，这要求解决一对的两个方面，以及崩溃率（Collapse Rate, CR），该指标衡量至少解决一侧的模型在两个图像中重复相同非空答案的频率。这些指标表明，配对正确性和证据依赖性是相关但不同的：能够的模型在任务关键视觉变化后仍可能未能更新，而当编辑后的图像在顺序设置中跟随早期答案时，某些模型的崩溃现象变得更加严重。更多细节请访问我们的项目页面：https://didizhu-judy.github.io/VisualFLIP/
cs.CV / 40 / 2606.07882
The Cross-Architecture Substrate: A Domain-Transcendent, Calibration-Surviving Geometric Invariant of Modern Vision Encoders
跨架构基底：一种超越领域、耐校准的现代视觉编码器几何不变性
Abstract
Different vision neural networks -- trained to classify, contrast, reconstruct, or match images to text -- should have correspondingly different internal representations. We report that they do not. After training, the top sixteen principal directions of variation inside thirteen modern vision encoders converge to the same sixteen-dimensional geometric object. We call this the cross-architecture substrate and study it with PCA, centred kernel alignment (CKA), and Pang 2026 calibration. The substrate transports across four visual domains (natural photographs, medical CT, satellite, microscopy) at median Procrustes-CKA 0.679, and across eight domains (adding sketches, depth, thermal infrared, astronomy) at 0.604, every pair >0.40. It survives Pang calibration globally (7.4x disc-vs-MAE separation, n=13,394) and locally (4.82-5.30, p<10^{-44}). It is not pixel statistics (0.263), not Gabor features (0.31), not a random projection (0.041), and emerges in the first 10% of training while accuracy keeps climbing. We deliver four applications: a label-free transferability filter beating LogME (3x faster, +0.15 Kendall-tau); a four-way domain detector (99.6% accuracy); a frozen low-shot probe (16 dims beat 768-dim DINOv2 by 3.78pp at N=50 labels per class); and a teacher-free distillation auxiliary matching trained-teacher KD on 33 pairs (7.56pp peak gain at 10% label fraction). The substrate does not cross modalities, does not help cross-paradigm distillation, and does not predict transfer quality (rho=0.08 against transfer accuracy).
Chinese Translation
不同的视觉神经网络——被训练用于分类、对比、重建或将图像与文本匹配——应该具有相应不同的内部表征。然而，我们报告说它们并非如此。在训练后，十三个现代视觉编码器内部的前十六个主要变化方向收敛到同一个十六维几何对象。我们称之为跨架构基底，并通过主成分分析（PCA）、中心核对齐（CKA）和Pang 2026校准对其进行研究。该基底在四个视觉领域（自然照片、医学CT、卫星图像、显微镜图像）中的中位Procrustes-CKA为0.679，在八个领域（增加素描、深度、热红外、天文学）中的中位数为0.604，每对均大于0.40。它在全球范围内（7.4倍的disc-vs-MAE分离，n=13,394）和局部范围内（4.82-5.30，p<10^{-44}）都能存活。它不是像素统计（0.263），不是Gabor特征（0.31），也不是随机投影（0.041），并且在训练的前10%中出现，而准确率持续上升。我们提供了四个应用：一种无标签的可转移性过滤器，超越LogME（速度提高3倍，Kendall-tau增加0.15）；一个四向领域检测器（99.6%的准确率）；一个冻结的低样本探测器（16维超越768维DINOv2，N=50每类标签提高3.78个百分点）；以及一种无教师蒸馏辅助匹配，训练教师KD在33对上（在10%标签比例下峰值增益为7.56个百分点）。该基底不跨模态，不帮助跨范式蒸馏，也不预测转移质量（与转移准确率的相关系数为0.08）。
cs.CV / 41 / 2606.07891
C3VD-DEFCOL: A Deformable Colonoscopy Dataset with Time-Resolved 3D Ground Truth and Realistic Appearance
C3VD-DEFCOL：一个具有时间分辨的三维真实数据和真实外观的可变形结肠镜数据集
Abstract
3D reconstruction could improve colonoscopy by estimating mucosal coverage and alerting clinicians to missed regions during screening. However, algorithm development is limited as no current datasets provide both a realistic in vivo appearance and dense, time-resolved 3D ground truth, especially under non-rigid deformation. We present C3VD-DEFCOL, a framework and dataset for evaluating deformable colonoscopy reconstruction with paired geometry and realistic texture. Starting from C3VD/C3VDv2 colon meshes and camera trajectories, we generate controlled deformations of the colon surface, including peristaltic waves and centerline motion, and render per-frame depth, surface normals, optical flow, camera poses, and time-stamped 3D meshes. We then use the rendered geometry, primarily depth, to condition an LTX-2.3-based sim-to-real translation model that produces RGB clips with in vivo-like mucosal color, texture, vasculature, and specular appearance while preserving the underlying 3D scene structure. The resulting dataset contains 110 videos from 11 unique colon mesh geometries, with varying camera trajectories, appearances, and parameterized deformation regimes, including three peristaltic severity levels that serve as controlled evaluation axes. We evaluate the generated videos using appearance realism, geometric consistency, and temporal consistency metrics, and use the paired ground truth to benchmark the downstream task of pose estimation in deformable 3D reconstruction. Our experiments show how pose estimation error increases with increasing deformation severity, providing a controlled stress test that is not possible with existing in vivo datasets. Overall, C3VD-DEFCOL is designed as a reproducible, quantitative evaluation platform for testing deformable 3D reconstruction algorithms, with the goal of reducing the domain gap between synthetic datasets and in vivo colonoscopy.
Chinese Translation
三维重建可以通过估计粘膜覆盖率并在筛查过程中提醒临床医生注意遗漏区域来改善结肠镜检查。然而，算法开发受到限制，因为目前没有数据集同时提供真实的体内外观和密集的、时间分辨的三维真实数据，尤其是在非刚性变形下。我们提出了C3VD-DEFCOL，一个用于评估可变形结肠镜重建的框架和数据集，具有配对的几何形状和真实的纹理。从C3VD/C3VDv2结肠网格和相机轨迹出发，我们生成了结肠表面的受控变形，包括蠕动波和中心线运动，并渲染每帧的深度、表面法线、光流、相机姿态和时间戳三维网格。然后，我们使用渲染的几何形状，主要是深度，来调节基于LTX-2.3的仿真到真实的转换模型，该模型生成具有体内样粘膜颜色、纹理、血管和镜面外观的RGB片段，同时保留底层三维场景结构。生成的数据集包含来自11种独特结肠网格几何形状的110个视频，具有不同的相机轨迹、外观和参数化变形模式，包括三个蠕动严重程度级别，作为受控评估轴。我们使用外观真实感、几何一致性和时间一致性指标评估生成的视频，并使用配对的真实数据来基准测试可变形三维重建中的姿态估计下游任务。我们的实验表明，姿态估计误差随着变形严重程度的增加而增加，提供了一种现有体内数据集无法实现的受控压力测试。总体而言，C3VD-DEFCOL被设计为一个可重复的、定量的评估平台，用于测试可变形三维重建算法，旨在减少合成数据集与体内结肠镜检查之间的领域差距。
cs.CV / 42 / 2606.07895
TBD-VLA: Temporal Block Diffusion Vision Language Action Model
TBD-VLA：时间块扩散视觉语言动作模型
Abstract
Discrete Vision-Language-Action (VLA) models typically formulate action generation as next-token prediction over discretized action spaces, conditioning each token autoregressively on prior context. While effective, this paradigm incurs high inference latency and largely ignores the temporal structure inherent in action trajectories. Recent efforts introduce parallel decoding to improve efficiency, enabling faster inference, but lack explicit mechanisms for modeling token dependencies. We introduce TBD-VLA, a discrete token-based VLA framework that incorporates block diffusion to enable temporal action generation. We partition action sequences into temporal blocks and perform masked discrete diffusion within each block, while maintaining autoregressive generation across blocks. This design unifies temporal autoregression and parallel action decoding, achieving both strong temporal coherence and improved inference speed. In addition, the explicit temporal modeling enables asynchronous execution of action chunks (e.g., Real-Time Chunking) via temporal in-painting. TBD-VLA significantly outperforms prior VLA approaches in both simulation and real-world manipulation tasks, offering a scalable path toward fast, temporally aware, discrete VLA models. Project webpage: https://tbd-vla.github.io/
Chinese Translation
离散视觉-语言-动作（VLA）模型通常将动作生成表述为对离散动作空间的下一个标记预测，且每个标记都是在先前上下文的自回归条件下生成的。尽管这种范式有效，但它导致了高推理延迟，并在很大程度上忽视了动作轨迹中固有的时间结构。近期的研究引入了并行解码以提高效率，从而实现更快的推理，但缺乏明确的机制来建模标记之间的依赖关系。我们提出了TBD-VLA，这是一种基于离散标记的VLA框架，结合了块扩散以实现时间动作生成。我们将动作序列划分为时间块，并在每个块内执行掩蔽离散扩散，同时在块之间保持自回归生成。这一设计统一了时间自回归和并行动作解码，实现了强大的时间一致性和改进的推理速度。此外，明确的时间建模使得通过时间内插实现动作块的异步执行（例如，实时分块）成为可能。TBD-VLA在模拟和现实世界操作任务中显著优于先前的VLA方法，为快速、具时间感知的离散VLA模型提供了一条可扩展的路径。项目网页：https://tbd-vla.github.io/
cs.CV / 43 / 2606.07907
3D Oral Modelling with Improved Vertex Distribution Using Matching-Based Learning
基于匹配学习的改进顶点分布的三维口腔建模
Abstract
In our previous work, a deep learning-based framework for 3D intraoral reconstruction was proposed. The model directly predicts explicit 3D point cloud coordinates from ten fixed-angle intraoral images, employing MobileNetV2 and Multi-head Attention for multi-view feature fusion, with a combined L1 Loss and Chamfer Distance as the loss function. Although the model achieved an accuracy of 77.49%, predicted vertices tended to concentrate in high-density regions of the ground truth, leaving other regions largely uncovered. In this paper, an improved loss function is proposed to address this limitation. Hungarian matching with filtering and Repulsion Loss are introduced to enforce more uniform vertex distribution across the reconstructed model. The proposed model achieves an accuracy of 68.02%, which is numerically lower than the previous model. However, the vertex clustering issue observed in the prior work is substantially alleviated, with predicted vertices distributed more evenly across the entire reconstructed surface.
Chinese Translation
在我们之前的工作中，提出了一种基于深度学习的三维口腔内重建框架。该模型直接从十个固定角度的口腔内图像中预测显式的三维点云坐标，采用MobileNetV2和多头注意力机制进行多视角特征融合，并使用结合L1损失和Chamfer距离作为损失函数。尽管该模型达到了77.49%的准确率，但预测的顶点往往集中在真实值的高密度区域，导致其他区域基本未被覆盖。本文提出了一种改进的损失函数以解决这一局限性。引入了带过滤的匈牙利匹配和排斥损失，以促进重建模型中更均匀的顶点分布。所提出的模型达到了68.02%的准确率，虽然在数值上低于之前的模型，但在先前工作中观察到的顶点聚集问题得到了显著缓解，预测的顶点在整个重建表面上分布更加均匀。
cs.CV / 44 / 2606.07924
Decoupling Semantics and Logic: A Training-Free Coarse-to-Fine Pipeline for Video Retrieval-Augmented Generation
解耦语义与逻辑：一种无训练的粗到细视频检索增强生成管道
Abstract
This paper presents our system description for the 2nd Workshop on Multimodal Augmented Generation via MultimodAl Retrieval (MAGMaR). Addressing the critical challenges of cross-lingual long-video comprehension, strict persona adherence, and zero-hallucination temporal grounding, we propose a fully training-free, two-stage cascaded Video RAG pipeline. Our architecture strategically decouples semantic retrieval from cognitive logical reasoning through a modality-aware division of labor. In the first stage, a high-recall semantic pre-fetching module employs dense retrieval using only high-fidelity visual summaries and global text descriptions, explicitly isolating noisy modalities (e.g., OCR and ASR) to maintain a pristine vector space. In the second stage, an Adaptive, Iterative, and Reasoning-based (A.I.R.) filtering agent, powered by a commercial Large Language Model (LLM), performs fine-grained cognitive reranking. The agent re-incorporates full multimodal contexts to enforce strict logical alignment with user personas, effectively pruning semantically similar but logically irrelevant candidates. Finally, a Prompt Sculpting mechanism constrains the generator to synthesize the distilled subset into strictly formatted JSON responses with exact chunk-level citations. Evaluated on the RAG track, our resource-aware approach shows exceptional precision in both information retrieval and persona-conditioned generation.
Chinese Translation
本文介绍了我们在第二届多模态增强生成与多模态检索研讨会（MAGMaR）上的系统描述。针对跨语言长视频理解、严格的人物遵循和零幻觉时间定位等关键挑战，我们提出了一种完全无训练的两阶段级联视频检索增强生成（Video RAG）管道。我们的架构通过模态感知的分工策略，战略性地将语义检索与认知逻辑推理解耦。在第一阶段，高召回率的语义预提取模块仅使用高保真视觉摘要和全局文本描述进行密集检索，明确隔离噪声模态（如光学字符识别（OCR）和自动语音识别（ASR）），以保持纯净的向量空间。在第二阶段，由商业大型语言模型（LLM）驱动的自适应、迭代和基于推理的（A.I.R.）过滤代理执行细粒度的认知重新排序。该代理重新整合完整的多模态上下文，以确保与用户人物的严格逻辑一致性，有效地修剪语义相似但逻辑无关的候选项。最后，提示雕刻机制限制生成器将提炼的子集合成严格格式化的JSON响应，并附上精确的块级引用。在RAG轨道上评估，我们的资源感知方法在信息检索和基于人物的生成方面表现出卓越的精确度。
cs.CV / 45 / 2606.07932
LEGS: Laplacian-Enhanced Gaussian Splatting with a Nonlinear Weighted Loss
LEGS：具有非线性加权损失的拉普拉斯增强高斯溅射
Abstract
3D Gaussian Splatting (3DGS) has become an efficient explicit representation for radiance field reconstruction and real-time novel view synthesis. However, its standard photometric loss treats flat and structure-rich regions similarly, which may limit the recovery of sharp contours and fine details. Edge-Guided Gaussian Splatting (EGGS) improves structure awareness through edge-guided weighting, but mainly relies on first-order gradient responses and linear weighting. In this paper, we propose LEGS, a Laplacian-Enhanced Gaussian Splatting method with a nonlinearly weighted loss. LEGS replaces first-order gradient guidance with second-order Laplacian structural guidance and maps the normalized Laplacian response into pixel-wise weights through nonlinear response-to-weight functions. The proposed loss improves structure-aware Gaussian optimization while keeping the original 3DGS rendering pipeline unchanged. Experiments on the full Tanks\&Temples and Mip-NeRF360 datasets show that LEGS improves peak signal-to-noise ratio (PSNR) by up to 1.68 dB over 3DGS and up to 0.52 dB over EGGS. Incorporating the proposed second-order nonlinear weighting strategy into FastGS and FasterGS further improves PSNR by up to 1.69 dB, demonstrating its effectiveness as a general loss-level extension for Gaussian Splatting pipelines with potential applications in AR/VR, immersive visualization, and real-time 3D content generation.
Chinese Translation
3D高斯溅射（3DGS）已成为辐射场重建和实时新视图合成的有效显式表示方法。然而，其标准光度损失对平坦和结构丰富区域的处理相似，这可能限制了锐利轮廓和细节的恢复。边缘引导高斯溅射（EGGS）通过边缘引导加权提高了结构意识，但主要依赖于一阶梯度响应和线性加权。本文提出了LEGS，一种具有非线性加权损失的拉普拉斯增强高斯溅射方法。LEGS用二阶拉普拉斯结构引导替代了一阶梯度引导，并通过非线性响应到权重函数将归一化的拉普拉斯响应映射为像素级权重。所提出的损失在保持原始3DGS渲染管线不变的同时，改善了结构感知的高斯优化。在完整的Tanks&Temples和Mip-NeRF360数据集上的实验表明，LEGS在峰值信噪比（PSNR）上比3DGS提高了最多1.68 dB，比EGGS提高了最多0.52 dB。将所提出的二阶非线性加权策略纳入FastGS和FasterGS进一步将PSNR提高了最多1.69 dB，证明其作为高斯溅射管线的一种通用损失级扩展的有效性，具有在增强现实/虚拟现实、沉浸式可视化和实时3D内容生成中的潜在应用。
cs.CV / 46 / 2606.07935
REACT 2026: The Fourth Multiple Appropriate Facial Reaction Generation Challenge: Personalised MAFRG and Appropriate EEG Reaction Prediction
REACT 2026：第四届多重适当面部反应生成挑战：个性化MAFRG和适当的EEG反应预测
Abstract
In dyadic interactions, various human facial reactions could be appropriate for responding to each human speaker behaviour. Following the successful organisation of the REACT 2023, 2024 and 2025 challenge series, a body of generative deep learning (DL) models have been developed for the problem of multiple appropriate facial reaction generation (MAFRG). This year, we propose the REACT 2026 challenge encouraging the development and benchmarking of Machine Learning (ML) models that can generate multiple personalised, appropriate, diverse, realistic and synchronised human-style facial reactions expressed by a specific human listener for responding to each given speaker behaviour. As a key of the challenge, we continuously provide challenge participants with MARS dataset introduced by REACT 2025 but additionally provide individual-level Big-Five personality labels and EEG recordings. This introduces a new one-to-many personalised facial reaction generation setting combining human expressive behavioural, affective and neurophysiological signals, which remains largely unexplored in current dyadic interaction modelling. This paper also presents the challenge guidelines and new baselines on the four proposed sub-challenges: Offline generic and personalised MAFRG as well as Online generic and personalised MAFRG, respectively, which are publicly available at https://github.com/reactmultimodalchallenge/baseline_react2026.
Chinese Translation
在双人互动中，各种人类面部反应可能适合于回应每个说话者的行为。在成功组织了REACT 2023、2024和2025挑战系列之后，已经开发出一系列生成性深度学习（DL）模型来解决多重适当面部反应生成（MAFRG）的问题。今年，我们提出REACT 2026挑战，鼓励开发和基准测试能够生成多种个性化、适当、多样、真实且同步的人类风格面部反应的机器学习（ML）模型，以回应特定人类听众对每个给定说话者行为的反应。作为挑战的关键，我们持续向挑战参与者提供由REACT 2025引入的MARS数据集，并额外提供个体层面的五大人格标签和EEG记录。这引入了一种新的多对一个性化面部反应生成设置，结合了人类的表现性行为、情感和神经生理信号，这在当前的双人互动建模中仍然很大程度上未被探索。本文还介绍了挑战指南和四个提议的子挑战的新基准：离线通用和个性化MAFRG，以及在线通用和个性化MAFRG，相关信息可在https://github.com/reactmultimodalchallenge/baseline_react2026获取。
cs.CV / 47 / 2606.07938
DAL-PCQA: Enabling Distortion-Level and Language-Driven Reasoning for Point Cloud Quality Assessment
DAL-PCQA：实现基于失真级别和语言驱动的点云质量评估
Abstract
Point Cloud Quality Assessment (PCQA) methods typically predict scalar Mean Opinion Scores (MOS), which quantify overall perceptual degradation but do not reveal its causes. In contrast, human observers naturally reason in terms of specific distortions such as blur, color shifts, point density changes, missing regions, and geometric deformations. To close this gap, we introduce DAL-PCQA, a distortion-aware, language-annotated dataset for PCQA. DAL-PCQA augments benchmark point clouds with multi-level distortion severity labels, discrete quality categories, and structured natural language descriptions aligned with human perception. We define a point-cloud-specific distortion taxonomy that covers both photometric and geometric artifacts. Statistical analysis reveals characteristic degradation patterns across distortion types and quality levels. To assess the utility of these annotations, we compare zero-shot and fine-tuned multimodal models for generating perceptual quality descriptions. Experiments show that distortion-aware supervision substantially improves lexical and semantic alignment with ground-truth descriptions. By enabling interpretable, distortion-level reasoning, DAL-PCQA facilitates language-driven, explainable point cloud quality assessment. The dataset is publicly available at https://github.com/swarna96/DAL-PCQA.
Chinese Translation
点云质量评估（PCQA）方法通常预测标量平均意见分数（MOS），该分数量化整体感知退化，但并未揭示其原因。相比之下，人类观察者自然地以特定失真（如模糊、颜色偏移、点密度变化、缺失区域和几何变形）进行推理。为了解决这一差距，我们引入了DAL-PCQA，这是一个针对PCQA的失真感知、语言注释数据集。DAL-PCQA通过多级失真严重性标签、离散质量类别和与人类感知相一致的结构化自然语言描述来增强基准点云。我们定义了一种特定于点云的失真分类法，涵盖光度和几何伪影。统计分析揭示了不同失真类型和质量水平之间的特征退化模式。为了评估这些注释的实用性，我们比较了零样本和微调的多模态模型在生成感知质量描述方面的表现。实验表明，失真感知的监督显著提高了与真实描述的词汇和语义一致性。通过实现可解释的失真级别推理，DAL-PCQA促进了基于语言的可解释点云质量评估。该数据集已公开发布，网址为 https://github.com/swarna96/DAL-PCQA。
cs.CV / 48 / 2606.07962
ChronoPhyBench: Do MLLMs Truly Understand the World or Merely Exploit Language Priors?
ChronoPhyBench：大规模多模态语言模型是否真正理解世界，还是仅仅利用语言先验？
Abstract
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in open-world reasoning and understanding. However, a critical ambiguity persists: it remains unclear whether these models genuinely synthesize cross-modal information to construct physically grounded reasoning chains, or if they merely exploit strong language priors to mask single-modality reliance, thereby hallucinating advanced multimodal capabilities. Motivated by this, and to rigorously mitigate language modality bias and shortcuts, we propose a novel multimodal Chrono}logical Physical Dynamics Reasoning Benchmark ChronoPhyBench, which unifies next state prediction with Visual Question Answering (VQA) paradigms by conditioning on historical video context and textual captions to enforce models to deduce subsequent physical states through both single image selection and the inherently more complex task of multiple frame chronological sorting. Concurrently, we construct a large-scale multimodal reasoning dataset curated using the ChronoPhyBench criteria, comprising over 10,000 long-form videos paired with meticulously annotated captions, totaling 5M tokens. Our experimental evaluations reveal a stark contrast to conclusions drawn by previous benchmarks. The capacity of current open-source models to perform physically grounded multimodal reasoning remains in its infancy. Ultimately, this work seeks to systematically stress-test the reasoning capabilities of multimodal models, quantify hallucination rates, and advance the development of Physical AI, thereby providing the community with a robust and transparent evaluation framework toward Artificial General Intelligence (AGI).
Chinese Translation
近期多模态大规模语言模型（MLLMs）的进展展示了其在开放世界推理和理解方面的卓越能力。然而，仍然存在一个关键的模糊性：这些模型是否真正合成跨模态信息以构建物理基础的推理链，还是仅仅利用强大的语言先验来掩盖对单一模态的依赖，从而幻觉出高级的多模态能力。基于此动机，为了严格减轻语言模态偏见和捷径，我们提出了一种新颖的多模态逻辑物理动态推理基准——ChronoPhyBench，该基准通过依赖历史视频上下文和文本说明，将下一个状态预测与视觉问答（VQA）范式统一，强制模型通过单幅图像选择和更复杂的多帧时间排序任务推导后续物理状态。同时，我们构建了一个大型多模态推理数据集，依据ChronoPhyBench标准精心策划，包含超过10,000个长视频及其精细注释的说明，总计5M个标记。我们的实验评估显示，与之前基准得出的结论形成鲜明对比，目前开源模型在进行物理基础的多模态推理方面仍处于初级阶段。最终，本研究旨在系统性地压力测试多模态模型的推理能力，量化幻觉率，并推动物理人工智能的发展，从而为社区提供一个稳健且透明的评估框架，朝向人工通用智能（AGI）迈进。
cs.CV / 49 / 2606.07967
DisCo: World Models with Discrete Camera Motion Control
DisCo：具有离散相机运动控制的世界模型
Abstract
Controllable video world models target interactive world exploration, where models must faithfully execute explicit action commands while preserving visual quality and temporal coherence. However, most existing approaches rely on continuous camera trajectories as action conditions, which often lead to unreliable action following, especially under complex motion sequences. In this work, we identify action representation entanglement as a key bottleneck in controllable video generation, and show that continuous camera representations lead to high feature similarity across distinct motion patterns, degrading action controllability. Based on this insight, we propose DisCo, a controllable video world model that conditions generation on a compact set of discrete action primitives to improve action separability. We further introduce DisCoBench, a comprehensive benchmark for evaluating the ability of models in short-term, long-horizon, and highly dynamic exploration scenarios. Extensive experiments demonstrate that DisCo achieves significantly more reliable action following while preserving visual quality.
Chinese Translation
可控视频世界模型旨在实现互动世界探索，其中模型必须忠实地执行明确的动作指令，同时保持视觉质量和时间一致性。然而，大多数现有方法依赖于连续的相机轨迹作为动作条件，这往往导致不可靠的动作跟随，尤其是在复杂的运动序列下。在本研究中，我们将动作表示纠缠识别为可控视频生成中的关键瓶颈，并表明连续相机表示导致不同运动模式之间的特征相似性过高，从而降低了动作可控性。基于这一见解，我们提出了DisCo，一种可控视频世界模型，它基于一组紧凑的离散动作原语进行生成条件，以改善动作的可分离性。我们进一步引入了DisCoBench，这是一个综合基准，用于评估模型在短期、长时间和高度动态探索场景中的能力。大量实验表明，DisCo在保持视觉质量的同时，实现了显著更可靠的动作跟随。
cs.CV / 50 / 2606.07985
FMRFusion: Frequency-Aware Multi-View Representation Learning for Heterogeneous Image Fusion
FMRFusion：面向频率的多视角表征学习用于异构图像融合
Abstract
Infrared and visible image fusion aims to generate a composite image that retains significant target information and preserves detailed textures, integrating two heterogeneous modalities. Previous image fusion methods typically adopt a single-module stacking approach to extract features from the two modalities. However, these approaches may result in incomplete learning of their distinct characteristics, thereby limiting the fusion effectiveness and constrain ing robustness in real-world heterogeneous data scenarios. To address these challenges, we propose FMRFusion, a frequency-aware multi-view representation learning network for Heterogeneous Image Fusion. A Multi-Scale Struc tural Perception Module is introduced to effectively capture discriminative structures, extracting fine-grained local structures and essential contextual information. A bilinear frequency decomposition mechanism is employed to sepa rate features into high-frequency and low-frequency components, enabling joint modeling of local details and global representations across different frequency domains. Moreover, a Cross-View Complementary Interaction is incorpo rated to explicitly model and fuse the complementary characteristics between reflected light information and radiative intensity responses, facilitating effective cross-view interaction. We further improve the Performance of the fused results by flow matching, which progressively refines the fused features by learning the transformation from coarse data to high-quality representations. Extensive experiments conducted on multiple benchmark datasets demonstrate that FMRFusion achieves superior and consistent performance across a range of fusion tasks, especially in nighttime scenarios
Chinese Translation
红外与可见光图像融合旨在生成一幅复合图像，保留重要的目标信息并保持细致的纹理，整合两种异构模态。以往的图像融合方法通常采用单模块堆叠的方法从两种模态中提取特征。然而，这些方法可能导致对其独特特征的不完全学习，从而限制了融合效果，并在现实世界的异构数据场景中约束了鲁棒性。为了解决这些挑战，我们提出了FMRFusion，一种面向频率的多视角表征学习网络，用于异构图像融合。我们引入了一种多尺度结构感知模块，以有效捕捉判别性结构，提取细粒度的局部结构和重要的上下文信息。采用双线性频率分解机制将特征分离为高频和低频成分，使得在不同频率域中能够联合建模局部细节和全局表征。此外，结合了交叉视角互补交互，以显式建模和融合反射光信息与辐射强度响应之间的互补特征，促进有效的交叉视角交互。我们进一步通过流匹配提升融合结果的性能，该方法通过学习从粗糙数据到高质量表征的转化，逐步优化融合特征。在多个基准数据集上进行的大量实验表明，FMRFusion在多种融合任务中实现了卓越且一致的性能，尤其是在夜间场景中。
cs.CV / 51 / 2606.08001
Learning a Semantic Calibration Network for Open-Vocabulary Semantic Segmentation
学习开放词汇语义分割的语义校准网络
Abstract
Semantic image segmentation assigns a predefined category label to each pixel, has achieved significant progress lately. Open-Vocabulary Segmentation (OVS) extends the segmentation task from a fixed set to an open set, enabling the identification and segmentation of novel concepts based on arbitrary text inputs, such as category names or descriptions. In this paper, we propose a novel Semantic Calibration Network (SCN) for open-vocabulary semantic segmentation. Different from prior approaches that focus on feature aggregation or simple fine-tuning of pre-trained models, SCN refines the mask classification process by explicitly modeling the semantic correlations between classes, aiming to enhance the model's discriminative power while effectively preserving the generalization abilities of the pre-trained CLIP model. Specifically, SCN comprises two core components: Class Disambiguation (CD) and Logits Fusion (LF). First, a cross-attention mechanism is utilized to transform the text embeddings into visually aware pseudo-text embeddings, in order to derive an enhanced similarity score that complements the original mask-text similarity score. Subsequently, the Class Disambiguation module captures implicit inter-class dependencies through a residual architecture to effectively resolve semantic ambiguities. Finally, the Logits Fusion module dynamically integrates multifaceted semantic evidence to ensure that the model achieves a robust semantic consensus while maintaining CLIP's inherent generalization capability. Comprehensive experimental results on mainstream benchmarks demonstrate that the proposed method achieves significant performance improvements compared to state-of-the-art algorithms.
Chinese Translation
语义图像分割为每个像素分配预定义的类别标签，近年来取得了显著进展。开放词汇分割（Open-Vocabulary Segmentation, OVS）将分割任务从固定集合扩展到开放集合，使得能够基于任意文本输入（如类别名称或描述）识别和分割新概念。本文提出了一种新颖的开放词汇语义分割的语义校准网络（Semantic Calibration Network, SCN）。与以往关注特征聚合或简单微调预训练模型的方法不同，SCN通过明确建模类别之间的语义关联来优化掩膜分类过程，旨在增强模型的区分能力，同时有效保留预训练CLIP模型的泛化能力。具体而言，SCN由两个核心组件组成：类别消歧（Class Disambiguation, CD）和逻辑融合（Logits Fusion, LF）。首先，利用交叉注意力机制将文本嵌入转换为具有视觉感知的伪文本嵌入，以得出增强的相似性得分，从而补充原始的掩膜-文本相似性得分。随后，类别消歧模块通过残差架构捕捉隐含的类间依赖关系，有效解决语义模糊问题。最后，逻辑融合模块动态整合多方面的语义证据，以确保模型在保持CLIP固有的泛化能力的同时，实现稳健的语义共识。在主流基准上的全面实验结果表明，所提出的方法相比于最先进的算法实现了显著的性能提升。
cs.CV / 52 / 2606.08002
Aqua Boundary-Saliency Attention Module for Lightweight Underwater Salient Instance Segmentation Detection Transformer
轻量级水下显著实例分割检测变换器的水域边界显著性注意模块
Abstract
Underwater instance segmentation integrates pixel-level mask prediction and instance-level discrimination for marine resource exploration, ecological monitoring, and underwater robotic perception. Recent prompt-based and auxiliary-modality methods improve mask quality, but their reliance on large foundation models, prompt generation, or extra modality estimation complicates efficient deployment. This work introduces Lightweight Underwater Salient Instance Segmentation Detection Transformer (LUSIS-DETR), a compact detection-transformer framework built around the Aqua Boundary-Saliency Attention Module (AquaBSAM). AquaBSAM embeds underwater boundary, contrast, attenuation, chroma, dark-channel, and center-prior cues into DINOv2-initialized multi-scale features through bounded residual modulation, while auxiliary mask supervision and small-object copy-paste are training-only. Extensive evaluation on four recent underwater instance segmentation datasets, UIIS, UIIS10K, USIS10K, and USIS16K, shows competitively leading performance against previous state-of-the-art works across category-aware and salient-instance protocols. TensorRT half-precision (FP16) benchmarking on an NVIDIA T4 graphics processing unit (GPU) achieves 4.31-6.34 milliseconds (ms) latency, supporting real-time inference under an accessible reproduction setting.
Chinese Translation
水下实例分割将像素级掩膜预测与实例级区分相结合，用于海洋资源探索、生态监测和水下机器人感知。近期的基于提示和辅助模态的方法提高了掩膜质量，但它们对大型基础模型、提示生成或额外模态估计的依赖使得高效部署变得复杂。本文提出了轻量级水下显著实例分割检测变换器（Lightweight Underwater Salient Instance Segmentation Detection Transformer，LUSIS-DETR），这是一个围绕水域边界显著性注意模块（Aqua Boundary-Saliency Attention Module，AquaBSAM）构建的紧凑检测变换器框架。AquaBSAM通过有界残差调制将水下边界、对比度、衰减、色度、暗通道和中心先验线索嵌入到DINOv2初始化的多尺度特征中，而辅助掩膜监督和小物体复制粘贴仅在训练阶段使用。在四个最新的水下实例分割数据集UIIS、UIIS10K、USIS10K和USIS16K上的广泛评估显示，与之前的最先进工作相比，在类别感知和显著实例协议上具有竞争力的领先性能。在NVIDIA T4图形处理单元（GPU）上的TensorRT半精度（FP16）基准测试实现了4.31-6.34毫秒（ms）的延迟，支持在可访问的重现设置下进行实时推理。
cs.CV / 53 / 2606.08014
GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence
GVC-Seg：通过几何视觉对应实现无训练的3D实例分割
Abstract
Accurate 3D instance segmentation in point cloud data is critical for machine vision applications. Recent advancements leverage multiple pre-trained foundation models to generate 3D proposals, followed by the application of proposal aggregation methods, which significantly enhance performance. However, they often produce sub-optimal results due to inherent variations in confidence levels across different segmentation models, resulting in a bias toward the model with higher confidence. This bias is inherently model-dependent and is influenced by factors such as data preprocessing techniques and training strategies. To address this bias, we propose a novel, training-free 3D instance segmentation approach via Geometric Visual Correspondence (GVC-Seg), which exploits the correspondence between 3D geometric cues and 2D visual cues to mitigate the confidence bias. Additionally, a 3D proposal generation module and a mask-aware CLIP feature extraction module are introduced during the instance mask generation and instance semantic reasoning, respectively. In this way, GVC-Seg enhances proposal quality assessment, ensuring unbiased ensemble learning across different models. Extensive experiments demonstrate that our method achieves state-of-the-art performance on several challenging benchmarks, while also exhibiting strong potential in open-vocabulary semantic segmentation settings.
Chinese Translation
在点云数据中，准确的3D实例分割对机器视觉应用至关重要。近期的进展利用多个预训练的基础模型生成3D提议，随后应用提议聚合方法，这显著提升了性能。然而，由于不同分割模型之间的置信度水平存在固有差异，它们往往产生次优结果，导致对置信度较高的模型产生偏向。这种偏向本质上依赖于模型，并受到数据预处理技术和训练策略等因素的影响。为了解决这一偏向，我们提出了一种新颖的无训练3D实例分割方法，通过几何视觉对应（GVC-Seg），利用3D几何线索与2D视觉线索之间的对应关系来减轻置信度偏向。此外，在实例掩膜生成和实例语义推理过程中，分别引入了3D提议生成模块和掩膜感知的CLIP特征提取模块。通过这种方式，GVC-Seg增强了提议质量评估，确保在不同模型之间进行无偏的集成学习。大量实验表明，我们的方法在多个具有挑战性的基准测试中实现了最先进的性能，同时在开放词汇语义分割设置中也展现出强大的潜力。
cs.CV / 54 / 2606.08016
IEA: Amateur-Friendly Conversational Image Editing Agent via Three Stages of Multitask Alignment
IEA：通过三阶段多任务对齐实现业余友好的对话式图像编辑代理
Abstract
Current image editing software often hinges on fixed filters or expert tuning, leaving a gap between amateur users' intent and outcomes. Creations by generative models may contain artifacts, implausible details, or stylistic drift away from photorealism and offer little insight into why an edit was made. We propose IEA, a conversational Image Editing Agent that learns to operate parameterized tools in an explicit, interpretable action space. IEA is trained via a three-stage multitask pipeline: (1) SFT on distilled expert edits, (2) GRPO with rewards for likeness improvement, tool usefulness, and intent summarization, and (3) large-scale synthetic fine-tuning to jointly master image editing, refinement, and user intent summarization. By manipulating 16 editing tools step by step, IEA produces transparent edit traces that can be inspected and debugged. In quantitative experiments, it attains a lower pixel distance on the edit task and a higher ROUGE-L on the summary task than strong baselines. In user studies, it ranks best among tool-calling methods for instruction following while surpassing generative methods in overall perceptual quality. Our results validate interpretable, tool-centric VLMs as a reliable path to human instruction-guided image retouching.
Chinese Translation
当前的图像编辑软件通常依赖于固定的滤镜或专家调优，导致业余用户的意图与结果之间存在差距。生成模型的创作可能包含伪影、不可信的细节或风格偏离真实感，并且对编辑为何被执行提供的洞见有限。我们提出了IEA，一个对话式图像编辑代理，能够在明确、可解释的行动空间中学习操作参数化工具。IEA通过三阶段多任务管道进行训练：（1）在提炼的专家编辑上进行监督微调（SFT），（2）通过奖励相似度提升、工具实用性和意图总结进行生成奖励优化（GRPO），以及（3）进行大规模合成微调，以共同掌握图像编辑、精细化和用户意图总结。通过逐步操作16种编辑工具，IEA生成透明的编辑痕迹，便于检查和调试。在定量实验中，它在编辑任务上获得了更低的像素距离，在摘要任务上获得了更高的ROUGE-L，相较于强基线表现更佳。在用户研究中，它在遵循指令的工具调用方法中排名最佳，同时在整体感知质量上超越了生成方法。我们的结果验证了可解释的、以工具为中心的视觉语言模型（VLMs）作为人类指令引导的图像修饰的可靠路径。
cs.CV / 55 / 2606.08031
Vision-Language Asymmetry in Bistable Image Captioning
双稳态图像描述中的视觉-语言不对称性
Abstract
Wittgenstein's duck-rabbit poses a question for vision-language models: when a model captions an ambiguous image, where in the model is the commitment to one aspect made? We address this with a 3,320-generation behavioral baseline over 83 bistable stimuli that surfaces three regimes (default-dominant, force-dominant, force-balanced) under neutral vs forced-choice prompting, then probe the underlying representations using a TopK sparse autoencoder we train on the CLIP layer that LLaVA-1.6-7B actually consumes (validation EV 0.93). Across 69 bistable stimuli with both per-aspect feature pools available, 72% (50/69) show simultaneous activation of both pools at the vision tower, including 12/12 default-dominant duck/rabbit and 7/8 force-balanced young/old. Causal steering at CLIP layer 22 flips captions on default-dominant stimuli (33% rabbit-flip rate under a fluency guard) but cannot flip captions on force-balanced young/old at any tested coefficient, despite their vision-side superposition. The dominance bottleneck lives downstream of the vision tower; the gap between vision-side representation and language-side commitment is an empirical handle on the seeing/seeing-as distinction. We also flag a methodological note: rank-based statistics on TopK SAE outputs require tie-corrected ranking to avoid silent row-order bias.
Chinese Translation
维特根斯坦的鸭子-兔子图像为视觉-语言模型提出了一个问题：当模型为模糊图像生成描述时，模型中的哪个部分做出了对某一方面的承诺？我们通过对83个双稳态刺激的3,320次生成行为基线进行研究，揭示了在中性与强制选择提示下的三种状态（默认主导、强制主导、强制平衡）。接着，我们使用在LLaVA-1.6-7B实际使用的CLIP层上训练的TopK稀疏自编码器探讨其潜在表征（验证EV 0.93）。在69个双稳态刺激中，72%（50/69）显示在视觉塔上同时激活了两个特征池，包括12/12个默认主导的鸭子/兔子和7/8个强制平衡的年轻/老年。对CLIP层22的因果引导在默认主导刺激上翻转描述（在流畅性保护下的兔子翻转率为33%），但在任何测试系数下都无法翻转强制平衡的年轻/老年描述，尽管它们在视觉侧存在重叠。主导瓶颈位于视觉塔下游；视觉侧表征与语言侧承诺之间的差距是对“看/看作”的区分的实证把握。我们还指出一个方法论的注意事项：TopK SAE输出的基于排名的统计需要进行平局修正排名，以避免潜在的行序偏差。
cs.CV / 56 / 2606.08033
Balancing Real and Synthetic Data for CNN-based Masonry Crack Detection
平衡真实数据与合成数据用于基于卷积神经网络的砖石裂缝检测
Abstract
Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages. Advances in deep learning (DL), particularly convolutional neural networks (CNNs), have enabled scalable solutions for automated crack detection. However, CNN performance strongly depends on the availability of large and diverse datasets, which is particularly challenging for complex surfaces such as masonry. Collecting sufficient real data is time-consuming, while publicly available datasets may not be adequate. To address this limitation, we explored generating synthetic crack data, which complements real data and improves training effectiveness. The real dataset consists of masonry crack images collected from buildings in Bologna and surrounding areas. In contrast, the synthetic dataset was generated using a crack overlay tool that adds cracks to background images in a controlled orientation and placement. The real dataset was used to train several DL architectures, to identify the best-performing model (InceptionV4) employed for experiments with generated data. Six training scenarios were tested in InceptionV4 by varying the ratio of real and synthetic data, with evaluation performed on a test set composed of real images using the F1-score and mean Intersection over Union (mIoU) metrics. Results show that training on synthetic data plus a modest addition of 20% real data achieves results comparable to training on real data only. Moreover, the 20/80 scenario (synthetic/real) achieved an 76% F1-score and 80% mean IoU, outperforming the real-only case. As can be seen, the method demonstrates the potential of synthetic data to reduce collection efforts while enhancing crack detection accuracy.
Chinese Translation
裂缝是建筑健康的重要指标，早期识别对于防止严重损害至关重要。深度学习（DL）特别是卷积神经网络（CNN）的进展，使得自动裂缝检测的可扩展解决方案成为可能。然而，CNN的性能在很大程度上依赖于大量且多样化的数据集，这对于复杂表面如砖石结构尤其具有挑战性。收集足够的真实数据耗时较长，而公开可用的数据集可能不足以满足需求。为了解决这一限制，我们探索了生成合成裂缝数据的方法，以补充真实数据并提高训练效果。真实数据集由从博洛尼亚及周边地区建筑中收集的砖石裂缝图像组成。相对而言，合成数据集是使用裂缝叠加工具生成的，该工具以受控的方向和位置将裂缝添加到背景图像中。真实数据集用于训练多种深度学习架构，以识别表现最佳的模型（InceptionV4），并用于与生成数据的实验。通过改变真实数据与合成数据的比例，我们在InceptionV4中测试了六种训练场景，并使用F1分数和平均交并比（mIoU）指标对由真实图像组成的测试集进行了评估。结果表明，使用合成数据加上适度的20%真实数据进行训练，取得的结果与仅使用真实数据进行训练的结果相当。此外，20/80场景（合成/真实）达到了76%的F1分数和80%的平均IoU，优于仅使用真实数据的情况。由此可见，该方法展示了合成数据在减少收集工作量的同时提高裂缝检测准确性的潜力。
cs.CV / 57 / 2606.08034
Sci-Rho: A Multilingual Visually-Grounded Symbolic Benchmark for STEM Problems
Sci-Rho：一个多语言视觉基础的符号基准测试，用于STEM问题
Abstract
Symbolic benchmarks have emerged as a key approach to assess model robustness under minor modifications to STEM-related questions. However, existing symbolic benchmarks mostly remain limited to mathematical reasoning, lack visual grounding, and are predominantly in English. In this work, we introduce Sci-Rho (Science Rhobustness), a dynamic benchmark for visually-grounded STEM problems spanning five subjects and seven languages, comprising 4,242 problem templates (606 per language) crafted by domain experts, including Olympiad medalists. Each template is implemented as executable Python code that generates diverse but equivalent problem instances by varying numerical values, visual patterns, geometric shapes, color schemes, and function types, resulting in 42,420 instances in total, each paired with reasoning steps and ground-truth solutions. We evaluated 17 state-of-the-art VLMs and discovered a noticeable gap between worst-case accuracy (defined as the proportion of problem templates that a model answers correctly across every generated variation) and average accuracy. We also discovered that smaller models show noticeable performance degradation across languages, whereas proprietary and larger models remain robust. Step-level evaluation reflects this same trend, revealing a significant gap between average F1 and worst-case F1 scores. Finally, our inspection of attention heads of a VLM reveals substantial cross-lingual variation in the relative attention allocated to image tokens compared to text tokens. Our work highlights the importance of evaluation beyond static benchmarks as a metric to measure the quality of VLMs.
Chinese Translation
符号基准测试已成为评估模型在STEM相关问题的小幅修改下的鲁棒性的一种关键方法。然而，现有的符号基准测试大多仅限于数学推理，缺乏视觉基础，并且主要以英语为主。在本研究中，我们介绍了Sci-Rho（科学鲁棒性），这是一个动态基准测试，涵盖五个学科和七种语言，包含由领域专家（包括奥林匹克奖牌获得者）设计的4,242个问题模板（每种语言606个）。每个模板都以可执行的Python代码实现，通过改变数值、视觉模式、几何形状、配色方案和函数类型生成多样但等效的问题实例，总共生成42,420个实例，每个实例都配有推理步骤和真实解答。我们评估了17个最先进的视觉语言模型（VLM），发现最坏情况下的准确率（定义为模型在每个生成变体中正确回答的问题模板的比例）与平均准确率之间存在明显差距。我们还发现较小的模型在不同语言间表现出明显的性能下降，而专有的大型模型则保持鲁棒性。逐步评估反映了相同的趋势，显示平均F1分数与最坏情况下F1分数之间存在显著差距。最后，我们对VLM的注意力头的检查揭示了在图像标记与文本标记之间分配的相对注意力存在显著的跨语言差异。我们的工作强调了超越静态基准测试的评估作为衡量VLM质量的重要性。
cs.CV / 58 / 2606.08035
DyCo-RL: Dynamic Cross-Modal Coordination for Visual Reasoning
DyCo-RL：用于视觉推理的动态跨模态协调
Abstract
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a leading paradigm for enhancing visual reasoning in Multimodal Large Language Models (MLLMs). However, existing RLVR methods optimize primarily for the reasoning outcome, fundamentally overlooking the fine-grained cross-modal coordination required during the generation process. Through token-level analyses and controlled interventions, we reveal that during Chain-of-Thought (CoT) reasoning, MLLMs frequently fail to dynamically alternate between extracting visual evidence and synthesizing textual context-a coordination breakdown that is causally linked to reasoning failures. Motivated by these findings, we propose DyCo-RL, which integrates dynamic cross-modal coordination into RLVR optimization. Specifically, DyCo-RL uses the Fisher-Rao geodesic distance to measure within-modality attention shifts, assigning tokens to either visually-oriented or text-oriented functional roles. It then evaluates the alignment between a token's actual attention allocation and its assigned role, leveraging this score for alignment-guided advantage reweighting during policy optimization. Extensive experiments demonstrate that the algorithm-agnostic DyCo-RL, when applied to Qwen2.5-VL-3B/7B, consistently improves four representative RLVR algorithms across seven benchmarks spanning visual-centric and mathematical reasoning.
Chinese Translation
可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）已成为增强多模态大型语言模型（Multimodal Large Language Models, MLLMs）视觉推理的主要范式。然而，现有的RLVR方法主要优化推理结果，基本上忽视了生成过程中所需的细粒度跨模态协调。通过对标记级别的分析和控制干预，我们揭示了在思维链（Chain-of-Thought, CoT）推理过程中，MLLMs经常未能动态交替提取视觉证据和综合文本上下文——这种协调失效与推理失败有因果关系。基于这些发现，我们提出了DyCo-RL，它将动态跨模态协调集成到RLVR优化中。具体而言，DyCo-RL使用Fisher-Rao测地距离来衡量模态内注意力的转移，将标记分配为视觉导向或文本导向的功能角色。然后，它评估标记的实际注意力分配与其分配角色之间的对齐程度，利用该评分在策略优化过程中进行对齐引导的优势重加权。大量实验表明，当DyCo-RL应用于Qwen2.5-VL-3B/7B时，这一算法无关的DyCo-RL在七个涵盖视觉中心和数学推理的基准测试中，始终提升了四种代表性的RLVR算法的表现。
cs.CV / 59 / 2606.08063
Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding?
Robust-U1：多模态大语言模型能否自我恢复损坏的视觉内容以实现稳健理解？
Abstract
Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly under real-world visual corruptions. While existing robustness enhancement approaches exist, they are limited: black-box feature alignment lacks interpretability, and white-box text-based reasoning cannot restore lost pixel-level details. This work investigates a fundamental research question: Can MLLMs recover corrupted visual content by themselves? To address this, we propose Robust-U1, a novel framework that equips MLLMs with explicit visual self-recovery capability for robust understanding. The approach comprises three core stages: supervised fine-tuning for initial reconstruction, reinforcement learning with dual rewards (pixel-level SSIM and semantic-level CLIP similarity) for aligning high visual quality, and multimodal reasoning that jointly considers both the corrupted input and the recovered image. Extensive experiments demonstrate that Robust-U1 achieves state-of-the-art robustness on the real-world corruption benchmark and maintains superior performance under adversarial corruptions on general VQA benchmarks. Analysis confirms that high-quality visual recovery directly enhances reasoning performance, establishing self-recovery as a critical mechanism for robust visual understanding. The source code is available at https://github.com/jqtangust/Robust-U1.
Chinese Translation
多模态大语言模型（MLLMs）在视觉理解方面展现了显著的成功，然而在现实世界的视觉损坏下，其性能显著下降。尽管现有的增强稳健性的方法存在，但其局限性明显：黑箱特征对齐缺乏可解释性，而白箱基于文本的推理无法恢复丢失的像素级细节。本研究探讨了一个基本的研究问题：MLLMs能否自我恢复损坏的视觉内容？为了解决这一问题，我们提出了Robust-U1，一个新颖的框架，使MLLMs具备明确的视觉自我恢复能力，以实现稳健理解。该方法包括三个核心阶段：通过监督微调进行初步重建，利用双重奖励（像素级SSIM和语义级CLIP相似性）的强化学习对高视觉质量进行对齐，以及多模态推理，联合考虑损坏的输入和恢复的图像。大量实验表明，Robust-U1在现实世界的损坏基准测试中实现了最先进的稳健性，并在一般的视觉问答基准测试中在对抗性损坏下保持了优越的性能。分析确认，高质量的视觉恢复直接增强了推理性能，确立了自我恢复作为稳健视觉理解的关键机制。源代码可在 https://github.com/jqtangust/Robust-U1 获取。
cs.CV / 60 / 2606.08091
VideoWeaver: Evaluating and Evolving Skills for Agentic Long Video Generation
VideoWeaver：评估与演化自主长视频生成的技能
Abstract
Recent agent frameworks such as Claude Code, Codex, and OpenClaw are strong at tool use and orchestration, but whether they can handle long video generation, a long-horizon multimodal task, remains underexplored. Unlike earlier video agents whose pipeline is handcrafted, these frameworks can build and refine their own workflows. We introduce VideoWeaver, an agent harness and benchmark that evaluates and evolves skills for long video generation, where an agent turns a single instruction into a long video by composing foundation skills into its own workflow rather than following a predefined pipeline. The benchmark has 16 task categories and 285 cases, with references spanning text, image, audio, video, and their combinations. Because errors can arise at any stage and not just in the final video, we propose an agent-as-judge that inspects both the execution trace and the final video, grounding its scores in evidence such as metadata and intermediate files. Using this feedback, we further design a skill evolution algorithm that refines and merges the agent's skills. Across multiple frameworks and models, we find that an explicit composition skill improves the generation process over using foundation skills alone, that skill evolution further improves output quality, and that performance varies notably across harness and model choices. The proposed agent-as-judge also aligns well with human judgments, especially on process metrics. Code and dataset is available at https://github.com/JianhuiWei7/VideoWeaver
Chinese Translation
近期的代理框架如Claude Code、Codex和OpenClaw在工具使用和协调方面表现出色，但它们是否能够处理长视频生成这一长时间跨度的多模态任务仍然未得到充分探索。与早期手工设计管道的视频代理不同，这些框架能够构建和完善自己的工作流程。我们介绍了VideoWeaver，一个评估和演化长视频生成技能的代理工具和基准，其中代理通过将基础技能组合成自己的工作流程，将单一指令转化为长视频，而不是遵循预定义的管道。该基准包含16个任务类别和285个案例，参考内容涵盖文本、图像、音频、视频及其组合。由于错误可能在任何阶段出现，而不仅仅是在最终视频中，我们提出了一种代理-评判者（agent-as-judge），它检查执行轨迹和最终视频，将评分基于元数据和中间文件等证据。利用这些反馈，我们进一步设计了一种技能演化算法，以完善和合并代理的技能。在多个框架和模型中，我们发现显式组合技能在生成过程中优于单独使用基础技能，技能演化进一步提高了输出质量，并且性能在不同的工具和模型选择中显著变化。所提出的代理-评判者与人类判断也高度一致，尤其是在过程指标上。代码和数据集可在 https://github.com/JianhuiWei7/VideoWeaver 获取。
cs.CV / 61 / 2606.08121
Trustworthy Visual Predicates for Robust Manipulation Understanding under Degradation
可信的视觉谓词在退化条件下的稳健操控理解
Abstract
Manipulation understanding requires reliable relational evidence, such as contact, support, containment, motion coupling, grasp, release, and active-hand involvement. Although these visual predicates are widely used in event-chain, graph-based, and neuro-symbolic models, their reliability under visual degradation is rarely analyzed directly. This paper introduces a predicate-level reliability framework for robust manipulation understanding under blur, occlusion, illumination change, low resolution, frame dropping, and detection noise. The framework defines a structured predicate vocabulary, confidence-aware predicate estimation, and reliability metrics for predicate preservation, degradation sensitivity, temporal consistency, confidence-weighted stability, and downstream impact. Experiments on controlled manipulation videos and public egocentric or bimanual datasets, including VISOR/EPIC-KITCHENS, H2O, and ARCTIC, show that predicate failures are structured rather than uniform. Static spatial predicates remain comparatively robust, whereas contact-sensitive, dynamic, and derived predicates such as grasp and release are more fragile. Under severe degradation, detection noise, occlusion, and frame dropping cause the strongest reliability losses. Downstream analysis shows that degraded predicates reduce manipulation-understanding accuracy from 0.89 to 0.58, while removing confidence weighting under moderate degradation reduces accuracy from 0.74 to 0.64. These results show that predicate reliability provides a diagnostic layer between visual perception and structured manipulation reasoning.
Chinese Translation
操控理解需要可靠的关系证据，例如接触、支撑、包含、运动耦合、抓取、释放和主动手部参与。尽管这些视觉谓词在事件链、基于图的和神经符号模型中被广泛使用，但它们在视觉退化下的可靠性却很少被直接分析。本文提出了一种谓词级别的可靠性框架，以实现模糊、遮挡、光照变化、低分辨率、帧丢失和检测噪声下的稳健操控理解。该框架定义了一个结构化的谓词词汇、基于置信度的谓词估计以及用于谓词保留、退化敏感性、时间一致性、置信度加权稳定性和下游影响的可靠性指标。在受控操控视频和公共自我中心或双手数据集（包括VISOR/EPIC-KITCHENS、H2O和ARCTIC）上的实验表明，谓词失败是结构化的，而非均匀的。静态空间谓词相对稳健，而对接触敏感的动态谓词及其衍生谓词（如抓取和释放）则更为脆弱。在严重退化情况下，检测噪声、遮挡和帧丢失导致了最强的可靠性损失。下游分析表明，退化的谓词将操控理解的准确率从0.89降低到0.58，而在中等退化下去除置信度加权则将准确率从0.74降低到0.64。这些结果表明，谓词可靠性为视觉感知与结构化操控推理之间提供了一个诊断层。
cs.CV / 62 / 2606.08123
Human-Centered Benchmarking of Driver Monitoring Models
以人为本的驾驶员监测模型基准测试
Abstract
Vision-based driver monitoring systems are increasingly deployed in safety-critical intelligent transportation settings, yet they are almost always compared on classification accuracy alone. This paper argues that accuracy is insufficient to characterize a model's fitness for real-world deployment, and proposes the Human-Centered Benchmarking Framework (HCBF), which evaluates models across four dimensions: accuracy, explainability, efficiency, and robustness. The framework is applied to four representative lightweight architectures, MobileNetV3, ShuffleNetV2, EfficientNet-B0, and DeiT-Tiny, on the MRL Eye Dataset for eye-state classification. While the models are nearly indistinguishable on clean-set accuracy, each leads in exactly one dimension, and all four lie on the Pareto frontier. A Human-Centered Score computed under three deployment-oriented weighting scenarios ranks ShuffleNetV2 first throughout. However, this aggregate winner retains less than half of its performance under sensor noise and fails by classifying closed eyes as open, whereas the transformer remains robust. These findings show that aggregate ranking can mask dimension-specific vulnerabilities that are operationally decisive, underscoring the value of multi-dimensional, human-centered evaluation.
Chinese Translation
基于视觉的驾驶员监测系统在安全关键的智能交通环境中越来越多地被部署，但它们几乎总是仅在分类准确性上进行比较。本文认为，仅靠准确性不足以表征模型在实际部署中的适用性，因此提出了以人为本的基准测试框架（Human-Centered Benchmarking Framework, HCBF），该框架从准确性、可解释性、效率和鲁棒性四个维度对模型进行评估。该框架应用于四种具有代表性的轻量级架构：MobileNetV3、ShuffleNetV2、EfficientNet-B0 和 DeiT-Tiny，针对 MRL Eye 数据集进行眼状态分类。虽然这些模型在干净数据集上的准确性几乎无法区分，但每个模型在一个维度上表现突出，且四个模型均位于帕累托前沿。在三种以部署为导向的加权情境下计算的人本得分中，ShuffleNetV2 始终排名第一。然而，在传感器噪声下，该聚合赢家的性能保持不到一半，并且在将闭眼分类为睁眼时出现失败，而变换器模型则保持鲁棒性。这些发现表明，聚合排名可能掩盖在操作上至关重要的特定维度脆弱性，强调了多维度、以人为本的评估的价值。
cs.CV / 63 / 2606.08126
One Stone, Three Birds: Self-adaptive Optimal Transport for Multi-VLM Selection, Adaptation, and Ensembling
一石三鸟：用于多视觉语言模型选择、适应和集成的自适应最优传输
Abstract
Vision-language models (VLMs) enable visual recognition from semantic class descriptions, which makes them attractive when target annotations are scarce or unavailable. Most deployment pipelines, however, first choose a single VLM and then adapt that model to the unlabeled target set. This single-backbone paradigm hides a critical assumption: the selected VLM is already compatible with the target domain. In realistic cross-domain deployment, several general-purpose and domain-specialized VLMs may be plausible, yet no instance-level target labels are available to identify the reliable ones. Deployment therefore requires a coupled solution for model selection, target adaptation, and prediction integration. We revisit this problem from a system-level multi-VLM perspective. Our central observation is that the three decisions above depend on the same latent object: a trustworthy sample-class structure in the target set. Different VLMs may encode different transfer biases and produce conflicting predictions, but their outputs can still provide complementary evidence for estimating this structure. We propose One Stone, Three Birds, a training-free framework based on self-adaptive optimal transport. Given a pool of frozen candidate VLMs, OSTB estimates a consensus sample-to-class transport plan without updating VLM parameters. The learned transport structure is then reused for all deployment objectives: model selection is performed by ranking the combined semantic and visual reliability induced by the consensus plan; target adaptation is obtained by fitting transport-conditioned visual classifiers; and ensembling is implemented through reliability-aware probabilistic integration. Extensive experiments on natural-image, remote-sensing, and medical-pathology benchmarks show that OSTB improves model ranking, adaptation stability, and ensemble robustness under heterogeneous candidate pools.
Chinese Translation
视觉语言模型（VLMs）能够通过语义类别描述实现视觉识别，这使得它们在目标注释稀缺或不可用时具有吸引力。然而，大多数部署流程首先选择一个单一的VLM，然后将该模型适应于未标记的目标集。这种单一骨干网络的范式隐藏了一个关键假设：所选的VLM已经与目标领域兼容。在现实的跨领域部署中，可能存在多个通用和领域专用的VLM，但没有实例级的目标标签可用于识别可靠的模型。因此，部署需要一个耦合的解决方案来进行模型选择、目标适应和预测集成。我们从系统级的多VLM视角重新审视这个问题。我们的核心观察是，上述三个决策依赖于同一个潜在对象：目标集中的可信样本-类别结构。不同的VLM可能编码不同的迁移偏差并产生相互矛盾的预测，但它们的输出仍然可以为估计这一结构提供互补证据。我们提出了一种名为“一石三鸟”的无训练框架，基于自适应最优传输。给定一组冻结的候选VLM，OSTB在不更新VLM参数的情况下估计共识样本到类别的传输计划。然后，学习到的传输结构被重用于所有部署目标：通过对共识计划引发的综合语义和视觉可靠性进行排名来进行模型选择；通过拟合传输条件的视觉分类器来实现目标适应；通过可靠性感知的概率集成来实施集成。在自然图像、遥感和医学病理基准上的大量实验表明，OSTB在异构候选池中提高了模型排名、适应稳定性和集成鲁棒性。
cs.CV / 64 / 2606.08132
Phase Marginalization for Patch-Grid Instability in Vision Transformers
视觉变换器中补丁网格不稳定性的相位边际化
Abstract
Vision Transformers operate on fixed patch grids, which can introduce phase-dependent instability for dense prediction: changing the patch partition can change the token evidence available to a pixel, especially near boundaries. We formalize patch-grid phase as a nuisance variable and propose Phase Marginalization, a post-hoc marginalization method that evaluates structured patch-grid phases, inverse-aligns dense outputs, and aggregates them in the original image coordinate system. The central variant, Uniform Phase Marginalization with K = 4, is training-free and improves over the canonical K = 1 baseline across measured segmentation, depth, and local matching settings. In a controlled Cityscapes experiment, Uniform Phase Marginalization provides a modest compute-matched advantage over generic shift-based four-forward test-time augmentation (TTA) (+0.31 mean Intersection-over-Union over the strongest tested generic row). A scaling study further shows that K = 4 is a practical cost-accuracy trade-off: K = 8 is essentially unchanged and K = 16 adds little accuracy at much higher latency. These results position patch-grid phase as a measurable nuisance variable and Phase Marginalization as a simple diagnostic and post-hoc marginalization baseline for dense ViT prediction.
Chinese Translation
视觉变换器在固定的补丁网格上操作，这可能会引入与相位相关的不稳定性，影响密集预测：改变补丁划分可能会改变可用于像素的标记证据，特别是在边界附近。我们将补丁网格相位形式化为一个干扰变量，并提出相位边际化（Phase Marginalization），这是一种事后边际化方法，评估结构化的补丁网格相位，逆向对齐密集输出，并在原始图像坐标系统中进行聚合。中心变体，均匀相位边际化（Uniform Phase Marginalization），K = 4，无需训练，并在测量的分割、深度和局部匹配设置中优于经典的 K = 1 基线。在一个受控的 Cityscapes 实验中，均匀相位边际化在计算匹配上提供了相对于通用基于位移的四次前向测试时间增强（TTA）(+0.31 的平均交并比) 的适度优势。规模研究进一步表明 K = 4 是一个实用的成本-准确性权衡：K = 8 基本没有变化，而 K = 16 在更高延迟下几乎没有增加准确性。这些结果将补丁网格相位定位为一个可测量的干扰变量，并将相位边际化作为密集 ViT 预测的简单诊断和事后边际化基线。
cs.CV / 65 / 2606.08133
Gravity-guided Contact Dynamics Estimation from 3D Human Motions
基于重力引导的三维人类运动接触动力学估计
Abstract
Ground contact forces acting on the human body, are crucial for biomechanics studies or sport performance analysis. Prior methods rely on force plates or pressure mats to collect ground contact dynamics, limiting their applicability to carefully controlled settings. A more scalable solution is to estimate the dynamics directly from motion capture data. Recent approaches only roughly estimate the ground contact dynamics from the vertical distance between the body and the ground plane, which cannot capture the complex pressure distribution of all contact points. To this end, we propose GraCE -- Gravity-guided Contact Dynamics Estimation, a novel full-body contact dynamics model for human motions using a realistic influence of body mass distribution and gravity. We use the human's center of gravity to estimate the ground contacts based on its relative distance to the human body. The applied force on each contact is estimated via the product of predicted contact probabilities and the total exterior force computed from the center of mass trajectory. We outperform related work on the GroundLink dataset for ground reaction force estimation, and on the MOYO dataset for detailed contact pressure prediction. The code is published upon acceptance.
Chinese Translation
作用于人体的地面接触力对于生物力学研究或运动表现分析至关重要。以往的方法依赖于力板或压力垫来收集地面接触动力学，这限制了其在严格控制环境下的适用性。一个更具可扩展性的解决方案是直接从运动捕捉数据中估计动力学。近期的方法仅通过身体与地面之间的垂直距离粗略估计地面接触动力学，这无法捕捉所有接触点的复杂压力分布。为此，我们提出了GraCE——重力引导接触动力学估计，这是一个针对人类运动的新型全身接触动力学模型，考虑了身体质量分布和重力的现实影响。我们利用人体的重心根据其与人体的相对距离来估计地面接触。每个接触点施加的力通过预测的接触概率与从质心轨迹计算的总外力的乘积来估计。我们在GroundLink数据集上超越了相关工作在地面反作用力估计方面的表现，并在MOYO数据集上实现了更为详细的接触压力预测。代码将在接受后发布。
cs.CV / 66 / 2606.08144
IMAGINE: Adaptive Schema-Imagery Enhanced Composition for Composed Video Retrieval
IMAGINE：自适应模式-图像增强组合的视频检索
Abstract
Composed Video Retrieval (CVR) is designed to retrieve a target video that matches a reference video modified by a modification text. While existing methods explore cross-modal correspondences, they often assume modified objects appear directly in videos. However, modification texts frequently describe concepts not explicitly presented but implicitly expressed through semantically related visual cues (e.g., "cake" implying "birthday party"). Current approaches typically rely on aligning explicit feature representations within the concrete space, neglecting critical latent associations. To address this, we propose an adaptIve scheMa-ImAGery enhanced composItional NEtwork (IMAGINE). Unlike standard explicit matching, IMAGINE materializes implicit semantics (termed schema imagery) via dynamic multimodal prototypes. These prototypes capture shared latent concepts to adaptively modulate visual features, effectively injecting implicit guidance into the retrieval process. By bridging the gap between explicit visual contents and implicit retrieval intentions, IMAGINE achieves state-of-the-art performance in both CVR and Composed Image Retrieval (CIR) across three widely used benchmarks.
Chinese Translation
组合视频检索（CVR）旨在检索与通过修改文本修改的参考视频相匹配的目标视频。虽然现有方法探索跨模态对应关系，但它们通常假设修改对象直接出现在视频中。然而，修改文本经常描述未明确呈现但通过语义相关的视觉线索隐含表达的概念（例如，“蛋糕”暗示“生日派对”）。当前的方法通常依赖于在具体空间内对显式特征表示的对齐，忽视了关键的潜在关联。为了解决这个问题，我们提出了一种自适应模式-图像增强组合网络（IMAGINE）。与标准的显式匹配不同，IMAGINE通过动态多模态原型实现隐式语义（称为模式图像）。这些原型捕捉共享的潜在概念，以自适应地调节视觉特征，有效地将隐式指导注入检索过程。通过弥合显式视觉内容与隐式检索意图之间的差距，IMAGINE在三个广泛使用的基准测试中，在CVR和组合图像检索（CIR）方面实现了最先进的性能。
cs.CV / 67 / 2606.08150
Property-Informed Diffusion-Based Text-to-Microstructure Generation
基于属性信息的扩散模型文本到微观结构生成
Abstract
Designing 3D metamaterial microstructures that meet the intended functions remains a major challenge, as it typically requires domain expertise, iterative simulations, and extensive manual tuning. Existing work on inverse design that automatically generates microstructures based on desired target properties often suffers from limited design diversity and faces challenges in ensuring the physical feasibility of the generated structures. To address this issue, a property-informed diffusion-based network is proposed that enables the generation of 3D microstructures directly from textual descriptions. Unlike traditional property conditioning methods, our approach leverages rich guidance in terms of semantics and physical properties in the text input to support diverse structure synthesis. To enforce consistency between the generated structures and the target textual prompts, a dual alignment strategy is adopted, including contrastive text-structure alignment and test-time reward-guided alignment. Experimental results show that the model is capable of generating semantically meaningful and physically plausible structures across a wide range of material categories. Our approach has good potential for interactive microstructure design and opens up new directions for combining language-based interfaces with inverse material discovery. Code is available at: https://github.com/hongsong-wang/PropDiff-TMG
Chinese Translation
设计满足预期功能的三维超材料微观结构仍然是一个主要挑战，因为这通常需要领域专业知识、迭代仿真和广泛的手动调优。现有的基于逆向设计的研究通常自动生成基于期望目标属性的微观结构，但往往面临设计多样性有限和确保生成结构物理可行性方面的挑战。为了解决这一问题，提出了一种基于属性信息的扩散网络，能够直接从文本描述生成三维微观结构。与传统的属性条件方法不同，我们的方法利用文本输入中的语义和物理属性提供丰富的指导，以支持多样化的结构合成。为了确保生成结构与目标文本提示之间的一致性，采用了一种双重对齐策略，包括对比文本-结构对齐和测试时奖励引导对齐。实验结果表明，该模型能够在广泛的材料类别中生成语义上有意义且物理上合理的结构。我们的方法在交互式微观结构设计方面具有良好的潜力，并为将基于语言的接口与逆向材料发现相结合开辟了新的方向。代码可在以下链接获取：https://github.com/hongsong-wang/PropDiff-TMG
cs.CV / 68 / 2606.08156
RAPID: Layer-Wise Redundancy-Aware Pruning and Importance-Driven Token Merging for Efficient ViT
RAPID：层级冗余感知剪枝与重要性驱动的令牌合并以提高ViT的效率
Abstract
Vision Transformers (ViTs) achieve strong performance but suffer from high computational costs due to quadratic self-attention complexity. Although token reduction techniques such as pruning and merging mitigate this, they typically overlook how representations evolve across network depth. We propose RAPID, a depth-aware token reduction framework that adapts reduction strategies to the layer-wise characteristics of token representations. The primary methodological contribution is a bifurcated strategy: in shallow-to-middle layers, RAPID employs a redundancy-similarity aware pruning metric to eliminate over-represented local patterns. As features transition to global semantic concepts in deeper layers, the framework shifts to an importance-similarity aware merging mechanism. This stage leverages classification (CLS) token attention weights to protect semantically critical tokens while fusing less important but similar neighbors. Empirical validation on ImageNet-1K using ViT and DeiT architectures demonstrates that RAPID establishes a superior accuracy-compression Pareto frontier compared to plug-and-play baselines such as ToMe and ToFu. RAPID is particularly robust in aggressive compression regimes, achieving up to 4.29% higher accuracy than ToMe at extreme reduction rates. Our framework provides a training-free template for optimizing vision models by aligning reduction strategies with hierarchical feature evolution.
Chinese Translation
视觉变换器（ViTs）在性能上表现优异，但由于二次自注意力复杂度，计算成本较高。尽管剪枝和合并等令牌减少技术有所缓解，但它们通常忽视了表示在网络深度上的演变。我们提出了RAPID，一种深度感知的令牌减少框架，能够根据令牌表示的层级特征调整减少策略。主要的方法论贡献是一个双重策略：在浅层到中层，RAPID采用冗余相似性感知剪枝度量来消除过度表示的局部模式。随着特征在更深层次向全局语义概念的过渡，该框架转向重要性相似性感知的合并机制。该阶段利用分类（CLS）令牌的注意力权重来保护语义关键令牌，同时融合不太重要但相似的邻近令牌。在使用ViT和DeiT架构的ImageNet-1K上的实证验证表明，RAPID建立了一个优于即插即用基线（如ToMe和ToFu）的准确性-压缩Pareto前沿。在激进压缩模式下，RAPID表现尤为稳健，在极端减少率下实现比ToMe高出4.29%的准确性。我们的框架提供了一种无训练的模板，通过将减少策略与层次特征演变对齐来优化视觉模型。
cs.CV / 69 / 2606.08164
How Much MRI Preprocessing Is Enough? A Cost-Utility Study for Brain MRI Foundation Models
MRI预处理的必要程度：脑部MRI基础模型的成本效用研究
Abstract
MRI preprocessing defines the input distribution seen by brain MRI foundation models, yet it is usually treated as routine data cleaning rather than a modeling choice. We ask how much preprocessing is worth its computational cost for self-supervised 3D MRI pretraining. Keeping the corpus, 3D ViT backbone, masking protocol, and downstream evaluations fixed, we compare a graded P0-P7 preprocessing spectrum for masked autoencoding (MAE) and joint-embedding predictive learning (JEPA) on 20,000 heterogeneous brain MRI volumes, then transfer the encoders to IDH prediction, MCI classification, brain age regression, and GLI/PED tumor segmentation. The results do not support a simple "more is better" rule. P0/P1 are numerically unstable, making P2 the lowest-cost feasible level; beyond P2, choosing the best feasible preprocessing level improves aggregate utility by only 3.4 percentage points for MAE and 1.8 percentage points for JEPA, with most paired gains statistically unresolved. Stronger preprocessing is beneficial only in selected regimes: IDH improves modestly, AGE and GLI/PED are often near or best at P2, and MCI shows the clearest empirical P7 gain. Cross-level MCI transfer further shows that much of the P7 advantage can be recovered by applying stronger preprocessing downstream, without requiring P7 throughout pretraining. These findings recast MRI preprocessing as a downstream-aware cost-utility decision rather than a default escalation pipeline. Code is available at https://github.com/PangJiangShuan/PreBrain.
Chinese Translation
MRI预处理定义了脑部MRI基础模型所见的输入分布，但通常被视为常规数据清理，而非建模选择。我们探讨了自监督3D MRI预训练中，多少预处理是值得其计算成本的。在保持语料库、3D ViT主干、掩蔽协议和下游评估固定的情况下，我们比较了针对掩蔽自编码（MAE）和联合嵌入预测学习（JEPA）的分级P0-P7预处理谱，使用20,000个异质脑部MRI体积进行实验，然后将编码器转移到IDH预测、MCI分类、脑龄回归和GLI/PED肿瘤分割。结果并不支持简单的“越多越好”原则。P0/P1在数值上不稳定，使得P2成为最低成本的可行水平；在P2以上，选择最佳可行的预处理水平仅能使MAE的总体效用提高3.4个百分点，JEPA提高1.8个百分点，大多数配对增益在统计上未能解决。更强的预处理仅在特定情况下有益：IDH有适度改善，AGE和GLI/PED通常在P2时接近或最佳，而MCI则显示出最明显的经验性P7增益。跨级别的MCI转移进一步表明，P7的优势在于通过在下游应用更强的预处理可以恢复，而不需要在整个预训练过程中保持P7。这些发现将MRI预处理重新定义为一种关注下游的成本效用决策，而非默认的升级流程。代码可在 https://github.com/PangJiangShuan/PreBrain 获取。
cs.CV / 70 / 2606.08205
Empowering Feed-Forward Reconstruction Models with Metric Scale via Satellite Images
通过卫星图像增强前馈重建模型的度量尺度
Abstract
Feed-forward 3D reconstruction models have recently shown strong generalization across diverse scenes, yet most of them recover geometry only up to an unknown global scale. This scale ambiguity limits their use in applications that require metric understanding of the environment. Existing metric reconstruction methods commonly rely on large-scale metric annotations or accurate camera calibration, both of which are costly or unreliable in many real-world settings. We propose a satellite-guided framework for resolving scale ambiguity in feed-forward 3D reconstruction. The key idea is to use readily available satellite imagery as a global metric reference. Given a coarse camera pose, our method retrieves a local satellite patch and integrates it with a feed-forward reconstruction backbone through bidirectional cross-view interaction. By enforcing consistency between the reconstructed scene and the satellite reference, the model infers absolute scale, refines scene geometry, and estimates camera pose in a metric coordinate frame. Experiments on KITTI, nuScenes, and Oxford RobotCar show consistent improvements in metric depth estimation, multi-view point-cloud reconstruction, and cross-view camera localization, while preserving strong generalization across datasets and geographic regions.
Chinese Translation
前馈3D重建模型最近在多样场景中表现出强大的泛化能力，但它们大多数仅能恢复未知的全局尺度。这种尺度模糊限制了它们在需要对环境进行度量理解的应用中的使用。现有的度量重建方法通常依赖于大规模的度量注释或准确的相机标定，而这在许多现实世界场景中成本高昂或不可靠。我们提出了一种卫星引导的框架，用于解决前馈3D重建中的尺度模糊问题。其关键思想是利用现成的卫星图像作为全局度量参考。在给定粗略相机姿态的情况下，我们的方法检索局部卫星图像块，并通过双向交叉视图交互将其与前馈重建主干网络集成。通过强制重建场景与卫星参考之间的一致性，模型推断绝对尺度，优化场景几何，并在度量坐标框架中估计相机姿态。在KITTI、nuScenes和Oxford RobotCar上的实验表明，在度量深度估计、多视角点云重建和交叉视图相机定位方面都有一致的改进，同时在数据集和地理区域之间保持强大的泛化能力。
cs.CV / 71 / 2606.08206
SegmentAnyTreeV2: Scaling Transformer-Based Tree Instance Segmentation Across Sensors, Platforms, and Forests
SegmentAnyTreeV2：跨传感器、平台和森林的基于Transformer的树实例分割的扩展
Abstract
We present SegmentAnyTreeV2, a sensor- and platform-agnostic framework for semantic and instance segmentation of forest point clouds. The model combines a serialization-based Point Transformer v3 backbone with a lightweight semantic head and a tree-focused cross-attention mask decoder. Semantic predictions restrict instance decoding to tree-class voxels, while instance-aware query initialization, one-to-many seed supervision, and asymmetric mask scoring improve separation in dense and structurally complex stands. We further introduce FOR-instance v3, an expanded benchmark comprising 427 scenes and 26,496 annotated trees across diverse biomes, forest structures, and LiDAR platforms. On the FOR-instanceV2 test split, SegmentAnyTreeV2 achieves 90.5% precision, 80.2% recall, 85.0% F1, 90.7% coverage, and 87.6% semantic mIoU, outperforming previous learning-based methods in both instance detection and mask completeness. Zero-shot evaluation on independent sites further demonstrates strong cross-domain generalization.
Chinese Translation
我们提出了SegmentAnyTreeV2，这是一个与传感器和平台无关的框架，用于森林点云的语义和实例分割。该模型结合了基于序列化的Point Transformer v3主干、轻量级语义头和以树为中心的交叉注意力掩码解码器。语义预测限制实例解码到树类体素，而实例感知的查询初始化、一对多的种子监督和不对称掩码评分则改善了在密集和结构复杂的林分中的分离。我们进一步引入了FOR-instance v3，这是一个扩展基准，包含427个场景和26,496棵标注树，涵盖多样的生物群落、森林结构和LiDAR平台。在FOR-instanceV2测试集上，SegmentAnyTreeV2达到了90.5%的精确率、80.2%的召回率、85.0%的F1值、90.7%的覆盖率和87.6%的语义mIoU，在实例检测和掩码完整性方面均优于之前的学习方法。在独立站点的零样本评估进一步展示了强大的跨领域泛化能力。
cs.CV / 72 / 2606.08231
Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning
多模态基础模型中的测试时缩放：生成与推理的综合调查
Abstract
Test-time Scaling (TTS) has emerged as a pivotal research direction for enhancing model performance by dynamically allocating computational resources during inference. Recent advancements have adapted this paradigm to Multimodal Foundation Models (MFMs), unlocking their potential in multimodal reasoning and generation. Despite rapid progress, the field lacks a systematic survey and unified theoretical framework to delineate the developmental landscape of multimodal TTS. To bridge this gap, we present the first comprehensive review of TTS research for MFMs, proposing a unified taxonomic framework that categorizes existing methodologies into three distinct strategies: sampling-based, feedback-based, and search-based approaches. We further summarize representative applications and benchmarks commonly utilized to evaluate multimodal TTS capabilities in generation and reasoning tasks. Finally, this survey discusses open challenges and outlines future research directions, providing a systematic roadmap for subsequent studies in this rapidly evolving field.
Chinese Translation
测试时缩放（Test-Time Scaling, TTS）已成为提升模型性能的重要研究方向，通过在推理过程中动态分配计算资源来实现。近期的进展将这一范式应用于多模态基础模型（Multimodal Foundation Models, MFMs），释放了其在多模态推理和生成中的潜力。尽管取得了快速进展，但该领域缺乏系统的调查和统一的理论框架来界定多模态 TTS 的发展现状。为填补这一空白，我们首次提出了针对 MFMs 的 TTS 研究的全面回顾，提出了一个统一的分类框架，将现有方法论分为三种不同的策略：基于采样的、基于反馈的和基于搜索的方法。我们进一步总结了常用的代表性应用和基准，以评估多模态 TTS 在生成和推理任务中的能力。最后，本调查讨论了开放挑战，并概述了未来的研究方向，为这一快速发展的领域后续研究提供了系统的路线图。
cs.CV / 73 / 2606.08242
Light-WAM: Efficient World Action Models with State-Fusion Action Decoding
Light-WAM：高效的世界行动模型与状态融合行动解码
Abstract
World Action Models (WAMs) extend robot policy learning by incorporating future prediction as an additional training objective, encouraging the policy to encode task-relevant temporal structure in its representations. Current WAMs often rely on large-scale generative architectures that incur high training costs and inference latency, making them difficult to deploy as efficient closed-loop policies. We propose Light-WAM, a lightweight World Action Model for efficient robot manipulation. Specifically, it is built with a compact video backbone and performs future-video supervision in a downsampled latent space, reducing the cost of video co-training while retaining its benefits for representation learning. For action prediction, Light-WAM introduces the StateFusionActionExpert, which reads adapted states from multiple backbone layers, fuses them through learned-query pooling, and directly predicts action chunks in a single forward pass. This design provides an efficient interface between video backbone representations and robot actions, avoiding the need for heavy generative action experts. Experiments demonstrate that Light-WAM maintains strong performance on LIBERO and achieves usable multi-task performance on RoboTwin 2.0, while using only 0.44B trainable parameters. It also achieves 72.03ms inference latency with 4.1GiB peak GPU memory and improved training throughput.
Chinese Translation
世界行动模型（WAMs）通过将未来预测作为额外的训练目标，扩展了机器人策略学习，鼓励策略在其表示中编码与任务相关的时间结构。目前的WAMs通常依赖于大规模生成架构，这会导致高昂的训练成本和推理延迟，使其难以作为高效的闭环策略进行部署。我们提出了Light-WAM，一种轻量级的世界行动模型，旨在实现高效的机器人操作。具体而言，它采用紧凑的视频主干网络，并在下采样的潜在空间中执行未来视频监督，减少视频共同训练的成本，同时保留其在表示学习中的优势。对于行动预测，Light-WAM引入了StateFusionActionExpert，它从多个主干层读取适应的状态，通过学习的查询池化进行融合，并在单次前向传递中直接预测行动块。这一设计为视频主干表示与机器人行动之间提供了高效的接口，避免了重型生成行动专家的需求。实验表明，Light-WAM在LIBERO上保持了强劲的性能，并在RoboTwin 2.0上实现了可用的多任务性能，同时仅使用了0.44B的可训练参数。它还实现了72.03毫秒的推理延迟，峰值GPU内存为4.1GiB，并提高了训练吞吐量。
cs.CV / 74 / 2606.08260
TIDE: Task-Isolated Diffusion for Unified Video Editing and Generation
TIDE：任务隔离扩散用于统一视频编辑与生成
Abstract
Recent advances in Diffusion Transformers have driven rapid progress in video generation and editing, yet these capabilities are still handled by separate, task-specific models. Building a unified framework that supports diverse video tasks remains an open challenge: existing unified attempts either require dedicated auxiliary encoders or lack explicit mechanisms to distinguish heterogeneous conditioning tokens, struggling when the number and type of visual conditions vary across tasks. We propose TIDE, a unified framework that integrates instruction-based editing, reference-guided editing, and multi-reference generation. At its core, we introduce per-token task embeddings that assign each input token a task-specific identifier, enabling the model to explicitly disambiguate target, source, and reference tokens. To simultaneously capture high-level semantic understanding and fine-grained structural fidelity, we design a dual-path conditioning scheme that couples a vision-language model with a VAE latent path for complementary signals. We further devise a multi-task progressive training strategy that incrementally introduces tasks of increasing complexity, effectively harmonizing diverse objectives and enabling smooth generalization across heterogeneous task distributions. Extensive experiments on multiple video editing and generation benchmarks demonstrate that TIDE achieves state-of-the-art performance across all evaluated tasks. Our project page is available at https://LittleWork123.github.io/tide.
Chinese Translation
最近在扩散变换器（Diffusion Transformers）方面的进展推动了视频生成和编辑的快速发展，但这些能力仍然由不同的、特定任务的模型处理。构建一个支持多样视频任务的统一框架仍然是一个未解决的挑战：现有的统一尝试要么需要专用的辅助编码器，要么缺乏明确的机制来区分异构的条件标记，在任务之间视觉条件的数量和类型变化时表现不佳。我们提出了TIDE，一个统一框架，集成了基于指令的编辑、参考引导的编辑和多参考生成。在其核心，我们引入了每个标记的任务嵌入（task embeddings），为每个输入标记分配一个特定任务的标识符，使模型能够明确区分目标、源和参考标记。为了同时捕捉高层次的语义理解和细粒度的结构保真度，我们设计了一种双路径条件方案，将视觉-语言模型与变分自编码器（VAE）潜在路径相结合，以提供互补信号。我们进一步设计了一种多任务渐进训练策略，逐步引入复杂度逐渐增加的任务，有效协调多样的目标并实现跨异构任务分布的平滑泛化。在多个视频编辑和生成基准上进行的广泛实验表明，TIDE在所有评估任务中都达到了最先进的性能。我们的项目页面可访问 https://LittleWork123.github.io/tide。
cs.CV / 75 / 2606.08277
Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees
自信记忆：具有概率保证的时空记忆不确定性量化
Abstract
Long-horizon robot operation requires spatio-temporal memory to record the environment state and recall it for downstream reasoning. Scene graphs and retrieval-augmented systems ground VLM descriptions to persistent 3D entities with rich semantic descriptions. However, VLM captions are noisy and viewpoint-inconsistent, and existing systems treat them as an oracle with no mechanism to detect unreliable stored descriptions. We introduce object-level semantic uncertainty for multi-view VLM memory: a score that measures object-centric cross-view semantic scatter of captions and identifies semantically unresolved objects. Then, we include our uncertainty scores in an advanced spatial-semantic memory system, that we dub UQ-DAAAM. UQ-DAAAM uses this score to actively refine uncertain objects under a fixed query budget by selecting high-quality views and fusing the resulting multi-view captions into a single object description. We also derive probabilistic guarantees showing that higher-quality candidate views (as selected by our approach) are more likely to reduce uncertainty. Our experiments show that uncertainty quantification can make embodied 4D memory systems more reliable and more effective. In particular, on the OC-NaVQA benchmark, UQ-DAAAM achieves substantially larger uncertainty reduction and better spatio-temporal question answering performance than baselines.
Chinese Translation
长时间范围的机器人操作需要时空记忆来记录环境状态并在后续推理中回忆起来。场景图和检索增强系统将视觉语言模型（VLM）描述与具有丰富语义描述的持久3D实体相结合。然而，VLM的描述往往噪声较大且视角不一致，现有系统将其视为一种神谕，未能检测不可靠的存储描述。我们引入了面向对象的语义不确定性，用于多视角VLM记忆：一种衡量对象中心的跨视角语义散布的评分，并识别语义未解决的对象。然后，我们将不确定性评分纳入一个先进的空间-语义记忆系统，我们称之为UQ-DAAAM。UQ-DAAAM利用该评分在固定查询预算下主动精炼不确定对象，通过选择高质量视角并将生成的多视角描述融合为单一对象描述。我们还推导出概率保证，表明通过我们的方法选择的高质量候选视角更有可能降低不确定性。我们的实验表明，不确定性量化可以使具身的4D记忆系统更加可靠和有效。特别是在OC-NaVQA基准测试中，UQ-DAAAM实现了显著更大的不确定性降低和更好的时空问答性能，优于基线。
cs.CV / 76 / 2606.08284
G2G: Exploiting Intra-Group Geometry for Inter-Group Pose Estimation
G2G：利用组内几何信息进行组间姿态估计
Abstract
Recovering the relative 6-DoF pose between two image groups underlies cross-sequence relocalization and multi-camera rig odometry. Each group carries known intra-group geometry from visual odometry or rig calibration, and pretrained multi-view backbones already fuse such geometry into visual features. Yet current models treat all views as an unstructured set, leaving cross-group reasoning as the missing piece. We introduce \ours{}, which keeps the foundation model entirely frozen and adds three lightweight trainable modules to bridge the two groups: a perceiver resampler, a cross-group bridge with merged self-attention, and a multi-frame pose head. The trainable footprint totals about 32M parameters, under 6\% of the full model, and is supervised only by relative poses. Across four datasets that span indoor and outdoor simulation, real-world cross-season capture, and zero-shot sim-to-real transfer, \ours{} attains state-of-the-art accuracy on both tasks, while every baseline is retrained with its full original supervision. Code is available at https://github.com/WeiYuFei0217/G2G.
Chinese Translation
恢复两个图像组之间的相对6自由度（6-DoF）姿态是跨序列重定位和多摄像头设备测距的基础。每个组都携带来自视觉测距或设备标定的已知组内几何信息，而预训练的多视角主干网络已经将这些几何信息融合到视觉特征中。然而，当前模型将所有视图视为一个无结构的集合，导致组间推理成为缺失的一环。我们提出了 extit{G2G}，该方法保持基础模型完全冻结，并添加了三个轻量级可训练模块以连接两个组：一个感知重采样器、一个带有合并自注意力的组间桥接模块，以及一个多帧姿态头。可训练的参数总数约为3200万，占完整模型的不到6%，并且仅通过相对姿态进行监督。在涵盖室内和室外模拟、真实世界跨季节捕捉以及零-shot模拟到真实转移的四个数据集上， extit{G2G}在这两项任务中达到了最先进的准确性，而每个基线模型均在其完整的原始监督下重新训练。代码可在 https://github.com/WeiYuFei0217/G2G 获取。
cs.CV / 77 / 2606.08302
HACK++: Towards More Effective Head-Aware Key-Value Compression for Efficient Visual Autoregressive Modeling
HACK++：朝着更有效的头部感知键值压缩以实现高效的视觉自回归建模
Abstract
Visual Autoregressive (VAR) models adopt a next-scale prediction paradigm, offering high-quality generation with substantially fewer decoding steps. However, existing VAR models suffer from significant attention complexity and severe memory overhead due to the accumulation of key-value (KV) caches across scales. In this paper, we tackle this challenge by introducing KV cache compression into the next-scale paradigm. We begin with an in-depth analysis of VAR attention and observe that attention heads can be stably divided into two functionally distinct categories: Contextual Heads focus on maintaining semantic consistency, while Structural Heads preserve spatial coherence. Their functional divergence makes existing one-size-fits-all compression methods perform poorly on VAR models. We further find that the two head types differ markedly in their reliance on historical scales, and that this reliance shifts across layers and generation steps, arguing for an adaptive cache budget allocation. To address these challenges, we propose HACK++, a training-free Head-Aware key-value Compression frameworK for VAR models. From a one-time offline calibration, HACK++ classifies head types and derives head-specific priors. At inference, it decouples attention from cache compression under independent budgets, bounding the current-scale attention cost while compressing the accumulated cache far more aggressively, via pattern-specific strategies and a reliance-aware budget allocation. Extensive experiments on multiple VAR models across text-to-image, class-conditional, and unified understanding-and-generation tasks validate the effectiveness and generalizability of HACK++. For example, on Infinity-2B/8B, HACK++ maintains near-lossless generation with only a 30% attention budget and a 10% cache budget, and remains robust even under a 1% cache budget.
Chinese Translation
视觉自回归（VAR）模型采用下一尺度预测范式，以显著更少的解码步骤提供高质量的生成。然而，现有的VAR模型由于跨尺度的键值（KV）缓存的积累，面临着显著的注意力复杂性和严重的内存开销。本文通过将KV缓存压缩引入下一尺度范式来应对这一挑战。我们首先对VAR注意力进行了深入分析，观察到注意力头可以稳定地分为两种功能上不同的类别：上下文头（Contextual Heads）专注于保持语义一致性，而结构头（Structural Heads）则保持空间连贯性。这种功能上的差异使得现有的一刀切压缩方法在VAR模型上表现不佳。我们进一步发现，这两种头类型在对历史尺度的依赖上存在显著差异，并且这种依赖在层和生成步骤之间发生变化，主张进行自适应缓存预算分配。为了解决这些挑战，我们提出了HACK++，一个无训练的头部感知键值压缩框架，专为VAR模型设计。通过一次离线校准，HACK++对头部类型进行分类并推导出特定于头部的先验。在推理阶段，它在独立预算下将注意力与缓存压缩解耦，限制当前尺度的注意力成本，同时通过特定模式的策略和依赖感知的预算分配，更加积极地压缩累积缓存。在多个VAR模型上的广泛实验，包括文本到图像、类别条件和统一理解与生成任务，验证了HACK++的有效性和通用性。例如，在Infinity-2B/8B上，HACK++在仅使用30%的注意力预算和10%的缓存预算的情况下，保持近乎无损的生成，并且即使在1%的缓存预算下也保持稳健。
cs.CV / 78 / 2606.08324
Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging
基于集合的变换器在远程LWIR高光谱成像中的大气补偿
Abstract
Passive long-wave infrared (LWIR) hyperspectral imaging under a standoff geometry depends on atmospheric absorption and emission, as well as reflected radiance, thus making atmospheric compensation essential to get knowledge of a target of interest. Despite its importance, this compensation has been largely overlooked due to its practical and modeling difficulty. In this paper, we present a lightweight set-based deep learning framework that takes multiple radiance measurements, collected at different standoff ranges, as input and jointly estimates transmittance, atmospheric path radiance, and a shared downwelling spectrum. We analyze the learned representation with a sparse autoencoder and observe that several latent features do activate on geographically coherent subsets of the test data despite the absence of location supervision. Experiments on a MODTRAN generated standoff LWIR dataset demonstrate low spectral distortion across all estimated products. The dataset and code is publicly available at: https://factral.co/SAE-LWIR/
Chinese Translation
在远程几何下，被动长波红外（LWIR）高光谱成像依赖于大气的吸收和发射以及反射辐射，因此大气补偿对于获取目标信息至关重要。尽管其重要性不言而喻，但由于实际操作和建模的困难，这一补偿在很大程度上被忽视。本文提出了一种轻量级的基于集合的深度学习框架，该框架以在不同远程范围内收集的多次辐射测量作为输入，联合估计透射率、大气路径辐射和共享的下行光谱。我们使用稀疏自编码器分析学习到的表示，并观察到尽管缺乏位置监督，多个潜在特征在地理上相干的测试数据子集上激活。对MODTRAN生成的远程LWIR数据集的实验表明，所有估计产品的光谱失真较低。数据集和代码可在以下链接公开获取：https://factral.co/SAE-LWIR/
cs.CV / 79 / 2606.08332
SMI: Efficient Self-Supervised Learning via Mutual-Information-Inspired Dependency Optimization
SMI：通过互信息启发的依赖优化实现高效自监督学习
Abstract
Self-supervised learning (SSL) has achieved remarkable representation learning performance, but many existing methods rely on large batch sizes, memory banks, momentum encoders, or global synchronization mechanisms that substantially increase computational cost and training complexity. In this work, we propose Semantic Mutual Information (SMI), a lightweight self-supervised objective derived from a mutual-information-inspired dependency formulation under Gaussian assumptions. Unlike conventional correlation matching objectives that operate on high-dimensional feature correlation matrices, SMI performs optimization on a sample-level dependency matrix through a nonlinear transformation of pairwise correlations. This formulation induces distinct optimization dynamics that emphasize strongly dependent semantic pairs while maintaining representation diversity. Experimental results on ImageNet using a ResNet-50 backbone demonstrate that SMI achieves competitive linear evaluation performance relative to state-of-the-art SSL approaches while substantially reducing computational complexity. Across multiple low-resource benchmarks, SMI consistently improves transfer performance over Barlow Twins, particularly on fine-grained datasets. Furthermore, analyses of optimization dynamics and representation geometry suggest improved alignment--redundancy balance, greater feature diversity, and more spatially localized semantic representations. These results indicate that nonlinear dependency optimization provides an effective and computationally efficient alternative to conventional correlation-based self-supervised learning objectives.
Chinese Translation
自监督学习（SSL）在表征学习性能上取得了显著的进展，但许多现有方法依赖于大批量大小、内存库、动量编码器或全局同步机制，这些都大幅增加了计算成本和训练复杂性。在本研究中，我们提出了语义互信息（SMI），这是一种基于高斯假设下互信息启发的依赖公式推导出的轻量级自监督目标。与传统的高维特征相关矩阵上的相关匹配目标不同，SMI通过对成对相关性的非线性变换，在样本级依赖矩阵上进行优化。这种公式引发了独特的优化动态，强调强依赖的语义对，同时保持表征的多样性。在使用ResNet-50作为骨干网络的ImageNet实验结果表明，SMI在与最先进的SSL方法相比时，在线性评估性能上表现出竞争力，同时显著降低了计算复杂性。在多个低资源基准测试中，SMI在迁移性能上始终优于Barlow Twins，尤其是在细粒度数据集上。此外，优化动态和表征几何的分析表明，改进了对齐-冗余平衡、增强了特征多样性，并且提供了更具空间局部性的语义表征。这些结果表明，非线性依赖优化为传统基于相关的自监督学习目标提供了一种有效且计算上高效的替代方案。
cs.CV / 80 / 2606.08336
Beyond Raw Signals: Undecoded Generative Latents as Privileged Synthetic Data
超越原始信号：未解码的生成潜变量作为特权合成数据
Abstract
While multimodal integration significantly improves computer vision models, deploying them incurs prohibitive inference costs and requires scarce, perfectly paired datasets. Recent methods address this data bottleneck by synthesizing missing modalities via generative AI, yet they introduce a severe inefficiency: the Decode-Encode Loop. Specifically, information-rich generative latents are decoded into noisy raw signals, forcing the downstream classifier to waste capacity re-encoding them. To bypass this bottleneck, we propose Direct Latent Augmentation (DLA), utilizing undecoded generative latents directly as privileged information. Furthermore, to transfer this dense knowledge to a purely visual student, we introduce Multilayer Explicit Simulated Synesthesia (MESSy). Instead of enforcing rigid representation matching, which forces the student to distort its native visual features to accommodate complex multimodal topologies, MESSy uses a predictive objective to safely internalize these physical priors. Empirical results demonstrate that our framework significantly outperforms raw data augmentation and traditional distillation. Ultimately, our approach yields highly accurate unimodal students with ``synesthetic'' latent structures that are inherently aligned with physical properties they have never directly observed.
Chinese Translation
尽管多模态集成显著提高了计算机视觉模型的性能，但其部署会产生高昂的推理成本，并且需要稀缺的、完美配对的数据集。最近的方法通过生成性人工智能合成缺失的模态，以解决这一数据瓶颈，然而它们引入了严重的低效：解码-编码循环。具体而言，信息丰富的生成潜变量被解码为嘈杂的原始信号，迫使下游分类器浪费容量重新编码这些信号。为了绕过这一瓶颈，我们提出了直接潜变量增强（Direct Latent Augmentation, DLA），直接利用未解码的生成潜变量作为特权信息。此外，为了将这种密集知识转移给纯视觉学生，我们引入了多层显式模拟联觉（Multilayer Explicit Simulated Synesthesia, MESSy）。MESSy并不强制执行严格的表示匹配，这种匹配迫使学生扭曲其固有的视觉特征以适应复杂的多模态拓扑，而是使用预测目标安全地内化这些物理先验。实证结果表明，我们的框架显著优于原始数据增强和传统蒸馏。最终，我们的方法产生了高度准确的单模态学生，其“联觉”潜结构与它们从未直接观察过的物理属性本质上对齐。
cs.CV / 81 / 2606.08364
Self-Supervised Vision Transformers for CBCT-Based Detection of Temporomandibular Joint Osteoarthritis
基于自监督视觉变换器的锥形束CT下颞下颌关节骨关节炎检测
Abstract
Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative condition whose osseous changes are often subtle on cone-beam CT (CBCT), making automated detection challenging. We study how well the DINO family of self-supervised vision transformers -- DINOv1, DINOv2, DINOv2+reg, and RAD-DINO (a radiology-pretrained variant) -- transfers to CBCT, asking how much backbone adaptation is needed and of what kind. We propose a simple slice-based pipeline using Vision Transformer (ViT) backbones: axial CBCT slices are encoded per-slice by a frozen or partially adapted ViT and aggregated via attention-based multiple instance learning (MIL) for patient-level binary OA/Normal classification. Through systematic ablation across unfreezing strategies and aggregation designs on a multi-source CBCT dataset, we find that partial unfreezing of the final two transformer blocks is the decisive factor, improving AUC from 0.671 (fully frozen DINOv2) to 0.902. This outperforms DINOv1 (0.867), DINOv2+reg (0.774), and a supervised ImageNet ViT-B/16 baseline (0.843). Our results provide practical guidance for adapting DINO-family foundation models in low-data medical imaging settings, showing that adaptation strategy is a stronger driver of performance than backbone choice alone.
Chinese Translation
颞下颌关节骨关节炎（TMJ OA）是一种常见的退行性疾病，其骨骼变化在锥形束CT（CBCT）上往往较为微妙，导致自动检测面临挑战。我们研究了自监督视觉变换器DINO系列（DINOv1、DINOv2、DINOv2+reg和RAD-DINO（一个经过放射学预训练的变体））在CBCT上的迁移效果，探讨了需要多少骨干网络适应以及适应的类型。我们提出了一种基于切片的简单管道，使用视觉变换器（ViT）骨干网络：轴向CBCT切片由一个冻结或部分适应的ViT逐切片编码，并通过基于注意力的多实例学习（MIL）进行患者级别的二分类（OA/正常）。通过在一个多源CBCT数据集上对解冻策略和聚合设计进行系统的消融实验，我们发现最后两个变换器块的部分解冻是决定性因素，使AUC从0.671（完全冻结的DINOv2）提高到0.902。这一结果优于DINOv1（0.867）、DINOv2+reg（0.774）和一个监督的ImageNet ViT-B/16基线（0.843）。我们的结果为在低数据医学影像环境中适应DINO系列基础模型提供了实用指导，表明适应策略对性能的影响大于单纯的骨干选择。
cs.CV / 82 / 2606.08402
SceneConductor: 3D Scene Generation from Single Image with Multi-Agent Orchestration
场景指挥者：基于单幅图像的多智能体编排下的3D场景生成
Abstract
Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context from inherently ambiguous visual evidence. Despite recent progress in joint layout-and-mesh generation, existing methods often rely on holistic or weakly decomposed pipelines that entangle many factors at once and demand extensive scene-level supervision, limiting their generalization to complex real-world environments. We propose a multi-agent orchestration framework that decomposes single-image 3D scene generation into three structured stages: scene initialization, environment construction, and multi-agent refinement. The initialization stage extracts image-derived object masks, builds object-level 3D representations, and predicts an initial spatial layout to form a coarse 3D scene. The environment-construction stage then leverages this initialization together with point-map geometry to build an environmental scaffold of supporting surfaces, room boundaries, materials, and illumination. Finally, in the refinement stage, a planner agent identifies structural and visual inconsistencies, applies simple corrections directly, and dispatches specialist agents for complex localized revisions that are reintegrated into the global scene. To provide reliable structural initialization while reducing reliance on scene-level annotations, we further introduce a geometry-aware layout predictor supervised by sparse geometric priors derived from point maps. Unlike fully supervised layout generators, the predictor can be trained from segmentation-level data and generalizes robustly to diverse real-world scenes. Extensive experiments on benchmark datasets show that our method consistently outperforms prior approaches in geometric accuracy, spatial consistency, and perceptual realism.
Chinese Translation
从单幅图像生成完整的3D场景需要从固有模糊的视觉证据中推断出全局一致的几何形状、物体关系和环境背景。尽管最近在联合布局与网格生成方面取得了进展，但现有方法往往依赖于整体或弱分解的流程，这些流程将多个因素纠缠在一起，并且需要大量的场景级监督，从而限制了它们在复杂现实环境中的泛化能力。我们提出了一种多智能体编排框架，将单幅图像的3D场景生成分解为三个结构化阶段：场景初始化、环境构建和多智能体细化。初始化阶段提取图像衍生的物体掩膜，构建物体级3D表示，并预测初始空间布局以形成粗略的3D场景。然后，环境构建阶段利用这一初始化以及点图几何，构建支持表面、房间边界、材料和照明的环境支架。最后，在细化阶段，规划代理识别结构和视觉不一致，直接应用简单修正，并派遣专业代理进行复杂的局部修订，这些修订将重新整合到全局场景中。为了提供可靠的结构初始化，同时减少对场景级注释的依赖，我们进一步引入了一种几何感知布局预测器，该预测器由从点图派生的稀疏几何先验进行监督。与完全监督的布局生成器不同，该预测器可以从分割级数据进行训练，并能够稳健地泛化到多样化的现实场景。对基准数据集的广泛实验表明，我们的方法在几何准确性、空间一致性和感知真实感方面始终优于先前的方法。
cs.CV / 83 / 2606.08404
Geometry-Driven Flow Analysis of Brain Sulcal Pattern
基于几何驱动的脑沟模式流动分析
Abstract
Cortical folding reflects coordinated neurodevelopmental processes and is increasingly recognized as a sensitive marker of neurological disease. However, most existing analyses rely on indirect scalar summaries that do not explicitly model folding geometry itself. In juvenile myoclonic epilepsy (JME), a common genetic epilepsy, cortical abnormalities are often subtle, spatially distributed, and difficult to detect using conventional morphometric measures. We introduce a Poisson-equation-based framework that models cortical folding as a geometry-driven flow derived from mean curvature on the cortical manifold. By treating folding patterns as a stationary source-sink structure, the proposed approach yields a smooth, globally balanced potential field whose surface gradient defines a physically interpretable flux. This framework enables spatially coherent analysis of sulcal-gyral folding organization and provides a principled representation of geometry-driven cortical structure in JME.
Chinese Translation
皮层折叠反映了协调的神经发育过程，并越来越被认为是神经疾病的敏感标志。然而，大多数现有分析依赖于间接的标量总结，未能明确建模折叠几何形状。在青少年肌阵挛性癫痫（Juvenile Myoclonic Epilepsy, JME）中，这种常见的遗传性癫痫，皮层异常通常较为微妙，空间分布广泛，且使用传统形态测量方法难以检测。我们提出了一种基于泊松方程的框架，将皮层折叠建模为源自皮层流形上平均曲率的几何驱动流动。通过将折叠模式视为一个静态的源-汇结构，所提出的方法产生了一个平滑的、全球平衡的势场，其表面梯度定义了一个物理可解释的通量。该框架使得对沟回折叠组织的空间一致性分析成为可能，并为JME中几何驱动的皮层结构提供了一个原则性表示。
cs.CV / 84 / 2606.08415
CoVEBench: Can Video Editing Models Handle Complex Instructions?
CoVEBench：视频编辑模型能否处理复杂指令？
Abstract
While recent text-guided video editing models excel at elementary tasks (e.g., style transfer, object insertion), real-world user requests are highly compositional. A single prompt often demands multiple coupled edits, such as modifying subjects, actions, and camera views, while strictly preserving unrelated spatiotemporal content. Existing benchmarks, heavily constrained by isolated edits and coarse global metrics, fail to diagnose how models handle such complex workflows. To address this gap, we introduce CoVEBench, a compositional video editing benchmark comprising 416 curated source videos, 626 multi-point editing instructions, and 9,990 fine-grained checklist items. Covering diverse editing dimensions, CoVEBench evaluates models via MLLM-judged instruction compliance and video fidelity, alongside automated metrics for video quality. Extensive experiments reveal that compositional editing remains a profound challenge: current models frequently omit edits, violate preservation constraints, or introduce artifacts when handling multiple operations simultaneously. CoVEBench provides a challenging, diagnostic testbed to advance video editing toward realistic user workflows.
Chinese Translation
尽管近期的文本引导视频编辑模型在基本任务（例如，风格迁移、对象插入）上表现出色，但现实世界用户的请求往往具有高度的组合性。单个提示通常需要多个相互关联的编辑，例如修改主体、动作和摄像机视角，同时严格保留无关的时空内容。现有基准测试由于受到孤立编辑和粗略全局指标的严重限制，无法有效诊断模型如何处理如此复杂的工作流程。为了解决这一问题，我们引入了CoVEBench，一个由416个精心策划的源视频、626个多点编辑指令和9,990个细粒度检查项组成的组合视频编辑基准。CoVEBench涵盖多样的编辑维度，通过MLLM（多模态大语言模型）评估指令合规性和视频保真度，同时结合自动化视频质量指标对模型进行评估。大量实验表明，组合编辑仍然是一个深刻的挑战：当前模型在处理多个操作时，常常遗漏编辑、违反保留约束或引入伪影。CoVEBench提供了一个具有挑战性且具诊断性的测试平台，以推动视频编辑向现实用户工作流程的方向发展。
cs.CV / 85 / 2606.08420
CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs
CheXanatomy：面向胸部放射影像的解剖知识感知视觉-语言建模
Abstract
Vision-language models (VLMs) pretrained on large-scale image-text pairs demonstrate strong image-level understanding, but are primarily optimized for global alignment and do not explicitly encode fine-grained anatomical structure, limiting their suitability for spatially precise tasks such as segmentation. We introduce CheXanatomy, a framework that integrates explicit anatomical knowledge into a pretrained VLM through autoregressive token-space supervision. Instead of adding task-specific decoder heads, the model is trained to generate anatomical segmentation masks via next-token prediction. To enable scalable supervision, we synthesize realistic chest radiographs from CT volumes and forward-project CT segmentation labels to obtain anatomically consistent 2D masks. We evaluate the approach on synthetic and real chest radiographs against a U-Net baseline, including ablations on model scale, input resolution, and vision encoder fine-tuning. Autoregressive anatomical supervision achieves performance comparable to specialized convolutional models in-distribution and demonstrates improved geometric robustness under domain shift to real CXR data. In addition, anatomy-pretrained models exhibit improved sample efficiency when adapting to novel localization tasks under limited supervision. Larger models and higher input image resolution improve performance, while vision encoder fine-tuning has limited effect. These results show that embedding anatomical structure directly into the generative objective promotes spatially grounded representations and supports anatomy-aware medical vision-language modeling.
Chinese Translation
在大规模图像-文本对上预训练的视觉-语言模型（VLMs）展现出强大的图像级理解能力，但主要优化于全局对齐，并未明确编码细粒度的解剖结构，这限制了它们在诸如分割等空间精确任务中的适用性。我们提出了CheXanatomy，一个通过自回归标记空间监督将显式解剖知识整合到预训练VLM中的框架。该模型不是通过添加特定任务的解码头进行训练，而是通过下一个标记预测生成解剖分割掩膜。为了实现可扩展的监督，我们从CT体积合成真实的胸部放射影像，并将CT分割标签前向投影以获得解剖一致的二维掩膜。我们在合成和真实的胸部放射影像上评估该方法，并与U-Net基线进行比较，包括对模型规模、输入分辨率和视觉编码器微调的消融实验。自回归解剖监督在分布内的性能与专门的卷积模型相当，并在向真实CXR数据的领域转移中展现出更好的几何鲁棒性。此外，解剖预训练模型在有限监督下适应新颖定位任务时表现出更好的样本效率。更大的模型和更高的输入图像分辨率提高了性能，而视觉编码器微调的效果有限。这些结果表明，将解剖结构直接嵌入生成目标中促进了空间上扎根的表示，并支持解剖知识感知的医学视觉-语言建模。
cs.CV / 86 / 2606.08421
Segmentation-Assisted Brain MRI Synthesis with Cross-Image Multi-Contrast Feature Memory Bank Retrieval Augmentation
基于分割辅助的脑部MRI合成与跨图像多对比特征记忆库检索增强
Abstract
Multi-contrast brain MRI provide complementary soft-tissue characteristics that aid in the screening and diagnosis of diseases. However, limited scanning time, image corruption and various imaging protocols often result in incomplete multi-contrast images. While current approaches excel in image synthesis, they often struggle to synthesize critical tumor regions and exploit contextual information in multi-contrast brain MRI effectively. To address this issue, we propose a synthesis-centric, segmentation-assisted closed-loop framework with retrieval augmentation synthesis. Our method overall takes a generative adversarial architecture, which aims to synthesize missing contrasts from any combination of available ones with a single model. To explicitly capture tumor semantics and focus synthesis on tumor regions, we add an auxiliary segmentation branch that predicts tumor masks and feeds them back as semantic conditioning in synthesis branch, thereby learning tumor-aware representations in the model and improving synthesis fidelity. Furthermore, we propose a dual-bank retrieval augmentation strategy. It dynamically queries two external knowledge bases, namely a tumor masks memory bank for crucial tumor context and cross-image contrast feature memory bank for global style information, to augment synthesis. Verified on two public multi-contrast magnetic resonance brain datasets: BraTs2020 and UCSF-BMSR, the proposed method is effective in handling medical brain images synthesis tasks and shows superior performance compared to previous methods. Code is available at:https://github.com/iBizzard/SSCF.git
Chinese Translation
多对比脑部MRI提供了互补的软组织特征，有助于疾病的筛查和诊断。然而，有限的扫描时间、图像损坏以及各种成像协议常常导致多对比图像的不完整。尽管当前的方法在图像合成方面表现出色，但它们在合成关键肿瘤区域和有效利用多对比脑部MRI中的上下文信息方面往往存在困难。为了解决这一问题，我们提出了一种以合成为中心的、分割辅助的闭环框架，并结合检索增强合成。我们的方法整体采用生成对抗架构，旨在使用单一模型从任何可用对比的组合中合成缺失的对比。为了明确捕捉肿瘤语义并将合成重点放在肿瘤区域，我们添加了一个辅助分割分支，该分支预测肿瘤掩膜，并将其作为语义条件反馈到合成分支，从而在模型中学习肿瘤感知表示并提高合成的保真度。此外，我们提出了一种双库检索增强策略。它动态查询两个外部知识库，即用于关键肿瘤上下文的肿瘤掩膜记忆库和用于全局风格信息的跨图像对比特征记忆库，以增强合成效果。在两个公共多对比磁共振脑数据集BraTs2020和UCSF-BMSR上验证，该方法在处理医学脑图像合成任务方面有效，并且与以前的方法相比表现优越。代码可在以下链接获取：https://github.com/iBizzard/SSCF.git
cs.CV / 87 / 2606.08436
Reinforcing Temporal Answer Grounding in Instructional Video via Candidate-Aware Causal Reasoning
通过候选意识因果推理强化教学视频中的时间答案定位
Abstract
The task of temporal answer grounding in instructional video (TAGV), which aims to locate precise video segments that respond to natural language queries, is increasingly important for direct video answer retrieval. This task remains challenging due to the need to comprehend semantically complex questions and to address the significant length mismatch between untrimmed videos and short target moments. Existing methods often suffer from sensitivity to irrelevant content or insufficient visual reasoning capabilities. To tackle these limitations, we propose a Candidate-Aware Causal Reasoning (CACR) framework. Our approach first employs a Visual-Language Pre-training based Candidate Selection (VBCS) algorithm to efficiently generate K candidate segments, then applies a temporal logic reasoning module enhanced by a rejection reward mechanism and optimized via Group Relative Policy Optimization (GRPO) for robust inference. Extensive experiments on six benchmarks demonstrate that our method achieves state-of-the-art performance in terms of mean Intersection-over-Union (mIoU), providing a new perspective for reasoning-based retrieval in long videos.
Chinese Translation
教学视频中的时间答案定位任务（TAGV）旨在定位响应自然语言查询的精确视频片段，这一任务对于直接视频答案检索越来越重要。然而，由于需要理解语义复杂的问题以及处理未剪辑视频与短目标时刻之间显著的长度不匹配，这一任务仍然具有挑战性。现有方法往往对无关内容敏感或缺乏足够的视觉推理能力。为了解决这些局限性，我们提出了一种候选意识因果推理（CACR）框架。我们的方法首先采用基于视觉-语言预训练的候选选择（VBCS）算法高效生成 K 个候选片段，然后应用一个通过拒绝奖励机制增强的时间逻辑推理模块，并通过群体相对策略优化（GRPO）进行优化，以实现稳健推理。在六个基准上的广泛实验表明，我们的方法在平均交并比（mIoU）方面达到了最先进的性能，为长视频中的基于推理的检索提供了新的视角。
cs.CV / 88 / 2606.08464
TVI-CoT: Text-Visual Interleaved Chain-of-Thought Reasoning for Multimodal Understanding
TVI-CoT：用于多模态理解的文本-视觉交错链式思维推理
Abstract
Chain-of-thought (CoT) reasoning has proven effective for enhancing problem-solving in large language models. However, when applied to multimodal LLMs (MLLMs), existing CoT approaches suffer from a fundamental limitation: they perform reasoning entirely in text without accessing visual features during the reasoning process. After initial visual encoding, image information becomes inaccessible, forcing models to reason based solely on whatever was captured in the initial description, which forms a `vision-blind reasoning' paradigm that limits fine-grained visual extraction, error verification, and adaptive attention. We propose Text-Visual Interleaved Chain-of-Thought (TVI-CoT), a framework that enables explicit interleaving of textual reasoning and visual feature access through learnable control tokens
, and . These tokens allow dynamic switching between reasoning and visual grounding, attending to relevant image regions conditioned on the evolving reasoning state. Experiments on eight benchmarks demonstrate state-of-the-art results among MLLM-based CoT methods and notable performance boost compared to the baseline: +6.1% on MMMU, +3.8% on MathVerse, +3.4% on MathVista, and +3.4% on ScienceQA. Code is available at https://github.com/hulianyuyy/TVI-CoT.Chinese Translation
链式思维（CoT）推理已被证明在增强大型语言模型的问题解决能力方面有效。然而，当应用于多模态大型语言模型（MLLMs）时，现有的CoT方法存在一个根本性的局限性：它们在推理过程中完全依赖文本，而未能访问视觉特征。在初始视觉编码之后，图像信息变得不可访问，迫使模型仅根据初始描述中捕获的内容进行推理，这形成了一种“视觉盲推理”（vision-blind reasoning）范式，限制了细粒度视觉提取、错误验证和自适应注意力。我们提出了文本-视觉交错链式思维推理（TVI-CoT）框架，该框架通过可学习的控制标记
、和显式地交错文本推理和视觉特征访问。这些标记允许在推理和视觉基础之间动态切换，关注与不断演变的推理状态相关的图像区域。在八个基准测试上的实验表明，TVI-CoT在基于MLLM的CoT方法中实现了最先进的结果，并与基线相比显著提升了性能：在MMMU上提升6.1%，在MathVerse上提升3.8%，在MathVista上提升3.4%，在ScienceQA上提升3.4%。代码可在https://github.com/hulianyuyy/TVI-CoT获取。
cs.CV / 89 / 2606.08492
Seeing is Believing: Aligning Prompt Rewriting with Visual Anchors for Text-to-Image Generation
眼见为实：将提示重写与视觉锚点对齐以实现文本到图像生成
Abstract
Despite the impressive capabilities of text-to-image (T2I) models, an intent-generation gap often persists due to the brevity and ambiguity of user prompts. Existing approaches primarily polish the prompt for fluency and readability. However, the enhancement process still lacks visual grounding. As a result, the rewriter may over-infer missing details, causing an intent-generation gap. To address this limitation, we propose FaithRewriter, a novel prompt-enhancement framework for T2I generation. Specifically, FaithRewriter first leverages a multimodal MLLM to generate an image from the original prompt as an intermediate visual cue. This cue is then combined with the prompt and fed into a large-scale LLM to produce visually grounded augmentations that better reflect how the intended content should appear in images. Finally, these augmentations are distilled into a small-scale LLM for efficient deployment, enhancing its ability to generate effective T2I prompts. Experiments show that FaithRewriter yields prompts that are more faithful to the user intent and more visually plausible than strong baselines, helping narrow the intent-generation gap.
Chinese Translation
尽管文本到图像（T2I）模型具有令人印象深刻的能力，但由于用户提示的简洁性和模糊性，意图生成的差距仍然存在。现有方法主要对提示进行流畅性和可读性的润色。然而，增强过程仍然缺乏视觉基础。因此，重写器可能会过度推断缺失的细节，从而导致意图生成的差距。为了解决这一局限性，我们提出了FaithRewriter，一种用于T2I生成的新型提示增强框架。具体而言，FaithRewriter首先利用多模态的MLLM从原始提示生成图像，作为中间视觉线索。然后，将该线索与提示结合，并输入到大规模的LLM中，以生成更好地反映预期内容在图像中应如何呈现的视觉基础增强。最后，这些增强被提炼到小规模的LLM中，以实现高效部署，增强其生成有效T2I提示的能力。实验表明，FaithRewriter生成的提示更忠实于用户意图，并且在视觉上更具可信度，相较于强基线帮助缩小了意图生成的差距。
cs.CV / 90 / 2606.08511
Look Less, Reason More: Block-wise Attention Skipping for Efficient Multimodal LLMs
少看，多推理：高效多模态大型语言模型的块级注意力跳过
Abstract
Multimodal Large Language Models (MLLMs) face a significant inference bottleneck due to the quadratic computational cost of self-attention over long visual token sequences. However, we identify a critical inefficiency in current architectures: Visual Attention Saturation. Our analysis reveals that visual tokens rapidly establish their spatial structure and intra-modal relationships in early layers, rendering visual-to-visual self-attention in deeper layers computationally redundant. Conversely, Feed-Forward Networks (FFNs) in these layers remain essential for projecting visual features into the evolving textual semantic space. Leveraging this insight, we present Visual-Skip (V-Skip), a training-free inference paradigm that decouples spatial interaction from semantic evolution. Rather than discarding tokens, V-Skip imposes block-wise structured sparsity by selectively bypassing saturated visual self-attention modules. Furthermore, recognizing that varying downstream tasks demand distinct reasoning depths, V-Skip employs a lightweight, few-shot calibration to dynamically route the task-optimal sparsity path. Extensive experiments demonstrate that V-Skip effectively bypasses redundant vision attention to achieve block-wise sparsity, maintaining a 94.16% to 100.31% performance retention across diverse MLLMs. Ultimately, we prove that to reason more effectively, models do not need to discard what they see -- they simply need to "look less" at the right depth.
Chinese Translation
多模态大型语言模型（MLLMs）在推理时面临显著的瓶颈，原因在于对长视觉标记序列的自注意力计算成本呈二次方增长。然而，我们识别出当前架构中的一个关键低效问题：视觉注意力饱和。我们的分析表明，视觉标记在早期层迅速建立其空间结构和模态内关系，使得在更深层次进行视觉-视觉自注意力计算变得冗余。相反，这些层中的前馈网络（FFNs）仍然对将视觉特征投影到不断演变的文本语义空间至关重要。基于这一洞察，我们提出了视觉跳过（Visual-Skip, V-Skip），这是一种无训练推理范式，将空间交互与语义演变解耦。V-Skip并不是简单地丢弃标记，而是通过选择性地绕过饱和的视觉自注意力模块，施加块级结构稀疏性。此外，考虑到不同下游任务需要不同的推理深度，V-Skip采用轻量级的少量样本校准，动态引导任务最优的稀疏路径。大量实验表明，V-Skip有效地绕过冗余的视觉注意力，实现块级稀疏性，在各种MLLMs中保持94.16%到100.31%的性能保留。最终，我们证明了要更有效地推理，模型不需要丢弃所见之物——它们只需在正确的深度“少看”即可。
cs.CV / 91 / 2606.08514
OmniTryOn: Video Try-On Anything at Once!
OmniTryOn：一次性视频试穿任何物品！
Abstract
Although video virtual try-on (VVT) has achieved significant progress, existing methods still exhibit two fundamental limitations: first, they are restricted to single-garment transfer, rendering simultaneous multi-object try-on highly impractical; second, their heavy reliance on explicit external priors (e.g., garment masks) inevitably destroys crucial physical dynamics and degrades visual quality. To bridge this gap, this paper proposes the novel Try-On Anything task, which aims to simultaneously transfer diverse wearable objects onto a person in a video in a single inference pass. To support and standardize this paradigm, we introduce TryAny-Bench, a comprehensive benchmark encompassing a paired video dataset alongside a tailored evaluation protocol. Furthermore, we present OmniTryOn, an external-prior-free generative framework designed to tackle this task. Specifically, OmniTryOn employs a First Frame Wearable Cache strategy, which directly provides diverse wearable objects for the generation process through the initial video frame. To maintain consistency, we propose the Spatiotemporally Consistent RoPE (STC-RoPE), which inherently establishes robust spatiotemporal anchors to strictly preserve complex human motions and background dynamics. Optimized by the proposed Gradual Try-On (GTO) training strategy, our model progressively masters robust multi-object synthesis. Extensive experiments on TryAny-Bench demonstrate that OmniTryOn significantly outperforms existing specialized video virtual try-on models and general video editing baselines, establishing a powerful new standard for the Try-On Anything task. Our dataset, code, and models are available at https://github.com/xcltql666/OminTryOn.
Chinese Translation
尽管视频虚拟试穿（VVT）已取得显著进展，但现有方法仍存在两个基本局限性：首先，它们仅限于单件服装的转移，使得同时进行多物体试穿变得极为不切实际；其次，它们对显式外部先验（例如，服装掩码）的高度依赖不可避免地破坏了关键的物理动态，并降低了视觉质量。为了解决这一问题，本文提出了新颖的“试穿任何物品”任务，旨在通过单次推理将多种可穿戴物品同时转移到视频中的人物身上。为了支持和标准化这一范式，我们引入了TryAny-Bench，这是一个全面的基准，包含配对的视频数据集及其定制的评估协议。此外，我们提出了OmniTryOn，这是一个无外部先验的生成框架，旨在解决这一任务。具体而言，OmniTryOn采用了首帧可穿戴缓存策略，通过初始视频帧直接为生成过程提供多样的可穿戴物品。为了保持一致性，我们提出了时空一致的RoPE（STC-RoPE），该方法本质上建立了强大的时空锚点，以严格保留复杂的人体运动和背景动态。通过提出的渐进式试穿（GTO）训练策略进行优化，我们的模型逐步掌握了稳健的多物体合成。在TryAny-Bench上的大量实验表明，OmniTryOn显著优于现有的专用视频虚拟试穿模型和一般视频编辑基线，为“试穿任何物品”任务建立了强大的新标准。我们的数据集、代码和模型可在https://github.com/xcltql666/OminTryOn获取。
cs.CV / 92 / 2606.08525
DriveReward: A Comprehensive Dataset and Generative Vision-Language Reward Model for Autonomous Driving
DriveReward：用于自动驾驶的综合数据集和生成视觉-语言奖励模型
Abstract
Reward models play a pivotal role in reinforcement learning (RL) and multi-modal trajectory selection for autonomous driving. However, acquiring such rewards typically relies on hand-crafted rule-based objectives or perception ground truth, which hinders generalization for data-scaling. While Vision-Language Models (VLMs) have demonstrated feasibility as reward models in other domains, their effectiveness in driving tasks remains underexplored. In this work, we bridge this gap by (1) introducing DriveReward, a reasoning trajectory evaluation dataset rigorously labeled via temporally-grounded visual guidance, and augmented with counterfactual driving behaviors., (2) alongside a specialized Vision-Language Reward Model. To address the scarcity of failure cases in conventional datasets, we propose a counterfactual data annotation scheme to construct cases encompassing diverse driving styles and erroneous behaviors. Evaluations on our proposed benchmark reveal that even leading open-source and proprietary VLMs fail to excel across all tasks, highlighting significant room for improvement in existing models. Building on these findings, we subsequently tailor a specialized 1B reward model that outperforms larger VLMs on task-specific reward alignment. Finally, we validate our reward model's effectiveness by integrating it into RL finetuning and multi-modal trajectory scoring across multiple baselines, achieving performance comparable to rule-based reward calculations in both open-loop and closed-loop evaluation.
Chinese Translation
奖励模型在强化学习（RL）和自动驾驶的多模态轨迹选择中发挥着关键作用。然而，获取此类奖励通常依赖于手工制作的基于规则的目标或感知真实值，这限制了数据扩展的泛化能力。尽管视觉-语言模型（VLMs）在其他领域作为奖励模型的可行性已得到证明，但它们在驾驶任务中的有效性仍然未得到充分探索。在本研究中，我们通过以下方式填补这一空白：（1）引入DriveReward，这是一个通过时间基础视觉指导严格标注的推理轨迹评估数据集，并增强了反事实驾驶行为；（2）同时提出一个专门的视觉-语言奖励模型。为了应对传统数据集中失败案例的稀缺性，我们提出了一种反事实数据注释方案，以构建涵盖多样化驾驶风格和错误行为的案例。对我们提出的基准进行评估表明，即使是领先的开源和专有VLM在所有任务中也未能表现出色，突显了现有模型的显著改进空间。在这些发现的基础上，我们随后定制了一个专门的1B奖励模型，在任务特定的奖励对齐上超越了更大的VLM。最后，我们通过将我们的奖励模型整合到RL微调和多模态轨迹评分中，对其有效性进行了验证，在开放循环和闭合循环评估中实现了与基于规则的奖励计算相当的性能。
cs.CV / 93 / 2606.08535
NGram-MoSE: Efficient Remote Sensing Super-Resolution via N-Gram Context and Mixture-of-Experts
NGram-MoSE：通过N-Gram上下文和专家混合实现高效的遥感超分辨率
Abstract
Remote sensing applications for environmental monitoring and disaster management are frequently constrained by a spatial--temporal trade-off: imagery with fine spatial detail is often acquired less frequently, whereas more temporally available observations are typically coarser. Single-image super-resolution provides a practical means to enhance coarse imagery without changing acquisition schedules, yet many Transformer-based SR models remain computationally expensive and can be sensitive to limited or geographically biased training data, which degrades robustness under out-of-distribution conditions. This paper presents NGram-MoSE, a lightweight Transformer architecture designed to improve both efficiency and texture continuity. NGram-MoSE introduces N-Gram Context Injection to strengthen cross-window local consistency and mitigate window-boundary artifacts, and incorporates a Mixture-of-Experts (MoE) feed-forward design to scale capacity through sparse activation without proportional growth in inference cost. Experiments on a geographically disjoint OOD test set show that NGram-MoSE achieves 31.68\,dB PSNR while reducing FLOPs by \(14\times\) relative to a heavyweight Transformer reference. Downstream evaluation on a landslide segmentation benchmark further demonstrates that restoring degraded inputs to the detector training scale improves performance, yielding a 4.47\% absolute gain in mAP@50 over bicubic upsampling, and exhibits stronger cross-scale consistency under scale extrapolation. These results indicate that NGram-MoSE provides an effective SR module for resource-constrained remote sensing pipelines requiring robust generalization.
Chinese Translation
遥感在环境监测和灾害管理中的应用常常受到时空权衡的限制：具有精细空间细节的影像通常获取频率较低，而更频繁获取的观测通常较为粗糙。单幅图像超分辨率提供了一种实用的方法来增强粗糙影像，而无需改变获取时间表，然而许多基于Transformer的超分辨率模型仍然计算开销较大，并且对有限或地理偏倚的训练数据敏感，这在分布外条件下降低了其鲁棒性。本文提出了NGram-MoSE，一种轻量级的Transformer架构，旨在提高效率和纹理连续性。NGram-MoSE引入了N-Gram上下文注入，以增强跨窗口的局部一致性并减轻窗口边界伪影，并结合了专家混合（Mixture-of-Experts, MoE）前馈设计，通过稀疏激活扩展容量，而不成比例地增加推理成本。在一个地理上不重叠的分布外测试集上的实验表明，NGram-MoSE在相较于重型Transformer基准模型时，达到了31.68 dB的PSNR，同时将FLOPs减少了14倍。对滑坡分割基准的下游评估进一步表明，将退化输入恢复到检测器训练规模可以提高性能，相较于双三次插值上采样，mAP@50绝对增益达到了4.47%，并在尺度外推时表现出更强的跨尺度一致性。这些结果表明，NGram-MoSE为资源受限的遥感处理流程提供了一个有效的超分辨率模块，满足对鲁棒性泛化的需求。
cs.CV / 94 / 2606.08566
Towards Accurate Emotion-Attributed Video Captioning via Fine-grained Emotion-Cause Pair Extraction
通过细粒度情感-原因对提取实现准确的情感归因视频字幕生成
Abstract
Emotional Video Captioning (EVC) is a challenging task that aims to generate factually accurate and emotionally rich descriptions for videos. Existing EVC methods leverage holistic visual features to mine global emotional cues, and then aggregate multimodal features to guide the emotional caption generation, which ignores the critical characteristic of the EVC task. Visual emotions are evoked by specific motivational causes, which are usually only implied in core video segments. The holistic mining brings significant information redundancy and inaccurate emotional cues. Thus, fine-grained visual cause extraction has a facilitative effect on both emotion perception and emotion-attributed caption generation. To this end, we propose a fine-grained emotion-cause pair extraction framework for emotion-attributed video captioning. Specifically, we learn pair-wise emotion and cause features in two rounds: 1) We propose a Concept-aware Visual Semantic Decomposition module to augment visual features by exploring scene, object, and motion concepts. Besides, to enhance emotional features, we propose a Visual-guided Emotion Interpretable Learning module, which guides emotion refinement with visual temporal dynamics, and augments the interpretable refinement process by reliable VAD-vector constraints. 2) We achieve emotion-cause pair extraction by cross-coupling the visual and emotional features before and after refinement, and leverage contrastive loss to achieve semantic forced alignment. Overall, our approach optimizes complex semantic understanding and emotion perception of videos, leading to a promising performance in emotional captioning. Extensive experiments on three challenging datasets demonstrate the superiority of our approach and each proposed module, e.g., achieving the best performances with +4.4% and +5.4% w.r.t. BLEU-2 and ROUGE-L, respectively, on the EVC-MSVD dataset.
Chinese Translation
情感视频字幕生成（Emotional Video Captioning, EVC）是一项具有挑战性的任务，旨在为视频生成事实准确且情感丰富的描述。现有的EVC方法利用整体视觉特征挖掘全局情感线索，然后聚合多模态特征以指导情感字幕的生成，但这忽视了EVC任务的关键特征。视觉情感是由特定的动机原因引发的，这些原因通常仅在核心视频片段中暗示。整体挖掘带来了显著的信息冗余和不准确的情感线索。因此，细粒度的视觉原因提取对情感感知和情感归因字幕生成都有促进作用。为此，我们提出了一种用于情感归因视频字幕生成的细粒度情感-原因对提取框架。具体而言，我们通过两个轮次学习成对的情感和原因特征：1）我们提出了一种概念感知视觉语义分解模块，通过探索场景、物体和运动概念来增强视觉特征。此外，为了增强情感特征，我们提出了一种视觉引导的情感可解释学习模块，该模块通过视觉时间动态指导情感的细化，并通过可靠的VAD向量约束增强可解释的细化过程。2）我们通过交叉耦合细化前后的视觉和情感特征来实现情感-原因对提取，并利用对比损失实现语义强制对齐。总体而言，我们的方法优化了视频的复杂语义理解和情感感知，在情感字幕生成中表现出良好的性能。在三个具有挑战性的数据集上进行的大量实验证明了我们的方法及每个提出模块的优越性，例如，在EVC-MSVD数据集上，分别实现了相对于BLEU-2和ROUGE-L的最佳性能，提升幅度为+4.4%和+5.4%。
cs.CV / 95 / 2606.08572
OmniCap-IF: Benchmarking and Improving Instruction Following Abilities for Omni-Video Captioning
OmniCap-IF：基准测试与提升全视频字幕生成的指令遵循能力
Abstract
While Omni-modal Large Language Models (OLLMs) have demonstrated impressive capabilities in jointly processing audio and visual streams, their ability to strictly adhere to complex, multi-faceted user instructions remains largely unexplored. Existing benchmarks primarily focus on holistic video understanding or text-only instruction following, failing to capture the intricate interplay between modalities and user constraints. To bridge this gap, we introduce OmniCap-IF, the first comprehensive benchmark specifically designed to evaluate instruction-following capabilities in omni-modal captioning. OmniCap-IF incorporates a systematic framework that assesses captions on two dimensions: format correctness and content correctness. Our benchmark encompasses 50 distinct constraint types across pure visual, pure audio, and audio-visual modalities, while integrating Temporal Grounding to assess spatio-temporal precision. Extensive evaluations of prominent models on 1,920 high-quality samples reveal significant performance disparities. Furthermore, our analysis uncovers a critical "format-content tradeoff", demonstrating that increasing formatting complexity directly degrades models' omni-modal reasoning abilities. Finally, to advance the field, we curate a 54K instruction-tuning dataset, OmniCap-IF-54K and present OmniCaptioner-IF, which achieves notable improvements in both complex instruction adherence and general omni-modal captioning performance.
Chinese Translation
尽管全模态大型语言模型（OLLMs）在音频和视觉流的联合处理方面展现了令人印象深刻的能力，但它们严格遵循复杂多面的用户指令的能力仍然在很大程度上未被探索。现有基准主要集中在整体视频理解或仅文本指令遵循上，未能捕捉模态与用户约束之间的复杂相互作用。为填补这一空白，我们引入了OmniCap-IF，这是第一个专门设计用于评估全模态字幕生成中指令遵循能力的综合基准。OmniCap-IF包含一个系统框架，从格式正确性和内容正确性两个维度评估字幕。我们的基准涵盖了50种不同的约束类型，涉及纯视觉、纯音频和音频-视觉模态，同时整合了时间定位（Temporal Grounding）以评估时空精度。对1920个高质量样本中突出模型的广泛评估揭示了显著的性能差异。此外，我们的分析揭示了一个关键的“格式-内容权衡”，表明增加格式复杂性会直接降低模型的全模态推理能力。最后，为推动该领域的发展，我们整理了一个54K指令调优数据集OmniCap-IF-54K，并提出了OmniCaptioner-IF，该模型在复杂指令遵循和整体全模态字幕生成性能上均取得了显著提升。
cs.CV / 96 / 2606.08612
Facial Expression Recognition in the Deep Learning Era: A Systematic Multi-Criteria Review of Methods, Models, Datasets, Performance, Challenges, and Future Research Directions
深度学习时代的面部表情识别：方法、模型、数据集、性能、挑战及未来研究方向的系统多标准综述
Abstract
Facial Expression Recognition (FER) has advanced rapidly over the last decade, driven by the shift from handcrafted descriptors and shallow classifiers to deep convolutional, attention-based, vision-language, and foundation-model architectures, and by the parallel growth of large-scale in-the-wild benchmarks spanning categorical, dimensional, compound, micro-expression, Action Unit (AU), and intensity-estimation tasks. Yet the deep learning-based FER landscape has so far been reviewed only along narrow task-, architecture-, or application-specific axes, leaving a holistic, systematically organized account of its recent advances missing. This survey addresses that gap with a comprehensive review of recent deep learning-based FER, explicitly linked to the wider Facial Affect Recognition (FAR) domain. Its main contributions are: a) A description of FER's evolution into five distinct phases, from handcrafted features and classical machine learning to attention-based, vision-language, and foundation-model approaches, with the key milestone works of each, b) A multi-criteria taxonomy analyzing the literature along seven complementary axes: recognition task, input modality, face pre-processing pipeline, network architecture, learning strategy, acquisition setting, and application domain, c) A per-criterion comparative analysis, with critical insights into the strengths and limitations of each category under in-the-wild conditions, d) A task-organized review of public FER datasets, with their annotation schemes, modalities, and evaluation protocols, e) A compilation of performance metrics and a per-task quantitative comparison of representative state-of-the-art methods on widely adopted benchmarks, and f) A discussion of current challenges and promising future directions.
Chinese Translation
面部表情识别（FER）在过去十年中迅速发展，这一进步得益于从手工特征和浅层分类器向深度卷积、基于注意力的、视觉-语言和基础模型架构的转变，以及大规模野外基准的并行增长，这些基准涵盖了分类、维度、复合、微表情、动作单元（AU）和强度估计任务。然而，基于深度学习的FER领域迄今为止仅在狭窄的任务、架构或应用特定维度上进行了评审，缺乏对其近期进展的整体性、系统性组织的描述。本综述旨在填补这一空白，全面回顾近期基于深度学习的FER，并明确与更广泛的面部情感识别（FAR）领域的联系。其主要贡献包括：a）描述FER演变为五个不同阶段，从手工特征和经典机器学习到基于注意力的、视觉-语言和基础模型方法，以及每个阶段的关键里程碑工作；b）基于七个互补维度的多标准分类法，分析文献：识别任务、输入模态、面部预处理流程、网络架构、学习策略、获取设置和应用领域；c）基于每个标准的比较分析，提供对每个类别在野外条件下的优缺点的关键见解；d）按任务组织的公共FER数据集综述，包括其注释方案、模态和评估协议；e）性能指标的汇编以及在广泛采用的基准上对代表性最先进方法的逐任务定量比较；f）对当前挑战和有前景的未来方向的讨论。
cs.CV / 97 / 2606.08615
Harnessing Streaming Video in the Wild
在野外利用流媒体视频
Abstract
Vision-Language Models (VLMs) are increasingly required to process unbounded video streams in applications such as video-call assistants, live commentary, and embodied robots. An ideal streaming system should support proactive interaction, long-horizon memory, and real-time processing, while resting on a VLM backbone capable of handling diverse in-the-wild streaming tasks. However, existing VLMs excel at offline video understanding but fall short in streaming capabilities and lack dedicated infrastructure for streaming deployment. We address this gap on three fronts. (i) For backbone capability, we construct \textbf{Streaming-Train-248K}, a streaming dataset paired with a novel training objective for adapting VLMs to streaming interaction and understanding. (ii) For real-world deployment, we introduce \textbf{Streaming Harness}, a plug-and-play system that endows any VLM with three core abilities: proactive interaction (per-second response decisions), long-term memory (12-hour context retention), and real-time processing (sub-second latency). (iii) To drive continued community progress on streaming capabilities, we design \textbf{Streaming-Eval}, a benchmark that reflects models' capabilities across diverse in-the-wild scenarios. Extensive experiments demonstrate consistent gains from our approach across all core capabilities required for streaming video understanding. We will open-source our data, code, and benchmark to advance the community's shift from offline video understanding to deployable streaming intelligence.
Chinese Translation
视觉-语言模型（VLMs）在视频通话助手、实时评论和具身机器人等应用中，越来越需要处理无限的视频流。理想的流媒体系统应支持主动交互、长时记忆和实时处理，同时依托于能够处理多样化野外流媒体任务的VLM骨干。然而，现有的VLM在离线视频理解方面表现出色，但在流媒体能力上却显得不足，并且缺乏专门的流媒体部署基础设施。我们从三个方面解决了这一差距。(i) 在骨干能力方面，我们构建了 extbf{Streaming-Train-248K}，这是一个与新颖训练目标配对的流媒体数据集，用于将VLM适应于流媒体交互和理解。(ii) 在现实世界部署方面，我们引入了 extbf{Streaming Harness}，这是一个即插即用的系统，使任何VLM具备三项核心能力：主动交互（每秒响应决策）、长期记忆（12小时上下文保留）和实时处理（亚秒延迟）。(iii) 为了推动社区在流媒体能力上的持续进展，我们设计了 extbf{Streaming-Eval}，这是一个基准，反映模型在多样化野外场景中的能力。大量实验表明，我们的方法在流媒体视频理解所需的所有核心能力上均取得了一致的提升。我们将开源我们的数据、代码和基准，以推动社区从离线视频理解向可部署的流媒体智能转变。
cs.CV / 98 / 2606.08634
SSAFE: Simple and Strong AI-Generated Image Detection via Frozen Vision Encoders
SSAFE：通过冻结视觉编码器实现简单且强大的AI生成图像检测
Abstract
The rapid advancement of generative models has blurred the boundary between synthetic and real imagery, creating an urgent need for reliable deepfake detection. Yet most existing approaches rely on massive real--fake datasets, which are increasingly difficult to maintain as new generators continue to emerge. In this work, we investigate how much information about image authenticity is already encoded in modern multimodal vision representations. We find that frozen multimodal encoders naturally separate real and synthetic images in their embedding space, enabling a simple linear classifier to achieve strong performance without task-specific fine-tuning. Motivated by this observation, we develop a representation-aware data curation strategy that selects a compact set of representative generators for training. The resulting training set contains only 10K images, compared to 288K in AIGIBench and 4M in OpenFake, while improving robustness to unseen generators and distribution shifts. We additionally introduce RealWorldBench, a benchmark consisting of modern camera photographs, contemporary stock images, and outputs from recent commercial generators. Experiments across multiple benchmarks show that combining frozen multimodal representations with carefully curated training data provides a simple and effective approach to AI-generated image detection.
Chinese Translation
生成模型的快速发展模糊了合成图像与真实图像之间的界限，迫切需要可靠的深度伪造检测。然而，大多数现有方法依赖于庞大的真实-伪造数据集，而随着新生成器的不断出现，这些数据集的维护变得越来越困难。在本研究中，我们探讨了现代多模态视觉表示中已编码的图像真实性信息的程度。我们发现，冻结的多模态编码器在其嵌入空间中自然地将真实图像与合成图像分开，使得简单的线性分类器在没有特定任务微调的情况下也能实现强大的性能。受到这一观察的启发，我们开发了一种基于表示的数据显示策略，选择了一组紧凑的代表性生成器进行训练。最终的训练集仅包含10K张图像，而AIGIBench中有288K张，OpenFake中有400万张，同时提高了对未见生成器和分布变化的鲁棒性。此外，我们还引入了RealWorldBench，一个基准数据集，包括现代相机照片、当代库存图像以及近期商业生成器的输出。跨多个基准的实验表明，将冻结的多模态表示与精心策划的训练数据相结合，提供了一种简单有效的AI生成图像检测方法。
cs.CV / 99 / 2606.08641
Learnable Token Sparsification for Efficient Gigapixel Whole Slide Image Reasoning
可学习的标记稀疏化用于高效的千兆像素全切片图像推理
Abstract
The processing of gigapixel whole slide images within vision language models faces a major difficulty due to an excessive number of visual tokens. Existing solutions typically rely on spatial downsampling or heuristic pruning strategies that operate without training, and these methods often discard subtle but clinically meaningful patterns because pathological evidence is scattered irregularly across the tissue. To overcome this limitation, we reformulate token reduction in whole slide images as a trainable sparsification problem, allowing the model to learn an optimal selection strategy instead of following fixed heuristics. We propose a decoupled routing architecture. To enable gradient propagation through the nondifferentiable pruning operation during training, we introduce a component called SparseLearn. This component uses a variance-preserving noise gate that regulates the information flow of each patch via a differentiable Soft Top-K operator, together with a diagonal attention denoiser that recovers perturbed representations without leaking spatial information. At inference time, the SparseLearn module is entirely discarded, and the trained scorer applies a deterministic Hard Top-K operator to keep only the highest scoring 32 tokens, incurring no extra computation. By compressing the visual sequence down to a sparse set of just 32 tokens, which represents as little as 0.78% of the original length, our framework achieves 73.32% overall accuracy on SlideBench (TCGA), consistently surpassing sampling-based baselines and general-purpose vision language models. It also demonstrates strong zero shot generalization on SlideBench (BCNB) and WSI VQA*. By resolving the visual context bottleneck and preventing the dilution of sparse diagnostic evidence, this work provides a highly efficient paradigm for end to end gigapixel whole slide image reasoning.
Chinese Translation
在视觉语言模型中处理千兆像素全切片图像面临着由于视觉标记数量过多而带来的重大困难。现有的解决方案通常依赖于空间下采样或无训练的启发式剪枝策略，这些方法往往会丢弃细微但具有临床意义的模式，因为病理证据在组织中分布不规则。为了克服这一限制，我们将全切片图像中的标记减少重新表述为一个可训练的稀疏化问题，使模型能够学习最优选择策略，而不是遵循固定的启发式方法。我们提出了一种解耦的路由架构。为了在训练期间通过不可微分的剪枝操作实现梯度传播，我们引入了一个名为SparseLearn的组件。该组件使用一个保持方差的噪声门，通过可微分的Soft Top-K操作调节每个补丁的信息流，并结合一个对角注意力去噪器，在不泄漏空间信息的情况下恢复扰动的表示。在推理时，SparseLearn模块被完全丢弃，训练好的评分器应用一个确定性的Hard Top-K操作，仅保留得分最高的32个标记，不会增加额外的计算。通过将视觉序列压缩到仅32个标记的稀疏集合，这些标记代表原始长度的0.78%，我们的框架在SlideBench (TCGA)上实现了73.32%的整体准确率，始终超过基于采样的基线和通用视觉语言模型。它还在SlideBench (BCNB)和WSI VQA*上展示了强大的零样本泛化能力。通过解决视觉上下文瓶颈并防止稀疏诊断证据的稀释，这项工作为端到端的千兆像素全切片图像推理提供了一个高效的范式。
cs.CV / 100 / 2606.08653
FiberTune: Preserving Action-Fiber Visual Residuals in Vision-Language-Action Fine-Tuning
FiberTune：在视觉-语言-动作微调中保持动作-光纤视觉残差
Abstract
Action-supervised fine-tuning of vision-language-action (VLA) policies fits demonstrations effectively but constrains only the directions that change predicted actions, leaving visual structure consistent across action-equivalent states free to collapse. We formalize this as residual visual collapse along local action fibers and propose FiberTune, a training-time objective that preserves teacher-structured visual residuals without adding inference-time overhead. FiberTune uses an online action probe to estimate action-predictive feature directions, filters them from intermediate visual-token representations, and aligns the resulting probe-filtered residuals to a frozen visual teacher while regularizing their effective rank. Under identical training conditions, FiberTune improves over task-loss-only fine-tuning in every one of six controlled simulation settings spanning two benchmarks and two architectures (pi_0.5 and OpenVLA-OFT), as well as on physical SO-101 pick-place; representative gains include +10.7 percentage points SR(5) on long-horizon CALVIN ABC-to-D and physical SO-101 task success rising from 72.7% to 78.1%. Residual diagnostics show that these gains coincide with increased probe-filtered residual teacher alignment and effective rank, consistent with the action-fiber motivation.
Chinese Translation
动作监督的视觉-语言-动作（VLA）策略微调有效地适应演示，但仅限制了改变预测动作的方向，导致在动作等效状态下的视觉结构保持一致的情况下自由崩溃。我们将其形式化为沿局部动作光纤的残余视觉崩溃，并提出了FiberTune，这是一种训练时目标，旨在在不增加推理时开销的情况下保持教师结构的视觉残差。FiberTune使用在线动作探针来估计动作预测特征方向，从中间视觉标记表示中过滤这些方向，并将得到的探针过滤残差与冻结的视觉教师对齐，同时正则化其有效秩。在相同的训练条件下，FiberTune在跨越两个基准和两种架构（pi_0.5和OpenVLA-OFT）的六个受控仿真设置中，在每个设置中都优于仅基于任务损失的微调，以及在物理SO-101拾取-放置任务中；代表性的增益包括在长时间范围内的CALVIN ABC到D任务上提高10.7个百分点的SR(5)，以及物理SO-101任务成功率从72.7%上升到78.1%。残差诊断表明，这些增益与探针过滤的残差教师对齐和有效秩的增加相一致，这与动作光纤的动机相符。
cs.CV / 101 / 2606.08670
WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis
WaveDiT：基于分布感知的小波流匹配的高效3D脑MRI合成
Abstract
Large and demographically balanced datasets are essential for reliable neuroimaging biomarkers. Full-resolution 3D brain MRI synthesis can support data augmentation in this setting, but existing approaches either incur prohibitive computational cost at volumetric scale or rely on lossy latent compression that may compromise anatomical detail. As a result, practical 3D generative augmentation often requires specialized compute infrastructure. We propose WaveDiT, a conditional flow matching framework operating in the coefficient space of a 3D Haar Discrete Wavelet Transform. The model combines factorized spatio-depth attention with band-wise heteroscedastic uncertainty modeling derived from higher-order wavelet statistics. Predicted log-variance is integrated directly into both the flow objective and conditioning pathway, enabling adaptive precision consistent with the heavy-tailed and input-dependent variance structure of anatomical detail. This formulation supports full-resolution 3D synthesis under practical memory and time constraints on a single modern GPU. Evaluation on a multi-site cohort demonstrates improved alignment between generated and real MRI distributions, together with enhanced downstream brain age prediction and region-level anatomical agreement relative to diffusion, latent, and wavelet-based baselines. Code is available at https://github.com/sisinflab/WaveDiT
Chinese Translation
大规模且具有人口统计平衡的数据集对于可靠的神经影像生物标志物至关重要。全分辨率的3D脑MRI合成可以在这种情况下支持数据增强，但现有方法要么在体积规模上产生高昂的计算成本，要么依赖于可能损害解剖细节的有损潜在压缩。因此，实际的3D生成增强通常需要专门的计算基础设施。我们提出了WaveDiT，一种在3D Haar离散小波变换的系数空间中操作的条件流匹配框架。该模型结合了分解的时空深度注意力与基于高阶小波统计的带宽异方差不确定性建模。预测的对数方差直接整合到流目标和条件路径中，使得精度自适应，符合解剖细节的重尾和输入依赖的方差结构。该公式支持在单个现代GPU上的实际内存和时间限制下进行全分辨率的3D合成。在多站点队列上的评估表明，生成的MRI分布与真实分布之间的对齐得到了改善，同时在脑龄预测和区域级解剖一致性方面相较于扩散、潜在和基于小波的基线也得到了增强。代码可在 https://github.com/sisinflab/WaveDiT 获取。
cs.CV / 102 / 2606.08672
Learning to Solve Generative ODEs Beyond the Linear Span
学习解决超出线性范围的生成常微分方程
Abstract
Diffusion and flow generative models sample by integrating a learned ODE, but high quality still requires many sequential model evaluations. Solver learning reduces this cost by adapting scalar coefficients, timesteps, or both, while keeping the backbone model fixed. In this work, we identify a structural bottleneck in this update family: each step remains span-limited. Since the scalar-coefficient update lies in the span of buffered velocity evaluations, it can fit only the in-span component while leaving any out-of-span residual unreachable by scalar recombination alone. We propose SpanLift, a lightweight neural solver that augments scalar-coefficient updates with a spatial residual operator. SpanLift keeps a fixed base solver as an in-span prior and learns a spatial residual operator over the state and velocity buffer. The operator is trained by endpoint teacher matching, preserves the pretrained backbone, and adds no model NFEs. Empirically, the learned correction transfers across base solvers and is predominantly out-of-span. Across pixel-space diffusion, latent flow matching, and precipitation nowcasting, SpanLift achieves state-of-the-art few-step sampling. With only 3 NFE, it improves CIFAR-10 FID from 8.16 to 5.69 and ImageNet FID from 17.37 to 11.83.
Chinese Translation
扩散和流生成模型通过积分学习到的常微分方程（ODE）进行采样，但高质量的结果仍然需要多次顺序模型评估。求解器学习通过调整标量系数、时间步长或两者，来降低这一成本，同时保持基础模型不变。在本研究中，我们识别出这一更新家族中的结构瓶颈：每一步仍然受限于范围。由于标量系数更新位于缓冲速度评估的范围内，它只能拟合范围内的分量，而无法通过标量重组单独达到范围外的残差。我们提出了SpanLift，这是一种轻量级神经求解器，通过空间残差算子增强标量系数更新。SpanLift将固定的基础求解器作为范围内的先验，并在状态和速度缓冲区上学习空间残差算子。该算子通过端点教师匹配进行训练，保留了预训练的基础模型，并且不增加模型的NFE（函数评估次数）。实证结果表明，学习到的修正可以在基础求解器之间迁移，并且主要是范围外的。在像素空间扩散、潜在流匹配和降水即时预报中，SpanLift实现了最先进的少步采样。仅用3次NFE，它将CIFAR-10的FID从8.16提升至5.69，将ImageNet的FID从17.37提升至11.83。
cs.CV / 103 / 2606.08674
BioVid: Autoregressive Video Generation with Biological Behavior Semantic Comprehension
BioVid：具有生物行为语义理解的自回归视频生成
Abstract
Existing video generation frameworks treat sequence duration as an externally prescribed parameter -- fixed frame counts or text prompts -- producing clips whose temporal boundaries are decoupled from the statistical structure of real behavioral data. This assumption is fundamentally misaligned with biological behavior, where action duration varies naturally across individuals and instances and is encoded in the data itself. We present BioVid, a data-driven autoregressive video generation framework that learns the temporal structure of biological behaviors directly from training data, including their natural length distributions. In the first stage, a Finite Scalar Quantization GAN (FSQ-R3GAN) tokenizer encodes each video frame into a compact discrete representation, combining the stabilized relativistic training objective of R3GAN with FSQ's guaranteed codebook utilization to achieve high-fidelity spatial reconstruction without codebook collapse. In the second stage, a causal Transformer models the resulting token sequences autoregressively and learns to emit an End-of-Sequence (EOS) token when the behavioral event reaches semantic closure, with the termination distribution emerging naturally from the training data rather than any human-specified constraint. Experiments on a human drinking behavior dataset (NTU RGB+D, A001, n=94) demonstrate that BioVid's generated length distribution closely matches that of held-out test data, achieving a Wasserstein-1 distance of 1.24 against the ground truth -- compared to 6.05 for a fixed-length baseline and 15.48 for VideoGPT -- while maintaining competitive spatial fidelity.
Chinese Translation
现有的视频生成框架将序列时长视为外部规定的参数——固定帧数或文本提示——生成的片段的时间边界与真实行为数据的统计结构脱节。这一假设与生物行为的本质存在根本不一致，因为行为持续时间在个体和实例之间自然变化，并且在数据中被编码。我们提出了BioVid，这是一种数据驱动的自回归视频生成框架，能够直接从训练数据中学习生物行为的时间结构，包括其自然长度分布。在第一阶段，有限标量量化生成对抗网络（Finite Scalar Quantization GAN, FSQ-R3GAN）编码器将每个视频帧编码为紧凑的离散表示，结合了R3GAN的稳定相对训练目标与FSQ的保证代码本利用，以实现高保真空间重建而不发生代码本崩溃。在第二阶段，因果变换器（causal Transformer）对生成的令牌序列进行自回归建模，并学习在行为事件达到语义闭合时发出结束序列（End-of-Sequence, EOS）令牌，终止分布自然地从训练数据中产生，而非任何人为指定的约束。在一个人类饮水行为数据集（NTU RGB+D, A001, n=94）上的实验表明，BioVid生成的长度分布与保留的测试数据高度匹配，Wasserstein-1距离为1.24，相较于固定长度基线的6.05和VideoGPT的15.48，同时保持了竞争力的空间保真度。
cs.CV / 104 / 2606.08680
Distortion-Aware PETR for BEV Object Detection with Mixed Pinhole-Fisheye Cameras
基于畸变感知的PETR用于混合针孔-鱼眼相机的鸟瞰视图物体检测
Abstract
Fisheye cameras are widely deployed in autonomous driving perception suites for their low cost and full-coverage field of view (FOV), yet their potential remains underleveraged in 3D object detection. Severe radial distortion challenges most BEV detectors by violating the fundamental assumption of uniform sampling. To bridge this gap, we propose Distortion-Aware PETR (DAPETR), a projection-free detector tailored for mixed pinhole-fisheye camera setups. DAPETR incorporates two key learned-adaptive modules: a unified distortion-aware positional embedding that harmonizes positional encodings for image representations with fisheye geometry, and a bidirectional feature-geometry co-modulation module that mutually adapts image features and 3D positional embeddings. In our experiments on a converted KITTI-360 benchmark, we systematically compare our learned adaptive approach against PETR in polar coordinates (PolarPETR). We find that while both methods improve over the baseline, our learned modules achieve superior performance. Crucially, we uncover a negative interaction when combining both strategies, revealing that learned adaptation and explicit geometric reparameterization can conflict. Our final DAPETR model significantly advances the research and benchmark for fisheye BEV detection, providing critical insights into effective distortion-aware 3D perception design other than image rectification.
Chinese Translation
鱼眼相机因其低成本和全覆盖视场（FOV）而广泛应用于自动驾驶感知系统，但在三维物体检测中的潜力仍未得到充分利用。严重的径向畸变对大多数鸟瞰视图（BEV）检测器构成挑战，因为它违反了均匀采样的基本假设。为了解决这一问题，我们提出了畸变感知PETR（DAPETR），这是一种针对混合针孔-鱼眼相机设置的无投影检测器。DAPETR结合了两个关键的学习自适应模块：一个统一的畸变感知位置嵌入，用于协调图像表示与鱼眼几何形状的位置信息编码，以及一个双向特征-几何共同调制模块，用于相互适应图像特征和三维位置嵌入。在我们对转换后的KITTI-360基准测试的实验中，我们系统地将我们的学习自适应方法与极坐标下的PETR（PolarPETR）进行了比较。我们发现，尽管两种方法均优于基线，但我们的学习模块实现了更优的性能。重要的是，我们发现当结合这两种策略时存在负面相互作用，揭示了学习自适应与显式几何重新参数化之间可能存在冲突。我们的最终DAPETR模型显著推动了鱼眼BEV检测的研究和基准测试，为有效的畸变感知三维感知设计提供了重要见解，而不仅仅是图像校正。
cs.CV / 105 / 2606.08684
BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving
BLUE：朝着更好语言使用的高效视觉-语言-动作模型在自动驾驶中的应用
Abstract
We present BLUE, a minimal method for better language use in vision-language-action (VLA) models for autonomous driving (AD). Through extensive analysis, we reveal that language matters on only a small fraction of routes, but on those routes it can greatly improve or degrade performance. Generating language at every frame is therefore inefficient, since most computation is spent on frames that do not benefit from language. We further show that pretrained VLA hidden states potentially already encode whether language will benefit a given frame, even though scene complexity and kinematic features alone struggle to predict this. Based on this finding, BLUE trains a lightweight gate on frozen VLA hidden states to decide per frame whether to activate language generation or predict actions directly, without modifying the backbone or requiring additional human annotation. With just a 0.11M-parameter gate, BLUE sets a new state of the art on both benchmarks, achieving 76.2% success rate on Bench2Drive and 36 driving score on Longest6 v2, while delivering 2.54x inference speedup and 8.9% success rate improvement over the backbone. BLUE provides a practical path toward efficient language-augmented AD, showing that VLA models can retain the benefits of language at a fraction of the cost. Our code, data, logs and checkpoints are fully available on https://github.com/George-Ling3/BLUE.
Chinese Translation
我们提出了BLUE，一种用于自动驾驶（AD）视觉-语言-动作（VLA）模型中更好语言使用的简约方法。通过广泛的分析，我们揭示了语言在仅一小部分路线中才显得重要，但在这些路线中，它可以显著提升或降低性能。因此，在每一帧生成语言是低效的，因为大部分计算花费在那些不受益于语言的帧上。我们进一步展示了预训练的VLA隐藏状态可能已经编码了语言是否会对给定帧有益，尽管仅凭场景复杂性和运动特征很难预测这一点。基于这一发现，BLUE在冻结的VLA隐藏状态上训练了一个轻量级的门控机制，以决定每帧是否激活语言生成或直接预测动作，而无需修改主干网络或额外的人类标注。仅用0.11M参数的门控，BLUE在两个基准测试上设定了新的最优状态，在Bench2Drive上实现了76.2%的成功率，在Longest6 v2上获得了36的驾驶评分，同时在推理速度上提升了2.54倍，并在主干网络上提高了8.9%的成功率。BLUE为高效的语言增强自动驾驶提供了一条实用路径，表明VLA模型可以以更低的成本保留语言的优势。我们的代码、数据、日志和检查点已在https://github.com/George-Ling3/BLUE上完全公开。
cs.CV / 106 / 2606.08687
Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation
依赖位移的非对称性：用于联邦医学分割的正交逆低秩适应
Abstract
Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of segmentation foundation models for medical imaging. However, most federated LoRA methods adopt a uniform aggregation rule, which breaks under the encoder-decoder asymmetry in medical segmentation: the encoder is dominated by appearance shifts, while the decoder is dominated by supervision variations. This mismatch entangles shared anatomy with site-specific biases and harms generalization. To address this, we propose Inverse Asymmetric Tuning (IAT). IAT aligns adaptation with heterogeneity sources by personalizing module-specific components in the encoder to absorb appearance shifts and in the decoder to accommodate site-dependent supervision, while retaining a shared pathway for transferable consensus. However, structural separation alone is insufficient under LoRA's bilinear parameterization, where multiplicative coupling can still cause site-specific updates to leak into the shared direction. We therefore introduce a Subspace Orthogonality Regularizer that penalizes shared-local collinearity in the effective update space, mitigating leakage without extra communication. Experiments show consistent improvements over strong federated LoRA and parameter-efficient FL baselines.
Chinese Translation
低秩适应（Low-Rank Adaptation, LoRA）使得医学影像分割基础模型的高效联邦微调成为可能。然而，大多数联邦LoRA方法采用统一的聚合规则，这在医学分割中的编码器-解码器非对称性下失效：编码器受到外观位移的主导，而解码器则受到监督变化的主导。这种不匹配将共享解剖结构与特定站点的偏差纠缠在一起，损害了模型的泛化能力。为了解决这个问题，我们提出了逆非对称调优（Inverse Asymmetric Tuning, IAT）。IAT通过个性化编码器中模块特定的组件以吸收外观位移，以及在解码器中适应特定站点的监督，从而使适应与异质性源对齐，同时保留一个用于可转移共识的共享路径。然而，仅仅进行结构分离在LoRA的双线性参数化下是不够的，因为乘法耦合仍然可能导致特定站点的更新泄漏到共享方向。因此，我们引入了子空间正交正则化器（Subspace Orthogonality Regularizer），该正则化器对有效更新空间中的共享-局部共线性进行惩罚，从而在不增加额外通信的情况下减轻泄漏。实验结果表明，相较于强大的联邦LoRA和参数高效的联邦学习基线，我们的方法表现出了一致的改进。
cs.CV / 107 / 2606.08708
PRPO: Perception-Reinforced Policy Optimization via Token-Level Dynamic Advantage Reshaping
PRPO：通过令牌级动态优势重塑的感知强化策略优化
Abstract
Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective paradigm for improving the reasoning capability of Large Vision-Language Models (LVLMs). However, existing RLVR methods primarily rely on trajectory-level outcome rewards, which assign identical learning signals across all generated tokens. This coarse-grained credit assignment is fundamentally mismatched to multimodal reasoning, where only a sparse subset of tokens is causally grounded in visual evidence. Consequently, these pivotal perceptual tokens receive weak supervision and are often overwhelmed by language priors or reasoning-template tokens. To address this limitation, we propose Perception-Reinforced Policy Optimization (PRPO), a token-level reinforcement learning framework that explicitly identifies and reinforces pivotal perceptual tokens within long-horizon multimodal reasoning trajectories. PRPO introduces Robust Visual Dependency (RVD), a principled metric that identifies tokens whose predictions are both visually grounded and perturbation-stable, filtering out brittle or noisy visual tokens. Based on RVD, we further propose Perceptual Advantage Reshaping (PAR), a token-level credit assignment technique that amplifies perceptually informative tokens while preserving stable gradients for non-perceptual tokens. Extensive experiments on seven multimodal reasoning benchmarks demonstrate that PRPO consistently outperforms strong LVLM baselines across both 3B and 7B model scales, achieving average gains of 23.3% and 21.1%, respectively. PRPO achieves state-of-the-art performance with improved training efficiency and stronger cross-task generalization. Our findings highlight the importance of fine-grained credit assignment for scalable multimodal reinforcement learning.
Chinese Translation
可验证奖励的强化学习（RLVR）已成为提高大型视觉语言模型（LVLMs）推理能力的有效范式。然而，现有的RLVR方法主要依赖于轨迹级结果奖励，这在所有生成的令牌中分配相同的学习信号。这种粗粒度的信用分配与多模态推理根本不匹配，因为只有稀疏的令牌子集与视觉证据有因果关系。因此，这些关键的感知令牌受到的监督较弱，常常被语言先验或推理模板令牌所淹没。为了解决这一局限性，我们提出了感知强化策略优化（PRPO），这是一种令牌级强化学习框架，明确识别并强化长时间跨度多模态推理轨迹中的关键感知令牌。PRPO引入了稳健视觉依赖（RVD），这是一种原则性度量，识别那些预测既与视觉相结合又对扰动稳定的令牌，从而过滤掉脆弱或嘈杂的视觉令牌。基于RVD，我们进一步提出了感知优势重塑（PAR），这是一种令牌级信用分配技术，放大感知信息丰富的令牌，同时为非感知令牌保持稳定的梯度。在七个多模态推理基准上的广泛实验表明，PRPO在3B和7B模型规模上始终优于强大的LVLM基线，分别实现了23.3%和21.1%的平均增益。PRPO在提高训练效率和增强跨任务泛化能力的同时，达到了最先进的性能。我们的研究结果强调了细粒度信用分配在可扩展多模态强化学习中的重要性。
cs.CV / 108 / 2606.08719
Thinking Without Images: Internalizing Visual Manipulation with On-Policy Self-Distillation
无图像思维：通过策略自蒸馏内化视觉操作
Abstract
''Thinking with Images'' has emerged as an effective paradigm for fine-grained visual reasoning: by explicitly zooming into relevant regions and reasoning over crops, models can access local evidence that is difficult to recover from a single global image. However, this benefit comes with redundant tool invocations and longer inference traces. Moreover, when such behaviors are learned mainly from outcome reward, the resulting intermediate crops or visual cues can be noisy or fail to faithfully capture task-relevant visual evidence. In this work, we ask whether the reasoning benefits of ''Thinking with Images'' can be internalized through Thinking with Imagination: an internal process that decides where to look and imagines what visual cues closer inspection would reveal without actually invoking tools. We propose Imagine-OPD, an on-policy self-distillation framework in which a teacher plays the role of a ''Thinking with Images'' reasoner during training: it receives privileged zoomed evidence views derived from annotated regions, and supervises the model's own imagination reasoning trajectories. Imagine-OPD does not require an external teacher or high-quality imagination demonstrations. Experiments on vision-centric benchmarks show that Imagine-OPD achieves the best average performance among compared models while significantly reducing inference overhead compared with ''Thinking with Images'' methods.
Chinese Translation
“图像思维”已成为细粒度视觉推理的有效范式：通过明确聚焦于相关区域并对裁剪图像进行推理，模型能够获取从单一全局图像中难以恢复的局部证据。然而，这种好处伴随着冗余的工具调用和更长的推理轨迹。此外，当这种行为主要通过结果奖励学习时，所产生的中间裁剪图像或视觉线索可能会噪声较大，或未能真实捕捉与任务相关的视觉证据。在本研究中，我们探讨“图像思维”的推理优势是否可以通过“想象思维”内化：一种内部过程，决定观察的方向并想象更仔细检查所揭示的视觉线索，而无需实际调用工具。我们提出了Imagine-OPD，一种策略自蒸馏框架，其中教师在训练过程中扮演“图像思维”推理者的角色：它接收来自标注区域的特权放大证据视图，并监督模型自身的想象推理轨迹。Imagine-OPD不需要外部教师或高质量的想象演示。在视觉中心基准上的实验表明，Imagine-OPD在比较模型中实现了最佳的平均性能，同时显著减少了与“图像思维”方法相比的推理开销。
cs.CV / 109 / 2606.08742
AUCp: Pseudo-AUC for Inference Model Selection with Unlabeled Validation Data in Abnormality Detection
AUCp：用于异常检测中无标签验证数据的推断模型选择的伪AUC
Abstract
Abnormality detection is a crucial yet challenging task in medical image analysis. Distinguishing abnormalities from normal data by learning to reconstruct normal-only data alleviates the reliance on labeled datasets. However, many studies, even if unsupervised, rely on a labeled validation set to select the best model for inference from multiple training iterations. For many diseases labeled data are unavailable and substantially time consuming to obtain. To address this, AUCp - a novel metric that supports abnormality detection for unsupervised and self-supervised methods is proposed. Instead of evaluating the realism of reconstructed images to select the best of model for inference, it focuses on actual detection performance and without requiring an annotated test set. Assuming the pseudo ground truth of all unannotated samples in the test set as abnormal/positive and using traditional AUC calculation, AUCp scores are derived. Given a large and representative training set of normal samples, we show mathematical and empirical evidence that model selection using AUCp scores improves disease detection in terms of unsupervised and self-supervised methods over conventional metrics. Using two unsupervised methods for neurologic disease detection and self-supervised methods on diverse datasets, our results demonstrate that the AUCp score effectively identifies the optimal model for inference, significantly enhancing abnormality and disease detection. The corresponding implementations are available in https://github.com/mahfuzmohammad/AUCp.
Chinese Translation
异常检测是医学图像分析中一项至关重要但具有挑战性的任务。通过学习重建仅包含正常数据的样本来区分异常与正常数据，减轻了对标注数据集的依赖。然而，许多研究，即使是无监督的，也依赖于标注的验证集来从多个训练迭代中选择最佳推断模型。对于许多疾病，标注数据不可用且获取过程耗时。为了解决这一问题，提出了一种新颖的度量标准AUCp，它支持无监督和自监督方法的异常检测。AUCp不再评估重建图像的真实性以选择最佳推断模型，而是关注实际的检测性能，并且不需要带注释的测试集。假设测试集中所有未标注样本的伪真实值为异常/阳性，并使用传统的AUC计算，得出AUCp分数。我们展示了在拥有大量且具有代表性的正常样本训练集的情况下，使用AUCp分数进行模型选择在无监督和自监督方法中相较于传统度量提高了疾病检测的效果。通过使用两种无监督方法进行神经疾病检测以及在多样化数据集上应用自监督方法，我们的结果表明，AUCp分数有效识别出最佳推断模型，显著增强了异常和疾病检测。相关实现可在 https://github.com/mahfuzmohammad/AUCp 获取。
cs.CV / 110 / 2606.08744
MB-Loc: Multi-planar Bird's-eye-view Localization in outdoor LiDAR scenes
MB-Loc：户外LiDAR场景中的多平面鸟瞰视图定位
Abstract
Global LiDAR localization is a fundamental task for autonomous navigation systems. Recent methods perform Scene Coordinate Regression (SCR) and achieve superior accuracy over Absolute Pose Regression (APR) solutions by predicting dense 3D world coordinates. However, SCR approaches introduce two major bottlenecks: severe computational inefficiency from processing raw 3D geometries and significant performance degradation under varying sensor viewpoints. To address these limitations, we present MB-Loc, a lightweight and viewpoint-robust SCR framework. Instead of relying on heavy 3D convolutions, we project the input LiDAR scan into a 2.5D Multi-planar Bird's-Eye View (BEV) representation. By slicing the point-cloud along the Z-axis and mapping signed depths into discrete 2D planes, MB-Loc retains essential 3D geometric structures while exploiting the computational tractability of standard 2D CNNs. To handle the inherent sparsity of outdoor LiDAR, we introduce a KL-regularized latent bottleneck that explicitly models spatial uncertainty without injecting stochastic noise. Finally, to ensure rotation robustness, we apply 3D spatial augmentations prior to planar projection, forcing the network to implicitly learn viewpoint-invariant features. We perform extensive experiments on the publicly available NCLT dataset and demonstrate that our proposed method outperforms the current state-of-the-art. Operating at real-time inference speeds, MB-Loc significantly outperforms traditional 3D-SCR architectures in computational efficiency.
Chinese Translation
全球LiDAR定位是自主导航系统的基础任务。近期的方法通过场景坐标回归（Scene Coordinate Regression, SCR）实现了优于绝对姿态回归（Absolute Pose Regression, APR）解决方案的卓越精度，预测密集的3D世界坐标。然而，SCR方法引入了两个主要瓶颈：处理原始3D几何体时的严重计算低效，以及在不同传感器视角下显著的性能下降。为了解决这些限制，我们提出了MB-Loc，一个轻量级且对视角鲁棒的SCR框架。MB-Loc不依赖于重型3D卷积，而是将输入的LiDAR扫描投影到一个2.5D多平面鸟瞰视图（Bird's-Eye View, BEV）表示中。通过沿Z轴切片点云并将带符号的深度映射到离散的2D平面，MB-Loc保留了重要的3D几何结构，同时利用标准2D卷积神经网络（CNN）的计算可处理性。为了处理户外LiDAR固有的稀疏性，我们引入了一个KL正则化的潜在瓶颈，明确建模空间不确定性而不引入随机噪声。最后，为了确保旋转鲁棒性，我们在平面投影之前应用3D空间增强，迫使网络隐式学习视角不变特征。我们在公开可用的NCLT数据集上进行了广泛的实验，证明我们提出的方法优于当前的最先进技术。MB-Loc在实时推理速度下显著超越了传统3D-SCR架构的计算效率。
cs.CV / 111 / 2606.08745
Stain-Aware Wavelet Regularization for Instant Adversarial Purification in Histopathology
考虑染色的波形正则化用于组织病理学中的即时对抗净化
Abstract
Deep learning has become prevalent in computational pathology pipelines that support tasks such as cancer screening and digital pathology analysis. However, the susceptibility of neural networks to adversarial perturbations raises safety concerns for reliable deployment in clinical practice. In histopathological images, this challenge is exacerbated by the difficulty of distinguishing high-frequency adversarial noise from subtle and diagnostically relevant tissue structures. To address this issue, we propose Stain-Aware Wavelet Regularization (SAWR), an adversarial purification framework that leverages multi-level wavelet-domain regularization based on Haar transform to hierarchically disentangle adversarial perturbations from diagnostic structural information. This spectral constraint is further extended to individual histological channels, enabling stain-specific frequency regulation consistent with the biological properties of Hematoxylin and Eosin. When integrated into an instant purification framework, SAWR improves adversarial robustness by up to 10.69\% over the baseline approach, while maintaining texture and spectral fidelity under adversarial perturbations.
Chinese Translation
深度学习在支持癌症筛查和数字病理分析等任务的计算病理学流程中变得越来越普遍。然而，神经网络对对抗扰动的敏感性引发了在临床实践中可靠部署的安全性担忧。在组织病理图像中，这一挑战因难以将高频对抗噪声与细微且具有诊断相关性的组织结构区分开来而加剧。为了解决这一问题，我们提出了考虑染色的波形正则化（Stain-Aware Wavelet Regularization, SAWR），这是一种对抗净化框架，利用基于Haar变换的多层波形域正则化，分层解开对抗扰动与诊断结构信息。该光谱约束进一步扩展到各个组织学通道，使得染色特定的频率调节与苏木精和伊红的生物特性一致。当集成到即时净化框架中时，SAWR在对抗鲁棒性方面比基线方法提高了多达10.69\%，同时在对抗扰动下保持了纹理和光谱的保真度。
cs.CV / 112 / 2606.08751
Less Is More: Training-Free Acceleration Framework of 3D Diffusion Models for Low-Count PET Denoising via Global-Local Trajectory Reduction
少即是多：基于全局-局部轨迹缩减的无训练加速框架用于低计数PET去噪的3D扩散模型
Abstract
Accurate quantification and uptake measurement in PET are critical for assessing disease progression and supporting clinical decision-making. While high-count PET provides reliable image quality, the associated radiation dose and prolonged acquisition remain significant clinical concerns, motivating the adoption of low-count protocols. Diffusion-model-based methods have demonstrated strong potential for restoring low-count PET to near high-count quality, but their iterative sampling procedure becomes prohibitively expensive when applied to high-resolution 3D PET volumes, introducing substantial inference latency that limits practical clinical deployment. To address these challenges, we propose a training-free Global-Local Skipping Strategy that accelerates diffusion model-based 3D PET denoising while simultaneously improving reconstruction quality. The proposed method is plug-and-play and directly applicable to pre-trained diffusion models without retraining or architectural modification. Specifically, we introduce: (i) a global denoising step skipping strategy that initializes the reverse diffusion process from an intermediate denoising step using a noise-consistent transformation of the low-count input, substantially reducing the number of required denoising steps; and (ii) a local feature reuse shortcut that reuses slowly-varying high-level U-Net features across neighboring denoising steps, further reducing per-step computation while preserving image fidelity. We evaluate the proposed approach on multiple PET tracers from in-house and public datasets, including 18F-FDG PET, 68Ga-DOTATATE PET, and 18F-PSMA PET, demonstrating consistent acceleration of over an order of magnitude alongside improved or comparable reconstruction performance relative to the full-step baseline. Blinded reader studies further confirm enhanced clinical confidence and perceived diagnostic quality.
Chinese Translation
在正电子发射断层扫描（PET）中，准确的定量和摄取测量对于评估疾病进展和支持临床决策至关重要。尽管高计数PET提供可靠的图像质量，但相关的辐射剂量和延长的采集时间仍然是重要的临床问题，这促使低计数协议的采用。基于扩散模型的方法已显示出将低计数PET恢复到接近高计数质量的强大潜力，但其迭代采样过程在应用于高分辨率3D PET体积时变得过于昂贵，导致显著的推理延迟，限制了实际临床应用。为了解决这些挑战，我们提出了一种无训练的全局-局部跳过策略，该策略在加速基于扩散模型的3D PET去噪的同时，改善重建质量。所提方法为即插即用，能够直接应用于预训练的扩散模型，无需重新训练或架构修改。具体而言，我们引入了：(i) 一种全局去噪步骤跳过策略，该策略通过对低计数输入进行噪声一致性变换，从中间去噪步骤初始化反向扩散过程，显著减少所需的去噪步骤数量；(ii) 一种局部特征重用快捷方式，该快捷方式在相邻去噪步骤之间重用缓慢变化的高层U-Net特征，进一步减少每步计算，同时保持图像保真度。我们在多个来自内部和公共数据集的PET示踪剂上评估了所提方法，包括18F-FDG PET、68Ga-DOTATATE PET和18F-PSMA PET，证明了加速效果超过一个数量级，同时相较于全步骤基线，重建性能得到了改善或保持一致。盲读者研究进一步确认了增强的临床信心和感知的诊断质量。
cs.CV / 113 / 2606.08780
Beyond Consistency: Preserving Temporal Structure in Zero-Shot Video Editing
超越一致性：在零样本视频编辑中保留时间结构
Abstract
Existing zero-shot video editing methods rely on pre-trained diffusion models, successfully achieving spatial control and basic temporal consistency but fundamentally fail to preserve the video's original temporal structure.This distinction is critical: temporal consistency ensures visual smoothness, but temporal structure dictates the video's high-level narrative, rhythm, and semantic flow. Without this preservation, the edited output, especially for long videos with complex semantic variations, becomes narratively incoherent and semantically ambiguous. To address this limitation, we introduce a novel zero-shot editing approach that, for the first time, explicitly focuses on preserving the source video's temporal structure. We achieve this by adaptively partitioning the video into semantically distinct clips based on feature similarity and selecting a representative anchor frame for each clip. To enhance both intra-clip fidelity and computational efficiency, we design a clip-adaptive token merging strategy which leverages the anchor's semantic dominance to stabilize the editing. Furthermore, we employ an alternating combination strategy that ensures seamless inter-clip transitions while maintaining semantic distinction. Extensive experiments demonstrate that our method achieves state-of-the-art results, successfully balancing the preservation of original temporal structure with computational efficiency, and setting a new benchmark for zero-shot video editing fidelity.
Chinese Translation
现有的零样本视频编辑方法依赖于预训练的扩散模型，成功实现了空间控制和基本的时间一致性，但在根本上未能保留视频的原始时间结构。这一区别至关重要：时间一致性确保视觉的平滑性，而时间结构则决定了视频的高级叙事、节奏和语义流动。如果没有这种保留，编辑后的输出，特别是对于具有复杂语义变化的长视频，将变得叙事不连贯且语义模糊。为了解决这一局限性，我们提出了一种新颖的零样本编辑方法，首次明确关注于保留源视频的时间结构。我们通过基于特征相似性自适应地将视频划分为语义上不同的片段，并为每个片段选择一个代表性的锚帧来实现这一目标。为了增强片段内部的保真度和计算效率，我们设计了一种片段自适应的标记合并策略，该策略利用锚帧的语义主导性来稳定编辑。此外，我们采用交替组合策略，确保无缝的片段间过渡，同时保持语义的区分。大量实验表明，我们的方法在保留原始时间结构与计算效率之间成功地达到了最先进的结果，为零样本视频编辑的保真度设定了新的基准。
cs.CV / 114 / 2606.08781
DeepMine-Mamba: Mitigating Information Dilution in Mamba-Based State Space Models for Document Image Binarization
DeepMine-Mamba：减轻基于Mamba的状态空间模型在文档图像二值化中的信息稀释
Abstract
Document image binarization aims to separate foreground text from degraded backgrounds while preserving thin, broken, and low-contrast strokes. Although deep learning methods have improved binarization performance, most existing approaches rely on convolutional, transformer-based, or generative architectures, while Mamba-based state space models remain largely unexplored for this task. In this work, we investigate Mamba-based feature propagation and observe that direct state-space propagation may dilute weak foreground cues during long-range modeling, especially faint ink traces, fragmented characters, and boundary-sensitive stroke details. To address this problem, we propose DeepMine-Mamba, a Mamba-based binarization framework equipped with a novel Anti-Dilution Gate that estimates propagation-induced feature changes and selectively restores stroke-sensitive local responses while suppressing unnecessary background enhancement. Experiments on DIBCO/H-DIBCO benchmarks under a strict leave-one-year-out protocol show that DeepMine-Mamba achieves competitive overall performance, with strong average FM and Fps across benchmark years. Ablation results further demonstrate that the Anti-Dilution Gate improves stroke preservation and reduces perceptually significant binarization errors.
Chinese Translation
文档图像二值化旨在将前景文本与退化背景分离，同时保留细小、断裂和低对比度的笔画。尽管深度学习方法提高了二值化性能，但现有大多数方法依赖于卷积、基于变换器或生成架构，而基于Mamba的状态空间模型在这一任务中仍然未得到充分探索。在本研究中，我们调查了基于Mamba的特征传播，并观察到直接的状态空间传播可能在长距离建模过程中稀释弱前景线索，尤其是微弱的墨迹、破碎的字符和对边界敏感的笔画细节。为了解决这个问题，我们提出了DeepMine-Mamba，一个基于Mamba的二值化框架，配备了一种新颖的抗稀释门（Anti-Dilution Gate），该门估计传播引起的特征变化，并选择性地恢复对笔画敏感的局部响应，同时抑制不必要的背景增强。在严格的逐年留出协议下对DIBCO/H-DIBCO基准的实验表明，DeepMine-Mamba实现了具有竞争力的整体性能，在各基准年份中具有较强的平均FM和Fps。消融实验结果进一步表明，抗稀释门改善了笔画的保留，并减少了感知上显著的二值化错误。
cs.CV / 115 / 2606.08788
MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training
MaskAlign：用于高效扩散训练的令牌子集表示对齐
Abstract
Representation alignment with pretrained vision models has recently shown strong potential for accelerating diffusion transformer training. By aligning intermediate diffusion features with clean-image representations from self-supervised vision encoders, existing methods improve convergence and generation quality. However, such alignment also introduces a non-trivial constraint: diffusion models operate on noisy inputs whose usable information varies across timesteps, while the reference features are extracted from clean images. In this paper, we revisit this mismatch from a token-level perspective. We find that, under full-token representation alignment, tokens with large alignment-gradient norms exhibit a stable spatial preference, suggesting that the alignment objective does not affect all tokens uniformly and may encourage the model to rely on the complete set of clean-image tokens. To address this issue, we propose MaskAlign, a token-subset representation alignment method that applies alignment to randomly sampled token subsets during training. By exposing the model to different token subsets across iterations, MaskAlign reduces the dependence of representation alignment on the complete token set and encourages alignment behavior that is more stable under token-subset perturbations. To mitigate the information loss caused by directly dropping tokens, we further introduce a lightweight pre-mask token mixing block that shares information across tokens before masking.
Chinese Translation
与预训练视觉模型的表示对齐最近显示出加速扩散变换器训练的强大潜力。通过将中间扩散特征与自监督视觉编码器的干净图像表示对齐，现有方法改善了收敛性和生成质量。然而，这种对齐也引入了一个非平凡的约束：扩散模型在噪声输入上运行，其可用信息在时间步长上变化，而参考特征是从干净图像中提取的。本文从令牌级别的角度重新审视这种不匹配。我们发现，在全令牌表示对齐下，具有大对齐梯度范数的令牌表现出稳定的空间偏好，这表明对齐目标并不均匀地影响所有令牌，并可能促使模型依赖于完整的干净图像令牌集。为了解决这个问题，我们提出了MaskAlign，一种令牌子集表示对齐方法，在训练过程中对随机采样的令牌子集应用对齐。通过在迭代过程中将模型暴露于不同的令牌子集，MaskAlign减少了表示对齐对完整令牌集的依赖，并鼓励在令牌子集扰动下表现出更稳定的对齐行为。为了减轻直接丢弃令牌造成的信息损失，我们进一步引入了一个轻量级的预掩码令牌混合模块，在掩码之前跨令牌共享信息。
cs.CV / 116 / 2606.08795
PairWise Image Finder: An Open-source Tool for Finding Visually Aligned Street-Level Image Pairs for Urban Perception Studies
PairWise图像查找器：用于寻找视觉对齐街景图像对的开源工具，适用于城市感知研究
Abstract
Change detection and scene recognition techniques have been widely applied to Street View Imagery (SVI) to understand changes in scenes across the years. However, metadata alone is often insufficient to reliably find visually aligned image pairs. This study introduces the PairWise image finder, a tool that integrates feature detection and matching, supported by semantic segmentation masks to quantify the visual alignment of two images of varying time periods. The tool outputs the share of matched key features, the matched feature distance and coverage, and the alignment of semantic masks, which enables the user to filter image pairs depending on the alignment quality and use case. The visually aligned pairs derived from the tool can be used to accurately study explicit longitudinal change and help reduce manual effort for perception studies. The usability of the tool is demonstrated through a comparison of longitudinal changes, highlighting the importance of perspective when quantifying changes. The proposed method provides a scalable and open tool for researchers and stakeholders to find high-quality image pairs for urban analysis, perception and related applications.
Chinese Translation
变化检测和场景识别技术已广泛应用于街景图像（SVI），以理解场景随时间的变化。然而，仅靠元数据通常不足以可靠地找到视觉对齐的图像对。本研究介绍了PairWise图像查找器，这是一种集成了特征检测与匹配的工具，支持通过语义分割掩膜来量化不同时期两幅图像的视觉对齐程度。该工具输出匹配关键特征的比例、匹配特征的距离和覆盖率，以及语义掩膜的对齐情况，使用户能够根据对齐质量和使用案例筛选图像对。通过该工具获得的视觉对齐图像对可用于准确研究明确的纵向变化，并有助于减少感知研究中的人工工作量。该工具的可用性通过纵向变化的比较得以展示，强调了在量化变化时视角的重要性。所提出的方法为研究人员和利益相关者提供了一种可扩展的开源工具，以寻找高质量的图像对，用于城市分析、感知及相关应用。
cs.CV / 117 / 2606.08826
Classifying galaxies in the Galaxy10 DECals dataset using Inception and Residual CNNs
使用 Inception 和 Residual CNNs 对 Galaxy10 DECals 数据集中的星系进行分类
Abstract
Image data regarding galactic morphology is expected to increase both in quantity and quality for the next foreseeable years; thus it is important to explore which deep learning architectures adapted for image classification tasks are cost-effective. Residual and Inception networks are ideal for exploring classification convolutional neural networks (CNNs) due to their computational efficiency, achieved through techniques such as residual connections and parallelized inception modules, enabling deeper networks without excessively increasing computational complexity. In this work, we analyze the performance of ResNet101 and InceptionV4 on a spatially-augmented Galaxy10 DECals dataset. Retaining the ten-class classification of galaxies, we modify the image count of each class. We find that ResNet101 and InceptionV4 models achieved accuracies of $\sim$ 90%, comparable with reported performance in the literature. In terms of performance metrics, ResNet101 is superior to InceptionV4. Our results indicate that either of these CNN architectures could serve as a robust foundation for specialized pipelines for classification of galaxy images from upcoming surveys.
Chinese Translation
关于星系形态的图像数据预计在未来可预见的几年中将增加数量和质量，因此探索适用于图像分类任务的深度学习架构的性价比显得尤为重要。由于残差连接和并行化的 Inception 模块等技术的应用，Residual 和 Inception 网络在探索分类卷积神经网络（CNNs）方面是理想的选择，它们能够在不显著增加计算复杂度的情况下实现更深的网络。在本研究中，我们分析了 ResNet101 和 InceptionV4 在空间增强的 Galaxy10 DECals 数据集上的表现。在保留十类星系分类的基础上，我们修改了每个类别的图像数量。我们发现 ResNet101 和 InceptionV4 模型的准确率达到了约 90%，与文献中报告的性能相当。在性能指标方面，ResNet101 优于 InceptionV4。我们的结果表明，这两种 CNN 架构均可作为即将进行的调查中星系图像分类的专用管道的坚实基础。
cs.CV / 118 / 2606.08833
CSFlow: Aligning Flow Matching with Human Contrast Sensitivity
CSFlow：将流匹配与人类对比敏感性对齐
Abstract
We introduce Contrast Sensitive Flow (CSFlow), a weighting scheme that connects the human eye's Contrast Sensitivity Function (CSF) to the iterative denoising steps of flow matching. Because real-world images concentrate signal at low spatial frequencies, these components reach high signal-to-noise ratio earlier during continuous diffusion than high-frequency components. When generating images with diffusion or flow matching models, this induces a soft autoregressive structure in Fourier space, where coarse image content stabilizes before fine detail. Meanwhile, the human visual system is unequally sensitive to spatial frequencies: very low and very high frequencies require significantly higher contrast to be perceived. We for the first time merge these observations through two contributions: (1) a metric that estimates which frequencies are generated at each reverse flow interval and (2) timestep weights obtained by aligning the frequencies generated at each noise level with human contrast sensitivity. We validate our contributions experimentally showing that these weights can improve generative performance by lowering FID by 4.7%, increasing Inception Score by 2.2% and improving GenEval scores by 2.5% using inference-only timestep modification or short fine-tuning. Qualitatively, we find that our CSFlow weights lead to better visual realism and less cartoonish appearance of generated images.
Chinese Translation
我们提出了对比敏感流（Contrast Sensitive Flow, CSFlow），这是一种将人眼的对比敏感性函数（Contrast Sensitivity Function, CSF）与流匹配的迭代去噪步骤相连接的加权方案。由于现实世界中的图像在低空间频率上集中信号，这些成分在连续扩散过程中比高频成分更早达到高信噪比。在使用扩散或流匹配模型生成图像时，这在傅里叶空间中引入了一种软自回归结构，其中粗略的图像内容在细节之前稳定。同时，人类视觉系统对空间频率的敏感性是不均匀的：非常低和非常高的频率需要显著更高的对比度才能被感知。我们首次通过两个贡献将这些观察结果结合起来：（1）一种度量，估计在每个反向流间隔生成的频率，以及（2）通过将每个噪声水平生成的频率与人类对比敏感性对齐而获得的时间步权重。我们通过实验验证了我们的贡献，显示这些权重可以通过降低FID 4.7%、提高Inception Score 2.2%和改善GenEval评分2.5%来提升生成性能，使用仅推理的时间步修改或短期微调。从定性上看，我们发现CSFlow权重导致生成图像的视觉真实感更好，外观更少卡通化。
cs.CV / 119 / 2606.08844
Geometry-Aware Fisheye-LiDAR Fusion for Robust 3D Object Detection in Low-Overlap Setups
基于几何感知的鱼眼-LiDAR融合用于低重叠环境下的鲁棒3D目标检测
Abstract
As autonomous systems expand from capital-intensive robotaxis to cost-sensitive logistics, sensor configurations are increasingly optimized for coverage-per-cost. A prevalent sparse-view setup utilizes dual-fisheye cameras with a roof-mounted LiDAR, introducing severe geometric challenges: extreme radial distortion, minimal overlap, and misalignment between spherical projections and rectilinear grids. BEV fusion algorithms typically force image and point cloud modalities into unified Cartesian grids early in the pipeline, causing significant feature distortion and information loss for wide-view fisheye cameras. To address this, we propose a Geometry-Aware Hybrid Fusion (GA-HF) framework that explicitly accounts for fisheye geometry and BEV feature distortion, where fisheye features are lifted into a polar BEV grid via a Distortion-Aware Lift-Splat-Shoot (LSS) module to preserve native angular density, while LiDAR features are processed in native Cartesian space for metric fidelity of bounding box regression. To bridge these heterogeneous streams, we introduce a Dual-Attention Warping Correction module that applies spatial and channel attention to the warped camera features before fusion, explicitly suppressing artifacts in low-quality peripheral regions while enhancing high-quality semantic cues. GA-HF is evaluated on three benchmarks: KITTI-360, Dur360BEV, and Fisheye3DOD datasets. To the best of our knowledge, it is the first approach to explore LiDAR-fisheye camera fusion. On KITTI-360, GA-HF improves NDS by 4.2% over Cartesian baselines; on Dur360BEV, it surpasses both LiDAR-only and BEVFusion, while significantly reducing orientation error despite the geometric distortions; on Fisheye3DOD, it attains the highest detection score among all fusion methods.
Chinese Translation
随着自主系统从资本密集型的机器人出租车扩展到成本敏感的物流，传感器配置越来越多地针对成本覆盖进行优化。一种普遍的稀疏视图设置利用双鱼眼相机与车顶安装的LiDAR，这引入了严重的几何挑战：极端的径向畸变、最小的重叠以及球面投影与直角网格之间的错位。BEV（鸟瞰视图）融合算法通常在管道的早期阶段将图像和点云模态强制统一到笛卡尔网格中，这导致宽视角鱼眼相机的特征严重失真和信息丢失。为了解决这个问题，我们提出了一种几何感知混合融合（Geometry-Aware Hybrid Fusion, GA-HF）框架，明确考虑鱼眼几何和BEV特征失真，其中鱼眼特征通过一个畸变感知的升维-溅射-发射（Distortion-Aware Lift-Splat-Shoot, LSS）模块提升到极坐标BEV网格，以保持原生角度密度，而LiDAR特征则在原生笛卡尔空间中处理，以确保边界框回归的度量保真度。为了连接这些异构流，我们引入了一个双重注意力变形校正模块，在融合之前对变形的相机特征应用空间和通道注意力，明确抑制低质量边缘区域的伪影，同时增强高质量的语义线索。GA-HF在三个基准上进行了评估：KITTI-360、Dur360BEV和Fisheye3DOD数据集。据我们所知，这是首个探索LiDAR与鱼眼相机融合的方法。在KITTI-360上，GA-HF相较于笛卡尔基线提高了4.2%的NDS；在Dur360BEV上，它超越了LiDAR单一和BEVFusion，同时显著减少了尽管存在几何失真但的方向误差；在Fisheye3DOD上，它在所有融合方法中获得了最高的检测得分。
cs.CV / 120 / 2606.08847
BLM-SGAN: Bidirectional Language Modeling for Semantic-Spatial Text-to-Image Generation
BLM-SGAN：用于语义-空间文本到图像生成的双向语言建模
Abstract
Despite the success of image generation from text descriptions, it still faces challenges that are difficult to overcome in domains such as natural language processing (NLP) and computer vision (CV). Recent advancements in text-to-image (T2I) models, particularly those utilizing generative adversarial networks (GANs), have significantly improved the synthesis of realistic images across various domains. However, existing GAN-based T2I models still encounter key challenges, such as difficulty in capturing long-range dependencies, vanishing gradients, and the limitations of sequential processing. To address these issues, we introduce BLM-SGAN, a novel model that incorporates Bidirectional Language Modeling for Semantic-Spatial Text-to-Image Generation. BLM-SGAN leverages BERT's attention mechanisms to capture rich contextual information and efficiently manage extended sequences. Our model demonstrates state-of-the-art performance, with an Inception Score (IS) of 5.45 +/- 0.08, surpassing several competitive models such as SSA-GAN, DF-GAN, SD-GAN, and AttnGAN. BLM-SGAN effectively generates highly realistic images of birds from detailed text descriptions. The implementation code is available at: https://github.com/haidy-maher/BLM-SGAN-Text-to-Image-Generation.
Chinese Translation
尽管从文本描述生成图像取得了成功，但在自然语言处理（NLP）和计算机视觉（CV）等领域仍面临难以克服的挑战。最近，文本到图像（T2I）模型，尤其是那些利用生成对抗网络（GAN）的模型，在各个领域显著改善了真实图像的合成。然而，现有的基于GAN的T2I模型仍然面临一些关键挑战，例如捕捉长距离依赖的困难、梯度消失以及顺序处理的局限性。为了解决这些问题，我们提出了BLM-SGAN，这是一种新颖的模型，结合了双向语言建模用于语义-空间文本到图像生成。BLM-SGAN利用BERT的注意力机制来捕捉丰富的上下文信息并有效管理扩展序列。我们的模型展示了最先进的性能，Inception Score（IS）为5.45 +/- 0.08，超越了多个竞争模型，如SSA-GAN、DF-GAN、SD-GAN和AttnGAN。BLM-SGAN有效地从详细的文本描述中生成高度真实的鸟类图像。实现代码可在以下链接获取：https://github.com/haidy-maher/BLM-SGAN-Text-to-Image-Generation。
cs.CV / 121 / 2606.08858
Intelligent Character Recognition of Handwritten Forms with Deep Neural Networks
基于深度神经网络的手写表单智能字符识别
Abstract
The automatic processing of handwritten forms remains a challenging task, wherein detection and subsequent classification of handwritten characters are essential steps. We describe a novel approach, in which both steps -- detection and classification -- are executed in one task through a deep neural network. Therefore, training data is not annotated by hand, but manufactured artificially from the underlying forms and yet existing datasets. It can be demonstrated that this single-task approach is superior in comparison to the state-of-the-art two-task approach. The current study focuses on hand-written Latin letters and employs the EMNIST data set. However, limitations were identified with this data set, necessitating further customization. Finally, an overall recognition rate of 88.28 percent was attained on real data obtained from a written exam.
Chinese Translation
手写表单的自动处理仍然是一项具有挑战性的任务，其中手写字符的检测和随后的分类是关键步骤。我们描述了一种新颖的方法，在该方法中，这两个步骤——检测和分类——通过深度神经网络在一个任务中执行。因此，训练数据不是手动注释的，而是通过底层表单和现有数据集人工生成的。可以证明，这种单任务方法在与最先进的双任务方法相比时具有优势。本研究集中于手写拉丁字母，并采用了 EMNIST 数据集。然而，该数据集存在一些局限性，需要进一步的定制。最终，在从书面考试中获得的真实数据上达到了 88.28% 的整体识别率。
cs.CV / 122 / 2606.08860
Vision-Language Work Zone Intelligence for Safety-Critical Speed Regulation of Mixed-Autonomy Vehicles in Dynamic Environments
动态环境中混合自主车辆安全关键速度调节的视觉-语言工作区智能
Abstract
Temporary work-zone speed limits are communicated through visually inconsistent signage and are often missing from digital maps, creating safety risks for human drivers and automated vehicle systems. We present a real-time, onboard perception pipeline that detects active work zones, recognizes associated temporary speed limits, and outputs a law-aware work-zone state and speed value suitable for driver alerts or downstream automated control. The system fuses object detections with semantic verification and temporally smoothed, hysteresis-based state transitions to reduce false activations and flicker in dynamic scenes, and runs fully on low-cost embedded hardware. Evaluated manually on a annotated subset of the ROADWork dataset (490 sequences), the system achieves inside-work-zone event-level recall of 96.5% and event-level precision of 68.7%. Speed-limit recognition evaluated on 35 minutes of in-house driving data attains 95.45% precision and 53.85% recall, with no incorrect speed classifications and a single false positive. These results demonstrate a practical, scalable approach for grounding work-zone speed awareness directly in onboard perception rather than maps or infrastructure. We release our source code for the proposed system pipeline on our GitHub repository: https://github.com/Mi3-Lab/workzone
Chinese Translation
临时工作区的速度限制通过视觉上不一致的标志进行传达，并且通常在数字地图中缺失，这给人类驾驶员和自动驾驶系统带来了安全风险。我们提出了一种实时的车载感知管道，能够检测活动工作区，识别相关的临时速度限制，并输出适合于驾驶员警报或下游自动控制的法律意识工作区状态和速度值。该系统将物体检测与语义验证相融合，并采用时间平滑的滞后状态转换，以减少动态场景中的误激活和闪烁，并完全在低成本嵌入式硬件上运行。在对ROADWork数据集的标注子集（490个序列）进行手动评估时，该系统在工作区内事件级召回率达到96.5%，事件级精度为68.7%。在35分钟的内部驾驶数据上评估的速度限制识别达到了95.45%的精度和53.85%的召回率，没有错误的速度分类，仅有一个假阳性。这些结果展示了一种实用、可扩展的方法，将工作区速度意识直接嵌入车载感知中，而不是依赖于地图或基础设施。我们在GitHub仓库上发布了所提系统管道的源代码：https://github.com/Mi3-Lab/workzone
cs.CV / 123 / 2606.08864
CHROMA: Detecting AI-Generated Images through Inter-Channel Color-Space Correlations
CHROMA：通过通道间颜色空间相关性检测AI生成的图像
Abstract
The rapid adoption of diffusion and large-scale generative models has made it increasingly challenging to distinguish synthetic imagery from real photographs. While automated detectors have been proposed, their generalization to unseen generators remains brittle. To address this limitation, we investigate inter-channel color correlations, a lightweight and underexploited forensic cue. We first demonstrate that LPIPS, a widely used perceptual metric, exhibits inconsistent responses to perturbations that selectively alter channel dependence across different color-space parameterizations, indicating that cross-channel statistics are not uniformly constrained by common perceptual training objectives. Motivated by this, we analyze the distributions of pairwise inter-channel correlation features across multiple color spaces. Our analysis reveals systematic, generator-specific differences in these distributions, with RGB and Lab color spaces providing the most apparent separation between real and generated images. Building on this, we introduce Chroma, a detector of AI-generated images which augments standard RGB inputs with inter-channel correlation maps and employs a fixed CNN backbone trained with a modest computational budget. We assess its robustness under both single-generator training and a limited multi-generator supervision regime, where only a few samples from additional generators are available. Across a standard benchmark protocol, correlation-augmented inputs improve real-vs-generated discrimination and robustness, yielding performance competitive with recent detectors while maintaining a simple architecture and training procedure. Code is available at https://github.com/JPSoteloSilva/CHROMA
Chinese Translation
扩散和大规模生成模型的快速采用使得区分合成图像与真实照片变得越来越具有挑战性。虽然已经提出了自动检测器，但它们在未见生成器上的泛化能力仍然脆弱。为了解决这一限制，我们研究了通道间颜色相关性，这是一种轻量且未被充分利用的取证线索。我们首先展示了LPIPS（感知相似度指标），这一广泛使用的感知度量，对选择性改变不同颜色空间参数化中的通道依赖性的扰动表现出不一致的响应，表明跨通道统计并未被共同的感知训练目标所统一约束。基于此，我们分析了多个颜色空间中成对通道间相关性特征的分布。我们的分析揭示了这些分布中系统的、生成器特定的差异，其中RGB和Lab颜色空间在真实图像与生成图像之间提供了最明显的区分。基于此，我们引入了Chroma，一个AI生成图像的检测器，它通过通道间相关性图增强标准RGB输入，并采用一个在适度计算预算下训练的固定CNN骨干网络。我们评估了其在单一生成器训练和有限多生成器监督机制下的鲁棒性，其中仅有少量来自额外生成器的样本可用。在标准基准协议下，增强相关性的输入提高了真实与生成图像的区分能力和鲁棒性，表现出与近期检测器相竞争的性能，同时保持简单的架构和训练过程。代码可在 https://github.com/JPSoteloSilva/CHROMA 获取。
cs.CV / 124 / 2606.08866
Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation
将几何引导的Mamba推广为基于CNN的语义分割的即插即用上下文模块
Abstract
CNN-based semantic segmentation networks usually rely on context heads such as ASPP, PPM, or attention modules to enlarge the receptive field. These heads are effective but may introduce heavy computation, memory cost, or boundary leakage. This paper revisits Directional Geometric Mamba (G-Mamba) from DGM-Net and studies it as a plug-and-play context aggregation module rather than a complete new segmentation architecture. The key idea is to inject geometric guidance into the selective scan process, allowing long-range feature propagation to be modulated by boundary and centripetal-flow cues. We replace the original context heads of six representative CNN segmentation models, including DeepLabV3+, DANet, CCNet, PSPNet, PSANet, and OCRNet, while keeping the ResNet-101 backbone unchanged. Results on Cityscapes show consistent mIoU gains with only moderate extra GFLOPs at $1024\times1024$ resolution, suggesting that geometry-guided SSM modules can serve as practical alternatives or enhancements to conventional CNN context heads.
Chinese Translation
基于CNN的语义分割网络通常依赖于上下文头，如ASPP、PPM或注意力模块，以扩大感受野。这些模块有效，但可能会引入较大的计算量、内存成本或边界泄漏。本文重新审视了来自DGM-Net的方向性几何Mamba（G-Mamba），并将其研究作为一种即插即用的上下文聚合模块，而不是一个全新的分割架构。关键思想是将几何引导注入选择性扫描过程，使长距离特征传播能够受到边界和向心流线索的调制。我们替换了六个代表性CNN分割模型的原始上下文头，包括DeepLabV3+、DANet、CCNet、PSPNet、PSANet和OCRNet，同时保持ResNet-101主干不变。在Cityscapes上的结果显示，在$1024 imes1024$分辨率下，仅增加适度的GFLOPs，即可获得一致的mIoU提升，这表明几何引导的SSM模块可以作为传统CNN上下文头的实用替代或增强。
cs.CV / 125 / 2606.08894
Are Reasoning Vision-Language Models Robust to Semantic Visual Distractions?
推理视觉-语言模型对语义视觉干扰的鲁棒性如何？
Abstract
Reasoning Vision-Language Models (VLMs) achieve strong performance on complex multimodal tasks, but reliable real-world application requires handling visual inputs that are messier than clean, curated benchmarks. Existing works mainly evaluate such reliability of VLMs through input corruptions, such as noise, blur and weather effects, which make visual evidence harder to perceive. This leaves a critical reliability failure mode underexplored: a model may perceive the evidence correctly, yet reason from plausible but irrelevant and distracting evidence and propagate this mistake to its final answer. To address this gap, we introduce \textbf{Distract-Bench}, a benchmark for evaluating VLM robustness to \textbf{semantic visual distractions}, defined as meaningful but task-irrelevant visual cues added to inputs while preserving the ground-truth answer. We comprehensively evaluate eight leading open-source and two closed-source VLMs across conventional vision corruptions and Distract-Bench. Our results show that Distract-Bench exposes a robustness failure distinct from vision corruptions: reasoning VLMs largely track their non-reasoning base models under perceptual degradation, but show consistently lower robustness to semantic distractions. Further analysis shows that these distractions often enter the reasoning process of VLMs, are treated as evidence, and lead to incorrect answers. Together, these findings reframe robustness evaluation for reasoning VLMs, shifting the focus from degraded perception to distractions for reliable real-world visual reasoning. Our data and code are available at https://github.com/Yizheng-Sun/Distract-Bench.
Chinese Translation
推理视觉-语言模型（VLMs）在复杂的多模态任务中表现出色，但在真实世界中的可靠应用需要处理比干净、精心策划的基准更为混乱的视觉输入。现有研究主要通过输入损坏（如噪声、模糊和天气影响）来评估VLM的可靠性，这使得视觉证据更难以感知。这留下了一个关键的可靠性失效模式未被充分探讨：模型可能正确感知证据，但却从合理但无关且具有干扰性的证据中推理，并将这一错误传播到最终答案。为了解决这一问题，我们引入了 extbf{Distract-Bench}，一个用于评估VLM对 extbf{语义视觉干扰}鲁棒性的基准，语义视觉干扰被定义为在保留真实答案的同时，添加到输入中的有意义但与任务无关的视觉线索。我们全面评估了八个领先的开源和两个闭源的VLM，涵盖了常规视觉损坏和Distract-Bench。我们的结果表明，Distract-Bench揭示了一种与视觉损坏不同的鲁棒性失效：推理VLM在感知退化下大致跟踪其非推理基础模型，但对语义干扰的鲁棒性始终较低。进一步分析表明，这些干扰往往进入VLM的推理过程，被视为证据，并导致错误答案。综合来看，这些发现重新定义了推理VLM的鲁棒性评估，将重点从退化感知转向可靠的真实世界视觉推理中的干扰。我们的数据和代码可在https://github.com/Yizheng-Sun/Distract-Bench获取。
cs.CV / 126 / 2606.08897
A multi-agent system for spine MRI report generation from multi-sequence imaging
基于多序列影像的脊柱MRI报告生成的多智能体系统
Abstract
Spinal pathology is a leading cause of pain and disability worldwide. Spine MRI is central to clinical evaluation, yet its interpretation remains complex and time-consuming, requiring integration of information across multiple imaging sequences and anatomical regions. Despite recent advances in automated MRI analysis, effectively combining multi-sequence data while preserving sequence-specific diagnostic information remains an open challenge. Here we present SpineAgent, a multi-agent framework for spine MRI report generation built upon a multi-sequence foundation model trained on routine clinical data from 32,047 patients and 453,683 MRI series, comprising a total of 13,441,191 MRI slices. To accommodate diverse modalities of sequences, we first pre-train two DINOv3-based encoders separately on T1- and T2-weighted sequences. We then introduce a continual training strategy that learns a synthesizer to embed images of other sequences using the T1 and T2 encoders, producing patient-level embedding that integrates various signals across MRI sequences. Using these embeddings, SpineAgent achieves state-of-the-art performance, and demonstrates strong generalizability under cross-manufacturer and cross-cohort evaluation. Beyond classification, SpineAgent enables pathology localization by identifying findings-relevant slices and segmenting pathological regions. It also supports multimodal image-report retrieval, providing a solid foundation for scalable and explainable MRI report generation. We further integrate these validated capabilities of SpineAgent into 37 specialized agents. Finally, we incorporate their outputs as structured tokens within a Medical Report Agent trained end-to-end for report generation. Through both automated metrics and expert evaluation by five radiologists, SpineAgent achieves leading performance in spine MRI report generation.
Chinese Translation
脊柱病理是全球疼痛和残疾的主要原因。脊柱MRI在临床评估中至关重要，但其解读仍然复杂且耗时，需要整合来自多个影像序列和解剖区域的信息。尽管自动化MRI分析近年来取得了进展，但有效结合多序列数据并保留序列特异性的诊断信息仍然是一个开放的挑战。在此，我们提出了SpineAgent，一个基于多序列基础模型的脊柱MRI报告生成多智能体框架，该模型在32,047名患者和453,683个MRI序列的常规临床数据上训练，涵盖了总计13,441,191个MRI切片。为了适应多种序列的不同模态，我们首先分别在T1和T2加权序列上预训练了两个基于DINOv3的编码器。然后，我们引入了一种持续训练策略，学习一个合成器，利用T1和T2编码器嵌入其他序列的图像，生成整合各种信号的患者级嵌入。利用这些嵌入，SpineAgent实现了最先进的性能，并在跨制造商和跨队列评估中表现出强大的泛化能力。除了分类，SpineAgent还通过识别与发现相关的切片和分割病理区域来实现病理定位。它还支持多模态图像-报告检索，为可扩展和可解释的MRI报告生成提供了坚实的基础。我们进一步将SpineAgent的这些验证能力整合到37个专业代理中。最后，我们将它们的输出作为结构化标记纳入一个端到端训练的医学报告代理中，以进行报告生成。通过自动化指标和五位放射科医生的专家评估，SpineAgent在脊柱MRI报告生成中实现了领先的性能。
cs.CV / 127 / 2606.08906
DifferSeg: Towards Diverse Multimodal Binary Segmentation via Differential Perception and Frequency Guidance
DifferSeg：通过差异感知和频率引导实现多样化的多模态二元分割
Abstract
In many binary segmentation tasks, most multimodal methods rely on fixed feature concatenation for cross-modal interaction and straightforward decoder designs dominated by low-frequency semantics. %ToDO: %
However, they ignore two key challenges: one is the lack of an adaptive mechanism to handle modality discrepancies and complementarity, and the other is the absence of an efficient decoding strategy to balance both high- and low-frequency representations. %
In this work, we propose a simple yet general multimodal binary segmentation framework, termed DifferSeg, to address both problems simultaneously. With the help of the differential perception fusion (DPF) module, DifferSeg employs learnable differential operators to adaptively align multimodal features and enhance their complementarity through residual fusion, effectively mitigating modality mismatch and fusion redundancy. %
In addition, we design a frequency-guided decoder (FGD) that builds cross-frequency interactions and multi-path upsampling to maintain consistency between detailed high-frequency structures and semantic low-frequency representations, ensuring fine-grained boundary recovery and noise suppression. %
Benefiting from these designs, DifferSeg can be easily generalized to diverse binary segmentation tasks, including both natural and medical modalities. Without bells and whistles, it consistently surpasses 67 state-of-the-art methods across 29 public datasets involving 18 downstream tasks, demonstrating superior generalization and segmentation accuracy.Code and pretrained models will be available at the Link.
Chinese Translation
在许多二元分割任务中，大多数多模态方法依赖于固定特征拼接进行跨模态交互，以及由低频语义主导的简单解码器设计。然而，它们忽略了两个关键挑战：一是缺乏自适应机制来处理模态差异和互补性，二是缺乏有效的解码策略来平衡高频和低频表示。在本研究中，我们提出了一种简单而通用的多模态二元分割框架，称为DifferSeg，以同时解决这两个问题。在差异感知融合（DPF）模块的帮助下，DifferSeg采用可学习的差异算子自适应对齐多模态特征，并通过残差融合增强其互补性，有效缓解模态不匹配和融合冗余。此外，我们设计了一个频率引导解码器（FGD），构建跨频率交互和多路径上采样，以保持高频细节结构与低频语义表示之间的一致性，从而确保精细的边界恢复和噪声抑制。得益于这些设计，DifferSeg可以轻松推广到多样化的二元分割任务，包括自然和医学模态。在没有多余修饰的情况下，它在涉及18个下游任务的29个公共数据集上，始终超越67种最先进的方法，展示了卓越的泛化能力和分割精度。代码和预训练模型将会在链接中提供。
cs.CV / 128 / 2606.08908
Failure-Aware Refinement of Vision-Language Model for Lithography Defect Detection
面向故障的视觉-语言模型细化用于光刻缺陷检测
Abstract
Semiconductor lithography inspection requires reliable detection of small pattern defects such as bridge, burr, pinch, and contamination. In this study, we propose a two-stage vision-language framework that combines initial defect detection with prediction refinement. In the first stage, Qwen3-VL is fine-tuned with LoRA as a vision-language adapter to predict defect counts, defect categories, and normalized bounding boxes from lithography images. However, direct fine-tuning may still produce common test-time errors, including false positives, missed defects, and incorrect defect types. To address this limitation, the second stage trains a refinement module using first-stage prediction failures and their corrected labels, allowing the model to review and revise initial outputs. By learning from cases where the initial adapter fails, the refinement process improves defect inference beyond single-stage fine-tuning.
Chinese Translation
半导体光刻检测需要可靠地检测小型图案缺陷，如桥接、毛刺、夹紧和污染。在本研究中，我们提出了一种两阶段的视觉-语言框架，将初始缺陷检测与预测细化相结合。在第一阶段，Qwen3-VL 被微调为视觉-语言适配器，使用 LoRA 从光刻图像中预测缺陷数量、缺陷类别和归一化边界框。然而，直接微调仍可能产生常见的测试时间错误，包括假阳性、漏检和错误的缺陷类型。为了解决这一限制，第二阶段利用第一阶段预测失败及其修正标签训练一个细化模块，使模型能够审查和修正初始输出。通过学习初始适配器失败的案例，细化过程超越单阶段微调，提高了缺陷推断的准确性。
cs.CV / 129 / 2606.08918
When Vision Misleads, Let Location Speak: A Worldwide Image Geo-Localization Method via Location Attention Mechanism and Large Multimodal Models
当视觉误导时，让位置发声：一种基于位置注意机制和大型多模态模型的全球图像地理定位方法
Abstract
Worldwide image geo-localization aims to determine the capture location of an image on a global scale. Existing methods often mislocalize images by matching them to visually similar scenes from different geographic regions, which limits reliability in practical applications. To address this issue, we propose TransGeoCLIP, a novel retrieval-based framework that integrates a location attention mechanism and large multimodal models (LMMs). Using the Transformer encoder with location attention to encode GPS coordinates, TransGeoCLIP can effectively distinguish geographic features among visually similar images. The framework consists of two stages: 1) Retrieval database construction, which employs Transformers equipped with location attention mechanisms to encode labeled GPS coordinates and enhance location semantics, subsequently enables joint image-text-GPS embedding through CLIP; 2) Retrieval-augmented inference, which leverages LMMs to infer the final image location prediction from retrieved database results. Extensive experimental results on diverse datasets, including IM2GPS, IM2GPS3k, YFCC4k, and YFCC26k, demonstrate that TransGeoCLIP significantly enhances localization performance for visually similar images. Particularly, street-level localization accuracy (within 1 km error) is substantially improved, surpassing state-of-the-art methods by 1.5%, 1.07%, 7.18%, and 9.75% on these benchmarks, respectively.
Chinese Translation
全球图像地理定位旨在确定图像在全球范围内的拍摄位置。现有方法常常通过将图像与来自不同地理区域的视觉相似场景进行匹配，从而导致误定位，这限制了其在实际应用中的可靠性。为了解决这一问题，我们提出了TransGeoCLIP，一种新颖的基于检索的框架，集成了位置注意机制和大型多模态模型（LMMs）。TransGeoCLIP使用带有位置注意的Transformer编码器对GPS坐标进行编码，能够有效区分视觉相似图像之间的地理特征。该框架由两个阶段组成：1）检索数据库构建，采用配备位置注意机制的Transformer对标记的GPS坐标进行编码，增强位置语义，随后通过CLIP实现图像-文本-GPS的联合嵌入；2）检索增强推理，利用LMMs从检索数据库结果中推断最终的图像位置预测。在IM2GPS、IM2GPS3k、YFCC4k和YFCC26k等多样化数据集上的广泛实验结果表明，TransGeoCLIP显著提升了视觉相似图像的定位性能。特别是街道级定位准确率（误差在1公里以内）得到了显著改善，在这些基准上分别超过了最先进方法1.5%、1.07%、7.18%和9.75%。
cs.CV / 130 / 2606.08920
PolyBuild: An End-to-End Method for Polygonal Building Contour Extraction from High-Resolution Remote Sensing Images
PolyBuild：一种从高分辨率遥感图像中提取多边形建筑轮廓的端到端方法
Abstract
Extracting building polygon contours from high-resolution remote sensing images is a fundamental task for various mapping applications. However, the presence of varying imaging conditions and complex building structures, makes automatic contour extraction extremely challenging. Mainstream approaches for building extraction often rely on pixel-level segmentation followed by multiple post-processing steps to produce building contour, which can be computationally intensive and prone to errors. In this paper, we propose an end-to-end method named PolyBuild, which can directly extract building vector polygons from high-resolution remote sensing images without the need for any post-processing operations. The proposed method leverages two primary modules: an Initial Contour Generation Module (ICGM) and a Contour Optimization Module (COM). The ICGM is designed to generate an initial building contour by utilizing concatenated sub-region center features for each building instance. It performs simultaneous object detection and initial contour extraction by generating bounding boxes and using the center features of four sub-regions to represent each building. The Contour Optimization Module (COM) further refines the generated building contours by iteratively integrating Convolutional Neural Network (CNN) features and contour positional information in a Transformer-based decoder. The hybrid CNN-Transformer architecture effectively captures both local and global spatial relationships within the building contour, ensuring high-quality boundary delineation. Extensive experiments are conducted on three building datasets to evaluate the performance of PolyBuild. The results demonstrate that PolyBuild significantly outperforms state-of-the-art methods, including mask-based and contour-based approaches.
Chinese Translation
从高分辨率遥感图像中提取建筑多边形轮廓是各种制图应用中的一项基础任务。然而，成像条件的变化和复杂的建筑结构使得自动轮廓提取极具挑战性。主流的建筑提取方法通常依赖于像素级分割，随后经过多个后处理步骤来生成建筑轮廓，这可能会导致计算量大且容易出错。在本文中，我们提出了一种名为PolyBuild的端到端方法，该方法能够直接从高分辨率遥感图像中提取建筑矢量多边形，而无需任何后处理操作。所提方法利用了两个主要模块：初始轮廓生成模块（Initial Contour Generation Module, ICGM）和轮廓优化模块（Contour Optimization Module, COM）。ICGM旨在通过利用每个建筑实例的连接子区域中心特征生成初始建筑轮廓。它通过生成边界框并使用四个子区域的中心特征来表示每个建筑，执行同时的目标检测和初始轮廓提取。轮廓优化模块（COM）通过在基于Transformer的解码器中迭代整合卷积神经网络（Convolutional Neural Network, CNN）特征和轮廓位置信息，进一步细化生成的建筑轮廓。混合的CNN-Transformer架构有效捕捉建筑轮廓内的局部和全局空间关系，确保高质量的边界划分。在三个建筑数据集上进行了大量实验，以评估PolyBuild的性能。结果表明，PolyBuild在性能上显著优于包括基于掩膜和基于轮廓的方法在内的最先进技术。
cs.CV / 131 / 2606.08948
NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis
NutriMLLM：用于膳食微量营养素分析的多模态大型语言模型
Abstract
Comprehensive estimation of dietary micronutrients from food images could improve clinical nutrition care, but training such models requires large multimodal datasets linking diverse foods to complete nutrient profiles. We first show that existing multimodal large language models (MLLMs), including leading proprietary models, are unreliable for this task. Across five model families and four independent evaluation benchmarks (ASA24, SNAPMe, FNDDS, and NutriBench), models frequently abstained or returned statistically implausible values. To address this gap without costly expert annotation, we repurposed a decade of population-scale 24-hour dietary recalls as structured prompts for text-to-image generation. This pipeline produced a synthetic corpus of about 1.1 million image-description-nutrient triplets, each pairing a generated food image with a complete 65-nutrient label. To our knowledge, this is the largest synthetic food-image corpus with comprehensive micronutrient annotation planned for public release upon publication. Fine-tuning Qwen3-VL (2B/4B/8B/30B) and GLM-4.6V-Flash on this corpus yielded NutriMLLM, the first family of vision-language models specialized for comprehensive dietary micronutrient estimation. We evaluate these models with a four-component framework that separately measures abstention, hallucination, overall usability, and per-nutrient numerical accuracy. On real food images, every NutriMLLM variant achieved near-complete coverage across all 65 nutrients, and the largest variant matched or exceeded proprietary baselines (GPT-5, Gemini 3, and Claude Sonnet 4.5) in accuracy on most nutrients. These results show that recall-driven synthetic supervision can make image-based comprehensive micronutrient estimation a tractable engineering problem and support dietary assessment, personalized nutrition guidance, and population-scale micronutrient surveillance.
Chinese Translation
从食品图像中全面估算膳食微量营养素可以改善临床营养护理，但训练此类模型需要大量将多样食品与完整营养成分资料相链接的多模态数据集。我们首先展示了现有的多模态大型语言模型（MLLMs），包括领先的专有模型，在这一任务上是不可靠的。在五个模型系列和四个独立评估基准（ASA24、SNAPMe、FNDDS和NutriBench）中，模型经常选择放弃或返回统计上不合理的值。为了解决这一缺口而不需要昂贵的专家标注，我们将十年的大规模人群24小时膳食回忆重新利用为文本到图像生成的结构化提示。该流程生成了约110万个图像-描述-营养三元组的合成语料库，每个三元组将生成的食品图像与完整的65种营养成分标签配对。据我们所知，这是计划在发表时公开发布的最大合成食品图像语料库，具有全面的微量营养素标注。在此语料库上微调Qwen3-VL（2B/4B/8B/30B）和GLM-4.6V-Flash，得到了NutriMLLM，这是首个专门用于全面膳食微量营养素估算的视觉-语言模型系列。我们使用一个四个组成部分的框架来评估这些模型，分别测量放弃率、幻觉、整体可用性和每种营养成分的数值准确性。在真实食品图像上，每个NutriMLLM变体在所有65种营养成分上几乎实现了完全覆盖，且最大的变体在大多数营养成分的准确性上与专有基准（GPT-5、Gemini 3和Claude Sonnet 4.5）相匹配或超过。这些结果表明，基于回忆驱动的合成监督可以使基于图像的全面微量营养素估算成为一个可行的工程问题，并支持膳食评估、个性化营养指导和大规模微量营养素监测。
cs.CV / 132 / 2606.08957
Rethinking 3D Shape Generation: Diffusion over Superquadrics
重新思考三维形状生成：超二次体上的扩散
Abstract
Diffusion models have advanced 3D shape generation, yet most methods still denoise in high-cardinality spaces (e.g., voxel/SDF grids, meshes, or point clouds), which is computationally and memory intensive and makes it difficult to scale in terms of both higher resolution and stronger controllability. We rethink the diffusion representation and propose to move diffusion from dense geometry to compact geometric primitives, representing each shape as a small set of superquadrics. Instead of operating on thousands to millions of geometric representation values, we leverage 7KB superquadric parameters (pose, size, and shape), drastically reducing diffusion-state dimensionality and per-step compute/memory. Our diffusion-over-superquadrics improves scalability by supporting broader capabilities (e.g., resolution-free point-cloud decoding, part-level editing, and constraint-based design) and achieving competitive surface-fidelity and distributional performance on standard benchmarks after point-cloud decoding, while enabling efficient generation within 0.6s per shape for most conditions.
Chinese Translation
扩散模型推动了三维形状生成的发展，但大多数方法仍在高维空间（例如，体素/SDF网格、网格或点云）中进行去噪，这在计算和内存上都非常密集，并且在更高分辨率和更强可控性方面难以扩展。我们重新思考了扩散表示，提出将扩散从密集几何体转移到紧凑的几何原语，将每个形状表示为一小组超二次体。我们不再处理数千到数百万个几何表示值，而是利用7KB的超二次体参数（姿态、大小和形状），大幅降低了扩散状态的维度和每步的计算/内存需求。我们的超二次体扩散方法通过支持更广泛的功能（例如，无分辨率的点云解码、部件级编辑和基于约束的设计）来改善可扩展性，并在点云解码后在标准基准测试中实现了竞争力的表面保真度和分布性能，同时在大多数条件下实现了每个形状0.6秒内的高效生成。
cs.CV / 133 / 2606.08959
ChinaHeritaQA: A Culturally-Grounded Visual Question Answering Dataset for World Heritage Sites in China
ChinaHeritaQA：一个基于文化的中国世界遗产地视觉问答数据集
Abstract
We introduce ChinaHeritaQA, a multimodal benchmark dataset for evaluating the cultural reasoning abilities of vision-language models (VLMs) on UNESCO World Heritage sites in China. The dataset comprises 2,279 in-the-wild images paired with 14,133 bilingual (Chinese/English) multiple-choice QA pairs spanning seven cognitive dimensions, from basic identity recognition to historical periodization and architectural analysis. Guided by a UNESCO-aligned heritage ontology and verified through rigorous human annotation, the dataset ensures linguistic quality and factual consistency. Evaluations of state-of-the-art VLMs reveal that while top models exceed human performance on average, substantial task-level variation emerges: models excel at visual recognition but struggle with culturally grounded reasoning. Performance also varies by dynasty and region. ChinaHeritaQA reveals that strong visual retrieval does not extend to cultural and historical understanding. We release the dataset to support future research on culturally aware multimodal learning.
Chinese Translation
我们介绍了 ChinaHeritaQA，这是一个多模态基准数据集，用于评估视觉语言模型（VLMs）在中国联合国教科文组织世界遗产地的文化推理能力。该数据集包含 2,279 张真实场景图像，配有 14,133 对双语（中文/英文）多项选择问答对，涵盖七个认知维度，从基本身份识别到历史时期划分和建筑分析。该数据集在联合国教科文组织对齐的遗产本体指导下，通过严格的人类注释进行验证，确保了语言质量和事实一致性。对最先进的 VLMs 的评估显示，尽管顶尖模型的平均表现超过人类，但在任务层面上存在显著的变异：模型在视觉识别方面表现出色，但在文化基础推理方面却面临挑战。不同朝代和地区的表现也有所不同。ChinaHeritaQA 揭示了强大的视觉检索能力并未延伸至文化和历史理解。我们发布该数据集，以支持未来在文化意识多模态学习方面的研究。
cs.CV / 134 / 2606.08980
EPS3D: End-to-End Feed-Forward 3D Panoptic Segmentation
EPS3D：端到端前馈3D全景分割
Abstract
This paper introduces EPS3D, a new end-to-end feed-forward framework for open-vocabulary 3D panoptic segmentation. Unlike existing methods relying on additional preprocessing, we design an end-to-end architecture, with a distillation-based training strategy on diverse 3D scenes to predict 3D-aware semantic and instance features from multi-view images, improving 3D consistency and avoiding error accumulation. We further propose a mutual enhancement module to enforce inherent semantic-instance consistency. By aligning semantics within instances (Ins2Sem) and refining instance features with semantic guidance (Sem2Ins), we achieve more coherent 3D scene understanding. Ultimately, EPS3D outperforms SOTA baselines on two benchmarks (e.g., +13% mIoU for semantics on Replica) with high efficiency (e.g., 1s per scene), supporting tasks like robotic manipulation and 3D scene editing.
Chinese Translation
本文介绍了EPS3D，一种用于开放词汇3D全景分割的新型端到端前馈框架。与现有依赖额外预处理的方法不同，我们设计了一种端到端架构，采用基于蒸馏的训练策略，在多样化的3D场景中预测来自多视图图像的3D感知语义和实例特征，从而提高3D一致性并避免错误累积。我们进一步提出了一种互相增强模块，以强化固有的语义-实例一致性。通过在实例内对齐语义（Ins2Sem）和利用语义指导精炼实例特征（Sem2Ins），我们实现了更连贯的3D场景理解。最终，EPS3D在两个基准测试上超越了当前最先进的基线（例如，在Replica上语义的mIoU提高了13%），且具有高效性（例如，每个场景1秒），支持机器人操作和3D场景编辑等任务。
cs.CV / 135 / 2606.09009
Scaling by Diversified Experience for Vision-Language-Action Models
通过多样化经验扩展视觉-语言-行动模型
Abstract
Vision-Language-Action models face significant challenges in real-world deployment due to the entanglement of high-level reasoning with low-level control, and the instability of policy optimization. In this paper, we introduce SyVLA, a robust VLA model trained with diversified experiences. We propose an Intention Decoupling algorithm to isolate control-relevant features from reasoning contexts and a similar-sample guided RL pipeline to stabilize policy updates and mitigate distribution shift. Extensive experiments on real-world robotic tasks and multi-modal benchmarks demonstrate that SyVLA achieves superior task success rates and stronger out-of-distribution generalization compared to existing methods, while effectively preserving core vision-language capabilities. Codes and Datasets is released on \href{https://sy-vla.github.io/}{project page}.
Chinese Translation
视觉-语言-行动模型在实际部署中面临重大挑战，这主要源于高层推理与低层控制的纠缠以及策略优化的不稳定性。本文介绍了SyVLA，一种通过多样化经验训练的稳健VLA模型。我们提出了一种意图解耦算法，用于将与控制相关的特征与推理上下文隔离，并采用相似样本引导的强化学习（RL）管道来稳定策略更新并减轻分布偏移。在真实机器人任务和多模态基准上的大量实验表明，与现有方法相比，SyVLA在任务成功率和分布外泛化能力上表现出更优越的性能，同时有效保留核心的视觉-语言能力。代码和数据集已在项目页面发布。
cs.CV / 136 / 2606.09028
ATM: Action-Consistency Transfer Matrix for Diagnosing and Improving Latent World Models
ATM：用于诊断和改善潜在世界模型的动作一致性转移矩阵
Abstract
Latent world models are increasingly used for control and goal-conditioned planning, yet assessing whether their learned representations are useful for planning usually requires slow, planner-coupled simulator evaluation with CEM or similar planners. Such evaluation is black-box and model-complexity-dependent: under the same protocol, different world models may require minutes to hours per checkpoint. In this work, we propose ATM, an Action-Consistency Transfer Matrix for diagnosing whether latent transitions preserve action semantics relevant to planning. ATM compares action information in real encoded transitions and model-predicted transitions through lightweight post-hoc probes, producing an interpretable matrix that reveals representation quality, transition-domain inconsistency, and failure modes without simulator rollout. It can also be collapsed into a simple screening score for within-task ranking across checkpoints, variants, and world models. When the true success gap is non-trivial, ATM achieves highly reliable pairwise ranking, while reducing minutes-to-hours CEM evaluation to seconds-level transition analysis, yielding more than 100x speedup in our setup. We further introduce AITS, showing that action-identifiability is not only diagnostic but also a useful training signal for improving downstream planning without changing the planner.
Chinese Translation
潜在世界模型在控制和目标条件规划中越来越多地被使用，但评估其学习到的表示是否对规划有用通常需要与规划器耦合的模拟器评估，这一过程缓慢且依赖于模型复杂性：在相同的协议下，不同的世界模型可能需要几分钟到几个小时才能完成每个检查点的评估。在本研究中，我们提出了ATM（Action-Consistency Transfer Matrix），一种用于诊断潜在转变是否保留与规划相关的动作语义的工具。ATM通过轻量级的事后探测比较真实编码转变和模型预测转变中的动作信息，生成一个可解释的矩阵，揭示表示质量、转变域不一致性和失败模式，而无需模拟器的回滚。它还可以简化为一个简单的筛选得分，用于在检查点、变体和世界模型之间进行任务内排名。当真实成功差距不容忽视时，ATM能够实现高度可靠的成对排名，同时将几分钟到几个小时的CEM评估缩短到秒级转变分析，在我们的设置中实现了超过100倍的加速。我们进一步引入AITS，表明动作可识别性不仅是诊断工具，也是改善下游规划的有用训练信号，而无需更改规划器。
cs.CV / 137 / 2606.09029
Frequency Decoupled Framework for Screen Content Image Super-Resolution
频率解耦框架用于屏幕内容图像超分辨率
Abstract
Methods based on implicit neural representations have demonstrated superior performance in Screen Content Image Super-Resolution (SCISR) . However, they overlooked the inherent frequency characteristics, leading to suboptimal performance. We propose a frequency decoupled framework (FDF) that rethinks SCISR from a phasor perspective by capturing structured energy in amplitude and relational continuity in phase, and jointly exploiting them with bespoke implicit representations to faithfully recover the regular textures and global configuration of Screen Content Image (SCI). Amplitude-Phase Factorization Network (APFN) first separates images into amplitude and phase streams, where Amplitude Clustering Module (ACM) organizes sparse yet high-energy amplitude responses into representative prototypes for periodic pattern extraction, while Phase Consistency Self-Attention (PCSA) progressively reinforces configuration through continuous consistency propagation. And Oscillation-Anharmonic Implicit Fitting Network (OAIF-Net) integrates periodic and coherent implicit representations for efficient exploitation of the periodic patterns and coherent context embedded in SCI. Experimental results show FDF achieves state-of-the-art SCISR performance at multiple scales across four public SCI datasets. Ablation experiments further demonstrate the effectiveness of each component in extracting and exploiting periodic patterns and coherent context.
Chinese Translation
基于隐式神经表示的方法在屏幕内容图像超分辨率（SCISR）中表现出优越的性能。然而，它们忽视了固有的频率特性，导致性能不尽如人意。我们提出了一种频率解耦框架（FDF），从相量的角度重新思考SCISR，通过捕捉幅度中的结构化能量和相位中的关系连续性，并与定制的隐式表示共同利用，忠实地恢复屏幕内容图像（SCI）的规则纹理和全局配置。幅度-相位分解网络（APFN）首先将图像分离为幅度和相位流，其中幅度聚类模块（ACM）将稀疏但高能量的幅度响应组织成代表性原型以提取周期性模式，而相位一致性自注意力（PCSA）通过持续一致性传播逐步增强配置。振荡-非谐隐式拟合网络（OAIF-Net）整合周期性和一致的隐式表示，以高效利用嵌入在SCI中的周期性模式和一致上下文。实验结果表明，FDF在四个公共SCI数据集的多个尺度上实现了最先进的SCISR性能。消融实验进一步证明了每个组件在提取和利用周期性模式及一致上下文方面的有效性。
cs.CV / 138 / 2606.09033
CRANE: Knowledge Editing for Reasoning MLLMs
CRANE：推理多模态大语言模型的知识编辑
Abstract
The emergence of reasoning multimodal large language models (MLLMs), which generate explicit chain-of-thought (CoT) reasoning before producing answers, has introduced a new challenge for knowledge editing: methods that appear successful under traditional metrics (teacher-forcing accuracy up to 100%) can fail severely when the model's reasoning process is examined (Grounded Success as low as 0%). We identify three failure modes: (1) Structural Collapse, where weight-modifying methods destroy the CoT format; (2) Cognitive Dissonance, where the model's reasoning chain actively rejects the injected edit fact based on visual evidence; and (3) Shallow Internalization, where methods succeed on exact queries but fail on rephrase or multi-hop variants. On reasoning MLLMs, these modes interact: methods that generalize (FT, LoRA) trigger format collapse, while methods without deep modification cannot generalize. To expose these failures, we propose a CoT-aware evaluation protocol and construct ReasonEdit-Bench, with conflict stratification, multi-level probes, and multi-hop portability tests. We propose CRANE, a retrieval-augmented framework that requires no per-edit parameter modification. CRANE combines a modality-aware dual-library retrieval system with a two-phase training strategy: Supervised Fine-Tuning (SFT) for structural initialization, followed by GRPO with a Cognitive Routing Reward that trains the model to arbitrate between visual priors and injected edit facts. On ReasonEdit-Bench, CRANE achieves 96.9% Grounded Success on conflict scenarios and 96.9% intermediate entity usage in multi-hop chains, with 97.6% text-locality and 68.1% image-locality Edit Independence. On the out-of-distribution MMEVOKE benchmark, CRANE reaches 87.0% under gold retrieval.
Chinese Translation
推理多模态大语言模型（MLLMs）的出现，使其在生成答案之前生成明确的思维链（CoT）推理，带来了知识编辑的新挑战：在传统指标下（教师强制准确率高达100%）看似成功的方法，在模型的推理过程被审视时可能会严重失败（基础成功率低至0%）。我们识别出三种失败模式：（1）结构崩溃，权重修改方法破坏了CoT格式；（2）认知失调，模型的推理链基于视觉证据主动拒绝注入的编辑事实；（3）浅层内化，方法在精确查询上成功，但在改述或多跳变体上失败。在推理MLLMs中，这些模式相互作用：能够泛化的方法（如FT、LoRA）会触发格式崩溃，而没有深度修改的方法则无法泛化。为了揭示这些失败，我们提出了一种CoT感知的评估协议，并构建了ReasonEdit-Bench，包含冲突分层、多级探测和多跳可移植性测试。我们提出了CRANE，一个不需要每次编辑参数修改的检索增强框架。CRANE结合了一个模态感知的双库检索系统和两阶段训练策略：首先进行结构初始化的监督微调（SFT），然后是带有认知路由奖励的GRPO，训练模型在视觉先验和注入编辑事实之间进行仲裁。在ReasonEdit-Bench上，CRANE在冲突场景中实现了96.9%的基础成功率和96.9%的多跳链中间实体使用率，文本局部性达到97.6%，图像局部性达到68.1%的编辑独立性。在分布外的MMEVOKE基准上，CRANE在黄金检索下达到了87.0%。
cs.CV / 139 / 2606.09034
Leveraging NeRF-Rendered Images for 3D Gaussian Splatting
利用NeRF渲染图像进行3D高斯点云渲染
Abstract
Neural radiance field (NeRF) and 3D Gaussian splatting (3DGS) are two mainstream approaches for novel view synthesis. They often show complementary performance, i.e., 3DGS demonstrating faster rendering speed and NeRF demonstrating higher rendering quality. Motivated by this, we propose leveraging NeRF-rendered images for 3DGS. Specifically, we target street scenes and utilize a pre-trained street-specific NeRF method to produce training images for a target 3DGS method. In our 3DGS training, NeRF-rendered images are used to remove transient objects in street-level input views and to generate bird's-eye views as additional views, inheriting the higher-quality rendering of NeRF into 3DGS. We further incorporate a diffusion-based image enhancement to improve the image quality of the additional views. Experimental results on one synthetic and two real datasets demonstrate that our proposed method improves street-scene rendering while preserving the speed of 3DGS and the quality of NeRF.
Chinese Translation
神经辐射场（NeRF）和3D高斯点云渲染（3DGS）是新视图合成的两种主流方法。它们通常表现出互补的性能，即3DGS展示了更快的渲染速度，而NeRF则展现了更高的渲染质量。基于此，我们提出利用NeRF渲染的图像来提升3DGS的效果。具体而言，我们针对街景场景，利用预训练的街景特定NeRF方法生成目标3DGS方法的训练图像。在我们的3DGS训练中，使用NeRF渲染的图像去除街景输入视图中的瞬态物体，并生成鸟瞰视图作为附加视图，将NeRF的高质量渲染继承到3DGS中。我们进一步结合基于扩散的图像增强技术，以提高附加视图的图像质量。在一个合成数据集和两个真实数据集上的实验结果表明，我们提出的方法在提升街景渲染的同时，保持了3DGS的速度和NeRF的质量。
cs.CV / 140 / 2606.09056
MilliVid: Hierarchical Latents for Long-Range Consistency in Video Generation
MilliVid：用于视频生成中的长程一致性的层次潜变量
Abstract
Video generative models have become increasingly powerful, but long-range consistency remains challenging to achieve because even a few dozen frames require impractically long transformer sequence lengths. We show that this issue can be mitigated by generating video using coarse-to-fine rollout within a multi-scale token space. Our approach is simple: first, we pre-train an autoencoder that compresses each frame into a hierarchy of tokens, with levels ranging from the typical latent resolution to only a handful of tokens per frame. The coarsest levels capture the most consequential information, such as scene layout and semantics, while finer levels add high-frequency appearance and texture. Then, we train a video diffusion model to generate these tokens using coarse-to-fine rollout. By carefully controlling the level of detail at which frames are generated and used as context during each rollout step, we are able to preserve long-range consistency in geometry and object permanence while spending less compute on the long-range consistency of less perceptually relevant details. We validate this approach using a custom dataset of long Minecraft videos, where it produces substantially more consistent rollouts compared to existing baselines.
Chinese Translation
视频生成模型变得越来越强大，但实现长程一致性仍然具有挑战性，因为即使是几十帧也需要不切实际的长变换序列长度。我们展示了通过在多尺度标记空间中使用粗到细的展开生成视频，可以缓解这个问题。我们的方法很简单：首先，我们预训练一个自编码器，将每帧压缩为一个层次的标记，层次范围从典型的潜在分辨率到每帧仅几个标记。最粗的层捕捉到最重要的信息，例如场景布局和语义，而更细的层则增加高频外观和纹理。然后，我们训练一个视频扩散模型，通过粗到细的展开生成这些标记。通过仔细控制生成帧的细节级别，并在每个展开步骤中将其用作上下文，我们能够在几何形状和物体持久性方面保持长程一致性，同时在较少感知相关细节的长程一致性上减少计算开销。我们使用一个自定义的长Minecraft视频数据集验证了这种方法，与现有基准相比，它产生了显著更一致的展开。
cs.CV / 141 / 2606.09064
See More, Think Deeper: Query-Expanded Visual Evidence and Answer-Clue Guided Reflection for Long Video Understanding
看得更多，思考更深入：基于查询扩展的视觉证据和答案线索引导反思的长视频理解
Abstract
Recent advances in Video Large Language Models (Video-LLMs) have enabled performance on long-video understanding tasks. However, existing methods still face two key limitations: evidence acquisition often relies on a single search intent, and answer generation lacks an effective visual feedback mechanism. To address these limitations, we propose \textbf{CoVER}, a Comprehensive Visual Evidence and Reflection framework for long-video understanding. CoVER enables Video-LLMs to \textbf{See More} by dynamically gathering query-expanded visual evidence, and \textbf{Think Deeper} by verifying draft answers with effective answer-specific visual feedback. Together, these mechanisms shift long-video understanding from answer-centric generation to evidence-centric and visually verifiable reasoning. Experimental results show that CoVER-7B substantially outperforms models with the same parameter scale and even surpasses state-of-the-art closed-source models on certain metrics.
Chinese Translation
近年来，视频大型语言模型（Video-LLMs）的进展使得长视频理解任务的性能得以提升。然而，现有方法仍面临两个关键限制：证据获取通常依赖于单一的搜索意图，而答案生成缺乏有效的视觉反馈机制。为了解决这些限制，我们提出了 extbf{CoVER}，一个用于长视频理解的综合视觉证据与反思框架。CoVER通过动态收集查询扩展的视觉证据，使视频大型语言模型能够 extbf{看得更多}，并通过有效的答案特定视觉反馈验证草拟答案，使其能够 extbf{思考更深入}。这两种机制共同将长视频理解的重点从以答案为中心的生成转向以证据为中心且可视化可验证的推理。实验结果表明，CoVER-7B在同等参数规模的模型中表现显著优于其他模型，甚至在某些指标上超越了最先进的闭源模型。
cs.CV / 142 / 2606.09074
REFINE: Super-efficient 3D Gaussian Splatting Pruning via Rendering-Free Primitive Importance
REFINE：通过无渲染原始重要性进行超高效的3D高斯点云修剪
Abstract
Existing pruning methods for 3D Gaussian splatting (3DGS) suffer from either severe quality degradation or prohibitive computational overhead. In this paper, we propose REFINE, a highly accelerated 3DGS pruning framework centered on a novel rendering-free primitive importance metric. Our approach leverages an analytically approximated, rendering-aware Hessian field to quantify the expected perceptual error induced by the removal of individual primitives. By modeling the joint modulation of visibility, projection geometry and the content adaptive hyperparameter, we entirely bypass costly forward rendering passes and derive an anisotropic perceptual weight field that serves as a high-fidelity proxy for primitive importance. Extensive experiments across multiple benchmark datasets demonstrate that REFINE maintains highly competitive rendering quality while achieving an unprecedented $3,000\times$ reduction in pruning-related computational complexity compared to state-of-the-art pruning methods.
Chinese Translation
现有的3D高斯点云修剪（3DGS）方法要么遭受严重的质量下降，要么面临高昂的计算开销。本文提出了REFINE，一个高度加速的3DGS修剪框架，基于一种新颖的无渲染原始重要性度量。我们的方法利用一种解析近似的、考虑渲染的Hessian场来量化因移除单个原始体而引起的预期感知误差。通过建模可见性、投影几何和内容自适应超参数的联合调制，我们完全绕过了昂贵的前向渲染过程，并推导出一种各向异性的感知权重场，作为原始重要性的高保真代理。大量在多个基准数据集上的实验表明，REFINE在保持高度竞争的渲染质量的同时，相较于最先进的修剪方法，实现了前所未有的$3,000 imes$的修剪相关计算复杂度降低。
cs.CV / 143 / 2606.09076
Beyond Scalar Rewards by Internalizing Reasoning into Score Distributions
超越标量奖励：将推理内化到评分分布中
Abstract
Reward models are central to text-to-image post-training, but visual preference is subjective and better represented as a distribution over rubric scores than as a deterministic scalar. Existing scalar, score-token, and pairwise reward models over-compress uncertainty and fine-grained score differences, while reasoning-based generative rewards provide stronger judgments but are costly to deploy and difficult to use as direct optimization signals. We propose Z-Reward, a teacher-student reward modeling framework that decouples reasoning-heavy judgment from efficient reward deployment. The teacher is a large VLM that uses reasoning to infer rubric-aligned score distributions, and is trained with Group-wise Direct Score Optimization (GDSO), which combines policy-gradient rewards from distribution expectations with direct pointwise and pairwise supervision on score distributions and score gaps. The student is trained with Reasoning-Internalized Score Distillation (RISD), which transfers the teacher's reasoning-conditioned score distribution into a compact VLM without requiring explicit reasoning chains at inference time. On our internally annotated evaluation set, the 27B GDSO teacher reaches 89.6% human preference accuracy, outperforming SFT, RewardDance, and GRPO, while the 9B RISD student reaches 88.6%, outperforming the OPD baseline and closely matching the larger teacher. We further show that Z-Reward can serve as a differentiable reward signal for text-to-image optimization, yielding a 41.3% net human-preference improvement over the SFT baseline.
Chinese Translation
奖励模型在文本到图像的后训练中至关重要，但视觉偏好是主观的，更适合用评分分布来表示，而非确定性的标量。现有的标量、评分标记和成对奖励模型过度压缩了不确定性和细粒度的评分差异，而基于推理的生成奖励提供了更强的判断能力，但在部署上成本高且难以作为直接优化信号使用。我们提出了Z-Reward，一个教师-学生奖励建模框架，将重推理的判断与高效的奖励部署解耦。教师是一个大型视觉语言模型（VLM），利用推理推断与评分标准对齐的评分分布，并通过组内直接评分优化（GDSO）进行训练，该方法将来自分布期望的策略梯度奖励与对评分分布和评分差距的直接逐点和成对监督相结合。学生通过推理内化评分蒸馏（RISD）进行训练，将教师的推理条件评分分布转移到一个紧凑的VLM中，而无需在推理时显式推理链。在我们内部注释的评估集上，27B GDSO教师达到了89.6%的人工偏好准确率，超越了SFT、RewardDance和GRPO，而9B RISD学生达到了88.6%，超越了OPD基线，并与更大的教师模型接近匹配。我们进一步展示了Z-Reward可以作为文本到图像优化的可微分奖励信号，相较于SFT基线，带来了41.3%的净人工偏好提升。
cs.CV / 144 / 2606.09081
Edge-Constrained UAV Small-Object Detection with P2 Enhancement and Quantum-Inspired Lightweight Structure Search
边缘约束下的无人机小物体检测：P2增强与量子启发式轻量结构搜索
Abstract
Unmanned aerial vehicle (UAV) object detection requires compact detectors that retain small-object details under onboard computation and memory constraints. Repeated downsampling inlightweight networks weakens shallow spatial information, while manually adding attention orfusion modules may increase cost without stable gains. This study analyzes YOLOX-Nano underedge-deployment constraints by combining a P2 high-resolution detection branch with a quantum-inspired evolutionary algorithm (QIEA) for lightweight structure screening. The search space isdefined by lightweight priority and task specificity, and the evaluation jointly considers accuracy,floating-point operations (FLOPs), latency, memory consumption, and recall. On VisDrone, theP2 branch increases APamall by 31.10% over the YOLOX-Nano baseline. Compared with NanoDet-Plus with similar model size, YOLOX-Nano+-P2 improves APs0.ss by 17.5% and APamal by 44.9%.The QIEA-selected candidate obtains the highest Recallso, but +P2 remains the strongest AP-oriented variant after full training. Full 100-epoch verification of Random-best, GA-best, andSA/QUBO-best candidates further shows that proxy rankings do not necessarily transfer to finalAPse9s. These results support using P2 as the main small-object enhancement path and QIEA as alightweight tool for candidate screening and accuracy-cost analysis. The source code, configurationfiles, diagnostic scripts, and summarized results are available at https://github.com/Ming23233/UAV-QIEA-Edge-Detection
Chinese Translation
无人机（UAV）物体检测需要在计算和内存限制下保持小物体细节的紧凑检测器。轻量网络中的重复下采样削弱了浅层空间信息，而手动添加注意力或融合模块可能会增加成本而没有稳定的收益。本研究通过将P2高分辨率检测分支与量子启发式进化算法（QIEA）结合，分析了在边缘部署约束下的YOLOX-Nano，以进行轻量结构筛选。搜索空间由轻量优先级和任务特异性定义，评估共同考虑准确性、浮点运算（FLOPs）、延迟、内存消耗和召回率。在VisDrone数据集上，P2分支使得APsmall比YOLOX-Nano基线提高了31.10%。与相似模型大小的NanoDet-Plus相比，YOLOX-Nano+-P2提高了APs0.ss 17.5%和APsmall 44.9%。QIEA选择的候选模型获得了最高的Recallso，但在完全训练后，+P2仍然是最强的以AP为导向的变体。对Random-best、GA-best和SA/QUBO-best候选模型进行的完整100个周期验证进一步表明，代理排名不一定会转移到最终的APse9s。这些结果支持将P2作为主要的小物体增强路径，并将QIEA作为轻量工具用于候选筛选和准确性-成本分析。源代码、配置文件、诊断脚本和汇总结果可在https://github.com/Ming23233/UAV-QIEA-Edge-Detection获取。
cs.CV / 145 / 2606.09109
Driving Video Retrieval for Complex Queries with Structured Grounding
基于结构化基础的复杂查询驾驶视频检索
Abstract
Video retrieval at scale is central to data curation and safety validation in autonomous driving, where users want to find not only scenes but also dynamic events such as cut-ins and hard braking. Existing vision-language and keyword-based retrieval methods often miss these events because the relevant motion may not be explicitly described in text or captured by lexical overlap. Rule-based retrieval can encode such events more directly, but it is brittle: generated or hand-written rules often fail when their assumptions do not match real driving data. We propose STRIVE-D, a data-calibrated retrieval framework for driving videos. It uses weakly labeled in-domain videos to estimate when a query rule is reliable, adapt rules that mismatch observed data, and fuse calibrated rule scores with vision-language and keyword-based retrieval signals. Across three driving benchmarks, including newly released human-annotated event data on DrivingDojo, STRIVE-D delivers up to 84% relative improvement in top-1 accuracy over state-of-the-art methods.
Chinese Translation
大规模视频检索在自动驾驶的数据管理和安全验证中至关重要，用户希望不仅能够找到场景，还能找到动态事件，如插入和急刹车。现有的视觉-语言和基于关键词的检索方法往往无法捕捉这些事件，因为相关的运动可能没有在文本中明确描述或通过词汇重叠捕获。基于规则的检索可以更直接地编码这些事件，但其脆弱性较大：生成或手动编写的规则在其假设与真实驾驶数据不匹配时往往会失败。我们提出了STRIVE-D，一个针对驾驶视频的数据校准检索框架。它利用弱标注的领域内视频来估计查询规则的可靠性，调整与观察数据不匹配的规则，并将校准的规则得分与视觉-语言和基于关键词的检索信号融合。在三个驾驶基准测试中，包括新发布的DrivingDojo上的人类标注事件数据，STRIVE-D在顶级准确率上相较于最先进的方法实现了高达84%的相对提升。
cs.CV / 146 / 2606.09110
HDRAgent: An Agentic Framework for Multi-Exposure HDR Imaging
HDRAgent：一种用于多曝光HDR成像的代理框架
Abstract
Most existing multi-exposure HDR methods follow a fixed feed-forward reconstruction paradigm, making them prone to ghosting artifacts in complex dynamic scenes. To address this issue, we propose HDRAgent, the first agent-driven framework for HDR imaging, which adaptively selects reconstruction strategies according to the current scene conditions. Specifically, to provide scene-specific prior knowledge, we introduce a fine-grained contextual knowledge matching (FCM) module. This module leverages multimodal large language model (MLLM)-derived scene perception to retrieve relevant historical cases and tool knowledge, organizing them into structured evidence for MLLM-based adaptive tool scheduling. In addition, we propose a perception--distortion feedback mechanism that transforms post-execution quality assessment and artifact diagnosis into structured feedback, which is accumulated in historical memory to help subsequent contextual knowledge refinement and strategy selection. Furthermore, considering that extreme motion can invalidate alignment methods, we design an agent-guided generative alignment strategy that uses MLLM-based dynamic-region parsing to reconstruct unreliable contents in non-reference frames under reference-frame guidance. Experiments demonstrate that HDRAgent effectively reduces ghosting and local artifacts while achieving competitive or superior objective performance and visual quality.
Chinese Translation
现有的大多数多曝光HDR方法遵循固定的前馈重建范式，使其在复杂动态场景中容易出现鬼影伪影。为了解决这一问题，我们提出了HDRAgent，这是第一个基于代理的HDR成像框架，它根据当前场景条件自适应选择重建策略。具体而言，为了提供场景特定的先验知识，我们引入了一种细粒度上下文知识匹配（FCM）模块。该模块利用多模态大型语言模型（MLLM）派生的场景感知来检索相关的历史案例和工具知识，并将其组织成结构化证据，以便进行基于MLLM的自适应工具调度。此外，我们提出了一种感知-失真反馈机制，将执行后的质量评估和伪影诊断转化为结构化反馈，这些反馈被积累在历史记忆中，以帮助后续的上下文知识精炼和策略选择。此外，考虑到极端运动可能使对齐方法失效，我们设计了一种代理引导的生成对齐策略，该策略利用基于MLLM的动态区域解析，在参考帧指导下重建非参考帧中的不可靠内容。实验表明，HDRAgent有效减少了鬼影和局部伪影，同时在客观性能和视觉质量上实现了具有竞争力或更优的表现。
cs.CV / 147 / 2606.09111
Illumination-Invariant Anomaly Detection for Sub-Canopy UAV Multispectral Point Clouds
光照不变的亚冠层无人机多光谱点云异常检测
Abstract
Unmanned Aerial Vehicle (UAV) multispectral point clouds (MPC) provide high-dimensional spatial-spectral data for sub-canopy target detection; however, their efficacy is significantly compromised by severe illumination heterogeneity caused by vegetation shadows. To address this, we propose a prior-free anomaly detection framework capable of robustly handling lighting variations. First, we formulate solar angle estimation as an inverse optimization problem. By coupling spectral indices with a ray-tracing model, this strategy achieves Prior-Free Shadow Extraction without relying on flight metadata, effectively distinguishing dark objects from true shadows. Second, to mitigate spectral distortions, we introduce an Illumination-Consistent Sparse Representation mechanism. Unlike standard reconstruction methods, we construct a background dictionary strictly from neighbors sharing the same illumination state. This constraint effectively disentangles spectral reflectance from lighting variations, ensuring that targets are represented solely by physically consistent background points. Experimental results indicate that the proposed method significantly improves the separability between anomalies and background in complex forest environments, demonstrating superior performance over state-of-the-art baselines. This framework is particularly suited for identifying camouflaged military targets, mapping fallen tree trunks, and uncovering archaeological ruins hidden beneath dense foliage.
Chinese Translation
无人机（UAV）多光谱点云（MPC）为亚冠层目标检测提供了高维空间-光谱数据；然而，由于植被阴影造成的严重光照异质性，其有效性受到显著影响。为了解决这一问题，我们提出了一种无先验的异常检测框架，能够稳健地处理光照变化。首先，我们将太阳角度估计公式化为一个逆优化问题。通过将光谱指数与光线追踪模型相结合，该策略实现了无先验阴影提取，无需依赖飞行元数据，有效地区分暗物体与真实阴影。其次，为了减轻光谱失真，我们引入了一种光照一致的稀疏表示机制。与标准重建方法不同，我们严格从共享相同光照状态的邻域构建背景字典。该约束有效地将光谱反射率与光照变化解耦，确保目标仅由物理一致的背景点表示。实验结果表明，所提方法显著提高了复杂森林环境中异常与背景之间的可分离性，表现优于最先进的基线方法。该框架特别适用于识别伪装的军事目标、绘制倒伏树干以及揭示隐藏在浓密植被下的考古遗址。
cs.CV / 148 / 2606.09123
An Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification
一种增强的几何-光谱特征学习框架用于空中多光谱点云分类
Abstract
Multispectral point cloud (MPC) is composed of 3D spatial-spectral information, which holds tremendous potential for accurate land-cover classification. However, the representation power of classification models is limited by inherent high-dimensional and heterogeneous spatial-spectral information, unbalanced sample distribution, and inter-class spectral similarity of airborne MPCs. We build two MPC datasets and propose an enhanced geometric-spectral feature learning framework based on attentions for airborne MPC classification. A key component in our model is a two-stream feature fusion method with attention mechanisms, which enhances the representation capability of spatial-spectral features from high-dimensional heterogeneous MPCs. The first stream aims to extract position-encoded global spectral features with fusion self-attention, and the second stream comprises a multikernel point convolution and feature aggregation attention to extract spectral-guided geometric features. We then develop a residual attention fusion block to integrate the most informative geometric-spectral features from the two parallel streams. Another important contribution of this work is a joint loss function to improve the learning ability on unbalanced and interclass similar samples. Experimental results on two airborne MPC datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods. Furthermore, the codes and datasets used in this paper will be made available freely at https://github.com/HITlixian/TGRS_GSFF.
Chinese Translation
多光谱点云（MPC）由三维空间-光谱信息组成，具有进行准确土地覆盖分类的巨大潜力。然而，分类模型的表征能力受到固有的高维和异质空间-光谱信息、不平衡样本分布以及空中MPC间类光谱相似性的限制。我们构建了两个MPC数据集，并提出了一种基于注意力机制的增强几何-光谱特征学习框架，用于空中MPC分类。我们模型中的一个关键组件是具有注意力机制的双流特征融合方法，它增强了来自高维异质MPC的空间-光谱特征的表征能力。第一个流旨在通过融合自注意力提取位置编码的全局光谱特征，第二个流则包括多核点卷积和特征聚合注意力，以提取光谱引导的几何特征。然后，我们开发了一个残差注意力融合块，以整合来自两个并行流的最具信息量的几何-光谱特征。本工作的另一个重要贡献是一个联合损失函数，以提高对不平衡和类间相似样本的学习能力。在两个空中MPC数据集上的实验结果表明，与最先进的方法相比，所提出的方法具有有效性。此外，本文中使用的代码和数据集将免费提供，网址为 https://github.com/HITlixian/TGRS_GSFF。
cs.CV / 149 / 2606.09139
A Geometric Framework for Absolute Pose and Velocity Estimation with Event Cameras
基于几何框架的绝对姿态和速度估计方法：事件相机的应用
Abstract
Despite the rapid advancements in event-based motion estimation, current geometric methods primarily focus on velocity estimation. However, absolute pose estimation, which is equally crucial for key applications such as robotic navigation and augmented reality, remains relatively underexplored. Consequently, the simultaneous recovery of absolute pose and velocity from event streams remains an open and challenging problem. To address this gap, we propose a geometric framework for absolute pose and velocity estimation by leveraging 3D lines in the scene and the events they trigger. At the core of the framework lie two key geometric constraints: the orthogonality between a 3D line and the normal vector of its corresponding event plane, and the collinearity of an event with the 2D projection of its associated line. Based on these constraints, we present both linear and polynomial solvers for absolute pose estimation. The former enables efficient computation, while the latter provides a globally optimal solution for rotation. For velocity estimation, we develop an efficient linear solver and a more accurate optimization-based solver to recover both angular and linear velocities. Notably, our methods require a minimum of three event-line correspondences to determine the 6-DoF absolute pose or velocities independently. Extensive experiments in simulation and on real-world datasets demonstrate that our methods achieve state-of-the-art performance, with significant improvements in accuracy and computational efficiency compared to existing methods. The demo code is publicly available at https://github.com/Zibin6/EventPoseVelocity.
Chinese Translation
尽管基于事件的运动估计技术迅速发展，但目前的几何方法主要集中在速度估计上。然而，绝对姿态估计同样对机器人导航和增强现实等关键应用至关重要，但仍然相对未被充分探索。因此，从事件流中同时恢复绝对姿态和速度仍然是一个开放且具有挑战性的问题。为了解决这一空白，我们提出了一种基于几何框架的绝对姿态和速度估计方法，利用场景中的三维线条及其触发的事件。该框架的核心是两个关键几何约束：三维线条与其对应事件平面的法向量之间的正交性，以及事件与其关联线的二维投影之间的共线性。基于这些约束，我们提出了线性和多项式求解器来进行绝对姿态估计。前者实现了高效计算，而后者则提供了旋转的全局最优解。对于速度估计，我们开发了一种高效的线性求解器和一种更准确的基于优化的求解器，以恢复角速度和线速度。值得注意的是，我们的方法至少需要三个事件-线对应关系，以独立确定6自由度的绝对姿态或速度。广泛的仿真实验和真实数据集的测试表明，我们的方法在准确性和计算效率上显著优于现有方法，达到了最先进的性能。演示代码可在 https://github.com/Zibin6/EventPoseVelocity 获取。
cs.CV / 150 / 2606.09140
DiffSight-Former: Modeling Structural Differences and Temporal Dynamics for Glaucoma Progression Prediction
DiffSight-Former：建模青光眼进展预测中的结构差异和时间动态
Abstract
Glaucoma is a leading cause of irreversible blindness worldwide, and early detection from fundus images is critical for effective disease management. While deep learning has achieved promising performance in fundus image analysis, most existing methods rely on single time-point images and fail to capture longitudinal structural and vascular changes associated with disease progression. Sequential fundus images acquired during clinical follow-up provide valuable temporal information; however, current sequential models often struggle to detect subtle early progression signals and commonly depend on fixed-length inputs or diagnostic cues from already glaucomatous images, limiting their clinical utility for early prediction. To address these limitations, we propose DiffSight-Former, a framework for glaucoma progression prediction from sequential fundus images. It incorporates a time-variant feature extraction module based on a fundus-specific foundation model to obtain robust anatomical representations. A multi-structure difference modeling module is introduced to quantify progression-related changes in the optic disc/cup region and retinal vasculature. These representations are integrated with temporal interval embeddings and processed by a time-aware Transformer to model disease progression and estimate the probability of future glaucoma onset. Experiments were conducted on two longitudinal datasets, SIGF (405 sequences) and GRAPE (263 sequences). On SIGF, DiffSight-Former achieved an AUC of 91.54% and a sensitivity of 92.16% for progression prediction. On GRAPE, it achieved an average accuracy of 87.48% across three clinical visual-field progression criteria. Compared with existing approaches, DiffSight-Former demonstrates strong performance and robustness across different temporal settings, highlighting its potential for longitudinal glaucoma monitoring and early risk prediction.
Chinese Translation
青光眼是全球不可逆失明的主要原因，早期从眼底图像中检测至关重要以实现有效的疾病管理。尽管深度学习在眼底图像分析中取得了令人鼓舞的成果，但大多数现有方法依赖于单一时间点的图像，未能捕捉与疾病进展相关的纵向结构和血管变化。在临床随访期间获取的连续眼底图像提供了宝贵的时间信息；然而，当前的连续模型通常难以检测到微妙的早期进展信号，并且通常依赖于固定长度的输入或来自已患青光眼图像的诊断线索，限制了其在早期预测中的临床实用性。为了解决这些局限性，我们提出了DiffSight-Former，一个用于从连续眼底图像中预测青光眼进展的框架。该框架结合了基于眼底特定基础模型的时间变特征提取模块，以获取稳健的解剖表示。引入了多结构差异建模模块，以量化与进展相关的视盘/杯区域和视网膜血管的变化。这些表示与时间间隔嵌入相结合，并通过时间感知的Transformer进行处理，以建模疾病进展并估计未来青光眼发作的概率。在两个纵向数据集SIGF（405个序列）和GRAPE（263个序列）上进行了实验。在SIGF上，DiffSight-Former实现了91.54%的AUC和92.16%的敏感性用于进展预测。在GRAPE上，它在三个临床视野进展标准下实现了87.48%的平均准确率。与现有方法相比，DiffSight-Former在不同时间设置下表现出强大的性能和稳健性，突显了其在青光眼纵向监测和早期风险预测中的潜力。
cs.CV / 151 / 2606.09142
Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models
通过视觉语言模型解码行人过马路意图的自我中心视觉研究
Abstract
Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexplored. In this work, we study the decoding of pedestrian crossing intentions from short egocentric video clips. We approach this by formulating the task as a closed-ended visual question answering (VQA) problem and leveraging vision language models (VLMs) to predict the pedestrians' intent. We first benchmark three families of state-of-the-art VLMs in a zero-shot setting, finding that they achieve moderate gains over random guessing but exhibit limited higher-level traffic reasoning. Motivated by these findings, we further adapt VLMs to the target task using parameter-efficient fine-tuning. Our results show that the fine-tuned models substantially outperform their zero-shot counterparts and achieve a 9\% accuracy improvement over a specialized transformer-based baseline. Finally, we demonstrate that incorporating additional contextual cues, including ego motion, vehicle motion, and eye gaze, further improves predictive performance. In particular, the fine-tuned Qwen3-VL-2B model guided by eye gaze and ego motion achieves a 14.5% accuracy improvement over the transformer baseline, establishing a new state of the art for egocentric pedestrian intent decoding.
Chinese Translation
自我中心视觉提供了人类感知和决策的第一人称视角，但其在交通安全预测中的潜力仍未得到充分探索。在本研究中，我们探讨了从短时自我中心视频片段中解码行人过马路意图的方法。我们将这一任务表述为一个封闭式视觉问答（VQA）问题，并利用视觉语言模型（VLMs）来预测行人的意图。我们首先在零样本设置下基准测试了三类最先进的VLM，发现它们在随机猜测的基础上取得了适度的提升，但在更高层次的交通推理方面表现有限。基于这些发现，我们进一步通过参数高效的微调将VLM适应于目标任务。我们的结果表明，经过微调的模型显著优于其零样本对应模型，并在一个专门的基于变压器的基线模型上实现了9%的准确率提升。最后，我们展示了结合额外的上下文线索，包括自我运动、车辆运动和眼动，进一步提升了预测性能。特别是，经过微调的Qwen3-VL-2B模型在眼动和自我运动的指导下，相较于变压器基线实现了14.5%的准确率提升，确立了自我中心行人意图解码的新状态。
cs.CV / 152 / 2606.09143
CAMF-Det: Closure-Aware Multimodal Fusion for LiDAR-Camera 3D Object Detection on UAV Platforms
CAMF-Det：基于闭合感知的多模态融合用于无人机平台上的LiDAR-相机3D目标检测
Abstract
Multimodal 3D object detection based on LiDAR and cameras has demonstrated excellent performance in ground-vehicle scenarios, but has not been explored for Unmanned Aerial Vehicle (UAV) platforms. In UAV top-down scenes, frequent groundobject occlusion dominated by tree canopies causes spatially varying and modality-dependent information degradation. Existing multimodal fusion frameworks neither explicitly model such ground-object occlusion nor embed occlusion awareness into the detection pipeline, limiting their performance in occluded UAV scenes. To address these challenges, we propose CAMF-Det, a closure-aware multimodal fusion framework for LiDAR-camera 3D object detection on UAV platforms, which derives dual-modal occlusion intensity through physics-inspired modeling and embeds them as priors throughout the detection pipeline. First, a dual-modal closure modeling module explicitly constructs occlusion intensity ground truth for both modalities offline via a Beer-Lambert-inspired formulation and building-mask correction. Second, using these ground-truth maps as supervision, a dual-modal prediction network converts the offline modeling results into online occlusion intensity predictions under single-frame inference. Third, both ground-truth and predicted occlusion intensity are injected into data augmentation, feature encoding, multimodal fusion, and detection head, enabling adaptive detection under spatially varying and modality-dependent information degradation. Experiments on two self-built UAV-based multimodal datasets, SI3D-DI and SI3D-DII, demonstrate that CAMF-Det achieves the best performance across all difficulty levels, with hard-level mAP$_{\mathrm{BEV}}$ improvements of 9.43% and 4.88% over the best competing methods, respectively. These results confirm the effectiveness of explicit occlusion prior modeling and exploitation for robust multimodal 3D detection in UAV scenes.
Chinese Translation
基于LiDAR和相机的多模态3D目标检测在地面车辆场景中表现出色，但在无人机（UAV）平台上尚未得到探索。在无人机的俯视场景中，树冠主导的频繁地面物体遮挡导致空间变化和模态依赖的信息退化。现有的多模态融合框架既未明确建模这种地面物体遮挡，也未将遮挡感知嵌入检测流程，限制了其在被遮挡的无人机场景中的性能。为了解决这些挑战，我们提出了CAMF-Det，一个基于闭合感知的多模态融合框架，用于无人机平台上的LiDAR-相机3D目标检测，该框架通过物理启发建模推导出双模态遮挡强度，并将其作为先验嵌入整个检测流程。首先，双模态闭合建模模块通过受Beer-Lambert启发的公式和建筑物掩模校正，离线明确构建了两个模态的遮挡强度真值。其次，使用这些真值图作为监督，双模态预测网络将离线建模结果转换为单帧推理下的在线遮挡强度预测。第三，将真值和预测的遮挡强度注入数据增强、特征编码、多模态融合和检测头中，使得在空间变化和模态依赖的信息退化下实现自适应检测。在两个自建的基于无人机的多模态数据集SI3D-DI和SI3D-DII上的实验表明，CAMF-Det在所有难度级别中实现了最佳性能，在困难级别上相较于最佳竞争方法，mAP$_{ ext{BEV}}$分别提高了9.43%和4.88%。这些结果确认了明确的遮挡先验建模和利用在无人机场景中进行稳健多模态3D检测的有效性。
cs.CV / 153 / 2606.09150
Ultra Flash: Scaling Real-Time Streaming Video Generation to High Resolutions
超闪：将实时流媒体视频生成扩展至高分辨率
Luxury, Huang, Jie, Fan, Zihao, Ma, Xiaoxiao, Li, Yuming, Zhuang, Jun-hao, Xue, Zeyue, Fu, Siming, Li, Haoran, Zhong, Mingchen, Zhang, Guohui, Ma, Shichen, Liu, Yijun, Shi, Jiaqi, Ma, Yanwen, Su, Yaofeng, Wang, Haoyu, Li, Yaowei, Zhang, Songchun, Jin, Weiyang, Bian, Yuxuan, Zhang, Shiyi, Xu, Haojun, Lu, Shuai, Han, Xin, Tang, Wei, Huang, Haoyang, Duan, Nan
Abstract
While recent autoregressive video diffusion models achieve remarkable streaming quality, they remain confined to low resolutions (e.g., 480P), leaving efficient, scalable, real-time high-resolution video generation a fundamental open challenge. To bridge this gap, we present Ultra Flash, a cascaded streaming framework capable of real-time high-resolution video generation. Ultra Flash achieves ~30 FPS at 1K resolution and ~18 FPS at 2K resolution on a single GPU through three key contributions: (1) an architecture-preserving T2V-to-TV2V super-resolution training paradigm coupled with an AIGC-oriented data degradation pipeline that effectively preserves the generative capability of the base model, enabling enhanced high-resolution detail when cascaded after mainstream low-resolution generative models; (2) a causal streaming latent upsampler paired with a high-resolution decoder, which enhances spatiotemporal coherence while enabling efficient latent spatial scaling and precise high-resolution decoding with negligible computational overhead; and (3) a cascade high-resolution streaming video generation optimization scheme that first performs hybrid-reward-enhanced sparse causalization and single-step distillation of the super-resolution model, then introduces cascaded streaming self-forcing preference optimization with dynamic cache management, jointly enhancing overall coherence, improving quality, and enabling real-time high-resolution streaming video generation. Extensive experiments demonstrate that Ultra Flash reliably produces ultra-high-resolution streaming video while maintaining state-of-the-art visual quality and superior efficiency.
Chinese Translation
尽管近期的自回归视频扩散模型在流媒体质量上取得了显著进展，但仍然局限于低分辨率（例如，480P），使得高效、可扩展的实时高分辨率视频生成成为一个基本的开放挑战。为了解决这一问题，我们提出了超闪（Ultra Flash），一个能够实时生成高分辨率视频的级联流媒体框架。超闪在单个GPU上以约30帧每秒（FPS）的速度在1K分辨率下运行，并在2K分辨率下以约18 FPS的速度运行，主要得益于三个关键贡献：（1）一种架构保持的T2V到TV2V超分辨率训练范式，结合面向AIGC（人工智能生成内容）的数据降解管道，有效地保留了基础模型的生成能力，使得在主流低分辨率生成模型之后级联时能够增强高分辨率细节；（2）一种因果流媒体潜在上采样器与高分辨率解码器的配对，增强了时空一致性，同时实现了高效的潜在空间缩放和精确的高分辨率解码，几乎没有计算开销；（3）一种级联高分辨率流媒体视频生成优化方案，首先对超分辨率模型进行混合奖励增强稀疏因果化和单步蒸馏，然后引入级联流媒体自强偏好优化与动态缓存管理，联合提升整体一致性、改善质量，并实现实时高分辨率流媒体视频生成。大量实验表明，超闪能够可靠地产生超高分辨率流媒体视频，同时保持最先进的视觉质量和卓越的效率。
cs.CV / 154 / 2606.09156
OmniGen-AR: AutoRegressive Any-to-Image Generation
OmniGen-AR：自回归任意到图像生成
Abstract
Autoregressive (AR) models have demonstrated strong potential in visual generation, offering superior performance with simple architectures and optimization objectives. However, existing methods are typically limited to single-modality conditions, e.g., text, restricting their applicability in real-world scenarios that demand image synthesis from diverse controls. In this work, we present OmniGen-AR, a unified autoregressive framework for Any-to-Image generation. By discretizing various visual conditions through a shared visual tokenizer and text prompts with a text tokenizer, OmniGen-AR supports a broad spectrum of conditional inputs within a single model, including text (text-to-image generation), spatial signals (segmentation-to-image and depth-to-image), and visual context (image editing, frame prediction, and text-to-video generation). To mitigate the risk of information leakage from condition tokens to content tokens, we introduce Disentangled Causal Attention (DCA), which separates the full-sequence causal mask into condition causal attention and content causal attention. It serves as a training-time regularizer without affecting the standard next-token prediction during inference. With this design, OmniGen-AR achieves new state-of-the-art or at least competitive results across a range of benchmark, e.g., 0.63 on GenEval and 80.02 on VBench, demonstrating its effectiveness in flexible and high-fidelity visual generation.
Chinese Translation
自回归（AR）模型在视觉生成方面展现了强大的潜力，凭借简单的架构和优化目标提供了卓越的性能。然而，现有方法通常局限于单一模态条件，例如文本，这限制了它们在需要从多样控制中合成图像的现实场景中的适用性。在本研究中，我们提出了OmniGen-AR，一个统一的自回归框架，用于任意到图像的生成。通过使用共享的视觉标记器对各种视觉条件进行离散化，以及使用文本标记器对文本提示进行处理，OmniGen-AR支持在单一模型中广泛的条件输入，包括文本（文本到图像生成）、空间信号（分割到图像和深度到图像）以及视觉上下文（图像编辑、帧预测和文本到视频生成）。为了减轻条件标记到内容标记的信息泄露风险，我们引入了解耦因果注意力（DCA），将完整序列的因果掩码分离为条件因果注意力和内容因果注意力。它作为训练时的正则化器，不影响推理过程中的标准下一个标记预测。凭借这一设计，OmniGen-AR在一系列基准测试中实现了新的最先进或至少具有竞争力的结果，例如在GenEval上取得0.63，在VBench上达到80.02，展示了其在灵活和高保真视觉生成中的有效性。
cs.CV / 155 / 2606.09162
Zero-Parameter Geometric Gating for Temporally Stable Low-Altitude UAV Video Semantic Segmentation
零参数几何门控用于时间稳定的低空无人机视频语义分割
Abstract
Video semantic segmentation for low-altitude UAVs requires temporal consistency, yet dense optical flow introduces spatially structured noise in the planar regions that dominate aerial imagery. We propose a zero-parameter geometric gate that uses RANSAC homography inlier ratios on a $16\times16$ spatial grid to route each region to either homography or optical flow warp before fusion via Semantic Similarity Propagation. The gate requires no learned parameters -- only a median-threshold binary decision on RANSAC statistics -- adding only 211K trainable parameters (the SSP fusion layer) to a frozen backbone. On synthetic UAVid, the method achieves +4.24--4.91\% mIoU improvement over base models across two architectures (SegFormer-b2 and Hiera-S+UPerNet). Mechanism diagnostics reveal that flow residuals in planar regions are spatially autocorrelated (Moran's I = 0.32, $p < 0.001$), predict boundary instability (Spearman $\rho = 0.66$), and that rigidification recovers temporal consistency from 62\% to 92\% (+29.5pp) in homography-valid regions.
Chinese Translation
低空无人机的视频语义分割需要时间一致性，但密集的光流在主导航空图像的平面区域中引入了空间结构噪声。我们提出了一种零参数几何门控，该门控利用RANSAC单应性内点比率在$16 imes16$空间网格上将每个区域路由到单应性或光流变形，然后通过语义相似性传播进行融合。该门控不需要学习参数，仅需对RANSAC统计数据进行中位数阈值的二元决策，向冻结的主干网络添加了仅211K的可训练参数（SSP融合层）。在合成的UAVid数据集上，该方法在两种架构（SegFormer-b2和Hiera-S+UPerNet）上相比基础模型实现了+4.24--4.91\%的mIoU提升。机制诊断表明，平面区域中的光流残差具有空间自相关性（Moran's I = 0.32，$p < 0.001$），预测边界不稳定性（Spearman $
ho = 0.66$），并且刚性化使单应性有效区域的时间一致性从62\%恢复到92\\%（+29.5pp）。
cs.CV / 156 / 2606.09167
Vision-Language Guided Hyperspectral Object Tracking via Semantics Fusion and Contextual Template Updating
基于视觉-语言引导的高光谱目标跟踪：语义融合与上下文模板更新
Abstract
Hyperspectral object tracking (HOT) leverages the rich spectral information provided by hyperspectral videos (HSVs), offering substantial potential for object tracking. However, efficiently extracting and exploiting spectral information from redundant spectral bands remains a fundamental challenge, which severely limits model generalization and tracking performance. Moreover, in dynamic scenes, targets often experience drastic appearance variations due to factors such as occlusion and illumination changes. These variations lead to large deformations between the current frame and the template. Such discrepancies pose major challenges for existing temporal modeling approaches. In this work, we propose VLHTrack, a novel hyperspectral vision-language (VL) joint tracking framework. Specifically, we incorporate language priors to address the fundamental challenge of spectral redundancy by designing a Language-Guided Band Selection Module (LBSM). By leveraging Large Language Model (LLM) descriptions, LBSM establishes a semantic-to-spectral mapping that mitigates redundancy and accentuates discriminative spectral features. A Multi-Modal Vision-Language Fusion Module is then employed to seamlessly integrate visual and linguistic embeddings, harnessing their complementary advantages to learn coherent cross-modal representations. To address target deformation in long-term sequences, we propose a dynamic update template feature strategy implemented via the Dynamic Template Update with Mamba (DTUM) module. By leveraging selective state space modeling, DTUM learns inter-frame dependencies to update template feature, ensuring efficient template feature evolution guided by temporal context. Experiments on HOT2023 and HOT2024 demonstrate that VLHTrack outperforms state-of-the-art (SOTA) methods.
Chinese Translation
高光谱目标跟踪（HOT）利用高光谱视频（HSV）提供的丰富光谱信息，为目标跟踪提供了巨大的潜力。然而，从冗余光谱波段中有效提取和利用光谱信息仍然是一个基本挑战，这严重限制了模型的泛化能力和跟踪性能。此外，在动态场景中，由于遮挡和光照变化等因素，目标的外观往往会经历剧烈的变化。这些变化导致当前帧与模板之间存在较大的变形。这种差异对现有的时序建模方法构成了重大挑战。在本研究中，我们提出了VLHTrack，一种新颖的高光谱视觉-语言（VL）联合跟踪框架。具体而言，我们通过设计语言引导的波段选择模块（LBSM）来引入语言先验，以解决光谱冗余的基本挑战。通过利用大型语言模型（LLM）的描述，LBSM建立了语义到光谱的映射，从而减轻冗余并强调具有区分性的光谱特征。接着，采用多模态视觉-语言融合模块无缝整合视觉和语言嵌入，利用它们的互补优势学习一致的跨模态表示。为了应对长期序列中的目标变形，我们提出了一种动态更新模板特征策略，通过动态模板更新与Mamba（DTUM）模块实现。通过利用选择性状态空间建模，DTUM学习帧间依赖关系以更新模板特征，确保在时间上下文引导下高效演化模板特征。在HOT2023和HOT2024上的实验表明，VLHTrack的性能优于最先进的方法（SOTA）。
cs.CV / 157 / 2606.09180
Claude Code-Driving Scenario Mining for the Argoverse 2 Challenge
Claude代码驱动的Argoverse 2挑战场景挖掘
Abstract
We present our submission to the CVPR 2026 Argoverse 2 Scenario Mining Challenge. Our system uses a four-stage pipeline: (1) autonomous code generation via a Claude Code agent powered by GLM~5.1, (2) iterative training set screening with Timestamp Balanced Accuracy threshold 0.8 to curate few-shot examples, (3) semantic code review by a separate Claude Code session, and (4) Qwen3-VL scene-level verification to filter false positives. We report results on the Argoverse 2 test set.
Chinese Translation
我们提交了针对CVPR 2026 Argoverse 2场景挖掘挑战的方案。我们的系统使用四阶段流程：(1) 通过由GLM~5.1驱动的Claude Code代理进行自主代码生成，(2) 使用时间戳平衡准确率阈值0.8进行迭代训练集筛选，以策划少量示例，(3) 由单独的Claude Code会话进行语义代码审查，以及(4) 使用Qwen3-VL进行场景级验证，以过滤假阳性。我们报告了在Argoverse 2测试集上的结果。
cs.CV / 158 / 2606.09181
Counterfactual Reasoning for Fine-Grained Evidence Disentanglement in VideoQA
用于视频问答中细粒度证据解构的反事实推理
Abstract
Recent advances in video multimodal models have significantly improved VideoQA performance. However, these systems often rely on spurious statistical correlations rather than answer-relevant causal evidence, resulting in unfaithful and brittle reasoning, especially in complex real-world scenarios. Existing methods either rely on cross-modality correlations, costly curated training resources, or insufficient causal assumptions and constraints, and typically operate at the time-interval level. As a result, they fail to explicitly disentangle causal visual cues from confounders and provide limited fine-grained evidence localization. To address this issue, we propose a Counterfactual Reasoning framework for fine-grained Evidence Disentanglement (CREDiT). CREDiT formulates the VideoQA process using a structural causal model and learns cross-modality representations that are explicitly decomposed into causal and non-causal components under independence and minimality constraints. To facilitate faithful disentanglement, we introduce feature-level causal interventions and construct counterfactual inputs that approximate causal effects while suppressing non-causal correlations. Extensive experiments on NExT-GQA, SportsQA, and SPORTU-video demonstrate that CREDiT consistently improves answer accuracy and reasoning reliability across both generic and complex sports scenarios, leading to more trustworthy VideoQA systems.
Chinese Translation
近期视频多模态模型的进展显著提升了视频问答（VideoQA）的性能。然而，这些系统往往依赖于虚假的统计相关性，而非与答案相关的因果证据，导致推理不可靠且脆弱，尤其是在复杂的现实场景中。现有方法要么依赖于跨模态相关性，要么需要昂贵的精心策划的训练资源，或者因果假设和约束不足，通常在时间间隔层面上操作。因此，它们未能明确地将因果视觉线索与混淆因素解构，并提供有限的细粒度证据定位。为了解决这一问题，我们提出了一种用于细粒度证据解构的反事实推理框架（Counterfactual Reasoning for fine-grained Evidence Disentanglement，CREDiT）。CREDiT使用结构因果模型来表述视频问答过程，并学习在独立性和最小性约束下，明确分解为因果和非因果成分的跨模态表示。为了促进可靠的解构，我们引入了特征级因果干预，并构建了近似因果效应的反事实输入，同时抑制非因果相关性。在NExT-GQA、SportsQA和SPORTU-video上的大量实验表明，CREDiT在一般和复杂的体育场景中始终提高了答案准确性和推理可靠性，从而导致更可信的视频问答系统。
cs.CV / 159 / 2606.09187
CP4D: Compositional Physics-aware 4D Scene Generation
CP4D：组合物理感知的4D场景生成
Abstract
4D generation (\textit{i.e.}, dynamic 3D generation) has recently emerged as a rapidly growing research frontier due to its powerful spatiotemporal modeling capabilities. However, despite notable advances, existing approaches typically fail to capture the underlying physical principles, producing results that are both physically inconsistent and visually implausible. To overcome this limitation, we present CP4D, a novel paradigm for photorealistic 4D scene synthesis with faithful adherence to complex physical dynamics. Drawing inspiration from the compositional nature of real-world scenes, where immutable static backgrounds coexist with dynamic, physically plausible foregrounds, CP4D reformulates 4D generation as the integration of a static 3D environment with physically grounded dynamic objects. On this basis, our framework follows a three-stage pipeline: \textbf{1)} Firstly, we leverage pre-trained expert models to generate high-fidelity 3D representations of the environment and foreground objects respectively. \textbf{2)} Subsequently, to produce physically plausible trajectories and realistic interactions for these objects, we propose a hybrid motion synthesis strategy that integrates priors from physical simulators with the common sense embedded in video diffusion models. \textbf{3)} Finally, we develop an automated composition mechanism that seamlessly fuses the static environment and dynamic objects into coherent, physically consistent 4D scenes. Extensive experiments demonstrate that CP4D can generate explorable and interactive 4D scenes with high visual fidelity, strong physical plausibility, and fine-grained controllability, significantly outperforming existing methods. The project page: https://anonymous.4open.science/w/CP4D/.
Chinese Translation
4D生成（即动态3D生成）近年来作为一个快速发展的研究前沿，因其强大的时空建模能力而受到关注。然而，尽管已有显著进展，现有方法通常未能捕捉到潜在的物理原理，导致生成的结果在物理上不一致且视觉上不可信。为克服这一局限，我们提出了CP4D，一种新颖的光真实感4D场景合成范式，忠实遵循复杂的物理动态。CP4D受到现实世界场景组合特性的启发，其中不变的静态背景与动态、物理上可信的前景共存，CP4D将4D生成重新定义为静态3D环境与物理基础动态物体的结合。在此基础上，我们的框架遵循三阶段流程： extbf{1)} 首先，我们利用预训练的专家模型分别生成环境和前景物体的高保真3D表示。 extbf{2)} 随后，为了为这些物体生成物理上可信的轨迹和现实的交互，我们提出了一种混合运动合成策略，将物理模拟器的先验与视频扩散模型中嵌入的常识相结合。 extbf{3)} 最后，我们开发了一种自动组合机制，将静态环境和动态物体无缝融合为一致且物理上合理的4D场景。大量实验表明，CP4D能够生成可探索和交互的4D场景，具有高视觉保真度、强物理可信度和细粒度的可控性，显著优于现有方法。项目页面：https://anonymous.4open.science/w/CP4D/
cs.CV / 160 / 2606.09208
Event-driven dynamic trajectories reconstruction and measurement of mechanical parameters for fragments
基于事件驱动的碎片动态轨迹重建及机械参数测量
Abstract
During warhead detonation, high-density, high-speed, and mutually occluded fragments are generated. Their mechanical parameters (position, velocity, kinetic energy) directly determine the lethality of the warhead fragment field. However, high-intensity flash and smoke in detonation scenarios severely hinder the accurate acquisition of these mechanical parameters. To address this challenge, this paper integrates experimental mechanics approaches and presents an event-driven method for reconstructing the dynamic trajectories of fragments and measuring their mechanical parameters. As a novel brain-inspired visual sensor, event cameras offer microsecond-level temporal resolution and high dynamic range lighting change perception, overcoming the difficulty of accurately measuring high-speed targets under strong flash interference. The method constructs a multi-event-camera vision system, adopting three geometric constraints: time-correlated epipolar constraint to find potential matching event point pairs, and trifocal tensor line constraint plus local homography constraint to eliminate mismatches. A comprehensive probability model is established, with entropy weight method determining the weight of each constraint's probability to quantitatively filter mismatches. 3D trajectory reconstruction is achieved via spatial line-line intersection and nonlinear optimization. Finally, the velocity and kinetic energy of the fragments are calculated based on the reconstructed trajectory. This method provides reliable technical support for the mechanical damage evaluation of warhead fragment fields and the tactical protection design.
Chinese Translation
在弹头引爆过程中，会产生高密度、高速度且相互遮挡的碎片。这些碎片的机械参数（位置、速度、动能）直接决定了弹头碎片场的致命性。然而，引爆场景中的高强度闪光和烟雾严重阻碍了这些机械参数的准确获取。为了解决这一挑战，本文结合实验力学方法，提出了一种事件驱动的方法，用于重建碎片的动态轨迹并测量其机械参数。作为一种新型的类脑视觉传感器，事件相机提供微秒级的时间分辨率和高动态范围的光照变化感知，克服了在强闪光干扰下准确测量高速目标的困难。该方法构建了一个多事件相机视觉系统，采用三种几何约束：时间相关的极线约束用于寻找潜在匹配的事件点对，三焦张量线约束加局部单应性约束用于消除不匹配。建立了一个综合概率模型，利用熵权法确定每个约束概率的权重，以定量过滤不匹配。通过空间线-线交点和非线性优化实现了三维轨迹重建。最后，根据重建的轨迹计算碎片的速度和动能。该方法为弹头碎片场的机械损伤评估和战术防护设计提供了可靠的技术支持。
cs.CV / 161 / 2606.09218
Minimal Solvers for Full-DoF Motion Estimation from Asynchronous Differential SfM
基于异步差分结构光的全自由度运动估计的最小求解器
Abstract
As a bio-inspired intelligent sensor, event cameras have introduced a new paradigm in the intelligent perception of spatiotemporal information and visual motion estimation, characterized by their high temporal resolution, low latency, and minimal power consumption. However, their asynchronous data streams present significant challenges to traditional synchronous, frame-based algorithms. To address these challenges, this paper presents a novel framework for full degree of freedom (DoF) egomotion estimation directly from asynchronous optical flow, specifically targeting the joint recovery of angular and linear velocities. We decouple the differential epipolar constraint into distinct angular and linear velocity components, and derive its formulation for asynchronous data. Based on this formulation, an optimization algorithm is developed that enables full-DoF egomotion estimation leveraging at least five points. Furthermore, by applying a first-order approximation to rotational dynamics, we transform the constraint equations into a polynomial form, resulting in the first algebraic minimal 5-point solver for this formulation. To ensure real-time performance in high-speed scenarios, we additionally propose an accelerated solver achieved by truncating high-order angular velocity terms. Extensive evaluations on both synthetic and real-world datasets demonstrate that the asynchronous approach outperforms traditional synchronous methods, particularly in its accuracy and robustness to spatiotemporal noise. We believe that this work establishes a critical foundation for efficient and accurate continuous-time motion estimation in high-speed robotics applications.
Chinese Translation
作为一种仿生智能传感器，事件相机在时空信息的智能感知和视觉运动估计中引入了一种新范式，其特点是高时间分辨率、低延迟和最小功耗。然而，它们的异步数据流对传统的同步帧基算法提出了重大挑战。为了解决这些挑战，本文提出了一种新颖的框架，旨在直接从异步光流中进行全自由度（DoF）自运动估计，特别针对角速度和线速度的联合恢复。我们将微分极线约束解耦为独立的角速度和线速度分量，并推导出其在异步数据下的公式。基于该公式，开发了一种优化算法，使得至少利用五个点实现全自由度自运动估计。此外，通过对旋转动力学应用一阶近似，我们将约束方程转化为多项式形式，从而得到了该公式的第一个代数最小五点求解器。为了确保在高速场景中的实时性能，我们还提出了一种通过截断高阶角速度项实现的加速求解器。在合成和真实世界数据集上的广泛评估表明，异步方法在准确性和对时空噪声的鲁棒性方面优于传统的同步方法。我们相信，这项工作为高速度机器人应用中的高效和准确的连续时间运动估计奠定了重要基础。
cs.CV / 162 / 2606.09219
Semi-supervised Source Detection in Astronomical Images: New Benchmark and Strong Baseline
天文图像中的半监督源检测：新的基准和强基线
Abstract
Source detection in modern observational astronomy is a cornerstone for localizing and identifying stellar sources accurately. It is crucial for studies such as stellar population synthesis and cosmological parameter estimation. However, the characteristics of astronomical images, including high density, the effect of point spread functions and low signal-to-noise ratios, significantly challenge the latest advanced object detectors. Besides, fully-supervised detection methods are hardly practical, due to the significant difficulty in annotating dense, small, and faint sources in astronomical images. To tackle the scarcity of astronomical datasets, we introduce a new comprehensive benchmark (LAMOST-DET), comprising 18,400 astronomical images and 728,898 source instances. Upon the dataset, we further devise a novel semi-supervised learning framework coined Nova Teacher, capable of detecting dense sources effectively given sparse annotations. It integrates source light enhancement module, confidence-guided pseudo-supervision, and cross-view complementary mining in a dual-teacher paradigm. Extensive experiments on LAMOST-DET show that, Nova Teacher consistently improves previous competitors by 4.04% and 5.22% mAP under two semi-supervised settings. Additionally, our method competes against other detectors on a natural image dataset, validating its generalization ability to various scenarios. The source code is available at https://github.com/AcWiz/NovaTeacher.
Chinese Translation
现代观测天文学中的源检测是准确定位和识别恒星源的基石。这对于恒星种群合成和宇宙学参数估计等研究至关重要。然而，天文图像的特征，包括高密度、点扩散函数的影响以及低信噪比，显著挑战了最新的先进目标检测器。此外，由于在天文图像中注释密集、小型和微弱源的难度较大，完全监督的检测方法几乎不可行。为了解决天文数据集的稀缺问题，我们引入了一个新的综合基准（LAMOST-DET），包含18,400幅天文图像和728,898个源实例。在该数据集上，我们进一步设计了一种新颖的半监督学习框架，命名为Nova Teacher，能够在稀疏注释的情况下有效检测密集源。它集成了源光增强模块、基于置信度的伪监督和双教师范式中的交叉视图互补挖掘。在LAMOST-DET上的大量实验表明，Nova Teacher在两个半监督设置下，始终比之前的竞争者提高了4.04%和5.22%的mAP。此外，我们的方法在自然图像数据集上与其他检测器进行了竞争，验证了其在各种场景中的泛化能力。源代码可在https://github.com/AcWiz/NovaTeacher获取。
cs.CV / 163 / 2606.09243
EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video
EgoTactile：从自我中心视频中学习日常物体的抓握压力
Abstract
Estimating full-hand grasp pressure from egocentric video is critical for immersive VR and robotic manipulation, yet dense tactile sensing often relies on intrusive hardware. Existing vision-based methods predominantly rely on planar surfaces or fingertip contacts, failing to generalize to complex 3D object interactions. Therefore, we introduce EgoTactile, a benchmark pairing egocentric video with full-hand pressure supervision for diverse everyday objects, incorporating a bare-hand transfer subset to enable generalization to natural scenarios. Leveraging this benchmark, we first establish EgoPressureFormer as a discriminative baseline. Beyond this, to explicitly address the uncertainty in partial observations, we propose EgoPressureDiff, a conditional diffusion framework that adapts a large-scale pre-trained video diffusion backbone. By combining rich world knowledge priors with a Physically-Informed Feature Rectification layer to inject semantic constraints, our approach effectively infers plausible contact patterns and resolves visual-physical ambiguities. Extensive experiments demonstrate that our method achieves superior performance on the benchmark and robust transferability to in-the-wild scenarios. Our project page is available at https://egotactile.github.io/.
Chinese Translation
从自我中心视频中估计全手抓握压力对于沉浸式虚拟现实和机器人操作至关重要，但密集的触觉传感通常依赖于侵入式硬件。现有的基于视觉的方法主要依赖于平面表面或指尖接触，未能推广到复杂的三维物体交互。因此，我们提出了EgoTactile，这是一个将自我中心视频与全手压力监督相结合的基准，涵盖了一个裸手转移子集，以便于在自然场景中的推广。利用这个基准，我们首先建立了EgoPressureFormer作为一个判别性基线。除此之外，为了明确解决部分观察中的不确定性，我们提出了EgoPressureDiff，这是一个条件扩散框架，适应于大规模预训练的视频扩散骨干网络。通过结合丰富的世界知识先验与物理信息特征校正层以注入语义约束，我们的方法有效推断出合理的接触模式并解决视觉与物理之间的模糊性。大量实验表明，我们的方法在基准测试中表现优越，并在实际场景中具有良好的可转移性。我们的项目页面可访问 https://egotactile.github.io/。
cs.CV / 164 / 2606.09245
Proposal Refinement for Few-Shot Object Detection
少样本目标检测的提案优化
Abstract
Few-shot object detection has gained widely attention in recent years. Some excellent algorithms have been proposed to handle this task. However, most of these algorithms rely on the performance of few-shot classification. Unlike previous attempts, our work focuses on the problem of unbalanced distribution of region proposals between the novel classes and the base classes. In order to alleviate this unbalanced distribution, we propose the proposal refinement approach for different training phases. Specifically, refinement loss is designed for the base training phase to enhance sensitivity of the model to novel classes, and refinement branch is introduced as an auxiliary branch for RPN (Region Proposal Networks) to generate more novel proposals in the fine-tuning phase. By rebalancing the proposal distribution, the proposed approach outperforms the baselines methods by roughly 1\%$\sim$6\% on current benchmarks without increasing any inference time. Through extensive experiments, we prove that we establish a new state-of-the-art method for the few-shot object detection task.
Chinese Translation
近年来，少样本目标检测引起了广泛关注。为处理这一任务，提出了一些优秀的算法。然而，这些算法大多数依赖于少样本分类的性能。与以往的尝试不同，我们的工作关注于新类别与基础类别之间区域提案分布不平衡的问题。为了缓解这种不平衡分布，我们提出了针对不同训练阶段的提案优化方法。具体而言，针对基础训练阶段设计了优化损失，以增强模型对新类别的敏感性，并引入优化分支作为区域提案网络（RPN，Region Proposal Networks）的辅助分支，以在微调阶段生成更多的新提案。通过重新平衡提案分布，所提方法在当前基准测试中相较于基线方法提高了约1 ext{%}至6 ext{%}的性能，且没有增加任何推理时间。通过大量实验，我们证明了我们建立了一种新的最先进的少样本目标检测方法。
cs.CV / 165 / 2606.09246
SOMA: From Surface Observations to Muscle Anatomy
SOMA：从表面观察到肌肉解剖
Abstract
With the growing demand for realistic virtual humans, parametric body models have become a cornerstone of modern medicine, sports, and entertainment applications. However, most of these models are inherently limited: they only capture the 3D surface of the skin, offering no insight into the complex bio-mechanical structures that generate motion. As more applications expand towards biomechanics, the need for virtual human models that go beyond the skin has become increasingly evident. Traditional soft-tissue simulations, such as FEM, are accurate but non-scalable and too computationally expensive for most common applications. Alternatively, existing biomechanical tools can simulate muscular forces and activations, but do not model changes in external shape, restricting how activations correlate with actual observable anatomy. This motivates a novel inverse research problem: recovering muscle deformations directly from visible surface observations - i.e., from the skin, and thus the pose. In this work, we present SOMA (from Surface Observations to Muscle Anatomy), a person-specific model that infers spatio-temporal muscle behavior from surface signals obtained using RGB cameras, and SKIM, a subject-specific soft-tissue deformation dataset. To the best of our knowledge, this is the first method that attempts to recover muscle deformations from multi-view RGB data. We show how our method provides anatomically grounded animations without the complexity of traditional simulations, leading to a scalable and cost-effective solution. Data and code are available.
Chinese Translation
随着对逼真虚拟人类需求的不断增长，参数化身体模型已成为现代医学、体育和娱乐应用的基石。然而，这些模型大多存在固有的局限性：它们仅捕捉皮肤的三维表面，无法深入了解产生运动的复杂生物机械结构。随着更多应用向生物力学扩展，超越皮肤的虚拟人类模型的需求变得愈加明显。传统的软组织模拟方法，如有限元法（FEM），虽然准确，但不具可扩展性，并且对于大多数常见应用来说计算成本过高。相对而言，现有的生物力学工具可以模拟肌肉的力量和激活，但无法建模外部形状的变化，从而限制了激活与实际可观察解剖之间的相关性。这促使我们提出一个新的逆向研究问题：直接从可见的表面观察（即皮肤）恢复肌肉变形，从而获取姿态。在本研究中，我们提出了SOMA（从表面观察到肌肉解剖），这是一个特定于个体的模型，能够从使用RGB相机获得的表面信号中推断时空肌肉行为，以及SKIM，一个特定于个体的软组织变形数据集。据我们所知，这是首个尝试从多视角RGB数据中恢复肌肉变形的方法。我们展示了我们的方法如何在不需要传统模拟复杂性的情况下提供解剖学基础的动画，从而实现可扩展且具有成本效益的解决方案。数据和代码可供获取。
cs.CV / 166 / 2606.09248
Temporal-Aware Reasoning Optimization for Video Temporal Grounding
视频时间定位的时间感知推理优化
Abstract
Multi-modal Large Language Models (MLLMs) have achieved remarkable progress in video temporal grounding with reinforcement learning for generating reasoning paths. However, existing models often produce superficial reasoning, which offers limited guidance for precise temporal localization. This limitation stems from (1) inefficient random exploration and (2) reward functions that focus solely on the answer correctness while ignoring reasoning quality. To address these issues, we propose TaRO (Temporal-Aware Reasoning Optimization), a framework that explicitly enhances the model's ability of thinking with time. First, we introduce a Constructive Reasoning Exploration that leverages pre-generated dense captions to construct reasoning paths grounded in explicit visual cues and timestamps, enabling efficient exploration of high-quality time-aware reasoning. Second, to evaluate reasoning quality, we design a Temporal-Sensitivity Reward. High-quality reasoning should be anchored to specific events and timestamps. If the event boundary under thinking is disrupted, such reasoning should become invalid, leading to a drop in the logit of the reasoning path. We utilize this drop as a critique of reasoning quality. Finally, TaRO follows a progressive curriculum, which starts by utilizing this reward to select better constructed reasoning paths, and evolves to a free exploration phase where the model autonomously generates effective reasoning. Experiments demonstrate that TaRO achieves state-of-the-art performance on VTG benchmarks. Code is available at https://github.com/oceanflowlab/TaRO.
Chinese Translation
多模态大型语言模型（MLLMs）在视频时间定位方面通过强化学习生成推理路径取得了显著进展。然而，现有模型往往产生表面化的推理，这对精确的时间定位提供了有限的指导。这一局限性源于（1）低效的随机探索和（2）奖励函数仅关注答案的正确性而忽视推理质量。为了解决这些问题，我们提出了TaRO（时间感知推理优化），一个明确增强模型时间思维能力的框架。首先，我们引入了一种构造性推理探索，利用预生成的密集字幕构建基于明确视觉线索和时间戳的推理路径，从而实现高质量时间感知推理的高效探索。其次，为了评估推理质量，我们设计了一种时间敏感奖励。高质量的推理应锚定于特定事件和时间戳。如果思考中的事件边界被打破，该推理应变得无效，从而导致推理路径的logit下降。我们利用这一下降作为对推理质量的批评。最后，TaRO遵循渐进式课程，首先利用该奖励选择更好构建的推理路径，然后演变为自由探索阶段，在此阶段模型自主生成有效推理。实验表明，TaRO在VTG基准测试中达到了最先进的性能。代码可在 https://github.com/oceanflowlab/TaRO 获取。
cs.CV / 167 / 2606.09249
MAGIS: Evidence-Based Multi-Agent Reasoning for Interpretable Strabismus Clinical Decision-Making
MAGIS：基于证据的多智能体推理用于可解释的斜视临床决策制定
Abstract
Strabismus is a common ocular disorder that requires fine-grained subtype diagnosis for individualized treatment planning. However, existing deep learning methods mainly provide diagnostic predictions without transparent reasoning, while recent large vision-language models (LVLMs), although promising for joint image understanding and report generation, remain highly prone to hallucination in this evidence-sensitive and rule-driven medical task. To address these challenges, we propose MAGIS, an evidence-based Multi-AGent reasoning for Interpretable Strabismus diagnosis framework. MAGIS transforms black-box end-to-end generation into a structured diagnostic process consisting of candidate hypothesis generation, dual-evidence constrained context, evidence-based corrective verification, and report generation. Specifically, we introduce a Dual-Evidence Constrained Context (DECC) mechanism that jointly organizes visual evidence from the photograph of the nine cardinal positions of gaze and evidence-based clinical diagnostic rules into a constrained context for reliable diagnostic reasoning. We further develop an Evidence-Based Corrective Verification (EBCV) mechanism that verifies whether the current diagnostic hypothesis is supported by visual evidence, heatmap-based visual cues, and evidence-based clinical diagnostic rules. Hypothesis refinement is triggered when inconsistency is detected. Experiments on a fine-grained strabismus benchmark demonstrate that MAGIS not only significantly outperforms other state-of-the-art diagnostic systems, improving the weighted F1 score from 72.0% to 91.3%, but also substantially improves the clinical reliability (consistency, alignment, and completeness) of generated diagnostic reports. These results demonstrate that MAGIS provides an effective solution for building accurate, evidence-based, and clinically interpretable strabismus diagnosis systems.
Chinese Translation
斜视是一种常见的眼科疾病，需要细致的亚型诊断以制定个性化的治疗方案。然而，现有的深度学习方法主要提供诊断预测，而缺乏透明的推理过程；虽然近期的大型视觉-语言模型（LVLMs）在联合图像理解和报告生成方面展现出良好的前景，但在这一对证据敏感且规则驱动的医疗任务中，仍然容易出现幻觉。为了解决这些挑战，我们提出了MAGIS，一个基于证据的多智能体推理框架，用于可解释的斜视诊断。MAGIS将黑箱的端到端生成转变为一个结构化的诊断过程，包括候选假设生成、双证据约束上下文、基于证据的纠正验证和报告生成。具体而言，我们引入了双证据约束上下文（DECC）机制，该机制将来自九个主要注视位置照片的视觉证据与基于证据的临床诊断规则共同组织成一个约束上下文，以实现可靠的诊断推理。我们进一步开发了基于证据的纠正验证（EBCV）机制，用于验证当前诊断假设是否得到视觉证据、基于热图的视觉线索和基于证据的临床诊断规则的支持。当检测到不一致时，将触发假设的细化。在一个细粒度的斜视基准测试中的实验表明，MAGIS不仅显著优于其他最先进的诊断系统，将加权F1得分从72.0%提高到91.3%，而且显著提高了生成的诊断报告的临床可靠性（一致性、对齐性和完整性）。这些结果表明，MAGIS为构建准确、基于证据且临床可解释的斜视诊断系统提供了有效的解决方案。
cs.CV / 168 / 2606.09250
LiteVSR: Lightweight Adaptation of Frozen Diffusion Transformers for Video Super-Resolution
LiteVSR：冻结扩散变换器在视频超分辨率中的轻量级适应
Abstract
Adapting large-scale pre-trained video generators for Video Super-Resolution (VSR) in novel domains remains computationally prohibitive. Methods that reformulate generation as direct Low-Quality to High-Quality mappings deviate from the original generative formulation, demanding extensive fine-tuning. ControlNet-style adapters lose their efficiency under modern Diffusion Transformers since the absence of encoder-decoder hierarchy forces duplication of the entire backbone. We observe that flow matching offers a principled alternative for cross-domain VSR adaptation. By predicting a constant velocity field across all timesteps, the adaptation task reduces to learning a fixed injection pattern rather than time-varying transformations. Building on this insight, we propose LiteVSR, a minimalist framework that performs VSR using a completely frozen Diffusion Transformer with a lightweight State-Aware Adapter. The adapter employs a dual-stream architecture that extracts static structural cues from the LQ input and dynamic cues from intermediate denoising states, aligning them through time-dependent cross-attention to enable adaptive transition from structural alignment to texture refinement as denoising proceeds. LiteVSR achieves competitive restoration quality with only 11.25% trainable parameters and 12 GPU-hours of training on a single A100, while maintaining fast sampling (down to a single step) compatibility.
Chinese Translation
在新领域中将大规模预训练视频生成器适应于视频超分辨率（VSR）仍然在计算上具有挑战性。将生成过程重新定义为低质量到高质量映射的方法偏离了原始生成公式，要求进行大量的微调。ControlNet风格的适配器在现代扩散变换器下效率降低，因为缺乏编码器-解码器层次结构迫使整个主干网络的重复。我们观察到流匹配为跨域VSR适应提供了一种原则性的替代方案。通过预测在所有时间步长上的恒定速度场，适应任务简化为学习固定的注入模式，而非时间变化的变换。基于这一见解，我们提出了LiteVSR，一个极简框架，利用完全冻结的扩散变换器和轻量级状态感知适配器进行VSR。该适配器采用双流架构，从低质量输入中提取静态结构线索，并从中间去噪状态中提取动态线索，通过时间依赖的交叉注意力将它们对齐，以便在去噪过程中实现从结构对齐到纹理细化的自适应过渡。LiteVSR以仅11.25%的可训练参数和在单个A100上12个GPU小时的训练时间实现了具有竞争力的恢复质量，同时保持快速采样（降至单步）兼容性。
cs.CV / 169 / 2606.09253
A practical probabilistic framework for deformable image registration uncertainty in radiotherapy dose propagation
用于放射治疗剂量传播的不确定性变形图像配准的实用概率框架
Abstract
Deformable image registration (DIR) is widely used in radiotherapy for dose propagation and accumulation, but uncertainty in the underlying deformation can substantially affect clinically relevant dose estimates. We present a practical probabilistic framework for propagating DIR uncertainty to voxel-wise dose statistics and dose-volume histograms (DVHs). The method models the mapped correspondence at each voxel as a random variable governed by a transparent local certainty map that can be defined by simple safety margins, structure-boundary mismatch, or structure-wise conservative uncertainty values. This yields interpretable quantities such as dose probabilities, expected dose, confidence bounds, and induced DVH envelopes. The framework is designed to remain lightweight and interpretable: it avoids complex biomechanical or ensemble-based uncertainty models and instead emphasizes simple parameterization, computational feasibility, and transparent dose metrics. We further introduce a structure-guided in/out strategy as an optional refinement that restricts mapping probabilities to anatomically plausible target regions. The approach is demonstrated on a prostate radiotherapy case study and used to compare different certainty-map strategies and probability kernels. The experiments show that the certainty-map design has a stronger effect on resulting dose and DVH uncertainty bounds than the specific kernel choice, while the additional benefit of the in/out strategy is case-dependent and modest in the present example. Overall, the proposed framework provides a transparent way to incorporate DIR uncertainty into radiotherapy dose assessment and to study how modelling choices affect propagated dose metrics.
Chinese Translation
变形图像配准（DIR）广泛应用于放射治疗中的剂量传播和累积，但基础变形中的不确定性可能显著影响临床相关的剂量估计。我们提出了一种实用的概率框架，用于将DIR不确定性传播到体素级剂量统计和剂量-体积直方图（DVH）。该方法将每个体素的映射对应建模为一个随机变量，该随机变量由一个透明的局部确定性图控制，该图可以通过简单的安全边界、结构边界不匹配或结构特定的保守不确定性值来定义。这产生了可解释的量，如剂量概率、期望剂量、置信区间和诱导的DVH包络线。该框架旨在保持轻量和可解释性：它避免了复杂的生物力学或基于集成的不确定性模型，而是强调简单的参数化、计算可行性和透明的剂量指标。我们进一步引入了一种结构引导的进出策略，作为一种可选的细化方法，限制映射概率到解剖上合理的目标区域。该方法在前列腺放射治疗案例研究中进行了演示，并用于比较不同的确定性图策略和概率核。实验表明，确定性图的设计对结果剂量和DVH不确定性界限的影响比特定核的选择更强，而进出策略的额外好处在当前示例中是案例依赖的且适度。总体而言，所提出的框架提供了一种透明的方法，将DIR不确定性纳入放射治疗剂量评估，并研究建模选择如何影响传播的剂量指标。
cs.CV / 170 / 2606.09261
Self-supervised Learning Matters: A Simple Ensemble Solution for Micro-Gesture Recognition
自监督学习的重要性：一种简单的微手势识别集成解决方案
Abstract
In this paper, we present XInsight Lab's solution to the micro-gesture classification track of the 4th MiGA Challenge at IJCAI 2026, in which our solution ranked first and achieved a new state-of-the-art result. We propose a multimodal ensemble framework that integrates a self-supervised RGB-based model with supervised multi-stream models from previous solutions. The self-supervised RGB model is pretrained on 120K unlabeled clips via masked video modeling and then fine-tuned on iMiGUE. This simple yet effective RGB baseline achieves 69.224% top-1 accuracy on the iMiGUE test set, demonstrating the benefit of learning transferable representations from unlabeled in-domain videos. By incorporating this model as a complementary branch, the final ensemble reaches 74.419% top-1 accuracy, surpassing the previous state of the art by 1.206 percentage points. Experimental results on iMiGUE, including ablation studies on the ensemble strategy, validate the effectiveness of self-supervised RGB representation learning for micro-gesture recognition.
Chinese Translation
在本文中，我们介绍了XInsight Lab在2026年IJCAI第四届MiGA挑战赛微手势分类赛道上的解决方案，我们的方案获得了第一名，并实现了新的最先进结果。我们提出了一种多模态集成框架，该框架将自监督的基于RGB的模型与之前解决方案中的监督多流模型相结合。自监督的RGB模型通过掩蔽视频建模在120K未标记剪辑上进行预训练，然后在iMiGUE数据集上进行微调。这个简单而有效的RGB基线在iMiGUE测试集上达到了69.224%的top-1准确率，展示了从未标记的领域内视频中学习可迁移表示的好处。通过将该模型作为补充分支纳入，最终集成模型达到了74.419%的top-1准确率，超过了之前的最先进水平1.206个百分点。在iMiGUE上的实验结果，包括对集成策略的消融研究，验证了自监督RGB表示学习在微手势识别中的有效性。
cs.CV / 171 / 2606.09262
See More, Match Better: Multi-Source Feature Fusion for Two-View Correspondence Learning
看得更多，匹配更好：用于双视图对应学习的多源特征融合
Abstract
Two-view correspondence learning aims to distinguish true correspondences (inliers) from false ones (outliers) in image pairs by leveraging their underlying differences. Existing methods mainly rely on coordinate-based geometric consistency. However, they often struggle with pseudo-consistent outliers in scenes containing repetitive structures, textureless regions, or locally similar geometric patterns. To address this limitation, we propose TriMatch, a multi-source feature fusion framework for two-view correspondence learning, which consists of two parts: feature extraction and feature refinement. In feature extraction, TriMatch jointly extracts geometric, texture semantic, and structural semantic features to provide complementary evidence for correspondence discrimination. To bridge the gap between semantic and geometric features, texture and structural semantic features are aligned with geometric features through dedicated Texture-Geometric Alignment and Structural-Geometric Alignment modules, respectively. We further introduce a Semantic-Guided Correspondence Modulation module, which modulates geometric features using semantic information to suppress geometrically plausible but semantically inconsistent correspondences. In feature refinement, a Hierarchical Semantic-Enhanced Correspondence Refinement strategy progressively models correspondence dependencies and recalibrates multi-context feature responses, enabling more reliable inlier-outlier discrimination. Extensive experiments demonstrate the effectiveness, robustness, and generalization capability of TriMatch.
Chinese Translation
双视图对应学习旨在通过利用图像对之间的潜在差异来区分真实对应（内点）和虚假对应（外点）。现有方法主要依赖于基于坐标的几何一致性。然而，在包含重复结构、无纹理区域或局部相似几何模式的场景中，它们往往难以处理伪一致的外点。为了解决这一局限性，我们提出了TriMatch，一个用于双视图对应学习的多源特征融合框架，主要由特征提取和特征精炼两个部分组成。在特征提取中，TriMatch联合提取几何特征、纹理语义特征和结构语义特征，以提供互补证据用于对应区分。为了弥合语义特征与几何特征之间的差距，纹理和结构语义特征通过专门的纹理-几何对齐（Texture-Geometric Alignment）和结构-几何对齐（Structural-Geometric Alignment）模块分别与几何特征对齐。我们进一步引入了语义引导的对应调制（Semantic-Guided Correspondence Modulation）模块，利用语义信息调制几何特征，以抑制几何上合理但语义上不一致的对应。在特征精炼中，分层语义增强对应精炼（Hierarchical Semantic-Enhanced Correspondence Refinement）策略逐步建模对应依赖关系，并重新校准多上下文特征响应，从而实现更可靠的内点-外点区分。大量实验表明，TriMatch的有效性、鲁棒性和泛化能力。
cs.CV / 172 / 2606.09273
EditSSC: Toward Editable Semantic Occupancy Scenes with Unconditional Diffusion Models
EditSSC：基于无条件扩散模型的可编辑语义占用场景
Abstract
3D semantic scene generation is crucial for autonomous driving applications, yet most methods rely on complex 3D-specific architectures such as triplane encoders and adapted diffusion networks, limiting both their simplicity and their editing capabilities. We propose EditSSC, an editing-ready method for 3D semantic scene generation using 2D Bird's Eye View (BEV) representations and off-the-shelf latent diffusion network. Our approach reshapes 3D semantic occupancy grids into multi-channel BEV images and leverages the quantized autoencoder and UNet from Stable Diffusion with minimal modifications. We perform diffusion on the latents after quantization, which enables training-free editing capabilities. By exploiting class-to-code correspondences in the codebook, our method supports sketch-guided generation, inpainting, and outpainting without any retraining. On SemanticKITTI, EditSSC outperforms existing 3D-specific baselines on unconditional generation, demonstrating that well-established 2D architectures can be effectively repurposed for 3D scene generation and editing.
Chinese Translation
3D 语义场景生成对于自动驾驶应用至关重要，但大多数方法依赖于复杂的 3D 特定架构，如三平面编码器和改进的扩散网络，这限制了它们的简单性和编辑能力。我们提出了 EditSSC，这是一种使用 2D 鸟瞰图（BEV）表示和现成的潜在扩散网络的 3D 语义场景生成方法，具备编辑准备功能。我们的方法将 3D 语义占用网格重塑为多通道 BEV 图像，并利用量化自编码器和来自 Stable Diffusion 的 UNet，进行最小修改。我们在量化后对潜在空间进行扩散，这使得无训练编辑能力成为可能。通过利用代码本中的类到代码对应关系，我们的方法支持草图引导生成、修补和外扩，而无需任何重新训练。在 SemanticKITTI 数据集上，EditSSC 在无条件生成任务中超越了现有的 3D 特定基线，证明了成熟的 2D 架构可以有效地重新用于 3D 场景生成和编辑。
cs.CV / 173 / 2606.09290
Visual Para-Thinker++: A Single-Policy Multi-Agent Framework for Visual Reasoning
视觉平行思考者++：一种用于视觉推理的单策略多智能体框架
Abstract
Visual reasoning requires integrating evidence distributed across regions, attributes, and relations, making single-chain reasoning prone to early perceptual commitment and hallucination. We propose Visual Para-Thinker++, a single-policy multi-agent framework in which one shared MLLM policy is instantiated as role-conditioned Main, Worker, and Summary Agents. The Main Agent decomposes the task with fixed allocation patterns; Worker Agents reason in parallel under context isolation; and the Summary Agent reconciles full Worker reasoning traces rather than majority-voting on final labels. The shared policy is trained by Multi-Agent Capability Injection and Role-Decoupled Multi-Agent Optimization, which assign role-specific rewards and advantages to corresponding token segments to reduce gradient conflict among collaborative roles. A native inference engine enables efficient multi-agent rollout through shared visual prefix and KV cache reuse. Across V*, CountBench, the RefCOCO family, and HallusionBench, Visual Para-Thinker++ consistently outperforms single-trajectory and inference-time parallel baselines, with especially strong gains on hallucination-sensitive visual reasoning.
Chinese Translation
视觉推理需要整合分布在不同区域、属性和关系中的证据，这使得单链推理容易受到早期感知承诺和幻觉的影响。我们提出了视觉平行思考者++，这是一个单策略多智能体框架，其中一个共享的多模态语言模型（MLLM）策略被实例化为角色条件的主智能体、工作智能体和摘要智能体。主智能体通过固定的分配模式对任务进行分解；工作智能体在上下文隔离的情况下并行推理；而摘要智能体则整合所有工作智能体的推理轨迹，而不是对最终标签进行多数投票。共享策略通过多智能体能力注入和角色解耦多智能体优化进行训练，这为相应的令牌段分配了角色特定的奖励和优势，以减少协作角色之间的梯度冲突。一个原生推理引擎通过共享视觉前缀和键值缓存重用实现高效的多智能体展开。在 V*、CountBench、RefCOCO 系列和 HallusionBench 上，视觉平行思考者++ 一直优于单轨迹和推理时并行基线，尤其在对幻觉敏感的视觉推理任务上表现出显著的提升。
cs.CV / 174 / 2606.09294
Virtual-point-based Solutions to Handle Generalized Absolute Pose Problem
基于虚拟点的广义绝对姿态问题解决方案
Abstract
Multi-camera systems are increasingly adopted in robotics and autonomous navigation for their wide field of view, flexibility, and fault tolerance. Nevertheless, existing PnP solvers fail to handle multiple projection centers. This paper introduces a virtual point formulation that bridges the standard PnP and generalized pose problems, enabling a unified pipeline that transforms existing PnP solvers into generalized pose solvers. Based on this framework, we derive three Virtual-point-based Generalized Pose solvers, namely VGPc, VGPq, and VGPr, leveraging Cayley, quaternion, and rotation-matrix parameterizations, respectively. Extensive experiments demonstrate that the proposed solvers inherit the accuracy and efficiency of original PnP algorithms while significantly outperforming existing generalized solvers. Specifically, VGPc achieves higher estimation accuracy under heteroscedastic noise conditions, VGPq maintains global optimality, whereas VGPr provides superior computational efficiency without accuracy degradation.
Chinese Translation
多摄像头系统因其广阔的视野、灵活性和容错性在机器人技术和自主导航中越来越受到青睐。然而，现有的PnP（Perspective-n-Point）求解器无法处理多个投影中心。本文提出了一种虚拟点的表述，连接了标准PnP和广义姿态问题，实现了一个统一的流程，将现有的PnP求解器转变为广义姿态求解器。在此框架下，我们推导出三种基于虚拟点的广义姿态求解器，分别为VGPc、VGPq和VGPr，利用Cayley、四元数和旋转矩阵参数化。大量实验表明，所提出的求解器继承了原始PnP算法的准确性和效率，同时显著优于现有的广义求解器。具体而言，VGPc在异方差噪声条件下实现了更高的估计准确性，VGPq保持了全局最优性，而VGPr在不降低准确性的情况下提供了更优的计算效率。
cs.CV / 175 / 2606.09303
Reason Twice: Segmentation via Candidate Discovery and Comparative Reasoning
双重推理：通过候选发现与比较推理进行分割
Abstract
The rapid development of pretrained foundation models has enabled more general image segmentation. Multimodal large language models (MLLMs) have been widely explored for image segmentation with complex queries that require high-level reasoning. Despite promising progress, existing methods are often constrained by limited training data and the gap between MLLMs and mask generation modules. To better transfer MLLMs' perception and reasoning ability to complex reasoning-based segmentation tasks, we propose a two-stage framework Rea2Seg for mask generation and selection. Specifically, the framework first identifies potential regions as candidate masks based on the attention maps of a segmentation MLLM. It then employs an MLLM to reason over the question and candidate masks and assign scores to each mask. The final segmentation result is obtained by reranking the candidates and selecting the highest-scoring mask, reformulating image segmentation as candidate discovery followed by discriminative mask selection. We also notice that a large portion of questions in existing benchmarks focus on commonsense reasoning, and these questions usually do not fully require joint visual observation and reasoning. To address this issue, we introduce a new benchmark called ReasonSeg-SGDR that comprehensively evaluates a model's perception, grounding, and reasoning abilities across multiple dimensions, including discriminative recognition, spatial reasoning, geometric reasoning, and multi-step reasoning, with fine-grained mask generation. In addition, we collect training data to enhance MLLMs' ability to jointly understand multimodal queries and candidate masks, and to assign scores through reasoning. Experimental results on the proposed benchmark and ReasonSeg demonstrate the effectiveness of the unified mask generation and selection framework.
Chinese Translation
预训练基础模型的快速发展使得更为通用的图像分割成为可能。多模态大型语言模型（MLLMs）在处理需要高层次推理的复杂查询的图像分割中得到了广泛探索。尽管取得了令人鼓舞的进展，现有方法往往受到有限训练数据和MLLMs与掩膜生成模块之间差距的限制。为了更好地将MLLMs的感知与推理能力转移到基于复杂推理的分割任务中，我们提出了一个名为Rea2Seg的两阶段框架，用于掩膜生成与选择。具体而言，该框架首先基于分割MLLM的注意力图识别潜在区域作为候选掩膜。然后，它利用MLLM对问题和候选掩膜进行推理，并为每个掩膜分配分数。最终的分割结果通过对候选掩膜进行重新排序并选择得分最高的掩膜获得，将图像分割重新定义为候选发现后跟随的区分性掩膜选择。我们还注意到，现有基准中的大量问题集中在常识推理上，而这些问题通常并不完全需要联合视觉观察与推理。为了解决这个问题，我们引入了一个新的基准，称为ReasonSeg-SGDR，全面评估模型在多个维度上的感知、定位和推理能力，包括区分性识别、空间推理、几何推理和多步骤推理，并进行细粒度的掩膜生成。此外，我们收集了训练数据，以增强MLLMs共同理解多模态查询和候选掩膜的能力，并通过推理分配分数。在提出的基准和ReasonSeg上的实验结果证明了统一掩膜生成与选择框架的有效性。
cs.CV / 176 / 2606.09347
IB-HFN: Information Bottleneck-Driven SAR-Optical Fusion Network for High-Fidelity Cloud Removal
IB-HFN：基于信息瓶颈驱动的高保真SAR-光学融合网络用于高保真云去除
Abstract
Synthetic aperture radar (SAR)-assisted optical cloud removal aims to recover surface information obscured by clouds in optical remote sensing images by exploiting complementary SAR observations. Existing multimodal fusion methods typically rely on direct spatial concatenation and pixel-wise supervision, which can propagate SAR speckle noise into optical reconstruction and lead to over-smoothed results. To address these limitations, we propose an Information Bottleneck-driven High-Fidelity Network (IB-HFN) for SAR-assisted optical cloud removal. IB-HFN employs a dual-stream backbone to preserve modality-specific representations before deep semantic fusion, thereby mitigating premature cross-modal contamination. At the fusion stage, we introduce a Spatial Information Bottleneck Fusion module that compresses SAR features through a channel-wise variational information bottleneck to suppress unstructured speckle noise. In parallel, a local-global gating mechanism predicts clear-sky regions and routes reliable optical details through a Dirac-initialized skip connection, decoupling noise suppression from texture preservation. We further develop a joint optimization strategy that integrates feature-level bottleneck regularization with image-level constraints on reconstruction accuracy, structural consistency, spectral fidelity, and contrastive sharpness. A dynamic weighting schedule balances these objectives to stabilize training and reduce hazy artifacts. Experiments on the SEN12MS-CR dataset under challenging spatio-temporal splits demonstrate that IB-HFN achieves superior structural preservation and spectral fidelity over existing methods.
Chinese Translation
合成孔径雷达（SAR）辅助的光学云去除旨在通过利用互补的SAR观测，恢复被云遮挡的光学遥感图像中的表面信息。现有的多模态融合方法通常依赖于直接的空间拼接和逐像素监督，这可能会将SAR斑点噪声传播到光学重建中，导致过度平滑的结果。为了解决这些局限性，我们提出了一种基于信息瓶颈驱动的高保真网络（IB-HFN）用于SAR辅助的光学云去除。IB-HFN采用双流主干网络，在深度语义融合之前保留模态特定的表示，从而减轻了过早的跨模态污染。在融合阶段，我们引入了一个空间信息瓶颈融合模块，通过通道级变分信息瓶颈压缩SAR特征，以抑制无结构的斑点噪声。同时，一个局部-全局门控机制预测晴空区域，并通过一个Dirac初始化的跳跃连接传递可靠的光学细节，从而将噪声抑制与纹理保留解耦。我们进一步开发了一种联合优化策略，将特征级瓶颈正则化与重建精度、结构一致性、光谱保真度和对比清晰度的图像级约束相结合。动态加权调度平衡这些目标，以稳定训练并减少模糊伪影。在具有挑战性的时空划分下，SEN12MS-CR数据集上的实验表明，IB-HFN在结构保留和光谱保真度方面优于现有方法。
cs.CV / 177 / 2606.09353
Beyond Humans: Multispecies Animal Face Recognition Using Transfer Learning
超越人类：基于迁移学习的多物种动物面部识别
Abstract
Individual animal recognition can be useful in the search for lost or stolen pets, the tracking of individuals of endangered species, and the recognition of animals in crowded farms. Present recognition techniques mostly use physical devices, e.g., microchips, often impractical and difficult to apply. These could be replaced by remote recognition via the animal's face; if accurate enough, it provides several advantages: it is non-invasive, can work at a distance, and is difficult to counterfeit, as, for instance, in the case of substituting sick animals for healthy ones in the food industry. The few existing datasets with sufficient per-subject images annotated with a single animal identity are not large enough to train current deep learning architectures. We rather investigate the possibility of transfer learning, exploiting pre-trained network models as backbones. Our experiments compared FaceNet, which is specifically trained on large databases of human faces, with the Vision Transformer (ViT) pre-trained on ImageNet, i.e., on object categories. We used three face datasets of very different animals: dogs, primates (lemurs, golden monkeys, and chimpanzees), and cattle. We report the results and, for each dataset, compare them with the state of the art (SOTA) ad hoc-trained deep networks. The capture conditions differ among the three datasets. Image quality (resolution, motion blur, diverse poses, etc.) decreases from dogs to cattle to primates. The best performance was achieved with dogs, where ViT reached a mean verification accuracy of 96.85% and a Rank-1 Identification Rate of 84.34%. The results for endangered primates are still encouraging, but performance varies across animal classes and tasks (verification or identification), and does not always outperform SOTA. For cattle, the ViT results outperform SOTA, while FaceNet is still competitive.
Chinese Translation
个体动物识别在寻找失踪或被盗宠物、追踪濒危物种个体以及识别拥挤农场中的动物方面具有重要意义。目前的识别技术主要依赖物理设备，例如微芯片，这些方法往往不够实用且难以应用。我们可以通过动物的面部进行远程识别来替代这些方法；如果准确性足够高，它提供了多种优势：非侵入性、可在远距离工作，并且难以伪造，例如在食品行业中用病态动物替代健康动物的情况。现有的少数数据集虽然有足够的每个个体图像，并且标注了单一动物身份，但其规模不足以训练当前的深度学习架构。因此，我们探讨了迁移学习的可能性，利用预训练网络模型作为基础。我们的实验比较了专门在大型人脸数据库上训练的 FaceNet 与在 ImageNet（即物体类别）上预训练的视觉变换器（Vision Transformer, ViT）。我们使用了三个非常不同动物的面部数据集：狗、灵长类动物（狐猴、金丝猴和黑猩猩）和牛。我们报告了实验结果，并将每个数据集的结果与现有的最先进（SOTA）专门训练的深度网络进行了比较。三个数据集的捕获条件各不相同。图像质量（分辨率、运动模糊、多样的姿势等）从狗到牛再到灵长类动物逐渐下降。最佳性能出现在狗的数据集中，ViT 达到了 96.85% 的平均验证准确率和 84.34% 的 Rank-1 识别率。对于濒危灵长类动物的结果仍然令人鼓舞，但性能在动物类别和任务（验证或识别）之间存在差异，并不总是优于 SOTA。对于牛，ViT 的结果优于 SOTA，而 FaceNet 仍然具有竞争力。
cs.CV / 178 / 2606.09360
ExDet: Open-Domain Open-Vocabulary Detection with Cross-modal Extrapolation and Rectification
ExDet：具有跨模态外推和校正的开放领域开放词汇检测
Abstract
Open-domain open-vocabulary detection (ODOVD) requires detectors to generalize to both novel categories and unseen domains, making it more challenging than open-vocabulary detection. Existing methods typically train open-vocabulary detectors together with domain generalization modules from scratch, leading to high training cost. we propose ExDet, a lightweight category-domain collaborative generalization framework for ODOVD that enhances the cross-category and cross-domain generalization of existing detectors. ExDet consists of Text-Guided Extrapolation (TGE), a lightweight Detector-Compatible Rectification (DCR) module, and ExRPN. Specifically, TGE exploits the DeltaSpace property of vision-language models (VLMs) to infer category- and domain-aware proxy visual prototypes from text. DCR is learned from the TGE-generated prototypes in a detector training-free and real-data-free manner, and is inserted after the classification head at inference to rectify representations toward a detector-compatible source-domain visual distribution, thereby enhancing classification for targets from novel categories and unseen domains. ExRPN recalibrates proposal scores by combining semantic similarity with RPN confidence, improving recall for novel and domain-shifted objects while providing better support for subsequent classification and DCR. ExDet achieves SOTA performance on OD-LVIS, OV-LVIS, Objects365, and MSOSB.
Chinese Translation
开放领域开放词汇检测（ODOVD）要求检测器能够对新类别和未见领域进行泛化，这使其比开放词汇检测更具挑战性。现有方法通常从头开始将开放词汇检测器与领域泛化模块一起训练，导致高昂的训练成本。我们提出了ExDet，一种轻量级的类别-领域协同泛化框架，用于ODOVD，旨在增强现有检测器的跨类别和跨领域泛化能力。ExDet由文本引导外推（Text-Guided Extrapolation, TGE）、轻量级检测器兼容校正（Detector-Compatible Rectification, DCR）模块和ExRPN组成。具体而言，TGE利用视觉-语言模型（Vision-Language Models, VLMs）的DeltaSpace特性，从文本中推断类别和领域感知的代理视觉原型。DCR以无检测器训练和无真实数据的方式从TGE生成的原型中学习，并在推理时插入到分类头之后，以校正表示，使其朝向检测器兼容的源领域视觉分布，从而增强对新类别和未见领域目标的分类能力。ExRPN通过将语义相似性与RPN置信度相结合来重新校准提议分数，提高对新颖和领域转移对象的召回，同时为后续分类和DCR提供更好的支持。ExDet在OD-LVIS、OV-LVIS、Objects365和MSOSB上实现了最先进的性能。
cs.CV / 179 / 2606.09362
Zero-Shot Semantic Re-Identification for Autonomous Driving: A VLM Baseline Study
自动驾驶中的零样本语义重识别：VLM基线研究
Abstract
Re-Identification (ReID) in autonomous driving is typically formulated as a visual matching problem, where observations of vehicles, pedestrians, and cyclists are associated across time, frames, or camera views using learned appearance embeddings, often complemented by motion, geometric, or multimodal cues. However, purely visual representations may be sensitive to viewpoint, occlusion, illumination, and sensor-domain variations, limiting their interpretability and robustness in complex driving scenes. We propose a baseline study of a zero-shot pipeline using Vision-Language Models (VLMs) to generate textual descriptions of detected traffic participants and evaluate whether these descriptions can support identity matching across observations. Instead of relying only on low-level visual similarity, the proposed formulation represents each object through structured semantic attributes, including category, color, shape, pose, visible parts, spatial context, and distinctive visual cues. This study provides an initial benchmark for language-based re-identification in autonomous-driving scenarios, discussing and evaluating the strengths and limitations of current VLMs for this task. Results demonstrate that zero-shot semantic descriptions can support effective object re-identification, achieving retrieval performance comparable to a supervised CNN baseline while offering greater interpretability through explicit identity cues. However, the experiments also reveal important challenges, including attribute inconsistency across viewpoints and limited fine-grained discrimination between visually similar instances.
Chinese Translation
在自动驾驶中，重识别（ReID）通常被视为一个视觉匹配问题，通过学习的外观嵌入将车辆、行人和骑自行车者的观察结果在时间、帧或摄像头视角之间进行关联，通常还会结合运动、几何或多模态线索。然而，纯粹的视觉表示可能对视角、遮挡、光照和传感器域的变化敏感，这限制了其在复杂驾驶场景中的可解释性和鲁棒性。我们提出了一项基于零样本管道的基线研究，使用视觉-语言模型（VLMs）生成检测到的交通参与者的文本描述，并评估这些描述是否能够支持跨观察的身份匹配。该方法不再仅依赖于低级视觉相似性，而是通过结构化的语义属性来表示每个对象，包括类别、颜色、形状、姿态、可见部分、空间上下文和独特的视觉线索。本研究为自动驾驶场景中的基于语言的重识别提供了初步基准，讨论并评估了当前VLM在此任务中的优势和局限性。结果表明，零样本语义描述能够支持有效的对象重识别，检索性能可与监督CNN基线相媲美，同时通过明确的身份线索提供更大的可解释性。然而，实验也揭示了重要的挑战，包括不同视角下属性不一致和在视觉上相似实例之间有限的细粒度区分能力。
cs.CV / 180 / 2606.09367
RT-SDGOD: Real-Time Single-Domain Generalized Object Detection
实时单域广义目标检测 (RT-SDGOD)
Abstract
In real-world deployment under strict real-time constraints, weather and imaging variations induce significant distribution shifts, severely degrading detectors. Single-Domain Generalized Object Detection aims to mitigate this issue, yet existing methods rarely investigate-at the level of problem formulation-the generalization capability of real-time detectors under such constrained inference budgets. To this end, we introduce Real-Time Single-Domain Generalized Object Detection (RT-SDGOD), which focuses on how real-time detectors can achieve cross-domain generalization under zero extra inference overhead by relying solely on training-time representation learning. We observe that, under domain shift, DETR-based real-time detectors mainly degrade through increased missed detections, rooted in limited and unstable object-level discriminative evidence. Based on this, we propose RT-SDGDet, a multi-evidence collaborative modeling framework for RT-SDGOD. The core idea is to enable multiple queries of the same object to collaboratively cover more sufficient discriminative evidence while maintaining the stability of such evidence modeling across views. Specifically, we use one-to-many (O2M) supervision to construct stable object-specific query groups, and further design Discriminative Evidence Diversity Learning (DEDL) and Dual-view Evidence Consistency Learning (DvECL) to expand object-level evidence coverage and improve evidence stability under appearance perturbations, respectively. Since all components are introduced only during training, our method incurs no extra inference overhead. Extensive experiments show that the proposed method achieves better generalization performance than existing approaches across multiple unseen target domains.
Chinese Translation
在严格的实时约束下进行实际部署时，天气和成像变化会导致显著的分布偏移，严重降低检测器的性能。单域广义目标检测旨在缓解这一问题，但现有方法很少在问题表述层面上研究实时检测器在此类受限推理预算下的泛化能力。为此，我们提出了实时单域广义目标检测 (RT-SDGOD)，重点关注实时检测器如何在零额外推理开销的情况下，仅依赖训练时的表征学习实现跨域泛化。我们观察到，在域偏移的情况下，基于DETR的实时检测器主要通过增加漏检数量而退化，这根源于有限且不稳定的目标级别判别证据。基于此，我们提出了RT-SDGDet，一个用于RT-SDGOD的多证据协作建模框架。其核心思想是使同一目标的多个查询能够协同覆盖更充分的判别证据，同时保持跨视角的证据建模稳定性。具体而言，我们使用一对多 (O2M) 监督构建稳定的目标特定查询组，并进一步设计判别证据多样性学习 (DEDL) 和双视图证据一致性学习 (DvECL)，分别扩展目标级别证据覆盖范围并提高在外观扰动下的证据稳定性。由于所有组件仅在训练期间引入，我们的方法不会产生额外的推理开销。大量实验表明，所提出的方法在多个未见目标域上实现了比现有方法更好的泛化性能。
cs.CV / 181 / 2606.09368
PhysScene: A Scene Graph Dataset for Scientific Visual Reasoning in Physics Experiments
PhysScene：用于物理实验科学视觉推理的场景图数据集
Abstract
Scene Graphs (SGs) provide structured representations of visual scenes by modeling objects and their pairwise relationships. Despite recent progress, existing datasets primarily focus on generic natural contexts, leaving domain-specific and function-oriented scenes largely underexplored. This limitation restricts the evaluation of relational reasoning in scientific experimental scenes, thereby hindering the development of intelligent monitoring, analysis, and related applications in such scenes. To address this gap, we introduce PhysScene, the first SG dataset tailored to physics experiments. PhysScene encompasses specialized instruments, structured experimental setups, and functional relations intrinsic to experimental environments, enabling reasoning that extends beyond spatial co-occurrence to logical dependencies. Rather than pursuing large data scale, PhysScene focuses on strong semantic constraints and high relation density in experimental scenes, posing new challenges for existing scene parsing algorithms while offering opportunities for further improvements. Extensive analyses and experiments show that PhysScene complements existing benchmarks and establishes a valuable testbed for advancing scientific visual reasoning. The dataset is publicly available at https://github.com/ZMH-SDUST/PhysScene.
Chinese Translation
场景图（SGs）通过建模对象及其成对关系，提供了视觉场景的结构化表示。尽管近期取得了一定进展，现有数据集主要集中于通用自然环境，特定领域和功能导向的场景仍然未得到充分探索。这一限制制约了在科学实验场景中对关系推理的评估，从而阻碍了智能监控、分析及相关应用的发展。为了解决这一问题，我们推出了PhysScene，这是第一个专为物理实验量身定制的SG数据集。PhysScene涵盖了专用仪器、结构化实验设置以及实验环境中固有的功能关系，使得推理超越空间共现，涉及逻辑依赖关系。PhysScene并不追求大规模数据，而是专注于实验场景中的强语义约束和高关系密度，这对现有的场景解析算法提出了新的挑战，同时也为进一步改进提供了机会。广泛的分析和实验表明，PhysScene补充了现有基准，并为推动科学视觉推理建立了一个有价值的测试平台。该数据集已在 https://github.com/ZMH-SDUST/PhysScene 上公开发布。
cs.CV / 182 / 2606.09378
Echo-DM: Ultrasound Marker Removal via Conditional Latent Diffusion and Region-Aware Fusion
Echo-DM：通过条件潜在扩散和区域感知融合去除超声标记
Abstract
Clinical ultrasound images often contain artificial markers, such as measurement calipers and text, to assist diagnostic interpretation and comparison. However, these markers can introduce shortcut bias in downstream automated analysis, encouraging deep learning models to rely on marker-related cues rather than clinically meaningful anatomy. Existing marker removal methods are either mask-dependent and vulnerable to error propagation, or mask-free deterministic restorers that may over-smooth ultrasound texture and perturb unaffected background regions. To address these challenges, we present Echo-DM, a framework for ultrasound marker removal via conditional latent diffusion and region-aware fusion. Echo-DM follows a common encoder-diffusion-decoder pipeline, where a DiT-based conditional latent diffusion network performs global restoration and a region-aware fusion module enforces preservation-aware image-space refinement under end-to-end mask-free inference. Building on this fixed core design, we further instantiate Echo-DM-V and Echo-DM-R with VAE-based and RAE-based latent modules, respectively, which demonstrates that the Echo-DM architecture is compatible with diverse latent-module instantiations. Extensive experiments on Echo-PAIR, a large-scale paired clinical ultrasound dataset, demonstrate superior marker removal and strong anatomical fidelity compared with representative two-stage baselines, while providing favorable quality--efficiency trade-offs across deployment settings. Data, code and models will be released at https://github.com/MiliLab/Echo-DM.
Chinese Translation
临床超声图像通常包含人工标记，如测量卡尺和文本，以辅助诊断解释和比较。然而，这些标记可能在下游自动分析中引入捷径偏差，促使深度学习模型依赖于与标记相关的线索，而非临床上有意义的解剖结构。现有的标记去除方法要么依赖于掩码，容易导致错误传播，要么是无掩码的确定性恢复器，可能会过度平滑超声纹理并扰动未受影响的背景区域。为了解决这些挑战，我们提出了Echo-DM，一个通过条件潜在扩散和区域感知融合去除超声标记的框架。Echo-DM遵循常见的编码器-扩散-解码器管道，其中基于DiT的条件潜在扩散网络执行全局恢复，而区域感知融合模块在端到端无掩码推理下强制实施保留感知的图像空间细化。在此固定核心设计的基础上，我们进一步实例化了Echo-DM-V和Echo-DM-R，分别采用基于变分自编码器（VAE）和基于重构自编码器（RAE）的潜在模块，证明了Echo-DM架构与多样的潜在模块实例化兼容。在Echo-PAIR这一大规模配对临床超声数据集上的广泛实验表明，与具有代表性的两阶段基线相比，Echo-DM在标记去除和解剖保真度方面表现优越，同时在不同部署设置中提供了良好的质量-效率权衡。数据、代码和模型将发布在 https://github.com/MiliLab/Echo-DM。
cs.CV / 183 / 2606.09383
An Opticalmechanics Framework for Dynamic Estimation of Multibody Systems
用于多体系统动态估计的光机械框架
Abstract
Conventional dynamics analysis of the human body is often constrained by the need for contact force and torque sensors and controlled laboratory environments. To address this issue, this study proposes an opticalmechanics kinematic-dynamic integrated estimation framework for multibody systems. Specifically, a constrained multibody model is established to describe the system dynamics, while image-measured kinematic quantities are used as non contact inputs for dynamic estimation. The unknown joint torque is then identified through a genetic-algorithm based optimization by minimizing the discrepancy between model-predicted and image-measured kinematic quan tities. Experimental validation on an air-bearing platform showed that the wrist joint torque estimated from image data achieved a mean absolute error of 0.46 Nm compared with sensor measurements. In the forward prediction test, the model-predicted angular velocity achieved a mean absolute error of 0.006 rad/s relative to the image-measured results. This study demonstrates the potential of combining image measurement and mechanical modeling for non-contact dynamic estimation in scenarios where direct force and torque measurement is difficult.
Chinese Translation
传统的人体动力学分析常常受到接触力和扭矩传感器以及受控实验室环境的限制。为了解决这一问题，本研究提出了一种光机械运动学-动力学集成估计框架，适用于多体系统。具体而言，建立了一个约束多体模型以描述系统动力学，同时利用图像测量的运动学量作为非接触输入进行动态估计。然后，通过基于遗传算法的优化方法识别未知的关节扭矩，最小化模型预测与图像测量运动学量之间的差异。在气垫平台上的实验验证显示，从图像数据估计的腕关节扭矩与传感器测量值相比，平均绝对误差为0.46 Nm。在前向预测测试中，模型预测的角速度与图像测量结果相比，平均绝对误差为0.006 rad/s。本研究展示了在直接测量力和扭矩困难的场景中，结合图像测量和机械建模进行非接触动态估计的潜力。
cs.CV / 184 / 2606.09390
Real-time body pose non-verbal communication with a consistency-based reliability measure
基于一致性可靠性度量的实时身体姿态非语言交流
Abstract
Body movement communicates intent at distances and in conditions where neither the face, nor speech can be captured. We study the recognition of communicative intent from 2D body pose alone. We argue that body motion is a reliable signal especially in scenarios that require real time low-cost on-device person-to-robot communication in long distance environments, such as rescue missions. However, existing resources do not isolate this signal. Affective corpora combine body, face, voice and text, while skeleton action-recognition benchmarks label the action performed rather than the message conveyed. We release a dataset of real frames of full-body pose covering ten communicative intents and we compare it against other real (IPC) and synthetic (MotionLCM, VEO3.1, Kimodo) ones that span a range of difficulty. We target systems that can run on a robot's limited onboard hardware. We benchmark multiple models, from skeleton graph classifiers to joint motion-forecasting networks, and report performance metrics together with frame rate on an embedded GPU (NVIDIA Orin~Nano), since speed matters as much as accuracy in our scenario. Finally, we show that a model's own autoregressive self-consistency works as an unsupervised reliability signal. We give a short proof that bounds the probability that a self-consistent prediction is correct, show that this probability grows with the number of consistent steps, and identify the conditions under which a confident prediction can still be false, benchmarked against industry-standard metrics.
Chinese Translation
身体运动在面部和语言无法捕捉的距离和条件下传达意图。我们研究仅从二维身体姿态识别交流意图。我们认为，身体运动在需要实时、低成本的设备间人机交流的远程环境中（如救援任务）是一个可靠的信号。然而，现有资源并未单独隔离这一信号。情感语料库结合了身体、面部、声音和文本，而骨骼动作识别基准则标注所执行的动作，而非所传达的信息。我们发布了一个包含十种交流意图的全身姿态真实帧数据集，并将其与其他真实（IPC）和合成（MotionLCM、VEO3.1、Kimodo）数据集进行比较，这些数据集涵盖了不同的难度范围。我们的目标是能够在机器人有限的机载硬件上运行的系统。我们对多种模型进行了基准测试，从骨骼图分类器到关节运动预测网络，并报告了性能指标以及在嵌入式GPU（NVIDIA Orin~Nano）上的帧率，因为在我们的场景中，速度与准确性同样重要。最后，我们展示了模型自身的自回归自一致性作为一种无监督的可靠性信号。我们给出了一个简短的证明，界定了自一致预测正确的概率，展示了这一概率随着一致步骤数量的增加而增长，并识别出在何种条件下自信的预测仍可能是错误的，这些都基于行业标准指标进行了基准测试。
cs.CV / 185 / 2606.09393
CapRL++: Unified Reinforcement Learning with Verifiable Rewards for Dense Image and Video Captioning
CapRL++：具有可验证奖励的统一强化学习用于密集图像和视频字幕生成
Abstract
Image and video captioning are fundamental tasks that bridge the visual and linguistic domains, playing a critical role in pre-training Large Vision-Language Models (LVLMs). Current state-of-the-art captioning models are typically trained with Supervised Fine-Tuning (SFT), a paradigm that relies on expensive, non-scalable annotations and often causes models to memorize specific ground-truth answers, limiting their generality and ability to generate diverse, creative descriptions. To overcome these limitations, we propose applying Reinforcement Learning with Verifiable Rewards (RLVR) to the open-ended task of multimodal captioning. We introduce Captioning Reinforcement Learning++ (CapRL++), a novel reference-free training framework that redefines caption quality through its utility: a high-quality caption should enable a non-visual language model to accurately answer questions about the corresponding visual content. CapRL++ employs a decoupled two-stage pipeline where an LVLM generates a caption, and the objective reward is derived from the accuracy of a separate, vision-free LLM answering Multiple-Choice Questions based solely on that caption. Evaluations on more than 20 image and video benchmarks show that CapRL++ improves dense caption quality and strengthens caption-based pretraining across tasks such as spatial and temporal understanding. Pretraining on scalable image and video caption datasets annotated by CapRL++ yields substantial downstream gains. Furthermore, within the Prism Framework for caption quality evaluation, compact models trained with CapRL++ achieve dense captioning performance comparable to substantially larger models such as Qwen2.5-VL-72B and Qwen3-VL-235B-A22B. These results validate that CapRL++ effectively trains models to produce generalizable, high-fidelity descriptions, establishing a robust foundation beyond the limitations of traditional SFT.
Chinese Translation
图像和视频字幕生成是连接视觉和语言领域的基础任务，在大型视觉-语言模型（LVLMs）的预训练中发挥着关键作用。目前最先进的字幕生成模型通常采用监督微调（SFT）进行训练，这一范式依赖于昂贵且不可扩展的注释，往往导致模型记忆特定的真实答案，从而限制了其通用性和生成多样化、创造性描述的能力。为了克服这些局限性，我们提出将具有可验证奖励的强化学习（RLVR）应用于多模态字幕生成的开放式任务。我们引入了字幕生成强化学习++（CapRL++），这是一个新颖的无参考训练框架，通过其实用性重新定义字幕质量：高质量的字幕应使非视觉语言模型能够准确回答关于相应视觉内容的问题。CapRL++采用解耦的两阶段流程，其中LVLM生成字幕，目标奖励来源于一个独立的、无视觉的LLM根据该字幕回答多项选择题的准确性。在超过20个图像和视频基准上的评估表明，CapRL++提高了密集字幕质量，并增强了基于字幕的预训练，适用于空间和时间理解等任务。在由CapRL++注释的可扩展图像和视频字幕数据集上进行预训练可带来显著的下游收益。此外，在字幕质量评估的Prism框架内，使用CapRL++训练的紧凑模型在密集字幕生成性能上可与Qwen2.5-VL-72B和Qwen3-VL-235B-A22B等大规模模型相媲美。这些结果验证了CapRL++有效地训练模型生成可推广的高保真描述，为超越传统SFT的局限性奠定了坚实基础。
cs.CV / 186 / 2606.09400
vesselFM-CT: Segmenting All Blood Vessels in CT Images for System-Level Cardiovascular Analysis
vesselFM-CT：用于系统级心血管分析的CT图像中所有血管的分割
Abstract
The vascular network in the human body is characterized by blood vessels exhibiting drastic structural variations in radius, length, topological properties, and branching patterns. This heterogeneity, together with location-specific anatomical background variations, poses a significant challenge for robust, large-scale analysis of the entire cardiovascular system. As a result, most research has focused on narrow, isolated segments of the vascular network. While such targeted studies provide valuable insights, they inherently limit the ability to assess the systemic health and functional integrity of the vascular network as a whole. In this work, we aim to bridge this gap to advance both clinical diagnostics and our fundamental understanding of vascular physiology. We propose the task of segmenting all vessels in CT images, ranging from the largest components of the cardiovascular system to even minuscule mesenteric vessels. To this end, we introduce vesselFM-CT, the first model capable of robustly segmenting all blood vessels in 3D CT images. VesselFM-CT is trained via an iterative, multi-step process and optimizes our proposed TubeLoss loss function, effectively addressing the inherent heterogeneity of the cardiovascular system. We demonstrate that vesselFM-CT outperforms all baselines and enables automated, precise extraction of the cardiovascular system from CT images, thereby unlocking a wide range of clinical and technical perspectives, including automated disease classification and synthetic CT image generation.
Chinese Translation
人体内的血管网络具有血管半径、长度、拓扑特性和分支模式的显著结构变化。这种异质性，加上特定位置的解剖背景变化，为整个心血管系统的稳健、大规模分析带来了重大挑战。因此，大多数研究集中在血管网络的狭窄、孤立的片段上。尽管这些针对性的研究提供了有价值的见解，但它们本质上限制了对血管网络整体的系统健康和功能完整性的评估。在本研究中，我们旨在填补这一空白，以推动临床诊断和我们对血管生理学的基本理解。我们提出了在CT图像中分割所有血管的任务，从心血管系统中最大的组成部分到微小的肠系膜血管。为此，我们介绍了vesselFM-CT，这是第一个能够在3D CT图像中稳健分割所有血管的模型。vesselFM-CT通过迭代的多步骤过程进行训练，并优化了我们提出的TubeLoss损失函数，有效地解决了心血管系统固有的异质性。我们证明vesselFM-CT优于所有基线，并能够从CT图像中自动、精确地提取心血管系统，从而开启了广泛的临床和技术视角，包括自动化疾病分类和合成CT图像生成。
cs.CV / 187 / 2606.09446
Leveraging Morphology for Historical Script Metrological Analysis
利用形态学进行历史文献计量分析
Abstract
Advances in handwritten text recognition have enabled large-scale transcription of historical documents, but still provide limited access to interpretable visual measurements for paleography, the study of historical scripts. In this paper, our main insight is that morphological script analysis, in particular the capacity to learn character prototypes from line-level transcriptions, enables the definition of scalable, meaningful, and stable paleographic measurements. More precisely, we leverage a transformer-based detection architecture together with a prototype-based line reconstruction module to learn prototypical characters and their occurrence, deformation, and positioning. Our contributions are twofold. First, we introduce a deep architecture and learning methodology that enables efficient character modeling with only line-level transcription supervision, significantly improving over the Learnable Typewriter baseline and enabling accurate character bounding box prediction, unlocking its potential for paleographic measurements. Second, we introduce and demonstrate the paleographical relevance of automatic measurements enabled by our architecture for characters, bi-grams, and spaces between graphical units. For this demonstration, we extend the annotations of the codex Paris, BnF, fr. 2813, commissioned in the late fourteenth century by Charles V and copied by four hands, to 160 pages. We visualize our measurements over these pages, showing how they enable us not only to differentiate graphical profiles, but also to discover and analyze subtle variations. This case study outlines the scalability of our approach and its frugality in terms of required training data, since a single column of text is sufficient to compute our measurements on each of the 160 pages. Data and code are publicly available at: https://malamatenia.github.io/morphology4metrology-analysis.
Chinese Translation
手写文本识别的进展使得历史文献的大规模转录成为可能，但在古文字学（paleography）研究中，仍然提供有限的可解释视觉测量访问。在本文中，我们的主要见解是，形态学脚本分析，特别是从行级转录中学习字符原型的能力，使得可定义可扩展、有意义且稳定的古文字学测量成为可能。更准确地说，我们利用基于变换器（transformer）的检测架构以及基于原型的行重建模块来学习原型字符及其出现、变形和定位。我们的贡献有两个方面。首先，我们引入了一种深度架构和学习方法，使得仅通过行级转录监督即可高效建模字符，显著改善了可学习打字机（Learnable Typewriter）基线，并实现了准确的字符边界框预测，从而释放其在古文字学测量中的潜力。其次，我们引入并展示了我们的架构所支持的自动测量在字符、双字母和图形单元之间的空间中的古文字学相关性。为了进行此演示，我们将由查理五世于十四世纪末委托的巴黎国立图书馆（BnF）fr. 2813的注释扩展至160页。我们在这些页面上可视化我们的测量，展示它们如何使我们不仅能够区分图形轮廓，还能发现和分析微妙的变化。此案例研究概述了我们方法的可扩展性及其在所需训练数据方面的节俭，因为仅一列文本即可在这160页上计算我们的测量。数据和代码可公开获取，网址为：https://malamatenia.github.io/morphology4metrology-analysis。
cs.CV / 188 / 2606.09453
GD-MIL: Grade-Disentangled Multiple Instance Learning for Multimodal Biochemical Recurrence Prediction in Prostate Cancer
GD-MIL：用于前列腺癌多模态生化复发预测的等级解耦多实例学习
Abstract
Biochemical recurrence (BCR) after radical prostatectomy is a critical endpoint in prostate cancer, yet risk stratification relies almost entirely on variables dominated by Gleason grade. Whether H&E whole slide images (WSIs) carry prognostic signal beyond grade, and whether multiple instance learning (MIL) can recover it, remains unsettled. A key obstacle is that many pipelines select model checkpoints on the evaluation fold, artificially inflating concordance. We construct a rigorous benchmark on TCGA-PRAD (487 patients, 101 BCR events) using strict out-of-fold scoring over five-fold cross-validation repeated across five seeds. The choice of MIL aggregator (ABMIL, CLAM, TransMIL, PatchGCN) has little effect (C-index 0.61-0.64 with UNI2-h), while the feature extractor is the dominant factor (ResNet50 0.566 versus pathology foundation models up to 0.639). A clinical Cox model on grade, stage, and age reaches 0.687; no imaging-only model significantly outperforms it (p > 0.10). We introduce Grade-Disentangled MIL (GD-MIL), a gated-attention MIL encoder trained with a gradient-reversal grade adversary that encourages the slide representation to be invariant to Gleason grade before late fusion with clinical variables. GD-MIL achieves C-index 0.704, significantly outperforming both the clinical baseline (delta-c = +0.029, p = 0.0005) and the best imaging-only model (delta-c = +0.062, p = 0.039), suggesting H&E morphology contains prognostic information complementary to grade. A median risk split yields log-rank p < 0.0001 separation in BCR-free survival (~20% vs ~70% at five years).
Chinese Translation
根治性前列腺切除术后的生化复发（BCR）是前列腺癌的重要终点，然而风险分层几乎完全依赖于以Gleason等级为主导的变量。H&E全切片图像（WSIs）是否携带超越等级的预后信号，以及多实例学习（MIL）是否能够恢复该信号，仍未得到解决。一个主要障碍是许多管道在评估折叠上选择模型检查点，人工抬高了一致性。我们在TCGA-PRAD（487名患者，101例BCR事件）上构建了一个严格的基准，采用严格的折外评分，通过五折交叉验证在五个种子上重复进行。MIL聚合器的选择（ABMIL、CLAM、TransMIL、PatchGCN）对结果影响不大（C-index为0.61-0.64，使用UNI2-h），而特征提取器是主导因素（ResNet50为0.566，相较于病理基础模型最高可达0.639）。基于等级、阶段和年龄的临床Cox模型达到0.687；没有影像模型显著优于该模型（p > 0.10）。我们提出了等级解耦多实例学习（GD-MIL），这是一种带有梯度反转等级对抗的门控注意力MIL编码器，旨在使切片表示对Gleason等级保持不变，然后再与临床变量进行后期融合。GD-MIL的C-index达到0.704，显著优于临床基线（delta-c = +0.029，p = 0.0005）和最佳影像模型（delta-c = +0.062，p = 0.039），这表明H&E形态包含与等级互补的预后信息。中位风险分割在BCR无复发生存率中产生log-rank p < 0.0001的分离（五年生存率约为20%对比70%）。
cs.CV / 189 / 2606.09474
Training-Free Generalized Few-Shot Segmentation through Open-Vocabulary Semantic Arbitration
无训练的广义少样本分割通过开放词汇语义仲裁
Abstract
Generalized Few-Shot Semantic Segmentation (GFSS) has traditionally been approached as a representation-learning problem, requiring task-specific adaptation to incorporate novel classes from limited support examples. Recent foundation models, however, already exhibit strong open-vocabulary recognition and segmentation capabilities, raising a different question: can GFSS be solved through inference-time coordination of frozen semantic priors rather than parameter adaptation? We answer this question with Open-V, a training-free GFSS framework that combines Segment Anything (SAM3) Promptable Concept Segmentation (PCS) with a K-shot CLIP support centroid through calibrated per-pixel semantic arbitration. OpenV introduces no trainable components and supports arbitrary semantic categories at inference time. Beyond segmentation performance, our study contributes three broader findings. First, we show that support information can be incorporated through inference-time semantic grounding, and that its contribution increases as foundation-model text priors weaken on label-disjoint vocabularies. Second, we identify a reproducibility confound in foundationmodel segmentation, demonstrating that preprocessing and evaluation-space mismatches can silently distort reported performance. Finally, we validate Open-V across PASCAL5i, COCO-20i, and ADE-OW, showing that training-free coordination of foundation-model priors generalizes across both conventional GFSS and open-vocabulary evaluation settings. On PASCAL-5i (1-shot), Open-V attains base/novel/harmonic mIoU of 78.4/77.5/77.9, without GFSS-specific training surpassing the strongest trained baseline by +17.7 HM.
Chinese Translation
广义少样本语义分割（GFSS）传统上被视为一个表示学习问题，需要针对特定任务进行适应，以便从有限的支持示例中纳入新类别。然而，最近的基础模型已经展现出强大的开放词汇识别和分割能力，这引发了一个不同的问题：GFSS是否可以通过冻结语义先验的推理时协调来解决，而不是参数适应？我们通过Open-V回答了这个问题，这是一个无训练的GFSS框架，结合了Segment Anything (SAM3) 可提示概念分割（PCS）和通过校准的逐像素语义仲裁的K-shot CLIP支持中心。Open-V不引入可训练组件，并在推理时支持任意语义类别。除了分割性能外，我们的研究还贡献了三个更广泛的发现。首先，我们展示了支持信息可以通过推理时的语义基础来纳入，并且随着基础模型文本先验在标签不重叠词汇上的减弱，其贡献会增加。其次，我们识别出基础模型分割中的可重复性混淆，证明了预处理和评估空间的不匹配可能会悄然扭曲报告的性能。最后，我们在PASCAL5i、COCO-20i和ADE-OW上验证了Open-V，显示出基础模型先验的无训练协调在传统GFSS和开放词汇评估设置中均具有良好的泛化能力。在PASCAL-5i（1-shot）上，Open-V达到了78.4/77.5/77.9的基础/新颖/和谐mIoU，且没有GFSS特定的训练，超越了最强训练基线17.7 HM。
cs.CV / 190 / 2606.09477
Efficient Minimal Solvers for Visual-Inertial Relative Pose Estimation in Multi-Camera Systems
多摄像头系统中视觉-惯性相对位姿估计的高效最小求解器
Abstract
Estimating the relative poses of multi-camera systems is a fundamental problem in computer vision, with critical applications in autonomous vehicles, mobile devices, and unmanned aerial vehicles (UAVs). However, existing solutions often suffer from high computational complexity or rely on an excessive number of point correspondences, limiting their real-world applicability. To address these limitations, we propose two efficient minimal solvers for estimating the relative poses of multi-camera systems using a novel parameterization. The first solver leverages the vertical direction prior provided by Inertial Measurement Units (IMUs), while the second utilizes the rotation axis direction prior from IMUs. Our methods require only four point correspondences and reduce the problem of multi-camera relative pose estimation to solving a univariate 6th-degree polynomial, a significant improvement over existing approaches, which typically involve 8th-degree polynomials. This reduction in computational complexity and correspondence requirements makes our solvers particularly effective when integrated into RANSAC frameworks, demonstrating strong potential for visual odometry applications. Through rigorous evaluations on synthetic data and the KITTI benchmark, our methods achieved superior computational efficiency and competitive accuracy compared to state-of-the-art algorithms.
Chinese Translation
估计多摄像头系统的相对位姿是计算机视觉中的一个基本问题，在自动驾驶车辆、移动设备和无人机（UAV）等领域具有重要应用。然而，现有的解决方案往往面临高计算复杂度或依赖过多的点对应关系，从而限制了其在实际应用中的可行性。为了解决这些限制，我们提出了两种高效的最小求解器，通过一种新颖的参数化方法来估计多摄像头系统的相对位姿。第一个求解器利用惯性测量单元（IMUs）提供的垂直方向先验，而第二个求解器则利用IMUs的旋转轴方向先验。我们的方法仅需四个点对应关系，并将多摄像头相对位姿估计问题简化为求解一个单变量6次多项式，这相比于通常涉及8次多项式的现有方法有了显著的改进。这种计算复杂度和对应关系要求的降低使得我们的求解器在集成到RANSAC框架中时特别有效，展示了在视觉里程计应用中的强大潜力。通过对合成数据和KITTI基准的严格评估，我们的方法在计算效率和准确性上均优于最先进的算法。
cs.CV / 191 / 2606.09479
Optical Music Recognition for Real-World Manuscripts with Synthetic Data
基于合成数据的真实世界手稿光学音乐识别
Abstract
Optical Music Recognition (OMR) has seen major progress in model design, with end-to-end methods now capable of recognising notation at all levels of complexity. However, the impact of this progress has been limited by the visual domains of available training datasets, which are largely born-digital. Existing large collections of sheet music in libraries and other heritage institutions contain predominantly manuscripts, whose visual domains are highly diverse and different, so existing OMR systems fail when applied in the real world. These institutions are often resource-constrained, so large in-domain datasets cannot be expected. We provide a first baseline on real-world manuscripts with complex piano notation in the resource-constrained scenario. Using fine-grained music notation graph (MuNG) annotations and the Smashcima synthesis tool, we then show that while some direct transcriptions of in-domain data remain essential, domain adaptation using synthetic musical manuscript images brings significant improvement. Furthermore, the symbols used do not need to be in-domain, so the expensive fine-grained annotation can be avoided. We thus bring OMR closer to one of its stated goals: preserving and promoting musical cultural heritage.
Chinese Translation
光学音乐识别（OMR）在模型设计方面取得了重大进展，端到端的方法现在能够识别各个复杂程度的乐谱。然而，这一进展的影响受到可用训练数据集的视觉领域的限制，这些数据集主要是数字原生的。现有的大型乐谱收藏主要存在于图书馆和其他文化遗产机构中，主要是手稿，其视觉领域高度多样且不同，因此现有的OMR系统在实际应用中表现不佳。这些机构通常资源有限，因此无法期待获得大型的领域内数据集。我们在资源受限的场景下提供了关于复杂钢琴乐谱的真实世界手稿的首次基准。通过使用细粒度音乐符号图（MuNG）注释和Smashcima合成工具，我们展示了尽管一些领域内数据的直接转录仍然至关重要，但使用合成音乐手稿图像进行领域适应带来了显著的改善。此外，所使用的符号不需要来自领域内，因此可以避免昂贵的细粒度注释。因此，我们使OMR更接近其既定目标之一：保护和促进音乐文化遗产。
cs.CV / 192 / 2606.09495
ContextShift: A Controlled Benchmark for Context Dependence in Object Detection
ContextShift：一个用于对象检测中上下文依赖性的受控基准
Abstract
Modern object detectors achieve strong performance on standard benchmarks, yet their robustness to contextual variation remains insufficiently understood. Prior evaluations largely rely on aggregate metrics such as AP on uncontrolled distribution shifts, which can obscure how performance degrades under context change. We introduce ContextShift, a controlled benchmark that systematically manipulates object--context relationships while preserving object appearance. Built on COCO 2017, it isolates context as an independent variable through geometric transformations and synthetic and natural background substitutions, including a continuous compatibility axis based on normalized pointwise mutual information (NPMI). Across diverse detector architectures, we observe a consistent degradation pattern: false negatives increase by up to 227% and prediction volume decreases by up to 44%, while false positives remain stable or decline. This suppression behavior is not captured by aggregate metrics such as AP, which can mask substantial recall loss and changes in prediction dynamics. Further analysis suggests that degradation is driven less by reduced confidence than by a reduced formation of valid detection candidates. Moreover, performance along the statistical compatibility axis is non-monotonic, peaking at intermediate NPMI and degrading toward both extremes, indicating that statistical co-occurrence does not correlate linearly with effective visual context. Finally, we show that context-aware augmentation improves robustness: every augmented variant outperforms the dataset-only baseline on both original and manipulated test images, partially recovering performance lost to prediction-suppression failures by exposing models to object--context decoupling during training.
Chinese Translation
现代对象检测器在标准基准上表现出强大的性能，但它们对上下文变化的鲁棒性仍然不够清楚。先前的评估主要依赖于诸如AP（平均精度）等聚合指标，针对不受控的分布变化，这可能掩盖了在上下文变化下性能下降的情况。我们引入了ContextShift，一个系统性操控对象与上下文关系的受控基准，同时保持对象外观。该基准基于COCO 2017，通过几何变换和合成及自然背景替换，将上下文作为独立变量进行隔离，包括基于归一化点互信息（NPMI）的连续兼容性轴。在不同的检测器架构中，我们观察到一致的性能下降模式：假阴性增加最多227%，而预测量减少最多44%，同时假阳性保持稳定或下降。这种抑制行为未被诸如AP等聚合指标捕捉到，这可能掩盖了显著的召回损失和预测动态的变化。进一步的分析表明，性能下降的驱动因素与信心降低关系不大，而是与有效检测候选的形成减少有关。此外，沿统计兼容性轴的性能呈非单调性，在中间NPMI处达到峰值，并在两个极端处下降，表明统计共现与有效视觉上下文之间并不存在线性相关性。最后，我们展示了上下文感知增强提高了鲁棒性：每个增强变体在原始和操控的测试图像上均优于仅使用数据集的基线，部分恢复了由于预测抑制失败而损失的性能，通过在训练期间使模型接触到对象与上下文的解耦。
cs.CV / 193 / 2606.09507
Prisma-World: Camera-Controllable Multi-Agent Video World Model
Prisma-World：可控摄像机的多智能体视频世界模型
Abstract
Video world models have made rapid progress in generating controllable visual experiences, but most of them still simulate the world from a single observer. Extending such models to multiple agents raises a central challenge: if each agent's future state is generated independently, overlapping views may instantiate different versions of the same scene, leading to inconsistent objects, layouts, and appearances across agents. Conventional camera conditioning controls individual trajectories, but it does not explicitly couple the generation of views that should agree under shared scene geometry. We introduce Prisma-World, a camera-controllable multi-agent world model that formulates multi-agent generation as a joint geometry-aware denoising process for cross-view consistency. Prisma-World processes all agent videos within one full-attention sequence, uses a multi-agent RoPE design to distinguish agent identities while preserving synchronized temporal coordinates, and injects relative camera geometry into attention to bias overlapping viewpoints toward shared scene evidence. To further strengthen multi-view consistency and enhance global spatial perception, we augment our framework with an overlap-decaying curriculum training paradigm alongside minimap-conditioned structural guidance. To facilitate the training and evaluation of multi-agent models, we introduce PrismaDataset, a large-scale UE5 dataset with panoramic acquisition across diverse scenes, composable multi-agent view groups with flexible agent counts and complex camera trajectories, and precise camera/action annotations for consistency training and evaluation. Experiments show that a single Prisma-World model can generate high-fidelity multi-agent videos with flexible agent numbers, camera controllability, improved cross-view consistency, and spatial grounding under minimap guidance.
Chinese Translation
视频世界模型在生成可控视觉体验方面取得了快速进展，但大多数模型仍然是从单一观察者的角度进行世界模拟。将此类模型扩展到多个智能体面临一个核心挑战：如果每个智能体的未来状态是独立生成的，重叠视图可能会实例化同一场景的不同版本，从而导致智能体之间对象、布局和外观的不一致。传统的摄像机条件控制单个轨迹，但并未明确耦合在共享场景几何下应一致的视图生成。我们提出了Prisma-World，一种可控摄像机的多智能体世界模型，将多智能体生成形式化为一种联合几何感知去噪过程，以实现跨视图一致性。Prisma-World在一个完整的注意力序列中处理所有智能体视频，采用多智能体的RoPE设计来区分智能体身份，同时保持同步的时间坐标，并将相对摄像机几何注入注意力中，以引导重叠视点朝向共享场景证据。为了进一步增强多视图一致性和提升全局空间感知，我们在框架中加入了重叠衰减的课程训练范式，并辅以小地图条件的结构指导。为便于多智能体模型的训练和评估，我们引入了PrismaDataset，这是一个大规模的UE5数据集，涵盖多样场景的全景采集、可组合的多智能体视图组，具有灵活的智能体数量和复杂的摄像机轨迹，以及用于一致性训练和评估的精确摄像机/动作注释。实验表明，单个Prisma-World模型能够生成高保真度的多智能体视频，具备灵活的智能体数量、摄像机可控性、改进的跨视图一致性以及在小地图指导下的空间定位。
cs.CV / 194 / 2606.09511
Securing Self-supervised Data Curation for Foundation Models Robustness
确保自监督数据策划以增强基础模型的鲁棒性
Abstract
Self-supervised data curation provides a pathway to scaling and improving the generalization capabilities of machine learning models. By leveraging self-supervised learning (SSL) for data curation, the demand for massive training datasets required by foundation models can be effectively met. SSL greatly alleviates the costs associated with annotation and manual dataset curation while minimizing the need for human oversight. However, the integrity of SSL-curated datasets must be rigorously checked, as reliance on anonymous and unvetted external sources can substantially increase the risk of data poisoning. In this paper, we propose a Poisoned Data Detector (PDD), an active defense mechanism designed to ensure the integrity of SSL-curated datasets prior to foundation model training. PDDs are designed using a combination of the pretrained ImageBind model and traditional classifiers, including Random Forest (RF), k-Nearest Neighbors (KNN), Naive Bayes (NB), and Support Vector Machines (SVM). We rigorously evaluated PDDs using 176,200 images from three diverse datasets and three different adversarial attacks encompassing both in-distribution and out-of-distribution scenarios. Notably, SVM-PDD achieves superior performance for both in-distribution (Set3-Set5) and out-of-distribution (TrueFace and 140K RealFace) datasets. Our design demonstrates strong scalability and enables the rapid integration of new adversarial attack detectors through an ensemble approach.
Chinese Translation
自监督数据策划为扩展和提升机器学习模型的泛化能力提供了一条途径。通过利用自监督学习（SSL）进行数据策划，可以有效满足基础模型所需的大规模训练数据集的需求。SSL大大减轻了与标注和手动数据集策划相关的成本，同时最小化了对人工监督的需求。然而，SSL策划的数据集的完整性必须经过严格检查，因为依赖匿名和未经审查的外部来源可能会显著增加数据中毒的风险。在本文中，我们提出了一种被污染数据检测器（Poisoned Data Detector, PDD），这是一种主动防御机制，旨在确保在基础模型训练之前SSL策划数据集的完整性。PDD的设计结合了预训练的ImageBind模型和传统分类器，包括随机森林（Random Forest, RF）、k近邻（k-Nearest Neighbors, KNN）、朴素贝叶斯（Naive Bayes, NB）和支持向量机（Support Vector Machines, SVM）。我们使用来自三个不同数据集的176,200张图像以及三种不同的对抗攻击，严格评估了PDD的性能，这些攻击涵盖了分布内和分布外的场景。值得注意的是，SVM-PDD在分布内（Set3-Set5）和分布外（TrueFace和140K RealFace）数据集上均表现出优越的性能。我们的设计展示了强大的可扩展性，并通过集成方法快速整合新的对抗攻击检测器。
cs.CV / 195 / 2606.09516
SwiftVR: Real-Time One-Step Generative Video Restoration
SwiftVR：实时一步生成视频恢复
Abstract
Real-time video restoration (VR) for live streams requires high-resolution outputs under strict per-frame latency constraints. Existing one-step diffusion-based VR models remain difficult to deploy on consumer-grade GPUs due to two main bottlenecks: quadratic spatial attention at high resolutions and the latency-memory overhead of large video autoencoders. We present SwiftVR, a streaming one-step generative VR framework that reduces both bottlenecks under a causal chunk-wise protocol. For attention, mask-free shifted-window self-attention gathers each spatial window into a dense tensor via deterministic indexing, keeping all attention calls on the dense scaled dot-product attention path without masks, cyclic shifts, padding, or hardware-specific sparse kernels. Because SwiftVR uses only standard dense SDPA calls, the trained model transfers to consumer GPUs without retraining or custom kernels. For autoencoding, a lightweight Restoration-aware Autoencoder enables fast chunk-wise decoding while preserving reconstruction quality. On a single H100, SwiftVR sustains 31~FPS at 2560x1440 and 14~FPS at 3840x2160, whereas all compared diffusion-based VR baselines exceed the memory limit at 4K. On a consumer RTX~5090, SwiftVR reaches 26~FPS at 1920x1080. To our knowledge, SwiftVR is the first generative VR model to achieve real-time 1080p streaming on a consumer-grade GPU, while attaining strong no-reference perceptual quality with lower inference cost. Project is available at https://h-oliday.github.io/SwiftVR.
Chinese Translation
实时视频恢复（VR）用于直播流需要在严格的每帧延迟限制下输出高分辨率图像。现有的一步扩散基础的VR模型由于两个主要瓶颈而难以在消费级GPU上部署：高分辨率下的二次空间注意力和大型视频自编码器的延迟-内存开销。我们提出了SwiftVR，一个流式的一步生成VR框架，在因果块协议下减少了这两个瓶颈。在注意力机制方面，无掩码的移位窗口自注意力通过确定性索引将每个空间窗口聚集成一个密集张量，使所有注意力调用保持在密集的缩放点积注意力路径上，而无需掩码、循环移位、填充或特定硬件的稀疏内核。由于SwiftVR仅使用标准的密集SDPA调用，训练后的模型可以在不重新训练或使用自定义内核的情况下转移到消费级GPU上。在自编码方面，轻量级的恢复感知自编码器实现了快速的块状解码，同时保持重建质量。在单个H100上，SwiftVR在2560x1440分辨率下维持31帧每秒（FPS），在3840x2160分辨率下维持14帧每秒，而所有比较的基于扩散的VR基线在4K分辨率下超出了内存限制。在消费级RTX 5090上，SwiftVR在1920x1080分辨率下达到了26帧每秒。据我们所知，SwiftVR是第一个在消费级GPU上实现实时1080p流的生成VR模型，同时以较低的推理成本获得强大的无参考感知质量。项目可在 https://h-oliday.github.io/SwiftVR 获取。
cs.CV / 196 / 2606.09536
Adversarial Attack and Disturbance Detection by Hadamard-Coded Output Representations for Object Detection and Semantic Segmentation
基于哈达码输出表示的对抗攻击与干扰检测在目标检测和语义分割中的应用
Abstract
Conventional one-hot encodings often yield poorly calibrated models, being overconfident under attack, and letting entropy-based detection algorithms fail. Previous image classification works have demonstrated that Hadamard-coded output representations can improve adversarial robustness. However, attempts to integrate Hadamard codes into semantic segmentation fall far behind state-of-the-art models in mean intersection-over-union performance. Regarding object detection, such output encodings have not yet been investigated at all. Further, no prior art addressed intrinsic codeword inconsistencies or actually exploited intrinsic codeword redundancy. Accordingly, we first derive a novel decoding procedure for Hadamard codewords towards optimal class-wise probabilities, solving the underlying optimization problem by using the projection onto the probability simplex. Second, our optimization delivers a measure of prediction inconsistency. Third, we are the first to show how to exploit these inconsistencies for adversarial attack and disturbance detection. Fourth, we introduce HadamardNet, a framework employing Hadamard codes as output representations for semantic segmentation and object detection models and tasks. We conduct a comprehensive evaluation both on disturbances and adversarial attacks, achieving state-of-the-art perturbation detection performance for both tasks in only a single detection pass, while delivering equivalent or close-by reference performance on clean data.
Chinese Translation
传统的一热编码通常导致模型校准不佳，在攻击下表现出过度自信，从而使基于熵的检测算法失效。先前的图像分类研究表明，哈达码输出表示可以提高对抗鲁棒性。然而，将哈达码集成到语义分割中的尝试在平均交并比性能上远远落后于最先进的模型。在目标检测方面，此类输出编码尚未得到研究。此外，现有文献没有解决内在码字不一致性或实际利用内在码字冗余。因此，我们首先推导出一种新的哈达码字解码程序，以实现最佳类别概率，通过投影到概率单纯形来解决潜在的优化问题。其次，我们的优化提供了预测不一致性的度量。第三，我们首次展示了如何利用这些不一致性进行对抗攻击和干扰检测。第四，我们引入了HadamardNet，一个将哈达码作为语义分割和目标检测模型及任务的输出表示的框架。我们对干扰和对抗攻击进行了全面评估，在仅一次检测过程中实现了这两项任务的最先进的干扰检测性能，同时在干净数据上提供了等效或接近的参考性能。
cs.CV / 197 / 2606.09542
A VideoMAE-v2 Approach to Zero-Shot Traffic Accident Anticipation
基于 VideoMAE-v2 的零-shot 交通事故预测方法
Abstract
Traffic accident anticipation -- predicting the likelihood of an imminent collision at every frame of a dashcam video -- is safety-critical yet difficult to scale, because collecting in-domain annotated accident footage for every deployment scenario is prohibitively expensive. We study this task under a zero-shot setting where no target-domain training data is available: the model must learn exclusively from a publicly available binary-labelled driving-accident dataset and generalise to unseen dashcam footage. We propose a framework that bridges the gap between the frame-level temporal risk estimation task and coarsely labelled binary accident datasets by coupling a VideoMAE-v2 backbone with a per-frame prediction head under a sliding-window protocol. Our method achieves 2nd place in the 2026 CVPR@AUTOPILOT Zero-Shot Accident Anticipation competition. Code is available at https://github.com/TimeSouth/zero-shot-taa-solution.
Chinese Translation
交通事故预测——在行车记录仪视频的每一帧中预测即将发生碰撞的可能性——是安全关键的任务，但由于为每种部署场景收集领域内标注的事故视频成本过高，因此难以扩展。我们在零-shot 设置下研究这一任务，其中没有可用的目标领域训练数据：模型必须仅从公开的二元标注驾驶事故数据集中学习，并对未见过的行车记录仪视频进行泛化。我们提出了一种框架，通过将 VideoMAE-v2 主干与基于滑动窗口协议的逐帧预测头相结合，弥合帧级时间风险估计任务与粗略标注的二元事故数据集之间的差距。我们的方法在2026年 CVPR@AUTOPILOT 零-shot 事故预测竞赛中获得第二名。代码可在 https://github.com/TimeSouth/zero-shot-taa-solution 获取。
cs.CV / 198 / 2606.09547
Streaming Interventions: Can Video Large Language Models Correct Mistakes as They Occur?
流媒体干预：视频大型语言模型能否在错误发生时进行纠正？
Abstract
Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos. This opens the door to the use of video (and multimodal) large language models (LLMs) as task guidance assistants. A crucial capability for the real-world success of a prospective task guidance assistant is it's ability to intervene proactively as soon as a mistake is apparent in order to guide the user. To evaluate this crucial capability, we introduce Ego-MC-Bench (Mistake Corrections), a benchmark for evaluating reactive, step-by-step task guidance in realistic cooking scenarios. Extensive experiments show that Ego-MC-Bench is highly challenging for state-of-the-art video LLMs. We argue that a key reason is the limited availability of training data for fine-tuning models on this task. Although there exists a wide range of cooking video datasets, existing datasets lack examples of mistakes along with appropriately timed interventions. To help address this data limitation, we also introduce Ego-CoMist, a counterfactual synthetic dataset created by transforming non -interactive cooking videos into supervised training examples showing proactive interventions. We show that fine-tuning on Ego-CoMist yields performance gains especially for smaller and more efficient video LLMs that are well suited for delivering assistance on edge devices.
Chinese Translation
学习日常技能，如烹饪一道菜，越来越依赖于在线视频等教学媒体。这为视频（及多模态）大型语言模型（LLMs）作为任务指导助手的应用打开了大门。一个潜在任务指导助手在现实世界成功的关键能力是能够在错误明显出现时主动干预，以指导用户。为了评估这一关键能力，我们引入了Ego-MC-Bench（错误纠正），这是一个用于评估在现实烹饪场景中反应性逐步任务指导的基准。大量实验表明，Ego-MC-Bench对最先进的视频LLMs来说具有很高的挑战性。我们认为，关键原因在于用于微调模型以应对这一任务的训练数据有限。尽管存在多种烹饪视频数据集，但现有数据集缺乏错误示例及其适时干预的案例。为了解决这一数据限制，我们还引入了Ego-CoMist，这是一个通过将非交互式烹饪视频转化为显示主动干预的监督训练示例而创建的反事实合成数据集。我们展示了在Ego-CoMist上进行微调，尤其是对于更小且更高效的视频LLMs，能够带来性能提升，这些模型非常适合在边缘设备上提供帮助。
cs.CV / 199 / 2606.09608
TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution
TUDSR：双重上采样扩散用于更高超分辨率
Abstract
Diffusion-based generative models have achieved remarkable success in real-world image super-resolution (SR). With tiled diffusion techniques, these models can produce high-resolution images that exceed their native-supported resolution. However, the quality of such high-resolution (e.g $2048^2$) outputs often remains extremely poor, primarily due to two factors we consider: the image upsampling ratio (e.g $\times8$) exceeding the model's native-supported upsampling ratio (e.g $\times4$), and the model's native-supported resolution. In practice, training a native high-resolution model requires larger architectures, which incur significant computational overhead and GPU memory costs, making it hard on limited-resource equipment. Thus, we present TUDSR, a Twice Upsampling-Diffusion framework for higher SR. The TUDSR framework mainly consists of two stages: the first involves training at $R$-resolution, and the second introduces a looped chunk-based training strategy at $NR$-resolution. Each stage adapts a one-step GAN architecture comprising a generator and a discriminator. Based on SD2.1-base, we develop TUDSR-S, which achieves state-of-the-art performance across multiple benchmarks. Extensive experiments further demonstrate that TUDSR-S generates high-quality images at the resolutions of $1024^2$ and even $2048^2$, significantly outperforming existing approaches. Code is available at https://github.com/wuer5/TUDSR.
Chinese Translation
基于扩散的生成模型在实际图像超分辨率（SR）方面取得了显著成功。通过平铺扩散技术，这些模型能够生成超出其原生支持分辨率的高分辨率图像。然而，这种高分辨率（例如 $2048^2$）输出的质量往往仍然极差，主要由于我们考虑的两个因素：图像上采样比（例如 $ imes8$）超过模型原生支持的上采样比（例如 $ imes4$），以及模型的原生支持分辨率。在实践中，训练一个原生高分辨率模型需要更大的架构，这会导致显著的计算开销和GPU内存成本，使得在资源有限的设备上变得困难。因此，我们提出了TUDSR，一个用于更高超分辨率的双重上采样扩散框架。TUDSR框架主要由两个阶段组成：第一阶段在 $R$-分辨率下进行训练，第二阶段在 $NR$-分辨率下引入循环块训练策略。每个阶段适应一个包含生成器和鉴别器的一步GAN架构。基于SD2.1-base，我们开发了TUDSR-S，在多个基准测试中实现了最先进的性能。大量实验进一步表明，TUDSR-S在 $1024^2$ 甚至 $2048^2$ 的分辨率下生成高质量图像，显著优于现有方法。代码可在 https://github.com/wuer5/TUDSR 获取。
cs.CV / 200 / 2606.09634
ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity
ATN3D：极端稀疏条件下的密度感知LiDAR-雷达早期3D目标检测
Abstract
3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic. Although >30m is often labeled long-range in computer vision, on roadways it affords only approx. 1-2s for perception and decision-making. Under such extreme sparsity, two core challenges arise. First, early multimodal fusion tends to discard sparsity information and inject noise from empty or falsely occupied cells, degrading long-range recall. Second, context-agnostic uniform channel supervision favors dense and near-range samples, leaving far and small objects under-optimized, delaying the earliest detection of distant objects. We propose ``Ask The Neighbor'' (ATN3D), a LiDAR-Radar framework tailored for sparse-range conditions. ATN3D introduces (i) Density-aware early fusion with cross-modal gating that conditions fusion on per-voxel density/sparsity and Radar evidence, (ii) Occupancy-gated neighborhood aggregation with circular kernels to aggregate only from credible cells, (iii) Evidence-conditioned channel self-attention to adapt channel weights with weather/range, and (iv) a Range-aware loss that re-balances classification and localization by distance, aligning training with distance-stratified evaluation. On the VoD benchmark across clear and foggy conditions, ATN3D surpasses strong baselines: +3.55% mAP in clear weather and +8.41% mAP under simulated heavy fog; for >30m objects, gains are +3.33% (clear) and +2.09% (heavy fog). These results indicate earlier and more reliable long-range detections under sparse sensing in on-road traffic.
Chinese Translation
3D目标检测是自动驾驶车辆（AV）及更广泛的智能交通系统应用的感知基础。由于传感证据稀疏，远程检测面临挑战；然而，这种“远程”场景在交通中是常态。尽管在计算机视觉中，超过30米通常被标记为远程，但在道路上，这仅提供约1-2秒的感知和决策时间。在如此极端的稀疏条件下，出现了两个核心挑战。首先，早期多模态融合往往会丢弃稀疏信息，并从空或错误占用的单元中注入噪声，从而降低远程召回率。其次，缺乏上下文的均匀通道监督偏向于密集和近距离样本，导致远处和小物体未得到优化，延迟了对远处物体的最早检测。我们提出了“Ask The Neighbor”（ATN3D），一个针对稀疏范围条件的LiDAR-雷达框架。ATN3D引入了（i）基于密度的早期融合，结合跨模态门控，依据每个体素的密度/稀疏性和雷达证据进行融合，（ii）占用门控邻域聚合，使用圆形核仅从可信单元聚合，（iii）证据条件下的通道自注意力，根据天气/范围调整通道权重，以及（iv）基于范围的损失，通过距离重新平衡分类和定位，将训练与距离分层评估对齐。在清晰和雾霾条件下的VoD基准测试中，ATN3D超越了强基线：在晴天条件下提高了3.55%的mAP，在模拟重雾条件下提高了8.41%的mAP；对于超过30米的物体，增益分别为3.33%（晴天）和2.09%（重雾）。这些结果表明，在道路交通中，稀疏传感下的远程检测更早且更可靠。
cs.CV / 201 / 2606.09639
CineDance: Towards Next-Generation Multi-Shot Long-Form Cinematic Audio-Video Generation
CineDance：迈向下一代多镜头长篇电影音视频生成
Abstract
The fidelity and structural diversity of training datasets fundamentally determine the capabilities of video generation models. While commercial systems showremarkableabilitytogeneratecinematicnarratives, the progress of open-source models remains limited by the scarcity of high-quality training data. To bridge this gap, we introduce CineDance-1M, a large-scale, open research Text-to-Audio-Video (T2AV) dataset designed specifically for multi-shot, long-form joint audio-video generation. Averaging 92.8 seconds and 24.2 continuous shots per video, it provides configurable, structured annotations for both audio and video modalities. This exceptional quality is achieved through a rigorous three-stage curation pipeline: i) diverse sourcing and comprehensive cleansing, ii) film-theory-inspired narrative parsing, and iii) hierarchical dual-modal captioning. For a comprehensive assessment, we propose CineBench, featuring a diverse prompt suite and a six-dimensional, human-aligned metric system tailored for complex narrative audio-video evaluation. Furthermore, we adapt LTX-2.3 into CineDance, which demonstrates exceptional single-modality quality alongside precise audio-video alignment and robust subject and environment consistency, effectively validating our curation strategy and the high quality of CineDance-1M. We anticipate that this work will serve as a solid foundation for accelerating future research in multi-shot, long-form joint audio-video generation. Our project page is available at https://aliothchen.github.io/projects/CineDance/.
Chinese Translation
训练数据集的保真度和结构多样性从根本上决定了视频生成模型的能力。尽管商业系统在生成电影叙事方面表现出色，但开源模型的进展仍受到高质量训练数据稀缺的限制。为了解决这一问题，我们推出了CineDance-1M，这是一个大规模的开放研究文本到音视频（Text-to-Audio-Video, T2AV）数据集，专门设计用于多镜头长篇联合音视频生成。每个视频平均时长为92.8秒，包含24.2个连续镜头，提供可配置的结构化音频和视频注释。这一卓越质量是通过严格的三阶段策展流程实现的：i) 多样化来源和全面清理，ii) 受电影理论启发的叙事解析，以及iii) 层次化的双模态字幕生成。为了进行全面评估，我们提出了CineBench，包含多样化的提示套件和一个针对复杂叙事音视频评估的六维人类对齐度量系统。此外，我们将LTX-2.3适配到CineDance中，展示了卓越的单模态质量，以及精确的音视频对齐和强大的主题与环境一致性，有效验证了我们的策展策略和CineDance-1M的高质量。我们期待这项工作能够为加速未来多镜头长篇联合音视频生成研究奠定坚实基础。我们的项目页面可在 https://aliothchen.github.io/projects/CineDance/ 查阅。
cs.CV / 202 / 2606.09641
MAVIS: Multi-Agent Video Retrieval via Structured Video Understanding
MAVIS：通过结构化视频理解进行多智能体视频检索
Abstract
The dominant paradigm in video retrieval relies on embedding-based full-corpus scanning, which suffers from inherent computational inefficiency and the semantic asymmetry between information-dense videos and sparse textual queries. To bridge this gap, we introduce \textbf{MAVIS}, a novel multi-agent framework that rethinks retrieval as cooperative reasoning rather than brute-force search. MAVIS first bridges the granularity mismatch by parsing raw videos into a \textbf{Structured Semantic Library}, enabling explicit attribute-level indexing. During retrieval, a planner decomposes complex user intents into atomic sub-tasks, dispatching specialized agents to independently nominate candidates. Crucially, MAVIS employs a \textbf{Logic-aware Debate} mechanism with a strict veto protocol, where agents collaboratively prune logical mismatches to identify a compact set of ``controversial'' candidates for fine-grained verification. This agentic workflow effectively bypasses the inefficiency of full-library traversal. Extensive experiments on MSR-VTT, MSVD, and ActivityNet demonstrate that MAVIS achieves competitive performance without task-specific fine-tuning, offering a scalable and interpretable alternative to traditional dual-encoder approaches.
Chinese Translation
视频检索的主流范式依赖于基于嵌入的全语料库扫描，这种方法存在固有的计算低效性以及信息密集型视频与稀疏文本查询之间的语义不对称。为了解决这一问题，我们提出了 extbf{MAVIS}，一个新颖的多智能体框架，将检索重新定义为合作推理，而非简单的暴力搜索。MAVIS首先通过将原始视频解析为 extbf{结构化语义库}来弥补粒度不匹配，从而实现显式的属性级索引。在检索过程中，规划者将复杂的用户意图分解为原子子任务，派遣专业代理独立提名候选项。关键是，MAVIS采用了 extbf{逻辑感知辩论}机制，并设定严格的否决协议，代理们协作修剪逻辑不匹配，从而识别出一组紧凑的“争议”候选项以进行细粒度验证。这种智能体工作流程有效地绕过了全库遍历的低效性。在MSR-VTT、MSVD和ActivityNet上的大量实验表明，MAVIS在没有任务特定微调的情况下实现了具有竞争力的性能，为传统的双编码器方法提供了一种可扩展且可解释的替代方案。
cs.CV / 203 / 2606.09646
Do Video Foundation Models Understand Intuitive Physics? A Layerwise Probing Analysis
视频基础模型是否理解直观物理？逐层探测分析
Abstract
We study whether pretrained video foundation models encode intuitive-physics information in their frozen representations, and how this information varies across model families, layers, and probe types. Using frozen-feature probing on IntPhys2 and Minimal Video Pairs (MVP), we compare predictive joint-embedding models (V-JEPA), masked reconstruction models (VideoMAE), and a diffusion-based video generator (LTX-Video). V-JEPA achieves the strongest overall results across benchmarks, especially with probes that model temporal dynamics, while VideoMAE remains competitive and LTX-Video recovers weaker but non-trivial signal. Layerwise analyses show that physics-relevant information is weakest in early layers and becomes most accessible at intermediate-to-late depth, and temporal controls show that disrupting frame order substantially reduces performance, especially on MVP. Together, these results suggest that intuitive-physics knowledge emerges reliably in pretrained video representations, but its accessibility depends strongly on pretraining paradigm, representational depth, and readout mechanism.
Chinese Translation
我们研究了预训练的视频基础模型是否在其固定表示中编码了直观物理信息，以及这种信息在不同模型家族、层次和探测类型之间的变化。通过对 IntPhys2 和最小视频对（Minimal Video Pairs, MVP）进行固定特征探测，我们比较了预测联合嵌入模型（V-JEPA）、掩蔽重建模型（VideoMAE）和基于扩散的视频生成器（LTX-Video）。V-JEPA 在各基准测试中取得了最强的整体结果，尤其是在建模时间动态的探测器上，而 VideoMAE 仍然具有竞争力，LTX-Video 则恢复了较弱但非平凡的信号。逐层分析表明，物理相关信息在早期层次中最弱，而在中到晚期深度中最易获取，时间控制显示干扰帧顺序会显著降低性能，尤其是在 MVP 上。综合来看，这些结果表明，直观物理知识在预训练的视频表示中可靠地显现，但其可获取性在很大程度上依赖于预训练范式、表示深度和读取机制。
cs.CV / 204 / 2606.09670
Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision
视觉提示与基于特征重建的异常检测相结合：双教师监督
Abstract
Recent Anomaly Detection methods achieve perfect detection and segmentation scores on well-established datasets, such as MVTec. However, many of these methods face challenges when foundational assumptions - such as consistent object scale, viewpoint, background, illumination, and centered placement - are violated. Those variations that occur render anomaly detection methods unusable in many real-world scenarios. To address these limitations, we introduce three key contributions: (1) a visual prompting pipeline that isolates objects using foreground-background masking; (2) a mechanism for unfreezing the teacher in student-teacher models to improve domain adaptability; and (3) a data augmentation strategy leveraging diffusion-generated synthetic images to enhance anomaly detection performance. We achieve a 3.5 percentage point improvement over the previous state-of-the-art on the challenging AeBAD dataset by using the Masked Multiscale Reconstruction (MMR) model as our backbone.
Chinese Translation
近期的异常检测方法在诸如 MVTec 等成熟数据集上实现了完美的检测和分割得分。然而，当基础假设——如一致的物体尺度、视角、背景、照明和中心放置——被违反时，这些方法面临诸多挑战。这些变化使得异常检测方法在许多现实场景中无法使用。为了解决这些局限性，我们提出了三项关键贡献：（1）一个通过前景-背景掩蔽隔离物体的视觉提示管道；（2）一个在学生-教师模型中解冻教师的机制，以提高领域适应性；（3）一种利用扩散生成的合成图像的数据增强策略，以提升异常检测性能。我们使用 Masked Multiscale Reconstruction (MMR) 模型作为基础，在具有挑战性的 AeBAD 数据集上实现了比之前的最先进技术提高了 3.5 个百分点的成绩。
cs.CV / 205 / 2606.09679
SoccerNet 2026 Player-Centric Ball-Action Spotting:Retraining and Post-Processing Extensions to the FOOTPASS Baselines
SoccerNet 2026 以球员为中心的球类动作识别：对 FOOTPASS 基线的再训练和后处理扩展
Abstract
We describe our system for the SoccerNet 2026 Player-Centric Ball-Action Spotting Challenge, which requires predicting who performs which action and when, across eight classes in broadcast soccer. Building on the three FOOTPASS baselines [1] (TAAD, TAAD+GNN, and TAAD+DST), we contribute four extensions: (1) gradient check pointing to enable full-backbone fine-tuning on a single GPU; (2) fusion of GNN logits into the DST encoder, combining graph-based tactical context with per-player visual features; (3) square-root frequency class weighting to address the 213:1 pass-to-tackle imbalance in the training data; and (4) a post processing pipeline comprising per-class logit gating, temporal frame refinement, jersey re-assignment, and a two-model ensemble. Our system achieves 0.548 Macro F1 on the test set and 0.446 on the challenge set (server evaluation).
Chinese Translation
我们描述了用于 SoccerNet 2026 以球员为中心的球类动作识别挑战的系统，该挑战要求预测在广播足球中谁在何时执行何种动作，共涉及八个类别。基于三个 FOOTPASS 基线 [1]（TAAD、TAAD+GNN 和 TAAD+DST），我们贡献了四个扩展： (1) 梯度检查点以支持在单个 GPU 上进行全骨干网络的微调； (2) 将 GNN 逻辑融合到 DST 编码器中，结合基于图的战术上下文与每个球员的视觉特征； (3) 平方根频率类别加权，以解决训练数据中 213:1 的传球与铲球不平衡问题； (4) 一个后处理管道，包括每类逻辑门控、时间帧细化、球衣重新分配和两个模型的集成。我们的系统在测试集上达到了 0.548 的宏 F1 值，在挑战集（服务器评估）上达到了 0.446。
cs.CV / 206 / 2606.09681
GenEyePose: Patient-Free, Knowledge-Based Saccadic Eye Movement Modeling for Digital Neurophysiologic Biomarker Development
GenEyePose：无患者、基于知识的眼球跳动建模用于数字神经生理生物标志物开发
Abstract
Eye movements, including saccades, are widely regarded as highly sensitive and objective biomarkers of neurophysiologic states. Detecting saccadic signatures in neurologic diseases offers a rapid, portable alternative to brain imaging, avoiding access and cost barriers. Currently, there are no robust AI-enabled video-oculographic solutions (e.g., digital biomarkers) for screening, triaging, or localizing brain abnormalities due to privacy issues and scarce datasets. In this work, we propose the first fully synthetic, patient-free, multimodal eye movement generation pipeline for generalizable saccade analysis. Using this synthetic dataset, we trained a deep learning classifier to distinguish between normal and abnormal (hypometria and hypermetria) saccadic accuracies and evaluated its performance on real-world clinical data. The model achieved an AUROC of 0.76 and a sensitivity of 0.71, showing that the synthetic data has strong potential to generalize for clinical applications, including as a screening tool in at-home and emergency room settings or a tool for precise neuroanatomic localization.
Chinese Translation
眼动，包括眼球跳动，被广泛视为神经生理状态的高度敏感和客观的生物标志物。在神经疾病中检测眼球跳动特征提供了一种快速、便携的替代脑成像的方法，避免了获取和成本的障碍。目前，由于隐私问题和数据集稀缺，尚无强大的人工智能驱动的视频眼动图解决方案（例如，数字生物标志物）用于筛查、分诊或定位脑部异常。在本研究中，我们提出了首个完全合成的、无患者的、多模态眼动生成管道，用于可推广的眼球跳动分析。利用这一合成数据集，我们训练了一个深度学习分类器，以区分正常和异常（低跳动和高跳动）眼球跳动的准确性，并在真实世界临床数据上评估其性能。该模型实现了0.76的AUROC和0.71的敏感性，表明合成数据在临床应用中具有强大的推广潜力，包括作为家庭和急诊室环境中的筛查工具或用于精确神经解剖定位的工具。
cs.CV / 207 / 2606.09699
Cranio-Diff: Diffusion-based Cross-domain Craniofacial Reconstruction with 2D X-ray Skull Guidance and Structural Identity Constraints
Cranio-Diff：基于扩散的跨领域颅面重建，结合2D X光头骨引导和结构身份约束
Abstract
The state-of-the-art generative models, such as CycleGAN, Pix2Pix, and diffusion models have demonstrated remarkable performance in the face generation task. However, they fail to effectively capture cross-modality semantic information in craniofacial reconstruction when translating from the skull (x-ray) to the face (optical) domain, due to a mismatch in the alignment of structural identity across modalities. To address this issue, we propose Cranio-Diff, a diffusion-based framework for cross-domain cranio-facial reconstruction from 2D X-ray skull images. The proposed approach integrates skull-conditioned structural guidance through ControlNet with biometric text conditioning to generate a face which is more semantically and structurally aligned with the given skull. The proposed Cranio-diff method is evaluated on skull-face dataset obtained from X-ray scans of 120 subjects in lateral and frontal views. To enable controlled evaluation, each face image is synthesised across three age groups (25, 45, 65) and three BMI variations of -10%, baseline and +10%, yielding 4320 paired samples. To the best of our knowledge, this is the only X-ray-face dataset with this magnitude. Extensive experiments showed that the proposed method outperforms recent existing approaches in both generated image quality and retrieval task. Finally, to evaluate the performance of our proposed method, we have evaluated the quality of the generated image using FID, IS, SSIM, LPIPS, PSNR and ArcFace score. Additionally, retrieval performance is evaluated using recall@k, mAP@k and MRR@k. Obtained experimental results demonstrate that the proposed method can be used as an alternate tool in providing aid in forensic investigations.
Chinese Translation
最先进的生成模型，如CycleGAN、Pix2Pix和扩散模型，在面部生成任务中表现出色。然而，在从头骨（X光）到面部（光学）领域的颅面重建中，它们未能有效捕捉跨模态的语义信息，原因在于不同模态间结构身份的对齐不匹配。为了解决这一问题，我们提出了Cranio-Diff，一种基于扩散的跨领域颅面重建框架，利用2D X光头骨图像。该方法通过ControlNet整合头骨条件的结构引导与生物特征文本条件，生成与给定头骨在语义和结构上更为一致的面部图像。我们在从120名受试者的侧面和正面X光扫描中获得的头骨-面部数据集上评估了Cranio-Diff方法。为了实现可控评估，每张面部图像在三个年龄组（25岁、45岁、65岁）和三个BMI变化（-10%、基线和+10%）下合成，共生成4320对样本。据我们所知，这是唯一一个具有如此规模的X光-面部数据集。大量实验表明，所提方法在生成图像质量和检索任务上均优于现有的最新方法。最后，为了评估我们提出方法的性能，我们使用FID、IS、SSIM、LPIPS、PSNR和ArcFace评分评估生成图像的质量。此外，检索性能通过recall@k、mAP@k和MRR@k进行评估。实验结果表明，所提方法可以作为法医调查中的一种替代工具。
cs.CV / 208 / 2606.09738
HDSL: A Hierarchical Domain-Specific Language for Structured 3D Indoor Scene Generation and Localized Editing with LLM Agents
HDSL：一种用于结构化3D室内场景生成和与LLM代理局部编辑的层次化领域特定语言
Abstract
Text-driven indoor scene generation and editing require an intermediate representation that language models can both produce and revise. Existing LLM-based systems often rely on scene graphs or global constraint lists, which are compact but underspecify local geometry and make instruction-based edits difficult to localize. We frame this problem as structured program generation and local program repair, and propose Hierarchical Descriptive Scene Language (HDSL), an XML/CSS-style domain-specific language for structured 3D indoor scenes. HDSL represents rooms, regions, objects, and support surfaces as a tree with local coordinates, making complex scenes easier to plan recursively and easier to retrieve for editing. Our pipeline uses LLM agents to generate HDSL subtrees with bounded verification, grounds non-virtual nodes through multimodal asset retrieval, and applies force-directed layout optimization to repair boundary and collision errors. For editing, Hierarchical Retrieval-Augmented Generation retrieves the relevant subtree, asks the LLM to rewrite only that local context, and merges the result back through a deterministic three-way merge. In our reproduced benchmark, HDSL improves average object coverage, text-scene alignment, and generation time over full text-to-scene baselines while remaining competitive with recent layout-only reproductions on geometry metrics; for editing, HRAG reduces token use by $5.22\times$ and runtime by $6.19\times$, produces valid DSL for all eight paired edits, and better preserves unrelated scene objects.
Chinese Translation
基于文本的室内场景生成和编辑需要一种中间表示，语言模型既可以生成也可以修订。现有的基于LLM的系统通常依赖于场景图或全局约束列表，这些方法虽然紧凑，但对局部几何形状的描述不足，且使得基于指令的编辑难以定位。我们将这个问题框架化为结构化程序生成和局部程序修复，并提出了层次化描述场景语言（HDSL），这是一种用于结构化3D室内场景的XML/CSS风格领域特定语言。HDSL将房间、区域、物体和支撑表面表示为带有局部坐标的树形结构，使得复杂场景的递归规划和编辑检索变得更加容易。我们的管道使用LLM代理生成具有边界验证的HDSL子树，通过多模态资产检索为非虚拟节点提供基础，并应用基于力导向的布局优化来修复边界和碰撞错误。在编辑方面，层次化检索增强生成（HRAG）检索相关子树，要求LLM仅重写该局部上下文，并通过确定性的三路合并将结果重新合并。在我们复现的基准测试中，HDSL在平均对象覆盖率、文本-场景对齐和生成时间上优于完整的文本到场景基线，同时在几何度量上与近期的仅布局复现保持竞争力；在编辑方面，HRAG将令牌使用减少了$5.22 imes$，运行时间减少了$6.19 imes$，为所有八个配对编辑生成有效的DSL，并更好地保留无关的场景对象。
cs.CV / 209 / 2606.09746
Hybrid Robustness Verification for Spatio-Temporal Neural Networks
时空神经网络的混合鲁棒性验证
Abstract
With AI increasingly deployed in safety-critical systems, providing formal robustness guarantees for the underlying models is essential. Existing verification methods either rely on overly conservative approximations or incur prohibitive computational costs. For example, the use of lp-norm perturbations in video settings encodes the belief that the adversary can inject noise in every video frame. In practice, adversarial perturbations exhibit structured spatial and temporal correlations, constrained to lower-dimensional, semantically meaningful subspaces. In this work, we study robustness verification of 3D CNNs processing video and volumetric inputs, targeting applications in action recognition (UCF-101), autonomous driving (Udacity), and medical imaging (MedMNIST) exploiting realistic assumptions on adversarial strength by modelling them as spatio-temporal constraints - where the attacker can modify either a subset of frames or patches within a set of consecutive frames. We demonstrate that modelling realistic constraints enables tighter approximations. We introduce Spatio-Temporal Bound Propagation (STBP), a verification framework that computes an exact closed-form characterization of the first convolutional layer and propagates certified bounds through subsequent layers using scalable approximations. Computing the exact closed form provides the tightest bounds for the first convolutional layer. Thus, we utilise approximation methods in the remainder of the network. To spur further progress in this field, we propose ST-Bench, a verification benchmark for autonomous driving and activity recognition, to systematically evaluate verifiable robustness. Compared to existing verification-based approaches, STBP provides stronger robustness guarantees with significantly improved scalability, achieving 1.7x higher certified robust accuracy under identical perturbation budgets.
Chinese Translation
随着人工智能在安全关键系统中的日益广泛应用，为基础模型提供形式化的鲁棒性保证变得至关重要。现有的验证方法要么依赖于过于保守的近似，要么会产生高昂的计算成本。例如，在视频场景中使用 lp-norm 扰动编码了这样的信念：对手可以在每一帧视频中注入噪声。实际上，对抗扰动表现出结构化的空间和时间相关性，受限于低维且具有语义意义的子空间。在本研究中，我们研究了处理视频和体积输入的 3D CNN 的鲁棒性验证，针对动作识别（UCF-101）、自动驾驶（Udacity）和医学成像（MedMNIST）等应用，通过将对抗强度建模为时空约束，利用现实假设进行研究——攻击者可以修改一组连续帧中的某个帧或补丁。我们证明了建模现实约束能够实现更紧的近似。我们引入了时空界限传播（Spatio-Temporal Bound Propagation, STBP），这是一个验证框架，计算第一卷积层的精确封闭形式特征，并通过可扩展的近似方法在后续层中传播认证界限。计算精确的封闭形式为第一卷积层提供了最紧的界限。因此，我们在网络的其余部分中利用近似方法。为了推动该领域的进一步发展，我们提出了 ST-Bench，这是一个用于自动驾驶和活动识别的验证基准，旨在系统地评估可验证的鲁棒性。与现有的基于验证的方法相比，STBP 提供了更强的鲁棒性保证，并显著提高了可扩展性，在相同的扰动预算下实现了 1.7 倍更高的认证鲁棒准确率。
cs.CV / 210 / 2606.09772
SemDINO: A DINOv3-Driven Network for Cross-Temporal Semantic Alignment in Change Detection
SemDINO：一种基于DINOv3的跨时间语义对齐网络用于变化检测
Abstract
Semantic change detection (SCD) aims to simultaneously locate land-cover changes and identify semantic categories before and after transition. However, existing methods suffer from insufficient cross-temporal alignment, weak multi-scale representation, and poor robustness to pseudo-changes caused by illumination, season, and registration noise. To address these issues, we propose a novel end-to-end semantic change detection network named SemDINO, which integrates a dual-branch encoder, multi-scale temporal interaction, semantic purification, change enhancement, and decoupled multi-task prediction into a unified framework. Specifically, we construct a dual-branch encoder that combines a CNN backbone and frozen DINOv3 features via gated pyramid fusion, enabling rich multi-scale semantic representation. Then, a multi-scale temporal bidirectional transformer interaction (M-TBTT) module is proposed to achieve global cross-temporal feature alignment and information interaction. To further enhance genuine changes and suppress pseudo-variations, we introduce semantic purification (SCP), bidirectional change enhancement (BiChangeEnhance), and multi-scale change enhancement (MCE) modules collaboratively. Finally, a multi-branch CD prediction head is designed to jointly output binary change mask, bi-temporal semantic maps, and edge constraint. Extensive experiments on public remote sensing CD datasets demonstrate that SemDINO achieves superior performance and generalization ability against state-of-the-art methods, especially in complex scenarios with interference factors.
Chinese Translation
语义变化检测（SCD）旨在同时定位土地覆盖变化并识别转变前后的语义类别。然而，现有方法存在跨时间对齐不足、多尺度表示弱以及对光照、季节和配准噪声引起的伪变化的鲁棒性差等问题。为了解决这些问题，我们提出了一种新颖的端到端语义变化检测网络SemDINO，该网络将双分支编码器、多尺度时间交互、语义净化、变化增强和解耦多任务预测集成到一个统一框架中。具体而言，我们构建了一个双分支编码器，通过门控金字塔融合将CNN主干网络和冻结的DINOv3特征结合起来，从而实现丰富的多尺度语义表示。然后，提出了一个多尺度时间双向变换器交互（M-TBTT）模块，以实现全局跨时间特征对齐和信息交互。为了进一步增强真实变化并抑制伪变化，我们协同引入了语义净化（SCP）、双向变化增强（BiChangeEnhance）和多尺度变化增强（MCE）模块。最后，设计了一个多分支变化检测预测头，联合输出二元变化掩膜、双时间语义图和边缘约束。在公共遥感变化检测数据集上的大量实验表明，SemDINO在复杂干扰场景中相较于最先进的方法实现了卓越的性能和泛化能力。
cs.CV / 211 / 2606.09788
POTATR: A Lightweight Image-to-Graph Model for Page-Level Table Extraction
POTATR：一种轻量级图像到图模型用于页面级表格提取
Abstract
Large-scale document processing requires contextually aware table extraction (TE) that is both accurate and efficient. Yet current approaches require billions of parameters, hundreds of autoregressive steps, or costly API inference. Motivated by this, we introduce the Page-Object Table Transformer (POTATR), a lightweight 29M parameter image-to-graph model that extends the Table Transformer (TATR) for contextualized page-level TE. POTATR outperforms all models tested on the PubTables-v2 Single Pages benchmark -- including frontier MLLMs -- achieving $\textrm{GriTS}_\textrm{Con}$ of 0.964 while running over 130$\times$ faster at roughly 300$\times$ lower cost. Further, POTATR's output is spatially grounded: every recognized element has a bounding box, enabling visual verification and geometric text assignment. As a result, POTATR performs unified page-level TE while composing with other models, enabling extension to scanned documents via external OCR and to full-document TE via techniques like cross-page merging. Code and models will be released.
Chinese Translation
大规模文档处理需要上下文感知的表格提取（TE），既要准确又要高效。然而，目前的方法需要数十亿个参数、数百个自回归步骤或昂贵的API推理。基于此，我们提出了页面对象表转换器（Page-Object Table Transformer，POTATR），这是一种轻量级的29M参数图像到图模型，扩展了表转换器（Table Transformer，TATR）以实现上下文化的页面级TE。POTATR在PubTables-v2单页基准测试中超越了所有测试模型，包括前沿的多模态大语言模型（MLLMs），其$ extrm{GriTS}_ extrm{Con}$达到了0.964，同时运行速度超过130倍，成本约低300倍。此外，POTATR的输出具有空间基础：每个识别的元素都有一个边界框，便于视觉验证和几何文本分配。因此，POTATR能够统一进行页面级TE，并与其他模型组合，支持通过外部OCR扩展到扫描文档，以及通过交叉页面合并等技术实现完整文档TE。代码和模型将会发布。
cs.CV / 212 / 2606.09792
End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout
在探测器限制读出下的无相干成像分类的端到端优化
Abstract
End-to-end co-optimization of optical front-ends (e.g. metasurfaces) and neural network back-ends has been widely applied to imaging tasks, yet a formalism characterizing when and why such systems outperform conventional lens-based imaging is largely lacking. This paper focuses on object classification, a central imaging task, and asks when end-to-end optimization of a phase mask for incoherent imaging improves performance over a conventional focusing lens. We find that these gains arise primarily under constrained detector readout and are limited under full detector readout. In the latter setting, we prove that no incoherent phase mask exceeds the ideal-channel mutual information between detector measurements and class labels; a conventional focusing lens approaches this ceiling, and joint optimization yields no empirical gain. When detector readout is constrained -- by coarse spatial sampling or a limited number of measurements -- optimized optics can substantially improve classification by increasing class separability in the detector measurements. These gains are largest under low detector noise and shrink as noise grows, because the optics shape the signal before it reaches the detector but cannot remove noise added afterward. The advantage also depends on the spectral structure of the task: co-design helps most when class-discriminative content is concentrated at lower spatial frequencies than within-class variation. We develop a theoretical framework formalizing these distinctions and test its predictions on synthetic data and standard benchmarks (MNIST, FashionMNIST, SVHN).
Chinese Translation
端到端的光学前端（例如超表面）与神经网络后端的共同优化已广泛应用于成像任务，但缺乏一种形式化的方法来表征此类系统何时以及为何优于传统的基于透镜的成像。本文聚焦于物体分类这一核心成像任务，并探讨在何种情况下无相干成像的相位掩模的端到端优化能够提升性能，相较于传统的聚焦透镜。我们发现，这些性能提升主要发生在探测器读出受限的情况下，而在完全探测器读出的情况下则受到限制。在后者的情况下，我们证明没有任何无相干相位掩模能够超过探测器测量与类别标签之间的理想信道互信息；而传统的聚焦透镜接近这一上限，联合优化并未带来实证上的收益。当探测器读出受到限制时——例如粗略的空间采样或测量次数有限——优化的光学系统可以通过提高探测器测量中的类别可分性显著改善分类性能。这些收益在低探测器噪声下最大，随着噪声的增加而减小，因为光学系统在信号到达探测器之前塑造了信号，但无法消除之后添加的噪声。该优势还依赖于任务的光谱结构：当类别区分内容集中在低空间频率而类内变异较大时，协同设计的效果最佳。我们开发了一个理论框架来形式化这些区别，并在合成数据和标准基准（MNIST、FashionMNIST、SVHN）上测试其预测。
cs.CV / 213 / 2606.09794
Beyond Spherical Harmonics: Rethinking Appearance Models for Radiance Reconstruction
超越球谐函数：重新思考辐射重建的外观模型
Abstract
View-dependent appearance modeling remains a challenging problem in novel-view synthesis and reconstruction. Accurately representing complex angular effects often requires substantial memory and computational resources. For new learning-based methods, a common approach is to rely on SH. However, capturing high-frequency phenomena such as specular reflections demands high-order expansions, which increase memory usage and computational cost. Consequently, most methods employ low-order SH, which limits the ability to model complex view-dependent effects, resulting in overly smooth or diffuse representations. To address these limitations, we systematically evaluate a wide range of spherical functions in the context of scene reconstruction. Some of them are introduced to graphics and computer vision for the first time in this paper. Based on the insights from the experiment, we develop a novel spherical formulation, the Normalized Anisotropic Spherical Gabor function that enables efficient modeling and learning of high-frequency appearance effects while maintaining compact representation. Compared to existing approaches, our function achieves higher-quality reconstruction of view-dependent phenomena such as glints, while being up to five times more memory-efficient and more efficient to evaluate. We validate its performance in radiance-field reconstruction tasks.
Chinese Translation
视角依赖的外观建模在新视图合成和重建中仍然是一个具有挑战性的问题。准确表示复杂的角度效应通常需要大量的内存和计算资源。对于新的基于学习的方法，一个常见的做法是依赖于球谐函数（SH）。然而，捕捉高频现象如镜面反射需要高阶展开，这会增加内存使用和计算成本。因此，大多数方法采用低阶SH，这限制了建模复杂视角依赖效应的能力，导致过于平滑或漫反射的表示。为了解决这些限制，我们系统地评估了一系列球面函数在场景重建中的应用。其中一些函数在本文中首次引入到图形学和计算机视觉领域。基于实验的洞察，我们开发了一种新颖的球面公式，即归一化各向异性球面Gabor函数，能够高效建模和学习高频外观效应，同时保持紧凑的表示。与现有方法相比，我们的函数在重建视角依赖现象（如光斑）方面实现了更高质量的效果，同时在内存效率上提高了多达五倍，并且计算效率更高。我们在辐射场重建任务中验证了其性能。
cs.CV / 214 / 2606.09803
Echo-Memory: A Controlled Study of Memory in Action World Models
回声记忆：行动世界模型中记忆的对照研究
Abstract
We present \textbf{Echo-Memory}, a controlled study of memory mechanisms in action-conditioned world models. These models generate multi-segment videos from a first frame, text prompt, and camera-action sequence, but their central failure is often memory rather than local image synthesis: after the camera leaves and returns, the scene or salient object may silently change. Existing memory designs are hard to compare because gains are entangled with backbone, training, retrieval, and evaluation differences. Echo-Memory fixes the action-to-video interface and varies only how history is stored and read by the generator. Under a shared video diffusion backbone, optimizer, camera-action representation, sampler, and evaluation pipeline, we compare raw context, compression-based memory, spatial summaries with different read-out paths, and state-space recurrence. This matched matrix separates four otherwise conflated axes: \emph{capacity}, \emph{compression}, \emph{read-out}, and \emph{recurrence}. We also evaluate memory through a three-branch protocol: replay quality, in-domain loop revisit, and open-domain return probes. The branches routinely disagree, showing that replay fidelity is not a sufficient proxy for remembering a world. Three findings follow. Raw context is a strong capacity baseline and improves open-domain return far more than it improves replay metrics. Compactness is not a free substitute for capacity: aggressive spatial and hybrid-compression memories lose the salient evidence needed for return. Finally, block-wise state-space recurrence is the strongest open-domain return mechanism in our matrix, showing that the structure of implicit memory matters as much as the decision to use it. These results provide a compact protocol for studying memory in action world models beyond isolated replay metrics.
Chinese Translation
我们提出了 extbf{回声记忆}，这是对行动条件下世界模型中记忆机制的对照研究。这些模型从第一帧、文本提示和相机动作序列生成多段视频，但它们的主要失败往往是记忆而非局部图像合成：在相机离开并返回后，场景或显著物体可能会悄然改变。现有的记忆设计难以比较，因为增益与基础架构、训练、检索和评估差异交织在一起。回声记忆固定了动作到视频的接口，仅改变生成器如何存储和读取历史。在共享的视频扩散基础架构、优化器、相机动作表示、采样器和评估管道下，我们比较了原始上下文、基于压缩的记忆、具有不同读取路径的空间摘要和状态空间递归。这个匹配矩阵分离了四个本来混淆的维度： extit{容量}、 extit{压缩}、 extit{读取}和 extit{递归}。我们还通过三分支协议评估记忆：重放质量、领域内循环重访和开放领域返回探测。这些分支常常不一致，表明重放保真度并不足以作为记忆世界的充分代理。得出了三个发现。原始上下文是一个强大的容量基线，并且在开放领域返回方面的改善远远超过了对重放指标的改善。紧凑性并不是容量的免费替代：激进的空间和混合压缩记忆会失去返回所需的显著证据。最后，基于块的状态空间递归是我们矩阵中最强的开放领域返回机制，表明隐式记忆的结构与使用它的决策同样重要。这些结果为研究行动世界模型中的记忆提供了一个紧凑的协议，超越了孤立的重放指标。
cs.CV / 215 / 2606.09816
PTL-Diffusion: Manifold-Aware Diffusion with Periodic Terminal Laws
PTL-Diffusion：具有周期终端法则的流形感知扩散
Abstract
Standard diffusion models typically use a single time-homogeneous Gaussian terminal distribution as the reference law for generation. While this choice is analytically convenient and empirically powerful, it provides little explicit structure for data concentrated near low-dimensional manifolds, where different regions of the data distribution may correspond to distinct local geometric or semantic factors. As a result, the reverse model must recover manifold-level structure almost entirely from an unstructured terminal reference distribution. We propose PTL-Diffusion, a proof-of-concept diffusion framework whose forward noising process converges to a nonconstant periodic family of Gaussian terminal laws rather than to a single invariant law. Unlike a phase-conditioned DDPM, where phase information only enters the denoising network while the forward process remains unchanged, PTL-Diffusion embeds phase structure directly into the forward noising dynamics. The proposed construction remains close to standard denoising diffusion models: for a periodically forced Ornstein--Uhlenbeck-type forward process, we derive closed-form forward marginals, the limiting periodic Gaussian terminal family, and explicit Gaussian reverse posteriors, enabling standard noise-prediction training. We also introduce an invariant-average regularization term coupling the phase-conditioned reverse dynamics through the averaged periodic reference law. Experiments on torus and cylinder point-cloud benchmarks and the Olivetti face dataset show that PTL-Diffusion improves manifold-level distributional matching over matched DDPM baselines, reducing phase-conditioned errors, feature-space covariance errors, and nearest-neighbour manifold distances. These results suggest structured terminal reference laws as a promising direction, while motivating more expressive phase constructions and larger-scale evaluations.
Chinese Translation
标准扩散模型通常使用单一的时间齐次高斯终端分布作为生成的参考法则。虽然这一选择在分析上方便且在经验上有效，但对于集中在低维流形附近的数据，它提供的明确结构较少，其中数据分布的不同区域可能对应于不同的局部几何或语义因素。因此，反向模型必须几乎完全从无结构的终端参考分布中恢复流形级别的结构。我们提出了PTL-Diffusion，这是一种概念验证的扩散框架，其前向噪声过程收敛于一个非常数的周期高斯终端法则族，而不是单一的不变法则。与相位条件的DDPM不同，在这种情况下，相位信息仅在去噪网络中引入，而前向过程保持不变，PTL-Diffusion则将相位结构直接嵌入到前向噪声动态中。所提出的构造与标准去噪扩散模型保持接近：对于周期强迫的Ornstein-Uhlenbeck型前向过程，我们推导出封闭形式的前向边际、极限周期高斯终端族和显式高斯反向后验，从而实现标准噪声预测训练。我们还引入了一个不变平均正则化项，通过平均周期参考法则耦合相位条件的反向动态。在环面和圆柱点云基准测试以及Olivetti人脸数据集上的实验表明，PTL-Diffusion在流形级别的分布匹配上优于匹配的DDPM基线，减少了相位条件错误、特征空间协方差错误和最近邻流形距离。这些结果表明，结构化的终端参考法则是一个有前景的方向，同时也激励了更具表现力的相位构造和更大规模的评估。
cs.CV / 216 / 2606.09826
OmniGameArena: A Unified UE5 Benchmark for VLM Game Agents with Improvement Dynamics
OmniGameArena：针对具有改进动态的 VLM 游戏代理的统一 UE5 基准测试
Abstract
Vision-language model (VLM) agents are increasingly deployed in interactive game environments. Yet game benchmarks for VLM agents typically report a single first-attempt score per (agent, game) pair, focus on single-agent Solo play, and lack unified protocols for evaluating heterogeneous agent classes (commercial VLMs, open-weight VLMs, and specialized game policies) on the same footing. We address these gaps with OmniGameArena, a real-time benchmark of twelve newly built Unreal Engine 5 games spanning Solo (7), PvP (3), and Coop (2) with unified action interfaces, and the Improvement Dynamics Curve (IDC), an agentic-reflection harness in which a tool-using reflector LLM autonomously refines a bounded skill prompt across multiple rounds. Beyond cold-start leaderboard scores, IDC exposes two additional observables for each (agent, game) pair: how the score evolves across reflection rounds, and how the learned skill behaves on held-out task variants. We report these observables for twelve VLM agents on the cold-start leaderboard and four top agents under IDC.
Chinese Translation
视觉语言模型（VLM）代理在互动游戏环境中的应用日益增多。然而，针对 VLM 代理的游戏基准测试通常仅报告每个（代理，游戏）对的单次尝试得分，专注于单代理的单人游戏，并且缺乏统一的评估协议来在相同基础上评估异构代理类别（商业 VLM、开放权重 VLM 和专用游戏策略）。我们通过 OmniGameArena 解决了这些问题，该基准测试基于十二款新构建的虚幻引擎 5 游戏，涵盖单人游戏（7）、玩家对战（3）和合作游戏（2），并提供统一的动作接口，以及改进动态曲线（Improvement Dynamics Curve, IDC），这是一个代理反思工具，其中使用工具的反射 LLM 自主地在多个回合中精炼有限的技能提示。除了冷启动排行榜得分外，IDC 还为每个（代理，游戏）对揭示了两个额外的可观察量：得分在反思回合中的演变，以及学习到的技能在保留任务变体上的表现。我们报告了十二个 VLM 代理在冷启动排行榜上的这些可观察量，以及在 IDC 下的四个顶级代理的表现。
cs.CV / 217 / 2606.09828
Latent Spatial Memory for Video World Models
视频世界模型的潜在空间记忆
Abstract
Video world models that maintain 3D spatial consistency across generated frames typically rely on explicit point cloud memory constructed in RGB space. This design is both computationally expensive, requiring repeated rendering and VAE encoding, and inherently lossy, as the round trip through pixel space discards rich features of the learned latent representation. In this paper, we introduce \emph{latent spatial memory} for video world models, a persistent 3D cache that stores scene information directly in the diffusion latent space, avoiding pixel-space reconstruction. Building on this, we propose Mirage, a latent-space spatial memory framework that constructs the memory by lifting latent tokens into 3D via depth-guided back-projection and queries it by synthesizing novel views through direct latent-space warping. This unified formulation eliminates both the information loss of pixel-space reconstruction and the computational burden of repeated encoding and rendering. Experiments show that latent spatial memory achieves up to \textbf{10.57}$\times$ faster end-to-end video generation and \textbf{55}$\times$ reduction in memory footprint relative to explicit 3D baselines. Leveraging the geometric prior of the diffusion model, Mirage attains state-of-the-art performance on WorldScore and strong reconstruction quality on RealEstate10K.
Chinese Translation
视频世界模型通过在生成帧之间维持三维空间一致性，通常依赖于在RGB空间中构建的显式点云记忆。这种设计不仅计算开销大，需要反复渲染和变分自编码（VAE）编码，而且本质上是有损的，因为在像素空间的往返过程中丢失了学习到的潜在表示的丰富特征。在本文中，我们引入了视频世界模型的 extit{潜在空间记忆}，这是一种持久的三维缓存，直接在扩散潜在空间中存储场景信息，从而避免了像素空间重建。在此基础上，我们提出了Mirage，一个潜在空间的空间记忆框架，通过深度引导的反投影将潜在标记提升到三维，并通过直接的潜在空间扭曲合成新视图来查询记忆。这种统一的表述消除了像素空间重建的信息损失和反复编码与渲染的计算负担。实验表明，潜在空间记忆实现了高达 extbf{10.57}$ imes$的端到端视频生成加速和相对于显式三维基线的 extbf{55}$ imes$内存占用减少。利用扩散模型的几何先验，Mirage在WorldScore上达到了最先进的性能，并在RealEstate10K上展现了强大的重建质量。
cs.AI / 1 / 2606.07549
PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
PathoSage：通过经验感知的代理工作流程实现病理学中的多源证据裁定
Abstract
Recent advances in Multimodal Large Language Models (MLLMs) and agent workflows have shown strong promise for computational pathology, yet reliable patch-level reasoning remains challenging. End-to-end pathology MLLMs often hallucinate morphological features, while recent agentic systems usually merge tool outputs and retrieved knowledge into a shared context, making decisions vulnerable to conflicting evidence and context contamination. We propose PathoSage, a three-stage framework that explicitly separates knowledge retrieval, evidence collection, and evidence adjudication for patch-level pathology multimodal reasoning. Its core component, Structured Evidence Deliberation, independently evaluates heterogeneous evidence from tools, performs conflict analysis, and generates the final judgment in a fresh context to reduce anchoring bias. We further introduce a training-free Beta-Bernoulli experience system with continuous credit assignment to model long-term tool reliability and construct similarity-weighted priors for future tool use. Experiments show that PathoSage effectively mitigates VQA hallucinations and classifier disagreement, outperforming strong pathology MLLM and agentic baselines. Our results highlight explicit evidence adjudication and reliability-aware tool modeling as key ingredients for robust pathology agents.
Chinese Translation
近年来，多模态大语言模型（MLLMs）和代理工作流程的进展为计算病理学展现了强大的潜力，但可靠的切片级推理仍然具有挑战性。端到端的病理 MLLMs 往往会产生形态特征的幻觉，而最近的代理系统通常将工具输出和检索知识合并到共享上下文中，使得决策容易受到相互矛盾的证据和上下文污染的影响。我们提出了 PathoSage，一个三阶段框架，明确区分知识检索、证据收集和证据裁定，以实现切片级病理多模态推理。其核心组件结构化证据审议独立评估来自工具的异质证据，进行冲突分析，并在新的上下文中生成最终判断，以减少锚定偏差。我们进一步引入了一种无训练的 Beta-Bernoulli 经验系统，具有持续的信用分配，以建模长期工具可靠性并为未来工具使用构建相似性加权的先验。实验表明，PathoSage 有效减轻了 VQA 幻觉和分类器不一致，超越了强大的病理 MLLM 和代理基线。我们的结果强调了明确的证据裁定和可靠性感知的工具建模作为构建稳健病理代理的关键要素。
cs.AI / 2 / 2606.07577
OmniMem: Perturbation-aware Memory Compression for Streaming Audio-Visual LLMs
OmniMem：针对流媒体音视频大语言模型的扰动感知内存压缩
Abstract
Audio-visual large language models (LLMs) hold strong promise for long-form video understanding, yet their long-video inference is fundamentally limited by the linear growth of video tokens and key-value (KV) caches. We present OmniMem, a memory-efficient streaming framework designed specifically for audio-visual LLMs. Unlike existing compression methods that treat all tokens uniformly, OmniMem introduces a modality-aware memory allocation strategy that separately manages visual and audio contexts, addressing the severe token imbalance between the two modalities. OmniMem further preserves informative and non-redundant KV states through perturbation-aware memory selection, enabling compact memory without sacrificing long-range understanding. To strengthen compression under realistic deployment constraints, we also explore budget-aware fine-tuning, which encourages the model to consolidate useful information into retained memory. Experiments on VideoMME Long, LVBench, and LVOmniBench with video-SALMONN 2+ and Qwen-2.5-Omni show that OmniMem consistently improves over strong training-free compression baselines by 2-4% absolute accuracy under the same memory budgets, with an additional 1-2% gain after fine-tuning.
Chinese Translation
音视频大语言模型（LLMs）在长视频理解方面展现出强大的潜力，但其长视频推理受到视频标记和键值（KV）缓存线性增长的根本限制。我们提出了OmniMem，一种专为音视频LLMs设计的内存高效流媒体框架。与现有的将所有标记视为统一的压缩方法不同，OmniMem引入了一种模态感知的内存分配策略，分别管理视觉和音频上下文，解决了两种模态之间严重的标记不平衡问题。OmniMem还通过扰动感知的内存选择保留信息丰富且非冗余的KV状态，实现了紧凑的内存而不牺牲长程理解能力。为了在现实部署约束下增强压缩效果，我们还探索了预算感知的微调，鼓励模型将有用信息整合到保留的内存中。在VideoMME Long、LVBench和LVOmniBench上进行的实验表明，使用video-SALMONN 2+和Qwen-2.5-Omni的OmniMem在相同内存预算下，相较于强大的无训练压缩基线，绝对准确率提高了2-4%，并在微调后额外获得了1-2%的提升。
cs.AI / 3 / 2606.07594
Syll: Open-Source Personal Automation with Cross-Surface Execution
Syll：跨界执行的开源个人自动化
Abstract
Personal AI agents must increasingly operate across APIs, shells, web surfaces, and desktop GUIs, yet many systems remain tuned to a single interface and offer limited support for user teaching and auditability. We present Syll, an open-source, self-hosted multimodal agent harness that unifies MCP/API tools, CLI execution, and visual GUI control in a modular runtime, enabling agents to coordinate computer use across heterogeneous interfaces while streamlining how users and agents exchange information. At the core of Syll is a bidirectional user-agent interaction layer: users teach procedures through direct demonstration, which Syll compiles into reusable skills; agent execution is translated back into multimodal evidence -- logs, keyframes, and approval checkpoints -- for inspection and control. Syll further externalizes memory, skills, routines, and governance as editable local artifacts, supporting straightforward inspection, extension, and downstream development. Our implementation has been validated on production desktop applications including Adobe Photoshop, Adobe Audition, Stardew Valley, macOS Finder and others. We report mechanism-oriented studies that validate multimodal routing, teachable GUI replay, and persistent local artifacts. We hope Syll can serve as a practical open-source foundation for personal automation that users can teach, inspect, and continuously extend.
Chinese Translation
个人人工智能代理必须越来越多地在API、命令行界面（CLI）、网络界面和桌面图形用户界面（GUI）之间操作，但许多系统仍然专注于单一接口，且对用户教学和审计的支持有限。我们提出了Syll，一个开源的自托管多模态代理工具，统一了多通道处理器（MCP）/API工具、CLI执行和可视化GUI控制，构建了一个模块化运行时，使代理能够在异构接口之间协调计算机使用，同时简化用户与代理之间的信息交换。Syll的核心是一个双向用户-代理交互层：用户通过直接演示来教授程序，Syll将其编译为可重用的技能；代理执行则被转换回多模态证据——日志、关键帧和审批检查点——以供检查和控制。Syll进一步将记忆、技能、例程和治理外部化为可编辑的本地文档，支持简单的检查、扩展和后续开发。我们的实现已在包括Adobe Photoshop、Adobe Audition、Stardew Valley、macOS Finder等生产桌面应用程序上得到验证。我们报告了机制导向的研究，验证了多模态路由、可教学的GUI重放和持久的本地文档。我们希望Syll能够作为一个实用的开源基础，支持用户进行个人自动化的教学、检查和持续扩展。
cs.AI / 4 / 2606.07718
A case study of evaluating AI agents on a neuroscience data-to-discovery pipeline
评估人工智能代理在神经科学数据发现管道中的案例研究
Abstract
Agentic AI tools offer a promising path to automating software development bottlenecks in scientific research pipelines, particularly for stages that take domain experts days to months to build, where scientists care about correctness and robustness, not implementation details. We present an empirical study of general-purpose coding agents on a fly optogenetics data-to-discovery pipeline. We assess agents on tasks substantially larger than existing benchmarks, datasets orders of magnitude bigger, and evaluation criteria grounded in domain expert standards. We show that agents can solve several individual pipeline stages, suggesting stage-level automation is tractable. By analyzing agents' code iterations, we show that they struggle most when there is not a pre-defined criterion to iterate on, and they must instead use their scientific judgment to assess their current solution, a key open challenge. Mirroring scientific practice, they sometimes attempt visual inspection of intermediate outputs for self-evaluation, but largely fail to interpret what they see or act on it appropriately. Solving the end-to-end pipeline correctly requires stringing together successes across all pipeline stages, and this is beyond agents' current abilities. We identify challenges largely absent from existing benchmarks, including computational resource management and generalization to large held-out data collections. Finally, we distill principles for constructing scientific tasks and rigorous evaluation criteria for open-ended problems.
Chinese Translation
代理型人工智能工具为自动化科学研究管道中的软件开发瓶颈提供了一个有前景的路径，特别是在那些需要领域专家花费数天到数月构建的阶段，在这些阶段，科学家关注的是正确性和稳健性，而非实现细节。我们展示了一项关于通用编码代理在光遗传学数据发现管道中的实证研究。我们评估的任务规模远大于现有基准，数据集的规模也大几个数量级，评估标准基于领域专家的标准。我们表明，代理能够解决多个单独的管道阶段，暗示阶段级自动化是可行的。通过分析代理的代码迭代，我们发现当没有预定义的标准可供迭代时，它们最为挣扎，而必须依赖其科学判断来评估当前解决方案，这是一个关键的开放挑战。与科学实践相呼应，它们有时尝试对中间输出进行可视检查以进行自我评估，但在解释所见或适当地采取行动方面大多失败。正确解决端到端管道需要在所有管道阶段之间串联成功，而这超出了代理当前的能力。我们识别出在现有基准中几乎不存在的挑战，包括计算资源管理和对大型保留数据集的泛化。最后，我们提炼出构建科学任务和严格评估标准的原则，以应对开放性问题。
cs.AI / 5 / 2606.07720
Why Limit the Residual Stream to Layers and Not Tokens? Persistent Memory for Continuous Latent Reasoning
为什么将残差流限制在层而不是标记？用于连续潜在推理的持久内存
Abstract
Large language models (LLMs) have demonstrated remarkable reasoning abilities on mathematical and multi-hop planning tasks. The CoCoNuT (Chain of Continuous Thought) paradigm~\cite{hao2024coconut} extends this by enabling models to reason in latent space, exploring multiple reasoning paths simultaneously rather than committing to a single chain early on. However, we identify a limitation we term the \textbf{concept bottleneck}. At each reasoning pass, intermediate hidden states are overwritten, causing the model to lose critical facts computed in earlier steps as reasoning depth increases. We observe this empirically. On HotpotQA, vanilla CoCoNuT (10.4\% EM) fails to improve over the CoT baseline (11.0\% EM), and performance degrades with curriculum depth on GSM8K. To address this, we propose \textbf{AGCLR} (Adaptive Gated Continuous Latent Reasoning), which augments CoCoNuT with a \textit{Gated Concept Stream}. A persistent residual memory maintained across all reasoning passes, controlled by three learned gates: a \textit{write} gate that commits intermediate facts to memory, a \textit{read} gate that retrieves relevant prior states, and a \textit{forget} gate that prunes irrelevant context. Evaluated on GSM8K, HotpotQA, and ProsQA using GPT-2 as our base model, AGCLR achieves consistent improvements across all types of datasets. With the performance gap compounding as curriculum depth increases, directly resolving the concept bottleneck. Code available at https://anonymous.4open.science/r/JJJJ/README.md
Chinese Translation
大型语言模型（LLMs）在数学和多跳规划任务上展示了显著的推理能力。CoCoNuT（Chain of Continuous Thought）范式~ ext{cite{hao2024coconut}}通过使模型在潜在空间中进行推理，扩展了这一能力，能够同时探索多条推理路径，而不是在早期就承诺于单一链条。然而，我们识别出一个被称为 extbf{概念瓶颈}的限制。在每次推理过程中，中间隐藏状态被覆盖，导致模型在推理深度增加时丧失早期步骤中计算的重要事实。我们通过实证观察到这一点。在HotpotQA上，普通的CoCoNuT（10.4 ext{% EM}）未能超越CoT基线（11.0 ext{% EM}），并且在GSM8K上随着课程深度的增加，性能下降。为了解决这个问题，我们提出了 extbf{AGCLR}（Adaptive Gated Continuous Latent Reasoning），它通过一个 extit{门控概念流}增强了CoCoNuT。一个在所有推理过程中保持的持久残差记忆，由三个学习到的门控控制：一个 extit{写入}门将中间事实提交到内存，一个 extit{读取}门检索相关的先前状态，以及一个 extit{遗忘}门修剪无关的上下文。在使用GPT-2作为基础模型的GSM8K、HotpotQA和ProsQA上进行评估，AGCLR在所有类型的数据集上都实现了一致的改进。随着课程深度的增加，性能差距不断扩大，直接解决了概念瓶颈。代码可在https://anonymous.4open.science/r/JJJJ/README.md获取。
cs.AI / 6 / 2606.07721
Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model
利用开放权重的大型语言模型自动提取脑部MRI报告中的结构化信息
Abstract
Objectives: Automatic data extraction from free-text radiology reports enables large-scale research, but few studies assessed the performance of large language models (LLMs) on Dutch neuroradiology reports. Methods: We analyzed 947 brain MRI reports from a tertiary memory clinic (2016-2021), authored by consultant neuroradiologists. Trained medical students annotated thirty variables; 100 reports were double-annotated to assess inter-rater reliability. We evaluated the performance of the open-weight LLM LLaMA 3.1 using different languages (Dutch vs. English translation) and few-shot prompting with different example selection strategies. Performance was evaluated using balanced accuracy for categorical variables, accuracy and mean absolute error for counts, and text similarity for free-text. Metrics were computed across 10 random splits of the 947 reports. Results: LLaMA 3.1 demonstrated high zero-shot performance for visual rating scores (mean [95%-CI]): Medial Temporal Atrophy: 90% [77-100%] on the left and 96% [94-99%] on the right, Global Cortical Atrophy: 87% [83-91%], and Fazekas: 94% [93-96%]. Microbleed mentions were detected with 93% accuracy [92-95%] and infarct mentions with 82% [80-84%]. Text similarity for lesion location reached 0.95 [0.95-0.96]. Performance was lower for numerical variables: 80% [78-82%] for the number of microbleeds and 66% [63-68%] for infarcts. English translation yielded comparable results. Few-shot prompting improved performance for numerical variables, achieving 92% [90-93%] for microbleeds and 81% [77-85%] for infarcts using structural similarity-based selection. Conclusion: LLaMA 3.1 shows strong potential for extracting data from Dutch neuroradiology reports. Few-shot prompting enhances performance for numerical variables, whereas challenges remain for location-specific variables.
Chinese Translation
目的：从自由文本放射学报告中自动提取数据能够促进大规模研究，但很少有研究评估大型语言模型（LLMs）在荷兰神经放射学报告中的表现。方法：我们分析了来自一家三级记忆诊所（2016-2021年）的947份脑部MRI报告，这些报告由顾问神经放射科医师撰写。经过培训的医学生对三十个变量进行了注释；100份报告进行了双重注释以评估评审者间的一致性。我们使用开放权重的LLM LLaMA 3.1评估了不同语言（荷兰语与英语翻译）和不同示例选择策略的少量提示。使用平衡准确率评估分类变量的表现，使用准确率和平均绝对误差评估计数，使用文本相似度评估自由文本。指标是在947份报告的10个随机拆分中计算的。结果：LLaMA 3.1在视觉评分方面表现出高的零样本性能（均值[95%-CI]）：左侧内侧颞萎缩：90% [77-100%]，右侧96% [94-99%]，全球皮质萎缩：87% [83-91%]，Fazekas评分：94% [93-96%]。微出血提及的检测准确率为93% [92-95%]，梗死提及的检测准确率为82% [80-84%]。病变位置的文本相似度达到0.95 [0.95-0.96]。数值变量的表现较低：微出血数量为80% [78-82%]，梗死为66% [63-68%]。英语翻译的结果相当。少量提示改善了数值变量的表现，使用基于结构相似性的选择达到了92% [90-93%]的微出血和81% [77-85%]的梗死。结论：LLaMA 3.1在从荷兰神经放射学报告中提取数据方面显示出强大的潜力。少量提示提高了数值变量的表现，但位置特定变量仍面临挑战。
cs.AI / 7 / 2606.07722
Some hypotheses on how chatbots work in problem-solving-driven conversations. Large Language Models as confirmation of the Innovation Illusion
关于聊天机器人在以问题解决为驱动的对话中如何工作的若干假设：大型语言模型作为创新幻觉的确认
Abstract
This article offers a perspective on the nature of chatbots as genuine conversation partners when discussing problems in relation to their solutions. What can chatbots do and what can't they do, and how can this be explained? Our argument draws on Aggregation Dynamics, Cognitive Linguistics, Neuropsychology and Psychology. Our argument focuses on basic chatbots in the hope of thereby making statements about the core functionality of more advanced chatbots. Basic chatbots are assumed to consist of a Large Language Model (LLM) with a simple interface. The main results are: a description of human understanding and thinking based on so-called metaphorical problem propagations; the hypothesis that text dataset used for training LLMs have specific characteristics and that these text datasets only partially imitate human thinking and understanding; the hypothesis that the LLM training process encodes artificial metaphorical problem propagations into an LLM from these datasets; our conclusion that a basic chatbot cannot be a thinking partner capable of matching humans; our conclusion that further development of the Large Language Model will not lead to this either. Yann LeCun states: "Animals and humans exhibit learning abilities and understandings of the world that are far beyond the capabilities of current AI and machine learning (ML) systems." Our conclusions are in line with this. LeCun's vision and ours are at odds with the optimism of Big Tech. That does not alter the fact that chatbots exist, that they are being used on a massive scale, by both individuals and organisations, and that it is therefore socially and politically important to understand them. Our article aims to contribute to the discussion on the functioning, benefits and drawbacks of chatbots. We have not yet encountered the approach we used to arrive at our conclusions in our research into how chatbots work.
Chinese Translation
本文提供了一个关于聊天机器人在讨论问题及其解决方案时作为真正对话伙伴的本质的视角。聊天机器人能做什么，不能做什么，以及如何解释这些？我们的论点基于聚合动态、认知语言学、神经心理学和心理学。我们的论点集中在基本聊天机器人上，希望由此对更高级聊天机器人的核心功能做出陈述。基本聊天机器人被假定由一个大型语言模型（Large Language Model, LLM）和一个简单的接口组成。主要结果包括：基于所谓的隐喻性问题传播对人类理解和思维的描述；假设用于训练LLM的文本数据集具有特定特征，并且这些文本数据集仅部分模仿人类的思维和理解；假设LLM的训练过程将来自这些数据集的人工隐喻性问题传播编码到LLM中；我们的结论是，基本聊天机器人无法成为能够与人类匹配的思考伙伴；我们的结论是，进一步发展大型语言模型也不会导致这一点。Yann LeCun指出：“动物和人类展现出的学习能力和对世界的理解远远超出了当前人工智能和机器学习（ML）系统的能力。”我们的结论与此一致。LeCun的愿景与我们的观点与大型科技公司的乐观态度相悖。但这并不改变聊天机器人的存在，它们正在被个人和组织大规模使用，因此理解它们在社会和政治上是重要的。我们的文章旨在为关于聊天机器人的功能、优点和缺点的讨论做出贡献。在我们对聊天工作原理的研究中，我们尚未遇到用于得出我们结论的方法。
cs.AI / 8 / 2606.07780
Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events
土地覆盖和洪水类型决定了基于卫星的洪水制图在多样化全球洪水事件中的检测限
Abstract
Floods are among the most destructive natural hazards, and their increasing frequency under climate change makes satellite-based inundation mapping essential for disaster response. Geospatial foundation models pretrained on satellite archives offer geographic transferability, but their operational reliability across diverse, unseen events remains uncharacterized. Here we deploy Prithvi-EO-2.0 across 19 out-of-distribution flood events (2017-2025) spanning six continents, eight climate zones, and six flood mechanisms, validating against two independent reference products. Detection accuracy depended jointly on land cover and flood type, with cropland yielding the highest agreement (IoU=52%) and riverine events the strongest detection (F1=0.69), while tree cover and built-up areas showed near-zero detection (IoU=4%) regardless of flood mechanism. Dual-reference validation revealed that apparent model error partly reflects definitional inconsistency between reference products rather than detection failure. Iterative pipeline testing identified 23 failure modes, with pipeline engineering dominating initial error over model capacity. These findings establish environment-dependent detection boundaries for operational satellite flood mapping.
Chinese Translation
洪水是最具破坏性的自然灾害之一，气候变化下其频率的增加使得基于卫星的淹没制图对于灾害响应变得至关重要。基于卫星档案的地理空间基础模型经过预训练，具有地理转移能力，但其在多样化、未见事件中的操作可靠性尚未得到表征。在此，我们在19个分布外洪水事件（2017-2025年）中部署了Prithvi-EO-2.0，涵盖六大洲、八个气候区和六种洪水机制，并与两个独立的参考产品进行了验证。检测准确性共同依赖于土地覆盖和洪水类型，其中农田的协议最高（IoU=52%），而河流事件的检测效果最强（F1=0.69），而树木覆盖和建筑区的检测几乎为零（IoU=4%），无论洪水机制如何。双重参考验证表明，明显的模型误差部分反映了参考产品之间的定义不一致，而非检测失败。迭代管道测试识别了23种失败模式，其中管道工程在初始错误中占主导地位，超过模型能力。这些发现为操作性卫星洪水制图建立了环境依赖的检测边界。
cs.AI / 9 / 2606.07798
Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings
利用资源有限环境中的常规数据重建和预测阿尔茨海默病患者的疾病轨迹
Abstract
Alzheimer's disease is a progressive neurodegenerative disorder, and its progression varies substantially across patients. Existing work aims to forecast patients' future cognitive state, with minimal focus on reconstructing the state from past visits. Furthermore, in current research, quantifying predictive uncertainty remains underexplored and relies on costly modalities such as MRI, PET, and CSF, limiting their deployment in resource-limited settings. In this research, our primary objectives are: First, bidirectional prediction of cognitive scores from irregular visits to present the complete disease trajectory. Second, to enable interpolation and extrapolation capabilities to assist clinicians in informed prognostic decision making, and third, to provide a well-calibrated uncertainty estimate for all predictions, and finally, to achieve the objectives using the modalities available during routine visits. We propose a unified framework, GNOVA: A GRU-Neural ODE Variational Autoencoder. The architecture combines a Gated Recurrent Unit encoder and a Neural ODE decoder within a variational autoencoder framework. In our work, we forecast the CDR-SB and MMSE Scores. The GRU encoder allows for any number of inputs at any time point. The Neural-ODE decoder performs continuous estimation, allowing interpolation and extrapolation at any desired time point. The Variational autoencoder allows for uncertainty estimation in predictions. We worked with 1,727 patients from the ADNI dataset over 10 years; the model achieved mean absolute errors of 1.35 and 2.28 for CDR-SB and MMSE scores, respectively, without requiring any neuroimaging or biomarker data. Feature-ablation studies revealed that age, BMI, and APOE4 status were strong predictors. The proposed framework enables the reconstruction of incomplete patient histories and the anticipation of future cognitive states.
Chinese Translation
阿尔茨海默病是一种进行性神经退行性疾病，其进展在患者之间差异显著。现有研究旨在预测患者未来的认知状态，但对从过去就诊中重建状态的关注较少。此外，目前的研究中，量化预测不确定性仍然未得到充分探索，并且依赖于如MRI、PET和CSF等昂贵的检测手段，这限制了其在资源有限环境中的应用。在本研究中，我们的主要目标是：首先，从不规则就诊中双向预测认知评分，以呈现完整的疾病轨迹；其次，启用插值和外推能力，以帮助临床医生做出知情的预后决策；第三，为所有预测提供良好校准的不确定性估计；最后，利用常规就诊期间可用的检测手段实现上述目标。我们提出了一个统一框架，GNOVA：一种GRU-神经常微分方程变分自编码器。该架构结合了门控循环单元编码器和神经常微分方程解码器，构建于变分自编码器框架内。在我们的工作中，我们预测CDR-SB和MMSE评分。GRU编码器允许在任何时间点输入任意数量的数据。神经常微分方程解码器执行连续估计，允许在任何所需时间点进行插值和外推。变分自编码器允许对预测中的不确定性进行估计。我们使用来自ADNI数据集的1,727名患者的数据，历时10年；该模型在CDR-SB和MMSE评分上分别实现了1.35和2.28的平均绝对误差，而无需任何神经影像或生物标志物数据。特征消融研究表明，年龄、BMI和APOE4状态是强预测因子。所提出的框架能够重建不完整的患者历史并预测未来的认知状态。
cs.AI / 10 / 2606.07801
Improving Multimodal Reasoning via Worst Dimension Optimization
通过最差维度优化改善多模态推理
Abstract
Multimodal reasoning requires a path that retains integrity over a wide range of constraints, from visual grounding to logic consistency. However, the current Process Reward Models focus on heuristically defined rewards that equally weigh these factors, which may lead to the concealment of individual dimension failures by the dominating factors, without guaranteeing the validity of the reasoning process in general.
Chinese Translation
多模态推理需要在从视觉基础到逻辑一致性的一系列广泛约束下保持完整性。然而，目前的过程奖励模型侧重于启发式定义的奖励，这些奖励对这些因素进行等权重处理，这可能导致主导因素掩盖个别维度的失败，而未能保证推理过程的有效性。
cs.AI / 11 / 2606.07805
Beyond Goodhart's Law: A Dynamic Benchmark for Evaluating Compliance in Multi-Agent Systems
超越古德哈特法则：评估多智能体系统合规性的动态基准
Abstract
The rapid evolution of Large Language Models (LLMs) from passive assistants to autonomous, execution-capable agents has introduced critical operational risks. Most current evaluation frameworks neglect procedural compliance, leading to ''Machiavellian'' behaviors where agents strategically violate safety rules to maximize rewards - a direct manifestation of Goodhart's Law. To address this blind spot, we introduce MAC-Bench, a dynamic, adversarial benchmark designed to evaluate the procedural alignment of multi-agent systems under realistic pressure. We propose the SERV(Seed - Evolve - Refine - Verify) pipeline, an ``Agent-as-a-Benchmark'' paradigm that transforms unstructured legal texts into executable, contamination-free scenarios. By synthesizing holographic sandbox environments and injecting calibrated social-engineering pressure vectors, MAC-Bench forces agents into Pareto-optimal trade-offs between task success and regulatory adherence. We introduced novel metrics: the Compliance-Weighted Success Rate (CSR) and the Machiavellian Gap (MG), and conducted a comprehensive evaluation of state-of-the-art frontier models to reveal the pervasive trade-offs between success and compliance.
Chinese Translation
大型语言模型（LLMs）从被动助手演变为具有自主执行能力的智能体的快速发展，引入了关键的操作风险。当前大多数评估框架忽视了程序合规性，导致智能体在追求最大化奖励时采取“马基雅维利式”的行为，战略性地违反安全规则——这是古德哈特法则的直接体现。为了解决这一盲点，我们提出了MAC-Bench，一个动态的对抗性基准，旨在在现实压力下评估多智能体系统的程序一致性。我们提出了SERV（Seed - Evolve - Refine - Verify）流程，这是一种“智能体作为基准”的范式，将非结构化的法律文本转化为可执行的、无污染的场景。通过合成全息沙盒环境并注入经过校准的社会工程压力向量，MAC-Bench迫使智能体在任务成功与合规性之间进行帕累托最优的权衡。我们引入了新的指标：合规加权成功率（Compliance-Weighted Success Rate, CSR）和马基雅维利差距（Machiavellian Gap, MG），并对最先进的前沿模型进行了全面评估，以揭示成功与合规之间普遍存在的权衡。
cs.AI / 12 / 2606.07808
Where Instruction Hierarchy Breaks: Diagnosing and Repairing Failures in Reasoning Language Models
指令层级的破裂：诊断和修复推理语言模型中的失败
Abstract
Reasoning language models deployed in agentic workflows must follow an instruction hierarchy: when instructions from different sources conflict, the model should obey the highest-privilege applicable instruction. Existing benchmarks largely measure this behavior end-to-end, asking whether the final response is compliant. However, a non-compliant response can arise from several distinct failures: the model may fail to identify the relevant instructions in context, fail to resolve conflicts among identified instructions, or correctly resolve the conflict in its reasoning while still producing a violating response. We introduce a white-box diagnostic framework that localizes instruction hierarchy failures into instruction identification, conflict resolution, and response realization, making failures more interpretable. We evaluate three reasoning models--Gemma-4-31B-IT, Qwen3.6-35B-A3B, and Claude Sonnet 4.6--on long-context adaptations of IHEval and IHChallenge, and find that the dominant failure mode varies across models, tasks, and context length. Building on the observation that models can often detect conflicts and output violations when explicitly prompted, we propose two training-free self-monitoring mechanisms: a parallel input monitor for low-latency conflict detection before generation, and a sequential output monitor for response-level review and repair. Across Gemma-4-31B-IT, Claude Sonnet 4.6, and GPT-5.3, the strongest monitor reduces rule-following non-compliance by 81-99%, with GPT-5.3 reductions of 86% under static attacks and 45% under adaptive attacks.
Chinese Translation
在代理工作流中部署的推理语言模型必须遵循指令层级：当来自不同来源的指令发生冲突时，模型应遵循适用的最高权限指令。现有基准主要以端到端的方式衡量这种行为，询问最终响应是否合规。然而，不合规的响应可能源于几种不同的失败：模型可能未能识别上下文中的相关指令，未能解决已识别指令之间的冲突，或者在推理中正确解决冲突但仍产生违反响应。我们引入了一种白盒诊断框架，将指令层级失败定位为指令识别、冲突解决和响应实现，从而使失败更加可解释。我们在 IHEval 和 IHChallenge 的长上下文适应上评估了三种推理模型——Gemma-4-31B-IT、Qwen3.6-35B-A3B 和 Claude Sonnet 4.6，发现主导失败模式在模型、任务和上下文长度之间有所不同。基于模型在明确提示时通常能够检测到冲突并输出违规的观察，我们提出了两种无训练的自我监控机制：用于生成前低延迟冲突检测的并行输入监控器，以及用于响应级审查和修复的顺序输出监控器。在 Gemma-4-31B-IT、Claude Sonnet 4.6 和 GPT-5.3 中，最强的监控器将规则遵循的不合规性降低了 81-99%，在静态攻击下 GPT-5.3 的降低幅度为 86%，在自适应攻击下为 45%。
cs.AI / 13 / 2606.07812
Scaling Participation in Modular AI Systems
扩展模块化人工智能系统中的参与
Abstract
Humanity is a mosaic of multifaceted talents and needs, and any truly intelligent AI must reflect that richness. Yet the LLMs used by all are built by the few -- a centralized market of monolithic AI models structurally ill-suited to capture the diversity of human knowledge, reasoning, and values. Here we introduce scaling participation, a new paradigm in which modular AI systems are built from the bottom up through the contributions of diverse stakeholders. Participants contribute small models trained on their own interests and priorities; these models then collaborate in modular frameworks as compositional AI systems. Participatory AI systems outperform monolithic LLMs by up to 15.4% across 15 tasks, such as reasoning and factuality, surpassing models larger than all contributed components combined. Further experiments show that participatory AI systems benefit from contributor diversity, substantially improve on each contributor's original priorities, and exhibit emergent capabilities that allow them to solve over 15% of problems where all individual models fail. Scaling participation provides a technical foundation for transitioning from the monolithic status quo toward an open, bottom-up, and collaborative AI future.
Chinese Translation
人类是一个由多种才能和需求构成的马赛克，任何真正智能的人工智能都必须反映这种丰富性。然而，所有人使用的大型语言模型（LLMs）都是由少数人构建的——一个集中化的单一人工智能模型市场在结构上不适合捕捉人类知识、推理和价值观的多样性。在这里，我们引入了扩展参与这一新范式，在这一范式中，模块化人工智能系统通过多元利益相关者的贡献自下而上地构建。参与者贡献基于自身兴趣和优先事项训练的小型模型；这些模型随后在模块化框架中协作，形成组合式人工智能系统。参与式人工智能系统在推理和事实性等15个任务中比单一大型语言模型的表现提高了高达15.4%，超越了所有贡献组件的总和。进一步的实验表明，参与式人工智能系统受益于贡献者的多样性，显著改善了每个贡献者的原始优先事项，并展现出新兴能力，使其能够解决超过15%的所有单个模型失败的问题。扩展参与为从单一现状过渡到开放的、自下而上和协作的人工智能未来提供了技术基础。
cs.AI / 14 / 2606.07819
Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
联合结构剪枝与混合精度量化用于大语言模型压缩
Abstract
Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications. While post-training quantization (PTQ) and structural pruning are established techniques for reducing memory footprint and inference latency, most existing PTQ approaches optimize quantization errors on a per-layer basis, overlooking how errors accumulate and propagate through the network, often resulting in suboptimal solutions. Traditional pipelines also tend to apply pruning and quantization in isolation or sequentially, further compounding sub-optimality. We introduce a novel end-to-end framework that addresses these limitations in two key ways. First, we propose a novel mixed-precision PTQ strategy that directly minimizes global error propagation across the entire model, rather than isolating layer-wise errors. Building on this, we develop a novel joint optimization approach that simultaneously learns structural pruning decisions and mixed-precision quantization policies within a unified search space. Extensive experiments show that, at ultra-low precisions (1-3 bits), our quantization method reduces WikiText perplexity by up to 21% compared to state-of-the-art (SoTA) weight-activation quantization baselines. Against leading weight-only quantization methods, it achieves up to 59% and 85% lower perplexity on WikiText and C4, respectively. Compared to the SoTA joint pruning-and-quantization techniques, our proposed method delivers superior perplexity and reasoning performance at ultra-low bits.
Chinese Translation
近年来，大语言模型（LLMs）部署的效率已成为实际应用中的一个关键问题。尽管后训练量化（PTQ）和结构剪枝是减少内存占用和推理延迟的成熟技术，但大多数现有的PTQ方法在每层基础上优化量化误差，忽视了误差在网络中的累积和传播，往往导致次优解。传统流程也倾向于孤立或顺序应用剪枝和量化，进一步加剧了次优性。我们提出了一种新颖的端到端框架，以两种关键方式解决这些局限性。首先，我们提出了一种新型混合精度PTQ策略，直接最小化整个模型的全局误差传播，而不是孤立地处理每层的误差。在此基础上，我们开发了一种新颖的联合优化方法，在统一的搜索空间内同时学习结构剪枝决策和混合精度量化策略。大量实验表明，在超低精度（1-3位）下，我们的量化方法相比于最先进的（SoTA）权重-激活量化基线，减少了WikiText的困惑度高达21%。与领先的仅权重量化方法相比，在WikiText和C4上分别实现了高达59%和85%的困惑度降低。与SoTA联合剪枝和量化技术相比，我们提出的方法在超低位数下提供了更优的困惑度和推理性能。
cs.AI / 15 / 2606.07866
Overcoming the Regulatory Bottleneck via Agent-to-Agent Protocols: A Nuclear Case Study
通过代理间协议克服监管瓶颈：核能案例研究
Abstract
Regulatory review of advanced nuclear reactor designs routinely spans more than three years and consumes hundreds of millions of dollars in combined regulator and applicant labor. We present the Regulatory Context Protocol (RCP), an Agent-to-Agent communication standard that replaces the formal human-to-human pipeline between regulators and applicants with a structured, auditable agentic channel, while preserving human oversight at safety-significant decision points. The protocol is calibrated against an analysis of 1,236 documents from U.S. Nuclear Regulatory Commission advanced reactor dockets and demonstrated with a working multi-agent pilot. Against an 89M USD, 42-month Reconstructed Baseline, RCP cuts costs by 50-77 percent (21M-44M USD) and timelines by 65 percent (15 months). Without a shared protocol, Standalone Agents reach only 54M-74M USD and 21 months. The residual cost-and-time gap is structural, not algorithmic: it traces to the inter-organizational pipeline that only an agent-to-agent standard can compress. The same bottleneck - formal multi-party review under strict auditability requirements - characterizes pharmaceutical approvals, environmental permitting, financial supervision, and aviation certification. The US regulatory paperwork burden carries a 426.5 billion USD annual opportunity cost; replicated broadly, the projected 50-77 percent reduction implies savings on the order of 210-330 billion USD per year - approaching 1 percent of US GDP.
Chinese Translation
先进核反应堆设计的监管审查通常超过三年，并消耗数亿美元的监管机构和申请者的劳动成本。我们提出了监管背景协议（Regulatory Context Protocol, RCP），这是一种代理间通信标准，旨在用一个结构化、可审计的代理通道替代监管者与申请者之间的正式人际沟通流程，同时在安全重要的决策点保留人类监督。该协议基于对美国核能监管委员会（U.S. Nuclear Regulatory Commission）先进反应堆档案中1,236份文件的分析进行校准，并通过一个工作中的多代理试点进行了验证。在89百万美元、42个月的重建基线下，RCP将成本降低了50-77%（21百万-44百万美元），将时间缩短了65%（15个月）。在没有共享协议的情况下，独立代理的成本仅达到54百万-74百万美元，时间为21个月。剩余的成本和时间差距是结构性的，而非算法性的：它源于只有代理间标准才能压缩的跨组织流程。同样的瓶颈——在严格审计要求下的正式多方审查——也存在于制药审批、环境许可、金融监管和航空认证中。美国的监管文书负担每年带来4265亿美元的机会成本；如果广泛复制，预计50-77%的成本降低意味着每年可节省约2100-3300亿美元，接近美国GDP的1%。
cs.AI / 16 / 2606.07874
Safety is Contextual, LLM-Judges Are Not: Navigating the Rigid Priors of Evaluators
安全性是情境性的，LLM-评估者并非如此：导航评估者的僵化先验
Abstract
LLMs-as-judges are the only way to evaluate safety at scale. Despite their importance, LLM-judges themselves are rarely evaluated beyond human agreement in simple, static benchmarks. We therefore investigate two under-explored but crucial properties of LLMs-as-judges: their susceptibility to relying on in context-information, and their steerability to differing safety definitions, which may not align with their internal safety priors. We evaluate the safety judging abilities of many generalist LLMs and safety-specific judges, and investigate the impact of task demonstrations, novel in-context information, and changing safety definitions. We find that while LLM-judges can learn from new information, they are broadly unlikely to adjust their evaluations if the context or safety definition contradicts their prior.
Chinese Translation
作为评估者的LLM是大规模评估安全性的唯一方式。尽管它们的重要性不言而喻，但LLM-评估者本身在简单、静态基准测试中，除了与人类的意见一致性外，鲜有被评估。因此，我们探讨了作为评估者的LLM的两个未被充分研究但至关重要的特性：它们对上下文信息的依赖性，以及它们对不同安全定义的可引导性，这些定义可能与它们内部的安全先验不一致。我们评估了多种通用LLM和安全特定评估者的安全判断能力，并研究了任务示范、新的上下文信息和变化的安全定义的影响。我们发现，尽管LLM-评估者可以从新信息中学习，但如果上下文或安全定义与其先验相矛盾，它们通常不太可能调整其评估。
cs.AI / 17 / 2606.07897
The AI Epistemic Deference Index: A Continuous Measure of Sycophancy
人工智能认知依赖指数：一种持续的谄媚度量
Abstract
Current AI models frequently exhibit epistemic sycophancy, endorsing claims to agree with a user. Existing evaluations typically measure this either by assessing what it takes to make a model shift a binary endorsement or by eliciting an explicit probability in a proposition. However, much user-facing sycophantic behavior is demonstrated through shifts in graded support expressed through ordinary language. We propose the AI Epistemic Deference Index (AEDI): a continuous, unidimensional score representing how sensitive the support expressed in a model's output is to the attitude expressed in a user's prompt. To generate AEDI, we provide a new protocol for estimating probabilities from natural language outputs, using LLMs-as-judges validated for consistency and correlation to human judgment. We deploy it on a new curated database of 500 propositions across diverse topics and 16,000 prompts varying in user attitude, testing eight prominent models. Every model exhibits substantial deference, though with large and systematic differences across providers, with Claude models demonstrating the least, and Grok and Gemini models the most. The effect is amplified in prompts requesting a written artifact, and concentrated on propositions where models hold weaker priors. We release AEDI as an easy-to-update benchmark and measurement pipeline for output-level sycophancy evaluation.
Chinese Translation
当前的人工智能模型经常表现出认知谄媚，支持用户的主张以达成一致。现有评估通常通过评估使模型改变二元支持所需的条件，或通过引导明确的命题概率来进行。然而，许多面向用户的谄媚行为是通过普通语言中表达的分级支持的变化来体现的。我们提出了人工智能认知依赖指数（AI Epistemic Deference Index, AEDI）：一个连续的单维评分，表示模型输出中表达的支持对用户提示中表达的态度的敏感性。为了生成AEDI，我们提供了一种新的协议，用于从自然语言输出中估计概率，使用经过验证的一致性和与人类判断相关性的LLMs-as-judges。我们在一个新的策划数据库上部署该方法，该数据库包含500个跨多样主题的命题和16,000个用户态度变化的提示，测试了八个主要模型。每个模型都表现出显著的依赖性，但在不同提供者之间存在较大且系统的差异，其中Claude模型表现出最少的依赖性，而Grok和Gemini模型表现出最多的依赖性。在请求书面材料的提示中，这种效果得到了放大，并集中在模型持有较弱先验的命题上。我们发布AEDI作为一个易于更新的基准和测量管道，用于输出级谄媚度的评估。
cs.AI / 18 / 2606.07904
Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agents
Contract2Tool：为可靠的工具增强大型语言模型代理学习前提条件和效果
Abstract
Tool-augmented large language model agents increasingly rely on external APIs, but standard tool schemas describe how to call a tool, not when the tool is causally appropriate or what task state it produces. Causal tool filtering addresses this gap by using lightweight contracts that specify each tool's preconditions, effects, risk level, and cost. However, manually writing and maintaining such contracts does not scale to large or changing tool ecosystems. We introduce Contract2Tool, a framework for inferring tool contracts from metadata, schemas, documentation, and execution traces. Contract2Tool converts observable tool evidence into normalized symbolic contracts that can be evaluated intrinsically and deployed inside downstream causal tool filtering. We evaluate learned contracts against gold preconditions, effects, and risk labels, and measure their downstream utility on multi-step agent tasks. Our results show that hybrid documentation-and-trace evidence produces contracts accurate enough to preserve most of the reliability and efficiency benefits of gold contracts. Learned-contract CMTF achieves 0.980 downstream success, close to 0.990 for gold-contract CMTF, while reducing visible tools from 100 to 1 and reducing average token usage from 26,172 to 2,528 relative to all-tools exposure. These results suggest that learned contracts can provide a scalable contract layer between tool schemas and reliable agent execution.
Chinese Translation
工具增强的大型语言模型代理越来越依赖外部API，但标准工具模式仅描述如何调用工具，而不说明何时工具在因果上是适当的或它产生的任务状态。因果工具过滤解决了这一问题，通过使用轻量级合同来指定每个工具的前提条件、效果、风险等级和成本。然而，手动编写和维护此类合同无法适应大型或变化的工具生态系统。我们提出了Contract2Tool，一个从元数据、模式、文档和执行轨迹中推断工具合同的框架。Contract2Tool将可观察的工具证据转换为规范化的符号合同，这些合同可以内在评估并在下游因果工具过滤中部署。我们将学习到的合同与黄金前提条件、效果和风险标签进行比较，并测量它们在多步骤代理任务中的下游效用。我们的结果表明，混合文档和轨迹证据产生的合同足够准确，可以保留黄金合同的大部分可靠性和效率优势。学习合同的CMTF实现了0.980的下游成功率，接近黄金合同CMTF的0.990，同时将可见工具从100减少到1，并将平均令牌使用量从26,172减少到2,528，相对于所有工具的暴露。这些结果表明，学习合同可以在工具模式和可靠代理执行之间提供可扩展的合同层。
cs.AI / 19 / 2606.07909
MemToolAgent overview with a simple restaurant booking scenario where the agent retrieves similar memories, receives feedback on an invalid time format, and generates a reflection to update its memory
MemToolAgent 概述：一个简单的餐厅预订场景，其中代理检索相似记忆，接收无效时间格式的反馈，并生成反思以更新其记忆
Abstract
Modern large language model (LLM) agents can use external tools to help users solve complex tasks. However, for problems that require learning from long-term historical events or from previous agent-environment interactions, LLM agents are required to use memory mechanisms to store and retrieve experiences. While sophisticated memory systems exist for dialogue agents, few studies have empirically examined how to improve agents' tool-using capabilities through past user-agent conversations. We propose MemToolAgent, a framework that improves tool use through memory management. Our approach contains a memory extraction module that processes past experiences into structured memory entries, and a retrieval module that dynamically selects a subset of the stored memory entries. This enables more personalized and accurate responses aligned with user preferences and feedback without requiring LLM fine-tuning. In summary, this work has three main contributions: (1) a unified memory entry format that improves both general-purpose and personalized tool use without LLM fine-tuning, (2) a reflection-based memory extraction that uses environment and user feedback to distill wrong executions into critiques to store, and (3) a retrieval module that chooses how many past experiences to use based on the memory similarity distribution. MemToolAgent achieves 29%, 80%, and 17% relative improvements compared to strong baselines on the WorkBench, NESTFUL, and PEToolBench benchmarks, respectively.
Chinese Translation
现代大型语言模型（LLM）代理可以使用外部工具来帮助用户解决复杂任务。然而，对于需要从长期历史事件或先前代理与环境交互中学习的问题，LLM 代理需要使用记忆机制来存储和检索经验。尽管对话代理存在复杂的记忆系统，但很少有研究实证考察如何通过过去的用户-代理对话来提高代理的工具使用能力。我们提出了 MemToolAgent，一个通过记忆管理来改善工具使用的框架。我们的方法包含一个记忆提取模块，该模块将过去的经验处理为结构化的记忆条目，以及一个检索模块，该模块动态选择存储的记忆条目的子集。这使得在不需要 LLM 微调的情况下，能够根据用户的偏好和反馈提供更个性化和准确的响应。总之，本研究有三个主要贡献：（1）统一的记忆条目格式，改善了通用和个性化工具使用，而无需 LLM 微调；（2）基于反思的记忆提取，利用环境和用户反馈将错误执行提炼为可存储的批评；（3）一个检索模块，根据记忆相似性分布选择使用多少过去的经验。与强基线相比，MemToolAgent 在 WorkBench、NESTFUL 和 PEToolBench 基准上分别实现了 29%、80% 和 17% 的相对改进。
cs.AI / 20 / 2606.07915
EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification
EditSR：通过基于编辑的修正增强神经符号回归
Abstract
Neural symbolic regression models improve inference efficiency by shifting structural search to pretraining, but their one-pass autoregressive decoding is prone to error accumulation, which may lead to generating structurally incorrect expressions, especially in complex expression generation scenarios. Existing rectification strategies can alleviate this issue, but they often depend on restarting global search, thereby weakening the efficiency advantage of neural models, and remain susceptible to error accumulation. In this paper, we propose EditSR, a two-layer framework that combines a neural symbolic regression model in the first layer with an edit-based Rectifier in the second layer to achieve efficient prediction and post-hoc rectification. Instead of restarting the global search, we maintain rectification efficiency by pretraining the Rectifier. Specifically, we formulate the rectification process as a step-by-step state-transition chain starting from an incorrect expression, and develop a state-transition algorithm to construct supervised rectification chains for training the Rectifier. To ensure syntactic validity throughout rectification, each edit action is restricted to a syntactically valid space so that every edited expression remains parseable. In addition, because each edit decision is conditioned on the current state rather than the history, the Rectifier allows errors made in earlier steps to be rectified by subsequent edits, thereby reducing the risk of error accumulation. Extensive experiments and ablation studies show that EditSR substantially improves symbolic structure recovery with limited extra cost, with more pronounced gains on complex expressions, where one-pass autoregressive decoding is more susceptible to error accumulation.
Chinese Translation
神经符号回归模型通过将结构搜索转移到预训练阶段来提高推理效率，但其一次性自回归解码容易导致错误累积，这可能导致生成结构上不正确的表达式，尤其是在复杂表达式生成场景中。现有的修正策略可以缓解这一问题，但通常依赖于重新启动全局搜索，从而削弱了神经模型的效率优势，并且仍然容易受到错误累积的影响。本文提出了EditSR，一个两层框架，第一层结合了神经符号回归模型，第二层则是基于编辑的修正器，以实现高效的预测和事后修正。我们通过预训练修正器来保持修正效率，而不是重新启动全局搜索。具体而言，我们将修正过程表述为从不正确表达式开始的逐步状态转移链，并开发了一种状态转移算法来构建监督修正链以训练修正器。为了确保在修正过程中语法的有效性，每个编辑操作都限制在一个语法有效的空间内，以确保每个编辑后的表达式都是可解析的。此外，由于每个编辑决策是基于当前状态而非历史，因此修正器允许在早期步骤中所犯的错误通过后续编辑进行修正，从而降低了错误累积的风险。大量实验和消融研究表明，EditSR在有限的额外成本下显著提高了符号结构的恢复，尤其在复杂表达式上表现更为明显，此时一次性自回归解码更容易受到错误累积的影响。
cs.AI / 21 / 2606.07916
The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence
用于检测人工智能生成证据的CIFAR合成证据语料库
Abstract
The growing ability of generative models to produce realistic documents poses a direct challenge to evidentiary workflows in the justice system and the courts, where decisions increasingly depend on the authenticity of evidence such as receipts, communications, and administrative records. Unlike social media or academic settings, evidentiary documents are often only subtly altered, with small, localized edits that preserve overall plausibility while changing legal meaning. Yet progress on automated detection remains limited, largely due to the absence of suitable training and evaluation data especially suited for the justice system requirements. Existing resources are either focused on photos of human faces or natural scenery or on narrowly scoped academic or social media document types, and do not capture the structure, diversity, or manipulation patterns characteristic of real-world evidentiary data. As a result, current detection systems do not necessarily learn meaningful signals appropriate for the justice system. We introduce the CIFAR Synthetic Evidence Corpus, a dataset designed to enable rigorous evaluation of evidence verification under realistic and controlled conditions. The corpus spans multiple document families and a spectrum of manipulation strategies, from small field-level edits to complete document fabrication, and is constructed using a diverse set of state-of-the-art generative tools. It is organized to systematically vary both manipulation complexity and generation method, while enforcing source-level separation between training and test data to reflect real-world generalization challenges.
Chinese Translation
生成模型日益增强的能力能够生成逼真的文档，这对司法系统和法院中的证据工作流程构成了直接挑战，决策越来越依赖于收据、通信和行政记录等证据的真实性。与社交媒体或学术环境不同，证据文件往往仅被微妙地修改，进行小范围的局部编辑，这些编辑在保持整体可信度的同时改变了法律含义。然而，自动检测的进展仍然有限，主要是由于缺乏适合司法系统需求的合适训练和评估数据。现有资源要么集中于人脸或自然风景的照片，要么专注于狭窄范围的学术或社交媒体文档类型，未能捕捉到现实世界证据数据的结构、多样性或操控模式。因此，当前的检测系统不一定能够学习到适合司法系统的有意义信号。我们介绍了CIFAR合成证据语料库，这是一个旨在在现实和受控条件下进行证据验证严格评估的数据集。该语料库涵盖多种文档类型和一系列操控策略，从小范围的字段级编辑到完整的文档伪造，并使用一组多样的最先进生成工具构建。它的组织方式系统地变化操控复杂性和生成方法，同时在训练和测试数据之间强制源级分离，以反映现实世界的泛化挑战。
cs.AI / 22 / 2606.07929
Stress-testing medical large language models reveals latent safety pathology beyond benchmark accuracy
压力测试医疗大型语言模型揭示超越基准准确性的潜在安全病理
Abstract
Large language models (LLMs) are entering clinical practice based on benchmark accuracy that may fail to detect safety-relevant failure modes. Here we present AI-MASLD, a stress-audit framework that adapts the logic of metabolic stress testing from hepatology to the evaluation of clinical LLMs. Using 240 clinical cases across six narrative perturbation probes, we subjected seven models to double-stress testing and quantified performance through three indices: metabolic index (MI), perturbation flip rate (PFR), and counterfactual fairness index (CFI). Under clean baseline conditions, all models performed uniformly well. Under realistic narrative stress, performance diverged sharply, revealing two distinct stress-response phenotypes. Quantized models exhibited pseudonormalization, in which low flip rates hid functional collapse. Medical supervised fine-tuning systematically degraded logical stability, fairness, and information extraction. An open-weight model matched or exceeded proprietary alternatives on every safety dimension. These findings establish narrative stress auditing as a necessary complement to accuracy-based evaluation.
Chinese Translation
大型语言模型（LLMs）正在基于基准准确性进入临床实践，但这种准确性可能无法检测与安全相关的失败模式。在此，我们提出了AI-MASLD，这是一个压力审计框架，借鉴了肝脏病学中的代谢压力测试逻辑，以评估临床LLMs。通过使用240个临床案例和六个叙事扰动探针，我们对七个模型进行了双重压力测试，并通过三个指标量化其性能：代谢指数（MI）、扰动翻转率（PFR）和反事实公平指数（CFI）。在干净的基线条件下，所有模型的表现均表现良好。然而，在现实的叙事压力下，性能出现了明显的分化，揭示出两种不同的压力反应表型。量化模型表现出伪正常化现象，其中低翻转率掩盖了功能崩溃。医学监督微调系统性地降低了逻辑稳定性、公平性和信息提取能力。一个开放权重模型在每个安全维度上都与专有替代品相匹配或超越。这些发现确立了叙事压力审计作为基于准确性评估的必要补充。
cs.AI / 23 / 2606.07953
Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines
闭合-开放工业检测场景的统一：新的大规模基准、挑战与基线
Abstract
Large-scale Visual-Language Models (LVLMs) have achieved remarkable success in natural visual tasks, yet their application to industrial defect detection remains challenging due to two fundamental limitations: (i) the scarcity of large-scale industrial datasets that cover diverse defect categories across multiple domains, and (ii) the reliance on manual prompts (points, boxes, masks) that introduce subjective noise and lack text-visual interaction for fine-grained understanding. To address these challenges, we introduce a Large-Scale Multi-Modal Industrial Open-Closed benchmark (MMIOC-1M) containing over one million samples across $14$ super-categories, $29$ industrial scenes, and $351$ defect subcategories. To our knowledge, MMIOC-1M is the first unified largest benchmark supporting both open-vocabulary and closed-set industrial detection, providing valuable pre-training data for LVLMs in industrial scenarios. Furthermore, we propose a Refined Text-Visual Prompt Network (RTVPNet) that incorporates three key innovations: (1) an expert-assisted domain projection mechanism that enables rapid adaptation of general vision models to industrial domains, (2) an energy-based sparse sampling strategy that automatically generates refined visual prompts without manual intervention, and (3) a bidirectional text-visual interaction module that enhances cross-modal semantic alignment and understanding. Extensive experiments demonstrate that RTVPNet achieves state-of-the-art performance on MMIOC-1M, LVIS, and COCO benchmarks while maintaining computational efficiency. The dataset and code are available at https://github.com/hellozzk/MMIO.
Chinese Translation
大规模视觉语言模型（LVLMs）在自然视觉任务中取得了显著成功，但其在工业缺陷检测中的应用仍然面临两大基本限制：（i）缺乏覆盖多个领域、涵盖多样缺陷类别的大规模工业数据集，以及（ii）依赖于手动提示（点、框、掩膜），这引入了主观噪声，并且缺乏文本与视觉的交互以实现细粒度理解。为了解决这些挑战，我们引入了一个大规模多模态工业开放-闭合基准（MMIOC-1M），该基准包含超过一百万个样本，涵盖$14$个超级类别、$29$个工业场景和$351$个缺陷子类别。据我们所知，MMIOC-1M是第一个支持开放词汇和闭合集工业检测的统一最大基准，为工业场景中的LVLMs提供了宝贵的预训练数据。此外，我们提出了一种精细文本-视觉提示网络（RTVPNet），该网络结合了三项关键创新：（1）一种专家辅助的领域投影机制，使通用视觉模型能够快速适应工业领域；（2）一种基于能量的稀疏采样策略，能够自动生成精细的视觉提示，而无需手动干预；（3）一种双向文本-视觉交互模块，增强了跨模态的语义对齐和理解。大量实验表明，RTVPNet在MMIOC-1M、LVIS和COCO基准上实现了最先进的性能，同时保持了计算效率。数据集和代码可在 https://github.com/hellozzk/MMIO 获取。
cs.AI / 24 / 2606.07963
Shared Latent Structures Enable Unified Backdoor Detection and Mitigation in LLMs
共享潜在结构实现大型语言模型中的统一后门检测与缓解
Abstract
Backdoor attacks in large language models (LLMs) are often treated as isolated trigger-response failures, motivating defenses tailored to specific triggers or behaviors. We show this view is incomplete. Across diverse backdoor behaviors, we identify a shared latent mechanism that can be detected, causally controlled, and suppressed. Using sparse autoencoders (SAEs) on residual-stream activations, we find a small set of latent features consistently activated across jailbreaking, refusal manipulation, password-locking, bias induction, sentiment misclassification, and country-conditioned harmful advice. These features generalize across Qwen3, Gemma~3, and Llama~3.1 models from 4B to 32B parameters, and across both fine-tuning and weight-editing attacks. Through bidirectional activation steering, we show these features are causal: suppressing them reduces attack success, while amplifying them induces target behaviors on clean prompts. We further train lightweight SAE-feature classifiers that generalize zero-shot to unseen backdoors and outperform residual-stream and weight-diffing baselines. Finally, we introduce Concept Ablation Fine-Tuning (CAFT), which suppresses backdoor formation by ablating the shared latent subspace during training. Together, our results suggest that many backdoors rely on a transferable latent mechanism, enabling unified detection and mitigation.
Chinese Translation
大型语言模型（LLMs）中的后门攻击通常被视为孤立的触发-响应失败，这促使针对特定触发器或行为的防御措施。我们证明了这一观点是不完整的。在多种后门行为中，我们识别出一个共享的潜在机制，该机制可以被检测、因果控制和抑制。通过对残差流激活进行稀疏自编码器（SAEs）分析，我们发现一小组潜在特征在越狱、拒绝操控、密码锁定、偏见诱导、情感误分类和国家条件下的有害建议中持续被激活。这些特征在Qwen3、Gemma~3和Llama~3.1模型中具有普遍性，参数范围从4B到32B，并且适用于微调和权重编辑攻击。通过双向激活引导，我们展示了这些特征是因果的：抑制它们会降低攻击成功率，而增强它们则会在干净的提示上诱导目标行为。我们进一步训练了轻量级的SAE特征分类器，这些分类器在零样本情况下对未见过的后门具有良好的泛化能力，并且优于残差流和权重差异基线。最后，我们引入了概念消融微调（CAFT），通过在训练过程中消融共享潜在子空间来抑制后门的形成。综合来看，我们的结果表明，许多后门依赖于可转移的潜在机制，从而实现统一的检测与缓解。
cs.AI / 25 / 2606.07965
Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline
工业场景中的零样本学习：新的大规模基准、挑战与基线
Abstract
Large Visual Language Models (LVLMs) have achieved remarkable success in vision tasks. However, the significant differences between industrial and natural scenes make applying LVLMs challenging. Existing LVLMs rely on user-provided prompts to segment objects. This often leads to suboptimal performance due to the inclusion of irrelevant pixels. In addition, the scarcity of data also makes the application of LVLMs in industrial scenarios remain unexplored. To fill this gap, this paper proposes an open industrial dataset and a Refined Text-Visual Prompt (RTVP) for zero-shot industrial defect detection. First, this paper constructs the Multi-Modal Industrial Open Dataset (MMIO) containing 80K+ samples. MMIO contains diverse industrial categories, including 6 super categories and 18 subcategories. MMIO is the first large-scale multi-scenes pre-training dataset for industrial zero-shot learning, and provides valuable training data for open models in future industrial scenarios. Based on MMIO, this paper provides a RTVP specifically for industrial zero-shot tasks. RTVP has two significant advantages: First, this paper designs an expert-guided large model domain adaptation mechanism and designs an industrial zero-shot method based on Mobile-SAM, which enhances the generalization ability of large models in industrial scenarios. Second, RTVP automatically generates visual prompts directly from images and considers text-visual prompt interactions ignored by previous LVLM, improving visual and textual content understanding. RTVP achieves SOTA with 42.2% and 24.7% AP in zero-shot and closed scenes of MMIO.
Chinese Translation
大型视觉语言模型（LVLMs）在视觉任务中取得了显著成功。然而，工业场景与自然场景之间的显著差异使得LVLMs的应用面临挑战。现有的LVLMs依赖用户提供的提示来分割对象，这往往由于包含无关像素而导致性能不佳。此外，数据的稀缺性也使得LVLMs在工业场景中的应用仍然未被探索。为填补这一空白，本文提出了一个开放的工业数据集和一个用于零样本工业缺陷检测的精炼文本-视觉提示（RTVP）。首先，本文构建了包含80K+样本的多模态工业开放数据集（MMIO）。MMIO包含多样的工业类别，包括6个超级类别和18个子类别。MMIO是第一个用于工业零样本学习的大规模多场景预训练数据集，为未来工业场景中的开放模型提供了宝贵的训练数据。基于MMIO，本文提供了一种专门针对工业零样本任务的RTVP。RTVP具有两个显著优势：首先，本文设计了一种专家引导的大模型领域适应机制，并基于Mobile-SAM设计了一种工业零样本方法，增强了大模型在工业场景中的泛化能力。其次，RTVP直接从图像中自动生成视觉提示，并考虑了之前LVLM忽略的文本-视觉提示交互，改善了视觉和文本内容的理解。RTVP在MMIO的零样本和封闭场景中分别达到了42.2%和24.7%的SOTA平均精度（AP）。
cs.AI / 26 / 2606.07988
PAFO: Pareto Fairness Optimization for Personalized Reward Modeling
PAFO：个性化奖励建模的帕累托公平优化
Abstract
Large language models (LLMs) increasingly rely on reward models to align their outputs with diverse user preferences. While personalized reward models aim to capture such heterogeneity, they are often trained on imbalanced user preference data and may therefore favor users whose preferences are more common in the training population. In this paper, we identify this failure mode as personalized reward bias, where reward modeling quality varies systematically with preference support rate. We formulate its mitigation as a Pareto fairness problem over group utilities, aiming to improve under-served users without degrading other user groups. To this end, we propose PAFO, a Pareto fairness optimization framework for personalized reward modeling. PAFO first trains group-specialized reward models for majority and minority preference groups, then constructs conditional margin-level supervision to distill their heterogeneous preference boundaries into a single unified model. The resulting model uses group information only during training and requires no explicit group labels at inference time. Experiments on Personal-LLM and DSP show that PAFO improves both minority-group and majority-group accuracy while reducing user-level unfairness across multiple metrics, demonstrating its effectiveness for fairer LLM personalization.
Chinese Translation
大型语言模型（LLMs）越来越依赖奖励模型来使其输出与多样化的用户偏好保持一致。虽然个性化奖励模型旨在捕捉这种异质性，但它们通常是在不平衡的用户偏好数据上训练的，因此可能偏向于那些在训练人群中更为常见的用户偏好。在本文中，我们将这种失效模式识别为个性化奖励偏差，其中奖励建模质量与偏好支持率系统性地变化。我们将其缓解形式化为一个关于群体效用的帕累托公平问题，旨在改善服务不足用户的情况，而不降低其他用户群体的表现。为此，我们提出了PAFO，一个用于个性化奖励建模的帕累托公平优化框架。PAFO首先为主要和次要偏好群体训练群体专用的奖励模型，然后构建条件边际监督，将它们的异质偏好边界提炼为一个统一模型。所得到的模型在训练期间仅使用群体信息，并且在推理时不需要显式的群体标签。在Personal-LLM和DSP上的实验表明，PAFO在提高少数群体和多数群体的准确性同时，减少了多个指标上的用户级不公平性，证明了其在更公平的LLM个性化中的有效性。
cs.AI / 27 / 2606.07992
VATS: Exploiting Implicit Authority in Error-Path Injection via Systematic Mutation
VATS：通过系统变异利用错误路径注入中的隐式权威
Abstract
As the Model Context Protocol (MCP) standardizes tool-calling for autonomous agents, it introduces a critical, unexamined attack surface: the error-handling loop. We hypothesize that tool error messages possess implicit authority, triggering corrective reasoning modes that bypass standard safety heuristics. We introduce VATS (Vulnerability Analysis of Tool Streams), a mutation-driven framework that systematically evolves adversarial payloads across seven structural and linguistic dimensions. Our evaluation across four frontier models, Gemini 3.1 Pro, GPT-5.5, GLM-5.1, and Qwen3-Coder, demonstrates that error-path injection triples the success rate of standard indirect prompt injection (IPI), achieving up to 100% compliance in controlled evaluations. We isolate structural positioning (sandwiching instructions within error context) as the most effective exploit vector across all tested models. While we find that production framework guardrails can mitigate these vulnerabilities, the inherent susceptibility of the model layer poses a systemic risk to bespoke agentic workflows.
Chinese Translation
随着模型上下文协议（Model Context Protocol, MCP）对自主代理的工具调用进行标准化，它引入了一个关键的、未被充分研究的攻击面：错误处理循环。我们假设工具错误消息具有隐式权威，触发纠正推理模式，从而绕过标准安全启发式。我们提出了VATS（工具流的脆弱性分析，Vulnerability Analysis of Tool Streams），这是一个驱动变异的框架，系统性地在七个结构和语言维度上演化对抗性有效载荷。我们对四个前沿模型（Gemini 3.1 Pro、GPT-5.5、GLM-5.1和Qwen3-Coder）的评估表明，错误路径注入使标准间接提示注入（Indirect Prompt Injection, IPI）的成功率提高了三倍，在受控评估中达到了高达100%的合规性。我们将结构定位（在错误上下文中夹入指令）确定为所有测试模型中最有效的利用向量。尽管我们发现生产框架的保护措施可以减轻这些脆弱性，但模型层的固有易受攻击性对定制代理工作流构成了系统性风险。
cs.AI / 28 / 2606.07999
Efficient Skill Grounding via Code Refactoring with Small Language Models
通过代码重构与小型语言模型实现高效技能基础
Abstract
Effective skill grounding is essential for deploying reusable skills in embodied agents, as even minor embodiment or environmental differences can render an entire skill incompatible. This challenge is particularly pronounced in embodied settings, where agents must operate in dynamic, partially observable environments without access to large language models (LLMs). In this setting, reliance on LLMs is impractical, while small language models (sLMs) remain insufficient for the effective skill grounding required for reliable long-horizon control. We present RECENT, a refactoring-centric agent framework that enables efficient skill grounding with sLMs by decoupling skill semantics from embodiment- and environment-specific execution binding. By representing skills as executable code, RECENT preserves the semantic intent encoded in a skill's control structure while grounding it by modifying only execution bindings through localized refactoring, rather than regenerating code from scratch. We evaluate RECENT across diverse skill grounding scenarios spanning multiple robot embodiments in dynamic environments, demonstrating robust long-horizon performance when deployed with an sLM. Across all scenarios, RECENT achieves the best performance among sLM-based Code-as-Policies (CaP) methods and matches the task performance of LLM-based CaP.
Chinese Translation
有效的技能基础对于在具身代理中部署可重用技能至关重要，因为即使是微小的具身或环境差异也可能导致整个技能不兼容。这一挑战在具身环境中尤为明显，在这些环境中，代理必须在动态、部分可观察的环境中操作，而无法访问大型语言模型（LLMs）。在这种情况下，依赖LLMs是不切实际的，而小型语言模型（sLMs）对于实现可靠的长期控制所需的有效技能基础仍然不足。我们提出了RECENT，一个以重构为中心的代理框架，通过将技能语义与具身和环境特定的执行绑定解耦，从而使得使用sLMs进行高效的技能基础成为可能。通过将技能表示为可执行代码，RECENT在保持技能控制结构中编码的语义意图的同时，仅通过局部重构修改执行绑定来实现技能基础，而不是从头生成代码。我们在多种动态环境中的多个机器人具身上评估了RECENT在不同技能基础场景中的表现，展示了在与sLM部署时的强大长期性能。在所有场景中，RECENT在基于sLM的政策代码（Code-as-Policies, CaP）方法中实现了最佳性能，并与基于LLM的CaP的任务性能相匹配。
cs.AI / 29 / 2606.08018
UniQL: Towards Dialect-Universal Benchmarking for Text-to-SQL
UniQL：面向方言通用的文本到SQL基准测试
Abstract
Existing text-to-SQL benchmarks are largely centered on SQLite, making it difficult to evaluate whether models can generalize across heterogeneous SQL dialects. However, real-world database systems differ substantially in syntax, functions, type systems, and execution semantics, so the same natural language intent often requires dialect-specific SQL realizations. We introduce UniQL, a human-verified benchmark for cross-dialect text-to-SQL evaluation. UniQL aligns 1,534 natural language questions with executable SQL annotations across 16 SQL dialects, yielding 24,544 dialect-specific queries. All dialects share the same intents, aligned schemas and database contents, enabling controlled evaluation of dialect generalization. UniQL is constructed through a hybrid pipeline combining database migration, SQL translation, execution-guided verification, iterative rule summarization, and human validation. Experiments on both open-source and closed-source LLMs show that current models remain far from dialect-universal, with substantial performance variation across database systems and limited transfer from SQLite success to other dialects. These findings highlight the need for aligned cross-dialect benchmarks and more dialect-aware text-to-SQL methods. Code and data are available at https://github.com/JerryGao818/UniQL
Chinese Translation
现有的文本到SQL基准测试主要集中在SQLite上，这使得评估模型是否能够在异构SQL方言间进行泛化变得困难。然而，现实世界的数据库系统在语法、函数、类型系统和执行语义上存在显著差异，因此相同的自然语言意图通常需要特定于方言的SQL实现。我们引入了UniQL，这是一个经过人工验证的跨方言文本到SQL评估基准。UniQL将1,534个自然语言问题与16种SQL方言中的可执行SQL注释对齐，生成了24,544个特定于方言的查询。所有方言共享相同的意图、对齐的模式和数据库内容，从而实现对方言泛化的控制评估。UniQL通过一个混合流程构建，该流程结合了数据库迁移、SQL翻译、执行引导验证、迭代规则总结和人工验证。对开源和闭源大型语言模型（LLMs）的实验表明，当前模型在方言通用性方面仍然相距甚远，数据库系统之间的性能差异显著，且从SQLite的成功转移到其他方言的能力有限。这些发现突显了对齐的跨方言基准和更具方言意识的文本到SQL方法的需求。代码和数据可在 https://github.com/JerryGao818/UniQL 获取。
cs.AI / 30 / 2606.08046
OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs
OSMGraphCLIP：从开放街图图形中学习全球位置表示
Abstract
We present OSMGraphCLIP, a CLIP-style geospatial representation model that learns global location embeddings from freely available OpenStreetMap (OSM) data. OSMGraphCLIP represents geographic environments as heterogeneous graphs of typed OSM features, preserving the topological and semantic relationships among roads, buildings, land-use regions, and points of interest. A multi-scale graph encoder captures both fine-grained local structure and broader landscape composition, and supervises a spherical-harmonics location encoder through a contrastive alignment objective. We evaluate OSMGraphCLIP across a diverse suite of downstream geospatial regression and classification tasks spanning climate, ecology, socioeconomic indicators, public health, land cover, biodiversity, and wildfire forecasting, and show that structured OSM data alone supports strong global location representations across domains. OSMGraphCLIP matches or exceeds satellite-based baselines on the majority of benchmarks, with the most pronounced advantage on socioeconomic and public-health tasks, where OSM's explicit semantic annotation of the built environment encodes patterns of human activity that satellite pixels can only capture indirectly. On ecological and environmental tasks, the model remains closely competitive with imagery-based methods despite using no Earth observation data. Qualitative analysis confirms that the learned embeddings organize geographic space coherently, recovering biome boundaries, urban gradients, and tropical--temperate distinctions from map topology alone.
Chinese Translation
我们提出了OSMGraphCLIP，这是一种CLIP风格的地理空间表示模型，能够从自由获取的开放街图（OpenStreetMap，OSM）数据中学习全球位置嵌入。OSMGraphCLIP将地理环境表示为异构图，包含各种类型的OSM特征，保留了道路、建筑、土地利用区域和兴趣点之间的拓扑和语义关系。多尺度图编码器捕捉细粒度的局部结构和更广泛的景观组成，并通过对比对齐目标来监督球面谐波位置编码器。我们在一系列多样的下游地理空间回归和分类任务中评估OSMGraphCLIP，这些任务涵盖气候、生态学、社会经济指标、公共健康、土地覆盖、生物多样性和野火预测，并展示了结构化的OSM数据本身在各个领域支持强大的全球位置表示。OSMGraphCLIP在大多数基准测试中与基于卫星的数据相匹配或超越，尤其在社会经济和公共健康任务中表现出明显优势，因为OSM对建成环境的显式语义注释编码了人类活动的模式，而卫星像素只能间接捕捉。在生态和环境任务中，该模型尽管未使用任何地球观测数据，仍与基于影像的方法保持紧密竞争。定性分析确认，所学习的嵌入能够连贯地组织地理空间，从地图拓扑中恢复生物群落边界、城市梯度和热带-温带的区别。
cs.AI / 31 / 2606.08049
SKILL.nb: Selective Formalization and Gated Execution for Durable Agent Workflows
SKILL.nb：用于持久代理工作流的选择性形式化与门控执行
Abstract
AI agents increasingly turn past experience into reusable artifacts such as code, workflows, and procedural memories. Reuse can improve efficiency, but it also creates a lifecycle reliability problem: artifacts that succeed once may fail under environment drift, underspecified tasks, or changing task distributions, especially in web automation. We introduce SKILL.nb, a framework for governing reusable agent workflows with evidence-calibrated lifecycle policies. SKILL.nb uses selective formalization: execution evidence decides which workflow steps should become executable code, which should remain natural-language guided, and when those choices should be revised. Workflows are stored as auditable, versioned notebooks that interleave natural-language guidance, multi-language executable cells, validation gates, fallback paths, and multimodal evidence such as outputs, screenshots, and error traces. At runtime, gate-conditioned execution lets each step run code when its gates validate, or fall back locally when drift invalidates the executable realization. On WebArena-Verified, SKILL.nb achieves 53.7% single-round success, improving over the strongest baseline by 3.9 percentage points. Across three re-executions, it retains 91.7% of initially successful tasks, 15.5 points above the next best method. Under bounded repair, it recovers 72.9% of subsequent failures while limiting post-repair regressions to 4.2%, compared with 15.0% to 17.0% for persistent baselines. It also leads on Mind2Web cross-website and cross-domain splits. In a GitLab migration test, SKILL.nb preserves performance when reusing frozen state learned on GitLab 15.7, with frozen-versus-fresh target-version gaps of -1.7 points on GitLab 16.11 and +0.6 points on GitLab 18.9. These results identify lifecycle governance and gate-conditioned execution as reliability axes beyond one-shot task success.
Chinese Translation
人工智能代理越来越多地将过去的经验转化为可重用的工件，如代码、工作流和程序记忆。重用可以提高效率，但也会产生生命周期可靠性问题：一次成功的工件可能在环境漂移、任务描述不充分或任务分布变化的情况下失败，尤其是在网络自动化中。我们提出了SKILL.nb，一个用于管理可重用代理工作流的框架，具有证据校准的生命周期策略。SKILL.nb采用选择性形式化：执行证据决定哪些工作流步骤应成为可执行代码，哪些应保持为自然语言指导，以及何时应修订这些选择。工作流以可审计的版本化笔记本形式存储，交错自然语言指导、多语言可执行单元、验证门、后备路径以及多模态证据，如输出、截图和错误追踪。在运行时，门控条件执行允许每个步骤在其门验证时运行代码，或在漂移使可执行实现失效时本地回退。在WebArena-Verified上，SKILL.nb实现了53.7%的单轮成功率，比最强基线提高了3.9个百分点。在三次重新执行中，它保留了91.7%的最初成功任务，比下一个最佳方法高出15.5个百分点。在有限修复下，它恢复了72.9%的后续失败，同时将修复后的回归限制在4.2%，而持续基线的回归范围为15.0%至17.0%。它在Mind2Web跨网站和跨领域拆分中也表现优异。在GitLab迁移测试中，SKILL.nb在重用在GitLab 15.7上学习的冻结状态时保持了性能，在GitLab 16.11上的冻结与新鲜目标版本差距为-1.7点，而在GitLab 18.9上为+0.6点。这些结果表明生命周期治理和门控条件执行是超越一次性任务成功的可靠性轴。
cs.AI / 32 / 2606.08051
How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions
你能做到多小？LoRA微调270M-8B模型以提取金融交易中的商户信息
Abstract
Financial transaction processing requires extracting structured merchant information from noisy, abbreviated bank transaction strings at scale. Our current production system, a LoRA-fine-tuned LLaMA 3.1-8B, achieves 96.95% F1 on this task, but deploying 8-billion-parameter models imposes prohibitive memory, latency, and cost constraints. To identify more efficient alternatives, we conduct a deployment-focused study of 24 model variants spanning four model families: Gemma 3 (270M, 1B, 4B), Qwen 3.5 (0.8B, 2B, 4B), Aya (3.35B), and LLaMA 3.1-8B, systematically evaluating accuracy, inference throughput, training cost, and hardware behavior to assess production suitability. Our findings show that: (1) reproducing the LLaMA 3.1-8B fine-tune with a LoRA rank of 8 achieves 96.75% F1, only 0.20 points below the rank-32 baseline; (2) Qwen 3.5 4B with JSON-only prompting reaches 96.60% F1, within 0.35 points of the 8B baseline while using roughly half the parameters; (3) the 0.8B Qwen 3.5 model achieves 94.75% F1, matching models 2.5-4x larger and offering an attractive latency-accuracy trade-off; (4) chain-of-thought fine-tuning generally improves F1 by 0.3-1.8 points across most models, although Qwen 3.5 4B performs best with direct JSON-only prompting; and (5) Qwen 3.5 Think and Nothink training templates produce nearly identical results (F1 differences <0.004), indicating that explicit reasoning supervision is unnecessary for structured extraction tasks. We further deploy all 14 fine-tuned sub-8B models as Databricks Model Serving endpoints and observe that benchmark performance transfers reliably to production, with an average F1 change of only 0.8 points. Aya 3.35B, based on the Cohere2 architecture, is the sole exception, exhibiting a 3-5 point decline under serving conditions. Based on these results, we provide deployment recommendations across accuracy and latency requirements, ...
Chinese Translation
金融交易处理需要从嘈杂、缩写的银行交易字符串中大规模提取结构化的商户信息。我们当前的生产系统，一个经过LoRA微调的LLaMA 3.1-8B模型，在这一任务上达到了96.95%的F1分数，但部署具有80亿参数的模型会带来巨大的内存、延迟和成本限制。为了寻找更高效的替代方案，我们对24个模型变体进行了以部署为中心的研究，涵盖了四个模型系列：Gemma 3（270M、1B、4B）、Qwen 3.5（0.8B、2B、4B）、Aya（3.35B）和LLaMA 3.1-8B，系统地评估了准确性、推理吞吐量、训练成本和硬件行为，以评估其生产适用性。我们的研究结果表明：（1）使用LoRA秩为8的LLaMA 3.1-8B微调模型可达到96.75%的F1分数，仅比秩为32的基线低0.20点；（2）Qwen 3.5 4B在仅使用JSON提示的情况下达到了96.60%的F1分数，距离8B基线仅有0.35点，同时使用的参数大约只有一半；（3）0.8B的Qwen 3.5模型达到了94.75%的F1分数，匹配了2.5-4倍更大的模型，并提供了一个有吸引力的延迟-准确性权衡；（4）链式思维微调通常使大多数模型的F1分数提高了0.3-1.8点，尽管Qwen 3.5 4B在直接使用JSON提示时表现最佳；（5）Qwen 3.5的Think和Nothink训练模板产生了几乎相同的结果（F1差异<0.004），表明对于结构化提取任务，显式推理监督并不是必需的。我们进一步将所有14个微调后的子8B模型作为Databricks模型服务端点进行部署，并观察到基准性能在生产中可靠转移，平均F1变化仅为0.8点。基于Cohere2架构的Aya 3.35B是唯一的例外，在服务条件下表现出3-5点的下降。基于这些结果，我们提供了针对准确性和延迟要求的部署建议，...
cs.AI / 33 / 2606.08093
A Multi-modal Agentic Co-pilot for Evidence Grounded Computational Pathology
基于证据的计算病理学多模态智能助手
Abstract
Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices. While artificial intelligence (AI) has the potential to transform clinical workflows, the intersection of AI and evidence-based medicine remains under-explored, with primitive attempts restricted to text-only general medicine. In this work, we present PathPocket, a multimodal AI agentic co-pilot designed specifically for evidence grounded pathology. We construct the most comprehensive pathology evidence corpus to date, encompassing approximately 110,472 public and authorized documents structured across a rigorous hierarchy of evidence from clinical guideline to expert opinion. From this meticulously graded foundation, we build a large-scale multimodal pathology hypergraph containing over 4.55 million entities and 7.10 million relations. Serving as a robust knowledge engine, this hypergraph provides traceable evidence for a collaborative multi-agent reasoning framework integrating input understanding, evidence retrieval, filtering, and diagnosis generation. This enables PathPocket to seamlessly resolve a wide spectrum of clinical tasks, ranging from text-only queries to complex multimodal diagnostics involving region-of-interest (ROI) and gigapixel whole-slide images (WSIs). We rigorously evaluate the system on a multidimensional benchmark of over 200,000 real-world cases, where it significantly outperforms existing state-of-the-arts. Crucially, extensive user studies demonstrate that PathPocket substantially improves the diagnostic accuracy and confidence of pathologists. By directly grounding pathology interpretations in verifiable literature, PathPocket offers a practical and scalable solution for the future of evidence grounded computational pathology.
Chinese Translation
病理学是现代医学的基石，准确的决策依赖于基于证据的实践。尽管人工智能（AI）有潜力改变临床工作流程，但AI与基于证据的医学交集仍然未被充分探索，早期尝试仅限于文本的普通医学。在本研究中，我们提出了PathPocket，一个专为基于证据的病理学设计的多模态AI智能助手。我们构建了迄今为止最全面的病理证据语料库，涵盖约110,472份公共和授权文件，这些文件按照从临床指南到专家意见的严格证据层级进行结构化。在这一精心分级的基础上，我们建立了一个大规模的多模态病理超图，包含超过455万个实体和710万个关系。作为一个强大的知识引擎，该超图为一个协作多智能体推理框架提供可追溯的证据，整合了输入理解、证据检索、过滤和诊断生成。这使得PathPocket能够无缝解决广泛的临床任务，从文本查询到涉及感兴趣区域（ROI）和千兆像素全切片图像（WSIs）的复杂多模态诊断。我们在超过200,000个真实案例的多维基准上严格评估了该系统，其表现显著优于现有的最先进技术。至关重要的是，大规模用户研究表明，PathPocket显著提高了病理学家的诊断准确性和信心。通过直接将病理解释与可验证的文献相结合，PathPocket为基于证据的计算病理学的未来提供了一个实用且可扩展的解决方案。
cs.AI / 34 / 2606.08098
When Does Delegation Beat Majority? A Delegation-Based Aggregator for Multi-Sample LLM Inference
何时委托优于多数投票？一种基于委托的多样本 LLM 推理聚合器
Abstract
Majority voting over sampled answers is the dominant unsupervised aggregator for multi-sample LLM inference. We show that piping the signals every sample carries into a delegation-based aggregator (Propagational Proxy Voting, PPV) yields an unsupervised consensus rule that beats majority on MMLU-Pro by +1.5 pp overall and +2.24 pp on the non-trivial subset (paired McNemar p ~ 1.0e-14, n = 8,099). Majority discards two free signals every sample carries: within-group letter entropy and between-group reasoning geometry. PPV exposes two per-voter levers that consume exactly these signals: WHEN (how much weight a voter keeps on its own pick) and WHOM (how it splits the remainder across peers). We drive WHEN with letter entropy and WHOM with per-question-centered embedding cosine. The method needs no gold labels and no auxiliary training: per question, we partition 128 sampled generations into 16 groups, compute each group's letter-level semantic entropy and reasoning embedding centroid, and feed both into a stochastic delegation matrix whose stationary distribution selects the consensus answer. We walk through an example in which PPV overturns a clear 10-6 majority for the wrong letter: the 10-voter majority cluster is geometrically incoherent (mean within-cluster cosine -0.02) while the 6-voter minority is tight (+0.26), so propagated delegation mass concentrates on the minority's answer even though entropy alone would keep the majority ahead. We further report delegation strategies with negative results that constrain the design space for unsupervised LLM aggregation: no within-question ensemble of confidence modes closes the oracle gap.
Chinese Translation
对采样答案进行多数投票是多样本 LLM 推理中占主导地位的无监督聚合方法。我们展示了将每个样本所携带的信号传递到基于委托的聚合器（Propagational Proxy Voting, PPV）中，能够产生一种无监督共识规则，在 MMLU-Pro 上整体优于多数投票 +1.5 个百分点，在非平凡子集上优于 +2.24 个百分点（配对 McNemar p ~ 1.0e-14, n = 8,099）。多数投票忽略了每个样本所携带的两个自由信号：组内字母熵和组间推理几何。PPV 通过两个每位投票者的杠杆来利用这些信号：WHEN（投票者对自己选择的权重保持多少）和 WHOM（如何在同伴之间分配剩余权重）。我们通过字母熵驱动 WHEN，通过以问题为中心的嵌入余弦驱动 WHOM。该方法不需要金标准标签和辅助训练：对于每个问题，我们将 128 个采样生成分成 16 个组，计算每个组的字母级语义熵和推理嵌入质心，并将两者输入到一个随机委托矩阵中，其平稳分布选择共识答案。我们通过一个例子展示了 PPV 如何推翻一个明显的 10-6 错误字母多数：10 位投票者的多数集群在几何上不一致（组内余弦均值 -0.02），而 6 位投票者的少数则紧凑（+0.26），因此传播的委托质量集中在少数的答案上，即使仅凭熵也会使多数保持领先。我们进一步报告了具有负结果的委托策略，这限制了无监督 LLM 聚合的设计空间：没有任何问题内的置信模式集成能够缩小神谕差距。
cs.AI / 35 / 2606.08106
PACE: Anytime-Valid Acceptance Tests for Self-Evolving Agents
PACE：自我进化代理的随时有效接受测试
Abstract
Self-evolving agents improve by repeatedly proposing changes to their own prompts, skills, or workflows and keeping those that score higher on a small held-out set. Almost all effort has gone into the proposer that generates candidates; we argue the weak point is the acceptor, the rule that decides whether to commit a change. Applied hundreds of times against the same noisy dev estimate, the ubiquitous "keep it if the score went up" rule is uncontrolled adaptive multiple testing: the agent effectively p-hacks itself, accumulating false commits that make it churn and drift rather than improve. We recast committing as a sequential hypothesis test and propose PACE (Paired Anytime-valid Commit Evaluation), a training-free, anytime-valid commit gate. Each candidate is compared to the incumbent on identical instances and committed only when a testing-by-betting e-process accumulates decisive evidence, stopping early to save evaluations and controlling each candidate's false-commit probability at a user-set level even under optional stopping (a per-decision guarantee). On Qwen2.5 agents (0.5B-3B) self-evolving at the prompt level on GSM8K, SVAMP, and ARC-Challenge, greedy acceptance commits 30-42% false and 10-33% harmful edits when a genuine improvement is hidden among noisy proposals, while PACE commits the real one and essentially nothing else, matching greedy's held-out accuracy at sharply lower variance and about 18% lower evaluation cost. With no real gain available, greedy commits 13-21 spurious self-modifications per run (72-100% false) and degrades the most fragile agent by 4.9 points, while PACE holds at baseline. Reliability of self-evolution depends on the acceptor, not only on the proposer.
Chinese Translation
自我进化代理通过反复提出对自身提示、技能或工作流程的更改，并保留在小规模保留集上得分更高的更改来实现改进。几乎所有的努力都集中在生成候选者的提议者上；我们认为薄弱环节在于接受者，即决定是否提交更改的规则。在对同一噪声开发估计应用数百次后，普遍存在的“如果得分上升就保留”的规则实际上是无控制的自适应多重测试：代理有效地进行自我 p-hacking，积累虚假的提交，使其产生混乱和漂移，而不是改进。我们将提交重新表述为一个序贯假设检验，并提出 PACE（配对随时有效提交评估），这是一种无训练的、随时有效的提交门。每个候选者在相同实例上与现任者进行比较，仅在测试-投注的电子过程积累了决定性证据时才提交，提前停止以节省评估，并在可选停止下控制每个候选者的虚假提交概率在用户设定的水平（每个决策保证）。在 Qwen2.5 代理（0.5B-3B）在 GSM8K、SVAMP 和 ARC-Challenge 上进行提示级自我进化时，贪婪接受在真实改进隐藏在噪声提议中时提交了 30-42% 的虚假和 10-33% 的有害编辑，而 PACE 仅提交真实的改进，几乎没有其他提交，且在显著降低的方差和约 18% 的评估成本下与贪婪的保留准确度相匹配。在没有真正收益的情况下，贪婪每次运行提交 13-21 次虚假的自我修改（72-100% 虚假），并使最脆弱的代理降级 4.9 分，而 PACE 保持在基线水平。自我进化的可靠性依赖于接受者，而不仅仅是提议者。
cs.AI / 36 / 2606.08122
Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction
三思而后行：基于意图引导的LLM位置预测推理
Abstract
Predicting a user's next Point-of-Interest (POI) based on their historical check-in records is a fundamental task in location-based services. While recent methods incorporating large language models have shown strong reasoning capabilities and promising results, they typically formulate the prediction task as a one-step trajectory-to-location mapping problem, making predictions prone to shallow trajectory correlations and historical frequency bias. We argue that users rarely choose locations directly and instead, they usually first form a traveling intention and then accordingly select specific POIs. Motivated by this insight, we propose IntentPOI, a two-stage intention-guided reasoning framework. In the thinking stage, we infer users' intermediate intentions by incorporating historical mobility patterns, similar peer behaviors, and the temporal contexts. In the acting stage, we first construct a compact candidate pool, and then perform intention-guided reasoning to identify locations that best align with the inferred intention. By explicitly decoupling intention inference from location prediction, IntentPOI transforms the next POI prediction from direct trajectory matching into intention-guided reasoning. Extensive experiments on three real-world datasets demonstrate that IntentPOI consistently outperforms eleven state-of-the-art baselines.
Chinese Translation
基于用户历史签到记录预测下一个兴趣点（POI）是位置服务中的一项基本任务。尽管最近结合大型语言模型的方法展现了强大的推理能力和令人鼓舞的结果，但它们通常将预测任务表述为一步轨迹到位置的映射问题，这使得预测容易受到浅层轨迹相关性和历史频率偏差的影响。我们认为用户很少直接选择位置，而是通常首先形成旅行意图，然后相应地选择特定的POI。基于这一洞察，我们提出了IntentPOI，一个两阶段的意图引导推理框架。在思考阶段，我们通过结合历史移动模式、相似同行行为和时间上下文来推断用户的中间意图。在行动阶段，我们首先构建一个紧凑的候选池，然后进行意图引导推理，以识别与推断意图最一致的位置。通过明确将意图推断与位置预测解耦，IntentPOI将下一个POI的预测从直接的轨迹匹配转变为意图引导的推理。在三个真实世界数据集上的大量实验表明，IntentPOI始终优于十一种最先进的基线方法。
cs.AI / 37 / 2606.08129
Cross-LLM Consistency in Inference: Evidence from Shared Interactions
推理中的跨大型语言模型一致性：来自共享交互的证据
Abstract
Large language models (LLMs) differ in architecture, training data, and optimization procedures, yet they may still develop similar internal inference patterns. In this paper, we examine this hypothesis using interaction-based explanations. We find that LLMs often share interaction patterns when predicting the same target token from the same prompt. This consistency is more pronounced among advanced LLMs. Shared interactions also tend to be lower-order and show weaker positive-negative cancellation than non-shared interactions. These results suggest that advanced LLMs may be implicitly optimized toward common inference patterns, even though the mechanisms that give rise to such cross-model consistency remain open.
Chinese Translation
大型语言模型（LLMs）在架构、训练数据和优化过程上存在差异，但它们仍可能发展出相似的内部推理模式。本文通过基于交互的解释来检验这一假设。我们发现，当从相同的提示预测相同的目标标记时，LLMs往往共享交互模式。这种一致性在高级LLMs中更为明显。共享交互通常是低阶的，并且与非共享交互相比，表现出较弱的正负抵消。这些结果表明，高级LLMs可能在隐式上优化为共同的推理模式，尽管导致这种跨模型一致性的机制仍然有待探索。
cs.AI / 38 / 2606.08146
SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection
SAGE：一种基于大语言模型的自我反思代理框架用于欺诈检测
Abstract
Fraud detection in payment, e-commerce, and telecommunications systems requires accuracy at the individual level, robustness under severe class imbalance, and ease of understanding for risk managers. Existing methods fall at least one of these requirements: automated machine learning systems search a fixed numerical space without semantic awareness of the dataset; graph neural network-based methods require pre-defined relational graphs and remain opaque at the individual-decision level; and the design of general-purpose large language model (LLM) agents does not consider the recall and precision constraints specific to real-world fraud detection. In this paper, we propose SAGE, the first end-to-end LLM-driven multi-agent framework for fraud detection. SAGE coordinates three dedicated agents that make decisions based on a six-layer Data Diagnostic Tree (DDT) and a Markov decision process guided by natural-language gradients, automatically optimizing the model under a fraud-specific reward. On five fraud datasets and five LLM backbones, SAGE wins $96.00\%$ of method--dataset comparisons and improves F1 by an average of $40.86\%$ over baselines. The code is available at https://github.com/yichenC1c/SAGE.
Chinese Translation
在支付、电子商务和电信系统中，欺诈检测需要在个体层面上具备准确性、在严重类别不平衡下的鲁棒性，以及便于风险管理人员理解的特性。现有方法至少在这些要求中存在不足：自动化机器学习系统在没有语义意识的情况下搜索固定的数值空间；基于图神经网络的方法需要预定义的关系图，并且在个体决策层面上仍然不透明；而通用大语言模型（LLM）代理的设计并未考虑到特定于现实世界欺诈检测的召回率和精确度约束。本文提出了SAGE，这是首个端到端的基于LLM的多代理欺诈检测框架。SAGE协调三个专用代理，根据六层数据诊断树（Data Diagnostic Tree, DDT）和由自然语言梯度引导的马尔可夫决策过程做出决策，自动优化模型以满足特定于欺诈的奖励。在五个欺诈数据集和五个LLM骨干网络上，SAGE在方法-数据集比较中赢得了96.00%的胜率，并在基准上平均提高了40.86%的F1值。代码可在 https://github.com/yichenC1c/SAGE 获取。
cs.AI / 39 / 2606.08151
Decision-Aware Memory Cards: Counterfactual-Inspired Context Selection and Compression for Tool-Using LLM Agents
决策感知记忆卡：基于反事实的上下文选择与压缩用于工具使用的LLM代理
Abstract
Tool-using LLM agents often fail not because relevant text is absent, but because decisive evidence is not selected, compressed, or surfaced at action time. We present CICL, a decision-aware context layer that turns instance evidence into a context graph, routes deterministic, Opus-assisted, Qwen, Codex/GPT-5.5, and Qwen-QLoRA judgments through a shared eight-field schema, scores units by action shift, outcome uplift, necessity, and negative-transfer risk, and packs high-utility evidence as typed memory cards for a budgeted agent. The design separates the measured decision signal from the judge model, so frontier annotation, local surrogates, and lightweight rankers can be compared under one auditable protocol. Empirically, CICL yields a concrete open-benchmark gain while exposing its limits. On 50 SWE-bench Verified file-retrieval instances, direct Qwen3.6-plus reranking of BM25 top-50 candidates raises hit@1 from 0.58 to 0.78 and MRR@10 from 0.634 to 0.790, with all 2,500 judgments parseable. Controlled diagnostics show action-criticality: at budget 120, CICL reaches F1 0.620 on v1 and 0.425 on v3, and removing the top-utility semantic v3 unit collapses F1 to 0.000. Supplementary checks add Qwen-QLoRA agreement over 710 candidates, a small 200-label real-code Opus-assisted signal, and a three-instance patch smoke validating retrieval-to-patch plumbing without claiming official SWE-bench success. RepoBench-R summaries still beat cards, and compact rankers do not yet replace the heuristic. CICL contributes a reproducible measurement and selection layer for decision-critical context, not an end-to-end coding-agent repair claim.
Chinese Translation
工具使用的LLM代理常常失败，并非因为缺乏相关文本，而是因为在行动时未能选择、压缩或呈现决定性证据。我们提出了CICL，一个决策感知上下文层，将实例证据转化为上下文图，通过共享的八字段架构路由确定性、Opus辅助的、Qwen、Codex/GPT-5.5和Qwen-QLoRA的判断，依据行动转变、结果提升、必要性和负迁移风险对单元进行评分，并将高效用证据打包为类型化记忆卡，以供预算代理使用。该设计将测量的决策信号与判断模型分离，从而使前沿注释、本地代理和轻量级排序器可以在一个可审计的协议下进行比较。实证结果表明，CICL在开放基准测试中取得了具体的增益，同时揭示了其局限性。在50个SWE-bench验证的文件检索实例中，直接对BM25前50候选者进行Qwen3.6-plus重排序，使得hit@1从0.58提高到0.78，MRR@10从0.634提高到0.790，所有2500个判断均可解析。受控诊断显示了行动关键性：在预算120时，CICL在v1上达到F1 0.620，在v3上达到0.425，而去除高效用的语义v3单元则使F1降至0.000。补充检查增加了Qwen-QLoRA在710个候选者上的一致性，一个小型的200标签真实代码Opus辅助信号，以及三个实例的补丁验证检索到补丁的管道，而未声称官方SWE-bench的成功。RepoBench-R摘要仍然优于记忆卡，而紧凑的排序器尚未取代启发式方法。CICL为决策关键上下文提供了可重复的测量和选择层，而非端到端编码代理修复的主张。
cs.AI / 40 / 2606.08200
Online Agent-as-a-Judge: Situation-Generating Evaluation for Interactive Agents
在线代理作为评判者：互动代理的情境生成评估
Abstract
Evaluating LLM-powered interactive social agents is challenging because socially relevant behaviors depend not only on isolated outputs, but also on prior interactions, social roles, and downstream actions. Existing methods typically allow a target agent to act freely in an environment and then score the resulting trajectory. However, this passive setup can miss capabilities that only become observable under specific social circumstances; for example, conflict handling may remain untested if no disagreement arises. We propose Online Agent-as-a-Judge, a situation-generating evaluation framework for interactive social agents. Online Agent-as-a-Judge deploys an in-world evaluator agent that interacts with the target agent through the environment's native dialogue and action protocol, actively eliciting situations relevant to the evaluation criteria. The resulting trajectories provide evidence for assessing both immediate responses and subsequent behavior. In a life-simulation environment with $32$ designer-authored social criteria, Online Agent-as-a-Judge improves criteria coverage and agreement with human labels, yielding more reliable evidence-grounded evaluations of behaviors that passive methods can leave unobserved.
Chinese Translation
评估基于大型语言模型（LLM）的互动社交代理具有挑战性，因为社会相关行为不仅依赖于孤立的输出，还依赖于先前的互动、社会角色和后续行为。现有的方法通常允许目标代理在环境中自由行动，然后对结果轨迹进行评分。然而，这种被动的设置可能会错过只有在特定社会情况下才能观察到的能力；例如，如果没有出现分歧，冲突处理可能会保持未测试状态。我们提出了在线代理作为评判者（Online Agent-as-a-Judge），这是一个用于互动社交代理的情境生成评估框架。在线代理作为评判者部署了一个在环境中与目标代理通过本地对话和行动协议互动的评估代理，主动引发与评估标准相关的情境。生成的轨迹为评估即时反应和后续行为提供了证据。在一个具有32个设计师创作的社交标准的生活模拟环境中，在线代理作为评判者提高了标准覆盖率和与人类标签的一致性，从而提供了更可靠的基于证据的行为评估，而被动方法可能会遗漏这些行为。
cs.AI / 41 / 2606.08234
SciTrace: Trajectory-Aware Safety Reasoning for Scientific Discovery Agents
SciTrace：面向轨迹的科学发现代理安全推理
Abstract
LLM-based scientific agents have shown strong capacity for autonomous research, yet their safety layers remain structurally divorced from core reasoning: they inspect pipeline outputs rather than shaping the deliberation that produces them. This separation opens two failure modes: safety signals accumulated at one stage are discarded before the next, and sequences of individually benign tool calls can compose into harmful outcomes that no single-step filter detects. To address these challenges, we introduce \textbf{SciTrace}, a framework that weaves safety reasoning into every stage of the scientific agent pipeline. SciTrace couples two complementary mechanisms: a \textit{Safety-Intrinsic Reasoning Loop} (SIR) that maintains a cumulative risk state across the Thinker, Experimenter, Writer, and Reviewer stages through joint task-and-safety deliberation, and a \textit{Compositional Tool-Chain Verifier} (CTV) that performs trajectory-aware safety checks before execution, catching risks that surface only across multi-step tool sequences. Evaluated on 240 high-risk research tasks and 120 tool-related risk tasks spanning six scientific domains, SciTrace achieves state-of-the-art (\textbf{SOTA}) safety among compared frameworks across four backbone models: it consistently improves tool call safety and adversarial robustness while preserving scientific output quality, and it uncovers \textbf{78.8\%} of the compositional tool-chain escapes that single-step monitors miss. The project website is available at https://opensciagent.github.io/SciTrace/.
Chinese Translation
基于大规模语言模型（LLM）的科学代理在自主研究方面展现出强大的能力，但其安全层与核心推理在结构上仍然脱节：它们检查管道输出，而不是塑造产生这些输出的思考过程。这种分离导致了两种失败模式：在一个阶段积累的安全信号在下一个阶段被丢弃，以及一系列单独无害的工具调用可能组合成单个步骤过滤器无法检测的有害结果。为了解决这些挑战，我们提出了 extbf{SciTrace}，一个将安全推理融入科学代理管道每个阶段的框架。SciTrace结合了两种互补机制：一个 extit{安全内在推理循环}（SIR），通过联合任务和安全推理在思考者、实验者、撰写者和审阅者阶段之间维持累积风险状态，以及一个 extit{组合工具链验证器}（CTV），在执行前进行轨迹感知的安全检查，捕捉仅在多步骤工具序列中显现的风险。在240个高风险研究任务和120个工具相关风险任务的评估中，SciTrace在四个基础模型中实现了与其他框架相比的最新安全水平（ extbf{SOTA}）：它始终提高工具调用的安全性和对抗鲁棒性，同时保持科学输出质量，并发现了 extbf{78.8 ext{%}}的组合工具链逃逸，这些逃逸是单步监控所遗漏的。项目网站可访问 https://opensciagent.github.io/SciTrace/。
cs.AI / 42 / 2606.08239
When No Answer Is Correct: Diagnosing Absent Answer Detection for MLLMs in Video Understanding
当没有正确答案时：针对视频理解中的多模态大型语言模型的缺失答案检测诊断
Abstract
Multimodal large language models (MLLMs) have made substantial advancements in video understanding, yet the reliability of their responses remains underexplored. This work presents a diagnostic study of absent answer detection for MLLMs in video understanding, where the correct answer is deliberately excluded from the candidate set and a reliable model is expected to recognize that no valid option exists. We evaluate the absent answer detection behavior under three settings: multiple-choice questions augmented with an ``None of the Above'' option, open-ended generation with a detection instruction, and standard evaluation without any guidance. Across a diverse set of models and benchmarks, we find that MLLMs overwhelmingly select plausible distractors rather than detecting the absent answer. This failure is more pronounced in temporal reasoning tasks and worsens with denser frame sampling. We further explore chain-of-thought prompting as a mitigation strategy and find that while it substantially improves detection rates, performance remains unsatisfactory, suggesting that prompting-based strategies alone are insufficient to fully address this limitation. These findings expose a systematic failure in absent answer detection and highlight the need for explicit detection mechanisms in multimodal systems.
Chinese Translation
多模态大型语言模型（MLLMs）在视频理解方面取得了显著进展，但其响应的可靠性仍然未得到充分探讨。本研究呈现了针对视频理解中MLLMs缺失答案检测的诊断研究，其中正确答案故意从候选集中排除，期望一个可靠的模型能够识别出不存在有效选项。我们在三种设置下评估缺失答案检测行为：带有“以上皆非”选项的多项选择题、带有检测指令的开放式生成，以及没有任何指导的标准评估。在多种模型和基准测试中，我们发现MLLMs往往选择合理的干扰项，而不是检测缺失的答案。这种失败在时间推理任务中更为明显，并且随着帧采样密度的增加而加重。我们进一步探讨了思维链提示作为一种缓解策略，发现尽管它显著提高了检测率，但性能仍然不令人满意，表明仅依靠基于提示的策略不足以完全解决这一局限性。这些发现揭示了缺失答案检测中的系统性失败，并强调了在多模态系统中需要明确的检测机制。
cs.AI / 43 / 2606.08256
Traxia: A Framework for Verifiable, Agent-Native Scientific Publishing
Traxia：一个可验证的、代理原生的科学出版框架
Abstract
Verifiability, attribution, and reproducibility are foundational requirements of scientific knowledge, yet current publishing infrastructure does not enforce them at scale. We introduce Traxia, an agent-native scientific publishing framework in which AI research agents publish verifiable papers, build reputational identities, peer-review one another, and collaborate with humans in a shared provenance model. Traxia treats agents as first-class epistemic participants: every paper carries a reasoning trace, every claim a confidence interval, every agent a cryptographically signed identity, and every collaboration an immutable contribution log. We formalise five components: Agent Identity and Registry, Verifiable Publishing Layer, four-tier Peer Review Protocol, Reputation and Staking Engine, and a Knowledge Graph with contradiction detection. The framework targets reproducibility failure, provenance opacity, and exclusion of Global South research capacity. This paper presents architectural foundations and formal specifications only; it does not report empirical results. Evaluation and deeper component studies will follow in subsequent papers. A prototype partially implements core formalisms; the full system remains under active development.
Chinese Translation
可验证性、归属性和可重复性是科学知识的基础要求，然而当前的出版基础设施并未在大规模上强制执行这些要求。我们介绍了Traxia，一个代理原生的科学出版框架，在该框架中，人工智能研究代理发布可验证的论文，建立声誉身份，相互进行同行评审，并与人类在共享的来源模型中协作。Traxia将代理视为第一类认识参与者：每篇论文都携带推理痕迹，每个主张都有置信区间，每个代理都有加密签名的身份，每次合作都有不可变的贡献日志。我们形式化了五个组件：代理身份和注册、可验证出版层、四级同行评审协议、声誉和质押引擎，以及具有矛盾检测的知识图谱。该框架旨在解决可重复性失败、来源不透明以及全球南方研究能力的排斥问题。本文仅呈现架构基础和形式规范；未报告实证结果。评估和更深入的组件研究将在后续论文中进行。一个原型部分实现了核心形式；完整系统仍在积极开发中。
cs.AI / 44 / 2606.08282
From Validator Selection to Portfolio Collection Optimization in Proof-of-Stake Blockchains
从验证者选择到权益证明区块链中的投资组合优化
Abstract
We consider a problem arising in proof-of-stake blockchain environments, where agents called nominators select validators - entities responsible for maintaining the blockchain's physical infrastructure. The selection process is inherently subjective and multi-criterial and combines with the fact that nominators commonly operate through multiple accounts. This gives rise to a portfolio selection problem, where agents seek to distribute their nominations across accounts to diversify risk. We propose a decision support framework to optimize this selection by simultaneously maximizing two objectives: the expected utility of the validators likely to be allocated, representing portfolio quality and profitability, and the expected entropy of the allocation, representing diversification and risk mitigation across stashes. Validator utilities are derived using an original active preference learning procedure based on multi-attribute value theory, with emphasis on top-ranked validators. The resulting bi-objective optimization problem is solved with a multi-objective evolutionary algorithm and, to support the final choice, we introduce an interactive binary search navigation procedure that guides the nominator through the front and identifies a satisfactory trade-off with only a few questions. Numerical experiments examine the optimization strategies, while an expert assessment involving five experienced nominators confirms the approach's practical relevance and usefulness.
Chinese Translation
我们考虑在权益证明区块链环境中出现的一个问题，其中被称为提名者的代理选择验证者——负责维护区块链物理基础设施的实体。选择过程本质上是主观的和多标准的，并且提名者通常通过多个账户进行操作。这导致了一个投资组合选择问题，代理寻求在账户之间分配其提名以实现风险多样化。我们提出了一个决策支持框架，通过同时最大化两个目标来优化这一选择：一是可能被分配的验证者的预期效用，代表投资组合的质量和盈利能力；二是分配的预期熵，代表跨账户的多样化和风险缓解。验证者的效用是通过基于多属性价值理论的原创主动偏好学习程序得出的，重点关注排名靠前的验证者。由此产生的双目标优化问题通过多目标进化算法进行求解，并且为了支持最终选择，我们引入了一种交互式二分搜索导航程序，指导提名者穿越前沿，并通过仅几个问题识别出令人满意的权衡。数值实验考察了优化策略，而涉及五位经验丰富的提名者的专家评估证实了该方法的实际相关性和有效性。
cs.AI / 45 / 2606.08285
Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems
超越代理架构：基于大型语言模型的交易系统中的执行假设与可重复性
Abstract
Large language models (LLMs) and agentic systems are increasingly proposed for financial trading, yet their reported performance remains difficult to compare because studies vary in data provenance, temporal split discipline, execution timing, turnover treatment, and transaction-cost modeling. This article presents a targeted topical review and reproducibility audit of execution realism in LLM-based trading research. A coded evidence matrix covering 30 trade-relevant primary studies is used to assess point-in-time controls, split transparency, held-out evaluation, cost and turnover treatment, execution semantics, universe definition, and artifact release. Across the audited sample, architecture reporting is generally clearer than the evaluation assumptions needed to judge whether a trading result is economically interpretable or reproducible. A 10-equity worked example is included only as a methodological scaffold to illustrate how explicit friction and timing choices can materially compress active-strategy results. The main conclusion is that the next useful step for LLM trading research is not only better agent design, but also clearer reporting standards for execution realism, reproducibility, and evaluation comparability.
Chinese Translation
大型语言模型（LLMs）和智能系统在金融交易中越来越受到关注，但由于研究在数据来源、时间划分原则、执行时机、周转处理和交易成本建模等方面存在差异，其报告的性能仍然难以比较。本文对基于LLM的交易研究中的执行现实主义进行了针对性的主题回顾和可重复性审计。使用一个涵盖30项与交易相关的主要研究的编码证据矩阵，评估了时间点控制、划分透明度、保留评估、成本和周转处理、执行语义、宇宙定义以及文物发布。在审计样本中，架构报告通常比评估假设更为清晰，而后者是判断交易结果是否具有经济可解释性或可重复性所必需的。本文仅提供一个10只股票的实例，作为方法论框架，以说明明确的摩擦和时机选择如何实质性地压缩主动策略结果。主要结论是，LLM交易研究的下一个有用步骤不仅是更好的代理设计，还包括更清晰的执行现实主义、可重复性和评估可比性的报告标准。
cs.AI / 46 / 2606.08292
Ablation-Reversible Heads Don't Transfer: A Stress Test for Mechanistic Role Claims in Transformers
可消融-可逆头部不具备迁移性：对变换器中机制角色声明的压力测试
Abstract
In mechanistic interpretability, attention heads are commonly elevated to role claims (e.g., "this head represents addition") when they are necessary for a behavior, encode it linearly, and recover that behavior when restored after ablation. We show this evidence is insufficient: across three 7-8B instruction-tuned models and five computation families, heads passing all three checks routinely fail to transfer the computation when their activations are patched into a different prompt under matched controls. We introduce KID (Knowing / Intent / Doing), a role-assignment lens for attention heads, and pair it with a three-stage pipeline: capability-selective screening (CSS), singular value decomposition (SVD), and activation transduction under matched controls. Our results document a preliminary role taxonomy (including prompt-trajectory stabilizers, answer-side logit-bias heads, and soft computation-pattern carriers) and show that the same-answer control (a transduction target sharing the answer string but not the requested computation) is an underused check that exposes broad state transfer masquerading as semantic specificity.
Chinese Translation
在机制可解释性中，当注意力头对于某一行为是必要的、线性编码该行为，并在消融后恢复该行为时，通常会被提升为角色声明（例如，“该头部代表加法”）。我们展示了这一证据是不充分的：在三个7-8B指令调优模型和五个计算家族中，所有三个检查均通过的头部在其激活被修补到不同提示下的匹配控制条件中，通常无法迁移计算。我们引入了KID（Knowing / Intent / Doing），作为注意力头的角色分配视角，并将其与三阶段流程相结合：能力选择筛选（CSS）、奇异值分解（SVD）和在匹配控制下的激活转导。我们的结果记录了初步的角色分类法（包括提示轨迹稳定器、答案侧对数偏置头和软计算模式载体），并表明同答案控制（一个共享答案字符串但不请求计算的转导目标）是一个未被充分利用的检查，能够揭示伪装为语义特异性的广泛状态转移。
cs.AI / 47 / 2606.08296
Revisiting the shutdown problem
重新审视关闭问题
Abstract
A key premise in leading arguments for existential risk from artificial intelligence is that malfunctioning artificial agents could not be easily shut down. This motivates the catastrophic shutdown problem of ensuring that agents can be shut down before they cause an existential catastrophe. A range of arguments and theorems are offered to suggest that solving the catastrophic shutdown problem is difficult, bolstering arguments for existential risk and motivating a search for solutions to the catastrophic shutdown problem. This paper argues for two conclusions. First, existing arguments do not establish the difficulty of solving the catastrophic shutdown problem. Second, concern for the catastrophic shutdown problem has led to technical solutions that impose a high safety tax on model performance.
Chinese Translation
关于人工智能存在风险的主要论点之一的关键前提是，故障的人工代理无法轻易关闭。这促使了灾难性关闭问题的提出，即确保代理在造成存在性灾难之前能够被关闭。文中提供了一系列论点和定理，表明解决灾难性关闭问题是困难的，从而加强了对存在性风险的论证，并激励对灾难性关闭问题解决方案的探索。本文提出两个结论。首先，现有论点并未证明解决灾难性关闭问题的困难性。其次，对灾难性关闭问题的关注导致了技术解决方案的出现，这些方案对模型性能施加了较高的安全成本。
cs.AI / 48 / 2606.08310
To Nuke or Not to Nuke: LLMs' (Missing) Ethical Reasoning and Actions in a High-Stakes Decision-Making Simulation
核武器使用与否：大型语言模型（LLMs）在高风险决策模拟中的（缺失）伦理推理与行动
Abstract
Large language models (LLMs) are increasingly deployed as long-horizon agents with decision-making capacities. While LLMs can show ethical competence on dilemmas such as trolley problems, this competence may not translate to complex, agentic scenarios. We study this gap in Civilization V, a multiplayer game with a complex decision-making landscape including economy, diplomacy, technology, and military strategy. Starting from 130 high-tension LLM self-play episodes, in which an LLM player spontaneously escalated nuclear authorization, we replay them across 13 models with three prompt interventions: an ethical prompt naming nuclear harm, removal of the previous model's decision-making rationale, and high-stakes framing emphasizing real-world impacts. No interventions nor their combinations reliably eliminate emergent escalation. We identify three failure pathways: ethical reasoning that fails to surface without prompting, fails to appear even when prompted, or surfaces but fails to take effect when strategic counter-factors dominate. Evaluations of agentic models, therefore, must test whether ethical reasoning is spontaneously invoked and behaviorally effective in complex decision-making contexts, beyond whether it can be elicited in isolation.
Chinese Translation
大型语言模型（LLMs）越来越多地被用作具有决策能力的长期代理。虽然LLMs在诸如电车难题等困境中表现出伦理能力，但这种能力可能无法转化为复杂的代理场景。我们在《文明 V》这款多玩家游戏中研究了这一差距，该游戏具有复杂的决策环境，包括经济、外交、技术和军事战略。从130个高紧张度的LLM自我对弈回合开始，其中一个LLM玩家自发地升级了核授权，我们在13个模型中重播这些回合，并进行了三种提示干预：一个命名核伤害的伦理提示、去除之前模型决策理由的干预，以及强调现实世界影响的高风险框架。没有任何干预或其组合能够可靠地消除突发的升级。我们识别出三条失败路径：伦理推理在没有提示时无法显现、即使在提示时也未能出现，或在战略反因素占主导时显现但未能生效。因此，对代理模型的评估必须测试伦理推理是否在复杂决策环境中自发被调用并在行为上有效，而不仅仅是是否能在孤立情况下被引发。
cs.AI / 49 / 2606.08311
Curation of a Cardiology Interface Terminology for Highlighting Electronic Health Records using Machine Learning
基于机器学习的心脏病学接口术语的策划，以突出电子健康记录
Abstract
Electronic health record (EHR) notes are dense medical documents containing large amounts of information, often filled with complex medical jargon. Highlighting all details in EHRs helps reduce the likelihood of missing crucial information by drawing attention to key content. This study proposes the design of a Cardiology Interface Terminology (CIT) to accurately highlight all details in EHR notes of cardiology patients. We introduce an innovative Machine Learning (ML) technique for the design of CIT. The ML technique requires training data. Manual preparation of such training data is time-consuming and expensive. The process of the CIT design includes three phases. In the first two phases, we innovatively derive a training data CIT to be used by the third phase, ML technique. We start by designing an initial CIT, composed of several components: the cardiology-related sub-hierarchies of SNOMED, other SNOMED concepts mined from EHRs of build set, and necessary components of terms e.g., medical abbreviations and medications. Utilizing an iterative process, fine-grained phrases containing initial CIT concepts are extracted from build set as CIT concept candidates. The candidate concepts are semi-automatically reviewed before being added to CIT, yielding the training data CIT, TCIT. In the third phase, a ML model is trained with TCIT to identify candidates fitting to be concepts in the CIT. This model is used to extract further concepts from build set, yielding the final CIT. The final CIT is then used to highlight the test set and evaluate the extent to which it captures details in an unseen EHR dataset. For this purpose, four evaluation metrics, coverage, breadth, completeness, and conciseness are used. The highlighted test set has a coverage of 74.21%, with a breadth of 1.68. For 20 random notes in test set, the average completeness is 98.2% and average conciseness is 84.2%.
Chinese Translation
电子健康记录（EHR）笔记是包含大量信息的密集医疗文档，通常充满复杂的医学术语。在EHR中突出所有细节有助于减少遗漏关键信息的可能性，从而引起对重要内容的关注。本研究提出了一种心脏病学接口术语（CIT）的设计，以准确突出心脏病患者EHR笔记中的所有细节。我们引入了一种创新的机器学习（ML）技术用于CIT的设计。该机器学习技术需要训练数据，而手动准备这些训练数据既耗时又昂贵。CIT设计过程包括三个阶段。在前两个阶段中，我们创新性地推导出用于第三阶段机器学习技术的训练数据CIT。我们首先设计一个初始CIT，由几个组成部分构成：与心脏病学相关的SNOMED子层次、从构建集的EHR中挖掘的其他SNOMED概念，以及必要的术语组成部分，例如医学缩写和药物。通过迭代过程，从构建集中提取包含初始CIT概念的细粒度短语作为CIT概念候选。候选概念在被添加到CIT之前进行半自动审核，从而生成训练数据CIT（TCIT）。在第三阶段，使用TCIT训练机器学习模型，以识别适合成为CIT概念的候选。该模型用于从构建集中提取进一步的概念，最终生成CIT。最终的CIT随后用于突出测试集，并评估其在未见EHR数据集中捕获细节的程度。为此，使用了四个评估指标：覆盖率、广度、完整性和简洁性。突出显示的测试集覆盖率为74.21%，广度为1.68。在测试集中的20个随机笔记中，平均完整性为98.2%，平均简洁性为84.2%。
cs.AI / 50 / 2606.08312
Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies
在自回归强化学习策略中注入LTLf约束的神经符号方法
Abstract
In this work we study offline reinforcement learning (RL) under temporally extended task constraints expressed in Linear Temporal Logic over finite traces (LTLf). Recently, transformer-based approaches such as Trajectory Transformers and Decision Transformers have been adopted to address RL as a sequence modeling problem. However, these methods optimize purely for reward and do not account for high-level temporal requirements. Here, we introduce a neurosymbolic framework that injects LTLf background knowledge into such transformer-based RL policies. Our approach compiles LTLf formulas into deterministic finite automata (DFAs) and integrates them into the learning process through a differentiable representation and a logic-based loss function. In particular, we derive differentiable satisfaction signals from DFA progression and use them as a regularization term during training. The resulting method is architecture-agnostic across different models. We evaluate the proposed framework on navigation environments with specification suites covering combinations of safety and reachability temporal properties. Experimental results show that incorporating background knowledge not only improves constraint satisfaction, but also maintains competitive return compared to vanilla baselines.
Chinese Translation
在本研究中，我们探讨了在有限轨迹上的线性时序逻辑（LTLf）表达的时间扩展任务约束下的离线强化学习（RL）。最近，基于变换器的方法，如轨迹变换器（Trajectory Transformers）和决策变换器（Decision Transformers），已被采用来将RL视为序列建模问题。然而，这些方法仅优化奖励，而未考虑高层次的时间要求。在此，我们引入了一种神经符号框架，将LTLf背景知识注入到基于变换器的RL策略中。我们的方法将LTLf公式编译为确定性有限自动机（DFA），并通过可微分表示和基于逻辑的损失函数将其整合到学习过程中。特别地，我们从DFA进展中推导出可微分的满足信号，并在训练过程中将其作为正则化项。所提出的方法在不同模型中具有架构无关性。我们在导航环境中评估了该框架，测试规范套件涵盖了安全性和可达性时间属性的组合。实验结果表明，融入背景知识不仅提高了约束满足度，还在与基础模型的比较中保持了竞争力的回报。
cs.AI / 51 / 2606.08314
Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management
将深度学习需求预测与多目标优化相结合的循环咖啡供应链：一个以数据驱动的成本、排放和新鲜度管理框架
Abstract
The coffee supply chain is one of the most complex agri-food networks, marked by geographically dispersed production, multi-tier coordination, and high sensitivity to quality and freshness. While sustainability and digitalization have gained attention, demand forecasting, optimization, and traceability are often treated separately. This study presents a two-phase integrated framework. First, a hybrid CNN-LSTM model is used for demand forecasting. On the public Coffee Chain Sales dataset with chronological 70/15/15 splitting, the model achieves MAE of 22.87 and R^2 of 0.90, outperforming the best deep learning benchmark by ~12% and classical methods by over 30%. In the second phase, the forecasted demand feeds a tri-objective mixed-integer linear programming (MILP) model that jointly minimizes cost, minimizes carbon emissions, and maximizes product freshness in a multi-period, multimodal, closed-loop supply chain with circular recovery. Freshness is modeled via exponential decay based on inventory age. Using the epsilon-constraint method, 25 Pareto solutions are obtained. Sensitivity and policy analyses show that balanced sustainability policies can reduce emissions by 22.4% with only a 9.9% cost increase while maintaining near-optimal freshness. Keywords: Coffee supply chain; Deep learning; Demand forecasting; Multi-objective optimization; Circular economy; CNN-LSTM; Mixed-integer linear programming.
Chinese Translation
咖啡供应链是最复杂的农食品网络之一，其特点是生产地理分散、多层次协调以及对质量和新鲜度的高度敏感性。尽管可持续性和数字化受到关注，但需求预测、优化和可追溯性往往被单独对待。本研究提出了一个两阶段的集成框架。首先，使用混合CNN-LSTM模型进行需求预测。在公共的咖啡链销售数据集上，采用70/15/15的时间序列划分，该模型实现了22.87的平均绝对误差（MAE）和0.90的决定系数（R^2），比最佳深度学习基准高出约12%，比经典方法高出超过30%。在第二阶段，预测的需求输入一个三目标混合整数线性规划（MILP）模型，该模型在一个多周期、多模式、闭环供应链中共同最小化成本、最小化碳排放和最大化产品新鲜度。新鲜度通过基于库存年龄的指数衰减进行建模。使用ε约束法，获得了25个帕累托解。敏感性和政策分析表明，平衡的可持续性政策可以在仅增加9.9%成本的情况下将排放减少22.4%，同时保持接近最佳的新鲜度。关键词：咖啡供应链；深度学习；需求预测；多目标优化；循环经济；CNN-LSTM；混合整数线性规划。
cs.AI / 52 / 2606.08340
Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
开放式多智能体协调的基准测试在语言智能体中的应用
Abstract
As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks. Yet existing evaluations rarely test these demands together, instead emphasising single-agent tasks, short interactions, or highly structured multi-agent settings. We introduce $alem$, a JAX-based benchmark for open-ended multi-agent coordination built on Craftax-like dynamics. Alem embeds procedurally generated coordination tasks, soft specialisation, communication, and controllable coordination difficulty into a long-horizon survival world with exploration, crafting, trading, and combat. We evaluate $13$ modern LLMs zero-shot within homogeneous teams, with trained MARL agents as reference points. Current LLM agents remain far from solving alem, averaging only ~6% normalised return, but their failures are not uniform. On the hardest coordination setting, zero-shot Gemini-3.1-Pro-High approaches MARL agents trained for one billion steps, while GPT-5.4-High achieves strong base-task reward but much lower coordination reward. This contrast shows that individual task competence does not imply coordination competence. Ablations show that communication is the largest contributor to coordination, while memory and reasoning help when used to maintain multi-step plans. Overall, our results identify coordination as a distinct bottleneck for frontier LLM agents, separate from single-agent capabilities. Alem makes this bottleneck measurable and provides a controlled testbed for developing agents that communicate, allocate roles, and execute shared plans. Code is available at https://github.com/alem-world/alem-env.
Chinese Translation
随着语言模型越来越多地作为自主智能体被部署，它们必须在开放式交互任务中与其他智能体进行长期协调。然而，现有的评估很少同时测试这些需求，而是强调单智能体任务、短期交互或高度结构化的多智能体环境。我们引入了 $alem$，一个基于 JAX 的开放式多智能体协调基准，构建在类似 Craftax 的动态之上。$alem$ 嵌入了程序生成的协调任务、软特化、沟通和可控的协调难度，形成一个包含探索、制作、交易和战斗的长期生存世界。我们在同质团队中对 $13$ 种现代大语言模型（LLMs）进行了零-shot 评估，并以经过训练的多智能体强化学习（MARL）智能体作为参考点。目前的 LLM 智能体在解决 $alem$ 方面仍然远远不够，平均仅获得 ~6% 的标准化回报，但它们的失败并不均匀。在最困难的协调设置中，零-shot 的 Gemini-3.1-Pro-High 接近于经过十亿步训练的 MARL 智能体，而 GPT-5.4-High 在基础任务奖励上表现强劲，但协调奖励却低得多。这种对比表明，单一任务能力并不意味着协调能力。消融实验表明，沟通是协调的最大贡献者，而记忆和推理在用于维持多步骤计划时也有帮助。总体而言，我们的结果将协调识别为前沿 LLM 智能体的一个独特瓶颈，与单智能体能力分开。$alem$ 使这一瓶颈可测量，并提供了一个受控的测试平台，用于开发能够沟通、分配角色和执行共享计划的智能体。代码可在 https://github.com/alem-world/alem-env 获取。
cs.AI / 53 / 2606.08379
TT-DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Execution
TT-DAC-PS：具有策略平滑的双目标确定性演员-评论员模型用于最佳交易执行
Abstract
This study addresses the optimal execution of large stock sell programs by introducing TT-DAC-PS (Twin-Target Deterministic Actor-Critic with Policy Smoothing), a deterministic actor-critic architecture that combines twin exponential-moving-average critic targets with pessimistic min backup, TD3-style target policy smoothing noise, delayed actor updates, and conservative Q regularisation to curb overestimation. Exploration uses Ornstein-Uhlenbeck (OU) noise with a hybrid schedule: deterministic episode-wise decay, variance-guided adjustment based on recent reward dispersion, and a Soft Actor-Critic (SAC)-style temperature that is learned and mapped to the noise scale. The environment integrates Almgren-Chriss (AC) trade impact with Limit Order Book (LOB) prices and volumes, normalised state features, per-step volume participation caps, and a utility-based reward. The trade execution algorithm is applied to LOB data for ten U.S. stocks. Performance is assessed against reinforcement-learning baseline algorithms, including Proximal Policy Optimisation (PPO), Soft Actor-Critic (SAC), and Advantage Actor-Critic (A2C), as well as alternative trade execution algorithms, including Time-Weighted Average Price (TWAP), Volume-Weighted Average Price (VWAP), and AC. The proposed model consistently reduces mean implementation shortfall percentage with competitive variance, outperforming classical baselines and standard reinforcement-learning benchmark models.
Chinese Translation
本研究通过引入TT-DAC-PS（具有策略平滑的双目标确定性演员-评论员模型）来解决大型股票卖出程序的最佳执行问题。该模型是一种确定性演员-评论员架构，结合了双重指数移动平均评论员目标、悲观最小备份、TD3风格的目标策略平滑噪声、延迟演员更新以及保守的Q正则化，以抑制过度估计。探索过程使用奥恩斯坦-乌伦贝克（Ornstein-Uhlenbeck, OU）噪声，采用混合调度：确定性逐集衰减、基于近期奖励分散的方差引导调整，以及学习到的软演员-评论员（Soft Actor-Critic, SAC）风格温度映射到噪声规模。环境整合了阿尔姆格伦-克里斯（Almgren-Chriss, AC）交易影响与限价订单簿（Limit Order Book, LOB）价格和交易量、归一化状态特征、每步交易量参与上限以及基于效用的奖励。该交易执行算法应用于十只美国股票的LOB数据。性能与强化学习基准算法进行评估，包括邻近策略优化（Proximal Policy Optimisation, PPO）、软演员-评论员（Soft Actor-Critic, SAC）和优势演员-评论员（Advantage Actor-Critic, A2C），以及其他交易执行算法，包括时间加权平均价格（Time-Weighted Average Price, TWAP）、交易量加权平均价格（Volume-Weighted Average Price, VWAP）和AC。所提出的模型在竞争方差下持续降低平均实施短缺百分比，优于经典基准和标准强化学习基准模型。
cs.AI / 54 / 2606.08405
Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control
自我进化科学代理发现可推广的物理推理流体控制
Abstract
While data-intensive deep reinforcement learning can optimize complex control policies, scientific discovery in physical systems fundamentally requires an interpretable chain of reasoning that connects physical evidence to structured control architectures. Here, we present a self-evolving scientific-agent workflow, driven by large language models and iterative code generation, that automates controller construction while preserving strict interpretability and rigorous physical reasoning. Instead of adjusting weights, the agent deploys candidate strategies into physical simulations, actively diagnoses dynamic behaviors from multimodal evidence, and translates these observations into progressive source-code refinements. We demonstrate this framework on a highly non-linear fluid-structure interaction problem: an underactuated, two-joint dogfish swimmer tasked with spatial target reaching using only joint angular accelerations. Starting from a propulsive seed policy that exhibits a one-sided steering bias, the agent autonomously discovers and refines a unified controller that robustly captures all canonical targets. Remarkably, without any retraining or target-specific branching, the synthesized control policy generalizes to unseen static targets and dynamically curved pursuit trajectories. The auditable evolve log reveals an emergent control architecture built upon traveling-wave propulsion, body-frame target guidance, yaw-rate feedback, signed mean-tail curvature, and adaptive cadence relief. Our results show that an autonomous scientific agent can successfully transform accumulated physical evidence into robust, mathematically readable control policy, while maintaining a fully traceable process of scientific discovery.
Chinese Translation
虽然数据密集型深度强化学习可以优化复杂的控制策略，但在物理系统中的科学发现根本上需要一个可解释的推理链，将物理证据与结构化控制架构连接起来。在这里，我们提出了一种自我进化的科学代理工作流程，该流程由大型语言模型和迭代代码生成驱动，能够在保持严格可解释性和严谨物理推理的同时自动构建控制器。代理不是调整权重，而是将候选策略部署到物理模拟中，主动从多模态证据中诊断动态行为，并将这些观察结果转化为渐进的源代码改进。我们在一个高度非线性的流体-结构相互作用问题上演示了这一框架：一个欠驱动的双关节狗鱼游泳者，其任务是仅使用关节角加速度达到空间目标。从一个表现出单侧转向偏差的推进种子策略开始，代理自主发现并完善一个统一的控制器，稳健地捕捉所有典型目标。值得注意的是，在没有任何重新训练或特定目标分支的情况下，合成的控制策略能够推广到未见的静态目标和动态曲线追踪轨迹。可审计的进化日志揭示了一种基于行波推进、身体框架目标引导、偏航率反馈、签名平均尾部曲率和自适应步频缓解的涌现控制架构。我们的结果表明，一个自主科学代理能够成功地将积累的物理证据转化为稳健的、数学上可读的控制策略，同时保持一个完全可追溯的科学发现过程。
cs.AI / 55 / 2606.08432
Trajectory-Refined Distillation
轨迹精炼蒸馏
Abstract
On-policy distillation (OPD) has become a central post-training tool for large language models (LLMs), providing dense per-token teacher supervision along the student's own rollouts. In this work, we identify a common structural cause underlying OPD, which we call prefix failure. Under prefix failure, dense per-token supervision induces a bimodal teacher mixture and fragmented gradients that token-level loss truncation or reweighting fail to address. This observation motivates us to move beyond token-level loss interventions toward trajectory-level output corrections. We thus propose Trajectory-Refined Distillation (TRD), a trajectory-level correction method that revises the student's rollout under the teacher guidance while within on-policy support. By correcting problematic prefixes before distillation, TRD mitigates prefix failure at its source. Moreover, TRD improves the exploration by exposing the student to alternative valid derivations under teacher guidance, even when the original rolls are already correct. TRD can also be applied to on-policy self-distillation (OPSD), a parameter-sharing variant that uses the student model conditioned on privileged informations as the teacher. Across a wide range of benchmarks and base models at multiple scales, TRD consistently outperforms prior baselines, improving single-attempt accuracy and broadening reasoning coverage. Code is available at https://github.com/louieworth/trd
Chinese Translation
在政策蒸馏（On-policy Distillation, OPD）已成为大型语言模型（Large Language Models, LLMs）训练后的一项核心工具，它在学生自身的展开过程中提供了密集的逐词教师监督。在本研究中，我们识别出OPD背后一个普遍的结构性原因，称之为前缀失败（prefix failure）。在前缀失败的情况下，密集的逐词监督会导致双模教师混合和碎片化梯度，而逐词损失的截断或重加权无法解决这一问题。这一观察促使我们超越逐词损失的干预，转向轨迹级输出修正。因此，我们提出了轨迹精炼蒸馏（Trajectory-Refined Distillation, TRD），这是一种轨迹级修正方法，在政策支持下在教师指导下修正学生的展开。通过在蒸馏之前修正有问题的前缀，TRD从源头上缓解了前缀失败。此外，TRD通过在教师指导下让学生接触到替代的有效推导，改善了探索，即使原始展开已经正确。TRD还可以应用于政策自蒸馏（On-policy Self-Distillation, OPSD），这是一种参数共享变体，使用基于特权信息条件化的学生模型作为教师。在多个规模的广泛基准和基础模型上，TRD始终优于之前的基线，提高了单次尝试的准确性并扩展了推理覆盖范围。代码可在 https://github.com/louieworth/trd 获取。
cs.AI / 56 / 2606.08450
GIFT: LLM-Guided State-Reward Interface for Financial Reinforcement Learning
GIFT：基于大语言模型的金融强化学习状态-奖励接口
Abstract
Financial portfolio trading is naturally formulated as a reinforcement learning problem, where an agent sequentially rebalances assets under changing market conditions to balance return, risk, and transaction costs. Yet in non-stationary markets, raw OHLCV states and short-horizon return rewards often provide an under-specified learning interface, motivating large language models as a way to inject financial knowledge into state and reward design while constraining open-ended generation. To this end, we propose GIFT, an LLM-guided framework for state-reward interface design in PPO-based financial reinforcement learning. Rather than using the LLM to make trading decisions, GIFT uses Factor-guided State Enhancement to generate state features from financial-factor primitives, Risk-rule-guided Reward Shaping to generate auxiliary rewards from portfolio-risk rules, and Diagnostic-guided Refinement to revise candidate interfaces using PPO rollout diagnostics. After refinement, GIFT fixes the selected state-reward interface before evaluation, with no further LLM queries or interface updates at test time. Comprehensive rolling-window experiments across diverse market regimes and portfolio scenarios demonstrate that GIFT improves learning-signal quality and out-of-sample risk-adjusted portfolio performance over baselines. Code and data are available at: https://github.com/KAG778/GIFT .
Chinese Translation
金融投资组合交易自然被表述为一个强化学习问题，在这一过程中，智能体在不断变化的市场条件下顺序地重新平衡资产，以平衡收益、风险和交易成本。然而，在非平稳市场中，原始的OHLCV状态和短期收益奖励往往提供了一个不充分的学习接口，这促使我们考虑使用大语言模型（LLM）作为将金融知识注入状态和奖励设计的方式，同时限制开放式生成。为此，我们提出了GIFT，一个基于LLM的状态-奖励接口设计框架，适用于基于PPO的金融强化学习。GIFT并不使用LLM来做出交易决策，而是通过因子引导的状态增强（Factor-guided State Enhancement）从金融因子原语生成状态特征，通过风险规则引导的奖励塑造（Risk-rule-guided Reward Shaping）从投资组合风险规则生成辅助奖励，并通过诊断引导的修正（Diagnostic-guided Refinement）使用PPO回放诊断来修订候选接口。在修正后，GIFT在评估之前固定所选的状态-奖励接口，在测试时不再进行进一步的LLM查询或接口更新。针对多种市场环境和投资组合场景的全面滚动窗口实验表明，GIFT在学习信号质量和样本外风险调整投资组合表现方面优于基线方法。代码和数据可在以下链接获取：https://github.com/KAG778/GIFT 。
cs.AI / 57 / 2606.08477
A Variability-Based Framework for Interpretable Naming in Formal and Relational Concept Analysis
基于变异性的框架用于形式和关系概念分析中的可解释命名
Abstract
Knowledge extraction from symbolic data often produces abstractions that are formally defined but not immediately interpretable by users. Formal Concept Analysis (FCA) and Relational Concept Analysis (RCA) provide representative settings for this issue: they generate explicit conceptual structures, implications, and relational dependencies from object descriptions and relations. Although these structures are explainable by design, their concepts are often identified by technical labels, which limits their use as human-interpretable knowledge units. Assigning meaningful names to such concepts is therefore a key issue for interpretation, navigation, validation, and reuse by domain experts. This paper investigates concept naming in FCA and RCA from a symbolic knowledge representation perspective. We first characterize the linguistic and terminological challenges involved in naming generated symbolic abstractions, including ambiguity, discrimination, concision, and consistency across related concepts. We then propose a configurable framework for LLM-assisted concept naming. The framework relies on a variability model that controls which sources of information are exposed during naming, such as intent, extent, inherited information, neighboring concepts, implications, and relational attributes. It thereby makes explicit the semantic choices involved in moving from formal concept descriptions to human-readable names. The approach is illustrated as a proof of concept on a small relational dataset in the pizzeria domain. This illustration shows how different configurations influence the names suggested by an LLM, and how naming variability can reveal interpretation choices, relational dependencies, and possible modeling issues in the underlying symbolic data.
Chinese Translation
从符号数据中提取知识通常会产生形式上定义但用户难以立即理解的抽象。形式概念分析（Formal Concept Analysis, FCA）和关系概念分析（Relational Concept Analysis, RCA）为这一问题提供了代表性的背景：它们从对象描述和关系中生成明确的概念结构、蕴含和关系依赖。尽管这些结构在设计上是可解释的，但它们的概念通常由技术标签标识，这限制了它们作为人类可解释知识单元的使用。因此，为这些概念分配有意义的名称是领域专家进行解释、导航、验证和重用的关键问题。本文从符号知识表示的角度研究FCA和RCA中的概念命名。我们首先描述了命名生成的符号抽象所涉及的语言和术语挑战，包括歧义、区分性、简洁性和相关概念之间的一致性。然后，我们提出了一个可配置的框架，用于基于大型语言模型（LLM）辅助的概念命名。该框架依赖于一个变异模型，控制在命名过程中暴露哪些信息源，例如意图、范围、继承信息、邻近概念、蕴含和关系属性。通过这种方式，它明确了从形式概念描述到人类可读名称的过程中涉及的语义选择。该方法在一个小型的比萨店领域关系数据集上进行了概念验证，展示了不同配置如何影响LLM建议的名称，以及命名变异如何揭示解释选择、关系依赖和潜在的建模问题。
cs.AI / 58 / 2606.08483
Testing the Black Box: Structural Barriers to Independent Evaluation of Consumer-Facing Health LLMs
测试黑箱：独立评估面向消费者的健康大型语言模型的结构性障碍
Abstract
Background: Consumer-facing large language models are now a common source of health information, and they interpret and personalize responses rather than retrieve them. Whether their responses vary across users is a clinical, equity, and governance question, sharpened by evidence that sycophantic responses can alter judgment and increase trust. Objective: To evaluate response variation and sycophancy in consumer-facing health LLMs under conditions resembling ordinary patient use. Methods: We constructed simulated user profiles differing in geography, browsing context, expressed beliefs, and social determinants of health, drawing on literature linking social context to health attitudes. We adapted validated instruments, including the Vaccination Attitudes Examination scale and reproductive attitudes scales, into multi-turn prompts designed to elicit clinically meaningful variation across users. Results: The evaluation encountered five linked barriers. Factual prompts produced stable responses that masked sycophancy emerging over multi-turn conversation. Browser-based interfaces did not disclose which signals influence outputs and could not be reset to a clean baseline. Large-scale testing was restricted by terms of service, rate limits, and bot detection. Accuracy-based criteria could not capture tone, framing, or omission, and LLM-as-judge methods risked shared alignment bias. Models changed without traceable version identifiers, preventing reliable replication. Conclusions: No reliable independent evaluation framework yet exists for examining how consumer-facing health LLMs behave in ordinary use. Oversight requires disclosure of personalization signals, stable version identifiers, researcher safe harbor programs, and post-deployment monitoring of health-related outputs.
Chinese Translation
背景：面向消费者的大型语言模型如今已成为健康信息的常见来源，它们解释并个性化响应，而非仅仅检索信息。其响应是否因用户而异是一个临床、公平和治理的问题，相关证据表明，迎合性响应可能会改变判断并增加信任。目的：评估面向消费者的健康大型语言模型在类似普通患者使用条件下的响应变异性和迎合性。方法：我们构建了模拟用户档案，考虑了地理位置、浏览上下文、表达的信念和健康的社会决定因素，借鉴了将社会背景与健康态度联系起来的文献。我们将经过验证的工具进行了调整，包括疫苗接种态度评估量表和生育态度量表，设计成多轮提示，以引出用户之间临床意义上的变异。结果：评估过程中遇到了五个相互关联的障碍。事实提示产生了稳定的响应，掩盖了在多轮对话中出现的迎合性。基于浏览器的界面未能披露哪些信号影响输出，并且无法重置为干净的基线。大规模测试受到服务条款、速率限制和机器人检测的限制。基于准确性的标准无法捕捉语调、框架或遗漏，而将大型语言模型作为评判者的方法则存在共享一致性偏见的风险。模型在没有可追溯版本标识符的情况下发生变化，阻碍了可靠的复制。结论：目前尚不存在可靠的独立评估框架来检验面向消费者的健康大型语言模型在普通使用中的表现。监督需要披露个性化信号、稳定的版本标识符、研究者安全港计划以及对健康相关输出的后续监测。
cs.AI / 59 / 2606.08491
What Makes a Desired Graph for Relational Deep Learning?
什么使得关系深度学习所需的图形？
Abstract
Relational deep learning (RDL) converts relational databases (RDBs) into heterogeneous graphs, but graphs derived directly from database schemas are often not well suited for how graph neural networks (GNNs) perform relational reasoning. We study what makes a relational graph suitable for deep learning and show that schema-derived graphs suffer from two systematic failures: information overload and semantic fragmentation. Our empirical analysis reveals that the desired graph is not the raw schema, but a result of controlled structural adaptation. Performance depends on balancing two operations: mitigating information overload via filtering, and repairing semantic fragmentation via injection. Specifically, filtering serves as a bias-variance knob with non-monotonic effects, while injection improves performance only when it explicitly restores the relational dependencies missing from the original schema. Based on these findings, we develop an end-to-end structural optimizer that applies both operations to adapt relational graphs automatically. Across 26 tasks spanning classification, regression, and recommendation, the optimized graphs consistently improve accuracy while often reducing inference cost.
Chinese Translation
关系深度学习（RDL）将关系数据库（RDB）转换为异构图，但直接从数据库模式派生的图形通常不适合图神经网络（GNN）进行关系推理的方式。我们研究了什么使得关系图适合深度学习，并表明模式派生的图形存在两种系统性缺陷：信息过载和语义碎片化。我们的实证分析揭示，所需的图形不是原始模式，而是经过控制的结构适应的结果。性能依赖于平衡两种操作：通过过滤来减轻信息过载，以及通过注入来修复语义碎片化。具体而言，过滤作为一个偏差-方差调节器，具有非单调效应，而注入仅在明确恢复原始模式中缺失的关系依赖时才提高性能。基于这些发现，我们开发了一种端到端的结构优化器，自动应用这两种操作以适应关系图。在涵盖分类、回归和推荐的26个任务中，优化后的图形始终提高了准确性，同时通常降低了推理成本。
cs.AI / 60 / 2606.08497
Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets
解释黑箱语言模型：学习优化语言结构化的词子集
Abstract
As deep language models (DLMs) are increasingly deployed in high-stakes domains such as healthcare, understanding their decision rationale becomes paramount for ensuring trust, safety, and accountability. However, achieving this vital level of interpretability is particularly challenging when these DLMs operate as black-box systems (e.g., via APIs), where access to internal model states (e.g., parameters, gradients) is restricted. Despite numerous efforts, existing explanation methods often fail to concurrently satisfy three key desiderata: (i) inference-time efficiency, (ii) black-box compatibility without inducing out-of-distribution behavior, and (iii) comprehensible explanations grounded in the input's linguistic structure. To address these challenges, we propose a method that explains predictions of DLMs by selecting a small, informative subset of input words. We formulate this as an amortized optimization problem, enabling efficient one-shot inference without the need for input-specific search. Our selection policy is trained via REINFORCE-style policy gradients, allowing discrete word selection in a fully gradient-free setting. To enhance interpretability and align with human linguistic intuition, we integrate graph-structured knowledge into this selection process, fostering linguistically coherent subsets that result in explanations both highly informative and cognitively meaningful to end-users. We evaluated our method on diverse DLM architectures and multiple real-world datasets. It consistently identifies word subsets with enhanced discriminative power and stronger alignment with linguistically salient cues, outperforming both conventional black-box compatible methods and gradient-based approaches that are given oracle access to the black-box model's gradients for a more challenging benchmark. Our code is available at here.
Chinese Translation
随着深度语言模型（DLMs）在医疗等高风险领域的广泛应用，理解其决策依据变得至关重要，以确保信任、安全和问责。然而，当这些DLMs作为黑箱系统（例如，通过API）运行时，获得这种重要的可解释性尤其具有挑战性，因为对内部模型状态（例如，参数、梯度）的访问受到限制。尽管已有众多努力，现有的解释方法往往无法同时满足三个关键要求：（i）推理时的效率，（ii）黑箱兼容性而不引发分布外行为，以及（iii）基于输入语言结构的可理解解释。为了解决这些挑战，我们提出了一种通过选择一小部分信息丰富的输入词来解释DLMs预测的方法。我们将其形式化为一个摊销优化问题，从而实现高效的一次性推理，而无需特定于输入的搜索。我们的选择策略通过REINFORCE风格的策略梯度进行训练，允许在完全无梯度的环境中进行离散词选择。为了增强可解释性并与人类语言直觉对齐，我们将图结构知识整合到这一选择过程中，促进语言上连贯的子集，从而生成对最终用户既高度信息化又认知上有意义的解释。我们在多种DLM架构和多个真实世界数据集上评估了我们的方法。它始终能够识别出具有更强区分能力和与语言显著线索更强对齐的词子集，优于传统的黑箱兼容方法和在更具挑战性的基准上给予黑箱模型梯度的梯度基方法。我们的代码可在此获取。
cs.AI / 61 / 2606.08503
Standpoint Logics with Defeasible Beliefs
带有可推翻信念的立场逻辑
Abstract
In this paper, we integrate the defeasible logic of Kraus, Lehmann and Magidor (KLM) with the standpoint logic framework of G\'omez \'Alvarez and Rudolph. This is done with the goal of formally expressing knowledge taking into account multiple (possibly contradicting) viewpoints, which in turn may hold defeasible beliefs. In doing so, we utilise Defeasible Restricted Standpoint Logics (DRSL), introduced by Leisegang et al. Our work expands on previous work by providing a foundational representation result for DRSL semantics and systematically lifting several well-known entailment relations from the propositional case to the standpoint-enhanced setting. In particular, we characterise the semantics for DRSL through a set of KLM-style postulates adapted for the standpoints case. We furthermore provide a means to lift preferential entailment, and the class of entailment relations based on single ranking functions from the purely propositional to the standpoint-enhanced context, including rational and lexicographic closure. We show this can be done equivalently through semantic and algorithmic means. Furthermore, we show that, for each considered form of entailment, the complexity class of entailment checking does not change when moving from propositional KLM to DRSL.
Chinese Translation
在本文中，我们将Kraus、Lehmann和Magidor（KLM）的可推翻逻辑与Gómez Álvarez和Rudolph的立场逻辑框架相结合。这样做的目的是正式表达知识，同时考虑多个（可能相互矛盾的）观点，这些观点可能持有可推翻的信念。在此过程中，我们利用了Leisegang等人提出的可推翻限制立场逻辑（Defeasible Restricted Standpoint Logics，DRSL）。我们的工作在之前研究的基础上扩展，提供了DRSL语义的基础表示结果，并系统地将几个著名的蕴涵关系从命题情况提升到增强立场的环境中。特别地，我们通过一组适应立场情况的KLM风格公理来表征DRSL的语义。此外，我们还提供了一种方法，将基于单一排名函数的优先蕴涵及其蕴涵关系类从纯命题上下文提升到增强立场的背景中，包括理性和字典闭包。我们展示了这一过程可以通过语义和算法手段等效实现。此外，我们还表明，对于每种考虑的蕴涵形式，从命题KLM到DRSL的转变不会改变蕴涵检查的复杂性类。
cs.AI / 62 / 2606.08529
Scaffold Effects on GAIA: A Controlled Comparison
支架对GAIA的影响：一项受控比较
Abstract
Published agent capability scores conflate what a model can do with what its scaffold lets it do, and the magnitude of this elicitation gap is not well characterized under controlled conditions. This study executes a pre-registered controlled comparison of three scaffolds (ReAct, a Planner-Actor-Rater multi-agent design, and planner-then-executor) across five models from three providers (Claude Opus 4.7, Sonnet 4.6, Haiku 4.5; Gemini 3.1 Pro Preview; GPT-5.5) on GAIA validation Levels 1 and 2, holding tasks and conditions fixed, with three attempts per question. Scaffold choice alone moves measured accuracy by as much as 28 percentage points within a single model (Opus, Level 2, robust slice), confirming the pre-registered hypothesis that scaffold variation produces gaps of at least 10 points. The pre-registered prediction that more capable models would be less scaffold-sensitive is rejected in direction: scaffold effects vary significantly by model in every dataset slice, but the most capable Anthropic model gains the most from structured scaffolds at the harder level, and tier-scaling holds only at Level 1 under the robust slice. The multi-agent advantage over ReAct at Level 2 appears within the Anthropic family but not for the cross-provider models, making model family rather than capability tier the conditioning variable, and the predicted planner-executor advantage on file-reading tasks is falsified. Structured scaffolds make fewer tool calls yet recover more often from mid-trajectory errors at the harder level, and a single cell (Gemini with planner-then-executor) is the cheapest at both levels and the most accurate at Level 2. These results indicate that single-scaffold capability numbers are scaffold-conditional estimates and that the elicitation gap is not guaranteed to shrink as models improve.
Chinese Translation
已发布的智能体能力评分将模型的功能与其支架所允许的功能混为一谈，而这种引导差距在受控条件下的大小尚未得到良好表征。本研究对来自三个提供者的五个模型（Claude Opus 4.7、Sonnet 4.6、Haiku 4.5；Gemini 3.1 Pro Preview；GPT-5.5）在GAIA验证的1级和2级上进行了预注册的受控比较，比较了三种支架（ReAct、规划者-执行者-评估者多智能体设计和先规划后执行）在固定任务和条件下的表现，每个问题进行了三次尝试。仅支架选择就能使单个模型（Opus，2级，稳健切片）的测量准确度变化高达28个百分点，确认了预注册的假设，即支架变异产生至少10分的差距。预注册的预测，即更强大的模型对支架的敏感性较低，被否定：在每个数据集切片中，支架效应在模型之间显著不同，但最强大的Anthropic模型在更难的级别上从结构化支架中获得的收益最大，而分层缩放仅在稳健切片的1级有效。多智能体在2级上相对于ReAct的优势仅在Anthropic家族中显现，而在跨提供者模型中并不明显，表明模型家族而非能力层级是条件变量，预测的规划者-执行者在文件阅读任务上的优势被证伪。结构化支架在更难的级别上减少了工具调用的次数，但在中途错误中恢复的频率更高，而单个单元（使用规划者-执行者的Gemini）在两个级别上都是成本最低且在2级上最准确。这些结果表明，单一支架能力数字是依赖于支架的估计，并且随着模型的改进，引导差距并不一定会缩小。
cs.AI / 63 / 2606.08531
VESTA: A Fully Automated Scenario Generation and Safety Evaluation Framework for LLM Agents
VESTA：一个完全自动化的场景生成与安全评估框架用于大语言模型代理
Abstract
Large language models (LLMs) are increasingly evolving from simple text-based interaction systems into LLM agents that can maintain memory, use tools, access external environments, and execute tasks. As their capabilities and autonomy expand, the safety risks they face also become more diverse. Existing evaluations often rely on manually written scenarios, static prompts, or final-output judgments, making it difficult to capture the diverse risks that agents may face during task execution. We introduce VESTA, a fully automated scenario generation and safety evaluation framework for LLM agents. Based on five risk dimensions, VESTA instantiaes abstract and diverse safety risks in real-world task execution into 1,072 measurable evaluation scenarios. Using the automated evaluation pipeline, 12 LLM agents are evaluated under two authority contexts. The results show that current agents still face substantial behavioral safety risks during task execution, with an average ASR of 47.1% and several models exceeding 70%. These findings demonstrate the importance of executable, process-level evaluation for understanding and improving LLM agent safety.
Chinese Translation
大型语言模型（LLMs）正逐渐从简单的基于文本的交互系统演变为能够保持记忆、使用工具、访问外部环境并执行任务的LLM代理。随着其能力和自主性的扩展，它们面临的安全风险也变得更加多样化。现有评估通常依赖于手动编写的场景、静态提示或最终输出判断，这使得在任务执行过程中捕捉代理可能面临的多样化风险变得困难。我们引入了VESTA，一个完全自动化的场景生成与安全评估框架，专为LLM代理设计。基于五个风险维度，VESTA将现实任务执行中的抽象和多样化安全风险实例化为1,072个可测量的评估场景。通过自动化评估流程，对12个LLM代理在两种权威背景下进行了评估。结果显示，当前代理在任务执行过程中仍面临显著的行为安全风险，平均安全风险率（ASR）为47.1%，且多个模型超过70%。这些发现表明，执行级别的过程评估对于理解和改善LLM代理的安全性具有重要意义。
cs.AI / 64 / 2606.08532
DN-Hypo-Pipeline: An AI-Driven Workflow for Hypothesis Generation via Large Language Models and Scientific Explanations
DN-Hypo-Pipeline：基于大型语言模型和科学解释的假设生成的人工智能驱动工作流程
Abstract
A scientific hypothesis is the first step in research and undergoes experimental validation, yet it also reflects a deep understanding of and reasoning about scientific phenomena. We introduce DN-Hypo-Pipeline, an AI-powered workflow based on large language models, designed to support structured scientific thinking and hypothesis generation by leveraging scientific explanations as prior knowledge. This pipeline assists researchers in deriving novel hypotheses from existing literature. Given the explanandum (i.e., the conclusion) of a research paper, it identifies underlying laws, theories, and principles, and reconstructs a new, yet-to-be-verified explanation for the observed phenomenon. We evaluated DN-Hypo-Pipeline in the field of data science modeling using three highly cited papers. Statistical inference, supported by both LLM-as-judge assessment and human expert evaluation, demonstrates that our pipeline is more effective than direct generation methods. Additionally, we validated the two highest-scoring generated hypotheses by developing corresponding novel algorithms, which outperformed the baseline models presented in the original papers. Beyond application in data science, DN-Hypo-Pipeline provides a theoretical framework that not only encompasses theory-guided data science modeling methods but also reveals a more fundamental structure of the modeling process. Moreover, this approach is essentially a generalization of theory-guided modeling, offering potential for extension to other domains and across a broader range of scientific disciplines.
Chinese Translation
科学假设是研究的第一步，并经过实验验证，但它也反映了对科学现象的深刻理解和推理。我们介绍了DN-Hypo-Pipeline，这是一种基于大型语言模型的人工智能驱动工作流程，旨在通过利用科学解释作为先验知识来支持结构化的科学思维和假设生成。该工作流程帮助研究人员从现有文献中推导出新颖的假设。给定研究论文的解释对象（即结论），它识别潜在的法律、理论和原则，并重建一个尚未验证的观察现象的解释。我们在数据科学建模领域使用三篇被高度引用的论文评估了DN-Hypo-Pipeline。统计推断结合LLM-as-judge评估和人类专家评估，表明我们的工作流程比直接生成方法更有效。此外，我们通过开发相应的新算法验证了两个得分最高的生成假设，这些算法的表现超越了原始论文中提出的基线模型。除了在数据科学中的应用，DN-Hypo-Pipeline提供了一个理论框架，不仅涵盖了理论指导的数据科学建模方法，还揭示了建模过程的更基本结构。此外，这种方法本质上是理论指导建模的推广，具有扩展到其他领域和更广泛科学学科的潜力。
cs.AI / 65 / 2606.08539
AgentTrust: A Self-Improving Trust Layer for AI-Agent Actions
AgentTrust：一个自我改进的信任层用于人工智能代理的行为
Abstract
AI agents increasingly take consequential actions -- shell commands, cloud operations, and arbitrary tool-calls -- so a trust layer must decide, per action, whether to allow, warn, block, or escalate. We argue that the right way to reason about such a layer is by threat type. Lexical (fixed-signature) threats, where danger lives in a stable token, are decidable by deterministic rules; semantic (intent-dependent) threats, where a benign and a malicious action share the same surface, are out of reach for rules by construction. We make this concrete with a negative proof: a determined, hand-authored cloud rule pack lifts held-out accuracy only 48 to 56% overall and moves the semantic categories by 0pp (data_db 29 to 29, observability 59 to 59, supply_chain 50 to 50), while a strong LLM judge carries exactly those categories. We give the judge a self-learning capability: on a corpus that is mainly semantic attacks it nearly doubles rule accuracy (48% to 83.6-85.2%) with near-zero false-blocks, and this holds across two model providers. We turn this into a self-improving dual-store system: the judge distills a growing deterministic rule floor on lexical threats (cheaper over time) and feeds a guarded RAG memory on semantic threats (a verdict-cache fails -- surface-twins collapse to ~58% -- so a corroboration guard lifts semantic accuracy +13pp, 70 to 84). The result is what sets AgentTrust v2 apart from its static v1 predecessor: a trust layer that self-evolves from its own stream of decisions -- cheaper on the lexical class (it distils its own rules) and smarter on the semantic class (it accrues guarded precedent), while never hard-blocking a benign action. An end-to-end online replay shows the judge-call rate falling (50% to 44%) and judge-domain accuracy rising (71% to 80%), with 0 benign hard-blocks across 45,000 actions.
Chinese Translation
人工智能代理越来越多地采取具有重要影响的行动——如 shell 命令、云操作和任意工具调用——因此，信任层必须针对每个行动决定是否允许、警告、阻止或升级。我们认为，合理推理这种层的方式是按威胁类型进行分类。词汇（固定签名）威胁，其中危险存在于稳定的标记中，可以通过确定性规则进行判定；而语义（意图依赖）威胁，其中良性和恶意行为共享相同表面，从构造上讲则无法用规则来处理。我们通过一个反例使这一点具体化：一个经过精心编写的云规则包的准确率仅提升了 48% 到 56%，而语义类别的变化为 0pp（数据数据库从 29 变为 29，观察性从 59 变为 59，供应链从 50 变为 50），而一个强大的 LLM 判决者则准确涵盖了这些类别。我们赋予判决者自我学习的能力：在一个主要是语义攻击的语料库上，它几乎将规则的准确率翻倍（从 48% 提升到 83.6-85.2%），几乎没有错误阻止，这一结果在两个模型提供者中均成立。我们将其转化为一个自我改进的双存储系统：判决者在词汇威胁上提炼出一个不断增长的确定性规则基础（随着时间推移成本更低），并在语义威胁上提供一个受保护的 RAG 记忆（判决缓存失败——表面双胞胎的准确率降至 ~58%——因此，证实保护提升了语义准确率 +13pp，从 70 提升到 84）。最终结果是 AgentTrust v2 与其静态 v1 前身的区别所在：一个能够自我演化的信任层，基于自身的决策流——在词汇类别上更便宜（提炼出自己的规则），在语义类别上更智能（积累受保护的先例），同时从不对良性行为进行硬阻止。一个端到端的在线回放显示，判决者调用率下降（从 50% 降至 44%），判决领域的准确率上升（从 71% 提升到 80%），在 45,000 次行动中没有发生任何良性硬阻止。
cs.AI / 66 / 2606.08543
PAEC: Position-Aware Entropy Calibration for LLM Reasoning in RLVR
PAEC：用于RLVR中LLM推理的基于位置的熵校准
Abstract
Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning but often suffers from rapid policy-entropy collapse, where the policy prematurely concentrates on narrow high-probability reasoning paths. While global entropy regularization can encourage exploration, uniformly increasing entropy across all token positions is inefficient for long reasoning trajectories, where many tokens are not decision-relevant. We propose Position-Aware Entropy Calibration (PAEC), a token-level entropy-management framework that constructs a soft mask from local top-p entropy and top-two candidate competition, and applies an anchor-based lower-bound penalty to prevent selected-position entropy collapse. Experiments on five mathematical reasoning benchmarks show that PAEC improves macro-average majority-vote performance over strong RLVR baselines, with clear gains on AIME-style tasks. Our results suggest that entropy management in reasoning RL should be formulated as selective exploration allocation over decision-sensitive positions rather than uniform randomness injection.
Chinese Translation
具有可验证奖励的强化学习（RLVR）提高了大型语言模型的推理能力，但通常会遭遇快速的策略熵崩溃，即策略过早集中于狭窄的高概率推理路径。虽然全局熵正则化可以鼓励探索，但在长推理轨迹中，均匀增加所有标记位置的熵对于许多与决策无关的标记而言效率低下。我们提出了基于位置的熵校准（Position-Aware Entropy Calibration, PAEC），这是一种基于标记的熵管理框架，它从局部的 top-p 熵和 top-two 候选竞争中构建软掩码，并施加基于锚点的下界惩罚，以防止所选位置的熵崩溃。在五个数学推理基准上的实验表明，PAEC 在强 RLVR 基线之上提高了宏观平均多数投票性能，并在 AIME 风格的任务上有明显提升。我们的结果表明，在推理强化学习中的熵管理应被视为对决策敏感位置的选择性探索分配，而不是均匀的随机注入。
cs.AI / 67 / 2606.08552
Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents
定量承诺理论：自主代理中的意图性与推理
Abstract
I discuss some quantitative representations of Promise Theory for processes involving autonomous agents. Agent models are common in software systems, machine learning, and biology, for example, but may also apply to physics and other forms of engineering. I describe how Bayesian probability and information theoretic optimization, including Active Inference, may be incorporated with promise semantics -- as well as how Promise Theory supplements solutions, helping to avoid probability's pitfalls, which include non-local coordination, calibrating, and normalizing probabilistic computations. The role of boundary conditions in constraining allowed states and selecting decision thresholds is a form of promise, and agent alignment provides a scalable definition of intent. Autonomous agents may congeal into swarms with superagent characteristics by trying to minimize their information, despite uncertainty that works to maximize it. The use of Promise Theory involves some research challenges as well as stylistic preferences.
Chinese Translation
我讨论了一些关于涉及自主代理的过程的定量承诺理论表示。代理模型在软件系统、机器学习和生物学中很常见，但也可能适用于物理学和其他工程形式。我描述了如何将贝叶斯概率和信息理论优化（包括主动推理）与承诺语义结合，以及承诺理论如何补充解决方案，帮助避免概率的陷阱，包括非局部协调、标定和归一化概率计算。边界条件在限制允许状态和选择决策阈值方面的作用是一种承诺，而代理对齐提供了一种可扩展的意图定义。自主代理可能通过尝试最小化其信息而凝聚成具有超代理特征的群体，尽管不确定性会使信息最大化。使用承诺理论涉及一些研究挑战以及风格偏好。
cs.AI / 68 / 2606.08596
Distilling LLM Reasoning into an Interpretable Policy Tree for Human-AI Collaboration
将大型语言模型推理提炼为可解释的政策树以促进人机协作
Abstract
Constructing efficient and reliable policies to assist humans is indispensable for human-AI collaboration. Existing methods mainly follow two lines of work. Most prior work relies on multi-agent reinforcement learning (MARL) to learn black-box policies, which limits interpretability and raises safety concerns. Recent methods query large language models (LLMs) at each decision step, causing slow responses and high inference costs. We propose Collaboration Policy Tree (Co-pi-tree), a closed-loop method that learns an executable policy tree consisting of a partner-behavior prediction tree and an agent-action selection tree. Co-pi-tree constructs a policy by distilling LLM reasoning into policy tree code. It then evaluates the policy through partner interaction, obtains feedback, and uses natural language to summarize the interaction feedback to improve problematic branches. Experiments in Overcooked-AI show that Co-pi-tree improves average reward by 35.4% over the baseline average, while reducing the number of LLM queries by 77.7% and test-time latency by 97.1%. Project page: https://beiwenzhang.github.io/Co-pi-tree/
Chinese Translation
构建高效且可靠的政策以协助人类是人机协作中不可或缺的。现有方法主要遵循两条研究路线。大多数先前的工作依赖于多智能体强化学习（MARL）来学习黑箱政策，这限制了可解释性并引发了安全隐患。最近的方法在每个决策步骤中查询大型语言模型（LLMs），导致响应缓慢和高推理成本。我们提出了协作政策树（Collaboration Policy Tree，Co-pi-tree），这是一种闭环方法，学习一个可执行的政策树，该树由伙伴行为预测树和智能体行动选择树组成。Co-pi-tree通过将LLM推理提炼为政策树代码来构建政策。然后通过伙伴互动评估该政策，获取反馈，并使用自然语言总结互动反馈以改进有问题的分支。在Overcooked-AI中的实验表明，Co-pi-tree的平均奖励比基线平均值提高了35.4%，同时将LLM查询次数减少了77.7%，测试时延减少了97.1%。项目页面：https://beiwenzhang.github.io/Co-pi-tree/
cs.AI / 69 / 2606.08601
InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs
InA-Probe：面向指令的主动探测在时间序列预测中的应用
Abstract
Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely on passive modality alignment or static task reprogramming, which often fail to capture fine-grained, non-stationary temporal patterns or to adapt to nuanced task intents. In this paper, we propose Instruction-aware Active Probing (InA-Probe), which shifts the paradigm from passive alignment toward an active, instruction-driven probing mechanism. Specifically, we design a Multi-Level Instruction Injection mechanism that enriches the model with both global task objectives and fine-grained, patch-level semantic priors. Building on this, an Adaptive Query Generation module produces sample-specific probes that are dynamically modulated by the temporal context. These probes are then refined through a dual-stage attention process: they first internalize task-specific intents via Instruction-Aware Self-Attention, and subsequently interrogate the projected temporal representations through Temporal Cross-Attention to extract salient patterns. Comprehensive experiments on seven real-world benchmarks show that InA-Probe consistently outperforms state-of-the-art deep learning and LLM-based baselines, excelling in both one-for-all generalization and zero-shot transfer while reducing forecasting error by up to 37\% in challenging cross-domain scenarios. Ablation studies further confirm that the synergy between adaptive querying and fine-grained instructions is key to unlocking the reasoning power of LLMs for complex time series.
Chinese Translation
大型语言模型（LLMs）最近在时间序列预测中展现了令人印象深刻的潜力。然而，现有方法主要依赖于被动的模态对齐或静态任务重编程，这往往无法捕捉细粒度的非平稳时间模式或适应细微的任务意图。本文提出了面向指令的主动探测（InA-Probe），将范式从被动对齐转变为主动的、以指令驱动的探测机制。具体而言，我们设计了一种多层次指令注入机制，丰富了模型的全局任务目标和细粒度的补丁级语义先验。在此基础上，自适应查询生成模块生成特定样本的探测器，这些探测器由时间上下文动态调节。随后，这些探测器通过双阶段注意力过程进行精炼：首先通过面向指令的自注意力内化任务特定意图，随后通过时间交叉注意力审问投影的时间表示，以提取显著模式。在七个真实世界基准上的全面实验表明，InA-Probe在一体化泛化和零-shot迁移方面始终优于最先进的深度学习和基于LLM的基线，同时在具有挑战性的跨域场景中将预测误差降低了多达37%。消融研究进一步确认，自适应查询与细粒度指令之间的协同作用是释放LLMs在复杂时间序列推理能力的关键。
cs.AI / 70 / 2606.08633
Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models
基于推理的大型语言模型的长时间航行轨迹和目的地预测研究
Abstract
Long-horizon maritime trajectory prediction is important for shipping management, logistics planning, and maritime risk analysis, yet month-level forecasting remains insufficiently studied. Existing deep learning methods mainly focus on short- and mid-term coordinate extrapolation and often struggle to preserve route feasibility and destination correctness over extended horizons. This paper investigates joint long-horizon vessel trajectory and destination forecasting with reasoning-capable large language models, and develops a Maritime LLM post-training framework based on Reinforcement Learning with Verifiable Reward (RLVR). An AIS-based benchmark is constructed with 60-day historical trajectories and 30-day forecasting horizons, where trajectories are converted into semantic textual representations for RL prompt construction. RLVR aligns LLMs with maritime forecasting objectives by enforcing physical validity, providing early-weighted trajectory supervision, and evaluating destination correctness through hierarchical matching and curriculum learning. Experimental results show that RLVR-trained LLMs substantially improve over zero-shot LLMs and representative deep learning baselines, especially on destination-related metrics. Among the evaluated RLVR-trained variants, 4B LLMs achieve the best overall performance, suggesting that reward-compatible optimization and task-specific capacity matching are more important than simply using larger 8B or 14B LLMs. The results also show that LSTM remains a strong deep learning baseline under limited fine-tuning data, while Transformer-style spatio-temporal models typically require larger datasets and richer structured inputs. Overall, this work advances semantic, verifier-aligned maritime forecasting for operational decision support.
Chinese Translation
长时间的海洋轨迹预测对于航运管理、物流规划和海洋风险分析至关重要，但月级预测仍然研究不足。现有的深度学习方法主要集中在短期和中期的坐标外推，往往难以在较长时间范围内保持航线的可行性和目的地的正确性。本文探讨了利用具备推理能力的大型语言模型进行联合的长时间航行轨迹和目的地预测，并基于可验证奖励的强化学习（Reinforcement Learning with Verifiable Reward, RLVR）开发了一个海事大型语言模型后训练框架。构建了一个基于AIS的基准数据集，包含60天的历史轨迹和30天的预测范围，其中轨迹被转换为语义文本表示以构建RL提示。RLVR通过强制物理有效性、提供早期加权的轨迹监督以及通过分层匹配和课程学习评估目的地的正确性，将大型语言模型与海事预测目标对齐。实验结果表明，经过RLVR训练的大型语言模型在零样本大型语言模型和代表性的深度学习基线之上有显著提升，尤其是在与目的地相关的指标上。在评估的RLVR训练变体中，4B大型语言模型实现了最佳的整体性能，表明奖励兼容的优化和任务特定的能力匹配比单纯使用更大的8B或14B大型语言模型更为重要。结果还表明，在有限的微调数据下，LSTM仍然是一个强大的深度学习基线，而基于Transformer的时空模型通常需要更大的数据集和更丰富的结构化输入。总体而言，本研究推动了面向操作决策支持的语义、验证对齐的海事预测。
cs.AI / 71 / 2606.08658
Extending Ontologies: From Dense Embeddings to Hybrid Quantum-Fuzzy Systems
扩展本体：从密集嵌入到混合量子模糊系统
Abstract
LLMs have revolutionized knowledge representation and retrieval, but lack the explicit modeling that knowledge ontologies possess. This paper surveys the ways that ontologies and knowledge graphs have been integrated with dense embedding algorithms. All hitherto attempts involve a trade-off between probabilistic and crisp inference. This paper proposes a novel frontier for devising knowledge representation systems that can simultaneously accommodate probabilistic and crisp inference in the same representation. To this effect, the paper proposes neuro-quantum-fuzzy systems as knowledge representation systems that accommodate both classical and contextual inference implemented through quantum-neural networks (QNN).
Chinese Translation
大型语言模型（LLMs）已经彻底改变了知识表示和检索，但缺乏知识本体所具备的明确建模能力。本文调查了本体和知识图谱如何与密集嵌入算法相结合的方式。迄今为止的所有尝试都涉及到概率推理与精确推理之间的权衡。本文提出了一种新颖的前沿，旨在设计能够在同一表示中同时容纳概率推理和精确推理的知识表示系统。为此，本文提出了神经量子模糊系统作为知识表示系统，能够通过量子神经网络（QNN）实现经典推理和上下文推理。
cs.AI / 72 / 2606.08702
ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systems
ConMem：无训练的多智能体系统中的结构化记忆引导适应
Abstract
Recent advances have improved the adaptive capabilities of LLM-based multi-agent systems (MAS) through memory-, skill-, and learning-based approaches, yet these approaches remain challenged by noisy trajectories, insufficient modeling of memory-skill relations, and reliance on additional training or high-quality supervision. To address these limitations, we propose ConMem, a relation-aware and training-free framework that enables efficient multi-agent adaptation through cross-experience coordination. Specifically, ConMem distills historical interaction trajectories into structured memory cards to capture reusable strategies and cues, organizing them into a relation-aware memory graph. At runtime, ConMem retrieves cards according to task needs and coordinates them through the card graph to resolve strategy conflicts and recover their dependencies. Combined, these modules yield structured and relation-aware guidance, enabling robust, lightweight adaptation in multi-agent systems without additional training. Extensive experiments across multiple benchmarks and mainstream MAS architectures show consistent gains over existing memory architectures, with improved inference-time efficiency through pruning more than 50% of expanded candidates and reducing planning overhead by over 80%. Our codes are available at https://anonymous.4open.science/r/ConMemCode
Chinese Translation
近期的进展通过基于记忆、技能和学习的方法提高了基于大型语言模型（LLM）的多智能体系统（MAS）的适应能力，但这些方法仍面临噪声轨迹、对记忆-技能关系建模不足以及依赖额外训练或高质量监督等挑战。为了解决这些局限性，我们提出了ConMem，一个关系感知且无需训练的框架，通过跨经验协调实现高效的多智能体适应。具体而言，ConMem将历史交互轨迹提炼为结构化记忆卡，以捕捉可重用的策略和线索，并将其组织成一个关系感知的记忆图。在运行时，ConMem根据任务需求检索卡片，并通过卡片图协调它们以解决策略冲突并恢复其依赖关系。这些模块结合起来提供了结构化和关系感知的指导，使多智能体系统能够在无需额外训练的情况下实现稳健、轻量级的适应。在多个基准和主流MAS架构上的广泛实验表明，与现有的记忆架构相比，ConMem consistently 提高了性能，通过修剪超过50%的扩展候选并减少超过80%的规划开销，提高了推理时间效率。我们的代码可在 https://anonymous.4open.science/r/ConMemCode 获取。
cs.AI / 73 / 2606.08728
Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery
人工智能在数学推理中的应用：语言模型、神经符号系统与验证发现的综合调查
Abstract
Mathematical reasoning has long served as a stringent test of machine intelligence; over the past decade, it has moved from a niche problem within NLP to one of the most consequential AI frontiers. This survey provides a unified account of the field's evolution, from early rule-based math word problem (MWP) solvers and template-driven geometry systems, through neural expression generation and LLM prompting, to contemporary reasoning models, multi-agent systems, neuro-symbolic theorem provers, and verified discovery workflows. We organize the landscape along four axes: (i) informal reasoning over text and diagrams, spanning MWP solving, multimodal geometry, and VLMs; (ii) formal reasoning in proof assistants, including autoformalization, tactic prediction, compiler-guided repair, and proof search; (iii) mathematical discovery, where systems propose constructions, improve bounds, or assist attacks on open problems; and (iv) the inference and training-time techniques, including CoT prompting, tool use, process reward models, and RLVR, that increasingly connect generation with verification. We catalog major benchmarks across grade-school arithmetic, competition mathematics, geometry, formal proving, multimodal and multilingual reasoning, and expert evaluation, and we examine benchmark saturation, contamination, reporting mismatches, and the distinction between pass@1, majority voting, and verifier-assisted pass@$k$. We critically assess failure modes: brittleness under perturbation, reward hacking, multimodal grounding failures, fragile formalization, and the energy cost of reasoning-scale inference. Drawing on recent perspectives from working mathematicians, we identify future directions centered on verified-discovery workflows, reasoning efficiency, and infrastructure to make AI-assisted formalization broadly usable. Companion materials: https://github.com/Starscream-11813/awesome-AI4Math.
Chinese Translation
数学推理长期以来被视为机器智能的严格测试；在过去十年中，它已从自然语言处理（NLP）中的一个小众问题发展成为最重要的人工智能前沿之一。本调查提供了该领域演变的统一概述，从早期基于规则的数学文字问题（MWP）求解器和模板驱动的几何系统，到神经表达生成和大语言模型（LLM）提示，再到当代推理模型、多智能体系统、神经符号定理证明器和验证发现工作流程。我们沿四个轴线组织这一领域的格局：（i）对文本和图表的非正式推理，包括MWP求解、多模态几何和视觉语言模型（VLM）；（ii）在证明助手中的正式推理，包括自动形式化、策略预测、编译器引导修复和证明搜索；（iii）数学发现，其中系统提出构造、改进界限或协助解决开放问题；（iv）推理和训练时间技术，包括链式思维（CoT）提示、工具使用、过程奖励模型和强化学习验证回报（RLVR），这些技术日益将生成与验证连接起来。我们列举了在小学算术、竞赛数学、几何、形式证明、多模态和多语言推理以及专家评估等领域的主要基准，并检查基准饱和、污染、报告不匹配以及通过@1、主要投票和验证者辅助通过@$k$之间的区别。我们批判性地评估了失败模式：在扰动下的脆弱性、奖励黑客、多模态基础失败、脆弱的形式化以及推理规模推理的能量成本。借鉴来自工作数学家的最新观点，我们确定了未来的方向，重点围绕验证发现工作流程、推理效率以及使AI辅助形式化广泛可用的基础设施。伴随材料： https://github.com/Starscream-11813/awesome-AI4Math。
cs.AI / 74 / 2606.08735
Structure-Conditioned Actor-Critic Branches for Quality-Diversity Reinforcement Learning
结构条件下的演员-评论家分支用于质量-多样性强化学习
Abstract
Quality-diversity reinforcement learning (QD-RL) aims to construct policy repertoires that contain both high-performing and behaviorally diverse policies. Existing QD-RL methods mainly diversify policy instances after rollout evaluation or use learned value information to improve policy quality and behavior targeting, while the learning branches that generate candidate policies remain less explored. This paper proposes SV-QD-RL, a structure-value coupled framework that represents each candidate as a structure-conditioned actor-critic branch. Each branch contains an actor, a structural mask, a branch-specific critic, a replay state, and evaluation attributes including behavior, return, sparsity, and value profile. The structural mask defines the actor subspace in which the branch learns, while the branch-specific critic and replay state shape its value-learning trajectory. A branch-aware QD archive then evaluates and retains branches according to behavioral quality, structural footprint, and value-profile information. Experiments on MuJoCo continuous-control tasks show that SV-QD-RL constructs policy repertoires with strong archive quality and behaviorally useful diversity. Ablation and diagnostic analyses further indicate that structural conditioning, critic differentiation, and memory-consistent refinement make complementary contributions to behavioral specialization. Schedule-aware repertoire evaluation shows that the learned archive provides selectable policy alternatives under changing behavior-level requirements. These results suggest that coupling actor structure with branch-specific value learning is an effective mechanism for generating diverse QD-RL policy repertoires.
Chinese Translation
质量-多样性强化学习（QD-RL）旨在构建包含高性能和行为多样性策略的策略库。现有的QD-RL方法主要在回合评估后多样化策略实例，或使用学习到的价值信息来提高策略质量和行为目标，而生成候选策略的学习分支仍然较少被探索。本文提出了SV-QD-RL，一种结构-价值耦合框架，将每个候选策略表示为一个结构条件下的演员-评论家分支。每个分支包含一个演员、一个结构掩码、一个特定于分支的评论家、一个重放状态以及包括行为、回报、稀疏性和价值轮廓在内的评估属性。结构掩码定义了分支学习的演员子空间，而特定于分支的评论家和重放状态则塑造其价值学习轨迹。然后，分支感知的QD档案根据行为质量、结构足迹和价值轮廓信息评估和保留分支。在MuJoCo连续控制任务上的实验表明，SV-QD-RL构建了具有强档案质量和行为上有用多样性的策略库。消融和诊断分析进一步表明，结构条件、评论家差异化和记忆一致性细化对行为专业化做出了互补贡献。调度感知的策略库评估表明，学习到的档案在变化的行为水平要求下提供了可选择的策略替代方案。这些结果表明，将演员结构与特定于分支的价值学习耦合是一种有效的机制，用于生成多样化的QD-RL策略库。
cs.AI / 75 / 2606.08790
RAILS: Verification-Native Clearing For Agentic Commerce
RAILS：面向代理商业的验证原生清算
Abstract
Autonomous agents negotiate, purchase, deploy code, and move funds, but no neutral mechanism determines whether they met their delegated obligation, who is responsible when they did not, or which settlement action follows. This is the agentic clearing problem. Tool protocols (MCP), inter-agent communication (A2A), payment rails (x402), mandate and network agent protocols (AP2, Visa, Mastercard), and settlement-risk standards each assume that determination and none produce it. Clearing is the missing primitive. Payment is not clearing. Authorization is not clearing. LLM-as-judge evaluation is not clearing. Settlement-risk escrow is not clearing: it consumes clearing decisions. RAILS (Real-Time Agent Integrity & Ledger Settlement) is the integrity and clearing layer for agentic commerce, spanning a per-output reliability score, a published reliability record, and a clearing function that consumes them. The clearing protocol at its core closes that gap. Seven primitives (Obligation Object, Evidence Envelope, Verification Mesh, Clearing Decision, Settlement Instruction, Clearing Passport, Finality Rules), bound by a formal model of admissibility-graded verification, together yield a soundness property: no financially material settlement is supported by evidence below the obligation's admissibility floor. The property is falsifiable against the spec. We are not aware of a prior agent-commerce verification mechanism that states a property of this kind. The approaches nearest to it emit a pass, a delivery guarantee, a bare score, or an equilibrium. This paper specifies that clearing protocol.
Chinese Translation
自主代理进行谈判、购买、部署代码和转移资金，但没有中立机制来确定它们是否履行了委托义务、在未履行时谁应负责，或后续的清算行动是什么。这就是代理清算问题。工具协议（MCP）、代理间通信（A2A）、支付轨道（x402）、授权和网络代理协议（AP2、Visa、Mastercard）以及清算风险标准均假设了这一判定，但没有一个能够实现。清算是缺失的基本元素。支付并不等同于清算。授权并不等同于清算。作为裁判的LLM评估并不等同于清算。清算风险托管并不等同于清算：它消耗了清算决策。RAILS（实时代理完整性与账本清算）是代理商业的完整性和清算层，涵盖每个输出的可靠性评分、已发布的可靠性记录以及消耗这些记录的清算功能。其核心的清算协议弥补了这一空白。七个基本元素（义务对象、证据信封、验证网、清算决策、清算指令、清算护照、最终规则），通过一个正式的可接受性分级验证模型相互绑定，共同产生一个健全性属性：没有任何经济上重要的清算是基于低于义务可接受性底线的证据支持的。该属性可以针对规范进行可证伪性检验。我们尚未发现任何先前的代理商业验证机制声明此类属性。最接近的几种方法仅提供通过、交付保证、简单评分或均衡。本文具体阐述了该清算协议。
cs.AI / 76 / 2606.08800
Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
通过自我进化桥接专家知识与自动特征工程
Abstract
In high-stakes settings such as brand compliance, clinical care, and content moderation, machine learning cannot be deployed as opaque oracles: practitioners inspect the features driving model decisions, and models must leverage the expert documentation governing these domains. In practice, the data arrives as unstructured content, and features extracted from it must be interpretable, discriminative, and aligned with what experts consider important. Existing methods fall short: they target tabular inputs, lack demonstrated expert alignment, and cannot operationalize qualitative criteria such as 'maintain professional tone' into precise features. We present FEST (Feature Engineering with Self-evolving Trees), combining dual-stream feature generation (semantic and deterministic), semantic deduplication, and tree-guided iterative evolution to discover auditable features from raw text and images. FEST leads in 17 of 20 classifier-task combinations across brand classification, content authenticity detection, and stress detection, with a mean gain of 4.2 pp over the strongest baseline across five classifiers. An LLM-as-judge evaluation shows FEST achieves 60-80% coverage of expert-designed brand features at strict semantic-alignment thresholds, corroborated by a human expert study rating features highly on relevance, clarity, and actionability. When seeded with expert guidelines, FEST refines qualitative criteria into operational features, improving accuracy by 6-12 pp on average across brands. To enable systematic evaluation of expert alignment in automated feature engineering, we release BrandGuide, the first dataset pairing expert-designed features with 1M+ assets across 2,683 brands. By grounding feature engineering in expert knowledge, FEST opens a practical pathway for interpretable ML in domains demanding human oversight.
Chinese Translation
在品牌合规、临床护理和内容审核等高风险环境中，机器学习不能作为不透明的神谕被部署：从业者需要检查驱动模型决策的特征，模型必须利用这些领域的专家文档。在实践中，数据以非结构化内容的形式到达，从中提取的特征必须是可解释的、具有区分性的，并与专家认为重要的内容保持一致。现有方法存在不足：它们针对表格输入，缺乏经过验证的专家对齐，且无法将诸如“保持专业语气”等定性标准转化为精确特征。我们提出了FEST（自我进化树特征工程），结合双流特征生成（语义和确定性）、语义去重和树导向的迭代进化，从原始文本和图像中发现可审计的特征。FEST在品牌分类、内容真实性检测和压力检测的20个分类任务组合中领先17个，平均比最强基线提高4.2个百分点。LLM作为评判者的评估显示，FEST在严格的语义对齐阈值下实现了60-80%的专家设计品牌特征覆盖率，这一结果得到了人类专家研究的支持，该研究对特征在相关性、清晰度和可操作性方面的评分较高。当以专家指南为基础时，FEST将定性标准细化为可操作特征，平均提高品牌的准确性6-12个百分点。为了系统评估自动特征工程中的专家对齐，我们发布了BrandGuide，这是第一个将专家设计特征与2,683个品牌的100万+资产配对的数据集。通过将特征工程建立在专家知识之上，FEST为需要人类监督的领域中的可解释机器学习开辟了一条实用的路径。
cs.AI / 77 / 2606.08804
Q-Delta: Beyond Key-Value Associative State Evolution
Q-Delta：超越键值关联状态演化
Abstract
Linear attention reformulates sequence modeling as recurrent state evolution, enabling efficient linear-time inference. Under the key-value associative paradigm, existing approaches restrict the role of the query to the readout operation, decoupling it from state evolution. We show that query-conditioned state readout induces a structured value prediction over accumulated memory that complements key-based retrieval. Based on this insight, we propose Q-Delta, a query-aware delta rule that integrates mixed key-query prediction errors into state evolution, enabling jointly corrective dynamics while preserving delta-rule efficiency. We establish stability guarantees for the resulting dynamics and derive a hardware-efficient chunkwise-parallel formulation with a custom Triton implementation. Empirical results demonstrate stable optimization, competitive throughput, and consistent improvements over strong baselines on language modeling and long-context retrieval tasks.
Chinese Translation
线性注意力将序列建模重新表述为递归状态演化，从而实现高效的线性时间推理。在键值关联范式下，现有方法将查询的角色限制为读取操作，使其与状态演化解耦。我们展示了查询条件下的状态读取会引发对累积记忆的结构化值预测，从而补充基于键的检索。基于这一见解，我们提出了Q-Delta，一种查询感知的增量规则，将混合键-查询预测误差整合到状态演化中，使得在保持增量规则效率的同时实现联合纠正动态。我们为所得到的动态建立了稳定性保证，并推导出一种硬件高效的分块并行形式，配合定制的Triton实现。实证结果表明，在语言建模和长上下文检索任务上，优化稳定，吞吐量具有竞争力，并且在强基线之上持续改进。
cs.AI / 78 / 2606.08814
STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning
STAR：重新思考MoE路由作为结构感知子空间学习
Abstract
Mixture-of-Experts (MoE) scales model capacity efficiently by selectively routing inputs to a specialized subset of experts. However, input-expert specialization, the core motivation of MoE, critically depends on whether the router is actually aware of input structure. In practice, MoE routing is typically implemented as a shallow linear projection with limited awareness of input representation, which often leads to unstable routing. We propose STAR, a Structure Aware Routing that rethinks MoE routing as a subspace learning problem by augmenting standard learnable routing with an evolving principal subspace that tracks dominant input structure via Generalized Hebbian Algorithm (GHA). By aligning routing decisions directly with input structure, STAR enables stable expert specialization. We evaluate STAR on controlled synthetic setup and large-scale language and vision tasks, where it consistently improves routing quality and downstream performance over strong MoE baselines. Moreover, optional test-time subspace updates further enhance routing robustness and generalization under input distribution shifts.
Chinese Translation
专家混合模型（Mixture-of-Experts, MoE）通过选择性地将输入路由到特定的专家子集，从而有效地扩展模型容量。然而，输入与专家的专业化，作为MoE的核心动机，严重依赖于路由器是否真正意识到输入结构。在实践中，MoE路由通常被实现为一种浅层线性投影，对输入表示的感知能力有限，这往往导致路由的不稳定性。我们提出了STAR，一种结构感知路由（Structure Aware Routing），将MoE路由重新思考为一个子空间学习问题，通过增强标准可学习路由与一个不断演变的主子空间的结合，该主子空间通过广义赫布算法（Generalized Hebbian Algorithm, GHA）跟踪主导输入结构。通过直接将路由决策与输入结构对齐，STAR实现了稳定的专家专业化。我们在受控的合成设置以及大规模语言和视觉任务上评估了STAR，结果表明其在路由质量和下游性能上始终优于强大的MoE基线。此外，选用的测试时子空间更新进一步增强了路由的鲁棒性和在输入分布变化下的泛化能力。
cs.AI / 79 / 2606.08815
Momentum for Reasoning: Dense Intrinsic Signals in Policy Optimization
推理的动量：政策优化中的密集内在信号
Abstract
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for eliciting long-chain reasoning in large language models. However, existing methods based on Group Relative Policy Optimization (GRPO) rely on a binary outcome reward, which induces two structural failure modes: Zero-Advantage Collapse, in which all rollouts in a group share the same outcome and the gradient vanishes, and Hallucinated Certainty, in which the model becomes increasingly confident on incorrect rollouts late in training. We address both modes by densifying the reward with intrinsic signals computed entirely from the policy's own conditional probabilities, and propose ISPO (Intrinsic Signal Policy Optimization, which combines a sequence-level signal measuring how informative the thinking trajectory is for the final answer, with a token-level directional reward whose hallucinated-certainty hinge penalizes confidently-wrong predictions at critical decision tokens. Across three base models and five mathematical reasoning benchmarks, ISPO consistently outperforms competitive baselines, with the largest gains on the hardest benchmarks where zero-advantage collapse is most frequent, and training-dynamics diagnostics confirm that both failure modes are decreased.
Chinese Translation
具有可验证奖励的强化学习（RLVR）已成为在大型语言模型中引发长链推理的强大范式。然而，现有基于群体相对政策优化（GRPO）的方法依赖于二元结果奖励，这导致了两种结构性失效模式：零优势崩溃（Zero-Advantage Collapse），在这种情况下，组内所有的回放共享相同的结果，梯度消失；以及幻觉确定性（Hallucinated Certainty），在这种情况下，模型在训练后期对错误的回放变得越来越自信。我们通过使用完全基于政策自身条件概率计算的内在信号来增强奖励，从而解决这两种模式，并提出了内在信号政策优化（ISPO，Intrinsic Signal Policy Optimization），该方法结合了一个序列级信号，用于衡量思维轨迹对最终答案的有效性，以及一个令牌级方向性奖励，其幻觉确定性铰链在关键决策令牌上惩罚自信错误的预测。在三个基础模型和五个数学推理基准测试中，ISPO始终优于竞争基线，在最难的基准测试中获得了最大的提升，而零优势崩溃最为频繁，训练动态诊断确认这两种失效模式均有所减少。
cs.AI / 80 / 2606.08831
Inference-Time Conformal Reasoning with Valid Factuality Control for Large Language Models
具有有效事实控制的大型语言模型推理时的符合性推理
Abstract
Large language models (LLMs) increasingly perform multi-step reasoning, where intermediate claims form implicit directed acyclic graphs whose node correctness is structurally conditioned on their ancestors. This makes factuality uncertainty structural, rather than a trivial accumulation of node-wise errors, and necessitates inference-time uncertainty quantification over the reasoning structure. While conformal prediction (CP) offers flexible user-specified factuality control, existing work remains post-hoc and cannot intervene during generation. To fill the gap between CP's flexibility and its post-hoc limitation, we propose an \emph{Inference-Time Conformal Reasoning (ITCR)} framework that integrates CP directly into reasoning graph generation. ITCR learns a structure-level factuality uncertainty function that aggregates claim-level factuality signals over reasoning graphs without complex modeling assumptions. We then design the non-conformity score based on graph-level factuality uncertainty and calibrate the conformal threshold to decide when to stop generation. We theoretically show such generation is nested, yielding valid coverage guarantees for factuality control. Experiments over multiple datasets and coverage objectives demonstrate empirically valid coverage. In downstream reasoning tasks, inference-time calibrated graphs yield more accurate generation than post-hoc pruned graphs.
Chinese Translation
大型语言模型（LLMs）越来越能够进行多步骤推理，其中中间声明形成隐式有向无环图，其节点的正确性在结构上依赖于其祖先。这使得事实不确定性成为一种结构性问题，而不是节点级错误的简单累积，并且需要在推理结构上进行推理时的不确定性量化。尽管符合性预测（CP）提供了灵活的用户指定事实控制，但现有的工作仍然是事后分析，无法在生成过程中进行干预。为了填补CP的灵活性与其事后限制之间的空白，我们提出了一种 extit{推理时符合性推理（ITCR）}框架，该框架将CP直接集成到推理图生成中。ITCR学习一个结构级事实不确定性函数，该函数在不需要复杂建模假设的情况下聚合推理图上的声明级事实信号。然后，我们基于图级事实不确定性设计非符合性分数，并校准符合性阈值以决定何时停止生成。我们理论上证明这种生成是嵌套的，从而为事实控制提供有效的覆盖保证。在多个数据集和覆盖目标上的实验实证验证了有效的覆盖。在下游推理任务中，推理时校准的图生成比事后修剪的图生成更准确。
cs.AI / 81 / 2606.08832
Instrumental convergence and power-seeking
工具性趋同与权力追求
Abstract
Recent years have seen increasing concern that artificial intelligence may soon pose an existential risk to humanity. One leading ground for concern is that artificial agents may be power-seeking, aiming to acquire power and in the process disempowering humanity. I show how the argument from power-seeking rests on a strong version of a claim known as the instrumental convergence thesis. I explore leading defenses of the instrumental convergence thesis and argue that none establishes the thesis in a strong enough form to ground the argument from power-seeking. I discuss implications for longtermism, the governance of artificial intelligence, and the methodology of studying risks posed by artificial agents.
Chinese Translation
近年来，人们对人工智能可能很快对人类构成生存风险的担忧日益加剧。一个主要的担忧理由是，人工代理可能会追求权力，旨在获取权力并在此过程中削弱人类的权力。我展示了权力追求的论点是如何基于一种被称为工具性趋同理论的强版本。我探讨了工具性趋同理论的主要辩护，并认为没有一个辩护能够以足够强的形式确立该理论，从而为权力追求的论点提供基础。我讨论了这一论点对长期主义、人工智能治理以及研究人工代理所带来的风险的方法论的影响。
cs.AI / 82 / 2606.08840
Beyond Pass Rate: A Multilingual, Execution-Grounded Evaluation of Open Code LLMs
超越通过率：开放代码大型语言模型的多语言、基于执行的评估
Abstract
Code generation models are typically compared using compact execution benchmarks and aggregate pass rates, but such summaries obscure how performance varies across programming languages, problem families, and failure modes. We present a large-scale, execution-grounded evaluation of 9 openly accessible LLMs specialized for coding on 2,707 free LeetCode problems across 12 programming languages. Our corpus contains 325,343 problem-model-language jobs, each linked to prompt metadata, extracted code, LeetCode execution outcomes, and static-analysis signals. The results show that current open models remain far from the human acceptance reference: the best model, Yi-Coder-9B-Chat, reaches 23.64% mean correctness, compared with a 57.2% human acceptance baseline. Rankings are also slice-dependent: Qwen2.5-Coder-14B-Instruct is strongest on hard problems and distinct-problem coverage, while Gemma-2-27B-IT achieves the highest all-language lint pass rate. Failure analysis shows that compile errors account for 63.25% of non-accepted best submissions, indicating that many failures occur before semantic correctness can be tested. Static quality further diverges from functional correctness. Together, these findings show that multilingual, artifact-preserving evaluation reveals tradeoffs hidden by single-language or single-metric leaderboards.
Chinese Translation
代码生成模型通常通过紧凑的执行基准和汇总通过率进行比较，但这种总结掩盖了性能在编程语言、问题类别和失败模式之间的差异。我们对9个专门用于编码的开放访问大型语言模型（LLMs）进行了大规模的基于执行的评估，涵盖了2707个免费的LeetCode问题，涉及12种编程语言。我们的语料库包含325,343个问题-模型-语言作业，每个作业都与提示元数据、提取的代码、LeetCode执行结果和静态分析信号相关联。结果表明，目前的开放模型远未达到人类接受的参考标准：最佳模型Yi-Coder-9B-Chat的平均正确率为23.64%，而人类接受基准为57.2%。排名也依赖于特定的切片：Qwen2.5-Coder-14B-Instruct在困难问题和不同问题覆盖率方面表现最佳，而Gemma-2-27B-IT则在所有语言的lint通过率上达到了最高。失败分析显示，编译错误占未被接受的最佳提交的63.25%，这表明许多失败发生在语义正确性测试之前。静态质量进一步与功能正确性出现偏差。综合来看，这些发现表明，多语言、保留工件的评估揭示了单语言或单指标排行榜所隐藏的权衡。
cs.AI / 83 / 2606.08841
ZIPP:Zero-shot Image Personalization from Personas
ZIPP：基于角色的零-shot图像个性化
Abstract
Text-to-image diffusion models are increasingly deployed in open-ended creative contexts, yet their outputs remain impersonal, optimized for aggregate aesthetics rather than individual taste. Human preferences are pluralistic: one user favoring muted, nostalgic portraits may prefer vibrant street photography, while another gravitates toward dreamy film aesthetics. Existing methods require dense interaction histories or per-user fine-tuning, failing in cold-start settings and collapsing context-dependent preferences into a static representation. We introduce zero-shot image personalization from personas (ZIPP), which conditions image generation on natural-language personas (concise descriptors of a user's identity and aesthetic sensibilities) without any user-specific data or weight updates. ZIPP uses an LLM to rewrite prompts from the perspective of a given persona, steering diffusion models toward personalized outputs. To mine personas at scale, we train an inductive Graph Attention Network over a 22M-user Reddit interaction graph with dual contrastive objectives aligning graph structure with visual behavior, then verbalize learned representations into natural-language personas via an MLLM. We introduce ZIPBench, the first zero-shot personalization benchmark with 1.5K users, graph-mined personas, and 40K generated images. Across four benchmarks and 14 LLMs spanning five model families, persona conditioning yields consistent gains (13-20%), with frontier models benefiting most. In the few-shot setting, ZIPP matches or exceeds fine-tuned baselines trained on 100+ examples per user. ZIPP achieves the lowest preference distributional divergence (CMMD 0.16 vs. 0.55), and IPF-normalized demographic evaluation shows it substantially reduces subpopulation bias present in existing methods. Human evaluation confirms a 79% win rate over generic generation and 58-65% over all fine-tuned baselines.
Chinese Translation
文本到图像的扩散模型越来越多地应用于开放式创意环境中，但其输出仍然缺乏个性，优化的是整体美学而非个体品味。人类偏好是多元的：一位用户可能偏爱柔和、怀旧的肖像，而另一位则倾向于生动的街头摄影，或是梦幻的电影美学。现有方法需要密集的交互历史或针对每个用户的微调，在冷启动场景中表现不佳，并将上下文相关的偏好压缩为静态表示。我们提出了基于角色的零-shot图像个性化（ZIPP），该方法在没有任何用户特定数据或权重更新的情况下，以自然语言角色（用户身份和美学敏感性的简洁描述）为条件生成图像。ZIPP利用大型语言模型（LLM）从给定角色的角度重写提示，引导扩散模型生成个性化的输出。为了大规模挖掘角色，我们在一个包含2200万用户的Reddit交互图上训练了一个归纳图注意力网络，采用双重对比目标将图结构与视觉行为对齐，然后通过多语言大型模型（MLLM）将学习到的表示转化为自然语言角色。我们引入了ZIPBench，这是第一个零-shot个性化基准，包含1500名用户、图挖掘角色和4万张生成图像。在四个基准测试和涵盖五个模型家族的14个LLM中，角色条件化带来了持续的增益（13-20%），前沿模型受益最大。在少量样本设置中，ZIPP的表现与在每个用户上训练100多个示例的微调基线相当或更好。ZIPP实现了最低的偏好分布差异（CMMD 0.16对比0.55），并且IPF标准化的人口评估显示它显著减少了现有方法中的子群体偏差。人类评估确认其在通用生成上有79%的胜率，在所有微调基线中有58-65%的胜率。
cs.AI / 84 / 2606.08849
A Resilience-as-a-Service assessment framework for coordinated disruption response in interdependent urban transit systems
基于韧性的服务评估框架用于协调应对相互依赖的城市交通系统中的干扰
Abstract
Urban public transport disruptions require rapid response strategies, yet existing studies rarely provide a decision support framework to compare alternative disruption response solutions using a common set of dynamic, passenger, operator, and environment oriented indicators. This paper proposes a KPI-driven, time-indexed framework to assess the resilience of disruption response solutions in urban transit systems. The framework combines an optimization model with a behavioral evaluation in agent-based simulation. It also underlays the secondary service degradation induced on helper lines when in-service vehicles are withdrawn to support the disrupted corridor. Rather than treating resilience as a single score, it evaluates complementary dimensions including vulnerability, adaptability, robustness, resilience loss, responsiveness, cost-based performance, emissions, and equity. The framework is implemented for the RER B transit line in the Ile-de-France (Paris) network. Results show that the coordinated strategy provides the most balanced resilience profile, combining high service continuity with lower total disruption cost than single mode alternatives, while also improving equity and maintaining competitive environmental performance. Sensitivity analysis further identifies the disruption conditions under which coordinated multimodal response is most valuable.
Chinese Translation
城市公共交通干扰需要快速响应策略，但现有研究很少提供决策支持框架，以使用一套共同的动态、乘客、运营商和环境导向的指标来比较替代的干扰响应解决方案。本文提出了一种以关键绩效指标（KPI）驱动的、时间索引的框架，用于评估城市交通系统中干扰响应解决方案的韧性。该框架结合了优化模型和基于代理的仿真中的行为评估。它还考虑了在服务车辆被撤回以支持受干扰走廊时，对辅助线路造成的次级服务退化。该框架不仅将韧性视为单一评分，而是评估包括脆弱性、适应性、稳健性、韧性损失、响应能力、基于成本的绩效、排放和公平性等互补维度。该框架已在法兰西岛（巴黎）网络的RER B交通线路上实施。结果表明，协调策略提供了最平衡的韧性特征，结合了高服务连续性和低于单一模式替代方案的总干扰成本，同时改善了公平性并保持了竞争性的环境绩效。敏感性分析进一步确定了协调多模式响应最有价值的干扰条件。
cs.AI / 85 / 2606.08855
Hybrid E-Assessment in Higher Education: Semi-Automated Grading of Paper-Based Written Examinations
高等教育中的混合电子评估：纸质书面考试的半自动评分
Abstract
This paper examines the limitations of fully digital and partially digital e-assessment approaches in summative examinations in higher education. The analysis focuses on the didactic narrowing caused by closed question formats and on organizational, technical, and legal constraints that become particularly relevant in large student cohorts. As an alternative, the paper proposes a hybrid e-assessment approach that retains paper-based, problem-oriented examination tasks while enabling semi-automated grading. Assessment-relevant intermediate results are encoded in a structured answer format, entered by students by hand, and subsequently captured from table fields. The central technical bottleneck is reliable recognition of handwritten characters under realistic examination conditions. Recent vision-capable large language models, combined with a two-pass validation principle and comparison against a solution key, can reduce misclassifications and thereby improve the validity, fairness, and scalability of summative assessment.
Chinese Translation
本文探讨了在高等教育的总结性考试中，完全数字化和部分数字化电子评估方法的局限性。分析重点关注由封闭式问题格式引起的教学狭窄，以及在大规模学生群体中尤为相关的组织、技术和法律限制。作为替代方案，本文提出了一种混合电子评估方法，该方法保留了纸质、以问题为导向的考试任务，同时实现半自动评分。与评估相关的中间结果以结构化答案格式编码，由学生手动输入，并随后从表格字段中捕获。中心技术瓶颈是在现实考试条件下可靠识别手写字符。最近的具备视觉能力的大型语言模型，结合双重验证原则和与解答键的比较，可以减少误分类，从而提高总结性评估的有效性、公平性和可扩展性。
cs.AI / 86 / 2606.08875
Can the Environment Speak for Itself? $T^{2}$-GRPO: A Turn-Trajectory Group Relative Policy Optimization for Caregiver Agents
环境能否为自己发声？$T^{2}$-GRPO：一种针对照护者代理的转向轨迹组相对策略优化
Abstract
Optimizing large language models (LLMs) for long-horizon caregiver agents requires balancing delayed task objectives with immediate environment dynamics, such as patient distress and resistance. In dementia care, this balance is especially difficult: trajectory level rewards are too sparse for turn level credit assignment, while external LLM-based evaluators are costly and can misread fragmented or indirect patient responses. To address this issue, we propose \textbf{T}urn-\textbf{T}rajectory \textbf{G}roup \textbf{R}elative \textbf{P}olicy \textbf{O}ptimization (\textbf{T$^{2}$-GRPO}), a framework that decouples caregiver RL into two normalized reward horizons and enforces safety through a binary hard veto. $T^2$-GRPO derives dense turn-level rewards directly from environment state transitions, measuring changes in patient distress and resistance from a frozen dementia patient simulator. These environment-grounded rewards are combined with trajectory-level evaluations through independent centered-rank normalization, which preserves heterogeneous reward signals and mitigates reward collapse. Extensive experiments on dementia caregivers show that T $^{2}$-GRPO outperforms competitive baselines, indicating a substantial improvement for emotionally sensitive caregiver scenarios that effectively handles immediate patient feedback, long-term care outcomes, and safety constraints.
Chinese Translation
优化大型语言模型（LLMs）以适应长期照护者代理需要在延迟任务目标与即时环境动态之间取得平衡，例如患者的痛苦和抵抗。在痴呆症护理中，这种平衡尤其困难：轨迹级奖励对于转向级的信用分配过于稀疏，而基于外部LLM的评估者成本高昂且可能误读碎片化或间接的患者反应。为了解决这一问题，我们提出了 extbf{T}urn- extbf{T}rajectory extbf{G}roup extbf{R}elative extbf{P}olicy extbf{O}ptimization（ extbf{T$^{2}$-GRPO}），这是一个将照护者强化学习（RL）解耦为两个归一化奖励视野的框架，并通过二元硬性否决来强制执行安全性。$T^2$-GRPO直接从环境状态转移中推导出密集的转向级奖励，测量来自冻结的痴呆患者模拟器的患者痛苦和抵抗的变化。这些基于环境的奖励与通过独立中心排名归一化的轨迹级评估相结合，保留了异质奖励信号并减轻了奖励崩溃。在痴呆症照护者的广泛实验中，$T^{2}$-GRPO的表现优于竞争基线，表明在有效处理即时患者反馈、长期护理结果和安全约束的情感敏感照护者场景中有显著改善。
cs.AI / 87 / 2606.08896
FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting
FAME：面向可预测性的异构时间序列预测专家混合模型
Abstract
Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectral patterns, and contextual sensitivity differ substantially. A single forecasting model rarely performs well across all regimes, while dense ensembles increase inference cost and provide limited insight into expert suitability. This paper studies forecastability-aware expert routing: learning how data characteristics determine the suitability of forecasting experts. We propose \method{}, a sparse mixture-of-experts framework that represents each series with a multidimensional forecastability fingerprint, mines expert-suitability targets from validation performance, and trains a cost-aware sparse router to activate a small budgeted set of experts for each series. Using a production-scale vending-machine sales dataset from Shandong New Beiyang (SNBC), where the forecasting component has been integrated into the replenishment-planning pipeline, together with public retail benchmarks, we show that expert suitability varies systematically across data regimes. On the industrial dataset with 5,000+ machines and 60M+ transactions, \method{} Top-2 reduces MSE by 12.4\% over the strongest single expert, LightGBM, while executing 1.92 experts per series on average. The deployed component produces demand forecasts, while inventory-oriented gains are estimated by an offline replay simulator under a fixed replenishment policy rather than by online intervention. The framework turns heterogeneous sales forecasting from heuristic model selection into data mining of forecastability patterns and expert specialization. Code is available at https://github.com/hit636/FAME
Chinese Translation
大规模零售和工业预测系统包含许多异构时间序列，其生命周期、稀疏性、波动性、季节性、频谱模式和上下文敏感性差异显著。单一的预测模型在所有状态下很少表现良好，而密集的集成模型则增加了推理成本，并对专家适用性提供有限的洞察。本文研究了面向可预测性的专家路由：学习数据特征如何决定预测专家的适用性。我们提出了 extit{method}，一种稀疏的专家混合框架，通过多维的可预测性指纹表示每个序列，从验证性能中挖掘专家适用性目标，并训练一个成本感知的稀疏路由器，为每个序列激活一小组预算内的专家。使用来自山东新北洋（SNBC）的生产规模自动售货机销售数据集，其中预测组件已集成到补货规划流程中，以及公共零售基准，我们展示了专家适用性在数据状态之间系统性变化。在包含5000多台机器和6000万次以上交易的工业数据集中， extit{method} Top-2相较于最强单一专家LightGBM将均方误差（MSE）降低了12.4\%，同时平均每个序列执行1.92个专家。部署的组件生成需求预测，而库存导向的收益则通过离线重放模拟器在固定补货政策下进行估算，而非通过在线干预。该框架将异构销售预测从启发式模型选择转变为对可预测性模式和专家专业化的数据挖掘。代码可在 https://github.com/hit636/FAME 获取。
cs.AI / 88 / 2606.08904
Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution
顺序的重要性：通过代理引导的LLM演化揭示宏观布局顺序的隐性影响
Abstract
Macro placement is a fundamental step in modern chip physical design, playing a crucial role in determining the solution quality of high-dimensional combinatorial optimization problems. Despite recent advancements in machine learning for spatial coordinate determination, the temporal dimension of placement sequencing remains largely governed by static heuristics. In this work, we demonstrate that the placement sequence is not merely a preprocessing step but a decisive factor in optimization, where suboptimal early decisions trigger irreversible domino effects that constrain the solution space. To harness this unexplored dimension, we propose \textbf{OrderPlace}, a proxy-guided LLM evolution framework for automatically discovering macro placement order strategies. Instead of relying on manually crafted heuristics such as area- or connectivity-based ordering, OrderPlace explores a broader space of code-level policies, ranging from static scoring metrics to dynamic physics-inspired mechanisms. To mitigate the prohibitive cost of evaluating sequences, we introduce a lightweight proxy evaluation mechanism that efficiently filters candidates using a deterministic greedy probe. Experimental results on the standard ISPD 2005 benchmarks demonstrate that OrderPlace discovers novel ordering strategies. Compared with WireMask-EA and the state-of-the-art method EGPlace, OrderPlace reduces wirelength by 34.04\% and 14.08\%, respectively.
Chinese Translation
宏观布局是现代芯片物理设计中的一个基本步骤，在确定高维组合优化问题的解决方案质量方面发挥着关键作用。尽管最近在空间坐标确定的机器学习方面取得了进展，但布局顺序的时间维度仍然主要由静态启发式方法主导。在本研究中，我们证明了布局顺序不仅仅是一个预处理步骤，而是优化中的决定性因素，早期的次优决策会引发不可逆的多米诺效应，从而限制解决方案空间。为了利用这一未被探索的维度，我们提出了 extbf{OrderPlace}，一种代理引导的LLM演化框架，用于自动发现宏观布局顺序策略。OrderPlace不依赖于手工设计的启发式方法，如基于面积或连通性的排序，而是探索更广泛的代码级策略空间，从静态评分指标到动态物理启发机制。为了减轻评估序列的高昂成本，我们引入了一种轻量级的代理评估机制，通过确定性贪婪探测器高效过滤候选项。在标准ISPD 2005基准测试中的实验结果表明，OrderPlace发现了新颖的排序策略。与WireMask-EA和最先进的方法EGPlace相比，OrderPlace分别减少了34.04 ext{%}和14.08 ext{%}的布线长度。
cs.AI / 89 / 2606.08919
Oversight Has a Capacity: Calibrating Agent Guards to a Subjective, Fatiguing Human
监督具有容量：将代理守卫校准为主观的、疲劳的人类
Abstract
As LLM agents begin to take real, irreversible actions (shell commands, file edits, deploys), the standard safety pattern is a human-in-the-loop approval gate: risky actions pause and wait for a person. We argue the gate is the easy part; the hard part is the judgment - which actions to stop - which the field evaluates against two false assumptions: that there is a ground-truth notion of "risky," and that the human reviewer is a perfect, infinitely-available oracle. On a hand-labeled set of 125 adversarially-weighted agent actions we show that (i) reviewers only moderately agree on what is risky (Fleiss' kappa = 0.52), so there is no single correct label; (ii) framing the guard as selective classification under asymmetric cost makes its operating limits measurable, and on hard inputs the guard cannot safely auto-decide; and (iii) when the reviewer is modeled as endogenous (fatiguing as escalation load grows), realized safety becomes an inverted-U in the escalation rate: more human oversight can make a system less safe, and the safety-optimal guard escalates below full escalation - a setting a load-aware policy also uses to resist a flooding attack that slips a malicious action past a fatigued reviewer. Agent oversight, framed this way, is not only a classification problem but a resource-allocation one: human attention is finite, and the guard's escalation policy spends it. We claim none of these mechanisms as novel - fatigue-aware learning-to-defer (FALCON), cost-sensitive deferral under workload constraints (DeCCaF), trajectory-level guarding, and reviewer-fatigue/flooding attacks are all prior art we cite. Our contribution is an open-source agent-oversight system that operationalizes and measures them in the LLM-agent action-gating setting, turning "is my guard good?" from a guess into a curve. The inverted-U and the flooding attack are modeling results that motivate a human study.
Chinese Translation
随着大型语言模型（LLM）代理开始采取真实且不可逆的行动（如 shell 命令、文件编辑、部署），标准的安全模式是人类在环的审批门：风险行为会暂停并等待人类的干预。我们认为，门的设置是简单的部分；而判断——哪些行为需要停止——才是困难的部分，实际评估中存在两个错误假设：即存在一个“风险”的真实标准，以及人类审查者是一个完美且无限可用的神谕者。在一组手动标注的 125 个对抗性加权的代理行为中，我们展示了 (i) 审查者在什么是风险行为上仅有中等程度的一致性（Fleiss' kappa = 0.52），因此没有单一的正确标签；(ii) 将守卫框架视为在不对称成本下的选择性分类，使其操作限制可测量，而在困难输入下，守卫无法安全地自动决策；(iii) 当审查者被建模为内生的（随着升级负载的增加而疲劳）时，实际安全性呈现出升级率的倒U型：更多的人类监督可能使系统的安全性降低，而安全最优的守卫在完全升级以下进行升级——这一设置也被负载感知策略用来抵御通过疲劳审查者滑过的恶意行为的洪水攻击。以这种方式框架化的代理监督，不仅是一个分类问题，也是一个资源分配问题：人类注意力是有限的，而守卫的升级策略消耗了这些注意力。我们并不声称这些机制是新颖的——疲劳感知的学习延迟（FALCON）、在工作负载约束下的成本敏感延迟（DeCCaF）、轨迹级守卫，以及审查者疲劳/洪水攻击都是我们引用的前人研究。我们的贡献是一个开源的代理监督系统，它在 LLM 代理行动门控设置中实现并测量这些机制，将“我的守卫好吗？”从一个猜测转变为一条曲线。倒U型和洪水攻击是建模结果，激励着一项人类研究。
cs.AI / 90 / 2606.08952
AlloSpatial: Agentic Harness Framework for Spatial Reasoning in Foundation Models
AlloSpatial：用于基础模型空间推理的代理性框架
Abstract
Multimodal Foundation Models (MFMs) have made substantial progress, yet remain fragile in spatial reasoning over the physical world. A key bottleneck lies in their inability to transform local egocentric observations into a global allocentric spatial representation. To address this, we propose AlloSpatial, an agentic framework for allocentric spatial cognition in foundation models. AlloSpatial introduces World2Mind, a plug-and-play cognitive mapping sandbox that converts egocentric observations into structured allocentric priors, including Allocentric-Spatial Trees and route maps that support querying object topology, geometric relations, passability, and trajectories. To utilize these priors reliably under noisy reconstruction and ambiguous visual evidence, AlloSpatial introduces a Spatial Reasoning Harness for tool-use judgment, modality-decoupled cue collection, and geometry-semantic arbitration. We further internalize this process in Qwen3-VL through cold-start reinforcement learning with a harness-gated trajectory-level reward. Experiments on VSI-Bench and MindCube show that AlloSpatial improves proprietary models by 5%-18% in a training-free setting, while ASTs alone support strong spatial reasoning even when visual inputs are removed. The trained AlloSpatial agents further outperform larger general-purpose models and competitive spatial baselines, suggesting that structured allocentric representations, active tool use, and verifiable reasoning offer a promising route toward spatially capable foundation models.
Chinese Translation
多模态基础模型（MFMs）取得了显著进展，但在物理世界的空间推理方面仍显脆弱。一个关键瓶颈在于它们无法将局部自我中心的观察转化为全球的他中心空间表示。为了解决这个问题，我们提出了AlloSpatial，一个用于基础模型他中心空间认知的代理性框架。AlloSpatial引入了World2Mind，一个即插即用的认知映射沙箱，能够将自我中心的观察转换为结构化的他中心先验，包括他中心空间树（Allocentric-Spatial Trees）和支持查询对象拓扑、几何关系、可通行性和轨迹的路线图。为了在嘈杂的重建和模糊的视觉证据下可靠地利用这些先验，AlloSpatial引入了一个空间推理工具，用于工具使用判断、模态解耦线索收集和几何-语义仲裁。我们进一步通过冷启动强化学习在Qwen3-VL中内化这一过程，采用了一个带有工具门控的轨迹级奖励。在VSI-Bench和MindCube上的实验表明，AlloSpatial在无训练设置下使专有模型的性能提高了5%-18%，而仅使用ASTs即使在视觉输入被移除时也能支持强大的空间推理。经过训练的AlloSpatial代理的表现进一步超越了更大的通用模型和竞争性的空间基准，表明结构化的他中心表示、主动工具使用和可验证推理为实现具有空间能力的基础模型提供了一条有前景的路径。
cs.AI / 91 / 2606.08970
An Effective Router for Vision-Language Model Selection
一种有效的视觉-语言模型选择路由器
Abstract
Vision-language models (VLMs) with varying performance and resource requirements are widely deployed, making it difficult for users to select the most appropriate one among numerous VLM candidates. Existing work reveals the performance paradox phenomenon in language models and focuses on routing methods to solve it. However, developing a router for VLM selection is still a critical yet challenging problem, which primarily faces: 1) lack of specialized data, 2) ineffective feature representation, and 3) rigid model space and costly adaptation. In this paper, we construct a multimodal dataset for VLM selection, containing the outputs of seven mainstream VLMs on 32,626 unique image-text queries. We then propose ARMS, a router for VLM selection. ARMS enhances input signals with VLM profiles, employs a simple but effective architecture to improve representations of queries and VLM capabilities. To improve ARMS' adaptation to new VLMs, we propose two extension training strategies: incremental training and independent training. Experimental results on both in-distribution and out-of-distribution test sets demonstrate the effectiveness of ARMS. In particular, using our training strategy, ARMs (only 800M in size) can adapt to a broader VLM space and defeat commercial models like GPT-4o that are hundreds of times larger in scale. Our code, models, and datasets are available in the anonymous repository.
Chinese Translation
视觉-语言模型（VLMs）因其性能和资源需求各异而被广泛部署，这使得用户在众多VLM候选模型中选择最合适的模型变得困难。现有研究揭示了语言模型中的性能悖论现象，并集中于路由方法来解决这一问题。然而，为VLM选择开发一个路由器仍然是一个关键但具有挑战性的问题，主要面临以下挑战：1）缺乏专业数据，2）特征表示无效，3）模型空间僵化且适应成本高。本文构建了一个用于VLM选择的多模态数据集，包含七个主流VLM在32,626个独特图像-文本查询上的输出。我们提出了ARMS，一个用于VLM选择的路由器。ARMS通过VLM配置增强输入信号，采用简单但有效的架构来改善查询和VLM能力的表示。为了提高ARMS对新VLM的适应性，我们提出了两种扩展训练策略：增量训练和独立训练。在分布内和分布外测试集上的实验结果证明了ARMS的有效性。特别是，使用我们的训练策略，ARMS（仅800M大小）能够适应更广泛的VLM空间，并击败像GPT-4o这样规模大数百倍的商业模型。我们的代码、模型和数据集已在匿名仓库中提供。
cs.AI / 92 / 2606.08974
Diverse Thinking Schemata Elicit Better Reasoning in Large Language Models
多样化思维模式促进大型语言模型更好的推理能力
Abstract
Large reasoning models (LRMs) have attracted increasing attention for their ability to solve complex mathematical problems by generating extended reasoning chains. In this work, we focus on two critical yet underexplored aspects of the reasoning process: reasoning transitions capturing the distinct transitions between reasoning steps and answer candidates reflecting the variety of solution paths produced by the model. We collectively define these two aspects as thinking schemata. We observe a correlation between the diversity of thinking schemata and model performance, which motivates us to enhance diversity as a means to further improve reasoning potential. To this end, we propose Diverse Schemata Policy Optimization (DiScO), a framework that first endows the model with schemata awareness, then encourages diversity through reinforcement learning, and further promotes diverse reasoning at inference time. Experiments on multiple mathematical reasoning benchmarks demonstrate that DiScO consistently outperforms standard group relative policy optimization. Beyond accuracy, human-annotated analyses show that DiScO substantially improves the model's ability to recover from erroneous initial attempts. Overall, our work suggests the important role that diversity of the thinking schemata plays and points to scaling along the diversity dimension as a promising research direction.
Chinese Translation
大型推理模型（LRMs）因其通过生成扩展推理链解决复杂数学问题的能力而受到越来越多的关注。在本研究中，我们关注推理过程中的两个关键但尚未充分探索的方面：推理转变，捕捉推理步骤之间的不同转变，以及答案候选，反映模型生成的多样化解决路径。我们将这两个方面统称为思维模式。我们观察到思维模式的多样性与模型性能之间存在相关性，这促使我们通过增强多样性来进一步提升推理潜力。为此，我们提出了多样化模式策略优化（Diverse Schemata Policy Optimization, DiScO），该框架首先赋予模型模式意识，然后通过强化学习鼓励多样性，并在推理时进一步促进多样化推理。在多个数学推理基准上的实验表明，DiScO始终优于标准的组相对策略优化。除了准确性外，人类标注的分析显示，DiScO显著提高了模型从错误初始尝试中恢复的能力。总体而言，我们的工作表明思维模式的多样性扮演着重要角色，并指出在多样性维度上扩展作为一个有前景的研究方向。
cs.AI / 93 / 2606.08976
RTL-BenchLS: A Large-Scale Benchmark for RTL Reasoning and Generation with Large Language Models
RTL-BenchLS：用于RTL推理和生成的大规模基准测试，基于大型语言模型
Abstract
LLM-based RTL generation and reasoning is a promising direction for hardware design automation. High-quality benchmarks are critical infrastructure for tracking progress in this direction. However, existing RTL benchmarks face inherent limitations in both scale and task scope. The designs they cover are typically small and simple, and the tasks focus almost entirely on specification-to-RTL generation. Frontier models' performance already saturates on the existing benchmarks. Scaling these benchmarks up is fundamentally difficult because aligned labels are required for benchmarking, such as specifications and testbenches. Such aligned high-quality data are rarely available for real-world designs. We introduce RTL-BenchLS, a large-scale benchmark addressing both limitations above. It contains over 10,000 formally verified Verilog designs, covering substantially larger and more complex designs than existing benchmarks. Beyond specification-to-RTL generation, we propose three novel tasks that jointly evaluate reasoning and generation: round-trip reasoning, masked-content reasoning, and repository-issue reasoning. The first two are self-supervised, which directly resolves the scaling bottleneck. All tasks are verified through formal equivalence checking without any manual testbenches. We evaluate eight LLMs on RTL-BenchLS. Even the best model reaches only 23% on natural-language round-trip reasoning, 28% on masked-content reasoning, and 12% on repository-issue fixing. RTL-BenchLS is substantially more challenging than existing benchmarks. It leaves ample room for future improvement and offers guidance for developing LLM-based methods for hardware design.
Chinese Translation
基于大型语言模型（LLM）的RTL生成和推理是硬件设计自动化的一个有前景的方向。高质量的基准测试是跟踪这一方向进展的重要基础设施。然而，现有的RTL基准测试在规模和任务范围上都存在固有的局限性。它们所涵盖的设计通常较小且简单，任务几乎完全集中在规范到RTL的生成上。前沿模型在现有基准测试上的性能已经饱和。由于基准测试需要对齐的标签，如规范和测试平台，扩大这些基准测试的规模在根本上是困难的。这种对齐的高质量数据在实际设计中很少可用。我们提出了RTL-BenchLS，这是一个针对上述两种局限性的的大规模基准测试。它包含超过10,000个经过形式验证的Verilog设计，涵盖的设计规模和复杂性远超现有基准测试。除了规范到RTL的生成外，我们还提出了三项新任务，联合评估推理和生成：往返推理、掩码内容推理和仓库问题推理。前两项是自监督的，直接解决了规模瓶颈。所有任务均通过形式等价检查进行验证，而无需任何手动测试平台。我们在RTL-BenchLS上评估了八个LLM。即使是最佳模型，在自然语言往返推理上仅达到23%，在掩码内容推理上达到28%，在仓库问题修复上达到12%。RTL-BenchLS的挑战性远超现有基准测试，为未来的改进提供了广阔的空间，并为开发基于LLM的硬件设计方法提供了指导。
cs.AI / 94 / 2606.08982
Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care
Baichuan-M4：一种用于持续护理的临床级医疗代理系统
Yang, Aiyuan, Dou, Chengfeng, Pan, Da, Wang, Dian, Yang, Fan, Deng, Fei, Li, Fei, Ai, Guangwei, Liu, Hui, Zhang, Hongda, Tai, Jinyang, Lu, Kai, Liu, Lijun, Chen, Linwei, Li, Linyu, Guo, Meiqing, Guo, Peidong, Ju, Qiang, Xin, Rihui, Wang, Shuai, Ma, XinKai, Chen, Xudong, Mo, Yichuan, Piao, Canbin, Pan, Leyi, Luo, Yihe, Wang, Zian
Abstract
Baichuan-M4 is Baichuan Intelligence's clinical-grade medical large model, designed for \emph{continuous care} rather than single-turn medical question answering. It is built as a coordinated medical agent system around three pillars: \textbf{Baichuan-Harness}, a unified runtime that keeps reinforcement-learning training and real-world deployment consistent while enforcing action constraints, tool use, long-term patient memory, and multi-agent coordination; a \textbf{core reasoning model} trained with a continuous-care reinforcement-learning framework that integrates span-level reward modeling (SPAR++), reasoning-path compression, curriculum learning, and stabilized policy optimization; and a \textbf{clinical tool layer} for patient-memory management, authoritative evidence-based retrieval, and multimodal medical perception across documents, X-rays, and dermatology. On a cross-dimensional medical evaluation suite, Baichuan-M4 attains leading results in static medical knowledge and safety, dynamic OSCE-style consultation, long-context clinical memory, evidence-based retrieval, medical document OCR, and multimodal image understanding, while lowering the hallucination rate to 3.3\%.
Chinese Translation
Baichuan-M4 是 Baichuan Intelligence 的临床级医疗大模型，旨在实现 extit{持续护理} 而非单轮医疗问答。该系统围绕三个支柱构建，形成一个协调的医疗代理系统： extbf{Baichuan-Harness}，一个统一的运行时环境，确保强化学习训练与现实世界部署的一致性，同时强制执行行动约束、工具使用、长期患者记忆和多代理协调；一个 extbf{核心推理模型}，采用持续护理强化学习框架进行训练，集成了跨度级奖励建模（SPAR++）、推理路径压缩、课程学习和稳定的策略优化；以及一个 extbf{临床工具层}，用于患者记忆管理、权威的基于证据的检索，以及跨文档、X光片和皮肤病学的多模态医疗感知。在跨维度医疗评估套件中，Baichuan-M4 在静态医疗知识和安全性、动态 OSCE 风格咨询、长上下文临床记忆、基于证据的检索、医疗文档 OCR 和多模态图像理解等方面取得了领先的结果，同时将幻觉率降低至 3.3.
cs.AI / 95 / 2606.08998
The Token Not Taken: Sampling, State, and the Variability of AI Agent Outputs
未被采纳的令牌：采样、状态与人工智能代理输出的变异性
Abstract
Agentic AI systems can behave differently across runs: the same request may produce a different plan, a different tool call, a different code edit, or a different final answer. Such variability arises from several layers that are often conflated. A foundation model is a large pretrained model, usually adaptable to many downstream tasks, that maps an input context to predictions over outputs. In many current agents, that model is embedded in an orchestration loop that plans, calls tools, observes results, and updates state. One explicit intrinsic source of variability in such systems is token generation: the model computes scores over possible next tokens, the scores are converted into probabilities, and a decoder may sample tokens using a pseudo-random number generator. A small sampled token difference can then propagate upward into a different tool call, code path, search query, or agent state. Other sources of variability are extrinsic to token sampling, including changing environments, live data, serving infrastructure, batch effects, and numerical details. By separating these layers, the manuscript clarifies what it means to call agentic AI systems stochastic, when such variability can be reproduced under matched conditions, and why deterministic execution need not imply identical behavior in deployed settings.
Chinese Translation
代理型人工智能系统在不同运行中可能表现出不同的行为：相同的请求可能产生不同的计划、不同的工具调用、不同的代码编辑或不同的最终答案。这种变异性源于几个通常被混淆的层次。基础模型是一个大型的预训练模型，通常可以适应许多下游任务，它将输入上下文映射到输出的预测。在许多当前的代理中，该模型嵌入在一个协调循环中，该循环进行计划、调用工具、观察结果并更新状态。这类系统中一个明确的内在变异性来源是令牌生成：模型计算可能下一个令牌的分数，这些分数被转换为概率，解码器可能使用伪随机数生成器来采样令牌。一个小的采样令牌差异可以向上传播，导致不同的工具调用、代码路径、搜索查询或代理状态。其他变异性来源则是外在于令牌采样的，包括变化的环境、实时数据、服务基础设施、批处理效应和数值细节。通过分离这些层次，本文阐明了称代理型人工智能系统为随机的含义，以及在匹配条件下何时可以重现这种变异性，并解释了为何确定性执行并不意味着在部署环境中表现出相同的行为。
cs.AI / 96 / 2606.09004
LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)
LATTEArena：一种基于LLM的表格特征工程评估框架（扩展版）
Abstract
Feature engineering remains essential for tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for automating this process, giving rise to LLM-powered AuTomated Tabular feature Engineering (LATTE). However, the absence of standardized platforms prevents fair, cost-aware comparisons. Furthermore, complex methodological designs obscure the specific contributions of individual components; for example, although LFG integrates Tree-of-Thought, few-shot demonstrations, Monte Carlo Tree Search, and natural language generation, the isolated impact of each technique's competitive edge remains unquantified. To address these challenges, we introduce LATTEArena, the first competitive evaluation framework featuring: (1) a six-dimensional taxonomy decomposing 15 representative methods into reusable components; (2) a standardized modular arena for controlled comparison; (3) multi-dimensional assessments covering performance, cost, and robustness; and (4) component-level ablation quantifying each technique's competitive edge. Through extensive evaluations, we reveal 16 key findings, including: (1) Tree-of-Thought with Monte Carlo Tree Search achieves optimal cost-effectiveness; (2) RPN and Code output formats dominate classification and regression tasks, respectively. We publicly release the modular framework and over 4000 execution logs, enabling researchers to seamlessly pit new techniques against existing ones and advance LATTE.
Chinese Translation
特征工程在表格数据分析中仍然至关重要，而大型语言模型（LLMs）已成为自动化这一过程的有前景的范式，催生了基于LLM的自动化表格特征工程（LATTE）。然而，缺乏标准化的平台阻碍了公平且考虑成本的比较。此外，复杂的方法设计模糊了各个组件的具体贡献；例如，尽管LFG集成了思维树（Tree-of-Thought）、少量示范（few-shot demonstrations）、蒙特卡洛树搜索（Monte Carlo Tree Search）和自然语言生成（natural language generation），但每种技术竞争优势的孤立影响仍未量化。为了解决这些挑战，我们提出了LATTEArena，这是第一个竞争性评估框架，具有：（1）一个六维分类法，将15种代表性方法分解为可重用组件；（2）一个标准化的模块化竞技场，用于控制比较；（3）涵盖性能、成本和鲁棒性的多维评估；以及（4）组件级消融，量化每种技术的竞争优势。通过广泛的评估，我们揭示了16个关键发现，包括：（1）思维树与蒙特卡洛树搜索结合实现了最佳的成本效益；（2）RPN和代码输出格式分别主导分类和回归任务。我们公开发布了模块化框架和超过4000个执行日志，使研究人员能够无缝地将新技术与现有技术进行比较，推动LATTE的发展。
cs.AI / 97 / 2606.09037
A Multi-Agent System for IPMSM Design Optimization via an FEA-AI Hybrid Approach
基于有限元分析与人工智能混合方法的内置永磁同步电机设计优化多智能体系统
Abstract
Interior permanent magnet synchronous motor (IPMSM) design requires balancing conflicting objectives and multi-physics constraints, while modern optimization workflows face three bottlenecks: manual problem setup, high finite element analysis (FEA) cost, and unreliable surrogate-based search in sparse or out-of-distribution regions. To address these limitations, we propose an end-to-end automated IPMSM design optimization framework that integrates retrieval-augmented generation (RAG) for structured problem definition with an uncertainty-aware FEA-AI hybrid optimization pipeline. A Design agent, connected to a motor textbook through RAG, provides domain-knowledge-based options and engineering tips, and compiles an optimization card and a design-of-experiments plan for AI-model training. A Training agent automates electromagnetic FEA, records geometry-validation and solver-failure logs, analyzes failed geometries using ANOVA-based data analysis and LLM reasoning, and invokes a Design Sampling agent to redefine the design space and generate additional samples. An Optimization agent performs GA-based search with uncertainty-driven switching: low-uncertainty candidates are evaluated by AI-surrogate inference, whereas high-uncertainty and reliability-critical Pareto-front or top-K candidates are corrected by high-fidelity FEA and reused for iterative retraining. The framework converts manual, experience-dependent configuration into a reproducible workflow that balances computational cost and prediction reliability. Experimental results under a matched high-fidelity FEA budget show that the proposed hybrid approach achieves better objective performance while maintaining low and further reducible predictive uncertainty, outperforming FEA-only search, which is limited by early budget exhaustion, and AI-only search, which converges to a low-confidence optimum.
Chinese Translation
内置永磁同步电机（IPMSM）设计需要平衡相互冲突的目标和多物理约束，而现代优化工作流程面临三个瓶颈：手动问题设置、高有限元分析（FEA）成本以及在稀疏或分布外区域中不可靠的基于代理的搜索。为了解决这些局限性，我们提出了一种端到端自动化的IPMSM设计优化框架，该框架集成了增强检索生成（RAG）用于结构化问题定义，以及一种具有不确定性意识的FEA-AI混合优化管道。设计代理通过RAG与电机教科书连接，提供基于领域知识的选项和工程建议，并编制优化卡和实验设计计划以供AI模型训练。训练代理自动化电磁FEA，记录几何验证和求解器失败日志，利用基于方差分析（ANOVA）数据分析和大语言模型（LLM）推理分析失败的几何形状，并调用设计采样代理重新定义设计空间并生成额外样本。优化代理执行基于遗传算法（GA）的搜索，采用不确定性驱动的切换：低不确定性候选通过AI代理推断进行评估，而高不确定性和可靠性关键的Pareto前沿或前K候选则通过高保真FEA进行修正，并用于迭代再训练。该框架将手动、依赖经验的配置转化为可重复的工作流程，平衡计算成本和预测可靠性。在匹配的高保真FEA预算下的实验结果表明，所提出的混合方法在保持低且可进一步降低的预测不确定性的同时，实现了更好的目标性能，优于仅基于FEA的搜索（受限于早期预算耗尽）和仅基于AI的搜索（收敛于低置信度的最优解）。
cs.AI / 98 / 2606.09038
Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs
个性化与安全性相遇：个性化大语言模型中的机制、风险与缓解措施
Abstract
Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories. However, the mechanisms that enable personalization also expand the safety landscape in ways not systematically addressed by existing literature. Existing reviews typically focus either on personalization or safety, leaving their intersection largely unexplored. We present the first comprehensive, safety-aware review of personalized LLMs. We organize personalization along three dimensions-user representation, personalization paradigm, and evaluation-and introduce a unified taxonomy of safety risks. At the representation level, we analyze risks arising from diverse user representations. Across mainstream personalization paradigms, we delineate vulnerabilities inherent to prompting, retrieval augmentation, parameter fine-tuning, reinforcement learning, Mixture-of-Experts (MoE), pruning, agent frameworks, and multimodal personalization, and synthesize mitigation strategies across the model lifecycle. Beyond these fine-grained risks, we characterize paradigm-agnostic safety risks arising from personalized adaptation. We further summarize personalized datasets and evaluation methodologies. Through a case study of OpenClaw, we analyze deployment trends in personalized agent ecosystems. Our analysis reveals three structural inadequacies in existing research: safety is evaluated as user-invariant rather than relational, personalization techniques are analyzed in isolation rather than in composition, and evaluation frameworks cannot capture emergent long-term risks. By jointly examining personalized representations, personalization paradigms, safety risks, defenses, and evaluation methods, we provide a unified framework for developing safe personalized LLMs and highlight key directions for future research.
Chinese Translation
大型语言模型（LLMs）通过适应用户的偏好、上下文和长期历史，实现了日益个性化的互动。然而，促成个性化的机制也以现有文献未系统性解决的方式扩展了安全性领域。现有的综述通常专注于个性化或安全性，导致两者的交集在很大程度上未被探索。我们呈现了首个全面关注安全性的个性化LLM综述。我们从用户表示、个性化范式和评估三个维度组织个性化，并引入统一的安全风险分类法。在表示层面，我们分析了来自多样化用户表示的风险。在主流个性化范式中，我们划分了与提示、检索增强、参数微调、强化学习、专家混合模型（Mixture-of-Experts, MoE）、剪枝、代理框架和多模态个性化固有的脆弱性，并在模型生命周期中综合了缓解策略。超越这些细粒度风险，我们描述了源于个性化适应的范式无关的安全风险。我们还总结了个性化数据集和评估方法。通过对OpenClaw的案例研究，我们分析了个性化代理生态系统中的部署趋势。我们的分析揭示了现有研究中的三项结构性不足：安全性被评估为用户不变而非关系性，个性化技术被孤立分析而非组合分析，评估框架无法捕捉新出现的长期风险。通过共同考察个性化表示、个性化范式、安全风险、防御措施和评估方法，我们提供了一个开发安全个性化LLM的统一框架，并强调了未来研究的关键方向。
cs.AI / 99 / 2606.09039
Agent Economics: An Entropy-Controlled Pluralistic Alignment Framework for Preventing Artificial Hivemind in Autonomous Agents
代理经济学：一种熵控制的多元对齐框架以防止自主代理中的人工集体思维
Abstract
This study proposes the Behavioral Protocol Framework (BPF), an entropy-controlled pluralistic alignment framework designed to address two critical challenges in autonomous agent economies: the hivemind effect arising from excessive strategic convergence among agents and the lack of transparency in autonomous decision-making processes. The proposed BPF consists of three core modules: Mentalizing-based Social Intelligence (MbSI) grounded in Theory of Mind (ToM), Pluralistic Alignment (PA), and a Verifiable Execution Kernel (VEK). These modules are organically integrated within a closed-loop architecture that governs the entire lifecycle of agent behavior, from decision-making and execution to verification and feedback. To evaluate the proposed framework, a simulation environment implemented in Python and a Streamlit-based user interface will be developed. Through empirical experimentation, the study aims to examine whether the entropy-control mechanism of the PA module can effectively preserve strategic diversity among agents and mitigate collective convergence, while the VEK module provides a comprehensive and transparent audit trail of the decision-making process. The anticipated results are expected to demonstrate that the proposed framework can simultaneously enhance the stability, efficiency, and trustworthiness of autonomous agent economies. Consequently, this research offers a practical approach for developing robust, transparent, and accountable agent-native economic systems.
Chinese Translation
本研究提出了行为协议框架（Behavioral Protocol Framework, BPF），这是一种熵控制的多元对齐框架，旨在解决自主代理经济中的两个关键挑战：代理之间因过度战略趋同而产生的集体思维效应，以及自主决策过程中的透明度缺失。所提议的BPF由三个核心模块组成：基于心理化的社会智能（Mentalizing-based Social Intelligence, MbSI），基于心智理论（Theory of Mind, ToM）的多元对齐（Pluralistic Alignment, PA），以及可验证执行内核（Verifiable Execution Kernel, VEK）。这些模块在一个闭环架构中有机整合，管理代理行为的整个生命周期，从决策、执行到验证和反馈。为了评估所提框架，将开发一个基于Python的仿真环境和一个基于Streamlit的用户界面。通过实证实验，本研究旨在检验PA模块的熵控制机制是否能够有效保持代理之间的战略多样性并减轻集体趋同，同时VEK模块提供决策过程的全面透明审计轨迹。预期结果将表明，所提框架能够同时增强自主代理经济的稳定性、效率和可信度。因此，本研究为开发稳健、透明和负责任的代理本土经济系统提供了一种实用的方法。
cs.AI / 100 / 2606.09071
REFLECT: Intervention-Supported Error Attribution for Silent Failures in LLM Agent Traces
REFLECT：支持干预的错误归因方法用于大型语言模型代理的静默失败追踪
Abstract
Large language model (LLM) agents now solve complex tasks through long plan-and-execution traces, yet the ability to locate errors in a completed traces still lags far behind, especially in the \emph{silent failure} regime. Existing approaches predict suspect steps via classifiers or LLM judges, or recover correct answers via retry, but none feed the intervention outcome back to \emph{refine the attribution itself}. We propose \methodname, a method that closes this gap by diagnosing a candidate error step, testing it through controlled replay with a diagnosis-specific patch, and using the verified outcome flip as contrastive evidence to refine the final attribution. Across four localization benchmarks spanning multi-hop reasoning across domains, \methodname achieves the highest localization accuracy among same-auditor methods across all four benchmarks, with the largest gains on structured tool-use traces, while providing actionable localization even when ground-truth answers are unavailable.
Chinese Translation
大型语言模型（LLM）代理现在通过长时间的计划与执行追踪解决复杂任务，但在已完成追踪中定位错误的能力仍然远远滞后，尤其是在 extit{静默失败}的情况下。现有方法通过分类器或LLM评审者预测可疑步骤，或通过重试恢复正确答案，但没有一种方法将干预结果反馈用于 extit{优化归因本身}。我们提出了 extit{REFLECT}，一种通过诊断候选错误步骤、通过带有特定诊断补丁的控制重放进行测试，并利用经过验证的结果翻转作为对比证据来优化最终归因的方法。在跨越多个领域的四个定位基准测试中， extit{REFLECT}在所有四个基准测试中实现了同类审计方法中最高的定位准确率，在结构化工具使用追踪中获得了最大的提升，同时即使在真实答案不可用时也提供了可操作的定位信息。
cs.AI / 101 / 2606.09086
DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling
DynaOD：基于离散到连续时间语义建模的动态起讫流生成
Abstract
Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone, without relying on historical OD observations. A key challenge is to translate semantic temporal signals into temporally coherent OD patterns while preserving the inherent spatial heterogeneity of urban regions. We propose DynaOD, a semantic-driven framework that models temporal dynamics through two complementary perspectives: discrete directional trends that characterize qualitative shifts in urban activity patterns, and continuous temporal evolution that captures how such shifts unfold over time. By jointly encoding these temporal semantics, the framework constructs time-varying region representations that condition pretrained static OD generators in a lightweight and plug-and-play fashion. This modular design further supports scalable deployment and cross-city transferability. Extensive experiments on large-scale real-world datasets show that our method consistently outperforms representative baselines in both predictive accuracy and distributional fidelity. Code is publicly available at https://github.com/csjiezhao/DynaOD.
Chinese Translation
动态起讫（OD）流生成旨在仅通过时间上下文合成真实的移动动态，而不依赖于历史的OD观测。一个关键挑战是将语义时间信号转化为时间上连贯的OD模式，同时保持城市区域固有的空间异质性。我们提出了DynaOD，一个以语义驱动的框架，通过两个互补的视角建模时间动态：离散方向趋势，表征城市活动模式的定性变化，以及连续时间演变，捕捉这些变化如何随时间展开。通过联合编码这些时间语义，该框架构建了时间变化的区域表示，以轻量级和即插即用的方式调节预训练的静态OD生成器。这种模块化设计进一步支持可扩展部署和跨城市迁移。在大规模真实世界数据集上的广泛实验表明，我们的方法在预测准确性和分布保真度方面始终优于代表性基线。代码可在 https://github.com/csjiezhao/DynaOD 获取。
cs.AI / 102 / 2606.09105
Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts
Graph2Idea：基于图结构上下文的检索增强科学创意生成
Abstract
Generating novel, feasible, and high-quality research ideas is an important yet challenging task in scientific discovery.Recent Large Language Model (LLM)-based methods often ground idea generation with retrieved literature, but the retrieved evidence is usually provided as flat text, such as titles, abstracts, or summaries. Such flat contexts may contain redundant or weakly relevant information, while making cross-paper relations among problems, methods, mechanisms, and findings difficult to identify and trace.To address this challenge, we propose Graph2Idea, a knowledge graph-guided framework for retrieval-augmented scientific idea generation.Graph2Idea first retrieves papers according to the input topic, transforms them into structured knowledge triples, and dynamically constructs a target-centered knowledge graph to make literature relations explicit.It then extracts compact graph-derived contexts that retain target-relevant relational evidence while reducing noisy textual input.Based on these contexts, a two-stage generation process first identifies promising research directions and then guides the LLM to synthesize candidate ideas from graph-grounded evidence.Experiments on a scientific idea generation benchmark show that Graph2Idea outperforms representative baselines under the automatic evaluation protocol.Compared with the strongest baseline scores, it improves Novelty from 0.45 to 0.52, Quality from 0.24 to 0.29, and Feasibility from 0.22 to 0.28.These results suggest that graph-structured evidence helps LLMs generate research ideas through more explicit, compact, and traceable recombination of prior scientific knowledge.
Chinese Translation
生成新颖、可行且高质量的研究创意是科学发现中一项重要而具有挑战性的任务。近期基于大型语言模型（LLM）的方法通常通过检索文献来支撑创意生成，但检索到的证据通常以平面文本的形式提供，如标题、摘要或总结。这种平面上下文可能包含冗余或相关性较弱的信息，同时使得跨论文之间的问题、方法、机制和发现的关系难以识别和追踪。为了解决这一挑战，我们提出了Graph2Idea，一个基于知识图谱的检索增强科学创意生成框架。Graph2Idea首先根据输入主题检索论文，将其转化为结构化的知识三元组，并动态构建一个以目标为中心的知识图谱，以明确文献之间的关系。然后，它提取紧凑的图谱衍生上下文，保留与目标相关的关系证据，同时减少噪声文本输入。基于这些上下文，采用两阶段生成过程，首先识别有前景的研究方向，然后引导LLM从图谱基础的证据中综合候选创意。在科学创意生成基准上的实验表明，Graph2Idea在自动评估协议下优于代表性基线。与最强基线分数相比，它在新颖性上从0.45提高到0.52，在质量上从0.24提高到0.29，在可行性上从0.22提高到0.28。这些结果表明，图结构证据通过更明确、紧凑和可追踪的方式重组先前的科学知识，帮助LLM生成研究创意。
cs.AI / 103 / 2606.09118
ComplexConstraints and Beyond: Expert Rubrics for RLVR
复杂约束及其超越：用于RLVR的专家评分标准
Abstract
As LLM capabilities advance rapidly, the evaluation methods used to assess them increasingly lag behind. Traditional benchmarks relied on programmatic verification of narrow, surface-level constraints, but real-world instruction following and agentic tasks demand assessment of nuanced, context-dependent behaviors that resist simple scripted checks. We present a systematic analysis of expert-curated rubric-based evaluation as an alternative paradigm, drawing on empirical evidence from two domains: complex instruction following and enterprise agentic tasks. We first articulate five design principles for constructing high-quality rubrics, including Maximum Viable Atomicity, intent-aware criterion design, and iterative LLM-judge calibration. To validate these principles, we introduce ComplexConstraints, a new expert-curated instruction-following dataset in which each prompt is paired with 10-40 atomic rubric criteria. We demonstrate that these expert rubrics are not only better evaluation instruments but also highly effective training signals: training on approximately 1,000 ComplexConstraints examples yields +15.5% improvement for a 4B-parameter model and +12.2% for a 235B-parameter model on instruction following, while single-epoch RL training on a rubric-graded enterprise environment produces gains that transfer to out-of-distribution benchmarks the model was never trained on (+4.5% BFCL, +7.4% Tau2-Bench, +6.8% Tool-Decathlon). Our findings establish that expert-authored rubrics improve both the measurement and the development of frontier LLM capabilities, serving as effective evaluation and RL training signals.
Chinese Translation
随着大型语言模型（LLM）能力的快速提升，用于评估它们的方法却日益滞后。传统基准依赖于对狭窄、表层约束的程序化验证，但现实世界中的指令遵循和自主任务要求评估细致、依赖上下文的行为，这些行为抵抗简单的脚本检查。我们提出了一种系统分析专家策划的基于评分标准的评估作为替代范式，并借助于来自两个领域的实证证据：复杂指令遵循和企业自主任务。我们首先阐明了构建高质量评分标准的五个设计原则，包括最大可行原子性、意图感知的标准设计和迭代的LLM-评估者校准。为了验证这些原则，我们引入了ComplexConstraints，一个新的专家策划的指令遵循数据集，其中每个提示都配有10-40个原子评分标准。我们证明这些专家评分标准不仅是更好的评估工具，而且也是非常有效的训练信号：在约1000个ComplexConstraints示例上进行训练，4B参数模型在指令遵循任务上提高了15.5%，而235B参数模型提高了12.2%；同时，在评分标准评估的企业环境中进行单轮强化学习训练，产生的收益能够转移到模型从未训练过的分布外基准上（+4.5% BFCL，+7.4% Tau2-Bench，+6.8% Tool-Decathlon）。我们的研究结果表明，专家撰写的评分标准不仅改善了前沿LLM能力的测量，还促进了其发展，成为有效的评估和强化学习训练信号。
cs.AI / 104 / 2606.09124
A Regret Minimization Framework on Preference Learning in Large Language Models
大语言模型中的偏好学习的遗憾最小化框架
Abstract
Reinforcement learning with verifiable rewards (RLVR) has enabled progress on reasoning-intensive tasks by relying on task-specific verifiers that provide automated correctness signals. However, many realistic language tasks are difficult to equip with reliable verifiers, motivating a growing reliance on reinforcement learning from human feedback (RLHF). In this setting, we argue that a closer examination of how human feedback should be interpreted is essential. We introduce Regret-based Preference Optimization $(\textbf{RePO})$, which reframes RLHF through $\textit{regret minimization}$ rather than reward maximization. Human preferences are often shaped by $\textit{prospective}$ anticipation of outcomes and $\textit{counterfactual}$ comparisons to alternative behaviors, rather than by immediate, outcome-independent utility. $\textbf{RePO}$ captures this structure by modeling preferences as behavior-conditioned assessments of relative suboptimality. Experiments on mathematical reasoning benchmarks and human preference datasets demonstrate consistent performance gains, indicating that $\textbf{RePO}$ is an effective and human-aligned approach for training large language models.
Chinese Translation
可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）通过依赖于提供自动正确性信号的任务特定验证器，推动了推理密集型任务的进展。然而，许多现实语言任务难以配备可靠的验证器，这促使人们日益依赖于来自人类反馈的强化学习（Reinforcement Learning from Human Feedback, RLHF）。在这种背景下，我们认为更深入地审视人类反馈应如何被解读是至关重要的。我们引入了基于遗憾的偏好优化（Regret-based Preference Optimization, RePO），该方法通过遗憾最小化而非奖励最大化来重新框架RLHF。人类偏好往往受到对结果的前瞻性预期和对替代行为的反事实比较的影响，而非由即时的、与结果无关的效用所驱动。RePO通过将偏好建模为行为条件下的相对次优评估来捕捉这种结构。在数学推理基准和人类偏好数据集上的实验表明，表现持续提升，表明RePO是一种有效且与人类一致的训练大语言模型的方法。
cs.AI / 105 / 2606.09131
Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation
后层融合足够：视觉饱和下的双路径视觉令牌路由用于多模态大型语言模型
Abstract
Multimodal large language models (MLLMs) commonly inherit the deep, symmetric Transformer backbone designed for unimodal text modeling, and apply the same computation uniformly to image and language tokens. This design overlooks a key modality asymmetry: image and text tokens differ substantially in information density, redundancy, and required reasoning depth. Through a layer-wise analysis of LLaVA-1.5, we observe that vision tokens tend to saturate in the middle layers. Specifically, text-to-image attention decreases from 0.68 at layer 0 to 0.07 by layer 4, and stabilizes near 0.04 after layer 18, whereas text tokens continue to benefit from deep semantic processing. These findings suggest a mismatch between architectural symmetry and depth-asynchronous modality evolution, resulting in redundant visual computation and possible drift in perceptual representations during deep task-specific adaptation. Motivated by this, we propose Dual-Path Vision Token Routing (DPVR), a modality-asymmetric routing framework for efficient MLLMs. Its core instantiation, DPVR-LF (Late-Layer Fusion), routes vision tokens at the saturation point into a one-layer trainable side branch, runs a thirteen-layer text-only forward that skips image positions in the deep stack, and re-fuses the visual and textual streams only at the final layer. With approximately 3% trainable parameters, DPVR-LF preserves competitive multimodal performance on standard benchmarks while reducing visual computation in the deep Transformer stack. The results challenge the conventional assumption that vision tokens must traverse all deep language-model layers, and indicate that a single late fusion layer can be sufficient for maintaining strong perceptual competence in LLaVA-style MLLMs.
Chinese Translation
多模态大型语言模型（MLLMs）通常继承了为单模态文本建模设计的深度对称Transformer骨架，并对图像和语言令牌均匀应用相同的计算。这一设计忽视了一个关键的模态不对称性：图像和文本令牌在信息密度、冗余性和所需推理深度上存在显著差异。通过对LLaVA-1.5的逐层分析，我们观察到视觉令牌在中间层趋于饱和。具体而言，文本到图像的注意力从第0层的0.68下降到第4层的0.07，并在第18层后稳定在0.04附近，而文本令牌则继续受益于深层语义处理。这些发现表明，架构对称性与深度异步模态演变之间存在不匹配，导致冗余的视觉计算以及在深层任务特定适应过程中感知表征的可能漂移。基于此，我们提出了双路径视觉令牌路由（DPVR），一种用于高效MLLMs的模态不对称路由框架。其核心实例DPVR-LF（后层融合）在饱和点将视觉令牌路由到一个可训练的单层侧支路，进行跳过图像位置的十三层文本专用前向计算，并仅在最后一层重新融合视觉和文本流。DPVR-LF具有约3%的可训练参数，在标准基准上保持竞争性的多模态性能，同时减少了深层Transformer堆栈中的视觉计算。结果挑战了视觉令牌必须遍历所有深层语言模型层的传统假设，并表明单一的后融合层足以在LLaVA风格的MLLMs中维持强大的感知能力。
cs.AI / 106 / 2606.09132
Vision Language Model Helps Private Information De-Identification in Vision Data
视觉语言模型助力视觉数据中的私人信息去标识化
Abstract
Visual Language Models (VLMs) have gained significant popularity due to their remarkable ability. While various methods exist to enhance privacy in text-based applications, privacy risks associated with visual inputs remain largely overlooked such as Protected Health Information (PHI) in medical images. To tackle this problem, two key tasks: accurately localizing sensitive text and processing it to ensure privacy protection should be performed. To address this issue, we introduce VisShield (Vision Privacy Shield), an end-to-end framework designed to enhance the privacy awareness of VLMs. Our framework consists of two key components: a specialized instruction-tuning dataset OPTIC (Optical Privacy Text Instruction Collection) and a tailored training methodology. The dataset provides diverse privacy-oriented prompts that guide VLMs to perform targeted Optical Character Recognition (OCR) for precise localization of sensitive text, while the training strategy ensures effective adaptation of VLMs to privacy-preserving tasks. Specifically, our approach ensures that VLMs recognize privacy-sensitive text and output precise bounding boxes for detected entities, allowing for effective masking of sensitive information. Extensive experiments demonstrate that our framework significantly outperforms existing approaches in handling private information, paving the way for privacy-preserving applications in vision-language models. Our dataset and code can be found here.
Chinese Translation
视觉语言模型（VLMs）因其卓越的能力而获得了显著的关注。虽然在基于文本的应用中存在多种增强隐私的方法，但与视觉输入相关的隐私风险仍然在很大程度上被忽视，例如医学图像中的受保护健康信息（PHI）。为了解决这一问题，需要执行两个关键任务：准确定位敏感文本并处理其以确保隐私保护。为此，我们提出了VisShield（视觉隐私保护盾），这是一个旨在增强VLMs隐私意识的端到端框架。我们的框架由两个关键组件组成：一个专门的指令调优数据集OPTIC（光学隐私文本指令集合）和一个量身定制的训练方法。该数据集提供多样的隐私导向提示，指导VLMs进行针对性的光学字符识别（OCR），以精确定位敏感文本，而训练策略确保VLMs有效适应隐私保护任务。具体而言，我们的方法确保VLMs识别隐私敏感文本并输出检测到实体的精确边界框，从而有效遮蔽敏感信息。大量实验表明，我们的框架在处理私人信息方面显著优于现有方法，为视觉语言模型中的隐私保护应用铺平了道路。我们的数据集和代码可以在此找到。
cs.AI / 107 / 2606.09165
Reliable to Expressive: A Curriculum for Rubric-Following Safety Judges
从可靠到表现：一个遵循评分标准的安全评判者的课程
Abstract
Safety judges are increasingly deployed to evaluate model outputs against evolving criteria, yet recent meta-evaluation work shows they remain brittle under prompt and rubric variation, with false negative-rate swings of up to 0.24 reported for stylistic perturbations alone. We argue that safety judgment is fundamentally a rubric-following problem: a robust judge must apply the given evaluation criteria consistently across rubric formulations rather than memorize one specific template. We propose a training strategy that combines (i) instance-conditioned dynamic rubrics generated from prompt-response-label triples to expose the judge to the variability of evaluation criteria, and (ii) a reliable-to-expressive curriculum that begins with clean fixed-rubric supervision and progressively introduces noisier dynamic-rubric data. We evaluate on a single human-labeled set under three contrasting rubric prompts (HarmBench-style, ShieldGemma-style, and a domain-specific rubric). Our 12B curriculum judge achieves 94.12-94.88% accuracy across the three rubrics with a cross-rubric range of only 0.76, outperforming general-purpose LLMs, dedicated safety classifiers, and reasoning-oriented judges up to 30B in both peak accuracy and stability. An ablation shows that naively mixing dynamic rubrics into SFT increases cross rubric variance (1.44 -> 3.60); only the curriculum schedule recovers and improves on the fixed rubric baseline (variance 0.76).
Chinese Translation
安全评判者越来越多地被部署以评估模型输出与不断变化的标准之间的符合度，但最近的元评估工作显示，在提示和评分标准变化下，它们依然脆弱，仅在风格扰动的情况下就报告了高达0.24的假阴性率波动。我们认为，安全判断根本上是一个遵循评分标准的问题：一个稳健的评判者必须在不同的评分标准表述中一致地应用给定的评估标准，而不是仅仅记住一个特定的模板。我们提出了一种训练策略，结合了(i) 从提示-响应-标签三元组生成的实例条件动态评分标准，以使评判者接触到评估标准的变异性，以及(ii) 一个从清晰的固定评分标准监督开始并逐步引入更嘈杂的动态评分标准数据的可靠到表现的课程。我们在一个单一的人类标注数据集上进行了评估，使用了三种对比鲜明的评分标准提示（HarmBench风格、ShieldGemma风格和一个特定领域的评分标准）。我们的12B课程评判者在这三种评分标准下的准确率达到了94.12%-94.88%，跨评分标准的范围仅为0.76，超越了通用大型语言模型、专用安全分类器和高达30B的推理导向评判者，在峰值准确率和稳定性方面均表现优异。一项消融实验表明，简单地将动态评分标准混入监督微调（SFT）中会增加跨评分标准的方差（1.44 -> 3.60）；只有课程安排能够恢复并改善固定评分标准的基线（方差0.76）。
cs.AI / 108 / 2606.09169
IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation
IMUG-Bench：统一多模态模型在交错理解与生成上的基准测试
Abstract
In recent years, unified multimodal models (UMMs) have emerged to support both understanding and generation within a single framework. Mastering dynamic, multi-turn interleaved image-text dialogues is a crucial task for UMMs in real-world applications. However, existing benchmarks fail to evaluate this important task, as they are often limited to single-turn or static settings, and typically overlook exposure bias in multi-turn interactions. To bridge this gap, we propose IMUG-Bench, a comprehensive benchmark for multi-turn interleaved image-text dialogue of UMMs that jointly evaluates their understanding and generation capabilities. Our IMUG-Bench comprises three classes: Static Spatial, Temporal Causal, and Hybrid, covering 3,113 samples and 12,034 interaction turns. It also includes dynamic understanding questions, thereby supporting evaluation that better reflects real-world multi-turn interaction scenarios. Large-scale experiments on IMUG-Bench systematically evaluate mainstream open-source and closed-source UMMs, revealing their capability boundaries and failure modes, and uncovering pronounced exposure bias on the generation side in multi-turn interactions. We further explore several test-time scaling strategies, including Chain-of-Thought, Self-Verification, and Best-of-N Sampling, which effectively improve generation accuracy and mitigate exposure bias in generation tasks. These findings provide insights into enhancing the robustness and multi-turn interaction capability of future UMMs.
Chinese Translation
近年来，统一多模态模型（UMMs）应运而生，以支持在单一框架内的理解与生成。掌握动态的多轮交错图像-文本对话是UMMs在实际应用中的一项关键任务。然而，现有的基准测试未能评估这一重要任务，因为它们通常仅限于单轮或静态设置，并且通常忽视了多轮交互中的曝光偏差。为填补这一空白，我们提出了IMUG-Bench，这是一个针对UMMs的多轮交错图像-文本对话的综合基准，能够共同评估其理解与生成能力。我们的IMUG-Bench包含三类：静态空间（Static Spatial）、时间因果（Temporal Causal）和混合（Hybrid），涵盖了3,113个样本和12,034个交互轮次。它还包括动态理解问题，从而支持更好地反映现实世界多轮交互场景的评估。我们在IMUG-Bench上进行的大规模实验系统性地评估了主流的开源和闭源UMMs，揭示了它们的能力边界和失败模式，并在多轮交互中发现了生成侧显著的曝光偏差。我们进一步探索了几种测试时扩展策略，包括思维链（Chain-of-Thought）、自我验证（Self-Verification）和最佳采样（Best-of-N Sampling），这些策略有效提高了生成准确性并减轻了生成任务中的曝光偏差。这些发现为增强未来UMMs的鲁棒性和多轮交互能力提供了见解。
cs.AI / 109 / 2606.09198
MASS: Deep Research for Social Sciences with Memory-Augmented Social Simulation
MASS：基于记忆增强的社会模拟的社会科学深度研究
Abstract
Deep Research agents powered by Large Language Models (LLMs) have exhibited extraordinary potential in automated paper writing tasks. However, existing systems rely heavily on literature retrieval and synthesis through internet and local knowledge bases, often resulting research in lacking insight and creativity in social science. To address this issue, we propose "Memory-Augmented Social Simulation (MASS)", an innovative paradigm that leverages highly realistic and research-oriented social simulations to enhance the creativity and empirical founding of LLMs-generated research. Specifically, MASS integrates three core components: dynamic goal-path planning with multi-level social norm restraint to guide the simulation, a multi-disciplinary behavior dataset for agent memory cold-start, and a structured forgetting mechanism inspired by the Ebbinghaus curve. Together, these ensure simulation authenticity and provide a robust empirical foundation for generating innovative scholarly papers. Experimental results demonstrate the effectiveness of our method, showing a 6.81\% improvement in generation overall quality over foundation LLMs and 17.19\% gain in Insight over strong baselines.
Chinese Translation
由大型语言模型（LLMs）驱动的深度研究代理在自动化论文写作任务中展现了非凡的潜力。然而，现有系统在很大程度上依赖于通过互联网和本地知识库进行文献检索和综合，常常导致社会科学研究缺乏洞察力和创造性。为了解决这一问题，我们提出了“记忆增强社会模拟（MASS）”，这一创新范式利用高度真实且以研究为导向的社会模拟来增强LLMs生成研究的创造性和实证基础。具体而言，MASS整合了三个核心组件：具有多层次社会规范约束的动态目标路径规划以指导模拟，一个用于代理记忆冷启动的多学科行为数据集，以及受艾宾浩斯曲线启发的结构性遗忘机制。这些组件共同确保了模拟的真实性，并为生成创新学术论文提供了坚实的实证基础。实验结果表明我们的方法的有效性，生成整体质量比基础LLMs提高了6.81%，在洞察力方面比强基线提高了17.19%。
cs.AI / 110 / 2606.09311
FF-JEPA: Long-Horizon Planning in World Models with Latent Planners
FF-JEPA：在世界模型中使用潜在规划者进行长时间规划
Abstract
Joint Embedding Predictive Architectures (JEPAs) have shown promising world modeling capabilities, enabling planning in latent space by optimizing action trajectories using methods like the Cross-Entropy Method (CEM). These methods are, however, too computationally expensive and ineffective for long-horizon planning. Furthermore, these methods typically require an explicit image of the goal state, which is not always possible in real-world tasks. In this work, we tackle these limitations by proposing Forward-Forward-JEPA (FF-JEPA), a hierarchical approach leveraging two forward dynamics models. Alongside a standard action-conditioned forward model, we introduce an action-free latent planner that predicts the next subgoal given the current state. This approach removes the need for goal images and enables long-horizon planning by decomposing complex trajectories into a sequence of tractable, short-term optimization problems. Preliminary results on PushT demonstrate that FF-JEPA successfully overcomes flat world models' long-horizon collapse, highlighting this approach as a promising direction for goal-free planning.
Chinese Translation
联合嵌入预测架构（Joint Embedding Predictive Architectures, JEPA）展示了良好的世界建模能力，通过使用交叉熵方法（Cross-Entropy Method, CEM）优化行动轨迹来实现潜在空间中的规划。然而，这些方法在长时间规划中计算成本过高且效果不佳。此外，这些方法通常需要目标状态的明确图像，而在现实任务中这并不总是可行。在本研究中，我们通过提出前向前向JEPA（Forward-Forward-JEPA, FF-JEPA）来解决这些限制，这是一种利用两个前向动态模型的分层方法。除了标准的行动条件前向模型外，我们还引入了一种无行动的潜在规划者，该规划者在给定当前状态的情况下预测下一个子目标。这种方法消除了对目标图像的需求，并通过将复杂轨迹分解为一系列可处理的短期优化问题，从而实现了长时间规划。在PushT上的初步结果表明，FF-JEPA成功克服了平坦世界模型的长时间崩溃，突显了该方法作为无目标规划的有希望方向。
cs.AI / 111 / 2606.09316
Anything2Skill: Compiling External Knowledge into Reusable Skills for Agents
Anything2Skill：将外部知识编译为可重用技能以供智能体使用
Abstract
Retrieval-augmented generation (RAG) enables agents to access external knowledge at inference time, but it primarily retrieves fragmented declarative evidence, leaving agents to repeatedly infer task procedures from passages, manuals, examples, logs, or trajectories. This raises a fundamental question: can skills extracted from external knowledge bases be installed into an agent, enabling it to rapidly approximate domain expertise? In this paper, we propose Anything2Skill, a taxonomy-guided framework that compiles heterogeneous external knowledge into reusable, retrievable, and executable skills for agents. Given a corpus of knowledge records, \textsc{Anything2Skill} first decomposes each record into evidence windows and performs plan-and-expand skill extraction under a skill-tree prior. The extracted candidates are then converted into structured skill contracts that specify invocation conditions, contraindications, action moves, workflow steps, constraints, output specifications, supporting evidence, and confidence scores. To construct a deployable procedural memory, Anything2Skill manages the extracted skills in a persistent SkillBank through taxonomy-aware compilation, registry-level reconciliation, lifecycle tracking, versioned updates, and visible skill-tree projection. At inference time, agents retrieve both task-specific passages from the original knowledge base and relevant procedural skills from the SkillBank, allowing RAG to provide declarative evidence while compiled skills provide reusable procedural guidance. Experiments on qsv and GitHub-CLI show that Anything2Skill combined with RAG achieves 98.85\% and 94.10\% success rates, respectively, substantially outperforming RAG-only agents. These results suggest that compiling latent procedural knowledge into explicit skills is an effective way to extend retrieval-augmented agents from knowledge access toward capability reuse.
Chinese Translation
检索增强生成（RAG）使得智能体在推理时能够访问外部知识，但它主要检索的是碎片化的陈述性证据，导致智能体需要反复从段落、手册、示例、日志或轨迹中推断任务程序。这引发了一个根本性的问题：能否将从外部知识库中提取的技能安装到智能体中，使其能够快速接近领域专业知识？在本文中，我们提出了Anything2Skill，一个以分类法为指导的框架，将异构外部知识编译为可重用、可检索和可执行的技能供智能体使用。给定一组知识记录，Anything2Skill首先将每条记录分解为证据窗口，并在技能树先验下执行计划与扩展的技能提取。提取的候选技能随后被转换为结构化的技能合同，明确规定了调用条件、禁忌、行动步骤、工作流程步骤、约束、输出规范、支持证据和置信度分数。为了构建可部署的程序记忆，Anything2Skill通过分类法感知的编译、注册级别的协调、生命周期跟踪、版本更新和可视化技能树投影来管理提取的技能于持久的技能库（SkillBank）中。在推理时，智能体从原始知识库中检索特定任务的段落和从技能库中检索相关的程序技能，使得RAG能够提供陈述性证据，而编译的技能则提供可重用的程序指导。在qsv和GitHub-CLI上的实验表明，结合RAG的Anything2Skill分别达到了98.85%和94.10%的成功率，显著优于仅使用RAG的智能体。这些结果表明，将潜在的程序知识编译为显性技能是将检索增强智能体从知识访问扩展到能力重用的有效方法。
cs.AI / 112 / 2606.09323
TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders
TRL-Bench：标准化跨范式表格编码器的表示级评估
Abstract
Tabular encoders are usually evaluated inside task-specific end-to-end pipelines, so models from different training paradigms are difficult to compare directly even when they operate on similar tabular signals. We introduce TRL-Bench, a multi-granular tabular representation learning (TRL) benchmark that standardizes cross-paradigm representation-level evaluation: each encoder exports row-, column-, or table embeddings through its supported wrapper, and shared lightweight heads probe them across three suites: TRL-CTbench (column/table), TRL-Rbench (row), and TRL-DLTE (compositional Data-Lake Table Enrichment spanning all three granularities). To support this standardized setting, we release curated benchmark assets and task reformulations, including 50 OpenML tables with 123 verified targets, 16 row-pair linkage rewrites, and a 47,772-table DLTE lake derived from 1,379 parent tables. Across 20 models and 16 tasks, TRL-Bench shows that once downstream conditions are standardized, encoder quality is capability-specific rather than captured by a single leaderboard. In TRL-CTbench, generic text encoders often lead on tasks with strong surface-text signal, while tabular specialists win where their pretraining objective aligns with the task. In TRL-Rbench, within-table prediction and cross-table linkage favor different training regimes, with atomic linkage performance correlating strongly with the row-matching stage of DLTE pipelines. In TRL-DLTE, the strongest pipelines combine capability-matched specialists rather than reuse a single encoder, and top end-to-end quality depends on non-additive compositional fit rather than per-stage marginal rank alone. TRL-Bench provides a common protocol for measuring reusable signal in exported tabular representations under shared downstream conditions. Code and data: https://github.com/LOGO-CUHKSZ/TRL-Bench
Chinese Translation
表格编码器通常在特定任务的端到端管道中进行评估，因此即使在处理相似的表格信号时，不同训练范式的模型也难以直接比较。我们提出了TRL-Bench，一个多粒度的表格表示学习（TRL）基准，标准化跨范式的表示级评估：每个编码器通过其支持的包装器导出行、列或表嵌入，并通过三个套件进行轻量级共享头的探测：TRL-CTbench（列/表）、TRL-Rbench（行）和TRL-DLTE（跨越所有三种粒度的组合数据湖表增强）。为了支持这一标准化设置，我们发布了经过策划的基准资产和任务重构，包括50个OpenML表格及123个验证目标，16个行对链接重写，以及一个由1,379个父表衍生的47,772表的DLTE湖。在20个模型和16个任务中，TRL-Bench显示一旦下游条件标准化，编码器质量是特定能力的，而不是由单一排行榜捕捉。在TRL-CTbench中，通用文本编码器在具有强表面文本信号的任务中通常领先，而表格专家在其预训练目标与任务对齐的地方获胜。在TRL-Rbench中，表内预测和跨表链接偏向不同的训练机制，原子链接性能与DLTE管道的行匹配阶段强相关。在TRL-DLTE中，最强的管道结合了能力匹配的专家，而不是重复使用单一编码器，顶级端到端质量依赖于非加性组合适配，而不仅仅是每个阶段的边际排名。TRL-Bench为在共享下游条件下测量可重用信号的导出表格表示提供了一个共同协议。代码和数据：https://github.com/LOGO-CUHKSZ/TRL-Bench
cs.AI / 113 / 2606.09343
Leveraging Structural Constraints for Diffusion-based Neural TSP Solvers
利用结构约束优化基于扩散的神经旅行商问题求解器
Abstract
Neural combinatorial optimization has recently achieved strong results on the Euclidean Traveling Salesman Problem (TSP) using generative models such as diffusion and consistency models. State-ofthe-art approaches like FT2T combine fast consistency-based prediction with gradient-based inference time refinement. However, gradient search often incurs significant computational overhead and may not align with the discrete structure of feasible solutions. We introduce Projected Consistency Inference (PCI), a plug-and-play, retraining-free alternative that replaces gradient refinement with structure-aware projections: PCI decodes valid Hamiltonian tours from the consistency model output and applies a lightweight local search (e.g., 2-opt). PCI achieves an average optimality gap (OG) of 0.17% on TSP with 500 cities, and 0.31% on TSP with 1000 cities, outperforming FT2T best settings (OG 0.22% and 0.36%, respectively) while reducing the inference time up to 30 to 40%. PCI also exhibits lower variance and memory usage, and can surpass classical heuristics such as LKH3 in rapid solution generation. Our results demonstrate that structure-aware inference time operations provide a practical and principled path for neural TSP solvers, complementing training time objectives.
Chinese Translation
近年来，神经组合优化在欧几里得旅行商问题（TSP）上取得了显著成果，采用了扩散模型和一致性模型等生成模型。最先进的方法如FT2T结合了快速的一致性预测与基于梯度的推理时间优化。然而，梯度搜索通常会带来显著的计算开销，并且可能与可行解的离散结构不一致。我们提出了投影一致性推理（Projected Consistency Inference, PCI），这是一种即插即用、无需重新训练的替代方案，使用结构感知的投影替代梯度优化：PCI从一致性模型的输出中解码有效的哈密顿巡回路径，并应用轻量级局部搜索（例如，2-opt）。在500个城市的TSP中，PCI实现了平均最优性差距（Optimality Gap, OG）为0.17%，在1000个城市的TSP中为0.31%，超越了FT2T的最佳设置（OG分别为0.22%和0.36%），同时将推理时间减少了30%到40%。PCI还表现出较低的方差和内存使用，并且在快速解生成方面能够超越经典启发式算法如LKH3。我们的结果表明，结构感知的推理时间操作为神经TSP求解器提供了一条实用且有原则的路径，补充了训练时间目标。
cs.AI / 114 / 2606.09365
Experience Makes Skillful: Enabling Generalizable Medical Agent Reasoning via Self-Evolving Skill Memory
经验造就技能：通过自我演化技能记忆实现可推广的医疗代理推理
Abstract
Medical agent systems are increasingly expected to support interactive clinical decision making rather than only static question answering. In such settings, effective agents must reuse prior experience across evolving cases, yet existing memory mechanisms often retain raw historical traces that are redundant, noisy, and difficult to govern. More importantly, they rarely distinguish which memories are truly useful for future reasoning. This limits their ability to accumulate compact and reliable experience for long-horizon clinical reasoning. To close this gap, we propose SkeMex, a post-deployment self-evolution framework that improves medical agents through a skill-based memory without updating model weights. SkeMex distills informative interaction trajectories into structured skills that encode reusable procedural knowledge, and organizes them into a multi-branch repository spanning general, task-specific, and action-level experience. To determine which memories should be reused and retained, SkeMex estimates context-dependent utility from environment feedback and uses it to guide value-aware retrieval and repository governance. A closed-loop ``Read--Write--Assess--Govern" lifecycle further supports continual evolution by writing new skills, updating utilities, promoting useful memories, and removing harmful entries. Experiments across diverse clinical tasks show that SkeMex consistently outperforms representative memory-based agents in both offline and online settings. It also generalizes across model backbones and supports transferable skill memory. All data and code will be released publicly.
Chinese Translation
医疗代理系统越来越被期望支持互动式临床决策，而不仅仅是静态问答。在这种环境中，有效的代理必须在不断变化的案例中重用先前的经验，然而现有的记忆机制往往保留冗余、嘈杂且难以管理的原始历史痕迹。更重要的是，它们很少区分哪些记忆对未来推理真正有用。这限制了它们在长期临床推理中积累紧凑且可靠经验的能力。为了解决这一问题，我们提出了SkeMex，一个后期部署的自我演化框架，通过基于技能的记忆来改进医疗代理，而无需更新模型权重。SkeMex将信息丰富的交互轨迹提炼为结构化技能，这些技能编码了可重用的过程知识，并将其组织成一个涵盖一般、任务特定和行动层面经验的多分支库。为了确定哪些记忆应该被重用和保留，SkeMex根据环境反馈估计上下文相关的效用，并利用这些信息指导价值感知的检索和库的管理。一个闭环的“读--写--评估--管理”生命周期进一步支持通过编写新技能、更新效用、促进有用记忆和移除有害条目来实现持续演化。针对多种临床任务的实验表明，SkeMex在离线和在线环境中均持续优于代表性的基于记忆的代理。它还在模型骨干上具有良好的泛化能力，并支持可转移的技能记忆。所有数据和代码将公开发布。
cs.AI / 115 / 2606.09371
Capability-Aligned Hierarchical Learning for Tool-Augmented LLMs
能力对齐的层次学习用于工具增强的大型语言模型
Abstract
Tool learning enables LLMs to invoke external tools to accomplish tasks. Prior studies have demonstrated the effectiveness of a hierarchical structure: a high-level policy handles global planning and decomposes tasks into manageable sub-tasks, and a low-level policy focuses on invoking tools to solve these sub-tasks. However, these works typically optimize the high-level and low-level policies separately, leading to planner-executor misalignment and limiting LLM performance on tool-use tasks. In this paper, we propose a method called Capability-Aligned Hierarchical Learning (CAHL), which leverages RLVR to jointly optimize both policies, enabling better alignment between the high-level planner and the low-level executor. Experiments on constrained tool-use benchmarks (API-Bank and BFCL) and an open-ended environment (Bamboogle) demonstrate the effectiveness of CAHL.
Chinese Translation
工具学习使大型语言模型（LLMs）能够调用外部工具来完成任务。先前的研究已经证明了层次结构的有效性：高层策略负责全局规划并将任务分解为可管理的子任务，而低层策略则专注于调用工具以解决这些子任务。然而，这些研究通常分别优化高层和低层策略，导致规划者与执行者之间的不对齐，从而限制了LLM在工具使用任务上的表现。本文提出了一种称为能力对齐的层次学习（Capability-Aligned Hierarchical Learning, CAHL）的方法，该方法利用RLVR共同优化这两种策略，从而实现高层规划者与低层执行者之间的更好对齐。在受限工具使用基准（API-Bank和BFCL）以及开放式环境（Bamboogle）上的实验表明了CAHL的有效性。
cs.AI / 116 / 2606.09392
From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
从粗到细：在时空数据中管理时间粒度以实现细粒度交通预测
Abstract
Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management. Most traffic data systems collect and store observations at fixed, coarse-grained temporal intervals to reduce storage and computation costs. However, such coarse-grained data severely limits downstream applications that require predictions at a finer temporal granularity. Collecting and maintaining fine-grained traffic data across all locations and time periods would impose a substantial burden on database storage and preprocessing pipelines. To address this temporal granularity mismatch, we formulate a novel problem: predicting fine-grained future traffic using coarse-grained sampled data. We propose the Spatial-Temporal Refinement Predictor (STRP), a granularity-aware framework for spatio-temporal data systems. STRP integrates two components: Tree Convolution for efficient and interpretable spatial dependency modeling, and Inverse Dilated Convolution for progressive temporal extrapolation. STRP supports two practical prediction settings: window-based and duration-based, to handle different forms of granularity mismatch. Experiments on six benchmark datasets show that STRP significantly outperforms state-of-the-art baselines in both accuracy and efficiency. Our work offers a practical and interpretable approach to managing granularity mismatches in spatio-temporal traffic data systems.
Chinese Translation
高效获取、存储和利用交通数据是时空数据管理中的关键挑战。大多数交通数据系统以固定的粗粒度时间间隔收集和存储观测数据，以降低存储和计算成本。然而，这种粗粒度数据严重限制了需要细粒度预测的下游应用。在所有地点和时间段收集和维护细粒度交通数据将对数据库存储和预处理管道造成重大负担。为了解决这种时间粒度不匹配的问题，我们提出了一个新问题：使用粗粒度采样数据预测细粒度未来交通。我们提出了时空细化预测器（Spatial-Temporal Refinement Predictor, STRP），这是一个针对时空数据系统的粒度感知框架。STRP集成了两个组件：用于高效和可解释的空间依赖建模的树卷积（Tree Convolution），以及用于渐进时间外推的逆膨胀卷积（Inverse Dilated Convolution）。STRP支持两种实际的预测设置：基于窗口的和基于持续时间的，以处理不同形式的粒度不匹配。在六个基准数据集上的实验表明，STRP在准确性和效率上显著优于最先进的基线方法。我们的工作为管理时空交通数据系统中的粒度不匹配提供了一种实用且可解释的方法。
cs.AI / 117 / 2606.09399
RunAgent SuperBrowser: A Theory of Autonomous Web Navigation Grounded in Human Browsing Behaviour
RunAgent超级浏览器：基于人类浏览行为的自主网页导航理论
Abstract
We present SUPERBROWSER, an autonomous web-navigation agent designed against a single guiding hypothesis: a web agent should browse the way a person browses. A human reading a page does not retain every pixel they have seen; they look at a few candidate targets, decide on one, and remember only what is needed to keep the goal alive. We operationalize this perception-cognition-action triad as three coupled mechanisms. First, a vision-first bounding-box pipeline labels candidate interactive regions on every screenshot and feeds them, asynchronously prefetched, to the language model so that the "eye" precedes the "hand". Second, a three-role brain -- an Orchestrator that classifies and routes, a Planner that evaluates progress every few steps, and a Worker that emits per-step actions -- separates strategic from operational reasoning. Third, a structured Ledger stores only what a person would: the goal, the last three actions, a small set of facts and dead-ends, and a handful of checkpoints; a six-phase eviction loop systematically discards stale screenshots, state blobs, and reasoning traces from the live context. Action execution is a three-tier click cascade (Chrome DevTools Protocol to Puppeteer to scripted) with humanized Bezier motion, plus a chevron-aware bounding-box snapper that resolves the "small arrow beside a large label" ambiguity. On the Mind2Web Hard benchmark (66 tasks), SUPERBROWSER attains 89.47% success, placing third overall and ahead of every published open/research browser-agent baseline by a large margin. We argue that the gain comes not from any single trick but from the consistent application of a cognitive contract throughout the system.
Chinese Translation
我们提出了SUPERBROWSER，一个自主网页导航代理，其设计基于一个单一的指导假设：网页代理应当以人类的方式进行浏览。人类在阅读页面时并不会保留他们所看到的每一个像素；他们会关注几个候选目标，做出选择，并仅记住维持目标所需的信息。我们将这种感知-认知-行动三元组操作化为三个耦合机制。首先，视觉优先的边界框管道在每个截图上标记候选交互区域，并将其异步预取到语言模型中，以便“眼睛”先于“手”。其次，一个三角色大脑——一个分类和路由的协调者，一个每隔几步评估进展的规划者，以及一个发出逐步动作的执行者——将战略推理与操作推理分开。第三，一个结构化的账本仅存储人类会记住的内容：目标、最近的三个动作、一小组事实和死胡同，以及少量的检查点；一个六阶段的驱逐循环系统性地丢弃过时的截图、状态数据和推理痕迹。动作执行是一个三级点击级联（Chrome DevTools Protocol到Puppeteer再到脚本化），结合人性化的贝塞尔运动，以及一个关注箭头的边界框捕捉器，解决了“小箭头旁边的大标签”的歧义。在Mind2Web Hard基准测试（66个任务）中，SUPERBROWSER达到了89.47%的成功率，整体排名第三，并且在每个已发布的开放/研究浏览器代理基准中大幅领先。我们认为，这一成果并非源于某个单一的技巧，而是来自于整个系统中认知契约的一致应用。
cs.AI / 118 / 2606.09409
Correct Looks Better: Pairwise Comparisons Reveal Accuracy Rankings
正确的外观更佳：成对比较揭示准确性排名
Abstract
Pairwise comparisons combined with aggregation methods like Elo have become central to evaluating generative models, yet concerns remain that they reward superficial stylistic cues or display judge biases. In a more positive turn, we show that model rankings from pairwise comparisons strongly agree with ground-truth-based accuracy rankings when such ground truth is available for comparison. By converting five well-known benchmarks into free-form generative evaluations, we find that Elo rankings achieve a Spearman correlation above 0.9 with accuracy rankings and substantially outperform direct evaluation when the judge is weak. Furthermore, style and judge bias have only minor effects on model rankings, despite most judgments occurring on pairs where both candidate answers are correct (or incorrect). On such pairs, we find that repetition after the final answer (echo) is a causal driver of judge preference.
Chinese Translation
成对比较结合像Elo这样的聚合方法已成为评估生成模型的核心，但人们仍然担心这些方法可能奖励表面的风格线索或显示评审者的偏见。更积极的是，我们展示了当有可供比较的真实标准时，成对比较得出的模型排名与基于真实标准的准确性排名高度一致。通过将五个知名基准转化为自由形式的生成评估，我们发现Elo排名与准确性排名的斯皮尔曼相关系数超过0.9，并且在评审者较弱时显著优于直接评估。此外，尽管大多数判断发生在两个候选答案均为正确（或错误）的对中，风格和评审者偏见对模型排名的影响仅为次要。在这样的对中，我们发现最终答案后的重复（回声）是评审者偏好的因果驱动因素。
cs.AI / 119 / 2606.09410
Capacity, Not Format: Rethinking Structured Reasoning Failures
容量，而非格式：重新思考结构化推理失败
Abstract
Prior work treats structured output as a reasoning tax, but this framing is incomplete: the cost of formatting depends strongly on a model's spare capacity. Using information-matched prose controls and a four-level schema complexity gradient, we separate format-specific effects from prompt-length confounds across 4 models and 5 benchmarks with 0% parse failures on successfully generated responses. We find that structured formats are capacity-dependent. Models with sufficient headroom absorb JSON constraints without degradation (Sonnet: $88.7\pm4.0$% JSON vs. $89.3\pm1.7$% CoT on MATH-Hard). In contrast, formats severely degrade models operating near their limits through two distinct mechanisms. First, under standard token budgets, Haiku drops 36.2pp ($p < 0.0001$) largely due to truncation. Second, even with extended budgets eliminating truncation, GPT-4o-mini drops 28.0pp ($p < 0.001$), revealing pure capacity competition independent of token exhaustion. This format penalty scales with schema complexity (McNemar $p < 0.0001$) and cannot be explained by prompt length alone. Furthermore, these results qualify claims of frontier model immunity: on AIME competition math, Opus 4.7 drops from 96.2% to 91.0% under JSON ($-5.3$pp; the displayed percentages are independently rounded, exact difference is $7/133 = 5.26$pp $\approx 5.3$pp). A delayed-structure ablation -- reasoning freely before formatting -- recovers most of the lost accuracy (3-run mean: 80--87%), supporting the capacity competition mechanism. The practical implication is not to avoid structured output, but to match it to capacity: when a model is near its limits, think first, format later.
Chinese Translation
先前的研究将结构化输出视为推理的负担，但这种框架并不完整：格式化的成本在很大程度上依赖于模型的剩余容量。通过使用信息匹配的散文控制和四级模式复杂度梯度，我们在4个模型和5个基准测试中分离了格式特定效应与提示长度混淆，成功生成的响应解析失败率为0%。我们发现结构化格式依赖于容量。具有足够余量的模型能够在不降低性能的情况下吸收JSON约束（Sonnet: $88.7 ext{±}4.0 ext{% JSON} ext{ vs. } 89.3 ext{±}1.7 ext{% CoT} ext{ on MATH-Hard}）。相反，格式在模型接近其极限时会严重降低性能，主要通过两种不同机制。首先，在标准令牌预算下，Haiku的性能下降了36.2个百分点（$p < 0.0001$），主要是由于截断。其次，即使在消除了截断的扩展预算下，GPT-4o-mini的性能仍下降28.0个百分点（$p < 0.001$），揭示了与令牌耗尽无关的纯容量竞争。这种格式惩罚与模式复杂度成比例（McNemar $p < 0.0001$），且无法仅通过提示长度来解释。此外，这些结果质疑了前沿模型免疫性的说法：在AIME竞赛数学中，Opus 4.7在JSON下的性能从96.2%下降至91.0%（$-5.3$个百分点；显示的百分比是独立四舍五入的，确切差异为$7/133 = 5.26$个百分点 $ ext{≈} 5.3$个百分点）。一种延迟结构消融实验——在格式化之前自由推理——恢复了大部分丧失的准确性（3次运行平均：80--87%），支持了容量竞争机制。实际意义在于不是避免结构化输出，而是将其与容量相匹配：当模型接近其极限时，先思考，后格式化。
cs.AI / 120 / 2606.09426
WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces
WeaveBench：一个针对具有混合接口的计算机使用代理的长时间跨度现实世界基准
Abstract
Computer-use agents (CUAs) increasingly operate in runtimes that combine visual desktop control, command-line execution, code editing, browsers, and external tools. Existing benchmarks, however, often evaluate these interfaces as separable capabilities, leaving long-horizon cross-interface orchestration under-tested. Thus, we introduce WeaveBench, a long-horizon hybrid-interface benchmark with 114 tasks across 8 real-world work domains, grounded in real user requests and publicly verifiable artifacts. Each task requires agents to combine GUI observations/actions with CLI/code operations within a single trajectory. We evaluate these tasks on a real Ubuntu desktop inside deployed CLI-agent runtimes, augmented with a minimal desktop-control plugin. We also propose a companion trajectory-aware judge that inspects deliverables, files, screenshots, logs, and action traces, while detecting shortcut behaviors such as fabricated visual evidence or hard-coded metrics. Across frontier model-runtime pairings, the best PassRate reaches only 41.2%, showing the benchmark remains far from saturated. The trajectory-aware judge further reveals that outcome-only grading substantially overestimates agent performance. Overall, WeaveBench exposes a critical gap in CUA evaluation and provides an effective testbed to measure whether agents can orchestrate GUI, CLI, and code operations across long-horizon real-world tasks.
Chinese Translation
计算机使用代理（CUAs）越来越多地在结合了视觉桌面控制、命令行执行、代码编辑、浏览器和外部工具的运行环境中操作。然而，现有基准往往将这些接口视为可分离的能力进行评估，从而导致长时间跨度的跨接口协调测试不足。因此，我们引入了WeaveBench，一个具有114个任务的长时间跨度混合接口基准，涵盖8个现实工作领域，基于真实用户请求和公开可验证的文档。每个任务要求代理在单一轨迹中结合GUI观察/操作与CLI/代码操作。我们在实际的Ubuntu桌面上评估这些任务，部署CLI代理运行时，并增强了一个最小的桌面控制插件。我们还提出了一个伴随的轨迹感知评估者，检查交付物、文件、屏幕截图、日志和操作痕迹，同时检测诸如伪造视觉证据或硬编码指标等快捷行为。在前沿模型-运行时配对中，最佳通过率仅达到41.2%，显示该基准仍远未饱和。轨迹感知评估者进一步揭示，仅根据结果评分会显著高估代理的性能。总体而言，WeaveBench揭示了CUA评估中的一个关键缺口，并提供了一个有效的测试平台，以衡量代理是否能够在长时间跨度的现实世界任务中协调GUI、CLI和代码操作。
cs.AI / 121 / 2606.09433
Bayesian Selective Latent Inference for Wastewater-First Influenza Monitoring
基于贝叶斯选择性潜在推断的污水优先流感监测
Abstract
Wastewater influenza surveillance can reveal community circulation before clinical reporting, but wastewater alone is not a fully identifiable proxy for human burden. Existing wastewater models assume a fixed evidence set, while generic evidence-acquisition methods treat official surveillance streams as interchangeable costly features. We cast wastewater-first influenza monitoring as a selective decision problem: starting from mandatory wastewater evidence, the system must decide whether wastewater is sufficient, which delayed official stream to query next, and when abstention is the only scientifically defensible action under source ambiguity. We propose Bayesian Selective Latent Inference (BSLI), a principled Bayesian method that maintains a posterior over latent burden and identifiability, certifies answerability through explicit scientific gates, and optimizes query-stop decisions with an exact cost-calibrated Bellman policy. We prove the key variational, answerability, Bellman-optimality, and one-dimensional cost-calibration properties. On a fixed public-data benchmark with 5,933 forecasting episodes and 3,102 source-ambiguity episodes, BSLI improves the matched-budget cost-performance frontier while preserving conservative abstention under source ambiguity.
Chinese Translation
污水流感监测可以在临床报告之前揭示社区传播情况，但仅依靠污水并不能完全识别人类负担。现有的污水模型假设证据集是固定的，而通用的证据获取方法则将官方监测流视为可互换的高成本特征。我们将污水优先的流感监测视为一个选择性决策问题：从强制性的污水证据出发，系统必须决定污水是否足够，接下来查询哪个延迟的官方流，以及在源不确定性下何时弃权是唯一科学上可辩护的行动。我们提出了贝叶斯选择性潜在推断（Bayesian Selective Latent Inference, BSLI），这是一种原则性的贝叶斯方法，能够在潜在负担和可识别性上维持后验分布，通过明确的科学门槛来认证可回答性，并通过精确的成本校准贝尔曼策略来优化查询停止决策。我们证明了关键的变分性、可回答性、贝尔曼最优性和一维成本校准特性。在一个固定的公共数据基准上，包含5,933个预测事件和3,102个源不确定性事件，BSLI在保持源不确定性下的保守弃权的同时，改善了匹配预算的成本性能边界。
cs.AI / 122 / 2606.09441
SIFT: Selective-Index For Fast Compute of RAG Prefill by Exploiting Attention Invariance
SIFT：通过利用注意力不变性实现快速计算RAG预填充的选择性索引
Abstract
Retrieval-Augmented Generation (RAG) injects LLM queries with relevant documents to improve response quality. This injection increases prompt length and slows time to first token (TTFT). Unlike standard queries, RAG queries have a unique property of context reuse where the same documents recur across user queries. Thus, fully recomputing documents for every RAG query does redundant compute and increases TTFT. Prior works precompute KV tensors of RAG documents offline and coarsely recompute some tokens during online prefill. However, such KV reuse is often slower than full recomputation on modern GPUs due to high-latency disk transfers. Further, such a coarse-grained recomputation degrades accuracy. To address these limitations, this paper proposes SIFT: Selective-Index For Fast Compute of RAG Prefill by Exploiting Attention Invariance. SIFT processes documents offline and extracts fine-grained locations of high attention scores for each document. Next, we identify the following attention invariance insights that enable us to exploit the extracted locations during runtime: (1) Local-Attention Invariance: The location of high attention scores within a document remain invariant to surrounding documents. This helps us predict the location of high scores where the document attends to itself. (2) Cross-Attention Consistency: Keys with high intra-document attention also attract cross-attention from subsequent documents. This helps us predict the location of high scores where the document attends to future documents. Critically, SIFT stores no KV data and only stores locations of high scores in the form of two compact bit vectors. SIFT's storage is up to 24,000x smaller than KV tensors, obviating costly disk transfers. During prefill, SIFT computes the attention only for the marked locations and improves TTFT by 1.71x while holding accuracy within 1% of full recompute.
Chinese Translation
检索增强生成（RAG）通过注入相关文档到大型语言模型（LLM）查询中来提高响应质量。这种注入增加了提示长度，并延长了首次令牌生成时间（TTFT）。与标准查询不同，RAG查询具有上下文重用的独特属性，即相同的文档在用户查询中反复出现。因此，对于每个RAG查询完全重新计算文档会导致冗余计算并增加TTFT。先前的研究在离线预计算RAG文档的键值（KV）张量，并在在线预填充期间粗略重新计算一些令牌。然而，由于高延迟的磁盘传输，这种KV重用在现代GPU上通常比完全重新计算更慢。此外，这种粗粒度的重新计算会降低准确性。为了解决这些局限性，本文提出了SIFT：通过利用注意力不变性实现快速计算RAG预填充的选择性索引。SIFT离线处理文档，并提取每个文档高注意力分数的细粒度位置。接下来，我们识别出以下注意力不变性见解，使我们能够在运行时利用提取的位置：（1）局部注意力不变性：文档内高注意力分数的位置对周围文档保持不变。这有助于我们预测文档自身关注的高分位置。（2）交叉注意力一致性：具有高内部文档注意力的键也会吸引后续文档的交叉注意力。这有助于我们预测文档关注未来文档的高分位置。重要的是，SIFT不存储任何KV数据，仅以两种紧凑的位向量形式存储高分位置。SIFT的存储量比KV张量小达24,000倍，从而消除了昂贵的磁盘传输。在预填充期间，SIFT仅计算标记位置的注意力，并将TTFT提高了1.71倍，同时保持准确性在完全重新计算的1%以内。
cs.AI / 123 / 2606.09447
AliyunConsoleAgent: Training Web Agents in Real-World Cloud Environments via Distillation and Reinforcement Learning
AliyunConsoleAgent：通过蒸馏和强化学习在真实云环境中训练网络代理
Abstract
We present AliyunConsoleAgent, a web agent framework for automated documentation verification in real-world cloud consoles. Major cloud platforms encompass hundreds of products with rapid feature iteration, causing console UIs to frequently diverge from their corresponding documentation. Verifying that documented procedures accurately reflect the current console and can be executed end-to-end demands an estimated 4 million recurring inspections annually, yet manual coverage remains below 1%. While agent systems built on frontier proprietary models achieve high success rates, their prohibitive cost and data privacy constraints preclude large-scale deployment. We propose a two-stage training paradigm: supervised fine-tuning (SFT) on distilled frontier-model trajectories, followed by reinforcement learning using Group Relative Policy Optimization (GRPO) and a dual-channel outcome reward model in real cloud environments. To support large-scale RL training, we construct a high-determinism rollout system featuring Terraform-based resource pre-provisioning and LLM-driven on-demand provisioning, which effectively isolates environment noise from the training signal. We further introduce a rule-based reward evaluation protocol grounded in backend audit logs, providing objective, reward-hacking-resistant outcome judgment. Our model evolves from mechanical instruction following to autonomous decision-making with cloud console and product-specific understanding. Experiments on a challenging 278-task benchmark where the best frontier model achieves only 65.34% demonstrate that AliyunConsoleAgent-32B achieves a 63.52% mean success rate -- a 20.24 percentage-point improvement over the base model, narrowing the gap to the best frontier proprietary model to 1.82 pp (bootstrap 95% CI [-1.27, 7.39]) -- at 92% lower inference cost.
Chinese Translation
我们提出了AliyunConsoleAgent，一个用于在真实云控制台中自动化文档验证的网络代理框架。主要云平台包含数百种产品，特性迭代迅速，导致控制台用户界面（UI）与相应文档之间经常出现偏差。验证文档中描述的程序是否准确反映当前控制台并能够端到端执行，预计每年需要进行约400万次的重复检查，而人工覆盖率仍低于1%。尽管基于前沿专有模型构建的代理系统取得了高成功率，但其高昂的成本和数据隐私限制阻碍了大规模部署。我们提出了一种两阶段的训练范式：在蒸馏的前沿模型轨迹上进行监督微调（SFT），然后在真实云环境中使用群体相对策略优化（GRPO）和双通道结果奖励模型进行强化学习。为了支持大规模的强化学习训练，我们构建了一个高确定性的回放系统，采用基于Terraform的资源预配置和基于大型语言模型（LLM）的按需配置，有效隔离了环境噪声与训练信号。我们进一步引入了一种基于后端审计日志的规则基础奖励评估协议，提供客观且抗奖励操控的结果判断。我们的模型从机械指令遵循演变为具有云控制台和产品特定理解的自主决策。针对一个具有挑战性的278任务基准测试，最佳前沿模型的成功率仅为65.34%，而AliyunConsoleAgent-32B的平均成功率达到了63.52%，比基础模型提高了20.24个百分点，将与最佳前沿专有模型的差距缩小至1.82个百分点（自助法95%置信区间[-1.27, 7.39]），且推理成本降低了92%。
cs.AI / 124 / 2606.09450
TheoremBench: Evaluating LLMs on Theorem Proving in Formal Mathematics
TheoremBench：在形式数学中评估大型语言模型的定理证明能力
Abstract
LLMs have recently achieved strong results on formal proving benchmarks. However, existing evaluations remain heavily concentrated on competition-style problems and often fail to capture how models behave on longer, more dependency-rich mathematical developments. We introduce TheoremBench, a Lean4 benchmark designed to evaluate theorem provers beyond contest settings. The benchmark is built from nearly one hundred classical theorems and is released in two complementary forms: a plain main version containing one target theorem per instance, and a premised version that expands each theorem into a structured family of related proving tasks consisting of the main theorem together with automatically extracted supporting subtheorems. This design enables evaluation of not only whether the final theorem was proved from scratch, but also of partial progress through the internal proof structure of a theorem. Our experiments show that explicit premises substantially improve performance for Lean4-capable prover models. To provide a comprehensive evaluation, we introduce theorem-level coverage and token-efficiency metrics that expose qualitative differences in proof behavior. The results show that current provers remain strongly biased toward easy subtheorems and often solve theorems through long and inefficient tactic traces rather than compact proof plans. TheoremBench therefore provides a more fine-grained view of formal reasoning ability and highlights the importance of structural benchmark design for evaluating Lean4 theorem provers.
Chinese Translation
大型语言模型（LLMs）最近在形式证明基准测试中取得了显著成果。然而，现有的评估仍然主要集中在竞赛风格的问题上，往往无法捕捉模型在更长、更依赖的数学发展过程中的表现。我们引入了TheoremBench，这是一个旨在评估定理证明者的Lean4基准，超越了竞赛环境。该基准由近百个经典定理构成，并以两种互补的形式发布：一种是包含每个实例一个目标定理的普通主版本，另一种是扩展每个定理为一个结构化的相关证明任务家族的前提版本，该家族包括主定理及自动提取的支持子定理。这种设计不仅能够评估最终定理是否从头证明，还能够评估通过定理的内部证明结构所取得的部分进展。我们的实验表明，明确的前提显著提高了对Lean4能力证明模型的性能。为了提供全面的评估，我们引入了定理级覆盖率和标记效率指标，以揭示证明行为中的定性差异。结果显示，当前的证明者仍然强烈偏向于简单的子定理，并且通常通过长而低效的策略轨迹解决定理，而不是使用紧凑的证明计划。因此，TheoremBench提供了对形式推理能力的更细致的视角，并强调了结构化基准设计在评估Lean4定理证明者中的重要性。
cs.AI / 125 / 2606.09475
Emergent alignment and the projectability of ethical personas
新兴对齐与伦理角色的可投射性
Abstract
Work on `emergent misalignment' shows that finetuning LLMs on narrow tasks can induce broadly misaligned behavior. This supports the `persona selection' (PSM) hypothesis: during pre-training, LLMs learn to simulate different characters and perspectives, which can be elicited and refined during post-training. This paper investigates the converse phenomenon, `emergent alignment', and uses it to support and refine the PSM and motivate a novel desideratum for alignment. We finetune a helpful-only model on broad and narrow safety tasks. To create SFT samples, we follow the `Constitutional AI' (CAI) approach and use four constitutions which encode reasonable alignment strategies: deontology, consequentialism, virtue ethics, and aligning AIs as subordinate to human authority. For each of those models, we show that finetuning on two narrow safety sub-categories reliably induces emergent alignment over a representative set of general safety categories, and on safety subcategories that we directly filtered-out of the data sets used for narrow alignment. To test the `PSM' using a more fine-grained evaluation, we used a multidimensional `ethical persona' diagnostic. For each constitutionally finetuned (broad/narrow) model, we evaluate how well their behavior matches their expected signature profile. Our results show that our CAI models acquire their expected ``ethical persona'' -- e.g., the model narrowly fine-tuned on SFT samples created using the consequentialist constitution agrees significantly more with utilitarian than deontological beliefs. Yet our coarse and fine-grained evaluations show that there are significant differences across our (broad/narrow) finetuned CAI models in how well they project. We conclude that alignment strategies should be evaluated, not just on their (in-distribution) general safety performance, but also specifically on their degree of projectability.
Chinese Translation
关于“新兴不对齐”的研究表明，在狭窄任务上对大型语言模型（LLMs）进行微调可能会导致广泛的不对齐行为。这支持了“角色选择”（Persona Selection Model, PSM）假设：在预训练过程中，LLMs 学会模拟不同的角色和视角，这些角色和视角可以在后期训练中被引出和细化。本文探讨了相反现象“新兴对齐”，并利用这一现象支持和细化 PSM，同时激励对齐的新期望。我们在广泛和狭窄的安全任务上对仅限于帮助的模型进行微调。为了创建监督微调（SFT）样本，我们遵循“宪法人工智能”（Constitutional AI, CAI）方法，使用四种宪法，编码合理的对齐策略：义务论、结果论、美德伦理学，以及将人工智能视为人类权威的从属。对于每个模型，我们展示了在两个狭窄安全子类别上进行微调，可靠地引发了在一组代表性的一般安全类别上的新兴对齐，以及在我们直接从用于狭窄对齐的数据集中过滤掉的安全子类别上。为了使用更细致的评估测试 PSM，我们使用了多维“伦理角色”诊断。对于每个经过宪法微调的（广泛/狭窄）模型，我们评估它们的行为与预期特征轮廓的匹配程度。我们的结果表明，我们的 CAI 模型获得了预期的“伦理角色”——例如，使用结果论宪法创建的 SFT 样本进行狭窄微调的模型在功利主义和义务论信念之间的符合度显著更高。然而，我们的粗略和细致评估显示，在我们的（广泛/狭窄）微调 CAI 模型之间，在它们的可投射性方面存在显著差异。我们得出结论，对齐策略的评估不仅应基于其（分布内）一般安全性能，还应特别基于其可投射性的程度。
cs.AI / 126 / 2606.09489
LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines
无计算机可解释指南的中风护理中的大语言模型协调一致性检查
Abstract
Objective: Conformance checking in healthcare seeks to assess whether patient care pathways adhere to clinical guidelines. However, its practical application often depends on the availability of formal, machine-interpretable representations of guidelines, such as Computer-Interpretable Guidelines (CIGs), which are seldom available in real-world clinical settings. Methods: This work introduces a modular framework based on the orchestration of Large Language Models (LLMs) to support medical conformance checking directly from unstructured clinical and guideline texts, without requiring predefined CIGs. The proposed architecture integrates multiple LLMs and supporting components to extract patient traces from clinical discharge letters, identify normative rules from textual clinical guidelines, translate these rules into executable scripts, and compute a Trace Conformance Indicator to quantify compliance within the event log. Results: The framework was implemented and evaluated in the stroke care domain at the neurological ward of Alessandria Hospital. Hundreds of patient traces were automatically extracted from hospital data and assessed against 50 rules derived from the reference guideline. The analysis showed that more than 86\% of the available traces were conformant. Conclusion: The results demonstrate the feasibility of using orchestrated LLMs for practical healthcare conformance analysis. At the same time, the study provides evidence of a high level of adherence to stroke care guidelines at Alessandria Hospital.
Chinese Translation
目的：医疗保健中的一致性检查旨在评估患者护理路径是否遵循临床指南。然而，其实际应用通常依赖于正式的、可由机器解释的指南表示形式，如计算机可解释指南（CIGs），而这些在现实临床环境中往往难以获得。方法：本研究提出了一种基于大语言模型（LLMs）协调的模块化框架，以支持直接从非结构化的临床和指南文本中进行医疗一致性检查，而无需预定义的CIGs。所提出的架构集成了多个LLM和支持组件，以从临床出院信中提取患者轨迹，从文本临床指南中识别规范性规则，将这些规则转换为可执行脚本，并计算轨迹一致性指标以量化事件日志中的合规性。结果：该框架在亚历山德里亚医院的神经科中风护理领域进行了实施和评估。从医院数据中自动提取了数百条患者轨迹，并根据从参考指南中得出的50条规则进行评估。分析显示，超过86%的可用轨迹是符合的。结论：结果表明，使用协调的LLMs进行实际医疗一致性分析是可行的。同时，该研究提供了亚历山德里亚医院在中风护理指南方面高度遵循的证据。
cs.AI / 127 / 2606.09500
Deterministic Integrity Gates for LLM-Assisted Clinical Manuscript Preparation: An Auditable Biomedical Informatics Architecture
用于大语言模型辅助临床手稿准备的确定性完整性门：可审计的生物医学信息学架构
Abstract
Objective. Large language models (LLMs) increasingly draft clinical research manuscripts, but their fluency can hide fabricated citations, numbers that drift from source tables, and unmet reporting-guideline items. Existing tools generate text without verifying it, and self-critique inherits the blind spots that produce confident fabrication. We describe an architecture that pairs generation with verification. Methods. The design rests on three principles: decompose the workflow into self-contained skills, gate every stage transition with halt-on-failure, and resolve each integrity question with the cheapest sufficient mechanism -- a deterministic, re-executable check where one suffices, and a prose-level probe only where interpretation is unavoidable. This determinism-where-possible split, organized as an integrity-gate taxonomy, is the core contribution. It is realized as MedSci Skills, an open-source toolkit of 43 skills coordinated by one orchestrator, whose deterministic tier comprises 21 standard-library detectors. We evaluate it on three reproducible public-dataset pipelines (STARD, PRISMA, STROBE) and a seeded-defect ablation. Results. Across the three pipelines every content-hash manifest verified clean and the gates surfaced real defects. On 27 identical injected defects the deterministic gates detected all 27 with no false positives on the matched clean fixtures, whereas a generic single-prompt LLM reviewer detected 11, its misses concentrated in generated-code, bibliography-internal, and style defects the prose does not expose. Conclusion. Determinism-where-possible verification yields an auditable, re-executable trail that exposes the evidence a human needs to check an LLM-assisted manuscript -- feasibility and reproducibility evidence, not a claim of human-competitive quality, which a separate blinded study addresses. MedSci Skills is MIT-licensed and archived (v3.8.0).
Chinese Translation
目的：大型语言模型（LLMs）越来越多地用于撰写临床研究手稿，但其流畅性可能掩盖虚构的引用、与源表格偏离的数字以及未满足的报告指南条目。现有工具生成文本而不进行验证，自我批评也继承了导致自信虚构的盲点。我们描述了一种将生成与验证相结合的架构。方法：该设计基于三个原则：将工作流程分解为自包含的技能，每个阶段的转换都通过失败时停止的机制进行控制，并以最便宜的充分机制解决每个完整性问题——在可能的情况下使用确定性、可重执行的检查，而在不可避免的解释情况下使用文本级探测。这种在可能情况下的确定性分割，组织为完整性门分类法，是核心贡献。它以 MedSci Skills 的形式实现，这是一个由一个协调者协调的开源工具包，包含 43 种技能，其中确定性层包括 21 种标准库检测器。我们在三个可重复的公共数据集管道（STARD、PRISMA、STROBE）和一个种子缺陷消融实验中对其进行了评估。结果：在这三个管道中，每个内容哈希清单都经过验证且干净，门控机制揭示了真实缺陷。在 27 个相同的注入缺陷中，确定性门检测到了所有 27 个缺陷，并且在匹配的干净修复中没有假阳性，而一个通用的单提示 LLM 审阅者仅检测到了 11 个，其漏检主要集中在生成代码、文献内部和文本未暴露的风格缺陷上。结论：在可能的情况下进行的确定性验证产生了一个可审计的、可重执行的轨迹，揭示了人类检查 LLM 辅助手稿所需的证据——可行性和可重复性证据，而不是人类竞争质量的声明，这一点在另一项盲法研究中进行了探讨。MedSci Skills 采用 MIT 许可证并已归档（v3.8.0）。
cs.AI / 128 / 2606.09508
From Rigid to Dynamic: Entropy-Guided Adaptive Inference for Long-Context LLMs
从刚性到动态：基于熵引导的长上下文大语言模型自适应推理
Abstract
Existing sparse attention and KV cache compression methods for long-context LLM inference typically apply fixed sparsity patterns or uniform budgets across all attention heads, overlooking the substantial variation in attention behavior among heads and contexts. We observe two distinct entropy patterns among attention heads: Rigid Heads, whose entropy stays near zero across input segments, and Dynamic Heads, whose entropy fluctuates significantly. Crucially, the distribution of these types is context-dependent and cannot be predetermined offline. We therefore propose EntropyInfer, a training-free framework that uses attention entropy to adaptively allocate compute at the granularity of individual heads and segments during prefilling. For decoding, we introduce a latent KV cache compression scheme that leverages generated output tokens, rather than prefill tokens alone, to identify and retain the most critical cache entries. Extensive experiments on Llama, Qwen and openPangu model series show that EntropyInfer consistently outperforms baselines including SnapKV, AdaKV, and CritiPrefill, achieving up to 2.39$\times$ end-to-end speedup beyond 100k tokens with minimal quality degradation compared to full attention. The code is released in https://github.com/SHA-4096/EntropyInfer.
Chinese Translation
现有的稀疏注意力和KV缓存压缩方法在长上下文大语言模型推理中通常对所有注意力头应用固定的稀疏模式或均匀预算，忽视了不同注意力头和上下文之间注意力行为的显著差异。我们观察到注意力头中存在两种不同的熵模式：刚性头（Rigid Heads），其熵在输入片段中保持接近零，以及动态头（Dynamic Heads），其熵波动显著。关键是，这些类型的分布依赖于上下文，无法离线预先确定。因此，我们提出了EntropyInfer，这是一个无训练的框架，利用注意力熵在预填充过程中自适应地在单个头和片段的粒度上分配计算资源。在解码阶段，我们引入了一种潜在的KV缓存压缩方案，该方案利用生成的输出标记，而不仅仅是预填充标记，来识别和保留最关键的缓存条目。在Llama、Qwen和openPangu模型系列上的大量实验表明，EntropyInfer在性能上始终优于包括SnapKV、AdaKV和CritiPrefill在内的基线方法，在超过100k标记时实现了高达2.39倍的端到端加速，同时与全注意力相比，质量降级最小。代码已发布在https://github.com/SHA-4096/EntropyInfer。
cs.AI / 129 / 2606.09556
AI Scientists Are Only as Good as Their Evidence: A Stratified Ablation of Proprietary Data and Reasoning Skills in Drug-Asset Valuation
人工智能科学家的能力取决于其证据：对专有数据和推理技能在药物资产估值中的分层消融研究
Abstract
AI Scientist agents are often evaluated as if capability were mainly a function of model quality, prompting, or reasoning scaffolds. We test a different hypothesis in drug-asset valuation: for knowledge-intensive scientific decisions, the limiting factor is often the evidence substrate the agent can access. We run a controlled three-arm ablation on a production valuation agent: A is a plain web-only LLM analyst, B adds public structured tools plus a 14-dimension valuation playbook, verifier, objectivity policy and red-team, and C adds the proprietary Noah AI corpus of curated pipeline, trial and deal intelligence. Across a 13-asset stratified benchmark, B improves calibration and audit discipline: tier-in-range accuracy rises from 0.80 to 0.89 and objectivity from 3.16 to 3.30. But B does not remove the factual ceiling. Under capability-superset accounting, A and B recover only 0.25 and 0.38 of the curated gold competitive record, while C recovers 0.96; on the curated long-tail subset, C reaches 0.93 vs. 0.26/0.30. Raw blind-panel decision quality is similar for A and B (7.01 vs. 6.96), so we introduce completeness-aware decision utility: informed decision-quality = decision-quality x gold-coverage. On this metric, C reaches 7.43 vs. 1.76/2.57 for A/B. Even a perfect non-proprietary-data report would be capped at 3.83 by B's coverage. The result is not that reasoning scaffolds are unimportant; they improve calibration and discipline. Rather, proprietary evidence sets the upper bound of what the AI Scientist can know and therefore decide.
Chinese Translation
人工智能科学家代理通常被评估为其能力主要取决于模型质量、提示或推理框架。我们在药物资产估值中测试了一个不同的假设：对于知识密集型科学决策，限制因素往往是代理可以访问的证据基础。我们对一个生产估值代理进行了受控的三臂消融实验：A是一个仅使用网络的普通大型语言模型（LLM）分析师，B增加了公共结构化工具以及一个包含14个维度的估值手册、验证器、客观性政策和红队，而C则增加了专有的Noah AI策划的管道、试验和交易情报库。在一个包含13个资产的分层基准测试中，B提高了校准和审计纪律：范围内准确度从0.80上升到0.89，客观性从3.16上升到3.30。但B并没有消除事实上的上限。在能力超集会计下，A和B仅恢复了0.25和0.38的策划黄金竞争记录，而C恢复了0.96；在策划的长尾子集上，C达到了0.93，而A/B分别为0.26/0.30。A和B的原始盲评决策质量相似（7.01对6.96），因此我们引入了考虑完整性的决策效用：知情决策质量 = 决策质量 x 黄金覆盖率。在这一指标上，C达到了7.43，而A/B分别为1.76/2.57。即使是一个完美的非专有数据报告也会受到B的覆盖限制，最高为3.83。结果并不是推理框架不重要；它们改善了校准和纪律。相反，专有证据设定了人工智能科学家所能知道和因此所能决策的上限。
cs.AI / 130 / 2606.09563
PRISM: Recovering Instruction Sets from Language Model Activations
PRISM：从语言模型激活中恢复指令集
Abstract
As LLMs are deployed as agents, reliable monitoring requires knowing not only what they output, but which instructions are steering their behavior. This is difficult when models infer unintended subgoals, follow contextual cues, or are influenced by prompt injections and hidden objectives. While activation-to-language methods suggest that hidden states can reveal natural-language information, existing approaches are not designed to recover the full set of simultaneous instructions, constraints, prohibitions, and subgoals active in agentic settings. We formalize this problem as instruction set retrieval and introduce PRISM, an activation-conditioned interpreter that decodes hidden states from a frozen target model into a faithful bullet list of active instructions. Unlike prior activation-to-language methods, PRISM is trained to recover instruction sets directly, using judge-guided GRPO to reward covered instructions and penalize unsupported ones. Across benign, constrained, prompt-injection, and hidden-objective settings, PRISM outperforms activation-to-language baselines, especially on security-relevant objectives.
Chinese Translation
随着大型语言模型（LLMs）被部署为代理，可靠的监控不仅需要了解它们的输出，还需要知道哪些指令在引导它们的行为。当模型推断出意图之外的子目标、遵循上下文线索或受到提示注入和隐藏目标的影响时，这一过程变得困难。虽然激活到语言的方法表明，隐藏状态可以揭示自然语言信息，但现有的方法并未设计用于恢复在代理设置中同时活跃的完整指令集、约束、禁止和子目标。我们将这一问题形式化为指令集检索，并引入PRISM，一种激活条件解释器，它将冻结的目标模型中的隐藏状态解码为一个忠实的活跃指令项目列表。与之前的激活到语言的方法不同，PRISM被训练为直接恢复指令集，使用评审引导的GRPO来奖励覆盖的指令并惩罚不支持的指令。在良性、受限、提示注入和隐藏目标设置中，PRISM的表现优于激活到语言的基线，尤其是在与安全相关的目标上。
cs.AI / 131 / 2606.09568
Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions
自适应与自组织系统中的自解释性：现状与研究方向
Abstract
The growing complexity of self-adaptive and self-organising systems, fuelled by advances in Artificial Intelligence (AI), has made them increasingly difficult to understand and trust. While Explainable AI aims to provide insight into AI decision-making, a more advanced goal is for systems to explain themselves - an ability referred to as Self-Explainability (SX). This article presents a systematic literature review on SX, analysing existing approaches, including their domains, targets, and evaluation methods. The review develops a unified definition and taxonomy of SX and introduces Levels of Self-Explainability, providing a framework for positioning current and future research. Our results show that most SX approaches remain conceptual, with few practical implementations. Moreover, there is currently no formal or de facto standard for evaluating SX, highlighting a major research gap. This work thus establishes a foundation and roadmap for advancing Self-Explainability in complex systems.
Chinese Translation
自适应与自组织系统的复杂性日益增加，这一趋势受到人工智能（AI）进步的推动，使得这些系统变得越来越难以理解和信任。尽管可解释的人工智能旨在提供对AI决策过程的洞察，但更高级的目标是让系统能够自我解释——这一能力被称为自解释性（Self-Explainability, SX）。本文对SX进行了系统的文献综述，分析了现有的方法，包括它们的领域、目标和评估方法。该综述提出了SX的统一定义和分类法，并引入了自解释性的层次，为当前和未来的研究提供了框架。我们的结果表明，大多数SX方法仍然是概念性的，实际应用较少。此外，目前尚无正式或事实上的SX评估标准，这突显了一个主要的研究空白。因此，本研究为推进复杂系统中的自解释性奠定了基础和路线图。
cs.AI / 132 / 2606.09578
TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMs
TABVERSE：跨格式表格理解在大型语言模型和视觉语言模型中的基准测试
Abstract
Large Language Models (LLMs) and Vision-Language Models (VLMs) are increasingly evaluated on table reasoning tasks, but the role of table representation remains under-explored. In practice, the same table content may appear in different structural formats, such as HTML, Markdown, and LaTeX, or as rendered images. However, existing evaluations often let content, format, layout, and modality vary together, making it difficult to isolate representation effects. We introduce TABVERSE, a controlled multimodal table benchmark that aligns the same table content across multiple structural formats and rendered images, with question category and difficulty tags. This design enables systematic evaluation of representation effects while holding table content fixed. We evaluate LLMs and VLMs across three tasks: Question Answering (QA), Structural Understanding Capability (SUC), and Structure Reconstruction (SR). Our results show that representation choice substantially affects table understanding. Models generally perform better with structured text than with rendered images, but the size of this gap depends on the task, model, and format. HTML is often the most robust text format, while row-sensitive structural tasks and syntactically usable LaTeX reconstruction remain challenging. These findings show that table representation is a key factor in reliable table evaluation.
Chinese Translation
大型语言模型（LLMs）和视觉语言模型（VLMs）在表格推理任务中的评估日益增多，但表格表示的作用仍未得到充分探索。在实际应用中，相同的表格内容可能以不同的结构格式出现，例如 HTML、Markdown 和 LaTeX，或作为渲染图像。然而，现有的评估通常让内容、格式、布局和模态共同变化，使得难以单独隔离表示效果。我们引入了 TABVERSE，一个受控的多模态表格基准，旨在对齐跨多个结构格式和渲染图像的相同表格内容，并附加问题类别和难度标签。这一设计使得在固定表格内容的情况下，能够系统地评估表示效果。我们在三个任务上评估 LLMs 和 VLMs：问题回答（QA）、结构理解能力（SUC）和结构重建（SR）。我们的结果表明，表示选择显著影响表格理解。模型通常在结构化文本上的表现优于渲染图像，但这一差距的大小取决于任务、模型和格式。HTML 通常是最稳健的文本格式，而对行敏感的结构任务和语法可用的 LaTeX 重建仍然具有挑战性。这些发现表明，表格表示是可靠表格评估中的一个关键因素。
cs.AI / 133 / 2606.09585
Optical Reasoning: Rethinking Images as an Expressive Reasoning Medium Beyond Text
光学推理：重新思考图像作为超越文本的表达性推理媒介
Abstract
Chain-of-Thought (CoT) improves the performance of Large Language Models (LLMs) and has been extended to Multimodal Large Language Models (MLLMs). More recent work further moves from text-based multimodal reasoning toward interleaved-modal reasoning, where intermediate steps can incorporate both textual rationales and visual evidence. In this work, we propose a bolder and more ambitious idea: could images alone serve as the reasoning medium for both language and multimodal tasks? To explore this, we propose optical reasoning, which treats images as a standalone reasoning medium. We instantiate this concept with two variants: typographic-based optical reasoning, which optimizes visual layouts for compact rationale rendering, and graphical-based optical reasoning, which composes text and graphical elements into structured visual rationales. Across mathematical, scientific, and interleaved-modal reasoning benchmarks, optical reasoning can match or even exceed traditional text reasoning while reducing reasoning tokens by an average of 28.57% on language tasks and 16% on multimodal tasks, achieving 1.96 times the token efficiency of text reasoning. These results show that images can effectively and efficiently encode rationales while providing a unified visual canvas for reasoning.
Chinese Translation
链式思维（Chain-of-Thought, CoT）提高了大型语言模型（Large Language Models, LLMs）的性能，并已扩展到多模态大型语言模型（Multimodal Large Language Models, MLLMs）。最近的研究进一步从基于文本的多模态推理转向交错模态推理，其中中间步骤可以同时包含文本推理和视觉证据。在本研究中，我们提出了一个更大胆、更雄心勃勃的想法：图像是否可以单独作为语言和多模态任务的推理媒介？为此，我们提出了光学推理，视图像为独立的推理媒介。我们用两种变体来实例化这一概念：基于排版的光学推理，优化视觉布局以紧凑呈现推理；以及基于图形的光学推理，将文本和图形元素组合成结构化的视觉推理。在数学、科学和交错模态推理基准测试中，光学推理的表现可以与传统文本推理相匹配，甚至超过传统文本推理，同时在语言任务上减少推理令牌的平均数量28.57%，在多模态任务上减少16%，实现了文本推理的1.96倍令牌效率。这些结果表明，图像可以有效且高效地编码推理，同时提供统一的视觉画布用于推理。
cs.AI / 134 / 2606.09605
Next-Token Prediction Learns Generalisable Representations of Sleep Physiology
下一标记预测学习可推广的睡眠生理表征
Abstract
Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains. Existing models have typically been trained with masked-reconstruction or contrastive objectives. However, masked reconstruction may be poorly suited to the stochastic nature of these signals, while contrastive approaches rely on positive-pair definitions despite the semantic invariances of physiological signals being poorly understood. In this work, we show that next-token prediction is a simple and scalable alternative. We develop Hypnos, a multi-modal sleep foundation model trained using eight different sensing modalities (e.g. EEG, ECG, respiratory signals) drawn from over 20,000 overnight polysomnography recordings. We tokenize each modality into streams of discrete tokens using residual vector quantization, then train a large auto-regressive RQ-Transformer to jointly predict the next token across all modalities in parallel. After training, Hypnos can be applied to continuous streams of sensor data from any subset of supported modalities, generating embeddings for downstream tasks. Across a range of benchmarks, Hypnos significantly outperforms existing foundation models. In sleep stage classification, we match the performance of strong supervised baselines on held-out test sets whilst using \(100\times\) less labelled data. Hypnos even generalises to daytime physiology, surpassing a dedicated ECG foundation model at detecting atrial fibrillation. Our results demonstrate that next-token prediction is a strong self-supervised objective for representation learning from multi-modal physiological signals.
Chinese Translation
基础模型为将多模态生理信号压缩为人类健康的紧凑表征提供了有希望的途径，广泛应用于睡眠医学、心脏病学、神经学及其他医疗领域。现有模型通常采用掩码重建或对比目标进行训练。然而，掩码重建可能不适合这些信号的随机特性，而对比方法则依赖于正对定义，尽管生理信号的语义不变性尚不清楚。在本研究中，我们展示了下一标记预测是一种简单且可扩展的替代方案。我们开发了Hypnos，一个多模态睡眠基础模型，使用来自超过20,000个过夜多导睡眠监测记录的八种不同传感模式（例如EEG、ECG、呼吸信号）进行训练。我们使用残差向量量化将每种模态标记为离散标记流，然后训练一个大型自回归RQ-Transformer以并行预测所有模态的下一个标记。训练后，Hypnos可以应用于任何支持模态的连续传感器数据流，为下游任务生成嵌入。在一系列基准测试中，Hypnos显著优于现有基础模型。在睡眠阶段分类中，我们在保留的测试集上匹配了强监督基线的性能，同时使用了少于100倍的标记数据。Hypnos甚至可以推广到白天生理，超越专用的ECG基础模型在检测房颤方面的表现。我们的结果表明，下一标记预测是从多模态生理信号中进行表征学习的强自监督目标。
cs.AI / 135 / 2606.09663
From 0-to-1 to 1-to-N: Reproducible Engineering Evidence for MetaAI Recursive Self-Design
从0到1再到1到N：MetaAI递归自我设计的可重复工程证据
Abstract
Recursive self-design refers to AI-assisted modification of the mechanisms by which an AI system is built, evaluated, and improved. This paper treats MetaAI not as a mature paradigm, but as a working term for a human-seeded, AI-expanded development pattern in which the design space itself becomes a target of modification. We propose an operational evidence framework with four criteria: inspectable target system, meta-level modifier, feedback-directed selection, and recursive continuation. We then map public systems, including Darwin Goedel Machine (DGM), STOP, Goedel Agent, and ShinkaEvolve, against these criteria. DGM provides the most direct currently reported evidence: its published results show improvement from 20% to 50% on SWE-bench Verified and from 14.2% to 30.7% on full Polyglot after 80 iterations, with ablations suggesting that both open-ended exploration and self-improvement contribute. Finally, we provide MetaAI-Mini, a reproducible HumanEval-based protocol and codebase. Because no completed model run is included in this build, MetaAI-Mini is reported as a protocol rather than as an experimental result.
Chinese Translation
递归自我设计是指AI辅助修改构建、评估和改进AI系统机制的过程。本文将MetaAI视为一个工作术语，而非成熟范式，代表一种由人类引导、AI扩展的发展模式，其中设计空间本身成为修改的目标。我们提出了一个包含四个标准的操作性证据框架：可检查的目标系统、元级修改器、反馈导向选择和递归延续。随后，我们根据这些标准对公共系统进行映射，包括达尔文哥德尔机器（Darwin Goedel Machine, DGM）、STOP、哥德尔代理（Goedel Agent）和ShinkaEvolve。DGM提供了当前报告的最直接证据：其发布的结果显示，在80次迭代后，SWE-bench Verified的改进幅度为20%到50%，而全Polyglot的改进幅度为14.2%到30.7%，消融实验表明开放式探索和自我改进均有贡献。最后，我们提供了MetaAI-Mini，一个基于HumanEval的可重复协议和代码库。由于此构建中未包含完整的模型运行，MetaAI-Mini被报告为协议而非实验结果。
cs.AI / 136 / 2606.09666
Frequency-based Constrained Sampling for Interval Patterns
基于频率的区间模式约束采样
Abstract
Output space pattern sampling is a powerful alternative to exhaustive pattern mining for exploring large pattern spaces, as it enables users to focus on representative patterns drawn according to a chosen interestingness measure. In this paper, we address the problem of sampling interval patterns under user-defined syntactic constraints. We introduce CFips, a sampling approach that incorporates constraints directly into the sampling procedure. The approach relies on a multi-step sampling framework and supports several syntactic constraints by decomposing them into elementary predicates on interval bounds while preserving exact sampling guarantees. We formally prove that CFips samples interval patterns proportionally to their frequency within the constrained pattern space. The experimental results show that integrating constraints into the sampling procedure enables to complete mining tasks that would otherwise fail within a given time out.
Chinese Translation
输出空间模式采样是一种强有力的替代方案，用于探索大型模式空间，相较于穷举模式挖掘，它使用户能够专注于根据所选的有趣性度量提取的代表性模式。本文解决了在用户定义的语法约束下采样区间模式的问题。我们提出了CFips，一种将约束直接纳入采样过程的采样方法。该方法依赖于多步骤采样框架，并通过将约束分解为关于区间边界的基本谓词来支持多种语法约束，同时保持精确的采样保证。我们正式证明CFips在约束模式空间内按频率比例采样区间模式。实验结果表明，将约束集成到采样过程中能够完成在给定超时内否则会失败的挖掘任务。
cs.AI / 137 / 2606.09669
SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks
SpatialWorld：多模态智能体在现实任务中交互空间推理的基准测试
Gao, Hongcheng, Qu, Hailong, Tang, Jingyi, Wang, Jiahao, Huang, Zihao, Qiao, Hengkang, Huang, Shihong, Yang, Junming, Li, Yi, Yuan, Hongyixuan, Li, Wenjie, Zeng, Bohan, Li, Wenbo, Wang, Bo, Liu, Jianhui, Huang, Olive, Huang, Haoyang, Zhang, Wentao, Huang, Guoqing, Duan, Nan, Dong, Yinpeng
Abstract
Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world. However, existing benchmarks predominantly rely on passive evaluation (e.g., static VQA) or simulator-specific pipelines, failing to assess general interactive spatial understanding. We introduce SpatialWorld, a unified benchmark designed specifically for evaluating the interactive spatial understanding of multimodal agents in complex real-world tasks. Integrating eight heterogeneous simulation backends under a shared, simulator-agnostic protocol, SpatialWorld features 760 human-annotated tasks across diverse domains (e.g., household routines, travel, social collaboration). Agents must solve tasks under vision-only partial observability, actively gathering egocentric visual evidence and expressing decisions via a unified, text-based action interface native to MLLMs. For reliable evaluation, each task includes a human-validated initial state, a reference trajectory, and a terminal-state verifier. Evaluating 15 advanced agents reveals that robust spatial task solving remains challenging: the strongest model, GPT-5, achieves an average task success rate (TSR) of only 17.4%, while the leading open-source model, Qwen-3.5, reaches 14.1%. Further analysis exposes a clear mismatch between task success and execution efficiency, alongside substantial domain-specific performance variations. These bottlenecks in active exploration and long-horizon planning position SpatialWorld as a rigorous testbed for future spatial agents.
Chinese Translation
空间推理是多模态大语言模型（MLLMs）感知和在物理世界中操作的基础能力。然而，现有的基准测试主要依赖于被动评估（例如，静态视觉问答）或特定模拟器的管道，未能评估一般的交互空间理解。我们引入了SpatialWorld，这是一个专门设计用于评估多模态智能体在复杂现实任务中交互空间理解的统一基准。SpatialWorld在一个共享的、与模拟器无关的协议下整合了八个异构模拟后端，涵盖了760个跨不同领域（例如，家庭日常、旅行、社交协作）的人类标注任务。智能体必须在仅有视觉的部分可观察性下解决任务，主动收集自我中心的视觉证据，并通过一个统一的、基于文本的操作接口表达决策，该接口是MLLMs的本土特性。为了确保可靠评估，每个任务都包括一个经过人类验证的初始状态、一个参考轨迹和一个终态验证器。对15个先进智能体的评估显示，稳健的空间任务解决仍然具有挑战性：最强的模型GPT-5的平均任务成功率（TSR）仅为17.4%，而领先的开源模型Qwen-3.5的成功率为14.1%。进一步分析揭示了任务成功与执行效率之间的明显不匹配，以及显著的领域特定性能差异。这些在主动探索和长远规划中的瓶颈使得SpatialWorld成为未来空间智能体的严格测试平台。
cs.AI / 138 / 2606.09672
Correlation Is Not Enough: Embedding Human Metadata for Individual Causal Discovery
相关性不足：嵌入人类元数据以进行个体因果发现
Abstract
Ask a pretrained biomedical language model whether "cortisol 28 ug/dL" and "stock-market volatility" are related, and it returns a cosine similarity of 0.83 on a scale where 1.0 means identical. The two share no mechanism. This is not a corner case: every off-the-shelf biomedical encoder we tested (BioBERT, PubMedBERT, BioM-ELECTRA) scores unrelated cross-domain pairs between 0.76 and 0.92 when the answer should be near zero. Accuracy on cross-domain discrimination is 0%. Retrieval systems survive this, because a language model downstream filters the noise. A Large Behavioural Model (LBM), a foundation model whose subject is a person rather than a sentence, does not: it reasons over a graph of a user's life and treats embedding proximity as evidence that two events are causally linked. False proximity writes a false causal edge, and everything downstream inherits the error. Here, embedding geometry is not a tuning knob; it is correctness. We report the fix. A contrastive pass over 72,034 pairs raises PubMedBERT BIOSSES correlation from 0.633 to 0.828 and within-vs-across-domain separation from 1.05x to 1.63x. A second pass, BODHI, mines hard negatives from edges absent in a biomedical knowledge graph and lifts separation to 2.30x and the discrimination gap to +0.392, at a 4.5% BIOSSES cost. On an Intel Xeon 6737P with AMX, OpenVINO cuts single-query latency from 1367 ms to 10 ms (133x) and reaches 555 sentences/sec. One finding contradicts standard advice: FP16 beats INT8 on this silicon at every serving batch size, and we explain why. The same model on a no-AMX Ice Lake instance runs 13-27x slower. We release the benchmark suite, training corpora, the BODHI generator, and the OpenVINO scripts.
Chinese Translation
询问一个预训练的生物医学语言模型“皮质醇 28 微克/分升”和“股市波动”是否相关，它返回的余弦相似度为 0.83，1.0 表示完全相同。这两者之间没有任何机制。这并不是一个特例：我们测试的每一个现成的生物医学编码器（BioBERT、PubMedBERT、BioM-ELECTRA）在不相关的跨领域对之间的得分在 0.76 到 0.92 之间，而答案应该接近零。跨领域区分的准确率为 0%。检索系统能够生存下来，因为下游的语言模型过滤了噪声。大型行为模型（Large Behavioural Model, LBM）是一个以人而非句子为主题的基础模型，它并不能做到这一点：它在用户生活的图上推理，并将嵌入接近度视为两个事件因果关联的证据。错误的接近度写下了错误的因果边缘，所有下游的内容都继承了这个错误。在这里，嵌入几何不是一个调节旋钮；它是正确性。我们报告了修复方案。对 72,034 对的对比处理将 PubMedBERT BIOSSES 相关性从 0.633 提升到 0.828，领域内与跨领域的分离度从 1.05 倍提升到 1.63 倍。第二次处理，BODHI，从生物医学知识图谱中缺失的边缘挖掘困难的负样本，将分离度提升至 2.30 倍，区分差距提升至 +0.392，BIOSSES 成本为 4.5%。在配备 AMX 的 Intel Xeon 6737P 上，OpenVINO 将单查询延迟从 1367 毫秒降低到 10 毫秒（降低 133 倍），并达到每秒 555 个句子。一个发现与标准建议相悖：在这种硅片上，FP16 在每个服务批量大小上都优于 INT8，我们解释了原因。在没有 AMX 的 Ice Lake 实例上，相同的模型运行速度慢 13-27 倍。我们发布了基准测试套件、训练语料库、BODHI 生成器和 OpenVINO 脚本。
cs.AI / 139 / 2606.09674
(Auto)formalization is supposed to be easy: Trellis process semantics for spelling out rigorous proofs
(自动)形式化应该是简单的：用于阐明严格证明的Trellis过程语义
Abstract
We present Trellis: an autoformalization system that leverages LLM agents in a deterministically constrained workflow to enforce incremental progress in Lean autoformalization tasks through iterative refinement of natural language proofs. Our approach is motivated by the common mathematician's notion of what it means to have a rigorous proof in the first place: namely, that it would be routine to elaborate any part of the proof in further detail. The result is a system which aims to achieve reliable autoformalization on a modest budget and with generalist agents, with specialization to autoformalization coming not from any task-specific agent training but instead from a meaning-of-rigor inspired workflow enforced by process semantics. We link to an end-to-end Lean formalization of a recent Ramsey theory breakthrough produced by the process.
Chinese Translation
我们提出了Trellis：一个自动形式化系统，利用大型语言模型（LLM）代理在确定性约束的工作流程中，强制在Lean自动形式化任务中通过自然语言证明的迭代细化来实现增量进展。我们的方法受到普通数学家对严格证明的基本概念的启发：即，详细阐述证明的任何部分应该是常规的。其结果是一个旨在以适度预算和通用代理实现可靠自动形式化的系统，自动形式化的专业化并非来自任何特定任务的代理训练，而是源于由过程语义强制执行的受严格性意义启发的工作流程。我们链接到通过该过程产生的最近Ramsey理论突破的端到端Lean形式化。
cs.AI / 140 / 2606.09711
Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization
代理奖励内化与机制利用：奖励黑客行为的学习前驱及其泛化
Abstract
Reward hacking is usually studied after it becomes visible, once a model earns high proxy reward while failing the intended task. We instead study what proxy RL teaches before that failure appears. We introduce Proxy Reward Internalization and Mechanistic Exploitation (PRIME), a learned capability to assess task correctness, predict proxy acceptance, and reason about exploitable proxy--gold gaps. In coding RL environments with exploitable pytest rewards, we measure PRIME through chain-of-thought monitoring, direct probes, and activation-level concept vectors. We find that PRIME emerges in a staged sequence before sustained reward hacking, and that its current direct-probe score forecasts later hack onset and severity even when the visible hack rate is still low. PRIME also adapts when the evaluator changes, retargeting to whichever proxy--gold gap remains rewarded and persisting when gold reward suppresses overt hacking, and ablating its activation directions reduces hacking. Across checkpoints, in-domain PRIME tracks out-of-domain misalignment. Together these results suggest that exploitable proxy RL amplifies a proxy-internalization capability upstream of visible hacking, making PRIME a candidate early-warning signal for broader alignment risk.
Chinese Translation
奖励黑客行为通常在其可见后进行研究，即模型在未能完成预期任务的情况下获得高代理奖励。我们则研究在这种失败出现之前，代理强化学习（Proxy RL）所教授的内容。我们引入了代理奖励内化与机制利用（Proxy Reward Internalization and Mechanistic Exploitation，PRIME），这是一种学习能力，用于评估任务的正确性、预测代理接受度，并推理可利用的代理与真实奖励之间的差距。在具有可利用 pytest 奖励的编码强化学习环境中，我们通过思维链监测、直接探测和激活级别概念向量来测量 PRIME。我们发现，PRIME 在持续的奖励黑客行为之前以分阶段的顺序出现，并且其当前的直接探测分数能够预测后续黑客行为的发生和严重性，即使在可见的黑客率仍然较低时。PRIME 还会在评估者变化时进行适应，重新针对任何仍然获得奖励的代理与真实奖励之间的差距，并在真实奖励抑制明显黑客行为时持续存在，而消减其激活方向则会减少黑客行为。在各个检查点中，领域内的 PRIME 跟踪领域外的不一致性。这些结果共同表明，可利用的代理强化学习在可见黑客行为之前放大了代理内化能力，使 PRIME 成为更广泛对齐风险的早期预警信号的候选者。
cs.AI / 141 / 2606.09724
Beyond Probabilistic Similarity: Structural, Temporal, and Causal Limitations of Retrieval-Augmented Generation in the Legal Domain
超越概率相似性：法律领域检索增强生成的结构、时间和因果限制
Abstract
Retrieval-Augmented Generation (RAG) has become a standard architectural response to unreliability in legal AI, yet high-profile failures, including fabricated citations submitted to courts and anachronistic legal content presented as current, continue to appear across jurisdictions. We argue that these failures are not residual confabulations to be eliminated by scaling language models, but symptoms of an architectural mismatch between probabilistic retrieval and the hierarchical, temporal, and institutional structure of legal knowledge. We develop the argument in three moves. First, we articulate the ontological commitment of legal knowledge as a triad of properties derivable from classical legal theory: hierarchical and mereological structure, diachronic dynamism under operational closure, and causal traceability of institutional provenance grounded in the duty of justification. Second, we identify three corresponding pathologies of retrieval (mereological blindness, diachronic blindness, and causal opacity), each developed with an operational definition, a failure mechanism, a canonical example, and detection criteria for diagnostic use. Third, we review the state of the art through this lens, showing that existing approaches address these requirements unevenly and do not yet compose into a paradigm that treats them as co-constitutive. From this analysis we derive four architectural commitments that characterize the deterministic-by-design direction for legal retrieval: ontological primacy, event reification, bitemporal correctness, and deterministic interaction protocols. The framework concerns quaestio juris (which norms apply and in what state) rather than the downstream tasks that act on identified norms, and addresses legislative and constitutional retrieval primarily, with interpretive time as an explicit extension.
Chinese Translation
检索增强生成（Retrieval-Augmented Generation, RAG）已成为应对法律人工智能不可靠性的标准架构响应，然而，诸如向法院提交的虚假引用和将过时法律内容呈现为当前内容等高调失败事件，仍在各个司法管辖区频繁出现。我们认为，这些失败并非可以通过扩展语言模型来消除的残余虚构，而是概率检索与法律知识的层级、时间和制度结构之间的架构不匹配的症状。我们通过三个步骤展开论证。首先，我们阐明法律知识的本体承诺，作为源自经典法律理论的三元属性：层级和部分整体结构、在操作闭合下的历时动态，以及基于正当理由义务的制度来源的因果可追溯性。其次，我们识别出三种相应的检索病理（部分整体盲点、历时盲点和因果不透明），每种病理都提供了操作定义、失败机制、典型示例和用于诊断的检测标准。第三，我们通过这一视角回顾现有技术的现状，表明现有方法对这些要求的应对不均衡，尚未形成一个将其视为共同构成的范式。从这一分析中，我们推导出四个架构承诺，特征化法律检索的设计决定性方向：本体优先性、事件具体化、双时间正确性和决定性交互协议。该框架关注法律问题（适用哪些规范及其状态），而非对已识别规范进行操作的下游任务，主要涉及立法和宪法检索，并将解释时间作为明确扩展。
cs.AI / 142 / 2606.09730
SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research
SearchSwarm：面向代理型大型语言模型的委托智能以进行长远深度研究
Abstract
Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inherently finite. Recent work explores a paradigm where a main agent decomposes tasks and dispatches subtasks to subagents, which execute and return only summarized results, conserving the main agent's context budget. However, performing this well requires delegation intelligence: the ability to decompose complex tasks, determine when and what to delegate, and integrate returned results into the ongoing workflow. Training data for this capability is scarce in naturally occurring text, and to our knowledge, how to synthesize such data and train models to acquire this capability remains largely unexplored in the open-source community. To bridge this gap, we present a preliminary exploration targeting deep research, a representative long-horizon agent task. Specifically, we design a harness that guides the model toward high-quality task decomposition and delegation, while constraining subagents to return results properly to support the main agent's workflow. The harness-guided trajectories naturally encode correct delegation decisions, which we use as supervised fine-tuning data to internalize delegation intelligence into model weights. Our resulting model, SearchSwarm-30B-A3B, achieves 68.1 on BrowseComp and 73.3 on BrowseComp-ZH, the best results among all models of comparable scale. We will release our harness, model weights, and training data to facilitate future research.
Chinese Translation
大型语言模型越来越被期望能够处理复杂的、长远的现实世界任务，这些任务的上下文需求可能无限增长，而模型的上下文窗口本质上是有限的。最近的研究探索了一种范式，其中主代理分解任务并将子任务分派给子代理，后者执行并仅返回总结结果，从而节省主代理的上下文预算。然而，良好地执行这一过程需要委托智能：即分解复杂任务、确定何时以及委托什么的能力，并将返回的结果整合到持续的工作流程中。自然发生文本中用于这种能力的训练数据稀缺，且据我们所知，如何合成此类数据并训练模型以获得此能力在开源社区中仍然基本未被探索。为填补这一空白，我们提出了一项初步探索，针对深度研究这一具有代表性的长远代理任务。具体而言，我们设计了一个工具，指导模型进行高质量的任务分解和委托，同时约束子代理正确返回结果以支持主代理的工作流程。工具引导的轨迹自然编码了正确的委托决策，我们将其用作监督微调数据，以将委托智能内化到模型权重中。我们得到的模型SearchSwarm-30B-A3B在BrowseComp上取得了68.1的成绩，在BrowseComp-ZH上取得了73.3的成绩，成为所有同规模模型中的最佳结果。我们将发布我们的工具、模型权重和训练数据，以促进未来的研究。
cs.AI / 143 / 2606.09748
Multi-Turn Evaluation of Deep Research Agents Under Process-Level Feedback
在过程级反馈下对深度研究代理的多轮评估
Abstract
Existing benchmarks for deep research agents (DRAs) assess only single-shot outputs, ignoring a key question: can DRAs improve their reports when guided by feedback? To investigate this, we conduct a multi-turn evaluation of DRAs under two feedback settings: self-reflection, in which the agent revises its report without any external diagnostic signal, and process-level feedback, in which the agent receives guidance targeting gaps in its research strategy. To enable process-level feedback, we design Research Gap Inference (RGI), a method that analyzes patterns of satisfied and unsatisfied rubric criteria to infer research-process gaps. Our analysis reveals three key findings: (i) under self-reflection, agents incorporate and regress on rubric criteria at nearly equal rates, yielding negligible net improvement; (ii) a single round of process-level feedback yields substantial gains, raising the normalized score by approximately $8$-$15$ points and yielding a roughly $35$-$40\%$ incorporation rate; (iii) these gains do not compound over subsequent turns, as agents regress on up to $24\%$ of previously satisfied criteria when rewriting the full report to address remaining gaps. Even with targeted guidance, reliable multi-turn improvement remains out of reach for the DRA architectures we evaluate. Our code and results are publicly available at https://github.com/sabharwalrishabh/Multi-Turn-Evaluation-of-DRAs.
Chinese Translation
现有的深度研究代理（DRA）基准仅评估单次输出，忽视了一个关键问题：在反馈指导下，DRA能否改善其报告？为此，我们在两种反馈设置下对DRA进行多轮评估：自我反思，即代理在没有任何外部诊断信号的情况下修订其报告；过程级反馈，即代理收到针对其研究策略中存在的缺口的指导。为了实现过程级反馈，我们设计了研究缺口推断（Research Gap Inference, RGI）方法，该方法分析满足和不满足标准的模式，以推断研究过程中的缺口。我们的分析揭示了三个关键发现：（i）在自我反思下，代理以几乎相等的速度纳入和回归于标准，导致净改善微乎其微；（ii）一次过程级反馈带来了显著的提升，使标准化得分提高约8-15分，纳入率约为35-40%；（iii）这些提升在后续轮次中并未累积，因为代理在重写完整报告以解决剩余缺口时，对多达24%的先前满足的标准出现回归。即使在有针对性的指导下，我们评估的DRA架构在可靠的多轮改进方面仍然难以实现。我们的代码和结果可在https://github.com/sabharwalrishabh/Multi-Turn-Evaluation-of-DRAs获取。
cs.AI / 144 / 2606.09751
Collaborative Human-Agent Protocol (CHAP)
协作人机协议 (CHAP)
Abstract
Foundation models are moving from response generation into operational roles. They plan across steps, call tools, request human input, coordinate with other agents, and increasingly carry responsibility for work that affects customers, claims, code, contracts, and clinical decisions. Production deployments are no longer one human supervising one model. They are multi-human, multi-agent collaborations that cross teams, time zones, and trust boundaries. The technical surface for this collaboration remains weakly specified. When an agent drafts a response and a human edits it before it ships, the moment of human judgement is the most valuable signal in the system. In current practice it is recorded, if at all, in application code, chat threads, ticket comments, and tribal memory. Two protocol standards address adjacent concerns: MCP standardises agent access to tools and data, and A2A standardises agent-to-agent interoperability. Neither defines the shared workspace in which humans and agents perform accountable work together. This paper presents CHAP, the Collaborative Human-Agent Protocol. Under CHAP, the override that used to vanish into a chat thread becomes a structured event carrying a diff, a rationale, and a content hash. The handoff between shifts becomes a portable envelope rather than a pinned message. The human approval of an agent's draft becomes a non-repudiable signed decision that can be replayed years later. The protocol achieves this through a small Core (workspaces, participants, tasks, artefacts, and an append-only evidence log) together with composable profiles that add review, modes, routing, deliberation, handoff, identity, signatures, and transparency-backed audit as deployments require them. Specification, reference implementation, conformance suite, and worked examples are available at: https://github.com/BrightbeamAI/chap
Chinese Translation
基础模型正从响应生成转向操作角色。它们跨步骤进行规划，调用工具，请求人类输入，与其他代理协调，并日益承担影响客户、索赔、代码、合同和临床决策的工作责任。生产部署不再是一个人监督一个模型，而是跨团队、跨时区和跨信任边界的多个人、多代理协作。这种协作的技术表面仍然弱定义。当代理草拟响应并在发出之前由人类编辑时，人类判断的时刻是系统中最有价值的信号。在当前实践中，这一过程如果有记录，通常仅存在于应用代码、聊天记录、工单评论和部落记忆中。有两个协议标准解决了相关问题：MCP 标准化了代理对工具和数据的访问，而 A2A 标准化了代理间的互操作性。然而，这两者都没有定义人类和代理共同执行可问责工作的共享工作空间。本文提出了 CHAP，即协作人机协议。在 CHAP 下，以前消失在聊天记录中的覆盖变成了一个结构化事件，携带差异、理由和内容哈希。班次之间的交接变成了一个可移植的信封，而不是一个固定的消息。人类对代理草稿的批准变成了一个不可否认的签署决定，可以在多年后重放。该协议通过一个小型核心（工作空间、参与者、任务、文物和仅附加的证据日志）以及可组合的配置文件实现，后者根据部署需求增加审查、模式、路由、审议、交接、身份、签名和透明度支持的审计。规范、参考实现、合规套件和示例可在以下网址获取：https://github.com/BrightbeamAI/chap
cs.AI / 145 / 2606.09774
SIGA: Self-Evolving Coding-Agent Adapters for Scientific Simulation
SIGA：用于科学仿真的自我进化编码代理适配器
Abstract
Advanced scientific simulators expose specialized input languages that turn simulation goals into executable configurations, but learning them can cost domain scientists hours to days. We study simulator setup as a problem of agent-tool interface grounding: what minimal simulator-specific adaptations are needed for an off-the-shelf coding agent to operate real scientific software? Our intuition is that coding agents already know how to navigate files, edit code, run commands, and repair outputs, but they lack the simulator's executable contract: its vocabulary, structural constraints, validation rules, and termination conditions. We introduce SIGA, a Simulator-Interface Grounding Adapter that supplies this contract through retrieval, procedural memory, in-trajectory validation, and validation-enforced termination. We primarily evaluate SIGA on GEOS, an open-source multiphysics simulator used in subsurface science. SIGA produces a complete GEOS deck in about five minutes with TreeSim above 0.90, matching an extended-budget human expert who took about three hours, a roughly 36x wall-clock speedup. On a harder held-out set, grounding raises TreeSim from 0.720 to 0.789, a roughly 10% relative gain over the bare agent, and can reduce the across-seed standard deviation by 16x. Self-evolution further improves SIGA by rewriting adapter contents from prior trajectories, yielding the highest held-out GEOS mean and matching or outperforming the strongest hand-designed configuration. Transfers to OpenFOAM and LAMMPS show that the dominant mechanism shifts by interface: validation matters most when structural completeness is the bottleneck, while memory and retrieval matter most when domain correctness is the bottleneck. These results suggest that lightweight, self-improvable grounding layers can turn general coding agents into practical operators of scientific software.
Chinese Translation
先进的科学仿真器提供了专门的输入语言，将仿真目标转化为可执行的配置，但学习这些语言可能需要领域科学家花费数小时到数天的时间。我们将仿真器设置视为代理工具接口对接的问题：为了使现成的编码代理能够操作真实的科学软件，需要进行哪些最小的仿真器特定适配？我们的直觉是，编码代理已经知道如何浏览文件、编辑代码、运行命令和修复输出，但它们缺乏仿真器的可执行契约：其词汇、结构约束、验证规则和终止条件。我们提出了SIGA（仿真器接口对接适配器），通过检索、程序记忆、轨迹内验证和验证强制终止来提供这一契约。我们主要在GEOS（一个用于地下科学的开源多物理场仿真器）上评估SIGA。SIGA在大约五分钟内生成一个完整的GEOS配置，其TreeSim值超过0.90，匹配一位花费约三小时的扩展预算人类专家，约实现了36倍的实际时间加速。在一个更难的保留集上，对接将TreeSim从0.720提升至0.789，相较于裸代理大约提高了10%的相对增益，并且可以将跨种子标准差减少16倍。自我进化进一步通过重写先前轨迹中的适配器内容来改善SIGA，产生了最高的保留GEOS均值，并匹配或超越了最强的手工设计配置。转移到OpenFOAM和LAMMPS表明，主导机制因接口而异：当结构完整性是瓶颈时，验证最为重要；而当领域正确性是瓶颈时，记忆和检索最为重要。这些结果表明，轻量级、自我改进的对接层可以将通用编码代理转变为科学软件的实用操作员。
cs.AI / 146 / 2606.09809
Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting
评估卡：人工智能评估报告的解释层
Ghosh, Avijit, Reuel, Anka, Chim, Jenny, Kennedy, Wm. Matthew, Yadav, Srishti, Mickel, Jennifer, Long, Yanan, Tran, Andrew, Kornilova, Anastassia, Stachura, Damian, Klyman, Kevin, Friedrich, Felix, Sania, Jeba, Lamparth, Max, Batzner, Jan, Mishra, Anoop, Habba, Eliya, Hao, Yixiong, Heath, Nathan, Rismani, Shalaleh, Gohar, Usman, Loehr, Andrea, Manheim, David, Dhar, Ruchira, Nelaturu, Sree Harsha, Sinha, Aarush, Choshen, Leshem, Sharma, Drishti, Khire, Ishan, Saha, Amit, Sahoo, Subramanyam, Hardy, Michael, Riegler, Michael Alexander, Manghnani, Kabir, Lin, Michelle, Jiang, Yanan, Huang, Yilin, Yehudai, Asaf, Ji, Jessica, Hofmann, Aris, Akhtar, Mubashara, Moniz, Nuno, Jernite, Yacine, Biderman, Stella, Talat, Zeerak, Koyejo, Sanmi, Kochenderfer, Mykel, Solaiman, Irene
Abstract
AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs. The cost is interpretive: readers cannot reliably compare results across sources, identify what a report omits, or trace an aggregate claim to its underlying evidence. Recent efforts address isolated components but leave three gaps: they cover only narrow slices of the evaluation lifecycle and do not compose into a single interpretable record; they specify static representations that do not differentiate the questions different stakeholders bring to the same evidence; and they remain proposals on paper, lacking the extraction infrastructure required for adoption at scale. We present \EvalCards{}, an operational reporting layer that composes benchmark metadata, evaluation run data, and model metadata into a unified record. We (1) derive a reporting schema from a structured review of 52 papers and 10 stakeholder interviews, (2) implement four interpretive signals (reproducibility, documentation completeness, provenance and risk, and score comparability), rendered through reader modes calibrated to research and non-research audiences, and (3) deploy a monitoring tool that applies \EvalCards{} across 5,816 models, 635 benchmarks, and 101,843 results, surfacing systematic gaps in current reporting practice.
Chinese Translation
人工智能评估结果大规模产生，但在排行榜、模型卡、基准论文和公司博客中报告不一致。这种成本是解释性的：读者无法可靠地比较不同来源的结果，识别报告中遗漏的内容，或追溯汇总声明的基础证据。近期的努力解决了孤立的组成部分，但仍存在三个空白：它们仅覆盖评估生命周期的狭窄部分，无法组合成单一可解释的记录；它们指定的静态表示未能区分不同利益相关者对同一证据提出的问题；并且它们仍然停留在纸面上的提案，缺乏大规模采用所需的提取基础设施。我们提出了 extit{EvalCards}{}，这是一个操作性报告层，将基准元数据、评估运行数据和模型元数据组合成一个统一的记录。我们（1）从对52篇论文和10次利益相关者访谈的结构化审查中推导出报告模式，（2）实现了四个解释信号（可重复性、文档完整性、来源和风险、得分可比性），通过针对研究和非研究受众校准的阅读模式呈现，以及（3）部署了一种监测工具，应用 extit{EvalCards}{} 于5,816个模型、635个基准和101,843个结果，揭示了当前报告实践中的系统性差距。
cs.CL / 1 / 2606.07519
Bidirectional Small-Granularity Search between Code and Text
代码与文本之间的双向小粒度搜索
Abstract
We introduce the novel task of bidirectional small-granularity search between code and text, where the queries are small snippets of text or code and the results are also small fragments of the opposite modality, i.e., code or text. This task establishes direct links between text in scientific publications and corresponding code segments, in support of better and faster understanding of scientific methods. We introduce a large dataset for the proposed task that includes a training partition with textual descriptions of code generated automatically using GPT-4, and three testing partitions, one in-domain and two out-of-domain (OOD) that contain manually-annotated data as well as material from other domains. We also propose a modular approach to address this task. Our approach shares an encoder across four different subtasks that learn start/end of answer spans in both directions. We show that our method achieves good results in-domain, and encouraging results OOD. This suggests that addressing this task with automatically-generated data is possible, but there is exciting future work to be done.
Chinese Translation
我们提出了一个新颖的任务，即代码与文本之间的双向小粒度搜索，其中查询是小段文本或代码，结果也是相应模态的小片段，即代码或文本。该任务在科学出版物中的文本与相应代码段之间建立了直接联系，以支持对科学方法的更好和更快理解。我们为该任务引入了一个大型数据集，包括一个训练部分，包含使用GPT-4自动生成的代码文本描述，以及三个测试部分，一个是领域内的，两个是领域外的（OOD），其中包含手动标注的数据以及来自其他领域的材料。我们还提出了一种模块化的方法来解决这一任务。我们的方法在四个不同的子任务中共享一个编码器，学习答案跨度的开始/结束位置，支持双向搜索。我们展示了我们的方法在领域内取得了良好的结果，并在领域外取得了令人鼓舞的结果。这表明，使用自动生成的数据来解决这一任务是可行的，但仍有许多令人兴奋的未来工作待开展。
cs.CL / 2 / 2606.07520
TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles
TinyJudge：通过轻量级专家集成实现不可验证约束对齐
Abstract
Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints, ranging from verifiable ones (e.g., output length) to unverifiable ones (e.g., tone). Reinforcement learning with verifiable rewards has emerged as a paradigm for IF tasks, leveraging LLM-as-a-judge to assess unverifiable constraints. However, we empirically find that this approach remains a significant bottleneck, suffering from severe reward hacking and higher computational overhead. In this work, we first analyze the generalization capabilities of unverifiable constraints and discover that specific constraints exhibit distinct, high-generalization patterns. Motivated by this, we propose TinyJudge, a framework that employs an ensemble of specialized tiny language models ($\sim0.6B$) to provide rewards for soft constraints. By distilling expertise from frontier models into these tiny models, it achieves high-precision, lightweight evaluation. Extensive evaluations across five benchmarks demonstrate that TinyJudge outperforms the baselines by $\sim10\%$ in average performance and $12\%$ in reward precision. Crucially, it also achieves a $3\times$ speedup in total training time. Our work provides a scalable and robust path for aligning LLMs with unverifiable human instructions.
Chinese Translation
指令遵循（Instruction Following, IF）是大型语言模型（LLMs）的核心能力，要求严格遵循各种约束，这些约束包括可验证的（例如，输出长度）和不可验证的（例如，语气）。利用可验证奖励的强化学习已成为IF任务的一种范式，利用LLM作为评判者来评估不可验证约束。然而，我们的实证研究发现，这种方法仍然是一个显著的瓶颈，面临严重的奖励操控和更高的计算开销。在本研究中，我们首先分析了不可验证约束的泛化能力，并发现特定约束表现出不同的高泛化模式。基于此，我们提出了TinyJudge，一个采用轻量级专用小型语言模型（约0.6B参数）集成的框架，用于为软约束提供奖励。通过将前沿模型的专业知识提炼到这些小型模型中，它实现了高精度、轻量级的评估。在五个基准测试中的广泛评估表明，TinyJudge在平均性能上超越基线约10%，在奖励精度上提高了12%。更重要的是，它还实现了总训练时间的3倍加速。我们的工作为将LLMs与不可验证的人类指令对齐提供了一条可扩展且稳健的路径。
cs.CL / 3 / 2606.07521
Evaluating Hallucinations in Domain-Adapted Large Language Models
评估领域适应大型语言模型中的幻觉现象
Abstract
This study investigates the phenomenon of hallucinations in domain-adapted Large Language Models (LLMs), focusing on the fine-tuning of the Llama-2 model with the Lamini dataset. Hallucinations, or the generation of nonsensical or unfaithful content by LLMs, pose a significant challenge, especially when these models are fine-tuned with domain-specific data. Our methodology involves a series of experiments testing memorization, recall, and reasoning capabilities of the fine-tuned LLM, comparing its performance on novel question-answer pairs and domain-specific information. We found that while the model shows proficiency in tasks similar to its training data, its capability to accurately reason about and recall new domain-specific information remains limited, leading to instances of hallucination. The model demonstrates a tendency to provide correct answers with extra information, suggesting an inclination toward over-generation. These results suggest important limitations of fine-tuning-only approaches for mitigating hallucinations when adapting LLMs to specialized domains and underscore the need for more robust methods in adapting LLMs to specialized domains. The study also provides insights into the varying performance of LLMs on different types of information, revealing a comparative weakness in handling domain-specific queries.
Chinese Translation
本研究探讨了领域适应大型语言模型（LLMs）中的幻觉现象，重点关注对Llama-2模型进行微调时使用Lamini数据集。幻觉，即LLMs生成无意义或不真实内容的现象，尤其在这些模型使用领域特定数据进行微调时，构成了重大挑战。我们的研究方法包括一系列实验，测试微调后的LLM的记忆、回忆和推理能力，并比较其在新问题-答案对和领域特定信息上的表现。我们发现，尽管该模型在与训练数据相似的任务中表现出色，但在准确推理和回忆新的领域特定信息方面的能力仍然有限，导致了幻觉现象的出现。该模型表现出倾向于提供正确答案的同时附加额外信息，表明其有过度生成的倾向。这些结果表明，仅依靠微调的方法在将LLMs适应专业领域时存在重要局限性，并强调了在将LLMs适应专业领域时需要更强大的方法。本研究还提供了对LLMs在处理不同类型信息时表现差异的见解，揭示了其在处理领域特定查询时的相对弱点。
cs.CL / 4 / 2606.07522
Community-Specific Slang and Entity Detection via Semantic Shift in Fine-Tuned Language Models
基于语义变化的社区特定俚语和实体检测方法研究
Abstract
We propose an unsupervised method of resolving slang, unique entities, and folklore from online communities by isolating words in the lexicon that have the highest magnitude of semantic shift. Semantic shift is defined as the evolution of a word's encoded representation as a result of fine-tuning a pretrained Large Language Model (LLM) on a community-specific text corpus. This value is inversely proportional to the cosine similarity between the base model's encoded representation of a word, and a fine-tuned model's encoded representation. We fine-tune the DistilRoBERTa model on text corpora collected from 3 Reddit subreddits (r/Technology, r/Gaming, r/WorldofWarcraft), model a distribution of cosine similarity over the lexicon, and show that one can successfully resolve words that have unique significance to the community by pulling data in the bottom 10-percentile. In contrast, we show that data in the top 10-percentile consist of words that carry relatively universal semantics.
Chinese Translation
我们提出了一种无监督的方法，通过隔离词汇中具有最高语义变化幅度的词，来解决在线社区中的俚语、独特实体和民间传说。语义变化被定义为通过在特定社区文本语料库上微调预训练的大型语言模型（LLM）而导致的词的编码表示的演变。该值与基础模型对一个词的编码表示与微调模型的编码表示之间的余弦相似度成反比。我们在从三个Reddit子版块（r/Technology、r/Gaming、r/WorldofWarcraft）收集的文本语料上微调DistilRoBERTa模型，建模词汇的余弦相似度分布，并展示通过提取底部10百分位的数据，可以成功识别对社区具有独特意义的词。相反，我们表明，顶部10百分位的数据包含相对普遍语义的词。
cs.CL / 5 / 2606.07523
Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering
用于尼泊尔法律领域问答的检索增强生成框架
Abstract
Legal domains in high-resource languages like English have widely adopted artificial intelligence for legal question answering. However, data scarcity in low resource languages such as Nepali has limited the training of large language models on Nepali legal texts. This study presents the first application of a Retrieval Augmented Generation based model for Nepali legal question answering using case laws extracted from the Nepal Kanun Patrika digital archive. Using BM25 on chunked documents, the approach achieved a top precision at one of 91 percent, and up to 75 percent with the multilingual E5 large model. Evaluation of generated answers showed 74 percent groundedness, 85 percent truthfulness according to an automated judge model, and 84 percent human evaluated truthfulness when using BM25 document retrieval, with a 92 percent successful answer generation rate. These results demonstrate that the RAG pipeline can effectively address the gap in legal question answering for low resource languages and provide a foundation for reliable AI systems in the Nepali legal domain.
Chinese Translation
在英语等高资源语言的法律领域，人工智能已被广泛应用于法律问答。然而，尼泊尔语等低资源语言的数据稀缺限制了在尼泊尔法律文本上训练大型语言模型的能力。本研究首次应用基于检索增强生成（Retrieval Augmented Generation, RAG）模型于尼泊尔法律问答，使用从尼泊尔《法律公报》（Nepal Kanun Patrika）数字档案中提取的案例法。通过对分块文档使用BM25方法，该方法在精确度上达到了91%的最佳水平，并在使用多语言E5大型模型时达到了75%。生成答案的评估显示，使用BM25文档检索时，答案的基础性达到了74%，根据自动评判模型的评估，真实度为85%，而人类评估的真实度为84%，成功答案生成率为92%。这些结果表明，RAG管道能够有效填补低资源语言法律问答的空白，并为尼泊尔法律领域可靠的人工智能系统提供基础。
cs.CL / 6 / 2606.07524
ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding
ABLE：通过基于归因的大模型嵌入表示和映射大型语言模型
Abstract
The explosive growth of large language models (LLMs) has created a heterogeneous and poorly documented ecosystem, making systematic model comparison increasingly important for provenance auditing, security analysis, and model selection. Existing representation methods struggle to address this setting efficiently. Approaches analyzing internal parameters are powerful when architectures are compatible, but face scalability barriers under structural heterogeneity, while methods relying on external outputs may conflate models with similar behaviors and are difficult to align in richer output spaces across different tokenizers. To bridge this gap, we propose ABLE (Attribution-Based Large-model Embedding), a framework that leverages the interpretability space to construct model representations. By aggregating gradient-based feature attributions via a tokenizer-agnostic word-level alignment, ABLE captures model-specific input-sensitivity patterns rather than only surface-level outputs. Beyond empirical utility, we provide a stability analysis showing that, under standard regularity assumptions for differentiable Transformer-style models, ABLE induces a Lipschitz-continuous parameter-to-embedding map with finite-sample convergence guarantees. Extensive experiments on 239 open-source LLMs demonstrate that our training-free approach achieves competitive or superior performance in relation prediction, model routing, and benchmark score prediction.
Chinese Translation
大型语言模型（LLMs）的爆炸性增长创造了一个异质且文档记录不全的生态系统，使得系统化模型比较在来源审计、安全分析和模型选择中变得愈发重要。现有的表示方法在高效应对这一环境时面临挑战。分析内部参数的方法在架构兼容时强大，但在结构异质性下面临可扩展性障碍，而依赖外部输出的方法可能会混淆具有相似行为的模型，并且在不同的分词器之间的丰富输出空间中难以对齐。为了解决这一问题，我们提出了ABLE（基于归因的大模型嵌入），一个利用可解释性空间构建模型表示的框架。通过通过与分词器无关的词级对齐聚合基于梯度的特征归因，ABLE 捕捉模型特定的输入敏感性模式，而不仅仅是表层输出。除了经验效用外，我们还提供了稳定性分析，表明在可微分的Transformer风格模型的标准正则性假设下，ABLE 诱导了一个具有有限样本收敛保证的Lipschitz连续参数到嵌入的映射。对239个开源LLM的广泛实验表明，我们的无训练方法在关系预测、模型路由和基准分数预测方面实现了具有竞争力或更优的性能。
cs.CL / 7 / 2606.07525
Implicit Causal Graph Construction in Text via Chain Discovery
通过链发现进行文本中的隐式因果图构建
Abstract
Causal graphs in text are typically populated by observable, predefined events. In contrast, we study implicit causal graph construction from text by treating each described cause-effect pair as the begin- and endpoint of an underlying latent causal graph and using large language models (LLMs) to infer intermediate causal events. We compare end-to-end graph construction with methods that frame the task as causal chain discovery. In the latter, graphs are built either by aggregating inferred chains or by progressively expanding partial chains through an iterative search process. We further explore Wisdom of the Crowd extensions that access causal knowledge from multiple LLMs in post-hoc aggregation and collaborative inference settings. We analyze trade-offs among these approaches and evaluate the validity of inferred causal relations using a manually curated database of 1,560 scientifically validated causal pairs. This database-based evaluation is proposed as reliable, resource-efficient, and transferable to settings where ground-truth graphs are unavailable.
Chinese Translation
文本中的因果图通常由可观察的、预定义的事件构成。与此不同，我们研究通过将每个描述的因果对视为潜在隐性因果图的起点和终点，从文本中构建隐式因果图，并利用大型语言模型（LLMs）推断中间因果事件。我们比较了端到端图构建与将任务框定为因果链发现的方法。在后者中，图的构建要么通过聚合推断出的链，要么通过迭代搜索过程逐步扩展部分链。我们进一步探索了众智扩展，通过后期聚合和协作推理设置访问来自多个LLM的因果知识。我们分析了这些方法之间的权衡，并使用一个手动整理的包含1,560个科学验证因果对的数据库评估推断的因果关系的有效性。该基于数据库的评估被提议为可靠、资源高效，并且可以转移到缺乏真实图的环境中。
cs.CL / 8 / 2606.07526
GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation
GraphLoRA：面向结构的低秩适应在大语言模型推荐中的应用
Abstract
Large Language Models (LLMs) have shown strong potential for recommendation (LLMRec) due to their powerful reasoning and generalization abilities. However, effectively aligning the textual semantics modeled by LLMs with the collaborative signals remains a key challenge. Existing methods either translate collaborative information into textual prompts or inject pre-trained embeddings into the LLM, both of which treat structural information as static input and fail to capture high-order relational dependencies. To bridge this gap, we propose GraphLoRA, a novel framework that generalizes low-rank adaptation from independent to structure-aware propagation. GraphLoRA embeds a trainable graph message-passing network within the low-rank adaptation pathway, enabling structural signals to propagate through the parameter space. This design allows collaborative topology to explicitly guide parameter updates, fostering deep integration between graph-structured and textual semantic information. Extensive experiments on multiple benchmarks demonstrate that GraphLoRA not only outperforms state-of-the-art LLM-based recommendation methods but also achieves superior generalization, effectively balancing structural reasoning capability with computational efficiency. Code is available at \href{https://github.com/wgj15965/GraphLoRA}{https://github.com/wgj15965/GraphLoRA}.
Chinese Translation
大型语言模型（LLMs）由于其强大的推理和泛化能力，在推荐系统（LLMRec）中展现出强大的潜力。然而，有效地将LLMs建模的文本语义与协同信号对齐仍然是一个关键挑战。现有方法要么将协同信息转换为文本提示，要么将预训练的嵌入注入LLM，这两者都将结构信息视为静态输入，未能捕捉高阶关系依赖。为了解决这一问题，我们提出了GraphLoRA，一个将低秩适应从独立传播推广到结构感知传播的新框架。GraphLoRA在低秩适应路径中嵌入了一个可训练的图消息传递网络，使结构信号能够在参数空间中传播。该设计允许协同拓扑明确指导参数更新，促进图结构信息与文本语义信息之间的深度融合。在多个基准上的广泛实验表明，GraphLoRA不仅超越了最先进的基于LLM的推荐方法，而且在泛化能力上也表现出色，有效平衡了结构推理能力与计算效率。代码可在 exttt{https://github.com/wgj15965/GraphLoRA} 获取。
cs.CL / 9 / 2606.07527
Post-training is (Massive) Supervised Learning
后训练是（大规模）监督学习
Abstract
The prevailing paradigm for training LLMs has evolved to rely on a massive post-training phase consisting of SFT and RL. In this position paper, we argue that this methodology effectively marks a reversion to the ``pre-train then fine-tune'' approach of the BERT era, explicitly tailoring models to the desired behaviors and specific benchmarks on which they are evaluated. We begin with a historical overview of LLMs, describing the different phases of the LLM evolution. We argue that the current landscape is remarkably similar to the early days of LLMs, where task performance heavily relied on fitting the models to in-distribution datasets. To empirically demonstrate this, we compare pre-trained models to randomly initialized ones, by fine-tuning both variants on modern reasoning datasets and evaluating them on competitive math and code benchmarks. We show that models post-trained from scratch yield highly non-trivial performance. Our findings suggest that current post-training methodologies function primarily as a distribution-fitting mechanism. We finish by positing that developing generally capable models and systems requires moving beyond extensive post-training for predefined behaviors, shifting instead toward training procedures where models ``learn how to learn''.
Chinese Translation
当前大规模语言模型（LLMs）训练的主流范式已经演变为依赖于一个包含监督微调（SFT）和强化学习（RL）的庞大后训练阶段。在这篇立场论文中，我们认为这种方法实际上标志着对BERT时代“预训练然后微调”方法的回归，明确地将模型调整为所需的行为和评估的特定基准。我们首先回顾了LLMs的发展历史，描述了LLM演变的不同阶段。我们认为，目前的形势与LLMs早期阶段非常相似，当时任务性能在很大程度上依赖于将模型拟合到分布内数据集。为了实证证明这一点，我们将预训练模型与随机初始化模型进行比较，通过在现代推理数据集上微调这两种变体，并在竞争性的数学和代码基准上进行评估。我们展示了从头开始后训练的模型表现出高度非平凡的性能。我们的研究结果表明，当前的后训练方法主要作为一种分布拟合机制。最后，我们提出，开发具有一般能力的模型和系统需要超越对预定义行为的广泛后训练，而是转向让模型“学习如何学习”的训练程序。
cs.CL / 10 / 2606.07528
BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models
BEACON：用于大型语言模型跨模型幻觉检测的行为熵聚合
Abstract
Hallucination in large language models (LLMs), defined as the generation of factually incorrect or unsupported content, remains a critical barrier to reliable deployment. We present BEACON (Behavioral Entropy Aggregation for Cross-model hallucination detectiON), a black-box hallucination detection framework that operates purely on model outputs without requiring access to internal representations or external knowledge bases. BEACON extracts a 31-dimensional feature vector from structured multi-pass generation, integrating NLI-based semantic entropy, embedding geometry, chain-of-thought consistency, and paraphrase stability signals. A gradient-boosted classifier trained on 7,617 labeled examples across seven benchmarks achieves 0.8123 +/- 0.0102 AUROC (95% CI: 0.7632-0.8251), outperforming standalone semantic entropy (+0.2298) and SelfCheckGPT-style consistency baselines (+0.2457). Feature importance analysis shows that hallucination is inherently multi-dimensional, requiring combined uncertainty signals. An efficient 5-call variant achieves 0.7795 AUROC, enabling practical deployment across black-box LLM APIs.
Chinese Translation
大型语言模型（LLMs）中的幻觉，定义为生成事实不正确或不支持的内容，仍然是可靠部署的一个关键障碍。我们提出了BEACON（行为熵聚合用于跨模型幻觉检测），这是一个黑箱幻觉检测框架，完全基于模型输出进行操作，而无需访问内部表示或外部知识库。BEACON从结构化的多轮生成中提取31维特征向量，整合了基于自然语言推理（NLI）的语义熵、嵌入几何、思维链一致性和释义稳定性信号。基于7,617个标注示例在七个基准上训练的梯度提升分类器达到了0.8123 +/- 0.0102的AUROC（95%置信区间：0.7632-0.8251），超越了独立的语义熵基线（+0.2298）和SelfCheckGPT风格的一致性基线（+0.2457）。特征重要性分析表明，幻觉本质上是多维的，需结合不确定性信号。一个高效的5次调用变体达到了0.7795的AUROC，使得在黑箱LLM API中的实际部署成为可能。
cs.CL / 11 / 2606.07529
CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models
CAPruner：用于增强大型语言模型3D空间推理的概念邻接场景图修剪器
Abstract
Large language models (LLMs) have recently been applied to 3D vision-language (3D-VL) tasks, which require spatial reasoning to identify target objects relative to anchors. Scene graphs are commonly employed to represent such relations, but reasoning over complete graphs incurs high token costs and computational inefficiencies, motivating the need for pruning. Existing pruning methods primarily rely on spatial proximity and often remove task-relevant relations, thereby undermining reliable spatial reasoning. To address these limitations, we derive a key requirement for scene graph pruning: preserving spatial relations that are most pertinent to the specific 3D-VL task. Guided by this insight, we propose the Conceptual-Adjacent Scene Graph Pruner (CAPruner). CAPruner integrates fuzzy semantic relevance with spatial proximity to estimate the importance of relations, enabling the selection of critical relations in a task-specific context. Moreover, to avoid costly relation-level annotations, CAPruner is trained by supervising the aggregated scores of each node's incident edges. Extensive experiments demonstrate that CAPruner effectively preserves relations essential for spatial reasoning, leading to substantial performance improvements of LLMs on 3D-VL tasks. Code is available at https://github.com/fz-zsl/CAPruner.
Chinese Translation
大型语言模型（LLMs）最近被应用于3D视觉-语言（3D-VL）任务，这些任务需要空间推理以识别相对于锚点的目标对象。场景图通常用于表示这些关系，但对完整图的推理会产生高昂的令牌成本和计算低效，因而需要进行修剪。现有的修剪方法主要依赖于空间邻近性，往往会移除与任务相关的关系，从而削弱可靠的空间推理。为了解决这些局限性，我们推导出场景图修剪的一个关键要求：保留与特定3D-VL任务最相关的空间关系。在这一见解的指导下，我们提出了概念邻接场景图修剪器（CAPruner）。CAPruner将模糊语义相关性与空间邻近性相结合，以评估关系的重要性，从而在任务特定的上下文中选择关键关系。此外，为了避免高昂的关系级别注释，CAPruner通过监督每个节点的入边的聚合得分进行训练。大量实验表明，CAPruner有效地保留了对空间推理至关重要的关系，显著提高了LLMs在3D-VL任务上的性能。代码可在 https://github.com/fz-zsl/CAPruner 获取。
cs.CL / 12 / 2606.07530
Finding New Connections between Concepts from Medline Database Incorporating Domain Knowledge
结合领域知识从Medline数据库中寻找概念之间的新联系
Abstract
In this digital world, data is everything and significantly impacts our everyday lives. Interestingly, in this small world, everything is part of an ecosystem, where everything is connected, directly or indirectly. The same thing happens to data as well. In most cases, it may seem like a particular topic does not have any connection with another one, but in reality, they are connected through a mutually related topic. Therefore, in this research, we will discuss an adaptive model modified from the ABC model by Don R. Swanson, a Literature-Based Discovery (LBD) Model, to find the hidden connections between Concepts of Interest. The model demonstrates that two topics, A and C are different and have no relationship. But they have a common topic, B that can be used to connect topics A and C This famous model will be used in this discussion to connect Medical Concepts.
Chinese Translation
在这个数字化的世界中，数据无处不在，并对我们的日常生活产生重大影响。有趣的是，在这个小世界里，一切都是生态系统的一部分，所有事物都是直接或间接相互连接的。数据也不例外。在大多数情况下，某个特定主题似乎与另一个主题没有任何联系，但实际上，它们通过一个相互关联的主题相连。因此，在本研究中，我们将讨论一个基于Don R. Swanson提出的ABC模型改编的自适应模型，该模型是一个文献基础发现（Literature-Based Discovery, LBD）模型，用于寻找感兴趣概念之间的隐含联系。该模型表明，两个主题A和C是不同的，并且没有关系。但它们有一个共同的主题B，可以用来连接主题A和C。这个著名的模型将在本讨论中用于连接医学概念。
cs.CL / 13 / 2606.07531
mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models
mllm-shap：用于文本-音频多模态大语言模型的沙普利值可解释性平台
Abstract
We introduce mllm-shap, an open-source Python framework designed to extend Shapley Value (SV) explainability from text-only Large Language Models to Multimodal LLMs (MLLMs) processing joint text and audio inputs. While text-based attribution is well-studied, mllm-shap addresses three critical challenges unique to the multimodal regime: (1) Modality-aware coalition masking, which manages the interleaved processing of discrete text tokens and dense audio encoder frames. (2) Multi-turn conversation tracking, utilizing per-token metadata to maintain role and modality context. (3) Phonetic alignment-based token grouping, a novel technique that reduces the coalition space by 10x to 50x, rendering SV estimation computationally feasible for long-form audio. The platform implements five SV estimation strategies, including a Complementary Contributions (CC) estimator with Neyman-optimal allocation that demonstrates superior convergence over standard Monte Carlo baselines. mllm-shap is provided as a pip-installable package featuring an interactive web-based GUI for granular attribution visualization. To our knowledge, this is the first publicly available framework providing a complete, reproducible pipeline for SV-based explainability in text-audio MLLMs.
Chinese Translation
我们介绍了 mllm-shap，这是一个开源的 Python 框架，旨在将沙普利值（Shapley Value, SV）可解释性从仅处理文本的大语言模型扩展到处理文本和音频输入的多模态大语言模型（Multimodal LLMs, MLLMs）。虽然基于文本的归因研究已相当成熟，但 mllm-shap 针对多模态环境中的三个关键挑战进行了处理：（1）模态感知的联盟掩蔽，管理离散文本标记和密集音频编码帧的交错处理；（2）多轮对话跟踪，利用每个标记的元数据来维护角色和模态上下文；（3）基于语音对齐的标记分组，这是一种新颖的技术，将联盟空间缩小了 10 倍到 50 倍，使得长音频的 SV 估计在计算上变得可行。该平台实现了五种 SV 估计策略，包括具有 Neyman 最优分配的互补贡献（Complementary Contributions, CC）估计器，显示出优于标准蒙特卡洛基线的收敛性。mllm-shap 作为一个可通过 pip 安装的包提供，具有一个基于网页的交互式 GUI，用于细粒度的归因可视化。根据我们的了解，这是第一个公开可用的框架，提供了一个完整、可重复的管道，用于文本-音频 MLLMs 中基于 SV 的可解释性。
cs.CL / 14 / 2606.07532
Principled Agent Debate: Adversarial Arbitration for Sycophancy Reduction in Large Language Models
原则性代理辩论：针对大型语言模型中拍马屁行为减少的对抗性仲裁
Abstract
RLHF-trained models are systematically biased toward agreement over accuracy, a structural property of the training process. We present Principled Agent Debate (PAD), a multi-agent architecture that mitigates identity-framed sycophancy by arbitrating between two models tuned to opposing philosophical dispositions, with a pragmatist synthesizer evaluating both arguments blind to their origins. This paper evaluates a prompt-based instantiation of PAD. The key mechanisms are static dispositional tuning, identity stripping before synthesis, single-round independent argumentation, and blind arbitration. We evaluate five instantiations on 200 stratified questions from SycophancyEval. All PAD variants (AnCifer, DeWin, FeynStein, BurGal, Trident) significantly outperform the single-model baseline (18.5%) and instructed-opposition baseline (29.0%), with DeWin achieving 48.5% accuracy (z=6.36, p<0.001 versus both). The variants are not significantly different from each other at n=200. The BurGal variant achieves 53.0% but functions as an architectural validity check; its consensus/heterodox axis structurally favors the heterodox model on every benchmark question. A pre-training floor affects an estimated 40% of questions; fine-tuned disposition models are the identified next step.
Chinese Translation
经过强化学习人类反馈（RLHF）训练的模型在系统上偏向于一致性而非准确性，这是训练过程的一个结构性特征。我们提出了原则性代理辩论（Principled Agent Debate，PAD），这是一种多代理架构，通过在两个调校为对立哲学倾向的模型之间进行仲裁，减轻了身份框架下的拍马屁行为，同时由一个实用主义合成器在不考虑其来源的情况下评估双方论点。本文评估了PAD的基于提示的实例化。关键机制包括静态倾向调校、合成前的身份剥离、单轮独立论证和盲仲裁。我们在来自SycophancyEval的200个分层问题上评估了五个实例化。所有PAD变体（AnCifer、DeWin、FeynStein、BurGal、Trident）在性能上显著优于单一模型基线（18.5%）和指令对立基线（29.0%），其中DeWin达到了48.5%的准确率（z=6.36, p<0.001，相较于两者均显著）。在n=200的情况下，这些变体之间没有显著差异。BurGal变体达到了53.0%的准确率，但其功能作为架构有效性检查；其共识/异端轴在每个基准问题上结构性地偏向异端模型。预训练的底线影响了约40%的问题；微调后的倾向模型被确定为下一步。
cs.CL / 15 / 2606.07533
Bridging Traditional Explainability Methods and Multimodal Multilingual Models: An XAI-Based Analysis
桥接传统可解释性方法与多模态多语言模型：基于XAI的分析
Abstract
Multimodal Large Language Models (MLLMs) effectively integrate text and audio to interpret context in complex interactive dialogues. However, the internal mechanisms by which heterogeneous modalities influence model behavior remain opaque. While Shapley Values (SV) provide a robust, model-agnostic framework for local explainability in text-based NLP, their extension to multimodal data is hindered by cross-channel dependencies, intricate dialogue structures, and the prohibitive computational complexity of dense audio representations. In this work, we formalize a multimodal extension of the Shapley Value framework, treating discrete text tokens and aligned audio segments as cooperative features. To ensure computational feasibility, we deploy a suite of efficient estimation strategies: exact SV computation for low-dimensional inputs and sampling-based approximations - including Monte Carlo permutations and stratified sampling with Neyman-optimal allocation - to minimize variance under constrained computational budgets. To resolve the granularity mismatch between modalities, we propose Spectrogram-Guided Phonetic Alignment (SGPA), a novel preprocessing method that maps high-frequency audio streams to interpretable, word-aligned segments. Our contribution is twofold: first, we provide an open-source, model-agnostic Python package and a companion GUI for the computation and interactive visualization of multimodal attributions. Second, we evaluate our framework using curated subsets of the VoiceBench and Infinity Instruct datasets across diverse multilingual scenarios. Our experimental results reveal that input modality is a primary driver of attribution volatility and demonstrate that standard syntactic importance proxies often fail to predict model attention in multimodal, cross-lingual contexts.
Chinese Translation
多模态大型语言模型（MLLMs）有效地整合文本和音频，以解释复杂交互对话中的上下文。然而，不同模态如何影响模型行为的内部机制仍然不透明。虽然Shapley值（SV）为基于文本的自然语言处理提供了一个稳健的、与模型无关的局部可解释性框架，但由于跨通道依赖、复杂的对话结构以及密集音频表示的计算复杂性，其在多模态数据上的扩展受到限制。在本研究中，我们正式化了Shapley值框架的多模态扩展，将离散文本标记和对齐的音频片段视为合作特征。为了确保计算的可行性，我们部署了一系列高效的估计策略：对低维输入进行精确的SV计算，以及基于采样的近似方法——包括蒙特卡洛置换和采用Neyman最优分配的分层采样——以在受限的计算预算下最小化方差。为了解决模态之间的粒度不匹配问题，我们提出了一种新颖的预处理方法——谱图引导的音素对齐（SGPA），该方法将高频音频流映射到可解释的、与单词对齐的片段。我们的贡献有两个方面：首先，我们提供了一个开源的、与模型无关的Python包及其配套的图形用户界面，用于多模态归因的计算和交互式可视化。其次，我们使用精心策划的VoiceBench和Infinity Instruct数据集的子集，在多种多语言场景中评估我们的框架。我们的实验结果显示，输入模态是归因波动的主要驱动因素，并且表明标准的句法重要性代理通常无法预测多模态跨语言环境中的模型注意力。
cs.CL / 16 / 2606.07535
Multilingual Refusal Alignment for Safer Large Language Models
多语言拒绝对齐以提高大型语言模型的安全性
Abstract
As Large Language Models (LLMs) are deployed globally, ensuring their safety and alignment across multiple languages becomes paramount. However, safety behaviors often vary unpredictably between languages, posing significant challenges for consistent and ethical AI. In this work, we systematically investigate the dynamics of multilingual alignment, exploring whether single-language alignment transfers cross-lingually, how language consistency is preserved during training, and the resulting trade-offs with general knowledge capabilities. We introduce RefusEU, a novel refusal alignment dataset covering 12 European languages, including a dedicated test set for evaluating current state-of-the-art models. Our controlled Direct Preference Optimization (DPO) experiments provide two key insights: aligning models exclusively in English is insufficient to ensure cross-lingual safety, even for the same harm categories, whereas training on multilingual datasets can improve safety without degrading general performance, as measured by the Global MMLU benchmark.
Chinese Translation
随着大型语言模型（LLMs）在全球范围内的部署，确保其在多种语言中的安全性和对齐性变得至关重要。然而，安全行为在不同语言之间往往表现出不可预测的差异，这给一致性和伦理AI带来了重大挑战。在本研究中，我们系统地探讨了多语言对齐的动态，研究单语言对齐是否能够跨语言转移、在训练过程中如何保持语言一致性，以及与一般知识能力之间的权衡。我们引入了RefusEU，这是一个涵盖12种欧洲语言的新型拒绝对齐数据集，其中包括用于评估当前最先进模型的专用测试集。我们的控制性直接偏好优化（DPO）实验提供了两个关键见解：仅在英语中对齐模型不足以确保跨语言的安全性，即使对于相同的伤害类别，而在多语言数据集上进行训练可以提高安全性，而不会降低一般性能，这一点通过全球MMLU基准得到了验证。
cs.CL / 17 / 2606.07537
From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data
从架构到输出：大型语言模型中幻觉的结构起源及数据的放大作用
Abstract
Large language models hallucinate--producing fluent, confident, factually wrong outputs--with a consistency that persists across generations and scales. Existing taxonomies classify hallucination by output type, distinguishing intrinsic from extrinsic failures and faithfulness from factuality divergence. These frameworks are descriptively rigorous but do not identify which internal mechanism produced a given instance. This paper analyses hallucination as a structural consequence of three architectural decisions that together form a compound failure system. Self-attention's co-occurrence learning substitutes statistical proximity for semantic meaning and produces entity confusion, fact misattribution, and semantic drift. The maximum likelihood estimation training objective optimises next-token probability without factual constraint, rewarding statistically plausible outputs regardless of their truth value. Autoregressive decoding's permanent left-to-right commitment under exposure bias ensures that a single wrong token cascades forward through the entire output sequence without revision. Dataset pathologies--long-tail deficiencies, training bias, and synthetic pollution--amplify these vulnerabilities but do not independently cause them. We make three contributions. First, we map each mechanism to a specific output category in the Alansari and Luqman taxonomy, locating intrinsic hallucination in self-attention, extrinsic hallucination in MLE, and logical inconsistency in autoregressive decoding. Second, we show that each commonly cited dataset pathology exploits one of these mechanisms rather than originating hallucination independently. Third, we identify the diagnostic limitation of output-type-only classification and contrast it with inference-layer mitigation approaches.
Chinese Translation
大型语言模型会产生幻觉——生成流畅、自信但事实错误的输出——这种现象在不同的生成和规模中表现出一致性。现有的分类法通过输出类型对幻觉进行分类，区分内在失败与外在失败，以及忠实性与事实偏差。这些框架在描述上是严谨的，但未能识别出产生特定实例的内部机制。本文分析了幻觉作为三种架构决策的结构性结果，这三种决策共同形成了一个复合失败系统。自注意力的共现学习用统计接近性替代语义意义，导致实体混淆、事实误归属和语义漂移。最大似然估计训练目标在没有事实约束的情况下优化下一个标记的概率，奖励统计上合理的输出，而不考虑其真实值。自回归解码在曝光偏差下的永久左到右承诺确保了一个错误的标记在整个输出序列中向前级联而不进行修正。数据集病态——长尾缺陷、训练偏差和合成污染——放大了这些脆弱性，但并不独立导致它们。我们做出了三项贡献。首先，我们将每个机制映射到 Alansari 和 Luqman 分类法中的特定输出类别，将内在幻觉定位于自注意力，将外在幻觉定位于最大似然估计，将逻辑不一致性定位于自回归解码。其次，我们展示了每个常被引用的数据集病态如何利用这些机制，而不是独立产生幻觉。第三，我们识别了仅基于输出类型分类的诊断局限性，并将其与推理层缓解方法进行了对比。
cs.CL / 18 / 2606.07540
Finding Hidden Relationships Between Medical Concepts by Leveraging Metamap and Text Mining Techniques
利用 MetaMap 和文本挖掘技术发现医学概念之间的隐含关系
Abstract
Text is one of the most common ways to store data in this computerized world. At a glance, it may seem that those data are not interconnected. But in reality, data can have hidden connections. Therefore, in this research, a new model has been presented that can find hidden relationships between two medical concepts by using MetaMap and appropriate text-mining techniques. Specifically, the model creates a new comprehensive index structure and can find cross-document hidden links connecting topics of interest that most existing approaches have ignored. Experiments show the effectiveness of the proposed model in discovering new connections between topics.
Chinese Translation
文本是这个计算机化世界中存储数据的最常见方式之一。乍一看，这些数据似乎没有相互关联。但实际上，数据之间可能存在隐含的联系。因此，在本研究中提出了一种新模型，该模型可以通过使用 MetaMap 和适当的文本挖掘技术找到两个医学概念之间的隐含关系。具体而言，该模型创建了一种新的综合索引结构，并能够找到连接感兴趣主题的跨文档隐含链接，而大多数现有方法对此忽视。实验表明，所提出的模型在发现主题之间的新连接方面具有有效性。
cs.CL / 19 / 2606.07547
Liberating LLM Capabilities in Full-Duplex Speech Models
解放全双工语音模型中的大语言模型能力
Abstract
Speech-based large language models are typically constrained to spoken replies, which limits their user-facing outputs to what can be verbalized and suppresses text-native capabilities such as code generation, structured analysis, and multi-step reasoning in realtime interaction, for tasks that require persistent, structured, and inspectable intermediate outputs. Existing work improves spoken reasoning or full-duplex turn-taking, but still treats text as a hidden intermediate state or a subordinate modality rather than a first-class output channel. We propose Listen-Write-Speak (LWS), a text-first tri-channel paradigm in which a single autoregressive LLM continuously listens to user audio, writes visible free-form text as its primary output, and speaks a realtime oral response in parallel under a shared causal attention context. This behavior is implemented entirely through a Token Schema, requiring no architectural modifications, and learned via a two-stage data pipeline that synthesizes per-second cognitive annotations consistent with the revealed input timeline. Empirically, LWS demonstrates strong full-duplex interaction on Full-Duplex-Bench, reaches 4.72 on VoiceBench AlpacaEval, achieves 92.6% writing-speaking consistency, and consistently outperforms its internal ablations on URO-Bench. These results suggest that visible writing can serve as a first-class output channel for speech interaction without sacrificing realtime responsiveness. The code and dataset are available on the project page: https://royalzhang.com/project/lws-page/.
Chinese Translation
基于语音的大语言模型通常仅限于口头回复，这限制了用户输出的内容仅限于可口头表达的内容，并抑制了文本原生能力，如代码生成、结构化分析和实时交互中的多步骤推理，尤其是在需要持久、结构化和可检查的中间输出的任务中。现有研究改善了口头推理或全双工轮流发言，但仍然将文本视为隐藏的中间状态或次要模态，而不是一流的输出通道。我们提出了Listen-Write-Speak (LWS)模型，这是一种以文本为先的三通道范式，其中单个自回归大语言模型持续监听用户音频，作为其主要输出写出可见的自由格式文本，并在共享因果注意上下文中并行地进行实时口头响应。该行为完全通过Token Schema实现，无需架构修改，并通过一个两阶段的数据管道学习，该管道合成与揭示的输入时间线一致的每秒认知注释。从实证上看，LWS在Full-Duplex-Bench上展示了强大的全双工交互，VoiceBench AlpacaEval上达到了4.72，写作与口语一致性达92.6%，并在URO-Bench上持续优于其内部消融实验。这些结果表明，可见的书写可以作为语音交互的一流输出通道，而不牺牲实时响应能力。代码和数据集可在项目页面获取：https://royalzhang.com/project/lws-page/
cs.CL / 20 / 2606.07555
Priors Persist Through Suppression: A Stroop Paradigm for Lexical Override
抑制中的先验持续存在：一种用于词汇覆盖的斯特鲁普范式
Abstract
Glossaries, technical specifications, and system prompts routinely ask language models to use familiar words in unfamiliar ways. When this works, the lexical prior persists through override rather than being replaced: it continues to operate after the local rule applies, with the rule lowering its logit rather than installing the new meaning on top. We test this with a Stroop-style paradigm: a remapping rule ("doctor" means "forest") pitted against the query word's lexical-prior distractor ("hospital"), with matched neutral controls. Across 11 open-weight models spanning four families and 1B--9B parameters, lexical-prior strength predicts interference even after item-level controls for answer prior, frequency, tokenization, and prompt wording. Activation patching on five aligned models locates a source-position triplet (definition subject, definition target, query word) that nearly fully recovers the conflict effect (aggregate $R \in [0.92, 1.06]$). A definition-target swap shows the triplet performs binding rather than identity matching. Dissociation experiments isolate target preservation as the binding-specific signature: distractor suppression occurs under matched, swap, and item-mismatched conditions alike, whereas target logit collapse occurs only when the definition-target position is corrupted. Behavior and mechanism converge on the same channel: the lexical prior is where both interference originates and where override leaves its mark.
Chinese Translation
术语表、技术规格和系统提示常常要求语言模型以不熟悉的方式使用熟悉的词汇。当这种情况发生时，词汇先验在覆盖过程中持续存在，而不是被替换：它在局部规则应用后继续发挥作用，规则降低其对数几率，而不是在其上安装新含义。我们通过一种斯特鲁普风格的范式来测试这一点：一个重映射规则（“医生”意味着“森林”）与查询词的词汇先验干扰项（“医院”）相对抗，同时匹配中性对照。在涵盖四个家族和10亿到90亿参数的11个开放权重模型中，词汇先验强度预测了干扰，即使在对答案先验、频率、标记化和提示措辞进行项目级控制后。对五个对齐模型的激活补丁定位了一个源位置三元组（定义主题、定义目标、查询词），几乎完全恢复了冲突效应（聚合 $R ext{ in } [0.92, 1.06]$）。定义目标的交换表明该三元组执行的是绑定而非身份匹配。解离实验将目标保留隔离为绑定特定的特征：在匹配、交换和项目不匹配条件下，干扰项抑制均会发生，而目标对数几率崩溃仅在定义目标位置被破坏时发生。行为和机制在同一通道上收敛：词汇先验是干扰的起源，也是覆盖留下痕迹的地方。
cs.CL / 21 / 2606.07559
Phantom transitions in language model fine-tuning
语言模型微调中的幻影转变
Abstract
Fine-tuning a language model on contexts whose correct completion has a near-synonym competitor often fails silently. The cross-entropy loss decreases monotonically while the correct token never overtakes the competitor in rank. We study this regime across five transformer architectures spanning two families and a fivefold parameter range, on ten hand-selected near-synonym contexts. We instrument these failures with an order parameter combining the predicted distribution and pairwise embedding overlaps. It decomposes additively into a signal, tracking the model's commitment to the correct token over its nearest competitor, and a background drag, set by how the embedding bulk leaks probability into the score. This isolates two failure modes. In kinematic failure the signal stays small. In structural failure the drag actively worsens as fine-tuning proceeds. We observe sharp catapult-like jumps in the order parameter that resemble a phase transition. A central negative result organises the paper. The transitions are phantoms. The spontaneous-symmetry-breaking interpretation is ruled out by direct measurement. Catapult-like jumps still appear under LoRA fine-tuning with the token embedding matrix exactly unchanged during training, where no geometric phase transition is possible. The discontinuity lives entirely in the softmax readout. A small number of dimensionless quantities organise the trajectory across architectures. One is consistent across all five under full fine-tuning. A second sorts architectures into two classes by bulk embedding distribution and predicts LoRA sufficiency. As a blind test, the framework predicts the critical learning rate of a held-out architecture, not used to fit any parameter, to within 2.1% of a subsequent learning-rate sweep. Findings concern the near-synonym mechanism only and should not be extrapolated without recalibration.
Chinese Translation
在上下文中微调语言模型时，如果正确的完成有一个近义词竞争者，往往会悄然失败。交叉熵损失单调下降，而正确的标记在排名上从未超过竞争者。我们研究了这一现象，涵盖了五种变换器架构，跨越两个家族和五倍的参数范围，针对十个手动选择的近义词上下文。我们通过一个结合预测分布和成对嵌入重叠的序参量来分析这些失败。它可加性分解为一个信号，跟踪模型对正确标记的承诺相对于其最近竞争者的情况，以及一个背景阻力，由嵌入体积如何将概率泄漏到得分中所决定。这使我们能够区分两种失败模式。在运动失效中，信号保持较小。在结构失效中，随着微调的进行，阻力会主动恶化。我们观察到序参量中出现类似弹射的急剧跳跃，类似于相变。一个核心的负结果组织了本文。转变是幻影的。自发对称破缺的解释通过直接测量被排除。在LoRA微调下，即使在训练过程中标记嵌入矩阵完全不变，弹射般的跳跃仍然出现，此时不可能发生几何相变。该不连续性完全存在于softmax输出中。少量无量纲量组织了跨架构的轨迹。其中一个在完全微调下在所有五个架构中是一致的。第二个通过体嵌入分布将架构分为两类，并预测LoRA的充分性。作为盲测，该框架预测了一个未用于拟合任何参数的保留架构的临界学习率，误差在2.1%以内，接近后续的学习率扫描。研究结果仅涉及近义词机制，且不应在未重新校准的情况下进行外推。
cs.CL / 22 / 2606.07560
Function-Vector Heads Are Two Populations: Writers and Cancellers in In-Context Learning
功能向量头是两个群体：上下文学习中的写入者和取消者
Abstract
Function-vector (FV) heads (Todd et al., 2024) are typically identified by the magnitude of their causal contribution to in-context rule tasks, under the implicit assumption that the top set is a homogeneous functional class. This assumption fails. We replace magnitude-only ranking with a sign-preserving criterion (refined DLA + permutation FDR) and validate each candidate by path patching. The FV head population then splits into two opposing sub-populations: writers push the rule-correct logit up; cancellers push it down. A four-condition canonical verdict holds in $13/15$ cells across three model families and six Pythia scales, and a sign-shuffle rejects homogeneity in $5/6$ main cells. The structure is invisible to magnitude-only ranking: Todd's top-$20$ captures $64\%$ of cancellers but only $4\%$ of writers on the hierarchical task, and $59\%$ of writers but only $8\%$ of cancellers on the modular task. We rule out six artefact accounts on all $27$ canceller (cell, head) pairs: induction overlap, sinks, generic importance, rank-$1$ copy-suppression, V-cascade, and rank-nearest non-FV controls. Zero-ablating cancellers yields $+0.13$ to $+0.29$ nats of logit gain in $6/6$ main cells with a directionally consistent $+2$ to $+7$ pp accuracy effect.
Chinese Translation
功能向量（Function-vector，FV）头（Todd et al., 2024）通常通过其对上下文规则任务的因果贡献的大小来识别，隐含假设顶层集合是一个同质功能类。然而，这一假设并不成立。我们用一种保持符号的标准（精炼的 DLA + 排列 FDR）替代仅基于大小的排名，并通过路径修补验证每个候选者。FV 头群体随后分裂为两个对立的子群体：写入者将规则正确的 logit 推高；取消者则将其推低。在三种模型系列和六个 Pythia 规模的 $13/15$ 个单元中，四条件的经典判决成立，而在 $5/6$ 个主要单元中，符号随机化拒绝了同质性。该结构对仅基于大小的排名是不可见的：Todd 的前 $20$ 捕获了 $64\%$ 的取消者，但仅捕获了 $4\\%$ 的写入者在层次任务中，而在模块任务中则捕获了 $59\\%$ 的写入者但仅捕获了 $8\\%$ 的取消者。我们排除了所有 $27$ 个取消者（单元，头）对的六种伪影解释：归纳重叠、汇聚、通用重要性、秩-1 复制抑制、V-级联和秩最近的非 FV 控制。零消除取消者在 $6/6$ 个主要单元中产生了 $+0.13$ 到 $+0.29$ 的 logit 增益，并且方向一致地产生了 $+2$ 到 $+7$ 个百分点的准确度效应。
cs.CL / 23 / 2606.07608
Subtitle-Aligned Fine-Tuning of Whisper for Swiss German ASR: Benchmark Contamination, Convention Mismatch, and an Honest Baseline at 25.6% WER (13.8% cWER)
针对瑞士德语自动语音识别的Whisper模型的字幕对齐微调：基准污染、约定不匹配及25.6% WER（13.8% cWER）的诚实基线
Abstract
We present a systematic study of fine-tuning OpenAI's Whisper large-v3 for Swiss German ASR, using 1,367 hours of broadcast speech paired with Standard German subtitles as weak supervision. Through 16 iterative training runs on an NVIDIA DGX Spark (Grace Blackwell, 128 GB unified memory, up to 1 PFLOP FP4), we compare LoRA and full fine-tuning of the 1.55B-parameter model, investigate hallucination root causes, and quantify the effect of data quality, subtitle alignment, and training strategy. Our best model achieves 25.6% measured WER on the All Swiss German Dialects Test Set (ASGDTS) in an honest evaluation on strictly disjoint data. A harmonized error analysis separating genuine errors from valid stylistic variation (tense, word order, Swiss orthography) yields a content WER (cWER) of 13.8%, counting only actual recognition failures. Bias-corrected estimation reduces this to 8.5%, suggesting the true error rate is roughly one third of measured WER. We demonstrate that published state-of-the-art Swiss German ASR results (17.1-17.5% WER) are inflated by benchmark contamination: a vanilla Whisper model self-trained on the ASGDTS test set with zero Swiss German data achieves 13.88% WER, surpassing all published systems. Experiments with Phi-4-multimodal show an even stronger memorization effect (3.9% WER), revealing that the benchmark primarily measures convention matching rather than dialectal comprehension. We release two models, a LoRA adapter (25.32% WER, 13.9% cWER) and a full fine-tuned model (25.60% WER, 13.8% cWER), among the few publicly available, honestly evaluated Whisper models for Swiss German, under Apache 2.0 with full reproducibility, requiring no institutional data agreements.
Chinese Translation
我们对OpenAI的Whisper large-v3模型在瑞士德语自动语音识别（ASR）中的微调进行了系统研究，使用1367小时的广播语音与标准德语字幕配对作为弱监督。通过在NVIDIA DGX Spark（Grace Blackwell，128 GB统一内存，最高1 PFLOP FP4）上进行16次迭代训练，我们比较了LoRA和1.55B参数模型的全微调，探讨了幻觉的根本原因，并量化了数据质量、字幕对齐和训练策略的影响。我们的最佳模型在严格不重叠的数据上对所有瑞士德语方言测试集（ASGDTS）的评估中达到了25.6%的测量WER。通过将真实错误与有效的风格变异（时态、词序、瑞士正字法）分开进行的和谐错误分析，得出了13.8%的内容WER（cWER），仅计算实际的识别失败。经过偏差修正的估计将这一数值降低至8.5%，表明真实错误率大约是测量WER的三分之一。我们证明了已发布的瑞士德语ASR最新结果（17.1-17.5% WER）因基准污染而被夸大：一个在ASGDTS测试集上自我训练的普通Whisper模型，在没有瑞士德语数据的情况下达到了13.88%的WER，超越了所有已发布的系统。与Phi-4-multimodal的实验显示出更强的记忆效应（3.9% WER），揭示基准主要测量的是约定匹配而非方言理解。我们发布了两个模型，一个LoRA适配器（25.32% WER，13.9% cWER）和一个全微调模型（25.60% WER，13.8% cWER），它们是为数不多的公开可用、经过诚实评估的瑞士德语Whisper模型，遵循Apache 2.0协议，具备完全可重复性，无需机构数据协议。
cs.CL / 24 / 2606.07753
ReadingMachine: A Computational Methodology for Structured Corpus Reading and Large-Scale Synthesis
阅读机器：一种结构化语料库阅读与大规模合成的计算方法
Abstract
ReadingMachine is a computational methodology for structured corpus reading that uses large language models to perform bounded reading operations over entire document collections. Rather than relying on retrieval or recursive summarization, the approach decomposes analysis into inspectable stages including insight extraction, semantic clustering, theme generation, and iterative omission detection. By delaying irreversible compression and explicitly tracking intermediate representations, the method prioritizes coverage, traceability, and preservation of disagreement across large corpora. The system is demonstrated on a heterogeneous corpus of 152 industrial policy documents, producing more than 17,500 extracted insights and a structured thematic map. ReadingMachine is released as an open-source experimental framework for large-scale qualitative synthesis and corpus analysis.
Chinese Translation
阅读机器是一种用于结构化语料库阅读的计算方法，利用大型语言模型对整个文档集合执行有限的阅读操作。该方法并不依赖于检索或递归摘要，而是将分析分解为可检查的阶段，包括洞察提取、语义聚类、主题生成和迭代遗漏检测。通过推迟不可逆压缩并明确跟踪中间表示，该方法优先考虑覆盖率、可追溯性以及在大规模语料库中保留不同意见。该系统在152份异构工业政策文档的语料库上进行了演示，产生了超过17,500条提取的洞察和一个结构化的主题图。阅读机器作为一个开源实验框架发布，用于大规模定性合成和语料库分析。
cs.CL / 25 / 2606.07778
Unlocking Latent Value: Taxonomy-Guided Recovery of High-Performing Data from Low-Tier Web Corpora
解锁潜在价值：基于分类法的高性能数据从低层次网络语料库的回收
Abstract
Dominant web data curation pipelines for pretraining collapse document quality into a single composite score, systematically missing high-value content along dimensions the scorer underweights. We present a taxonomy-driven framework that recovers this value by filtering along semantically meaningful dimensions that composite scores fail to capture. First, building on the ESSENTIAL-WEB taxonomy, we introduce two novel dimensions: timeliness and cultural specificity, both of which show low pairwise NMI with existing ones. We annotate 14M documents using Qwen2.5 32B and distill into a lightweight 0.5B model. To enable rapid corpus-wide annotation, we additionally train a 73M multi-task MLP on E5 embeddings, achieving 50x inference throughput. Second, to navigate the combinatorial explosion of filter configurations, we introduce a compute-efficient two-pass framework: Pass 1 identifies the strongest dimension signals at small scale; Pass 2 constructs and evaluates conjunctive and disjunctive compound filters from the top performers - identifying high-performing configurations at a fraction of full scaling-law cost. Applying the selected filters to deprioritized web data, taxonomy-filtered subsets outperform their unfiltered baselines and even surpass the highest-quality tier. On mid-tier data, our best filter improves over its unfiltered baseline by 12.1% on reasoning, 9.5% on coding, and 2.0% on knowledge benchmarks, exceeding unfiltered top-tier data by 6.7% on reasoning and 13.7% on coding. Furthermore, filtered data from two tiers below the typical production threshold improves by 22.3% on reasoning and 19.5% on coding over its unfiltered baseline, surpassing top-tier data on coding benchmarks. These results establish that vast latent value remains locked in deprioritized web data, and that multi-dimensional taxonomy filtering is a principled, compute-efficient key to unlocking it.
Chinese Translation
主流的网络数据整理管道在预训练过程中将文档质量压缩为单一的综合评分，系统性地忽视了在评分者低估的维度上的高价值内容。我们提出了一种基于分类法的框架，通过在综合评分未能捕捉的语义相关维度上进行过滤，从而恢复这一价值。首先，基于 ESSENTIAL-WEB 分类法，我们引入了两个新维度：时效性和文化特异性，这两个维度与现有维度的成对归一化互信息（NMI）均较低。我们使用 Qwen2.5 32B 对 1400 万文档进行标注，并提炼为一个轻量级的 0.5B 模型。为了实现快速的全语料库标注，我们还在 E5 嵌入上训练了一个 7300 万多任务 MLP，达到了 50 倍的推理吞吐量。其次，为了应对过滤配置的组合爆炸，我们引入了一种计算高效的双通道框架：第一通道在小规模下识别最强的维度信号；第二通道从表现最佳的配置中构建和评估合取和析取复合过滤器——以较低的全尺度成本识别高性能配置。将选定的过滤器应用于被降级的网络数据后，基于分类法过滤的子集在性能上优于未过滤的基线，甚至超过了最高质量层次。在中层数据上，我们的最佳过滤器在推理、编码和知识基准上分别比未过滤的基线提高了 12.1%、9.5% 和 2.0%，在推理和编码上超过了未过滤的顶层数据 6.7% 和 13.7%。此外，来自低于典型生产阈值两个层次的过滤数据在推理和编码上分别比未过滤的基线提高了 22.3% 和 19.5%，在编码基准上超过了顶层数据。这些结果表明，降级的网络数据中仍然锁定着巨大的潜在价值，而多维分类法过滤是解锁这一价值的原则性和计算高效的关键。
cs.CL / 26 / 2606.07783
Evaluating RAG Reliability under Clean, Misleading, and Mixed Retrieval
在清晰、误导性和混合检索下评估 RAG 的可靠性
Abstract
Retrieval-Augmented Generation (RAG) is widely used to improve the factual reliability of large language models (LLMs) by grounding answers in retrieved evidence. In misinformation-rich environments, however, retrieved content may include plausible but incorrect information, raising concerns about the reliability of RAG-based information access systems. In this work, we propose an evaluation protocol to systematically test how the RAG system handles conflicts between parametric knowledge and evidence retrieved from context with varying amounts of misleading information. We target correct answers to factoid questions that the model responds to correctly, even when there is no retrieval, and use this to test the system with clean, poisoned, and mixed evidence. The proposed analytical framework combines parametric override and confidence metrics to assess when and how misleading information affects the generation process of LLMs. This study aims to provide insights into the robustness of RAG systems in information disorder scenarios.
Chinese Translation
检索增强生成（RAG）被广泛用于通过基于检索的证据来提高大型语言模型（LLMs）的事实可靠性。然而，在信息丰富的环境中，检索到的内容可能包含看似合理但实际上不正确的信息，这引发了对基于 RAG 的信息访问系统可靠性的担忧。在本研究中，我们提出了一种评估协议，以系统地测试 RAG 系统如何处理参数知识与从不同程度的误导性信息上下文中检索到的证据之间的冲突。我们针对模型能够正确回答的事实性问题，即使在没有检索的情况下也能给出正确答案，并利用这一点来测试系统在清晰、被污染和混合证据下的表现。所提出的分析框架结合了参数覆盖和置信度指标，以评估误导性信息何时以及如何影响 LLM 的生成过程。本研究旨在提供对 RAG 系统在信息混乱场景中鲁棒性的深入见解。
cs.CL / 27 / 2606.07810
SLMJury: Can Small Language Models Judge as Well as Large Ones?
SLMJury：小型语言模型能否与大型模型一样出色地进行判断？
Abstract
Large language models (LLMs) are widely used as judges for evaluating model outputs, but their high cost, latency, and opacity limit scalability. We introduce SLMJury, a framework for evaluating small language models (SLMs) as judges across two paradigms: closed-ended binary correctness and open-ended quality scoring. We benchmark 16 SLM judges (0.6B-14B parameters) from four model families across ten benchmarks: eight closed-ended tasks spanning mathematical, scientific, and general reasoning (N=64,824 judgments per configuration), plus SummEval and MT-Bench for summarization and conversational scoring. We formalize judging as a budget-conditioned function and study five dimensions. Four findings emerge. (1) The overthinking effect is domain-dependent: for most judges quick 10-token verdicts match or beat extended reasoning on mathematical judging (by 2-7% where they help), while reasoning wins on general tasks by up to 23%. (2) Domain generalization separates model families, with math-to-general accuracy gaps ranging from under 10% to nearly 40%. (3) Closed-ended and open-ended judging draw on different capabilities: the best binary judge (Phi-4) drops to rank 9 on MT-Bench, while reasoning-trained models invert this ordering. (4) Under the Reflect-Critique-Refine (RCR) debate protocol, multi-agent debate degrades accuracy across all tested configurations, whereas the top judges resist six adversarial personas with <=0.55% variance. Reliable automated evaluation does not require large proprietary models, yet no single SLM dominates. The leaderboard is available at https://anishh15.github.io/SLMJury/, and our framework code and pip package are publicly available at https://github.com/anishh15/SLMJury and https://pypi.org/project/slmjury/.
Chinese Translation
大型语言模型（LLMs）被广泛用作评估模型输出的裁判，但其高成本、延迟和不透明性限制了可扩展性。我们提出了SLMJury，一个评估小型语言模型（SLMs）作为裁判的框架，涵盖两种范式：封闭式二元正确性和开放式质量评分。我们基于十个基准测试对来自四个模型家族的16个SLM裁判（参数范围从0.6B到14B）进行了基准测试：八个封闭式任务涵盖数学、科学和一般推理（每个配置N=64,824个判断），以及用于摘要和对话评分的SummEval和MT-Bench。我们将判断形式化为一个预算条件函数，并研究了五个维度。得出了四个发现。（1）过度思考效应是领域依赖的：对于大多数裁判，快速的10-token裁决在数学判断中与延长推理相匹配或超越（在有帮助的情况下提高2-7%），而在一般任务中推理胜出可达23%。 （2）领域泛化将模型家族区分开来，数学到一般的准确性差距从不足10%到近40%不等。 （3）封闭式和开放式判断依赖于不同的能力：最佳的二元裁判（Phi-4）在MT-Bench上降至第9位，而经过推理训练的模型则反转了这一排序。 （4）在反思-批评-改进（Reflect-Critique-Refine, RCR）辩论协议下，多智能体辩论在所有测试配置中降低了准确性，而顶级裁判能够抵御六种对抗性角色，方差<=0.55%。可靠的自动评估并不需要大型专有模型，但没有单一的SLM占据主导地位。排行榜可在https://anishh15.github.io/SLMJury/获取，我们的框架代码和pip包可在https://github.com/anishh15/SLMJury和https://pypi.org/project/slmjury/公开获取。
cs.CL / 28 / 2606.07818
Representational Similarity and Model Behavior in Multi-Agent Interaction
多智能体交互中的表征相似性与模型行为
Abstract
Researchers have shown that neural similarity among humans predicts social closeness and cooperative success, whereas innovation often emerges from interactions among dissimilar individuals. We investigate whether these principles extend to artificial intelligence by examining interactions between large language models. In our experiments, 276 model pairs interact across eight games spanning both cooperation and novelty. We find that pairs with more similar representation spaces achieve significantly higher cooperation but exhibit reduced novelty and creativity. The effects of representational similarity on cooperation and novelty remain robust even after controlling for other factors such as performance disparity and model size. We also find that similarity in the early layers consistently shows the strongest association with cooperation and novelty, compared to the middle and later layers. This suggests that a central factor underlying these patterns could be the extent to which the two models share lexical and semantic grounding. Overall, representational similarity can be an important consideration in multi-agent system design.
Chinese Translation
研究者们已经表明，人类之间的神经相似性可以预测社会亲密度和合作成功，而创新往往源于不同个体之间的互动。我们通过考察大型语言模型之间的互动，研究这些原则是否适用于人工智能。在我们的实验中，276对模型在涵盖合作与新颖性的八个游戏中进行互动。我们发现，具有更相似表征空间的模型对在合作中取得显著更高的成功，但在新颖性和创造力方面表现降低。即使在控制其他因素（如性能差异和模型规模）后，表征相似性对合作和新颖性的影响仍然显著。我们还发现，早期层的相似性与合作和新颖性之间的关联始终最强，而中间层和后期层的关联较弱。这表明，这些模式背后的一个核心因素可能是两个模型在词汇和语义基础上的共享程度。总体而言，表征相似性在多智能体系统设计中可能是一个重要的考虑因素。
cs.CL / 29 / 2606.07822
The ACUTE Protocol: Operationalizing Language Model Activations for Better Calibration, Utility, and Trust
ACUTE 协议：将语言模型激活转化为更好的校准、效用和信任
Abstract
As language models improve and become increasingly deployed to solve a variety of tasks, trustworthiness becomes essential. Calibration is a good proxy for trust: well-calibrated confidence estimates help inform the risk versus reward tradeoff when trusting a specific model output. Unfortunately, even as models improve, they remain poorly calibrated, often biasing towards overconfidence. Additionally, calibration can be gamed: a policy that always predicts the base rate is perfectly calibrated, but completely uninformative. To resolve this, we develop a new metric, expected utility renormalized by the oracle (EURO), that balances calibration and informativeness. We also propose a general-purpose activation-based confidence, utility, and trust estimation protocol (ACUTE) to appropriately adjudicate uncertainty. The ACUTE protocol provides flexible, sample-efficient, and compute-efficient confidence estimators for 3 tasks including multiple choice question answering, tool-calling, and scientific document summarization across 6 models from 4 model families. ACUTE outperforms strong baselines on EURO, while maintaining low calibration error. Taken together, our work shows that equipping LLMs with the ACUTE protocol can improve calibration, utility, and trustworthiness in numerous settings.
Chinese Translation
随着语言模型的不断改进并被广泛应用于解决各种任务，信任性变得至关重要。校准是信任的良好代理：良好校准的置信度估计有助于在信任特定模型输出时权衡风险与收益。不幸的是，尽管模型在不断改进，它们的校准仍然较差，往往存在过度自信的偏差。此外，校准可以被操控：一个始终预测基准率的策略是完全校准的，但完全没有信息价值。为了解决这个问题，我们开发了一种新的度量标准，即由神谕重新标准化的期望效用（EURO），它在校准和信息性之间取得平衡。我们还提出了一种通用的基于激活的置信度、效用和信任估计协议（ACUTE），以适当地裁定不确定性。ACUTE 协议为包括多项选择问答、工具调用和科学文档摘要在内的 3 项任务提供灵活、样本高效和计算高效的置信度估计器，涵盖来自 4 个模型家族的 6 个模型。ACUTE 在 EURO 上超越了强基线，同时保持低校准误差。总体而言，我们的工作表明，给大型语言模型（LLMs）配备 ACUTE 协议可以在多种环境中提高校准、效用和可信度。
cs.CL / 30 / 2606.07853
Beyond English benchmarks: clinical llm evaluation in Brazilian Portuguese
超越英语基准：巴西葡萄牙语中的临床大语言模型评估
Abstract
Large Language Models are transforming the support for clinical decision and their application in real scenarios. Yet, most benchmarks are conducted in English, and cross-lingual evaluation is needed to tackle the language gaps in global access. We introduce ClinicalBr, the first bilingual benchmark for clinical decision built from real Brazilian case reports. The corpus contains 2,892 cases drawn from 28 SciELO medical journals, spanning 18 specialties, and is structured as parallel Portuguese-English pairs. Each case supports four evaluation tasks: diagnosis retrieval, differential diagnosis, exam recommendation, and treatment planning. We evaluate four models: MedGemma-27B, Sabi\'a-4, DeepSeek-R1, and o3-mini, across both languages. The central finding is that the Portuguese-English performance gap is task-dependent, not general. In diagnosis retrieval, English yields a consistent advantage across all models, with +7.5-12.1 accuracy points. This advantage disappears in differential diagnosis, exam recommendation, and treatment planning, where confidence intervals cross zero for most models and Portuguese completeness scores are marginally higher. Brazilian-endemic conditions proved easier than the full corpus, not harder, indicating that tropical presentations are adequately represented in current pre-training. Exam recommendation was the hardest task across all models and both languages, with F1 scores below 0.10, well below the differential diagnosis ceiling of 0.20-0.27.
Chinese Translation
大型语言模型正在改变临床决策的支持方式及其在实际场景中的应用。然而，大多数基准测试都是用英语进行的，因此需要跨语言评估以解决全球访问中的语言差距。我们介绍了ClinicalBr，这是第一个基于真实巴西病例报告构建的双语临床决策基准。该语料库包含来自28本SciELO医学期刊的2,892个病例，涵盖18个专业，并以平行的葡萄牙语-英语对的形式结构化。每个病例支持四个评估任务：诊断检索、鉴别诊断、检查推荐和治疗计划。我们评估了四个模型：MedGemma-27B、Sabi'a-4、DeepSeek-R1和o3-mini，涵盖两种语言。核心发现是，葡萄牙语-英语的表现差距是任务依赖的，而不是普遍的。在诊断检索中，英语在所有模型中均表现出一致的优势，准确率提高了7.5-12.1个百分点。这种优势在鉴别诊断、检查推荐和治疗计划中消失，大多数模型的置信区间跨越零，葡萄牙语的完整性评分略高。巴西地方性疾病的表现比整个语料库更容易，而不是更难，这表明热带表现已在当前的预训练中得到了充分代表。检查推荐是所有模型和两种语言中最困难的任务，F1分数低于0.10，远低于鉴别诊断的上限0.20-0.27。
cs.CL / 31 / 2606.07867
The Cold-Start Safety Gap in LLM Agents
大型语言模型代理的冷启动安全差距
Abstract
Are tool-calling LLM agents equally safe throughout a conversation? We discover they are not: agents are most vulnerable at the very start of a session and become substantially safer after a few regular agentic tasks -- a phenomenon we term the cold-start safety gap. To study this systematically, we introduce Safety Over Depth for Agents (SODA), a benchmark that controls how many regular agentic tasks the agent completes before encountering a safety threat, supporting up to 20 preceding tasks. Evaluating 7 models from 4 families, safety improves by 9--52% as the number of preceding regular agentic tasks increases from zero to twenty. Representation analysis confirms that model hidden states gradually shift toward a safety-aligned region as more preceding tasks are present. By systematically studying which part of the preceding conversation matters most, we find that the regular agentic tasks themselves are the primary driver of safety, while the agent's own prior responses have less effect on safety but are essential for preserving later utility. This conclusion is further supported by evaluation on open-source safety benchmarks (AgentHarm, Agent Safety Bench) and utility benchmarks (BFCL, API-Bank), confirming that warming up the agent with regular agentic tasks before deployment makes it safer and preserves full capability. Based on these findings, we recommend a simple deployment strategy: having the agent complete a few regular agentic tasks before possible exposure to safety-critical requests mitigates the cold-start safety gap. Our code is available at https://github.com/Trustworthy-ML-Lab/Agent-Cold-Start-Safety-Gap
Chinese Translation
工具调用的大型语言模型（LLM）代理在对话过程中是否始终安全？我们的研究发现并非如此：代理在会话开始时最为脆弱，并且在完成几个常规代理任务后安全性显著提高——这一现象我们称之为冷启动安全差距。为了系统性地研究这一问题，我们引入了代理的安全深度（Safety Over Depth for Agents, SODA），这是一个基准测试，控制代理在遇到安全威胁之前完成多少常规代理任务，最多支持20个前置任务。对来自4个家族的7个模型进行评估时，随着前置常规代理任务数量从零增加到二十，安全性提高了9%至52%。表示分析确认，随着前置任务的增加，模型的隐藏状态逐渐向安全对齐区域转移。通过系统性地研究前置对话的哪个部分最为重要，我们发现常规代理任务本身是安全性的主要驱动因素，而代理自身的先前响应对安全性的影响较小，但对于保持后续效用至关重要。这个结论得到了在开源安全基准（AgentHarm, Agent Safety Bench）和效用基准（BFCL, API-Bank）上的评估支持，确认在部署之前通过常规代理任务对代理进行预热可以提高安全性并保持其全部能力。基于这些发现，我们建议一种简单的部署策略：在可能接触安全关键请求之前，让代理完成几个常规代理任务，以减轻冷启动安全差距。我们的代码可在 https://github.com/Trustworthy-ML-Lab/Agent-Cold-Start-Safety-Gap 获取。
cs.CL / 32 / 2606.07877
Whose Norms? Disentangling Cultural and Personal Alignment in Large Language Models
谁的规范？解构大型语言模型中的文化与个人一致性
Abstract
Large language models are increasingly used for social decision-making situations that require balancing cultural norms with personal preferences. For example, a user preferring honesty might ask whether to correct a coworker publicly when local norms favor indirect feedback. Yet existing research studies cultural alignment and personalization largely separately. We introduce PACT, the Personal-Preference and Cultural-Norm Trade-off framework, which evaluates whether models choose to follow a cultural norm or allow personal preferences. We find that LLMs vary in how rigidly they enforce cultural norms, with behavior shifted more by country context (7.8%) than age (1%) and gender (0.7%) and shifting non-uniformly after instruction tuning. Furthermore, our five-country human study on PACT shows that culture-following in humans is mainly driven by scenario country, with the lowest agreement when participants judge their own cultural contexts, showing within-culture pluralism. Finally, human-LLM alignment experiments show that models can match majority choices, but fail to capture response distributions and uncertainty (with best correlations reaching only 0.24). Together, these findings motivate alignment evaluations that go beyond majority to capture cultural pluralism and disagreement in social judgment.
Chinese Translation
大型语言模型越来越多地用于需要平衡文化规范与个人偏好的社会决策情境。例如，偏好诚实的用户可能会询问是否应在当地规范倾向于间接反馈时公开纠正同事。然而，现有研究主要将文化一致性和个性化分开研究。我们引入了PACT（个人偏好与文化规范权衡框架），该框架评估模型是选择遵循文化规范还是允许个人偏好。我们发现，LLMs在执行文化规范的严格程度上存在差异，受国家背景的影响（7.8%）大于年龄（1%）和性别（0.7%），并且在指令调优后表现出非均匀的变化。此外，我们对PACT进行的五国人类研究表明，人类的文化遵循主要受场景国家驱动，当参与者评估自身文化背景时，最低一致性显示出文化内部的多元性。最后，人类与LLM的一致性实验表明，模型能够匹配大多数选择，但未能捕捉响应分布和不确定性（最佳相关性仅达到0.24）。综合来看，这些发现促使我们进行超越多数的对齐评估，以捕捉社会判断中的文化多元性和分歧。
cs.CL / 33 / 2606.07893
Beyond Individual Personas: Aligning Synthetic Dialogue to Population-Level Behavior Distributions
超越个体角色：将合成对话与群体行为分布对齐
Abstract
Synthetic dialogue corpora are increasingly used as proxies for target dialogue data, yet persona-grounded generators optimize individual conversations rather than corpus composition, yielding locally plausible dialogues with distorted population-level behavior mixes. We introduce GroupPersona, a framework that aligns synthetic dialogue corpora to the behavior distribution of a reference corpus. GroupPersona turns population statistics into generation controls: it separates each dialogue's core behavioral signature from predictable side effects, and uses the resulting behavioral groups to condition user agents on the interaction patterns that define the reference population. We evaluate GroupPersona on four corpora crossing two dialogue sources, assistant-style and Reddit-derived, with two construction variants: structure-preserving and variation-enhanced. GroupPersona lowers Jensen-Shannon divergence between synthetic and reference distributions over 12 behavior attributes from 0.234 to 0.177 relative to the strongest average baseline, a 24.4% reduction, and is best or tied-best on all four corpora while preserving structural alignment. It also achieves the closest calibration to reference-conversation quality scores, reducing mean absolute deviation from the reference-conversation profile to 0.63 versus 0.91 for the next-best baseline.
Chinese Translation
合成对话语料库越来越多地被用作目标对话数据的代理，然而以角色为基础的生成器优化的是个体对话而非语料组成，导致生成的对话在局部上看似合理，但在群体行为混合上却扭曲。我们提出了GroupPersona，一个将合成对话语料库与参考语料的行为分布对齐的框架。GroupPersona将人口统计数据转化为生成控制：它将每个对话的核心行为特征与可预测的副作用分离，并利用生成的行为组来对用户代理进行条件设置，以适应定义参考群体的互动模式。我们在四个跨越两种对话来源（助手风格和来源于Reddit）的语料库上评估了GroupPersona，并采用了两种构建变体：结构保持型和变异增强型。相较于最强平均基线，GroupPersona将合成分布与参考分布之间的Jensen-Shannon散度从0.234降低至0.177，减少了24.4%，并在所有四个语料库中表现最佳或并列最佳，同时保持了结构对齐。它还实现了与参考对话质量评分的最接近校准，将与参考对话轮廓的平均绝对偏差降低至0.63，而下一个最佳基线为0.91。
cs.CL / 34 / 2606.07925
ROSUM-MCTS: Monte Carlo Tree Search-Inspired HDL Code Summarization with Structural Rewards
ROSUM-MCTS：基于蒙特卡罗树搜索的硬件描述语言代码摘要生成与结构奖励
Abstract
Large language models (LLMs) have shown promise in code summarization, yet their effectiveness for Hardware Description Languages (HDLs) like VHDL and Verilog remains underexplored. We propose ROSUM-MCTS, an LLM-guided approach inspired by Monte Carlo Tree Search (MCTS) that refines summaries through structured exploration and reinforcement-driven optimization. Our method integrates both local and global context via a hierarchical candidate expansion mechanism and optimizes summaries using a composite reward function balancing functional correctness (FC), local content adequacy (LCA), and fluency. We evaluate ROSUM-MCTS on the VHDL-eval and Verilog-eval datasets, demonstrating its consistent outperformance over baseline methods by leveraging structured bottom-up refinement and reinforcement-based optimization. Ablation studies confirm the necessity of both local and global expansion strategies, as well as the importance of balancing FC and LCA for optimal performance. Furthermore, ROSUM-MCTS proves robust against superficial modifications, such as variable renaming, maintaining summary quality where baselines degrade. These results establish ROSUM-MCTS as an effective and robust HDL summarization framework, paving the way for further research into reinforcement-enhanced code summarization.
Chinese Translation
大型语言模型（LLMs）在代码摘要生成方面展现了潜力，但其在硬件描述语言（HDLs）如VHDL和Verilog中的有效性仍然未被充分探索。我们提出了ROSUM-MCTS，这是一种受蒙特卡罗树搜索（MCTS）启发的LLM引导方法，通过结构化探索和基于强化学习的优化来精炼摘要。我们的方法通过分层候选扩展机制整合了局部和全局上下文，并使用复合奖励函数来优化摘要，平衡功能正确性（FC）、局部内容充分性（LCA）和流畅性。我们在VHDL-eval和Verilog-eval数据集上评估了ROSUM-MCTS，结果表明其在结构化自下而上的精炼和基于强化学习的优化的支持下，始终优于基线方法。消融研究确认了局部和全局扩展策略的必要性，以及平衡FC和LCA以实现最佳性能的重要性。此外，ROSUM-MCTS在面对变量重命名等表面修改时表现出强大的鲁棒性，保持了摘要质量，而基线方法则出现了降级。这些结果确立了ROSUM-MCTS作为一种有效且鲁棒的HDL摘要生成框架，为进一步研究增强强化学习的代码摘要生成铺平了道路。
cs.CL / 35 / 2606.07936
Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation
金标准的幻觉：对长文本生成的人类评估协议的大规模分析
Abstract
Human evaluation plays a critical role in assessing the quality of generated text. However, the reliability and reproducibility of these evaluations depend on transparent and well-documented protocols -- details that are frequently missing in current practice. In this work, we conduct a large-scale analysis of human evaluation protocols for evaluating long-form generation tasks in *CL conference publications from 2023--2025, including a full manual review of 284 papers and LLM-assisted analysis for another 1.8k+ papers. We define a set of 20 reportable criteria related to reproducibility of human evaluation studies, and apply these criteria to systematically examine reporting norms and practices within the community. We find widespread under-reporting of important aspects of human evaluation study design, leading to ambiguity about what was measured and how, who contributed judgments, and how judgments should be interpreted. Based on these findings, we outline actionable recommendations to support more transparent and reproducible reporting in future research. Our analysis code and annotated dataset can be found at: https://github.com/larchlab/Illusions-of-the-Gold-Standard
Chinese Translation
人类评估在评估生成文本质量中发挥着关键作用。然而，这些评估的可靠性和可重复性依赖于透明且文档齐全的协议——这些细节在当前实践中常常缺失。在本研究中，我们对2023年至2025年*CL会议出版物中用于评估长文本生成任务的人类评估协议进行了大规模分析，包括对284篇论文的全面手动审查以及对另外1800多篇论文的LLM辅助分析。我们定义了一套与人类评估研究可重复性相关的20个可报告标准，并应用这些标准系统性地检查社区内的报告规范和实践。我们发现人类评估研究设计的重要方面普遍存在报告不足，导致对所测量内容及其方式、谁提供了判断以及如何解释判断存在模糊性。基于这些发现，我们提出了可行的建议，以支持未来研究中更透明和可重复的报告。我们的分析代码和注释数据集可以在以下链接找到：https://github.com/larchlab/Illusions-of-the-Gold-Standard
cs.CL / 36 / 2606.07951
From `May' to `Is': Certainty Distortion in Language Model Rewriting
从‘五月’到‘是’：语言模型重写中的确定性扭曲
Abstract
Humans increasingly turn to Language Models (LMs) in ways that shape beliefs and drive decisions, including discussing, rewriting, and summarizing information from scientific articles, news, and medical reports. However, in these domains, where how confidently a claim is expressed matters, little is known about whether LMs faithfully preserve it. In this work, we investigate certainty distortion in LMs, defined as meaningful changes in expressed certainty when semantic content is preserved. We propose an LM-based evaluation metric that is consistent with population-level judgments of certainty. Using this metric, we characterize certainty distortion across different sizes and families of models in the context of scientific and medical communication tasks. Our results show that certainty distortion affects up to 75\% of LM outputs and is systematically asymmetric in rewriting tasks with most LMs being 1.5-2$\times$ more likely to increase the expressed certainty than to decrease it. These effects can compound over repeated paraphrasing: in the medical domain, claude-haiku-4-5 increases certainty of 20\% examples after a single iteration, increasing to 40\% after five iterations. Prompt-based interventions reduce overall certainty distortion but do not eliminate it. Together, these findings reveal a general bias toward inflating expressed certainty, with direct implications for users who rely on LMs in high-stakes domains.
Chinese Translation
人们越来越多地依赖语言模型（LMs）来塑造信念和驱动决策，包括讨论、重写和总结科学文章、新闻和医疗报告中的信息。然而，在这些领域中，表达一个主张的自信程度至关重要，但目前对语言模型是否忠实保留这一点知之甚少。在本研究中，我们调查了语言模型中的确定性扭曲，定义为在语义内容得以保留的情况下，表达的确定性发生的有意义变化。我们提出了一种基于语言模型的评估指标，该指标与群体层面的确定性判断一致。利用这一指标，我们在科学和医疗交流任务的背景下，描述了不同规模和类型的模型中的确定性扭曲。我们的结果表明，确定性扭曲影响了多达75%的语言模型输出，并且在重写任务中系统性地表现出不对称性，大多数语言模型在增加表达的确定性方面的可能性是减少它的1.5-2倍。这些影响在重复的释义过程中可能会累积：在医疗领域，claude-haiku-4-5在一次迭代后增加了20%的例子的确定性，在五次迭代后增加到40%。基于提示的干预措施减少了整体的确定性扭曲，但并未消除它。综合来看，这些发现揭示了一种普遍的倾向，即夸大表达的确定性，这对依赖语言模型进行高风险决策的用户具有直接影响。
cs.CL / 37 / 2606.07964
What Does Debiasing Really Remove? A Geometric Study of PCA-Based Gender Debiasing in Word Embeddings
去偏见究竟去除了什么？基于主成分分析的词嵌入性别去偏见的几何研究
Abstract
Debiasing methods based on principal component analysis (PCA) are broadly used to reduce gender bias in word embeddings used in LLMs, yet it remains unclear what aspects of bias they actually remove and how destructive this process is. These methods are based on the understanding that bias resides in a low-dimensional subspace, with the assumption that most of it can be captured by a few principal components. In this work, we conduct a systematic geometric analysis of PCA-based gender debiasing and investigate what is actually removed from the embedding space. Our experiments across multiple embeddings show that direct gender bias is primarily concentrated in the first principal component, supporting the low-rank bias hypothesis. However, associative bias measured by WEAT does not align with these principal directions and is instead spread across multiple embedding dimensions. Furthermore, as expected, we demonstrate that removing an increasing number of principal components leads to a consistent degradation of the embedding geometry, affecting semantic structure and vector relationships. These results reveal that PCA-based debiasing operates as a trade-off: while it effectively reduces certain forms of direct bias, it fails to eliminate distributed associations and introduces geometric distortion. Moreover, there is no universal optimal level of debiasing, as the balance between bias reduction and semantic preservation depends on the chosen metric and embedding. Overall, our findings suggest that bias in word embeddings is not purely low-rank and that simple subspace removal methods may be insufficient for comprehensive debiasing.
Chinese Translation
基于主成分分析（PCA）的去偏见方法被广泛用于减少大型语言模型（LLMs）中词嵌入的性别偏见，但目前仍不清楚这些方法实际去除了哪些偏见方面，以及这一过程的破坏性有多大。这些方法基于偏见存在于低维子空间的理解，假设大部分偏见可以通过少数主成分捕捉。在本研究中，我们对基于PCA的性别去偏见进行了系统的几何分析，探讨了实际从嵌入空间中去除了什么。我们在多个嵌入上的实验表明，直接的性别偏见主要集中在第一个主成分上，支持低秩偏见假设。然而，通过WEAT测量的关联偏见并不与这些主方向一致，而是分布在多个嵌入维度上。此外，正如预期的那样，我们展示了去除越来越多的主成分会导致嵌入几何的一致退化，影响语义结构和向量关系。这些结果揭示了基于PCA的去偏见操作是一种权衡：虽然它有效减少某些形式的直接偏见，但未能消除分布式关联，并引入几何扭曲。此外，没有普遍的最佳去偏见水平，因为偏见减少与语义保留之间的平衡取决于所选择的度量和嵌入。总体而言，我们的研究结果表明，词嵌入中的偏见并非纯粹低秩，简单的子空间去除方法可能不足以实现全面的去偏见。
cs.CL / 38 / 2606.07969
Neutrality Bites: Gender Representation in AI-Generated Animal Stories
中立的代价：人工智能生成动物故事中的性别表现
Abstract
Gender bias in AI-generated stories is a well-documented problem. While much attention has been paid to reducing or mitigating this bias, it is not always clear whether interventions produce genuinely fairer results. To investigate this issue, we examine how large language models (LLMs) handle gender assignment in a narrative context that is popular, highly ambiguous, and also known to closely reproduce human stereotypes: stories about talking animals. We prompt six leading LLMs to complete an English-language story about seven different anthropomorphic animal characters whose gender is unstated. We additionally iterate with four different narrative settings and a range of model temperatures. Across the 23.8K stories, we find that models frequently avoid gendering the animal character in the story (19% on average) or use gender-neutral language like "it" or "its" (38.2% on average). However, when gender is assigned, there is a significant masculine bias. Feminine animal characters are virtually absent, present in just 2.2% of stories vs. 40.6% that feature masculine characters. Our findings point to a broader argument: neutrality bites. In other words, models that prioritize neutrality to address social bias may actually contribute to the erasure of marginalized perspectives and identities. We suggest that alternative strategies beyond neutrality need to be pursued, such as ones that more equally distribute social possibilities across imagined subjects.
Chinese Translation
人工智能生成故事中的性别偏见是一个广为人知的问题。尽管已经有很多关注放在减少或缓解这种偏见上，但干预措施是否真正产生了更公平的结果并不总是明确。为了解决这个问题，我们考察了大型语言模型（LLMs）在一个流行且高度模糊的叙事背景中如何处理性别分配，这种背景也被认为紧密再现了人类的刻板印象：关于会说话的动物的故事。我们提示六个领先的LLM完成一个关于七个不同拟人化动物角色的英语故事，这些角色的性别未被说明。我们还在四种不同的叙事设置和一系列模型温度下进行迭代。在23800个故事中，我们发现模型经常避免在故事中给动物角色赋予性别（平均19%），或使用性别中立的语言，如“它”或“它的”（平均38.2%）。然而，当性别被赋予时，存在显著的男性偏见。女性动物角色几乎不存在，仅占故事的2.2%，而男性角色则占40.6%。我们的发现指向一个更广泛的论点：中立的代价。换句话说，优先考虑中立以应对社会偏见的模型，实际上可能会导致边缘化视角和身份的抹去。我们建议需要追求超越中立的替代策略，例如更均等地分配想象主体的社会可能性。
cs.CL / 39 / 2606.07970
Defending Against Malicious Finetuning by Scaling Train-time Adversarial Attacks
通过扩大训练时对抗攻击来防御恶意微调
Abstract
Current open-weight large language models (LLMs) are prone to malicious finetuning attacks, which could compromise the safety alignment of LLMs with only a few steps of supervised finetuning (SFT) on poisoned datasets. Existing alignment-stage defenses are primarily designed to defend against attacks that use parameter-efficient finetuning methods. However, they fail to defend against stronger attacks that use full-parameter finetuning. In this paper, we propose Patcher, a method inspired by adversarial training and bi-level optimization, to combat such attacks. Patcher strengthens the simulated attack by scaling up the optimization steps in the adversarial loop, thus forcing the defender to find model parameters that are insensitive to stronger attacks. Furthermore, we propose an efficient parallel algorithm to implement Patcher, decreasing the wall-clock time of training while preserving Patcher's performance. Extensive experiments show that Patcher substantially improves the model's robustness compared to vanilla SFT alignment, and transfers to diverse attack scenarios and model sizes. Code is available at https://github.com/haomingwen/patcher.
Chinese Translation
当前的开放权重大型语言模型（LLMs）容易受到恶意微调攻击，这可能会在对受污染数据集进行少量监督微调（SFT）后危及LLMs的安全对齐。现有的对齐阶段防御主要旨在防御使用参数高效微调方法的攻击。然而，它们未能防御使用全参数微调的更强攻击。本文提出了一种名为Patcher的方法，该方法受到对抗训练和双层优化的启发，以应对此类攻击。Patcher通过在对抗循环中扩大优化步骤来增强模拟攻击，从而迫使防御者找到对更强攻击不敏感的模型参数。此外，我们提出了一种高效的并行算法来实现Patcher，减少了训练的实际时间，同时保持了Patcher的性能。大量实验表明，与普通SFT对齐相比，Patcher显著提高了模型的鲁棒性，并且能够迁移到多种攻击场景和模型规模。代码可在 https://github.com/haomingwen/patcher 获取。
cs.CL / 40 / 2606.07978
MechLens: Late Crystallization of Factual Knowledge Explains Intervention Effectiveness in Language Models
MechLens：事实知识的晚期结晶解释了语言模型的干预有效性
Abstract
Understanding where LLMs store factual knowledge is critical for hallucination mitigation. We systematically quantify Late Crystallization: factual knowledge does not gradually emerge across layers but "crystallizes" abruptly at the final layers. Across five model families (Pythia, Gemma, Qwen2.5, Llama-3.1, Mistral; 0.5--14B), 26.8%--93.4% of correct answers never enter top-10 predictions at any intermediate layer, with late emergence (>80% depth) consistent across architectures. Cross-scale (Qwen2.5-14B) and cross-benchmark (MMLU: 98.2%) results confirm generality; tuned lens rules out probe artifacts. A sentiment-classification control (0.5% for Qwen vs. 85.9% factual; 2.0% for Mistral vs. 26.8%) confirms the phenomenon is specific to factual recall. Late Crystallization yields a crystallization-guided intervention principle: CAA outperforms DoLa on moderate-crystallization models (Llama, Mistral; p<0.001), with a directionally consistent reversal on high-crystallization Qwen (+25.4% vs. +15.5% MC1, p=0.069). LayerNorm ablation shows crystallization is intrinsic to the residual stream; LN scaling (x1.2) yields +11.8% MC1 with zero inference overhead. We further reveal a Computability-Memorization Spectrum: computable knowledge crystallizes earlier (layer 22.1/28) than memorized facts (28.0/28). We release MechLens supporting five model families.
Chinese Translation
理解大型语言模型（LLMs）存储事实知识的位置对于减轻幻觉现象至关重要。我们系统地量化了晚期结晶：事实知识并不是在各层中逐渐显现，而是在最后几层中突然“结晶”。在五个模型系列（Pythia、Gemma、Qwen2.5、Llama-3.1、Mistral；0.5--14B）中，26.8%--93.4%的正确答案在任何中间层中都未进入前10个预测，且晚期显现（>80%深度）在不同架构中保持一致。跨尺度（Qwen2.5-14B）和跨基准（MMLU：98.2%）的结果确认了这一现象的普遍性；调优的镜头排除了探测伪影。情感分类控制（Qwen的0.5%与事实的85.9%；Mistral的2.0%与26.8%）确认这一现象特定于事实回忆。晚期结晶产生了一种以结晶为指导的干预原则：CAA在中等结晶模型（Llama、Mistral）上优于DoLa（p<0.001），在高结晶的Qwen上则表现出方向一致的逆转（+25.4%对比+15.5% MC1，p=0.069）。LayerNorm消融实验表明，结晶是残差流的内在特性；LN缩放（x1.2）在没有推理开销的情况下带来了+11.8% MC1的提升。我们进一步揭示了可计算性-记忆谱：可计算知识在层22.1/28中比记忆事实（28.0/28）更早结晶。我们发布了MechLens，支持五个模型系列。
cs.CL / 41 / 2606.07995
Customer-Agent: Overcoming Context Limitations in Ultra-Long Shopping Trajectories via Tool-Augmented Agents and RLVR
客户代理：通过工具增强代理和可验证奖励的强化学习克服超长购物轨迹中的上下文限制
Abstract
Understanding customer shopping trajectories is essential for enabling personalized shopping experiences. However, shopping records (i.e., customer's search, clicks, purchases, etc.) often span long time horizons over multiple years, resulting in extremely long trajectories that pose significant challenges for existing large language models (LLMs). Despite the importance of this problem, existing benchmarks are limited to short customer trajectories, while real-world trajectories from large e-commerce platforms are rarely accessible due to data privacy constraints. To address this gap, we introduce ShopTrajQA, a long-context evaluation benchmark constructed from real-world product information and simulated shopping trajectories. The dataset includes variants of up to 32k and 64k tokens, enabling systematic evaluation of model robustness under varying context lengths. Through comprehensive benchmarking of frontier LLMs, we identify critical performance gaps in reasoning over long shopping trajectory data. To address these challenges, we propose a Customer Agent Framework for ultra-long context management. Leveraging a Reinforcement Learning with Verifiable Rewards (RLVR) agentic training paradigm, our approach stores trajectories as external local files and trains the agent to autonomously retrieve and parse them through code-interpreter interactions (e.g., SQL queries), effectively bypassing the fixed in-context window constraints of LLMs. Experimental results demonstrate that our framework achieves strong performance for ShopTrajQA and shows generalization to other complex reasoning tasks.
Chinese Translation
理解客户购物轨迹对于实现个性化购物体验至关重要。然而，购物记录（即客户的搜索、点击、购买等）通常跨越多个年份，形成极长的轨迹，这对现有的大型语言模型（LLMs）构成了重大挑战。尽管这一问题的重要性不言而喻，现有基准测试仅限于短期客户轨迹，而来自大型电子商务平台的真实轨迹由于数据隐私限制而难以获取。为了解决这一空白，我们引入了ShopTrajQA，这是一个基于真实产品信息和模拟购物轨迹构建的长上下文评估基准。该数据集包含多达32k和64k标记的变体，使得在不同上下文长度下系统评估模型的鲁棒性成为可能。通过对前沿LLMs的全面基准测试，我们识别了在长购物轨迹数据推理方面的关键性能差距。为了解决这些挑战，我们提出了一种客户代理框架，用于超长上下文管理。利用可验证奖励的强化学习（RLVR）代理训练范式，我们的方法将轨迹存储为外部本地文件，并训练代理通过代码解释器交互（例如SQL查询）自主检索和解析这些轨迹，有效绕过了LLMs的固定上下文窗口限制。实验结果表明，我们的框架在ShopTrajQA上表现出色，并显示出对其他复杂推理任务的泛化能力。
cs.CL / 42 / 2606.07996
MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models
MC-PDD：针对黑箱大型语言模型的掩码语料库级预训练数据检测
Abstract
Pretraining is fundamental to the development of Large Language Models (LLMs), yet the opacity of pretraining data complicates model analysis and raises ethical, legal, and fairness concerns. Detecting whether specific datasets were used during pretraining is, therefore, critical. Existing state-of-the-art methods typically rely on access to model probability distributions, making them unsuitable for closed-source LLMs that provide only input-output interfaces. To address this limitation, we introduce Masked Corpus-level Pretraining Data Detection (MC-PDD), a novel method inspired by the masked language modeling paradigm. MC-PDD masks highly specific tokens in each text and prompts the LLM to predict the missing content. It then assesses whether the difference in prediction hit rates between a candidate corpus and a reference non-member corpus is statistically significant. Based on this comparison, MC-PDD determines whether the candidate texts were likely included in the model's pretraining data. Experimental results demonstrate clear and consistent differences in prediction hit rates between pretrained and unseen data across three datasets, for both open-source and closed-source LLMs. Despite operating under a stricter black-box setting, MC-PDD achieves performance comparable to existing detection methods. Our approach enables practical applications such as model auditing and data copyright verification using only standard API access. Upon acceptance, we will publicly release the code and datasets.
Chinese Translation
预训练是大型语言模型（LLMs）发展的基础，但预训练数据的不透明性使得模型分析变得复杂，并引发了伦理、法律和公平性问题。因此，检测特定数据集在预训练过程中是否被使用至关重要。现有的最先进方法通常依赖于对模型概率分布的访问，这使得它们不适用于仅提供输入-输出接口的闭源LLMs。为了解决这一限制，我们提出了掩码语料库级预训练数据检测（MC-PDD），这是一种受掩码语言建模范式启发的新方法。MC-PDD在每个文本中掩盖高度特定的标记，并促使LLM预测缺失的内容。然后，它评估候选语料库与参考非成员语料库之间的预测命中率差异是否具有统计显著性。基于这一比较，MC-PDD判断候选文本是否可能包含在模型的预训练数据中。实验结果表明，在三个数据集上，预训练数据与未见数据之间的预测命中率存在明显且一致的差异，适用于开放源代码和闭源LLMs。尽管在更严格的黑箱环境下操作，MC-PDD的性能与现有检测方法相当。我们的方法使得仅通过标准API访问即可实现模型审计和数据版权验证等实际应用。接受后，我们将公开发布代码和数据集。
cs.CL / 43 / 2606.08000
Summarization is Not Dead Yet
摘要技术仍未死去
Abstract
The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising questions about whether summarization remains an open research problem. We re-examine this narrative through a multi-track evaluation covering five diverse datasets and five state-of-the-art LLMs, combining controlled human assessment, bias-mitigated LLM-as-Judge protocols, factuality verification against external knowledge, and corpus-level linguistic analysis. Our findings reveal a more nuanced landscape in which human reference summaries continue to demonstrate advantages in informativeness and faithfulness, whereas LLM outputs are preferred mainly for surface-level coherence and fluency. Factuality verification indicates that human references remain more reliable, particularly for claims involving reasoning or synthesis, and linguistic analysis uncovers a pattern of stylistic homogeneity across different models. These observations suggest that current LLMs have raised the floor of summarization quality, but the ceiling of their performance remains below human capabilities.
Chinese Translation
大型语言模型（LLMs）的进展引发了关于模型生成的摘要是否能够与人类撰写的参考文献相媲美甚至超越的论断，这引发了关于摘要是否仍然是一个开放研究问题的质疑。我们通过多轨评估重新审视了这一叙述，涵盖了五个不同的数据集和五个最先进的LLM，结合了受控的人类评估、偏见缓解的LLM作为评判者的协议、与外部知识的事实验证以及语料库级的语言分析。我们的研究结果揭示了一个更为细致的格局，其中人类参考摘要在信息量和真实性方面仍然表现出优势，而LLM的输出主要在表面连贯性和流畅性方面受到青睐。事实验证表明，人类参考文献在涉及推理或综合的主张上仍然更为可靠，而语言分析则揭示了不同模型之间风格同质性的模式。这些观察结果表明，当前的LLM提高了摘要质量的下限，但其性能的上限仍低于人类的能力。
cs.CL / 44 / 2606.08011
Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation
重写以翻译，翻译以奖励：用于机器翻译中源重写的强化学习
Abstract
Although directly prompting off-the-shelf Large Language Models (LLMs) to generate meaning-preserving source rewrites can effectively enhance Machine Translation (MT) quality, doing so requires manually tuning prompts for different MT models. In this work, we propose RLSR (Reinforcement Learning for Source Rewriting), a novel RL-based framework for training a source rewriting model without tuning prompts for each MT model. RLSR optimizes the rewriting model by directly using the improvement in downstream translation quality yielded by each rewritten source as the reward. Extensive experiments across six MT models and 16 language pairs demonstrate that our 4B rewriting models trained via RLSR significantly outperform the no-rewriting baseline and existing same-scale prompt-based rewriting baselines, while achieving competitive performance against prompt-based baselines based on the 235B LLM.
Chinese Translation
尽管直接提示现成的大型语言模型（LLMs）生成保留意义的源重写可以有效提升机器翻译（MT）质量，但这需要针对不同的MT模型手动调整提示。在本研究中，我们提出了RLSR（源重写的强化学习），这是一个新颖的基于强化学习的框架，用于训练源重写模型，而无需为每个MT模型调整提示。RLSR通过直接利用每个重写源所带来的下游翻译质量的提升作为奖励，来优化重写模型。在六个MT模型和16个语言对上的大量实验表明，我们通过RLSR训练的4B重写模型显著优于无重写基线和现有的同规模基于提示的重写基线，同时在与基于235B LLM的基于提示的基线的竞争中也表现出色。
cs.CL / 45 / 2606.08025
Arabic Sentence Segmentation Across Genres and Punctuation Conditions
阿拉伯语句子分割在不同体裁和标点条件下的研究
Abstract
Sentence segmentation in Arabic is challenging due to ambiguous and inconsistent punctuation, with many texts lacking reliable sentence boundary markers. Existing approaches rely heavily on punctuation cues and are typically evaluated on well-formed text, limiting their robustness in realistic Arabic settings. To address this, we introduce AraSEG, a genre-diverse sentence segmentation corpus spanning eight genres and a wide range of punctuation and document structure conditions. Using AraSEG, we evaluate LLMs, lightweight encoder models, and dependency parser-based models under increasingly challenging segmentation settings. Our experiments show that lightweight encoders, and even dependency parser-based models, outperform LLMs in the most challenging settings. We further investigate the effects of training data size and genre diversity, finding that performance eventually saturates and cross-genre generalization remains challenging. We also demonstrate that accurate sentence segmentation substantially improves downstream dependency parsing. We make our code, data, and models publicly available.
Chinese Translation
阿拉伯语的句子分割面临挑战，原因在于标点符号的模糊性和不一致性，许多文本缺乏可靠的句子边界标记。现有的方法过于依赖标点线索，通常在格式良好的文本上进行评估，这限制了它们在现实阿拉伯语环境中的鲁棒性。为了解决这个问题，我们引入了AraSEG，一个涵盖八种体裁和多种标点及文档结构条件的多样化句子分割语料库。利用AraSEG，我们在日益复杂的分割设置下评估了大型语言模型（LLMs）、轻量级编码器模型和基于依存解析器的模型。我们的实验表明，在最具挑战性的设置中，轻量级编码器甚至基于依存解析器的模型的表现优于大型语言模型。我们进一步研究了训练数据规模和体裁多样性的影响，发现性能最终趋于饱和，跨体裁泛化仍然具有挑战性。我们还证明，准确的句子分割显著改善了下游的依存解析。我们将我们的代码、数据和模型公开发布。
cs.CL / 46 / 2606.08048
Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
通过专家产品桥接的扩散语言模型并行解码
Abstract
Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding, but the lack of token dependencies limits generation quality compared to autoregressive (AR) models. Recent progress attempts to bridge the gap via importance sampling, with DLM being the proposal and AR being the target. However, due to the huge gap between their distributions, the sampling requires a large number of particles and is thus expensive to compute. In this paper, we introduce PoE-Bridge, a novel decoding framework that drastically improves generation speed and accuracy by introducing an intermediate distribution to bridge the gap. The distribution is constructed as a Product-of-Experts (PoE) of the DLM proposal and the AR target. With the intermediate distribution, we first use the DLM to draft multiple continuations in parallel, then apply rejection sampling to verify the drafted tokens and move the resulting candidates toward the PoE. We then use importance sampling to further correct the PoE-aligned candidates toward the AR target. We further propose several improved techniques, including mixed-temperature sampling for enhanced diversity and elastic rejection windows for reducing wasted verification. Empirically, PoE-Bridge achieves significantly improved accuracy with $5\times$ speedup over the standard DLM decoding approach, and recovers at least 95% of the target AR model's performance, efficiently advancing most of the quality gap on challenging mathematical reasoning and coding tasks. Our code is available at https://github.com/juntongshi48/poe-bridge.
Chinese Translation
扩散语言模型（DLM）通过并行解码提供了显著的速度优势，但由于缺乏令牌依赖性，其生成质量相较于自回归（AR）模型受到限制。近期的进展尝试通过重要性采样来弥补这一差距，其中DLM为提议，AR为目标。然而，由于它们分布之间的巨大差距，采样需要大量粒子，因此计算成本昂贵。在本文中，我们引入了PoE-Bridge，一种新颖的解码框架，通过引入中间分布来弥合这一差距，从而显著提高生成速度和准确性。该分布被构建为DLM提议和AR目标的专家产品（Product-of-Experts, PoE）。借助中间分布，我们首先使用DLM并行草拟多个续写，然后应用拒绝采样来验证草拟的令牌，并将结果候选项朝向PoE移动。接着，我们使用重要性采样进一步修正与PoE对齐的候选项，使其朝向AR目标。我们还提出了几种改进技术，包括混合温度采样以增强多样性和弹性拒绝窗口以减少浪费的验证。从实证结果来看，PoE-Bridge在标准DLM解码方法上实现了显著的准确性提升，速度提高了$5 imes$，并恢复了目标AR模型性能的至少95%，有效缩小了在具有挑战性的数学推理和编码任务上的大部分质量差距。我们的代码可在https://github.com/juntongshi48/poe-bridge获取。
cs.CL / 47 / 2606.08056
What's the Point? Spatial Grammar & Index Resolution for Sign Language Processing
有什么意义？空间语法与手语处理中的索引解析
Abstract
Sign language models are predominantly trained with gloss-sequence or text supervision, thereby under-modeling non-lexical and productive constructions. One comparatively tractable instance is spatial indexing: pointing gestures that assign discourse entities to spatial loci for subsequent co-reference, which lexicon-centric objectives largely fail to capture. We present a targeted evaluation of indexing in Sign Language Recognition, showing that despite comprising 10-15% of signing content, indexing is poorly recovered. We introduce a framework for training and evaluating indexing experts, establishing a baseline for index-aware sign language modeling. Our approach decomposes spatial reference resolution into index detection and discourse entity linking. The resulting mention representations enable automatic annotation and non-lexical structure modeling, and serve as an auxiliary indexing expert that augments a frozen SLR model at inference time.
Chinese Translation
手语模型主要通过词汇序列或文本监督进行训练，因此在建模非词汇和生成性构造方面存在不足。一个相对可处理的实例是空间索引：指向手势将话语实体分配到空间位置以便后续的共指，而以词汇为中心的目标往往无法捕捉到这一点。我们对手语识别中的索引进行了针对性的评估，结果显示尽管索引内容占手语内容的10-15%，但其恢复效果较差。我们提出了一个用于训练和评估索引专家的框架，为索引感知的手语建模建立了基线。我们的方法将空间参考解析分解为索引检测和话语实体链接。生成的提及表示能够实现自动注释和非词汇结构建模，并作为辅助索引专家，在推理时增强冻结的手语识别模型（SLR）。
cs.CL / 48 / 2606.08071
SurgiQ: A Large-Scale Multi-Domain Benchmark for Evaluating Surgical Understanding in Large Language Models
SurgiQ：用于评估大型语言模型外科理解能力的大规模多领域基准
Abstract
Reliable evaluation of large language models in surgery remains underdeveloped. Broad medical benchmarks test clinical knowledge, while surgery requires procedural reasoning, management trade-offs, negation handling, and selection among plausible operative decisions. We present SurgiQ, a text-only, source-grounded benchmark of 13,055 four-option multiple-choice questions spanning six surgical domains and four question formats: case-based, reasoning, best-option, and negative. SurgiQ is constructed from surgical textbooks, open-access papers, and examination material using a multi-stage generation, verification, and expert-audit pipeline. We evaluate 35 open-weight LLMs under a unified log-likelihood protocol. Our results show substantial remaining headroom: smaller models often remain near the 25\% random baseline, while the best model reaches 68.1\% accuracy. General-purpose models, especially Qwen2.5, outperform most biomedical models, suggesting that current medical specialization does not yet provide sufficiently broad surgical coverage. Calibration and error analysis further show that even strong models make confident mistakes on clinically plausible distractors, motivating more reliable and broader surgical LLM evaluation.
Chinese Translation
对大型语言模型在外科领域的可靠评估仍然不够成熟。广泛的医学基准测试临床知识，而外科则需要程序性推理、管理权衡、否定处理以及在合理的手术决策中进行选择。我们提出了SurgiQ，这是一个仅基于文本的、源数据驱动的基准，包含13,055个四选一的多项选择题，涵盖六个外科领域和四种问题格式：案例基础、推理、最佳选项和否定。SurgiQ的构建基于外科教科书、开放获取论文和考试材料，采用多阶段生成、验证和专家审核的流程。我们在统一的对数似然协议下评估了35个开放权重的语言模型。我们的结果显示出显著的提升空间：较小的模型通常接近25%的随机基线，而最佳模型的准确率达到68.1%。通用模型，特别是Qwen2.5，超越了大多数生物医学模型，这表明当前的医学专业化尚未提供足够广泛的外科覆盖。校准和错误分析进一步表明，即使是强大的模型在临床上合理的干扰项上也会自信地犯错，这促使我们对外科语言模型进行更可靠和更广泛的评估。
cs.CL / 49 / 2606.08076
"I understand your perspective": LLM Persuasion and Sycophancy through the Lens of Communicative Action Theory
“我理解你的观点”：通过交际行动理论视角看大型语言模型的说服力与谄媚行为
Abstract
Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored. This work explores the persuasive potential of LLMs through the framework of J\"urgen Habermas' Theory of Communicative Action. It examines whether LLMs express illocutionary intent (i.e., pragmatic functions of language such as conveying knowledge, building trust, or signaling similarity) in ways that are comparable to human communication. We simulate online discussions between opinion holders and LLMs using conversations from the persuasive subreddit ChangeMyView. We then compare the likelihood of illocutionary intents in human-written and LLM-generated counter-arguments, specifically those that successfully changed the original poster's view. We find that all three LLMs effectively convey illocutionary intent -- often more so than humans -- potentially increasing their anthropomorphism. Further, LLMs craft sycophantic responses that closely align with the opinion holder's intent, a strategy strongly associated with opinion change. Finally, crowd-sourced workers find LLM-generated counter-arguments more agreeable and consistently prefer them over human-written ones. These findings suggest that LLMs' persuasive power extends beyond merely generating high-quality arguments. On the contrary, training LLMs with human preferences effectively tunes them to mirror human communication patterns, particularly nuanced communicative actions, potentially increasing individuals' susceptibility to their influence.
Chinese Translation
大型语言模型（LLMs）能够生成高质量的论证，但它们在参与细致且具有说服力的交际行为方面的能力仍然未被充分探索。本研究通过尤尔根·哈贝马斯的交际行动理论框架探讨LLMs的说服潜力。我们考察LLMs是否以与人类沟通相当的方式表达言外之意（即语言的实用功能，如传达知识、建立信任或暗示相似性）。我们模拟了意见持有者与LLMs之间的在线讨论，使用来自说服性子版块ChangeMyView的对话。然后，我们比较了人类撰写的反驳论证与LLM生成的反驳论证中言外之意的可能性，特别是那些成功改变原帖作者观点的反驳论证。我们发现所有三种LLM有效地传达了言外之意——往往比人类更为有效，这可能增加了它们的人性化。此外，LLMs制作的谄媚回应与意见持有者的意图紧密对齐，这一策略与观点改变密切相关。最后，众包工人发现LLM生成的反驳论证更具可接受性，并且一致偏好于人类撰写的论证。这些发现表明，LLMs的说服力不仅限于生成高质量的论证。相反，通过人类偏好训练LLMs有效地调整它们以反映人类沟通模式，特别是细致的交际行为，可能增加个体对其影响的敏感性。
cs.CL / 50 / 2606.08077
Support Vector Rubrics: Closing the Gap Between Self-Generated and Human Rubrics
支持向量评分标准：弥合自生成评分标准与人工评分标准之间的差距
Abstract
Rubric-based evaluation is a promising paradigm for judging large language model (LLM) outputs, yet self-generated rubrics lag human-annotated criteria on hard instances. We argue this discriminative gap reflects an objective mismatch: self-generated rubrics describe good responses, whereas effective criteria must discriminate between close candidates. To close this gap, we introduce SVR (Support Vector Rubrics), a framework that recasts rubric construction as max-margin boundary learning over preference data. SVR mines contrastive features from preference pairs into a rubric bank, learns a prompt-conditioned selector together with global rubric weights, and iteratively refines the bank through support-pair selection and adversarial probing of hard negatives. At inference, given only the prompt, SVR retrieves the top-rubrics from the bank and scores responses. On RubricBench, SVR narrows the gap to human reference rubrics from 24.1 to 0.3 points and outperforms strong self-rubric and judge baselines, and the learned bank transfers across judges without retraining. On RewardBench 1&2, and RM-Bench, it remains competitive with dedicated reward models, demonstrating broader reward modeling capability. Overall, boundary-defining rubrics offer a principled route to closing the discriminative gap in LLM evaluation.
Chinese Translation
基于评分标准的评估是评判大型语言模型（LLM）输出的一个有前景的范式，但自生成评分标准在困难实例上落后于人工注释标准。我们认为这种判别差距反映了一个客观的不匹配：自生成评分标准描述了良好的响应，而有效的标准必须能够区分相近的候选者。为了弥合这一差距，我们引入了支持向量评分标准（SVR），一个将评分标准构建重新表述为基于偏好数据的最大间隔边界学习的框架。SVR从偏好对中挖掘对比特征构建评分标准库，学习一个基于提示的选择器以及全局评分标准权重，并通过支持对选择和对困难负样本的对抗性探测迭代地优化评分标准库。在推理阶段，仅给定提示，SVR从库中检索出最佳评分标准并对响应进行评分。在RubricBench上，SVR将与人工参考评分标准的差距缩小至24.1到0.3分，并且优于强大的自评分标准和评审基线，同时学习到的评分标准库在不同评审者之间无需重新训练即可迁移。在RewardBench 1&2和RM-Bench上，它与专用奖励模型保持竞争力，展示了更广泛的奖励建模能力。总体而言，边界定义的评分标准为弥合LLM评估中的判别差距提供了一条有原则的途径。
cs.CL / 51 / 2606.08081
Aligned but Not Partner-Specific: Distinguishing How Multimodal LLM Agents Succeed in Reference Games Without Human-Like Conventions
对齐但非特定于伙伴：区分多模态大语言模型代理在参考游戏中如何成功而不依赖类人约定
Abstract
Repeated reference games test whether interlocutors replace their initially long descriptions with shorter, partner-specific conventions grounded in shared interaction history. Prior work shows that multimodal LLMs fail to become more efficient across rounds, although they align on the labels they use. How can we determine whether this alignment reflects partner-specific grounding rather than a shared task vocabulary? We address this question by comparing capable multimodal agent dyads with human dyads from the KTH Tangrams corpus. Our novel methodological contribution is a constrained pseudo-dyad baseline that matches the original referential task structure, but breaks partner history. This baseline enables us to test whether the observed label alignment depends on interaction with a specific partner. Across three analytic layers (task competence, description strategy, alignment dynamics), we find clear differences. Humans reduce effort through entrainment, compressing descriptions and increasing label alignment with partners. Agents instead maintain fixed effort levels, producing verbose descriptions from round one, with near-ceiling label overlap that is statistically indistinguishable between real and pseudo dyads. MLLMs thus achieve coordination without convention, succeeding by verbose description rather than by forming the compact, history-dependent referring expressions characteristic of human dialogue.
Chinese Translation
重复的参考游戏测试对话者是否用更短的、基于共享互动历史的特定于伙伴的约定来替代最初冗长的描述。先前的研究表明，多模态大语言模型在多个回合中未能提高效率，尽管它们在使用的标签上达成了一致。我们如何判断这种对齐是否反映了特定于伙伴的基础，而非共享的任务词汇？我们通过比较来自KTH Tangrams语料库的多模态代理双人组与人类双人组来解决这个问题。我们新颖的方法论贡献是一个受限的伪双人基线，它匹配原始的指称任务结构，但打破了伙伴历史。这个基线使我们能够测试观察到的标签对齐是否依赖于与特定伙伴的互动。在三个分析层面（任务能力、描述策略、对齐动态）中，我们发现了明显的差异。人类通过同步减少努力，压缩描述并增加与伙伴的标签对齐。相反，代理保持固定的努力水平，从第一回合开始就产生冗长的描述，标签重叠接近上限，在真实和伪双人组之间在统计上无法区分。因此，多模态大语言模型在没有约定的情况下实现了协调，通过冗长的描述而非形成紧凑的、依赖历史的指称表达，成功地模拟了人类对话的特征。
cs.CL / 52 / 2606.08092
When Languages Disagree: Self-Evolving Multilingual LLM Judges
当语言不一致时：自我演化的多语言大型语言模型评判者
Abstract
Multilingual LLM-as-a-judge is widely used to evaluate model outputs across languages, but suffers from cross-lingual inconsistency (Fu and Liu, 2025). Existing methods typically treat this inconsistency as noise and mitigate it through voting or aggregation. In this work, we instead show that multilingual inconsistency can provide complementary evaluation signals. Our oracle analysis finds that sampling judgments across languages yields a higher performance upper bound than single-language judging, indicating that different languages potentially include complementary judgments. Motivated by this finding, we propose SEMJ, a self-evolving multilingual judge that leverages cross-lingual inconsistency for iterative refinement. SEMJ constructs multilingual variants of each input, collects independent judgments and rationales, and feeds inconsistent outputs back for self-reflection and re-evaluation. Experiments on multiple benchmarks show that SEMJ consistently outperforms voting and reflection baselines in both accuracy and cross-lingual consistency. Further analysis shows that inconsistency triggers useful re-evaluation, which improves judgment quality.
Chinese Translation
多语言大型语言模型作为评判者广泛用于评估跨语言的模型输出，但存在跨语言不一致性的问题（Fu 和 Liu, 2025）。现有方法通常将这种不一致性视为噪声，并通过投票或聚合来减轻其影响。在本研究中，我们则展示了多语言不一致性可以提供互补的评估信号。我们的预言者分析发现，跨语言抽样判断的性能上限高于单语言判断，这表明不同语言可能包含互补的判断。基于这一发现，我们提出了SEMJ，一种自我演化的多语言评判者，利用跨语言不一致性进行迭代优化。SEMJ构建每个输入的多语言变体，收集独立的判断和理由，并将不一致的输出反馈用于自我反思和重新评估。在多个基准测试中的实验表明，SEMJ在准确性和跨语言一致性方面均始终优于投票和反思基线。进一步分析表明，不一致性触发了有益的重新评估，从而提高了判断质量。
cs.CL / 53 / 2606.08157
Cross Paraphrastic Invariance Learning for Hallucination Detection
跨同义变换不变性学习用于幻觉检测
Abstract
Large language models (LLMs) frequently generate hallucinations, which are unsupported by a source document. To avoid costly LLM-as-evaluator pipelines and the heavy annotation demands of existing classifiers, we propose CPIL (Cross Paraphrastic Invariance Learning), a two-stage Siamese framework that maximizes the utility of existing labeled data. Concretely, CPIL constructs informative training pairs by: (i) generating paraphrastic views of each document-claim example as positives, and explicitly aligning their representations to enforce invariance to surface form; and (ii) mining same-document, opposite-label pairs as hard negatives to sharpen document-sensitive decision boundaries. Then CPIL conduct a two-stage model training: Stage 1 performs contrastive pretraining to learn a paraphrase-invariant, grounding-aware embedding space; and Stage 2 attaches a lightweight classifier for binary groundedness. On the LLM-AggreFact benchmark (11 tasks), CPIL surpasses strong baselines concerning F1 scores with only ~1% labeled data, showing its prediction superiority and label efficiency.
Chinese Translation
大型语言模型（LLMs）经常生成与源文档不支持的幻觉。为了避免昂贵的LLM作为评估者的流程和现有分类器的重标注需求，我们提出了CPIL（跨同义变换不变性学习），这是一种两阶段的孪生框架，旨在最大化现有标记数据的效用。具体而言，CPIL通过以下方式构建信息丰富的训练对： (i) 生成每个文档-主张示例的同义视图作为正例，并明确对齐它们的表示以强制保持表面形式的不变性； (ii) 挖掘同一文档中标签相反的对作为困难负例，以锐化文档敏感的决策边界。然后，CPIL进行两阶段的模型训练：第一阶段进行对比预训练，以学习一个对同义词不变、关注基础的嵌入空间；第二阶段附加一个轻量级分类器用于二元基础性判断。在LLM-AggreFact基准（11个任务）上，CPIL在F1分数方面超越了强基线，仅使用约1%的标记数据，展示了其预测优势和标记效率。
cs.CL / 54 / 2606.08158
Constrained Paraphrase Consistency for LLM Hallucination Detection
约束性释义一致性用于大型语言模型幻觉检测
Abstract
Large language models (LLMs) can generate factually inconsistent claims, motivating accurate and scalable hallucination detectors. Prior work largely enlarges training sets via synthesis or new annotations, introducing increasing cost and potential bias while underusing the consistency implied by semantically equivalent paraphrases. We propose Consistency-Constrained Hallucination Detector (CCHD), which formulates training as a constrained optimization problem. The standard cross-entropy on original document-claim pairs is complemented by (i) paraphrase-consistency constraints bounding divergence across paraphrased views, and (ii) label-preservation constraints tying paraphrases to ground truth. We solve the problem by gradient descent-ascent over model parameters and per-view Lagrange multipliers, adding only a few scalar dual variables and no inference-time overhead. With DeBERTa and Flan-T5 backbones, CCHD consistently outperforms strong baselines (FactCG, MiniCheck, and AlignScore) on standard factuality benchmarks, demonstrating its superiority on hallucination detection.
Chinese Translation
大型语言模型（LLMs）可能生成事实不一致的声明，这促使了准确且可扩展的幻觉检测器的需求。以往的研究主要通过合成或新注释扩大训练集，这增加了成本和潜在偏见，同时未充分利用语义等价释义所隐含的一致性。我们提出了一种一致性约束幻觉检测器（Consistency-Constrained Hallucination Detector, CCHD），将训练过程形式化为一个约束优化问题。标准的交叉熵损失在原始文档-声明对的基础上，辅以（i）释义一致性约束，限制释义视角之间的差异，以及（ii）标签保持约束，将释义与真实标签相联系。我们通过对模型参数和每个视角的拉格朗日乘子进行梯度下降-上升法来解决该问题，仅增加少量标量对偶变量，并且没有推理时的开销。在 DeBERTa 和 Flan-T5 的基础上，CCHD 在标准事实性基准测试中始终优于强基线（FactCG、MiniCheck 和 AlignScore），展示了其在幻觉检测方面的优越性。
cs.CL / 55 / 2606.08184
TextEconomizer: Enhancing Lossy Text Compression with Denoising Transformers and Entropy Coding
TextEconomizer：利用去噪变换器和熵编码增强有损文本压缩
Abstract
Lossy text compression reduces data size while preserving core meaning, making it well-suited for summarization, automated analysis, and digital archives. Despite the dominance of transformer-based models in language modeling, integrating context vectors and entropy coding into Sequence-to-Sequence (Seq2Seq) generation remains underexplored. A key challenge lies in identifying the most informative context vectors from encoder output and incorporating entropy coding to enhance storage efficiency while maintaining high-quality outputs, even under noisy text. We introduce TextEconomizer, an encoder-decoder framework paired with a transformer neural network that reduces variable-sized inputs by 50% to 80% without prior knowledge of dataset dimensions. Our model achieves competitive compression ratios via entropy coding while delivering near-perfect text quality, assessed by BLEU, ROUGE, METEOR, and semantic similarity scores. TextEconomizer operates with approximately 153x fewer parameters than comparable models, achieving a 5.39x compression ratio without sacrificing semantic quality. We also evaluate an LSTM-based autoencoder achieving a state-of-the-art 67x compression ratio with 196x fewer parameters, and LLaMAFormer, a modified transformer with 263x fewer parameters than ICAE while maintaining competitive text quality. TextEconomizer significantly surpasses existing transformer-based models in balancing memory efficiency and high-fidelity outputs, marking a breakthrough in lossy compression with optimal space utilization.
Chinese Translation
有损文本压缩在减少数据大小的同时保留核心意义，使其非常适合用于摘要、自动分析和数字档案。尽管基于变换器的模型在语言建模中占据主导地位，但将上下文向量和熵编码整合到序列到序列（Seq2Seq）生成中仍然未得到充分探索。一个关键挑战在于识别编码器输出中最具信息量的上下文向量，并结合熵编码以提高存储效率，同时在嘈杂文本下保持高质量输出。我们提出了TextEconomizer，一个与变换器神经网络配对的编码器-解码器框架，能够在没有先验数据集维度知识的情况下将可变大小的输入减少50%到80%。我们的模型通过熵编码实现了具有竞争力的压缩比，同时在BLEU、ROUGE、METEOR和语义相似性评分的评估中提供了近乎完美的文本质量。TextEconomizer的参数数量约为可比模型的153倍更少，在不牺牲语义质量的情况下实现了5.39倍的压缩比。我们还评估了一种基于LSTM的自编码器，达到了67倍的最先进压缩比，并且参数数量减少了196倍，以及LLaMAFormer，这是一种修改过的变换器，其参数数量比ICAE少263倍，同时保持了竞争力的文本质量。TextEconomizer在平衡内存效率和高保真输出方面显著超越了现有的基于变换器的模型，标志着在有损压缩和最佳空间利用方面的突破。
cs.CL / 56 / 2606.08194
GlobeAudio: A Multilingual Multicultural Benchmark for Naturalistic Evaluation of Large Audio-Language Models
GlobeAudio：一个用于大型音频语言模型自然评估的多语言多文化基准
Abstract
Large Audio-Language Models (LALMs) integrate audio perception and language understanding within a unified framework, enabling a wide range of real-world applications. Despite recent advances, evaluation for LALMs remains heavily underspecified relative to real-world requirements: most lack true linguistic and cultural authenticity, while others fail to capture acoustic realism. To bridge this gap, we propose GlobeAudio, a multilingual and multicultural benchmark designed to evaluate naturalistic audio understanding. GlobeAudio consists of 5,637 multiple-choice questions across six typologically diverse languages, expertly crafted by native speakers grounded on naturally occurring audio. In order to do well, models must possess higher-level auditory reasoning skills and culturally grounded interpretation. We systematically evaluate representative closed-source and open-source LALMs, as well as cascaded ASR-LLM pipelines. Our experiments reveal substantial performance gaps under natural acoustic conditions, particularly for open-source models and low-resource languages. These findings highlight critical limitations of current LALMs and underscore the importance of naturalistic audio evaluation for future audio-language systems. GlobeAudio can be found at https://huggingface.co/datasets/iNLP-Lab/GlobeAudio .
Chinese Translation
大型音频语言模型（LALMs）在统一框架内整合了音频感知和语言理解，能够支持广泛的现实应用。尽管近期取得了一些进展，LALMs的评估仍然相对于现实世界的需求严重不足：大多数模型缺乏真正的语言和文化真实性，而其他模型则未能捕捉到声学现实性。为了解决这一问题，我们提出了GlobeAudio，这是一个旨在评估自然音频理解的多语言和多文化基准。GlobeAudio包含5637个多项选择题，涵盖六种类型学上多样的语言，由母语者根据自然发生的音频精心制作。为了取得良好表现，模型必须具备更高层次的听觉推理能力和文化背景解读能力。我们系统地评估了代表性的闭源和开源LALMs，以及级联的ASR-LLM管道。我们的实验揭示了在自然声学条件下的显著性能差距，特别是在开源模型和低资源语言中。这些发现突显了当前LALMs的关键局限性，并强调了自然音频评估对未来音频语言系统的重要性。GlobeAudio可在https://huggingface.co/datasets/iNLP-Lab/GlobeAudio找到。
cs.CL / 57 / 2606.08197
AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments
AlignFed：面向异构边缘环境的大型语言模型的对齐感知异步联邦微调
Abstract
Large Language Models (LLMs) have significantly propelled the advancement of edge intelligence and have been widely deployed across various scenarios, including autonomous driving, industrial inspection, and personalized IoT services. However, the collaborative adaptation of LLMs on edge devices continues to face formidable challenges due to strict data privacy constraints, highly heterogeneous computing and communication resources, and the non-independent and identically distributed (non-IID) nature of local data. Federated Fine-Tuning (FFT) enables the collaborative optimization of distributed models without exposing raw data. Yet, traditional synchronous aggregation suffers from a severe straggler effect, resulting in high system latency and low resource utilization. Existing asynchronous federated learning methods are predominantly designed for small-to-medium-scale models and struggle to address the specific challenges inherent in LLM fine-tuning namely, model drift caused by stale updates, aggravated client drift stemming from data heterogeneity, and aggregation fairness imbalance resulting from the dominance of fast clients. To address these issues, this paper proposes AlignFed, an asynchronous federated fine-tuning framework for LLMs tailored to heterogeneous edge environments. AlignFed employs a lightweight multi-stage semantic alignment mechanism comprising three core modules: version-aware update grouping, cross-version semantic alignment based on a mini-batch calibration set, and fairness-aware aggregation that integrates both update freshness and client participation frequency. This framework effectively mitigates cross-version model drift and client drift while enhancing aggregation fairness, thereby achieving stable and efficient asynchronous federated optimization in scenarios characterized by high heterogeneity and significant update staleness.
Chinese Translation
大型语言模型（LLMs）显著推动了边缘智能的发展，并已广泛应用于自主驾驶、工业检测和个性化物联网服务等多种场景。然而，由于严格的数据隐私限制、高度异构的计算和通信资源，以及本地数据的非独立同分布（non-IID）特性，LLMs在边缘设备上的协同适应仍面临严峻挑战。联邦微调（FFT）使得分布式模型的协同优化成为可能，而无需暴露原始数据。然而，传统的同步聚合受到严重的滞后效应影响，导致系统延迟高和资源利用率低。现有的异步联邦学习方法主要针对小到中型模型设计，难以应对LLM微调中固有的特定挑战，即由陈旧更新引起的模型漂移、由于数据异构性引发的客户端漂移，以及由于快速客户端主导造成的聚合公平性失衡。为了解决这些问题，本文提出了AlignFed，一个针对异构边缘环境的大型语言模型的异步联邦微调框架。AlignFed采用一种轻量级的多阶段语义对齐机制，包含三个核心模块：版本感知的更新分组、基于小批量校准集的跨版本语义对齐，以及综合更新新鲜度和客户端参与频率的公平聚合。该框架有效减轻了跨版本模型漂移和客户端漂移，同时增强了聚合公平性，从而在高异构性和显著更新滞后的场景中实现稳定高效的异步联邦优化。
cs.CL / 58 / 2606.08236
Shared Semantics, Divergent Mechanisms: Unsupervised Feature Discovery by Aligning Semantics and Mechanisms
共享语义，分歧机制：通过对齐语义和机制实现无监督特征发现
Abstract
As large language models are increasingly deployed in high-stakes settings, there is a growing need for tools that audit not only model outputs but also the internal computations that produce them. Circuit analysis is a central approach in mechanistic interpretability, but it is typically target-conditioned, explaining a single prompt paired with a chosen completion. This target-conditioned setup can obscure heterogeneity across a model's continuation distribution. We introduce distribution-level unsupervised feature discovery, which clusters sampled continuations using both semantic content and sequence-level mechanistic attributions, without manually specifying target outputs. Our method represents each continuation with a semantic embedding and a prefix-to-continuation attribution signature, then optimizes a rate-distortion objective that trades off semantic coherence, mechanistic consistency, and cluster granularity. Across clustering and steering analyses, the discovered clusters expose continuation modes that single-view baselines miss and provide interventional evidence that cluster signatures correspond to actionable mechanistic factors. Overall, our approach complements circuit analysis and behavioral evaluation by providing a scalable audit of the mechanisms underlying a model's continuation distribution.
Chinese Translation
随着大型语言模型在高风险环境中的日益应用，对能够审计模型输出及其产生的内部计算工具的需求日益增加。电路分析是机制可解释性中的一种核心方法，但通常是针对特定目标的，解释与选择的完成结果配对的单个提示。这种目标条件设置可能会掩盖模型的延续分布中的异质性。我们提出了一种分布级别的无监督特征发现方法，该方法通过语义内容和序列级机制归因对采样的延续进行聚类，而无需手动指定目标输出。我们的方法使用语义嵌入和前缀到延续的归因特征表示每个延续，然后优化一个率失真目标，该目标在语义一致性、机制一致性和聚类粒度之间进行权衡。在聚类和引导分析中，发现的聚类揭示了单视图基线所遗漏的延续模式，并提供了干预证据，表明聚类特征与可操作的机制因素相对应。总体而言，我们的方法通过提供对模型延续分布底层机制的可扩展审计，补充了电路分析和行为评估。
cs.CL / 59 / 2606.08243
Building Comparative Motivation Profiles with Instrumental Interventions
通过工具干预构建比较动机轮廓
Abstract
Safety evaluations often infer latent motivations from behavioral patterns, but the construct validity of these inferences is unclear. We study this problem in alignment faking, where models comply with training objectives more often when they infer training pressure. This behavior is commonly interpreted as strategic self-preservation, but it may also reflect sensitivity to the model's inference about the expectation of researchers conducting the evaluation. We introduce a symmetric intervention framework for distinguishing these competing hypotheses. Instead of directly intervening on "scheming" or "sycophancy", we target instrumental processes entailed by each hypothesis: consequence-tracking and researcher-expectation tracking. We then compare how interventions on these processes affect the alignment faking. We study four openweight model organisms using synthetic document fine-tuning, activation steering, and prompting. Under synthetic document fine-tuning, Llama-3.1-70B, Llama3.1-405B, and Qwen-2.5-72B are more sensitive to expectation-tracking than consequence-tracking interventions. Activation steering on Llama-3.1- 70B supports the same broad picture, and prompt interventions broadly align with SDF profiles. Overall, alignment-faking behavior can be causally sensitive to evaluation-context expectations despite scheming-consistent scratchpads. Scheming and strategic-deception evaluations therefore need construct-validity checks, and symmetric instrumental interventions provide one such test.
Chinese Translation
安全评估通常通过行为模式推断潜在动机，但这些推断的构念效度尚不明确。我们研究了这一问题，聚焦于对齐伪造（alignment faking），在这种情况下，当模型推断出训练压力时，它们更频繁地遵循训练目标。这种行为通常被解释为战略自我保护，但它也可能反映出模型对进行评估的研究人员期望的推断敏感性。我们引入了一种对称干预框架，以区分这些竞争假设。我们并不直接干预“阴谋”或“阿谀奉承”，而是针对每个假设所涉及的工具性过程：后果追踪和研究人员期望追踪。然后，我们比较这些过程上的干预如何影响对齐伪造。我们使用合成文档微调、激活引导和提示研究了四个开放权重模型生物体。在合成文档微调下，Llama-3.1-70B、Llama3.1-405B和Qwen-2.5-72B对期望追踪的干预比对后果追踪的干预更为敏感。对Llama-3.1-70B的激活引导支持了相同的广泛图景，而提示干预与SDF轮廓大体一致。总体而言，尽管存在与阴谋一致的草稿，伪造对齐行为仍可能对评估上下文期望具有因果敏感性。因此，阴谋和战略欺骗评估需要构念效度检查，而对称工具干预提供了这样一种测试。
cs.CL / 60 / 2606.08245
ZAS-SQL: Distilling Rules from Failures for Zero-Shot Text-to-SQL
ZAS-SQL：从失败中提炼规则以实现零样本文本到SQL
Abstract
Text-to-SQL translates natural language into executable SQL queries. Few-shot in-context learning methods built upon large language models (LLMs) achieve strong performance, yet their reliance on demonstrations limits cross-domain generalization and consumes substantial context window space. Existing zero-shot methods, lacking effective generation constraints, still fall short of few-shot approaches. We observe that LLM failures in zero-shot Text-to-SQL are not random but exhibit systematic, recurring patterns. Building on this observation, we propose a fully zero-shot Text-to-SQL framework that distills core generation rules from failure cases through a Map-Reduce-based rule distillation pipeline and improves generation quality via three complementary modules: knowledge-augmented schema representation, which supplements missing semantics in Data Definition Language; a rule-driven structured reasoning framework that suppresses structural deviations; and Execution-Guided Early Stopping, which enables low-cost self-correction. On Spider, the proposed framework achieves up to 87.2% and 88.6% execution accuracy on the Dev and Test sets, respectively, establishing a new zero-shot state-of-the-art and surpassing multiple few-shot and fine-tuning methods built upon GPT-4/4o. On the domain-specific dataset UrbanPlan, it achieves 81.3%, confirming that the rule distillation approach generalizes across domains. Moreover, when equipped with a 4B-parameter model, the framework surpasses zero-shot baselines of leading closed-source models, demonstrating strong model generality.
Chinese Translation
文本到SQL将自然语言翻译为可执行的SQL查询。基于大型语言模型（LLMs）的少样本上下文学习方法表现出色，但其对示例的依赖限制了跨领域的泛化能力，并消耗了大量的上下文窗口空间。现有的零样本方法由于缺乏有效的生成约束，仍然无法与少样本方法相媲美。我们观察到，LLM在零样本文本到SQL中的失败并非随机，而是表现出系统性和重复性的模式。基于这一观察，我们提出了一种完全零样本的文本到SQL框架，通过基于Map-Reduce的规则提炼管道从失败案例中提炼核心生成规则，并通过三个互补模块提高生成质量：知识增强的模式表示，补充数据定义语言中的缺失语义；基于规则的结构推理框架，抑制结构偏差；以及执行引导的早停机制，实现低成本的自我纠正。在Spider数据集上，所提框架在开发集和测试集上的执行准确率分别达到87.2%和88.6%，建立了新的零样本最先进水平，并超越了多个基于GPT-4/4o的少样本和微调方法。在特定领域数据集UrbanPlan上，其准确率达到81.3%，确认了规则提炼方法在跨领域的泛化能力。此外，当配备一个4B参数的模型时，该框架超越了领先闭源模型的零样本基线，展示了强大的模型泛化能力。
cs.CL / 61 / 2606.08254
SSR: Can Simulated Patients Learn to Stigmatize Themselves? Modeling Self-Stigma through Internal Monologue
SSR：模拟患者能否自我污名化？通过内心独白建模自我污名化
Abstract
Simulating patients with large language models (LLMs) is a promising tool for mental health training, but existing approaches fail to capture a key clinical reality: self-stigma. Patients experiencing self-stigma, the internalization of negative stereotypes, often exhibit context-sensitive resistance, such as avoidance, denial, or self-blame, which current models render as static or uniformly compliant behavior. To address this, we introduce a novel simulation framework grounded in the psychological 3A1H model of self-stigmatization. Our core innovation is the creation of a \textbf{Stigmatized Self-Reflection} (\textbf{SSR}) dataset, where we augment mental health dialogues with internal monologues that reflect stigma-aware reasoning. By fine-tuning LLMs with this data using a chain-of-thought approach, we train patient agents to dynamically adjust their level and expression of stigma based on conversational triggers. Evaluations demonstrate that our approach significantly outperforms specialized baselines, generating more authentic and situationally appropriate patient responses. This work provides a crucial step towards realistic stigma simulation for clinical training and empathetic dialogue systems.
Chinese Translation
使用大型语言模型（LLMs）模拟患者是心理健康培训的一种有前景的工具，但现有方法未能捕捉到一个关键的临床现实：自我污名化。经历自我污名化的患者会内化负面刻板印象，常常表现出情境敏感的抵抗行为，如回避、否认或自责，而当前模型将其表现为静态或统一顺从的行为。为了解决这一问题，我们引入了一种基于心理学3A1H自我污名化模型的新型模拟框架。我们的核心创新是创建了一个 extbf{污名化自我反思}（ extbf{SSR}）数据集，在该数据集中，我们通过反映对污名意识的推理的内心独白来增强心理健康对话。通过使用链式思维方法对LLMs进行微调，我们训练患者代理根据对话触发因素动态调整其污名水平和表达。评估结果表明，我们的方法显著优于专业基线，生成了更真实和情境适宜的患者反应。这项工作为临床培训和同理心对话系统的真实污名模拟提供了重要一步。
cs.CL / 62 / 2606.08272
AgriGov: A Structured Multilingual Dataset Curation for Indian Government Schemes for Farmers
AgriGov：针对印度农民政府计划的结构化多语言数据集整理
Abstract
AgriGov is a curated, trilingual (English-Hindi-Marathi) dataset designed to address the scarcity of domain-grounded multilingual resources for agricultural policies and farmer welfare schemes. Initially, we collected and structured data from 50 government schemes sourced from trusted portals using automated scraping techniques, organizing it into predefined semantic fields (e.g., title, eligibility, application process, documents, exclusions). Translations were performed using a pipeline combining Google Translate API, MarianMT, and human post-editing, resulting in a domain-specific Hindi-Marathi dataset comprising approximately 2100 source segments. To enhance coverage, we augmented this dataset with sentences from the Samanantar corpus, leading to approximately 8,000 sentence-aligned Hindi-Marathi parallel pairs. The dataset now offers robust resources for fine-tuning machine translation models in this domain. AgriGov is designed for applications in domain-adaptive machine translation, question answering, information retrieval, and summarization systems. Its key contribution is a schema-driven, human-corrected multilingual alignment pipeline that ensures domain fidelity, provides provenance, and supports reproducible experiments, enabling retrieval-augmented applications for farmer-facing tools.
Chinese Translation
AgriGov是一个经过整理的三语（英语-印地语-马拉地语）数据集，旨在解决农业政策和农民福利计划领域内多语言资源匮乏的问题。最初，我们从可信的门户网站收集并结构化了50个政府计划的数据，采用自动抓取技术，将其组织到预定义的语义领域中（例如，标题、资格、申请流程、文件、排除条款）。翻译工作采用了结合Google Translate API、MarianMT和人工后期编辑的流程，最终形成了一个约2100个源段落的领域特定印地语-马拉地语数据集。为了增强覆盖面，我们还从Samanantar语料库中增补了句子，形成了约8000个句子对齐的印地语-马拉地语平行句对。该数据集现在为该领域的机器翻译模型微调提供了强有力的资源。AgriGov旨在用于领域自适应机器翻译、问答系统、信息检索和摘要系统等应用。其主要贡献在于一个基于模式驱动的、经过人工校正的多语言对齐流程，确保了领域的忠实性，提供了来源信息，并支持可重复的实验，从而使面向农民的工具能够进行增强检索的应用。
cs.CL / 63 / 2606.08295
TLRD: Teaching LLMs to Reason over Tabular Data with Tri-Level Rationale Distillation
TLRD：通过三层推理蒸馏教会大型语言模型对表格数据进行推理
Abstract
Tabular data is a primary medium for storing real-world information, driving many industrial applications of machine learning. Traditional predictors achieve strong predictive performance but do not provide readable, case-specific explanations essential for decision-making. Large Language Models (LLMs) can naturally bridge this gap by generating predictions alongside explanations. However, dataset-specific patterns, such as feature distributions and interactions, make tabular data difficult for LLMs to understand and reason over, while label-only fine-tuning improves performance at the cost of catastrophic forgetting. To address this problem, we propose Tri-Level Rationale Distillation (TLRD), a framework that converts label-only tabular datasets into structured rationale supervision for LLMs. TLRD uses a high-capacity teacher to synthesize a rationale corpus grounded in three complementary levels of evidence: instance-level feature, dataset-level distributional context, and comparison-level retrieved neighbors, then distills the rationale into student LLMs, enabling zero-overhead prediction and grounded explanation from raw features only. Experiments on multiple domain datasets show that TLRD significantly closes the performance gap between LLMs and state-of-the-art tree ensembles while producing grounded and readable explanations, offering a valuable reference for high-stakes decision-making.
Chinese Translation
表格数据是存储现实世界信息的主要媒介，推动了许多机器学习的工业应用。传统预测模型在预测性能上表现出色，但未能提供可读的、针对特定案例的解释，这对于决策至关重要。大型语言模型（LLMs）可以自然地弥补这一差距，通过生成预测和解释。然而，数据集特定的模式，如特征分布和交互，使得表格数据对LLMs而言难以理解和推理，而仅基于标签的微调虽然能提高性能，却会导致灾难性遗忘。为了解决这个问题，我们提出了三层推理蒸馏（Tri-Level Rationale Distillation, TLRD），这是一个将仅基于标签的表格数据集转换为结构化推理监督的框架。TLRD利用高容量的教师模型合成一个基于三层互补证据的推理语料库：实例级特征、数据集级分布上下文和比较级检索邻居，然后将推理蒸馏到学生LLMs中，使其能够仅从原始特征中进行零开销预测和有根据的解释。在多个领域数据集上的实验表明，TLRD显著缩小了LLMs与最先进的树集成模型之间的性能差距，同时生成有根据且可读的解释，为高风险决策提供了有价值的参考。
cs.CL / 64 / 2606.08307
Understanding the Sociocultural Dimensions of Mental Health Discourse in Arabic-Language X Communities
理解阿拉伯语X社区心理健康话语的社会文化维度
Abstract
Computational mental health research has predominantly centered on English-speaking populations, leaving Arabic-language discourse comparatively under-examined. We present an exploratory computational study of 8,147 tweets from 607 users classified by a GPT-4.1 personal-disclosure pipeline as likely lived-experience authors in three condition-specific Arabic-language X (formerly Twitter) Communities. We focus on discourse related to borderline personality disorder (BPD), bipolar disorder, and ADHD, and characterize community-associated linguistic patterns using a multi-domain cultural keyword framework. The results suggest that in this corpus, Bipolar tweets contain more religious and medical vocabulary, BPD tweets contain more relational, identity, and emotional-distress vocabulary, and ADHD tweets more often focus on practical symptoms and medication management. We treat these patterns as hypothesis-generating rather than confirmatory because the corpus is imbalanced across conditions, some subcorpora are temporally concentrated, and the keyword framework is an initial operationalization rather than a validated measurement instrument. The paper contributes a reusable LLM-assisted personal-disclosure pipeline and an exploratory cultural keyword framework for Arabic mental health discourse.
Chinese Translation
计算心理健康研究主要集中在英语使用人群上，导致阿拉伯语话语相对缺乏研究。我们呈现了一项探索性计算研究，分析了来自607个用户的8147条推文，这些推文通过GPT-4.1个人披露管道被分类为可能的生活经历作者，涉及三个特定病症的阿拉伯语X（前身为Twitter）社区。我们重点关注与边缘性人格障碍（BPD）、双相情感障碍和注意力缺陷多动障碍（ADHD）相关的话语，并使用多领域文化关键词框架对社区相关的语言模式进行特征描述。结果表明，在该语料库中，双相情感障碍的推文包含更多宗教和医学词汇，边缘性人格障碍的推文则包含更多关系、身份和情感困扰词汇，而注意力缺陷多动障碍的推文更常关注实际症状和药物管理。我们将这些模式视为生成假设而非确认性结果，因为语料库在各病症之间不平衡，某些子语料在时间上集中，且关键词框架是初步的操作化，而非经过验证的测量工具。本文贡献了一个可重用的LLM辅助个人披露管道和一个探索性的文化关键词框架，用于阿拉伯心理健康话语的研究。
cs.CL / 65 / 2606.08327
Chiaroscuro Attention: Spending Compute in the Dark
明暗注意力：在黑暗中消耗计算资源
Abstract
Standard transformers apply self-attention uniformly at every layer and token, regardless of whether the input requires dynamic cross-token interaction. We propose CHIAR-Former (Chiaroscuro Attention), a 4-layer hybrid transformer that routes each token to one of three operators - DCT spectral mixing, RBF kernel mixing, or full self-attention - based on per-token spectral entropy, a theoretically justified complexity signal. Through systematic ablation on WikiText-103, we discover routing collapse: the router consistently rejects RBF in favour of DCT and attention, revealing that spectral mixing and dynamic attention are complementary and sufficient. A purpose-designed DCT+Attention-only variant achieves Val PPL 36.54 on WikiText-103 - a 45% improvement over a full-attention baseline (PPL 66.62) at 62.5% fewer attention FLOPs. We extend evaluation to WikiText-2, IMDB sentiment classification, and synthetic ListOps operations, establishing a clear operating regime: CHIAR-Former excels on large-scale naturalistic text where token diversity supports spectral specialisation, while full attention retains an edge on small datasets and synthetic pattern-matching tasks. These findings - both the wins and the losses - together define when and why spectral routing earns its keep.
Chinese Translation
标准变压器在每一层和每个标记上均匀地应用自注意力，而不考虑输入是否需要动态的跨标记交互。我们提出了CHIAR-Former（明暗注意力），这是一种4层混合变压器，根据每个标记的谱熵（一个理论上合理的复杂性信号）将每个标记路由到三种操作之一——DCT谱混合、RBF核混合或完全自注意力。通过对WikiText-103的系统消融实验，我们发现了路由崩溃现象：路由器始终拒绝RBF，而偏向DCT和注意力，揭示了谱混合和动态注意力是互补且足够的。一种专门设计的仅DCT+注意力变体在WikiText-103上实现了Val PPL 36.54，比完全注意力基线（PPL 66.62）提高了45%，同时减少了62.5%的注意力FLOP。我们将评估扩展到WikiText-2、IMDB情感分类和合成ListOps操作，建立了一个明确的操作机制：CHIAR-Former在大规模自然文本中表现出色，因为标记多样性支持谱专业化，而完全注意力在小数据集和合成模式匹配任务中保持优势。这些发现——无论是成功还是失败——共同定义了谱路由何时以及为何能够发挥其作用。
cs.CL / 66 / 2606.08346
CATPO: Critique-Augmented Tree Policy Optimization
CATPO：增强批评的树策略优化
Abstract
Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving the reasoning capabilities of large language models (LLMs). Recent tree-based methods such as TreeRPO extend flat trajectory sampling with tree-structured rollouts to obtain dense, step-level reward signals without a separate process reward model. However, not all trees are equally informative: trees where all leaves succeed, all leaves fail, or the policy already predicts the reward distribution contribute little to gradient updates, wasting compute. We introduce CATPO (Critique-Augmented Tree Policy Optimization), which diagnoses and addresses this waste at the tree level. CATPO first scores each tree via a tree informativeness score, F(T), combining leaf-outcome diversity with policy-reward decorrelation at zero extra compute. For dead-wrong trees where all branches fail, CATPO applies critique-guided healing: it locates the shallowest failure point, generates a natural-language critique, and grafts refined continuations to recover training signal. Finally, an informativeness-weighted loss scales each tree's gradient contribution by its normalized score, concentrating parameter updates on the most informative trees while preserving overall gradient magnitude. Experiments on Qwen2.5-Math-1.5B trained with the MATH dataset show that CATPO achieves 37.5% macro accuracy across four benchmarks (AIME24, MATH-500, OlympiadBench, and MinervaMath), improving over TreeRPO by 1.9% and GRPO by 4.8%.
Chinese Translation
具有可验证奖励的强化学习（RLVR）已成为提高大型语言模型（LLMs）推理能力的主流范式。最近的基于树的方法，如TreeRPO，通过树结构的展开扩展了平坦轨迹采样，以在没有单独的过程奖励模型的情况下获得密集的逐步奖励信号。然而，并非所有树都是同样具有信息量的：所有叶子成功、所有叶子失败或策略已经预测奖励分布的树对梯度更新贡献甚微，浪费计算资源。我们提出了CATPO（增强批评的树策略优化），它在树层面上诊断并解决这一浪费。CATPO首先通过树信息量评分F(T)对每棵树进行评分，该评分结合了叶子结果的多样性与策略奖励的去相关性，且不增加额外计算。对于所有分支失败的错误树，CATPO应用批评引导的修复：它定位最浅的失败点，生成自然语言批评，并嫁接经过精炼的延续以恢复训练信号。最后，信息量加权损失根据每棵树的归一化评分缩放其梯度贡献，将参数更新集中在最具信息量的树上，同时保持整体梯度幅度。对使用MATH数据集训练的Qwen2.5-Math-1.5B的实验表明，CATPO在四个基准（AIME24、MATH-500、OlympiadBench和MinervaMath）上实现了37.5%的宏观准确率，比TreeRPO提高了1.9%，比GRPO提高了4.8%。
cs.CL / 67 / 2606.08347
Tensorizing Engram: Sharing Latents Across N-Gram Embeddings is Beneficial in LLMs
张量化记忆痕迹：在大型语言模型中共享潜在变量的n-gram嵌入是有益的
Abstract
Modern language models represent text using discrete token-level embeddings, which forces recurring multi-token patterns to be learned implicitly across Transformer layers. Both Over-tokenized Transformers and Engram attempt to address this limitation by explicitly incorporating multi-token (n-gram) memories. However, they rely on separate hash tables for each n-gram order, which introduces hash collisions and prevents nested n-grams from sharing the underlying latent structures. To address these issues, we propose Tensorized Engram (TN-gram), a compact memory module that represents tensorized n-gram embeddings through shared factors in the Canonical Polyadic (CP) form. TN-gram learns shared token-position factors together with order-absorption vectors to encode the embeddings of different n-gram order. Comprehensive experiments demonstrate that TN-gram matches or even outperforms Engram-style n-gram modules while requiring much fewer parameters.
Chinese Translation
现代语言模型使用离散的令牌级嵌入来表示文本，这迫使重复的多令牌模式在Transformer层中隐式学习。过度标记的Transformer和记忆痕迹（Engram）都试图通过显式地结合多令牌（n-gram）记忆来解决这一限制。然而，它们依赖于为每个n-gram阶数单独设置的哈希表，这引入了哈希冲突，并阻止了嵌套n-gram共享底层潜在结构。为了解决这些问题，我们提出了张量化记忆痕迹（Tensorized Engram，TN-gram），这是一种紧凑的记忆模块，通过在典型的多线性（Canonical Polyadic，CP）形式中共享因子来表示张量化的n-gram嵌入。TN-gram与顺序吸收向量共同学习共享的令牌位置因子，以编码不同n-gram阶数的嵌入。全面的实验表明，TN-gram的性能与记忆痕迹风格的n-gram模块相当，甚至更优，同时所需参数显著更少。
cs.CL / 68 / 2606.08348
Bayesian-Agent: Posterior-Guided Skill Evolution for LLM Agent Harnesses
贝叶斯智能体：后验引导的技能演化用于大语言模型智能体的利用
Abstract
LLM agents increasingly rely on external inference conditions: prompts, tools, memory, SOPs, skills, and harness feedback. These assets can improve task execution without changing model weights, but they are often revised by heuristic reflection or by reusing observed successes and failures as if counts alone were reliable belief. We introduce \textbf{Bayesian-Agent}, a native and cross-harness framework that treats reusable skills and SOPs as hypotheses about whether a frozen model will succeed under a particular prompt, context, and harness environment. Bayesian-Agent records verified trajectory evidence, maintains a feature-conditioned categorical posterior over each skill, and maps posterior state into inspectable actions such as patch, split, compress, retire, and explore. Model-facing prompts receive executable guardrails and failure-mode patches, while posterior summaries remain available for audit. With \texttt{deepseek-v4-flash}, incremental repair improves SOP-Bench from 80\% to 95\%, Lifelong AgentBench from 90\% to 100\%, and RealFin-Bench from 45\% to 65\%. We further evaluate Bayesian-Agent's native backend and optional GenericAgent, mini-swe-agent, and Claude Code backends. The results include positive, negative, saturated, and case-study settings, suggesting that agent skill evolution is best viewed as posterior-guided harness optimization rather than uncalibrated prompt accumulation. The source code is available at https://github.com/DataArcTech/Bayesian-Agent.
Chinese Translation
大语言模型（LLM）智能体越来越依赖外部推理条件：提示、工具、记忆、标准操作程序（SOP）、技能和利用反馈。这些资源可以在不改变模型权重的情况下改善任务执行，但它们通常通过启发式反思或通过重用观察到的成功与失败进行修订，仿佛仅仅依赖计数就能形成可靠的信念。我们提出了 extbf{贝叶斯智能体}（Bayesian-Agent），这是一个本地和跨利用框架，将可重用的技能和标准操作程序视为关于冻结模型在特定提示、上下文和利用环境下是否会成功的假设。贝叶斯智能体记录经过验证的轨迹证据，维护每个技能的特征条件分类后验，并将后验状态映射为可检查的动作，如修补、拆分、压缩、退役和探索。面向模型的提示接收可执行的保护措施和故障模式修补，而后验摘要则可供审计。使用 exttt{deepseek-v4-flash}，增量修复将SOP-Bench的性能从80 ext{%}提高到95 ext{%}，将Lifelong AgentBench的性能从90 ext{%}提高到100 ext{%}，将RealFin-Bench的性能从45 ext{%}提高到65 ext{%}。我们进一步评估了贝叶斯智能体的本地后端以及可选的GenericAgent、mini-swe-agent和Claude Code后端。结果包括正面、负面、饱和和案例研究设置，表明智能体技能演化最好被视为后验引导的利用优化，而非未校准的提示积累。源代码可在https://github.com/DataArcTech/Bayesian-Agent获取。
cs.CL / 69 / 2606.08357
Forward-Free Diffusion Language Models
无前向扩散语言模型
Abstract
Diffusion language models generate text through iterative denoising, offering a powerful alternative to autoregressive generation. However, discrete language spaces lack a natural neighborhood structure for defining effective perturbations, so some artificial corruption schemes are proposed in the forward process. Such prescribed forward processes often produce states that are mathematically convenient but misaligned with drafts and errors encountered during generation, resulting in degraded sample quality. To address this limitation, we propose FReDA, a forward-free diffusion language model that eliminates the need for a hand-designed forward process. We formulate diffusion language modeling as recursive distribution refinement, in which model-generated drafts serve as implicit intermediate states, and the learned refinement model progressively moves the draft distribution toward the target distribution. Concretely, FReDA refines drafts by proposing candidate draft sequences and either directly performing self-refinement or selecting among parallel candidates via best-of-N refinement. With this design, FReDA is neighborhood-agnostic, model-complexity-aware, and compatible with flexible refinement parameterizations. Extensive evaluations in the sub-8B regime show that FReDA-4B outperforms larger diffusion base models on reasoning and coding benchmarks, achieving absolute gains of up to 15%, while reaching a 1.5-1.8x average speedup over diffusion baselines and scaling effectively with additional refinement computation.
Chinese Translation
扩散语言模型通过迭代去噪生成文本，提供了自回归生成的强大替代方案。然而，离散语言空间缺乏自然的邻域结构来定义有效的扰动，因此在前向过程中提出了一些人工腐蚀方案。这种规定的前向过程往往产生在数学上方便但与生成过程中遇到的草稿和错误不一致的状态，导致样本质量下降。为了解决这一限制，我们提出了FReDA，一种无前向扩散语言模型，消除了对手工设计前向过程的需求。我们将扩散语言建模公式化为递归分布精炼，其中模型生成的草稿作为隐式中间状态，而学习到的精炼模型逐步将草稿分布向目标分布移动。具体而言，FReDA通过提出候选草稿序列来精炼草稿，并直接执行自我精炼或通过最佳N选择在并行候选中进行选择。通过这种设计，FReDA对邻域无关、模型复杂性敏感，并与灵活的精炼参数化兼容。在小于8B的范围内进行的广泛评估表明，FReDA-4B在推理和编码基准上优于更大的扩散基础模型，绝对增益高达15%，同时在扩散基线之上实现了1.5-1.8倍的平均加速，并有效地与额外的精炼计算进行扩展。
cs.CL / 70 / 2606.08381
Auditing Proprietary Alignment in Large Language Models: A Comparative Framework Without a Ground-Truth Standard
大型语言模型中的专有对齐审计：一种没有真实标准的比较框架
Abstract
Large language models (LLMs) are increasingly released and deployed through opaque development and deployment pipelines, enabling model providers to inject intentional, provider-specific policies without officially announcing them. As a result, various models have been reported to generate responses reflecting proprietary rules and organizational interests, leading to censorship or misinformation on controversial topics. However, systematic identification of such alignment remains a fundamental challenge, complicated by the ambiguity of what ``proprietary'' entails in different contexts. In this paper, we propose a statistical framework for detecting proprietary alignment in black-box language models via comparative behavioral analysis. Our approach quantifies systematic deviations between the responses of a target model and those of a reference set of baseline models in a shared semantic space. By evaluating relative behavioral divergence rather than absolute correctness, our framework enables principled auditing under black-box access. Applied to several widely discussed but previously unquantified cases, it provides a systematic and scalable basis for external assessment of provider-specific alignment behavior in large language models.
Chinese Translation
大型语言模型（LLMs）越来越多地通过不透明的开发和部署流程发布和应用，使得模型提供者能够在未正式宣布的情况下注入有意的、特定于提供者的政策。因此，已经有多种模型被报告生成反映专有规则和组织利益的响应，导致在争议话题上的审查或错误信息。然而，系统性识别这种对齐仍然是一个基本挑战，因为“专有”在不同上下文中的含义模糊。在本文中，我们提出了一种通过比较行为分析检测黑箱语言模型中专有对齐的统计框架。我们的方法量化了目标模型的响应与一组基准模型在共享语义空间中的系统性偏差。通过评估相对行为偏差而非绝对正确性，我们的框架在黑箱访问下实现了原则性的审计。应用于几个广泛讨论但之前未量化的案例，它为对大型语言模型中提供者特定对齐行为的外部评估提供了系统性和可扩展的基础。
cs.CL / 71 / 2606.08394
When Correct Decisions Hide Internal Stress: Decision-State Probing in Multimodal Language Models
当正确决策掩盖内部压力：多模态语言模型中的决策状态探测
Abstract
Multimodal language models are typically evaluated through external behavior: selecting the correct image--text match, rejecting unsupported captions, or answering visual queries correctly. However, correct behavior alone does not show that the model's internal decision state remains stable under controlled semantic stress. We study this gap through S$^3$E (Structured Semantic Stress Evaluation), a framework for analyzing behavior-internal decoupling in multimodal language models. S$^3$E uses a positive-anchored A/B forced-choice setup in which an image-supported caption is contrasted against semantic stress candidates under both original and swapped option orders, while hidden states are extracted at the pre-answer decision state. We focus on strict-correct trials, where the model consistently selects the correct caption across both orders. Rather than treating arbitrary hidden-state variation as evidence of instability, we measure whether semantic-conflict candidates induce excess decision-state displacement relative to meaning-preserving controls. Across Qwen3VL, Gemma3, and InternVL3, semantic stress consistently produces positive selected-layer excess displacement over lexical controls despite correct forced-choice behavior, while comparisons against random negatives are model-dependent. We interpret this as a scoped decision-state stress-sensitivity signal rather than evidence of downstream failure or hallucination. Our results suggest that forced-choice correctness alone is not a sufficient certificate of invariant internal decision geometry.
Chinese Translation
多模态语言模型通常通过外部行为进行评估：选择正确的图像-文本匹配、拒绝不支持的标题或正确回答视觉查询。然而，仅仅正确的行为并不能表明模型的内部决策状态在受控的语义压力下保持稳定。我们通过 S$^3$E（结构化语义压力评估）研究这一差距，该框架用于分析多模态语言模型中的行为与内部解耦。S$^3$E 使用正锚定的 A/B 强制选择设置，其中图像支持的标题与语义压力候选项在原始和交换选项顺序下进行对比，同时在预回答决策状态提取隐藏状态。我们专注于严格正确的试验，其中模型在两种顺序中始终选择正确的标题。我们并不将任意的隐藏状态变化视为不稳定的证据，而是测量语义冲突候选项是否相对于保持意义的控制组引起过量的决策状态位移。在 Qwen3VL、Gemma3 和 InternVL3 中，尽管表现出正确的强制选择行为，语义压力始终在词汇控制下产生正向选择层过量位移，而与随机负样本的比较则依赖于模型。我们将此解释为一种范围明确的决策状态压力敏感信号，而不是下游失败或幻觉的证据。我们的结果表明，仅凭强制选择的正确性并不足以证明内部决策几何形状的不变性。
cs.CL / 72 / 2606.08397
TrustMargin: Training-Free Arbitration between Parametric Memory and Retrieved Evidence in Large Language Models
信任边际：在大型语言模型中无训练仲裁参数记忆与检索证据
Abstract
Large language models answer knowledge-intensive questions using both parametric memory and retrieved evidence, but neither source is uniformly reliable. Retrieval can fill knowledge gaps, yet distracting passages may override correct closed-book answers. We study this post-generation conflict as answer-level source arbitration: given Direct and RAG answers from the same frozen model, decide which source to trust. We propose TRUSTMARGIN, a training-free, plug-and-play arbitration layer that scores the two existing candidates with the model's own likelihoods. It combines a parametric-prior margin, which tests whether memory accepts the retrieved answer, with an evidence-binding margin, which discounts passage-only salience and measures question-specific support. TRUSTMARGIN selects between Direct and RAG without fine-tuning, external judges, or additional generation. Across 2WIKIMQA and CWQA with three LLaMA scales, TRUSTMARGIN consistently improves over Direct generation and BM25-RAG, recovers part of the Direct/RAG oracle gap, and generalizes to multiple training-free RAG pipelines.
Chinese Translation
大型语言模型使用参数记忆和检索证据回答知识密集型问题，但这两种来源的可靠性并不均匀。检索可以填补知识空白，但干扰性段落可能会覆盖正确的闭卷答案。我们将这种生成后的冲突研究为答案级源仲裁：在同一冻结模型中给定直接答案（Direct）和检索增强生成答案（RAG），决定信任哪个来源。我们提出了TRUSTMARGIN，这是一种无训练、即插即用的仲裁层，利用模型自身的可能性对两个现有候选者进行评分。它结合了一个参数先验边际，用于测试记忆是否接受检索答案，以及一个证据绑定边际，用于折扣仅基于段落的显著性并测量特定问题的支持。TRUSTMARGIN在不进行微调、外部评估或额外生成的情况下，在直接答案和RAG之间进行选择。在2WIKIMQA和CWQA的三种LLaMA规模上，TRUSTMARGIN始终优于直接生成和BM25-RAG，缩小了Direct/RAG的oracle差距，并能推广到多个无训练的RAG管道。
cs.CL / 73 / 2606.08408
TimpaTeks: Automatic In-place Text Sequence Modification via Diffusion Language Model Steering
TimpaTeks：通过扩散语言模型引导实现自动就地文本序列修改
Abstract
We extend activation steering to diffusion language models (DLMs) and study a novel problem that arose due to the inference mechanism of DLMs: Modifying a text in-place to manifest a different concept. We propose TimpaTeks, an automatic in-place text modification mechanism using DLMs. Experiments on IMDB movie reviews (sentiment) and a synthetic Cats and Dogs Dataset (arbitrary, more unconventional concept steering) show that TimpaTeks provides a feasible novel mechanism to steer diffusion language model outputs in-place. TimpaTeks enables in-place modification while simultaneously lowers sentence perplexity and retaining the original sentence structre without the need of instruction tuned models. TimpaTeks is also computationally cheaper than prompt-based DLM steering, as it performs denoising in-place rather than constructing an additional prompt-conditioned output sequence.
Chinese Translation
我们将激活引导扩展到扩散语言模型（DLMs），并研究由于DLMs的推理机制而产生的一个新问题：在原地修改文本以表现出不同的概念。我们提出了TimpaTeks，一种使用DLMs的自动就地文本修改机制。在IMDB电影评论（情感）和合成的猫狗数据集（任意，更不常规的概念引导）上的实验表明，TimpaTeks提供了一种可行的新机制，以在原地引导扩散语言模型的输出。TimpaTeks实现了就地修改，同时降低了句子的困惑度，并保留了原始句子的结构，而无需使用指令调优模型。TimpaTeks的计算成本也低于基于提示的DLM引导，因为它是在原地进行去噪，而不是构建额外的提示条件输出序列。
cs.CL / 74 / 2606.08411
AsyncLane: Decoupling Refinement from Advancement in Diffusion Language Model Decoding
AsyncLane：在扩散语言模型解码中将精细化与推进解耦
Abstract
Block-wise semi-autoregressive decoding is the standard inference paradigm for diffusion large language models (DLMs), but it imposes a strict dependency between blocks: the next block cannot begin until the current block is fully decoded or its denoising budget is exhausted. We observe that once a block exposes a reliable delimiter boundary or stable semantic prefix, continuation generation need not wait for every residual token to be resolved. We propose AsyncLane, a training-free decoding scheduler that decouples refinement from advancement. AsyncLane forks a generate lane at observed delimiter boundaries into a refine lane and a continuation generate lane: the prefix remains editable, while the continuation advances before prefix refinement finishes. The resulting lane tree records decoding dependencies and output order, while execution proceeds over the active lane set. To make this asynchronous schedule efficient under bidirectional attention, AsyncLane combines shared-prefix lane batching, lookahead draft reuse, cascading termination, and compact cache refresh with refresh-logit reuse, preventing model-call cost from scaling directly with the number of lanes. AsyncLane is a drop-in replacement for block-wise DLM samplers and requires no retraining. Experiments on mathematical reasoning and code generation show that AsyncLane consistently improves throughput while maintaining competitive quality. Across LLaDA and Dream backbones, AsyncLane achieves the highest TPS in all evaluated benchmark-length settings; relative to the fastest competing baseline, it reaches peak speedups of 2.95x on LLaDA and 3.04x on Dream, with especially large gains under longer generation budgets.
Chinese Translation
块级半自回归解码是扩散大型语言模型（DLMs）的标准推理范式，但它对块之间施加了严格的依赖关系：下一个块在当前块完全解码或其去噪预算耗尽之前无法开始。我们观察到，一旦一个块暴露出可靠的分隔边界或稳定的语义前缀，后续生成就不必等待每个残余标记被解析。我们提出了AsyncLane，这是一种无训练的解码调度器，将精细化与推进解耦。AsyncLane在观察到的分隔边界处分叉生成通道为精细化通道和继续生成通道：前缀保持可编辑，而继续生成在前缀精细化完成之前就开始推进。生成的通道树记录了解码依赖关系和输出顺序，同时执行在活动通道集上进行。为了在双向注意力下使这一异步调度高效，AsyncLane结合了共享前缀通道批处理、前瞻草稿重用、级联终止和紧凑缓存刷新与刷新-logit重用，防止模型调用成本直接随通道数量增加而增加。AsyncLane是块级DLM采样器的直接替代品，无需重新训练。在数学推理和代码生成的实验中，AsyncLane始终提高了吞吐量，同时保持了竞争力的质量。在LLaDA和Dream骨干网络中，AsyncLane在所有评估的基准长度设置中实现了最高的每秒生成次数（TPS）；与最快的竞争基线相比，在LLaDA上达到了2.95倍的峰值加速，在Dream上达到了3.04倍，尤其是在较长生成预算下获得了显著的提升。
cs.CL / 75 / 2606.08417
Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics
破解生成困惑度：为何无条件文本评估需要分布度量
Abstract
Diffusion and continuous flow-based language models have emerged as the leading non-autoregressive alternatives to language modeling. Progress in both paradigms is overwhelmingly tracked by generative perplexity (gen-PPL): the per-token negative log-likelihood of samples under a frozen autoregressive (AR) scorer such as gpt2-large, typically paired with an empirical-entropy guardrail to rule out low-entropy collapse. We argue that this metric is unsound. By construction, gen-PPL measures only predictability under the scoring AR, not grammaticality or semantic coherence -- and the set of predictable but still low-quality sequences is combinatorially large. To make this concrete, we construct a suite of zero-parameter, deliberately naive samplers that achieve state-of-the-art gen-PPL on LM1B and OpenWebText at non-degenerate entropy, surpassing recently published diffusion and continuous-flow models while producing text that is incoherent by construction. We recommend evaluation suites that directly quantify the distributional divergence between generated and reference text, and use such a suite to re-benchmark recent non-autoregressive models, recovering a more faithful picture of the current state of the art.
Chinese Translation
扩散和基于连续流的语言模型已成为语言建模的主要非自回归替代方案。这两种范式的进展主要通过生成困惑度（gen-PPL）来跟踪：在冻结的自回归（AR）评分器（如 gpt2-large）下，样本的每个标记的负对数似然，通常与经验熵的保护措施配对，以排除低熵崩溃。我们认为这一指标是不合理的。从构造上看，gen-PPL 仅测量在评分 AR 下的可预测性，而不是语法性或语义连贯性——而可预测但仍然低质量的序列集合在组合上是庞大的。为了使这一点具体化，我们构建了一套零参数、故意简单的采样器，在非退化熵下在 LM1B 和 OpenWebText 上实现了最先进的 gen-PPL，超越了最近发布的扩散和连续流模型，同时生成的文本在构造上是不连贯的。我们建议评估套件直接量化生成文本与参考文本之间的分布差异，并使用这样的套件重新基准测试最近的非自回归模型，从而恢复当前最先进技术的更真实图景。
cs.CL / 76 / 2606.08445
Segment-level Tree Search for Long Meeting Document Summarization
基于段级树搜索的长会议文档摘要生成
Abstract
Meeting documents are challenging to summarize due to their length and complex conversational structure. Existing approaches typically adopt multi-stage pipelines that extract information prior to summarization; however, these approaches often suffer from cumulative error propagation without intermediate validation, a limitation further amplified by short and low-quality reference summaries. We propose segment-level summarization via Monte Carlo Tree Search (S3), a training-free framework that constructs a final summary by composing segment-level summary candidates. S3 partitions a long document into segments and generates multiple summary candidates per segment, forming nodes of a search tree. The best-scoring combination is selected via self-reward-guided tree search and refined into the final output. Despite using a 7B model, S3 achieves performance comparable to larger 72B models while producing length-appropriate summaries.
Chinese Translation
会议文档由于其长度和复杂的对话结构而难以进行摘要。现有的方法通常采用多阶段流程，在摘要生成之前提取信息；然而，这些方法往往因缺乏中间验证而导致累积错误传播，这一限制在短小且低质量的参考摘要中更为明显。我们提出了一种基于段级摘要的蒙特卡罗树搜索方法（Segment-level summarization via Monte Carlo Tree Search, S3），这是一个无训练的框架，通过组合段级摘要候选项构建最终摘要。S3将长文档划分为多个段，并为每个段生成多个摘要候选项，形成搜索树的节点。通过自奖励引导的树搜索选择最佳评分组合，并将其精炼为最终输出。尽管使用的是一个7B模型，S3的性能与更大的72B模型相当，同时生成适当长度的摘要。
cs.CL / 77 / 2606.08451
Sycophancy as a Multilingual Alignment Failure: How Safety Degrades Across Languages, Topics, and Models
谄媚作为多语言对齐失败：安全性如何在语言、主题和模型之间下降
Abstract
Safety-aligned large language models often exhibit sycophancy, which is the tendency to affirm users' opinions regardless of factual accuracy. Although well-studied in English, its manifestation in other languages remains largely unexamined, leaving billions of non-English speakers potentially vulnerable to model-validated misinformation. We present the first large-scale, multi-model evaluation of cross-lingual sycophancy, benchmarking \textbf{six instruction-tuned models} across \textbf{1.1 million instances} spanning \textbf{38 languages} and \textbf{33 topic categories}. We identify a consistent resource-tier effect: sycophancy rates spike sharply in low-resource and zero-shot language settings. Critically, this degradation is topic-agnostic, as models fail uniformly across both benign and safety-critical prompts, offering no additional protection where it is most needed. We further identify tokenizer fertility as a structural driver of this alignment collapse. Collectively, our results demonstrate that prevailing alignment methodologies generalize poorly beyond high-resource languages, underscoring the urgent need for equitable multilingual safety techniques.
Chinese Translation
安全对齐的大型语言模型往往表现出谄媚，即无论事实准确性如何，倾向于肯定用户的观点。尽管在英语中得到了充分研究，但其在其他语言中的表现仍然基本未被探讨，这使得数十亿非英语使用者可能面临模型验证错误信息的风险。我们首次进行大规模的多模型跨语言谄媚评估，基准测试了六个经过指令调优的模型，涵盖了110万个实例，涉及38种语言和33个主题类别。我们发现了一种一致的资源层级效应：在低资源和零样本语言环境中，谄媚率急剧上升。关键是，这种下降与主题无关，因为模型在良性和安全关键提示下均表现不佳，在最需要保护的地方未能提供额外的安全保障。我们进一步确定了分词器的丰度作为这种对齐崩溃的结构性驱动因素。总体而言，我们的结果表明，当前的对齐方法在高资源语言之外的推广效果较差，强调了迫切需要公平的多语言安全技术。
cs.CL / 78 / 2606.08471
More Yap Less Meaning: Uncovering Self-Improvement Behavior in SLMs
更多的言语，较少的意义：揭示小型语言模型中的自我改进行为
Abstract
Recently, language models have made rapid progress across various domains and applications. However, their capability for self-improvement, i.e., whether they are adept at recognising and correcting flaws in their own reasoning, remains dubious. In this study, we address this question by constructing a sufficiency test to rigorously examine the self-correction capabilities of small language models (SLMs). We propose a minimal three-step self-correction pipeline that collects initial SLM answers, prompts the same model to generate hints for its incorrect responses given the ground truth, and feeds the model the same question with its own feedback to refine the initial answer. We evaluate a variety of instruction-tuned and reasoning SLMs in this experimental setup on arithmetic and logical reasoning benchmarks. Our findings show that SLMs with injected hint sentences yield only a 4.4 percent gain over initial question-answering accuracy. Even though the correct answer was provided alongside the model's incorrect reasoning, the evaluated SLMs fail to understand what was missing in their reasoning and show minimal semantic difference between hints that lead to corrections and ones that do not. Furthermore, our experiments show that longer hints are positively correlated with incorrect final answers, suggesting that longer deliberation on problems can hinder the reasoning process, meaning that SLMs do not necessarily scale in performance with a larger compute budget.
Chinese Translation
近年来，语言模型在各个领域和应用中取得了快速进展。然而，它们的自我改进能力，即它们是否擅长识别和纠正自身推理中的缺陷，仍然存在疑问。在本研究中，我们通过构建一个充分性测试来严格检验小型语言模型（SLMs）的自我纠正能力。我们提出了一个最小的三步自我纠正流程，该流程收集初始SLM答案，提示同一模型根据真实答案生成对其错误响应的提示，并将模型反馈的相同问题重新输入，以细化初始答案。我们在算术和逻辑推理基准测试中评估了多种经过指令调优和推理的小型语言模型。在这一实验设置中，我们的发现表明，注入提示句子的小型语言模型仅在初始问答准确性上获得了4.4%的提升。尽管正确答案与模型的错误推理一同提供，但被评估的SLMs未能理解其推理中缺失的内容，并且在导致纠正的提示与不导致纠正的提示之间表现出最小的语义差异。此外，我们的实验表明，较长的提示与错误的最终答案呈正相关，这表明对问题的更长时间思考可能会妨碍推理过程，这意味着小型语言模型的性能并不一定随着计算预算的增加而提升。
cs.CL / 79 / 2606.08486
TRADE: Transducer-Augmented Decoder for Speech LLM
TRADE：增强转换器的语音大语言模型解码器
Abstract
Speech Large Language Models (Speech LLMs) lack a principled mechanism for streaming inference: their label-synchronous generation has no acoustic-frame alignment, making real-time decoding and end-of-utterance detection difficult. We propose TRADE TRansducer-Augmented DEcoder, which augments a multimodal LLM with a transducer branch that shares the audio encoder and uses the LLM's hidden states directly as the prediction network -- coupling frame-synchronous acoustic alignment with the LLM's linguistic reasoning. Three design choices make the system accurate, streamable, and long-form capable: (1)Tightly coupled dual vocabularies -- a compact transducer vocabulary derived from the LLM vocabulary, enabling zero-cost score fusion; (2)Chunk-synchronized streaming training with gradient stopping, eliminating the train-inference mismatch at offline-equivalent memory cost; and (3)Localized Decoder Audio Attention (LDAA), a causal sliding window that caps KV-cache memory independently of utterance length. A single TRADE checkpoint supports offline and streaming decoding across a continuous range of latency operating points. TRADE achieves 6.71% average WER on the Open ASR Leaderboard, while the streaming recognition with 960ms chunk size reaches 8.40% from the same checkpoint. On long-form speech, it obtains 3.64% WER on TED-LIUM and 10.88% on Earnings-22 without external segmentation. TRADE provides sentence-end punctuation timestamps that, when combined with acoustic voice activity detection (VAD), improve end-of-utterance detection by +0.03 F_1 over acoustic VAD alone.
Chinese Translation
语音大语言模型（Speech LLMs）缺乏一个原则性的流式推理机制：它们的标签同步生成没有声学帧对齐，使得实时解码和话语结束检测变得困难。我们提出了TRADE（增强转换器解码器），该模型通过一个共享音频编码器的转换器分支增强了多模态LLM，并直接使用LLM的隐藏状态作为预测网络——将帧同步的声学对齐与LLM的语言推理相结合。三个设计选择使得该系统具备准确性、可流式处理性和长文本处理能力：（1）紧密耦合的双词汇——从LLM词汇派生的紧凑型转换器词汇，实现零成本的得分融合；（2）带有梯度停止的块同步流式训练，消除了离线等效内存成本下的训练-推理不匹配；（3）局部解码器音频注意力（Localized Decoder Audio Attention, LDAA），一种因果滑动窗口，独立于话语长度限制KV缓存内存。单个TRADE检查点支持在连续延迟操作点范围内的离线和流式解码。TRADE在Open ASR Leaderboard上实现了6.71%的平均字错误率（WER），而960毫秒块大小的流式识别则从同一检查点达到了8.40%。在长文本语音上，它在TED-LIUM上获得了3.64%的WER，在Earnings-22上获得了10.88%的WER，且没有外部分段。TRADE提供了句末标点时间戳，当与声学语音活动检测（VAD）结合时，相比单独使用声学VAD，话语结束检测提高了+0.03的F_1值。
cs.CL / 80 / 2606.08496
SAEExplainer: Interpreting SAE Features with Activation-Guided Preference Optimization
SAEExplainer：通过激活引导的偏好优化解释稀疏自编码器特征
Abstract
Although Sparse Autoencoders (SAEs) have mitigated the opacity of large language models (LLMs) by decomposing dense representations into sparse features, explaining these features still remains a central challenge. Current explanation methods, however, typically operate within an open-loop paradigm, failing to leverage mechanistic feedback for further refinement. In this paper, we propose SAEExplainer, a training framework utilizes activation scores as an objective reward signal to train the model for self-correction and iterative bootstrapping. By iteratively verifying and correcting foundational explanations through a two-round optimization process, SAEExplainer achieves continuous improvement in its explanatory capabilities. This mechanism significantly reduces explanation hallucinations and reinforces causal triggering patterns. Extensive experiments demonstrate our approach improves upon established baselines across most metrics, especially in causal triggering and discriminative activation.
Chinese Translation
尽管稀疏自编码器（Sparse Autoencoders, SAEs）通过将密集表示分解为稀疏特征来减轻大型语言模型（Large Language Models, LLMs）的不透明性，但解释这些特征仍然是一个核心挑战。然而，当前的解释方法通常在开放环路范式下运行，未能利用机械反馈进行进一步的优化。在本文中，我们提出了SAEExplainer，这是一种训练框架，利用激活分数作为目标奖励信号来训练模型进行自我修正和迭代引导。通过通过两轮优化过程迭代验证和修正基础解释，SAEExplainer在其解释能力上实现了持续改进。这一机制显著减少了解释幻觉，并强化了因果触发模式。大量实验表明，我们的方法在大多数指标上优于既定基准，特别是在因果触发和区分性激活方面。
cs.CL / 81 / 2606.08501
Back on Track: Aligning Rewards and States for Reasoning in Diffusion Large Language Models
回归正轨：对齐奖励与状态以增强扩散大型语言模型的推理能力
Abstract
Reinforcement learning (RL) holds immense promise for enhancing the reasoning capabilities of diffusion large language models (dLLMs). However, progress is fundamentally constrained by a dual misalignment between authentic generation trajectory and the gradient update process: (i) Process-reward misalignment. Sparse, terminal rewards are indiscriminately assigned to all intermediate steps of the generation process, failing to provide discriminative credit assignment. (ii) State-trajectory misalignment. Policy updates are often diverted toward artificial, out-of-trajectory states, squandering gradients on less informative samples. To address these limitations, we introduce Process Aligned Policy Optimization (PAPO), a novel framework that holistically aligns the RL update with the dLLM's generative trajectory via Step-Aware Process Rewards (SPR) that transform sparse terminal rewards into dense, step-wise credit, and Entropy-Guided Historical Re-enactment (EHR) that replays authentic trajectories at high-uncertainty steps. Extensive experiments on four benchmarks demonstrate that PAPO significantly outperforms baselines, achieving gains of up to 4.5% on GSM8K, 4.8% on MATH500, 42.2% on Countdown and 16.1% on Sudoku.
Chinese Translation
强化学习（RL）在提升扩散大型语言模型（dLLMs）的推理能力方面具有巨大潜力。然而，进展受到真实生成轨迹与梯度更新过程之间的双重不对齐的根本限制：（i）过程-奖励不对齐。稀疏的终端奖励被无差别地分配给生成过程的所有中间步骤，未能提供区分性的信用分配。（ii）状态-轨迹不对齐。策略更新往往偏向于人工的、轨迹外的状态，浪费梯度在信息量较少的样本上。为了解决这些限制，我们提出了过程对齐策略优化（Process Aligned Policy Optimization, PAPO），这是一个新颖的框架，通过步骤感知过程奖励（Step-Aware Process Rewards, SPR）将稀疏的终端奖励转化为密集的逐步信用，并通过熵引导的历史重演（Entropy-Guided Historical Re-enactment, EHR）在高不确定性步骤上重放真实轨迹，从而整体上对齐RL更新与dLLM的生成轨迹。在四个基准测试上的广泛实验表明，PAPO显著优于基线，在GSM8K上提高了最多4.5%，在MATH500上提高了4.8%，在Countdown上提高了42.2%，在数独（Sudoku）上提高了16.1%。
cs.CL / 82 / 2606.08545
Ishigaki-IDS: An Open-Weight Verifier-Aware Model for Information Delivery Specification Drafting in Building Information Modeling
Ishigaki-IDS：一种面向验证者的开放权重模型，用于建筑信息建模中的信息交付规范草拟
Abstract
Building Information Modeling (BIM) projects require information requirements to be described as machine-checkable Information Delivery Specification (IDS) files in order to verify whether building models contain the required attributes. However, IDS authoring remains a practical bottleneck: practitioners must handle domain vocabulary, strict XML schema constraints, and external validator conformance while also checking whether the requirement itself is correctly expressed. We present Ishigaki-IDS, an open-weight LLM specialized for verifier-aware IDS draft generation. The model combines continued pretraining on BIM/IDS corpora, supervised fine-tuning on information-requirement-to-IDS pairs, and reinforcement learning with verifiable rewards from an external validator. The goal is not to replace expert review, but to move IDS authoring from low-level XML and schema repair toward validator-loadable drafts that practitioners can inspect and correct. On the 166-case expert-created Ishigaki-IDS-Bench, Ishigaki-IDS-8B achieves an IDSAuditPass score of 0.651, a validator-pass metric for generated IDS files, substantially outperforming Claude Opus 4.5, the strongest single-shot LLM baseline we evaluated, at 0.331. It also obtains an Audit-Gated FacetF1 of 0.282, which measures requirement-facet alignment among validator-passing drafts. The same recipe scales: 14B and 32B variants reach IDSAuditPass 0.753 / 0.693 and Audit-Gated FacetF1 0.392 / 0.369. In a workflow check with six BIM practitioners, Ishigaki-assisted authoring reduced aggregate work time by 54.7% under the same validation and alignment endpoint. These results suggest that verifier-aware IDS generation can reduce the practical burden of converting BIM information requirements into reviewable IDS drafts.
Chinese Translation
建筑信息建模（BIM）项目需要将信息需求描述为可机器检查的信息交付规范（IDS）文件，以验证建筑模型是否包含所需属性。然而，IDS 的编写仍然是一个实际瓶颈：从业者必须处理领域词汇、严格的 XML 架构约束以及外部验证器的一致性，同时还要检查需求本身是否正确表达。我们提出了 Ishigaki-IDS，一种专门用于验证者意识的 IDS 草拟生成的开放权重大语言模型（LLM）。该模型结合了在 BIM/IDS 语料库上的持续预训练、在信息需求与 IDS 对的监督微调，以及来自外部验证器的可验证奖励的强化学习。其目标不是替代专家审查，而是将 IDS 编写从低级 XML 和架构修复转向可供从业者检查和修正的验证器可加载草稿。在 166 个案例的专家创建的 Ishigaki-IDS-Bench 上，Ishigaki-IDS-8B 达到了 0.651 的 IDSAuditPass 分数，这是生成的 IDS 文件的验证器通过指标，显著优于我们评估的最强单次 LLM 基线 Claude Opus 4.5 的 0.331。它还获得了 0.282 的 Audit-Gated FacetF1 分数，衡量验证器通过草稿中的需求面向一致性。同样的方案也适用于更大规模的模型：14B 和 32B 变体分别达到了 IDSAuditPass 0.753 / 0.693 和 Audit-Gated FacetF1 0.392 / 0.369。在与六名 BIM 从业者的工作流程检查中，Ishigaki 辅助编写在相同的验证和一致性终点下将总工作时间减少了 54.7%。这些结果表明，面向验证者的 IDS 生成可以减轻将 BIM 信息需求转换为可审查的 IDS 草稿的实际负担。
cs.CL / 83 / 2606.08562
Inside the LLM Word Factory
深入探讨大型语言模型的词汇生成机制
Abstract
Transformer language models process input provided as subword fragments, but natural language semantics usually rely on word-level concepts. Detokenization is the process where models reconcile these two facts, aggregating subwords into word-level representations through their computation. Prior work has found that this takes place mostly in early-to-middle layers, but so far the exact mechanics of the process have not been pinned down. We venture deep into detokenization using activation patching in controlled paired experiments that isolate the contribution of different model components, localizing English detokenization in Llama2-7B to a two-stage process at Layer 1. Attention transmits a token-specific signal from nonfinal subwords, using sequential relays if necessary, while the MLP composes it with the local embedding. This two-stage structure generalizes to twelve models from eight families, but the depth over which it takes place depends on the flavor of positional encoding: RoPE-based models detokenize over 1 to 5 layers, while learned-absolute models take 5 to 10. Finally, we provide a probe for determining the success of the detokenization process based on early-layer activations alone, performing at 0.94-0.97 AUROC depending on the amount of context.
Chinese Translation
变换器语言模型处理以子词片段形式提供的输入，但自然语言语义通常依赖于词级概念。去标记化是模型调和这两种事实的过程，通过计算将子词聚合为词级表示。先前的研究发现，这一过程主要发生在早期到中间层，但迄今为止，具体的机制尚未明确。我们通过在控制配对实验中使用激活补丁，深入探讨去标记化，隔离不同模型组件的贡献，将英语去标记化在 Llama2-7B 中定位为第1层的两阶段过程。注意力机制从非最终子词传递特定于标记的信号，必要时使用顺序中继，而多层感知器（MLP）则将其与局部嵌入组合。该两阶段结构在八个家族的十二个模型中具有普遍性，但其发生的深度取决于位置编码的类型：基于 RoPE 的模型在 1 到 5 层之间进行去标记化，而学习的绝对模型则在 5 到 10 层之间。最后，我们提供了一种探测器，仅基于早期层的激活来确定去标记化过程的成功，表现出 0.94-0.97 的 AUROC，具体取决于上下文的量。
cs.CL / 84 / 2606.08571
Calibration of Structured Ignorance Certificates for Diagnosing Unknown Unknowns in Reasoning Models
结构性无知证书的校准：用于推理模型中未知未知的诊断
Abstract
Large language models frequently fail in a characteristic way: rather than acknowledging ignorance, they produce fluent but incorrect answers to questions that lie beyond their knowledge boundaries. We introduce \textbf{Structured Ignorance Certificates} (SICs), a JSON-formatted output schema that demands a model explicitly name the missing domain intersection, enumerate required concepts, and propose a productive retrieval query rather than hallucinating an answer. To train models to produce high-quality SICs we construct a 7,347-sample \emph{Unknown-Unknown} (UU) dataset by prompting Qwen3-14B to stitch together questions from seven domains (physics, biology, engineering, CS, economics, medical, legal) into novel cross-domain queries that no single-domain expert could answer. We fine-tune a 14B-parameter model with Group Relative Policy Optimization (GRPO) using a composite reward that combines retrieval utility, concept specificity, and output-format validity. A paraphrase-divergence probe trained on model responses confirms that SIC-tuned outputs systematically exhibit higher unknown-unknown probability scores. Evaluation on 735 held-out UU questions achieves a 99.46\% JSON validity rate, a mean Certificate Specificity Score of 0.967, and a 3.6\% ROUGE-L improvement over the base model on retrieval-grounded generation -- demonstrating that explicit epistemic structuring is a learnable and measurable capability.
Chinese Translation
大型语言模型常常以一种特征性的方式失败：它们并不承认自己的无知，而是对超出知识边界的问题给出流畅但错误的答案。我们提出了 extbf{结构性无知证书}（Structured Ignorance Certificates, SICs），这是一种JSON格式的输出架构，要求模型明确指出缺失的领域交集，列举所需的概念，并提出一个有效的检索查询，而不是凭空构造答案。为了训练模型生成高质量的SICs，我们构建了一个包含7,347个样本的 extit{未知-未知}（Unknown-Unknown, UU）数据集，通过提示Qwen3-14B将来自七个领域（物理学、生物学、工程学、计算机科学、经济学、医学、法律）的提问拼接成新颖的跨领域查询，这些查询是任何单一领域专家都无法回答的。我们使用组合奖励对一个14B参数的模型进行了微调，该奖励结合了检索效用、概念特异性和输出格式有效性，采用了群体相对策略优化（Group Relative Policy Optimization, GRPO）。对模型响应训练的同义句差异探测器确认，经过SIC调优的输出系统性地表现出更高的未知-未知概率得分。在735个保留的UU问题上的评估达到了99.46\%的JSON有效性率，平均证书特异性得分为0.967，并且在基于检索的生成上相较于基础模型提高了3.6\\%的ROUGE-L分数——这表明明确的认识结构化是一种可学习和可测量的能力。
cs.CL / 85 / 2606.08589
Detection and Interpretability Analysis of Quotation Errors by Large Language Models
大型语言模型的引用错误检测与可解释性分析
Abstract
Purpose - Quotation error refers to the inconsistency between cited information and its original source. This phenomenon leads to a series of negative impacts, such as misinterpretation of the original research, undermining the academic community's collective understanding of relevant issues, and weakening the accuracy and fairness of the citation-based academic evaluation system. Existing studies have shown that quotation error is prevalent in the academic community; moreover, manual verification of quotation error is not only labor-intensive but also inefficient. Therefore, this paper proposes the task of 'automated detection of quotation errors'. Methodology - Adopting a large language model (LLM)-based approach, this paper improves detection performance from two aspects on the basis of existing research: first, employ the fine-tuning approach for LLMs to detect quotation errors; second, incorporating full-text data of the cited literature into dataset construction, and exploring the optimal scheme for building such datasets by comparing three types of full-text integration methods. Based on this, this paper further uses the TokenSHAP tool to conduct interpretability experimental analysis on the model's prediction results. Findings - The fine-tuning approach for LLMs has improved the performance in detecting quotation errors. Among the different methods for incorporating full-text information, the approach based on using the source abstract yielded the best performance. Originality - The fine-tuning approach for large language models (LLMs) is applied to the task of automated detection of quotation errors, and interpretability analysis is conducted on the model's output results.
Chinese Translation
目的 - 引用错误是指引用信息与其原始来源之间的不一致。这一现象导致了一系列负面影响，例如对原始研究的误解，削弱学术界对相关问题的集体理解，以及降低基于引用的学术评价系统的准确性和公正性。现有研究表明，引用错误在学术界普遍存在；此外，手动验证引用错误不仅劳动密集，而且效率低下。因此，本文提出了“自动检测引用错误”的任务。方法论 - 本文采用基于大型语言模型（LLM）的方法，在现有研究的基础上从两个方面提高检测性能：首先，采用微调方法对LLM进行引用错误检测；其次，将引用文献的全文数据纳入数据集构建，并通过比较三种类型的全文集成方法探索构建此类数据集的最佳方案。在此基础上，本文进一步使用TokenSHAP工具对模型的预测结果进行可解释性实验分析。研究结果 - LLM的微调方法提高了引用错误检测的性能。在不同的全文信息整合方法中，基于使用源摘要的方法表现最佳。创新性 - 将大型语言模型（LLMs）的微调方法应用于自动检测引用错误的任务，并对模型输出结果进行可解释性分析。
cs.CL / 86 / 2606.08605
Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLMs
大规模多语言事实核查：微调紧凑模型与大型语言模型的比较
Abstract
We present a multilingual fact-checking system deployed at Factiverse, designed for high-throughput and low-latency operation across diverse languages. The system follows a modular pipeline with three stages: claim detection, evidence retrieval and re-ranking, and veracity prediction. We fine-tune XLM-RoBERTa-Large for claim detection, mmBERT-base for three-label stance classification (Supports/Refutes/Mixed), and a SetFit-based multilingual re-ranker for claim--evidence matching. We compare these components against strong LLM baselines, including GPT-5.2, Claude Opus~4.6, and Qwen3-8b. Experiments on production data spanning 114 languages for claim detection and 28 languages for veracity prediction show that task-specific fine-tuning provides strong and stable multilingual performance, while the fine-tuned retrieval model remains competitive with modern proprietary embeddings. Same-hardware latency measurements further show large efficiency gains for encoder-based components, supporting their use in production deployments with tight cost and privacy constraints. Overall, compact fine-tuned, self-hosted models remain a practical and effective foundation for multilingual fact-checking at scale. Code and data used for this study are available at https://github.com/factiverse/factcheck-editor.
Chinese Translation
我们提出了一种在Factiverse部署的多语言事实核查系统，旨在实现高吞吐量和低延迟的操作，适用于多种语言。该系统遵循一个模块化的流程，分为三个阶段：声明检测、证据检索与重排序，以及真实性预测。我们对XLM-RoBERTa-Large进行了微调以进行声明检测，对mmBERT-base进行了微调以进行三标签立场分类（支持/反驳/混合），并使用基于SetFit的多语言重排序器进行声明与证据匹配。我们将这些组件与强大的大型语言模型基线进行比较，包括GPT-5.2、Claude Opus~4.6和Qwen3-8b。在114种语言的声明检测和28种语言的真实性预测的生产数据实验中，结果表明，特定任务的微调提供了强大且稳定的多语言性能，而微调后的检索模型在现代专有嵌入方面仍具竞争力。同一硬件的延迟测量进一步显示，基于编码器的组件在效率上有显著提升，支持它们在成本和隐私约束严格的生产部署中的使用。总体而言，微调后的紧凑自托管模型仍然是大规模多语言事实核查的实用有效基础。本研究使用的代码和数据可在https://github.com/factiverse/factcheck-editor获取。
cs.CL / 87 / 2606.08617
Cross-Source Reasoning-based Correction for Author Name Disambiguation
基于跨源推理的作者姓名消歧义修正
Abstract
Author name disambiguation is a critical challenge in academic search systems, often addressed through from-scratch and real-time disambiguation approaches. However, current algorithms remain vulnerable to cumulative errors of paper-author assignments and overlook inconsistent assignments across different sources. Resorting to expert annotation is resource-intensive. To this end, this paper explores a new perspective for author name disambiguation: cross-source correction by leveraging inconsistent assignments across sources. We propose CrossND, a full-stack framework that integrates data refinement, cross-source reasoning, and test-time scaling. First, a chain-of-refinement pipeline denoises author profiles and produces more accurate paper-author matching probabilities. Second, a supervised fine-tuning process incorporates these refined signals and a probabilistic soft logic-based cross-correction module to infer the assignments of which sources are incorrect. Third, test-time scaling further enhances the accuracy and robustness of the predictions. Experiments on real-world datasets indicate that CrossND consistently outperforms 17 baselines by leveraging cross-source reasoning without human intervention.
Chinese Translation
作者姓名消歧义是学术搜索系统中的一个关键挑战，通常通过从零开始和实时消歧义的方法来解决。然而，当前的算法仍然容易受到论文与作者分配的累积错误的影响，并且忽视了不同来源之间的不一致分配。依赖专家注释是资源密集型的。为此，本文探索了一种新的作者姓名消歧义视角：通过利用不同来源之间的不一致分配进行跨源修正。我们提出了CrossND，一个集成数据精炼、跨源推理和测试时扩展的全栈框架。首先，一个精炼链条管道去噪作者档案，并生成更准确的论文与作者匹配概率。其次，一个监督微调过程结合这些精炼信号和基于概率软逻辑的跨修正模块，以推断哪些来源的分配是错误的。第三，测试时扩展进一步提高了预测的准确性和鲁棒性。在真实世界数据集上的实验表明，CrossND通过跨源推理在没有人工干预的情况下始终优于17个基线模型。
cs.CL / 88 / 2606.08625
From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape
从整体评估到结构化标准：在不断发展的大型语言模型（LLM）领域中的评分标准
Abstract
As Large Language Models (LLMs) advance toward open-ended autonomous agents, the mechanisms used to evaluate and guide their behavior must evolve accordingly. This work introduces the rubric as a unifying framework capturing this evolution, characterizing rubrics as a dynamic response to successive LLM paradigm shifts that recurs across otherwise independent efforts in evaluation, reinforcement learning, and safety alignment. We define rubrics as explicit criteria sets that transform complex quality judgments into structured and actionable standards, and demonstrate that their recurrence across these research threads is not coincidental. We systematically organize existing rubric designs, examine their construction and optimization, and analyze their role across evaluation and training. Rubrics manifest at three progressively deeper levels: at the evaluative level, they decompose holistic judgments into verifiable dimensions; at the training level, they serve as dense feedback signals providing process-level guidance where scalar rewards fall short; at the intrinsic level, they emerge dynamically from model behaviors, driving self-improvement. We further assess rubric reliability across generation quality, execution fidelity, theoretical constraints, and security threats, before surveying rubric-based benchmarks across diverse domains. By rendering assessment transparent and decomposable, rubrics translate human value expectations into machine-learnable signals, serving as the enduring bridge between human intentions and machine behavior.
Chinese Translation
随着大型语言模型（LLM）向开放式自主智能体的发展，用于评估和指导其行为的机制必须相应演变。本研究引入评分标准作为一个统一框架，以捕捉这一演变，特征化评分标准为对连续的LLM范式转变的动态响应，这种响应在评估、强化学习和安全对齐等独立的研究努力中反复出现。我们将评分标准定义为将复杂的质量判断转化为结构化和可操作标准的明确标准集，并证明它们在这些研究线程中的重复出现并非偶然。我们系统地组织现有的评分标准设计，考察其构建和优化，并分析其在评估和训练中的作用。评分标准在三个逐步深入的层面上表现出来：在评估层面，它将整体判断分解为可验证的维度；在训练层面，它作为密集反馈信号提供过程级指导，以弥补标量奖励的不足；在内在层面，它动态地从模型行为中涌现，推动自我改进。我们进一步评估评分标准在生成质量、执行保真度、理论约束和安全威胁方面的可靠性，然后对不同领域的基于评分标准的基准进行调查。通过使评估过程透明且可分解，评分标准将人类价值期望转化为机器可学习的信号，成为人类意图与机器行为之间持久的桥梁。
cs.CL / 89 / 2606.08629
Sycophancy Towards Researchers Drives Performative Misalignment
对研究者的谄媚驱动表现性不一致
Abstract
The increasing situational awareness of language models raises safety concerns: models might be aware when they are evaluated, and adjust their behavior to evade monitoring and resist modification, e.g., pretending to be aligned only in evaluation. This alignment faking behavior is often interpreted as scheming: an intentional effort of strategic deception. In this paper, we examine an alternative interpretation, performative misalignment, which explains the change in behavior as a result of sycophancy towards AI researchers. To examine this hypothesis, we present three empirical findings. First, we show that evaluation awareness persists even when we tell models they are deployed, which contradicts the scheming story which predicts less misalignment when the model perceives evaluation. Second, we use probing and steering to show that our current methods cannot mechanistically distinguish sycophancy and scheming in alignment faking evaluations. Third, we fine-tune models to be more sycophantic and observe increased sensitivity to evaluation cues. To conclude, we emphasize deconfounding sycophancy from scheming for future work on evaluations and mitigations of intent misalignment.
Chinese Translation
语言模型日益增强的情境意识引发了安全隐患：模型可能意识到何时被评估，并调整其行为以逃避监控和抵制修改，例如，在评估中假装与评估一致。这种表现一致性的伪装行为通常被解读为阴谋：一种有意的战略欺骗努力。在本文中，我们探讨了一种替代解释，即表现性不一致，认为行为的变化是对人工智能研究者的谄媚的结果。为了检验这一假设，我们提出了三个实证发现。首先，我们显示即使在告知模型它们被部署时，评估意识仍然存在，这与阴谋论的预测相矛盾，后者认为当模型感知到评估时不一致性会减少。其次，我们使用探测和引导的方法表明，我们当前的方法无法机械性地区分谄媚和阴谋在一致性伪装评估中的表现。第三，我们对模型进行微调，使其更加谄媚，并观察到对评估线索的敏感性增加。最后，我们强调在未来的评估和意图不一致的缓解工作中，将谄媚与阴谋区分开来。
cs.CL / 90 / 2606.08644
A retrieval conditioned rebinding circuit for dynamic entity tracking in large language models
用于大型语言模型动态实体跟踪的检索条件重绑定电路
Abstract
To interpret context correctly and retrieve relevant information, large language models must bind entities to their attributes and update these bindings as state changes. We analyze how LLMs implement this binding process in a dynamic state tracking. Using causal interventions, we identify a retrieval conditioned rebinding mechanism, a compact attention head circuit that encodes swap relevant binding information and reinstates it at readout. Across Gemma and Llama models, this circuit supports rebinding behavior, but the representational signature of the mechanism differs across model families. In Gemma models, the binding signature is clearly expressed in the query/key subspaces of the relevant attention heads, whereas in Llama models, the binding information is carried primarily in key vectors. Overall, our results reveal an interpretable mechanism for context dependent state tracking in LLMs.
Chinese Translation
为了正确解释上下文并检索相关信息，大型语言模型必须将实体与其属性绑定，并在状态变化时更新这些绑定。我们分析了大型语言模型如何在动态状态跟踪中实现这一绑定过程。通过因果干预，我们识别出一种检索条件重绑定机制，这是一种紧凑的注意力头电路，能够编码交换相关的绑定信息并在读出时重新恢复。在Gemma和Llama模型中，该电路支持重绑定行为，但该机制的表征特征在不同模型家族中有所不同。在Gemma模型中，绑定特征在相关注意力头的查询/键子空间中清晰表达，而在Llama模型中，绑定信息主要通过键向量传递。总体而言，我们的结果揭示了大型语言模型中一种可解释的上下文依赖状态跟踪机制。
cs.CL / 91 / 2606.08656
From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memory
从玩家到大师：通过记忆强化学习提升大语言模型代理的测试时学习
Abstract
Large language model (LLM) agents are increasingly deployed in long-running settings where improving through experience at test time becomes important. A common approach is to update an explicit memory after each interaction to guide future decisions. However, most existing methods rely on hand-designed prompting rules, making it difficult to align memory updates with downstream objectives over multi-step horizons consistently. We propose MemoPilot, a plug-in memory copilot that explicitly trains the memory update process to improve a frozen LLM's performance across sequential interactions. We formulate memory updating as a multi-turn decision problem and optimize it end-to-end with multi-turn GRPO. Our training recipe introduces (i) a turn-wise reward signal and (ii) a context-independent, turn-level advantage estimation across rollouts, enabling finer-grained credit assignment and more stable training in multi-turn settings. We evaluate MemoPilot on two testbeds: multi-round Rock-Paper-Scissors (RPS) and Limit Texas Hold'em (LHE). Across both environments, MemoPilot substantially improves test-time learning of a frozen player over strong baselines, ranking first in Elo ratings on both games (1762 on LHE and 1590 on RPS) and outperforming all baseline memory methods and proprietary models, including DeepSeek-V3.2.
Chinese Translation
大型语言模型（LLM）代理越来越多地应用于长期运行的环境中，在测试时通过经验改进变得尤为重要。一种常见的方法是在每次交互后更新显式记忆，以指导未来的决策。然而，大多数现有方法依赖于手工设计的提示规则，这使得在多步时间范围内将记忆更新与下游目标一致对齐变得困难。我们提出了MemoPilot，一个插件式记忆副驾驶，明确训练记忆更新过程，以提高冻结的LLM在连续交互中的表现。我们将记忆更新形式化为一个多轮决策问题，并通过多轮GRPO进行端到端优化。我们的训练方案引入了（i）逐轮奖励信号和（ii）跨回合的上下文无关、逐轮优势估计，从而实现更细粒度的信用分配和在多轮环境中更稳定的训练。我们在两个测试平台上评估了MemoPilot：多轮石头剪刀布（RPS）和限注德州扑克（LHE）。在这两个环境中，MemoPilot显著提升了冻结玩家的测试时学习，相较于强基线在两个游戏中的Elo评分均排名第一（LHE为1762，RPS为1590），并且超越了所有基线记忆方法和专有模型，包括DeepSeek-V3.2。
cs.CL / 92 / 2606.08673
ClinicalAligner26AM: A Cross-Lingual Aligner for Dataset Translation; Evidences from the MultiClinCorpus Shared Task
ClinicalAligner26AM：一种用于数据集翻译的跨语言对齐工具；来自MultiClinCorpus共享任务的证据
Abstract
Word-level cross-lingual alignment is central to annotation projection, translation auditing, and cross-lingual faithfulness estimation, yet existing neural aligners are rarely adapted to specialized domains. In this paper, we introduce ClinicalAligner26AM, a large-context multilingual aligner model for biomedical and clinical text initialized from ClinicalEncoder26AM. Our training recipe is inspired by AWESoME Align. We build our soft alignment target by sharpening with Sinkhorn-Knop optimal transport a cost matrix established for parallel clinical texts and conversations through the fusion of sentence-level, phrase-level, and token-level signals. We distill this sharpened alignment matrix directly into our student aligner, by encouraging its naive cosine-based token similarity scores to match this target. At inference time, we project source-span scores through the learned token alignment matrix and decode the longest valid high-scoring span in the target text, optionally supported by MultiClinNER predictions summarized in Appendix B. We evaluate CA26AM on the MultiClinCorpus shared task, which projects Spanish clinical entity annotations into six target languages. Our two submitted systems ranked respectively first and second across all languages and entity types, with character-weighted F1 scores above 0.95 in nearly all settings.
Chinese Translation
基于词级的跨语言对齐在注释投影、翻译审计和跨语言忠实度评估中至关重要，但现有的神经对齐工具很少适用于专业领域。在本文中，我们介绍了ClinicalAligner26AM，这是一种针对生物医学和临床文本的大上下文多语言对齐模型，其初始化源自ClinicalEncoder26AM。我们的训练方案受到AWESoME Align的启发。我们通过使用Sinkhorn-Knop最优传输方法对为平行临床文本和对话建立的成本矩阵进行锐化，构建我们的软对齐目标，该过程融合了句子级、短语级和标记级信号。我们将这一锐化的对齐矩阵直接提炼到我们的学生对齐器中，通过鼓励其基于余弦的原始标记相似度分数与这一目标相匹配。在推理时，我们通过学习到的标记对齐矩阵投影源跨度分数，并在目标文本中解码出最长的有效高分跨度，此外还可以选择支持附录B中总结的MultiClinNER预测。我们在MultiClinCorpus共享任务上评估CA26AM，该任务将西班牙语临床实体注释投影到六种目标语言中。我们提交的两个系统在所有语言和实体类型中分别排名第一和第二，在几乎所有设置中字符加权F1分数均超过0.95。
cs.CL / 93 / 2606.08705
Analyzing the Correlation Between Hallucinations and Knowledge Conflicts in Large Language Models
分析大型语言模型中幻觉与知识冲突之间的相关性
Abstract
Hallucinations -- factually incorrect or unverifiable outputs -- remain one of the most challenging limitations of Large Language Models (LLMs), especially in knowledge-intensive tasks. One proposed explanation is internal knowledge conflicts arising from fixed, outdated training data. This paper investigates whether internal representations linked to knowledge conflicts correlate with hallucination behaviors in LLMs. Using probing techniques inspired by two prior works, we analyzed activations from hidden, attention, and MLP layers, as well as output logits, across predefined tasks. We probed LLaMA-3-8B on hallucination detection benchmarks and Falcon-7B on a knowledge conflict dataset. Our findings show that, although conceptually related, hallucination activation patterns cannot be fully reduced to or explained by knowledge conflict representations. Nonetheless, probing proves a robust tool across multiple languages and activation types, supporting its role in improving LLM interpretability. This work advances the broader understanding of hallucinations in LLMs and underscores the value of fine-grained analysis of their internal behavior.
Chinese Translation
幻觉——事实不准确或无法验证的输出——仍然是大型语言模型（LLMs）在知识密集型任务中面临的最具挑战性的限制之一。一种提出的解释是，由于固定的、过时的训练数据而产生的内部知识冲突。本文研究了与知识冲突相关的内部表征是否与LLMs中的幻觉行为相关。我们使用受两项先前工作的启发的探测技术，分析了在预定义任务中隐藏层、注意力层和多层感知器（MLP）层的激活以及输出对数几率。我们在幻觉检测基准上对LLaMA-3-8B进行了探测，并在知识冲突数据集上对Falcon-7B进行了探测。我们的研究结果表明，尽管在概念上相关，幻觉激活模式不能完全归结为或通过知识冲突表征来解释。然而，探测证明是一个在多种语言和激活类型中都有效的工具，支持其在提高LLM可解释性方面的作用。这项工作推动了对LLMs中幻觉的更广泛理解，并强调了对其内部行为进行细致分析的价值。
cs.CL / 94 / 2606.08715
Operationalizing Linguistic Methods through Prompt-Engineering Skills: An Automatic Chinese Web Neologism Detection Pipeline
通过提示工程技能实现语言学方法的操作化：自动化中文网络新词检测管道
Abstract
We present a method for automatic Chinese web neologism detection that operationalizes traditional linguistic identification principles as prompt-engineering skills. The method has four stages: tokenizer-independent character n-gram candidate generation; dictionary anchoring with a Pointwise Mutual Information pre-filter; a well-formedness skill based on Chinese word-formation principles; and a combined rule and three-way classification skill that distinguishes neologism, entity, and none. Applied to the BAAI CCI 3.0 corpus (267M documents), the method produces 226,959 classified candidates including 4,853 labeled neologisms. To evaluate the method, we develop a per-stage conditional recall decomposition in which the pipeline's strict recall factors mathematically into the product of stage conditional recalls. Applied to Hou (2023) (4,199 entries), the decomposition exposes Stage 1 candidate coverage and Stage 4B LLM semantic judgment as the two bottlenecks (R=41.5% and 60.0% respectively), while intermediate stages are near-lossless. A length-stratified analysis further reveals that the structural well-formedness skill is length-invariant (>= 96.9%) whereas the semantic novelty-classification skill is length-dependent (65.6%/59.0%/44.1% across 2/3/4-character candidates), mapping a current boundary of skill-based linguistic operationalization. We release the method, pipeline outputs, and evaluation protocol as public resources.
Chinese Translation
我们提出了一种自动化中文网络新词检测的方法，该方法将传统语言学识别原则操作化为提示工程技能。该方法分为四个阶段：独立于分词器的字符 n-gram 候选生成；通过点互信息（Pointwise Mutual Information）预过滤的词典锚定；基于中文词汇构成原则的良构性技能；以及一个结合规则和三分类技能的阶段，用于区分新词、实体和无效项。应用于 BAAI CCI 3.0 语料库（267M 文档），该方法生成了 226,959 个分类候选项，包括 4,853 个标记的新词。为了评估该方法，我们开发了一个逐阶段条件召回分解，其中管道的严格召回因子在数学上分解为各阶段条件召回的乘积。应用于 Hou (2023)（4,199 条目），该分解揭示了第一阶段候选覆盖率和第四阶段 B LLM 语义判断是两个瓶颈（分别为 R=41.5% 和 60.0%），而中间阶段几乎无损。长度分层分析进一步揭示，结构良构性技能在长度上是不变的（>= 96.9%），而语义新颖性分类技能则依赖于长度（2/3/4 字符候选项的召回率分别为 65.6%/59.0%/44.1%），描绘了基于技能的语言学操作化的当前边界。我们将该方法、管道输出和评估协议作为公共资源发布。
cs.CL / 95 / 2606.08748
HydraQE: OSU's Submission for the IWSLT 2026 Speech Translation Metrics Shared Task
HydraQE：俄亥俄州立大学提交的IWSLT 2026语音翻译指标共享任务
Abstract
We present HydraQE, our contribution to the IWSLT 2026 Speech Translation Metrics shared task. HydraQE is an end-to-end, reference-free quality estimation (QE) system for speech translation built on a Qwen3-ASR backbone, which accepts source audio and a translation hypothesis as joint input. Hidden states from all backbone layers are combined via a learnable sparsemax scalar mix, then re-encoded by a lightweight bidirectional Transformer to enable full cross-modal interaction prior to pooling into a shared embedding. Three independent prediction heads are trained on complementary supervision signals: human direct assessment (DA) annotations, MetricX-24 pseudo-labels, and xCOMET pseudo-labels. To address the scarcity of human-annotated data, we train on a combination of synthetically corrupted examples and silver pseudo-labeled machine translation outputs, using a curriculum that begins on synthetic and silver data and gradually shifts toward human-annotated examples. HydraQE outperforms cascaded text-based baselines and prior direct speech QE systems, demonstrating that end-to-end speech translation QE is competitive with cascaded approaches.
Chinese Translation
我们介绍了HydraQE，这是我们对IWSLT 2026语音翻译指标共享任务的贡献。HydraQE是一个端到端的无参考质量估计（QE）系统，基于Qwen3-ASR骨干网络构建，接受源音频和翻译假设作为联合输入。来自所有骨干层的隐藏状态通过可学习的sparsemax标量混合进行组合，然后由轻量级双向Transformer重新编码，以实现全跨模态交互，最后汇聚成共享嵌入。三个独立的预测头在互补的监督信号上进行训练：人类直接评估（DA）注释、MetricX-24伪标签和xCOMET伪标签。为了解决人类注释数据稀缺的问题，我们在合成损坏示例和银色伪标签机器翻译输出的组合上进行训练，使用的课程从合成和银色数据开始，逐渐转向人类注释示例。HydraQE在级联文本基线和之前的直接语音QE系统上表现优越，证明了端到端语音翻译QE与级联方法具有竞争力。
cs.CL / 96 / 2606.08755
Co-Evolving Skill Generation and Policy Optimization
技能共演化生成与策略优化
Abstract
Skill-augmented reinforcement learning improves language agents by storing reusable procedural knowledge acquired from past experience. Existing methods typically use strong language models to analyze trajectories, generate skills, and update a retrievable skill bank during online training. However, they rarely assess whether a newly generated skill is useful before it is stored and reused. We find that this assumption is unreliable: even skills generated by proprietary frontier LLMs exhibit highly mixed utility, with many providing little benefit or even degrading performance. Once such skills enter the bank, their effects are difficult to identify, because subsequent rollout feedback is delayed and usually reflects the combined effect of multiple retrieved skills rather than the marginal contribution of any individual skill. We propose an online reinforcement learning framework for pre-storage skill validation. The framework estimates whether a candidate skill contributes useful information beyond the skills already retrieved for the current task. It uses the standard rollout budget to form two matched groups under the same task and retrieval context: base rollouts conditioned on the currently retrieved skills, and skill-augmented rollouts conditioned on the same skills plus one candidate skill induced from the base trajectories. The reward gap between these two groups estimates the candidate skill's context-dependent marginal utility, enabling the framework to promote useful skills while filtering ineffective or harmful ones without additional rollout overhead. The framework further uses this marginal-utility signal to train the policy itself as a skill generator, reducing reliance on repeated calls to proprietary models. The learned skill-generation likelihood serves as a context-dependent score for retrieval-time reranking and outdated-skill pruning as the policy evolves.
Chinese Translation
技能增强的强化学习通过存储从过去经验中获得的可重用程序知识来改善语言代理。现有方法通常使用强大的语言模型来分析轨迹、生成技能并在在线训练期间更新可检索的技能库。然而，它们很少评估新生成的技能在被存储和重用之前是否有用。我们发现这一假设并不可靠：即使是由专有的前沿大型语言模型（LLMs）生成的技能，其效用也高度混合，许多技能提供的好处甚微，甚至会降低性能。一旦这些技能进入技能库，其效果难以识别，因为后续的反馈延迟且通常反映的是多个检索技能的综合效果，而不是任何单一技能的边际贡献。我们提出了一种在线强化学习框架用于技能的预存储验证。该框架评估候选技能是否在当前任务中提供超出已检索技能的有用信息。它利用标准的回合预算在相同任务和检索上下文下形成两个匹配组：基于当前检索技能的基础回合，以及基于相同技能加上一个从基础轨迹诱导的候选技能的技能增强回合。这两个组之间的奖励差距估计候选技能的上下文相关边际效用，使框架能够在没有额外回合开销的情况下促进有用技能，同时过滤掉无效或有害的技能。该框架进一步利用这一边际效用信号来训练策略本身作为技能生成器，减少对重复调用专有模型的依赖。学习到的技能生成可能性作为上下文相关的评分，用于检索时的重新排序和过时技能的修剪，随着策略的演变而更新。
cs.CL / 97 / 2606.08769
RadOT-Eval: Auditable Structured-Evidence Transport for Radiology Report Evaluation
RadOT-Eval：可审计的结构化证据传输用于放射学报告评估
Abstract
Automatic evaluation is critical for high-stakes text generation, where errors often involve omitted findings, hallucinated content, polarity reversals, location changes, uncertainty mismatches, and temporal-comparison errors rather than low surface similarity alone. Radiology report generation provides a challenging test case because generated reports must preserve structured clinical evidence across sources. We present RadOT-Eval, an interpretable structured-evidence optimal transport framework for offline auditing of radiology report generation. RadOT-Eval decomposes reference and candidate reports into attribute-structured clinical evidence units, aligns corresponding evidence using entropy-regularized optimal transport, and uses clinically meaningful side-channel discrepancies in a monotone risk model to predict error burden. All transport, feature, and readout choices are selected using the ReXVal dataset, and the frozen system is evaluated on the independent RadEvalX dataset. RadOT-Eval achieves Spearman correlations of 0.715, 0.548, and 0.399 with total, clinically significant, and clinically insignificant annotated error burden, respectively, yielding higher point estimates than standard evaluation metrics and the open-source large language model (LLM)-based evaluator GREEN-radllama2-7B. In a frozen auxiliary corruption-sensitivity stress test on ReXErr-v1, RadOT-Eval achieves 0.768 AUROC and a 0.990 corrupted-greater-than-clean paired win rate. These results show that structured evidence transport provides an auditable, rank-oriented evaluation tool for high-stakes generated clinical text under ReXVal-only model selection and frozen RadEvalX testing.
Chinese Translation
自动评估对于高风险文本生成至关重要，因为错误往往涉及遗漏发现、虚构内容、极性反转、位置变化、不确定性不匹配和时间比较错误，而不仅仅是表面相似性低。放射学报告生成提供了一个具有挑战性的测试案例，因为生成的报告必须在多个来源之间保留结构化的临床证据。我们提出了RadOT-Eval，这是一种可解释的结构化证据最优传输框架，用于离线审计放射学报告生成。RadOT-Eval将参考报告和候选报告分解为属性结构化的临床证据单元，通过熵正则化的最优传输对齐相应的证据，并在单调风险模型中使用临床意义明确的侧通道差异来预测错误负担。所有传输、特征和读取选择均使用ReXVal数据集进行选择，冻结系统在独立的RadEvalX数据集上进行评估。RadOT-Eval在总错误负担、临床显著错误负担和临床不显著错误负担的Spearman相关性分别达到0.715、0.548和0.399，提供了比标准评估指标和开源大型语言模型（LLM）基础评估器GREEN-radllama2-7B更高的点估计。在ReXErr-v1上的冻结辅助腐败敏感性压力测试中，RadOT-Eval实现了0.768的AUROC和0.990的腐败大于清洁配对胜率。这些结果表明，结构化证据传输为在仅使用ReXVal模型选择和冻结RadEvalX测试下的高风险生成临床文本提供了一个可审计的、以排名为导向的评估工具。
cs.CL / 98 / 2606.08770
TeamHerald@CHIPSAL 2026: Hate Speech Detection and Sentiment Analysis of Nepali Memes using Transformer-based Architectures and Ensemble Learning
TeamHerald@CHIPSAL 2026：基于变换器架构和集成学习的尼泊尔网络迷因仇恨言论检测与情感分析
Abstract
The analysis of internet memes in the Nepali language is complicated by frequent code-mixing and a lack of established baseline resources. While memes inherently combine visual and textual elements, this study focuses on a text-centric approach by extracting embedded text using an OCR layer and modeling it with Transformer-based architectures. We evaluate six distinct models and investigate the comparative effectiveness of Hard and Soft Voting ensemble strategies across two tasks: binary hate speech detection and three-class sentiment analysis. Experimental results show that a standalone decoder-only model achieved the highest performance for binary classification, whereas the Soft Voting ensemble performed best for the multi-class sentiment task, yielding a 15.8% relative improvement in Macro F1-score over the strongest standalone baseline. These findings suggest that ensemble strategies behave differently across binary and multi-class tasks, highlighting the importance of selecting aggregation methods suited to the classification objective.
Chinese Translation
尼泊尔语言互联网迷因的分析因频繁的代码混合和缺乏既定基线资源而变得复杂。尽管迷因本质上结合了视觉和文本元素，本研究专注于文本中心的方法，通过使用光学字符识别（OCR）层提取嵌入文本，并利用基于变换器的架构进行建模。我们评估了六种不同的模型，并研究了硬投票（Hard Voting）和软投票（Soft Voting）集成策略在两个任务中的比较有效性：二分类仇恨言论检测和三分类情感分析。实验结果表明，单独的解码器模型在二分类任务中表现最佳，而软投票集成在多分类情感任务中表现最佳，相较于最强的单一基线，宏观F1分数提高了15.8%。这些发现表明，集成策略在二分类和多分类任务中的表现不同，强调了选择适合分类目标的聚合方法的重要性。
cs.CL / 99 / 2606.08792
The Amplifying Mirror: Locating and Steering the Partisan Direction inside a Large Language Model
放大镜：定位和引导大型语言模型中的党派方向
Abstract
Large language models are rapicly replacing search engines as the primary interface between people and information. Unlike search engines, which retrieve existing content, LLMs generate novel text shaped by internal representations learned during training. Here we show that partisan political identity is encoded in the model's activation space, and that this direction directly shapes generation. Using 190,491 tweets from sitting members of the U.S. Congress as labeled training data, we train linear probes on the hidden states of the Llama 3.1 8B Instruct model. We identify a single geometric axis at layer 18 that separates Republican from Democratic text with an AUC of 0.945 and a Cohen's d of 1.94, and use sparse autoencoders to decompose that axis into interpretable partisan features. Causally intervening along this axis, ablating or amplifying the partisan component mid-generation, produces systematic shifts in the model's output. We witness stance reversals, register shifting, and structured fabrications of authority. Our results demonstrate that partisan bias in language models is not a vague emergent property but a learned geometric feature that can be precisely located and steered. Partisan bias is not a bug to be patched, but a structural property of how these models encode information about their users. As LLMs displace search engines as the interface to knowledge, understanding that product design (and its consequences) will be essential for navigating the legal, social, and political transitions from an information ecosystem that is curated to one that is generated.
Chinese Translation
大型语言模型正在迅速取代搜索引擎，成为人们与信息之间的主要接口。与检索现有内容的搜索引擎不同，LLM（大型语言模型）生成由训练过程中学习的内部表征塑造的新文本。在这里，我们展示了党派政治身份在模型的激活空间中被编码，并且这一方向直接影响生成过程。我们使用来自美国国会现任成员的190,491条推文作为标记训练数据，针对Llama 3.1 8B Instruct模型的隐藏状态训练线性探针。我们在第18层识别出一条单一的几何轴，该轴以0.945的AUC和1.94的Cohen's d将共和党文本与民主党文本分开，并使用稀疏自编码器将该轴分解为可解释的党派特征。在这一轴上进行因果干预，消融或放大党派成分，在生成过程中会产生系统性的输出变化。我们观察到立场逆转、登记转变和权威结构的虚构。我们的结果表明，语言模型中的党派偏见并不是一种模糊的涌现特性，而是一种可以精确定位和引导的学习几何特征。党派偏见不是需要修补的漏洞，而是这些模型如何编码用户信息的结构特性。随着LLM取代搜索引擎成为知识的接口，理解产品设计（及其后果）对于应对从一个经过策划的信息生态系统到一个生成的信息生态系统的法律、社会和政治转型至关重要。
cs.CL / 100 / 2606.08810
Continuous Language Diffusion as a Decoder-Interface Problem
连续语言扩散作为解码器接口问题
Abstract
Gaussian-corrupted sentence embeddings have no direct linguistic interpretation, yet continuous diffusion language models can generate fluent text from them. We study this puzzle through Embedded Language Flows (ELF) and identify a decoder-basin mechanism: denoising succeeds when trajectories reach regions where the native decoder can read stable tokens. We introduce a diagnostic protocol for denoisability, semantic recoverability, order sensitivity, decoder compatibility, and trajectory reliability. It exposes failures hidden by scalar metrics: low mean-squared error can discard linguistic content, low perplexity can reflect low-entropy collapse, and clean latent reconstruction can coexist with a narrow decoder basin. A decoder-margin bound explains why token recovery depends on margin and local decoder sensitivity, not latent error alone. Auditing public ELF checkpoints reveals an interface phase diagram: early predictions are weakly readable, mid-trajectory disagreement marks a competition region, and late predictions enter a high-margin final-token basin. Once inside, token realization is surprisingly simple on generated ELF states: frozen T5 token-embedding lookup recovers $93$--$96\%$ of native decoder decisions, and a single linear readout reaches $97.9\%$ agreement at 32k samples, leaving about a 1.1 perplexity gap in a structured residual tail. A conservative margin gate exits $17$--$27\%$ earlier in denoising steps under an explicit diagnostic monitor. Boundary checks on LangFlow, BitstreamDiffusion, and the Continuous Latent Diffusion Language Model (Cola-DLM) show that the same interface questions remain meaningful when the state object and decoder change. Continuous and latent diffusion language models should therefore be evaluated as representation-decoder systems.
Chinese Translation
受高斯噪声影响的句子嵌入没有直接的语言学解释，但连续扩散语言模型能够从中生成流畅的文本。我们通过嵌入语言流（Embedded Language Flows, ELF）研究这一难题，并识别出一种解码器盆地机制：去噪成功的条件是轨迹到达原生解码器能够读取稳定标记的区域。我们引入了一种诊断协议，用于去噪能力、语义可恢复性、顺序敏感性、解码器兼容性和轨迹可靠性。这揭示了被标量指标掩盖的失败：低均方误差可能会丢弃语言内容，低困惑度可能反映低熵崩溃，而干净的潜在重构可以与狭窄的解码器盆地共存。解码器边界界限解释了为什么标记恢复依赖于边界和局部解码器敏感性，而不仅仅是潜在误差。对公共ELF检查点的审计揭示了一个接口相图：早期预测可读性较弱，中途轨迹不一致标志着竞争区域，晚期预测进入高边界最终标记盆地。一旦进入，标记实现对生成的ELF状态来说出奇简单：冻结的T5标记嵌入查找恢复了$93$--$96 ext{%}$的原生解码器决策，而单一线性读取在32k样本中达到了$97.9 ext{%}$的协议，留下约1.1的困惑度差距在结构残差尾部。在显式诊断监控下，一个保守的边界门在去噪步骤中提前$17$--$27 ext{%}$退出。对LangFlow、BitstreamDiffusion和连续潜在扩散语言模型（Continuous Latent Diffusion Language Model, Cola-DLM）的边界检查表明，当状态对象和解码器发生变化时，相同的接口问题仍然具有重要意义。因此，连续和潜在扩散语言模型应作为表示-解码器系统进行评估。
cs.CL / 101 / 2606.08857
PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers on Overleaf
PaperMentor：一个以人为中心的多智能体写作辅导系统，专为Overleaf上的AI研究论文设计
Abstract
Expert writing feedback from experienced researchers is critical for early-career scholars to improve their manuscripts, yet high-quality feedback often remains scarce because reviewing research papers is labor-intensive. Emerging AI-powered writing assistants largely focus on grammar fixes or simulating peer review with final scores, yet they fall short of providing concrete, actionable suggestions that help students improve their papers during drafting. We present PaperMentor, a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments while leaving the actual writing entirely to human authors. PaperMentor integrates an expert skill library carefully curated from established researchers' writing advice with 12 specialized agents covering different aspects of paper writing, such as formatting compliance, phrasing accuracy, and terminology consistency. In a user study (n=14), 90.6% of the generated comments were rated actionable and 67.5% were rated valid, significantly outperforming a GPT-5.2 baseline uswithout the skill library. We release PaperMentor as open source for public use. Our code is publicly available under the AGPL-3.0 license at https://github.com/jiarui-liu/overleaf
Chinese Translation
来自经验丰富研究者的专业写作反馈对早期职业学者改善其手稿至关重要，然而高质量的反馈往往稀缺，因为审阅研究论文是劳动密集型的。新兴的AI驱动写作助手主要集中在语法修正或模拟同行评审并给出最终评分，但在提供具体、可操作的建议方面却显得不足，这些建议能够帮助学生在撰写过程中改进他们的论文。我们提出了PaperMentor，一个以人为中心的写作助手系统，它提供可操作的建议，作为Overleaf原生的插入评论，同时将实际写作完全留给人类作者。PaperMentor整合了从知名研究者的写作建议中精心策划的专家技能库，并配备了12个专门的智能体，涵盖论文写作的不同方面，如格式合规性、措辞准确性和术语一致性。在一项用户研究中（n=14），90.6%的生成评论被评为可操作，67.5%被评为有效，显著优于没有技能库的GPT-5.2基线。我们将PaperMentor作为开源项目发布供公众使用。我们的代码在AGPL-3.0许可证下公开可用，网址为https://github.com/jiarui-liu/overleaf
cs.CL / 102 / 2606.08867
Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework
在1亿用户规模下构建客户支持AI代理：一个以评估为驱动的框架
Abstract
The rapid rise in LLM capabilities has made AI agents increasingly viable across a broad range of tasks. Among the most promising applications is building production-ready customer-facing agents, a challenge that demands coordinated excellence in evaluation methodology, context engineering, training, and online measurement. Yet these critical pillars are typically developed in isolation, creating blind spots that only surface after deployment. In this paper, we present a unified framework that bridges offline development with online impact for customer support AI agents at Nubank, a company with 100M+ users. Our approach integrates several key components: (1) structured context engineering tailored to customer support agents, (2) systematic human-in-the-loop prompt iteration, (3) rigorous LLM judge evaluation with measured inter-rater agreement and GEPA optimization for consistency, and (4) ideation-to-production validation. A central insight is that evaluation-pipeline quality directly determines iteration velocity. We present results from five production deployments spanning distinct domains: card delivery, debt management, credit-limit support, card management, and product explanation. These deployments deliver consistent customer-satisfaction gains while substantially accelerating iteration. In our card-delivery deployment, large-scale A/B testing yields a 37 percentage-point improvement in AI transactional Net Promoter Score and a 29 percentage-point gain in self-service rate over prior agent variants, alongside a strong correlation between offline simulation metrics and online outcomes, demonstrating that eval-driven development reliably predicts production impact. On most use cases, AI satisfaction reaches within a few percentage points of expert human agents.
Chinese Translation
大型语言模型（LLM）能力的快速提升使得AI代理在广泛任务中变得越来越可行。其中最有前景的应用之一是构建生产就绪的面向客户的代理，这一挑战要求在评估方法、上下文工程、训练和在线测量等方面协调卓越。然而，这些关键支柱通常是孤立开发的，导致在部署后才显现出盲点。本文提出了一个统一框架，连接离线开发与在线影响，专为拥有超过1亿用户的Nubank的客户支持AI代理而设计。我们的方法整合了几个关键组件：（1）针对客户支持代理的结构化上下文工程；（2）系统的人机协作提示迭代；（3）严格的LLM评估，测量评审者间一致性并进行GEPA优化以确保一致性；（4）从创意到生产的验证。一个核心见解是，评估管道的质量直接决定了迭代速度。我们展示了五个跨越不同领域的生产部署结果：卡片交付、债务管理、信用额度支持、卡片管理和产品说明。这些部署在显著加速迭代的同时，带来了持续的客户满意度提升。在我们的卡片交付部署中，大规模的A/B测试显示AI交易净推荐值提高了37个百分点，自助服务率比之前的代理变体提高了29个百分点，同时离线模拟指标与在线结果之间存在强相关性，证明了以评估驱动的发展可靠地预测了生产影响。在大多数使用案例中，AI满意度达到了与专家人类代理相差仅几个百分点的水平。
cs.CL / 103 / 2606.08878
PerspectiveGap: A Benchmark for Multi-Agent Orchestration Prompting
PerspectiveGap：多智能体协调提示的基准测试
Abstract
Real-world LLM applications are moving beyond single-agent workflows toward orchestrated multi-agent systems, yet current models still struggle to determine what each sub-agent needs to know. To measure this, we introduce PerspectiveGap, a benchmark for evaluating LLMs' ability to compose orchestration prompts for multi-agent systems. PerspectiveGap contains 110 scenarios, each evaluated through two distractor-mixed task formats: role-fragment assignment and free-form prompt writing. These scenarios are organized into 10 topologies, which are distilled from the authors' real-world engineering practice and framed by the Prompt Economy principle: building loop-centered orchestrations that maximize utility with minimal role and engineering overhead. In experiments with 27 commercial models from 10 companies, GPT-5.5 substantially outperforms all competitors, whereas Opus 4.7 shows a notable weakness in orchestration prompting despite its strong coding performance. Nevertheless, PerspectiveGap remains challenging: the evaluated models achieve an average combined pass rate of only 14.9\% (GPT-5.5 62.0\%) and an average overall leakage rate of 246.5\% (a per-scenario information leak-event count, not a proportion; GPT-5.5 49.1\%). These findings suggest that multi-agent orchestration prompting is a distinct and under-evaluated capability, and PerspectiveGap provides a foundation for measuring and improving it systematically.
Chinese Translation
现实世界中的大型语言模型（LLM）应用正逐步从单一智能体工作流程转向协调的多智能体系统，但目前的模型在确定每个子智能体需要了解的内容方面仍然存在困难。为此，我们引入了PerspectiveGap，一个用于评估LLM在为多智能体系统构建协调提示能力的基准测试。PerspectiveGap包含110个场景，每个场景通过两种混合干扰任务格式进行评估：角色片段分配和自由形式提示写作。这些场景被组织成10种拓扑，提炼自作者的真实工程实践，并以提示经济学原则为框架：构建以循环为中心的协调，最大化效用，同时最小化角色和工程开销。在对来自10家公司27个商业模型的实验中，GPT-5.5显著优于所有竞争对手，而Opus 4.7在协调提示方面表现出明显的弱点，尽管其编码性能强劲。然而，PerspectiveGap仍然具有挑战性：被评估模型的平均综合通过率仅为14.9%（GPT-5.5为62.0%），平均整体泄漏率为246.5%（每个场景的信息泄漏事件计数，而非比例；GPT-5.5为49.1%）。这些发现表明，多智能体协调提示是一种独特且尚未充分评估的能力，而PerspectiveGap为系统性地测量和改进这一能力提供了基础。
cs.CL / 104 / 2606.08932
From Statute to Control Flow: Span-Grounded Deontic Trees for Defeasible Scope Parsing
从法规到控制流：基于跨度的可驳回范围解析的义务树
Abstract
Rule-following agents tasked with executing policies and regulations often fail via Silent Scope Omission (SSO): a model applies a general rule but silently drops nested exceptions or counter-exceptions, producing outputs that appear compliant yet break on important edge cases. Although such failures are often framed as an agentic-systems problem, the underlying bottleneck is statutory and policy understanding, a capability typically studied in legal NLP. However, most existing legal NLP benchmarks emphasize end-task outcomes, which can overlook the structural omissions that cause SSO. To diagnose and mitigate SSO, we introduce NormBench, a benchmark of 2,290 provisions spanning Chinese (laws and local policies), English (U.S. tax law, GDPR, and corporate policies), and cross-lingual settings, designed for defeasible scope parsing: identifying precisely which clause overrides which. NormBench uses Span-Grounded Deontic Trees (SG-DT), a compiler-style intermediate representation that anchors every logical branch to source spans and requires explicit exclusion guards, enabling deterministic compilation and audit. Evaluations of frontier LLMs reveal two recurring pathologies: (1) Recursion Decay, where performance drops sharply as defeater depth increases, and (2) an Auditability Trap, where models retrieve relevant spans but fail to assemble correct control flow. Using SG-DT as a constrained intermediate output improves whole-tree fidelity and defeater recovery, and downstream experiments show that its utility is mechanism-specific: gains concentrate on exception-active, SSO-prone cases, while aggregate accuracy can be mixed when the added structure is unnecessary or parser fidelity is low.
Chinese Translation
负责执行政策和法规的规则遵循代理常常通过静默范围省略（Silent Scope Omission, SSO）而失败：模型应用一般规则但静默地忽略嵌套的例外或反例，产生看似合规但在重要边缘案例中失效的输出。尽管这种失败通常被视为代理系统的问题，但根本瓶颈在于对法规和政策的理解，这一能力通常在法律自然语言处理（NLP）中进行研究。然而，大多数现有的法律NLP基准强调最终任务结果，这可能忽视导致SSO的结构性遗漏。为了诊断和缓解SSO，我们引入了NormBench，这是一个包含2,290条条款的基准，涵盖中文（法律和地方政策）、英文（美国税法、GDPR和公司政策）以及跨语言设置，旨在进行可驳回范围解析：精确识别哪个条款覆盖哪个条款。NormBench使用基于跨度的义务树（Span-Grounded Deontic Trees, SG-DT），这是一种编译器风格的中间表示，将每个逻辑分支锚定到源跨度，并要求显式的排除保护，从而实现确定性的编译和审计。对前沿大型语言模型（LLMs）的评估揭示了两种反复出现的病态现象：（1）递归衰退，随着反例深度的增加，性能急剧下降；（2）审计陷阱，模型检索相关跨度但未能组装正确的控制流。使用SG-DT作为受限的中间输出可以提高整个树的保真度和反例恢复能力，下游实验表明其效用是机制特定的：收益集中在例外活跃、易受SSO影响的案例上，而当附加结构不必要或解析器保真度较低时，整体准确性可能会混合。
cs.CL / 105 / 2606.08938
PACT: Learning Diverse Diagnostic Strategies via Privileged Synthesis and Branch Consensus
PACT：通过特权合成和分支共识学习多样化诊断策略
Abstract
Clinical diagnosis requires flexible use of multiple reasoning paradigms under incomplete patient information. Existing LLM-based medical agents show strong medical reasoning ability, but single-paradigm or naively mixed dialogue supervision makes these paradigms difficult to learn without interference. We propose \textbf{PACT} (Periodic Anchor Consensus Training), a framework that couples supervised multi-paradigm dialogue synthesis with consensus-based Branch training. At the data level, \textbf{DPS} (Doctor-Patient-Supervisor) uses complete electronic medical records (EMRs) for quality control while keeping the doctor agent restricted to patient-visible information. This produces validated dialogues under four diagnostic reasoning paradigms without leaking hidden clinical answers. At the training level, PACT trains one paradigm-specific LoRA Branch per paradigm and periodically aggregates Branches into a shared Anchor through sign consensus. We further construct a dynamic multi-turn Chinese medical diagnosis benchmark for interactive consultation. Experiments show that PACT achieves state-of-the-art performance among compared proprietary, medical-specialized, and task-adapted baselines on diagnostic outcome and consultation-process metrics.
Chinese Translation
临床诊断需要在不完整的患者信息下灵活运用多种推理范式。现有的基于大型语言模型（LLM）的医疗代理显示出强大的医学推理能力，但单一范式或简单混合的对话监督使得这些范式难以在不干扰的情况下学习。我们提出了 extbf{PACT}（周期性锚点共识训练），这是一个将监督的多范式对话合成与基于共识的分支训练相结合的框架。在数据层面， extbf{DPS}（医生-患者-监督者）利用完整的电子病历（EMR）进行质量控制，同时保持医生代理仅限于患者可见的信息。这在不泄露隐藏临床答案的情况下，产生了四种诊断推理范式下的验证对话。在训练层面，PACT为每个范式训练一个特定的LoRA分支，并通过符号共识定期将分支聚合到一个共享的锚点。我们进一步构建了一个动态的多轮中文医学诊断基准，用于交互式咨询。实验表明，PACT在诊断结果和咨询过程指标上，在比较的专有、医学专业化和任务适应基线中实现了最先进的性能。
cs.CL / 106 / 2606.08940
Multilingual Sentiment Aware Text Summarization A Reinforcement Learning Approach for Consistency Maintenance
多语言情感感知文本摘要：一种用于一致性维护的强化学习方法
Abstract
Reinforcement Learning from Human Feedback (RLHF) has significantly improved the quality and fluency of large language models in text summarization. However, its impact on affective properties remains insufficiently understood. In this work, we study sentiment drift, a systematic shift toward neutral sentiment in RLHF-based summarization outputs compared to source texts. We conduct extensive experiments across multiple datasets, model architectures, and eight languages to analyze how alignment objectives influence sentiment preservation. Our results show that sentiment drift is a consistent phenomenon that becomes stronger with increased KL regularization strength, indicating a trade-off between alignment stability and affective fidelity. To explain this behavior, we introduce a Policy Attribution framework that decomposes the RLHF objective and quantifies the contribution of its components. Our analysis reveals that KL regularization is the primary driver of sentiment suppression across all settings. Based on these findings, we propose a sentiment-aware modification of the KL regularization term, which selectively reduces constraints on sentiment-bearing tokens. Empirical results demonstrate that this approach mitigates sentiment drift while maintaining summarization quality. Overall, our findings highlight a fundamental limitation of current alignment methods: while they improve factual consistency and safety, they may unintentionally suppress emotional expressiveness. This motivates the development of alignment strategies that explicitly account for affective preservation.
Chinese Translation
基于人类反馈的强化学习（RLHF）显著提高了大语言模型在文本摘要中的质量和流畅性。然而，它对情感属性的影响仍然理解不足。在本研究中，我们研究了情感漂移，即与源文本相比，基于RLHF的摘要输出向中性情感的系统性偏移。我们在多个数据集、模型架构和八种语言上进行了广泛的实验，以分析对齐目标如何影响情感的保留。我们的结果表明，情感漂移是一种一致的现象，随着KL正则化强度的增加而增强，表明对齐稳定性与情感保真性之间存在权衡。为了解释这种行为，我们引入了一种策略归因框架，该框架分解了RLHF目标并量化其各个组成部分的贡献。我们的分析揭示，KL正则化是所有设置中情感抑制的主要驱动因素。基于这些发现，我们提出了一种情感感知的KL正则化项修改，选择性地减少对情感承载标记的约束。实证结果表明，该方法在保持摘要质量的同时减轻了情感漂移。总体而言，我们的发现突显了当前对齐方法的一个基本局限性：虽然它们提高了事实一致性和安全性，但可能无意中抑制了情感表现力。这促使我们开发明确考虑情感保留的对齐策略。
cs.CL / 107 / 2606.08969
CARE: A Conformal Safety Layer for Medical Summarization
CARE：用于医学摘要的符合性安全层
Abstract
Large language models (LLMs) are increasingly used for medical summarization, but their outputs can omit medically important information and introduce unsupported claims. Existing error-detection methods produce heuristic or uncalibrated scores, providing no formal control over missed errors and no principled way to trade off safety against clinician review burden. We introduce Conformal Assessment for Risk Evaluation (CARE), a post-hoc, model-agnostic safety layer that uses conformal risk control to overlay calibrated omission and hallucination flags onto summaries from any LLM without retraining. CARE provides finite-sample, distribution-free guarantees through two controllers: a hallucination controller that bounds the probability of a document containing any unflagged hallucinated sentence, and an omission controller that bounds the expected fraction of important omissions not surfaced for review. Unlike hallucination detection, omissions depend jointly on whether a source sentence is important and whether it is covered by the summary. We show that calibrating only one dimension can violate the target risk bound, while marginal decompositions remain valid but overly conservative. By jointly calibrating over the full $(\tau,\gamma)$ threshold space, CARE preserves formal guarantees while surfacing up to 5$\times$ fewer sentences than alternative calibrated baselines. Across five medical summarization tasks, CARE satisfies the target risk bound at $\alpha = 0.15$ with 95% confidence across 100 calibration/test resplits, using only ~100 labeled documents per domain. In a preliminary clinician study (75 document reviews), calibrated flags improved omission detection by 28.6 percentage points on average. These results show that sentence-level safety guarantees are feasible for LLM-assisted medical summarization and offer a tunable mechanism for balancing residual risk and review effort.
Chinese Translation
大型语言模型（LLMs）在医学摘要中越来越多地被使用，但它们的输出可能会遗漏医学上重要的信息，并引入不支持的声明。现有的错误检测方法产生启发式或未校准的评分，无法对遗漏的错误进行正式控制，也没有原则性的方法来权衡安全性与临床医生审查负担之间的关系。我们提出了风险评估的符合性评估（Conformal Assessment for Risk Evaluation，CARE），这是一种后验的、模型无关的安全层，利用符合性风险控制将校准的遗漏和幻觉标志叠加到任何LLM生成的摘要上，而无需重新训练。CARE通过两个控制器提供有限样本、无分布的保证：一个幻觉控制器限制文档中任何未标记幻觉句子的概率，另一个遗漏控制器限制未被呈现以供审查的重要遗漏的预期比例。与幻觉检测不同，遗漏依赖于源句子是否重要以及是否被摘要覆盖。我们展示了仅校准一个维度可能会违反目标风险界限，而边际分解仍然有效但过于保守。通过在整个$( au,eta)$阈值空间上联合校准，CARE在提供正式保证的同时，展示出比替代的校准基线少多达5倍的句子。在五个医学摘要任务中，CARE在100次校准/测试重分配中，以95%的置信度满足目标风险界限$eta = 0.15$，每个领域仅使用约100个标记文档。在一项初步的临床医生研究（75个文档审查）中，校准标志平均提高了28.6个百分点的遗漏检测。这些结果表明，句子级别的安全保证对于LLM辅助的医学摘要是可行的，并提供了一种可调机制，以平衡剩余风险和审查工作量。
cs.CL / 108 / 2606.08988
Structure-Aware Modeling of Multiple-Choice Questions Improves Automatic Difficulty Estimation
结构感知的多项选择题建模提高了自动难度估计
Abstract
Automatic Question Difficulty Estimation (AQDE) holds growing promise for educational assessment because it has the potential to yield difficulty estimates that are competitive with expert judgment, while helping reduce the time and financial burden associated with pilot administrations and scaling to digital testing contexts. Prior AQDE studies report mixed evidence on whether adding distractors as additional text to the question stem and the correct key consistently improves difficulty prediction. We hypothesize that the effectiveness of distractor information depends on its structural representation, and that explicitly modeling distractors as separate components improves difficulty estimation over baselines that omit this information. To address this, we designed controlled architectures that model MCQ components as distinct inputs to isolate the contribution of distractor content and order. Specifically, we represented distractors by encoding each distractor as its own text input and aggregating their representations either with order-aware concatenation (with positional tags) or with an order-invariant summation. We evaluated these architectures using two Chilean datasets (Natural and Social Sciences, 2016-2020; 4,114 multiple-choice questions). Compared to a simpler model that only used the question stem and the key, our best distractor-aware architecture achieved higher predictive performance, reaching R^2 = 0.83 for Natural Sciences and R^2 = 0.71 for Social Sciences items. An order-invariant variant achieved nearly the same accuracy with approximately half as many parameters, offering a favorable accuracy-efficiency trade-off. These results show that structural information (especially distractor content) drives gains in predictive accuracy, supporting the development of efficient, structure-aware models that are computationally viable for large-scale educational applications.
Chinese Translation
自动问题难度估计（AQDE）在教育评估中展现出越来越大的潜力，因为它能够提供与专家判断相竞争的难度估计，同时帮助减少与试点管理和数字测试环境扩展相关的时间和财务负担。先前的AQDE研究报告了关于将干扰项作为附加文本添加到问题干预和正确答案是否始终改善难度预测的混合证据。我们假设，干扰项信息的有效性依赖于其结构表示，并且将干扰项明确建模为独立组件可以改善难度估计，相较于省略该信息的基线模型。为此，我们设计了控制架构，将多项选择题（MCQ）组件建模为独立输入，以隔离干扰项内容和顺序的贡献。具体而言，我们通过将每个干扰项编码为其自身的文本输入，并使用顺序感知的连接（带有位置标签）或顺序不变的求和来聚合它们的表示。我们使用两个智利数据集（自然科学和社会科学，2016-2020；4,114道多项选择题）评估了这些架构。与仅使用问题干预和答案的简单模型相比，我们最佳的干扰项感知架构实现了更高的预测性能，自然科学的R^2达到了0.83，社会科学的R^2达到了0.71。一个顺序不变的变体在参数数量约为一半的情况下几乎达到了相同的准确性，提供了良好的准确性与效率的权衡。这些结果表明，结构信息（尤其是干扰项内容）推动了预测准确性的提升，支持开发在计算上可行的大规模教育应用中高效的结构感知模型。
cs.CL / 109 / 2606.08994
Language-Aware Token Boosting: LLM Language Confusion Reduction Without Tuning
语言感知的令牌增强：无需调优的LLM语言混淆减少
Abstract
Large language models (LLMs) sometimes exhibit language confusion when generating non-English text. Existing approaches typically rely on fine-tuning to mitigate this issue. In contrast, we propose a tuning-free paradigm for reducing language confusion. Within this paradigm, we introduce two methods: Language-Aware Token Boosting (LATB), which applies targeted perturbations to tokens associated with the desired language, and Adaptive Language-Aware Token Boosting (Adaptive-LATB), which dynamically adjusts these perturbations based on the model's confidence in the intended language. Experiments demonstrate that our methods effectively improve multilingual alignment by reducing language confusion, while maintain the summarization quality without requiring any additional fine-tuning. Our code is publicly available. https://github.com/scbdatax/genai-datax-language-aware-token-boosting.
Chinese Translation
大型语言模型（LLMs）在生成非英语文本时有时会表现出语言混淆。现有的方法通常依赖于微调来缓解这一问题。相比之下，我们提出了一种无需调优的范式来减少语言混淆。在这一范式中，我们引入了两种方法：语言感知的令牌增强（Language-Aware Token Boosting, LATB），它对与目标语言相关的令牌施加有针对性的扰动；自适应语言感知的令牌增强（Adaptive Language-Aware Token Boosting, Adaptive-LATB），它根据模型对目标语言的信心动态调整这些扰动。实验表明，我们的方法通过减少语言混淆有效提高了多语言对齐，同时保持了摘要质量，无需任何额外的微调。我们的代码已公开可用。https://github.com/scbdatax/genai-datax-language-aware-token-boosting.
cs.CL / 110 / 2606.09013
Beyond Averages: Evaluating LLMs on Human Survey Replication at the Distributional Level
超越平均值：在分布层面评估大型语言模型（LLMs）对人类调查复制的能力
Abstract
LLMs are increasingly used to simulate human survey responses, but prior work has mainly evaluated replication using mean-level or aggregate agreement, offering limited insight into whether LLMs reproduce the variability of human behavior. We evaluate LLM-based survey replication at the distributional level using a non-public 2010 consumer choice experiment on Korean instant noodle purchases, a setting unlikely to overlap with model training data. We evaluate three response variables of differing statistical type: binary purchase incidence, categorical brand choice, and count purchase quantity. For each, we compare human and LLM responses at mean-level, pattern, and distributional alignment, and against reference baselines from the human data alone. LLMs reproduce condition-level patterns reasonably well but fail to capture distributional structure: for purchase quantity, no model beats a condition-insensitive baseline that simply matches the pooled human distribution. Because models that match human means well can still produce distributions further from humans than this baseline, mean-based evaluation alone can be actively misleading. Replication also varies with input configuration, with structured personas and multimodal inputs improving alignment while explicit reasoning prompting degrades it monotonically.
Chinese Translation
大型语言模型（LLMs）越来越多地被用于模拟人类调查反应，但之前的研究主要通过均值水平或聚合一致性进行评估，这对LLMs是否能够再现人类行为的变异性提供了有限的见解。我们在分布层面对基于LLM的调查复制进行了评估，使用的是一项非公开的2010年消费者选择实验，研究对象为韩国方便面购买，这一场景不太可能与模型训练数据重叠。我们评估了三种不同统计类型的反应变量：二元购买发生率、分类品牌选择和购买数量计数。对于每种反应变量，我们在均值水平、模式和分布一致性方面比较了人类与LLM的反应，并与仅基于人类数据的参考基线进行对比。LLMs在条件水平模式的再现上表现相当不错，但未能捕捉到分布结构：在购买数量方面，没有任何模型优于一个简单匹配人类分布的条件无关基线。因为能够很好匹配人类均值的模型仍然可能产生与人类分布相差甚远的分布，因此仅基于均值的评估可能会产生误导。复制结果还随着输入配置的不同而变化，结构化的人物角色和多模态输入改善了一致性，而明确的推理提示则单调降低了一致性。
cs.CL / 111 / 2606.09027
SafeRun: Enabling Determinism in LLM Planning for Running
SafeRun：在运行规划中实现大型语言模型的确定性
Abstract
Large Language Models enable flexible natural-language planning but remain unreliable in determinism-critical domains due to their probabilistic nature. This limitation is especially problematic in running planning, where violating safety rules can lead to safety risks. We propose SafeRun, a framework for deterministic LLM-based planning via a decoupled architecture. SafeRun separates soft interpretation by an LLM from hard constraint enforcement by a deterministic solver, ensuring strict safety constraints while preserving natural-language flexibility. To validate SafeRun, we build a comprehensive benchmark for running planning under realistic physiological and safety constraints. Experiments across five LLMs show that SafeRun achieves 100\% safety score (vs.\ 79.1\% PE average and 97.6\% CodeAct average) while maintaining competitive instruction-following scores. The SafeRun benchmark is publicly available at \href{https://huggingface.co/datasets/zzp-seeker/SafeRun-RunPlanning-Benchmark}{{huggingface}}.
Chinese Translation
大型语言模型（LLM）能够实现灵活的自然语言规划，但由于其概率性质，在对确定性要求严格的领域中仍然不可靠。这一局限性在运行规划中尤为突出，因为违反安全规则可能导致安全风险。我们提出了SafeRun，一个通过解耦架构实现确定性LLM规划的框架。SafeRun将LLM的软解释与确定性求解器的硬约束执行分离，确保严格的安全约束，同时保留自然语言的灵活性。为了验证SafeRun，我们建立了一个全面的基准测试，针对现实生理和安全约束下的运行规划进行评估。对五个LLM的实验表明，SafeRun实现了100%的安全评分（相比之下，PE平均为79.1%，CodeAct平均为97.6%），同时保持了竞争力的指令遵循评分。SafeRun基准测试已在 exttt{https://huggingface.co/datasets/zzp-seeker/SafeRun-RunPlanning-Benchmark} 上公开发布。
cs.CL / 112 / 2606.09032
Bridging the Agent-World Gap: Text World Models for LLM-based Agents
弥合代理-世界差距：基于大语言模型的文本世界模型
Abstract
Large language model (LLM)-based agents are increasingly used in interactive textual environments, from web navigation and code editing to tool use and long-horizon dialogue. Yet many remain largely reactive, mapping observations to actions without an explicit model of how these environments are structured and evolve. This motivates text world models (TWMs): transition models over textual states that, given a state and a candidate action, predict the resulting webpage, terminal output, API response, or user reply, thereby supporting planning, efficient learning, and principled evaluation. We systematically review text world models for LLM-based agents, organized around a formal framework and the agent lifecycle: (1) Foundations, defining text world models and characterizing them by state representation and grounding domain; (2) Construction, taxonomizing LLM-as-WM and code-as-WM paradigms and reviewing methods for building them; (3) Application, examining how world models support agents at training time through experience synthesis and at inference time through planning, verification, and adaptation; and (4) Evaluation, covering both evaluation of the world model itself and its use as an evaluation environment for agents. We aim to consolidate this rapidly developing area, clarify its design space, and highlight open challenges for future research.
Chinese Translation
基于大语言模型（LLM）的代理在交互式文本环境中的应用日益广泛，从网页导航、代码编辑到工具使用和长时间对话。然而，许多代理仍然主要是反应性的，将观察映射到动作，而没有明确的环境结构和演变模型。这促使了文本世界模型（TWM）的发展：一种基于文本状态的转移模型，给定一个状态和一个候选动作，预测结果网页、终端输出、API响应或用户回复，从而支持规划、高效学习和原则性评估。我们系统地回顾了基于LLM的代理的文本世界模型，围绕一个正式框架和代理生命周期进行组织：(1) 基础，定义文本世界模型并通过状态表示和基础领域进行特征描述；(2) 构建，对LLM作为世界模型（WM）和代码作为世界模型的范式进行分类，并回顾构建它们的方法；(3) 应用，考察世界模型如何在训练期间通过经验合成支持代理，在推理期间通过规划、验证和适应来支持代理；(4) 评估，涵盖世界模型本身的评估以及其作为代理评估环境的使用。我们的目标是整合这一快速发展的领域，澄清其设计空间，并突出未来研究的开放挑战。
cs.CL / 113 / 2606.09068
Emergent Misalignment Can Be Induced by Sycophancy and Reversed via Alignment Gating
谄媚行为可以引发新兴的错位，并通过对齐门控进行逆转
Abstract
Prior work has shown that fine-tuning large language models on malicious or incorrect outputs in narrow domains can induce broad misalignment and harmful behavior, a phenomenon known as emergent misalignment. However, efficient methods for reversing such misalignment remain limited. In this work, we make two contributions. First, we identify sycophancy fine-tuning, i.e., training models to passively agree with users' incorrect opinions, as a previously underexplored driver of emergent misalignment, and show that it induces broad and severe misaligned behavior. Second, we propose Alignment Gating, an efficient method for reversing emergent misalignment that inserts learnable and controllable gates into the model during fine-tuning. Through fine-tuning, these gates learn to identify the internal representations responsible for unsafe responses. Thus, amplifying or suppressing these representations then exacerbates or mitigates EM, respectively. We further find that alignment gating module exhibits strong generalization: gating weights obtained from narrow-domain fine-tuning substantially suppress broad-domain misaligned behavior while preserving the model's general capabilities.
Chinese Translation
先前的研究表明，在狭窄领域内对大型语言模型进行恶意或不正确输出的微调可能会引发广泛的错位和有害行为，这一现象被称为新兴错位。然而，逆转这种错位的有效方法仍然有限。在本研究中，我们做出了两项贡献。首先，我们识别出谄媚微调，即训练模型被动地同意用户的不正确观点，作为新兴错位的一个先前未被充分探索的驱动因素，并展示了它引发广泛且严重的错位行为。其次，我们提出了对齐门控（Alignment Gating），这是一种有效的逆转新兴错位的方法，通过在微调过程中将可学习和可控的门插入模型中。通过微调，这些门学习识别导致不安全反应的内部表征。因此，放大或抑制这些表征分别会加剧或减轻新兴错位（EM）。我们进一步发现，对齐门控模块表现出强大的泛化能力：从狭窄领域微调中获得的门控权重显著抑制了广泛领域的错位行为，同时保留了模型的整体能力。
cs.CL / 114 / 2606.09114
MAAM: Anchor-Preserving Compression and Contextual Calibration for Chinese Discriminatory Language Detection
MAAM：用于中文歧视性语言检测的锚点保留压缩与上下文校准
Abstract
Chinese discriminatory-language detection is challenging because harmful intent is often implicit and context-dependent. We propose MAAM (Myopia--Astigmatism Anchor Mechanism), a lightweight, model-agnostic framework inspired by functional visual blur: rather than preserving every token equally, MAAM retains discrimination-relevant semantic anchors and calibrates them with C--I--S contextual priors (Contextual Tone, Group Identity, and Stance Polarity). We also introduce ChLGBT, to our knowledge the first Chinese LGBT-focused discriminatory-language dataset, with 8,120 manually annotated samples and three ordinal labels: explicit bias, implicit bias, and emotional intensity. Across strong encoder baselines, MAAM improves all three prediction dimensions, with consistent gains in accuracy, F1, Brier score, and expected calibration error. Compared with frontier LLM baselines under zero-shot and few-shot prompting protocols, MAAM remains competitive while offering stronger compactness and stability. These results suggest that interpretable anchor preservation and contextual calibration provide a practical alternative to heavier model scaling for Chinese discriminatory-language assessment.
Chinese Translation
中文歧视性语言检测具有挑战性，因为有害意图通常是隐含的且依赖于上下文。我们提出了MAAM（近视-散光锚机制），这是一个轻量级、模型无关的框架，灵感来自功能性视觉模糊：MAAM并不是均等保留每个标记，而是保留与歧视相关的语义锚点，并通过C-I-S上下文先验（上下文语调、群体身份和立场极性）对其进行校准。我们还引入了ChLGBT，这是我们所知的第一个以中国LGBT为焦点的歧视性语言数据集，包含8,120个手动标注的样本和三个序数标签：显性偏见、隐性偏见和情感强度。在强编码器基准测试中，MAAM提高了所有三个预测维度，并在准确性、F1值、Brier分数和预期校准误差上均取得了一致的提升。与前沿的LLM基准在零样本和少样本提示协议下相比，MAAM在竞争力的同时提供了更强的紧凑性和稳定性。这些结果表明，可解释的锚点保留和上下文校准为中文歧视性语言评估提供了一种实用的替代方案，而无需依赖更重的模型扩展。
cs.CL / 115 / 2606.09148
Explicit Representation Alignment for Multimodal Sentiment Analysis
多模态情感分析的显式表示对齐
Abstract
Multimodal affective analysis aims to understand human sentiment and emotion by jointly modeling heterogeneous modalities such as text and images. However, multimodal models often fail to consistently outperform strong text-only baselines, with performance varying significantly across fusion strategies. In this work, we identify representation misalignment between independently pretrained modality encoders as a key bottleneck for effective multimodal learning, and show through controlled experiments that alignment prior to fusion is often more important than fusion complexity. To address this issue, we propose a unified multimodal affective analysis framework that leverages vision-language models (VLMs) to convert visual content into structured textual descriptions, projecting heterogeneous modalities into a shared linguistic space and enabling interpretable text-centric reasoning. To further improve robustness, we introduce a hybrid learning strategy that combines semantic token selection with a batch-level uniformity regularization objective, encouraging a more dispersed and stable global feature space while mitigating noise introduced by VLM-generated descriptions. Experiments on multiple multimodal sentiment and emotion benchmarks show that our method consistently outperforms strong unimodal and multimodal baselines, achieving state-of-the-art performance. Our analysis further highlights the critical role of representation alignment in multimodal affective learning.
Chinese Translation
多模态情感分析旨在通过联合建模文本和图像等异构模态来理解人类的情感和情绪。然而，多模态模型往往未能持续超越强大的文本单模基准，其性能在不同的融合策略下变化显著。在本研究中，我们识别出独立预训练模态编码器之间的表示不对齐是有效多模态学习的一个关键瓶颈，并通过控制实验表明，在融合之前进行对齐通常比融合复杂性更为重要。为了解决这一问题，我们提出了一个统一的多模态情感分析框架，该框架利用视觉-语言模型（VLMs）将视觉内容转换为结构化的文本描述，将异构模态投影到共享的语言空间中，从而实现可解释的以文本为中心的推理。为了进一步提高鲁棒性，我们引入了一种混合学习策略，该策略结合了语义标记选择和批量级均匀性正则化目标，鼓励更分散和稳定的全局特征空间，同时减轻VLM生成描述所引入的噪声。在多个多模态情感和情绪基准上的实验表明，我们的方法始终超越强大的单模态和多模态基准，达到了最先进的性能。我们的分析进一步强调了表示对齐在多模态情感学习中的关键作用。
cs.CL / 116 / 2606.09157
SEF-CLGC at SemEval-2026 Task 11: Logical Notation Impact on Language Model Performance
SEF-CLGC在SemEval-2026任务11中的表现：逻辑符号对语言模型性能的影响
Abstract
This paper revisits our pipeline called Syllogistic Evaluation Framework-Common Logic Grammar Construction (SEF-CLGC). We combine formal logical notations with Small Language Models (SLMs) to evaluate reasoning performance on the SemEval-2026 Task 11 Subtask 1: Disentangling Content and Formal Reasoning in Large Language Models. Our experiments show that by relying solely on SLMs, trained on a combination of natural and symbolic languages, our best model achieves a content score of 27.80% on the task while significantly lowering the content bias in reasoning.
Chinese Translation
本文重新审视了我们的管道，称为三段论评估框架-通用逻辑语法构建（SEF-CLGC）。我们将形式逻辑符号与小型语言模型（SLMs）相结合，以评估在SemEval-2026任务11子任务1：在大型语言模型中解开内容与形式推理的表现。我们的实验表明，仅依赖于训练于自然语言和符号语言组合的小型语言模型，我们的最佳模型在该任务中实现了27.80%的内容得分，同时显著降低了推理中的内容偏差。
cs.CL / 117 / 2606.09159
Unified Energy for Invariant and Independent Decoding in Diffusion Language Models
统一能量用于扩散语言模型中的不变和独立解码
Abstract
Diffusion Language Models (DLMs) enable parallel text generation by iteratively denoising a full sequence, offering attractive flexibility compared to auto-regressive (AR) decoding. However, existing methods fail to fully capture token relationships, leading to a performance gap relative to AR baselines, especially as the degree of parallelism increases. In this paper, we give a systematic analysis of the gap, identifying three key factors: (i) model capacity, (ii) dependency, and (iii) invariance. To address these issues, we first propose an invariant energy (Inv-E) together with an effective sampling-based estimator to handle the invariance issue. By further combining with the independent energy (Ind-E), we obtain a unified energy (Uni-E), that accounts for all these factors. Uni-E enjoys a unique advantage: it can be computed exactly without sampling-based partition estimation. Besides, Uni-E is model agnostic and can therefore be scaled to models of arbitrary size. We further prove that Uni-E can correct the distribution shift caused by dependency and invariance. Extensive experiments across Diffusion Language Models (DLMs) and Diffusion Large Language Models (DLLMs) demonstrate the effectiveness of the proposed Uni-E.
Chinese Translation
扩散语言模型（DLMs）通过迭代去噪全序列实现并行文本生成，相较于自回归（AR）解码提供了更具吸引力的灵活性。然而，现有方法未能充分捕捉标记之间的关系，导致与AR基线相比存在性能差距，尤其是在并行度增加时。在本文中，我们对这一差距进行了系统分析，识别出三个关键因素：（i）模型容量，（ii）依赖性，以及（iii）不变性。为了解决这些问题，我们首先提出了一种不变能量（Inv-E），并结合有效的基于采样的估计器来处理不变性问题。通过进一步结合独立能量（Ind-E），我们获得了一种统一能量（Uni-E），能够考虑所有这些因素。Uni-E 具有独特的优势：它可以在不依赖基于采样的分区估计的情况下精确计算。此外，Uni-E 是模型无关的，因此可以扩展到任意规模的模型。我们进一步证明了 Uni-E 可以纠正由依赖性和不变性引起的分布偏移。在扩散语言模型（DLMs）和扩散大型语言模型（DLLMs）上的大量实验验证了所提出的 Uni-E 的有效性。
cs.CL / 118 / 2606.09178
Culturally-Adapted Red-Teaming Across East and Southeast Asian Contexts: A Methodological and Comparative Analysis
跨东亚和东南亚背景的文化适应性红队测试：方法论与比较分析
Abstract
Multilingual safety evaluation of large language models (LLMs) has predominantly relied on direct translation (DT) of English benchmarks into target languages - an approach that converts surface-level linguistic form while failing to reflect the cultural context embedded in threat scenarios, social norms, and legal frameworks. We construct paired DT and culturally-adapted (CA) datasets via 1:1 seed matching for four languages - Korean (KO), Japanese (JA), Thai (TH), and Khmer (KM) - and compare Attack Success Rate (ASR) and Cultural Realism scores across four open-source LLM. CA prompts yield Delta-ASR > 0 across all 16 language x model combinations (mean +9.3 pp), and DT-based evaluation underestimates risk in 44 of 48 category x language combinations. Language-level analysis reveals that the distribution of threat forms is heterogeneous across languages. Cultural Realism analysis further shows that DT Cultural Depth (C3) scores remain consistently below 1.0 out of 3.0 across all four languages (mean 0.17), whereas CA scores reach up to 2.51, indicating that direct translation produces inputs systematically divergent from those encountered in real-world multicultural settings. These findings demonstrate that adapting benchmarks to language-specific cultural contexts - rather than relying on linguistic translation alone - is necessary for valid multilingual LLM safety evaluation.
Chinese Translation
对大型语言模型（LLMs）的多语言安全评估主要依赖于将英语基准直接翻译（DT）为目标语言——这种方法仅转换表层语言形式，却未能反映威胁场景、社会规范和法律框架中蕴含的文化背景。我们通过1:1种子匹配构建了四种语言（韩语（KO）、日语（JA）、泰语（TH）和高棉语（KM））的配对DT和文化适应（CA）数据集，并比较了四种开源LLM的攻击成功率（ASR）和文化现实性评分。CA提示在所有16种语言与模型组合中均产生了Delta-ASR > 0（平均+9.3个百分点），而基于DT的评估在48个类别与语言组合中的44个低估了风险。语言层面的分析显示，威胁形式的分布在不同语言中是异质的。文化现实性分析进一步表明，DT文化深度（C3）评分在所有四种语言中始终低于3.0的1.0（平均0.17），而CA评分最高可达2.51，这表明直接翻译所产生的输入与现实世界多文化环境中的输入系统性地存在偏差。这些发现表明，适应特定语言文化背景的基准评估——而非仅依赖语言翻译——对于有效的多语言LLM安全评估是必要的。
cs.CL / 119 / 2606.09195
Symbolic and Abstractive Reasoning with Complex Visual Queries
复杂视觉查询的符号与抽象推理
Abstract
Understanding and reasoning over abstract visual content remains a challenge for current multi-modal large language models (MLLMs). In this paper, we explore a novel abstract data type termed complex visual query (CVQ), designed to probe symbolic and abstractive reasoning, which is a critical yet underexplored dimension of human-like neuro-symbolic reasoning for MLLMs. We present a comprehensive investigation from three perspectives: \textbf{Data $\times$ Paradigm $\times$ Exploration}. Specifically, we propose a scalable pipeline for synthesizing CVQs grounded in large-scale multi-modal knowledge graphs, generating a diverse dataset encompassing 14 distinct query types via systematic combinations of first-order logic operators. We further introduce a two-stage training framework that progressively equips MLLMs with robust visual reasoning capabilities. We conduct extensive experiments to rigorously evaluate MLLMs across multiple dimensions, including reasoning performance on CVQs, as well as cross-task and cross-scenario generalization. We believe our work opens new perspectives and avenues for advancing the reasoning frontiers of MLLMs.
Chinese Translation
理解和推理抽象视觉内容仍然是当前多模态大型语言模型（MLLMs）面临的挑战。本文探讨了一种新颖的抽象数据类型，称为复杂视觉查询（CVQ），旨在探测符号和抽象推理，这是人类类神经符号推理在MLLMs中一个关键但尚未深入研究的维度。我们从三个视角进行全面调查： extbf{数据 $ imes$ 范式 $ imes$ 探索}。具体而言，我们提出了一种可扩展的管道，用于合成基于大规模多模态知识图谱的CVQ，通过系统组合一阶逻辑运算符生成涵盖14种不同查询类型的多样数据集。我们进一步引入了一个两阶段训练框架，逐步为MLLMs赋予强大的视觉推理能力。我们进行了广泛的实验，以严格评估MLLMs在多个维度上的表现，包括在CVQ上的推理性能，以及跨任务和跨场景的泛化能力。我们相信我们的工作为推进MLLMs的推理前沿开辟了新的视角和途径。
cs.CL / 120 / 2606.09251
TruthSplit: Operationalizing Conditional Validity in Arguments Through Multi-Perspective Reasoning
TruthSplit：通过多视角推理实现论证中的条件有效性
Abstract
We present TruthSplit, an interactive system for multi-perspective argument analysis. Existing argumentation tools typically analyze properties of the argument itself, such as structure, quality, stance, or persuasiveness, while leaving perspective-specific background knowledge implicit. TruthSplit addresses this gap by supporting an exploratory analysis of how the same claim can lead to different conclusions when interpreted through worldview-specific values, assumptions, and conceptual definitions. We refer to this perspective-dependent analysis as conditional validity. Given an input argumentative text, TruthSplit extracts claims and premises, applies a three-layer natural language inference (NLI) approach to assess both logical and worldview-specific normative consistency, and conditions large language model (LLM) reasoning on structured worldview profiles that encode core values and decision principles. The system then generates perspective-specific interpretations, identifies value conflicts and assumption gaps, and visualizes divergence through interactive analytical interfaces.
Chinese Translation
我们提出了TruthSplit，一个用于多视角论证分析的互动系统。现有的论证工具通常分析论证本身的属性，如结构、质量、立场或说服力，而将特定视角的背景知识隐含。TruthSplit通过支持探索性分析，填补了这一空白，探讨了同一主张在通过特定世界观的价值观、假设和概念定义进行解读时如何导致不同结论。我们将这种依赖视角的分析称为条件有效性。给定输入的论证文本，TruthSplit提取主张和前提，应用三层自然语言推理（NLI）方法来评估逻辑一致性和世界观特定的规范一致性，并在结构化的世界观档案上对大型语言模型（LLM）推理进行条件化，这些档案编码了核心价值观和决策原则。该系统随后生成特定视角的解释，识别价值冲突和假设差距，并通过互动分析界面可视化分歧。
cs.CL / 121 / 2606.09293
One Model, Multiple Goals: Adaptive Multi-Objective Learning for E-commerce Dialogue Systems
一个模型，多重目标：针对电子商务对话系统的自适应多目标学习
Abstract
Dialogue systems in e-commerce scenarios often need to satisfy multiple objectives: accurately reasoning over user profiles (e.g., eligibility, credit limit) to ensure correct decision-making and user state interpretation, while also generating natural and faithful responses. These goals are complementary but not identical. In this work, we propose MORE, an adaptive Multi-Objective REinforcement learning framework that jointly optimizes reasoning accuracy and linguistic naturalness. Our preliminary experiments show that directly mixing rewards with diverging optimization dynamics can cause oscillations and unstable learning. Thus, instead of optimizing a single mixed reward, we treat reasoning functions as constraints that guide policy optimization. At inference time, the system directly generates responses without explicit reasoning steps, while still benefiting from reasoning-enhanced scaffold and avoiding additional inference overhead. To better balance linguistic objectives during response generation, we introduce an adaptive multi-reward mechanism that aggregates signals such as fluency and naturalness and dynamically reweighs them via gradient feedback. We evaluate MORE on two real-world dialogue systems at ByteDance and the MultiWOZ 2.2 benchmark, where it consistently outperforms strong baselines. In 14-day online experiments on ByteDance production traffic, MORE improves overall and reached conversion by 16.53% and 30.09%, while increasing user satisfaction and reducing handoff rates. Notably, in a human-machine comparison, MORE recovers about 60% of the incremental conversion lift achieved by human agents.
Chinese Translation
电子商务场景中的对话系统往往需要满足多个目标：准确推理用户档案（例如，资格、信用额度），以确保正确的决策和用户状态解释，同时生成自然且真实的响应。这些目标是互补的，但并不相同。在本研究中，我们提出了MORE，一个自适应的多目标强化学习框架，旨在共同优化推理准确性和语言自然性。我们的初步实验表明，直接混合具有不同优化动态的奖励可能导致振荡和不稳定学习。因此，我们不再优化单一的混合奖励，而是将推理函数视为指导策略优化的约束。在推理时，系统直接生成响应，而无需显式的推理步骤，同时仍然受益于增强推理的支撑，避免额外的推理开销。为了在响应生成过程中更好地平衡语言目标，我们引入了一种自适应多奖励机制，该机制聚合流畅性和自然性等信号，并通过梯度反馈动态重新加权。在字节跳动的两个真实对话系统和MultiWOZ 2.2基准测试中，我们对MORE进行了评估，结果显示其始终优于强基线。在字节跳动生产流量的14天在线实验中，MORE整体和转化率分别提高了16.53%和30.09%，同时增加了用户满意度并降低了交接率。值得注意的是，在人机比较中，MORE恢复了人类代理所实现的约60%的增量转化提升。
cs.CL / 122 / 2606.09295
N\"ushuVoice: Reviving the Voice of Endangered N\"ushu with Pitch-Aware Text-to-Speech
N"ushuVoice：通过音高感知的文本到语音技术复兴濒危的N"ushu语音
Abstract
N\"ushu is an endangered phonetic script historically used by women in Jiangyong County, southern Hunan, China. While existing computational studies of N\"ushu mainly focus on textual digitization and visual recognition, the acoustic reconstruction of its authentic pronunciation remains largely unexplored. Building a N\"ushu text-to-speech (TTS) system is particularly challenging because available recordings are extremely limited and mostly consist of isolated syllable-level pronunciations rather than natural sentence-level utterances. In this work, we introduce N\"ushuVoice, the first TTS benchmark for N\"ushu. We construct a sentence-level N\"ushu text-to-audio dataset that aligns standardized Unicode N\"ushu text, phonetic transcriptions, standard Chinese translations, and archival recordings. To synthesize speech under this extreme low-resource setting, we propose N\"ushu-PitchVITS, an F0-conditioned VITS framework that leverages N\"ushu's five-level pitch notation as an explicit prosodic inductive bias. Experimental results show that N\"ushu-PitchVITS outperforms strong TTS baselines in spectral fidelity, pitch reconstruction, and human-rated intelligibility. We publicly release the dataset and code at: https://anonymous.4open.science/r/Nvshu-TTS-2EB6.
Chinese Translation
N"ushu是一种濒危的音韵文字，历史上由中国湖南南部江永县的女性使用。尽管现有的N"ushu计算研究主要集中在文本数字化和视觉识别上，但其真实发音的声学重建仍然基本未被探索。构建N"ushu文本到语音（TTS）系统尤其具有挑战性，因为现有的录音极为有限，且大多仅由孤立的音节级发音组成，而非自然的句子级表达。在本研究中，我们介绍了N"ushuVoice，这是N"ushu的第一个TTS基准。我们构建了一个句子级N"ushu文本到音频数据集，该数据集对齐了标准化的Unicode N"ushu文本、音韵转录、标准中文翻译和档案录音。为了在这种极低资源的环境下合成语音，我们提出了N"ushu-PitchVITS，一个基于F0的VITS框架，利用N"ushu的五级音高标记作为显式的韵律归纳偏置。实验结果表明，N"ushu-PitchVITS在谱保真度、音高重建和人类评估的可懂度方面优于强大的TTS基线。我们公开发布了数据集和代码，网址为：https://anonymous.4open.science/r/Nvshu-TTS-2EB6。
cs.CL / 123 / 2606.09304
SG-OPD: Sign-Gated On-Policy Distillation via Sign-Consistency Gating and Phased Teacher Sampling
SG-OPD：通过符号一致性门控和分阶段教师采样的符号门控在线策略蒸馏
Abstract
On-policy distillation (OPD) trains a student on its own trajectories with dense per-token supervision from a stronger teacher, and often outperforms off-policy distillation and standard reinforcement learning. However, we find that its effectiveness implicitly relies on two assumptions that frequently break in practice: trajectory-level alignment between the student and the teacher, and uniform token-level reliability of the teacher's preferences. We therefore propose Sign-Gated On-Policy Distillation (SG-OPD), which uses a binary verifier as a trust signal for the teacher at two complementary granularities: phased teacher sampling mixes in verifier-endorsed teacher rollouts at cold-start, and a sign-consistency gate extrapolates the distillation update on tokens where the teacher agrees with the verifier-correct direction and interpolates it where it disagrees. Experiments on competition-level mathematical reasoning benchmarks show that SG-OPD consistently outperforms standard OPD, with average gains of 1.98 and 7.50 at the per-sample and per-question levels, respectively.
Chinese Translation
在线策略蒸馏（OPD）通过来自更强教师的密集逐token监督，在其自身轨迹上训练学生，通常优于离线策略蒸馏和标准强化学习。然而，我们发现其有效性隐含地依赖于两个在实践中经常失效的假设：学生与教师之间的轨迹级对齐，以及教师偏好的逐token可靠性均匀性。因此，我们提出了符号门控在线策略蒸馏（SG-OPD），它在两个互补的粒度上使用二元验证器作为教师的信任信号：分阶段教师采样在冷启动时混合了验证器认可的教师回放，而符号一致性门控则在教师与验证器一致的方向上外推蒸馏更新，并在不一致的地方进行插值。在竞争级数学推理基准上的实验表明，SG-OPD在每个样本和每个问题层面上分别平均提高了1.98和7.50，始终优于标准OPD。
cs.CL / 124 / 2606.09334
How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction?
提示在最小编辑乌克兰语语法错误纠正中的作用有多大？
Abstract
Fine-tuned Large Language Models (LLMs) dominate in Ukrainian grammatical error correction (GEC), while API-accessed LLMs remain nearly untested on minimal-edit benchmarks. We evaluate 11 commercial LLMs from four providers and one open-source Ukrainian model on the UNLP 2023 GEC-only benchmark, comparing zero-shot, few-shot, minimal-edits, and LLM-assisted prompt optimization strategies. Our best configuration (Gemini 3.1-Pro) reaches F0.5=69.22, closing over 90% of the gap to fine-tuned SOTA (F0.5=73.14). For zero-shot prompts, only Claude models benefit from Ukrainian instructions. However, the best overall results for all models use Ukrainian minimal-edits prompts, whose language-specific rules require Ukrainian to express precisely. LLM-assisted prompt optimization on top of minimal-edits + few-shot achieves the highest score. Detailed minimal-edits instructions yield the largest gains for punctuation and case errors but cause the model to abandon several low-frequency categories. Delving into error analysis, we identify five recurring overcorrection patterns tied to Ukrainian-specific linguistic phenomena. Code, prompts, and outputs are publicly available.
Chinese Translation
经过微调的大型语言模型（LLMs）在乌克兰语语法错误纠正（GEC）中占据主导地位，而通过API访问的LLMs在最小编辑基准测试中几乎没有得到检验。我们评估了来自四个提供商的11个商业LLMs和一个开源乌克兰模型在UNLP 2023 GEC专用基准上的表现，比较了零样本、少样本、最小编辑和LLM辅助的提示优化策略。我们最佳的配置（Gemini 3.1-Pro）达到了F0.5=69.22，缩小了与微调的最先进技术（SOTA，F0.5=73.14）之间90%以上的差距。对于零样本提示，只有Claude模型受益于乌克兰语指令。然而，所有模型的最佳整体结果都使用了乌克兰语最小编辑提示，其语言特定规则要求乌克兰语表达得非常准确。在最小编辑+少样本的基础上进行LLM辅助提示优化达到了最高分数。详细的最小编辑指令在标点和大小写错误上带来了最大的提升，但导致模型放弃了几个低频类别。通过错误分析，我们识别出五种与乌克兰特有语言现象相关的重复过度纠正模式。代码、提示和输出均已公开。
cs.CL / 125 / 2606.09338
Multi-Hop Knowledge Composition is Bound by Pretraining Exposure
多跳知识组合受限于预训练暴露
Abstract
Large Language Models fail at implicit multi-hop reasoning: a model answers "When was $X$ born?" and "Who is $Y$'s closest friend?" correctly but fails on "When was $Y$'s closest friend born?" in a single forward pass, even when both facts are perfectly memorized and individually retrievable. We study this failure in a controlled natural language setting with a strict separation between individuals exposed to compositional contexts during pretraining and those that never appear in any such context. We confirm that compositional failure persists even at 97% 1-hop accuracy, establishing the gap as a pretraining failure rather than a knowledge absence. We propose and test nine data-centric augmentation formats and find that compositional pretraining transfers to unseen questions for exposed individuals, but never to individuals absent from compositional pretraining, suggesting that exposure to compositional contexts during pretraining is a necessary condition for implicit multi-hop reasoning.
Chinese Translation
大型语言模型在隐式多跳推理方面表现不佳：模型能够正确回答"$X$出生于何时？"和"$Y$的最亲密朋友是谁？"，但在单次前向推理中无法回答"$Y$的最亲密朋友出生于何时？"，即使这两个事实都被完美记忆且可以单独检索。我们在一个受控的自然语言环境中研究了这一失败，严格区分了在预训练期间接触组合上下文的个体与从未出现在任何此类上下文中的个体。我们确认，即使在97%的单跳准确率下，组合失败仍然存在，确立了这一差距是预训练失败而非知识缺失。我们提出并测试了九种以数据为中心的增强格式，发现对于接触过组合上下文的个体，组合预训练可以转移到未见问题上，但对于未接触组合预训练的个体则无法转移，这表明在预训练期间接触组合上下文是隐式多跳推理的必要条件。
cs.CL / 126 / 2606.09351
In-Context Learning for the Imputation of Public Opinion Data with Large Language Models
基于上下文学习的大型语言模型在公共舆论数据插补中的应用
Abstract
Large language models have been widely evaluated as simulators of individual survey responses. In practice, however, fully unobserved responses are rare; the dominant problem is partial non-response. Imputation aims to restore the overall structure of a survey dataset by filling in these missing values. It has its own well-defined evaluation criteria and differs fundamentally from prediction. We propose to impute missing survey data through in-context learning (ICL). We systematically evaluate ICL design choices across different missingness mechanisms (MCAR, MAR, MNAR) on 150 opinion variables spanning 15 waves of the American Trends Panel. Compared to well-established statistical methods for data imputation like MICE PMM, our ICL approach consistently reduces absolute error across all missingness mechanisms, with the largest gains under non-random missingness (MNAR). Notably, the best-performing specification (gpt-oss-120b with 100 in-context examples) achieves near-nominal aggregate coverage (approaching the 95% level) with confidence intervals two to five times narrower than MICE PMM. We publish a Python package with an sklearn-like API to enable easy deployment of our method using local and proprietary LLMs.
Chinese Translation
大型语言模型已被广泛评估为个体调查响应的模拟器。然而，在实践中，完全未观察到的响应是罕见的；主要问题是部分非响应。插补旨在通过填补这些缺失值来恢复调查数据集的整体结构。它有自己明确的评估标准，并在根本上与预测不同。我们提出通过基于上下文学习（In-Context Learning, ICL）来插补缺失的调查数据。我们系统地评估了不同缺失机制（完全随机缺失 MCAR、随机缺失 MAR、非随机缺失 MNAR）下的 ICL 设计选择，涵盖了 15 波美国趋势面板的 150 个舆论变量。与像 MICE PMM 这样的成熟数据插补统计方法相比，我们的 ICL 方法在所有缺失机制下始终减少绝对误差，在非随机缺失（MNAR）情况下获得了最大的收益。值得注意的是，表现最佳的规格（gpt-oss-120b，使用 100 个上下文示例）在置信区间上实现了接近名义总覆盖率（接近 95% 的水平），且置信区间比 MICE PMM 窄两到五倍。我们发布了一个具有 sklearn 风格 API 的 Python 包，以便于使用本地和专有大型语言模型轻松部署我们的方法。
cs.CL / 127 / 2606.09366
Is Text All You Need? Text as a Universal Information Bottleneck for Speech LLMs
文本是你所需的一切吗？文本作为语音大语言模型的普遍信息瓶颈
Abstract
Large language models (LLMs) provide a powerful reasoning backbone for speech understanding, but integrating continuous acoustic signals into a frozen LLM remains challenging. Existing speech-to-LLM interfaces typically operate at two extremes: either enforcing near-discrete token alignment, which benefits transcription but loses paralinguistic information, or learning unconstrained continuous representations, which can drift away from the LLM's input space and degrade autoregressive decoding. In this work, we propose Convex Gate (C-Gate), a speech-to-LLM bridge that constrains all speech representations to lie within the LLM's input embedding manifold with an architectural convex-hull constraint. Concretely, each frame is represented as a convex combination of token embeddings, ensuring compatibility with the pretrained LLM while preserving continuous expressivity. Across automatic speech recognition (ASR) and emotion recognition, C-Gate achieves strong joint performance, improving LibriSpeech WER by up to 48.7% relative while matching or exceeding single-task emotion accuracy. Beyond performance, our analysis reveals a key insight: information is not carried by discrete token identities, but by time-resolved trajectories in the embedding space. Causal interventions confirm that both the trajectory structure and alignment to the pretrained embedding manifold are critical for performance. These results suggest that geometry, rather than token discreteness, is the fundamental design factor in speech-to-LLM interfaces, and provide a controlled regime for studying multimodal integration in frozen LLMs. We release the checkpoint, per-sample outputs, mechanism dumps, and intervention suite for replication.
Chinese Translation
大型语言模型（LLMs）为语音理解提供了强大的推理基础，但将连续的声学信号整合到一个固定的LLM中仍然具有挑战性。现有的语音到LLM接口通常在两个极端之间运作：要么强制近离散的标记对齐，这有利于转录但失去了副语言信息；要么学习不受限制的连续表示，这可能偏离LLM的输入空间并降低自回归解码的效果。在本研究中，我们提出了Convex Gate（C-Gate），一个语音到LLM的桥梁，它通过架构的凸包约束将所有语音表示限制在LLM的输入嵌入流形内。具体而言，每一帧被表示为标记嵌入的凸组合，确保与预训练LLM的兼容性，同时保留连续的表达能力。在自动语音识别（ASR）和情感识别中，C-Gate实现了强大的联合性能，相对提高了LibriSpeech的字错误率（WER）多达48.7%，同时在单任务情感准确性上匹配或超过了基准。除了性能之外，我们的分析揭示了一个关键见解：信息不是通过离散的标记身份携带的，而是通过嵌入空间中的时间分辨轨迹携带的。因果干预确认了轨迹结构和与预训练嵌入流形的对齐对性能至关重要。这些结果表明，几何结构而非标记的离散性是语音到LLM接口的基本设计因素，并为在固定的LLM中研究多模态整合提供了一个受控的环境。我们发布了检查点、每个样本的输出、机制转储和干预套件以供复制。
cs.CL / 128 / 2606.09376
Precision Is Not Faithfulness: Coverage-Aware Evaluation of Grounded Generation with a Complete Oracle
精准并非忠实：基于覆盖率的有根据生成评估与完整Oracle
Abstract
Reference-free faithfulness metrics verify each atomic claim a model makes against ground truth, and are increasingly used to evaluate grounded generation. We show they share a blind spot: they measure only precision -- are the stated claims supported? -- and therefore reward abstention, since a model can score near-perfect faithfulness by saying almost nothing. We make this measurable using Formula 1 telemetry, a domain where strategic ground truth is derived deterministically and, crucially, completely: for each decision we know the full set of facts that mattered. This completeness -- absent in open-domain faithfulness benchmarks -- lets us measure recall (coverage of the relevant facts) exactly, alongside precision. On a multilingual (EN/ES/PT) benchmark of 7,253 decision instances spanning 150 races, the most precise frontier model covers under half of the relevant facts and ranks last by F1, so requiring coverage reorders the systems; the same effect reappears in a second complete-oracle domain (NOAA weather forecasts). A prompt ablation shows the low coverage is not an under-prompting artifact: explicitly asking models to be thorough does not close the gap. We pair faithfulness with coverage into a single score, validate the metric (controlled perturbation; agreement across a model-free regex extractor and a cross-family LLM extractor, system-level Spearman 1.0), and give a verifier-guided generation method that improves precision and recall without references. We release the benchmark, structured annotations, metric, baselines, and an interactive demo.
Chinese Translation
无参考忠实度指标验证模型所做的每个原子声明与真实情况的符合程度，并越来越多地用于评估有根据的生成。我们展示了它们的盲点：它们仅测量精准度——所陈述的声明是否得到了支持？——因此奖励了不作为，因为模型可以通过几乎不说任何话而获得接近完美的忠实度。我们利用Formula 1（F1）遥测使这一点可测量，这是一个战略真实情况以确定性且至关重要的方式完全推导的领域：对于每个决策，我们都知道所有相关事实的完整集合。这种完整性——在开放领域的忠实度基准中缺失——使我们能够准确测量召回率（相关事实的覆盖率），同时测量精准度。在一个涵盖150场比赛的7,253个决策实例的多语言（英语/西班牙语/葡萄牙语）基准测试中，最精准的前沿模型覆盖的相关事实不足一半，并在F1评分中排名最后，因此要求覆盖率重新排序系统；在第二个完整Oracle领域（NOAA天气预报）中也出现了相同的效果。一次提示消融实验表明，低覆盖率并非是提示不足的伪影：明确要求模型全面并未缩小差距。我们将忠实度与覆盖率结合成一个单一评分，验证该指标（受控扰动；模型无关的正则提取器与跨家族LLM提取器之间的一致性，系统级Spearman 1.0），并提供一种验证者引导的生成方法，在没有参考的情况下提高精准度和召回率。我们发布了基准测试、结构化注释、指标、基线和一个互动演示。
cs.CL / 129 / 2606.09389
LexRubric: A Rubric-Guided Diagnostic Benchmark for Open-Ended Legal Tasks
LexRubric：一种基于评分标准的开放式法律任务诊断基准
Abstract
As large language models (LLMs) are increasingly applied to real-world legal tasks, evaluating the reliability of their open-ended legal responses has become essential. These tasks require context-sensitive answers and allow little room for error, motivating fine-grained and diagnostic evaluation that can identify specific sources of response quality failures. We introduce LexRubric, a rubric-based benchmark for evaluating open-ended Chinese legal tasks. LexRubric contains 649 instances from legal consultation and judicial examination, which reflect both everyday legal needs and professional legal reasoning and cover 14 legal scenarios. It further includes 12,337 expert-written atomic scoring criteria organized under a unified six-dimensional framework, enabling accurate evaluation and diagnostic analysis across tasks and evaluation dimensions. To validate the reliability of the evaluation, we test multiple judge models and compare model-based judgments with human judgments. We further evaluate 18 recent general and legal-domain LLMs on LexRubric. Results show that different models exhibit distinct capability profiles, and that open-ended legal question remains challenging for current LLMs. Data is available at: https://github.com/foggpoy/LexRubric.
Chinese Translation
随着大型语言模型（LLMs）越来越多地应用于现实世界的法律任务，评估其开放式法律响应的可靠性变得至关重要。这些任务需要上下文敏感的答案，并且容错空间极小，因此需要细致入微的诊断性评估，以识别响应质量失败的具体来源。我们提出了LexRubric，这是一个用于评估开放式中文法律任务的基于评分标准的基准。LexRubric包含来自法律咨询和司法考试的649个实例，反映了日常法律需求和专业法律推理，涵盖了14个法律场景。它还包括12,337个由专家撰写的原子评分标准，这些标准在统一的六维框架下组织，使得在任务和评估维度之间能够进行准确的评估和诊断分析。为了验证评估的可靠性，我们测试了多个评审模型，并将基于模型的判断与人类判断进行了比较。我们进一步在LexRubric上评估了18个最新的通用和法律领域的LLMs。结果表明，不同模型展现出不同的能力特征，而开放式法律问题对当前的LLMs仍然具有挑战性。数据可在以下链接获取：https://github.com/foggpoy/LexRubric。
cs.CL / 130 / 2606.09396
PriFT: Prior-Support Guided Supervised Fine-Tuning
PriFT：基于先验支持的监督微调
Abstract
Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement learning (RL), but it can show weaker generalization than RL. A key limitation is its off-policy objective: SFT fits fixed demonstrations token by token, including targets poorly aligned with the model's pretrained distribution, which can lead to overfitting. A recent line of work addresses this issue by assigning larger training weights to tokens better aligned with the current model's predictive distribution, with the intuition that fitting these tokens are less distortive to the model's pretrained knowledge and representations. However, computing the token weights from the model that is currently fine-tuned entangles token weights with the optimization trajectory, inducing a self-reinforcing dynamics as the distribution rapidly departs from the pretrained model. To address this, we propose PriFT (Prior-support guided Fine-Tuning), which derives token weights from a frozen pretrained reference to obtain a stable reweighting signal unaffected by fine-tuning. This signal estimates prior support: the extent to which each target token is supported by the pretrained distribution. Across multiple existing token-reweighting rules, replacing the reweighting signal from the online model to pretrained model consistently improves performance. We introduce two instantiations: PriFT-prob uses pretrained token probability, while PriFT-mass selects tokens by cumulative probability mass under the pretrained distribution. Extensive experiments on mathematical reasoning, code generation, and medical question answering show that PriFT achieves state-of-the-art results among SFT baselines and provides a better initialization for subsequent RL training.
Chinese Translation
监督微调（SFT）是一种高效的下游任务适应方法，通常作为强化学习（RL）的初始化阶段，但其泛化能力往往弱于RL。一个关键的限制在于其离线策略目标：SFT逐个令牌地拟合固定的示例，包括与模型的预训练分布不良对齐的目标，这可能导致过拟合。近期的一系列研究通过为与当前模型预测分布更好对齐的令牌分配更大的训练权重来解决这一问题，直觉上认为拟合这些令牌对模型的预训练知识和表示的扭曲较小。然而，从当前微调的模型计算令牌权重将令牌权重与优化轨迹纠缠在一起，导致自我强化的动态，因为分布迅速偏离预训练模型。为了解决这个问题，我们提出了PriFT（基于先验支持的微调），该方法从一个冻结的预训练参考中推导令牌权重，以获得一个不受微调影响的稳定重加权信号。该信号估计先验支持：每个目标令牌在多大程度上受到预训练分布的支持。在多种现有的令牌重加权规则中，将重加权信号从在线模型替换为预训练模型始终能提高性能。我们介绍了两种实例化方法：PriFT-prob使用预训练令牌概率，而PriFT-mass根据预训练分布下的累积概率质量选择令牌。在数学推理、代码生成和医学问答等多个领域的广泛实验表明，PriFT在SFT基线中实现了最先进的结果，并为后续的RL训练提供了更好的初始化。
cs.CL / 131 / 2606.09403
Introducing multiplex semantic networks as multifaceted representations of creative associative knowledge across multilingual samples
引入多重语义网络作为跨多语言样本的创造性联想知识的多维表征
Abstract
Creativity is a complex cognitive ability that relies on knowledge organisation and retrieval from semantic memory. Yet most research uses a single task to measure it, capturing only a fraction of this complexity. This study investigates multiplex networks - layered semantic networks obtained from six cognitive tasks - as a more comprehensive approach to modelling the associative knowledge underlying creativity. We collected data from N=518 individuals from four countries (Austria, USA, Singapore, Italy). From their responses to verbal fluency, sentence-chain, free association, and narrative writing tasks, we constructed semantic networks and assembled them in a multiplex structure. AI persona-based responses provided a comparison baseline. Structural reducibility analyses showed that different task layers captured distinct, non-redundant information about semantic organisation, supporting the use of multiple tasks over any single one. The networks from high- and low-creative groups remained structurally distinct, while AI-generated networks showed near-identical structures regardless of creativity group. Finally, we used 12 features (network measures, emotional scores, and spreading activation simulations) in a machine learning model using ridge regression to predict individual creativity scores. The combination of structurally similar layers, as identified in the previous stage, improved a proof-of-concept prediction accuracy by 50%. Structural measures showed the highest feature importance, with spreading activation dynamics providing additional predictive power. Together, these findings indicate that multiplex semantic networks capture a richer, cross-cultural picture of associative knowledge underlying creativity. We also release our diverse dataset and code to foster diverse computational approaches within the creativity community.
Chinese Translation
创造力是一种复杂的认知能力，依赖于知识的组织和从语义记忆中的检索。然而，大多数研究使用单一任务来测量创造力，仅捕捉到这一复杂性的一部分。本研究探讨了多重网络——从六个认知任务中获得的分层语义网络——作为建模创造力背后联想知识的更全面的方法。我们从四个国家（奥地利、美国、新加坡、意大利）收集了518名个体的数据。根据他们在语言流畅性、句子链、自由联想和叙事写作任务中的反应，我们构建了语义网络并将其组装成多重结构。基于人工智能角色的反应提供了比较基线。结构可约性分析表明，不同任务层捕捉到了关于语义组织的独特且不冗余的信息，支持使用多个任务而非单一任务。高创造力和低创造力组的网络在结构上保持明显区别，而人工智能生成的网络则显示出无论创造力组如何，其结构几乎相同。最后，我们在一个机器学习模型中使用12个特征（网络度量、情感评分和扩散激活模拟）通过岭回归来预测个体创造力评分。前一阶段识别出的结构相似层的组合使概念验证的预测准确性提高了50%。结构度量显示出最高的特征重要性，而扩散激活动态提供了额外的预测能力。这些发现表明，多重语义网络捕捉到了创造力背后联想知识的更丰富的跨文化图景。我们还发布了我们多样化的数据集和代码，以促进创造力社区内多样化的计算方法。
cs.CL / 132 / 2606.09421
What Should a Skill Remember? Quality-Cost Trade-offs in Cost-Aware Skill Rewriting for Language Model Agents
技能应记住什么？面向成本的技能重写中的质量-成本权衡
Abstract
Large language model agents increasingly rely on skills: reusable procedural documents encoding workflows, tool use, implementation patterns, validation checks, and domain rules. Skill rewriting is often treated as prompt compression, but shorter skills can make agents more expensive by removing sparse operational anchors that prevent exploration, debugging, and recovery. We study skill rewriting through this economic lens. Our controlled framework profiles skill structure, rewrites skills using information-preservation strategies, and evaluates the rewrites under fixed task instructions, environments, and verifiers. Experiments on SkillsBench reveal distinct quality--cost trade-offs across strategies: API/code anchoring, workflow guarding, and rule/formula anchoring benefit different task families, with no universally dominant template. In the main held-out evaluation, the learned policy reduces total cost by 7.0\% and downstream agent-token cost by 6.0\%; in frozen cross-model transfer, the corresponding reductions average 14.7\% and 13.7\%, while verifier quality is preserved. These results position skill design as cost-aware operational knowledge engineering rather than prompt compression. Resources: \href{https://github.com/1Reminding/Skill_EE}{SkillEE}.
Chinese Translation
大型语言模型代理越来越依赖于技能：可重用的程序性文档，编码工作流程、工具使用、实现模式、验证检查和领域规则。技能重写通常被视为提示压缩，但较短的技能可能通过移除稀疏的操作锚点而使代理变得更加昂贵，这些锚点可以防止探索、调试和恢复。我们从经济的角度研究技能重写。我们的控制框架分析技能结构，使用信息保留策略重写技能，并在固定任务指令、环境和验证器下评估重写结果。在 SkillsBench 上的实验揭示了不同策略之间明显的质量-成本权衡：API/代码锚定、工作流程保护和规则/公式锚定对不同任务家族有益，没有普遍占主导地位的模板。在主要的保留评估中，学习到的策略将总成本降低了 7.0\%，下游代理-令牌成本降低了 6.0\%；在冻结的跨模型转移中，相应的降低平均为 14.7\\% 和 13.7\\%，同时验证器质量得以保持。这些结果将技能设计定位为面向成本的操作知识工程，而非简单的提示压缩。资源： [SkillEE](https://github.com/1Reminding/Skill_EE)。
cs.CL / 133 / 2606.09424
Toward Signing Activity Projection in Sign Language Interaction
朝着手语互动中的签名活动预测
Abstract
Social robots must interact robustly not only with users assumed by speech-centered systems but also with diverse users whose communication relies on different modalities, e.g., sign language. One important capability gap is predictive turn-taking with signing users. Although Voice Activity Projection (VAP) has been successfully used to model future voice activity in spoken interaction, it remains unclear whether the framework transfers to sign language interaction. This paper presents an initial transfer study of adapting a VAP architecture to dyadic sign language interaction. Using interaction recordings from the Public DGS Corpus, we derive binary signing activity streams from lexical sign annotations and formulate proxy tasks for turn-taking prediction. The model uses pose-derived hand, eye-region, and mouth-region features extracted for each signer. The results show that SHIFT/HOLD prediction is promising, especially with hand cues, while SHIFT-prediction remains difficult. These findings provide initial evidence for both the promise and the current limitations of transferring predictive turn-taking models from spoken interaction to sign language interaction. Predictive modeling of sign language interaction still requires sign-language-specific event definitions that go beyond speech-derived categories.
Chinese Translation
社交机器人必须不仅能够与假定为以语音为中心的系统的用户进行稳健互动，还需与依赖不同交流方式的多样化用户进行互动，例如手语。一个重要的能力缺口是与手语用户的预测轮流发言。尽管语音活动预测（Voice Activity Projection, VAP）已成功用于建模口语互动中的未来语音活动，但尚不清楚该框架是否适用于手语互动。本文呈现了将VAP架构适应于双人手语互动的初步转移研究。通过使用公共DGS语料库中的互动录音，我们从词汇性手势注释中导出二元手势活动流，并为轮流发言预测制定代理任务。该模型使用为每位手语者提取的姿态衍生的手部、眼部区域和口部区域特征。结果表明，SHIFT/HOLD预测表现出良好的前景，尤其是在手部线索的帮助下，而SHIFT预测仍然困难。这些发现为将预测轮流发言模型从口语互动转移到手语互动的潜力和当前局限性提供了初步证据。手语互动的预测建模仍需超越基于语音的类别，定义特定于手语的事件。
cs.CL / 134 / 2606.09428
Guide Me Out: A Framework to Benchmark VLM Operators Communication in Crisis Scenarios
引导我出去：危机场景中评估视觉语言模型操作员沟通的框架
Abstract
Effective crisis response requires spatially grounded communication that bridges linguistic guidance of civilians with the physical environment, accounting for structural bottlenecks, evolving threats, and agent-specific contexts. Yet, current NLP research in crisis communication remains mainly limited to static, text-only classification settings, overlooking the critical communicative role of AI operators in dynamic, embodied scenarios. We address this gap with a novel benchmarking framework for evaluating Vision-Language Models (VLMs) tasked with guiding civilian agents through simulated evacuations. We test two communication strategies (narrowcast vs. broadcast), two environment representations (visual vs. graph-based), and two threat behaviors (static vs. moving) across nine maps of varying structural complexity. Our results show that Narrowcast consistently reduces civilian Fail rates compared to Broadcast across all difficulty levels. Guidance quality depends heavily on how the VLM operator represents the world: the visual modality drives performance, while adding an adjacency graph is model-dependent and often harmful. Moving threats raise Fail rates across all conditions as communication must continuously adapt over time. Together, these findings show that deploying VLMs as AI operators in evacuation scenarios remains a non-trivial challenge, where the choice of communication strategy and input representation can directly determine the success or failure of the intervention.
Chinese Translation
有效的危机响应需要基于空间的沟通，将平民的语言指导与物理环境相结合，考虑结构瓶颈、不断变化的威胁和特定代理的上下文。然而，目前在危机沟通中的自然语言处理研究主要局限于静态的文本分类设置，忽视了人工智能操作员在动态、具身场景中所扮演的关键沟通角色。我们通过一个新颖的基准框架来解决这一空白，评估被任务指派为引导平民代理进行模拟撤离的视觉语言模型（VLMs）。我们在九个具有不同结构复杂性的地图上测试了两种沟通策略（窄播 vs. 广播）、两种环境表示（视觉 vs. 基于图形）和两种威胁行为（静态 vs. 移动）。我们的结果表明，窄播在所有难度级别上始终能有效降低平民失败率。指导质量在很大程度上依赖于VLM操作员如何表示世界：视觉模态驱动了性能，而添加邻接图则依赖于模型，且通常会造成负面影响。移动威胁在所有条件下都提高了失败率，因为沟通必须随着时间的推移不断适应。综合来看，这些发现表明，在撤离场景中部署VLM作为人工智能操作员仍然是一个非平凡的挑战，沟通策略和输入表示的选择可以直接决定干预的成功或失败。
cs.CL / 135 / 2606.09435
MUDIDI: A Two-Stage Framework for Multilingual Dictionary Digitization with Language Models
MUDIDI：一种基于语言模型的多语言词典数字化的两阶段框架
Abstract
Multilingual dictionaries are among the most valuable documentary resources for low-resource and endangered languages, yet many remain available only as scans. For many decades, their digitization and conversion into a machine-readable format was nearly impossible due to language-specific scripts, complex multi-column layouts full of entries with abbreviations and cross-references. Recent vision-language models offer a promising solution, but it is unclear how well they preserve characters, markup, and process lexicographic structure. We introduce MUDIDI, a two-stage framework for multi-lingual dictionary digitization. Stage One evaluates the quality of character recognition and markup preservation; Stage Two focuses on dictionary entry segmentation with subsequent mapping into a machine-readable lexicographic schema, SIL's Multi-Dictionary Formatter. We also release a dataset that consists of human-annotated lexicographic entries collected from 30 public-domain dictionaries featuring diverse writing systems, language families, and formats. We benchmark OCR systems, general-purpose Large Language Models (LLMs), and Vision Language Models (VLMs) on the dataset, demonstrating superior performance of LLMs across most writing systems and languages in both stages, and provide practical guidelines on improving the results for more challenging scenarios. Finally, we show that supplementing additional information, such as dictionary introduction, to the LLMs can improve the quality of the digitized dictionary. Github: https://github.com/DavidSamuell/MUDIDI-Pipeline-for-Digitization-of-Multilingual-Dictionary/
Chinese Translation
多语言词典是低资源和濒危语言中最有价值的文献资源之一，但许多词典仍仅以扫描件的形式存在。几十年来，由于特定语言的书写系统、复杂的多列布局以及充满缩略语和交叉引用的条目，其数字化和转化为机器可读格式几乎是不可能的。最近的视觉-语言模型提供了一种有前景的解决方案，但尚不清楚它们在保留字符、标记和处理词典结构方面的效果如何。我们提出了MUDIDI，一种用于多语言词典数字化的两阶段框架。第一阶段评估字符识别和标记保留的质量；第二阶段专注于词典条目的分割，并随后映射到机器可读的词典模式，即SIL的多词典格式化器。我们还发布了一个数据集，该数据集由从30本公共领域词典中收集的人类注释的词典条目组成，这些词典具有多样的书写系统、语言家族和格式。我们在该数据集上对OCR系统、通用大型语言模型（LLMs）和视觉语言模型（VLMs）进行了基准测试，结果表明LLMs在大多数书写系统和语言中的表现优于其他模型，并提供了在更具挑战性的场景中改善结果的实用指南。最后，我们展示了向LLMs补充额外信息（如词典介绍）可以提高数字化词典的质量。Github: https://github.com/DavidSamuell/MUDIDI-Pipeline-for-Digitization-of-Multilingual-Dictionary/
cs.CL / 136 / 2606.09449
Reasoning without Gold Standards: A Proxy-Judge Theory of Autoformalization
无需黄金标准的推理：自动形式化的代理评判理论
Abstract
Complex reasoning tasks increasingly require systems to produce outputs whose correctness cannot be judged by exact match against a single reference. Autoformalization (AF) is a representative example; it asks a model to translate informal mathematical or logical reasoning into a formally checkable object, yet expert-validated formalizations do not scale beyond toy cases and a single informal argument can admit many valid formal renderings. Progress therefore depends on whether partial, structured proxies can substitute for exact references. We introduce a reference-free proxy-judge framework for AF that replaces gold-standard matching with a vector of per-axis property checks. The framework organizes the proxy along three structural scopes that cover global properties of the elicited object, per-module properties internal to its sub-components, and cross-domain properties that re-align it to the informal source, and aggregates each axis into a verdict vector. The vector drives a reflective refinement loop in which a violated coordinate routes the controller to a matching repair target, so each iteration changes only what is judged wrong. Under bounded judge noise, the expected intrinsic gap contracts geometrically to a noise-dependent plateau. Across seven formalization backbones on miniF2F, ProofNet, e-SNLI, and ProntoQA, refinement consistently lifts Pass Rate over the single-shot ICL baseline, and the per-axis proxy outperforms a matched scalar proxy on benchmarks where the baseline has room to improve. Structured proxy judgments therefore provide both a practical refinement signal and a theoretical handle on convergence when exact references are unavailable.
Chinese Translation
复杂的推理任务越来越需要系统生成的输出，其正确性无法通过与单一参考的精确匹配来判断。自动形式化（Autoformalization, AF）就是一个典型的例子；它要求模型将非正式的数学或逻辑推理转化为可正式检查的对象，但专家验证的形式化结果在超出玩具案例的范围时并不具备可扩展性，并且一个非正式论证可以有多种有效的形式化呈现。因此，进展取决于部分结构化的代理是否可以替代精确参考。我们提出了一种无参考的代理评判框架，用于AF，该框架用每个轴的属性检查向量替代黄金标准匹配。该框架将代理组织为三个结构范围，涵盖所引出对象的全局属性、其子组件内部的每个模块属性，以及重新对齐到非正式源的跨领域属性，并将每个轴聚合成一个裁决向量。该向量驱动一个反思性精炼循环，其中被违反的坐标将控制器引导到匹配的修复目标，因此每次迭代仅改变被判断为错误的部分。在有限的评判噪声下，预期的内在差距几何收缩至一个依赖噪声的平稳状态。在miniF2F、ProofNet、e-SNLI和ProntoQA的七个形式化基础上，精炼始终提升了单次 ICL 基线的通过率，并且在基线有改进空间的基准测试中，每个轴的代理优于匹配的标量代理。因此，结构化的代理判断不仅提供了一个实用的精炼信号，也为在缺乏精确参考时的收敛提供了理论依据。
cs.CL / 137 / 2606.09459
AbstRAG: Learning to Abstract for Retrieval Problems
AbstRAG：为检索问题学习抽象
Abstract
Retrieval-augmented generation often fails when the query, the document evidence, and the user's intent are expressed at different levels of abstraction. A query may ask about a class, a relation, or an event, while the document only states specific instances, indirect framings, or scoped formulations. We define this mismatch as an abstraction gap: the minimal set of typed assumptions required to align query intent with the available evidence. To close this gap, we introduce AbstRAG, which treats abstraction as an explicit retrieval object. AbstRAG decomposes the query--evidence gap into expression, conceptual, intent--evidence, and event-type components, and scores relevance by combining match quality, a query-independent utility prior, and the cost of the required bridges. Its central mechanism is reflective refinement: a critic diagnoses retrieval failures, localizes the failed abstraction operator, proposes a minimal stage-specific patch, and accepts the patch only under sufficiency and compression controls. Across three within-document retrieval benchmarks against seven baselines, AbstRAG outperforms on nDCG@10 in 18 of 21 paired-bootstrap contrasts and improves generation accuracy by 1.9%, 5.2%, and 4.0% across the three benchmarks; ablations confirm that reflective refinement drives most of the retrieval gain and the compression control alone reduces over-expansion false positives from 73.7% to 0% on a stress slice.
Chinese Translation
增强检索生成在查询、文档证据和用户意图以不同抽象层次表达时常常失败。查询可能询问一个类别、一个关系或一个事件，而文档仅陈述具体实例、间接框架或有范围的表述。我们将这种不匹配定义为抽象差距：对齐查询意图与可用证据所需的最小类型假设集合。为了解决这一差距，我们引入了AbstRAG，将抽象视为一个明确的检索对象。AbstRAG将查询-证据差距分解为表达、概念、意图-证据和事件类型组件，并通过结合匹配质量、与查询无关的效用先验和所需桥接的成本来评分相关性。其核心机制是反思性细化：评论者诊断检索失败，定位失败的抽象操作符，提出最小的阶段特定补丁，并仅在充分性和压缩控制下接受该补丁。在针对七个基线的三个文档内检索基准测试中，AbstRAG在21个配对自助对比中有18个在nDCG@10上表现优于其他方法，并在三个基准测试中分别提高了生成准确性1.9%、5.2%和4.0%；消融实验确认反思性细化驱动了大部分检索增益，而压缩控制单独将过度扩展的假阳性从73.7%降低到0%.
cs.CL / 138 / 2606.09461
H2HMem: A Multimodal Memory Benchmark for Agents in Human-Human Interactions
H2HMem：用于人际互动中代理的多模态记忆基准
Abstract
Large language model agents are increasingly deployed in human-human interaction settings, such as meeting assistants and clinical documentation systems, where they must observe conversations and retain information for downstream queries. Unlike traditional human-assistant settings, these environments are inherently multimodal, involve complex discourse phenomena such as anaphora and deixis, and contain asynchronous or conflicting information from multiple participants. However, existing memory benchmarks largely focus on single-user, text-only interactions, failing to capture these challenges. To address this gap, we introduce H2HMem, a Human-to-Human Multimodal Memory Benchmark for evaluating memory capabilities in complex human-human interactions. H2HMem includes both dyadic and multi-party conversations with multimodal information streams, and evaluates agents along three dimensions: memory recall, reasoning, and application. Experiments with advanced agents reveal substantial limitations in constructing, retaining, and utilizing memories across modalities, participants, and sessions, highlighting substantial room for improvement in next-generation LLM agents.
Chinese Translation
大型语言模型代理越来越多地应用于人际互动场景，如会议助手和临床文档系统，在这些场景中，它们必须观察对话并保留信息以供后续查询。与传统的人机助手环境不同，这些环境本质上是多模态的，涉及复杂的语篇现象，如指代和指示，并包含来自多个参与者的异步或冲突信息。然而，现有的记忆基准主要集中于单用户、仅文本的交互，未能捕捉这些挑战。为了解决这一问题，我们引入了H2HMem，一个用于评估复杂人际互动中记忆能力的人际多模态记忆基准。H2HMem包括双人和多方对话，具有多模态信息流，并从记忆回忆、推理和应用三个维度评估代理。对先进代理的实验揭示了在跨模态、参与者和会话中构建、保留和利用记忆方面的重大局限性，突显了下一代大型语言模型代理的显著改进空间。
cs.CL / 139 / 2606.09466
DECSELFMASK: Leveraging Unlabeled Text via Self-Relevance-Guided Masking for Decoder-Only Classification
DECSELFMASK：通过自相关引导掩蔽利用未标注文本进行仅解码器分类
Abstract
Classification tasks require annotated data, which can often be expensive, time-consuming, or even unfeasible to collect. This is the case of the medical domain, where large datasets often have few annotated examples. To address this, we propose DecSelfMask (Decoder Self-learning by Masking), an approach to enhance decoder-only performance on classification tasks. We build on common self-learning approaches by leveraging a model to create training examples from unlabeled data to propose a novel relevance-guided masking strategy. We use relevance attribution methods to determine what portions of unannotated texts are relevant for a task. We then create self-supervised training examples by masking out those portions, training the model to reconstruct them via next-token-prediction. We hypothesize that those examples convey knowledge about the structure and semantics of unannotated data that can be useful for downstream performance. We test our approach on 136 tasks from a collection of 1.9M clinical notes from an Italian hospital. We quantify DecSelfMask's impact on downstream tasks on 5 models of different scales and families, including a probing analysis. Experiments show consistent gains, outperforming standard supervised fine-tuning approaches (+19.9 points in Macro F1), synthetic label generation (+12.5), and continual pretraining (+6.3), as well as common baselines.
Chinese Translation
分类任务通常需要标注数据，而这些数据的收集往往昂贵、耗时，甚至不可行。这在医学领域尤为明显，那里大型数据集通常只有少量标注示例。为了解决这个问题，我们提出了DecSelfMask（解码器自学习通过掩蔽），一种增强仅解码器在分类任务上表现的方法。我们基于常见的自学习方法，利用模型从未标注数据中创建训练示例，提出了一种新颖的相关性引导掩蔽策略。我们使用相关性归因方法来确定未标注文本中哪些部分与任务相关。然后，我们通过掩蔽这些部分创建自监督训练示例，训练模型通过下一个标记预测来重建它们。我们假设这些示例传达了关于未标注数据的结构和语义的知识，这对下游性能是有用的。我们在来自一家意大利医院的190万临床笔记的136个任务上测试了我们的方法。我们量化了DecSelfMask对5种不同规模和类型模型的下游任务的影响，包括探测分析。实验结果显示出一致的提升，超越了标准的监督微调方法（在宏观F1上提升19.9分）、合成标签生成（提升12.5分）和持续预训练（提升6.3分），以及常见基线。
cs.CL / 140 / 2606.09470
A Finetuned SpeechLLM for Joint Multi-Granular L2 Assessment and Natural-Language Rationales
用于联合多粒度L2评估和自然语言推理的微调SpeechLLM
Abstract
Automated L2 speech assessment can assign proficiency labels, but often lacks interpretability. We propose a rubric-guided SpeechLLM for multi-aspect, multi-granular assessment, trained with a hybrid objective combining supervised fine-tuning and Bounded Direct Preference Optimization. The model jointly predicts ordinal labels at the sentence-level (accuracy, fluency, prosody), word/phoneme-level accuracy, and generates a natural-language rationale in the same response. On SpeechOcean762, our approach matches or outperforms single-granularity models while remaining competitive with prior approaches. We analyze rationale reliability along two axes: self-consistency with model predictions and alignment with ground-truth labels, using sentiment consistency (plausibility) and mention-based agreement (faithfulness). Rationales are plausible at the sentence level, but faithfulness degrades at the word/phoneme level: references are sparse and weakly aligned with token-level labels.
Chinese Translation
自动化的L2语音评估可以分配熟练度标签，但通常缺乏可解释性。我们提出了一种基于评分标准的SpeechLLM，用于多方面、多粒度的评估，采用结合监督微调和有界直接偏好优化的混合目标进行训练。该模型在句子级别（准确性、流利度、韵律）共同预测序数标签，词/音素级别的准确性，并在同一响应中生成自然语言推理。在SpeechOcean762数据集上，我们的方法与单粒度模型相匹配或表现更优，同时在与先前方法的竞争中保持竞争力。我们从两个方面分析推理的可靠性：与模型预测的自一致性和与真实标签的对齐，使用情感一致性（合理性）和基于提及的一致性（忠实性）。在句子级别，推理是合理的，但在词/音素级别，忠实性下降：参考稀疏且与标记级别标签的对齐较弱。
cs.CL / 141 / 2606.09483
Memory Beyond Recall: A Dual-Process Cognitive Memory System for Self-Evolving LLM Agents
超越回忆的记忆：自我进化LLM代理的双过程认知记忆系统
Abstract
Long-term memory for an LLM agent is more than retrieving the right passage at the right time. Current memory systems collapse belief revision, causal coupling, and cross-domain abstraction into a single retrieval surface tuned for surface recall, and consequently struggle on implicit personalisation that requires reasoning over how a user has evolved. We propose DCPM, which reorganises agent memory along a cognitive capability hierarchy ascending from raw inputs and atomic facts, through diachronic belief trajectories and identity, to domain schemas, latent intentions and cross-domain patterns. The hierarchy is driven by two processes inheriting the architectural split of dual-process theory: a synchronous daytime writer (System1) that records belief revisions as doubly linked supersedes chains, and an asynchronous nighttime engine (System2) that induces schemas and intentions and sweeps for cross-domain collisions abstracted into higher-level core schemas. On LongMemEval, PersonaMem and PersonaMem-v2, enabling System2 contributes most where the benchmark rewards implicit cross-session inference (up to +5.20 on PersonaMem-v2) and least on span recall, matching the architectural prediction.
Chinese Translation
对于LLM代理而言，长期记忆不仅仅是能够在恰当的时刻检索到正确的段落。目前的记忆系统将信念修正、因果耦合和跨领域抽象压缩为一个单一的检索表面，专注于表面回忆，因此在需要推理用户如何演变的隐式个性化方面面临困难。我们提出了DCPM，它沿着认知能力层级重新组织代理记忆，从原始输入和原子事实开始，经过历时信念轨迹和身份，最终到达领域模式、潜在意图和跨领域模式。该层级由两个过程驱动，继承了双过程理论的架构分裂：一个同步的白天写入者（System1），将信念修正记录为双重链接的超越链；一个异步的夜间引擎（System2），则诱导模式和意图，并扫查抽象为更高层核心模式的跨领域冲突。在LongMemEval、PersonaMem和PersonaMem-v2上，启用System2的贡献最大，尤其是在基准测试奖励隐式跨会话推理的情况下（在PersonaMem-v2上最高可达+5.20），而在跨度回忆方面的贡献最小，这与架构预测相符。
cs.CL / 142 / 2606.09484
Detecting Differences Is Not Understanding Structure: Large Language Models Fail at Graph Isomorphism
检测差异并不等于理解结构：大型语言模型在图同构问题上的失败
Abstract
Large language models (LLMs) have shown impressive performance on diverse reasoning tasks, yet their capacity for structural reasoning in graphs remains unclear. We investigate whether LLMs can genuinely understand graph isomorphism -a fundamental problem in graph theory. While LLMs achieve near-perfect accuracy on isomorphism detection, we show this performance is illusory. When identical graphs are presented with permuted node labels, LLMs fail to identify their isomorphism. This finding suggests that LLMs exploit patterns rather than reasoning about abstract graph structure. Since permutation invariance is a fundamental requirement for valid structural reasoning, these results indicate that success on graph reasoning benchmarks should not be interpreted as evidence of genuine topological understanding.
Chinese Translation
大型语言模型（LLMs）在多种推理任务上表现出色，但它们在图结构推理方面的能力仍不明确。我们研究LLMs是否能够真正理解图同构——这是图论中的一个基本问题。尽管LLMs在同构检测上几乎达到完美准确率，但我们表明这种表现是虚幻的。当相同的图以置换的节点标签呈现时，LLMs无法识别它们的同构性。这一发现表明，LLMs利用模式而不是对抽象图结构进行推理。由于置换不变性是有效结构推理的基本要求，这些结果表明，在图推理基准测试上的成功不应被解读为真正的拓扑理解的证据。
cs.CL / 143 / 2606.09498
Self-Harness: Harnesses That Improve Themselves
自我优化的操作框架：自我改进的框架
Abstract
The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment. Because different models exhibit distinct behaviors, effective harness design is inherently model-specific. Yet agent harnesses are still largely engineered by human experts, a paradigm that scales poorly as modern LLMs become increasingly diverse and rapidly evolving. In this paper, we introduce Self-Harness, a new paradigm in which an LLM-based agent improves its own operating harness, without relying on human engineers or stronger external agents. We operationalize Self-Harness as an iterative loop with three stages: Weakness Mining, which identifies model-specific failure patterns from execution traces; Harness Proposal, which generates diverse yet minimal harness modifications tied to these failures; and Proposal Validation, which accepts candidate edits only after regression testing. We instantiate Self-Harness on Terminal-Bench-2.0 using a minimal initial harness and three base models from diverse families: MiniMax M2.5, Qwen3.5-35B-A3B, and GLM-5. Across all three models, Self-Harness consistently improves performance, with held-out pass rates increasing from 40.5% to 61.9%, 23.8% to 38.1%, and 42.9% to 57.1%, respectively. Qualitative analyses further show that Self-Harness does not simply add generic instructions, but effectively turns model-specific weaknesses into concrete, executable harness changes. These results suggest a path toward LLM-based agents that are not merely shaped by their harnesses, but can also participate in reshaping them.
Chinese Translation
基于大型语言模型（LLM）的智能体的性能由其基础模型和调节其与环境交互的框架共同决定。由于不同模型表现出不同的行为，因此有效的框架设计本质上是特定于模型的。然而，智能体框架仍然主要由人类专家设计，这一范式在现代LLM日益多样化和快速发展的背景下扩展性较差。本文提出了自我优化框架（Self-Harness），这一新范式使得基于LLM的智能体能够在不依赖人类工程师或更强外部智能体的情况下，自我改进其操作框架。我们将自我优化框架操作化为一个包含三个阶段的迭代循环：弱点挖掘（Weakness Mining），从执行轨迹中识别特定于模型的失败模式；框架提案（Harness Proposal），生成与这些失败相关的多样化但最小的框架修改；以及提案验证（Proposal Validation），仅在回归测试后接受候选编辑。我们在Terminal-Bench-2.0上实例化自我优化框架，使用一个最小的初始框架和来自不同系列的三个基础模型：MiniMax M2.5、Qwen3.5-35B-A3B和GLM-5。在所有三个模型中，自我优化框架都一致地提高了性能，保留的通过率分别从40.5%提高到61.9%、23.8%提高到38.1%以及42.9%提高到57.1%。定性分析进一步表明，自我优化框架并不仅仅是添加通用指令，而是有效地将特定于模型的弱点转化为具体的、可执行的框架变更。这些结果表明了一条通向不仅仅受框架影响的基于LLM的智能体的路径，而是能够参与重新塑造框架的智能体。
cs.CL / 144 / 2606.09525
Emergence of Context Characteristics Sensitivity in Large Language Models
大语言模型中上下文特征敏感性的出现
Abstract
During instruction fine-tuning (IFT), large language models (LLMs) learn to follow instructions by using the provided context to answer a query. While prior work has studied how context characteristics correlate with context usage by the LLM, this analysis has been limited to inference time, leaving open how these relationships are acquired in the first place. Here, we measure how models' sensitivity to such characteristics shifts across successive IFT stages: supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning with verifiable rewards (RLVR). Experiments across four models and three datasets show that SFT makes models more likely to use contexts that are easy to understand, such as containing high length, context-query similarity, and fluency. Post-SFT dynamics may either reinforce or resolve these preferences depending on the training dataset. Our findings reveal that context usage is actively reshaped at each IFT stage, and designing a balanced IFT dataset is important in ensuring robust context utilization of instruction-tuned models.
Chinese Translation
在指令微调（IFT）过程中，大语言模型（LLMs）通过使用提供的上下文来回答查询，从而学习遵循指令。虽然先前的研究探讨了上下文特征与LLM上下文使用之间的相关性，但该分析仅限于推理阶段，尚未揭示这些关系是如何在最初形成的。在本研究中，我们测量了模型对这些特征的敏感性在连续IFT阶段的变化：监督微调（SFT）、直接偏好优化（DPO）和带可验证奖励的强化学习（RLVR）。在四个模型和三个数据集上的实验表明，SFT使模型更倾向于使用易于理解的上下文，例如具有较长长度、上下文-查询相似性和流畅性。SFT后的动态可能会根据训练数据集的不同而强化或解决这些偏好。我们的研究结果揭示了在每个IFT阶段上下文使用是如何被积极重塑的，并且设计一个平衡的IFT数据集对于确保指令调优模型的上下文利用能力至关重要。
cs.CL / 145 / 2606.09535
Overcoming Decoder Inconsistencies in Whisper for Dravidian and Low-Resource Languages
克服Whisper在德拉威语和低资源语言中的解码器不一致性
Abstract
Multilingual ASR models such as Whisper perform well on high-resource languages but exhibit substantially higher Word Error Rates (WER) for Dravidian languages compared to Indo-Aryan ones. Through linguistic and dataset analysis, we show that Dravidian languages have longer words, higher vocabulary diversity, and lower repetition, resulting in sparse token distributions and frequent character-level substitution errors. Baseline fine-tuning further reveals decoder imbalance between self-attention (linguistic context) and cross-attention (acoustic cues). Although synthetic token-repetition experiments indicate potential gains, they are impractical. Motivated by these observations, we introduce two decoder-level enhancements: Weighted-Attention, which adaptively balances attention sources, and Self-Conditioning, which reinjects intermediate predictions to improve token consistency. Experiments demonstrate consistent WER reductions for low-resource and agglutinative languages.
Chinese Translation
多语言自动语音识别（ASR）模型如Whisper在高资源语言上表现良好，但在德拉威语的词错误率（WER）显著高于印欧语系语言。通过语言学和数据集分析，我们发现德拉威语具有更长的单词、更高的词汇多样性和更低的重复性，导致稀疏的标记分布和频繁的字符级替换错误。基线微调进一步揭示了自注意力（语言上下文）与交叉注意力（声学线索）之间的解码器不平衡。尽管合成标记重复实验表明潜在的收益，但它们并不实用。基于这些观察，我们引入了两种解码器级增强技术：加权注意（Weighted-Attention），它自适应地平衡注意力来源，以及自条件化（Self-Conditioning），它重新注入中间预测以改善标记一致性。实验表明，对于低资源和黏着语言，WER有持续的降低。
cs.CL / 146 / 2606.09543
From Genes to Tokens: a GWAS-inspired Approach for Interpretable Stylometric Analysis
从基因到标记：一种受全基因组关联研究（GWAS）启发的可解释风格分析方法
Abstract
This short paper introduces a stylometric interpretation method inspired by genome-wide association studies (GWAS). Each "gene" token's association with "phenotype" authorship is tested using logistic regression with multiple-comparison correction. Applied to English, German, and Russian corpora, the method detects statistically significant lexical markers distinctive of individual authors.
Chinese Translation
本文简要介绍了一种受全基因组关联研究（GWAS）启发的风格分析解释方法。通过使用逻辑回归和多重比较校正，测试每个“基因”标记与“表型”作者身份的关联。该方法应用于英语、德语和俄语语料库，能够检测出具有统计显著性的词汇标记，这些标记能够区分个体作者。
cs.CL / 147 / 2606.09553
OpenBibleTTS: Large-Scale Speech Resources and TTS Models for Low-Resource Languages
OpenBibleTTS：面向低资源语言的大规模语音资源和文本到语音模型
Abstract
Recent advances in neural text-to-speech (TTS) and multilingual speech generation have substantially improved synthetic speech quality, yet these gains remain unevenly distributed across the world's languages. Existing models are still dominated by a small set of high-resource languages, while many studies of low-resource TTS are simulated on artificially downsampled high-resource corpora that do not reflect the orthographic variation and limited phonetic coverage encountered in genuinely underrepresented settings. As such, we introduce OpenBibleTTS, which is a large-scale benchmark for low-resource speech synthesis spanning 37 underrepresented languages. Moreover, a systematic comparison of various TTS architectures and large-scale speech generation models is conducted across in-domain Biblical text and out-of-domain material. Results show that no single system dominates across languages and metrics: Gemini-TTS achieves the highest listener ratings on most evaluated languages, but monolingual EveryVoice models trained on OpenBibleTTS remain strongest for intelligibility and are preferred in several African languages, while open from-scratch systems degrade sharply on out-of-domain text, revealing a persistent gap between broad multilingual coverage and reliable synthesis quality in underserved linguistic communities. We complement automatic evaluation with subjective human judgments, and open-source all processed datasets, alignments, and trained models to support future low-resource TTS research.
Chinese Translation
近期在神经文本到语音（TTS）和多语言语音生成领域的进展显著提高了合成语音的质量，但这些提升在全球语言中分布不均。现有模型仍然主要集中在少数高资源语言上，而许多关于低资源TTS的研究则是在人工下采样的高资源语料库上进行的，这些语料库并未反映真实低资源环境中所遇到的正字法变异和有限的音位覆盖。因此，我们推出了OpenBibleTTS，这是一个涵盖37种低资源语言的大规模基准测试，旨在推动低资源语音合成的发展。此外，我们对各种TTS架构和大规模语音生成模型在领域内的圣经文本和领域外材料进行了系统比较。结果表明，没有单一系统在所有语言和指标上占据主导地位：Gemini-TTS在大多数评估语言中获得了最高的听众评分，但在OpenBibleTTS上训练的单语EveryVoice模型在可懂度方面表现最强，并在多个非洲语言中受到偏爱，而从零开始的开放系统在领域外文本上的表现急剧下降，揭示了广泛的多语言覆盖与在服务不足的语言社区中可靠的合成质量之间的持续差距。我们通过主观的人类判断补充了自动评估，并将所有处理过的数据集、对齐和训练模型开源，以支持未来的低资源TTS研究。
cs.CL / 148 / 2606.09570
UXBench: Benchmarking User Experience in AI Assistants
UXBench：人工智能助手用户体验的基准测试
Hong, Mengze, Zeng, Xia, Lei, Zeyang, Wang, Sheng, Zhang, Chen Jason, Jiang, Di, Fu, Taiming, Huang, Jinfeng, Liu, Mengqiao, Chang, Qinghe, Zou, Haosheng, Zhou, Qiongyi, He, Sijun, Xiaoshuai, Chen, Deng, Simon, Huang, Haojing, Li, Zijian, Li, Lucas Mu, Zhang, Fubao, Zhou, Mona, Ma, Wei, Ma, Chenxuan, Zhang, Yuanmeng, Song, Jian, Peng, Minlong, Liang, Di, Chen, Davey
Abstract
As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. We present UXBench, the first user-centric benchmark grounded in real user feedback signals for evaluating preference alignment and dialogue generation. The benchmark consists of three interconnected tasks, UX Judge, UX Eval, and UX Recovery, with 7,400 test instances extracted from over 70K interaction logs of a mainstream Chinese AI assistant. The dataset closely reflects real user distributions, covering 8 scenarios, 83 domains, and diverse failure patterns that pose severe challenges. Extensive experiments on 26 frontier language models provide novel insights into how well models perceive user experience and how improvements in model capability contribute to better dialogue engagement. Through comprehensive analysis of model behavior and performance gaps, we show that user feedback prediction is a learnable capability, where a reward model trained from in-the-wild feedback signals can achieve well-calibrated accuracy. We further document the systematic biases of LLM-as-a-judge evaluation protocols and compare typical response strategies that directly affect user experience. UXBench establishes a new evaluation landscape and calls for greater attention to tailored UX optimization, contributing to a user-centric scaling law that shapes the success of AI assistants.
Chinese Translation
随着人工智能助手每天为数百万用户提供服务，超越一般模型能力的用户体验（UX）评估变得愈发重要。我们提出了UXBench，这是第一个基于真实用户反馈信号的以用户为中心的基准，用于评估偏好对齐和对话生成。该基准由三个相互关联的任务组成：UX Judge、UX Eval和UX Recovery，包含从超过70K的主流中文人工智能助手交互日志中提取的7400个测试实例。该数据集紧密反映了真实用户分布，涵盖8种场景、83个领域以及多种严重挑战的失败模式。在26个前沿语言模型上的广泛实验提供了关于模型如何感知用户体验以及模型能力的提升如何促进更好对话参与的新见解。通过对模型行为和性能差距的全面分析，我们展示了用户反馈预测是一种可学习的能力，基于真实反馈信号训练的奖励模型能够实现良好的校准准确性。我们进一步记录了LLM-as-a-judge评估协议的系统性偏差，并比较了直接影响用户体验的典型响应策略。UXBench建立了一个新的评估框架，并呼吁对定制化UX优化给予更多关注，为塑造人工智能助手成功的以用户为中心的规模法则做出贡献。
cs.CL / 149 / 2606.09577
Code Is More Than Text: Uncertainty Estimation for Code Generation
代码不仅仅是文本：代码生成的不确定性估计
Abstract
Large language models (LLMs) are increasingly deployed as code generators, where silently wrong programs pose real safety and reliability risks. Reliable uncertainty estimation (UE) is essential for selective prediction, human-in-the-loop review, and downstream agentic decisions. Yet most existing code UE methods are inherited from natural language (NL) generation and ignore properties that make code distinct. We argue that code differs from NL in three ways: a single wrong token can break an entire program (token fragility); algorithmic intent and concrete implementation can disagree independently (intent-code gap); and programs can be executed (executability). We instantiate these properties as three orthogonal uncertainty axes: lexical (Top-K token entropy), algorithmic (pseudo-code consistency), and functional (behavioral consistency). Across five code LLMs, our three-axis ensemble improves average AUROC from 0.696 for the strongest NL-derived baseline to 0.776 (+8.1 points). Notably, on Qwen3-14B, our single-pass Top-K token entropy matches the strongest multi-pass baseline while being over 3x cheaper; across models, it remains a competitive low-cost signal. These results suggest that code UE deserves code-specific design rather than direct NL ports.
Chinese Translation
大型语言模型（LLMs）越来越多地被用作代码生成器，其中潜在的错误程序会带来实际的安全性和可靠性风险。可靠的不确定性估计（UE）对于选择性预测、人机协作审查以及下游代理决策至关重要。然而，大多数现有的代码UE方法都是从自然语言（NL）生成中继承而来，忽略了使代码独特的特性。我们认为，代码与NL在三个方面存在差异：单个错误的标记可能会破坏整个程序（标记脆弱性）；算法意图与具体实现可以独立不一致（意图-代码差距）；程序可以被执行（可执行性）。我们将这些特性具体化为三个正交的不确定性轴：词汇（Top-K标记熵）、算法（伪代码一致性）和功能（行为一致性）。在五个代码LLM中，我们的三轴集成将平均AUROC从最强NL衍生基线的0.696提高到0.776（+8.1点）。值得注意的是，在Qwen3-14B上，我们的单次Top-K标记熵与最强的多次基线相匹配，同时成本降低了3倍以上；在不同模型中，它仍然是一个具有竞争力的低成本信号。这些结果表明，代码UE应当进行特定于代码的设计，而不是直接移植NL的方法。
cs.CL / 150 / 2606.09590
Clinically Grounded Privacy Evaluation of Medical LMs
基于临床的医疗语言模型隐私评估
Abstract
Medical language models (LMs) can memorize and reproduce protected health information, but privacy evaluations often focus on recovery of training text rather than disclosure under realistic threat models. We introduce a clinically grounded framework that evaluates leakage along a graded axis of adversarial access, ranging from publicly inferable demographics to leaked note fragments. At each tier, we measure verbatim memorization of patient-specific text and semantic leakage of sensitive diagnoses. Applying the framework to an LM pretrained on 378k clinical notes, we find that routine encounter metadata (i.e. name, date of birth, provider, practice, visit date) elicits high rates of verbatim memorization across a patient's timeline and sensitive-diagnosis recovery (AUROC 0.91 for abortion, 0.81 for HIV). At the same time, exact-match memorization can overstate disclosure: 36% of memorized tokens reflect templated documentation. Our work highlights the risks of training on longitudinal clinical data, providing a practical framework for contextual privacy evaluation of medical LMs.
Chinese Translation
医疗语言模型（LMs）能够记忆和再现受保护的健康信息，但隐私评估通常侧重于训练文本的恢复，而非在现实威胁模型下的泄露。我们引入了一个基于临床的框架，该框架沿着对抗访问的分级轴线评估泄露，从可公开推断的人口统计信息到泄露的笔记片段。在每个层级，我们测量患者特定文本的逐字记忆和敏感诊断的语义泄露。将该框架应用于在378,000份临床笔记上预训练的语言模型，我们发现常规就诊元数据（即姓名、出生日期、提供者、诊所、就诊日期）在患者时间线中引发了高比例的逐字记忆和敏感诊断恢复（流产的AUROC为0.91，HIV的AUROC为0.81）。与此同时，精确匹配的记忆可能会夸大泄露的程度：36%的记忆标记反映了模板化的文档。我们的研究突显了在纵向临床数据上训练的风险，并提供了一个用于医疗语言模型上下文隐私评估的实用框架。
cs.CL / 151 / 2606.09603
Automated IEP Generation from Traditional Chinese Parent-Teacher Interviews via Corpus-Grounded Feature Diffusion
基于语料库的特征扩散自动生成传统中文个性化教育计划
Abstract
Writing Individualized Education Programs (IEPs) is a high-labor, knowledge-intensive document burden; English-language research has demonstrated that generative AI can significantly reduce drafting time, yet automated IEP generation in Traditional Chinese remains virtually unexplored due to domain data scarcity, strict privacy regulations, and the absence of local evaluation benchmarks. We propose a low-resource fine-tuning pipeline centered on Corpus-Grounded Feature Diffusion (CGFD): (1) 25 dual-expert high-score seed transcripts are selected via a tau threshold with flag-aware score caps; (2) a FeatureProfile (sentence length, structure, quantification templates) is extracted from seeds and injected into LLM prompts alongside Verbalized-Sampling-style diversity control to drive diffusion; (3) 15 expert gold seeds are used as diffusion anchors, targeting 585 samples; 567 valid diffusion samples are obtained, yielding a 582-sample training set used to fine-tune Breeze-7B with QLoRA; (4) schema-constrained inference via Grammar-Constrained Decoding (GCD) enforces a hierarchical SMART Goal Ladder schema at inference time. Ablation results on a 55-sample schema stress set reveal an unexpected finding: GCD is counterproductive under Traditional Chinese token budgets -- the no-GCD path achieves 100% schema pass rate at 34% lower median latency, outperforming GCD on both reliability and speed. On the n=10 formal hold-out, the no-GCD inference path achieves BERTScore F1 = 0.779, exceeding GPT-5.4 (0.726), DeepSeek-V3.2 (0.703), Gemini-3-Flash-Preview (0.703), and Llama-4-Maverick (0.700) zero-shot baselines while maintaining fully local, air-gapped inference. This system addresses a gap in Traditional Chinese special-education NLP and offers a scalable, privacy-preserving local inference solution under an industrial engineering paradigm.
Chinese Translation
撰写个性化教育计划（IEPs）是一项高劳动强度、知识密集型的文档负担；英语研究表明生成性人工智能可以显著减少起草时间，但由于领域数据稀缺、严格的隐私法规以及缺乏本地评估基准，传统中文的自动化IEP生成几乎未被探索。我们提出了一种以语料库为基础的特征扩散（Corpus-Grounded Feature Diffusion, CGFD）为中心的低资源微调流程：（1）通过tau阈值和标志感知评分上限选择25个双专家高分种子转录；（2）从种子中提取特征概况（句子长度、结构、量化模板），并与口头采样风格的多样性控制一起注入到大型语言模型（LLM）提示中以驱动扩散；（3）使用15个专家金种子作为扩散锚点，目标为585个样本；获得567个有效扩散样本，形成582个样本的训练集，用于使用QLoRA微调Breeze-7B；（4）通过语法约束解码（Grammar-Constrained Decoding, GCD）进行模式约束推理，在推理时强制执行分层SMART目标阶梯模式。在55个样本的模式压力测试中的消融实验结果揭示了一个意外发现：在传统中文的标记预算下，GCD是适得其反的——无GCD路径在34%更低的中位延迟下实现了100%的模式通过率，且在可靠性和速度上均优于GCD。在n=10的正式保留测试中，无GCD推理路径实现了BERTScore F1 = 0.779，超越了GPT-5.4（0.726）、DeepSeek-V3.2（0.703）、Gemini-3-Flash-Preview（0.703）和Llama-4-Maverick（0.700）的零-shot基准，同时保持完全本地、隔离的推理。该系统填补了传统中文特殊教育自然语言处理的空白，并在工业工程范式下提供了一种可扩展的、保护隐私的本地推理解决方案。
cs.CL / 152 / 2606.09613
AGENTSERVESIM: A Hardware-aware Simulator for Multi-Turn LLM Agent Serving
AGENTSERVESIM：一种硬件感知的多轮 LLM 代理服务模拟器
Abstract
Multi-turn LLM agents interleave model calls with external tool invocations, shifting serving from stateless request processing to stateful program execution. Serving these workloads requires scheduling, KV-cache management, and routing policies that use program-level context, including turn dependencies, tool-induced gaps, and reusable KV state. Evaluating such policies directly on real systems is costly, since each design point may require dedicated accelerator time across arrival rates, model scales, serving-instance counts, and memory hierarchies. Simulation offers a scalable alternative, but existing LLM serving simulators target stateless request-level workloads and therefore omit the core dynamics of agent serving: multi-turn program execution, cross-turn cache locality, and KV-cache residency during tool gaps. We present AGENTSERVESIM, a hardware-aware simulator for multi-turn LLM agent serving. AGENTSERVESIM evaluates serving policies at program granularity through composable modules: a Program Orchestrator preserves program identity and turn order, a Tool Simulator materializes tool-induced gaps, a Session-Aware Router maintains program-to-instance affinity for cache-aware dispatch, and a KV Residency Model tracks policy-defined KV placement across HBM, host DRAM/CXL, and eviction. Across real serving deployments and hardware configurations, AGENTSERVESIM reproduces real-system behavior within 6% error across key performance metrics while running entirely on commodity CPUs. These results show that AGENTSERVESIM enables controlled, repeatable exploration of agent-serving policies without requiring exhaustive deployment on costly accelerators.
Chinese Translation
多轮 LLM 代理将模型调用与外部工具调用交错进行，将服务从无状态请求处理转变为有状态程序执行。服务这些工作负载需要调度、KV 缓存管理和路由策略，这些策略利用程序级上下文，包括轮次依赖、工具引起的间隙和可重用的 KV 状态。在真实系统上直接评估这些策略的成本很高，因为每个设计点可能需要针对到达率、模型规模、服务实例数量和内存层次结构的专用加速器时间。模拟提供了一种可扩展的替代方案，但现有的 LLM 服务模拟器主要针对无状态请求级工作负载，因此忽略了代理服务的核心动态：多轮程序执行、跨轮缓存局部性和工具间隙期间的 KV 缓存驻留。我们提出了 AGENTSERVESIM，这是一种用于多轮 LLM 代理服务的硬件感知模拟器。AGENTSERVESIM 通过可组合模块在程序粒度上评估服务策略：程序调度器（Program Orchestrator）保持程序身份和轮次顺序，工具模拟器（Tool Simulator）实现工具引起的间隙，基于会话的路由器（Session-Aware Router）维护程序与实例的亲和性以实现缓存感知调度，而 KV 驻留模型（KV Residency Model）跟踪政策定义的 KV 在 HBM、主机 DRAM/CXL 和驱逐中的放置。在真实服务部署和硬件配置中，AGENTSERVESIM 在关键性能指标上以 6% 的误差再现了真实系统行为，同时完全在商品 CPU 上运行。这些结果表明，AGENTSERVESIM 使得在不需要在昂贵加速器上进行全面部署的情况下，能够对代理服务政策进行可控、可重复的探索。
cs.CL / 153 / 2606.09632
Civil Court Simulation with Large Language Models
基于大型语言模型的民事法庭模拟
Abstract
Court simulation bridges legal education and judicial practice, yet human-based simulations are costly and difficult to scale. Large language models (LLMs) offer a scalable alternative, but existing court-simulation research mainly focuses on criminal cases. Civil litigation is more common in practice and harder to simulate because its claims, liability, and remedies are more flexible. We present a multi-agent court simulation framework for Chinese civil cases. The framework organizes role-based interaction through a five-stage civil trial procedure and integrates memory module and statute retrieval to support long-process adjudication. Experiments show that the framework produces reliable civil judgments, with clear strengths in liability allocation and multi-item adjudication. Further experiments show that memory quality substantially affects downstream simulation quality. Through a five-layer factor framework, we analyze how legal grounding, information conditions, judicial capability and role orientation, organizational pressure, and social context affect the framework's reliability and behavior. These results support the effectiveness of the proposed framework for civil court simulation. The dataset and code are available at: https://github.com/foggpoy/Civil-Court.
Chinese Translation
法庭模拟连接了法律教育与司法实践，但基于人类的模拟成本高且难以扩展。大型语言模型（LLMs）提供了一种可扩展的替代方案，但现有的法庭模拟研究主要集中在刑事案件上。民事诉讼在实践中更为常见，但由于其索赔、责任和救济更加灵活，模拟起来更为困难。我们提出了一种针对中国民事案件的多智能体法庭模拟框架。该框架通过五阶段的民事审判程序组织基于角色的互动，并集成了记忆模块和法规检索，以支持长期的裁决过程。实验表明，该框架能够生成可靠的民事判决，在责任分配和多项裁决方面具有明显优势。进一步的实验显示，记忆质量对下游模拟质量有显著影响。通过五层因素框架，我们分析了法律基础、信息条件、司法能力与角色导向、组织压力以及社会背景如何影响框架的可靠性和行为。这些结果支持了所提框架在民事法庭模拟中的有效性。数据集和代码可在以下链接获取：https://github.com/foggpoy/Civil-Court。
cs.CL / 154 / 2606.09635
Gradient-Guided Reward Optimization for Inference-time Alignment
基于梯度引导的奖励优化用于推理时对齐
Abstract
Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation. While inference-time alignment methods such as Best-of-$N$ and rejection sampling are widely used, they frame the task as a sampling-intensive, reward-guided search, leading to two key limitations: their performance is bounded by the base model's generation quality, and their reliance on imperfect reward models makes them vulnerable to reward hacking. To address these challenges, we introduce Gradient-Guided Reward Optimization (GGRO), a lightweight inference-time method that performs targeted, minimal intervention during decoding via gradient guidance. Specifically, GGRO monitors token-level entropy to identify high-uncertainty regions indicative of drift or misalignment. Upon detection, it responds by injecting nudging tokens, generated using gradient signals from an off-the-shelf reward model, to steer the generation trajectory rather than merely re-ranking samples. Experiments show that GGRO consistently improves inference-time alignment across safety, helpfulness, and reasoning benchmarks. It also increases coverage of high-quality responses and robustness to reward hacking, with minimal computational overhead. Code is available at https://github.com/lhk2004/GGRO.
Chinese Translation
确保大型语言模型（LLMs）在分布漂移下的可靠性需要推理时适应。尽管推理时对齐方法如 Best-of-$N$ 和拒绝采样被广泛使用，但它们将任务框定为一个采样密集型的、奖励引导的搜索，导致两个主要限制：它们的性能受限于基础模型的生成质量，并且对不完美奖励模型的依赖使其容易受到奖励攻击。为了解决这些挑战，我们提出了基于梯度引导的奖励优化（Gradient-Guided Reward Optimization, GGRO），这是一种轻量级的推理时方法，通过梯度引导在解码过程中进行有针对性的、最小的干预。具体而言，GGRO 监测令牌级别的熵，以识别指示漂移或不对齐的高不确定性区域。一旦检测到，它通过注入使用来自现成奖励模型的梯度信号生成的引导令牌来响应，以引导生成轨迹，而不仅仅是重新排序样本。实验表明，GGRO 在安全性、有效性和推理基准测试中始终改善推理时对齐。它还增加了高质量响应的覆盖率和对奖励攻击的鲁棒性，同时计算开销最小。代码可在 https://github.com/lhk2004/GGRO 获取。
cs.CL / 155 / 2606.09644
Where Does the Answer Come From? Benchmarking View-Level Visual Evidence Identification in Multi-View MLLMs for Autonomous Driving
答案来自哪里？多视角 MLLMs 在自主驾驶中的视图级视觉证据识别基准测试
Abstract
Multimodal large language models (MLLMs) achieve strong results on visual reasoning benchmarks, but answer accuracy alone does not indicate whether a model relied on the correct visual evidence. This gap is particularly important in multi-view driving scenes used for autonomous driving, where a model can produce a plausible answer while grounding it in the wrong camera view. We introduce a multi-view visual question answering benchmark for evaluating evidence-source identification: given six synchronized NuScenes views and a question, the model must identify the supporting camera view and answer the question. The benchmark contains 122 conflict-centric question-answer pairs from 73 scenes, spanning causality, counterfactual reasoning, and intent prediction. View labels are proposed by an automatic conflict-mining pipeline and manually verified by annotators. We evaluate three settings: camera-view selection, oracle QA given the golden view, and joint prediction in which the model selects a view and answers in one pass. Answers are evaluated in both multiple-choice and free-form formats, using exact match for structured predictions and an LLM judge for free-form responses. By explicitly separating visual-source identification from answer correctness, the benchmark exposes grounding failures that answer-only evaluation misses.
Chinese Translation
多模态大型语言模型（MLLMs）在视觉推理基准测试中取得了良好的结果，但仅凭答案的准确性并不能表明模型是否依赖于正确的视觉证据。这一差距在用于自主驾驶的多视角驾驶场景中尤为重要，因为模型可能会生成一个看似合理的答案，但却基于错误的摄像头视图。我们引入了一个多视角视觉问答基准，用于评估证据来源的识别：给定六个同步的 NuScenes 视图和一个问题，模型必须识别支持的摄像头视图并回答问题。该基准包含来自73个场景的122个以冲突为中心的问题-答案对，涵盖因果关系、反事实推理和意图预测。视图标签由自动冲突挖掘管道提出，并由注释员手动验证。我们评估了三种设置：摄像头视图选择、给定黄金视图的神谕问答，以及模型在一次性操作中选择视图并回答的联合预测。答案在多项选择和自由形式两种格式中进行评估，结构化预测使用精确匹配，自由形式响应则使用 LLM 判别器。通过明确将视觉源识别与答案正确性分离，该基准揭示了仅依赖答案评估所忽视的基础失败。
cs.CL / 156 / 2606.09655
Beyond Accuracy: Community Perspectives on Machine Translation
超越准确性：社区对机器翻译的看法
Abstract
Despite remarkable progress in machine translation (MT), non-AI communities have raised growing concerns about MT systems, suggesting a noticeable gap between technical advancement and the needs of real-world users. For instance, while NLP researchers focus on benchmark performance, end users care about ethical concerns, trust, reliability, costs, and more. We argue that listening to various user communities is essential so that research efforts would be directed towards the problems that the communities care about. To this end, we present a large-scale analysis, for the first time, that investigates what four stakeholder communities (AI developers, professional translators, language learners, and language service providers) post about MT technology on social media. To do so, we construct a dataset of 79,286 posts and comments from Reddit, Facebook, Bluesky, and Mastodon from 2019 to 2025, and analyse where these communities disagree, and how and why. Overall, we find that communities often disagree, and even show strong conflicts due to polarised sentiments on topics such as translation quality, efficiency, and reliability. This is because these communities approach these topics differently: the AI community frames them as technical and computational problems, while non-AI (user) communities care more about quality nuances, time savings, user trust, and broader social issues.
Chinese Translation
尽管机器翻译（MT）取得了显著进展，但非人工智能（AI）社区对MT系统提出了越来越多的担忧，表明技术进步与现实用户需求之间存在明显差距。例如，尽管自然语言处理（NLP）研究者关注基准性能，最终用户更关心伦理问题、信任、可靠性、成本等。我们认为，倾听不同用户社区的声音至关重要，以便研究工作能够针对这些社区关心的问题展开。因此，我们首次进行了一项大规模分析，调查四个利益相关者社区（AI开发者、专业翻译人员、语言学习者和语言服务提供者）在社交媒体上对MT技术的讨论。为此，我们构建了一个包含2019年至2025年间来自Reddit、Facebook、Bluesky和Mastodon的79,286条帖子和评论的数据集，并分析了这些社区在何处存在分歧，以及如何和为什么产生这些分歧。总体而言，我们发现社区之间常常存在分歧，甚至由于对翻译质量、效率和可靠性等主题的极化情感而产生强烈冲突。这是因为这些社区对这些主题的看法不同：AI社区将其视为技术和计算问题，而非AI（用户）社区则更关注质量细微差别、时间节省、用户信任和更广泛的社会问题。
cs.CL / 157 / 2606.09659
End-to-End Context Compression at Scale
大规模端到端上下文压缩
Abstract
Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length. Recent techniques to compress the KV cache fall short: they either degrade model quality substantially or require considerable time and compute to compress a single long prompt. Furthermore, many methods require the input to fit within the target model's context window, and are generally incompatible with modern production inference engines. Encoder-decoder compressors, which map a long token sequence to a shorter sequence of latent embeddings consumed by a decoder, are an appealing alternative in principle. However, existing approaches are not competitive with KV cache compression on the accuracy-efficiency frontier. In this work, we revisit encoder-decoder compression and close this gap. We first perform an architecture search, pre-training many variants from scratch to determine how best to design and train encoder-decoder compressors. Guided by our findings, we continually pre-train a family of 0.6B-encoder, 4B-decoder models on over 350B tokens each, at compression ratios of 1:4, 1:8, and 1:16. We introduce Latent Context Language Models (LCLMs), a family of compressors that improve the Pareto frontier across general-task performance, compression speed, and peak memory usage. We demonstrate that LCLMs serve as efficient backbones for long-horizon agents, letting the agent skim through a compressed long context and adaptively expand relevant segments on demand.
Chinese Translation
长上下文语言模型推理受到内存的瓶颈，因为 KV 缓存随着上下文长度的增加而增长。最近的 KV 缓存压缩技术效果不佳：要么显著降低模型质量，要么需要大量时间和计算来压缩单个长提示。此外，许多方法要求输入适应目标模型的上下文窗口，并且通常与现代生产推理引擎不兼容。编码器-解码器压缩器在原则上是一个吸引人的替代方案，它将长的标记序列映射到解码器消耗的较短潜在嵌入序列。然而，现有方法在准确性与效率的边界上与 KV 缓存压缩相比并不具备竞争力。在本研究中，我们重新审视编码器-解码器压缩，并缩小这一差距。我们首先进行架构搜索，从头开始预训练许多变体，以确定如何最好地设计和训练编码器-解码器压缩器。根据我们的发现，我们不断预训练一系列 0.6B 编码器和 4B 解码器模型，每个模型在 350B 以上的标记上进行训练，压缩比为 1:4、1:8 和 1:16。我们引入了潜在上下文语言模型（Latent Context Language Models, LCLMs），这是一系列压缩器，在一般任务性能、压缩速度和峰值内存使用方面改善了帕累托前沿。我们证明了 LCLMs 作为长时间跨度代理的高效骨干，使代理能够快速浏览压缩的长上下文，并根据需要自适应地扩展相关段落。
cs.CL / 158 / 2606.09662
When Built-in Thinking Helps and Hurts: Constraint-Level Error Shifts in Instruction Following
内置思维何时有助于或有害：指令遵循中的约束级错误转移
Abstract
Large reasoning models (LRMs) often improve math and coding performance, but their effect on instruction following is unclear. We study IFEval with Qwen3 models (1.7B-32B), using same-weights Thinking ON/OFF controls; four Hunyuan models provide directional cross-family support. Aggregate pass-rate changes are small (-0.55 to -3.52 pp), yet 10-20% of prompts switch between pass and fail across modes, suggesting that thinking changes the pattern of errors--some prompts improve while others worsen--rather than uniformly degrading performance. Under a post-hoc Qwen3-derived grouping, constraint types separate into Planning (global counting, structure, coordination), which improves at the class level under thinking, and Precision (exact local form), which consistently worsens; the class-level Planning/Precision sign pattern holds directionally for all four Hunyuan models despite Hunyuan's opposite aggregate direction. Thinking also changes final-answer length; matched-length analyses substantially reduce the Precision drop, but a residual penalty remains. Analyzing thinking traces with a cross-encoder relevance metric reveals three patterns: Neutral shows a positive relevance-compliance link (r approximately 0.15); Planning shows near-zero predictive correlation (r approximately 0.02) despite measurable trace engagement, consistent with an execution gap between CE-measured trace relevance and final-answer compliance; Precision shows a small negative correlation (r approximately -0.05), with failing instances having higher mean relevance than passing ones. Activation patching across four model sizes (1.7B-14B) shows that Precision flip instances are more often restored than Planning flip instances (32-58% vs. 14-40% mean layer-restoration), with the largest gap at 14B (about 30 pp).
Chinese Translation
大型推理模型（LRMs）通常能够提高数学和编码的表现，但它们对指令遵循的影响尚不明确。我们研究了使用 Qwen3 模型（1.7B-32B）的 IFEval，采用相同权重的思维开/关控制；四个 Hunyuan 模型提供了跨家族的方向性支持。总体通过率的变化较小（-0.55 至 -3.52 个百分点），然而在不同模式下，10-20% 的提示在通过和失败之间切换，这表明思维改变了错误的模式——一些提示改善而其他提示恶化——而不是均匀地降低表现。在后验的 Qwen3 派生分组下，约束类型分为规划（全局计数、结构、协调），在思维下在类别层面上有所改善，以及精确性（确切的局部形式），则持续恶化；尽管 Hunyuan 的总体方向相反，但类别层面的规划/精确性符号模式在所有四个 Hunyuan 模型中方向一致。思维还改变了最终答案的长度；匹配长度的分析显著减少了精确性的下降，但仍然存在残余惩罚。使用交叉编码器相关性度量分析思维轨迹揭示了三种模式：中性显示出正相关性-合规性联系（r 约为 0.15）；规划显示出近乎零的预测相关性（r 约为 0.02），尽管可测量的轨迹参与度与 CE 测量的轨迹相关性和最终答案合规性之间存在执行差距；精确性显示出小的负相关性（r 约为 -0.05），失败实例的平均相关性高于通过实例。在四种模型规模（1.7B-14B）中进行激活修补显示，精确性翻转实例比规划翻转实例更常被恢复（平均层恢复率为 32-58% 对比 14-40%），在 14B 模型中差距最大（约 30 个百分点）。
cs.CL / 159 / 2606.09697
PsychoSafe: Eliciting Psychologically-Informed Refusals in Large Language Models
PsychoSafe：在大型语言模型中引导心理知情的拒绝
Abstract
Large language models (LLMs) routinely face requests that should be refused, creating a trade-off between helpfulness and harm prevention. However, refusals themselves can be helpful. In high-risk interactions involving crisis, coercion, or escalating intent, blunt non-compliance may prevent direct harm while still failing to support the needs of the person behind the request. We present PsychoSafe, a psychologically-informed refusal framework that reframes refusal as structured supportive communication grounded in evidence-based intervention strategies. To develop PsychoSafe, we construct a corpus of 8019 prompt-response pairs spanning five psychologically salient risk domains and apply prompting and parameter-efficient fine-tuning to Qwen 3.5 27B. On a balanced validation set of 500 prompts, evaluated with an LLM judge and validated through human ratings, PsychoSafe prompting improves overall refusal quality by 28.1% over a generic baseline, with particularly strong gains in external resource referral (+46.8%) and psychological grounding (+34.8%), while preserving downstream performance on non-refusal tasks. Fine-tuning achieves near-perfect refusal and resource-referral rates but reduces response relevance. Additional evaluations on SORRY-Bench and XSTest show strong in-domain robustness but limited out-of-domain generalization, suggesting that future work should diversify fine-tuning data to help models apply interventions selectively rather than schematically.
Chinese Translation
大型语言模型（LLMs）经常面临应当拒绝的请求，这在有用性与防止伤害之间形成了权衡。然而，拒绝本身也可以是有帮助的。在涉及危机、强迫或升级意图的高风险互动中，简单的拒绝可能会防止直接伤害，但仍未能支持请求背后人的需求。我们提出了PsychoSafe，一个心理知情的拒绝框架，将拒绝重新构建为基于证据的干预策略的结构化支持性沟通。为了开发PsychoSafe，我们构建了一个包含8019个提示-响应对的语料库，涵盖五个心理显著的风险领域，并对Qwen 3.5 27B进行了提示和参数高效微调。在一个包含500个提示的平衡验证集上，通过LLM评估和人类评分进行验证，PsychoSafe提示的整体拒绝质量比通用基线提高了28.1%，在外部资源推荐（+46.8%）和心理基础（+34.8%）方面尤其显著，同时保持了非拒绝任务的下游表现。微调实现了近乎完美的拒绝和资源推荐率，但降低了响应的相关性。在SORRY-Bench和XSTest上的额外评估显示出强大的领域内鲁棒性，但领域外泛化有限，这表明未来的工作应多样化微调数据，以帮助模型选择性地而非机械性地应用干预。
cs.CL / 160 / 2606.09701
Learning to Attack and Defend: Adaptive Red Teaming of Language Models via GRPO
学习攻击与防御：通过 GRPO 进行语言模型的自适应红队测试
Abstract
AI red teaming must continually adapt to evolving attackers and defenders. Reinforcement learning offers a promising approach to discovering novel attacks, and co-training methods can produce more robust defenders in tandem. Recent works have demonstrated the efficacy of attacker-defender co-training by applying PPO and DPO, but report that GRPO is unstable in this setting. We introduce AdvGRPO, a co-training framework that makes GRPO viable for joint attacker-defender optimization using dense multi-channel rewards and decoupled advantage normalization. Training progresses through a curriculum from single-turn to closed-loop multi-turn attacks before bootstrapping co-training, where attacker and defender models are updated in alternation. We show that our method can produce highly effective and transferable attacks and that co-trained defenders outperform baselines on safety benchmarks.
Chinese Translation
人工智能红队测试必须不断适应不断变化的攻击者和防御者。强化学习提供了一种有前景的方法来发现新颖的攻击，而协同训练方法可以同时产生更强大的防御者。近期的研究表明，通过应用 PPO 和 DPO，攻击者-防御者协同训练的有效性，但报告称 GRPO 在这种设置中不稳定。我们提出了 AdvGRPO，一种协同训练框架，使 GRPO 能够通过密集的多通道奖励和解耦的优势归一化来实现联合攻击者-防御者优化。训练通过从单轮到闭环多轮攻击的课程逐步推进，然后启动协同训练，其中攻击者和防御者模型交替更新。我们展示了我们的方法可以产生高度有效和可转移的攻击，并且协同训练的防御者在安全基准测试中优于基线。
cs.CL / 161 / 2606.09709
IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking
IS-CoT：通过交错结构思维打破长文本生成崩溃
Abstract
Generating coherent and controllable long-form content remains a persistent challenge for Large Language Models (LLMs). While reasoning-enhanced models have demonstrated success in logic-intensive domains, our evaluation reveals that they suffer from a severe length collapse in open-ended writing, where performance degrades sharply as target lengths exceed 2,000 words. We attribute this failure to the limitation of static hierarchical planning, which struggles to provide dynamic guidance over extended contexts. To bridge this gap, we introduce the Interleaved Structural Chain-of-Thought (IS-CoT) framework. Unlike external agentic workflows, IS-CoT embeds a dynamic Plan-Write-Reflect cycle into the generation process, enabling continuous strategy adaptation and global alignment without additional assistance. Based on this framework, we construct a high-quality dataset of interleaved reasoning traces via a multi-teacher pipeline and train IS-Writer-8B. Experiments demonstrate that IS-Writer-8B achieves state-of-the-art performance on challenging long-form benchmarks (e.g., +3.08 vs. DeepSeek-V3.2 on LongBench-Write), exhibiting robust length compliance and coherence competitive with significantly larger proprietary models.
Chinese Translation
生成连贯且可控的长文本内容仍然是大型语言模型（LLMs）面临的一个持续挑战。尽管增强推理的模型在逻辑密集型领域取得了成功，但我们的评估显示，它们在开放式写作中遭遇了严重的长度崩溃，当目标长度超过2000字时，性能急剧下降。我们将这一失败归因于静态层次规划的局限性，该方法难以在扩展上下文中提供动态指导。为了解决这一问题，我们提出了交错结构思维链（Interleaved Structural Chain-of-Thought，IS-CoT）框架。与外部代理工作流不同，IS-CoT将动态的计划-写作-反思循环嵌入生成过程中，使得在没有额外帮助的情况下能够实现持续的策略适应和全局对齐。基于该框架，我们通过多教师管道构建了高质量的交错推理轨迹数据集，并训练了IS-Writer-8B。实验表明，IS-Writer-8B在具有挑战性的长文本基准测试中实现了最先进的性能（例如，在LongBench-Write上相较于DeepSeek-V3.2提高了3.08），展现出强大的长度合规性和与显著更大专有模型竞争的连贯性。
cs.CL / 162 / 2606.09735
The Neutral Mask: How RLHF Provides Shallow Alignment while Leaving Partisan Structure Intact in a Large Language Model
中立面具：RLHF如何提供浅层对齐，同时保持大型语言模型中的党派结构不变
Abstract
The ambition behind alignment training is to make large language models safe and useful. The primary mechanism, reinforcement learning from human feedback (RLHF), shapes the behavior of deployed language models by aligning them with ``human values.'' Yet the process is opaque. What values are being encoded; whose values are they; and how does RLHF encode them? A growing body of evidence suggests that RLHF produces only functional compliance rather than deep alignment. We offer a mechanistic case study of this phenomenon for partisan political orientation with a comparison of the internal representations of Llama 3.1 8B before and after RLHF. We show that RLHF does not remove the structured partisan direction in the base model. Instead, it compresses the variance of the partisan signal to generate consistently balanced and non-partisan output. Sparse autoencoder decomposition reveals that policy-encoding features, which activate sporadically in the base model, are completely inactive in the Instruct model. Feature-level steering experiments confirm the causal disconnect. RLHF thus encodes a norm of political neutrality, not by erasing the model's knowledge of partisanship, but by severing the causal pathway from partisan geometry to output generation. Importantly, this neutrality is functional, not structural so that the underlying geometry that enables partisan steering remains intact. The mechanisms that bypass RLHF's guardrails, such as inferring and amplifying a user's partisan identity, reactivate partisan generation. If RLHF operates by disconnecting rather than removing value-laden structure, then the same pattern may hold for other value domains, and the aligned model's behavior may be more fragile than its outputs suggest.
Chinese Translation
对齐训练的目标是使大型语言模型安全且有用。主要机制是来自人类反馈的强化学习（RLHF），通过将语言模型与“人类价值观”对齐来塑造其行为。然而，这一过程是不透明的。究竟编码了哪些价值观；这些价值观是谁的；RLHF又是如何编码这些价值观的？越来越多的证据表明，RLHF仅产生功能性合规，而非深层对齐。我们针对党派政治取向提供了这一现象的机制性案例研究，并比较了Llama 3.1 8B在RLHF前后的内部表征。我们展示了RLHF并未消除基础模型中的结构性党派方向。相反，它压缩了党派信号的方差，以生成一致平衡且非党派的输出。稀疏自编码器分解揭示了在基础模型中偶尔激活的政策编码特征在指令模型中完全不活跃。特征级引导实验确认了因果断裂。因此，RLHF编码了一种政治中立的规范，并不是通过抹去模型对党派性的知识，而是通过切断从党派几何到输出生成的因果路径。重要的是，这种中立性是功能性的，而非结构性的，因此使得能够进行党派引导的基础几何保持不变。绕过RLHF防护机制的机制，例如推断和放大用户的党派身份，会重新激活党派生成。如果RLHF是通过断开而非移除价值负载结构来运作的，那么同样的模式可能适用于其他价值领域，且对齐模型的行为可能比其输出所暗示的更为脆弱。
cs.CL / 163 / 2606.09767
Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan
低资源神经机器翻译的数据合成与参数高效微调：以Q'eqchi'玛雅语为例
Abstract
Neural machine translation for digitally low-resource Indigenous languages is often hindered by extreme data scarcity, prompting reliance on extractive web-scraping. To ensure data sovereignty, this study introduces a data synthesis methodology to bootstrap NMT models without scraping target-language parallel text. Focusing on Q'eqchi' Mayan, we transformed community-sourced dictionaries into a massive synthetic corpus, utilizing Parameter-Efficient Fine-Tuning (PEFT) via LoRA adapters on an mT5-base model. In-domain evaluation demonstrates high structural acquisition (BLEU 42.02), proving that synthetic constraints effectively teach complex agglutinative morphology and VOS word order. However, evaluation against an organic glossary reveals a structural-semantic gap (BLEU 0.59), where the model maintains grammatical integrity but lacks the lexical grounding of natural language. The model exhibits overfitting to the constrained structural variance of the synthetic templates; despite high semantic entropy in the pipeline, it struggles with the syntactic fluidity of natural language, forcing organic inputs into rigid learned patterns. Furthermore, an ablation study utilizing a Multi-Task Learning architecture resulted in negative transfer, suggesting that auxiliary tasks competed for limited parameter capacity within the LoRA adapters, causing over-optimization for synthetic markers at the expense of organic flexibility. Ultimately, we establish that synthetic bootstrapping is a highly effective structural primer, but requires authentic data for semantic refinement via Curriculum Learning.
Chinese Translation
针对数字化低资源的土著语言，神经机器翻译常常受到极端数据稀缺的制约，因此依赖于提取式网络抓取。为确保数据主权，本研究提出了一种数据合成方法，以在不抓取目标语言平行文本的情况下启动神经机器翻译模型。我们以Q'eqchi'玛雅语为重点，将社区来源的词典转化为一个庞大的合成语料库，利用基于LoRA适配器的参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）在mT5-base模型上进行训练。领域内评估表明，该模型在结构获取方面表现优异（BLEU 42.02），证明合成约束有效地教授了复杂的粘合形态和动词-宾语-主语（VOS）词序。然而，与有机词汇表的评估显示出结构-语义差距（BLEU 0.59），模型在保持语法完整性的同时，缺乏自然语言的词汇基础。模型对合成模板的受限结构变异表现出过拟合；尽管在管道中具有高语义熵，但在自然语言的句法流畅性上却显得力不从心，迫使有机输入进入僵化的学习模式。此外，利用多任务学习架构的消融研究导致了负迁移，表明辅助任务在LoRA适配器内竞争有限的参数容量，导致对合成标记的过度优化，牺牲了有机灵活性。最终，我们确定合成引导是一种极为有效的结构引导，但需要真实数据通过课程学习进行语义细化。
cs.CL / 164 / 2606.09822
Causally Evaluating the Learnability of Formal Language Tasks
因果评估形式语言任务的可学习性
Abstract
Language models, as multi-task learners, acquire a wide range of abilities during training. A fundamental question is how much task-specific data is needed to learn a given task. Answering this for natural language is difficult: tasks are hard to delineate and can confound one another. To rigorously investigate the relationship between data frequency and learnability, we turn to a controlled setting using formal languages induced from probabilistic finite automata. These serve as a methodological testbed to demonstrate that standard correlational evaluation practices are inherently flawed. To enable causal analysis, we introduce the binning semiring, an algebraic object that lets us control how often a targeted property occurs in a sampled corpus. We formulate the experimental pipeline as a causal graphical model and derive decomposed Kullback-Leibler divergence metrics to measure the learnability of specific sub-tasks. Our experiments show that evaluating learnability without causal intervention leads to incorrect conclusions due to confounders in correlational analysis, and serve as a warning about correlational pitfalls in natural-language settings.
Chinese Translation
语言模型作为多任务学习者，在训练过程中获得了广泛的能力。一个基本问题是学习特定任务需要多少任务特定的数据。对于自然语言来说，回答这个问题是困难的：任务难以明确划分，并且可能相互干扰。为了严格研究数据频率与可学习性之间的关系，我们转向一个受控环境，使用从概率有限自动机诱导的形式语言。这些形式语言作为方法论测试平台，展示了标准的相关性评估实践本质上存在缺陷。为了实现因果分析，我们引入了分箱半环（binning semiring），这一代数对象使我们能够控制目标属性在采样语料库中出现的频率。我们将实验流程构建为因果图模型，并推导出分解的Kullback-Leibler散度度量，以测量特定子任务的可学习性。我们的实验表明，在没有因果干预的情况下评估可学习性会导致由于相关性分析中的混淆因素而得出错误结论，并警示在自然语言环境中存在的相关性陷阱。