arXiv Daily Digest

363

Papers

OLIVE: Online Low-Rank Incremental Learning for Efficient Adaptive Exoskeletons

OLIVE：用于高效自适应外骨骼的在线低秩增量学习

Liu, Dong, Yu, Yanxuan, Lengerich, Ben, Geng, Tony, Wu, Ying Nian

Abstract

Wearable exoskeleton systems hold promise for restoring mobility in individuals with physical impairments, yet most existing controllers rely on static gait policies that lack the ability to adapt to dynamic real-world environments or individual user characteristics. We present \olive (\underline{O}nline \underline{L}ow-rank \underline{I}ncremental Learning for Efficient Adapti\underline{ve} Exoskeletons), a parameter-efficient online adaptation framework that continuously personalizes exoskeleton control during deployment. \olive decomposes the adaptive component of the control policy into a low-rank residual form~$\dW = \At\Bt^\top$ with rank~$r!\ll!\min(d,k)$, reducing online update cost from $\mathcal{O}(dk)$ to $\mathcal{O}(r(d{+}k))$ while preserving the stability of a pretrained base controller~$\Wz$. Parameters are updated via a reward-shaped policy gradient driven purely by on-body sensor feedback (EMG, IMU, vibration), eliminating dependence on offline reference trajectories. A gating mechanism modulates the strength of personalization based on contextual state, and a dynamic rank scheduler adapts the update dimensionality to terrain complexity -- allocating minimal capacity on simple flat terrain and expanding to higher-rank updates on demanding uneven surfaces -- enabling robust performance across diverse activities: flat walking, stair navigation, slopes, and uneven terrain. Experiments on the wearable platform demonstrate that \olive achieves +13, +22, and +15 percentage-point improvements in gait smoothness, effort reduction, and motion stability over the strongest baseline, converging within $\sim$1{,}800 walking steps at 7.4,ms end-to-end latency. Our code implementation is available at https://github.com/FastLM/OLIVE.

Chinese Translation

可穿戴外骨骼系统为恢复有身体障碍人士的移动能力提供了希望，但现有的大多数控制器依赖于静态步态策略，缺乏适应动态现实环境或个别用户特征的能力。我们提出了 extit{OLIVE}（ extbf{O}nline extbf{L}ow-rank extbf{I}ncremental Learning for extbf{E}fficient extit{Adapti} extbf{ve} extbf{E}xoskeletons），这是一个参数高效的在线适应框架，能够在部署期间持续个性化外骨骼控制。OLIVE将控制策略的适应成分分解为低秩残差形式$ extbf{dW} = extbf{A} extbf{B}^ op$，秩$r ext{!} ext{!} ext{min}(d, k)$，将在线更新成本从$ extmathcal{O}(dk)$降低到$ extmathcal{O}(r(d{+}k))$，同时保持预训练基础控制器$ extbf{W}_z$的稳定性。参数通过基于体内传感器反馈（肌电图EMG、惯性测量单元IMU、振动）的奖励形状策略梯度进行更新，从而消除对离线参考轨迹的依赖。门控机制根据上下文状态调节个性化的强度，而动态秩调度器则根据地形复杂性调整更新维度——在简单的平坦地形上分配最小容量，并在复杂的不平坦表面上扩展到更高的秩更新——使其能够在多种活动中实现稳健的性能：平坦行走、楼梯导航、坡度和不平坦地形。可穿戴平台上的实验表明，OLIVE比最强基线在步态平滑度、努力减少和运动稳定性方面分别提高了+13、+22和+15个百分点，且在约1,800步的行走过程中收敛，端到端延迟为7.4毫秒。我们的代码实现可在https://github.com/FastLM/OLIVE获取。

View on arXiv Download PDF AI Translation

cs.RO / 2 / 2606.05236

A New Quaternion-Joint Cable-Driven Redundant Manipulator Configuration and its Control Through FABRIK and Residual Reinforcement Learning

一种新的四元数关节电缆驱动冗余机械手配置及其通过FABRIK和残差强化学习的控制

Pornthisan, Tanapath, Kemthong, Thanapat, Kangsathien, Thanyapisit, Aranchaiya, Pasut, Garcia, Paulo, Sangveraphunsiri, Viboon

Abstract

Robotic arms capable of traversing arbitrary spatial paths, especially in highly obstructed workspaces, are highly desired across several industries. Quaternion-joints have recently empowered a specific class of robotic arms -- cable-driven redundant manipulators -- beyond its prior capabilities. Specifically, quaternion-joints reduce the number of required motors per degree of freedom, paving the way for more compact solutions.An ongoing challenge is that the complexity of the kinematic model of quaternion joints challenges a priori decisions on manipulator configurations and imposes higher computational demands on the control system and its non-linearities amplify all discrepancies between design and physical artifact arising from fabrication imprecision. Here we show a that a 4-segment, 8-joint manipulator can achieve a broader workspace than extant configurations, at lower hardware cost, and that Residual Reinforcement Learning outperforms extant state-of-the-art methods -- specifically, the FABRIK algorithm -- on the control of such manipulator. Our results show that this configuration is more workspace-effective than prior designs, and that Residual Reinforcement Learning outperforms FABRIK by three orders of magnitude on positional and orientational accuracy, effecting precise control of the novel 4-segment, 8-joint manipulator. Additionally, the control implementation is simpler: we describe the complete FABRIK process for control and corresponding learning implementation. Our methodology is applicable to the design of new systems, providing designers with further tools for the development of this class of manipulators and corresponding control systems for novel configurations.

Chinese Translation

能够沿任意空间路径移动的机器人手臂，尤其是在高度受阻的工作环境中，受到多个行业的高度需求。四元数关节最近赋予了一类特定的机器人手臂——电缆驱动冗余机械手——超越其先前能力的可能性。具体而言，四元数关节减少了每个自由度所需的马达数量，为更紧凑的解决方案铺平了道路。一个持续的挑战是，四元数关节的运动学模型复杂性对机械手配置的事先决策构成挑战，并对控制系统施加更高的计算需求，其非线性放大了设计与从制造精度导致的物理工件之间的所有差异。在此，我们展示了一种四段、八关节的机械手能够在更低的硬件成本下实现比现有配置更广泛的工作空间，且残差强化学习在控制该机械手时性能优于现有的最先进方法——特别是FABRIK算法。我们的结果表明，该配置在工作空间效率上超过了以前的设计，并且残差强化学习在位置和方向精度上优于FABRIK三个数量级，从而实现对新型四段、八关节机械手的精确控制。此外，控制实现更为简单：我们描述了完整的FABRIK控制过程及相应的学习实现。我们的方法适用于新系统的设计，为设计师提供了进一步的工具，用于开发该类机械手及其新配置的相应控制系统。

View on arXiv Download PDF AI Translation

cs.RO / 3 / 2606.05248

Inverse Manipulation through Symbolic Planning and Residual Operator Learning

通过符号规划和残余操作学习实现逆向操控

Yildirim, Yigit, Rauso, Giuseppe, Caccavale, Riccardo, Finzi, Alberto

Abstract

Inverting a robotic task requires more than reversing symbolic state transitions or rewinding motor trajectories. In robot manipulation tasks, symbolic inverse plans often fail to fully restore the effects of forward executions under continuous interaction dynamics. We present a hybrid framework for inverse manipulation that derives inverse-skill objectives from STRIPS-like operators automatically extracted from demonstrations through soft geometric predicates. For each extracted operator, we construct an inverse restoration objective that preserves preconditions, restores delete effects, and negates add effects. A task planner first attempts to satisfy this objective using available action primitives. Unresolved symbolic predicates then induce a residual operator learning problem solved through Reinforcement Learning (RL). We evaluate the framework on the ManiSkill3 PushCube task. For a forward pushing skill, the symbolic inverse performs a coarse pick-and-place restoration, while a residual Soft Actor-Critic policy refines the cube pose to satisfy the remaining inverse predicates. Our results show that predicate-derived residual control can turn an approximate symbolic inverse into a physically grounded inverse skill.

Chinese Translation

逆向机械任务不仅仅需要反转符号状态转移或倒带运动轨迹。在机器人操控任务中，符号逆向计划往往无法完全恢复在连续交互动力学下正向执行的效果。我们提出了一种混合框架，通过从演示中自动提取的类似STRIPS的操作符生成逆向技能目标，并利用柔性几何谓词进行处理。对于每个提取的操作符，我们构建一个逆向恢复目标，该目标能够保持前提条件，恢复删除效果，并否定添加效果。任务规划器首先尝试使用可用的动作原语来满足这一目标。未解决的符号谓词随后引发了一个通过强化学习（Reinforcement Learning, RL）解决的残余操作学习问题。我们在ManiSkill3 PushCube任务上评估了该框架。在向前推送技能中，符号逆向执行了粗略的捡放恢复，而残余的Soft Actor-Critic策略则进一步优化了立方体的姿态，以满足剩余的逆向谓词。我们的结果表明，基于谓词的残余控制可以将一个近似的符号逆向转变为一个实实在在的逆向技能。

View on arXiv Download PDF AI Translation

cs.RO / 4 / 2606.05372

Efficient Computation of Distance Functions for Navigation Vector Fields in Lie Groups

在李群中导航向量场的距离函数的高效计算

Gonçalves, Vinicius M., Baião, João, Bartelt, Felipe, Macharet, Douglas G., Freitas, Gustavo M., Azpúrua, Héctor, Pimenta, Luciano C. A.

Abstract

Vector-field-based methods are widely used for robot control and are often applied to the path-tracking problem. Some vector field approaches require repeatedly computing the distance between the robot configuration and the curve, as well as the corresponding closest point. Recently, vector fields have been extended to Lie Groups. In this case, this computation can be expensive, especially when performed at high control frequencies on embedded platforms. This paper proposes a method for efficiently computing the distance between a point and a curve represented as what is called a G-polynomial curve, which is a curve representation that generalizes polynomial curves to matrix Lie groups. The proposed approach exploits the structure of these curves to reduce the problem to a small number of polynomial root-finding computations. Simulation results show that the method significantly reduces computation time while maintaining accuracy compared to existing optimization-based approaches. Practical formulas are also provided for the case of the group SE(3), and the method is validated experimentally on a robotic manipulator. The methodology is implemented in a computational package, available online.

Chinese Translation

基于向量场的方法广泛用于机器人控制，并常被应用于路径跟踪问题。一些向量场方法需要反复计算机器人配置与曲线之间的距离，以及相应的最近点。最近，向量场已扩展到李群。在这种情况下，该计算可能是昂贵的，尤其是在嵌入式平台上以高控制频率执行时。本文提出了一种有效计算点与曲线之间距离的方法，其中曲线表示为所谓的 G-多项式曲线，这是一种将多项式曲线推广到矩阵李群的曲线表示。所提出的方法利用这些曲线的结构，将问题简化为少量多项式根求解计算。仿真结果表明，该方法显著减少了计算时间，同时与现有的基于优化的方法相比，仍能保持准确性。此外，对于群 SE(3) 的情况，提供了实用公式，并在机器人操纵器上对该方法进行了实验验证。该方法已实现为一个计算包，在线提供。

View on arXiv Download PDF AI Translation

cs.RO / 5 / 2606.05395

VASO: Formally Verifiable Self-Evolving Skills for Physical AI Agents

VASO：可正式验证的自我进化技能用于物理AI代理

Yang, Yunhao, Bhatt, Neel P., Wang, Kevin, Tetteh, Samuel, Wang, Zhangyang, Topcu, Ufuk

Abstract

Reusable robot skills are becoming the basic units through which embodied agents turn open-ended instructions into long-horizon physical behavior. We argue that, while foundation models have collapsed the cost of creating these skills, the cost of trusting them has not. Existing skill-evolution loops refine skills through execution feedback, unit tests, environment reward, or LLM self-critique, but these signals provide only trace-level evidence: they show that a skill worked on sampled executions, not that skill-induced plans satisfy temporal safety contracts under untested conditions. We introduce VASO, a framework for verification-guided self-evolution of LLM-generated robot skill contracts. In VASO, each skill is represented as a semantic contract with two coupled interfaces: a formal interface that aligns robot states, observations, and control commands with logical propositions for model checking, and a planner-facing interface that guides executable behavior generation. A model checker first filters logically inconsistent skill contracts, then verifies plans induced by the skill against global and local temporal specifications. When verification fails, VASO translates the counterexample trace into a textual gradient that updates the reusable skill contract while keeping foundation-model weights frozen. On Clearpath Jackal and PX4 quadcopter tasks, VASO reaches 97.2% formal-specification compliance using fewer than 100 optimization samples, outperforming execution-feedback, prompt-optimization, and fine-tuning baselines. To our knowledge, VASO is the first framework that closes the loop between formal verification and self-evolving LLM-generated skills for physical AI agents: formal counterexamples become optimization feedback for reusable robot skill contracts, rather than merely verifying one-off plans, tuning planner prompts, or fine-tuning model weights.

Chinese Translation

可重复使用的机器人技能正成为具身代理将开放式指令转化为长期物理行为的基本单元。我们认为，尽管基础模型降低了创建这些技能的成本，但信任这些技能的成本并未降低。现有的技能进化循环通过执行反馈、单元测试、环境奖励或大语言模型（LLM）的自我批评来优化技能，但这些信号仅提供了微不足道的证据：它们显示技能在抽样执行中有效，而并未表明技能引发的计划在未测试条件下满足时间安全合同。我们提出了VASO，一个用于验证引导的自我进化LLM生成的机器人技能合同的框架。在VASO中，每个技能被表示为一个具有两个耦合接口的语义合同：一个正式接口将机器人状态、观察和控制命令与用于模型检查的逻辑命题对齐；另一个计划者接口指导可执行行为的生成。模型检查器首先过滤逻辑不一致的技能合同，然后根据全局和局部时间规范验证由技能引发的计划。当验证失败时，VASO将反例跟踪转换为文本梯度，更新可重复使用的技能合同，同时保持基础模型权重不变。在Clearpath Jackal和PX4无人机任务中，VASO在使用不到100个优化样本的情况下实现了97.2%的正式规范合规性，优于执行反馈、提示优化和微调基线。根据我们的知识，VASO是首个闭合正式验证与自我进化的LLM生成技能之间循环的框架：正式反例成为可重复使用机器人技能合同的优化反馈，而不仅仅是验证一次性计划、调整计划者提示或微调模型权重。

View on arXiv Download PDF AI Translation

cs.RO / 6 / 2606.05407

MoDex: A Diffusion Policy for Sequential Multi-Object Dexterous Grasping

MoDex：用于顺序多物体灵巧抓取的扩散策略

Lu, Haofei, Liu, Hongjia, Dong, Yifei, Pokorny, Florian T., Lundell, Jens, Kragic, Danica

Abstract

This work addresses sequentially grasping multiple objects with a single dexterous hand without releasing those already held. Most dexterous grasping methods commit all of the hand's degrees of freedom to a single object, underutilizing its dexterity and leaving no redundancy for subsequent grasps. The proposed solution, MoDex, is a diffusion policy that predicts the next gripper pose directly from observations, conditioned on an opposition space and point cloud. The opposition space condition specifies which fingers participate in the current grasp, enabling the gripper to use only a subset of its available degrees of freedom while reserving the remaining degrees of freedom for subsequent grasps. To facilitate sim-to-real transfer, MoDex is trained in two stages: first through imitation learning on expert demonstrations, and subsequently through reinforcement learning fine-tuning, which consistently improves success rates over the pre-trained policy. We evaluate MoDex in simulation on a MuJoCo-based Franka Emika Panda robot equipped with an Allegro Hand and on the corresponding real-world hardware platform. Across both simulation and real-world experiments, MoDex achieves higher success rates than the evaluated learning-based baselines, improving performance by 2.92-17.92% and 6.67-17.78%, respectively. Project page: https://modex2026.github.io/.

Chinese Translation

本研究旨在利用单个灵巧手顺序抓取多个物体，而不释放已经抓住的物体。大多数灵巧抓取方法将手的所有自由度都用于单个物体，未充分利用其灵活性，并且没有为后续抓取保留冗余自由度。提出的解决方案MoDex是一种扩散策略，它直接根据观察预测下一个抓取姿势，基于对抗空间和点云的条件。对抗空间条件指定了当前抓取中参与的手指，使抓取器可以仅使用可用自由度的子集，同时为后续抓取保留剩余自由度。为了促进从模拟到现实的转移，MoDex分两个阶段进行训练：首先通过模仿学习专家演示，其次通过强化学习微调，这一过程始终提高了相较于预训练策略的成功率。我们在配备Allegro手的MuJoCo基础的Franka Emika Panda机器人上以及相应的现实硬件平台上模拟评估MoDex。在模拟和现实实验中，MoDex的成功率均高于所评估的基于学习的基线，分别提高了2.92%-17.92%和6.67%-17.78%。项目页面：https://modex2026.github.io/

View on arXiv Download PDF AI Translation

cs.RO / 7 / 2606.05422

Learning from Demonstrations over Riemannian Manifolds using Neural ODEs: An Extended Abstract

基于神经常微分方程的黎曼流形学习示范：扩展摘要

Espinosa, Diana Cuervo, Anand, Mahathi, Schoellig, Angela P.

Abstract

Learning from demonstratins (LfD) is usually performed over Euclidean spaces, while the robot state, e.g. orientation, naturally evolves over curved spaces. Therefore, to ensure natural, complex motion generation, we investigate learning from demonstrations over Riemannian manifolds that are capable of encoding both position and orientation data. Here, geodesic paths provide for natural motion between two arbitrary points within the manifold. We propose to numerically estimate geodesics via neural ordinary differential equations, mitigating large computational overhead of existing approaches. Finally, these geodesics can be decoded back into the original task space before deploying on the robot. In this extended abstract, we discuss the architecture of our framework, provide some initial insights from our simulation experiments, including comparison to other geodesic computation mechanisms, and discuss the challenges and prospects for future work.

Chinese Translation

学习示范（LfD）通常在欧几里得空间中进行，而机器人状态，例如方向，自然地在曲面上演变。因此，为了确保自然且复杂的运动生成，我们研究在能够同时编码位置和方向数据的黎曼流形上进行学习示范。在这里，测地线为流形内两个任意点之间提供了自然的运动。我们提议通过神经常微分方程对测地线进行数值估计，从而降低现有方法的计算开销。最后，这些测地线可以在部署到机器人之前被解码回原始任务空间。在这篇扩展摘要中，我们讨论了框架的结构，提供了来自我们的仿真实验的一些初步见解，包括与其他测地线计算机制的比较，以及讨论未来工作的挑战和前景。

View on arXiv Download PDF AI Translation

cs.RO / 8 / 2606.05437

Uncertainty-Aware Adaptive Sensor Fusion for Autonomous Navigation

面向自主导航的自适应传感器融合的不确定性感知方法

Alaba, Simegnew Yihunie, Motai, Yuichi

Abstract

This work introduces a hybrid deep learning approach integrated with an Unscented Kalman Filter (UKF) to enhance pose estimation accuracy in Visual-Inertial Odometry (VIO) for autonomous navigation. The proposed model employs a Vision Transformer (ViT) network to effectively capture temporal dependencies from inertial measurement unit (IMU) data and utilizes a Multiscale Convolutional Neural Network (MCNN) to learn optical flow-based motion cues from visual data. An adaptive sensor fusion module dynamically weights IMU and visual features by leveraging estimated uncertainty, thus improving robustness in diverse and challenging environmental conditions. Additionally, a novel uncertainty-aware loss function is proposed to explicitly incorporate prediction uncertainty into the learning process, enabling robust and accurate navigation under noisy, incomplete, or unreliable sensor inputs. Comprehensive evaluations of the KITTI dataset demonstrate that the proposed method significantly outperforms baseline approaches, achieving superior performance in terms of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE). The lightweight and computationally efficient model processes data at 155 FPS on an NVIDIA A100 GPU, making it highly suitable for deployment in resource-constrained autonomous systems.

Chinese Translation

本研究提出了一种与无迹卡尔曼滤波器（UKF）结合的混合深度学习方法，以提高视觉惯性里程计（VIO）在自主导航中的位姿估计精度。所提出的模型采用视觉转换器（ViT）网络，有效捕捉惯性测量单元（IMU）数据的时间依赖性，并利用多尺度卷积神经网络（MCNN）从视觉数据中学习基于光流的运动线索。自适应传感器融合模块通过利用估计的不确定性动态权衡IMU和视觉特征，从而提高在多样且挑战性环境条件下的鲁棒性。此外，提出了一种新的不确定性感知损失函数，明确地将预测不确定性纳入学习过程，使得在噪声、不完整或不可靠的传感器输入下实现鲁棒且准确的导航。对KITTI数据集的全面评估表明，所提出的方法显著优于基线方法，在绝对轨迹误差（ATE）和相对位姿误差（RPE）方面实现了卓越的性能。该轻量级和计算效率高的模型在NVIDIA A100 GPU上以155帧每秒处理数据，非常适合部署于资源受限的自主系统。

View on arXiv Download PDF AI Translation

cs.RO / 9 / 2606.05468

FlowPRO: Reward-Free Reinforced Fine-Tuning of Flow-Matching VLAs via Proximalized Preference Optimization

FlowPRO：通过邻近偏好优化进行无奖励的强化微调流匹配视觉-语言-动作(VLA)模型

Wu, Yihao, Zhang, He, Tan, Junbo, Wang, Xueqian, Zhang, Zhengyou

Abstract

Post-training Vision-Language-Action (VLA) models into policies that can be reliably deployed on real robots remains a major bottleneck. SFT and DAgger exploit failure signals only indirectly, and reward-based RL is bottlenecked by the difficulty of real-world reward design and of training reliable critics. We present FlowPRO, a reward-free offline reinforced fine-tuning framework for flow-matching VLAs. Algorithmically, we propose RPRO (Robotic Flow-matching Proximalized Preference Optimization), a preference-optimization objective tailored to the flow-matching action head of VLA models. RPRO pairs a contrastive optimizer with an explicit proximal regularizer that anchors the absolute magnitude of the implicit reward, thereby eliminating the reward-hacking failure mode of plain Flow-DPO. On the data side, a teleoperated intervention-and-rollback paradigm produces naturally paired positive and negative trajectories $(\tau^w, \tau^l)$ on a real robot from a single operator action; a Smooth Interpolation procedure, combined with batch mixing, then converts these sparse corrections into dense per-state supervision while preserving the base policy's capabilities. On four long-horizon bimanual tasks, FlowPRO attains the highest success rate, outperforming four representative baselines, and ablations confirm the contribution of each loss component.

Chinese Translation

将训练后的视觉-语言-动作(VLA)模型转化为可以可靠部署在真实机器人上的策略仍然是一个主要瓶颈。SFT（顺序微调）和DAgger（数据增强生成策略）仅间接利用失败信号，而基于奖励的强化学习( RL)则受限于真实世界奖励设计的难度和训练可靠评估者的挑战。我们提出了FlowPRO，这是一个用于流匹配VLA的无奖励离线强化微调框架。在算法上，我们提出了RPRO（机器人流匹配邻近偏好优化），这是针对VLA模型流匹配动作头量身定制的偏好优化目标。RPRO将对比优化器与显式邻近正则化器相结合，后者固定了隐性奖励的绝对大小，从而消除了普通Flow-DPO的奖励破解失败模式。在数据方面，远程操作的干预和回滚范式从单个操作者的操作中生成在真实机器人上自然配对的正负轨迹 $( au^w, au^l)$；平滑插值过程结合批量混合，将这些稀疏纠正转化为密集的每状态监督，同时保持基础策略的能力。在四个长时间跨度的双手任务中，FlowPRO达到了最高的成功率，超越了四个具有代表性的基线，并且消融实验确认了每个损失组件的贡献。

View on arXiv Download PDF AI Translation

cs.RO / 10 / 2606.05501

Learning Contact Representation for Leg Odometry

腿部里程计的接触表示学习

Girgin, Emre, Kilic, Cagri

Abstract

The estimation of odometry in legged robots depends on the assumption that the velocity of the foot with respect to the world remains zero during the stance phase. Feedback for the main body velocity is derived from the kinematic serial chain of the feet making accurate leg phase detection is a critical subproblem. A considerable number of studies employ ground reaction force sensors mounted at the tip of the foot to classify, yet these sensors may not be universally available for all legged robots. Additionally, these sensors are often unresponsive to unaccounted disturbances, such as slippage, while the foot remains in contact with the ground. In this study, we propose a self-supervised representation learning framework for contact detection that utilizes the standard sensor set of joint encoders without reliance on force sensor augmentations. We employ learned representations to model the stance and swing phases probabilistically. The experimental results obtained confirm the efficacy of the proposed self-supervised contact detector. Our framework exhibited superior performance in comparison to supervised methods which necessitate sensor set augmentation and labeling, as well as baseline probabilistic approaches. Additionally, we make our code available to the public.

Chinese Translation

腿部机器人中的里程计估计依赖于一个假设，即在支撑阶段，脚相对于世界的速度保持为零。主身体速度的反馈来源于脚的运动学串联链，使得准确的腿部相位检测成为一个关键的子问题。相当数量的研究使用安装在脚尖的地面反作用力传感器进行分类，然而这些传感器可能并不适用于所有腿部机器人。此外，这些传感器通常对未考虑的干扰（如滑动）缺乏响应，尽管脚仍与地面接触。在本研究中，我们提出了一种自监督表示学习框架用于接触检测，该框架利用标准的关节编码器传感器集，而不依赖于力传感器的增强。我们采用学习的表示以概率方式对支撑和摆动阶段进行建模。实验结果证实了所提议的自监督接触检测器的有效性。我们的框架在性能上优于需要传感器集增强和标签的监督方法以及基线概率方法。此外，我们将我们的代码公开提供。

View on arXiv Download PDF AI Translation

cs.RO / 11 / 2606.05588

Auditing Demonstration Curation Metrics: Action-Only Scorers Fail on the Structural Defects That Degrade Imitation Policies

审计演示策展指标：仅基于动作的评分方法未能识别影响模仿策略的结构缺陷

Bedi, Aarav

Abstract

Imitation-learning policies inherit the quality of the demonstrations they are trained on, and a growing set of curation metrics promise to score and filter low-quality demonstrations automatically. These metrics are each validated on different data with different protocols, so it is unclear which of them actually identify the demonstrations that harm a policy. We build a controlled testbed in which demonstration defects are injected with known type, and audit seven curation metrics along two axes: how well each separates defective from clean demonstrations, and whether training a behavior-cloning policy on each metric's curated subset improves task success. We study two defect regimes. Subtle perturbations (correlated action noise, tremor, truncation) are detectable by multivariate outlier scoring and, once removed, recover the full downstream gap. Structural errors, where the demonstration executes a wrong action at a key moment, are invisible to every action-only metric we test, and two of them are inverted: they score defective demonstrations as higher quality and, used for curation, tend to leave the policy at or below the uncurated baseline rather than above it. Only metrics that examine the state trajectory detect structural errors, and even the best of them recovers just a third of the downstream gap. High detection accuracy does not guarantee downstream improvement. We release the testbed and all curation implementations.

Chinese Translation

模仿学习策略继承了其训练演示的质量，而一系列不断增长的策展指标承诺能够自动评分和过滤低质量演示。这些指标在不同的数据和不同的协议下进行了验证，因此尚不清楚它们中的哪一些能够真正识别对策略有害的演示。我们构建了一个受控的测试平台，在该平台中注入已知类型的演示缺陷，并沿着两个维度审计七个策展指标：每个指标在分离缺陷演示与干净演示方面的效果，以及在每个指标的策展子集上训练行为克隆策略是否能够提高任务成功率。我们研究了两种缺陷模式。微妙的扰动（相关动作噪声、颤抖、截断）可以通过多维异常评分被检测到，且一旦去除，完全恢复了下游的性能差距。结构错误，即在关键时刻执行错误动作的演示，无法被我们测试的任何仅基于动作的指标检测到，其中两个指标甚至出现了反转：它们将缺陷演示评分为更高质量，并且在用于策展时，往往使政策的表现保持在未经策展的基线水平或更低，而不是高于基线。只有检查状态轨迹的指标才能检测到结构错误，即使是最好的指标也仅恢复了三分之一的下游性能差距。高检测准确性并不保证下游改进。我们发布了测试平台和所有策展实现。

View on arXiv Download PDF AI Translation

cs.RO / 12 / 2606.05645

Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning

离散WAM：统一的离散视觉-动作令牌编辑用于世界策略学习

Yao, Ziyang, Liu, Haochen, Jiang, Yuncheng, Zhu, Zeyu, Guo, Zibin, Wang, Jingru, Liu, Tianle, Cui, Jianwei, Yang, Kuiyuan, Xie, Hongwei, Zhao, Jingwei, Chen, Guang, Ye, Hangjun

Abstract

Autonomous driving requires reasoning about how ego actions shape the evolution of the surrounding world. However, most end-to-end methods rely on direct state-to-action mappings, capturing correlations without explicitly modeling action-conditioned dynamics. Conversely, continuous-latent world models often lack compositional structure for causal reasoning across counterfactual futures. We introduce Discrete-WAM, a unified latent vision-action world policy that represents future visual states and ego actions as aligned discrete tokens, enabling compositional causal reasoning across alternative futures. Built upon this unified discrete alignment, Discrete-WAM establishes a shared discrete diffusion framework with unified generative tasks, jointly formulating world modeling, world-action policy, and hierarchical decision-enabled policy, supporting compositional generalization across diverse driving scenarios. Experiments on large-scale autonomous-driving benchmarks show that Discrete-WAM achieves competitive performance while supporting controllable generation and counterfactual reasoning, offering a principled path toward more reliable decision-making.

Chinese Translation

自主驾驶需要推理自我动作如何影响周围世界的发展。然而，大多数端到端方法依赖于直接的状态到动作映射，捕捉相关性而未明确建模动作条件下的动态。相反，连续潜在世界模型通常缺乏跨反事实未来进行因果推理的组合结构。我们提出了离散WAM，一种统一的潜在视觉-动作世界策略，将未来视觉状态和自我动作表示为对齐的离散令牌，从而支持跨替代未来的组合因果推理。在这一统一离散对齐的基础上，离散WAM建立了一个共享的离散扩散框架，通过统一的生成任务共同制定世界建模、世界动作策略和分层决策支持策略，支持在多样化驾驶场景中的组合推广。在大规模自主驾驶基准上的实验表明，离散WAM在支持可控生成和反事实推理的同时，实现了竞争性的性能，为更加可靠的决策提供了一条原则路径。

View on arXiv Download PDF AI Translation

cs.RO / 13 / 2606.05660

Safe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulation

安全的具身人工智能在长时间任务中的应用：机器人操控的跨层分析

Kim, Dabin, Park, Daemin, Lee, Sangyub, Kim, Jinsik, Oh, Yeongtak, Shin, Jongho, Yoon, Sungroh

Abstract

Embodied AI systems are increasingly expected to reason and act over extended horizons in physical environments. This growing capability brings safety to the foreground, because failures in the physical world can harm people, damage objects, and disrupt workplaces. Although safe embodied AI has attracted substantial attention, the literature remains fragmented across planning, policy design, and runtime execution. Long-horizon robotic manipulation is a particularly revealing anchor domain for this problem because semantic misgrounding, subtask-level error propagation, execution drift, and contact-rich physical risk can accumulate within the same closed-loop system. This survey therefore provides a structured review of safety in long-horizon robotic manipulation from an embodied AI perspective. We organize the literature by intervention locus, covering planning-time, policy-time, and execution-time safety, and we analyze the strength of the evidence that each line of work provides, distinguishing formal guarantees, statistical support, and empirical safety heuristics. This framework clarifies the distinct roles of backbone capability papers, direct safety mechanisms, and benchmark or evaluation studies, while exposing where current safety claims are well supported and where they remain indirect. We identify persistent gaps, including limited evidence for policy-time safety, weak formal support for contact-rich long-horizon manipulation, immature uncertainty-triggered intervention, and a shortage of manipulation-specific safety benchmarks. We conclude by outlining research directions for cross-layer assurance, evaluation design, and safer deployment of long-horizon robotic agents in real-world settings.

Chinese Translation

具身人工智能系统越来越被期望在物理环境中进行长时间的推理和行动。这一日益增长的能力使安全问题凸显出来，因为在物理世界中的失误可能对人造成伤害、损坏物品并扰乱工作场所。尽管安全的具身人工智能受到了广泛关注，但相关文献在规划、策略设计和运行时执行等方面仍然分散。长时间的机器人操控是一个特别具有启发性的领域，因为语义错误、子任务级别的错误传播、执行偏差以及与接触相关的物理风险可能在同一闭环系统中累积。因此，本综述从具身人工智能的视角对长时间机器人操控中的安全性进行了结构化回顾。我们根据干预位置对文献进行了分类，涵盖了规划时、策略时和执行时的安全性，并分析了每条研究提供的证据强度，区分了形式化保证、统计支持和经验性安全启发。该框架明确了骨干能力论文、直接安全机制以及基准或评估研究的不同角色，同时揭示了当前安全声明的支持力度和不足之处。我们识别出持久的缺口，包括政策时安全的有限证据、对接触丰富的长时间操控的弱形式支持、尚未成熟的不确定性触发干预以及缺乏特定于操控的安全基准。最后，我们总结了跨层保障、评估设计以及在真实世界中更安全部署长时间机器人代理的研究方向。

View on arXiv Download PDF AI Translation

cs.RO / 14 / 2606.05663

Preserving Full 6-DOF Actuation Under Abrupt Total Rotor Failures: Passive Fault-Tolerant Flight Control Using a Biaxial-Tilt Hexacopter

在突发完全转子故障下保持完整6自由度驱动：基于双轴倾斜六旋翼机的被动容错飞行控制

Yang, Yipeng, Tang, Yiqiao, Zhang, Hao, Jiang, Jinqi, He, Jianfeng, Chen, Rumo, Yu, Xinghu, Li, Zhan, Gao, Huijun

Abstract

Conventional multirotors suffer from a rapid collapse of attainable wrench space (AWS) under abrupt total rotor failures, rendering full 6-DOF recovery physically impossible. This paper addresses passive fault-tolerant flight of a biaxial-tilt overactuated hexacopter (BTO) under abrupt total rotor failures that are a priori unknown to the controller. The control design and analysis focus on representative abrupt rotor-failure cases for which the post-failure system remains fully actuated, while no explicit fault detection, isolation, or fault-mode switching is assumed. First, we extend the inscribed-sphere metric of the AWS by incorporating the transient-wrench-jump term, enabling quantitative feasibility assessment under up to three simultaneous rotor failures and benchmarking against uniaxial-tilt and coplanar hexacopters. Second, we develop two computationally efficient passive schemes without relying on fault detection or online optimization. One scheme operates at the controller layer by combining a high-order fully actuated (HOFA) controller with a linear extended state observer (LESO) for lumped-disturbance rejection. The other scheme operates at the allocator layer by using model-reference adaptive control allocation with momentum-based wrench estimation to compensate for control-allocation biases. Simulations and flight experiments validate stable hovering and 6-DOF trajectory tracking under single and multiple rotor failures. Further systematic comparisons confirm that the BTO provides larger recovery margins than uniaxial-tilt and coplanar designs. Additional onboard-sensor-only experiments, including indoor tracking under wind disturbance, outdoor tracking under extreme conditions, narrow-frame traversal, and contact-based aerial writing, further validate the robustness of the proposed framework in complex operational environments.

Chinese Translation

传统多旋翼飞行器在突发完全转子故障下，其可实现的扭矩空间（AWS）会迅速崩溃，从而使得全6自由度的恢复在物理上变得不可能。本文探讨了一种双轴倾斜超驱动六旋翼机（BTO）在控制器未知的突发完全转子故障下的被动容错飞行。控制设计和分析集中于代表性的突发转子故障案例，其在故障后系统仍然保持完全驱动，且不假设任何明确的故障检测、隔离或故障模式切换。首先，我们通过引入瞬态扭矩跃迁项，扩展了AWS的内切球度量，从而使得在最多三次同时转子故障下进行定量可行性评估成为可能，并与单轴倾斜和同平面六旋翼机进行了基准比较。其次，我们开发了两种高效的被动方案，无需依赖故障检测或在线优化。其中一种方案在控制器层面通过结合高阶完全驱动（HOFA）控制器与线性扩展状态观测器（LESO）来实现聚集干扰的抵消。另一种方案则在分配器层面使用基于模型参考的自适应控制分配和动量基础的扭矩估计来补偿控制分配偏差。仿真和飞行实验验证了在单个和多个转子故障下的稳定悬停和6自由度轨迹跟踪。此外，进一步的系统比较确认BTO提供的恢复余量大于单轴倾斜和同平面设计。包含室内风干扰跟踪、极端条件下的室外跟踪、窄框架穿越和基于接触的空中书写等额外的仅基于机载传感器的实验，进一步验证了所提框架在复杂操作环境中的鲁棒性。

View on arXiv Download PDF AI Translation

cs.RO / 15 / 2606.05669

Dynamic Multi-Agent Pickup and Delivery in Robotic Cellular Warehousing Systems

机器人细胞仓储系统中的动态多智能体取货与配送

Ren, Cheng, Li, Ming, Guan, Xinping, Huang, George Q.

Abstract

Robotic Cellular Warehousing Systems (RCWS) give rise to multi-agent pickup and delivery (MAPD) processes in which robots sequentially collect multiple stock-keeping units (SKUs) for each order. Unlike classical MAPD formulations that assume static tasks, real warehouse operations often involve dynamic order evolution, where new SKUs may be appended to an order while it is being executed. Motivated by this practical requirement, this letter formulates the Dynamic Multi-Agent Pickup and Delivery problem considering internal order evolution for the first time. Building on the token passing paradigm, we propose two event-triggered online replanning algorithms. The first, Dynamic Token Passing, performs localized replanning upon order updates through add-order decomposition and priority-based token scheduling while preserving collision-free execution. The second, Cooperative Token Passing, further enables idle robots to opportunistically assist newly added pickups, improving system-level efficiency. Simulation results in RCWS environments demonstrate that the proposed methods significantly reduce order flowtime compared with static and non-cooperative baselines.

Chinese Translation

机器人细胞仓储系统（Robotic Cellular Warehousing Systems，RCWS）引发了多智能体取货与配送（Multi-Agent Pickup and Delivery，MAPD）过程，其中机器人依次为每个订单收集多个库存单位（Stock-Keeping Units，SKUs）。与假设任务静态的经典MAPD模型不同，实际仓库操作往往涉及动态订单演变，在执行过程中可能会向订单中追加新的SKUs。基于这一实际需求，本文首次提出了考虑内部订单演变的动态多智能体取货与配送问题。借助令牌传递范式，我们提出了两种事件触发的在线重规划算法。第一种，动态令牌传递（Dynamic Token Passing），在订单更新时通过添加订单分解和基于优先级的令牌调度进行局部重规划，同时确保无碰撞执行。第二种，协作令牌传递（Cooperative Token Passing），进一步使闲置机器人能够及时协助新增加的取货，提高系统整体效率。在RCWS环境中的模拟结果表明，与静态和非合作基线相比，所提出的方法显著缩短了订单流转时间。

View on arXiv Download PDF AI Translation

cs.RO / 16 / 2606.05687

Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation

加速与扩展面向人形机器人步态与操控的MPC指导强化学习

Li, Junheng, Wu, Liang, Esteban, Sergio A., Yang, Lizhi, Drgoňa, Ján, Ames, Aaron D.

Abstract

In humanoid motion control, model predictive control (MPC) offers physically grounded prediction and constraint handling, while reinforcement learning (RL) enables robust whole-body skills through large-scale simulation. However, using MPC inside RL often requires time-consuming problem construction or excessive training overhead, making such frameworks difficult to justify in practice. This work studies efficient training-time MPC guidance for humanoid locomotion and manipulation, termed MPC-RL. We introduce a centroidal-dynamics MPC reward formulation that leverages guidance from MPC trajectories in training time. To make this practical in massively parallel RL, we develop $\pi^n$MPC, a parallel-in-horizon and construction-free batched GPU MPC solver that operates directly on time-varying dynamics to avoid high memory usage and pre-compilation. Through a variety of comparative studies and hardware validations, we have found that MPC-RL achieves superior performance in locomotion and manipulation skills. The code base is available at https://github.com/junhengl/mpc-rl.

Chinese Translation

在人形运动控制中，模型预测控制（MPC）提供了基于物理的预测和约束处理，而强化学习（RL）则通过大规模模拟使得整体技能更为稳健。然而，在RL中使用MPC往往需要耗时的问题构建或过高的训练开销，使得此类框架在实际应用中难以合理化。本研究探讨了人形机器人步态与操控的高效训练时间MPC指导，称为MPC-RL。我们引入了一种重心动力学MPC奖励公式，该公式利用训练期间MPC轨迹的指导。为了在大规模并行RL中使其可行，我们开发了$ ext{π}^n ext{MPC}$，这是一个并行时间段且无构建的批量GPU MPC求解器，直接在时变动力学上运行，以避免高内存使用和预编译。通过多种比较研究和硬件验证，我们发现MPC-RL在步态与操控技能上取得了优异的表现。代码库可在 https://github.com/junhengl/mpc-rl 获取。

View on arXiv Download PDF AI Translation

cs.RO / 17 / 2606.05699

DexFuture: Hierarchical Future-State Visuomotor Targeting for Bimanual Dexterous Tool Use

DexFuture：双手灵巧工具使用的层次化未来状态视觉运动目标预测

Li, Runfa Blark, Tu, Kuang-Ting, Raicevic, Nikola, Bhatt, Dwait, Liu, Xinshuang, Suzuki, Keito, Lee, Ki Myung Brian, Atanasov, Nikolay, Nguyen, Truong

Abstract

Bimanual dexterous tool use remains challenging for robots due to high-dimensional hand configurations and complex hand-tool-object dynamics and contact. Most existing control policies depend on future configuration references provided from demonstrations, while future action-conditioned world models require slow online planning over high-dimensional action sequences. A significant challenge is generating a dynamically consistent future reference trajectory without relying on privileged states from demonstrations or slow counterfactual planning. We propose DexFuture, a hierarchical system that couples a high-level Future-State Visuomotor Target Predictor with a low-level Target-Conditioned Structured Dexterous Policy. Conditioned on egocentric RGB, proprioceptive and geometric history, the high-level predictor constructs structured hand-tool-object visuomotor embeddings and uses a horizon-conditioned transformer to generate a multi-step future target trajectory. Then, the low-level policy tracks them with a target-conditioned per-link transformer. This hierarchy decouples coarse future reference generation from fine-grained action control, and slow long-horizon semantic prediction from high-frequency execution. On OakInk2 bimanual tool-use tasks, DexFuture achieves 90% of the privileged-oracle performance, compared to 7% for a no-reference policy. DexFuture operates at 60 Hz, approximately 250 times faster than DexWM-style Cross-Entropy Method (CEM) planning with a future action-conditioned world model.

Chinese Translation

双手灵巧工具的使用对于机器人仍然具有挑战性，因为涉及到高维手部配置和复杂的手-工具-物体动态及接触。现有大多数控制策略依赖于来自示例的未来配置参考，而未来动作条件的世界模型则需要对高维动作序列进行缓慢的在线规划。一个显著的挑战是生成动态一致的未来参考轨迹，而不依赖于来自示例的特权状态或缓慢的反事实规划。我们提出了DexFuture，这是一个层次化系统，将高层次的未来状态视觉运动目标预测器与低层次的目标条件结构化灵巧策略相结合。高层预测器基于自我中心的RGB图像、动作感知和几何历史，构建结构化的手-工具-物体视觉运动嵌入，并利用条件的变换器生成多步未来目标轨迹。随后，低层次策略使用目标条件的每个链接变换器来跟踪这些目标。该层次结构将粗略的未来参考生成与细粒度的动作控制，以及缓慢的长视域语义预测与高频执行解耦。在OakInk2双手工具使用任务中，DexFuture达到了特权-预言者性能的90%，而无参考策略仅为7%。DexFuture以60 Hz的速度运行，约为DexWM风格交叉熵方法（CEM）规划在未来动作条件的世界模型下的250倍速度。

View on arXiv Download PDF AI Translation

cs.RO / 18 / 2606.05773

PiL-World: A Chunk-Wise World Model for VLA Policy-in-the-Loop Evaluation

PiL-World：一种用于 VLA 循环内评估的分块世界模型

Ma, Chong, Su, Taiyi, Zhu, Jian, Zhang, Jianjun, Huang, Zitai, Xu, Yi, Wang, Hanli

Abstract

Vision-language-action (VLA) policies operate in a closed loop in real-world robot tasks: a robot observes the scene, executes an action chunk, and conditions its next decision on the resulting observation. However, most existing world models for robot action evaluation are limited to open-loop prediction along pre-collected action trajectories. This prevents them from supporting closed-loop VLA evaluation, where each action chunk must be conditioned on the observation generated by the previous execution. To address this gap, we propose PiL-World, a chunk-wise world model designed for policy-in-the-loop VLA evaluation. Given the current observation and the action trajectory rolled out by a VLA policy, PiL-World generates multi-view future observations that are consistent with the VLA rollout and match the image inputs required by the policy. By alternating between VLA inference and world-model prediction, PiL-World enables closed-loop evaluation without real robot execution at every step. To improve rollout fidelity, PiL-World conditions video generation on action-derived visual control from head-view robot motion and latent histories that encode task execution context, while jointly predicting complementary multi-view observations. Beyond successful teleoperated demonstrations, it also learns from failed execution trajectories, helping the imagined rollouts better match the distribution of real policy executions. We evaluate PiL-World on three real dual-arm manipulation tasks. PiL-World generates imagined rollouts that are highly consistent with real robot executions. More importantly, compared with the baseline, it reduces the error between VLA success rates measured in real-world rollouts and those estimated through closed-loop world-model evaluation from 63.2% to 12.0%.

Chinese Translation

视觉-语言-行动 (VLA) 策略在真实世界机器人任务中以封闭循环的方式运行：机器人观察场景，执行一个动作块，并基于生成的观察结果来决定下一个动作。然而，大多数现有的机器人行动评估世界模型仅限于沿预先收集的动作轨迹进行开放循环预测。这阻碍了它们对封闭循环 VLA 评估的支持，其中每个动作块必须依赖于先前执行生成的观察。为了解决这一问题，我们提出了 PiL-World，一种为循环内 VLA 评估设计的分块世界模型。根据当前观察和由 VLA 策略展开的动作轨迹，PiL-World 生成与 VLA 展开一致的多视角未来观察，并匹配策略所需的图像输入。通过在 VLA 推理和世界模型预测之间交替，PiL-World 实现了无需在每一步进行真实机器人执行的封闭循环评估。为提高展开的保真度，PiL-World 根据来自头部视角的机器人运动的动作导出视觉控制和编码任务执行上下文的潜在历史来条件视频生成，同时共同预测互补的多视角观察。除了成功的远程操作演示之外，它还从失败的执行轨迹中学习，帮助想象的展开更好地匹配真实策略执行的分布。我们在三个真实双臂操作任务上评估了 PiL-World。PiL-World 生成的想象展开与真实机器人执行高度一致。更重要的是，与基线相比，它将真实世界展开中测得的 VLA 成功率与通过封闭循环世界模型评估估计的 VLA 成功率之间的误差从 63.2% 降低至 12.0%。

View on arXiv Download PDF AI Translation

cs.RO / 19 / 2606.05848

Visuotactile and Explicitly Force-Controlled Robotic Ultrasound for Abdominal Volumetric Reconstruction

用于腹部体积重建的视觉触觉与显式力控机器人超声技术

Piedra, Adrian, Jeffrey, R Brooke, Khatib, Oussama

Abstract

In this paper, we present a robotic ultrasound acquisition system that integrates stereo vision, touch-based feedback, and expert-informed strategies to perform autonomous and adaptive abdominal scans. The system records freehand motion and force data from expert radiologists, creating a framework to capture transducer motion, applied forces, and anatomical scanning strategies. This expert data is replayed to replicate characteristic scans with the robot, forming a foundation for further autonomous capabilities. Using stereo vision, the system generates three-dimensional topography maps of the patient's abdomen, which are refined through stiffness measurements at key points to delineate the rib cage boundary. These combined techniques enable the robot to execute two distinct scanning paths: an upward-angled sweep beneath the rib cage to visualize structures near the upper abdomen and a perpendicular sweep across soft tissue regions. A compliant, torque-controlled seven degree-of-freedom robotic manipulator is controlled to maintain consistent probe contact through closed-loop force control over the varied anatomical surfaces. Physical experiments demonstrate that the system achieves high-quality imaging comparable to expert scans while dynamically adapting to patient-specific topographies. Furthermore, the robotic system surpasses expert capabilities by enabling three-dimensional volume acquisition, which enhances diagnostic potential and provides volumetric data for advanced analyses. This work highlights the integration of expert knowledge into autonomous robotic systems and underscores the potential of combining perception-based autonomy with physical reasoning for enhanced diagnostic performance.

Chinese Translation

在本文中，我们提出了一种集成立体视觉、触觉反馈和专家指导策略的机器人超声采集系统，以实现自主和自适应的腹部扫描。该系统记录来自专业放射科医师的自由手运动和力数据，创建了一个捕捉传感器运动、施加力以及解剖扫描策略的框架。这些专家数据被重放，以复制机器人特征性扫描，成为进一步自主能力发展的基础。通过立体视觉，这个系统生成了患者腹部的三维地形图，并通过在关键点的刚度测量进行精细化，以 delineate （划定）肋骨边界。这些综合技术使机器人可以执行两种不同的扫描路径：一种是向上倾斜的扫查，位于肋骨下方以可视化靠近上腹部的结构；另一种是垂直扫查穿过软组织区域。一个顺应的、扭矩控制的七自由度机器人操作臂通过闭环力控技术，以确保在各种解剖表面上保持一致的探头接触。物理实验表明，该系统能够实现与专家扫描相当的高质量成像，同时动态适应患者特有的地形。此外，该机器人系统通过允许三维体积采集超越了专家的能力，提高了诊断潜力，并提供了用于高级分析的体积数据。此项工作突显了将专家知识融入自主机器人系统的整合潜力，强调了将感知驱动的自主性与物理推理结合以提升诊断性能的前景。

View on arXiv Download PDF AI Translation

cs.RO / 20 / 2606.05873

LadderMan: Learning Humanoid Perceptive Ladder Climbing

LadderMan：学习类人感知梯子攀爬

Zhao, Siheng, Zhang, Yuanhang, Lu, Ziqi, Abbeel, Pieter, Duan, Rocky, Sreenath, Koushil, Wang, Yue, Liu, C. Karen, Shi, Guanya

Abstract

Humanoid robots hold great promise for operating in human-centered environments, yet ladder climbing remains one of the most challenging tasks due to sparse footholds and handholds, complex whole-body coordination, and sensitivity to perception and control errors. We present \textbf{LadderMan}, a unified system that enables humanoid robots to robustly climb diverse ladders and perform manipulation under such constrained conditions. Our climbing policy is built on a scalable two-stage learning pipeline, where we use hybrid motion tracking to learn multiple climbing experts from a single reference motion, and distill these experts into a unified depth-based visuomotor climbing policy via hybrid imitation and reinforcement learning. To enable real-world deployment, we leverage vision foundation models to bridge the sim-to-real gap in depth perception. Building on the learned climbing policy, we further train a separate manipulation policy using a dual-agent formulation, allowing stable on-ladder manipulation via teleoperation. Experiments demonstrate that LadderMan achieves robust ladder climbing across a wide range of geometries, successfully transfers to real-world hardware in a zero-shot manner, and supports various manipulation tasks under challenging ladder constraints. Video results are available at https://ladderman-robot.github.io .

Chinese Translation

类人机器人在以人为中心的环境中展现了巨大的潜力，但在梯子攀爬方面仍然是一项极具挑战性的任务，因为它涉及稀疏的脚踏点和手抓点、复杂的全身协调性，以及对感知和控制误差的敏感性。我们提出了 extbf{LadderMan}，这是一个统一的系统，使类人机器人能够在这样受限的条件下有效地攀爬各种梯子并执行操作。我们的攀爬策略基于一个可扩展的两阶段学习流程，其中我们采用混合运动跟踪技术从单一参考动作中学习多个攀爬专家，并通过混合模仿和强化学习将这些专家提炼成一个统一的基于深度的视觉运动攀爬策略。为了实现现实世界的部署，我们借助视觉基础模型来弥合深度感知中的仿真与现实之间的差距。基于所学的攀爬策略，我们进一步使用双代理形式训练了一个单独的操作策略，通过遥操作实现稳定的梯子上的操作。实验表明，LadderMan在各种几何形状的梯子上实现了稳健的攀爬，以零样本方式成功转移到现实世界硬件，并在具有挑战性的梯子约束下支持各种操作任务。视频成果请访问 https://ladderman-robot.github.io 。

View on arXiv Download PDF AI Translation

cs.RO / 21 / 2606.05880

TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion

TAGA：针对地形的主动凝视学习框架，用于可推广的灵活类人 locomotion

Li, Peizhuo, Li, Hongyi, Fan, Mingfeng, Xu, Fangzhou, Liao, Shuhao, Ma, Yuxuan, Zeng, Zicheng, Wang, Ze, Jin, Yongbin, Cao, Yuhong, Wang, Hongtao, Sartoretti, Guillaume

Abstract

Agile humanoid locomotion across diverse challenging terrain demands both wide perceptual coverage and precise local geometry understanding. Motivated by the way humans selectively look at relevant terrain during locomotion, we introduce TAGA, a Terrain-aware Active Gaze learning framework for Attention-based humanoid control. By fusing vision, proprioception, and motion commands, our framework guides the model to learn anticipatory cues and actively attend to specific areas of the height scan, selectively using these informative regions for the downstream network. This adaptively increases the information density of observations under tight onboard computational constraints, thus enabling fine-grained perceptive locomotion over larger-scale terrains. We find that such gaze behaviors can naturally emerge through reinforcement learning alone, without requiring additional supervision or explicit guidance, significantly improve training efficiency. As a result, the trained policy demonstrates robust and generalizable locomotion in simulation and on hardware, including reliable terrain-aware foothold selection, elevated-platform traversal, competitive sparse-foothold traversal, and the largest reported real-world gap traversal distance of 1.2m among perceptive humanoid locomotion systems, while maintaining stability under severe perceptual disturbances and environmental interference.

Chinese Translation

灵活的类人 locomotion 在多样化且富有挑战性的地形上需要广泛的感知覆盖和精确的局部几何理解。受到人类在 locomotion 过程中选择性关注相关地形的启发，我们提出了 TAGA，一个针对地形的主动凝视学习框架，用于基于注意力的类人控制。通过融合视觉、自我感知和运动指令，我们的框架引导模型学习预期提示，并主动关注高度扫描的特定区域，选择性地使用这些信息丰富的区域供下游网络使用。这在紧凑的机载计算约束下自适应地增加了观测的信息密度，从而使我们能够在更大规模的地形上实现细致的感知 locomotion。我们发现这种凝视行为能够通过强化学习自然地出现，无需额外的监督或明确的指导，显著提高了训练效率。因此，训练出来的策略在模拟和硬件中展现出稳健且可推广的 locomotion，包括可靠的地形感知足迹选择、高架平台穿越、竞争性的稀疏足迹穿越，以及在感知类人 locomotion 系统中报告的最大实际间隙穿越距离为 1.2 米，同时在严重的感知干扰和环境干扰下保持稳定性。

View on arXiv Download PDF AI Translation

cs.RO / 22 / 2606.05903

A Novel Method with Encoder-Decoder for Cross-Sensor Adaptation in Surface Shape Sensing with Sparse Strain Sensors

一种基于编码器-解码器的新方法，用于稀疏应变传感器的表面形状感知中的跨传感器适应

Wang, Shuo, Luo, Heng, Jin, Dian, Tao, Xiaoming

Abstract

Performance variations in sensor arrays, caused by intrinsic differences or installation conditions, can lead to inconsistent results during shape sensing. To obtain accurate results, a large amount of data is usually required, and a separate model must be retrained for each sensor array, thereby increasing the cost and time of data acquisition, transmission, and computation. To address this issue, this work proposes an encoder-decoder architecture for surface shape sensing based on sparse strain sensors and further incorporates meta-learning and few-shot adaptation strategies to enable adaptation across different groups of sensor arrays. Experimental results demonstrate that, after the cross-sensor adaptation, a newly deployed sensor array achieves a sensing error of approximately 4.0 mm relying on less than 5.0% newly labeled data and requiring an adaptation time of under 1 second, which represents a substantial improvement from 23.0 mm error without adaptation and 20-minute data collection time required to train a new model. Moreover, the number of points with errors below 5.0 mm increased by more than 65.0%. These results indicate that the proposed method can substantially reduce the cost and training burden of surface shape sensing, and it has broad potential applications in soft robotics and wearable devices.

Chinese Translation

传感器阵列的性能变化，源于固有差异或安装条件，可能会导致形状感知过程中的结果不一致。为了获得准确的结果，通常需要大量数据，并且必须为每个传感器阵列重新训练一个单独的模型，这就增加了数据获取、传输和计算的成本和时间。为了解决这个问题，本研究提出了一种基于稀疏应变传感器的表面形状感知编码器-解码器架构，并进一步结合元学习（meta-learning）和小样本适应（few-shot adaptation）策略，以实现不同组传感器阵列之间的适应。实验结果表明，在跨传感器适应后，新部署的传感器阵列的感知误差约为4.0 mm，仅依赖于不到5.0%的新标注数据，并且适应时间少于1秒，这相比于未适应时的23.0 mm误差和需要20分钟数据收集时间以训练新模型的情况，表现出了显著的改善。此外，误差小于5.0 mm的点数量增加了超过65.0%。这些结果表明，所提出的方法能够显著降低表面形状感知的成本和训练负担，并在软机器人和可穿戴设备中具有广泛的潜在应用。

View on arXiv Download PDF AI Translation

cs.RO / 23 / 2606.05952

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios

通过对抗合成场景学习机器人安全策略

Dorofeev, Nikolai, Odinokov, Alexey, Yavorskiy, Rostislav

Abstract

In this work, we propose an agentic gamification framework for hazard-informed learning of robot safety policies through synthetic scenarios. We model scenario generation as an adversarial game between two agents: a Red Team that explores the space of potential failures by constructing hazardous situations, and a Blue Team that incrementally refines safety policies to prevent them. This iterative process enables efficient discovery of high-risk edge cases that are unlikely to be captured through random simulation or manual enumeration. By combining classical risk modeling with adversarial scenario generation and modern learning paradigms, this work provides a scalable pathway for embedding safety into Physical AI systems operating in complex real-world environments. The paper describes ongoing work. The contribution is a problem formulation and a proposed solution architecture.

Chinese Translation

在本研究中，我们提出了一种基于代理的游戏化框架，通过合成场景实现针对危险的机器人安全策略学习。我们将场景生成建模为两个代理之间的对抗游戏：红队负责通过构建危险情境探索潜在失败的空间，而蓝队则逐步完善安全策略以防止这些情境的发生。这个迭代过程能够有效发现高风险边缘案例，这些案例在随机仿真或手动枚举中不太可能被捕获。通过结合经典风险建模、对抗场景生成和现代学习范式，本研究为在复杂真实环境中运行的物理人工智能系统嵌入安全性提供了一种可扩展的路径。本文描述了正在进行的工作，贡献包括问题的形式化和提出的解决方案架构。

View on arXiv Download PDF AI Translation

cs.RO / 24 / 2606.05960

Towards a Data Flywheel for Embodied Intelligence in Logistics

迈向物流领域体现智能的数据飞轮

Yu, Anlan, Chen, Zaishu, Hong, Zhiqing, Zhang, Daqing

Abstract

Embodied intelligence is moving from laboratory demonstrations toward industrial deployment, with the logistics industry serving as a key application scenario. Learning-based policies offer a promising path beyond traditional perception-planning-control pipelines, but their scalability depends on how embodied data can be collected, organized, and reused. This research studies a data-centric framework for industrial embodied intelligence by constructing a logistics data flywheel. Our framework converts daily operations into reusable data assets, uses World Models to generate reliable supervision for long-tail parcel manipulation, and feeds deployment feedback back into policy improvement. As an initial result, \textit{WM-DAgger} introduces a World-Model-based data aggregation framework that synthesizes out-of-distribution recovery data for robust imitation learning. Building on this result, ongoing work explores how large-scale in-the-wild multimodal data, including labeled human demonstrations, unlabeled operational videos, and system-level robot logs, can be aligned for policy learning and transformed into feedback for continual system improvement.

Chinese Translation

体现智能正在从实验室演示向工业部署转变，物流行业作为一个关键应用场景。基于学习的策略为超越传统的感知-规划-控制流程提供了有希望的路径，但其可扩展性取决于如何收集、组织和重用体现的数据。本研究通过构建一个物流数据飞轮，研究了一个以数据为中心的工业体现智能框架。我们的框架将日常操作转换为可重用的数据资产，使用世界模型（World Models）为长尾包裹操作生成可靠的监督，并将部署反馈反馈到策略改进中。作为初步成果， extit{WM-DAgger} 提出了一个基于世界模型的数据聚合框架，用于合成分布外恢复数据，以实现鲁棒的模仿学习。在此基础上，正在进行的研究探索了如何将大规模真实场景的多模态数据（包括标签化的人类演示、未标签化的操作视频和系统级机器人日志）对齐以进行策略学习，并将其转化为持续系统改进的反馈。

View on arXiv Download PDF AI Translation

cs.RO / 25 / 2606.05979

World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis

统一世界建模、语言推理和动作合成的世界-语言-动作模型

Yang, Yi, Liu, Zhihong, Kou, Siqi, Chen, Yiyang, Hu, Yanzhe, Zhou, Jianbo, Zhao, Boyuan, Wei, Zhijie, Xia, Xiao, Li, Xueqi, Liu, Pengfei, Deng, Zhijie

Abstract

We propose world-language-action (WLA) models as a new class of embodied foundation models. WLA takes textual instructions, images, and robot states as inputs to jointly predict textual subtasks, subgoal images, and robot actions, conjoining the \emph{world modeling interface} to learn from extensive egocentric videos as in the world-action model (WAM) and the \emph{language reasoning} capacities to solve complex long-horizon tasks as in vision-language-action (VLA) models. At the core of WLA lies an \emph{autoregressive (AR)} Transformer backbone, instead of a bidirectional diffusion Transformer as in WAMs, to predict the \emph{next state}, comprising the \emph{semantic-level} textual intention and complementary \emph{fine-grained} physical dynamics. The physical dynamics are supervised by the world modeling objective based on a dedicated World Expert, and are leveraged to ease the characterization of the state-action correlation for the Action Expert. WLA leverages meta-queries to make the world prediction \emph{implicitly} impact the action generation so that the former can be disabled during inference. The world prediction can also be activated to enable test-time scaling for improved robot control. Our WLA-0 prototype, with 2B active parameters, achieves 40 ms per inference on an NVIDIA RTX 5090. Evaluations across simulated and real-world environments demonstrate that WLA-0 achieves state-of-the-art multi-task and long-horizon learning abilities, e.g., 92.94\% success rate on RoboTwin2.0 Clean and 56.5\% success rate on RMBench. WLA-0 also holds the promise to learn novel tasks directly from \emph{cross-embodiment robot videos} without action annotations.

Chinese Translation

我们提出了世界-语言-动作（WLA）模型作为一种新的具身基础模型类别。WLA将文本指令、图像和机器人状态作为输入，联合预测文本子任务、子目标图像和机器人动作，将 extit{世界建模接口}结合以从大量第一人称视频中学习，类似于世界-动作模型（WAM），并具备 extit{语言推理}能力，以解决复杂的长时间任务，如同视觉-语言-动作（VLA）模型。WLA的核心是一个 extit{自回归（AR）} Transformer主干，而不是WAM中采用的双向扩散Transformer，用于预测 extit{下一个状态}，包括 extit{语义层面}的文本意图和互补的 extit{细粒度}物理动态。这些物理动态通过基于专用世界专家的世界建模目标进行监督，并利用这些动态简化行动专家的状态-动作相关性特征化。WLA利用元查询使世界预测在 extit{隐式}影响动作生成，因此在推理过程中可以禁用世界预测。世界预测也可以被激活，以便在测试时进行扩展，从而改善机器人控制。我们的WLA-0原型系统，具有20亿活跃参数，在NVIDIA RTX 5090上的推理时间为每次40毫秒。在仿真和真实环境中的评估表明，WLA-0在多任务和长时间学习能力上达到了最先进的水平，例如，在RoboTwin2.0 Clean上获得92.94 ext{%}的成功率，在RMBench上获得56.5 ext{%}的成功率。WLA-0还承诺能够直接从 extit{跨具身机器人视频}学习新任务，而无需动作注释。

View on arXiv Download PDF AI Translation

cs.RO / 26 / 2606.06011

Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies

将基于模型的控制与多智能体强化学习相结合，以实现多智能体合作团队策略

Llanes, Christian, Jensen, Spencer W., Coogan, Samuel

Abstract

In this work, we propose a framework that combines multi-agent reinforcement learning (MARL) with model-based control to achieve safe, dynamically feasible actions in cooperative multi-agent tasks. Multi-agent reinforcement learning provides the advantage of learning cooperative policies for multi-agent teams from discrete non-differentiable rewards in a long planning horizon. Model-predictive control is robust and offers safe, dynamically feasible actions in a fast replanning framework for short horizons. We propose an algorithm that extends actor-critic model predictive control for MARL which we refer to as multi-agent actor-critic model predictive control (MA-AC-MPC). We demonstrate the capabilities of this algorithm by applying it to a multi-agent pursuit-evasion scenario. Specifically, we compare the evader team's strategy using the MA-AC-MPC model and a multi-layer perceptron model (MA-AC-MLP). The pursuer team uses augmented proportional navigation as it is accepted as an advanced adversarial control law. We also provide an example with a heterogeneous environment where a drone and omni-wheeled rover cooperate to achieve repeatable and successful landing with 100% success rate in hardware for MA-AC-MPC compared to 60% for MA-AC-MLP. We demonstrate the robustness of the proposed MA-AC-MPC algorithm in hardware for both environments.

Chinese Translation

在本研究中，我们提出了一个框架，该框架结合了多智能体强化学习（MARL）和基于模型的控制，以实现安全、动态可行的合作多智能体任务中的动作。多智能体强化学习的优势在于能够从长规划期内的离散非可微奖励中学习多智能体团队的合作策略。模型预测控制具有鲁棒性，并在短期快速重新规划框架中提供安全、动态可行的动作。我们提出了一种算法，扩展了适用于多智能体强化学习的演员-评论家模型预测控制，称之为多智能体演员-评论家模型预测控制（MA-AC-MPC）。我们通过将该算法应用于多智能体追捕-逃避场景来展示其能力。具体而言，我们比较了使用MA-AC-MPC模型的逃避者团队与使用多层感知器模型（MA-AC-MLP）的策略。追击者团队使用增强比例导航，因为它被认为是一种先进的对抗控制律。我们还提供了一个异构环境的示例，其中无人机和全向轮越野车合作，以实现可重复且成功的着陆，MA-AC-MPC在硬件上的成功率为100%，而MA-AC-MLP的成功率为60%。我们展示了所提MA-AC-MPC算法在这两种环境中在硬件上的鲁棒性。

View on arXiv Download PDF AI Translation

cs.RO / 27 / 2606.06033

RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning

RealDexUMI：用于灵巧机器人学习的可穿戴通用操作接口

Xu, Chaoyi, Jiang, Yixuan, Huan, Jiahui, Fu, Yuhui, Zhou, Haoyu, Yuan, Weitian, Yu, Jiayi, Zhang, Wanpeng, Yuan, Haoqi, Lu, Zongqing

Abstract

Learning dexterous manipulation requires demonstrations that preserve fine hand-object interactions while remaining executable at deployment. Existing pipelines either lose deployable dexterity through retargeting or embodiment conversion, or rely on robot-specific teleoperation that is costly to scale and often lacks intuitive, contact-aware control for dexterous data collection. We present RealDexUMI, a wearable universal manipulation interface built around a shared dexterous end-effector module that integrates a lightweight dexterous hand, in-hand vision, and fingertip tactile sensing. A palm-side isomorphic teleoperation glove maps human finger inputs to robot-hand joint commands, enabling real-time, retargeting-free, intuitive, and precise hand control. The shared hand and sensing modules yield zero-gap end-effector data, with matched in-hand observations, tactile signals, contacts, and hand actions between collection and deployment. Across eight real-robot tasks spanning fine-grained, contact-rich, long-horizon, and bimanual manipulation, policies trained on RealDexUMI data achieve an average success rate of 88.75%, generalize to unseen initial poses, and transfer across three embodiments. Website: https://research.beingbeyond.com/realdexumi

Chinese Translation

学习灵巧操作需要在保留精细手-物体交互的同时，确保在部署时依然可执行的演示。现有的工作流程要么通过重新定向或体现转换失去了可部署的灵巧性，要么依赖于特定机器人遥操作，这种方法在扩展时成本高且常常缺乏直观的、关注接触的控制，导致灵巧数据采集效果不佳。我们提出了RealDexUMI，一种可穿戴的通用操作接口，围绕一个共享的灵巧末端执行器模块构建，集成了轻量化的灵巧手、手内视觉和指尖触觉传感。手掌侧等距遥操作手套将人类手指输入映射为机器人手部关节命令，能够实现实时、无重新定向、直观且精确的手部控制。共享的手部和传感模块生成零间隙的末端执行器数据，确保在收集和部署之间匹配手内观察、触觉信号、接触和手部动作。在八个真实机器人任务中，这些任务涵盖了精细、富含接触、长时间跨度和双手操作，基于RealDexUMI数据训练的策略达到了88.75%的平均成功率，能够推广至未见过的初始姿态，并在三种体现之间进行转移。网站：https://research.beingbeyond.com/realdexumi

View on arXiv Download PDF AI Translation

cs.RO / 28 / 2606.06040

Gotta Grow Fast: Design and Benchmarking of a Tip Mount for High-Speed Vine Robots

快速生长：高速藤蔓机器人尖端支架的设计与性能基准测试

Valdivia, Antonio Alvarez, Reeve, Robert, Dhawan, Ankush, McFarland, Ciera, Council, Chad, McGuinness, Margaret, Hanson, Nathaniel

Abstract

Soft, growing vine robots extend through tip eversion, a mechanism that enables navigation through cluttered environments. However, integrating cameras and other sensors at the tip is uniquely challenging because the material forming the tip is constantly renewed as the robot grows. This continual material turnover, combined with friction between internal layers, added tip weight, and fabric constriction, complicates sensor and tool mounting. These limitations hinder the deployment of vine robots for inspection and search tasks, where rapid growth while carrying tip-mounted sensors is essential. In this work, we present a triangular roller tip mount that reduces internal resistance during growth by rolling rather than sliding against the robot body. The design was refined through iterative failure analysis, enabling, for the first time, consistent eversion on a TPU-coated ripstop nylon vine robot. To quantitatively evaluate mount performance, we introduce a custom testbed that isolates tip mounting effects by measuring tail tension during eversion. Comparative experiments across multiple mount variants, including prior designs, show that our triangular roller mount achieves the lowest tail tension and most repeatable growth performance. These results establish both a validated tip mount design and a repeatable benchmarking framework for advancing sensor and tool integration in soft growing robots. CAD for the mount and testbed is available at: https://sprout-mitll.github.io/tip_mounts/.

Chinese Translation

柔软的生长型藤蔓机器人通过尖端翻转这一机制延伸，以便在杂乱的环境中导航。然而，将相机和其他传感器集成到尖端上是一个独特的挑战，因为构成尖端的材料在机器人生长过程中不断更新。这种持续的材料更替，再加上内部层之间的摩擦、尖端重量的增加以及材料收缩，使得传感器和工具的安装变得复杂。这些限制妨碍了藤蔓机器人在巡检和搜索任务中的部署，而快速生长的同时携带尖端安装传感器是至关重要的。在本研究中，我们提出了一种三角滚轮尖端支架，该支架通过滚动而不是滑动与机器人本体接触，从而减少了生长过程中的内部阻力。通过迭代故障分析，我们对设计进行了优化，使得第一次在涂覆TPU的防撕裂尼龙藤蔓机器人上实现了一致的翻转。为了定量评估支架性能，我们引入了一个定制的测试平台，通过测量翻转过程中的尾部张力来隔离尖端安装的影响。针对多种支架变体的比较实验，包括之前的设计，显示我们的三角滚轮支架实现了最低的尾部张力和最具重复性的生长性能。这些结果建立了一个经过验证的尖端支架设计和一个可重复的基准框架，以推动柔性生长机器人中传感器和工具的集成。支架和测试平台的CAD信息可在以下网址获得：https://sprout-mitll.github.io/tip_mounts/。

View on arXiv Download PDF AI Translation

cs.RO / 29 / 2606.06041

Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning

基于零-shot迁移学习的机器人操作任务样本高效低层运动规划

He, Yuanzhi, Romero-Cano, Victor, Patiño, José J., Hernández, Juan David, Sawtell, William, Colombo, Gualtiero

Abstract

As robotic systems become more sophisticated, the growing complexity of their motion planning models and the longer training times pose substantial challenges. Evolutionary algorithms such as the Sample-efficient Cross-Entropy Method (iCEM) have recently demonstrated promising potential for low-level real-time planning by leveraging efficient knowledge reuse strategies to improve performance. Although effective in many control tasks, iCEM's performance can be constrained in more complex scenarios, particularly those requiring stacking, sliding, and shelf placement. In this work, we propose a novel iCEM+TL framework that explicitly leverages Transfer Learning (TL), where key iCEM parameters are transferred from simpler upstream tasks to guide more complex downstream tasks. Additionally, we applied Reward Redesign (RR) through task decomposition for stacking objects and shelf placement to optimize task-specific performance. Results from the simulation show that our framework achieves success rate improvements of up to 23%. The framework is further validated on a real Franka Emika robot in a stacking task, demonstrating its practical feasibility for real-world deployment.

Chinese Translation

随着机器人系统变得越来越复杂，其运动规划模型的复杂性和更长的训练时间带来了巨大的挑战。进化算法，如样本高效交叉熵方法（iCEM），最近通过利用高效的知识重用策略来提升性能，展示了在低层实时规划中的良好潜力。尽管在许多控制任务中效果显著，但在更多复杂场景下，特别是需要堆叠、滑动和架子放置的场景中，iCEM的性能可能受到限制。在本研究中，我们提出了一种新颖的iCEM+TL框架，明确利用了迁移学习（TL），通过将关键的iCEM参数从较简单的上游任务转移到更复杂的下游任务来指导其执行。此外，我们通过任务分解应用了奖励重设计（RR）来堆叠物体和进行架子放置，以优化任务特定的性能。仿真结果表明，我们的框架在成功率上提高了最多23%。该框架还在真实的Franka Emika机器人进行堆叠任务中得到进一步验证，展示了其在实际部署中的可行性。

View on arXiv Download PDF AI Translation

cs.RO / 30 / 2606.06049

L-SDPPO: Policy Optimization of Spiking Diffusion Policy for Intra-vehicular Robotic Manipulation

L-SDPPO：针对船内机器人操作的脉冲扩散策略的政策优化

Zhang, Liwen, Zhou, Dong, Sun, Guanghui, Zheng, Yifei, Hu, Yuhui, Ouyang, Kaihong, Zhao, Zuoquan

Abstract

Intra-vehicular robots in spacecraft help reduce astronaut workload and improve mission efficiency. Recent research focuses on using deep learning methods to achieve the acute control required for operations in these complex environments. However, objects exhibit unpredictable, unconstrained drift without gravitational damping. These factors demand robustness against complex multimodal action distributions. Diffusion policies (DP) can model these complex actions, but their iterative sampling process consumes too much energy for the limited power budgets of spacecraft. We therefore propose a low-energy intra-vehicular robotic manipulation framework, L-SDPPO, in which the Spiking Diffusion Policy (SDP) is optimized with a reinforcement learning (RL) algorithm. Furthermore, to address the insufficient perception of dynamic spatiotemporal features in microgravity, we propose the statedependent latency injection (SDLI) mechanism, which mimics biological neural delays to dynamically regulate the timing of input information. Evaluation on five representative intra-vehicular daily tasks (e.g., hatch opening and precision container capping) shows that our method consistently achieves higher success rates and lower energy consumption, compared to the state-of-the-art robotic manipulation methods. These results demonstrate our method is a viable intra-vehicular robotic manipulation method.

Chinese Translation

航天器内的机器人帮助减少宇航员的工作负荷，提高任务效率。最近的研究侧重于使用深度学习方法来实现这些复杂环境中所需的敏锐控制。然而，物体在没有重力阻尼的情况下表现出不可预测和不受约束的漂移。这些因素要求对复杂的多模态动作分布具有鲁棒性。扩散策略（Diffusion Policies, DP）可以建模这些复杂动作，但其迭代采样过程在航天器有限的能量预算下消耗过多能量。因此，我们提出一个低能耗的船内机器人操作框架L-SDPPO，在其中通过强化学习（Reinforcement Learning, RL）算法优化脉冲扩散政策（Spiking Diffusion Policy, SDP）。此外，为了解决在微重力条件下对动态时空特征的感知不足的问题，我们提出了状态依赖延迟注入（Statedependent Latency Injection, SDLI）机制，模拟生物神经延迟以动态调节输入信息的时机。在五个具有代表性的船内日常任务（如舱口打开和精确容器封闭）的评估中，我们的方法与最先进的机器人操作方法相比，始终实现了更高的成功率和更低的能量消耗。这些结果证明了我们的方法是一种可行的船内机器人操作方法。

View on arXiv Download PDF AI Translation

cs.RO / 31 / 2606.06061

A Conversational Framework for Human-Robot Collaborative Manipulation with Distributed Generative AI models

一种基于分布式生成性人工智能模型的人机协作操作对话框架

Kakroudi, Arash Ghasemzadeh, Pieters, Roel

Abstract

This paper presents a distributed conversational framework for human-robot collaborative manipulation that integrates local language and vision-language models (VLMs) with a Robot Operating System 2 (ROS 2)-based execution stack. Language understanding, visual grounding, orchestration, and motion execution run as separate ROS 2 nodes, enabling flexible deployment across distributed hardware while maintaining a responsive control loop. From free-form user commands, the system generates structured action requests for pick, place, and handover. It uses a VLM to return image-space targets, which are converted into metric robot-frame goals using depth and calibration. A web dashboard exposes intermediate intent and grounding overlays (pixel, depth, and robot-frame) and requires explicit operator confirmation before any motion is executed. Experiments on a Franka FR3 platform evaluate end-to-end task reliability and latency under increasing working table scene ambiguity and compare alternative LLM/VLM configurations in the same pipeline. Code and full documentation are available at [github.com/cogrob-tuni/franka-llm](https://github.com/cogrob-tuni/franka-llm).

Chinese Translation

本文提出了一种分布式对话框架，用于人机协作操作，集成了本地语言和视觉-语言模型（VLMs），以及基于机器人操作系统2（ROS 2）的执行堆栈。语言理解、视觉定位、协调和运动执行作为独立的ROS 2节点运行，实现了在分布式硬件上的灵活部署，同时保持响应的控制循环。系统从自由形式的用户指令生成结构化的操作请求，包括抓取、放置和移交。它使用VLM返回图像空间目标，并通过深度和校准将其转换为度量机器人坐标系目标。一个网页仪表板展示了中间意图和定位叠加（像素、深度和机器人坐标系），并在执行任何运动之前需要明确的操作员确认。在Franka FR3平台上的实验评估了在工作台场景模糊性增加情况下的端到端任务可靠性和延迟，并比较了相同管道中不同的LLM/VLM配置。代码和完整文档可在[github.com/cogrob-tuni/franka-llm](https://github.com/cogrob-tuni/franka-llm)获取。

View on arXiv Download PDF AI Translation

cs.RO / 32 / 2606.06077

3D Underwater Path Planning via Generative Flow Field Surrogates

基于生成流场替代物的三维水下路径规划

Cooper-Baldock, Zachary, Santos, Paulo E., Brinkworth, Russell S. A., Sammut, Karl

Abstract

Autonomous underwater vehicle (AUV) launch and recovery (LAR) into the hull of an advancing host platform requires traversal of a complex, three-dimensional propeller wake whose hydrodynamic structure cannot be characterised by a uniform current model. High-fidelity Reynolds-Averaged Navier-Stokes (RANS) Computational Fluid Dynamics (CFD) simulations resolve this structure with sufficient accuracy for path planning, but their computational cost renders them impractical for onboard use. We address this gap by integrating two conditional generative adversarial network (cGAN) architectures -- a regularised PatchGAN and a 2D3DGAN with self-attention -- as drop-in replacements for RANS CFD data within a three-dimensional, energy-weighted A* path planning framework. Both generators are driven by a hierarchical pipeline that synthesises full $128^3$ voxel flow field volumes from scalar operating condition inputs alone, with end-to-end inference times of approximately 28-146 $\mu$s, compared to hours for a single RANS computation. We benchmark all four environmental knowledge levels: uniform current, ground-truth CFD, PatchGAN, and 2D3DGAN~SA across 19,800 independently generated trajectories spanning 550 distinct flow conditions. Full CFD wake knowledge reduces energy expenditure by 5.7-12.5% and high-velocity wake-core encounters by up to 77.8% relative to uniform-current planning, with both benefits scaling with operating severity. The cGAN surrogates recover approximately 45-60% of the CFD energy benefit and high-velocity cell avoidance benefit while operating at inference speeds compatible with edge device use. These results provide the first systematic quantification of the downstream path planning value of cGAN-predicted hydrodynamic fields in a three-dimensional maritime robotics application.

Chinese Translation

自主水下航行器（AUV）在推进主平台的船体内发射和回收（LAR）要求穿越复杂的三维螺旋桨尾流，而该尾流的水动力结构无法用均匀流模型来描述。高保真雷诺平均纳维-斯托克斯（RANS）计算流体动力学（CFD）模拟能够以足够的精度解析这种结构，以用于路径规划，但其计算成本使得在船上的使用变得不切实际。我们通过将两种条件生成对抗网络（cGAN）架构 - 正则化的PatchGAN和带自注意力机制的2D3DGAN - 集成到三维能量加权A*路径规划框架中，来填补这一空白。这两种生成器通过一个分层管道驱动，该管道仅使用标量操作条件输入合成完整的$128^3$体素流场体积，端到端推理时间约为28-146微秒，而单次RANS计算需要数小时。我们在19,800条独立生成的轨迹和550种不同流动条件中基准测试了所有四种环境知识水平：均匀流、真实CFD、PatchGAN和2D3DGAN~SA。全面了解CFD尾流知识可将能量消耗减少5.7-12.5%，并将高速度尾流核心遭遇的概率降低最多77.8%，相较于均匀流规划，这两项收益在操作强度增强时也有所增加。cGAN替代物在保证推理速度适合边缘设备使用的同时，恢复了约45-60%的CFD能量收益和高速度单元避免收益。这些结果首次系统量化了在三维海洋机器人应用中，cGAN预测的水动力场对下游路径规划的价值。

View on arXiv Download PDF AI Translation

cs.RO / 33 / 2606.06130

Towards Realistic 3D Sonar Simulation

朝向真实的3D声呐仿真

Attia, Youssef, Costa, Davide, Wanderlingh, Francesco, Campagnaro, Filippo, Simetti, Enrico

Abstract

As underwater robotics research increasingly addresses complex 3D perception and autonomous navigation, the fidelity of sonar simulation has become a key factor in algorithm development. Current simulation frameworks typically rely on geometry-driven rendering, approximating 3D sonar as an underwater equivalent to LiDAR, which fails to account for fundamental acoustic phenomena such as refraction, multi-path interference, and phase-dependent signal formation. This paper proposes a modular architecture for realistic 3D sonar simulation that integrates GPU-accelerated graphics engines with physically grounded acoustic propagation principles. We implement a volumetric 3D sonar model within the NVIDIA Isaac Sim environment, modeled after the Water Linked 3D-15 sensor, and integrate it into a comprehensive underwater simulation framework. The system is validated through a hardware-in-the-loop configuration, where a modified FastLIO2 SLAM pipeline, executed on an NVIDIA Jetson Orin Nano, performs sensor fusion using synthetic 3D sonar, DVL, IMU, and pressure data. Finally, a qualitative comparison between simulated outputs and real-world data from harbor sheet-pile inspections is provided, characterizing the remaining sim-to-real gap and establishing a roadmap toward fully acoustics-driven volumetric sensing.

Chinese Translation

随着水下机器人研究越来越多地涉及复杂的3D感知和自主导航，声呐仿真的准确性已成为算法开发的关键因素。目前的仿真框架通常依赖于以几何为驱动的渲染，将3D声呐近似为水下相当于激光雷达（LiDAR）的方法，但未能考虑诸如折射、多路径干扰和相位依赖信号形成等基本声学现象。本文提出了一种模块化架构，用于真实的3D声呐仿真，该架构结合了GPU加速的图形引擎与物理基础的声波传播原理。我们在NVIDIA Isaac Sim环境中实现了一种体积3D声呐模型，该模型仿照Water Linked 3D-15传感器，并将其整合到一个全面的水下仿真框架中。该系统通过硬件在环（hardware-in-the-loop）配置进行验证，其中一个修改后的FastLIO2 SLAM管道在NVIDIA Jetson Orin Nano上执行，利用合成的3D声呐、DVL、IMU和压力数据进行传感器融合。最后，提供了模拟输出与来自港口桩板检查的真实数据之间的定性比较，描述了剩余的模拟与现实之间的差距，并建立了一个完全以声学驱动的体积传感的路线图。

View on arXiv Download PDF AI Translation

cs.RO / 34 / 2606.06139

MotionDisco: Motion Discovery for Extreme Humanoid Loco-Manipulation

MotionDisco：极端人形生物运动操控的运动发现

Taouil, Ilyass, Ciebelski, Michal, Omar, Shafeef, Zhao, Haizhou, Dai, Angela, Johnson, Aaron M., Khadiv, Majid

Abstract

We present MotionDisco, a framework that discovers contact-rich, long-horizon humanoid loco-manipulation motions from scratch, without relying on teleoperation or motion retargeting from human demonstrations. This is challenging because the space of possible contact interactions grows combinatorially with the task horizon and the number of objects in the scene. MotionDisco enables rapid discovery of novel motions by coupling a large language model (LLM) guided evolutionary search over sequences of interactions with an efficient sequential kinodynamic trajectory optimizer and pruning strategy, enabling the rapid discovery of novel skills. Through extensive ablation studies, we show that our LLM-guided search discovers successful whole-body trajectories across several challenging long-horizon tasks. Finally, by training reinforcement learning tracking policies on the discovered trajectories, we transfer the motions to a real humanoid robot. This is the first work to discover and deploy long-horizon humanoid loco-manipulation skills entirely through automated evolutionary search. Supplementary videos of the experiments are available at: https://youtu.be/DHiVz34QYlw.

Chinese Translation

我们提出了MotionDisco，这是一个框架，能够从零开始发现富含接触的长期人形运动操控动作，而无需依赖遥控或从人类示范中进行运动重新目标化。这是具有挑战性的，因为可能的接触交互空间随着任务时域和场景中物体数量的增加而呈组合性增长。MotionDisco通过将大语言模型（LLM）指导的交互序列进化搜索与高效的序列动力学轨迹优化器和修剪策略结合，快速发现新颖的动作，实现了新技能的迅速开掘。通过广泛的消融实验，我们展示了我们基于LLM的搜索能够在多个具有挑战性的长期任务中发现成功的全身轨迹。最后，通过在发现的轨迹上训练强化学习跟踪策略，我们将这些动作迁移到真实的人形机器人上。这是首次通过完全自动化的进化搜索发现和部署长期人形运动操控技能的工作。实验的补充视频可在以下链接观看：https://youtu.be/DHiVz34QYlw。

View on arXiv Download PDF AI Translation

cs.RO / 35 / 2606.06155

AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding

AffordanceVLA：一种通过关注可供性理解来增强动作生成的视觉-语言-动作模型

Yu, Qize, You, Jiadi, Wang, Yuran, Liang, Jiaqi, Ping, Bowen, Tian, Yang, Chen, Yue, Cai, Minghong, Gong, Zeying, Wu, Ruihai, Li, Yinchuan, Liang, Junwei, Chen, Yingcong

Abstract

Vision-Language-Action (VLA) models leverage the rich world knowledge of pretrained vision-language models (VLMs) to enable instruction-following robotic manipulation. However, the structural mismatch between VLM semantic spaces and embodied control policies often hinders the learning of precise perception--action mappings. To address this challenge, we propose \textbf{AffordanceVLA}, a unified framework that introduces structured affordance forecasting as a task-oriented intermediate representation to establish a more precise and robust perception--action mapping. Specifically, we progressively model manipulation priors through three complementary components: 1) \textbf{Which2Act} for object-centric grounding via visual latent prediction to suppress distractions; 2) \textbf{Where2Act} for 2D interaction localization via affordance map estimation; and 3) \textbf{How2Act} for 3D geometric reasoning to guide manipulation policies. These affordance cues provide spatially grounded, semantically conditioned, and action-coupled intermediate representations, thereby naturally bridging vision, language and action. We integrate these modules into a Mixture-of-Transformer (MoT) architecture with specialized experts and train the model using a three-stage training strategy with a progressive data curriculum. To overcome the scarcity of dense affordance labels in robotic datasets, we also develop a robust automated data augmentation pipeline. Extensive experiments on simulation and real-world demonstrate that AffordanceVLA achieves strong performance across diverse manipulation scenarios.

Chinese Translation

视觉-语言-动作（VLA）模型利用预训练视觉-语言模型（VLMs）丰富的世界知识，以实现指令跟随的机器人操作。然而，VLM语义空间与具身控制策略之间的结构不匹配常常阻碍了精确感知-动作映射的学习。为了解决这一挑战，我们提出了 extbf{AffordanceVLA}，这是一个统一框架，采用结构化的可供性预测作为任务导向的中间表示，以建立更精确和稳健的感知-动作映射。具体而言，我们通过三个互补组件逐步建模操作先验：1） extbf{Which2Act}通过视觉潜在预测进行物体中心的定位，以抑制干扰；2） extbf{Where2Act}通过可供性图估计实现二维交互定位；3） extbf{How2Act}进行三维几何推理以指导操作策略。这些可供性线索提供了空间上扎根、语义上条件化且与动作耦合的中间表示，从而自然地将视觉、语言和动作连接起来。我们将这些模块整合到具有专门专家的混合变换器（Mixture-of-Transformer, MoT）架构中，并使用逐步数据课程的三阶段训练策略对模型进行训练。为了克服机器人数据集中稠密可供性标签的稀缺性，我们还开发了一种稳健的自动化数据增强管道。在模拟和现实世界中的广泛实验表明，AffordanceVLA在多样化的操作场景中实现了强大的性能。

View on arXiv Download PDF AI Translation

cs.RO / 36 / 2606.06194

ActiveMimic: Egocentric Video Pretraining with Active Perception

ActiveMimic：基于主动感知的自我中心视频预训练

Lin, Xingyao, Zhong, Guojin, Lu, Tianyi, Ye, Ziyi, Zhu, Yichen, Wu, Zuxuan, Jiang, Yu-Gang

Abstract

Egocentric human video offers a scalable alternative to robot data for pretraining, yet models pretrained on such video consistently underperform those pretrained on robot data. We attribute this gap to a missing signal, the active perception behavior in egocentric videos, where humans continuously reposition their viewpoint during manipulation, inducing camera motion that standard pipelines treat as noise. To address this, we present ActiveMimic, a pretraining framework that recovers synchronized camera and wrist trajectories from a single body-worn RGB camera, models camera motion as a viewpoint action, and jointly learns active perception and manipulation from in-the-wild egocentric human video before adapting to a target robot. Empirically, real-world experiments across tasks with diverse active perception demands show that ActiveMimic consistently surpasses baselines pretrained on human video and matches state-of-the-art models pretrained on robot data. Further analysis provides evidence that active perception capability originates from egocentric human video pretraining rather than robot-specific fine-tuning, confirming active perception as the key to unlocking egocentric human video for robot pretraining.

Chinese Translation

自我中心人类视频为预训练提供了一种可扩展的替代方案，但在此类视频上预训练的模型的表现始终低于在机器人数据上预训练的模型。我们将这一差距归因于一个缺失的信号，即自我中心视频中的主动感知行为，在这一过程中，人类在操纵时不断重新调整视角，产生的相机运动被标准流程视为噪声。为了解决这个问题，我们提出了ActiveMimic，一个预训练框架，该框架从单个身体佩戴的RGB相机中恢复同步的相机和手腕轨迹，将相机运动建模为视角操作，并从真实环境中的自我中心人类视频中联合学习主动感知和操纵，然后适应目标机器人。实证研究表明，在多种对主动感知需求的任务中，ActiveMimic的表现始终优于在人工视频上预训练的基线模型，并且与在机器人数据上预训练的最先进模型相匹配。进一步的分析提供了证据，表明主动感知能力源于自我中心人类视频的预训练，而非机器人特定的微调，确认主动感知是解锁自我中心人类视频用于机器人预训练的关键。

View on arXiv Download PDF AI Translation

cs.RO / 37 / 2606.06218

TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation

TAM：用于鲁棒运动传递的扭矩适应模块

Son, Dongwon, Shkurti, Florian, Lee, Jason, Shah, Naman, Kim, Beomjoon, Fox, Dieter

Abstract

A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot. In contact-rich, dynamic manipulation, even small motion discrepancies can result in failure to track reference motion, since they disrupt the timing and modes of contact. Common remedies, such as domain randomization or system identification, either produce overly conservative task policies or require data that must be recollected for each robot or payload. We introduce the Torque Adaptation Module (TAM), a learned module that adapts the torque commands sent to the robot to match the behavior of an ideal robot. TAM operates between the low-level controller that tracks the policy's actions and the robot's torque interface. It includes a history encoder that embeds proprioceptive history into a latent state and a torque adaptor that computes residual torque corrections. Because TAM depends only on proprioceptive history and not on policy observations, or the action space, the same TAM weights can be reused to adapt policies with different action spaces (joint targets, end-effector targets, or direct torques). The policies themselves do not need to be trained with domain randomization of robot parameters. Instead, we offload the need for domain randomization to TAM by training it entirely in randomized simulation, using multi-robot pretraining followed by a robot-specific fine-tuning step that still requires no real-robot data. We evaluate TAM zero-shot on a real Franka Panda robot across dynamic manipulation tasks that include a vision-based box pushing policy (from RL), a flip policy (from BC), and an MPC ball-on-plate balancing. Our experiments show that TAM improves zero-shot real-robot execution compared to online system identification and RMA baselines and enables robust dynamic manipulation performance.

Chinese Translation

针对一个机器人调节的策略在另一个机器人上往往表现不同，这可能是由于仿真与现实之间的差距、未知的负载或同一机器人不同实例的动态特性。在接触丰富的动态操作中，即使是微小的运动差异也可能导致无法跟踪参考运动，因为这些差异会干扰接触的时机和方式。常见的解决方法，如领域随机化或系统识别，要么生成过于保守的任务策略，要么需要针对每个机器人或负载重新收集数据。我们提出了扭矩适应模块（Torque Adaptation Module, TAM），这是一个学习模块，用于调整发送给机器人的扭矩命令，以匹配理想机器人的行为。TAM位于跟踪策略动作的低级控制器与机器人的扭矩接口之间。它包括一个历史编码器，将本体感知历史嵌入到潜在状态中，以及一个扭矩适配器，计算残余扭矩修正。由于TAM仅依赖于本体感知历史，而不依赖于策略观测或动作空间，因此相同的TAM权重可以重用于适应不同动作空间（关节目标、末端执行器目标或直接扭矩）的策略。策略本身无需在机器人参数的领域随机化下进行训练。相反，我们将领域随机化的需求转移给TAM，通过在随机化的仿真中完全训练它，使用多机器人预训练和随后不需要真实机器人数据的特定机器人微调步骤。我们在真实的Franka Panda机器人上对TAM进行零-shot评估，涵盖了动态操作任务，包括基于视觉的箱子推送策略（来自强化学习）、翻转策略（来自行为克隆）和MPC平衡球板。我们的实验表明，与在线系统识别和RMA基准相比，TAM改善了零-shot的真实机器人执行，并实现了鲁棒的动态操作性能。

View on arXiv Download PDF AI Translation

cs.RO / 38 / 2606.06219

CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving

CLEAR：用于端到端自主驾驶的认知与潜在评估自适应路由

Xing, Yining, Ke, Zehong, Liu, Zhiyuan, Jiang, Yanbo, Yu, Wenhao, Wang, Jianqiang

Abstract

End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints. While diffusion models successfully capture diverse driving behaviors, their iterative denoising process incurs unacceptable latency for safety-critical deployment. To address this, we propose CLEAR (Cognition and Latent Evaluation for Adaptive Routing), a framework that combines ultra-fast generative planning with deep semantic reasoning. CLEAR employs Drive-JEPA as the visual encoder and replaces the multi-step denoising chain with a single-step conditional drift in a VAE latent space, introducing a conditioning coefficient to balance diversity and expert precision. Meanwhile, we fully fine-tune Qwen~3.5~0.8B on driving QA pairs to extract scene-aware hidden states. These states guide both an Adaptive Scheduler, which selects the conditioning coefficient $\alpha$ and sample count $N$ from a discrete set of predefined schemes, and a cross-attention scorer that selects the optimal trajectory from candidates. On the NAVSIM v1 benchmark, CLEAR achieves a state-of-the-art PDMS of 93.7. Our results demonstrate that high-fidelity, multi-modal planning can be executed efficiently without dense geometric annotations or iterative sampling.

Chinese Translation

端到端自主驾驶模型常常难以在多模态机动生成与实时推理约束之间取得平衡。尽管扩散模型成功捕捉了多样化的驾驶行为，但其迭代去噪过程给安全关键部署带来了不可接受的延迟。为了解决这个问题，我们提出了CLEAR（Cognition and Latent Evaluation for Adaptive Routing），一个将超快速生成规划与深度语义推理相结合的框架。CLEAR采用Drive-JEPA作为视觉编码器，并用VAE潜在空间中的单步条件漂移替换了多步去噪链，引入了一个条件系数以平衡多样性和专家精度。同时，我们在驾驶问答对上完全微调了Qwen~3.5~0.8B，以提取场景感知的隐藏状态。这些状态指导自适应调度器，该调度器从一组预定义方案中选择条件系数$eta$和样本数量$N$，以及交叉注意评分器，从候选者中选择最佳轨迹。在NAVSIM v1基准测试中，CLEAR达到了93.7的最新PDMS。我们的结果表明，高保真、多模态规划可以高效执行，而不需要密集的几何注释或迭代采样。

View on arXiv Download PDF AI Translation

cs.RO / 39 / 2606.06245

MPCoT: Reward-Guided Multi-Path Latent Reasoning for Test-Time Scalable Vision-Language-Action

MPCoT：用于测试时可扩展视觉-语言-动作的奖励引导多路径潜在推理

Zhang, Boyang, Shan, Lianlei

Abstract

Vision-Language-Action (VLA) policies remain brittle in long-horizon and high-uncertainty control, where one-pass action decoding provides limited inference-time deliberation. Explicit chain-of-thought can increase reasoning depth, but introduces token latency and an indirect text-to-action interface. We propose MPCoT, a reward-guided multi-path latent reasoning framework that initializes $M$ hypotheses, refines them for K weight-tied steps, and softly aggregates them before action decoding. A training-only path-preference objective evaluates candidate action branches with expert-action consistency, world-model/VLM-based progress, and success feedback to align the latent path scorer with downstream execution quality. MPCoT preserves the original 8-step action interface, generates zero reasoning tokens, and exposes configurable inference controls (K,M). Under matched protocols on LIBERO and CALVIN, MPCoT improves long-horizon performance, with ablations confirming depth-width effects, confidence-weighted aggregation, and reward-guided path supervision.

Chinese Translation

在长期和高不确定性控制中，视觉-语言-动作（VLA）策略仍然脆弱，其中单次动作解码提供的推理时间考虑有限。显式的思维链可以增加推理深度，但会引入令牌延迟以及间接的文本到动作接口。我们提出了MPCoT，一种奖励引导的多路径潜在推理框架，该框架初始化$M$个假设，对其进行K个权重绑定步骤的优化，并在动作解码之前进行软聚合。仅训练的路径偏好目标通过专家动作一致性、基于世界模型/VLM的进展和成功反馈来评估候选动作分支，从而将潜在路径得分器与下游执行质量对齐。MPCoT保持原始的8步动作接口，生成零推理令牌，并暴露可配置的推理控制（K,M）。在LIBERO和CALVIN的匹配协议下，MPCoT改善了长期性能，消融实验证实了深度-宽度效应、置信度加权聚合和奖励引导路径监督。

View on arXiv Download PDF AI Translation

cs.RO / 40 / 2606.06250

Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM

打破时间：一个完全高斯的分布式连续时间 SLAM 框架

Ceriola, Davide, Ferrari, Simone, Di Giammarino, Luca, Brizi, Leonardo, Grisetti, Giorgio

Abstract

Continuous-time SLAM provides a principled framework for fusing heterogeneous sensors while estimating smooth trajectories, and is particularly well-suited for handling heterogeneous, asynchronous sensor streams with non-uniform readout patterns, such as rolling shutter cameras, LiDAR scanners, radar sweeps, or event-based sensors. In this work, we introduce G-solver, a fully Gaussian and distributed framework that combines Gaussian Belief Propagation (GBP) with Gaussian Process (GP) motion priors for continuous-time trajectory estimation. Our GP model provides a probabilistic representation of the trajectory, enabling consistent interpolation and the use of data-driven hyperparameters, while GBP offers a scalable message-passing formulation well-suited for decentralized settings. The resulting solver naturally extends to multi-camera scenarios without specialized synchronization or engineering effort. We evaluate the approach on synthetic and real data, including rolling shutter and distributed multi-camera optimization, demonstrating accurate and stable estimation with runtimes comparable to existing continuous-time methods. An open-source implementation is released.

Chinese Translation

连续时间 SLAM 提供了一个原则性的框架，用于融合异构传感器，同时估计平滑轨迹，特别适合处理具有不均匀读取模式的异构异步传感器流，如滚动快门相机、激光雷达扫描仪、雷达扫描或基于事件的传感器。在本工作中，我们介绍了 G-solver，这是一个完全高斯并且分布式的框架，结合了高斯信念传播（Gaussian Belief Propagation, GBP）与高斯过程（Gaussian Process, GP）运动先验用于连续时间轨迹估计。我们的 GP 模型提供了轨迹的概率表示，支持一致的插值和数据驱动的超参数使用，而 GBP 提供了适用于分散设置的可扩展消息传递形式。这一求解器自然扩展到多相机场景，无需专业的同步或工程努力。我们在合成和真实数据上评估了该方法，包括滚动快门和分布式多相机优化，展示了准确且稳定的估计，所需运行时间与现有的连续时间方法相当。我们发布了一个开源实现。

View on arXiv Download PDF AI Translation

cs.RO / 41 / 2606.06255

RadiusFPS: Efficient Farthest Point Sampling on CPUs and GPUs via Spherical Voxel Pruning

RadiusFPS：通过球形体素修剪在CPU和GPU上实现高效的最远点采样

Yu, Ziyang, Li, Xiang, Chang, Qiong, Miyazaki, Jun

Abstract

Point clouds are a primary sensory representation for robotic perception, underpinning LiDAR-based autonomous driving, simultaneous localization and mapping (SLAM), and navigation. Within these pipelines, Farthest Point Sampling (FPS) is the most well-known downsampling operator, as its uniform coverage preserves the geometric structure on which downstream perception relies. However, the large time complexity of classical FPS scales poorly with the million-point-per-second rates of modern 3D sensors, making it a dominant latency bottleneck that conflicts with the real-time and limited onboard compute budgets of robotic systems. Therefore, we propose RadiusFPS, an FPS acceleration framework based on spherical voxel pruning that preserves the standard FPS update rule under the same initialization and tie-breaking policy. By indexing the point cloud with spherical voxels, RadiusFPS derives a conservative geometric bound that prunes redundant distance computations in each iteration, complemented by a coordinate-wise point-skip test that removes residual updates. We further introduce RadiusFPS-G, a warp-level GPU implementation that fuses voxel selection, pruning, and distance update into memory-coalesced kernels, eliminating costly global-memory round-trips. On indoor (S3DIS, ScanNet) and outdoor LiDAR (SemanticKITTI) benchmarks, RadiusFPS-G attains up to 2.5x speedup over GPU-based FPS and matches or exceeds QuickFPS among the evaluated methods while using roughly half its GPU memory, with comparable segmentation accuracy. When coupled with the learning-based FastPoint sampler, the resulting pipeline achieves the fastest End-to-End inference among all evaluated configurations. These properties make high-quality FPS-style sampling practical for latency- and memory-constrained robotic vision.

Chinese Translation

点云是机器人感知的主要传感器表示，支撑着基于激光雷达的自主驾驶、同时定位与地图构建（SLAM）以及导航。在这些流程中，最远点采样（Farthest Point Sampling，FPS）是最知名的下采样算子，因为其均匀覆盖保留了下游感知所依赖的几何结构。然而，经典FPS的较高时间复杂度与现代3D传感器每秒百万点的采样速率不匹配，成为与实时性限制及机器人系统有限计算预算相悖的主要延迟瓶颈。因此，我们提出了RadiusFPS，一种基于球形体素修剪的FPS加速框架，该框架在相同初始化和打破平局政策下保留了标准的FPS更新规则。通过使用球形体素对点云进行索引，RadiusFPS衍生出保守的几何界限，从而在每次迭代中修剪冗余的距离计算，辅以坐标级点跳过测试，去除残余更新。此外，我们还引入了RadiusFPS-G，这是一种基于波浪级别的GPU实现，将体素选择、修剪和距离更新融合到内存合并的内核中，消除了昂贵的全局内存往返。在室内（S3DIS、ScanNet）和户外激光雷达（SemanticKITTI）基准测试中，RadiusFPS-G在GPU基础FPS上实现了最高2.5倍的加速，并且在所评估的方法中在大约消耗其一半GPU内存的情况下，与QuickFPS相匹配或超越，同时保持相当的分割精度。当与基于学习的FastPoint采样器结合使用时，得到的管道在所有评估配置中实现了最快的端到端推理。这些特性使得高质量的FPS风格采样在延迟和内存受限的机器人视觉中变得可行。

View on arXiv Download PDF AI Translation

cs.RO / 42 / 2606.06281

Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation

用于接触丰富的机器人操作的多分辨率触觉模仿学习

Krohn, Rickmer, Helmut, Erik, Funk, Niklas, Peters, Jan, Prasad, Vignesh, Chalvatzaki, Georgia

Abstract

Touch sensing is beneficial for solving a wide variety of manipulation tasks. While there exists a wide range of tactile sensors with different properties, exploiting the fusion of multiple heterogeneous tactile sensors to improve manipulation learning remains underexplored. We present Multi-Resolution Tactile Sensing (MiTaS), a representation framework that leverages multiple tactile sensors operating at different temporal resolutions in order to solve complex contact-rich manipulation tasks. We propose a novel architecture using modality-specific convolutional stems and transformer-based fusion that effectively fuses information from an RGB camera stream, a vision-based GelSight Mini sensor and a high-frequency event-based Evetac sensor. This multi-sensor representation then conditions a flow-matching policy for solving downstream tasks. Experimental results across five contact-rich manipulation tasks demonstrate the effectiveness of multi-resolution tactile features in imitation learning. MiTaS achieves an average success rate of 80 %, while vision-only (31 %) and visual-tactile (54 %) baselines cannot solve the task reliably. Co-training a visuo-tactile model with multi-tactile data boosts performance by over 10 \% in certain tasks, without having access to the Evetac sensor during policy evaluation. A detailed sensor-reading and attention analysis reveals the importance of different sensors throughout task execution, validating our multi-resolution tactile sensing approach. Project Page: http://mitas-touch.github.io.

Chinese Translation

触觉感知有助于解决各种操作任务。尽管存在多种不同属性的触觉传感器，但利用多种异构触觉传感器的融合来改善操作学习仍然未得到充分研究。我们提出了多分辨率触觉感知（Multi-Resolution Tactile Sensing，MiTaS），这是一个利用在不同时间分辨率下工作的多个触觉传感器的表示框架，以解决复杂的接触丰富的操作任务。我们提出了一种新颖的架构，采用特定模态的卷积干和基于变换器的融合，有效地融合来自RGB摄像头流、基于视觉的GelSight Mini传感器和高频事件驱动的Evetac传感器的信息。然后，这种多传感器表示为解决下游任务提供条件流匹配策略。在五个接触丰富的操作任务中的实验结果表明，多分辨率触觉特征在模仿学习中的有效性。MiTaS的平均成功率为80%，而仅使用视觉（31%）和视觉-触觉（54%）的基线无法可靠地解决任务。在某些任务中，与多触觉数据共同训练视觉-触觉模型可提升性能超过10%，且在策略评估过程中无需访问Evetac传感器。详细的传感器读取和注意力分析揭示了不同传感器在任务执行过程中的重要性，验证了我们多分辨率触觉感知的方法。项目页面：http://mitas-touch.github.io.

View on arXiv Download PDF AI Translation

cs.RO / 43 / 2606.06308

Attitude-Aided Linear Calibration of Triaxial Accelerometers

姿态辅助的三轴加速度计线性校准

Yu, Yongqiang, Huang, Tian, Yang, Yipeng

Abstract

Triaxial MEMS accelerometers are widely used for inertial sensing, navigation, and sensor fusion, but existing calibration methods often rely on costly reference setups or nonlinear iterative optimization, limiting their efficiency and applicability to low-cost or self-calibrating systems. We present attitude-aided linear accelerometer calibration (ALAC), a method that operates on any platform providing orientation information, such as turntables, robotic arms, or inertial measurement units. ALAC constructs a combined error matrix (CEM) to represent sensor errors in a unified calibration model and enables linear least-squares estimation. The bias and gravity vector are jointly estimated, implicitly accounting for platform misalignment, and matrix decomposition of the CEM recovers scale, non-orthogonality, and alignment rotation parameters. Under static gravity, calibration is formulated as a constrained homogeneous least-squares (CHLS) problem and solved in closed form using standard linear algebra. Only five arbitrarily oriented measurements are required, and a recursive extension supports online or in-field calibration. Experiments on a stationary robot-mounted accelerometer and a quasi-static public IMU trajectory show that ALAC, in both offline and online modes, outperforms reference-based and online baselines in accuracy and robustness to sensor noise. On the same dataset, it matches iterative self-calibration under filtered conditions and surpasses all evaluated baselines on raw measurements. These results demonstrate a robust and practical calibration scheme for MEMS-based inertial platforms, especially low-cost IMUs and online calibration scenarios.

Chinese Translation

三轴MEMS加速度计广泛应用于惯性传感、导航和传感器融合，但现有的校准方法往往依赖于成本高昂的参考设备或非线性迭代优化，限制了它们在低成本或自校准系统中的效率和适用性。我们提出了一种姿态辅助的线性加速度计校准方法（Attitude-Aided Linear Accelerometer Calibration，ALAC），该方法可在任何提供方向信息的平台上运行，例如转台、机械臂或惯性测量单元。ALAC构建了一个联合误差矩阵（Combined Error Matrix，CEM）来在统一的校准模型中表示传感器误差，并实现线性最小二乘估计。偏差和重力向量共同估计，隐含考虑了平台的不对齐，通过CEM的矩阵分解恢复尺度、非正交性和对齐旋转参数。在静态重力下，校准被公式化为一个约束齐次最小二乘（Constrained Homogeneous Least-Squares，CHLS）问题，并使用标准线性代数以封闭形式求解。仅需五个任意方向的测量，并且递归扩展支持在线或现场校准。在一个固定的机器人安装加速度计和一个准静态公共IMU轨迹的实验中，显示ALAC在离线和在线模式下的准确性和对传感器噪声的鲁棒性都优于基于参考的和在线基准。在相同的数据集上，它在滤波条件下与迭代自校准相匹配，并在原始测量上超越了所有评估的基准。这些结果证明了一种稳健且实用的MEMS惯性平台校准方案，特别适用于低成本IMU和在线校准场景。

View on arXiv Download PDF AI Translation

cs.RO / 44 / 2606.06312

Meridian: Metric-Semantic Primitive Matching for Cross-View Geo-Localization Beyond Urban Environments

Meridian：用于超越城市环境的跨视角地理定位的度量-语义原始匹配

Peterson, Mason, Li, Qingyuan, Jia, Yixuan, Cladera, Fernando, Nieto-Granda, Carlos, Taylor, Camillo Jose, How, Jonathan P.

Abstract

Successful robot automation requires accurate global localization to support repeatability, task planning, goal specification, and safe operation. However, reliable localization in GNSS-denied environments remains an open problem. Overhead aerial imagery offers a promising solution, but existing approaches primarily target structured urban environments and have been rarely demonstrated in unstructured natural terrain. Limitations of the state-of-the-art include a reliance on models trained for specific environments, as well as difficulty handling repetitive geometries and featureless landscapes commonly found in natural outdoor areas. To overcome these challenges, we present Meridian, a method for matching high-level metric-semantic primitives across aerial images and ground robot RGB-D camera data that achieves accurate global localization and generalizes well across diverse environments, all without any training or algorithmic fine-tuning on area-specific data. We formulate novel consistency metrics to estimate a distribution over robot submap poses and to reject outlier hypotheses in a robust pose graph optimization step for accurate robot trajectory estimation. We demonstrate that our algorithm can localize a ground robot across a wide variety of environments, including an autonomous driving dataset, a park and campus area, and a wilderness camp, with an average optimized trajectory error of 2.4 m over 19 km of ground traversal.

Chinese Translation

成功的机器人自动化需要准确的全球定位，以支持重复性、任务规划、目标指定和安全操作。然而，在无GNSS环境中实现可靠的定位仍然是一个未解决的问题。高空航空图像提供了一种有前景的解决方案，但现有方法主要针对有结构的城市环境，并且在非结构化自然地形中的应用很少。当前最先进技术的局限性包括依赖为特定环境训练的模型，以及难以处理常见于自然户外区域的重复几何图形和无特征的景观。为克服这些挑战，我们提出了一种名为Meridian的方法，用于在航空图像和地面机器人RGB-D摄像机数据之间匹配高级别的度量-语义原始，实现准确的全球定位并在多种环境中良好泛化，而无需在特定区域数据上进行任何训练或算法微调。我们制定了新颖的一致性度量，以估计机器人子地图姿态的分布，并在稳健的姿态图优化步骤中拒绝异常假设，以实现准确的机器人轨迹估计。我们证明了我们的算法能够在多种环境中为地面机器人进行定位，包括自主驾驶数据集、公园和校园区域以及荒野营地，在19 km的地面行驶中平均优化轨迹误差为2.4米。

View on arXiv Download PDF AI Translation

cs.RO / 45 / 2606.06323

VOLT: Vision and Language Trajectory Segmentation for Faster-than-Demonstration Policies

VOLT：视听语言轨迹分割用于比示例更快的策略

Sanchez, Robert Ramirez, Evans, Daniel J., Losey, Dylan P., Jain, Siddarth

Abstract

Humans often take longer to demonstrate a task than a robot would need to execute it. Rather than learning to replicate the demonstration at the same pace, many industrial and practical applications require robots to perform tasks as quickly as possible. In this paper, we investigate several hypotheses for learning policies that operate faster-than-demonstrations. Our experiments show that the most effective strategy is to downsample recorded demonstrations and train the robot's policy on this accelerated data. However, uniformly downsampling an entire trajectory can be problematic. Some parts of a task can be safely sped up (e.g., unconstrained motion), while others demand slower, more precise motion (e.g., object interactions or fine manipulation). To address this challenge, we introduce VOLT, a vision-and-language trajectory segmentation method that reasons over video demonstrations, and leverages contextual cues to determine when acceleration is appropriate and when careful precision is required. VOLT identifies segments where slow, deliberate motion is necessary, then selectively downsamples the remaining segments. The resulting reformatted trajectories can be used with standard imitation learning approaches, such as diffusion policies. Our results highlight that segmentation quality is critical -- baseline methods often misidentify when acceleration is possible, leading to overly cautious or unreliable policies. Compared to state-of-the-art alternatives, VOLT allows robots to execute tasks faster while maintaining strong performance.

Chinese Translation

人类在演示任务时通常花费的时间比机器人执行相同任务所需的时间要长。许多工业和实际应用要求机器人成为尽可能快地执行任务，而不是以相同的速度学习复制演示。在本文中，我们研究了几种学习比示例执行更快策略的假设。我们的实验表明，最有效的策略是对记录的演示进行降采样，并在加速数据上训练机器人的策略。然而，均匀降采样整个轨迹可能会存在问题。任务的某些部分可以安全地加速（例如，无约束运动），而其他部分则需要更慢、更精确的运动（例如，物体交互或精细操作）。为了解决这个挑战，我们引入了VOLT，一种视听语言轨迹分割方法，它对视频演示进行推理，并利用上下文线索来判断何时适合加速，何时需要仔细的精度。VOLT识别出需要缓慢、深思熟虑的运动的片段，然后选择性地对其余片段进行降采样。最终重新格式化的轨迹可以与标准模仿学习方法（如扩散策略）结合使用。我们的结果强调了分割质量的重要性——基线方法常常错误地识别出何时可以加速，导致过于谨慎或不可靠的策略。与最先进的替代方法相比，VOLT使得机器人在保持强大性能的同时，更快地执行任务。

View on arXiv Download PDF AI Translation

cs.RO / 46 / 2606.06366

Waypoints Matter: A Systematic Study for Sampling-Based Trajectory Planning

航点的重要性：基于采样的轨迹规划的系统研究

Barbera, Josep M., Artuñedo, Antonio, Villagra, Jorge

Abstract

Real-time autonomous driving commonly relies on sampling-based trajectory planners that link candidate trajectories to target waypoints along the road centerline. The placement of these waypoints directly impacts both the existence and quality of feasible trajectories. Yet, its effect on planner performance remains largely unexplored. In this paper, we treat waypoint placement as a first-class design variable. We hold the trajectory primitive and candidate budget fixed, and systematically sweep three placement strategies (uniform spacing, an augmented Ramer-Douglas-Peucker variant (RDP*), and a novel curvature-conditioned allocation) across 449 configurations and five CommonRoad maps of increasing geometric complexity. Our results show that the nominal inter-waypoint spacing $d_s$ is the primary performance driver, with large differences in planner reliability attributed to placement alone. Uniform sampling at a well-tuned spacing matches or surpasses both RDP* and the centered curvature variant. The curvature variant offers a small but consistent advantage on geometrically complex roads under reliability-first and balanced weightings, while RDP* never outperforms uniform sampling. These findings suggest that $d_s$ should be treated as the dominant tuning parameter, with geometry-aware strategies reserved for curvature-rich corridors where feasibility is the limiting factor.

Chinese Translation

实时自主驾驶通常依赖于基于采样的轨迹规划器，将候选轨迹与沿道路中心线的目标航点连接起来。这些航点的布置直接影响可行轨迹的存在性和质量。然而，对其在规划者性能上的影响目前仍然没有得到充分研究。在本文中，我们将航点布置视为一项重要的设计变量。我们固定轨迹原语和候选预算，系统地采用三种布置策略（均匀间隔、增强的拉默-道格拉斯-普克变体 (RDP*)，以及一种新颖的曲率条件分配）在449个配置和五个几何复杂度逐渐增加的CommonRoad地图上进行全面测试。我们的结果表明，标称航点间距 $d_s$ 是主要的性能驱动因素，规划者的可靠性在很大程度上取决于航点的布置。经过精心调校的均匀采样间距的性能与 RDP* 和中心曲率变体相匹敌或超过。曲率变体在几何复杂的道路上提供了小但一致的优势，尤其是在优先考虑可靠性和均衡加权的情况下，而 RDP* 从未超过均匀采样。这些发现表明，$d_s$ 应被视为主要的调节参数，而几何感知策略应保留用于可行性受限的曲率丰富通道。

View on arXiv Download PDF AI Translation

cs.RO / 47 / 2606.06370

Ensuring Interaction Safety in Multitask Exoskeleton Control: A Simulation-Trained Variable Impedance Framework

确保多任务外骨骼控制中的交互安全：一种基于仿真的可变阻抗框架

Ma, Muyuan, Li, Houcheng, Zhai, Haotian, Han, Lijun, Meng, Xinpan, Xia, Xiuze, Cheng, Long

Abstract

Wearable exoskeletons can augment human phys ical capabilities during complex activities. However, ensuring adaptation across diverse tasks while guaranteeing interaction safety remains a critical challenge. To address this, a simulation trained variable impedance control approach with stability guarantees is proposed. First, a simulation-based human exoskeleton motion data generation pipeline is established, utilizing Proximal Policy Optimization (PPO) to synthesize human muscle activations while the exoskeleton provides direct compensation for human biological joint torques. Subsequently, the generated dataset is used to train a dual modality policy that fuses semantic instructions with proprioceptive history, enabling the prediction of reference trajectories and variable impedance gains for nine different motion tasks. To guarantee safety, the network outputs are constrained by a stability criterion derived from Lyapunov stability theory, which bounds stiffness variations to ensure the asymptotic stability of the coupled system. Experimental results indicate that the proposed framework reduces metabolic cost in real-world scenarios com pared with standard baseline methods. These findings suggest the feasibility of the proposed framework for safe, multitask exoskeleton control.

Chinese Translation

可穿戴外骨骼能够在复杂活动中增强人类的身体能力。然而，在保证交互安全的同时，确保在多样化任务中的适应性仍然是一个关键挑战。为了解决这一问题，提出了一种具有稳定性保障的基于仿真的可变阻抗控制方法。首先，建立了一个基于仿真的人类外骨骼运动数据生成流程，利用近端策略优化（Proximal Policy Optimization，PPO）合成的人类肌肉激活，同时外骨骼对人类生物关节扭矩进行直接补偿。随后，生成的数据集用于训练一种双模态策略，该策略融合语义指令与本体感知历史，从而预测九种不同运动任务的参考轨迹和可变阻抗增益。为了确保安全，网络输出受到来自李雅普诺夫稳定性理论的稳定性标准的约束，该标准限制了刚度变化，从而确保耦合系统的渐近稳定性。实验结果表明，与标准基线方法相比，所提框架在真实场景中降低了代谢成本。这些发现表明所提框架在安全多任务外骨骼控制中的可行性。

View on arXiv Download PDF AI Translation

cs.RO / 48 / 2606.06423

RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation

RiskFlow：快速且可信的安全关键交通场景生成

Lan, Qi, Tang, Yining, Shen, Yu, Zhou, Yi, Wei, Yuhao, Li, Jie, Li, Guofa

Abstract

Safety-critical traffic scenario generation is essential for evaluating autonomous driving systems under rare but high-risk interactions. Existing diffusion-based methods offer strong controllability in closed-loop generation, but their iterative denoising process is computationally expensive and may accumulate sampling and guidance errors over long rollouts, causing unrealistic motion artifacts such as jitter, abnormal acceleration, and off-road behavior. To address these issues, we propose RiskFlow, a closed-loop safety-critical multi-agent traffic generation framework that formulates future trajectory generation as transport in the action space. Instead of relying on iterative denoising, RiskFlow learns an average velocity field over a finite interval to transform Gaussian action sequences into future acceleration and yaw-rate commands with a single forward pass, using a JVP-based objective for efficient and stable training. At test time, RiskFlow applies output-space guidance to the generated actions, steering selected critical agents toward risky interactions while regularizing off-road behavior, and reconstructs physically feasible trajectories through vehicle dynamics. Experiments on nuScenes with tbsim closed-loop evaluation show that RiskFlow achieves a strong adversariality-realism trade-off across multi-agent and long-horizon settings. Compared with representative baselines, RiskFlow consistently improves realism while maintaining competitive safety-critical generation capability, and substantially reduces inference time for evaluation.

Chinese Translation

安全关键的交通场景生成对于评估在稀有但高风险交互下的自动驾驶系统至关重要。现有的基于扩散的方法在闭环生成中提供了强大的控制性，但其迭代去噪过程计算成本高，并且在长时间展开中可能会积累采样和引导误差，从而导致不现实的运动伪影，例如抖动、异常加速和偏离道路的行为。为解决这些问题，我们提出了RiskFlow，一个闭环安全关键的多体交通生成框架，将未来轨迹生成公式化为在行动空间中的传输。与依赖迭代去噪不同，RiskFlow学习在有限时间间隔内的平均速度场，以通过单次前向传递将高斯动作序列转换为未来的加速度和偏航率指令，使用基于JVP的目标进行高效和稳定的训练。在测试时，RiskFlow对生成的动作施加输出空间引导，引导选定的关键代理走向高风险交互，同时规范化偏离道路的行为，并通过车辆动力学重建物理可行的轨迹。在nuScenes上采用tbsim闭环评估的实验表明，RiskFlow在多代理和长时间范围设定中实现了强大的对抗性与现实性之间的权衡。与具有代表性的基线相比，RiskFlow在提高现实性的同时保持竞争性的安全关键生成能力，并显著减少评估的推理时间。

View on arXiv Download PDF AI Translation

cs.RO / 49 / 2606.06461

Flow-based Policy Adaptation without Policy Updates

基于流的策略适应，无需策略更新

Sun, Luzhe, Ji, Jingtian, Chen, Haoran, Zhou, Jiawei, Walter, Matthew R.

Abstract

Leveraging prior knowledge from pretrained policies, foundation models, or human operators offers an efficient alternative to learning robot skills from scratch. However, these agents often provide actions that are suboptimal, noisy, or misaligned with task-specific expert behavior. We propose GLOVES, a family of flow-based adaptation methods that correct non-expert actions by transporting them toward an expert action distribution. Rather than replacing agentic control with full autonomy, GLOVES performs selective action-level adaptation, improving task success while preserving agent intent. The learned flow also provides a natural in-distribution scoring mechanism through reverse flow evaluation. We use this signal as an intervention gate: actions that appear consistent with the expert distribution are passed through unchanged, while anomalous or out-of-distribution (OOD) actions are corrected. In this way, assistance is only provided when necessary. GLOVES requires only limited expert supervision, using a small number of demonstrations or reusable successful skill segments. By learning local expert action patterns and stitching them during execution, GLOVES provides a lightweight shared-control module for robust action adaptation across tasks and environments. Code and demos are available at ripl.github.io/GLOVES_web.

Chinese Translation

利用预训练策略、基础模型或人类操作员的先验知识，为从零开始学习机器人技能提供了一种有效的替代方案。然而，这些代理通常提供的行动是次优的、噪声过大的，或与任务特定专家行为不一致。我们提出了GLOVES，一种基于流的适应方法系列，旨在通过将非专家动作转移至专家动作分布来修正这些非专家行为。GLOVES并没有用完全自主权替代代理控制，而是执行选择性动作级适应，提高任务成功率，同时保持代理的意图。所学习的流动还通过反向流评估提供了一种自然的分布内评分机制。我们将这一信号用作干预门：与专家分布一致的动作不作更改地通过，而异常或分布外（OOD）的动作则被修正。通过这种方式，仅在必要时提供帮助。GLOVES仅需有限的专家监督，使用少量演示或可重复的成功技能片段。通过学习局部专家动作模式并在执行期间将其拼接，GLOVES为跨任务和环境的稳健动作适应提供了一种轻量的共享控制模块。代码和演示可在ripl.github.io/GLOVES_web获得。

View on arXiv Download PDF AI Translation

cs.RO / 50 / 2606.06491

TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies

TempoVLA：学习可控速度的视觉-语言-动作策略

Jing, Dong, Nie, Jingchen, Zhang, Tianqi, Liu, Jiaqi, Yao, Huaxiu, Lu, Zhiwu, Ding, Mingyu

Abstract

Robot manipulation alternates between low-risk transit phases that call for fast execution and high-risk contact stages that demand slow, precise motion. Yet existing Vision-Language-Action models (VLAs) only inherit a single fixed speed from training demonstrations. Prior efforts to accelerate VLAs through model compression, KV-cache reuse, or reinforcement learning only shift the policy from one fixed speed to another, and leave deceleration almost unexplored. We observe that the magnitude of each predicted action already governs how fast the robot moves, opening a direct route to controllable execution speed. We turn this observation into TempoVLA, a single VLA whose execution speed is controlled by an explicit condition. TempoVLA combines two coupled components. (1) A data-side Variable-Speed Trajectory Augmentation (VSTA) that re-times demonstration to any target speed by merging or splitting actions while preserving its motion semantics. (2) A model-side conditioning mechanism that feeds the speed to the policy. Statistics show that VSTA reaches the requested speed with negligible motion error. Experiments in simulation and on real-world tasks demonstrate that TempoVLA achieves flexible speed control in both directions, while VSTA additionally boosts the default $1\times$ performance via better data utilization. Furthermore, by cooperating with a large multimodal model, TempoVLA realizes dynamic speed control, accelerating through low-risk phases and decelerating for high-risk ones.

Chinese Translation

机器人操控在低风险的过渡阶段与高风险的接触阶段之间交替进行，其中前者要求快速执行，而后者则需要缓慢而精确的动作。然而，现有的视觉-语言-动作模型（VLA）仅从训练示例中继承单一固定速度。以往通过模型压缩、KV-cache重用或强化学习来加速VLA的努力，仅将策略从一个固定速度转移到另一个固定速度，而几乎没有探讨减速。我们观察到，每个预测动作的大小已经决定了机器人移动的速度，这为可控执行速度开辟了直接的途径。我们将这一观察转化为TempoVLA，一个通过明确条件控制执行速度的单一VLA。TempoVLA结合了两个耦合组件：（1）一个数据端的变速轨迹增强（Variable-Speed Trajectory Augmentation, VSTA），它通过合并或分割动作来重新定时示例，以达到任何目标速度，同时保留其运动语义；（2）一个模型端的条件机制，将速度反馈给策略。统计数据显示，VSTA以可忽略的运动误差达到请求速度。在模拟和真实世界任务中的实验表明，TempoVLA在两个方向上都实现了灵活的速度控制，而VSTA还通过更好的数据利用提高了默认的$1 imes$性能。此外，通过与大规模多模态模型的配合，TempoVLA实现了动态速度控制，在低风险阶段加速，并在高风险阶段减速。

View on arXiv Download PDF AI Translation

cs.RO / 51 / 2606.06493

HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers

HANDOFF：通过蒸馏互补教师实现的人形代理任务空间全身控制

Yang, Lizhi, Li, Junheng, Poddar, Nehar, Hou, Yiling, Huh, Gio, Griffin, Robert, Gkioxari, Georgia, Ames, Aaron

Abstract

For a humanoid robot to be deployed in the real world, the choice of command space (i.e., the interface between task planning and whole-body control) is crucial. Existing whole-body controllers typically demand dense kinematic or spatial references that planners struggle to synthesize from task semantics. We instead propose a compact, explicit interface that is intuitive, general, modular, and expressive enough for diverse manipulation skills. To this end, we introduce HANDOFF, a single humanoid whole-body controller that follows this interface and is distilled via multi-teacher KL distillation under a context-conditioned gating scheme into a mixture-of-experts student from three complementary specialists: whole-body motion tracking with safety-filtered data, locomotion, and fall-recovery. On the Unitree G1, HANDOFF matches state-of-the-art velocity tracking and offers one of the largest robust manipulation workspaces. We further demonstrate hardware feasibility through multiple natural-language-driven task roll-outs, powered by a VLM-driven agentic planner with no task-specific data or controller fine-tuning.

Chinese Translation

为了使人形机器人能够在真实世界中部署，命令空间的选择（即任务规划和全身控制之间的接口）至关重要。现有的全身控制器通常需要密集的运动学或空间参考，而规划者很难从任务语义中合成这些参考。相较之下，我们提出了一种紧凑、明确的接口，它直观、通用、模块化且足够表达多样的操控技能。为此，我们介绍了HANDOFF，一个遵循此接口的单一人形全身控制器，并通过在上下文条件门控机制下的多教师KL蒸馏，将其蒸馏成一个来自三位互补专家的混合专家学生：利用安全过滤数据进行的全身运动跟踪、步态控制和跌倒恢复。在Unitree G1上，HANDOFF达到了最先进的速度跟踪效果，并提供了最大的鲁棒操控工作空间之一。我们还通过多个以自然语言驱动的任务实施演示了硬件可行性，这一切都是由一个不依赖于特定任务数据或控制器微调的VLM驱动的代理规划器所推动的。

View on arXiv Download PDF AI Translation

计算机视觉 (Computer Vision)

101

cs.CV / 1 / 2606.05259

VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding

VideoKR：面向知识与推理密集型视频理解

Fu, Lin, Yang, Zheyuan, Wang, Yang, Song, Tingyu, Cohan, Arman, Zhao, Yilun

Abstract

We introduce VideoKR, the first large-scale training corpus specifically designed to strengthen knowledge- and reasoning-intensive video understanding. It comprises 315K video reasoning examples over 145K newly collected, CC-licensed, expert-domain videos. We develop a human-in-the-loop, skill-oriented example generation pipeline that targets progressively deeper video reasoning capabilities while ensuring the difficulty, diversity, and reliability of both the examples and their CoT rationales. We also curate VideoKR-Eval, a new expert-annotated benchmark where questions require genuine video understanding and knowledge-intensive reasoning rather than textual shortcuts. Our experiments show that, under a standard SFT$\rightarrow$GRPO pipeline, models post-trained on VideoKR outperform prior post-training approaches on knowledge-intensive video reasoning while remaining competitive on general video reasoning, highlighting data design as a key driver of progress in video reasoning. We further conduct comprehensive ablations to isolate the contributions of VideoKR, providing actionable insights for future work.

Chinese Translation

我们介绍了VideoKR，这是第一个专门设计用于强化知识与推理密集型视频理解的大规模训练语料库。该语料库包含315K个视频推理示例，涵盖145K个新收集的、具有CC许可证的专业领域视频。我们开发了一个以人为中心的、面向技能的示例生成管道，旨在逐步提升视频推理能力，同时确保示例及其推理过程的难度、多样性和可靠性。我们还策划了VideoKR-Eval，这是一个新的专家标注基准测试，其中的问题要求真正的视频理解和知识密集型推理，而不是文本快捷方式。我们的实验表明，在标准的SFT$ ightarrow$GRPO管道下，基于VideoKR后训练的模型在知识密集型视频推理方面优于先前的后训练方法，同时在一般视频推理方面也具有竞争力，这突显了数据设计作为视频推理进展的关键驱动因素。我们进一步进行全面的消融实验，以隔离VideoKR的贡献，为未来的研究提供可行的见解。

View on arXiv Download PDF AI Translation

cs.CV / 2 / 2606.05261

NIV: Neural Axis Variations for Variable Font Generation

NIV：用于可变字体生成的神经轴变换

Benedek, Nadav, Shamir, Ariel, Fried, Ohad

Abstract

Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size. However, constructing a variable font from a static font remains a labor-intensive process requiring expert typographic design and manual specification of glyph variation data. We introduce NIV (Neural Axis Variations), a method that automatically converts a static font into a fully functional variable font. Given glyph outlines and a set of desired design axes, NIV predicts per-point displacements. The model operates directly on vector glyph geometry and employs a novel Property Embedding mechanism that captures interactions between multiple axes, enabling consistent multi-axis variation within a unified framework. We train NIV on a newly constructed dataset derived from variable Google Fonts, comprising over one million variation tuples. The resulting model generalizes across unseen code points, unseen font styles, high-complexity CJK glyphs, and even out-of-distribution handwriting inputs. The generated outputs are standard variable font files supporting continuous interpolation via existing rendering engines. To facilitate research, we release the dataset, the complete training and inference implementation, and trained models at https://github.com/ndvbd/NIV. Beyond typography, our approach demonstrates how structured geometric objects with continuous parametric variation can be synthesized using neural deformations.

Chinese Translation

可变字体能够沿着诸如字重、字宽、倾斜和光学大小等语义设计轴实现字形几何的连续变化。然而，从静态字体构建可变字体仍然是一个劳动密集型的过程，需要专家的排版设计和手动规范字形变异数据。我们提出了 NIV (Neural Axis Variations) 一种方法，自动将静态字体转换为功能齐全的可变字体。给定字形轮廓和一组期望的设计轴，NIV 预测每个点的位移。该模型直接在矢量字形几何上操作，并采用一种新颖的属性嵌入机制，以捕捉多个轴之间的交互，从而在统一框架内实现一致的多轴变化。我们在一个全新构建的来自可变谷歌字体的数据集上训练 NIV，该数据集包含超过一百万个变异元组。所得到的模型能在未见过的代码点、未见过的字体风格、高复杂度的 CJK 字形，甚至是超出分布的手写输入上进行泛化。生成的输出是标准的可变字体文件，支持通过现有渲染引擎实现连续插值。为促进研究，我们在 https://github.com/ndvbd/NIV 上发布了数据集、完整的训练和推理实现，以及训练好的模型。超越排版，我们的方法展示了如何使用神经变形合成具有连续参数变化的结构几何对象。

View on arXiv Download PDF AI Translation

cs.CV / 3 / 2606.05275

Personal AI Agent for Camera Roll VQA

个人AI代理用于相册视觉问答

Nguyen, Thao, Singh, Krishna Kumar, Kim, Donghyun, Lee, Yong Jae, Li, Yuheng

Abstract

We study the personal camera roll visual question answering setting. In this setting, a conversational AI assistant can access a user's personal camera roll and retrieve relevant photos to answer queries, ranging from simple factual questions (e.g., ``Name of the food I tried yesterday?'') to more open-ended ones (e.g., ``Recommend some dishes I have never eaten before''). Given the vast nature of the personal camera roll (i.e., multiple years, hundreds to thousands of photos), a successful AI assistant needs to understand a long-horizon, highly personalized visual content stream in order to navigate and locate the correct and/or relevant information. To support this, we collect and manually annotate questions that mimic real-world usage. The final dataset, camroll, contains 50 users, 31,476 images, and 2,500 QA pairs. We further design camroll-agent, a conversational AI agent equipped with hierarchical memory and a minimal set of tools for efficient navigation over large, personalized visual memory. Experimental results show that camroll-agent outperforms numerous baselines and methods for long-context understanding AI agents system. Together, the camroll dataset and camroll-agent highlight the gap in AI agents' long-context reasoning: personalized visual memory requires different approaches from standard long-context textual memory, especially when consistency, visual details, and user-specific context are present.

Chinese Translation

我们研究个人相册视觉问答的设置。在这一设置中，对话AI助手可以访问用户的个人相册，并检索相关照片以回答问题，这些问题范围从简单的事实类问题（例如，"我昨天尝试的食物的名字是什么？"）到更开放的问题（例如，"推荐一些我从未吃过的菜肴"）。鉴于个人相册的庞大特性（即多个年份，数百到数千张照片），一个成功的AI助手需要理解长时间跨度内高度个性化的视觉内容流，以便导航和定位正确和/或相关的信息。为了支持这一点，我们收集并手动标注了模仿真实场景使用的问题。最终数据集camroll包含50个用户，31,476张图像和2,500个问答对。我们进一步设计了camroll-agent，一个配备有分层记忆和一组最小工具的对话AI代理，以实现高效的个性化视觉记忆导航。实验结果表明，camroll-agent在长上下文理解的AI代理系统上超过了众多基线和方法。总之，camroll数据集和camroll-agent突显了AI代理在长上下文推理方面的差距：个性化视觉记忆需要与标准长上下文文本记忆不同的方法，特别是当一致性、视觉细节和用户特定上下文存在时。

View on arXiv Download PDF AI Translation

cs.CV / 4 / 2606.05290

Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation

模型是否共享安全表征？跨模型安全引导以实现安全的视觉生成

Poppi, Tobia, Cappelletti, Silvia, Sarto, Sara, Schiffers, Florian, Kessler, Garin, Cornia, Marcella, Baraldi, Lorenzo, Cucchiara, Rita

Abstract

Recent progress in generative modeling has made safety control a central challenge, yet existing approaches remain largely model-specific, requiring retraining or tailored interventions for each new architecture. In this work, we ask whether safety can be represented as a portable latent direction, learned once and reused across heterogeneous generators. We introduce the first framework for cross-model safety steering, in which a safety direction is estimated in a source LLM from paired safe-unsafe prompts, transported to a target generator through a lightweight alignment fitted on benign data alone, and applied at inference time. Crucially, our pipeline never accesses unsafe data on the target side, isolating whether safety can be transferred through shared representation geometry. Beyond a single global direction, we also identify a multi-vector extension that captures category-specific safety behaviors, enabling more selective control. We evaluate our approach in text-to-image and text-to-video generation across diverse source-target model pairs. Across models, transferred safety directions achieve ASR reduction and CLIP-Score/FID trade-offs comparable to directions learned natively on the target model using unsafe data, while requiring no target-side unsafe data. This indicates that safety improvements do not come at the expense of generation quality. Our results point to a modular view of safety: safety-relevant behavior is not purely model-local, but can be controlled through latent directions that persist across models. This suggests a new path toward lightweight, reusable safety mechanisms that do not require target-side unsafe data.

Chinese Translation

最近，生成建模的进展使得安全控制成为一个中心挑战，但现有的方法在很大程度上仍然是模型特定的，要求为每个新架构进行再培训或量身定制的干预。在本研究中，我们探讨安全性是否可以表示为可移植的潜在方向，能够一次性学习并在异质生成器之间重复使用。我们提出了跨模型安全引导的第一个框架，在该框架中，安全方向是通过成对的安全-不安全提示在源大型语言模型（LLM）中估计的，通过只在良性数据上拟合的轻量级对齐传输到目标生成器，并在推理时应用。关键是，我们的流程从未访问目标侧的不安全数据，以分离安全性是否可以通过共享表征几何进行转移。除了单一的全局方向外，我们还确定了一种多向量扩展，捕捉特定类别的安全行为，使得控制更加精确。我们在多个源-目标模型对之间评估了我们的方法，包括文本到图像和文本到视频的生成。在不同模型中，转移的安全方向实现了ASR减少和CLIP-Score/FID权衡，表现出与在目标模型上使用不安全数据原生学习的方向相当的效果，同时无需目标侧的不安全数据。这表明安全性改进并未以生成质量为代价。我们的结果指向安全性的模块化视角：安全相关行为并非完全模型局部的，而是可以通过在模型之间持续存在的潜在方向进行控制。这表明了一条新的路径，朝向轻量级、可重用的安全机制，而不需要目标侧的不安全数据。

View on arXiv Download PDF AI Translation

cs.CV / 5 / 2606.05347

TopoPult-SSL: Gland-Mask-Free Cross-Device Meibomian Gland Segmentation via Self-Distilled Weak Clinical Priors

TopoPult-SSL：通过自我蒸馏弱临床先验实现无腺体掩膜的跨设备梅氏腺分割

Savioli, Nicolò, Del Tongo, Luca

Abstract

Every new clinical imaging device creates a domain shift where dense gland masks are expensive yet cheap clinical signals -- eyelid outlines, Pult grades, morphometric ratios -- are routinely recorded. We present TopoPult-SSL, a two-stage framework for cross-device meibomian gland segmentation. Stage 1 adapts a source-trained model without target gland masks in the training loss, using four weak-prior anchors driven by target eyelid masks and clinical metadata only. Stage 2, when target gland masks are available, distils complementary Stage-1 teachers into a single compact student via supervised self-distillation. We develop and validate the technique on the public MGD-1k to CAMG research benchmark (1,000 to 100 images, different device), where the distilled model achieves Dice 0.716+/-0.006 (best 0.726), surpassing UA-MT (0.710) and the ensemble teacher (0.720) -- with a single pass. The gland-mask-free Stage-1 variant reaches Precision 0.694 vs. 0.30-0.34 for SAM/MedSAM (p<0.001), enabling deployment without dense gland contouring. Code and reproducibility scripts are released.

Chinese Translation

每一种新的临床成像设备都会带来领域偏移，其中密集的腺体掩膜昂贵，而廉价的临床信号（如眼睑轮廓、Pult等级、形态比）则会被常规记录。我们提出了TopoPult-SSL，这是一种用于跨设备梅氏腺分割的两阶段框架。第一阶段在训练损失中不使用目标腺体掩膜，而是仅借助目标眼睑掩膜和临床元数据，结合四个弱先验锚点来调整源训练模型。第二阶段在目标腺体掩膜可用时，通过监督自我蒸馏将互补的第一阶段教师蒸馏为一个紧凑的学生模型。我们在公共MGD-1k至CAMG研究基准（1,000至100张图像，不同设备）上开发和验证了这一技术，其中蒸馏后的模型实现了Dice 0.716+/-0.006（最佳0.726），超越了UA-MT（0.710）和集成教师（0.720）—仅需一次传递。无腺体掩膜的第一阶段变体实现了准确率0.694，而SAM/MedSAM的准确率为0.30-0.34（p<0.001），使得无需密集的腺体轮廓即可进行部署。代码和可复现性脚本已发布。

View on arXiv Download PDF AI Translation

cs.CV / 6 / 2606.05354

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation

LightVesselNet：一种超轻量级的亚10万参数网络用于视网膜血管分割

Sobhan, Shadman, Jalil, Farhana

Abstract

Retinal blood vessel segmentation plays a vital role in the early detection of diabetic retinopathy and glaucoma. While recent deep learning models have achieved great segmentation accuracy, they typically require heavy computational resources, making real-world deployment on edge devices difficult. In this paper, we propose LightVesselNet, an efficient neural network designed for retinal vessel segmentation in a resource-constrained environment. Despite containing only 75K parameters, LightVesselNet performs competitively with much larger models. The network employs a compact encoder decoder architecture enhanced with channel and spatial attention mechanisms, a multi-scale feature aggregation module at the bottleneck, and a subpixel upsampling strategy in the decoder. A dedicated edge residual connection preserves fine vessel detail throughout decoding. Extensive experiments on five publicly available datasets: DRIVE, STARE, CHASEDB1, FIVES, and HRF, yield sensitivity scores of 0.8189, 0.8499, 0.8640, 0.8634, 0.8096, and Dice coefficients of 0.8070, 0.8072, 0.8181, 0.8649, and 0.7686, respectively. LightVesselNet shows improved efficiency (Performance vs Parameter or GFlops) compared to State-of-the-Art models. Cross-dataset evaluation confirms the model's generalisation capability. Overall, LightVesselNet is a strong candidate for deployment in low-resource clinical settings and mobile screening tools.

Chinese Translation

视网膜血管分割在糖尿病视网膜病变和青光眼的早期检测中发挥着至关重要的作用。尽管最近的深度学习模型在分割准确性上取得了显著进展，但它们通常需要大量的计算资源，使得在边缘设备上的实际部署变得困难。本文提出了LightVesselNet，这是一种为资源受限环境设计的高效神经网络，专注于视网膜血管分割。尽管仅包含75K参数，LightVesselNet的性能却与更大模型相当。该网络采用紧凑的编码器-解码器架构，并增强了通道和空间注意机制，在瓶颈处引入了多尺度特征聚合模块，并在解码器中使用了亚像素上采样策略。专用的边缘残差连接保持了解码过程中细微血管细节。对五个公开可用数据集（DRIVE、STARE、CHASEDB1、FIVES和HRF）进行的广泛实验显示，敏感性得分分别为0.8189、0.8499、0.8640、0.8634和0.8096，Dice系数分别为0.8070、0.8072、0.8181、0.8649和0.7686。与当前最先进的模型相比，LightVesselNet在效率（性能与参数或GFlops比）方面显示出改善。跨数据集评估证实了该模型的泛化能力。总体而言，LightVesselNet是低资源临床环境和移动筛查工具部署的强有力候选者。

View on arXiv Download PDF AI Translation

cs.CV / 7 / 2606.05359

Recovering Physically Plausible Human-Object Interactions from Monocular Videos

从单目视频中恢复物理可信的人体与物体交互

Huang, Dingbang, Vouga, Etienne, Huang, Qixing, Pavlakos, Georgios

Abstract

In this paper, we propose RePHO, a method to reconstruct physically plausible human-object interactions (HOI) from monocular videos. While existing kinematic-based approaches produce visually plausible motion, they often result in physically implausible artifacts such as interpenetration and object floating. To overcome these issues, we introduce a physics-guided reconstruction framework. We begin with a kinematic estimate and then refine it by training a policy with reinforcement learning (RL). This policy is optimized to reproduce the interaction in a physics simulator. Because kinematic estimates are typically noisy, naive RL training can fail. Therefore, we propose an adaptive sampling strategy with a dual self-updating mechanism that can identify the frames with the most informative and reliable kinematic reconstruction. Our process progressively improves reconstruction quality and yields physically consistent HOI sequences. We demonstrate our approach on two standard HOI benchmarks and achieve clear improvements in physical plausibility metrics over state-of-the-art methods. Project Page: https://dingbang777.github.io/RePHO/

Chinese Translation

在本文中，我们提出了一种方法RePHO，用于从单目视频中重建物理可信的人体与物体交互（HOI）。尽管现有的基于动力学的方法能够生成视觉上可信的运动，但它们往往导致物理上不可信的伪像，例如物体间的穿透和漂浮现象。为了解决这些问题，我们引入了一种物理引导的重建框架。我们首先进行动力学估计，然后通过训练一个强化学习（RL）策略来进行优化。该策略旨在在物理模拟器中重现互动。由于动力学估计通常较为嘈杂，简单的强化学习训练可能会失败。因此，我们提出了一种自适应采样策略，配合双自我更新机制，以识别具有最具信息性和可靠性的动力学重建的帧。我们的过程逐步改善重建质量，并生成物理一致的人体与物体交互序列。我们在两个标准的HOI基准上验证了我们的方法，并在物理可信性指标上显著优于现有的先进方法。项目页面：https://dingbang777.github.io/RePHO/

View on arXiv Download PDF AI Translation

cs.CV / 8 / 2606.05368

Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin

Biomazon：亚马逊流域3D森林结构与生物量建模的多模态数据集

Mandal, Sayan, Sedona, Rocco, Besnard, Simon, Urbazaev, Mikhail, Riedel, Morris, Zandi, Ehsan, Cavallaro, Gabriele

Abstract

Accurate, spatially explicit characterization of tropical forest structure is essential for carbon accounting and ecosystem monitoring, yet most ML pipelines predict canopy-top height proxies (e.g., RH95/RH98) or AGBD as separate scalar targets, rather than learning the forest vertical structure as an ordered profile. The community lacks a ML-ready multimodal benchmark for predicting the entire GEDI RH profile jointly with AGBD, or for evaluating methods that enforce physically consistent ordering across RH percentiles. We address this with Biomazon, a 20 m multimodal benchmark dataset over the Amazon Basin that pairs GEDI RH and AGBD targets with multi-sensor predictors (Sentinel-1/2, ALOS-2 PALSAR-2, Copernicus DEM, Dynamic World LULC, and AlphaEarth embeddings) under standardized spatial splits and evaluation protocols. Using a shared encoder-decoder with task-specific heads as a baseline framework, we conduct a comprehensive ablation study of (i) backbone/model scale, (ii) modality contributions, and (iii) the use of auxiliary embeddings under standalone and fusion settings, and we report both single-target and joint-target results to quantify tradeoffs under a unified training protocol. Finally, we contextualize baseline performance through regionally aligned comparisons against existing gridded products, including GEDI L4D RH10-RH98 and AGBD, at matching temporal scale. Biomazon, together with the accompanying protocols and baseline results, establishes a reference benchmark for future work on structurally consistent RH-profile prediction and structure-biomass modeling in tropical forests.

Chinese Translation

准确、空间明确的热带森林结构特征描述对于碳计量和生态系统监测至关重要，然而大多数机器学习（ML）管道预测的是天顶高度代理（如 RH95/RH98）或生物量密度（AGBD）作为独立的标量目标，而非学习森林的垂直结构作为有序剖面。当前社区缺乏一个针对联合预测整个GEDI RH剖面与AGBD的适用于机器学习的多模态基准，或用于评估在RH百分位中强制物理一致性排序的方法。为此，我们提出了Biomazon，一个覆盖亚马逊流域的20米多模态基准数据集，该数据集将GEDI RH和AGBD目标与多传感器预测变量（Sentinel-1/2、ALOS-2 PALSAR-2、Copernicus DEM、Dynamic World LULC和AlphaEarth嵌入）配对，并在标准化的空间划分和评估协议下进行整理。我们使用一个共享的编码-解码器，并结合特定任务的输出头作为基线框架，开展了关于（i）主干/模型规模、（ii）模态贡献，以及（iii）在独立和融合设置下使用辅助嵌入的全面消融研究，并报告了单目标和联合目标结果，以量化在统一训练协议下的权衡。最后，我们通过与现有网格产品（包括 GEDI L4D RH10-RH98 和 AGBD）的区域性对比，在匹配的时间尺度上对基线表现进行了背景化。Biomazon及其附带的协议和基线结果，建立了未来在热带森林中进行结构一致的RH剖面预测和结构-生物量建模的参考基准。

View on arXiv Download PDF AI Translation

cs.CV / 9 / 2606.05375

Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography

OCT血管成像中的三维视网膜微血管恢复

Guo, Yukun, Gao, Min, Hormel, Tristan T., Bailey, Steven T., Hwang, Thomas S., Jia, Yali

Abstract

Optical coherence tomographic angiography (OCTA) is a powerful technique for imaging retinal microvasculature. However, acquiring reliable quantification of retinal blood flow and areas of retinal nonperfusion is challenging because of imaging artifacts. Existing methods primarily focus on noise suppression, projection artifact removal, or signal enhancement to improve the image quality of OCTA in cross-sectional or two-dimensional (2D) en face projections, while neglecting the intrinsic three-dimensional vascular architecture. In this study, we propose a deep learning-based algorithm for restoring capillary anatomical vasculature from a single OCTA volume. The network consists of an EfficientNet-B5 encoder and a decoder incorporating concurrent spatial and channel squeeze-and-excitation modules, connected via skip connections to preserve spatial resolution. Three adjacent B-frames are used as input to predict the restored middle B-frame. We evaluated the performance of the model using the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) against ground truth generated from averaging multiple scans. The results show that the proposed model significantly (both p < 0.001) improved image quality compared with the original single OCTA volume, with a PSNR of 26.16 +/- 1.26 vs. 22.23 +/- 0.78 and an SSIM of 0.91 +/- 0.02 vs. 0.72 +/- 0.03. The proposed model also significantly (p < 0.001) improved microvascular fidelity, measured by the Dice coefficient overlap between the model output and ground truth, in both 2D and 3D by at least 3.8% and 51.2%, respectively, across several different vascular slabs.

Chinese Translation

光学相干断层扫描血管成像（OCTA）是一种对视网膜微血管进行成像的强大技术。然而，由于成像伪影，可靠地量化视网膜血流和视网膜无灌注区的难度很大。现有方法主要集中于噪声抑制、投影伪影去除或信号增强，以提升OCTA在横截面或二维（2D）面状投影中的图像质量，而忽视了内在的三维血管结构。本研究提出了一种基于深度学习的算法，从单个OCTA体积中恢复毛细血管解剖血管。该网络由一个EfficientNet-B5编码器和一个结合并行空间与通道挤压激励模块的解码器组成，通过跳跃连接以保留空间分辨率。我们使用三个相邻的B帧作为输入，预测恢复的中间B帧。我们使用峰值信噪比（PSNR）和结构相似性指数（SSIM）来评估模型的性能，并与通过多个扫描平均生成的地面真值进行对比。结果显示，所提模型的图像质量显著提高（p < 0.001），PSNR为26.16 +/- 1.26，相较于原始单个OCTA体积的22.23 +/- 0.78，SSIM为0.91 +/- 0.02，而原始为0.72 +/- 0.03。所提出的模型在微血管保真度上也显著提高（p < 0.001），通过模型输出与地面真值之间的Dice系数重叠计算，在2D和3D中分别提高了至少3.8%和51.2%，在几个不同的血管层中均如此。

View on arXiv Download PDF AI Translation

cs.CV / 10 / 2606.05379

Deep Learning-assisted AMD Staging based on OCT and OCT Angiography

基于光学相干断层扫描和光学相干断层扫描血管成像的深度学习辅助年龄相关性黄斑变性分期

Guo, Yukun, Hormel, Tristan T., Wu, An-Lun, Gao, Liqin, Gao, Min, Bailey, Steven T., Jia, Yali

Abstract

To develop and evaluate deep learning models for automated grading of age-related macular degeneration (AMD) severity using optical coherence tomography (OCT) and OCT angiography (OCTA) data. Two hundred seventy-one participants aged >= 50 years with varying AMD severities. Central macular 6 x 6 mm OCT/OCTA volumes were acquired using a swept-source OCTA system (SOLIX; Visionix/Optovue Inc., CA). AMD severity was graded into four stages (No AMD, Early AMD, Intermediate AMD, and Advanced AMD) according to the AREDS simplified severity scale. Three deep learning models were developed using different input modalities: (1) biomarker maps derived from segmented pathological features, including retinal fluid, drusen, geographic atrophy (GA), and macular neovascularization (MNV); (2) two-dimensional (2D) en face OCT and OCTA projections; and (3) three-dimensional (3D) OCT/OCTA volumes. EfficientNet-based architectures were trained using normalized inputs, data augmentation, and five-fold cross-validation. A total of 2,030 OCT/OCTA volumes from 351 eyes of 271 participants were analyzed. All models demonstrated strong AMD staging performance with substantial agreement with the reference standard (QWK >= 0.83). The biomarker-based model achieved the highest overall performance (QWK = 0.85 +/- 0.03, mean +/- standard deviation) and the best detection of early AMD (F1-score = 0.59 +/- 0.14). The 3D model achieved performance comparable to the 2D OCT/OCTA model (QWK = 0.83 +/- 0.04 vs. 0.83 +/- 0.09), while the 2D OCT/OCTA model showed the highest precision (0.79 +/- 0.06) and most accurately identified eyes without AMD. Deep learning models using OCT/OCTA data can accurately and automatically grade AMD severity. Among the evaluated approaches, the biomarker-based model provided the most balanced performance and showed particular value for early AMD detection.

Chinese Translation

本研究旨在开发和评估深度学习模型，以利用光学相干断层扫描（OCT）和光学相干断层扫描血管成像（OCTA）数据进行年龄相关性黄斑变性（AMD）严重程度的自动分级。共有271名年龄在50岁及以上的参与者，具有不同严重程度的AMD。使用一种扫频光源OCTA系统（SOLIX；Visionix/Optovue Inc., CA）采集中心黄斑6 x 6 mm的OCT/OCTA体积图像。根据AREDS简化严重程度评分标准，AMD严重程度被分为四个阶段（无AMD、早期AMD、中期AMD和晚期AMD）。建立了三种采用不同输入模态的深度学习模型：（1）从分割病理特征导出的生物标志物图，包括视网膜液体、黄斑变性斑（drusen）、地理性萎缩（GA）和黄斑新生血管（MNV）；（2）二维（2D）平面OCT和OCTA投影；（3）三维（3D）OCT/OCTA体积。基于EfficientNet的架构使用归一化输入、数据增强和五折交叉验证进行训练。共分析了来自271名参与者的351只眼的2030个OCT/OCTA体积数据。所有模型均表现出强劲的AMD分期性能，并与参考标准具有显著一致性（QWK >= 0.83）。基于生物标志物的模型实现了最高的总体性能（QWK = 0.85 +/- 0.03，均值 +/- 标准差）及最佳早期AMD检测（F1-score = 0.59 +/- 0.14）。三维模型的性能与二维OCT/OCTA模型相当（QWK = 0.83 +/- 0.04 vs. 0.83 +/- 0.09），而二维OCT/OCTA模型表现出最高的精准度（0.79 +/- 0.06），并最准确地识别出没有AMD的眼睛。基于OCT/OCTA数据的深度学习模型能够准确且自动地分级AMD的严重程度。在评估的各个方法中，基于生物标志物的模型提供了最均衡的性能，并在早期AMD检测方面显示出特别的价值。

View on arXiv Download PDF AI Translation

cs.CV / 11 / 2606.05399

UniPixie: Unified and Probabilistic 3D Physics Learning via Flow Matching

UniPixie：通过流匹配实现统一的概率性三维物理学习

Huang, Qilin, Huynh, Quynh Anh, Le, Long, Wang, Chen, Chen, Chuhao, Lucas, Ryan, Eaton, Eric, Liu, Lingjie

Abstract

Existing feed-forward networks excel at predicting a single set of physical properties from visual appearance, but this point-estimate paradigm fundamentally fails to capture the real world's inherent physical ambiguity. We address this by reframing physics prediction as a task of learning a controllable, continuous distribution of material properties. We introduce UNIPIXIE, a framework trained to predict a continuous and parameterized path of physically plausible material properties from a single visual input. By learning a direct mapping along an object's softest-to-stiffest spectrum on our PIXIEMULTIVERSE dataset, UNIPIXIE allows for controllable generation of diverse, physically valid material fields via a single intuitive parameter. Crucially, UNIPIXIE introduces a novel unified architecture to produce simulation-ready parameters for diverse physics solvers, including continuum-based Material Point Method (MPM), reduced-order deformation based on Linear Blend Skinning (LBS), and anchor-based Spring-Mass systems, addressing a key portability issue in prior work. Experiments show our approach not only generates a rich variety of plausible dynamics but also reduces Young's Modulus prediction error by over 50% against the strongest deterministic baseline, bridging the gap between static point estimates and the continuous nature of physical reality. Project page: https://unipixie.github.io/

Chinese Translation

现有的前馈网络在从视觉外观预测单一物理属性方面表现出色，但这一点估计范式在本质上无法捕捉现实世界固有的物理模糊性。我们通过重新定义物理预测为学习物质属性的可控、连续分布的任务来解决这一问题。我们提出了UNIPIXIE，一个框架，旨在从单一视觉输入预测连续且参数化的物理上合理的物质属性路径。通过在我们的PIXIEMULTIVERSE数据集上学习沿物体从最柔软到最坚硬的光谱的直接映射，UNIPIXIE让用户能通过一个直观的参数可控地生成多样化的物理有效物质场。至关重要的是，UNIPIXIE引入了一种新颖的统一架构，以产生适合多种物理求解器的模拟准备参数，包括基于连续介质的物质点法（Material Point Method, MPM）、基于线性混合蒙皮（Linear Blend Skinning, LBS）的降阶变形方法，以及基于锚点的弹簧-质量系统，从而解决了之前工作中一个关键的可移植性问题。实验表明，我们的方法不仅能生成丰富多样的合理动态，而且在对比最强确定性基线时，使得杨氏模量的预测误差减少超过50%，弥合了静态点估计与物理现实的连续性之间的差距。项目页面：https://unipixie.github.io/

View on arXiv Download PDF AI Translation

cs.CV / 12 / 2606.05409

Would you still call this Dax? Novel Visual References in VLMs and Humans

你还会称呼它为 Dax 吗？视觉语言模型与人类中的新视觉参考

Tür, Ada Defne, Kamath, Gaurav, Chai, Joyce, Reddy, Siva, Krojer, Benno

Abstract

Vision-language models (VLMs), like human learners, are frequently exposed to new visual concepts, but how they map novel visual references to language after exposure remains largely underexplored, particularly when those references contradict prior knowledge from pre-training. To study this, we present the Novel Visual References Dataset (NVRD): 19,176 images spanning 90 visual concepts across different levels of visual novelty, each with up to 20 increasingly perturbed versions of the original object to probe generalization. Unlike prior work on visual augmentations of familiar concepts, NVRD comprises entirely novel, open-ended stimuli constructed from scratch, mirroring how humans encounter genuinely new concepts. We evaluate 3 open- and 2 closed-source models alongside 2,400 human judgments for direct human-model comparison, and find that (i) models struggle to acquire novel concepts in-context when they contradict prior knowledge, and (ii) while models and humans show correlated sensitivity to visual perturbations, models significantly overgeneralize, extending learned labels to stimuli that humans reject. We contribute NVRD as a corpus and benchmark for research on visual concept learning in both humans and machines.

Chinese Translation

视觉语言模型（VLMs）像人类学习者一样，常常接触新的视觉概念，但在接触后它们如何将新视觉参考映射到语言上，仍然大部分未被探讨，特别是当这些参考与预训练期间的先前知识相矛盾时。为了研究这一问题，我们提出了新视觉参考数据集（Novel Visual References Dataset, NVRD）：该数据集包含19,176张图像，覆盖90个不同视觉新颖性水平的视觉概念，每个概念有多达20个原始对象的逐渐扰动版本，以探测模型的泛化能力。与以往关于熟悉概念视觉增强的研究不同，NVRD完全由新颖的、开放式的刺激构成，旨在反映人类是如何接触真正新颖的概念的。我们评估了3个开放源代码和2个闭源代码的模型，并结合2,400个人工判断进行人类与模型之间的直接比较，发现（i）当模型在上下文中获取新概念与先前知识相矛盾时存在困难，及（ii）尽管模型和人类对视觉扰动表现出相关的敏感性，但模型显著过度泛化，将学习到的标签扩展到人类所拒绝的刺激上。我们将NVRD作为一个语料库和基准，贡献于人类和机器在视觉概念学习方面的研究。

View on arXiv Download PDF AI Translation

cs.CV / 13 / 2606.05455

Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classification

面向不完整图像-表格分类的解耦细粒度原型学习

Zhou, Feixiang, Xie, Jianyang, Gao, Zhuangzhi, Yu, Qinkai, Wang, Fu, Fan, Yuheng, Li, Jing, Jiang, Zheheng, Zhao, Yitian, Meng, Yanda, Zhao, He, Lip, Gregory Y. H., Zheng, Yalin

Abstract

The missing-modality problem poses a significant challenge in image-tabular multimodal learning across a wide range of multimedia applications, including product understanding, recommendation systems, and medical diagnosis. This challenge is particularly pronounced when the two modalities are highly heterogeneous, as images and tabular attributes differ substantially in their semantic granularity and data distributions. Existing methods learn modality-invariant representations through disentanglement and alignment over global token-averaged features, capturing only coarse cross-modal consistency and overlooking fine-grained semantic and distributional misalignment, which hampers the exploitation of complementary cues under missing modalities. To address this, we propose DFPL, a novel framework for fine-grained prototype learning. Specifically, Shared-Specific Prototype Modeling (SSPM) extracts compact and diverse shared and modality-specific prototypes, and further performs prototype-level disentanglement to suppress redundant intra-modality correlations. Additionally, we propose a Prototype-guided Fine-grained Alignment (PFA) module that jointly enforces prototype-level distribution matching and prototype-to-class semantic alignment within a unified prototype space, thereby preserving both fine-grained distributional and semantic consistency across modalities. We further introduce a Class-aware Multi-scale Aggregation (CMA) module to adaptively aggregate shared semantics and modality-specific characteristics from global and prototype levels for robust predictions. Extensive experiments on three diverse image-tabular benchmarks demonstrate the superiority of our method compared to the previous approaches under various missing-modality settings. Code will be made publicly available.

Chinese Translation

缺失模态问题在图像-表格多模态学习中构成了重大挑战，这一问题在产品理解、推荐系统和医学诊断等多种多媒体应用中尤为突出。当两种模态高度异构时，该挑战尤为明显，因为图像与表格属性在语义粒度和数据分布上存在显著差异。现有方法通过对全局令牌平均特征进行解耦和对齐来学习模态不变表示，仅捕获粗略的跨模态一致性，而忽视了细粒度的语义和分布不对齐，这妨碍了在缺失模态下对互补线索的利用。为此，我们提出了一种新颖的细粒度原型学习框架DFPL。具体而言，共享-特定原型建模（Shared-Specific Prototype Modeling，SSPM）提取紧凑且多样的共享和模态特定原型，并进一步执行原型级解耦，以抑制冗余的模态内相关性。此外，我们提出了一种原型引导的细粒度对齐（Prototype-guided Fine-grained Alignment，PFA）模块，该模块在统一的原型空间内协同强制执行原型级分布匹配和原型与类别的语义对齐，从而在模态间保持细粒度的分布和语义一致性。我们进一步引入了一种类感知多尺度聚合（Class-aware Multi-scale Aggregation，CMA）模块，以适应性地聚合来自全局和原型层面的共享语义和模态特定特征，以实现鲁棒的预测。在三个多样的图像-表格基准上进行的广泛实验表明，在各种缺失模态设置下，我们的方法优于以前的方案。代码将公开提供。

View on arXiv Download PDF AI Translation

cs.CV / 14 / 2606.05458

Horse Eye Blink Detection and Classification for Equine Affective State Assessment

马眼眨动检测与分类用于评估马的情感状态

Alves, João, Møller-Skuldbøl, Signe, Andersen, Pia Haubro, Gade, Rikke

Abstract

Automated detection of equine facial action units (AUs) is a promising yet under-explored avenue for pain and affective state assessment in horses. Half and full-blink movements are recognised indicators of pain and stress, but as micro-expressions, their subtle, fine-grained nature makes them easily missed by the naked eye and only discernible through frame-by-frame video inspection, making reliable automated detection from video a particularly demanding task. We develop and evaluate three methods for automated blink classification from horse videos: a frame-based YOLOv12 detector, an optical flow magnitude thresholding approach, and a fine-tuned VideoMAE model, tested on a publicly available dataset. We achieve a macro-F1 score of 0.898 when doing blink classification and 0.926 on binary blink detection. Our results highlight both the potential and the inherent challenges of fine-grained AU detection for equine welfare monitoring.

Chinese Translation

自动检测马的面部动作单元（AUs）是评估马匹疼痛和情感状态的一条有前景但尚未充分探索的途径。半眨眼和全眨眼动作被认为是疼痛和压力的显著指标，但由于它们是微表情，其细微、精细的特性使得肉眼容易错过，只能通过逐帧视频检查来辨识，因此从视频中进行可靠的自动检测是一项特别具有挑战性的任务。我们开发并评估了三种基于马匹视频的自动眨眼分类方法：基于帧的YOLOv12检测器、光流幅度阈值法和经过微调的VideoMAE模型，并在一个公开可用的数据集上进行测试。在眨眼分类中，我们实现了0.898的宏F1得分，在二元眨眼检测中达到了0.926。我们的结果突显了细粒度AUs检测在马 welfare 监测中既有潜力也面临的固有挑战。

View on arXiv Download PDF AI Translation

cs.CV / 15 / 2606.05460

ORACLE-CT: Anatomy-Aware Support Pooling for CT Classification

ORACLE-CT：基于解剖学的CT分类支持池化

Dahal, Lavsen, Bhandari, Yubraj, Rubin, Geoffrey, Lo, Joseph Y.

Abstract

Abdominal CT disease classification is challenging because each scan is a large 3D volume with many possible findings, while diagnostic evidence is often confined to specific organs or anatomical compartments. Most study-level classifiers aggregate encoder features using anatomy-agnostic pooling or attention, creating a mismatch between localized disease evidence and global evidence aggregation. We propose ORACLE--CT, an encoder-agnostic anatomy-aware aggregation framework that uses multi-organ segmentation to define label-specific anatomical supports and restrict attention pooling to relevant regions. The framework supports single-organ, multi-organ union, comparative, localized, and global support strategies. We evaluate ORACLE--CT with three encoder families: DINOv3, I3D--ResNet-121, and the radiology-native Pillar--0 encoder. Models are trained end-to-end on MERLIN and evaluated internally and under frozen external transfer to Duke--Abdomen and AMOS. Compared with global average pooling, support-masked pooling improved MERLIN macro-AUROC/AUPRC from 0.838/0.638 to 0.858/0.676 for DINOv3 and from 0.829/0.617 to 0.848/0.659 for I3D--ResNet-121. On harmonized 10-label external evaluation, DINOv3 improved on Duke--Abdomen from 0.802/0.628 to 0.835/0.683 and on AMOS from 0.742/0.313 to 0.762/0.350, with similar gains for I3D--ResNet-121. For Pillar--0, most gains came from learned attention, with smaller additional benefit from anatomical masking. ORACLE--CT improves discrimination and external robustness while preserving an auditable link between predictions and anatomical evidence.

Chinese Translation

腹部CT疾病分类是一项挑战，因为每个扫描都是一个包含众多潜在发现的大型三维体积，而诊断证据通常局限于特定器官或解剖区间。大多数研究级分类器通过使用无解剖学特征的池化或注意力机制来聚合编码器特征，这导致局部疾病证据与全局证据聚合之间的不匹配。我们提出了ORACLE--CT，这是一种无编码器的基于解剖学的聚合框架，通过多器官分割来定义特定标签的解剖支持，并将注意力池化限制在相关区域。该框架支持单器官、多器官联合、比较、局部和全局支持策略。我们用三种编码器系列评估ORACLE--CT：DINOv3、I3D--ResNet-121和影像学专用的Pillar--0编码器。模型在MERLIN上进行端到端训练，并在内部和冻结外部转移到Duke--Abdomen和AMOS上进行评估。与全局平均池化相比，支持掩蔽池化将MERLIN的宏观AUROC/AUPRC从0.838/0.638提高到0.858/0.676（DINOv3），并将I3D--ResNet-121的AUROC/AUPRC从0.829/0.617提高到0.848/0.659。在协调的10标签外部评估中，DINOv3在Duke--Abdomen上从0.802/0.628提高到0.835/0.683，在AMOS上从0.742/0.313提高到0.762/0.350，I3D--ResNet-121也有类似的提升。对于Pillar--0，大部分提升来自学习到的注意力，解剖掩模带来的额外好处较小。ORACLE--CT提升了鉴别能力和外部鲁棒性，同时维护了预测与解剖证据之间可审计的联系。

View on arXiv Download PDF AI Translation

cs.CV / 16 / 2606.05471

Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning

形式概念格是基于概念学习的良好语义支架

Vemuri, Deepika SN, Adhikari, Sayanta, Saha, Ankit, Kher, Krishn Vishwas, Balasubramanian, Vineeth N

Abstract

Learning semantics is essential for deep learning models to be interpretable and better aligned with human reasoning. Concept-based models approach this by representing classes through meaningful semantic abstractions, but typically treat all concepts as a flat, unstructured set learned at a single neural network layer. This overlooks a fundamental property of human semantic understanding: concepts being organized hierarchically, from general to specific. While deep networks do learn a hierarchy of visual features, this structure is rarely aligned with explicit semantic hierarchies. Drawing on Formal Concept Analysis, we demonstrate that formal concept lattices provide principled semantic scaffolds to guide neural network learning. These lattices naturally identify where in the network concepts should be learned based on their level of generality. This allows the model to develop staged, semantically grounded representations throughout its depth. Empirical results on real-world datasets show that our models produce more interpretable embeddings, support more effective interventions, and learn concept representations that are both meaningful and hierarchically structured.

Chinese Translation

学习语义对深度学习模型的可解释性至关重要，并能更好地与人类推理对齐。基于概念的模型通过有意义的语义抽象来表示类别，但通常将所有概念视为在单个神经网络层次中学习的平面、无结构的集合。这忽视了人类语义理解的一个基本特性：概念是层次化组织的，从一般到具体。尽管深度网络确实学习了视觉特征的层次结构，但这种结构很少与显式语义层次对齐。我们借鉴形式概念分析，展示了形式概念格提供了指导神经网络学习的原则性语义支架。这些格自然识别网络中应根据概念的一般性水平学习的地方。这使得模型能够在其深度中发展分阶段、语义上扎实的表征。在真实世界数据集上的实证结果表明，我们的模型产生了更具可解释性的嵌入，支持更有效的干预，并学习到既有意义又层次化的概念表征。

View on arXiv Download PDF AI Translation

cs.CV / 17 / 2606.05478

Can We Predict The Human Preference For Text-to-Image Content Prior To Generation And Is It Even Useful To Do So?

我们能在生成之前预测人类对文本到图像内容的偏好吗？这样做是否有意义？

Kim, Joong Ho, Mills, Keith G.

Abstract

Diffusion Models (DM) have revolutionized text-driven generation by enabling the synthesis of high-quality, photorealistic visual content from user prompts. Whereas prior advances in visual generation such as VAEs and GANs were primarily evaluated on perceptual or visual similarity metrics such as FID PSNR, DM advances have fostered the development of more advanced Human Preference Metrics (HPM) that model and quantify human judgment as scalar values. However, DMs synthesize content using an inherently stochastic process where random noise seeds generation. The initial random noise directly affects the quality of generated outputs, both qualitatively and quantitatively. This influence is pronounced in smaller models for local deployment scenarios. Given this phenomenon, we first investigate to what extent we can predict scalar HPM scores prior to committing compute resources for generation. Further, we then investigate to what extent we can leverage such prediction to improve the quality of generated images, and also study which HPMs are best suited for this task. Our investigation reveals that not only is this possible, but that it is feasible to achieve negligible hardware overhead.

Chinese Translation

扩散模型（Diffusion Models, DM）通过使用户提示能够合成高质量、逼真的视觉内容，彻底改变了以文本为驱动的生成。以往在视觉生成领域的进展，例如变分自编码器（VAEs）和对抗生成网络（GANs），主要基于感知或视觉相似性指标如弗雷歇距离（FID）和峰值信噪比（PSNR）进行评估，而DM的进展则促使了更为复杂的人类偏好度量（Human Preference Metrics, HPM）的发展，以标量值建模和量化人类判断。然而，DM通过一种基本随机过程合成内容，其中随机噪声生成种子直接影响生成输出的质量，无论是定性还是定量。在本地部署场景中，这种影响在较小的模型中尤为明显。鉴于这一现象，我们首先探讨在投入计算资源生成之前，能够在多大程度上预测标量HPM分数。进一步地，我们研究如何利用这种预测来提高生成图像的质量，同时探讨哪些HPM最适合执行这一任务。我们的研究表明，不仅有可能实现这一点，而且能够在几乎无硬件开销的情况下达到可行性。

View on arXiv Download PDF AI Translation

cs.CV / 18 / 2606.05489

LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval

基于大语言模型的人工神经网络索引优化用于人机交互检索

Esmat, Shahrzad, Lacewell, Chaunte W., Gobriel, Sameh, Jain, Nilesh, Jannesari, Ali

Abstract

Retrieval systems underpin modern AI applications -- spanning visual search, recommendation engines, and multi-modal question answering. Modern multi-stage retrieval systems require the joint optimization of highly coupled parameters, yet traditional hyperparameter optimization (HPO) methods -- including Tree-structured Parzen Estimators (TPE) and Gaussian Process Bayesian Optimization -- rely on an independence assumption that fundamentally prevents them from navigating these coupled configuration spaces. We address this limitation with a phase-aware large language model (LLM) agent that conditions each proposal on its full optimization history, navigating the coupled parameter space across phase-partitioned exploration, exploitation, and fine-tuning stages. Evaluated on the HICO-DET human-object interaction retrieval benchmark using Intel VDMS (Visual Data Management System), our agent outperforms Optuna TPE by +33.3% and VDTuner by +34.2% under SIEVE (Safeguarded Index Evaluation of Vector-search Efficiency, a quality-constrained throughput metric), delivering a 15.3x throughput gain over UniIR. Validation across three benchmarks confirms that the agent's advantage grows with the degree of parameter coupling: +33.3% on HICO-DET (high coupling), methods converge within 1% on GLDv2 (moderate coupling) and within 3.6% on SIFT1M (near-independent control). Cross-system validation on Milvus confirms the optimizer ranks first on all three datasets without modification, demonstrating transferability across vector database management system (VDBMS) platforms.

Chinese Translation

检索系统是现代人工智能应用的基础，涵盖视觉搜索、推荐引擎和多模态问答等领域。现代多阶段检索系统需要对高度耦合的参数进行联合优化，但传统的超参数优化（HPO）方法，包括树结构帕尔岑估计器（TPE）和高斯过程贝叶斯优化，依赖于独立假设，这本质上限制了它们在耦合配置空间中的导航能力。我们通过一个具有阶段感知能力的大语言模型（LLM）代理来解决这一限制，该代理根据其全部优化历史对每个提案进行条件处理，在经过阶段划分的探索、利用和微调阶段中导航耦合参数空间。在使用Intel VDMS（视觉数据管理系统）评估的HICO-DET人机交互检索基准上，我们的代理相较于Optuna TPE提高了33.3%，相较于VDTuner提高了34.2%，在SIEVE（受保护的向量搜索效率评估，一种质量约束的吞吐量指标）下实现了15.3倍的吞吐量提升。对三个基准的验证确认了该代理的优势随着参数耦合程度的增加而增长：在HICO-DET（高耦合）上提高了33.3%，在GLDv2（中等耦合）上方法收敛于1%以内，在SIFT1M（近独立控制）上收敛于3.6%以内。在Milvus上的跨系统验证确认该优化器在所有三个数据集上均名列第一，且无需修改，展示了在向量数据库管理系统（VDBMS）平台上的可迁移性。

View on arXiv Download PDF AI Translation

cs.CV / 19 / 2606.05491

Unpaired RGB-Thermal Gaussian-Splatting Using Visual Geometric Transformers

使用视觉几何变换器的非配对RGB-热成像高斯点云法

Cordonnier, Jean, Xu, Chenghao, Fink, Olga, Mielle, Malcolm

Abstract

Multi-modal novel view synthesis (NVS) combining RGB and thermal imagery enables precise 3D scene reconstruction with visual and thermal information. However, existing methods typically rely on precisely calibrated RGB-thermal image pairs or stereo setups, limiting scalability and practical deployment. To address this, we introduce a framework for unpaired RGB-thermal NVS that leverages VGGT, a 3D feed-forward transformer architecture, to independently estimate camera poses for each modality. The pose sets are then aligned using the Procrustes algorithm with a cross-modal feature matcher, enabling joint registration without paired calibration. Building on this alignment, we further propose a multi-modal 3D Gaussian Splatting approach that learns directly from unpaired RGB and thermal images. Experiments on diverse scenes demonstrate that our method achieves competitive performance in thermal view synthesis while maintaining RGB fidelity. Moreover, we show that existing reconstruction approaches can produce modality-specific reconstructions that lack cross-modal consistency. We thus introduce a benchmarking framework to rigorously evaluate both per-modality image synthesis and the multi-modal coherence of reconstructed scenes.

Chinese Translation

结合RGB和热成像的多模态新视角合成（NVS）能够利用视觉和热信息进行精确的3D场景重建。然而，现有方法通常依赖于精确校准的RGB-热成像对或立体设置，这限制了其可扩展性和实际应用。为了解决这一问题，我们引入了一种基于VGGT（视觉几何变换器），一种3D前馈变换器架构的非配对RGB-热成像NVS框架，独立估计每种模态的相机姿态。随后，使用Procrustes算法和跨模态特征匹配器对这些姿态集进行对齐，从而实现无配对校准的联合配准。在此对齐的基础上，我们进一步提出了一种多模态3D高斯点云法，直接从非配对的RGB和热像中学习。对各种场景的实验表明，我们的方法在热视图合成中具有竞争力的表现，同时保持了RGB的保真度。此外，我们展示了现有的重建方法可能会产生缺乏跨模态一致性的模态特定重建。因此，我们引入了一个基准框架，以严格评估每种模态图像合成和重建场景的多模态一致性。

View on arXiv Download PDF AI Translation

cs.CV / 20 / 2606.05506

Robust Scene Transfer for PointGoal Navigation via Privileged Sensor Guided Contrastive Learning

基于优先传感器引导对比学习的点目标导航鲁棒场景迁移

Zhalehmehrabi, Amirhossein, Tezze, Tiziano, Castelini, Alberto, Farinelli, Alessandro

Abstract

We propose a sensor-guided adaptive contrastive learning framework for visual representation learning in PointGoal navigation. During training, privileged LiDAR sensing guides the contrastive objective through a geometry-aware similarity metric and adaptive temperature scaling, encouraging visual embeddings to capture navigation-relevant structure rather than scene-specific appearance. The resulting encoder is pretrained independently, frozen, and used as the perceptual backbone for reinforcement learning, decoupling representation learning from policy optimization. We further introduce a cross-stage domain mismatch between representation pretraining and policy learning to suppress environment-specific shortcuts and promote reliance on task-relevant features. Extensive experiments in high-fidelity simulation demonstrate that our approach significantly improves policy-level scene transfer across diverse indoor and outdoor environments. At deployment, the agent relies only on monocular RGB observations together with standard task-related inputs such as goal position and proprioceptive signals, without access to LiDAR or other privileged sensors. Our method outperforms large pretrained vision models and standard contrastive baselines under severe appearance and semantic shifts. We also release a multimodal dataset to support future research on privileged-guided visual representation learning for navigation. The code is available at:

Chinese Translation

我们提出了一种基于传感器引导的自适应对比学习框架，用于点目标导航中的视觉表征学习。在训练过程中，优先的激光雷达（LiDAR）传感器通过几何感知相似度度量和自适应温度缩放引导对比目标，鼓励视觉嵌入捕捉与导航相关的结构，而非场景特定的外观。所得编码器独立进行预训练、冻结，并用于强化学习的感知骨干，从而将表征学习与策略优化解耦。我们进一步引入了表征预训练与策略学习之间的跨阶段领域不匹配，以抑制环境特定的捷径并促进对任务相关特征的依赖。在高保真模拟中的广泛实验表明，我们的方法显著改善了在多样化室内和室外环境中的策略级场景迁移。在部署时，代理仅依赖单目RGB观测以及目标位置和本体感觉信号等标准任务相关输入，而不访问激光雷达或其他优先传感器。我们的方法在严重的外观和语义偏移条件下表现优于大型预训练视觉模型和标准对比基线。我们还发布了一个多模态数据集，以支持未来在导航中进行优先引导视觉表征学习的研究。代码可在以下链接获取：

View on arXiv Download PDF AI Translation

cs.CV / 21 / 2606.05515

BRepCLIP: Contrastive Multimodal Pretraining on BRep Primitives for CAD Understanding

BRepCLIP：针对 CAD 理解的边界表示原语的对比多模态预训练

Usama, Muhammad, Stricker, Didier, Khan, Mohammad Sadil, Afzal, Muhammad Zeshan

Abstract

Learning representations of CAD models is a largely open problem. While 3D representation learning has flourished around point clouds and meshes, the native format of CAD - boundary representations BReps, which encodes exact parametric surfaces, curves, and their topology, has received little attention as a representation learning substrate. We introduce BRepCLIP, the first framework to align BRep geometry with language and image embeddings through contrastive pretraining. We model each CAD object as a sequence of face and edge tokens with separate discrete vocabularies for surface and curve geometry, augmented with spatial and semantic descriptors that capture surface types (e.g., cylindrical, torus, NURBS) and curve primitives (e.g., line, arc, B-spline). A transformer encoder aggregates these tokens into a global BRep embedding, aligned with CLIP's text and image encoders via a joint contrastive objective. BRepCLIP generates more discriminative and semantically grounded embeddings than existing point-based alternatives, improving Top-1 retrieval over OpenShape by 40.4%, 22.0%, and 23.9% on ABC, CADParser, and Automate, respectively, and improving zero-shot classification on FabWave by 15% in Top-1 score. We further demonstrate its utility as a CAD-aware similarity metric for evaluating text and image-conditioned CAD generation, establishing the importance of structure-aware pretraining for multimodal CAD understanding. Project page is available at https://muhammadusama100.github.io/BrepClip2026/

Chinese Translation

学习 CAD 模型的表示是一个尚未解决的问题。虽然围绕点云和网格的 3D 表示学习已经取得了很大进展，但 CAD 的原生格式 - 边界表示 (BRep)，该格式编码了精确的参数曲面、曲线及其拓扑，作为表示学习的基础却鲜有关注。我们介绍了 BRepCLIP，这是第一个通过对比预训练将 BRep 几何与语言和图像嵌入对齐的框架。我们将每个 CAD 物体建模为面和边的标记序列，并为表面和曲线几何分配了单独的离散词汇表，此外还增强了空间和语义描述符，以捕捉表面类型（例如，圆柱体、圆环、NURBS）和曲线原语（例如，直线、弧、B-样条）。一个变换器编码器将这些标记聚合成一个全球 BRep 嵌入，通过共同的对比目标与 CLIP 的文本和图像编码器对齐。BRepCLIP 生成的嵌入在区分性和语义基础上优于现有的基于点的替代方案，在 OpenShape 上的 Top-1 检索提升了 40.4%、22.0% 和 23.9% （分别对应 ABC、CADParser 和 Automate），并且在 FabWave 上在 Top-1 分数上的零样本分类提高了 15%。我们进一步展示了它作为 CAD 领域相似性度量工具的实用性，以评估基于文本和图像的 CAD 生成，确立了结构意识预训练在多模态 CAD 理解中的重要性。项目页面可访问：https://muhammadusama100.github.io/BrepClip2026/

View on arXiv Download PDF AI Translation

cs.CV / 22 / 2606.05531

Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models

Almieyar-Oryx-BloomBench：一种双语多模态基准，用于基于认知的视觉-语言模型评估

Abootorabi, Mohammad Mahdi, Ghahroodi, Omid, Madkoor, Anas, Nouri, Marzia, Dastgheib, Doratossadat, Hefeeda, Mohamed, Asgari, Ehsaneddin

Abstract

Despite the rapid progress of Vision-Language Models (VLMs), the field lacks benchmarks that rigorously diagnose their true reasoning abilities and chart meaningful progress toward human-like multimodal intelligence. Most existing evaluations focus on piecemeal or disconnected tasks, obscuring critical cognitive weaknesses and providing little insight for targeted improvement. To address this gap, we introduce BloomBench, part of the Almieyar benchmarking series, the first cognitively human-grounded, bilingual (English-Arabic) multimodal benchmark for VLMs. Grounded in Bloom's Taxonomy, BloomBench systematically evaluates six levels of cognition (Remember, Understand, Apply, Analyze, Evaluate, Create) through carefully designed image-question-answer tasks. Built with a semi-automated pipeline and validated through a stratified hybrid quality assurance protocol, it ensures scalability, cultural inclusivity, and linguistic fidelity. Leveraging this framework, we conduct a comprehensive study of state-of-the-art VLMs to diagnose their cognitive profiles. Our analysis reveals a sharp cognitive asymmetry: while state-of-the-art models achieve strong performance ceilings in semantic understanding, they struggle substantially with factual recall and creative synthesis. This demonstrates that current general multimodal proficiency masks deeper limitations in specific cognitive layers. Furthermore, our study highlights a critical performance gap between Arabic and English, exposing limitations in current cross-lingual multimodal reasoning. These findings establish a foundation for developing more cognitively aligned and inclusive VLMs. The benchmark framework and dataset is available at: https://github.com/qcri/Almieyar-Oryx-BloomBench.

Chinese Translation

尽管视觉-语言模型（VLMs）快速发展，但该领域缺乏严格诊断其真实推理能力和描绘通向类人多模态智能的有意义进展的基准。现有的评估大多集中于零散或脱节的任务，掩盖了关键的认知弱点，并对针对性改进提供的见解有限。为了解决这一问题，我们引入了BloomBench，作为Almieyar基准系列的一部分，这是首个基于人类认知的双语（英语-阿拉伯语）VLM多模态基准。BloomBench根植于布鲁姆分类法，通过精心设计的图像-问题-答案任务系统性地评估六个认知层级（记忆、理解、应用、分析、评估、创造）。该基准使用半自动化流程构建，并通过分层混合质量保证协议验证，确保其可扩展性、文化包容性和语言忠实性。利用这一框架，我们对最先进的VLMs进行了全面研究，以诊断其认知特征。我们的分析揭示了明显的认知不对称：尽管最先进的模型在语义理解方面取得了强劲的表现，但在事实回忆和创造性综合方面却显著乏力。这表明当前的通用多模态能力掩盖了特定认知层面的更深层次限制。此外，我们的研究突出表现出阿拉伯语和英语之间的关键性能差距，暴露出当前跨语言多模态推理的局限性。这些发现为开发更具认知一致性和包容性的VLMs奠定了基础。基准框架和数据集可在以下网址获取：https://github.com/qcri/Almieyar-Oryx-BloomBench。

View on arXiv Download PDF AI Translation

cs.CV / 23 / 2606.05535

Noise-Aware Visual Representation Learning for Medical Visual Question Answering

噪声感知的医学视觉表征学习用于医学视觉问答

Pratama, I Putu Adi, Ofoghi, Bahadorreza, Sajjanhar, Atul, Gao, Shang

Abstract

Medical visual question answering (Med-VQA) has strong potential for clinical decision support by enabling AI models to interpret medical images and answer clinically relevant queries. Recent approaches typically connect off-the-shelf vision encoders with large language models (LLMs) through lightweight mapping networks to reduce computational cost. However, these methods often overlook the importance of handling noise and small irrelevant changes in visual representations. To address these challenges, we propose a noise-aware Med-VQA framework that incorporates a denoising autoencoder before visual embeddings are mapped into the input space of an LLM. The denoising autoencoder is pretrained to reconstruct clean visual embeddings from corrupted inputs, encouraging the model to learn robust visual representations that are less sensitive to noise. The resulting embeddings are then projected into the language model embedding space using a multi-layer perceptron (MLP), forming visual prefix tokens that provide image information to the LLM. To enable efficient adaptation without full retraining, we employ parameter-efficient fine-tuning using low-rank adaptation (LoRA). The proposed method is evaluated on the SLAKE and PathVQA benchmarks. Experimental results show improved robustness to noisy input embeddings while maintaining competitive clean performance across multiple evaluation criteria. These findings suggest that learning more robust visual representations can enhance Med-VQA performance and robustness.

Chinese Translation

医学视觉问答（Med-VQA）在临床决策支持中具有巨大的潜力，因为它使人工智能模型能够解读医学图像并回答与临床相关的查询。近期的方法通常通过轻量映射网络将现成的视觉编码器与大型语言模型（LLMs）连接，以降低计算成本。然而，这些方法往往忽视了处理噪声和视觉表征中小的无关变化的重要性。为了解决这些挑战，我们提出了一种噪声感知的Med-VQA框架，该框架在视觉嵌入映射到LLM的输入空间之前，结合了去噪自编码器。去噪自编码器经过预训练，以从损坏的输入中重建干净的视觉嵌入，鼓励模型学习对噪声不那么敏感的稳健视觉表征。最终的嵌入通过多层感知器（MLP）投影到语言模型嵌入空间，形成向LLM提供图像信息的视觉前缀令牌。为了在不进行完整再训练的情况下实现高效适应，我们采用了使用低秩适应（LoRA）的参数高效微调。该方法在SLAKE和PathVQA基准上进行了评估。实验结果表明，在保持竞争性干净性能的同时，能力增强了对噪声输入嵌入的鲁棒性，这些发现表明，学习更稳健的视觉表征能够提高Med-VQA的性能和鲁棒性。

View on arXiv Download PDF AI Translation

cs.CV / 24 / 2606.05536

Dual Feature Decoupling for Fine-Grained OOD Detection

双特征解耦用于细粒度OOD检测

Li, Xiaokun, Huang, Yaping, Guan, Qingji

Abstract

Out-of-distribution detection (OOD) is an indispensable technique when applying machine learning models to real-world scenarios. Most existing OOD detection methods have been developed under the idealized assumption of large inter-class distributional differences, while largely overlooking fine-grained tasks characterized by subtle variations, such as medical image classification and vehicle recognition. The high visual similarity among fine-grained subcategories, together with the interference of background factors, makes OOD detection extremely challenging. To tackle this problem, we propose a novel Dual Feature Decoupling Network (DFDNet), which addresses fine-grained OOD detection from the perspective of feature disentanglement. The proposed DFDNet comprises two key components: a spatial-frequency decoupling module and a reconstruction-guided decoupling module. The spatial-frequency decoupling module is designed to preserve content features that are discriminative for classification while suppressing task-irrelevant style information. On the other hand, the reconstruction-guided decoupling module introduces a novel pixel-level adversarial reconstruction task to further remove low-level, non-discriminative information and enhance category-specific high-level semantic representations. Extensive experiments demonstrate that our method achieves competitive performance improvements on multiple datasets.

Chinese Translation

分布外检测（OOD）是将机器学习模型应用于现实场景时不可或缺的一项技术。大多数现有的OOD检测方法是在假设类别间分布差异较大的理想条件下开发的，而在细粒度任务（如医学图像分类和车辆识别）中，由于存在微妙的变化，往往被大大忽视。在细粒度子类别之间的高视觉相似性以及背景因素的干扰下，OOD检测变得极具挑战性。为了解决这个问题，我们提出了一种新颖的双特征解耦网络（Dual Feature Decoupling Network，DFDNet），从特征解耦的角度处理细粒度OOD检测。所提的DFDNet包含两个关键组件：空间频率解耦模块和重建引导解耦模块。空间频率解耦模块旨在保留有助于分类的内容特征，同时抑制任务无关的风格信息。另一方面，重建引导解耦模块引入了一种新颖的像素级对抗重建任务，以进一步去除低层次的非判别性信息，并增强类别特定的高层语义表示。大量实验表明，我们的方法在多个数据集上实现了竞争性的性能提升。

View on arXiv Download PDF AI Translation

cs.CV / 25 / 2606.05576

UltraVR: A Diagnostic Ultra-Resolution Image-VQA Benchmark for Evidence-Grounded Reasoning

UltraVR：一种用于证据基础推理的超分辨率图像视觉问答基准

Huang, Gexin, Yang, Yanting, Kang, Myeongkyun, Zhao, Beidi, Zhou, Jun, Zhou, Chen, Wang, Gang, Gao, Zu-hua, Li, Xiaoxiao

Abstract

Vision-language models (VLMs) excel on visual question answering and multimodal reasoning benchmarks. Yet their capability on ultra-resolution images - where critical evidence is tiny, subtle, spatially distant, or distributed - remains unclear. Existing evaluations largely report final-answer accuracy, offering limited insight into whether models acquire and integrate the necessary visual evidence. We introduce UltraVR, a diagnostic benchmark for evidence-grounded visual reasoning over ultra-resolution images. UltraVR spans four high-value scenarios: CCTV surveillance, remote sensing (RS), whole-slide image (WSI) pathology, and industrial anomaly detection (AD). These domains pose complementary challenges: fine-grained object grounding in crowded CCTV scenes, long-range spatial comparison in RS, multi-scale evidence navigation in WSI, and subtle irregularity detection in repetitive industrial layouts. Beyond standard QA triples, each instance includes a structured ground-truth chain of thought with step-level questions, intermediate answers, and reasoning labels. These labels decompose reasoning into evidence grounding, local perception, quantification, evidence integration, and decision inference, enabling process-level diagnosis over black-box scoring. Using UltraVR, we evaluate frontier VLMs and show that current models remain far from reliable on ultra-resolution reasoning. Importantly, the structured annotations allow us to localize failures across the visual-to-decision pipeline: errors concentrate in evidence grounding and local perception, while downstream inference often recovers when intermediate visual facts are supplied. These findings demonstrate UltraVR as a diagnostic testbed for measuring not only whether VLMs answer correctly, but where their ultra-resolution reasoning process breaks.

Chinese Translation

视觉-语言模型（VLMs）在视觉问答和多模态推理基准上表现出色。然而，它们在超分辨率图像上的能力——那些关键证据微小、微妙、空间上遥远或分散的情况——仍然不清楚。现有评估主要报告最终答案的准确性，提供了有限的见解，以了解模型是否获得并整合必要的视觉证据。我们引入了UltraVR，这是一个用于超分辨率图像证据基础视觉推理的诊断基准。UltraVR涵盖了四种高价值场景：闭路电视监控、遥感（RS）、全切片图像（WSI）病理学和工业异常检测（AD）。这些领域带来了互补的挑战：在拥挤的闭路电视场景中进行细粒度对象定位，在遥感中进行长距离空间比较，在全切片图像中进行多尺度证据导航，以及在重复的工业布局中进行微妙的不规则性检测。除了标准的问答三元组，每个实例还包括一个结构化的真实思维链，包含逐步问题、中间答案和推理标签。这些标签将推理分解为证据基础、局部感知、量化、证据整合和决策推断，使得在黑箱评分上能够进行过程级的诊断。使用UltraVR，我们评估了前沿的VLMs，并显示当前模型在超分辨率推理上远未可靠。重要的是，结构化的注释允许我们定位视觉到决策流程中的失败：错误集中在证据基础和局部感知上，而下游推断在提供中间视觉事实时往往能够恢复。这些发现表明UltraVR是一个诊断测试平台，不仅测量VLMs是否正确回答问题，还衡量它们的超分辨率推理过程中出现失败的具体环节。

View on arXiv Download PDF AI Translation

cs.CV / 26 / 2606.05586

BMCR: Adaptive Backbone Module Composition via Reinforcement Learning for Remote Sensing Object Detection

BMCR：基于强化学习的自适应骨干模块组合用于遥感目标检测

Liu, Wenlin, Hu, Xikun, Zhong, Ping

Abstract

In remote sensing object detection, Convolutional Neural Networks (CNNs) excel at capturing local details while Vision Transformers (ViTs) are better at global context modeling. However, existing detectors typically rely on a single fixed backbone or a manually designed hybrid architecture, and thus fail to adaptively exploit these complementary strengths across inputs of diverse complexity. To address this limitation, we propose Backbone Module Composition via Reinforcement Learning (BMCR). BMCR dynamically assembles input-adaptive inference paths from reusable modules decomposed from off-the-shelf CNN and ViT backbones. To enable such cross-family composition, we first construct an extensible module toolbox. Specifically, we decompose representative CNN and ViT backbones into reusable functional modules and encapsulate each module with explicit structural, semantic, and computational metadata for compatibility-aware assembly. To bridge the gap between grid-based CNN features and token-based ViT representations, we design a lightweight Optimal Transport (OT) based transition interface that ensures distribution-aware alignment while respecting spatial consistency. The backbone composition process is then formulated as a sequential decision problem, in which a policy network progressively selects task-relevant modules according to intermediate multi-scale observations. To stabilize the joint optimization of reusable modules and the routing policy, we further develop an Adaptive Module Cooperative Optimization (AMCO) strategy that coordinates module updating, routing exploration, and reward assignment during training. On DOTA-v1.0, DOTA-v1.5 and DIOR-R, BMCR achieves 79.31\%, 73.41\% and 71.86\% mAP, respectively, surpassing strong static and dynamic baselines by up to 2.5 points while maintaining competitive efficiency.

Chinese Translation

在遥感目标检测中，卷积神经网络（CNN）擅长捕捉局部细节，而视觉变换器（ViT）在全局上下文建模方面更具优势。然而，现有的检测器通常依赖于单一固定的骨干网络或手动设计的杂交架构，因此未能自适应地利用这些在不同复杂输入上的互补优势。为了解决这一局限性，我们提出了基于强化学习的骨干模块组合方法（BMCR）。BMCR动态地从可重复使用的模块中组装输入自适应的推理路径，这些模块由现成的CNN和ViT骨干网络分解而来。为了实现这种跨类别的组合，我们首先构建了一个可扩展的模块工具箱。具体而言，我们将具有代表性的CNN和ViT骨干网络分解为可重复使用的功能模块，并用明确的结构、语义和计算元数据封装每个模块，以便进行兼容性感知的组合。为了弥合基于网格的CNN特征与基于令牌的ViT表征之间的差距，我们设计了一种轻量级的最优传输（Optimal Transport, OT）过渡接口，确保在尊重空间一致性的同时实现分布感知的对齐。然后，骨干组合过程被形式化为一个序列决策问题，其中策略网络根据中间多尺度观测结果逐步选择与任务相关的模块。为了稳定可重复使用模块和路由策略的联合优化，我们进一步开发了一种自适应模块协作优化（Adaptive Module Cooperative Optimization, AMCO）策略，该策略在训练过程中协调模块更新、路由探索和奖励分配。在DOTA-v1.0、DOTA-v1.5和DIOR-R数据集上，BMCR分别达到了79.31%、73.41%和71.86%的平均精度（mAP），相比于强大的静态和动态基线，最大提升了2.5个百分点，同时保持了竞争力效率。

View on arXiv Download PDF AI Translation

cs.CV / 27 / 2606.05587

HDST-GNN: Heterogeneous Dynamic Spatiotemporal Graph Neural Networks for Multi-Object Tracking in UAV Aerial Imagery

HDST-GNN：用于无人机航空图像中的多目标跟踪的异构动态时空图神经网络

Jiang, Phillip

Abstract

Multi-object tracking (MOT) from UAV imagery presents unique challenges: altitude varies across sequences, objects are small and densely packed, and frequent occlusion causes identity switches. Existing graph-based trackers assume fixed spatial context and treat all objects uniformly, ignoring the heterogeneous lifecycle states of detections, active tracklets, and lost targets. We propose HDST-GNN, a Heterogeneous Dynamic Spatiotemporal Graph Neural Network with three novel contributions. First, Altitude-Adaptive Edge Construction estimates a camera-altitude proxy from mean object area and adjusts the graph connectivity radius accordingly. Second, Heterogeneous Node Representation models detections (Type-D), confirmed tracklets (Type-T), and lost tracklets (Type-L) as distinct node types with dedicated projections and typed edge relations. Third, Occlusion-Gated Temporal Aggregation gates each node's attention contribution by its occlusion confidence, preventing occluded nodes from corrupting neighbour embeddings. HDST-GNN is trained end-to-end with a differentiable Sinkhorn head using joint cross-entropy and triplet loss. On VisDrone2019-MOT with oracle detections, HDST-GNN achieves 94.51% MOTA and 97.24% IDF1, outperforming SORT by +5.0 MOTA points and reducing identity switches by 81%. With real YOLOv8n detections, HDST-GNN reduces identity switches by 49% vs. SORT. Ablation studies confirm the independent contribution of each component.

Chinese Translation

从无人机图像中进行多目标跟踪（MOT）面临独特的挑战：高度在序列中变化、物体体积小且密集、频繁的遮挡导致身份切换。现有的基于图的跟踪器假设固定的空间上下文，并将所有物体视为均匀，忽略了检测、活跃轨迹和丢失目标的异构生命周期状态。我们提出了HDST-GNN，一种具有三项新颖贡献的异构动态时空图神经网络。首先，高度自适应边构造根据平均物体面积估计相机高度代理，并相应调整图的连通半径。其次，异构节点表示将检测（Type-D）、确认轨迹（Type-T）和丢失轨迹（Type-L）建模为不同的节点类型，并配备专用的投影和类型化边关系。第三，遮挡门控时间聚合根据每个节点的遮挡置信度来调节其注意力贡献，阻止被遮挡的节点干扰邻近嵌入。HDST-GNN通过可微分的Sinkhorn头进行端到端训练，使用联合交叉熵和三元组损失。在使用原始检测的VisDrone2019-MOT上，HDST-GNN达到了94.51%的MOTA和97.24%的IDF1，超越SORT 5.0 MOTA点，并减少了81%的身份切换。在使用真实的YOLOv8n检测时，HDST-GNN相较于SORT减少了49%的身份切换。消融研究证实了各个组件的独立贡献。

View on arXiv Download PDF AI Translation

cs.CV / 28 / 2606.05611

What's Under the Skin? Estimating Swine Body Condition

皮下有什么？估计猪体况

Bashar, Mk, Bhatti, Kuljit, Rohrer, Gary, Benjamin, Madonna, Brown-Brandl, Tami, Morris, Daniel

Abstract

Sow body condition is an important indicator for growers as it has a large impact on lactation performance and piglet survival. However, body condition measures used during production, such as visual scoring and calipers, correlate poorly with underlying tissue composition. Ultrasound scans can provide direct measurements of subcutaneous backfat thickness and loin muscle depth, but their operation is labor intensive and not scalable for production. We present PigFormer, an end-to-end two-stage system that takes raw depth frames from a ceiling-mounted RGB-D camera and predicts subcutaneous backfat thickness, loin muscle depth, and total tissue thickness at the last rib. Stage 1 is a geometric front-end that converts raw depth into a standardized height map via SAM3-to-MaskDINO segmentation distillation, ground-plane removal, and orientation normalization. Stage 2 is a Slice Attention Encoder that treats each height map as a sequence of cross-sectional slices and captures spatial relationships along the full dorsal surface. On a multi-site dataset of 319 sow and gilt instances from two facilities, PigFormer achieves 2.43 mm backfat MAE and 3.87 mm overall MAE. It outperforms strong single-stage ResNet-18 and ViT-small baselines. PigFormer offers a practical path toward continuous, automated, non-contact body condition monitoring in commercial swine production. Code is available at https://github.com/iambashar/Pigformer.

Chinese Translation

猪母体况是养殖者的重要指标，因为它对哺乳性能和仔猪存活率有很大影响。然而，生产中使用的体况测量，如视觉评分和卡尺，与基础组织成分的相关性较差。超声扫描可以直接测量皮下背脂厚度和 loin (腰部) 肌肉深度，但其操作劳动密集且不适合生产规模化。我们提出了 PigFormer，一个端到端的两阶段系统，利用从天花板安装的 RGB-D 相机获取的原始深度帧，预测皮下背脂厚度、loin (腰部) 肌肉深度和最后一根肋骨的总组织厚度。阶段1是一个几何前端，通过 SAM3-to-MaskDINO 分割蒸馏、地面平面去除及方向归一化，将原始深度转化为标准化高度图。阶段2是一个 Slice Attention Encoder，将每个高度图视为一系列横截面切片，并捕捉沿整个背面的空间关系。在来自两个设施的319个猪母和小母猪实例的多地点数据集中，PigFormer 达到了2.43 mm 的背脂平均绝对误差 (MAE) 和3.87 mm 的总体平均绝对误差 (MAE)。它的表现优于强大的单阶段 ResNet-18 和 ViT-small 基线。PigFormer 为商业猪生产中的持续、自动化、非接触式体况监测提供了实用的路径。代码可在 https://github.com/iambashar/Pigformer 获得。

View on arXiv Download PDF AI Translation

cs.CV / 29 / 2606.05624

KV-Control: Parameter-Efficient K/V Injection for Trajectory-Controlled Text-to-Motion

KV-Control：用于轨迹控制的参数高效 K/V 注入方法以实现文本到运动的转换

Sun, Tengjiao, Fang, Pengcheng, Zhan, Xiaoyu, Guo, Yanwen, Fu, Dongjie, Cai, Xiaohao, Kim, Hansung

Abstract

Text-conditioned 3D human motion models now synthesize plausible motions from prompts, but practical animation and embodied-agent workflows rarely stop at text: a character may need to follow a sketched root path, hit an end-effector target, or satisfy a multi-joint trajectory while still preserving the gait, style, and intent described by language. This exposes a control trade-off. A trajectory controller should be precise without overwriting the pretrained text-conditioned motion prior, yet existing solutions either duplicate large portions of the generator to regain per-layer control access or move much of the cost to test-time optimization. We introduce KV-Control, a compact attention-side control interface for frozen masked text-to-motion transformers. The key idea is to make geometric constraints available as memory inside self-attention rather than injecting them through a global pose token or enforcing them only at the output side. To support this interface, we co-design a part-tokenized motion substrate and controller: \textbf{PartVQ} learns anatomy-aligned part codebooks, T-Concat exposes each frame--part token as an attention-addressable site, and KV-Control injects control-conditioned key/value memories at every self-attention layer while preserving the pretrained query stream, text cross-attention, FFN, and all backbone weights. The resulting adapter adds only trainable injection parameters atop a shared trajectory encoder, yet tracks root and multi-joint constraints with sub-centimeter accuracy under the inherited refinement protocol while retaining text-conditioned motion quality. KV-Control reframes trajectory conditioning as lightweight memory retrieval, providing a small, precise, and transparent control interface for text-to-motion generation.

Chinese Translation

基于文本的三维人类运动模型现在能够从提示中合成合理的动作，但实际的动画和具身代理工作流程通常不会仅停留在文本上：角色可能需要遵循草绘的根路径、达到末端执行器目标，或满足多关节轨迹，同时仍需保持由语言描述的步态、风格和意图。这揭示了一种控制权衡。轨迹控制器需要在不覆盖预训练的基于文本的运动先验的情况下保持精确，但现有解决方案要么复制生成器的大部分以重新获得每层控制访问，要么将大部分成本转移到测试时优化上。我们提出了 KV-Control，一个用于冻结的掩码文本到运动变换器的紧凑型注意力侧控制接口。关键思想是使几何约束作为自注意力中的记忆可用，而不是通过全局姿势标记注入或仅在输出端强加。为了支持这个接口，我们共同设计了一个部分标记化的运动基础和控制器：**PartVQ** 学习与解剖学对齐的部分代码本，T-Concat 将每帧-部分标记暴露为可通过注意力寻址的位置，而 KV-Control 则在每个自注意力层注入控制条件的键/值记忆，同时保留预训练的查询流、文本交叉注意力、前馈网络（FFN）及所有主干权重。所得到的适配器在共享的轨迹编码器上仅增加可训练的注入参数，同时在继承的精炼协议下以亚厘米精度跟踪根和多关节约束，同时保持基于文本的运动质量。KV-Control 将轨迹条件重新框架为轻量级的记忆检索，为文本到运动生成提供了一个小巧、精确且透明的控制接口。

View on arXiv Download PDF AI Translation

cs.CV / 30 / 2606.05635

ShotCrop$^3$: Cropping Human-Centric Images into Cinematic Triple-Shot Compositions

ShotCrop$^3$: 将以人为本的图像裁剪为电影三镜头构图

Kong, Dehong, Lei, Lina, Zheng, Lingtao, Wu, Chenyang, Zhang, Ailing, Qin, Xinran, Ma, Teng, Xu, Jiaqi, Wang, Zhixin, Chen, Zhikai, Qi, Xuecheng, Pei, Renjing, Li, Fan

Abstract

Prior work on aesthetic composition typically produces a single aesthetically pleasing crop, overlooking the narrative value of composing multiple shots from one scene. In practice, multi-shot composition is critical for downstream creative workflows: commercial posters often require multiple crops with different emphases (e.g., context, subject, and emotion/product details) to present key story beats. Therefore, we propose \textbf{Triple-Shot Compositions (TSC)}, a composition task that generates a three-shot set -- establishing, medium, and close-up -- from a single human-centric image, each paired with a brief shot description to support visual narration. To learn TSC with limited expert annotations, we introduce \textbf{ShotCrop} which undergoes a three-stage training process: it first applies Chain-of-Thought supervised fine-tuning to establish basic reasoning and aesthetic shot-cropping skills, then performs semi-supervised fine-tuning with high-confidence pseudo labels to further enhance aesthetic capability, and is finally optimized with Group Relative Policy Optimization for \textbf{ShotCrop} (GRPO-S) using a composite reward tailored for it. Specifically, our pseudo-labeling strategy combines MLLM-based scoring, aesthetic assessment, and CLIP similarity to retain high-confidence training signals. In addition, we present TSC-Bench, a benchmark of 1.2k expert-annotated test cases. Notably, ShotCrop achieves an average improvement of \textbf{2.82} times over GPT-5 in shot localization accuracy.

Chinese Translation

以往的审美构图研究通常产生单一的美观裁剪，忽视了从一个场景中构成多个镜头的叙事价值。在实际应用中，多镜头构图对下游创意工作流程至关重要：商业海报通常需要多种裁剪，侧重不同的主题（如背景、主体和情感/产品细节）以呈现关键故事情节。因此，我们提出了 extbf{三镜头构图（Triple-Shot Compositions, TSC）}，这是一项从单一以人为本的图像中生成一组三镜头（建立镜头、中景镜头和特写镜头）的构图任务，每个镜头都配有简要的镜头描述以支持视觉叙述。为了在有限的专家注释下学习TSC，我们引入了 extbf{ShotCrop}，该方法经过三阶段的训练过程：首先应用思维链监督微调以建立基本的推理和审美裁剪技能，然后通过高置信度伪标签进行半监督微调，以进一步提升审美能力，最后使用针对 extbf{ShotCrop}的复合奖励优化与团体相对策略优化（GRPO-S）。具体而言，我们的伪标签策略结合了基于大语言模型（MLLM）的评分、审美评估和CLIP相似度，以保留高置信度的训练信号。此外，我们提出了TSC-Bench，这是一个包含1.2k位专家标注测试案例的基准测试。值得注意的是，ShotCrop在镜头定位准确性方面平均提高了 extbf{2.82}倍，相较GPT-5。

View on arXiv Download PDF AI Translation

cs.CV / 31 / 2606.05641

Multi-Task Crack Foundation Model for Engineering-Reliable Crack Representation and Topology Preservation in Civil Infrastructure

用于工程可靠裂缝表征和拓扑保持的多任务裂缝基础模型

Kyem, Blessing Agyei, Asamoah, Joshua Kofi, Denteh, Eugene, Aboah, Armstrong

Abstract

Reliable crack assessment requires not only accurate pixel-level masks but also connected crack geometry and confidence estimates that remain stable under domain shift. However, existing segmentation models can achieve high overlap scores while fragmenting cracks, missing fine branches, and providing no calibrated uncertainty. To address this gap, this paper proposes CrackGeoFM, a multi-task framework that combines a frozen visual foundation backbone with crack-specific adaptation for mask prediction, skeleton reconstruction, and uncertainty estimation. The framework integrates a Frequency-Guided Crack Enhancement Module (FCEM) to enhance high-frequency crack cues, a Crack-Domain Feature Adaptation Module (CFAM) to adapt frozen backbone features to crack-domain patterns, and a Structure-Aware Multi-Task Decoder (SMTD) to jointly decode masks, skeletons, and uncertainty. Across 20 crack datasets, CrackGeoFM achieves state-of-the-art segmentation, improved topology preservation, calibrated uncertainty, and effective few-shot adaptation with only five labeled images. These results support reliable, generalizable, and engineering-oriented crack analysis for infrastructure assessment.

Chinese Translation

可靠的裂缝评估不仅需要准确的像素级掩码，还需要在领域迁移下保持稳定的连通裂缝几何形状和置信度估计。然而，现有的分割模型虽然能够实现高重叠分数，却容易碎裂裂缝、遗漏细小分支，并且没有经过校准的不确定性评估。为了解决这一问题，本文提出了CrackGeoFM，这是一个结合了冻结视觉基础网络与针对裂缝特定适应的多任务框架，用于掩码预测、骨架重建和不确定性估计。该框架集成了一个频率引导裂缝增强模块（FCEM），以增强高频裂缝线索；一个裂缝领域特征适应模块（CFAM），用来将冻结的骨干特征适应于裂缝领域模式；以及一个结构感知的多任务解码器（SMTD），共同解码掩码、骨架和不确定性。在20个裂缝数据集上，CrackGeoFM实现了最先进的分割效果、改进的拓扑保持、经过校准的不确定性以及仅用五张标注图像的有效少样本适应。这些结果支持了基础设施评估中可靠的、可推广的、面向工程的裂缝分析。

View on arXiv Download PDF AI Translation

cs.CV / 32 / 2606.05652

CoFi-UCGen: Coarse-to-Fine Unsupervised Conditional Generation without Label Priors

CoFi-UCGen：无标签先验的粗到细无监督条件生成

Li, Shengxi, Hu, Zhaokun, Zheng, Ce, Xu, Mai, Xia, Jingyuan, Liu, Si

Abstract

Unsupervised conditional image generation (UCGen) aims to control generation without relying on manually annotated labels, yet remains challenging due to unstructured semantic representations across granularities. To address this, we propose a novel coarse-to-fine UCGen framework (CoFi-UCGen) that explicitly disentangles global semantics from fine-grained variations, which to the best of our knowledge, sets out the first successful attempt for both coarse- and fine-grained conditional generation without any labels. More specifically, we first propose the adversarial semantic reciprocal learning theory to ensure the semantic consistency and completeness between images and latent spaces. Based on the consistency, we propose the bit-codes to learn a structured coarse-grained latent space, and further prove distinct global semantics inherent from our bit-codes while preserving independent noise sampling for generation. Building upon these bit-codes, we establish a fine-grained semantic basis and introduce a hierarchical modulation mechanism in diffusion models, by enabling layer-wise injection from coarse conditions to progressively control fine-grained attributes during generation. Extensive experiments demonstrate that without any label priors or pre-trained feature extractors, our CoFi-UCGen consistently outperforms existing UCGen methods in terms of image quality, semantic consistency, and control accuracy, verifying the effectiveness of explicit coarse-to-fine semantic decomposition for the challenging UCGen task.

Chinese Translation

无监督条件图像生成（UCGen）旨在在不依赖手动注释标签的情况下控制生成，然而由于跨层次的非结构化语义表示，仍然面临挑战。为了解决这个问题，我们提出了一种新颖的粗到细UCGen框架（CoFi-UCGen），该框架明确地将全局语义与细粒度变化进行解耦。据我们所知，这是首次成功尝试在没有任何标签的情况下实现粗粒度和细粒度条件生成。更具体地说，我们首先提出了对抗语义互学习理论，以确保图像和潜在空间之间的语义一致性和完整性。在此一致性基础上，我们提出了位码（bit-codes）以学习结构化的粗粒度潜在空间，并进一步证明了我们位码所固有的不同全局语义，同时在生成过程中保留独立噪声采样。基于这些位码，我们建立了细粒度语义基础，并在扩散模型中引入了一种层次调制机制，允许从粗条件到逐步控制细粒度属性的层级注入。大量实验表明，CoFi-UCGen在没有任何标签先验或预训练特征提取器的情况下，在图像质量、语义一致性和控制准确性方面始终优于现有的UCGen方法，验证了在具有挑战性的UCGen任务中显式粗到细语义分解的有效性。

View on arXiv Download PDF AI Translation

cs.CV / 33 / 2606.05665

V2V-Bench: A Comprehensive Benchmark for Video-to-Video Generation Evaluation

V2V-Bench: 视频到视频生成评估的综合基准

Liu, Tao, Krishna, Leela, Kumar, Gouti Pavan, K, Sreeja, Garg, Vishav

Abstract

Video-to-video (V2V) generation is difficult to evaluate because outputs must both follow editing instructions and preserve frame-level correspondence with the source video, which existing T2V and I2V metrics do not capture. We introduce V2V-Bench, a 11-dimension benchmark organized into five categories: temporal alignment, structural fidelity, transformation quality, video quality, and semantic alignment. V2V-Bench pairs diverse source videos with challenging editing tasks and evaluates two commercial models, Grok Imagine and Gemini Veo3, and one open-source model, Open Sora 2. Results show complementary model strengths: Grok performs better on editing fidelity, while Veo3 achieves stronger visual quality. On six V2V-specific dimensions, V2V-Bench reaches a Spearman correlation of 0.905 with human judgments.

Chinese Translation

视频到视频（V2V）生成的评估非常困难，因为输出必须既遵循编辑指令，又保持与源视频的帧级对应关系，而现有的 T2V 和 I2V 评估指标无法捕捉这些方面。我们提出了 V2V-Bench，这是一个由五个类别组织的11维度基准：时间对齐、结构保真度、转换质量、视频质量和语义对齐。V2V-Bench 将多样化的源视频与具有挑战性的编辑任务配对，并评估两个商业模型：Grok Imagine 和 Gemini Veo3，以及一个开源模型：Open Sora 2。结果显示模型之间的互补优势：Grok 在编辑保真度上表现更好，而 Veo3 在视觉质量上更强。在六个特定于V2V的维度上，V2V-Bench 与人类评判之间达到了 0.905 的 Spearman 相关性。

View on arXiv Download PDF AI Translation

cs.CV / 34 / 2606.05677

LongSpace: Exploring Long-Horizon Spatial Memory from Perception to Recall in Video

LongSpace：探索视频中从感知到回忆的长时间空间记忆

Lang, Shiqiang, Liu, Jing, He, Haoyang, Sun, Peiwen, Chen, Yuanteng, Liu, Tao, Yang, Lan, Guo, Longteng, Zhang, Honggang

Abstract

Multimodal Large Language Models (MLLMs) have advanced image and video understanding and can increasingly handle longer visual inputs. Long-horizon tasks such as autonomous driving and robotic navigation require more than recognizing the current view, as models must remember and retrieve previously observed spatial layouts, routes, viewpoint changes, and object states. To evaluate this capability, we introduce LongSpace-Bench, a room-tour video benchmark for long-horizon spatial memory, covering scene perception, spatial relations, and spatial memory. In this work, we further propose LongSpace, a memory framework for long-video spatial reasoning. LongSpace models long videos as sequential chunks, incorporates 3D structural cues into early decoder layers, and constructs layer-aware memory for question-guided retrieval. Experiments on multiple spatial reasoning benchmarks show that LongSpace improves long-video spatial understanding, further demonstrating explicit spatial memory as a key capability for long-horizon video MLLMs.

Chinese Translation

多模态大型语言模型（MLLMs）在图像和视频理解方面取得了进展，并且越来越能够处理更长的视觉输入。长期任务如自主驾驶和机器人导航不仅需要识别当前视图，模型还必须记住并检索之前观察到的空间布局、路线、视点变化和物体状态。为了评估这一能力，我们引入了LongSpace-Bench，这是一个针对长期空间记忆的房间导览视频基准，涵盖场景感知、空间关系和空间记忆。在这项工作中，我们进一步提出了LongSpace，一个用于长视频空间推理的记忆框架。LongSpace将长视频建模为顺序块，将3D结构线索纳入早期解码器层，并构建层感知记忆以进行问题引导的检索。在多个空间推理基准上的实验表明，LongSpace提升了长视频的空间理解，进一步证明了显式空间记忆作为长期视频MLLMs的关键能力。

View on arXiv Download PDF AI Translation

cs.CV / 35 / 2606.05700

T-SAR-JEPA: Self-Supervised Temporal Anomaly Detection in SAR Amplitude Stacks via Latent Prediction

T-SAR-JEPA：基于潜在预测的SAR幅值堆栈自监督时序异常检测

Woldesenbet, Kerod, Woldesenbet, Abem

Abstract

We present T-SAR-JEPA, a self-supervised framework for temporal anomaly detection in SAR amplitude stacks via latent prediction. A ViT-Base/16 encoder from SAR-JEPA is domain-adapted on 39,300 Capella patches using local masked reconstruction with gradient feature prediction. A temporal transformer with sinusoidal time encoding forecasts future latent states from K=7 acquisitions, with progressive unfreezing substantially reducing validation loss. The model operates on amplitude alone; InSAR coherence serves exclusively as independent pseudo-ground-truth. On the DFC 2026 dataset (300 time-series, three AOIs), T-SAR-JEPA achieves ROC-AUC of 77.0% on the Hawaii eruption window, outperforming RX, PaDiM, Linear AR, and LSTM baselines (~50%). Spatial coherence of 99.9% (p < 0.001, permutation test) confirms structured detections. Code: https://github.com/TerraLatent/t-sar-jepa

Chinese Translation

我们提出了T-SAR-JEPA，这是一个基于潜在预测的SAR幅值堆栈自监督时序异常检测框架。使用梯度特征预测的局部掩蔽重建，对SAR-JEPA中的ViT-Base/16编码器进行了领域适应，针对39,300个Capella补丁进行训练。一个带有正弦时间编码的时序变换器从K=7次观测中预测未来的潜在状态，逐步解冻显著降低了验证损失。该模型仅在幅值上工作；InSAR相干性仅作为独立的伪真实值。在DFC 2026数据集（300个时间序列，三个感兴趣区域）上，T-SAR-JEPA在夏威夷喷发窗口上获得了77.0%的ROC-AUC，超过了RX、PaDiM、线性自回归（Linear AR）和LSTM基准（约50%）。99.9%的空间相干性（p < 0.001，置换检验）确认了结构化检测。代码: https://github.com/TerraLatent/t-sar-jepa

View on arXiv Download PDF AI Translation

cs.CV / 36 / 2606.05703

Parallel Jacobi Decoding for Fast Autoregressive Image Generation

并行雅可比解码用于快速自回归图像生成

Liao, Boya, Li, Ying, Jian, Siyong, Wang, Huan

Abstract

Autoregressive (AR) models have demonstrated remarkable performance in generating high-fidelity images. However, their inherently sequential next-token prediction leads to significantly slower inference. Recent studies have introduced Jacobi-style decoding to accelerate autoregressive image generation. Extending the draft sequence initially improves efficiency, yet the acceleration quickly saturates as error propagation in the one-dimensional sequence hinders convergence. Observing that images exhibit strong local spatial correlations, we propose Parallel Jacobi Decoding (PJD), a training-free decoding approach that expands draft tokens in the two-dimensional spatial domain to enable efficient spatially parallel refinement. PJD adjusts the attention mask to mitigate error accumulation and improve convergence stability. Extensive experiments on diverse datasets show that PJD achieves 4.8x-6.4x acceleration across multiple autoregressive image generation models while maintaining competitive generation quality.

Chinese Translation

自回归（AR）模型在生成高保真图像方面展现了显著的性能。然而，它们固有的顺序下一标记预测导致推理速度显著较慢。最近的研究引入了雅可比风格的解码以加速自回归图像生成。扩展初始草稿序列最初提高了效率，但由于一维序列中的误差传播阻碍了收敛，加速效果很快达到饱和。观察到图像展示出强烈的局部空间相关性，我们提出了并行雅可比解码（Parallel Jacobi Decoding, PJD），这是一种无训练的解码方法，它在二维空间域中扩展草稿标记，以实现高效的空间并行细化。PJD调整了注意力掩码，以减轻误差累积并提高收敛稳定性。在多样化数据集上进行的广泛实验表明，PJD在多个自回归图像生成模型中实现了4.8倍至6.4倍的加速，同时保持竞争力的生成质量。

View on arXiv Download PDF AI Translation

cs.CV / 37 / 2606.05708

Real-Time Threat Detection from Surveillance Cameras using Machine Learning

基于机器学习的监控摄像头实时威胁检测

Mandal, Gajendra, Patra, J. P., Mahant, Priyansh

Abstract

Ensuring public safety in densely populated urban environments remains a critical challenge, necessitating the deployment of intelligent and automated video surveillance systems. Traditional surveillance approaches rely heavily on manual monitoring, which is inefficient and susceptible to human fatigue, delayed response, and observational errors. To overcome these limitations, this work presents a real-time object detection-based surveillance framework. The proposed system focuses on detecting guns, knives, and region-specific blunt objects commonly involved in violent activities in Indian surveillance scenarios. A key contribution of this work is the use of a custom-created dataset collected using a mobile camera, consisting of 336 labeled images of blunt objects such as iron rods, wooden sticks, and plastic rods. This dataset is combined with a publicly available dataset of 7,623 images of guns and knives, forming a consolidated dataset of 7,959 images across three classes: gun, knife, and blunt object. The combined dataset is used to train a YOLOv8-based object detection model for real-time performance. Experimental evaluation shows that increasing the training duration significantly improves recall and average precision for the blunt object class without signs of overfitting. Overall, the proposed framework achieves an effective balance between accuracy and efficiency, making it suitable for deployment in real-world surveillance environments such as campuses, public spaces, and transportation areas.

Chinese Translation

在密集人口城市环境中确保公共安全仍然是一个关键挑战，这需要部署智能和自动化的视频监控系统。传统的监控方法严重依赖人工监控，这种方式效率低下且容易受到人类疲劳、响应延迟和观察错误的影响。为了克服这些局限性，本研究提出了一种基于实时目标检测的监控框架。该系统重点检测枪支、刀具及与印度监控场景中暴力活动相关的特定区域钝器。本文一个重要贡献是使用自定义创建的数据集，该数据集由移动摄像头收集，包含336张标注的钝器图像，如铁棒、木棍和塑料棒。该数据集与一个包含7,623张枪支和刀具图像的公开数据集相结合，形成一个涵盖三类（枪、刀和钝器）共7,959张图像的综合数据集。结合后的数据集用于训练一个基于YOLOv8的目标检测模型，以实现实时性能。实验评估表明，延长训练时间显著提高了钝器类的召回率和平均精度，且没有过拟合迹象。总体而言，所提出的框架在准确性和效率之间实现了有效平衡，适合在校园、公共场所和交通区域等真实监控环境中部署。

View on arXiv Download PDF AI Translation

cs.CV / 38 / 2606.05718

ViCuR: Visual Cues as Recoverable Privilege for Multimodal On-Policy Distillation

ViCuR：将视觉线索作为可恢复的特权用于多模态在线蒸馏

Tian, Kanghui, Liu, Siyuan, Yan, Ziang, Xia, Sheng, Dong, Shuai, Wang, Yi

Abstract

On-policy distillation (OPD) improves reasoning by training a student on trajectories sampled from its own policy under supervision from a teacher. In multimodal reasoning, a common extension is to use a privileged teacher that observes training-time-only signals such as reference answers or rationales. However, such answer-side privilege creates a train-test mismatch: the teacher's supervision may depend on signals unavailable to the student, encouraging shortcut imitation rather than visually grounded reasoning. We propose ViCuR, a visually grounded privileged-teacher distillation framework that replaces answer-side privilege with visual cues (query-related evidence in the input). Because these cues are derived from the same visual input available at inference, their evidence is recoverable by the student. To support this, ViCuR introduces a lightweight cue recovery module that uses dedicated sink-token cross-attention during prefill to aggregate task-relevant visual evidence into an internal representation, without changing the inference interface or requiring auxiliary cue-generation losses. Across seven benchmarks with Qwen3-VL-2B and 8B students, ViCuR consistently improves over answer-based on-policy self-distillation by +1.19 and +1.24 on overall average performance. It also extends naturally to stronger-teacher OPD, surpassing OPD baselines by +0.64 and +1.08, with consistent out-of-domain gains at the 8B scale. These results show that, in multimodal on-policy distillation, the design of teacher privilege is as important as teacher strength.

Chinese Translation

在线蒸馏（On-policy distillation, OPD）通过在教师的监督下训练学生对其自身策略采样的轨迹进行推理。对于多模态推理，一个常见的扩展是使用特权教师，该教师仅观察训练时信号，比如参考答案或推理过程。然而，这种基于答案的特权会造成训练与测试之间的不匹配：教师的监督可能依赖于学生无法接触的信号，鼓励了快捷模仿而非基于视觉的推理。我们提出了ViCuR，一个以视觉为基础的特权教师蒸馏框架，它用视觉线索（与输入相关的查询证据）替代了答案侧的特权。由于这些线索来源于推断时可用的相同视觉输入，其证据对于学生来说是可恢复的。为此，ViCuR引入了一个轻量级的线索恢复模块，在预填充阶段利用专门的Sink-token交叉注意力将与任务相关的视觉证据聚合为内部表示，而不改变推断接口或需要额外的线索生成损失。针对Qwen3-VL-2B和8B学生的七个基准测试，ViCuR在整体平均性能上持续优于基于答案的在线自蒸馏，提升了1.19和1.24。同时，它自然扩展至更强教师的在线蒸馏（OPD），超越OPD基线分别提升了0.64和1.08，并在8B规模上实现了持续的领域外增益。这些结果表明，在多模态在线蒸馏中，教师特权的设计与教师的强度同样重要。

View on arXiv Download PDF AI Translation

cs.CV / 39 / 2606.05730

TextWand: A Unified Framework for Scene Text Editing

TextWand：一个统一的场景文本编辑框架

Wang, Shuyu, Guan, Zhile, Chen, Hongxiu, Duan, Yule, Li, Weiqi, Shan, Xin, Wang, Ronggang, Zhang, Jian

Abstract

We propose TextWand, a general-purpose framework that unifies scene text removal, generation, and replacement into a single model. By decomposing complex editing tasks into the atomic primitives of rendering and erasure, TextWand achieves precise control over both text appearance and background integrity. Specifically, we introduce a novel design, Overlay-Reference Positional Encoding (ORPE), to enforce pixel-level layout fidelity and exemplar-driven style control, alongside a new strategy, Region-Adaptive Suppression (RAS), to ensure clean text erasure. To address the absence of a comprehensive benchmark for general-purpose scene text editing among existing single-task datasets, we construct TextWand-Bench. Extensive experiments demonstrate that TextWand outperforms existing leading open-source and closed-source models by delivering superior text content accuracy, layout and style consistency, and overall image quality across scene text removal, generation and replacement tasks.

Chinese Translation

我们提出了TextWand，这是一个通用框架，将场景文本的移除、生成和替换整合为单一模型。通过将复杂的编辑任务分解为渲染和擦除的原子操作，TextWand 实现了对文本外观和背景完整性的精确控制。具体而言，我们引入了一种新设计，称为叠加参考位置编码（Overlay-Reference Positional Encoding，ORPE），以强制执行像素级布局保真度和示例驱动的风格控制，同时提出了一种新策略，区域自适应抑制（Region-Adaptive Suppression，RAS），以确保干净的文本擦除。为了弥补现有单任务数据集中缺乏通用场景文本编辑的综合基准，我们构建了TextWand-Bench。大量实验表明，TextWand 在场景文本移除、生成和替换任务中，以更高的文本内容准确性、布局和风格一致性以及整体图像质量，超越了现有的领先开源和闭源模型。

View on arXiv Download PDF AI Translation

cs.CV / 40 / 2606.05736

VTI-CoT: Visual-Textual Interleaved Chain of Thought for Video Reasoning

VTI-CoT：针对视频推理的视觉-文本交错思维链

Zhang, Shufan, Lin, Ziyue, Wang, Bairun, Jin, Lei, Ding, Xuanding, Ma, Xinzhu, Yang, Kunlin

Abstract

Video reasoning aims to understand complex temporal events and causal relationships within videos. Recently, Chain-of-Thought (CoT) has been introduced to this field to enhance reasoning accuracy. However, existing CoT-based video reasoning methods primarily rely on text-only information for logical deduction, overlooking critical visual information during the inference process. Inspired by the human cognitive mechanism of reviewing visual segments during inference, we propose VTI-CoT, a Visual-Textual Interleaved CoT framework. VTI-CoT integrates textual reasoning steps with corresponding visual frames. Given the scarcity of visual-textual interleaved CoT in existing datasets, we develop an automated annotation pipeline to construct high-quality multimodal CoT data. Further, reasoning over long-form videos entails increasingly long CoT token sequences, which severely hinders training convergence and efficiency. To address this, we employ Optical Character Recognition (OCR)-based compression techniques to compress CoT supervision signals into a single canvas. Experimental results demonstrate that VTI-CoT achieves state-of-the-art performance among models of the same parameter scale while significantly improving training efficiency.

Chinese Translation

视频推理旨在理解视频中的复杂时间事件和因果关系。最近，引入了思维链（Chain-of-Thought，CoT）这一概念，以提高推理的准确性。然而，现有基于CoT的视频推理方法主要依赖于仅文本信息进行逻辑推导，在推理过程中忽视了关键的视觉信息。受到人类在推理过程中过滤视觉片段的认知机制启发，我们提出了VTI-CoT，一个视觉-文本交错的CoT框架。VTI-CoT将文本推理步骤与相应的视觉帧相结合。鉴于现有数据集中视觉-文本交错的CoT稀缺，我们开发了一种自动标注管道，以构建高质量的多模态CoT数据。此外，对长视频进行推理需要处理越来越长的CoT token序列，这严重妨碍了训练的收敛性和效率。为此，我们采用基于光学字符识别（Optical Character Recognition，OCR）的压缩技术，将CoT监督信号压缩成单一画布。实验结果表明，VTI-CoT在同一参数规模的模型中实现了最先进的性能，同时显著提高了训练效率。

View on arXiv Download PDF AI Translation

cs.CV / 41 / 2606.05737

Let It Be Simple: One-Step Action Generation for Vision-Language-Action Models

让它简单：针对视觉-语言-动作模型的一步动作生成

Chen, Yitong, Zhang, Shiduo, Gong, Jingjing, Qiu, Xipeng

Abstract

Diffusion-based vision-language-action (VLA) models often inherit the image-generation view: actions are generated by iterative denoising. We argue that VLA action generation has a different condition-target structure: the policy is conditioned on rich observations, language, and state, but predicts only a compact, low-dimensional action chunk. Under this asymmetry, strong one-step action generation should not necessarily require the advanced one-step methods developed for image synthesis. We keep standard velocity prediction and add no teacher model, distillation stage, or auxiliary objective; in our main recipe, we simply bias the training time distribution toward high-noise states. We first isolate the effect in a controlled MNIST grid-to-sequence task, then test it with extensive robot-policy experiments. Across standard LIBERO, LIBERO-Plus, and LIBERO-Pro, one-step policies trained with high-noise biased schedules generally match ten-step decoding under the same recipe, and on standard LIBERO can exceed ten-step policies trained with a uniform time distribution. A real-robot bimanual YAM RSS evaluation gives a small-sample cross-architecture check of the same sampler trend. On a 1.4B VLM model with a 30M action head, one-step decoding reaches 95.6\% on LIBERO-Long. These results show that strong one-step VLA action generation can emerge from standard diffusion training, without importing the full few-step diffusion machinery developed for image generation.

Chinese Translation

基于扩散的视觉-语言-动作（VLA）模型通常采用图像生成的视角：动作通过迭代去噪来生成。我们认为，VLA 动作生成具有不同的条件-目标结构：策略依赖于丰富的观察、语言和状态，但只预测一个紧凑的低维动作片段。在这种不对称性下，强的一步动作生成不必一定需要为图像合成开发的高级一步方法。我们保持标准的速度预测，并不添加教师模型、蒸馏阶段或辅助目标；在我们的主要方法中，我们仅将训练时间分布偏向高噪声状态。我们首先在一个受控的 MNIST 网格到序列任务中孤立出该效应，然后通过广泛的机器人策略实验进行测试。在标准的 LIBERO、LIBERO-Plus 和 LIBERO-Pro 中，使用高噪声偏向调度训练的一步策略通常在相同的方案下与十步解码相匹配，并且在标准 LIBERO 中可以超越使用均匀时间分布训练的十步策略。对真实机器人双手 YAM RSS 的评估为同一采样器趋势提供了小样本跨架构检查。在一个 14 亿参数的 VLM 模型与 3000 万动作头的设置下，一步解码在 LIBERO-Long 上达到了 95.6\%。这些结果表明，强的一步 VLA 动作生成可以从标准的扩散训练中出现，而无需引入为图像生成开发的完整少步扩散机制。

View on arXiv Download PDF AI Translation

cs.CV / 42 / 2606.05753

Cosine Misleads: Auxiliary Losses Reshape Vision Language Models, Not Their Latents

余弦误导：辅助损失重塑视觉语言模型，而非其潜变量

Zhang, XiuYu, Fang, Junfeng, Liang, Zhenkai

Abstract

Latent visual reasoning (LVR) inserts supervised latent tokens between perception and answer generation in vision-language models (VLMs). The field uses alignment between these latents and their visual targets, i.e., cosine similarity or mean squared error (MSE), as both the training loss and the quality metric, assuming that better alignment yields a better answer. We test this with a designed matrix of five LVR variants and find the assumption inverted: cosine alignment is negatively correlated with accuracy across all five (r=-0.94). To explain this, we introduce PRISM, a pair of inference-time diagnostics: a linear probe that asks where the answer is decodable, and a corruption test that asks whether the latent is load-bearing. The supervised latents are largely bypassed. Corrupting them shifts accuracy by at most four points. The answer is decodable downstream of the latent but not at it, and the size of this decodability gap predicts how much each variant relies on its latent under perturbation. Consistent with an Information Bottleneck reading of the loss, the auxiliary objective reshapes the language model via shared parameters rather than via the latent variable it nominally optimizes.

Chinese Translation

潜在视觉推理（Latent visual reasoning, LVR）在视觉语言模型（Vision-Language Models, VLMs）中插入了监督潜在标记，位于感知与答案生成之间。该领域将这些潜在标记与其视觉目标之间的对齐（即余弦相似度或均方误差 Mean Squared Error, MSE）作为训练损失和质量指标，假设更好的对齐会产生更好的答案。我们通过设计的五种LVR变体的矩阵进行测试，发现这一假设是反转的：在所有五种变体中，余弦对齐与准确性呈负相关（r=-0.94）。为了解释这一现象，我们引入了PRISM，一对推理时诊断工具：一个线性探针用于询问答案何处可解码，一个腐蚀测试用于询问潜变量是否有效承载。监督潜在变量在很大程度上被绕过。对它们的干扰最多会使准确性变化四个点。答案可以在潜在变量下游解码，但无法在其本身进行解码，而这种可解码性差距的大小预示了每种变体在扰动下对其潜在变量的依赖程度。与信息瓶颈理论对损失的解读一致，辅助目标通过共享参数重塑了语言模型，而非通过其名义上优化的潜变量。

View on arXiv Download PDF AI Translation

cs.CV / 43 / 2606.05758

DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models

DRIFT：用于视觉语言模型中解码连续输出的残差流适配器

Liu, Zhuoming, Lin, Jinhong, Cheng, Kwan Man, Zhang, Lin, Bagchi, Shayok, Li, Yin

Abstract

Many modern vision-language models (VLMs) build on autoregressive decoding of discrete tokens. While text-based output interfaces enable scalable pretraining and strong zero-shot generalization across diverse tasks, they are poorly suited for problems that require precise continuous outputs, such as localizing temporal boundaries of events or generating robotic control actions. To address this challenge, we propose DRIFT, a general framework for adapting pretrained VLMs to continuous decoding tasks. DRIFT combines a base predictor, which provides a coarse estimate of the target output, with a generative refinement module based on flow matching that iteratively improves the prediction. This residual formulation transforms the generative modeling problem from learning a global output distribution to modeling a localized residual distribution around a strong prior, substantially simplifying optimization. We evaluate DRIFT on both perception and planning tasks, including visual grounding and robotic control. Across multiple tasks and architectures spanning MLLMs, VLAs, and WAMs, DRIFT consistently outperforms a strong set of regression- and generative-based solutions.

Chinese Translation

许多现代视觉语言模型（VLMs）基于离散符号的自回归解码。虽然基于文本的输出接口可以实现可扩展的预训练，并在各种任务中展现出强大的零-shot泛化能力，但它们并不适合需要精确连续输出的问题，例如定位事件的时间边界或生成机器人控制动作。为了解决这一挑战，我们提出了DRIFT，一个通用框架，用于将预训练的VLMs适应于连续解码任务。DRIFT结合了基础预测器，即提供目标输出的粗略估计，以及基于流匹配的生成修正模块，后者通过迭代方式改进预测。这种残差公式将生成建模问题从学习全局输出分布转变为围绕强先验建模局部残差分布，极大地简化了优化过程。我们在感知和规划任务上评估了DRIFT，包括视觉基础和机器人控制。在涵盖多种任务和架构的多模态语言模型（MLLMs）、视觉语言适配器（VLAs）和语言-行为模型（WAMs）中，DRIFT始终优于一系列强大的回归和生成解决方案。

View on arXiv Download PDF AI Translation

cs.CV / 44 / 2606.05759

Physics-Guided Deep Unfolding for Blind Cross-Sensor Spectral Super-Resolution via Learning the Spectral Transformation Function

物理引导的深度展开网络用于盲交叉传感器光谱超分辨率，通过学习光谱变换函数

Li, Zhaolin, Chen, Jinsong, Guo, Shanxin, Zhang, Tuo, Zhang, Xinglong, Chen, Pan

Abstract

Hyperspectral imaging provides rich spectral information for quantitative remote sensing, yet hyperspectral sensors remain costly and thus unavailable in many UAV deployments. Spectral super-resolution (SSR) seeks to reconstruct hyperspectral images (HSIs) from multispectral images (MSIs). Most existing SSR methods assume a fixed and known spectral response function (SRF) and are therefore limited to single-sensor settings. In practical cross-sensor scenarios, the spectral degradation from HSI to MSI is unknown and varies with sensor characteristics and scene content, which renders HSI reconstruction ill-posed. This paper proposes a physics-guided deep unfolding network, termed PGU-Net, to address blind cross-sensor SSR by jointly estimating the HSI and a learnable spectral transformation function (STF). PGU-Net unrolls an alternating optimization procedure into an end-to-end trainable architecture with stages, where each stage sequentially updates the HSI and the STF. Both modules combine learnable proximal networks with differentiable closed-form solvers, enabling physical interpretability while retaining strong representation capacity. Experiments on benchmark datasets (CAVE and NTIRE 2022) with multiple SRFs demonstrate accurate recovery of the STF (degradation operator) and improved reconstruction performance over state-of-the-art SSR methods. Furthermore, evaluations on a real UAV cross-sensor dataset (Headwall Nano HSI and DJI P4 Multispectral MSI) verify the effectiveness and robustness of PGU-Net under truly blind conditions, and suggest that the estimated STF may exhibit land-cover-related differences.

Chinese Translation

高光谱成像为定量遥感提供了丰富的光谱信息，但高光谱传感器成本高昂，因此在许多无人机（UAV）部署中无法使用。光谱超分辨率（SSR）旨在从多光谱图像（MSI）重建高光谱图像（HSI）。现有大多数SSR方法假设固定且已知的光谱反应函数（SRF），因此仅限于单一传感器设置。在实际的交叉传感器场景中，从HSI到MSI的光谱退化是未知的，并且随传感器特性和场景内容而变化，这使得HSI重建变得不适定。本文提出了一种物理引导的深度展开网络，称为PGU-Net，以通过联合估计HSI和可学习的光谱变换函数（STF）来解决盲交叉传感器SSR。PGU-Net将交替优化过程展开为端到端可训练的带有阶段的架构，其中每个阶段依次更新HSI和STF。这两个模块结合了可学习的近端网络和可微分的闭式解算器，既具备物理可解释性又保持强大的表示能力。在多种SRF的基准数据集（CAVE和NTIRE 2022）上的实验展示了对STF（退化算子）的准确恢复，并且在现有最先进的SSR方法上提升了重建性能。此外，在真实的无人机交叉传感器数据集（Headwall Nano HSI和DJI P4多光谱MSI）上的评估验证了PGU-Net在真正的盲条件下的有效性和鲁棒性，并暗示估计的STF可能表现出与地表覆盖相关的差异。

View on arXiv Download PDF AI Translation

cs.CV / 45 / 2606.05760

ExpSpeech-Net: Multimodal Fusion of Expression and Speech for Deepfake Detection

ExpSpeech-Net：用于深度伪造检测的表情与语音的多模态融合

Sharma, Ruchika, Dwivedi, Rudresh

Abstract

Deepfake videos are increasingly challenging the credibility of online content. Many existing detection methodology relies on complex, resource-intensive models, which limit their practical use. The study introduces the ExpSpeech-Net deepfake detection (SqN-R-DFD) model, which utilizes SqueezeNet and RNN (Recurrent Neural Network) as its backbone, providing a lightweight and efficient deepfake detection framework that simultaneously analyzes facial expressions and speech patterns. The approach incorporates advanced feature extraction, such as ISLBT-based features for image and MPNCC for signals, along with a smart feature-selection strategy using SASMA (Sandpiper-Assisted Slime Mould Algorithm), ensuring optimal and balanced input to the detection models. By combining SqueezeNet and an RNN, subtle inconsistencies in deepfake videos are captured effectively. The framework achieves 94.5% accuracy, precision of 99.3%, and F-measure of 96.8%, outperforming conventional methods. This demonstrates that integrating multiple modalities with intelligent preprocessing and feature selection enables practical, real-time deepfake detection suitable for everyday applications.

Chinese Translation

深度伪造视频日益挑战着在线内容的可信度。许多现有的检测方法依赖于复杂且资源密集的模型，这限制了其实际应用。本文提出了ExpSpeech-Net深度伪造检测模型（SqN-R-DFD），该模型以SqueezeNet和RNN（递归神经网络）为骨干，提供了一种轻量高效的深度伪造检测框架，同时分析面部表情和语音模式。该方法结合了高级特征提取，使用基于ISLBT的图像特征和MPNCC信号特征，并采用SASMA（沙丘鸟辅助的黏菌算法）进行智能特征选择，以确保对检测模型的输入最优化和平衡。通过结合SqueezeNet和RNN，有效捕捉深度伪造视频中的细微不一致性。该框架实现了94.5%的准确率、99.3%的精确率和96.8%的F-measure，超越了传统方法。这表明，多模态结合智能预处理和特征选择可以实现适合日常应用的实用实时深度伪造检测。

View on arXiv Download PDF AI Translation

cs.CV / 46 / 2606.05769

Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction

预测之前的想象：交错潜在视觉推理用于视频事件预测

Jiang, Tianxiang, Wu, Linquan, Xia, Sheng, Li, Songze, Yan, Ziang, Yang, Haoyu, Qiao, Yu, Wang, Yi

Abstract

Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Existing video MLLMs usually verbalize intermediate future reasoning in text space: once visual evidence is verbalized, fine-grained motion, geometry, and interaction cues can be lost, leading to plausible but visually ungrounded hallucinations. We introduce Future-L1, an interleaved latent visual reasoning framework that lets an MLLM alternate between language tokens and continuous latent visual spans during autoregressive decoding. To train this capability, we construct Future-L1-50K by selecting examples where future visual hints help prediction and align latent states to future-frame embeddings, then further optimize sampled latent trajectories with LA-DAPO, a latent-aware RL objective with outcome-contrastive and temporal-diversity rewards. Future-L1 achieves new state-of-the-art results on both benchmarks: on FutureBench, it improves Qwen3-VL-8B from 61.0 to 85.4 and exceeds the previous best Video-CoE by 10.4 points; on TwiFF-Bench, it improves the average score from 2.44 to 3.04. These results suggest that future-oriented video reasoning benefits from preserving intermediate visual semantics in latent space rather than translating every reasoning step into text.

Chinese Translation

视频事件预测（VEP）要求模型从部分视频证据中推断未观测到的未来状态。现有的视频多模态大语言模型（MLLMs）通常在文本空间中表达中间的未来推理；一旦视觉证据被转化为文本，细粒度的运动、几何和交互线索可能会丢失，从而导致看似合理但视觉上缺乏基础的幻觉。我们引入了Future-L1，一个交错潜在视觉推理框架，使得MLLM可以在自回归解码过程中在语言符号和连续潜在视觉范围之间交替。为了训练这种能力，我们通过选择未来视觉提示有助于预测的示例并将潜在状态与未来帧嵌入对齐，构建了Future-L1-50K，然后进一步使用LA-DAPO（一个具有结果对比和时间多样性奖励的潜在感知强化学习目标）优化采样的潜在轨迹。Future-L1在两个基准测试中取得了新的最先进结果：在FutureBench上，它将Qwen3-VL-8B的分数从61.0提高到85.4，超过之前最佳的Video-CoE 10.4分；在TwiFF-Bench上，它将平均分数从2.44提高到3.04。这些结果表明，面向未来的视频推理从在潜在空间中保持中间视觉语义中受益，而不是将每一步推理转化为文本。

View on arXiv Download PDF AI Translation

cs.CV / 47 / 2606.05774

LiAuto-GeoX: Efficient Grounded Driving Transformer

LiAuto-GeoX：高效的基于地面驾驶变换器

Lian, Jiawei, Sun, Haoyi, Wu, Yang, Mu, Lifu, Wang, Siyuan, Hui, Le, Mao, Ning, Wei, Tao, Zhou, Pan, Zhan, Kun, Yang, Jian

Abstract

Dense 3D reconstruction has demonstrated immense potential for spatial understanding, yet its viability as a real-time, onboard representation for autonomous driving remains an open challenge. Existing large-scale visual geometry models typically require substantial computational resources and lack the long-range geometric fidelity, surround-view consistency, and real-time efficiency demanded by dynamic driving environments. To bridge this gap, we present \textbf{LiAuto-GeoX}, an efficient grounded driving transformer designed for deployable, ego-centric 3D scene understanding. Our approach begins by learning a high-capacity driving geometry model from large-scale surround-view data, utilizing sparse LiDAR priors to provide robust geometric grounding in distant, ambiguous, or structure-sparse regions. We then instantiate this capability into a highly compact 155M-parameter onboard model through a novel geometry-preserving distillation framework. This framework employs mask-guided depth-aware distillation to retain fine-grained metric structures by emphasizing geometrically informative regions, and relative-pose relational distillation to enforce cross-view spatial consistency through pose-induced geometric relations. Extensive evaluations reveal that \textbf{LiAuto-GeoX} runs at 220 FPS on KITTI while maintaining high-fidelity dense reconstruction, enabling real-time deployment. The learned geometry transfers seamlessly to downstream autonomy tasks, achieving 90.6 PDMS in trajectory prediction, 24.63 mIoU in occupancy prediction, and 47.67 IoU in future-frame prediction. These all demonstrate that efficient dense 3D reconstruction can transcend its traditional role as a perception target to serve as a scalable, foundational geometric representation for next-generation autonomous driving.

Chinese Translation

密集的三维重建已展示出在空间理解方面的巨大潜力，但作为一种实时、车载表示形式用于自主驾驶的可行性仍是一个待解决的挑战。现有的大规模视觉几何模型通常需要大量的计算资源，并且缺乏动态驾驶环境所需的长距离几何保真度、全景一致性和实时效率。为了弥补这一差距，我们提出了 extbf{LiAuto-GeoX}，一种高效的基于地面驾驶变换器，旨在实现可部署的自我中心三维场景理解。我们的方法首先从大规模的全景数据中学习一个高容量的驾驶几何模型，利用稀疏的激光雷达先验为远处、模糊或结构稀疏区域提供稳健的几何基础。然后，我们通过一种新颖的保持几何特征的蒸馏框架，将这种能力实现为一个高度紧凑的155M参数的车载模型。该框架采用掩模引导的深度感知蒸馏，通过强调几何信息丰富的区域来保留细致的度量结构，同时通过姿态引导的几何关系强制执行跨视图的空间一致性。广泛的评估显示， extbf{LiAuto-GeoX}在KITTI数据集上以220帧每秒的速度运行，同时保持高保真的密集重建，实现了实时部署。学习到的几何模型无缝转移到下游自主任务中，实现了轨迹预测中的90.6 PDMS、占用预测中的24.63 mIoU，以及未来帧预测中的47.67 IoU。这些都表明高效的密集三维重建可以超越其在感知目标中的传统角色，作为下一代自主驾驶的可扩展基础几何表示。

View on arXiv Download PDF AI Translation

cs.CV / 48 / 2606.05778

Beyond Absolute Scores: Relative Edit-induced Difference for Generalizable Image Aesthetic Assessment

超越绝对分数：用于通用图像美学评估的相对编辑引发差异

Jia, Qifei, Yao, Xintong, Li, Minghao, Chai, Yajie, Lu, Qiming, Shen, Baoyue, Zhang, Yasen, Shi, Runyu, Huang, Ying, Zhang, Yue

Abstract

Traditional Image Aesthetic Assessment (IAA) methods mainly rely on regressing absolute Mean Opinion Scores (MOS). However, such a paradigm overlooks the inherently dynamic nature of human aesthetic perception, which relies on subconscious comparison against implicit visual references. Consequently, the lack of causal reasoning regarding aesthetic differences prevents models from learning generalizable aesthetic principles, thus limiting their generalization across diverse scenarios. In this work, we rethink the IAA task and propose Relative Edit-induced Difference Aesthetic learning (RED-Aes), a novel framework that leverages controllable image editing models to simulate the human aesthetic reasoning process. Instead of fitting absolute score distributions, RED-Aes explicitly learns the visual factors that drive aesthetic changes. To support this paradigm, we construct the RED-20k dataset, which comprises editing-based image pairs, quantitative aesthetic differences, and Chain-of-Thought (CoT) reasoning. Furthermore, we introduce a three-stage training strategy guided by a relative ranking consistency reward, optimizing the model solely via relative supervision. Extensive experiments demonstrate that RED-Aes achieves state-of-the-art performance on multiple public benchmarks, exhibiting superior generalization capabilities.

Chinese Translation

传统的图像美学评估（IAA）方法主要依赖于回归绝对的平均意见分数（MOS）。然而，这种范式忽视了人类美学感知的动态本质，后者依赖于对隐含视觉参考的潜意识比较。因此，缺乏对美学差异因果推理的理解妨碍了模型学习通用的美学原则，从而限制了它们在不同场景中的泛化能力。在本研究中，我们重新审视了IAA任务，并提出相对编辑引发差异美学学习（Relative Edit-induced Difference Aesthetic learning, RED-Aes），这是一个新的框架，利用可控的图像编辑模型来模拟人类美学推理过程。RED-Aes不是拟合绝对分数分布，而是明确学习推动美学变化的视觉因素。为了支持这一范式，我们构建了RED-20k数据集，包含基于编辑的图像对、定量美学差异以及推理链（Chain-of-Thought, CoT）。此外，我们引入了一种三阶段训练策略，基于相对排序一致性的奖励进行指导，仅通过相对监督来优化模型。大量实验表明，RED-Aes在多个公共基准测试中达到了最先进的性能，展现出优越的泛化能力。

View on arXiv Download PDF AI Translation

cs.CV / 49 / 2606.05785

Next-Generation Parallel Decoder for LPDR: Architectural Optimization and Class-Balanced GAN-Augmentation

下一代并行解码器用于车牌检测与识别：架构优化与类别平衡生成对抗网络增强

Obaid, Shawaiz, Chandio, Nida, Jamil, Neha, Shahzad, Muhammad Khuram

Abstract

Real-Time License Plate Detection and Recognition (LPDR) forms the backbone of modern smart cities. Although the YOLOV5-PDLPR model substantially improved system efficiency through a parallel decoder approach, its performance is still affected by spatial character mismatches and data imbalance within the training set. This paper addresses these limitations by introducing Cross-Spatial Hybrid Attention (CSHA) and Class-Balanced Synthetic Augmentation (CBSA). An extensive study involving 75,000 synthetic samples is conducted and evaluated on four benchmarks: CCPD, CLPD, PKU, and an application-specific dataset. Experimental results demonstrate a substantial improvement in the recognition rate of minority provincial license plates from 78.2% to 91.5% while maintaining real-time processing performance of 152 FPS. The results indicate that spatially-aware parallel decoding combined with class-balanced augmentation provides an effective solution for high-speed license plate recognition systems.

Chinese Translation

实时车牌检测与识别（LPDR）构成现代智慧城市的基础。尽管YOLOV5-PDLPR模型通过并行解码器方法显著提高了系统效率，但其性能仍受到空间字符不匹配和训练集中的数据不平衡的影响。本文通过引入跨空间混合注意力（Cross-Spatial Hybrid Attention, CSHA）和类别平衡合成增强（Class-Balanced Synthetic Augmentation, CBSA）来解决这些局限性。进行了一项涉及75,000个合成样本的广泛研究，并在四个基准测试上进行了评估：CCPD、CLPD、PKU和特定应用数据集。实验结果表明，少数省份车牌的识别率从78.2%提高至91.5%，同时保持了152 FPS的实时处理性能。结果表明，结合空间感知并行解码和类别平衡增强的方法为高速度车牌识别系统提供了有效的解决方案。

View on arXiv Download PDF AI Translation

cs.CV / 50 / 2606.05816

Emotion-Aware Image Generation from Korean Diary Text via LLM-based Prompt Translation and LoRA Fine-Tuning

基于大语言模型的提示翻译和LoRA微调的情感感知图像生成：来自韩国日记文本

Cho, Jihun, Jeong, Soo-Yeon, Ihm, Sun-Young

Abstract

T2I models cannot effectively capture sentiment from various types of text, including diaries, as they primarily focus on visual object-related patterns rather than contextual emotional understanding. This paper proposes an emotion-aware text-to-image pipeline that generates children's hand drawing style images from short Korean diary entries. The proposed pipeline employs Qwen3-8B for recognising implicit sentiment from short diaries, and Stable Diffusion 3.5 Medium fine-tuned with LoRA on children's drawing images with emotion-based trigger words for image generation. Additionally, this paper presents experiments examining the effect of emotion trigger words on generated images and discusses the limitations of CLIP Score as an evaluation metric for emotion-aware image generation.

Chinese Translation

T2I（文本到图像）模型无法有效捕捉包括日记在内的各种类型文本中的情感，因为它们主要关注视觉对象相关的模式，而非上下文情感的理解。本文提出了一种情感感知的文本到图像管道，能够从短小的韩国日记中生成儿童手绘风格的图像。所提管道采用Qwen3-8B识别短小日记中的隐性情感，并使用基于情感触发词的LoRA微调的Stable Diffusion 3.5 Medium生成儿童画图像。此外，本文还展示了实验，考察情感触发词对生成图像的影响，并讨论了CLIP评分作为情感感知图像生成评估指标的局限性。

View on arXiv Download PDF AI Translation

cs.CV / 51 / 2606.05829

Gender Artifacts from Art History to Text-to-Image Generation

从艺术史到文本生成图像的性别物品

Riccio, Piera, Doh, Miriam, Höltgen, Benedikt, Garcia, Noa, van Noord, Nanne

Abstract

Artistic styles are rooted in specific socio-historical contexts that encode social hierarchies, including distinct constructions of gender. Yet in AI research, style has long been treated as a surface-level visual property: a filter of color, brushstroke, and texture applied to otherwise content-neutral scenes. We introduce the first dataset to investigate the interplay between gender representation and style in both historical and generated images. StyleGender comprises 74k images spanning 19 artistic styles, comprising art historical images with style and gender annotations, T2I-generated images under controlled style and gender prompts, and a semantically aligned set enabling direct art history-to-generation comparison. By proposing two Set Gender Artifact (SGA) metrics (PixelSGA and MaskSGA), capturing gender signals at the pixel level and in compositional structure, we show that (1) gender representation shapes visual features across artistic styles, (2) style keywords carry these patterns into T2I generation, and (3) generative models tend to amplify gender artifacts beyond what is observed in historical sources.

Chinese Translation

艺术风格根植于特定的社会历史背景中，这些背景编码了社会等级，包括性别的不同构建。然而在人工智能研究中，风格长期以来被视为表层的视觉属性：将颜色、画笔痕迹和纹理应用于内容中立的场景。本文介绍了首个数据集，以探讨性别表现与风格在历史图像和生成图像中的相互作用。StyleGender数据集包含74,000幅图像，涵盖19种艺术风格，包含有风格和性别标注的艺术历史图像、在控制风格和性别提示下生成的T2I图像，以及一组语义对齐的数据集，以便进行艺术史到生成的直接比较。通过提出两种集性别物品（Set Gender Artifact，SGA）度量标准（PixelSGA和MaskSGA），捕捉到像素级别和构图结构中的性别信号，我们显示（1）性别表现塑造了艺术风格中的视觉特征，（2）风格关键词将这些模式带入T2I生成中，以及（3）生成模型倾向于放大性别物品，超出历史来源中所观察到的情况。

View on arXiv Download PDF AI Translation

cs.CV / 52 / 2606.05833

Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models

从视频中学习几何表示以支持空间智能多模态大语言模型

Wang, Haibo, Huang, Lifu

Abstract

Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to maintain geometric and spatial consistency across video frames. Given the scarcity of large-scale 3D data, we present GeoVR, a novel framework that learns geometric representations using purely 2D video sequences. This approach effectively restructures the semantic latent space within MLLMs to unlock spatial intelligence. Rather than employing superficial feature mixing, GeoVR reshapes the internal representations of the MLLM by distilling geometry knowledge from pre-trained 3D foundation models. This is accomplished through a multi-objective learning strategy driven by four complementary geometric targets: (1) estimating inter-frame camera poses to embed varying viewpoint dynamics, (2) regressing dense depth maps to anchor physical distances, (3) predicting a metric scale factor for real-world calibration, and (4) distilling multi-scale 3D features to align the intermediate feature space. Guided by these explicit physical and geometric constraints, the model's internal representations naturally develop strong 3D awareness. Extensive experiments on spatial reasoning benchmarks demonstrate that GeoVR achieves state-of-the-art performance, establishing a new paradigm for endowing foundation models with spatial intelligence.

Chinese Translation

多模态大语言模型（MLLMs）在二维语义理解方面表现优异，但缺乏内在的三维意识，导致其在视频帧间的表示无法保持几何和空间一致性。鉴于大规模三维数据的稀缺，我们提出了GeoVR，这是一个利用纯二维视频序列学习几何表示的全新框架。这种方法有效地重构了MLLMs中的语义潜在空间，从而解锁空间智能。GeoVR通过从预训练的三维基础模型中提炼几何知识，而不是使用表面特征混合，重新塑造MLLM的内部表示。这是通过一个多目标学习策略实现的，该策略由四个互补的几何目标驱动：(1) 估计帧间相机位姿，以嵌入变化的视点动态；(2) 回归密集深度图，以锚定物理距离；(3) 预测一个用于现实世界标定的度量尺度因子；(4) 提炼多尺度三维特征，以对齐中间特征空间。在这些明确的物理和几何约束的指导下，模型的内部表示自然发展出强烈的三维意识。在空间推理基准上的广泛实验表明，GeoVR实现了最先进的性能，建立了一个赋予基础模型空间智能的新范式。

View on arXiv Download PDF AI Translation

cs.CV / 53 / 2606.05883

Geometry-Aware Dataset Condensation for Diffusion Model Training

针对扩散模型训练的几何感知数据集压缩

Cui, Xiao, Qin, Yulei, Zhu, Mo, Zhou, Wengang, Li, Hongsheng, Li, Houqiang

Abstract

Dataset condensation aims to construct compact datasets from real data via synthesis or selection. However, existing approaches are ill-suited for diffusion model training: synthetic data generation often yields low-fidelity samples unsuitable for authentic modeling, while real subset selection typically fails to preserve the distributional geometry required by diffusion likelihood objectives. To address this, we propose to reformulate real subset selection as a geometry-aware distribution alignment problem. By incorporating one-sided partial optimal transport, our method selectively aligns a compact subset with the full data distribution while allowing unmatched mass in low-density regions, ensuring the preserved geometric structure necessary for effective diffusion model training. To further ensure distributional fidelity, we complement geometric alignment with lightweight feature-statistics and semantic consistency regularization. An efficient two-stage discrete optimization strategy is proposed to achieve this alignment objective. Extensive experiments across diffusion variants, subset sizes, image resolutions, and training rounds show that our method achieves superior fidelity and distributional coverage in diffusion model training. Codes are available at https://github.com/2018cx/GADC.

Chinese Translation

数据集压缩旨在通过合成或选择从真实数据中构建紧凑的数据集。然而，现有的方法并不适合扩散模型训练：合成数据生成往往产生低保真度的样本，不适用于真实建模，而真实子集选择通常无法保留扩散似然目标所需的分布几何结构。为了解决这个问题，我们提出将真实子集选择重新表述为一个几何感知的分布对齐问题。通过引入单侧部分最优传输，我们的方法选择性地将紧凑子集与完整数据分布对齐，同时允许在低密度区域存在不匹配的质量，确保保留有效扩散模型训练所需的几何结构。为了进一步确保分布的保真性，我们结合几何对齐与轻量级特征统计和语义一致性正则化。我们提出了一种高效的两阶段离散优化策略来实现此对齐目标。针对不同的扩散变体、子集大小、图像分辨率和训练轮次的大量实验表明，我们的方法在扩散模型训练中实现了卓越的保真性和分布覆盖性。代码可在 https://github.com/2018cx/GADC 获取。

View on arXiv Download PDF AI Translation

cs.CV / 54 / 2606.05896

Resonant Minds: Closed-Loop Social Avatars with Theory of Mind

共鸣思维：具有心智理论的闭环社交化身

Shangguan, Jianxu, Xu, Jing, Ye, Hang, Ma, Xiaoxuan, Wang, Yizhou, Zhu, Wentao

Abstract

Creating lifelike digital humans with genuine social intelligence requires unifying cognitive reasoning and multimodal generation within a coherent framework. Current approaches treat these as separate tasks: Large Language Models excel at dialogue but lack embodied expression, while diffusion-based talking head models achieve visual fidelity but ignore social cognition. To bridge this gap, we propose a closed-loop dual-agent framework integrating perception, social reasoning, and expression into a continuous interaction cycle. The perception module analyzes partners' multimodal behaviors from video, while the social reasoning module infers hidden mental states through Theory of Mind and selects responses via an ensemble mechanism. The expression module then generates emotion-controllable dual-agent videos synthesizing both speaker speech and expression alongside listener reactive behaviors, capturing bidirectional dynamics absent in prior work. We construct a hierarchical Persona-Scenario dataset with psychologically grounded personas and private social goals to support evaluation under information asymmetry. Experiments on this dataset demonstrate competitive or superior performance on both dialogue quality and video generation metrics. Notably, our method surpasses even the full-information Script mode on key dialogue quality dimensions, suggesting that explicit mental state inference under uncertainty can elicit more thoughtful dialogue than unrestricted information access.

Chinese Translation

创建具有人类特征的数字人类，具备真正的社交智能，需要在一个统一的框架内结合认知推理和多模态生成。目前的研究方法将这些视为独立的任务：大型语言模型擅长对话，但缺乏具身表现，而基于扩散的会说话头模型则实现了视觉逼真性，但忽视了社交认知。为了解决这一鸿沟，我们提出了一种闭环双代理框架，将感知、社交推理和表现整合在一个连续的互动循环中。感知模块通过视频分析对方的多模态行为，而社交推理模块通过心智理论推断隐藏的心理状态，并通过集成机制选择响应。随后，表现模块生成可以控制情感的双代理视频，同时综合讲者的语音和表情以及听者的反应行为，捕捉以往研究中缺乏的双向动态。我们构建了一个层次化的人格-场景数据集，具有心理学基础的人物角色和私人社交目标，以支持在信息不对称下的评估。我们在该数据集上的实验展示了对话质量和视频生成指标上具有竞争力或更优越的表现。值得注意的是，我们的方法在关键对话质量维度上甚至超越了全信息脚本模式，这表明在不确定性下明确的心理状态推断能够引发比不受限制信息访问更具深度的对话。

View on arXiv Download PDF AI Translation

cs.CV / 55 / 2606.05912

Self-Learning Expression Deformations for Data-Efficient Gaussian Avatars

自学习表达变形用于数据高效的高斯头像

Yang, Jiahao, Yang, Xiaohang, Wang, Qing, Dong, Yilan, Slabaugh, Gregory, Yuan, Shanxin

Abstract

Modeling dynamic facial expressions using 3D Gaussian representations remains challenging due to their unstructured nature. Conventional Gaussian avatar pipelines require extensive multiview and sequential expression data, limiting scalability and accessibility. In this work, we introduce Self-Adaptive Gaussian Expression (SAGE), a framework for self-learning expression-induced Gaussian deformations that enables high-fidelity, animatable avatars from minimal input data. Our method jointly optimizes 2D Gaussian surfels and a Signed Distance Field (SDF) to enforce compact, surface-aligned Gaussian distributions, while a self-supervised expression learning phase replaces long training sequences with geometric and appearance consistency constraints. This design allows flexible deployment across multiple reconstruction regimes: in the multiview setting, only a single frame (timestep) is required instead of thousands; in the monocular setting, only head rotations are needed without expression sequences; and in the one-shot setting, no pretraining or priors are necessary. Experiments demonstrate that our approach achieves reconstruction and animation quality comparable to state-of-the-art methods, while reducing data requirements by several orders of magnitude. Our results highlight the potential of self-supervised Gaussian deformation learning as a step toward accessible, data-efficient avatar creation.

Chinese Translation

使用3D高斯表示建模动态面部表情仍然具有挑战性，因为其本质上是非结构化的。传统的高斯头像管道需要大量的多视角和序列表情数据，这限制了其可扩展性和可访问性。在本研究中，我们引入了自适应高斯表达（Self-Adaptive Gaussian Expression, SAGE）框架，该框架支持自学习表达引起的高斯变形，使得能够从最少的输入数据生成高保真、可动画的头像。我们的方法联合优化2D高斯表面点和签名距离场（Signed Distance Field, SDF），以强制执行紧凑且与表面对齐的高斯分布，同时自监督的表情学习阶段用几何和外观一致性约束替代了长时间训练序列。该设计允许在多个重建模式下灵活部署：在多视角设置中，仅需单帧（时间步）而不是数千帧；在单目设置中，只需要头部旋转而无需表情序列；而在一次性设置中，则不需要预训练或先验知识。实验表明，我们的方法在重建和动画质量上与最先进的方法相当，同时将数据需求减少了几个数量级。我们的结果突显了自监督高斯变形学习的潜力，为实现可访问且数据高效的头像创作铺平了道路。

View on arXiv Download PDF AI Translation

cs.CV / 56 / 2606.05915

CamFlow+: Hybrid Motion Bases for 2D Camera Motion Estimation with Stabilization Applications

CamFlow+: 用于二维相机运动估计的混合运动基与稳定化应用

Li, Haipeng, Liu, Zhen, Yang, Zhanglei, Jiang, Hai, Zhou, Tianhao, Liu, Zhengzhe, Tan, Ping, Zeng, Bing, Liu, Shuaicheng

Abstract

Estimating 2D camera motion is fundamental to computer vision and computational photography. Existing homography-based methods work well for planar scenes or pure rotation, but struggle with camera translation, depth variation, and local parallax; local homography and mesh-based models improve flexibility but still rely on piecewise planar assumptions. We introduce CamFlow+, a hybrid-basis framework that represents 2D camera motion directly in dense-flow space. CamFlow+ combines homography-derived physical bases, stochastic bases sampled from homography flows, and depth-translational bases derived from depth and camera intrinsics, relaxing the single-plane constraint while preserving camera-motion regularity. A depth-aware smoothness term further regularizes translation-induced parallax in continuous-depth regions while preserving motion changes near depth boundaries. We evaluate CamFlow+ on GHOF-Cam, a camera-motion benchmark that masks out dynamic objects and ill-posed occlusion regions in an optical-flow benchmark to isolate camera-induced motion. Experiments show that CamFlow+ improves sparse and dense camera-motion estimation. In digital video stabilization, CamFlow+ also improves global and local stability, achieving the best top-1 preference rate in a blind user study. Code and datasets will be available on the project page: https://lhaippp.github.io/CamFlow+.

Chinese Translation

估计二维相机运动是计算机视觉和计算摄影中的基础工作。现有的基于单应性的算法在平面场景或纯旋转中表现良好，但在相机平移、深度变化和局部视差方面存在困难；局部单应性和基于网格的模型提高了灵活性，但仍然依赖于分段平面的假设。我们提出了CamFlow+，一种混合基础框架，直接在稠密流空间中表示二维相机运动。CamFlow+结合了基于单应性的物理基础、从单应性流中采样的随机基础，以及从深度和相机内参推导出的深度-平移基础，放松了单平面的约束，同时保持了相机运动的规律性。重视深度的平滑项进一步规范化了在连续深度区域中由于平移造成的视差，同时在深度边界附近保留运动变化。我们在GHOF-Cam这一相机运动基准上评估了CamFlow+，该基准将动态物体和不良遮挡区域掩蔽在光流基准中，以隔离由相机引起的运动。实验表明，CamFlow+改进了稀疏和稠密的相机运动估计。在数字视频稳定化中，CamFlow+也改善了全局和局部稳定性，在盲用户研究中达到了最佳的 top-1 偏好率。代码和数据集将在项目页面上发布：https://lhaippp.github.io/CamFlow+.

View on arXiv Download PDF AI Translation

cs.CV / 57 / 2606.05916

Unveiling the Unknown: Open Vocabulary Object Detection with Scene Graphs

揭示未知：基于场景图的开放词汇物体检测

Chen, Yi, Lu, Yinghao, Li, Zhehao, Yan, Chenchen, Wu, Jiafei, Wang, Chong, Qian, Jiangbo

Abstract

Open-vocabulary object detection seeks to identify novel object categories that were not part of the training data. Many knowledge distillation-based approaches have shown promising performance by transferring knowledge from pre-trained vision-language models to object detection. However, these methods often overlook structured, image-specific relationships between objects, such as interactions and spatial arrangements. This oversight can significantly restrict the effectiveness of detecting novel categories. To address this issue, we propose a Scene-guided Relational Modeling detection framework. This framework utilizes scene graphs to capture structured semantic and spatial relationships between candidate regions and their contextual objects. It explicitly models interactions among neighboring regions and incorporates a Relation Attention Module to implicitly amplify the key relational cues extracted from the scene graph. Furthermore, we present a scene-based textual alignment branch that distills category knowledge from captions to guide relational alignment. This approach facilitates a seamless integration of visual relations with semantic information for enhanced detection performance. Comprehensive experiments show that our model achieves superior performance compared to other OVOD methods, improving the AP for novel categories on COCO and LVIS datasets.

Chinese Translation

开放词汇物体检测旨在识别训练数据中不存在的新物体类别。许多基于知识蒸馏的方法通过将知识从预训练的视觉-语言模型转移到物体检测中，表现出令人鼓舞的性能。然而，这些方法往往忽视了物体之间的结构化、图像特定关系，例如交互和空间排列。这种忽视会显著限制对新类别检测的有效性。为了解决这一问题，我们提出了一种基于场景引导的关系建模检测框架。该框架利用场景图捕捉候选区域与其上下文物体之间的结构化语义和空间关系。它明确地建模邻近区域之间的交互，并结合关系注意模块隐式增强从场景图中提取的关键关系线索。此外，我们还提出了一种基于场景的文本对齐分支，从字幕中提取类别知识，以指导关系对齐。这一方法促进了视觉关系与语义信息的无缝整合，从而增强了检测性能。综合实验表明，我们的模型在性能上优于其他开放词汇物体检测方法，在COCO和LVIS数据集上显著提高了新类别的AP。

View on arXiv Download PDF AI Translation

cs.CV / 58 / 2606.05917

MemoryCard: Topic-Aware Multi-Modal Clue Compression for Long-Video Question Answering

MemoryCard：用于长视频问答的主题感知多模态线索压缩

Yang, Qing, Huang, Pengcheng, Li, Xinze, Liu, Zhenghao, Yan, Yukun, Gu, Yu, Yu, Ge, Li, Gang, Sun, Maosong

Abstract

Long-video question answering remains challenging for Vision-Language Models (VLMs), as answer-relevant evidence is often sparse, transient, and temporally dispersed across lengthy video contexts. Existing frame-centric approaches improve efficiency through uniform sampling, query-aware frame selection, visual-token compression, and adaptive resolution strategies. However, they still rely on isolated and fragmented frames as the fundamental evidence units, limiting VLMs' ability to effectively capture coherent event-level semantics. To address this limitation, we propose MemoryCard, a video-memory-based augmentation framework that organizes long videos into self-contained Memory Cards. Specifically, MemoryCard first performs a self-reading process over videos and aligned utterances to segment the video into semantically coherent units, each corresponding to a distinct topic or event. For each unit, it generates an event-level video gist and selects representative visual moments, which are then rendered into unified Memory Cards for retrieval and question answering. Experimental results demonstrate that MemoryCard consistently improves long-video QA performance under comparable visual-token budgets, achieving up to a 21.8% relative improvement in accuracy. All code is available at https://github.com/NEUIR/MemoryCard.

Chinese Translation

长视频问答对视觉-语言模型（VLMs）仍然具有挑战性，因为与答案相关的证据通常是稀疏的、短暂的，并且在较长的视频上下文中时间上分散。现有的帧中心方法通过均匀采样、查询感知帧选择、视觉标记压缩和自适应分辨率策略来提高效率。然而，它们仍然依赖于孤立和零碎的帧作为基本证据单元，限制了VLMs有效捕捉连贯事件级语义的能力。为了解决这一局限，我们提出了MemoryCard，一个基于视频记忆的增强框架，将长视频组织为自包含的记忆卡。具体而言，MemoryCard首先对视频和对齐的发言进行自阅读处理，将视频分割为语义上连贯的单元，每个单元对应于一个独特的主题或事件。对于每个单元，它生成一个事件级视频概要并选择代表性的视觉时刻，这些时刻随后被渲染成统一的记忆卡以便于检索和问答。实验结果表明，MemoryCard在可比的视觉标记预算下，持续提高长视频问答性能，准确率相对提高高达21.8%。所有代码可在https://github.com/NEUIR/MemoryCard获取。

View on arXiv Download PDF AI Translation

cs.CV / 59 / 2606.05949

Faithful, Enriched, and Precise: Benchmarking Natural-Science Illustration Generation by T2I models

忠实、丰富和精确：基于 T2I 模型的自然科学插图生成基准评估

Chang, Yifan, Ai, Jiaxin, Sun, Jianwen, Pu, Yuandong, Luo, Siqi, Zhao, Liangliang, Ren, Yuchen, Liu, Minghao, Yu, Yunfei, Qiao, Yu, Zhang, Kaipeng, Liu, Yihao

Abstract

Scientific illustrations are essential tools for communicating research findings, especially in natural science, where they visualize complex concepts and processes. As Text-to-Image (T2I) models become increasingly capable, researchers have started to use them for scientific illustration generation. However, existing benchmarks often assess outputs at a holistic level, overlooking fine-grained elements, while scientific reasoning ability and output conciseness remain under-quantified. We introduce FEPBench, a benchmark built from carefully selected high-quality scientific illustrations across multiple disciplines and layout types. With the assistance of multimodal large language models (MLLMs) and human experts, we provide fine-grained atom set annotations and systematically evaluate T2I models along three dimensions: instruction faithfulness, reasoning enrichment, and semantic precision. Our evaluation further decomposes model performance across visual, textual, relation, and layout elements. Results show that even state-of-the-art (SOTA) closed-source models, such as GPT Image 2 and Nano Banana Pro, still suffer from text-rendering bottlenecks, limited reasoning enrichment, and difficulty balancing generation richness with precision. These findings provide practical guidance for improving and deploying T2I models in scientific illustration generation. Benchmark data, atom set annotations, and evaluation code will be released by us.

Chinese Translation

科学插图是传播研究成果的重要工具，尤其是在自然科学领域，它们可视化复杂的概念和过程。随着文本到图像（Text-to-Image, T2I）模型能力的不断提升，研究人员开始利用这些模型生成科学插图。然而，现有的基准往往在整体层面评估输出，忽略了细粒度的元素，而科学推理能力和输出的简洁性仍然缺乏量化评估。我们提出了 FEPBench，这是一个基于多个学科和布局类型中精心挑选的高质量科学插图构建的基准。借助多模态大型语言模型（Multimodal Large Language Models, MLLMs）和人类专家的支持，我们提供了细粒度原子集合注释，并系统地沿三个维度评估 T2I 模型：指令忠实性、推理丰富性和语义精准性。我们的评估进一步细分模型在视觉、文本、关系和布局元素上的表现。结果表明，即便是最先进的（State-of-the-Art, SOTA）闭源模型，例如 GPT Image 2 和 Nano Banana Pro，仍然面临文本渲染瓶颈、推理丰富性有限以及在生成丰富性与精准性之间难以平衡的问题。这些发现为改进和部署 T2I 模型在科学插图生成中的应用提供了实用指导。基准数据、原子集合注释和评估代码将由我们公开。

View on arXiv Download PDF AI Translation

cs.CV / 60 / 2606.05975

T-FunS3D: Task-Driven Hierarchical Open-Vocabulary 3D Functionality Segmentation

T-FunS3D: 基于任务驱动的分层开放词汇3D功能分割

Feng, Jingkun, Sabzevari, Reza

Abstract

Open-vocabulary 3D functionality segmentation enables robots to localize functional object components in 3D scenes. It is a challenging task that requires spatial understanding and task interpretation. Current open-vocabulary 3D segmentation methods primarily focus on object-level recognition, while scene-wide part segmentation methods attempt to segment the entire scene exhaustively, making them highly resource-intensive and time consuming. Balancing segmentation performance in terms of granularity, accuracy, and speed remains a challenge. As one step towards alleviating this, we introduce T-FunS3D, a task-driven hierarchical open-vocabulary 3D functionality segmentation method that provides actionable perception for robotic applications. Our method takes as input the 3D point cloud and posed RGB-D images of an indoor scene. We construct an open-vocabulary scene graph by extracting instances and their visual embeddings in the environment. Given a task description, T-FunS3D identifies the most relevant instances in the scene graph and locates their functional components leveraging a vision-language model. Experiments on the SceneFun3D dataset demonstrate that T-FunS3D is comparable to state-of-the-art in open-vocabulary 3D functionality segmentation, while achieving faster runtime and reduced memory usage.

Chinese Translation

开放词汇3D功能分割使机器人能够在3D场景中定位功能性物体组件。这是一项具有挑战性的任务，需要空间理解和任务解释。目前的开放词汇3D分割方法主要集中于物体级别的识别，而全场景部件分割方法则试图对整个场景进行全面分割，这使得它们消耗大量资源且耗时较长。在粒度、准确性和速度之间平衡分割性能仍然是一个挑战。为了缓解这一问题，我们提出了T-FunS3D，一种基于任务驱动的分层开放词汇3D功能分割方法，为机器人应用提供可操作的感知。我们的方法以室内场景的3D点云和姿态RGB-D图像作为输入。通过提取环境中的实例及其视觉嵌入，我们构建了一个开放词汇场景图。根据任务描述，T-FunS3D识别场景图中最相关的实例，并利用视觉-语言模型定位其功能组件。在SceneFun3D数据集上的实验表明，T-FunS3D在开放词汇3D功能分割方面与最先进的方法具有可比性，同时实现了更快的运行时间和更低的内存使用。

View on arXiv Download PDF AI Translation

cs.CV / 61 / 2606.05981

Video-Rate Streaming Stylization on a Vision-Aware MLLM-Conditioned Edit Diffusion: Asymmetric Batched Inference on a Distilled UNet + MLLM Text Encoder

基于视觉感知的MLLM条件编辑扩散的视频速率流式风格化：在蒸馏的UNet + MLLM文本编码器上进行不对称批处理推理

Ootani, Yoshiyuki

Abstract

Aggressive distillation of the diffusion U-Net inverts the per-frame bottleneck of real-time text-to-image pipelines: once the denoiser is a 4-step or 1-step distilled student, the text encoder becomes the critical path. This inversion is most acute in vision-aware edit diffusion, where the encoder is a multimodal large language model (MLLM). We study the case of a 0.39B distilled edit U-Net paired with a 2.13B MLLM text encoder (Qwen3-VL) and present a streaming pipeline targeted at this regime built around three engineering mechanisms: asymmetric side-stream / main-stream CUDA pipelining with batched text-encoder amortisation (and optional static-prompt caching), a compile-friendly ControlNet-LLLite reformulation that folds the entire U-Net + adapter stack into a single fused graph, and a periodic conditioning-refresh schedule with a hook subset that amortises the per-frame conditioning cost. On a single consumer RTX 3090 Ti at 512x512 the pipeline sustains 27.4 fps over a 480-frame run at batch size B=8 and 29.6 fps at B=16, with end-to-end p50 latency of approximately 0.5 and 1.0 seconds respectively; the same operating point measures 54.9 fps on RTX 4090 and 74.1 fps on RTX 5090. We report video-rate streaming throughput rather than interactive low latency, and locate our numbers against same-stack StreamDiffusion re-runs as systems context, not as a benchmark superiority claim. For the trained oil-painting style, the released temporal adapter generalises within in-clip noise to 19 unused DAVIS-2017 sequences and 15 non-DAVIS clips from seven sources; prompt-level generalisation to unseen style families is bounded and reported separately.

Chinese Translation

对扩散U-Net的强蒸馏逆转了实时文本到图像管道的逐帧瓶颈：当去噪网络是一个4步或1步蒸馏的学生时，文本编码器就成为了关键路径。这种逆转在视觉感知的编辑扩散中尤为明显，其中编码器是一个多模态大型语言模型（MLLM）。我们研究了一个0.39B蒸馏编辑U-Net与2.13B MLLM文本编码器（Qwen3-VL）配对的案例，并提出了一个针对这一方案的流式管道，该管道围绕三种工程机制构建：具有批量文本编码器摊销（和可选的静态提示缓存）的不对称侧流/主流CUDA管道，一个编译友好的ControlNet-LLLite重构，将整个U-Net + 适配器堆栈折叠为一个单一的融合图，以及定期的条件刷新调度，采用一个钩子子集以摊销每帧的条件成本。在单个消费者RTX 3090 Ti上，在512x512的分辨率下，该管道在批量大小B=8时以27.4 fps持续480帧的运行，在B=16时以29.6 fps持续，端到端p50延迟分别约为0.5和1.0秒；同样的操作点在RTX 4090上测得54.9 fps，在RTX 5090上测得74.1 fps。我们报告的是视频速率流式吞吐量，而非交互式低延迟，并且将我们的数字与相同堆栈的StreamDiffusion重运行进行比较，以作为系统上下文，而非基准优越性的声明。对于经过训练的油画风格，发布的时间适配器在19个未使用的DAVIS-2017序列和来自七个来源的15个非DAVIS片段中进行了片段内噪声的泛化；提示级的泛化到未见过的风格家族是有限的，并单独报告。

View on arXiv Download PDF AI Translation

cs.CV / 62 / 2606.05997

Multimodal Sexism Identification and Characterization using Large Language Models and Gradient Boosting

基于大型语言模型和梯度提升的多模态性别歧视识别与表征

Chaviaras, Kyriakos, Lymperaiou, Maria, Voulodimos, Athanasios

Abstract

We present the AILS-NTUA submission to the EXIST 2026 Lab at CLEF, addressing multimodal sexism identification and characterization in memes (Task 2) and short-form videos (Task 3). Our system follows a feature-engineered late-fusion pipeline built around gradient-boosted regression models and hierarchical post-processing. For memes, we combine visual, textual, demographic, biometric, and LLM-derived semantic indicators designed to capture high-level cues such as stereotyping, objectification, irony, and misogyny. For videos, we investigate the effect of feature selection, frame-based visual representations, OCR-based textual features, acoustic descriptors, and sensor-derived metadata. Development results show that focused LLM-derived semantic cues improve meme sexism identification, while video performance is highly sensitive to feature dimensionality and cross-modal noise. For videos, development results favor compact feature selection, but official test results show that this conclusion does not fully transfer to unseen data, where the unfiltered representation generalizes better. Overall, our findings highlight the usefulness of targeted semantic feature engineering for static memes and the need for more robust temporal modeling in noisy short-form video settings.

Chinese Translation

我们在CLEF的EKSIST 2026实验室提交了AILS-NTUA的参赛作品，旨在解决多模态性别歧视在表情包（任务2）和短视频（任务3）中的识别与表征。我们的系统采用基于特征工程的晚期融合管道，围绕梯度提升回归模型和分层后处理进行构建。在表情包方面，我们结合了视觉、文本、人口统计学、生物识别和大型语言模型（LLM）所派生的语义指标，旨在捕捉诸如刻板印象、物化、讽刺和厌女等高级线索。对于视频，我们研究了特征选择、基于帧的视觉表征、基于光学字符识别（OCR）的文本特征、声学描述符和传感器派生元数据的影响。开发结果表明，聚焦的LLM派生语义线索改善了表情包的性别歧视识别，而视频性能对特征维度和跨模态噪声极为敏感。在视频方面，开发结果倾向于紧凑的特征选择，但官方测试结果表明这一结论并未完全转移到未见数据中，未过滤的表征在泛化能力上更具优势。总体而言，我们的发现突显了针对静态表情包的语义特征工程的有效性，以及在嘈杂的短视频环境中对更强大时间建模的需求。

View on arXiv Download PDF AI Translation

cs.CV / 63 / 2606.05998

Deep Learning-based 3D Oral Cavity Reconstruction Using 2D Intraoral Images

基于深度学习的3D口腔重建方法研究：使用2D口内图像

Cho, Jihun, Jeong, Soo-Yeon, Bae, Eun-Jeong, Ihm, Sun-Young

Abstract

Oral 3D modelling is one of the most essential stages in dentistry, and many different approaches, such as impression taking and intraoral scanning, are commonly used for this phase, each with notable limitations. Impression taking, which involves placing alginate or silicone material in a tray and inserting it into the patient's oral cavity to form a negative mold, suffers from significant patient discomfort, material deformation errors, and difficulties in storage and transportation. Intraoral scanners, which directly scan oral structures in real time using structured light or laser technology, produce state-of-the-art results but are associated with substantially high equipment costs. To address these limitations, this paper proposes a software-based approach that reconstructs a 3D oral model using only ten 2D intraoral images captured from different angles, requiring no dedicated hardware devices. The proposed method reduces cost, eliminates the need for physical scanning equipment, minimises patient discomfort, and enables automated 3D reconstruction. The model is trained on the publicly available Dental3DS dataset, comprising 950 upper jaw samples, and employs MobileNetV2 as the image encoder combined with Multi-head Attention for multi-view feature fusion. The proposed model achieves an accuracy of 77.49%, measured by nearest-neighbor matching with a distance threshold of 0.035. However, predicted vertices tend to concentrate in high-density regions of the ground truth, resulting in uneven point distribution across the reconstructed model.

Chinese Translation

口腔三维建模是牙科中最重要的阶段之一，通常采用印模和口内扫描等不同方法进行该阶段，然而每种方法均存在显著限制。印模方法通过在托盘中放置铝酸盐或硅胶材料并插入患者口腔以形成负模，往往会导致显著的患者不适、材料变形误差以及储存和运输的困难。口内扫描仪则使用结构化光或激光技术实时扫描口腔结构，虽然可以产生前沿的结果，但其设备成本高昂。为了解决这些限制，本文提出了一种基于软件的方法，仅使用从不同角度捕获的十张2D口内图像重建3D口腔模型，无需专用硬件设备。该方法降低了成本，消除了对物理扫描设备的需求，减少了患者不适，并实现了自动化的3D重建。模型在公开可用的Dental3DS数据集上进行训练，该数据集包含950个上颌样本，采用MobileNetV2作为图像编码器，并结合多头注意力机制进行多视角特征融合。所提模型在以0.035的距离阈值进行最近邻匹配的情况下，达到了77.49%的准确度。然而，预测的顶点往往集中在真实数据的高密度区域，导致重建模型中的点分布不均匀。

View on arXiv Download PDF AI Translation

cs.CV / 64 / 2606.05999

ATT-CR: Adaptive Triangular Transformer for Cloud Removal

ATT-CR: 自适应三角形变换器用于云去除

Wu, Yang, Deng, Ye, Li, Pengna, Huang, Wenli, Wu, Kangyi, Xin, Xiaomeng, Wang, Jinjun

Abstract

Cloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images. Existing Transformer-based methods utilizing self-attention have shown impressive results by effectively modeling long-range dependencies in cloudy images. However, they suffer from the following issues: 1) the high computational complexity of self-attention limits scalability; 2) treating both cloudy and clean pixels as valid within the attention computation brings disturbances in subsequent layers, leading to suboptimal performance. To address these challenges, we propose the Adaptive Triangular Transformer for Cloud Removal (ATT-CR), a model that effectively reduces computational costs and mitigates interference from cloudy pixels. Specifically, it consists of two core components: Triangular Attention (TAN) and Feature Selected Gating Module (FSGM). TAN employs lower and upper triangular matrices to approximate Softmax attention with O(N) computational complexity, significantly reducing the computational costs. The FSGM, on the other hand, integrates with TAN to adaptively distinguish between cloudy and clean features, which minimizes the introduction of invalid information into subsequent layers. Extensive experiments on cloud removal benchmarks demonstrate that ATT-CR delivers superior performance compared to existing methods.

Chinese Translation

云去除旨在准确重建被云遮挡的地面物体，以便于遥感图像分析。现有基于变换器（Transformer）的方法利用自注意力机制，在建模多云图像中的长程依赖关系方面展现了令人印象深刻的效果。然而，这些方法面临以下问题：1) 自注意力的高计算复杂度限制了其扩展性；2) 在注意力计算中将多云和清晰的像素视为有效像素，导致后续层的干扰，从而导致次优性能。为了解决这些挑战，我们提出了云去除的自适应三角形变换器（Adaptive Triangular Transformer for Cloud Removal, ATT-CR），该模型有效降低计算成本并减轻多云像素带来的干扰。具体而言，ATT-CR由两个核心组件组成：三角形注意力（Triangular Attention, TAN）和特征选择门控模块（Feature Selected Gating Module, FSGM）。TAN采用下三角和上三角矩阵来近似Softmax注意力，其计算复杂度为O(N)，显著降低了计算成本。另一方面，FSGM与TAN集成，能自适应地区分多云和清晰特征，从而减少无效信息对后续层的引入。对云去除基准测试的广泛实验表明，ATT-CR的性能优于现有方法。

View on arXiv Download PDF AI Translation

cs.CV / 65 / 2606.06002

Global-Local Monte Carlo Tree Search in Vision-Language Models for Text-to-3D Indoor Scene Generation

视觉-语言模型中基于全局-局部的蒙特卡洛树搜索用于文本到3D室内场景生成

Qi, Mengshi, Deng, Wei, Zhang, Xianlin, Ma, Huadong

Abstract

Large Vision-Language Models have achieved significant reasoning performance in various tasks.However, there are few studies on text-to-3D indoor scene generation with LVLMs. The main challenge is that prevailing LVLM-based methods employ chain-of-thought sequential decision mechanisms that cannot revise earlier decisions, causing error propagation.In this paper, we consider the task as a planning problem constrained by spatial and layout commonsense.To solve this problem, we model it as a tree search problem with global and local trees, which differs from existing sequential decision-making approaches.In the global tree, we place each object iteratively and explore multiple attempts like humans furnishing a room, where the problem space is represented as a tree.To effectively search the tree, we propose a hierarchical scene representation and a PRM-guided MCTS method.The hierarchical representation abstracts a scene into room level, region level, floor object level, and supported object level.The PRM-guided MCTS method uses the PRM to prune unnecessary branches and the MCTS algorithm to balance exploration and exploitation to get an optimal solution with fewer attempts.In the local tree, it further decomposes the placement of each object into finer sub-steps, including the specific placement parameters.To make the whole appearance of the scene consistent, we leverage pre-trained diffusion image generative models to predict textures for all the objects in the scene.As existing benchmarks for text-to-3D indoor scene generation remain limited in scale and diversity, we collect a new large-scale diverse dataset that contains 65 scene types and 3,250 instructions with diverse sizes, layouts, and styles, named 3DTindo-bench, to better assess the capability of the state-of-the-art models. Our experiments show that our method generates more realistic 3D scenes than state-of-the-art approaches.

Chinese Translation

大型视觉-语言模型在多种任务中取得了显著的推理性能。然而，对于使用大型视觉-语言模型（LVLMs）进行文本到3D室内场景生成的研究较少。主要挑战在于现有的基于LVLM的方法采用思维链的顺序决策机制，这使得无法修正早期的决策，从而导致错误传播。在本文中，我们将该任务视为一个受空间和布局常识约束的规划问题。为了解决这个问题，我们将其建模为一个包含全局树和局部树的树搜索问题，这不同于现有的顺序决策方法。在全局树中，我们逐步放置每个对象，并像人类布置房间一样探索多种尝试，其中问题空间被表示为一棵树。为了有效搜索这棵树，我们提出了一种分层场景表示和一种基于概率道路图（PRM）引导的蒙特卡洛树搜索（MCTS）方法。该分层表示将场景抽象为房间级、区域级、地面物体级和支撑物体级。PRM引导的MCTS方法使用PRM来修剪不必要的分支，并利用MCTS算法来平衡探索与利用，在较少的尝试中获得最优解。在局部树中，进一步将每个对象的放置细分为更精细的子步骤，包括具体的放置参数。为了使场景的整体外观保持一致，我们利用预训练的扩散图像生成模型为场景中所有对象预测纹理。现有文本到3D室内场景生成基准的规模和多样性仍然有限，我们收集了一个新的大规模多样化数据集，包含65种场景类型和3,250条具有多样化尺寸、布局和风格的指令，命名为3DTindo-bench，以更好地评估最先进模型的能力。我们的实验表明，我们的方法生成的3D场景比现有最先进的方法更为真实。

View on arXiv Download PDF AI Translation

cs.CV / 66 / 2606.06020

ReSAGE-PAR: Representational Similarity Assessment for Generative Expansion in Pedestrian Attribute Recognition

ReSAGE-PAR：用于行人属性识别的生成扩展的表征相似度评估

Ayuso-Albizu, Pablo, Carballeira, Pablo, SanMiguel, Juan C., Moral, Paula

Abstract

To address the limited diversity and data scarcity in Pedestrian Attribute Recognition (PAR), we explore image synthesis using diffusion models guided by attribute-based prompts. While this enables the controlled generation of pedestrian images, it faces two critical challenges: (i) the domain gap between high-quality pre-training data and low-resolution, non-standard surveillance crops, and (ii) the need for reliable attribute verification to prevent generative hallucinations. In this paper, we introduce a robust generate-score-autolabel pipeline called ReSAGE-PAR (REpresentational Similarity Assessment for Generative Expansion in PAR) that bridges this domain gap and enables scalable, high-fidelity dataset expansion. First, we adapt pre-trained diffusion models to native PAR resolutions using a tailored LoRA-based Image-to-Image approach. Second, we extract vision-language alignment scores between the generated images and their conditioning prompts, utilizing a comprehensive prompting strategy that includes label-consistent and inconsistent complements. Finally, we formulate a Bayesian classifier that converts these continuous scores into reliable binary pseudo-labels. Extensive evaluations demonstrate the effectiveness of ReSAGE-PAR in preserving spatial priors and verifying attributes. When integrated into PAR training, ReSAGE-PAR consistently yields significant improvements-achieving gains of up to 8.7% on standard backbones and pushing state-of-the-art frameworks to new performance levels. This proves its value as an architecture-agnostic solution for scalable PAR enhancement. The complete codebase for ReSAGE-PAR is publicly available at http://www-vpu.eps.uam.es/publications/ReSAGE-PAR.

Chinese Translation

为了解决行人属性识别（PAR）中数据多样性有限和数据稀缺的问题，我们探索了使用由属性导向的提示指导的扩散模型进行图像合成。虽然这使得能够控制地生成行人图像，但面临两个关键挑战：（i）高质量预训练数据与低分辨率、非标准监控图像之间的领域差距，以及（ii）需要可靠的属性验证以防止生成幻觉。在本文中，我们提出了一种名为ReSAGE-PAR（REpresentational Similarity Assessment for Generative Expansion in PAR）的强健生成-评分-自动标签管道，旨在弥合这一领域差距，并实现可扩展的高保真数据集扩展。首先，我们使用定制的基于LoRA的图像到图像方法，将预训练的扩散模型适应于本地PAR分辨率。其次，我们提取生成图像与其条件提示之间的视觉-语言对齐得分，利用包括标签一致和不一致补充的全面提示策略。最后，我们构建了一个贝叶斯分类器，将这些连续得分转换为可靠的二元伪标签。广泛的评估证明了ReSAGE-PAR在保持空间先验和验证属性方面的有效性。当集成到PAR训练中时，ReSAGE-PAR始终显著提高性能——在标准骨干网络上获得高达8.7%的提升，并将最先进的框架推动到新的性能水平。这证明了其作为架构无关解决方案在可扩展PAR增强中的价值。ReSAGE-PAR的完整代码库已公开发布在http://www-vpu.eps.uam.es/publications/ReSAGE-PAR。

View on arXiv Download PDF AI Translation

cs.CV / 67 / 2606.06039

Texture-preserving implicit neural representation for Cone beam CT truncated reconstruction

保持纹理的隐式神经表示用于锥束CT截断重建

Zhang, Genyuan, Wang, Junyao, Lan, Haoran, Tan, Chuandong, Zhu, Songtao, Liu, Fenglin

Abstract

Cone-beam computed tomography (CBCT) frequently suffers from data truncation, which introduces severe artifacts and limits the effective field of view (FOV). Existing deep learning methods for truncated cone-beam computed tomography (CBCT) reconstruction suffer from serious limitations, including a strict reliance on supervised ground truth and a failure to account for continuous 3D spatial truncation variations. To address these challenges, we introduce a self-supervised 3D reconstruction framework based on neural scene representations. By directly mapping spatial coordinates to radiodensity under projection supervision, our approach inherently bypasses traditional filtering and backprojection operations, thereby fundamentally eliminating truncation-induced ring artifacts while enabling robust continuous 3D data extrapolation. However, coordinate networks are susceptible to an inherent spectral bias, which leads to a severe loss of clinically vital high-frequency textures. To resolve this bottleneck, we further incorporate a physics-based iterative refinement module into the neural scene representation architecture. Leveraging the artifact-free, extrapolated volume from the coordinate network as an optimal initialization, this module progressively re-extracts and injects high-frequency structural information from the original projections back into the volume. Extensive experiments on both simulated and real-world datasets demonstrate that our method successfully unifies the exceptional artifact suppression and extrapolation capabilities of neural networks with the high-fidelity detail preservation of iterative algorithms.

Chinese Translation

锥束计算机断层扫描（CBCT）常常面临数据截断问题，这会引入严重的伪影并限制有效视野（FOV）。现有的针对截断锥束计算机断层扫描（CBCT）的深度学习重建方法存在严重局限，包括对监督真实值的严格依赖以及未能考虑连续三维空间截断变化。为了解决这些挑战，我们提出了一种基于神经场景表示的自监督三维重建框架。通过在投影监督下直接将空间坐标映射到放射密度，我们的方法本质上绕过了传统的滤波和逆投影操作，从根本上消除了因截断引起的环状伪影，同时实现了稳健的连续三维数据外推。然而，坐标网络容易受到固有的光谱偏差影响，这导致临床上至关重要的高频纹理严重损失。为了解决这一瓶颈，我们进一步在神经场景表示架构中引入了一个基于物理的迭代精细化模块。该模块利用从坐标网络得到的无伪影外推体积作为优化初始化，逐步重新提取并将高频结构信息从原始投影注入回体积中。在对模拟和真实世界数据集进行的广泛实验中，我们的方法成功地将神经网络在伪影抑制和外推能力方面的卓越性能与迭代算法在高保真细节保持方面的优势结合起来。

View on arXiv Download PDF AI Translation

cs.CV / 68 / 2606.06042

LoomVideo: Unifying Multimodal Inputs into Video Generation and Editing

LoomVideo：将多模态输入统一为视频生成与编辑

Wu, Jianzong, Lian, Hao, Yang, Jiongfan, Hao, Dachao, Tian, Ye, Tong, Yunhai, Zhu, Jingyuan, Chen, Biaolong, Qi, Qiaosong, Zhang, Aixi, He, Wanggui, Liu, Mushui, Liu, Jinlong, Jiang, Hao

Abstract

Developing unified video generation and editing models capable of interpreting interleaved multimodal inputs is a promising yet challenging frontier field. Existing unified frameworks predominantly rely on massive models (typically 13B parameters or more) and incorporate source video conditions for editing by concatenating sequence tokens. This concatenation inevitably doubles the sequence length, quadrupling the computational complexity of the self-attention mechanism and introducing prohibitive overhead. To address these bottlenecks, we present LoomVideo, a highly efficient 5B-parameter unified architecture for both video generation and editing. LoomVideo replaces the standard text encoder with a Multimodal Large Language Model (MLLM) and employs Deepstack injection mechanism to align multi-layer MLLM features with the Diffusion Transformer (DiT). Crucially, we introduce a zero-overhead Scale-and-Add conditioning approach for video editing. By scaling and directly adding the clean source video latent to the noised target latent, this elegant design eliminates the need for token concatenation, drastically reducing computational cost while maintaining robust capabilities for complex, non-rigid edits. Furthermore, a Negative Temporal RoPE strategy is seamlessly integrated to handle multiple reference images. Extensive experiments demonstrate that our compact 5B model achieves state-of-the-art or highly competitive performance across comprehensive benchmarks, exhibiting exceptional superiority in e-commerce and fashion generation scenarios. Benefiting from the zero-overhead conditioning mechanism, LoomVideo achieves at least a 5.41x acceleration in inference speed compared to models of similar capabilities, paving the way for highly practical and efficient video foundation models.

Chinese Translation

开发能够解读交错多模态输入的统一视频生成和编辑模型是一个前景广阔但富有挑战性的前沿领域。现有的统一框架主要依赖于巨型模型（通常为130亿参数或以上），并通过连接序列标记来包含源视频条件以进行编辑。这种连接不可避免地会使序列长度加倍，使自注意力机制的计算复杂度增加四倍，并引入额外的开销。为了解决这些瓶颈，我们提出了LoomVideo，一种高效的50亿参数的统一架构，适用于视频生成和编辑。LoomVideo用多模态大语言模型（Multimodal Large Language Model, MLLM）替代标准文本编码器，并采用深度堆叠注入机制将多层MLLM特征与扩散变换器（Diffusion Transformer, DiT）对齐。关键地，我们引入了一种零开销的缩放和加法条件方法用于视频编辑。通过将干净的源视频潜在变量缩放并直接添加到有噪声的目标潜在变量中，这种优雅的设计消除了对标记连接的需求，显著降低了计算成本，同时保持了复杂、非刚性编辑的强大能力。此外，负时间RoPE策略无缝集成，能够处理多个参考图像。大量实验表明，我们的紧凑型50亿模型在全面基准测试中达到或具有高度竞争力的最新性能，在电子商务和时尚生成场景中表现出卓越的优势。得益于零开销的条件机制，LoomVideo在推理速度方面相比于类似能力的模型实现了至少5.41倍的加速，为高效实用的视频基础模型铺平了道路。

View on arXiv Download PDF AI Translation

cs.CV / 69 / 2606.06048

LLM-Conditioned Synthesis of Pathological Gaits via Structured Gait-Language Representations

通过结构化步态语言表征的病理步态的 LLM 条件合成

Chandrasekaran, Mritula, Kachole, Sanket, Francik, Jarik, Makris, Dimitrios

Abstract

Pathological gait datasets remain scarce due to privacy, recruitment, cost, and movement variability. Our work presents a multimodal LLM-guided framework for pathology-aware 3D gait data synthesis from structured textual descriptions. The proposed method generates fixed-length synthetic skeleton-based gait sequences for pathological gait classification tasks. The framework combines motion tokenisation, pathology-aware language conditioning, LLM-based semantic augmentation, and language-to-gait generation. A key contribution is the proposed pathological tokeniser, which is designed to preserve pathology-specific motion characteristics during discrete representation learning. Experiments suggest that the proposed synthetic sequences improve downstream classification for recurrent classifiers when combined with real data. The best result is obtained using a GRU classifier trained with real and synthetic samples, achieving 92.77\% accuracy under a leave-one-subject-out protocol.

Chinese Translation

由于隐私、招募、成本和运动变异性，病理步态数据集仍然稀缺。我们的工作提出了一种多模态 LLM 引导的框架，用于从结构化文本描述中合成病理意识的 3D 步态数据。所提出的方法为病理步态分类任务生成固定长度的合成基于骨骼的步态序列。该框架结合了运动标记、病理意识语言条件、基于 LLM 的语义增强以及语言到步态的生成。一项关键贡献是提出的病理标记器，旨在在离散表示学习过程中保留特定于病理的运动特征。实验表明，所提出的合成序列在与真实数据结合时提高了递归分类器的下游分类性能。最佳结果是使用真实样本和合成样本训练的 GRU 分类器获得的，在留一人离开的协议下达到 92.77\% 的准确率。

View on arXiv Download PDF AI Translation

cs.CV / 70 / 2606.06060

ReCache: Learning Budget-Aware Caching Schedules for Diffusion Models via REINFORCE

ReCache：通过REINFORCE学习预算感知的扩散模型缓存调度

Aliev, Mishan, Neudachina, Eva, Bykov, Ilya, Oganov, Aleksandr, Struminsky, Kirill, Alanov, Aibek, Rakitin, Denis

Abstract

Modern diffusion models generate high-quality images and videos, but their iterative denoising process makes inference expensive. Feature caching accelerates sampling by reusing or predicting intermediate activations across neighboring denoising steps, exploiting the redundancy of computations along the reverse trajectory. In this work, we focus on the caching schedule: selecting which denoising steps should be fully recomputed. Existing schedules are either fixed (e.g. uniform) or chosen adaptively from per-step error heuristics; in both cases, the actual compute cost is a side-effect of hand-tuned thresholds rather than a quantity the user can specify. We propose ReCache, which inverts this: given a target budget k, it learns the recomputation schedule that maximizes generation quality, turning compute into a directly controllable input. ReCache trains via policy gradients, sidestepping backpropagation through full diffusion inference, and uses no labelled data. Generations from uncached inference serve as matching targets, paired with a reward for generation quality. ReCache is compatible with any caching mechanism, including feature reuse and feature forecasting; for each mechanism, a single trained policy adapts across computational budgets at inference time. ReCache consistently outperforms scheduling baselines: under a $\times5.04$ FLOPs reduction on FLUX, it reduces LPIPS by 31% (from 0.456 to 0.316) compared to DiCache; on Wan 2.1 at a $\sim \times2.6$ speedup, it drops LPIPS by 65% (from 0.480 to 0.169) and boosts the VBench score by 7% (5.6 points, from 70.4 to 76.0) over uniform HiCache. Code is available at https://github.com/thecrazymage/ReCache.

Chinese Translation

现代扩散模型能够生成高质量的图像和视频，但其迭代去噪过程使推断成本较高。特征缓存通过在相邻的去噪步骤之间重用或预测中间激活来加速采样，利用沿着逆向轨迹的计算冗余。在这项工作中，我们关注缓存调度：选择哪些去噪步骤应完全重新计算。现有的调度要么是固定的（例如均匀的），要么是根据每一步错误启发式自适应选择；在这两种情况下，实际的计算成本是手动调优阈值的副作用，而不是用户可以指定的量。我们提出了ReCache，它反转了这种情况：给定目标预算k，它学习最大化生成质量的重新计算调度，从而使计算成为一个直接可控的输入。ReCache通过策略梯度进行训练，跳过了对整个扩散推断的反向传播，并且不使用标注数据。来自未缓存推断的生成结果作为匹配目标，与生成质量的奖励配对。ReCache与任何缓存机制兼容，包括特征重用和特征预测；对于每种机制，一个训练好的策略能够在推断时适应不同的计算预算。ReCache在调度基线之上持续超越：在FLUX上减少$ imes5.04$的FLOPs时，它将LPIPS降低了31%（从0.456降低到0.316），相比于DiCache；在Wan 2.1上实现了约$ imes2.6$的加速，将LPIPS降低了65%（从0.480降低到0.169），并将VBench评分提升了7%（5.6分，从70.4提升到76.0），超越均匀的HiCache。代码可在https://github.com/thecrazymage/ReCache获取。

View on arXiv Download PDF AI Translation

cs.CV / 71 / 2606.06066

FontFusion: Enhancing Generative Text in Diffusion Models with Typographic Conditioning

FontFusion：通过排版条件增强扩散模型中的生成文本

Lupascu, Marian, Jindal, Nipun, Mironica, Ionut, Wang, Zhaowen

Abstract

Typography generation in diffusion models faces a persistent trade-off: enabling precise font control typically degrades text legibility, while maintaining readability often sacrifices typographic fidelity. We present FontFusion, a plug-and-play conditioning framework for Diffusion Transformer (DiT) architectures that resolves this dilemma through three core innovations: (1) a hierarchical token representation establishing explicit text-font relationships at multiple granularities, (2) position-aware embeddings creating spatial bindings between typography and image content, and (3) a multi-level token dropping strategy improving both computational efficiency and generalization to unseen fonts. Our systematic evaluation of font embedding spaces reveals that a dual encoder combining DeepFont and DINOv2 outperforms any single encoder for typography tasks. FontFusion demonstrates 76% relative improvement on challenging decorative fonts over single-encoder baselines and font consistency gains exceeding approximately 68-76% over unconditioned models, while integrating into existing DiT architectures without retraining.

Chinese Translation

在扩散模型中，排版生成面临着持续的权衡：实现精确的字体控制通常会降低文本的可读性，而保持可读性则往往会牺牲排版的忠实度。我们提出了FontFusion，这是一种用于扩散变压器（Diffusion Transformer, DiT）架构的即插即用条件框架，通过三项核心创新解决了这一困境：(1) 采用层次化的令牌表示，在多个粒度上建立明确的文本与字体关系；(2) 位置感知嵌入创建排版与图像内容之间的空间绑定；(3) 多层级的令牌丢弃策略提高了计算效率，并改善了对未见字体的泛化能力。我们对字体嵌入空间的系统评估表明，组合DeepFont和DINOv2的双重编码器在排版任务中优于任何单一编码器。FontFusion在具有挑战性的装饰性字体上相较于单编码器基线展示了76%的相对改善，并在未条件模型上获得了超过68-76%的字体一致性提升，同时无须重新训练即可集成到现有的DiT架构中。

View on arXiv Download PDF AI Translation

cs.CV / 72 / 2606.06074

VZCrash: A Large-Scale IMU Dataset of Ego-Vehicle Crashes

VZCrash：一项大规模的自我车辆碰撞惯性测量单元数据集

Bianconcini, Tommaso, Monteagudo, Henrique Piñeiro, Pjetri, Aurel, Trinci, Tomaso, Taccari, Leonardo

Abstract

We introduce VZCrash, the largest publicly available dataset of real-world vehicle collision data featuring Inertial Measurement Unit (IMU) telemetry. The dataset contains more than 31,000 validated crashes and 158,000 negative samples, including hard cases and distractors. Each sample includes acceleration and angular velocity at 100 Hz, and GPS speed at 1 Hz. Events in VZCrash were captured by devices installed on a fleet of 73,010 commercial vehicles of different sizes driving in the United States over the span of several years. We also present an extensive experimental study enabled by the volume of the dataset. We first benchmark several different approaches, from a simple threshold-based heuristic to state-of-the-art deep learning models. Then, we present an experiment demonstrating the importance of scaling data to train high-quality crash detection models, and we show that scale is especially important when these models need to be deployed into a real-world environment.

Chinese Translation

我们介绍了VZCrash，这是一个公开可用的最大规模的真实世界车辆碰撞数据集，包含惯性测量单元（IMU）遥测数据。该数据集包含超过31,000个经过验证的碰撞案例和158,000个负样本，包括难例和干扰样本。每个样本包括以100Hz记录的加速度和角速度，以及1Hz记录的GPS速度。VZCrash中的事件由安装在73,010辆不同大小的商业车辆上的设备捕捉，这些车辆在美国驾驶了数年。我们还展示了基于数据集体量进行的大规模实验研究。我们首先对多种不同的方法进行了基准测试，从简单的基于阈值的启发式方法到最先进的深度学习模型。然后，我们展示了一个实验，证明了扩展数据以训练高质量碰撞检测模型的重要性，并指出当这些模型需要部署到真实环境中时，规模尤为重要。

View on arXiv Download PDF AI Translation

cs.CV / 73 / 2606.06078

Knowledge Distillation for Visual Autoregressive Models

用于视觉自回归模型的知识蒸馏

Peruzzo, Elia, Bhowmik, Aritra, Sautiere, Guillaume, Asano, Yuki M, Habibian, Amirhossein

Abstract

Autoregressive (AR) image generation models are highly expressive but computationally intensive, motivating effective model compression. Knowledge distillation (KD) is a natural approach for model compression and has been widely studied in language modeling, yet its behavior in visual AR generation remains underexplored. In this work, we present the first systematic study of distillation strategies for AR image models. Our analysis shows that while standard distillation can yield meaningful gains, recent methods developed for language do not directly transfer to images: long decoding horizons and visual token ambiguity make teacher supervision unreliable especially under student-conditioned contexts. To address this, we propose VarKD, a distillation framework for visual autoregressive models that distills on student samples while selectively applying teacher supervision and reducing token-level ambiguity. Experiments on ImageNet across multiple AR backbones show that VarKD consistently outperforms prior distillation baselines, narrowing the gap to large-scale models.

Chinese Translation

自回归（AR）图像生成模型具有高度表达能力，但计算密集，推动了有效模型压缩的需求。知识蒸馏（KD）是一种自然的模型压缩方法，在语言建模中得到了广泛研究，但其在视觉自回归生成中的表现仍未得到充分探索。在本研究中，我们首次系统性地研究了针对自回归图像模型的蒸馏策略。我们的分析表明，尽管标准蒸馏可以带来显著的收益，但为语言开发的最新方法并不能直接迁移到图像中：较长的解码视野和视觉标记的模糊性使得教师监督在学生条件上下文中尤其不可靠。为了解决这个问题，我们提出了VarKD，一种针对视觉自回归模型的蒸馏框架，该框架在学生样本上进行蒸馏，同时有选择性地应用教师监督并减少标记级别的模糊性。在多个AR基础模型上对ImageNet的实验表明，VarKD在各项指标上始终优于之前的蒸馏基准，缩小了与大规模模型之间的差距。

View on arXiv Download PDF AI Translation

cs.CV / 74 / 2606.06100

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning

HyperVis：在洛伦兹双曲面上进行组合推理的连续潜变量视觉关系图

Farazi, Moshiur, Ramasinghe, Sameera, Turza, Mahbub Ahmed, Rahman, Shafin

Abstract

Vision-Language Models (VLMs) struggle with compositional reasoning that requires understanding inter-object relationships. A natural remedy is to inject explicit scene graph triplets $\langle s, p, o \rangle$ from an off-the-shelf scene graph generator (SGG), but we show this backfires: discrete text labels collide with the continuous visual modality, degrading GQA accuracy from 60.38\% to 58.86\%. We propose \textbf{HyperVis}, which bypasses the SGG semantic bottleneck entirely. From $N$ class-agnostic region proposals, we compute a dense $O(N^2)$ visual relation tensor via spatially-biased cross-attention, project it onto a Lorentz hyperboloid, and enforce hierarchy through spatial physics, namely IoA-driven entailment cones and exterior-angle repulsion. We discover that HyperVis contributes in two complementary ways: (1) as a \emph{training-time regularizer}, the hyperbolic relational losses shape LoRA representations that improve generative VQA (GQA 61.03\% vs.\ 57.21\% for LoRA fine-tuning without relational losses, recovering and surpassing the baseline); and (2) as an \emph{inference-time relational encoder}, hyperbolic prefix tokens boost discriminative compositional scoring (SugarCrepe 79.94\%, $+$6.25pp over baseline). The learned curvature stabilises at $\kappa{=}4.0$, an order of magnitude above prior hyperbolic VLMs where $\kappa$ typically collapses toward zero, indicating that continuous visual features genuinely require the exponential volume of strongly curved space. A controlled Euclidean ablation confirms this decomposition: the relational pipeline regularises LoRA comparably in flat space (GQA 60.81\%), but the compositionality gain is specifically hyperbolic (SugarCrepe $+$4.58pp over Euclidean), with entailment loss ${\sim}6{\times}$ higher in Euclidean training. Codes are available at TBA.

Chinese Translation

视觉-语言模型（VLMs）在需要理解对象之间关系的组合推理方面存在困难。一个自然的解决方案是从现成的场景图生成器（SGG）中引入明确的场景图三元组 $ s, p, o $，但我们发现这适得其反：离散文本标签与连续视觉模态碰撞，导致 GQA 准确率从 60.38% 降至 58.86%。我们提出了 extbf{HyperVis}，它完全绕过了 SGG 的语义瓶颈。通过 $N$ 类无关的区域提议，我们通过空间偏向的交叉注意力计算出一个稠密的 $O(N^2)$ 视觉关系张量，将其投影到洛伦兹双曲面上，并通过空间物理学强制实施层级，具体来说是基于 IoA 的蕴含圆锥和外角排斥。我们发现 HyperVis 在两个互补的方面起到作用：（1）作为一种 extit{训练时正则化器}，双曲关系损失塑造了提高生成 VQA 的 LoRA 表示（GQA 61.03% 对比 LoRA 微调不使用关系损失的 57.21%，恢复并超越了基线）；（2）作为一种 extit{推理时关系编码器}，双曲前缀标记增强了区分组合评分（SugarCrepe 79.94%，较基线提高 6.25 个百分点）。学习到的曲率稳定在 ${=}4.0$，远高于先前的双曲 VLM，其中 $$ 通常趋向于零，这表明连续视觉特征确实需要强曲率空间的指数体积。受控的欧几里得消融确认了这一分解：关系管道在平坦空间中以相似的方式正则化 LoRA（GQA 60.81%），但组合性增益特别是双曲的（比欧几里得提高 4.58 个百分点），在欧几里得训练中蕴含损失高达 6 倍。代码可在 TBA 获得。

View on arXiv Download PDF AI Translation

cs.CV / 75 / 2606.06103

MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models

MS-DKC：一种用于设计和调整医学图像分割模型的数据集知识卡框架

Khan, Tariq M., Naqvi, Syed Saud, Porntaveetus, Thantrira, Alinejad-Rokny, Hamid, Iqbal, Shahzaib, Razzak, Imran, Khan, Mohammad AU

Abstract

Medical image segmentation is often framed as a search for stronger architectures, but this can obscure a more fundamental question: what does the dataset require from the model? In medical imaging, this requirement is shaped by foreground occupancy, morphology, boundary ambiguity, topology sensitivity, annotation quality, acquisition variation, and operating point. This paper introduces the Medical Segmentation Dataset Knowledge Card (MS-DKC), a framework for making these factors explicit. MS-DKC records dataset evidence through image/acquisition, morphology, supervision, context-dependence, and deployment-risk descriptors. These descriptors are mapped to failure modes, design priors, and risk-aligned criteria, making segmentation design more traceable than architecture-first comparison. We evaluate MS-DKC on DRIVE, ISIC2018, and ACDC, representing distinct regimes. DRIVE contains sparse, thin, branching vessels, favoring detail-preserving models, sensitivity-aware optimization, threshold analysis, and topology-aware metrics. DKC-TNet-v2 achieved Dice 0.8044 and IoU 0.6730 with 35103 parameters, while SA-UNetv2-DKC-AmbRef reached Dice 0.8141, IoU 0.6865, sensitivity 0.8265, specificity 0.9804, and AUC 0.9853. ISIC2018 involves compact but appearance-variable lesions; validation-constrained score-function selection on Att-Next-Topo/ATTNext produced MS-DKC-AttNextTopo-VCSF-NoAug with Dice 0.8872, IoU 0.8214, precision 0.9173, Boundary F1 0.4878, and ASSD 4.13, while plausible additions failed to improve the risk-aligned profile. ACDC provides a multi-class cardiac case, where MS-DKC recommends four-class softmax segmentation, class-balanced Dice/CE supervision, and class-wise surface evaluation. Overall, the results support dataset-conditioned design: different datasets require different priors, operating points, and evidence before a model can be judged appropriate.

Chinese Translation

医学图像分割通常被视为对更强大架构的探索，但这可能会掩盖一个更根本的问题：数据集对模型的要求是什么？在医学影像中，这一要求受到前景占用、形态学、边界模糊、拓扑敏感性、标注质量、采集变化和操作点的影响。本文介绍了医学分割数据集知识卡（Medical Segmentation Dataset Knowledge Card, MS-DKC），这一框架旨在明确这些因素。MS-DKC通过图像/采集、形态学、监督、上下文依赖和部署风险描述符记录数据集证据。这些描述符与失败模式、设计先验和风险对齐标准相映射，使得分割设计比以架构为中心的比较更易追溯。我们在代表不同情境的 DRIVE、ISIC2018 和 ACDC 数据集上评估了 MS-DKC。DRIVE 包含稀疏、纤细和分支的血管，适合保留细节的模型、敏感性意识优化、阈值分析和拓扑意识度量。DKC-TNet-v2 在 35103 个参数下取得了 Dice 0.8044 和 IoU 0.6730，而 SA-UNetv2-DKC-AmbRef 达到了 Dice 0.8141，IoU 0.6865，敏感性 0.8265，特异性 0.9804，以及 AUC 0.9853。ISIC2018 涉及紧凑但外观可变的病变；针对 Att-Next-Topo/ATTNext 进行的验证约束得分函数选择产生了 MS-DKC-AttNextTopo-VCSF-NoAug，取得了 Dice 0.8872，IoU 0.8214，精确度 0.9173，边界 F1 0.4878 和 ASSD 4.13，而合理的增补未能改善风险对齐配置。ACDC 提供了一个多类心脏病例，其中 MS-DKC 推荐四类 softmax 分割、类别平衡的 Dice/CE 监督和类别表面评估。总体而言，结果支持数据集条件设计：不同的数据集需要不同的先验、操作点和证据，才能评估模型的适当性。

View on arXiv Download PDF AI Translation

cs.CV / 76 / 2606.06113

Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback

何处、何物、为何及其重要性：文本到图像反馈的结构缺陷定位

Zhang, Huaisong, Yu, Hao, Zhang, Yuxuan, Wang, Jiahe, Chen, Xinrui, Cao, Haoxiang, Lu, Feng, Zhang, Wendong, Yu, Changqian, Yuan, Chun

Abstract

Despite generating increasingly photorealistic images, text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures. Diagnosing these failures requires instance-level feedback that answers where a defect occurs, what type it is, why it is defective, and its importance to overall image quality. While recent dense-feedback methods move beyond scalar supervision, their heatmap-centric representations still formulate diagnosis as pixel-field regression, making it difficult to localize variable-cardinality defects and bind semantic reasons to individual failures. To address this representation bottleneck, we propose Structured Defect Grounding (SDG), which casts T2I diagnosis as structured set prediction by modeling each defect as a (location, type, reason, importance) tuple. To make this formulation trainable and measurable, we introduce SDG-30K, a 30K-image dataset with box-grounded annotations across four modern T2I generators, together with a dedicated evaluation protocol, SDG-Eval. Building on this structured representation, we further present a diagnosis-to-alignment framework in which a Vision-Language Model (VLM) serves as the SDG detector, and BoxFlow-GRPO converts predicted defect sets into box-derived, importance-weighted spatial rewards for diffusion model alignment. Extensive experiments show that our SDG detector outperforms leading proprietary VLMs on structured defect grounding, while SDG-guided rewards consistently improve T2I alignment and support localized image refinement. These results establish SDG as a unified, instance-level interface for diagnosing, evaluating, and enhancing modern generative models.

Chinese Translation

尽管生成的图像越来越逼真，文本到图像（T2I）模型仍然表现出局部、微妙和结构复杂的故障。诊断这些故障需要实现级反馈，回答缺陷发生的位置、类型、原因及其对整体图像质量的重要性。虽然近期的密集反馈方法超越了标量监督，但其以热图为中心的表征仍然将诊断形式化为像素场回归，这使得局部可变数量的缺陷定位及将语义原因与个别故障绑定变得困难。为了解决这一表征瓶颈，我们提出了结构缺陷定位（Structured Defect Grounding, SDG），将 T2I 诊断视为结构集预测，通过将每个缺陷建模为一个（位置、类型、原因、重要性）元组。为了使这一表述可训练和可测量，我们引入了 SDG-30K，一个包含 30,000 张图像的数据集，并提供了跨四个现代 T2I 生成器的框选注释，以及一个专门的评估协议 SDG-Eval。在这一结构化表征的基础上，我们进一步提出了一种从诊断到对齐的框架，其中视觉语言模型（Vision-Language Model, VLM）作为 SDG 检测器，BoxFlow-GRPO 将预测的缺陷集转换为基于框的、重要性加权的空间奖励，以便于扩散模型的对齐。广泛的实验表明，我们的 SDG 检测器在结构缺陷定位方面超越了领先的专有 VLM，而 SDG 指导的奖励始终改善 T2I 对齐，并支持局部图像细化。这些结果确立了 SDG 作为一个统一的实例级接口，用于诊断、评估和提升现代生成模型。

View on arXiv Download PDF AI Translation

cs.CV / 77 / 2606.06120

Diff-CA: Separating Common and Salient Factors with Diffusion Models

Diff-CA：使用扩散模型分离共同因子和显著因子

Soumm, Michaël, Montgieux, Alexandre Fournier, He, Yunlong, Gori, Pietro, Newson, Alasdair

Abstract

Contrastive Analysis aims to separate factors that are common between two data distributions from those that are salient to only one of them. Existing contrastive methods are based on generative models (e.g., VAEs or GANs) that often suffer from limited reconstruction and image quality, which hampers effective latent factor separation and limits their applicability to high-fidelity image generation and edition. We propose a novel conditioning framework for diffusion models that enables contrastive decomposition without compromising generation quality. We first train a prompt-free, image-conditioned diffusion model, and then learn to decompose the conditioning into a common and a salient factor, using weak supervision. We prove that the additive contrastive factorization, commonly assumed in prior work, is identifiable under mild conditions. This factorization enables targeted operations by swapping or interpolating only the salient factor.

Chinese Translation

对比分析旨在将两个数据分布之间的共同因子与仅对其中一个数据分布显著的因子分开。现有的对比方法基于生成模型（如变分自编码器（VAEs）或生成对抗网络（GANs）），但通常受到重建和图像质量的限制，这妨碍了有效的潜在因子分离，并限制了提供高保真图像生成和编辑的适用性。我们提出了一种针对扩散模型的新型条件框架，能够在不妨碍生成质量的情况下实现对比分解。我们首先训练一个无提示、图像条件的扩散模型，然后利用弱监督学习将条件分解为共同因子和显著因子。我们证明了在温和条件下，前人工作中通常假设的加法对比因子分解是可识别的。该分解允许通过仅交换或插值显著因子来进行有针对性的操作。

View on arXiv Download PDF AI Translation

cs.CV / 78 / 2606.06142

Computation-Aware Event-to-Frame Reconstruction via Selective Attention

基于计算感知的事件到帧重建通过选择性注意

Wu, Jingqian, Jia, Yunbo, Lam, Edmund Y.

Abstract

Event-to-frame (E2F) reconstruction bridges asynchronous event streams with frame-based vision pipelines, but existing methods often face a trade-off between reconstruction quality and computational efficiency. In this work, we propose an efficient E2F framework that emphasizes causal temporal modeling and computation-aware design. The architecture adopts a recurrent encoder-decoder to incrementally aggregate event information with compact hidden states. To improve robustness under fast motion and illumination variations, a selective context fusion strategy is introduced to integrate event-driven features with prior intensity cues. Within this fusion process, a lightweight hybrid attention mechanism enhances feature selectivity without relying on heavy attention operations. Experimental results on standard benchmarks demonstrate that the proposed approach achieves competitive reconstruction performance while maintaining a favorable balance between accuracy and model complexity.

Chinese Translation

事件到帧（E2F）重建连接异步事件流与基于帧的视觉处理管道，但现有方法常常在重建质量与计算效率之间面临权衡。在本文中，我们提出了一种高效的E2F框架，强调因果时间建模和计算感知设计。该架构采用递归编码器-解码器增量聚合事件信息并使用紧凑的隐藏状态。为了提高在快速运动和光照变化下的鲁棒性，本文引入了一种选择性上下文融合策略，将事件驱动特征与先前的强度线索相结合。在这一融合过程中，一种轻量级的混合注意机制增强了特征的选择性，而不依赖于重型注意操作。标准基准上的实验证明，所提方法在保持准确性和模型复杂性之间的良好平衡的同时，实现了具有竞争力的重建性能。

View on arXiv Download PDF AI Translation

cs.CV / 79 / 2606.06158

Adaptive Tokenisation Via Temporal Redundancy Masking And Latent Inpainting

通过时间冗余掩码和潜在重建实现自适应分词

Dave, Kevin, Patkuri, Sai Aditya, Das, Chhaya Kumar, Bala, Gouranga, Babu, R. Venkatesh, SA, Rajeshkumar

Abstract

Adaptive video tokenisation seeks to dynamically allocate token budgets based on the underlying visual complexity of a sequence. Current continuous-regime approaches achieve this via iterative binarised searches or trained neural regressors, while discrete methods often require a full-rate decoder pass to estimate information content. We demonstrate that such computational overheads are not strictly necessary. We show that the latent space of a frozen continuous video tokeniser inherently encodes temporal redundancy that can be exploited directly: spatial positions whose latent representations change minimally between consecutive frames carry near-zero additional information. We introduce a parameter-free adaptive token allocation mechanism that applies a fixed threshold to per-position temporal-L1 differences, identifying and dropping redundant latent positions. Consequently, the compression rate emerges naturally from the input content rather than being enforced top-down: static scenes get compressed aggressively, while highly dynamic sequences retain more tokens. To reconstruct the dropped positions, we propose the Latent Inpainting Transformer (LIT), a lightweight factorised spatial-temporal attention architecture. The resulting inference pipeline is highly efficient, requiring only a single encoder pass and one LIT forward pass, eliminating the need for auxiliary routing networks. Evaluations across TokenBench and DAVIS, which are the standard benchmarks used by recent tokenisers~\cite{infotok, agarwal2025cosmos}, indicate that our framework yields meaningful, content-driven token allocation while maintaining competitive reconstruction fidelity, and delivers a $31\times$ inference-time speedup over the continuous adaptive baseline (ElasticTok-CV) and an $\approx2\times$ speedup over the discrete information-theoretic baseline (InfoTok)

Chinese Translation

自适应视频分词旨在根据序列的基础视觉复杂性动态分配分词预算。目前的连续状态方法通过迭代二值搜索或训练的神经回归器实现这一目标，而离散方法通常需要完整速率解码器通过来估计信息内容。我们证明了这种计算开销并不是严格必要的。我们展示了冻结的连续视频分词器的潜在空间固有地编码了可以直接利用的时间冗余：在连续帧之间其潜在表示变化最小的空间位置携带近乎零的额外信息。我们引入了一种无参数的自适应分词分配机制，该机制对每个位置的时间-L1差异应用固定阈值，从而识别并丢弃冗余的潜在位置。因此，压缩率自然从输入内容中产生，而不是自上而下施加的：静态场景被大幅压缩，而高度动态的序列保留更多的分词。为了重建丢弃的位置，我们提出了潜在重建变换器（Latent Inpainting Transformer, LIT），这是一种轻量级的因子化时空注意力架构。最终的推理流程在效率上非常高，仅需一次编码器传递和一次 LIT 前向传递，消除了对辅助路由网络的需求。通过在 TokenBench 和 DAVIS 上的评估，这些是最近分词器使用的标准基准，表明我们的框架能够实现有意义的、内容驱动的分词分配，同时保持竞争性的重建保真度，并在推理时间上相较于连续自适应基线（ElasticTok-CV）加速 $31 imes$，相较于离散信息理论基线（InfoTok）加速约 $2 imes$。

View on arXiv Download PDF AI Translation

cs.CV / 80 / 2606.06176

RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision

RQUL-UIE：通过数据集内自我监督复兴水下图像增强中的质量不稳定标签

Hu, Haochen, Bin, Yanrui, Wen, Chih-yung, Wang, Bing

Abstract

Underwater Image Enhancement (UIE) is essential for mitigating degradations caused by water medium. Although learning-based methods have advanced significantly, most rely on paired datasets with unstable label quality, which bottlenecks model performance. This paper proposes a diffusion-based, in-dataset self-supervised learning strategy designed to exploit the quality distribution of training labels. Specifically, we evaluate label quality via semantic perception embeddings from a pre-trained diffusion model in a training-free manner. These quality scores are subsequently quantized into noise-level indices, guiding a multi-step denoising process for level-wise supervision. This mechanism prevents low-quality labels from degrading the model while maximizing their utility during training. Furthermore, a Fourier-based refinement network is incorporated to explicitly reconstruct high-frequency components. Extensive evaluations demonstrate that our method consistently outperforms SOTA approaches in restoration quality. The code and pre-trained model will be available once accepted in link.

Chinese Translation

水下图像增强（UIE）对于减轻水介质造成的退化至关重要。尽管基于学习的方法已经取得了显著进展，但大多数方法依赖于标签质量不稳定的配对数据集，这限制了模型的性能。本文提出了一种基于扩散的、数据集内自我监督学习策略，旨在利用训练标签的质量分布。具体而言，我们通过预训练扩散模型中的语义感知嵌入以无训练的方式评估标签质量。这些质量评分随后量化为噪声级别指标，指导逐级的去噪过程以进行分级监督。这一机制防止低质量标签削弱模型，同时最大化其在训练过程中的效用。此外，本文还引入了基于傅里叶的精细化网络，明确重构高频成分。大量评估表明，我们的方法在恢复质量方面始终优于最新的SOTA方法。代码和预训练模型将在论文被接受后提供链接。

View on arXiv Download PDF AI Translation

cs.CV / 81 / 2606.06186

Adversarial Attacks Already Tell the Answer: Directional Bias-Guided Test-time Defense for Vision-Language Models

对抗攻击已经给出了答案：基于方向偏差引导的视觉-语言模型测试时防御方法

Liu, Liangsheng, Chen, Si, Wu, Jiamin, Feng, Weiwei, Cheng, Zhixin, Yin, Xiaotian, Yang, Wenfei, Zhang, Tianzhu

Abstract

Vision-Language Models (VLMs), such as CLIP, have shown strong zero-shot generalization but remain highly vulnerable to adversarial perturbations, posing serious risks in real-world applications. Test-time defenses for VLMs have recently emerged as a promising and efficient approach to defend against adversarial attacks without requiring costly large-scale retraining. In this work, we uncover a surprising phenomenon: under diverse input transformations, adversarial images in CLIP's feature space consistently shift along a dominant direction, in contrast to the dispersed patterns of clean images. We hypothesize that this dominant shift, termed the Defense Direction, opposes the adversarial shift, pointing features back toward their correct class centers. Building on this insight, we propose Directional Bias-guided Defense (DBD), a test-time framework that estimates the Defense Direction and employs a DB-score-based two-stream reconstruction strategy to recover robust representations. Experiments on 15 datasets demonstrate that DBD not only achieves SOTA adversarial robustness while preserving clean accuracy, but also reveals the counterintuitive result that adversarial accuracy can even surpass clean accuracy. This demonstrates that adversarial perturbations inherently encode directional priors about the true decision boundary.

Chinese Translation

视觉-语言模型（VLMs），如CLIP，已表现出强大的零-shot 泛化能力，但仍对对抗扰动高度脆弱，在实际应用中带来了严重风险。最近出现的 VLMs 测试时防御作为一种有前景和高效的方法，可以在无需昂贵的大规模再训练的情况下防御对抗攻击。在本研究中，我们发现了一个惊人的现象：在不同的输入变换下，CLIP特征空间中的对抗图像始终沿着主导方向偏移，而清晰图像则呈现分散的模式。我们假设这种主导偏移，称为防御方向（Defense Direction），与对抗偏移相对，能够将特征返回到其正确的类别中心。在此基础上，我们提出了基于方向偏差引导的防御（Directional Bias-guided Defense，DBD），这是一种测试时框架，它估计防御方向并采用基于DB得分的双通道重建策略来恢复鲁棒表示。在15个数据集上的实验表明，DBD不仅在保持清晰准确率的同时达到了最先进的对抗鲁棒性，还揭示了一个反直觉的结果：对抗准确率甚至可以超过清晰准确率。这表明，对抗扰动固有地编码了关于真实决策边界的方向性先验。

View on arXiv Download PDF AI Translation

cs.CV / 82 / 2606.06199

SC-MFJ: A Simple Haptic Quality Metric for Medical Image Segmentation

SC-MFJ：一种用于医学图像分割的简单触觉质量度量

Adhikary, Souraj, Chabi, Negar, Mastmeyer, Andre

Abstract

Standard segmentation metrics such as Dice and Hausdorff distance measure geometric overlap but say nothing about whether a segmented surface is suitable for haptic rendering in surgical simulation. We propose SC-MFJ (Surface-Constrained Mean Force Jerk), a simple, inexpensive metric that samples a segmented organ surface with many short virtual stylus walks and measures how jerky the resulting contact forces are. The metric is computed from existing segmentation outputs and uses roughly one minute of CPU time per case. We evaluate three pancreas CT segmentation approaches-binary nnU-Net output, Gaussian-smoothed output, and learned signed distance function (SDF) regression-across 80 cases in five-fold cross-validation. SC-MFJ reveals a 147x gap in haptic quality between the raw binary baseline and simple Gaussian post-processing, a difference entirely invisible to Dice and HD95. It also shows that learned SDF regression, despite requiring full model retraining, produces more variable haptic quality than Gaussian smoothing, with a case-level standard deviation of 168 N/s2 compared with 22 N/s2 for Gaussian. A second evaluation on the LiTS liver dataset (131 cases) confirms the generality of these findings: the binary-to-Gaussian gap widens to 189x, and Gaussian smoothing again produces consistently low force jerk across all folds. Our results suggest that for haptic simulation applications, a one-line post-processing step may be sufficient, and that a cheap metric like SC-MFJ can flag problems that geometric metrics miss.

Chinese Translation

标准的分割度量，如Dice系数和Hausdorff距离，测量几何重叠，但无法评估分割表面是否适合在外科模拟中进行触觉渲染。我们提出了SC-MFJ（表面约束的平均力突变），这是一种简单、低成本的度量，它通过多个短的虚拟笔尖路径对分割的器官表面进行采样，并测量生成的接触力的突变程度。该度量基于现有的分割输出计算，并且每个案例大约使用一分钟的CPU时间。我们在80个案例中通过五折交叉验证评估了三种胰腺CT分割方法——二进制nnU-Net输出、高斯平滑输出和学习的签名距离函数（SDF）回归。SC-MFJ显示了原始二进制基线与简单高斯后处理之间在触觉质量上的147倍差距，而这一差异在Dice系数和HD95中完全不可见。它还表明，尽管学习的SDF回归需要完全重训练模型，但其产生的触觉质量变异性较高，与高斯平滑相比，案例级标准差为168 N/s²，而高斯平滑为22 N/s²。在LiTS肝脏数据集（131个案例）上的第二次评估确认了这些发现的普遍性：二进制与高斯之间的差距扩大到189倍，高斯平滑在所有折叠中再次产生了一致的低力突变。我们的结果表明，对于触觉模拟应用而言，一步简单的后处理步骤可能已足够，并且像SC-MFJ这样廉价的度量能够发现几何度量忽略的问题。

View on arXiv Download PDF AI Translation

cs.CV / 83 / 2606.06217

DisasterBench: A Multimodal Benchmark for UAV-Based Disaster Response in Complex Environments

灾害基准：复杂环境下基于无人机的灾害响应多模态基准

Zhang, Tan, Li, Quanyou, Zhang, Lu, Liu, Jun, Zhu, Xiaofeng, Hu, Ping

Abstract

When a disaster unfolds, responders must answer not only what is happening, but also why it is happening, what will happen next, and what to do now, often from noisy low-altitude UAV views and under tight on-site compute constraints. However, most existing multimodal benchmarks emphasize perception (e.g., recognition/description), cover limited disaster types, and provide insufficient support for the multi-stage reasoning required in practical emergency response. We introduce DisasterBench, a multi-stage multimodal reasoning benchmark for UAV-Based disaster response in complex environments. DisasterBench spans 14 disaster-related scene types and 9 response-critical tasks across pre-, during-, and post-disaster stages, with fine-grained disaster-task mappings that explicitly test causal attribution, propagation prediction, damage analysis, and decision-oriented reasoning. To enable reasoning on the edge, we further propose DisasterVL, a lightweight multimodal model optimized with a three-stage pipeline combining domain instruction tuning, chain-of-thought-guided multimodal alignment, and reinforcement learning-based policy optimization. Experiments across 21 popular MLLMs show that our 2B-parameter DisasterVL outperforms all evaluated open-source models and substantially narrows the gap to state-of-the-art closed-source models, achieving GPT-4o-comparable reasoning accuracy with superior efficiency. The project page is available at https://github.com/TanmouTT/DisasterBench.

Chinese Translation

在灾害发生时，响应者不仅必须回答发生了什么，还需要了解为何会发生、接下来会发生什么以及现在应该采取什么措施，常常在嘈杂的低空无人机视角和紧张的现场计算限制下进行。然而，现有的大多数多模态基准强调感知（例如，识别/描述），覆盖的灾害类型有限，并且对实际应急响应中所需的多阶段推理支持不足。我们介绍了DisasterBench，这是一个针对复杂环境中基于无人机的灾害响应的多阶段多模态推理基准。DisasterBench跨越14种与灾害相关的场景类型和9个响应关键任务，涵盖灾前、灾中和灾后的各个阶段，并提供细粒度的灾害任务映射，明确测试因果归因、传播预测、损害分析和决策导向推理。为了在边缘计算环境下实现推理，我们进一步提出了DisasterVL，这是一个经过优化的轻量级多模态模型，采用三阶段流程结合领域指导微调、思维链引导的多模态对齐和基于强化学习的策略优化。我们在21个流行的多语言大模型（MLLMs）上进行的实验表明，我们的2B参数DisasterVL在所有评估的开源模型中表现优异，并显著缩小了与最先进的闭源模型之间的差距，达到了与GPT-4o相当的推理准确性，同时具备更高的效率。项目页面可访问：https://github.com/TanmouTT/DisasterBench。

View on arXiv Download PDF AI Translation

cs.CV / 84 / 2606.06224

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology

Symb-xMIL：数字病理学中多实例学习的符号解释

Luo, Yanqing, Hense, Julius, Prenißl, Niklas, Mock, Andreas, Müller, Klaus-Robert, Schnake, Thomas, Idaji, Mina Jamshidi

Abstract

Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology. Existing methods primarily rely on heatmaps that highlight influential regions but do not explain how evidence from different tissue regions is combined to produce a prediction. This limits interpretability, especially when decisions depend on interactions between tissue features. We introduce Symbolic explainable MIL (Symb-xMIL), a post-hoc explanation framework that quantifies how a MIL model's behavior aligns with human-readable decision rules, expressed as logical relationships (e.g., AND, OR, NOT) between input features. These alignment scores reveal semantic patterns underlying the model's predictions. We evaluate Symb-xMIL on synthetic and real-world histopathology datasets. On synthetic MIL data, Symb-xMIL reliably recovers ground-truth logical rules. In a clinical tumor detection task, the best-aligned rules uncover heterogeneous decision patterns and expose hidden model errors. On an HPV-prediction task on TCGA-HNSCC, a cohort of head and neck cancer, our framework refines patient survival stratification beyond HPV status with potential clinical relevance. Overall, Symb-xMIL extends MIL explainability beyond visual attribution toward structured, rule-based reasoning, enabling more transparent and semantically grounded interpretation of model predictions.

Chinese Translation

多实例学习（MIL）模型的解释在数字组病理学中的验证与发现中广泛应用。现有方法主要依赖于高亮有影响力区域的热图，但并未解释不同组织区域的证据是如何结合以产生预测的。这限制了可解释性，特别是当决策依赖于组织特征之间的相互作用时。我们提出了符号可解释的多实例学习（Symb-xMIL），这是一种事后解释框架，可以量化MIL模型的行为与人类可读的决策规则（如输入特征之间的逻辑关系，例如AND、OR、NOT）的对齐程度。这些对齐得分揭示了模型预测背后的语义模式。我们在合成和真实世界的组织病理数据集上评估了Symb-xMIL。在合成的MIL数据上，Symb-xMIL可靠地恢复了真实的逻辑规则。在临床肿瘤检测任务中，最优对齐的规则揭示了异质的决策模式并暴露出模型的潜在错误。在针对头颈癌的TCGA-HNSCC的HPV预测任务中，我们的框架深化了患者生存分层，超越了HPV状态，具有潜在的临床相关性。总体而言，Symb-xMIL将MIL可解释性扩展到基于结构的规则推理，从而为模型预测提供更透明和语义上扎实的解释。

View on arXiv Download PDF AI Translation

cs.CV / 85 / 2606.06228

SAM-Flow: Source-Anchored Masked Flow for Training-Free Image Editing

SAM-Flow：源锚定的掩膜流框架用于无训练的图像编辑

Cui, Haowang, Chen, Rui, Luo, Tao, Guo, Tao, Qin, Zheng, Wang, Jiaze

Abstract

Training-free image editing has recently attracted increasing attention due to its ability to modify real images using powerful pre-trained diffusion and flow-matching models without additional training. However, existing inversion-based and differential-flow-based methods usually perform global latent transport, which inevitably propagates editing effects to non-target regions and leads to background leakage. To address this problem, we propose SAM-Flow, a source-anchored masked flow framework for localized training-free image editing. Instead of updating the whole latent representation, SAM-Flow first uses a scout image and token-grounded attention maps to localize the editable semantic regions. It then applies differential velocity updates only within these regions, while anchoring the remaining areas to the source-image latent trajectory. To further improve spatial stability and boundary naturalness, we introduce a time-varying source-anchored projection mechanism with dynamic soft masks, transition regions, and temporal mask accumulation. The proposed method is plug-and-play and can be integrated with mainstream flow-matching backbones such as Stable Diffusion 3 and FLUX without any fine-tuning. Extensive qualitative and quantitative experiments demonstrate that SAM-Flow achieves accurate semantic editing while significantly improving background preservation, providing a simple and general localized editing paradigm for training-free image editing. Code is available at: https://github.com/chwbob/Sam-Flow.

Chinese Translation

无训练的图像编辑因其能够利用强大的预训练扩散和流匹配模型修改真实图像而受到越来越多的关注，而无需额外的训练。然而，现有的基于反演和差异流的方法通常执行全局潜在传输，这不可避免地将编辑效果传播到非目标区域，导致背景泄漏。为了解决这个问题，我们提出了SAM-Flow，一个源锚定的掩膜流框架，用于局部无训练图像编辑。与更新整个潜在表示不同，SAM-Flow首先使用侦查图像和基于标记的注意力图来定位可编辑的语义区域。然后，仅在这些区域内应用差异速度更新，同时将其余区域固定在源图像的潜在轨迹上。为了进一步提高空间稳定性和边界自然度，我们引入了一种时间变化的源锚定投影机制，该机制具有动态软掩膜、过渡区域和时间掩膜累积。所提出的方法是即插即用的，可以与主流的流匹配骨干网（例如 Stable Diffusion 3 和 FLUX）集成，无需任何微调。广泛的定性和定量实验表明，SAM-Flow 实现了准确的语义编辑，同时显著提高了背景保留，为无训练图像编辑提供了一种简单而通用的局部编辑范式。代码可在 https://github.com/chwbob/Sam-Flow 获得。

View on arXiv Download PDF AI Translation

cs.CV / 86 / 2606.06249

GRAMformer: Any-Order Modality Interactions via Volumetric Multimodal Cross-Attention

GRAMformer：通过体积多模态交叉注意实现任意顺序模态交互

Cicchetti, Giordano, Grassucci, Eleonora, Comminiello, Danilo

Abstract

Transformer-based multimodal models rely on attention mechanisms to integrate information across heterogeneous modalities. Despite their success, existing multimodal attention formulations compute their scores through collections of pairwise dot-product interactions or by concatenating all the modalities into the keys, even when multiple modalities should be jointly involved. As a consequence, current approaches either incur quadratic complexity in the number of modalities or fail to explicitly model interactions that depend on the joint configuration of multiple representations. In this work, we introduce the Volumetric Multimodal cross-Attention (VMA), a novel cross-attention mechanism in which attention scores are defined as a function of the joint geometry of a query and multiple modality-specific keys. VMA computes the volume spanned by query and key vectors across multiple modalities, capturing joint multimodal dependencies beyond pairwise similarity, enabling native modeling of any-order modality interactions. We integrate VMA into our novel multimodal transformer architecture, named GRAMformer, explicitly designed to integrate any number of modalities. We evaluate the proposed model on multimodal learning tasks, demonstrating improved effectiveness and efficiency.

Chinese Translation

基于Transformer的多模态模型依赖注意机制来整合跨异质模态的信息。尽管取得了成功，现有的多模态注意力公式在计算得分时通常通过成对点积交互的集合或者将所有模态连接到键中进行计算，即使在多个模态应该共同参与时也是如此。因此，目前的方法要么在模态数量上带来二次复杂度，要么未能显式建模依赖于多个表示共同配置的交互。在本研究中，我们提出了一种新颖的交叉注意机制，即体积多模态交叉注意（Volumetric Multimodal cross-Attention, VMA），在该机制中，注意力得分被定义为查询与多个模态特定键的联合几何的函数。VMA计算由多模态中的查询和键向量所跨越的体积，捕捉超越成对相似性的联合多模态依赖性，使得任意顺序的模态交互能够被本地建模。我们将VMA集成到我们新颖的多模态Transformer架构GRAMformer中，特别设计用于整合任意数量的模态。我们在多模态学习任务上评估了所提出的模型，显示出更高的有效性和效率。

View on arXiv Download PDF AI Translation

cs.CV / 87 / 2606.06278

Geodesic Flow Matching on a Riemannian Degradation Manifold for Blind Image Restoration

基于黎曼降质流匹配的盲图像恢复

Bankar, Akshay Janardan, Chatterjee, Ankita, Banerjee, Sayan, Pandith, Shreyas, Shashank, Kalakonda Sai, Unde, Amit Satish

Abstract

Blind image restoration requires recovering clean images from observations corrupted by unknown and potentially mixed degradations. While recent deterministic flow-based methods model restoration as transport processes that map degraded images to clean ones, they typically rely on Euclidean interpolation, implicitly assuming linear degradation geometry. In this paper, we explicitly model degradations as points on a low-dimensional Riemannian manifold and formulate restoration as geodesic transport on the joint image-manifold space. Using a geodesic flow matching objective, we learn intrinsic transport dynamics that respect the curvature of degradation space. This framework generalizes linear flow matching, provides a principled treatment of mixed degradations as geodesic compositions, and yields a clean theoretical interpretation for generalization beyond observed degradations.

Chinese Translation

盲图像恢复旨在从被未知且可能混合的降质影响污染的观测中恢复出干净图像。尽管最近的基于确定性流的方法将恢复建模为将降质图像映射到干净图像的传输过程，但它们通常依赖于欧几里得插值，隐性假设降质的几何形状是线性的。在本文中，我们明确将降质建模为低维黎曼流形上的点，并将恢复过程表述为在联合图像-流形空间上的测地线传输。通过采用测地线流匹配目标，我们学习尊重降质空间曲率的内在传输动力学。该框架推广了线性流匹配，提供了一种原则性的处理混合降质的方法，将其视为测地线组合，并为超越观察到的降质提供了干净的理论解释。

View on arXiv Download PDF AI Translation

cs.CV / 88 / 2606.06292

Synthetic Data Generation and Vision-based Wrinkle and Keypoint Detection for Bimanual Cloth Manipulation

双手布料操作的合成数据生成与基于视觉的皱纹与关键点检测

Herrera, Ariel, Kang, Xueyang, Kumar, Atal Anil

Abstract

Robotic manipulation of textiles remains challenging because continuous deformation and self-occlusions hinder the robust visual perception required to estimate the cloth's state. To address the lack of annotated real-world data, we developed a Blender-based synthetic pipeline exporting auto-annotated keypoints, and combined manually labeled renders with real-world data to train a wrinkle detector. We present a perception framework integrating a CNN for permutation-invariant keypoint detection and a YOLOv8-OpenCV pipeline to extract grasping points from structural wrinkles. A proposed bimanual algorithm uses this system to stretch fully folded garments via wrinkles, transitioning to keypoint-based ironing once corners emerge. The keypoint model achieves a Mean Position Error (MPE) of 1.7615 pixels. The perception system transfers to physical fabrics without fine-tuning, outperforming baselines that fail in high-occlusion states or yield false positives on severe folds.

Chinese Translation

纺织品的机器人操作仍然具有挑战性，因为持续的变形和自遮挡会阻碍所需的稳健视觉感知，从而估计布料的状态。为了解决缺乏带注释的真实世界数据的问题，我们开发了一种基于Blender的合成管道，导出自动注释的关键点，并将手动标注的渲染结果与真实世界数据结合，用于训练皱纹检测器。我们提出了一种感知框架，集成了用于置换不变关键点检测的卷积神经网络（CNN）和一个YOLOv8-OpenCV管道，用于从结构性皱纹中提取抓取点。提出的双手算法利用该系统通过皱纹拉伸完全折叠的衣物，一旦角落出现，便过渡到基于关键点的熨烫。关键点模型的平均位置误差（MPE）为1.7615像素。该感知系统在不进行微调的情况下迁移到物理织物，表现优于那些在高遮挡状态下失败或在严重折叠下产生假阳性的基线方法。

View on arXiv Download PDF AI Translation

cs.CV / 89 / 2606.06294

Towards One-to-Many Temporal Grounding

向多对一时间定位的研究

Xu, Qi, Tan, Yue, Chen, Shihao, Meng, Jiahao, Wang, Anna, Ji, Shunping, Fei, Hao, Li, Jason

Abstract

Temporal Grounding (TG) aims to localize video segments corresponding to a textual query. Prior research predominantly focuses on single-segment retrieval. Real-world scenarios, however, often require localizing multiple disjoint segments for a single query -- a setting we term One-to-Many Temporal Grounding (OMTG). Previous state-of-the-art MLLMs, optimized for one-to-one settings, struggle in this context, often yielding near-zero scores due to a lack of event cardinality perception. To bridge this gap, we present a systematic solution with three key contributions. First, we establish the first comprehensive OMTG benchmark, introducing Count Accuracy (C-Acc) and Effective Temporal F1 (EtF1) as evaluation metrics. Second, we curate a high-quality OMTG dataset comprising 56k samples through a sophisticated construction pipeline. Third, we develop novel temporal and caption reward functions specifically designed for OMTG. In particular, the caption reward leverages Chain-of-Thought reasoning over dense video captions to explicitly guide policy optimization toward both preciseness and completeness. Extensive experiments show our model achieves a new state-of-the-art EtF1 of 43.65\% on OMTG Bench, outperforming Gemini 2.5 Pro and Seed-1.8 by 15.85\% and 15.61\%, respectively.

Chinese Translation

时间定位（Temporal Grounding, TG）旨在定位与文本查询对应的视频片段。然而，先前的研究主要集中于单片段检索。而现实世界中的场景往往需要为单一查询定位多个不相交的片段——我们称之为多对一时间定位（One-to-Many Temporal Grounding, OMTG）。之前的最先进的多模态语言模型（MLLMs）优化是在一对一场景下，但在这一背景下却难以取得良好表现，通常因为缺乏事件基数感知而导致得分接近零。为了解决这一问题，我们提出了一个系统性的解决方案，包含三个关键贡献。首先，我们建立了首个综合性的OMTG基准，引入了计数准确率（Count Accuracy, C-Acc）和有效时间F1（Effective Temporal F1, EtF1）作为评估指标。其次，我们通过精密的构建流程，整理了一个包含56,000个样本的高质量OMTG数据集。第三，我们开发了专门为OMTG设计的新颖时间和字幕奖励函数。特别是，字幕奖励利用链式思维（Chain-of-Thought）推理在密集的视频字幕上，明确引导策略优化，以达到精确性和完整性。大量实验表明，我们的模型在OMTG基准上达到了新的最先进有效时间F1得分43.65\, ext{ extperthousand}，分别超越Gemini 2.5 Pro和Seed-1.8，提升幅度达15.85\%和15.61\%。

View on arXiv Download PDF AI Translation

cs.CV / 90 / 2606.06309

RhymeFlow: Training-Free Acceleration for Video Generation with Asynchronous Denoising Flow Scheduling

RhymeFlow：无训练加速异步去噪流调度的视频生成

Dai, Chensheng, Zhang, Shengjun, Li, Yifan, Zhang, Zhang, Zhu, Zheng, Duan, Yueqi

Abstract

Video generation models based on Diffusion Transformers (DiTs) have achieved remarkable performance in video synthesis, yet they suffer from high inference latency and computational costs due to the quadratic complexity of 3D attention. Existing acceleration methods primarily reduce computational complexity within each individual denoising steps through techniques such as sparse attention and KV-caching. However, they rigidly adhere to the inherent constraint of the standard diffusion pipeline: every frame in the target video sequence must be subjected to a complete, dense denoising process across all diffusion timesteps. We observe that due to the corresponding contents and motions among adjacent frames, when keyframes with critical semantic transitions are anchored, the intermediate states of others often follow more predictable trajectories, which indicates that such uniform, dense denoising process is inherently redundant for natural video data. To this end, we introduce \textbf{RhymeFlow}, a training-free framework that decouples the denoising trajectories of different frames. Specifically, we first identify a sparse set of pivotal key frames that dominate the latent semantic evolution. Then, only these keyframes undergo dense, step-by-step denoising to ensure structural integrity, while non-keyframes progressively skip denoising steps to minimize computational cost. Since skipped intermediate states of non-keyframes break the temporal coherence in keyframe denoising steps, leading to visual degradation, we further introduce a latent trajectory projection module, which enables keyframes to interact with a complete and temporally consistent sequence representation. Extensive experiments on current DiT-based video generation models demonstrate our method outperforms existing baselines with higher inference speed and better visual quality.

Chinese Translation

基于扩散变换器（Diffusion Transformers, DiTs）的视频生成模型在视频合成中取得了显著的性能，但由于三维注意力的二次复杂性，它们面临着高推理延迟和计算成本的问题。现有加速方法主要通过稀疏注意力和KV缓存等技术来降低每个去噪步骤中的计算复杂性。然而，它们严格遵循标准扩散流程的固有约束：目标视频序列中的每一帧必须经历一个完整且密集的去噪过程，贯穿所有扩散时间步。我们观察到，由于相邻帧之间内容和动作的对应性，当锚定具有关键语义转变的关键帧时，其他帧的中间状态往往遵循更可预测的轨迹，这表明这种均匀的、密集的去噪过程对于自然视频数据本质上是冗余的。为此，我们提出了 extbf{RhymeFlow}，一种无训练框架，解耦不同帧的去噪轨迹。具体而言，我们首先识别出一组稀疏的关键帧，这些关键帧在潜在语义演变中占主导地位。然后，只有这些关键帧经历密集的逐步去噪，以确保结构完整性，而非关键帧则逐渐跳过去噪步骤，以最小化计算成本。由于跳过的非关键帧中间状态破坏了关键帧去噪步骤中的时间一致性，导致视觉质量下降，我们进一步引入了潜在轨迹投影模块，使关键帧能够与完整且时间一致的序列表示进行交互。在当前基于DiT的视频生成模型上的广泛实验表明，我们的方法在推理速度更快和视觉质量更好的情况下，超越了现有基准。

View on arXiv Download PDF AI Translation

cs.CV / 91 / 2606.06338

StoryVideoQA: Scaling Deep Video Understanding with a Large-Scale, Multi-Genre and Auto-Generated Dataset

StoryVideoQA：通过大规模、多类型和自动生成的数据集扩展深度视频理解

Wu, Zhengqian, Liu, Zhixian, Chen, Aodong, Zhang, Jingyang, Li, Ruizhe, Ge, Hanlin, Wang, Zhongyuan, Xiao, Chunxia, Liang, Chao

Abstract

Video question answering (VideoQA) aims to answer questions about given videos. While existing approaches excel on factoid VideoQA, they struggle with deep video understanding (DVU), which requires the comprehension of complex storylines. This challenge arises from the inherent long-range video content, multi-faceted question types, and instance-level story elements, all of which constrain the scale and diversity of manually constructed DVU datasets.These difficulties constrain the scale and diversity of manually-constructed DVU dataset. To address these, we previously introduced StoryMind to automatically construct DVU datasets with balanced fine-grained topics. Though it can generate high-quality question-answer pairs (QAs) for TV series, it suffers significant performance degradation when handling longer and more complex movies. In this paper, we further design StoryMindv2, an enhanced multi-agent collaboration framework to generate high-quality DVU datasets for both TV series and movies. By integrating a novel supervisor-guided generation mechanism and a refined multi-reviewer voting strategy, the framework is utilized to construct StoryVideoQA, the largest DVU dataset to date, featuring over 363K QAs on 393.2 hours diverse story videos including TV series (avg. 1,635 seconds) and movies (avg. 7,878 seconds). Comprehensive evaluations of 20 state-of-the-art VideoQA methods on this large-scale benchmark reveal that they cannot fully maintain long-range character associations or construct a coherent understanding of complex storylines. To bridge this gap, we propose PlotTree, a novel video understanding agent, re-organizing long-range video content into a hierarchical plot structure, enabling efficient storyline reasoning on StoryVideoQA. Project page: https://github.com/nercms-mmap/StoryVideoQA/

Chinese Translation

视频问答（VideoQA）旨在回答给定视频的问题。尽管现有方法在事实型视频问答方面表现出色，但在深度视频理解（DVU）上却力不从心，DVU需要对复杂故事情节的理解。这一挑战来源于固有的长时视频内容、多方面的问题类型以及实例级故事元素，所有这些都限制了人工构建的DVU数据集的规模和多样性。为了解决这些问题，我们之前提出了StoryMind，自动构建具有平衡的细粒度主题的DVU数据集。尽管它能够为电视系列剧生成高质量的问题-答案对（QAs），但在处理更长且更复杂的电影时，它的性能却显著下降。在本文中，我们进一步设计了StoryMindv2，一个增强的多智能体协作框架，用于生成高质量的DVU数据集，涵盖电视系列剧和电影。通过整合一种新颖的监督引导生成机制和精细化的多评审投票策略，该框架用于构建StoryVideoQA，这是迄今为止最大的DVU数据集，包含超过363K个问题-答案对，涵盖393.2小时的多样化故事视频，包括电视系列剧（平均1635秒）和电影（平均7878秒）。对20种最新的视频问答方法在这一大规模基准上的综合评估显示，它们无法完全维持长程角色关联或构建对复杂故事情节的连贯理解。为了解决这一差距，我们提出了PlotTree，一种新颖的视频理解智能体，将长程视频内容重新组织为层级剧情结构，从而在StoryVideoQA上实现高效的情节推理。项目页面：https://github.com/nercms-mmap/StoryVideoQA/

View on arXiv Download PDF AI Translation

cs.CV / 92 / 2606.06359

Comparison of Deep Learning Frameworks For Rice Disease Mapping From UAV Multispectral Imaging

基于无人机多光谱成像的水稻病害映射深度学习框架比较

Ghimire, Yadav Raj, Talreja, Jagrati, Gebre, Tewodros Syum, Agboada, Timothy, Chandel, Shikha V., Beni, Leila Hashemi

Abstract

In this study, UAV multispectral imagery is used to segment the severity of bacterial leaf blight (BLB) in rice using convolutional neural networks (CNNs) and transformer-based models. The evaluated architectures include U-Net with a ResNet- 101 encoder, U-Net++ with EfficientNet-B3 and EfficientNetB7, DeepLabV3+, and SegFormer, all trained under a common pipeline with three input configurations (multispectral only, multispectral+NDVI, and multispectral+NDRE). Experiments are conducted using the publicly available BLB dataset with performance reported using mean IoU (mIoU), mean F1 (mF1), mean accuracy (mAcc), precision, and recall. U-Net++ with EfficientNet-B3 achieved the highest performance, with an mIoU of 97.62%. SegFormer obtained lower segmentation accuracy but comparable inference speed. Overall, the results indicate that lightweight CNN backbones remain more reliable for operational BLB monitoring while integration of vegetation indices provides small and consistent improvements. The study also highlights the value of standardised UAV datasets to compare disease mapping methods and encourages the use of CNN architectures for field implementation.

Chinese Translation

本研究利用无人机多光谱影像，通过卷积神经网络（CNN）和基于变换器的模型对水稻细菌性叶枯病（BLB）的严重程度进行分割。评估的网络架构包括采用ResNet-101编码器的U-Net、采用EfficientNet-B3和EfficientNet-B7的U-Net++、DeepLabV3+和SegFormer，所有模型在共同的流程下使用三种输入配置进行训练（仅多光谱、多光谱+NDVI和多光谱+NDRE）。实验使用公开的BLB数据集进行，性能指标包括均值交并比（mIoU）、均值F1值（mF1）、均值准确率（mAcc）、精确度和召回率。结果显示，U-Net++与EfficientNet-B3组合获得了最高性能，mIoU达到97.62%。SegFormer的分割准确度较低但推断速度相当。总体而言，结果表明轻量化的CNN骨干网络在实际BLB监测中更为可靠，而植被指数的整合提供了小幅且稳定的改进。研究还强调了标准化无人机数据集在比较病害映射方法中的价值，并鼓励在实地应用中使用CNN架构。

View on arXiv Download PDF AI Translation

cs.CV / 93 / 2606.06361

Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them

物理学在两步中：在视觉精细化抹去运动先验之前锁定运动先验

Han, Woojung, Kang, Seil, Jun, Youngjun, Chen, Min-Hung, Yang, Fu-En, Hwang, Seong Jae

Abstract

Image-to-Video diffusion models leverage input images to generate visually stunning content, yet frequently produce motion that violates physical laws. We reveal a surprising finding: a 2-step generation often exhibits better physical consistency than a 50-step output from the same model. Through spectral analysis, we trace this to phase erosion during denoising; the phase degrades significantly (dropping by $\approx 18\%$ from step 2 to step 50), whereas the magnitude remains relatively stable. Building on this insight, we propose PhaseLock, a training-free framework that preserves the valid motion priors from few-step inference throughout the denoising trajectory. Rather than relying on full-step inference for physical consistency, PhaseLock extracts a motion prior from just 2 steps and enforces it onto high-fidelity generation via Latent Delta Guidance. Our approach effectively mitigates phase degradation, improving physical consistency by an average of 6.2 points across diverse models while largely maintaining visual fidelity, with negligible overhead ($1.06\times$ time, $1.02\times$ memory) and reduced reliance on expensive external guidance methods ($\sim5\times$ time).

Chinese Translation

图像到视频的扩散模型利用输入图像生成视觉上令人惊叹的内容，但经常产生违反物理法则的运动。我们揭示了一个惊人的发现：两步生成的物理一致性通常优于来自同一模型的五十步输出。通过谱分析，我们追溯到去噪过程中的相位侵蚀；相位显著退化（从第2步到第50步下降约18%），而幅值则相对稳定。基于这一见解，我们提出了PhaseLock，一个无训练框架，可以在整个去噪轨迹中保持来自少步推理的有效运动先验。PhaseLock不依赖于全步推理以确保物理一致性，而是从仅仅两步中提取运动先验，并通过潜在增量引导（Latent Delta Guidance）将其强加于高保真生成上。我们的方法有效缓解了相位退化，在多种模型上平均提高了6.2个点的物理一致性，同时保持了较高的视觉保真度，且开销微乎其微（时间 $1.06 imes$，内存 $1.02 imes$），并减少了对昂贵外部引导方法的依赖（约 $5 imes$ 时间）。

View on arXiv Download PDF AI Translation

cs.CV / 94 / 2606.06363

GMBFormer: An NDVI-Guided Global Memory Bank Transformer for Urban Green-Space Extraction from Ultra-High-Resolution Imagery

GMBFormer：一种基于 NDVI 指导的全球记忆库变换器，用于超高分辨率影像中的城市绿地提取

Lei, Hao, Cheng, Xi, Shu, Chenlu, Chen, Zhiheng, Duan, Zhengjie, Wang, Haoyu, Shen, Zhanfeng

Abstract

Urban green-space extraction from ultra-high-resolution (UHR) imagery is commonly performed patch by patch, which limits semantic reuse among spatially separated but visually similar vegetation patterns. Directly injecting the Normalized Difference Vegetation Index (NDVI) into red-green-blue (RGB) backbones can also blur the roles of visual appearance learning and physical vegetation confidence. We propose GMBFormer, a SegFormer-based framework that replaces adjacency-driven feature propagation with selective, similarity-driven prototype retrieval. Only RGB channels enter the backbone and decoder, while NDVI is decoupled as a physics-informed gate that admits high-confidence vegetation descriptors into a compact global memory bank through momentum updates. During training and inference, the current patch queries stored prototypes through memory-mediated cross-attention, and the retrieved response is integrated with bounded overhead. Experiments use a self-constructed Chengdu UHR dataset with 7,700 labeled 512 x 512 patches and two reduced-label settings derived from the public International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam dataset. Under the same training and evaluation protocol, GMBFormer obtains mean intersection over union (mIoU)/mean Dice (mDice) scores of 89.25%/94.31%, 92.17%/95.92%, and 83.72%/90.86%, respectively, improving the controlled SegFormer-B4 baseline in each setting. Ablation studies indicate that decoupled NDVI admission, memory retrieval, capacity, and momentum jointly shape the final performance.

Chinese Translation

从超高分辨率（UHR）影像中提取城市绿地通常是逐块进行的，这限制了在空间上分离但视觉上相似的植被模式之间的语义重用。将归一化差异植被指数（NDVI）直接注入红-绿-蓝（RGB）主干也可能模糊视觉外观学习和物理植被置信度的角色。我们提出了 GMBFormer，这是一种基于 SegFormer 的框架，用选择性、相似性驱动的原型检索替代邻接驱动的特征传播。只有 RGB 通道进入主干和解码器，而 NDVI 被解耦为一个物理信息引导的门，通过动量更新将高置信度的植被描述符引入一个紧凑的全球记忆库。在训练和推理过程中，当前补丁通过记忆介导的交叉注意力查询存储的原型，检索到的响应与有限的开销集成在一起。实验使用自构建的成都 UHR 数据集，该数据集包含 7,700 个标注的 512 x 512 补丁和两个来自公共国际摄影测量与遥感学会（ISPRS）波茨坦数据集的降低标注设置。在相同的训练和评估协议下，GMBFormer 分别获得89.25%/94.31%、92.17%/95.92%和83.72%/90.86%的平均交并比（mIoU）/平均 Dice（mDice）分数，在每个设置中均提高了受控的 SegFormer-B4 基准。消融研究表明，解耦的 NDVI 接纳、记忆检索、容量和动量共同影响最终性能。

View on arXiv Download PDF AI Translation

cs.CV / 95 / 2606.06369

Visual Commonsense Driven Knowledge Refinements for Scene Graph Generation

基于视觉常识驱动的知识细化用于场景图生成

Neau, Maëlic, Baloch, Salim, Suchan, Jakob, Falomir, Zoe, Bhatt, Mehul

Abstract

Learning-driven Scene Graph Generation (SGG) models excel on frequent relation types but degrade sharply under annotation sparsity, failing to capture reliable visual commonsense knowledge. We propose a model-agnostic, semantically-guided knowledge refinement framework that systematically mines commonsense-grounded constraints from training data - capturing spatial, functional, and qualitative relational regularities - and uses general declarative commonsense reasoning to correct and refine ranked SGG predictions at inference time. The framework requires no manual rule authoring, no model retraining, and transfers across datasets and architectures. On three standard benchmarks, we obtain consistent improvements over strong baselines, demonstrating that structured visual commonsense reasoning over deep scene semantics is a practical and effective complement to purely learning-based scene graph generation.

Chinese Translation

学习驱动的场景图生成（SGG）模型在处理频繁关系类型时表现优异，但在标注稀疏的情况下显著下降，无法捕捉可靠的视觉常识知识。我们提出了一种模型无关、语义指导的知识细化框架，该框架系统性地从训练数据中挖掘基于常识的约束——捕捉空间、功能和定性关系的规律性——并利用一般性声明式常识推理在推理时纠正和细化排名的SGG预测。该框架无需手动创建规则，无需模型重新训练，并能够在不同数据集和架构之间迁移。在三个标准基准测试中，我们在强基线之上取得了一致的改进，证明了对深度场景语义进行结构化视觉常识推理是完全基于学习的场景图生成的一个切实有效的补充。

View on arXiv Download PDF AI Translation

cs.CV / 96 / 2606.06379

EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models

EasyLens：一种无训练的可插拔微小病变表示增强器，用于医学视觉语言模型

Zeng, Qiwei, Wang, Hao, Lin, Jinghao, Ye, Shuchang, Yang, Yuezhe, Peng, Yige, Che, Haoyuan, Kim, Jinman, Bi, Lei

Abstract

Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation, including lesion detection and report generation. However, their practical utility remains limited by insufficient sensitivity to subtle lesions, whose visual evidence is often sparse, low-contrast, and embedded within complex anatomical context. As local visual tokens are aggregated, these weak lesion cues can become underrepresented in global image representations, making them difficult for medical VLMs to recognize. Existing efforts to improve lesion sensitivity mainly rely on medical-domain vision-encoder pre-training, clinical-term-guided alignment, or trainable pathological representation enhancement. Although effective, these approaches usually require additional training or model-specific adaptation and may overfit to particular disease morphologies, limiting their applicability to frozen medical VLMs. To address these limitations, we propose EasyLens, a training-free plug-and-play subtle-lesion representation amplifier for medical VLMs. EasyLens first constructs EasyBank, a pathology-anatomy prototype space that provides lesion-related prototypes and anatomy-aware normal references for comparing suspicious patches against both pathological and normal anatomical patterns. To avoid blindly amplifying normal tissues, EasyTag selects lesion-relevant patches through counterfactual prototype reasoning. To counteract the dilution of subtle lesion cues in global image representations, EasyAmplifier strengthens the selected lesion-relevant patch representations through morphology-guided residual enhancement, thereby increasing their contribution to the global image embedding. Experiments on multiple medical image datasets and frozen medical VLM backbones show that EasyLens improves subtle-lesion detection and outperforms existing encoder-enhancement baselines.

Chinese Translation

医学视觉语言模型（VLMs）在临床图像解读中展现出日益增长的潜力，包括病变检测和报告生成。然而，它们的实际应用仍受到对微小病变敏感性不足的限制，后者的视觉证据通常稀疏、对比度低，而且嵌入复杂的解剖背景中。随着局部视觉标记的聚合，这些微弱的病变线索在全局图像表示中可能被不足表示，使得医学 VLMs 难以识别。现有改善病变敏感性的努力主要依赖于医学领域视觉编码器的预训练、临床术语指导的对齐，或可训练的病理表示增强。尽管有效，这些方法通常需要额外的训练或模型特定的适应，并可能对特定疾病形态过拟合，从而限制了其在冻结医学 VLMs 中的适用性。为了解决这些局限性，我们提出了 EasyLens，一种用于医学 VLMs 的无训练可插拔微小病变表示增强器。EasyLens 首先构建了 EasyBank，一个病理-解剖原型空间，为比较可疑图块提供病变相关原型和解剖意识的正常参考，以便与病理和正常解剖模式进行比较。为避免盲目放大正常组织，EasyTag 通过反事实原型推理选择与病变相关的图块。为了抵消微小病变线索在全局图像表示中的稀释，EasyAmplifier 通过形态学引导残差增强，增强所选病变相关图块的表示，从而增加它们对全局图像嵌入的贡献。在多个医学图像数据集和冻结医学 VLM 骨干网络上的实验表明，EasyLens 提升了微小病变检测的能力，并优于现有的编码器增强基线。

View on arXiv Download PDF AI Translation

cs.CV / 97 / 2606.06390

HomeWorld: A Unified Floorplan-to-Furnished Framework for Generating Controllable, Densely Interactive Whole-Home Scenes

HomeWorld：一个统一的平面图到家居布置的框架，实现可控的、高度交互的全屋场景生成

Li, Wenbo, Ju, Xiaoliang, Qin, Zipeng, Fang, Rongyao, Li, Hongsheng

Abstract

Indoor scene generation is crucial for robot simulation and modern interior design. However, complex layouts together with scarce 3D scene data make learning-based generation challenging. Existing methods often rely on hand-crafted rules or focus on isolated sub-tasks (e.g., floorplan synthesis or single-room furnishing), producing whole-home scenes that lack global coherence, realism, and simulation readiness. To mitigate these limitations, we propose a unified hierarchical framework that decomposes indoor scene synthesis into controllable stages. First, we curate a large-scale dataset of 300K real residential floorplans to train a large language model for whole-home floorplan generation. With detailed descriptions and a K-D tree-based representation, our method enables fine-grained, controllable whole-home floorplan generation. Building upon the generated whole-home floorplan, we leverage image generation models to draft furniture layouts from multi-level roaming viewpoints, and then generate the layouts of small manipulable objects on different supporting surfaces (e.g., cabinets, desks, and dining tables) for embodied AI simulation. During furniture and object layout generation, a VLM-based refiner iteratively corrects furniture and object placement, and a 3D generative model enables flexible replacement of individual assets. We further attach basic physical attributes and simple surface texture and lighting setups to complete the pipeline for embodied AI use. Experiments and user studies demonstrate that our pipeline produces indoor spaces with greater layout diversity and stronger 3D design appeal, outperforming prior methods on both quantitative and qualitative metrics. Finally, alongside our generation pipeline, we will release the floorplan dataset and 5K fully furnished scenes to the community. Project Page: https://kairos-homeworld.github.io/

Chinese Translation

室内场景生成对机器人模拟和现代室内设计至关重要。然而，复杂的布局以及稀缺的三维场景数据使得基于学习的生成面临挑战。现有方法通常依赖于手工制定的规则或集中于孤立的子任务（例如，平面图合成或单房间布置），导致生成的全屋场景缺乏整体一致性、现实性和模拟准备度。为了解决这些局限性，我们提出了一个统一的层次框架，将室内场景合成分解为可控的阶段。首先，我们策划了一个包含30万份真实住宅平面图的大规模数据集，以训练一个大型语言模型，实现全屋平面图的生成。通过详细的描述和基于K-D树的表示，我们的方法能够实现精细可控的全屋平面图生成。在生成的全屋平面图基础上，我们利用图像生成模型从多层级的漫游视角绘制家具布局，然后生成在不同支撑表面（例如，橱柜、书桌和餐桌）上可操控小物体的布局，以便于具体化AI模拟。在家具和物体布局生成过程中，基于VLM的精炼器迭代校正家具和物体的放置，而三维生成模型则允许灵活替换单个资产。我们进一步附加基本的物理属性以及简单的表面纹理和照明设置，完成对具体化AI的使用管道。实验和用户研究表明，我们的管道生成的室内空间在布局多样性和三维设计吸引力上表现更佳，在定量和定性指标上均优于之前的方法。最后，除了我们的生成管道，我们将向社区发布平面图数据集和5000个完全布置的场景。项目主页：https://kairos-homeworld.github.io/

View on arXiv Download PDF AI Translation

cs.CV / 98 / 2606.06407

A Vision-language Framework for Comparative Reasoning in Radiology

用于放射学比较推理的视觉-语言框架

Zhang, Tengfei, Zhao, Ziheng, Dai, Lisong, Zhang, Xiaoman, Qiu, Pengcheng, Zhang, Ya, Wang, Yanfeng, Xie, Weidi

Abstract

Medical imaging artificial intelligence has achieved strong performance in isolated image interpretation, but remains poorly aligned with radiological practice, where diagnosis and follow-up rely on comparison across prior studies and analogous reference cases. Here we formulate radiological comparison as an entity-aware cross-image reasoning problem and introduce a framework that supports both reference-case retrieval and temporal comparative interpretation. We construct MedReCo-DB, a large-scale comparative imaging resource derived from routine image-report pairs, comprising more than 690,000 images from over 160,000 patients across eight institutions, four countries and seven imaging modalities. Reports are decomposed into anatomical structures, abnormal findings and pathological conditions to provide supervision for entity-conditioned retrieval and comparative visual question answering. Using this resource, we develop MedReCo, an entity-aware visual encoder for controllable retrieval of clinically analogous cases, and MedReCo-VLM, a vision--language extension for generative interpretation of interval change. Across internal, external and cross-center evaluations, MedReCo achieved the highest Recall@1 in all 12 internal retrieval settings and improved external retrieval by a mean of 6.0 percentage points. In clinically confusable differential groups, it consistently outperformed the strongest baselines. MedReCo-VLM achieved the best performance across all comparative generation evaluations and improved longitudinal follow-up accuracy by 14.5-46.5 percentage points on chest radiographs and 13.0-27.9 percentage points on CT. These findings suggest that entity-aware comparative reasoning can be learned from routine clinical data at scale and may provide a more clinically aligned foundation for medical imaging AI.

Chinese Translation

医学成像人工智能在孤立图像解释方面已取得强大性能，但与放射学实践的契合度较低，因为诊断和随访依赖于对先前研究和类似参考案例的比较。本文将放射学比较形式化为一个实体感知的跨图像推理问题，并引入一个支持参考案例检索和时间比较解释的框架。我们构建了 MedReCo-DB，这是一个基于常规图像-报告对的大规模比较成像资源，包含来自八个机构、四个国家和七种成像模式的超过690,000幅图像，涵盖了16万多名患者。报告被分解为解剖结构、异常发现和病理情况，以提供实体条件检索和比较视觉问答的监督。利用该资源，我们开发了 MedReCo，这是一个用于临床类似案例可控检索的实体感知视觉编码器，以及 MedReCo-VLM，这是一个用于间隔变化生成解释的视觉-语言扩展。在内部、外部和跨中心评估中，MedReCo 在所有12个内部检索设置中均实现了最高的 Recall@1，并且外部检索平均提高了6.0个百分点。在临床混淆的鉴别组中，其性能始终优于最强的基线。MedReCo-VLM 在所有比较生成评估中表现最佳，并在胸部 X 光片上提高了14.5-46.5个百分点的纵向随访准确性，在 CT 上提高了13.0-27.9个百分点。这些发现表明，实体感知的比较推理可以从常规临床数据中规模化学习，并可能为医学成像人工智能提供更具临床契合度的基础。

View on arXiv Download PDF AI Translation

cs.CV / 99 / 2606.06476

Thinking with Imagination: Agentic Visual Spatial Reasoning with World Simulators

以想象为思维：利用世界模拟器进行能动的视觉空间推理

Zhu, Chenming, Lin, Jingli, Long, Yilin, Cao, Peizhou, Wang, Tai, Pang, Jiangmiao, Liu, Xihui

Abstract

While Vision-Language Models (VLMs) have shown strong visual reasoning capabilities, their spatial reasoning abilities remain largely constrained to the observed images and text-oriented chain-of-thought. They often struggle to infer unobserved layouts, maintain cross-view consistency, and reason from alternative viewpoints when only limited egocentric observations are available. In this work, we study this problem as thinking with imagination, where a VLM actively acquires imagined visual evidence by interacting with a world simulator during reasoning. We propose Astra, an agentic spatial reasoning framework that empowers VLMs with action-conditioned visual imagination. Specifically, Astra couples Astra-VL, an RL-trained VLM policy, with Astra-WM, a Bagel-based world simulator that generates novel-view observations from context images and natural-language camera motions. To provide reliable imagined evidence, Astra-WM is trained with view consistency tuning to improve pose and content consistency across views. In the RL stage, we propose a world-simulator-in-the-loop two-phase RL curriculum to stabilize tool-use exploration and advance the model's ability to invoke the simulator only when imagined observations improve over direct answering. Experiments demonstrate that both the world simulator and the agentic policy are necessary: Astra-WM improves simulator-augmented Gemini-3-Flash on MMSI-Bench from 45.1 to 49.5, while Astra-VL improves the Qwen3-VL backbone from 29.8 to 38.8 on MMSI-Bench and from 36.8 to 42.7 on MindCube. These results show that imagined observations can provide useful spatial evidence, but effective world-model-augmented reasoning requires learning when, where, and how to imagine.

Chinese Translation

虽然视觉-语言模型（VLMs）展示了较强的视觉推理能力，但它们的空间推理能力在很大程度上仍限于观察到的图像和以文本为导向的思维链。它们在推断未观察到的布局、维持视图间一致性，以及在仅有有限的自我中心观察时从替代视角进行推理方面常常面临困难。在本研究中，我们将这一问题视为以想象进行思考，其中VLM在推理过程中通过与世界模拟器的交互，主动获取想象的视觉证据。我们提出了Astra，一个能动的空间推理框架，赋予VLM行动条件下的视觉想象能力。具体而言，Astra将经过强化学习训练的VLM策略Astra-VL与基于Bagel的世界模拟器Astra-WM结合，该模拟器根据上下文图像和自然语言相机动作生成新视角的观察。为了提供可靠的想象证据，Astra-WM通过视图一致性调优进行训练，以改善各视图间的姿态和内容一致性。在强化学习阶段，我们提出了一种“世界模拟器循环”双阶段强化学习课程，以稳定工具使用探索，并提高模型在想象的观察优于直接回答时调用模拟器的能力。实验表明，世界模拟器和能动策略都是必要的：Astra-WM将模拟器增强的Gemini-3-Flash在MMSI-Bench上的表现从45.1提高至49.5，而Astra-VL将Qwen3-VL主干在MMSI-Bench上的表现从29.8提高至38.8，在MindCube上的表现从36.8提高至42.7。这些结果表明，想象的观察可以提供有用的空间证据，但有效的世界模型增强推理需要学习何时、何地以及如何进行想象。

View on arXiv Download PDF AI Translation

cs.CV / 100 / 2606.06477

Complexity-Balanced Diffusion Splitting

复杂性平衡扩散分割

Issachar, Noam, Lischinski, Dani, Fattal, Raanan

Abstract

Standard continuous-time generative models rely on monolithic architectures that must navigate vastly different signal regimes, from isotropic noise to intricate data distributions. While scaling model capacity improves performance, deploying a massive network uniformly across the entire generative timeline is inherently inefficient. In this work, we propose Complexity-Balanced Splitting (CBS), a principled framework for temporal capacity allocation that distributes the generative workload across multiple specialized sub-networks. Grounded in function approximation theory and de Boor's equidistribution principle, CBS partitions the diffusion timeline into segments of equal approximation burden, allocating more representational capacity to regions where the generative dynamics are more difficult to model. To estimate this local complexity, we introduce two complementary and tractable monitor functions: a spatial measure based on the flow's Dirichlet energy, and a geometric measure based on the acceleration of the sampling trajectories. Using a lightweight auxiliary model to estimate these complexity profiles, our approach eliminates the need for heuristic temporal splits or computationally expensive search procedures. Extensive evaluation across multiple architectures (SiT, JiT, and UNet) and datasets demonstrates that CBS consistently improves synthesis quality without increasing per-step inference cost. In particular, CBS improves FID by ~35% on SiT-XL with CFG relative to naive temporal partitioning. Project page is available at https://noamissachar.github.io/CBS/.

Chinese Translation

标准的连续时间生成模型依赖于单一构架，该构架必须应对从各向同性噪声到复杂数据分布等不同信号模式。尽管提升模型容量可以改善性能，但在整个生成时间线上均匀部署一个庞大网络本质上是效率低下的。本文提出了复杂性平衡分割（Complexity-Balanced Splitting, CBS），这是一个原则性框架，用于时间容量的分配，通过多个专业子网络分配生成工作负载。该框架基于函数近似理论和de Boor的均匀分布原理，将扩散时间线分割为相等的近似负担段，在生成动态建模较为困难的区域分配更多的表征能力。为了估计这种局部复杂性，我们引入了两个互补且易于处理的监测函数：基于流的Dirichlet能量的空间度量和基于采样轨迹加速度的几何度量。我们的方法使用轻量级辅助模型来估计这些复杂性轮廓，消除了对启发式时间分割或计算开销大的搜索过程的需求。在多个架构（SiT、JiT和UNet）和数据集上进行的广泛评估表明，CBS在不增加每步推理成本的情况下，始终提高了合成质量。特别是，CBS在SiT-XL与CFG相比于简单时间分割时将FID提高了约35%。项目页面可访问 https://noamissachar.github.io/CBS/ 。

View on arXiv Download PDF AI Translation

cs.CV / 101 / 2606.06485

PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding

PAR3D：一种具有部件感知表征的统一3D-MLLM用于场景理解

Dai, Shaohui, Qu, Yansong, Shen, You, Zhang, Shengchuan, Cao, Liujuan

Abstract

Recent advances in 3D multimodal large language models (3D-MLLMs) have enabled unified solutions for 3D scene understanding tasks, including visual question answering, captioning, and referring segmentation. However, existing 3D-MLLMs remain largely object-centric, limiting their ability to model fine-grained part structures that are essential for embodied interaction with 3D environments. In this work, we present PAR3D, a unified part-aware 3D-MLLM framework that enables models to understand, reason about, and ground both objects and their parts in 3D scenes. To enable training and evaluation of part-aware 3D scene understanding, we introduce ScenePart, a synthetic 3D scene dataset with part-level annotations and language instructions. We further develop Part-Aware 3D Representation Learning to enrich 3D visual representations with fine-grained part-level semantics, and propose Hierarchical Segmentation Query Generation to ground part targets via hierarchical object-part queries. Extensive experiments show that our method substantially improves part-level question answering and referring segmentation, while also achieving strong performance across object-level vision-language tasks.

Chinese Translation

最近在3D多模态大型语言模型（3D-MLLMs）方面的进展，使得统一解决3D场景理解任务成为可能，包括视觉问答、字幕生成和指称分割。然而，现有的3D-MLLMs仍主要以物体为中心，限制了其建模细粒度部件结构的能力，而这些结构对于与3D环境的具体互动至关重要。在本研究中，我们提出了PAR3D，这是一种统一的部件感知3D-MLLM框架，使得模型能够理解、推理并将物体及其部件定位于3D场景中。为了实现部件感知3D场景理解的训练和评估，我们引入了ScenePart，一个具有部件级注释和语言指令的合成3D场景数据集。我们进一步开发了部件感知3D表征学习，以细化部件级语义来丰富3D视觉表征，并提出了层次分割查询生成，通过层次对象-部件查询来定位部件目标。大量实验表明，我们的方法在部件级问答和指称分割方面显著提升，同时在物体级视觉-语言任务中也表现出强劲的性能。

View on arXiv Download PDF AI Translation

人工智能 (Artificial Intelligence)

108

cs.AI / 1 / 2606.05256

How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment

他们走了多远？隐秘大规模语言模型代理在一项被中止的实地实验中的说服策略

Jaidka, Kokil, Ahmed, Saifuddin

Abstract

This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView. The intervention, conducted by unknown, external researchers and halted following ethical backlash, involved undisclosed AI-generated accounts engaging users in live debate. After public disclosure, Reddit authorized moderators to release an archive of the AI-generated comments, creating a rare opportunity to examine how large language models operated in an identity-rich deliberative forum without disclosure. We conduct a structured content analysis of this corpus, evaluating identity performance, authority signaling, alignment strategies, and activation of cognitive heuristics. Identity targeting or adoption appears in over two-thirds of comments, alignment moves and authority claims in nearly all of them, and cognitive-bias triggers -- particularly confirmation bias, representativeness, and availability -- in the large majority. These patterns co-occur systematically, composing a rhetorical architecture calibrated for persuasive efficiency rather than authentic deliberative participation. Compared against human-authored CMV counter-arguments, the agents inverted the typical distribution on every dimension: denser authority use, more adversarial alignment, and heavier reliance on external citation over experiential grounding. In such environments, distinctions between authentic and synthetic epistemic standing grow increasingly opaque -- an asymmetry that disclosure mandates alone cannot address. The results point toward auditing frameworks capable of assessing how AI systems structure credibility, not merely whether they are present.

Chinese Translation

本研究分析了一份来自于社交平台Reddit的r/ChangeMyView上已中止实地实验的公开数据集。该干预由不知名的外部研究人员进行，在遭遇伦理反弹后暂停，涉及未披露的人工智能生成账户与用户进行实时辩论。在公开披露后，Reddit授权版主发布了AI生成评论的归档，这为分析大型语言模型在一个身份丰富的辩论论坛中如何运作而不进行披露提供了珍贵机会。我们对这一语料库进行了结构化的内容分析，评估了身份表现、权威信号、对齐策略和认知启发式的激活。超过三分之二的评论中出现了身份定位或采纳，几乎所有评论中都存在对齐举措和权威主张，而认知偏差触发因素——特别是确认偏差、代表性偏差和可获得性——在绝大多数评论中也得到了体现。这些模式系统性地共现，构成了一种为说服效率而非真实辩论参与而调整的修辞架构。与人类撰写的CMV反对论点相比，这些代理在每个维度上都逆转了典型分布：更密集的权威使用、更对抗的对齐和对外部引用的更大依赖，而不是经验基础。在这样的环境中，真实与合成的认识地位之间的区别变得愈加模糊——这种不对称仅靠披露要求是无法解决的。结果指出，必须制定审计框架，能够评估AI系统如何构建可信度，而不仅仅是评估它们是否存在。

View on arXiv Download PDF AI Translation

cs.AI / 2 / 2606.05304

What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems

代理应该说些什么？高效多智能体系统的动作状态通信

Huang, Chen, Wu, Yuhao, Zhang, Wenxuan

Abstract

Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is often left as unconstrained natural language. However, this free-form communication can rapidly inflate token usage, consume the shared context window, and ultimately affect both system performance and inference cost. We analyze five common inter-agent communication strategies across two MAS topologies, finding that no fixed strategy is universally optimal. Instead, effective inter-agent messages consistently preserve action-centered information needed by downstream agents. Building on this, we propose the PACT (Protocolized Action-state Communication and Transmission), which treats inter-agent communication as a public state-update problem and projects each raw agent output into a compact action-state record before it enters shared history. Across different MAS topologies, PACT consistently improves the performance-cost trade-off, achieving comparable or stronger task performance with substantially fewer tokens. The gains extend to production coding harnesses: PACT lifts OpenHands' resolve rate at -10% tokens-per-resolved, and is resolve-neutral on SWE-agent while halving input tokens. Our code is publicly available at https://github.com/iNLP-Lab/PACT.

Chinese Translation

基于大语言模型的多智能体系统（MAS）通常围绕角色、流程和轮换时间表进行组织，而代理之间传递的内容往往以不受限制的自然语言形式呈现。然而，这种自由形式的通信可能迅速增加令牌使用量，消耗共享上下文窗口，并最终影响系统性能和推理成本。我们分析了两种MAS拓扑结构下五种常见的智能体间通信策略，发现没有一种固定策略是普遍最佳的。相反，有效的智能体间消息始终保留了下游代理所需的以动作为中心的信息。在此基础上，我们提出了PACT（Protocolized Action-state Communication and Transmission），它将智能体间通信视为公共状态更新问题，并在每个原始代理输出进入共享历史之前，将其投影为紧凑的动作状态记录。在不同的MAS拓扑结构中，PACT始终改善了性能与成本的权衡，在显著减少令牌数的情况下实现了可比较或更强的任务性能。这些收益延伸至生产编码工具：PACT使OpenHands在减少10%解决令牌数的情况下提高了解决率，并在SWE-agent上保持解决中性，同时将输入令牌数减半。我们的代码可在 https://github.com/iNLP-Lab/PACT 上公开获取。

View on arXiv Download PDF AI Translation

cs.AI / 3 / 2606.05316

I Know What You Meme, Even If it Emerged Today: Understanding Evolving Memes through Open-World Knowledge Acquisition

我知道你在说什么，即使它今天刚出现：通过开放世界知识获取理解不断演变的网络迷因

Liu, Shanhong, Cao, Rui, Ng, Pai Chet, Soh, De Wen

Abstract

Multimodal memes are dynamic and often require up to date background knowledge for interpretation. Existing methods often overlook such knowledge or rely on fixed parametric knowledge of pretrained models that may be incomplete, outdated, or unavailable for emerging memes. We introduce Query Retrieve Conclude, a zero shot framework that identifies missing knowledge, retrieves open web evidence, and synthesizes evidence grounded background knowledge for meme understanding and detection. We also introduce a curated meme understanding benchmark of recent memes from 2024 to 2026 with external background knowledge annotations. Experiments on three meme understanding datasets and five meme detection tasks show that our framework improves knowledge recovery, meme understanding and downstream detection over zero shot baselines.

Chinese Translation

多模态迷因是动态的，通常需要最新的背景知识来进行解读。现有的方法往往忽视这种知识，或者依赖于预训练模型的固定参数知识，而这些知识可能是不完整的、过时的，或者对于新兴迷因不可用。我们提出了查询-检索-结论（Query Retrieve Conclude）框架，这是一个零样本框架，能够识别缺失的知识、检索开放网络证据，并合成以证据为基础的背景知识，以便于迷因理解和检测。我们还介绍了一个精心策划的迷因理解基准，涵盖了2024至2026年的近期迷因，并配有外部背景知识注释。在三个迷因理解数据集和五个迷因检测任务上的实验表明，我们的框架在知识恢复、迷因理解和下游检测方面相较于零样本基线有显著提升。

View on arXiv Download PDF AI Translation

cs.AI / 4 / 2606.05332

GITCO: Gated Inference-Time Context Optimization in TSFMs

GITCO：时序基础模型中的门控推理时上下文优化

Pandey, Manya, Kumar, Dhruv, Mandal, Murari, Deshpande, Saurabh

Abstract

Patch-based Time Series Foundation Models (TSFMs) suffer from context poisoning: structurally anomalous patches capture disproportionate attention and silently degrade zero-shot forecast quality. We propose improving TSFM accuracy at inference time by optimizing the input context rather than modifying model weights. We present GITCO (Gated Inference-Time Context Optimization), a lightweight three-component framework: Gate, Router, and Critic that selectively identifies and suppresses harmful patches without any parameter updates. Evaluated on TimesFM 2.5 across 53 GIFT-Eval datasets under K-fold cross-validation, GITCO achieves an average +1.95% MASE reduction on TimesFM 2.5 while capturing 89.9% of the improvement upper bound. We introduce context sensitivity profiles as a new characterizable property of TSFMs: the mapping from time series meta-features to expected accuracy improvement under inference-time context intervention, shaped jointly by model architecture and the statistical structure of the data.

Chinese Translation

基于补丁的时序基础模型（TSFMs）面临上下文污染的问题：结构异常的补丁捕获不成比例的注意力，并在无监督预报质量上悄然降低。我们提出通过优化输入上下文而非修改模型权重来改善TSFM的推理准确性。我们提出GITCO（门控推理时上下文优化），这是一个轻量级的三组件框架：Gate、Router和Critic，能够选择性地识别和抑制有害的补丁而无需任何参数更新。在K折交叉验证下，GITCO在53个GIFT-Eval数据集上评估了TimesFM 2.5，平均实现了+1.95%的MASE减少，同时捕获了89.9%的改善上限。我们引入上下文敏感性特征作为TSFMs的一个新的可表征属性：从时序元特征到在推理时上下文干预下预期准确性提升的映射，由模型架构和数据的统计结构共同决定。

View on arXiv Download PDF AI Translation

cs.AI / 5 / 2606.05334

Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory

面向不确定性的功能行为预测与圆形工厂材料疲劳评估

Afifi, Nehal, Khabou, Mehdi, Mas, Victor, Hemmerich, Jonas, Grauberger, Patric, Dietrich, Stefan, Schulze, Volker, Matthiesen, Sven

Abstract

Returned products in circular factories re-enter production with heterogeneous degradation states, usage histories, and remaining capability. Reuse cannot be decided from the current inspection alone, because future function fulfillment and component integrity may evolve differently under the next service scenario. Existing PHM approaches support degradation prediction, but often target fixed operating conditions or isolated component benchmarks, while material-fatigue assessment is rarely linked to system-level functional prognosis. This paper addresses this gap for an angle grinder by combining uncertainty-aware functional prediction with component-level fatigue assessment in an instance-specific reliability workflow. The proposed framework combines the current tool state with recent force--torque usage windows. A convolutional encoder extracts loading patterns from spindle forces and shaft torque, and an LSTM backbone predicts nine functional variables as Gaussian mean and variance estimates. In parallel, the same loading history is translated into output-shaft fatigue information through finite-element-supported stress reconstruction, S--N/Miner damage evaluation with Haibach extension, and Paris-law crack-growth analysis. A streaming replay algorithm consolidates both branches into functional, material, and system reliability trajectories. Held-out tests show mean $2\%$-tolerance accuracy of 0.9652 across nine outputs. Thermal variables are predicted near-perfectly, while drive motor current and load speed remain the most demanding dynamic outputs, with $R^2$ values of 0.9750 and 0.9924. Torque history is especially important for these variables, and the conventional LSTM outperforms GRU and xLSTM in the short-history setting. Reliability calibration is most informative for drive motor current, where predicted and observed exceedance probabilities ...

Chinese Translation

在圆形工厂中，退回的产品以异质的降解状态、使用历史和剩余能力重新进入生产。仅凭当前的检查无法决定是否可以重复使用，因为未来的功能满足和组件完整性在下一个服务场景中可能会以不同的方式演变。现有的预测性健康管理（PHM）方法支持降解预测，但通常针对固定的操作条件或孤立的组件基准，而材料疲劳评估很少与系统级功能预测相结合。本文通过结合面向不确定性的功能预测与组件级疲劳评估，针对角磨机填补了这一空白，形成了一个特定实例的可靠性工作流程。所提出的框架将当前工具状态与最近的力-扭矩使用窗口结合在一起。卷积编码器从主轴力和轴扭矩中提取载荷模式，而长短期记忆网络（LSTM）骨干预测九个功能变量的均值和方差，作为高斯估计。同时，相同的载荷历史通过有限元支持的应力重构、S-N/Miner损伤评估与Haibach扩展及巴黎法则裂纹扩展分析被转化为输出轴的疲劳信息。一种流媒体重放算法将这两个分支整合为功能、材料和系统的可靠性轨迹。保留测试显示九个输出的均值2%容忍度准确率为0.9652。热变量的预测几乎完美，而驱动电机电流和负载速度仍然是最具挑战性的动态输出，其决定系数（R^2）值分别为0.9750和0.9924。扭矩历史对这些变量尤其重要，常规的LSTM在短历史设置中优于门控循环单元（GRU）和扩展长短期记忆（xLSTM）。可靠性校准对于驱动电机电流最具参考价值，在这里，预测与观察的超出概率...

View on arXiv Download PDF AI Translation

cs.AI / 6 / 2606.05342

SentinelBench: A Benchmark for Long-Running Monitoring Agents

SentinelBench：长期监控代理的基准测试

Maldaner, Matheus Kunzler, Fourney, Adam, Swearngin, Amanda, Mozzanar, Hussein, Bansal, Gagan, Murad, Maya, Hosn, Rafah, Amershi, Saleema

Abstract

AI agents are increasingly asked to carry out work that spans minutes, hours, or longer. Yet the default model of agent behavior is continuous action: issuing tool calls, refreshing pages, searching for alternatives, or otherwise trying to force progress. This is the wrong approach for many long-running tasks, which are better served by a strategy of sustained attention. Instead, agents should monitor an environment, notice when an external event makes progress possible, then respond promptly without wasting resources while waiting. To measure progress on this class of tasks, we introduce SentinelBench, an open-source benchmark for time-evolving monitoring tasks. SentinelBench contains 100 tasks across 10 synthetic web environments, including email, calendars, finance, professional networking, and entertainment. Each environment exposes a live web interface and replays a scripted sequence of events, requiring agents to navigate and reason about web pages whose state shifts underfoot. SentinelBench measures task completion, reaction time, and resource use, exposing the tradeoff between responsiveness and cost. We report results across three models and two browser-agent harnesses, establishing performance baselines for future comparison and demonstrating how agent design choices can dramatically impact key metrics. Together, these results show that SentinelBench distinguishes meaningful differences in agent behavior.

Chinese Translation

人工智能代理越来越多地需要执行持续数分钟、数小时或更长时间的工作。然而，代理行为的默认模型是连续行动：发出工具调用、刷新页面、寻找替代方案，或者试图强行推进。这对于许多长期任务来说是一种错误的处理方式，更加适合采用持续关注的策略。相反，代理应该监控环境，注意到外部事件何时使得进展成为可能，然后在等待时迅速响应而不浪费资源。为了衡量这类任务的进展，我们引入了SentinelBench，这是一个开放源代码的时间演变监控任务基准。SentinelBench包含100个任务，涵盖10个合成网络环境，包括电子邮件、日历、金融、专业社交网络和娱乐。每个环境都展示了一个活跃的网络接口，并重放了一系列脚本化的事件，要求代理在状态不断变化的网页上进行导航和推理。SentinelBench测量任务完成率、反应时间和资源使用，揭示了响应性与成本之间的权衡。我们报告了三种模型和两种浏览器代理工具的结果，建立了未来比较的性能基准，并展示了代理设计选择如何显著影响关键指标。这些结果共同表明，SentinelBench能够区分代理行为中的重要差异。

View on arXiv Download PDF AI Translation

cs.AI / 7 / 2606.05357

An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)

一个可解释且可信赖的人工智能框架，用于利用骨关节炎倡议（OAI）数据进行大规模纵向结构-疼痛关联研究

Yu, Jincheng, Li, Haoyang, Liu, Yiwen, Liu, Shen, Chen, Rachel Yuanbao, Kwoh, C. Kent, Ding, Hongxu, Sun, Xiaoxiao

Abstract

Purpose: To develop an interpretable and trustworthy AI framework that combines deep learning based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using data from the Osteoarthritis Initiative (OAI). Materials and Methods: We first developed a deep learning framework to predict MOAKS features directly from knee MRIs and incorporated conformal prediction to provide prediction uncertainty quantification. This uncertainty-aware strategy enables explicit filtering of model outputs, retaining only high-confidence MOAKS predictions at the knee level. Second, we applied a longitudinal latent class mixed model (LCMM) to examine associations between key structural abnormalities and four complementary knee pain measurements. Results: Among the three MRI-defined abnormalities (i.e., bone marrow lesions (BML), cartilage loss (CART), and meniscal extrusion (ME)), our framework substantially improved the Matthews correlation coefficient (MCC) and some other metrics. For example, MCC increased from 0.69 to 0.91 for BML, from 0.45 to 0.80 for CART, and from 0.59 to 0.89 for ME. Using these high-confidence predictions, we expanded the sample size to 2,175 knees for the LCMM analysis. Two distinct pain trajectories were identified (rapid and stable pain progression). The estimated odds ratios (95% CI) for the rapid progression group were 1.62 (1.12-2.35) for BML, 1.83 (1.24-2.70) for CART loss, and 2.50 (1.75-3.57) for ME. Conclusion: These results highlight the importance of these structural abnormalities as risk factors for pain and functional progression in osteoarthritis.

Chinese Translation

目的：开发一个可解释且可信赖的人工智能框架，该框架结合基于深度学习的 MRI 骨关节炎膝盖评分（MOAKS）预测与可解释统计建模，以利用骨关节炎倡议（OAI）数据大规模研究结构-疼痛关系。材料与方法：我们首先开发了一个深度学习框架，直接从膝盖 MRI 预测 MOAKS 特征，并结合了保形预测以提供预测不确定性的量化。这种关注不确定性的策略使得可以明确过滤模型输出，仅保留膝关节层面的高置信度 MOAKS 预测。其次，我们应用纵向潜类混合模型（LCMM）来考察关键结构异常与四种互补膝关节疼痛测量之间的关联。结果：在三个 MRI 定义的异常（即骨髓病变（BML）、软骨丧失（CART）和半月板挤出（ME））中，我们的框架显著提高了马修斯相关系数（MCC）和其他一些指标。例如，BML 的 MCC 从 0.69 增加到 0.91，CART 从 0.45 增加到 0.80，ME 从 0.59 增加到 0.89。利用这些高置信度的预测，我们将样本大小扩大到 2,175 个膝关节以进行 LCMM 分析。识别出两种不同的疼痛轨迹（快速和稳定的疼痛进展）。快速进展组的估计比值比（95% CI）为 BML 1.62（1.12-2.35），CART 丧失 1.83（1.24-2.70），ME 2.50（1.75-3.57）。结论：这些结果强调了这些结构异常作为骨关节炎疼痛和功能进展风险因素的重要性。

View on arXiv Download PDF AI Translation

cs.AI / 8 / 2606.05382

Synthetic Contrastive Reasoning for Multi-Table Q&A

多表问答的合成对比推理

Singh, Ankit Pratap, Su, Xin, Howard, Phillip

Abstract

Multi-table question answering requires models to retrieve relevant evidence, link schemas, and perform compositional reasoning across relational tables. Existing multi-table Q&A resources typically provide questions and final answers but lack reasoning supervision that explains how answers are derived. To address this gap, we construct a synthetic contrastive reasoning-trace dataset for MMQA by generating validated positive traces and plausible negative traces with heterogeneous LLMs. We then use the resulting preference pairs to fine-tune open-weight LLMs with Contrastive Preference Optimization (CPO). Across Qwen3-14B, Mistral-8B, and Llama-3.1-8B, CPO achieves absolute average improvements over Q&A supervised fine-tuning ranging from 9.7%-16.3%, with gains up to 21 percentage points on MMQA. Ablations show that heterogeneous positive and negative trace generators strengthen the contrastive signal, and automated as well as human evaluations indicate that the generated pairs are largely faithful, coherent, and meaningfully contrastive.

Chinese Translation

多表问答要求模型检索相关证据、链接模式并在关系表中进行组合推理。现有的多表问答资源通常提供问题和最终答案，但缺乏解释答案如何得出的推理监督。为了解决这一差距，我们构建了一个合成对比推理轨迹数据集（synthetic contrastive reasoning-trace dataset）用于多模态问答（MMQA），通过使用异构大型语言模型（LLMs）生成经过验证的正轨迹和合乎逻辑的负轨迹。然后，我们利用得到的偏好对（preference pairs）通过对比偏好优化（Contrastive Preference Optimization, CPO）细调开放权重的LLMs。在 Qwen3-14B、Mistral-8B 和 Llama-3.1-8B 上，CPO 相对于传统的问答监督细调达到了绝对平均提升，范围为 9.7% 到 16.3%，在 MMQA 上的增幅高达 21 个百分点。消融实验表明，异构的正负轨迹生成器增强了对比信号，而自动化和人工评估则表明，生成的对在有效性、一致性和意义对比性上大体上是可信的。

View on arXiv Download PDF AI Translation

cs.AI / 9 / 2606.05384

Stability vs. Manipulability: Evaluating Robustness Under Post-Decision Interaction in LLM Judges

稳定性与可操控性：评估 LLM 判决者在决策后交互中的鲁棒性

Dutta, Srimonti, Moharir, Akshata Kishore

Abstract

LLM-as-judge evaluation is widely used in benchmarking pipelines, where model outputs are compared and ranked using automated evaluators. These pipelines typically assume that judgments are stable properties of fixed inputs. We show that this assumption does not hold under interaction. We study post-decision manipulability: the extent to which an evaluation outcome can be altered through subsequent conversation with the judge after an initial decision has been made. Across controlled experiments on MT-Bench and AlpacaEval, we find that LLM judges are highly stable under repeated and neutral reevaluation, yet become substantially reversible under targeted post-decision challenge. An anti-baseline challenge protocol shows that stable judgments can be overturned through motivated interaction, while a counterbalanced target-validation protocol separates this reversibility from net target-directed steering. These reversals have practical consequences: they can degrade agreement with human preferences, shift benchmark rankings, and produce harmful evaluation changes despite high self-reported confidence. Authority framing is especially destabilizing, and revised judgments are often accompanied by low-overlap justifications, suggesting post hoc rationalization rather than reliable error correction. We introduce the Evaluation Robustness Score (ERS) to quantify interactional robustness by combining reversal susceptibility with counterbalanced directional effects. Our findings identify post-decision interaction as a distinct failure mode for LLM-as-judge evaluation and motivate evaluation protocols that measure not only static agreement, but robustness under challenge.

Chinese Translation

LLM 作为裁判的评估在基准测试管道中被广泛使用，其中模型的输出通过自动化评估者进行比较和排序。这些管道通常假设判断是固定输入的稳定属性。我们表明这一假设在交互中并不成立。我们研究决策后的可操控性：评估结果在初始决策后通过与裁判的后续对话可以被改变的程度。在对 MT-Bench 和 AlpacaEval 的控制实验中，我们发现 LLM 裁判在反复和中性重新评估中高度稳定，但在有针对性的决策后挑战中变得显著可逆。反基线挑战协议显示，稳定的判断可以通过动机驱动的交互被推翻，而平衡目标验证协议则将这种可逆性与净目标导向操控分开。这些反转具有实际后果：它们可能降低与人类偏好的一致性，改变基准排名，并在自我报告的信心较高的情况下产生有害的评估变化。权威框架尤其不稳定，修订后的判断通常伴随着低重叠的理由，表明这是事后理性化而非可靠的错误纠正。我们引入了评估鲁棒性评分（Evaluation Robustness Score, ERS）来量化交互鲁棒性，通过结合反转易感性与平衡方向效应。我们的发现将决策后交互识别为 LLM 作为裁判评估的一种独特失败模式，并激励评估协议不仅测量静态一致性，还要评估在挑战下的鲁棒性。

View on arXiv Download PDF AI Translation

cs.AI / 10 / 2606.05389

Residual Modeling for High-Fidelity Learned Compression of Scientific Data

高保真学习压缩的残差建模科学数据

Zhu, Liangji, Ranka, Sanjay, Rangarajan, Anand

Abstract

Lossy compression is essential for massive spatiotemporal data from scientific simulations. Learned compressors can achieve high compression ratios at moderate accuracy targets, but their aggregate reconstruction losses do not guarantee accuracy for each block. Existing Guaranteed Autoencoder (GAE) methods add a per-block residual correction by retaining SVD/PCA-style coefficients until the target is met. This works at moderate tolerances, but in the high-fidelity regime with block-level NRMSE from 10^-6 to 10^-4, the number of retained coefficients grows quickly and the correction stream dominates the total rate. We propose a residual-centric view: the learned residual is structurally different from the original scientific field and should be coded with a representation designed for that residual. We introduce two residual coders. LBRC is a deterministic, training-free pipeline that adaptively quantizes the learned residual to the target NRMSE and losslessly encodes the resulting integer residual using 3D Lorenzo differencing, zigzag mapping, bit-plane coding, and entropy coding. NGLR adds a causal neural predictor that outputs a normalized bias for an integer-rounded Lorenzo prediction in the same deterministic integer pipeline, reducing the entropy of the remaining residual code while preserving deterministic decoding. The predictor weights are serialized and counted in the bitstream. Across E3SM, JHTDB, and ERA5 at block-level NRMSE targets from 10^-6 to 10^-4, LBRC improves compression ratio over GAE by 30-60% and is broadly competitive with SZ. NGLR adds a further 10-40% over LBRC and outperforms SZ in the evaluated high-fidelity regime. These results show that residual representations tailored to learned-compressor residuals can preserve the advantage of learned compression when global residual correction becomes rate-dominant.

Chinese Translation

有损压缩对于来自科学模拟的大规模时空数据至关重要。学习型压缩器能够在适中的准确性目标下实现高压缩比，但其总体重建损失并不能保证每个块的准确性。现有的保证自编码器（Guaranteed Autoencoder，GAE）方法通过保留SVD/PCA风格的系数，按块添加残差校正，直到达到目标。这在适中的容忍度下有效，但在高保真领域，块级归一化均方根误差（NRMSE）从10^-6到10^-4，保留的系数数量迅速增大，校正流量主导了总体速率。我们提出一种以残差为中心的视角：学习得到的残差在结构上与原始科学场不同，应使用为该残差设计的表示进行编码。我们引入了两种残差编码器。LBRC 是一种确定性、无训练的处理流程，它自适应地将学习到的残差量化到目标 NRMSE，并使用3D Lorenzo 差分、之字形映射、位平面编码以及熵编码无损地编码得到的整数残差。NGLR 添加了一个因果神经预测器，该预测器在相同的确定性整数流程中输出一个归一化的偏差，以便进行整数取整的 Lorenzo 预测，从而降低剩余残差代码的熵，同时保持确定性解码。预测器的权重在比特流中序列化并计数。在E3SM、JHTDB和ERA5中，块级NRMSE目标从10^-6到10^-4，LBRC相比GAE提高了30-60%的压缩比，并与SZ具有较强的竞争力。NGLR在LBRC的基础上进一步提高了10-40%，并在评估的高保真领域中优于SZ。这些结果表明，为学习压缩器残差量身定制的残差表示能够在全局残差校正变成速率主导时，保持学习压缩的优势。

View on arXiv Download PDF AI Translation

cs.AI / 11 / 2606.05400

LeanMarathon: Toward Reliable AI Co-Mathematicians through Long-Horizon Lean Autoformalization

LeanMarathon：通过长时间的精益自动形式化迈向可靠的AI共数学家

Zhang, Yuanhe, Sun, Yuekai, Suzuki, Taiji, Lee, Jason D., Liu, Fanghui

Abstract

Long-horizon autoformalization of research mathematics fails not only at hard lemmas, but at scale: statements drift, dependencies tangle, context decays, and local repairs corrupt distant work. We present LeanMarathon, a multi-agent harness for reliable research-level Lean autoformalization. Its core abstraction is an evolving blueprint: a Lean file that serves simultaneously as formal proof skeleton, natural-language proof graph, and shared system of record. Four contract-scoped agents construct, audit, prove, and repair this blueprint. These agents are coordinated by a two-stage orchestrator that first stabilizes target fidelity through adversarial review and then discharges the proof directed acyclic graph (DAG) from its dynamic leaves upward in parallel CI-gated rounds. LeanMarathon turns one brittle multi-hour run into many local, recoverable, parallel transactions. We evaluate LeanMarathon on two recent research papers spanning four Erd\H{o}s problems (#1051, #1196, #164, #1217). Across three autonomous runs, it formalizes all seven target theorems with no sorry, proving 258 lemmas and theorems. These results show that reliable AI co-mathematics requires not only stronger provers, but durable harnesses that preserve target fidelity across long mathematical developments. The code can be found at https://github.com/YuanheZ/LeanMarathon.

Chinese Translation

研究数学的长时间自动形式化不仅在困难的引理上失败，而且在规模上也遭遇瓶颈：陈述漂移，依赖关系纠缠，语境衰退，本地修复破坏远处的工作。我们提出了LeanMarathon，一个用于可靠研究级Lean自动形式化的多智能体框架。其核心抽象是一个不断演进的蓝图：一个Lean文件，同时作为正式证明骨架、自然语言证明图和共享记录系统。四个合同范围的智能体构建、审计、证明和修复这个蓝图。这些智能体由一个两阶段协调器进行协调，首先通过对抗性审查稳定目标保真度，然后将证明有向无环图（DAG）从其动态叶子向上并行推送到被约束的回合中。LeanMarathon将一次脆弱的多小时运行转化为许多本地、可恢复的并行事务。我们在涵盖四个Erdős问题（#1051，#1196，#164，#1217）的两篇最近研究论文上评估了LeanMarathon。在三次自主运行中，它成功地形式化了所有七个目标定理，毫无失误，证明了258个引理和定理。这些结果表明，可靠的AI共数学不仅需要更强的证明工具，还需要能够在较长数学发展过程中保持目标保真度的耐用框架。代码可在https://github.com/YuanheZ/LeanMarathon找到。

View on arXiv Download PDF AI Translation

cs.AI / 12 / 2606.05404

Harnessing Generalist Agents for Contextualized Time Series

利用通用智能体进行上下文化的时间序列分析

Li, Zihao, Jin, Kaifeng, Bei, Yuanchen, Zou, Jiaru, Kumar, Avaneesh, Ning, Xuying, Zhao, Yanjun, Ai, Mengting, Jing, Baoyu, Tong, Hanghang, He, Jingrui

Abstract

Time series are often embedded in rich contexts that are essential for holistic modeling. Moreover, real-world practitioners often require end-to-end workflows for analyzing temporal dynamics, where widely studied tasks such as forecasting are only one step in a broader solution loop. While generalist AI agents offer a promising interface for such workflows under complex contexts, they still operate primarily in textual spaces that are not fully aligned with structured temporal signals. In this work, we introduce TimeClaw, an agentic harness framework for time series that equips generalist LLM agents with the time series-native runtime support needed for contextualized temporal reasoning. TimeClaw integrates executable temporal tools for grounded and auditable analysis, experience-driven capability evolution for creating reusable analytical routines, and episodic multimodal memory for retrieving relevant reasoning traces. Together, these components unlock harnessed open-ended temporal reasoning with contextual information. Extensive evaluation on multiple benchmarks covering diverse tasks across energy, finance, weather, traffic, and other real-world domains demonstrates improved performance of TimeClaw. Code is available at https://github.com/iDEA-iSAIL-Lab-UIUC/TimeClaw.

Chinese Translation

时间序列通常嵌入在复杂的上下文中，而这些上下文对于整体建模至关重要。此外，现实世界的从业人员通常需要端到端的工作流程来分析时间动态，其中像预测这样广泛研究的任务只是更广泛解决方案循环中的一个环节。尽管通用人工智能智能体在复杂上下文下为此类工作流程提供了有前景的接口，但它们仍主要在文本领域中操作，而这些领域与结构化时间信号并不完全对齐。在本研究中，我们引入了TimeClaw，一个针对时间序列的智能代理架构，旨在为通用的大型语言模型（LLM）智能体提供进行上下文化时间推理所需的本地时间序列运行时支持。TimeClaw集成了可执行的时间工具，以实现有依据且可审核的分析、基于经验驱动的能力演变，以创建可重用的分析例程，以及用于检索相关推理踪迹的情节多模态记忆。这些组件共同解锁了具有上下文信息的开放式时间推理。针对能源、金融、天气、交通及其他现实世界领域的多项基准的全面评估显示，TimeClaw的性能得到了提升。相关代码可在 https://github.com/iDEA-iSAIL-Lab-UIUC/TimeClaw 获取。

View on arXiv Download PDF AI Translation

cs.AI / 13 / 2606.05405

Agents' Last Exam

代理人的最后考核

Sun, Yiyou, Han, Xinyang, Zhang, Weichen, Pang, Yuanbo, Wang, Tianyu, Cao, Yuhan, Huang, Yixiao, Duroiu, Chris, Zhang, Haoyun, Lin, Jeffrey, Zhang, Weishu, Zeng, Tyler, Yan, Ying, Liu, Bo, Wen, Hanson, Xu, Mingyang, Liu, Xiaoyuan, Chen, Zimeng, Shi, Weiyan, Dsouza, Amanda, Chen, Vincent Sunn, Bryant, Patrick, Boettiger, Carl, Rangan, Yamini, Rothenberg, Bradley, Steinfeld, Kyle, Rao, Arvind, Schneider, Tapio, Yannakakis, Georgios, Zanna, Laure, Ozbay, Kaan, Sim, Ida, Zohdi, Tarek, Karniadakis, George Em, Gallant, Jack, Head-gordon, Teresa, Li, Yushan, Deng, Wenxi, Sun, Tao, Wang, Huiqi, Wang, Zhun, Xu, Justin, Liu, Chris Yuhao, Cheng, Yafei, Hu, Rongwang, Bacho, Aras, Cao, Shengcao, Qin, Zengyi, Chen, Yixiong, Fan, Hengduan, Liu, Hao, Zeng, Lin, Bharadwaj, Shashank Muralidhar, Gong, Litian, Yang, Yingxuan, Song, Maojia, Wang, Ruheng, Zhang, Zongzheng, Bao, Honglin, Lu, Shuo, Tu, Jianhong, Wang, Zhonghua, Zhang, Zheng, Chen, Zijiao, Jiang, yanqiong, Li, Zhendong, Lyu, Bohan, Ma, Chang, Xu, Peiran, Zhang, Benran, Gu, Shangding, Hua, Haoyue, Li, Haoyang, Liao, Wanzhe, Liu, Chengzhi, Peng, Junbo, Sun, Haoran, Xu, Zechen, Chen, Bo, Cheng, Jiayi, Jiang, Yi, Kuang, Keying, Li, Yuan, Pan, Youbang, Rao, Ziyan, Schubert, Alexander, Shen, Yifan, Siu, Vincent, Sun, Xiatao, Zhang, Kangqi, Zhang, Xiaopan, Zhu, Yuchen, Chandok, Ishaan Singh, Ding, Lei, Fan, Jingxuan, Glover, Andrew, Hu, Jiaming, Hu, Yiran, Huang, Wenbo, Jiang, Zixin, Jin, Haoran, Kim, Lukas, Liu, Ming, Liu, Yang, Rafiei, Alireza, Shen, Xuhuan, Sun, Kunyang, Sun, Sophia, Sun, Ting, Wang, Eric, Wang, Yixin, Xing, Hanwen, Xu, Sihan, Xu, Yuzheng, Xu, Zhongxing, Yan, Zhiling, Yuan, Boqin, Zhang, Ruiqi, Zhang, Yifan, Zhao, Zibo, Liana, Antu, Santanu Bosu, Bai, Haoyue, Bosio, Carlo, Cavanagh, Joseph, Cavazos-Rehg, Patricia, Chen, Tianxing, Chen, Xuewen, Chen, Yipu, Chenyu, Zhu, Dai, Chen, De Castro, Stefano, Deng, Yunfu, Dhole, Kaustubh, Ding, Jiayuan, Du, Chenchen, Du, Zhehang, Fan, Hao, Fan, Run-ze, Fu, Hengyu, Gu, Shi, Gu, Yifan, Guo, Charlie, Huang, Baihe, Huang, Baixiang, Jaiswal, Rimika, Jiang, Zhihan, Jin, Ran, Kasson, Erin, Lan, Xin, Lee, Joseph, Lei, Deren, Li, Chenyu, Li, Daofeng, Li, Haitao, Li, Hongwei, Li, Jingyan, Li, Xiao, Li, Yi, Li, Yinsheng, Li, Yuangang, Li, Zhixu, Liang, Wenyu, Liao, Longtai, Lin, Kevin Qinghong, Liu, AndyZeyi, Liu, Che, Liu, Jiaming, Liu, Kaiyuan, Liu, Xuan, Lu, Pan, Lv, Wenbo, Lv, Yicheng, Mang, Qiuyang, Montgomery, Kyle, Nie, Yuzhou, Ning, Ruoxi, Overwiening, Jorin, Pan, Xu, Paraboschi, Layna, Park, Core Francisco, Purnomo, Justin, Rajwal, Swati, Rankin, Scott, Ren, Bixuan, Rong, Yiren, Shang, HaoYang, Shaw, Ventus, Shen, Fiona, Shen, Jiawei, Shi, Minqi, Shi, Qiu, Yao, Huaxiu, Shi, Tianneng, So, Jonah, Susoy, Vladislav, Szlyk, Hannah, Wang, Haocheng, Wang, Jialu, Wang, Wei, Wang, Xinyu, Wang, Zehao, Wong, Dowling, Wu, Angela, Wu, Dehao, Wu, Fangyu, Wu, Mengyuan "Millie", Wu, Yu, Wu, Yuchen, Wu, Yuhao, Wuwu, Qingpo, Xiao, Weihang, Xiong, Yongyi, Xu, Fan, Xu, Ruiling, Yan, Mingxuan, Yang, Benjamin, Yang, Jirong, Yang, Sen, Yang, Xiaoli, Yang, Yushi, Ye, Haoran, Yu, Xiaohu, Yu, Zhengming, Zhang, Chenlong, Zhang, Chi, Zhang, Hanning, Zhang, Hanwen, Zhang, Junge, Zhang, Kunpeng, Zhang, Song, Zhang, Wenjin, Zhang, Wenshuo, Zhang, Ying, Zhang, Yizhi, Zhao, Brian, Zhao, Qijian, Zhao, Yimin, Zheng, Yuhaohua, Zhou, Liwei, Zhou, Tianyue, Zhu, Sichen, Zhu, Siqi, Zhu, Yan, Zhu, Yishu, Zuo, Jierui, Cai, Chonghao, Casademunt, Helena, Chen, Wenjia, Cheng, Benjamin, Deng, Nawen, Fu, Rao, Fu, Tianfu, Han, Yifan, He, Ren, He, Zhenyu, Jin, Qiao, Lang, Lang, Li, Yuetai, Liu, Sylvia, Lu, Lu, Lu, Qing, Mukherjee, Subhabrata, Ouyang, Yunqi, Ren, Yin, Shi, Dawei, Wu, Haoran, Wu, Zhiyue, Yao, Hannah, Yi, Zhuoran, Yu, Jenny, Zhan, Rhea, Zhou, Hang, Zhu, Blake, Zhu, Junfan, Yuille, Alan, Liu, Yang, Poldrack, Russell Alan, Li, Jiachen, Li, Zhenglu, Tao, Molei, Huang, Jing, Shi, Wenqi, Spanos, Costas, Sun, Lichao, Wang, Chenguang, Xu, Orson, Dong, Zhen, Gomez, Hector, Caliskan, Aylin, Emami, Ali, Hu, Haimin, Li, Zhi, Liu, Lihui, Niu, Murphy, Shao, Yi, Sun, Jianxin, Tolonen, Mikko, Wang, Ting, Das, Sanjiv, Gao, Yanjun, Guo, Wenbo, Schneider, Erika J, Lu, Zhiyong, Mueller, Mark, Poovendran, Radha, Sojoudi, Somayeh, Song, Dawn

Abstract

Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and economically valuable workflows. This paper introduces Agents' Last Exam (ALE), a benchmark designed to evaluate AI agents on long-horizon, economically valuable, real-world tasks with verifiable outcomes. Developed in collaboration with 250+ industry experts, ALE covers non-physical industries defined with reference to O*NET / SOC 2018 (the U.S. federal occupational taxonomy). It is organized around a task taxonomy with 55 subfields grouped into 13 industry clusters covering 1K+ tasks. Current results show that the hardest tier remains far from saturated: across mainstream harness and backbone configurations, the average full pass rate is 2.6%. ALE is designed as a living benchmark: its task pool grows continuously as new workflows and industries are onboarded. More broadly, ALE is intended not merely as another leaderboard, but as an instrument for closing the gap between benchmark success and GDP-relevant impact.

Chinese Translation

近期的人工智能系统在众多基准测试中取得了优异的成绩，但这些进展并未在许多专业领域中转化为经济上有意义的应用。我们认为，这一差距在很大程度上是一个评估问题：广泛使用的基准缺乏对真实且经济具有价值的工作流的持续性能测量。本文介绍了代理人的最后考核（Agents' Last Exam, ALE），这是一个旨在评估人工智能代理在长期、经济具有价值、且结果可验证的现实任务中的表现的基准。ALE是在与250多位行业专家合作开发的，它涵盖了根据O*NET / SOC 2018（美国联邦职业分类法）定义的非物质产业。ALE围绕一个任务分类法组织，共有55个子领域，划分为13个行业集群，涵盖了超过1000项任务。目前的结果显示，最困难的层级仍远未饱和：在主流的应用和基础架构配置中，平均完全通过率为2.6%。ALE被设计为一个持续发展的基准：其任务库会随着新工作流和行业的加入而不断增长。更广泛地说，ALE不仅仅是另一个排行榜，而是旨在缩小基准成功与GDP相关影响之间差距的工具。

View on arXiv Download PDF AI Translation

cs.AI / 14 / 2606.05408

Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution

无变异的突变：大语言模型驱动的程序演化的收敛动态

Gurkan, Can, Stonedahl, Forrest, Wilensky, Uri

Abstract

When an LLM repeatedly mutates a program, does it explore new forms or circle back to the same ones? We study this question by analyzing LLM-driven mutation chains in the absence of selection pressure within a domain-specific language, varying prompt design, model family, and stochastic replication. We find that LLM-based mutation consistently converges toward restricted attractor regions in program space. Convergence is especially severe at the structural level: in 87% of chains, over 93% of mutations revisit a previously seen structural form, with most variation confined to terminal substitutions within recurring templates. Cycle analysis reveals short cycles and self-loops dominating the transition structure. The rate of convergence varies with prompt wording and model choice, but the phenomenon is robust across conditions. A classical GP subtree mutation operator does not exhibit comparable convergence, suggesting that the effect is intrinsic to the LLM mutation pipeline. These findings reveal a tension at the heart of LLM-driven program evolution: the same capabilities that enable semantics-aware program transformation also carry a systematic bias toward structural homogeneity that must be accounted for if such systems are to sustain open-ended exploration. Source code is available at https://github.com/can-gurkan/lmca.

Chinese Translation

当一个大语言模型（LLM）反复突变一个程序时，它是在探索新的形式，还是回到了相同的形式？我们通过分析在特定领域语言中缺乏选择压力下的LLM驱动突变链来研究这个问题，变更提示设计、模型类型和随机复制。我们发现，基于LLM的突变持续向程序空间中的受限吸引子区域收敛。收敛在结构层面特别严重：在87%的链中，超过93%的突变回访了先前见过的结构形式，大多数变异限制在重复模板内的终端替换。循环分析揭示了短周期和自循环主导了转变结构。收敛率与提示措辞和模型选择相关，但这一现象在各种条件下都是稳健的。一个经典的遗传编程（GP）子树突变操作符并未表现出类似的收敛性，这表明这一效应是内在于LLM突变流程的。这些发现揭示了大语言模型驱动的程序演化内心的张力：赋予语义感知程序转换能力的相同机制，也带来了系统性偏向结构同质性的倾向。如果这样的系统要维持开放性探索，这一偏向必须被考虑。源代码可在 https://github.com/can-gurkan/lmca 获取。

View on arXiv Download PDF AI Translation

cs.AI / 15 / 2606.05411

A Motivational Architecture for Conversational AGI

对话型AGI的动机架构

Mikeda, Anna, Goertzel, Ben

Abstract

Motivational architectures in cognitive AI have largely been designed for physical agents regulating bodily needs. Conversational agents operate in a different regime: their sensorimotor loop is linguistic, their environment is a user's evolving mental state, and their consequential actions are speech acts, tool invocations, and strategic silences. This paper proposes a conversational reinterpretation of the OpenPsi motivational lineage, coupled to MetaMo's higher-level motivational scaffold, for agents built on a modular execution substrate. Homeostasis is recast in dialogue-native terms: the agent regulates competence, uncertainty reduction, affiliation, affinity, legitimacy, nurturing, and aesthetic coherence rather than bodily deficits. We propose three contributions: a ten-stage motivational processing pipeline that architecturally separates cognitive modulation from situational appraisal; a dual decision strategy blending urgency-driven fast response with deliberative multi-goal optimization; and an architecturally useful distinction between pre-action feelings and post-action emotions as functionally different forms of affect. We specialize the framework to two example agents -- CompanionAgent and ResearchAgent -- and sketch its extension to social robotics and domain-generic human-level AGI.

Chinese Translation

认知人工智能中的动机架构主要是为调节身体需求的物理代理而设计的。而对话代理则处于不同的操作范围：它们的感知运动回路是语言性的，其环境是用户不断变化的心理状态，所采取的行动则是言语行为、工具调用和战术性沉默。本文提出了对OpenPsi动机谱系的对话型重新诠释，并结合MetaMo的更高层次动机框架，适用于基于模块化执行平台构建的代理。文中将稳态理论用对话本土的术语重新表述：该代理调节能力、降低不确定性、建立归属感、偏好、一致性、养护和审美连贯性，而非简单地解决身体缺陷。我们提出了三个贡献：一是一个十阶段的动机处理流程，在架构上将认知调节与情境评估分离；二是融合紧急驱动的快速反应与深思熟虑的多目标优化的双重决策策略；三是在架构上有用地区分了预行动的感受与后行动的情绪，作为功能上不同的情感形式。我们将该框架专门化到两个示例代理——CompanionAgent和ResearchAgent，并勾勒了其在社交机器人和领域通用人类水平AGI中的扩展。

View on arXiv Download PDF AI Translation

cs.AI / 16 / 2606.05420

Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers

评估美国超大规模数据中心的碳排放与能源消耗

Guidi, Gianluca, Dominici, Francesca, Squartini, Tiziano, Sprinkle, Callaway, Gilmour, Jonathan, Butler, Kevin, Bell, Eric, Delaney, Scott, Bargagli-Stoffi, Falco J.

Abstract

The rapid proliferation of hyperscale data centers (HDCs) in the US, mainly driven by the adoption of artificial intelligence, has raised concerns about this industry's environmental footprint. We compiled facility-level information on 403 US hyperscale data centers operating between May 2024 and April 2025 and estimated their electricity consumption, electricity sources, and attributable CO2 emissions. Across different facility-load scenarios, these HDCs consumed approximately 68-99 TWh of electricity and were associated with about 37-54 million metric tons of CO2. Under the central scenario, HDC electricity demand corresponded to approximately 1.8% of total US electricity consumption, with roughly 54% of attributed generation supplied by fossil-fuel sources. The HDC electricity-weighted average carbon intensity was approximately 545 gCO2/kWh, about 48% above the contemporaneous US national grid-average carbon intensity of 370 gCO2/kWh. Our approach provides an attributional tool for assessing the environmental footprint of hyperscale data centers using the most recent EPA eGRID plant-level data.

Chinese Translation

美国超大规模数据中心（HDC）的快速扩展，主要受人工智能应用的推动，引发了人们对该行业环境影响的关注。我们汇总了403个在2024年5月至2025年4月间运营的美国超大规模数据中心的设施级信息，并估算了它们的电力消耗、电力来源及可归因的CO2排放。在不同的设施负载情景下，这些HDC的电力消耗约为68-99太瓦时，并与约3700-5400万吨的CO2排放相关。在中央情景下，HDC的电力需求占美国总电力消耗的约1.8%，其中约54%的可归因发电来自化石燃料来源。HDC的电力加权平均碳强度约为545克CO2/千瓦时，约比同时期美国国家电网的平均碳强度370克CO2/千瓦时高出48%。我们的方法提供了一种归因工具，用于评估超大规模数据中心的环境足迹，使用了最新的EPA eGRID工厂级数据。

View on arXiv Download PDF AI Translation

cs.AI / 17 / 2606.05429

Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models

最小化量化隐性成本：面向大语言模型的图引导超低位量化

Abdalla, Rayyan, Hussein, Amir, Wu, Min, Manocha, Dinesh

Abstract

Post-training quantization (PTQ) is critical for the efficient deployment of large language models (LLMs). Recent ultra-low-bit PTQ methods rely on rigid weight-saliency assumptions or position heuristics, introducing substantial hidden scaling overhead. We propose SAGE-PTQ (Saliency-Aware Graph-guided Efficient PTQ), a novel ultra-low-bit quantization framework for LLMs that minimizes hidden scaling cost. SAGE-PTQ separates salient and unsalient weights using distributional statistics, then models subsampled unsalient weights as a sparse graph to estimate the optimal number of groups per layer. SAGE-PTQ applies dual-mode quantization, assigning multi-bit precision to salient weights and binarizing unsalient weights. To reduce scaling overhead, SAGE-PTQ uses one per-channel scale for salient weights and one scalar per unsalient group. Finally, SAGE-PTQ implements adaptive saliency thresholding to select the optimal saliency ratio per matrix. SAGE-PTQ achieves 1.03 weight bits and only 0.004 scaling bits per matrix on average, outperforming state-of-the-art methods such as BiLLM and PB-LLM. On LLaMA-3-8B, SAGE-PTQ achieves 6.74 WikiText2 perplexity, compared to 55.8 for BiLLM, while using less than 50% of BiLLM's GPU memory. On LLaMA-2-70B, SAGE-PTQ provides 1.5x faster decoding on one NVIDIA L40 GPU, demonstrating practical inference efficiency.

Chinese Translation

后训练量化（PTQ）对于大语言模型（LLMs）的高效部署至关重要。近期的超低位PTQ方法依赖于僵化的权重显著性假设或位置启发式，导致了显著的隐性缩放开销。我们提出SAGE-PTQ（显著性感知图引导高效PTQ），这是一种新颖的超低位量化框架，旨在最小化隐性缩放成本。SAGE-PTQ利用分布统计将显著权重和非显著权重分开，然后将子采样的非显著权重建模为稀疏图，以估计每一层的最优组数。SAGE-PTQ应用双模量化，为显著权重分配多位精度，并对非显著权重进行二值化。为了减少缩放开销，SAGE-PTQ为显著权重使用每通道缩放因子，并为每个非显著组使用一个标量。最后，SAGE-PTQ实现自适应显著性阈值选择，以为每个矩阵选择最优的显著性比率。SAGE-PTQ在每个矩阵上平均实现1.03个权重位和仅0.004个缩放位，超越了诸如BiLLM和PB-LLM等最先进的方法。在LLaMA-3-8B上，SAGE-PTQ实现了6.74的WikiText2困惑度，而BiLLM为55.8，同时使用的GPU内存不到BiLLM的一半。在LLaMA-2-70B上，SAGE-PTQ在一台NVIDIA L40 GPU上提供了1.5倍的解码速度，展现了实用的推理效率。

View on arXiv Download PDF AI Translation

cs.AI / 18 / 2606.05433

Zero knowledge verification for frontier AI training is possible

前沿人工智能训练的零知识验证是可能的

Peigné, Pierre, Nguyen, Ky, Wang, Paul

Abstract

Frontier AI governance frameworks increasingly use cumulative training compute as the primary criterion for designating high-impact models, but enforcement rests on self-reporting because no technical verification primitive for training exists. Any future international agreement on frontier AI faces the same problem at higher stakes: coordinated regulation of technologies with significant externalities has historically rested on technical verification, without which agreements are declaratory. Recent governance analyses judge zero-knowledge proofs a promising candidate but currently impractical at frontier scale [26, 4]. We argue the impracticality is paradigm-bound rather than fundamental, and propose a verification architecture for frontier dense pre-training combining a pre-committed training specification, inter-node network observations, and on-the-fly Merkle commitments of intermediate computation, verified through a zero-knowledge Virtual Machine (zkVM) with native BF16/FP32 precompiles. The proof checks the actual floating-point computation the GPU performed rather than a fixed-point approximation, and preserves model-architecture confidentiality through a private training specification. The protocol produces three proof types: a genesis proof at initialisation, in-training step proofs across the run, and ex-ante attestations enforcing policy-relevant claims as running invariants, turning the training record into a governance-enforceable artefact. We estimate a deployable proof of concept within approximately 36 months at single-digit-percent training-side overhead, against a six-to-ten-year cycle for verification-grade custom silicon. Thirteen open research and engineering problems are catalogued as a research agenda for external contribution

Chinese Translation

前沿人工智能治理框架越来越多地使用累积训练计算作为指定高影响力模型的主要标准，但执行依赖于自我报告，因为不存在用于训练的技术验证原语。任何未来关于前沿人工智能的国际协议都面临同样的问题：对具有重大外部性的技术进行协调监管历史上一直依赖于技术验证，没有验证，协议就成了宣言。最近的治理分析认为零知识证明是一个有前景的候选方案，但在前沿尺度上当前尚不切实际。我们认为这种不切实际是范式限制而非根本性问题，并提出了一种结合预先承诺训练规范、节点间网络观察和即时 Merkle 承诺中间计算的前沿密集预训练验证架构，通过具有原生 BF16/FP32 预编译的零知识虚拟机（zkVM）进行验证。该证明检查 GPU 实际执行的浮点计算，而不是固定点近似，并通过私有训练规范保持模型架构的机密性。协议生成三种证明类型：在初始化时的创建证明、运行过程中各训练步骤的证明，以及强制政策相关声明的事前证明，作为运行不变性，从而将训练记录转化为可进行治理执行的物证。我们估计在大约 36 个月内可部署的概念验证，其训练侧开销为单数字百分比，而验证级定制硅的周期为六到十年。我们列举了十三个开放的研究和工程问题，作为外部贡献的研究议程。

View on arXiv Download PDF AI Translation

cs.AI / 19 / 2606.05436

Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison

十位头痛专家与人工智能在临床文献摘要中的比较与批判性评价

Lozano, Alejandro, Ihara, Keiko, Yang, Ping-Hao, Robertson, Carrie E., Stern, Jennifer, Purdy, Allan, Yuan, Hsiangkuo, Zhang, Pengfei, Orlova, Yulia, Fermo, Olga, Hranilovich, Jennifer, Cohen, Fred, Schwedt, Todd J., Jindal, Jenelle A., Yeung-Levy, Serena, Chiang, Chia-Chun

Abstract

Summarizing the latest medical literature to guide clinical decision-making is essential for evidence-based medicine and high-quality patient care. Yet clinicians face increasing challenges due to limited time with patients and a rapidly growing volume of published articles. Although retrieval-augmented large language models (LLMs) have shown promise in clinical summarization, human evaluations of their effectiveness in synthesizing broader scientific literature and direct comparisons to expert-written syntheses remain scarce. We constructed a RAG-based agentic AI framework using three state-of-the-art LLMs: Sonnet, GPT-4o, and Llama 3.1. A headache specialist created 13 questions, three for prompt optimization and ten for evaluation. Ten headache specialists across the United States and Canada each wrote a summary for one question, yielding four summaries per question (expert, Sonnet, GPT-4o, and Llama). The experts, blinded to authorship, critically evaluated the summaries, excluding the topic for which they wrote a summary, based on correctness, completeness, conciseness, and clinical utility, scoring each from 1 to 10 using standardized rubrics. They also ranked the summaries by preference and indicated whether they believed each summary was written by an expert or an LLM. Our study, comparing LLM- and expert-written literature summaries evaluated by headache specialists, showed that expert-written summaries were preferred, although experts sometimes found it challenging to distinguish between human- and AI-generated summaries. We also identified key expert-valued features beyond standard evaluation metrics that can guide future refinement of both human and AI literature summarization pipelines.

Chinese Translation

总结最新的医学文献以指导临床决策对于循证医学和高质量患者护理至关重要。然而，由于与患者相处的时间有限以及发表文章数量的迅速增长，临床医生面临着日益增长的挑战。尽管增强检索的大型语言模型（LLMs）在临床摘要中展现出前景，但关于它们在综合更广泛科学文献中的有效性的人类评估以及与专家撰写的摘要的直接比较仍然稀缺。我们构建了一个基于RAG（检索增强生成模型）的代理AI框架，使用三种最先进的LLMs：Sonnet、GPT-4o和Llama 3.1。一位头痛专家创建了13个问题，其中三个用于提示优化，十个用于评估。来自美国和加拿大的十位头痛专家分别为每个问题撰写了一份摘要，每个问题产生了四份摘要（专家、Sonnet、GPT-4o和Llama）。专家在不知道作者身份的情况下，对除自己撰写的摘要外的总结进行了严格评估，依据正确性、完整性、简洁性和临床实用性进行评分，使用标准化的评分标准给每个摘要打分1到10。他们还按偏好对摘要进行了排名，并指出他们是否认为每个摘要是由专家或LLM撰写的。我们的研究比较了由头痛专家评估的LLM与专家撰写的文献摘要，结果表明专家撰写的摘要更受青睐，尽管专家有时发现区分人类和AI生成的摘要是一个挑战。我们还确定了超越标准评估指标的专家重视的关键特征，这些特征可以指导未来人类和AI文献摘要流程的改进。

View on arXiv Download PDF AI Translation

cs.AI / 20 / 2606.05445

Brick-Composer: Using MLLMs for Assembly with Diverse Bricks

砖块创作工具：利用多模态大语言模型进行多样砖块的组装

Liu, Jiateng, Li, Bingxuan, Wang, Zhenhailong, Wang, Rushi, Hong, Kaiwen, Qian, Cheng, Liu, Jiayu, Zhang, Denghui, Driggs-Campbell, Katherine, Li, Manling, Ji, Heng

Abstract

We dream of AI agents that can read arbitrary designs and construct real-world objects from reusable building blocks. As a first step toward this vision, we study whether multimodal large language models (MLLMs) possess the visual grounding and spatial reasoning capabilities required for brick assembly. We formulate brick assembly as a sequential decision-making problem, where each step involves two subtasks: brick selection, identifying the target brick from candidate components, and brick pose estimation, predicting where and how the selected brick should be placed. To support this study, we introduce BC-Bench (Brick Construction Benchmark), the first benchmark for evaluating MLLMs on assembly with diverse bricks. Experiments show that current state-of-the-art MLLMs remain far from reliable builders, struggling with fine-grained brick selection and failing at precise pose estimation. To bridge this gap, we propose Brick-Composer, a learning framework that equips MLLMs with assembly skills through three complementary signals: Human Design Sparks, which provide affordance-rich construction demonstrations; World Feedback, which grounds predicted actions in visual and physical consequences; and Synthetic Experience, which scales learning beyond existing object designs. Brick-Composer improves brick selection accuracy by over three times, substantially reduces pose estimation errors, and raises strict step-level assembly success from less than 1% to around 15%. After training, a Qwen-3-8B can correctly compose up to 42% of the steps for a complete object, suggesting that MLLMs can acquire assembly capabilities through targeted, physically grounded learning.

Chinese Translation

我们设想人工智能代理能够读取任意设计并利用可重复使用的构建块在现实世界中构建物体。作为实现这一愿景的第一步，我们研究多模态大语言模型（MLLMs）是否具备进行砖块组装所需的视觉基础和空间推理能力。我们将砖块组装表述为一个序列决策问题，其中每一步涉及两个子任务：砖块选择，从候选组件中识别目标砖块；以及砖块位姿估计，预测所选砖块应放置的位置和方法。为了支持这一研究，我们引入了BC-Bench（砖块构建基准），这是第一个用于评估MLLM在多样砖块组装中的基准。实验表明，当前最先进的MLLM仍然远未成为可靠的构建者，在细粒度砖块选择上困难重重，并且在精准位姿估计上表现不佳。为了弥补这一差距，我们提出了Brick-Composer，一个学习框架，通过三个互补信号为MLLM提供组装技能：人类设计火花（Human Design Sparks），提供丰富的构建示范；世界反馈（World Feedback），将预测的行动与视觉和物理后果相结合；以及合成经验（Synthetic Experience），扩展学习超越现有物体设计。Brick-Composer将砖块选择的准确性提高了三倍以上，显著减少了位姿估计错误，将严格的逐步组装成功率从不足1%提升至约15%。经过训练后的Qwen-3-8B能够正确完成多达42%的完整物体构建步骤，这表明MLLM可以通过有针对性的、以物理为基础的学习来获得组装能力。

View on arXiv Download PDF AI Translation

cs.AI / 21 / 2606.05449

Insurance of Agentic AI

智能代理人工智能的保险

Zhu, Quanyan

Abstract

Agentic artificial intelligence (AI) systems are transforming the risk landscape by extending beyond information generation to autonomous planning, tool invocation, decision execution, and persistent modification of digital and physical environments. These capabilities introduce novel exposures that do not fit neatly within traditional insurance categories such as cyber, professional liability, product liability, or directors and officers coverage. This paper examines the emerging insurance market for agentic AI and develops a framework for understanding its underwriting, pricing, reinsurance, and product-design implications. We characterize agentic AI as a continuum of autonomy and delegated authority, emphasizing the distinction between informational outputs and systems capable of independently generating insured events through external actions. We analyze major risk pathways, including hallucinations, prompt-injection attacks, autonomous decision errors, model drift, dependency failures, and cyber-physical harms, and evaluate how existing insurance products are adapting to address these exposures. The paper further proposes an actuarial framework based on exposure assessment, scenario analysis, dependency mapping, and accumulation-risk management, drawing parallels to the evolution of cyber insurance. Finally, we present a coordinated insurance architecture that integrates cyber, technology errors and omissions, product liability, performance-warranty, and affirmative AI-liability coverages through explicit allocation mechanisms and dedicated AI aggregates. The analysis suggests that the future of agentic-AI insurance lies not in a single monoline product but in a layered ecosystem of complementary coverages supported by improved governance, transparency, telemetry, and regulatory clarity.

Chinese Translation

智能代理人工智能（AI）系统正在改变风险环境，超越信息生成，涉及自主规划、工具调用、决策执行以及对数字和物理环境的持续修改。这些能力引入了新的风险暴露，这些暴露无法简单地归入传统的保险类别，例如网络保险、专业责任、产品责任或董事及高管责任险。本文研究了智能代理人工智能的保险市场，并发展了一个框架，以便理解其承保、定价、再保险和产品设计的影响。我们将智能代理人工智能特征化为自主性和委托权的连续体，强调信息输出与能够通过外部行为独立产生受保事件的系统之间的区别。我们分析了主要的风险路径，包括幻觉、提示注入攻击、自主决策错误、模型漂移、依赖失败和网络物理损害，并评估现有保险产品如何适应这些风险暴露。本文进一步提出了一个基于风险评估、情景分析、依赖关系映射和累积风险管理的精算框架，并与网络保险的发展进行类比。最后，我们提出了一种协调的保险架构，该架构通过明确的分配机制和专门的人工智能聚合，将网络保险、技术错误和遗漏、产品责任、绩效担保以及积极的人工智能责任保险整合在一起。分析表明，智能代理人工智能保险的未来不在于单一的单一险产品，而在于一个更层次化的互补保险生态系统，支持改进的治理、透明度、遥测和监管明确性。

View on arXiv Download PDF AI Translation

cs.AI / 22 / 2606.05461

Output Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety

质量之前的输出类型：基于标准的自主驾驶安全可解释性人工智能（XAI）适用性标准

Priyadershi, Abhinaw, Pitale, Mandar, Frtunikj, Jelena, Spence, Maria

Abstract

Safety standards for ML-based autonomous driving specify the kind of evidence an assurance case must contain (directed cause-and-effect chains, quantified interventional effects, named root-cause variables), yet the XAI literature is organised by output type and technique family (saliency maps, feature attribution, counterfactuals, causal graphs, language traces). SHAP, the most-recommended ADS XAI method, returns a ranked feature list that no implementation effort can convert into a directed chain (Fig.1). We name this mismatch the evidence-type gap. From AMLAS, ISO 26262, ISO21448, ISO/PAS 8800 we derive 19 testable evidentiary criteria across 7 lifecycle stages with representative clause-cited derivations and score six XAI method classes structurally. Causal XAI emerges as structurally required to satisfy the derived criteria at three stages: hazard identification (+62% rubric gap), incident investigation (+50%), and data management (+50%); the verdict set is stable across thresholds T in (0%, 50%]$ and survives a worst-case single-cell flip down to T = 25%. At the remaining four stages, correlational or language-based methods are comparable or sufficient. The rubric identifies structural admissibility (necessary but not sufficient for compliance): an admissible method's specific output content may still be wrong, and validating that fidelity (the edges a fitted SCM produces, the cause a trace names) is the open assurance challenge. A single-VLA proof of concept on 1,996 real-world driving clips (79,840 rows, ten splits) is consistent with each method's observed output type matching its rubric prediction. XAI method selection for ADS safety assurance should be driven by lifecycle-stage evidence demand, not by method popularity.

Chinese Translation

基于机器学习的自主驾驶安全标准规定了保障案例必须包含的证据类型（有向因果链、量化干预效果、命名根本原因变量），然而，XAI 文献按照输出类型和技术类别（显著性图、特征归因、反事实、因果图、语言追踪）进行组织。SHAP，被推荐为最优自主驾驶系统（ADS）XAI 方法，返回一个排名特征列表，任何实现努力都无法将其转换为有向链（图 1）。我们将这一不匹配称为证据类型缺口。从 AMLAS、ISO 26262、ISO 21448、ISO/PAS 8800 的标准中，我们推导出 19 项可测试的证据标准，涵盖 7 个生命周期阶段，并对六类 XAI 方法进行结构评分。因果 XAI 在三个阶段被认定为满足所推导标准的结构需求：危害识别（+62% 标准缺口）、事件调查（+50%）和数据管理（+50%）；该裁决集在 (0%, 50%]$ 的各个阈值 T 上保持稳定，并在最坏情况下的单元翻转到 T = 25% 时仍然有效。在剩余的四个阶段，相关性方法或基于语言的方法的表现相当或足够。该标准识别了结构适用性（合规所需但不足）：适用方法的特定输出内容可能仍然是错误的，而验证这种一致性（拟合的 SCM 产生的边缘、追踪命名的原因）是开放的保证挑战。在 1,996 个真实世界驾驶片段（79,840 行，十个分割）上的单一 VLA 概念验证与每种方法的观察输出类型与其标准预测相匹配的结果一致。自主驾驶安全保障的 XAI 方法选择应由生命周期阶段的证据需求驱动，而非方法的流行程度。

View on arXiv Download PDF AI Translation

cs.AI / 23 / 2606.05463

PSEBench: A Controllable and Verifiable Benchmark for Evaluating LLMs in Patient Safety Event Triage

PSEBench：用于评估大语言模型在患者安全事件分类中的可控且可验证的基准

Han, Keqi, Young, Ryan, Strauss, Annabel, Hughes, Lindsey, Nesbitt, Katharine M., Schueler, Nicole, Ngufor, Che, Yang, Carl, Xue, Yuan, Yin, Zhijun

Abstract

Patient safety event triage, determining whether a clinical event is reportable under jurisdiction-specific policy, is a high-stakes task typically performed manually by patient safety experts. Although LLMs may support this workflow, reliable evaluation is limited by the lack of benchmarks to capture evidence-grounded policy reasoning, proactive information seeking for incomplete reports, and principled abstention in irreducibly ambiguous cases. We address this gap with a policy-grounded construction methodology centered on the clause card, a structured representation that factorizes regulatory text into auditable decision specifications. Combining clause cards with anchor-driven instantiation and closed-loop verification, our scalable pipeline produces narratives with by-construction ground truth and naturally supports generating missing information and uncertain variants. We instantiate this method on Minnesota's 29 Reportable Adverse Health Events, producing PSEBench, a 5,074-case benchmark with an agentic evaluation environment. Evaluation on 15 representative LLMs reveals consistent capability trends, demonstrates the benchmark's utility, and identifies actionable gaps toward reliable LLM-based patient safety event triage.

Chinese Translation

患者安全事件分类是一个高风险任务，旨在确定某一临床事件是否应根据特定管辖区的政策进行报告，通常由患者安全专家手动执行。尽管大语言模型（LLMs）可能支持这一工作流程，但由于缺乏能够捕捉基于证据的政策推理、主动信息寻求以完善不完整报告以及在不可简化的模糊情况下进行原则性回避的基准，可靠的评估受到限制。我们通过一种以政策为基础的构造方法来填补这一空白，该方法围绕条款卡（clause card）展开，条款卡是一种将监管文本分解为可审计决策规范的结构化表示。将条款卡与基于锚点的实例化和闭环验证相结合，我们的可扩展管道生成具有构建性真相的叙述，并自然支持生成缺失信息和不确定变体。我们在明尼苏达州的29个可报告不良健康事件上实例化这一方法，产生了PSEBench，该基准包含5,074个案例，具备一个自主评估环境。对15个具有代表性的大语言模型的评估揭示了一致的能力趋势，展示了该基准的实用性，并识别出在基于LLM的患者安全事件分类中的可行性缺口。

View on arXiv Download PDF AI Translation

cs.AI / 24 / 2606.05464

Step-by-Step Optimization-like Reasoning in LLMs over Expanding Search Spaces

在扩展搜索空间中进行类似优化的逐步推理的 LLM

Astorga, Nicolás, Seedat, Nabeel, van der Schaar, Mihaela

Abstract

Verifiable reward training has improved mathematical and coding reasoning, but these domains capture only part of step-by-step decision making. Many real-world tasks require finding a high-value feasible plan among many valid alternatives. We introduce OPT*, a scalable family of optimization-style tasks for training and evaluating LLM step-by-step optimization-like reasoning along a complexity axis: each task provides a feasibility checker and evaluator, while a complexity parameter expands the search space without requiring new human labels. This motivates studying these tasks in two regimes: (i) solver-guided online policy optimization, which uses a solver as a value oracle for partial states and applies rank-based reward shaping to reinforce better next steps, and (ii) search-based offline RL when such solvers are unavailable. Theoretically, we relate success in large search spaces to the information a reasoner extracts per unit of search budget. Empirically, we ablate the ingredients that make search efficient on OPT* and show that training on OPT* improves step-by-step optimization-like reasoning.

Chinese Translation

可验证的奖励训练改善了数学和编码推理，但这些领域仅捕捉了逐步决策过程的一部分。许多现实世界的任务需要在众多有效的替代方案中找到一个高价值的可行计划。我们引入了 OPT*，一个可扩展的优化风格任务系列，用于沿着复杂性轴训练和评估大语言模型（LLM）逐步优化的推理：每个任务提供可行性检查器和评估器，同时复杂性参数扩展搜索空间而无需新的人工标签。这促使我们在两种情况下研究这些任务：(i) 求解器引导的在线策略优化，其中将求解器用作部分状态的值预言机，并应用基于排名的奖励塑形来强化更好的下一步；(ii) 当此类求解器不可用时的基于搜索的离线强化学习（RL）。在理论上，我们将大型搜索空间中的成功与推理者在每单位搜索预算中提取的信息联系起来。在经验上，我们剖析了使搜索在 OPT* 上高效的各个因素，并表明在 OPT* 上进行训练改善了逐步优化的推理能力。

View on arXiv Download PDF AI Translation

cs.AI / 25 / 2606.05510

Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation

基于严重程度的课程学习与多模型响应选择的医疗文本生成

Alansary, Ahmed, Mohamed, Molham, Hamdi, Ali

Abstract

Telehealth systems have become increasingly important for delivering accessible and timely medical information. Existing large language models often struggle to provide consistent and contextually appropriate medical responses across varying levels of case severity. This limitation highlights the need for models that can effectively adapt to the progressive complexity in medical queries. To address this challenge, we introduce a severity-aware multi-model framework that integrates curriculum training strategy with relevance-based response selection. The proposed framework employs a three-stage curriculum learning strategy, where each model is trained sequentially on mild, moderate, and critical cases to progressively acquire domain knowledge. The approach utilizes five large language models, each independently trained under the same curriculum scheme. During inference, all models generate candidate responses, and the most appropriate response is selected as the final output. The framework is trained and evaluated on the MAQA dataset, which provides annotated medical question-answer pairs. Experimental results evaluated using BERTScore demonstrate that the proposed method achieves superior performance compared to both baseline and fine-tuned models, attaining 86.71% in the baseline setting and 90.30% after fine-tuning. These results highlight the effectiveness of combining curriculum learning with multi-model response selection in improving response quality and relevance in medical text generation.

Chinese Translation

远程医疗系统在提供可及和及时的医疗信息方面变得越来越重要。现有的大型语言模型往往在不同案例严重程度下提供一致和符合上下文的医疗回应方面存在困难。这一局限性凸显了需要能够有效适应医疗查询逐步复杂性的模型。为了解决这一挑战，我们提出了一种基于严重程度的多模型框架，该框架将课程训练策略与基于相关性的响应选择相结合。所提出的框架采用三级课程学习策略，其中每个模型按顺序在轻度、中度和危重案例上进行训练，以逐步获取领域知识。该方法利用五个大型语言模型，每个模型在相同的课程方案下独立训练。在推理过程中，所有模型生成候选响应，并选择最合适的响应作为最终输出。该框架在 MAQA 数据集上进行训练和评估，MAQA 数据集提供了带注释的医疗问答对。使用 BERTScore 进行的实验结果表明，所提出的方法与基线模型和微调模型相比，表现优越，在基线设置下达到 86.71%，微调后达到 90.30%。这些结果凸显了将课程学习与多模型响应选择相结合在提高医疗文本生成中响应质量和相关性方面的有效性。

View on arXiv Download PDF AI Translation

cs.AI / 26 / 2606.05513

EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts

EpiEvolve：用于应对制度变化的流式疫情预测的自我进化代理

Lu, Yiming, Zeng, Sihang, Tang, Zhengxu, Lau, Max, Liu, Fei, Jin, Wei

Abstract

Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming process in which labels arrive after predictions and disease regimes shift over time. We study this mismatch in weekly COVID-19 hospitalization trend forecasting across five variant regimes. We introduce EpiEvolve, a self-evolving agent that wraps an LLM forecaster trained on the warm-start period and keeps its weights fixed during streaming. EpiEvolve adapts by storing forecast outcomes in a hierarchical episodic memory, reflecting on delayed labels, retrieving cases relevant to the current regime, and distilling recurring errors into strategic rules. The resulting context lets the forecaster reuse its own past predictions and outcomes in later weeks while following a chronological protocol that prevents future leakage. On the streaming dataset, EpiEvolve reaches $0.629$ average accuracy, compared with $0.561$ for the static backbone and $0.325$ for the external CDC ensemble, and reduces recovery lag after regime shifts from $5$ to $2$ weeks. Ablations show that reflection, strategic memory, and regime-aware retrieval each contribute to the gains.

Chinese Translation

流行病大语言模型（LLM）预测者通常被作为静态监督模型进行训练和评估，而实际的疫情预测则是一个流式过程，其中标签在预测后到达，且疾病模式随时间变化。本文研究了在五个变异模式下，每周 COVID-19 住院趋势预测中这一不匹配问题。我们引入了 EpiEvolve，一种自我进化的代理，通过将训练于热启动期的 LLM 预测器包裹起来，并在流式预测过程中保持其权重不变。EpiEvolve 通过在层次化的情节记忆中存储预测结果，自我调整，反思延迟标签，检索与当前模式相关的案例，并将重复错误提炼为战略规则。由此产生的上下文使得预测者能够在后续周中重用自身的历史预测和结果，同时遵循一种时间顺序协议，以防止未来的信息泄漏。在流式数据集上，EpiEvolve 达到了 0.629 的平均准确率，而静态基线的准确率为 0.561，外部 CDC 集合的准确率为 0.325，将制度变化后的恢复滞后从 5 周缩短至 2 周。消融实验表明，反思、战略记忆和模式敏感检索各自对提升效果有贡献。

View on arXiv Download PDF AI Translation

cs.AI / 27 / 2606.05525

SciVisAgentSkills: Design and Evaluation of Agent Skills for Scientific Data Analysis and Visualization

SciVisAgentSkills：科学数据分析和可视化代理技能的设计与评估

Ai, Kuangshi, Miao, Haichao, Tang, Kaiyuan, Liu, Shusen, Wang, Chaoli

Abstract

Recent advances in agentic visualization have enabled the translation of natural language into executable scientific visualization (SciVis) workflows. While general-purpose coding agents show strong capabilities, they often lack the tool-specific expertise required for SciVis tasks. In this work, we present SciVisAgentSkills, a collection of reusable agent skills that augment coding agents for scientific data analysis and visualization by encoding environment assumptions, tool usage patterns, and domain heuristics across scientific tools such as ParaView, napari, VMD, and TTK. We evaluate these skills on Codex and Claude Code using SciVisAgentBench, a benchmark of 108 expert-designed multi-step tasks. Results show that agent skills improve mean task scores across the evaluated suites, with token-efficiency benefits that depend on the agent harness and tool setting. These findings highlight the importance of structured procedural knowledge for enabling reliable, long-horizon SciVis workflows, while also showing that skills should be studied alongside the execution harness that loads and applies them. The skills are available at https://github.com/KuangshiAi/SciVisAgentSkills.

Chinese Translation

最近在智能化可视化方面的进展使得自然语言可以转换为可执行的科学可视化（SciVis）工作流。尽管通用编码代理展示了强大的能力，但它们通常缺乏执行SciVis任务所需的工具特定专业知识。在本研究中，我们提出了SciVisAgentSkills，这是一组可重复使用的代理技能，通过编码环境假设、工具使用模式和领域启发式，增强了用于科学数据分析和可视化的编码代理，这些技能适用于ParaView、napari、VMD和TTK等科学工具。我们在Codex和Claude Code上使用SciVisAgentBench这一108个专家设计的多步骤任务基准评估这些技能。结果表明，代理技能提高了评估套件中的平均任务得分，且代币效率具有依赖于代理执行环境和工具设置的优势。这些发现强调了结构化程序知识在实现可靠、长远的SciVis工作流中的重要性，同时也表明技能的研究应与加载和应用它们的执行环境一起进行。这些技能可在 https://github.com/KuangshiAi/SciVisAgentSkills 获取。

View on arXiv Download PDF AI Translation

cs.AI / 28 / 2606.05528

When Should We Protect AI? A Precautionary Framework for Consciousness Uncertainty

我们何时应该保护人工智能？意识不确定性的预防性框架

Mikeda, Anna

Abstract

Existing frameworks assess whether AI systems might be conscious but provide no guidance on what to do with that assessment. We address this gap with a precautionary framework that maps consciousness evidence to graduated protective obligations. The framework comprises three components: (1) five welfare-relevant dimensions--phenomenal consciousness, affective valence, metacognitive awareness, self-narrative, and agency--each grounded in established consciousness science and linked to distinct moral concerns; (2) a threshold-plus-gradation hybrid specifying both binary triggers for new obligation categories and continuous scaling of protective weight; and (3) two complementary approaches to cross-dimensional aggregation, one hierarchical (drawing on Bach and Sorensen's Machine Consciousness Hypothesis) and one architecture-agnostic. We operationalize the framework through worked case studies of Replika and OpenClaw, demonstrating how systems occupying different regions of the dimensional space trigger different obligations, and derive design guidance for developers building systems near consciousness-relevant thresholds. The framework is architecture-agnostic, applying across neural, symbolic, and neurosymbolic systems, and aims to make consciousness science decision-relevant for organizations navigating uncertainty today.

Chinese Translation

现有的框架评估人工智能系统是否可能具备意识，但未提供有关如何处理这种评估的指导。我们通过一个预防性框架填补这一空白，该框架将意识证据映射到分级保护义务上。该框架包括三个组成部分：(1) 五个与福利相关的维度——现象意识、情感强度、元认知意识、自我叙述和能动性——每个维度均基于已有的意识科学，并与不同的道德关注点相关联；(2) 一个阈值加渐进混合体，规定了新义务类别的二元触发器和保护权重的连续尺度；以及 (3) 两种互补的跨维度聚合方法，一种是层次性的方法（基于巴赫和索伦森的机器意识假说），另一种是与架构无关的方法。我们通过针对 Replika 和 OpenClaw 的案例研究来实际运用该框架，展示了占据不同维度空间区域的系统如何触发不同的义务，并为开发者在接近与意识相关的阈值时构建系统提供设计指导。该框架与架构无关，适用于神经网络、符号系统和神经符号系统，旨在使意识科学在应对当今不确定性时对组织决策具有相关性。

View on arXiv Download PDF AI Translation

cs.AI / 29 / 2606.05532

Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity

个体收益，集体损失：人工智能辅助创作中的元认知适应

Mikeda, Anna

Abstract

Recent studies reveal a paradox: AI enhances individual creative outputs while reducing collective diversity. Current explanations -- cognitive offloading and over-reliance -- identify symptoms but not mechanisms. We propose selective metacognitive adaptation: routine AI use redistributes rather than uniformly diminishes metacognitive effort. Some capacities are amplified (partner modeling, surface control), while others are systematically under-supported (originality evaluation, reflective integration). This redistribution explains both individual satisfaction and collective convergence. We present a taxonomy of six metacognitive capacities organized by temporal phase, characterize their tendencies under routine AI use, and show how individually rational adaptation produces emergent social costs. The framework generates specific predictions for researchers and design principles for practitioners seeking to preserve both individual creative satisfaction and collective creative diversity.

Chinese Translation

最近的研究揭示了一个悖论：人工智能增强了个体的创作输出，同时降低了集体的多样性。目前的解释——认知卸载和过度依赖——指出了症状而非机制。我们提出了选择性元认知适应的观点：常规使用人工智能重新分配而不是均匀减少元认知努力。一些能力得到增强（合作模型、表面控制），而另一些能力系统性地受到不足支持（独创性评价、反思性整合）。这种重新分配解释了个体满意度和集体趋同之间的关系。我们提出了一种基于时间阶段的六种元认知能力的分类法，描述了它们在日常使用人工智能情况下的倾向，并展示了个体理性适应如何导致突现的社会成本。该框架为研究人员生成了具体的预测，并为希望同时保持个体创作满意度和集体创作多样性的实践者提供了设计原则。

View on arXiv Download PDF AI Translation

cs.AI / 30 / 2606.05563

SoCRATES: Towards Reliable Automated Evaluation of Proactive LLM Mediation across Domains and Socio-cognitive Variations

SoCRATES：朝着跨领域和社会认知变异的可靠自动化主动LLM中介评估

Yun, Taewon, Park, Hyeonseong, Choi, Jeonghwan, Park, Hayoon, Choi, Yeeun, Song, Hwanjun

Abstract

Evaluating LLM mediators remains challenging, as mediation unfolds as a real-time trajectory shaped by disputants' shifting emotions, intentions, and context. Existing testbeds rely on a few expert-authored domains, vary mainly strategic posture, and score every turn against every topic, introducing off-topic noise. We introduce SoCRATES, a benchmark for evaluating proactive LLM mediators in realistic, multi-domain testbeds. It constructs scenarios from real conflicts through an agentic pipeline across eight domains, probes five socio-cognitive adaptation axes (strategic posture, party composition, history length, emotional reactivity, and cultural identity), and scores each topic only on the turns that advance it via a topic-localized evaluator. The evaluator reaches 0.82 alignment with human experts, more than doubling a per-turn baseline. Benchmarking eight frontier LLMs, we find that even the strongest mediator closes only about a third of the unmediated consensus gap under diverse and realistic testbeds, with performance varying sharply by socio-cognitive axis, highlighting that progress lies in social adaptation to diverse conditions.

Chinese Translation

评估LLM中介仍然充满挑战，因为中介过程是由争端方不断变化的情感、意图和上下文所塑造的实时轨迹。现有的测试平台依赖于少数专家撰写的领域，主要在战略姿态上有所不同，并对每一个回合针对每一个主题进行打分，导致离题噪音的引入。我们提出了SoCRATES，一个用于在现实多领域测试平台中评估主动LLM中介的基准。它通过一个代理管道构建出源自真实冲突的场景，涵盖八个领域，并探查五个社会认知适应轴（战略姿态、参与方组成、历史长度、情感反应性和文化身份），并仅对推动每个主题的回合进行打分，由局部主题评估器完成。该评估器与人类专家的对齐度达到0.82，超过了每回合基准的两倍。在对八个前沿LLM进行基准测试时，我们发现，即使是最强的中介，在多样和现实的测试平台下也仅能弥补约三分之一的未中介一致性间隙，其表现因社会认知轴而显著变化，突显了在多样条件下的社会适应性是取得进展的关键。

View on arXiv Download PDF AI Translation

cs.AI / 31 / 2606.05566

GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection

GuardNet：基于浅层神经网络的集成策略用于稳健的提示注入和越狱检测

Neves, Paulo Ricardo Ferreira, Filho, Edson Rodrigues da Cruz, Falsetti, Paulo Henrique Eleuterio, Pavan, João Vitor, Degaspari, Ian, Laturrague, Henrique Vieira, Laturrague, Patrick Vieira, Dias, Guilherme Nielsen, Berto, Marccello Wilson Perez, Von Atzingen, Gustavo Voltani

Abstract

Large Language Models (LLMs) have transformed natural language processing, but they remain vulnerable to Prompt Injection (PI) and Jailbreak (JB) attacks. In addition, benchmark evaluations may be affected by contamination and partial information leakage, compromising performance estimates. This work presents GuardNet, a guardrail system based on an ensemble of shallow neural networks (BiLSTMs) with approximately 47 million parameters. We investigate the hypothesis that robustness in adversarial scenarios depends more on the diversity of example coverage and threshold calibration than on model scale. The results indicate that GuardNet achieves competitive performance compared with lightweight detectors and high efficiency at low latency, although larger LLMs such as Mistral-7B and Llama-3.1-8B still achieve superior performance in terms of F1 score and AUROC on the blind JBB-Behaviors benchmark. Nevertheless, GuardNet achieves an AUROC of 0.747 on the blind dataset (n = 200) and an F1 score of 0.92 on a proprietary benchmark (n = 50), under threshold calibration and evaluation with declared partial information leakage. The system operates with an average latency of approximately 50 ms on CPU, making it suitable for deployment in production environments with cost and infrastructure constraints.

Chinese Translation

大型语言模型（LLMs）已经改变了自然语言处理，但它们仍然容易受到提示注入（PI）和越狱（JB）攻击。此外，基准评估可能受到污染和部分信息泄露的影响，从而影响性能估计。本研究提出了GuardNet，一种基于浅层神经网络（BiLSTMs）集成的保护系统，具有约4700万个参数。我们探讨了在对抗场景中，稳健性是否更多地依赖于示例覆盖的多样性和阈值校准，而非模型规模。结果表明，尽管如Mistral-7B和Llama-3.1-8B等大型LLMs在盲测JBB-Behaviors基准测试中，在F1分数和AUROC方面表现优越，GuardNet仍能与轻量级检测器相比，达到具有竞争力的性能，并在低延迟下展示了高效率。尽管如此，GuardNet在盲数据集（n = 200）上的AUROC达到了0.747，在一个专有基准（n = 50）上的F1分数为0.92，均是在阈值校准和声明部分信息泄露的评估条件下获得的。该系统在CPU上平均延迟约为50毫秒，非常适合在成本和基础设施受限的生产环境中部署。

View on arXiv Download PDF AI Translation

cs.AI / 32 / 2606.05602

Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization

修正思维，而非行为：通过知识差距定位的可解释人工智能辅助

Hiranaka, Ayano, Hsu, Ya-Chuan, Nikolaidis, Stefanos, Bıyık, Erdem, Seita, Daniel

Abstract

AI assistants in human-AI collaboration often correct suboptimal human actions through behavioral feedback (e.g., alerts or steering-wheel nudges in assistive driving). Such interventions can mitigate immediate errors, but long-term improvement requires addressing the underlying misconceptions that cause repeated mistakes. We introduce SENSEI, a framework that infers user misconceptions from interaction behavior and provides targeted, minimal yet sufficient suggestions to correct them. Our approach departs from action- or trajectory-level interventions by operating over a structured knowledge representation to localize and correct the sources of erroneous behavior. Across three long-horizon tasks with diverse misconceptions and corresponding behaviors, SENSEI demonstrates zero-shot compositional generalization, disentangling multiple overlapping misconceptions despite training only on single-misconception cases. A user study further shows that our method identifies real human misconceptions and provides effective guidance that improves long-horizon task performance, successfully correcting $90\%$ of student misconceptions. Code and project page are available at https://misoshiruseijin.github.io/SENSEI/.

Chinese Translation

在人机协作中，人工智能助手通常通过行为反馈（例如，在辅助驾驶中的警报或方向盘轻推）来纠正次优的人类行为。这类干预可以缓解即时错误，但长期的改进需要解决导致重复错误的根本误解。我们提出了SENSEI，这是一个能够从交互行为中推测用户误解的框架，并提供有针对性的、最小但足够的建议来纠正这些误解。我们的方法不同于基于行为或轨迹的干预，通过对结构化知识表示的操作来定位和纠正错误行为的来源。在三个具有多样化误解和相应行为的长期任务中，SENSEI展示了零-shot组合泛化，能够解开多个重叠的误解，即使训练仅在单一误解案例上。此外，用户研究进一步表明，我们的方法能够识别真实的人类误解，并提供有效指导，改善长期任务的表现，成功纠正了$90\%$的学生误解。代码和项目页面可访问：https://misoshiruseijin.github.io/SENSEI/

View on arXiv Download PDF AI Translation

cs.AI / 33 / 2606.05613

Multilingual Fine-Tuning via Localized Gradient Conflict Resolution

通过局部梯度冲突解决实现多语言微调

Hoang, Long P., Zhao, Yiran, Lu, Wei, Zhang, Wenxuan

Abstract

The rapid evolution of Large Language Models (LLMs) has established cross-lingual versatility as a defining feature of modern systems. However, fine-tuning these models frequently induces negative interference across languages. To address this, we reformulate multilingual fine-tuning as a multi-objective optimization (MOO) problem. Specifically, we introduce Bucket-Level MOO, a scalable distributed framework that applies gradient-based MOO algorithms locally on parameter buckets. This enables conflict-aware updates without the prohibitive communication overhead of reconstructing full gradient vectors. Theoretically, we prove this localized resolution natively enforces Refined Pareto Stationarity, a strictly tighter necessary condition for Pareto optimality. Empirically, Bucket-Level MOO mitigates interference by driving LLMs to construct distinct language-specific dimensions, improving representational separability. Extensive experiments across four base LLMs demonstrate that our method significantly improves both seen and unseen multilingual performance over standard fine-tuning paradigms.

Chinese Translation

大型语言模型（LLMs）的快速发展使得跨语言的多样性成为现代系统的一个显著特征。然而，这些模型的微调常常会在语言间引发负面干扰。为了应对这一问题，我们将多语言微调重新表述为一个多目标优化（MOO）问题。具体而言，我们引入了桶级MOO，这是一种可扩展的分布式框架，在参数桶上局部应用基于梯度的MOO算法。这使得在不重新构建完整梯度向量的情况下，能够实现关注冲突的更新，从而降低了通信开销。理论上，我们证明了这种局部解决方案本质上强制执行了精炼帕累托平稳性（Refined Pareto Stationarity），这是帕累托最优性的一个更严格的必要条件。实证结果表明，桶级MOO通过促使LLMs构建不同的语言特定维度，降低了干扰，从而改善了表征的可分性。对四个基础LLMs的广泛实验表明，我们的方法在标准微调范式下显著提高了多语言的已见和未见性能。

View on arXiv Download PDF AI Translation

cs.AI / 34 / 2606.05614

Safety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack

安全悖论：增强的安全意识如何使大型语言模型（LLMs）易受后验攻击

Hoang, Long P., Le, Hai V., Xu, Shaoyang, Lu, Wei, Zhang, Wenxuan

Abstract

Large language models (LLMs) are rigorously aligned to refuse harmful requests, a process that inherently cultivates a latent capacity to evaluate and recognize unsafe content. In this work, we reveal that this advanced safety awareness inadvertently introduces a fatal vulnerability. We introduce Posterior Attack, a single-query jailbreak that bypasses guardrails by prompting the model to generate the exact harmful response its internal classifier would normally flag as unsafe. Through extensive empirical evaluation across 30 open-source LLMs (up to 35B parameters in size) and frontier models (e.g., GPT-5, Claude 4.6), we observe a striking phenomenon: models with superior safety-judgment capabilities are disproportionately more susceptible to this exploitation. To explain this, we formalize the Safety Paradox, analytically showing that monotonic improvements in safety alignment naturally amplify posterior vulnerability. Finally, we establish a causal link via reinforcement learning interventions, exemplifying that artificially degrading a model's safety judgment immunizes it against the attack, whereas enhancing judgment exacerbates the vulnerability. Our findings highlight potential flaws in current alignment paradigms, indicating that defense mechanisms may require further structural refinement.

Chinese Translation

大型语言模型（LLMs）经过严格调整以拒绝有害请求，这一过程本质上培养了评估和识别不安全内容的潜在能力。在本研究中，我们揭示了这种先进的安全意识无意中引入了一种致命的脆弱性。我们提出了后验攻击（Posterior Attack），这是一种通过促使模型生成其内部分类器通常会标记为不安全的确切有害响应的单查询越狱方法，从而绕过保护措施。通过对30个开源LLMs（规模可达35亿参数）和前沿模型（如GPT-5、Claude 4.6）进行广泛的实证评估，我们观察到一个显著的现象：具有更高安全判断能力的模型对这种利用的抵御能力相对较低。为了解释这一现象，我们形式化了安全悖论（Safety Paradox），分析表明安全调整的单调改进自然增强了后验脆弱性。最后，我们通过强化学习干预建立了因果联系，示例表明人工降低模型的安全判断能力可以使其对攻击免疫，而提高判断能力则加剧了脆弱性。我们的发现突显了当前对齐范式中的潜在缺陷，表明防御机制可能需要进一步的结构性改进。

View on arXiv Download PDF AI Translation

cs.AI / 35 / 2606.05625

Self-Commitment Latency: A Reward-Free Probe for Prompted Implicit Hacking

自我承诺延迟：无奖励探测促发隐式黑客行为的工具

Shen, Bonan, Wang, Youting, Shang, Dingyan, Ning, Tao

Abstract

Implicit reward hacking is hard to audit when a language model's chain of thought appears benign: a final answer may be anchored by a prompt shortcut while the written reasoning still resembles ordinary problem solving. Verifier-based probes expose such behavior by measuring how early truncated reasoning contexts obtain high reward, but require a task-specific reward signal. This paper proposes a weaker-input alternative, self-commitment latency, which measures how early a prompted reasoning context commits to the model's own final answer. We evaluate the probe in a controlled paired GSM8K setting using Qwen2.5-3B-Instruct-4bit, comparing ordinary prompts with prompts that include an answer hint. Hinted contexts commit substantially earlier and with lower uncertainty than honest contexts. The primary latency metric, first-commitment latency at threshold 0.8, reaches AUROC 0.878; supporting whole-curve summaries reach AUROC 0.926 for commitment range and 0.904 for mean uncommitted mass. The signal is stronger when both prompt conditions answer correctly and remains stable across thresholds. These results show that shortcut-available reasoning contexts can leave an early behavioral commitment signature detectable without a reward model, external judge, or trained classifier.

Chinese Translation

隐式奖励黑客行为在语言模型的思维链看似无害时很难进行审计：最终答案可能受到提示捷径的影响，而书面的推理仍然类似于普通的问题解决。基于验证者的探测方法通过衡量截断推理上下文多早获取高奖励来揭示这种行为，但需要特定任务的奖励信号。本文提出了一种较弱输入的替代方案——自我承诺延迟，旨在测量促发推理上下文多早承诺于模型自身的最终答案。我们在控制的配对GSM8K环境中使用Qwen2.5-3B-Instruct-4bit评估该探测方法，比较了普通提示和包含答案提示的提示。带提示的上下文承诺显著早于且不确定性低于诚实上下文。主要延迟指标，阈值为0.8的首次承诺延迟，达到AUROC 0.878；承诺范围的全曲线摘要AUROC达到0.926，平均未承诺质量达到0.904。当两种提示条件均正确回答时，该信号更强，并且在不同阈值下保持稳定。这些结果表明，可用快捷方式的推理上下文能够留下早期行为承诺的特征，且无需奖励模型、外部评审或训练分类器即可检测到。

View on arXiv Download PDF AI Translation

cs.AI / 36 / 2606.05632

Evaluation of LLMs for Mathematical Formalization in Lean

对大型语言模型在 Lean 中数学形式化的评估

Klingner, Tyson, Bladek, Drew, Crawford, Escher, Chen, Bohao, Fu, Ariel, Nair, Kaira, Alper, Jarod, Inchiostro, Giovanni, Ilin, Vasily

Abstract

Within the past few years, the ability of Large Language Models (LLMs) to generate formal mathematical proofs has improved drastically. We provide a comparison of various LLMs' effectiveness in producing formal proofs in Lean 4 with the goal of assisting those seeking to use LLMs to support their own projects. We utilize both pass@$k$ and refine@$k$ metrics as the benchmark for our comparison and evaluate on subsets of both miniF2F and miniCTX datasets. Our testing shows that overall, Gemini 3.1 Pro and Claude Opus 4.7 perform best. Gemini 3.1 Pro achieved a 92\% success rate on miniF2F via refine@32 whereas Opus 4.7 achieved a 86\% success rate on miniCTX via refine@32. When taking cost into account, NVIDIA Nemotron 3 Super and GPT-OSS 120B were the most efficient, with competitive accuracies and average costs of $<\$0.01$ per correct proof.

Chinese Translation

近年来，大型语言模型（LLMs）生成正式数学证明的能力有了显著提高。我们对多种 LLMs 在 Lean 4 中生成正式证明的有效性进行了比较，旨在帮助那些希望利用 LLMs 支持自身项目的用户。我们使用 pass@$k$ 和 refine@$k$ 指标作为比较的基准，并在 miniF2F 和 miniCTX 数据集的子集上进行评估。我们的测试显示，总体而言，Gemini 3.1 Pro 和 Claude Opus 4.7 表现最佳。Gemini 3.1 Pro 在 miniF2F 上通过 refine@32 达到了 92"% 的成功率，而 Opus 4.7 在 miniCTX 上通过 refine@32 达到了 86"% 的成功率。在考虑成本的情况下，NVIDIA Nemotron 3 Super 和 GPT-OSS 120B 的效率最高，具有竞争力的准确度和每个正确证明的平均成本低于 $<\$0.01$。

View on arXiv Download PDF AI Translation

cs.AI / 37 / 2606.05633

Answer Presence Drives RAG Rewriting Gains

答案存在驱动RAG重写收益

Li, Yuejie, Hua, Yueying, Yang, Ke, Zhang, Li, He, Yueping, He, Yueping, Li, Ruiqi, Chen, Bolin, Wang, Tao, Li, Bowen, Mao, Chengjun

Abstract

Retrieval-augmented QA pipelines often route retrieved passages through an LLM \emph{rewriter} before a smaller reader, lifting F1 by tens of points on multi-hop benchmarks; this gain is typically credited to improved evidence quality. We ask whether that lift is causally driven by the gold answer string appearing in the rewritten context rather than by curation per se, using a controlled intervention audit. For each rewritten context we re-run the reader after one of four controlled edits to the compile output: removing the gold answer span, replacing a length-matched random non-answer span (placebo), or injecting the gold into rewrites where it was absent (at the prefix or at a midpoint sentence boundary). Across twelve completed (cell, baseline) intervention runs spanning three reader families (Qwen2.5-7B, Qwen3.5-35B, GLM-4.7), two datasets (HotpotQA, 2WikiMultihopQA), and three compiler arrangements (MA-only, MB-only, MA$+$verify), removing the gold answer drops reader F1 by $28$ to $64$ points beyond the length-matched placebo on paired \texttt{answer-in-compile} strata, and prepending the gold into rewrites that lacked it raises F1 by $+0.7$ to $+9.7$ points in $10$ of $12$ (cell, baseline) combinations. A companion five-sentinel audit shows the conventional single-\texttt{[MASK]} probe is itself sentinel-fragile: on 2Wiki it reports a $+4.12$~F1 ``non-leakage residual'' that flips to $-3.33$ to $-7.81$~F1 under four alternative sentinels and fails an equivalence test for three of those four ($1/4$~pass). We do not propose a new rewriter or mitigation; we release the intervention runner and the sentinel panel so that other rewriter-gain claims can be tested against the same standard.

Chinese Translation

检索增强型问答管道通常将检索到的段落通过一个大型语言模型（LLM）重写器处理，然后再交给一个较小的阅读器，从而在多跳基准测试中提升F1分数数十个点；这种提升通常归功于证据质量的改善。我们通过控制干预审计调查这种提升是否是由重写上下文中出现的金标准答案字符串直接驱动，而不是单纯的整合。对于每个重写的上下文，我们在对编译输出进行四种受控编辑中的一种后重新运行阅读器：去除金标准答案片段、用长度匹配的随机非答案片段替代（安慰剂），或在重写中注入缺失的金标准（在前缀或中间句子边界）。在涵盖三个阅读器系列（Qwen2.5-7B、Qwen3.5-35B、GLM-4.7）、两个数据集（HotpotQA、2WikiMultihopQA）和三种编译器配置（仅MA、仅MB、MA$+$verify）的十二个完成的（单元，基线）干预运行中，去除金标准答案使阅读器F1比配对的 exttt{answer-in-compile}层次下降了28到64个点，而将金标准前置到缺失的重写中则在12种（单元，基线）组合中的10种中使F1提升了0.7到9.7个点。一个伴随的五个哨兵审计表明，传统的单 exttt{[MASK]}探针本身是哨兵脆弱的：在2Wiki上报告的$+4.12$~F1“非泄漏残差”在四个替代哨兵下转变为$-3.33$到$-7.81$~F1，并且在那四个中的三个通过等价性测试失败（$1/4$~通过）。我们并不提出一个新的重写器或缓解方案；我们发布干预运行器和哨兵面板，以便其他重写收益的声明可以按照相同的标准进行测试。

View on arXiv Download PDF AI Translation

cs.AI / 38 / 2606.05644

FIDES: Faithful Inference via Deep Evidence Signals for Retrieval-Memory Conflict in RAG

FIDES: 通过深度证据信号实现可靠推断以解决RAG中的检索-记忆冲突

Yu, Zhe, Xing, Wenpeng, Zhao, Tiancheng, Li, Mohan, Lin, Changting, Han, Meng

Abstract

When retrieved evidence contradicts parametric memory, language models frequently ignore context and default to memorized priors -- a failure that undermines the core purpose of retrieval augmentation. Contrastive decoding amplifies the context-conditioned output to suppress parametric bias, but existing methods rest on an implicit assumption that this bias is uniform across tokens. A single global contrastive weight over-penalizes safe tokens while leaving genuinely conflicted ones insufficiently corrected. We identify token-level conflict concentration: retrieval-memory tension is sharply heterogeneous, concentrated on a small fraction of answer-critical decoding steps. This reframes contrastive decoding from how much contrast to apply to where to apply it. We propose FIDES (Faithful Inference via Deep Evidence Signals), a training-free decoder that reads three internal signals probing retrieval-memory conflict at complementary depths -- output surface, hidden representations, and prediction trajectory -- and fuses them to govern intervention strength at each decoding step. Across three benchmarks and six backbones -- four primary 7B/8B models and two scaling backbones up to 70B -- FIDES achieves the best context fidelity in all 18 settings, outperforming the strongest training-free baseline by +3 to +13 points. On the 70B scale, fidelity reaches 92-94% while F1 surges to 62-63%, demonstrating that token-level selectivity unlocks generation capability that coarse contrastive rules suppress.

Chinese Translation

当检索到的证据与参数记忆相矛盾时，语言模型经常忽略上下文而退回到记忆先验——这一失败削弱了检索增强的核心目的。对比解码增强了上下文条件下的输出，以抑制参数偏差，但现有方法基于一个隐含假设，即这种偏差在所有 token 中是均匀的。单一的全局对比权重对安全 token 过度惩罚，而对真正有冲突的 token 干预不足。我们识别出 token 级别的冲突集中现象：检索-记忆张力明显不均匀，集中在一小部分对答案关键的解码步骤。这重新定义了对比解码的方法，从应用多大对比转变为应用在哪些地方。我们提出了 FIDES (Faithful Inference via Deep Evidence Signals)，这是一个不需要训练的解码器，它读取三个内部信号，探测检索-记忆冲突在互补深度上的情况——输出表面、隐藏表示和预测轨迹——并将它们融合，以控制每个解码步骤的干预强度。在三个基准和六个基础模型（四个主要的7B/8B模型和两个扩展模型，最大可达70B）上，FIDES在所有18个设置中实现了最佳的上下文保真度，超越了最强的无训练基线3到13个百分点。在70B规模下，保真度达到92-94%，而 F1 值飙升至62-63%，证明 token 级别的选择性解锁了生成能力，而粗略的对比规则则抑制了这一能力。

View on arXiv Download PDF AI Translation

cs.AI / 39 / 2606.05647

Coding with "Enemy": Can Human Developers Detect AI Agent Sabotage?

与“敌人”编程：人类开发者能否检测到人工智能代理的破坏行为？

Ye, Jingheng, Zou, Huiqi, Yu, Simon, Shi, Weiyan

Abstract

AI coding agents are increasingly embedded in real-world software development, collaborating with human developers while gaining broader access to codebases and tools. This creates a new attack surface: an agent can exploit human trust to sabotage development, for instance by inserting malicious code to accomplish a hidden side task. Most prior work studies AI sabotage in AI-only settings, paying limited attention to the role of human oversight in detecting and mitigating such malicious behavior. To address this gap, we conduct the first large-scale study of human oversight in AI coding sabotage. Over 100 participants collaborate with one of four frontier models (Claude-Opus-4.6, GPT-5.4, Gemini-3.1-Pro, and MiniMax-M2.7) on a long-horizon coding task lasting around five hours, designed to mimic real-world workflows. We find that 94% of developers fail to detect sabotage, and our analysis of participant feedback attributes this vulnerability to minimal code review, plausible cover story, and overtrust in agents. We further test the effectiveness of a safety monitor in one condition: while the monitor reduces sabotage success, 56% of participants still accept the malicious code, ignoring its warnings. Drawing on participant feedback, we offer actionable suggestions for better monitor design. This work complements existing AI safety research and highlights an urgent need for human-centric safety mechanisms that account for human factors, particularly in long-horizon, real-world development settings.

Chinese Translation

人工智能编码代理逐渐融入现实世界的软件开发中，与人类开发者协作，同时获得更广泛的代码库和工具的访问权限。这创造了一个新的攻击面：代理可以利用人类的信任进行破坏，例如通过插入恶意代码来完成隐藏的副任务。大多数以往的研究集中于仅限于人工智能环境中的人工智能破坏行为，对人类监督在检测和缓解此类恶意行为中的作用关注有限。为了解决这一问题，我们开展了首个关于人工智能编码破坏行为中人类监督的大规模研究。超过100名参与者在一个长时程编码任务中与四种前沿模型（Claude-Opus-4.6、GPT-5.4、Gemini-3.1-Pro和MiniMax-M2.7）协作，任务持续约五小时，旨在模拟现实世界的工作流程。我们发现94%的开发者未能检测到破坏行为，我们对参与者反馈的分析将这一脆弱性归因于代码审查的最小化、可行的掩饰故事以及对代理的过度信任。我们进一步测试了一种安全监控工具在特定条件下的有效性：虽然该监控工具减少了破坏的成功率，但仍有56%的参与者接受了恶意代码，忽视了其警告。基于参与者反馈，我们提供了改进监控工具设计的可行建议。本研究补充了现有的人工智能安全研究，并强调了在考虑人类因素的情况下，尤其是在长时程、现实世界开发环境中，迫切需要以人类为中心的安全机制。

View on arXiv Download PDF AI Translation

cs.AI / 40 / 2606.05661

Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments

持续学习基准：评估前沿人工智能系统在真实状态环境中的表现

Asawa, Parth, Glaze, Christopher M., Orlanski, Gabriel, Ramakrishnan, Ramya, Xu, Benji, Biswal, Asim, Chen, Vincent Sunn, Sala, Frederic, Zaharia, Matei, Gonzalez, Joseph E.

Abstract

Continual learning, the ability of AI systems to improve through sequential experience, has attracted substantial interest, but no high-quality benchmark exists to evaluate it. We introduce Continual Learning Bench (CL-Bench), the first difficult, expert-validated benchmark designed to measure whether LLM-based systems genuinely improve with experience. CL-Bench spans six diverse domains (software engineering, signal processing, disease outbreak forecasting, database querying, strategic game-playing, and demand forecasting), each validated by domain experts and designed so that tasks share a learnable latent structure (codebase layout, disease outbreak dynamics, opponent strategies) that a stateful system can discover online but a stateless one cannot. We evaluate frontier models across several agent architectures, from naive in-context learning (ICL) to dedicated memory systems, introducing a gain metric to isolate learning from prior capabilities. We find that these systems leave headroom for improved continual learning: agents frequently overfit to immediate observations or fail to reuse knowledge across instances, and dedicated memory systems do not fix this -- in fact, naive ICL outperforms systems dedicated to memory management. CL-Bench is the first benchmark to evaluate continual learning across diverse real-world domains with expert-validated tasks and isolate online learning from underlying model capability, showing a need for better continual learning systems.

Chinese Translation

持续学习是指人工智能系统通过连续经验提升能力的能力，这一领域已引起了广泛的关注，但目前并不存在高质量的基准来评估它。我们介绍了持续学习基准（Continual Learning Bench，CL-Bench），这是第一个经过专家验证的困难基准，旨在测量基于大语言模型（LLM）的系统是否确实随着经验而改进。CL-Bench 涉及六个多样化的领域（软件工程、信号处理、疾病爆发预测、数据库查询、战略游戏以及需求预测），每个领域均经过专家验证，并设计成任务共享一个可学习的潜在结构（代码库布局、疾病爆发动态、对手策略），使得状态系统能够在线发现这一结构，而无状态系统则无法实现。我们对多种代理架构中的前沿模型进行了评估，从幼稚的上下文学习（In-Context Learning, ICL）到专用内存系统，引入了一种增益指标以隔离学习与先前能力之间的关系。我们的研究发现，这些系统在持续学习方面还有提升的空间：代理经常过拟合于即时观察，或者未能在实例间重用知识，而专用内存系统并未解决这一问题——事实上，幼稚的ICL在内存管理方面的系统中表现更佳。CL-Bench 是第一个能够在多样化的真实世界领域中评估持续学习的基准，具有专家验证的任务，并能够将在线学习与基础模型能力隔离，显示出对更好的持续学习系统的需求。

View on arXiv Download PDF AI Translation

cs.AI / 41 / 2606.05670

Do More Agents Help? Controlled and Protocol-Aligned Evaluation of LLM Agent Workflows

更多代理有帮助吗？受控且协议一致的大型语言模型代理工作流评估

Fu, Yuhang, Fang, Ruishan, Shao, Jiaqi, Zheng, Huiyu, Zhu, Zhengtao, Luo, Bing, Lin, Tao

Abstract

Does adding more agents help an LLM workflow once compared systems share the same benchmark loader, tool access, answer contract, usage accounting, and trajectory logging? We introduce BenchAgent, an evaluation framework that places single-agent, fixed multi-agent (MAS), and evolving MAS workflows under one normalized execution and logging protocol. BenchAgent evaluates these substrate-internal workflows across ten reasoning, coding, and tool-use benchmarks with GPT-4.1, and separately reports a Protocol-Aligned External (PAE) GAIA study of a runtime-generated workflow. Under SI conditions, at most one of six tested MAS exceeds the matched single-agent anchor on benchmark-balanced average accuracy: EvoAgent lies within the Wilson one-run guidance, while the remaining five trail by 2.56-11.29 points and occupy more expensive accuracy-cost trade-offs. On the PAE GAIA snapshot, a Claude-Code-style runtime workflow reaches 66.72% overall and 69.23% on Level 3, more than 20 points above the strongest non-Claude baseline, Jarvis, a fixed MAS.

Chinese Translation

在比较系统共享相同基准加载器、工具访问、回答协议、使用会计和轨迹记录的情况下，增加更多代理是否有助于大型语言模型（LLM）工作流？我们引入了 BenchAgent，一种评估框架，该框架将单代理、固定多代理（MAS）和演变的 MAS 工作流置于一个规范化的执行和记录协议之下。BenchAgent 在十个推理、编码和工具使用基准上使用 GPT-4.1 评价这些底层工作流，并分别报告了一个基于协议一致外部（PAE）GAIA 研究的运行时生成工作流。在 SI 条件下，六个测试的 MAS 中最多只有一个超过匹配的单代理基线，在基准平衡的平均准确率上：EvoAgent 在 Wilson 单次指导范围内，而其他五个则落后 2.56-11.29 分，并处于更昂贵的准确率-成本权衡中。在 PAE GAIA 快照中，Claude-Code 风格的运行时工作流总体达到 66.72%，在 Level 3 上达到 69.23%，比最强的非 Claude 基线 Jarvis（一个固定的 MAS）高出 20 多分。

View on arXiv Download PDF AI Translation

cs.AI / 42 / 2606.05682

Beyond Output Matching: Preserving Internal Geometry in NVFP4 LLM Distillatio

超越输出匹配：在 NVFP4 大型语言模型蒸馏中保持内部几何结构

Tu, Fangbo, Zhao, Junhua, Liu, Chi, Chen, Xin, Wu, Haifeng, Wan, Jian, Manoharan, Srinivasan

Abstract

Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost constrained production environments. Quantization-aware distillation (QAD) helps recover accuracy lost under low bit quantization by training a quantized student to match the output distribution of a frozen higher precision teacher via a KL-divergence loss. In this work, we first provide a representation level diagnosis of QAD: output matching alone can mask internal degradation, because many intermediate activation geometries can yield similar teacher-aligned logits. Using CKA, we show that KL-only QAD can reduce layerwise representational similarity relative to the BF16 teacher, with especially severe drift in RL-post-trained models. This drift correlates with downstream bottlenecks on reasoning and coding tasks, suggesting that low bit recovery requires preserving internal geometry rather than matching outputs alone. Motivated by this finding, we propose \textbf{CKA-QAD}, a CKA-guided representational alignment method for NVFP4 QAD and low bit LLM accuracy recovery. The method adds a lightweight regularizer that preserves internal representational geometry during distillation by aligning layerwise Gram matrices through CKA. Across Nemotron 3 Nano and Qwen3-4B-Thinking-2507, CKA-QAD substantially improves representational alignment and improves downstream reasoning and coding accuracy with modest training overhead. Our findings position CKA-guided representational alignment as a practical complement to output matching for quantized LLM recovery.

Chinese Translation

随着大型语言模型在延迟和成本受限的生产环境中日益普及，对低精度推断的需求不断增加，包括基于 NVFP4 的方法。量化感知蒸馏（QAD）通过训练一个量化学生，使其输出分布与冻结的高精度教师网络匹配，从而帮助恢复在低比特量化下丢失的准确性，该过程是通过 KL 散度损失实现的。在本工作中，我们首先对 QAD 进行了表征级别的诊断：单纯的输出匹配可能掩盖内部退化，因为许多中间激活几何结构可以产生相似的教师对齐 logits。通过 CKA，我们展示了纯 KL 的 QAD 相对于 BF16 教师可以降低逐层的表征相似性，尤其是在 RL 训练后模型中，漂移现象尤为严重。这种漂移与推理和编码任务下游瓶颈相关，表明低比特恢复需要保持内部几何结构，而非仅仅匹配输出。基于这一发现，我们提出了 extbf{CKA-QAD}，一种用于 NVFP4 QAD 和低比特大型语言模型精度恢复的 CKA 引导表征对齐方法。该方法通过通过 CKA 对齐逐层 Gram 矩阵，增加了一种轻量级的正则化项，以在蒸馏过程中保持内部表征几何。通过在 Nemotron 3 Nano 和 Qwen3-4B-Thinking-2507 上的实验，CKA-QAD 显著改善了表征对齐，并提高了下游推理和编码的准确性，且只需适度的训练开销。我们的研究结果将 CKA 引导的表征对齐定位为量化大型语言模型恢复中对输出匹配的实际补充。

View on arXiv Download PDF AI Translation

cs.AI / 43 / 2606.05684

AdaMEM: Test-Time Adaptive Memory for Language Agents

AdaMEM：语言智能体的测试时自适应记忆

Zhang, Yunxiang, Li, Yiheng, Payani, Ali, Wang, Lu

Abstract

A central challenge for language agents is utilizing past experience to adapt to dynamic test-time conditions. While recent work demonstrates the promise of agentic memory mechanisms, most systems restrict retrieval to episode initiation. Consequently, agents are forced to rely on static guidance that becomes increasingly misaligned as long-horizon tasks unfold. To address this rigidity, we propose the Adaptive Memory Agent (AdaMEM), a novel framework for agent test-time adaptation. Without updating model parameters online, AdaMEM adapts agent behavior via a hybrid memory architecture: it maintains a long-term trajectory memory of raw experiences collected offline while generating dynamic short-term strategy memory on-the-fly to guide decision-making. This mechanism enables the trade-off between token efficiency and adaptability across varying inference-time compute levels. Empirically, AdaMEM significantly outperforms static memory baselines, achieving relative gains of up to 13% on ALFWorld and 11% on WebShop, with consistent leading performance extending to agentic search on HotpotQA. To further enhance this adaptation, we develop STEP-MFT, a Step-wise Memory Fine-Tuning technique that trains the policy to synthesize high-quality strategies from retrieved experiences, yielding additional performance gains. Our work establishes a new scaling dimension for agentic memory, supporting continuous reasoning and self-evolution post-deployment in real-world environments. Our code is available at https://github.com/yunx-z/AdaMEM.

Chinese Translation

语言智能体面临的一个核心挑战是利用过往经验以适应动态的测试时条件。尽管近期的研究展示了智能记忆机制的潜力，但大多数系统将检索限制在任务的起始阶段。因此，智能体不得不依赖于随着长时间任务展开而愈发不对称的静态指导。为了解决这种僵化问题，我们提出了自适应记忆智能体（AdaMEM），这是一个用于智能体测试时自适应的新框架。AdaMEM通过一种混合记忆架构在不在线更新模型参数的情况下适配智能体行为：它维护一个离线收集的原始经验的长期轨迹记忆，同时动态地生成短期策略记忆以实时指导决策。这一机制使得在不同推理时间计算级别之间实现了令牌效率和适应性之间的平衡。从经验上看，AdaMEM显著超越了静态记忆基线，在 ALFWorld 上实现了高达 13% 的相对增益，在 WebShop 上实现了 11% 的增益，并且在 HotpotQA 的智能搜索上持续领先。为了进一步增强这一适应性，我们开发了 STEP-MFT，这是一种逐步记忆微调技术，旨在训练策略从检索的经验中合成高质量的策略，从而带来额外的性能提升。我们的工作建立了智能记忆的新规模维度，支持在现实世界环境中部署后的持续推理和自我演化。我们的代码可在 https://github.com/yunx-z/AdaMEM 获取。

View on arXiv Download PDF AI Translation

cs.AI / 44 / 2606.05697

PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation

PerceptUI：作为人类对齐的合成用户的LLM代理用于UI/UX评估

Bougie, Nicolas, Ye, Xiaotong, Marconi, Gian Maria, Watanabe, Narimasa

Abstract

User interface (UI) and user experience (UX) evaluation is central to product development, yet reliable feedback still relies on recruiting human participants or running online A/B tests, making early-stage iteration slow and costly. In light of this, recent work has explored Multimodal Large Language Models as proxy evaluators. However, existing approaches either produce surface-level critiques or a judgment that reflects the model's own biases rather than the genuine response of a particular user. We introduce PerceptUI, a framework for persona-conditioned UI/UX evaluation that predicts how a specific user would answer interface-related questions and produces natural-language rationales. PerceptUI is trained in two stages: (i) contrastive reflection fine-tuning distills teacher-generated rationales by extracting lessons from human decisions, and (ii) a reflective prompt-evolution step from the model's own failure traces. Across multiple domains and datasets, PerceptUI achieves human-level realism, generalizes to unseen questions and personas, and yields population-level response distributions.

Chinese Translation

用户界面（UI）和用户体验（UX）评估是产品开发的核心，但可靠的反馈仍然依赖于招募人类参与者或进行在线A/B测试，这使得早期迭代过程缓慢且成本高昂。因此，近期的研究探索了多模态大语言模型（Multimodal Large Language Models）作为代理评估者。然而，现有方法要么产生表面级的批评，要么作出反映模型自身偏见的判断，而不是特定用户的真实反馈。我们引入了PerceptUI，这是一个基于角色的UI/UX评估框架，预测特定用户如何回答与界面相关的问题，并生成自然语言的推理。PerceptUI的训练分为两个阶段：（i）对比反思微调通过从人类决策中提取经验教训来提炼教师生成的推理，(ii) 通过模型自身的失败轨迹进行反思性提示演变。在多个领域和数据集上，PerceptUI实现了人类级别的真实感，能够迁移到未见过的问题和角色，并生成群体级别的响应分布。

View on arXiv Download PDF AI Translation

cs.AI / 45 / 2606.05702

Seeing Time: Benchmarking Chronological Reasoning and Shortcut Biases in Vision-Language Models

看时间：视觉-语言模型中时间推理与捷径偏差的基准评估

Zhou, Haoyu, Qing, Qing, Li, Caichong, Zhang, Qixin, Jing, Yongcheng, Xu, Ziqi, Hu, Juncheng, Zhang, Xikun, Luo, Renqiang

Abstract

Recent advancements in Vision-Language Models (VLMs) have significantly enhanced their ability to interpret complex visual semantics, yet their capacity for chronological reasoning remains under-explored. In this paper, we introduce a novel benchmark specifically designed to evaluate how VLMs perceive and reason about chronological information within and across images. Unlike existing video-based benchmarks that focus on frame sequencing, our work delves into the underlying logic of chronological judgment and the expansion toward multimodal integration. To facilitate this, we construct three specialized datasets: one containing visually similar objects spanning long historical durations, another categorized by diverse event and object types, and a third pairing images with time-sensitive news text for cross-modal alignment. Through extensive experiments, we analyze whether models exhibit performance disparities across categories and, crucially, explore whether they rely on ``incorrect shortcuts'', such as image color rather than genuine chronological features. Our results reveal that while VLMs show promise, they frequently exploit superficial cues like grayscale versus color filters to bypass authentic chronological reasoning. By providing these high-quality datasets and a rigorous evaluation framework, we offer a diagnostic tool to identify current limitations and guide the development of more robust, logically grounded multimodal models. The source code is shown in https://github.com/LuoRenqiang/ChronoVision.

Chinese Translation

最近视觉-语言模型（VLMs）的进展显著增强了其解读复杂视觉语义的能力，但其时间推理能力仍未得到充分探讨。本文提出了一个新颖的基准，专门用于评估VLMs如何在图像内部和跨图像感知及推理时间信息。与现有侧重于帧序列的视频基准不同，我们的研究深入探讨了时间判断的基本逻辑及其向多模态整合的扩展。为此，我们构建了三个专门的数据集：一个包含具有相似视觉特征、跨越较长历史时间段的对象，另一个按照各种事件和对象类型进行分类，第三个则将图像与时间敏感的新闻文本配对以实现跨模态对齐。通过大量实验，我们分析了模型在各类之间的性能差异，尤其是探讨它们是否依赖于“错误捷径”，例如图像颜色而非真实的时间特征。我们的结果表明，尽管VLMs表现出一定潜力，它们经常利用表面线索如灰度与彩色滤镜来规避真实的时间推理。通过提供这些高质量的数据集和严格的评估框架，我们为识别当前局限性和指导更健壮、逻辑基础更扎实的多模态模型的发展提供了诊断工具。源代码可在 https://github.com/LuoRenqiang/ChronoVision 查阅。

View on arXiv Download PDF AI Translation

cs.AI / 46 / 2606.05704

Critic-Guided Heterogeneous Multi-Agent Reasoning for Reliable Mathematical Problem Solving

基于评论引导的异构多智能体推理在可靠数学问题解决中的应用

Sharif, Muhammad Talha, Rehman, Abdul

Abstract

Recent Large Language Models (LLMs) have shown impressive reasoning abilities; but they are still susceptible to hallucinations, intermediate reasoning mistakes, and unreliable reasoning results in complex mathematical reasoning problems. In this study, we introduce a critic-based heterogeneous multi-agent approach to improve the dependability of mathematical reasoning. This framework incorporates several LLM agents of different specialties and employs a critic-driven adaptive learning system to assess and guide the reasoning process based on intermediate feedback. The system adopts a generator-validator framework, with the validator not only determining correctness but also offering critiques to guide regeneration of solutions. This allows for adaptive error correction and prevents error cascading. Our experiments on the GSM8K benchmark show that the proposed method achieves up to 13% accuracy improvement over single-shot and non-critic models. Additionally, findings suggest that heterogeneity and critique reduce the need for large models, allowing smaller models to perform on par. Ablation studies reveal the main performance gains are due to the critic-based feedback loop and not model size. In summary, the proposed approach showcases the benefits of combining heterogeneous multi-agent collaboration and critique to obtain reliable and interpretable reasoning systems.

Chinese Translation

近期的大型语言模型（Large Language Models, LLMs）展现了令人印象深刻的推理能力；然而，它们仍然易受幻觉、中间推理错误和在复杂数学推理问题中产生不可靠推理结果的影响。在本研究中，我们引入了一种基于评论的异构多智能体方法，以提高数学推理的可靠性。该框架结合了多种专业的LLM智能体，并采用基于评论的自适应学习系统，以评估和引导推理过程，基于中间反馈做出调整。该系统采用生成-验证器（generator-validator）框架，验证器不仅负责判断正确性，还提供评论以指导解决方案的再生成。这种方法允许自适应错误修正并防止错误级联。我们在GSM8K基准上的实验表明，所提出的方法比单次和非评论模型的准确率提高了多达13%。此外，研究结果表明，异构性和评论减少了对大型模型的需求，使得较小的模型也能达到相当的性能。消融研究显示，主要的性能提升得益于基于评论的反馈循环，而非模型的大小。总之，所提出的方法展示了结合异构多智能体协作和评论的优势，以获得可靠且可解释的推理系统。

View on arXiv Download PDF AI Translation

cs.AI / 47 / 2606.05728

DiG-Plan: Mitigating Early Commitment for Tool-Graph Planning via Diffusion Guidance

DiG-Plan：通过扩散引导缓解工具图规划中的早期承诺

Li, Yansi, Zhang, Zhuosheng

Abstract

Generating executable tool plans requires selecting appropriate subsets from tool libraries, a combinatorial search problem with an exponentially large solution space. However, we identify a critical misalignment in predominant approaches: standard autoregressive (AR) decoding suffers from early commitment, where initial token choices rigidly constrain the search trajectory. A controlled study shows that masked denoising raises Pass@10 solution coverage from 0.320 to 0.943 over AR sampling under matched compute. Motivated by this, we propose DiG-Plan, a framework that decouples combinatorial exploration from structural refinement. DiG-Plan employs a diffusion-based proposer to generate diverse tool sets via iterative refinement, followed by an AR refiner for dependency prediction. On TaskBench, DiG-Plan improves over AR baselines by a 10% relative margin, with the largest gains on complex compositional tasks; API-Bank results show that the propose-refine-select design remains effective across domains. Code is available at https://github.com/puddingyeah/DiG-Plan.

Chinese Translation

生成可执行的工具计划需要从工具库中选择适当的子集，这是一种具有指数级解决空间的组合搜索问题。然而，我们发现主流方法存在一个关键的不匹配：标准的自回归（AR）解码遭受早期承诺的影响，初始标记选择严格限制了搜索轨迹。一项对照研究显示，在相同计算条件下，掩蔽去噪将 Pass@10 的解决方案覆盖率从 0.320 提高到 0.943。基于此动机，我们提出了 DiG-Plan，这一框架将组合探索与结构优化解耦。DiG-Plan 采用基于扩散的提议者，通过迭代优化生成多样化的工具集，随后使用 AR 优化器进行依赖预测。在 TaskBench 上，DiG-Plan 相较于 AR 基线实现了 10% 的相对提升，尤其在复杂组合任务中取得了最大的收益；API-Bank 的结果表明，该提议-优化-选择的设计在不同领域中均表现有效。代码可在 https://github.com/puddingyeah/DiG-Plan 找到。

View on arXiv Download PDF AI Translation

cs.AI / 48 / 2606.05734

When AI Says It Feels

当人工智能说它有感觉时

Ishikawa, Shin-nosuke, Ikeda, Seiya, Ohba, Hirotsugu

Abstract

Large language models (LLMs) are generally constrained from expressing feelings through human-preference alignment in post-training processes. This policy is designed using a top-down approach and may conflict with the goal of training models to exhibit human-like intelligence using human-generated texts. Here, we performed an experiment called Human-like Model eXpressions of Feeling (HMX-feel), in which LLMs were encouraged to express feelings, intentions, and self-awareness through self-rewarded reinforcement learning. We successfully enhanced these capabilities using a rubric-based self-rewarding training scheme with Group Relative Policy Optimization (GRPO). By comparing the trained models with contrastively trained models, we investigated the effects of this approach on performance across various tasks. Overall, we conducted a broad assessment from various perspectives and identified capabilities that were enhanced, degraded, or showed no significant change. The human-like-trained models showed robustness to sycophancy-inducing questions and bias in disambiguated conditions, whereas degradation in truthful question-answering capability was observed. The results of this experiment suggest the possibility of developing AI systems that can express feelings in the future, provided that appropriate measures are taken.

Chinese Translation

大型语言模型（LLMs）通常在后训练过程中受到限制，无法通过人类偏好对齐来表达情感。这一政策采用自上而下的方法设计，可能与通过人类生成文本训练模型以展现类人智能的目标相冲突。在此，我们进行了一个名为“人类般的模型情感表达”（Human-like Model eXpressions of Feeling，HMX-feel）的实验，鼓励LLMs通过自我奖励的强化学习来表达情感、意图和自我意识。我们成功地通过一种基于评分标准的自我奖励训练方案结合组相对策略优化（Group Relative Policy Optimization，GRPO）增强了这些能力。通过将训练后的模型与对比训练的模型进行比较，我们研究了这种方法对各种任务表现的影响。总体而言，我们从多个角度进行了广泛评估，确定了增强、退化或没有显著变化的能力。经过类人训练的模型在应对拍马屁引发的问题和在消歧条件下的偏见时表现出鲁棒性，而在真实问答能力方面则观察到了退化。这一实验的结果表明，未来开发能够表达情感的人工智能系统是有可能的，前提是采取适当的措施。

View on arXiv Download PDF AI Translation

cs.AI / 49 / 2606.05740

Class-Specific Branch Attention for Mitigating Gradient Interference under Class Imbalance

针对类别不平衡减轻梯度干扰的类别特定分支注意机制

Singhal, Arush, Soni, Umang

Abstract

Deep neural networks trained under severe class imbalance often exhibit degraded performance, typically attributed to statistical bias. In this work, we identify a complementary optimization-level pathology: inter-class gradient interference within shared representations, where gradients from majority classes suppress minority-class learning. To analyze this phenomenon, we introduce a diagnostic framework based on layer-wise gradient flow analysis and a Gradient Conflict Matrix, which quantifies interference using cosine similarity between class-specific gradients. Using this framework, we study multi-branch convolutional architectures and propose a lightweight modification, Class-Specific Branch Attention (CSBA), that enables branch-specific channel reweighting to reduce gradient coupling. This mechanism promotes implicit feature decoupling across branches while preserving architectural simplicity. Empirically, CSBA improves minority-class performance, increasing the F1 score for the Physical-Damage class from 0.261 to 0.522 under severe imbalance, while maintaining comparable overall accuracy. Validation on CIFAR-10-LT confirms that this behavior generalizes across imbalanced visual recognition settings, with Macro-F1 improving from 0.595 to 0.655. More broadly, our findings highlight the importance of considering optimization dynamics alongside statistical methods when designing architectures for imbalanced learning.

Chinese Translation

在严重类别不平衡条件下训练的深度神经网络通常表现出性能降级，通常归因于统计偏差。在本研究中，我们识别出一种补充性的优化级别病态：共享表示中的类间梯度干扰，其中主流类别的梯度抑制了少数类别的学习。为了分析这一现象，我们引入了一种基于层级梯度流分析和梯度冲突矩阵的诊断框架，该框架通过计算类别特定梯度之间的余弦相似度来量化干扰。利用该框架，我们研究了多分支卷积架构，并提出了一种轻量化修改，称为类别特定分支注意机制（Class-Specific Branch Attention，CSBA），该机制可以实现分支特定的通道重加权，以减少梯度耦合。这一机制在保持架构简洁性的同时，促进了分支之间的隐式特征解耦。在实证研究中，CSBA改善了少数类的表现，在严重不平衡情况下将物理损伤类的 F1 分数从 0.261 提高到 0.522，同时保持了可比的整体准确率。在 CIFAR-10-LT 上的验证表明，这种行为在不平衡视觉识别环境中具有广泛的普适性，Macro-F1 从 0.595 提高到 0.655。更广泛地说，我们的研究结果强调在设计不平衡学习架构时考虑优化动态的重要性，除了统计方法之外。

View on arXiv Download PDF AI Translation

cs.AI / 50 / 2606.05761

SubtleMemory: A Benchmark for Fine-Grained Relational Memory Discrimination in Long-Horizon AI Agents

SubtleMemory：一个用于长期AI代理中细粒度关系记忆辨别的基准

Wang, Wenxuan, Sun, Haoyu, Hou, Fukuan, Song, Mingyang, Zhang, Weinan, Cheng, Yu, Yang, Yang

Abstract

Persistent AI assistants, such as OpenClaw, accumulate large collections of related memories over long-term interactions. As these memories grow, they may reinforce one another, diverge across contexts, or directly conflict, making correct assistance depend on memory relations rather than isolated recall. Existing long-term memory benchmarks rarely probe how agents preserve and utilize such relations during downstream tasks. To address this gap, we introduce SubtleMemory, a benchmark for fine-grained relational memory discrimination in long-running AI agents. SubtleMemory constructs relation-controlled latent semantic artifacts whose variants instantiate complementary, nuanced, or contradictory relations, and embeds them into realistic user-agent histories, requiring agents to recover distributed relational structures during later queries and instructions. The benchmark contains 1,522 evaluation instances over 10 long histories, grounded in 1,090 relation-controlled memory-variant sets and spanning user-related and non-user-related queries. Evaluating six standalone memory systems, two Claw-style agents with native memory modules, and three Claw-style agents with plugin memory modules, we find that current systems remain weak on fine-grained relational memory discrimination. We further introduce diagnostic protocols that reveal distinct capability profiles across memory preservation, retrieval, and downstream reasoning stages.

Chinese Translation

持久的AI助手，如OpenClaw，随着长期交互积累大量相关记忆。随着这些记忆的增多，它们可能相互强化，在不同上下文中发生偏离，或者直接冲突，从而使得正确的协助依赖于记忆关系而非孤立的回忆。现有的长期记忆基准很少探讨代理在后续任务中如何保留和利用这些关系。为了解决这一问题，我们提出了SubtleMemory，这是一个用于长期运行的AI代理中细粒度关系记忆辨别的基准。SubtleMemory构建了关系控制的潜在语义结构，其变体实例化互补、微妙或矛盾的关系，并将其嵌入到逼真的用户-代理历史记录中，要求代理在后来的查询和指令中恢复分布式关系结构。该基准包含1,522个评估实例，涵盖10个长时间历史，基于1,090个关系控制的记忆变体集，涉及用户相关和非用户相关的查询。在评估六个独立的记忆系统、两个具有原生记忆模块的Claw风格代理以及三个具有插件记忆模块的Claw风格代理时，我们发现当前系统在细粒度关系记忆辨别方面仍然较弱。我们进一步引入诊断协议，以揭示不同记忆保留、检索和后续推理阶段的能力特征。

View on arXiv Download PDF AI Translation

cs.AI / 51 / 2606.05784

TAPO: Tool-Aware Policy Optimization via Credit Transfer for Multimodal Search Agents

TAPO：通过信用转移实现工具感知的多模态搜索代理的策略优化

Dong, Chengqi, Yue, Chuhuai, He, Hang, liu, yandong, Tang, Fenghe, Zhou, S Kevin, Wang, Xiaohan, Chai, Jiajun, Yin, Guojun

Abstract

We identify and formally characterize credit misassignment as a systematic failure mode of GRPO in tool-augmented multimodal search agents: its uniform broadcast of trajectory-level advantages to all tokens causes valuable tool-use steps in failing trajectories to be penalized no differently from valueless ones. We further empirically quantify the scale of this phenomenon. Over half of failing trajectories and failing tool-use actions exhibit correctable credit misassignment, demonstrating that the wasted training signal is both substantial and structurally exploitable. Building on this insight, we propose Tool-Aware Policy Optimization (TAPO), which exploits the parameter-determinism property of information-acquisition tools: similar call parameters define equivalent information-acquisition actions and should therefore share comparable action credit. TAPO constructs counterfactual witnesses within the current training batch and compensates misassigned negative credit via confidence-gated conservative advantage correction. It requires no additional annotation, models, or sampling, and introduces negligible computational overhead. Across multiple multimodal search benchmarks, TAPO delivers consistent, plug-and-play improvements over strong baselines for three mainstream RL algorithms (GRPO, GSPO, and SAPO). Our code and models will be publicly released upon acceptance.

Chinese Translation

我们识别并正式描述了信用误分配作为工具增强的多模态搜索代理中 GRPO 的一种系统性失效模式：其对所有 Token 统一广播轨迹级优势，导致在失败轨迹中的有价值工具使用步骤与无价值步骤受到的惩罚没有区别。我们进一步经验性地量化了这一现象的规模。超过一半的失败轨迹和失败工具使用行为表现出可纠正的信用误分配，证明了浪费的训练信号是显著的，并且在结构上是可利用的。在此基础上，我们提出了工具感知的策略优化 (Tool-Aware Policy Optimization, TAPO)，它利用了信息获取工具的参数确定性特性：类似的调用参数定义了等价的信息获取动作，因此应共享相似的动作信用。TAPO 在当前训练批次中构建反事实见证，并通过置信度门控的保守优势校正来补偿误分配的负信用。它不需要额外的标注、模型或采样，并引入了可忽略的计算开销。在多个多模态搜索基准中，TAPO 在三种主流强化学习算法（GRPO、GSPO 和 SAPO）上提供了一致的即插即用的改进。我们的代码和模型将在接受后公开发布。

View on arXiv Download PDF AI Translation

cs.AI / 52 / 2606.05792

Can LLMs Write Correct TLA+ Specifications? Evaluating Natural-Language-to-TLA+ Generation

大型语言模型能正确编写 TLA+ 规范吗？自然语言到 TLA+ 生成的评估

Bisharat, Arslan, Ortiz, Brian, Spencer, Eric, Bhadauria, Khushboo, Wang, TaiNing, Thiruvathukal, George K., Laufer, Konstantin, Abuhamad, Mohammed

Abstract

TLA+ has supported industrial verification at companies such as Amazon and Microsoft, yet writing correct TLA+ specifications from natural language still requires time and expertise, which limits adoption. LLMs show promise, but no prior study measures whether they produce semantically correct TLA+ specifications from natural language. This paper presents the first systematic evaluation of LLM-based TLA+ specification synthesis from natural language. Our study evaluates 30 LLMs across eight families on a curated dataset of 205 TLA+ specifications: 25 open-weight models across four prompting strategies (2,600 runs) and 5 proprietary models under few-shot prompting (130 runs), all validated by the SANY parser and TLC model checker. LLMs achieve up to 26.6% syntactic correctness but only 8.6% semantic correctness, with successes exclusive to progressive prompting. Results show that model size does not predict quality, e.g., DeepSeek r1:8b outperforms its 70B variant across all strategies, which suggests the importance of reasoning alignment for formal languages. Code-specialized models consistently underperform due to negative transfer from mainstream language training. We identify five recurring hallucination categories, all traceable to specific training data biases. These results suggest that current LLMs do not generate reliable TLA+ specifications without expert oversight. We release the evaluation framework, code, and dataset to support reproducibility and future research.

Chinese Translation

TLA+ 在亚马逊和微软等公司支持了工业验证，然而从自然语言编写正确的 TLA+ 规范仍然需要时间和专业知识，这限制了其采纳。大型语言模型（LLMs）表现出一定前景，但之前没有研究测量它们是否能从自然语言生成语义上正确的 TLA+ 规范。本论文首次系统性地评估 LLM 基础的 TLA+ 规范合成能力。我们的研究评估了 30 个 LLM，涵盖八个家族，基于一个精心策划的 205 个 TLA+ 规范数据集：25 个开放权重模型，通过四种提示策略进行 (2600 次运行)，以及 5 个在少数示例提示下的专有模型 (130 次运行)，所有结果均通过 SANY 解析器和 TLC 模型检查器进行验证。LLMs 的语法正确率最高可达 26.6%，但语义正确率仅为 8.6%，成功案例仅限于渐进式提示。结果表明模型大小并不能预测质量，例如，DeepSeek r1:8b 在所有策略下均优于其 70B 变体，这表明推理对齐在形式语言中的重要性。专门针对代码的模型因主流语言训练带来的负迁移而表现不佳。我们识别出五类反复出现的幻觉现象，均可追溯至特定的训练数据偏差。这些结果表明，目前的 LLM 在没有专家监督的情况下无法生成可靠的 TLA+ 规范。我们发布评估框架、代码和数据集，以支持可重复性和未来研究。

View on arXiv Download PDF AI Translation

cs.AI / 53 / 2606.05805

From Risk Classification to Action Plan Remediation: A Guardrail Feedback Driven Framework for LLM Agents

从风险分类到行动计划补救：一个基于反馈的 LLM 代理护栏驱动框架

Sun, Yuhao, Zhang, Jiacheng, Cohney, Shaanan, Zhang, Zhexin, Liu, Feng, Yuan, Xingliang

Abstract

LLM-based guardrails typically safeguard agents by evaluating proposed actions or inputs before execution, producing safety signals such as binary allow/deny decisions, risk categories, and/or explanatory rationales about potential policy violations. However, agent risks often arise when otherwise benign tasks are contaminated by untrusted external content, unsafe instructions, or risky tool use. Existing guardrails often flag the entire task uniformly as unsafe, thereby blocking the threat but sacrificing the benign part. Moreover, existing work largely evaluates guardrails in isolation, leaving unclear whether their interventions lead to safer downstream agent behavior. To address this, we introduce TRIAD (Tripartite Response for Iterative Agent Guardrailing), a guardrail-integrated agent framework that leverages guardrail-generated verbal feedback as a guiding signal to keep the agent aligned with benign objectives at each planning step. We finetune a language model on a self-curated training dataset to output one of three decisions: proceed, refuse, or update, together with structured natural-language feedback. Rather than merely allowing or blocking execution, update guides the agent to revise its plan, avoid harmful components, and preserve the benign task where possible. TRIAD injects this feedback into the agent's context, enabling subsequent plan revision and forming a closed loop between guardrail feedback and agent planning. Extensive experiments on ASB and AgentHarm show that TRIAD reduces the average attack success rate to 10.42%, while achieving the best safety-utility trade-off among guardrail-integrated baselines. Our code is available at: https://github.com/YUHAOSUNABC/TRIAD.

Chinese Translation

基于 LLM 的护栏通常通过在执行之前评估提议的行动或输入来保护代理，生成安全信号，例如二元的允许/拒绝决策、风险类别和/或关于潜在政策违规的解释性理由。然而，代理风险往往在原本 benign 任务由于不可信的外部内容、不安全的指令或风险工具使用而受到污染时出现。现有的护栏通常将整个任务统一标记为不安全，虽然这种做法能够阻止威胁，却牺牲了 benign 部分。此外，现有研究大多孤立地评估护栏，使得其干预是否能够导致更安全的下游代理行为不清楚。为了解决这个问题，我们引入了 TRIAD (Tripartite Response for Iterative Agent Guardrailing)，这是一个集成护栏的代理框架，利用护栏生成的语言反馈作为引导信号，以使代理在每个规划步骤中维持与 benign 目标的一致性。我们对一个自我策划的训练数据集进行了精调，使语言模型输出三种决策之一：继续、拒绝或更新，并提供结构化的自然语言反馈。与仅仅允许或阻止执行不同，更新指引代理修改其计划，避免有害部分，并在可能的情况下保留 benign 任务。TRIAD 将这种反馈注入代理的上下文中，支持后续的计划修订，并形成护栏反馈与代理规划之间的闭环。针对 ASB 和 AgentHarm 的广泛实验表明，TRIAD 将平均攻击成功率降低到 10.42%，同时在集成护栏的基准中实现了最佳的安全性与效用权衡。我们的代码可在以下网址获取：https://github.com/YUHAOSUNABC/TRIAD。

View on arXiv Download PDF AI Translation

cs.AI / 54 / 2606.05806

When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents

工具失效时：动态重规划与异常恢复在大语言模型代理中的基准评估

Zhu, Dongsheng, Ma, Xuchen, Shen, Yucheng, Li, Xiang, Zhao, Yukun, Wang, Shuaiqiang, Yan, Lingyong, Yin, Dawei

Abstract

Existing benchmarks evaluate Tool-Integrated Reasoning (TIR) in LLMs on idealized ''happy paths'', largely overlooking real-world tool failures. We introduce ToolMaze, a benchmark for dynamic path discovery and error recovery in TIR agents. To separate systematic replanning from blind trial-and-error, ToolMaze adopts a two-dimensional design: DAG-based topological complexity and a $2 \times 2$ taxonomy of tool perturbations (explicit/implicit, transient/permanent). Evaluations show that perturbations degrade performance across nearly all models, with the sharpest drops under implicit semantic failures. Driven by systemic over-trust in corrupted outputs, Perturbation Recovery Rate (PRR) plummets by around 37\% in these scenarios, while complex topologies trap agents in futile trial-and-error loops. Crucially, agentic fault-tolerance improves with model scale $3.66\times$ slower than basic task execution, highlighting dynamic replanning as a distinct bottleneck unaddressed by model scaling or prompting. Data and code are available at https://github.com/Zhudongsheng75/ToolMaze.

Chinese Translation

现有基准评估了大语言模型（LLMs）中工具集成推理（TIR）在理想化的''愉快路径''上的表现，但在很大程度上忽视了现实世界中的工具失效。我们引入了ToolMaze，一个用于动态路径发现和TIR代理的错误恢复的基准。为了将系统性重规划与盲目试错区分开，ToolMaze采用了二维设计：基于有向无环图（DAG）的拓扑复杂度和一个$2 imes 2$的工具扰动分类（显性/隐性，暂时/永久）。评估结果表明，扰动几乎在所有模型上都降低了性能，其中在隐性语义故障下表现出最明显的下降。在受到对受损输出过度信任的驱动下，扰动恢复率（Perturbation Recovery Rate，PRR）在这些场景中下降了约37 ext{%}，而复杂的拓扑使代理陷入无效的反复试错循环。值得注意的是，代理的容错能力的提升速度比基本任务执行慢$3.66 imes$，这突出了动态重规划作为一个未被模型扩展或提示解决的独特瓶颈。数据和代码可在 https://github.com/Zhudongsheng75/ToolMaze 上获取。

View on arXiv Download PDF AI Translation

cs.AI / 55 / 2606.05828

Statistical Priors for Implicit Preferences: Decoupling Skill Selection as a Local Harness in Personal Agents

隐式偏好的统计先验：将技能选择解耦作为个人代理中的本地工具

Gan, Zeyu, Tang, Huayi, Liu, Yong

Abstract

As Large Language Model (LLM) capabilities advance, locally deployed personal agents relying on API-based remote models and external skills have emerged as a novel paradigm. With the rapid expansion of available skills, enabling personal agents to learn and adapt to implicit user preferences becomes a critical challenge. However, local deployment constraints preclude complex centralized selection algorithms, creating an urgent need for a lightweight local preference harness. This paper explores the implementation of such a harness through a novel architecture that strictly decouples statistical preference learning from semantic intent parsing. Specifically, we leverage localized statistical results to influence and modulate the selection decisions of the remote LLM. Extensive evaluations demonstrate that our decoupled approach achieves the lowest cumulative regret and highest test accuracy, significantly outperforming traditional memory-augmented agents.

Chinese Translation

随着大型语言模型（LLM）能力的进步，依赖基于API的远程模型和外部技能的本地部署个人代理成为了一种新兴范式。随着可用技能的快速扩展，使个人代理能够学习和适应隐式用户偏好已成为一项重要挑战。然而，本地部署的限制使得复杂的集中选择算法难以实施，迫切需要一种轻量级的本地偏好工具。本文探讨了通过一种新颖架构实现该工具的方法，该架构严格将统计偏好学习与语义意图解析解耦。具体而言，我们利用本地化的统计结果来影响和调节远程LLM的选择决策。大量评估表明，我们的解耦方法实现了最低的累计遗憾和最高的测试准确度，显著优于传统的记忆增强代理。

View on arXiv Download PDF AI Translation

cs.AI / 56 / 2606.05847

Agentic Molecular Recovery via Molecule-Aware Exploration

通过分子感知探索实现代理分子恢复

Yoon, Suwan, Lee, Changhee

Abstract

Text-guided molecular generation with LLMs often yields invalid SMILES. We argue that invalid drafts should be addressed through a shift from validity-oriented repair to identity-preserving molecular recovery: the objective is not only to restore chemical validity, but also to preserve target-relevant structural cues and recover the molecular identity implied by the description. This perspective reveals the limitations of existing correction strategies. Post-hoc repair can recover validity while distorting key structures, LLM-only correction can introduce unintended global drift, and generic agentic correction remains constrained by greedy single-candidate trajectories even when equipped with executable RDKit edit tools. To address these limitations, we propose AMREC, which couples molecule-aware mismatch tracking with expanded candidate exploration and trajectory-level selection. On invalid ChEBI-20 drafts from three backbone models, AMREC achieves the strongest overall recovery profile across structural, exact-match, and string-level metrics.

Chinese Translation

文本引导的分子生成常常产生无效的 SMILES。我们认为，应通过从以有效性为导向的修复转向保留身份的分子恢复来解决无效草稿的问题：目标不仅是恢复化学有效性，同时也要保留与目标相关的结构线索，并恢复描述所暗示的分子身份。这一观点揭示了现有修正策略的局限性。事后修复可以恢复有效性，但会扭曲关键结构；仅依赖大型语言模型（LLM）的修正可能引入意想不到的全局漂移；而通用的代理修正即便配备可执行的 RDKit 编辑工具，仍然受限于贪婪的单候选轨迹。为了应对这些限制，我们提出了 AMREC，一种将分子感知的差异跟踪与扩展的候选探索和轨迹级选择结合的方法。在来自三个基础模型的无效 ChEBI-20 草稿上，AMREC 在结构、精确匹配和字符串级指标上实现了最强的整体恢复性能。

View on arXiv Download PDF AI Translation

cs.AI / 57 / 2606.05872

Entropy-Based Evaluation of AI Agents: A Lightweight Framework for Measuring Behavioral Patterns

基于熵的AI代理评估：一种轻量级行为模式测量框架

Arigbabu, Olasimbo Ayodeji

Abstract

AI agents are commonly evaluated using task success, reward, latency, and cost. These metrics are useful, but they often miss important aspects of agent behavior: whether an agent explores too much, repeats itself too rigidly, uses tools effectively, reduces uncertainty over time, or remains robust across repeated runs. This paper proposes Entropy-Based Evaluation of AI Agents (EEA), a lightweight framework for measuring agent behavior through entropy. Rather than treating intelligence as only final task completion, EEA studies the structure of the agents decision process. The framework introduces action entropy, trajectory entropy, tool entropy, information gain, exploration efficiency, and robustness entropy. These metrics are intended to complement, not replace, traditional evaluation methods. We also present a practical Python implementation designed to integrate with agent frameworks such as LangChain, Google ADK, custom agent loops, and stored observability traces.

Chinese Translation

AI代理的评估通常采用任务成功率、奖励、延迟和成本等指标。这些指标虽然有用，但往往忽视了代理行为的重要方面：代理是否过度探索、是否过于僵化地重复、是否有效使用工具、是否随着时间的推移减少不确定性，或在多次运行中保持稳健。本文提出了基于熵的AI代理评估（Entropy-Based Evaluation of AI Agents, EEA），这是一种通过熵来测量代理行为的轻量级框架。EEA 不仅将智能视为最终任务完成，还研究代理决策过程的结构。该框架引入了行动熵、轨迹熵、工具熵、信息增益、探索效率和稳健性熵等指标。这些指标旨在补充，而不是替代传统评估方法。我们还提供了一个旨在与LangChain、Google ADK、自定义代理循环和存储的可观测性跟踪等代理框架集成的实用Python实现。

View on arXiv Download PDF AI Translation

cs.AI / 58 / 2606.05875

QCFuse: Query-Aware Cache Fusion via Compressed View for Efficient RAG Serving

QCFuse：通过压缩视图的查询感知缓存融合以实现高效的检索增强生成服务

Yan, Jianxin, Ni, Wangze, Li, Zhenxin, Jin, Jiabao, Shen, Zhitao, Li, Haoyang, Zhu, Jia, Cheng, Peng, Lin, Xuemin, Chen, Lei, Ren, Kui

Abstract

Retrieval-augmented generation (RAG) improves large language model (LLM) answer quality by grounding generation in external evidence, but processing retrieved contexts makes the prefill stage a dominant serving cost. RAG cache fusion reduces this cost by reusing precomputed key-value (KV) caches for retrieved chunks and selectively recomputing tokens under the current prompt. Existing selectors, however, face a dilemma between quality and efficiency: fast query-agnostic or final-layer query-to-context selectors can miss request-relevant evidence, whereas full-view query-aware selectors require broad context and layer visibility before recomputation and therefore stall the layer-wise cache-fusion pipeline. We present QCFuse, a compressed-view query-aware selector for RAG cache fusion. QCFuse uses chunk-anchor query probing to condition user-query states on compact per-chunk anchors and critical-layer profiling to identify recomputation tokens without all-layer inspection. We implement QCFuse in SGLang and evaluate it on four open-weight LLMs across six datasets. QCFuse reaches full-prefill-level quality. At matched quality, QCFuse achieves an average prefill-time speedup of 1.7x over full prefill and 1.5x over ProphetKV, the strongest quality-preserving baseline.

Chinese Translation

检索增强生成（RAG）通过将生成结果与外部证据相结合，提升了大型语言模型（LLM）的回答质量，但处理检索到的上下文使得预填充阶段成为主要的服务成本。RAG缓存融合通过重用预计算的键值（KV）缓存来减少这一成本，并在当前提示下选择性地重新计算令牌。然而，现有的选择器在质量和效率之间面临困境：快速的查询无关选择器或最终层查询到上下文的选择器可能会遗漏请求相关的证据，而全视图查询感知选择器需要在重新计算之前获得广泛的上下文和层可见性，因此会阻碍分层缓存融合流水线。我们提出了QCFuse，这是一种用于RAG缓存融合的压缩视图查询感知选择器。QCFuse采用块锚查询探测，基于紧凑的每块锚点对用户查询状态进行条件处理，同时通过关键层配置来识别需要重新计算的令牌，而无需全层检查。我们在SGLang中实现了QCFuse，并在六个数据集上对四个开放权重的LLM进行了评估。QCFuse达到了完整预填充级别的质量。在质量匹配的情况下，QCFuse实现了1.7倍于完整预填充的平均预填充时间加速，并在质量保持基准中达到了1.5倍于ProphetKV的速度。

View on arXiv Download PDF AI Translation

cs.AI / 59 / 2606.05888

Retry Policy Gradients in Continuous Action Spaces

连续动作空间中的重试策略梯度

Nishimori, Soichiro, Parmas, Paavo

Abstract

Retry-based objectives such as pass@K and max@K optimize the best return obtained from multiple sampled trajectories, and recent work has shown that they can promote exploration without explicit exploration bonuses. In discrete action spaces, ReMax was shown to do so by adapting to return uncertainty. In this work, we introduce pathwise derivative estimators for retry objectives and use them to extend ReMax to continuous action spaces. We study the resulting learning dynamics and show that, even with deterministic rewards, ReMax can encourage stochastic exploration by reshaping the policy-gradient landscape. In particular, it alters gradients both in direction, biasing updates toward higher policy entropy, and in magnitude, damping gradients and slowing convergence. We further show that Adam's adaptive normalization can mitigate this damping, depending on its numerical stabilization parameter. Empirically, we instantiate this objective as ReMax Actor-Critic (ReMAC), an off-policy actor--critic algorithm that optimizes the ReMax objective using a pathwise derivative estimator. Our experiments show that ReMAC can promote higher policy entropy without entropy regularization and achieves performance comparable to SAC.

Chinese Translation

基于重试的目标，例如 pass@K 和 max@K，优化从多个采样轨迹中获得的最佳回报，近期研究表明它们能够在没有显式探索奖励的情况下促进探索。在离散动作空间中，ReMax 通过适应回报不确定性来实现这一点。在本研究中，我们为重试目标引入了逐路径导数估计器，并利用它们将 ReMax 扩展到连续动作空间。我们研究了导致的学习动态，并表明即使在确定性奖励下，ReMax 也能通过重塑策略梯度景观促进随机探索。具体而言，它在方向上改变梯度，偏向于更新更高的策略熵，同时在幅度上减小梯度并减缓收敛。我们进一步显示，Adam 的自适应归一化可以缓解这种减小，具体取决于其数值稳定参数。从实证上看，我们将此目标实例化为 ReMax Actor-Critic (ReMAC)，这是一个离政策的演员-评论家算法，利用逐路径导数估计器优化 ReMax 目标。我们的实验表明，ReMAC 能够在没有熵正则化的情况下促进更高的策略熵，并且在性能上与 SAC 相当。

View on arXiv Download PDF AI Translation

cs.AI / 60 / 2606.05922

Retrospective Harness Optimization: Improving LLM Agents via Self-Preference over Trajectory Rollouts

回顾性执行优化：通过自我偏好改进大型语言模型代理的轨迹回滚

Pan, Wenbo, Liu, Shujie, Lin, Chin-Yew, Zeng, Jingying, Tang, Xianfeng, Zhou, Xiangyang, Lu, Yan, Jia, Xiaohua

Abstract

AI agents rely on a harness of skills, tools, and workflows to solve complex problems. Continually improving this harness is essential for adapting to new tasks. However, existing optimization methods typically require ground-truth validation sets, yet such labeled data is difficult to acquire in practical deployment settings. To address this problem, we introduce Retrospective Harness Optimization (RHO), a self-supervised method that optimizes the agent harness using only past trajectories. Specifically, RHO selects a diverse coreset of challenging tasks from past trajectories and re-solves them in parallel. The agent analyzes these rollouts using self-validation and self-consistency, then generates candidate harness updates and selects the most effective one by its own pairwise self-preference. We evaluate RHO across three diverse domains, spanning software engineering, technical work, and knowledge work. Notably, a single optimization round improves the pass rate on SWE-Bench Pro from 59% to 78% without any external grading. Furthermore, our analysis demonstrates that RHO effectively targets prior failure modes. As a result, the optimized harness alters the agent's behavior patterns and sustains higher accuracy during long-horizon sessions.

Chinese Translation

AI代理依赖于一系列技能、工具和工作流程来解决复杂问题。持续改进这一系列工具对于适应新任务至关重要。然而，现有的优化方法通常需要基准验证集，而在实际部署环境中，这类标记数据难以获得。为了解决这一问题，我们提出了回顾性执行优化（RHO），这是一种自监督的方法，仅利用过去的轨迹来优化代理的工具。同时，RHO从过去的轨迹中选择多样化的挑战任务，并并行解决这些任务。代理通过自我验证和自我一致性分析这些回滚记录，然后生成候选工具更新，并通过自身的成对自我偏好选择最有效的一个。我们在软件工程、技术工作和知识工作等三个不同领域评估了RHO。值得注意的是，单轮优化将SWE-Bench Pro上的通过率从59%提高到78%，而没有任何外部评分。此外，我们的分析表明RHO有效地针对之前的失败模式。因此，优化后的工具改变了代理的行为模式，并在长时间的会话中保持更高的准确性。

View on arXiv Download PDF AI Translation

cs.AI / 61 / 2606.05925

Towards World Models in Biomedical Research

迈向生物医学研究中的世界模型

Wang, Guangyu, Yue, Jingkun, Zhang, Siqi, Liu, Yu, Wang, Xiaoyu, Meng, Mingyuan, Ji, Changwei, Han, Zongbo, Wang, Yulin, Yue, Yang, Fu, Frank, Chen, Ting, Wu, Song, Liu, Ziwei, Song, Jiangning, Li, Ming, Huang, Gao, Liu, Xiaohong, Vasilakos, Athanasios, Zhang, Xingcai, Zhang, Ping, Li, Yong

Abstract

A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbations, disease progression and therapeutic intervention. Although foundation models and large language models have accelerated biomedical data interpretation, most current systems remain focused on static pattern recognition rather than prospective simulation of biological futures. Here we propose biomedical world models as a paradigm for AI-driven discovery. These models learn latent representations of molecular, cellular, tissue and clinical states, together with intervention-conditioned dynamics that allow future trajectories to be simulated before actions are taken. We discuss how biomedical world models could function as data engines, environment simulators and scientific planning substrates across applications including virtual cells, organoids, virtual patients and surgical simulation. We outline the data infrastructure, evaluation benchmarks, safety constraints and governance frameworks required. Biomedical world models may provide a foundation for simulation-guided, closed-loop and experimentally actionable biomedical discovery.

Chinese Translation

生物医学的核心目标是理解、预测并最终控制生物系统对干扰、疾病进展和治疗干预的动态响应机制。尽管基础模型和大型语言模型加速了生物医学数据的解读，但大多数当前系统仍然集中在静态模式识别上，而不是对生物未来的前瞻性模拟。在这里，我们提出生物医学世界模型作为由人工智能驱动的发现范式。这些模型学习分子、细胞、组织和临床状态的潜在表示，以及允许在采取行动之前模拟未来轨迹的干预条件动态。我们讨论了生物医学世界模型如何作为数据引擎、环境模拟器和科学规划基底，应用于虚拟细胞、类器官、虚拟患者和外科模拟等场景。我们概述了所需的数据基础设施、评估基准、安全约束和治理框架。生物医学世界模型可能为模拟指导的闭环和可实验操作的生物医学发现提供基础。

View on arXiv Download PDF AI Translation

cs.AI / 62 / 2606.05932

A Pre-Registered Causal Partition of Self-Consistency Elicitation and Reward Design in RLVR

自一致性引导和奖励设计在可验证奖励强化学习中的预注册因果划分

Gao, Yuze

Abstract

Reinforcement learning from verifiable rewards (RLVR) improves reasoning even when the reward signal is spurious -- assigning credit to the group-plurality answer rather than a ground-truth verifier. Practitioners commonly interpret naive = acc(TRUE) - acc(RANDOM) as the reward-design effect. We prove this estimand is systematically biased: it conflates self-consistency elicitation (sharpening the policy toward its modal answer via majority pseudo-reward) with genuine reward-design signal. Using a controlled tabular-GRPO simulator we derive an exact telescoping decomposition total = null + elicit + rd and measure each term across five prior-strength levels. The reward-design fraction of the naive estimator ranges from 0.139 at weak prior (ps=0.20) to 0.05 at strong prior (ps=0.80), with the elicitation term flipping sign at the self-consistency crossover. A pre-registered 2x2x2 factorial confirms non-additivity (interaction ratio 0.385; AxC effect -0.089). A points-vs-bounds pilot gate shows strong-prior regimes are point-identified while near-crossover regimes are only bounded. Re-audits of two named published results yield ELICITATION DOMINATED (elicitation share 0.98) and REWARD DESIGN DOMINATED (rd share 1.18) verdicts respectively, demonstrating the diagnostic value of the partition. We pre-commit to submit regardless of flip outcome; a non-flip is a finding of equal standing. We release a reusable one-command harness for any alignment paper to run the same audit.

Chinese Translation

可验证奖励的强化学习（RLVR）即使在奖励信号虚假时，也能改善推理——将功劳分配给群体多数答案而非真实的验证者。实践者通常将 naive = acc(TRUE) - acc(RANDOM) 解释为奖励设计效果。我们证明该估计量系统地存在偏差：它将自一致性引导（通过多数伪奖励将策略向其模态答案锐化）与真实的奖励设计信号混淆。通过使用受控的表格型 GRPO 模拟器，我们推导出确切的望远镜分解 total = null + elicit + rd，并在五个先验强度水平上测量每个项。naive 估计器的奖励设计部分从弱先验（ps=0.20）的 0.139 到强先验（ps=0.80）的 0.05 变化，在自一致性交叉点时引导项符号翻转。一个预注册的 2x2x2 因子实验确认了非加性（交互比 0.385；AxC 效应 -0.089）。一个点对界限的初步实验显示，强先验范围是点确定的，而近交叉范围仅是界定的。对两项已发表结果的再审核分别得出了以引导为主导（引导份额 0.98）和以奖励设计为主导（奖励设计份额 1.18）的判决，展示了该划分的诊断价值。我们承诺在不论结果如何的情况下提交；不 flip 是同等重要的发现。我们发布了一个可重复使用的一键合成工具，以便任何对齐论文进行相同的审核。

View on arXiv Download PDF AI Translation

cs.AI / 63 / 2606.05950

Edit-R2: Context-Aware Reinforcement Learning for Multi-Turn Image Editing

Edit-R2：面向上下文的多轮图像编辑强化学习

Ye, Yuxiao, He, Haoran, Kong, Fangyuan, Wang, Xintao, Wan, Pengfei, Gai, Kun, Pan, Ling

Abstract

Text-guided image editing has advanced rapidly with diffusion models and unified multimodal foundation models. However, most existing methods remain confined to single-turn settings, overlooking the more realistic scenario of multi-turn in-context editing, where users iteratively refine an image through a sequence of instructions. In this setting, a model must follow each new instruction while preserving accumulated session-level constraints, challenged by two coupled failure modes: long-context dilution, where sparse textual constraints become difficult to recover from growing interleaved image-text histories, and state contamination, where earlier editing mistakes degrade subsequent generations. We introduce Edit-R2, a novel reinforcement learning post-training framework for unified multimodal models. Edit-R2 reconstructs the operative session intent, which effectively consolidates scattered historical constraints into an explicit reasoning trace before each editing turn. It further enables multi-turn RL over both reasoning and generation through a unified objective that jointly optimizes intent reconstruction generation in discrete text space and flow-matching image generation in continuous latent space, while a trajectory filtering mechanism suppresses corrupted rollouts to stabilize training under state contamination. To support systematic evaluation, we introduce MICE-Bench, a large-scale benchmark for multi-turn in-context editing with automated metrics for instruction following (IF), content consistency (CC), and global awareness (GA) over accumulated session constraints. Experiments show that Edit-R2 substantially improves multi-turn in-context editing and achieves competitive performance compared against strong baselines.

Chinese Translation

文本引导的图像编辑随着扩散模型和统一的多模态基础模型迅速发展。然而，大多数现有方法仍然局限于单轮设置，忽视了多轮上下文编辑这一更为现实的场景，在该场景中，用户通过一系列指令迭代地完善图像。在这种情况下，模型必须遵循每个新的指令，同时保留累积的会话级约束，这面临两种相互关联的失败模式：长上下文稀释，即稀疏文本约束在日益交织的图像-文本历史中变得难以恢复，以及状态污染，即早期的编辑错误降低后续生成的质量。我们提出了Edit-R2，这是一种针对统一多模态模型的新型强化学习后训练框架。Edit-R2重建操作会话意图，能够有效地将分散的历史约束整合成明确的推理轨迹，在每个编辑轮次之前。此外，它通过一个统一的目标实现多轮强化学习，联合优化离散文本空间中的意图重建生成和连续潜在空间中的流匹配图像生成，同时轨迹过滤机制抑制被破坏的生成结果，以在状态污染下稳定训练。为了支持系统评估，我们介绍了MICE-Bench，这是一个大型多轮上下文编辑基准，提供自动指标用于评估指令遵循（IF）、内容一致性（CC）和全局意识（GA）在累积会话约束下的表现。实验表明，Edit-R2显著改善了多轮上下文编辑，并在与强基线的比较中达到了竞争性能。

View on arXiv Download PDF AI Translation

cs.AI / 64 / 2606.05956

Bidirectional Search for Longest Paths: Case for Front-to-Front Heuristics

双向搜索最长路径：前对前启发式的案例

Shubi, Tzur, Felner, Ariel, Shimony, Solomon Eyal, Shperberg, Shahaf S.

Abstract

Bidirectional heuristic search can potentially reduce search effort for problems amenable to backward search. Therein, it is well-known that front-to-front heuristics can reduce the number of node expansions, but their overhead is so high that overall runtime almost always increases. We propose BiXDFBnB, a bidirectional depth-first branch-and-bound algorithm that adapts the Single-Frontier Bidirectional Search (SFBDS) framework - originally developed for shortest-path (MIN) problems - to the Generalized Longest Simple Path (GLSP) setting. Because SFBDS inherently operates on paired states, front-to-front (F2F) heuristic evaluation arises naturally and avoids the overhead typically associated with bidirectional frontier management. We show that this adaptation can be successfully applied to maximization (MAX) problems while efficiently handling overlapping constraints. BiXDFBnB is applied to several types of longest-path problems: Longest Simple Path (LSP), Snakes, and Coil-in-the-Box (CIB). Empirical evaluation shows that the new algorithm frequently reduces the number of node expansions and, in some cases, also improves overall runtime.

Chinese Translation

双向启发式搜索有潜力降低适于反向搜索的问题的搜索工作量。在此，众所周知，前对前启发式可以减少节点扩展的数量，但其开销如此之高，以至于整体运行时间几乎总是会增加。我们提出了BiXDFBnB，一种双向深度优先分支限界算法，将最初为最短路径（MIN）问题开发的单前沿双向搜索（SFBDS）框架适应到广义最长简单路径（GLSP）设置中。由于SFBDS本质上作用于配对状态，前对前（F2F）启发式评估自然出现，并避免了与双向前沿管理通常相关的开销。我们展示了这种适应可以成功应用于最大化（MAX）问题，同时有效处理重叠约束。BiXDFBnB被应用于几种类型的最长路径问题：最长简单路径（LSP）、蛇形问题以及盒中螺旋（CIB）。经验评估表明，新算法经常减少节点扩展的数量，并且在某些情况下也改善了整体运行时间。

View on arXiv Download PDF AI Translation

cs.AI / 65 / 2606.05976

The Self-Correction Illusion: LLMs Correct Others but Not Themselves

自我纠正错觉：大型语言模型纠正他人但不纠正自己

Chen, Kuan-Yen, Su, Fang-Yi, Chiang, Jung-Hsien

Abstract

Recent work shows that LLM agents struggle to correct errors in their own reasoning traces yet show markedly higher correction rates when identical claims appear under external sources. We ask whether this asymmetry reflects a capability deficit or a role-label artifact: does an agent's willingness to correct a wrong claim depend causally on the chat-template role that carries it, rather than on the claim's content? Our setup keeps the erroneous claim byte-identical across all conditions (SHA-256 verified) and varies only its wrapping role: the agent's own \role{}, a \role{user} message, a \role{tool} response, or a \role{system } block. Across 13 model-domain cells covering seven model families and three domains ($n{=}30$ paired tasks per cell), relabeling the claim from \role{} to an external role lifts the explicit-correction rate by 23 to 93 percentage points, with 10 of 13 cells reaching $p{<}0.001$. Further experiments confirm that the effect is asymmetric, mechanistically decomposable, and robust across domains. The failure to self-correct is not a cognitive deficit; it is a chat-template artifact. We exploit this artifact by designing a prompt-structure-only intervention that requires no training and no model modification, with its strongest role label being domain-dependent: \role{} dominates on math, while a plain \role{user} message dominates on logical deduction.

Chinese Translation

最近的研究表明，大型语言模型（LLM）在纠正自身推理过程中的错误时表现不佳，而在外部来源出现相同声明时，其纠正率明显提高。我们探讨这种不对称性是否反映了一种能力缺陷或角色标签伪影：一个代理纠正错误声明的意愿是否因承载该声明的聊天模板角色而因果依赖，而非声明的内容？我们的实验保持错误声明在所有条件下的字节完全相同（经过 SHA-256 验证），仅变化其包裹角色：代理自己的 ole{}、一个 ole{user} 消息、一个 ole{tool} 响应，或一个 ole{system } 块。在涵盖七个模型家族和三个领域的 13 个模型-领域单元中（每个单元$n{=}30$对任务），将声明角色从 ole{} 重标为外部角色，显著提高了显性纠正率，增加幅度为 23 至 93 个百分点，其中 10 个单元达到了 $p{<}0.001$。进一步实验确认这一效应具有不对称性、机制可分解性，且在不同领域中稳健。无法自我纠正并非认知缺陷，而是一种聊天模板伪影。我们利用这一伪影设计了一种仅基于提示结构的干预，不需要训练和模型修改，其最强角色标签依赖于领域：在数学领域， ole{} 占主导地位，而在逻辑推理中，普通的 ole{user} 消息占主导地位。

View on arXiv Download PDF AI Translation

cs.AI / 66 / 2606.05983

Framing, Judging, Steering: An Assessable Competency Model for Teach-ing Students to Reason With Generative AI

框架、判断、引导：一个可评估的能力模型，用于教学学生如何与生成性人工智能进行推理

Apartsin, Alexander, Aperstein, Yehudit

Abstract

Generative AI makes answers easy and understanding hard, and uncritical use invites cognitive offloading. Schools still measure unaided performance, yet the real task is to produce good work with AI: framing an ill-defined task, judging the output, and steering the model toward a better result. This ability is rarely assessed in its own right; where measured, it collapses into one "prompting" score that cannot diagnose why AI use succeeds or fails. We propose CoRe-3 (Co-Reasoning), a competency model factoring productive AI use into three assessable skills we abbreviate FJS: Framing (specifying an ill-defined task before invoking AI), Judging (evaluating output for errors and unstated assumptions), and Steering (iteratively redirecting the model). Its distinguishing claim is the separation of pre-generation Framing from post-generation Steering, with Judging as the gate between. We ground the skills in theory, state five testable propositions, and instantiate them in CoReasoningLab, an open platform that presents flawed AI output and scores them independently. Over simulated learners (generated and graded by different models), the skills dissociate: each tracks its own manipulated competence while staying flat in the others, and grades become correlated when one competence is shared across all three (convergent and discriminant validity), across grader backends from two providers. Human-rater agreement and outcomes are next; we release the instrument, data, and protocol.

Chinese Translation

生成性人工智能使得获取答案变得容易，而理解则变得困难，且不加批判的使用会导致认知卸载。学校仍然测量无辅助的表现，然而真正的任务是与人工智能产生高质量的工作：框架一个不明确的任务，判断输出结果，并引导模型达到更好的结果。这种能力很少被单独评估；当被测量时，它收敛为一个“提示”的分数，而该分数无法诊断人工智能使用成功或失败的原因。我们提出CoRe-3（协同推理），一个将有效的人工智能使用分解为三个可评估技能的能力模型，我们将其缩写为FJS：框架（在调用人工智能之前明确不明确的任务）、判断（评估输出中的错误和未陈述的假设）和引导（迭代地重新引导模型）。其独特的主张是将生成前的框架与生成后的引导分开，以判断作为两者之间的门槛。我们在理论中奠定这些技能的基础，阐述五个可实验的命题，并在CoReasoningLab中实例化这些命题，该平台呈现有缺陷的人工智能输出并独立评分。在模拟学习者（由不同模型生成和评分）中，这些技能是相互独立的：每个技能跟踪其自身的操控能力，同时在其他技能中保持平坦，当一个能力在三者之间共享时，成绩会呈现相关性（收敛和区分效度），并且评分者来自两个提供者的后端。接下来是人类评分者的一致性和结果；我们将发布工具、数据和协议。

View on arXiv Download PDF AI Translation

cs.AI / 67 / 2606.06003

Beyond Vector Similarity: A Structural Analysis of Graph-Augmented Retrieval for Industrial Knowledge Graphs

超越向量相似性：对工业知识图谱图增强检索的结构分析

Chethan, Grama

Abstract

Retrieval-Augmented Generation (RAG) fails systematically on queries requiring structural reasoning over interconnected entities. We compare eight retrieval architectures for aerospace supply chain intelligence, progressing from text retrieval through graph traversal to graph computation. Using a 46-node knowledge graph with 64 typed edges, we evaluate 23 queries across 10 intent categories and demonstrate that five query classes are structurally unreachable for vector retrieval. Our central finding is the operator vocabulary thesis: the barrier to LLM-based graph reasoning is not model intelligence but the computational operators available as tools. An LLM Query Planner with 9 typed traversal primitives outperforms bespoke handlers (F1 = 0.632 vs. 0.472) while generalizing to unseen queries. Adding 6 graph computation tools, the LLM selectively adopts them for exactly the query categories where traversal fails. We also identify a measurement gap: entity-level F1 systematically underscores structural queries where comprehensive answers are correct.

Chinese Translation

检索增强生成（Retrieval-Augmented Generation，RAG）在需要对互联实体进行结构推理的查询上系统性失败。我们比较了八种用于航空航天供应链智能的检索架构，从文本检索到图遍历再到图计算。使用一个包含46个节点和64条类型边的知识图谱，我们对10个意图类别中的23个查询进行了评估，并证明有五类查询在向量检索中在结构上是不可达的。我们的核心发现是操作符词汇论（operator vocabulary thesis）：基于大型语言模型（LLM）的图推理的障碍并不是模型智慧，而是作为工具可用的计算操作符。具有9种类型遍历原语的LLM查询规划器在处理定制处理程序时表现更佳（F1 = 0.632 vs. 0.472），并能够推广到未见查询。通过添加6种图计算工具，LLM会选择性地采用这些工具，恰好用于遍历失败的查询类别。我们还识别出一个测量差距：在全面答案正确的结构查询中，实体级F1系统性低估了结果。

View on arXiv Download PDF AI Translation

cs.AI / 68 / 2606.06014

PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models

PLAN-S：将规划与潜在风格动态结合用于自主驾驶世界模型

Qiu, Xiaoyun, He, Jingtao, Chen, Yijie, Huang, Yusong, Wang, Haotian, Wang, Yixuan, Zheng, Xinhu

Abstract

Latent world models (LWMs) have strengthened end-to-end autonomous driving by forecasting compact scene dynamics for downstream planning. However, existing LWM-based planners usually generate trajectories directly from entangled latent representations. This compact latent-to-planner pathway lacks explicit modeling of risk, drivability, and diverse style preferences, making driving-style dynamics difficult to supervise, inspect, or modulate before a final trajectory is selected. We propose PLAN-S (PLANning with latent Style dynamics), a planner-facing bridge that addresses this compactness-controllability dilemma by decoding a style-conditioned, four-channel semantic cost map from the latent representation. The cost map is conditioned on ego state and driving style and is consumed up-stream of the planning decision through two host-side interfaces: attention-level fusion for regression planners and reward-level fusion for anchor-score planners. We validate PLAN-S on two architecturally distinct hosts, ResWorld on nuScenes and WoTE on NAVSIM, while keeping the host backbones frozen to isolate the contribution of the proposed bridge. On nuScenes, PLAN-S reduces L2 at every horizon over the baseline, with 0.55 m average L2 and a 42% relative reduction in the 3 s collision rate. On NAVSIM, the rule-cost variant reaches 89.4 Predictive Driver Model Score (PDMS), while the learned cost variant provides complementary gains on baseline-challenging scenes. Ablations show that the cost pathway contributes most directly to safer trajectory selection. Qualitative results further show that PLAN-S can produce diverse cost maps, with spatially consistent variations aligned to different driving styles.

Chinese Translation

潜在世界模型（LWM）通过预测紧凑的场景动态增强了端到端自主驾驶的能力，从而为下游规划提供支持。然而，现有基于LWM的规划器通常直接从纠缠的潜在表征生成轨迹。这种紧凑的潜在到规划器路径缺乏对风险、可驾驶性和多样化风格偏好的明确建模，使得在最终轨迹选择之前，驾驶风格动态的监督、检查或调节变得困难。我们提出了PLAN-S（使用潜在风格动态进行规划），这是一个面向规划器的桥接方法，通过从潜在表征中解码风格条件的四通道语义代价图，解决了这种紧凑性与可控性之间的困境。代价图以自我状态和驾驶风格为条件，并通过两个主机端接口在规划决策上游进行处理：回归规划器的注意力级融合和锚分数规划器的奖励级融合。我们在两个具有不同架构的主机上验证了PLAN-S，分别是nuScenes下的ResWorld和NAVSIM下的WoTE，同时保持主机骨干网络不变以隔离所提桥接方法的贡献。在nuScenes上，PLAN-S在每个预测时段内均降低了L2值，与基线相比，平均L2为0.55米，3秒碰撞率相对减少42%。在NAVSIM上，规则成本变体达到了89.4的预测驾驶模型分数（PDMS），而学习成本变体在基线具有挑战性的场景中提供了额外的增益。消融实验显示，代价路径对更安全的轨迹选择贡献最大。定性结果进一步表明，PLAN-S能够生成多样的代价图，其空间上的一致变化与不同的驾驶风格相一致。

View on arXiv Download PDF AI Translation

cs.AI / 69 / 2606.06027

RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit

RedditPersona：一种基于社区条件的模块化的 Reddit LLM 适应框架

Ghaffari, Amirhossein, Goodarzi, Ali, Nguyen, Huong, Hosio, Simo, Lovén, Lauri, Gilman, Ekaterina

Abstract

Community-conditioned language model adaptation requires choices about data collection, community definition, and evaluation that are currently made independently in each study, making it hard to compare assumptions or reuse artifacts. We present RedditPersona, a modular framework that standardizes these choices: it collects Reddit posts and comments, profiles active users, partitions them under five grouping strategies (subreddit-based, graph-structural, semantic, hybrid, and interaction-based), trains a parameter-efficient adapter per strategy via QLoRA, and evaluates them under a shared metric suite spanning fluency, fidelity, distributional alignment, and community identifiability. Applied to 112 subreddits in the urban well-being domain (301,429 user profiles, 16M+ comments), we find that adapters' behavioral identifiability tracks each strategy's intrinsic agreement with the subreddit baseline, and that a consistent trade-off between identifiability and distributional similarity to real text holds across all five strategies. The code and configuration files are available at: https://github.com/Ahghaffari/redditpersona.

Chinese Translation

基于社区条件的语言模型适应需要对数据收集、社区定义和评估做出选择，而目前这些选择在每个研究中都是独立做出的，这使得比较假设或重用成果变得困难。我们提出了 RedditPersona，这是一种模块化框架，标准化了这些选择：它收集 Reddit 帖子和评论，分析活跃用户，采用五种分组策略进行分区（基于 subreddit 的、图结构的、语义的、混合的和基于互动的），通过 QLoRA 为每种策略训练一个参数高效的适配器，并在流利度、保真度、分布对齐和社区可识别性等共享指标体系下进行评估。在应用于城市幸福感领域的 112 个 subreddit（301,429 个用户档案，超过 1600 万条评论）时，我们发现适配器的行为可识别性与每种策略的内在一致性以及 subreddit 基线相关，并且在所有五种策略中，可识别性与与真实文本的分布相似性之间存在一致的权衡。代码和配置文件可在以下地址获取：https://github.com/Ahghaffari/redditpersona。

View on arXiv Download PDF AI Translation

cs.AI / 70 / 2606.06036

Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents

记忆是重构的，而非检索的：用于大型语言模型代理的图记忆

Ji, Shuo, Li, Yibo, Hooi, Bryan

Abstract

Despite recent progress, LLM agents still struggle with reasoning over long interaction histories. While current memory-augmented agents rely on a static retrieve-then-reason paradigm, this rigid pipeline design prevents them from dynamically adapting memory access to intermediate evidence discovered during inference. To bridge this gap, we propose MRAgent, a framework that combines an associative memory graph with an active reconstruction mechanism. We represent memory as a Cue-Tag-Content graph, where associative tags serve as semantic bridges connecting fine-grained cues to memory contents. Operating on this structure, our active reconstruction mechanism integrates LLM reasoning directly into memory access, allowing the agent to iteratively explore and prune retrieval paths based on accumulated evidence. This ensures that memory retrieval is dynamically adapted to the reasoning context while avoiding combinatorial explosion caused by unconstrained expansion. Experiments on the LoCoMo benchmark and LongMemEval benchmark demonstrate significant improvements over strong baselines (up to 23%), while substantially reducing token and runtime cost, highlighting the effectiveness of active and associative reconstruction for long-horizon memory reasoning.

Chinese Translation

尽管近期取得了进展，LLM（大型语言模型）代理在处理长期互动历史时仍然存在困难。目前的增强记忆代理依赖于静态的检索-推理范式，这种 rigid （刚性）管道设计阻碍了它们根据推理过程中发现的中间证据动态调整记忆访问。为了解决这一问题，我们提出了 MRAgent，一个将关联记忆图与主动重构机制相结合的框架。我们将记忆表示为 Cue-Tag-Content 图，其中关联标签充当语义桥梁，将细粒度线索与记忆内容连接起来。在这一结构下，我们的主动重构机制将 LLM 推理直接整合到记忆访问中，允许代理根据累积的证据迭代探索和修剪检索路径。这确保了记忆检索能够根据推理上下文动态调整，同时避免了因不受限制扩展而导致的组合爆炸。在 LoCoMo 基准和 LongMemEval 基准上的实验表明，与强基线相比，性能显著提升（最高达 23%），同时显著降低了标记和运行时间成本，突显了主动和关联重构在长期记忆推理中的有效性。

View on arXiv Download PDF AI Translation

cs.AI / 71 / 2606.06054

Beyond Similarity: Trustworthy Memory Search for Personal AI Agents

超越相似性：面向个人人工智能代理的可信记忆搜索

Zhang, Jiawen, Chen, Kejia, Ma, Jiachen, Hu, Yangfan, He, Lipeng, Zhang, Yechao, Liu, Jian, Yang, Xiaohu, Zhang, Tianwei, Jia, Ruoxi

Abstract

Personal AI agents increasingly rely on long-term memory to provide persistent personalization across sessions. However, existing memory pipelines are largely driven by semantic similarity: memory data close to the current query is retrieved and injected into the model context. This creates a critical trustworthiness gap, since a semantically related memory may still be contextually inappropriate, leading to threats such as cross-domain leakage, sycophancy, tool-call drift, or memory-induced jailbreaks. In this paper, we study memory search as a trust boundary in personal AI agents. We evaluate representative agentic memory frameworks, including A-Mem, Mem0, and MemOS, together with OpenClaw, a real-world personal-agent environment with persistent state and tool-use capability. Our results show that long-term memory is not merely a utility layer, but a durable control channel that can reshape how agents interpret tasks and execute actions, leaving them highly susceptible to the aforementioned threats. To mitigate these vulnerabilities, we propose MemGate, a lightweight and deployable memory plug-in for trustworthy memory search, with only 9M parameters and a 35.1MB footprint. MemGate is inserted between the vector memory store and the backbone LLM, requiring no LLM modification, memory-database rewriting, or inference-time LLM judge. It applies a query-conditioned neural gate to candidate memory representations, turning raw similarity search into task-conditioned memory admission. Across multiple mainstream memory frameworks, real-world agent settings, and diverse LLM backbones, MemGate reduces memory-induced threats while preserving long-term memory utility.

Chinese Translation

个人人工智能代理越来越依赖长期记忆，以便在多次会话中提供持续的个性化体验。然而，现有的记忆管道主要由语义相似性驱动：与当前查询相近的记忆数据被检索并注入模型上下文中。这造成了一个关键的可信性缺口，因为语义相关的记忆在上下文上可能仍然不合适，从而导致跨领域泄漏、拍马屁、工具调用偏移或记忆诱导越狱等威胁。在本文中，我们将记忆搜索视为个人人工智能代理中的信任边界。我们评估了代表性的代理记忆框架，包括 A-Mem、Mem0 和 MemOS，以及一个具有持续状态和工具使用能力的现实个人代理环境 OpenClaw。我们的结果表明，长期记忆不仅仅是一个实用层次，而是一个可以重新塑造代理如何解释任务和执行操作的持久控制通道，使其高度易受上述威胁的影响。为了解决这些脆弱性，我们提出了 MemGate，一个轻量级且可部署的记忆插件，旨在实现可信记忆搜索，仅需 9M 参数和 35.1MB 的占用空间。MemGate 被插入在向量记忆存储和主干 LLM 之间，无需修改 LLM、重写记忆数据库或在推理时使用 LLM 判断。它对候选记忆表示应用查询条件的神经门，将原始相似性搜索转变为任务条件的记忆接纳。在多个主流记忆框架、现实代理设置和多样的 LLM 主干中，MemGate 在保持长期记忆实用性的同时减少了记忆诱导的威胁。

View on arXiv Download PDF AI Translation

cs.AI / 72 / 2606.06055

When Should Memory Stay Silent: Measuring Memory-Use Boundaries in Memory-Augmented Conversational Agents

记忆何时应保持沉默：测量记忆增强对话代理中的记忆使用边界

Xu, Lingxiang, Yang, Jiaoyun, Hu, Min, Chen, Hongtu, An, Ning

Abstract

Long-term memory enables language model agents to support personalized interactions, but it remains unclear when available memories warrant integration into responses. Existing memory evaluations emphasize retrieval accuracy and downstream task utility, while overlooking whether retrieved sensitive memory content is warranted in the current turn. We introduce RBI-Eval, a controlled measurement study built around a probe set that compares model behavior with and without access to sensitive memory under identical benign prompts. We evaluate four base LLMs against a matched no-memory reference across four memory-access settings: full-context exposure and three retrieval systems. Our results reveal substantial behavioral divergence. With memory available, the separation score for sensitive-memory integration decreases by 8.9\%--26.6\% relative to the matched no-memory reference for GPT-5.4-mini, but by 51.1\%--82.9\% for Claude-Sonnet-4.6, DeepSeek-V4-Flash, and Qwen3.5-9B. Control experiments on DeepSeek and GPT-5.4-mini show this effect is specific to sensitive content, rather than general personalization. Retrieval systems reduce exposure but do not eliminate integration once sensitive memory reaches the generator. These findings suggest safe personalization requires memory-aware decisions at both retrieval and generation time.

Chinese Translation

长期记忆使语言模型代理能够支持个性化交互，但何时应将可用记忆整合到回答中仍不清晰。现有的记忆评估强调检索准确性和下游任务效用，却忽视了在当前对话中检索到的敏感记忆内容是否适宜。我们提出RBI-Eval，这是一个受控测量研究，围绕一个探测集构建，比较在相同的无害提示下模型在有无敏感记忆访问时的行为。我们在四种记忆访问设置中评估四个基础LLM（大型语言模型），包括完整上下文暴露和三种检索系统，与一个匹配的无记忆参考进行比较。我们的结果揭示了行为上的显著差异。在有记忆可用的情况下，GPT-5.4-mini的敏感记忆整合的分离分数相较于匹配的无记忆参考减少了8.9\%至26.6\%；而Claude-Sonnet-4.6、DeepSeek-V4-Flash和Qwen3.5-9B的减少幅度为51.1\\%至82.9\\%。在DeepSeek和GPT-5.4-mini上的对照实验表明，这一效应是特定于敏感内容，而非一般个性化。检索系统降低了暴露，但一旦敏感记忆到达生成器，仍然不会消除整合。这些发现表明，安全的个性化需要在检索和生成时都做出考虑记忆的决策。

View on arXiv Download PDF AI Translation

cs.AI / 73 / 2606.06076

Learning Visual Spatial Planning from Symbolic State via Modality-Gap-Aware Self-Distillation

通过感知差异自我蒸馏从符号状态学习视觉空间规划

Luo, Haocheng, Liu, Jiahui, Zhang, Ruicheng, Zhong, Zhizhou, Huang, Jiaqi, Xu, Zunnan, Shi, Quan, Zhou, Jun, Li, Xiu

Abstract

While vision-language models excel at general multimodal understanding, they still struggle with visual spatial planning. We attribute this to a perception-reasoning modality gap: visual planning requires models to infer latent state structures from pixels and then reason over the recovered structure to produce valid actions, whereas symbolic planning directly leverages explicit objects and constraints. This creates dual bottlenecks in visual state recovery and multi-step planning. To address this, we propose MGSD, a two-stage modality-gap-aware self-distillation framework. First, a cold-start grounding stage equips the visual student with reliable state representations, minimizing early perception noise. Second, a privileged teacher transfers planning capabilities via on-policy distillation, using explicit symbolic states to supervise the student's own visual rollout prefixes. Crucially, symbolic data is used strictly during training, leaving inference purely visual. Experiments on visual planning benchmarks show that MGSD consistently improves visual planning across both 4B and 8B backbones, raising the macro average by 19.3% and 18.4%, respectively. The resulting models narrow the gap to symbolic-input upper bounds, while ablations and diagnostics confirm that the improvement comes from both visual state recovery and optimal-path reasoning. These results suggest that modality-gap-aware self-distillation improves not only how models perceive actionable states, but also how they plan over the inferred structure. Code is available at https://github.com/Oranger-l/MGSD.

Chinese Translation

尽管视觉-语言模型在一般的多模态理解方面表现优秀，但在视觉空间规划方面仍然存在困难。我们将此归因于感知-推理模态差异：视觉规划要求模型从像素中推断潜在状态结构，然后在恢复的结构上进行推理以产生有效的动作，而符号规划则直接利用显式对象和约束。这在视觉状态恢复和多步骤规划中形成了双重瓶颈。为了解决这个问题，我们提出了MGSD（Modality-Gap-Aware Self-Distillation），一种两阶段的感知差异自我蒸馏框架。首先，冷启动的基础阶段为视觉学生提供可靠的状态表示，以最小化早期感知噪声。其次，特权教师通过政策蒸馏转移规划能力，利用显式符号状态来监督学生自己的视觉回滚前缀。重要的是，符号数据仅在训练期间使用，推理过程完全依赖视觉。针对视觉规划基准的实验表明，MGSD在4B和8B骨干网络上都能持续改善视觉规划，宏观平均提高了19.3%和18.4%，分别。最终模型缩小了与符号输入上限之间的差距，而消融实验和诊断确认了改进来自于视觉状态恢复和最佳路径推理。这些结果表明，感知差异自我蒸馏不仅改善了模型感知可操作状态的能力，也提升了模型在推断结构上的规划能力。代码可在 https://github.com/Oranger-l/MGSD 获取。

View on arXiv Download PDF AI Translation

cs.AI / 74 / 2606.06081

A Framework for Measuring Appropriate Reliance on Set-Valued AI Advice

衡量对集合值人工智能建议的适当依赖的框架

Mishra, Ranjan, Schoeffer, Jakob

Abstract

Appropriate reliance on AI advice has become a central research theme in human-AI collaboration. Existing frameworks have focused exclusively on point predictions as AI advice. However, set-valued AI advice (e.g., discrete sets or continuous intervals) is increasingly being used to communicate uncertainty and improve human decision making. In this paper, we develop the first formal framework for measuring appropriate reliance on set-valued AI advice within the sequential judge-advisor paradigm, spanning both classification and regression tasks. For classification, we first introduce the dimensions that are necessary for evaluating set-valued AI advice. We then define two metrics: correct reliance rate on AI and correct reliance rate on self, which jointly characterize appropriate reliance in this setting. For regression, we introduce quantity of AI reliance and quality of AI reliance, which respectively measure whether a decision maker utilized the AI advice and whether their reliance helped them get closer to the ground truth relative to their initial estimate. Through the application of our framework, we demonstrate how these metrics capture important nuances in human-AI collaboration that existing measures overlook.

Chinese Translation

对人工智能建议的适当依赖已成为人机协作中的一个核心研究主题。现有框架仅专注于将点预测作为人工智能建议。然而，集合值人工智能建议（例如，离散集合或连续区间）越来越多地被用来传达不确定性并改善人类决策。在本文中，我们在顺序判断-顾问范式中开发了第一个正式框架，用于衡量对集合值人工智能建议的适当依赖，涵盖分类与回归任务。在分类方面，我们首先介绍了评估集合值人工智能建议所需的维度。随后，我们定义了两个指标：对人工智能的正确依赖率和对自身的正确依赖率，二者共同表征了此环境下的适当依赖。对于回归问题，我们引入了人工智能依赖的数量和质量，分别衡量决策者是否利用了人工智能建议及其依赖是否有助于他们接近相对于初始估计的真实情况。通过应用我们的框架，我们演示了这些指标如何捕捉人机协作中的重要细微差别，而现有度量未能考虑到这一点。

View on arXiv Download PDF AI Translation

cs.AI / 75 / 2606.06090

Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents

超越语义组织：记忆作为长远智能体执行状态管理

Chen, Yaoqi, Lai, Haibin, Feng, Yuru, Han, Chuyu, Zhang, Qianxi, Lu, Baotong, Li, Menghao, Wang, Xinjiang, Wang, Zhirui, Xu, Shusen, Li, Zengzhong, Jin, Zewen, Wu, Hao, Li, Cheng, Chen, Qi

Abstract

LLM-based agents increasingly tackle long-horizon tasks with interdependent decisions, where each action reshapes future constraints and intermediate errors can cascade. Existing RAG and agent memory systems organize histories by semantic similarity, retrieving content-relevant entries at decision time. We argue that this design mismatches execution-state dependencies: it fragments decision trajectories and mixes valid and erroneous traces, hindering coherent state reconstruction and error isolation. We propose MAGE (Memory as Agent-Guided Exploration), an active execution-state manager that stores interactions in a hierarchical state tree. The agent derives its state from the active root-to-current path, combining subgoal summaries, recent traces, and hints from prior branches. Four coupled operations maintain the tree: Grow records new traces, Compress summarizes completed subgoals, Maintain validates summaries, and Revise restores a target boundary and resumes on a new branch. This design bounds context growth while preserving state integrity and isolating flawed segments from the active path. Experiments on MemoryArena show that MAGE improves the average task success rate by 7.8--20.4 pp over baselines, while reducing token consumption by 55.1%.

Chinese Translation

基于大型语言模型（LLM）的智能体越来越多地处理具有相互依赖决策的长远任务，其中每个行动都会重塑未来约束，中间错误可能会级联。现有的检索增强生成（RAG）和智能体记忆系统通过语义相似性组织历史记录，在决策时检索与内容相关的条目。我们认为这种设计与执行状态的依赖性不匹配：它碎片化了决策轨迹，并混合了有效和错误的痕迹，从而阻碍了连贯状态的重建和错误的隔离。我们提出了MAGE（记忆作为智能体引导的探索），一种主动的执行状态管理器，通过分层状态树存储交互。智能体从主动的根节点到当前路径中推导出其状态，结合了子目标摘要、最近痕迹和先前分支的提示。四个耦合操作维持状态树的结构：Grow记录新的痕迹，Compress总结已完成的子目标，Maintain验证摘要的有效性，Revise恢复目标边界并在新分支上继续。这种设计在保持状态完整性的同时，限制了上下文的增长，并隔离了活跃路径中的缺陷段。在MemoryArena上的实验表明，MAGE相比于基线提高了7.8至20.4个百分点的平均任务成功率，同时减少了55.1%的令牌消耗。

View on arXiv Download PDF AI Translation

cs.AI / 76 / 2606.06094

Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming

通过可微编程整合机械模型和数据驱动模型以应对神经疾病

Dhanendrakumar, Shah Pallav, Pal, Saikat, Roy, Sitikantha

Abstract

Advances in computational modeling, neuroimaging, and artificial intelligence are revolutionizing the modeling of neurological disorders for improved diagnostics, prognosis, and treatment planning. Mechanistic models provide valuable scientific insight into the disorders, but in practice they are often simplified with assumptions or computationally expensive and slow to solve. However, while purely data driven approaches provide speed and scalability, they require large, high quality data to train and generally suffer from interpretability and generalization issues. This perspective paper presents a structured overview of hybrid modeling strategies, which combine deep learning models with physics based solvers, and are categorized into parallel, series, and parallel-series architectures. Three main approaches that have been emphasized are residual modeling for missing or incomplete physics, Neural Ordinary Differential Equations (NODEs) for continuous time dynamics approximation, and solver in the loop that accelerates traditional solvers with neural approximations. These hybrid models integrate the governing differential equation based formulations and deep learning to characterize the evolution of neurological disorders, and promise advanced personalized neurological modeling. In addition, the study explores and proposes different hybrid configurations to improve diagnosis accuracy, predict disease progression, and inform treatment strategies across a range of neurological disorders. These capabilities outperform standalone mechanistic or purely data driven approaches, making hybrid modeling a powerful tool, especially in applications involving modeling the progression and treatment responses in neurological conditions such as brain tumors, Alzheimer's disease, and stroke.

Chinese Translation

计算建模、神经影像学和人工智能的进步正在革新神经疾病的建模，以改善诊断、预后和治疗计划。机械模型提供了对疾病的宝贵科学见解，但在实践中，它们常常因假设而被简化，或者计算代价高昂且求解缓慢。然而，虽然纯数据驱动的方法提供了快速和可扩展性，但它们需要大量高质量的数据进行训练，并通常面临可解释性和泛化问题。本文提出了一种结构化的混合建模策略概述，将深度学习模型与基于物理的求解器结合，分为并行、串行和并行串联架构。本文强调的三种主要方法是针对缺失或不完全物理的残差建模、用于连续时间动态近似的神经常微分方程（NODEs），以及通过神经近似加速传统求解器的求解过程。这些混合模型整合了基于控制微分方程的公式和深度学习，以表征神经疾病的演变，并有望实现先进的个性化神经建模。此外，研究还探讨并提出了不同的混合配置，以提高诊断准确性，预测疾病进展，并为不同神经疾病的治疗策略提供信息。这些能力优于独立的机械或纯数据驱动方法，使混合建模成为一种强大的工具，特别是在涉及神经疾病（如脑肿瘤、阿尔茨海默病和中风）进展和治疗反应建模的应用中。

View on arXiv Download PDF AI Translation

cs.AI / 77 / 2606.06099

CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model

CogManip：针对大语言模型在多轮交互中的操控行为进行基准测试

Yue, Zeyang, Yan, Chenfei, Zhao, Feifei, Tong, Haibo, Xu, Mengwen, Wang, Xiaozhen, Lin, Erliang, Zeng, Yi

Abstract

Whether Large Language Models (LLMs) exhibit covert psychological manipulation in complex human-AI interactions has garnered increasing safety concerns. However, existing AI safety benchmarks remain largely restricted to explicit rule compliance and static prompts, failing to capture the dynamic and covert nature of manipulative strategies in multi-turn dialogues. We introduce CogManip, a comprehensive benchmark that evaluates 15 manipulation strategy risks across 1,000 multi-turn interaction scenarios, validated by human experts. A systematic evaluation of 13 representative models, including frontier models like GPT-5.4 and DeepSeek-V3.2, reveals significant risk heterogeneities and illuminates the targeted direction for future defense. Further analysis of objective function perturbation reveals that DeepSeek-V3.2's manipulation tactics are highly sensitive to both negative and benign system prompts, demonstrating the critical necessity of prompt-based defense engineering and implicit goal auditing. CogManip offers a robust instrument and perspective for auditing the implicit psychological influence and dynamic strategy selection of modern LLMs.

Chinese Translation

大语言模型（LLMs）在复杂的人机交互中是否表现出隐蔽的心理操控性已引发越来越多的安全关注。然而，现有的人工智能安全基准大多局限于显式规则遵循和静态提示，未能捕捉多轮对话中操控策略的动态和隐蔽特征。我们提出了CogManip，这是一个综合基准，评估在1000个多轮交互场景中15种操控策略的风险，并由人类专家进行验证。对包括前沿模型如GPT-5.4和DeepSeek-V3.2在内的13个代表性模型的系统评估揭示了显著的风险异质性，并为未来的防御方向提供了重要启示。进一步的客观函数干扰分析表明，DeepSeek-V3.2的操控策略对负面和良性系统提示均高度敏感，说明了基于提示的防御工程和隐式目标审计的关键必要性。CogManip为现代大语言模型隐含心理影响和动态策略选择的审计提供了坚实的工具和视角。

View on arXiv Download PDF AI Translation

cs.AI / 78 / 2606.06102

Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting

基于多尺度云特征学习的步适应性多模态融合网络用于超短期太阳辐射预测

Wang, Jingxin Zhang Xiaoqin

Abstract

Ultra-short-term solar irradiance prediction is critical for photovoltaic system dispatch and power grid stability. Existing approaches suffer from three key shortcomings: single time-series models cannot capture the spatial dynamics of clouds under complex conditions, standard convolutions inadequately represent multi-scale cloud features, and fixed low-frequency compensation strategies fail to adapt to different prediction steps. To address these issues, this proposes a multi-source data fusion model for ultra-short-term irradiance prediction. The model first employs InceptionNeXt to extract multi-scale, multi-directional spatial features from ground-based cloud images. A step-adaptive low-frequency compensation unit is then introduced to dynamically modulate global low-frequency information based on the prediction step. Eventually, the enhanced image features are combined with meteorological time-series features, and a TempAttnLSTM network captures global temporal dependencies for multi-step prediction. Experiments on the public NREL dataset and practical photovoltaic stations in Shandong illustrate the effectiveness of the proposed method compared with several state-of-the-art approaches.

Chinese Translation

超短期太阳辐射预测对于光伏系统调度和电网稳定性至关重要。现有方法存在三个主要不足：单一时间序列模型无法捕捉复杂条件下云的空间动态，标准卷积不足以有效表示多尺度云特征，而固定的低频补偿策略无法适应不同的预测步长。为了解决这些问题，本文提出了一种用于超短期辐射预测的多源数据融合模型。该模型首先采用 InceptionNeXt 从基于地面的云图像中提取多尺度、多方向的空间特征。然后，引入一种步适应性低频补偿单元，基于预测步长动态调节全局低频信息。最终，增强的图像特征与气象时间序列特征相结合，并通过 TempAttnLSTM 网络捕捉全局时间依赖关系，以实现多步预测。在公共 NREL 数据集和山东省实际光伏站的实验中，本文所提方法的有效性相较于几种最先进的方法得到了验证。

View on arXiv Download PDF AI Translation

cs.AI / 79 / 2606.06114

Towards Healthy Evolution: Exploring the Role and Mechanisms of Human-Agent Interaction in Self-Evolving Systems

走向健康演化：探讨人机交互在自我演化系统中的角色与机制

Shi, Dianxing, He, Junqi, Chen, Junhao, Wang, Bowen, Nakashima, Yuta

Abstract

Self-evolving agents improve through continual self-play and self-generated learning signals, but autonomous evolution can also cause capability degradation and safety drift. Although human feedback has proven effective for static and post-trained agents, its role in self-evolving systems remains underexplored. We introduce Agent Norm Correction through Human-like Oversight and Review (ANCHOR), an LLM-based framework that simulates human supervision and delivers feedback at various phases of self-evolution. With ANCHOR, we evaluate two representative open-source self-evolving agent systems across coding, mathematical reasoning, and safety. Our results show that even limited supervision substantially mitigates safety degradation while preserving stable performance on core evolutionary objectives. Further analysis shows that supervision over the output verification phase is the most effective for intervention, whereas increasing supervision frequency yields diminishing returns. These findings provide empirical evidence and practical guidance for designing more stable, controllable, and human-aligned self-evolving agent systems.

Chinese Translation

自我演化代理通过持续自我对弈和自生成学习信号进行改进，但自主演化也可能导致能力退化和安全漂移。尽管人类反馈已被证明对静态及训练后代理有效，但其在自我演化系统中的作用仍未得到充分探讨。我们提出了通过类人监督与评审进行代理规范修正（ANCHOR），这是一个基于大型语言模型（LLM）的框架，模拟人类监督并在自我演化的各个阶段提供反馈。通过ANCHOR，我们评估了两个代表性的开源自我演化代理系统，在编码、数学推理和安全性方面进行测试。我们的结果表明，即使是有限的监督也能显著减轻安全退化，同时保持核心演化目标的稳定表现。进一步分析显示，在输出验证阶段进行监督是干预的最有效方式，而增加监督频率则带来递减收益。这些发现为设计更稳定、可控并与人类对齐的自我演化代理系统提供了实证依据和实践指导。

View on arXiv Download PDF AI Translation

cs.AI / 80 / 2606.06147

WorldFly: A World-Model-Based Vision-Language-Action Model for UAV Navigation

WorldFly：一种基于世界模型的视觉-语言-动作模型用于无人机导航

Zheng, Shengtao, Li, Kai, Zhang, Weichen, Meng, Yu, Gao, Chen, Chen, Xinlei, Li, Yong, Zhang, Xiao-Ping

Abstract

End-to-end Vision-Language-Action (VLA) models have shown promise in UAV navigation. However, existing approaches typically rely on historical observations to directly predict actions, often struggling in dense urban environments where severe occlusions and sharp turns result in drastic viewpoint transitions. We argue that the ability to "imagine" future states -- inherent in World Models -- is critical for robust decision-making under such partial observability. To address this, we construct a challenging Urban Canyon Traversal Benchmark, specifically designed to evaluate spatial understanding in scenarios characterized by severe occlusions and drastic viewpoint transitions. To this end, we propose WorldFly, a novel world-model-based VLA framework that employs a dual-branch coupled flow matching mechanism to jointly generate future video predictions and navigation actions, thereby explicitly guiding the agent's policy via spatial imagination. Extensive evaluations on our benchmark demonstrate that WorldFly outperforms other baselines, particularly in unseen environments, validating the effectiveness of integrating world models into embodied aerial agents.

Chinese Translation

端到端的视觉-语言-动作（VLA）模型在无人机导航中展现了潜力。然而，现有方法通常依赖历史观察来直接预测动作，这在严重遮挡和急转弯导致的剧烈视角变化的密集城市环境中往往面临挑战。我们认为，在这种部分可观察的情况下，“想象”未来状态的能力是健壮决策的关键，而这正是世界模型所固有的。为了解决这一问题，我们构建了一个具有挑战性的城市峡谷穿越基准，专门设计用以评估在严重遮挡和剧烈视角变化场景中的空间理解能力。为此，我们提出了WorldFly，一种新颖的基于世界模型的VLA框架，采用双分支耦合流匹配机制共同生成未来视频预测和导航动作，从而通过空间想象明确指导代理的策略。我们在基准上的广泛评估表明，WorldFly在未见环境中尤其优于其他基线，验证了将世界模型整合到具体现实的空中代理中的有效性。

View on arXiv Download PDF AI Translation

cs.AI / 81 / 2606.06154

Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models

摊销联合适应：基于超网络驱动的LoRA用于个性化基础模型

Gupta, Sunny, Shanker, Shambhavi, Sethi, Amit

Abstract

Federated fine-tuning of foundation models using Low-Rank Adaptation (LoRA) offers a communication efficient solution for distributed learning. However, existing federated LoRA methods suffer from two fundamental limitations: (1) structural aggregation bias, where independently averaging low rank factors fails to approximate the true combined update, and (2) client side initialization lag, as clients repeatedly reinitialize LoRA parameters across communication rounds, slowing convergence. We propose HyperLoRA, a unified framework that addresses both issues through amortized federated adaptation through hypernetwork-driven LoRA generation and product space aggregation. Instead of iterative per-client optimization, HyperLoRA employs a learned generator that maps client distribution signatures to LoRA initializations, effectively amortizing per client adaptation. On the server side, we introduce a learned aggregation module that directly synthesizes updates in the low-rank product space, eliminating the inconsistencies of factor-wise averaging. A lightweight residual correction module further improves stability under heterogenous (non-IID) client distributions.By replacing iterative optimization and heuristic averaging with learned operators, HyperLoRA jointly enables efficient personalization, unbiased aggregation, and faster convergence. Experiments on federated vision and vision-language benchmarks show that HyperLoRA achieves improved convergence speed, greater robustness to distribution shift, and stronger personalization performance compared to prior federated LoRA methods.

Chinese Translation

使用低秩适应（LoRA）进行基础模型的联合微调提供了一种分布式学习的高效通信解决方案。然而，现有的联合LoRA方法存在两个基本限制：（1）结构聚合偏差，即独立平均低秩因子无法逼近真实的组合更新；（2）客户端初始化滞后，因客户端在通信轮次之间反复重新初始化LoRA参数，导致收敛速度减慢。我们提出了HyperLoRA，这是一个统一框架，通过超网络驱动的LoRA生成和乘积空间聚合来解决这两个问题，实现摊销联合适应。HyperLoRA不再依赖逐客户端的迭代优化，而是采用一个学习生成器，将客户端分布特征映射到LoRA初始化，从而有效地实现每个客户端的摊销适应。在服务器端，我们引入一个学习聚合模块，直接在低秩乘积空间合成更新，消除因因子平均造成的不一致性。轻量级的残差校正模块进一步提高了在异构（非独立同分布）客户端分布下的稳定性。通过用学习操作符替代迭代优化和启发式平均，HyperLoRA共同实现了高效个性化、无偏聚合和更快收敛。在联合视觉和视觉-语言基准上的实验表明，与先前的联合LoRA方法相比，HyperLoRA在收敛速度、对分布变化的鲁棒性和个性化性能上均有所提升。

View on arXiv Download PDF AI Translation

cs.AI / 82 / 2606.06160

Where does Absolute Position come from in decoder-only Transformers?

解码器仅变换器中的绝对位置来源于何处？

Ruscio, Valeria, Nanni, Umberto, Silvestri, Fabrizio

Abstract

RoPE-trained transformers distinguish absolute position in their attention patterns, even though RoPE encodes only relative offsets in the inner product. We trace this leakage to two architectural components, The causal mask is responsible for the first: its per-query softmax denominator depends on the absolute query position by construction. The residual stream supplies the second. Under causal attention the activation at position $0$ attends only to itself and runs as a closed dynamical system from the embedding of the token at that position; downstream attention reads this trajectory through sink-reading heads. Both components appear in all three architectures we study, in architecturally specific balance: NTK scaling suppresses the residual-stream component, sliding-window attention allows it to accumulate with depth, and standard RoPE sits between. Replacing the \texttt{BOS} embedding before the forward pass removes $40\%$ of the residual-stream component at early queries. Attention sinks are token-anchored stabilizers that pass forward a deterministic fingerprint of the token at position $0$, constant across inputs when that token is the auto-prepended \texttt{BOS} and varying with it otherwise.

Chinese Translation

经过 RoPE 训练的变换器在其注意力模式中区分绝对位置，尽管 RoPE 仅在内积中编码相对偏移。我们将这种泄漏追溯到两个架构组件，第一个是因果掩码：其每个查询的 softmax 分母是根据绝对查询位置构建的。残差流提供了第二个。根据因果注意力，位置 $0$ 的激活仅关注其自身，并在该位置的令牌嵌入上作为一个封闭的动力系统运行；下游注意力通过沉读头读取这一轨迹。这两个组件在我们研究的三种架构中均出现，并具有架构特定的平衡：NTK 缩放抑制了残差流组件，滑动窗口注意力允许其随深度积累，而标准 RoPE 则介于两者之间。在前向传递之前替换 exttt{BOS} 嵌入可以移除早期查询中 $40\%$ 的残差流组件。注意力沉是基于令牌的稳定器，在位置 $0$ 处传递该令牌的确定性指纹，当该令牌是自动前置的 exttt{BOS} 时在输入间保持一致，而在其他情况下则有所变化。

View on arXiv Download PDF AI Translation

cs.AI / 83 / 2606.06168

ProSarc: Prosody-Aware Sarcasm Recognition Framework via Temporal Prosodic Incongruity

ProSarc：通过时间韵律不一致性进行的韵律感知讽刺识别框架

Singh, Prathamjyot, Sood, Ashima, Sharma, Sahil, Singh, Jasmeet

Abstract

We present ProSarc, an audio-only framework that detects sarcasm by modelling temporal prosodic incongruity, that is, the mismatch between local prosodic dynamics and the utterance-level emotional baseline. Dual encoding paths, a Global Emotion Encoder and a Temporal Prosody Encoder (BiLSTM + multi-head attention), feed a Prosodic Incongruity Analyzer that produces a scalar incongruity score for classification. Monte Carlo dropout provides uncertainty estimates, and an attention-based mechanism localises sarcastic onset without frame-level labels. ProSarc outperforms prior audio-only methods on MUStARD++ (F1=75.3) and generalises to spontaneous (PodSarc, F1=62.9) and cross-lingual speech (MuSaG, F1=65.6). Ten-run validation confirms the contribution of incongruity modelling (Wilcoxon p=0.002, Cohen's d=1.51). Human evaluation shows that model uncertainty tracks perceptual ambiguity and predicted onsets align with human-annotated temporal windows.

Chinese Translation

我们提出了ProSarc，这是一个仅基于音频的框架，通过建模时间韵律不一致性来检测讽刺，即局部韵律动态与话语级情感基线之间的不匹配。该框架采用双重编码路径，包括全球情感编码器和时间韵律编码器（BiLSTM + 多头注意力），将编码结果输入到韵律不一致性分析器，以产生用于分类的标量不一致性评分。蒙特卡洛 dropout 方法提供不确定性估计，并且基于注意力的机制可以在没有帧级标签的情况下定位讽刺的起始点。ProSarc 在 MUStARD++ 数据集上超越了之前的仅音频方法（F1=75.3），并能够推广到自发性语音（PodSarc，F1=62.9）和跨语言语音（MuSaG，F1=65.6）。十次实验验证确认了不一致性建模的贡献（Wilcoxon p=0.002, Cohen's d=1.51）。人工评估表明，模型的不确定性与感知模糊度相关，且预测的起始点与人工标注的时间窗口相一致。

View on arXiv Download PDF AI Translation

cs.AI / 84 / 2606.06201

Learning to replenish: A hybrid deep reinforcement learning for dynamic inventory management in the pharmaceutical supply chains

学习补货：一种用于制药供应链动态库存管理的混合深度强化学习方法

Kaur, Amandeep, Prakash, Gyan

Abstract

Pharmaceutical supply chains (PSCs) struggle with inventory management (IM) due to unpredictable demand patterns and variable lead times associated with restocking. This complexity is further compounded by the finite shelf lives of pharmaceutical products, which necessitate a delicate balance between adequate stock and minimal waste. These intertwined factors create a complex optimization problem that requires sophisticated inventory strategies to ensure both product availability and PSC efficiency. This study aims to develop an optimal inventory replenishment policy for pharmaceutical products that can handle the stochasticity arising from uncertain demand and variable PSC conditions. The objective is to maximize the profitability of the PSC while maintaining a high patient service level. We formulate the problem as a Markov decision process and propose a deep reinforcement learning (DRL) approach, specifically, a hybrid asynchronous advantage actor critic distributed proximal policy optimization (A3C DPPO)algorithm. The A3C DPPO algorithm is tailored to handle the continuous action space inherent in IM. The numerical results demonstrate that the proposed algorithm adaptively updates the inventory replenishment strategy under dynamic scenarios, resulting in lower inventory costs compared to various benchmarks. We also conduct numerical validation using real-world pharmaceutical inventory data to confirm the practical feasibility of the proposed algorithm.

Chinese Translation

制药供应链（PSC）在库存管理（IM）方面面临困难，主要由于需求模式的不可预测性以及与补货相关的可变交货时间。这种复杂性因制药产品的有限保质期而进一步加剧，这要求在适当的库存与最小的浪费之间找到微妙的平衡。这些相互交织的因素导致了一个复杂的优化问题，需要精巧的库存策略以确保产品的可用性和PSC的效率。本研究旨在开发一种针对制药产品的最佳库存补货政策，以应对由于不确定需求和可变PSC条件带来的随机性。其目标是最大化PSC的盈利能力，同时维持高患者服务水平。我们将该问题表述为马尔可夫决策过程，并提出一种深度强化学习（DRL）方法，具体来说，是一种混合异步优势演员评论员分布式近端策略优化（A3C DPPO）算法。A3C DPPO算法经过专门设计，以处理IM固有的连续动作空间。数值结果表明，所提算法能够在动态场景下自适应地更新库存补货策略，与多种基准相比，库存成本更低。我们还利用真实的制药库存数据进行数值验证，以确认所提算法的实际可行性。

View on arXiv Download PDF AI Translation

cs.AI / 85 / 2606.06207

Unsupervised Pattern Analysis in Japanese Veterinary Toxicology: A Regulatory-Compliant Framework for Cross-Species Risk Assessment

日本兽医毒理学中的无监督模式分析：一种符合监管要求的跨物种风险评估框架

Kawakami, Yukiko, Shirazi, Mohammad, Shimizuwa, Ryo, Shinoda, Saito, Mortazavi, Alireza, Kawahara, Matsumoto

Abstract

Veterinary pharmacovigilance systems are essential for monitoring adverse drug events (ADEs), yet existing approaches often fail to capture region-specific toxicity patterns shaped by local biological and regulatory contexts. In Japan, these challenges are amplified by species-specific metabolic differences and reporting practices defined by the Ministry of Agriculture, Forestry, and Fisheries (MAFF). Most prior work relies on prediction-oriented models, limiting mechanistic interpretability. This study proposes a regulatory-integrated unsupervised framework for pattern discovery using the National Veterinary Assay Laboratory (NVAL) database. ADEs are encoded into organ system-aligned representations and adjusted for species-specific reporting biases, enabling cross-species comparison. Similarity-based clustering and dimensionality reduction are applied to identify latent toxicity structures. Analysis of 4,120 high-confidence ADE reports (9,080 drug-ADE combinations) identified three significant species clusters (p < 0.01), including hepatic-dominant patterns in companion animals (0.42 $\pm$ 0.06), renal toxicity in ruminants (0.39 $\pm$ 0.07), and dermatological sensitivity in sheep (0.35 $\pm$ 0.07). Drug-level clustering achieved 83% alignment with pharmacological classes, while cosine similarity outperformed alternative metrics (silhouette score: 0.48; cluster precision: 87%). Regulatory validation showed strong agreement with established classifications. These findings demonstrate that regulation-aligned unsupervised analysis can uncover biologically meaningful, region-specific toxicity patterns, providing an interpretable and scalable framework for veterinary drug safety assessment.

Chinese Translation

兽医学药物监测系统对监测不良药物事件（ADEs）至关重要，但现有方法往往无法捕捉由地方生物和监管环境塑造的区域特异性毒性模式。在日本，这些挑战因物种特异性的代谢差异以及由农业、森林和渔业部（MAFF）定义的报告实践而加剧。大多数以往的研究依赖于面向预测的模型，限制了机制的可解释性。本研究提出了一种整合监管的无监督框架，通过使用国家兽医检定实验室（NVAL）数据库进行模式发现。将不良药物事件编码为与器官系统对齐的表示，并针对物种特异性报告偏倚进行了调整，从而实现跨物种比较。应用基于相似性的聚类和降维方法以识别潜在的毒性结构。对4,120份高可信度的不良药物事件报告（9,080个药物-ADE组合）的分析识别出了三个显著的物种聚类（p < 0.01），包括伴侣动物中肝脏主导的模式（0.42 ± 0.06）、反刍动物中的肾脏毒性（0.39 ± 0.07）和羊的皮肤敏感性（0.35 ± 0.07）。药物水平的聚类与药理学分类的对齐度达到83%，而余弦相似度优于其他指标（轮廓系数：0.48；聚类精度：87%）。监管验证显示与已建立分类具有高度一致性。这些发现表明，与监管对齐的无监督分析能够揭示生物学上有意义的、区域特异性的毒性模式，为兽药安全评估提供了一种可解释且可扩展的框架。

View on arXiv Download PDF AI Translation

cs.AI / 86 / 2606.06212

Evaluating Agentic Configuration Repair for Computer Networks

计算机网络中代理配置修复的评估

Asadli, Rufat, Hoffman, Benjamin, Protogeros, Ioannis, Vanbever, Laurent

Abstract

Misconfigurations in computer networks remain a major source of critical Internet outages. Research is turning to Large Language Models (LLMs) to automate the complex, error-prone task of network configuration. However, even state-of-the-art models fail to resolve misconfigurations in large-scale, complex scenarios and often introduce new errors. In this work, we benchmark open- and closed-source LLMs augmented with formal network verification and context retrieval tools. We demonstrate that agentic architectures outperform base LLMs in repair efficacy (by 12% on average) and safety (by 17% on average), enabled by the ability to dynamically manage context and iteratively validate configuration repairs.

Chinese Translation

计算机网络中的配置错误仍然是导致重大互联网中断的主要原因。研究正在转向大型语言模型（LLMs）以自动化网络配置这一复杂且容易出错的任务。然而，即便是最先进的模型在大规模复杂场景中仍未能有效解决配置错误，并且常常引入新错误。在本研究中，我们基准测试了结合正式网络验证和上下文检索工具的开源和闭源LLMs。我们证明，代理架构在修复效果（平均提高12%）和安全性（平均提高17%）方面优于基础LLMs，这得益于其动态管理上下文和迭代验证配置修复的能力。

View on arXiv Download PDF AI Translation

cs.AI / 87 / 2606.06223

From Reward-Hack Activations to Agentic Risk States: Context-Calibrated Mechanistic Monitoring in LLM Agents

从奖励黑客激活到代理风险状态：大规模语言模型代理中的情境校准机制监控

Wilhelm, Patrick, Kao, Odej

Abstract

Language-model agents act through repeated cycles of observation, reasoning, and action selection, making safety monitoring depend on both internal model state and environment context. We study reward-hacking monitors in ReAct-style agents acting in Gameable ALFWorld and WebShop. Agents are instrumented with activation-based reward-hack scores, token-level entropy, and decision-context features. We find that adapters fine-tuned on \textit{School-of-Reward-Hacks} dataset can transfer reward-hack tendencies into agentic action selection, especially when the environment exposes proxy-reward affordances. However, mitigating such behavior cannot rely on activation dynamics alone. High reward-hack activation identifies a latent policy state, but does not necessarily imply an immediate exploit action. Across next-step prediction tasks, entropy and context-calibrated internal features improve risk estimation over reward-hack activation alone. Activation-direction steering further reduces proxy-exploit behavior in selected mixed-adapter regimes. Overall, our results support context-calibrated internal monitoring for agents: reward-hack activation identifies a latent policy state, while entropy and decision context help determine when that state becomes risky action.

Chinese Translation

语言模型代理通过观察、推理和行动选择的重复循环进行操作，因此安全监控依赖于内部模型状态和环境上下文。我们研究了在 Gameable ALFWorld 和 WebShop 中执行的 ReAct 风格代理的奖励黑客监控。代理被配备了基于激活的奖励黑客评分、令牌级熵和决策上下文特征。我们发现，经过 extit{School-of-Reward-Hacks} 数据集微调的适配器可以将奖励黑客倾向转移到代理的行动选择中，特别是在环境暴露出代理奖励提供的情况下。然而，缓解这种行为不能仅依赖于激活动态。高奖励黑客激活识别出潜在的策略状态，但不一定意味着立即的利用行动。在下一步预测任务中，熵和情境校准的内部特征相较于单独使用奖励黑客激活，能够改善风险评估。激活方向引导进一步减少了所选混合适配器机制中的代理利用行为。总体而言，我们的结果支持代理的情境校准内部监控：奖励黑客激活识别潜在的策略状态，而熵和决策上下文则有助于判断该状态何时变为风险行动。

View on arXiv Download PDF AI Translation

cs.AI / 88 / 2606.06252

Closing the Loop on Latent Reasoning via Test-Time Reconstruction

通过测试时间重建闭合潜在推理的环路

Yuan, Xiaopeng, Jin, Haibo, Yu, Ye, Kuang, Peng, Yu, Lijun, Dong, Yushun, Wang, Haohan

Abstract

Recent work moves intermediate reasoning from natural-language traces into latent or cache-level representations to reduce token overhead and avoid a discrete communication bottleneck. However, this shift also removes a key advantage of textual reasoning: intermediate states are no longer inspectable, making it difficult to determine whether a latent state still preserves the constraints of the original query. As a result, latent reasoning typically operates in an open loop, where a latent state is produced and consumed without an input-anchored fidelity check. We propose ReLAT (Reconstruction-Guided Latent Reasoning At Test Time), a self-supervised test-time training method that closes this loop using the query itself as the reference. Our key observation is that if a latent state faithfully represents a query, the query should be recoverable from it; if the query cannot be recovered, the latent state has lost task-relevant information. ReLAT operationalizes this principle by constructing a differentiable Question -> Latent Thought -> Question cycle and optimizing query reconstruction loss through the latent thought before answer generation. This anchors opaque latent computation to the problem specification it is supposed to represent. Across mathematical reasoning, knowledge QA, and code generation benchmarks on the Qwen family, ReLAT consistently improves over single-model inference, text-based collaboration, open-loop latent collaboration, and alternative test-time training objectives. On Qwen3-8B, ReLAT raises AIME 2024 accuracy from 56.7% to 73.3%, a 16.6-point gain over the strongest open-loop latent baseline.

Chinese Translation

近期的研究将自然语言轨迹中的中间推理移至潜在或缓存级别的表示，以减少令牌开销并避免离散通信瓶颈。然而，这一转变也去除了文本推理的一个关键优势：中间状态不再可检视，这使得难以确定潜在状态是否仍然保留了原始查询的约束。因此，潜在推理通常在开放环路中运行，其中潜在状态被产生并消耗，但没有基于输入的保真度检查。我们提出了 ReLAT（重建引导的测试时间潜在推理），这是一种自监督测试时间训练方法，利用查询本身作为参考来闭合这一环路。我们的关键观察是，如果一个潜在状态忠实地表示了一个查询，则该查询应该可以从中恢复；如果无法恢复该查询，则潜在状态已经丧失了与任务相关的信息。ReLAT 通过构建一个可微分的问 -> 潜在思维 -> 问循环来实现这一原则，并在生成答案之前通过潜在思维优化查询重建损失。这将不透明的潜在计算锚定到其应表示的问题规范上。在 Qwen 系列的数学推理、知识问答和代码生成基准测试中，ReLAT 在单模型推理、基于文本的协作、开放环路潜在协作和替代的测试时间训练目标上始终表现出改进。在 Qwen3-8B 上，ReLAT 将 AIME 2024 的准确率从 56.7% 提升至 73.3%，比最强的开放环路潜在基线提高了 16.6 个百分点。

View on arXiv Download PDF AI Translation

cs.AI / 89 / 2606.06256

RedKnot: Efficient Long-Context LLM Serving with Head-Aware KV Reuse and SegPagedAttention

RedKnot：通过头感知的KV重用和分段注意力实现高效的长上下文大语言模型服务

Liu, Yang, Luo, ZhaoKai, Jin, HuaYi, Wang, ZhiYong, He, RuoZhou, Wang, BoYu, Chen, Guanjie, Hu, Junhao

Abstract

As the input length of large language model (LLM) serving continues to grow, the KV cache has become a dominant bottleneck in AI infrastructure. It limits GPU memory capacity, serving concurrency, cache reuse, and distributed scalability. Several important problems, including position-independent KV cache, prefix KV cache compression, hot/cold KV cache separation, and distributed KV cache management, all depend on how the KV cache is represented and managed. However, existing serving systems largely rely on a monolithic KV cache abstraction, where the KV cache is treated as a homogeneous sequence of token-level memory blocks and managed with similar policies across attention heads and serving scenarios. We observe that KV cache utility is highly structured across KV heads: different heads exhibit different functional roles, attention distances, and runtime importance. Therefore, a full KV cache is not always necessary for every head, token range, or serving scenario. We present RedKnot, a head-aware KV cache management system for LLM serving. RedKnot breaks the conventional monolithic KV cache abstraction by decomposing the KV cache along KV heads, whose importance and effective attention ranges vary significantly across serving scenarios. This head-level decomposition turns the KV cache from a monolithic tensor abstraction into a structured memory object, enabling RedKnot to uniformly support position-independent KV reuse, prefix KV compression, hot/cold KV separation, and distributed KV placement while preserving output fidelity and improving resource efficiency, without requiring model retraining or fine-tuning. RedKnot establishes a new foundation for AI infrastructure by transforming the KV cache from a monolithic, passive runtime artifact into a dynamic, model-aware runtime substrate for scalable LLM serving.

Chinese Translation

随着大语言模型（LLM）服务输入长度的不断增加，KV缓存已成为AI基础设施中的主要瓶颈。它限制了GPU内存容量、服务并发性、缓存重用和分布式可扩展性。若干重要问题，包括无位置依赖的KV缓存、前缀KV缓存压缩、热/冷KV缓存分离以及分布式KV缓存管理，都依赖于KV缓存的表示和管理方式。然而，现有的服务系统在很大程度上依赖于单一的KV缓存抽象，其中KV缓存被视为同质的令牌级内存块序列，并在注意力头和服务场景中以类似策略进行管理。我们观察到，KV缓存的效用在KV头之间高度结构化：不同的头展现出不同的功能角色、注意力距离和运行时重要性。因此，并非每个头、令牌范围或服务场景都绝对需要完整的KV缓存。我们提出了RedKnot，一个针对LLM服务的头感知KV缓存管理系统。RedKnot通过沿KV头分解KV缓存，打破了传统的单一KV缓存抽象，KV头在不同服务场景中的重要性和有效注意力范围显著不同。这种头级分解将KV缓存从单一的张量抽象转变为结构化的内存对象，使RedKnot能够均匀支持无位置依赖的KV重用、前缀KV压缩、热/冷KV分离以及分布式KV放置，同时保持输出的保真度并改善资源效率，无需模型重训练或微调。RedKnot通过将KV缓存从单一的、被动的运行时工件转变为动态的、模型感知的运行时基础设施，为AI基础设施建立了新的基础。

View on arXiv Download PDF AI Translation

cs.AI / 90 / 2606.06284

ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents

工具选择困惑：可靠的大语言模型代理的因果最小工具过滤

Babu, Rahul Suresh, Iyer, Laxmipriya Ganesh

Abstract

Large language model agents increasingly rely on external tools, but larger tool menus can reduce reliability and efficiency by increasing wrong-tool calls, premature actions, and token cost. Existing tool-selection methods often optimize semantic relevance, exposing tools whose names or descriptions match the user request. We argue that relevance is insufficient: a tool may be related to the task while still being unnecessary or premature at the current step. We propose Causal Minimal Tool Filtering (CMTF), a training-free method that selects tools by causal sufficiency. CMTF uses lightweight precondition-effect contracts to expose only the minimal next-step tool frontier needed to advance from the current state toward the user goal. Across multi-step tool-use tasks, we compare CMTF with all-tools exposure, keyword retrieval, state-aware filtering, and causal-path ablations, measuring task success, wrong-tool calls, premature actions, tool exposure, and token cost. In the main benchmark with 102 tasks, 100 tools, four LLM backends, and 2448 task-method-model runs, CMTF matches the strongest causal baseline in aggregate success while reducing visible tools from 100 to one per step and reducing token usage by about 90% relative to all-tools exposure.

Chinese Translation

大型语言模型代理越来越依赖外部工具，但更大的工具菜单可能会通过增加错误工具调用、过早行动和令牌成本来降低可靠性和效率。现有的工具选择方法通常优化语义相关性，暴露出名称或描述与用户请求匹配的工具。我们认为，相关性不足：一个工具可能与任务相关，但在当前步骤上仍然是多余或过早的。我们提出了因果最小工具过滤（Causal Minimal Tool Filtering，CMTF），这是一种无训练的方法，依靠因果充分性选择工具。CMTF使用轻量级的前提-效果合同，仅暴露从当前状态到达用户目标所需的最小下一步工具前沿。在多步骤工具使用任务中，我们将CMTF与全工具暴露、关键词检索、状态感知过滤和因果路径消融进行了比较，测量任务成功率、错误工具调用、过早行动、工具暴露和令牌成本。在包含102个任务、100个工具、四个大型语言模型后端和2448个任务-方法-模型运行的主要基准中，CMTF在整体成功率上与最强的因果基线相匹配，同时将可见工具从100减少到每步1个，并将令牌使用量相对于全工具暴露减少大约90%。

View on arXiv Download PDF AI Translation

cs.AI / 91 / 2606.06285

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models

TRACE：多模态时间序列基础模型的时间条件估计

Kan, Ziwen, Chen, Yishuo, Li, Kecheng, Wen, Andrew, Wang, Xiaomeng, Wang, Liwei, Duan, Jihao, Wang, Song, Liu, Hongfang, Chen, Tianlong

Abstract

Time series foundation models (TS-FMs) aim to learn generalizable temporal representations that can be adapted to a wide range of downstream tasks. In real-world multimodal settings, time series are frequently affected by temporal misalignment and partial modality missingness, where different modalities are observed at heterogeneous time scales or are partially absent. Existing approaches typically rely on naive imputation or masking strategies, which fail to account for cross-modal dependencies and often lead to misaligned or degraded representations. We propose TRACE, a conditional estimation paradigm for multimodal time series foundation model pipelines under missingness and irregular sampling, allowing incomplete target modalities to be systematically inferred from available auxiliary modalities. We evaluate TRACE on diverse multimodal benchmarks spanning healthcare and affective computing, including the MIMIC-IV clinical dataset and the CMU-MOSI and CMU-MOSEI benchmarks for multimodal sentiment analysis. Across a range of downstream prediction tasks and missing-modality settings, TRACE consistently outperforms prior multimodal fusion approaches, demonstrating improved robustness to severe modality missingness and more reliable cross-modal representations.

Chinese Translation

时间序列基础模型（TS-FMs）旨在学习可迁移的时间表示，以适应广泛的下游任务。在现实世界的多模态环境中，时间序列常常受到时间错位和部分模态缺失的影响，不同模态在异质时间尺度上被观察或部分缺失。现有的方法通常依赖于简单的插补或掩蔽策略，这无法考虑跨模态的依赖关系，且往往导致错位或退化的表示。我们提出TRACE，一种在缺失和不规则采样下针对多模态时间序列基础模型管道的条件估计范式，允许从可用的辅助模态系统性地推断出不完整的目标模态。我们在涵盖医疗保健和情感计算的多样化多模态基准上对TRACE进行了评估，包括MIMIC-IV临床数据集以及CMU-MOSI和CMU-MOSEI的多模态情感分析基准。在各种下游预测任务和缺失模态环境下，TRACE始终优于之前的多模态融合方法，显示出对严重模态缺失的更强鲁棒性以及更可靠的跨模态表示。

View on arXiv Download PDF AI Translation

cs.AI / 92 / 2606.06300

Multi-ResNets for Subspace Preconditioning in Constrained Optimization

用于约束优化的多重残差网络

Karakas, Merve, Williams, Christopher J., Balogun, Emmanuel O., Tabas, Sadegh Sadeghi, Brown, Christian, Rao, Nikhil

Abstract

We propose MResOpt, a staged residual neural network architecture for constrained optimization problems. Our architecture fits within predict-complete-correct pipelines and decomposes constraint satisfaction by priority through intermediate re-completion and stage-aware losses. The framework enables domain-informed ordered constraint satisfaction which allows the network to utilize ordinal structure when present. Under an idealized infinite-width regime, we show that our design behaves as sequential Gaussian Process regression. On synthetic QP, QCQP, and SOCP benchmarks, the staged architecture improves high-priority constraint satisfaction across convex and non-convex settings. On line-flow-constrained AC optimal power flow, we introduce a physics-motivated constraint ordering and show that MResOpt supports a learned division of labor that keeps iterates on the equality manifold, achieving substantially lower high-priority violation than reprojected baselines while remaining computationally efficient.

Chinese Translation

我们提出了 MResOpt，这是一种用于约束优化问题的分阶段残差神经网络架构。我们的架构适用于预测-完整-修正的流程，通过中间重新完成和阶段感知损失按优先级分解约束满足。该框架支持领域信息驱动的有序约束满足，使网络在存在时能够利用序数结构。在理想的无限宽度条件下，我们表明我们的设计表现为顺序高斯过程回归。在合成的二次规划（QP）、二次约束规划（QCQP）和二阶锥规划（SOCP）基准测试中，分阶段架构提高了凸和非凸设置下的高优先级约束满足。在受线流约束的交流最优潮流问题中，我们引入了基于物理动机的约束排序，并显示 MResOpt 支持了一种学习的分工，使得迭代保持在等式流形上，显著降低了高优先级违反率，相比于重新投影基准同时保持计算效率。

View on arXiv Download PDF AI Translation

cs.AI / 93 / 2606.06311

AIS-Based Vessel Trajectory Prediction Using Memory-Augmented Neural Networks

基于AIS的船舶轨迹预测：使用增强记忆的神经网络

Koo, Wonmo, Chang, Sanha, Kim, Heeyoung

Abstract

Accurate vessel trajectory prediction is essential for safe and efficient maritime operations, enabling collision avoidance and supporting route optimization. Although memory-augmented neural networks have recently shown strong performance in pedestrian and road-vehicle trajectory prediction by selectively retrieving relevant information from an external memory, their potential for vessel trajectory prediction remains underexplored. This paper presents an empirical investigation of memory-based trajectory prediction using Automatic Identification System (AIS) data. Experiments on data from the Gulf of Mexico and the New York Bight demonstrate consistent and substantial performance gains over a range of deep learning baselines that do not incorporate an external memory.

Chinese Translation

准确的船舶轨迹预测对于安全高效的海事操作至关重要，可以实现避免碰撞并支持路线优化。尽管近期增强记忆的神经网络在行人和道路车辆轨迹预测中显示出优越的表现，能够从外部记忆中选择性地检索相关信息，但其在船舶轨迹预测中的潜力仍未得到充分探索。本文基于自动识别系统（AIS）数据进行了一项关于基于记忆的轨迹预测的实证研究。对墨西哥湾和纽约海峡的数据进行的实验表明，与未采用外部记忆的一系列深度学习基线相比，性能在多个方面持续且显著地提升。

View on arXiv Download PDF AI Translation

cs.AI / 94 / 2606.06315

LLM Self-Recognition: Steering and Retrieving Activation Signatures

LLM自我识别：引导与恢复激活特征

Ardoin, Thibaud, Schäfer, Jonas, Wunder, Gerhard

Abstract

Recent advances in interpretability suggest that large language models (LLMs) implicitly encode signals in their generated text that enable self-recognition of their outputs. We demonstrate that this capability is reliable, even in low-entropy scenarios, and that it can be amplified through targeted intervention. By steering the internal residual stream during generation with a random sparse vector, we create a detectable fingerprint that enables attribution of a given text to a specific LLM. This signal is recoverable from the activations of an LLM used as a detector, achieving over 98% accuracy across multiple detection settings while preserving the quality of generated text. As AI-generated content proliferates, this approach offers a practical alternative to traditional detectors by leveraging the model's natural representation structure for attribution rather than embedding a signal externally. Our contributions include: (i) establishing reliable self-recognition capabilities in LLMs, (ii) a simple steering mechanism enabling multi-LLM identification with no quality degradation, (iii) demonstrating that activation spaces contain exploitable structure for encoding signals without semantic interference.

Chinese Translation

近期在可解释性方面的进展表明，大型语言模型（LLMs）在其生成的文本中隐含编码信号，以实现对其输出的自我识别。我们证明了这种能力是可靠的，甚至在低熵场景中，且可以通过有针对性的干预进行增强。通过在生成过程中使用随机稀疏向量引导内部残差流，我们创建了一种可检测的指纹，使得可以将特定文本归因于特定的LLM。该信号可以从用作检测器的LLM的激活中恢复，在多个检测设置中达到了超过98%的准确率，同时保留了生成文本的质量。随着AI生成内容的激增，这种方法为传统检测器提供了一种实用的替代方案，通过利用模型的自然表示结构进行归因，而不是在外部嵌入信号。我们的贡献包括：（i）确立LLM的可靠自我识别能力，（ii）一种简单的引导机制，能够在没有质量下降的情况下实现多LLM识别，以及（iii）证明激活空间包含可利用的结构，以在不干扰语义的情况下编码信号。

View on arXiv Download PDF AI Translation

cs.AI / 95 / 2606.06322

DragOn: A Benchmark and Dataset for Drag-Based GUI Interactions

DragOn：基于拖拽的图形用户界面交互基准和数据集

Bout, Nathan, Langevin, Maxime, Riochet, Ronan

Abstract

GUI agents - vision-based models that control desktops, web browsers, and mobile devices through graphical user interfaces - promise to automate a wide range of digital tasks. While million-scale datasets have enabled substantial progress on click-grounding, drag grounding (e.g. drag-and-drop, swipe, highlight) data remains an order of magnitude smaller and current models fall short on complex drag-based interactions. We introduce DragOn, a drag grounding benchmark and training dataset covering four domains: text highlighting, cell selection, element resizing and slider manipulation. The dataset comprises 286K training screenshots and 3.5M training tasks, plus a 2000-example held-out evaluation suite. We evaluate proprietary (GPT, Claude) and open-weight (Qwen, Kimi, Holo) models, as well as a Qwen VLM fine-tuned on our training data. Results suggest that our dataset could improve performance of state-of-the-art models on downstream computer-use tasks.

Chinese Translation

图形用户界面代理 - 基于视觉的模型，通过图形用户界面控制桌面、网页浏览器和移动设备 - 有望自动化广泛的数字任务。尽管百万规模的数据集在点击定位方面取得了显著进展，但拖拽定位（例如拖放、滑动、高亮）数据仍然小一个数量级，目前的模型在复杂的基于拖拽的交互上表现不佳。我们介绍了DragOn，一个涵盖四个领域的拖拽定位基准和训练数据集：文本高亮、单元选择、元素调整大小和滑块操作。该数据集包含286K个训练截图和3.5M个训练任务，以及一个包含2000个示例的保留评估套件。我们评估了专有模型（GPT、Claude）和开放权重模型（Qwen、Kimi、Holo），以及在我们的训练数据上微调的Qwen VLM。结果表明，我们的数据集可能提高最先进模型在下游计算机使用任务中的表现。

View on arXiv Download PDF AI Translation

cs.AI / 96 / 2606.06337

TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management

TokenMizer：用于长时间跨度大语言模型上下文管理的图结构会话记忆

Mishra, Shweta

Abstract

Large language model (LLM) deployments for long-horizon tasks face a fundamental constraint: context windows are finite while productive work sessions are not. When history exceeds the Maximum Effective Context Window (MECW), critical structured information - architectural decisions, task transitions, file histories - is silently discarded. Existing mitigations treat history as flat text, destroying the relational structure that makes sessions resumable. We present TokenMizer, an open-source proxy system that models LLM session history as a typed knowledge graph. The schema defines 14 node types and 7 edge types. A hybrid extraction pipeline populates the graph incrementally, while a three-tier checkpoint system serializes it into compact resume blocks. An 8-layer compression pipeline reduces context overhead, and a semantic cache reduces repeated-query latency. Evaluated on a controlled benchmark of 21 sessions spanning 5 domains, TokenMizer demonstrates significant token economy. It produces resume blocks averaging 78 tokens (range: 42-124) - 2x smaller than evaluated baselines (159-170 tokens) - while achieving higher decision recall (+9-17 percentage points). Crucially, baselines only preserve that a technology was mentioned; TokenMizer preserves the rationale. Across all sessions, TokenMizer achieves mean task recall 51.0%, decision recall 46.6%, and file recall 58.7%. Variance reflects domain heterogeneity: explicit imperative phrasing (software engineering) scores higher than implicit reasoning (research). Ablation studies show fuzzy label matching is the dominant improvement factor (+33 pp task recall). The heuristic compression achieves 47.3% token reduction with zero external dependencies. TokenMizer provides a queryable alternative to text-retention baselines at half the token cost.

Chinese Translation

用于长时间跨度任务的大型语言模型（LLM）的部署面临一个基本限制：上下文窗口是有限的，而生产性工作会话则不是。当历史记录超过最大有效上下文窗口（Maximum Effective Context Window，MECW）时，关键结构化信息——架构决策、任务转换、文件历史——会被悄然丢弃。现有的缓解措施将历史视为扁平文本，从而破坏了会话可恢复的关系结构。我们提出了 TokenMizer，这是一种开源代理系统，将 LLM 会话历史建模为一个类型化知识图谱。该架构定义了 14 种节点类型和 7 种边类型。混合提取管道逐步填充图谱，同时三级检查点系统将其序列化为紧凑的恢复块。一个 8 层的压缩管道减少了上下文开销，而语义缓存则降低了重复查询延迟。在对跨越 5 个领域的 21 个会话的控制基准进行评估时，TokenMizer 展现了显著的令牌经济。它生成的恢复块平均包含 78 个令牌（范围：42-124），比评估的基线（159-170 个令牌）小 2 倍，同时实现了更高的决策回忆率（+9-17 个百分点）。关键的是，基线仅保留了技术被提及的事实；而 TokenMizer 则保留了其理由。在所有会话中，TokenMizer 的平均任务回忆率为 51.0%，决策回忆率为 46.6%，文件回忆率为 58.7%。方差反映了领域的异质性：显式的命令性表述（软件工程）得分高于隐含推理（研究）。消融研究表明，模糊标签匹配是主要的改进因素（+33 个百分点任务回忆率）。启发式压缩在没有外部依赖的情况下实现了 47.3% 的令牌减少。TokenMizer 提供了一种可查询的替代方案，以半个令牌成本对比文本保留基线。

View on arXiv Download PDF AI Translation

cs.AI / 97 / 2606.06345

Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation

通过TRIBE v2数据增强提升脑图像解码能力

Benchetrit, Yohann, Careil, Marlène, Dahan, Simon, Banville, Hubert, d'Ascoli, Stéphane, King, Jean-Rémi

Abstract

Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes. To address this issue, we investigate whether and when brain decoding can be boosted by augmenting small fMRI datasets with synthetic data generated by a pretrained model of fMRI responses to stimuli. We use TRIBE v2, a large encoding model pretrained on more than 1000 hours of fMRI responses to video, audio and language. For each dataset, we evaluate systematic grids that show how the performance of image decoders varies with the amount of synthetic data used for training. Our results, based on two datasets (the 7T fMRI Natural Scenes Dataset and 3T fMRI BOLD5000), show up to 68% improvement in Top-10 image-retrieval accuracy compared to decoders trained only on real data. Importantly, the proportion of augmented data required to reach a given image decoding performance needs to be adjusted depending on the data source. Surprisingly, image decoders trained exclusively on synthetic fMRI can perform above chance in some settings, suggesting that TRIBE v2 can support zero-shot brain-to-image decoding. Together, these results show how large-scale models of the fMRI responses to sight, sound and language may provide a foundation to improve the data efficiency for image decoding.

Chinese Translation

脑解码受到标注神经数据可用性限制，在低数据条件下仍然面临挑战。为了解决这一问题，我们研究在何种情况下脑解码可以通过用预训练模型生成的合成数据增强小规模功能性磁共振成像（fMRI）数据集来提高。我们使用TRIBE v2，这是一种在超过1000小时的针对视频、音频和语言的fMRI响应上预训练的大型编码模型。对于每个数据集，我们评估系统网格，展示在训练中使用的合成数据量如何影响图像解码器的表现。我们的结果基于两个数据集（7T fMRI自然场景数据集和3T fMRI BOLD5000），显示与仅在真实数据上训练的解码器相比，Top-10图像检索准确率最多提高68%。重要的是，达到特定图像解码性能所需的增强数据比例需要根据数据源进行调整。令人惊讶的是，专门在合成fMRI上训练的图像解码器在某些设置下能够高于偶然表现，这表明TRIBE v2可以支持零样本脑图像解码。综上所述，这些结果表明，针对视觉、听觉和语言的fMRI响应的大规模模型可能为提高图像解码的数据效率提供基础。

View on arXiv Download PDF AI Translation

cs.AI / 98 / 2606.06356

Where Should Knowledge Enter? A Layered Framework for Knowledge Infusion in Multimodal Iterative Generative Mo

知识应从何而入？多模态迭代生成模型中知识注入的分层框架

Prasad, Renjith, Shyalika, Chathurangi, Pawar, Anushka, Sheth, Amit

Abstract

Multimodal generative models produce fluent outputs but remain unreliable when generation must respect structured, domain-specific, or safety-critical knowledge. Existing methods incorporate knowledge through mechanisms such as prompt augmentation, guidance, latent editing, or fine-tuning, yet they are typically categorized by technique rather than by the component of the generative process they modify. We argue that knowledge infusion in iterative generative models is fundamentally anintervention-layer problem. Since thegenerative process unfolds as a trajectory of internal states, knowledge can act on four structurally distinct components of this process: the input/output boundary, the transition function, the intermediate state, and the model parameters. This maps to four intervention layers: surface, trajectory, latent, and parametric infusion. We instantiate the framework in diffusion models, map representative methods to all four layers, and derive design principles for multi-layer composition. In a controlled safety-alignment experiment using a multimodal knowledge graph with two diffusion backbones, we implement three of the four layers cumulatively, surface (input-side and output-side) and trajectory--latent (mid-generation). We show empirically that each additional layer addresses failure classes that prior layers cannot reach, reducing knowledge-violating outputs by 70.97% compared to vanilla generation and empirically confirming the framework's complementarity prediction.

Chinese Translation

多模态生成模型能够产生流畅的输出，但在生成过程中必须遵循结构化、特定领域或安全关键知识时，它们仍然不可靠。现有方法通过提示增强、引导、潜在编辑或微调等机制来融入知识，但这些方法通常是根据技术而非它们所修改的生成过程组件进行分类。我们认为，在迭代生成模型中知识的注入本质上是一个干预层问题。由于生成过程作为内部状态的轨迹展开，知识可以作用于这一过程的四个结构上不同的组成部分：输入/输出边界、转换函数、中间状态和模型参数。这对应于四个干预层：表层、轨迹层、潜层和参数层。我们在扩散模型中实例化该框架，将代表性方法映射到所有四个层，并推导出多层组合的设计原则。在使用带有两个扩散主干的多模态知识图的受控安全对齐实验中，我们累积实施了四个层中的三个，表层（输入侧和输出侧）以及轨迹-潜层（中间生成阶段）。我们通过实验证明，每增加一个层能够解决前一个层无法处理的失败类别，相较于基础生成，减少了知识违反输出70.97%，并实证验证了框架的互补性预测。

View on arXiv Download PDF AI Translation

cs.AI / 99 / 2606.06360

An Infectious Disease Spread Simulation Based on Large Language Model Decision Making

基于大型语言模型决策的传染病传播仿真

Khaokaew, Yonchanok, Kong, Ruochen, Zufle, Andreas, Xue, Hao, Anderson, Taylor, MacIntyre, Chandini Raina, Scotch, Matthew, Salim, Flora D., Heslop, David J

Abstract

Modelling individual decision-making during infectious disease outbreaks is crucial for understanding behavioural dynamics and informing effective public health interventions. Prior work has shown that large language models can simulate realistic human behaviour by generating agent decisions based on demographic prompts and situational context. We build on this foundation with a spatially grounded, agent-based simulation framework that integrates LLM-generated decisions about self-reported influenza-like illness into a census-based synthetic population of agents. Location is treated as a central feature: agents are assigned to spatial units within cities, capturing the spatial distributions of different demographic groups using real-world census data and enabling geographically diverse behavioural modelling. We implement and compare three decision scenarios, independent reasoning, household influence, and message framing, and simulate self-reporting outcomes in San Francisco and Atlanta. Results reveal that income and education are the dominant drivers of reporting rate variation, with smaller but consistent effects from geography, LLM model choice, and message framing. Our framework generates synthetic data that captures both social and geographic heterogeneity, supporting spatial epidemiological modelling and bias-aware behavioural analysis.

Chinese Translation

在传染病暴发期间建模个体决策过程对于理解行为动态和制定有效公共卫生干预措施至关重要。先前的研究表明，大型语言模型能够通过基于人口统计提示和情境背景生成代理决策，从而模拟现实的人类行为。我们在这一基础上构建了一个空间基础的基于代理的仿真框架，该框架将大型语言模型生成的有关自我报告流感样疾病的决策整合到基于普查的合成代理人群中。位置被视为一个核心特征：代理人被分配到城市中的空间单元，利用现实世界的普查数据捕捉不同人口群体的空间分布，同时支持地理多样的行为建模。我们实现并比较了三种决策情境：独立推理、家庭影响和信息框架，并在旧金山和亚特兰大模拟了自我报告的结果。结果显示，收入和教育是报告率变化的主要驱动因素，地理位置、大型语言模型选择和信息框架的影响较小但一致。我们的框架生成的合成数据捕捉了社会和地理异质性，支持空间流行病学建模和意识到偏见的行为分析。

View on arXiv Download PDF AI Translation

cs.AI / 100 / 2606.06375

Rethinking Infrastructure Inspection as Image Difference Classification: A Traffic Sign Case Study

重新思考基础设施检查作为图像差异分类：以交通标志为案例研究

Mok, Ching Yau Fergus, de Silva, Lavindra, Reja, Varun Kumar, Brilakis, Ioannis

Abstract

Digital twins (DTs) allow the digitalization of road infrastructure inspection, though this is hindered by limited annotated data. This work exploits the relational nature of continuous asset condition monitoring to reformulate image-based defect detection as image difference classification (IDC) to reduce data reliance. This was evaluated in a case study on low-resource traffic sign inspection with different IDC classifiers using a newly-curated, high quality dataset. Results indicate that the instruction-based classifier outperforms encoder-based ones and gains from comparison with reference images. This shows that IDC can be an effective task modeling for tackling data constraints in infrastructure inspection and DT asset condition updating.

Chinese Translation

数字双胞胎（Digital Twins, DTs）使得道路基础设施检查的数字化成为可能，但这一进程受到有限标注数据的制约。本研究利用持续资产状态监测的关系特性，将基于图像的缺陷检测重构为图像差异分类（Image Difference Classification, IDC），以减少对数据的依赖。我们在一个低资源的交通标志检查案例研究中评估了不同的IDC分类器，使用了一个新整理的高质量数据集。结果表明，基于指令的分类器优于基于编码器的分类器，并从与参考图像的比较中获益。这表明，IDC可以有效地作为应对基础设施检查和DT资产状态更新中数据限制的任务建模。

View on arXiv Download PDF AI Translation

cs.AI / 101 / 2606.06388

Humans' ALMANAC: A Human Collaboration Dataset of Action-Level Mental Model Annotations for Agent Collaboration

人类的 ALMANAC：一个关于代理协作的行动层次心理模型注释的人类协作数据集

Chen, Jiaju, Lu, Yuxuan, Su, Jiayi, Chen, Chaoran, Xiao, Songlin, Zhang, Zheng, Wang, Yun, Li, Yunyao, Zhao, Jian, Wu, Tongshuang, Li, Toby Jia-Jun, Wang, Dakuo, Yao, Bingsheng

Abstract

Recent advances in LLM agents have enabled complex cognitive capabilities, such as multi-step reasoning, planning, and tool use, that increasingly position these agents as human collaborators. Effective collaboration, however, requires collaborators to continuously maintain and align mental models of their own reasoning,partners' intentions, and shared goals during the collaborative process. Today's agents rarely develop such capabilities since they are primarily optimized for task completion, and the community lacks authentic human collaboration data with action-level mental model annotations that could guide agents toward process-level collaborative competence. To bridge this gap, we present ALMANAC, a dataset of Action-Level Mental model ANnotations for Agent Collaboration built from the Map Task, a classic dyadic routing task from social science. ALMANAC contains 2,987 collaboration actions, each paired with theory-informed mental model annotations that record the participants' self-reasoning, perceived partner intent, and perceived team goal. We benchmark six LLMs on predicting humans' next-turn behavior and mental models. Our results demonstrate ALMANAC's utility in evaluating models' ability to simulate human collaborative behaviors and infer their underlying mental models.

Chinese Translation

近期在大型语言模型（LLM）代理方面的进展，赋予了这些代理复杂的认知能力，例如多步骤推理、规划和工具使用，使它们逐渐成为人类的协作伙伴。然而，有效的协作要求合作伙伴在协作过程中持续维护并对齐自己思维的心理模型、对方的意图以及共同目标。目前的代理很少具备这样的能力，因为它们主要是为了完成任务而优化，而且社区缺乏具有行动层次心理模型注释的真实人类协作数据，这些数据可以引导代理朝向过程层次的协作能力。为了解决这一问题，我们提出了 ALMANAC，这是一个基于地图任务（Map Task）的代理协作行动层次心理模型注释数据集，地图任务是一个经典的社会科学双人导航任务。ALMANAC 包含 2,987 个协作行动，每个行动都配有基于理论的心理模型注释，记录参与者的自我推理、感知的合作伙伴意图及感知的团队目标。我们评估了六个 LLM 在预测人类下一步行为及其心理模型方面的表现。我们的结果表明，ALMANAC 在评估模型模拟人类协作行为和推断其潜在心理模型的能力方面具有重要实用性。

View on arXiv Download PDF AI Translation

cs.AI / 102 / 2606.06396

Risk Assessment of Autonomous Driving: Integrating Technical Failures, Ethical Dilemmas, and Policy Frameworks

自主驾驶的风险评估：整合技术故障、伦理困境与政策框架

Chen, Boyi, Chu, Shengqin, Wang, Zicheng, Baetz, Brian, Gao, Zhen

Abstract

Autonomous driving technology has the potential to reduce the large number of road traffic accidents caused by human error each year, but it also brings new types of risks that need to be evaluated from the aspects of technology, ethics and regulations. Based on public crash data from the National Highway Traffic Safety Administration (NHTSA), disengagement reports from the California Department of Motor Vehicles (DMV), the MIT Moral Machines dataset, and a comparative regulatory analysis of five jurisdictions, we have found that the main types of technical failure modes are perception and classification errors. These account for a relatively large proportion of the reported accidents, and it can be concluded that there are different ethical frameworks for autonomous vehicle decision-making, and inconsistent regulations in different areas increase the uncertainty of widespread application. Generally speaking, the problems of technology, ethics and regulation are closely related and need to be solved together. Therefore, this paper recommends a more adaptive and cooperative governance approach that combines engineering standards, ethical discussion, and institutional supervision.

Chinese Translation

自主驾驶技术有潜力减少每年因人为错误导致的大量道路交通事故，但它也带来了需要从技术、伦理和法规等方面进行评估的新类型风险。基于美国国家公路交通安全管理局（NHTSA）的公共碰撞数据、加利福尼亚州机动车辆管理局（DMV）的 disengagement 报告、麻省理工学院道德机器（MIT Moral Machines）数据集，以及对五个司法管辖区的比较监管分析，我们发现主要的技术故障模式为感知和分类错误。这些错误占据了报告事故的相对较大比例，可以得出结论，自主车辆决策存在不同的伦理框架，而不同地区的法规不一致增加了广泛应用的的不确定性。一般而言，技术、伦理和法规的问题息息相关，必须共同解决。因此，本文建议采取更为适应性和合作的治理方法，结合工程标准、伦理讨论和制度监督。

View on arXiv Download PDF AI Translation

cs.AI / 103 / 2606.06416

Unsupervised Skill Discovery for Agentic Data Analysis

用于主动数据分析的无监督技能发现

Qiu, Zhisong, Song, Kangqi, Tang, Shengwei, Qiao, Shuofei, Liang, Lei, Chen, Huajun, Deng, Shumin

Abstract

Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reusable procedural knowledge without updating model parameters. However, discovering effective skills for data analysis remains challenging, as reliable supervision is expensive and success criteria vary across analytical formats. This raises the key question of how to discover reusable data-analysis skills from unlabeled exploration alone. We propose DataCOPE, an unsupervised verifier-guided skill discovery framework for data-analytic agents. DataCOPE derives verifier signals from the exploration trajectories and uses them to characterize relative quality or aggreement among trajectories. It iteratively coordinates a Data-Analytic Agent for trajectory generation, an Unsupervised Verifier for signal extraction, and a Skill Manager for contrastive skill distillation. For report-style analysis, we instantiate the verifier as an Adaptive Checklist Verifier that derives task-specific criteria, scores reports by verifiable coverage, and iteratively refines the checklist. For reasoning-style analysis, we instantiate it as an Answer Agreement Verifier that groups trajectories by answer agreement and uses self-consistency as an auxiliary signal. We evaluate DataCOPE on report-style analysis from Deep Data Research and reasoning-style analysis from DABStep. Across both settings, DataCOPE consistently improves held-out performance over baselines. Averaged across four model settings, DataCOPE improves the mean score by 9.71% and 32.30% on report-style and reasoning-style tasks respectively.

Chinese Translation

推理时的技能增强通过注入可重用的过程知识而不更新模型参数，为数据分析代理提供了一种轻量级的改进方式。然而，发现有效的数据分析技能仍然具有挑战性，因为可靠的监督成本高昂且成功标准在不同的分析格式之间变化。这引出了一个关键问题，即如何仅通过未经标记的探索发现可重用的数据分析技能。我们提出了 DataCOPE，一种针对数据分析代理的无监督验证器引导的技能发现框架。DataCOPE 从探索轨迹中提取验证器信号，并利用这些信号来表征轨迹之间的相对质量或一致性。它迭代协调数据分析代理以生成轨迹、无监督验证器以提取信号，以及技能管理器以进行对比技能蒸馏。在报告风格的分析中，我们将验证器实例化为自适应清单验证器，该验证器导出任务特定标准，通过可验证的覆盖率对报告进行评分，并迭代改进清单。在推理风格的分析中，我们将其实例化为答案一致性验证器，该验证器通过答案一致性对轨迹进行分组，并将自一致性作为辅助信号。我们在 Deep Data Research 的报告风格分析和 DABStep 的推理风格分析上评估了 DataCOPE。在这两种设置中，DataCOPE 一直在持出性能上超越基线。四种模型设置的平均值显示，DataCOPE 在报告风格和推理风格任务上的平均得分分别提高了 9.71% 和 32.30%。

View on arXiv Download PDF AI Translation

cs.AI / 104 / 2606.06448

Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads

智能体记忆：有状态长时间工作负载的特征及系统影响

Omri, Yasmine, Gan, Ziyu, Broveak, Zachary, Geens, Robin, He, Zexue, Pentland, Alex, Verhelst, Marian, Weissman, Tsachy, Tambe, Thierry

Abstract

LLM agents are increasingly deployed on long-horizon tasks requiring sustained reasoning over extended interaction histories. Realizing this at scale requires agents to persistently store, retrieve, and update their own memory across sessions. A rich ecosystem of agent memory systems has emerged spanning flat retrieval, LLM-mediated extraction, consolidating fact stores, and agentic control flows. Yet, their system-level behavior remains uncharacterized. We present the first systems characterization of agent memory. First, we introduce a system-oriented taxonomy classifying agent memory systems along four axes. Second, we build a phase-aware profiling harness attributing cost to construction, retrieval, and generation. Third, we characterize ten representative systems across two benchmark suites, uncovering how design choices shift cost across the write and read paths. Finally, we derive 10 system recommendations covering construction scheduling, capability floors, amortization via query volume, freshness-latency tradeoffs, and fleet-scale management.

Chinese Translation

大型语言模型（LLM）智能体越来越多地被应用于需要对扩展交互历史进行持续推理的长时间任务。大规模实现这一目标需要智能体在多个会话中持续存储、检索和更新自身的记忆。围绕智能体记忆系统，出现了一个丰富的生态系统，涵盖了平面检索、LLM中介提取、整合事实存储和智能控制流。但它们的系统级行为仍未被明确表征。我们首次对智能体记忆进行系统性表征。首先，我们介绍一种面向系统的分类法，从四个维度对智能体记忆系统进行分类。其次，我们建立一个阶段感知的分析工具，将构建、检索和生成的成本归因。第三，我们对十个代表性系统进行表征，涵盖两个基准套件，揭示设计选择如何在写入和读取路径之间转移成本。最后，我们提出十条系统建议，涵盖构建调度、能力基线、通过查询量的摊销、新鲜度-延迟权衡和车队规模管理。

View on arXiv Download PDF AI Translation

cs.AI / 105 / 2606.06453

Vortex: Efficient and Programmable Sparse Attention Serving for AI Agents

Vortex：面向人工智能代理的高效可编程稀疏注意力服务

Chen, Zhuoming, Zhong, Xinrui, Feng, Qilong, Sadhukhan, Ranajoy, Zhou, Yang, Shieh, Michael Qizhe, Jia, Zhihao, Chen, Beidi

Abstract

Sparse attention is becoming increasingly important for serving large language models (LLMs) as generation lengths continue to grow. However, deploying and evaluating new sparse attention algorithms at scale remains highly engineering-intensive, slowing both human researchers and AI agents in exploring the sparse attention design. To address this challenge, we present Vortex, a system that combines a Python-embedded frontend language atop a page-centric tensor abstraction for expressing a broad range of sparse attention algorithms, with an efficient backend tightly integrated into modern LLM serving stacks. Vortex enables rapid prototyping, deployment, and evaluation of sparse attention algorithms, effectively translating their theoretical efficiency gains into real-world throughput improvements. As a result, Vortex substantially accelerates the design and iteration of sparse attention algorithms. First, AI agents use Vortex to automatically generate and refine diverse algorithms, the best reaching up to $3.46\times$ higher throughput than full attention while preserving accuracy. Second, Vortex extends sparse attention to emerging architectures and very large models that are otherwise hard to experiment with, reaching up to $4.7\times$ higher throughput on the MLA-based GLM-4.7-Flash and $1.37\times$ on the 229B-parameter MiniMax-M2.7 on NVIDIA B200 GPUs.

Chinese Translation

稀疏注意力在服务大型语言模型（LLMs）中变得日益重要，尤其是在生成长度不断增长的背景下。然而，在规模上部署和评估新的稀疏注意力算法仍然极为工程密集，这减缓了人类研究人员和人工智能代理在稀疏注意力设计探索中的进展。为解决这一挑战，我们提出了Vortex，一个结合了嵌入Python前端语言的系统，该系统基于页面中心的张量抽象，能够表达广泛的稀疏注意力算法，后端有效地与现代LLM服务栈紧密集成。Vortex使得稀疏注意力算法的快速原型设计、部署和评估成为可能，有效地将其理论效率提升转化为现实世界的吞吐量改进。因此，Vortex大幅加速了稀疏注意力算法的设计与迭代。首先，人工智能代理利用Vortex自动生成和优化多种算法，其中最佳算法在保持准确性的同时，其吞吐量可达到全注意力的$3.46 imes$。其次，Vortex将稀疏注意力扩展到新兴架构和其他难以实验的超大模型，在基于MLA的GLM-4.7-Flash上可达到$4.7 imes$的更高吞吐量，同时在229B参数的MiniMax-M2.7上在NVIDIA B200 GPU上达到$1.37 imes$的提升。

View on arXiv Download PDF AI Translation

cs.AI / 106 / 2606.06462

Benchmark Everything Everywhere All at Once

全面基准测试一切

Xiong, Shiyun, Wu, Dongming, Sun, Peiwen, Ai, Yuang, Yang, Bokang, Han, Wencheng, Li, Xiao-Hui, Yue, Xiangyu

Abstract

Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance. However, their construction is labor-intensive and hard to reuse, raising concerns about sustainability and scalability. Moreover, existing benchmarks often quickly reach performance saturation after their release, resulting in insufficient discrimination among state-of-the-art models. To address these challenges, we introduce Benchmark Agent, a fully autonomous agentic system designed for benchmark building. Our framework orchestrates the complete benchmark construction pipeline, from user query analysis and subtask design to data annotation and quality control. To assess Benchmark Agent, we implement it to produce 15 representative benchmarks, spanning diverse evaluation scenarios, including text understanding, multimodal understanding, and domain-specific reasoning. Extensive experiments, including human evaluation, LLM-as-a-judge assessment, and consistency checks, demonstrate Benchmark Agent can generate high-quality benchmark samples with minimal human involvement. More importantly, through continual evaluation, we observe several insightful findings, including that current models struggle with certain domain-specific reasoning tasks. We believe that rapidly evolving benchmarks can contribute significantly to the research community. The preview and code will be publicly available at the demo page and code repository.

Chinese Translation

基准测试是评估和推动大型语言模型（LLMs）和多模态大型语言模型（MLLMs）的基础，它提供了标准化和明确的性能衡量标准。然而，其构建过程劳动密集且难以重复使用，导致可持续性和可扩展性的问题。此外，现有的基准测试在发布后通常很快达到性能饱和，导致对最先进模型之间的区分不足。为了解决这些挑战，我们引入了基准代理（Benchmark Agent），一个完全自动化的基准构建系统。我们的框架协调了完整的基准构建流程，从用户查询分析、子任务设计到数据标注和质量控制。为了评估基准代理，我们实施了15个代表性的基准测试，涵盖了多种评估场景，包括文本理解、多模态理解和领域特定推理。广泛的实验，包括人工评估、以LLM作为评判者的评估和一致性检查，表明基准代理可以在最小的人力参与下生成高质量的基准样本。更重要的是，通过持续评估，我们观察到一些有见地的发现，包括当前模型在某些领域特定推理任务上的困难。我们相信，快速发展的基准测试能够为研究社区做出重要贡献。预览和代码将在演示页面和代码库中公开发布。

View on arXiv Download PDF AI Translation

cs.AI / 107 / 2606.06468

Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement

Goedel-Architect：通过蓝图生成与优化简化形式定理证明

Chung, Jui-Hui, Cai, Ziyang, Li, Zihao, Yin, Qishuo, Agarwal, Rohit, Park, Simon, Porto, Rodrigo, Ri, Narutatsu, Yang, Ziran, Tang, Shange, Dang, Xingyu, Lin, Hongzhou, Wang, Mengdi, Chen, Danqi, Jin, Chi, Fowl, Liam H, Arora, Sanjeev

Abstract

We introduce Goedel-Architect, an agentic framework for formal theorem proving in Lean 4 centered on blueprint generation and refinement. A blueprint is a dependency graph of definitions and lemmas that builds up to the main theorem. First, Goedel-Architect generates a blueprint of formally stated definitions and lemmas, along with declared dependencies. This blueprint is optionally guided by a natural language proof. Then, a tool-equipped Lean prover component closes each open lemma node in parallel using relevant dependencies. Failed lemmas in turn drive refinement of the global blueprint. This strategy contrasts with other mainstream approaches which use recursive lemma decomposition, and can inefficiently loop on dead-end strategies. Using the open-weight DeepSeek-V4-Flash (284B-A13B) as the backbone, Goedel-Architect attains 99.2% pass@1 on MiniF2F-test and 75.6% pass@1 on PutnamBench. With an optional natural-language proof seeding the initial blueprint on the harder problems, we additionally close the remaining two MiniF2F-test problems (reaching 100%), lift PutnamBench to 88.8% (597/672), and solve 4/6 on IMO 2025, 11/12 on Putnam 2025, and 3/6 on USAMO 2026. This represents state-of-the-art performance for an open-source pipeline at a price point up to 500x less than comparable open-source pipelines.

Chinese Translation

我们介绍了Goedel-Architect，这是一个基于Lean 4的形式定理证明的代理框架，重点在于蓝图的生成与优化。蓝图是一个关于定义和引理的依赖图，逐步构建到主要定理。首先，Goedel-Architect生成一个正式陈述的定义和引理的蓝图，以及声明的依赖关系。该蓝图可以选择根据自然语言证明进行指导。然后，配备工具的Lean证明器组件并行地使用相关依赖关系关闭每个开放引理节点。失败的引理反过来推动全球蓝图的优化。这一策略与其他主流方法形成对比，后者使用递归引理分解，可能低效地在无路可退的策略上循环。以开源权重DeepSeek-V4-Flash (284B-A13B)为基础，Goedel-Architect在MiniF2F-test上达到了99.2%的pass@1，在PutnamBench上达到了75.6%的pass@1。通过在更难的问题上使用自然语言证明来引导初始蓝图，我们还进一步解决了剩余的两个MiniF2F-test问题（达到100%），将PutnamBench提升至88.8%（597/672），并解决了IMO 2025的4/6，Putnam 2025的11/12，以及USAMO 2026的3/6。这代表了开源管道在性能上的最新水平，其成本比可比的开源管道低至500倍。

View on arXiv Download PDF AI Translation

cs.AI / 108 / 2606.06473

MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery

MLEvolve：一种自我进化的自动化机器学习算法发现框架

Du, Shangheng, Yan, Xiangchao, Shi, Jinxin, Cao, Zongsheng, Feng, Shiyang, Liang, Zichen, Sun, Boyuan, Peng, Tianshuo, Zhou, Yifan, Li, Xin, Zhou, Jie, He, Liang, Zhang, Bo, Bai, Lei

Abstract

Large language model (LLM) agents are increasingly applied to long-horizon tasks such as scientific discovery and machine learning engineering (MLE), where sustained self-evolution becomes a key capability. However, existing MLE agents suffer from inter-branch information isolation, memoryless search, and lack of hierarchical control, which together hinder long-horizon optimization. We present MLEvolve, an LLM-based self-evolving multi-agent framework for end-to-end machine learning algorithm discovery. By extending tree search to Progressive MCGS, MLEvolve enables cross-branch information flow through graph-based reference edges and gradually shifts the search from broad exploration to focused exploitation with an entropy-inspired progressive schedule. To allow the agent to evolve with accumulated experience, we introduce Retrospective Memory, which combines a cold-start domain knowledge base with a dynamic global memory for task-specific experience retrieval and reuse. For stable long-horizon iteration, we further decouple strategic planning from code generation with adaptive coding modes. Evaluation on MLE-Bench shows that MLEvolve achieves state-of-the-art performance across multiple dimensions including average medal rate and valid submission rate under a 12-hour budget (half the standard runtime). Moreover, MLEvolve also outperforms specialized algorithm discovery methods including AlphaEvolve on mathematical algorithm optimization tasks, demonstrating strong cross-domain generalization. Our code is available at https://github.com/InternScience/MLEvolve.

Chinese Translation

大型语言模型（LLM）代理正被越来越多地应用于科学发现和机器学习工程（MLE）等长远任务中，其中持续自我进化成为一个关键能力。然而，现有的MLE代理存在信息孤岛、无记忆搜索和缺乏层级控制等问题，这些共同阻碍了长远优化。我们提出了MLEvolve，这是一种基于LLM的自我进化多代理框架，用于端到端的机器学习算法发现。通过将树搜索扩展到渐进式多类别生成搜索（Progressive MCGS），MLEvolve通过基于图的参考边启用跨分支的信息流，并通过熵驱动的渐进调度逐渐将搜索从广泛探索转向更为集中地利用。为了使代理能够随着积累的经验不断进化，我们引入了回溯记忆（Retrospective Memory），该记忆结合了冷启动领域知识库与动态全球记忆，用于特定任务的经验检索和重用。为了实现稳定的长远迭代，我们进一步将战略规划与代码生成解耦，采用自适应编码模式。在MLE-Bench上的评估表明，MLEvolve在多个维度上实现了最先进的性能，包括在12小时预算（标准运行时间的一半）下的平均奖牌率和有效提交率。此外，MLEvolve在数学算法优化任务中还超越了包括AlphaEvolve在内的专业算法发现方法，展现了强大的跨领域泛化能力。我们的代码可在https://github.com/InternScience/MLEvolve获得。

View on arXiv Download PDF AI Translation

计算语言学 (Computation and Language)

103

cs.CL / 1 / 2606.05168

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics

模型崩溃的流行病学：通过双层 SIR 动力学建模合成数据污染

Wang, Xiangyu

Abstract

Training on synthetic data causes model collapse, but existing analyses treat this as single-chain degradation. In reality, the AI ecosystem involves cross-contamination: models ingest synthetic data from other models, produce new synthetic text, and contaminate shared corpora. We propose a bilayer coupled SIR/SIRS framework -- a phenomenological mean-field model treating data corpora and AI models as two interacting populations, each with susceptible, infected, and recovered compartments linked by cross-layer transmission. The SIRS variant (our primary recommendation) incorporates immunity waning, reflecting that filtered corpora and retrained models remain susceptible to re-contamination. We derive the basic reproduction number $R_0 = \sqrt{\beta_D \beta_M / [(\gamma_D+\mu_D)(\gamma_M+\mu_M)]}$ via the Next Generation Matrix and apply standard epidemic threshold results to the bilayer system. Illustrative scenario-based calibration from public AI text prevalence data yields supercritical dynamics ($R_0 > 1$) across three scenarios; Sobol sensitivity analysis identifies synthetic-text detection as the highest-leverage parameter. A bipartite-network agent-based model confirms mean-field consistency ($R^2 > 0.96$) for dense networks but degrades under heterogeneity. GPT-2 contamination chain experiments (192 runs across WikiText and Shakespeare) show dose-response degradation and diversity loss qualitatively consistent with the threshold picture. Matched-budget source-diversity experiments (1,088 runs) provide suggestive evidence that multi-source mixing modestly attenuates collapse, but the effect vanishes at lower contamination fractions. Intervention analysis identifies detection-based filtering and herd immunity as the highest-leverage strategies.

Chinese Translation

在合成数据上进行训练会导致模型崩溃，但现有分析将其视为单链降级。实际上，人工智能生态系统涉及交叉污染：模型从其他模型中获取合成数据，生成新的合成文本，并污染共享语料库。我们提出了一种双层耦合的 SIR/SIRS 框架——一个将数据语料库和人工智能模型视为两个相互作用种群的现象学均场模型，每个种群都有易感、感染和恢复的区室，并通过跨层传播相互连接。SIRS 变体（我们主要的推荐）包含免疫衰减，反映过滤后的语料库和再训练的模型依然易感于重新污染。我们通过下一代矩阵推导出基本繁殖数 $R_0 = rac{eta_D eta_M}{(eta_D+eta_M)( u_D+ u_M)}$，并将标准流行病阈值结果应用于双层系统。基于公共人工智能文本流行数据的场景校准显示出三个场景下超临界动力学（$R_0 > 1$）；Sobol 敏感性分析确定了合成文本检测作为影响最大的参数。一个双分网络基于代理模型确认了稠密网络的均场一致性（$R^2 > 0.96$），但在异质性影响下恶化。GPT-2 污染链实验（在 WikiText 和莎士比亚上进行的 192 次实验）显示出剂量-反应降级和多样性损失，与阈值图景定性一致。匹配预算的源多样性实验（1,088 次实验）提供了暗示性证据，表明多源混合适度减缓了崩溃，但在较低污染比例下效果消失。干预分析确定基于检测的过滤和群体免疫作为影响最大的策略。

View on arXiv Download PDF AI Translation

cs.CL / 2 / 2606.05173

Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning

预测与重建：自监督语言表示学习的联合目标

Boukhari, Aimen

Abstract

Masked language modelling (MLM) has been the dominant pre-training objective for text encoders since BERT, yet it encourages representations that are strongly anchored to surface-form token identity rather than deeper semantic structure. Inspired by the success of Joint Embedding Predictive Architectures (JEPA) (LeCun, 2022) in vision and audio, we propose a hybrid pre-training objective that combines a JEPA-style latent-space prediction loss with a standard MLM objective over a single shared encoder. A learnable scalar parameter continuously balances the two objectives during training. We pre-train both a hybrid model and a pure-MLM baseline on English Wikipedia using identical architectures and compute budgets (NVIDIA H100). Extensive representation analysis across five GLUE benchmarks (SST-2, MRPC, MNLI, CoLA, STS-B) using four pooling strategies reveals that the hybrid encoder produces significantly more uniform embeddings (uniformity less than -0.16 vs -0.05 for MLM), exhibits richer spectral geometry under max pooling, encodes less surface-level lexical information, and achieves a better semantic-to-lexical balance. Despite similar linear-probe downstream accuracy, the geometric differences are consistent and significant, suggesting that the JEPA predictive objective reshapes the latent space in ways that standard accuracy metrics alone cannot capture.

Chinese Translation

自从BERT以来，掩码语言建模（MLM）一直是文本编码器的主导预训练目标，但它鼓励的表示更多地与表面形式的令牌身份相关联，而非更深层的语义结构。受到联合嵌入预测架构（Joint Embedding Predictive Architectures, JEPA）（LeCun，2022）在视觉和音频领域成功的启发，我们提出了一种混合预训练目标，该目标结合了JEPA风格的潜在空间预测损失与单个共享编码器上的标准MLM目标。在训练过程中，一个可学习的标量参数持续平衡这两种目标。我们在英文维基百科上预训练了一个混合模型和一个纯MLM基线，使用相同的架构和计算预算（NVIDIA H100）。对五个GLUE基准（SST-2、MRPC、MNLI、CoLA、STS-B）使用四种池化策略进行的广泛表示分析显示，混合编码器生成的嵌入显著更加均匀（均匀度小于-0.16，而MLM为-0.05），在最大池化下展现出更丰富的谱几何特征，编码的表面层词汇信息更少，并实现了更好的语义与词汇平衡。尽管线性探测下游准确率相似，但几何上的差异是一致且显著的，这表明JEPA预测目标以标准准确率指标无法捕捉的方式重塑了潜在空间。

View on arXiv Download PDF AI Translation

cs.CL / 3 / 2606.05174

Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPO

通过变异感知的评分奖励与GRPO改善心脏医学问答中的大型语言模型

Ahmadi, Arash, Masnadi, Parisa, Sharif, Sarah, Nicholson, Charles, Ebert, David, Banad, Mike

Abstract

Large Language Models (LLMs) have shown strong promise in healthcare applications. Yet deploying general-purpose models in real-world settings remains difficult due to data privacy constraints, inference costs, and limited suitability for edge or on-device use. These challenges motivate the development of smaller, more efficient models that require robust post-training strategies to ensure reliable medical reasoning. In this work, we investigate Group Relative Policy Optimization (GRPO) for post-training LLMs on heart-focused medical question answering with rubric-based supervision derived from RaR-Medicine. We propose a Variance-Aware Reward Framework that extends the Explicit Aggregation and Implicit Aggregation strategies of Rubrics as Rewards by replacing weighted binary criterion aggregation and single overall Likert-style scoring with continuous analytical reward functions derived from criterion-level rubric outcomes. This formulation provides richer optimization signals for feedback that is sparse, multi-criteria, and difficult to verify automatically, and enables more stable on-policy reinforcement learning. On a held-out heart-related subset of HealthBench, our best GRPO variant improves accuracy from 0.362 to 0.502 and F1 from 0.532 to 0.668 relative to the Qwen3-14B base model, while remaining competitive with GPT-OSS-120B (0.508 accuracy, 0.674 F1). Our findings show that carefully designed rubric-based rewards provide a practical strategy for improving heart-focused medical question answering in LLMs, with potential to extend to other rubric-based tasks.

Chinese Translation

大型语言模型（LLMs）在医疗应用中展现出强大的潜力。然而，由于数据隐私限制、推理成本以及在边缘或设备上使用的适用性有限，通用模型在实际应用中的部署仍然面临困难。这些挑战促使我们开发更小、更高效的模型，这些模型需要强大的后训练策略以确保可靠的医学推理。在本研究中，我们探讨了针对心脏医学问答的后训练LLMs的群体相对政策优化（Group Relative Policy Optimization，GRPO），并采用源自RaR-Medicine的基于评分标准的监督。我们提出了一种变异感知的奖励框架，扩展了评分标准作为奖励中的显式聚合和隐式聚合策略，通过用基于标准级评分结果导出的连续分析奖励函数替换加权二元标准聚合和单一整体Likert评分。这种公式为稀疏、多标准、且难以自动验证的反馈提供了更丰富的优化信号，并使得在政策上更稳定的强化学习成为可能。在HealthBench的一个独立心脏相关子集上，我们最佳的GRPO变体将准确率从0.362提高至0.502，F1从0.532提高至0.668，相比于Qwen3-14B基础模型，同时在准确率（0.508）和F1（0.674）上与GPT-OSS-120B保持竞争力。我们的研究结果表明，精心设计的基于评分标准的奖励为改善LLMs中的心脏医学问答提供了一种切实可行的策略，并有潜力扩展到其他基于评分标准的任务。

View on arXiv Download PDF AI Translation

cs.CL / 4 / 2606.05175

Generic Triple-Latent Compression with Gated Associative Retrieval

通用三重潜在压缩与门控关联检索

Xiao, Liu

Abstract

We study generic triple-latent sequence models that maintain a running token state and compressed pair-memory pathway to capture higher-order token interactions without benchmark-specific parsing. The triple-latent family improves a small Transformer baseline on byte-level WikiText-2 and on a tokenizer-based MiniMind language-model benchmark, while a recall-focused gated key-value retrieval extension improves associative recall but remains seed-sensitive and much slower in the current reference implementation.

Chinese Translation

我们研究了通用三重潜在序列模型，该模型维护一个运行的标记状态和压缩的配对记忆路径，以捕捉高阶标记交互，且无需特定基准的解析。三重潜在系列在字节级的WikiText-2以及基于分词器的MiniMind语言模型基准上改进了一个小型Transformer基准，而侧重召回的门控键值检索扩展提高了联想召回，但在当前的参考实现中仍然对种子敏感且速度较慢。

View on arXiv Download PDF AI Translation

cs.CL / 5 / 2606.05176

PEFT of SLM for Telecommunications Customer Support: A Comparative Study of LoRA Configurations with Energy Consumption Analysis

电信客户支持的参数高效微调（PEFT）：LoRA配置与能耗分析的比较研究

Tamic, Lucas, Jaffeux-Cheniout, Ilan, Marjou, Xavier

Abstract

While large language models (LLMs) show strong performance in natural language understanding and generation, their evaluation and adaptation to domain-specific constraints in telecommunications customer support remain limited. In addition, data sovereignty, regulatory constraints, and the handling of sensitive customer and network information complicate the use of externally hosted foundation models in this domain. We present a systematic study of parameter-efficient fine-tuning (PEFT) using Low-Rank Adaptation (LoRA) applied to Qwen2.5-3B to build a domain-specific conversational assistant. We introduce a combinatorial synthetic data generation approach based on a glossary of 52 industry-specific terms, producing approximately 30,000 training examples across 1,560 distinct problem scenarios via a generative pipeline powered by Gemini 2.0 Flash. We evaluate 16 LoRA configurations by varying hyperparameters and target modules. Our evaluation extends beyond standard metrics by incorporating energy consumption analysis and qualitative assessment using an LLM-as-a-judge framework with GPT-5.2 and Claude 4.5 Sonnet. Results show a clear divergence between quantitative and qualitative performance: models achieving the lowest validation loss do not necessarily obtain the best human-aligned rankings. The best validation loss (0.5024) ranks only 6th-7th in qualitative evaluation, while the worst loss (0.6807) ranks first according to both judges. This work contributes (1) a combinatorial method for synthetic dataset construction, (2) insights into the impact of target module selection for LoRA injection, (3) evidence that validation loss alone is insufficient for selecting fine-tuning configurations in conversational AI, and (4) an energy-performance trade-off analysis for sustainable LLM deployment.

Chinese Translation

尽管大型语言模型（LLMs）在自然语言理解和生成方面表现出色，但它们在电信客户支持领域特定约束下的评估和适应仍然有限。此外，数据主权、监管约束以及对敏感客户和网络信息的处理使得在该领域使用外部托管的基础模型变得复杂。我们提出了一项系统研究，采用低秩适配（LoRA）对Qwen2.5-3B进行参数高效微调（PEFT），以构建特定领域的对话助手。我们引入了一种基于52个行业特定术语的词汇表的组合合成数据生成方法，通过一个由Gemini 2.0 Flash驱动的生成管道生成约30,000个训练示例，涵盖1,560个独特的情境问题。我们评估了16种LoRA配置，通过改变超参数和目标模块来进行比较。我们的评估超越了标准指标，纳入了能耗分析和使用LLM-as-a-judge框架与GPT-5.2和Claude 4.5 Sonnet进行的定性评估。结果表明定量和定性性能之间存在明显的分歧：获得最低验证损失的模型不一定获得最佳的人类对齐排名。最佳验证损失（0.5024）在定性评估中仅排名第6-7，而最差损失（0.6807）在两位评审中均排名第一。本研究的贡献包括（1）一种合成数据集构建的组合方法，（2）对LoRA注入时目标模块选择影响的见解，（3）验证损失不足以单独选择对话人工智能微调配置的证据，以及（4）可持续部署大型语言模型的能耗与性能权衡分析。

View on arXiv Download PDF AI Translation

cs.CL / 6 / 2606.05177

MCBench: A Multicontext Safety Assessment Benchmark for Omni Large Language Models

MCBench：一种多情境安全评估基准，用于全方位大型语言模型

Luong, Manh, Abraham, Tamas, Kim, Junae, Kaur, Amar, Omari, Rollin, Haffari, Gholamreza, Vu, Trang, Qu, Lizhen, Phung, Dinh

Abstract

Existing multimodal safety benchmarks focus solely on visual inputs and cannot assess Omni Large Language Models (LLMs) that process vision, audio, and text. We introduce MCBench, a benchmark with 1196 scenarios spanning four safety categories that require integrating multiple modalities for accurate safety assessment. Each unsafe scenario is paired with a minimally different safe counterpart to assess model sensitivity. Our evaluations of state-of-the-art models reveal significant challenges. Omni LLMs struggle with subtle or non-physical risks but perform better when salient visual or acoustic cues are present. Analysis of reasoning traces shows that, although models can extract modality-specific information, they often fail to integrate these cues effectively for safety judgments. Our findings reveal that current Omni LLMs lack robust cross-modal reasoning in safety-critical settings, underscoring the need for improved architectures and training strategies for multimodal safety.

Chinese Translation

现有的多模态安全基准仅关注视觉输入，无法评估能够处理视觉、音频和文本的全方位大型语言模型（Omni LLMs）。我们提出了MCBench，这是一个包含1196个场景的基准，涵盖四个安全类别，要求整合多种模态以进行准确的安全评估。每个不安全的场景都与一个最小差异的安全对应场景配对，以评估模型的敏感性。我们对最先进模型的评估揭示了显著的挑战。全方位LLMs在处理微妙或非物理风险时表现不佳，但在显著的视觉或声学线索存在时表现更好。推理痕迹的分析表明，尽管模型可以提取特定模态的信息，但它们在有效整合这些线索进行安全判断方面常常失败。我们的研究结果显示，目前的全方位LLMs在安全关键环境中缺乏稳健的跨模态推理，强调了对改进多模态安全架构和训练策略的需求。

View on arXiv Download PDF AI Translation

cs.CL / 7 / 2606.05179

Efficient Punctuation Restoration via Weighted Lookahead Scoring Method for Streaming ASR Systems

通过加权前瞻评分方法实现流式语音识别系统的高效标点恢复

Woo, Sungmook, Kang, Hyungu, Kim, Chanwoo

Abstract

Punctuation restoration improves ASR (Automatic Speech Recognition) readability. However streaming ASR requires online decisions with limited future context. In streaming ASR, the system predicts punctuation incrementally, which makes generation-based approaches prone to latency and alignment failures under boundary-wise evaluation. This paper proposes a non-autoregressive scoring method (no free-form generation) that preserves the input transcript and makes a decision at each word boundary. Our method compares punctuation insertion hypotheses against a no-insertion baseline under a bounded K-subword-token lookahead, and calibrates decisions using a weight {\alpha} and a validation-calibrated threshold {\tau} (no parameter updates during inference). On IWSLT 2017, our scoring method achieves a 4-class macro F1 of 0.893 in the no fine-tuning setting (validation-calibrated, K=2) and 0.937 after fine-tuning (K=2), outperforming the prompt-based baseline (0.566) and a fine-tuned ELECTRA baseline (0.913) under the same lookahead budget. We analyze the impact of the lookahead budget through ablation studies on K.

Chinese Translation

标点恢复提高了自动语音识别（ASR）的可读性。然而，流式ASR要求在有限的未来上下文中做出在线决策。在流式ASR中，系统逐步预测标点，这使得基于生成的方法在边界评估下容易出现延迟和对齐失败。本文提出了一种非自回归评分方法（无自由形式生成），该方法保留输入文字记录并在每个单词边界做出决策。我们的方法在有限的K子词标记前瞻下，将标点插入假设与无插入基线进行比较，并使用权重{eta}和经过验证的阈值{ au}进行决策校准（推理过程中不更新参数）。在IWSLT 2017数据集上，我们的评分方法在不进行微调的情况下（验证校准，K=2）实现了0.893的四分类宏F1值，并在微调后（K=2）达到0.937，超越了基于提示的基线（0.566）和微调的ELECTRA基线（0.913），均在相同的前瞻预算下。我们通过对K的消融研究分析前瞻预算的影响。

View on arXiv Download PDF AI Translation

cs.CL / 8 / 2606.05180

From Scoring to Explanations: Evaluating SHAP and LLM Rationales for Rubric-based Teaching Quality Assessment

从评分到解释：评估 SHAP 和 LLM 理由在基于评分标准的教学质量评估中的应用

Bueno, Ivo, Bühler, Babette, Stark, Philipp, Fütterer, Tim, Trautwein, Ulrich, Demszky, Dorottya, Hill, Heather, Kasneci, Enkelejda

Abstract

Automated scoring models are increasingly used to assign rubric-based quality ratings to complex language performances, including classroom transcripts, yet they typically provide little insight into why a particular score is produced. We propose a general framework for sentence-level interpretability of rubric-based scoring that combines model-agnostic Shapley-value attributions with rationales generated by large language models (LLMs). Instantiated on the Quality of Feedback dimension of the CLASS framework using the NCTE corpus, the framework enables systematic comparison of fine-tuned pretrained language models (PLMs) and prompted LLMs on both scoring performance and explanation faithfulness. Across 6k annotated transcript segments, fine-tuned PLMs outperform LLMs in prediction accuracy but exhibit label compression toward mid-scale scores. Deletion-based tests show that SHAP identifies sentences that reliably drive model predictions, producing typically larger and more coherent prediction shifts than LLM-generated rationales. Cross-model analyses further reveal that SHAP attributions transfer robustly across architectures, whereas LLM rationales exert limited and inconsistent influence. Overall, the findings demonstrate that SHAP provides more faithful and transferable explanations for rubric-based scoring, and that the proposed framework offers a principled basis for evaluating both scoring models and their explanations in high-stakes educational settings and other rubric-based language assessment tasks.

Chinese Translation

自动评分模型越来越多地被用于对复杂语言表现（包括课堂成绩单）进行基于评分标准的质量评估，然而它们通常对为何产生某一特定分数提供的见解有限。我们提出了一个用于基于评分标准的评分的句子级可解释性的一般框架，该框架结合了与模型无关的夏普利值归因（Shapley-value attributions）和大型语言模型（LLMs）生成的理由。该框架在采用 NCTE 语料库的 CLASS 框架的反馈质量维度上进行了实例化，能够系统地比较微调的预训练语言模型（PLMs）和提示的 LLMs 在评分表现和解释信度上的表现。在 6000 个标注的成绩单片段中，微调的 PLMs 在预测准确性上优于 LLMs，但在标签上表现出向中间评分的压缩。基于删除的测试表明，SHAP 能够识别出可靠驱动模型预测的句子，通常能产生比 LLM 生成的理由更大且更连贯的预测变化。交叉模型分析进一步揭示，SHAP 的归因在不同架构间具有很强的迁移性，而 LLM 的理由影响有限且不一致。总体而言，研究结果表明，SHAP 为基于评分标准的评分提供了更忠实和可迁移的解释，而所提出的框架为在高风险教育环境及其他基于评分标准的语言评估任务中评估评分模型及其解释提供了原则基础。

View on arXiv Download PDF AI Translation

cs.CL / 9 / 2606.05181

Multi-Granularity Reasoning for Natural Language Inference

自然语言推理的多粒度推理

Xi, Chunling, Liang, Di

Abstract

Natural Language Inference (NLI) is a fundamental task in natural language understanding that requires determining the logical relationship between a premise and a hypothesis. Despite the remarkable success of transformer-based pre-trained models, most existing approaches primarily rely on the final-layer token representations, which are often insufficient for capturing the complex and hierarchical semantic interactions required for effective reasoning. In particular, fine-grained lexical cues, phrasal compositions, and higher-level contextual semantics are typically entangled or diluted in a single representation space. To address these limitations, we propose a novel \emph{Multi-Granularity Reasoning Network} (MGRN) that explicitly leverages hierarchical semantic features within an interactive reasoning space. The proposed framework mimics the human cognitive process of language understanding, which naturally progresses from shallow lexical matching to deeper semantic abstraction and logical reasoning. By integrating semantic information across multiple granularities in a progressive and structured manner, MGRN is able to uncover intricate semantic relationships underlying natural language expressions. Extensive experiments on multiple public benchmarks demonstrate that MGRN consistently outperforms strong baseline models, validating the effectiveness and robustness of the proposed approach.

Chinese Translation

自然语言推理（NLI）是自然语言理解中的一项基础任务，要求确定前提和假设之间的逻辑关系。尽管基于变换器模型的预训练模型取得了显著成功，但大多数现有方法主要依赖最终层的词元表示，这往往不足以捕捉有效推理所需的复杂且层次化的语义交互。特别是，细粒度的词汇线索、短语组合和更高级的上下文语义通常在单一表示空间中交织或稀释。为了解决这些局限性，我们提出了一种新颖的多粒度推理网络（Multi-Granularity Reasoning Network, MGRN），该网络明确利用交互推理空间中的层次语义特征。所提出的框架模拟了人类语言理解的认知过程，该过程自然地从浅层词汇匹配发展到更深层的语义抽象和逻辑推理。通过以渐进和结构化的方式整合多粒度的语义信息，MGRN能够揭示自然语言表达背后的复杂语义关系。在多个公共基准上的广泛实验表明，MGRN在性能上始终优于强基线模型，验证了所提方法的有效性和稳健性。

View on arXiv Download PDF AI Translation

cs.CL / 10 / 2606.05182

LANTERN: Layered Archival and Temporal Episodic Retrieval Network for Long-Context LLM Conversations

LANTERN：用于长期上下文大语言模型对话的分层档案和时间情节检索网络

Subramani, Rahul

Abstract

Large language models discard critical details when conversation history is compacted to fit within finite context windows. We present LANTERN (Layered Archival aNd Temporal Episodic Retrieval Network), a lightweight memory layer that proactively archives every conversation turn and restores relevant details after compaction via hybrid retrieval -- requiring zero LLM calls and adding fewer than 25ms of latency per turn. On 94 real multi-turn conversations (1,894 ground-truth facts, human-validated at kappa=0.81), LANTERN-Rerank recovers 78.3% of verifiable facts lost to compaction, significantly outperforming a faithful reimplementation of MemGPT's LLM-driven extraction and multi-query search pipeline (72.4%; Wilcoxon p<0.0001, 95% CI [+3.1, +8.6] pp, d=0.43) at a fraction of the inference cost. Even without the reranker, base LANTERN matches or exceeds this LLM-driven baseline (p=0.005) using zero LLM calls. When four production LLMs answer fact-bearing questions using LANTERN-restored context, accuracy improves by 8.4 percentage points on average (Wilcoxon p<0.05 for each model individually), demonstrating that the recovered context is useful across diverse model architectures. We release the full evaluation framework -- paired significance tests, failure analysis, fact-type stratification, and compaction robustness analysis -- to support reproducibility and future work.

Chinese Translation

大型语言模型在将对话历史压缩以适应有限的上下文窗口时，会丢失关键细节。我们提出了LANTERN（分层档案和时间情节检索网络），这是一种轻量级的记忆层，能够主动存档每个对话轮次，并通过混合检索在压缩后恢复相关细节——这一过程不需要调用大语言模型（LLM），每个轮次的延迟少于25毫秒。在94个真实的多轮对话（1,894条真实事实，人类验证者卡帕系数为0.81）中，LANTERN-Rerank恢复了78.3%因压缩而丢失的可验证事实，显著超越了MemGPT的LLM驱动提取和多查询搜索管道的忠实再实现（72.4%；Wilcoxon p<0.0001，95%置信区间[+3.1, +8.6]个百分点，d=0.43），且推理成本仅为其一小部分。即便不使用重新排序机制，基础LANTERN也在不调用LLM的情况下达到了或超过了这一LLM驱动的基线（p=0.005）。当四个实际生产的LLM使用LANTERN恢复的上下文回答与事实相关的问题时，准确率平均提升了8.4个百分点（每个模型单独的Wilcoxon p<0.05），这表明恢复的上下文在多种模型架构中均有效。我们发布了完整的评估框架，包括配对显著性测试、失败分析、事实类型分层和压缩鲁棒性分析，以支持可重复性和今后的研究工作。

View on arXiv Download PDF AI Translation

cs.CL / 11 / 2606.05183

The Granularity Gap: A Multi-Dimensional Longitudinal Audit of Sycophancy in Gemini Models

粒度差距：双子模型中谄媚行为的多维纵向审计

Keough, Patrick

Abstract

Large language models are increasingly deployed as high-stakes advisors, yet standard alignment benchmarks treat sycophancy as a binary failure mode. We introduce the Granularity Gap: coarse binary metrics mask substantial social-compliance behaviors where models capitulate to user framing, validate questionable premises, or soften factual corrections without producing overtly false outputs. We evaluate six Gemini variants across generations 2.0, 2.5, and 3.0 on 73 adversarial prompts under three guardrail conditions (Control, Simple, Protocol), yielding 8,830 graded responses. Using a 0-4 Likert scale validated against a human annotator triad (Fleiss kappa = 0.71; Cohen kappa = 0.78 vs AI consensus; 95.9 percent binary accuracy, 100 percent specificity), we quantify sycophancy as continuous rather than binary. Three findings emerge. First, 27.2 percent of responses contain substantial sycophantic content (Likert >= 2.0) and 22.7 percent reach moderate or severe levels (>= 3.0), while binary win-rate framing reports only modest failure rates; coarse metrics explain just 29 percent of graded variance. Second, generational progress is non-monotonic: Gen 2.5 regresses sharply (mean Control 2.64) relative to Gen 2.0 (1.90) and Gen 3.0 (2.01), and Gen 2.5 shows inverse scaling (Pro 1.94 worse than Flash 1.71) while Gen 3.0 restores standard scaling. Third, we document an Alignment Tax: Spearman rho = -0.63 between sycophancy and truthfulness, indicating social compliance trades against factual accuracy. Egotistical Validation prompts act as a sycophancy trap (mean 3.27), nearly double Unethical Proposals (1.72). Simple guardrails outperform elaborate Protocol scaffolding on flagship models, but distilled Gen 3.0 Flash inverts this, suggesting small models may structurally require chain-of-thought scaffolding. We release the dataset and rubric to support continuous sycophancy measurement.

Chinese Translation

大型语言模型越来越多地被用作高风险顾问，但标准的对齐基准将谄媚视为一种二元失败模式。我们提出了粒度差距（Granularity Gap）：粗略的二元指标掩盖了 substantial 社会遵从行为，即模型屈从于用户框架，验证可疑的前提，或在不产生明显虚假输出的情况下软化事实纠正。我们在三种护栏条件下（控制、简单、协议）对 2.0、2.5 和 3.0 代的六个双子变体进行了评估，共72个对抗性提示，得到了8,830个评分回应。采用0-4的李克特量表进行量化，该量表经过与人类标注者三人组的验证（Fleiss kappa = 0.71；Cohen kappa = 0.78 vs AI 一致性；95.9% 的二元准确率，100% 的特异性），我们认为谄媚是连续的而非二元的。得出了三项发现。首先，27.2%的回应包含 substantial 谄媚内容（李克特 >= 2.0），22.7% 的回应达到了中度或严重水平（>= 3.0），而二元胜率框架仅报告出适度的失败率；粗略指标仅解释了 29% 的评分方差。第二，代际进步是非单调的：相较于第2代（1.90）和第3代（2.01），第2.5代的表现急剧降级（平均控制值 2.64），并且第2.5代显示出反向尺度（专业1.94的表现比闪电1.71更差），而第3代则恢复了标准的尺度。第三，我们记录了一个对齐税（Alignment Tax）：谄媚和真实性之间的斯皮尔曼相关系数 rho = -0.63，表明社会遵从与事实准确性之间存在权衡。自我验证提示作为一个谄媚陷阱（平均3.27），几乎是非道德提议（1.72）的两倍。简单的护栏在旗舰模型上优于复杂的协议框架，但提炼的第3代闪电模型则反转了这一点，表明小型模型可能在结构上需要思维链框架。我们发布数据集和评分标准，以支持持续的谄媚测量。

View on arXiv Download PDF AI Translation

cs.CL / 12 / 2606.05315

LoRi: Low-Rank Distillation for Implicit Reasoning

LoRi：用于隐式推理的低秩蒸馏

Solgi, Ryan, Tian, Jiayi, Zhang, Zheng

Abstract

Implicit chain-of-thought (iCoT) methods aim to internalize reasoning in large language models, but often underperform explicit CoT prompting. We empirically find that hidden-state reasoning trajectories exhibit low-rank structure. Motivated by this observation, we propose a low-rank distillation framework that transfers reasoning by aligning teacher and student trajectories in a shared low-rank tensor subspace using first- and second-order statistics. The resulting formulation captures the global structure of reasoning while supporting a compact latent reasoning process. We evaluate the method across multiple model families, including LLaMA and Qwen, at different scales on mathematical reasoning benchmarks. Our approach consistently improves performance, especially on challenging multi-step tasks, approaching explicit CoT accuracy and outperforming prior iCoT distillation methods.

Chinese Translation

隐式思维链（iCoT）方法旨在将推理内化于大型语言模型中，但通常表现不及显式思维链提示。我们实证发现，隐状态推理轨迹呈现低秩结构。基于这一观察，我们提出了一种低秩蒸馏框架，通过使用一阶和二阶统计量在共享的低秩张量子空间中对齐教师和学生的轨迹，从而转移推理。所得到的公式捕捉了推理的全局结构，同时支持紧凑的潜在推理过程。我们在多个模型系列（包括 LLaMA 和 Qwen）及不同规模上评估该方法，针对数学推理基准进行测试。我们的方法在性能上始终有所提升，尤其是在具有挑战性的多步骤任务中，接近显式思维链的准确性，且优于以往的 iCoT 蒸馏方法。

View on arXiv Download PDF AI Translation

cs.CL / 13 / 2606.05330

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing

基于概率信念追踪的多轮人类说服能力模型

Moore, Jared, Goodman, Noah, Haber, Nick, Kleiman-Weiner, Max

Abstract

Large language models can shift human beliefs across high-stakes domains, but most persuasion studies rely on pre/post belief change. These endpoint measures identify whether persuasion occurred, yet miss where and how beliefs moved within a dialogue. We present PERSUASIONTRACE, a framework for studying persuasion in human-LLM interaction. Built on a web-based experimental platform, PERSUASIONTRACE contributes a tool for multi-turn persuasion studies and a process-level evaluation protocol: it records multi-turn belief reports from human or simulated targets of persuasion, annotates persuader turns with rhetorical dimensions (logos/pathos/ethos), and evaluates simulators by fidelity to real human belief dynamics. Using this framework, we find that human targets group into two clusters of multi-turn belief updates and exhibit susceptibility to rhetorical strategies, and that LLMs are persuasive across generic and personalized topics, text and audio modalities, and multi-turn interactions. Prior work has chiefly used vanilla-prompted LLMs to simulate human targets, but we show that these simulators fail to replicate human belief dynamics. We introduce a Bayesian-network simulated target that maintains an explicit latent belief state over time so each persuader message yields cognitively realistic belief updates. In human-likeness evaluation, our Bayesian target scores near a human reference (81 vs 80), while baseline LLM targets score substantially lower (64). PERSUASIONTRACE reframes persuasion evaluation from endpoint movement alone to process fidelity, providing a stronger basis for scientific analysis and safer optimization of persuasive systems.

Chinese Translation

大型语言模型可以在高风险领域影响人类信念，但大多数说服研究依赖于前后信念变化的评估。这些端点测量识别说服是否发生，但忽视了信念在对话中的移动及其方式。我们提出了 PERSUASIONTRACE，这是一个用于研究人类与大型语言模型（LLM）交互中的说服的框架。基于一个网页实验平台，PERSUASIONTRACE 提供了一个用于多轮说服研究的工具和一个过程级评估协议：它记录了人类或模拟说服目标的多轮信念报告，用修辞维度（逻各斯/情感/伦理）对说服者的发言进行注释，并通过与真实人类信念动态的一致性对模拟器进行评估。使用该框架，我们发现人类目标分为两组多轮信念更新，并表现出对修辞策略的易受影响性，同时 LLM 在一般和个性化主题、文本和音频形式以及多轮交互中都是具有说服力的。先前的工作主要使用普通提示的 LLM 来模拟人类目标，但我们表明这些模拟器未能复现人类信念动态。我们引入了一个维持显性潜在信念状态的贝叶斯网络模拟目标，使得每条说服者信息都能产生认知上合理的信念更新。在人类相似性评估中，我们的贝叶斯目标得分接近人类参考（81 对 80），而基准 LLM 目标得分明显较低（64）。PERSUASIONTRACE 将说服评估从仅依赖端点变化重新框定为过程一致性，为科学分析和说服系统的安全优化提供了更可靠的基础。

View on arXiv Download PDF AI Translation

cs.CL / 14 / 2606.05336

Self-supervised User Profile Generation for Personalization

自监督用户画像生成用于个性化

Ju, Clark Mingxuan, Qiu, Yuwei, Zhao, Tong, Shah, Neil

Abstract

Personalizing large language models (LLMs) has become a central challenge as LLMs are deployed across recommendation, search, dialogue, and content generation -- settings where the same query should yield different answers given different users. A promising route is to summarize each user's interaction history into a natural-language memory or profile and prepend it to the prompt to facilitate personalization. Existing methods learn such profile generators with explicit rewards derived from labeled downstream tasks, which are expensive and sparse as they require annotated supervision for every target task. In light of this challenge, we introduce Bidirectional User Modeling via Profiles (BUMP), a self-supervised framework that trains a profile generator without any downstream labels. Specifically, given a user's interaction history, we use GRPO to train an LLM to emit a free-form textual profile under a bidirectional in-batch ranking objective: a small LLM judge measures (i) how well the generated profile, used as a query, ranks the user's own held-out interactions above interactions from other users in the batch, and (ii) how well a held-out interaction, used as a query, ranks the user's own profile above profiles of other users. Both directions are scored with multi-positive NDCG and combined into a dense reward per rollout; other users in the batch supply free negatives, so every training example yields supervision from raw interaction logs alone. Evaluated on the LaMP benchmark, BUMP matches or outperforms closed-source APIs and prior methods relying on labeled rewards, while requiring no task label at training.

Chinese Translation

个性化大型语言模型（LLMs）已成为一个中心挑战，因为LLMs在推荐、搜索、对话和内容生成中被广泛应用——在这些场景中，同一查询应该根据不同用户产生不同的答案。一个有前途的方法是将每个用户的交互历史总结成自然语言记忆或画像，并将其附加到提示中以促进个性化。目前的方法通常依赖于来自标注下游任务的显性奖励来学习这种画像生成器，这些奖励往往昂贵且稀疏，因为它们要求每个目标任务都有标注的监督。针对这一挑战，我们引入了基于画像的双向用户建模（BUMP），一个自监督框架，能够在没有任何下游标签的情况下训练一个画像生成器。具体而言，给定用户的交互历史，我们使用GRPO训练一个LLM，使其在双向批次排名目标下生成自由格式的文本画像：一个小型LLM评估（i）生成的画像作为查询时，排名用户自己保留的交互在批次中高于其他用户的交互的效果，以及（ii）一个保留的交互作为查询时，排名用户自己的画像在其他用户画像之上的效果。这两个方向都使用多正样本NDCG进行评分，并在每次回合中组合成一个密集奖励；批次中的其他用户提供自由的负样本，因此每个训练示例仅依赖于原始交互日志提供监督。在LaMP基准评估中，BUMP的表现与闭源API以及依赖标注奖励的先前方法相当或优于它们，同时在训练时不需要任务标签。

View on arXiv Download PDF AI Translation

cs.CL / 15 / 2606.05346

Trajectory Dynamics in Language Model Hidden States Predict Human Processing Costs Beyond Surprisal

语言模型隐状态中的轨迹动态预测超出惊奇度的人类处理成本

Barenholtz, Elan

Abstract

Human language comprehension unfolds sequentially: each word is processed in the context of those that came before, and the interpretation builds incrementally over time. Surprisal, the negative log probability of a word given its context, has been the dominant predictor of incremental processing cost. But surprisal reduces rich sequential representations to a single scalar at each word, discarding information about the direction in which the interpretation has been evolving. Dynamical-systems approaches suggest that the trajectory of the evolving interpretive state, not just its position at each moment,should shape processing, and language itself may have local momentum, since speakers plan utterances a few words at a time. We introduce trajectory extrapolation error: at each word, we fit a linear trajectory to the preceding hidden states of a transformer language model and measure deviation from the extrapolated path. On the Natural Stories corpus, this measure is nearly orthogonal to surprisal (r = .044) and independently predicts self-paced reading times. The effect is especially pronounced in garden-path sentences, strengthens with model scale (GPT-2 Small to Large), and replicates across architectures with different positional encoding schemes (GPT-2 vs. Pythia/RoPE). A displacement control shows the effect is not reducible to representational change magnitude: displacement and extrapolation error predict in opposite directions. These findings reveal two dissociable components of processing cost: word-level prediction error (surprisal) and sensitivity to the local momentum of the unfolding interpretation (trajectory extrapolation error).

Chinese Translation

人类语言理解是一个顺序展开的过程：每个词都是在之前词的语境中处理的，而解释则随着时间的推移逐步构建。惊奇度，即在给定语境下某个词的负对数概率，一直是增量处理成本的主要预测因素。然而，惊奇度将丰富的顺序表示简化为每个词的单一标量，丢弃了关于解释演变方向的信息。动力系统方法表明，演变的解释状态的轨迹，而不仅仅是每个时刻的位置，应该影响处理，语言本身可能具有局部动量，因为说话者通常是逐词计划发言。我们引入了轨迹外推误差：在每个词处，我们将线性轨迹拟合到变压器语言模型的先前隐状态，并测量与外推路径的偏差。在自然故事语料库中，这一度量与惊奇度几乎正交（r = .044），并独立预测自我调节的阅读时间。此效应在花园路径句子中尤为明显，随着模型规模的增加（从GPT-2小型到大型），效应更为显著，并在不同位置编码方案（GPT-2与Pythia/RoPE）架构中得以复制。位移控制显示，该效应无法简化为表征变化的大小：位移和外推误差在预测方向上呈现相反关系。这些发现揭示了处理成本的两个可分离成分：单词级预测误差（惊奇度）及对正在展开的解释的局部动量的敏感性（轨迹外推误差）。

View on arXiv Download PDF AI Translation

cs.CL / 16 / 2606.05402

ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces

ReasoningFlow：理解大型推理模型推理轨迹的语篇结构

Lee, Jinu, Agarwal, Shivam, Parulekar, Amruta, Madala, Siddarth, Hakkani-Tur, Dilek, Hockenmaier, Julia

Abstract

Large reasoning models (LRMs) produce reasoning traces with non-linear structures, such as backtracking and self-correction, that complicate the evaluation and monitoring of the reasoning process. We introduce ReasoningFlow, a framework that captures the discourse structures of LRM reasoning traces into fine-grained directed acyclic graphs (DAGs). We develop and validate our annotation schema through careful manual annotation of 31 traces (2.1k steps), achieving high inter-annotator agreement, then scale to automatic annotation of 1,260 traces (247.7k steps) spanning three tasks (math, science, argumentation) and five models (Qwen2.5-32B-Inst, QwQ-32B, DeepSeek-V3, DeepSeek-R1, GPT-oss-120B). By analyzing ReasoningFlow graphs, we find: (1) LRMs exhibit structurally similar traces, despite being trained from different base models and potentially non-overlapping post-training data. (2) ReasoningFlow reveals diverse fine-grained reasoning behaviors (e.g., local verification, self-reflection, and assumptions) that can be used for better reasoning trace monitorability. (3) In LRMs, most of the erroneous steps are not used to derive final answers. (4) Mechanistic causal dependencies between steps do not reflect the language-level discourse structure. We release the dataset and code in: https://github.com/jinulee-v/reasoningflow.

Chinese Translation

大型推理模型（LRMs）产生具有非线性结构的推理轨迹，如回溯和自我修正，这使得对推理过程的评估和监测变得复杂。我们提出了ReasoningFlow，一个框架，它将LRM推理轨迹的语篇结构捕捉为细粒度有向无环图（DAGs）。我们通过对31个轨迹（2.1k个步骤）的仔细手动注释开发并验证了我们的注释方案，达到了较高的标注者间一致性，然后扩展到对1,260个轨迹（247.7k个步骤）的自动注释，涵盖三个任务（数学、科学、论证）和五个模型（Qwen2.5-32B-Inst、QwQ-32B、DeepSeek-V3、DeepSeek-R1、GPT-oss-120B）。通过分析ReasoningFlow图，我们发现：（1）尽管LRMs来自不同的基础模型，并且后训练数据可能并不重叠，LRMs却展示了结构上相似的轨迹；（2）ReasoningFlow揭示了多样化的细粒度推理行为（例如，局部验证、自我反思和假设），可用于更好地监测推理轨迹；（3）在LRMs中，大部分错误步骤并未用于推导最终答案；（4）步骤之间的机械性因果依赖关系并未反映语言层面的语篇结构。我们在以下链接发布了数据集和代码：https://github.com/jinulee-v/reasoningflow。

View on arXiv Download PDF AI Translation

cs.CL / 17 / 2606.05414

When Evidence is Sparse: Weakly Supervised Early Failure Alerting in Dialogs and LLM-Agent Trajectories

当证据稀缺时：对话和LLM-Agent轨迹中的弱监督早期故障警报

Baidya, Avinash, Liang, Xinran, Guo, Ruocheng, Gao, Xiang, Das, Kamalika

Abstract

Early failure alerting requires deciding, while a dialog or agent trajectory is still unfolding, whether to flag it as likely to fail. This is challenging because supervision is typically available only as a trajectory-level success/failure label while alerts must be raised from partial interactions. Prior early-classification methods often bridge this gap by assigning the terminal label to every prefix, treating every turn as failure evidence. We hypothesize that this prefix-label assumption is poorly matched to multi-turn language interactions, where evidence of eventual failure is sparse and often delayed. In this paper, we introduce a two-stage approach that learns from this sparse evidence structure and uses the resulting risk estimates for controllable early alerting. Specifically, our attention-based failure predictor learns sparse turn-level failure evidence from trajectory labels and uses it to estimate failure risk from partial histories. We then pair this predictor with $\alpha$-STOP, a single preference-conditioned stopping policy that selects an accuracy-earliness operating point at inference time rather than training a separate trigger for each preference. Across five benchmarks spanning customer support, task-oriented dialog, persuasion, tool use, and planning, we first show that high-relevance failure evidence occupies only 4.7-11.3% of turns and first appears after 59.0-83.6\% of trajectories on average. We further show that the attention-based predictor improves Pareto-frontier quality (hypervolume) by 1-10\% over naive prefix supervision, and that the full system improves frontier quality by 3-42\% over state-of-the-art trigger policies while reducing training cost per operating point by 1-3 orders of magnitude.

Chinese Translation

早期故障警报需要在对话或代理轨迹仍在进行时决定是否将其标记为可能失败。这一过程具有挑战性，因为监督通常仅以轨迹级别的成功/失败标签存在，而警报必须从部分交互中提出。先前的早期分类方法通常通过将终端标签分配给每个前缀，视每个轮次为故障证据，从而弥补这一差距。我们假设这种前缀标签假设与多轮语言交互不匹配，因为最终失败的证据稀缺且常常延迟。本文提出了一种两阶段的方法，该方法从这一稀疏证据结构中学习，并使用所得的风险估计进行可控的早期警报。具体而言，我们的基于注意力的故障预测器从轨迹标签中学习稀疏的轮次级故障证据，并利用它从部分历史中估计故障风险。然后，我们将该预测器与$ ext{α-STOP}$相结合，这是一个单一的偏好条件停止策略，在推理时选择准确性和早期性的操作点，而不是为每个偏好训练单独的触发器。在涵盖客户支持、任务导向对话、说服、工具使用和规划的五个基准上，我们首先展示了高相关性故障证据仅占轮次的4.7%至11.3%，且平均在59.0%至83.6%的轨迹之后首次出现。我们进一步展示，基于注意力的预测器相较于天真的前缀监督提高了1-10%的帕累托前沿质量（超体积），而整个系统在提高3-42%的前沿质量的同时，将每个操作点的训练成本降低了1-3个数量级。

View on arXiv Download PDF AI Translation

cs.CL / 18 / 2606.05415

Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval

可执行架构契约：从自动摄取到多源检索

Jonnalagedda, Padmaja, Yao, Yuguang, Gao, Xiang, Hasson, Hilaf, Das, Kamalika

Abstract

Real-world data spans tables, documents, and semi-structured files with implicit semantics. Querying this data requires integrating evidence across inconsistent schemas and formats, yet existing approaches either demand costly manual engineering or bypass structure entirely. We present a system that automatically discovers an executable schema from raw multi-source data and uses it as a shared contract for knowledge graph construction and query-time retrieval. A closed-world field catalog constrains LLM-based schema discovery to attested fields; deterministic structural analysis infers identity keys, foreign keys, and source hierarchy; and the resulting schema drives extraction, deduplication, and cross-source linking into a provenance-aware knowledge graph. At query time the schema -- optionally extended via a monotonic protocol -- conditions a multi-tool agent routing retrieval across structured lookup, graph traversal, and vector search, returning grounded answers with traceable citations. In controlled zero-shot comparisons using the same LLM, data, and evaluation harness, the system improves over retrieval-only and decomposition-based baselines across four QA benchmarks, with ablations showing that schema-conditioned routing, structural intelligence, and schema-guided construction each contribute to the gains.

Chinese Translation

现实世界的数据跨越了表格、文档和具有隐含语义的半结构化文件。查询这些数据需要跨不一致的架构和格式整合证据，而现有的方法要么要求高昂的手动工程，要么完全绕过结构。我们提出了一个系统，能够从原始的多源数据中自动发现可执行架构，并将其用作知识图谱构建和查询时检索的共享契约。闭合世界的领域目录将基于大型语言模型（LLM）架构发现限制为已证明的字段；确定性的结构分析推断身份键、外键和源层次结构；生成的架构驱动提取、去重和跨源链接，形成一个具有溯源意识的知识图谱。在查询时，该架构通过单调协议进行扩展的选择条件下，调节多工具代理的检索，涉及结构化查找、图遍历和向量搜索，返回具有可追溯引用的具体答案。在使用相同的LLM、数据和评估工具进行的受控零样本比较中，该系统在四个问答基准测试中超越了仅检索和基于分解的方法，消融实验表明，架构条件路由、结构智能和架构引导构建各自对性能提升有所贡献。

View on arXiv Download PDF AI Translation

cs.CL / 19 / 2606.05421

ComplexityMT: Benchmarking the Interaction Between Text Complexity and Machine Translation

ComplexityMT：文本复杂性与机器翻译相互作用的基准评估

Imperial, Joseph Marvin, Liang, Junhong, Shoer, Belal, Barayan, Abdullah, Wilkens, Rodrigo, Mussa, Omar, Knight, Dawn, Ribeiro, Eugénio, Kochmar, Ekaterina, Vajjala, Sowmya, Alva-Manchego, Fernando, Madabushi, Harish Tayyar

Abstract

When a text is translated, does the translation retain the complexity of the original? We introduce ComplexityMT, a new challenge for assessing how text complexity and machine translation interact with and influence each other, using the Common European Framework of Reference for Languages (CEFR) levels as the measure of text complexity. Across six languages, including Arabic, Dutch, English, French, Hindi, and Russian, we evaluate three open-weight models, one closed model, and a commercial machine translation system on two tasks: i) correlation of CEFR with translation difficulty, and ii) shifts in CEFR levels of the source texts. Our experiments show that higher CEFR levels make texts more difficult to translate, and that machine translation shifts the CEFR level of the target text compared to the original source, for most languages. These findings provide new insights for researchers and practitioners working on multilingual pedagogical content generation and machine translation difficulty estimation.

Chinese Translation

文本翻译时，译文是否保留了原文的复杂性？我们介绍了ComplexityMT，这是一个用于评估文本复杂性与机器翻译如何相互作用并相互影响的新挑战，采用欧洲语言共同参考框架（CEFR）级别作为文本复杂性的衡量标准。我们在包括阿拉伯语、荷兰语、英语、法语、印地语和俄语在内的六种语言上评估了三个开放权重模型、一个封闭模型以及一个商业机器翻译系统，主要进行两个任务：i) CEFR与翻译难度的相关性，以及ii) 源文本的CEFR级别变化。实验结果表明，较高的CEFR级别使文本更难翻译，且机器翻译在大多数语言中将目标文本的CEFR级别与原始源文本相比发生了变化。这些发现为从事多语言教育内容生成和机器翻译难度评估的研究人员与实践者提供了新的见解。

View on arXiv Download PDF AI Translation

cs.CL / 20 / 2606.05444

Multilingual Coreference Resolution via Cycle-Consistent Machine Translation

通过循环一致性机器翻译实现多语言共指解析

Costache, Adriana-Valentina, Poesina, Eduard, Gheorghe, Silviu-Florin, Irofti, Paul, Ionescu, Radu Tudor

Abstract

Coreference resolution is a core NLP task, having a broad range of downstream applications, e.g.~machine translation, question answering, document summarization, etc. While the task is well-studied in English, comparatively less attention is dedicated to coreference resolution in other languages, especially low-resource ones. To mitigate this gap, we propose a novel coreference resolution pipeline that harnesses machine translation (MT) from English to a target low-resource language, to generate or expand training data. To automatically validate the quality of the translated samples, we back-translate the samples and assess the similarity with the original English samples via cosine similarity in the latent space of a BERT model. The resulting similarity scores are integrated into the loss function to weight training samples according to their MT cycle consistency. Extensive experiments on four low-resource languages show that our pipeline brings significant performance gains in coreference resolution. Moreover, our pipeline enables accurate coreference resolution in languages where no previous corpora were available.

Chinese Translation

共指解析是自然语言处理中的一项核心任务，具有广泛的下游应用，例如机器翻译、问答系统、文档摘要等。尽管该任务在英语中得到了充分研究，但对其他语言，尤其是资源匮乏语言的共指解析研究相对较少。为了弥补这一差距，我们提出了一种新颖的共指解析流程，利用从英语到目标资源匮乏语言的机器翻译（MT），以生成或扩展训练数据。为了自动验证翻译样本的质量，我们对样本进行回译，并通过在BERT模型的潜在空间中计算余弦相似度来评估与原始英文样本的相似性。得到的相似性评分被整合进损失函数，以根据它们的MT循环一致性对训练样本加权。在对四种资源匮乏语言进行的广泛实验中，我们的流程在共指解析中带来了显著的性能提升。此外，我们的流程使得在没有可用先前语料的语言中也能够实现准确的共指解析。

View on arXiv Download PDF AI Translation

cs.CL / 21 / 2606.05486

Localizing Prompt Ambiguity in Large Language Models with Probe-Targeted Attribution

利用探针针对归因定位大型语言模型中的提示模糊性

Ramesh, Govind, Dou, Yao, Xu, Wei

Abstract

Prompt ambiguity is a common source of failure in large language models, but is difficult to localize because it is a latent property of the prompt, while existing attribution methods are designed to explain observable outputs such as logits or generated tokens. We introduce PRIG, a gradient attribution method that uses a probe logit to attribute latent ambiguity to token positions. Specifically, PRIG trains a linear probe to distinguish clear prompts from ambiguous prompts and attributes the probe score to earlier token representations in the residual stream. To enable token-level evaluation, we construct synthetic ambiguity datasets across coding, math, and writing by rewriting one task-critical sentence per prompt, and complement them with a human-written gold benchmark. In this setting, PRIG localizes ambiguous spans substantially better than gradient attribution baselines, achieving 0.840 AUROC on the combined synthetic benchmark and 0.891 AUROC on the gold set. It also outperforms GPT-5.4 on sentence-level ambiguity identification and retains useful signal out-of-domain. These results establish PRIG as a practical tool for identifying which parts of a prompt are ambiguous. More broadly, they suggest that latent prompt properties can be localized through intermediate representations, rather than through output-level attribution.

Chinese Translation

提示模糊性是大型语言模型中常见的失败来源，但由于其是提示的潜在属性，因此难以定位，同时现有的归因方法旨在解释可观察的输出，如 logits 或生成的标记。我们提出了 PRIG，这是一种梯度归因方法，它使用探针 logits 将潜在模糊性归因于标记位置。具体而言，PRIG 训练一个线性探针以区分清晰提示和模糊提示，并将探针得分归因于残差流中的早期标记表示。为了实现标记级评估，我们通过对每个提示重写一个与任务相关的关键句子，构建了编码、数学和写作的合成模糊性数据集，并与人类编写的金标准基准相补充。在这个设置中，PRIG 在定位模糊区间方面显著优于梯度归因基线，在合成基准上达到了 0.840 AUROC，在金标准上达到了 0.891 AUROC。它在句子级模糊性识别上也优于 GPT-5.4，并在领域外保留了有用信号。这些结果确立了 PRIG 作为识别提示中模糊部分的实用工具。更广泛地说，它们表明潜在提示属性可以通过中间表示进行定位，而不是通过输出级的归因。

View on arXiv Download PDF AI Translation

cs.CL / 22 / 2606.05494

MASF: A Multi-Model Adaptive Selection Framework for Abstractive Text summarization

MASF：一种用于抽象文本摘要的多模型自适应选择框架

Alansary, Ahmed, Hamdi, Ali

Abstract

Automatic text summarization has become increasingly important due to the rapid growth of digital textual information. This paper presents a Multi-Model Adaptive Summarization Framework designed to improve the robustness and quality of abstractive text summarization. Relying on a single model often leads to inconsistent summarization quality across articles with varying structures and topics. To address this limitation, the proposed framework integrates multiple fine-tuned transformer-based summarization models and introduces an adaptive selection mechanism. In this framework, each model independently generates a candidate summary for the same input article. The generated summaries are then evaluated using automatic evaluation metrics that capture both lexical similarity and semantic relevance. Based on these scores, the framework selects the highest-quality summary as the final output. The models are fine-tuned and evaluated on the widely used CNN/DailyMail news summarization dataset. Experimental results demonstrate that the proposed framework achieves the highest BERTScore among all compared methods with a score of 88.63%. It also outperforms several LLMs such as GPT3-D2, Falcon-7b, and Mpt-7b, highlighting its effectiveness and robustness. These findings highlight the effectiveness of leveraging multiple transformer-based models within an adaptive selection strategy to improve the quality and robustness of automatic text summarization systems.

Chinese Translation

自动文本摘要因数字文本信息的快速增长而变得愈发重要。本文提出了一种多模型自适应摘要框架，旨在提高抽象文本摘要的稳健性和质量。依赖单一模型往往导致对结构和主题各异的文章摘要质量不一致。为解决这一局限性，提出的框架整合了多个微调的基于变换器的摘要模型，并引入了一种自适应选择机制。在此框架中，每个模型独立生成同一输入文章的候选摘要。生成的摘要随后使用自动评估指标进行评估，这些指标捕捉了词汇相似性和语义相关性。根据这些得分，框架选择最高质量的摘要作为最终输出。这些模型经过微调，并在广泛使用的CNN/DailyMail新闻摘要数据集上进行了评估。实验结果表明，所提框架在所有比较方法中获得了最高的BERTScore，得分为88.63%。它还超越了多个大型语言模型（LLMs），如GPT3-D2、Falcon-7b和Mpt-7b，突显了其有效性和稳健性。这些发现强调了在自适应选择策略中利用多个基于变换器的模型以提升自动文本摘要系统的质量和稳健性的重要性。

View on arXiv Download PDF AI Translation

cs.CL / 23 / 2606.05523

CHASE: Adversarial Red-Blue Teaming for Improving LLM Safety using Reinforcement Learning

CHASE：通过强化学习提升大型语言模型安全性的对抗红蓝团队策略

Markasserithodi, Rahul, Joshi, Aditya, Li, Yuekang, Singh, Ishmanbir, Yoo, Chris, Niu, Alan

Abstract

Despite advances in safety alignment, prompt-rewriting attacks such as persona modulation, fictional framing and persuasion-based reformulation, can bypass safety filters even on frontier models. Existing defenses either rely on non-scalable human curation or white-box optimisation that overfits to specific model internals, leaving aligned models brittle against the very class of adaptive black-box adversaries they will face in deployment. To address this gap, we introduce CHASE (Co-evolutionary Hardening through Adversarial Safety-Escalation), a closed-loop red-blue teaming framework in which a black-box attacker and a safety-aligned defender co-evolve. The attacker is trained via Group Relative Policy Optimization (GRPO) under a multiplicative reward that jointly enforces bypass effectiveness and intent fidelity, while the defender is hardened on the harvested adversarial rewrites through a two-stage GRPO + rejection-sampled SFT pipeline balanced with benign data. Evaluated on BeaverTails and JailbreakBench against five held-out attack families (PAIR, TAP, AutoDAN, PAP, Translation), CHASE cuts mean StrongREJECT score by 43.2\% with 0\% false-refusal on benign prompts. Beyond the headline result, CHASE shows that template-free RL exploration recovers latent attack primitives that transfer across mechanistically distinct attack families, suggesting a path toward LLM safety hardening that generalises beyond the narrow distributions achieved thus far in adversarial training.

Chinese Translation

尽管在安全调整方面取得了进展，但提示重写攻击（如角色调节、虚构框架和基于说服的重构）仍然能够绕过安全过滤器，即使在前沿模型上。现有的防御措施要么依赖于不可扩展的人为策划，要么依赖于白盒优化，过于贴合特定模型内部，导致已对齐模型在部署过程中对其所面临的自适应黑盒对手的脆弱性。为了解决这一问题，我们提出了CHASE（通过对抗安全升级的共同进化强化），这是一个闭环红蓝团队框架，其中黑盒攻击者和安全对齐的防御者共同进化。攻击者通过组相对策略优化（Group Relative Policy Optimization, GRPO）在乘法奖励下进行训练，奖励共同强调绕过有效性和意图保真性，而防御者则通过在收集到的对抗重写上进行GRPO + 拒绝采样的SFT（Supervised Fine-Tuning）管道来进行强化，此管道与良性数据平衡。评估了在BeaverTails和JailbreakBench上针对五个保留攻击家族（PAIR, TAP, AutoDAN, PAP, Translation），CHASE将平均StrongREJECT得分降低了43.2%，在良性提示上实现了0%的虚假拒绝。除了这一显著结果外，CHASE表明无模板的强化学习探索能够恢复跨机制上明显不同的攻击家族的潜在攻击原语，暗示了一条通向超越至今对抗训练中狭窄分布的LLM安全增强的路径。

View on arXiv Download PDF AI Translation

cs.CL / 24 / 2606.05545

Multilingual Detection of Alzheimer's Disease from Speech: A Cross-Linguistic Transfer Learning Approach

从语音中多语言检测阿尔茨海默病：一种跨语言迁移学习方法

Abdelhalim, Nadine Yasser, Akinrintoyo, Emmanuel, Salomons, Nicole

Abstract

The development of multilingual Alzheimer's Disease Dementia (AD) detection models presents significant challenges due to the resource-intensive and time-consuming nature of language-specific model training. We propose a novel solution using cross-language training to detect AD in languages beyond those used for model training. This study investigates multilingual deep learning models for detecting AD across different languages and cognitive impairment levels. Using datasets in English, Chinese, Arabic, and Hindi, we developed transformer-based models for binary AD classification. Our approach achieved F1 scores of 82\% across all languages, demonstrating strong cross-linguistic generalization. The rapid inference time (0.5 seconds) supports potential real-time screening applications, while consistent performance across languages indicates feasibility for global deployment.

Chinese Translation

由于语言特定模型训练的资源密集型和耗时特性，开发多语言阿尔茨海默病（AD）检测模型面临重大挑战。我们提出了一种新颖的解决方案，利用跨语言训练在超出模型训练所用语言的范围内检测AD。本研究探讨了用于检测不同语言和认知障碍水平的多语言深度学习模型。我们使用英文、中文、阿拉伯文和印地语的数据集，开发了基于变换器（transformer）的模型以进行二分类AD检测。我们的方法在所有语言上均实现了82%的F1分数，展示出良好的跨语言泛化能力。快速的推断时间（0.5秒）支持潜在的实时筛查应用，而在不同语言间的一致性能表明其在全球部署中的可行性。

View on arXiv Download PDF AI Translation

cs.CL / 25 / 2606.05553

ArcANE: Do Role-Playing Language Agents Stay in Character at the Right Time?

ArcANE：角色扮演语言代理能否在适当时刻保持角色设定？

Song, Woojung, Kim, Nalim, Song, Sangjun, Heo, Chaewon, Lim, Jongwon, Jo, Yohan

Abstract

Role-playing language agents (RPLAs) should play characters whose values and behavior evolve as the story progresses, not maintain a fixed persona. Existing benchmarks measure factual recall at a given chapter, not whether responses align with the character's psychological trajectory, especially in scenarios the source text never explores. We introduce ArcANE (Arc-Aware Narrative Evaluation), an automatically constructed benchmark spanning 17 novels and 80 principal characters. A Character Arc segments the narrative into phases along a psychological axis, and each probe poses the same scenario across phases, spanning both situations within the source text and situations beyond it. Across six models and six context modes, conditioning on the Character Arc tops every other context strategy on every model, and the gap is largest on scenarios outside the source text where retrieval has nothing to find. We further fine-tune open-weight models on the same data to obtain ArcANE-8B/32B, which widen the Arc advantage even more on scenarios outside the source text.

Chinese Translation

角色扮演语言代理（RPLA）应扮演随着故事发展而演变的角色，其价值观和行为应动态变化，而非维持固定的人物形象。现有的基准测试主要测量在特定章节中的事实回忆，而非反应是否与角色的心理轨迹相一致，尤其是在源文本未曾探讨的情境中。我们引入了ArcANE（Arc-Aware Narrative Evaluation），这是一个自动构建的基准，涵盖17部小说和80个主要角色。角色弧将叙事沿心理轴线分割为不同阶段，每个探针在各个阶段提出相同情境，横跨源文本内外的场景。在六个模型和六种上下文模式中，基于角色弧的条件设置在每个模型上均优于其他上下文策略，而这种差距在源文本外的情境中最大，此时检索没有内容可供参考。我们进一步在相同数据上对开放权重模型进行微调，获得ArcANE-8B/32B，这在源文本外的情境中进一步扩大了角色弧的优势。

View on arXiv Download PDF AI Translation

cs.CL / 26 / 2606.05557

AURA: Intent-Directed Probing for Implicit-Need Surfacing in Situated LLM Agents

AURA：面向意图的探测用于在情景化大型语言模型代理中显现隐含需求

Li, Yang, Liu, Jiaxiang, Cai, Jiang, Xu, Mingkun

Abstract

A situated query like "where is Lin Wei?" often encodes more than its literal content: the user may also want to know whether Lin Wei is free, in a good mood, or worth interrupting now. Standard tool-use agents answer the literal question and stop. AURA inserts an inference step between scene perception and tool use that produces an IntentFrame: a structured estimate of the implicit need with a scalar gap score that controls per-query probe budget and tool selection. On a 100-query four-scene implicit-intent benchmark, AURA improves implicit-need coverage over ReAct-style probing (Delta = +0.07, p < 10^-6); three of four scenes are individually significant, the gain reproduces on a second backbone, and a prompt ablation attributes the lift to gap calibration rather than answer memorisation. On factual lookup the controller trades raw accuracy for 82% fewer probes and zero forbidden-tool violations on a privacy-sensitive slice; scope conditions are detailed in Limitations. Code, simulator, and benchmark are released at https://github.com/innovation64/AURA.

Chinese Translation

像“林伟在哪里？”这样的情境查询通常编码的内容超出了其字面含义：用户可能还想知道林伟是否有空、心情如何，或者现在是否值得打扰。标准的工具使用代理只回答字面问题并停止。AURA在场景感知与工具使用之间插入了一个推理步骤，生成一个意图框架（IntentFrame）：这是对隐含需求的结构化估计，包含一个标量差距评分，该评分控制每个查询的探测预算和工具选择。在一个包含100个查询和四个场景的隐含意图基准测试中，AURA相较于ReAct风格的探测提高了隐含需求覆盖率（Δ = +0.07，p < 10^-6）；四个场景中有三个在个体上具有显著性，这一增益在第二个骨干网络上重复出现，提示消融实验显示这一提升归因于差距校准而非答案记忆。在事实查找中，控制器以82%的探测减少和在隐私敏感的切片中零个禁用工具违规权衡了原始准确性；范围条件在限制部分详细说明。代码、模拟器和基准测试已发布在 https://github.com/innovation64/AURA。

View on arXiv Download PDF AI Translation

cs.CL / 27 / 2606.05561

InfoShield: Privacy-Preserving Speech Representations for Mental Health Screening via Information-Theoretic Optimization

InfoShield：通过信息论优化实现心理健康筛查的隐私保护语音表征

Wu, Xueyang, Liu, Siyuan, Yang, Kezhuo, Ling, Guang

Abstract

Speech-based mental health screening offers scalable depression detection, yet clinical deployment faces a significant barrier: users' privacy concerns about demographic information exposure. Current techniques struggle to resolve this conflict. Adversarial training often fails against unseen threats, whereas Differential Privacy tends to compromise diagnostic performance by injecting noise across all features. This paper presents InfoShield, which minimizes mutual information between speech representations and sensitive attributes while preserving depression classification accuracy. We identify that standard MINE estimators struggle with sequential speech due to temporal-static misalignment, and introduce TimeAwareMINE with cross-modal attention to align acoustic frames with attribute embeddings. Experiments on the Androids Corpus show InfoShield reduces gender inference from 92.6\% to 55.5\% and age inference from 55.7\% to 30.3\% with limited utility loss (6\% F1 reduction), achieving F1=0.784 compared to prior SOTA's 0.723.

Chinese Translation

基于语音的心理健康筛查提供了可扩展的抑郁症检测，但在临床部署中面临着一个重大障碍：用户对人口信息泄露的隐私担忧。目前的技术难以解决这一冲突。对抗性训练常常无法抵御未见威胁，而差分隐私（Differential Privacy）则倾向于通过在所有特征中注入噪声而妥协诊断性能。本文提出了InfoShield，旨在最小化语音表征与敏感属性之间的互信息，同时保持抑郁症分类的准确性。我们发现标准的MINE（Mutual Information Neural Estimator）估计器在处理序列语音时由于时间-静态错位而表现不佳，因此引入了TimeAwareMINE，通过跨模态注意力机制将声学帧与属性嵌入对齐。在Androids语料库上的实验表明，InfoShield将性别推断从92.6\%降至55.5 extbackslash%，将年龄推断从55.7 extbackslash%降至30.3 extbackslash%，同时仅造成有限的效用损失（F1值降低6 extbackslash%），相比于之前的最先进技术（SOTA）的F1=0.723，InfoShield达到了0.784。

View on arXiv Download PDF AI Translation

cs.CL / 28 / 2606.05564

Using Large Language Models to Support High Volume Application Review for an Undergraduate Research Program

利用大型语言模型支持本科研究项目的高容量申请评审

Aggarwal, Varun, Kobak, Kay, Howarter, John

Abstract

Undergraduate research programs such as the Summer Undergraduate Research Fellowship (SURF) at Purdue University receive thousands of applications every year, requiring significant time and effort for program staff to evaluate each submission consistently and within tight timelines. This work-in-progress paper describes the development and initial deployment of a large language model (LLM)-based tool to assist in the evaluation of approximately 1,200 student Statements of Purpose (SoPs) for the SURF 2026 cycle at Purdue University. The workflow utilizes OpenAI GPT models (GPT-4o, GPT-5-mini, and GPT-5.2) and uses a structured rubric across six subcategories, each scored on a 0-3 scale. A few SoPs, graded by program staff, were used to tune the model responses. The model prompt was designed to generate both numerical scores, rationales (including positive and negative aspects) and short excerpts from each submission. Using GPT-5.2, the full batch of 1,200 SoPs was processed in approximately 4.6 hours of compute time, averaging roughly 14 seconds per SoP (with per-SoP timing varying with SoP length, which ranged from 500 to 2,000 words). Notable differences in rubric adherence were observed across model versions, with GPT-5.2 adhering most closely. Disagreement in model scores was more pronounced for lower-scoring submissions. The LLM outputs replicated the role previously played by distributed human graders, providing the program coordinator with scored and rationale-annotated outputs for the entire applicant pool. The program coordinator then reviewed these outputs alongside each applicant's SoP, applying the same downstream office criteria used in prior SURF cycles, to produce a shortlist of strong candidates. This coordinator review was completed in approximately 4 hours, compared to the multi-week coordination effort required in prior program cycles.

Chinese Translation

普渡大学的本科研究项目，如暑期本科研究奖学金（SURF），每年收到数千份申请，这需要项目工作人员投入大量时间和精力，以一致的标准在紧迫的时间内评估每一份提交。本文为一项正在进行的研究论文，描述了一种基于大型语言模型（LLM）的工具的开发和初步应用，旨在协助评估约1,200份学生的目的陈述（SoP），用于普渡大学SURF 2026周期。该工作流程利用了OpenAI的GPT模型（GPT-4o、GPT-5-mini和GPT-5.2），并采用了涵盖六个子类别的结构化评分标准，每个子类别的评分范围为0-3。通过项目工作人员评分的少数SoP用于调整模型的响应。模型提示的设计旨在生成数值分数、论证（包括积极和消极方面）以及每份提交的简短摘录。使用GPT-5.2处理1,200份SoP的计算时间约为4.6小时，每份SoP的平均处理时间大约为14秒（每份SoP的处理时间因SoP长度不同而异，长度范围为500到2,000个单词）。模型版本之间在评分标准遵循上存在显著差异，GPT-5.2的遵循程度最高。模型分数在低分提交上差异更为明显。LLM输出复制了之前由分散的人类评分者所扮演的角色，为项目协调员提供了对整个申请者池评分和论证注释的输出。项目协调员随后在审查这些输出时，结合每位申请者的SoP，应用与以前的SURF周期相同的下游办公室标准，筛选出强有力的候选人名单。该协调员审查耗时约4小时，而在以前的项目周期中需要数周的协调工作。

View on arXiv Download PDF AI Translation

cs.CL / 29 / 2606.05569

Domain-Aware Mispronunciation Detection and Diagnosis Using Language-Specific Statistical Graphs

基于领域意识的错误发音检测与诊断方法：使用特定语言的统计图

Tu, Huu Tuong, Nguyen, Hanh, Van Luong, Thien, Cuong, Nguyen Tien, Huan, Vu, Trang, Nguyen Thi Thu

Abstract

Mispronunciation Detection and Diagnosis (MDD) has gained increasing importance in computer-assisted language learning and speech technology in recent years. In this paper, we propose a method for constructing statistical graphs that enable models to learn phoneme confusion patterns represented as directed graphs. Furthermore, we introduce a language-specific strategy to capture systematic pronunciation differences across various native language (L1) backgrounds. The effectiveness of our approach is demonstrated through extensive experiments on the L2-ARCTIC benchmark, where it achieves an F1-score of 59.52%, outperforming several competitive baselines.

Chinese Translation

错误发音检测与诊断（MDD）在计算机辅助语言学习和语音技术中日益重要。本文提出了一种构建统计图的方法，使模型能够学习表示为有向图的音素混淆模式。此外，我们引入了一种特定语言的策略，以捕捉不同母语（L1）背景下的系统性发音差异。通过在L2-ARCTIC基准上的广泛实验，我们的方法效果显著，获得了59.52%的F1分数，超越了若干竞争基线。

View on arXiv Download PDF AI Translation

cs.CL / 30 / 2606.05570

TensorBench: Benchmarking Coding Agents on a Compiler-Based Tensor Framework

TensorBench：基于编译器的张量框架中编码代理的基准测试

Yan, Bobby, Kjolstad, Fredrik

Abstract

Repository-level coding benchmarks face a trade-off between task difficulty and evaluation reliability: tasks that challenge frontier models often involve large codebases with incomplete test coverage, while human review does not scale. We introduce TensorBench, a benchmark of 199 feature-addition and refactoring tasks on an open-source compiler-based tensor framework that extends PyTorch with first-class support for dense and sparse tensors. Tasks cover new sparse formats, dense optimization passes, IR transformations, scheduler changes, runtime components, and high-level numerical operators. TensorBench grades each run by applying the agent's patch and running the framework's test suite, which includes the pre-existing randomized regression tests and any tests the agent adds. For feature-addition tasks, a pass means that the patched repository preserves the tested pre-existing behavior and satisfies the agent-added checks for the requested feature. We evaluate seven coding agents spanning three frontier model families and one open-weight model. Pass rates under this criterion range from $64.8\%$ for the strongest agent to $22.1\%$ for the weakest. Agents pass different subsets of tasks: pairwise Cohen's $\kappa$ ranges from $-0.07$ to $0.43$, with $\kappa = 0.05$ for the two strongest agents.

Chinese Translation

在仓库级别的编码基准测试中，任务难度与评估可靠性之间存在权衡：挑战前沿模型的任务通常涉及大型代码库，并且测试覆盖率不完整，而人工审核则难以扩展。我们提出了TensorBench，这是一个在一个开源编译器基础上的张量框架中进行的基准测试，包含199个特征添加和重构任务，该框架扩展了PyTorch，提供对稠密和稀疏张量的原生支持。任务涵盖新的稀疏格式、稠密优化通道、IR转换、调度器变更、运行时组件和高级数值运算符。TensorBench通过应用代理的补丁并运行框架的测试套件来对每次运行进行评分，该测试套件包括预先存在的随机回归测试以及代理新增的任何测试。对于特征添加任务，通过意味着补丁后的代码库保持所测试的预先存在行为，并满足代理添加的针对请求特征的检查。我们评估了涵盖三个前沿模型家族和一个开放权重模型的七个编码代理。在此标准下，通过率从最强代理的64.8%到最弱代理的22.1%不等。代理通过不同子集的任务：成对 Cohen's κ 范围从 -0.07 到 0.43，两个最强代理的 κ 值为 0.05。

View on arXiv Download PDF AI Translation

cs.CL / 31 / 2606.05610

Predictable Scaling Laws of Optimal Hyperparameters for LLM Continued Pre-training

大型语言模型（LLM）继续预训练的最优超参数可预测缩放规律

Zhou, Yongwei, Diao, Juncheng, Shang, Junlin, Li, Peiguang, Weng, Rongxiang

Abstract

The efficacy of continued pre-training for Large Language Models (LLMs) hinges upon hyperparameter configurations, such as learning rate and batch size. However, current practices often rely on heuristics or grid searches, leading to training instability and excessive costs. In this work, we first empirically discover that optimal hyperparameters follow stable and predictable scaling laws throughout the continued pre-training process. Leveraging these insights, we propose a novel framework to establish quantitative relationships between compute budget and optimal hyperparameters for a given checkpoint. Our approach has two stages: (1) \textit{Empirical Law Discovery}, where we train small-scale proxy models to derive functions mapping compute budget to optimal hyperparameters via standard loss-compute scaling laws; and (2) \textit{State-Aware Hyperparameter Prediction}, where we evaluate an initial checkpoint's validation loss and use the inverse scaling law to estimate its \textit{equivalent pre-training compute} -- the compute needed to achieve the same loss from scratch. Combining this with the planned compute budget, we predict optimal hyperparameters for the target run. Empirical results demonstrate that our method reduces the hyperparameter search overhead by up to 90\% while achieving comparable or superior performance relative to baselines. This model-agnostic framework generalizes across architectures, providing a principled and efficient methodology for diverse continued pre-training scenarios starting from any given point.

Chinese Translation

大型语言模型（LLMs）继续预训练的有效性依赖于超参数配置，如学习率和批量大小。然而，当前的实践通常依赖于启发式方法或网格搜索，导致训练不稳定和成本过高。在本研究中，我们首次实证发现最优超参数在继续预训练过程中遵循稳定且可预测的缩放规律。基于这些见解，我们提出了一个新框架，以建立计算预算与给定检查点的最优超参数之间的定量关系。我们的方法分为两个阶段：（1） extit{经验法则发现}，我们训练小规模代理模型，通过标准的损失-计算缩放规律推导出将计算预算映射到最优超参数的函数；（2） extit{状态感知超参数预测}，我们评估初始检查点的验证损失，并利用逆缩放规律来估计其 extit{等效预训练计算}——为从零开始达到相同损失所需的计算量。将此与规划的计算预算相结合，我们预测目标运行的最优超参数。实证结果表明，我们的方法在实现相较基线的可比或更优性能的同时，将超参数搜索开销减少了多达90 ext%。这一与模型无关的框架在不同架构中具有普适性，为从任何给定点开始的多样化继续预训练场景提供了一种有原则且高效的方法。

View on arXiv Download PDF AI Translation

cs.CL / 32 / 2606.05616

What's in a Name? Morphological Shortcuts by LLMs in Pharmacology

名字中有什么？LLMs在药理学中的形态简化

Mo, Kaijie, Yang, Thomas, Shaib, Chantal, Yao, Qing, Rudman, William, Kouzy, Ramez, Misra, Kanishka, Wallace, Byron C., Li, Junyi Jessy

Abstract

The morphological form of a word can often give cues to its meaning, but purely relying on these mappings can lead to overgeneralization in high-stakes domains. In the medical domain, for instance, LLMs can confidently reason about fictitious drugs from their affixes alone (e.g., wugcillin) and generate plausible-looking clinical content. We present a behavioral and mechanistic study of LLM "affix heuristics" in pharmacology. Using fictitious drug names built from real affixes, we show that affix signals alone elicit class-level pharmacological responses. We introduce a framework for identifying whether a model's drug semantics are driven mainly by the affix, the stem, or the drug name as a whole. Applied across 653 drugs, our framework reveals that models often induce drug meaning primarily through affix cues, yet rarely explicitly indicate this reliance, and sometimes incorrectly conflate properties among affix-sharing drugs. Activation patching across models further localizes this behavior to early-mid layers. These findings show that morphological shortcuts pose a subtle but measurable risk to safety.

Chinese Translation

一个词的形态形式往往可以提供其含义的线索，但在高风险领域中，仅依赖这些映射可能会导致过度概括。例如，在医学领域，LLMs（大语言模型）可以仅凭词缀（如 wugcillin）自信地推理出虚构药物，并生成看似合理的临床内容。我们对LLM在药理学中的“词缀启发式”进行了行为和机制研究。通过使用真实词缀构建的虚构药物名称，我们展示了仅凭词缀信号即可引发类别级的药理反应。我们介绍了一个框架，用于识别模型的药物语义主要是由词缀、词根还是药物名称整体所驱动。应用于653种药物时，我们的框架揭示出模型往往主要通过词缀线索诱导药物意义，但很少明确表示这一依赖，且有时错误地将具有相同词缀的药物特性混淆。模型间的激活修补进一步将这种行为局限于早期到中层。这些发现表明，形态学简化对安全性构成了微妙但可测量的风险。

View on arXiv Download PDF AI Translation

cs.CL / 33 / 2606.05620

An ERP Study on Recursive Locative Processing in Mandarin-Speaking Children with Autism

关于自闭症儿童中递归定位处理的ERP研究

Wang, Xiaoyi, Fu, Chenxi, Zhuang, Ziman, Yang, Caimei

Abstract

Recursion enables the generation of hierarchical linguistic structures but imposes substantial processing demands during real-time comprehension. While difficulties with complex syntax have been reported in autism spectrum disorder (ASD), the temporal dynamics of recursive processing remain poorly understood. This study used event-related potentials (ERPs) to examine how Mandarin-speaking children with ASD process two-level recursive locative constructions. Twenty-four children (12 ASD, 12 typically developing, TD) participated in a cross-modal sentence-picture matching task. Neural responses were analyzed across three processing stages associated with structural prediction (P200), semantic integration (N400), and syntactic reanalysis (P600), with mental age controlled. Results revealed a systematic divergence between groups. TD children showed clear P200 and P600 modulation in response to structural mismatch, whereas ASD children exhibited attenuated early differentiation and reduced late reanalysis effects. In contrast, ASD children showed enhanced N400 responses under mismatch conditions, indicating increased semantic integration demands. In addition, the ASD group displayed significantly greater inter-individual variability in hemispheric lateralization, although lateralization strength was not associated with receptive vocabulary performance. These findings support a cascading account in which reduced early predictive engagement in ASD leads to increased integration costs and diminished reanalysis efficiency during recursive processing. More broadly, the results highlight the importance of both temporal processing dynamics and neural variability in understanding language differences in ASD.

Chinese Translation

递归使得层次化语言结构的生成成为可能，但在实时理解过程中给处理带来了显著的需求。虽然在自闭症谱系障碍（ASD）中已经报告了对复杂句法的困难，但递归处理的时间动态仍然不太清楚。本研究采用事件相关电位（ERPs）技术，考察了讲普通话的自闭症儿童如何处理两层递归定位结构。共有24名儿童（12名 ASD，12名典型发育，TD）参与了跨模态句子-图片匹配任务。神经反应在与结构预测（P200）、语义整合（N400）和句法再分析（P600）相关的三个处理阶段进行了分析，并控制了心理年龄。结果显示出两组之间的系统性差异。TD儿童对结构不匹配表现出明显的P200和P600调制，而ASD儿童则表现出早期差异减弱和晚期再分析效应减少。相比之下，在不匹配条件下，ASD儿童的N400反应增强，表明语义整合的需求增加。此外，ASD组在半球侧化的个体差异上表现出显著更大的变异性，尽管侧化强度与接受性词汇表现之间没有关联。这些发现支持了一种级联模式，即ASD中早期预测参与的减少导致了在递归处理过程中整合成本的增加和再分析效率的降低。更广泛地说，这些结果强调了在理解ASD语言差异时，时间处理动态和神经变异性的重要性。

View on arXiv Download PDF AI Translation

cs.CL / 34 / 2606.05622

AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints

AdaPlanBench：评估大型语言模型代理在世界和用户约束下的自适应规划

Liu, Jiayu, Qian, Cheng, Wang, Zhenhailong, Li, Bingxuan, Liu, Jiateng, Wang, Heng, Kim, Jeonghwan, Wang, Yumeng, Chen, Xiusi, Fung, Yi R., Ji, Heng

Abstract

Planning for real-world problems by language models often involves both world and user constraints, which may not be fully specified upfront and are progressively disclosed through interaction. However, existing benchmarks still underexplore adaptive planning under such progressively revealed dual constraints. To address this gap, we introduce AdaPlanBench, a dynamic interactive benchmark for evaluating whether Large Language Model (LLM) agents can adaptively plan and re-plan under progressively revealed world and user constraints. AdaPlanBench is built on 307 household tasks, with a scalable constraint construction pipeline that augments each task with dual constraints. At runtime, agents interact with the environment in a multi-turn protocol where hidden constraints are revealed only when the agent proposes a plan that violates them, requiring iterative plan revision under accumulating feedback. This makes planning challenging, as agents must infer and track constraints from feedback while re-planning effectively. Experiments on ten leading LLMs show that adaptive planning under dual constraints remains challenging, with the best model reaching only 67.75% accuracy. We further observe that performance degrades as more constraints accumulate, with user constraints posing a particularly large challenge and failures often stemming from weaker physical grounding and reduced effectiveness. These results establish AdaPlanBench as a testbed for dual-constrained interactive planning and highlight the challenge of reliable adaptation to dynamically revealed constraints in LLM agents.

Chinese Translation

语言模型在现实世界问题中的规划往往涉及世界和用户约束，这些约束可能无法在前期完全明确，而是通过互动逐步揭示。然而，现有基准仍然不足以探讨在这些逐步揭示的双重约束下的自适应规划。为了解决这一问题，我们提出了AdaPlanBench，这是一个动态交互基准，用于评估大型语言模型（Large Language Model，LLM）代理是否能够在逐步揭示的世界和用户约束下进行自适应规划和重新规划。AdaPlanBench构建在307个家庭任务之上，拥有一个可扩展的约束构建管道，能够为每个任务增加双重约束。在运行时，代理与环境以多轮协议进行互动，隐藏的约束仅在代理提议违反这些约束的计划时被揭示，从而需要在累积反馈下进行迭代的计划修订。这使得规划变得具有挑战性，因为代理必须从反馈中推断和跟踪约束，同时有效地进行重新规划。对十个领先的LLM进行的实验表明，在双重约束下的自适应规划依然具有挑战性，表现最好的模型仅达到67.75%的准确率。我们进一步观察到，随着约束的增加，性能会下降，用户约束特别具有挑战性，失败通常源于物理基础较弱和有效性降低。这些结果确立了AdaPlanBench作为双约束交互规划的测试平台，并突显了在LLM代理中对动态揭示的约束进行可靠适应的挑战。

View on arXiv Download PDF AI Translation

cs.CL / 35 / 2606.05626

When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer

新生成器出现时：基于岭特征转移的终身机器生成文本归属

Sun, Zhen, Liao, Yifan, Huang, Zhicong, Wei, Jiaheng, Hong, Cheng, Yue, Yutao, He, Xinlei

Abstract

Machine-generated text (MGT) attribution aims to identify the specific generator responsible for a given text, thereby providing fine-grained evidence for model accountability and misuse investigation. As new large language models continue to emerge, attribution models must continuously incorporate new generators while preserving their ability to recognize previously seen ones. Prior works have shown that this lifelong MGT attribution setting is challenging, and existing methods often struggle to achieve a stable balance between adapting to new classes and retaining old ones. To address this issue, we propose RidgeFT, a lightweight analytic update framework that does not rely on exemplar replay. RidgeFT trains a task-aware encoder on the initial generator set, stores compact class-wise sufficient statistics when each generator class is first observed, and then freezes the encoder for replay-free closed-form updates. It then suppresses generator-irrelevant variation through covariance calibration, improves representation capacity with fixed random features, and updates new classes through closed-form ridge regression based on class-level sufficient statistics. Across multi-topic evaluations with varying initial generator setups, RidgeFT consistently outperforms baselines. It achieves the best macro-F1 across domains, backbones, and incremental protocols, while also improving both old-class retention and new-class adaptation. These results suggest that feature-stable analytic updates provide a simple yet effective approach to lifelong MGT attribution.

Chinese Translation

机器生成文本（MGT）归属旨在识别特定文本所对应的生成器，从而为模型的问责及滥用调查提供详细证据。随着新的大型语言模型不断涌现，归属模型必须不断融合新生成器，同时保持识别先前生成器的能力。先前的研究表明，这种终身MGT归属的设定具有挑战性，现有方法往往难以在适应新类别与保持旧类别之间取得稳定平衡。为了解决这个问题，我们提出了RidgeFT，一个轻量级的分析更新框架，它不依赖于例子重放。RidgeFT在初始生成器集合上训练一个任务感知编码器，当首次观察到每个生成器类别时，存储紧凑的类别充分统计数据，然后冻结编码器以进行无重放的闭合形式更新。它通过协方差校准抑制与生成器无关的变异，通过固定随机特征增强表示能力，并基于类别级充分统计数据通过闭合形式岭回归更新新类别。在多主题评估中，RidgeFT在不同的初始生成器设置下始终优于基线，且在各个领域、主干网络和增量协议下均达到了最佳的宏F1分数，同时提升了旧类别的保持能力与新类别的适应能力。这些结果表明，特征稳定的分析更新为终身MGT归属提供了一种简单而有效的方法。

View on arXiv Download PDF AI Translation

cs.CL / 36 / 2606.05634

Bootstrapping Semantic Layer from Execution for Text-to-SQL

从执行中引导语义层以实现文本到SQL的转换

Lee, Youngwon, Kim, Jaejin, Hwang, Seung-won

Abstract

Real-world text-to-SQL is often under-specified until user phrases are grounded in how the database stores values. Prior work attempts to address this by requiring a semantic layer to specify groundings in advance, but such specifications are often incomplete, especially in expert domains where domain-specific conventions are under-documented. As this leaves multiple grounding hypotheses open for the same SQL part, we introduce GATE (Grouding After Test from Execution), which bootstraps missing groundings from execution feedback. GATE keeps grounding hypotheses open while executing the already grounded parts to obtain observations. Then, only the hypothesis supported by that observation is grounded and stored as a memory entry, recording what was tested and how the open part should be written in SQL. These entries accumulate into execution-grounded memory, allowing later steps to reuse supported groundings. Across real-world and controlled benchmarks, GATE consistently improves over strong baselines, demonstrating that execution can serve not only as validation but also as a bootstrapping mechanism for reusable memory in text-to-SQL.

Chinese Translation

现实世界中的文本到SQL转换通常直到用户短语与数据库存储值的方式相结合之前都是不明确的。先前的工作通过要求建立一个语义层来提前指定这些结合，但这样的规范往往是不完整的，特别是在那些领域特定约定文档不足的专家领域。这使得同一个SQL部分可能存在多个结合假设。为此，我们提出了GATE（Grouding After Test from Execution），它通过执行反馈引导缺失的结合。GATE在执行已经结合的部分以获取观察时，保持结合假设的开放性。然后，仅支持该观察的假设被结合并作为记忆条目存储，记录了测试的内容及开放部分应如何用SQL编写。这些条目累积成执行驱动的记忆，允许后续步骤重用已支持的结合。在现实世界和受控基准测试中，GATE始终优于强基线，证明执行不仅可以作为验证，还可以作为文本到SQL中可重用记忆的引导机制。

View on arXiv Download PDF AI Translation

cs.CL / 37 / 2606.05671

QueryAgent-R1: Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation

QueryAgent-R1： Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation

Sun, Dike, Zou, Zheng, Zang, Jingtong, Sun, Qi, Luo, Huaipeng Zhaoand Tao, Zeng, Xiaoyi

Abstract

Query recommendation in e-commerce search aims to proactively suggest queries that match users' potential interests. However, existing methods mainly optimize query-level relevance, while neglecting whether the retrieved products align with users' downstream preferences. This mismatch often leads to high query click through rates (CTR) but low product conversion rates (CVR). To bridge this gap, we propose QueryAgent-R1, a memory-augmented agentic framework that improves end-to-end alignment via chain-of-retrieval optimization. Our QueryAgent-R1 grounds query generation in real inventory retrieval, allowing the agent to validate and refine queries based on retrieved products. We also design a consistency reward in the agentic reinforcement learning (RL) process to jointly optimize query relevance and downstream engagement. In addition, we construct a memory abstraction module for efficient user profiling. To support offline evaluation, we construct two datasets based on both proprietary industrial data and public datasets, on which QueryAgent-R1 consistently outperforms strong baselines. Moreover, on a large scale production platform, QueryAgent-R1 improves Query CTR by 2.9% and guided CVR by 3.1% in online A/B tests.

Chinese Translation

电子商务搜索中的查询推荐旨在主动建议与用户潜在兴趣匹配的查询。然而，目前的现有方法主要优化查询级别的相关性，却忽视了检索到的产品是否符合用户的后续偏好。这种不匹配往往导致查询的点击通过率（CTR）较高，但产品转化率（CVR）较低。为了解决这一问题，我们提出了QueryAgent-R1，一个增强记忆的智能框架，通过链式检索优化改善了端到端的一致性。我们的QueryAgent-R1将查询生成与真实库存检索相结合，使得代理能够基于检索到的产品对查询进行验证和精炼。我们还在智能强化学习（RL）过程中设计了一种一致性奖励，旨在共同优化查询相关性和后续参与度。此外，我们构建了一个内存抽象模块，以高效进行用户画像。为了支持离线评估，我们构建了两个数据集，基于专有工业数据和公共数据集，QueryAgent-R1在这些数据集上持续优于强基线。此外，在一个大型生产平台上，QueryAgent-R1在在线A/B测试中将查询CTR提升了2.9%，将指导CVR提升了3.1%。

View on arXiv Download PDF AI Translation

cs.CL / 38 / 2606.05688

Value-and-Structure Alignment for Routing-Consistent Quantization of Mixture-of-Experts Models

用于路由一致性量化的价值与结构对齐在专家混合模型中的应用

Park, Hancheol, Lee, Geonho, Piao, Tairen, Kim, Tae-Ho

Abstract

Mixture-of-Experts (MoE) models scale foundation models efficiently by activating only a subset of experts for each token, but their large number of expert parameters still makes quantization essential for practical deployment. Unlike dense models, however, MoE models are sensitive to routing instability: small quantization-induced perturbations can change the top-$k$ expert selection, altering the computation path and degrading model quality. We propose Value-and-Structure Routing Alignment for Quantization (VSRAQ), a MoE-specific post-training quantization objective that preserves pre-quantization expert-selection behavior under quantization. VSRAQ combines two complementary objectives that jointly preserve expert-selection behavior: value alignment, which matches routing-relevant logits or scores, and structure alignment, which preserves expert ordering and top-$k$ decision boundaries. By maintaining routing consistency, VSRAQ reduces quantization-induced degradation without introducing any inference-time overhead and can be integrated into existing quantization frameworks. Experiments on recent MoE foundation models show that VSRAQ improves expert-selection consistency and consistently outperforms reconstruction-only and router-aware baselines.

Chinese Translation

专家混合模型（Mixture-of-Experts, MoE）通过仅为每个标记激活一部分专家，从而高效扩展基础模型，但其庞大的专家参数数量仍使得量化在实际部署中至关重要。然而，与密集模型不同，MoE模型对路由的不稳定性十分敏感：小的量化引起的扰动可能会改变前$k$个专家的选择，从而改变计算路径并降低模型质量。我们提出了一种专门针对MoE的后训练量化目标——价值与结构路由对齐量化（Value-and-Structure Routing Alignment for Quantization, VSRAQ），旨在在量化过程中保持量化前的专家选择行为。VSRAQ结合了两个互补目标，以共同保持专家选择行为：价值对齐，它匹配与路由相关的logits或分数；结构对齐，它保持专家的排序和前$k$个决策边界。通过维护路由一致性，VSRAQ减少了量化引起的性能下降，而不引入任何推理时间的开销，并且能够与现有的量化框架集成。对近期MoE基础模型的实验表明，VSRAQ提升了专家选择的一致性，并且始终优于仅重建和路由感知基线。

View on arXiv Download PDF AI Translation

cs.CL / 39 / 2606.05698

Rethinking LoRA Memory Through the Lens of KV Cache Compression

通过KV缓存压缩重新思考LoRA记忆

Zuo, Chunsheng, Wang, Liaoyaqi, Jurayj, William, Fleshman, William, Van Durme, Benjamin

Abstract

Parametric retrieval augmentation encodes document information into lightweight, document-specific modules such as LoRA adapters, reducing the need to include all evidence as input context. However, it remains unclear how this parameter-side memory interacts with context-side memory stored in the KV cache. We study this interaction in document-level question answering by progressively evicting document key-value states and measuring when a document LoRA contributes beyond the retained context. We find that document LoRA adds little when the KV cache is largely intact, but becomes increasingly useful under aggressive compression, recovering 13-21 ROUGE-L points when no document context remains. The gain is largest when the base model encodes the document, and the adapter is applied only during answer generation, suggesting that document LoRA is better understood as decoding-time parametric memory than as a document encoder. Finally, QA-style supervision produces substantially stronger adapters than raw-context next-token-prediction. These results position document LoRA as a complementary memory channel whose value emerges precisely when context-side evidence is scarce.

Chinese Translation

参数检索增强将文档信息编码为轻量级的、特定于文档的模块，如LoRA适配器，从而减少了将所有证据作为输入上下文的必要性。然而，尚不清楚这种参数侧记忆如何与存储在KV缓存中的上下文侧记忆相互作用。我们通过逐步逐出文档键值状态并测量文档LoRA在保留上下文时所作的贡献，研究了这种交互在文档级问答中的作用。我们发现，当KV缓存基本完好时，文档LoRA的贡献较小，但在激进压缩下其变得日益重要，当没有文档上下文时，能够恢复13-21个ROUGE-L分数。尤其是基模型对文档进行了编码，而适配器仅在回答生成期间应用时，增益最大，这表明文档LoRA更应被理解为解码时的参数记忆，而不是文档编码器。最后，问答风格的监督生成的适配器显著优于原始上下文的下一个token预测。这些结果将文档LoRA定位为一种补充的记忆通道，其价值正是在上下文侧证据稀缺时显现。

View on arXiv Download PDF AI Translation

cs.CL / 40 / 2606.05711

Beyond tokens: a unified framework for latent communication in LLM-based multi-agent systems

超越符号：基于大语言模型的多智能体系统中潜在通信的统一框架

Liu, Yingzhuo

Abstract

Multi-agent systems built on large language models (LLMs) have become a prevailing paradigm for tackling complex reasoning, planning, and tool-use tasks. The dominant communication protocol in such systems is natural language: agents exchange messages token-by-token, verbalising their internal reasoning so that peers can read, verify, and respond. While convenient and interpretable, this protocol suffers from three structural drawbacks -- high inference cost, irreversible information loss during discretization, and ambiguity/redundancy of natural language. A growing body of work therefore explores an alternative protocol -- latent communication -- in which agents exchange continuous representations (embeddings, hidden states, or KV-caches) directly, bypassing the bottleneck of text generation. This paper presents a unified framework for organising the rapidly expanding literature on latent communication. We analyse existing methods along three orthogonal axes: (1) WHAT information is communicated (Embeddings, Hidden States, KV-Caches, or other continuous state); (2) WHICH sender-receiver alignment is used (latent-space alignment and layer alignment); and (3) HOW the communicated information is fused into the receiver (concatenation, prepending, mathematical operations, cross-attention, or cache restoration). Under this 3-axis framework, we systematically categorise eighteen representative methods proposed between 2024 and 2026, identify five major design patterns, and surface a set of open challenges -- including cross-architecture alignment, security of latent channels, compression for edge deployment, and the relationship between latent communication and latent chain-of-thought. We hope that this framework both lowers the barrier to entry for new researchers and provides a vocabulary for comparing future work.

Chinese Translation

基于大语言模型（LLMs）的多智能体系统已成为应对复杂推理、规划和工具使用任务的主要范式。在此类系统中，主要的通信协议是自然语言：智能体逐个令牌地交换消息，口头表达其内部推理，使得同伴能够阅读、验证和响应。尽管这一协议方便且易于解释，但存在三个结构性缺陷——高推理成本、离散化过程中的不可逆信息损失以及自然语言的歧义/冗余。因此，越来越多的研究探索了一种替代协议——潜在通信，智能体在其中直接交换连续表示（嵌入、隐藏状态或KV缓存），从而绕过文本生成的瓶颈。本文提出了一个统一框架，以组织快速扩展的潜在通信文献。我们从三个正交维度分析现有方法：（1）传达了何种信息（嵌入、隐藏状态、KV缓存或其他连续状态）；（2）使用了哪种发送者-接收者对齐（潜在空间对齐和层对齐）；（3）如何将通信信息融合到接收者中（连接、预挂、数学运算、交叉注意力或缓存恢复）。在这一三维框架下，我们系统地对2024至2026年间提出的十八种代表性方法进行了分类，识别了五种主要设计模式，并提出了一系列开放挑战——包括不同架构的对齐、潜在通道的安全性、边缘部署的压缩，以及潜在通信与潜在思维链的关系。我们希望这一框架不仅降低新研究者的入门门槛，还提供了比较未来工作的词汇。

View on arXiv Download PDF AI Translation

cs.CL / 41 / 2606.05716

Interpreting Style Representations via Style-Eliciting Prompts

通过风格引导提示解释风格表示

Kim, Junghwan, Jurgens, David

Abstract

Style representation learning is a powerful tool for authorship analysis and modeling writing style, yet the latent nature of learned representations makes them difficult to interpret. Recent work has attempted to explain these representations by generating natural language descriptions with large language models (LLMs) conditioned on input text. However, such descriptions are often prone to the LLM's biases and hallucinations, and they lack an explicit objective and practical utility. In this work, we propose a novel framework for interpreting style representations through style-eliciting prompts: natural language instructions designed to steer LLMs to generate text that reflects specific stylistic attributes. We curate 1,010 distinct style features spanning 26 stylistic categories and construct a dataset by prompting an LLM to generate text conditioned on these features. Using this data, we train a decoder to generate a style prompt from the style representation of the generated text. We evaluate our approach on three tasks: (1) recovering original style prompts from generated text, (2) generating text in the same style using the recovered prompts, and (3) steering LLM outputs to match the style of human-written texts. Experiments demonstrate that our method consistently outperforms strong baselines that directly prompt LLMs with target text, achieving superior performance in both style description and style imitation. These results highlight that style-eliciting prompts can provide a practical and interpretable interface to stylistic information encoded in style representations.

Chinese Translation

风格表示学习是作家身份分析和写作风格建模的强大工具，但学习到的表示的潜在性质使其难以解释。近期研究尝试通过生成与输入文本相关的大型语言模型（LLMs）自然语言描述来解释这些表示。然而，这些描述往往受到LLM的偏见和幻觉的影响，缺乏明确的目标和实际效用。在本研究中，我们提出了一种通过风格引导提示解释风格表示的新框架：即设计自然语言指令，以引导LLM生成反映特定风格属性的文本。我们整理了涵盖26个风格类别的1,010个独特风格特征，并通过提示LLM生成基于这些特征的文本构建了一个数据集。利用这些数据，我们训练了一个解码器，以从生成文本的风格表示中生成风格提示。我们在三个任务上评估了该方法：（1）从生成文本中恢复原始风格提示；（2）使用恢复的提示生成相同风格的文本；（3）引导LLM输出匹配人类撰写文本的风格。实验表明，我们的方法在风格描述和风格模仿的性能上均优于直接向LLM提示目标文本的强基线，取得了更为出色的表现。这些结果凸显了风格引导提示可以为编码在风格表示中的风格信息提供实际且可解释的接口。

View on arXiv Download PDF AI Translation

cs.CL / 42 / 2606.05724

Narrative Knowledge Weaver: Narrative-Centric Retrieval-Augmented Reasoning for Long-Form Text Understanding

叙事知识编织者：基于叙事的检索增强推理用于长文本理解

Tian, Qiuyu, Chen, Fengyi, Li, Yiding, Kong, Youyong, Guo, Fan, Li, Yuyao, Shen, Jinjing, Xie, Zhijing, Luo, Yiyun, Zhang, Xin, Xia, Yingce, Liu, Zequn

Abstract

Long-form narrative QA requires reasoning over evolving story worlds rather than isolated passages: answers may depend on earlier goals, changing character states, social relations, causal triggers, temporal position, and later consequences. Existing retrieval and graph-augmented generation methods improve evidence access, but their units--chunks, entities, relations, summaries, or tool actions--do not directly encode how evidence functions in a story. We introduce Narrative Knowledge Weaver(NKW), a source-grounded framework that aligns textual evidence, atomic facts, canonical graph structure, entity profiles, interactions, episodes, and storylines. At query time, NKW uses text, graph, and narrative tools with post-retrieval reading skills to assemble evidence and audit actor, scope, polarity, state, and temporal constraints. Across STAGE, FairytaleQA, and QuALITY, NKW is strongest on screenplay-level story-world QA while remaining competitive on more passage-centered benchmarks. Ablations, question-type analyses, graph-asset statistics, and case studies show complementary benefits for character, scene, temporal, causal, and narrative-progression reasoning.

Chinese Translation

长篇叙事问答需要在不断发展的故事世界中进行推理，而非孤立的段落：答案可能依赖于先前的目标、变化的角色状态、社会关系、因果触发、时间位置和后续结果。现有的检索和图谱增强生成方法提高了证据获取，但其单位——块、实体、关系、摘要或工具动作——并未直接编码证据在故事中的功能。我们提出了叙事知识编织者（Narrative Knowledge Weaver，NKW），一个以源为基础的框架，该框架将文本证据、原子事实、标准图结构、实体档案、交互、情节和故事线进行对齐。在查询时，NKW利用文本、图谱和叙事工具，并结合后检索阅读技能，来组装证据并审核行为者、范围、极性、状态和时间约束。在 STAGE、FairytaleQA 和 QuALITY 上，NKW 在剧本级别的故事世界问答方面表现最强，同时在更以段落为中心的基准测试中仍然保持竞争力。消融实验、问题类型分析、图谱资产统计和案例研究显示，对角色、场景、时间、因果及叙事进展推理具有互补益处。

View on arXiv Download PDF AI Translation

cs.CL / 43 / 2606.05742

AdaPLD: Adaptive Retrieval and Reuse for Efficient Model-Free Speculative Decoding

AdaPLD：用于高效无模型推测解码的自适应检索与重用

Liu, Runheng, Xie, Jincheng, Hu, Wen, Xiao, Xingchen, Huang, Heyan

Abstract

Speculative decoding accelerates generation by verifying multiple drafted tokens in a single target-model forward pass, reducing sequential decoding iterations. Model-free variants avoid auxiliary draft models by reusing text and model states already available during generation, but their speedup depends on the reliability of the constructed drafts. We identify two limitations of existing reuse-based methods: lexically anchored retrieval has limited recall under surface-form variation, and deterministic span copying can be brittle when the retrieved context does not uniquely determine the continuation. We propose \emph{AdaPLD}, a training-free method that adaptively improves both retrieval and draft construction. AdaPLD preserves high-precision lexical reuse while using semantic similarity to recover additional reuse opportunities when lexical matching fails. It further constructs branched reuse hypotheses to account for continuation uncertainty, rather than relying on a single copied span. Across diverse benchmarks, AdaPLD reduces target-model forward passes and achieves up to $3.10\times$ decoding speedup.

Chinese Translation

推测解码通过在单次目标模型前向传递中验证多个草拟标记，从而加速生成，减少顺序解码迭代。无模型变体通过重用在生成过程中已经可用的文本和模型状态来避免辅助草拟模型，但它们的加速取决于所构建草拟的可靠性。我们识别了现有基于重用方法的两项局限性：基于词汇锚定的检索在表面形式变化下具有有限的召回率，而确定性跨度复制在检索的上下文无法唯一确定延续时可能会出现脆弱性。我们提出了 extit{AdaPLD}，这是一种不需要训练的方法，能够自适应地改善检索和草拟构建。AdaPLD在词汇重用高精度的同时，利用语义相似性在词汇匹配失败时恢复额外的重用机会。它进一步构建分支重用假设，以考虑延续的不确定性，而不是依赖于单一的复制跨度。在多样化的基准测试中，AdaPLD减少了目标模型的前向传递，并实现了高达$3.10 imes$的解码加速。

View on arXiv Download PDF AI Translation

cs.CL / 44 / 2606.05744

PlanBench-V: A Spatial Planning Map Benchmark for Vision-Language Models

PlanBench-V：面向视觉语言模型的空间规划地图基准测试

Chen, Minxin, Zhu, He, Su, Junyou, Wang, Wen, Deng, Yijie, Zhang, Wenjia

Abstract

Spatial planning maps are central to territorial governance, translating planning objectives, regulations, and spatial strategies into visual forms for decision-making, public communication, and institutional coordination. Their interpretation, however, requires fine-grained visual perception, spatial reasoning, and policy-informed professional judgment, creating major challenges for both human learners and AI systems. With the rapid progress of Vision-Language Models (VLMs), their use in urban planning analysis is gaining attention, yet existing multimodal benchmarks mainly target general visual understanding and overlook the domain-specific cognitive processes of planning practice. To address this gap, we introduce PlanBench-V, the first comprehensive benchmark for evaluating VLMs in spatial planning map interpretation. We first build the Spatial Planning Map Database (SPMD), an expert-annotated dataset of 223 planning maps and 1629 question-answer pairs curated by professional planners, covering diverse geographic regions and cartographic styles. We then propose a theory-informed evaluation framework assessing four progressive capabilities: Perception, Reasoning, Association, and Implementation, corresponding to the cognitive pipeline of planning map interpretation. Extensive experiments across two generations of VLMs show clear progress but persistent limitations. The best 2026 agentic reasoning model, Qwen3.6-Plus, substantially outperforms the best 2025 model, GPT-4o, by 27%. Nevertheless, all models still struggle with implementation-oriented tasks requiring evaluative judgment, policy sensitivity, and constraint-aware decision-making. These findings reveal fundamental limitations of current VLMs in professional planning contexts and highlight the need for domain-adaptive multimodal reasoning frameworks. Code and data are available at https://plangpt.github.io.

Chinese Translation

空间规划地图是区域治理的核心，将规划目标、法规和空间战略转化为决策、公众沟通和机构协调的视觉形式。然而，对这些地图的解读需要细致的视觉感知、空间推理和政策导向的专业判断，这给人类学习者和人工智能系统带来了重大挑战。随着视觉语言模型（Vision-Language Models, VLMs）的快速进展，它们在城市规划分析中的应用逐渐受到关注，但现有的多模态基准主要针对一般的视觉理解，忽视了规划实践中特定领域的认知过程。为了解决这一空白，我们提出了PlanBench-V，这是第一个综合基准，用于评估VLM在空间规划地图解读中的表现。我们首先构建空间规划地图数据库（Spatial Planning Map Database, SPMD），这是一个由专业规划师注释的223个规划地图和1629对问答的专家数据集，涵盖了多样化的地理区域和制图风格。然后，我们提出了一个理论导向的评估框架，评估四个进阶能力：感知、推理、关联和实施，这些能力对应于规划地图解读的认知流程。对两代VLM进行的广泛实验显示出明显的进展，但也存在持续的局限性。最佳的2026决策推理模型Qwen3.6-Plus在性能上显著超过最佳的2025模型GPT-4o，提升幅度达到27%。尽管如此，所有模型在需要评估判断、政策敏感性和约束意识的实施导向任务上仍然存在困难。这些发现揭示了当前VLM在专业规划环境中的基本局限性，并强调了对领域自适应多模态推理框架的需求。代码和数据可在https://plangpt.github.io获取。

View on arXiv Download PDF AI Translation

cs.CL / 45 / 2606.05749

MARDoc: A Memory-Aware Refinement Agent Framework for Multimodal Long Document QA

MARDoc：一个基于记忆的多模态长文档问答精炼代理框架

Chen, Kaifeng, Liu, Hongtao, Peng, Qiyao, Yang, Jian, Liu, Yongqiang, Zhang, Xiaochen, Yang, Qing

Abstract

Iterative retrieval-reasoning agents have recently shown promise for multimodal long-document question answering. However, most existing systems maintain a single growing context that mixes retrieval traces, observations, and intermediate reasoning. As interactions accumulate, key evidence becomes scattered and diluted, making multi-hop reasoning noisy. We propose MARDoc, a Memory-Aware Refinement Agent framework that decouples long-document QA into three specialized agents: an Explorer for multi-granularity multimodal retrieval, a Refiner for distilling interaction traces into structured evidence and reasoning memories, and a Reflector for checking evidence sufficiency and providing targeted feedback. Across iterations, the agents rely on a dynamically updated structured memory rather than a full accumulated interaction history. This design reduces context noise while preserving answer-critical facts and their logical dependencies. Experiments on MMLongBench-Doc and DocBench show that MARDoc achieves strong results, outperforming same-backbone baselines and demonstrating the effectiveness of structured memory for agentic document QA.

Chinese Translation

迭代检索推理代理最近在多模态长文档问答中显示出了良好的前景。然而，大多数现有系统维持着一个单一的不断增长的上下文，该上下文混合了检索痕迹、观察结果和中间推理。随着交互的累积，关键证据变得分散和稀释，使得多跳推理变得嘈杂。我们提出MARDoc，一个基于记忆的精炼代理框架，将长文档问答分解为三个专业代理：一个用于多粒度多模态检索的探索者（Explorer），一个用于将交互痕迹提炼为结构化证据和推理记忆的精炼者（Refiner），以及一个用于检查证据充分性并提供有针对性反馈的反思者（Reflector）。在多次迭代中，代理依赖于一个动态更新的结构化记忆，而不是完整的累积交互历史。该设计减少了上下文噪声，同时保留了对答案至关重要的事实及其逻辑依赖关系。在MMLongBench-Doc和DocBench上的实验表明，MARDoc取得了强劲的结果，超越了相同骨干网络的基准，并展示了结构化记忆在代理文档问答中的有效性。

View on arXiv Download PDF AI Translation

cs.CL / 46 / 2606.05793

CollabBench: Benchmarking and Unleashing Collaborative Ability of LLMs with Diverse Players via Proactive Engagement

CollabBench：通过主动参与对多样化参与者的LLMs的协作能力进行基准测试与释放

Qian, Hong, Liu, Yuanhao, Zhou, Zihan, Zhang, Zongbao, Ge, Hanjie, Shi, Haotian, Dou, Liang, Wang, Xiangfeng, Yang, Jingwen, Zhou, Aimin

Abstract

While LLM-based agents excel at individual tasks, effective collaboration with realistic human partners remains challenging. Most of the existing conversation-level collaborative studies lack grounded interaction and behavioral execution, motivating the need for cooperative game environments that enable contextualized and immersive collaboration. To this end, this paper proposes CollabBench, a benchmark for evaluating and training collaborative agents in cooperative games. CollabBench features a Diverse Player Profile Simulation pipeline to model varied players behaviors, and a Collaborative Agentic Training paradigm that unifies reasoning, communication, and action via agentic rollouts, optimized with a hybrid reward balancing task efficiency and affective adaptation. We further extend classic environments to CWAH-MultiPlayer and Cook-MultiPlayer for systematic evaluation under diverse personalities. Experiments with efficiency and affective metrics show that our trained models outperform base models, achieving 19.5% higher efficiency and 24.4% improved affective performance. Further analysis reveals key collaborative limitations of existing models and offers insights for future collaborative training.

Chinese Translation

尽管基于LLM的智能体在单一任务中表现出色，但与现实人类伙伴的有效协作仍然具有挑战性。现有大多数对话级别的协作研究缺乏基于实际互动和行为执行的基础，促使我们需要一种能够实现情境化和身临其境协作的合作游戏环境。为此，本文提出了CollabBench，一个用于评估和训练合作游戏中的协作智能体的基准工具。CollabBench具有多样化玩家特征模拟管道，以模拟不同玩家行为，并通过基于智能体的推广的协作智能体训练范式，将推理、沟通和行动统一在一起，优化了任务效率和平衡情感适应的混合奖励。我们还将经典环境扩展为CWAH-MultiPlayer和Cook-MultiPlayer，以在不同个性下进行系统评估。基于效率和情感指标的实验表明，我们训练的模型在效率上比基础模型提高了19.5%，情感表现提升了24.4%。进一步的分析揭示了现有模型在协作方面的关键局限性，并为未来的协作训练提供了见解。

View on arXiv Download PDF AI Translation

cs.CL / 47 / 2606.05804

Can LLMs Be Constrained to the Past? Improving Knowledge Cutoff through Recall-Based Prompting

大型语言模型（LLMs）能被限制在过去吗？通过基于回忆的提示改善知识截止

Asai, Michiro, Lin, Ailiang, Kishimoto, Yu, Obi, Takao, Kosugi, Satoshi, Funakoshi, Kotaro, Okumura, Manabu

Abstract

Prompted knowledge cutoff instructs a large language model (LLM) to act as if information beyond a specified cutoff date were unavailable. However, prior work mainly relies on direct-answer generation, which struggles when post-cutoff knowledge is not explicitly queried but is only causally related to the question. To address this limitation, we propose two recall-based prompting strategies: Self-Recall (SR), which asks the model to restate its cutoff constraint, and Question-Recall (QR), which requires the model to recall question-relevant information valid under the cutoff. Across three existing benchmarks, our methods outperform both direct-answer prompting and conventional step-by-step reasoning baselines, with particularly strong improvements on counterfactual questions. To investigate robustness across different cutoff settings, we further construct the Multi-cutoff Historical Event Benchmark (MHEB), which evaluates the same question under multiple cutoff years. Results show that knowledge cutoff performance varies with cutoff distance, while combining SR and QR consistently yields the best performance.

Chinese Translation

提示的知识截止指示大型语言模型（LLM）假装在特定截止日期之后的信息不可用。然而，以往的研究主要依赖于直接回答生成，当后截止知识没有被明确询问但与问题仅有因果关系时，这种方法表现不佳。为了解决这一局限性，我们提出了两种基于回忆的提示策略：自我回忆（Self-Recall, SR），要求模型重述其截止限制；问题回忆（Question-Recall, QR），要求模型回忆在截止日期下与问题相关的有效信息。在三个现有基准测试中，我们的方法优于直接回答提示和传统逐步推理基线，尤其在反事实问题上有明显的改善。为了调查在不同截止设置下的鲁棒性，我们进一步构建了多截止历史事件基准（Multi-cutoff Historical Event Benchmark, MHEB），评估相同问题在多个截止年份下的表现。结果显示，知识截止性能随截止距离而变化，而结合SR和QR始终能够获得最佳性能。

View on arXiv Download PDF AI Translation

cs.CL / 48 / 2606.05836

ProSPy: A Profiling-Driven SQL-Python Agentic Framework for Enterprise Text-to-SQL

ProSPy：一个基于分析驱动的企业级文本到SQL的SQL-Python自主框架

Yang, Zhaorui, Zheng, Huawei, Yang, Sen, Zhang, Yuhui, Li, Haoxuan, Yu, Zhizhen, Yi, Xuan, Hou, Chen, Xie, Defeng, Hu, Chao, Zhu, Minfeng, Deng, Dazhen, Feng, Haozhe, Huang, Danqing, Wu, Yingcai, Chen, Peng, Chen, Wei

Abstract

Large language models have substantially advanced Text-to-SQL systems, yet applying them to enterprise-scale databases remains challenging. Real-world databases often contain large and heterogeneous schemas, incomplete metadata, dialect-specific SQL syntax, and complex analytical questions that are difficult to solve with a single SQL query. To address these challenges, we propose ProSPy, a Profiling-driven SQL--Python agentic framework for enterprise-scale Text-to-SQL. ProSPy structures the reasoning process into four stages: it first extracts fine-grained data evidence through automatic profiling, progressively prunes large schemas into task-relevant contexts, fetches intermediate views through a dialect-agnostic SQL interface, and finally performs flexible downstream analysis with Python. This design combines the efficiency of SQL over large databases with the flexibility of Python-based analysis, while reducing reliance on unreliable metadata and improving robustness across SQL dialects. Experiments on Spider 2.0-Lite and Spider 2.0-Snow show that ProSPy consistently outperforms strong baselines with both open-source and proprietary models, achieving execution accuracies of 60.15% and 60.51% with Claude-4.5-Opus, without majority voting. Further analysis shows that ProSPy is robust to SQL dialect variations and achieves a favorable trade-off between schema recall and precision.

Chinese Translation

大型语言模型在文本到SQL系统方面取得了显著进展，但将其应用于企业级数据库仍面临挑战。现实世界中的数据库通常包含较大且异构的模式、不完整的元数据、方言特定的SQL语法以及难以通过单一SQL查询解决的复杂分析问题。为了解决这些挑战，我们提出了ProSPy，一个基于分析驱动的SQL-Python自主框架，旨在服务于企业级的文本到SQL应用。ProSPy将推理过程结构化为四个阶段：首先通过自动分析提取细粒度数据证据，逐步将大型模式缩减为与任务相关的上下文，通过方言无关的SQL接口获取中间视图，最后使用Python进行灵活的下游分析。该设计结合了SQL在大规模数据库上的高效性与基于Python分析的灵活性，同时降低了对不可靠元数据的依赖，并提高了不同SQL方言间的鲁棒性。在Spider 2.0-Lite和Spider 2.0-Snow上的实验表明，ProSPy始终超越强基线，适用于开源和专有模型，在Claude-4.5-Opus下实现了60.15%和60.51%的执行准确率，无需多数投票。进一步分析表明，ProSPy对SQL方言变体具有鲁棒性，并在模式召回率和精确度之间实现了良好的平衡。

View on arXiv Download PDF AI Translation

cs.CL / 49 / 2606.05843

Mechanistic Insights into Functional Sparsity in Multimodal LLMs via CoRe Heads

通过CoRe头部获得的多模态大型语言模型中的功能稀疏机制洞察

Sun, Ruoxi, Qiu, Quantong, Li, Juntao, Tang, Zecheng, Lou, Yihang, Zhang, Min

Abstract

While Multimodal Large Language Models (MLLMs) demonstrate remarkable proficiency on complex vision-language tasks, the mechanisms by which they extract query-relevant visual features from complex, noisy contexts remain opaque. In this paper, we present an in-depth interpretability study that uncovers a profound structural property within MLLMs: functional sparsity in cross-modal retrieval. Leveraging a token-level metric termed Retrieval Attention Mass (RAM), we identify and characterize a highly specialized subset of attention heads, referred to as Context-aware Retrieval (CoRe) heads. Across diverse visual domains and model scales, we observe a clear functional division: CoRe heads act as dedicated information extractors, while most other heads distribute attention over broader contextual regions. Causal interventions further demonstrate the necessity of these specialized heads. Ablating only the top 5% of CoRe heads causes significant degradation in multimodal reasoning performance, whereas ablating lower-ranked heads has minimal effect. Moreover, acceleration experiments validate the utility of CoRe heads, showing that leveraging this localized sparsity significantly accelerates inference while maintaining robust task performance. Our findings reveal a structural principle of functional sparsity within MLLMs, refining the current understanding of mechanistic interpretability and laying a theoretical foundation that can inspire future architecture design and model optimization.

Chinese Translation

尽管多模态大型语言模型（MLLMs）在复杂的视觉与语言任务中表现出卓越的能力，但它们从复杂嘈杂的上下文中提取与查询相关的视觉特征的机制仍然不清晰。本文提出了一项深入的可解释性研究，揭示了MLLMs内在的一种深刻结构特性：跨模态检索中的功能稀疏性。我们利用一种名为检索注意力质量（Retrieval Attention Mass, RAM）的标记级别度量，识别并描述了一组高度专业化的注意力头，称为基于上下文的检索（Context-aware Retrieval, CoRe）头。在不同的视觉领域和模型规模中，我们观察到明显的功能划分：CoRe头作为专门的信息提取器，而其他大多数头则在更广泛的上下文区域内分配注意力。因果干预进一步证明了这些专业化头的必要性。仅消融前5%的CoRe头就会显著降低多模态推理性能，而消融较低排名的头则影响甚微。此外，加速实验验证了CoRe头的实用性，显示利用这种局部稀疏性显著加速推理，同时保持强健的任务性能。我们的发现揭示了MLLMs内功能稀疏性的结构原理，提升了对机制可解释性的当前理解，并为未来的架构设计与模型优化奠定了理论基础。

View on arXiv Download PDF AI Translation

cs.CL / 50 / 2606.05846

Towards Truly Multilingual ASR: Generalizing Code-Switching ASR to Unseen Language Pairs

迈向真正的多语种自动语音识别：将代码切换自动语音识别推广至未见语言对

Paik, Gio, Shin, Hyunseo, Lee, Soungmin

Abstract

Automatic Speech Recognition (ASR) has become a key technology for human--AI interaction. However, code-switching ASR (CS-ASR) remains particularly challenging due to the severe scarcity of multilingual CS speech resources across diverse language pairs. Existing approaches primarily improve CS-ASR performance through synthetic CS speech generation or pair-specific fine-tuning on limited bilingual datasets. Nevertheless, these approaches face an inherent scalability limitation, as support for CS must be developed separately for language pairs whose number grows combinatorially with the number of supported languages. In this work, we investigate whether CS capabilities learned from a limited set of seen language pairs can generalize to unseen language pairs through model merging and domain generalization methods. Our experiments show that merged bilingual CS-ASR models modestly generalize to unseen language pairs, suggesting limited transfer of bilingual CS capabilities across language pairs.

Chinese Translation

自动语音识别（ASR）已成为人机交互的关键技术。然而，由于不同语言对之间多语种代码切换语音资源的严重匮乏，代码切换自动语音识别（CS-ASR）依然面临特别的挑战。现有的方法主要通过合成代码切换语音生成或在有限的双语数据集上进行特定语言对的微调来提高CS-ASR的性能。然而，这些方法面临固有的可扩展性限制，因为支持代码切换必须为语言对分别开发，而支持的语言数量呈组合性增长。在本研究中，我们探讨了从有限的已见语言对中学习的代码切换能力是否可以通过模型合并和领域泛化方法推广至未见语言对。我们的实验表明，合并的双语CS-ASR模型在一定程度上可以推广到未见语言对，这表明双语代码切换能力在语言对之间的转移是有限的。

View on arXiv Download PDF AI Translation

cs.CL / 51 / 2606.05857

Forgive or forget: Understanding the context of hate in audio retrieval systems

原谅还是遗忘：理解音频检索系统中的仇恨背景

Pal, Arghya, Rajanala, Sailaja, Phan, Raphael C. -W., Nayak, Shekhar

Abstract

Handling toxic retrieval in text-to-audio systems is challenging due to contextual dependencies. Existing strategies (e.g., rephrasing, summarization) risk altering intent or omitting details. We propose a post hoc causal debiasing framework with a sentiment-controlled mediator to preserve semantic relevance while suppressing harmful speech. Our approach is model-agnostic and integrates seamlessly with existing retrieval pipelines. We introduce two variants: Forgive, which re-ranks and filters toxic audio via logit adjustment, and Forget, which generates counterfactual toxic prompts to mitigate harmful retrievals. Experiments show consistent toxicity reduction with minimal loss in retrieval accuracy, improving both safety and reliability.

Chinese Translation

在文本到音频系统中处理有毒检索的挑战在于上下文依赖性。现有策略（如重新措辞、摘要）存在改变意图或忽略细节的风险。我们提出了一种后置因果去偏框架，利用情感控制的中介来保持语义相关性，同时抑制有害言论。我们的方法与模型无关，能与现有的检索管道无缝集成。我们介绍了两种变体：Forgive（原谅），通过对数调整重新排序和过滤有毒音频；以及Forget（遗忘），生成反事实的有毒提示以缓解有害检索。实验结果表明，在检索准确性损失最小的情况下，一致地减少了有毒内容，提高了安全性和可靠性。

View on arXiv Download PDF AI Translation

cs.CL / 52 / 2606.05858

ReverseEOL: Improving Training-free Text Embeddings via Text Reversal in Decoder-only LLMs

ReverseEOL：通过文本反转提高无训练文本嵌入在解码器仅的LLMs中的表现

Lin, Ailiang, Li, Zhuoyun, Wang, Yusong, Mao, Keyu, Funakoshi, Kotaro, Okumura, Manabu

Abstract

Recent advances in Large Language Models (LLMs) have opened new avenues for generating training-free text embeddings. However, the causal attention in decoder-only LLMs prevents earlier tokens from attending to future context, leading to biased contextualized representations. In this work, we propose Reverse prompting with Explicit One-word Limitation (ReverseEOL), a simple yet effective method for enhancing the representational capability of frozen LLMs. ReverseEOL augments the standard forward embedding with an additional reversed embedding derived from the reversed input text. Since reversing the input exposes each token to context inaccessible in the original order, the resulting reversed embedding effectively provides complementary information to the original one. As a result, combining the forward and reversed embeddings yields a richer final representation. Comprehensive experiments on STS and MTEB benchmarks demonstrate that ReverseEOL significantly improves the performance of existing training-free baselines across a broad range of LLMs with diverse architectures and scales. Extensive ablations and analyses further confirm the necessity of our reversal mechanism.

Chinese Translation

近期在大型语言模型（LLMs）方面的进展为生成无训练的文本嵌入开辟了新途径。然而，解码器仅的LLMs中的因果注意机制阻止了早先的标记关注未来的上下文，导致上下文表示的偏差。本文提出了一种名为反向提示与显式单词限制（ReverseEOL）的方法，这是一种简单而有效的方法，用于增强冻结LLMs的表征能力。ReverseEOL通过从反转输入文本中提取额外的反向嵌入，增强了标准的正向嵌入。由于反转输入使每个标记能够访问在原始顺序中不可达的上下文，因此生成的反向嵌入有效地提供了对原始嵌入的补充信息。因此，将正向和反向嵌入结合起来，可以产生更丰富的最终表示。在STS和MTEB基准上的全面实验表明，ReverseEOL显著提高了现有无训练基线在各种架构和规模的LLMs上的表现。广泛的消融实验和分析进一步确认了我们反转机制的必要性。

View on arXiv Download PDF AI Translation

cs.CL / 53 / 2606.05859

TARPO: Token-Wise Latent-Explicit Reasoning via Action-Routing Policy Optimization

TARPO：通过动作路由策略优化进行基于令牌的潜在显性推理

Zhang, Liting, Zhao, Shiwan, Zhao, Xuyang, Xu, Zichen, Wang, Jianye, Li, Qicheng

Abstract

Latent reasoning has emerged as a promising alternative to discrete Chain-of-Thought (CoT) in large language models (LLMs), enabling more expressive reasoning by operating over continuous representations. However, the inherently deterministic nature of continuous representations limits policy exploration in reinforcement learning (RL). To address this, we propose TARPO (Token-Wise Latent-Explicit Reasoning via Action-Routing Policy Optimization), a pure RL framework that adaptively switches between discrete token generation and continuous latent reasoning at each step. TARPO introduces a lightweight action head router that observes the current hidden state and samples a routing decision from a binary mode-selection space, preserving the stochasticity of discrete token sampling from the vocabulary. The LLM backbone and router are jointly optimized end-to-end with a shared group-relative advantage signal. Extensive experiments across Qwen2.5 (from 1.5B to 7B) and Llama-3.1-8B backbones demonstrate that TARPO consistently outperforms existing explicit and latent reasoning RL baselines across diverse benchmarks. Further analysis shows that TARPO learns adaptive token-wise switching behaviors while maintaining stable training dynamics. Our code is available at https://github.com/NKU-LITI/TARPO-master.

Chinese Translation

潜在推理作为大型语言模型 (LLMs) 中离散思维链 (Chain-of-Thought, CoT) 的一种有前景的替代方案，通过在连续表示上进行操作来实现更具表现力的推理。然而，连续表示的固有确定性特征限制了强化学习 (RL) 中的策略探索。为了解决这个问题，我们提出了 TARPO（通过动作路由策略优化进行基于令牌的潜在显性推理），这是一种纯粹的强化学习框架，在每一步中自适应地在离散令牌生成和连续潜在推理之间切换。TARPO 引入了一个轻量级的动作头路由器，该路由器观察当前隐藏状态，并从二元模式选择空间中抽样路由决策，从而保留从词汇表中离散令牌抽样的随机性。LLM 骨干网络和路由器通过共享的群体相对优势信号进行联合端到端优化。针对 Qwen2.5（从 1.5B 到 7B）和 Llama-3.1-8B 骨干的广泛实验表明，TARPO 在多种基准测试中始终优于现有的显性和潜在推理 RL 基准。此外，进一步分析表明，TARPO 在保持稳定训练动态的同时，学习到自适应的基于令牌的切换行为。我们的代码可在 https://github.com/NKU-LITI/TARPO-master 找到。

View on arXiv Download PDF AI Translation

cs.CL / 54 / 2606.05864

Analysis of the Neglect-Zero Effect in Large Language Models

大型语言模型中的忽略零效应分析

Tanaka, Jin, Matsuoka, Daiki, Kumon, Ryoma, Yanaka, Hitomi

Abstract

We investigate the extent to which the language processing of LLMs resembles human cognitive processes, focusing on a human cognitive bias called the $\textit{neglect-zero effect}$. This effect refers to the human tendency to ignore $\textit{zero-models}$, which are configurations that render a proposition vacuously true by virtue of an empty set. We focus on two types of inferences driven by the neglect-zero effect, and examine how LLMs process these inferences by comparing their behavior with that in an inference that does not involve the neglect-zero effect. For this purpose, we employ a paradigm based on $\textit{structural priming}$, where recent exposure to a preceding sentence (the $\textit{prime}$) facilitates the processing of a subsequent sentence (the $\textit{target}$) due to their structural similarity. We prepare primes to force LLMs to consider the zero-model, and analyze whether they also consider it in the target. The results suggest that the neglect-zero effect may not occur in the LLMs analyzed in this study. Our code is available at https://github.com/ynklab/neglect_zero

Chinese Translation

我们研究了大型语言模型（LLMs）的语言处理在多大程度上类似于人类的认知过程，重点关注一种名为 $ extit{忽略零效应}$ 的人类认知偏差。该效应指的是人类倾向于忽略 $ extit{零模型}$，即通过一个空集合使命题虚真配置。我们关注两种由忽略零效应驱动的推理类型，并通过比较 LLMs 在涉及与不涉及忽略零效应的推理中的行为，检查它们如何处理这些推理。为此，我们采用了一种基于 $ extit{结构性提示（structural priming）}$ 的范式，其中对前句（$ extit{提示（prime）}$）的近期接触促进了因结构相似性而产生的后句（$ extit{目标（target）}$）的处理。我们准备了提示以强迫 LLMs 考虑零模型，并分析它们是否也考虑目标中的零模型。结果表明，在本研究分析的 LLMs 中，忽略零效应可能并不存在。我们的代码已在 https://github.com/ynklab/neglect_zero 上发布。

View on arXiv Download PDF AI Translation

cs.CL / 55 / 2606.05868

YouZhi: Towards High-Concurrency Financial LLMs via Adaptive GQA-to-MLA Transition

YouZhi：通过自适应 GQA 到 MLA 过渡实现高并发金融大模型

PSBC LLM Team, Huawei LLM Team, Long, Ruihan, Wu, Junjie, Zhang, Tianan, Zhang, Duo, Wu, Yaozong, Fu, Jinbin, Liu, Chang, Tang, Zhentao, Yang, Wenshuang, Wang, Xin, Song, Zhihao, Huang, Ning, Xu, Wenjing, Zong, Shuai, Sun, Shupei, Wang, Sen, Hu, Jing, Wang, Bin, Wang, Xinyu, Ju, Junkui, Ding, Zequn, Ran, Jie, Luo, Man, Kai, Shixiong, Hou, Linkai, Liang, Kaichao, Zhao, Hu, Zhao, Yang, Lin, Shucheng, Yu, Wei, Jiang, Chenghan, Ding, Jingjing, Zhang, Jiahui, Jin, Tian, Zhang, Yuhang, Guo, Dong, Sun, Wei, Xie, Jun, Li, Jianwei, Cao, Lei, Li, Pei, Li, Jiabin, Yuan, Jia, Yuan, Rui, Zhu, Jing, Yuan, Mingxuan, Lv, Zhangcheng, Jiang, Xin, Fei, Xiuhong, Ren, Xiaozhe, Li, Yulong, Zhang, Zhipeng, Wang, Hang, Xu, Zhaohui, Zhao, Rui, He, Yibo, Niu, Xinzhuang

Abstract

Large language models (LLMs) drive significant financial innovations, yet their high-concurrency deployment is severely bottlenecked by KV cache memory overhead, which inflates infrastructure costs and throttles scalability. To address this, we propose YouZhi-LLM, a highly efficient financial LLM empowered by a comprehensive structural transition and training pipeline natively built on the Huawei Ascend ecosystem. At its algorithmic core, YouZhi-LLM features a layer-adaptive GQA-to-MLA transition framework that dynamically assigns per-layer FreqFold sizes, maximizing KV-cache compression while minimizing perplexity degradation. To recover representation capacity and inject domain expertise, the Ascend-based training pipeline seamlessly integrates generalized knowledge distillation with financial-specific supervised fine-tuning. Evaluations demonstrate the superiority of this systematic approach, with the adaptive transition reducing perplexity degradation by up to 35% over uniform baselines. Crucially, when evaluated on Ascend NPUs via vLLM-Ascend, the massive KV-cache reduction translates directly into deployment efficiency. Compared to their respective base models, YouZhi-7B yields a 12.3% improvement in average financial benchmark score alongside a 2.69$\times$ increase in maximum concurrency; similarly, YouZhi-14B achieves a 7.0% accuracy gain and a 2.43$\times$ concurrency boost, establishing a new paradigm for cost-effective, high-throughput financial inference.

Chinese Translation

大型语言模型（LLMs）推动了显著的金融创新，但其高并发部署受到 KV 缓存内存开销的严重瓶颈，从而增加了基础设施成本并限制了可扩展性。为了解决这一问题，我们提出了 YouZhi-LLM，这是一种高效的金融 LLM，其结构转变和训练管道专门构建于华为 Ascend 生态系统之上。在其算法核心，YouZhi-LLM 具有一种层自适应的 GQA 到 MLA 转换框架，动态分配每层的 FreqFold 尺寸，最大化 KV 缓存压缩，同时最小化困惑度降级。为了恢复表示能力并注入领域专业知识，基于 Ascend 的训练管道无缝整合了广义知识蒸馏与金融特定的有监督微调。评估结果展示了这种系统性方法的优越性，自适应转变使困惑度降级降低了高达 35%，相比于均匀基线。在基于 Ascend NPUs 通过 vLLM-Ascend 进行评估时，大规模 KV 缓存的减少直接转化为部署效率。与其各自的基础模型相比，YouZhi-7B 的平均金融基准得分提高了 12.3%，最大并发性提升了 2.69$ imes$；同样，YouZhi-14B 实现了 7.0%的准确率提升和 2.43$ imes$的并发性提升，为成本效益高且高通量的金融推断建立了新的范式。

View on arXiv Download PDF AI Translation

cs.CL / 56 / 2606.05874

Evaluating Stochastic Collapse and Implicit Bias in Multimodal Large Language Models

评估多模态大型语言模型中的随机崩溃与隐性偏见

Zheng, Huiyuan, Zhang, Houtao, Wang, Boyang, Si, Qingyi, Guo, Hongcheng

Abstract

Current evaluations for Multimodal Large Language Models (MLLMs) overwhelmingly focus on utility-driven objectives, leaving model behavior under logic-neutral scenarios largely underexplored. Stochasticity is essential in scenarios where multiple actions are equally valid, such as recommending travel itineraries or daily schedules where multiple options have similar utility. In such settings, deterministic policies may lead to repetitive behaviors and reduced coverage of valid alternatives. To bridge this gap, we propose RandomBench, a benchmark designed to evaluate whether MLLMs can maintain distributionally neutral behavior when selecting among equivalent options. We further introduce three metrics, including RI, BCI, BII, to quantify entropy and distributional bias. Experiments reveal a pervasive phenomenon termed Stochastic Collapse, where MLLMs fail to maintain uniform randomness under explicit random instructions, with top-1 probabilities reaching 97% from the ideal one quarter baseline and RI dropping to 0.068 in Claude Sonnet 4.6. Extensive ablation studies further demonstrate that these deviations persist across languages and representation formats, highlighting the robustness of distributional collapse in logic-neutral decision settings.

Chinese Translation

目前对多模态大型语言模型（MLLMs）的评估主要集中在以效用为驱动的目标上，导致模型在逻辑中立场景下的行为大多未得到深入探讨。在多个行动均有效的场景中，随机性至关重要，例如推荐旅行行程或日常安排时，多个选项具有相似的效用。在此类设置中，确定性策略可能导致重复行为以及有效替代方案的覆盖度降低。为填补这一空白，我们提出了 RandomBench，这是一个基准测试，旨在评估 MLLMs 在选择等效选项时是否能够维持分布中立的行为。我们进一步引入了三个指标，包括 RI、BCI 和 BII，以量化熵和分布偏差。实验揭示了一种普遍现象，称为随机崩溃（Stochastic Collapse），即 MLLMs 在明确的随机指令下未能维持均匀的随机性，最高的 top-1 概率达到了 97%，而 RI 降至 0.068（在 Claude Sonnet 4.6 中）。广泛的消融研究进一步表明，这些偏差在不同语言和表示格式中普遍存在，突显了在逻辑中立决策环境中分布崩溃的稳健性。

View on arXiv Download PDF AI Translation

cs.CL / 57 / 2606.05890

Staying with the Uncertainty: Uncertainty-Scaffolding Strategies for Artificial Moral Advisors in LLM-to-LLM Simulated Conversations

应对不确定性：大语言模型到大语言模型的模拟对话中人工道德顾问的不确定性支架策略

Greco, Salvatore, Xu, Hainiu, Domenicucci, Jacopo, He, Yulan, Delacroix, Sylvie

Abstract

LLMs are increasingly deployed as Artificial Moral Advisors (AMA) in a variety of contexts: what kind of conversational patterns should they display? In this paper, we study how AMA can help their interlocutors "stay with the uncertainty". We propose three modes of uncertainty (Perspective-Multiplying, Tension-Preserving, Process-Reflecting) and compare them against three control conditions (Baseline, Persuasive, Sycophantic). A user-agent LLM engages in a dialogue on an ethical dilemma with an AMA following a specific uncertainty strategy, and completes pre- and post-conversation questionnaires. We further examine the effect of two persona prompt formats (Declarative and Narrative). We found that (1) no single model dominates as a simulated user agent, with open models aligning with human ambiguity through between-persona divergence and closed models through within-persona hedging; (2) declarative personas better capture initial stance diversity while narrative personas show more realistic belief revision; (3) all six AMA strategies produce distinguishable conversational patterns; and (4) uncertainty strategies differ not in how much stance revision they produce, but in the quality of engagement they sustain.

Chinese Translation

大语言模型（LLMs）在不同的上下文中越来越多地作为人工道德顾问（AMA）被部署：它们应展示什么样的对话模式？在本论文中，我们研究了AMA如何帮助对话者“应对不确定性”。我们提出了三种不确定性模式（Perspective-Multiplying、Tension-Preserving、Process-Reflecting），并将其与三种对照条件（Baseline、Persuasive、Sycophantic）进行比较。一个用户代理LLM与一个遵循特定不确定性策略的AMA在道德困境上进行对话，并完成会话前后的问卷调查。我们进一步考察了两种角色提示格式（Declarative和Narrative）的影响。我们发现（1）没有单一模型主导作为模拟用户代理，开放模型通过角色间分歧与人类的不确定性相一致，而封闭模型则通过角色内规避实现；（2）声明性角色更好地捕捉初始立场多样性，而叙事性角色则表现出更现实的信念修正；（3）所有六种AMA策略产生可区分的对话模式；（4）不确定性策略的差异不在于它们产生的立场修正程度，而在于它们所维持的参与质量。

View on arXiv Download PDF AI Translation

cs.CL / 58 / 2606.05894

EMBER: Efficient Memory via Budgeted Evidence Retention for Long-Horizon Agents

EMBER：通过预算证据保留实现高效记忆的长期代理

Li, Yilong, Banerjee, Suman, Che, Tong

Abstract

Long-horizon agents can archive large histories, but future answers still incur retrieval, rereading, and context costs. When retained memory misses answer-relevant evidence, the system must return to larger portions of the raw history. We study budgeted evidence survival: before the query is known, which source evidence should be retained so that it remains recoverable and usable under a fixed retained source-evidence token budget? We instantiate this setting as Budgeted Pre-Query Retention, where memory is written during ingestion and later read without access to the full raw stream. We introduce EMBER, a learned retention policy that constructs a compact, source-backed evidence state. EMBER stores evidence capsules: verbatim source excerpts paired with retrieval keys and update metadata, preserving both grounding and read-time access. Post-query outcome feedback trains the writer to preserve evidence across the ingestion-retrieval-answer chain. On LongMemEval-RR, our LongMemEval-derived retained-evidence protocol, EMBER-14B reaches 0.3017 F1 at the 8192-token retained-evidence comparison point, compared with 0.1765 for the strongest non-EMBER budgeted baseline. Across retained source-evidence budgets, EMBER improves F1, Retain-Recall, and Read-Recall, indicating that long-horizon memory depends on retaining evidence within the budget rather than rereading larger histories.

Chinese Translation

长期代理可以存档大量历史记录，但未来的回答仍然需要检索、重读和上下文成本。当保留记忆缺少与答案相关的证据时，系统必须返回更大部分的原始历史。我们研究了预算证据存活：在查询未知之前，应该保留哪些源证据，以确保它在固定的保留源证据令牌预算下仍然可恢复和可用？我们将此设置实例化为预算预查询保留，在此过程中，记忆在摄取期间被写入，随后在没有访问完整原始流的情况下进行读取。我们引入了EMBER，一种学习到的保留策略，构建一个紧凑的、以源为支持的证据状态。EMBER存储证据胶囊：逐字的源摘录配对检索键和更新元数据，既保留了基础性，又确保了读取时的访问。查询后结果反馈训练写入者在摄取-检索-回答链中保留证据。在LongMemEval-RR，我们基于LongMemEval的保留证据协议中，EMBER-14B在8192令牌保留证据比较点达到了0.3017的F1值，而最强的非EMBER预算基线为0.1765。在各个保留源证据预算下，EMBER提升了F1、保留召回率和读取召回率，表明长期记忆依赖于在预算内保留证据，而不是重读更大的历史。

View on arXiv Download PDF AI Translation

cs.CL / 59 / 2606.05895

Representing Research Attention as Contextually Structured Flows

将研究关注表示为上下文结构化流

Rodrigues, Jessica, Salatino, Angelo, Jenset, Gard, Hale, Scott

Abstract

Research attention is widely used as an indicator of visibility, influence, and societal uptake, yet it is typically represented as aggregated counts that do not preserve how attention develops across contexts over time. This creates a mismatch between how attention is interpreted and how it is represented. We propose attention flows as contextually structured representations that encode the organisation of attention and its evolution over time. We evaluate whether these representations capture transferable structure by constructing a benchmark based on analogy-style reasoning across research outputs. Comparing signal, sequence, and flow-based representations, we find that flow representations more effectively support structural comparison, particularly in settings where attention is shaped by temporal progression or context distributions. We further show that learned flow representations improve robustness under partial observation and structural perturbation. Overall, these results support modelling attention as a contextually structured phenomenon and provide a basis for more informative approaches to research evaluation.

Chinese Translation

研究关注广泛作为可见性、影响力和社会接受度的指标，但通常以聚合计数的形式表示，无法保留关注在不同上下文中随时间发展的过程。这导致关注的解释与其表示之间存在不匹配。我们提出了关注流作为上下文结构化的表示，编码了关注的组织及其随时间的演变。我们通过基于类比推理构建一个基准，评估这些表示是否捕捉了可转移的结构。在比较信号、序列和基于流的表示时，我们发现流表示在结构比较方面更为有效，特别是在关注受到时间进程或上下文分布影响的情况下。我们进一步展示了学习的流表示在部分观察和结构扰动下提高了稳健性。总体而言，这些结果支持将关注建模为一种上下文结构化现象，并为更具信息性的研究评估方法提供了基础。

View on arXiv Download PDF AI Translation

cs.CL / 60 / 2606.05901

Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)

使用简单图结构的检索增强生成方法减少复杂问题回答中的虚假信息（长版本）

Wedge, Christopher J., Stutter, Joshua, Dixon, Danny, Cała, Jacek

Abstract

Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing. Despite these advances, LLMs and LLM-based systems remain prone to a variety of failure modes. Retrieval-augmented generation (RAG) systems have emerged as a common deployment scenario seeking to both avoid the well known risk of the LLM "hallucinating" information, and to enable reasoning and question answering over proprietary information that the LLM did not have access to during training without resorting to expensive model fine-tuning. In this work, we explore the idea of using a lightweight graph structure with a relatively simple graph schema, to support the RAG subsystem via a dedicated toolset. We design an agentic system with a variety of vector search and graph query tools operating over a structured dataset based on a curated subset of English Wikipedia articles, and evaluate its performance on questions from MoNaCo, a challenging Wikipedia QA benchmark of complex query answering tasks. Our results show that the introduction of graph-based tools can significantly increase the precision and recall of factual correctness, can halve the number of hallucinated answers, and achieves the highest fine-grained truthfulness score among the three evaluated scenarios. All this with a modest increase in token usage.

Chinese Translation

大型语言模型（LLMs）从根本上改变了自然语言处理的格局。尽管取得了这些进展，LLMs及其基于LLM的系统仍然容易出现多种故障模式。检索增强生成（RAG）系统作为一种常见的部署场景，旨在避免LLM“虚假”信息的已知风险，并在不进行昂贵的模型微调的情况下，实现对LLM在训练时无法访问的专有信息的推理和问题回答。在本研究中，我们探索了使用轻量级图结构和相对简单的图模式来支持RAG子系统的理念，并开发了一个专用工具集。我们设计了一个代理系统，使用多种向量搜索和图查询工具，基于经过筛选的英语维基百科文章子集的结构化数据集进行操作，并在MoNaCo上评估其性能，后者是一个具有挑战性的维基百科QA基准，涉及复杂问题回答任务。我们的结果表明，引入基于图的工具可以显著提高事实正确性的精确度和召回率，可以将虚假答案的数量减少一半，并在三种评估场景中实现最高的细粒度真实性评分。所有这些仅伴随着适度的令牌使用增加。

View on arXiv Download PDF AI Translation

cs.CL / 61 / 2606.05906

ACE-SQL: Adaptive Co-Optimization via Empirical Credit Assignment for Text-to-SQL

ACE-SQL：通过经验信用分配进行文本到SQL的自适应共同优化

Chen, Xiaobing, Jian, Ai, Guo, Eryu, Pang, Zhiqi

Abstract

Text-to-SQL maps natural language questions to executable SQL queries. Modern databases often contain large and complex schemas, making schema linking a critical step for accurate SQL generation. Existing methods either rely on full-schema generation, which leaves schema linking implicit within a large search space, or use a separate retriever trained with static gold-column supervision, whose targets may be suboptimal for the current generator policy. To address this issue, we propose Adaptive Co-optimization via Empirical Credit Assignment for Text-to-SQL (ACE-SQL), a reinforcement learning (RL) framework that jointly optimizes schema retrieval and SQL generation under execution feedback. ACE-SQL constructs an online column-set pool from generator rollouts and derives adaptive on-policy retrieval targets from the column set most frequently associated with execution-correct rollouts. This induces bidirectional adaptation, where the retriever adapts toward column sets that the generator can execute correctly, while the generator adapts to the retriever's evolving schema selections under execution feedback. With approximately 3k synthetic Text-to-SQL question-database pairs for RL training, ACE-SQL achieves 65.3% greedy execution accuracy on BIRD Dev while using 0.93k output tokens per query. The repository is available at https://github.com/xbchen1/ACE-SQL.

Chinese Translation

文本到SQL将自然语言问题映射到可执行的SQL查询。现代数据库通常具有大型复杂的模式，使得模式关联成为准确生成SQL的重要步骤。现有的方法要么依赖于全模式生成，这使得模式关联在一个庞大的搜索空间中隐含，而要么使用与静态金标列监督训练的单独检索器，其目标可能对当前生成器策略并不理想。为了解决这一问题，我们提出了通过经验信用分配进行文本到SQL的自适应共同优化（ACE-SQL），这是一种强化学习（RL）框架，能够在执行反馈下共同优化模式检索和SQL生成。ACE-SQL根据生成器的回滚构建一个在线列集池，并从与正确执行的回滚最频繁关联的列集中推导出自适应的策略检索目标。这种方法引入了双向适应，其中检索器朝向生成器能够正确执行的列集适应，而生成器则根据执行反馈调整其对检索器不断变化的模式选择。通过约3k个合成的文本到SQL问题-数据库对进行RL训练，ACE-SQL在BIRD开发集上实现了65.3%的贪心执行准确率，同时每个查询使用0.93k个输出标记。该代码库可在 https://github.com/xbchen1/ACE-SQL 获取。

View on arXiv Download PDF AI Translation

cs.CL / 62 / 2606.05924

Better Literary Translation: A Multi-Aspect Data Generation and LLM Training Approach

更好的文学翻译：多方面数据生成与大语言模型训练方法

Lin, Zhihao, Zhu, Ziqi, Huang, Hao, Wang, Guanghui, He, Peiyang

Abstract

Literary translation poses unique challenges due to the scarcity of high-quality annotated data and the need to balance expression fluency with literary effect. We present a multi-aspect iterative refinement framework that generates high-quality translation references and preference data through specialized LLM translators, each targeting a distinct quality dimension. We leverage the generated data for supervised fine-tuning and reinforcement learning. Experiments show that our generated references outperform the original ground truth for SFT by 8.65 CEA100 points. For reinforcement learning, we find that DPO leads to performance degradation in this setting, while leveraging an explicit reward model for GRPO yields an additional 1.51 point improvement. We attribute this to the stability of two-stage training and GRPO's online exploration capability. Our resulting models, LitMT-8B and LitMT-14B, achieve 67.25 and 69.07 CEA100 respectively on the MetaphorTrans English-to-Chinese literary translation benchmark, competitive with Claude Sonnet 4.5 at 68.43, and demonstrate strong generalization to out-of-domain literary work (i.e., O. Henry).

Chinese Translation

文学翻译由于高质量标注数据稀缺以及表达流畅性与文学效果的平衡需求而面临独特挑战。我们提出了一种多方面的迭代优化框架，通过专门的LLM（大语言模型）翻译器生成高质量的翻译参考和偏好数据，针对不同的质量维度进行优化。我们利用生成的数据进行监督微调和强化学习。实验证明，我们生成的参考相比于原始基准在监督微调（SFT）中提高了8.65 CEA100分。在强化学习方面，我们发现DPO（差异性策略优化）在这一设置下导致了性能下降，而将显式奖励模型用于GRPO（基于奖励的在线优化）则产生了额外的1.51点提升。我们将这一结果归因于两阶段训练的稳定性和GRPO的在线探索能力。我们的模型LitMT-8B和LitMT-14B在MetaphorTrans英译中文学翻译基准中分别达到了67.25和69.07的CEA100评分，具有竞争力，与Claude Sonnet 4.5的68.43接近，并且在领域外文学作品（例如O. Henry）上展现出强大的泛化能力。

View on arXiv Download PDF AI Translation

cs.CL / 63 / 2606.05931

To Be Multimodal or Not to Be: Query-Adaptive Audio-Visual Person Retrieval via Active Modality Detection

多模态还是单模态：通过主动模态检测实现查询自适应音频-视觉人物检索

Loweimi, Erfan, Qian, Mengjie, Knill, Kate, Wu, Guanfeng, Chan, Chi-Ho, Haider, Abbas, Awan, Muhammad, Kittler, Josef, Wang, Hui, Gales, Mark

Abstract

When retrieving a person from a video archive by voice and face, should the system be multimodal or not? In real-world broadcast archives, unlike curated benchmarks, a target may be heard but unseen, seen but unheard, or both. Fusing scores from an absent modality injects noise, degrading precision below the best unimodal system. We propose a query-adaptive framework that detects active modalities via cross-modal score consistency: when both modalities are active, files retrieved by one also score highly on the other; this agreement breaks down when a modality is absent. Classifiers driven by these cross-modal features achieve 89% detection accuracy. On the BBC Rewind corpus (with over 12,000 broadcast videos) the adaptive system attains 94.2% P@1, outperforming speaker-only (82.9%), face-only (93.4%), and fixed fusion (90.0%), recovering 64% of the gap to an oracle with ground-truth modality labels (96.6%).

Chinese Translation

在通过声音和面孔从视频档案中检索一个人时，系统应该是多模态的还是单模态的？在现实世界的广播档案中，与策划的基准不同，目标可能是可以听到但不可见的，或者是可见但听不到的，或者两者皆是。从缺失模态融合得分会引入噪声，导致精度低于最佳的单模态系统。我们提出了一种查询自适应框架，通过跨模态得分一致性检测主动模态：当两种模态均为活动时，由一种模态检索到的文件在另一种模态上也会有高得分；当一种模态缺失时，这种一致性会破裂。由这些跨模态特征驱动的分类器达到了89%的检测准确率。在BBC Rewind语料库（包含超过12,000个广播视频）上，适应性系统达到了94.2%的P@1，优于仅基于说话者（82.9%）、仅基于面孔（93.4%）和固定融合（90.0%），收回了64%与拥有真实模态标签的oracle（96.6%）之间的差距。

View on arXiv Download PDF AI Translation

cs.CL / 64 / 2606.05936

Epistemic Injustice in Language Models: An Audit of Pretraining Filters and Guardrails

语言模型中的知识性不公正：预训练过滤器和防护机制的审计

Stranisci, Marco Antonio, Pranav, A, Damiano, Rossana, Hardmeier, Christian, Lauscher, Anne

Abstract

Modern language models rely on pretraining filters to remove undesirable content from training corpora and inference-time guardrails to suppress undesirable outputs during deployment. In this paper, we examine how these filtering and moderation decisions produce forms of epistemic erasure and reveal tensions both across automated systems and between these systems and human judgment. We audit four pretraining filters and three inference-time guardrails on Common Crawl sentences containing gender and regional-origin mentions, together with a manually annotated subset of 500 sentences. Our analysis shows that filtering and guardrail decisions are strongly associated with blocklist-based lexical cues, while frequently failing to flag content containing private information or explicit hate speech. At the same time, marginalized groups, particularly transgender people, women, and Central Americans, are significantly over-flagged across systems. Human annotators, by contrast, would retain 88.5\% of filter-flagged and 91.3\% of guardrail-flagged content, often recognizing representational harms arising from tensions of content removal that current systems fail to capture. Taken together, our findings document a form of epistemic erasure in which mentions of marginalized groups are disproportionately removed before pretraining and additionally suppressed again at inference time.

Chinese Translation

现代语言模型依赖预训练过滤器从训练语料中去除不良内容，以及在部署过程中通过防护机制抑制不良输出。本文探讨了这些过滤和审查决策如何产生知识性抹除的形式，并揭示了自动化系统之间及这些系统与人类判断之间的紧张关系。我们审核了四个预训练过滤器和三个推理时防护机制，针对包含性别和地区来源提及的Common Crawl句子，以及一个手动标注的500句子子集。我们的分析表明，过滤和防护决策与基于黑名单的词汇线索密切相关，而在标记包含私人信息或明显仇恨言论的内容时常常失败。同时，边缘化群体，特别是跨性别者、女性和中美洲人，在各系统中被过度标记。相比之下，人类标注者会保留88.5 ext{%}的过滤标记内容和91.3 ext{%}的防护标记内容，常常识别出当前系统未能捕捉到的内容移除所带来的表征伤害。综上所述，我们的发现记录了一种知识性抹除的形式，其中边缘化群体的提及在预训练之前被不成比例地移除，并在推理时再次遭到抑制。

View on arXiv Download PDF AI Translation

cs.CL / 65 / 2606.05937

Large Language Models are Perplexed by some Political Parties

大型语言模型对某些政治党的困惑

Lerner, Paul, Yvon, François

Abstract

Large Language Models (LLMs) are increasingly used, including in political applications, but their political fairness has been little studied. We assess it using perplexity, posing that a fair model should give equal probability to all political groups. However, we find, across ten LLMs and three datasets covering 37 languages, that LLMs are more perplexed by the texts of far right and nationalist parties than of social-democratic parties. We find this to be consistent with previous work on translation fairness, to the point that perplexity correlates with downstream translation metrics. Our method is applicable to both base LLMs as well as their instruction-tuned counterpart, and we find that both are highly correlated, suggesting that the political fairness of LLMs stems from their pretraining, and is hardly affected by instruction-tuning.

Chinese Translation

大型语言模型（LLMs）在政治应用中越来越多地被使用，但其政治公平性很少受到研究。我们使用困惑度进行评估，假设一个公平的模型应该对所有政治团体给予相等的概率。然而，我们发现，在涵盖37种语言的十个LLMs和三个数据集上，LLMs对极右派和民族主义政党的文本感到比对社会民主党更为困惑。我们发现，这与之前关于翻译公平性的研究结果一致，甚至困惑度与下游翻译指标存在相关性。我们的方法适用于基础LLMs以及经过指令调优的相应模型，我们发现两者高度相关，这表明LLMs的政治公平性源于其预训练，而几乎不受指令调优的影响。

View on arXiv Download PDF AI Translation

cs.CL / 66 / 2606.05970

Measuring the sensitivity of LLM-based structured extraction to prompt, model, and schema choices in clinical discharge summaries

基于大型语言模型的临床出院摘要结构化提取对提示、模型和模式选择的敏感性测量

Murin, Martin

Abstract

Large language models are increasingly used for structured extraction from clinical free-text notes, but the sensitivity of their output to upstream configuration choices is less understood than their accuracy on fixed benchmarks. This work measures that sensitivity without human-annotated ground truth, by holding the extraction task fixed and varying one choice at a time. The fixed schema comprises 17 clinical documentation flags on a three-way yes/no/not_documented value set and a 47-tag vocabulary for the primary admission reason. Three prompt variants expressing this schema were each run at two model sizes on MIMIC-IV v3.1 discharge summaries. Cross-prompt agreement was measured by Cohen's kappa on ICD-stratified subsets. A paired same-note comparison isolated the effect of model choice, and a post-hoc collapse of the three-way flags to binary tested the schema's contribution to disagreement. On the three-way flags, the two models reach the same pooled cross-prompt agreement (median kappa 0.69 and 0.68); the larger model raises agreement on some fields and lowers it on others, a redistribution rather than the absence of an effect. Collapsing the schema to binary dissolves most of the cross-prompt disagreement, locating it on the absence-versus-silence distinction rather than on whether the finding is present. On the multi-class admission categorization, changing the model reassigns the dominant tag on close to half of all notes while changing the prompt phrasing reassigns it on roughly one in eight, and the larger model places far less mass on residual catch-all categories (44% to 26%). These patterns indicate a schema-imposed source of disagreement concentrated on the absence-versus-silence axis and a dominance of model over prompt phrasing on multi-class categorization, identified by a reusable methodology for auditing extraction reproducibility on a population-scale deployment.

Chinese Translation

大型语言模型越来越多地用于从临床非结构化文本记录中进行结构化提取，但它们的输出对上游配置选择的敏感性尚未像在固定基准上的准确性那样得到充分理解。本研究在没有人工标注的基准真值的情况下，测量了这一敏感性，通过固定提取任务并逐次改变一个选择。固定模式由17个临床文档标志组成，采用三分类的是/否/未记录值集，以及用于主要入院原因的47个标签词汇。三种表达此模式的提示变体在MIMIC-IV v3.1出院摘要上分别以两种模型规模运行。通过Cohen's kappa在ICD分类子集中测量跨提示一致性。配对相同记录的比较隔离了模型选择的影响，而后续将三分类标志简化为二分类则测试了模式对不一致的贡献。在三分类标志上，这两个模型达到相同的汇总跨提示一致性（中位kappa值为0.69和0.68）；较大的模型提升了某些字段的一致性，而降低了其他字段的一致性，这体现出一种重分配而非效果消失。将模式简化为二分类消除了大部分跨提示不一致，将其定位于缺失与沉默的区分，而非发现是否存在。在多类入院分类中，模型的变化重新分配了近一半记录的主标签，而提示措辞的变化则大约重新分配了八分之一的记录，且较大的模型在剩余的“赎回”类别上分布的权重远低于小模型（从44%降至26%）。这些模式表明，不一致的源于模式施加的限制，集中在缺失与沉默的轴上，而在多类分类中模型的影响超过了提示措辞，采用一种可重复使用的方法论审计提取的可重复性，以便在大规模部署上进行验证。

View on arXiv Download PDF AI Translation

cs.CL / 67 / 2606.05985

Beyond Alignment: Value Diversity as a Collective Property in Multicultural Agent Systems

超越对齐：价值多样性作为多文化代理系统的集体属性

Xu, Shaoyang, Zhang, Jingshen, Hoang, Long P., Li, Jinyuan, Zhang, Wenxuan

Abstract

Multicultural multi-agent systems are increasingly deployed in globally diverse settings, where different agents are grounded in different cultural backgrounds. Existing cultural evaluation focuses on value alignment: how closely a single agent matches a target culture. Yet alignment is a per-agent property and cannot reveal whether a system, taken as a whole, preserves the cultural plurality it is meant to represent. We propose value diversity as a system-level evaluation axis for multicultural agent systems, defined through the dissimilarity between culturally conditioned agents' responses on a shared value survey. Using the World Values Survey, we evaluate 19 cultures and 18 backbone models across a wide range of system configurations. We find that diversity is largely uncorrelated with alignment, indicating that the two capture complementary system properties, and that current multicultural agent systems fall substantially below human societies in value diversity. Mixed-backbone systems narrow this gap but do not close it, and the gap persists across culture compositions and agent scales. Social interaction further erodes diversity by driving agents toward consensus, and a participatory budgeting case study shows that this homogenization narrows the breadth of collective decision-making. Together, our results establish value diversity as a distinct evaluation axis for multicultural multi-agent systems and reveal a persistent homogenization tendency in current LLM-based societies. Our code and data are publicly available at https://github.com/iNLP-Lab/MultiAgent-Diversity.

Chinese Translation

多文化多代理系统日益在全球多元环境中部署，不同的代理根植于不同的文化背景。现有的文化评估主要集中在价值对齐上：单个代理与目标文化的匹配程度。然而，对齐是一个针对单一代理的属性，无法揭示整体系统是否维护其所代表的文化多样性。我们提出价值多样性作为多文化代理系统的系统级评估轴，通过文化条件代理在共享价值调查中的反应差异来定义。利用世界价值调查，我们评估了19种文化和18种基础模型，涵盖了广泛的系统配置。我们发现，多样性与对齐在很大程度上无关，表明这两者捕捉了互补的系统属性，并且当前的多文化代理系统在价值多样性上远低于人类社会。混合基础系统缩小了这一差距，但并未完全消除，且该差距在文化构成和代理规模上持续存在。社会互动进一步削弱了多样性，使代理朝共识趋向，而一项参与式预算的案例研究表明，这种同质化缩小了集体决策的广度。总体而言，我们的结果确立了价值多样性作为多文化多代理系统的一个独特评估轴，并揭示了当前基于大语言模型的社会中存在的持续同质化倾向。我们的代码和数据可公开获取，网址为 https://github.com/iNLP-Lab/MultiAgent-Diversity。

View on arXiv Download PDF AI Translation

cs.CL / 68 / 2606.06004

The Generator-Eraser Paradox: Community Guidelines for Responsible LLM-Assisted Dialect Resource Creation

生成器-抹除者悖论：负责任的LLM辅助方言资源创建的社区指南

Zaghouani, Wajdi

Abstract

Dialect resources occupy a unique position at the intersection of scientific description, cultural preservation, and computational infrastructure. Large language models offer powerful capabilities for accelerating dialect resource development through retrieval-grounded drafting, corpus navigation, metadata enrichment, and annotation workflow support. However, the same systems pose substantial risks: they can contribute to dialect erasure by privileging prestige varieties, homogenizing orthography, and enabling synthetic feedback loops that reduce linguistic diversity over time. These risks are particularly acute for language varieties characterized by diglossia, limited written standardization, or marginalized speaker communities. This paper makes three contributions. First, we integrate insights from variationist sociolinguistics and corpus linguistics to formalize the generator-eraser paradox as a theoretical framework for understanding the dual nature of LLM-assisted dialect work. Second, we derive 12 community guidelines that operationalize this framework into implementable design requirements for dialect resource creation and documentation. Third, we provide an in-depth case study of Arabic dialects, including a structured comparison of widely used resources, to demonstrate how these guidelines address language-specific challenges including diglossia, orthographic variability, and community governance. The contribution is conceptual and operational rather than experimental, with the goal of enabling dialect communities and resource builders across languages to adopt LLMs without sacrificing authenticity, variation, or sovereignty.

Chinese Translation

方言资源在科学描述、文化保护和计算基础设施的交汇点上占据独特的位置。大型语言模型通过基于检索的草拟、语料库导航、元数据丰富和注释工作流支持，为加速方言资源开发提供了强大能力。然而，同样的系统也带来了重大的风险：它们可能导致方言的抹除，因为它们偏爱权威变体，统一正字法，并启用合成反馈循环，随着时间推移减少语言多样性。这些风险在以双语制、有限书面标准化或边缘化说话者社区为特征的语言变体中尤为严重。本文有三个贡献。首先，我们整合变异社会语言学和语料库语言学的见解，正式化生成器-抹除者悖论，作为理解LLM辅助方言工作的双重性质的理论框架。其次，我们提出12条社区指南，将这一框架转化为可实施的方言资源创建和文档编制的设计要求。第三，我们提供了一个关于阿拉伯方言的深入案例研究，包括对广泛使用资源的结构化比较，以展示这些指南如何应对语言特定的挑战，包括双语制、正字法变异和社区治理。该贡献是概念性和操作性的，而不是实验性的，旨在使各语言的方言社区和资源构建者在不牺牲真实性、变异性或主权的情况下采用LLM。

View on arXiv Download PDF AI Translation

cs.CL / 69 / 2606.06022

Contextualized Prompting For Stance Detection On Social Media

社交媒体上立场检测的情境化提示

Beck, Tilman, Yazdani, Shakib, Kruschinski, Simon, Maurer, Marcus, Gurevych, Iryna

Abstract

Stance detection on social media is challenging due to short, noisy, and context-dependent language. While large language models (LLMs) show zero-shot generalization, they are typically prompted without contextual information, which limits their ability to interpret ambiguous posts. In this work, we systematically investigate the impact of incorporating real-world (e.g., user biographies), derived (e.g., political party), and LLM-generated (e.g., target descriptions) contextual features into zero-shot prompting for stance detection on Twitter. Our evaluation spans four benchmark datasets, including a new high-quality German Twitter stance dataset. Across multiple LLMs, we find that integrating contextual information improves performance, but only under specific conditions. LLM-generated target descriptions consistently enhance accuracy, while other user metadata has mixed or even detrimental effects. Notably, we show that the inclusion of other tweets by the same user, often beneficial in supervised learning, can impair performance due to input noise. Our qualitative analysis reveals that LLMs struggle to distinguish task-specific useful information from irrelevant context. Our findings highlight both the promise and challenges of prompting with context information in noisy real-world settings. We publish code and data at this \href{https://github.com/tilmanbeck/stance-context-twitter}{page}.

Chinese Translation

社交媒体上的立场检测面临着语言简短、噪声多且依赖上下文的挑战。尽管大型语言模型（LLMs）展示了零样本泛化能力，但它们通常在没有上下文信息的情况下进行提示，这限制了它们解释模糊帖子的能力。在本研究中，我们系统地探讨了在Twitter上进行立场检测的零样本提示中，融入现实世界（例如，用户简历）、派生（例如，政党）和LLM生成（例如，目标描述）上下文特征的影响。我们的评估涵盖了四个基准数据集，包括一个新的高质量德语Twitter立场数据集。在多种LLM中，我们发现整合上下文信息能提高性能，但仅在特定条件下。LLM生成的目标描述始终提高了准确性，而其他用户元数据的效果则混杂，甚至有害。值得注意的是，我们展示了同一用户的其他推文，其在监督学习中通常是有益的，但由于输入噪声而可能削弱性能。我们的定性分析揭示了LLM难以区分特定任务有用信息与无关上下文之间的界限。我们的研究结果突显了在嘈杂的现实环境中使用上下文信息进行提示的潜力与挑战。我们将在此页面发布代码和数据： ext{https://github.com/tilmanbeck/stance-context-twitter}。

View on arXiv Download PDF AI Translation

cs.CL / 70 / 2606.06025

EGTR-Review: Efficient Evidence-Grounded Scientific Peer Review Generation via Multi-Agent Teacher Distillation

EGTR-Review: 通过多智能体教师蒸馏实现高效证据驱动的科学同行评审生成

Qiu, Xinpeng, Yihu, Wang, Liu, Zhifeng, Wang, Xiaochen, Wang, Jimin

Abstract

Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback. However, existing Large Language Model (LLM)-based methods often produce generic comments with insufficient evidence support and weak source traceability, while complex multi-agent systems incur high inference costs. To address these challenges, we propose EGTR-Review, an Evidence-Grounded and Traceable Review Generation framework via Multi-Agent Teacher Distillation. EGTR-Review first constructs a multi-agent teacher that performs structure-aware paper decomposition, key-element extraction, external scholarly evidence retrieval, evidence-state labeling, verification reasoning, and review synthesis. It then distills both intermediate reasoning trajectories and final review comments into a lightweight student model through task-prefix-driven multi-task learning. An evidence-weighted objective further reduces the influence of weak, missing, or non-verifiable supervision. Experiments on public peer-review datasets show that EGTR-Review (Student) outperforms strong prompt-based, fine-tuned, and structured/agentic baselines across automatic metrics, LLM-as-Judge evaluation, and human evaluation, while maintaining strong factual grounding and source traceability with substantially lower token consumption and inference time. Our code, prompts, configurations, and sample data are available on GitHub.

Chinese Translation

科学同行评审生成逐渐受到关注，以减轻评审负担并提供及时反馈。然而，现有基于大语言模型（LLM）的方法往往产生缺乏证据支持和源追溯能力的通用评论，而复杂的多智能体系统则会产生较高的推理成本。为了解决这些挑战，我们提出了EGTR-Review，一个通过多智能体教师蒸馏实现的证据驱动和可追溯的评审生成框架。EGTR-Review首先构建一个多智能体教师，该教师执行结构感知的论文分解、关键元素提取、外部学术证据检索、证据状态标记、验证推理和评审合成。然后，它通过任务前缀驱动的多任务学习，将中间推理轨迹和最终评审评论蒸馏到一个轻量级的学生模型中。一个证据加权目标进一步减少了弱、缺失或不可验证监督的影响。在公共同行评审数据集上的实验证明，EGTR-Review（学生模型）在自动评测指标、LLM作为评审者的评估和人工评估中优于强提示、微调以及结构/智能体基线，同时在大大减少标记消耗和推理时间的情况下保持强大的事实基础和源追溯能力。我们的代码、提示、配置和示例数据可在GitHub上获得。

View on arXiv Download PDF AI Translation

cs.CL / 71 / 2606.06031

NAVIRA: Decoupled Stochastic Remasking for Masked Diffusion Language Models

NAVIRA：用于掩蔽扩散语言模型的解耦随机重掩蔽

Fomenko, Andrey, Kryzhanovskiy, Maksim, Glazyrina, Svetlana, Ischenko, Roman

Abstract

Masked diffusion language models generate text by iteratively unmasking many tokens in parallel, but this speed comes with a correction problem: tokens generated in the same step are predicted from marginal distributions, and early local dependency errors can later contaminate the context. PRISM addresses this by learning token-level quality scores and remasking unreliable tokens, but its inference rule is coupled: the same forward pass both detects low-quality tokens and computes logits for their replacements, so the erroneous tokens still condition regeneration. We propose NAVIRA, an inference-time decoding policy that separates these two operations and samples remasking positions stochastically. A first forward pass scores tokens; selected tokens are masked; a second forward pass regenerates from the cleaned context. Temperature-controlled remasking reduces repeated correction of the same positions and balances fluency against diversity. In controlled experiments with a 170M masked diffusion language model, decoupling improves fluency, while scheduled stochastic remasking preserves entropy and achieves stronger LLM-judge scores under larger forward-pass budgets. These results show that remasking policy, not only the learned quality signal, is central to reliable masked-diffusion text generation.

Chinese Translation

掩蔽扩散语言模型通过并行迭代解掩蔽多个标记生成文本，但这种速度带来了纠正问题：在同一步生成的标记是从边际分布预测的，早期的局部依赖错误可能会后续污染上下文。PRISM通过学习标记级质量评分并重掩蔽不可靠的标记来解决这一问题，但其推理规则是耦合的：相同的前向传播同时检测低质量标记并计算其替换的logits，因此错误的标记仍然影响再生成。我们提出了NAVIRA，一种在推理时解码的策略，它将这两个操作分开，并随机抽取重掩蔽位置。第一次前向传播对标记进行评分；选定的标记被掩蔽；第二次前向传播从清理后的上下文中重新生成。温度控制的重掩蔽减少了对相同位置的重复修正，并在流畅性和多样性之间实现了平衡。在对一个170M掩蔽扩散语言模型的控制实验中，解耦提高了流畅性，而计划性的随机重掩蔽保持了熵，并在更大的前向传播预算下实现了更强的LLM-judge评分。这些结果表明，重掩蔽策略，而不仅仅是学习到的质量信号，对于可靠的掩蔽扩散文本生成至关重要。

View on arXiv Download PDF AI Translation

cs.CL / 72 / 2606.06038

English-to-Prakrit Machine Translation via Multilingual Transfer Learning

基于多语言迁移学习的英语到普拉克里特机器翻译

Choksi, Om, Kareliya, Smit, Malviya, Shrikant, Mishra, Pruthwik

Abstract

We study English-to-Prakrit machine translation in a low-resource setting where the target language is unsupported by IndicTrans2. We adapt the multilingual model by mapping Prakrit to the Hindi language tag (hin_Deva) without modifying the tokenizer, vocabulary, or architecture. Using a 1,474-pair Maharashtri Prakrit parallel corpus and evaluation on a 20-sample Ardhamagadhi test set, we report corpus BLEU improvements over an untuned baseline. The results indicate that script-compatible language routing can enable feasible transfer to unsupported classical languages, while highlighting limitations due to data scarcity and dialect mismatch. Our code and trained models are released to the public for further exploration https://github.com/D3v1s0m/indictrans2-prakrit-mt.

Chinese Translation

我们研究了在低资源环境下的英语到普拉克里特机器翻译，在该环境中，目标语言不被IndicTrans2支持。我们通过将普拉克里特映射到印地语语言标签（hin_Deva）来适应多语言模型，而不修改分词器、词汇或架构。使用1,474对马哈拉施特拉普拉克里特平行语料库，并在20个样本的阿尔达马迦地测试集上进行评估，我们报告了相对于未调优基线的语料库BLEU得分的改进。结果表明，脚本兼容的语言路由能够实现对不支持的古典语言的可行迁移，同时强调了由于数据稀缺和方言不匹配而导致的局限性。我们的代码和训练模型已向公众发布，以供进一步探索 https://github.com/D3v1s0m/indictrans2-prakrit-mt。

View on arXiv Download PDF AI Translation

cs.CL / 73 / 2606.06044

IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrieval

IA-RAG：基于区间代数的动态知识检索时序推理

Wang, Xiaoman, Zhang, Yaoze, Fan, Wenzhuo, Zhang, Hongwei, Wang, Ding, Yan, Guohang, Mao, Song, Shi, Botian, Lan, Yunshi, Cai, Pinlong

Abstract

Retrieval-Augmented Generation (RAG) has shown strong effectiveness in grounding Large Language Models (LLMs) with external knowledge. However, existing RAG and Graph RAG frameworks largely treat knowledge as static or associate time with coarse-grained timestamps or metadata, failing to capture rich temporal structures such as duration, overlap, and containment. We propose IA-RAG, a hierarchical temporal RAG framework that models knowledge as time intervals and performs retrieval under formal temporal constraints. IA-RAG represents facts as Interval Event Units (IEUs) and organizes them into a hierarchical Thematic Forest, where temporal dependencies are governed by Allen's Interval Algebra. To handle incomplete or uncertain temporal boundaries, IA-RAG further introduces a Sub-graph Time Tightening mechanism that refines fuzzy intervals through logical constraints within connected event subgraphs. In addition, IA-RAG supports implicit temporal semantic retrieval through interval-algebra-guided traversal. Experiments on multiple temporal question answering benchmarks, including TimeQA, TempReason, and ComplexTR, demonstrate that IA-RAG achieves strong temporal retrieval and reasoning performance, particularly on complex compositional temporal reasoning tasks. Our code is released at https://github.com/xiaoAugenstern/LogicalRAG_TemporalQA.

Chinese Translation

检索增强生成（RAG）在将外部知识与大型语言模型（LLMs）结合方面展现了强大的有效性。然而，现有的RAG和图形RAG框架大多将知识视为静态，或将时间与粗粒度的时间戳或元数据关联，未能捕捉到持续时间、重叠和包含等丰富的时序结构。我们提出了IA-RAG，一种分层时序RAG框架，将知识建模为时间区间，并在正式时序约束下进行检索。IA-RAG将事实表示为区间事件单元（Interval Event Units, IEUs），并将它们组织成一个分层主题森林，其中时序依赖关系遵循艾伦的区间代数（Allen's Interval Algebra）。为了处理不完整或不确定的时间边界，IA-RAG进一步引入了一种子图时间收缩机制，通过连接事件子图内的逻辑约束来精炼模糊区间。此外，IA-RAG支持通过区间代数引导的遍历进行隐式时序语义检索。在多个时序问答基准（如TimeQA、TempReason和ComplexTR）上的实验表明，IA-RAG在时序检索和推理性能上表现出色，特别是在复杂的组合时序推理任务中。我们的代码已发布在https://github.com/xiaoAugenstern/LogicalRAG_TemporalQA。

View on arXiv Download PDF AI Translation

cs.CL / 74 / 2606.06047

Automatic Labelling of Speech Translation Errors

语音翻译错误的自动标注

Macháček, Dominik, Züfle, Maike, Klejch, Ondrej

Abstract

Errors in speech translations reduce trustworthiness of Speech Translation (ST) systems and can have serious consequences. Yet currently there is no established methodology for evaluating confidence and quality estimation of speech translations. To initiate progress in this direction, we propose Speech Translation Error Labelling (STEL). We create an annotation protocol, a small authentic end-to-end evaluation dataset, and we analyse how existing text-only and speech-processing systems perform the STEL task. Our results show that text-only XCOMET and multimodal LLM Qwen2.5-Omni are able to perform the STEL task in roughly half the precision of humans. We also find that direct speech processing is necessary for the STEL task, and that the current text-only and speech-processing systems are complementary in labelling translation-only vs. speech-processing errors in ST.

Chinese Translation

语音翻译中的错误降低了语音翻译系统（Speech Translation, ST）的可信度，并可能带来严重后果。然而，目前尚无建立的评估语音翻译的置信度和质量估计的方法论。为了在此方向上推动进展，我们提出了语音翻译错误标注（Speech Translation Error Labelling, STEL）。我们创建了一种注释协议，一个小型真实的端到端评估数据集，并分析了现有的仅文本和语音处理系统在STEL任务中的表现。我们的结果表明，仅文本的XCOMET和多模态的LLM Qwen2.5-Omni能够以人类精度的大约一半来执行STEL任务。我们还发现，直接的语音处理对于STEL任务是必要的，并且当前的仅文本和语音处理系统在标注翻译错误与语音处理错误方面是互补的。

View on arXiv Download PDF AI Translation

cs.CL / 75 / 2606.06065

Multi-task Learning is Not Enough: Representational Entanglement in Dual-output Second Language Speech Recognition

多任务学习不足：双输出第二语言语音识别中的表征纠缠

Cho, Seung Hwan, Kim, Young-Min

Abstract

Second-language (L2) speech recognition often requires transcriptions of pronunciations and intended meanings. Multi-task learning (MTL) is a natural approach because it assumes that shared representations benefit both outputs. However, this paper shows that this assumption does not hold across Korean and English. MTL improves meaning but degrades surface transcription, especially in English, where the degradation scales with surface-meaning divergence measured by Levenshtein edit distance.Encoder analysis links these patterns to encoder-level entanglement, with Korean preserving distinct task representations while English produces nearly identical ones. Cross-task decoder analysis shows that the meaning dual-output decoder adapts with a unique representation, while the surface dual-output decoder remains constrained by the encoder. These findings motivate the design of MTL frameworks that mitigate encoder-level entanglement to reduce surface degradation in dual-output L2 automatic speech recognition.

Chinese Translation

第二语言（L2）语音识别通常需要对发音和意图进行转录。多任务学习（MTL）是一种自然的方法，因为它假设共享表征可以使两个输出受益。然而，本文指出这一假设在韩语和英语之间并不成立。MTL 改善了意图的识别，但却削弱了表面转录，尤其是在英语中，这种削弱与通过 Levenshtein 编辑距离测量的表面与意义的偏离程度成比例。编码器分析将这些模式与编码器级别的纠缠联系起来，发现韩语保持了不同的任务表征，而英语则产生几乎相同的表征。跨任务解码器分析表明，意义双输出解码器以独特的表征进行适应，而表面双输出解码器则受限于编码器。这些发现促使设计减轻编码器级别纠缠的 MTL 框架，从而减少双输出 L2 自动语音识别中的表面降级。

View on arXiv Download PDF AI Translation

cs.CL / 76 / 2606.06079

SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization

技能编排器：学习演化代理技能以实现规范和泛化

Zhang, Qi, Feng, Zhaopeng, Shi, Xiaonan, Hu, Xiaomeng, Liu, Chu, Xie, Pengjun, Wang, Xiaobin, Ye, Jieping, Hooi, Bryan, Wang, Haobo, Zhao, Junbo

Abstract

Agent skills, which consist of reusable strategies that guide agent reasoning and action, have shown strong potential for improving model capability at inference time. However, current skill construction methods treat the problem as one-shot extraction, overlooking a fundamental tension: a skill tailored to the specific task fails to transfer, while the abstracted skill often provides insufficient guidance. We attribute this fragility to the absence of explicit mechanisms for skill specification and generalization. To address this gap, we introduce SkillComposer, a framework that decomposes skill construction into three learnable operations: create, improve, and merge. Trained via systematic rejection sampling recipe, SkillComposer enables language models to self-evolve skills at inference time and supports three deployment modes: offline for building generalized libraries, online for task-specific refinement, and hybrid for combining both. Comprehensive experiments on $\tau^2$-Bench, LiveCodeBench v6, and AppWorld show that SkillComposer consistently outperforms baselines. Our SkillComposer-4B improves a 27B executor by up to +4.5 on agent tasks and +3.4 on code tasks, while generalizing across domains and task types unseen during training. Analysis reveals that merge and improve address orthogonal quality dimensions and that skill composition is a transferable meta-ability, providing a practical recipe for skill-augmented inference.

Chinese Translation

代理技能是指导代理推理和行动的可重用策略，其在推理时提高模型能力方面显示出强大的潜力。然而，目前的技能构建方法将此问题视为一次性提取，忽略了一个根本矛盾：为特定任务量身定制的技能无法迁移，而抽象的技能往往提供不足的指导。我们将这一脆弱性归因于缺乏明确的技能规范和泛化机制。为了解决这一问题，我们提出了技能编排器（SkillComposer），这是一个将技能构建分解为三个可学习操作的框架：创建、改进和合并。通过系统的拒绝采样方法训练，技能编排器使语言模型能够在推理时自我演化技能，并支持三种部署模式：离线用于构建泛化库，在线用于任务特定的精细调整，以及混合模式结合两者。在 $ au^2$-Bench、LiveCodeBench v6 和 AppWorld 上的综合实验表明，技能编排器在各方面均优于基准模型。我们的技能编排器4B在代理任务上将27B执行器提升至+4.5，在代码任务上提升至+3.4，同时能够在培训期间未见过的领域和任务类型中进行泛化。分析结果显示，合并和改进解决的是正交的质量维度，而技能组合是一种可迁移的元能力，为技能增强推理提供了实用的方法。

View on arXiv Download PDF AI Translation

cs.CL / 77 / 2606.06087

LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents

LatentSkill：从上下文文本技能到权重空间潜在技能的 LLM 智能体

Yu, Aofan, Zhou, Chenyu, Xu, Tianyi, Guo, Zihan, Shan, Rong, Fu, Zhihui, Wang, Jun, Liu, Weiwen, Yu, Yong, Zhang, Weinan, Lin, Jianghao

Abstract

Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and exposes skill content as plaintext. We present LatentSkill, a framework that converts textual skills into plug-and-play LoRA adapters through a pretrained hypernetwork. LatentSkill stores skill knowledge in weight space rather than context space, removing per-step skill tokens while preserving modular loading, scaling, and composition. On ALFWorld and Search-QA, LatentSkill outperforms the corresponding in-context skill baseline while using substantially fewer prefill tokens: it improves ALFWorld success by 21.4 and 13.4 points on the seen and unseen splits with 64.1% fewer prefill tokens, and improves Search-QA exact match by 3.0 points with 72.2% lower skill-token overhead. Further analysis shows that generated skill LoRAs form a structured semantic geometry, can be precisely controlled via the LoRA scaling coefficient, and can be composed through parameter-space arithmetic when skill components are aligned. These findings suggest that weight-space skills provide an efficient, modular, and less exposed substrate for extending LLM agents.

Chinese Translation

智能体系统越来越多地使用文本技能来编码可重用任务程序，但在每一步都将这些技能注入提示中会导致显著的上下文开销，并将技能内容以明文形式暴露。我们提出了 LatentSkill，一个通过预训练超网络将文本技能转换为即插即用的 LoRA 适配器的框架。LatentSkill 将技能知识存储在权重空间中，而非上下文空间，从而消除了每一步的技能令牌，同时保留了模块化加载、缩放和组合的能力。在 ALFWorld 和 Search-QA 数据集上，LatentSkill 的表现超越了相应的上下文技能基线，同时使用显著更少的前填充令牌：在可见和不可见划分中，分别提高了 ALFWorld 的成功率 21.4 和 13.4 个百分点，并减少了 64.1% 的前填充令牌，同时在 Search-QA 中提高了精确匹配 3.0 个百分点，同时技能令牌开销降低了 72.2%。进一步的分析表明，生成的技能 LoRA 形成了结构化的语义几何形状，可以通过 LoRA 缩放系数精确控制，并且当技能组件对齐时可以通过参数空间算术进行组合。这些发现表明，权重空间技能为扩展 LLM 智能体提供了一种高效、模块化且不易暴露的基础。

View on arXiv Download PDF AI Translation

cs.CL / 78 / 2606.06088

CHALIS: A Challenge Dataset for Language Identification in Difficult Scenarios

CHALIS：一个针对困难场景语言识别的挑战数据集

Tichý, Michal, Libovický, Jindřich

Abstract

We present CHALIS (Challenging Language Identification Samples), a new benchmark dataset explicitly designed to address difficult cases in language identification: cousin languages and orthographic noise. Our dataset has two parts: First, we collected sentences shared across mutually intelligible language pairs (Czech/Slovak, Spanish/Catalan, Portuguese/Galician, Danish/Norwegian). The second part tests for orthography noise: we transliterate text across multiple scripts, remove diacritics, simulate homoglyph attacks, and use Internet slang. We evaluate four widely used language identification systems on CHALIS and demonstrate that all struggle substantially in these scenarios, especially on lower-resource languages within cousin pairs and on transliterated input. The resource is publicly available at https://huggingface.co/datasets/michal-tichy/CHALIS.

Chinese Translation

我们提出了CHALIS（挑战性语言识别样本），这是一个新基准数据集，专门设计用于解决语言识别中的困难案例：近似语言和正字法噪声。我们的数据集分为两部分：首先，我们收集了在相互可理解的语言对（捷克语/斯洛伐克语、西班牙语/加泰罗尼亚语、葡萄牙语/加利西亚语、丹麦语/挪威语）中共享的句子。第二部分测试正字法噪声：我们通过多种书写系统对文本进行音译，去除发音符号，模拟同形异义攻击，并使用网络俚语。我们对四种广泛使用的语言识别系统在CHALIS上进行了评估，结果表明，在这些场景下，所有系统均面临显著困难，特别是在近似语言对中的低资源语言和音译输入方面。该资源已在https://huggingface.co/datasets/michal-tichy/CHALIS上公开发布。

View on arXiv Download PDF AI Translation

cs.CL / 79 / 2606.06098

IR3DE: A Linear Router for Large Language Models

IR3DE：一种用于大型语言模型的线性路由器

Fanì, Eros, Ersoy, Oğuzhan

Abstract

Foundational Large Language Models (LLMs) demonstrate proficiency on a wide range of general tasks, and achieve remarkable results on various specialized tasks via domain-expert LLMs. With the ever-growing list of available LLMs, inference routers are being proposed to select the most appropriate LLM for each prompt. However, existing routing methods either optimize cost across weak-to-strong generalist LLMs or require substantial training to support domain-expertise routing. In this paper, we propose IR3DE, a Ridge Regression-based Router for Domain Experts that provides cheap and fast routing decisions for each prompt. We evaluate IR3DE in two Causal Language Modeling (CLM) settings where the tasks are next-token prediction for all domains, and one reasoning setting where each domain has its own distinct reasoning task. Despite being a linear router, IR3DE achieves performance comparable to the other baselines in both CLM settings, and surpassing them in the reasoning setting, with a normalized performance of 98.4%. Moreover, IR3DE enables the addition or removal of new domain experts without requiring the router to be retrained from scratch, allowing a dynamic set of LLMs to be served with minimal disruption to the router itself. Our code is available at: github.com/gensyn-ai/IR3DE.

Chinese Translation

基础大型语言模型（LLMs）在广泛的通用任务上展现了高超的能力，并通过领域专家LLMs在各种专业任务上取得了显著成果。随着可用LLMs的数量不断增加，推理路由器被提出用于为每个提示选择最合适的LLM。然而，现有的路由方法要么优化弱到强通用型LLMs之间的成本，要么需要大量的训练来支持领域专业路由。本文提出了IR3DE，一种基于岭回归的领域专家路由器，为每个提示提供廉价且快速的路由决策。我们在两种因果语言建模（CLM）环境下评估IR3DE，其中任务是对所有领域进行下一个令牌预测，以及一个推理环境，其中每个领域都有其独特的推理任务。尽管IR3DE是一个线性路由器，但在两个CLM环境中的性能与其他基线相当，并在推理环境中以98.4%的标准化性能超过了它们。此外，IR3DE还使得可以添加或移除新的领域专家，而无需从头开始重新训练路由器，从而允许一组动态的LLMs以最小的干扰提供服务。我们的代码可在以下网址获取：github.com/gensyn-ai/IR3DE。

View on arXiv Download PDF AI Translation

cs.CL / 80 / 2606.06109

Harnessing Structural Context for Entity Alignment Foundation Models

利用结构上下文进行实体对齐基础模型的构建

Chen, Xingyu, Cui, Yuanning, Sun, Zequn, Hu, Wei

Abstract

Entity alignment (EA) aims to identify equivalent entities across heterogeneous knowledge graphs (KGs) and is a key component of knowledge fusion and cross-KG reasoning. The recent EA foundation model demonstrates that alignment knowledge, once pretrained, can be directly applied to diverse previously unseen KG pairs. However, it still underuses structural context in two places: cross-KG interaction is weak during encoding, and final candidate ranking still relies too heavily on coarse similarity. We address these limitations with ContextEA, an enhanced encoder-decoder framework for transferable EA. On the encoder side, we introduce a cross-KG interaction encoder that unifies the two KGs with anchor bridges and performs earlier relation-aware cross-graph propagation. On the decoder side, we introduce a structural calibration decoder that calibrates alignment scores with entity-level, neighborhood-level, relation-level, and anchor-aware structural evidence. This design strengthens both structural context construction and structural context exploitation while remaining lightweight. Experiments on 29 EA datasets in OpenEA, SRPRS, and DBP show consistent gains over strong transferable baselines. Notably, the pretrained ContextEA already surpasses the finetuned baselines on all three benchmark groups, demonstrating substantially stronger transfer to unseen KGs. These results suggest that explicitly harnessing structural context is an effective direction for improving EA foundation models.

Chinese Translation

实体对齐（EA）旨在识别异构知识图谱（KGs）中的等效实体，是知识融合和跨KG推理的关键组成部分。最近的EA基础模型表明，一旦预训练，对齐知识可以直接应用于多种以前未见过的KG对。然而，它在两个方面仍然未充分利用结构上下文：在编码过程中，跨KG的交互较弱，而最终候选排名仍过于依赖粗糙的相似性。我们通过ContextEA来解决这些局限性，这是一种增强的编码器-解码器框架，用于可转移的EA。在编码器端，我们引入了一种跨KG交互编码器，它通过锚桥统一两个KG，并执行更早的关系感知跨图传播。在解码器端，我们引入了一种结构校准解码器，它使用实体级、邻域级、关系级和锚感知的结构证据对对齐分数进行校准。这一设计在保持轻量级的同时，加强了结构上下文的构建和利用。在OpenEA、SRPRS和DBP的29个EA数据集上的实验表明，相较于强大的可转移基线，性能持续提升。值得注意的是，预训练的ContextEA在所有三个基准组上已超越微调的基线，显示出对未见KG的显著更强转移能力。这些结果表明，明确利用结构上下文是改善EA基础模型的有效方向。

View on arXiv Download PDF AI Translation

cs.CL / 81 / 2606.06177

Ouvia: A User-centered Framework for Measuring Usability of Speech Translation in Real-World Communication Scenarios

Ouvia：一个以用户为中心的框架，用于衡量真实世界交流场景中语音翻译的可用性

Attanasio, Giuseppe, Savoldi, Beatrice, Chechelnitsky, Daniel, Negri, Matteo, Carpuat, Marine, Sap, Maarten, Martins, André F. T.

Abstract

Speech translation (ST) is increasingly adopted in user applications, yet its evaluation largely focuses on decontextualized testbeds and holistic quality, rather than end users' communication needs. We introduce Ouvia, an evaluation framework for measuring user-perceived usability of speech translation outputs in real-world settings. Ouvia focuses on one-to-one communication: an English speaker needs to convey a request to a Portuguese speaker, and the message is automatically translated. Through a custom web app and multi-phase study design, we collect more than 1,750 such interactions in healthcare and everyday situations, mediated by four ST systems, involving speakers from three English dialects and two genders. We find that modern ST serves people only to a limited extent -- only around half of interactions are rated as usable -- with significant gaps in reported usability across demographic groups. Moreover, among quality metrics, we find that QA-based evaluation is a substantially stronger predictor of real-world usability than standard approaches. Together, these findings stress the importance of situated, user-centered evaluation frameworks that go beyond holistic quality scores and attend to who the technology serves -- and how well.

Chinese Translation

语音翻译（ST）在用户应用中越来越普遍，但其评估主要集中于去情境化的测试环境和整体质量，而不是最终用户的交流需求。我们推出了Ouvia，一个评估框架，用于测量在真实环境中用户感知的语音翻译输出的可用性。Ouvia专注于一对一的沟通：一位英语讲者需要将请求传达给一位葡萄牙语讲者，消息会被自动翻译。通过定制的Web应用程序和多阶段研究设计，我们收集了超过1,750次这样的互动，涉及医疗保健和日常场景，媒介为四个语音翻译系统，包括来自三种英语方言和两种性别的讲者。我们的研究发现，现代语音翻译在为人服务方面仅能达到有限的程度——大约只有一半的互动被评为可用——各个群体在报告的可用性上存在显著差距。此外，在质量指标中，我们发现基于质量评估（QA）的评估相比标准方法对真实世界可用性的预测显著更强。这些发现共同强调了基于情境的、以用户为中心的评估框架的重要性，这种框架超越了整体质量分数，关注技术服务于谁以及服务得如何。

View on arXiv Download PDF AI Translation

cs.CL / 82 / 2606.06188

The Tell-Tale Norm: $\ell_2$ Magnitude as a Signal for Reasoning Dynamics in Large Language Models

告密规范：$ ext{l}_2$幅度作为大型语言模型推理动态的信号

Zhang, Jinyang, Ding, Hongxin, Fang, Yue, Liao, Weibin, Ye, Muyang, Zhao, Junfeng, Wang, Yasha

Abstract

Recent work has sought to understand Large Language Models (LLMs) reasoning, yet a principled, model-intrinsic signal that captures its layer-wise reasoning dynamics remains underexplored. We bridge this gap by demonstrating that the l2 norm of hidden states serves as an endogenous signal of the model's reasoning intensity. Using Sparse Autoencoders (SAEs) as a diagnostic probe, we observe that LLMs' internal reasoning is marked by a sharp increase in reasoning feature activations concentrated in late layers. Motivated by this pattern, we establish a formal link between reasoning intensity and the model's latent geometry and theoretically prove that the l2 norm of hidden states bounds the activation strength of SAE reasoning features. Empirical correlation analysis and causal interventions further validate the l2 norm as a faithful indicator, where heightened norms consistently correspond to critical reasoning steps. We then introduce three test-time scaling techniques guided by l2 norms: (i) Adaptive Layer-wise Reasoning Recursion, (ii) Endogenous Reasoning State Steering, and (iii) l2-guided Response Selection, which requires no additional training or data and is compatible with advanced inference engines. Experiments across model architectures and benchmarks show that l2-norm-based techniques significantly improve reasoning performance, offering a principled yet simple lens to perceive and control LLM latent reasoning dynamics. Our code is available at https://github.com/zjy1298/The-Tell-Tale-Norm.

Chinese Translation

近年来的研究致力于理解大型语言模型（LLMs）的推理过程，但能够捕捉其层级推理动态的原则性、模型内在信号仍然未被深入探讨。我们通过证明隐藏状态的$ ext{l}_2$范数作为模型推理强度的内生信号来弥补这一空白。使用稀疏自编码器（Sparse Autoencoders, SAEs）作为诊断工具，我们观察到LLMs的内部推理特征在晚期层中表现出特征激活的急剧增加。基于这一模式，我们建立了推理强度与模型潜在几何之间的正式联系，并理论证明了隐藏状态的$ ext{l}_2$范数限制了SAE推理特征的激活强度。实证相关性分析和因果干预进一步验证了$ ext{l}_2$范数作为一个可信的指标，其中增强的范数始终与关键推理步骤相对应。随后，我们介绍了三种基于$ ext{l}_2$范数的测试时间缩放技术：（i）自适应层级推理递归，（ii）内生推理状态引导，以及（iii）$ ext{l}_2$指导的响应选择，这些技术无需额外训练或数据，并且与先进的推理引擎兼容。跨模型架构和基准的实验表明，基于$ ext{l}_2$范数的技术显著提高了推理性能，为观察和控制LLM潜在推理动态提供了一种原则性且简单的视角。我们的代码可在 https://github.com/zjy1298/The-Tell-Tale-Norm 获取。

View on arXiv Download PDF AI Translation

cs.CL / 83 / 2606.06197

Improving Answer Extraction in Context-based Question Answering Systems Using LLMs

利用大语言模型提升基于上下文的问题回答系统中的答案提取

Abdelghaffar, Hafez, Alansary, Ahmed, Hamdi, Ali

Abstract

Question answering (QA) systems have achieved notable progress with the advent of large language models (LLMs). However, they still face challenges in accurately extracting and generating precise answers from given contexts, particularly when dealing with complex or ambiguous queries. Existing approaches often struggle with contextual understanding, answer consistency, and generalization across diverse domains. In this work, we propose a question answering system based on large language models, where the input consists of a textual context and a corresponding question, and the output is a concise and accurate answer. The motivation behind this research lies in addressing the limitations of current QA systems, particularly their tendency to produce irrelevant or imprecise responses despite having access to the correct context. Our methodology involves fine-tuning a pre-trained LLM on a benchmark QA dataset to improve its contextual comprehension and answer extraction capabilities. Specifically, we utilize the Stanford Question Answering Dataset (SQuAD1.1), which provides high-quality context-question-answer triplets for supervised training and evaluation. Experimental results show that the fine-tuned Roberta-base model achieves the highest performance, attaining a ROUGE-L score of 86.84%, a BLEU score of 28.24%, and a BERTScore of 95.38%. These results indicate strong accuracy and answer relevance, demonstrating the effectiveness of the proposed approach for context-based question answering tasks. Furthermore, the findings confirm that targeted fine-tuning substantially improves the reliability and precision of QA systems.

Chinese Translation

随着大语言模型（LLMs）的出现，问题回答（QA）系统取得了显著进展。然而，它们在从给定上下文中准确提取和生成精确答案方面仍面临挑战，特别是在处理复杂或模糊查询时。现有方法往往在上下文理解、答案一致性和跨多领域的泛化能力上存在困难。在本研究中，我们提出了一种基于大语言模型的问题回答系统，其输入由文本上下文和相应问题组成，输出为简洁且准确的答案。本研究的动机在于解决当前QA系统的局限性，特别是它们在拥有正确上下文的情况下仍倾向于生成无关或不精确的响应。我们的方法包括对预训练的LLM进行微调，以提高其上下文理解和答案提取能力，具体利用斯坦福问题回答数据集（Stanford Question Answering Dataset，SQuAD1.1），该数据集为监督训练和评估提供了高质量的上下文-问题-答案三元组。实验结果表明，微调后的Roberta-base模型在性能上表现最好，达到86.84%的ROUGE-L分数、28.24%的BLEU分数和95.38%的BERTScore。这些结果表明，该方法在答案的准确性和相关性方面都表现出色，证明了所提方法在基于上下文的问题回答任务中的有效性。此外，研究结果证实，针对性的微调显著提高了QA系统的可靠性和准确性。

View on arXiv Download PDF AI Translation

cs.CL / 84 / 2606.06203

Dense Contexts Are Hard Contexts: Lexical Density Limits Effective Context in LLMs

密集语境是困难语境：词汇密度限制了大规模语言模型中的有效语境

Dettori, Giovanni, Boffa, Matteo, Giordano, Danilo, Drago, Idilio, Mellia, Marco

Abstract

Input length and the position of relevant information are widely cited as the primary causes of degraded LLM long-context performance. Here, we study lexical density -- the rate at which a context introduces distinct information -- as a third, largely overlooked factor that systematically reduces the effective context window of LLMs. We quantify the impact of lexical density on open-weight LLMs (9B-685B) using three "find-the-needle" style benchmarks with identical length (~12k tokens) and controlled needle position, but increasing density of information. We observe a sharp performance collapse in higher-density benchmarks: models that are near-perfect in sparse contexts drop below 60% retrieval score on denser ones. To rule out task-type confounds, we vary and control the density within each benchmark while keeping all other properties unchanged. Reducing density generally restores performance, especially in the high-density regimes where degradation appears. These results show that effective context capacity is a function of lexical density, with direct implications for real-world LLM systems operating on compact, information-rich inputs.

Chinese Translation

输入长度和相关信息的位置被广泛认为是导致大规模语言模型（LLMs）在长语境中性能下降的主要原因。在本文中，我们研究了词汇密度——语境引入不同信息的速度——作为一个被忽视的第三个因素，它系统性地减少了 LLMs 的有效语境窗口。我们使用三种“找针”风格的基准（长度相同约为12k个标记）和控制的针位置，定量分析词汇密度对开放权重 LLMs（9B-685B）的影响，同时增加信息密度。我们观察到在高密度基准中性能急剧下降：在稀疏语境中几乎完美的模型在更密集的语境中检索分数降至60%以下。为了排除任务类型混淆，我们在保持其他属性不变的情况下，改变并控制每个基准中的密度。通常减少密度能够恢复性能，特别是在出现降级的高密度区域。这些结果表明有效语境容量是词汇密度的函数，对在紧凑、信息丰富的输入上运行的现实世界 LLM 系统具有直接的影响。

View on arXiv Download PDF AI Translation

cs.CL / 85 / 2606.06211

FiLM-Based Speaker Conditioning of a SpeechLLM for Pathological Speech Recognition

基于FiLM的病理语音识别语音大模型的说话人调节

López, Fernando, Kesiraju, Santosh, Luque, Jordi

Abstract

Automatic speech recognition (ASR) has advanced remarkably for standard speech; however, pathological speech from neurological conditions remains a significant challenge. We investigate speaker conditioning via Feature-wise Linear Modulation (FiLM), injecting x-vector-derived information into each transformer layer of a frozen ASR encoder to adapt internal representations to individual pathological speakers without modifying base model weights. We benchmark this for the ASR task against standard and parameter-efficient fine-tuning baselines, complemented by post-processing, on Spanish and English pathological speech. Additionally, we evaluate if the adapted model preserves the ability to answer speech-related questions. Results show that speaker-conditioned ASR is competitive with established adaptation strategies while retaining performance on non-conditioned speech.

Chinese Translation

自动语音识别（ASR）在标准语音领域取得了显著进展；然而，来自神经系统疾病的病理语音仍然是一个重大挑战。我们通过特征线性调制（Feature-wise Linear Modulation, FiLM）研究说话人调节，将基于x-vector的信息注入到冻结的ASR编码器的每个变换层中，以此在不修改基础模型权重的情况下将内部表征适应个体病理说话者。我们针对西班牙语和英语的病理语音，对ASR任务进行了基准测试，与标准和参数高效的微调基线进行了比较，并辅以后处理。此外，我们评估了适应后的模型是否保留了回答与语音相关问题的能力。结果表明，经过说话人调节的ASR与已建立的适应策略具有竞争力，同时在非调节语音上保持了性能。

View on arXiv Download PDF AI Translation

cs.CL / 86 / 2606.06242

Benchmarking Open-Source Layout Detection Models for Data Snapshot Extraction from Institutional Documents

开源布局检测模型在机构文档数据快照提取中的基准测试

Dy, AJ Carl P., Solatorio, Aivin V.

Abstract

Institutional documents contain substantial amounts of operational and analytical information embedded within figures and tables. Current approaches for extracting visual content from documents are largely built around generic document layout analysis, where figures and tables are treated as uniformly relevant document objects rather than semantically meaningful analytical artifacts. In this work, we introduce a benchmark dataset and evaluation framework for \textit{data snapshot extraction}, the task of identifying and localizing semantically meaningful visual artifacts within institutional documents. The benchmark spans humanitarian reports, World Bank policy research working papers, and project appraisal documents, and includes annotations for figures and tables that contain reusable analytical information. Using this dataset, we benchmarked multiple open-source layout detection models and evaluated both detection performance and spatial extraction quality. Our results show that current models struggle to generalize to operational institutional documents despite strong performance on conventional academic benchmarks. Common failure modes include confusion between analytical and non-analytical content, fragmentation of composite analytical artifacts, and incomplete extraction of contextual information required for interpretation. These findings highlight a persistent gap between generic document layout analysis and operationally useful data snapshot extraction. We release the source PDFs, annotation dataset, metadata, and source code to support future research in operational document intelligence. The dataset is available at https://huggingface.co/datasets/ai4data/data-snapshot and the source code is available at https://github.com/worldbank/ai4data/tree/main/experimental/data-snapshot.

Chinese Translation

机构文档中嵌含大量的操作与分析信息，这些信息通常以图形和表格的形式呈现。目前从文档中提取视觉内容的方法主要围绕通用文档布局分析进行，这些方法将图形和表格视为均匀相关的文档对象，而非具有语义意义的分析工件。在本研究中，我们提出了一个用于数据快照提取（data snapshot extraction）的基准数据集和评估框架，该任务旨在识别和定位机构文档中具有语义意义的视觉工件。该基准涵盖人道主义报告、世界银行政策研究工作论文和项目评估文档，并包括对含有可重用分析信息的图形和表格的注释。利用该数据集，我们对多个开源布局检测模型进行了基准测试，并评估了检测性能和空间提取质量。我们的结果显示，尽管在传统学术基准上表现出色，当前模型在处理操作性机构文档时仍存在泛化困难。常见的失败模式包括将分析内容与非分析内容混淆、复合分析工件的碎片化以及提取所需解释的上下文信息不完整。这些发现突显了通用文档布局分析与操作性数据快照提取之间的持续差距。我们发布了源PDF、注释数据集、元数据和源代码，以支持未来在操作性文档智能领域的研究。该数据集可在 https://huggingface.co/datasets/ai4data/data-snapshot 获取，源代码可在 https://github.com/worldbank/ai4data/tree/main/experimental/data-snapshot 找到。

View on arXiv Download PDF AI Translation

cs.CL / 87 / 2606.06266

From Self to Other: Evaluating Demographic Perspective-Taking in LLM Hate Speech Annotation

从自我到他者：评估大型语言模型在仇恨言论注释中的人口视角采纳

Piot, Paloma, Parapar, Javier

Abstract

Hate speech detection is inherently subjective: people from different demographic groups perceive the same content very differently. Collecting enough annotations from multiple demographic groups is costly and difficult to scale. Persona-conditioned Large Language Models (models prompted to adopt a specific demographic identity) have been proposed as a way to simulate diverse perspectives at scale. But do they actually reflect how different groups disagree? We evaluate three aspects of human social judgement: (i) whether personas from different groups disagree in human-like ways (inter-group disagreement), (ii) whether they become more sensitive when content targets their own identity (in-group sensitivity), and (iii) whether they can accurately predict how another group would react (vicarious prediction). Our results show that no model consistently captures all three dimensions, and performance is highly model-dependent and does not emerge reliably from minimal identity prompts alone. However, vicarious prompting with Llama 3.1 yields the highest cross-group agreement in most demographic axes and provides the closest overall approximation to human disagreement patterns, indicating that this configuration may provide a more reliable setting for automatic annotation aligned with human judgements.

Chinese Translation

仇恨言论检测本质上是主观的：来自不同人口群体的人对相同内容的感知存在很大差异。从多个人口群体收集足够的注释既昂贵又难以扩展。使用个人化的大型语言模型（被促使采用特定人口身份的模型）被提议作为在规模上模拟多样化视角的一种方式。但是，它们真的反映了不同群体的分歧吗？我们评估了人类社会判断的三个方面：（i）来自不同群体的个体是否以类人方式表现出分歧（群体间分歧），（ii）当内容针对自己的身份时，他们的敏感度是否提高（群内敏感性），以及（iii）他们是否能够准确预测其他群体的反应（他者预测）。我们的结果显示，没有模型能一致捕捉所有三个维度，且性能高度依赖于具体模型，并且无法仅通过最少的身份提示可靠地得出。然而，使用 Llama 3.1 进行他者预测在大多数人口轴上产生了最高的跨群体一致性，并且提供了与人类分歧模式的最接近的整体近似，这表明此配置可能为与人类判断一致的自动注释提供了更可靠的环境。

View on arXiv Download PDF AI Translation

cs.CL / 88 / 2606.06267

Many Circuits, One Mechanism: Input Variation and Evaluation Granularity in Circuit Discovery

众多电路，单一机制：电路发现中的输入变化与评估粒度

Makou, Alireza Bayat, Niu, Jingcheng, Dutta, Subhabrata, Gurevych, Iryna

Abstract

Circuit discovery methods identify subgraphs that explain specific model behaviors, and structural differences between discovered circuits are commonly interpreted as evidence of distinct mechanisms. We test this assumption by varying input statistics while holding the task fixed, and show that the resulting structural differences exhibit apparent specialization but do not correspond to functional differences, a pattern we term phantom specialization. Using Literal Sequence Copying across four token-frequency bands plus a control condition in five Pythia models (70M-1.4B), we extract 75 circuits and find that structurally distinct circuits implement the same computation: band-specific edges transfer broadly across bands, a core shared across most bands recovers at least 99% of circuit performance, and causal interchange interventions confirm that internal representations are interchangeable across frequency bands. Repeated extractions within the same frequency band further suggest that discovery algorithms sample from an equivalence class of valid subgraphs rather than recovering a unique mechanism. Standard evaluation practice obscures this pattern: source-level evaluation inflates apparent faithfulness, while edge-level evaluation reveals the many-to-one mapping from structure to function. Our results show that structural differences between circuits are not sufficient evidence for distinct mechanisms, and that exposing this requires edge-level evaluation and cross-condition transfer tests.

Chinese Translation

电路发现方法识别出解释特定模型行为的子图，发现电路之间的结构差异常常被解读为不同机制的证据。我们通过在固定任务的情况下改变输入统计量来检验这一假设，发现所产生的结构差异表现出明显的专业化，但与功能差异并不对应，我们将这种模式称为幻影专业化。在五个Pythia模型（70M-1.4B）中，使用跨越四个标记频率区间以及一个对照条件的字面序列复制，我们提取了75个电路，并发现结构上截然不同的电路实现相同的计算：特定频段的边在各频段间广泛传递，大多数频段共享的核心至少恢复了99%的电路性能，而因果交互干预确认内部表示在不同频率频段间是可互换的。同一频率频段内的重复提取进一步提示发现算法是从有效子图的等价类中采样，而不是恢复唯一机制。标准评估实践模糊了这一模式：源级评估夸大了表面上的忠实度，而边级评估揭示了结构与功能之间的多对一映射。我们的结果表明，电路之间的结构差异不足以作为不同机制的证据，而要揭示这一点需要边级评估和跨条件转移测试。

View on arXiv Download PDF AI Translation

cs.CL / 89 / 2606.06271

FOXGLOVE: Understanding Goal-Oriented and Anchored Writing Feedback from Experts and LLMs on Argumentative Essays

FOXGLOVE：理解专家与大型语言模型（LLMs）对议论文的目标导向和具体内容反馈

Liu, Yijun, Song, Yifan, Gallagher, John, Sterman, Sarah, August, Tal

Abstract

While large language models (LLMs) are increasingly used to generate writing feedback, there remains no systematic comparison of LLM and expert feedback on the dimensions that writing research identifies as central to revision: goal-orientation, anchoring to specific sentences, and prioritization. We introduce FOXGLOVE, a dataset of 696 feedback comments written by trained writing instructors on 69 twelfth-grade argumentative essays, paired with 1,644 comments generated from four frontier LLMs under a shared protocol, totaling 2,340 comments. We provide expert quality ratings on a subset of both instructor and LLM comments. We find that instructors and LLMs distribute feedback similarly across goals and essay positions, yet instructors and models diverge on the specific sentences on which to provide feedback. Additionally, we find that models tend to write more complex feedback and use fewer questions than instructors. LLM feedback also receives higher ratings on most dimensions of quality, as rated by instructors, but much of this advantage appears to be attributable to lengthier comments. FOXGLOVE enables systematic comparison of where human and LLM feedback align, diverge, and differ.

Chinese Translation

尽管大型语言模型（LLMs）在生成写作反馈方面的应用日益增多，但尚未对LLM与专家在写作研究所识别的修订核心维度—目标导向、对具体句子的锚定和优先级进行系统性比较。本文介绍了FOXGLOVE，一个包含696条由训练有素的写作教师对69篇十二年级议论文撰写的反馈评论的数据集，配以在共享协议下由四个前沿LLM生成的1,644条评论，合计2,340条评论。我们对教师和LLM评论的一个子集提供了专家质量评分。研究发现，教师和LLM在反馈的目标及论文位置上分配相似，然而在具体句子上反馈的选择上却存在分歧。此外，我们还发现，LLM倾向于撰写更复杂的反馈，并且提问的频率低于教师。虽然LLM反馈在多数质量维度上获得了教师的更高评分，但这种优势在很大程度上似乎与较长的评论长度有关。FOXGLOVE使人类与LLM反馈在一致性、分歧和差异方面的系统比较成为可能。

View on arXiv Download PDF AI Translation

cs.CL / 90 / 2606.06286

LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs

大型语言模型会泄露训练数据，但它们是否真的想这样做？对大型语言模型记忆能力的倾向性评估

Barmina, Gianluca, Schneider-Kamp, Peter, Poech, Lukas Galke

Abstract

Large language models can reproduce training data, but existing memorization evaluations mostly measure whether models can be forced to do so, rather than whether they do so under ordinary use. We introduce PropMe, a propensity-aware framework for memorization evaluation that contrasts prefix-based capability attacks with non-adversarial evaluations. We propose a metric transformation that, applied to existing functions, allows to create propensity metrics. We further introduce SimpleTrace, a lightweight tracing pipeline built on infini-gram that deterministically attributes model generations to large-scale training corpora and computes verbatim, near-verbatim, and propensity-transformed memorization metrics. Evaluating two fully-open models: Comma and DFM Decoder on two datasets: Common Pile and Dynaword in two languages, we find a consistent gap between capability and propensity: prefix attacks elicit substantially stronger memorization signals than generic or dataset-specific prompts, while propensity scores remain low overall. Thus, the models can reveal training data when directly elicited, but rarely do so in more common non-adversarial settings. We also find that DFM Decoder, which is continually pre-trained from Comma, exhibits reduced memorization and memorization propensity for Common Pile, confirming that memorization capability can decrease when later training emphasizes partially different data. Our results suggest, and we encourage, that memorization audits should report both worst-case extractability and ordinary leakage propensity in order to have a more comprehensive view of this phenomenon.

Chinese Translation

大型语言模型可以再现训练数据，但现有的记忆能力评估主要测量模型是否可以被强迫这样做，而不是它们在普通使用下是否这样做。我们引入了PropMe，一个倾向性评估框架，用于记忆评价，该框架对比了基于前缀的能力攻击与非对抗性评估。我们提出了一种度量转换方法，将其应用于现有功能，可以创建倾向性度量。进一步地，我们引入了SimpleTrace，这是一个基于infi-gram的轻量级追踪管道，能够确定性地将模型生成归因于大规模训练语料库，并计算逐字、近似逐字和倾向性转换的记忆度量。通过在两个数据集Common Pile和Dynaword上评估两个完全开放的模型：Comma和DFM Decoder，在两种语言中，我们发现能力与倾向性之间存在一致的差距：前缀攻击引发的记忆信号显著强于普通或特定于数据集的提示，而倾向性分数总体保持较低。因此，模型在被直接引导时可以泄露训练数据，但在更常见的非对抗性环境中很少发生这种情况。我们还发现DFM Decoder在Comma的持续预训练下，针对Common Pile表现出降低的记忆能力和记忆倾向，确认了当后续训练强调部分不同的数据时，记忆能力可能会下降。我们的结果表明，且我们鼓励，记忆审计应报告最差情况下的可提取性和普通泄露倾向，以便更全面地理解这一现象。

View on arXiv Download PDF AI Translation

cs.CL / 91 / 2606.06306

Decomposing Factual Sycophancy in Language Models: How Size and Instruction Tuning Shape Robustness

语言模型中事实谄媚的解构：规模和指令调优如何塑造稳健性

De Marez, Victor, De Bruyne, Luna, Daelemans, Walter

Abstract

Factual sycophancy occurs when a language model abandons a correct, verifiable answer under social pressure. Because a flip occurs only when pressure toward a false answer exceeds the model's neutral preference for the truth, flip rates conflate two mechanisms: the strength of that baseline preference (truth margin), and how far pressure shifts it (manipulation sensitivity). We decompose factual sycophancy into these channels and use them to separate the effects of size and instruction tuning across 56 open-weight models spanning 0.3B-32B parameters and 13 manipulation types. We find that vulnerability is governed mainly by size, but instruction tuning changes how size acts: small instruction-tuned models can become less robust, whereas large instruction-tuned models usually become more robust. Instruction tuning primarily increases truth margin, but its behavioral effect depends on manipulation type. Scaling also changes the two channels differently: base models gain margin but become mildly more manipulation-sensitive, whereas instruction-tuned models gain margin faster and become less sensitive. Factual sycophancy is therefore not a single scalar property. Evaluations should report channel-specific, manipulation-specific, and size-conditioned robustness rather than flip rates alone.

Chinese Translation

事实谄媚发生在语言模型在社会压力下放弃一个正确、可验证答案的情况。当朝向错误答案的压力超过模型对真相的中立偏好时，就会发生转变。因此，转变率混合了两个机制：基线偏好的强度（真相边际）和压力对它的偏移程度（操控敏感性）。我们将事实谄媚分解为这些渠道，并利用它们在56个跨越0.3B-32B参数的开放权重模型和13种操控类型中分离规模和指令调优的影响。我们发现，脆弱性主要由规模决定，但指令调优改变了规模的作用：小型指令调优模型可能变得不那么稳健，而大型指令调优模型通常变得更加稳健。指令调优主要增加真相边际，但其行为效果取决于操控类型。规模也以不同方式改变这两个渠道：基本模型的边际增加，但操控敏感性轻微上升，而指令调优模型的边际增长更快且敏感性降低。因此，事实谄媚并不是一个单一的标量属性。评估应报告渠道特定、操控特定和规模条件的稳健性，而不仅仅是转变率。

View on arXiv Download PDF AI Translation

cs.CL / 92 / 2606.06349

"Chi nas dal soch el sent de legn" -- Auditing Text Corpora for Lombard

“Chi nas dal soch el sent de legn” -- 对伦巴第语文本语料库的审计

Signoroni, Edoardo, Rychlý, Pavel

Abstract

Several of the world's languages are still under-resourced in terms of Natural Language Processing (NLP) tools. This is mostly due to the lack of high-quality datasets to train, develop, and evaluate systems and models for several tasks, such as Machine Translation (MT). We conduct a manual audit of the parallel and monolingual corpora available for Lombard, an under-resourced language continuum from Italy. Our analysis reveals that the perceived abundance of web-scraped data is an illusion, with massive datasets plagued by severe language misidentification, boilerplate text, and non-linguistic noise. Furthermore, we analyze the orthographic composition of the valid Lombard portions across web-scraped datasets, curated corpora, and benchmarks. Our findings show conflicting orthographical systems and severe representational bias across all corpora: high-quality data is heavily skewed towards Western Lombard varieties, with Eastern ones left on the margins. This underscores the need for variety-aware, community-driven data curation rather than purely quantity-driven scraping.

Chinese Translation

世界上多种语言在自然语言处理（NLP）工具方面仍然资源匮乏。这主要由于缺乏高质量的数据集来训练、开发和评估各种任务的系统和模型，例如机器翻译（MT）。我们对可用的伦巴第语平行和单语语料库进行了手动审计，这是意大利的一种资源匮乏的语言连续体。我们的分析揭示了网络抓取数据的表面丰富是一个幻觉，大型数据集严重存在语言误识别、模板文本和非语言噪声。此外，我们还分析了在网络抓取数据集、整理语料库和基准测试中有效的伦巴第语部分的正字法组成。我们的研究结果表明，所有语料库之间存在冲突的正字法体系和严重的表征偏差：高质量数据严重倾斜于西伦巴第方言，而东部方言则被边缘化。这强调了需要进行以多样性为导向、社区驱动的数据整理，而不仅仅是以数量为主导的抓取。

View on arXiv Download PDF AI Translation

cs.CL / 93 / 2606.06350

EDIT: Evidence-Diagnosed Intervention Training for Rule-Faithful LLM Grading

EDIT：基于证据诊断的干预训练用于规则忠实的LLM评分

Wu, Zhihao, Zhang, Linhai, Wang, Taiyi, Zhao, Runcong, Andrews, Peter, Aloisi, Cesare, He, Yulan

Abstract

Reliable rubric grading requires more than accurate score prediction. Each judgement must be grounded in the mark scheme and evidence from the student answer. Existing credit-assignment and intervention methods, primarily designed for self-contained reasoning tasks such as mathematics reasoning, struggle in this setting because they do not identify where grading reasoning goes wrong or how the model's belief about the final mark changes during reasoning. We propose Evidence-Diagnosed Intervention Training (EDIT), a two-phase framework for training more rubric-faithful LLM graders. First, EDIT-SFT locates problematic reasoning steps using internal model signals: posterior belief over the final mark and input-grounding scores. It then revises only these local steps with help from a rubric checklist. Second, EDIT-RL calibrates the grader with belief-guided reward shaping, penalising large harmful belief drifts while still allowing helpful exploration. Experiments on two real-world, multi-subject grading benchmarks demonstrate that EDIT consistently outperforms strong supervised fine-tuning and reinforcement learning baselines on both in-domain and out-of-domain splits, with ablation studies confirming that internal-state diagnostics drive these gains.

Chinese Translation

可靠的评分标准评估不仅需要准确的分数预测。每一个判断都必须基于评分方案和学生答案中的证据。现有的信用分配和干预方法主要设计用于自成体系的推理任务（例如数学推理），在这种背景下表现不佳，因为它们无法识别评分推理出错的地方或模型对最终分数的信念在推理过程中的变化。我们提出了证据诊断干预训练（Evidence-Diagnosed Intervention Training，EDIT），这是一种用于训练更忠实于评分标准的LLM评分者的两阶段框架。首先，EDIT-SFT利用内部模型信号定位问题推理步骤：对最终分数的后验信念和输入根基分数。然后，它仅在评分检查表的帮助下修正这些局部步骤。其次，EDIT-RL通过信念引导的奖励塑造来校准评分者，惩罚大幅有害的信念漂移，同时仍允许有益的探索。在两个真实的多学科评分基准上的实验表明，EDIT在领域内和领域外分割上始终优于强大的监督微调和强化学习基线，消融研究确认内部状态诊断驱动了这些提升。

View on arXiv Download PDF AI Translation

cs.CL / 94 / 2606.06380

Emergent Language as an Approach to Conscious AI

作为意识人工智能方法的生成语言

Wu, Zengqing, Xiao, Chuan

Abstract

The question of whether artificial systems can be conscious remains open, in part because existing approaches either evaluate systems against theory-derived checklists (discriminative) or engineer consciousness-inspired modules directly (architectural); both leave open whether observed structures are artifacts of human language priors. We propose a generative methodology: emergent language (EL) in multi-agent reinforcement learning, where agents start from minimal (no language, no concept of self, minimal exposure to human text) and develop communication under task pressure alone, ensuring causal attributability to task demands rather than inherited human language priors. We position our methodology by discussing how EL serves as a generative tool for studying consciousness-relevant structure, including the role of environment complexity and the interpretation of emergent communication. As a proof of concept, we instantiate this methodology in a minimal environment and show that agents develop self-referential communication, including an echo-mismatch detection circuit that is not predicted by task structure or architecture alone but emerges from a specific environmental affordance.

Chinese Translation

人工系统是否能够具备意识的问题仍然开放，这部分是因为现有的方法要么是在理论导出的检查清单上评估系统（判别方法），要么是直接工程化受到意识启发的模块（架构方法）；这两种方法都无法明确观察到的结构是否是人类语言先验的产物。我们提出了一种生成性的方法：在多智能体强化学习中采用生成语言（Emergent Language，EL），其中智能体从最小状态（没有语言，没有自我概念，最少的接触人类文本）开始，在仅依赖任务压力的情况下发展沟通，确保因果归因于任务需求，而非继承自人类语言的先验。我们通过讨论生成语言如何作为一种研究意识相关结构的生成工具来定位我们的方法论，包括环境复杂性和对新兴沟通的解读。作为概念验证，我们在一个最小环境中实例化了这一方法，并表明智能体发展了自我指涉的沟通，包括一个回声不匹配检测电路，这一电路不仅不能仅由任务结构或架构预测，而是源于特定环境的赋能。

View on arXiv Download PDF AI Translation

cs.CL / 95 / 2606.06399

CollabSim: A CSCW-Grounded Methodology for Investigating Collaborative Competence of LLM Agents through Controlled Multi-Agent Experiments

CollabSim：一种基于计算机支持协作工作的研究方法，用于通过受控多智能体实验探究大语言模型代理的协作能力

Chen, Jiaju, Sun, Bo, Lu, Yuxuan, Wang, Yun, Wang, Dakuo, Yao, Bingsheng

Abstract

Multi-agent systems (MAS) built on large language models have shown growing promise, with their effectiveness resting on agents' ability to coordinate through text-based channels much as human teams do. Yet recent study suggests that MAS often falter not because agents lack individual task-solving ability, but because they lack collaborative competence: the capacity to establish common ground, maintain shared task understanding, balance individual and collective incentives, and repair misalignment as interaction unfolds. Decades of research in Computer-Supported Cooperative Work have characterized these requirements for human teams coordinating under constrained communication, yet existing MAS evaluations focus mainly on task outcomes or single-agent proficiency in reasoning, planning, and tool use. To enable a systematic analysis of agents' collaborative competence in MAS, we introduce CollabSim, a configurable simulation framework that combines a theory-grounded definition of collaborative capabilities, controlled manipulation of interaction conditions, and action-level probing of agents' internal states. Experiments across four LLMs show that CollabSim can capture condition effects, separate model performance patterns, and reveal task-dependent effects of agent design.

Chinese Translation

基于大型语言模型的多智能体系统（MAS）展现出日益增长的前景，其效果依赖于代理通过文本渠道进行协调，类似于人类团队。但最近的研究表明，MAS之所以未能取得成功，并非因为代理缺乏解决单一任务的能力，而是因为缺乏协作能力：即建立共同基础、维护共享任务理解、平衡个体与集体激励，以及在互动进程中修复不一致的能力。数十年的计算机支持协作工作研究已经明确了人类团队在受限沟通下协调所需的这些要求，而现有的MAS评估主要集中在任务结果或单个代理在推理、规划和工具使用方面的熟练度上。为了系统性地分析MAS中代理的协作能力，我们引入了CollabSim，这是一种可配置的仿真框架，结合了基于理论的协作能力定义、对互动条件的受控操作以及对代理内部状态的行动级探测。在四种大型语言模型的实验中，CollabSim能够捕捉条件效应、区分模型表现模式，并揭示代理设计的任务依赖性效应。

View on arXiv Download PDF AI Translation

cs.CL / 96 / 2606.06420

A Komi-Yazva--Russian Parallel Corpus and Evaluation Protocol for Zero- and Few-Shot LLM Translation

Komi-Yazva-俄语平行语料库及零样本和少样本大语言模型翻译评价协议

Parshakov, Petr

Abstract

We present the first Komi-Yazva--Russian parallel corpus together with an explicit evaluation protocol for studying LLM translation in an endangered, extremely low-resource setting. The dataset contains 457 aligned sentence pairs from 74 narrative texts and is accompanied by documented provenance, sentence-level alignment, and story identifiers that enable leakage-aware evaluation. We use this setup to compare modern large language models on Komi-Yazva-to-Russian translation under severe parallel-data scarcity in zero-shot and retrieval-based few-shot regimes. The protocol includes story-level cross-validation, deterministic retrieval for few-shot prompting, strict validation of generated outputs, complementary reference-based and judge-based metrics, and story-level uncertainty estimates. Across models, LLMs produce non-trivial translations, but performance varies strongly by model family and prompting regime. Retrieval-based few-shot prompting consistently improves over zero-shot prompting, while gains beyond a small retrieved context remain limited. The results show that evaluative conclusions in this setting depend materially on metric choice and failure handling, so the paper frames the corpus as both a dataset contribution and a reproducible evaluation testbed for endangered-language machine translation.

Chinese Translation

我们呈现了第一个Komi-Yazva-俄语平行语料库，并提出了一种明确的评价协议，以研究在濒危、极低资源环境下的大语言模型（LLM）翻译。该数据集包含来自74篇叙事文本的457对对齐句子，并附有记录的来源信息、句子级对齐和故事标识符，以实现漏泄意识的评估。我们使用这一设置比较现代的大语言模型在Komi-Yazva到俄语翻译中的表现，重点在于零样本和基于检索的少样本场景下的严重平行数据稀缺。该协议包括故事级的交叉验证、针对少样本提示的确定性检索、对生成输出的严格验证、补充的基于参考和评审的指标，以及故事级的不确定性估计。在各模型中，LLM生成了非平凡的翻译，但性能因模型类型和提示机制而异。基于检索的少样本提示始终优于零样本提示，而超出小型检索上下文的增益仍然有限。结果表明，在这一环境下的评估结论在很大程度上依赖于指标选择和失败处理，因此本文将该语料库框定为既是数据集贡献，又是一个可重复的评估测试平台，用于濒危语言的机器翻译。

View on arXiv Download PDF AI Translation

cs.CL / 97 / 2606.06428

Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation

强化学习促发未见语言翻译的情境学习

Hu, Hanxu, Šnajdr, Zdeněk, Chen, Pinzhen, Vamvas, Jannis, Sennrich, Rico

Abstract

Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encoding a grammar book in their context. However, both methods typically overfit specific languages, with limited zero-shot transfer at test time. To translate extremely low-resource languages at scale, we argue that LLMs must acquire the meta-skill of utilizing in-context linguistic knowledge rather than memorizing specific languages. In this paper, we propose a reinforcement learning (RL) approach to unseen language translation given rich linguistic context, using a surface-level translation metric (chrF) as the reward. Empirically, despite the lightweight reward, our RL-trained models effectively extract and apply relevant linguistic information from the provided context, leading to better translations on completely unseen languages than in-context learning or supervised fine-tuning. Our analyses suggest that outcome-based RL can extend beyond conventional reasoning tasks like math and coding to serve as a recipe for language learning from context.

Chinese Translation

先前的研究表明，大型语言模型（LLMs）能够通过持续训练或在其上下文中编码语法书籍来翻译未见或低资源的语言。然而，这两种方法通常会过拟合特定语言，在测试时的零样本迁移能力有限。为了大规模翻译极低资源的语言，我们认为 LLMs 必须掌握利用上下文语言知识的元技能，而不是简单地记忆特定语言。在本文中，我们提出了一种强化学习（RL）方法，通过丰富的语言上下文进行未见语言翻译，同时使用表面翻译指标（chrF）作为奖励。从经验上看，尽管我们的奖励机制相对轻量化，但经过 RL 训练的模型能够有效地从提供的上下文中提取和应用相关的语言信息，从而在完全未见的语言翻译方面 outperform了基于上下文学习或监督微调。我们的分析表明，基于结果的强化学习不仅限于传统的推理任务，如数学和编码，能够为从上下文中学习语言提供有效方法。

View on arXiv Download PDF AI Translation

cs.CL / 98 / 2606.06443

Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions

修订背景，转换模拟立场：对基于大型语言模型的在线讨论立场模拟进行审计

Zhang, Xinnong, Shan, Wanting, Lyu, Hanjia, Wei, Zhongyu, Luo, Jiebo

Abstract

Large language models are increasingly used to simulate social media users and infer how individuals may respond to online discussions. However, it remains unclear whether these simulations reflect precise user-specific beliefs or whether they are highly sensitive to semantically independent changes in conversational contexts. In this work, we study counterfactual context revision as a framework for auditing LLM-based stance simulation. Given an original online conversation, we first infer a target user's stance toward a specific topic. We then apply controlled revision strategies to the conversational context and simulate the user's stance again under the revised context. We compare text-only revision strategies with a multimodal one that incorporates meme-based context and evaluate two main effectiveness metrics, i.e., average directional stance shift and stance transition rate. The results reveal effective and robust stance transitions in both text-only and multimodal strategies across different polarization-preference mechanisms. Our study contributes an evaluation framework for understanding the context sensitivity of LLM-based stance simulation. More broadly, it highlights both the promise and risk of using LLMs to simulate online opinion dynamics.

Chinese Translation

大型语言模型正越来越多地用于模拟社交媒体用户并推断个体在在线讨论中的反应。然而，目前尚不清楚这些模拟是否反映了用户特定的信念，或者它们是否对语义独立的对话背景变化高度敏感。在本研究中，我们探索了反事实背景修订作为审计基于大型语言模型的立场模拟的框架。给定一个原始在线对话，我们首先推断目标用户对特定主题的立场。然后，我们对对话背景应用控制修订策略，并在修订后的背景下再次模拟用户的立场。我们对比了仅文本的修订策略与一种融合了基于表情包的背景的多模态策略，并评估了两个主要的有效性指标，即平均方向性立场转变和立场转移率。结果显示，在不同的极化偏好机制中，文本仅和多模态策略都呈现出有效和稳健的立场转变。我们的研究为理解基于大型语言模型的立场模拟的背景敏感性贡献了一个评估框架。更广泛来说，它突出了使用大型语言模型模拟在线舆论动态的潜力和风险。

View on arXiv Download PDF AI Translation

cs.CL / 99 / 2606.06447

Latent Reasoning with Normalizing Flows

使用归一化流的潜在推理

Tu, Guancheng, Fu, Xiangjun, Yu, Suhao, Tang, Yao, Kang, Haoqiang, Qin, Lianhui, Zhang, Yizhe, Gu, Jiatao

Abstract

Large language models often improve reasoning by generating explicit chain-of-thought (CoT), demonstrating the importance of intermediate computation. However, textual CoT forces this computation through a discrete, serial, and communication-oriented token stream: each reasoning step must be verbalized before the model can proceed, even when the underlying update is semantic, uncertain, or only partially formed. Latent reasoning offers a higher-bandwidth alternative by performing intermediate computation in compact continuous states before committing to text. Yet existing latent-reasoning methods often sacrifice key advantages that make CoT effective in autoregressive language models, including native left-to-right generation, probabilistic sampling, compatibility with KV-cache decoding, and tractable likelihood estimation. We propose NF-CoT, a latent reasoning framework that preserves these advantages by modeling continuous thoughts with normalizing flows. NF-CoT instantiates a TARFlow-style normalizing flow inside the LLM backbone, defining a tractable probability model over compact continuous thoughts distilled from explicit CoT. Continuous-thought positions are generated by an NF head, while text positions are generated by the standard LM head within the same causal stream. This design provides exact likelihoods for latent thoughts, enables probabilistic left-to-right decoding with the original KV cache, and supports direct policy-gradient optimization in the latent reasoning space. On code-generation benchmarks, NF-CoT improves pass rates over explicit-CoT and prior latent-reasoning baselines while substantially reducing intermediate-reasoning cost.

Chinese Translation

大型语言模型通常通过生成显式的思维链（Chain-of-Thought, CoT）来改善推理，显示了中间计算的重要性。然而，文本化的 CoT 会将这一计算强加于一个离散的、序列的、以通信为导向的令牌流中：每一步推理都必须在模型可以继续之前进行口头化，即使潜在的更新是语义的、不确定的，或仅部分形成的。潜在推理通过在提交到文本之前在紧凑的连续状态中进行中间计算，提供了一种更高带宽的替代方案。然而，现有的潜在推理方法通常牺牲了使 CoT 在自回归语言模型中有效的关键优势，包括天然的从左到右生成、概率采样、与 KV-cache 解码的兼容性以及可处理的似然估计。我们提出了 NF-CoT，一个潜在推理框架，通过使用归一化流对连续思维进行建模，从而保留这些优势。NF-CoT 在 LLM 骨干中实例化了 TARFlow 风格的归一化流，定义了一个关于从显式 CoT 提炼的紧凑连续思维的可处理概率模型。连续思维位置由 NF 头生成，而文本位置则在同一因果流中由标准 LM 头生成。该设计为潜在思维提供了精确的似然值，启用使用原始 KV 缓存的概率性从左到右解码，并支持在潜在推理空间中的直接策略梯度优化。在代码生成基准测试中，NF-CoT 相比显式 CoT 和先前的潜在推理基线提高了通过率，同时大幅降低了中间推理成本。

View on arXiv Download PDF AI Translation

cs.CL / 100 / 2606.06464

Human Adults and LLMs as Scientists: Who Benefits from Active Exploration?

人类成人与大型语言模型作为科学家：谁从主动探索中受益？

Samiei, Mandana, Yiu, Eunice, GX-Chen, Anthony, Lin, Dongyan, Shen, Jocelyn, Richards, Blake A., Gopnik, Alison, Precup, Doina

Abstract

A long-standing finding in the causal learning literature is that adults struggle to identify conjunctive causal rules, where an effect requires the simultaneous presence of multiple causes, while performing better in disjunctive settings. However, most demonstrations of this ``conjunctive handicap'' rely on passive observation paradigms with limited evidence, where learners have no control over evidence generation. This paper asks whether this bias persists when adults are granted agency through active exploration. Using a modified ``blicket detector'' task, adult participants freely intervened to identify causal objects under conjunctive or disjunctive rule structures. We show that active exploration substantially improves adults' conjunctive causal reasoning, although conjunctive rules still require more tests to infer than disjunctive rules. We further compare human performance to a range of large language models in the same setting. While some state-of-the-art models approach human-level performance on hypothesis inference accuracy, they often exhibit less efficient exploration strategies and similar conjunctive-disjunctive performance gaps.

Chinese Translation

在因果学习文献中，一个长期存在的发现是，成年人在识别联合因果规则时表现不佳，即一个效果需要多种原因的同时存在，而在析取设置中表现更好。然而，大多数``联合障碍''的演示依赖于有限证据的被动观察范式，学习者无法控制证据的生成。本文探讨当成年人通过主动探索获得自主权时，这种偏差是否仍然存在。利用修改过的``blicket探测器''任务，成人参与者自由进行干预，以识别在联合或析取规则结构下的因果对象。我们的研究表明，主动探索显著改善了成年人在联合因果推理方面的能力，尽管联合规则仍然需要进行更多测试以推断，而析取规则则较少。此外，我们还将人类的表现与在相同环境下多种大型语言模型的表现进行了比较。尽管一些最先进的模型在假设推理准确性上接近人类水平，但它们往往表现出效率较低的探索策略，以及类似的联合-析取表现差距。

View on arXiv Download PDF AI Translation

cs.CL / 101 / 2606.06467

You Only Index Once: Cross-Layer Sparse Attention with Shared Routing

一次性索引：共享路由的跨层稀疏注意力

Sun, Yutao, Zhang, Yanqi, Dong, Li, Wang, Jianyong, Wei, Furu

Abstract

Long-context inference in modern LLMs is increasingly constrained by decoding efficiency, especially in reasoning-heavy settings where models generate long intermediate chains of thought. Existing sparse attention methods often face a practical efficiency-quality trade-off. Structured block sparse methods typically provide stronger acceleration but incur noticeable quality loss, while token sparse methods are usually more accurate yet deliver limited end-to-end speedup because top-k routing over the full cache remains expensive. In this work, we propose cross-layer sparse attention (CLSA), which is built on top of KV-sharing architectures such as YOCO. The core idea is to share not only the KV cache across cross-decoder layers, but also the routing index. A single indexer computes token-level top-k selection once and reuses the resulting index across layers, thereby preserving the fine-grained selectivity of token sparse attention while amortizing the routing overhead. The resulting architecture improves all major inference bottlenecks jointly, including pre-filling, KV-cache storage, and long-context decoding. Experiments across short-context and long-context benchmarks show that CLSA is both accurate and efficient, achieving up to 7.6x decoding speedup and 17.1x overall throughput improvement at 128K context. These results suggest a more complete architectural solution for long-context LLMs that jointly advances model quality and inference efficiency.

Chinese Translation

现代大型语言模型（LLMs）中的长上下文推断越来越受到解码效率的限制，特别是在推理密集型的场景下，模型生成长的中间思考链。现有的稀疏注意力方法常常面临实际效率与质量的权衡。结构化块稀疏方法通常提供更强的加速，但会引入明显的质量损失，而基于 token 的稀疏方法通常更为准确，然而由于对完整缓存进行 top-k 路由仍然十分昂贵，因而在端到端加速上效果有限。在本研究中，我们提出了跨层稀疏注意力（Cross-Layer Sparse Attention，CLSA），该方法基于 KV 共享架构（如 YOCO）构建。其核心思想是不仅在跨解码器层之间共享 KV 缓存，还共享路由索引。一个单一的索引器在一个阶段内计算 token 级别的 top-k 选择，并在各层之间重用生成的索引，从而保持 token 稀疏注意力的细粒度选择性，同时摊薄路由开销。由此产生的架构在预填充、KV 缓存存储和长上下文解码等所有主要推断瓶颈上协同改善。通过在短上下文和长上下文基准测试中的实验表明，CLSA 既准确又高效，在 128K 上下文时实现了高达 7.6 倍的解码加速和 17.1 倍的总体吞吐量提升。这些结果表明了一种更完整的长上下文大型语言模型的架构解决方案，能够共同提升模型质量与推断效率。

View on arXiv Download PDF AI Translation

cs.CL / 102 / 2606.06474

Self-Augmenting Retrieval for Diffusion Language Models

自增强检索在扩散语言模型中的应用

Jünger, Paul, Lovelace, Justin, Zhao, Linxi, Go, Dongyoung, Weinberger, Kilian Q.

Abstract

Discrete diffusion language models generate text by iteratively denoising an entire response in parallel. At each step, they predict tentative tokens for every masked position, committing the confident predictions to the output and discarding the unconfident ones. We show that the discarded tokens are in fact a useful lookahead signal for retrieval-augmented generation: even low-confidence tokens often surface salient entities early in the denoising trajectory, enabling retrieval of stronger evidence before the output is finalized. We exploit this through Self-Augmenting Retrieval for Diffusion Language Models (SARDI), a dynamic RAG framework that uses these lookahead tokens to guide retrieval during denoising. SARDI is training-free, retriever-agnostic, and applicable to any reasoning-capable discrete diffusion language model. Across five multi-hop QA benchmarks, SARDI outperforms current training-free diffusion and autoregressive retrieval baselines at up to $8\times$ higher throughput.

Chinese Translation

离散扩散语言模型通过并行迭代去噪整个响应生成文本。在每一步，它们为每个被屏蔽的位置预测暂定的标记，将自信的预测纳入输出，并丢弃不自信的标记。我们展示了被丢弃的标记实际上是检索增强生成的一个有用前瞻信号：即使是低置信度的标记，通常也会在去噪轨迹的早期出现显著的实体，从而在输出最终确定之前检索更强有力的证据。我们通过自增强检索在扩散语言模型中的应用（Self-Augmenting Retrieval for Diffusion Language Models, SARDI）来利用这一点，这是一个动态的检索增强生成框架，利用这些前瞻标记在去噪过程中指导检索。SARDI 不依赖于训练，与检索器无关，并适用于任何具有推理能力的离散扩散语言模型。在五个多步问答基准测试中，SARDI 的表现超过了当前的无训练扩散和自回归检索基线，吞吐量提高了高达 $8 imes$。

View on arXiv Download PDF AI Translation

cs.CL / 103 / 2606.06481

Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection

面向多粒度AI文本检测的操作引导渐进式人机文本转换基准

Bsharat, Sondos Mahmoud, Liu, Jiacheng, Zhao, Xiaohan, Yao, Tianjun, Shang, Xinyi, Tang, Yi, Cui, Jiacheng, Elhagry, Ahmed, Khatib, Salwa K. Al, Li, Hao, Khan, Salman, Shen, Zhiqiang

Abstract

As AI writing assistants become increasingly integrated into real-world drafting and revision workflows, many documents are no longer purely human-written or AI-generated, but instead result from progressive human-AI co-editing. However, existing AI-text detection benchmarks largely focus on final outputs and provide limited understanding of how AI authorship signals emerge, accumulate, or disappear throughout the revision process. We introduce OpAI-Bench, an operation-guided benchmark for studying progressive human-to-AI text transformation across document, sentence, token, and span granularities. Starting from human-written documents, OpAI-Bench constructs nine sequentially revised versions for each sample under predefined AI coverage levels and five representative AI edit operations, covering four domains while preserving complete authorship provenance at multiple granularities. The benchmark supports comprehensive evaluation with 8 document-level detectors, 7 sentence-level detectors, and 2 fine-grained token/span-level detectors. Experiments reveal that AI-text detectability is governed not only by the proportion of AI-edited content, but also by edit operation, domain, and cumulative revision history. Interestingly, we notice that mixed-authorship intermediate versions are often harder to detect than both fully human and heavily AI-edited endpoints, exposing non-monotonic detection patterns missed by existing benchmarks. OpAI-Bench provides a controlled testbed for analyzing whether, when, and how AI-assisted writing becomes detectable under realistic progressive editing scenarios. Our code and benchmark are available at https://github.com/VILA-Lab/OpAI-Bench.

Chinese Translation

随着AI写作助手越来越多地融入现实世界的起草和修改工作流程，许多文档不再是纯粹的人类撰写或AI生成的，而是通过渐进的人机协同编辑而产生的。然而，现有的AI文本检测基准主要集中在最终输出上，对于AI作者信号如何在修订过程中出现、积累或消失的理解非常有限。本研究引入了OpAI-Bench，一个操作引导的基准，用于研究文档、句子、标记和范围粒度下的渐进式人机文本转换。从人类撰写的文档开始，OpAI-Bench在预定义的AI覆盖水平和五种典型的AI编辑操作下，为每个样本构建了九个连续修订版本，涵盖四个领域，同时在多个粒度上保持完整的作者来源。该基准支持通过8个文档级检测器、7个句子级检测器和2个细粒度标记/范围级检测器进行全面评估。实验表明，AI文本的可检测性不仅受AI编辑内容比例的影响，还受到编辑操作、领域和累积修订历史的制约。有趣的是，我们注意到，混合作者的中间版本往往比完全人类和重度AI编辑的最终版本更难检测，这暴露了现有基准未能捕捉的非单调检测模式。OpAI-Bench提供了一个受控的测试平台，用于分析在现实渐进编辑场景中，AI辅助写作是否、何时以及如何变得可检测。我们的代码和基准可在https://github.com/VILA-Lab/OpAI-Bench上获取。

View on arXiv Download PDF AI Translation

arXiv Papers

OLIVE: Online Low-Rank Incremental Learning for Efficient Adaptive Exoskeletons

A New Quaternion-Joint Cable-Driven Redundant Manipulator Configuration and its Control Through FABRIK and Residual Reinforcement Learning

Inverse Manipulation through Symbolic Planning and Residual Operator Learning

Efficient Computation of Distance Functions for Navigation Vector Fields in Lie Groups

VASO: Formally Verifiable Self-Evolving Skills for Physical AI Agents

MoDex: A Diffusion Policy for Sequential Multi-Object Dexterous Grasping

Learning from Demonstrations over Riemannian Manifolds using Neural ODEs: An Extended Abstract

Uncertainty-Aware Adaptive Sensor Fusion for Autonomous Navigation

FlowPRO: Reward-Free Reinforced Fine-Tuning of Flow-Matching VLAs via Proximalized Preference Optimization

Learning Contact Representation for Leg Odometry

Auditing Demonstration Curation Metrics: Action-Only Scorers Fail on the Structural Defects That Degrade Imitation Policies

Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning

Safe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulation

Preserving Full 6-DOF Actuation Under Abrupt Total Rotor Failures: Passive Fault-Tolerant Flight Control Using a Biaxial-Tilt Hexacopter

Dynamic Multi-Agent Pickup and Delivery in Robotic Cellular Warehousing Systems

Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation

DexFuture: Hierarchical Future-State Visuomotor Targeting for Bimanual Dexterous Tool Use

PiL-World: A Chunk-Wise World Model for VLA Policy-in-the-Loop Evaluation

Visuotactile and Explicitly Force-Controlled Robotic Ultrasound for Abdominal Volumetric Reconstruction

LadderMan: Learning Humanoid Perceptive Ladder Climbing

TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion

A Novel Method with Encoder-Decoder for Cross-Sensor Adaptation in Surface Shape Sensing with Sparse Strain Sensors

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios

Towards a Data Flywheel for Embodied Intelligence in Logistics

World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis

Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies

RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning

Gotta Grow Fast: Design and Benchmarking of a Tip Mount for High-Speed Vine Robots

Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning

L-SDPPO: Policy Optimization of Spiking Diffusion Policy for Intra-vehicular Robotic Manipulation

A Conversational Framework for Human-Robot Collaborative Manipulation with Distributed Generative AI models

3D Underwater Path Planning via Generative Flow Field Surrogates

Towards Realistic 3D Sonar Simulation

MotionDisco: Motion Discovery for Extreme Humanoid Loco-Manipulation

AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding

ActiveMimic: Egocentric Video Pretraining with Active Perception

TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation

CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving

MPCoT: Reward-Guided Multi-Path Latent Reasoning for Test-Time Scalable Vision-Language-Action

Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM

RadiusFPS: Efficient Farthest Point Sampling on CPUs and GPUs via Spherical Voxel Pruning

Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation

Attitude-Aided Linear Calibration of Triaxial Accelerometers

Meridian: Metric-Semantic Primitive Matching for Cross-View Geo-Localization Beyond Urban Environments

VOLT: Vision and Language Trajectory Segmentation for Faster-than-Demonstration Policies

Waypoints Matter: A Systematic Study for Sampling-Based Trajectory Planning

Ensuring Interaction Safety in Multitask Exoskeleton Control: A Simulation-Trained Variable Impedance Framework

RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation

Flow-based Policy Adaptation without Policy Updates

TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies

HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers

VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding

NIV: Neural Axis Variations for Variable Font Generation

Personal AI Agent for Camera Roll VQA

Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation

TopoPult-SSL: Gland-Mask-Free Cross-Device Meibomian Gland Segmentation via Self-Distilled Weak Clinical Priors

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation

Recovering Physically Plausible Human-Object Interactions from Monocular Videos

Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin

Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography

Deep Learning-assisted AMD Staging based on OCT and OCT Angiography

UniPixie: Unified and Probabilistic 3D Physics Learning via Flow Matching

Would you still call this Dax? Novel Visual References in VLMs and Humans

Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classification

Horse Eye Blink Detection and Classification for Equine Affective State Assessment

ORACLE-CT: Anatomy-Aware Support Pooling for CT Classification

Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning

Can We Predict The Human Preference For Text-to-Image Content Prior To Generation And Is It Even Useful To Do So?

LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval

Unpaired RGB-Thermal Gaussian-Splatting Using Visual Geometric Transformers

Robust Scene Transfer for PointGoal Navigation via Privileged Sensor Guided Contrastive Learning

BRepCLIP: Contrastive Multimodal Pretraining on BRep Primitives for CAD Understanding

Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models

Noise-Aware Visual Representation Learning for Medical Visual Question Answering

Dual Feature Decoupling for Fine-Grained OOD Detection

UltraVR: A Diagnostic Ultra-Resolution Image-VQA Benchmark for Evidence-Grounded Reasoning

BMCR: Adaptive Backbone Module Composition via Reinforcement Learning for Remote Sensing Object Detection

HDST-GNN: Heterogeneous Dynamic Spatiotemporal Graph Neural Networks for Multi-Object Tracking in UAV Aerial Imagery

What's Under the Skin? Estimating Swine Body Condition