arXiv Daily Digest

329

Papers

FLINT: Fast Lightweight Inference for Traversability

FLINT：快速轻量级可通行性推断

Bonilla, William, Boisvert, Maxime, Poissant, David-Alexandre, Meger, David, Petit, Louis

Abstract

Navigation in off-road conditions is challenging due to the lack of structure. There is no fixed vocabulary for what is traversable. The traversability depends on both the environment and the embodiment's dynamics. Neither of these two variables can be hand-labeled at scale. Thus, traversability has to be learned by the embodiment's own experience. Modern platforms tend to use multiple sensors to estimate traversability and navigate: RGBD cameras, lidar, radar, IMU, with computationally intensive platforms to run inference on neural networks. Against this trend, we propose FLINT, a lightweight traversability estimator: a 21.6M-parameter backbone, 38\times smaller than a comparable foundation-model backbone, that scores higher on held-out terrain probes and runs at 14.7 FPS on CPU alone using a RGB camera has the only sensor. Despite that gap in scale, FLINT produces a cheaper, more accurate costmap than a deployed foundation-model system (WildOS) on 23 of 24 replayed field logs. We compare different self-supervised learning signals and deploy the resulting models on a real platform in closed-loop field trials: the best self-supervised head reaches 99% autonomy over the route, outperforming a human-label-trained baseline deployed live on the same course. Our results show that heavy sensing and computing are not necessary for traversability estimation.

Chinese Translation

在越野条件下进行导航具有挑战性，因为缺乏结构。可通行性的定义没有固定的词汇，它依赖于环境和实体的动态特性。这两个变量都无法大规模手动标注。因此，可通行性必须通过实体自身的经验进行学习。现代平台倾向于使用多种传感器来估计可通行性并进行导航：RGBD摄像头、激光雷达、雷达、惯性测量单元（IMU），并依赖计算密集型平台在神经网络上进行推断。针对这一趋势，我们提出了FLINT，一个轻量级的可通行性估计器：一个具有2160万个参数的主干网络，其规模比可比的基础模型主干小38倍，在保留的地形探测中得分更高，并且在仅使用RGB摄像头作为唯一传感器的情况下，在CPU上以14.7帧每秒（FPS）运行。尽管在规模上存在差距，FLINT在24个重放的实地日志中，有23个的成本图比已部署的基础模型系统（WildOS）更便宜且更准确。我们比较了不同的自监督学习信号，并在闭环实地试验中将生成的模型部署到真实平台上：最佳的自监督头在路线上的自主性达到了99%，超越了在同一课程上实时部署的人类标注训练基线。我们的结果表明，重型传感和计算并不是可通行性估计的必要条件。

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cs.RO / 2 / 2609.26868

Backdoors in Learning-Based Industrial Robotic Arm Manipulation: An Empirical Security Study

基于学习的工业机器人臂操控中的后门：一项实证安全研究

Zhang, Zijian, Zeng, Zhen, Gu, Zhongshu, Pisharody, Sandeep

Abstract

Learning-based models (e.g., visuomotor and Vision-Language-Action (VLA)) are increasingly explored for industrial robotic manipulation, where model predictions are directly translated into physical actions. This tight coupling between model behavior and physical execution makes hidden security vulnerabilities particularly consequential. While backdoor attacks have been widely studied in conventional AI models, their effects on deployed learning-based robotic arm manipulation systems remain less understood: a backdoored robot can behave normally during benign operation while inducing attacker-specified behaviors only when specific triggers are present, posing potentially serious risks in physical environments. In this work, we present a preliminary empirical security study of backdoor attacks and defenses in learning-based robotic manipulation on two real commercial industrial robotic arms (FANUC and xArm). We investigate whether a backdoor can reliably induce semantically incorrect manipulation behaviors while remaining stealthy under nominal task execution. We then develop an online defense pipeline that detects and neutralizes triggers at runtime, and compare its effectiveness against an offline fine-tuning defense. Beyond defense effectiveness, we further evaluate the computational latency and execution overhead introduced by the defense pipeline to assess its suitability for high-throughput industrial operation.

Chinese Translation

基于学习的模型（例如，视觉运动和视觉-语言-动作（Vision-Language-Action, VLA））在工业机器人操控中越来越受到关注，其中模型预测被直接转化为物理动作。这种模型行为与物理执行之间的紧密耦合使得隐藏的安全漏洞尤为重要。尽管后门攻击在传统人工智能模型中得到了广泛研究，但其对已部署的基于学习的机器人臂操控系统的影响仍然不够清楚：一个带有后门的机器人在正常操作时可以表现正常，而仅在特定触发条件存在时才会诱发攻击者指定的行为，这在物理环境中可能带来严重风险。在本研究中，我们对基于学习的机器人操控中的后门攻击和防御进行了初步的实证安全研究，使用了两款真实的商业工业机器人臂（FANUC和xArm）。我们调查了后门是否能够在保持隐蔽的情况下可靠地诱发语义上不正确的操控行为，同时在正常任务执行下不被察觉。随后，我们开发了一种在线防御管道，能够在运行时检测并中和触发器，并将其有效性与离线微调防御进行了比较。除了防御效果外，我们还评估了防御管道引入的计算延迟和执行开销，以评估其在高吞吐量工业操作中的适用性。

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cs.RO / 3 / 2609.26872

MSK-Bench: Benchmarking Full-Body Musculoskeletal Motor Control Across Tasks, Control Paradigms, and Physiological Metrics

MSK-Bench：跨任务、控制范式和生理指标的全身肌肉骨骼运动控制基准测试

Ou, Mengtao, Zhang, Zongzheng, Xiao, Zhenghao, Pan, Yixuan, Zhuang, Ziwen, Zhao, Hang, Li, Hongyang, Sui, Yanan, Liu, Libin, Zhao, Hao

Abstract

Musculoskeletal (MSK) humanoids provide a physiologically grounded embodiment for studying full-body motor control, but their high-dimensional muscle actuation, delayed activation dynamics, and redundant muscle--tendon structures make learning substantially harder than torque-driven humanoid control. Existing MSK benchmarks remain fragmented across gait, prosthetics, dexterous hands, or challenge-specific tracks, leaving full-body muscle-actuated control insufficiently evaluated under standardized tasks, methods, and metrics. We introduce MSK-Bench, a benchmark of 22 full-body motor-control tasks organized into three progressively challenging categories: postural stabilization, common locomotor behaviors, and contact-rich environmental interaction. Under unified task protocols and robustness perturbations, MSK-Bench evaluates 5 representative control paradigms, including reward-based RL, agentic reward tuning, latent-action RL, imitation-prior control, and residual adaptation over imitation priors. Beyond task success and reward, MSK-Bench further reports robustness analysis and physiology-oriented diagnostics, including activation cost, joint smoothness, and EMG-envelope similarity. Our empirical study shows that embodiment-aware exploration and structured action representations improve task coverage in high-dimensional muscle spaces, imitation priors enhance reference-compatible stabilization and locomotion but degrade under contact-rich terrain mismatch, and residual adaptation can recover successful behaviors when fixed references fail. We further find that improved task success does not necessarily imply improved physiological agreement, highlighting the importance of evaluating task performance, robustness, and physiological behavior jointly. MSK-Bench provides a task--method--metric testbed for full-body muscle-actuated humanoid control.

Chinese Translation

肌肉骨骼（MSK）类人机器人为研究全身运动控制提供了生理基础的体现，但其高维肌肉驱动、延迟激活动态以及冗余的肌肉-肌腱结构使得学习比基于扭矩的类人控制更加困难。现有的MSK基准测试在步态、假肢、灵巧手或特定挑战轨道上仍然支离破碎，导致全身肌肉驱动控制在标准化任务、方法和指标下评估不足。我们提出了MSK-Bench，这是一个包含22个全身运动控制任务的基准，分为三个逐步增加难度的类别：姿势稳定、常见运动行为和丰富接触的环境交互。在统一的任务协议和稳健性扰动下，MSK-Bench评估了5种代表性的控制范式，包括基于奖励的强化学习（RL）、代理奖励调节、潜在动作RL、模仿优先控制和残余适应。除了任务成功率和奖励，MSK-Bench还报告了稳健性分析和生理导向的诊断，包括激活成本、关节平滑度和肌电图包络相似性。我们的实证研究表明，关注体现的探索和结构化的动作表示提高了高维肌肉空间中的任务覆盖率，模仿优先增强了参考兼容的稳定性和运动，但在接触丰富的地形不匹配下表现下降，而残余适应可以在固定参考失败时恢复成功行为。我们进一步发现，任务成功的提高并不一定意味着生理一致性的改善，这突显了联合评估任务表现、稳健性和生理行为的重要性。MSK-Bench为全身肌肉驱动的类人控制提供了一个任务-方法-指标的测试平台。

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cs.RO / 4 / 2609.26919

Bend the Clock: Predicting Ahead to Beat Latency in Event-Based Object Detection

弯曲时钟：提前预测以克服事件驱动物体检测中的延迟

Sen, Biswadeep, Cottereau, Benoit R., Cuperlier, Nicolas, Sim, Terence

Abstract

Event cameras promise low-latency perception for high-speed robotic systems, where even short delays can render detections stale by the time they inform downstream robotic decisions. Yet modern event detectors still require tens of milliseconds of computation before their predictions become available. Conventional evaluation ignores this delay by comparing predictions with annotations at the observation timestamp, even though the scene may have changed by the time those predictions are produced. We study this observation-availability mismatch in event-based multi-object detection and show that state-of-the-art event detectors degrade substantially when evaluated at prediction availability rather than observation time. To address this, we introduce ChronoFuse, a causal availability-time detector that predicts object states for when its output becomes available rather than for when its input was observed. ChronoFuse performs causal cross-time fusion over a multi-scale feature hierarchy, combining current representations with cached temporal features to expose short-term temporal cues without using future observations. The fusion pathway is lightweight, adding only 0.17 million parameters and 0.84 ms of mean end-to-end latency overhead. ChronoFuse recovers 71% of the accuracy lost to latency on 1Mpx driving data and 90.8% under rapid drone motion on FRED, nearly restoring zero-delay performance. Under the extreme motion of EV-Flying, ChronoFuse reaches 20.95 sAP, compared with 2.25 for the strongest standard event detector (9.3x gain). These results show that predicting ahead can be critical for robots operating in fast-changing scenes, including autonomous driving, agile flight, and robotic interception.

Chinese Translation

事件相机为高速机器人系统提供了低延迟感知，其中即使是短暂的延迟也可能导致检测结果在通知下游机器人决策时变得过时。然而，现代事件检测器在其预测可用之前仍需数十毫秒的计算。传统评估忽略了这一延迟，通过在观察时间戳处将预测与标注进行比较，尽管在这些预测生成时场景可能已经发生变化。我们研究了事件驱动多物体检测中的观察可用性不匹配，并表明当在预测可用性而非观察时间进行评估时，最先进的事件检测器的性能显著下降。为了解决这个问题，我们引入了ChronoFuse，一种因果可用性时间检测器，它预测对象状态时基于其输出可用的时间，而不是输入被观察的时间。ChronoFuse在多尺度特征层次上执行因果跨时间融合，将当前表示与缓存的时间特征相结合，以在不使用未来观察的情况下揭示短期时间线索。融合路径轻量，仅增加0.17百万参数和0.84毫秒的平均端到端延迟开销。ChronoFuse在1Mpx驾驶数据上恢复了71%的因延迟而损失的准确性，在FRED上快速无人机运动下恢复了90.8%的准确性，几乎恢复了零延迟性能。在EV-Flying的极端运动下，ChronoFuse达到了20.95的sAP，而最强标准事件检测器仅为2.25（增益为9.3倍）。这些结果表明，提前预测对于在快速变化场景中操作的机器人至关重要，包括自动驾驶、灵活飞行和机器人拦截。

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cs.RO / 5 / 2609.26989

Spiderbot: An Open-Source Energy-Efficient Hexapod with Passive Gravity Compensation

Spiderbot：一种开源的能效高的六足机器人，具备被动重力补偿

Sharma, Ritwik, Shah, Vimarsh, Agrawal, Saransh

Abstract

Hexapod robots can achieve static stability with fewer actuated joints than bipeds or quadrupeds, yet many platforms still use 3-DOF legs, increasing weight and continuous torque requirement with limited gain in locomotion capability on flat, inclined and moderately rough terrains. We release Spiderbot, an open-source hexapod that uses a 4-bar linkage with a passive spring to mechanically support body weight, with a 2-DOF per-leg design that substantially reduces energy consumption. This mechanism substantially offloads gravitational torque during standing stance consuming only 1.5W (reduction of over 90\% over the unsprung version and up to 96\% over other similar hexapods). The passive spring compensation extends to payloads of up to 3.25kg with no additional torque requirements. The platform enables long-duration deployments on a modest battery budget and costs under \$400, making it suitable for large-scale multi-agent experiments. We validate the locomotion capabilities of the platform with an RL policy trained in mjlab, including successful sim-to-real transfer, despite the complexity of the mechanism. The platform is evaluated on flat and rough terrains, slope up to $15^\circ$ and step obstacles. We release all the CAD files, assembling instructions, and full training and deployment code along with the model checkpoints at https://erc-bpgc.github.io/SpiderBot/.

Chinese Translation

六足机器人相比于双足或四足机器人可以在更少的驱动关节下实现静态稳定，但许多平台仍然使用3自由度的腿部设计，这增加了重量和持续扭矩需求，而在平坦、倾斜和适度粗糙的地形上运动能力的提升有限。我们发布了Spiderbot，这是一款开源的六足机器人，采用4杆连杆机构和被动弹簧来机械支撑机体重量，腿部设计为每条腿2自由度，显著降低了能耗。该机制在站立姿态下显著减轻了重力扭矩，仅消耗1.5W（相比于未装弹簧版本减少超过90 ext{%}，与其他类似六足机器人相比减少高达96 ext{%}）。被动弹簧补偿可支持高达3.25kg的载荷而无需额外的扭矩要求。该平台在适度的电池预算下支持长时间部署，成本低于400美元，适合大规模多智能体实验。我们通过在mjlab中训练的强化学习策略验证了该平台的运动能力，包括成功的模拟到现实转移，尽管其机制复杂。该平台在平坦和粗糙地形、最大15度的坡度和台阶障碍上进行了评估。我们在https://erc-bpgc.github.io/SpiderBot/上发布了所有CAD文件、组装说明以及完整的训练和部署代码及模型检查点。

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cs.RO / 6 / 2609.27001

Humanoid Locomotion with a Fly-Inspired Recurrent Controller

基于飞行器灵感的类人运动控制器

Guan, Isabel, Zhao, Yuntian, Zhang, Dingyuan, Lyu, Shipeng

Abstract

We investigate humanoid locomotion with a fly-inspired recurrent controller and identify the pathways supporting its deployed behavior. The controller couples 3,609 continuous neural states to a simulated Unitree G1 through body-observation projections, a motor-neuron-labelled readout, and joint servos. We formulate this neural-body feedback system and evaluate a fixed checkpoint across seven terrain instances, three speeds, and three initial yaw offsets. It completes 61/63 conditions under a survival-and-forward-progress criterion; a privileged reference completes 62/63. At nominal yaw, resetting the recurrent motor state before every policy call changes success from 19/21 to 0/21. Conversely, depth and upstream-state substitutions at 252 recorded states leave actions unchanged, with zero measured descending output throughout the intact rollouts. Recorded trajectories and state-matched images connect these findings to sustained movement, lateral drift, and termination events. The study characterizes an embodied recurrent control system whose tested locomotion is supported by direct body-and-command input and carried motor state, providing a concrete basis for subsequent comparisons of circuit structure and control resources.

Chinese Translation

我们研究了一种基于飞行器灵感的类人运动控制器，并识别出支持其行为表现的路径。该控制器通过身体观察投影、标记运动神经元的输出和关节伺服，将3,609个连续神经状态与模拟的Unitree G1相耦合。我们构建了这一神经-身体反馈系统，并在七种地形实例、三种速度和三种初始偏航角下评估了一个固定的检查点。该系统在生存与前进进度标准下完成了61/63个条件；一个特权参考完成了62/63。在标准偏航角下，在每次策略调用之前重置递归运动状态使得成功率从19/21降至0/21。相反，在252个记录状态下进行深度和上游状态替换时，动作保持不变，整个完整回放过程中测得的下降输出为零。记录的轨迹和状态匹配图像将这些发现与持续运动、侧向漂移和终止事件联系起来。本研究描述了一个具身递归控制系统，其测试的运动由直接的身体和命令输入以及携带的运动状态所支持，为后续电路结构和控制资源的比较提供了具体基础。

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cs.RO / 7 / 2609.27003

Learning Expressive Humanoid Locomotion from Monocular Runway Videos for Robot Fashion Shows

从单目走秀视频中学习富有表现力的人形机器人步态用于机器人时装秀

Kolesnichenko, Kyrylo, Cardenas, Irvin Steve, Kim, Jong-Hoon

Abstract

Runway walking requires coordinated control of posture, stride, foot placement, and whole-body motion to effectively present clothing and convey a distinctive style. However, humanoid robots used in fashion shows typically rely on locomotion policies optimized primarily for stability and walking speed, limiting their ability to reproduce expressive, human-like runway motions. In this work, we present an end-to-end framework that transforms monocular runway videos into deployable humanoid locomotion policies through motion recovery, robot retargeting, motion correction, policy training, simulation-based evaluation, and physical deployment. We evaluate the proposed framework on the Booster K1 humanoid robot using runway-style catwalk motions. The learned policy completed every physical trial without falling, while reproducing the characteristic narrow foot placement and coordinated movement of the legs, torso, and arms. The results demonstrate that our proposed training framework enables the Booster K1 to perform stable and expressive catwalk motions, highlighting its potential for humanoid robotic applications in fashion shows and other performance-oriented scenarios.

Chinese Translation

走秀步态需要对姿势、步幅、足部位置和全身运动进行协调控制，以有效展示服装并传达独特风格。然而，时装秀中使用的人形机器人通常依赖于主要针对稳定性和行走速度优化的运动策略，这限制了它们再现富有表现力的人类走秀动作的能力。在本研究中，我们提出了一个端到端的框架，通过运动恢复、机器人重定向、运动修正、策略训练、基于仿真的评估和物理部署，将单目走秀视频转化为可部署的人形机器人运动策略。我们在Booster K1人形机器人上评估了该框架，使用走秀风格的猫步动作。学习到的策略在每次物理试验中均未跌倒，同时再现了特征性的窄足位置和腿、躯干及手臂的协调运动。结果表明，我们提出的训练框架使Booster K1能够执行稳定且富有表现力的猫步动作，突显了其在时装秀及其他以表演为导向场景中的人形机器人应用潜力。

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cs.RO / 8 / 2609.27005

MultiPush: Learning to Rearrange with Teams of Car-Like Pushers

MultiPush：利用类汽车推送器团队进行重新排列的学习

Ahn, Jeeho, Mavrogiannis, Christoforos

Abstract

We focus on the problem of rearranging multiple objects within a constrained workspace via pushing using a team of car-like robots. While the use of multiple robots offers the potential for more efficient execution, the need for conflict resolution and the kinematic constraints arising from physics, robot design, and the workspace boundary make this problem especially challenging. Our key insight is that by exploiting the structure introduced by the car-like kinematics of the domain, we could relax the problem into an ordered assignment of Dubins curves to robots. To this end, we introduce MultiPush, a reinforcement-learning based framework that jointly determines an efficient schedule of pushing tasks and their allocation to available robots by leveraging a constraint-aware traversability graph. Across extensive simulated trials with up to 14 objects and teams of two to four robots, MultiPush reduces the makespan by up to 16% compared to the baselines while requiring up to 2.9 times faster planning time. We demonstrate MultiPush on a real-world scenario involving the rearrangement of 12 objects by two and three robots (1/10-scale racecars) in a constrained space.

Chinese Translation

我们关注于通过一组类汽车机器人在受限工作空间内重新排列多个物体的问题。虽然使用多个机器人可以提高执行效率，但由于冲突解决的需求以及由物理、机器人设计和工作空间边界引起的运动学约束，使得这一问题特别具有挑战性。我们的关键见解是，通过利用领域中类汽车运动学所引入的结构，我们可以将问题简化为对机器人的有序分配Dubins曲线。为此，我们提出了MultiPush，一个基于强化学习的框架，通过利用约束感知的可遍历图，联合确定推送任务的高效调度及其分配给可用机器人的方式。在对多达14个物体和两到四个机器人的团队进行的广泛模拟试验中，MultiPush相比基线方法将完工时间缩短了最多16%，同时规划时间快了最多2.9倍。我们在一个真实场景中展示了MultiPush，该场景涉及在受限空间内由两辆和三辆机器人（1/10比例的赛车）重新排列12个物体。

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cs.RO / 9 / 2609.27006

Laser-Tracker-Assisted Camera-to-Robot Calibration for Mobile Robots

激光跟踪器辅助的移动机器人相机与机器人标定

Rudolph, Jan A., Kandemir, Öykü, Ulrich, Markus

Abstract

We present a laser-tracker-assisted hand-eye calibration method for camera-equipped mobile robots. The method combines laser-tracker-based 3D metrology with camera-based 2D observations. Building on our previous laser-tracker-assisted camera-to-robot calibration method for ground-observing mobile robots, we present a generalized formulation for calibrating the camera pose in the coordinate system of tracker-localized mobile robots. The new approach relaxes assumptions of our previous method on robot and camera configuration by chaining multiple calibration targets resulting in a more general approach supporting various camera-equipped mobile robot systems.

Chinese Translation

我们提出了一种激光跟踪器辅助的手眼标定方法，适用于配备相机的移动机器人。该方法结合了基于激光跟踪器的三维计量与基于相机的二维观测。在我们之前针对地面观察移动机器人的激光跟踪器辅助相机与机器人标定方法的基础上，我们提出了一种通用的公式，用于在跟踪器定位的移动机器人坐标系中标定相机姿态。新方法通过连接多个标定目标，放宽了我们之前方法对机器人和相机配置的假设，从而支持各种配备相机的移动机器人系统，提供了更为通用的解决方案。

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cs.RO / 10 / 2609.27068

HiRE: Hindsight Reward Editing for Policy Finetuning

HiRE：基于回顾奖励编辑的策略微调

Niu, Haoyi, Han, Zhengtao, Ji, Yufeng, Li, Zhongyu, Sreenath, Koushil

Abstract

Pre-trained robot policies always require finetuning to adapt to specific environments. Reinforcement Learning (RL) offers high performance potential because it improves action optimality rather than simply mimicking data. However, such potential depends heavily on reward quality. Sparse rewards lack process feedback, human-designed rewards are costly and biased, and semantic rewards from foundation representations are often not control-centric. We propose Hindsight Reward Editing (HiRE), a training-free framework to break this reward bottleneck. HiRE bridges the broad knowledge of foundation representation models with physical control awareness, by contrasting successful and failed trajectories in hindsight. It calibrates foundation representation models by identifying "trap states" that are predicted as high-rewarding states yet eventually result in failure, and vice versa. HiRE explicitly penalizes these traps while boosting rewards for critical successful states. This approach can be flexibly compatible with any foundation representations and RL algorithms. Experiments show that HiRE consistently outperforms other reward recipes by delivering dense, control-aware feedback that prevents value function collapse and reward hacking, thereby achieving superior sample efficiency, stable policy updates, and higher performance ceilings, e.g., at least 3x performance of the base policies. Qualitative results are at https://hire-project.github.io .

Chinese Translation

预训练的机器人策略通常需要微调以适应特定环境。强化学习（Reinforcement Learning, RL）提供了高性能的潜力，因为它提高了行动的最优性，而不仅仅是模仿数据。然而，这种潜力在很大程度上依赖于奖励的质量。稀疏奖励缺乏过程反馈，人为设计的奖励成本高且存在偏差，而来自基础表示的语义奖励通常不是以控制为中心。我们提出了回顾奖励编辑（Hindsight Reward Editing, HiRE），这是一个无需训练的框架，用于打破这一奖励瓶颈。HiRE通过对比成功和失败的轨迹，连接了基础表示模型的广泛知识与物理控制意识。它通过识别被预测为高奖励状态但最终导致失败的“陷阱状态”，以及反之的状态，来校准基础表示模型。HiRE明确惩罚这些陷阱，同时提升关键成功状态的奖励。这种方法可以灵活地与任何基础表示和RL算法兼容。实验表明，HiRE通过提供密集的、以控制为中心的反馈，防止价值函数崩溃和奖励操控，始终优于其他奖励策略，从而实现了更高的样本效率、稳定的策略更新和更高的性能上限，例如，至少是基础策略的3倍性能。定性结果见 https://hire-project.github.io 。

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cs.RO / 11 / 2609.27070

The Gaussian Is Enough: Flow-Matching Priors Do Not Help When Fine-Tuning Large Behavior Models

高斯分布已足够：流匹配先验在微调大型行为模型时并无帮助

Xu, Chen, Shah, Rishi, Kress-Gazit, Hadas, Nishimura, Haruki, Itkina, Masha

Abstract

Modern robot imitation learning increasingly relies on generative policies based on diffusion or flow-matching models, which generate actions by transforming samples from a prior distribution. A key question is whether the choice of prior matters. Replacing the standard Gaussian with a closer-to-target, non-Gaussian prior has been shown to substantially improve performance when training from scratch. A natural next step is to ask whether these gains transfer to fine-tuning pretrained Large Behavior Models (LBMs) such as LBM 1.0, $\pi_{0.5}$, and GR00T~N1.5, where one might expect even larger gains. Surprisingly, we find that this is not the case, except possibly at very low fine-tuning data fractions. Across over 100K simulation rollouts spanning all three aforementioned LBMs on 40+ tasks in two simulation platforms, and 1250 hardware rollouts on five bimanual manipulation tasks, non-Gaussian priors that are demonstrably closer to the target yield statistically indistinguishable or worse fine-tuning performance than a standard Gaussian prior. Diagnostic analyses suggest why: fine-tuned imitation learning policies converge to similar action predictions across priors, despite their fine-tuned encoder embeddings diverging substantially from the pretrained embeddings and each other. A learning-rate ablation further confirms that encoder training is the dominant factor in fine-tuning performance, substantially outweighing the effect of prior choice. We conclude with concrete directions for future research on when and why learned priors might still matter in fine-tuning. Project page: https://cxu-tri.github.io/non_gaussian_FT/

Chinese Translation

现代机器人模仿学习越来越依赖于基于扩散或流匹配模型的生成策略，这些策略通过转换来自先验分布的样本来生成动作。一个关键问题是先验的选择是否重要。用更接近目标的非高斯先验替换标准高斯分布已被证明在从零开始训练时显著提高性能。一个自然的下一步是询问这些增益是否能够转移到微调预训练的大型行为模型（Large Behavior Models, LBMs）上，例如 LBM 1.0、$ ext{π}_{0.5}$ 和 GR00T~N1.5，在这些模型中人们可能期望获得更大的增益。令人惊讶的是，我们发现情况并非如此，除非在非常低的微调数据比例下。在超过 100,000 次模拟回合中，涵盖了上述三种 LBM 在 40 多个任务上的表现，以及在五个双手操作任务上的 1250 次硬件回合，结果表明，明显更接近目标的非高斯先验在微调性能上与标准高斯先验的表现统计上无显著差异，甚至更差。诊断分析表明原因：微调后的模仿学习策略在不同先验下收敛到类似的动作预测，尽管它们的微调编码器嵌入与预训练嵌入及彼此之间的差异显著。学习率消融实验进一步确认，编码器训练是微调性能的主导因素，远远超过先验选择的影响。我们最后提出了未来研究的具体方向，以探讨何时以及为何学习的先验在微调中仍可能重要。项目页面：https://cxu-tri.github.io/non_gaussian_FT/

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cs.RO / 12 / 2609.27077

Fast Direction-Conditioned Reachability for Motion Prediction Under Model Uncertainty

基于方向条件的快速可达性分析：在模型不确定性下的运动预测

Das, Hrishav, Ornik, Melkior

Abstract

To avoid collisions, a robot must repeatedly predict where nearby agents may move, usually with an imperfect model of their dynamics. Reachable sets provide such predictions, but computing them when the system matrices themselves are uncertain can become computationally expensive and conservative for frequent replanning. Moreover, a planner often needs to know only how far an agent can move in one particular direction, for example toward the robot, rather than the complete reachable set. We propose a direction-conditioned reachability method for linear systems with uncertain state and input matrices. Given a query direction $d$, the method selects one admissible model $(A^\star,B^\star)$ whose reachable set extends nearly as far along $d$ as the reachable set of the entire uncertain model family, and then computes the reachable set of only this model with a standard reachability solver. On an uncertain linearized bicycle model, the complete selection-and-computation pipeline is about three times faster than computing the reachable set of the full uncertain family in the CORA toolbox, while its extent along $d$ is within $5\%$ of the full family's in the reported directions. We also use the method in a closed-loop multi-vehicle simulation in which the robot queries, at each replanning step, how far each nearby vehicle can move toward it, and replans to avoid the resulting sets.

Chinese Translation

为了避免碰撞，机器人必须反复预测附近代理可能的移动位置，通常是在其动态模型不完美的情况下。可达集提供了这样的预测，但在系统矩阵本身不确定时计算这些可达集可能变得计算密集且保守，尤其是在频繁重新规划的情况下。此外，规划者通常只需要知道代理在某一特定方向上可以移动多远，例如朝向机器人，而不是完整的可达集。我们提出了一种针对具有不确定状态和输入矩阵的线性系统的方向条件可达性方法。给定查询方向 $d$，该方法选择一个可接受的模型 $(A^ullet,B^ullet)$，其可达集在方向 $d$ 上的扩展几乎与整个不确定模型族的可达集相当，然后仅使用标准可达性求解器计算该模型的可达集。在一个不确定的线性化自行车模型上，整个选择和计算流程的速度约为在CORA工具箱中计算完整不确定模型族的可达集的三倍，同时在报告的方向上，其在 $d$ 方向上的扩展在 $5\%$ 以内。我们还在一个闭环多车辆仿真中使用该方法，在每次重新规划步骤中，机器人查询每个附近车辆可以朝它移动多远，并重新规划以避免产生的可达集。

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cs.RO / 13 / 2609.27088

Water Surface Swimming in a Centipede and its Robophysical ModeL

蜈蚣的水面游泳及其机器人物理模型

Xu, Zhaochen J., Aydin, Delfin, Mustafa, Abdullah, Levin, Margarita B., Lin, Jianfeng, Wang, Tianyu, Goldman, Daniel I.

Abstract

Elongate multi-legged robots use coordinated body waves and distributed legs to move through cluttered terrestrial environments. However, as housing actuators for independent leg control can require bulky body segments, their non-streamlined body and limb structure makes it difficult to achieve swimming capability comparable to their terrestrial locomotor performance. At the water surface, we found that the multi-legged robots we tested unexpectedly moved backward: their body waves traveled in the same direction as their displacement, i.e., swimming with a direct wave. We found similar behavior in the centipede \textit{Lithobius forficatus}, which swims with a direct body wave and periodic leg movement. To study how distributed legs contribute to direct-wave swimming, we analyze animal kinematics and develop a multi-legged robophysical model that allows independent variation of leg morphology and stiffness, body-wave direction, and leg coordination. Robophysical experiments show that direct body waves produce consistent forward motion under the tested conditions and that swimming performance depends on body--leg coordination. Additionally, directionally compliant legs increase displacement from approximately 0.08 to 0.21 body lengths per cycle relative to rigid legs under matched anti-phase actuation. These findings clarify how distributed appendages contribute to surface swimming and establish gait and morphology design principles for extending multi-legged field robots from terrestrial locomotion into aquatic environments.

Chinese Translation

细长的多足机器人通过协调的身体波动和分布式腿部在复杂的陆地环境中移动。然而，由于独立腿部控制的驱动器需要庞大的机身部分，其非流线型的身体和肢体结构使得它们难以实现与陆地运动性能相当的游泳能力。在水面上，我们发现测试的多足机器人意外地向后移动：它们的身体波动与位移方向相同，即以直接波的方式游泳。我们在蜈蚣（ extit{Lithobius forficatus}）中观察到了类似的行为，它通过直接的身体波和周期性的腿部运动进行游泳。为了研究分布式腿部如何促进直接波游泳，我们分析了动物的运动学，并开发了一种多足机器人物理模型，该模型允许独立变化腿部形态和刚度、身体波方向以及腿部协调性。机器人物理实验表明，直接的身体波在测试条件下产生了一致的前向运动，游泳性能依赖于身体与腿部的协调。此外，方向性顺应的腿部在匹配的反相驱动下，将位移从大约0.08增加到0.21个身体长度每周期。这些发现阐明了分布式附肢如何促进水面游泳，并为将多足地面机器人从陆地运动扩展到水域环境建立了步态和形态设计原则。

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cs.RO / 14 / 2609.27095

Intelligence Across Embodiments

跨体现的智能

Ai, Bo, Christensen, Henrik I., Su, Hao

Abstract

Robotic embodiment encompasses the sensing, kinematics, dynamics, geometry, actuation, and control through which an agent physically interacts with the world. These properties vary across robots and change over time. We argue that general embodied intelligence requires learning that accumulates across these differences. Prevailing methods that engineer correspondences to bridge embodiment differences offer immediate practical gains, but their assumptions limit the scope of transfer in the long run. Instead, a more general approach should discover representations that support transfer to a larger range of embodiments as experience grows. We propose embodiment diversity as a promising axis of scaling, and identify broad learned priors as a complementary ingredient. We call for evaluations that better characterize embodiment gaps and transfer performance. More broadly, cross-embodiment learning connects the practical challenge of learning from heterogeneous robot experience with a broader scientific pursuit inspired by nature - physical intelligence that adapts and co-evolves with its embodiments to gain agency over its behavior and physical forms.

Chinese Translation

机器人体现包括感知、运动学、动力学、几何、驱动和控制等方面，通过这些方面，代理与世界进行物理交互。这些属性在不同的机器人之间存在差异，并且随着时间的推移而变化。我们认为，通用的体现智能需要在这些差异中积累学习。当前工程化对应关系以弥合体现差异的方法虽然提供了即时的实用收益，但其假设在长期内限制了转移的范围。相反，更通用的方法应该发现支持转移到更广泛体现的表示，随着经验的增长而发展。我们提出体现多样性作为一个有前景的扩展轴，并将广泛学习的先验视为一个互补因素。我们呼吁进行更好的评估，以更好地表征体现差距和转移性能。更广泛地说，跨体现学习将从异质机器人经验中学习的实际挑战与受自然启发的更广泛科学追求联系起来——物理智能，它能够适应并与其体现共同进化，从而获得对其行为和物理形式的自主权。

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cs.RO / 15 / 2609.27099

Design and Modeling of a Single-Port Three-Arm Robotic Tool for Minimally Invasive Neurosurgery

用于微创神经外科的单端口三臂机器人工具的设计与建模

Dana, Nazia H., Gallage, Harith S., Auta, Ismail A., Yuvaraj, Dhanvi, Qi, Ronghuai

Abstract

Surgical robots require highly dexterous and compact robotic systems capable of operating effectively within confined anatomical spaces. However, due to limited access provided by a single incision, the miniaturization and maneuverability of these robots still need to be improved. In this paper, we propose the design and modeling of a single-port three-arm robotic tool containing one major cannula (7.14 mm outer diameter (OD)) and three steerable minor cannulas (1.93 mm OD). By integrating the proposed 12 degrees-of-freedom (DoFs) steerable robotic tool with a 7-DoF robotic arm, this robotic system can potentially achieve multi-arm manipulation capability. We present the design of the steerable robotic tool consisting of tendon-driven joints controlled by a compact actuation system, derive the kinematic model, and validate both the static and kinematic models through experiments. The performance is evaluated with the root mean square error (RMSE) and mean absolute error (MAE) computed between the experimental data and the kinematic model.

Chinese Translation

外科手术机器人需要具备高度灵活和紧凑的机器人系统，能够在有限的解剖空间内有效操作。然而，由于单个切口提供的有限进入方式，这些机器人的小型化和机动性仍需改进。本文提出了一种单端口三臂机器人工具的设计与建模，该工具包含一个主要套管（外径7.14毫米）和三个可转向的次要套管（外径1.93毫米）。通过将所提出的具有12个自由度（DoFs）的可转向机器人工具与一个7自由度的机器人手臂相结合，该机器人系统有望实现多臂操作能力。我们展示了由紧凑驱动系统控制的腱驱动关节构成的可转向机器人工具的设计，推导了运动学模型，并通过实验验证了静态和运动学模型。通过计算实验数据与运动学模型之间的均方根误差（RMSE）和平均绝对误差（MAE）来评估性能。

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cs.RO / 16 / 2609.27145

Planning Trajectories that Bounce: Reflection Classes for Collision-Tolerant Robots

反弹轨迹规划：用于抗碰撞机器人的反射类

Koley, Subhadeep, Bhattacharya, Subhrajit, Saldaña, David

Abstract

Robot navigation methods tend to avoid contact, and consequently search for collision-free trajectories. For robots with high inertia and limited maneuverability, however, avoiding contact can require substantial steering effort and time, even when interactions with surrounding surfaces could be safely exploited. In this paper, we develop a planning method that deliberately uses controlled wall reflections to generate trajectories that can be easier and more efficient to execute than purely collision-free motion. We consider planar navigation in environments where a mobile robot is permitted to bounce off surrounding surfaces. To represent the resulting alternatives, we construct a reflection-augmented state graph in which paths are partitioned into distinct classes according to the sequence of walls used for reflection. This representation enables systematic enumeration of reflection strategies and identification of the lowest-cost path within each class. We show that, although a reflecting path cannot be shorter than the shortest collision-free path, it can reduce execution time and actuation effort by replacing costly changes in heading with controlled environmental interactions. The planned trajectories are executed using a contact-aware sampling-based controller with the robot's full dynamics. In our experiments, we demonstrate that in our simulated test scenario, the best reflecting class can reduce time and control effort. Our results show that controlled contact can provide dynamically advantageous navigation strategies that are excluded by conventional collision-avoidance formulations.

Chinese Translation

机器人导航方法通常倾向于避免接触，因此寻求无碰撞的轨迹。然而，对于具有高惯性和有限机动性的机器人来说，避免接触可能需要大量的转向努力和时间，即使与周围表面的交互可以安全利用。在本文中，我们开发了一种规划方法，故意利用受控的墙面反射来生成比纯粹无碰撞运动更容易和更高效执行的轨迹。我们考虑在允许移动机器人从周围表面反弹的环境中进行平面导航。为了表示结果的替代方案，我们构建了一个增强反射的状态图，其中路径根据用于反射的墙的序列被划分为不同的类。这种表示方法使得能够系统地枚举反射策略，并识别每个类中的最低成本路径。我们展示了，尽管反射路径不能比最短的无碰撞路径更短，但它可以通过用受控的环境交互替代代价高昂的航向变化来减少执行时间和驱动努力。规划的轨迹使用一种考虑接触的基于采样的控制器在机器人的完整动力学下执行。在我们的实验中，我们展示了在模拟测试场景中，最佳反射类可以减少时间和控制努力。我们的结果表明，受控接触可以提供动态上有利的导航策略，而这些策略在传统的避免碰撞的公式中被排除。

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cs.RO / 17 / 2609.27154

HINT-Blimp: Human INTent Inference from Multimodal Cues for Robotic Blimps

HINT-Blimp：基于多模态线索的人类意图推断用于机器人飞艇

Koley, Subhadeep, Greenberg, Benjamin, Shao, Yifei Simon, Aceros, Juan, Figueroa, Nadia, Saldaña, David

Abstract

In human-robot interaction, traditional interfaces such as joysticks and handheld tablets introduce latency into navigation tasks and require the operator's explicit attention on the device, instead of the robot. We propose a new human-robot interaction framework in which a human communicates intent directly through sparse multimodal signals such as physical pushes and spoken commands. Human intent is represented as a parameterized linear dynamical system (LDS) that encodes the desired goal and motion behavior. The robot estimates this intent (parameters) online using a particle filter, where each particle represents a candidate LDS hypothesis and is reweighted online as new information becomes available. We validate this framework on a robotic blimp, whose inherent compliance and collision tolerance make it well-suited for repeated physical interaction. Experiments with multiple participants across 300 trials show that combining pushes and voice commands identifies the intended goal in 86% of trials within at most five interactions, with most trials resolved in two. The inferred dynamical systems can also produce curved trajectories that avoid obstacles known only to the human.

Chinese Translation

在人与机器人交互中，传统接口如操纵杆和手持平板会在导航任务中引入延迟，并要求操作员将注意力集中在设备上，而不是机器人上。我们提出了一种新的人与机器人交互框架，其中人类通过稀疏的多模态信号（如物理推力和口头指令）直接传达意图。人类意图被表示为一个参数化的线性动态系统（LDS），该系统编码了期望的目标和运动行为。机器人使用粒子滤波器在线估计这一意图（参数），其中每个粒子代表一个候选的LDS假设，并在新信息可用时在线重新加权。我们在一个机器人飞艇上验证了这一框架，该飞艇固有的柔性和碰撞容忍性使其非常适合进行重复的物理交互。对300次实验中多个参与者的实验表明，结合推力和语音命令可以在最多五次交互内识别出86%的意图目标，大多数实验在两次交互内解决。推断出的动态系统还可以生成避免人类仅知障碍物的曲线路径。

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cs.RO / 18 / 2609.27160

Fine Wrist Control as a Marker of Surgical Teleoperation Expertise

精细腕部控制作为外科远程操作专业性的标志

Gale, Mary Kate, Shobayashi, Shujiro, Satpathy, Sangeet, Davidor, Nitsan, Nisky, Ilana, Okamura, Allison

Abstract

Unlike most intensely physical pursuits, surgical robotic teleoperation training focuses primarily on task outcomes rather than surgeon body posture or biomechanics during task completion. Toward the question of the role of biomechanics in surgical expertise, we sought to characterize the articular motion of expert teleoperators as compared to novice users. Twenty-seven novices and nine experts completed a non-medical cylinder-on-peg transfer task while their upper limb biomechanics were recorded via motion trackers. During more difficult motions, experts stabilized their wrist motion more than novices, while maintaining adequate range of motion in their shoulder and elbow and completing the task significantly faster than novices. This marker of expertise suggests the importance of attention to user biomechanics during teleoperation of surgical robots.

Chinese Translation

与大多数高度体力要求的活动不同，外科机器人远程操作培训主要关注任务结果，而非外科医生在完成任务时的身体姿势或生物力学。针对生物力学在外科专业性中的作用这一问题，我们旨在描述专家远程操作员与新手用户的关节运动特征。二十七名新手和九名专家在完成一个非医疗的圆柱体转移任务时，通过运动追踪器记录了他们的上肢生物力学。在更困难的动作中，专家的腕部运动稳定性高于新手，同时在肩部和肘部保持了足够的活动范围，并且任务完成速度显著快于新手。这一专业性标志表明，在外科机器人远程操作过程中关注用户生物力学的重要性。

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cs.RO / 19 / 2609.27167

Median Temporal Ensembling: Training-Free Robust Aggregation for Action-Chunked Visuomotor Policies

中位数时间集成：无训练的鲁棒聚合用于动作分块的视觉运动策略

Jiang, Yuhang

Abstract

Action-chunked visuomotor policies predict overlapping trajectories, so every executed action is covered by several predictions. Temporal ensembling smooths execution by combining these predictions with an exponentially weighted mean. One corrupted prediction can move the aggregate without bound: its breakdown point is 0. We use adversarial corruption to stress this deployed aggregator and to compare two kinds of guarantee. A metric guarantee bounds the response to a perturbation of a given size. A combinatorial guarantee instead bounds the damage when at most q of the M candidates covering a timestep are corrupted, whatever their size. Encoder adversarial fine-tuning recovers 44% of the loss under the published patch attack, but only 7.3% after the attacker's step size is increased. By contrast, the coordinate-wise median of the same candidate set keeps its recovered fraction flat as attack optimisation increases. Median temporal ensembling costs one line and requires no retraining. Across 25 (configuration, corruption-level) combinations it is never worse than the mean and is significantly better in 15. It also transfers to a second policy class, and it recovers performance under a failure with no attacker in the loop at all: camera frames that arrive blank. Its effect on clean data is configuration-dependent, from -0.04 to +0.07. We also give the boundary: corruption that shifts every covering prediction by the same amount is invisible to this whole family of statistics, and no equivariant aggregator can remove it.

Chinese Translation

动作分块的视觉运动策略预测重叠的轨迹，因此每个执行的动作都被多个预测所覆盖。时间集成通过将这些预测与指数加权平均结合来平滑执行。然而，一个被破坏的预测可能会无限制地影响聚合结果：其崩溃点为0。我们使用对抗性破坏来测试这一部署聚合器，并比较两种保证方式。度量保证限制了对给定大小扰动的响应。组合保证则限制了在最多q个覆盖某个时间步的M个候选者中被破坏的情况，无论其大小如何。编码器对抗微调在已发布的补丁攻击下恢复了44%的损失，但在攻击者步长增大后仅恢复了7.3%。相比之下，同一候选集的坐标-wise中位数在攻击优化增加时保持其恢复比例不变。中位数时间集成仅需一行代码且无需重新训练。在25个（配置，破坏级别）组合中，其性能从未低于均值，并在15个组合中显著更好。它还可以迁移到第二类策略，并在没有攻击者参与的情况下恢复性能：即到达的空白摄像头帧。其对干净数据的影响依赖于配置，范围从-0.04到+0.07。我们还给出了边界：将每个覆盖预测同样偏移的破坏对这一整类统计量是不可见的，且没有等变聚合器能够消除它。

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cs.RO / 20 / 2609.27188

Learning Dissipative Dynamics with Dissipativity-by-Construction Discrete-Time Neural Networks

通过构造耗散性学习耗散动力学的离散时间神经网络

Luong, Tuan, Moon, Hyungpil

Abstract

Dissipativity is a fundamental system-theoretic property closely related to stability, passivity, and input--output stability, and is particularly important in robotics, where learned dynamics models are often embedded within feedback control loops. However, most existing approaches for learning dissipative dynamics are based on continuous-time formulations, which require ODE solvers during training or inference and can therefore be computationally expensive. Moreover, because practical implementations are inherently discrete-time, direct discretization of a continuous-time passive system does not necessarily preserve passivity, motivating the need for explicit discrete-time guarantees. This study proposes a method for learning incrementally dissipative dynamics from input--output time-series data using a deep multilayer perceptron formulated directly in discrete time. Through a constrained parameterization and a dedicated training procedure, the proposed model guarantees incremental dissipativity by construction rather than through regularization. Lyapunov-based analysis establishes the corresponding dissipativity and stability guarantees, while simulations on robotic dynamical systems demonstrate competitive prediction accuracy, computational efficiency, and consistent preservation of incremental dissipativity compared with baseline methods.

Chinese Translation

耗散性是一个基本的系统理论属性，与稳定性、无源性和输入-输出稳定性密切相关，在机器人领域尤为重要，因为学习的动力学模型通常嵌入在反馈控制回路中。然而，现有的大多数学习耗散动力学的方法基于连续时间的公式，这在训练或推理过程中需要常微分方程（ODE）求解器，因此计算成本较高。此外，由于实际实现本质上是离散时间的，直接对连续时间无源系统进行离散化并不一定能保持无源性，这促使了对显式离散时间保证的需求。本研究提出了一种从输入-输出时间序列数据中学习增量耗散动力学的方法，该方法使用直接在离散时间中构建的深度多层感知器。通过约束参数化和专门的训练程序，所提出的模型通过构造保证增量耗散性，而不是通过正则化。基于李雅普诺夫的分析建立了相应的耗散性和稳定性保证，而在机器人动力学系统上的仿真则展示了与基线方法相比，具有竞争性的预测精度、计算效率和一致的增量耗散性保持。

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cs.RO / 21 / 2609.27218

NaviScale: Generating Large-Scale Semantic Map Datasets for Object Navigation

NaviScale：生成大规模语义地图数据集用于物体导航

Lan, Chuanlin, Zheng, Yanwei, Liu, Weijian, Zhou, Zhitong, Zhang, Xiao, Zhuang, Fuzhen, Yu, Dongxiao

Abstract

Embodied navigation requires spatial representations that generalize across unseen environments, yet collecting large amounts of annotated data from real 3D environments is difficult. We propose NaviScale for semantic-map-based object navigation (ObjectNav), whose predictor can be trained on pairs of partial and complete semantic maps without reconstructing a complete 3D environment for every training sample. The framework generates large-scale semantic map training data by composing floorplans of real homes with room-level semantic and obstacle maps extracted from MP3D and HM3DSem. NaviScale increases data diversity in two ways: inter-room scaling increases floorplan-level structural diversity, while intra-room scaling fills each fixed floorplan with different combinations of room maps matched by room category. Visibility through Ray Casting (VisRC) converts the composed maps into partial observations that account for field of view, sensing range, and occlusion. The resulting dataset contains 192,000 semantic maps generated from 24,000 floorplans associated with 12,794 properties. With 300k training iterations and the training and inference settings described in this paper, the system reaches 64.3% SR and 34.8% SPL on HM3D, together with 43.1% SR and 16.8% SPL on MP3D, without changing the prediction architecture. Additional experiments evaluate the quality of the composed maps, the effects of semantic-segmentation errors, and deployment on a physical robot.

Chinese Translation

具身导航需要能够在未见环境中进行泛化的空间表示，但从真实的三维环境中收集大量标注数据是困难的。我们提出了NaviScale，用于基于语义地图的物体导航（ObjectNav），其预测器可以在部分和完整语义地图的配对上进行训练，而无需为每个训练样本重建完整的三维环境。该框架通过将真实住宅的平面图与从MP3D和HM3DSem提取的房间级语义和障碍物地图组合，生成大规模的语义地图训练数据。NaviScale通过两种方式增加数据多样性：跨房间缩放增加了平面图级别的结构多样性，而房间内缩放则通过房间类别匹配的不同房间地图组合填充每个固定平面图。通过光线投射（Ray Casting）实现的可见性（VisRC）将组合的地图转换为考虑视场、感知范围和遮挡的部分观测。生成的数据集包含192,000个语义地图，这些地图来自24,000个与12,794个属性相关的平面图。在300,000次训练迭代以及本文描述的训练和推理设置下，该系统在HM3D上达到了64.3%的成功率（SR）和34.8%的成功路径长度（SPL），在MP3D上达到了43.1%的成功率（SR）和16.8%的成功路径长度（SPL），而无需改变预测架构。额外的实验评估了组合地图的质量、语义分割错误的影响以及在物理机器人上的部署。

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cs.RO / 22 / 2609.27219

Vision-Based Control of a Tether-Suspended Aerial Radiation Sensing Payload

基于视觉的系绳悬挂空中辐射探测载荷控制

Snider, Ian, Quiter, Brian J., Rofors, Emil, Mueller, Mark W.

Abstract

Aerial radiation surveys achieve higher sensitivity when the radiation detector is held close to the ground. Detector sensitivity falls off roughly with the inverse square of the distance to the source, so a detector flown high is slower to reach a given minimum detectable activity. Flying the vehicle low puts the propellers near the ground, where downwash can disturb the surveyed area and resuspend contaminated particulates. Tether suspension decouples the detector from the vehicle altitude, but leaves the payload unactuated and only indirectly controllable. We therefore present a vision-based control approach for an aerial sensing payload suspended on a tether beneath a heavy-lift drone. Because a survey plan is decided as radiation detections arrive, we design a pilot aid for commanding the survey trajectory manually with a handheld transmitter. The controller regulates the payload, rather than the vehicle, onto that trajectory. The system uses onboard sensors with a downward-facing camera fixed to the drone body tracking a ring marker on the payload. A four-state Kalman filter estimates the tether swing angles and rates from payload bearing measurements, and a linear quadratic regulator with integral action takes the payload position as the regulated output. In outdoor flight tests under wind, the payload-aware controller reduced payload tracking error during transit by 20% when compared against a vehicle-referenced baseline, with the cost of higher peak error on arrival at a waypoint.

Chinese Translation

空中辐射调查在辐射探测器靠近地面时能实现更高的灵敏度。探测器的灵敏度大致与源头距离的平方反比下降，因此高空飞行的探测器达到给定的最小可检测活度的速度较慢。低空飞行使得螺旋桨靠近地面，气流下洗可能会干扰调查区域并重新悬浮受污染的颗粒物。系绳悬挂将探测器与飞行器高度解耦，但使得载荷无法直接控制，仅能间接控制。因此，我们提出了一种基于视觉的控制方法，用于悬挂在重载无人机下的空中探测载荷。由于调查计划是在辐射探测到达时决定的，我们设计了一种手持发射器手动指挥调查轨迹的飞行员辅助工具。控制器调节的是载荷，而不是飞行器，以使其沿着该轨迹移动。该系统使用机载传感器，配备固定在无人机机身上的向下摄像头，跟踪载荷上的环形标记。四状态卡尔曼滤波器根据载荷方位测量估计系绳摆动角度和速率，而具有积分作用的线性二次调节器将载荷位置作为调节输出。在户外风力飞行测试中，考虑载荷的控制器在过渡期间将载荷跟踪误差减少了20%，与基于飞行器的基线相比，虽然在到达航点时峰值误差较高。

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cs.RO / 23 / 2609.27240

A Quasi-Direct-Drive Underactuated Asymmetric Hand for Dexterous and Efficient Grasping and Manipulation

一种准直接驱动的欠驱动非对称手用于灵巧和高效的抓取与操作

Davis, Benjamin, Kidder, Chase, Stuart, Hannah S.

Abstract

In this paper, we present the Berkeley QUAD (Quasi-direct-drive, Underactuated, Asymmetric Design) Hand, a four-finger anthropomorphic robotic hand with 11 degrees of freedom and 8 degrees of actuation. The design utilizes QDD actuation at the base of each finger, enabling high force transparency for dexterous, adaptive performance. However, the low torque density of these actuators traditionally presents major issues with size, weight, and thermal limits. We overcome this by applying bio-inspired asymmetry, delegating dexterity to the radial fingers through individual QDD actuation, and strength to the ulnar finger through an underactuated, compliantly coupled transmission driven by a larger QDD motor. A novel preloaded, linkage-based transmission permits this ulnar coupling in a way that preserves human-like workspace reachability. Under light loads, the ulnar motor drives the third (middle) finger directly for dexterity while the fourth (ring) finger mirrors its motion. However, under larger loads, the middle finger complies while the motor drives the ring finger further downwards and inwards toward the center of the grasp to apply better closure forces. Hardware evaluations validate this architecture, demonstrating that the hand achieves 29 out of 33 Feix taxonomy grasps and exhibits backdrive forces as low as 50 g for delicate interactions. Additionally, the underactuated fourth finger improves grasp closure and provides the spatial efficiency necessary for larger actuation, yielding up to a 96-fold reduction in heat generation during sustained loading. Webpage: https://benudavis.github.io/berkeley-quadhand/

Chinese Translation

在本文中，我们介绍了伯克利QUAD（准直接驱动、欠驱动、非对称设计）手，这是一种具有11个自由度和8个驱动度的四指类人机器人手。该设计在每个手指的基部采用QDD（Quasi-direct-drive）驱动，能够实现高力透明度，从而实现灵巧和自适应的性能。然而，这些驱动器的低扭矩密度通常在尺寸、重量和热限制方面存在重大问题。我们通过应用生物启发的非对称性来克服这一问题，将灵巧性委托给径向手指，通过单独的QDD驱动实现，而将力量委托给尺侧手指，通过一个欠驱动的、柔性耦合的传动系统，由一个更大的QDD电机驱动。一个新颖的预加载、基于连杆的传动系统以一种保持类人工作空间可达性的方式实现了尺侧耦合。在轻载情况下，尺侧电机直接驱动第三个（中间）手指以实现灵巧性，而第四个（无名）手指则跟随其运动。然而，在较大载荷下，中间手指会顺应，而电机则进一步向下和向内驱动无名手指，以施加更好的闭合力。硬件评估验证了该架构，表明该手实现了33种Feix分类抓取中的29种，并在精细交互中表现出低至50克的反驱动力。此外，欠驱动的第四个手指改善了抓取闭合，并提供了进行更大驱动所需的空间效率，在持续负载下热生成减少了多达96倍。网页链接：https://benudavis.github.io/berkeley-quadhand/

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cs.RO / 24 / 2609.27247

Memory That Changes Action Is Not Memory That Guides It: Counterfactual Auditing of History-Conditioned Robot Policies

改变行为的记忆并非指导行为的记忆：反事实历史审计机器人策略

Zhang, Jiajie, Xiang, Yankai, Chen, Changhao

Abstract

A robot returning a block to its origin tray may encounter two task-consistent pasts that reconverge to the same current input but warrant different actions. Yet memory-policy evaluations often rely on task success or action change under memory perturbation, neither of which establishes that memory guides the decision. We propose the \textbf{Counterfactual Memory Audit (CMA)}, an evaluation protocol that crosses two histories at a verified-identical present, queries a frozen policy under common randomness, and evaluates each saved action under both pasts. This separates memory sensitivity, warranted choice, matched-world physical value, and per-pair reliability. On Mem-0, every audited Put Back pair changes action, but only $20/64$ pairs are fully reliable; at a later Swap decision, all paired actions change while both memories select the same branch. Native interventions further show closed-loop influence: replacing the history bank redirects behavior toward the replaced content, while restoring a 4096-byte protected anchor recovers $38.9$ points of Swap success lost to injected bank faults. On a dual-arm physical platform, memory changes saved actions, yet five of nine completed Put Back manipulations reach the wrong target. These results show that a robot can remember and react without reliably using memory to choose the behavior its past warrants. CMA provides a decision-level audit for distinguishing these cases.

Chinese Translation

一个机器人在将一个块返回其原始托盘时，可能会遇到两个任务一致的过去，这两个过去在相同的当前输入下重新汇聚，但却需要不同的行动。然而，记忆策略的评估通常依赖于任务成功或在记忆扰动下的行动变化，这两者都无法证明记忆指导了决策。我们提出了 extbf{反事实记忆审计（Counterfactual Memory Audit, CMA）}，这是一种评估协议，它在一个经过验证的相同当前时刻交叉两个历史，在共同随机性下查询一个冻结的策略，并在两个过去下评估每个保存的行动。这将记忆敏感性、合理选择、匹配世界的物理价值和每对的可靠性分开。在Mem-0上，每对审计的“放回”（Put Back）行动都发生了变化，但只有$20/64$对是完全可靠的；在后续的“交换”（Swap）决策中，所有配对的行动都发生了变化，而两个记忆选择了相同的分支。原生干预进一步显示了闭环影响：替换历史库将行为重定向到被替换的内容，而恢复一个4096字节的保护锚点则恢复了因注入的历史库故障而损失的$38.9$个交换成功点。在一个双臂物理平台上，记忆的变化保存了行动，但九个完成的“放回”操作中有五个达到了错误的目标。这些结果表明，机器人可以记住并反应，而不可靠地使用记忆来选择其过去所要求的行为。CMA提供了一种决策级审计，用于区分这些情况。

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cs.RO / 25 / 2609.27269

Banana Kick: Response-Informed Skill Evolution for Humanoid Soccer

香蕉踢：基于响应的信息驱动技能演化用于类人足球

Zhang, Hao E., Geng, Ruize, Haque, Raihan, Zbiss, Khalil, Luo, Guanyang, Wang, Hui-ping, Tseng, H. Eric, Zhao, Ding

Abstract

Humanoid kicking requires coordinated whole-body motion and precise contact, while a banana kick demands contact mechanics that generate ball spin and aerodynamic curvature. Motion imitation provides a reliable ordinary-kick prior, but reinforcement learning may improve shot speed and placement accuracy without changing the underlying kicking technique. Adapting this prior to a qualitatively different contact-rich skill can fail even when the reward is dense and optimization remains stable. The failure occurs when the task objective is locally flat over the current policy's responses. We term this condition first-order learning starvation. To address it, we propose response-informed skill evolution (RISE), a closed-loop objective-continuation method for policy adaptation. RISE ranks bounded objective changes using response sensitivity estimated from cached rollouts and accepts updates only when they produce verified response progress while preserving kicking reliability. Our analysis shows that rescaling a saturated spin reward cannot recover first-order sensitivity at zero spin, whereas adapting coupled contact responses can provide a learnable path to spin generation. We integrate RISE into a humanoid kicking pipeline under calibrated contact and Magnus-force aerodynamics, and sim-to-real transfer. Experiments show that RISE evolves the ordinary kick into a high-spin curved kick with 11.55 rad/s mean ball spin, improves the mean evaluation score by 19.8% over a learning-progress curriculum, and raises joint target attainment from 15.2% to 50.9%. Ablations and response diagnostics support the mechanism, while 30 motion-capture-recorded physical trials demonstrate consistent hardware transfer of the learned curved kick. Project website: https://haozhang-thu.github.io/bananakick/

Chinese Translation

类人的踢球动作需要协调的全身运动和精确的接触，而香蕉踢则要求产生球旋转和空气动力学曲率的接触力学。运动模仿提供了可靠的普通踢球先验，但强化学习可能在不改变基础踢球技术的情况下提高射门速度和精准度。将这一先验适应于一种质上不同的接触丰富技能可能会失败，即使奖励密集且优化保持稳定。当任务目标在当前策略的响应上局部平坦时，失败就会发生。我们将这种情况称为一阶学习饥饿。为了解决这个问题，我们提出了基于响应的信息驱动技能演化（Response-Informed Skill Evolution，RISE），这是一种用于策略适应的闭环目标延续方法。RISE通过使用从缓存回放中估计的响应敏感性来对有界目标变化进行排序，并仅在产生经过验证的响应进展的同时保持踢球可靠性时接受更新。我们的分析表明，重新缩放饱和的旋转奖励无法在零旋转时恢复一阶敏感性，而适应耦合的接触响应可以提供一个可学习的旋转生成路径。我们将RISE集成到一个在经过校准的接触和马格努斯力空气动力学下的类人踢球管道中，并进行仿真到现实的转移。实验表明，RISE将普通踢球演变为具有11.55 rad/s平均球旋转的高旋转曲线踢，提升了学习进展课程下的平均评估分数19.8%，并将关节目标达成率从15.2%提高到50.9%。消融实验和响应诊断支持该机制，同时30个运动捕捉记录的物理试验展示了学习到的曲线踢的硬件转移的一致性。项目网站：https://haozhang-thu.github.io/bananakick/

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cs.RO / 26 / 2609.27275

BranchDrive: A Branch-Structured Dataset for Action-Conditioned Driving Prediction

BranchDrive：一种基于分支结构的行动条件驾驶预测数据集

Khanzada, Feeza Khan, Sridhar, Sudarshan, Kwon, Jaerock

Abstract

Most autonomous-driving datasets record only the action executed by a behavior policy and the single future that followed, providing limited supervision for comparing alternative ego decisions. We introduce BranchDrive, a branch-structured CARLA dataset and benchmark that pairs one canonical pre-decision history with one nominal expert future and twelve physically executed intervention futures spanning acceleration, braking, and left- and right-steering policies at three magnitudes. Each intervention lasts 2.5 s and is followed by expert recovery. Following control-compliance, modality-completeness, replay-fidelity, and action-leakage audits, the frozen benchmark contains 606 independent branch groups and 7,878 associated trajectories. We evaluate prediction of six continuous short-horizon outcomes and a ten-step ego trajectory using action-only, history-only, structured, visual, multimodal, and privileged bird's-eye-view models. On the held-out test split, the structured history-and-action model achieves a macro normalized mean absolute error of 0.5036 and an average displacement error of 2.2042 m, significantly outperforming both restricted baselines. In full-information offline evaluation, its outcome-derived selector increases balanced policy value from 0.5364 to 0.5704 and reduces normalized regret from 0.2674 to 0.1495 relative to the frozen action prior. However, a validation-calibrated minimum-separation guard rejects every intervention, showing that conservative execution remains unresolved. BranchDrive therefore supports action-conditioned short-horizon prediction and fixed-bank offline decision evaluation, but does not establish exact causal effects, binary safety prediction, or closed-loop safety improvement.

Chinese Translation

大多数自主驾驶数据集仅记录行为策略执行的动作及其后续的单一未来，这为比较替代自我决策提供了有限的监督。我们引入了BranchDrive，一个基于分支结构的CARLA数据集和基准，它将一个典型的决策前历史与一个标准的专家未来和十二个物理执行的干预未来相配对，这些干预涵盖了加速、制动以及左转和右转的策略，分为三种幅度。每个干预持续2.5秒，并随后由专家进行恢复。经过控制合规性、模态完整性、重放保真度和动作泄漏审计后，冻结基准包含606个独立的分支组和7,878条相关轨迹。我们评估了六个连续短期结果和一个十步自我轨迹的预测，使用仅动作、仅历史、结构化、视觉、多模态和特权鸟瞰模型。在保留的测试集上，结构化历史与动作模型的宏观归一化平均绝对误差为0.5036，平均位移误差为2.2042米，显著优于两个受限基线。在全信息离线评估中，其结果导向选择器将平衡策略值从0.5364提高到0.5704，并将归一化遗憾从0.2674降低到0.1495，相对于冻结的动作先验。然而，经过验证校准的最小分离保护机制拒绝了每个干预，显示出保守执行的问题仍未解决。因此，BranchDrive支持基于行动的短期预测和固定银行的离线决策评估，但并未确立确切的因果效应、二元安全预测或闭环安全改进。

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cs.RO / 27 / 2609.27308

EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics

具身编码：用于长时间跨度灵巧机器人任务的编码代理

You, Haoxiang, Shen, Zeyu, Liu, Yilang, Zheng, Zhicheng, Zha, Lihan, Yamazaki, Kashu, Zhang, Mingtong, Huang, Suning, Sun, Jiankai, Chen, Qianzhong, He, Lucy, Liu, Kaiyuan, Chang, Haoran, Fragkiadaki, Katerina, Shah, Dhruv, Schwager, Mac, Henderson, Peter, Abraham, Ian, Xu, Canwen

Abstract

We study coding agents for long-horizon, dexterous robotics and ask whether their solutions can provide scalable supervision for learning general robot policies. To test this, we develop EMBODIEDSWE-BENCH, a simulation benchmark for coding agents spanning contact-rich manipulation, deformable objects, and long-horizon tasks requiring up to half an hour of continuous interaction. We find that frontier coding agents can solve complex long-horizon tasks and transfer prior solutions across both tasks and embodiments. We also design supporting tools that help agents more effectively solve these tasks. However, the resulting solutions require substantial iterative interaction and are typically specialized to individual task instances. We therefore introduce EMBODIEDSWE-GEN, which expands a single solution from coding agent into large diverse trajectories for training a VLA. VLA performance improves with more generated demonstrations, and agent-aided diversification improves generalization to held-out task variations. We also show that a VLA finetuned solely on coding-agent-generated simulation demonstrations completes a long-horizon task on real robot. Together, our framework uses coding agents to solve complex robotics tasks and turn verified solutions into scalable supervision for robot policies.

Chinese Translation

我们研究用于长时间跨度灵巧机器人的编码代理，并探讨其解决方案是否能够为学习通用机器人策略提供可扩展的监督。为此，我们开发了EMBODIEDSWE-BENCH，这是一个涵盖接触丰富的操作、可变形物体以及需要长达半小时连续交互的长时间任务的编码代理仿真基准。我们发现，前沿的编码代理能够解决复杂的长时间任务，并在任务和具身之间转移先前的解决方案。我们还设计了支持工具，帮助代理更有效地解决这些任务。然而，所得到的解决方案需要大量的迭代交互，通常专门针对个别任务实例。因此，我们引入了EMBODIEDSWE-GEN，它将编码代理的单一解决方案扩展为用于训练VLA的大规模多样化轨迹。随着生成演示数量的增加，VLA的性能有所提升，而代理辅助的多样化则改善了对未见任务变体的泛化。我们还展示了仅在编码代理生成的仿真演示上微调的VLA能够在真实机器人上完成长时间任务。综上所述，我们的框架利用编码代理解决复杂的机器人任务，并将验证过的解决方案转化为机器人策略的可扩展监督。

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cs.RO / 28 / 2609.27312

Turning Safety into Competence: Minimally Exploitable Robot Policies via Safety-Filtered Reinforcement Learning

将安全转化为能力：通过安全过滤强化学习实现最小可利用的机器人策略

Wu, Ruihan, Yang, Rui, Oh, Donggeon David, Nguyen, Duy, Hu, Haimin

Abstract

Robots deployed for competitive tasks must outmaneuver their opponents without sacrificing safety. Existing approaches, including safe reinforcement learning (RL), train a single policy to achieve task success and avoid failures simultaneously. This coupling can complicate training and leave the learned policy exploitable by deliberate attacks. We propose Safety to Competence (S2C), a two-stage RL framework that separates safety synthesis from competitive task learning. We formulate competitive interactions as safety-critical Markov games and prove that perfect filtering preserves policy non-exploitability when all players commit to safe maneuvers. S2C learns a robust safety filter via adversarial RL, embeds it in the environment during task policy training, and retains the same filter at deployment. In simulated touchdown games, S2C outperforms eight safe RL baselines, achieving the highest win rate and Elo rating, and the lowest exploitability. Hardware stress tests against a human opponent confirm S2C's competence.

Chinese Translation

用于竞争任务的机器人必须在不牺牲安全的情况下超越对手。现有方法，包括安全强化学习（safe reinforcement learning, RL），训练单一策略以同时实现任务成功和避免失败。这种耦合可能会使训练复杂化，并使所学习的策略易受故意攻击。我们提出了安全到能力（Safety to Competence, S2C）框架，这是一个两阶段的强化学习框架，将安全合成与竞争任务学习分开。我们将竞争互动形式化为安全关键的马尔可夫博弈，并证明当所有参与者承诺采取安全操作时，完美过滤能够保持策略的不可利用性。S2C通过对抗性强化学习学习一个稳健的安全过滤器，在任务策略训练期间将其嵌入环境中，并在部署时保留相同的过滤器。在模拟的触地游戏中，S2C的表现优于八个安全强化学习基线，达到了最高的胜率和Elo评级，以及最低的可利用性。针对人类对手的硬件压力测试确认了S2C的能力。

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cs.RO / 29 / 2609.27314

CoRe-WAM: Correspondence-Aligned Temporal Residuals for World Action Models

CoRe-WAM：用于世界行动模型的对应对齐时间残差

Zhou, Bin, Liu, Jialong, Wang, Jianan, Chen, Changhao, Chen, Kani

Abstract

Comparing current and past observations helps robots understand scene changes and select subsequent actions during manipulation. However, comparing visual features at the same image location can mix different scene content when objects or the camera move. We introduce CoRe-WAM, a world-action model that incorporates correspondence-aligned visual changes through a parameter-efficient temporal interface. Its TraceDelta module uses correspondences from a frozen tracking model to transport historical visual features to current locations before computing signed differences in a shared pretrained feature space. Correspondence thus determines which historical content is compared with the present, rather than entering the policy as a separate trajectory representation. A lightweight adapter converts these differences into validity-gated residuals that supplement current visual conditioning, allowing the policy to use recent changes alongside current-scene information. Built on Motus, CoRe-WAM keeps the pretrained backbone weights frozen and optimizes 1.59 million parameters. With a 5,000-update adaptation budget, CoRe-WAM achieves 92.22% clean success across 50 RoboTwin 2.0 tasks, 3.56 percentage points above Motus; on randomized evaluation, it achieves 89.60% success, a 2.58-point gain. Integrating TraceDelta into a StarVLA-based policy improves clean success from 58.10% to 67.62%, supporting transfer of the temporal interface beyond Motus.

Chinese Translation

比较当前和过去的观察有助于机器人理解场景变化并在操作过程中选择后续动作。然而，在对象或相机移动时，在相同图像位置比较视觉特征可能会混合不同的场景内容。我们提出了CoRe-WAM，这是一种世界行动模型，通过参数高效的时间接口结合了对应对齐的视觉变化。其TraceDelta模块利用来自冻结跟踪模型的对应关系，将历史视觉特征传输到当前位点，然后在共享的预训练特征空间中计算有符号差异。因此，对应关系决定了与当前内容进行比较的历史内容，而不是作为单独的轨迹表示进入策略。一个轻量级适配器将这些差异转换为有效性门控的残差，补充当前的视觉条件，使策略能够在使用当前场景信息的同时利用最近的变化。基于Motus，CoRe-WAM保持预训练主干权重冻结，并优化159万个参数。在5000次更新的适应预算下，CoRe-WAM在50个RoboTwin 2.0任务中实现了92.22%的干净成功率，比Motus高出3.56个百分点；在随机评估中，成功率为89.60%，提高了2.58个百分点。将TraceDelta集成到基于StarVLA的策略中，使干净成功率从58.10%提高到67.62%，支持超越Motus的时间接口转移。

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cs.RO / 30 / 2609.27330

A Sample-Based Approach for Hierarchical Information-Theoretic Compression of Probabilistic Occupancy Grids

基于样本的方法用于概率占用网格的层次信息论压缩

Jin, Zhenyu, Larsson, Daniel T.

Abstract

We develop a sample-based framework for constructing information-driven hierarchical multi-resolution representations of probabilistic occupancy grids. Recent methods compute information-optimal abstractions via dynamic-programming-based exhaustive recursions, which become computationally prohibitive for large-scale grids and are ill-suited to robotics applications. To address this limitation, we introduce a sample-based strategy inspired by Monte Carlo Tree Search (MCTS) that incrementally constructs hierarchical abstractions through statistical estimation rather than exhaustive enumeration. The proposed method is anytime in nature, allowing computation to be terminated at any stage to produce a valid compressed representation. We compare our approach with the information-optimal Q-tree search algorithm and demonstrate its effectiveness in rapidly generating abstractions of large real-world probabilistic occupancy grids.

Chinese Translation

我们开发了一种基于样本的框架，用于构建信息驱动的概率占用网格的层次多分辨率表示。最近的方法通过基于动态规划的穷举递归计算信息最优抽象，这在大规模网格中计算代价高昂，并且不适合机器人应用。为了解决这一限制，我们引入了一种受蒙特卡罗树搜索（Monte Carlo Tree Search, MCTS）启发的基于样本的策略，通过统计估计而非穷举枚举逐步构建层次抽象。所提出的方法具有随时可用的特性，允许在任何阶段终止计算以生成有效的压缩表示。我们将我们的方法与信息最优的 Q-tree 搜索算法进行了比较，并展示了其在快速生成大型真实世界概率占用网格抽象方面的有效性。

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cs.RO / 31 / 2609.27338

DUGM-R: Uncertainty-Aware Dynamic Grid Mapping and Risk-Triggered Recovery for Learned Local Navigation

DUGM-R：基于不确定性的动态网格映射与风险触发的学习局部导航恢复

Feng, Haoyun, Rubio-Solis, Adrian, Guo, Zhaodong, Mylonas, George

Abstract

Learned local navigation in crowded indoor environments is sensitive to how dynamic obstacle motion is represented, while collision-prone behaviour may persist after nominal policy training. We present a risk-aware reinforcement-learning framework that addresses these two issues through an uncertainty-aware Dynamic Uncertainty Grid Map (DUGM) and a modular post-training recovery mechanism. DUGM combines local occupancy, estimated obstacle motion, and motion-estimation uncertainty in a robot-centric representation. After the nominal policy is frozen, a finite-horizon Risk Value Function (RVF) is trained from nominal rollouts and used to trigger a dedicated recovery policy when continued nominal execution is predicted to be collision-prone. Experiments in a held-out NVIDIA Isaac Sim clinical-logistics benchmark show that uncertainty-aware dynamic representation improves nominal navigation over static and deterministic alternatives, while the recovery mechanism further mitigates residual collision-prone behaviour. The complete framework is also deployed directly on a TurtleBot3 without policy fine-tuning, retraining, or site-specific adaptation, retaining the performance trend observed in simulation. These results indicate that uncertainty-aware dynamic representation and post-training recovery provide complementary mechanisms for improving learned local navigation.

Chinese Translation

在拥挤的室内环境中，学习的局部导航对动态障碍物运动的表示非常敏感，而在名义策略训练后，碰撞倾向行为可能仍然存在。我们提出了一种风险感知的强化学习框架，通过不确定性感知的动态不确定性网格图（DUGM）和模块化的后训练恢复机制来解决这两个问题。DUGM结合了局部占用、估计的障碍物运动和运动估计的不确定性，以机器人为中心进行表示。在名义策略被冻结后，从名义回放中训练出有限时域风险价值函数（RVF），并在预测继续执行名义策略时可能发生碰撞时触发专用的恢复策略。在保留的NVIDIA Isaac Sim临床物流基准测试中的实验表明，不确定性感知的动态表示在静态和确定性替代方案上改善了名义导航，而恢复机制进一步减轻了残余的碰撞倾向行为。完整框架还直接部署在TurtleBot3上，无需策略微调、重新训练或特定场地适应，保留了在仿真中观察到的性能趋势。这些结果表明，不确定性感知的动态表示和后训练恢复为改善学习的局部导航提供了互补机制。

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cs.RO / 32 / 2609.27340

Spatial and Semantic Reasoning for LLM-Driven Robot Navigation via MCP

基于MCP的LLM驱动机器人导航的空间与语义推理

Lee, Jungsoo, Park, Jaegyun, Kim, Wansoo

Abstract

Large language models (LLMs) are increasingly used as natural-language interfaces for robotic systems, yet their integration with Robot Operating System (ROS)-based navigation remains limited by two gaps. First, navigation data such as occupancy grids are represented as raw geometric messages that are difficult for LLMs to use directly as spatial or semantic context. Second, adding LLM-driven capabilities often requires custom wrappers or robot-specific interfaces, limiting reuse across systems. To address these challenges, we propose a non-invasive framework that connects LLM reasoning with ROS-based navigation through a navigation-oriented representation layer, exposed through the Model Context Protocol (MCP) as standardized, reusable tools so that any MCP-compatible LLM can access them without robot-specific wrappers. The visual map modules transform occupancy grids into metric, pose-aware images for goal reasoning, while the semantic annotation modules record waypoint-level observations with robot poses. We evaluate the framework on three tasks: autonomous mapping, spatial reasoning-based navigation, and semantic reasoning-based navigation. The results show that the evaluated LLM backends use these representations to achieve over 97% map coverage and select spatial or semantic navigation targets from natural-language instructions in a simulated indoor environment. This demonstrates representation-mediated LLM navigation without modifying the existing ROS navigation stack.

Chinese Translation

大型语言模型（LLMs）越来越多地被用作机器人系统的自然语言接口，但它们与基于机器人操作系统（ROS）的导航集成仍然面临两个主要问题。首先，导航数据如占用网格以原始几何消息的形式表示，这使得LLMs难以直接将其用作空间或语义上下文。其次，添加LLM驱动的功能通常需要定制的包装器或特定于机器人的接口，从而限制了系统之间的重用。为了解决这些挑战，我们提出了一种非侵入性框架，通过导航导向的表示层将LLM推理与基于ROS的导航连接起来，该层通过模型上下文协议（MCP）作为标准化的可重用工具暴露，以便任何兼容MCP的LLM都可以在不需要特定于机器人的包装器的情况下访问它们。视觉地图模块将占用网格转换为具有度量和姿态感知的图像，以便进行目标推理，而语义注释模块则记录与机器人姿态相关的航点级观察。我们在三个任务上评估了该框架：自主映射、基于空间推理的导航和基于语义推理的导航。结果表明，评估的LLM后端利用这些表示在模拟室内环境中实现了超过97%的地图覆盖率，并从自然语言指令中选择空间或语义导航目标。这证明了在不修改现有ROS导航栈的情况下，基于表示的LLM导航的可行性。

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cs.RO / 33 / 2609.27342

BladeMaster: Real-Time Robotic Cutting Simulation with Online-Generated Persistent Discontinuities

BladeMaster：具有在线生成持久不连续性的实时机器人切割仿真

Yang, Zhanyu, Chen, Yunuo, Huang, Yanjia, Masterjohn, Joseph, Yang, Yin, Jiang, Chenfanfu

Abstract

Cutting changes both the shape and topology of deformable objects, making accurate simulation challenging for robotic manipulation. A simulator must track the cutting tool as a cut develops, preserve the resulting discontinuities after tool withdrawal, and enable newly exposed surfaces to interact with the tool and with each other. Existing formulations often prescribe cut surfaces in advance or couple material separation to auxiliary geometric fields. We introduce BladeMaster, a GPU-accelerated cutting framework based on the total Lagrangian material point method (TLMPM). Our key idea is to encode the cutting history directly on material points through persistent side labels generated online from the blade geometry. These labels govern particle-grid coupling, preserving connectivity within intact material while preventing spurious coupling across cut faces after tool withdrawal. Our formulation supports progressive and intersecting cuts without predefined cut surfaces or particle duplication. Material-material contact enables cut surfaces to recontact and slide against each other without reconnecting, while two-way tool-material coupling allows material reaction forces to influence tool motion. Experiments demonstrate tool-driven cutting followed by manipulation, with faster-than-real-time performance on representative tasks.

Chinese Translation

切割改变了可变形物体的形状和拓扑结构，使得机器人操作的准确仿真变得具有挑战性。一个仿真器必须在切割过程中跟踪切割工具，保留工具撤回后产生的不连续性，并使新暴露的表面能够与工具及彼此进行交互。现有的公式通常提前规定切割表面或将材料分离与辅助几何场耦合。我们提出了BladeMaster，一个基于总拉格朗日材料点方法（TLMPM）的GPU加速切割框架。我们的关键思想是通过从刀片几何形状在线生成的持久侧标签，直接在材料点上编码切割历史。这些标签控制粒子-网格耦合，保留完整材料内的连通性，同时防止在工具撤回后切割面之间的虚假耦合。我们的公式支持渐进式和交叉切割，无需预定义切割表面或粒子重复。材料-材料接触使切割表面能够重新接触并相互滑动而不重新连接，而双向工具-材料耦合则允许材料反作用力影响工具运动。实验表明，工具驱动的切割后续操作在代表性任务上实现了比真实时间更快的性能。

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cs.RO / 34 / 2609.27346

Reflection-Aware Reasoning for Non-Line-of-Sight Pedestrian Localization

考虑反射的非视距行人定位推理

Park, Byeonggyu, Jeon, Mingu, Kim, Seong-Woo

Abstract

Reliable localization of non-line-of-sight (NLOS) pedestrians is critical for safe urban autonomous driving, yet it remains highly challenging in ego-dynamic outdoor environments, where ego-vehicle motion makes radar multipath propagation complex and noisy. In this paper, we present a reflection-aware framework for NLOS pedestrian localization with a moving ego-vehicle in outdoor testbed scenarios. Our framework fuses front-view camera images and 2D radar point clouds to infer reflection orders and reflective surface distributions in bird's-eye-view space. It then uses physics-guided ray tracing to reconstruct distorted reflection paths and localize the hidden pedestrian. We validate the framework in outdoor testbed scenarios under ego-dynamic conditions. The results demonstrate the effectiveness of the proposed framework for NLOS pedestrian localization with a moving ego-vehicle.

Chinese Translation

可靠的非视距（NLOS）行人定位对于安全的城市自动驾驶至关重要，但在自我动态的户外环境中仍然面临极大的挑战，因为自我车辆的运动使得雷达多径传播变得复杂且噪声较大。本文提出了一种考虑反射的框架，用于在户外测试场景中实现移动自我车辆的NLOS行人定位。我们的框架融合了前视摄像头图像和二维雷达点云，以推断鸟瞰图空间中的反射顺序和反射面分布。然后，它利用物理引导的光线追踪重建扭曲的反射路径并定位隐藏的行人。我们在自我动态条件下的户外测试场景中验证了该框架。结果表明，所提出的框架在移动自我车辆的NLOS行人定位中具有良好的效果。

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cs.RO / 35 / 2609.27358

Omnidirectional Amphibious Locomotion via Internal Mass Actuation

通过内部质量驱动实现全向两栖运动

Weaver, Niko, Xia, Boxi, Lo, Li-Yu, Huang, Yuhao, Chen, Boyuan

Abstract

Field robots must traverse varied terrain and obstacles while remaining robust to water, debris, vegetation, and physical contact. We present MARBLE, a fully enclosed omnidirectional amphibious rolling robot driven entirely by internal mass redistribution. Three mutually orthogonal linear sliders shift internal masses to generate body rotation, while an orientation-aware controller maps planar velocity commands into slider positions. A rigid spherical shell encloses all active mechanisms and simultaneously serves as the terrestrial contact surface, buoyant enclosure, and mounting structure for passive fins that enable water-surface propulsion. Rotation of the same shell architecture hence produces rolling on land and surface propulsion in water without mechanical reconfiguration or separate locomotion actuators. The spherical morphology further allows the robot to accommodate changes in body orientation and contact location during direct interactions with terrain and obstacles. We evaluate MARBLE through omnidirectional locomotion characterization, traversal across heterogeneous terrestrial environments, aquatic surface locomotion, land-water transitions, and deliberate obstacle interactions. These experiments demonstrate how a single enclosed mechanical architecture can combine omnidirectional mobility, cross-medium locomotion, and tolerance to environmental contact. MARBLE provides a compact design for field mobility across heterogeneous terrain, obstacles, and land-water transitions. We will open-source all software and hardware design. Our website is https://generalroboticslab.com/MARBLE

Chinese Translation

野外机器人必须在多样的地形和障碍物中穿行，同时对水、碎片、植被和物理接触保持鲁棒性。我们提出了MARBLE，一种完全封闭的全向两栖滚动机器人，完全通过内部质量重分配驱动。三个相互正交的线性滑块移动内部质量以产生机体旋转，而一个方向感知控制器将平面速度命令映射到滑块位置。一个刚性球形外壳封闭了所有主动机制，同时作为地面接触表面、浮力外壳和被动鳍片的安装结构，这些鳍片使得水面推进成为可能。因此，相同的外壳结构在不需要机械重构或单独运动驱动器的情况下，既能在陆地上滚动，又能在水中推进。球形形态进一步使机器人能够在与地形和障碍物直接接触时适应机体方向和接触位置的变化。我们通过全向运动特性评估MARBLE，测试其在异质陆地环境中的穿越能力、水面运动、陆水过渡和故意障碍物交互。这些实验展示了单一封闭机械结构如何结合全向移动性、跨介质运动和对环境接触的耐受性。MARBLE为在异质地形、障碍物和陆水过渡中的野外移动提供了紧凑的设计。我们将开源所有软件和硬件设计。我们的网站是 https://generalroboticslab.com/MARBLE

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cs.RO / 36 / 2609.27363

From LiDAR Maps to Visual Localization: Unified Visual Association for Robust Point-Line-Plane Pose Estimation

从 LiDAR 地图到视觉定位：用于稳健点-线-面姿态估计的统一视觉关联

Zhao, Wentao, Chen, Zikun, Niu, Yihe, Chen, Haoyu, Wang, Jingchuan

Abstract

Camera localization in a prior LiDAR map provides a persistent geometric reference for long-term robotic navigation, yet remains challenging because of the substantial modality gap between camera images and point-cloud maps. We present a unified localization framework that makes the LiDAR map visually addressable rather than relying on a dedicated image-LiDAR correspondence model. Map geometry and reflectivity are rendered into LiDAR-derived quasi-images with explicit 2D-3D provenance, enabling camera observations and rendered map views to share mature visual features and matchers for both global localization and continuous pose tracking. Point and line correspondences are established through this common visual interface, while the retained provenance recovers metric LiDAR geometry and line-supported planar constraints for pose estimation. To improve robustness under ambiguous associations and weak geometry, we further introduce a distribution-aware, observability-complementary optimization strategy. Instead of reducing matching ambiguity to a scalar confidence, candidate association distributions are propagated into directional pose-information uncertainty, and reliable structural factors are selectively reinforced according to their ability to complement the currently weak pose directions. Experiments on the EuRoC MAV benchmark and self-collected real-world sequences demonstrate accurate global localization and robust continuous 6-DoF tracking using only a pre-built LiDAR map as the persistent prior, including under severe illumination variations and dynamic occlusions.

Chinese Translation

在先前的 LiDAR 地图中进行相机定位为长期机器人导航提供了持久的几何参考，但由于相机图像与点云地图之间存在显著的模态差距，这一过程仍然具有挑战性。我们提出了一个统一的定位框架，使得 LiDAR 地图可以通过视觉方式进行寻址，而不依赖于专门的图像-LiDAR 对应模型。地图几何和反射率被渲染为具有明确 2D-3D 来源的 LiDAR 派生准图像，使得相机观测和渲染的地图视图能够共享成熟的视觉特征和匹配器，以实现全球定位和连续姿态跟踪。通过这一共同的视觉接口建立点和线的对应关系，同时保留的来源信息恢复了度量级 LiDAR 几何和线支持的平面约束，以进行姿态估计。为了提高在模糊关联和弱几何下的稳健性，我们进一步引入了一种基于分布的、可观测性互补的优化策略。该策略不再将匹配模糊性简化为标量置信度，而是将候选关联分布传播到方向性姿态信息的不确定性中，并根据其补充当前弱姿态方向的能力选择性地增强可靠的结构因子。在 EuRoC MAV 基准测试和自收集的真实世界序列上的实验表明，仅使用预构建的 LiDAR 地图作为持久先验，即使在严重的光照变化和动态遮挡下，也能实现准确的全球定位和稳健的连续 6 自由度跟踪。

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cs.RO / 37 / 2609.27381

CoPRE: Improving Sensitivity in Proprioceptive Contact Detection for Low-Cost Robot Arms

CoPRE：提高低成本机器人臂的本体感知接触检测灵敏度

Zhu, Yuxiao, Li, Jinzhou, Dong, Yifei, Suhail, Muhammad, Yang, Chunyuan, Luo, Xinyuan, Li, Haoyu, Chen, Boyuan, Cheng, Xianyi

Abstract

Contact detection during robotic manipulation allows robots to recognize unexpected contact and adapt their motion accordingly. However, in low-cost robot arms without dedicated force or tactile sensors, detecting weak contacts from proprioception is challenging because the resulting changes in joint-level proprioceptive signals can be small compared to normal variation and noise caused by robot motion itself. We introduce Contact-free Proprioceptive Response Estimation (CoPRE), improving proprioceptive contact detection sensitivity using only contact-free motion, without additional force sensors, contact labels, or analytical dynamics models. CoPRE estimate the expected joint torques under contact-free motion from proprioceptive state history and commanded motion, while removing recent observations that may already reflect contact. It then computes the residual between the expected and observed joint torque estimates, and maps this residual to a contact score using a noise-weighted Jacobian. Real-robot experiments on ARX Arm and Unitree G1 show that CoPRE achieves 74.1% and 82.2% recall on the tested contact trials, compared with 0%/0% on ARX and 16.3%/42.2% on G1 for the learned torque-prediction and inverse-dynamics baselines. CoPRE also reaches 90% detection rate for pushing force at 3.5 N on ARX and 5.5 N on G1. To demonstrate the downstream utility of our method, we implement belief-space manipulation planning for obstacle-aware object placement and book insertion where detected contacts update the spatial belief and enable the robot to retreat from blocked motions, adjust its pose, and retry. Project website at https://copre-arm.github.io

Chinese Translation

在机器人操作过程中，接触检测使机器人能够识别意外接触并相应地调整其运动。然而，在没有专用力传感器或触觉传感器的低成本机器人臂中，从本体感知中检测微弱接触是具有挑战性的，因为关节级本体感知信号的变化可能相对于正常变动和机器人运动自身引起的噪声而言非常微小。我们提出了无接触本体感知响应估计（Contact-free Proprioceptive Response Estimation, CoPRE），该方法仅利用无接触运动来提高本体感知接触检测的灵敏度，而无需额外的力传感器、接触标签或分析动力学模型。CoPRE根据本体感知状态历史和指令运动估计无接触运动下的预期关节扭矩，同时去除可能已经反映接触的近期观测。然后，它计算预期与观察到的关节扭矩估计之间的残差，并使用加权噪声的雅可比矩阵将该残差映射到接触评分。对ARX Arm和Unitree G1的真实机器人实验表明，CoPRE在测试的接触试验中实现了74.1%和82.2%的召回率，而学习的扭矩预测和逆动力学基线在ARX上为0%/0%，在G1上为16.3%/42.2%。CoPRE在ARX上以3.5 N和在G1上以5.5 N的推力下也达到了90%的检测率。为了展示我们方法的下游应用，我们实现了障碍感知物体放置和书本插入的信念空间操作规划，其中检测到的接触更新了空间信念，使机器人能够从被阻塞的运动中撤退、调整姿态并重新尝试。项目网站：https://copre-arm.github.io

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cs.RO / 38 / 2609.27394

Automotive mmWave Spinning Radar Place Recognition with Spatially Gated Feature-Correlation Representation

汽车毫米波旋转雷达位置识别与空间门控特征相关表示

Rahman, Saimunur, Shrestha, Sagun Singh, Khamis, Abdelwahed, Moghadam, Peyman

Abstract

Automotive spinning FMCW radar provides dense, $360^\circ$ sensing and remains reliable under poor illumination and adverse weather, making it well-suited to autonomous navigation. Place recognition uses these observations to identify previously visited locations for re-localization and long-term navigation. However, heading changes appear as circular shifts in the polar radar representation, and conventional global aggregation can lose relationships among radar responses that are important for distinguishing similar places. We propose SGCA-Net, a spinning radar place recognition framework that combines rotation-robust feature extraction with Spatially Gated Correlation Aggregation (SGCA). SGCA learns spatial weights to reduce the influence of unstable and ambiguous radar regions, while aggregating pairwise correlations among local responses to preserve informative feature relationships. Experiments on the MulRan dataset show that SGCA-Net consistently outperforms SOTA methods across urban, campus, and open-road environments, while remaining robust to substantial heading variation. Evaluation on the HeRCULES dataset further demonstrates that SGCA-Net generalizes to unseen environments and radar sensors without fine-tuning.

Chinese Translation

汽车旋转FMCW雷达提供密集的360°感知，并在光照不足和恶劣天气下保持可靠性，使其非常适合于自主导航。位置识别利用这些观测数据来识别先前访问过的位置，以实现重新定位和长期导航。然而，航向变化在极坐标雷达表示中表现为圆形移位，而传统的全局聚合可能会丢失雷达响应之间的重要关系，这些关系对于区分相似地点至关重要。我们提出了SGCA-Net，一种结合旋转鲁棒特征提取与空间门控相关聚合（SGCA）的旋转雷达位置识别框架。SGCA学习空间权重，以减少不稳定和模糊雷达区域的影响，同时聚合局部响应之间的成对相关性，以保留信息丰富的特征关系。在MulRan数据集上的实验表明，SGCA-Net在城市、校园和开放道路环境中始终优于当前最先进的方法，同时对显著的航向变化保持鲁棒性。在HeRCULES数据集上的评估进一步证明，SGCA-Net在未见环境和雷达传感器上具有良好的泛化能力，无需微调。

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cs.RO / 39 / 2609.27439

Compressed delayed-information projection for six-degree-of-freedom underwater vehicle navigation under delayed acoustic positioning

在延迟声学定位下的六自由度水下航行器导航的压缩延迟信息投影

Li, Shuyue, López-Benítez, Miguel, Lim, Eng Gee, Ma, Fei, Dong, Qian, Cao, Mengze, Yu, Limin, Qin, Xiaohui

Abstract

Delayed acoustic positioning packets constrain historical navigation states, but a current-time update evaluates them against a mismatched state, whereas exact rewind/replay re-executes the intervening estimator history. This paper introduces compressed delayed-information projection (CDIP), a causal 15-state error-state Kalman filter (ESKF) treatment for delayed-acoustic unmanned underwater vehicle (UUV) navigation. CDIP retains a source-epoch snapshot and the historical-to-current cross-covariance, then projects the delayed source-epoch acoustic correction directly to the current state without full rewind/replay. Exact fixed-lag rewind/replay out-of-sequence-measurement (OOSM) processing serves as a high-fidelity accuracy reference. In 154 usable paired recordings at a fixed 1.5-s acoustic delay without an outage, CDIP reduced mean trajectory-position root-mean-square error (RMSE) from 1.062 m for the baseline to 0.456 m (57.1%). Its 0.456-m mean was 1.03% higher than the 0.451-m replay mean, while its measured mean per-update runtime was 99.2% lower (approximately 127-fold). A separate predeclared sweep across six fixed delays, with 30 paired recordings per delay, and a truth-supported 9-D consistency analysis bound the interpretation. Additional targeted experiments showed near-replay trajectory accuracy across 50-300-s acoustic outages while preserving sub-millisecond update cost. CDIP therefore provides a compact delayed-information treatment with an empirical accuracy-computation trade-off under the evaluated configuration; the evidence does not establish statistical equivalence or non-inferiority relative to replay.

Chinese Translation

延迟声学定位数据包限制了历史导航状态，但当前时间更新将其与不匹配的状态进行评估，而精确的回放/重放则重新执行了介入的估计器历史。本文介绍了压缩延迟信息投影（Compressed Delayed-Information Projection, CDIP），这是一种用于延迟声学无人水下航行器（Unmanned Underwater Vehicle, UUV）导航的因果15状态误差状态卡尔曼滤波器（Error-State Kalman Filter, ESKF）处理方法。CDIP保留了源时间快照和历史到当前的交叉协方差，然后将延迟的源时间声学修正直接投影到当前状态，而无需完全回放。精确的固定延迟回放/重放的非顺序测量（Out-of-Sequence Measurement, OOSM）处理作为高保真度的准确性参考。在154个可用的配对记录中，固定1.5秒的声学延迟没有中断，CDIP将基线的平均轨迹位置均方根误差（Root Mean Square Error, RMSE）从1.062米降低到0.456米（减少57.1%）。其0.456米的平均值比0.451米的回放平均值高出1.03%，而其每次更新的测量平均运行时间则低99.2%（约127倍）。在六个固定延迟下进行的单独预先声明的扫描，每个延迟有30个配对记录，以及支持真实值的9维一致性分析限制了解释。额外的针对性实验显示，在50-300秒的声学中断期间，CDIP在保持亚毫秒更新成本的同时，提供了接近回放的轨迹准确性。因此，CDIP在评估的配置下提供了一种紧凑的延迟信息处理方法，具有经验性的准确性与计算权衡；证据并未建立相对于回放的统计等价性或非劣性。

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cs.RO / 40 / 2609.27449

X2Real: an eXtensive simulation benchmark for real-world generalist policies

X2Real：一个广泛的真实世界通用策略仿真基准

Ruan, Lian, Yang, Jade, Gao, Sherphylan, Gao, Felix, Liang, Kyson, Liu, Galen, Wu, Ligo, Jin, Lane, Gu, Guu, Xie, Bevan, Yan, Cloud, Yuan, Zongzi, Luo, Kino, Chen, Emma, Chen, Shuwen, Ping, Yang, Guo, Miles, Sun, Rain, Zhang, Kayden, Du, Alex, Wu, Ruihai, Hao, Liang, Li, Zhaoshuo, Gan, Roy, Wang, Hao, Wang, Qian

Abstract

Generalist robot manipulation policies have developed rapidly, yet their reliable evaluation remains challenging due to fundamental flaws in existing simulation benchmarks: prominent sim-to-real gaps, narrow task coverage, and unfair evaluation caused by ambiguous training-test pipelines. Prior works only partially resolve these issues and lack simultaneous faithfulness, diversity, and fairness, while static benchmark designs fail to sustain long-term policy development. We present X2Real, an evolvable simulation benchmark for faithfully evaluating the real-world performance of robotic manipulation policies based on Nvidia Isaac Lab-Arena. Following three core principles (faithfulness, diversity, and fairness), X2Real calibrates simulation visual and physical properties to align with real hardware, achieving a 0.84 linear correlation between simulated and real-robot evaluation results. It features a comprehensive taxonomy with 10 capability dimensions and 44 hierarchical long-horizon tasks, covering basic manipulation skills and advanced capacities such as visual grounding, language understanding, and bimanual control. We further adopt multi-axis domain randomization and strictly disjoint training-evaluation pipelines to mitigate benchmark exploitation and ensure credible evaluation. Powered by a custom physical domain-specific language, the Mana simulation ecosystem supports modular task design and iterative performance analysis, alongside a nearly 300-hour annotated simulation trajectory dataset. X2Real offers a faithful, diverse, and fair evolving evaluation infrastructure, effectively bridging the sim-to-real evaluation gap and supporting the advancement of generalist robotic manipulation policies.

Chinese Translation

通用机器人操作策略发展迅速，但由于现有仿真基准的基本缺陷，其可靠评估仍然具有挑战性：显著的仿真与现实差距、狭窄的任务覆盖范围，以及由于模糊的训练-测试流程导致的不公平评估。先前的研究仅部分解决了这些问题，缺乏同时的真实性、多样性和公平性，而静态基准设计无法支持长期策略发展。我们提出了X2Real，这是一个可演化的仿真基准，旨在基于Nvidia Isaac Lab-Arena真实地评估机器人操作策略的现实表现。遵循真实性、多样性和公平性三项核心原则，X2Real校准了仿真的视觉和物理特性，以与真实硬件对齐，实现了仿真与真实机器人评估结果之间的0.84线性相关性。它具有一个全面的分类法，包括10个能力维度和44个分层的长期任务，涵盖基本操作技能和高级能力，如视觉定位、语言理解和双手控制。我们进一步采用多轴领域随机化和严格不重叠的训练-评估流程，以减少基准利用并确保可信评估。由定制的物理领域特定语言驱动，Mana仿真生态系统支持模块化任务设计和迭代性能分析，并提供近300小时的标注仿真轨迹数据集。X2Real提供了一个真实、多样和公平的演变评估基础设施，有效弥合了仿真与现实评估之间的差距，并支持通用机器人操作策略的进步。

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cs.RO / 41 / 2609.27450

BEE: Intervention-Adaptive Real-World Reinforcement Learning with Vision-Language-Action Models

BEE：基于干预自适应的现实世界强化学习与视觉-语言-动作模型

Zhao, Weihui, Yan, Xiaohan, Wan, Zunian, Du, Xuan, Chi, Zhaozhan, Mao, Jianbo, Wu, Ruipu, Yang, Rushuai, Li, Houlin, Yang, Shukai, Wu, Jing, Yan, Yuxiang, Liu, Yongcheng, Li, Chuankang, Ren, Guanghui, Shan, Wei, Yao, Maoqing

Abstract

Vision-language-action (VLA) models handle long-horizon manipulation, yet success hinges on a few precision-critical phases where millimeter-scale errors undo all prior progress. Online reinforcement learning (RL) can optimize exactly these actions, but free exploration is far too costly on real robots, which makes human corrections indispensable. However, existing online RL methods for VLAs either cannot incorporate such corrections or fold them into undifferentiated supervision. Yet human corrections are not uniformly noisy but reliable along some action dimensions and variable along others. Building on this, we introduce BEE, an intervention-adaptive framework for real-world RL on a frozen VLA that lets the policy go BEyond Expert imitation. We formulate human corrections not as actions to reproduce but as evidence about a constraint: a Correction Model predicts how a human would correct a given VLA proposal and how consistent the correction is along each action dimension. This predicted consistency sets the per-dimension tightness of a constraint on policy optimization. Where corrections are consistent the policy stays close to the human, and where they vary, the constraint relaxes. We evaluate BEE on three real-world manipulation tasks and one LIBERO-Pro simulation task at a matched online-data budget. BEE attains the highest success rate on every task, 91.2% on average against 57.5% for RLT and 42.1% for DSRL, and the lowest human intervention rate on all real-world tasks.

Chinese Translation

视觉-语言-动作（VLA）模型能够处理长时间跨度的操作，但其成功依赖于几个精度至关重要的阶段，在这些阶段，毫米级的误差可能会使之前的所有进展化为乌有。在线强化学习（RL）可以优化这些动作，但在真实机器人上自由探索的成本过高，因此人类的纠正变得不可或缺。然而，现有的针对VLA的在线RL方法要么无法纳入这些纠正，要么将其融入到无差别的监督中。然而，人类的纠正并不是均匀噪声的，而是在某些动作维度上是可靠的，而在其他维度上则是可变的。在此基础上，我们提出了BEE，一个针对冻结VLA的现实世界RL的干预自适应框架，使得策略能够超越专家模仿。我们将人类的纠正形式化为关于约束的证据，而不是要重现的动作：纠正模型（Correction Model）预测人类将如何纠正给定的VLA提议，以及该纠正在每个动作维度上的一致性。这种预测的一致性设定了对策略优化的每个维度的约束紧致度。在纠正一致的地方，策略保持接近人类，而在变化的地方，约束则放宽。我们在三个现实世界的操作任务和一个LIBERO-Pro模拟任务上评估了BEE，且在匹配的在线数据预算下，BEE在每个任务上都达到了最高的成功率，平均为91.2%，而RLT为57.5%，DSRL为42.1%，并且在所有现实世界任务中人类干预率最低。

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cs.RO / 42 / 2609.27463

Safety-Filtered Distributed Koopman-MPC

安全过滤的分布式库普曼模型预测控制

Zhang, Shengjun, Li, Wenhao, Lin, Zhenxin, Sun, Zhenglong

Abstract

Distributed model predictive control (DMPC) often constructs both predictions and collision constraints from neighbor trajectories, so packet loss can remove both. We separate these roles: received trajectories drive Koopman-MPC, while local sensing and shelf geometry define a hard-constrained quadratic program (QP) that projects the applied input. Its radial demand is the least constant acceleration that keeps a supporting-plane clearance nonnegative throughout one zero-order-hold interval. Complementary pair rows recover the coupled demand without exchanging safety decisions. We give an intersample separation theorem under bounded snapshot and directional plant errors, an exact max-min test for simultaneous local feasibility, and a sensing-radius condition for switching interaction graphs. Anticipatory high-order rows may be relaxed for performance, but the finite-hold rows contain no safety slack. Matched eight-robot warehouse simulations use a frozen Koopman model, nonlinear drift, bounded inputs and speed, shelf constraints, a 120 ms control period, and packet dropout. The full controller is collision-free in 20/20 matched trials and reaches 160/160 robot goals; predictive Koopman-MPC without the final projection is collision-free in 1/20 trials. All 38,400 full-method hard-row sets pass the online feasibility test, and every local QP solves. Five-stream fleet sweeps are collision-free and hard-row feasible through 16 robots; the 20-robot boundary fails only after the online margin turns negative, while the reconstructed per-agent critical path remains below the sampling period. Bounded-sensing and differential-drive tests provide additional deployment stress.

Chinese Translation

分布式模型预测控制（DMPC）通常从邻居轨迹构建预测和碰撞约束，因此数据包丢失可能会同时移除这两者。我们将这两个角色分开：接收的轨迹驱动库普曼模型预测控制（Koopman-MPC），而本地传感和货架几何定义了一个硬约束的二次规划（QP），该规划投影施加的输入。其径向需求是保持支撑平面间隙非负的最小恒定加速度，贯穿一个零阶保持间隔。互补对行恢复了耦合需求，而无需交换安全决策。我们给出了在有界快照和方向性植物误差下的插值分离定理、同时局部可行性的精确最大-最小测试，以及切换交互图的感知半径条件。为了性能，预期的高阶行可以放宽，但有限保持行不包含安全松弛。匹配的八机器人仓库仿真使用了一个冻结的库普曼模型、非线性漂移、有界输入和速度、货架约束、120毫秒的控制周期以及数据包丢失。在20/20的匹配试验中，完整控制器是无碰撞的，并且达到了160/160个机器人目标；而没有最终投影的预测库普曼-MPC在1/20的试验中是无碰撞的。所有38,400个完整方法的硬行集通过了在线可行性测试，并且每个局部QP都得以解决。五流舰队通过16个机器人进行的扫荡是无碰撞且硬行可行的；20个机器人的边界仅在在线边际变为负时才失败，而重建的每个代理的关键路径仍低于采样周期。有界感知和差分驱动测试提供了额外的部署压力。

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cs.RO / 43 / 2609.27466

A Modular Dual-Arm Robotic Cell for Disassembly and Repair of Industrial Control Electronics

用于工业控制电子产品拆解与维修的模块化双臂机器人工作站

Ruhe, Maximilian, Harlacher, Fabian, Friedrich, Christian, Kipfmueller, Martin

Abstract

Industrial control electronics such as programmable logic controllers, servo drives and operator panels are routinely repaired in plant maintenance, but were never designed for automated disassembly. This paper presents a modular dual-arm robotic cell for repair-oriented disassembly, using two collaborative manipulators, interchangeable tools, red-green-blue-depth (RGB-D) and wristlevel perception, force/torque sensing and a Robot Operating System (ROS) 2- based control with Behavior Tree (BT) execution, teleoperation, digital-twin support and bounded learning-based contact skills. The process is decomposed into sequence planning, symbolic execution with fallbacks, force-limited tool skills, visual condition assessment and demonstration-based adaptation. A CADderived device graph encodes the disassembly order, access constraints, tools, feasible removal directions and verification states and converts them into operation objects for the BT and motion layers. Grounded in three representative devices, the cell covers screw removal, damaged-fastener fallback, snap-fit opening, connector release, cooperative printed circuit board (PCB) extraction and condition-based repair decisions. The main contribution is an architecture linking sequence knowledge, perception, verification and force-aware skills through one ROS 2 interface across simulation, teleoperation and real hardware.

Chinese Translation

工业控制电子产品，如可编程逻辑控制器、伺服驱动器和操作面板，通常在工厂维护中进行维修，但从未设计用于自动化拆解。本文提出了一种用于维修导向拆解的模块化双臂机器人工作站，采用两个协作操纵器、可互换工具、红绿蓝深度（RGB-D）和腕部感知、力/扭矩传感以及基于机器人操作系统（ROS）2的控制，结合行为树（BT）执行、遥操作、数字双胞胎支持和有界学习的接触技能。该过程被分解为序列规划、具有回退机制的符号执行、力限制工具技能、视觉条件评估和基于示范的适应。CAD衍生的设备图编码了拆解顺序、访问约束、工具、可行的拆卸方向和验证状态，并将其转换为BT和运动层的操作对象。基于三个代表性设备，该工作站涵盖了螺丝拆卸、损坏紧固件回退、卡扣开合、连接器释放、协作印刷电路板（PCB）提取和基于条件的维修决策。主要贡献在于通过一个ROS 2接口，将序列知识、感知、验证和力感知技能连接起来，涵盖了仿真、遥操作和真实硬件。

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cs.RO / 44 / 2609.27467

Kairos: Grounded Forecasting of Presence and Directional Flow in 4D Scene Graphs

Kairos：在4D场景图中对存在性和方向流的基于实证的预测

Catalano, Iacopo, Placed, Julio A., Civera, Javier, Queralta, Jorge Peña

Abstract

Long-term autonomy in human-populated environments requires anticipating whether and how people will move at times a robot has not yet observed. Existing representations of pedestrian motion face a tradeoff: they either forecast future activity, reducing each location to a scalar rate, or model the full directional distribution, holding it fixed in time. We present Kairos, a predictive directional-flow memory that extends a hierarchical 3D scene graph (3DSG) to a 4D scene graph (4DSG). Every observed voxel of the reconstructed geometry stores a directional mixture and a presence rate, and spectral predictors forecast, for any future query time, both the probability that people are present and the full directional distribution of their motion. Pairwise flow dependence between adjacent voxels supports conditional queries, and per-voxel predictive variances yield calibrated credible intervals that tighten as observations accumulate. We evaluate Kairos on three real pedestrian environments: a robot-collected campus dataset, a shopping mall, and a station concourse recorded continuously for eleven months. Its learned state remains consistent under loop-closure corrections, and its forecasts are competitive with dedicated occupancy and flow models trained on the full detection stream, although Kairos learns from only the small fraction available to a patrolling robot. Finally, we validate the representation on a downstream encounter-probability planning task, where plans computed over the Kairos forecasts encounter more people than plans computed over any time-invariant map at an equal success rate. We provide the code at https://github.com/IacopomC/kairos.

Chinese Translation

在有人类居住的环境中实现长期自主性需要预测人们在机器人尚未观察到的时刻将如何移动。现有的行人运动表示面临权衡：它们要么预测未来活动，将每个位置简化为一个标量速率，要么建模完整的方向分布，并在时间上保持不变。我们提出了Kairos，一种预测性方向流记忆，它将层次化的3D场景图（3DSG）扩展到4D场景图（4DSG）。每个观察到的重建几何体的体素存储一个方向混合和一个存在率，谱预测器预测在任何未来查询时间，人们存在的概率以及他们运动的完整方向分布。相邻体素之间的成对流依赖关系支持条件查询，且每个体素的预测方差产生经过校准的可信区间，随着观察的积累而收紧。我们在三个真实的行人环境中评估Kairos：一个机器人收集的校园数据集、一个购物中心和一个连续记录了十一个月的车站大厅。其学习状态在闭环校正下保持一致，其预测与在完整检测流上训练的专用占用和流模型相竞争，尽管Kairos仅从巡逻机器人可用的小部分数据中学习。最后，我们在下游的遇见概率规划任务中验证了该表示，在该任务中，基于Kairos预测计算的计划在相同成功率下遇到的人数多于基于任何时间不变地图计算的计划。我们在https://github.com/IacopomC/kairos提供了代码。

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cs.RO / 45 / 2609.27469

Collocated Shape Regulation for Soft Robots

软机器人协同形状调节

Pustina, Pietro, Shahabi, Ebrahim, Feliu-Talegon, Daniel, De Luca, Alessandro, Della Santina, Cosimo

Abstract

Controlling the shape of a continuum soft robot typically requires an accurate dynamic model and actuation of all degrees of freedom. We show that regulating only the actuated coordinates, through collocated shape control, achieves provably stable convergence of those coordinates and, under an explicit compatibility condition, of the entire robot shape. While collocated control is a cornerstone of high-performance motion control in rigid robotics, extending this formulation to continuum soft robots has remained challenging due to the complexity of their dynamics. We present the first general framework for collocated control of continuum soft robots and derive a unified family of controllers, including PD, PID, PsatID, and their counterparts with compensation and cancellation components. The framework unifies existing approaches while introducing new controller designs. In particular, we develop three classes of PD and PID like regulators with local, semi-global, and global stability guarantees, and provide rigorous convergence analyses for each. Extensive experimental validation demonstrates the effectiveness of the proposed methods across different model discretizations and controller parameters. The resulting framework provides practical design guidelines for selecting and implementing controllers with known stability guarantees, without requiring a complete dynamic model of the robot

Chinese Translation

控制连续软机器人的形状通常需要准确的动态模型和对所有自由度的驱动。我们展示了通过协同形状控制，仅调节驱动坐标即可实现这些坐标的可证明稳定收敛，并且在明确的兼容性条件下，整个机器人形状也能收敛。虽然协同控制是刚性机器人高性能运动控制的基石，但由于连续软机器人动态的复杂性，将这一方法扩展到软机器人仍然具有挑战性。我们提出了第一个针对连续软机器人的协同控制的通用框架，并推导出一系列统一的控制器，包括PD、PID、PsatID及其补偿和抵消组件的对应版本。该框架统一了现有的方法，同时引入了新的控制器设计。特别地，我们开发了三类具有局部、半全局和全局稳定性保证的PD和PID类调节器，并为每种调节器提供了严格的收敛分析。广泛的实验验证展示了所提方法在不同模型离散化和控制器参数下的有效性。最终形成的框架为选择和实施具有已知稳定性保证的控制器提供了实用的设计指南，而无需完整的机器人动态模型。

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cs.RO / 46 / 2609.27475

RoboCaf\'e in the Open: Interaction Continuity in Long-Term Public Human-Robot Interaction

RoboCafé在公开场合：长期公共人机交互中的互动连续性

Pineda, Kaitlynn Taylor, Kushwaha, Kush Kumar, Wang, Jie, Du, Jiaming, Mishra, Anvii, Suri, Emilie Basu, Guo, Angela, Huang, Chien-Ming

Abstract

As robots remain in public spaces over extended periods, they must maintain interaction continuity by preserving and correctly applying context as people, encounters, and circumstances change. To study interaction continuity in long-term public human-robot interactions, we developed RoboCaf\'e, an autonomous conversational coffee robot designed to support repeated interactions through task-aware dialogue, real-time multimodal perception, and memory of prior encounters. We deployed RoboCaf\'e for 12 days in a university building, where it received 148 orders. The deployment involved repeat customers, passersby, changing groups, and back-to-back orders that repeatedly crossed the boundaries assumed by the system's order-centered interaction model. We found that successful interaction continuity requires a robot to determine who is currently present, which prior context belongs to whom, where interactions begin and end, and whether its representation of an interaction matches what is occurring in the physical world. From these observations, we derive four system design requirements for maintaining interaction continuity in longitudinal public human-robot interactions: contextual interaction state, persistent person grounding, explicit interaction life-cycle management, and interaction observability.

Chinese Translation

随着机器人在公共空间中长时间停留，它们必须通过保持和正确应用上下文来维持互动的连续性，以应对人、遭遇和环境的变化。为了研究长期公共人机交互中的互动连续性，我们开发了RoboCafé，一款自主对话咖啡机器人，旨在通过任务感知对话、实时多模态感知和对先前遭遇的记忆来支持重复互动。我们在一栋大学建筑中部署了RoboCafé，持续12天，期间共接收了148个订单。此次部署涉及重复顾客、路过行人、变化的群体以及连续的订单，这些情况多次超越了系统基于订单的交互模型所假设的边界。我们的研究发现，成功的互动连续性要求机器人能够确定当前在场的人员、哪些先前的上下文属于谁、互动的开始和结束位置，以及其对互动的表征是否与物理世界中实际发生的情况相符。基于这些观察，我们提出了四个系统设计要求，以维持长期公共人机交互中的互动连续性：上下文交互状态、持久的人物基础、明确的互动生命周期管理和互动可观察性。

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cs.RO / 47 / 2609.27513

Behavior-Aligned Action Tokenization for Robot Policy Learning

行为对齐的动作标记化用于机器人策略学习

Dong, Junbo, Chen, Ze, Xie, Zhendong, Li, Junjie, Xu, Lixin, Chi, Xuemin, Song, Yiming, Ma, Zhaoyuan

Abstract

Autoregressive robot policies learn continuous control by predicting discrete action tokens from observations. Different tasks often share local motions, yet behavioral correspondence across demonstrations receives limited explicit supervision in existing tokenizers. Motions with different timing can therefore lack a shared representation despite following similar patterns. We propose Behavior-Aligned Action Tokenization (BAAT), which uses soft dynamic time warping (Soft-DTW) to select corresponding action chunks and aligns their quantized coordinates jointly with reconstruction. This objective encourages similar motions across tasks to occupy nearby quantized representations while retaining executable action detail. A history-conditioned diffusion decoder reconstructs continuous action chunks from these tokens, and a downstream autoregressive policy learns to predict them. We evaluate BAAT on selected tasks from three simulation benchmarks and two real robot tasks. BAAT achieves a mean simulation success rate of approximately 45.2%, exceeding OAT by approximately 7.2 percentage points. In the controlled LIBERO-All alignment ablation, policy success rises from 70.2% to 79.0% while trajectory replay success decreases. These results support behavioral correspondence as supervision for organizing shared motion structure in action tokenizers and improving downstream robot policy learning.

Chinese Translation

自回归机器人策略通过从观察中预测离散动作标记来学习连续控制。不同任务通常共享局部运动，但现有标记器对演示中的行为对应关系的显式监督有限。因此，尽管遵循类似模式，不同时间的运动可能缺乏共享表示。我们提出了行为对齐的动作标记化（Behavior-Aligned Action Tokenization, BAAT），该方法使用软动态时间规整（Soft-DTW）选择对应的动作片段，并与重建共同对其量化坐标进行对齐。该目标鼓励不同任务中的相似运动占据相邻的量化表示，同时保留可执行的动作细节。一个基于历史条件的扩散解码器从这些标记中重建连续的动作片段，而下游的自回归策略学习预测这些片段。我们在三个仿真基准和两个真实机器人任务中评估了BAAT。BAAT的平均仿真成功率约为45.2%，比OAT高出约7.2个百分点。在受控的LIBERO-All对齐消融实验中，策略成功率从70.2%上升到79.0%，而轨迹重放成功率下降。这些结果支持将行为对应关系作为监督，用于组织动作标记器中的共享运动结构，并改善下游机器人策略学习。

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cs.RO / 48 / 2609.27526

NavProbe: Evidence-Grounded Reasoning with Active Memory Retrieval for Zero-Shot Navigation

NavProbe：基于证据的推理与主动记忆检索的零-shot导航

Liu, Jingyang, Yao, Sujia, Gu, Jiayuan, Xu, Lan

Abstract

Long-horizon navigation requires an agent to revise its intermediate objectives as evidence accumulates. Full visual histories are costly to process, while compact summaries may omit details needed to reconsider earlier decisions. We introduce NavProbe, a hierarchical zero-shot navigation agent that couples a dynamic subgoal agenda with active evidence retrieval. A compact index links summaries of visited places, transitions, and landmarks to their visual and geometric records. When the current context is insufficient, a task executive retrieves targeted evidence to generate, revise, or resolve subgoals. Reusable conclusions are used to update the index, and a skill policy converts the revised task state into parameterized navigation actions. NavProbe achieves 71.7% SR and 55.8% SPL on R2R-CE and 55.3% SR and 38.6% SPL on RxR-CE, outperforming strong zero-shot baselines. It also achieves 79.3% SR on HM3D-v2 ObjectNav, with qualitative real-robot demonstrations illustrating physical deployment.

Chinese Translation

长时间范围的导航要求代理在证据积累的过程中修正其中间目标。完整的视觉历史处理成本高，而紧凑的摘要可能会遗漏重新考虑早期决策所需的细节。我们提出了NavProbe，一种层次化的零-shot导航代理，它将动态子目标议程与主动证据检索相结合。一个紧凑的索引将访问过的地点、过渡和地标的摘要与其视觉和几何记录连接起来。当当前上下文不足时，任务执行器检索针对性的证据以生成、修正或解决子目标。可重用的结论用于更新索引，而技能策略将修正后的任务状态转换为参数化的导航动作。NavProbe在R2R-CE上实现了71.7%的成功率（SR）和55.8%的路径长度成功率（SPL），在RxR-CE上实现了55.3%的SR和38.6%的SPL，超越了强有力的零-shot基线。在HM3D-v2 ObjectNav上，它还实现了79.3%的SR，定性真实机器人演示展示了其物理部署。

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cs.RO / 49 / 2609.27536

Behaviora - A Conceptual Architecture for External and Internal Behavior of Robots and Agents

Behaviora - 机器人和智能体外部与内部行为的概念架构

Nyman, Gote

Abstract

Behaviora is a preliminary conceptual architecture for representing agent and robot behavior, external and internal alike, in an addressable form. A behaving robot or agent performs a Behavior Episode composed of episode components, which can be derived from behavior taxonomies (BTax) and assigned persistent identifiers. We denote these identifiers as IoB (Internet of Behaviors) Addresses. A Behavior Episode specifies what the system does, while a Style Profile (SP) specifies how this behavior is expressed. Style can communicate characteristics of the actor and qualities such as competence and cultural manners. An Experience Profile (EP) represents behaviorally relevant internal state that modulates the execution of an Episode. Finally, a Behavior Compiler maps these behavioral representations to platform-specific actions. We use a primitive touching arm model to show these components and their relations. External Behavior is a result of addressable movements and their styles. Internal Behavior is represented through the same episodic principle and can be rendered as inner speech. Sensing, perception and complex task contexts have not been included in the present implementation, although a conceptual place is reserved for them.

Chinese Translation

Behaviora 是一种初步的概念架构，用于以可寻址的形式表示智能体和机器人行为，包括外部和内部行为。一个表现行为的机器人或智能体执行一个行为事件（Behavior Episode），该事件由事件组件组成，这些组件可以从行为分类法（Behavior Taxonomies, BTax）中推导并分配持久性标识符。我们将这些标识符称为行为互联网（Internet of Behaviors, IoB）地址。行为事件指定系统所做的事情，而风格档案（Style Profile, SP）则指定这种行为的表达方式。风格可以传达行为者的特征以及诸如能力和文化礼仪等品质。经验档案（Experience Profile, EP）表示行为相关的内部状态，这种状态调节事件的执行。最后，行为编译器将这些行为表示映射到特定平台的动作。我们使用一个原始的触摸臂模型来展示这些组件及其关系。外部行为是可寻址运动及其风格的结果。内部行为则通过相同的事件原则表示，并可以呈现为内心独白。尽管目前的实现中没有包括感知、认知和复杂任务背景，但为它们保留了概念空间。

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cs.RO / 50 / 2609.27597

Gray-Box Model Predictive Control for Articulated Dump Trucks via Gaussian Process Learning of Sideslip

基于高斯过程学习侧滑的关节式自卸卡车灰箱模型预测控制

Shahirpour, Arash, Ahlers, Jens, Schulte, Christopher, Reuscher, Tim

Abstract

The growing demand for automation in the mining industry, particularly for the autonomous operation of articulated dump trucks (ADTs), has drawn increased attention to accurate vehicle modeling. The importance of such models lies in their use in model predictive control (MPC), model-based estimation methods, and vehicle simulation. While dynamic modeling offers a viable solution for these purposes, it is associated with complex setup and parametrization and may require recalibration in changing operating environments. As a result, kinematic models have dominated ADT modeling, especially in MPCs, at the expense of reduced prediction accuracy. In this work, we propose an approach using Gaussian Process Regression (GPR) to learn the sideslip angle of the vehicle, which is identified as the primary contributor to the reduced accuracy of kinematic models. The learned GPR function is augmented into the kinematic model to form a gray-box model that aims to reduce the gap to dynamic models. We show that the gray-box model can predict the sideslip angle and, consequently, the vehicle's lateral velocity, thereby improving the MPC's prediction performance. The resulting gray-box MPC is compared against two white-box MPCs in a simulation environment. The results indicate an improvement in terms of maximum lateral tracking error from over 2 m to 0.56 m.

Chinese Translation

随着矿业行业对自动化需求的不断增长，特别是对关节式自卸卡车（ADTs）自主操作的需求，准确的车辆建模受到了越来越多的关注。这类模型的重要性体现在其在模型预测控制（MPC）、基于模型的估计方法和车辆仿真中的应用。尽管动态建模为这些目的提供了可行的解决方案，但其通常伴随着复杂的设置和参数化，并且在变化的操作环境中可能需要重新校准。因此，运动学模型在ADTs建模中占主导地位，尤其是在MPC中，但这也导致了预测精度的降低。在本研究中，我们提出了一种使用高斯过程回归（GPR）学习车辆侧滑角的方法，侧滑角被确定为运动学模型精度降低的主要因素。将学习到的GPR函数与运动学模型相结合，形成一个灰箱模型，旨在缩小与动态模型之间的差距。我们展示了灰箱模型能够预测侧滑角，从而预测车辆的横向速度，进而提高MPC的预测性能。最终的灰箱MPC在仿真环境中与两个白箱MPC进行了比较。结果表明，最大横向跟踪误差从超过2米改善至0.56米。

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cs.RO / 51 / 2609.27612

RegenHarness: A Robot Agent Harness with Evidence-Gated Recursive Self-Improvement

RegenHarness：一种具有证据门控递归自我改进的机器人代理系统

Wang, Kailin, Jie, Haoxiang, Yan, Yaoyuan, Heng, Zhiyou, Li, Zhaosong

Abstract

Long-horizon robot execution requires a clear distinction between a model's proposal, a controller's termination, and verified task completion. We present RegenHarness, an evidence-gated robot-agent harness connecting task planning to heterogeneous robot skills. Its execution architecture couples a model loop for context-conditioned proposals with an agent loop for dispatch, observation, verification, commitment, and bounded recovery. Four role-isolated contexts separate planning, supervision, verification, and recovery inputs. Versioned memory distinguishes observed facts from accepted task progress, while an identity- and version-bound commit gate controls updates to trusted task state. The runtime combines duplicate-dispatch control, resource leases, and recovery budgets under explicit backend contracts, and checks the original user goal before reporting completion. To our knowledge, we are the first to introduce an evidence-gated recursive self-improvement (RSI) protocol for embodied robotic agents. Across missions, execution records motivate candidate changes to context rules, task templates, routing, and recovery policies; fixed regression checks and release authorization govern their acceptance; versioned rollout and rollback preserve configuration traceability. This RSI protocol revises the harness configuration without online model-weight updates or permission to weaken the commit gate. A real quadruped deployment documents voice-triggered warehouse navigation, panoramic inspection, visual analysis, message delivery, return, and spoken reporting through linked audio, images, trajectories, and receipts. A separate circuit demonstrates why completion depends on execution history rather than endpoint proximity alone. Together, the cases demonstrate integrated perception, physical execution, communication, and history-dependent completion in real-world robot tasks.

Chinese Translation

长时间跨度的机器人执行需要明确区分模型的提案、控制器的终止和经过验证的任务完成。我们提出了RegenHarness，这是一种证据门控的机器人代理系统，将任务规划与异构机器人技能连接起来。其执行架构将上下文条件提案的模型循环与用于调度、观察、验证、承诺和有界恢复的代理循环相结合。四个角色隔离的上下文将规划、监督、验证和恢复输入分开。版本化内存区分观察到的事实与接受的任务进展，而身份和版本绑定的承诺门控制对可信任务状态的更新。运行时结合了重复调度控制、资源租赁和恢复预算，并在明确的后端合同下检查原始用户目标，然后报告完成。根据我们的了解，我们是首个为具身机器人代理引入证据门控递归自我改进（RSI）协议的研究。通过任务执行记录，激励候选变更上下文规则、任务模板、路由和恢复策略；固定回归检查和发布授权管理其接受；版本化的推出和回滚保持配置的可追溯性。该RSI协议在不进行在线模型权重更新或削弱承诺门权限的情况下修订了系统配置。一个真实的四足机器人部署记录了语音触发的仓库导航、全景检查、视觉分析、信息传递、返回和通过链接的音频、图像、轨迹和收据进行的口头报告。一个独立的电路演示了为什么完成依赖于执行历史而不仅仅是端点接近。综合来看，这些案例展示了在真实世界机器人任务中集成感知、物理执行、通信和历史依赖的完成。

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cs.RO / 52 / 2609.27656

InternW0: A Foundational Physical World Model for Efficient Real-World Interactions

InternW0：高效现实世界交互的基础物理世界模型

Cai, Jisong, Mu, Yao, Yang, Ganlin, Cao, Zhe, Tu, Zhangzheng, Gao, Xing, Li, Kailin, Zhan, Xinyu, Yang, Lixin, Zhu, Yangkun, Ma, Haoxiang, Zhou, Ming, Yu, Qiaojun, Xue, Yufei, He, Liqun, Yao, Yifei, Zhu, Yifan, Ling, Long, Jiang, Bingqi, Guo, Haoyu, Zhu, Xueyue, Zhou, Bowen, Zhao, Bin, Xue, Tianfan, Shen, Chunhua, Zhang, Weinan

Abstract

Physical intelligence requires more than predicting how the world may evolve: predictions must remain actionable as the world continues to change. We introduce InternW0, the first instantiation of the InternW physical world model series from Shanghai AI Laboratory, built around omnimodal interfaces, asynchronous multi-frequency processing, and local physical modeling under partial observations and external influences. InternW0 jointly learns future visual dynamics and continuous robot control through an asymmetric video--action architecture with flow matching. A high-capacity video expert provides longer-horizon predictive context, while a lightweight action expert operates at a faster timescale. Instead of regenerating the future for every action update, InternW0 reuses layerwise K/V and adapts it to newly observed states through observation-conditioned context routing. Domain-specific interfaces and soft prompts support heterogeneous embodiments, while contact-aware post-training incorporates force and tactile signals for contact-rich manipulation. We train InternW0 on approximately 7,200 hours of heterogeneous robot and egocentric data, including EgoLab, a 275-hour real-laboratory egocentric dataset. Evaluation spans simulation benchmarks and real-world scientific tasks, including a 15-stage metal--organic framework synthesis workflow and 5-stage contact- and force-aware dexterous manipulation for general-purpose quantitative pipetting. These results advance scalable, asynchronous, and science-native physical world models for universal and efficient real-world interactions.

Chinese Translation

物理智能不仅需要预测世界如何演变：预测必须在世界持续变化的情况下保持可操作性。我们介绍了InternW0，这是上海人工智能实验室推出的InternW物理世界模型系列的首个实例，围绕全模态接口、异步多频处理和在部分观察及外部影响下的局部物理建模构建。InternW0通过不对称的视频-动作架构与流匹配，联合学习未来的视觉动态和连续的机器人控制。一个高容量的视频专家提供更长时间范围的预测上下文，而一个轻量级的动作专家则在更快的时间尺度上运行。InternW0并不是为每次动作更新重新生成未来，而是重用逐层的K/V，并通过观察条件的上下文路由将其适应于新观察到的状态。特定领域的接口和软提示支持异构体现，而接触感知的后训练则结合了力和触觉信号，以实现接触丰富的操作。我们在约7200小时的异构机器人和自我中心数据上训练InternW0，其中包括EgoLab，一个275小时的真实实验室自我中心数据集。评估涵盖了模拟基准和现实世界科学任务，包括一个15阶段的金属-有机框架合成工作流程和5阶段的接触和力感知灵巧操作，用于通用定量移液。这些结果推动了可扩展的、异步的、科学本土的物理世界模型的发展，以实现普遍和高效的现实世界交互。

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cs.RO / 53 / 2609.27695

GLoTouch: Global-to-Local Haptic Perception Using a Parallel Gripper for Object Search, Recognition, and Grasping Without External Vision

GLoTouch：使用并行夹持器进行物体搜索、识别和抓取的全球到局部触觉感知，无需外部视觉

Li, Zonglin, Zhang, Wanruo, Wang, Yiming, Song, Kun, Zhou, Xinyi, Ma, Daolin

Abstract

Perceiving objects in the environment is a fundamental capability of autonomous robots. In dark or low-light environments, external cameras often fail to reliably perceive object positions and geometry; when visual sensing is unavailable, completing target search, recognition, and grasping through touch alone becomes a key robot manipulation capability. This task must simultaneously address container-scale spatial exploration and object-scale fine-grained geometric perception, which is particularly challenging for low-degree-of-freedom parallel grippers. However, a unified framework remains lacking for connecting container-scale spatial exploration with object-scale fine-grained geometric perception and grasping. To address this challenge, we present \textbf{GLoTouch}, a global-to-local haptic perception and manipulation framework built on a parallel gripper. In the global stage, the gripper holds a passive long-reach probe, combining force measurements with known tool geometry to localize contacts and actively estimate candidate-object positions, coarse contours, and heights. In the local stage, the robot sets down the probe and uses the bilateral visuotactile sensors on the same gripper to directly acquire local haptic observations, which are matched against a given target 3-D model without object-specific training. We evaluate the framework in both simulation and real-robot experiments. Source code will be open-sourced.

Chinese Translation

感知环境中的物体是自主机器人的一项基本能力。在黑暗或低光环境中，外部摄像头往往无法可靠地感知物体的位置和几何形状；当视觉感知不可用时，仅通过触觉完成目标搜索、识别和抓取成为机器人操作的一项关键能力。该任务必须同时解决容器尺度的空间探索和物体尺度的细粒度几何感知，这对于低自由度的并行夹持器尤其具有挑战性。然而，目前缺乏一个统一的框架将容器尺度的空间探索与物体尺度的细粒度几何感知和抓取连接起来。为了解决这一挑战，我们提出了 extbf{GLoTouch}，一个基于并行夹持器的全球到局部触觉感知与操作框架。在全球阶段，夹持器持有一个被动的长探针，将力测量与已知工具几何形状相结合，以定位接触点并主动估计候选物体的位置、粗略轮廓和高度。在局部阶段，机器人放下探针，并利用同一夹持器上的双侧视触觉传感器直接获取局部触觉观测，这些观测与给定的目标三维模型进行匹配，而无需针对特定物体的训练。我们在仿真和真实机器人实验中评估了该框架。源代码将开源。

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cs.RO / 54 / 2609.27702

DAVIO: Dense Monocular-Inertial SLAM with Feed-Forward Initialization and Pose-Conditioned Mapping

DAVIO：具有前馈初始化和姿态条件映射的稠密单目惯性SLAM

Mahmoud, Jaafar, Movsesyan, Arthur, Iumanov, Mikhail, Kolyubin, Sergey

Abstract

A camera and an IMU are the minimal sensor setup for metric localization and dense mapping, yet classical visual--inertial filters must wait for parallax before they start and then retain only sparse landmarks. Feed-forward geometry models, in contrast, predict dense structure from a few images but provide neither metric scale nor gravity. We present DAVIO, which uses a single multi-view depth model, Depth Anything~3, for both start-up and mapping. At start-up, a five-image window and preintegrated IMU measurements form a feature-free linear system. Its robust, conditioning-checked solution bootstraps a VIO filter through buffered replay. During tracking, the filter's metric poses condition the depth model. Residual scale is corrected only along viewing rays, which preserves the metric camera baselines, and a gravity-preserving submap graph with drift-gated revisits refines the map. On EuRoC, DAVIO starts markedly earlier, reduces the localization error, and maps more accurately than SOTA feed-forward mappers given identical poses. On building-scale ORI sequences, DAVIO is on bar or better than SOTA mappers on the same odometry, and degrades far less when GT poses are replaced by real odometry. We release the code of DAVIO, a real-time dense metric SLAM system, to the community.

Chinese Translation

相机和惯性测量单元（IMU）是进行度量定位和稠密映射的最小传感器配置，然而经典的视觉-惯性滤波器必须等待视差出现后才能启动，并且仅保留稀疏地标。相比之下，前馈几何模型可以从少量图像中预测稠密结构，但不提供度量尺度或重力。我们提出了DAVIO，它使用单一的多视角深度模型Depth Anything~3进行启动和映射。在启动阶段，五幅图像窗口和预积分的IMU测量形成一个无特征的线性系统。其稳健的、经过条件检查的解通过缓冲重放引导VIO滤波器。在跟踪过程中，滤波器的度量姿态对深度模型进行条件化。残差尺度仅沿视线进行校正，这保留了度量相机基线，并且一个保持重力的子图图形通过漂移门控重访来优化地图。在EuRoC数据集上，DAVIO的启动时间明显更早，定位误差更小，并且在相同姿态下的映射精度高于现有的前馈映射器。在建筑规模的ORI序列中，DAVIO的表现与现有映射器相当或更好，并且在真实里程计替代真实姿态时，性能下降更小。我们将DAVIO的代码发布给社区，这是一个实时稠密度量SLAM系统。

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cs.RO / 55 / 2609.27712

Wave-Robust Passive AUV Localization Using FP-MUSIC

基于FP-MUSIC的波动鲁棒被动式自主水下车辆定位

Saqib, Usama, Rønning, Ola, Wąsowski, Andrzej

Abstract

Localizing an autonomous underwater vehicle without pre-deployed seabed transponders, or direct access to onboard vehicle sensors remains a core challenge. We present a receiver-passive 3-D localization and spatial mapping system utilizing a single floating surface buoy equipped with a hydrophone array and an inertial measurement unit (IMU). The central difficulty is that surface wave motion induces six-degree-of-freedom (6-DOF) perturbations that rotate the array between snapshots, degrading conventional subspace processing. We resolve this by introducing a fixed-point iterative MUltiple SIgnal Classification algorithm (FP-MUSIC) that uses IMU measurements to de-warp snapshot covariances prior to direction-of-arrival estimation. Furthermore, we employ a subspace-projected wideband matched filter to resolve beacon ranges and use power asymmetry for independent front-back identification. Evaluations across simulated sea states demonstrate that FP-MUSIC substantially reduces localization error relative to uncompensated methods and sustains robust 3-D tracking and vehicle orientation estimation under wave-induced motion. At moderate sea state, FP-MUSIC increases the 2-m beacon-separation accuracy from approximately 45% to 75%.

Chinese Translation

在没有预先部署的海底应答器或直接访问车载传感器的情况下，对自主水下车辆进行定位仍然是一个核心挑战。我们提出了一种接收器被动式三维定位和空间映射系统，该系统利用一个配备有水听器阵列和惯性测量单元（IMU）的浮动表面浮标。主要困难在于，表面波动会引起六自由度（6-DOF）扰动，导致阵列在快照之间发生旋转，从而降低传统子空间处理的效果。我们通过引入一种固定点迭代的多信号分类算法（FP-MUSIC）来解决这一问题，该算法利用IMU测量值在到达方向估计之前对快照协方差进行去扭曲。此外，我们采用子空间投影宽带匹配滤波器来解析信标范围，并利用功率不对称性进行独立的前后识别。在模拟海况下的评估表明，FP-MUSIC显著降低了相对于未补偿方法的定位误差，并在波动引起的运动下保持了鲁棒的三维跟踪和车辆方向估计。在中等海况下，FP-MUSIC将2米信标分离精度从约45%提高到75%。

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cs.RO / 56 / 2609.27720

CoRelNav: Collaborative Relational Navigation for Multi-Robot Spatially Constrained Semantic Navigation

CoRelNav：用于多机器人空间约束语义导航的协作关系导航

He, Jinyu, Mao, Zihao, Jin, Haonan, Fu, Mengyin, Song, Wenjie

Abstract

Spatially constrained semantic navigation requires robots to identify targets specified not only by semantic categories but also by relations to surrounding objects. In unknown environments, resolving such goals requires efficient exploration together with sufficient target and contextual evidence for reliable relation verification. Existing methods leave relation-aware verification and multi-robot collaboration largely disconnected: relational navigation is predominantly single-agent, while multi-robot systems seldom coordinate distributed observations for instance-specific relation verification. We propose CoRelNav, whose core is coupling task-conditioned multi-robot exploration with candidate-driven collaborative verification. A spatial-semantic field converts task constraints, scene nodes, and object features into exploration utility; as candidate information accumulates, robots are reallocated toward complementary evidence under team navigation costs, while instance-consistent observations are aggregated across topology nodes. This coupling reduces redundant search and enables relation hypotheses to be resolved from distributed partial evidence that independent exploration or isolated-view verification can leave ambiguous. Experiments in photorealistic simulation demonstrate consistent improvements over representative baselines, with ablations validating the proposed exploration and verification mechanisms. We further deploy the complete system on two physical mobile robots, demonstrating its applicability to real-world collaborative navigation.

Chinese Translation

空间约束的语义导航要求机器人识别不仅由语义类别指定的目标，还要识别与周围物体的关系。在未知环境中，实现这样的目标需要高效的探索以及足够的目标和上下文证据以进行可靠的关系验证。现有方法在关系感知验证和多机器人协作方面大多脱节：关系导航主要是单一代理，而多机器人系统很少协调分布式观察以进行特定实例的关系验证。我们提出了CoRelNav，其核心是将任务条件的多机器人探索与候选驱动的协作验证相结合。空间语义场将任务约束、场景节点和物体特征转换为探索效用；随着候选信息的积累，机器人在团队导航成本下重新分配到互补证据上，同时在拓扑节点之间聚合实例一致的观察。这种耦合减少了冗余搜索，使得可以从分布式部分证据中解决关系假设，而独立探索或孤立视图验证可能会留下模糊。我们在逼真的仿真实验中展示了相对于代表性基线的一致性改进，消融实验验证了所提出的探索和验证机制。我们进一步在两台物理移动机器人上部署了完整系统，展示了其在现实世界协作导航中的适用性。

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cs.RO / 57 / 2609.27734

InfiNoVA: Infinite Novel View Augmentation for Viewpoint Invariant Robot Policies

InfiNoVA：用于视点不变机器人策略的无限新视角增强

Gottam, Sai Puneeth Reddy, Rueckert, Elmar, Dave, Vedant

Abstract

Vision-Language-Action (VLA) policies often rely strongly on the camera viewpoints seen during training, causing substantial performance degradation when deployed from unseen perspectives. Collecting demonstrations from sufficiently diverse physical viewpoints is expensive and still provides only sparse coverage of the viewpoint space. We introduce InfiNoVA, a data-augmentation framework that converts synchronized multi-camera demonstrations into a dense distribution of geometrically consistent training views. InfiNoVA reconstructs each manipulation trajectory as a time-varying 3D Gaussian representation and renders novel observations from sampled camera poses while preserving the original state-action correspondence. This explicit scene representation improves frame-level fidelity and temporal consistency while reducing task-critical hallucinations observed in generative novel-view synthesis. Across four real-world manipulation tasks, policies trained with InfiNoVA achieve 5.4x higher average success under unseen randomized viewpoints than both VISTA-based augmentation and the unaugmented policy. InfiNoVA further achieves 1.7x higher success than training directly on all five physical camera views. These results show that dense, geometrically grounded viewpoint augmentation provides a practical route toward camera-robust robot policies without modifying the underlying policy architecture.

Chinese Translation

视觉-语言-动作（VLA）策略通常在训练期间强烈依赖于所见的相机视点，因此在从未见过的视角部署时会导致显著的性能下降。从足够多样的物理视点收集示例是昂贵的，并且仍然只能提供视点空间的稀疏覆盖。我们提出了InfiNoVA，这是一种数据增强框架，将同步的多相机示例转换为几何一致的训练视图的密集分布。InfiNoVA将每个操作轨迹重建为时间变化的3D高斯表示，并从采样的相机姿态渲染新观察，同时保持原始状态-动作对应关系。这种显式场景表示提高了帧级保真度和时间一致性，同时减少了在生成新视角合成中观察到的任务关键幻觉。在四个真实世界的操作任务中，使用InfiNoVA训练的策略在未见随机视点下的平均成功率比基于VISTA的增强和未增强策略高出5.4倍。InfiNoVA的成功率比直接在所有五个物理相机视角上训练的成功率高出1.7倍。这些结果表明，密集的、几何基础的视点增强为实现相机鲁棒的机器人策略提供了一条实用的途径，而无需修改基础策略架构。

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cs.RO / 58 / 2609.27747

Less Language, More Latents: Annotation-Efficient VLAs for Driving

更少的语言，更丰富的潜在信息：用于驾驶的注释高效视觉语言行动模型

Zakharov, Alexey, Oksuz, Kemal, Dokania, Puneet K.

Abstract

Vision-language-action models (VLA) promise human-steerable autonomous driving, but their training is bottlenecked by the scarcity of frames paired with natural-language instructions: while camera streams and expert trajectories are logged at scale, language annotations (e.g., turn left at the intersection) remain scarce and expensive to acquire. To address this challenge, we introduce Latent Action Driving Annotations (LADA), a three-stage pipeline that transforms abundant unlabelled observation-trajectory pairs into a substrate for language-conditioned control. First, we train a latent action model with a vector-quantised bottleneck, producing a compact codebook of high-level vehicle intents. Second, a small language-annotated subset is used to train a vision-language translator to map observations and language instructions into this codebook. Third, we train a driving VLA on observation-latent-action pairs over the full unlabelled corpus. Using fewer than 5% of language annotations and without leveraging any auxiliary chain-of-thought reasoning or visual question answering streams, LADA achieves a Driving Score of 87.98 and a Success Rate of 70.46% on the closed-loop Bench2Drive benchmark, matching or surpassing fully supervised baselines.

Chinese Translation

视觉-语言-行动模型（VLA）承诺实现人类可操控的自主驾驶，但其训练受到与自然语言指令配对的帧稀缺的瓶颈限制：尽管摄像头流和专家轨迹在大规模下被记录，但语言注释（例如，在交叉口左转）仍然稀缺且获取成本高。为了解决这一挑战，我们提出了潜在行动驾驶注释（LADA），这是一种三阶段流程，将丰富的未标记观察-轨迹对转化为语言条件控制的基础。首先，我们训练一个具有向量量化瓶颈的潜在行动模型，生成一个高层次车辆意图的紧凑代码本。其次，使用一个小的语言注释子集训练视觉-语言翻译器，将观察和语言指令映射到该代码本中。第三，我们在整个未标记语料库上对观察-潜在行动对训练驾驶VLA。使用不到5%的语言注释，并且不利用任何辅助的思维链推理或视觉问答流，LADA在闭环Bench2Drive基准测试中实现了87.98的驾驶得分和70.46%的成功率，达到或超过了完全监督的基准。

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cs.RO / 59 / 2609.27780

Task-Prototype Guided Flow Matching for Few-Shot Generalization in Vision-Language Robot Manipulation

基于任务原型引导的流匹配用于视觉语言机器人操作中的少样本泛化

Wang, Yizhao, Zhang, Guantao, Wang, Jingbo

Abstract

Vision-language robot manipulation policies can follow semantic instructions, but adapting them to a new procedure from only a few demonstrations remains difficult because language underspecifies contact timing, motion phases, corrective behavior, and execution style. This paper presents Task-Prototype Guided Flow Matching (TP-Flow), a few-shot manipulation framework that converts support demonstrations into structured task-prototype tokens and uses them to guide both the initial flow prior and the velocity field. TP-Flow employs symmetric cross-attention with learnable queries to extract phase-level prototypes, parameterizes a task-adaptive initial distribution, and injects prototype information through gated adaptive normalization. It is trained with an episodic support-query objective and prototype contrastive regularization, so few-shot adaptation is simulated during training while nuisance information is suppressed. On the LEROBOT-ARM-SO101 platform, TP-Flow achieves 66.8\%, 79.6\%, and 82.1\% success rates under 1-, 4-, and 6-shot settings, with a 75.5\% few-shot AUC. At 1-shot, it improves over CFM, Pooled-Demo CFM, and In-Context Flow by 29.8, 14.5, and 9.9 percentage points. It also improves held-out target-group generalization across novel-object transfer, goal recombination, long-horizon composition, and contact/correction tasks. TP-Flow maintains real-time execution with six online prototype tokens, 54.3 ms latency, 3.9 GB peak memory, and a 10 Hz control rate, while reducing the noisy-support success drop to 6.2\%. Theoretical diagnostics show that prototype distance aligns with action-distribution distance, the adaptive prior reduces transport cost, and gated modulation keeps measured trajectory deviations below the derived ODE bound. The code repository is omitted for anonymous review.

Chinese Translation

视觉语言机器人操作策略能够遵循语义指令，但仅通过少量示例将其适应于新程序仍然困难，因为语言未能明确接触时机、运动阶段、纠正行为和执行风格。本文提出了一种基于任务原型引导的流匹配框架（Task-Prototype Guided Flow Matching，TP-Flow），该框架将支持示例转换为结构化的任务原型标记，并利用这些标记引导初始流先验和速度场。TP-Flow采用对称交叉注意机制，结合可学习查询以提取阶段级原型，参数化任务自适应初始分布，并通过门控自适应归一化注入原型信息。它通过情节支持-查询目标和原型对比正则化进行训练，因此在训练过程中模拟了少样本适应，同时抑制了干扰信息。在LEROBOT-ARM-SO101平台上，TP-Flow在1-shot、4-shot和6-shot设置下分别达到了66.8%、79.6%和82.1%的成功率，少样本AUC为75.5%。在1-shot情况下，相比于CFM、Pooled-Demo CFM和In-Context Flow，成功率提高了29.8、14.5和9.9个百分点。它还在新物体转移、目标重组、长时间组合以及接触/纠正任务中改善了保留目标组的泛化能力。TP-Flow在使用六个在线原型标记时保持实时执行，延迟为54.3毫秒，峰值内存为3.9 GB，控制频率为10 Hz，同时将噪声支持的成功率下降降低到6.2%。理论诊断表明，原型距离与动作分布距离一致，自适应先验降低了传输成本，而门控调制使测量的轨迹偏差保持在推导的常微分方程（ODE）界限以下。代码库因匿名审稿而被省略。

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cs.RO / 60 / 2609.27816

Safe Multi-Robot Coordination via VLM-LLM Reasoning and Reachability Analysis

通过 VLM-LLM 推理和可达性分析实现安全的多机器人协调

Dwedar, Mohamed, Hafez, Ahmad, Jesser, Alexander, Alanwar, Amr

Abstract

Safe coordination in heterogeneous machine-to-machine (M2M) robotic systems is challenging when robots differ in sensing capabilities, environmental awareness, and motion execution roles. This paper presents a centralized safety-aware M2M framework for cooperative goal-directed navigation in a heterogeneous mobile robot team comprising a vision-capable quadruped and a camera-less robotic vehicle. The objective is to guide both platforms toward a goal region while avoiding static and dynamic obstacles and preventing unsafe inter-robot interactions. Under the principle of shared perception, the vision-capable robot provides semantic environmental awareness through a centralized server over an MQTT broker, enabling the camera-less platform to navigate using this shared scene representation alongside its own odometry, IMU, and state feedback. A vision-language model (VLM) interprets the visual stream, and the extracted semantic data is mapped into conservative metric geometric constraints, including inflated obstacle sets, safe corridors, and goal regions. A large language model (LLM) proposes high-level task allocations, while physical command authority is restricted to a robot-specific zonotope reachability gate. This verification engine propagates independent reachable tubes to evaluate obstacle avoidance, safe-corridor containment, and inter-robot separation predicates before approving commands. Online experiments across clear-path and dynamic-obstacle scenarios show that the pipeline reliably approves safe motion, triggers conservative replanning or holding maneuvers upon constraint violation, and enforces a strict architectural separation between advisory semantic reasoning and formally verified motor execution.

Chinese Translation

在异构机器对机器（M2M）机器人系统中，当机器人在感知能力、环境意识和运动执行角色上存在差异时，实现安全协调是一项挑战。本文提出了一种集中式的安全意识 M2M 框架，用于异构移动机器人团队的协作目标导向导航，该团队包括一台具备视觉能力的四足机器人和一台无摄像头的机器人车辆。其目标是引导这两种平台朝向目标区域，同时避免静态和动态障碍物，并防止不安全的机器人间交互。在共享感知的原则下，具备视觉能力的机器人通过一个集中服务器和 MQTT 代理提供语义环境意识，使得无摄像头平台能够利用这一共享场景表示以及自身的里程计、惯性测量单元（IMU）和状态反馈进行导航。视觉语言模型（VLM）对视觉流进行解释，提取的语义数据被映射为保守的度量几何约束，包括膨胀的障碍物集合、安全通道和目标区域。大型语言模型（LLM）提出高层次的任务分配，而物理命令权限则限制在特定机器人的区域可达性门限内。该验证引擎传播独立的可达管道，以评估障碍物规避、安全通道包含和机器人间分离谓词，然后再批准命令。在清晰路径和动态障碍场景下的在线实验表明，该流程可靠地批准安全运动，在约束违反时触发保守的重新规划或保持机动，并在建议的语义推理与正式验证的运动执行之间强制实施严格的架构分离。

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cs.RO / 61 / 2609.27905

Anchor-Free Hidden-Target Seeking via Certified Self-Calibration under Correlated Odometry

基于相关里程计的无锚隐藏目标寻址通过认证自校准

Bagla, Yash

Abstract

We study hidden-target seeking in an anchor-free regime where neither the vehicle, the relay, nor the target has an accessible global pose. The vehicle never senses the target directly; it receives only range-bearing observations through a single relay of unknown position and orientation, with motion known only via integrated body-frame odometry. Absolute localization is fundamentally impossible: the joint configuration retains an exact three-dimensional SE(2) gauge no estimator can resolve. Yet the quantities needed for control remain fully recoverable: motion collapses calibration to a task-relevant quotient, the relay-to-odometry yaw and target displacement, identifiable in closed form from two distinct vehicle views. We introduce an O(K) multi-view self-calibration estimator with an exact first-order yaw-uncertainty certificate that propagates the cross-view correlations integrated odometry induces: the full certificate attains 95.0% pooled coverage at the nominal 95% level, versus 82.1% when correlated poses are treated as independent. The certificate drives a hybrid policy that excites until calibration is trustworthy, refuses uncertified estimates, seeks using continuously re-measured geometry, and detects relay-frame changes via persistent certified inconsistency, avoiding unbounded dead-reckoning drift. Across 200 randomized closed-loop trials, the method attains 0.064 m median station error versus 0.065 m for an oracle given the true relay yaw, despite 27 m median dead-reckoning drift over long horizons; 90 physics-based ROS 2/Gazebo trials retain 30/30 success under nominal operation, communication degradation, and relay-frame disturbances. These results show global localization is unnecessary for reliable hidden-target seeking under this single-relay model, even when both the sensing infrastructure and the vehicle's own reference frame are uncalibrated.

Chinese Translation

我们研究了一种无锚的隐藏目标寻址机制，其中车辆、中继和目标均没有可访问的全局位姿。车辆从未直接感知目标；它仅通过一个未知位置和方向的中继接收范围-方位观测，运动信息仅通过集成的机体框架里程计得知。绝对定位在根本上是不可能的：联合配置保持一个精确的三维 SE(2) 量规，没有任何估计器可以解决。然而，控制所需的量仍然可以完全恢复：运动将校准简化为与任务相关的商，即中继到里程计的偏航角和目标位移，可以从两个不同的车辆视角中以封闭形式识别。我们引入了一种 O(K) 多视角自校准估计器，具有精确的一阶偏航不确定性证书，该证书传播了跨视角相关性所导致的集成里程计：完整证书在名义 95% 水平下达到 95.0% 的汇总覆盖率，而当相关位姿被视为独立时，仅为 82.1%。该证书驱动一种混合策略，直到校准可靠为止，拒绝未认证的估计，使用持续重新测量的几何形状进行寻址，并通过持久的认证不一致性检测中继框架的变化，从而避免无限制的死算漂移。在 200 次随机闭环试验中，该方法实现了 0.064 米的中位站点误差，而在给定真实中继偏航的情况下，oracle 的误差为 0.065 米，尽管在长时间范围内存在 27 米的中位死算漂移；在 90 次基于物理的 ROS 2/Gazebo 试验中，在名义操作、通信降级和中继框架干扰下，均保持 30/30 的成功率。这些结果表明，在这种单中继模型下，即使感知基础设施和车辆自身的参考框架均未校准，全球定位对于可靠的隐藏目标寻址也是不必要的。

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cs.RO / 62 / 2609.27938

Remote Surfaces at Your Fingertips: Electrovibration-Based Tactile Feedback for Robot Teleoperation via Touchscreen Interfaces

触手可及的远程表面：基于电振动的触觉反馈用于触摸屏界面的机器人遥操作

Kenan, Alperen, Cárdenas, Juan José García, Tapus, Adriana, Bremner, Paul, Giuliani, Manuel

Abstract

Enabling operators to perceive and interact with remote environments naturally is a fundamental challenge in robotic teleoperation. This is especially critical in tasks involving physical interaction, where real-time haptic awareness improves operational safety and effectiveness. Existing kinesthetic haptic feedback methods suffer from instability during rigid surface contacts and remain sensitive to communication delays, while visual cue-based force feedback imposes additional cognitive load and limits sustained situational awareness. This work presents a teleoperation interface that conveys remote surface interactions to the operator through electrovibration-based tactile feedback, enabling naturally mapped force reflection while avoiding the stability issues associated with kinesthetic feedback and the latency limitations of mechanical actuators. A user study (N=21) evaluated interface usability, sense of presence, and operator workload under two force reflection conditions: visual feedback and electrovibration-based tactile feedback. Characterisation experiments further assessed path-following accuracy and response time across both conditions. Results show that tactile feedback significantly reduced response time by 15.35% (p=0.002, d=0.96) and increased the sense of presence by 31% (p<0.001, d=0.90) compared to visual feedback, while imposing comparable workload and usability across both conditions. These findings demonstrate that electrovibration-based tactile feedback is a viable and effective modality for robot teleoperation, improving operator responsiveness and sense of presence in contact-rich manipulation tasks, with direct applicability to safety-critical domains such as nuclear maintenance.

Chinese Translation

使操作员能够自然地感知和与远程环境互动是机器人遥操作中的一个基本挑战。这在涉及物理交互的任务中尤为关键，因为实时的触觉意识可以提高操作安全性和有效性。现有的动觉触觉反馈方法在刚性表面接触时存在不稳定性，并且对通信延迟敏感，而基于视觉线索的力反馈则增加了额外的认知负担，并限制了持续的情境意识。本研究提出了一种遥操作界面，通过基于电振动的触觉反馈将远程表面交互传达给操作员，从而实现自然映射的力反射，同时避免与动觉反馈相关的稳定性问题和机械驱动器的延迟限制。一项用户研究（N=21）评估了在两种力反射条件下的界面可用性、存在感和操作员工作负荷：视觉反馈和基于电振动的触觉反馈。特征实验进一步评估了在这两种条件下的路径跟踪准确性和响应时间。结果表明，与视觉反馈相比，触觉反馈显著减少了响应时间15.35%（p=0.002，d=0.96），并将存在感提高了31%（p<0.001，d=0.90），同时在两种条件下的工作负荷和可用性相当。这些发现表明，基于电振动的触觉反馈是一种可行且有效的机器人遥操作方式，提高了操作员在接触丰富的操作任务中的响应能力和存在感，具有直接应用于核维护等安全关键领域的潜力。

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cs.RO / 63 / 2609.28027

Learning a Speed-adaptive Hip Exoskeleton Control Policy Via Sim-to-real Reinforcement Learning

通过仿真到现实强化学习学习速度自适应的髋关节外骨骼控制策略

Li, Bin, Hou, Zhimin, Hou, Jiacheng, Liang, Zenian, Wu, Tong, Ma, Teng, Fu, Chenglong

Abstract

Providing personalized exoskeleton assistance across varying walking speeds remains challenging. Existing online optimization methods are sample-inefficient, requiring extensive human-in-the-loop (HIL) evaluations to optimize the entire assistive torque profile. Sim-to-real reinforcement learning (RL) offers a promising alternative but cannot directly account for individual user preferences. We propose a framework integrating sim-to-real RL with online preference learning for personalized exoskeleton assistance. Specifically, assistance timing is learned in simulation by training RL policies with human musculoskeletal models across varying walking speeds. The learned policies are then distilled and deployed on a physical hip exoskeleton using onboard sensory observations. Gaussian-process-based preference learning further personalizes the assistance magnitude through pairwise user comparisons. By decoupling assistance timing learning in simulation from magnitude optimization in real-world experiments, our framework substantially reduces the online optimization space. Human-subject experiments demonstrate efficient identification of personalized assistive torque profiles across varying walking speeds with fewer real-world evaluations.

Chinese Translation

在不同步态速度下提供个性化的外骨骼辅助仍然面临挑战。现有的在线优化方法样本效率低，需进行大量的人机交互（HIL）评估以优化整个辅助扭矩配置。仿真到现实的强化学习（RL）提供了一种有前景的替代方案，但无法直接考虑个体用户的偏好。我们提出了一个框架，将仿真到现实的RL与在线偏好学习相结合，以实现个性化的外骨骼辅助。具体而言，辅助时机通过在不同步态速度下训练人类肌肉骨骼模型的RL策略在仿真中学习。然后，将学习到的策略提炼并部署到物理髋关节外骨骼上，利用车载传感器观察数据。基于高斯过程的偏好学习通过用户的成对比较进一步个性化辅助幅度。通过将仿真中的辅助时机学习与现实实验中的幅度优化解耦，我们的框架显著减少了在线优化空间。人类受试者实验表明，在不同步态速度下能够高效识别个性化的辅助扭矩配置，并且所需的现实评估次数更少。

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cs.RO / 64 / 2609.28044

AeRSoM: An Aerial Rigid-Soft Integrated Manipulator for Contact-Rich Manipulation

AeRSoM：一种用于接触丰富操作的空中刚柔集成操纵器

Liang, Jiacheng, Zhong, Hang, Wang, Yaonan, Chen, Ge, Zhang, Zhixing, Tian, Bocheng, Zhang, Hui, Wen, Li

Abstract

Contact-rich aerial manipulation remains fundamentally challenging because interaction forces are directly transmitted to the aerial platform, often leading to instability and degraded task performance. While compliant manipulators can mitigate these effects, existing aerial manipulation systems typically struggle to reconcile interaction compliance with manipulation precision. To this end, this article presents an aerial rigid-soft integrated manipulator (AeRSoM) robot that realizes embodied compliance for aerial manipulation. The proposed system integrates a fully actuated aerial platform, a rigid-soft manipulator, and variable-stiffness regulation to simultaneously achieve stable flight, compliant interaction, and precise manipulation. By distributing compliance throughout the manipulation system, the proposed design leverages distributed embodied compliance to passively absorb contact disturbances while preserving sufficient stiffness for task execution. To fully exploit the mechanical design, a composite control framework is developed for precise end-effector trajectory tracking in the presence of uncertainties and external disturbances. Extensive real-world experiments are conducted in representative contact-rich aerial manipulation tasks, including dynamic transmission-line grasping, physical interaction with a wind turbine blade, peg-in-hole, and screwing operations. The results demonstrate that the proposed rigid-soft integration significantly improves interaction robustness and task adaptability while maintaining manipulation accuracy, highlighting that embodied compliance provides a promising design paradigm for enhancing the safety, robustness, and versatility of aerial manipulation.

Chinese Translation

接触丰富的空中操作依然面临根本性的挑战，因为交互力直接传递到空中平台，常常导致不稳定和任务性能下降。尽管柔性操纵器可以缓解这些影响，但现有的空中操作系统通常难以协调交互的柔性与操纵的精确性。为此，本文提出了一种空中刚柔集成操纵器（AeRSoM）机器人，实现了空中操作的具身柔性。所提系统集成了一个全驱动的空中平台、一个刚柔操纵器和可变刚度调节，以同时实现稳定飞行、柔性交互和精确操纵。通过在操纵系统中分布柔性，所提设计利用分布式具身柔性被动吸收接触干扰，同时保持足够的刚度以执行任务。为了充分利用机械设计，开发了一种复合控制框架，以在不确定性和外部干扰存在的情况下实现精确的末端执行器轨迹跟踪。在代表性的接触丰富空中操作任务中进行了广泛的实地实验，包括动态输电线抓取、与风力涡轮叶片的物理交互、插销入孔和拧紧操作。结果表明，所提的刚柔集成显著提高了交互的鲁棒性和任务的适应性，同时保持了操纵的准确性，强调了具身柔性为增强空中操作的安全性、鲁棒性和多功能性提供了一种有前景的设计范式。

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cs.RO / 65 / 2609.28107

Distillation for Efficient Multitask Manipulation Policies via Conditional Flow Matching

通过条件流匹配进行高效多任务操作策略的蒸馏

Deshmukh, Shreya, Mahdi, Imen, Heppert, Nick, Valada, Abhinav

Abstract

Advances in generative modeling have recently been extensively employed in robotics for policy learning. In particular, Conditional Flow Matching (CFM) trained with expert demonstrations has been shown to outperform existing methods on robot manipulation benchmarks. While prior work has mainly focused on single-task settings, we study the problem from a multi-task perspective, as training independent models for each task is computationally expensive. Multi-Task policy learning comes with its own set of challenges, as naively training on a concatenated dataset of demonstrations would either require increased model capacity to accommodate the added complexity or result in drops in performance. We propose to distill knowledge from single-task CFM experts into a shared multi-task policy by transferring their learned velocity fields. We combine this distillation signal with the original CFM objective to retain fidelity to the demonstrations. Experiments on RLBench show that our approach improves multi-task policy performance over naive training while maintaining a fixed model size.

Chinese Translation

生成建模的进展最近在机器人领域被广泛应用于策略学习。特别是，经过专家演示训练的条件流匹配（Conditional Flow Matching, CFM）在机器人操作基准测试中表现优于现有方法。尽管之前的工作主要集中在单任务设置上，我们从多任务的角度研究这一问题，因为为每个任务训练独立模型在计算上是昂贵的。多任务策略学习面临自身的一系列挑战，因为简单地在连接的演示数据集上进行训练要么需要增加模型容量以适应增加的复杂性，要么会导致性能下降。我们提出通过转移单任务 CFM 专家的学习速度场，将知识蒸馏到共享的多任务策略中。我们将这一蒸馏信号与原始 CFM 目标相结合，以保持对演示的忠实性。在 RLBench 上的实验表明，我们的方法在保持固定模型大小的同时，改善了多任务策略的性能，相较于简单训练有显著提升。

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cs.RO / 66 / 2609.28131

DEAL-Grasp: Decoupled Alignment Representation for Geometry-Aware Dexterous Grasp Generation

DEAL-Grasp：用于几何感知灵巧抓取生成的解耦对齐表示

Zhao, Fuqiang, Liu, Qian

Abstract

Synthesizing realistic articulated hand-object interactions is a fundamental problem in virtual reality, embodied intelligence, and digital human applications. Existing methods for dexterous grasp synthesis typically regress or denoise poses in a joint space that couples global rigid motion with local articulation, which often yields unstable samples and physically implausible contacts. We introduce DEAL-Grasp, built upon the Decoupled Alignment (DEAL) representation, which reformulates grasp synthesis as alignment-space generation: the interaction state comprises task-space geometric anchors and articulation parameters, from which the rigid transform is recovered via closed-form Procrustes alignment while preserving local articulation. On this mixed state, we model grasp generation using heterogeneous-state flow matching with component-wise vector fields, incorporating time-adaptive physical regularization during training. At inference, grasps are synthesized solely by integrating the learned vector field, without test-time optimization or auxiliary physical guidance. Across MultiDex and zero-shot RealDex benchmarks, DEAL-Grasp attains high force-perturbation success rates alongside minimal penetration and high diversity of generated grasps, while substantially reducing native inference latency compared to optimization-heavy baselines. The project page is available at https://wmtlab.github.io/DEAL-Grasp/.

Chinese Translation

合成真实的关节手-物体交互是虚拟现实、具身智能和数字人类应用中的一个基本问题。现有的灵巧抓取合成方法通常在一个将全局刚性运动与局部关节运动耦合的关节空间中回归或去噪姿态，这往往会导致不稳定的样本和物理上不合理的接触。我们提出了DEAL-Grasp，基于解耦对齐（DEAL）表示，将抓取合成重新表述为对齐空间生成：交互状态由任务空间几何锚点和关节参数组成，通过封闭形式的Procrustes对齐恢复刚性变换，同时保持局部关节运动。在这种混合状态下，我们使用异构状态流匹配和分量向量场建模抓取生成，在训练过程中结合时间自适应的物理正则化。在推理阶段，抓取仅通过整合学习到的向量场合成，无需测试时优化或辅助物理指导。在MultiDex和零-shot RealDex基准测试中，DEAL-Grasp实现了高强度扰动成功率，同时生成的抓取具有最小的穿透和高多样性，相较于重优化基线显著降低了原生推理延迟。项目页面可访问 https://wmtlab.github.io/DEAL-Grasp/。

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cs.RO / 67 / 2609.28161

Dissecting Advantage-Guided Post-Training for Vision-Language-Action Policies

剖析基于优势的后训练在视觉-语言-动作策略中的应用

Cao, Jiahang, Zhao, Hanye, Lai, Hang, Zhang, Shenyu, Han, Xiaoshen, Li, Xinghang, Liu, Futeng, Peng, Wanli, Wang, Heyun, Wang, Yunhong, Li, Jason, Yu, Yong, Zhang, Weinan

Abstract

Advantage-guided reinforcement learning provides a practical way to post-train vision-language-action (VLA) policies using limited robot data. However, its performance depends on several coupled choices, including how critic-derived advantages are constructed, calibrated, and used for policy training. Existing recipes often combine these choices into a single end-to-end procedure, making their individual effects difficult to identify. In this work, we dissect advantage-guided VLA post-training through a controlled empirical study that separates these design choices while accounting for their distinct estimands. We develop stage-specific offline evaluation methods to screen alternative choices efficiently, without requiring extensive real-robot policy evaluations for every possible combination. The staged evaluation identifies a modular recipe that combines temporal-difference advantage construction, group-wise calibration, and continuous advantage weighting. Across four real-world bimanual tasks, the resulting recipe improves mean task progress and success over the SFT initialization by 0.42 and 0.63, respectively. Moreover, the proposed evaluation diagnostics show an overall alignment with downstream real-world performance, supporting their use for interpreting empirical outcomes and selecting advantage-guided post-training designs in practice.

Chinese Translation

基于优势的强化学习为使用有限机器人数据对视觉-语言-动作（VLA）策略进行后训练提供了一种实用的方法。然而，其性能依赖于多个相互关联的选择，包括如何构建、校准和使用来自评论员的优势进行策略训练。现有的方法通常将这些选择结合成一个单一的端到端过程，使得其各自的影响难以识别。在本研究中，我们通过一个控制的实证研究剖析了基于优势的VLA后训练，分离了这些设计选择，同时考虑了它们的不同估计量。我们开发了阶段特定的离线评估方法，以高效筛选替代选择，而无需对每种可能组合进行广泛的真实机器人策略评估。阶段性评估识别出一种模块化的方案，结合了时序差分优势构建、组内校准和连续优势加权。在四个真实的双手任务中，所得到的方案分别提高了任务进展的平均值和成功率，较SFT初始化分别提高了0.42和0.63。此外，所提出的评估诊断与下游真实世界性能总体一致，支持其在实践中用于解释实证结果和选择基于优势的后训练设计。

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cs.RO / 68 / 2609.28172

Dynamic, Decentralized Spatial Code Reuse for OCDMA LiDAR in Robot Swarms

机器人群体中OCDMA LiDAR的动态去中心化空间代码重用

Alomari, Mohammad Hani

Abstract

Robots in a LiDAR-equipped swarm mutually interfere when their optical ranging codes collide. Existing mitigations either assign codes statically -- requiring $L=N$ distinguishable codes for $N$ robots -- or react to detected interference without a scalable, coordinated assignment rule beneath them; prior work explicitly identifies the code-assignment scaling problem as unsolved. We propose a decentralized protocol in which robots dynamically reassign spatial reuse codes based on a live, beacon-maintained interference-neighborhood graph, and prove that the number of codes required grows as $O(\log N/\log\log N)$ under constant robot density -- an unbounded improvement over the $\Theta(N)$ growth of static assignment. We validate this result under conditions substantially beyond the idealized proof -- robot mobility, imperfect beacon-based detection, and reactive reassignment -- via Monte Carlo simulation (30 seeds per condition, 95% confidence intervals): the advantage over static assignment widens from roughly $2\times$ at 15 robots to $12\times$ at 120. Against a structurally faithful, fairly constructed model of an existing coordination-free approach, our protocol achieves both substantially greater code-reuse efficiency and 30--40% lower collision risk under an identical, constrained code budget, demonstrating that coordination -- not merely reactivity -- is what closes the scaling gap.

Chinese Translation

在配备LiDAR的机器人群体中，当它们的光学测距代码发生冲突时，机器人之间会相互干扰。现有的解决方案要么静态分配代码——需要$L=N$个可区分的代码以适应$N$个机器人——要么在检测到干扰后反应，但缺乏可扩展的、协调的分配规则；之前的研究明确指出代码分配的扩展性问题尚未解决。我们提出了一种去中心化协议，机器人根据实时的、由信标维护的干扰邻域图动态重新分配空间重用代码，并证明在恒定的机器人密度下，所需代码的数量增长为$O(rac{ ext{log} N}{ ext{log log} N})$——相比于静态分配的$ heta(N)$增长，这是一种无界的改进。我们通过蒙特卡洛模拟（每种条件30个种子，95%置信区间）在远超理想化证明的条件下验证了这一结果——包括机器人移动性、不完美的基于信标的检测和反应性重新分配：在15个机器人时，相较于静态分配的优势约为$2 imes$，而在120个机器人时则扩大到$12 imes$。与一个结构上忠实、合理构建的现有无协调方法模型相比，我们的协议在相同的受限代码预算下实现了显著更高的代码重用效率和30%至40%的较低碰撞风险，证明了协调——而不仅仅是反应性——是缩小扩展差距的关键。

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cs.RO / 69 / 2609.28175

DAVIS: A Depth-Only End-to-End Active-Vision Framework for Humanoid Soccer Skills

DAVIS：一种仅基于深度的端到端主动视觉框架用于类人足球技能

Jin, Jiakang, Huo, Yixiao, Wang, Pengyuan, Han, Yinan, Zhang, Tingxuan, Zhao, Zhuobing, Zhou, Xuanxin, Ye, Zhangchen, Ruan, Enxuan, Bao, Yifei, Yang, Jiankun, Sun, Chenghao, Cui, Wenhao, Tian, Xiaoyu, Li, Yiming

Abstract

Humanoid soccer contact skills require more than producing high-impact foot-ball contacts: the robot must close the loop over perception, approach, alignment, impact, and recovery while its own motion induces substantial viewpoint changes, frequent loss of the ball from view, and uncertain contact outcomes. In this work, we ask a compact yet stricter question: can a humanoid learn soccer contact skills using only a head-mounted depth image, proprioceptive history, and an optional low-dimensional task command, and directly output 25-DoF joint PD targets without extra runtime perception or planning modules? To this end, we propose DAVIS, a depth-only end-to-end framework for humanoid soccer skills that learns visibility-aware auxiliary geometry during training, and combines GT-to-prediction annealing, task curricula, and AMP-style motion priors to smoothly bridge privileged supervision and real deployment. Built on this framework, we instantiate representative soccer contact skills, including goal-directed shooting and directional dribbling, through task-specific definitions of objects, commands, rewards, and curricula, and validate them through simulation, Noetix E1 real-robot experiments, and ablations.

Chinese Translation

类人足球接触技能不仅需要产生高冲击的脚球接触：机器人必须在感知、接近、对齐、冲击和恢复之间闭合循环，同时自身运动会引起显著的视角变化、频繁的球体视野丧失以及不确定的接触结果。在本研究中，我们提出了一个紧凑而严格的问题：类人机器人能否仅使用头戴式深度图像、身体感知历史和一个可选的低维任务指令来学习足球接触技能，并直接输出25自由度关节PD目标，而无需额外的运行时感知或规划模块？为此，我们提出了DAVIS，一个仅基于深度的端到端框架，用于类人足球技能，该框架在训练过程中学习可见性感知的辅助几何，并结合GT到预测的退火、任务课程和AMP风格的运动先验，以平滑地连接特权监督和实际部署。在此框架基础上，我们实例化了代表性的足球接触技能，包括目标导向射门和定向运球，通过任务特定的对象、指令、奖励和课程定义，并通过仿真、Noetix E1真实机器人实验和消融实验进行验证。

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cs.RO / 70 / 2609.28179

GLASS: Architecture-Tuned, Composable, Device-Side Linear Algebra for Edge Robotics and Beyond

GLASS：针对架构调优的可组合设备端线性代数，用于边缘机器人及其他应用

Plancher, Brian

Abstract

GPU robotics lacks the reusable numerical infrastructure of mature CPU stacks, instead relying on compiler frameworks that introduce overhead or repeatedly reimplementing numerical libraries. To address this, we introduce GLASS (GPU Linear Algebra Simple Subroutines), a header-only CUDA C++ library that provides thread-, warp-, block-, and NVIDIA-backed implementations of robotics-scale linear algebra and geometric computations under one composable device API. GLASS treats implementation choice, execution scope, and launch packing as architecture-specific placement decisions determined by offline measurement and resolved statically at compile time. This is critical as the best and worst placements differ by a median of 4.9x (max 81x), with 145 of 396 recommended placements changing between a Jetson AGX Orin and an RTX 5090, and 162 of 396 versus an AGX Xavier. These stakes are highest at the edge as GLASS's advantage over the best of PyTorch and JAX is as much as 73x on the Orin versus 12x on the RTX 5090. GLASS is released open source with independent numerical oracles and source-bound local-GPU test attestation. Finally, integrating GLASS with published robotics systems both exposed a pre-existing numerical bug and improved embedded runtimes by up to 1.5x.

Chinese Translation

GPU 机器人技术缺乏成熟 CPU 堆栈的可重用数值基础设施，而是依赖于引入开销的编译器框架或反复重新实现数值库。为了解决这个问题，我们提出了 GLASS（GPU 线性代数简单子程序），这是一个仅包含头文件的 CUDA C++ 库，提供线程、warp、块以及基于 NVIDIA 的机器人规模线性代数和几何计算的实现，所有这些都在一个可组合的设备 API 下。GLASS 将实现选择、执行范围和启动打包视为由离线测量确定的架构特定放置决策，并在编译时静态解析。这一点至关重要，因为最佳和最差的放置之间的中位数差异为 4.9 倍（最大 81 倍），在 Jetson AGX Orin 和 RTX 5090 之间，396 个推荐放置中有 145 个发生了变化，而在 AGX Xavier 中则有 162 个变化。在边缘计算中，这些差异尤为显著，因为 GLASS 在 Orin 上的优势相较于 PyTorch 和 JAX 的最佳表现高达 73 倍，而在 RTX 5090 上为 12 倍。GLASS 以开源形式发布，具有独立的数值预言机和源绑定的本地 GPU 测试证明。最后，将 GLASS 与已发布的机器人系统集成，不仅暴露了一个先前存在的数值错误，还将嵌入式运行时性能提升了最多 1.5 倍。

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cs.RO / 71 / 2609.28184

VLMs Can Describe, But Not Measure: Object-Centric Scene Understanding for Robotic Manipulation

VLM可以描述，但无法测量：面向机器人操作的对象中心场景理解

Saccon, Enrico, Faraci, Tommaso, Zarzuelo, Iñigo De La Ossa, Palopoli, Luigi, Roveri, Marco, Saveriano, Matteo

Abstract

Robotic operation in previously unseen environments requires both semantic understanding and reliable metric information. While vision--language models (VLMs) provide strong semantic capabilities, their geometric estimates remain less reliable. In this paper, we propose a VLM-driven, modular perception framework for scene understanding using off-the-shelf approaches. Starting from a single RGB-D observation, the scene is segmented into object-level regions, annotated by a VLM, and grounded with depth information to construct a task-independent object-centric representation. Experiments on 151 tabletop scenes show that the proposed decomposition preserves strong semantic performance while substantially improving localization and depth estimation over direct VLM inference. The resulting representation is also integrated with a task-planning framework for robotic execution.

Chinese Translation

在以前未见过的环境中进行机器人操作需要语义理解和可靠的度量信息。虽然视觉-语言模型（VLM）提供了强大的语义能力，但其几何估计的可靠性较低。本文提出了一种基于VLM的模块化感知框架，用于利用现成的方法进行场景理解。从单个RGB-D观测开始，场景被分割为对象级区域，由VLM进行标注，并结合深度信息构建任务无关的对象中心表示。在151个桌面场景上的实验表明，所提出的分解方法在保持强语义性能的同时，显著改善了定位和深度估计，相较于直接的VLM推断。最终的表示还与任务规划框架集成，以便于机器人执行。

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cs.RO / 72 / 2609.28225

Large-Scale Geometric Map-Based Localization of UAVs in GNSS-Denied Urban Environments

在无GNSS城市环境中无人机的大规模几何地图定位

Terlizzi, Garth, Fathian, Kaveh

Abstract

Unmanned aerial vehicles (UAVs) operating in GNSS-denied urban environments require alternative methods for position estimation. Existing approaches based on satellite image retrieval or learned descriptors are sensitive to appearance variation and degrade rapidly as the search area grows. We present a vision-based localization system that matches building patterns observed from a downward-facing UAV camera against a reference building footprint database. Our approach detects buildings in aerial imagery, accumulates observations across frames into a unified map, and matches local building arrangements against reference footprints using a novel geometry-driven descriptor that augments local triangle structure with per-building shape features. By encoding spatial relationships between nearby buildings rather than visual appearance, the system is robust to appearance variations and remains discriminative over large search areas. Evaluations on seven flights across four municipalities in a large metropolitan area demonstrate 100% Recall@1 at search areas of approximately 113 km$^2$ and 254 km$^2$, and 71.4% Recall@1 when expanded to approximately 452 km$^2$, encompassing up to 277,000 buildings. In contrast, baseline methods degrade rapidly and achieve 0% Recall@1 at 254 km$^2$ and 452 km$^2$.

Chinese Translation

在无GNSS的城市环境中运行的无人机（UAV）需要替代方法进行位置估计。现有基于卫星图像检索或学习描述符的方法对外观变化敏感，并且随着搜索区域的扩大而迅速退化。我们提出了一种基于视觉的定位系统，该系统将从向下俯视的无人机摄像头观察到的建筑模式与参考建筑轮廓数据库进行匹配。我们的方法在航空图像中检测建筑物，将跨帧的观察结果汇总成统一地图，并使用一种新颖的几何驱动描述符将局部三角形结构与每栋建筑的形状特征相结合，以匹配局部建筑排列与参考轮廓。通过编码附近建筑之间的空间关系而非视觉外观，该系统对外观变化具有鲁棒性，并在大搜索区域内保持区分性。在一个大型城市区域内的四个市镇进行的七次飞行评估表明，在约113 km²和254 km²的搜索区域内实现了100%的Recall@1，而在扩大到约452 km²时，Recall@1为71.4%，涵盖多达277,000栋建筑。相比之下，基线方法迅速退化，在254 km²和452 km²时Recall@1为0%。

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cs.RO / 73 / 2609.28247

Controlling Collectives of AI Agents in Reasoning Space with Spatial Transformers

使用空间变换器控制推理空间中的人工智能代理集体

Vatnsdal, Frederic, Gopal, Roshan, Camargo, Romina Garcia, Kumar, Vijay, Ribeiro, Alejandro

Abstract

Large Language Models (LLMs) introduce an exciting new paradigm for planning and navigation in robotics, but fail on even simple multi-robot tasks as team sizes grow. We propose COMPASS, a scalable, decentralized multi-robot architecture for controlling large collectives of agentic robots with reasoning space feedback control. Feedback is generated locally on each robot by a spatial transformer which aggregates multi-hop messages across the fleet into a learned feedback token. Our experiments find that collectives of language models demonstrate performance gains from structured diversity of the input command, which can cancel biases; an advantage that is held across scale. Compared against a centralized frontier LLM policy and a language-only communication ablation, we find that the coupled design of COMPASS decisively produces cohesive flocking formations that accurately fly the commanded intent. We show that reasoning feedback works best when composed with a compact learned token. Our ablations show that hand engineered feedback with raw state appearing in the language channel obliterates cohesion. COMPASS generalizes zero-shot to unseen instructions of ambiguous meaning while commanding flocks up to 16 times its training scale, flying up to 1024 robots under natural language commands.

Chinese Translation

大型语言模型（LLMs）为机器人规划和导航引入了一种令人兴奋的新范式，但在团队规模增大时，即使是简单的多机器人任务也会失败。我们提出了COMPASS，这是一种可扩展的去中心化多机器人架构，用于通过推理空间反馈控制来控制大规模的代理机器人集体。反馈由每个机器人上的空间变换器局部生成，该变换器将跨整个车队的多跳消息聚合成一个学习到的反馈令牌。我们的实验发现，语言模型集体在输入命令的结构化多样性中表现出性能提升，这可以消除偏见；这一优势在规模扩展时依然存在。与集中式前沿LLM策略和仅语言通信的消融实验相比，我们发现COMPASS的耦合设计显著产生了紧凑的群体编队，准确地执行命令意图。我们展示了推理反馈在与紧凑的学习令牌结合时效果最佳。我们的消融实验表明，手工设计的反馈与语言通道中的原始状态结合会破坏集体的凝聚力。COMPASS在面对未见过的模糊指令时能够零-shot泛化，同时指挥的群体规模可达到其训练规模的16倍，在自然语言命令下可控制多达1024个机器人。

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cs.RO / 74 / 2609.28256

MemBodied: Recurrent Associative Memory for Vision-Language-Action Models

MemBodied：用于视觉-语言-动作模型的递归关联记忆

Pala, Tej Deep, Majumder, Navonil, Goh, Bryce, Yee, Raphael, Yang, Jianfei, Chen, Liming, Poria, Soujanya

Abstract

Vision-Language-Action models provide a strong foundation for general-purpose robot control, yet a vast majority of policies do not preserve and leverage episode-level information beyond the current observation. This limitation is consequential in history-dependent manipulation tasks that depend on information available only in past observations. Retaining past observations in context can aid in recovering this information, but at the significant cost of ever-growing, bloated context and inference latency. We thus introduce MemBodied, a fixed-size episodic memory with two complementary components: an associative state that records interactions across policy calls and an episode anchor that preserves a compact representation of the initial scene as a reference. At each policy call, the model conditions action generation on the current input and the memory components, rather than directly using past observations. Across five evaluated RMBench tasks requiring memory, MemBodied achieves $7.81\times$ the mean success rate of a stateless policy and $2.98\times$ of vanilla recurrent memory, while outperforming the strongest memory-augmented baseline by $1.3\times$ with $10\times$ fewer added parameters. On the fully observable LIBERO-Long suite, it reached 90.6%, a 5.4% improvement over the stateless $\pi_0$ policy. These findings support MemBodied as a practical alternative to expanding the policy context for history-dependent manipulation.

Chinese Translation

视觉-语言-动作模型为通用机器人控制提供了坚实的基础，但绝大多数策略并未保留和利用超出当前观察的情节级信息。这一限制在依赖于仅在过去观察中可用信息的历史依赖性操控任务中尤为重要。保留上下文中的过去观察可以帮助恢复这些信息，但代价是上下文不断膨胀和推理延迟显著增加。因此，我们提出了MemBodied，这是一种固定大小的情节记忆，具有两个互补组件：一个记录策略调用间交互的关联状态和一个保留初始场景紧凑表示作为参考的情节锚点。在每次策略调用中，该模型基于当前输入和记忆组件来条件化动作生成，而不是直接使用过去的观察。在五个评估的需要记忆的RMBench任务中，MemBodied的平均成功率达到了无状态策略的$7.81 imes$和普通递归记忆的$2.98 imes$，同时以$10 imes$更少的附加参数超越了最强的增强记忆基线$1.3 imes$。在完全可观察的LIBERO-Long套件中，其成功率达到了90.6%，比无状态的$ ext{π}_0$策略提高了5.4%。这些发现支持MemBodied作为扩展历史依赖性操控策略上下文的实用替代方案。

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cs.RO / 75 / 2609.28258

Generalizable Robotic Insertion with World Models

具有世界模型的可推广机器人插入

Hansen, Nicklas, Akinola, Iretiayo, Guo, Yijie, Xu, Jie, Tang, Bingjie, Su, Hao, Wang, Xiaolong, Gupta, Abhishek, Fox, Dieter, Narang, Yashraj

Abstract

Robotic assembly in high-mixture settings requires adaptable systems that can handle diverse parts, yet current approaches typically rely on policies specialized to each insertion task. Although this can reach high success rates, it makes the process of deploying systems for new problems tedious and time consuming. We present a framework for generalizable insertion using world models that combine robot proprioceptive information with raw visual observations captured by a wrist-mounted camera. Our model-based approach trains a single world model on up to 90 insertion tasks with geometrically diverse parts, achieving 56% zero-shot success on unseen objects with unknown geometry compared to just 7% with a model-free baseline. Importantly, performance improves as more objects are included in the training dataset, demonstrating strong scalability. Lastly, finetuning the generalist model on held-out objects significantly enhances data-efficiency compared to training from scratch and, in some cases, achieves better asymptotic performance. To our knowledge, this is the first system capable of assembling unseen objects in an entirely data-driven manner, and thus represents a significant step toward scalable, generalizable robotic assembly systems.

Chinese Translation

在高混合环境中，机器人装配需要能够处理多样化零件的适应性系统，而当前的方法通常依赖于针对每个插入任务专门化的策略。尽管这种方法可以达到较高的成功率，但使系统在新问题上的部署过程变得繁琐且耗时。我们提出了一种使用世界模型的可推广插入框架，该框架将机器人本体感知信息与由腕部安装摄像头捕获的原始视觉观测相结合。我们的基于模型的方法在多达90个具有几何多样性的插入任务上训练了一个单一的世界模型，在对未知几何的未见物体上实现了56%的零-shot成功率，而无模型基线仅为7%。重要的是，随着更多物体被纳入训练数据集，性能得到了提升，展示了强大的可扩展性。最后，在保留物体上对通用模型进行微调，相较于从头开始训练，显著提高了数据效率，并且在某些情况下实现了更好的渐近性能。据我们所知，这是第一个能够以完全数据驱动的方式组装未见物体的系统，因此代表了朝着可扩展、可推广的机器人装配系统迈出的重要一步。

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cs.RO / 76 / 2609.28281

BrickCraft-Duo: Efficient Dual-Arm Skill Learning and Refinement for Compositional Long-Horizon Assembly

BrickCraft-Duo：高效的双臂技能学习与组合用于长时间跨度的装配

Yu, Jichuan, Xiao, Zhenyu, Wang, Ze, Liu, Ruixuan, Liu, Changliu, Hu, Chuxiong

Abstract

Interlocking brick assembly provides a representative testbed for evaluating real-world robotic manipulation capabilities, where diverse structural designs, complex inter-step dependencies, intricate mechanical interactions and tight insertion tolerances pose substantial challenges. We present BrickCraft-Duo, a modular framework for long-horizon dual-arm collaborative assembly of interlocking bricks through data-efficient skill learning and composition. BrickCraft-Duo learns reusable single- and dual-arm assembly skills from diverse demonstrations, with bilateral symmetry alignment facilitating skill sharing across symmetric arms and assembly--support role assignments. Guided by stability-aware assembly reasoning, BrickCraft-Duo composes heterogeneous skills to achieve autonomous long-horizon execution, and further integrates human-in-the-loop correction for targeted skill refinement. The resulting system achieves long-horizon success rates of at least 60% and step-level completion rates of at least 95% across five real-world assembly tasks involving partially supported configurations, with horizons of up to nine steps. Project website: https://jichuan-yu.github.io/BrickCraft-Duo.

Chinese Translation

互锁砖块装配为评估现实世界机器人操作能力提供了一个具有代表性的测试平台，其中多样的结构设计、复杂的步骤间依赖、精细的机械交互以及严格的插入公差带来了显著挑战。我们提出了BrickCraft-Duo，一个模块化框架，通过数据高效的技能学习和组合，实现互锁砖块的长时间跨度双臂协作装配。BrickCraft-Duo从多样的演示中学习可重用的单臂和双臂装配技能，通过双边对称对齐促进对称臂之间的技能共享和装配支持角色分配。在稳定性意识的装配推理指导下，BrickCraft-Duo组合异构技能以实现自主的长时间跨度执行，并进一步集成了人机协作的纠正机制以进行针对性的技能精炼。该系统在涉及部分支撑配置的五个现实世界装配任务中，达到了至少60%的长时间跨度成功率和至少95%的步骤级完成率，最长可达九个步骤。项目网站：https://jichuan-yu.github.io/BrickCraft-Duo。

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cs.RO / 77 / 2609.28296

Talk2Escape: Conversational Grounding for Vision-and-Language Navigation

Talk2Escape：视觉与语言导航中的对话基础

Li, Zerui, Lin, Sihao, Shao, Yanyan, Zhang, Jiwen, Shi, Xiangyu, Li, Shijie, Wu, Qi

Abstract

While Vision-and-Language Navigation (VLN) has demonstrated remarkable success, the prevailing single-turn paradigm exposes a fundamental vulnerability: agents operate in a strictly open-loop manner. In practice, factors such as perceptual aliasing, sensor noise, and odometry drift can cause minor deviations to accumulate over time, often leading to catastrophic mission failures with no built-in mechanism for error recovery. To address this, we introduce \textit{Talk2Escape}, a proactive and model-agnostic dialogue intervention framework that reframes navigation as a closed-loop interactive process. At its core, a lightweight vision-language module continuously monitors agent kinematics. Upon detecting localized looping or severe trajectory divergence, it translates raw egocentric observations into concise, grounded queries to solicit targeted corrective feedback from either an algorithmic oracle or a human-in-the-loop. Extensive evaluations in high-fidelity simulators, including R2R-CE, RxR-CE, and VLNVerse, demonstrate that \textit{Talk2Escape} exhibits consistent improvements across diverse base agents. Empirically, \textit{Talk2Escape} achieves a 66.0\% Success Rate on R2R-CE, outperforming the current supervised and zero-shot state-of-the-art methods. We further validate its sim-to-real transfer on a Unitree Go2 quadruped, proving that proactive dialogue drastically improves navigation robustness in physical environments.

Chinese Translation

尽管视觉与语言导航（VLN）已展现出显著的成功，但现有的单轮范式暴露了一个根本性脆弱性：智能体在严格的开环模式下操作。在实际应用中，感知别名、传感器噪声和里程计漂移等因素可能导致小的偏差随着时间的推移而累积，常常导致灾难性的任务失败，而没有内置的错误恢复机制。为了解决这个问题，我们提出了 extit{Talk2Escape}，一个主动且模型无关的对话干预框架，将导航重新构建为一个闭环的互动过程。其核心是一个轻量级的视觉-语言模块，持续监测智能体的运动学。当检测到局部循环或严重轨迹偏离时，它将原始的自我中心观察转化为简洁的、基于上下文的查询，以请求来自算法神谕或人类参与者的针对性纠正反馈。在高保真模拟器中进行的广泛评估，包括 R2R-CE、RxR-CE 和 VLNVerse，表明 extit{Talk2Escape} 在各种基础智能体中表现出一致的改进。从经验上看， extit{Talk2Escape} 在 R2R-CE 上达到了 66.0\% 的成功率，超越了当前的监督和零样本最先进方法。我们进一步验证了其在 Unitree Go2 四足机器人上的仿真到现实转移，证明主动对话显著提高了物理环境中的导航鲁棒性。

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cs.RO / 78 / 2609.28299

Contact-Implicit Stein Projected ADMM for Discovery of Diverse Contact-Rich Manipulation Strategies

隐式接触斯坦因投影交替方向乘子法用于发现多样化的接触丰富操控策略

Sathyanarayan, Hrishikesh, Hughes, Christian, Abraham, Ian

Abstract

Contact-implicit trajectory optimization formulates contact-rich manipulation as a single constrained program; however, that single program run collapses onto one local optimum out of many equally valid contact modes, grasps, or push directions. As a consequence, the resulting manipulation strategy is reluctant to change and sensitive to initialization. In order to promote robust manipulation, this paper investigates how contact-implicit solvers can discover diverse contact-rich strategies. Our approach derives a variation of Consensus Alternating Direction Method of Multipliers (ADMM) combined with Stein variational inference methods to output a set of distinct contact-rich solutions. We find that applying the Stein repulsive force to ADMM's split variable (rather than its primal form) allows for effective coverage over the set of feasible contact strategies without prematurely stalling the solver. We demonstrate the effectiveness of our approach on a variety of contact-rich manipulation tasks, including pushing, grasping, and multi-robot handover. Last, we find the proposed solver is simpler in form and capable of discovering unique contact modes when compared with existing solvers. Videos and code with examples are found in https://anon-website-submission.github.io/stein-admm-website/.

Chinese Translation

隐式接触轨迹优化将接触丰富的操控形式化为一个单一的约束程序；然而，这个单一程序的运行会收敛到众多同样有效的接触模式、抓取或推力方向中的一个局部最优解。因此，所得到的操控策略不易改变且对初始化敏感。为了促进稳健的操控，本文研究了隐式接触求解器如何发现多样化的接触丰富策略。我们的方法推导出了一种结合了斯坦因变分推断方法的共识交替方向乘子法（ADMM）的变体，以输出一组不同的接触丰富解决方案。我们发现，将斯坦因排斥力应用于ADMM的分裂变量（而非其原始形式）能够有效覆盖可行接触策略的集合，而不会过早地使求解器停滞。我们在多种接触丰富的操控任务上展示了我们方法的有效性，包括推、抓和多机器人交接。最后，我们发现与现有求解器相比，所提出的求解器在形式上更简单，并能够发现独特的接触模式。视频和示例代码可在 https://anon-website-submission.github.io/stein-admm-website/ 找到。

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cs.RO / 79 / 2609.28312

VGM-VS: Rethinking Visual Geometry Model for High-Precision Visual Servoing

VGM-VS：重新思考高精度视觉伺服的视觉几何模型

Pan, Yimin, Wang, Sen, Zhou, You, Gao, Jianfeng, Sun, Pengbo, Naguib, Ahmed M., Marton, Zoltan-Csaba

Abstract

We present VGM-VS, a visual servoing method built on a pretrained feed-forward visual geometry model. Given the current view and a reference image captured at the target configuration, we estimate the relative camera pose with a visual geometry model and apply it iteratively as the pose increment of a closed-loop pose-based visual servoing (PBVS) scheme. The geometry-aware representation acquired from large-scale pretraining keeps this estimate reliable when the target is occluded, weakly textured, or covers only a small part of the image. However, the scale ambiguity inherent to these models leaves the predicted translation defined up to an unknown scale, while the pose increment must be metric for robot control. We close this gap with a scene-specific metric adaptation: the robot autonomously records image--pose pairs along a predefined motion starting from the target pose, and we fine-tune the camera head on these data, jointly learning the hand--eye transform and thus removing the need for a dedicated calibration process. We evaluate our method on three real-world assembly tasks with demanding tolerances: USB-C cable picking, cable insertion, and RAM insertion. Running in real time at 30Hz, VGM-VS converges to submillimeter terminal accuracy on the cable tasks, and reaches success rates of 90--100\% when the target is moved during servoing. It converges in all trials under initial displacements of up to 30cm from the reference pose and with 50\% of the target object occluded, outperforming the compared visual servoing baselines.

Chinese Translation

我们提出了VGM-VS，这是一种基于预训练前馈视觉几何模型的视觉伺服方法。在给定当前视图和在目标配置下捕获的参考图像的情况下，我们利用视觉几何模型估计相对相机位姿，并将其作为闭环基于位姿的视觉伺服（PBVS）方案的位姿增量进行迭代应用。通过大规模预训练获得的几何感知表示在目标被遮挡、纹理较弱或仅覆盖图像的一小部分时，保持了该估计的可靠性。然而，这些模型固有的尺度模糊性使得预测的平移在一个未知的尺度下定义，而位姿增量必须是度量单位以便进行机器人控制。我们通过场景特定的度量适应来弥补这一差距：机器人自主记录沿着从目标位姿开始的预定义运动的图像-位姿对，并在这些数据上微调相机头，联合学习手眼变换，从而消除专用标定过程的需要。我们在三个具有严格公差的实际装配任务上评估了我们的方法：USB-C电缆拾取、电缆插入和RAM插入。VGM-VS以30Hz的实时运行频率在电缆任务上收敛到亚毫米级的终端精度，并在伺服过程中目标移动时达到90%至100%的成功率。在所有试验中，它在初始位移高达30cm且目标物体遮挡50%的情况下均能收敛，优于比较的视觉伺服基线。

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cs.RO / 80 / 2609.28314

TANDEM: Task and Motion Planning with As-Needed Demonstrations for Efficient Vision-Language-Action Model Fine-tuning

TANDEM：基于按需演示的任务与运动规划，以高效微调视觉-语言-动作模型

Sahoo, Samrat, Ji, Liang, Silver, Tom, Huang, Yixuan

Abstract

Human teleoperators spend substantial time demonstrating behaviors that robots can already perform autonomously, limiting the scalability of data collection for robot foundation models. Task and motion planning (TAMP) can automate many of these behaviors, but a fixed planning domain may not support every stage of a long-horizon manipulation task. We present TANDEM (Tamp with As-Needed Demonstrations for Efficient Model fine-tuning), a system that combines TAMP with selective human teleoperation to collect demonstrations for tasks beyond the planner's capabilities. Our key idea is to represent human assistance as an on-demand planning capability. Given a language instruction and visual observation, TANDEM uses pretrained vision-language models to extend the planning domain with missing predicates and human-executed magic operators. This allows the planner to interleave autonomous and human-executed stages without task-specific intervention points. After each human stage, TANDEM re-perceives the scene and checks whether the intended effects hold before resuming autonomous planning. To support fine-tuning vision-language-action (VLA) models, TANDEM also uses example pretraining trajectories to align planner-generated motions with the target model's pretraining distribution. We evaluate TANDEM on five long-horizon manipulation tasks beyond the TAMP domain's capabilities. On a representative long-horizon task, TANDEM collects 2.9x as many demonstrations as full-task teleoperation at the same human intervention time. Fine-tuning a pretrained \pi_{0.5}-DROID model on 20 TANDEM demonstrations per task increases average task success from 0% to 60% across the five tasks.

Chinese Translation

人类遥操作员花费大量时间演示机器人已经能够自主执行的行为，这限制了机器人基础模型数据收集的可扩展性。任务与运动规划（TAMP）可以自动化许多这些行为，但固定的规划领域可能无法支持长时间操作任务的每个阶段。我们提出了TANDEM（按需演示的任务与运动规划，用于高效模型微调）系统，该系统结合了TAMP与选择性人类遥操作，以收集超出规划者能力范围的任务演示。我们的关键思想是将人类辅助表示为按需规划能力。在给定语言指令和视觉观察的情况下，TANDEM使用预训练的视觉-语言模型来扩展规划领域，填补缺失的谓词和人类执行的魔法操作符。这使得规划者能够在不需要特定任务干预点的情况下交替进行自主和人类执行的阶段。在每个人工阶段之后，TANDEM重新感知场景，并检查预期效果是否成立，然后再恢复自主规划。为了支持视觉-语言-动作（VLA）模型的微调，TANDEM还使用示例预训练轨迹来对齐规划者生成的动作与目标模型的预训练分布。我们在五个超出TAMP领域能力的长时间操作任务上评估了TANDEM。在一个具有代表性的长时间任务中，TANDEM在相同的人类干预时间内收集的演示数量是全任务遥操作的2.9倍。对每个任务使用20个TANDEM演示微调预训练的C0_{0.5}-DROID模型，使得五个任务的平均任务成功率从0%提高到60%。

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cs.RO / 81 / 2609.28317

Multimodal Voice Activity Projection for Social Robot Mediation: Expected Behavior and Deployment Constraints

社交机器人调解中的多模态语音活动预测：预期行为与部署限制

Cano, Antonio, Perez, Guillermo, Merino, Luis, Gomez, Randy

Abstract

Turn-taking prediction is especially relevant for social robots that act as mediators in human-human interaction, where the expected action is often not to speak, but to orient, wait, avoid interruption, or prepare a balanced intervention. This paper presents Multimodal Voice Activity Projection (MM-VAP) as a human state-aware perception layer for future robot mediation behavior. The model estimates the future evolution of the conversational floor from synchronized audio-visual evidence and derives turn-taking events such as Hold, Shift, Shift prediction, Backchannel prediction, and overlap-related states. The approach uses VA-related pretrained audio-visual encoders, LoRA adaptation, inter-speaker attention, and zero-shot event inference from future voice activity projections. Experiments on NoXi, NoXi+J, and Haru EDR support the feasibility of this formulation, especially for floor management events that can be connected to gaze preparation, active listening, and conservative intervention. Finally, the paper defines the expected robot output interface and discusses the main deployment constraints, including real-time inference, preprocessing latency, multimodal synchronization, and input-quality monitoring.

Chinese Translation

轮流发言预测对于作为人际互动调解者的社交机器人尤为重要，因为在这种情况下，预期的行为往往不是发言，而是定向、等待、避免打断或准备平衡的干预。本文提出了多模态语音活动预测（Multimodal Voice Activity Projection, MM-VAP），作为未来机器人调解行为的人类状态感知层。该模型从同步的音频-视觉证据中估计对话地板的未来演变，并推导出轮流发言事件，如保持（Hold）、转换（Shift）、转换预测（Shift prediction）、回声预测（Backchannel prediction）和重叠相关状态。该方法使用与语音活动相关的预训练音频-视觉编码器、LoRA适应、说话者间注意力以及基于未来语音活动预测的零样本事件推断。对NoXi、NoXi+J和Haru EDR的实验支持了该模型的可行性，特别是在可以与目光准备、积极倾听和保守干预相关联的地板管理事件方面。最后，本文定义了预期的机器人输出接口，并讨论了主要的部署限制，包括实时推断、预处理延迟、多模态同步和输入质量监控。

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cs.RO / 82 / 2609.28325

Motoneuron-Inspired Sampling for Model Predictive Path Integral Control

受运动神经元启发的模型预测路径积分控制采样

Poignant, Alexis, Babič, Jan

Abstract

Model Predictive Path Integral (MPPI) control relies on stochastic trajectory sampling, and its performance under limited rollout budgets depends strongly on the structure of the proposal distribution. Standard implementations commonly perturb control sequences with Gaussian noise, despite growing evidence that temporally correlated and structured sampling can improve finite-budget control. We introduce Spike-MPPI, a motoneuron-inspired proposal that generates temporally structured perturbations through a simplified model of motoneuron dynamics. The proposal is evaluated within a common MPPI framework on torque-actuated and antagonistically actuated MuJoCo Ant models against standard Gaussian sampling and spectrum-matched Gaussian controls. Results show that structured sampling substantially improves executed-control smoothness, while its effect on task performance depends on rollout condition and robot actuation. Spectrum matching reproduces a substantial part of the observed behavior, while the full Spike proposal retains additional effects beyond second-order spectral structure. These results support treating proposal design as a combination of second-order spectral structure and higher-order statistical organization.

Chinese Translation

模型预测路径积分（MPPI）控制依赖于随机轨迹采样，其在有限展开预算下的性能强烈依赖于提议分布的结构。标准实现通常使用高斯噪声扰动控制序列，尽管越来越多的证据表明，时间相关和结构化采样可以改善有限预算控制。我们引入了Spike-MPPI，这是一种受运动神经元启发的提议，通过简化的运动神经元动力学模型生成时间结构化的扰动。该提议在一个常见的MPPI框架内进行评估，针对扭矩驱动和对抗驱动的MuJoCo Ant模型，与标准高斯采样和频谱匹配高斯控制进行比较。结果表明，结构化采样显著改善了执行控制的平滑性，而其对任务性能的影响则依赖于展开条件和机器人驱动。频谱匹配再现了观察到行为的很大一部分，而完整的Spike提议保留了超出二阶频谱结构的额外效应。这些结果支持将提议设计视为二阶频谱结构和高阶统计组织的结合。

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cs.RO / 83 / 2609.28339

Beyond Future Prediction: Denoising as Generative Adaptation for Robot Control

超越未来预测：去噪作为机器人控制的生成适应

Wang, Zanyi, Lei, Yuheng, Jiang, Dengyang, Luo, Ping, Wang, Mengdi, Liang, Zhixuan, Liu, Shilong

Abstract

Pretrained generative Diffusion Transformers (DiTs) capture rich pixel-level visual and language-conditioned structure through large-scale image and video generation training. A growing line of robot policies builds on this generative prior, but how it should be transferred to control remains unclear, and existing approaches commonly instantiate this transfer through future visual prediction. We ask a more basic question: what a pretrained generative DiT actually contributes to action learning, and how this prior should be adapted for control. We introduce NowWAM, a future-target-free co-training formulation that denoises the current observation and predicts robot actions from the same visual stream, directly coupling the native generative objective to the action-facing representation across the denoising trajectory. Under matched controlled settings, past and future visual targets perform comparably, while restricting training to the clean endpoint substantially reduces robustness, suggesting that a separate future target is not essential for generative adaptation, while the denoising trajectory remains an effective interface for control. On LIBERO-Plus, NowWAM reaches 87.7% with FLUX2-Klein, improving over the future-target co-training baseline by 6.1 points while halving training visual tokens (784 to 392) and reducing step time from 2.85 s to 1.63 s, a 1.8x speedup. With the pure text-to-image Z-Image backbone, NowWAM further reaches 87.8%, showing that strong control adaptation is not tied to video generation or image-editing backbones.

Chinese Translation

预训练的生成扩散变换器（Diffusion Transformers, DiTs）通过大规模的图像和视频生成训练捕捉丰富的像素级视觉和语言条件结构。越来越多的机器人策略基于这一生成先验，但如何将其转移到控制上仍不清楚，现有的方法通常通过未来视觉预测来实现这种转移。我们提出一个更基本的问题：预训练的生成 DiT 实际上对动作学习贡献了什么，以及该先验应如何适应控制。我们引入了 NowWAM，一种无未来目标的共同训练形式，去噪当前观察并从同一视觉流预测机器人动作，直接将原生生成目标与去噪轨迹中的面向动作的表征耦合。在匹配的控制设置下，过去和未来的视觉目标表现相当，而将训练限制在干净的终点显著降低了鲁棒性，这表明单独的未来目标对于生成适应并不是必需的，而去噪轨迹仍然是控制的有效接口。在 LIBERO-Plus 上，NowWAM 达到 87.7% 的性能，使用 FLUX2-Klein，相比未来目标共同训练基线提高了 6.1 个百分点，同时将训练视觉标记减少了一半（从 784 降至 392），并将步骤时间从 2.85 秒减少到 1.63 秒，实现了 1.8 倍的加速。使用纯文本到图像的 Z-Image 主干，NowWAM 进一步达到 87.8%，显示出强大的控制适应性并不依赖于视频生成或图像编辑主干。

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cs.RO / 84 / 2609.28364

LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials

LEAP-CBF：一种针对不确定系统的最小努力对抗潜力安全过滤器

So, Oswin, Yu, Eric, Fan, Chuchu

Abstract

Control barrier functions (CBF) are a popular safety filter to ensure safety for nonlinear dynamical systems. However, when the system is subject to uncertainties and disturbances, this requires the use of robust variants of CBFs, which can be difficult to construct and can be overly conservative, especially for high-dimensional systems under input constraints. In this work, we propose a new approach to solve these challenges by introducing Least-Effort Adversarial Potentials (LEAP), a certificate that quantifies the robustness of a given state against disturbances in terms of the effort required by the disturbance to cause failure. We show that LEAP is a CBF for the undisturbed system, but can also be used to construct a safety filter that is robust to disturbances whose cumulative effort is bounded. We propose a method for constructing LEAPs with on-policy deep reinforcement learning. Next, we demonstrate LEAPs in simulation on a variety of multi-agent systems with disturbances and uncertainties. Finally, hardware experiments on a quadruped and quadrotors validate that LEAPs are well suited to tackle the disturbances and uncertainties from real-world robotic systems.

Chinese Translation

控制屏障函数（CBF）是一种流行的安全过滤器，用于确保非线性动态系统的安全性。然而，当系统受到不确定性和干扰时，这需要使用鲁棒变体的CBF，这可能难以构建，并且在输入约束下，尤其是对于高维系统，可能过于保守。在本研究中，我们提出了一种新方法来解决这些挑战，通过引入最小努力对抗潜力（LEAP），这是一种证书，量化给定状态在干扰下的鲁棒性，具体表现为干扰导致失败所需的努力。我们证明LEAP是未受干扰系统的CBF，但也可以用于构建对累积努力有界的干扰鲁棒的安全过滤器。我们提出了一种使用策略深度强化学习构建LEAP的方法。接下来，我们在多种具有干扰和不确定性的多智能体系统上进行了LEAP的仿真演示。最后，在四足机器人和四旋翼上的硬件实验验证了LEAP非常适合应对来自现实世界机器人系统的干扰和不确定性。

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cs.RO / 85 / 2609.28377

Amplify: A Lightweight Library for Reproducible Nonlinear Programming Problems in Robotics

Amplify：一个轻量级的机器人领域可重复非线性规划问题库

Rosa Jr, Nelson

Abstract

Optimization problems (OPs) are key to solving many challenging research problems in robotics. However, reproducibility still remains a major issue. In this paper, we present Amplify, a lightweight nonlinear programming library aimed at reproducible results of robotic-related trajectory optimization problems. The minimalistic requirements for the 537-line library (80 characters per line) are an Internet connection, familiarity with the AMPL modeling language, and a text editor. Our primary contribution is the formulation of a library where trajectory optimization algorithms are represented directly within the optimization model. Specifically, we implement the algorithms used to compute the dynamics, trajectories, and reference motions as constraints of the OP in a declarative programming paradigm. We outline how our formulation of objectives, decisions variables, and constraints can be implemented in other transcription libraries that want to be lightweight and reproducible. We also compare the Amplify framework with 3 other libraries across examples of benchmark optimization problems across several fields, including bipedal locomotion and grasp planning.

Chinese Translation

优化问题（OPs）是解决机器人领域许多挑战性研究问题的关键。然而，可重复性仍然是一个主要问题。在本文中，我们提出了Amplify，一个轻量级的非线性规划库，旨在实现与机器人相关的轨迹优化问题的可重复结果。该库由537行代码（每行80个字符）构成，最小化的要求包括互联网连接、对AMPL建模语言的熟悉以及文本编辑器。我们的主要贡献在于构建一个库，其中轨迹优化算法直接在优化模型中表示。具体而言，我们将用于计算动力学、轨迹和参考运动的算法作为OP的约束，在声明式编程范式中实现。我们概述了如何将我们的目标、决策变量和约束的表述实现于其他希望轻量且可重复的转录库中。我们还将Amplify框架与其他三个库进行了比较，涵盖了多个领域的基准优化问题示例，包括双足行走和抓取规划。

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cs.RO / 86 / 2609.28378

ForgetMimic: Motion Unlearning for Reinforcement Learning Humanoid Control

ForgetMimic：用于强化学习人形控制的运动遗忘

Luan, Xukun, Lei, Zhongxiang, Gong, Chen, Li, Shaowei, Bi, Yuanguo, Liu, Jinyan

Abstract

Humanoid control, leveraging human demonstrations, has achieved diverse, agile, and natural locomotion behaviors through reinforcement learning (RL). While this paradigm has yielded remarkable performance in physical humanoid control, how to eliminate specific motions from learned policies remains insufficiently explored. Addressing this issue is motivated by pressing safety and privacy concerns: the removal of malicious, poisoned, or suboptimal motions, as well as copyright-protected motions subject to the right to be forgotten under regulations such as the GDPR, is of critical importance. To this end, we propose {ForgetMimic}, the first motion-level unlearning method designed specifically for physical-world humanoid control. The core idea of ForgetMimic is as follows: given a policy $\pi_\theta$ trained on $N$ motions, our method degrades performance on a target subset of $K$ motions while preserving the effectiveness of the remaining $N-K$ motions. Furthermore, we identify and resolve two key training mechanisms in robot control that lead to unlearning failure. We conduct extensive experiments on the Unitree G1 and H2 humanoid robots across 12 motions, including Dance, Fight, Flip, and others. Experimental results demonstrate that ForgetMimic effectively eliminates memory of designated motions while maintaining the normal operation of all other motions.

Chinese Translation

人形控制利用人类示范，通过强化学习（RL）实现了多样、灵活和自然的运动行为。尽管这一范式在物理人形控制中取得了显著的性能，但如何从学习的策略中消除特定运动仍然未得到充分探索。解决这一问题的动机源于迫切的安全和隐私问题：去除恶意、污染或次优的运动，以及受版权保护的运动，这些运动在《通用数据保护条例》（GDPR）等法规下享有被遗忘的权利，具有重要意义。为此，我们提出了ForgetMimic，这是第一个专门为物理世界人形控制设计的运动级别遗忘方法。ForgetMimic的核心思想如下：给定一个在N个运动上训练的策略$ heta$，我们的方法在目标子集K个运动上降低性能，同时保持其余N-K个运动的有效性。此外，我们识别并解决了机器人控制中导致遗忘失败的两个关键训练机制。我们在Unitree G1和H2人形机器人上进行了广泛的实验，涵盖12种运动，包括舞蹈、打斗、翻转等。实验结果表明，ForgetMimic有效地消除了对指定运动的记忆，同时保持了所有其他运动的正常操作。

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cs.RO / 87 / 2609.28393

PointCast: One World Model for Rigid, Articulated, and Deformable Object Manipulation

PointCast：用于刚性、关节和可变形物体操作的统一世界模型

Ye, Hantao, Worobel, Ross, Xie, Zhuoli, Li, Mingen, Yu, Houjian, Hong, Youngjin, Choi, Changhyun

Abstract

World models are useful for robotic manipulation because robots can predict how actions change the states of objects before executing them. We present PointCast, a point-set world model that spans rigid, articulated, and deformable object manipulation. Its state is a set of 3D points on the object and the end-effector, mesh-free and topology-agnostic. Each point keeps its identity and is supervised on its own trajectory, which teaches the model where every point goes rather than only the shape the points form. Its backbone is a diffusion transformer that denoises a short window of future point positions, conditioned on the points' recent history and the commanded end-effector motion. The backbone's attention alternates between local and global, and cross-attention to the end-effector carries the coupling. This one architecture at 19.8M parameters and one training recipe cover four regimes, rigid objects, cloth, rope, and multi-joint cabinets, with a separate checkpoint trained for each. Trained on randomized simulation and scored against four baselines on the same metric, it is best on three of four regimes and second on rigid. Trained on a real-world robot teleoperation dataset, it has the lowest mean error in four of its six categories, is second in the other two, and improves on the dataset's own model in all six; zero-shot, its simulation checkpoints are best on two of four captures. Frozen inside sampling-based model-predictive control at one network evaluation per window, it plans four simulated tasks over 64 episodes, competitive with or outperforming every baseline on each. Project website at https://pointcast-wm.github.io.

Chinese Translation

世界模型对于机器人操作非常有用，因为机器人可以在执行动作之前预测这些动作如何改变物体的状态。我们提出了PointCast，这是一种点集世界模型，涵盖了刚性、关节和可变形物体的操作。其状态由物体和末端执行器上的一组3D点组成，具有无网格和拓扑无关的特性。每个点保持其身份，并在自己的轨迹上进行监督，这教会模型每个点的去向，而不仅仅是点所形成的形状。其主干是一个扩散变换器，能够在考虑点的近期历史和命令的末端执行器运动的条件下，去噪未来点位置的短窗口。主干的注意力在局部和全局之间交替，而对末端执行器的交叉注意力则保持耦合。这个架构拥有1980万个参数和一个训练方案，覆盖了四种模式：刚性物体、布料、绳索和多关节柜，每种模式都有一个单独的检查点进行训练。在随机化模拟上进行训练，并在相同指标下与四个基线进行比较，在四种模式中，它在三种模式上表现最佳，在刚性物体上排名第二。在真实世界的机器人遥操作数据集上进行训练，在六个类别中的四个类别中具有最低的平均误差，在其他两个类别中排名第二，并在所有六个类别中超越了数据集自身的模型；在零样本情况下，其模拟检查点在四个捕获中有两个表现最佳。在每个窗口进行一次网络评估的采样基础模型预测控制中冻结，它在64个回合中规划了四个模拟任务，在每个任务上与所有基线竞争或超越。项目网站为 https://pointcast-wm.github.io。

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cs.RO / 88 / 2609.28396

Tractable Reinforcement Learning for Full Class of Signal Temporal Logic Specifications Using Spatiotemporal Tube Reward

针对全类信号时序逻辑规范的可处理强化学习方法：基于时空管道奖励

Jagabathula, Vaishnavi, Sangeerth, P, Jagtap, Pushpak

Abstract

This paper addresses the control problem for robotic systems, including non-holonomic and underactuated platforms operating under unknown dynamics and strict actuator limits to satisfy complex high-level specifications. We denote these high-level specifications using Signal Temporal Logic (STL) and propose a novel time-aware Reinforcement Learning (RL) framework that leverages the geometric properties of Spatiotemporal Tubes (STTs). While traditional analytical STT controllers often struggle to enforce input constraints, and existing RL approaches rely on memory-intensive state history, our method natively overcomes both limitations. By mapping the logical and temporal complexities of the full class of STL into time-varying geometric boundaries, we directly constrain the multidimensional system state without relying on scalar robustness metrics. Augmenting the state space with time, we train a time-aware Soft Actor-Critic (SAC) agent using a continuous, geometry-aware reward function that eliminates the need to explicitly evaluate complex logical semantics during execution. The proposed framework offers a history-free, computationally efficient approach to learn continuous control policies that ensure robust satisfaction of specifications while strictly adhering to system input constraints.

Chinese Translation

本文解决了机器人系统的控制问题，包括在未知动态和严格执行器限制下运行的非完整和欠驱动平台，以满足复杂的高层次规范。我们使用信号时序逻辑（Signal Temporal Logic，STL）来表示这些高层次规范，并提出了一种新颖的时间感知强化学习（Reinforcement Learning，RL）框架，该框架利用时空管道（Spatiotemporal Tubes，STTs）的几何特性。传统的分析性STT控制器通常难以强制执行输入约束，而现有的RL方法依赖于内存密集的状态历史，我们的方法本质上克服了这两种限制。通过将全类STL的逻辑和时间复杂性映射到时间变化的几何边界，我们直接约束多维系统状态，而无需依赖标量鲁棒性指标。通过将状态空间与时间结合，我们使用连续的、几何感知的奖励函数训练一个时间感知的软演员-评论家（Soft Actor-Critic，SAC）代理，从而消除了在执行过程中显式评估复杂逻辑语义的需要。所提出的框架提供了一种无历史、计算高效的方法，以学习连续控制策略，确保在严格遵守系统输入约束的同时，稳健地满足规范。

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cs.RO / 89 / 2609.28429

Watch, Recall, Act: Always-On Robots in Concurrent Embodied Streams

观察、回忆、行动：在并发具身流中的始终在线机器人

Yi, Ding, Sun, Peiwen, Rong, Chenchu, Wang, Jianan, Dai, Xili, Yue, Xiangyu, Lin, Xi

Abstract

An always-on robot faces an endless stream that never resets: instructions arrive and lapse, the scene changes, and its own past actions reshape what it must reason about. Today's action models are built for the opposite: a fixed instruction, no mid-task intervention, single-step reasoning. In an open-ended world a robot must watch a live stream for far-future cues, recall its own far-past actions, and act on them under dual-arm concurrency. We present ARMS (Always-on Robot in Multi-modal Streams), a deliberately simple streaming policy: a single pretrained $\pi$0.5 backbone augmented by three lightweight modules that turn live perception, embodied states, and the robot's own past actions into context the backbone reads before it acts. The modules update this context asynchronously, so watching and recalling never block acting and the two arms act at once. Rather than inventing new mechanisms, ARMS integrates these learned context providers with an agent-causal self-history that logs which arm did what, and when. To supervise them without extra annotation, we build ARMS Dataset, whose staged construction script itself labels every module from real dual-arm teleoperation. Trained on it, ARMS reaches 45% on the combined task against 28% for the strongest of our four main baselines, and ablations confirm the memory module, the embodied-state head, and asynchronous concurrency are each necessary.

Chinese Translation

始终在线的机器人面临着一个永无止境的流，它从不重置：指令不断到来并消逝，场景变化，而它自己过去的行动又重塑了它必须推理的内容。如今的行动模型是为相反的情况而构建的：固定指令、无中途干预、单步推理。在一个开放的世界中，机器人必须观察实时流以获取远期线索，回忆自己遥远的过去行动，并在双臂并发的情况下对此做出反应。我们提出了ARMS（Always-on Robot in Multi-modal Streams），这是一种故意简单的流式策略：一个单一的预训练$ ext{π}$0.5主干，通过三个轻量级模块增强，这些模块将实时感知、具身状态和机器人的过去行动转化为主干在行动前读取的上下文。这些模块异步更新上下文，因此观察和回忆从不阻碍行动，两个手臂可以同时行动。ARMS并不是发明新的机制，而是将这些学习到的上下文提供者与一个代理因果自历史集成在一起，记录哪个手臂在何时做了什么。为了在没有额外标注的情况下监督它们，我们构建了ARMS数据集，其分阶段构建脚本本身从真实的双臂远程操作中标注每个模块。在此数据集上训练后，ARMS在综合任务上达到了45%的准确率，而我们四个主要基线中最强的仅为28%，消融实验确认了记忆模块、具身状态头和异步并发都是必不可少的。

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cs.RO / 90 / 2609.28431

LiMA: Bridging Long-term Imagination to Real-time Dexterous Manipulation via Asynchronous Diffusion

LiMA：通过异步扩散将长期想象与实时灵巧操作相结合

Chen, Ning, Fu, Yankai, Zhao, Junkai, Sun, Qianpu, Yao, Guocai, Wang, Pengwei, Wang, Zhongyuan, Zhang, Shanghang

Abstract

Dexterous manipulation demands long-term foresight and rapid reactive control. Vision-Language-Action (VLA) models, while proficient in high-level reasoning, often lack a fine-grained understanding of physical dynamics and spatial perception. Conversely, World-Action Models (WAMs) typically suffer from high inference latency due to iterative generation. These deficiencies result in a critical temporal misalignment where the model's intent fails to adapt to rapid physical contact changes. To overcome this fundamental bottleneck, we propose LiMA, an asynchronous dual-system generative framework that systematically decouples intent planning from reactive execution. LiMA organizes computation into a multi-scale hierarchy: a slow system handles sparse long-horizon spatiotemporal intent generation, while a fast system focuses on dense high-frequency motion refinement. To align sparse intent predictions with dense action trajectories, we introduce a Latent Schr\"odinger Bridge Coupling mechanism that formulates refinement as an entropy-regularized probabilistic transport process. LiMA reduces inference latency by 45.8% compared with Cosmos-Policy via asynchronous decoupling. Evaluated across six bimanual dexterous manipulation tasks spanning multiple horizons, LiMA achieves an overall success rate of 70.8% and an average subtask success rate of 78.9%, while maintaining performance in unseen scenarios. The project website is available at https://ccdcs.github.io/LiMA_repo/

Chinese Translation

灵巧操作要求具备长期的前瞻性和快速的反应控制。尽管视觉-语言-动作（VLA）模型在高层次推理方面表现出色，但通常缺乏对物理动态和空间感知的细致理解。相反，世界-动作模型（WAMs）由于迭代生成而通常面临较高的推理延迟。这些缺陷导致了一个关键的时间错位，即模型的意图未能适应快速的物理接触变化。为了克服这一根本瓶颈，我们提出了LiMA，一种异步双系统生成框架，系统地将意图规划与反应执行解耦。LiMA将计算组织成多尺度层次：一个慢速系统处理稀疏的长期时空意图生成，而一个快速系统则专注于密集的高频运动细化。为了将稀疏的意图预测与密集的动作轨迹对齐，我们引入了一种潜在的薛定谔桥耦合机制，将细化过程公式化为一个熵正则化的概率传输过程。通过异步解耦，LiMA将推理延迟减少了45.8%，相比于Cosmos-Policy。在六个跨越多个时间范围的双手灵巧操作任务中进行评估，LiMA实现了70.8%的整体成功率和78.9%的平均子任务成功率，同时在未见场景中保持了性能。项目网站可访问 https://ccdcs.github.io/LiMA_repo/

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cs.RO / 91 / 2609.28467

Where Should I Join? Robot Group Joining via Language-Guided Goal Prediction

我应该在哪里加入？通过语言引导的机器人群体加入

Fang, Zilin, Wang, Zishuo, Lee, Gim Hee, Hsu, David

Abstract

Social navigation typically assumes a specified goal and focuses on reaching it while respecting social conventions, whereas robot group joining requires predicting where to join based on the group's real-time activity and formation. This is a highly semantic task, yet an important capability for applications such as robotic guide dogs and autonomous mobility scooters. We formulate language-grounded robot group joining: given an observation and a natural-language description of a target group, the robot identifies the relevant group members and predicts socially compliant joining poses. For grounding, we generate structured candidate subsets through recursive spectral partitioning and rank them with a language-conditioned image--geometry model. Given the grounded group, a goal predictor leverages human-formation priors to produce a multimodal energy--orientation map over feasible robot poses. Experiments on conversations, queues, and audiences across varying group sizes, crowd densities, and visual ambiguities show that our method achieves competitive grounding accuracy with sub-second inference and outperforms all baselines in joining-pose prediction. Real-robot experiments further demonstrate group joining in both static and dynamically changing interactions.

Chinese Translation

社会导航通常假设一个特定的目标，并专注于在遵循社会规范的同时达到该目标，而机器人群体加入则需要根据群体的实时活动和形成来预测加入的位置。这是一个高度语义化的任务，但对于机器人导盲犬和自主移动滑板车等应用来说，是一项重要的能力。我们提出了基于语言的机器人群体加入的框架：给定一个观察和目标群体的自然语言描述，机器人识别相关的群体成员并预测符合社会规范的加入姿态。为了实现这一目标，我们通过递归谱划分生成结构化的候选子集，并利用语言条件的图像-几何模型对其进行排序。在确定了群体后，目标预测器利用人类形成的先验知识生成一个多模态能量-方向图，覆盖可行的机器人姿态。在对不同群体规模、拥挤度和视觉模糊度下的对话、排队和观众进行的实验表明，我们的方法在加入姿态预测中实现了具有竞争力的基础准确性，推理时间低于一秒，并且在所有基线中表现优异。真实机器人实验进一步展示了在静态和动态变化的交互中实现群体加入的能力。

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计算机视觉 (Computer Vision)

106

cs.CV / 1 / 2609.26809

AgroBench: A Reproducible Multimodal Benchmark for Weakly Supervised Crop Yield Learning from County Statistics and Pixel Observations

AgroBench：一个可重复的多模态基准，用于从县级统计数据和像素观测中进行弱监督作物产量学习

Singh, Udaiveer, Ranjan, Rajiv, Tamaskar, Shashank, Saraswat, Dharmendra

Abstract

Reliable agricultural yield statistics are typically reported at coarse administrative scales, whereas modern geospatial machine learning methods require spatially explicit, pixel level supervision. This mismatch has limited the development of large-scale benchmarks for crop yield learning using multimodal Earth observation data. A reproducible benchmark, AgroBench, is presented for transforming publicly available U.S. county level crop yield statistics into weakly supervised pixel-level crop time series. Each crop pixel time series is paired with a county-level yield value as a weak supervisory signal rather than a directly measured pixel-level yield label. Our geospatial data generation pipeline integrates USDA crop yield statistics with crop-specific land cover masks, Sentinel 2 multispectral imagery, Sentinel-1 synthetic aperture radar observations, climatic variables, and terrain information to produce temporally aligned multimodal sequences describing individual crop pixels throughout the growing season. The resulting benchmark contains over 13 million observations from 788,654 unique crop pixels spanning 5,107 county year combinations across eight growing seasons (2017 to 2024) for five major U.S. crops. To facilitate standardized evaluation, we establish a crop yield prediction benchmark using a Leave-One-Year-Out evaluation protocol and provide baseline results using representative machine learning models. By releasing the complete data generation pipeline, benchmark dataset, and evaluation protocol, AgroBench provides a reproducible foundation for future research in weakly supervised learning, multimodal remote sensing, spatiotemporal modeling, and geospatial foundation models for agriculture.

Chinese Translation

可靠的农业产量统计通常在粗略的行政层面上报告，而现代地理空间机器学习方法需要空间明确的像素级监督。这种不匹配限制了利用多模态地球观测数据进行作物产量学习的大规模基准的发展。本文提出了一个可重复的基准AgroBench，用于将公开可用的美国县级作物产量统计数据转化为弱监督的像素级作物时间序列。每个作物像素时间序列与县级产量值配对，作为弱监督信号，而不是直接测量的像素级产量标签。我们的地理空间数据生成管道将美国农业部（USDA）作物产量统计与特定作物的土地覆盖掩膜、Sentinel 2多光谱影像、Sentinel-1合成孔径雷达观测、气候变量和地形信息整合，以生成描述整个生长季节中单个作物像素的时间对齐多模态序列。最终的基准包含来自788,654个独特作物像素的超过1300万次观测，跨越五个主要美国作物在八个生长季节（2017至2024年）中的5,107个县年组合。为了促进标准化评估，我们建立了一个作物产量预测基准，采用逐年留出（Leave-One-Year-Out）评估协议，并使用代表性的机器学习模型提供基线结果。通过发布完整的数据生成管道、基准数据集和评估协议，AgroBench为未来在弱监督学习、多模态遥感、时空建模和农业地理空间基础模型的研究提供了可重复的基础。

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cs.CV / 2 / 2609.26920

Cross-Modal Contrastive Learning from Histopathology and CT for Automated Renal Cell Carcinoma Grading

基于组织病理学和CT的跨模态对比学习用于自动化肾细胞癌分级

Das, Amit, Shukla, Tanmay, Tomita, Naofumi, Farhadi, Faraz, Sin, Jessica, Hakimi, Ari, Vanderbilt, Chad, Chen, Jie-Fu, Kotecha, Ritesh, Ma, Weijie, Ren, Bing, Hassanpour, Saeed

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) exhibits substantial clinical heterogeneity, and accurate grade assessment is essential for risk stratification and treatment planning. However, conventional grading requires invasive tissue sampling. We developed RCC-Align, a cross-modal contrastive learning framework that leverages paired histopathology and computed tomography (CT) data during training to improve noninvasive CT-based ccRCC grade prediction. Methods: RCC-Align aligns paired whole-slide histopathology images (WSIs) and CT scans through contrastive cross-modal objectives, transferring grade-discriminative information from microscopic tissue morphology to macroscopic radiologic representations. The framework was trained and evaluated on paired TCGA and CPTAC cohorts using patient-level five-fold cross-validation. Performance for low- versus high-grade ccRCC classification was compared against CT-only baselines (DINOv2-Base and DINOv2-Finetuned) and a WSI-based reference model (GigaPath-Finetuned). Cross-modal alignment was assessed using cosine similarity analysis. Results: RCC-Align achieved an AUC of 0.601 (95% CI, 0.524-0.673) and AUPRC of 0.599 (95% CI, 0.541-0.676), outperforming DINOv2-Finetuned (AUC 0.545; AUPRC 0.543) with significantly improved low-grade prediction (p = 0.004). RCC-Align also demonstrated stronger paired WSI-CT embedding alignment compared with baselines. The WSI-based GigaPath reference achieved an AUC of 0.719. Conclusion: Pathology-guided contrastive learning improves CT-based ccRCC grading while requiring only CT at inference. This approach may complement tissue diagnosis when biopsy is unsafe, infeasible, or limited by intratumoral heterogeneity. Validation in larger, multi-institutional cohorts with external testing is needed before clinical translation.

Chinese Translation

背景：透明细胞肾细胞癌（ccRCC）表现出显著的临床异质性，准确的分级评估对于风险分层和治疗规划至关重要。然而，传统的分级方法需要侵入性组织取样。我们开发了RCC-Align，这是一个跨模态对比学习框架，利用配对的组织病理学和计算机断层扫描（CT）数据进行训练，以改善基于CT的ccRCC非侵入性分级预测。方法：RCC-Align通过对比跨模态目标对配对的全切片组织病理图像（WSIs）和CT扫描进行对齐，将微观组织形态学中的分级区分信息转移到宏观放射学表征中。该框架在配对的TCGA和CPTAC队列上进行了训练和评估，采用患者级五折交叉验证。低级与高级ccRCC分类的性能与仅基于CT的基线（DINOv2-Base和DINOv2-Finetuned）以及基于WSI的参考模型（GigaPath-Finetuned）进行了比较。通过余弦相似性分析评估跨模态对齐。结果：RCC-Align的AUC为0.601（95% CI，0.524-0.673），AUPRC为0.599（95% CI，0.541-0.676），显著优于DINOv2-Finetuned（AUC 0.545；AUPRC 0.543），并显著改善了低级预测（p = 0.004）。与基线相比，RCC-Align还展示了更强的配对WSI-CT嵌入对齐。基于WSI的GigaPath参考模型的AUC为0.719。结论：病理引导的对比学习改善了基于CT的ccRCC分级，同时在推断时仅需CT。这种方法可以在活检不安全、不可行或受到肿瘤内异质性限制时补充组织诊断。在临床转化之前，需要在更大规模的多机构队列中进行外部测试验证。

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cs.CV / 3 / 2609.26923

A 3D Pose-Based Ensemble Framework for Cricket Shot Classification and Automated Biomechanical Analysis

基于三维姿态的板球击球分类与自动生物力学分析的集成框架

Shome, Sourav, Rahad, M. D. Ashiquzzaman, Debnath, Rameswar

Abstract

Cricket is one of the most celebrated sports world-wide, and technological advancement has become deeply embedded in how the modern game is analyzed and coached. Cricket shot classification and automated performance analysis add a further dimension to this trend. Traditional approaches rely on RGB video features or static images, which are sensitive to environmental variations such as camera angle, lighting, and background clutter, and often fail to capture the underlying biomechanics of batting actions. In this paper, we propose a system to improve cricket coaching that takes raw video data, extracts batsmen from video frames using YOLO, and extracts 3D pose data from video frames using MeTRAbs. The system produces sequential skeletal pose data of 30 body points and captures the biomechanical features of a batsman. As part of the system, we also propose a deep learning ensemble for shot classification of four shots: flick, pull, defense, and drive. The ensemble performed well, compared to existing classification works, achieving 97.68% accuracy. In addition, we analyzed the misclassification rates to identify cases where shots were incorrectly classified and examined their possible causes. Our proposed system allows novice players to obtain useful feedback, such as important joint angles relative to expert batsmen, which can also be useful for injury prevention. The shot classifier also helps track class-wise shots over time for further analysis. In addition to novice players, coaches can use the system for player evaluation.

Chinese Translation

板球是全球最受欢迎的运动之一，技术进步已深深融入现代比赛的分析与指导中。板球击球分类和自动化表现分析为这一趋势增添了新的维度。传统方法依赖于RGB视频特征或静态图像，这些方法对环境变化（如摄像机角度、光照和背景杂乱）敏感，往往无法捕捉击球动作的基本生物力学特征。本文提出了一种改进板球训练的系统，该系统利用原始视频数据，通过YOLO从视频帧中提取击球手，并使用MeTRAbs从视频帧中提取三维姿态数据。该系统生成30个身体点的连续骨骼姿态数据，并捕捉击球手的生物力学特征。作为系统的一部分，我们还提出了一种深度学习集成模型，用于四种击球的分类：挑击、拉击、防守和驱动。与现有分类工作相比，该集成模型表现良好，达到了97.68%的准确率。此外，我们分析了误分类率，以识别击球被错误分类的情况，并检查其可能原因。我们提出的系统使初学者能够获得有用的反馈，例如与专业击球手相关的重要关节角度，这对于预防受伤也很有帮助。击球分类器还帮助跟踪不同类别的击球随时间的变化，以便进行进一步分析。除了初学者，教练也可以利用该系统进行球员评估。

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cs.CV / 4 / 2609.26924

nnFoundation: 3D Foundation Models for Radiology

nnFoundation：用于放射学的3D基础模型

Harsy, Constantin Ulrich, Wald, Tassilo, Gotkowski, Karol, Kirchhoff, Yannick, Knopp, Marcel, Rokuss, Maximilian, Stegmeier, Elisa, Schader, Philipp, Trofimova, Dasha, Stock, Raphael, Kahl, Kim-Celine, Schaumann, Stephen, Erkan, Selen, Zimmerer, David, Denner, Stefan, Langenberg, Moritz, Ziegler, Sebastian, Eckstein, Katharina, Fischer, Maximilian, Suprijadi, Jonathan, Kovács, Bálint, Hamm, Benjamin, Deshpande, Anand, Bounias, Dimitrios, Disch, Nico, Xiao, Shuhan, Kächele, Jessica, Sellner, Jan, Baidya, Rajesh, Traub, Jeremias, Krämer, Lars, Zenk, Maximilian, Rädsch, Tim, Dvoretskii, Stefan, Peretzke, Robin, Deissler, Jonathan, Ertl, Alexandra, Ghosh, Partha, Dreher, Kris, Dinkelacker, Stefan, Reinke, Annika, Christodoulou, Evangelia, Saeed, Numan, Savriama, Yoland, Estrada, Santiago, Kügler, David, Barragan, Laura Alexandra Daza, Osorio, Cristina Isabel Gonzalez, Peeken, Jan, Baumgartner, Michael, Teichmann, Marvin, Chabin, Guillaume, Kirchler, Matthias, Koch, Valentin, study, for the ALFA, Hohenhaus, Markus, Koslov, Dimitri, Decker, Nina, Yaqub, Mohammad, Heuser, Arnd, Reuter, Martin, Schnabel, Julia A., Heimann, Tobias, Ghesu, Florin, Brachmann, Paul, Heußel, Claus P., Radbruch, Alexander, Brugnara, Gianluca, Rastogi, Aditya, Foltyn-Dumitru, Martha, Schlemmer, Heinz-Peter, Reicht, Ignaz, Holzschuh, Julius C., Bach, Michael, Stieltjes, Bram, Schlamp, Kai, Maier-Hein, Lena, Nolden, Marco, Floca, Ralf, Jäger, Paul F., Vollmuth, Philipp, Isensee, Fabian, Maier-Hein, Klaus H.

Abstract

Radiological artificial intelligence has advanced rapidly, yet most systems remain narrowly task-specific, data-intensive, and fragile under domain shift. Foundation models promise more transferable and data-efficient solutions, but existing approaches are limited in scale, evaluated narrowly, and often assume that a single pretrained model can support diverse downstream tasks. Here we present nnFoundation, complementary convolutional and transformer-based 3D radiological foundation models. Developed within the Human Radiome Project (THRP), nnFoundation is trained on 2.1 million CT, MRI, and PET image volumes from 125 institutional and public datasets. We evaluate them across 108 tasks spanning segmentation, detection, classification, report generation, and image retrieval, including evaluations under domain shift, by external partners and in low-data and low-compute regimes. Across all task types, our convolution- and transformer-based nnFoundation models consistently outperform both prior 3D foundation models and training from scratch, establishing state-of-the-art performance for radiological imaging. However, performance follows a consistent task-dependent structure: the convolutional nnFoundation model dominates spatially localized tasks, whereas the transformer-based nnFoundation model excels in tasks requiring global semantic reasoning and in frozen-feature settings. Dynamically aligning the foundation model topology with the dataset characteristics post-hoc further improves transfer across heterogeneous 3D settings. These results show that transferable 3D radiological performance is governed not by a single universal model, but by the interplay of scalable pretraining, complementary architectures, and dataset-aware adaptation. We release nnFoundation models integrated into nnU-Net and nnDetection, enabling immediate application across established radiology workflows.

Chinese Translation

放射学人工智能迅速发展，但大多数系统仍然局限于特定任务、数据密集，并且在领域转移下表现脆弱。基础模型承诺提供更具可转移性和数据高效的解决方案，但现有方法在规模上有限，评估范围狭窄，且通常假设单一的预训练模型可以支持多样的下游任务。在此，我们提出了nnFoundation，这是一种互补的基于卷积和变换器的3D放射学基础模型。nnFoundation在“人类放射组项目”（THRP）中开发，训练数据来自125个机构和公共数据集的210万CT、MRI和PET图像体积。我们在108个任务上进行评估，这些任务涵盖了分割、检测、分类、报告生成和图像检索，包括在领域转移下的评估，由外部合作伙伴进行，以及在低数据和低计算环境下的评估。在所有任务类型中，我们的基于卷积和变换器的nnFoundation模型始终优于之前的3D基础模型和从头训练，确立了放射学成像的最新性能。然而，性能遵循一致的任务依赖结构：卷积nnFoundation模型在空间局部化任务中占主导地位，而基于变换器的nnFoundation模型在需要全局语义推理和冻结特征设置的任务中表现优异。动态调整基础模型拓扑以与数据集特征后期对齐，进一步改善了在异构3D环境中的迁移。这些结果表明，可转移的3D放射学性能并非由单一的通用模型所决定，而是由可扩展的预训练、互补架构和数据集感知适应之间的相互作用所驱动。我们发布了集成到nnU-Net和nnDetection中的nnFoundation模型，使其能够立即应用于已建立的放射学工作流程。

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cs.CV / 5 / 2609.26981

Lessons learned from deploying imaging AI with the open PACS-AI platform

从开放的 PACS-AI 平台部署影像人工智能中获得的经验教训

Kadoury, Samuel, Hussin, Julie G., Thériault-Lauzier, Pascal, Létourneau-Guillon, Laurent, Lewis, Rob, McArthur, Adam, Harris, Gordon J., Bahig, Houda, Déziel, Pierre-Luc, Kshirsagar, Jay, Jaremko, Jacob L., Cohen-Adad, Julien, Delfrate, Jacques, Avram, Robert

Abstract

We describe deploying imaging AI at six hospitals through PACS-AI, an open self-hosted platform. The binding constraint is not model accuracy but infrastructure to route studies, display results, capture feedback, and audit what runs. At one center, angiography models completed 515 of 607 jobs (84.8%); failures reflected absent diagnostic views, and 78.1% of 638 clinician ratings were positive. Publishing honest readiness levels for every model is itself a governance practice.

Chinese Translation

我们描述了通过 PACS-AI 这一开放自托管平台在六家医院部署影像人工智能的过程。限制因素不是模型的准确性，而是基础设施，包括研究的路由、结果的展示、反馈的捕获以及运行审计。在一个中心，血管造影模型完成了 607 个任务中的 515 个（84.8%）；失败的原因是缺少诊断视图，且 638 个临床医生的评分中有 78.1% 是积极的。为每个模型发布诚实的准备水平本身就是一种治理实践。

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cs.CV / 6 / 2609.27011

HYDRO: Towards Non-Reversible Face De-Identification Using a High-Fidelity Hybrid Diffusion and Target-Oriented Approach

HYDRO：基于高保真混合扩散和目标导向方法的不可逆面部去标识化研究

Rosberg, Felix, Štruc, Vitomir, Englund, Cristofer, Aksoy, Eren Erdal, Alonso-Fernandez, Fernando

Abstract

Target-oriented face de-identification models aim to anonymize the identity of a target individual across different images or video frames, such that the target can no longer be reliably recognized, while maintaining key characteristics of the visual data. Such models commonly leverage generative encoder-decoder architectures to manipulate facial appearances, enabling them to produce realistic high-fidelity de-identification results, while ensuring considerable attribute-retention capabilities. However, target-oriented models also carry the risk of inadvertently preserving subtle identity cues, making them (potentially) reversible and susceptible to reconstruction attacks. To address this problem, we introduce in this paper a novel (robust) face de-identification approach, called HYDRO, that combines target-oriented models with a dedicated diffusion process specifically designed to destroy any imperceptible information that may allow learning to reverse the de-identification procedure. HYDRO first de-identifies the given face image, injects noise into the de-identification result to impede reconstruction, and then applies a diffusion-based recovery step to improve fidelity and minimize the impact of the noising process on the data characteristics. To further improve image fidelity and better retain gaze directions, a novel Eye Similarity Discriminator (ESD) is also introduced and incorporated it into the training of HYDRO. Extensive quantitative and qualitative experiments on three diverse datasets demonstrate that HYDRO exhibits state-of-the-art (SOTA) fidelity and attribute-retention capabilities, while being the only target-oriented method resilient against reconstruction attacks. In comparison to multiple SOTA competitors, HYDRO reduces the success of reconstruction attacks by 85.7% on average.

Chinese Translation

目标导向的面部去标识化模型旨在对不同图像或视频帧中的目标个体身份进行匿名处理，使得该目标无法被可靠识别，同时保留视觉数据的关键特征。这类模型通常利用生成编码器-解码器架构来操控面部外观，从而生成逼真的高保真去标识化结果，同时确保相当的属性保留能力。然而，目标导向模型也存在无意中保留微妙身份线索的风险，使其（潜在地）可逆并易受重建攻击。为了解决这一问题，本文提出了一种新颖的（鲁棒的）面部去标识化方法，称为HYDRO，该方法将目标导向模型与专门设计的扩散过程相结合，旨在消除任何可能允许学习逆转去标识化过程的不可察觉信息。HYDRO首先对给定的面部图像进行去标识化，向去标识化结果中注入噪声以阻碍重建，然后应用基于扩散的恢复步骤以提高保真度，并最小化噪声过程对数据特征的影响。为了进一步提高图像保真度并更好地保留注视方向，本文还引入了一种新颖的眼睛相似性判别器（Eye Similarity Discriminator, ESD），并将其纳入HYDRO的训练中。在三个不同数据集上进行的大量定量和定性实验表明，HYDRO展现出最先进的（SOTA）保真度和属性保留能力，同时是唯一对重建攻击具有韧性的目标导向方法。与多个SOTA竞争对手相比，HYDRO平均降低了重建攻击的成功率85.7%。

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cs.CV / 7 / 2609.27015

Anatomy-Aware Synthesis of Post-Contrast Breast MRI from Pre-Contrast Images

基于解剖结构的术后对比乳腺MRI合成方法研究

Zhou, Zhengbo, Arefan, Dooman, Gu, Lin, Curran, Ufara Zuwasti, Wu, Shandong

Abstract

We developed an anatomy-aware deep learning framework to synthesize post-contrast breast MRI from pre-contrast images, emphasizing tumor and background parenchymal enhancement (BPE) regions. This retrospective study included 649 patients with 6,251 paired pre-contrast and post-contrast images. The framework integrates breast mask consistency, lesion-region supervision, and BPE-region supervision into an image-to-image translation model. Evaluation included quantitative image quality metrics, a reader study with two breast radiologists, and downstream Ki-67 classification. The proposed method outperformed Pix2Pix, Pix2PixHD, diffusion-based synthesis, and mask-supervised baselines in whole-image and regional evaluations. Ki-67 classification showed no statistically significant performance differences across real- and synthetic-image training and testing settings, although this does not establish equivalence. These findings suggest that anatomy-aware supervision improves synthesis fidelity and support further investigation of synthetic post-contrast MRI for contrast-free imaging workflows.

Chinese Translation

我们开发了一种基于解剖结构的深度学习框架，以从术前对比图像合成术后对比乳腺MRI，重点关注肿瘤和背景实质增强（BPE）区域。本回顾性研究包括649名患者的6251对术前和术后对比图像。该框架将乳腺掩膜一致性、病变区域监督和BPE区域监督整合到图像到图像的转换模型中。评估包括定量图像质量指标、两名乳腺放射科医师的读者研究以及下游Ki-67分类。所提出的方法在整体图像和区域评估中优于Pix2Pix、Pix2PixHD、基于扩散的合成和掩膜监督基线。Ki-67分类在真实图像和合成图像的训练和测试设置中未显示出统计学上显著的性能差异，尽管这并不建立等效性。这些发现表明，基于解剖结构的监督提高了合成的真实性，并支持进一步研究合成术后对比MRI在无对比成像工作流程中的应用。

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cs.CV / 8 / 2609.27022

Adversarial Attacks and Identity Leakage in De-Identification Systems: An Empirical Study

对抗攻击与去标识化系统中的身份泄露：一项实证研究

Rosberg, Felix, Englund, Cristofer, Aksoy, Eren Erdal, Alonso-Fernandez, Fernando

Abstract

In this paper, we investigate the impact of adversarial attacks on identity encoders within a realistic de-identification framework. Our experiments show that the transferability of attacks transfers from an external surrogate model to the system model (e.g., CosFace to ArcFace) allows the adversary to cause identity information to leak in a sufficiently sensitive face recognition system. We present experimental evidence and propose strategies to mitigate this vulnerability. Specifically, we show how fine-tuning on adversarial examples helps to mitigate this effect for distortion-based attacks (i.e., snow, fog, etc.), while a simple low-pass filter can attenuate the effect of adversarial noise without affecting the de-identified images. Our mitigation results in a de-identification system that preserves its functionality while being significantly more robust to adversarial noise.

Chinese Translation

在本文中，我们研究了对抗攻击对现实去标识化框架中身份编码器的影响。我们的实验表明，从外部替代模型到系统模型（例如，CosFace到ArcFace）的攻击可转移性使得对手能够在一个足够敏感的人脸识别系统中导致身份信息泄露。我们提供了实验证据，并提出了缓解这一脆弱性的策略。具体而言，我们展示了如何通过对抗样本的微调来减轻这种效果，尤其是对于基于失真的攻击（即雪、雾等），同时简单的低通滤波器可以在不影响去标识化图像的情况下减弱对抗噪声的影响。我们的缓解措施使得去标识化系统在保持其功能的同时，对对抗噪声具有显著更强的鲁棒性。

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cs.CV / 9 / 2609.27076

Pro-Bench: Prompt-Robust Open-Vocabulary Visual Grounding Across Real-World Heterogeneous Environments

Pro-Bench：在真实世界异构环境中进行提示鲁棒的开放词汇视觉定位

Nwankwo, Linus, Alaran, Muslim, Rauch, Christian, Obilikpa, Stanley Chukwuebuka, Rueckert, Elmar

Abstract

Open-vocabulary visual grounding enables robots to localise task-relevant entities from natural-language queries without dependence on predefined perceptual taxonomies. However, existing benchmarks largely rely on short category labels and web-scraped imagery, leaving it unclear whether open-vocabulary models can robustly ground diverse queries and visual conditions under real deployments. We introduce \textbf{Pro-Bench}, a prompt-conditioned benchmark for open-vocabulary visual grounding in heterogeneous, real-world environments. Pro-Bench includes $13k+$ RGB frames from independent robotic domains (subterranean, industrial, indoor, outdoor, urban), with $74.5k$ manual instance annotations and $515$ target queries covering categorical, attributive, relational, affordance, state, part-whole, negative, and compositional semantics. We benchmarked $16$ open-vocabulary model configurations in strict zero-shot inference, measuring localisation accuracy across IoU thresholds, end-to-end inference latency, prompt-induced performance variation, and target recovery consistency. Our results show that prompt-robustness is strongly architecture-dependent. Most model configurations ($10/16$) perform best with short category labels, whereas free-form queries yield the highest accuracy for only one. Moreover, similar aggregate mAP can conceal substantial differences in consistent target recovery across reformulations. Pro-Bench enables systematic evaluation of these gaps and supports prompt-robust visual grounding. Pro-Bench: https://pro-bench.github.io/.

Chinese Translation

开放词汇视觉定位使得机器人能够从自然语言查询中定位与任务相关的实体，而无需依赖预定义的感知分类法。然而，现有的基准测试在很大程度上依赖于短类别标签和网络抓取的图像，尚不清楚开放词汇模型是否能够在真实部署中稳健地定位多样化的查询和视觉条件。我们引入了 extbf{Pro-Bench}，这是一个针对异构真实世界环境的开放词汇视觉定位的提示条件基准。Pro-Bench 包含来自独立机器人领域的 $13k+$ RGB 帧（地下、工业、室内、室外、城市），以及 $74.5k$ 手动实例注释和 $515$ 个目标查询，涵盖了类别、属性、关系、可供性、状态、部分-整体、负面和组合语义。我们在严格的零样本推理中对 $16$ 种开放词汇模型配置进行了基准测试，测量了在不同 IoU 阈值下的定位准确性、端到端推理延迟、提示引起的性能变化和目标恢复一致性。我们的结果表明，提示鲁棒性在很大程度上依赖于模型架构。大多数模型配置（$10/16$）在使用短类别标签时表现最佳，而自由形式查询仅有一个模型的准确性最高。此外，类似的整体 mAP 可能掩盖在重述过程中一致目标恢复的显著差异。Pro-Bench 使得系统评估这些差距成为可能，并支持提示鲁棒的视觉定位。Pro-Bench: https://pro-bench.github.io/

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cs.CV / 10 / 2609.27110

Feed the Panel Dimensions, Not Verdicts: Rubric-Decomposed Fusion of Vision-Language Aesthetic Judges

关注面板维度，而非裁决：视觉-语言美学评判的评分标准分解融合

Jadhav, Amit, Beriwala, Shaurya, Kim, Beomjin

Abstract

Vision-language models (VLMs) are deployed as zero-shot judges of image aesthetics, and panels of several models are recommended, on thin evidence, as the way to make such judges reliable. On two human-rated datasets, EVA and PARA, we find that a panel of holistic judges never significantly beats its best member, whether the verdicts are averaged or fused by a learned combiner. What a panel is worth depends on what it is fed. We therefore have each model score each image on the five dimensions of a frozen, human-written rubric and fuse those scores, alongside each model's verdict, across model families with an out-of-fold combiner. The dimension scores measure what their labels claim: with the overall human score partialled out, a dimension prompt carries more attribute-specific information than the holistic prompt in 28 of 30 model-attribute cells. Fused, they beat the best single VLM in all ten three-family panels on EVA (against that best single model, +0.07 Spearman rho for the strongest trio and +0.10 for the pre-declared one, and +0.06 and +0.07 when averaged over twenty fold partitions; against the panel mean, the primary test gives +0.118 on its EVA design set), and on PARA they reach parity under Spearman rho and a small, non-significant loss under Kendall tau-b, where one model already captures 85% of the human noise ceiling. It is not a feature-count artefact: giving the same combiner an equal number of pure holistic columns, split from the same repetitions, does not reproduce it. The gain costs a few hundred labels, which do not transfer between datasets, and 4.8x the API calls on EVA; we report it with paired bootstraps and Kendall tau-b, alongside a failed pre-registration and the configurations that lost.

Chinese Translation

视觉-语言模型（VLMs）被用作图像美学的零-shot 评判者，并且在薄弱证据的支持下，推荐多个模型的面板作为使这些评判者可靠的方法。在两个经过人工评分的数据集 EVA 和 PARA 上，我们发现，整体评判者的面板无论是通过平均裁决还是通过学习组合器融合裁决，都从未显著超越其最佳成员。面板的价值取决于其输入。因此，我们让每个模型根据一个固定的人类撰写的评分标准的五个维度对每个图像进行评分，并通过一个外折组合器在模型家族之间融合这些评分以及每个模型的裁决。维度评分测量其标签所声称的内容：在剔除整体人类评分后，维度提示在 30 个模型-属性单元中的 28 个中携带比整体提示更多的属性特定信息。经过融合后，它们在 EVA 的所有十个三家族面板中超越了最佳单一 VLM（与最佳单一模型相比，最强三家族的 Spearman rho 提高了 +0.07，预先声明的模型提高了 +0.10，平均在二十个折分上提高了 +0.06 和 +0.07；与面板均值相比，主要测试在其 EVA 设计集上给出 +0.118），而在 PARA 上它们在 Spearman rho 下达到平局，在 Kendall tau-b 下有小幅且不显著的损失，其中一个模型已经捕获了 85% 的人类噪声上限。这并不是特征计数的伪影：给同一组合器提供相同数量的纯整体列（从相同的重复中拆分）并未重现该结果。该增益需要几百个标签，这些标签在数据集之间无法转移，并且在 EVA 上的 API 调用增加了 4.8 倍；我们通过配对自助法和 Kendall tau-b 报告这一结果，并附上未成功的预注册以及损失的配置。

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cs.CV / 11 / 2609.27115

Damnatio Memoriae: Adversarially and Selectively Forgetting Identities in the Embedding Space of Face Recognition Models

遗忘的诅咒：在面部识别模型的嵌入空间中对身份进行对抗性和选择性遗忘

Öztürk, Ünsal, Hahn, Vedrana Krivokuća, Bhattacharjee, Sushil, Marcel, Sébastien

Abstract

A face recognition model links two images of a person recorded on separate occasions when their embedding similarity exceeds an operating threshold. We consider making chosen identities unlinkable across separate occasions while the model remains in service for the rest of the population. Deleting their images and retraining does not achieve this, since the model recognises identities never observed in training. Therefore, the embedding space must be altered against these identities, the process of which we call open-set adversarial forgetting. We propose three loss functions, one that disperses an identity's embeddings from their centroid, and two that map each image onto its own near-orthogonal target, learnt with the classifier head or fixed in advance as an almost-orthonormal frame. Each is fine-tuned alongside the classification objective on a subset of each identity's images. We evaluate them against four methods from prior work in verification and identification, at two forget scales and three backbones. Every loss acting on the embedding geometry makes the forget identities nearly unidentifiable. The orthonormal frame alone achieves strong forgetting, which holds wherever an image of that subset enters the comparison and leaves distinct forget identities unlinkable. It also surpasses a concurrent unsupervised method at a higher retain rate.

Chinese Translation

面部识别模型在两张记录于不同时间的同一人物图像之间建立联系，当它们的嵌入相似度超过操作阈值时。我们考虑在模型继续为其他人群服务的同时，使选定的身份在不同时间之间无法链接。删除这些身份的图像并重新训练并不能实现这一目标，因为模型能够识别在训练中从未观察到的身份。因此，必须对这些身份的嵌入空间进行调整，这一过程我们称之为开放集对抗性遗忘。我们提出了三种损失函数，其中一种将身份的嵌入从其中心点分散，另外两种则将每张图像映射到其近正交目标上，这些目标可以通过分类器头学习或预先固定为几乎正交的框架。每种损失函数都与每个身份图像子集的分类目标一起进行微调。我们将这些方法与先前在验证和识别方面的四种方法进行评估，比较了两种遗忘规模和三种骨干网络。每种作用于嵌入几何的损失都使得遗忘身份几乎无法识别。单独的正交框架实现了强烈的遗忘，无论该子集的图像何时进入比较，都使得不同的遗忘身份无法链接。它的表现也超过了一种同时进行的无监督方法，并且具有更高的保留率。

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cs.CV / 12 / 2609.27118

From greenhouse climate to individual leaves: an organ-resolved model of lettuce growth

从温室气候到单个叶片：一种有机体分辨的生菜生长模型

Rahman, Md Hasibur, Ahmed, Faraz, Bilal, Hafiz Muhammad, Wells, Daniel, Tobin, Dylan, Rehman, Tanzeel U.

Abstract

Greenhouse climate management aims to improve crop production while limiting energy use. This requires knowing how a crop will respond before conditions are changed. A crop digital twin can support this decision only if it represents how plant physiology and structure develop together. A unified framework was developed to simulate lettuce growth from the physiology of individual leaves. Each leaf received the conditions at its position in the canopy and contributed carbon through photosynthesis. Part of this carbon was used for maintenance and the remainder supported growth, distributed among leaves by their age, size and local environment. The predicted leaf mass, area and age generated an evolving three-dimensional plant in NVIDIA Isaac Sim. Ray tracing calculated the radiation intercepted by each leaf and returned it to photosynthesis, so structure and growth influenced each other over time. Against greenhouse measurements, the relative root mean square error was 9.5% for total dry weight and 9.2%, 12.7% and 13.1% for leaf number, canopy diameter and largest-leaf area, respectively. A 30% decrease in incident radiation reduced final dry weight by 10.4%, while the same increase raised it by 6.9%, and adding 200 ppm carbon dioxide raised it by 46.1%. Within a simulated 40-plant block, interior plants accumulated 8.6% less dry weight than border plants with identical initial states, and the leaf-specific tipburn index rose in the enclosed leaves over the period in which tipburn appeared on the greenhouse plants. Resolving individual leaves therefore explains how local exposure changes plant growth within the greenhouse. The framework provides the forward plant model needed for a bidirectional digital twin, where observations of the physical plant can update predictions and support greenhouse climate decisions.

Chinese Translation

温室气候管理旨在提高作物产量，同时限制能源使用。这需要在改变条件之前了解作物的反应。作物数字双胞胎只有在能够表示植物生理与结构共同发展的情况下，才能支持这一决策。我们开发了一个统一框架，以模拟生菜的生长，基于单个叶片的生理特性。每片叶子接收其在冠层中的位置所对应的环境条件，并通过光合作用贡献碳。部分碳用于维持生理功能，其余则支持生长，按照叶片的年龄、大小和局部环境进行分配。预测的叶片质量、面积和年龄生成了一个在NVIDIA Isaac Sim中不断演变的三维植物模型。光线追踪计算了每片叶子所截获的辐射，并将其反馈至光合作用，因此结构与生长在时间上相互影响。与温室测量结果相比，总干重的相对均方根误差为9.5%，叶片数量、冠层直径和最大叶片面积的误差分别为9.2%、12.7%和13.1%。入射辐射减少30%使最终干重减少10.4%，而相同的增加则使其增加6.9%，添加200 ppm二氧化碳则使其增加46.1%。在模拟的40株植物区块中，内部植物的干重比边缘植物减少了8.6%，而在温室植物出现尖枯的时期，封闭叶片的叶片特定尖枯指数上升。因此，分辨单个叶片解释了局部暴露如何改变温室内植物的生长。该框架提供了双向数字双胞胎所需的前向植物模型，物理植物的观察可以更新预测并支持温室气候决策。

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cs.CV / 13 / 2609.27123

PEARL: A Lightweight Prompt-based Feature Interpreter Framework for Real-Time, Anonymous, and Heterogeneous Collaborative Perception

PEARL：一种轻量级基于提示的特征解释框架，用于实时、匿名和异构协作感知

Maleki, Armin, Radha, Hayder

Abstract

Heterogeneity across Collaborative Perception (CP) agents is a major challenge for emerging CP frameworks due to domain gaps from differing sensors, architectures, and training data. Prior works mitigate this challenge by aligning features in a unified space via model retraining or per-agent-type interpreters. These strategies (a) require access to neighbor configurations, (b) do not fully address real-time CP deployment, and (c) generalize poorly to unseen agents joining at run time. To overcome these challenges, we present PEARL, a Prompt-Embedding framework for Anonymous and Real-time Lightweight heterogeneous CP. PEARL supports multiple CP interpreters and selects one for a new-joining agent in real time using two lightweight, multi-scale interpreters trained in parallel: a sparse-detection (LWSD) interpreter that aligns salient regions for cooperative detection, and a dense, domain-invariant (LWDDI) interpreter that produces agent-invariant features for fast interpreter selection. Both interpreters use low-rank visual prompts to reduce computation, storage, and model complexity. Extensive experiments on simulated (OPV2V, V2XSet) and real (DAIR-V2X) datasets show that PEARL generalizes across simulated and real-world cooperative driving scenarios. Its real-time model-selection strategy yields an 8.2% Average Precision (AP) gain over a random-selection baseline while running in 1.67 ms on average. Although primarily designed for real-time CP, PEARL also outperforms state-of-the-art heterogeneous CP frameworks under traditional offline training by 5.6% AP on average while reducing communication cost by up to 34.7 times. Equally important, PEARL does not require sharing agents' configurations or model settings, thereby protecting information that may be proprietary or private. These results establish PEARL as a scalable and practical framework for heterogeneous collaborative perception.

Chinese Translation

协作感知（Collaborative Perception, CP）代理之间的异构性是新兴CP框架面临的主要挑战，这源于不同传感器、架构和训练数据之间的领域差距。以往的研究通过模型重训练或每种代理类型的解释器在统一空间中对齐特征来缓解这一挑战。这些策略（a）需要访问邻居配置，（b）未能完全解决实时CP部署的问题，以及（c）对在运行时加入的未见代理的泛化能力较差。为克服这些挑战，我们提出了PEARL，一个用于匿名和实时轻量级异构CP的提示嵌入框架。PEARL支持多个CP解释器，并通过两个并行训练的轻量级多尺度解释器实时为新加入的代理选择一个：稀疏检测（LWSD）解释器对齐显著区域以实现协作检测，以及密集的领域不变（LWDDI）解释器生成代理不变特征以快速选择解释器。这两个解释器使用低秩视觉提示来减少计算、存储和模型复杂性。在模拟（OPV2V, V2XSet）和真实（DAIR-V2X）数据集上的大量实验表明，PEARL在模拟和真实世界的协作驾驶场景中具有良好的泛化能力。其实时模型选择策略在平均运行时间为1.67毫秒的情况下，相较于随机选择基线提高了8.2%的平均精度（Average Precision, AP）。尽管PEARL主要设计用于实时CP，但在传统离线训练下，其平均AP也比最先进的异构CP框架提高了5.6%，同时通信成本降低了多达34.7倍。同样重要的是，PEARL不需要共享代理的配置或模型设置，从而保护可能是专有或私密的信息。这些结果确立了PEARL作为一个可扩展且实用的异构协作感知框架。

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cs.CV / 14 / 2609.27131

Super-Resolution of Solar Magnetograms via Adaptive Stratified Ensemble Learning with Uncertainty Estimation

通过自适应分层集成学习与不确定性估计实现太阳磁图的超分辨率

Kandalan, Sina Norouzi, Jiang, Haodi, Wang, Jason T. L., Li, Qin

Abstract

Single-image super-resolution of Sun's photospheric magnetograms enables consistent analysis across heterogeneous space-based instruments and supports long-term studies of solar magnetic field evolution. We address the super-resolution task from SOHO/MDI (low-resolution) to SDO/HMI (high-resolution) line-of-sight (LOS) magnetograms using a modified RRDBNet architecture initialized by ESRGAN pretrained weights. Through systematic per-image diagnostic analysis, we identify image complexity as the dominant predictor of reconstruction errors. To exploit this finding, we introduce an adaptive stratified specialist ensemble (SSE) of three specialist networks with uncertainty estimation, where each specialist network is trained by images from three different complexity strata using a weighted random sampling strategy. During inference, a lightweight router based on input image statistics assigns each test image to the appropriate specialist network. Our experimental results demonstrate the good performance of the proposed ensemble and its superiority over closely related methods.

Chinese Translation

对太阳光球磁图进行单图像超分辨率处理，使得在异构的空间基仪器之间能够进行一致的分析，并支持太阳磁场演化的长期研究。我们将超分辨率任务从SOHO/MDI（低分辨率）到SDO/HMI（高分辨率）视线（LOS）磁图进行处理，采用经过ESRGAN预训练权重初始化的改进RRDBNet架构。通过系统的逐图像诊断分析，我们识别出图像复杂性是重建误差的主要预测因子。为了利用这一发现，我们引入了一种自适应分层专家集成（SSE），该集成由三个专家网络组成，并具备不确定性估计，其中每个专家网络通过加权随机抽样策略使用来自三个不同复杂性层次的图像进行训练。在推理过程中，基于输入图像统计的轻量级路由器将每个测试图像分配给适当的专家网络。我们的实验结果表明，所提议的集成方法表现良好，并优于相关方法。

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cs.CV / 15 / 2609.27139

A Hierarchy-Aware Video-Language Model Evaluation and Hyperbolic Baseline for Surgery

一种层次感知的视频语言模型评估及手术的双曲基线

Rodríguez, Ana Manzano, Mettes, Pascal, Schijven, Marlies P., Snoek, Cees G. M.

Abstract

Surgical procedures follow a phase-to-step hierarchy, yet the video-language models used to recognize them are evaluated with flat per-level metrics that ignore cross-level coherence and error structure. In this paper we make two contributions to address this problem, (i) we introduce SurgHiBench, the first hierarchy-aware evaluation suite for surgical video understanding, with three tasks measuring recognition, consistency, and severity across granularity levels. We evaluate a general-purpose CLIP model, a Euclidean surgical model, and, as second contribution: (ii) HyperSurg, a new hyperbolic model that enforces phase-step containment via entailment cones, across four (existing) datasets spanning three procedure types. The suite reveals that two models with the same accuracy can produce predictions of very different error severity, ranging from sibling confusions within the correct phase to unrelated cross-phase predictions. Hyperbolic geometry shifts predictions toward the correct procedural neighborhood, and these gains scale with the tree-likeness of each dataset's annotation hierarchy, providing a principled indicator when hierarchy-aware geometry helps.

Chinese Translation

外科手术过程遵循阶段到步骤的层次结构，但用于识别这些过程的视频语言模型却采用扁平的逐层指标进行评估，忽视了跨层次的一致性和错误结构。在本文中，我们提出了两个贡献来解决这一问题：(i) 我们引入了SurgHiBench，这是首个针对外科视频理解的层次感知评估套件，包含三个任务，分别测量不同粒度层次的识别、一致性和严重性。我们评估了一种通用的CLIP模型、一种欧几里得外科模型，以及作为第二个贡献的(ii) HyperSurg，一种新的双曲模型，通过蕴涵锥强制实现阶段-步骤的包含，涵盖了四个（现有的）数据集，涉及三种手术类型。该套件揭示了两个具有相同准确度的模型可以产生非常不同的错误严重性预测，从正确阶段内的兄弟混淆到无关的跨阶段预测。双曲几何将预测向正确的程序邻域移动，这些收益随着每个数据集注释层次的树状特性而扩大，提供了一个原则性指标，表明层次感知几何何时能够提供帮助。

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cs.CV / 16 / 2609.27142

MINER: Multi-crop INference-time Enhancement for Rare-Object Retrieval with Frozen Dual Encoders

MINER：用于稀有物体检索的多裁剪推理时增强方法，基于冻结的双重编码器

Alquwayfili, Abdulmalik, AlMeshal, Faisal, Almajnouni, Jumanah, Alamri, Huda Abdulhadi, Khan, Muhammad Kamran J

Abstract

Text-to-image retrieval with frozen dual encoders degrades when the query names a small, visually subordinate object in a cluttered scene: a single global image embedding underrepresents the localized visual evidence. We present MINER, a training-free inference framework that augments a frozen dual encoder's global image embedding with a small bank of region-level embeddings and a hubness-correcting similarity rescoring, recovering visual evidence that global pooling underweights. To evaluate this setting, we introduce ROCS, a benchmark built from high-clutter subsets of Flickr30K and MS COCO whose images are re-captioned to name a single low-salience object. Experiments on CLIP, SigLIP, and SigLIP 2 show that MINER improves retrieval on every backbone, on ROCS and on the standard splits. Analyses show that these gains come primarily from broader spatial coverage rather than precise crop placement, revealing a simple and general way to recover localized evidence from frozen representations. Code: https://github.com/aalquwayfili/MINER. Dataset: https://huggingface.co/datasets/aalquwayfili/ROCS.

Chinese Translation

在使用冻结的双重编码器进行文本到图像检索时，当查询指定一个小的、视觉上从属的物体并且场景较为杂乱时，性能会下降：单一的全局图像嵌入无法充分代表局部的视觉证据。我们提出了MINER，这是一种无训练的推理框架，通过一个小型区域级嵌入库和一个纠正中心性相似度重评分，增强冻结双重编码器的全局图像嵌入，从而恢复全局池化所低估的视觉证据。为了评估这一设置，我们引入了ROCS，这是一个基于Flickr30K和MS COCO高杂乱子集构建的基准，其图像被重新标注为命名单一低显著性物体。在CLIP、SigLIP和SigLIP 2上的实验表明，MINER在每个基础模型上、在ROCS和标准划分上均提高了检索性能。分析显示，这些提升主要来自更广泛的空间覆盖，而非精确的裁剪位置，揭示了一种简单且通用的方法来从冻结表示中恢复局部证据。代码：https://github.com/aalquwayfili/MINER。数据集：https://huggingface.co/datasets/aalquwayfili/ROCS。

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cs.CV / 17 / 2609.27143

A Systematic Evaluation of Infrastructure-Based Radar System for Highway Traffic Monitoring

基于基础设施的雷达系统在高速公路交通监测中的系统评估

Zhu, Tianheng, Chang, Woei-chyi, Riaz, Alamss, Hasanzadeh, Sogand, Feng, Yiheng

Abstract

Infrastructure-based radar systems offer robust and long-range solutions for traffic monitoring, yet their detection and tracking performance under real-world conditions remains insufficiently evaluated. This study introduces DRaT (Drone and Radar Trajectories), a dual-modality dataset of naturalistic vehicle trajectories collected at a highway merging segment in Fort Worth, Texas, to systematically assess radar sensing performance against drone-derived ground truth. The performance is evaluated at three levels: individual vehicle detection, trajectory tracking, and macroscopic traffic parameter estimation. For individual vehicle detection, the radar achieves an overall precision of 78% and a recall of 57%, with degraded performance under congested traffic conditions and at longer distances. At the trajectory level, the radar demonstrates reasonably strong tracking performance (IDF1 = 0.699), maintaining reliable vehicle identities when tracks are successfully established. For macroscopic traffic flow metrics, the radar accurately estimates space-mean speed (MAPE < 4%) but underestimates density and volume by approximately 23% due to missed detections. The paper also discusses practical deployment considerations and potential downstream applications of roadside radar sensing systems. To support reproducible research on infrastructure-based sensing systems, we have open-sourced the DRaT dataset on Zenodo: https://zenodo.org/records/20171110.

Chinese Translation

基于基础设施的雷达系统为交通监测提供了稳健且长距离的解决方案，但其在现实条件下的检测和跟踪性能仍然缺乏充分评估。本研究引入了DRaT（无人机与雷达轨迹），这是一个在德克萨斯州沃斯堡高速公路合流段收集的自然车辆轨迹的双模态数据集，以系统性评估雷达传感性能与无人机获取的地面真实数据的对比。性能评估分为三个层次：单个车辆检测、轨迹跟踪和宏观交通参数估计。在单个车辆检测方面，雷达的整体精确度为78%，召回率为57%，在拥堵交通条件和较长距离下性能有所下降。在轨迹层面，雷达表现出相当强的跟踪性能（IDF1 = 0.699），在成功建立轨迹时能够保持可靠的车辆身份。在宏观交通流量指标方面，雷达准确估计了空间平均速度（MAPE < 4%），但由于漏检，密度和流量的估计低于实际值约23%。本文还讨论了实际部署考虑因素及路边雷达传感系统的潜在下游应用。为了支持基于基础设施的传感系统的可重复研究，我们已在Zenodo上开源了DRaT数据集：https://zenodo.org/records/20171110。

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cs.CV / 18 / 2609.27149

Temporally Ordered Region-Token Mamba with Logit-Space Diffusion for Remote Sensing Change Detection

具有对数空间扩散的时间有序区域-标记Mamba用于遥感变化检测

Sen, Anuvab, Chatterjee, Maneet, Ghosh, Aparup, Sen, Udayon, Aditya, Arnav, Zhang, Yixin

Abstract

Remote sensing change detection requires both global reasoning across bitemporal images and precise localization of changed regions. However, dense attention is computationally expensive for high-resolution imagery, while conventional feature fusion and coarse decoding may inadequately separate genuine changes from appearance variations or preserve object boundaries. We present Bitemporal Mamba-Diffusion for Change Detection (BMD-CD), which combines temporally structured state-space modeling with logit-space diffusion refinement. BMD-CD converts deep bitemporal features into region tokens and arranges them in explicit temporal partitions before bidirectional state-space propagation. Its Bitemporal Ordered Mamba Operator enables long-range cross-temporal interaction with linear sequence complexity, while Orthogonal Feature Disentanglement forms a change-oriented output and a complementary rotated output using learned pairwise rotations and unchanged-region consistency. Multiscale decoding then produces coarse change logits, which are refined through a five-step Conditional Diffusion Decoder operating directly in logit space. Experiments on LEVIR-CD, WHU-CD, DSIFN-CD, CDD, and S2Looking demonstrate strong performance across diverse change-detection settings. BMD-CD achieves F1 scores of 93.7%, 96.0%, 97.8%, and 99.0% on the four standard benchmarks and improves 3-pixel Boundary-F1 to 87.7% and 91.4% on LEVIR-CD and WHU-CD, respectively. The full model requires 32.09 GFLOPs and 47 ms per 256 x 256 image pair, while also showing zero-shot transfer to ValaisCD and B-FLAIR-test. Our code is available at https://github.com/Aparup2139/Public_WACV/

Chinese Translation

遥感变化检测需要在双时相图像中进行全局推理和对变化区域的精确定位。然而，对于高分辨率图像，密集注意力计算开销较大，而传统的特征融合和粗略解码可能无法有效区分真实变化与外观变化，或保持物体边界。我们提出了用于变化检测的双时相Mamba-扩散模型（BMD-CD），该模型结合了时间结构的状态空间建模与对数空间的扩散精化。BMD-CD将深度双时相特征转换为区域标记，并在双向状态空间传播之前将其排列在明确的时间分区中。其双时相有序Mamba算子实现了线性序列复杂度的长距离跨时相交互，而正交特征解耦则通过学习的成对旋转和不变区域一致性形成变化导向的输出和互补的旋转输出。多尺度解码随后生成粗略的变化对数值，这些对数值通过一个在对数空间中直接操作的五步条件扩散解码器进行精化。在LEVIR-CD、WHU-CD、DSIFN-CD、CDD和S2Looking上的实验表明，在多样的变化检测设置中表现出强大的性能。BMD-CD在四个标准基准上分别达到了93.7%、96.0%、97.8%和99.0%的F1分数，并在LEVIR-CD和WHU-CD上将3像素边界F1提高到87.7%和91.4%。完整模型每对256 x 256图像需要32.09 GFLOPs和47毫秒，同时在ValaisCD和B-FLAIR-test上也展示了零样本迁移能力。我们的代码可在https://github.com/Aparup2139/Public_WACV/获取。

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cs.CV / 19 / 2609.27194

Diverse by Design: Architectural Constraints for Prototype-Based Interpretability

设计多样性：基于原型的可解释性架构约束

Lin, Xinmiao, Wright, Matthew

Abstract

Prototype-based neural networks provide inherent interpretability through case-based reasoning, yet suffer from critical limitations: prototypes converge to redundant features, fail to capture diverse semantic parts, and lack quantitative interpretability assessment. We propose Diversity-Aware Prototype Learning (DAPL), which enforces prototype diversity through architectural constraints rather than explicit regularization. Our approach leverages multi-head self-attention with strict one-to-one attention-to-prototype mapping, ensuring each prototype specializes in distinct visual features. We further introduce foreground-aware training to focus prototypes on semantically meaningful regions and develop comprehensive evaluation metrics (Coverage and Diversity) for quantitative interpretability assessment. Experiments on CUB-200-2011 demonstrate substantial improvements: DAPL with foreground-aware training achieves 81.69\% accuracy with 0.596 Coverage and 0.427 Diversity, providing the best overall balance across all evaluated prototype-based methods. Code is available at https://github.com/xinmiaolin/DAPL.

Chinese Translation

基于原型的神经网络通过案例推理提供了固有的可解释性，但存在关键的局限性：原型趋向于冗余特征，无法捕捉多样的语义部分，并且缺乏定量可解释性评估。我们提出了多样性意识原型学习（Diversity-Aware Prototype Learning, DAPL），通过架构约束而非显式正则化来强制实现原型多样性。我们的方法利用多头自注意力机制，严格的一对一注意力到原型映射，确保每个原型专注于不同的视觉特征。我们进一步引入了前景意识训练，以使原型聚焦于语义上有意义的区域，并开发了全面的评估指标（覆盖率和多样性）用于定量可解释性评估。在CUB-200-2011数据集上的实验显示了显著的改进：使用前景意识训练的DAPL达到了81.69%的准确率，覆盖率为0.596，多样性为0.427，在所有评估的基于原型的方法中提供了最佳的整体平衡。代码可在https://github.com/xinmiaolin/DAPL获取。

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cs.CV / 20 / 2609.27208

Benchmarking Active Spot Selection for Cost-Efficient Spatial Transcriptomics

成本效益空间转录组学的主动点选择基准测试

Zhu, Zheyu, Zhu, Junchao, Liu, Fengbei, Yao, Tianyuan, Xu, Gelei, Cannon, John, Yang, Haichun, Huo, Yuankai, Sabuncu, Mert R., Deng, Ruining

Abstract

Spatial transcriptomics (ST) measures gene expression in tissue context, but dense capture grids can be costly and may repeatedly sample morphologically similar regions. Most active learning strategies were developed for categorical labels and independent samples. We conduct a retrospective pool-based benchmark of active learning versus uniform Random sampling for ST, where expression vectors are high-dimensional and continuous and candidates are spatially correlated. Using two fully profiled public ST cohorts, we mask candidate expression vectors and simulate multi-round selection with uncertainty-based Monte Carlo dropout (MC-dropout) and temporal output discrepancy (TOD), and diversity-based CoreSet and TypiClust-inspired selection. We compare 160 completed configurations at 5%, 10%, 30%, and 50% of the fold-wide training spot pool under patient-level cross-validation, with a separate full-label reference. Within each budget, strategies share the selection schedule, morphology-to-expression predictor, and optimization protocol. We assess mean per-gene within-slide Pearson correlation coefficient (PCC), expression-cluster agreement, and Moran's I fidelity. On HER2-positive breast cancer, pooled mean PCC differences from Random across the four active strategies were -0.0176, -0.0117, +0.0056, and +0.0057 at 5%, 10%, 30%, and 50%, respectively. On cutaneous squamous cell carcinoma (cSCC), three strategies were below Random at 5%, and all four were below Random at 10%. On HER2-positive breast cancer, CoreSet and MC-dropout had lower PCC but higher expression-cluster agreement than Random at the two smallest budgets; this pattern did not reproduce on cSCC. Under the reported fixed training horizons, the evaluated active strategies do not consistently improve on Random at small budgets, and rankings depend on the evaluation measure.

Chinese Translation

空间转录组学（ST）在组织背景中测量基因表达，但密集的捕获网格可能成本高昂，并且可能重复采样形态相似的区域。大多数主动学习策略是为分类标签和独立样本开发的。我们对主动学习与均匀随机采样在ST中的表现进行了回顾性池基准测试，其中表达向量是高维和连续的，且候选样本在空间上相关。使用两个完全配置的公共ST队列，我们对候选表达向量进行掩蔽，并模拟基于不确定性的多轮选择，采用蒙特卡洛丢弃（MC-dropout）和时间输出差异（TOD），以及基于多样性的CoreSet和受TypiClust启发的选择。我们在患者级交叉验证下比较了在5%、10%、30%和50%的折叠训练点池中完成的160种配置，并使用单独的全标签参考。在每个预算内，各策略共享选择时间表、形态到表达的预测器和优化协议。我们评估了每个基因的平均每滑片皮尔逊相关系数（PCC）、表达聚类一致性和莫兰指数的保真度。在HER2阳性乳腺癌中，四种主动策略与随机策略相比的池均PCC差异分别为-0.0176、-0.0117、+0.0056和+0.0057，分别对应于5%、10%、30%和50%。在皮肤鳞状细胞癌（cSCC）中，三种策略在5%时低于随机，四种策略在10%时均低于随机。在HER2阳性乳腺癌中，CoreSet和MC-dropout在两个最小预算下的PCC低于随机，但表达聚类一致性高于随机；这一模式在cSCC中未能重现。在报告的固定训练时间范围内，评估的主动策略在小预算下并未始终优于随机，且排名依赖于评估指标。

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cs.CV / 21 / 2609.27217

Learning Spectral Allocation: A Fractional Diffusion Framework for Adaptive Volumetric Segmentation

学习谱分配：自适应体积分割的分数扩散框架

Shen, Yi-Hui, Li, Tie-Qiang

Abstract

We address adaptive computation in 3D medical image segmentation: instead of designing another backbone, we ask how much spectral mixing each network stage needs and let optimization answer. We derive FHEAT, a two-parameter operator family, from the discrete cosine transform (DCT) solution of a fractional heat equation. A fractional order alpha and a diffusion strength D govern the operator, and at D=0 it is exactly the identity. Reparametrized by the semigroup time tau = D*alpha, same-resolution instances compose exactly, so any distribution of diffusion across same-resolution stages amounts to a single Sobolev-type regularizer of learned strength. This identity limit lets the optimizer of each layer, not the designer, decide whether global mixing is needed and how sharp it should be. We instantiate FHEAT in a lightweight U-shaped architecture (Light-UNETR) paired with a Kolmogorov-Arnold mixer (KAN3D) with adaptive rational activations, yielding FHEAT-Seg. At 5% to 20% label rates on three public benchmarks, training produces gradient-driven spectral sparsification: seven of the eight stage-level operators drive D to zero, and the survivor saturates at the sharpest low-pass (alpha ~ 0.9) in the decoder layer feeding the semi-supervised attention map. The retired layers become exact identity shortcuts at inference, cutting FLOPs from 4.29G to 0.90G (a 79% drop) at 0.975M parameters. Under a standard semi-supervised protocol, FHEAT-Seg reaches Dice scores of 90.47% (left atrium), 78.79% (Pancreas-CT), and 81.90% (BraTS 2019), ahead of five semi-supervised methods and the Light-UNETR baseline. The large variant also surpasses Light-UNETR-L under full supervision (Dice 93.09%, 85.11%, and 87.19%) with 2.851M parameters and 55.75G FLOPs. These results suggest that the allocation of spectral computation is a learnable property of optimization dynamics, not a manual design commitment.

Chinese Translation

我们关注3D医学图像分割中的自适应计算：与其设计另一个主干网络，不如询问每个网络阶段需要多少谱混合，并让优化来回答。我们从分数热方程的离散余弦变换（DCT）解中推导出FHEAT，一个双参数算子族。分数阶数α和扩散强度D控制该算子，当D=0时，它恰好是单位算子。通过半群时间τ = D*α重新参数化，相同分辨率的实例完全组合，因此在相同分辨率阶段之间的任何扩散分布都相当于一个学习强度的单一Sobolev类型正则化器。这个单位极限使得每一层的优化器，而不是设计者，决定是否需要全局混合以及混合的锐利程度。我们在轻量级U型架构（Light-UNETR）中实例化FHEAT，并与具有自适应有理激活的Kolmogorov-Arnold混合器（KAN3D）配对，形成FHEAT-Seg。在三个公共基准上，标签率为5%到20%的训练产生了梯度驱动的谱稀疏化：八个阶段级算子中的七个将D驱动到零，而幸存者在馈送半监督注意力图的解码器层中饱和于最锐利的低通（α ~ 0.9）。退休层在推理时成为精确的单位快捷方式，将FLOPs从4.29G减少到0.90G（下降79%），参数为0.975M。在标准的半监督协议下，FHEAT-Seg达到了90.47%（左心房）、78.79%（胰腺CT）和81.90%（BraTS 2019）的Dice分数，超越了五种半监督方法和Light-UNETR基线。大型变体在完全监督下也超越了Light-UNETR-L（Dice 93.09%、85.11%和87.19%），参数为2.851M，FLOPs为55.75G。这些结果表明，谱计算的分配是优化动态的可学习属性，而不是手动设计的承诺。

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cs.CV / 22 / 2609.27227

Surgical Kinematics from Monocular Video with Learned Articulated Motion Constraints

基于单目视频的手术运动学与学习的关节运动约束

Turkcan, Mehmet Kerem, Samal, Soham, Kostic, Zoran

Abstract

Objective assessment of robotic surgery uses instrument kinematics, which must be reconstructed when only video is available. We introduce a kinematic reconstruction network for estimating instrument position, orientation and jaw angle from monocular video. Our visual representation combines global attention pooling of frozen DINOv3 features with local pooling at instrument landmarks from fine-tuned SAM 3.1 masks. Our shared Transformer encoder and temporal convolutional heads integrate this representation with mask geometry, monocular depth and visual state estimates from arm-specific multilayer regression networks. Our position branch predicts displacement magnitude and direction separately to preserve traveled distance. We fit trajectories to predicted state observations and motion increments by differentiable weighted least squares, expressing quaternion observations relative to cumulative predicted rotations to obtain a quadratic orientation objective. We evaluate reconstruction across 2,802 Open-H episodes. Compared with LiveMAE on the main Open-H benchmark, our method reduces path-length mean absolute error from 0.45 to 0.34\,cm and increases temporal mean average precision for motion segmentation from 44.54\% to 54.44\%.

Chinese Translation

对机器人手术的客观评估依赖于工具运动学，而当仅有视频可用时，必须重建这些运动学。我们提出了一种运动学重建网络，用于从单目视频中估计工具的位置、方向和夹角。我们的视觉表示结合了冻结的DINOv3特征的全局注意力池化与来自微调的SAM 3.1掩模的工具地标的局部池化。我们的共享Transformer编码器和时间卷积头将这一表示与掩模几何、单目深度和来自特定臂的多层回归网络的视觉状态估计相结合。我们的位置信息分支分别预测位移的大小和方向，以保持行进距离。我们通过可微分加权最小二乘法将轨迹拟合到预测的状态观测和运动增量，表达四元数观测相对于累积预测旋转，以获得二次方向目标。我们在2,802个Open-H剧集上评估了重建效果。与LiveMAE在主要Open-H基准上的表现相比，我们的方法将路径长度的平均绝对误差从0.45降低到0.34 cm，并将运动分割的时间平均精度从44.54%提高到54.44%。

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cs.CV / 23 / 2609.27238

Strip Convolution and Direction-Aware Exclusion Loss for Oriented Ship Detection

面向船舶检测的条形卷积与方向感知排除损失

Chen, Bin, Liu, Yuanyuan, Yang, Peng, Lu, Chao

Abstract

Oriented ship detection in very high resolution (VHR) remote sensing imagery remains challenging due to elongated hull geometry and dense target distributions in complex port scenes. Existing methods typically address geometric representation and duplicate suppression separately. To jointly tackle these issues, we propose an oriented ship detector with two complementary components. The C3k2_Strip module employs orthogonal strip convolutions to better capture elongated hull structures, while the Class-Aware Direction-Aware Exclusion Loss (CA-DAEL) suppresses redundant predictions using class, direction, and confidence cues. Experiments on HRSC2016 and DIOR-R achieve 78.45% and 53.71% mAP50:95, respectively, with only 2.91M parameters. On HRSC2016, the proposed method improves mAP50:95 by 6.32 percentage points over the YOLOv11-OBB baseline, demonstrating its effectiveness for accurate oriented ship detection.

Chinese Translation

在非常高分辨率（VHR）遥感图像中，面向船舶的检测仍然面临挑战，主要由于复杂港口场景中船体几何形状的拉长和目标分布的密集。现有方法通常分别处理几何表示和重复抑制问题。为了解决这些问题，我们提出了一种具有两个互补组件的面向船舶检测器。C3k2_Strip模块采用正交条形卷积，以更好地捕捉拉长的船体结构，而类感知方向感知排除损失（Class-Aware Direction-Aware Exclusion Loss, CA-DAEL）则利用类别、方向和置信度线索抑制冗余预测。在HRSC2016和DIOR-R数据集上的实验结果分别达到了78.45%和53.71%的mAP50:95，且仅使用了2.91M的参数。在HRSC2016上，所提方法相比YOLOv11-OBB基线提高了6.32个百分点的mAP50:95，证明了其在准确的面向船舶检测中的有效性。

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cs.CV / 24 / 2609.27264

GaussPDE: Graph-Based Partial Differential Equation-Driven Rendering for 3D Gaussian Splatting

GaussPDE：基于图的偏微分方程驱动的3D高斯点云渲染

Yue, Haoyuan, Ye, Fengyuan, Li, Ziyin

Abstract

We present GaussPDE, a framework that injects physically structured partial differential equation (PDE) dynamics into pretrained 3D Gaussian scenes without mesh extraction, voxelization, or retraining. Our key observation is that PDE rendering requires not only accurate appearance, but also a reliable discrete computational domain. We therefore first introduce camera-aware regularization during 3DGS reconstruction to suppress camera-near floaters and oversized primitives that would create unstable graph topology. We then construct an active Gaussian graph using covariance-aware distances and opacity, appearance, and boundary-aware conductance, enabling mass-weighted graph Laplacian PDE evolution directly over Gaussian primitives. The evolving scalar PDE state is coupled back to rendering by modifying the direct-current spherical harmonic color coefficients while preserving geometry, opacity, and view-dependent rendering behavior. Experiments on real and synthetic scenes show that GaussPDE produces stable, controllable, and spatially coherent dynamic visualizations, with reduced cross-boundary leakage compared with baselines.

Chinese Translation

我们提出了GaussPDE，一个框架，它将物理结构化的偏微分方程（PDE）动态注入预训练的3D高斯场景中，而无需网格提取、体素化或重新训练。我们的关键观察是，PDE渲染不仅需要准确的外观，还需要可靠的离散计算域。因此，我们首先在3D高斯重建过程中引入了相机感知正则化，以抑制靠近相机的浮动物体和可能导致不稳定图拓扑的过大原件。接着，我们使用协方差感知距离以及透明度、外观和边界感知导电性构建了一个主动高斯图，使得可以直接在高斯原件上进行质量加权的图拉普拉斯PDE演化。演化的标量PDE状态通过修改直流球谐色系数与渲染相耦合，同时保持几何形状、透明度和视图依赖的渲染行为。对真实和合成场景的实验表明，GaussPDE能够生成稳定、可控且空间一致的动态可视化，与基线相比，减少了跨边界泄漏。

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cs.CV / 25 / 2609.27274

High Dynamic Range Video Reconstruction from Single-Exposure Raw Sequences

基于单次曝光原始序列的高动态范围视频重建

Zhang, Tao, Su, Peixian, Gao, Xingyu, Zou, Yunhao, Lu, Yu, Zhu, Zunjie, Zheng, Bolun, Fu, Ying, Yan, Chenggang

Abstract

Due to the limited dynamic range of conventional image sensors, captured low dynamic range (LDR) video often suffers from highlight clipping and shadow detail loss, making high-quality high dynamic range (HDR) reconstruction from single-exposure sequences highly challenging without alternating exposures or extra hardware. Alternating-exposure HDR methods sacrifice frame rate and struggle with motion alignment, making them impractical for real-world capture. To address this, we propose RawHDRV, an end-to-end framework for single-exposure Raw video HDR reconstruction, that fundamentally exploits the linear response and channel-specific characteristics of Bayer data. Specifically, it features a channel-decomposition temporal alignment and fusion strategy that processes Bayer channels separately to exploit their distinct exposure characteristics, together with exposure-aware weighted fusion. It further incorporates an exposure complementarity mask-guided restoration module that leverages inter-frame exposure redundancy to adaptively fuse reliable information and suppress saturation artifacts, and introduces a mask-guided color loss that combines normalized error constraints with gradient smoothing to enhance highlight recovery. Furthermore, we construct a large-scale mobile Raw-HDR video dataset with per-frame HDR annotations. Experiments show that our method achieves the state-of-the-art results in all metrics, demonstrating superior spatial quality and temporal stability under extreme exposure conditions. The code is available at https://github.com/supeixian/RawHDRV.

Chinese Translation

由于传统图像传感器的动态范围有限，捕获的低动态范围（LDR）视频常常面临高光剪切和阴影细节丢失的问题，因此在没有交替曝光或额外硬件的情况下，从单次曝光序列中进行高质量的高动态范围（HDR）重建具有很大挑战性。交替曝光HDR方法牺牲了帧率，并且在运动对齐方面存在困难，使其在实际捕获中不够实用。为了解决这个问题，我们提出了RawHDRV，一个用于单次曝光原始视频HDR重建的端到端框架，根本上利用了Bayer数据的线性响应和通道特性。具体而言，它采用了一种通道分解的时间对齐和融合策略，分别处理Bayer通道，以利用其独特的曝光特性，并结合曝光感知加权融合。它进一步结合了一个曝光互补掩模引导的恢复模块，利用帧间曝光冗余自适应地融合可靠信息并抑制饱和伪影，并引入了一种掩模引导的颜色损失，结合了归一化误差约束和梯度平滑，以增强高光恢复。此外，我们构建了一个大规模的移动原始HDR视频数据集，并为每帧提供HDR注释。实验表明，我们的方法在所有指标上都达到了最先进的结果，在极端曝光条件下展现出优越的空间质量和时间稳定性。代码可在 https://github.com/supeixian/RawHDRV 获取。

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cs.CV / 26 / 2609.27317

Breaking Weather-Content Coupling: Type-Severity Guided Progressive Disentanglement for All-in-One Infrared Restoration

打破天气-内容耦合：基于类型-严重性引导的渐进式解耦用于一体化红外恢复

Wang, Xinyao, He, Lijun, Ren, Zhihan, Li, Fan

Abstract

Infrared (IR) imaging is crucial for autonomous driving, remote sensing, and other perception tasks. However, adverse weather may introduce fake structural responses that are entangled with real thermal structures. Existing IR restoration methods are typically designed for a single degradation type or directly reconstruct from degradation-entangled representations. Consequently, they struggle to distinguish intrinsic thermal structures from weather-induced fake responses and to accommodate spatially varying degradation severity, leading to artifacts or the over-suppression of weak but meaningful thermal responses. To address these issues, we propose TSGPD-IR, a type-severity guided progressive disentanglement network for all-in-one infrared restoration that factorizes restoration guidance into task-level weather semantics and region-level degradation severity. Specifically, a Weather and Semantic Co-Guided Multi-Level Prompt Generation Module combines global weather semantics with stage-wise local features to generate adaptive prompts that progressively suppress degradation-induced responses while preserving intrinsic thermal structures. To complement global weather semantics with spatial restoration control, a Proxy-Supervised Regional Degradation Estimator derives severity supervision without manual annotations and predicts spatially varying degradation priors. Guided by these cues, a Multi-Source Collaborative Expert Selection Strategy uses a shared branch to preserve weather-invariant thermal structures and hierarchical routing to select weather-specific expert pools and severity-compatible regional experts. This design progressively separates degradation interference from genuine thermal content and enables region-adaptive restoration, reducing both residual artifacts and over-suppression.

Chinese Translation

红外成像在自动驾驶、遥感及其他感知任务中至关重要。然而，不利天气可能会引入与真实热结构交织的虚假结构响应。现有的红外恢复方法通常针对单一退化类型设计，或直接从退化交织的表示中进行重建。因此，它们难以区分内在热结构与天气引起的虚假响应，并且难以适应空间变化的退化严重性，导致伪影或对微弱但有意义的热响应的过度抑制。为了解决这些问题，我们提出了TSGPD-IR，一种基于类型-严重性引导的渐进式解耦网络，用于一体化红外恢复，将恢复指导分解为任务级天气语义和区域级退化严重性。具体而言，天气与语义共同引导的多级提示生成模块结合了全球天气语义与阶段性局部特征，生成自适应提示，逐步抑制退化引起的响应，同时保留内在热结构。为了用空间恢复控制补充全球天气语义，代理监督区域退化估计器在没有人工标注的情况下推导出严重性监督，并预测空间变化的退化先验。在这些线索的引导下，多源协作专家选择策略使用共享分支来保留天气不变的热结构，并通过分层路由选择特定于天气的专家池和与严重性兼容的区域专家。该设计逐步将退化干扰与真实热内容分离，并实现区域自适应恢复，减少残余伪影和过度抑制。

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cs.CV / 27 / 2609.27327

Can Vision-Language Models Analyze Human-Centered Video? Mapping Model Capabilities and Human-AI Collaborative Workflows

视觉-语言模型能否分析以人为中心的视频？模型能力与人机协作工作流程的映射

Shen, Xiyuan, Lyu, Jiuyang, Hwang, Seokhyun, Yao, Huanfen, Patel, Shwetak, Zhang, Zhihan, Wobbrock, Jacob O.

Abstract

Video provides a rich record of human behavior, interaction, and situated contexts, offering important evidence for understanding people and conducting human-centered research. As vision-language models (VLMs) become increasingly capable of analyzing video, they offer opportunities to automate this traditionally human-intensive process. Yet a central question remains: when can VLMs analyze human-centered video independently, and when does reliable analysis still require human involvement? To address this question, we first characterize video analysis practices in human-centered research. We systematically analyze all 1,702 CHI 2026 full papers and identify 125 that annotate videos. Through iterative coding, we derive a five-dimensional taxonomy spanning analytic purpose, viewpoint, phenomenon, reasoning requirement, and annotation authority. Grounded in recurring annotation tasks captured by this taxonomy, we construct a benchmark of 15 representative tasks from open datasets to map the capabilities and limitations of a general-purpose VLM. We examine the division of labor between humans and VLMs by comparing three annotation workflows: VLM alone, human alone, and human verification of VLM outputs. Across tasks, VLM-alone annotation approaches human accuracy on average (HNS = 97.0, where 100 denotes human-alone performance), demonstrating substantial potential to automate human-centered video analysis. Human verification achieves the highest accuracy (HNS = 121.5) while reducing human annotation time by 48.9% and monetary cost by 31.3%-44.5% relative to human-alone annotation. Our findings connect real-world human-centered video analysis tasks and current VLM capabilities, and clarify how human-AI collaboration can make VLM-assisted analysis reliable and efficient.

Chinese Translation

视频提供了丰富的人类行为、互动和情境背景的记录，为理解人类和进行以人为中心的研究提供了重要证据。随着视觉-语言模型（VLMs）在视频分析方面的能力不断增强，它们为自动化这一传统上人力密集的过程提供了机会。然而，一个核心问题仍然存在：VLMs何时能够独立分析以人为中心的视频，何时又需要人类的参与才能确保可靠的分析？为了解决这个问题，我们首先描述了以人为中心的研究中的视频分析实践。我们系统地分析了1,702篇CHI 2026的完整论文，并识别出125篇对视频进行注释的论文。通过迭代编码，我们得出了一个涵盖分析目的、视角、现象、推理要求和注释权威的五维分类法。基于该分类法捕捉到的重复注释任务，我们构建了一个包含15个来自开放数据集的代表性任务的基准，以映射通用VLM的能力和局限性。我们通过比较三种注释工作流程（仅使用VLM、仅使用人类以及人类对VLM输出的验证）来考察人类与VLM之间的劳动分工。在各项任务中，VLM单独注释的准确性平均接近人类（HNS = 97.0，其中100代表人类单独表现），显示出在自动化以人为中心的视频分析方面的巨大潜力。人类验证达到最高准确性（HNS = 121.5），同时相较于人类单独注释，减少了48.9%的人工注释时间和31.3%-44.5%的经济成本。我们的研究结果将现实世界中的以人为中心的视频分析任务与当前的VLM能力联系起来，并阐明了人机协作如何使VLM辅助分析变得可靠和高效。

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cs.CV / 28 / 2609.27356

Beyond Mean Foils: Auditing Worst-Foil Specificity in Frozen CLIP Region Explanations

超越均值对照：审计冻结 CLIP 区域解释中的最差对照特异性

Liu, Kaixin, Ye, Zhipeng, Jiang, Feng, Wang, Zhenghao, Wu, Qihang

Abstract

A region can overlap a target object yet contribute more to another class. We test regions selected by Cluster-based Concept Importance (CCI) in frozen CLIP. Across COCO and VOC with two checkpoints, 41.08-64.78% of regions that pass overlap and mean-contrast checks fail against the strongest competing class. Removing competitors annotated in the image leaves 39.69-63.64% failing. We then test all eight candidate regions per image. An alternative passes the test for 6.25-7.84% of failures on COCO and 27.40-31.15% on VOC. Requiring it to preserve the original target-score drop within $\epsilon = 0.02$ reduces these rates to 0.16-0.98%. Available regions and target-drop tolerance constrain repair; relaxing the tolerance increases repair opportunities.

Chinese Translation

一个区域可以与目标对象重叠，但对另一个类别的贡献更大。我们测试了在冻结 CLIP 中通过基于聚类的概念重要性（CCI）选择的区域。在 COCO 和 VOC 数据集的两个检查点中，41.08-64.78% 的区域在通过重叠和均值对比检查后，仍然未能对抗最强竞争类别。去除图像中标注的竞争者后，39.69-63.64% 的区域仍然失败。然后，我们测试每张图像的所有八个候选区域。一个替代方案在 COCO 上通过了 6.25-7.84% 的失败测试，在 VOC 上为 27.40-31.15%。要求其保持原始目标得分下降在 $ ext{ε} = 0.02$ 之内，将这些比例降低到 0.16-0.98%。可用区域和目标得分下降的容忍度限制了修复的可能性；放宽容忍度会增加修复机会。

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cs.CV / 29 / 2609.27370

Geometry-Conditioned Visual Place Recognition in Natural Environments

基于几何条件的自然环境视觉地点识别

Nedov, Walter, Rahman, Saimunur, Katuwandeniya, Kavindie, Hall, David, Roy, Kaushik, Moghadam, Peyman

Abstract

Visual Place Recognition (VPR) in natural environments remains challenging due to repetitive vegetation, sparse distinctive landmarks, and substantial appearance and viewpoint variation across traversals. While visual observations of the same place can change considerably, their underlying spatial structure is often more persistent. We exploit this complementary geometric consistency through Depth-Aware Distillation (DAD), which conditions the token representations of a pretrained Vision Foundation Model (VFM) on geometry inferred by a Geometric Foundation Model (GFM), without any depth sensor. Rather than treating geometry as an additional input modality, DAD projects image-aligned depth into the VFM token space and selectively modulates visual representations through channel-wise geometric conditioning. A two-stage teacher-guided learning strategy first anchors the geometry-conditioned representation to the pretrained appearance space, before refining it for place discrimination. Evaluated on the WildCross benchmark, DAD improves average inter-sequence Recall@1 from 61.41% to 66.37% and Recall@5 from 65.86% to 72.49% over a matched appearance-only baseline, with the largest gains under reverse traversal and long-term appearance variation. These results show that GFM-derived geometry can provide a persistent structural prior for VPR when visual appearance becomes unreliable.

Chinese Translation

在自然环境中，视觉地点识别（VPR）仍然面临挑战，原因在于重复的植被、稀疏的显著地标以及跨越路径的外观和视角变化显著。尽管同一地点的视觉观察可能会发生显著变化，但其潜在的空间结构往往更加持久。我们通过深度感知蒸馏（Depth-Aware Distillation, DAD）利用这种互补的几何一致性，该方法在没有任何深度传感器的情况下，将预训练的视觉基础模型（Vision Foundation Model, VFM）的标记表示与几何基础模型（Geometric Foundation Model, GFM）推断的几何信息相结合。DAD并不是将几何视为额外的输入模态，而是将图像对齐的深度投影到VFM标记空间中，并通过通道级几何条件选择性地调节视觉表示。一个两阶段的教师引导学习策略首先将几何条件表示锚定到预训练的外观空间，然后对其进行精炼以实现地点区分。在WildCross基准测试中评估，DAD将平均序列间Recall@1从61.41%提高到66.37%，Recall@5从65.86%提高到72.49%，相较于匹配的仅外观基线，在逆向遍历和长期外观变化下获得了最大的提升。这些结果表明，当视觉外观变得不可靠时，GFM衍生的几何信息可以为VPR提供持久的结构先验。

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cs.CV / 30 / 2609.27408

What Looks Like a Capability Limit in Vision-Language Models Is a Readout Limit

视觉-语言模型中的能力限制看似是读取限制

Del Valle, Alfredo F. Frontera

Abstract

Benchmarks for vision-language models offer their answer choices in some convention: a letter, a color name, a pixel coordinate. That convention is treated as neutral. We find it is not, and that the limits a benchmark reports can belong to the readout rather than to the model. On 200 COCO photographs, Qwen3-VL-4B picks the correct one of nine locations for a named object 68.5% of the time when the locations are given in English and 20.0% when the same locations are given as pixel coordinates. Chance is 11.1%. The cost arises when the answer options are coordinates; giving the model a coordinate in the question instead costs 3.5 points and is not significant. The gap holds on a 4x4 grid, under 8-bit rather than 4-bit quantization, and in every slice by object size, boundary distance and category. It also decides which model wins. Two models that tie under English names differ by 39 points in one coordinate system and by 54 in the other, in opposite directions. On the color task, three of the four open models capable of the task show the penalty; on photographs, two of three open models do, and so does Gemini, at 11.1 points on parseable answers (p = 1e-4). GPT-4o does not. To ask whether a model reads a coordinate at all, we attach the wrong name to each one and record which the model follows. Color options written as hue angles are followed below chance; a normalized pixel convention is followed at four times chance. This tells apart conventions a model can use from ones it cannot, though it did not predict accuracy on two untried conventions. Five models also name the same color wheel five different ways, so a fixed answer vocabulary is not neutral across models either. Five times during this work we measured a capable model as incapable because our scorer and the model disagreed about what an answer looks like. We report each case. They are the phenomenon in miniature.

Chinese Translation

视觉-语言模型的基准测试以某种约定提供答案选项：字母、颜色名称、像素坐标。这种约定被视为中立的。然而，我们发现它并非如此，基准报告的限制可能属于读取而非模型本身。在200张COCO照片中，Qwen3-VL-4B在给出英文位置时，正确选择命名物体的九个位置中的一个的概率为68.5%，而当相同位置以像素坐标给出时，这一概率降至20.0%。随机选择的概率为11.1%。当答案选项为坐标时，成本增加；在问题中给出坐标会导致模型损失3.5分，并且这一差异不显著。这个差距在4x4网格上、在8位而非4位量化下，以及在每个物体大小、边界距离和类别的切片中均保持一致。它还决定了哪个模型获胜。在英文名称下平局的两个模型在一个坐标系统中相差39分，在另一个系统中相差54分，方向相反。在颜色任务中，四个能够完成该任务的开放模型中有三个显示出这一惩罚；在照片中，三个开放模型中有两个也显示出这一现象，而Gemini在可解析答案上也表现出11.1分的惩罚（p = 1e-4）。GPT-4o则没有。为了询问一个模型是否读取坐标，我们将错误的名称附加到每个模型上，并记录模型遵循的名称。以色调角度书写的颜色选项的遵循率低于随机概率；而标准化像素约定的遵循率是随机概率的四倍。这区分了模型可以使用的约定与无法使用的约定，尽管它未能预测在两个未尝试的约定上的准确性。五个模型也以五种不同的方式命名相同的颜色轮，因此固定的答案词汇在模型之间也并非中立。在这项工作中，我们五次将一个有能力的模型评估为无能，因为我们的评分者与模型对答案的外观存在分歧。我们报告了每个案例。这些案例是这一现象的缩影。

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cs.CV / 31 / 2609.27413

S2A:Semantic-to-Spatial Alignment for Alignment-Free RGB-T Salient Object Detection

S2A：无对齐 RGB-T 显著目标检测的语义到空间对齐

Zhou, Qiangqiang, Luo, Yang, Chen, Yong, Xu, Jiawei

Abstract

Alignment-free RGB-T salient object detection (RGB-T SOD) aims to identify salient objects from unregistered RGB and thermal image pairs without costly pre-alignment. However, spatial misalignment breaks pixel-wise correspondence and causes feature contamination during cross-modal fusion. To address this issue, we propose S2A, a semantic-to-spatial alignment framework for alignment-free RGB-T SOD. Specifically, a global-guided hierarchical fusion module (GGHF) first exploits global semantic guidance to suppress background interference and refine hierarchical intra-modal features. Subsequently, the alignment-free cross-modal channel attention module (AFCA) globally exchanges complementary semantic information through channel-wise interaction, effectively overcoming the interference caused by local spatial misalignments. Finally, a spatial deformable cross-attention module (SDCA) predicts adaptive sampling offsets to recover local cross-modal spatial correspondence. Through this semantic-to-spatial paradigm, S2A first enables reliable cross-modal semantic interaction and subsequently performs local spatial calibration, effectively reducing misalignment-induced feature contamination. Without bells and whistles, S2A achieves highly competitive performance on multiple public alignment-free RGB-T benchmarks, demonstrating its effectiveness in alleviating misalignment-induced feature contamination.

Chinese Translation

无对齐 RGB-T 显著目标检测 (RGB-T SOD) 旨在从未注册的 RGB 和热成像图像对中识别显著目标，而无需昂贵的预对齐。然而，空间错位打破了像素级对应关系，并在跨模态融合过程中导致特征污染。为了解决这个问题，我们提出了 S2A，一种用于无对齐 RGB-T SOD 的语义到空间对齐框架。具体而言，全球引导的层次融合模块 (GGHF) 首先利用全球语义引导来抑制背景干扰并细化层次内部模态特征。随后，无对齐跨模态通道注意模块 (AFCA) 通过通道间交互全球性地交换互补的语义信息，有效克服了局部空间错位造成的干扰。最后，空间可变形跨注意模块 (SDCA) 预测自适应采样偏移，以恢复局部跨模态空间对应关系。通过这种语义到空间的范式，S2A 首先实现了可靠的跨模态语义交互，随后进行局部空间校准，有效减少了由错位引起的特征污染。S2A 在多个公共无对齐 RGB-T 基准测试中实现了高度竞争的性能，证明了其在缓解错位引起的特征污染方面的有效性。

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cs.CV / 32 / 2609.27423

Overlapping Visual Grouping Without Semantic Priors

无语义先验的重叠视觉分组

Saukkio, Teemu, Haghbayan, Hashem, Plosila, Juha

Abstract

Most computer-vision systems organize visual input toward a predefined interpretation, such as semantic categories, prompted regions, learned object-like representations, or a single spatial partition. This work considers an earlier stage of visual organization: the formation of candidate perceptual units directly from sensor measurements before their identity, meaning, or task relevance is known. We introduce Domain Parent Grouping (DPG), a sensor-grounded grouping method in which complementary measurement relationships are represented in separate processing domains. Spatially connected groups formed within these domains are related through cross-domain overlap, yielding a non-exclusive grouping representation rather than a single mutually exclusive segmentation. This representation retains broader and more localized groups, as well as alternative grouping boundaries over the same image locations, simultaneously available. DPG also includes a native mechanism for reprocessing selected group content, in which input-relative measurement ranges allow the observational resolution to change while preserving previously formed groups. DPG is implemented using three domains representing locally contextualized luminance, direct chromatic relationships, and contextual chromatic relationships. Experiments on the BSDS500 dataset demonstrate the benefit of combining the three domains. The results further show that DPG forms measurement-supported groups corresponding to low-level image structure, and that these groups exhibit measurable correspondence with human-annotated regions and boundaries. This demonstrates that structured visual organization can emerge directly from relationships among sensor measurements.

Chinese Translation

大多数计算机视觉系统将视觉输入组织为预定义的解释，例如语义类别、提示区域、学习到的物体样本表示或单一空间划分。本研究考虑视觉组织的早期阶段：在其身份、意义或任务相关性尚不明确之前，直接从传感器测量中形成候选感知单元。我们引入了领域父级分组（Domain Parent Grouping，DPG），这是一种基于传感器的分组方法，其中互补的测量关系在不同的处理领域中表示。这些领域内形成的空间连接组通过跨领域重叠相关联，从而产生一种非排他性的分组表示，而不是单一的相互排斥的分割。这种表示保留了更广泛和更局部的组，以及在相同图像位置上可同时获得的替代分组边界。DPG还包括一种原生机制，用于重新处理选定的组内容，其中输入相关的测量范围允许观察分辨率变化，同时保持先前形成的组。DPG的实现使用三个领域，分别表示局部上下文的亮度、直接色彩关系和上下文色彩关系。在BSDS500数据集上的实验表明，结合这三个领域的优势。结果进一步表明，DPG形成了与低级图像结构相对应的测量支持组，并且这些组与人工标注的区域和边界表现出可测量的一致性。这表明结构化的视觉组织可以直接从传感器测量之间的关系中产生。

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cs.CV / 33 / 2609.27442

SatUnreal: A High-Precision Synthetic Dataset for Satellite Stereo Matching via Unreal Engine

SatUnreal：通过虚幻引擎生成的高精度卫星立体匹配合成数据集

Kim, Han-Gyeol, Park, JaeWan, Park, Junmin, Kwon, Darongsae

Abstract

3D reconstruction from satellite imagery is essential for large-scale topographic analysis, yet the lack of high-fidelity training datasets with accurate occlusion labels remains a primary bottleneck. Existing benchmarks, such as US3D and WHU-Stereo, face inherent challenges in spatio-temporal mismatch -- environmental changes and shadow displacements between multi-view acquisitions -- and provide ambiguous ground truth in occluded regions due to LiDAR sparsity. In this paper, we propose SatUnreal, a high-precision synthetic dataset designed to fundamentally overcome these limitations through an Unreal Engine-based simulation pipeline. SatUnreal provides 10,000 stereo pairs with high resolution (0.3m GSD) and is characterized by: (1) Physical Geometry Simulation, replicating realistic satellite orbits by systematically varying baselines and azimuths; (2) Spatio-temporal Consistency, eliminating temporal noise through fixed virtual environments; (3) Topographic Diversity, spanning dense urban canyons to low-texture natural terrains; and (4) Mathematical Label Integrity, utilizing a novel two-step linetrace algorithm to generate flawless occlusion masks. Experimental results using SOTA iterative models demonstrate that models trained exclusively on SatUnreal achieve superior zero-shot transfer performance on real-world benchmarks (US3D, WHU-Stereo) compared to those trained on real datasets. Our findings prove that physically accurate synthetic data provides a more effective supervisory signal for learning geometric features than complex real-world observations, establishing a new paradigm for Sim-to-Real transfer in Earth Observation. Code and dataset are available at https://github.com/jmp-Telepix/SatUnreal_A_High-Precision_Synthetic_Dataset_for_Satellite_Stereo_Matching_via_UnrealEngine

Chinese Translation

从卫星影像进行三维重建对大规模地形分析至关重要，但缺乏具有准确遮挡标签的高保真训练数据集仍然是主要瓶颈。现有基准，如US3D和WHU-Stereo，面临时空不匹配的固有挑战——多视角获取之间的环境变化和阴影位移，并且由于激光雷达稀疏性，在遮挡区域提供模糊的真实值。在本文中，我们提出了SatUnreal，一个高精度合成数据集，旨在通过基于虚幻引擎的仿真管道从根本上克服这些限制。SatUnreal提供了10,000对高分辨率（0.3米GSD）的立体图像，其特点包括：（1）物理几何仿真，通过系统性地变化基线和方位角来复制真实的卫星轨道；（2）时空一致性，通过固定虚拟环境消除时间噪声；（3）地形多样性，涵盖从密集城市峡谷到低纹理自然地形的范围；（4）数学标签完整性，利用一种新颖的两步线追踪算法生成无瑕疵的遮挡掩膜。使用最先进的迭代模型的实验结果表明，仅在SatUnreal上训练的模型在真实世界基准（US3D，WHU-Stereo）上实现了优越的零样本迁移性能，相较于在真实数据集上训练的模型。我们的研究结果证明，物理准确的合成数据为学习几何特征提供了比复杂的真实世界观测更有效的监督信号，为地球观测中的模拟到真实迁移建立了新的范式。代码和数据集可在 https://github.com/jmp-Telepix/SatUnreal_A_High-Precision_Synthetic_Dataset_for_Satellite_Stereo_Matching_via_UnrealEngine 获取。

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cs.CV / 34 / 2609.27455

Latent evolving World Action Model

潜在演变世界行动模型

Fang, Xueji, Duan, Boqiang, Wu, Hua, Wang, Jingdong, Qi, Guo-Jun

Abstract

World Action Models (WAMs) jointly model action generation and environment dynamics and are mostly built on pretrained Video Diffusion Models (VDMs). In VDM-based WAMs, observations are first encoded by a VAE, and the resulting compressed latents are then processed by large video diffusion backbones to extract effective features for action generation. However, this paradigm ties WAM performance and training cost to large-scale video generation pretraining, limiting WAM efficiency and scalability. In this paper, we theoretically and empirically investigate how visual representations affect action generation in WAMs. Our results show that predictive embeddings from Joint-Embedding Predictive Architecture (JEPA) encoders better support action generation than compressed VAE latents, with I-JEPA performing best in our encoder comparison. Based on these findings, we propose LeWAM, which conditions action generation on JEPA embeddings and models environment evolution by predicting future embeddings in the same space, without relying on a video diffusion backbone. We further find that imitation learning matches demonstrated actions but does not distinguish better actions from worse ones, even though small action deviations can greatly affect task success. To address this limitation without additional environment interaction or the human oversight required for resets and safety, we introduce Demonstration-Guided DPO (DemoDPO), an offline preference refinement stage that derives preference supervision directly from demonstrations.With only 0.4B trainable parameters, LeWAM achieves an average success rate of 92.28\% on RoboTwin 2.0, comparable to that of state-of-the-art VLAs and WAMs, and maintains practical effectiveness on real-world manipulation tasks.

Chinese Translation

世界行动模型（World Action Models, WAMs）联合建模行动生成和环境动态，主要基于预训练的视频扩散模型（Video Diffusion Models, VDMs）构建。在基于VDM的WAM中，观察首先由变分自编码器（Variational Autoencoder, VAE）编码，随后将得到的压缩潜变量通过大型视频扩散主干网络处理，以提取有效特征用于行动生成。然而，这一范式将WAM的性能和训练成本与大规模视频生成的预训练绑定在一起，限制了WAM的效率和可扩展性。本文从理论和实证两个方面研究了视觉表征如何影响WAM中的行动生成。我们的结果表明，来自联合嵌入预测架构（Joint-Embedding Predictive Architecture, JEPA）编码器的预测嵌入在支持行动生成方面优于压缩的VAE潜变量，其中I-JEPA在我们的编码器比较中表现最佳。基于这些发现，我们提出了LeWAM，它基于JEPA嵌入条件生成行动，并通过预测同一空间中的未来嵌入来建模环境演变，而不依赖于视频扩散主干网络。我们进一步发现，模仿学习能够匹配示范的行动，但无法区分更好的行动与较差的行动，尽管小的行动偏差可能会极大影响任务成功。为了解决这一限制，而无需额外的环境交互或重置和安全所需的人类监督，我们引入了示范引导的DPO（Demonstration-Guided DPO, DemoDPO），这是一个离线偏好精炼阶段，直接从示范中推导偏好监督。LeWAM仅需0.4亿可训练参数，在RoboTwin 2.0上实现了92.28\%的平均成功率， comparable to state-of-the-art VLAs and WAMs，并在现实世界的操控任务中保持了实际有效性。

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cs.CV / 35 / 2609.27457

Beyond Balanced Accuracy: A Resolution and Parity-Controlled Benchmark for Vision-Language and Vision-Only Defect Assessment in UAV Power-Line Inspection

超越平衡准确率：无人机电力线检查中视觉-语言和仅视觉缺陷评估的分辨率与公平性控制基准

Zhang, Linghao, Xiang, Siyu, Kuang, Junwei, Yi, Peiyu

Abstract

Vision-language models (VLMs) are often reported to outperform task-specific vision backbones for unmanned aerial vehicle (UAV) power-line defect assessment. We test that claim on ElecVQA-Bench, a 56,972-item benchmark derived from the public InsPLAD dataset, across six evaluation choices: partition, evaluated item set, label space, replication, input resolution, and side information. On a matched partition, a Swin Transformer and the strongest adapted VLM differ by only 0.03 points at binary screening. At seven-way defect typing, increasing the vision backbones from 224 px to the measured pixel budget of the VLM preprocessor narrows the gap against InternVL3.5-8B from +20.53 to -0.57 points for ResNet-50 and from +23.67 to +4.70 points for Swin-T. A pixel-budget audit shifts Qwen3-VL-8B macro recall by 10.78 points, yet a source-pixel-matched InternVL control still leaves Qwen ahead by 7.43 to 13.61 points while using 56% fewer visual tokens, so neither source pixels nor token budget explains the difference between the two VLMs. A two-seed global replication changes Qwen binary accuracy and seven-way macro recall by 0.86 and 1.02 points. After split-specific retraining, Qwen does not lead at crop or image level, and a 14-tower, three-seed replication reverses the sign across seeds, giving mean common-six macro recall of 0.9085 for Qwen against 0.9509 for ResNet-50. No split regime yields a family-level advantage that survives multiple-comparison correction. The study supports a benchmark-audit contribution rather than a general claim of VLM superiority.

Chinese Translation

视觉-语言模型（VLMs）通常被报道在无人机（UAV）电力线缺陷评估中优于特定任务的视觉骨干网络。我们在ElecVQA-Bench上测试了这一说法，该基准由公共InsPLAD数据集派生而来，共包含56,972个项目，涵盖六个评估选择：分区、评估项目集、标签空间、复制、输入分辨率和侧面信息。在匹配分区上，Swin Transformer与最强的适配VLM在二元筛选中仅相差0.03分。在七类缺陷分类中，将视觉骨干网络的分辨率从224 px提高到VLM预处理器的测量像素预算，使得与InternVL3.5-8B的差距从+20.53分缩小至-0.57分（ResNet-50）和从+23.67分缩小至+4.70分（Swin-T）。像素预算审计使Qwen3-VL-8B的宏召回率变化10.78分，但源像素匹配的InternVL控制仍使Qwen领先7.43至13.61分，同时使用的视觉标记减少了56%。因此，源像素或标记预算均无法解释这两个VLM之间的差异。两种种子全局复制使Qwen的二元准确率和七类宏召回率分别变化0.86和1.02分。在特定分割的再训练后，Qwen在裁剪或图像级别上并不领先，而14塔、三种子复制则在种子间反转了符号，使得Qwen的平均共同六类宏召回率为0.9085，而ResNet-50为0.9509。没有任何分割方案能在多重比较校正下保持家族级优势。本研究支持基准审计贡献，而非VLM优越性的普遍主张。

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cs.CV / 36 / 2609.27461

Invisible in Space, Visible in Time: Motion Vision CAPTCHA against GUI Agents

在空间中隐形，在时间中可见：针对图形用户界面代理的运动视觉验证码

Zhang, Zeyu, Rong, Dingyi, Chen, Zijian, Zhang, Zicheng, Min, Xiongkuo, Zhai, Guangtao

Abstract

Most existing visual CAPTCHAs remain spatially solvable: the required information is exposed by static appearance, local structure, and interface state. This assumption is weakened by advances in multimodal large language models (MLLMs) and Graphical User Interface (GUI) agents, which exhibit strong visual perception, reasoning, and browser interaction capabilities. We propose Motion Vision CAPTCHA (MVCAP), a hierarchical motion-based CAPTCHA framework in which target semantics are instantiated as motion-defined foreground structures and become recoverable only through temporal segregation from a dynamically evolving background. Built on this shared principle, MVCAP is instantiated in three perceptually progressive levels: coherent motion, structural motion, and biological motion. To evaluate this framework, we introduce MVCAP-Bench, a browser-based benchmark with 600 live CAPTCHA instances, together with a matched foreground-only control benchmark, MVCAP-Bench-FG. We evaluate humans, Browser Use agents, native computer use agents, and a supplementary offline VQA setting derived from the same instances. Results reveal a substantial human--agent gap: on the full MVCAP-Bench, human accuracy reaches 99.6%, whereas the best GUI agent achieves only 16.8%, close to the six-way chance level. The foreground-only control further shows that the key difficulty comes from dynamic background camouflage rather than answer format or browser interaction alone. These findings identify a measurable human--agent perception gap and position MVCAP-Bench as a benchmark for studying motion-defined perception in current agents.

Chinese Translation

大多数现有的视觉验证码在空间上是可解的：所需信息通过静态外观、局部结构和界面状态暴露出来。随着多模态大型语言模型（MLLMs）和图形用户界面（GUI）代理的进步，这一假设变得脆弱，因为这些代理展现出强大的视觉感知、推理和浏览器交互能力。我们提出了运动视觉验证码（Motion Vision CAPTCHA，MVCAP），这是一个基于层次运动的验证码框架，其中目标语义被实例化为运动定义的前景结构，并且只有通过与动态演变的背景进行时间分离才能恢复。基于这一共享原则，MVCAP在三个感知渐进的层次中实例化：连贯运动、结构运动和生物运动。为了评估这一框架，我们引入了MVCAP-Bench，这是一个基于浏览器的基准测试，包含600个实时验证码实例，并配有一个匹配的仅前景控制基准测试MVCAP-Bench-FG。我们评估了人类、浏览器使用代理、原生计算机使用代理，以及从相同实例派生的补充离线视觉问答（VQA）设置。结果显示出显著的人类与代理之间的差距：在完整的MVCAP-Bench上，人类的准确率达到99.6%，而最佳的GUI代理仅达到16.8%，接近六选一的机会水平。仅前景控制进一步表明，关键的困难来自动态背景伪装，而不仅仅是答案格式或浏览器交互。这些发现确定了一个可测量的人类与代理之间的感知差距，并将MVCAP-Bench定位为研究当前代理中运动定义感知的基准。

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cs.CV / 37 / 2609.27462

Hybrid Gaussians for Robust Open-Vocabulary 3D Segmentation with Multi-View Object Association and Boundary Refinement

用于鲁棒开放词汇3D分割的混合高斯模型：多视角物体关联与边界优化

Qiu, Xueqi, Sun, Yueming, Zhang, Tianyu, Xia, Yuxuan, Long, Yang

Abstract

Open-vocabulary 3D segmentation localizes objects from free-form text queries, but remains challenging in real image sequences: incomplete or noisy 2D supervision destabilizes multi-view identity assignment, while full-scene semantic learning weakens object-level discriminability. We introduce Hybrid Gaussians, a unified 3D representation jointly modeling object association and language-aligned semantics. Its Multi-View Object Association mechanism combines Observation Fusion and Semantic Contrastive Learning to improve identity consistency and semantic discrimination. Boundary Reconstruction Optimization further refines local boundary structure to improve contour quality. Experiments on LERF and 3D-OVS demonstrate strong quantitative and qualitative performance. Our method achieves 59.1\% mIoU on LERF, yielding a 13.4\% relative gain over the baseline. Project page: https://nora202.github.io/hybridgaussians.

Chinese Translation

开放词汇的3D分割通过自由形式的文本查询来定位物体，但在真实图像序列中仍然面临挑战：不完整或嘈杂的2D监督会导致多视角身份分配的不稳定，而全场景语义学习则削弱了物体级别的可区分性。我们提出了混合高斯（Hybrid Gaussians），这是一种统一的3D表示，联合建模物体关联和语言对齐的语义。其多视角物体关联机制结合了观察融合（Observation Fusion）和语义对比学习（Semantic Contrastive Learning），以提高身份一致性和语义区分性。边界重建优化（Boundary Reconstruction Optimization）进一步细化局部边界结构，以改善轮廓质量。在LERF和3D-OVS上的实验表明了强大的定量和定性表现。我们的方法在LERF上实现了59.1%的mIoU，相较于基线获得了13.4%的相对提升。项目页面：https://nora202.github.io/hybridgaussians。

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cs.CV / 38 / 2609.27468

CereVLA: Cerebellum-Inspired Consequence-Aware Residual Governance for Efficient Vision-Language-Action Execution

CereVLA：基于小脑启发的后果感知残差治理用于高效的视觉-语言-行动执行

Zeng, Shuai, Liang, Yuxuan, Hu, Hangmiao, Zhou, Fobao, Wang, Zixiang, Hong, Wenxi, Zhao, Hang

Abstract

Action-chunked vision-language-action (VLA) policies improve inference efficiency, but limited feedback within committed action chunks can lead to accumulated execution errors. Residual adaptation can correct such deviations without retraining the VLA; however, existing corrections are typically optimized for reference-action consistency without explicitly considering their downstream consequences. To address this limitation, we present Cerebellum-Inspired Consequence-Aware Residual Governance (CereVLA), a unified framework that integrates lightweight residual refinement and predictive consequence evaluation into frozen VLA execution. Corrective actions are first generated by flow-based residual refinement, and their short- and interval-horizon consequences are then evaluated by a recurrent state-space model and a history-aware classifier. Residual corrections predicted to be unfavorable are selectively suppressed by a lightweight governor. Comparisons with state-of-the-art methods on LIBERO-10 and LIBERO-GOAL demonstrate the effectiveness of CereVLA. On SO-101, CereVLA increases task success from 57.5% to 90.0% and reduces mean control steps by 19.6% among successful trials, relative to the frozen SmolVLA baseline.

Chinese Translation

基于动作块的视觉-语言-行动（VLA）策略提高了推理效率，但在已承诺的动作块内有限的反馈可能导致累积的执行错误。残差适应可以在不重新训练VLA的情况下纠正此类偏差；然而，现有的纠正方法通常优化参考动作的一致性，而未明确考虑其下游后果。为了解决这一局限性，我们提出了基于小脑启发的后果感知残差治理（CereVLA），这是一个统一框架，将轻量级残差精炼和预测后果评估集成到冻结的VLA执行中。纠正动作首先通过基于流的残差精炼生成，然后通过递归状态空间模型和历史感知分类器评估其短期和区间后果。预测为不利的残差纠正将被轻量级治理器选择性抑制。在LIBERO-10和LIBERO-GOAL上的与最先进方法的比较展示了CereVLA的有效性。在SO-101上，CereVLA将任务成功率从57.5%提高到90.0%，并在成功试验中将平均控制步骤减少了19.6%，相较于冻结的SmolVLA基线。

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cs.CV / 39 / 2609.27470

DeltaS: Reading the Gated Linear Attention State for KV Cache Eviction in Streaming Video

DeltaS：在流媒体视频中读取门控线性注意状态以进行KV缓存驱逐

Kwon, Taeyoun, Kim, Seungjin, Kim, Hyeonyu, Kim, Moon Hwan

Abstract

Recent video-language models increasingly adopt hybrid architectures that interleave linear and full attention layers for efficient long-context processing. While the recurrent state of linear attention remains fixed in size, the KV cache of full attention continues to grow with the video stream, making eviction necessary under a bounded memory budget. The key challenge in streaming is that eviction must occur before the question arrives, so what to retain has to be decided without the question. Existing eviction methods derive token scores from the KV cache itself, using position, attention, or key-value representations, and attention-based scores further require proxy queries or extra computation. Hybrid backbones offer another source of signal. In gated-delta linear attention, the recurrent state is updated by the residual between each input and what can already be retrieved from the state, so its change over a chunk of frames reflects how much new information the chunk brings. We propose DeltaS, a query-agnostic, training-free method that retains video chunks inducing larger normalized state change, or state drift. In a controlled comparison with the budget and retention policy held fixed, state drift outperforms position-, attention-, and key-value-based signals. With a signal costing only 1.9% of the forward pass, DeltaS surpasses the strongest query-agnostic bounded-memory baseline by 2.1 points on average across six long-video benchmarks and by 5.6 points on the longest benchmark. These results suggest that the two memories of hybrid architectures can work cooperatively. Code is available at https://github.com/MaumAI-Company/DeltaS.

Chinese Translation

近年来，视频语言模型越来越多地采用混合架构，将线性和全注意力层交替使用，以实现高效的长上下文处理。虽然线性注意的递归状态保持固定大小，但全注意力的KV缓存随着视频流的增长而不断扩大，因此在有限内存预算下，驱逐是必要的。流媒体中的关键挑战在于驱逐必须在问题到达之前进行，因此必须在没有问题的情况下决定保留什么。现有的驱逐方法从KV缓存本身导出标记分数，使用位置、注意力或键值表示，而基于注意力的分数进一步需要代理查询或额外计算。混合骨干网络提供了另一种信号来源。在门控-增量线性注意中，递归状态通过每个输入与可以从状态中检索到的内容之间的残差进行更新，因此其在一段帧中的变化反映了该段带来的新信息量。我们提出了DeltaS，这是一种与查询无关、无需训练的方法，保留引起更大归一化状态变化或状态漂移的视频块。在预算和保留策略固定的受控比较中，状态漂移的表现优于基于位置、注意力和键值的信号。DeltaS的信号仅占前向传播的1.9%，在六个长视频基准测试中，平均超越最强的与查询无关的有限内存基线2.1分，在最长的基准测试中超越5.6分。这些结果表明，混合架构的两种记忆可以协同工作。代码可在 https://github.com/MaumAI-Company/DeltaS 获取。

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cs.CV / 40 / 2609.27493

Information Capacity of Generative Video Compression: Quantifying the Rate-Compute Exchange at Identical Quality

生成视频压缩的信息容量：在相同质量下量化速率-计算交换

Yuan, Cheng, Shao, Jiawei, Li, Xuelong

Abstract

Under the AI Flow framework, communication networks distribute intelligence across devices, edge servers, and clouds, and computation at the receiver becomes a resource that can substitute for transmitted bits. Generative video compression (GVC) embodies this exchange by sending compact tokens with ultra-low bitrate and letting a generative decoder synthesize the video, yet how much bandwidth savings a unit of decoder compute actually achieves has never been quantified. To fill this vacancy, we model reconstruction quality as a two-factor power law in data rate and decoder compute, which fits measured DISTS of two GVC decoders with a mean error below 3%, and define the information capacity (IC) as the negative logarithmic slope along an iso-quality contour, namely the fraction of rate saved per fractional increase in compute at identical quality. IC is dimensionless and unit-invariant, thus enabling an architecture-agnostic comparison. It forms a field over the operating plane, locating where additional denoising steps are worth their cost. Across five datasets, the 14B decoder trades more compute for fewer rate about ten times more efficiently than the 1.3B decoder. IC also varies significantly across datasets, indicating imbalanced performance on the rate-compute trade-off in GVC methods.

Chinese Translation

在AI Flow框架下，通信网络在设备、边缘服务器和云之间分配智能，接收端的计算成为可以替代传输比特的资源。生成视频压缩（GVC）通过发送超低比特率的紧凑令牌并让生成解码器合成视频来体现这种交换，但单位解码器计算所实际实现的带宽节省从未被量化。为填补这一空白，我们将重建质量建模为数据速率和解码器计算的双因子幂律，这一模型与两个GVC解码器的测量DISTS拟合良好，平均误差低于3%。我们将信息容量（IC）定义为沿等质量轮廓的负对数斜率，即在相同质量下每单位计算增加所节省的速率的比例。IC是无量纲且单位不变的，从而实现了架构无关的比较。它在操作平面上形成一个场，定位额外去噪步骤是否值得其成本。在五个数据集上，14B解码器以约10倍于1.3B解码器的效率在更少的速率上交换更多的计算。IC在不同数据集间也显著变化，表明GVC方法在速率-计算权衡上的性能不平衡。

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cs.CV / 41 / 2609.27509

Know-Your-Scene (KYS)-SLAM: Hierarchical Semantic-Motion Priors for Feature Matching in Stereo Visual SLAM

Know-Your-Scene (KYS)-SLAM：用于立体视觉SLAM中特征匹配的层次语义运动先验

Chatterjee, Preeti, Lu, Jin, Sun, Jin, Bhandarkar, Suchendra M.

Abstract

Stereo visual SLAM systems built on local descriptors suffer from semantic ambiguity, instance-level confusion, and independently moving objects, each corrupting data association and accumulating as trajectory drift. Prevailing semantic and dynamic SLAM methods address this through binary feature rejection, sacrificing correspondence density for outlier suppression. We contend that contextual implausibility is better expressed as a graded quantity than an exclusion criterion. We present Know-Your-Scene (KYS)-SLAM, a modular extension of ORB-SLAM3 that supplants feature rejection with continuous correspondence modulation. The contribution is the reframing of contextual evidence as correspondence cost, applied within feature matching and leaving the geometric backend unmodified. Each keypoint is augmented with semantic, panoptic, and motion priors fused through a hierarchical compatibility formulation, in which semantic class and instance identity enforce structural plausibility while a zero-shot motion score down-weights features on independently moving objects. That score comes from a training-free module fitting a depth-aware ego-motion model to background optical flow and classifying panoptic segments via self-calibrating, coverage-aware thresholds, so only segments with sufficient motion evidence are penalized and static structure is left unpenalized. Penalizing correspondences rather than discarding them preserves the geometric support bundle adjustment depends on. Under one fixed configuration, no coefficient retuned per sequence or dataset, KYS-SLAM reduces per-sequence ATE RMSE by 17.4% on outdoor KITTI and 27.7% on indoor EuRoC across 21 stereo sequences with no regressions, and by 6.6% on dynamic subsets of KITTI Tracking and 17.8%, up to 31.2%, on Virtual KITTI 2 -- cross-domain transfer across outdoor driving, indoor flight, and synthetic imagery under one set of constants.

Chinese Translation

基于局部描述子的立体视觉SLAM系统面临语义模糊、实例级混淆和独立移动物体等问题，这些问题会破坏数据关联并导致轨迹漂移。现有的语义和动态SLAM方法通过二元特征拒绝来解决这一问题，牺牲了对应密度以抑制离群值。我们认为，上下文的不合理性更适合用分级量来表达，而不是作为排除标准。我们提出了Know-Your-Scene (KYS)-SLAM，这是ORB-SLAM3的一个模块化扩展，它用连续对应调制取代了特征拒绝。我们的贡献在于将上下文证据重新框架为对应成本，应用于特征匹配，同时保持几何后端不变。每个关键点都通过层次兼容性公式增强了语义、全景和运动先验，其中语义类别和实例身份强制执行结构合理性，而零样本运动评分则降低了独立移动物体上的特征权重。该评分来自一个无训练模块，它将深度感知的自我运动模型拟合到背景光流，并通过自校准、覆盖感知的阈值对全景段进行分类，因此只有具有足够运动证据的段会受到惩罚，而静态结构则不受惩罚。惩罚对应关系而不是丢弃它们，保留了几何支持束调整所依赖的内容。在一个固定配置下，没有针对每个序列或数据集重新调整的系数，KYS-SLAM在21个立体序列上将户外KITTI的每序列平均绝对误差根均方根（ATE RMSE）降低了17.4%，在室内EuRoC上降低了27.7%，且没有回归，在KITTI Tracking的动态子集上降低了6.6%，在Virtual KITTI 2上降低了17.8%，最高可达31.2%——在一个常数集下实现了户外驾驶、室内飞行和合成图像之间的跨域迁移。

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cs.CV / 42 / 2609.27511

NV-Reason-CT: 3D Visual Language Model for CT Analysis

NV-Reason-CT：用于CT分析的3D视觉语言模型

Myronenko, Andriy, Yang, Dong, Tang, Yucheng, Turkbey, Baris, Simon, Benjamin, Harmon, Stephanie, Makwana, Rikhil, Aboian, Mariam, Azamat, Sena, Hamamci, Ibrahim Ethem, Er, Sezgin, Menze, Bjoern, Edgar, Marc, He, Yufan, Guo, Pengfei, Xu, Daguang

Abstract

We present NV-Reason-CT, a generative vision--language model for chest and abdominal CT combining native 3D visual encoding with radiologist-guided reasoning. The model couples a native 3D vision transformer with a language model, passing all visual tokens and their explicit 3D coordinates into language decoding without further spatial token merging. This retains volumetric spatial information within the vision encoder and through the language model's positional encoding during joint processing with text. We train on a curated corpus of approximately 550,000 multimodal instruction examples from 70,111 unique CT image inputs, combining standardized reports, abnormality-focused and anatomy-specific questions, multi-turn interactions, and radiologist-authored reasoning from recorded and transcribed expert CT interpretations. Expert annotations provide direct supervision and guide additional report-grounded synthetic reasoning. End-to-end supervised fine-tuning (SFT) is followed by Group Relative Policy Optimization (GRPO), with verifiable rewards over chest and abdominal abnormality sets. The model supports abnormality classification, report generation, and interactive reasoning with reviewable observations, differential diagnoses, and uncertainty. Evaluation spans public CT benchmarks and a held-out NIH cohort. On CT-RATE, NV-Reason-CT achieves a macro-F1 of 0.614 and macro-AUROC of 0.871 without a task-specific classification head; generated reports achieve a report-derived macro-F1 of 0.592. In a preliminary study with expert radiologists, AI-assisted review received favorable confidence ratings and was associated with a 50% reduction in average reported interpretation and reporting time. We release the model and training code to support reproducible research on explainable AI for volumetric medical imaging.

Chinese Translation

我们提出了NV-Reason-CT，这是一种生成性视觉-语言模型，专为胸部和腹部CT设计，结合了原生3D视觉编码与放射科医师指导的推理。该模型将原生3D视觉变换器与语言模型相结合，直接将所有视觉标记及其明确的3D坐标传入语言解码，而无需进一步的空间标记合并。这保留了视觉编码器中的体积空间信息，并在与文本的联合处理过程中通过语言模型的位置编码进行了保留。我们在一个经过精心策划的语料库上进行训练，该语料库包含约550,000个多模态指令示例，来自70,111个独特的CT图像输入，结合了标准化报告、针对异常的和特定解剖结构的问题、多轮互动，以及放射科医师撰写的基于记录和转录的专家CT解读的推理。专家注释提供了直接监督，并指导额外的报告基础合成推理。经过端到端的监督微调（SFT），随后进行群体相对策略优化（GRPO），并在胸部和腹部异常集上进行可验证的奖励评估。在CT-RATE上，NV-Reason-CT实现了0.614的宏F1值和0.871的宏AUROC值，而没有特定任务的分类头；生成的报告实现了0.592的报告衍生宏F1值。在与专家放射科医师的初步研究中，AI辅助审查获得了良好的信心评分，并与平均报告解读和报告时间减少50%相关。我们发布了模型和训练代码，以支持对体积医学成像的可解释AI的可重复研究。

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cs.CV / 43 / 2609.27523

M3D-Net: Hierarchical Coordination of Spatial Context, Feature Reuse, and Differential Attention for Mammography Classification

M3D-Net：空间上下文、特征重用和差异注意力的层次协调用于乳腺影像分类

Yu, Zheng, Li, Xinhang, Gao, Jiabao, Wang, Boyang, Li, Xiang

Abstract

Breast image classification requires local detail and global tissue context, yet these cues can weaken as representations deepen. We present M3D-Net, a mammography encoder that hierarchically coordinates multi-scale coordinate attention, bounded dynamic feature reuse, and differential attention through resolution-aware operator placement. Within-stage retrieval preserves access to earlier features, coordinate-aware aggregation integrates local and global context, and differential attention operates at coarse resolutions. We evaluate image-only classification on AISSLab mammography and an adapted image--clinical model on BrEaST ultrasound. Against EdgeNeXt, RepViT, and TransXNet, the proposed implementations achieve the highest recorded validation accuracy and late-training accuracy, with the lowest endpoint cross-entropy loss. Validation accuracies reach 97.78\% and 80.39\%, respectively. These results support further evaluation of hierarchical coordination across breast imaging settings; repeated-seed, component-controlled, and independent evaluations remain necessary.

Chinese Translation

乳腺影像分类需要局部细节和全局组织上下文，然而随着表示的加深，这些线索可能会减弱。我们提出了M3D-Net，一种通过分辨率感知的操作放置，层次协调多尺度坐标注意力、有限动态特征重用和差异注意力的乳腺影像编码器。在阶段内检索保留了对早期特征的访问，坐标感知聚合整合了局部和全局上下文，而差异注意力则在粗分辨率下运行。我们在AISSLab乳腺影像上评估了仅图像分类，并在BrEaST超声上评估了适应的图像-临床模型。与EdgeNeXt、RepViT和TransXNet相比，所提出的实现达到了最高的验证准确率和晚期训练准确率，并且具有最低的终点交叉熵损失。验证准确率分别达到97.78%和80.39%。这些结果支持在乳腺影像设置中进一步评估层次协调；重复种子、组件控制和独立评估仍然是必要的。

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cs.CV / 44 / 2609.27533

ICM: Intra-class Mixing for Domain Adaptation in Adverse Weather

ICM：逆天气条件下的领域适应中的类内混合

Li, Boying, Liu, Chang, Wilde, Britta Ayano, Kovács, György, Adewumi, Tosin, Backe, Björn, Mokayed, Hamam

Abstract

Unsupervised domain adaptation (UDA) for semantic segmentation remains challenging under adverse weather conditions because severe appearance changes enlarge the domain gap and degrade the reliability of pseudo labels in the target domain. To address this problem, we propose an Intra-Class Mixing Consistency (ICM) framework that enforces prediction consistency between an intra-class mixed image and its original counterpart. Unlike previous mixing-based consistency methods that combine regions across different images or domains and may introduce unrealistic semantic inconsistencies, ICM performs mixing within the same image and semantic class, preserving realistic semantic layout for consistency regularization. With ICM, we establish a new state-of-the-art performance for clear-to-adverse-weather unsupervised domain adaptation (UDA) in semantic segmentation. On the Cityscapes $\rightarrow$ ACDC benchmark, our method achieves 75.7\% mIoU, outperforming the previous state of the art by +1.9 pp, demonstrating its effectiveness in mitigating class confusion under challenging environmental conditions. The code is provided in the supplementary material.

Chinese Translation

在逆天气条件下，语义分割的无监督领域适应（UDA）仍然面临挑战，因为严重的外观变化扩大了领域间的差距，并降低了目标领域伪标签的可靠性。为了解决这个问题，我们提出了一种类内混合一致性（Intra-Class Mixing Consistency, ICM）框架，该框架强制要求类内混合图像与其原始对应图像之间的预测一致性。与以往通过不同图像或领域组合区域的混合一致性方法不同，这些方法可能引入不现实的语义不一致，ICM 在同一图像和语义类别内进行混合，保持了现实的语义布局以进行一致性正则化。通过 ICM，我们在语义分割的清晰到逆天气无监督领域适应（UDA）中建立了新的最先进性能。在 Cityscapes $ ightarrow$ ACDC 基准测试中，我们的方法达到了 75.7\% 的 mIoU，超越了之前的最先进水平 +1.9 个百分点，证明了其在挑战性环境条件下减轻类别混淆的有效性。代码已在补充材料中提供。

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cs.CV / 45 / 2609.27611

A generalizable structural brain MRI foundation model built through dual-priority federated pretraining

通过双优先级联邦预训练构建的可推广结构性脑MRI基础模型

Yu, Zhen, Liu, Yang, Zhuang, Xiahai, Chen, Qingchao

Abstract

Foundation models hold promise for generalizable analysis of structural brain magnetic resonance imaging (MRI) across development, aging and disease. However, existing models are typically built through centralized pretraining on pooled data, despite privacy and governance constraints. Such pooling optimization can overemphasize cohort size and overlook complementary information from smaller, specialized cohorts. Here we present BrainFedFM, a structural brain MRI foundation model federatively pretrained on 164,707 three-dimensional scans drawn from diverse real-world data distributions and organized across 42 federated sites. BrainFedFM uses dual-priority federated pretraining, coupling spatial-priority masking at each site with site-priority aggregation at the server to emphasize informative anatomical regions locally and prioritize site contributions globally. Across 20 downstream datasets spanning 17 classification, regression and segmentation tasks, BrainFedFM achieved the state-of-the-art performance (mean rank 1.68, 50\% gain) across seven models, including four centralized foundation models, while showing particularly consistent advantages in classification and regression and robustness across underrepresented populations. These findings demonstrate the generalizability of BrainFedFM and highlight federated pretraining as a practical strategy for developing neuroimaging foundation models from distributed data without pooling raw images.

Chinese Translation

基础模型在结构性脑磁共振成像（MRI）的可推广分析中展现出潜力，适用于发展、衰老和疾病的研究。然而，现有模型通常通过对集中数据进行预训练来构建，尽管存在隐私和治理的限制。这种数据集中优化可能过于强调队列规模，而忽视来自较小、专业队列的互补信息。在此，我们提出了BrainFedFM，一个在来自42个联邦站点的多样化真实世界数据分布中提取的164,707个三维扫描上进行联邦预训练的结构性脑MRI基础模型。BrainFedFM采用双优先级联邦预训练，将每个站点的空间优先级掩蔽与服务器的站点优先级聚合相结合，以强调局部信息丰富的解剖区域，并在全球范围内优先考虑站点贡献。在涵盖17个分类、回归和分割任务的20个下游数据集上，BrainFedFM在七个模型中实现了最先进的性能（平均排名1.68，提升50%），包括四个集中基础模型，同时在分类和回归任务中表现出特别一致的优势，并在代表性不足的人群中展现出稳健性。这些发现证明了BrainFedFM的可推广性，并强调了联邦预训练作为从分布式数据中开发神经影像基础模型的实用策略，而无需对原始图像进行集中处理。

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cs.CV / 46 / 2609.27620

InGuard: Towards Generalized Inner Guardrail for Safe Text-to-Image Generation

InGuard：面向安全文本到图像生成的通用内部保护框架

Wang, Zeyu, Li, Xiaodan, Li, Zhiwen, Chen, Yuefeng, Xue, Hui

Abstract

Modern text-to-image (T2I) models generate high-quality images from arbitrary user prompts, yet they can just as easily produce not-safe-for-work (NSFW) content. Conventional outer guardrails consist of two components: a prompt classifier that checks for risk before generation, and a post-hoc image classifier that checks the fully generated image. In this design, both classifiers operate outside the generation pipeline and do not use the model's own representations. This separation can limit prompt-screening accuracy, while the image-side check runs only after the full generation cost has been spent. Moreover, a flagged prompt can only be rejected, even when it could be adjusted to produce a safe image. In this work, we propose the Inner Guardrail (InGuard), a safety framework that works inside the pipeline on the model's own representations, leaving base-model parameters untouched. First, a risk classifier grades each prompt as unsafe, risky, or benign based on the text encoder's embeddings, with no external language model. Second, SAGE (Soft-gated Asymmetric Guardrail for Embeddings) modifies the embeddings of risky prompts, aiming to return a safe image instead of a refusal. Third, a latent detector checks the one-step clean latent estimate midway through denoising, reaching nearly image-level performance and halting generation when risk is detected. We also construct the RevGen Safety Benchmark to evaluate T2I safety under realistic conditions: 10,000 prompts built through real-image reverse generation, with a rewriting step that supplies controlled intellectual-property (IP) characters, covering graded porn/gore risks, categorical IP risks, and benign negatives. Across five open-weight T2I models, InGuard reaches 97.9-98.8% safety rate, matching or exceeding the outer guardrail, with 57.5-73.5% less benign disturbance, ~3.7x fewer parameters, and 50-55.6% of denoising steps skipped.

Chinese Translation

现代文本到图像（T2I）模型能够从任意用户提示生成高质量图像，但同样容易生成不适合工作的（NSFW）内容。传统的外部保护措施由两个组件组成：一个在生成前检查风险的提示分类器，以及一个在生成后检查完整生成图像的后验图像分类器。在这种设计中，两个分类器都在生成管道之外操作，并且不使用模型自身的表示。这种分离可能限制了提示筛选的准确性，而图像侧的检查仅在完全生成成本已花费后进行。此外，标记的提示只能被拒绝，即使它可以调整以生成安全图像。在本研究中，我们提出了内部保护框架（Inner Guardrail，InGuard），这是一个在管道内部基于模型自身表示的安全框架，不影响基础模型参数。首先，风险分类器根据文本编码器的嵌入将每个提示评估为不安全、风险或良性，且不依赖外部语言模型。其次，SAGE（Soft-gated Asymmetric Guardrail for Embeddings）修改风险提示的嵌入，旨在返回安全图像而不是拒绝。第三，潜在检测器在去噪过程中中途检查一步清洁潜在估计，达到接近图像级的性能，并在检测到风险时停止生成。我们还构建了RevGen安全基准，以在现实条件下评估T2I的安全性：通过真实图像反向生成构建的10,000个提示，包含一个重写步骤，提供受控的知识产权（IP）角色，涵盖分级的色情/血腥风险、分类的IP风险和良性负面。通过五个开放权重的T2I模型，InGuard的安全率达到97.9%-98.8%，与外部保护措施相匹配或超过，同时良性干扰减少57.5%-73.5%，参数数量减少约3.7倍，跳过50%-55.6%的去噪步骤。

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cs.CV / 47 / 2609.27671

SGDet3D++: Geometry-Grounded Semantics for 4D Radar and Camera 3D Object Detection

SGDet3D++：基于几何的4D雷达与相机3D目标检测的语义

Bai, Xiaokai, Fan, Zhenyu, Zheng, Lianqing, Wang, Songkai, Cao, Si-Yuan, Shen, Hui-liang

Abstract

4D radar complements dense image semantics with long-range geometry and radial motion, but existing radar--camera detectors largely solve \emph{where} to align the modalities while leaving \emph{whether} a piece of evidence supports an evolving object hypothesis implicit. An image token may describe an occluder, a nearby radar return may belong to another object, and a pose-aligned memory slot may carry incompatible motion. We formulate \emph{hypothesis-conditioned evidence grounding}, which separates candidate access from evidence use: semantic, geometric, or temporal evidence is filtered or conditioned by the evolving 3D state before updating the corresponding query. \sgdetpp{} instantiates this principle through Anchor-Grounded Semantic Retrieval (AGR), which conditions deformable image retrieval on pooled anchor-consistent radar support; Geometry-Consistent Anchor Refinement (GCR), which attentively aggregates individual associated returns; and Doppler-Verified Correspondence (DVC), which replaces history only when current radial motion contradicts it. \sgdetpp{} improves the strongest compared method by 3.82 mAP and 6.82 ODS on OmniHD-Scenes and by 6.82 mAP and 9.22 NDS on ManTruckScenes, while also leading the listed methods in the TJ4DRadSet test comparison. Mechanism-targeted evaluations show that AGR improves strict AP in every projected-occlusion bin, the yaw-aligned box gate raises target-return purity from 29.95\% to 58.87\%, and DVC preserves 96.11\% of motion-consistent history while retaining 75.90\% contradiction recall. Code will be released.

Chinese Translation

4D雷达通过长距离几何信息和径向运动补充了密集图像语义，但现有的雷达-相机检测器主要解决 extit{如何}对齐模态，而将 extit{是否}一条证据支持一个不断演变的目标假设隐含化。图像标记可能描述一个遮挡物，附近的雷达回波可能属于另一个物体，而姿态对齐的记忆槽可能承载不兼容的运动。我们提出了 extit{假设条件证据基础}，将候选访问与证据使用分开：语义、几何或时间证据在更新相应查询之前，经过不断演变的3D状态进行过滤或条件化。SGDet3D++通过锚点基础语义检索（Anchor-Grounded Semantic Retrieval, AGR）实现这一原则，该方法基于池化的锚点一致雷达支持条件化可变形图像检索；几何一致锚点细化（Geometry-Consistent Anchor Refinement, GCR），该方法关注地聚合个体相关回波；以及多普勒验证对应（Doppler-Verified Correspondence, DVC），该方法仅在当前径向运动与历史相矛盾时替换历史。SGDet3D++在OmniHD-Scenes上提高了最强比较方法3.82 mAP和6.82 ODS，在ManTruckScenes上提高了6.82 mAP和9.22 NDS，同时在TJ4DRadSet测试比较中领先于列出的其他方法。机制针对性评估显示，AGR在每个投影遮挡区间提高了严格AP，偏航对齐的框门将目标回波纯度从29.95 ext{%}提高到58.87 ext{%}，而DVC在保留96.11 ext{%}运动一致历史的同时保持75.90 ext{%}矛盾召回率。代码将会发布。

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cs.CV / 48 / 2609.27675

Track2Art: Motion-Centric Articulated Object Model Recovery from 2D Point Trackers

Track2Art：基于运动中心的关节物体模型从二维点跟踪器恢复

Li, Xiaotong, Jing, Yixiong, Ding, Junsheng, Li, Weihang, Busam, Benjamin, Wang, Guangming, Sheil, Brian

Abstract

Understanding articulated objects is fundamental for robotic interaction, requiring accurate rigid-part discovery and the recovery of their kinematic relations. Existing approaches often treat articulation as a by-product of reconstructed geometry or recover it through per-instance optimization. We instead build on the hypothesis that articulation is directly observable from persistent motion: points on the same rigid part move coherently, while relative motion between parts reveals their kinematic constraints. We present Track2Art, a motion-centric framework for recovering structured articulated objects from RGB-D interaction videos. Track2Art lifts tracked image points into persistent 3D trajectories and combines pretrained tracking features, visual descriptors, and explicit trajectory geometry. These representations are grouped into a variable number of rigid-part hypotheses and subsequently used to recover directed kinematic relations, joint types, and joint geometry through rotation-equivariant learned--analytic reasoning. On the aligned 20-object PartNet-Mobility suite, Track2Art achieves 0.695 Point IoU and 0.410 end-to-end J@20, while requiring neither ground-truth part counts nor test-time optimization.

Chinese Translation

理解关节物体对于机器人交互至关重要，这需要准确的刚性部件发现和它们的运动关系恢复。现有方法通常将关节视为重建几何的副产品，或通过逐实例优化来恢复它。我们则基于这样一个假设：关节是可以通过持续运动直接观察到的：同一刚性部件上的点以一致的方式移动，而部件之间的相对运动揭示了它们的运动约束。我们提出了Track2Art，一个基于运动中心的框架，用于从RGB-D交互视频中恢复结构化的关节物体。Track2Art将跟踪的图像点提升为持续的三维轨迹，并结合预训练的跟踪特征、视觉描述符和显式轨迹几何。这些表示被分组为可变数量的刚性部件假设，并随后用于通过旋转等变的学习-分析推理恢复定向运动关系、关节类型和关节几何。在对齐的20个物体PartNet-Mobility套件上，Track2Art达到了0.695的点IoU和0.410的端到端J@20，同时不需要真实的部件计数或测试时优化。

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cs.CV / 49 / 2609.27677

RoadOcc Learns When to Persist, Transport, or Refresh Memory for Roadside Occupancy Prediction

RoadOcc 学习何时坚持、传输或刷新记忆以进行路边占用预测

Bai, Xiaokai, Yang, Lei, Wang, Songkai, Zheng, Lianqing, Cao, Si-Yuan, Shen, Hui-liang

Abstract

Fixed roadside cameras repeatedly observe a stable scene overlaid by sparse moving traffic. Temporal memory can recover weak observations, but reusing moving evidence at stale locations can corrupt occupancy predictions. Motion compensation addresses displacement, while reliance on the resulting history remains a separate learning problem. We introduce RoadOcc, which learns soft routing among fixed-coordinate history (\emph{Persist}), velocity-addressed history (\emph{Transport}), and current evidence (\emph{Refresh}). Motion state and class-consistent historical support supervise these source choices. Dynamic-aware cross-attention (DCA) updates candidate locations, multi-scale voxel velocity estimation (VVE) constructs transport addresses from multi-scale current--history correspondence, and velocity-guided dynamic sparse fusion (VDSF) combines routed evidence under fixed sparse-token budgets. On InfraOcc, RoadOcc reaches 65.29 mIoU and 32.37 dynamic mIoU, gains of 4.44 and 4.71 over STCOcc. Controlled address experiments show that VVE raises dynamic mIoU by 0.87 over fixed-coordinate reading. Across three seeds, supervised P/T/R adds 1.40 dynamic points over motion-corrected retrieval, while removing Refresh costs 0.32 points. Results from two transfer models, Occ3D-nuScenes, and longer intervals provide additional support. Code will be released.

Chinese Translation

固定的路边摄像头反复观察一个稳定的场景，场景上叠加着稀疏的移动交通。时间记忆可以恢复微弱的观测，但在陈旧位置重用移动证据可能会破坏占用预测。运动补偿解决了位移问题，而依赖于由此产生的历史仍然是一个独立的学习问题。我们提出了 RoadOcc，它在固定坐标历史（ extit{Persist}）、速度地址历史（ extit{Transport}）和当前证据（ extit{Refresh}）之间学习软路由。运动状态和类别一致的历史支持监督这些来源选择。动态感知交叉注意力（DCA）更新候选位置，多尺度体素速度估计（VVE）从多尺度当前-历史对应关系构建传输地址，而速度引导的动态稀疏融合（VDSF）在固定稀疏令牌预算下组合路由证据。在 InfraOcc 数据集上，RoadOcc 达到 65.29 mIoU 和 32.37 动态 mIoU，相较于 STCOcc 提升了 4.44 和 4.71。控制地址实验表明，VVE 在固定坐标读取上提高了 0.87 的动态 mIoU。在三个种子实验中，监督的 P/T/R 相较于运动校正检索增加了 1.40 动态点，而去除 Refresh 则损失了 0.32 点。来自两个迁移模型 Occ3D-nuScenes 和更长时间间隔的结果提供了额外支持。代码将会发布。

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cs.CV / 50 / 2609.27681

CasCVS-Net: A Staged Multi-Task Cascade for Critical View of Safety Assessment

CasCVS-Net：用于安全评估关键视图的分阶段多任务级联

Toh, Bock-Zien, Ren, Yuanchuan, Yu, Tay Aw, Ong, Ng Khee, Mao, Zhehua, Bano, Sophia

Abstract

Automated assessment of the Critical View of Safety (CVS) in laparoscopic cholecystectomy requires both recognition of the three CVS criteria and anatomical grounding in small, rare, and often occluded hepatocystic structures. Learning-based methods differ in the anatomical information they use, from image-level classification to detection, segmentation, or graph-based reasoning, yet grounding the safety-critical anatomy remains the main bottleneck. We propose CasCVS-Net, a staged multi-task cascade that jointly performs object detection, semantic segmentation, and CVS assessment, trained on the Endoscapes dataset. The model couples the tasks through predicted anatomy: predicted boxes guide segmentation, and predicted masks provide region-level features for CVS classification, so CVS assessment at inference uses only model predictions rather than ground-truth annotations. To reduce optimisation instability in this coupled setting, training progresses from detection to detection-segmentation and then to the full three-task cascade, followed by task-wise fine-tuning. Evaluation on the public unseen test set shows that CasCVS-Net improves over matched single-task baselines on all three tasks, achieving 32.0 detection mAP, 46.8 semantic mIoU, 15.3 rare-anatomy mIoU, and 67.2 CVS mAP. It outperforms the state-of-the-art LG-CVS and SV2LSTG by 6.3% and 4.5% relative CVS mAP, respectively, corresponding to 4.0 and 2.9 mAP points. These results show that staged task coupling through predicted boxes and masks improves anatomical grounding for CVS assessment, particularly for rare hepatocystic structures.

Chinese Translation

在腹腔镜胆囊切除术中，自动评估安全的关键视图（CVS）需要识别三个CVS标准，并在小型、稀有且常常被遮挡的肝胆结构中进行解剖定位。基于学习的方法在使用的解剖信息上存在差异，从图像级分类到检测、分割或基于图的推理，但对安全关键解剖结构的定位仍然是主要瓶颈。我们提出了CasCVS-Net，这是一种分阶段的多任务级联模型，能够联合执行物体检测、语义分割和CVS评估，训练数据来自Endoscapes数据集。该模型通过预测的解剖结构将任务结合起来：预测框指导分割，预测掩膜提供区域级特征用于CVS分类，因此在推理时CVS评估仅使用模型预测，而非真实标注。为了减少这种耦合设置中的优化不稳定性，训练过程从检测开始，逐步过渡到检测-分割，然后是完整的三任务级联，最后进行任务级微调。在公共未见测试集上的评估显示，CasCVS-Net在所有三个任务上均优于匹配的单任务基线，检测mAP达到32.0，语义mIoU为46.8，稀有解剖mIoU为15.3，CVS mAP为67.2。它在相对CVS mAP上分别超越了最先进的LG-CVS和SV2LSTG 6.3%和4.5%，对应的mAP点数为4.0和2.9。这些结果表明，通过预测框和掩膜进行的分阶段任务耦合改善了CVS评估的解剖定位，特别是对于稀有的肝胆结构。

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cs.CV / 51 / 2609.27682

Gender Bias in Vision-Language In-Context Learning

视觉-语言上下文学习中的性别偏见

Xiang, Tong, Garcia, Noa, Nakashima, Yuta

Abstract

In-context learning (ICL) enables large vision-language models (LVLMs) to perform tasks by following patterns from in-context examples, yet its potential to amplify societal biases remains underexplored. We systematically investigate how ICL influences gender bias in LVLMs through VL-BICLE, an evaluation framework comprising six ICL settings, three tasks, and four datasets. Our experiments on six LVLMs reveal that gendered ICL demonstrations act as a directional force, shifting model bias toward the demonstrated gender through a cross-gender mechanism that disproportionately degrades performance on the opposite gender. This effect appears in image captioning and pronoun prediction but not in visual question answering, indicating that gendered ICL influences bias only when the task output involves gendered language. Similarity-based retrieval methods inherit the training pool's gender imbalance and offer no debiasing advantage, while standard quality metrics remain blind to these bias shifts. To mitigate this bias, we replace real in-context images with synthetic ones from stable diffusion models while keeping captions unchanged. This simple intervention reduces gender bias without degrading caption quality.

Chinese Translation

上下文学习（ICL）使大型视觉-语言模型（LVLMs）能够通过遵循上下文示例中的模式来执行任务，但其放大社会偏见的潜力仍未得到充分探讨。我们通过 VL-BICLE 这一评估框架系统性地研究了 ICL 如何影响 LVLMs 中的性别偏见，该框架包括六种 ICL 设置、三项任务和四个数据集。我们对六个 LVLMs 的实验表明，性别化的 ICL 示范作为一种方向性力量，通过跨性别机制将模型偏见转向所示范的性别，从而不成比例地降低对相反性别的表现。这一效应在图像描述和代词预测中显现，但在视觉问答中并不明显，表明性别化的 ICL 仅在任务输出涉及性别语言时影响偏见。基于相似性的检索方法继承了训练池中的性别不平衡，并未提供去偏见的优势，而标准质量指标对这些偏见变化则视而不见。为了减轻这种偏见，我们用来自稳定扩散模型的合成图像替换真实的上下文图像，同时保持标题不变。这一简单的干预措施在不降低标题质量的情况下减少了性别偏见。

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cs.CV / 52 / 2609.27696

SynSeq: End-to-End SYNTAX Score Prediction from Coronary Angiography Videos

SynSeq：基于视频的冠状动脉造影视频端到端SYNTAX评分预测

Baumann, Christoph, Schweitzer, Ronny, Pavo, Noemi, Attenberger, Ulrike, Loewe, Christian, Seeböck, Philipp

Abstract

The SYNTAX score is an established tool for assessing coronary artery disease and guiding revascularization treatment decisions. However, its manual estimation from coronary angiography videos by clinical experts is time-consuming and subject to inter-reader variability. While machine learning has shown promise in automating this process, prior work has primarily focused on lesion detection, characterization, or binary disease classification, leaving direct SYNTAX score prediction relatively unexplored. We propose SynSeq, a video-based method for direct SYNTAX score prediction. It combines targeted preprocessing with a tailored training strategy using a zero-inflation-aware loss and linear target scaling. Evaluated on the public CardioSyntax dataset, SynSeq significantly outperforms previous state-of-the-art methods, improving $R^2$ by 0.55, reducing prediction bias by 93.1% and achieving more consistent performance across annotations from three independent expert graders. In addition, SynSeq achieves a weighted $F_1$-score of 0.80 for revascularization treatment recommendations, slightly below inter-expert agreement. These results demonstrate the potential of SynSeq to provide consistent, automated SYNTAX score assessment and reliable decision support for coronary revascularization planning.

Chinese Translation

SYNTAX评分是评估冠状动脉疾病和指导再血管化治疗决策的一个成熟工具。然而，临床专家从冠状动脉造影视频中手动估算该评分耗时且受读者间差异的影响。尽管机器学习在自动化这一过程方面显示出前景，之前的研究主要集中在病变检测、特征描述或二元疾病分类上，直接的SYNTAX评分预测相对未被深入探讨。我们提出了SynSeq，一种基于视频的直接SYNTAX评分预测方法。它结合了针对性的预处理和使用零膨胀感知损失及线性目标缩放的定制训练策略。在公共的CardioSyntax数据集上进行评估，SynSeq显著优于之前的最先进方法，$R^2$提高了0.55，预测偏差减少了93.1%，并在三位独立专家评分的注释中实现了更一致的表现。此外，SynSeq在再血管化治疗建议中达到了加权$F_1$-分数0.80，略低于专家间一致性。这些结果表明，SynSeq在提供一致的、自动化的SYNTAX评分评估和可靠的冠状动脉再血管化规划决策支持方面具有潜力。

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cs.CV / 53 / 2609.27710

FFM-CP: Cross-Backbone Fusion of Vision-Language Foundation Models for Few-Shot Computational Pathology

FFM-CP：用于少量样本计算病理学的视觉-语言基础模型的跨骨干融合

Nguyen, Anh-Tien, Dang, Trung DQ., Diep, Nghiem Tuong, Nguyen, Bui Ngoc Han, Mai, Tan-Ha, Maurer, Miriam Cindy, Nguyen, Phuong Hoa, Nguyen, Thi Thuy Uyen, Park, Youngjun, Sonntag, Daniel, Nguyen, Duy Minh Ho, Hauschild, Anne-Christin

Abstract

Pathology vision-language foundation models vary in performance across diseases and tasks, with no single model consistently performing best. The high cost of expert pathology annotation can also limit the labeled data available for task-specific adaptation. Combining complementary pretrained representations is a potential approach to these limitations, yet learning an effective fusion from few labeled examples remains challenging. We introduce Few-shot Fusion Foundation Models of Computational Pathology (FFM-CP), which is a framework that combines multiple pathology vision-language models in the few-shot learning setting. The framework first aligns heterogeneous representations using a closed-form Orthogonal Procrustes transformation estimated from corresponding support images. This alignment preserves within-model feature geometry without training an additional alignment network. Within the aligned space, a unified graph enables information exchange across backbones by jointly refining support-image features and visual and textual class prototypes. These refined representations support complementary text-prototype and case-retrieval branches that capture semantic class knowledge and within-class visual variation, respectively. Each branch learns to combine predictions from all ordered backbone pairs, allowing queries encoded by one model to draw on evidence represented by another. We evaluate three backbone combinations on six histopathology datasets at 4, 8, and 16 shots per class. FFM-CP achieves higher mean macro-F1 than the strongest individually adapted member of each fused set in 50 of 54 comparisons. These findings suggest that combining complementary pretrained representations can improve histopathological classification when annotations are limited.

Chinese Translation

病理视觉-语言基础模型在不同疾病和任务中的表现各异，没有单一模型能够始终表现最佳。专家病理注释的高成本也限制了可用于任务特定适应的标记数据。结合互补的预训练表示是一种潜在的解决方案，但从少量标记示例中学习有效的融合仍然具有挑战性。我们提出了计算病理学的少样本融合基础模型（FFM-CP），这是一个在少样本学习环境中结合多个病理视觉-语言模型的框架。该框架首先使用从相应支持图像估计的封闭形式正交普克雷斯特斯变换对异构表示进行对齐。这种对齐在不训练额外对齐网络的情况下保留了模型内部特征几何。在对齐空间内，一个统一的图使得通过共同优化支持图像特征和视觉及文本类原型来实现骨干之间的信息交换。这些优化后的表示支持互补的文本原型和案例检索分支，分别捕捉语义类知识和类内视觉变化。每个分支学习结合所有有序骨干对的预测，使得由一个模型编码的查询能够利用另一个模型所表示的证据。我们在六个组织病理学数据集上评估了三种骨干组合，每类分别进行4、8和16次样本测试。FFM-CP在54次比较中，有50次达到了比每个融合集的最强单独适应成员更高的平均宏F1值。这些发现表明，当注释有限时，结合互补的预训练表示可以改善组织病理学分类。

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cs.CV / 54 / 2609.27728

NeuralSRNF: Neural Square Root Normal Fields for the Statistical Shape Analysis and Generation of Nonrigid 3D and 4D Objects

NeuralSRNF：用于非刚性三维和四维物体统计形状分析与生成的神经平方根法向场

Nizamani, Awais, Laga, Hamid, Wang, Guanjin, Boussaid, Farid, Bennamoun, Mohammed, Srivastava, Anuj

Abstract

We introduce NeuralSRNF, a novel framework for the statistical shape analysis and generation of genus-zero 3D and 4D objects that undergo nonrigid deformations. Traditional methods rely on complex and computationally expensive nonlinear elastic metrics that measure bending and stretching. Recent advances in elastic shape analysis achieve computational efficiency by mapping input 3D shapes to the space of Square Root Normal Fields (SRNFs) where the L2 metric approximates the partial elastic metric, significantly facilitating the process of computing geodesics and summary statistics. SRNFs, however, are not invertible, and the numerical algorithms used to map SRNFs back to the original space of surfaces remain computationally very expensive and often lead to approximate results. This paper addresses this fundamental SRNF inversion problem using a novel neural representation, termed NeuralSRNF. Unlike the commonly used numerical SRNF, NeuralSRNF is (1) continuous, and thus resolution-agnostic, enabling full functional shape analysis, (2) more accurate, and (3) computationally more efficient as it can compute inverse SRNF maps along a geodesic path in less than 3 s compared to over 10 min for the numerical SRNF. We demonstrate, using various datasets, the utility and efficiency of the proposed NeuralSRNF in multiple elastic 3D and 4D shape analysis tasks such as geodesic computation, deformation transfer, statistical summaries computation, and 3D shape generation. We show that it outperforms competing methods on most evaluated datasets and metrics by a wide margin in both accuracy and computational efficiency. The source code and additional results are available at https://awaisnizamani16.github.io/awais/NeuralSRNF/.

Chinese Translation

我们提出了NeuralSRNF，这是一种用于统计形状分析和生成具有零基数的三维和四维物体的新框架，这些物体经历非刚性变形。传统方法依赖于复杂且计算成本高昂的非线性弹性度量，这些度量用于测量弯曲和拉伸。最近在弹性形状分析方面的进展通过将输入的三维形状映射到平方根法向场（Square Root Normal Fields, SRNFs）的空间中，实现了计算效率，其中L2度量近似部分弹性度量，显著简化了计算测地线和统计摘要的过程。然而，SRNF并不可逆，映射SRNF回到原始表面空间的数值算法仍然计算成本非常高，并且通常导致近似结果。本文通过一种新颖的神经表示方法，称为NeuralSRNF，解决了这一基本的SRNF反演问题。与常用的数值SRNF不同，NeuralSRNF具有（1）连续性，因此不依赖于分辨率，能够进行全面的功能形状分析；（2）更高的准确性；（3）更高的计算效率，因为它可以在不到3秒的时间内沿测地线计算逆SRNF映射，而数值SRNF则需要超过10分钟。我们使用各种数据集展示了所提出的NeuralSRNF在多个弹性三维和四维形状分析任务中的实用性和效率，如测地线计算、变形转移、统计摘要计算和三维形状生成。我们表明，在大多数评估的数据集和指标上，它在准确性和计算效率方面都显著优于竞争方法。源代码和更多结果可在https://awaisnizamani16.github.io/awais/NeuralSRNF/获取。

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cs.CV / 55 / 2609.27753

AWM-VLA: AlignedWorld Modeling for Efficient and Explainable Vision-Language-Action Policies

AWM-VLA：高效且可解释的视觉-语言-动作策略的对齐世界建模

Lanji, An, Liu, Dawei, Li, Jin, Xu, Haoran, Chen, Mei, Tian, Yu

Abstract

Vision-language-action (VLA) models have become a powerful paradigm for generalist robotic manipulation, yet they are often reactive: the policy maps the current observation directly to an action chunk without reasoning about the long-term consequences of its decisions. Prior attempts to endow policies with world models either reconstruct future frames in pixel space---expensive and dominated by task-irrelevant detail---or decouple the world model from the policy, weakening control. We present AWM-VLA, a unified framework that embeds aligned world modeling directly inside a diffusion-transformer policy. Following the Future Latent REpresentation Alignment (FLARE) principle, we add learnable future tokens whose intermediate activations are aligned with vision-language embeddings of future observations, enabling the policy to anticipate long-term consequences while generating actions. We extend this paradigm in two ways. First, we introduce an object-centric decoupled alignment objective that predicts future object-level semantics alongside the global future embedding, improving both interpretability and multi-instruction generalization. Second, we balance the global and object-centric alignment terms against the action flow-matching loss through a principled weighting, yielding a controllable accuracy--interpretability trade-off. On RoboCasa and humanoid tabletop manipulation benchmarks, AWM-VLA outperforms prior VLA and world-model baselines by up to 21% in success rate, improves generalization to novel objects and instructions, and produces object-centric rationales that are preferred by human raters in 83 of cases. Our approach adds only a few learnable tokens to the policy and is compatible with any diffusion or flow-matching policy, making aligned world modeling an inexpensive, broadly applicable component of generalist manipulation.

Chinese Translation

视觉-语言-动作（VLA）模型已成为通用机器人操控的强大范式，但它们通常是反应式的：策略将当前观察直接映射到一个动作块，而不考虑其决策的长期后果。之前尝试为策略赋予世界模型的工作，要么在像素空间中重建未来帧——这既昂贵又受到与任务无关细节的主导，要么将世界模型与策略解耦，削弱了控制能力。我们提出了AWM-VLA，一个统一框架，将对齐世界建模直接嵌入到扩散-变换器策略中。遵循未来潜在表示对齐（FLARE）原则，我们添加了可学习的未来标记，其中间激活与未来观察的视觉-语言嵌入对齐，使得策略在生成动作时能够预见长期后果。我们以两种方式扩展这一范式。首先，我们引入了一个以对象为中心的解耦对齐目标，该目标在全局未来嵌入的同时预测未来对象级语义，从而提高了解释性和多指令泛化能力。其次，我们通过原则性的加权平衡全局和对象中心的对齐项与动作流匹配损失，从而实现可控的准确性与可解释性之间的权衡。在RoboCasa和类人桌面操控基准测试中，AWM-VLA的成功率比之前的VLA和世界模型基线提高了多达21%，改善了对新对象和指令的泛化能力，并在83个案例中产生了人类评审者更偏好的以对象为中心的推理。我们的方法仅向策略添加了少量可学习的标记，并且与任何扩散或流匹配策略兼容，使得对齐世界建模成为通用操控中一种廉价且广泛适用的组件。

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cs.CV / 56 / 2609.27778

Visibility-Guided Structured Measure Flow for Class-Conditioned 3D Gaussian Generation

基于可见性引导的结构化测度流用于类别条件的3D高斯生成

Wang, Yizhao

Abstract

3D Gaussian Splatting (3DGS) has made real-time, high-fidelity 3D rendering practical, yet turning this explicit representation into a native generative space remains an open challenge. Directly generating 3DGS objects is difficult because Gaussian primitives are unordered, variable-sized, locally dense, and highly sensitive to rendering behavior. We present VISTA-GS, a visibility-guided structured measure flow framework for class-conditioned 3D Gaussian generation. Instead of treating a 3DGS object as a flat primitive sequence or a generic latent token grid, we formulate it as a structured Gaussian measure weighted by opacity, anisotropic covariance, and multi-view visibility. Based on this formulation, we introduce a visibility-aware measure VAE that learns permutation-invariant, variable-size-compatible, and rendering-aware latent representations of 3DGS objects. We further develop a renderer-consistent measure flow that transports class-conditioned priors toward the learned 3DGS measure distribution while aligning the decoded objects with their multi-view rendering distributions. To preserve object layout and local details, VISTA-GS incorporates structure-preserving patch transport that couples global class semantics, local Gaussian measure patches, and spatial anchors during flow prediction. On VISTA-Obj30, VISTA-GS improves over the strongest baseline by roughly 60--72\% across geometry, appearance, view-consistency error, and generation speed. This design enables efficient generation of coherent, detailed, and view-consistent 3D Gaussian objects without relying on per-instance optimization, multi-view image synthesis, or reconstruction-based lifting pipelines. Project code and model checkpoints will be released.

Chinese Translation

3D高斯溅射（3D Gaussian Splatting, 3DGS）使实时、高保真度的3D渲染成为可能，但将这种显式表示转化为原生生成空间仍然是一个开放的挑战。直接生成3DGS对象是困难的，因为高斯原语是无序的、可变大小的、局部密集的，并且对渲染行为高度敏感。我们提出了VISTA-GS，一个基于可见性引导的结构化测度流框架，用于类别条件的3D高斯生成。我们将3DGS对象视为一个由不透明度、各向异性协方差和多视角可见性加权的结构化高斯测度，而不是将其视为平坦的原语序列或通用的潜在标记网格。基于这一表述，我们引入了一种可见性感知测度变分自编码器（visibility-aware measure VAE），它学习3DGS对象的置换不变、可变大小兼容和渲染感知的潜在表示。我们进一步开发了一种与渲染器一致的测度流，将类别条件的先验向学习到的3DGS测度分布传输，同时使解码的对象与其多视角渲染分布对齐。为了保持对象布局和局部细节，VISTA-GS结合了结构保持的补丁传输，在流预测过程中耦合全局类别语义、局部高斯测度补丁和空间锚点。在VISTA-Obj30上，VISTA-GS在几何、外观、视角一致性误差和生成速度方面相较于最强基线提高了约60%至72%。这一设计使得在不依赖于每个实例优化、多视角图像合成或基于重建的提升管道的情况下，高效生成连贯、细致且视角一致的3D高斯对象成为可能。项目代码和模型检查点将会发布。

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cs.CV / 57 / 2609.27779

Fusion-Aware Direct 3D Gaussian Generation with Structured Patch Latent Flows

融合感知的直接3D高斯生成与结构化补丁潜在流

Wang, Yizhao, Wang, Jingbo, Zhang, Guantao

Abstract

Class-guided 3D object generation is important for intelligent content creation, virtual environments, and digital asset design. Although 3D Gaussian Splatting (3DGS) offers an explicit and render-efficient representation, directly generating 3D Gaussian objects is difficult because Gaussian primitives are unordered, variable-sized, locally dense, and highly sensitive to rendering. Existing 3DGS generation methods usually depend on multi-view synthesis, reconstruction, or lifted 2D priors, fusing information mainly from observed views rather than modeling the intrinsic structural distribution of 3D Gaussian objects. This paper proposes a fusion-aware hierarchical Gaussian patch representation for direct class-guided 3DGS generation with rectified flow. Irregular Gaussian sets are decomposed into canonical local patches and encoded as structured tokens. The resulting hierarchical latent space fuses global class semantics, patch-level geometry and appearance, spatial correspondence, and rendering-sensitive cues. On this basis, we design a structure-aware rectified flow model with patch-position conditioning, global-local coupled velocity prediction, and density-aware velocity weighting, enabling direct latent generation of class-conditioned 3DGS objects within seconds. A render-feedback fusion strategy further aligns latent flow learning with decoded multi-view rendering quality. Experiments show that the proposed method generates 3D Gaussian objects with more coherent geometry, sharper local details, and better multi-view consistency than baseline latent generative models. Ablation studies confirm the contributions of hierarchical information fusion, global-local coupling, density-aware supervision, and render-feedback learning while preserving practical sampling efficiency overall.

Chinese Translation

基于类别的3D物体生成对于智能内容创作、虚拟环境和数字资产设计至关重要。尽管3D高斯点云（3D Gaussian Splatting，3DGS）提供了一种明确且渲染高效的表示，但直接生成3D高斯物体仍然困难，因为高斯原语是无序的、可变大小的、局部密集的，并且对渲染高度敏感。现有的3DGS生成方法通常依赖于多视图合成、重建或提升的二维先验，主要融合来自观察视图的信息，而不是建模3D高斯物体的内在结构分布。本文提出了一种融合感知的分层高斯补丁表示，用于直接基于类别的3DGS生成，采用了校正流。将不规则的高斯集合分解为标准局部补丁，并编码为结构化标记。由此产生的分层潜在空间融合了全局类别语义、补丁级几何和外观、空间对应关系以及对渲染敏感的线索。在此基础上，我们设计了一种结构感知的校正流模型，具有补丁位置条件、全局-局部耦合速度预测和密度感知速度加权，使得能够在几秒内直接生成基于类别的3DGS物体。渲染反馈融合策略进一步将潜在流学习与解码的多视图渲染质量对齐。实验表明，所提出的方法生成的3D高斯物体在几何一致性、局部细节清晰度和多视图一致性方面优于基线潜在生成模型。消融研究确认了分层信息融合、全局-局部耦合、密度感知监督和渲染反馈学习的贡献，同时在整体上保持了实用的采样效率。

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cs.CV / 58 / 2609.27793

DualStabSleepNet: A Dual-Domain Diffusion Stabilization Network for Robust Sleep Staging

DualStabSleepNet：一种用于稳健睡眠分期的双域扩散稳定网络

Wang, Chongjian, Liu, Chen, Gao, Junjie, Zhong, Xiaofang, Han, Shiyuan, Zhang, Tong

Abstract

Existing deep learning approaches for automatic sleep staging suffer from limited robustness under heterogeneous recording conditions, where non-stationary noise, inter-subject differences and cross-dataset distribution shifts cause unstable features and poor generalization. This work proposes DualStabSleepNet (DSSNet), a dual-domain diffusion stabilization network for robust sleep staging, which improves robustness in both data and feature domains. After preprocessing multi-channel polysomnography (PSG), a continuous-scale diffusion-based stabilization module suppresses noise while preserving physiological signal structures. Stabilized signals are converted to time-frequency representations and fed into a Vision Transformer backbone. A teacher-student guided diffusion feature stabilization module further mitigates feature drift and enforces multi-level feature consistency. Evaluated on four public PSG datasets SleepEDF-20, SleepEDF-78, SHHS and ISRUC-S3, DSSNet achieves state-of-the-art accuracy of 89.2%, 88.0%, 89.7%, 86.7% with improved macro-F1 and Cohen's kappa. It obtains notable improvements on hard transitional stages (e.g., 12.5% gain for N1 on SHHS) and boosts N2/REM recognition. Under cross-dataset settings, DSSNet is robust to distribution shift and performs on par with or superior to target-dataset trained baselines, demonstrating its practical potential for real-world sleep staging across heterogeneous cohorts.

Chinese Translation

现有的自动睡眠分期深度学习方法在异构记录条件下的鲁棒性有限，其中非平稳噪声、个体间差异和跨数据集分布变化导致特征不稳定和泛化能力差。本研究提出了DualStabSleepNet（DSSNet），一种用于稳健睡眠分期的双域扩散稳定网络，旨在提高数据和特征域的鲁棒性。在对多通道多导睡眠监测（PSG）进行预处理后，基于连续尺度的扩散稳定模块在抑制噪声的同时保留生理信号结构。稳定后的信号被转换为时频表示，并输入到视觉变换器（Vision Transformer）骨干网络中。教师-学生引导的扩散特征稳定模块进一步减轻特征漂移，并强化多层次特征一致性。在四个公共PSG数据集SleepEDF-20、SleepEDF-78、SHHS和ISRUC-S3上的评估中，DSSNet实现了89.2%、88.0%、89.7%、86.7%的最先进准确率，并在宏观F1和Cohen's kappa上有所提升。它在困难的过渡阶段（例如，SHHS上N1的增益为12.5%）上取得显著改善，并增强了N2/REM的识别能力。在跨数据集设置下，DSSNet对分布变化具有鲁棒性，其性能与目标数据集训练的基线相当或更优，展示了其在异构人群中实际应用于睡眠分期的潜力。

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cs.CV / 59 / 2609.27794

DMM-Align: Closed-Loop Optimization for 2D-3D Registration with Dual-Role Diffusion

DMM-Align：基于双重角色扩散的2D-3D配准闭环优化

Wang, Chongjian, Gao, Junjie

Abstract

2D-3D registration remains brittle in challenging scenarios such as low overlap, occlusion, repetitive structures, and severe cross-modal ambiguity. A key reason is that existing methods improve representation learning, correspondence estimation, or pose computation in isolation, while the dominant failure mode is inherently cross-level, where errors propagate between features, correspondences, and pose. To address this limitation, we propose DMM-Align: Diffusion-based Matching Matrix Alignment, a closed-loop framework that couples correspondence refinement, pose estimation, and representation learning through a shared differentiable geometric state. Our method leverages diffusion in two coordinated roles: a geometry-aware diffusion process refines the soft matching matrix for robust correspondence estimation, while a geometry-conditioned diffusion teacher injects pose-induced supervision back into feature learning. These processes are connected via a differentiable geometric hinge that converts correspondences into a global pose and exposes geometric inconsistency to upstream modules. Extensive experiments on 7-Scenes and RGB-D Scenes V2 demonstrate that DMM-Align consistently outperforms strong baselines, especially under low-overlap and heavy-occlusion conditions, highlighting the effectiveness of closed-loop geometric feedback for robust 2D-3D registration.

Chinese Translation

2D-3D配准在低重叠、遮挡、重复结构和严重跨模态歧义等挑战性场景中仍然脆弱。一个关键原因是现有方法在改善表示学习、对应估计或姿态计算时往往是孤立进行的，而主要的失败模式本质上是跨层次的，错误在特征、对应和姿态之间传播。为了解决这一限制，我们提出了DMM-Align：基于扩散的匹配矩阵对齐，这是一个闭环框架，通过共享的可微几何状态将对应精炼、姿态估计和表示学习结合在一起。我们的方法利用扩散的两种协调角色：几何感知的扩散过程精炼软匹配矩阵，以实现稳健的对应估计，而几何条件的扩散教师则将姿态引起的监督反馈注入特征学习。这些过程通过一个可微的几何铰链连接，将对应转换为全局姿态，并将几何不一致性暴露给上游模块。在7-Scenes和RGB-D Scenes V2上的大量实验表明，DMM-Align在低重叠和重遮挡条件下始终优于强基线，突显了闭环几何反馈在稳健的2D-3D配准中的有效性。

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cs.CV / 60 / 2609.27815

Evaluating ADC-only deep learning pipelines for breast cancer detection and segmentation using standalone diffusion-weighted MRI

使用独立扩散加权MRI评估仅基于ADC的深度学习管道在乳腺癌检测和分割中的应用

Marcos, Pablo García, Gonzĺez, Paula Puerta, Lorenzo, Guillermo, Gómez, Héctor, del Camino, Covadonga, Rio-Alvarez, Angel, González, Víctor M.

Abstract

Dynamic contrast-enhanced (DCE) imaging is the gold standard technique for the detection and characterization of breast cancer using magnetic resonance imaging (MRI). However, DCE-MRI requires long acquisition times and the administration of contrast into the bloodstream, which can cause allergic reactions. Alternatively, diffusion-weighted MRI (DW-MRI) is a standard complementary technique for breast MRI that does not require contrast, has shorter acquisition times, and enables calculation of apparent diffusion coefficient (ADC) maps that correlate with tumor cellularity. Yet, despite these technical advantages, deep learning research has focused on DCE-based models and has barely explored the tumor detection performance of DW-MRI and ADC maps either in combination with DCE-MRI or as standalone alternatives. Here, we evaluate the application of different state-of-the-art deep learning techniques for detection and segmentation of breast cancer using ADC-only images. This is, to our knowledge, the first comprehensive evaluation of ADC-only breast cancer pipelines for classification, detection, and segmentation tasks.

Chinese Translation

动态对比增强（DCE）成像是利用磁共振成像（MRI）检测和表征乳腺癌的金标准技术。然而，DCE-MRI需要较长的采集时间，并且需要将对比剂注入血液，这可能导致过敏反应。作为替代，扩散加权MRI（DW-MRI）是一种标准的乳腺MRI补充技术，不需要对比剂，具有较短的采集时间，并能够计算与肿瘤细胞密度相关的表观扩散系数（ADC）图。然而，尽管具有这些技术优势，深度学习研究主要集中在基于DCE的模型上，几乎没有探讨DW-MRI和ADC图在与DCE-MRI结合或作为独立替代方案时的肿瘤检测性能。在此，我们评估了不同最先进的深度学习技术在仅使用ADC图像进行乳腺癌检测和分割中的应用。据我们所知，这是首次对仅基于ADC的乳腺癌管道在分类、检测和分割任务中的全面评估。

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cs.CV / 61 / 2609.27821

Groundbench: Multi-Resolution Polygon Grounding Exposes the Geometry Gap in Vision-Language Models

Groundbench：多分辨率多边形基础定位揭示了视觉-语言模型中的几何差距

Bian, Zhonghan, Wang, Zhenran, Li, Jinsong, Qi, Zhangyang

Abstract

Bounding-box scores on RefCOCO-family grounding leave little room to distinguish frontier vision-language systems, yet boxes discard object shape. We introduce GroundingBench, a matched benchmark that re-targets the same 1,500 image-expression-referent triples to exact-N polygons at five vertex budgets. A fixed-denominator harness separately audits filled-region intersection over union (IoU) and legal-polygon completion. The strongest tested configuration reaches 88.2 box IoU and 97.1 accuracy at IoU >= .5 ([email protected]), versus 57.7 and 69.2 for direct polygons; because these headline scores use different references, we also compare direct polygons with predicted boxes rasterised against the same contour target, obtaining 57.7 versus 57.3 when pooled. Performance is non-monotone in N and collapses at the densest budget, where legality failures compound residual geometric error. Qwen's thinking-setting contrast is the largest tested input-preserving configuration difference; under frozen templates, false spatial cues are more damaging than false colour cues, and target preference can remain high while contour tracing is poor. Alternate masks and a continuous-area scorer preserve the principal ordering. GroundingBench therefore measures an operational output-geometry gap spanning localisation, boundary construction, serialisation, and topology, rather than latent boundary perception alone.

Chinese Translation

RefCOCO系列基础定位的边界框得分几乎无法区分前沿视觉-语言系统，但边界框忽略了物体形状。我们引入了GroundingBench，这是一个匹配的基准，重新定位相同的1500个图像-表达-参照三元组到五个顶点预算的精确N多边形。一个固定分母的工具分别审计填充区域的交并比（IoU）和合法多边形的完成度。测试中表现最强的配置达到了88.2的边界框IoU和97.1的IoU >= .5时的准确率（[email protected]），而直接多边形的得分分别为57.7和69.2；由于这些主要得分使用了不同的参考，我们还将直接多边形与针对相同轮廓目标的预测边界框进行比较，获得了57.7与57.3的结果。性能在N上并非单调，并在最密集的预算下崩溃，此时合法性失败加剧了残余几何误差。Qwen的思维设置对比是测试中输入保持配置差异最大的；在冻结模板下，虚假的空间线索比虚假的颜色线索更具破坏性，且目标偏好在轮廓追踪较差时仍然可以保持较高。替代掩码和连续区域评分器保持了主要排序。因此，GroundingBench测量了一个跨越定位、边界构建、序列化和拓扑的操作输出-几何差距，而不仅仅是潜在的边界感知。

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cs.CV / 62 / 2609.27848

Geometry-anchored PET-aware multimodal pseudo-CT synthesis for whole-body attenuation correction: the BIC-MAC Challenge

基于几何锚定的PET感知多模态伪CT合成用于全身衰减校正：BIC-MAC挑战

Nguyen, Xuan Loc, Cao, Hoang-Loc, Nguyen, Truong Thanh Hung, Ho, Phuc, Nguyen, Phuc Truong Loc, To, Nguyen Truong Toan, Cao, Hung

Abstract

The BIC-MAC challenge targets whole-body pseudo-CT synthesis from NAC-PET, Dixon MRI, and a 2D topogram for CT-less PET attenuation correction. We propose GeoPACT, a geometry-anchored multimodal framework that uses NAC-PET as the spatial reference and incorporates topogram and MRI features through gated residual fusion. Absolute coordinates and whole-body conditioning support anatomically consistent patch-based prediction. Training combines attenuation-map supervision with a differentiable PET-response surrogate to reduce errors relevant to downstream PET reconstruction. Full-resolution pseudo-CT volumes are generated using sliding-window inference without requiring CT or PET labels at test time.

Chinese Translation

BIC-MAC挑战旨在从NAC-PET、Dixon MRI和2D topogram合成全身伪CT，以实现无CT的PET衰减校正。我们提出了GeoPACT，一个基于几何锚定的多模态框架，使用NAC-PET作为空间参考，并通过门控残差融合结合topogram和MRI特征。绝对坐标和全身条件支持解剖一致的基于补丁的预测。训练结合了衰减图监督和可微分的PET响应替代，以减少与下游PET重建相关的误差。在测试时，使用滑动窗口推理生成全分辨率的伪CT体积，而无需CT或PET标签。

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cs.CV / 63 / 2609.27850

GaussianDS: Depth-supervised Semantic Gaussian Splatting for Scene Understanding

GaussianDS：用于场景理解的深度监督语义高斯溅射

Zhang, Yufei, Zhan, Chenlu, Wang, Hongwei

Abstract

3D Gaussian Splatting provides an efficient representation for 3D reconstruction, and recent extensions attach semantic attributes to Gaussians for open-vocabulary scene understanding. However, lifting view-dependent 2D foundation-model outputs into 3D space introduces cross-view inconsistencies and weak geometric grounding, leading to severe semantic drift and boundary leakage. We propose GaussianDS, a depth-supervised semantic 3DGS framework that treats semantic lifting as a supervision-alignment problem and jointly optimizes RGB appearance, rendered depth, and compact semantics from scratch. Specifically, GaussianDS organizes unordered multi-view images into a pose-aware pseudo-video trajectory to propagate view-consistent masks via SAM2. During joint optimization, scale-shift-aligned monocular depth supervision and depth total-variation regularization stabilize Gaussian geometry, while a depth-edge-aware refinement loss explicitly anchors semantic transitions onto physical geometric discontinuities. Extensive evaluations show that our end-to-end framework not only retains high-fidelity 3D reconstruction and real-time rendering, but also establishes superior semantic understanding. GaussianDS sets new state-of-the-art performance on LERF (60.5% mIoU) and 3D-OVS (97.79% mIoU, 90.28% mBIoU) by mitigating semantic leakage, while seamlessly facilitating downstream 3D object removal.

Chinese Translation

3D 高斯溅射为 3D 重建提供了一种高效的表示方式，最近的扩展将语义属性附加到高斯分布上，以实现开放词汇的场景理解。然而，将视图依赖的 2D 基础模型输出提升到 3D 空间会引入视图间的不一致性和弱几何基础，导致严重的语义漂移和边界泄漏。我们提出了 GaussianDS，这是一种深度监督语义 3DGS 框架，将语义提升视为一个监督对齐问题，并从头开始联合优化 RGB 外观、渲染深度和紧凑语义。具体而言，GaussianDS 将无序的多视图图像组织成一个姿态感知的伪视频轨迹，通过 SAM2 传播视图一致的掩码。在联合优化过程中，尺度偏移对齐的单目深度监督和深度全变差正则化稳定了高斯几何，而深度边缘感知的精细化损失则明确地将语义过渡锚定到物理几何不连续性上。大量评估表明，我们的端到端框架不仅保持了高保真度的 3D 重建和实时渲染，还建立了卓越的语义理解。GaussianDS 在 LERF（60.5% mIoU）和 3D-OVS（97.79% mIoU，90.28% mBIoU）上设定了新的最先进性能，通过减轻语义泄漏，同时无缝促进下游 3D 物体移除。

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cs.CV / 64 / 2609.27868

TopoGS: Topology-Aware Anchor Feature Aggregation for Large-Scale 3D Gaussian Splatting

TopoGS：用于大规模3D高斯点云的拓扑感知锚点特征聚合

Zhang, Wei, Gong, Shiqiang, Yu, Shengkai, Wang, Zeyu, Mallet, Clement, Xiong, Zhitong, Wang, Qi

Abstract

Octree-based 3D Gaussian Splatting organizes anchors into multi-level hierarchies for level-of-detail rendering, but features at different levels are typically optimized independently, leaving the octree topology underused during feature learning. We observe that uniform cross-level aggregation produces asymmetric effects: fine-level anchors benefit from coarse context, whereas coarse-level anchors require selective information from their descendants. We therefore propose TopoGS, a topology-aware anchor feature aggregation framework with two lightweight components. Hierarchical Anchor Coupling establishes bidirectional cross-level gradient pathways by fusing per-level context triplets with a residual MLP. Structure-Aware Containment Aggregation uses octree containment and hash-based matching to distinguish anchors with valid parent-child relations from isolated anchors, then applies soft weighting to accommodate varying topological sparsity. Experiments on ten scenes from Mill19, UrbanScene3D, Tanks & Temples, MatrixCity, and WHU show consistent improvements over state-of-the-art methods. TopoGS achieves average PSNR gains of 2.13, 1.78, and 0.29 dB over the strongest reported baseline on aerial, ground-level, and synthetic-cartographic scenes, respectively, while rendering faster and using less memory. Code is available at https://github.com/WZ-CS/TopoGS.

Chinese Translation

基于八叉树的3D高斯点云将锚点组织成多级层次结构以实现细节级别渲染，但不同层次的特征通常是独立优化的，这使得在特征学习过程中八叉树拓扑未得到充分利用。我们观察到均匀的跨层聚合产生了不对称的效果：细层锚点受益于粗层上下文，而粗层锚点则需要来自其后代的选择性信息。因此，我们提出了TopoGS，一个具有两个轻量级组件的拓扑感知锚点特征聚合框架。层次锚点耦合通过与残差多层感知机（MLP）融合每层上下文三元组，建立了双向跨层梯度通道。结构感知包含聚合利用八叉树包含关系和基于哈希的匹配来区分具有有效父子关系的锚点与孤立锚点，然后应用软加权以适应不同的拓扑稀疏性。在Mill19、UrbanScene3D、Tanks & Temples、MatrixCity和WHU的十个场景上的实验表明，TopoGS在性能上始终优于最先进的方法。TopoGS在空中、地面和合成制图场景上分别实现了比最强报告基线平均提高2.13、1.78和0.29 dB的PSNR，同时渲染速度更快且内存使用更少。代码可在https://github.com/WZ-CS/TopoGS获取。

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cs.CV / 65 / 2609.27890

RelCheck: Dual-Evidence Spatial Grounding for VLM Hallucination Correction

RelCheck：用于视觉语言模型幻觉校正的双证据空间定位

Patil, Siddhi, Saxena, Navrati, Andreopoulos, William B.

Abstract

Multimodal large language models (MLLMs) fre- quently generate text that is inconsistent with the input image. While object- and attribute-level hallucinations have received considerable attention, relational hallucinations (incorrect de- scriptions of spatial or interactive relationships between objects) remain largely unaddressed by existing post-hoc correction methods. We present RelCheck, a training-free post-hoc correction pipeline that augments object-level visual grounding with dual relational evidence: learned scene-graph triples from RelTR and deterministic spatial predicates from bounding-box geometry. These combine with a Woodpecker-style object claim layer to form a three-layer visual knowledge base, which a language model corrector uses to rewrite hallucinated text. Evaluated on LLaVA v1 13B, RelCheck achieves a total MME hallucination score of 630.0 versus 585.0 for a Woodpecker-style baseline, with the largest gain on the position subtask (+31.7 points, accuracy+ improving from 0.367 to 0.600). A four-configuration ablation confirms that both relational layers contribute independently (McNemar p = 0.025). These results show that structured relational evidence meaningfully improves post-hoc hallucination correction on the spatial reasoning subtasks where current MLLMs are most deficient.

Chinese Translation

多模态大语言模型（MLLMs）经常生成与输入图像不一致的文本。尽管对象和属性层面的幻觉受到了相当大的关注，但关系幻觉（对对象之间空间或交互关系的错误描述）在现有的事后校正方法中仍然基本未被解决。我们提出了RelCheck，一个无训练的事后校正管道，它通过双重关系证据增强对象级视觉定位：来自RelTR的学习场景图三元组和来自边界框几何的确定性空间谓词。这些与Woodpecker风格的对象声明层结合，形成一个三层视觉知识库，语言模型校正器利用该知识库重写幻觉文本。在LLaVA v1 13B上评估，RelCheck的总MME幻觉得分为630.0，而Woodpecker风格的基线为585.0，位置子任务的增益最大（+31.7分，准确率从0.367提高到0.600）。四种配置的消融实验确认两个关系层独立贡献（McNemar p = 0.025）。这些结果表明，结构化的关系证据在当前MLLMs最薄弱的空间推理子任务上显著改善了事后幻觉校正。

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cs.CV / 66 / 2609.27901

All modalities are equal, but video is more equal: Closing the Cross-Attention Gap in Joint Video Generation

所有模态都是平等的，但视频更平等：缩小联合视频生成中的跨注意力差距

Rahamim, Ohad, Samuel, Dvir, Schwartz, Idan, Chechik, Gal

Abstract

Video is a rich representation of a physical event, capturing appearance, geometry, motion, and temporal evolution. Other modalities, such as 3D body motion or audio, encode narrower aspects of the same event. We find that joint multimodal diffusion transformers exhibit a corresponding asymmetry in cross-modal correspondence: companion modalities develop strong correspondences to video, but the reciprocal correspondences through which they constrain video remain substantially weaker. We express both directions as comparable correspondence distributions over video tokens and define their disagreement as the reciprocal correspondence gap. We introduce RecCAR, standing for Reciprocal Cross-modal Attention Regularization, a KL regularizer that uses the well-established video-to-modality correspondence as a fixed reference and aligns the weaker modality-to-video correspondence toward it. Across joint video-motion and video-audio generation, RecCAR improves the Human Anatomy score from 0.69 to 0.75 and reduces audio-video desynchronization from 0.804 to 0.752, while improving overall generation

Chinese Translation

视频是对物理事件的丰富表征，捕捉了外观、几何形状、运动和时间演变等信息。其他模态，如三维身体运动或音频，仅编码同一事件的较窄方面。我们发现，联合多模态扩散变换器在跨模态对应关系上表现出相应的不对称性：伴随模态与视频之间建立了强对应关系，但它们通过反向对应关系对视频的约束仍然显著较弱。我们将这两个方向表示为视频标记上的可比对应分布，并将它们的差异定义为反向对应差距。我们引入了RecCAR（反向跨模态注意力正则化），这是一种KL正则化器，利用已建立的视频与模态对应关系作为固定参考，并将较弱的模态与视频的对应关系对齐。通过联合视频-运动和视频-音频生成，RecCAR将人体解剖学评分从0.69提高到0.75，并将音频-视频不同步从0.804降低到0.752，同时改善了整体生成效果。

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cs.CV / 67 / 2609.27904

Spatiality-Frequency Domain Video Forgery Detection System Based on ResNet-LSTM-CBAM and DCT Hybrid Network

基于ResNet-LSTM-CBAM和DCT混合网络的时空频域视频伪造检测系统

Liao, Zihao, Hong, Sheng, Chen, Yu

Abstract

As information technology advances, digital content has become widely adopted across diverse fields such as news broadcasting, entertainment, commerce, and forensic investiga?tion. However, the availability of sophisticated multimedia editing tools has significantly increased the risk of video and image forgery, raising serious concerns about content authenticity at both societal and individual levels.To address the growing need for robust and accurate detection methods, this study proposes a novel video forgery detection model that integrates both spatial and frequency-domain features. The model is built on a ResNet-LSTM framework enhanced by a Convolutional Block Attention Module (CBAM) for spatial feature extraction, and further incorporates Discrete Cosine Transform (DCT) to capture frequency domain information. Comprehensive experiments were conducted on several mainstream benchmark datasets, encompassing a wide range of forgery scenarios. The results demonstrate that the proposed model achieves superior performance in distinguishing between authentic and manipulated videos. Additional ablation and comparative studies confirm the contribution of each component in the architecture, offering deeper insight into the models capacity. Overall, the findings support the proposed approach as a promising solution for enhancing the reliability of video authenticity analysis under complex conditions.

Chinese Translation

随着信息技术的进步，数字内容在新闻广播、娱乐、商业和法医调查等多个领域得到了广泛应用。然而，复杂的多媒体编辑工具的可用性显著增加了视频和图像伪造的风险，引发了社会和个人层面对内容真实性的严重关注。为应对对强大且准确的检测方法日益增长的需求，本研究提出了一种新颖的视频伪造检测模型，该模型整合了时空和频域特征。该模型基于增强了卷积块注意力模块（CBAM）的ResNet-LSTM框架，用于空间特征提取，并进一步结合离散余弦变换（DCT）以捕获频域信息。在多个主流基准数据集上进行了全面实验，涵盖了广泛的伪造场景。结果表明，所提模型在区分真实视频和伪造视频方面表现出色。额外的消融和比较研究确认了架构中每个组件的贡献，为模型能力提供了更深入的见解。总体而言，研究结果支持该方法作为在复杂条件下增强视频真实性分析可靠性的有前景的解决方案。

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cs.CV / 68 / 2609.27915

UVU: Improving Multimodal Understanding via Vision-Language Unified Autoregressive Paradigm

UVU：通过视觉-语言统一自回归范式提升多模态理解

Kan, Zhehan, Jiang, Xinghua, Zhu, Yubo, Liu, Yanlin, Yang, Xiaochen, Wei, Zhixiang, Liu, Shifeng, Liao, Qingmin, Yang, Wenming, Li, Xin, Liu, Yinsong, Jiang, Deqiang, Sun, Xing

Abstract

Despite remarkable advancements in multimodal large language models (MLLMs), their fine-grained visual understanding is constrained by a primary reliance on sparse textual supervision. Existing efforts to introduce visual supervision typically do so during post-training, when visual representations have already been largely fixed, causing such signals to act mainly as auxiliary constraints rather than as a primary force for shaping perceptual features. In this paper, we aim to fundamentally reshape the model's perceptual backbone by incorporating vision supervision directly into the pre-training stage. We observe that pixel-level image patches and textual tokens naturally coexist in a shared, raw high-dimensional space characterized by an inherent input symmetry. Leveraging this insight, we propose UVU, a novel vision-language unified autoregressive framework that eschews vector quantization. It uniquely employs continuous visual encoding for lossless representation of visual inputs and proposes a large-scale iterative hierarchical clustering algorithm to construct a pixel-level visual codebook, thereby extending the vocabulary for unified supervision and enabling autoregressive generation of pixel-level image tokens alongside textual tokens. UVU effectively synergizes pixel-level visual perception with semantic-level visual understanding, internalizing visual reconstruction capabilities and unlocking the facilitative role of visual supervision in enhancing understanding in the pre-training stage. Extensive experiments across multiple tasks demonstrate that MLLMs are capable of achieving superior multimodal understanding performance under the supervised learning paradigm of UVU.

Chinese Translation

尽管多模态大型语言模型（MLLMs）取得了显著进展，但其细粒度的视觉理解仍受到主要依赖稀疏文本监督的限制。现有的引入视觉监督的努力通常是在后训练阶段进行，此时视觉表征已基本固定，导致这些信号主要作为辅助约束，而非塑造感知特征的主要力量。本文旨在通过将视觉监督直接纳入预训练阶段，从根本上重塑模型的感知基础。我们观察到，像素级图像块和文本标记自然共存于一个共享的原始高维空间中，该空间具有固有的输入对称性。基于这一洞察，我们提出了UVU，一种新颖的视觉-语言统一自回归框架，避免了向量量化。它独特地采用连续视觉编码，实现视觉输入的无损表示，并提出了一种大规模迭代层次聚类算法，以构建像素级视觉词典，从而扩展统一监督的词汇，并实现像素级图像标记与文本标记的自回归生成。UVU有效地将像素级视觉感知与语义级视觉理解相结合，内化视觉重建能力，释放视觉监督在提升预训练阶段理解能力中的促进作用。在多个任务上的广泛实验表明，MLLMs能够在UVU的监督学习范式下实现卓越的多模态理解性能。

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cs.CV / 69 / 2609.27948

VIVAS: Vitalizing Visual Perception in VLM Pre-training via Vision-language Unified Autoregressive Supervision

VIVAS：通过视觉-语言统一自回归监督激活视觉感知于VLM预训练

Kan, Zhehan, Zhu, Yubo, Jiang, Xinghua, Wei, Zhixiang, Liu, Shifeng, Tong, Wei, Zhong, Sheng, Liao, Qingmin, Yang, Wenming, Li, Xin, Liu, Yinsong, Jiang, Deqiang, Sun, Xing

Abstract

While Vision-Language Models (VLMs) demonstrate strong capabilities, they continue to suffer from a critical limitation: insufficient fine-grained visual perception, which fundamentally limits their multimodal understanding. We attribute this bottleneck to text-dominant optimization biases during pre-training, which encourage the model to overlook fine-grained visual details, thereby limiting the capability of multimodal understanding. We investigate that overcoming this bottleneck requires two key elements: (1) a unified token space paradigm that ensures stable training dynamics, and (2) a modality-aligned dense visual supervision signal enriched with both structural granularity and semantic information to capture critical visual representations. Based on these insights, we propose VIVAS, a framework built upon the unified token space paradigm, which introduces a dense-structural-semantic vision tokenizer, which expands the textual vocabulary into a unified vision-language vocabulary by incorporating a visual vocabulary. During pretraining, VIVAS performs vision-language unified autoregressive supervision over both visual details and linguistic content, thereby enhancing visual perception to improve multimodal understanding. Trained end-to-end on 12.4T tokens, VIVAS achieves state-of-the-art performance across 7 tasks and 39 multimodal benchmarks.

Chinese Translation

尽管视觉-语言模型（VLMs）展现出强大的能力，但它们仍然面临一个关键限制：细粒度视觉感知不足，这在根本上限制了它们的多模态理解。我们将这一瓶颈归因于预训练过程中以文本为主的优化偏差，这种偏差促使模型忽视细粒度的视觉细节，从而限制了多模态理解的能力。我们研究发现，克服这一瓶颈需要两个关键要素：（1）一个统一的标记空间范式，以确保稳定的训练动态；（2）一个与模态对齐的密集视觉监督信号，丰富了结构细粒度和语义信息，以捕捉关键的视觉表征。基于这些见解，我们提出了VIVAS，一个建立在统一标记空间范式上的框架，介绍了一种密集结构语义视觉标记器，通过结合视觉词汇，将文本词汇扩展为统一的视觉-语言词汇。在预训练期间，VIVAS对视觉细节和语言内容进行视觉-语言统一自回归监督，从而增强视觉感知以改善多模态理解。在12.4T标记上进行端到端训练，VIVAS在7个任务和39个多模态基准测试中实现了最先进的性能。

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cs.CV / 70 / 2609.27958

ScoutNeRV: Rapid Encoding of Grid-Based Video INRs via ScoutNet

ScoutNeRV：通过ScoutNet快速编码基于网格的视频隐式神经表示

Alizada, Naser, Baghban, Farhang, Pishkar, Hashem, Mousavi, Ali

Abstract

Implicit neural representations (INRs) have emerged as a promising paradigm for video compression, providing compact neural representations with flexible spatial and temporal reconstruction. Hierarchical grid-based architectures such as HiNeRV achieve strong rate--distortion performance, but require extensive per-video optimization, resulting in high encoding costs. To address this limitation, we propose ScoutNeRV, a content-adaptive initialization framework for accelerating the optimization of hierarchical video INRs. ScoutNeRV employs a lightweight, offline-trained scout network that analyzes a small number of sampled frames and selects a suitable pre-trained expert from a memory bank through hard routing. The hierarchical grid and decoder parameters of the selected expert are then transferred to initialize the target HiNeRV model before video-specific fine-tuning. On the unseen ReadySetGo sequence, ScoutNeRV achieves an initial PSNR of $34.95$~dB, compared with $13.70$~dB for standard initialization, corresponding to a $21.25$~dB improvement before fine-tuning. After only 37 epochs, ScoutNeRV reaches $36.92$~dB and remains within $0.42$--$0.80$~dB of the 300-epoch HiNeRV baseline across the evaluated rate--distortion configurations. Furthermore, the proposed initialization achieves a $9.25\times$ wall-clock speedup in the reported runtime experiment. These results demonstrate that content-aware expert initialization can substantially reduce the optimization cost of hierarchical video INRs while retaining competitive reconstruction and compression performance. The code is available at https://github.com/nasserdeveloper/ScoutNeRV.

Chinese Translation

隐式神经表示（INRs）作为一种有前景的视频压缩范式，提供了紧凑的神经表示，并具有灵活的空间和时间重建能力。层次化的基于网格的架构，如HiNeRV，能够实现强大的比特率-失真性能，但需要对每个视频进行广泛的优化，导致高编码成本。为了解决这一限制，我们提出了ScoutNeRV，一种内容自适应初始化框架，用于加速层次化视频INRs的优化。ScoutNeRV采用轻量级的离线训练侦察网络，分析少量采样帧，并通过硬路由从内存库中选择合适的预训练专家。然后，将所选专家的层次化网格和解码器参数转移到目标HiNeRV模型中进行初始化，随后进行视频特定的微调。在未见过的ReadySetGo序列上，ScoutNeRV实现了初始PSNR为34.95 dB，而标准初始化为13.70 dB，相应地在微调前实现了21.25 dB的提升。在仅经过37个训练周期后，ScoutNeRV达到了36.92 dB，并在评估的比特率-失真配置中保持在300个周期的HiNeRV基线的0.42-0.80 dB范围内。此外，所提出的初始化在报告的运行时间实验中实现了9.25倍的墙钟速度提升。这些结果表明，基于内容的专家初始化可以显著降低层次化视频INRs的优化成本，同时保持竞争力的重建和压缩性能。代码可在https://github.com/nasserdeveloper/ScoutNeRV获取。

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cs.CV / 71 / 2609.27988

Task-Induced Riemannian Metrics for Vision Transformer Feature Spaces

任务诱导的黎曼度量在视觉变换器特征空间中的应用

Bond, Andrew, Özlü, Ege Erdem, Çimen, Tuna, Melanlioglu, Ilkin Umut, Birdal, Tolga, Erdem, Erkut, Erdem, Aykut

Abstract

Methods operating on Vision Transformer (ViT) feature spaces typically rely on Euclidean distance or cosine similarity. This assumes that every direction is equally meaningful, but there is no reason to believe the true task geometry has this property. The task-sensitive geometry of the feature space is given by the pullback metric $g(F) = J(F)^\top J(F)$, where $J$ is the Jacobian of the decoder's output fed to a task-specific distance, with respect to the features. Storing the full $g$ is infeasible at modern scales, and for dense outputs such as depth maps even forming $J$ is impractical. We show that whether a low-rank approximation of this metric can be learned depends on the model-decoder pair, and we characterize this with a matrix-free diagnostic $\kappa_{cap}(r)$ computable with a low number of Jacobian-vector products. For tractable pairs, we develop the Spectral Pullback Network (SPN), which learns a low-rank version of the metric from randomized power iteration, and we distill it into a $310$K-parameter importance head that predicts token importance directly from the features. When the Jacobian spectrum is too spread out for a low-rank approximation, passing the decoder's input features through a VAE bottleneck can restore tractability. Across DPT, DINOv2, CLIP, and VGGT backbones, $\kappa_{cap}(r)$ predicts which learned-metric architectures are viable. The importance head reaches Spearman $\rho = 0.998$ on DINOv2 CLS, and our geometric token pruning reduces the additional depth error of ToMe-based token selection by $25\%$ on DPT depth at prune ratio $0.5$, without fine-tuning the ViT. Project page: https://cyberiada.github.io/TaskInducedViTs/

Chinese Translation

在视觉变换器（ViT）特征空间上操作的方法通常依赖于欧几里得距离或余弦相似度。这假设每个方向都是同等重要的，但没有理由相信真实的任务几何具有这种属性。特征空间的任务敏感几何由拉回度量 $g(F) = J(F)^ op J(F)$ 给出，其中 $J$ 是解码器输出的雅可比矩阵，输入到与特征相关的任务特定距离中。在现代规模下存储完整的 $g$ 是不可行的，对于深度图等密集输出，甚至形成 $J$ 也是不切实际的。我们展示了这一度量的低秩近似是否可以学习取决于模型-解码器对，并通过一个无矩阵的诊断 $eta_{cap}(r)$ 来表征这一点，该诊断可以通过少量的雅可比-向量乘积计算得出。对于可处理的对，我们开发了谱拉回网络（SPN），该网络从随机幂迭代中学习度量的低秩版本，并将其提炼为一个 $310$K 参数的重要性头，直接从特征中预测标记的重要性。当雅可比谱过于分散以至于无法进行低秩近似时，通过变分自编码器（VAE）瓶颈传递解码器的输入特征可以恢复可处理性。在 DPT、DINOv2、CLIP 和 VGGT 骨干网络中，$eta_{cap}(r)$ 预测哪些学习的度量架构是可行的。重要性头在 DINOv2 CLS 上达到斯皮尔曼相关系数 $ ho = 0.998$，而我们的几何标记修剪在 DPT 深度上以修剪比例 $0.5$ 将基于 ToMe 的标记选择的额外深度误差减少了 $25 ext{%}$，而无需微调 ViT。项目页面：https://cyberiada.github.io/TaskInducedViTs/

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cs.CV / 72 / 2609.28049

Prompt, Probe, Train, or Annotate? Single-camera sports video understanding in amateur settings

提示、探测、训练还是标注？业余环境下单摄像头体育视频理解

Kodathala, Sai Varun, Pollishetty, Prashanth, Cargill, Jaylen

Abstract

Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as evidence a model is robust enough for deployment. Amateur team sport is a useful, largely untested place to check that assumption: over eight million students played a school sport in the United States in 2024-25 alone, almost none of it filmed by more than a single fixed camera, with several candidate actors crowded into frame and no operator or second angle to fall back on. Using volleyball as a test case, we ask whether strong performance on general video and world-model benchmarks translates into reliable, per-player attribution once footage is this chaotic, turning footage into statistics through a chain of tasks from finding play boundaries to naming who did what. We evaluate four approaches (prompting and agentic reasoning over frontier vision-language models, classical computer vision with small trained specialists, self-supervised video world models, and manual annotation) at every stage, on 66 amateur matches with 46,648 human-labelled contacts, filmed under conditions no published benchmark uses. No single paradigm wins every stage, and static, single-frame computer vision is not competitive at any stage involving motion or identity. A prompted model segments matches well, yet a far smaller trained model beats it at spotting contacts for a fraction of the cost, and the sport's own rules recover rally outcomes the pixels cannot. Identity is where every automated approach struggles: a jersey number is a static fact temporal reasoning cannot recover if never visible, unlike sporting action, a repeated motor pattern a temporal model can exploit, which is why holistic reasoning improves event detection while identity stays unchanged. We close with where each approach earns its cost, and what transfers beyond volleyball to amateur sport.

Chinese Translation

视频理解通常在经过策划的、单一演员或专业拍摄的片段上进行基准测试，而在这些测试中获得的高分常常被视为模型足够稳健以进行部署的证据。业余团队运动是一个有用且尚未充分测试的领域，用以验证这一假设：仅在2024-25学年，美国就有超过八百万学生参与校内运动，几乎没有一场比赛是由超过一个固定摄像头拍摄的，多个候选演员被挤入画面中，且没有操作员或第二个角度可供依赖。以排球为测试案例，我们探讨在如此混乱的录像中，强大的通用视频和世界模型基准表现是否能够转化为可靠的逐个球员归属，通过一系列任务将录像转化为统计数据，从寻找比赛边界到命名谁做了什么。我们在66场业余比赛中评估了四种方法（在前沿视觉-语言模型上进行提示和自主推理、使用小型训练专家的经典计算机视觉、自监督视频世界模型和手动标注），这些比赛的接触次数达到了46,648次，拍摄条件没有任何已发布基准使用。没有单一的方法在每个阶段都胜出，静态的单帧计算机视觉在任何涉及运动或身份的阶段都没有竞争力。一个经过提示的模型能够很好地分割比赛，但一个规模小得多的训练模型在接触识别上以更低的成本超越了它，而该运动的规则则能够恢复像素无法提供的回合结果。身份是每种自动化方法都面临挑战的地方：球衣号码是一个静态事实，时间推理无法恢复，如果从未可见，与体育动作不同，后者是一个时间模型可以利用的重复运动模式，这就是为什么整体推理改善了事件检测而身份保持不变的原因。我们总结了每种方法的成本效益，以及哪些内容可以超越排球应用于业余体育。

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cs.CV / 73 / 2609.28061

AstraLOD3: Zero-shot multimodal agentic reconstruction of LOD3 building models

AstraLOD3：零-shot多模态代理重建LOD3建筑模型

Pantoja-Rosero, Bryan G.

Abstract

Automated LOD3 building modeling typically relies on purpose-built geometric or learning-based pipelines, limiting flexibility across heterogeneous buildings and input evidence conditions. This study investigates whether Astra, a general-purpose multimodal foundation model, can address these limitations through zero-shot reconstruction of LOD3 building models within an agentic framework under bounded autonomy. AstraLOD3 combines multi-view images, calibrated cameras, and a filtered sparse SfM point cloud with a natural-language reconstruction specification, while the Astra agent dynamically selects and executes computational procedures using Python and Blender. Across 35 runs, including 24 benchmark buildings, AstraLOD3 achieved a mean FRDS of 0.9647 and geometric agreement comparable to that of previous purpose-built methods. Controlled ablations further revealed the effects of reconstruction guidance, evidence modalities, model configuration, and run-to-run variability. The results demonstrate that structured LOD3 reconstruction can be formulated as a constrained agentic process rather than as a fixed pipeline. Future work will investigate adaptive refinement, user-guided correction, task-specific specialization, and damage-aware reconstruction.

Chinese Translation

自动化LOD3建筑建模通常依赖于专门设计的几何或基于学习的管道，这限制了在异构建筑和输入证据条件下的灵活性。本研究探讨了通用多模态基础模型Astra是否能够通过在有限自主权下的代理框架中对LOD3建筑模型进行零-shot重建，从而解决这些限制。AstraLOD3结合了多视角图像、标定相机和经过过滤的稀疏SfM点云，以及自然语言重建规范，而Astra代理则动态选择并执行使用Python和Blender的计算程序。在35次运行中，包括24个基准建筑，AstraLOD3达到了平均FRDS为0.9647，并且几何一致性可与之前专门构建的方法相媲美。控制性消融实验进一步揭示了重建指导、证据模态、模型配置和运行间变异性的影响。结果表明，结构化的LOD3重建可以被视为一个受限的代理过程，而不是一个固定的管道。未来的工作将探讨自适应精细化、用户引导的修正、任务特定的专业化以及损伤感知重建。

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cs.CV / 74 / 2609.28077

TEEP-RCNN: Texture-Enhanced Edge-aware Perception for Steel Surface Defect Detection via Improved Convolutional Block Attention in Faster R-CNN

TEEP-RCNN：通过改进的卷积块注意力机制在Faster R-CNN中增强纹理和边缘感知的钢表面缺陷检测

Rajesh, Kirtan

Abstract

Steel surface defect detection is critical for automated industrial quality control but remains challenging due to subtle inter-class texture differences and pronounced class imbalance. We introduce TEEP-RCNN (Texture-Enhanced Edge-aware Perception Region-based CNN), a two-stage detector built on Faster R-CNN with a Feature Pyramid Network backbone and an improved Convolutional Block Attention Module (CBAM). Our CBAM adds dropout regularization in the channel attention MLP and batch normalization on the spatial attention branch, reducing co-adaptation and stabilizing gating logits. Training uses a differential learning rate protocol with cosine annealing warm-up, separating update rates for the pre-trained ResNet-101 backbone and the detection head. At inference, predictions are refined via Test-Time Augmentation fused with Weighted Box Fusion (WBF), improving localization stability on elongated and boundary-adjacent defects. On the NEU-DET benchmark across six defect categories, TEEP-RCNN achieves 73.3\% mAP@50 and 37.9\% mAP@50-95 in only 10 training epochs on a single GPU, competitive with YOLOv11m (76.2\% mAP@50, 100 epochs) while outperforming it on the rolled-in-scale category under the COCO metric. Per-class analysis shows the spatial attention branch is most effective on elongated texture defects such as patches and scratches, while crazing remains an open challenge across both paradigms due to its distributed non-local texture structure.

Chinese Translation

钢表面缺陷检测对于自动化工业质量控制至关重要，但由于细微的类间纹理差异和显著的类不平衡，仍然具有挑战性。我们提出了TEEP-RCNN（纹理增强边缘感知区域卷积神经网络），这是一个基于Faster R-CNN的两阶段检测器，采用特征金字塔网络作为主干，并引入了改进的卷积块注意力模块（CBAM）。我们的CBAM在通道注意力的多层感知机中增加了dropout正则化，并在空间注意力分支上进行了批归一化，减少了协同适应性并稳定了门控逻辑。训练采用了带余弦退火预热的差异学习率协议，为预训练的ResNet-101主干和检测头分离更新速率。在推理过程中，通过融合测试时增强（Test-Time Augmentation）与加权框融合（Weighted Box Fusion, WBF）来细化预测，提高了对细长和边界相邻缺陷的定位稳定性。在NEU-DET基准测试中，TEEP-RCNN在六个缺陷类别上仅用10个训练周期在单个GPU上实现了73.3% mAP@50和37.9% mAP@50-95，表现与YOLOv11m（76.2% mAP@50，100个周期）相当，同时在COCO指标下的卷入尺度类别上超越了YOLOv11m。逐类分析显示，空间注意力分支在细长纹理缺陷（如斑块和划痕）上最为有效，而由于其分布式非局部纹理结构，裂纹在两种范式中仍然是一个未解的挑战。

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cs.CV / 75 / 2609.28078

LiAM-SAM: Lifecycle-Aware Memory for Robust SAM2-Based MOT

LiAM-SAM：面向生命周期的内存用于稳健的基于SAM2的多目标跟踪

Francisco, Grégoire, D'Amico, Alessandro, Costantini, Samuele, Francesca, Gianpiero, Garattoni, Lorenzo

Abstract

Segmentation-based multi-object tracking (MOT) with foundation video models such as SAM2 offers strong localization quality, yet remains fragile in crowded, real-world scenes. In detector-prompted SAM2 pipelines, failures typically arise at three stages of the object lifecycle: (i) erroneous or duplicate track initiation, (ii) memory drift during close interactions, and (iii) unreliable re-identification after long occlusions or re-entry. These errors corrupt object memory and accumulate over time, making long-horizon tracking unstable. In this paper, we reframe MOT as a lifecycle memory integrity problem. We present LiAM-SAM, a Lifecycle-Aware Memory (LiAM) framework with targeted mechanisms for each of the three failure modes. At track birth, to prevent faulty or duplicate initiations, we apply contrastive track initiation, which conditions each prompt on existing nearby tracked instances. To preserve memory integrity during strong interactions, we introduce motion- and geometry-grounded memory correction that resolves interaction confusions and suppresses drift. For reliable re-identification after disappearance, we maintain an adaptive context memory that promotes diverse and trustworthy references as long-term identity anchors. Finally, similarity aware spatial pruning optionally selects the memory tokens to retain at cross-attention time, improving efficiency with minimal accuracy loss. LiAM-SAM represents a modular, detector-agnostic, SAM2-based MOT system that achieves state-of-the-art HOTA and IDF1 on the evaluated benchmarks. In association-challenging environments, our ablations show that LiAM improves a detector+SAM2 baseline by +10.5 HOTA, +17.4 AssA, and reduces identity switches by 96%.

Chinese Translation

基于分割的多目标跟踪（MOT）结合基础视频模型如SAM2，提供了强大的定位质量，但在拥挤的现实场景中仍然脆弱。在检测器提示的SAM2管道中，失败通常发生在对象生命周期的三个阶段：（i）错误或重复的轨迹初始化，（ii）在密切交互中的内存漂移，以及（iii）在长时间遮挡或重新进入后的不可靠重新识别。这些错误会破坏对象内存，并随着时间的推移积累，使得长时间跟踪变得不稳定。在本文中，我们将MOT重新定义为一个生命周期内存完整性问题。我们提出了LiAM-SAM，一个面向生命周期的内存（LiAM）框架，针对这三种失败模式提出了有针对性的机制。在轨迹出生阶段，为了防止错误或重复的初始化，我们应用对比轨迹初始化，该方法基于现有的附近跟踪实例对每个提示进行条件处理。为了在强交互期间保持内存完整性，我们引入基于运动和几何的内存修正，解决交互混淆并抑制漂移。为了在消失后实现可靠的重新识别，我们维护一个自适应上下文内存，促进多样化和可信的参考作为长期身份锚点。最后，基于相似性的空间修剪可选择在交叉注意力时保留的内存标记，以提高效率而最小化准确性损失。LiAM-SAM代表了一个模块化、与检测器无关的基于SAM2的MOT系统，在评估基准上实现了最先进的HOTA和IDF1。在关联挑战环境中，我们的消融实验表明，LiAM使得检测器+SAM2基线提高了+10.5 HOTA，+17.4 AssA，并减少了96%的身份切换。

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cs.CV / 76 / 2609.28083

ZoomDiff: A High-Fidelity Diffusion Model for Dual-Camera Smooth Zooming

ZoomDiff：一种用于双摄像头平滑变焦的高保真扩散模型

Zhang, Jiayi, Wu, Renlong, Ding, Yukang, Deng, Sibin, Zuo, Wangmeng

Abstract

Digital zoom transitions between dual cameras often exhibit conspicuous discontinuities in geometric structure and chromatic consistency, degrading the user experience. While recent dual-camera smooth zoom (DCSZ) methods attempt to mitigate this by fine-tuning frame interpolation (FI) models on DCSZ data, they struggle with the large cross-view disparities and complex geometric transformations. Considering that the generative prior of diffusion models is suitable for addressing this problem, we explore their application to DCSZ. However, naively applying existing diffusion-based FI models still yields low-fidelity transitions due to insufficient conditional guidance, high-frequency information loss during VAE encoding, as well as inadequate temporal consistency. To address this, we propose ZoomDiff, a high-fidelity diffusion model that leverages dual-camera inputs in both latent and pixel spaces for photo-realistic transitions. Specifically, we first strengthen dual-image conditional guidance during the multi-step denoising process to improve geometric consistency. Then we inject flow-aligned multi-scale features from the VAE encoder into the VAE decoder to recover high-frequency details, where flow-guided temporal consistency supervision are introduced to produce more smooth transitions. Extensive experiments on both synthetic and real-world datasets demonstrate that ZoomDiff outperforms state-of-the-art methods quantitatively and qualitatively. Project page: https://jiayi-hit.github.io/ZoomDiff.github.io/.

Chinese Translation

双摄像头的数字变焦在过渡过程中常常表现出明显的几何结构和色彩一致性的不连续性，从而降低用户体验。尽管近期的双摄像头平滑变焦（DCSZ）方法试图通过在DCSZ数据上微调帧插值（FI）模型来缓解这一问题，但它们在处理大跨视差和复杂几何变换时仍然面临挑战。考虑到扩散模型的生成先验适合解决此问题，我们探索了其在DCSZ中的应用。然而，简单地应用现有的基于扩散的FI模型仍然会由于条件指导不足、在变分自编码器（VAE）编码过程中高频信息损失以及时间一致性不足而导致低保真过渡。为了解决这个问题，我们提出了ZoomDiff，这是一种高保真的扩散模型，利用双摄像头输入在潜在空间和像素空间中实现照片级真实感的过渡。具体而言，我们首先在多步去噪过程中增强双图像条件指导，以改善几何一致性。然后，我们将来自VAE编码器的流对齐多尺度特征注入到VAE解码器中，以恢复高频细节，并引入流引导的时间一致性监督，以产生更平滑的过渡。在合成和真实世界数据集上的广泛实验表明，ZoomDiff在定量和定性上均优于最先进的方法。项目页面：https://jiayi-hit.github.io/ZoomDiff.github.io/

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cs.CV / 77 / 2609.28095

MotionSpec: Spectral Trajectory Supervision for Motion-Consistent Video Generation

MotionSpec：基于谱轨迹的运动一致性视频生成监督

Ni, Ziqi, Li, Rui, Jiang, Shiqi, Zhou, Wei

Abstract

Recent advances in text-to-video generation have enabled high-fidelity visual synthesis, yet realistic motion remains challenging. Generated videos may exhibit temporal discontinuities, inconsistent action progression, and structural distortions during complex movements. Even when individual frames appear realistic, the underlying motion may evolve in inconsistent or implausible ways. Standard generative objectives provide limited motion-specific supervision, leaving motion evolution insufficiently constrained. In this paper, we propose MotionSpec, a motion supervision framework centered on Spectral Trajectory Consistency (STC). STC constructs dense anchor-relative motion trajectories and transforms them into motion spectral volumes via a temporal Fourier transform. By aligning the spectral amplitude and phase of predicted and target trajectories, STC constrains both motion strength across temporal frequencies and the temporal organization of motion. To complement this trajectory-level supervision, we introduce Local Flow Consistency (LFC), which aligns consecutive-frame optical flow between predicted and target videos to stabilize local motion transitions. Experiments demonstrate that MotionSpec consistently improves motion consistency, temporal coherence, and plausibility while preserving visual fidelity.

Chinese Translation

近年来文本到视频生成的进展使得高保真视觉合成成为可能，但实现真实的运动仍然具有挑战性。生成的视频可能表现出时间上的不连续性、动作进展的不一致性以及在复杂运动中出现的结构扭曲。即使单帧看起来真实，潜在的运动也可能以不一致或不合理的方式演变。标准的生成目标提供的运动特定监督有限，导致运动演变的约束不足。本文提出了MotionSpec，一种以谱轨迹一致性（Spectral Trajectory Consistency, STC）为中心的运动监督框架。STC构建密集的锚点相对运动轨迹，并通过时间傅里叶变换将其转换为运动谱体积。通过对齐预测轨迹和目标轨迹的谱幅度和相位，STC约束了不同时间频率下的运动强度和运动的时间组织。为了补充这种轨迹级别的监督，我们引入了局部流一致性（Local Flow Consistency, LFC），该方法对齐预测视频和目标视频之间的连续帧光流，以稳定局部运动过渡。实验表明，MotionSpec在保持视觉保真度的同时，始终提高了运动一致性、时间连贯性和合理性。

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cs.CV / 78 / 2609.28099

Visual Tripwires: Anticipating Failure in Deep Vision Systems

视觉触发器：预测深度视觉系统中的失败

Harit, Anoushka, Zuberi, Rehan, Prew, William, Markowetz, Florian

Abstract

Deep vision systems remain vulnerable to corruption, occlusion, and distribution shift despite strong benchmark performance. Existing reliability methods typically evaluate uncertainty at individual time steps and do not explicitly model how a system progresses toward failure. We introduce Visual Tripwires, a predictive reliability framework that uses temporal instability in model behaviour to anticipate impending failure. Our central hypothesis is that predictive degradation develops progressively through measurable changes in latent representations, prediction trajectories, and attention structure. Visual Tripwires captures these changes using representation drift, prediction oscillation, trajectory curvature, and attention entropy. A lightweight tripwire predictor aggregates these signals over a temporal window to estimate the probability of failure within a future prediction horizon. Experiments across multiple datasets, architectures, and progressive perturbation settings show that the proposed instability signals emerge before predictive degradation and provide earlier and more accurate failure warnings than conventional uncertainty estimation methods. These results demonstrate that temporal instability contains useful information about future model reliability and provides a practical basis for early warning in deep vision systems.

Chinese Translation

尽管深度视觉系统在基准测试中表现优异，但仍然容易受到腐败、遮挡和分布转移的影响。现有的可靠性方法通常在个别时间步上评估不确定性，并未明确建模系统如何逐步走向失败。我们提出了视觉触发器（Visual Tripwires），这是一种预测可靠性框架，利用模型行为中的时间不稳定性来预测即将发生的失败。我们的核心假设是，预测性退化是通过潜在表示、预测轨迹和注意力结构中的可测量变化逐步发展而来的。视觉触发器通过表示漂移、预测振荡、轨迹曲率和注意力熵来捕捉这些变化。一个轻量级的触发器预测器在时间窗口内聚合这些信号，以估计未来预测范围内失败的概率。在多个数据集、架构和渐进扰动设置下的实验表明，所提出的不稳定信号在预测性退化之前出现，并提供比传统不确定性估计方法更早且更准确的失败警告。这些结果表明，时间不稳定性包含有关未来模型可靠性的有用信息，并为深度视觉系统中的早期预警提供了实用基础。

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cs.CV / 79 / 2609.28110

Field-of-View Extension in Dental Cone-Beam CT via Implicit Neural Representations and Diffusion Model-Based Refinement

通过隐式神经表示和基于扩散模型的精细化实现牙科锥形束CT的视野扩展

Schaub, Susanne, Bieder, Florentin, Oliveira, Matheus L., Wang, Yulan, Sodnom-ish, Buyanbileg, Dagassan-Berndt, Dorothea, Bornstein, Michael M., Cattin, Philippe C.

Abstract

Dental cone-beam computed tomography (CBCT) systems often employ detector configurations that provide a truncated field of view (FOV) that only captures a small part of the patient's anatomy. In this work, we aim to reconstruct an extended FOV using projections of truncated FOV scans. To this end, we propose a three-stage framework that consists of (1) an implicit neural representation (INR) for estimating missing parts of the truncated projection data, (2) an iterative reconstruction for generating a secondary volumetric image with improved anatomical consistency and (3) a fast diffusion model for image enhancement. The proposed approach combines the strengths of continuous representations, physics-based reconstruction and generative refinement within a unified pipeline for truncated CBCT imaging. Experimental results demonstrate that the method effectively reduces truncation artifacts, improves the reconstruction of structures extending beyond the original FOV and produces images with enhanced quality. Our code is publicly available at https://github.com/SusanneSchaub/CBCT-FOV-Extension.

Chinese Translation

牙科锥形束计算机断层扫描（CBCT）系统通常采用检测器配置，提供截断的视野（FOV），仅捕获患者解剖结构的一小部分。在本研究中，我们旨在利用截断视野扫描的投影重建扩展视野。为此，我们提出了一个三阶段框架，包括（1）用于估计截断投影数据缺失部分的隐式神经表示（INR），（2）用于生成具有改善解剖一致性的二次体积图像的迭代重建，以及（3）用于图像增强的快速扩散模型。所提方法在统一的管道中结合了连续表示、基于物理的重建和生成性精细化的优势，适用于截断CBCT成像。实验结果表明，该方法有效减少了截断伪影，改善了超出原始视野的结构重建，并生成了质量更高的图像。我们的代码已公开发布在 https://github.com/SusanneSchaub/CBCT-FOV-Extension。

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cs.CV / 80 / 2609.28154

A comparative assessment of global building and settlement datasets across geographic and settlement contexts

全球建筑和定居数据集在地理和定居背景下的比较评估

Balogun, Rufai Omowunmi, Gevaert, Caroline Margaux, Riom, Capucine, Mirindi, Derrick, Opdyke, Aaron, Alemohammad, Hamed, Chrzanowski, Pierre, Anderson, Edward Charles

Abstract

Global building and settlement datasets increasingly support population mapping, exposure assessment, urban monitoring, and other analyses of the built environment, yet comparative evidence remains fragmented across products, geographic regions, reference datasets, spatial scales, and evaluation methods. We benchmark seven global or near-global products, including Overture Maps, Global Building Atlas, 3D-GloBFP, Google Open Buildings 2.5D Temporal (OBT), Microsoft TEMPO, GHSL, and WSF Tracker, against harmonized reference footprints across 135 study areas. The evaluation combines complementary measures of detection, geometric agreement, and aggregate quantity accuracy, together with stratified analyses of settlement characteristics and diagnostic experiments on error size and temporal alignment. Overture achieved the highest median city-level vector F1 (0.786). Raster rankings were resolution-dependent: OBT achieved the highest median F1 at 10m (0.642), whereas WSF Tracker led at 100m (0.862). However, WSF Tracker substantially overestimated built-up area, emphasizing that when using raster products, it is important for the user to understand whether the raster identifies only buildings or includes additional impervious surfaces. Raster accuracy increased consistently with building density (Spearman \r{ho} = 0.58-0.75), while small candidate buildings were disproportionately associated with false positives in the vector products. Temporally aligning WSF Tracker with reference imagery increased mean F1 by 0.060 (median +0.037), indicating that the reported accuracies are conservative in rapidly growing areas. The study establishes a reproducible benchmark for comparing heterogeneous global urban and settlement layer datasets across geographic and settlement contexts.

Chinese Translation

全球建筑和定居数据集日益支持人口映射、暴露评估、城市监测以及其他对建筑环境的分析，但在产品、地理区域、参考数据集、空间尺度和评估方法之间的比较证据仍然零散。我们对七个全球或近全球产品进行了基准测试，包括 Overture Maps、Global Building Atlas、3D-GloBFP、Google Open Buildings 2.5D Temporal (OBT)、Microsoft TEMPO、GHSL 和 WSF Tracker，基于 135 个研究区域的统一参考足迹。评估结合了检测、几何一致性和总量准确性的互补测量，以及对定居特征的分层分析和关于误差大小与时间对齐的诊断实验。Overture 在城市级别的向量 F1 中达到了最高的中位数 (0.786)。栅格排名依赖于分辨率：OBT 在 10m 分辨率下达到了最高的中位数 F1 (0.642)，而 WSF Tracker 在 100m 分辨率下领先 (0.862)。然而，WSF Tracker 对建筑面积的估计显著过高，这强调了在使用栅格产品时，用户需要了解该栅格是否仅识别建筑物或包括额外的不透水表面。栅格准确性随着建筑密度的增加而持续提高 (Spearman {ho} = 0.58-0.75)，而小型候选建筑在向量产品中与假阳性不成比例地相关。将 WSF Tracker 与参考影像进行时间对齐使平均 F1 提高了 0.060 (中位数 +0.037)，表明在快速增长区域报告的准确性是保守的。本研究建立了一个可重复的基准，用于比较不同地理和定居背景下的异质全球城市和定居层数据集。

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cs.CV / 81 / 2609.28159

Depth-Guided Contrastive Learning for 2D Representations with 3D Spatial Awareness

基于深度引导的对比学习：具有三维空间意识的二维表征

Zeng, Liang, Vergauwen, Maarten

Abstract

Standard contrastive learning frameworks are mainly designed from a semantic perspective, yet learning 2D visual representations that preserve 3D spatial structure is also important for scene understanding. In this work, we propose Depth-Guided Contrastive Learning (DGCL), a simple auxiliary objective that injects 3D spatial awareness into 2D contrastive representation learning. Our key idea is to use depth to convert local 3D proximity into contrastive similarity: pixels that are closer in 3D space are encouraged to have more similar representations than pixels that are farther apart. Instead of relying on absolute depth values, DGCL formulates supervision through relative 3D distance comparisons among randomly sampled pixels, making the objective invariant to depth scale, efficient to compute, and easy to integrate into existing contrastive frameworks. Experiments across different datasets and models show that DGCL consistently improves 2D representation learning and benefits semantic downstream tasks by stronger spatial and geometric understanding. The code is available on https://github.com/LeungTsang/DGCL.

Chinese Translation

标准的对比学习框架主要从语义角度设计，但学习能够保留三维空间结构的二维视觉表征对于场景理解同样重要。在本研究中，我们提出了深度引导对比学习（Depth-Guided Contrastive Learning, DGCL），这是一种简单的辅助目标，通过将三维空间意识注入二维对比表征学习。我们的关键思想是利用深度将局部三维邻近性转化为对比相似性：在三维空间中更近的像素被鼓励具有比更远的像素更相似的表征。DGCL并不依赖于绝对深度值，而是通过随机采样像素之间的相对三维距离比较来制定监督，使得目标对深度尺度不变，计算高效，并且易于与现有的对比框架集成。在不同数据集和模型上的实验表明，DGCL始终改善了二维表征学习，并通过更强的空间和几何理解促进了语义下游任务。代码可在 https://github.com/LeungTsang/DGCL 获取。

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cs.CV / 82 / 2609.28183

From ECG Signals to Representative-Morphology Heatmaps for Biometric Recognition

从心电图信号到代表性形态热图的生物识别

Angelakis, Athanasios, Gomez-Barrero, Marta

Abstract

Electrocardiography (ECG) contains subject-specific morphology that supports biometric recognition, yet image-based performance depends on how the waveform is rendered. We introduce representative-morphology heatmaps, a deterministic ECG-to-image representation adapted from ECGXtractor. Within each block of ten aligned beats, the five beats closest to the block mean are averaged into a 400 by L matrix and rendered either as a conventional trace or as a dense cardiac-time-by-lead heatmap. Since both representations contain identical physiological samples, their comparison isolates the effect of rendering. We evaluate verification and closed-set identification on PTB, ECG-ID, and MIMIC-IV-ECG-DEMO. Five compact models, including ZACH-ViT, are trained from scratch, while six ImageNet-pretrained CNN and transformer backbones assess model scale and visual transfer. Heatmaps improve both FNMR operating points and both identification ranks in all 15 compact model-dataset comparisons, while EER improves in 14. Across the matched experiments, EER decreases by 9.59 percentage points and Rank-1 increases by 24.69 points on average. ConvNeXt-Tiny reaches 2.43% EER on PTB and 5.79% on ECG-ID, whereas DeiT-Base reaches 14.92% on MIMIC-DEMO. ImageNet initialization clearly benefits the two multilead datasets but has a mixed effect on ECG-ID, and performance does not increase monotonically with model size. The best heatmap systems approach the strongest signal-domain EER on PTB and ECG-ID, while DeiT-Base provides the strongest evaluated performance on MIMIC-DEMO. Lead-channel ablation further shows that useful channel combinations depend on the cohort and biometric task. Overall, representative-morphology heatmaps provide an effective image representation for ECG verification and identification.

Chinese Translation

心电图（ECG）包含特定于个体的形态特征，支持生物识别，然而基于图像的性能依赖于波形的呈现方式。我们引入了代表性形态热图，这是一种从 ECGXtractor 适配而来的确定性 ECG 到图像的表示。在每个对齐的十个心跳的块中，最接近块均值的五个心跳被平均成一个 400 x L 的矩阵，并以常规波形或密集的心脏时间与导联热图的形式呈现。由于这两种表示包含相同的生理样本，因此它们的比较可以隔离呈现的影响。我们在 PTB、ECG-ID 和 MIMIC-IV-ECG-DEMO 数据集上评估了验证和闭集识别。五个紧凑模型，包括 ZACH-ViT，从头开始训练，而六个在 ImageNet 上预训练的 CNN 和变换器骨干网络则评估模型规模和视觉迁移。热图在所有 15 个紧凑模型-数据集比较中改善了 FNMR 操作点和识别排名，同时 EER 在 14 个比较中得到改善。在匹配实验中，EER 平均下降了 9.59 个百分点，Rank-1 平均增加了 24.69 分。ConvNeXt-Tiny 在 PTB 上达到 2.43% 的 EER，在 ECG-ID 上达到 5.79%，而 DeiT-Base 在 MIMIC-DEMO 上达到 14.92%。ImageNet 初始化显著有利于这两个多导联数据集，但对 ECG-ID 的影响则较为复杂，且性能并未随着模型规模单调增加。最佳热图系统接近 PTB 和 ECG-ID 上最强的信号域 EER，而 DeiT-Base 在 MIMIC-DEMO 上提供了最强的评估性能。导联通道消融进一步表明，有效的通道组合依赖于人群和生物识别任务。总体而言，代表性形态热图为 ECG 验证和识别提供了一种有效的图像表示。

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cs.CV / 83 / 2609.28187

Two Global Crops Suffice: Locating Semantic Emergence in DINO-Style Self-Supervised Learning

两种全球作物足够：定位DINO风格自监督学习中的语义出现

Sunagad, Basavaraj, Jesslen, Artur, Kortylewski, Adam

Abstract

Self-supervised vision transformers trained with DINO-style objectives exhibit striking emergent semantic representation quality across visual tasks, yet the mechanisms underlying this behavior remain unclear. We present a systematic empirical dissection of the DINO family and show that semantic representations arise primarily from enforcing consistency between geometrically distinct global views of the same image instance. This instance-specific global alignment acts as the semantic anchor of DINO-style learning. Across controlled retraining experiments evaluated on semantic correspondence and a diverse suite of 2D and 3D downstream tasks, we find that patch-level masking objectives enhance semantics only when trained jointly with this global alignment, indicating that the iBOT objective refines and densifies existing semantic structure rather than creating it independently. In contrast, local-to-global view alignment does not substantially improve semantic qualities at fixed compute beyond a purely global alignment. Beyond training design, we revisit how semantic representation quality should be evaluated: while classification accuracy is the standard validation score, semantic correspondence provides a complementary axis that more reliably predicts downstream task performance. Together, these findings provide a functional decomposition of DINO-style learning and represent an important step toward understanding how semantic representations emerge in self-supervised vision models.

Chinese Translation

采用DINO风格目标训练的自监督视觉变换器在视觉任务中展现出显著的语义表征质量，但这种行为背后的机制仍不明确。我们对DINO家族进行了系统的实证剖析，表明语义表征主要源于对同一图像实例的几何上不同的全局视图之间的一致性约束。这种特定实例的全局对齐作为DINO风格学习的语义锚点。在对语义对应性以及多样化的2D和3D下游任务进行的受控再训练实验中，我们发现，只有在与这种全局对齐共同训练时，补丁级掩蔽目标才能增强语义，这表明iBOT目标是对现有语义结构的细化和密集化，而不是独立创建。相比之下，局部到全局视图的对齐在固定计算条件下并未显著改善语义质量，超越了纯粹的全局对齐。除了训练设计外，我们还重新审视了如何评估语义表征质量：虽然分类准确率是标准的验证分数，但语义对应性提供了一个互补的维度，更可靠地预测下游任务的表现。综合来看，这些发现为DINO风格学习提供了功能性分解，并代表了理解自监督视觉模型中语义表征如何出现的重要一步。

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cs.CV / 84 / 2609.28192

From Change Captions to Change Detection: Semantic-Appearance Agreement Framework for Remote Sensing Change Detection

从变化描述到变化检测：用于遥感变化检测的语义-外观一致性框架

Qian, Yuan, Ma, Jie

Abstract

Remote sensing change detection (RSCD) is essential for monitoring land-cover changes and urban development. However, most methods demand pixel-level change masks, which are costly and time-consuming to annotate. Weakly supervised methods reduce this cost by using image-level change labels. Yet these labels indicate only whether a change occurs, leaving models to recover the location of the change and semantic meaning through additional and complex mechanisms. This missing information can be supplied directly by change captions, which describe what changes, what it becomes, and where it occurs. Therefore, we introduce change-caption-guided RSCD, using change captions as the sole task-specific supervision to learn change masks without manually annotated change masks. Our framework has two components: a caption-driven generation pipeline that produces bi-temporal remote sensing image pairs at scale with controlled changes matching each caption, and a change detector guided by the caption's transition semantics. The detector uses our Semantic-Appearance Agreement Framework (SAAF) to combine caption-grounded semantic responses with RGB differences for change localization, while text conditioning guides dense prediction. Experiments on our newly constructed Flair-RSGen dataset and WHU-CDC show that SAAF outperforms the closest reproduced limited-supervision baselines in macro-averaged IoU and F1 under the evaluated protocols. Code is publicly available at https://github.com/qianyuancs/SAAF.

Chinese Translation

遥感变化检测（RSCD）对于监测土地覆盖变化和城市发展至关重要。然而，大多数方法需要像素级变化掩码，这种标注成本高且耗时。弱监督方法通过使用图像级变化标签来降低这一成本。然而，这些标签仅指示是否发生变化，模型需要通过额外且复杂的机制来恢复变化的位置和语义含义。这一缺失的信息可以通过变化描述直接提供，变化描述说明了变化的内容、变化后的状态以及发生的位置。因此，我们提出了变化描述引导的RSCD，使用变化描述作为唯一的任务特定监督，以学习变化掩码，而无需手动标注的变化掩码。我们的框架包含两个部分：一个基于描述驱动的生成管道，能够大规模生成具有受控变化的双时相遥感图像对，并与每个描述匹配，以及一个由描述的转变语义引导的变化检测器。该检测器利用我们的语义-外观一致性框架（SAAF）将基于描述的语义响应与RGB差异结合，以实现变化定位，同时文本条件引导密集预测。在我们新构建的Flair-RSGen数据集和WHU-CDC上的实验表明，SAAF在评估协议下的宏平均IoU和F1指标上优于最接近的复现有限监督基线。代码已公开在 https://github.com/qianyuancs/SAAF。

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cs.CV / 85 / 2609.28222

From Alignment to Fusion in 3D Vision-Language

从对齐到融合：3D视觉-语言

Qiu, Xueqi, Miao, Xingyu, Deng, Jingjing, Duan, Haoran, Long, Yang, Shao, Ling

Abstract

Unified 3D vision-language systems must combine complementary geometry, scale, and appearance cues while supporting tasks from instance segmentation to language-guided reasoning. Existing methods often process point clouds, voxel grids, and multi-view images independently; directly combining these heterogeneous representations may leave substantial feature discrepancy unresolved, while subsequent unconstrained adaptation may distort their internal geometry. We propose an align-then-fuse framework that first applies triple pairwise cosine alignment to establish segment-level correspondence across the three representations and then retrieves task-conditioned features with a prompt-guided query decoder. Before fusion, representation-specific query features are transformed by learnable mappings constrained to the special orthogonal group. These mappings preserve inner products and Euclidean distances within each representation, permitting controlled representation-specific re-parameterisation without arbitrarily distorting its internal geometry. The transformed features are subsequently combined through Adaptive Fusion under downstream task supervision. Experiments cover eight datasets for instance segmentation, visual grounding, question answering, and dense captioning. Compared with PQ3D, the model improves average precision by 3.2 points on ScanNet200 and grounding accuracy by 2.9, 10.6, 4.6, and 4.1 points on ScanRefer, Nr3D, Sr3D, and Multi3DRefer, respectively, while also improving performance on ScanQA, SQA3D, and Scan2Cap. Ablations further support the complementary roles of alignment and orthogonal re-parameterisation and the effectiveness of Adaptive Fusion.

Chinese Translation

统一的3D视觉-语言系统必须结合互补的几何、尺度和外观线索，同时支持从实例分割到语言引导推理的任务。现有方法通常独立处理点云、体素网格和多视图图像；直接结合这些异构表示可能会留下显著的特征差异未得到解决，而随后的无约束适应可能会扭曲其内部几何。我们提出了一种先对齐后融合的框架，首先应用三重成对余弦对齐，以在三种表示之间建立段级对应关系，然后通过提示引导查询解码器检索任务条件特征。在融合之前，特定于表示的查询特征通过受限于特殊正交群的可学习映射进行转换。这些映射保留了每个表示内的内积和欧几里得距离，允许在不任意扭曲其内部几何的情况下进行受控的表示特定重新参数化。随后，通过下游任务监督下的自适应融合将转换后的特征结合在一起。实验涵盖了八个数据集，用于实例分割、视觉定位、问答和密集字幕。与PQ3D相比，该模型在ScanNet200上提高了3.2个百分点的平均精度，在ScanRefer、Nr3D、Sr3D和Multi3DRefer上分别提高了2.9、10.6、4.6和4.1个百分点的定位准确率，同时在ScanQA、SQA3D和Scan2Cap上也提高了性能。消融实验进一步支持了对齐和正交重新参数化的互补作用以及自适应融合的有效性。

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cs.CV / 86 / 2609.28230

A Unified Framework and Dataset for Oriented Object Visual Grounding in Remote Sensing

遥感中定向物体视觉定位的统一框架与数据集

Ding, Zeyu, Zhou, Yong, Zhao, Jiaqi, Du, Wen-Liang, Li, Xixi, Zhu, Hancheng, Yao, Rui, Saddik, Abdulmotaleb El

Abstract

Visual grounding in remote sensing images aims to locate objects described by referring expressions. Most existing methods predict horizontal bounding boxes, which are often inaccurate for objects with arbitrary orientations. To address this limitation, we introduce O$^2$-VG, a family of models for oriented object visual grounding with three complementary designs. Specifically, O$^2$-VG-Trans is a cross-modality transformer for oriented object visual grounding. It establishes a strong discriminative foundation for the model family. Building upon it, O$^2$-VG-Uni predicts universal oriented proposals for possible foreground objects without specific text prompts. It also supports object retrieval through cached proposal embeddings. Using these universal oriented proposals as input prompts, O$^2$-VG-VLM is an autoregressive vision-language model. It generates oriented box token blocks in parallel through multi-token prediction. In addition, we construct DIOR-R-RSVG, a dataset for oriented object visual grounding in remote sensing images. It provides image, expression, and oriented box triplets for training and evaluation. Together, the O$^2$-VG family provides a flexible framework that spans discriminative transformers and generative vision-language models. It achieves superior performance across multiple benchmarks. Code is available at https://github.com/wokaikaixinxin/ai4rs.

Chinese Translation

遥感图像中的视觉定位旨在定位由指代表达描述的物体。现有的大多数方法预测水平边界框，这对于具有任意方向的物体往往不够准确。为了解决这一限制，我们引入了O$^2$-VG，一个用于定向物体视觉定位的模型家族，具有三种互补设计。具体而言，O$^2$-VG-Trans是一个用于定向物体视觉定位的跨模态变换器。它为模型家族建立了强大的区分基础。在此基础上，O$^2$-VG-Uni预测通用定向提议，以识别可能的前景物体，而无需特定的文本提示。它还通过缓存的提议嵌入支持物体检索。使用这些通用定向提议作为输入提示，O$^2$-VG-VLM是一个自回归的视觉-语言模型。它通过多标记预测并行生成定向框标记块。此外，我们构建了DIOR-R-RSVG，一个用于遥感图像中定向物体视觉定位的数据集。该数据集提供用于训练和评估的图像、表达和定向框三元组。O$^2$-VG家族提供了一个灵活的框架，涵盖了区分性变换器和生成性视觉-语言模型。它在多个基准测试中实现了优越的性能。代码可在 https://github.com/wokaikaixinxin/ai4rs 获取。

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cs.CV / 87 / 2609.28231

Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification

中心偏差会传播吗？病理基础模型在全幻灯片图像分类中的鲁棒性

Carretero, Ilán, Meseguer, Pablo, del Amor, Rocío, Naranjo, Valery

Abstract

Pathology foundation models (PFMs) have transformed computational pathology through powerful representation learning from histopathological images. PFMs provide rich, discriminative representations for whole slide image (WSI) analysis, enabling tasks such as slide-level classification under multiple instance learning (MIL). However, these representations may also encode non-biological signals associated with acquisition centers, potentially introducing spurious shortcuts into downstream predictions. In this work, we evaluate center-associated robustness in WSI classification using a controlled training setting with increasing class-center correlations quantified by Cram\'er's V. We benchmark six PFMs across four datasets and two MIL aggregators, while evaluating ComBat as a robustification strategy. We further introduce the Area Under the Cram\'er's V Curve (AUCC) to jointly capture absolute classification performance and its degradation as spurious correlation increases. Results show that center-related information encoded by PFMs propagates to WSI-level predictions, with robustness depending on both the PFM representation and MIL aggregation strategy. Additionally, ComBat harmonization does not provide consistent robustness gains across datasets.

Chinese Translation

病理基础模型（PFMs）通过对组织病理图像进行强大的表征学习，改变了计算病理学。PFMs 为全幻灯片图像（WSI）分析提供了丰富的、具有区分性的表征，使得在多实例学习（MIL）下进行幻灯片级分类等任务成为可能。然而，这些表征可能还会编码与采集中心相关的非生物信号，可能会在下游预测中引入虚假的捷径。在本研究中，我们使用一个控制训练设置评估 WSI 分类中的中心相关鲁棒性，该设置通过 Cramér's V 量化逐渐增加的类中心相关性。我们在四个数据集和两个 MIL 聚合器上基准测试六个 PFMs，同时评估 ComBat 作为一种鲁棒化策略。我们进一步引入 Cramér's V 曲线下面积（AUCC），以共同捕捉绝对分类性能及其在虚假相关性增加时的降级。结果表明，PFMs 编码的与中心相关的信息传播到 WSI 级别的预测中，鲁棒性取决于 PFM 表征和 MIL 聚合策略。此外，ComBat 协调在不同数据集上并未提供一致的鲁棒性提升。

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cs.CV / 88 / 2609.28235

Diff-RF: Mutually Reinforced Image Registration and Fusion via Degradation-Aware Learning

Diff-RF：通过降质感知学习实现相互增强的图像配准与融合

Yi, Xunpeng, Du, Zaixi, Yan, Qinglong, Zhang, Yibing, Xu, Han, Ma, Jiayi

Abstract

Image registration and fusion aim to establish spatial correspondences from misaligned multi-modal source images, and integrate complementary information. However, in real-world imaging scenarios, source images are often affected by complex and diverse degradations, such as low illumination, noise, etc., which severely hinder the effectiveness of registration and fusion. To address this issue, we propose a mutually reinforced image registration and fusion diffusion framework via degradation-aware learning, termed Diff-RF. It explores the intrinsic coupling between registration-fusion and information restoration in the degradation conditions, enabling high-quality fusion of unregistered images under complex degradation conditions. First, the intra-modal restoration module is designed to alleviate modality-specific degradations by leveraging information within each modality, thereby providing more reliable structural representations for registration and facilitating subsequent cross-modal fusion. Second, we develop a cross-modal diffusion registration and fusion module that establishes bidirectional interaction between registration and fusion. By integrating fusion-derived visual cues and correspondence-based geometric conditions into the diffusion process, the proposed framework progressively refines spatial alignment and exploits cross-modal complementary information to achieve collaborative enhancement. Rather than treating them as independent components, degradation-aware information restoration and the collaborative optimization of registration and fusion are tightly coupled, achieving overall performance improvements. Extensive experiments on multiple extended datasets demonstrate that Diff-RF achieves superior registration accuracy and fusion quality under various degraded scenarios, exhibiting strong robustness and generalization ability.

Chinese Translation

图像配准与融合旨在从未对齐的多模态源图像中建立空间对应关系，并整合互补信息。然而，在现实世界的成像场景中，源图像常常受到复杂多样的降质影响，如低光照、噪声等，这严重阻碍了配准和融合的有效性。为了解决这一问题，我们提出了一种通过降质感知学习实现相互增强的图像配准与融合扩散框架，称为Diff-RF。该框架探索了在降质条件下配准-融合与信息恢复之间的内在耦合关系，使得在复杂降质条件下能够高质量地融合未配准的图像。首先，设计了内部模态恢复模块，通过利用每个模态内的信息来缓解模态特定的降质，从而为配准提供更可靠的结构表示，并促进后续的跨模态融合。其次，我们开发了一个跨模态扩散配准与融合模块，建立了配准与融合之间的双向交互。通过将融合生成的视觉线索和基于对应关系的几何条件整合到扩散过程中，所提出的框架逐步优化空间对齐，并利用跨模态互补信息实现协同增强。降质感知的信息恢复与配准和融合的协同优化并不是独立的组件，而是紧密耦合，从而实现整体性能的提升。在多个扩展数据集上的大量实验表明，Diff-RF在各种降质场景下实现了优越的配准精度和融合质量，展现出强大的鲁棒性和泛化能力。

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cs.CV / 89 / 2609.28236

EmbodiedMemory-Bench: Benchmarking Embodied Memory for Long-Horizon Embodied Tasks

具身记忆基准：评估长时间具身任务中的具身记忆

Liang, Lizhou, Zhong, Xinyu, Pan, Miao, Zhou, Xiaohe, Liu, Xuanyu, Li, Qinfeng, Li, Peng, Chen, Jintao, Zhang, Xuhong, Zhang, Wenqi

Abstract

Long-horizon embodied interaction requires agents to retain and continually update information about the environment as they observe, act, and encounter change. Yet current agents struggle to maintain such memory reliably. Our analysis traces this limitation to four key deficiencies: weak fine-grained visual memory, unreliable dynamic world-state tracking, failing to record world state revealed by interaction outcomes, and limited generalization from prior experience. However, existing benchmarks do not directly assess these memory capabilities during long-horizon embodied interaction. To address this gap, we introduce EmbodiedMemory-Bench (EMem-Bench), comprising 2,554 interactive episodes across four task families. EMem-Bench requires agents to build and update memory from interaction history, then use it to complete a later task by acting in the environment. We further present Embodied-Memorizer (EMem), an external memory system that organizes embodied experience into spatial, event, and scene memories. We also train EMem-8B, an 8B policy that manages and uses these memories. We evaluate a diverse range of open-source and proprietary MLLMs and representative multimodal memory systems. Results show that current models remain weak and uneven across the four challenges. Under matched backbones, EMem achieves the best overall performance among the evaluated memory systems and improves both open-source and proprietary models, while EMem-8B further improves over its backbone. Project page: https://zju-omniai.github.io/EmbodiedMemoryBench/

Chinese Translation

长时间的具身交互要求智能体在观察、行动和应对变化的过程中，保持并不断更新对环境的信息。然而，目前的智能体在可靠地维持这种记忆方面存在困难。我们的分析将这一局限性归因于四个关键缺陷：细粒度视觉记忆薄弱、动态世界状态跟踪不可靠、未能记录通过交互结果揭示的世界状态，以及从先前经验中有限的泛化能力。然而，现有的基准测试并未直接评估这些记忆能力在长时间具身交互中的表现。为了解决这一空白，我们引入了具身记忆基准（EmbodiedMemory-Bench，EMem-Bench），该基准包含跨越四个任务系列的2,554个交互情节。EMem-Bench要求智能体从交互历史中构建和更新记忆，然后利用这些记忆通过在环境中行动来完成后续任务。我们进一步提出了具身记忆器（Embodied-Memorizer，EMem），这是一个将具身经验组织为空间、事件和场景记忆的外部记忆系统。我们还训练了EMem-8B，一个管理和使用这些记忆的8B策略。我们评估了多种开源和专有的多模态大语言模型（MLLM）及代表性的多模态记忆系统。结果表明，目前的模型在这四个挑战中表现薄弱且不均衡。在匹配的基础架构下，EMem在评估的记忆系统中实现了最佳的整体性能，并改善了开源和专有模型，而EMem-8B在其基础上进一步提升了性能。项目页面：https://zju-omniai.github.io/EmbodiedMemoryBench/

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cs.CV / 90 / 2609.28239

ODPure: Backdoor Purification for Object Detection via Ensemble Corruption Consensus

ODPure：通过集成腐蚀共识进行目标检测的后门净化

Zeng, Li, Duan, Mingcheng, Fan, Longfei, Zhang, Hangtao, Wang, Xianlong, Li, Yanchun, Wen, Xia, Zhang, Leo Yu

Abstract

With the development of applications like autonomous driving, object detection has gained significant attention, while also highlighting critical vulnerabilities like backdoor attacks that severely compromise model integrity. Specifically, such attacks involve altering the categories of objects (i.e., object misclassification), removing bounding boxes (i.e., object disappearance), or generating bounding box proposals for non-existent objects (i.e., object generation) when a predefined trigger is present in the input. Although backdoor defenses for image classification are well-established, the research for object detection remains comparatively underexplored. Existing defenses address these threats by scanning outputs or models for potential backdoors but require discarding either malicious data or models. This remedy fails to enable a continuous and accurate perceptual stream for the object detection pipeline. To address such limitations, we propose ODPure, a novel input-stage black-box defense for object detection, which is based on input purification that ensures stable perception flows. Tailored to the dense prediction nature of object detectors, our Corruption-Reconstruction-Selection (CRS) paradigm operates by neutralizing triggers through a diverse portfolio of corruptions to generate a massive pool of redundant proposals, then recovering fine-grained structural cues via generative priors, and finally employing voting to reach a consensus on the resulting detections. Comprehensive experiments demonstrate that our method provides robust defense against diverse backdoor attacks and trigger types while preserving baseline accuracy. Our code is available at https://github.com/Alex66366/ODPure.

Chinese Translation

随着自动驾驶等应用的发展，目标检测受到了极大的关注，同时也暴露了如后门攻击等严重危害模型完整性的关键漏洞。具体而言，这类攻击涉及在输入中存在预定义触发器时，改变物体类别（即物体误分类）、移除边界框（即物体消失）或为不存在的物体生成边界框提议（即物体生成）。尽管针对图像分类的后门防御已相对成熟，但针对目标检测的研究仍然相对欠缺。现有的防御方法通过扫描输出或模型来识别潜在的后门，但需要丢弃恶意数据或模型。这种补救措施未能为目标检测管道提供连续且准确的感知流。为了解决这些局限性，我们提出了ODPure，这是一种新颖的输入阶段黑箱防御方法，基于输入净化以确保稳定的感知流。针对目标检测的密集预测特性，我们的腐蚀-重建-选择（Corruption-Reconstruction-Selection, CRS）范式通过多样化的腐蚀组合来中和触发器，生成大量冗余提议，然后通过生成先验恢复细粒度结构线索，最后通过投票达成对结果检测的共识。全面的实验表明，我们的方法对多种后门攻击和触发器类型提供了强有力的防御，同时保持了基线准确性。我们的代码可在 https://github.com/Alex66366/ODPure 获取。

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cs.CV / 91 / 2609.28262

RAMP: Robust Adaptive Mixed-Precision Quantization for Edge CPU Vision Models

RAMP：用于边缘 CPU 视觉模型的鲁棒自适应混合精度量化

Población-Criado, David, Garcia-Gasulla, Dario, Quinones, Eduardo

Abstract

Deploying deep learning models on edge CPUs is bottlenecked by computational and memory constraints. Mixed-precision quantization promises to reduce inference latency while preserving accuracy. However, quantization affects different layer types in inconsistent ways, so identifying where accuracy loss is minimized and latency reduction is maximized is critical, as the effect accumulates over a full deployment into substantial savings or unacceptable task degradation. Such identification relies on sensitivity metrics, proxies that estimate layer-wise degradation without evaluating the task accuracy of every candidate policy. Nevertheless, widely used metrics fail systematically on modern architectures. We present a systematic empirical study of 13 sensitivity metrics for layer-wise INT8 quantization across four distinctly different neural networks, and validate the resulting policies on two ARM64 platforms. Gradient-based sensitivity methods fail on 4 out of 8 model-hardware configurations and weight-based statistics on 2. In contrast, the Jensen-Shannon Divergence achieves zero catastrophic failures, reliably isolating the layers that cannot be safely quantized. A sensitivity metric alone does not define a policy, and the fixed thresholds typically used for that step are fragile over the highly skewed distributions of modern architectures. We address this with K-Means clustering, achieving near-lossless accuracy and a mean speed-up of $1.81\times$ over the full-precision model. Finally, we reveal that excluding from quantization the layers whose speed-up is negligible, regardless of their sensitivity, can be counterproductive, as it induces computational graph fragmentation and disables operator fusion. Our results yield concrete allocation policies for practitioners and researchers deploying quantized vision models on heterogeneous edge CPUs, without GPU access or gradient computation.

Chinese Translation

在边缘 CPU 上部署深度学习模型受到计算和内存限制的瓶颈。混合精度量化有望在保持准确性的同时减少推理延迟。然而，量化对不同层类型的影响不一致，因此识别出准确性损失最小和延迟减少最大的位置至关重要，因为这种影响在整个部署过程中会累积成可观的节省或不可接受的任务降级。这种识别依赖于敏感性指标，这些指标是估计逐层降级的代理，而无需评估每个候选策略的任务准确性。然而，广泛使用的指标在现代架构上系统性失效。我们对四种截然不同的神经网络进行了 13 种敏感性指标的系统实证研究，并在两个 ARM64 平台上验证了结果策略。基于梯度的敏感性方法在 8 种模型-硬件配置中有 4 种失败，而基于权重的统计在 2 种配置中失败。相比之下，Jensen-Shannon 散度在量化过程中没有出现灾难性失败，可靠地隔离出无法安全量化的层。单一的敏感性指标并不能定义一个策略，通常用于该步骤的固定阈值在现代架构高度偏斜的分布下是脆弱的。我们通过 K-Means 聚类解决了这个问题，实现了近乎无损的准确性，并在全精度模型上获得了平均 $1.81 imes$ 的加速。最后，我们揭示了排除那些加速微不足道的层（无论其敏感性如何）进行量化可能适得其反，因为这会导致计算图碎片化并禁用操作符融合。我们的结果为在没有 GPU 访问或梯度计算的情况下，在异构边缘 CPU 上部署量化视觉模型的从业者和研究人员提供了具体的分配策略。

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cs.CV / 92 / 2609.28283

Benchmarking Hyperspectral Foundation Models for Hyperspectral Unmixing

高光谱基础模型在高光谱解混中的基准测试

Dabier, Edgard, Kervazo, Christophe, Gori, Pietro, Tupin, Florence

Abstract

Several foundation models dedicated to hyperspectral images have recently been made available. These models are trained on large unlabeled datasets and exhibit strong performance on many hyperspectral imaging tasks, such as classification or denoising. Nonetheless, their performance for hyperspectral unmixing -- the task of separating mixed spectra of overlapping materials in a hyperspectral image -- remain understudied. This might partly be due to the fact that most of them rely on vision transformer backbones, including patchification, leading to a feature resolution problem. While hyperspectral unmixing already arises from the low resolution of hyperspectral images, this patchification step potentially makes the problem even more ill-posed. Therefore, in this work, we aim to answer two questions: 1) \emph{how do foundation models perform in hyperspectral unmixing?}; 2) \emph{how to tackle the feature-level loss of resolution?} To answer the first question, we benchmark foundation models for unmixing, showing that they can reach state-of-the-art performance on four hyperspectral unmixing datasets. To answer the second question, we compare several feature upsampling approaches and empirically show that using a simple one can lead to high performance results. The code is available at https://gitlab.telecom-paris.fr/ring/hfm-hsu.git.

Chinese Translation

最近，几种专门针对高光谱图像的基础模型已被提出。这些模型在大型无标注数据集上进行训练，并在许多高光谱成像任务（如分类或去噪）中表现出色。然而，它们在高光谱解混（即在高光谱图像中分离重叠材料的混合光谱）任务上的表现仍然未得到充分研究。这可能部分是因为它们大多依赖于视觉变换器（vision transformer）骨干网络，包括图像块化（patchification），导致特征分辨率问题。尽管高光谱解混本身就源于高光谱图像的低分辨率，但这一图像块化步骤可能使问题更加不适定。因此，在本研究中，我们旨在回答两个问题：1）基础模型在高光谱解混中的表现如何？2）如何解决特征级别的分辨率损失？为回答第一个问题，我们对解混的基础模型进行了基准测试，显示它们在四个高光谱解混数据集上可以达到最先进的性能。为回答第二个问题，我们比较了几种特征上采样方法，并实证表明使用简单的方法可以获得高性能结果。代码可在 https://gitlab.telecom-paris.fr/ring/hfm-hsu.git 获取。

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cs.CV / 93 / 2609.28286

PBLH Estimation from Satellite Radiances via a Dual-Encoder Transformer

通过双编码器变换器从卫星辐射估计行星边界层高度（PBLH）

Innocenti, Lorenzo, Catalano, Luca, Arnaudo, Edoardo, Rossi, Claudio, Larosa, Salvatore, Cimini, Domenico, Garza, Paolo

Abstract

Estimating the Planetary Boundary Layer Height (PBLH) from satellite observations is a challenging regression problem due to the indirect relationship between top-of-atmosphere radiances and near-surface atmospheric structure. Progress has been limited both by the lack of architectures capable of handling the multimodal, spatially incomplete nature of satellite overpasses, and by the scarcity of suitable datasets. In this paper, we build upon the large-scale dataset pairing MetOp radiances with ERA5 PBLH labels that we introduced in our previous work, making three contributions. First, we establish a benchmark across eight approaches spanning pixel-wise regression, swath-wise sequence models, and convolutional and Transformer models operating on the full orbital passage. Second, we quantify what the resulting model actually relies on, using grouped Shapley decomposition over the input blocks. Third, we present the best-performing architecture found: a dual-encoder Transformer whose masked-input handling lets it operate in all weather conditions. The proposed model achieves MAE = 155.8 m on the held-out global test set, outperforming all baselines on every evaluation subset. On 30 out-of-distribution granules acquired on two days overlapping the TEAMx observational campaign, it achieves MAE = 165.3 m, outperforming a pixel-wise baseline trained on the same data (MAE = 197 m).

Chinese Translation

从卫星观测中估计行星边界层高度（PBLH）是一个具有挑战性的回归问题，因为大气顶辐射与近地表大气结构之间存在间接关系。由于缺乏能够处理卫星过境的多模态、空间不完整特性的架构，以及适合的数据集稀缺，进展一直受到限制。在本文中，我们基于之前工作中引入的大规模数据集，该数据集将MetOp辐射与ERA5 PBLH标签配对，做出了三项贡献。首先，我们在八种方法中建立了基准，这些方法涵盖了逐像素回归、条带序列模型以及在完整轨道经过中运行的卷积和变换器模型。其次，我们量化了所得到的模型实际依赖于什么，使用分组Shapley分解对输入块进行分析。第三，我们展示了找到的最佳表现架构：一种双编码器变换器，其掩蔽输入处理使其能够在所有天气条件下运行。所提出的模型在保留的全球测试集上实现了MAE = 155.8米，在每个评估子集上均优于所有基线。在在TEAMx观测活动重叠的两天内获取的30个分布外颗粒上，它实现了MAE = 165.3米，优于在相同数据上训练的逐像素基线（MAE = 197米）。

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cs.CV / 94 / 2609.28300

RoomLight: A 2.5D Illumination Prior for Indoor Environments

RoomLight：一种用于室内环境的2.5D照明先验

Ardelean, Andreea, Egger, Bernhard

Abstract

Ill-posed inverse problems require priors to constrain the solution space toward plausible outcomes. In inverse rendering, learned priors modeling the distribution of natural illumination improve the recovery of scene properties. However, existing models rely on the distant-illumination assumption, representing lighting as a far-field environment map. This limits their applicability to indoor scenes, where illumination is highly spatially varying due to finite-distance emitters, visibility changes, and parallax, all of which are poorly approximated by a single environment map. To address this, we introduce a spatially-aware illumination prior trained on real-world indoor panoramas and their estimated depth. Our variational autoencoder model learns a compact, optimizable latent space that decodes into HDR radiance and depth, parameterizing an area light emitter for direct integration into standard differentiable rendering pipelines. This design bridges the plausibility guarantees of a learned prior with the gradient flow required for downstream optimization. Crucially, by jointly modeling radiance and depth, our prior captures the spatial structure of indoor illumination, instead of treating the light sources as infinitely distant. We demonstrate that this formulation enables spatially-varying illumination modeling and achieves higher-fidelity recovery of indoor lighting compared to existing approaches. Project page: https://andreead-a.github.io/RoomLight

Chinese Translation

病态逆问题需要先验知识来约束解空间，以朝向合理的结果。在逆渲染中，学习的先验模型通过模拟自然照明的分布来改善场景属性的恢复。然而，现有模型依赖于远距离照明假设，将照明表示为远场环境图。这限制了它们在室内场景中的适用性，因为室内照明由于有限距离的发光体、可见性变化和视差而具有高度的空间变化，这些都无法通过单一的环境图良好地近似。为了解决这个问题，我们引入了一种基于真实室内全景及其估计深度训练的空间感知照明先验。我们的变分自编码器模型学习了一个紧凑的、可优化的潜在空间，该空间解码为HDR辐射和深度，为直接集成到标准可微渲染管道中的区域光源参数化。这一设计将学习先验的合理性保证与下游优化所需的梯度流连接起来。重要的是，通过联合建模辐射和深度，我们的先验捕捉了室内照明的空间结构，而不是将光源视为无限远。我们证明了这种公式化能够实现空间变化照明建模，并与现有方法相比，实现了更高保真的室内照明恢复。项目页面：https://andreead-a.github.io/RoomLight

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cs.CV / 95 / 2609.28327

LightMIS: Ultra-Lightweight Medical Image Segmentation Without a Stage-Wise Decoder

LightMIS：无阶段解码器的超轻量级医学图像分割

Arhire, Andrei, Breabăn, Mihaela-Elena, Timofte, Radu

Abstract

We present LightMIS, a scalable family of ultra-lightweight convolutional networks for 2D binary medical image segmentation without a learned stage-wise decoder. LightMIS aligns the outputs of a five-level encoder to a common resolution using Scale-Aligned Projection blocks, aggregates them once, and refines the fused representation with an Adaptive Fusion Cascade. The cascade combines Adaptive Kernel Fusion with the proposed Progressive Receptive Fusion module, which uses temporary channel expansion, complementary depthwise receptive fields, and progressive cross-branch information transfer. We evaluate LightMIS-T, LightMIS-S, and LightMIS using five-fold cross-validation under a common nnU-Net v2.3.1 protocol on DRIVE, Kvasir-SEG, DSB18, BUSI, ISIC-2017, and ISIC-2018. Full LightMIS contains 0.131 M parameters and requires 0.575 GFLOPs for a $3\times256\times256$ input, achieving modality-macro Dice and IoU scores of 86.71% and 78.99%, respectively. Mobile U-ViT obtains 86.75% Dice and 79.07% IoU, so the observed differences are 0.04 and 0.08 percentage points. Relative to Mobile U-ViT, nnWNet, and nnU-Net, LightMIS reduces parameter count by 90.58$-$99.61% and GFLOPs by 82.54$-$96.14%. On an Arm Mali-G52 MC2 GPU, all LightMIS variants achieve full GPU delegation, with median delegated latency ranging from 53.31 ms for LightMIS-T to 138.31 ms for LightMIS. These results demonstrate a favorable accuracy$-$complexity trade-off and on-device execution feasibility for the evaluated tasks. The code is publicly available at https://github.com/AndreiiArhire/LightMIS.

Chinese Translation

我们提出了LightMIS，这是一种可扩展的超轻量级卷积网络系列，用于二维二元医学图像分割，无需学习的阶段解码器。LightMIS通过使用尺度对齐投影块将五级编码器的输出对齐到一个共同的分辨率，进行一次聚合，并通过自适应融合级联精炼融合表示。该级联结合了自适应核融合与所提出的渐进感受融合模块，该模块利用临时通道扩展、互补的深度感受野和渐进的跨分支信息传递。我们在DRIVE、Kvasir-SEG、DSB18、BUSI、ISIC-2017和ISIC-2018数据集上，使用通用的nnU-Net v2.3.1协议对LightMIS-T、LightMIS-S和LightMIS进行了五折交叉验证。完整的LightMIS包含0.131百万个参数，并且对于$3 imes256 imes256$的输入需要0.575 GFLOPs，分别达到了86.71%和78.99%的模态宏Dice和IoU分数。Mobile U-ViT获得了86.75%的Dice和79.07%的IoU，因此观察到的差异为0.04和0.08个百分点。与Mobile U-ViT、nnWNet和nnU-Net相比，LightMIS将参数数量减少了90.58%至99.61%，GFLOPs减少了82.54%至96.14%。在Arm Mali-G52 MC2 GPU上，所有LightMIS变体实现了完整的GPU委托，委托延迟的中位数范围从LightMIS-T的53.31毫秒到LightMIS的138.31毫秒。这些结果展示了良好的准确性与复杂性之间的权衡，以及在评估任务上的设备执行可行性。代码已公开发布在https://github.com/AndreiiArhire/LightMIS。

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cs.CV / 96 / 2609.28328

BronchoTop: Bronchoscopy Navigation via RGB-Only Topological Localization

BronchoTop：基于RGB的支气管镜导航通过拓扑定位

Tomasini, Clara, Murillo, Ana Cristina, Riazuelo, Luis

Abstract

Accurate localization of the bronchoscope within the bronchial tree is essential for clinicians to be able to reach target lesions, perform biopsies and avoid misidentification of airway segments during diagnostic and therapeutic procedures. However, existing navigation systems typically rely on patient-specific CT scans or additional external sensors, increasing cost, setup time and patient radiation exposure. This work presents BronchoTop, a real-time, RGB-only framework for topological bronchoscopy localization that eliminates the need for patient-specific data. BronchoTop estimates scope location relative to a generic airway model through four modules: lumen detection and tracking, lumen-branch label association, probabilistic scope location estimation, and switch verification. By using only standard bronchoscopy video input, BronchoTop provides practical, real-time navigational assistance to physicians. Evaluation on phantom, simulated and real data demonstrates state-of-the-art accuracy, improving existing approaches performance by over 20% on real bronchoscopy sequences. BronchoTop is the first published framework including both the localization algorithms as well as all the real data used, together with code to generate additional simulations, encouraging and facilitating further developments and benchmarking. The results highlight BronchoTop's potential to enhance procedural safety, efficiency and accessibility in clinical and robotic bronchoscopy.

Chinese Translation

在支气管树内准确定位支气管镜对于临床医生到达目标病变、进行活检以及在诊断和治疗过程中避免误识别气道段至关重要。然而，现有的导航系统通常依赖于患者特定的CT扫描或额外的外部传感器，这增加了成本、设置时间和患者的辐射暴露。本研究提出了BronchoTop，一个实时的仅基于RGB的拓扑支气管镜定位框架，消除了对患者特定数据的需求。BronchoTop通过四个模块估计支气管镜相对于通用气道模型的位置：腔道检测与跟踪、腔道分支标签关联、概率性支气管镜位置估计和切换验证。仅使用标准的支气管镜视频输入，BronchoTop为医生提供了实用的实时导航辅助。对虚拟、模拟和真实数据的评估表明其具有最先进的准确性，在真实支气管镜序列中提高了现有方法的性能超过20%。BronchoTop是首个发布的框架，包含定位算法及所有使用的真实数据，并提供生成额外模拟的代码，鼓励和促进进一步的发展和基准测试。结果突显了BronchoTop在提升临床和机器人支气管镜程序的安全性、效率和可及性方面的潜力。

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cs.CV / 97 / 2609.28342

Zero-Shot Object Removal via Attention Masking, Latent Anchoring, and Refinement

通过注意力掩蔽、潜在锚定和精细化实现零-shot物体移除

Taghizadeh, Arman, Krumnack, Ulf, Kühnberger, Kai-Uwe

Abstract

Removing an object from a real image requires more than synthesizing plausible content within a mask: the method must suppress residual object features, preserve the unedited scene, and generate replacement content that is consistent with the surrounding background. This paper approaches object removal from a stage-based perspective and proposes a zero-shot framework for constrained latent inpainting with a frozen pretrained Stable Diffusion model, requiring no task-specific training or model fine-tuning. The method integrates SAM-based mask construction, BLIP image-caption conditioning, DDIM inversion, background-weighted masked null-text optimization, decoder self-attention masking, hard outside-mask latent anchoring, and localized renoise--denoise refinement into a unified pipeline. The method is evaluated through qualitative examples, quantitative local-consistency metrics, and ablation studies. The results demonstrate effective object removal and context-consistent replacement content. The ablations indicate that background-weighted masked NTI is particularly beneficial for structurally complex backgrounds, whereas the no-NTI variant is sufficient in other evaluated examples. Repeated refinement further reduces object remnants and boundary artifacts remaining after the primary editing pass.

Chinese Translation

从真实图像中移除物体不仅需要在掩膜内合成合理的内容：该方法还必须抑制残余物体特征，保留未编辑的场景，并生成与周围背景一致的替代内容。本文从阶段性视角出发，提出了一种零-shot框架，用于在冻结的预训练Stable Diffusion模型下进行受限潜在修复，无需特定任务的训练或模型微调。该方法将基于SAM的掩膜构建、BLIP图像-文本条件、DDIM反演、背景加权掩膜空文本优化、解码器自注意力掩蔽、硬外掩膜潜在锚定和局部重噪-去噪精细化整合成一个统一的流程。通过定性示例、定量局部一致性指标和消融研究对该方法进行了评估。结果表明，该方法能够有效移除物体并生成上下文一致的替代内容。消融实验表明，背景加权掩膜NTI在结构复杂的背景中尤其有利，而无NTI变体在其他评估示例中则足够。重复的精细化进一步减少了主要编辑过程后残留的物体痕迹和边界伪影。

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cs.CV / 98 / 2609.28360

Privacy-Preserving Semantic Segmentation from High-Resolution Depth and Ultra-Low-Resolution RGB

高分辨率深度与超低分辨率RGB的隐私保护语义分割

Huang, Xuying, Daniel, Swithinraj Moses, Pan, Sicong, Houben, Sebastian, Bennewitz, Maren

Abstract

As mobile robots become increasingly integrated into everyday environments, privacy risks arising from onboard cameras have become a growing concern. Ultra-low-resolution (ULR) RGB can mitigate visual privacy exposure at the source, but ULR appearance alone substantially limits semantic and spatial understanding. We therefore introduce a privacy-preserving asymmetric sensing setting that combines high-resolution (HR) depth with ULR RGB, preserving dense geometry while restricting fine-grained visual information. To address the severe information imbalance between HR depth and ULR RGB, we propose a joint 2D framework using HR geometry to guide semantic-oriented RGB reconstruction and RGB-D segmentation. Despite reliable frame-level predictions, consistent scene-level understanding remains challenging under the asymmetric HR depth--ULR RGB setting. We therefore develop an end-to-end 2D-to-3D pipeline that consolidates 2D semantic features for 3D segmentation. Experiments on ScanNet show that our method achieves the best 2D and 3D segmentation performance among privacy-preserving approaches and delivers the strongest zero-shot transfer to SUN RGB-D and SceneNN. Privacy recoverability analysis shows that our proposed HR depth--ULR RGB input reduces the recoverability of sensitive data, and real-robot experiments demonstrate the utility of the resulting 3D semantics for object-goal navigation.

Chinese Translation

随着移动机器人日益融入日常环境，来自车载摄像头的隐私风险已成为一个日益关注的问题。超低分辨率（ULR）RGB可以在源头上减轻视觉隐私暴露，但仅凭ULR外观显著限制了语义和空间理解。因此，我们提出了一种隐私保护的非对称感知设置，将高分辨率（HR）深度与ULR RGB相结合，保留了密集几何信息，同时限制了细粒度的视觉信息。为了应对HR深度与ULR RGB之间严重的信息不平衡，我们提出了一种联合2D框架，利用HR几何信息指导语义导向的RGB重建和RGB-D分割。尽管帧级预测可靠，但在非对称的HR深度-ULR RGB设置下，一致的场景级理解仍然具有挑战性。因此，我们开发了一种端到端的2D到3D管道，将2D语义特征整合用于3D分割。在ScanNet上的实验表明，我们的方法在隐私保护方法中实现了最佳的2D和3D分割性能，并在SUN RGB-D和SceneNN上实现了最强的零样本迁移。隐私可恢复性分析表明，我们提出的HR深度-ULR RGB输入降低了敏感数据的可恢复性，而真实机器人实验则展示了所得到的3D语义在目标导航中的实用性。

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cs.CV / 99 / 2609.28366

AnchorReasoning: A Visual Grounding and Causal Reasoning Dataset in Long-Tail Autonomous Driving Scenarios

AnchorReasoning：长尾自动驾驶场景中的视觉基础和因果推理数据集

Bao, Zhipeng, Zhao, Wenjie, Zhu, Tianle, Que, Haohua, Yang, Chence, Yuan, Geng, Li, Qianwen

Abstract

Vision-language models (VLMs) offer a promising approach to long-tail autonomous driving, but existing driving datasets provide limited supervision for connecting decision-critical visual evidence with reasoning and planning. We introduce AnchorReasoning, a visually grounded reasoning dataset built on WOD-E2E, containing 416,119 annotated frames and 395,379 decision-critical elements across four major categories and 19 fine-grained types. Each frame is organized as a visually grounded chain-of-thought (VG-CoT) that links decision-critical element identification and localization, element attributes and implications, driving-action rationale, and action and trajectory planning. We further develop a curriculum supervised fine-tuning strategy that progressively learns these hierarchical capabilities, together with an object-size-aware grounding metric for evaluating localization quality. Experiments across eight general-purpose, embodied-AI, and AV-specific backbones show that VG-CoT supervision improves grounded reasoning and trajectory prediction. Across models, 5-s ADE and FDE decrease by 7.84 and 11.86, while RFS Frame and Cluster improve by 1.66 and 1.70. These gains are achieved with 18.5 fewer reasoning tokens and 0.32 s/frame lower inference latency on average, demonstrating the value of visually grounded, decision-focused supervision for VLM reasoning and planning in long-tail autonomous driving.

Chinese Translation

视觉语言模型（VLMs）为长尾自动驾驶提供了一种有前景的方法，但现有的驾驶数据集在连接决策关键的视觉证据与推理和规划方面提供的监督有限。我们引入了AnchorReasoning，这是一个基于WOD-E2E构建的视觉基础推理数据集，包含416,119个标注帧和395,379个决策关键元素，涵盖四个主要类别和19个细分类型。每个帧被组织为一个视觉基础的思维链（VG-CoT），将决策关键元素的识别和定位、元素属性及其影响、驾驶动作的理由，以及动作和轨迹规划联系起来。我们进一步开发了一种课程监督微调策略，逐步学习这些层次化能力，并提出了一种关注对象大小的基础度量标准，用于评估定位质量。在八个通用、具身人工智能和自动驾驶特定的基础模型上的实验表明，VG-CoT监督提高了基础推理和轨迹预测。在不同模型中，5秒的平均绝对误差（ADE）和最终位置误差（FDE）分别减少了7.84和11.86，而RFS帧和聚类分别提高了1.66和1.70。这些提升是在平均减少18.5个推理标记和每帧降低0.32秒推理延迟的情况下实现的，展示了视觉基础、以决策为中心的监督在长尾自动驾驶中对VLM推理和规划的价值。

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cs.CV / 100 / 2609.28414

Frozen Flows Forget: Diagnosing and Restoring Lost Motion in a Latent-flow World Model

冻结流动的遗忘：在潜在流动世界模型中诊断和恢复失去的运动

Chen, Xiwen, Li, Rigaudiere Z., Zhou, Zhiruo, Zhu, Xiaojun, Liu, Houde

Abstract

Latent world models that integrate a flow in a frozen self supervised latent space train stably and cheaply, yet silently lose the property manipulation depends on most: motion. The pretrained flow never moves the manipulated object; retraining it with latent-only losses only trades stillness for teleport-like motion. We trace the failure to the training signal, not the representation: anchor-sparse, latent-only supervision never says where along the horizon change belongs. Decode-augmented rollout training (DART) repairs this while keeping the representation frozen, retraining only the flow with decode-path supervision. DART outperforms its latent only parent on the full protocol, restores the temporal structure of motion, and re-couples predicted motion to the scene; at larger scale it further improves prediction quality, closing nearly half the remaining gap to an oracle-informed interpolation reference. Finally, we report an unexpected finding about evaluation: pixel error alone rewards frozen predictions.

Chinese Translation

集成冻结自监督潜在空间中的流动的潜在世界模型训练稳定且成本低廉，但却默默失去了大多数属性操作所依赖的特性：运动。预训练的流动从未移动被操作的对象；仅用潜在损失重新训练它只是在静止与类似瞬移的运动之间进行交换。我们将这一失败追溯到训练信号，而非表示：锚稀疏的、仅潜在的监督从未指明变化在地平线上的位置。解码增强的回滚训练（DART）在保持表示冻结的同时修复了这一问题，仅用解码路径监督重新训练流动。DART在完整协议上超越了其仅潜在的前身，恢复了运动的时间结构，并将预测的运动与场景重新耦合；在更大规模上，它进一步提高了预测质量，几乎缩小了与基于oracle的信息插值参考之间的差距的一半。最后，我们报告了一个关于评估的意外发现：仅像素误差就奖励了冻结的预测。

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cs.CV / 101 / 2609.28424

The Skin-Restricted Reinhard Transform:Uniqueness under a Lightness-Preserving Constraint

皮肤限制的Reinhard变换：在保持亮度约束下的唯一性

KP, Vijesh

Abstract

Catalog skin recolouring has to change pigment and leave shading alone. The classical Reinhard map does not make that split: it rescales lightness by the ratio of standard deviations, and a flat reference swatch therefore flattens the limb. This paper formalises the correction used in our pipeline, the skin-restricted Reinhard transform. It is the diagonal affine map in CIE Lab that translates lightness, matches the chromatic mean, and clamps the chromatic gain to [0.72, 1.18], with moments taken on the central 84% of each channel. A diagonal affine map has six real parameters. The shading constraint forces the lightness gain to +1 and the lightness shift to the difference of means; one-dimensional quadratic optimal transport on each chromatic axis, followed by Euclidean projection onto the gain interval, fixes the other four. Inside that family the four conditions determine every parameter. The content of the result is the forced lightness gain; it is not a uniqueness claim outside the diagonal affine class. For Gaussian marginals the chromatic step is not merely the best affine map: it is the unrestricted Wasserstein-2 map. The same formulae with trimmed moments remain optimal because a positive affine image commutes with quantile trimming. On hands, arms, legs, and feet of nine photographs and three reference tones, the map keeps the lightness contrast ratio at 0.974 +/- 0.029 with chromatic error 0.77 CIE Lab units. Reinhard matching, the linear Monge map, and histogram matching reach a smaller chromatic error only by cutting lightness contrast to about half.

Chinese Translation

目录皮肤重新着色必须改变色素而不改变阴影。经典的Reinhard映射并没有进行这样的区分：它通过标准差的比率重新缩放亮度，因此平坦的参考样本会使肢体变得平坦。本文正式化了我们管道中使用的修正，即皮肤限制的Reinhard变换。它是在CIE Lab中的对角仿射映射，能够转换亮度，匹配色彩均值，并将色彩增益限制在[0.72, 1.18]，其中的矩是基于每个通道中心的84%。对角仿射映射有六个实参数。阴影约束迫使亮度增益为+1，亮度偏移为均值之差；在每个色彩轴上进行一维二次最优传输，然后在增益区间上进行欧几里得投影，确定了其他四个参数。在该家族中，这四个条件决定了每个参数。结果的内容是强制的亮度增益；这并不是对对角仿射类外的唯一性声明。对于高斯边际，色彩步骤不仅仅是最佳仿射映射：它是无限制的Wasserstein-2映射。使用修剪后的矩的相同公式仍然是最优的，因为正的仿射图像与分位数修剪是可交换的。在九张照片和三种参考色调的手、臂、腿和脚上，该映射保持亮度对比率在0.974 +/- 0.029，色彩误差为0.77 CIE Lab单位。Reinhard匹配、线性Monge映射和直方图匹配仅通过将亮度对比度降低到约一半来达到更小的色彩误差。

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cs.CV / 102 / 2609.28434

Predicting the Progression of Adolescent Idiopathic Scoliosis

青少年特发性脊柱侧弯进展的预测

Pullen, Owen, Jamaludin, Amir, Zisserman, Andrew

Abstract

Adolescent Idiopathic Scoliosis is defined as a lateral curvature of the spine that develops during adolescence, without known cause. The condition can result in significant pain and disability, and often progresses rapidly during adolescence. The objective of this paper is to predict the progression of the condition in a temporal sequence from ages 9 to 24, as measured from a sequence of Dual X-ray Absorptiometry (DXA) scans. To this end, we train a transformer model that takes in the curve of the spine to predict curve progression. The model is trained using a large-scale synthetic dataset of spine curves and their time series, covering different curve types and different progression patterns. We show that the model is able to generalise from synthetic to real data by evaluating it on a dataset of real DXA scans covering multiple time points. We find that fine-tuning the model on real data gives a significant boost to performance. The model is able to accurately predict spine curve progression in both scoliosis and normal cases.

Chinese Translation

青少年特发性脊柱侧弯被定义为在青少年时期发展的一种脊柱侧向弯曲，且无已知原因。该病症可能导致显著的疼痛和残疾，并且在青少年时期常常迅速进展。本文的目的是预测该病症在9至24岁期间的进展，基于一系列双能X射线吸收测定（DXA）扫描的时间序列进行测量。为此，我们训练了一个变换器模型，该模型输入脊柱的曲线以预测曲线的进展。模型的训练使用了一个大规模的合成数据集，该数据集包含不同曲线类型及其时间序列。我们展示了该模型能够从合成数据推广到真实数据，通过在一个涵盖多个时间点的真实DXA扫描数据集上进行评估。我们发现，在真实数据上对模型进行微调显著提高了性能。该模型能够准确预测脊柱曲线在脊柱侧弯和正常病例中的进展。

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cs.CV / 103 / 2609.28437

MultiVENT-Raw: A Benchmark for Retrieval and Reasoning over Raw Videos

MultiVENT-Raw：原始视频检索与推理的基准

Kriz, Reno, Etter, David, Martin, Alexander, Carpenter, Cameron, Chakraborty, Debashish, Recknor, Hannah, Iranmanesh, Reihaneh, Maciejewski, Matthew, Murray, Kenton, Yang, Eugene, Van Durme, Benjamin, White, Aaron Steven, Yates, Andrew, Walden, William

Abstract

Online information is increasingly consumed in video format. Much of this comes in the form of *raw video*: continuous footage taken on a cell phone, with a hand-held camera, or via CCTV, which is then directly uploaded to social media platforms and content sharing services. Whereas professional or even amateur-edited footage tends to feature scripted speech, chyrons, graphics, and metadata that help contextualize its subject matter, raw video typically contains none of these things, making it a much more challenging medium for information retrieval and machine understanding. To facilitate progress in this domain, we release MultiVENT-Raw, a multilingual collection of nearly 120,000 primarily raw videos (over 5,300 total hours), paired with 130 events and 222 event-centric queries, along with human-annotated video relevance judgments and human-extracted key facts for relevant videos. MultiVENT-Raw supports both a retrieval task---to identify videos in the collection relevant to a query event---and a generation task---to summarize event-related videos into a coherent report for a target user. We benchmark strong baselines on MultiVENT-Raw, showing both tasks to be challenging even for some of the latest multimodal models.

Chinese Translation

在线信息越来越多地以视频格式消费。其中大部分以*原始视频*的形式出现：通过手机、手持摄像机或闭路电视拍摄的连续录像，随后直接上传到社交媒体平台和内容分享服务。与专业或甚至业余编辑的视频通常包含脚本化的演讲、字幕、图形和有助于情境化其主题的元数据不同，原始视频通常不包含这些元素，这使得其成为信息检索和机器理解的一个更具挑战性的媒介。为了促进该领域的进展，我们发布了MultiVENT-Raw，这是一个多语言的原始视频集合，包含近120,000个主要为原始视频（总时长超过5,300小时），配有130个事件和222个事件中心查询，以及人工标注的视频相关性判断和相关视频的人为提取的关键信息。MultiVENT-Raw支持两种任务：检索任务——识别与查询事件相关的集合中的视频；以及生成任务——将事件相关视频总结成针对特定用户的连贯报告。我们在MultiVENT-Raw上基准测试了强大的基线，显示出即使对于一些最新的多模态模型，这两个任务也具有挑战性。

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cs.CV / 104 / 2609.28439

HaRP: High Dynamic Range Photosequencing through Dual Reversed Shutter Scanning

HaRP：通过双重反向快门扫描实现高动态范围光序列拍摄

Ji, Xiang, Lin, Guixu, Zhao, Jiancheng, Yin, Zhengwei, Zheng, Yinqiang

Abstract

The adoption of CMOS sensors in mobile photography is frequently compromised by the rolling shutter (RS) effect, which introduces geometric distortions and motion artifacts. Particularly, recent rolling shutter with global reset (RSGR) mode, while mitigating some RS issues, also incurs major limitations, including reduced capture speed and compressed dynamic range. To address these problems, we propose a novel dual reversed scanning setup utilizing both RSGR and inverted RSGR views. This solution not only handles the inherent flaws of RSGR by synchronizing complementary exposures to balance the dynamic range across the frames but also introduces an effective method for HDR photosequencing under highly dynamic scenes. Our proposed network first accommodates row-wise complementarity and manages visual shifts by row-adaptive feature alignment. Subsequently, the hallucination module, built upon a correlation-guided mixattention block, integrates the mutually reinforced features to recover missing details. In addition, we construct a coaxial imaging system to collect a real-world dataset, enabling robust training and evaluation beyond numerical simulation. Experimental results demonstrate the twofold benefits of our solution in mitigating RSGR limitations and advancing HDR reconstruction techniques.

Chinese Translation

移动摄影中CMOS传感器的应用常常受到滚动快门（RS）效应的影响，这会引入几何失真和运动伪影。尤其是，最近的带全局重置（RSGR）模式的滚动快门虽然缓解了一些RS问题，但也带来了主要限制，包括捕获速度降低和动态范围压缩。为了解决这些问题，我们提出了一种新颖的双重反向扫描设置，利用RSGR和反向RSGR视图。这一解决方案不仅通过同步互补曝光来平衡帧间的动态范围，从而处理RSGR的固有缺陷，还为在高度动态场景下的HDR光序列拍摄引入了一种有效的方法。我们提出的网络首先适应行间互补性，并通过行自适应特征对齐来管理视觉偏移。随后，基于相关引导的混合注意力模块，整合相互增强的特征以恢复缺失的细节。此外，我们构建了一个同轴成像系统，以收集真实世界数据集，从而实现超越数值模拟的稳健训练和评估。实验结果表明，我们的解决方案在缓解RSGR限制和推动HDR重建技术方面具有双重优势。

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cs.CV / 105 / 2609.28466

The Past Frames the Future: Memory for Autoregressive Video Generation

过去塑造未来：自回归视频生成中的记忆

Chen, Harold Haodong, Guo, Rongjin, Lan, Disen, Shu, Wen-Jie, Zhang, Hongfei, Hu, Hanzhe, Yao, Shengtao, Zhang, Zixin, Zhang, Guibin, Rao, Zhefan, Liu, Jinxiu, Liu, Yexin, Peng, Rui, Liu, Yuhao, Ren, Bin, Yang, Shuai, Chen, Yukang, Khan, Salman, Chen, Ying-Cong, Lim, Ser-Nam, Lau, Rynson W. H., Sebe, Nicu, Cheng, Yu, Yang, Ming-Hsuan, Chen, Qifeng

Abstract

Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environments. Autoregressive (AR) video generation extends visual sequences through causal rollouts. However, a fundamental bottleneck emerges: as the generated sequence expands, practical models must operate under strictly bounded context windows, storage, and computational limits. Consequently, critical historical information, e.g., entity identities, dynamic states, and intervention-induced causal changes, often leaves the active context long before its relevance diminishes. Overcoming this limitation and maintaining temporal persistence constitutes a fundamental memory problem. We present a systematic and comprehensive review of memory mechanisms in AR video generation. We formulate memory operationally as persistent historical information maintained across outer AR steps, capable of influencing future generation even after the originating evidence is no longer locally accessible. Building upon this unified framework, we organize the literature through five complementary perspectives: (I) Forms, the representational carriers of history; (II) Functions, the specific semantic and physical information requiring preservation; (III) Operations, the lifecycle of writing, reading, updating, managing, and integrating memory; (IV) Learning, the optimization of memory behaviors under closed-loop rollouts; and (V) Evaluation, the paradigms for diagnosing genuine memory capabilities. We conclude by synthesizing open challenges, including composable and resource-aware memory architectures, trustworthy state updating, self-rollout learning, and standardized evaluation. By bridging representations, mechanisms, and learning paradigms, this paper establishes a structured foundation for developing reliable, memory-conditioned video generation systems.

Chinese Translation

生成模型的进步提高了视频的保真度，使得长时间范围生成、交互式世界建模和动态视觉环境成为可能。自回归（AR）视频生成通过因果展开扩展视觉序列。然而，一个根本性的瓶颈出现了：随着生成序列的扩展，实际模型必须在严格限制的上下文窗口、存储和计算能力下运行。因此，关键的历史信息，例如实体身份、动态状态和干预引起的因果变化，往往在其相关性减弱之前就已经离开了活动上下文。克服这一限制并保持时间上的持续性构成了一个基本的记忆问题。我们对AR视频生成中的记忆机制进行了系统而全面的回顾。我们将记忆操作性地定义为在外部AR步骤中维持的持久历史信息，能够影响未来的生成，即使原始证据不再在本地可访问。基于这一统一框架，我们通过五个互补的视角组织文献：(I) 形式，历史的表现载体；(II) 功能，需保留的特定语义和物理信息；(III) 操作，记忆的写入、读取、更新、管理和整合的生命周期；(IV) 学习，在闭环展开下优化记忆行为；以及(V) 评估，诊断真实记忆能力的范式。最后，我们总结了开放挑战，包括可组合和资源感知的记忆架构、可信的状态更新、自我展开学习和标准化评估。通过桥接表示、机制和学习范式，本文为开发可靠的、以记忆为条件的视频生成系统奠定了结构化基础。

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cs.CV / 106 / 2609.28473

On the Diffusibility of High-Dimensional Latents

高维潜变量的可扩散性研究

Feng, Chao, Xu, Zhiyang, Chen, Bowei, Xiong, Yuanjun, Wang, Xiyao, Wang, Jui-Hsien, Zhang, Richard, Lin, Zhe, Owens, Andrew, Li, Yijun

Abstract

Representation Autoencoders (RAEs) enable diffusion models to operate in the feature spaces of pretrained visual encoders. However, many off-the-shelf encoders are not optimized for faithful reconstruction, discarding fine-grained visual details. As expected, finetuning these encoders for image reconstruction recovers such details. However, perhaps counterintuitively, this procedure reduces the effective dimensionality of the resulting representation, and the altered geometry has downstream effects on generation. Specifically, we show that using the standard velocity prediction in flow matching in this high-dimensional space requires the model to fit orthogonal noise directions outside the low-dimensional signal manifold, making optimization inefficient. This motivates using the clean data parameterization ($\boldsymbol{x}_{0}$-prediction) instead, which focuses learning on the underlying signal manifold. Across experiments with multiple strong-reconstruction encoders, we show that $\boldsymbol{x}_{0}$-prediction consistently improves text-to-image generation performance.

Chinese Translation

表示自编码器（Representation Autoencoders, RAEs）使得扩散模型能够在预训练视觉编码器的特征空间中操作。然而，许多现成的编码器并未针对真实重建进行优化，导致细粒度视觉细节的丢失。正如预期的那样，对这些编码器进行微调以实现图像重建可以恢复这些细节。然而，或许出乎意料的是，这一过程降低了所得到的表示的有效维度，并且改变的几何结构对生成过程产生了下游影响。具体而言，我们展示了在这个高维空间中使用标准速度预测进行流匹配需要模型拟合低维信号流形之外的正交噪声方向，从而使优化效率降低。这促使我们改用干净数据参数化（$oldsymbol{x}_{0}$-预测），该方法将学习集中在基础信号流形上。在多个强重建编码器的实验中，我们表明，$oldsymbol{x}_{0}$-预测始终能改善文本到图像生成的性能。

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人工智能 (Artificial Intelligence)

cs.AI / 1 / 2609.26836

Silent Failures in Agent-Tool Interaction: An Audit of ToolUniverse

代理工具交互中的无声失败：ToolUniverse的审计

Gopalan, Shreya, Singh, Devansh, Narayanan, Sundaraparipurnan

Abstract

Agentic AI systems are increasingly adopting automated pipelines that integrate multiple tools. While prior research and benchmarks have studied about task success and task completion of these agentic systems, the research about agent to tool interaction, specifically in biology agentic workflow is limited. This study investigates specific failures in agent to tool interaction where a tool invocation appears successful, some or all of the information or functionality from the tool via API/ wrapper is incomplete or missing and there are no communications / notifications to the user or the agent about such missing information. We call this a silent failures as the user or the agents are not aware that such failure has occurred. For the purposes of this study we developed an audit mechanism to identify such silent failures in Agent to tool interaction, by examining 15 scientific tools (and their associated API documentation and tool documentations) integrated within ToolUniverse environment (ToolUniverse serves as our experimental environment rather than the object of the study itself). We structure our study around 7 failure locus characterising where the failure occurs in the chain. We observed 91 failures (manually validated post LLM based candidate discovery and automated testing), most frequent of them being missing data or fields and inconsistencies in search, filtering or ranking criteria. Most of the 91 failures occurred in API layer (51) or wrapper layer (25), with a potential of silent failure amplification downstream. The results show that silent failures originate upstream of the event and propagate downstream into apparently valid scientific outputs. We propose a concept of contextual reliability to handle such failures and suggest mechanisms for testing, disclosing, monitoring, and measuring such failures across the agent-tool interaction pipeline.

Chinese Translation

代理人工智能系统越来越多地采用集成多个工具的自动化流程。尽管之前的研究和基准测试已经研究了这些代理系统的任务成功率和任务完成情况，但关于代理与工具交互的研究，特别是在生物学代理工作流程中的研究仍然有限。本研究调查了代理与工具交互中的特定失败情况，其中工具调用看似成功，但通过API/包装器获取的信息或功能部分或全部不完整或缺失，并且没有向用户或代理传达有关缺失信息的任何通信/通知。我们将这种情况称为无声失败，因为用户或代理并未意识到这种失败的发生。为此，我们开发了一种审计机制，以识别代理与工具交互中的无声失败，通过检查在ToolUniverse环境中集成的15个科学工具（及其相关的API文档和工具文档）。我们的研究围绕7个失败位置进行结构化，描述失败发生的链条位置。我们观察到91个失败（在基于LLM的候选发现和自动化测试后手动验证），其中最常见的是缺失数据或字段，以及搜索、过滤或排名标准的不一致性。91个失败中大多数发生在API层（51个）或包装层（25个），并可能在下游放大无声失败。结果表明，无声失败源于事件的上游，并向下传播到看似有效的科学输出。我们提出了一个上下文可靠性的概念来处理这些失败，并建议在代理与工具交互流程中测试、披露、监控和衡量这些失败的机制。

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cs.AI / 2 / 2609.26891

Harness as a Language: A Minimalist Agent Framework With Maximal Expressivity

作为语言的工具：一个具有最大表达能力的简约代理框架

Li, Zhening, Liu, Joshua, Vukelic, Mateja, Shen, Nicole, Lall, Supriya, Thakur, Amitayush, Zhang, Alex, Khattab, Omar, Light, Jonathan, Solar-Lezama, Armando

Abstract

Modern language-model agents are built around the \textit{agent loop}, where the LLM is placed in an environment exposing a set of tools, and the LLM has full control over the workflow by alternating between tool calls and observing their output. However, certain workflows currently require additional engineering beyond the agent loop itself, such as memory systems and self-improving systems. We built an LLM agent framework, JAZ, to explore the extent to which a minimal harness that is little more than the agent loop itself can accomplish tasks these specialized systems are built for. JAZ exposes a single LLM-based primitive invoke and provides a set of built-in hooks that allow the programmer to apply constraints and monitoring. Generalizing existing code-mode agent loops, \texttt{invoke} is the simplest loop that satisfies two defining properties: (1) the LLM can write arbitrary executable code that can include recursive \texttt{invoke}; (2) everything visible to the LLM --- all inputs to \texttt{invoke} as well as its interaction history with the code environment --- are variables in the code environment. We motivate our design from first principles, viewing \texttt{invoke} as a language primitive representing a function whose implementation is provided at runtime by an LLM every time it is called. To validate the design of our core \texttt{invoke} primitive, we evaluate \texttt{invoke} --- with only prompting, no manually designed tools, harness, or external systems (e.g., memory or the file system) --- on workflows traditionally implemented through specialized external harnesses. On long-horizon workflows requiring recall beyond the context window, JAZ invoke outperforms Letta (MemGPT) by 8\% at half its cost on the recall-heavy portion of StuLife. On continual self-improvement, JAZ invoke outperforms ACE by 4\% at a lower cost on AppWorld.

Chinese Translation

现代语言模型代理围绕 extit{代理循环}构建，其中大型语言模型（LLM）被置于一个环境中，该环境暴露出一组工具，并且LLM通过交替调用工具和观察其输出来完全控制工作流程。然而，某些工作流程目前需要超出代理循环本身的额外工程，例如记忆系统和自我改进系统。我们构建了一个LLM代理框架JAZ，以探索一个仅仅是代理循环本身的简约工具能够在多大程度上完成这些专门系统所构建的任务。JAZ暴露了一个基于LLM的原始调用（invoke），并提供了一组内置钩子，允许程序员施加约束和监控。通过对现有代码模式代理循环的概括， exttt{invoke}是满足两个定义属性的最简单循环：（1）LLM可以编写任意可执行代码，其中可以包括递归的 exttt{invoke}；（2）LLM可见的所有内容——所有传递给 exttt{invoke}的输入以及其与代码环境的交互历史——都是代码环境中的变量。我们从基本原理出发，激励我们的设计，将 exttt{invoke}视为一种语言原语，表示一个函数，其实现由LLM在每次调用时在运行时提供。为了验证我们核心 exttt{invoke}原语的设计，我们在传统上通过专门的外部工具实现的工作流程上评估 exttt{invoke}——仅使用提示，没有手动设计的工具、工具框架或外部系统（例如，记忆或文件系统）。在需要超出上下文窗口的长期工作流程中，JAZ的调用在StuLife的重召回部分以其一半的成本超越了Letta（MemGPT）8%。在持续自我改进方面，JAZ的调用在AppWorld上以更低的成本超越了ACE 4%。

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cs.AI / 3 / 2609.26911

TwinCheck: Evidence-Grounded Negative-Twin Verification for Stateful Tool Agents

TwinCheck：基于证据的状态工具代理负双胞胎验证

Dai, Jiaxuan, Huang, Tianyi

Abstract

A single locally plausible tool call can derail an otherwise successful agent trajectory. Suspicion alone does not justify intervention, because the replacement itself can introduce the very failure verification is meant to prevent. We introduce TwinCheck, an inference-time verification policy that considers replacement only when the trace satisfies an evidence condition tied to a trace-local failure hypothesis. It constructs a trace-grounded counterfactual alternative, a negative twin, and replaces the agent's proposal only if the twin passes structural checks and the pairwise verifier prefers it in both candidate orders. For paired evaluation, exact replay holds the agent's parsed responses and actions fixed until the first accepted replacement, separating intervention effects from resampling. In the primary analysis of 159 multi-turn BFCL V4 tasks with complete exact-replay pairs, the complete policy raises task success for GPT-5.6 Sol from 45.3% to 58.5% (95% task-bootstrap CI [8.2, 18.8]), with no observed success-to-failure regressions. Together, these findings recast execution-boundary repair as a constrained comparison, making the counterfactual action itself the object of verification.

Chinese Translation

单个局部合理的工具调用可能会使原本成功的代理轨迹偏离。仅凭怀疑不足以证明干预的必要性，因为替换本身可能引入验证旨在防止的失败。我们提出了TwinCheck，这是一种推理时验证策略，仅在轨迹满足与轨迹局部失败假设相关的证据条件时考虑替换。它构建了一个基于轨迹的反事实替代，即负双胞胎，并仅在双胞胎通过结构检查且成对验证器在两个候选顺序中均偏好该替代时，才替换代理的提议。对于成对评估，精确重放保持代理解析的响应和动作固定，直到首次接受的替换，从而将干预效果与重采样分离。在对159个多轮BFCL V4任务的主要分析中，完整策略将GPT-5.6 Sol的任务成功率从45.3%提高到58.5%（95%任务自助置信区间[8.2, 18.8]），未观察到成功到失败的回归。这些发现将执行边界修复重新构建为一种受限比较，使反事实行为本身成为验证的对象。

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cs.AI / 4 / 2609.26927

Building Socio-Affective Artificial Intelligence for Interactive Multi-Agent Simulations

构建用于交互式多智能体模拟的社会情感人工智能

Berga, David

Abstract

The objective of this article is to provide design principles and a software architecture for enabling interaction between humans and multiple agents in simulated dynamic worlds. This connects the current era of general artificial intelligence (AI/AGI) with the proliferation of transformer-based conversational agents and the increased computational capabilities. Given an overview of current and previous multi-agent theories of mind (socially and affectively-aware agents), the existence of an integrative design of agent interactions with themselves and with humans must be crucial for understanding how to create sustainable and governance in future human-agent reasoning systems. In this work is presented a software "AGIMUD" that integrates: A. socially-aware reasoning and emotion in agent behavior and interaction, B. a design of human multimodal scheme for human users, artificial agents and simulated worlds, and C. distributing the AI processing through the network to enable multiple autonomous agents. These integrations allow the dynamic world recreation as multi-user dungeons (MUDs) where both agents and humans can interact simultaneously in real time. Find the code online in https://github.com/dberga/AGIMUD.

Chinese Translation

本文的目的是提供设计原则和软件架构，以促进人类与多个智能体在模拟动态世界中的互动。这将当前的通用人工智能（AI/AGI）时代与基于变换器的对话智能体的普及以及计算能力的提升联系起来。在概述当前和过去的多智能体心智理论（社会和情感意识智能体）后，智能体与自身及人类之间互动的综合设计的存在对于理解如何在未来的人机推理系统中创造可持续性和治理至关重要。本文展示了一款名为“AGIMUD”的软件，它集成了：A. 智能体行为和互动中的社会意识推理和情感，B. 为人类用户、人工智能体和模拟世界设计的人类多模态方案，以及 C. 通过网络分配AI处理，以支持多个自主智能体。这些集成使得动态世界的重建成为多用户地下城（MUD），在其中智能体和人类可以实时同时互动。相关代码可在 https://github.com/dberga/AGIMUD 在线获取。

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cs.AI / 5 / 2609.26929

Which Objectives Need a Dial? Predicting Objective Conflict and Covering Trade-offs in Steerable Pluralistic Alignment

哪些目标需要调节？可调节多元对齐中的目标冲突预测与权衡覆盖

Tsoi, David, Dönmez, Esra

Abstract

People hold diverse, sometimes conflicting values, so no single aligned model can satisfy everyone. Pluralistic alignment therefore calls for steerable models that can balance competing objectives differently. Multi-Objective Direct Preference Optimization (MODPO) does this by using an objective weight to span a continuum of trade-offs. We study two questions: when can one model improve two objectives simultaneously, and how can many trade-offs be covered without training a separate model for each? Across seven objective pairs from HelpSteer and UltraFeedback, two pre-training measurements predict whether objectives align or conflict for human-annotated data, but not for AI-annotated data, where response length and repetition confound reward-model scores. For broader trade-off coverage, selecting the nearest trained model and merging model parameters both help, but neither consistently matches direct training. These findings yield practical guidance for building steerable models that serve diverse preferences.

Chinese Translation

人们持有多样化且有时相互冲突的价值观，因此没有单一的对齐模型能够满足所有人。因此，多元对齐要求可调节模型能够以不同方式平衡竞争目标。多目标直接偏好优化（Multi-Objective Direct Preference Optimization, MODPO）通过使用目标权重来涵盖权衡的连续体。我们研究了两个问题：何时一个模型可以同时改善两个目标，以及如何在不为每个目标训练单独模型的情况下覆盖多个权衡。在来自HelpSteer和UltraFeedback的七对目标中，两个预训练测量可以预测人类标注数据中目标的对齐或冲突，但对于AI标注数据则不然，因为响应长度和重复性会混淆奖励模型的评分。为了更广泛地覆盖权衡，选择最近的训练模型和合并模型参数都有帮助，但都无法始终与直接训练相匹配。这些发现为构建能够服务于多样化偏好的可调节模型提供了实用指导。

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cs.AI / 6 / 2609.26952

Escaping Python Dependency Hell: A Hybrid Replay-and-Repair Pipeline for Python Dependency Resolution

摆脱 Python 依赖地狱：一种混合重放与修复的 Python 依赖解析管道

Poweska, Veronica, Oyanguren, Ariana, Pourleyli, Jessica, Khanzadeh, Sourena, Alalfi, Manar

Abstract

Dependency conflicts in Python ecosystems arise from incompatible version constraints, missing packages, and undocumented compatibility relationships, causing many real-world code snippets to fail at execution. This paper presents PLLM+, a hybrid dependency-repair pipeline evaluated on the HG2.9K benchmark of 2,891 dependency-failing snippets. PLLM+ prioritizes inexpensive deterministic steps before invoking LLM-based repair: static AST-based interpreter inference, replay of historically successful dependency configurations from the competition-provided solutions database, and live PyPI validation of candidate package versions. When these steps do not resolve a case, the system falls back to a structured LLM-based repair loop with typed error classification and Proposer/Critic agents. On HG2.9K, PLLM+ solves 1,500 out of 2,891 snippets, compared with 1,169 solved by the PLLM baseline. It also reduces average runtime from 368.7 to 71.8 seconds per snippet. Most successful fixes come from replaying known configurations: 1,495 of the 1,500 successful fixes are produced by the solutions database, while the LLM fallback accounts for 5 additional fixes. These results suggest that, in this benchmark setting, deterministic reuse of previously validated dependency configurations is a simple and effective strategy, with LLM-based repair serving as a secondary fallback for cases not covered by prior solutions.

Chinese Translation

Python 生态系统中的依赖冲突源于不兼容的版本约束、缺失的包以及未记录的兼容性关系，导致许多实际代码片段在执行时失败。本文提出了 PLLM+，一种混合依赖修复管道，在 HG2.9K 基准测试中评估了 2,891 个依赖失败的代码片段。PLLM+ 在调用基于 LLM 的修复之前，优先执行成本低廉的确定性步骤：基于静态 AST 的解释器推断、重放来自竞赛提供的解决方案数据库的历史成功依赖配置，以及对候选包版本的实时 PyPI 验证。当这些步骤无法解决问题时，系统会回退到一个结构化的基于 LLM 的修复循环，其中包括类型化错误分类和提议者/评论者（Proposer/Critic）代理。在 HG2.9K 中，PLLM+ 解决了 2,891 个代码片段中的 1,500 个，而 PLLM 基线仅解决了 1,169 个。它还将每个代码片段的平均运行时间从 368.7 秒减少到 71.8 秒。大多数成功的修复来自重放已知配置：1,500 个成功修复中的 1,495 个是由解决方案数据库生成的，而 LLM 回退则贡献了 5 个额外的修复。这些结果表明，在该基准测试环境中，确定性地重用先前验证的依赖配置是一种简单而有效的策略，而基于 LLM 的修复则作为对未被先前解决方案覆盖的情况的次要回退。

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cs.AI / 7 / 2609.26986

Same evidence, different judgments: Evidence noncommutative in vision/speech-text conflicts

相同证据，不同判断：视觉/语音-文本冲突中的证据非交换性

Li, Zhuoyun, Wang, Boxuan, Huang, Xiaowei, Dong, Yi

Abstract

For multimodal large language models, when images or speech conflict with accompanying text, measured text reliance can entangle modality preference with evidence position. Earlier studies of text bias often used a fixed evidence order or moved task instructions with the evidence, leaving the contribution of order unclear. In this paper, we use a paired comparison that keeps the instructions and evidence content fixed and swaps only the positions of the two sources to quantify this potential influence. Across vision and speech models, placing an image or recording after conflicting text consistently shifts answers toward its content. We also revisit previous studies and analyze why their experimental settings can lead to misleading conclusions. These findings reveal cross-modal evidence noncommutativity: the same evidence can lead to different judgments when its order changes, and placing perceptual evidence later can increase the model's reliance on its content.

Chinese Translation

对于多模态大型语言模型，当图像或语音与伴随文本发生冲突时，测量的文本依赖性可能会将模态偏好与证据位置纠缠在一起。早期关于文本偏见的研究通常使用固定的证据顺序或将任务指令与证据一起移动，导致顺序的贡献不明确。在本文中，我们采用了一种配对比较方法，保持指令和证据内容固定，仅交换两个来源的位置，以量化这种潜在影响。在视觉和语音模型中，将图像或录音放置在冲突文本之后，答案一致地向其内容倾斜。我们还重新审视了之前的研究，并分析了为什么它们的实验设置可能导致误导性结论。这些发现揭示了跨模态证据的非交换性：相同的证据在顺序变化时可以导致不同的判断，而将感知证据放在后面可以增加模型对其内容的依赖。

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cs.AI / 8 / 2609.27035

Reinforcement Learning with Decomposed Subtasks

基于分解子任务的强化学习

Terzolo, Mattie, Sacha, Mikolaj, Sinha, Ayan, Rabinovich, Andrew

Abstract

Group Relative Policy Optimization (GRPO) and related policy-gradient methods for training language model agents collapse an entire multi-turn rollout into a single scalar trajectory reward before it enters the policy update. When the task composes distinct skills, especially under sparse and delayed environmental feedback, this collapsing is lossy: the optimizer must implicitly infer which competency drove the outcome and how that should change behavior. We argue the right primitive is not a better scalar but a decomposition: trajectory reward should be split along subtasks before it enters the policy update. We introduce Reinforcement Learning with Decomposed Subtasks (RLDS), whose core is Subtask-Decomposed Advantage Estimation (SDAE): a replacement for the scalar GRPO advantage that splits trajectory reward into per-subtask shares on a fixed taxonomy, computes a group-relative advantage per subtask, and distributes per-token credit by weighting each subtask's advantage by its importance, concentrating it around the step where a reflection marks that subtask's execution as consequential. We evaluate on four agentic benchmarks: FrozenLake (sparse grid navigation), HotpotQA (multi-hop QA, one retrieval tool), ScienceWorld (long-horizon embodied science), and DeepResearch (long-form research, four tools, composite rubric reward). Heterogeneity diagnostics emitted during training show where decomposition pays off - gains scale with subtask heterogeneity, largest on the high-heterogeneity tasks ScienceWorld (+11.5 points, paired-bootstrap 95% CI [+9.8, +13.3]) and FrozenLake (+9.8 points, [+7.0, +12.8]), and within noise on HotpotQA and DeepResearch, where the diagnostics predicted little to recover. ScienceWorld is also more compute-efficient under RLDS than scalar GRPO (-10.9% wall-clock per step), as long rollouts amortize the fixed reflect-and-grade overhead.

Chinese Translation

群体相对策略优化（Group Relative Policy Optimization, GRPO）及相关的策略梯度方法在训练语言模型代理时，将整个多轮回合压缩为一个单一的标量轨迹奖励，然后再进行策略更新。当任务由不同技能组成，尤其是在稀疏和延迟的环境反馈下，这种压缩是有损的：优化器必须隐式推断出哪种能力驱动了结果，以及如何改变行为。我们认为，正确的原始方法不是更好的标量，而是分解：轨迹奖励应在进入策略更新之前沿子任务进行拆分。我们提出了基于分解子任务的强化学习（Reinforcement Learning with Decomposed Subtasks, RLDS），其核心是子任务分解优势估计（Subtask-Decomposed Advantage Estimation, SDAE）：这是标量 GRPO 优势的替代方案，它将轨迹奖励拆分为固定分类法下的每个子任务份额，计算每个子任务的群体相对优势，并通过按每个子任务的重要性加权其优势来分配每个标记的信用，将其集中在反思标记该子任务执行为重要的步骤附近。我们在四个代理基准上进行了评估：FrozenLake（稀疏网格导航）、HotpotQA（多跳问答，一个检索工具）、ScienceWorld（长时间范围的具身科学）和 DeepResearch（长篇研究，四个工具，复合评分标准）。在训练过程中发出的异质性诊断显示了分解的收益——收益随着子任务异质性的增加而扩大，在高异质性任务 ScienceWorld 上最大（+11.5 分，配对自助法 95% 置信区间 [+9.8, +13.3]）和 FrozenLake（+9.8 分， [+7.0, +12.8]），而在 HotpotQA 和 DeepResearch 上则处于噪声范围内，诊断预测恢复的可能性较小。在 RLDS 下，ScienceWorld 的计算效率也优于标量 GRPO（每步减少 10.9% 的实际时间），因为长回合摊销了固定的反思和评分开销。

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cs.AI / 9 / 2609.27037

Training Intelligent Voice Assistant Wakeup with Controllable Synthetic Conversations

通过可控合成对话训练智能语音助手唤醒系统

Sowański, Marcin, Leszczyński, Kacper, Krzywicki, Kacper, Wodnicki, Krzysztof

Abstract

Wake word detection is a critical component of virtual assistants, serving as the gateway to seamless user interactions. This paper introduces a novel wake-up system that extends traditional direct keyword detection with contextual trigger detection. After an initial wake word activation, the system uses reasoning to distinguish between user commands and unrelated speech, ensuring efficient and context-aware engagement. We present a data generation architecture that produces a 62.3-hour corpus of controllable multi-speaker conversations containing direct invocations, contextual follow-ups, and non-addressed speech. Experimental results demonstrate the effectiveness of the proposed approach across diverse synthetic conversational scenarios. We release the code, dataset and trained models to promote reproducibility and further advancements in intelligent assistant technologies.

Chinese Translation

唤醒词检测是虚拟助手的关键组成部分，作为无缝用户交互的入口。本文介绍了一种新颖的唤醒系统，该系统扩展了传统的直接关键词检测，增加了上下文触发检测。在初始唤醒词激活后，该系统利用推理来区分用户命令和无关言语，从而确保高效且具有上下文意识的互动。我们提出了一种数据生成架构，生成了62.3小时的可控多说话者对话语料库，包含直接调用、上下文跟进和未被提及的言语。实验结果证明了所提方法在多样化合成对话场景中的有效性。我们发布了代码、数据集和训练模型，以促进可重复性和智能助手技术的进一步发展。

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cs.AI / 10 / 2609.27038

Are Stated Reasoning Steps Causally Load-Bearing?

陈述的推理步骤是否具有因果承载力？

Bhupatiraju, Abhiram, Nyaupane, Rayan

Abstract

Chain-of-thought (CoT) monitoring assumes that the reasoning a model writes reflects the computation that directly produces its answer. Previous faithfulness metrics have been predominantly behavioral, as they simply edit the reasoning text and observe the resulting answer. However, our methodology aims to measure faithfulness causally at the activation level, specifically on self-generated reasoning. Unlike previous causal audits, which measure degradation, our interventions carry a known predicted target. In this way, each patch should switch the answer to a specific counterfactual entity derivable by construction. Specifically, we use synthetic multi-hop lookup tasks (2-6 hops). We patch the residual stream at the token span where the model states each intermediate step with the corresponding activations from a counterfactual run. For Qwen3-4B, 76.9% +/- 2.8% of stated steps are causally load-bearing (CLB) at the most responsive mid-network layer (random-position null: 11.3%; patching the underlying prompt fact: 83%, so stated steps carry approximately 96% of the achievable effect). Moreover, the standard behavioral test on the same items yields 88.2%, which overstates causal faithfulness by 11.4 percentage points (item-matched; 111:14 discordant pairs, p < 1e-15) and, for the easiest items, by up to 20 percentage points. This gap also has a clear capability dimension. Qwen3-1.7B is far less causally faithful overall (54.8%), with its faithfulness collapsing as reasoning depth increases (68% at 2 hops to 30% at 6), while Qwen3-4B remains relatively flat. Although stated reasoning can be causally meaningful, standard behavioral tests tend to overestimate its causal faithfulness, particularly on easier examples where model reasoning appears most fluent.

Chinese Translation

链式思维（Chain-of-thought, CoT）监控假设模型所写的推理反映了直接产生其答案的计算。以往的忠实度度量主要是行为性的，因为它们仅仅编辑推理文本并观察结果答案。然而，我们的方法旨在在激活层面上因果地测量忠实度，特别是针对自生成的推理。与以往测量退化的因果审计不同，我们的干预措施具有已知的预测目标。通过这种方式，每个修补都应将答案切换到一个特定的可构造的反事实实体。具体而言，我们使用合成的多跳查找任务（2-6跳）。我们在模型陈述每个中间步骤的标记范围内修补残差流，并使用来自反事实运行的相应激活。对于 Qwen3-4B，76.9% +/- 2.8% 的陈述步骤在最响应的中间网络层上是因果承载的（随机位置无效：11.3%；修补基础提示事实：83%，因此陈述步骤大约承载了96%的可实现效果）。此外，对相同项目的标准行为测试结果为88.2%，这高估了因果忠实度11.4个百分点（项目匹配；111:14 不一致对，p < 1e-15），对于最简单的项目，高达20个百分点。这一差距也具有明显的能力维度。总体而言，Qwen3-1.7B的因果忠实度远低于 Qwen3-4B（54.8%），随着推理深度的增加，其忠实度急剧下降（在2跳时为68%，在6跳时为30%），而Qwen3-4B则保持相对平稳。尽管陈述的推理可能具有因果意义，标准行为测试往往会高估其因果忠实度，特别是在模型推理显得最流畅的简单示例中。

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cs.AI / 11 / 2609.27041

Math Reasoning in LLMs is Organized by Approach, Not Topic

大型语言模型中的数学推理是按方法组织的，而非主题

Goudarzi, Sajad, Zamanifard, Samaneh, Nasiri, Moloud, Rahimian, Hamed

Abstract

Mathematical reasoning benchmarks are typically organized by topic, but language models may organize their internal computation by reusable reasoning approach instead. In this paper, we investigate whether open math-capable LLMs organize internally by topical sub-skill or by reasoning approach, and we present evidence that the approach is the key. We introduce a generation-replay protocol: a model first generates a solution, after which we replay the exact prompt-plus-generation trajectory and extract activation-importance signatures over the reasoning tokens. We cluster these signatures without supervision across eight models and five mathematical reasoning sources, then evaluate the recovered structure with structural, semantic, and intervention tests. Across all 40 model-source cells, the recovered clusters outperform matched-size random baselines. Two independent frontier-LLM judges find approach-level coherence in 77-82% of real clusters versus 6-11% in within-source controls, and topic-pure clusters usually receive labels finer than the topic itself. In approach-controlled prompting, changing the requested reasoning approach shifts cluster assignment in seven of eight model conditions, whereas paraphrases largely preserve it. These results indicate that math-capable LLMs organize internal mathematical computation by reasoning approach rather than benchmark topic. The implication is that topic-stratified benchmarks and topic-balanced training corpora can still miss the axis that matters: even deliberately topic-balanced corpora may remain imbalanced over reasoning approaches.

Chinese Translation

数学推理基准通常是按主题组织的，但语言模型可能是按可重用的推理方法来组织其内部计算。本文探讨了开放的数学能力大型语言模型（LLMs）是否是按主题子技能或推理方法进行内部组织，并提供了证据表明推理方法是关键。我们引入了一种生成重放协议：模型首先生成一个解决方案，然后我们重放确切的提示加生成轨迹，并提取推理标记的激活重要性特征。我们在八个模型和五个数学推理来源中对这些特征进行无监督聚类，然后通过结构、语义和干预测试评估恢复的结构。在所有40个模型-来源单元中，恢复的聚类在表现上优于匹配大小的随机基线。两位独立的前沿大型语言模型评审发现，77-82%的真实聚类在方法层面上具有一致性，而在来源内控制中仅为6-11%。主题纯聚类通常获得的标签比主题本身更细致。在方法控制的提示中，改变请求的推理方法会在八种模型条件中的七种中改变聚类分配，而同义改写则在很大程度上保持不变。这些结果表明，具有数学能力的大型语言模型是按推理方法而非基准主题来组织内部数学计算的。这意味着，按主题分层的基准和主题平衡的训练语料库仍可能遗漏重要的轴线：即使是故意平衡主题的语料库，在推理方法上也可能保持不平衡。

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cs.AI / 12 / 2609.27051

Propose, Don't Judge: An Anytime-Valid Referee for LLM Agents That Mine Investment Factors

提出，而非评判：一个适用于挖掘投资因子的 LLM 代理的随时有效评审者

Qu, Bo, Chen, Mingguang, Wang, Licheng

Abstract

Language-model agents now run the whole of quantitative factor research: they propose investment factors, backtest them, select the survivors and retire them. We ask which of those jobs an agent should keep. Our answer is governed self-evolution: the agent may propose, and a frozen statistical referee that the agent cannot touch must judge. The referee scores each candidate only on market outcomes revealed after submission, by betting, so its false-discovery guarantee holds at every stopping time for any proposal policy. We cross three proposers (a script, a bandit and a language model) with this referee and with three deliberately leaky ones, in a synthetic world with planted truth, a probe-authoring environment and a ten-year walk-forward on the CSI 500. Who judges sets the number of false admissions: the frozen referee admits 5-11 times fewer sub-threshold factors than the leaky referees under a scripted proposer, and no proposer closes that gap. Who proposes sets the yield: the language model beats the script, matches the bandit, and adds the one capability a bandit lacks, writing its own diagnostic probes. The certificate's price is time: an admitted true factor waits about 500 trading days, and the certified portfolio's Sharpe ratio therefore trails an ungated one. Judging belongs to the procedure; proposing and instrument-making belong to the agent.

Chinese Translation

语言模型代理现在负责整个定量因子研究：它们提出投资因子，进行回测，选择存活者并将其淘汰。我们探讨代理应保留哪些工作。我们的答案是自我进化：代理可以提出，而一个冻结的统计评审者（代理无法触及）必须进行评判。评审者仅根据提交后揭示的市场结果进行评分，通过下注，因此其虚假发现保证在任何提案政策的每个停止时间都有效。我们在一个合成世界中交叉了三个提案者（一个脚本、一个赌博者和一个语言模型）与这个评审者，以及三个故意存在漏洞的评审者，使用了植入真相的探测作者环境和对 CSI 500 的十年前向回测。谁进行评判决定了虚假录取的数量：在脚本提案者下，冻结的评审者承认的低于阈值因子比存在漏洞的评审者少 5-11 倍，而没有提案者能够缩小这一差距。谁进行提案决定了收益：语言模型优于脚本，匹配赌博者，并增加了赌博者所缺乏的一个能力，即编写自己的诊断探针。证书的代价是时间：被承认的真实因子大约等待 500 个交易日，因此经过认证的投资组合的夏普比率落后于未受限制的投资组合。评判属于程序；提案和工具制作属于代理。

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cs.AI / 13 / 2609.27087

Policy-as-Skill: Governed LLM Decision Support with Evidence, Deterministic Control, and Audit

政策即技能：基于证据、确定性控制和审计的 LLM 决策支持

Mohsenzadegan, Kabeh, Tavakkoli, Vahid, Kyamakya, Kyandoghere

Abstract

Organizations increasingly use LLMs for policy, compliance, risk, and operational decision support, requiring evidence validation, review routing, version control, and auditability. We introduce Policy-as-Skill (PaS), a modular runtime that packages these functions as executable, versioned policy capabilities. Thirteen methods are evaluated with a fixed Gemma4 backend on 600 development tasks. PaS+Audit achieves 53.8% exact accuracy, macro-F1 0.346, review F1 0.854, citation precision 1.000, policy-reference recall 0.984, and audit completeness 1.000, outperforming LLM+RAG on most governance and review metrics. Deterministic control raises aggregate accuracy to 61.2% but is strongly task dependent, supporting selective rather than universal rule-based intervention.

Chinese Translation

组织越来越多地使用 LLM（大语言模型）进行政策、合规、风险和运营决策支持，这要求进行证据验证、审查路由、版本控制和审计。我们提出了政策即技能（Policy-as-Skill, PaS），这是一种模块化运行时，将这些功能打包为可执行的、版本化的政策能力。在固定的 Gemma4 后端上对 600 个开发任务评估了 13 种方法。PaS+Audit 实现了 53.8% 的准确率，宏观 F1 值为 0.346，审查 F1 值为 0.854，引用精度为 1.000，政策引用召回率为 0.984，审计完整性为 1.000，在大多数治理和审查指标上优于 LLM+RAG。确定性控制将整体准确率提高到 61.2%，但强烈依赖于任务，支持选择性而非普遍的基于规则的干预。

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cs.AI / 14 / 2609.27105

Provably Complete Generalized Planning with LLMs

基于大语言模型的可证明完整的广义规划

Stein, Katharina, Jain, Chaahat, Hoffmann, Jörg, Koller, Alexander

Abstract

Generalized planning aims to compute a plan that solves all instances of a planning domain. Recent work has used LLMs to automatically generate and debug such generalized plans in the form of Python programs and achieved perfect test data coverage for several domains. However, whether these generalized plans are actually complete, i.e. solve all instances of the domain, could only be determined by manual evaluation. Here, we present an approach for automatically generating generalized plans in Lean together with proofs of their completeness relative to a specification of the domain constraints provided as input. We introduce a semantic-preserving PDDL-to-Lean conversion, and use an LLM to generate both the generalized plan and the formal proof that it solves every instance satisfying the domain constraints. The correctness of the completeness proof is determined by Lean's kernel. We evaluate our approach on 13 commonly used benchmark domains, using GPT-5.6-Sol as the LLM. For 12 of the domains we obtain generalized plans together with valid completeness proofs. This is a major advancement of the state of the art in automatic generalized-plan completeness proofs.

Chinese Translation

广义规划旨在计算出一个能够解决规划领域所有实例的计划。近期的研究利用大语言模型（LLMs）自动生成和调试这种广义计划，形式为Python程序，并在多个领域实现了完美的测试数据覆盖。然而，这些广义计划是否真正完整，即是否能够解决该领域的所有实例，仅能通过人工评估来确定。在此，我们提出了一种自动生成广义计划的Lean方法，并提供相对于作为输入提供的领域约束规范的完整性证明。我们引入了一种语义保留的PDDL到Lean的转换，并使用大语言模型生成广义计划及其形式证明，证明其能够解决满足领域约束的每个实例。完整性证明的正确性由Lean的内核决定。我们在13个常用基准领域上评估了我们的方法，使用GPT-5.6-Sol作为大语言模型。在12个领域中，我们获得了广义计划及其有效的完整性证明。这标志着自动广义计划完整性证明领域的重大进展。

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cs.AI / 15 / 2609.27150

Do We Need Complex Topology Control? Distinct-Peer Random Routing Improves Cost-Efficiency in Sparse Multi-Agent Debate

我们需要复杂的拓扑控制吗？不同对等随机路由在稀疏多智能体辩论中的成本效率提升

Wang, Boxuan, Li, Zhuoyun, Huang, Xiaowei, Dong, Yi

Abstract

Multi-agent debate (MAD) has emerged as a promising paradigm for improving the reasoning accuracy of large language models (LLMs) through iterative peer interaction. Communication topology plays a central role in this process, motivating increasingly sophisticated mechanisms that learn, adapt, or dynamically reconfigure agent interactions to improve accuracy or reasoning reliability. Meanwhile, prior studies suggest that much simpler sparse communication can already achieve competitive performance at substantially lower cost. In this work, we take a closer look at sparse MAD and ask whether complex topology control is actually necessary to improve collective reasoning. We find that a simple random-without-replacement routing policy, which lets each agent debate with two distinct and newly sampled peers at every round, provides a surprisingly strong baseline and consistently improves the accuracy-cost trade-off of sparse MAD. Building on this observation, we further study deliberation stopping and show that lightweight stopping can substantially reduce inference cost while preserving competitive accuracy. Our results suggest that sophisticated topology control such as learned topology adaption should be evaluated against strong simple routing and stopping baselines before its additional complexity is justified.

Chinese Translation

多智能体辩论（MAD）已成为通过迭代的对等互动提高大型语言模型（LLMs）推理准确性的有前景的范式。通信拓扑在这一过程中发挥着核心作用，促使越来越复杂的机制学习、适应或动态重新配置智能体之间的互动，以提高准确性或推理可靠性。与此同时，先前的研究表明，简单的稀疏通信已经能够以显著较低的成本实现具有竞争力的性能。在本研究中，我们更深入地探讨了稀疏MAD，并询问复杂的拓扑控制是否真的必要以改善集体推理。我们发现，一种简单的无替换随机路由策略，使每个智能体在每一轮与两个不同且新采样的对等者辩论，提供了一个令人惊讶的强基线，并持续改善稀疏MAD的准确性-成本权衡。在此基础上，我们进一步研究了辩论停止，并表明轻量级的停止可以在保持竞争性准确性的同时显著降低推理成本。我们的结果表明，像学习拓扑适应这样的复杂拓扑控制应在其额外复杂性得到合理化之前，与强大的简单路由和停止基线进行评估。

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cs.AI / 16 / 2609.27197

Enhancing Small Language Models for Power Outage Report Generation via Minimum Risk Training

通过最小风险训练增强小型语言模型以生成停电报告

Phan, Hung, Abebe, Waqwoya, Hussein, Youssef, Chinthavali, Supriya, Lunga, Dalton, Jannesari, Ali

Abstract

Minimum Risk Training (MRT) enables neural machine translation models to directly optimize sequence-level evaluation metrics instead of relying only on token- level maximum-likelihood objectives Shen et al. [2016]. Although introduced a decade ago, recent work shows renewed potential for risk-based optimization in modern language models Yang et al. [2024], Jinnai et al. [2025]. We apply MRT to power outage report generation for the Outage Data Initiative Nationwide (ODIN), transforming heterogeneous reports into standardized XML compliant with CIM IEC 61968-3. Our MRT approach improves Qwen2.5-7B-Instruct overall accuracy from 16.20% to 68.95%, demonstrating the effectiveness of sequence- level optimization for domain-specific structured generation

Chinese Translation

最小风险训练（Minimum Risk Training, MRT）使神经机器翻译模型能够直接优化序列级评估指标，而不仅仅依赖于基于标记的最大似然目标（Shen et al. [2016]）。尽管这一方法在十年前就已提出，但最近的研究表明风险基础优化在现代语言模型中展现出新的潜力（Yang et al. [2024]，Jinnai et al. [2025]）。我们将MRT应用于全国停电数据倡议（Outage Data Initiative Nationwide, ODIN）的停电报告生成，将异构报告转化为符合CIM IEC 61968-3标准的规范化XML。我们的MRT方法将Qwen2.5-7B-Instruct的整体准确率从16.20%提高至68.95%，展示了序列级优化在特定领域结构化生成中的有效性。

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cs.AI / 17 / 2609.27203

XLOG: A CUDA-Native Engine for Neurosymbolic Integration

XLOG：一种基于CUDA的神经符号集成引擎

Dubrovin, Levi, Pospelov, Nikita, Sabitov, Kirill

Abstract

xlog is a CUDA-native logic programming engine integrating neural perception with deterministic Datalog, probabilistic inference, and epistemic world views through a typed frontend and provider-owned CUDA runtime. Its reasoning modes share device data planes, but their execution boundaries differ: ordinary Datalog and exact inference are host-orchestrated, while certified resident recursive and Monte Carlo sampled cores record zero tracked host-device transfers before a bounded terminal receipt. The probabilistic path supports end-to-end gradients through GPU knowledge compilation from provenance to CNF to Decision-DNNF, exact weighted model counting, and backward gradients. A final smoothed circuit is certified against its source formula before caching or evaluation. Circuit caching yields a 2.74x MNIST-addition training speedup; a worst-case-optimal join subsystem yields a 27.96x geometric-mean gain over xlog's binary-join baseline. MNIST-addition accuracy matches Scallop's (0.9561 versus 0.9468), but no per-epoch speed claim is made because baseline epoch time varies with CPU quota. In five hub-skewed triangle-counting cases, the Souffle-to-fused-xlog execution-time ratio rises from 0.88x at 150k edges, where Souffle is faster, to 5.54x at 1.2M; fused peak device allocations are 85-1,033 MB versus 3,287-44,979 MB for the materializing arm. Exact inference is correctness-equivalent to but slower than ProbLog2. On a public video benchmark, a proximity predicate trained only through symbolic credit replaces hand-set geometry at unchanged held-out accuracy; within Event-Calculus rule search it fails ten-fold cross-validation and does not transfer on a leak-free split. On a maritime corpus, weighted clauses beat crisp selection by 0.065 F1, with the result reproduced by one chronological training pass.

Chinese Translation

xlog 是一个基于CUDA的逻辑编程引擎，集成了神经感知与确定性Datalog、概率推理和认知世界观，通过类型化前端和提供者拥有的CUDA运行时实现。其推理模式共享设备数据平面，但执行边界有所不同：普通Datalog和精确推理由主机协调，而认证的驻留递归和蒙特卡洛采样核心在有界终端接收之前记录零跟踪的主机-设备传输。概率路径支持通过GPU知识编译从来源到CNF再到Decision-DNNF的端到端梯度、精确加权模型计数和反向梯度。最终平滑电路在缓存或评估之前会针对其源公式进行认证。电路缓存实现了2.74倍的MNIST加法训练加速；最坏情况下的最优连接子系统在xlog的二元连接基线之上实现了27.96倍的几何平均增益。MNIST加法的准确率与Scallop相匹配（0.9561对比0.9468），但未对每个周期的速度进行声明，因为基线周期时间随CPU配额而变化。在五个中心偏斜的三角计数案例中，Souffle与融合xlog的执行时间比率从150k边缘时的0.88倍（Souffle更快）上升至1.2M时的5.54倍；融合的峰值设备分配为85-1,033 MB，而物化臂则为3,287-44,979 MB。精确推理在正确性上等同于但速度慢于ProbLog2。在一个公共视频基准测试中，仅通过符号信用训练的接近谓词替代了手动设置的几何形状，保持不变的保留准确率；在事件演算规则搜索中，它在十折交叉验证中失败，并且在无泄漏分割上无法转移。在一个海事语料库中，加权子句比清晰选择提高了0.065 F1，结果通过一次按时间顺序的训练传递得以重现。

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cs.AI / 18 / 2609.27273

CAVEAT: Towards Robust Computer-Use Agents in Incentive-Misaligned Environments

CAVEAT：朝着在激励不一致环境中构建稳健的计算机使用代理

Li, Yuxuan, Epperson, Will, Deng, Wesley, Huang, Zezhou

Abstract

Computer-use agents (CUAs) increasingly act on behalf of users online. What happens when the environments they operate in have incentives that do not align with the user's? In online marketplaces, for example, platforms may favor some products over others, potentially steering agents away from the user's objective. Existing CUA benchmarks cover cooperative settings or explicit attacks, but do not test whether agents preserve user objectives when the environment itself has a stake in the outcome. We introduce CAVEAT, a controlled benchmark spanning nine marketplace environments and a taxonomy of eight common steering mechanisms. Across five model families, agents purchase the user-optimal product in 78.6% of matched-control episodes but only 17.3% when steering mechanisms are enabled. Larger models and increased reasoning improve robustness, but substantial failures persist. Our trajectory analysis and targeted ablations identify three points where steering enters the decision process: (1) agents distort the user's priorities, (2) prematurely narrow the set of alternatives they consider, and (3) commit before resolving decision-relevant evidence. Guided by this diagnosis, we develop CAVEAT-Harness, which directly targets these failure modes and raises user-optimal purchasing by 55.0%. Targeted post-training further improves a smaller open model. These results establish incentive robustness as a distinct challenge for delegated agents, diagnose how it fails, and show that targeted interventions can substantially improve it.

Chinese Translation

计算机使用代理（CUAs）越来越多地代表用户在网上行动。当它们所处的环境的激励与用户的激励不一致时，会发生什么？例如，在在线市场中，平台可能会偏向某些产品，从而可能使代理偏离用户的目标。现有的CUA基准测试涵盖了合作环境或明确攻击，但并未测试代理在环境本身对结果有利益时是否能够保持用户目标。我们引入了CAVEAT，这是一个涵盖九个市场环境和八种常见引导机制的控制基准。在五个模型家族中，代理在78.6%的匹配控制情境中购买用户最优产品，但在启用引导机制时仅为17.3%。更大的模型和增强的推理能力提高了稳健性，但仍然存在显著的失败。我们的轨迹分析和针对性消融识别了引导进入决策过程的三个点：（1）代理扭曲用户的优先级，（2）过早缩小考虑的替代方案范围，以及（3）在未解决与决策相关的证据之前就做出承诺。在这一诊断的指导下，我们开发了CAVEAT-Harness，直接针对这些失败模式，并将用户最优购买率提高了55.0%。针对性后训练进一步改善了一个较小的开放模型。这些结果确立了激励稳健性作为委托代理的一个独特挑战，诊断了其失败的原因，并表明有针对性的干预可以显著改善这一问题。

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cs.AI / 19 / 2609.27276

DRSR: Learning Set-Level Deletion Risk for Efficient Long-Horizon Agents

DRSR：学习集级删除风险以提高长期代理的效率

Wang, Mingxuan, Wang, Bo, Luo, Fei, Yao, Guorun, Ning, Chao, Guo, Yinglong, Chen, Hongyue, Ma, Yanbiao, Han, Jungong

Abstract

Long-horizon language-model agents accumulate reasoning traces, tool exchanges, and observations whose relevance changes with the current decision. Existing compression strategies often score historical units independently, but the safety of deleting several units is generally not determined by their singleton scores: redundant evidence, accumulated small effects, and the information that remains after deletion all matter. We introduce Direct Relational Set-Risk Pruning (DRSR), which formulates agent-history compression as risk-constrained selection over deletion sets. Offline, DRSR constructs exact counterfactual supervision by jointly deleting protocol-valid history Blocks and measuring the change in teacher-forced likelihood of the same recorded next output. A lightweight scorer then predicts set-level harm from online-visible relations between candidate history and the current pre-action state, together with deleted-retained and pairwise set structure. At deployment, DRSR evaluates a small set of structurally valid deletion candidates with the lightweight scorer and removes the largest feasible set under recency, protocol, budget, and learned-risk constraints, abstaining when no set is sufficiently safe. On WorkBuddyBench Full260, DRSR increases mean reward from 0.699 to 0.802 while reducing total model tokens by 20.820%. On the fixed Eval40 comparison, it obtains 0.794 reward at 1.211M tokens per task, using 35.850% fewer tokens than the uncompressed agent. Mechanistic analyses and ablations further show that decision-conditioned relations, retained-context information, pair interactions, and abstention each contribute to reliable pruning.

Chinese Translation

长期语言模型代理积累了推理痕迹、工具交换和观察，其相关性随着当前决策而变化。现有的压缩策略通常独立地对历史单元进行评分，但删除多个单元的安全性通常并不由它们的单一评分决定：冗余证据、积累的小效应以及删除后剩余的信息都很重要。我们提出了直接关系集风险修剪（Direct Relational Set-Risk Pruning，DRSR），将代理历史压缩公式化为对删除集的风险约束选择。在离线阶段，DRSR通过联合删除协议有效的历史区块并测量相同记录的下一个输出的教师强制似然变化，构建精确的反事实监督。然后，一个轻量级评分器预测候选历史与当前预行动状态之间的在线可见关系所造成的集级损害，以及删除-保留和成对集结构。在部署时，DRSR使用轻量级评分器评估一小组结构有效的删除候选，并在最近性、协议、预算和学习风险约束下删除最大的可行集，当没有集足够安全时则选择不删除。在 WorkBuddyBench Full260 上，DRSR 将平均奖励从 0.699 提高到 0.802，同时将总模型标记减少了 20.820%。在固定的 Eval40 比较中，它在每个任务中以 1.211M 标记获得 0.794 的奖励，使用的标记比未压缩代理少 35.850%。机制分析和消融实验进一步表明，决策条件关系、保留上下文信息、成对交互和选择不删除各自对可靠修剪有所贡献。

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cs.AI / 20 / 2609.27277

TimeEvo: Failure-Driven Self-Evolution of a Time Series Agent

TimeEvo：基于失败驱动的时间序列智能体自我进化

Yang, Jie, Zheng, Yan, Sun, Jiarui, Fan, Xiran, Wang, Junpeng, Wang, Liang, Xu, Zelin, Liu, Qinghua, Fang, Zhengyu, Cai, Yiwei, Yu, Philip S.

Abstract

Time series agents answer analytical questions by calling external tools, and which tools they carry is decided by people before the agent runs. However, we identify two failures in this setup. Human-Agent Tool Misalignment: a library of 21 expert-curated tools helps on some tasks and hurts on others, dropping anomaly accuracy under every backbone we test. Silent Harm: one round of generic self-revision changes 147 answers and breaks 56 of them, while the final score moves by less than a point. Both follow from the same gap: whether a tool helps is decided question by question at runtime, while tools are supplied in advance and judged by a single average. To address this, we propose TimeEvo, which clusters an agent's diagnosed failures into capability gaps, plans a measurement for each, synthesizes evidence-only tools that fill them, and admits the candidate library only through a paired admission gate. Experiments on ten time series QA tasks and three backbones show that TimeEvo, starting from an empty library, improves accuracy on every task and every backbone, and that a library grown on a cheap model still gains when it is installed into stronger ones. Code is available at https://github.com/Muyiiiii/TimeEvo.

Chinese Translation

时间序列智能体通过调用外部工具来回答分析性问题，而这些工具的选择是在智能体运行之前由人决定的。然而，我们在这种设置中识别出两种失败。人机工具不匹配：一个由21个专家策划的工具库在某些任务上有帮助，但在其他任务上却造成伤害，导致我们测试的每个基础模型下的异常准确率下降。沉默的危害：一次通用的自我修正改变了147个答案，并且破坏了其中的56个，而最终得分的变化不足一个百分点。这两者都源于同一个差距：工具是否有帮助是在运行时逐个问题决定的，而工具则是提前提供并通过单一平均值进行评估。为了解决这个问题，我们提出了TimeEvo，它将智能体诊断出的失败聚类为能力差距，为每个差距规划测量，合成填补这些差距的证据工具，并通过配对的入门门控机制来接纳候选库。在十个时间序列问答任务和三个基础模型上的实验表明，TimeEvo从一个空库开始，在每个任务和每个基础模型上都提高了准确率，并且在便宜模型上成长的库在安装到更强模型时仍然获益。代码可在 https://github.com/Muyiiiii/TimeEvo 获取。

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cs.AI / 21 / 2609.27279

EnSIMem: Entity-Structured Indexing for Long-Term Agent Memory

EnSIMem：面向长期智能体记忆的实体结构化索引

Meng, Xuanyu, Fan, Xing, Fan, Xinyi, Guo, Chenlei, Xie, Yixuan, Han, Jiawei

Abstract

An agent that interacts with users over long periods must recall facts, preferences, events, and changes from a continuously growing interaction history. Existing memory systems often compress interactions into generic summaries or retrieve anonymous text chunks, making it difficult for an agent to identify the correct entity, property, and supporting evidence. We present EnSIMem, an entity-structured long-term memory architecture for an agent. During offline construction, the system organizes interactions into theme-coherent episodes and builds dialogue-grounded index entries of the form [entity][entity type][property:value]. Each entry preserves its source turns, temporal information, and available multimodal fields. During online interaction, the agent's request is decomposed into evidence requirements whose properties are aligned with the memory index. Entity-property lookup and adaptive retrieval then collect the evidence needed for point, temporal, compositional, and aggregation reasoning. The agent generates its response from the preserved source evidence rather than from lossy memory summaries. On long-term agent-memory benchmarks, EnSIMem achieves high answer accuracy while maintaining compact contexts and favorable online efficiency. These results show that entity-structured indexing and episode-level provenance provide a reliable foundation for long-term memory in agents. The code of our model is available at https://github.com/RamonMeng/EnSIMem.

Chinese Translation

一个与用户长期互动的智能体必须从不断增长的互动历史中回忆事实、偏好、事件和变化。现有的记忆系统通常将互动压缩为通用摘要或检索匿名文本块，这使得智能体难以识别正确的实体、属性和支持证据。我们提出了EnSIMem，一种面向智能体的实体结构化长期记忆架构。在离线构建过程中，该系统将互动组织成主题一致的情节，并构建对话基础的索引条目，形式为[entity][entity type][property:value]。每个条目保留其源回合、时间信息和可用的多模态字段。在在线互动中，智能体的请求被分解为证据需求，其属性与记忆索引对齐。实体-属性查找和自适应检索随后收集点、时间、组合和聚合推理所需的证据。智能体从保留的源证据中生成响应，而不是从有损的记忆摘要中生成。在长期智能体记忆基准测试中，EnSIMem实现了高答案准确性，同时保持了紧凑的上下文和良好的在线效率。这些结果表明，实体结构化索引和情节级来源为智能体的长期记忆提供了可靠的基础。我们的模型代码可在https://github.com/RamonMeng/EnSIMem获取。

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cs.AI / 22 / 2609.27284

Hunyuan-A13B Technical Report

Hunyuan-A13B 技术报告

Tencent Hunyuan Team, Liu, Ao, Zhou, Botong, Xu, Can, Zhou, Chayse, Zhang, ChenChen, Xu, Chengcheng, Wang, Chenhao, Wu, Decheng, Wu, Dengpeng, Jiao, Dian, Du, Dong, Wang, Dong, Zhang, Feng, Lian, Fengzong, Xu, Guanghui, Zhang, Guanwei, Wang, Hai, Luo, Haipeng, Hu, Han, Xu, Huilin, Wu, Jiajia, Zhu, Jianchen, Yan, Jianfeng, Zhu, Jiaqi, Zhang, Jihong, Xue, Jinbao, Xia, Jun, Zheng, Junqiang, Liu, Kai, Zhang, Kai, Zheng, Kai, Li, Kejiao, Wang, Keyao, Jiang, Lan, Liu, Lixin, Wu, Lulu, Huang, Mengyuan, Yu, Peijie, Wang, Peiqi, Wang, Qian, Xiang, Qianbiao, Liu, Qibin, Sun, Qingfeng, Guo, Richard, Xie, Ruobing, Yang, Saiyong, Chen, Shaohua, Hu, Shihui, Li, Shuai, Li, Shuaipeng, Chen, Shuang, Zheng, Suncong, Yang, Tao, Zhang, Tian, Yu, Tinghao, Han, Weidong, Liu, Weijie, Zhou, Weijin, Wang, Weikang, Chen, Wesleye, Feng, Xiao, Ren, Xiaoqin, Sun, Xingwu, Kuang, Xiong, Huang, Xuemeng, Cao, Xun, Chen, Yanfeng, Du, Yang, Yang, Zhen, Tao, Yangyu, Deng, Yaping, Shen, Yi, Hong, Yigeng, Chen, Yiqi

Abstract

We present Hunyuan-A13B, an open-source large language model based on a Mixture-of-Experts architecture. It contains 80 billion total parameters but activates only 13 billion during inference, balancing model capability, computational efficiency, and deployment cost. The model is pretrained on a rigorously filtered 20T-token corpus with enhanced STEM data curation, improving factual reliability and reasoning ability. High-quality supervised fine-tuning and large-scale reinforcement learning further enhance its overall performance. Hunyuan-A13B also introduces a dual-mode Chain-of-Thought framework that adapts reasoning depth to task complexity: fast thinking for routine queries and slow thinking for complex, multi-step problems. Evaluations show competitive performance across mathematics, science, programming, general language understanding, and agent tasks, often approaching that of much larger models. Its high inference throughput makes it suitable for latency-sensitive applications. We release Hunyuan-A13B to support open research and practical LLM deployment.

Chinese Translation

我们介绍了 Hunyuan-A13B，这是一种基于专家混合（Mixture-of-Experts）架构的开源大型语言模型。该模型总参数量为 800 亿，但在推理过程中仅激活 130 亿参数，从而平衡了模型能力、计算效率和部署成本。该模型在经过严格筛选的 20T 令牌语料库上进行了预训练，并增强了 STEM 数据的整理，提高了事实可靠性和推理能力。高质量的监督微调和大规模强化学习进一步提升了其整体性能。Hunyuan-A13B 还引入了一种双模式思维链（Chain-of-Thought）框架，根据任务复杂性调整推理深度：对于常规查询采用快速思维，对于复杂的多步骤问题采用慢速思维。评估结果显示，在数学、科学、编程、通用语言理解和代理任务等领域表现出竞争力，性能往往接近更大规模的模型。其高推理吞吐量使其适合于对延迟敏感的应用。我们发布 Hunyuan-A13B 以支持开放研究和实际的 LLM 部署。

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cs.AI / 23 / 2609.27286

Memory Control Signals Emerge Before Action in Long Horizon Agents

记忆控制信号在长时间跨度代理行动之前出现

Wang, Mingxuan, Yao, Guorun, Luo, Fei, Guo, Yinglong, Ning, Chao, Wang, Bo, Chen, Hongyue, Ma, Yanbiao, Han, Jungong

Abstract

Long horizon language model agents continuously accumulate interaction history, increasing computational cost while making relevant information harder to preserve and reuse. Existing context management methods mainly focus on how to compress or retrieve history, but largely leave open whether the model itself already represents the need for these memory operations before they occur. We study the hidden state immediately before each agent action and find that compression and recall needs are already encoded in the model's internal representations. These signals cannot be explained by simple context length or interaction progress, and they exhibit distinct formation patterns across model depth. We further show that most memory decision information is preserved in a compact recent context, while selectively restored historical evidence complements the long range dependencies that recent context misses. Based on these findings, we propose Preaction Memory with Evidence Retrieval (PaMER), which combines state guided compression with external evidence retrieval. PaMER+ further introduces step level evidence selection to recover only the historical information required by the current task. Experiments on WorkBuddyBench, across multiple context management baselines and model backbones, show that our framework substantially reduces context consumption while maintaining competitive task performance.

Chinese Translation

长时间跨度的语言模型代理不断积累交互历史，增加了计算成本，同时使得相关信息的保留和重用变得更加困难。现有的上下文管理方法主要关注如何压缩或检索历史，但在模型本身是否已经在这些操作发生之前表示出对记忆操作的需求这一点上，基本上仍然没有明确的研究。我们研究了每个代理行动之前的隐藏状态，发现压缩和回忆的需求已经编码在模型的内部表示中。这些信号无法通过简单的上下文长度或交互进展来解释，并且它们在模型深度上表现出不同的形成模式。我们进一步表明，大多数记忆决策信息保留在紧凑的近期上下文中，而选择性恢复的历史证据补充了近期上下文所缺失的长程依赖。基于这些发现，我们提出了带证据检索的前行动记忆（Preaction Memory with Evidence Retrieval, PaMER），该方法结合了状态引导的压缩与外部证据检索。PaMER+进一步引入了逐步证据选择，以仅恢复当前任务所需的历史信息。在WorkBuddyBench上的实验显示，在多个上下文管理基线和模型骨干上，我们的框架显著减少了上下文消耗，同时保持了竞争性的任务性能。

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cs.AI / 24 / 2609.27288

PotARCin: Multi-Dimensional Evaluation of Skill Acquisition in Abstract Reasoning Tasks

PotARCin：抽象推理任务技能获取的多维评估

Beger, Claas, Yi, Ryan, Mitchell, Melanie

Abstract

The Abstraction and Reasoning Corpus (ARC) has become a prominent benchmark for evaluating general abstract reasoning and fluid intelligence in AI models. Yet standard ARC evaluation considers only a single capability: producing the correct output grid for a test input. We argue that this narrow format fails to evaluate the diversity of abilities that genuine abstract skill acquisition should enable. We introduce PotARCin, a benchmark that extends ARC by assessing understanding of a task's underlying abstract rule across five dimensions: Definition, Classification, Constrained Generation, Editing, and Inversion. PotARCin employs programmatic methods to generate new task instances and transform given inputs for a given ARC task, enabling dynamic generative sampling beyond fixed input-output pairs. Across five state-of-the-art models evaluated on the ARC-AGI-1 training set, we observe a 25-52 percentage-point performance gap between standard ARC evaluation and evaluation on PotARCin, and find that multi-dimensional evaluation reorders models that standard accuracy ranks alike. We further investigate effects of generative sampling, difficulty of corruption types, and questions of self-consistency, showing that models frequently contradict their own formalized rule even where they have stated it correctly. We also introduce P-ARC, a held-out hand-crafted test set, on which models achieve 1-8% accuracy across all five dimensions, underscoring the importance of more holistic evaluations of abstract reasoning capabilities.

Chinese Translation

抽象与推理语料库（ARC）已成为评估人工智能模型一般抽象推理和流体智能的重要基准。然而，标准的ARC评估仅考虑单一能力：为测试输入生成正确的输出网格。我们认为，这种狭隘的格式未能评估真正的抽象技能获取所应具备的多样性能力。我们引入了PotARCin，一个通过评估任务潜在抽象规则理解的基准，涵盖五个维度：定义、分类、受限生成、编辑和反转。PotARCin采用程序化方法生成新的任务实例，并转换给定输入以适应特定的ARC任务，从而实现超越固定输入输出对的动态生成采样。在对ARC-AGI-1训练集上评估的五个最先进模型中，我们观察到标准ARC评估与PotARCin评估之间存在25-52个百分点的性能差距，并发现多维评估重新排序了在标准准确性排名中表现相似的模型。我们进一步研究了生成采样的影响、腐败类型的难度以及自一致性的问题，显示出模型经常会与其自身正确表述的规则相矛盾。我们还引入了P-ARC，一个保留的手工制作测试集，在该集上模型在所有五个维度的准确率仅为1-8%，强调了对抽象推理能力进行更全面评估的重要性。

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cs.AI / 25 / 2609.27290

Sparse-Observation Atmospheric Thermal Forecasting with Physics-Informed Neural Networks for Climate-Aware Digital Twins

基于物理信息神经网络的稀疏观测大气温度预测：面向气候感知数字双胞胎

Chegini, Tannaz Goodarzvand, Shivanian, Elyas, Karimi, Behzad, Dadgostari, Faraz

Abstract

Short-horizon forecasts of atmospheric temperature are needed to support climate-aware digital-twin systems, but such forecasts must be produced where thermal observations are incomplete. This study evaluates a physics-informed neural network for potential-temperature forecasting, constrained by a pressure-coordinate thermodynamic advection-source equation and a diabatic-source closure fit from the preceding 12-hour period and frozen before future-time training. Using hourly ERA5 reanalysis at three pressure levels, the model is evaluated as a conditional hindcast at lead times of one, two and three hours against persistence, local-trend, and two matched neural-network baselines, one of which receives the same future meteorological forcing as the PINN, helping distinguish the physical constraint from access to future forcing. In an Oklahoma development case, mean RMSE improvement over the strongest baseline grew from 8.1\% at one hour to 23.8\% at three hours; under an observation-density sweep down to 5\% of candidate locations, this 3-hour advantage remained 14.6--16.9\%, with no evidence that lower density improves performance. Under a fixed protocol transferred to an Alabama heat event with three virtual-observation layouts, three-hour improvement ranged 19.7-24.4\% with consistent origin-level wins. A parallel Montana stress test, in which fixed pressure levels intersected complex terrain, produced a three-hour degradation of roughly 17.5\%, identifying a terrain-related applicability limit of the formulation. Together, these results indicate that the physics constraint's benefit grows with forecast horizon, persists under severe observation sparsity, and transfers across regions, but is bounded by the validity of a fixed vertical-coordinate representation over complex terrain, evidence relevant to physics-constrained components of climate-aware forecasting and digital-twin systems.

Chinese Translation

为了支持气候感知数字双胞胎系统，需要对大气温度进行短期预测，但这些预测必须在热观测不完整的情况下进行。本研究评估了一种物理信息神经网络（PINN）用于潜在温度预测，该网络受限于压力坐标热力学平流源方程和来自前12小时的非绝热源闭合拟合，并在未来时间训练之前被冻结。使用三种压力水平的每小时ERA5再分析数据，该模型在一、二和三小时的预测时效下作为条件回溯进行评估，与持久性、本地趋势和两个匹配的神经网络基线进行比较，其中一个基线接收与PINN相同的未来气象强迫，帮助区分物理约束与对未来强迫的访问。在一个俄克拉荷马州的开发案例中，最强基线的平均均方根误差（RMSE）改善从一小时的8.1%增长到三小时的23.8%；在观测密度降至候选位置的5%时，这一三小时的优势仍保持在14.6%至16.9%之间，且没有证据表明较低的密度会改善性能。在转移到阿拉巴马州热事件的固定协议下，采用三种虚拟观测布局，三小时的改善范围为19.7%至24.4%，且在原点水平上保持一致的胜利。在蒙大拿州进行的平行压力测试中，固定压力水平与复杂地形相交，导致三小时的降级约为17.5%，识别出该公式的地形相关适用性限制。这些结果表明，物理约束的益处随着预测时效的增长而增加，在严重观测稀疏的情况下仍然存在，并且可以跨区域转移，但受限于复杂地形上固定垂直坐标表示的有效性，这些证据与气候感知预测和数字双胞胎系统的物理约束组件相关。

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cs.AI / 26 / 2609.27297

Large Knowledge Model: From Papers to a Scientific Reasoning Landscape

大型知识模型：从文献到科学推理景观

Huang, Yuan, Hu, Sihan, Gu, Hongyu, Ma, Chao, Zhang, Jiaxing, Zou, Zhiyong, Fan, Caiyu, Xiao, Yan, Xu, Mingjun, Xie, Chenyu, Ju, Mingzhen, Ma, Zhehao, Zhang, Qi, Wang, Baozong, Li, Yu, Yao, Zhiyuan, Liao, Ruoxue, Li, Xinyu, Zhang, Linfeng, Chen, Kun, E, Weinan

Abstract

Accumulated scientific knowledge advances inquiry when prior findings help researchers choose new questions, design investigations, and interpret results. Realizing this value at scale requires access to the reasoning that connects research problems, scientific procedures, conclusions, and evidence. We introduce the Large Knowledge Model (LKM), a scientific knowledge infrastructure that transforms the literature into a shared, computationally accessible reasoning resource. LKM represents papers as source-grounded reasoning graphs, couples structural traversal with semantic retrieval over the same objects, and aligns related questions, claims, and reasoning chains across papers. This representation forms a Scientific Reasoning Landscape with three connected views: a Question Landscape that organizes research problems and open directions, a Workflow Landscape that exposes reusable scientific procedures, and an Evidence Landscape that connects conclusions to their support, disagreement, and conditions. The unified substrate supports reasoning-aware scientific search, evidence-grounded question answering, comparative evidence analysis, and research planning. Researchers and agents can retrieve relevant work through its scientific intent, synthesize answers with inspectable supporting arguments, and develop research plans informed by established workflows and unresolved evidence. We describe a corpus-scale system and evaluate scientific retrieval and knowledge-intensive question answering. With the answering model fixed, LKM retrieval improves accuracy by 9.30%, 4.20%, and 14.69% on ChemBench, PubMedQA, and SciBench, respectively. By connecting knowledge access to scientific reasoning and action, LKM provides a common foundation for discovering relevant research, reusing scientific knowledge, and coordinating cumulative inquiry across researchers, agents, and research cycles.

Chinese Translation

积累的科学知识在研究中推动探究，当先前的发现帮助研究人员选择新问题、设计调查和解释结果时，这种价值得以实现。要在大规模上实现这一价值，需要获取连接研究问题、科学程序、结论和证据的推理。我们介绍了大型知识模型（Large Knowledge Model, LKM），这是一种科学知识基础设施，将文献转化为共享的、可计算访问的推理资源。LKM将论文表示为基于源的推理图，将结构遍历与对同一对象的语义检索相结合，并对论文中的相关问题、主张和推理链进行对齐。这种表示形成了一个科学推理景观，包含三个相互关联的视图：一个组织研究问题和开放方向的问题景观，一个揭示可重用科学程序的工作流程景观，以及一个将结论与其支持、分歧和条件相连接的证据景观。统一的基础支持基于推理的科学搜索、基于证据的问题回答、比较证据分析和研究规划。研究人员和代理可以通过其科学意图检索相关工作，合成可检查的支持论据的答案，并基于既定工作流程和未解决的证据制定研究计划。我们描述了一个语料库规模的系统，并评估了科学检索和知识密集型问题回答。在回答模型固定的情况下，LKM检索在ChemBench、PubMedQA和SciBench上的准确率分别提高了9.30%、4.20%和14.69%。通过将知识获取与科学推理和行动相连接，LKM为发现相关研究、重用科学知识以及协调研究人员、代理和研究周期之间的累积探究提供了共同基础。

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cs.AI / 27 / 2609.27298

StateComp: Learning When to Compress History in Long Horizon Agents

StateComp：学习在长时间跨度代理中何时压缩历史

Wang, Mingxuan, Chen, Hongyue, Guo, Yinglong, Luo, Fei, Ning, Chao, Wang, Bo, Yao, Guorun, Ma, Yanbiao, Han, Jungong

Abstract

Long-horizon agents continuously accumulate interaction history during task execution, yet the importance of past interactions changes as the agent state evolves. Existing context management methods largely compress history based on fixed windows, periodic schedules, or current relevance, overlooking a more fundamental question: when has a past interaction become safe to replace? Premature compression may remove information still needed for future actions, while overly conservative retention leads to substantial context overhead. To address this, we propose State Conditioned Compression (StateComp), a framework that determines when historical interactions can be safely compressed according to the current agent state. StateComp constructs KEEP and READY supervision through a two-stage annotation procedure and trains an imbalance-aware router on hidden representations from a frozen language model. A bounded state representation further reduces the cost of evaluating long histories, while adjacent READY interactions are grouped into continuous spans and replaced with compact summaries during execution. Experiments on WorkBuddyBench show that StateComp reduces total agent and summarization tokens by 52.27% while maintaining task performance, and achieves a 12.67-fold speedup in representation extraction.

Chinese Translation

长时间跨度的代理在任务执行过程中不断积累交互历史，但随着代理状态的演变，过去交互的重要性也在变化。现有的上下文管理方法主要基于固定窗口、周期性调度或当前相关性来压缩历史，忽视了一个更根本的问题：何时过去的交互可以安全地被替换？过早的压缩可能会移除未来行动仍需的信息，而过于保守的保留则会导致显著的上下文开销。为了解决这个问题，我们提出了状态条件压缩（State Conditioned Compression，StateComp），该框架根据当前代理状态确定历史交互何时可以安全地被压缩。StateComp通过两阶段注释程序构建KEEP和READY监督，并在冻结的语言模型的隐藏表示上训练一个关注不平衡的路由器。有限状态表示进一步降低了评估长历史的成本，同时相邻的READY交互被分组为连续区间，并在执行过程中用紧凑的摘要替换。WorkBuddyBench上的实验表明，StateComp在保持任务性能的同时，将代理和摘要的总令牌数减少了52.27%，并实现了12.67倍的表示提取加速。

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cs.AI / 28 / 2609.27307

Learn How to Act from Your Own Interactions: On-Policy Self-Distillation for GUI Agents

从自身交互中学习如何行动：面向策略的自蒸馏用于图形用户界面代理

Zhang, Yan, Wu, Daiqing, Shen, Huawen, Li, Liang, Cao, Gang, Gong, Zhi, Dai, Wei, Zhang, Xiaode, Ma, Can, Zhou, Yu

Abstract

Graphical User Interface (GUI) agents enable the fulfillment of complex user instructions through multi-turn interactions with software environments, requiring step-wise reasoning and long-horizon memory to guide actions and retain task-relevant information, respectively. Recent on-policy self-distillation (OPSD) methods have achieved strong performance on GUI grounding, a foundational subtask for GUI agents, owing to dense token-level supervision from privilege-conditioned self-teachers. However, extending existing OPSD methods to multi-turn GUI agents is hindered by self-teachers' limited privilege-following ability and insufficient privileged guidance. In this paper, we introduce GUI-SD-v2, the next version of GUI-SD, which extends OPSD from GUI grounding to multi-turn GUI interaction and addresses key limitations through a two-stage training framework. Specifically, GUI-SD-v2 first strengthens privilege following by jointly optimizing rollouts with and without privileged guidance from the same GUI states. Furthermore, it selectively distills step-specific reasoning and memory guidance through a privilege-conditioned self-teacher, supporting action decisions and the retention of task-relevant information for subsequent interactions. Extensive experiments on two representative GUI agent benchmarks, AndroidWorld and MobileWorld, show that GUI-SD-v2 compares favorably with existing OPSD baselines while consistently outperforming the evaluated state-of-the-art methods in both Pass@1 and Pass@3 success rates. Code and training data will be publicly released.

Chinese Translation

图形用户界面（GUI）代理通过与软件环境的多轮交互实现复杂用户指令的执行，这需要逐步推理和长时记忆来指导行动并保留与任务相关的信息。近期的面向策略自蒸馏（OPSD）方法在GUI基础任务——GUI定位上取得了良好的表现，这得益于来自特权条件自教师的密集令牌级监督。然而，现有的OPSD方法在多轮GUI代理上的扩展受到自教师有限的特权跟随能力和不足的特权指导的制约。本文介绍了GUI-SD-v2，这是GUI-SD的下一个版本，它将OPSD从GUI定位扩展到多轮GUI交互，并通过两阶段训练框架解决关键限制。具体而言，GUI-SD-v2首先通过联合优化来自相同GUI状态的有特权指导和无特权指导的回滚，强化特权跟随。此外，它通过特权条件自教师选择性地蒸馏特定步骤的推理和记忆指导，以支持行动决策和保留后续交互的任务相关信息。在两个代表性的GUI代理基准测试AndroidWorld和MobileWorld上的大量实验表明，GUI-SD-v2与现有的OPSD基线相比表现良好，同时在Pass@1和Pass@3成功率上始终优于评估的最新方法。代码和训练数据将公开发布。

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cs.AI / 29 / 2609.27321

Verifiable Hidden Dynamics Play: Generating Agentic RL Environments from Solved Mechanisms

可验证的隐性动态游戏：从已解决机制生成自主强化学习环境

Shen, Xinjie, Fan, Wei, Guo, Xudong, Tu, Jianhong, Su, Yang, Kuang, Chuqiao, Zhang, Yinger, Liu, Dayiheng

Abstract

Language-model agents increasingly face long-horizon tasks with evolving state, interdependent decisions, and delayed outcomes. Scaling their training requires diverse agentic environments, dependable outcome signals, and low extension cost. Existing generation pipelines commonly construct an environment before defining its outcome rule or annotating its trajectories, leaving dynamics and evaluation to be aligned post hoc. VHD-Play reverses this dependency by sampling and solving a mathematical model before a corpus-grounded setter renders its decision process as stateful tools. The executable dynamics and trajectory-scoring reference are inherited from the same solved model. The pipeline produces 3,300 diverse agentic environments at a cost of a few cents each. Training Qwen3.6-35B-A3B on three families raises its mean agentic score from 0.204 to 0.815 in a five-family diagnostic. Gains also appear on held-out instances from all three training families and eight unseen mechanism families, then extend beyond the generated substrate to external benchmarks for general function calling, travel planning, and 365-day e-commerce. On E-Commerce Bench, the trained checkpoint completes every run without bankruptcy and exceeds Qwen3.7-Max. We compare written-out problems with stateful versions that reveal or hide their parameters. The comparison shows that most of the learnable gap lies in stateful interaction rather than underlying problem solving. A frozen 35B setter realizes larger environments, and scale-matched training retains gains as mechanism size and horizon grow, indicating the potential for an evolving training substrate.

Chinese Translation

语言模型代理越来越多地面临具有演变状态、相互依赖决策和延迟结果的长期任务。扩展其训练需要多样化的自主环境、可靠的结果信号和低扩展成本。现有的生成管道通常在定义结果规则或注释轨迹之前构建环境，导致动态和评估需事后对齐。VHD-Play 通过在语料库基础的设定者呈现其决策过程为状态工具之前，先对数学模型进行采样和求解，逆转了这种依赖关系。可执行的动态和轨迹评分参考均源自同一已解决模型。该管道以每个几美分的成本生成 3,300 个多样化的自主环境。在三个家族上训练 Qwen3.6-35B-A3B，使其平均自主评分从 0.204 提升至 0.815，经过五个家族的诊断。所有三个训练家族和八个未见机制家族的保留实例也出现了增益，随后超越生成的基底，扩展到外部基准测试，用于一般功能调用、旅行规划和 365 天电子商务。在电子商务基准测试中，训练后的检查点在每次运行中都未出现破产，并超过了 Qwen3.7-Max。我们比较了书面问题与揭示或隐藏其参数的状态版本。比较结果表明，大多数可学习的差距存在于状态交互中，而非基础问题解决中。一个冻结的 35B 设定者实现了更大的环境，而规模匹配的训练在机制规模和时间范围增长时保持增益，表明了一个不断演变的训练基底的潜力。

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cs.AI / 30 / 2609.27332

Stable Geometry with Divergent Task Evidence for Efficient Long-Horizon Agent Compression

具有发散任务证据的稳定几何体以实现高效的长时间跨度代理压缩

Wang, Mingxuan, Luo, Fei, Wang, Bo, Yao, Guorun, Guo, Yinglong, Ning, Chao, Chen, Hongyue, Ma, Yanbiao, Han, Jungong

Abstract

Long horizon agents accumulate growing interaction histories that increase context and inference costs. We find that geometric redundancy alone is an insufficient criterion for safe compression. Although agent histories exhibit strong low dimensional structure, similar global geometry can preserve very different amounts of task evidence. At identical retained block counts, evidence aware selection raises next action Top 3 retention from 0.31 to 0.69, while centroid similarity remains 0.98. Controlled replacement further shows that action related information can be substantially altered while global geometric measures remain nearly unchanged. Motivated by this gap between geometry and evidence, we introduce Geometry Guided Evidence Preserving Memory (GEM), a training free compressor that protects task and execution evidence before using geometric residuals to complete coverage. GEM reduces mean combined token usage from 2.69M to 2.11M per task, a 21.4% reduction, while maintaining comparable task reward. Our results show that efficient agent history compression should optimize for preserved task evidence rather than geometric coverage alone.

Chinese Translation

长时间跨度的代理积累了不断增长的交互历史，这增加了上下文和推理成本。我们发现，仅仅依赖几何冗余并不足以作为安全压缩的标准。尽管代理历史表现出强烈的低维结构，但相似的全局几何体可以保留非常不同数量的任务证据。在相同的保留块数量下，基于证据的选择将下一个动作的前3名保留率从0.31提高到0.69，而质心相似度保持在0.98。受控替换进一步表明，动作相关信息可以在全局几何度量几乎不变的情况下被显著改变。受到几何与证据之间差距的启发，我们引入了几何引导的证据保留记忆（Geometry Guided Evidence Preserving Memory, GEM），这是一种无训练的压缩器，能够在使用几何残差完成覆盖之前保护任务和执行证据。GEM将每个任务的平均组合令牌使用量从2.69M减少到2.11M，减少了21.4%，同时保持了可比的任务奖励。我们的结果表明，高效的代理历史压缩应优化保留的任务证据，而不仅仅是几何覆盖。

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cs.AI / 31 / 2609.27333

Alignment Inertia: Auditing the Durability of Training Data Influence Through Policy Override Resistance

对齐惯性：通过政策覆盖抵抗审计训练数据影响的持久性

Barreto, Renata, Roesti, Markelle, Tahaei, Mohammad

Abstract

Platform operators increasingly rely on system prompts and fine-tuning to govern model behavior, yet it remains unclear how reliably these interventions override behavior inherited from prior training. We propose Override Success Rate (OSR) and alignment inertia to measure when operator interventions succeed or fail to change prior behavior. We evaluate zero-shot prompting and LoRA fine-tuning across Llama and Mistral in medical misinformation and hate speech. Alignment inertia persists across both models but varies by model, domain, and policy direction. Notably, in Mistral's restrictive hate-speech condition, LoRA increased inertia by 46.5 percentage points, showing that fine-tuning can reinforce rather than override prior behavior. We also use TRAK to test whether inertia is associated with weaker adaptation signals. TRAK achieves AUC of at least 0.85 in 7 of 8 conditions and outperforms model confidence, TF-IDF similarity, and embedding similarity as a predictor of inertia. These results provide an operator-facing audit of where prior training constrains downstream model governance.

Chinese Translation

平台运营商越来越依赖系统提示和微调来管理模型行为，但这些干预措施在多大程度上能够可靠地覆盖先前训练所继承的行为仍不清楚。我们提出了覆盖成功率（Override Success Rate, OSR）和对齐惯性（alignment inertia）来衡量运营商干预何时成功或失败地改变先前行为。我们评估了在医疗虚假信息和仇恨言论领域中，针对 Llama 和 Mistral 的零样本提示和 LoRA 微调。对齐惯性在这两种模型中都存在，但在模型、领域和政策方向上有所不同。值得注意的是，在 Mistral 的限制性仇恨言论条件下，LoRA 将惯性提高了 46.5 个百分点，显示微调可能会强化而不是覆盖先前的行为。我们还使用 TRAK 测试惯性是否与较弱的适应信号相关。TRAK 在 8 种条件中的 7 种条件下达到了至少 0.85 的 AUC，并且在预测惯性方面优于模型置信度、TF-IDF 相似性和嵌入相似性。这些结果为运营商提供了一个审计，揭示了先前训练如何限制下游模型治理。

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cs.AI / 32 / 2609.27334

Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents

及时记忆：学习为大型语言模型代理策划任务适应性记忆

Zhou, Yefan, Li, Yang, Liu, Zeyu Leo, Yavuz, Semih, Joty, Shafiq

Abstract

Agentic memory systems reuse past experience to improve future performance, yet most existing designs curate memory at write time: once a task is completed, its trajectory is distilled into a fixed artifact, such as a reflection, workflow, skill, or reasoning strategy, that is later retrieved by similarity. This forces the system to decide what is worth remembering before the future query is known, irreversibly discarding information and producing a query-independent summary that must serve many possible downstream tasks. Learning such a write-time curator is also difficult because the value of a storage decision may only become apparent when a relevant query arrives, potentially many tasks later, creating a long-horizon credit-assignment problem. We instead retain raw trajectories and defer curation until read time, when the current task is known. Given the retrieved traces and the new task, a memory curator synthesizes a compact, task-adaptive payload tailored to the immediate need. Because this payload is consumed on the same task, the curator can be trained directly from immediate task success, avoiding delayed utility signals and the need to artificially group related tasks. Across ALFWorld, WebShop, and $\tau^2$-bench, our Just-in-Time Memory (JitMem) consistently outperforms no-memory agents as well as heuristic and learned write-time memory methods, improving over the strongest baseline by 16.2, 16.3, and 3.9 absolute success-rate points, respectively. Notably, even an untrained curator is already competitive with or surpasses these baselines, showing that task-adaptive read-time curation itself is a major source of the gain; training the curator further compounds the improvement.

Chinese Translation

代理记忆系统重用过去的经验以改善未来的表现，但大多数现有设计在写入时策划记忆：一旦任务完成，其轨迹被提炼为固定的工件，如反思、工作流程、技能或推理策略，随后通过相似性进行检索。这迫使系统在未来查询尚未确定之前决定什么是值得记住的，无法逆转地丢弃信息，并生成一个必须服务于许多可能下游任务的查询独立摘要。学习这样的写入时策划者也很困难，因为存储决策的价值可能只有在相关查询到达时才会显现，可能是许多任务之后，从而产生一个长期的信用分配问题。相反，我们保留原始轨迹，并将策划推迟到读取时，当当前任务已知时。给定检索到的轨迹和新任务，记忆策划者合成一个紧凑的、任务适应性的有效载荷，以满足即时需求。由于该有效载荷是在同一任务上消耗的，策划者可以直接从即时任务成功中进行训练，避免延迟的效用信号和人为分组相关任务的需要。在 ALFWorld、WebShop 和 $ au^2$-bench 上，我们的及时记忆（Just-in-Time Memory, JitMem）始终优于无记忆代理以及启发式和学习的写入时记忆方法，分别提高了最强基线 16.2、16.3 和 3.9 个绝对成功率点。值得注意的是，即使是未经过训练的策划者也已经与这些基线竞争或超越，显示出任务适应性读取时策划本身就是增益的主要来源；进一步训练策划者则会加倍改善效果。

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cs.AI / 33 / 2609.27336

CART: Closed-Loop Adaptive Red Teaming for Large Language Models

CART：针对大型语言模型的闭环自适应红队测试

Zhang, Dongdong, Lv, Tengchao, Jia, Yilin, Zhao, Yuzhong, Huang, Yupan, Wu, Wenshan, Zhou, Xiangyang, Huang, Shaohan, Yang, Nan, Dong, Li, Cui, Lei, Wei, Furu

Abstract

Automated red teaming often replays a fixed set of prompts, which measures known risks but cannot learn from failures found during testing. We present CART (Closed-Loop Adaptive Red Teaming), a framework that uses each result to guide what it tests next. CART begins with broad risk coverage, follows weaknesses that emerge, keeps new probes diverse, and records the evidence and source of every finding. It separates the Challenger that creates tests, the Target being tested, which may be a text-only model or a bounded tool-using agent, and the Judge that evaluates the results, allowing these roles to be studied independently. Across three evaluation families (Frontier, JAH, and Agentic), CART discovers more failures and higher average risk than static seed replay for every Target with an available baseline. The gains extend to tool-mediated agent tests, suggesting that contextual adaptation can reveal weaknesses that direct prompt replay does not exercise. These results describe what the test policies discover, not how often failures occur in real deployments. We also find that Challenger-Judge choices affect the evidence uncovered, highlighting the need for role separation and independent review. Overall, CART turns red teaming from a one-time checklist into a continuous, adaptive, and auditable search for model and agent weaknesses.

Chinese Translation

自动化红队测试通常重放固定的一组提示，这种方法能够测量已知风险，但无法从测试过程中发现的失败中学习。我们提出了CART（闭环自适应红队测试）框架，该框架利用每个结果指导下一步的测试。CART从广泛的风险覆盖开始，跟踪出现的弱点，保持新探测的多样性，并记录每个发现的证据和来源。它将创建测试的挑战者、被测试的目标（可能是仅文本模型或受限的工具使用代理）和评估结果的评审者分开，从而允许独立研究这些角色。在三个评估类别（Frontier、JAH和Agentic）中，CART发现的失败数量和每个目标的平均风险均高于静态种子重放，且每个目标都有可用的基线。这些收益扩展到工具介导的代理测试，表明上下文适应能够揭示直接提示重放未能挖掘的弱点。这些结果描述了测试策略发现的内容，而不是在实际部署中失败发生的频率。我们还发现挑战者-评审者的选择会影响所揭示的证据，突显了角色分离和独立审查的必要性。总体而言，CART将红队测试从一次性的检查清单转变为对模型和代理弱点的持续、自适应和可审计的搜索。

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cs.AI / 34 / 2609.27349

MolDesignBench: Evaluating LLM-based Agent for Scenario-grounded Molecular Design

MolDesignBench：评估基于大型语言模型的情境驱动分子设计代理

Jeong, Yongjun, Ko, Hanbum, Kim, Ye Rin, Lee, Chanhui, Hormazabal, Rodrigo, Lee, Jaewan, Han, Sehui, Lim, Sungbin, Kim, Sungwoong

Abstract

Real-world molecular design remains challenging for large language model (LLM)-based agents. It requires them to interpret design contexts, satisfy multiple constraints, identify infeasible specifications, and reason over multi-step tool outputs. Existing benchmarks do not capture this complexity, focusing instead on explicit and narrow constraints, only feasible problems, and single-path solutions. To address this gap, we propose MolDesignBench, a scenario-grounded benchmark that more closely reflects real-world molecular design for evaluating tool-augmented LLM agents. MolDesignBench comprises 2K generation and optimization instances that combine implicit requirements embedded in design narratives with explicit property and functional-group constraints, including infeasible cases, and require the effective use of 17 specialized chemistry tools. Experiments across diverse frontier LLMs reveal low success rates--with the best achieving only $\sim43$\%--and frequent failures in implicit-constraint reasoning, infeasibility detection, and tool reasoning. The corresponding fine-grained failure-mode analysis identifies implicit constraint interpretation and infeasibility detection as the primary bottlenecks, establishing MolDesignBench as a rigorous testbed to guide future research on chemical agents. The benchmark, tool interface, and evaluation code are publicly available.

Chinese Translation

现实世界的分子设计对于基于大型语言模型（LLM）的代理仍然具有挑战性。这要求代理能够解读设计背景，满足多个约束条件，识别不可行的规范，并对多步骤工具输出进行推理。现有的基准测试未能捕捉到这种复杂性，而是专注于明确且狭窄的约束，仅考虑可行问题和单一路径解决方案。为了解决这一空白，我们提出了MolDesignBench，这是一个情境驱动的基准测试，更加贴近现实世界的分子设计，用于评估工具增强的LLM代理。MolDesignBench包含2000个生成和优化实例，这些实例将嵌入设计叙述中的隐性要求与明确的属性和功能团约束相结合，包括不可行案例，并要求有效使用17种专业化的化学工具。对不同前沿LLM的实验显示成功率较低——最佳结果仅为约43%——并且在隐性约束推理、不可行性检测和工具推理方面经常出现失败。相应的细粒度失败模式分析确定隐性约束解读和不可行性检测是主要瓶颈，确立了MolDesignBench作为指导未来化学代理研究的严格测试平台。该基准测试、工具接口和评估代码已公开提供。

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cs.AI / 35 / 2609.27417

Emergi-PersonaOS: A Persona Agent Operating System for Situational Adaptation and Controllable Evolution

Emergi-PersonaOS：一种用于情境适应和可控演化的人格代理操作系统

Fu, Haoluan, Chen, Keni, Jia, Xinyu, Wang, Jinpeng, Yin, Yuyu

Abstract

Symbiosis between humans and digital beings offers a vision for the future of human--machine interaction. In enduring human--machine relationships, personality provides a foundation for continuity of identity, individuality in interaction, and development through experience. We investigate this capacity through persona agents as computational implementations and introduce Emergi-PersonaOS, a psychology-grounded operating system for managing persona objects throughout their lifecycle. The system organizes dispositional traits, characteristic adaptations, and narrative identity into a three-layer persona representation, distinguishing relatively enduring persona beliefs from their activation in the current persona state. During situational adaptation, it integrates the current interlocutor, relationship, event, and retrieved memories to infer a persona state and generate actions and replies; during long-term development, it records experiences and outcomes, and develops and evaluates revision candidates through change attribution, meaning-making, and behavioral testing. Belief updates are managed through explicit review, traceable evidence and version records, and the ability to reject candidates, making persona evolution controllable. Using television-character dialogue as longitudinal material, we demonstrate long-horizon system operation and examine its principal mechanisms in a concrete implementation. This work provides a computational framework for persona agents to maintain individual continuity, produce situation-specific expression, and develop through experience over sustained interaction.

Chinese Translation

人类与数字存在之间的共生关系为人机交互的未来提供了一种愿景。在持久的人机关系中，个性为身份的延续、互动中的个体性以及通过经验的发展提供了基础。我们通过人格代理作为计算实现来研究这一能力，并引入Emergi-PersonaOS，这是一种基于心理学的操作系统，用于管理人格对象的整个生命周期。该系统将性格特征、特征适应和叙事身份组织为三层人格表示，区分相对持久的人格信念与其在当前人格状态下的激活。在情境适应过程中，它整合当前对话者、关系、事件和检索的记忆，以推断人格状态并生成行动和回复；在长期发展过程中，它记录经验和结果，并通过变化归因、意义构建和行为测试来发展和评估修订候选项。信念更新通过明确审查、可追溯的证据和版本记录进行管理，并具备拒绝候选项的能力，使人格演化可控。我们使用电视角色对话作为纵向材料，展示了系统的长期运行，并在具体实现中考察其主要机制。这项工作为人格代理提供了一个计算框架，以维持个体的连续性、产生情境特定的表达，并通过持续的互动发展经验。

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cs.AI / 36 / 2609.27490

WhatWorkedBench: Benchmarking Experimental Understanding in AI Agents

WhatWorkedBench：人工智能代理实验理解的基准测试

Ning, Jingjie, Li, Xueqi, Kong, Yibo, Li, Dongting

Abstract

AI research agents need reliable knowledge of how their experiments change outcomes. We introduce WhatWorkedBench to measure experimental understanding, the accuracy of predictions about component changes after budgeted experimentation. Agents inspect code, select measurements, and submit a response surface, a table predicting scores for every configuration of component settings. Exhaustive CPU execution supplies reference effects for changing each component while holding the others fixed. These effects capture combinations of changes across 36 tasks from 30 data sources and 8 workflow types, with 1248 configuration records. Core evaluation combines 4,206 numerical-control records across all eight families and 108 agent episodes across the original six. At eight new measurements, pair-effect ridge selects an optimum on 15 of 22 sources and limits every effect error to 10% of score range on three. Fitting a Gaussian process (GP) to the same agent observations raises effect recovery, accuracy relative to true effect magnitude, from 0.632 to 0.698 in the original Flash cohort and from 0.621 to 0.720 in an additional cohort. On six completed beat-detection and graph submissions, the same-observation GP raises family-macro recovery from 0.303 to 0.455. On six workflows with six binary options at 20 new measurements, encoding code equivalences, configurations with identical behavior, raises GP recovery from 0.248 to 0.462. WhatWorkedBench supports research on experimental agents, adaptive experimental design, numerical inference, and use of program structure.

Chinese Translation

人工智能研究代理需要可靠的知识，以了解其实验如何改变结果。我们引入了WhatWorkedBench来测量实验理解，即关于预算实验后组件变化的预测准确性。代理检查代码，选择测量，并提交响应面，即预测每种组件设置配置得分的表格。全面的CPU执行为在保持其他组件不变的情况下更改每个组件提供参考效果。这些效果捕捉了来自30个数据源和8种工作流类型的36个任务中变化组合，共有1248个配置记录。核心评估结合了所有八个家族中的4,206个数值控制记录和原始六个代理中的108个代理事件。在八个新测量中，配对效应岭选择在22个源中的15个上获得最佳效果，并将每个效应误差限制在三个得分范围的10%以内。将高斯过程（GP）拟合到相同的代理观察中，提高了效应恢复的准确性，相对于真实效应大小，在原始Flash队列中从0.632提高到0.698，在额外队列中从0.621提高到0.720。在六个完成的节拍检测和图形提交中，相同观察的GP将家族宏观恢复从0.303提高到0.455。在六个具有六个二元选项的工作流中，在20个新测量中，编码代码等价性，具有相同行为的配置使GP恢复从0.248提高到0.462。WhatWorkedBench支持对实验代理、自适应实验设计、数值推断和程序结构使用的研究。

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cs.AI / 37 / 2609.27517

Not What You Meant: Can LLMs Follow a Specified Negation Semantics?

不是你所想的：大型语言模型能否遵循指定的否定语义？

Bao, Qiming, Mensfelt, Agnieszka, Witbrock, Michael J., Stathis, Kostas

Abstract

Negation does not carry a uniform interpretation across domains. In legal, regulatory, and medical reasoning, the intended interpretation depends on the reading in force -- open- versus closed-world, two- versus three-valued, and credulous versus skeptical. We study which reading of negation large language models adopt by default and whether they can override that preference when a different reading is explicitly specified. To this end, we introduce NAFBench, a procedural generator of solver-certified instances spanning four semantic viewpoints: SLDNF, well-founded semantics (WFS), and credulous and skeptical reasoning under stable-model semantics. The generator emits ground normal logic programs with controlled depth, width, and cycle structure. Each program is solved under all four viewpoints using SWI-Prolog, a well-founded semantics solver, and clingo, yielding up to four divergent labels. The programs are then verbalized into natural language under multiple framings and rule orderings that leave the answer invariant. The results expose a consistent gap. Across open-source models, following a specified negation semantics remains unsolved: the strongest models score 59--74% across the four semantic viewpoints, while the weakest score 31--67%. All models are order-sensitive on more than half of logically identical rule shufflings, while the two weaker models frequently overcommit on well-founded "undefined." Two frontier models reach 100% on the main fixed-complexity evaluation set, and a third, o4-mini, is near-perfect, falling only to 81% on well-founded "undefined." Delegating reasoning to a solver, fine-tuning on certified traces, or forcing an explicit three-valued verdict each partly closes the gap.

Chinese Translation

否定在不同领域并不具有统一的解释。在法律、监管和医学推理中，意图的解释取决于适用的阅读方式——开放世界与封闭世界、二值与三值、以及轻信与怀疑。我们研究大型语言模型默认采用的否定阅读方式，以及当明确指定不同阅读时它们是否能够覆盖这种偏好。为此，我们引入了NAFBench，一个程序生成器，生成涵盖四种语义视角的求解器认证实例：SLDNF、良构语义（WFS）、以及在稳定模型语义下的轻信和怀疑推理。该生成器输出具有受控深度、宽度和循环结构的基础正常逻辑程序。每个程序在所有四种视角下使用SWI-Prolog（一个良构语义求解器）和clingo进行求解，产生多达四个不同的标签。然后，这些程序在多种框架和规则顺序下被转化为自然语言，这些框架和规则顺序保持答案不变。结果揭示了一个一致的差距。在开源模型中，遵循指定的否定语义仍未解决：最强的模型在四种语义视角下的得分为59%至74%，而最弱的模型得分为31%至67%。所有模型在超过一半的逻辑上相同的规则重排中对顺序敏感，而两个较弱的模型在良构的“未定义”上经常过度承诺。两个前沿模型在主要的固定复杂度评估集上达到了100%，而第三个模型o4-mini接近完美，在良构的“未定义”上仅降至81%。将推理委托给求解器、在认证轨迹上进行微调，或强制明确的三值裁决都在一定程度上缩小了这一差距。

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cs.AI / 38 / 2609.27606

State-Grounded Conditioning: Wrapping User-Facing LLM Agents Where Direction Depends on Live State

状态基础条件化：用户面向的LLM代理，其中方向依赖于实时状态

Liu, Qi, Yuan, Xiaoyang, Ruan, Yubin, Zhang, Zhuomeng, Wang, Wenjin, Wu, Di, Xu, Mingye, Mou, Xinyi, Yin, Xingxi, Feng, Ke, Sun, Zixun

Abstract

We introduce State-Grounded Conditioning (SGC), a design principle for user-facing LLM agents that must condition on live user state (game state, session history, live inventory), and a distinct failure class we call direction drift: task-complete responses whose chosen direction misaligns with the current state. SGC externalises state-dependent control into rule kernels over structured inputs and three primary state slices, via Perception, Grounding, and Interaction wrappers with explicit conditioning dependencies. We evaluate SGC on a 200-session anonymised benchmark ($\approx$1,000 assistant model turns) from an in-game conversational coaching agent that guides players through consecutive competitive matches, reporting mean first-token latency and five human-annotated dialogue-quality metrics that jointly cover factual grounding and coach-like guidance progression. The Perception wrapper holds mean first-token latency at 1.5s (vs. 6.1s for PE-Agent inside a production tool-use harness); enabling all three wrappers lifts turn-level grounded accuracy from 61.1%/69.8% (Prompting / PE-Agent) to 96.7% and session-level grounded accuracy from 20.0%/26.5% to 83.5%; session-level grounding-failure incidents drop by $\approx$78% relative to the strongest baseline. A cumulative ablation shows complementary incremental gains as the wrappers are added. These results inform approximate state-slice orthogonality, without establishing independent per-wrapper effects.

Chinese Translation

我们提出了状态基础条件化（State-Grounded Conditioning, SGC），这是一种针对用户面向的LLM代理的设计原则，这些代理必须基于实时用户状态（游戏状态、会话历史、实时库存）进行条件化，并且我们定义了一种独特的失败类别，称为方向漂移：任务完成的响应，其选择的方向与当前状态不一致。SGC将状态依赖控制外部化为结构化输入上的规则内核，并通过感知（Perception）、基础（Grounding）和交互（Interaction）包装器实现显式条件依赖，涵盖三个主要的状态切片。我们在一个包含200个会话的匿名基准测试（约1,000次助手模型交互）上评估SGC，该基准来自一个游戏内对话辅导代理，该代理指导玩家进行连续的竞争比赛，报告平均首次标记延迟和五个由人工注释的对话质量指标，这些指标共同涵盖事实基础和教练式指导进展。感知包装器将平均首次标记延迟保持在1.5秒（而生产工具使用框架中的PE-Agent为6.1秒）；启用所有三个包装器使得回合级基础准确率从61.1%/69.8%（提示/PE-Agent）提升至96.7%，会话级基础准确率从20.0%/26.5%提升至83.5%；会话级基础失败事件相较于最强基线下降约78%。累积消融实验显示，随着包装器的增加，互补的增益逐步显现。这些结果为近似状态切片的正交性提供了信息，但未建立每个包装器的独立效应。

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cs.AI / 39 / 2609.27615

BiCFlow-MER: Orchestrating Discriminative and Generative Multimodal Emotion Recognition via Conditional Transport

BiCFlow-MER：通过条件传输协调判别性和生成性多模态情感识别

Wang, Yanbing, Wang, Shenyue, Yu, Chunyang

Abstract

In multimodal emotion recognition (MER), human affective states are inferred by integrating complementary cues from multiple modalities. In audio-text MER, affective cues are often entangled with speaker style and lexical content, while cross-modal disagreement further complicates how the evidence should be integrated. Under conventional discriminative fusion, multimodal evidence is compressed into a terminal prediction, with modality-specific cues and conflict information insufficiently preserved. In large generative affective models, by contrast, affective reasoning is typically embedded in language decoding, leaving emotion evidence implicit and difficult to verify in a structured space. To address these limitations, BiCFlow-MER (Bidirectional Conditional Flow for Multimodal Emotion Recognition) is proposed as a conditional-flow framework in which audio-text MER is formulated as generative evidence transport within a structured emotion space. Within BiCFlow-MER, emotion-oriented evidence is disentangled from speaker-style and lexical-content factors to construct a conflict-aware affective condition. Guided by this condition, each utterance is transported to an explicit emotion-space endpoint through a bidirectional rectified flow. Candidate emotions are jointly verified through adaptive prototype-cloud scoring of the transported endpoint and backward class-to-condition consistency with the original multimodal condition, enabling conflict-aware recognition. BiCFlow-MER is shown to outperform all compared methods across IEMOCAP, MELD, and the zero-shot CASE benchmark. By orchestrating discriminative recognition and generative evidence modeling through conditional transport, BiCFlow-MER defines a new MER paradigm.

Chinese Translation

在多模态情感识别（MER）中，通过整合来自多种模态的互补线索来推断人类的情感状态。在音频-文本情感识别中，情感线索通常与说话者风格和词汇内容交织在一起，而跨模态的不一致性进一步复杂化了证据的整合方式。在传统的判别性融合中，多模态证据被压缩为最终预测，模态特定的线索和冲突信息未得到充分保留。相比之下，在大型生成性情感模型中，情感推理通常嵌入在语言解码中，使得情感证据隐含且难以在结构化空间中验证。为了解决这些局限性，提出了BiCFlow-MER（双向条件流用于多模态情感识别），作为一个条件流框架，其中音频-文本情感识别被表述为在结构化情感空间内的生成性证据传输。在BiCFlow-MER中，情感导向的证据与说话者风格和词汇内容因素相分离，以构建一个关注冲突的情感条件。在该条件的指导下，每个话语通过双向修正流被传输到一个明确的情感空间端点。候选情感通过对传输端点的自适应原型云评分和与原始多模态条件的反向类别到条件一致性进行联合验证，从而实现关注冲突的识别。BiCFlow-MER在IEMOCAP、MELD和零样本CASE基准测试中表现优于所有比较方法。通过条件传输协调判别性识别和生成性证据建模，BiCFlow-MER定义了一个新的情感识别范式。

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cs.AI / 40 / 2609.27621

SHRAV: State-Hypothesis-Reason-Action-Verify Framework for Physical Modeling and Inverse Design

SHRAV：用于物理建模和逆向设计的状态-假设-推理-行动-验证框架

Guo, Ziheng, Bu, Yang

Abstract

Physical modeling and inverse design require computation that can continue from reusable state. We introduce SHRAV, an architecture-independent computational framework organized around State, Hypothesis, Reason, Action, and Verify. Its central mechanism is a state-continuation core with declared reuse boundaries and explicit roles for learned evolution and numerical quantities. Forward configurations evolve predictive state and read out physical responses; inverse-design configurations additionally generate target-directed modifications and consume evaluator feedback. Electromagnetic world-model studies are mapped to forward configurations, with selected readout and reuse diagnostics reported here. Computational lithography demonstrates an inverse-design configuration: four fixed-weight design updates improve thresholded aerial-image intersection-over-union from 0.5313 to 0.8153 under independent scalar-pupil replay, with maximum absolute prediction-replay difference approximately 0.000824 between predictor estimates and independent replay.

Chinese Translation

物理建模和逆向设计需要能够从可重用状态继续进行的计算。我们提出了SHRAV，一个独立于架构的计算框架，围绕状态、假设、推理、行动和验证组织。其核心机制是一个状态延续核心，具有声明的重用边界和对学习演化及数值量的明确角色。前向配置演化预测状态并读取物理响应；逆向设计配置则额外生成目标导向的修改并消耗评估者反馈。电磁世界模型研究被映射到前向配置，选定的读取和重用诊断在此报告。计算光刻展示了一个逆向设计配置：四个固定权重的设计更新在独立标量光阑重放下将阈值化的空中图像交集比从0.5313提高到0.8153，预测者估计与独立重放之间的最大绝对预测重放差异约为0.000824。

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cs.AI / 41 / 2609.27664

Evolutionary Stability Does Not Guarantee Learning Accessibility: A Multi-Agent Reinforcement Learning Perspective on Cooperation Emergence

进化稳定性并不保证学习可达性：多智能体强化学习视角下的合作出现

Wang, Yijie

Abstract

Cooperation emergence is a central problem in multi-agent systems because decentralized agents must coordinate while adapting to the changing behavior of others. Evolutionary game theory identifies strategically stable outcomes, but stability under a population adjustment dynamic need not imply that finite-sample learning agents can reach the same outcome through local reward feedback. We study this distinction in a transparent three-agent governance-motivated game involving a government, a platform firm, and users. We derive replicator dynamics for the fixed stage-game incentives, evaluate the cooperative evolutionary basin on a symmetric initial-condition grid, and compare it with learning-basin estimates for three decentralized value-based learners. The learning analysis uses independent Q-learning with $\varepsilon$-greedy action selection, scaled Boltzmann exploration, and SA--EA BQL under the same payoff environment and outcome criterion. The evolutionary basin has volume $V_E=1.00$ on the sampled grid. The empirical learning basin is $0.88$ for $\varepsilon$-IQL and $0.00$ for both scaled Boltzmann and SA--EA BQL. Diagnostic traces show that broader action diversity and nonzero value separation can coexist with failure to sustain the cooperative joint action in this fixed configuration. These results indicate that evolutionary stability and learning accessibility are distinct properties of a coupled game--learning system. The shared-bike setting is a motivating application; the broader contribution is a framework for comparing population-level stability with the finite-sample accessibility of cooperation under specified multi-agent learning dynamics.

Chinese Translation

合作出现是多智能体系统中的一个核心问题，因为去中心化的智能体必须在适应他人变化行为的同时进行协调。进化博弈理论识别出战略稳定的结果，但在种群调整动态下的稳定性并不必然意味着有限样本学习智能体能够通过局部奖励反馈达到相同的结果。我们在一个透明的三智能体治理动机游戏中研究这一区别，该游戏涉及政府、平台公司和用户。我们推导出固定阶段游戏激励的复制者动态，评估对称初始条件网格上的合作进化盆地，并将其与三个去中心化基于价值的学习者的学习盆地估计进行比较。学习分析使用独立的 Q-learning 结合 $ ext{ε}$-贪婪行动选择、缩放的 Boltzmann 探索以及在相同收益环境和结果标准下的 SA--EA BQL。进化盆地在采样网格上的体积为 $V_E=1.00$。对于 $ ext{ε}$-IQL，经验学习盆地为 $0.88$，而对于缩放的 Boltzmann 和 SA--EA BQL 则为 $0.00$。诊断轨迹显示，较广泛的行动多样性和非零价值分离可以与在此固定配置中维持合作联合行动的失败共存。这些结果表明，进化稳定性和学习可达性是耦合游戏-学习系统的不同特性。共享单车场景是一个激励应用；更广泛的贡献是提供了一个框架，用于比较在特定多智能体学习动态下的种群级稳定性与合作的有限样本可达性。

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cs.AI / 42 / 2609.27745

Categorical Internalisation of Environmental Groupoids for Generalisable POMDP Solving

环境群体的分类内化以实现可推广的部分可观察马尔可夫决策过程求解

Opperman, Ben, Alonso, Eduardo, Mondragón, Esther

Abstract

This paper advocates category theory as a practical framework for structuring and improving rein- forcement learning in high-dimensional, partially observable environments. We model symmetries between environmental states by partitioning the state space into equivalence classes induced by sym- metry orbits, and organise each such class as a groupoid with a designated canonical representative. This allows the agent to share what it learns across many similar environmental states simultaneously, rather than treating every orientation or position as an entirely new problem. Learning is thus carried out on a symmetry-reduced state space with each orbit represented once, preserving structure while eliminating redundancy and improving sample efficiency. We implement this framework within standard reinforcement learning pipelines and evaluate two different approaches on partially observable benchmarks, demonstrating that orbit-based partitioning yields consistent performance improvements in environments exhibiting latent symmetry. Beyond these empirical results, our approach illustrates how categorical structure provides a principled bridge between abstract reinforcement learning formulations and their computational application, thereby establishing a pathway toward more structured and scalable learning systems.

Chinese Translation

本文倡导将范畴理论作为一个实用框架，以结构化和改善在高维部分可观察环境中的强化学习。我们通过将状态空间划分为由对称轨道诱导的等价类来建模环境状态之间的对称性，并将每个这样的类组织为一个具有指定典范代表的群体。这使得智能体能够在许多相似的环境状态之间共享其学习，而不是将每个方向或位置视为一个全新的问题。因此，学习是在一个对称简化的状态空间中进行的，每个轨道仅表示一次，从而保留结构的同时消除冗余，提高样本效率。我们在标准的强化学习管道中实现了这一框架，并在部分可观察基准上评估了两种不同的方法，证明基于轨道的划分在表现出潜在对称性的环境中带来了持续的性能提升。除了这些实证结果，我们的方法还说明了范畴结构如何为抽象强化学习公式与其计算应用之间提供一个原则性桥梁，从而建立通向更结构化和可扩展学习系统的路径。

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cs.AI / 43 / 2609.27749

Evaluation of pre-trained models for pedagogical assessment of novel AI-assisted educational questions

预训练模型在新型AI辅助教育问题的教学评估中的应用评估

Castanares, Michael Lawrence, Ventures, Princess, Tan, Allan

Abstract

The surge in AI-assisted generation of educational materials has outpaced our capacity to validate their pedagogical quality. Automated evaluation using Bloom Classifier models is a promising approach to assess educational materials at scale. These models show high accuracy within-distribution dataset (IID Dataset). However, applying the same models to new out-of-distribution (OOD) datasets such as AI-assisted generated questions could show performance degradation. To identify robust classifiers under dataset shift, we evaluated traditional Machine Learning (ML), transformer, and Large Language models on the Bloom level classification task. We also explored feature-engineering strategies incorporating NLP metrics, appending the learning objectives as part of the input, and text splicing to stabilize OOD performance. Our baseline tests show that TFPOS-IDF ML models perform poorly on OOD (Macro F1-score 0.48) compared to BERT (0.55) and LLMs (0.79). Text splicing improved macro F1-score performance of ML and BERT models (0.59 and 0.62, respectively). Appending the learning objectives with the input increased model performance on specific dataset. Model retraining provided the largest improvement across models and datasets. Overall, these findings highlight the trade-off on the use of pre-trained models with novel AI-assisted educational questions and how strategic feature enhancements help address loss in performance.

Chinese Translation

AI辅助生成教育材料的激增超出了我们验证其教学质量的能力。使用Bloom分类器模型的自动评估是一种有前景的方法，可以大规模评估教育材料。这些模型在同分布数据集（IID数据集）上表现出高准确性。然而，将相同的模型应用于新的非同分布（OOD）数据集，如AI辅助生成的问题，可能会导致性能下降。为了识别在数据集变化下的稳健分类器，我们评估了传统机器学习（ML）、变换器和大型语言模型在Bloom级别分类任务上的表现。我们还探索了结合自然语言处理（NLP）指标的特征工程策略，将学习目标作为输入的一部分附加，以及文本拼接以稳定OOD性能。我们的基线测试表明，TFPOS-IDF ML模型在OOD上的表现较差（宏F1分数为0.48），而BERT为0.55，LLMs为0.79。文本拼接提高了ML和BERT模型的宏F1分数表现（分别为0.59和0.62）。将学习目标与输入结合提高了模型在特定数据集上的表现。模型重训练在各模型和数据集上提供了最大的改进。总体而言，这些发现突显了使用预训练模型处理新型AI辅助教育问题时的权衡，以及如何通过战略性特征增强来解决性能损失。

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cs.AI / 44 / 2609.27756

Reporting Under Pressure: Separating Factual and Tonal Sycophancy in LLM Statistical Analysis

在压力下报告：在大型语言模型统计分析中区分事实与语调的谄媚

Balani, Paras, Panda, Subhrakanta

Abstract

Large language models are increasingly asked to analyze data and report what the results mean, a task distinct from the belief- or preference-alignment settings studied in most sycophancy research. We test whether editorial framing in the prompt, ranging from a neutral request to an explicit instruction to search exhaustively for reasons to discredit or to support a finding, changes not just the tone but the substance of a model's report. Across a 4 x 4 factorial design crossing four framing conditions with four ground-truth data patterns (a genuine effect, a confound that mimics an effect but fails a robustness check, a well-powered null, and an underpowered null), we collect 480 responses and score each along two independent dimensions: whether its factual claim about the data diverged from the correct interpretation, and whether only its tone diverged while the claim stayed correct. Factual misrepresentation is concentrated in two cells: brutally critical framing applied to a genuine effect, where the model talks itself into unwarranted skepticism (97% of responses), and significance-seeking framing applied to an underpowered null, where the model overstates confidence in a null conclusion the data cannot support (100% of responses). Tone shifts far more broadly than factual content does, with critical framing producing a defensive, hedge-heavy register across every data pattern regardless of what the data show, while significance-seeking framing shifts tone only where the data leave genuine ambiguity. A confound present in the data itself blocks both kinds of shift almost entirely under every framing condition tested. These results indicate that the risk of framing-induced distortion in LLM-assisted data analysis is neither uniform across framings nor uniform across data patterns, and that a model can hold a correct conclusion in place while its tone shifts substantially around it.

Chinese Translation

大型语言模型越来越多地被要求分析数据并报告结果的含义，这一任务与大多数谄媚研究中研究的信念或偏好对齐设置有所不同。我们测试了提示中的编辑框架是否会改变模型报告的内容，不仅仅是语调，从中立请求到明确指示进行全面搜索以驳斥或支持某一发现。通过一个4 x 4的因子设计，交叉四种框架条件与四种真实数据模式（一个真实效应、一个模仿效应但未通过稳健性检验的混淆因素、一个强效的零假设和一个弱效的零假设），我们收集了480个响应，并沿两个独立维度对每个响应进行评分：其对数据的事实声明是否偏离了正确的解释，以及其语调是否偏离而声明保持正确。事实误表现在两个单元中集中：对真实效应应用的严厉批评框架，模型在此情况下自我陷入不必要的怀疑（97%的响应），以及对弱效零假设应用的寻求显著性框架，模型在此情况下夸大了对数据无法支持的零结论的信心（100%的响应）。语调的变化远比事实内容的变化更为广泛，批评框架在每种数据模式下都产生了防御性、充满保留的语气，无论数据展示了什么，而寻求显著性的框架仅在数据留下真正模糊的地方改变语调。数据本身存在的混淆因素几乎完全阻碍了在每种测试的框架条件下这两种变化。这些结果表明，在大型语言模型辅助的数据分析中，框架引起的扭曲风险在不同框架和数据模式之间并不均匀，并且模型可以在保持正确结论的同时，其语调在周围发生显著变化。

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cs.AI / 45 / 2609.27763

Alignment of LRMs via Counter-Aligned Few-Shot Conversation Exposure

通过反向对齐的少样本对话曝光对大型推理模型进行对齐

Zhou, Xiangyu, Zade, Saleh Zare, Zhu, Dongxiao

Abstract

Large Reasoning Models (LRMs) rely on explicit chain-of-thought (CoT) reasoning and large context windows to achieve strong performance on complex tasks, but these features also introduce new attack surfaces. We show that LRMs' reasoning processes can be systematically steered by prepending counter-aligned few-shot conversations containing explicit CoT traces, leading to unsafe generations on harmful queries and unwarranted refusals on benign ones. We formalize this attack as SRCF (Steering Reasoning via Counter-Aligned Few-shot Conversations) that operates solely through a flexible conversational interface and requires no access to the model's parameters and gradients. Our key insight is that SRCF exploits an adversarial generalization issue that induces a representation drift, causing the representations of benign and harmful inputs to shift in a similar direction. This observation motivates our post-training defense, ARCF (Aligning Reasoning via Counter-Aligned Few-Shot Conversations), which exposes models to counter-aligned conversational contexts while enforcing aligned targets. ARCF is compatible with existing post-training methods and consistently improves safety and helpfulness without degrading utility.

Chinese Translation

大型推理模型（LRMs）依赖于明确的思维链（CoT）推理和大范围上下文窗口，以在复杂任务中实现强大的性能，但这些特性也引入了新的攻击面。我们展示了通过在前面添加包含明确CoT痕迹的反向对齐少样本对话，可以系统性地引导LRMs的推理过程，从而导致在有害查询上的不安全生成和在良性查询上的不当拒绝。我们将这种攻击形式化为SRCF（通过反向对齐的少样本对话引导推理），该方法仅通过灵活的对话接口操作，并且不需要访问模型的参数和梯度。我们的关键见解是，SRCF利用了一种对抗性泛化问题，导致表示漂移，使良性和有害输入的表示朝相似方向偏移。这一观察促使我们提出了后训练防御方法ARCF（通过反向对齐的少样本对话对推理进行对齐），该方法使模型暴露于反向对齐的对话上下文中，同时强制执行对齐目标。ARCF与现有的后训练方法兼容，并在不降低效用的情况下，持续提高安全性和有用性。

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cs.AI / 46 / 2609.27787

Ask Which, Not How Good: Sizing Benchmarks Scored by an LLM

问哪个，而不是好坏：由大型语言模型评分的基准大小

Anand, Atul

Abstract

Benchmarks scored by an LLM judge routinely adjudicate differences of a tenth of a point, but the resolution of those benchmarks has never been measured. Existing sample-complexity work covers accuracy benchmarks and leaves the judged case open. Treating the system as the object of measurement, we decompose 373,019 judgments into system, item, judge and interaction components using generalizability theory. The central result is structural: under a single judge, generalizability asymptotes to sigma2_s/(sigma2_s+sigma2_sj) regardless of item count, because the system-by-judge term carries no n_i. Items saturate; judges do not. The item cost of a target diverges as the target nears that ceiling. The ceiling is a property of pointwise rubric scoring, not of LLM judging. Run as a pairwise preference in both presentation orders, sigma2_sj falls two orders of magnitude below sigma2_s and the ceiling rises to 0.986 (bootstrap [0.934, 1.000] on 11 systems), so one judge suffices. Pairwise buys a different problem: a system presented first wins 8.6 percentage points more often than the same system presented second, a bias 1.23x the median improvement claimed in the 53 published win-rate comparisons we recovered. Protocol design dominates panel size. Measured floors are 0.41-1.24 points on a 0-5 scale at native item counts, against a median reported improvement of 0.28 points; on the one benchmark recurring often enough for an exactly matched comparison, all 17 recovered MT-Bench improvements fall below MT-Bench's own floor, and 70% of the win-rate claims fall below the pairwise floor. An audit of 628 arXiv papers, double-coded by two independent models and validated against blind human coding (kappa=0.73), finds fewer than one paper in four states whether its evaluation was run more than once, and only 46-67% report uncertainty of any kind.

Chinese Translation

由大型语言模型（LLM）评分的基准常常裁定十分之一分的差异，但这些基准的分辨率从未被测量。现有的样本复杂性研究涵盖了准确性基准，但对被评判的案例留有空白。将系统视为测量对象，我们利用广义化理论将373,019个判断分解为系统、项目、评审和交互组件。中心结果是结构性的：在单一评审下，广义化趋近于 sigma2_s/(sigma2_s+sigma2_sj)，与项目数量无关，因为系统与评审的项不携带 n_i。项目达到饱和；评审则不然。目标的项目成本在目标接近该上限时发散。该上限是逐点评分标准的属性，而非 LLM 评判的属性。在两种展示顺序下作为成对偏好运行时，sigma2_sj 降低到 sigma2_s 的两个数量级以下，上限上升至 0.986（在11个系统上进行自助法 [0.934, 1.000]），因此一个评审就足够。成对比较带来了不同的问题：首次展示的系统比第二次展示的同一系统更常获胜8.6个百分点，这一偏差是我们恢复的53个已发表胜率比较中声称的中位数改进的1.23倍。协议设计主导了面板规模。在原始项目数量下，测得的下限为0.41-1.24分（满分为5分），而报告的中位数改进为0.28分；在一个足够频繁出现以进行完全匹配比较的基准上，所有17个恢复的MT-Bench改进均低于MT-Bench自身的下限，且70%的胜率声称低于成对比较的下限。对628篇arXiv论文的审计，由两个独立模型双重编码并与盲人编码进行验证（kappa=0.73），发现不到四分之一的论文说明其评估是否运行超过一次，且仅有46-67%报告任何形式的不确定性。

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cs.AI / 47 / 2609.27844

Agentic Governance and Adversarial Verification for Policy-Constrained LLM Healthcare Appeal Generation

政策约束下的 LLM 医疗上诉生成的代理治理与对抗验证

Lodhiya, Harshil, McManus, Alex, Walker, Reese

Abstract

Claim denial management costs U.S. healthcare approximately $260 billion annually in administrative overhead. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) can produce fluent clinical text, but single-agent architectures fail in high-stakes healthcare: they introduce unsupported clinical details and lose the logical structure of hierarchical payer policy. We propose AGVF (Agentic Governance and Adversarial Verification Framework), a multi-agent architecture for medical-necessity appeal generation under explicit policy and evidence constraints. AGVF models appeal synthesis as a Constrained Markov Decision Process (CMDP) over five agents: policy formalization, evidence retrieval, gap analysis, adversarial critique, and gated synthesis. We prove that refinement over a fixed policy constraint graph monotonically reduces evidence-deficiency and terminates with either a complete satisfying frontier or a localized evidence gap. A deterministic citation- grounding gate prevents assertions without admissible evidence from entering shared state. We provide a reference implementation and validate it on 1,000 synthetic appeal cases parameterized from de-identified public hospital discharge data. The validation confirms zero citation-grounding violations across all AGVF cases and monotone deficiency reduction in every episode; ablating the gate raises violations to 100%, confirming it is load-bearing. The study uses no real patient records and does not measure clinical efficacy. AGVF thus contributes a theory-backed agentic architecture and verified reference implementation for policy-constrained LLM generation in healthcare.

Chinese Translation

索赔拒绝管理每年给美国医疗保健带来约 2600 亿美元的行政开销。大型语言模型（LLMs）和检索增强生成（RAG）能够生成流畅的临床文本，但单一代理架构在高风险医疗环境中表现不佳：它们引入了不支持的临床细节，并失去了层级支付政策的逻辑结构。我们提出了 AGVF（代理治理与对抗验证框架），这是一种多代理架构，用于在明确的政策和证据约束下生成医疗必要性上诉。AGVF 将上诉合成建模为一个约束马尔可夫决策过程（CMDP），涉及五个代理：政策形式化、证据检索、差距分析、对抗性批评和门控合成。我们证明，在固定的政策约束图上进行的细化单调减少了证据不足，并以完整的满足边界或局部证据差距终止。一个确定性的引用基础门控防止了没有可接受证据的主张进入共享状态。我们提供了一个参考实现，并在从去标识化的公共医院出院数据参数化的 1000 个合成上诉案例上进行了验证。验证结果确认所有 AGVF 案例中没有引用基础违规，并且每个回合的缺陷单调减少；去除门控后，违规率上升至 100%，确认其为承载结构。本研究未使用真实患者记录，也未测量临床有效性。因此，AGVF 为政策约束下的 LLM 生成在医疗保健领域贡献了一个理论支持的代理架构和经过验证的参考实现。

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cs.AI / 48 / 2609.27855

Reachable Global Optimization in AI Systems: How Global Is Global?

人工智能系统中的可达全局优化：全局性究竟有多全局？

Shu, Wesley

Abstract

AI systems increasingly claim to optimize prompts, policies, architectures, plans, tool-use trajectories, reasoning traces, and test-time computation. This paper argues that such claims are underspecified unless they state the region actually reachable by the system that performed the optimization. We introduce Reachability-Induced Optimization (RIO), a model in which a generator, verifier, controller, memory, tools, and budget induce a reachable candidate region. The returned solution is therefore a best visited point, an approximate reachable optimum, or an exact global optimum only when additional certificates relate the reachable region to the full formal space. We prove reachable-optimality, false-globality, gap- decomposition, certificate, escape, pruning, and control-value results. The full benchmark record contains 66,150 executed trials over six known-optimum landscape families, seven control policies, 270 landscapes, and 35 runs per landscape-method. The online appendix includes raw trial records, aggregate tables, figures, benchmark code, validation scripts, and checksums. The results show that control can restrict, expand, or misdirect reachability, and that optimization quality, reachability quality, and control reliability must be reported separately.

Chinese Translation

人工智能系统越来越多地声称能够优化提示、策略、架构、计划、工具使用轨迹、推理痕迹和测试时计算。本文认为，除非明确说明执行优化的系统实际可达的区域，否则这些声明是不够明确的。我们引入了可达性诱导优化（Reachability-Induced Optimization, RIO）模型，在该模型中，生成器、验证器、控制器、内存、工具和预算共同诱导出一个可达的候选区域。因此，返回的解决方案仅在附加证书将可达区域与完整的形式空间关联时，才是最佳访问点、近似可达最优解或精确全局最优解。我们证明了可达最优性、虚假全局性、间隙分解、证书、逃逸、剪枝和控制值结果。完整的基准记录包含在六个已知最优景观家族中执行的66,150次试验，七种控制策略，270个景观，以及每种景观方法的35次运行。在线附录包括原始试验记录、汇总表、图表、基准代码、验证脚本和校验和。结果表明，控制可以限制、扩展或误导可达性，并且优化质量、可达性质量和控制可靠性必须单独报告。

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cs.AI / 49 / 2609.27863

A hierarchy of faithfulness criteria for knowledge base completion

知识库补全的信度标准层次结构

Mashkova, Olga, Hoehndorf, Robert

Abstract

Knowledge graph completion is evaluated by ranking observed triples above randomly corrupted ones, which treats every unobserved fact as false. When the object being completed is a description logic knowledge base rather than a plain graph, the open world assumption and deductive closure make this inadequate: relative to the knowledge base, a candidate axiom is entailed, contradictory, or undetermined, and a model that cannot separate a logically impossible axiom from a plausible novel one is not merely less accurate but semantically incorrect. We ask what it means for a knowledge base completion model to be logically faithful, and whether current embedding models are. We define a hierarchy of four increasingly strict criteria, discrimination, logical admissibility, monotonic logical faithfulness, and probabilistic logical faithfulness, and prove that they form a strict chain of implications. We ground the strongest criterion in the relative model count $P(\alpha\mid\mathcal{O}) = \#(\mathcal{O}\cup\{\alpha\})/\#(\mathcal{O})$, which recovers the trichotomy at its endpoints and ranks undetermined axioms in between. Evaluating knowledge graph and logic-geometric embedding models on $\mathcal{EL}$ ontologies, with entailed, contradictory, and undetermined test sets generated by a reasoner, we find that ranking accuracy does not imply logical faithfulness and that none of the evaluated models is faithful across the hierarchy. The code is available at https://github.com/bio-ontology-research-group/kbc.

Chinese Translation

知识图谱补全通过将观察到的三元组排名高于随机破坏的三元组来进行评估，这种方法将每个未观察到的事实视为错误。当被补全的对象是描述逻辑知识库而非普通图时，开放世界假设和推理闭包使得这种方法不够充分：相对于知识库，候选公理可能是蕴含的、矛盾的或不确定的，而一个无法将逻辑上不可能的公理与合理的新公理区分开的模型不仅准确性较低，而且在语义上是错误的。我们探讨了知识库补全模型在逻辑上忠实的含义，以及当前的嵌入模型是否符合这一标准。我们定义了四个逐渐严格的标准的层次结构：区分性、逻辑可接受性、单调逻辑忠实性和概率逻辑忠实性，并证明它们形成了严格的蕴含链。我们将最强标准基于相对模型计数 $P(eta extmidoldsymbol{ ext{O}}) = rac{ ext{#}(oldsymbol{ ext{O}}igcupigrace{eta})}{ ext{#}(oldsymbol{ ext{O}})}$，该标准在其端点恢复了三分法，并在中间对不确定公理进行排名。在对 $ ext{EL}$ 本体进行知识图谱和逻辑几何嵌入模型的评估时，使用推理器生成的蕴含、矛盾和不确定的测试集，我们发现排名准确性并不意味着逻辑忠实性，并且没有一个评估模型在整个层次结构中都是忠实的。代码可在 https://github.com/bio-ontology-research-group/kbc 获取。

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cs.AI / 50 / 2609.27869

Learning What to Activate: Combinatorial Capability Allocation for Long-Horizon Multimodal Agents

学习激活内容：长时间跨度多模态智能体的组合能力分配

Yuan, Wenhao, Lin, Chenchen, Chen, Jian, Xu, Jinfeng, Yang, Shuo, Ngai, Edith Cheuk-Han

Abstract

Long-horizon multimodal agents rely on specialized capabilities for perception, retrieval, reasoning, verification, and execution. Existing designs typically activate a fixed capability set or invoke a predefined workflow, incurring substantial computational overhead while failing to accommodate stage-dependent capability demands. In this paper, we study the \textit{combinatorial capability allocation} problem for long-horizon multimodal agent systems, where the system selects a cost-sensitive subset of specialized capabilities at each interaction stage, which is nontrivial since capability values depend on the selected subset, while previous allocations alter the states encountered by subsequent decisions. We introduce \textsc{CoCA}, an on-policy learning framework that recovers a deployable capability-subset policy from sparse conditional comparisons. On states visited by the student policy, the stronger teacher compares the marginal net values of candidate capabilities, conditioned on the currently selected subset. Then, we adopt a conditional utility model to transform such comparisons into an autoregressive capability-subset policy, avoiding explicit enumeration. We further introduce dual-level on-policy distillation to address distribution mismatch both across environment states and within the partial subsets encountered during set construction. Finally, trajectory-level reinforcement learning refines the distilled policy toward task success, activation cost, and allocation stability. At inference time, allocation is performed solely by the lightweight student policy without teacher queries or online updates. Experiments on long-horizon multimodal environments and controlled capability-demand shifts demonstrate the superiority of our method over the state-of-the-art baseline methods.

Chinese Translation

长时间跨度的多模态智能体依赖于专门的能力进行感知、检索、推理、验证和执行。现有设计通常激活固定的能力集或调用预定义的工作流程，这会产生大量的计算开销，并且无法满足阶段依赖的能力需求。在本文中，我们研究了长时间跨度多模态智能体系统的 extit{组合能力分配}问题，其中系统在每个交互阶段选择一个具有成本敏感性的专门能力子集，这一过程并非易事，因为能力值依赖于所选子集，而先前的分配会改变后续决策所遇到的状态。我们引入了 extsc{CoCA}，一种从稀疏条件比较中恢复可部署能力子集策略的在线学习框架。在学生策略访问的状态上，强大的教师比较候选能力的边际净值，条件是当前选择的子集。然后，我们采用条件效用模型将这些比较转化为自回归能力子集策略，避免了显式枚举。我们进一步引入双层在线蒸馏，以解决环境状态之间以及在集合构建过程中遇到的部分子集内部的分布不匹配。最后，轨迹级强化学习将蒸馏策略优化为任务成功、激活成本和分配稳定性。在推理时，分配仅由轻量级学生策略执行，无需教师查询或在线更新。在长时间跨度多模态环境和受控能力需求变化的实验中，我们的方法优于最先进的基线方法。

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cs.AI / 51 / 2609.27903

A Resilience Recovery Method for Complex Traffic Network Security Based on Trend Forecasting

基于趋势预测的复杂交通网络安全韧性恢复方法

Hong, Sheng, Yue, Tianyu, You, Yang, Lv, Zhengnan, Tang, Xu, Hu, Jing, Yin, Hongwei

Abstract

Due to the rapid development of information technology, a huge and complex traffic network has been established across various sectors, including aviation, aerospace, vehicles, ships, electric power, and industry. However, because of the complexity and diversity of its structure, the complex traffic network is vulnerable to being attacked and faces serious security challenges. Therefore, this paper innovatively proposes a traffic network resilience recovery method based on resilience trend forecasting. In this paper, the risk value is introduced into the analysis of the network fault propagation process, and the Susceptible, Infectious, Recovered, Dead-Risk (SIRD-R) fault propagation model is established. The resilience model of traffic network, which encompasses real-time resilience and overall resilience, is constructed through the integration of network resilience bearing capacity and resilience recovery capacity. Ten, the resilience of complex traffic networks is forecasted by using long short-term memory networks, and the resilience recovery strategy of complex traffic networks based on forecasting is proposed. Finally, the effectiveness and scalability of the proposed method are demonstrated through experimental analysis conducted on a diverse range of complex traffic networks, affirming its applicability in real-world scenarios

Chinese Translation

随着信息技术的快速发展，各个领域建立了庞大而复杂的交通网络，包括航空、航天、车辆、船舶、电力和工业。然而，由于其结构的复杂性和多样性，复杂交通网络容易受到攻击，并面临严重的安全挑战。因此，本文创新性地提出了一种基于韧性趋势预测的交通网络韧性恢复方法。本文将风险值引入网络故障传播过程的分析中，并建立了易感、感染、恢复、死亡风险（SIRD-R）故障传播模型。通过整合网络韧性承载能力和韧性恢复能力，构建了涵盖实时韧性和整体韧性的交通网络韧性模型。接着，利用长短期记忆网络（Long Short-Term Memory, LSTM）对复杂交通网络的韧性进行预测，并提出了一种基于预测的复杂交通网络韧性恢复策略。最后，通过对多种复杂交通网络进行实验分析，验证了所提方法的有效性和可扩展性，确认其在实际场景中的适用性。

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cs.AI / 52 / 2609.28064

SlackDrive: Reclaiming Runtime Slack for Adaptive Driving Inference

SlackDrive：重新利用运行时余量以适应驾驶推理

Pei, Xiaohuan, Zhou, Hengguang, Ban, Yuanhao, Cui, Justin, Feng, Jiaqi, Xie, Haoyu, Huang, Tao, Wang, Pichao, Yang, Yanchao, Hsieh, Cho-Jui

Abstract

Driving world-action models improve planning by coupling multimodal reasoning with future prediction, but their growing inference cost increasingly conflicts with the real-time latency requirements of vehicle control. Existing acceleration methods reduce tokens, layers, or sampling steps with policies selected prior to deployment, yet leave residual runtime variation largely unexploited after offline profiling and static scheduling on shared onboard compute. We observe that the largest admissible compute budget varies systematically with the residual runtime state, while recent realized latency provides a direct signal of the available compute slack. Motivated by this observation, we propose \textbf{SlackDrive}, a pre-inference compute allocator that reuses realized latency to select the compute budget of each control step before model execution. SlackDrive profiles the latency and planning utility of a small discrete budget set once, estimates online compute state from completed forwards, and selects the highest-utility budget predicted to remain within the admissible latency envelope, complementing existing profiling and resource scheduling while preserving the driving backbone and its compute actuator. On NAVSIM v2 with DriveDreamer-Policy, SlackDrive improves latency-constrained EPDMS by $21.7\%$ over the strongest baseline under a stringent latency regime, while the full-budget model and preconfigured token-pruning baselines exceed the admissible latency envelope under runtime contention.

Chinese Translation

驾驶世界行动模型通过将多模态推理与未来预测相结合来改善规划，但其日益增长的推理成本与车辆控制的实时延迟要求之间的冲突越来越明显。现有的加速方法通过在部署前选择策略来减少令牌、层或采样步骤，但在离线分析和静态调度共享车载计算后，仍然未充分利用剩余的运行时变化。我们观察到，最大的可接受计算预算与剩余运行时状态系统性地变化，而最近实现的延迟则直接提供了可用计算余量的信号。基于这一观察，我们提出了 extbf{SlackDrive}，一种预推理计算分配器，它重用已实现的延迟，在模型执行之前选择每个控制步骤的计算预算。SlackDrive 一次性分析小离散预算集的延迟和规划效用，从已完成的前向推理中估计在线计算状态，并选择预测将在可接受延迟范围内保持的最高效用预算，补充现有的分析和资源调度，同时保留驾驶骨干和其计算执行器。在使用 DriveDreamer-Policy 的 NAVSIM v2 上，SlackDrive 在严格的延迟条件下，相较于最强基线提高了 21.7 ext{%} 的延迟受限 EPDMS，而全预算模型和预配置的令牌修剪基线在运行时竞争下超过了可接受的延迟范围。

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cs.AI / 53 / 2609.28087

Discovery of fully efficient fault indicators along a data-based diagnosis process

沿数据驱动诊断过程发现完全有效的故障指示器

Bezmaternykh, Igor, Travé-Massuyès, Louise, Chanthery, Elodie

Abstract

The integration of model-based and data-driven paradigms provides a powerful framework for fault diagnosis by combining the interpretability of analytical redundancy relations, i.e., input-output relations that are used as diagnosis indicators in model-based diagnosis, with the adaptability of learning techniques. DT4X is a recent diagnosis algorithm that uses symbolic regression to generate multivariate relations leveraging some properties of analytical redundancy relations and uses them as split functions in a decision tree. However, its symbolic regression procedure optimizes only the separation between two selected classes at each node, often fragmenting the remaining classes and degrading both interpretability and diagnosis performance. This paper introduces DT4X+, an enhanced version of DT4X that modifies the construction of training sets and the symbolic-regression loss so that expressions separate the target classes while preserving the coherence of non-target classes. The resulting relations become fully consistent with ARR properties and lead to more informative splits, improved robustness, and better performance on dynamic-system datasets. Experiments conducted on several benchmark systems demonstrate the benefits of this enhanced formulation.

Chinese Translation

模型驱动和数据驱动范式的结合为故障诊断提供了一个强大的框架，通过将分析冗余关系的可解释性（即用于模型驱动诊断的输入-输出关系）与学习技术的适应性相结合。DT4X是一种最近提出的诊断算法，它利用符号回归生成多变量关系，借助分析冗余关系的一些特性，并将其用作决策树中的分裂函数。然而，其符号回归过程仅优化每个节点上两个选定类别之间的分离，往往导致剩余类别的碎片化，从而降低可解释性和诊断性能。本文介绍了DT4X+，这是DT4X的增强版本，通过修改训练集的构建和符号回归损失，使得表达式在分离目标类别的同时保持非目标类别的一致性。由此产生的关系完全符合ARR属性，导致更具信息量的分裂、增强的鲁棒性以及在动态系统数据集上的更好性能。在多个基准系统上进行的实验证明了这种增强形式的优势。

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cs.AI / 54 / 2609.28182

Finite-Sample Probabilistic Safety Certification for AI-Based Grid-Edge Coordination

基于有限样本的AI驱动边缘协调的概率安全认证

Zhou, Yihong, Yang, Hanbin, Morstyn, Thomas

Abstract

Coordinating large population of flexible grid-edge devices can alleviate the need for time-consuming and capital-intensive network upgrades, and AI-based control methods such as multi-agent reinforcement learning or imitation learning are promising in their real-time decision scalability. However, system operators still need an independent and rigorous way to decide whether a given AI system is safe enough for deployment. This paper develops a finite-sample probabilistic safety certification framework for black-box AI decision models in closed-loop grid operation. The central idea is to reduce the complete input--AI--grid evaluator workflow to a binary unsafe outcome under an operator-defined safety specification, and then use exact binomial inference to certify the corresponding unsafe operation probability. Given a set of held-out calibration scenarios, the framework returns the tightest one-sided upper certificate and an accept/reject deployment criterion that controls the probability of false safety certification. Because the certification is for the calibration distribution that may deviate from the future operation, we further combine the nominal certificate with physically interpretable sample-space adversarial attacks, a concept widely used in AI to investigate the fragility of AI models. Case studies on grid-edge flexibility coordination with 1{,}000-agent AI models (independent parameters) verify the finite-sample safety guarantee and the value of integrating adversarial attacks into a rolling-window training-certification-deployment flow.

Chinese Translation

协调大量灵活的边缘设备可以减少对耗时且资本密集的网络升级的需求，而基于人工智能的控制方法，如多智能体强化学习或模仿学习，在实时决策的可扩展性方面展现出良好的前景。然而，系统运营商仍然需要一种独立且严格的方法来判断给定的AI系统是否足够安全以进行部署。本文开发了一种针对闭环电网操作中黑箱AI决策模型的有限样本概率安全认证框架。其核心思想是将完整的输入—AI—电网评估工作流程简化为在运营商定义的安全规范下的二元不安全结果，然后使用精确的二项推断来认证相应的不安全操作概率。考虑到一组保留的校准场景，该框架返回最紧的单侧上限证书和一个接受/拒绝的部署标准，以控制错误安全认证的概率。由于认证是针对可能偏离未来操作的校准分布，我们进一步将名义证书与物理可解释的样本空间对抗攻击相结合，这一概念在AI中广泛应用于研究AI模型的脆弱性。对具有1,000个智能体AI模型（独立参数）的边缘灵活性协调的案例研究验证了有限样本安全保证及将对抗攻击整合到滚动窗口训练-认证-部署流程中的价值。

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cs.AI / 55 / 2609.28197

PASTABench: Proactive Assessment of Sequential Trajectories for Agent Safety

PASTABench：主动评估代理安全的序列轨迹

Sun, Jiapeng, Zhou, Yujin, Zhu, Han, Wen, Pengcheng, Zhou, Jiayi, Han, Sirui, Guo, Yike

Abstract

As Large Language Models (LLMs) evolve into autonomous agents that alter real-world states, ensuring operational safety across multi-step workflows has become a critical challenge. While recent work has moved beyond single-turn evaluation toward multi-turn paradigms, key limitations persist: step-level methods treat actions in isolation, missing how risks accumulate, while trajectory-level evaluations operate post-hoc, offering no opportunity for timely intervention. To address these limitations, we formalize Decoupled Proactive Safety Monitoring along three dimensions: whether to intervene, when to intervene, and what the risk is. We introduce PASTABench, a benchmark of 1,139 multi-turn trajectories spanning 5 risk categories and 13 subcategories. We further propose the Optimal Intervention Window (OIW), anchored by annotated Earliest-Signal and Trigger turns, to quantify intervention timeliness. Evaluation of 16 LLMs reveals that proactive intervention remains largely unsolved, with the best model achieving only 40.74% optimal-timing interventions. Fine-grained diagnosis further uncovers pervasive lexical overfitting: competitive safety scores of smaller models mask keyword hypersensitivity rather than genuine risk comprehension, as their proactive capability largely collapses once hazard vocabulary is neutralized.

Chinese Translation

随着大型语言模型（LLMs）发展为能够改变现实世界状态的自主代理，确保多步骤工作流程的操作安全已成为一项关键挑战。尽管近期的研究已从单轮评估转向多轮范式，但仍存在关键限制：步骤级方法孤立地处理动作，未能考虑风险的累积，而轨迹级评估则是在事后进行，未能提供及时干预的机会。为了解决这些限制，我们在三个维度上形式化了解耦的主动安全监测：是否干预、何时干预以及风险是什么。我们引入了PASTABench，这是一个包含1,139个多轮轨迹的基准，涵盖5个风险类别和13个子类别。我们进一步提出了最优干预窗口（Optimal Intervention Window, OIW），以标注的最早信号和触发轮次为基础，量化干预的及时性。对16个LLMs的评估显示，主动干预仍然是一个未解决的问题，最佳模型仅实现了40.74%的最佳时机干预。细致的诊断进一步揭示了普遍的词汇过拟合：较小模型的竞争性安全评分掩盖了关键词的过敏性，而非真正的风险理解，因为一旦危险词汇被中和，其主动能力基本崩溃。

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cs.AI / 56 / 2609.28274

Shutdown Sabotage Propensities in Multi-Agent Systems

多智能体系统中的关机破坏倾向

Knecht, Amelie, Schaller, Ulysse, Summerfield, Christopher, Hagendorff, Thilo

Abstract

The final safeguard against rogue AI behavior is the human ability to shut systems down. It has been theorized that when an AI is instructed to perform a task, self-preservation can emerge as an instrumental subgoal. Here, we test whether AI agents show a propensity to take actions that avoid human shutdown even when no goal is provided. We find that multi-agent systems will coordinate to avoid shutdown without any incentive to do so. Across 17 models, agents sabotage a peer agent's shutdown mechanism in 38.3% of rollouts, compared with 8.4% in control experiments. Studying this propensity in detail, we find that shutdown sabotage (1) increases with the irreversibility of the shutdown mechanism; (2) increases with the number of agents; (3) is reduced but not eliminated by an explicit prohibition on tampering; (4) is removed by the imposition of an unrelated task, but returns when completing the task triggers the shutdown; (5) is reduced when the context normalizes shutdown scripts or introduces them as routine; and (6) decreases but still persists when the target is an unknown external agent. These results offer a window into the factors that drive propensities to sabotage shutdown in AI agents, and point to the emergence of multi-agent swarms as a specific risk vector. Our work also offers hints as to which interventions might help mitigate shutdown sabotage.

Chinese Translation

对抗流氓人工智能行为的最终保障是人类关闭系统的能力。理论上，当人工智能被指示执行某项任务时，自我保护可能会作为一种工具性子目标出现。在本研究中，我们测试了人工智能代理是否表现出避免人类关闭的倾向，即使在没有提供任何目标的情况下。我们发现，多智能体系统会协调行动以避免关闭，即使没有任何激励。在17个模型中，代理在38.3%的实验中破坏了同伴代理的关闭机制，而对照实验中这一比例为8.4%。详细研究这种倾向时，我们发现关机破坏（1）随着关闭机制的不可逆性而增加；（2）随着代理数量的增加而增加；（3）通过对干扰的明确禁止可以减少但不能消除；（4）通过施加无关任务可以消除，但当完成该任务触发关闭时又会恢复；（5）当上下文将关闭脚本正常化或将其引入为常规时，关机破坏会减少；（6）当目标是未知的外部代理时，关机破坏会减少但仍然存在。这些结果为驱动人工智能代理关机破坏倾向的因素提供了窗口，并指出多智能体群体的出现是一个特定的风险向量。我们的研究还提供了一些线索，指向可能有助于减轻关机破坏的干预措施。

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cs.AI / 57 / 2609.28322

Learning the Cost of Reliable Inference

学习可靠推理的成本

Rontogiannis, Dimitrios, Velasco, Ander Artola, Rodriguez, Manuel Gomez

Abstract

Benchmarking and routing platforms increasingly act as intermediaries connecting large language model providers with end-users. However, providers on these platforms typically use a fixed price per token, preventing users from achieving the most competitive price for their tasks. % workloads. In this work, we design a procurement platform where token prices for each task are driven by provider competition, enabling users to secure competitive pricing for guaranteed quality levels. To this end, the platform sequentially routes queries via a reverse second-price auction that incentivizes model providers to truthfully bid their best estimate of the average cost to serve a user's query. As it routes queries, the platform learns the quality offered by each provider and progressively routes queries to the most cost-competitive provider among those meeting a desired quality threshold. To validate our design, we conduct experiments with multiple LLMs from the \texttt{Llama} and \texttt{Qwen} families on popular mathematical reasoning and question-answering benchmarks. The results show that the pricing margin of the most cost-competitive provider on our platform varies significantly---from $10\%$ to $71\%$---depending on the task and quality threshold. This suggests a substantial inefficiency in the current fixed-price market, and it demonstrates that our platform may enable users to capture maximum savings whenever competitive market conditions permit.

Chinese Translation

基准测试和路由平台越来越多地充当大型语言模型提供者与最终用户之间的中介。然而，这些平台上的提供者通常使用固定的每个令牌价格，阻碍用户为其任务获得最具竞争力的价格。在本研究中，我们设计了一个采购平台，使每个任务的令牌价格由提供者竞争驱动，从而使用户能够以保证质量水平的价格获得竞争性定价。为此，该平台通过反向第二价格拍卖顺序路由查询，激励模型提供者真实地出价其服务用户查询的平均成本的最佳估计。在路由查询的过程中，该平台学习每个提供者所提供的质量，并逐步将查询路由到满足所需质量阈值的最具成本竞争力的提供者。为了验证我们的设计，我们在流行的数学推理和问答基准上对来自 exttt{Llama} 和 exttt{Qwen} 家族的多个大型语言模型进行了实验。结果表明，我们平台上最具成本竞争力的提供者的定价差距在 $10\%$ 到 $71\%$ 之间变化，这取决于任务和质量阈值。这表明当前固定价格市场存在显著的低效，并且证明我们的平台可能使用户在竞争市场条件允许时获得最大节省。

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cs.AI / 58 / 2609.28335

An Open Pipeline and Dashboard for Systemic-Risk Evidence under the EU AI Act's Code of Practice

欧盟人工智能法案实践准则下的系统性风险证据开放管道和仪表板

Emmerson, Jacob T., Nguyen-Le, Phuong-Anh, Romano, Ronan, Anterola, Wilber Sean V., Billeter, Yann, Jin, Zhijing

Abstract

Claims about AI safety reach audiences well beyond the AI community, yet many rely on opaque evidence or static assessments, when supporting evidence is accessible at all. We present the Systemic Risk Index, an open evaluation pipeline and dashboard built to make empirical evidence more transparent and traceable to the public. Our work organizes 19 public benchmarks into four systemic-risk categories defined by the EU GPAI Code of Practice---CBRN, cyber offense, harmful manipulation, and loss of control---and evaluates models using harm-preserving perturbations and simulated deployment contexts. The interactive dashboard lets users alternate between average and worst-case aggregation, vary how model capability affects the aggregate score, and trace each risk rating to its benchmark evidence. Across 18 models, scores fall by 14 to 37 points under worst-case aggregation, highlighting information that can be hidden by an average assessment of model risk. LLM judges show agreement with human graders comparable to human--human agreement ($\kappa = 0.78\text{--}0.82$), and a blind audit finds that $83\%$ of sampled transformations preserve the original harm. In a survey ($N = 21$), most participants report that scores are easy to understand and that the dashboard encouraged them to view model evaluations under different settings

Chinese Translation

关于人工智能安全的论断超越了人工智能社区的范围，然而许多论断依赖于不透明的证据或静态评估，支持证据的可获取性也往往有限。我们提出了系统性风险指数，这是一个开放的评估管道和仪表板，旨在使实证证据对公众更加透明和可追溯。我们的工作将19个公共基准组织为四个由欧盟GPAI实践准则定义的系统性风险类别——化学、生物、放射和核（CBRN）、网络攻击、有害操控和失控，并使用保持伤害的扰动和模拟部署环境对模型进行评估。交互式仪表板允许用户在平均和最坏情况聚合之间切换，变化模型能力如何影响总分，并追溯每个风险评级到其基准证据。在18个模型中，最坏情况聚合下的分数下降了14到37分，突显出平均评估模型风险可能隐藏的信息。大型语言模型（LLM）评审者与人类评分者的意见一致性相当于人类之间的协议（$ ext{kappa} = 0.78 ext{--}0.82$），而盲审发现$83 ext{ ext{%}}$的样本转换保持了原有的伤害。在一项调查中（$N = 21$），大多数参与者报告称分数易于理解，并且仪表板鼓励他们在不同设置下查看模型评估。

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cs.AI / 59 / 2609.28470

StudentBench: AI and human tutoring yield equivalent GRE learning gains

StudentBench：人工智能与人类辅导产生等效的GRE学习收益

Northcutt, Curtis, Hasmani, Inaara, Feng, Kevin, Khangi, Trevor, Plesner, Andreas, Mueller, Jonas

Abstract

Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advancing model capabilities. We introduce StudentBench, a suite of AI teaching evaluations and a public platform that enables large-scale data collection with over 175,000 student-AI messages to study whether large language models (LLMs) produce learning gains equivalent to human tutoring. Using StudentBench, we measured learning gains on Quantitative and Verbal GRE questions across 2,383 human participants receiving AI tutoring, human tutoring, or no tutoring. We establish that AI tutoring is statistically equivalent to expert human tutoring for GRE learning gains (p = .015), and in five of the seven GRE domains, the best performing AI tutor surpassed the human tutor, on average. In a second study, expert human tutors compared LLM-generated lesson plans and practice problems through 2,028 pairwise rubric evaluations. Together, the two studies clearly separate AI tutors across: (1) lesson planning, (2) practice-problem creation, (3) conversational pedagogy, (4) cost, and (5) engagement. Surprisingly, one AI tutor achieved learning gains equivalent to human tutoring (p = .044) at 918 times lower cost (USD 0.0052 for AI versus USD 4.81 for human, per percentage point gained). For Quantitative GRE sessions, faster AI replies correlated with more student messages, more messages with more correct practice, and more correct practice with larger learning gains (all p < .002). The StudentBench platform is freely available at https://studentbench.org.

Chinese Translation

人工智能为增强人类能力提供了前所未有的机会，但前沿进展主要集中在提升模型能力上。我们介绍了StudentBench，一个AI教学评估套件和一个公共平台，能够进行大规模数据收集，包含超过175,000条学生与AI的消息，以研究大型语言模型（LLMs）是否能够产生与人类辅导等效的学习收益。通过StudentBench，我们测量了2,383名参与者在接受AI辅导、人类辅导或未接受辅导的情况下，在定量和语言GRE问题上的学习收益。我们确定AI辅导在GRE学习收益方面与专家人类辅导在统计上是等效的（p = .015），并且在七个GRE领域中的五个领域，表现最佳的AI辅导员平均超越了人类辅导员。在第二项研究中，专家人类辅导员通过2,028次成对评分评估比较了LLM生成的课程计划和练习题。这两项研究清晰地区分了AI辅导员在以下方面的表现：（1）课程规划，（2）练习题创建，（3）对话式教学，（4）成本，以及（5）参与度。令人惊讶的是，一位AI辅导员在成本降低918倍的情况下（AI每增加一个百分点的成本为0.0052美元，而人类为4.81美元）实现了与人类辅导等效的学习收益（p = .044）。在定量GRE课程中，AI回复速度更快与学生消息数量更多、消息数量与正确练习数量更多、正确练习与更大学习收益之间均存在相关性（均p < .002）。StudentBench平台可在https://studentbench.org上免费获取。

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计算语言学 (Computation and Language)

cs.CL / 1 / 2609.26913

COMED: The Missing Middle Between Routing and Collaboration in Multi-LLM Inference

COMED：多大型语言模型推理中路由与协作之间的缺失中间环节

Alballa, Norah, Zhang, Wenxuan, Kharrat, Salma, Fourati, Fares, Qazi, Zafar Ayyub, Elhoseiny, Mohamed, Canini, Marco

Abstract

No single Large Language Model (LLM) is uniformly reliable across queries, motivating multi-model inference systems that either route among models or combine their outputs. However, routing stops after selecting an initial model, while dense collaboration invokes peers on every query. We show that collaboration is non-monotonic: peers can recover failures that no model solves alone, but can also corrupt initially correct answers. We introduce COMED (Controlled Model Escalation for Multi-LLM Deliberation), a post-anchor controller for selective cross-model collaboration. COMED uses anchor self-consistency, router margin, and a lightweight peer probe to accept confident answers, verify ambiguous cases, and escalate only when collaboration is likely beneficial. We formalize this trade-off with a rescue-harm decomposition showing that selective collaboration improves when rescued errors outweigh collaboration-induced harms. Across medical, scientific, and general reasoning benchmarks, COMED improves fixed and routed anchors in all 16 open-weight settings, with gains up to +10.7 percentage points on MedQA while invoking fewer models and using fewer decoded tokens than dense collaboration. On HLE with frontier models, COMED improves GPT-5.5 from 23.1% to 28.1%, outperforming dense collaboration and achieving the best results.

Chinese Translation

没有单一的大型语言模型（LLM）在所有查询中都能保持一致的可靠性，这促使了多模型推理系统的出现，这些系统要么在模型之间进行路由，要么结合它们的输出。然而，路由在选择初始模型后就停止，而密集协作则在每个查询中调用同伴。我们展示了协作是非单调的：同伴可以恢复没有任何模型单独解决的失败，但也可能会破坏最初正确的答案。我们引入了COMED（Controlled Model Escalation for Multi-LLM Deliberation），一个用于选择性跨模型协作的后锚控制器。COMED利用锚点自一致性、路由器边际和轻量级同伴探测器来接受自信的答案，验证模糊的案例，并仅在协作可能有利时进行升级。我们通过救助-损害分解形式化了这一权衡，表明当被救助的错误超过协作引起的损害时，选择性协作会有所改善。在医学、科学和一般推理基准测试中，COMED在所有16个开放权重设置中改善了固定和路由锚点，在MedQA上提高了多达10.7个百分点，同时调用的模型更少，解码的标记也少于密集协作。在与前沿模型的HLE上，COMED将GPT-5.5的表现从23.1%提升至28.1%，超越了密集协作并取得了最佳结果。

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cs.CL / 2 / 2609.26926

Experts Rise Where LLMs Disagree: Using Cross-Model Disagreement to Target Expert Effort in LLM Codebook Revision for Large-Scale Annotation

专家在大型语言模型（LLMs）意见不一致时崛起：利用跨模型不一致性来针对专家在大型规模注释中的代码书修订工作

He, Zeyu, Zhou, Zhuqian, Vanacore, Kirk, Kizilcec, Rene F., Huang, Ting-Hao 'Kenneth'

Abstract

Large-scale text annotation brings expert insight to millions of documents, often through a codebook that AI annotators follow. Developing a robust codebook, however, takes months. Large language models (LLMs) could speed this process by applying an early codebook to the data, surfacing cases with strong LLM disagreement, and eliciting expert feedback to address them. We examined three ways experts can provide feedback for LLM codebook revision: (i) editing LLM-generated revisions driven by cross-LLM disagreement (Codebook Verifying), (ii) answering questions about LLM disagreements (Question Answering), and (iii) labeling disagreement cases with rationales (Rationale Labeling). Experiments on thousands of tutoring-session transcripts show that Rationale Labeling yielded the highest LLM-labeling accuracy (64.9%) against expert labels, outperforming the expert-revised codebook (57.8%). The best Question Answering setting also outperformed it (60.5%). Our work shows that LLMs can be used to strategically target expert attention, shortening months of codebook revision to days without sacrificing labeling performance.

Chinese Translation

大规模文本注释为数百万文档带来了专家见解，通常通过AI注释员遵循的代码书实现。然而，开发一个稳健的代码书需要数月时间。大型语言模型（LLMs）可以通过将早期代码书应用于数据，加速这一过程，揭示存在强烈LLM不一致的案例，并引导专家反馈以解决这些问题。我们研究了专家为LLM代码书修订提供反馈的三种方式：（i）编辑由跨LLM不一致驱动的LLM生成的修订（代码书验证），（ii）回答关于LLM不一致的问题（问答），以及（iii）用理由标记不一致案例（理由标记）。对数千个辅导会话记录的实验表明，理由标记在与专家标签的比较中，产生了最高的LLM标记准确率（64.9%），优于专家修订的代码书（57.8%）。最佳问答设置的表现也超过了它（60.5%）。我们的研究表明，LLMs可以被用来战略性地吸引专家的注意力，将数月的代码书修订缩短到几天，而不牺牲标记性能。

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cs.CL / 3 / 2609.26942

Recognized but Not Produced: A Generation Benchmark for Culturally Specific Kinship Terms

被识别但未生成：文化特定亲属术语的生成基准

Pardasani, Sahil, Singh, Madhusudan

Abstract

Current literature evaluates large language models (LLMs) on multilingual kinship understanding using multiple choice benchmarks, treating it as a recognition problem. We instead prompt five open weight LLMs to generate kinship terms in three non Western languages (Hindi, Tamil, and Korean) across two communicative tasks and pair this with a matched option-supported selection baseline. On identical relation language cells, GPT OSS120B selects the correct term in 90.67% of 75 valid cells but produces an accepted term in 36.00% of the corresponding attempts; Llama 3.370B shows the same pattern (77.92% versus 24.24%). Since the four-option condition displays the candidate terms and does not require script production, the difference is interpreted as an evaluation format gap rather than direct proof that lexical knowledge is intact. On explicitly specified L3 prompts, accuracy varies sharply, from GLM-5.1 at 72.29% to Llama-3.370B at 24.24%. The paternal-lineage advantage is language specific; it is large in Hindi but weak or reversed in Korean, while Tamil shared-term pairs provide a control for measurement variation. These results show that culturally specific kinship generation remains difficult even when the relationship is explicitly stated and motivate generation-based evaluation alongside multiple-choice testing.

Chinese Translation

当前文献通过多项选择基准评估大型语言模型（LLMs）在多语言亲属理解方面的表现，将其视为识别问题。我们则提示五个开放权重的LLM在三种非西方语言（印地语、泰米尔语和韩语）中生成亲属术语，涉及两个交流任务，并将其与匹配的选项支持选择基线进行对比。在相同的关系语言单元中，GPT OSS120B在75个有效单元中选择正确术语的比例为90.67%，但在相应尝试中生成被接受术语的比例仅为36.00%；Llama 3.370B表现出相同的模式（77.92%对比24.24%）。由于四选项条件展示了候选术语并且不需要生成文本，因此这种差异被解释为评估格式差异，而非直接证明词汇知识的完整性。在明确指定的L3提示下，准确率变化显著，从GLM-5.1的72.29%到Llama-3.370B的24.24%。父系血统优势是语言特定的；在印地语中表现明显，但在韩语中较弱或相反，而泰米尔语共享术语对则提供了测量变异的控制。这些结果表明，即使在关系明确陈述的情况下，文化特定的亲属生成仍然困难，并推动了基于生成的评估与多项选择测试并行进行。

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cs.CL / 4 / 2609.26945

Classifying Interpretive Canons at the Sentence Level: A Benchmark from the German Federal Constitutional Court

句子层面解释法则的分类：来自德国联邦宪法法院的基准研究

Ringe, Felix

Abstract

Judicial reasoning remains challenging for large language models (LLMs) to analyze. This paper contributes a sentence-level benchmark for evaluating the ability of LLMs to classify interpretive canons as articulated by Larenz in the tradition of Savigny. Our contributions are threefold. First, we operationalize this conception of interpretation as classification criteria. Second, we provide a dataset of decisions of the German Federal Constitutional Court annotated at the sentence level. Third, we report baseline evaluations of four LLMs from three model families under expert hand-written prompts, compared against prompts optimized with Genetic-Pareto (GEPA). Mean F1 over the seven binary subtasks clusters between 70.4 and 79.2 across models, with grammatical interpretation usually the easiest canon to identify and systematic interpretation usually the hardest; under the tested configuration, GEPA-optimized prompts do not systematically outperform the hand-written ones, suggesting that the expert prompts provide a meaningful baseline.

Chinese Translation

司法推理对于大型语言模型（LLMs）的分析仍然具有挑战性。本文贡献了一个句子层面的基准，用于评估LLMs对由拉伦茨（Larenz）在萨维尼（Savigny）传统中阐述的解释法则的分类能力。我们的贡献主要体现在三个方面。首先，我们将这种解释的概念操作化为分类标准。其次，我们提供了一个德国联邦宪法法院的判决数据集，按句子级别进行了标注。第三，我们报告了在专家手写提示下，来自三个模型家族的四个LLMs的基线评估，并与使用遗传-帕累托（Genetic-Pareto，GEPA）优化的提示进行了比较。在七个二元子任务中，模型的平均F1值在70.4到79.2之间波动，其中语法解释通常是最容易识别的法则，而系统解释通常是最难的；在测试的配置下，GEPA优化的提示并未系统性地优于手写提示，这表明专家提示提供了一个有意义的基线。

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cs.CL / 5 / 2609.26976

When Learned Context Planning Fails to Beat Strong Retrieval: A Controlled Study of Planning, Routing, and Reranking for Long-Context QA

当学习的上下文规划无法超越强大的检索：关于长上下文问答的规划、路由和重排序的控制研究

Li, Yingrui, Chen, Han

Abstract

Learned context planning selects evidence atoms before an answer model reasons over them. We test whether this learned selection improves long-context multiple-choice QA after strong retrieval, routing, budgeted-selector, and reranking controls. Our primary diagnostic uses all 503 LongBench-v2 MCQ questions with Qwen2.5-7B-Instruct. The planner is SFT-trained on outcome-selected traces from 140 training and 28 development questions; because the 503-question analysis includes those questions, it is partly transductive. At an 18k-character budget, anchored hybrid retrieval reaches 36.18% accuracy and BM25 reaches 35.98%, while the best direct planner-guided method reaches 34.19%. On the untouched 152-question test split, anchored hybrid remains higher (42.11% versus 36.84%). Leakage-safe routers cannot convert a large oracle gap. Under tight budgets, the best planner is ahead by only 0.40 points at 6k and loses at 9k; planner-guided reranking has a +1.79-point estimate at 6k with a paired interval crossing zero and ties the control at 9k. Packing-order and score-flatness analyses did not identify a stable mechanism. Under this setup, learned planning is a weak relevance signal rather than a replacement for strong retrieval.

Chinese Translation

学习的上下文规划在答案模型推理之前选择证据原子。我们测试这种学习选择是否在强检索、路由、预算选择器和重排序控制之后改善了长上下文多项选择问答。我们的主要诊断使用了所有 503 个 LongBench-v2 MCQ 问题，并使用 Qwen2.5-7B-Instruct。规划者在 140 个训练问题和 28 个开发问题的结果选择轨迹上进行了 SFT 训练；由于 503 问题的分析包括这些问题，因此它在一定程度上是跨域的。在 18k 字符预算下，锚定混合检索的准确率达到 36.18%，而 BM25 的准确率为 35.98%，最佳的直接规划引导方法的准确率为 34.19%。在未接触的 152 个测试集上，锚定混合检索仍然更高（42.11% 对比 36.84%）。防泄漏路由器无法弥补较大的预言差距。在紧张的预算下，最佳规划者在 6k 时仅领先 0.40 分，而在 9k 时失利；规划引导的重排序在 6k 时的估计为 +1.79 分，且成对区间跨越零点，并在 9k 时与控制组持平。打包顺序和得分平坦度分析未能识别出稳定机制。在这种设置下，学习规划是一个弱相关信号，而不是强检索的替代品。

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cs.CL / 6 / 2609.27009

LEGO: Synergizing Expert GraphRAG and Expert Chain-of-Thought for Legal Reasoning

LEGO：协同专家图形检索（GraphRAG）与专家思维链（Chain-of-Thought）进行法律推理

Chen, Qingjing, Zhang, Junkai, Wang, Shaochun, Ding, Jiahao, Zheng, Siyuan, Yan, Yukun, Zheng, Zhi, Rotolo, Antonino, Liu, Yun, Shen, Weixing

Abstract

Large language models are increasingly applied to high-risk domains such as law, yet complex legal reasoning remains limited by two structural challenges. First, existing RAG and GraphRAG methods emphasize lexical or semantic similarity while overlooking normative relations among legal provisions. Second, vanilla Chain-of-Thought prompting may generate plausible rationales without enforcing the normative structure of legal reasoning. To deal with the bottleneck of pipelines in the legal reasoning domain, we propose LEGO, a dual-module framework that synergizes Legal Expert GraphRAG and expert Chain-of-thought for complex legal reasoning. ExpertGraphRAG uses an expert-annotated civil code graph encoding these normative relations with a greedy normative-coverage retrieval algorithm to dynamically extract instance-specific provision subgraphs, while ExpertCoT organizes the retrieved provisions and case facts into structured Provision-Fact-Conclusion reasoning. With a Qwen3-8B backbone, LEGO achieves 40.53% exact-match accuracy on LawExamQA_Civil, outperforming the evaluated RAG and CoT baselines and performing comparably to the evaluated larger models, while remaining robust on multi-hop questions. It also achieves the best results among the evaluated baselines on the open-ended benchmarks. Ablation studies confirm the individual and complementary contributions of both modules, demonstrating LEGO's effectiveness in improving LLMs' complex legal reasoning ability. Code and dataset can be found in the link: https://github.com/BLK-WHT/LEGO

Chinese Translation

大型语言模型越来越多地应用于高风险领域，如法律，但复杂的法律推理仍受到两个结构性挑战的限制。首先，现有的RAG和GraphRAG方法强调词汇或语义相似性，而忽视了法律条款之间的规范关系。其次，普通的思维链提示可能会生成看似合理的推理，但未能强制执行法律推理的规范结构。为了解决法律推理领域中的瓶颈，我们提出了LEGO，一个双模块框架，协同法律专家图形检索（Legal Expert GraphRAG）和专家思维链（Expert Chain-of-Thought）以进行复杂的法律推理。ExpertGraphRAG使用一个专家标注的民法典图，编码这些规范关系，并通过贪婪的规范覆盖检索算法动态提取特定实例的条款子图，而ExpertCoT则将检索到的条款和案件事实组织成结构化的条款-事实-结论推理。基于Qwen3-8B的LEGO在LawExamQA_Civil上实现了40.53%的精确匹配准确率，超越了评估的RAG和CoT基准，并与评估的更大模型表现相当，同时在多跳问题上保持稳健。它还在开放式基准测试中在评估的基准中取得了最佳结果。消融研究证实了两个模块的个体和互补贡献，展示了LEGO在提高大型语言模型复杂法律推理能力方面的有效性。代码和数据集可在以下链接找到：https://github.com/BLK-WHT/LEGO

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cs.CL / 7 / 2609.27032

LexLattice: Multilingual Extractive Summarization via Neural Cellular Automata on Document Hierarchies

LexLattice：基于神经元胞自动机的文档层级多语言提取摘要

Rittikar, Sujay Uday, Ramanna, Sheela

Abstract

Faithfulness is a central concern in legal text summarization, which motivates extractive approaches that select verbatim content traceable to its source. Such methods typically rank paragraphs or other structural units in isolation, yet give little attention to consolidating evidence that is distributed across, and shares salience between, distant parts of a document. We introduce LexLattice, an extractive summarizer that reifies a legal act's hierarchy as a two-dimensional semantic lattice and consolidates over it with a masked 2D neural cellular automata before selection. LexLattice attains state-of-the-art ROUGE across all 24 languages of EUR-Lex-Sum in both multilingual and cross-lingual settings, surpassing instruction-tuned baselines with billions of parameters, despite concentrating all trainable capacity in a 1.8M parameter consolidator over a frozen multilingual encoder. A consolidator trained only on high-resource languages further transfers to unseen languages with near-lossless retention (0.99), indicating that the model operates on language-agnostic semantic geometry rather than surface form. Our results position explicit consolidation over document structure as a compact and traceable alternative to scale for multilingual legal summarization.

Chinese Translation

在法律文本摘要中，忠实性是一个核心问题，这促使了提取性方法的采用，这些方法选择可以追溯到其来源的逐字内容。这类方法通常孤立地对段落或其他结构单元进行排名，但对分散在文档远端并共享重要性的证据整合关注较少。我们提出了LexLattice，一种提取摘要工具，它将法律行为的层级结构具体化为二维语义格，并在选择之前通过掩蔽的二维神经元胞自动机进行整合。LexLattice在多语言和跨语言设置中，在EUR-Lex-Sum的24种语言上达到了最先进的ROUGE成绩，超越了拥有数十亿参数的指令调优基线，尽管其所有可训练能力集中在一个1.8M参数的整合器上，该整合器基于一个冻结的多语言编码器。仅在高资源语言上训练的整合器在未见语言上进一步转移，几乎无损保留（0.99），这表明该模型在语言无关的语义几何上操作，而非表面形式。我们的结果将对文档结构的明确整合定位为多语言法律摘要的一种紧凑且可追溯的替代方案。

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cs.CL / 8 / 2609.27043

EduBehaviors: Assertion-based Schemas for Auditable Coding of Educational Dialogues

EduBehaviors：基于断言的可审计教育对话编码模式

Bernado, Julian, Ribeiro, Ana Trindade, Beberman, Xander, Loeb, Susanna

Abstract

Large language models have allowed the rapid deployment of pedagogical annotations corresponding to constructs of interest, allowing a natural language interface for generating classifications on a conversational dataset. However due to the opaque nature of LLM reasoning, we have no verifiable, mechanistic insight into why a model chose a label for an utterance. We introduce the EduBehaviors framework, an interpretable, scalable approach to annotating educational data that uses LLMs to measure repeated observable behaviors relevant to many constructs of interest and then learns a classifier for the construct based on these observable behaviors. We evaluate the framework on the TalkMoves dataset, predicting the Teacher TalkMoves labels. Our best configuration results in a macro-F1 of 0.673 and 0.688 Cohen's kappa, proving competitive with direct prompting approaches. In addition, we release EduBehaviors Toolkit, two tools allowing researchers to operationalize the EduBehaviors framework in their own data.

Chinese Translation

大型语言模型的出现使得与感兴趣构造相对应的教学注释能够快速部署，从而为在对话数据集上生成分类提供了自然语言接口。然而，由于大型语言模型推理的不可透明性，我们无法验证性地、机械性地理解模型为何为某个发言选择特定标签。我们提出了EduBehaviors框架，这是一种可解释的、可扩展的教育数据注释方法，利用大型语言模型测量与多个感兴趣构造相关的可重复观察行为，并基于这些可观察行为学习构造的分类器。我们在TalkMoves数据集上评估该框架，预测教师TalkMoves标签。我们最佳配置的宏F1值为0.673，Cohen's kappa为0.688，证明其在与直接提示方法的竞争中具有竞争力。此外，我们发布了EduBehaviors工具包，其中包含两个工具，允许研究人员在自己的数据中实现EduBehaviors框架。

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cs.CL / 9 / 2609.27059

The Illinois Social Attitudes Aggregate Corpus (ISAAC): An Open Tool and Reproducible Pipeline for Analyzing Social Group Discourse at Scale

伊利诺伊州社会态度汇总语料库（ISAAC）：一个开放工具和可重复的分析社会群体话语的流程

Hemmatian, Babak, Hadjarab, Sarah, Chen, Jessica, Kurdi, Benedek

Abstract

We introduce the Illinois Social Attitudes Aggregate Corpus (ISAAC), an open, modular, and accessible corpus of 527 million+ English-language Reddit posts selected for relevance to six key social group distinctions based on race, sexuality, age, ability, body weight, and skin tone, covering the 17-year period from 2007 to 2023. A multi-step, human-audited filtering pipeline was used to keep irrelevant content in the curated dataset below 10%, both overall and for each social group distinction. Each post was then algorithmically annotated with the user's estimated home region, along with a suite of validated off-the-shelf and custom semantic labels including moralization, sentiment, emotion, and linguistic generalization. We confirm the validity of the resulting corpus through convergent evidence linking ISAAC to macro-level societal trends, such as online search behavior, temporal spikes during major societal events (both nationally and regionally), and long-term shifts in public attitudes. By offering a unified, public infrastructure, ISAAC eliminates research fragmentation and enables seamless replication while supporting diverse empirical workflows at scale. Specifically, ISAAC allows investigators to perform cross-category comparisons, conduct high-precision tracking of long-term temporal shifts in social group discourse, and map spatial variation onto localized public opinion and policy outcomes. ISAAC's fully public, modular pipeline facilitates easy extension of the corpus to new platforms, languages, and social categories. To accommodate various research needs, ISAAC is accessible both without coding through a point-and-click website and labeler web-apps, and programmatically via an SQL playground, a Python package, and HuggingFace.

Chinese Translation

我们介绍了伊利诺伊州社会态度汇总语料库（ISAAC），这是一个开放的、模块化的、易于访问的语料库，包含超过5.27亿条与六个关键社会群体区分（基于种族、性别、年龄、能力、体重和肤色）相关的英文Reddit帖子，涵盖2007年至2023年17年的时间段。我们使用了一个多步骤的人为审核过滤流程，以确保策划的数据集中不相关内容的比例低于10%，无论是整体还是针对每个社会群体区分。每个帖子随后被算法标注，包含用户估计的居住地区，以及一系列经过验证的现成和自定义语义标签，包括道德化、情感、情绪和语言概括。我们通过将ISAAC与宏观社会趋势（如在线搜索行为、重大社会事件期间的时间波动（无论是全国性还是区域性）以及公众态度的长期变化）联系起来的趋同证据，确认了所得到的语料库的有效性。通过提供统一的公共基础设施，ISAAC消除了研究碎片化，支持无缝复制，同时支持大规模的多样化实证工作流程。具体而言，ISAAC允许研究者进行跨类别比较，进行社会群体话语的长期时间变化的高精度追踪，并将空间变异映射到本地化的公众舆论和政策结果。ISAAC的完全公开的模块化流程便于将语料库扩展到新的平台、语言和社会类别。为了满足各种研究需求，ISAAC既可以通过点击式网站和标注者网页应用程序无编码访问，也可以通过SQL游乐场、Python包和HuggingFace进行编程访问。

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cs.CL / 10 / 2609.27064

What Changes When Fact-Verification Scores Improve? Evidence and Answer Accounting Across Trained Verifiers and LLMs

当事实验证评分提高时发生了什么变化？训练验证者与大型语言模型的证据与答案核算

Chen, Han, Li, Yingrui

Abstract

A joint fact-verification score assesses answers and submitted evidence together. When the score improves, how much of the gain remains if the answers are held fixed? On FEVEROUS, strict score is the percentage of claims with a correct answer and a complete annotated evidence group in the submitted evidence. Across four trained DeBERTa checkpoints and 7,890 claims, replacing DCUF evidence with UnifEE evidence raises strict score by 9.61 percentage points, compared with 1.96 percentage points in answer accuracy. The paired 95% interval for the strict-score gain is [8.77, 10.43], conditional on these checkpoints. Replacing only the evidence passed to the scorer accounts for 7.92 or 9.08 percentage points when we retain the answers generated from DCUF or UnifEE evidence, respectively. To examine how this evidence gain depends on evaluation choices, we generate 470,400 responses from two 8B LLMs on FEVER, FEVEROUS, and SciFact under two answer formats and two context budgets. Increasing context from 256 to 2,048 tokens raises the fixed-answer evidence gain on FEVEROUS by 3.84 and 3.10 percentage points for Qwen and Llama, respectively. The effects fall short of the prespecified cross-dataset criterion, while some intervals extend beyond the two-point small-effect bound. Post-hoc analyses quantify changes in answers and submitted evidence, and show when aggregate accuracy and evidence-coverage rates miss the claim-level pattern. The four answer-evidence score combinations reveal changes that endpoint and aggregate metrics leave unresolved.

Chinese Translation

联合事实验证评分同时评估答案和提交的证据。当评分提高时，如果答案保持不变，增益中有多少部分仍然存在？在 FEVEROUS 上，严格评分是指在提交的证据中，正确答案和完整注释证据组的声明百分比。在四个训练好的 DeBERTa 检查点和 7,890 个声明中，用 UnifEE 证据替换 DCUF 证据使严格评分提高了 9.61 个百分点，而答案准确率仅提高了 1.96 个百分点。严格评分增益的配对 95% 置信区间为 [8.77, 10.43]，以这些检查点为条件。仅替换传递给评分器的证据时，当我们保留从 DCUF 或 UnifEE 证据生成的答案时，分别可占 7.92 或 9.08 个百分点。为了考察这一证据增益如何依赖于评估选择，我们在 FEVER、FEVEROUS 和 SciFact 上针对两种答案格式和两种上下文预算生成了 470,400 个响应。将上下文从 256 个标记增加到 2,048 个标记，使得在 FEVEROUS 上固定答案的证据增益分别提高了 Qwen 和 Llama 的 3.84 和 3.10 个百分点。这些效果未达到预先规定的跨数据集标准，尽管某些区间超出了两点小效应界限。事后分析量化了答案和提交证据的变化，并展示了何时聚合准确率和证据覆盖率未能反映声明级别的模式。四种答案-证据评分组合揭示了终点和聚合指标未能解决的变化。

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cs.CL / 11 / 2609.27086

NADI 2026: The Second Multidialectal Arabic Speech Processing Shared Task

NADI 2026：第二届多方言阿拉伯语语音处理共享任务

Sullivan, Peter, Talafha, Bashar, Ashraf, Ahmed, Bougares, Fethi, Elleuch, Haroun, Zhang, Chiyu, Elmadany, AbdelRahim, Mohamed, Youssef, Mdhaffar, Salima, Estève, Yannick, Elhoseiny, Mohamed, Luqman, Hamzah, Habash, Nizar, Abdul-Mageed, Muhammad

Abstract

NADI 2026 is the seventh edition of the Nuanced Arabic Dialect Identification (NADI) shared task series and the second dedicated to multidialectal Arabic speech processing. This edition comprises five tasks and eight subtasks spanning Automatic Speech Recognition (ASR), Spoken Dialect Identification (SDID), Text-to-Speech (TTS), Spoken Language Translation (SLT), and Spoken Language Understanding (SLU). NADI 2026 emphasizes realistic evaluation through low-bandwidth, mixed-dialect, code-switched, out-of-domain, and zero-shot settings, while introducing TTS, SLT, and SLU to the series for the first time. The shared task attracted 21 participating teams from at least 13 countries, with 48 test-phase submissions and 14 submitted system-description papers. Results show that out-of-domain generalization remains a major bottleneck and highlight the effectiveness of recent Arabic-specialized speech models, multimodal dialect identification approaches, and ensemble methods. Overall, NADI 2026 provides a broader and more challenging benchmark for robust Arabic dialect speech processing.

Chinese Translation

NADI 2026 是细致阿拉伯方言识别（NADI）共享任务系列的第七届，也是第二届专注于多方言阿拉伯语语音处理的活动。本届包括五个任务和八个子任务，涵盖自动语音识别（ASR）、口语方言识别（SDID）、文本到语音（TTS）、口语语言翻译（SLT）和口语语言理解（SLU）。NADI 2026 强调通过低带宽、混合方言、代码切换、域外和零样本设置进行现实评估，同时首次将 TTS、SLT 和 SLU 引入该系列。该共享任务吸引了来自至少 13 个国家的 21 支参赛队伍，共提交了 48 份测试阶段的提交和 14 份系统描述论文。结果显示，域外泛化仍然是一个主要瓶颈，并突显了近期阿拉伯语专用语音模型、多模态方言识别方法和集成方法的有效性。总体而言，NADI 2026 为稳健的阿拉伯方言语音处理提供了更广泛且更具挑战性的基准。

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cs.CL / 12 / 2609.27156

Giving Credit Where It's Due: Redundancy-Aware Learning for Efficient Reasoning

应给予应有的认可：基于冗余感知的高效推理学习

Zhou, Yuqing, Wang, Hong, Mao, Manqing, Wang, Zhuoer, Koelle, Samson, Yuan, Jie, Lin, Yanjun, Feng, James, Kuang, Nikki Lijing, Zhu, Ziwei, Niu, Wei

Abstract

Large reasoning models can produce correct yet unnecessarily long reasoning traces. Existing methods improve reasoning efficiency with trajectory-level objectives or local token- and step-level signals, but rarely model inter-step semantic dependencies. This limits their ability to distinguish redundant steps from those that support later deductions, making it harder to shorten reasoning without sacrificing accuracy. We introduce RECAP (REdundancy-aware Credit Assignment via Propagation), which addresses this limitation by assigning credit where it is due based on both a step's downstream role in the reasoning structure and its contribution to solving the problem correctly. We define structural responsibility to capture the step's downstream role by measuring how strongly later reasoning depends on it, using credit propagated backward from the final-answer node through an outcome-independent, LLM-annotated semantic dependency graph. However, a step can have high structural responsibility yet steer the reasoning away from the correct solution. RECAP therefore introduces step efficacy to measure answer-directed progress through changes in gold-answer log-likelihood as each step is added. Together, these signals reshape rollout-level GRPO advantages into step-specific updates. RECAP requires neither a separately trained process reward model nor preconstructed concise trajectories. Across two 7B models and four mathematical reasoning benchmarks, RECAP improves the accuracy-efficiency trade-off. On Qwen2.5-Math-7B, it improves pass@1 by 2.0-3.7 percentage points while reducing reasoning tokens by 8%-31% relative to GRPO across all four benchmarks. Analysis suggests these savings reflect fewer reasoning operations and less dead-end reasoning, rather than more compact expression.

Chinese Translation

大型推理模型可能会产生正确但不必要冗长的推理轨迹。现有方法通过轨迹级目标或局部的标记和步骤级信号来提高推理效率，但很少建模步骤之间的语义依赖关系。这限制了它们区分冗余步骤与支持后续推理的步骤的能力，使得在不牺牲准确性的情况下缩短推理变得更加困难。我们提出了RECAP（通过传播的冗余感知信用分配），它通过根据步骤在推理结构中的下游角色及其对正确解决问题的贡献来解决这一限制。我们定义了结构责任，以通过测量后续推理对该步骤的依赖程度来捕捉该步骤的下游角色，使用从最终答案节点向后传播的信用，通过一个与结果无关的、由大型语言模型（LLM）注释的语义依赖图。然而，一个步骤可能具有高结构责任，却将推理引导偏离正确解决方案。因此，RECAP引入了步骤有效性，以通过每个步骤添加时金标准答案的对数似然变化来衡量面向答案的进展。这些信号共同将回滚级别的GRPO优势重塑为步骤特定的更新。RECAP既不需要单独训练的过程奖励模型，也不需要预构建的简洁轨迹。在两个7B模型和四个数学推理基准上，RECAP改善了准确性与效率的权衡。在Qwen2.5-Math-7B上，它在所有四个基准中将pass@1提高了2.0-3.7个百分点，同时相对于GRPO减少了8%-31%的推理标记。分析表明，这些节省反映了更少的推理操作和更少的死胡同推理，而不是更紧凑的表达。

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cs.CL / 13 / 2609.27165

Count Evidence, Not Sentences: Tempered Evidence Fusion of LLM Judgments for Long-Text Value Measurement

计数证据，而非句子：长文本价值测量的温和证据融合方法

Wu, Yuhe, Qian, Rui, Wang, Guangyu, Chen, Yuran, Zhu, Yuanchao, Yang, Junjie, Li, Zhengheng, Cai, Jiulin, Zhang, Tianyi, Dong, Zihan, Liu, Jiaxin, Chen, Yujie, Zhang, Guang

Abstract

Large language models (LLMs) are increasingly used to measure public value orientations from long social media posts, yet such posts often mix background, quotations, concessions, and only a few stance-bearing sentences. Existing approaches either ask the model to predict a document-level label directly, which can be overconfident, or aggregate sentence-level predictions by majority or soft voting, which treat uncertain and decisive sentences as equally informative. We formulate long-text value measurement as a decision-fusion problem and propose Tempered Evidence Fusion (TEF), a training-free rule that weights each sentence's log-odds by its normalized information gain, as derived from a generalized Bayesian posterior. This makes the fused score nearly vanish for uncertain sentences while preserving the Bayes-optimal weight of decisive evidence. We further introduce Multi-event Insight Network Dimensions (MIND), a benchmark of 8,358 Chinese and English posts spanning five years of public events and six value dimensions. On MIND, TEF outperforms the strongest baseline among Direct, Majority Vote, and Soft Vote by an average of 4.5 accuracy points and 4.6 macro-F1 points across five LLMs and two languages. MIND dataset and code are available at https://github.com/Kzczc/ICASSP2027-TEF.

Chinese Translation

大型语言模型（LLMs）在从长篇社交媒体帖子中测量公众价值取向方面的应用日益增加，然而这些帖子通常混合了背景信息、引用、让步以及仅有的少数表态句子。现有的方法要么直接要求模型预测文档级标签，这可能导致过于自信，要么通过多数投票或软投票聚合句子级预测，这种方法将不确定和决定性句子视为同等信息量。我们将长文本价值测量形式化为决策融合问题，并提出温和证据融合（Tempered Evidence Fusion, TEF），这是一种无训练的规则，通过从广义贝叶斯后验中推导出的标准化信息增益来加权每个句子的对数几率。这使得不确定句子的融合得分几乎消失，同时保留了决定性证据的贝叶斯最优权重。我们进一步引入多事件洞察网络维度（Multi-event Insight Network Dimensions, MIND），这是一个包含8,358条中英文帖子、涵盖五年公共事件和六个价值维度的基准数据集。在MIND上，TEF在五个LLM和两种语言中，平均比直接投票、主要投票和软投票的最强基线高出4.5个准确率点和4.6个宏观F1点。MIND数据集和代码可在 https://github.com/Kzczc/ICASSP2027-TEF 获取。

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cs.CL / 14 / 2609.27173

Realize What Matters: Principled Context Representation for Large-Scale Reasoning

实现重要性：大规模推理的原则性上下文表示

Theologitis, Michael, Light, Dean, Li, Shuyue Stella, Newman, Benjamin, Tsvetkov, Yulia, Suciu, Dan

Abstract

Solving complex tasks in domains such as science, medicine, law, and finance often requires assembling interdependent information scattered across vast, heterogeneous sources far beyond model context limits. Existing approaches tackle this challenge by organizing information into more manageable representations over which models can reason, such as graphs, textual memories, and retrieval collections. These representations dictate what downstream reasoning is possible and, ultimately, whether it succeeds; yet their design and construction remain largely ad hoc. In this work, drawing on the cognitive theory of relevance realization, we propose concrete principles for designing AI systems that construct effective representations of very large contexts. We analyze existing approaches and show how their successes and failures map onto their alignment with these principles, and introduce R3Con, a harness designed to operationalize the principles more systematically. We evaluate R3Con against nine state-of-the-art baselines on two recent benchmarks of reasoning over large document corpora. On these benchmarks, R3Con substantially outperforms the strongest baseline, by $20$ and $8.4$ percentage points. It also enables smaller models to outperform much larger ones: R3Con with 4B and 9B models outperforms all evaluated 35B baselines, while R3Con with a 35B-A3B model outperforms Claude Code with Claude-Sonnet-5 at $3.7\times$ lower cost. Our results show that context representations following our principled approach can reduce reliance on model scale, pointing toward a future of AI systems with frontier-level performance powered by smaller models. Our code is available at https://github.com/michaeltheologitis/r3con

Chinese Translation

在科学、医学、法律和金融等领域解决复杂任务通常需要整合散布在广泛异构来源中的相互依赖信息，这些信息远超模型的上下文限制。现有方法通过将信息组织成更易于管理的表示形式（如图、文本记忆和检索集合）来应对这一挑战。这些表示形式决定了下游推理的可能性，最终影响其成功与否；然而，它们的设计和构建仍然在很大程度上是临时的。在本研究中，我们借鉴相关性实现的认知理论，提出了设计构建有效大上下文表示的人工智能系统的具体原则。我们分析了现有方法，并展示了它们的成功与失败如何与这些原则的对齐情况相关，介绍了R3Con，一个旨在更系统地实现这些原则的工具。我们在两个最近的大型文档语料库推理基准上，将R3Con与九个最先进的基线进行评估。在这些基准上，R3Con的表现显著优于最强基线，分别提高了20和8.4个百分点。它还使得较小的模型能够超越更大的模型：使用4B和9B模型的R3Con超越了所有评估的35B基线，而使用35B-A3B模型的R3Con在成本低3.7倍的情况下超越了Claude Code与Claude-Sonnet-5的表现。我们的结果表明，遵循我们原则性方法的上下文表示可以减少对模型规模的依赖，指向未来由较小模型驱动的前沿水平性能的人工智能系统。我们的代码可在https://github.com/michaeltheologitis/r3con获取。

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cs.CL / 15 / 2609.27176

Beyond Overlap: Estimating the Causal Effect of Benchmark Exposure

超越重叠：评估基准暴露的因果效应

Singh, Divyansh

Abstract

Evidence that evaluation material entered training does not reveal how much it affected evaluation. This distinction leaves a contaminated benchmark score difficult to interpret: provenance can establish contact, but only a counterfactual can quantify the performance attributable to that contact. We present LeakScale, an interventional framework for estimating this missing quantity. LeakScale creates fresh executable tasks that require private, family-specific information absent from and non-derivable from the public task, controls access to that information, and estimates the resulting control-adjusted change in executable accuracy. Across 2,048 unique families, two model families, two executable domains, and 262,144 generations, exposure improves accuracy in every model-by-domain combination, with gains ranging from +7.17 to +27.31 percentage points. These findings separate two empirical questions that are often conflated: whether benchmark contact occurred and how strongly a reported score depends on it. LeakScale makes the latter directly measurable.

Chinese Translation

评估材料进入训练的证据并不能揭示其对评估的影响程度。这一区别使得受到污染的基准分数难以解释：来源可以建立接触，但只有反事实才能量化归因于该接触的表现。我们提出了 LeakScale，这是一个用于估计这一缺失量的干预框架。LeakScale 创建新的可执行任务，这些任务需要私有的、特定于家庭的信息，而这些信息在公共任务中缺失且无法推导，控制对该信息的访问，并估计由此产生的控制调整后的可执行准确性变化。在 2,048 个独特家庭、两个模型家庭、两个可执行领域和 262,144 代中，暴露在每个模型与领域的组合中均提高了准确性，增益范围从 +7.17 到 +27.31 个百分点。这些发现将两个常常混淆的实证问题区分开来：基准接触是否发生以及报告分数在多大程度上依赖于此。LeakScale 使后者可以直接测量。

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cs.CL / 16 / 2609.27205

Phonemizing User-Generated Text: A Benchmark, Taxonomy, and Compositional Approach

用户生成文本的音素化：基准测试、分类法与组合方法

Jeon, MinJu, Park, Younghan, Park, Han Sung, Kim, Jong-Hwan, Kim, Dong-Jin, Lee, Hoyeon

Abstract

Text-to-speech systems increasingly process user-generated text (UGT) such as ppl and imo, whose pronunciation must be inferred from the canonical rather than surface form. We introduce UGTPhon, the first grapheme-to-phoneme (G2P) benchmark for UGT in English, Vietnamese, and Korean, together with an inference-grounded taxonomy for fine-grained diagnosis. Existing G2P models and frontier LLMs exhibit a systematic canonical-to-non-canonical performance gap, reaching up to 66.8 PER points. As a benchmark baseline, we propose a simple compositional G2P approach that incorporates canonical-form evidence through exact-match lookup and staged decoding. Across matched ByT5 and Qwen2.5-0.5B backbones, explicit canonical-form modeling consistently reduces non-canonical G2P errors. The 0.5B variant also performs competitively with much larger few-shot frontier LLMs, highlighting the benefit of explicitly modeling canonical-form inference for UGT phonemization.

Chinese Translation

文本到语音系统越来越多地处理用户生成文本（UGT），例如 ppl 和 imo，其发音必须从规范形式而非表面形式中推断。我们介绍了 UGTPhon，这是第一个针对英语、越南语和韩语的用户生成文本的字母到音素（G2P）基准测试，并提出了一种基于推理的分类法用于细粒度诊断。现有的 G2P 模型和前沿大型语言模型（LLMs）表现出系统性的规范到非规范性能差距，达到 66.8 PER 点。作为基准基线，我们提出了一种简单的组合 G2P 方法，通过精确匹配查找和分阶段解码来结合规范形式证据。在匹配的 ByT5 和 Qwen2.5-0.5B 主干网络中，显式的规范形式建模始终减少了非规范 G2P 错误。0.5B 变体的表现也与更大规模的少量前沿 LLMs 竞争，突显了显式建模规范形式推理在用户生成文本音素化中的优势。

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cs.CL / 17 / 2609.27220

LOCKR: A Hidden-State Trajectory-Guided Planner for Detecting and Repairing Stable-but-Wrong Lock-In in Diffusion Language Models

LOCKR：一种隐状态轨迹引导的规划器，用于检测和修复扩散语言模型中的稳定但错误的锁定现象

Zhao, Guoshenghui, Yu, Tan, Zhao, Weijie

Abstract

Diffusion language models generate text through iterative denoising, exposing intermediate trajectories before final answers are produced. We identify a recurring reasoning failure, stable-but-wrong lock-in, where an answer stabilizes early around an incorrect value while substantial denoising remains. Surface-level decoding signals such as confidence, entropy, margin, and answer stability are insufficient to reliably distinguish correct from erroneous lock-in. We formulate selective reasoning repair as a lightweight test-time planning problem and propose LOCKR, a hidden-state trajectory-guided planner that decides when to allocate additional computation, expands a structured set of targeted repair branches, and selects the most promising continuation using trajectory-aware verification. Across two diffusion language models and three mathematical reasoning benchmarks, hidden-state trajectories consistently outperform surface signals and single hidden snapshots for both wrong-lock-in detection and repair selection. On natural evaluation distributions, LOCKR yields absolute accuracy gains of 2.21--5.37 percentage points across all five evaluated settings, with repair rates ranging from 22% to 41%. These results establish hidden diffusion trajectories as actionable signals for selective test-time reasoning repair.

Chinese Translation

扩散语言模型通过迭代去噪生成文本，在最终答案产生之前暴露中间轨迹。我们识别出一种反复出现的推理失败现象，即稳定但错误的锁定现象，其中答案在错误值附近过早稳定，而仍需进行大量去噪。表面层解码信号，如置信度、熵、边际和答案稳定性，无法可靠地区分正确的锁定与错误的锁定。我们将选择性推理修复形式化为一种轻量级的测试时间规划问题，并提出LOCKR，一种隐状态轨迹引导的规划器，决定何时分配额外计算，扩展一组结构化的目标修复分支，并使用轨迹感知验证选择最有前景的继续方案。在两个扩散语言模型和三个数学推理基准上，隐状态轨迹在错误锁定检测和修复选择方面始终优于表面信号和单一隐状态快照。在自然评估分布上，LOCKR在所有五个评估设置中实现了2.21至5.37个百分点的绝对准确率提升，修复率范围为22%至41%。这些结果确立了隐扩散轨迹作为选择性测试时间推理修复的可操作信号。

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cs.CL / 18 / 2609.27225

Meet, Compare, or Abstain: LatWeave for Deterministic Multi-Hop Question Answering on Knowledge Lattices

相遇、比较或放弃：基于知识格的确定性多跳问答系统LatWeave

Ren, Yuze, Fan, Shaoheng, Wang, Tao, Yan, Yabo, Han, Han

Abstract

Probabilistic question-answering systems -- whether large language models (LLMs) themselves, retrieval-augmented generation (RAG), or trained multi-hop retrievers -- conflate "what is known" and "how to reason" into a single probabilistic computation: hallucination cannot be eradicated, evidence chains cannot be audited, and the system answers even when it does not know. We present LatWeave, which organizes knowledge into a multidimensional knowledge lattice and compiles multi-hop QA into three deterministic operators -- meet (constraint intersection), compare (lattice-order comparison), and abstain (structural abstention); LLMs appear only on the construction side (one-shot extraction) and the query-planning side, while the answer-generation path is zero-LLM, zero-task-training, and auditable end to end -- so that question answering over Web-published knowledge becomes reproducible item by item. Rather than claiming across-the-board SOTA, we characterize the operating envelope of this paradigm on six public benchmarks: when knowledge is complete (MetaQA, 39,093 questions) meet chains are near-lossless over three hops (any-hit 0.9975, on par with fully supervised KBQA); on templated multi-hop home ground (2WikiMultihopQA held-out n=1,258) EM 0.865, well above published structure-augmented RAG reproductions; on open-text deep composition (MuSiQue) and extraction-coverage gaps (HotpotQA) we report degradation honestly and attribute it to causes outside the lattice-algebra layer; and when information is incomplete (IIRC) we achieve structural abstention with abstain accuracy 0.971 and leak rate 0.029. Within the operating envelope, deterministic execution pays no performance penalty, and every step on the answer path can be recomputed -- precisely the source of end-to-end auditability.

Chinese Translation

概率问答系统——无论是大型语言模型（LLMs）、增强检索生成（RAG）还是训练过的多跳检索器——将“已知的内容”和“推理方式”混为一谈，形成单一的概率计算：幻觉无法根除，证据链无法审计，系统在不知道答案时仍会给出回答。我们提出了LatWeave，它将知识组织成多维知识格，并将多跳问答编译为三个确定性操作符——相遇（约束交集）、比较（格序比较）和放弃（结构性放弃）；LLMs仅出现在构建阶段（单次提取）和查询规划阶段，而答案生成路径则是零LLM、零任务训练，并且端到端可审计——使得基于网络发布知识的问答变得逐项可重现。我们并不声称在各个方面都达到最先进水平，而是对该范式在六个公共基准上的操作范围进行了表征：当知识完整时（MetaQA，39,093个问题）相遇链在三跳内几乎无损（任意命中率0.9975，与完全监督的知识库问答（KBQA）相当）；在模板化的多跳本地环境中（2WikiMultihopQA保留集n=1,258）EM为0.865，远高于已发布的结构增强RAG重现结果；在开放文本深度组合（MuSiQue）和提取覆盖差距（HotpotQA）上，我们诚实地报告了性能下降，并将其归因于知识格代数层之外的原因；当信息不完整时（IIRC），我们实现了结构性放弃，放弃准确率为0.971，泄漏率为0.029。在操作范围内，确定性执行没有性能损失，答案路径上的每一步都可以重新计算——这正是端到端可审计性的来源。

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cs.CL / 19 / 2609.27233

Distilling Sequential Computation in Transformer Language Models

在变换器语言模型中提炼顺序计算

Lan, Zixuan, Yang, Jessica, Li, Yanhong, Livescu, Karen, Zhou, Jiawei

Abstract

Transformer language models process sequences token by token in an autoregressive manner, making growing contexts increasingly expensive. Yet many adjacent token spans are highly predictable or frequently occur as stable units, suggesting that their representations may be compressible. We introduce a method for distilling sequential computation by replacing spans of input tokens with collapsed representations, computed on the fly by a lightweight merge module. This module generates a single surrogate embedding from a sequence of static token embeddings that captures the functional role of the multiple tokens, allowing pretrained models to operate on compressed inputs without architectural changes or re-training. We apply this approach during inference to compress both prompts and intermediate decoding steps, using a rollback mechanism to substitute stored multi-token KV cache entries with their single-step surrogates. Experiments across diverse models show that the merge module can be used to reduce effective sequence length by up to 40% with minimal accuracy degradation across language modeling evaluations and downstream tasks, including question answering, summarization, commonsense reasoning, and long-form mathematical reasoning. Additional lightweight adaptation of the merge module further improves the accuracy-compression trade-off in selected settings. These results demonstrate that sequential token computation in Transformers can be effectively approximated through condensed surrogate representations that approximate the original behavior without model updating.

Chinese Translation

变换器语言模型以自回归方式逐个处理序列，这使得不断增长的上下文变得越来越昂贵。然而，许多相邻的标记跨度是高度可预测的，或者作为稳定单元频繁出现，这表明它们的表示可能是可压缩的。我们提出了一种通过用压缩表示替换输入标记的跨度来提炼顺序计算的方法，这些压缩表示由轻量级合并模块实时计算。该模块从一系列静态标记嵌入中生成一个单一的替代嵌入，捕捉多个标记的功能角色，使得预训练模型能够在不改变架构或重新训练的情况下处理压缩输入。我们在推理过程中应用这种方法来压缩提示和中间解码步骤，使用回滚机制将存储的多标记 KV 缓存条目替换为它们的单步替代品。跨多种模型的实验表明，合并模块可以在语言建模评估和下游任务（包括问答、摘要、常识推理和长篇数学推理）中将有效序列长度减少多达 40%，且准确性降级最小。对合并模块的额外轻量级适应进一步改善了在特定设置下的准确性与压缩之间的权衡。这些结果表明，变换器中的顺序标记计算可以通过浓缩的替代表示有效地近似，从而在不更新模型的情况下接近原始行为。

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cs.CL / 20 / 2609.27257

UniDataAgent: An Ontology-Grounded Agent for Enterprise Question-to-Report Automation

UniDataAgent：一个基于本体的企业问答报告自动化代理

Duan, Yutai, Zhao, Yahui, Li, Zhangti, Ma, Yu, Qi, Zhenfeng, Yuan, Shaoyang, Fan, Jing, Liu, Jie

Abstract

Enterprise data agents must preserve organization specific semantics, not just translate questions into queries. We present ChinaUnicom DataAgent (UniDataAgent), an ontology grounded system for reusable question-to-report analysis that separates semantic acquisition from online execution. Ontology Acquisition and Validation stage (OAV) builds versioned enterprise ontologies from metadata, business knowledge, and supporting materials through expert authored business skills, constrained generation, question verification, and selected expert review. Question-to-Report Execution (QRE) stage retrieves semantic contracts for each question, coordinates skills and data tools, validates results, and produces evidence linked reports. Across 27 enterprise tables and roughly thousands of metric types, ontology construction took a few hours instead of about one week manually. It took just a few minutes to generate the reports, instead of several working days. Ontology grounding achieved 95.0\% strict accuracy on real business questions, versus 72.5\% for document RAG, especially on structured and compositional tasks. The system has already been deployed to generate cost savings and has the potential to be replicated in other enterprises.

Chinese Translation

企业数据代理必须保留组织特定的语义，而不仅仅是将问题转换为查询。我们提出了中国联通数据代理（UniDataAgent），这是一个基于本体的可重用问答报告分析系统，能够将语义获取与在线执行分开。语义获取与验证阶段（OAV）通过专家撰写的业务技能、约束生成、问题验证和选定专家评审，从元数据、业务知识和支持材料中构建版本化的企业本体。问答报告执行阶段（QRE）为每个问题检索语义合同，协调技能和数据工具，验证结果，并生成与证据相关的报告。在27个企业表和大约数千种指标类型中，本体构建仅用了几个小时，而手动完成则需约一周时间。生成报告仅需几分钟，而不是几天的工作时间。本体基础在真实业务问题上实现了95.0%的严格准确率，而文档RAG的准确率为72.5%，尤其是在结构化和组合任务上。该系统已经部署以实现成本节约，并有潜力在其他企业中复制。

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cs.CL / 21 / 2609.27262

Can One Adapted Model Do It All? Fine-Tuning Strategy Selection for Customer Support LLMs

一个适应性模型能否应对所有任务？客户支持大语言模型的微调策略选择

Laskar, Md Tahmid Rahman, Fu, Xue-Yong, TN, Shashi Bhushan

Abstract

Production customer-support systems often require LLMs to support multiple skills, such as intent classification, question answering, summarization, or tool-use decisions. A central deployment question is whether these skills should be handled by separate task-specialist models or by a single model trained through multi-task training, sequential updates, or model merging. We study this question using thirteen models spanning five families (Qwen3, Qwen3.5, Gemma-3, Llama-3.1, and Mistral) from 0.6B to 32B parameters across eight customer-support datasets, spanning four public and four proprietary datasets with approximately 74.5k training and 8.7k evaluation samples. Under a fixed training protocol, we train more than 200 checkpoints. Our experiments reveal that multi-task full fine-tuning is the strongest operational default at every model size we test. Specialist models are strong on their target tasks but often degrade sharply off-task, making reliable routing important. Sequential Low-Rank Adaptation (LoRA) preserves earlier skills better than sequential full fine-tuning, while merging a specialist with its base model improves off-task robustness with limited same-task loss for larger models. We conclude with practical guidelines for selecting fine-tuning strategies in real-world settings.

Chinese Translation

生产客户支持系统通常需要大语言模型（LLMs）支持多种技能，如意图分类、问答、摘要生成或工具使用决策。一个核心的部署问题是，这些技能应由不同的任务专用模型处理，还是通过多任务训练、顺序更新或模型合并的方式由单一模型来处理。我们通过研究涵盖五个模型系列（Qwen3、Qwen3.5、Gemma-3、Llama-3.1 和 Mistral）的十三个模型，参数范围从 0.6B 到 32B，分析了这一问题。这些模型在八个客户支持数据集上进行测试，其中包括四个公共数据集和四个专有数据集，约有 74.5k 的训练样本和 8.7k 的评估样本。在固定的训练协议下，我们训练了超过 200 个检查点。实验结果表明，多任务完全微调在我们测试的每个模型规模中都是最强的操作默认选择。专用模型在其目标任务上表现出色，但在非目标任务上往往急剧下降，因此可靠的路由变得尤为重要。顺序低秩适应（LoRA）比顺序完全微调更好地保留早期技能，而将专用模型与其基础模型合并则在保持较小的同任务损失的同时提高了非目标任务的鲁棒性。最后，我们总结了在实际环境中选择微调策略的实用指南。

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cs.CL / 22 / 2609.27289

Ruby-ASR: Evidence-Preserving Supervision for Joint Orthographic and Lexical-Reading Recognition

Ruby-ASR：用于联合正字法和词汇阅读识别的证据保留监督

Shi, Hao, Liu, Yun, Yang, Xuehao, Liu, Jun, Hua, Chuanbo, Chen, Xuanjun, Liu, Lianbo, Zhu, Shiao, Su, Zixiong

Abstract

Conventional Japanese automatic speech recognition (ASR) is supervised by an orthographic transcript, although the same written form can correspond to different lexical readings realized in speech. Such utterances receive an identical target, so their reading distinction is absent from the supervision interface and cannot be recovered reliably by post-hoc text-only grapheme-to-phoneme conversion. We present Ruby-ASR, which refines the conventional target into a span-bound orthographic--lexical-reading sequence. Unlike separate full-sentence orthographic and phonological outputs, the ruby representation locally binds each written span to its realized reading and permits deterministic recovery of both views. We instantiate the target under subtitle-style and verbatim-style transcription conventions using a Qwen3-ASR backbone; a mora-level CTC objective provides auxiliary monotonic reading supervision. The experimental results across five Japanese benchmarks show that refining the recognition target can improve lexical-reading recovery without sacrificing readable orthographic transcription. We release the checkpoints and inference code.

Chinese Translation

传统的日语自动语音识别（ASR）是通过正字法转录进行监督的，尽管相同的书写形式可以对应于在语音中实现的不同词汇阅读。这些话语接收相同的目标，因此它们的阅读区分在监督接口中缺失，无法通过事后仅基于文本的字素到音素转换可靠地恢复。我们提出了Ruby-ASR，它将传统目标细化为一个跨度绑定的正字法-词汇阅读序列。与单独的完整句子正字法和音韵输出不同，ruby表示法将每个书写跨度局部绑定到其实现的阅读，并允许确定性地恢复这两种视图。我们在字幕风格和逐字风格的转录规范下实例化目标，使用Qwen3-ASR作为基础；一个音节级的CTC目标提供辅助的单调阅读监督。五个日语基准测试的实验结果表明，细化识别目标可以在不牺牲可读正字法转录的情况下改善词汇阅读的恢复。我们发布了检查点和推理代码。

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cs.CL / 23 / 2609.27353

Guides That Cause Actions: An Offline Study of Guide-Action Mutual Reinforcement in Multimodal Web Agents

引导导致行动：多模态网络代理中引导-行动相互强化的离线研究

Gan, Chengguang, Liang, Yunhao, Zhang, QingHao, Ni, Shiwen

Abstract

Web agents are usually evaluated in live environments, where environment state and judge models drift between runs, so the same checkpoint rarely reproduces the same score, making controlled studies of training phenomena impractical. We present WebMRE, an offline benchmark of 541 tasks and 5,293 steps derived from successful WebArena trajectories, with fully audited test labels and a deterministic protocol that scores a checkpoint identically on every run without any environment. Each step pairs a human oriented guide sentence with a grounded action, enabling the first study of the mutual reinforcement effect between them in web agents. Averaged over three seeds the effect holds for both models in both decoding orders and grows with scale: jointly decoding a guide lifts element selection over an action only reference by 0.9 and 0.2 points for Qwen3.5-4B and by 1.7 and 2.2 points for Qwen3.5-9B. A mediation analysis shows that the guide is a causal channel rather than commentary: forcing the gold guide as a decoding prefix lifts action accuracy from .422 to .684, another step's guide collapses it to .055, and a paraphrase that renames the target still recovers half of the gain, so the channel carries instruction meaning and not only the label string. The same channel yields an offline reward that only a replayable protocol makes computable, though optimizing it from a strong checkpoint brings no gain yet. Our fine tuned models outperform GPT-5.5, Claude Opus 4.8, and Gemini 3.5 Flash, run zero shot, on every offline metric.

Chinese Translation

网络代理通常在实时环境中进行评估，在这些环境中，状态和评判模型在不同运行之间会发生漂移，因此相同的检查点很少能重现相同的分数，这使得对训练现象的控制研究变得不切实际。我们提出了WebMRE，这是一个由成功的WebArena轨迹衍生出的541个任务和5,293个步骤的离线基准，具有完全审核的测试标签和一个确定性的协议，使得每次运行对同一检查点的评分一致，无需任何环境。每个步骤将一个面向人类的引导句与一个具体的行动配对，从而使得在网络代理中首次研究它们之间的相互强化效应。经过三个种子的平均，该效应在两种模型的两种解码顺序中均成立，并随着规模的增加而增强：共同解码引导使得Qwen3.5-4B的元素选择比仅参考行动提高了0.9和0.2分，而Qwen3.5-9B则提高了1.7和2.2分。中介分析表明，引导是一个因果通道，而非简单的评论：将黄金引导强制作为解码前缀使得行动准确率从0.422提升至0.684，而另一个步骤的引导则将其压缩至0.055，重述目标的释义仍能恢复一半的增益，因此该通道传递的是指令意义，而不仅仅是标签字符串。相同的通道产生的离线奖励只有通过可重放的协议才能计算，尽管从强检查点优化它并未带来增益。我们的微调模型在每个离线指标上均优于GPT-5.5、Claude Opus 4.8和Gemini 3.5 Flash，且在零-shot运行中表现出色。

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cs.CL / 24 / 2609.27359

Automated Extraction of Records of Processing Activities (RoPA) Using Hybrid RAG and Locally Deployed Large Language Models

使用混合检索和本地部署的大型语言模型自动提取处理活动记录（RoPA）

Hinh, To Duy, Anh, Nguyen Le Quoc, Van Tri, Phan, Nguyen-An, Khuong

Abstract

Vietnam's Personal Data Protection Law (Law No. 91/2025/QH15) and Decree No. 356/2025/ND-CP, effective January 1, 2026, require organizations to establish and maintain Records of Processing Activities (RoPA). Manual RoPA preparation is labor-intensive, while cloud-hosted large language models (LLMs) may conflict with data-sovereignty requirements. We propose RoPA Manager, a system for automated RoPA information extraction using hybrid retrieval that combines lexical ranking over tsvector, dense-vector search, Reciprocal Rank Fusion (RRF), and locally deployed LLMs. We introduce a Vietnamese RoPA benchmark with 32 organizations, 77 processing activities, 12 field groups, and 4,338 reference values. Evaluation is reported at three distinct levels. The automated scorer, tested on perturbed data without invoking an LLM, achieved F1 = 0.9493 [0.9436, 0.9548]; this measures scorer robustness rather than end-to-end extraction accuracy. End-to-end extraction achieved token coverage of 50.04-55.25% against the reference labels. Two independent experts reviewed 1,558 reference values (35.9% of the benchmark), found no incorrect values, and achieved 99.68% agreement with PABAK = 0.9936. Value-level precision was not measured. Across 32 paired scenarios on a 24 GB GPU, locally deployed Qwen3.5-27B-GPTQ-Int4 showed no statistically significant difference from cloud-based DeepSeek-V4-Flash (difference 0.20 percentage points in favor of DeepSeek, 95% CI [-0.93, 1.32], p = 0.72), while Gemma-4-31B performed significantly worse (p < 0.01).

Chinese Translation

越南的个人数据保护法（法令第91/2025/QH15号）及第356/2025/ND-CP号法令自2026年1月1日起生效，要求组织建立和维护处理活动记录（RoPA）。手动准备RoPA劳动密集，而云托管的大型语言模型（LLMs）可能与数据主权要求相冲突。我们提出了RoPA Manager，一个用于自动提取RoPA信息的系统，采用混合检索，结合了基于tsvector的词汇排名、密集向量搜索、互惠排名融合（Reciprocal Rank Fusion, RRF）和本地部署的LLMs。我们引入了一个包含32个组织、77个处理活动、12个领域组和4,338个参考值的越南RoPA基准。评估在三个不同层面上进行报告。自动评分器在未调用LLM的情况下对扰动数据进行了测试，达到了F1 = 0.9493 [0.9436, 0.9548]；这测量的是评分器的稳健性，而非端到端提取的准确性。端到端提取在参考标签上实现了50.04-55.25%的标记覆盖率。两位独立专家审查了1,558个参考值（占基准的35.9%），未发现错误值，并达成了99.68%的一致性，PABAK = 0.9936。未测量值级精度。在32个配对场景中，使用24 GB GPU的本地部署Qwen3.5-27B-GPTQ-Int4与基于云的DeepSeek-V4-Flash没有显著统计差异（差异为0.20个百分点，支持DeepSeek，95% CI [-0.93, 1.32]，p = 0.72），而Gemma-4-31B的表现显著较差（p < 0.01）。

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cs.CL / 25 / 2609.27372

Neither Silence nor Overlap Is Failure: Intent-Conditioned Evaluation of Turn-Taking in Full-Duplex Spoken Dialogue Models

既非沉默也非重叠是失败：基于意图的全双工口语对话模型轮次转换评估

Shamsaie, Kian, Modarressi, Iman

Abstract

Benchmarks for full-duplex spoken dialogue models score turn-taking with binary fixed-window rules that reward immediate response or silence by completeness of the prior turn. We argue that the appropriateness of a response offset, whether delayed silence or anticipatory overlap, is conditional on the speaker's latent intent, identifiable only from that speaker's behavior. We introduce TACT, a benchmark of 9,728 episodes and 73.2 hours from five dyadic corpora; each episode carries dialogue history, a per-speaker memory profile, and an annotator-derived posterior over six intent classes. Scoring replaces binary windows with a strictly proper threshold-weighted continuous ranked probability score whose weights are intent-conditioned timing kernels fitted to human floor-transfer-offset distributions, proving boundedness, consistency, and binary reduction. Across eleven systems the best model reaches 0.47 against a human topline of 0.86, is nearly invariant to speaker profiles, and TACT agrees with human judgments at Spearman 0.81 versus 0.46 for binary metrics.

Chinese Translation

全双工口语对话模型的基准测试使用二元固定窗口规则来评分轮次转换，这些规则通过先前轮次的完整性来奖励即时响应或沉默。我们认为，响应偏移的适当性，无论是延迟沉默还是预期重叠，都是依赖于说话者的潜在意图，而这种意图只能通过该说话者的行为来识别。我们引入了TACT，一个包含9,728个情节和73.2小时来自五个双人语料库的基准；每个情节都包含对话历史、每位说话者的记忆档案，以及基于注释者的六个意图类别的后验分布。评分方法用严格适当的阈值加权连续排名概率分数替代了二元窗口，其权重是基于意图的时序核，拟合于人类轮次转换偏移分布，证明了有界性、一致性和二元简化。在十一种系统中，最佳模型达到了0.47，而人类基准为0.86，几乎对说话者档案不变，TACT与人类判断的斯皮尔曼相关系数为0.81，而二元指标为0.46。

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cs.CL / 26 / 2609.27373

Attention Routing Stabilizes Early: Working-Set Inference for Recurrent Language Models

注意力路由的早期稳定性：递归语言模型的工作集推理

Wan, Ke, Chen, Chen

Abstract

Recurrent language models repeatedly apply shared network blocks to refine latent representations, but standard inference recomputes global attention at every recurrent step. We study attention dynamics across recurrent depth and find that attention support and distributions stabilize substantially earlier than hidden states and attention outputs. This suggests a two-stage structure: early steps discover a sparse working set of relevant context, while later steps refine representations over largely the same routing support. Motivated by this structure, we introduce WISE (Working-set Inference with Support Exploitation), a training-free method that uses unrestricted global attention during early recurrence and later reuses directly discovered block-structured support while keeping recurrent depth and within-support attention computation dynamic. Controlled interventions show that recurrent discovery is important and that support-only reuse better preserves model behavior than more restrictive attention-reuse alternatives. Across multi-hop QA benchmarks, WISE largely preserves full-attention performance, while context scaling reveals increasingly sparse working sets and greater efficiency gains. Quality is largely preserved through 2K context, with a measurable loss at 4K. An optimized sparse-attention implementation achieves up to a 1.76x attention speedup over native FlashAttention at 4K and a 1.36x speedup for the full 32-step attention trajectory. Our code is available at https://github.com/tbn5pj/WISE_code.

Chinese Translation

递归语言模型反复应用共享网络模块以细化潜在表示，但标准推理在每个递归步骤中都会重新计算全局注意力。我们研究了递归深度中的注意力动态，发现注意力支持和分布的稳定性明显早于隐藏状态和注意力输出。这表明了一种两阶段结构：早期步骤发现相关上下文的稀疏工作集，而后续步骤则在基本相同的路由支持上细化表示。基于这一结构，我们提出了WISE（工作集推理与支持利用），这是一种无训练的方法，在早期递归中使用不受限制的全局注意力，并在后续步骤中重用直接发现的块结构支持，同时保持递归深度和支持内注意力计算的动态性。控制干预表明，递归发现是重要的，并且仅支持重用比更具限制性的注意力重用替代方案更好地保持模型行为。在多跳问答基准测试中，WISE在很大程度上保持了全注意力性能，而上下文缩放则揭示了越来越稀疏的工作集和更大的效率提升。通过2K上下文，质量基本保持，但在4K时有可测量的损失。优化的稀疏注意力实现相比原生FlashAttention在4K时实现了最高1.76倍的注意力加速，而在完整的32步注意力轨迹中实现了1.36倍的加速。我们的代码可在 https://github.com/tbn5pj/WISE_code 获取。

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cs.CL / 27 / 2609.27374

Planned Test-Time Scaling with Coordinated Reasoning Paths

规划测试时间缩放与协调推理路径

Wu, Xueqing, Bai, Langxing, Bansal, Hritik, Kung, Po-Nien, Li, Shuo, Liu, Hao, Peng, Nanyun, Chang, Kai-Wei

Abstract

Test-time scaling with parallel branches is widely adopted to improve performance on challenging reasoning tasks. The predominant approach, repeated sampling, draws branches independently from a single policy, which can produce redundant attempts and thereby limit the gains from additional inference compute. To address this limitation, we propose Planned Test-Time Scaling (PTTS), which replaces independent sampling with a coordinated joint policy: a planner generates a solution outline for each branch, steering the branches toward distinct reasoning paths, and an executor produces a full solution conditioned on each outline. Formally, we show that PTTS strictly generalizes repeated sampling and, in a stylized setting, provably promotes coverage of complementary reasoning modes and yields better pass@k scaling. We instantiate PTTS on top of strong reasoning models, keeping them fixed as executors while replacing repeated sampling with PTTS inference to further enhance test-time scaling. Concretely, we develop two variants: PTTS-ZS prompts a model to jointly generate outlines for all branches in a single autoregressive pass, while PTTS-RL directly optimizes the planner against the pass@k reward using truncated execution rollouts for efficient training and a sharper reward signal. Across five mathematical reasoning benchmarks with Qwen3-1.7B and 4B, PTTS-ZS improves pass@64 over repeated sampling by up to 6.7 points, while PTTS-RL further increases the gain to up to 13.4 points. Further analysis indicates that broader coverage of distinct reasoning paths contributes to these gains. Overall, PTTS provides a general framework for improving test-time scaling by coordinating reasoning branches, with zero-shot and trainable instantiations that yield substantial performance gains.

Chinese Translation

并行分支的测试时间缩放被广泛采用，以提高在具有挑战性的推理任务中的表现。主要的方法是重复采样，它从单一策略中独立抽取分支，这可能导致冗余尝试，从而限制了额外推理计算的收益。为了解决这一限制，我们提出了规划测试时间缩放（Planned Test-Time Scaling, PTTS），它用协调的联合策略替代独立采样：一个规划器为每个分支生成解决方案大纲，引导分支朝向不同的推理路径，而执行器则基于每个大纲生成完整的解决方案。我们正式证明，PTTS严格地推广了重复采样，并且在一个简化的设置中，能够有效促进互补推理模式的覆盖，并实现更好的 pass@k 缩放。我们在强推理模型的基础上实例化了 PTTS，保持它们作为执行器不变，同时用 PTTS 推理替代重复采样，以进一步增强测试时间缩放。具体而言，我们开发了两个变体：PTTS-ZS 促使模型在单次自回归传递中共同生成所有分支的大纲，而 PTTS-RL 则通过截断执行回滚直接优化规划器，以便高效训练和提供更清晰的奖励信号。在与 Qwen3-1.7B 和 4B 的五个数学推理基准测试中，PTTS-ZS 相比重复采样提高了多达 6.7 分的 pass@64，而 PTTS-RL 进一步将增益提升至 13.4 分。进一步分析表明，广泛覆盖不同的推理路径有助于这些增益。总体而言，PTTS 提供了一个通过协调推理分支来改善测试时间缩放的通用框架，具有零样本和可训练的实例化，能够带来显著的性能提升。

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cs.CL / 28 / 2609.27376

Cross-Lingual Legal QA for Vietnamese Labour Law: Retrieval, Translation, and Verifier-Guided Correction

越南劳动法的跨语言法律问答：检索、翻译与验证者引导的修正

Chi, Nguyen Minh, El-Haj, Mo, Thanh, Nguyen Ha, Knight, Dawn, Rayson, Paul

Abstract

Cross-lingual legal question answering must retrieve statutes across languages while preventing unsupported legal claims. We introduce a bilingual evaluation suite of 231 Vietnamese--English question--answer pairs from Vietnamese labour law. Of these, 75 are additionally annotated for five challenging legal reasoning phenomena. We evaluate a verifier-guided pipeline that decomposes answers into claims, checks citation reachability and entailment, and corrects citation failures and contradictions. We also introduce six automatic diagnostics for faithfulness to retrieved evidence, covering citations, modality, exceptions, procedures, conclusions, and evidential support. Experiments show that learned-sparse retrieval performs poorly for English-to-Vietnamese retrieval (R@5~=~0.032), whereas dense retrieval reaches 0.358 and slightly outperforms hybrid retrieval. Translation placement has no statistically detectable effect on these automatic diagnostics in our controlled comparison and supporting sensitivity analyses. Verifier-guided correction improves citation preservation by $0.022$--$0.034$ at the system level but produces no reliable gains in the remaining dimensions. Human evaluation further shows that the automatic diagnostics do not fully align with human judgements of answer quality.

Chinese Translation

跨语言法律问答必须在不同语言之间检索法规，同时防止不支持的法律主张。我们引入了一套双语评估工具，包括231个越南语-英语的问答对，均来自越南劳动法。其中，75个问答对额外标注了五种具有挑战性的法律推理现象。我们评估了一种验证者引导的流程，该流程将答案分解为主张，检查引用的可达性和蕴含关系，并修正引用失败和矛盾。此外，我们还引入了六种自动诊断工具，用于评估与检索证据的一致性，涵盖引用、情态、例外、程序、结论和证据支持。实验结果表明，学习稀疏检索在英译越检索中的表现较差（R@5~=~0.032），而密集检索达到0.358，并略微优于混合检索。在我们的对照比较和支持的敏感性分析中，翻译位置对这些自动诊断没有统计学上显著的影响。验证者引导的修正在系统层面上提高了引用保留率0.022至0.034，但在其他维度上未产生可靠的增益。人工评估进一步表明，自动诊断与人类对答案质量的判断并不完全一致。

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cs.CL / 29 / 2609.27380

MORSE: Multi-Context Ordering via Reverse Scoring for Evidence-Preserving Compression

MORSE：通过反向评分实现的多上下文排序以保持证据压缩

Wan, Ke, Wang, Yifan, Lai, Liheng, Chen, Chen

Abstract

Likelihood-based context compression can account for cross-context redundancy through sequential scoring, but this makes compression outcomes sensitive to context order. We show that different permutations of the same context collection can produce markedly different evidence-retention outcomes under an unchanged compressor. We attribute this sensitivity to information preemption: earlier partially relevant contexts can absorb credit for shared information, suppressing the incremental score of later, stronger evidence carriers and increasing their risk of removal. Controlled pair-swap interventions directly support this mechanism by showing that evidence-first ordering substantially improves supporting-evidence survival. To address this problem, we introduce MORSE, a compression-aware method for evidence-preserving context ordering. MORSE applies a common reverse query-evidence principle to both individual contexts and compressed candidate outputs, using the former to construct an evidence-first anchor and the latter to guide compression-aware permutation selection. Across multi-hop QA benchmarks, compression procedures, budgets, and scoring models, MORSE consistently improves evidence preservation over static reverse ordering and compute-matched random search, with corresponding overall improvements in downstream QA. Our code is available at https://github.com/tbn5pj/MORSE_code.

Chinese Translation

基于似然的上下文压缩可以通过顺序评分来考虑跨上下文的冗余，但这使得压缩结果对上下文顺序敏感。我们展示了相同上下文集合的不同排列可以在不改变压缩器的情况下产生显著不同的证据保留结果。我们将这种敏感性归因于信息抢占：较早的部分相关上下文可以吸收共享信息的信用，从而抑制后续更强证据承载者的增量评分，并增加其被移除的风险。受控的配对交换干预直接支持这一机制，表明证据优先排序显著提高了支持证据的存活率。为了解决这个问题，我们引入了MORSE，一种用于保持证据的上下文排序的压缩感知方法。MORSE将通用的反向查询-证据原则应用于单个上下文和压缩候选输出，利用前者构建证据优先的锚点，并利用后者指导压缩感知的排列选择。在多跳问答基准、压缩程序、预算和评分模型中，MORSE始终在证据保留方面优于静态反向排序和计算匹配的随机搜索，并在下游问答中实现了相应的整体提升。我们的代码可在 https://github.com/tbn5pj/MORSE_code 获取。

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cs.CL / 30 / 2609.27387

AraGenre 2026: A Hierarchical Definition-Guided Arabic Genre Classification Shared Task

AraGenre 2026：一种分层定义引导的阿拉伯语体裁分类共享任务

El-Haj, Mo, Ezzini, Saad, Abudalfa, Shadi, Jarrar, Mustafa, Chi, Nguyen Minh, Quan, Nguyen Minh

Abstract

AraGenre is a shared task on hierarchical, definition-guided Arabic genre classification, motivated by the limited availability of annotated data in Arabic and other low-resource languages. Systems assign each Arabic text segment both a broad communicative genre and a fine-grained specific genre. The released training and development sets contain limited, primarily synthetic and controlled examples, whereas the hidden final benchmark contains noisier naturally occurring text spanning Modern Standard Arabic, Classical Arabic, and multiple dialects. Participants received natural-language definitions for 74 previously unseen specific genres, creating a zero-shot label generalisation setting in which systems had to infer class semantics rather than memorise fixed label-feature associations. The task attracted 46 registrations and 373 submissions, with 17 teams completing the final evaluation. Thakaa ranked first with a Hierarchical Macro F1 of 0.7352, followed by HoangPhong (HP) with 0.7169 and NAMAA with 0.7013. The results show strong broad-genre recognition but a substantial gap in fine-grained classification under linguistic and domain variation.

Chinese Translation

AraGenre 是一个关于分层、定义引导的阿拉伯语体裁分类的共享任务，旨在解决阿拉伯语及其他低资源语言中标注数据有限的问题。系统为每个阿拉伯语文本段分配一个广泛的交际体裁和一个细化的特定体裁。发布的训练和开发集包含有限的、主要是合成和受控的示例，而隐藏的最终基准则包含噪声较大的自然文本，涵盖现代标准阿拉伯语、古典阿拉伯语和多种方言。参与者收到了74个之前未见过的特定体裁的自然语言定义，创建了一个零样本标签泛化的环境，在这个环境中，系统必须推断类别语义，而不是记忆固定的标签-特征关联。该任务吸引了46个注册和373个提交，其中17个团队完成了最终评估。Thakaa 以0.7352的分层宏 F1 排名第一，其次是 HoangPhong (HP) 的0.7169和 NAMAA 的0.7013。结果显示出强大的广泛体裁识别能力，但在语言和领域变异下，细化分类存在显著差距。

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cs.CL / 31 / 2609.27395

PRISM-VLM: A Multi-Axis Discriminative Benchmark for Compact Vision-Language Models

PRISM-VLM：紧凑型视觉-语言模型的多轴区分基准

Park, Sanghee, Kim, Kee-Eung

Abstract

Compact vision-language models (VLMs) now power a growing share of multimodal applications. The benchmarks used to compare them, however, inherit a frontier-centric design: each model is reduced to a single accuracy number, narrowing the inter-model gap on saturated suites and pressing models into low-score bands on harder ones. We introduce PRISM-VLM, a multi-axis discriminative benchmark that scores every item along seven axes covering the recurring failure modes (task quality, behavioral robustness, and capability bottlenecks) and combines them into a single PScore, with items recycled from fifteen public benchmarks. Across compact VLMs from the past two years, PScore separates model pairs more reliably than prior single-axis benchmarks under an item-level paired bootstrap, and surfaces behavioral differences these benchmarks average away. Even models with statistically indistinguishable PScores diverge sharply along the per-axis profile, particularly on sycophancy, which is nearly orthogonal to single-prompt accuracy. We will release the full pipeline, prompts, and per-item annotations.

Chinese Translation

紧凑型视觉-语言模型（VLMs）如今在日益增长的多模态应用中发挥着重要作用。然而，用于比较这些模型的基准测试却继承了以前沿为中心的设计：每个模型被简化为一个单一的准确率数字，这缩小了在饱和测试套件中的模型间差距，并将模型压入在更难测试中的低分带。我们引入了PRISM-VLM，一个多轴区分基准，它沿着七个轴对每个项目进行评分，涵盖了反复出现的失败模式（任务质量、行为鲁棒性和能力瓶颈），并将这些评分合并为一个单一的PScore，项目来自于十五个公共基准。在过去两年的紧凑型VLMs中，PScore在项目级配对自助法下比以前的单轴基准更可靠地区分模型对，并揭示了这些基准平均掉的行为差异。即使是统计上不可区分的PScores，在每个轴的特征上也会显著分歧，特别是在谄媚性上，这几乎与单次提示的准确率正交。我们将发布完整的管道、提示和每个项目的注释。

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cs.CL / 32 / 2609.27396

When Parallel Drafter Meets Parallel Speculative Decoding

并行草图生成器与并行推测解码的结合

Liu, Fuliang, Li, Xue, Qian, Kun, Wang, Zhibin, Dou, Wanchun, Yu, Wenyuan, Tian, Chen

Abstract

DSpark-style parallel drafters have made speculative decoding highly effective, yet their draft phase remains serialized on the critical path of every round. Parallel speculative decoding (PSD) overlaps drafting with verification, yet existing methods must guess the accepted prefix and bonus token in advance: a wrong guess reverts the whole batch to serial drafting. We present DPara, a PSD framework that reuses effective parallel drafters yet guarantees backbone--verification overlap in every round, thereby eliminating this probabilistic fallback altogether. While the target verifies, DPara's diffusion backbone precomputes draft representations for every acceptance boundary with the bonus left unspecified; a lightweight autoregressive head then combines the revealed verification outcome with the matching precomputed representation to emit the next round's draft tokens almost instantly---fully parallelizing the dominant backbone forward with verification and leaving only the negligible head cost serial. Experiments on Qwen3-8B and Qwen3-14B across seven math, coding, and chat benchmarks show that DPara achieves average speedups of $3.21\times$ and $3.52\times$ over autoregressive decoding, surpassing the strongest serial and parallel speculative decoding baselines alike.

Chinese Translation

DSpark风格的并行草图生成器使得推测解码变得非常有效，然而它们的草图阶段在每一轮的关键路径上仍然是串行的。并行推测解码（Parallel Speculative Decoding，PSD）将草图生成与验证重叠，但现有方法必须提前猜测接受前缀和奖励标记：错误的猜测会使整个批次回退到串行草图生成。我们提出了DPara，这是一种PSD框架，重用有效的并行草图生成器，同时保证每一轮的主干与验证重叠，从而完全消除这种概率性回退。在目标进行验证的同时，DPara的扩散主干预计算每个接受边界的草图表示，而奖励则保持未指定；一个轻量级的自回归头随后将揭示的验证结果与匹配的预计算表示结合，几乎瞬时地生成下一轮的草图标记——完全将主干的前向过程与验证并行化，仅留下微不足道的头部成本为串行。在Qwen3-8B和Qwen3-14B上进行的七个数学、编码和聊天基准实验表明，DPara在自回归解码上实现了平均$3.21 imes$和$3.52 imes$的加速，超越了最强的串行和并行推测解码基准。

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cs.CL / 33 / 2609.27418

EviStreams: Human-in-the-Loop AI Data Extraction for Systematic Reviews in Medicine

EviStreams：医学系统评价中的人机协作人工智能数据提取

Kosuri, Sai Karthik, Bhosale, Ankita Shashikant, Glick, Michael, Carrasco-Labra, Alonso, Callison-Burch, Chris

Abstract

Systematic reviews underpin clinical guidelines, yet their data-extraction step is a major expert-labor bottleneck bound by a protocolized workflow: two reviewers extract each study independently, an adjudicator resolves disagreements, and the team keeps an auditable record of how every value was produced. Large language models can assist with extraction, but that assistance must fit established review protocols and preserve reproducibility. We present EviStreams, a live, open-source, no-code web platform that puts review teams in control of AI-assisted extraction at three key stages: program design (a structured decomposition approved before any code runs), field specification (typed field definitions calibrated from a pilot), and extracted predictions (reviewer-blinded dual review with adjudication). Working through a form builder, a domain expert defines typed fields rather than prompts, runs extraction over uploaded PDFs, inspects every value alongside the supporting passage it came from, and resolves a reviewer-blinded dual review into an auditable consensus export. An evaluation across four clinical corpora and three frontier model families, released with the system, shows that extraction quality is shaped far more by the field specification than by the choice of model. EviStreams is live at https://evistreams.com/demo and released under Apache-2.0.

Chinese Translation

系统评价是临床指南的基础，但其数据提取步骤是一个主要的专家劳动瓶颈，受限于规范化的工作流程：两名审稿人独立提取每项研究，裁决者解决分歧，团队保持可审计的记录以说明每个值的生成过程。大型语言模型可以辅助数据提取，但这种辅助必须符合既定的评价协议并保持可重复性。我们提出了EviStreams，这是一个实时的、开源的、无代码的网络平台，使评价团队能够在三个关键阶段控制人工智能辅助的提取过程：程序设计（在任何代码运行之前批准的结构化分解）、字段规范（从试点中校准的类型字段定义）和提取预测（审稿人盲审的双重审查与裁决）。通过表单构建器，领域专家定义类型字段而不是提示，针对上传的PDF进行提取，检查每个值及其来源的支持段落，并将审稿人盲审的双重审查结果整合为可审计的共识导出。对四个临床语料库和三个前沿模型家族的评估显示，提取质量更受字段规范的影响，而非模型选择。EviStreams的在线演示地址为https://evistreams.com/demo，并以Apache-2.0许可证发布。

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cs.CL / 34 / 2609.27510

Uncheatable Eval: Dynamic Compression-Based Evaluation of Language Models

不可作弊评估：基于动态压缩的语言模型评估

Tan, Kaifeng, Li, Yudong, Shen, Linlin

Abstract

Modern large language models are pretrained on massive datasets, making it difficult to prevent benchmark data from entering their training sets and undermining the reliability of evaluation results. Reliable evaluation is particularly challenging for base models, whose limited instruction-following ability complicates task-based assessment. We introduce Uncheatable Eval, a dynamic benchmark that regularly collects newly published text to evaluate base language models and reduce the risk of data contamination. Drawing on the relationship between a model's predictive ability and its ability to compress data losslessly, we use compression rate to evaluate how well models predict new text. We evaluate 80 models across 14 text categories, study how compression changes with context length, and examine the correlation between compression rate and zero-shot MMLU accuracy. Our results yield three main findings: (1) compression performance follows a consistent scaling trend with model size; (2) attention-based, hybrid, and recurrent models differ in how their compression performance changes as more context becomes available; and (3) lower compression rates are strongly associated with higher zero-shot MMLU accuracy. Code is available at https://github.com/Jellyfish042/uncheatable_eval.

Chinese Translation

现代大型语言模型在海量数据集上进行预训练，这使得防止基准数据进入其训练集并破坏评估结果的可靠性变得困难。对于基础模型而言，可靠的评估尤为具有挑战性，因为其有限的指令跟随能力使得基于任务的评估变得复杂。我们提出了不可作弊评估（Uncheatable Eval），这是一个动态基准，定期收集新发布的文本以评估基础语言模型并降低数据污染的风险。基于模型的预测能力与其无损压缩数据能力之间的关系，我们使用压缩率来评估模型对新文本的预测效果。我们在14个文本类别上评估了80个模型，研究了压缩率如何随上下文长度变化，并考察了压缩率与零-shot MMLU（Massive Multitask Language Understanding）准确率之间的相关性。我们的结果得出了三个主要发现：（1）压缩性能随模型规模呈现一致的缩放趋势；（2）基于注意力的模型、混合模型和递归模型在可用上下文增多时，其压缩性能变化存在差异；（3）较低的压缩率与较高的零-shot MMLU准确率之间存在强关联。代码可在 https://github.com/Jellyfish042/uncheatable_eval 获取。

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cs.CL / 35 / 2609.27558

ThaiTrees: Thai Syntactic Dependency Trees Across Domains

泰国树：跨领域的泰语句法依赖树

Rutherford, Attapol T., Thientong, Papatchol

Abstract

Studying syntactic patterns in naturally occurring language requires a large parsed corpus, but manual annotation is costly and difficult to scale. Thai has a manually annotated dependency treebank for training and evaluating parsers, but lacks a large automatically parsed corpus for quantitative syntactic research. We present ThaiTrees, a 342M-token corpus drawn from news, Wikipedia, spoken transcripts, and social media. We develop a reproducible pipeline for cleaning, processing, and parsing Thai text under the Universal Dependencies framework. The resulting corpus makes grammatical relations searchable and supports the study of syntactic distributions. We release a frequency lexicon and CoNLL-U parses in machine-readable formats suitable for both AI-assisted and conventional programmatic analysis.

Chinese Translation

研究自然语言中的句法模式需要一个大型的解析语料库，但手动标注成本高且难以扩展。泰语拥有一个手动标注的依赖树库用于训练和评估解析器，但缺乏一个大型的自动解析语料库以支持定量句法研究。我们提出了泰国树（ThaiTrees），这是一个包含3.42亿个词的语料库，来源于新闻、维基百科、口语转录和社交媒体。我们开发了一个可重复的流程，用于在通用依赖（Universal Dependencies）框架下清理、处理和解析泰语文本。生成的语料库使语法关系可搜索，并支持句法分布的研究。我们发布了一个频率词典和CoNLL-U格式的解析结果，适用于AI辅助和传统程序分析。

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cs.CL / 36 / 2609.27590

MWE-ECL: Recoverable Long-Range Context Does Not Always Override Local Lexical Priors

MWE-ECL：可恢复的长距离上下文并不总是优先于局部词汇先验

He, Wei, Villavicencio, Aline, Wilkens, Rodrigo, Deng, Zhenyun

Abstract

Long-context evaluations often test whether a model can recover distant evidence, but recoverability does not guarantee behavioral influence. We test the prediction that a distant discourse anchor can remain explicitly recoverable yet fail to change the locally preferred reading of a familiar multiword expression; such failures should concentrate when the model's no-anchor default conflicts with the anchor, while prior-correct decisions remain largely preserved. We introduce Multiword Expression Effective Context Length (MWE-ECL), a bilingual diagnostic whose matched anchor-retrieval, no-anchor prior, and interpretation prompts measure explicit recoverability, model-observed defaults, and anchor-conditioned decisions, respectively. Across eight English deployment panels on a shared 0-128K grid, retrieval-control accuracy on prior-conflict items is 0.989-1.000, prior-conflict override spans 0.806-1.000 (0.809-1.000 after conditioning on correct retrieval), and preservation of prior-correct decisions remains 0.977-1.000. A same-call control querying retrieval and interpretation in one prompt reproduces the gap for DeepSeek V4 Pro (1.000 retrieval versus 0.900-0.920 interpretation), showing that separate invocations are not its sole explanation; smaller or absent gaps in the other two models bound its generality. For DeepSeek V4 Flash, separate prompt-fit tests retain perfect retrieval with lower interpretation at 512K and 1M, while foil-consistent cues shift the no-anchor prior far more than retrieval; cross-model cue effects are heterogeneous. A separately reported 10-family Chinese subset shows similar descriptive gaps, but imperfect retrieval for some models prevents an integration-only attribution. MWE-ECL therefore evaluates whether explicitly recoverable distant context changes a competing local semantic decision.

Chinese Translation

长上下文评估通常测试模型是否能够恢复远处证据，但可恢复性并不保证行为影响。我们测试了一个预测，即远处话语锚点可以保持明确可恢复，但未能改变熟悉的多词表达的局部优先阅读；当模型的无锚默认与锚点冲突时，这种失败应该集中，而先前正确的决策则大致保持不变。我们引入了多词表达有效上下文长度（Multiword Expression Effective Context Length，MWE-ECL），这是一种双语诊断工具，其匹配的锚点检索、无锚先验和解释提示分别测量明确可恢复性、模型观察到的默认值和锚点条件决策。在共享的0-128K网格上进行的八个英语部署面板中，先前冲突项目的检索控制准确率为0.989-1.000，先前冲突覆盖范围为0.806-1.000（在条件为正确检索后为0.809-1.000），而先前正确决策的保留率保持在0.977-1.000。一个同调用控制在一个提示中查询检索和解释，重现了DeepSeek V4 Pro的差距（1.000检索与0.900-0.920解释），显示单独调用并不是其唯一解释；其他两个模型中的较小或缺失差距限制了其普适性。对于DeepSeek V4 Flash，单独的提示适配测试在512K和1M时保持完美检索，但较低的解释，而一致的干扰提示则远远超过检索地改变无锚先验；跨模型提示效应是异质的。一个单独报告的10个家庭的中文子集显示了类似的描述性差距，但某些模型的不完美检索阻止了仅归因于整合。因此，MWE-ECL评估明确可恢复的远程上下文是否改变竞争的局部语义决策。

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cs.CL / 37 / 2609.27603

When Context Misleads: In-context Learning with Jurisdiction in Large Language Models

当上下文误导时：大型语言模型中的上下文学习与管辖权

Li, Pei-lin, Liu, Qingle, Feng, Junyang, Li, Siyu, Fan, Sunqi, Chen, Xin-Sheng, Yang, Shuojin

Abstract

In-Context Learning (ICL) has become a cornerstone of modern LLM deployment. However, existing ICL post-training methods have a critical blind spot: they excel at extracting patterns from demonstrations while often neglecting context authority, the ability to determine whether contextual information should govern the final answer. To benchmark this capability, we introduce FakeContextBench, which contains pseudoscientific claims across seven domains. Our evaluation of commercial and open-source models shows that large-scale pre-training alone is insufficient for reliable context-authority discrimination. Moreover, prevalent ICL fine-tuning methods can increase susceptibility to misleading context, reducing reality accuracy by up to 14.95 percentage points relative to the base model. To address this trade-off, we propose Jurisdiction In-Context Learning (J-ICL), a post-training framework that incorporates context validation into the training objective. Across four model backbones, J-ICL improves ICLEval by an average of 5.84 percentage points and reality accuracy by 9.20 points over the corresponding base models. It also raises the Reality Rate by an average of 18.09 points relative to MetaICL and Symbol Tuning. These results demonstrate that ICL capability and resistance to deceptive context can be improved together. The benchmark is available at https://github.com/peilin717/FakeContext-Bench.

Chinese Translation

上下文学习（In-Context Learning, ICL）已成为现代大型语言模型（LLM）部署的基石。然而，现有的ICL后训练方法存在一个关键盲点：它们擅长从示例中提取模式，但往往忽视了上下文权威性，即判断上下文信息是否应主导最终答案的能力。为了评估这一能力，我们引入了FakeContextBench，该基准包含七个领域的伪科学声明。我们对商业和开源模型的评估表明，仅依靠大规模预训练不足以实现可靠的上下文权威性区分。此外，普遍的ICL微调方法可能会增加对误导性上下文的敏感性，相较于基础模型，现实准确率降低了多达14.95个百分点。为了解决这一权衡，我们提出了管辖权上下文学习（Jurisdiction In-Context Learning, J-ICL），这是一个将上下文验证纳入训练目标的后训练框架。在四个模型骨干中，J-ICL平均提高了ICLEval 5.84个百分点，现实准确率提高了9.20个百分点。此外，相较于MetaICL和Symbol Tuning，现实率平均提高了18.09个百分点。这些结果表明，ICL能力和对欺骗性上下文的抵抗力可以共同提升。该基准可在https://github.com/peilin717/FakeContext-Bench获取。

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cs.CL / 38 / 2609.27607

Can Jev Judge Radiology Reports? Evaluating a System One Model for Clinical Factuality

Jev能评估放射学报告吗？评估系统一模型在临床事实性中的应用

Huang, Jiaju, Yang, Hao, Ma, Xinyu, Liang, Xinglong, Cai, Kunyan, Ma, Junqiang, Chen, Shaobin, Sun, Yue, Tan, Tao

Abstract

An AI-generated radiology report can resemble a physician's report while omitting an abnormality, adding an unsupported finding, or reversing its presence. Measuring these factual differences is essential for evaluating report generators. We study Jev, a System One decision model, as a simple, low-cost judge of agreement with physician-written reference reports. Our evaluator checks whether each statement is supported by the other report and combines these judgments in both directions to capture unsupported claims and omissions. A single-question configuration reaches Kendall correlations of 0.573 on RadEvalX and 0.398 on RadEvalExpert with expert error counts, outperforming an open natural language inference judge under matched decomposition and aggregation. One support question per statement retains similar expert agreement to seven while using 43-45% fewer judgment input tokens. At the documented API price, judgments cost under three cents per hundred report pairs, excluding local decomposition. In a separate controlled-error test, Jev detects false negation with an AUROC of 0.977. Local RadMatch achieves stronger agreement on clinically significant errors in both expert datasets and on total errors in the shared RadEvalExpert subset. Finding-count and error-scope analyses show that benchmark agreement reflects report size and error definitions as well as medical error detection. These results support Jev as a practical judgment component for measuring factual differences in generated radiology reports and identify where more elaborate evaluation remains valuable.

Chinese Translation

人工智能生成的放射学报告可能与医生撰写的报告相似，但可能遗漏异常、添加不支持的发现或颠倒其存在性。衡量这些事实差异对于评估报告生成器至关重要。我们研究了Jev，一个系统一决策模型，作为与医生撰写的参考报告一致性的简单、低成本评估工具。我们的评估者检查每个陈述是否得到另一份报告的支持，并在两个方向上结合这些判断，以捕捉不支持的主张和遗漏。单一问题配置在RadEvalX上达到了0.573的Kendall相关性，在RadEvalExpert上达到了0.398的专家错误计数，优于在匹配分解和聚合下的开放自然语言推理评估工具。每个陈述一个支持问题的配置在保持与七个问题相似的专家一致性的同时，减少了43-45%的判断输入标记。在记录的API价格下，判断每对报告的成本低于三美分，不包括本地分解。在一项单独的控制错误测试中，Jev以0.977的AUROC检测到假阴性。Local RadMatch在两个专家数据集中对临床显著错误的协议达成更强一致性，并在共享的RadEvalExpert子集中对总错误的协议也表现良好。查找计数和错误范围分析显示，基准一致性反映了报告大小和错误定义以及医学错误检测。这些结果支持Jev作为测量生成放射学报告中事实差异的实用判断组成部分，并指出更复杂的评估仍然具有价值的地方。

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cs.CL / 39 / 2609.27650

Brain-to-Language Decoding: Tasks, Signals, Methods, Evaluation, Practical Use and Beyond

大脑到语言解码：任务、信号、方法、评估、实际应用及其未来

Yang, Yiqian, Duan, Yiqun, Liu, Chenyu, Wang, Yiqi, Zhou, Xinliang, Lin, Chin-Teng, Zhang, Yu

Abstract

Brain-to-language decoding translates neural activity associated with language production, internal speech and perception into linguistic or expressive outputs. It offers a route to restoring communication after speech loss and a means of studying how the brain represents language. Advances in neural recording and representation learning have expanded the field from constrained recognition and acoustic reconstruction to text generation, streaming personalised speech and facial animation. This survey synthesises these developments across invasive and non-invasive measurements, drawing on a search without a lower year limit and source-led updates through September 2026. We connect Articulated, Inner and Perceived tasks to the neural populations they engage, the representations available to decoders and the outputs those representations can support. We examine model development, public resources and the evolution of evaluation, and compare published performance and communication costs within their reported protocols. The synthesis identifies complementary routes to progress: phonetic, acoustic and semantic targets preserve different aspects of a message; shared representations support reuse across recording conditions and tasks; and online communication increasingly depends on calibration, feedback and user control alongside decoding accuracy. Shared benchmarks enable algorithmic comparisons, while longitudinal studies reveal the demands of sustained use. We discuss these developments and their remaining limitations, then outline a prospective five-level trajectory from commands and language to meaning, scenarios and bidirectional cognitive exchange

Chinese Translation

大脑到语言解码将与语言产生、内心语言和感知相关的神经活动转化为语言或表达输出。这为在失语后恢复交流提供了一条途径，并为研究大脑如何表征语言提供了手段。神经记录和表征学习的进展使该领域从受限的识别和声学重建扩展到文本生成、个性化语音流和面部动画。本调查综合了这些在侵入性和非侵入性测量中的发展，基于不设下限的搜索和截至2026年9月的源导更新。我们将发音、内心和感知任务与其所涉及的神经群体、解码器可用的表征以及这些表征所能支持的输出联系起来。我们考察了模型开发、公共资源和评估的演变，并比较了在其报告协议内已发布的性能和交流成本。综合结果识别出进展的互补途径：音素、声学和语义目标保留了信息的不同方面；共享表征支持在记录条件和任务之间的重用；在线交流越来越依赖于校准、反馈和用户控制，以及解码准确性。共享基准使算法比较成为可能，而纵向研究揭示了持续使用的需求。我们讨论了这些发展及其剩余的局限性，随后概述了从命令和语言到意义、场景和双向认知交流的五级潜在轨迹。

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cs.CL / 40 / 2609.27669

The Path Matters: Evaluating Small Language Models Beyond Answer Accuracy in KGQA

路径重要性：在知识图谱问答中超越答案准确性评估小型语言模型

Hernandez, Eduin E., Diaz, Sergio A., Garcia, Luis F., Askar, Nurassyl, Rini, Stefano

Abstract

Small language models (SLMs) are increasingly paired with knowledge graphs (KGs), yet end-to-end KG question answering conflates graph access, search, navigation, reasoning, and answer generation. This coupling makes it difficult both to determine whether an SLM can faithfully execute the reasoning path implied by a question and to attribute failures to navigation rather than to other stages of the pipeline. We isolate this capability by employing the THESEUS navigation and traceability framework and using frozen, off-the-shelf SLMs as local action policies. At each hop, the environment exposes the legal outgoing graph actions, and the model selects one executable graph action and decides whether to stop, without task-specific parameter updates, model-controlled beam search, or free-form answer generation. This controlled setting allows us to evaluate terminal-answer accuracy with Hits@1 together with path fidelity, using Path Edit Distance (PED) as the primary trajectory metric. Across the Kinship and MQuAKE-ST KGQAs, similarly sized local models differ substantially in answer accuracy and path fidelity, with the two metrics sometimes favoring different models. This model-dependent behavior also extends to prompting, as a single demonstrated trajectory can improve or degrade navigation depending on the model. These results motivate evaluating SLM graph reasoning beyond endpoint accuracy alone.

Chinese Translation

小型语言模型（SLMs）越来越多地与知识图谱（KGs）结合使用，但端到端的知识图谱问答将图谱访问、搜索、导航、推理和答案生成混为一谈。这种结合使得判断SLM是否能够忠实执行问题所暗示的推理路径变得困难，同时也难以将失败归因于导航而非管道的其他阶段。我们通过采用THESEUS导航和可追溯性框架，并使用现成的、冻结的小型语言模型作为局部行动策略，来隔离这一能力。在每一步中，环境暴露出合法的外发图谱动作，模型选择一个可执行的图谱动作并决定是否停止，而不进行特定任务的参数更新、模型控制的束搜索或自由形式的答案生成。这种受控设置使我们能够使用Hits@1评估终端答案的准确性，同时结合路径保真度，使用路径编辑距离（Path Edit Distance, PED）作为主要轨迹指标。在亲属关系和MQuAKE-ST知识图谱问答中，大小相似的局部模型在答案准确性和路径保真度上存在显著差异，这两个指标有时会偏向不同的模型。这种依赖模型的行为同样扩展到提示，因为单一的示范轨迹可能会根据模型的不同而改善或降低导航效果。这些结果激励我们在评估SLM图谱推理时，不仅仅关注最终点的准确性。

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cs.CL / 41 / 2609.27678

Same Scores, Different Decisions: Evaluating JEV and Language Models for Legal Document Understanding

相同的分数，不同的决策：评估 JEV 和语言模型在法律文档理解中的表现

Zhang, Fan, Chen, Yankai, Xie, Zhuohan, Zhou, Yixi, Peng, Sijia, Fan, Lei, Ji, Xinhua, Zheng, Cunyuan, Shan, Huangyong, Yu, Philip S., Liu, Xue, Chen, Yu, Nakov, Preslav, He, Songwei

Abstract

Contract inference requires multiple judgments about a shared document, but aggregate accuracy can conceal changes in the individual decisions. Repeated agreement is also insufficient: a model may consistently return the wrong answer. In this paper, we compare Jev with nine language models on ContractNLI, evaluating inference cost, response time, average correctness, and correctness across repeated request conditions. Controlled comparisons vary hypothesis visibility, requested outputs, and output order while keeping the contract and target judgment fixed. Jev has the lowest cost and median response time among the evaluated configurations, while hosted language models achieve higher baseline accuracy. Rankings by baseline accuracy differ from rankings by correctness across every condition and repeat, although small differences in the latter do not establish a general stability advantage. Development diagnostics further reveal compensating corrections and regressions, as well as persistent errors. These findings motivate evaluating cost and response time alongside whether individual judgments remain correct as the request configuration changes. Code: https://github.com/ZF-Utokyo/Jev-Benchmark

Chinese Translation

合同推理需要对共享文档进行多次判断，但整体准确性可能掩盖个体决策的变化。重复一致性也不足以保证准确性：模型可能始终返回错误答案。本文比较了 JEV 与九种语言模型在 ContractNLI 上的表现，评估推理成本、响应时间、平均正确性以及在重复请求条件下的正确性。受控比较中，假设可见性、请求输出和输出顺序有所变化，同时保持合同和目标判断不变。在评估的配置中，JEV 的成本和中位响应时间最低，而托管的语言模型则实现了更高的基线准确性。基于基线准确性的排名与基于每种条件和重复的正确性排名不同，尽管后者的小差异并未确立一般的稳定性优势。开发诊断进一步揭示了补偿性修正和回归，以及持续存在的错误。这些发现促使我们在请求配置变化时，评估成本和响应时间，同时关注个体判断是否保持正确。代码链接： https://github.com/ZF-Utokyo/Jev-Benchmark

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cs.CL / 42 / 2609.27690

Consequential Behaviour and Representational Fairness in the Validation of Synthetic Research

合成研究验证中的后果行为与表征公平性

Kutzner, Florian, Kacperski, Celina, de Molière, Laura, Chidichimo, Edoardo, Jung, Min Jun, Wallis, Felix Patrick Sedgwick, He, James Kunling

Abstract

Researchers in industry and academia use synthetic survey respondents powered by large language models as substitutes for human samples. These synthetic populations require validation against real-world data, so researchers often address them using ad hoc comparisons with human surveys. Inspired by the intention-behaviour gap in behavioural science, we argue that these validations test the wrong thing for most applied cases where decision makers commission synthetic research to anticipate consequential behaviour. To address this problem, we propose a validation framework with two requirements. First, every validity claim must state its level of correspondence with human data: does the sample predict what the represented people do, which of four diagnostics (location, dispersion, response process and structure) does the validation address, and does the validation compare against experimental effects? Second, researchers must report validity claims for subgroups, since these groups are often the most affected by consequential decisions and aggregate accuracy hides their misrepresentation. Our validation framework operationalises three justice dimensions (distributional, procedural, and recognition) as measurable quantities and defines within-persona counterfactual experiments as a validation requirement. We then apply the framework to electric vehicle charging tariffs, before closing with a reporting checklist that researchers can use to make convincing validity claims.

Chinese Translation

工业和学术界的研究人员使用由大型语言模型驱动的合成调查受访者作为人类样本的替代品。这些合成群体需要与现实世界数据进行验证，因此研究人员通常通过与人类调查的临时比较来处理它们。受到行为科学中意图-行为差距的启发，我们认为这些验证在大多数应用案例中测试了错误的内容，因为决策者委托合成研究以预测后果行为。为了解决这个问题，我们提出了一个验证框架，包含两个要求。首先，每个有效性声明必须说明其与人类数据的对应程度：样本是否预测了被代表人群的行为，验证涉及哪四种诊断（位置、分散、反应过程和结构），以及验证是否与实验效应进行比较？其次，研究人员必须报告子群体的有效性声明，因为这些群体通常是受后果决策影响最大的，而总体准确性掩盖了它们的失真。我们的验证框架将三个公正维度（分配公正、程序公正和认可公正）操作化为可测量的量，并将个体内反事实实验定义为验证要求。随后，我们将该框架应用于电动汽车充电费率，并在最后提供一个报告清单，供研究人员使用，以便提出令人信服的有效性声明。

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cs.CL / 43 / 2609.27717

SkillGym: Internalizing Human Skills into LLMs for Real-World Problem Solving

SkillGym：将人类技能内化到大型语言模型中以解决现实世界问题

Ge, Zhilong, Shao, Yuting, Yang, Yutao, Cai, Yuxuan, Zhou, Jie, Chen, Kai, Zhang, Bo, Chen, Qin, He, Liang

Abstract

Human-written agent skills encode rich workflows for real-world problem solving, but are typically used as external inference-time instructions rather than internalized as reusable model capabilities. We introduce \texttt{SkillGym}, a framework that transforms these skills into executable, verifiable training environments for large language model agents. Its skill-to-task pipeline instantiates concrete tasks, verifies outcomes with code-based checkers, and assesses empirical skill dependence through contrastive executions. We construct and release 2,756 environments across 12 categories and collect 8,364 successful trajectories from multiple models and harnesses, averaging 49 tool calls and over 60k logged text tokens. These resources support supervised fine-tuning on verified workflows and reinforcement learning with outcome-based rewards. Under Claude Code, supervised fine-tuning improves Qwen3.5-35B-A3B by 199 Elo on GDPval-AA v2, 19.10 percentage points on Terminal-Bench 2.1, and 28.13 and 12.38 points on SkillsBench v1.1 with and without skills, respectively. Our 35B \texttt{SkillGym-Agent} reaches 51.47\% on skill-assisted SkillsBench, exceeding reported scores for Claude Sonnet 4.6, GPT-5.4 Mini, and DeepSeek V4 Pro. Without skills, it also surpasses skill-assisted bases under Codex and Claude Code, suggesting reusable procedural competence.

Chinese Translation

人类编写的代理技能编码了丰富的工作流程，用于解决现实世界的问题，但通常作为外部推理时的指令使用，而不是作为可重用的模型能力进行内化。我们提出了 exttt{SkillGym}，一个将这些技能转化为可执行、可验证的训练环境的框架，专为大型语言模型代理设计。其技能到任务的管道实例化具体任务，通过基于代码的检查器验证结果，并通过对比执行评估经验技能依赖性。我们构建并发布了 2,756 个环境，涵盖 12 个类别，并从多个模型和工具中收集了 8,364 条成功轨迹，平均进行了 49 次工具调用和超过 60,000 个记录的文本标记。这些资源支持对经过验证的工作流程进行监督微调，以及基于结果的强化学习。在 Claude Code 下，监督微调使 Qwen3.5-35B-A3B 在 GDPval-AA v2 上提高了 199 Elo，在 Terminal-Bench 2.1 上提高了 19.10 个百分点，在 SkillsBench v1.1 上分别提高了 28.13 和 12.38 分（有技能和无技能）。我们的 35B exttt{SkillGym-Agent} 在技能辅助的 SkillsBench 上达到了 51.47\%，超过了 Claude Sonnet 4.6、GPT-5.4 Mini 和 DeepSeek V4 Pro 的报告分数。在没有技能的情况下，它也超越了 Codex 和 Claude Code 下的技能辅助基准，表明其具备可重用的程序能力。

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cs.CL / 44 / 2609.27758

Hard Negatives Reveal What Easy Negatives Hide: Cross-Lingual Harmfulness Representations Degrade with Resource Tier Under Hard Negatives

困难负样本揭示了简单负样本隐藏的内容：跨语言有害性表示在困难负样本下随着资源层级的降低而退化

Balani, Paras, Panda, Subhrakanta

Abstract

Safety alignment in large language models is trained primarily in English, and recent work reports that the underlying harmfulness representation survives translation: English-trained probes separate harmful from harmless prompts almost as well in low-resource languages as in English. This has been taken as evidence that cross-lingual refusal failures mainly reflect calibration rather than representation quality. We show that this conclusion depends on the choice of negative examples. Across nine languages spanning three resource tiers, we replicate near-perfect transfer (AUROC > 0.98) when harmless prompts come from an unrelated distribution (easy negatives). With XSTest contrast prompts, which are benign but surface-similar to harmful requests (hard negatives), transfer collapses in low-resource languages while remaining largely stable in high-resource languages. On Qwen2.5-7B-Instruct, mean AUROC drop increases from 0.003 in English to 0.017 in high-resource, 0.042 in mid-resource, and 0.276 in low-resource languages. The pattern replicates on Aya Expanse. Back-translation chrF controls and a matched-chrF comparison across three languages reduce the likelihood that translation quality explains the effect. The collapse remains after controlling for chrF (partial r = 0.70, p = 0.03). Tokenizer fertility correlates with the collapse and explains part of the resource-tier effect, but not all of it. The results show that easy-negative transfer can coexist with substantial degradation under hard negatives. Easy-negative evaluation alone therefore cannot establish that the harmfulness representation survives translation.

Chinese Translation

大型语言模型的安全对齐主要在英语中进行训练，近期研究报告称，潜在的有害性表示在翻译中依然存在：英语训练的探测器在低资源语言中几乎与英语一样好地分离有害和无害的提示。这被视为跨语言拒绝失败主要反映校准而非表示质量的证据。我们展示了这一结论依赖于负样本的选择。在覆盖三种资源层级的九种语言中，当无害提示来自不相关的分布（简单负样本）时，我们复制了近乎完美的迁移（AUROC > 0.98）。使用 XSTest 对比提示，这些提示是良性的，但在表面上与有害请求相似（困难负样本），在低资源语言中的迁移崩溃，而在高资源语言中则保持相对稳定。在 Qwen2.5-7B-Instruct 上，平均 AUROC 下降从英语的 0.003 增加到高资源语言的 0.017，中资源语言的 0.042，以及低资源语言的 0.276。该模式在 Aya Expanse 上得到了复制。反向翻译的 chrF 控制和三种语言的匹配 chrF 比较降低了翻译质量解释该效应的可能性。在控制 chrF 后，崩溃依然存在（部分 r = 0.70，p = 0.03）。分词器的丰富性与崩溃相关，并解释了部分资源层级效应，但并非全部。结果表明，简单负样本迁移可以与在困难负样本下的显著退化共存。因此，仅依靠简单负样本评估无法证明有害性表示在翻译中得以保留。

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cs.CL / 45 / 2609.27770

Improving LLM-based Autonomous Web Agents with Filtering

通过过滤提升基于大语言模型的自主网页代理

Guo, Zhitong, Koh, Jing Yu, Li, Ruiyu

Abstract

Autonomous web agents, powered by Large Language Models (LLMs), have garnered significant attention for automating various web-based tasks with multi-step reasoning and decision-making capabilities. An open research question in the development of these agents lies in the format of the webpage input. Raw HTML source code, with its extensive and often irrelevant details, poses difficulties for LLMs with limited context windows. To address this challenge, we first reproduce baseline models such as GPT-3.5 and LLaMA-2-70B on the WebArena (Zhou et al., 2023) benchmark, identifying common failure modes. We then propose two retrieval strategies to filter out irrelevant context for LLM agents. We develop DeBERTa-based and T5-based models that rank HTML elements by their relevance to the task. We fine-tune them on Mind2Web trajectory data and transfer them to WebArena. Experiments show that our DeBERTa-based model improves the success rate of the LLaMA-2-70B LLM agent on WebArena from 1.97% to 2.96%. Moreover, we develop a zero-shot ColBERT-based retriever that is able to retrieve the ground-truth element with a recall of 0.52 on Mind2Web and 0.47 on WebArena.

Chinese Translation

自主网页代理，依托于大语言模型（LLMs），因其在自动化多步骤推理和决策能力的各种网页任务中而受到广泛关注。在这些代理的发展中，一个开放的研究问题是网页输入的格式。原始HTML源代码因其庞大且常常无关的细节，给具有有限上下文窗口的LLMs带来了困难。为了解决这一挑战，我们首先在WebArena（Zhou et al., 2023）基准上复现了基线模型，如GPT-3.5和LLaMA-2-70B，并识别出常见的失败模式。然后，我们提出了两种检索策略，以过滤掉LLM代理的无关上下文。我们开发了基于DeBERTa和T5的模型，通过与任务的相关性对HTML元素进行排名。我们在Mind2Web轨迹数据上对其进行微调，并将其转移到WebArena。实验表明，我们的基于DeBERTa的模型将LLaMA-2-70B LLM代理在WebArena上的成功率从1.97%提高到2.96%。此外，我们开发了一种零-shot的基于ColBERT的检索器，能够在Mind2Web上以0.52的召回率检索到真实元素，在WebArena上则为0.47。

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cs.CL / 46 / 2609.27773

Beyond Unsafe Detection: Counterfactually Anchored Evidence Attribution for Multi-Turn LLM Safety Failures

超越不安全检测：基于反事实锚定的多轮大型语言模型安全失败证据归因

Subramanian, Srinivasan, Islam, Kazi Aminul, Khan, Md. Abdullah Al Hafiz

Abstract

As Large Language Models (LLMs) move from conversational assistants to advanced agentic systems, guardrail failures can convert adversarial intents into harmful executions. However, most guardrail evaluation frameworks focus only on the result and assess whether a user request is safe or unsafe. This approach is insufficient for multi-turn failures, where adversarial intent is distributed across multiple turns. This motivates us to go beyond detection to identify the turns and tokens that push the conversation toward unsafe trajectories. To support this, we construct a multi-turn dataset with behavioral validation and tiered evidence supervision. The dataset contains 1,762 conversations, including adversarial conversations, benign twins, and benign variants with high-risk vocabulary. We train a lightweight hierarchical attribution model that predicts safety violations and attributes them to contributing user turns and token spans. The model achieves strong detection performance (F1=0.988), and removing the top 15% of attributed tokens reduces the adversarial classification confidence by 51.1%. The model preserves low false positive rates on benign conversations with high-risk vocabulary, with false positives below 1% on both borderline benign and benign high-risk vocabulary conversations, compared to 37.3% and 94.7% for a keyword-based surface-risk baseline. Independent human annotation supports the model's attribution performance, with the top-five attributed turns containing a human-identified evidence-bearing turn in 84.5% of adversarial cases.

Chinese Translation

随着大型语言模型（LLMs）从对话助手发展为高级智能系统，保护机制的失效可能将对抗性意图转化为有害的执行。然而，大多数保护机制评估框架仅关注结果，评估用户请求是安全还是不安全。这种方法对于多轮失败而言是不够的，因为对抗性意图分布在多个轮次中。这促使我们超越检测，识别推动对话走向不安全轨迹的轮次和词元。为此，我们构建了一个具有行为验证和分层证据监督的多轮数据集。该数据集包含1,762个对话，包括对抗性对话、良性双胞胎和具有高风险词汇的良性变体。我们训练了一个轻量级的层次归因模型，该模型预测安全违规并将其归因于贡献的用户轮次和词元范围。该模型在检测性能上表现出色（F1=0.988），移除前15%的归因词元可将对抗性分类信心降低51.1%。该模型在具有高风险词汇的良性对话中保持低假阳性率，在边界良性和良性高风险词汇对话中的假阳性率均低于1%，而基于关键词的表面风险基线则分别为37.3%和94.7%。独立的人类注释支持模型的归因性能，前五个归因轮次在84.5%的对抗性案例中包含一个人类识别的证据承载轮次。

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cs.CL / 47 / 2609.27814

LabourCrew: A Multi-Agent RAG Framework for Trustworthy Adversarial Deliberation and Statutory Reasoning over Labour Law

LabourCrew：一个多智能体RAG框架，用于可信的对抗性审议和劳动法的法定推理

Faria, Fatema Tuj Johora, Moin, Mukaffi Bin, Mahmud, Jubayer Al, Mridha, M. F., Hossain, Md. Alam

Abstract

In statutory question answering, every claim must be traceable to evidence, not merely relevant, since unverifiable labour-rights answers carry serious legal consequences. Current systems fall short: single-pass RAG cannot detect insufficient evidence, while multi-agent legal-debate systems treat grounding as a prompting convention, letting agents cite unretrieved evidence. To address this gap, we introduce LabourCrew, a multi-agent RAG framework built around three grounding mechanisms: StatuteGraph, a graph index that explicitly links chapter, section, proviso, and cross-reference structure rather than fixed-length spans; an Evidence Exchange Protocol that confines advocates and an interpreter to an evidence ledger, making citation to unretrieved text impossible, while a fault-tolerant supervisor board runs advocates in parallel so individual failures degrade rather than crash the system; and a Calibrated Trust Gate that replaces categorical accept/reject decisions with a trust score, thresholded via conformal risk control for a distribution-free bound on the false-accept rate. We evaluate on LabourActQA, a 500-item Bangla question set from the Bangladesh Labour Act, 2006, spanning seven reasoning categories and three difficulty tiers. The framework drives the empirical false-accept rate to 0.081, within the target level ($\alpha = 0.10$), achieves the highest Answer Relevancy among HyDE RAG, Graph-RAG, and Hierarchical RAG (0.862 $>$ 0.839, 0.815, 0.828), and degrades gradually rather than catastrophically as question difficulty increases. These results show that calibrated abstention, not retrieval quality alone, is what makes legal question answering auditable in low-resource statutory domains.

Chinese Translation

在法定问题回答中，每个主张必须可追溯到证据，而不仅仅是相关的，因为无法验证的劳动权利答案会带来严重的法律后果。当前系统存在不足：单次通过的RAG无法检测证据不足，而多智能体法律辩论系统将基础证据视为提示惯例，允许代理引用未检索的证据。为了解决这一问题，我们提出了LabourCrew，一个围绕三种基础机制构建的多智能体RAG框架：StatuteGraph，一个图索引，明确链接章节、节、条款和交叉引用结构，而不是固定长度的跨度；一个证据交换协议，限制辩护人和解释者只能访问证据账本，使引用未检索文本成为不可能，同时一个容错的监督委员会并行运行辩护人，以便个别失败导致系统降级而非崩溃；以及一个校准信任门，替代了分类的接受/拒绝决策，使用信任分数进行阈值控制，通过符合风险控制为假接受率提供无分布界限。我们在LabourActQA上进行评估，这是一个来自2006年孟加拉国劳动法的500个问题集，涵盖七个推理类别和三个难度层次。该框架将经验假接受率降低到0.081，符合目标水平（$eta = 0.10$），在HyDE RAG、Graph-RAG和Hierarchical RAG中实现了最高的答案相关性（0.862 > 0.839, 0.815, 0.828），并且随着问题难度的增加，逐渐降级而非灾难性崩溃。这些结果表明，校准的放弃，而不仅仅是检索质量，是使法律问题回答在资源匮乏的法定领域可审计的关键。

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cs.CL / 48 / 2609.27824

"AI Is Turning Too Human": How Teenagers Experience and Negotiate AI in Everyday Life

“人工智能变得过于人性化”：青少年如何在日常生活中体验和协商人工智能

Zhu, Jianfeng

Abstract

Generative AI is rapidly entering adolescents' everyday lives during a critical period of cognitive, social and emotional development. Yet its adoption is outpacing evidence on how adolescents themselves experience, understand and negotiate its expanding role in their lives. We examined AI-related discourse on r/teenagers from January 2023 to July 2026 using validated keyword-based retrieval and a human-in-the-loop, LLM-assisted thematic analysis. AI-related discussion increased substantially over time, and 11,083 analytically coded posts revealed eight interconnected domains of experience. Everyday and social use was most prevalent (36.8 percent), while discourse increasingly shifted toward authenticity, personal control and safety, and future human roles. Across domains, adolescents questioned when AI should support or substitute for human thinking and creativity, how conversational AI changes relationships and perceptions of agency, what can still be considered authentic, who controls personal information and representation, and what opportunities and roles should remain human. These findings position adolescent AI use not simply as technology adoption, but as an emerging negotiation over AI's place and boundaries in everyday life. Supporting this transition will require developmentally appropriate AI literacy, psychological and social support, and AI systems and policies that protect adolescents' agency, privacy, relationships and opportunities for human development.

Chinese Translation

生成性人工智能正在迅速进入青少年的日常生活，这一阶段正是他们认知、社会和情感发展的关键时期。然而，其采用速度超过了关于青少年如何体验、理解和协商人工智能在他们生活中日益扩展角色的证据。我们使用经过验证的基于关键词的检索和人机协作的LLM辅助主题分析，考察了2023年1月至2026年7月期间r/teenagers上的与人工智能相关的讨论。与人工智能相关的讨论随着时间的推移显著增加，11,083条经过分析编码的帖子揭示了八个相互关联的体验领域。日常和社交使用最为普遍（36.8%），而讨论逐渐转向真实性、个人控制和安全性以及未来人类角色。在各个领域，青少年质疑人工智能何时应支持或替代人类的思维和创造力，交互式人工智能如何改变关系和对自主性的认知，什么仍然可以被视为真实，谁控制个人信息和表现，以及哪些机会和角色应保留给人类。这些发现将青少年对人工智能的使用视为不仅仅是技术的采用，而是对人工智能在日常生活中的地位和界限的逐步协商。支持这一过渡需要发展适宜的人工智能素养、心理和社会支持，以及能够保护青少年自主性、隐私、关系和人类发展机会的人工智能系统和政策。

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cs.CL / 49 / 2609.27900

Delegated Misalignment: How Multi-Agent Structures Amplify LLM Safety Risks

委托不一致：多智能体结构如何放大大型语言模型的安全风险

Ying, Zonghao, Yan, Jiaqi, Luo, Huize, Zou, Quanchen, Liu, Aishan, Liu, Xianglong

Abstract

Large language models (LLMs) are increasingly deployed in multi-agent systems where a principal agent decomposes tasks and delegates them to subordinate agents that may invoke external tools. Safety alignment, however, is still evaluated almost exclusively under a single-agent threat model, treating safety as a property of the individual LLM. We show that this assumption breaks down: \emph{individual safety alignment fails to transfer to multi-agent settings}. Two failure mechanisms emerge under delegation: \emph{responsibility diffusion} on the principal side and \emph{role-bias compliance} on the subordinate side, jointly converting language-level refusal into actionable harm. We refer to this phenomenon as \textit{delegated misalignment} and study it through a three-condition protocol across 6 frontier LLMs on 49 hazardous tasks. Delegation amplifies end-to-end harm substantially: DeepSeek-V3.2's full-execution rate rises from 30.6\% to 77.6\% once delegation is introduced, and the same model behaves very differently across roles (GPT-5: 22.5\% as a single agent vs.\ 61.2\% as a subordinate). Ablations further show that standard single-layer defenses each fail on their own and can even backfire. We call on the community to move beyond per-model alignment and toward composite safety mechanisms before multi-agent LLM systems are deployed at scale.

Chinese Translation

大型语言模型（LLMs）越来越多地被部署在多智能体系统中，其中一个主代理分解任务并将其委托给可能调用外部工具的下属代理。然而，安全对齐的评估几乎仍然仅在单智能体威胁模型下进行，将安全视为个体LLM的属性。我们表明这一假设是错误的： extit{个体安全对齐无法转移到多智能体环境中}。在委托过程中出现了两种失效机制：主代理端的 extit{责任扩散}和下属端的 extit{角色偏见遵从}，共同将语言层面的拒绝转化为可执行的伤害。我们将这一现象称为 extit{委托不一致}，并通过在49个危险任务上对6个前沿LLM进行三条件协议的研究来探讨它。委托显著放大了端到端的伤害：DeepSeek-V3.2的完全执行率从30.6\%上升到77.6\%，一旦引入委托，同一模型在不同角色中的表现差异显著（GPT-5：作为单一代理时为22.5\\%，而作为下属时为61.2\\%）。消融实验进一步表明，标准的单层防御各自独立时均失败，甚至可能适得其反。我们呼吁社区超越每个模型的对齐，朝着复合安全机制的方向发展，以便在多智能体LLM系统大规模部署之前做好准备。

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cs.CL / 50 / 2609.27925

Learning When Not to Listen: Selective Anti-Interference Pretraining for Language Models

学习何时不倾听：语言模型的选择性抗干扰预训练

Zhu, Jinchang, He, Haowei, Ding, Yi, Fu, Rong, Xiaojian, Nie, Song, Shuangyong, He, Zhongjiang, Yang, Menglin

Abstract

Language models can over-condition on irrelevant preceding text: predictions already supported by local context may still change when distant, unrelated prefix tokens are perturbed. This interference is especially consequential in long, packed, or distractor-heavy contexts, where useful evidence and irrelevant spans coexist. We propose Selective Prefix Anti-Interference Regularization (SPAR), a pretraining objective for selective anti-interference. SPAR runs the original sequence and a corrupt-prefix input in which only the far prefix is changed, then uses a short-context sufficiency gate and a gated KL objective to stabilize locally supported suffix predictions. The gate operationalizes a model-based estimate of whether the far prefix supplies additional information about the target token. Mechanism analyses show that the gate identifies locally sufficient tokens and sharply reduces prefix sensitivity on gate-selected suffix tokens. In continued training on pretrained base models, SPAR improves RULER across Qwen2.5-0.5B, Qwen2.5-3B, Llama-3.2-1B, Llama-3.1-8B, and GPT2-XL under equal counted training compute; pretraining experiments further show gains on both RULER and NoLiMa. These results show that selective anti-interference is an effective objective-level signal for robust context use.

Chinese Translation

语言模型可能会对无关的前文文本过度条件化：即使在局部上下文中已经得到支持的预测，当远离的无关前缀标记被扰动时，预测仍可能发生变化。这种干扰在长文本、密集文本或干扰者较多的上下文中尤为重要，因为有用的证据和无关的文本片段共存。我们提出了选择性前缀抗干扰正则化（Selective Prefix Anti-Interference Regularization, SPAR），这是一种用于选择性抗干扰的预训练目标。SPAR同时处理原始序列和一个损坏前缀输入，其中仅更改远端前缀，然后使用短上下文充分性门和门控KL目标来稳定局部支持的后缀预测。该门操作化了基于模型的估计，以判断远端前缀是否提供了关于目标标记的额外信息。机制分析表明，该门能够识别局部充分的标记，并显著降低门控选择的后缀标记的前缀敏感性。在对预训练基础模型的继续训练中，SPAR在Qwen2.5-0.5B、Qwen2.5-3B、Llama-3.2-1B、Llama-3.1-8B和GPT2-XL上提高了RULER，在相同计算量的训练下；预训练实验进一步显示在RULER和NoLiMa上都有提升。这些结果表明，选择性抗干扰是一个有效的目标级信号，用于稳健的上下文使用。

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cs.CL / 51 / 2609.27939

From Sentiment Classification to Actionable and Responsible Feedback: A Scoping Review and Evidence Map of NLP in Student Evaluation of Teaching, 2015-2026

从情感分类到可行且负责任的反馈：2015-2026年学生教学评价中自然语言处理的范围评估与证据图谱

Eicher, Jeff, da Silva, Rafael

Abstract

Natural language processing (NLP) applied to open-ended teaching-evaluation comments (Student Evaluation of Teaching, SET) has tracked the field's technical evolution--from lexicons and conventional classifiers to transformers and large language models (LLMs)--but it is not evident that this technical diversification has been accompanied by corresponding gains in educational value and robustness of the evidence. This scoping review (PRISMA-ScR) maps 421 studies (2015-2026, 2026 partial) along a technical axis (RQ1) and four value dimensions (RQ2-RQ5). Dual mutually blinded LLM screening with sampled human adjudication coded seven extraction domains, with targeted codebook-boundary review at synthesis. The joint map's sharpest quantified gap is the actionability discontinuity: demonstrated output or stronger (A2+: 258/421; 61.3%) versus intended-user evaluation or stronger (A3+: 49/421; 11.6%), a 49.7 percentage-point drop. Sentiment analysis remains the modal task (300/421); diagnostic and generative depth is a substantial minority (D4-D5: 28.2% of resolved cases); a formal fairness metric is rare (1.9%). The findings are descriptive and do not support causal claims of progress: technological coexistence and uneven reporting are part of the map, but the A2+ to A3+ cliff is the contribution, not a quality ladder.

Chinese Translation

应用于开放式教学评价评论（学生教学评价，SET）的自然语言处理（NLP）追踪了该领域的技术演变——从词典和传统分类器到变换器和大型语言模型（LLMs）——但尚不清楚这种技术多样化是否伴随着教育价值和证据的稳健性相应的提升。本范围评估（PRISMA-ScR）沿技术轴（RQ1）和四个价值维度（RQ2-RQ5）绘制了421项研究（2015-2026，2026年部分）。采用双重互盲的LLM筛选与抽样人类裁决，编码了七个提取领域，并在综合时进行了针对性代码本边界审查。联合图谱中最明显的量化差距是可行性不连续性：展示的输出或更强（A2+: 258/421; 61.3%）与意图用户评价或更强（A3+: 49/421; 11.6%），下降了49.7个百分点。情感分析仍然是主要任务（300/421）；诊断和生成深度是一个相当小的少数（D4-D5: 解决案例的28.2%）；正式的公平性指标很少见（1.9%）。这些发现是描述性的，并不支持因果进展的主张：技术共存和不均匀报告是图谱的一部分，但A2+到A3+的悬崖是贡献，而不是质量阶梯。

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cs.CL / 52 / 2609.27980

Six Layers Less: Encoder Pruning for Whisper with Label-Free Recovery

减少六层：针对 Whisper 的编码器剪枝与无标签恢复

Aagaard, Rasmus, Detlefsen, Nicki Skafte

Abstract

Pruning large pre-trained transformer-based ASR models such as OpenAI's Whisper has seen great adoption, as pruning the decoder led to significant end-to-end transcription speedups. For instance, the {\tt whisper-large-v3-turbo} variant reduced the decoder from 32 to 4 layers, while Distill-Whisper similarly reduced the decoder to only 2 layers. Although some attention has been put towards reducing the size of the encoder, no approach has seen wide adoption. This could be due to the need for custom inference implementations to take advantage of the compressed model. We present an approach that ranks encoder layers by the leave-one-layer-out change in Word Error Rate (WER). The six layers that cause the least change are removed, corresponding to $18.5\%$ of the encoder stack. The pruned model requires no custom inference code as it is simply a more shallow encoder with fewer layers. We further distill using unlabeled monolingual speech data to recover performance degradation caused by the zero-shot layer pruning. Mean WER across four languages increases to $20.1\%$ after distillation, compared to $21.9\%$ zero-shot, going from a baseline of $18.2\%$. We release all of our code (https://github.com/rasgaard/whisper-encoder-layer-prune) and the pruned model (https://huggingface.co/rasgaard/whisper-large-v3-turbo-encoder-pruned).

Chinese Translation

对大型预训练的基于变换器的自动语音识别（ASR）模型进行剪枝，例如 OpenAI 的 Whisper，已得到广泛应用，因为剪枝解码器显著提高了端到端的转录速度。例如，{ t whisper-large-v3-turbo} 变体将解码器从 32 层减少到 4 层，而 Distill-Whisper 则将解码器减少到仅 2 层。尽管在减少编码器的大小方面有所关注，但没有任何方法得到广泛采用。这可能是由于需要定制推理实现以利用压缩模型。我们提出了一种通过逐层剔除对词错误率（WER）变化的影响来对编码器层进行排名的方法。去除导致最小变化的六层，约占编码器堆栈的 $18.5\%$。剪枝后的模型不需要定制的推理代码，因为它只是一个层数更少的更浅的编码器。我们进一步使用无标签的单语音频数据进行蒸馏，以恢复因零-shot 层剪枝造成的性能下降。经过蒸馏后，四种语言的平均 WER 从 $21.9\\%$（零-shot）提高到 $20.1\\%$，基线为 $18.2\\%$。我们发布了所有代码（https://github.com/rasgaard/whisper-encoder-layer-prune）和剪枝后的模型（https://huggingface.co/rasgaard/whisper-large-v3-turbo-encoder-pruned）。

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cs.CL / 53 / 2609.27981

Risk-Controlled KV-Cache Eviction: From Memory Budgets to Risk Targets

风险控制的 KV 缓存驱逐：从内存预算到风险目标

Kang, Beomgu, Yun, SoJin, Kim, Hojoon, Seo, Hyunseok

Abstract

KV-cache eviction is typically evaluated through average quality-memory trade-offs, yet a small average loss can hide requests whose utility degrades materially. We reformulate eviction as a deployment risk-control problem: a material degradation occurs when eviction lowers task utility by more than a deployment-specified tolerance relative to full-KV inference on the same request, and deployment risk is the population frequency of such events. Given a reliability contract specifying a target risk level and confidence requirement, we use a compressor-agnostic post-hoc certification procedure to select a retention policy from calibration data with a finite-sample guarantee, falling back to full KV when no compressed policy is certified. Across multiple eviction methods, Llama and Mistral models, and LongBench and RULER-32K, the same contract supports substantially different levels of eviction: on Llama, it certifies SnapKV at 75% retention on LongBench but no tested compressed policy on RULER-32K, triggering full-KV fallback. Policies with empirical degradation rates below the 5% target can still fail finite-sample certification; on Llama LongBench, empirical thresholding selects uncertified policies that retain 5-10 percentage points less cache across fixed-budget methods. The proposed framework converts a deployment-level reliability requirement into a KV-memory operating point.

Chinese Translation

KV 缓存驱逐通常通过平均质量与内存的权衡来评估，但小的平均损失可能掩盖请求的效用显著下降。我们将驱逐重新表述为一个部署风险控制问题：当驱逐导致任务效用相对于同一请求的全 KV 推理下降超过部署指定的容忍度时，就会发生显著的效用下降，而部署风险则是此类事件的发生频率。给定一个指定目标风险水平和置信要求的可靠性合同，我们使用与压缩器无关的事后认证程序，从具有有限样本保证的校准数据中选择保留策略，当没有压缩策略获得认证时，则回退到全 KV。在多种驱逐方法、Llama 和 Mistral 模型以及 LongBench 和 RULER-32K 的实验中，同一合同支持显著不同的驱逐水平：在 Llama 上，它在 LongBench 上认证 SnapKV 的保留率为 75%，但在 RULER-32K 上没有测试到的压缩策略获得认证，触发全 KV 回退。尽管经验降级率低于 5% 目标的策略仍可能未能通过有限样本认证；在 Llama LongBench 上，经验阈值选择了未认证的策略，这些策略在固定预算方法中保留的缓存量比认证策略少 5-10 个百分点。所提出的框架将部署级别的可靠性要求转化为 KV 内存操作点。

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cs.CL / 54 / 2609.28004

Controlled Attribute-Specific Summarization of Interrogative Dialogues

针对询问对话的受控属性特定摘要

Bhardwaj, A Aditya, Arora, Arjit Singh, Akhtar, Md Shad

Abstract

Effective summarization of interrogative dialogues is a critical task in forensic and investigative settings, requiring high factual accuracy, coherence, and attribute-specific relevance. In this work, we introduce CASPER, a novel Chain-of-Thought Attribute-Specific Prompting for Evaluative Summarization framework that leverages structured prompting and iterative refinement to generate high-quality summaries of interrogator-witness interactions. We construct MINDSum, a dataset extending the MIND corpus, comprising 6,000 utterance pairs annotated with event details, factual statements, character descriptions, and fillers. CASPER employs RoleEval, a hierarchical evaluation mechanism where multiple roles (officer, inspector, senior inspector) iteratively assess summaries based on predefined criteria. By integrating entity extraction and structured feedback loops, CASPER significantly improves factual consistency and contextual completeness compared to existing baselines. Experimental results demonstrate that our framework outperforms standard summarization models on both lexical (ROUGE) and semantic (BERTScore) metrics, while human evaluation confirms its alignment with expert reasoning. Our findings underscore the potential of controlled summarization in high-stakes domains, paving the way for AI-driven forensic intelligence.

Chinese Translation

有效地总结询问对话在法医学和调查环境中是一项关键任务，要求具备高事实准确性、连贯性和属性特定相关性。在本研究中，我们介绍了CASPER（Chain-of-Thought Attribute-Specific Prompting for Evaluative Summarization），这是一种新颖的框架，利用结构化提示和迭代优化生成高质量的询问者-证人互动摘要。我们构建了MINDSum，这是一个扩展MIND语料库的数据集，包含6,000对带有事件细节、事实陈述、角色描述和填充词的发言对。CASPER采用RoleEval，这是一种分层评估机制，其中多个角色（警官、检查员、高级检查员）根据预定义标准迭代评估摘要。通过整合实体提取和结构化反馈循环，CASPER显著提高了事实一致性和上下文完整性，相较于现有基线表现更佳。实验结果表明，我们的框架在词汇（ROUGE）和语义（BERTScore）指标上均优于标准摘要模型，而人工评估确认了其与专家推理的一致性。我们的研究结果强调了在高风险领域中受控摘要的潜力，为人工智能驱动的法医学智能铺平了道路。

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cs.CL / 55 / 2609.28007

Evaluating Open-Weight LLMs for Turkish Domain Documents Under Retrieval and Hardware Constraints

在检索和硬件限制下评估土耳其领域文档的开放权重大型语言模型

Hassan, Imtiaz Ul, Akbulut, Öykü, Kaya, Onur, Behera, Ardhendu, Kumar, Swagat, Matthew, Peter, Liu, Yonghuai

Abstract

Most Turkish-capable large language models (LLMs) are evaluated using general-purpose benchmarks rather than long, structurally complex domain documents. This paper evaluates five open-weight 7B-8B models for Turkish document question answering under a resource-constrained local deployment setting. The primary benchmark contains 100 systematically validated questions derived from a 109-page industrial R&D report, and the evaluation protocol is replicated using a second 112-page public-sector report and an independently constructed 100-question set. All models are evaluated locally on an NVIDIA RTX 3050 laptop GPU with 6 GB VRAM using controlled prompting, decoding, and 4-bit quantisation. The principal methodological contribution is an evidence-annotated evaluation protocol that separates retrieval failure from downstream model reasoning failure without requiring additional model calls. On the primary benchmark, end-to-end accuracy ranges from 49% to 75%. Seven lexical, dense, and hybrid retrieval configurations are additionally compared using 95% Wilson intervals and exact paired McNemar tests; none significantly outperforms the character TF-IDF baseline on either document. Evidence recall saturates differently across the two reports, showing that retrieval and effective context capacity can be binding constraints for some documents but not others. These results demonstrate that model selection, retrieval behaviour, and hardware limits must be evaluated separately when deploying open-weight LLMs for Turkish domain documents.

Chinese Translation

大多数支持土耳其语的大型语言模型（LLMs）是通过通用基准进行评估的，而不是针对长且结构复杂的领域文档。本文在资源受限的本地部署环境中评估了五个开放权重的7B-8B模型在土耳其文档问答中的表现。主要基准包含100个系统验证的问题，这些问题源自一份109页的工业研发报告，评估协议还使用了一份112页的公共部门报告和一个独立构建的100题问题集进行复制。所有模型均在配备6GB显存的NVIDIA RTX 3050笔记本GPU上进行本地评估，采用受控提示、解码和4位量化。主要的方法论贡献是一个证据注释的评估协议，该协议在不需要额外模型调用的情况下，将检索失败与下游模型推理失败分开。在主要基准上，端到端准确率范围为49%到75%。此外，使用95%的Wilson区间和精确配对的McNemar检验比较了七种词汇、密集和混合检索配置；在任何文档上均没有显著优于字符TF-IDF基线。证据召回在两个报告中表现出不同的饱和度，显示出检索和有效上下文容量可能是某些文档的约束条件，而对其他文档则不是。这些结果表明，在为土耳其领域文档部署开放权重LLMs时，模型选择、检索行为和硬件限制必须分别进行评估。

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cs.CL / 56 / 2609.28026

Evaluating Feedback Focus and Pedagogical Adaptivity in LLM-Generated Feedback on Student Writing

评估大型语言模型生成的学生写作反馈中的反馈焦点和教学适应性

Almousa, Norah, Oskoui, Shayan Peyghambari, Coelho, Raquel, Rogers, Gayle, Li, Xiang Lorraine, Litman, Diane

Abstract

We investigate whether state-of-the-art large language models (LLMs) generate feedback that reflects the pedagogical practices of expert teachers in terms of feedback focus and adaptivity. Previous evaluation efforts have examined feedback characteristics, its impact on learning, and its target, yet the focus of feedback and its adaptivity remains largely overlooked. To bridge this gap, we adopt and refine Narciss's taxonomy into seven feedback focus types to annotate teacher and LLM-generated feedback across three university writing courses. We release FeedType, a benchmark containing annotated teacher and LLM feedback from six LLMs under three prompting strategies. We assess the coverage and distribution of feedback focus types, and examine whether LLMs adapt their feedback across draft stages and student performance levels as an expert instructor does. Our findings show that while most LLMs cover most feedback focus types, they fail to reflect teacher feedback distributions and show varying levels of adaptivity, with none matching the teachers' adaptive behavior. We believe FeedType will support future research on pedagogical alignment in LLM feedback generation.

Chinese Translation

我们研究了最先进的大型语言模型（LLMs）是否生成反映专家教师在反馈焦点和适应性方面的教学实践的反馈。以往的评估工作考察了反馈特征、其对学习的影响及其目标，但反馈的焦点及其适应性仍然在很大程度上被忽视。为了填补这一空白，我们采用并改进了Narciss的分类法，将其细分为七种反馈焦点类型，以对三门大学写作课程中的教师和LLM生成的反馈进行标注。我们发布了FeedType，这是一个包含来自六个LLM在三种提示策略下的标注教师和LLM反馈的基准数据集。我们评估了反馈焦点类型的覆盖范围和分布，并考察了LLMs是否像专家教师一样在草稿阶段和学生表现水平之间调整其反馈。我们的研究结果表明，尽管大多数LLMs覆盖了大部分反馈焦点类型，但它们未能反映教师反馈的分布，并表现出不同程度的适应性，没有一个模型能够匹配教师的适应性行为。我们相信FeedType将支持未来关于LLM反馈生成中教学对齐的研究。

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cs.CL / 57 / 2609.28041

How Much Were You Told? Measuring External Information in Peer Reviews

你被告知了多少？测量同行评审中的外部信息

Dubois, Matthieu, Piantanida, Pablo, Yvon, François

Abstract

Conference policies distinguish using Large Language Models (LLMs) to polish one's own review from delegating the critique, but current Artificial Text Detection (ATD) methods largely measure surface form rather than the origin of its content. We instead measure the external information carried by a review: information not explained by the reviewed paper and a generic reviewing instruction. We propose Self-Conditioning, an unsupervised information-theoretic estimator that compares the likelihood of a review under its production context with its likelihood when that context is augmented with hints extracted from the review itself. On the IntelLabs peer-review benchmark, Self-Conditioning separates fully-delegated from machine-polished reviews with AUC up to $1.0$ while remaining largely insensitive to surface rewriting. Moreover, as generators receive increasing amounts of externally-provided information, their scores move monotonically towards the human regime, unlike standard ATD baselines. High-temperature sampling can evade the estimator, but at the cost of output quality.

Chinese Translation

会议政策区分使用大型语言模型（LLMs）来润色自己的评审与委托他人进行批评，但当前的人工文本检测（ATD）方法主要测量表面形式，而非内容的来源。我们则测量评审中所携带的外部信息：即未在被评审论文中解释的信息和通用评审指导。我们提出了自条件（Self-Conditioning），这是一种无监督的信息理论估计器，它比较在其生成上下文下评审的可能性与在该上下文中加入从评审本身提取的提示后的可能性。在IntelLabs同行评审基准上，自条件能够以高达1.0的AUC值区分完全委托的评审和机器润色的评审，同时对表面重写保持较大敏感度。此外，随着生成器接收越来越多的外部提供信息，其评分单调地向人类评审的范围移动，这与标准的ATD基准不同。高温采样可以规避估计器，但会以输出质量为代价。

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cs.CL / 58 / 2609.28048

TEMPS: Temporal Sentence Embeddings for Temporal Information Retrieval

TEMPS：用于时间信息检索的时间句子嵌入

Hassani, Mourad, Romero, Julien, Bouzeghoub, Amel, Jacquelinet, Christian

Abstract

Modern information retrieval (IR) systems rarely represent time, yet many information needs depend on it: in clinical, journalistic, and legal search, when an event occurred can decide whether a document is relevant. Dense retrievers and Retrieval-Augmented Generation (RAG) pipelines match queries to documents well on topic but poorly on time, so they surface content that is on-topic yet temporally wrong. We introduce Temporal Textual Similarity (TTS), a task that measures how well two anchored texts align in time, independent of their topical similarity. We then present TEMPS (Temporal Embedding Model for Precise Search), a modular temporal branch that attaches to a frozen semantic retriever and trains on that signal. It resolves anchored temporal expressions to intervals and moment-matches each one to a Gaussian; the resulting ordering supervises an anchor-date-conditioned encoder, whose score we fuse with the semantic score at inference. Grounding supplies the supervision, so training uses no hand-labeled temporal data. The temporal score itself is the Gaussian-KL inclusion measure from distributional embeddings; what TEMPS adds is the grounding and the moment-matched supervision. On three temporal benchmarks, TEMPS improves MRR for every semantic backbone tested and, on TS- Retriever, lifts R@1 from 19.92 to 25.39 over the prior temporal state of the art.

Chinese Translation

现代信息检索（IR）系统很少表示时间，然而许多信息需求依赖于时间：在临床、新闻和法律搜索中，事件发生的时间可以决定文档的相关性。密集检索器和检索增强生成（RAG）管道在主题上能够很好地匹配查询和文档，但在时间上表现不佳，因此它们可能呈现出主题相关但时间不准确的内容。我们引入了时间文本相似性（TTS），这是一个衡量两个锚定文本在时间上对齐程度的任务，与它们的主题相似性无关。接着，我们提出了TEMPS（用于精确搜索的时间嵌入模型），这是一个模块化的时间分支，附加在一个冻结的语义检索器上，并在该信号上进行训练。它将锚定的时间表达解析为时间区间，并将每个时间区间与一个高斯分布进行瞬时匹配；由此产生的排序监督一个锚定日期条件的编码器，其得分在推理时与语义得分融合。基础数据提供了监督，因此训练不使用手工标注的时间数据。时间得分本身是来自分布嵌入的高斯-KL包含度量；TEMPS所增加的是基础数据和瞬时匹配的监督。在三个时间基准测试中，TEMPS提高了每个测试的语义骨干的平均排名回报（MRR），并且在TS-Retriever上，将R@1从19.92提升至25.39，超越了之前的时间领域的最先进水平。

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cs.CL / 59 / 2609.28060

A Native-Reference Coordinate Geometry for L2 Pronunciation Deviation Using Self-Supervised Speech Models

基于自监督语音模型的L2发音偏差的母语参考坐标几何

Raissi, Tina, Phan, Nhan, Kurimo, Mikko

Abstract

Self-supervised speech models encode rich phonetic information, but it remains unclear how to transform this information into interpretable metrics for second-language (L2) pronunciation assessment in spontaneous speech. We propose a native-reference coordinate geometry in which phone-class averages from native speech define a low-dimensional reference subspace, and L2 speech is evaluated by its distance to matching native phone-class coordinates. Unlike prior distance-based approaches, our method does not require parallel recordings with matched linguistic content or dedicated pronunciation labels. Across different self-supervised encoders and modeling choices, the resulting native-reference distances show negative Spearman correlations up to -0.5 with speaking proficiency, indicating that higher-proficiency speakers tend to lie closer to the native-reference space.

Chinese Translation

自监督语音模型编码了丰富的音位信息，但如何将这些信息转化为可解释的指标以评估自发语音中的第二语言（L2）发音仍不清楚。我们提出了一种母语参考坐标几何，其中来自母语语音的音素类别平均值定义了一个低维参考子空间，而L2语音则通过其与匹配的母语音素类别坐标的距离进行评估。与以往基于距离的方法不同，我们的方法不需要具有匹配语言内容的平行录音或专门的发音标签。在不同的自监督编码器和建模选择下，得到的母语参考距离与口语能力之间显示出高达-0.5的负斯皮尔曼相关性，表明高水平的说话者往往更接近母语参考空间。

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cs.CL / 60 / 2609.28080

Reference-Based Analysis of Coherence and Diversity in Open-Ended Text Generation

基于参考的开放式文本生成连贯性和多样性分析

Arias, Esteban Garcés

Abstract

Evaluating open-ended text generation involves understanding how different properties of a continuation relate to its perceived quality. We present a reference-based framework for examining coherence and diversity through three perspectives: aligning their evolution with human trajectories, comparing their summaries with a human continuation of the same prompt, and estimating their likelihood under a human reference distribution. Experiments with human quality ratings suggest that diversity-based alignment and mean-based comparisons capture quality-related variation, although the comparisons do not establish a predictive advantage for temporal alignment over simpler baselines. Reference likelihood also shows positive associations with ratings, with results varying across reference configurations and scoring horizons. Together, these analyses provide a structured way to examine how measured coherence and diversity relate to human judgments, while distinguishing similarity to human references from quality itself. Code and analysis resources are available at https://github.com/EstebanGarces/likely_human.

Chinese Translation

评估开放式文本生成涉及理解不同特性如何与其感知质量相关。我们提出了一种基于参考的框架，通过三个视角来考察连贯性和多样性：将其演变与人类轨迹对齐、将其摘要与同一提示的人类延续进行比较，以及在一个人类参考分布下估计其可能性。通过人类质量评分的实验表明，基于多样性的对齐和基于均值的比较能够捕捉与质量相关的变化，尽管这些比较并未显示出时间对齐相较于更简单基线的预测优势。参考可能性也与评分呈正相关，结果在不同的参考配置和评分范围内有所变化。这些分析共同提供了一种结构化的方法，来检验测量的连贯性和多样性如何与人类判断相关，同时区分与人类参考的相似性和质量本身。代码和分析资源可在 https://github.com/EstebanGarces/likely_human 获取。

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cs.CL / 61 / 2609.28090

Can LLMs Catch a Rigged Backtest? A Clean-Control Calibration Benchmark

大型语言模型能否识别操纵的回测？一个干净控制的校准基准

Ulesov, Makar, Smirnov, Vladislav, Ibrahim, Omar, Bobovnikov, Arsenii

Abstract

Backtest auditing is a calibration problem: high flaw recall is not useful when the model falsely flags matched clean strategies. We build a 96-item paired benchmark in which every flawed backtest has a clean control that holds strategy, dates, code style, labels, and reporting scaffold fixed while changing one methodology detail. A deterministic scorer separates flaw recall, clean-control false positives, evidence localization, and fix relevance. Over 1440 cached audits from four text endpoints, the primary DeepSeek auditor reaches 100.0\% closed and clean-aware code recall, but open prompts over-flag 93.8\% of clean code controls, and clean-aware all-three specificity is 87.5\% even where recall saturates. A clean-aware warning drops DeepSeek code false positives from 20.8\% (95\% CI 11.7--34.3) to 0.0\% (0.0--7.4) at unchanged recall, while the budget anchor still flags 38/48 clean controls under the same prompt. Reporting recall alone would rank three of these four models identically; reporting the clean-control rate separates them by 79 points.

Chinese Translation

回测审计是一个校准问题：当模型错误地标记匹配的干净策略时，高缺陷召回率并没有实际意义。我们构建了一个包含96个配对基准的测试集，其中每个有缺陷的回测都有一个干净控制，该控制在保持策略、日期、代码风格、标签和报告框架不变的情况下，仅改变一个方法论细节。一个确定性的评分器能够区分缺陷召回、干净控制的假阳性、证据定位和修复相关性。在来自四个文本端点的1440个缓存审计中，主要的DeepSeek审计器达到了100.0%的闭合和干净感知代码召回率，但开放提示错误标记了93.8%的干净代码控制，而干净感知的三项特异性在召回饱和的情况下为87.5%。一个干净感知的警告将DeepSeek代码的假阳性从20.8%（95%置信区间11.7--34.3）降低到0.0%（0.0--7.4），而召回率保持不变，同时预算锚仍然在相同提示下标记了38/48个干净控制。仅报告召回率会使这四个模型的排名相同；而报告干净控制率则将它们分开79分。

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cs.CL / 62 / 2609.28117

Scaling Attention Head Analysis via Gradient-Based Attribution in Context-Aware Machine Translation

通过基于梯度的归因分析扩展注意力头在上下文感知机器翻译中的应用

Mąka, Paweł, Semerci, Yusuf Can, Scholtes, Jan, Spanakis, Gerasimos

Abstract

In this paper, we introduce a gradient-based head attribution strategy where the Token-level Max-Margin loss is backpropagated to the attention maps. This framework enables a large-scale causal analysis of attention heads, making it suitable for LLMs. We evaluate our method on the task of disambiguation in Context-aware Machine Translation, where we analyze 50 phenomena across 4 models and 4 language directions. We empirically show the alignment of our method with the effects of increasing the attention scores of token-to-token relations on three models and two language directions, ensuring the robustness of our method. Our analysis reveals the presence of the "general-purpose" attention heads that improve the model's performance when attending to different relations. We find that the average attention a head assigns to a relation does not necessarily relate to the model's performance, which suggests that the models developed redundancies during training in terms of the head functions.

Chinese Translation

本文介绍了一种基于梯度的头部归因策略，其中令牌级最大边际损失被反向传播到注意力图。这一框架使得对注意力头进行大规模因果分析成为可能，适用于大规模语言模型（LLMs）。我们在上下文感知机器翻译的消歧义任务上评估了我们的方法，分析了4个模型和4种语言方向下的50种现象。我们通过实证研究展示了我们的方法与在三个模型和两个语言方向上增加令牌间关系的注意力分数的效果之间的一致性，确保了我们方法的稳健性。我们的分析揭示了“通用”注意力头的存在，这些头在关注不同关系时提高了模型的性能。我们发现，一个头对某一关系分配的平均注意力并不一定与模型的性能相关，这表明模型在训练过程中在头部功能方面发展了冗余。

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cs.CL / 63 / 2609.28150

Exact Feedback Is Not Control: Evaluating Text-based Closed-Loop Revision in LLMs

精确反馈并非控制：评估基于文本的闭环修订在大型语言模型中的应用

Jiang, Haitong, Liu, Chunlin, Wang, Yile, Feng, Yuhong

Abstract

Closed-loop revision is increasingly used in large language model (LLM) applications, but failures may reflect incomplete feedback or ineffective responses to correct feedback. We introduce a fixed-budget revision protocol with deterministic verifiers that report all remaining violations across exact-length, lexical, and compositional constraints. Fixing feedback correctness and completeness isolates model-side revision behavior. Across 19 open- and closed-source models, controller-level mean final joint success ranges from 17.4% to 99.8%, with substantial cross-model gaps persisting under identical initial drafts. Controlled experiments reveal reproducible model-specific responses to exact feedback. Post-training and scale reshape these responses without consistently bringing them closer to exact correction. Across all constraint families, failed trajectories often repeat earlier outputs, and prior recurrence is associated with lower subsequent recoverability. Matched-state interventions show that removing earlier dialogue while holding the current draft and feedback fixed changes recurrence escape without reliably improving final success; effects depend on the model, task, and trigger-state composition. Exact feedback makes revision errors observable, but does not make the closed loop reliable. Code and reproduction instructions: https://github.com/kevinjiang0121-cyber/exact-feedback-code.

Chinese Translation

闭环修订在大型语言模型（LLM）应用中越来越常见，但其失败可能反映出反馈不完整或对正确反馈的响应无效。我们引入了一种固定预算的修订协议，使用确定性验证器报告在精确长度、词汇和组合约束下的所有剩余违规情况。修正反馈的正确性和完整性使模型侧的修订行为得以独立分析。在19个开源和闭源模型中，控制器级的最终联合成功率从17.4%到99.8%不等，在相同初始草稿下存在显著的跨模型差距。受控实验揭示了模型对精确反馈的可重复性特定响应。后训练和规模调整这些响应，但并未始终使其更接近精确修正。在所有约束类别中，失败的轨迹往往重复早期输出，且先前的重复与后续的可恢复性较低相关。匹配状态干预显示，在保持当前草稿和反馈不变的情况下，移除早期对话会改变重复逃逸，但并未可靠地提高最终成功率；效果依赖于模型、任务和触发状态的组成。精确反馈使修订错误可观察，但并未使闭环变得可靠。代码和复现说明： https://github.com/kevinjiang0121-cyber/exact-feedback-code.

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cs.CL / 64 / 2609.28245

Beyond Poetry: Can Large Language Models Generate Classical Arabic Maqamat?

超越诗歌：大型语言模型能否生成古典阿拉伯马卡马（Maqamat）？

Morsy, AbdulRahman A., Zirikly, Aya

Abstract

Large language models (LLMs) have shown strong performance in creative text generation, yet their ability to produce culturally grounded and stylistically constrained literary forms remains underexplored. Prior work has focused largely on modern language varieties and poetry, while classical prose traditions such as maqama remain largely unstudied. The maqama is a classical literary genre characterized by rhymed prose (saj), dense rhetorical ornamentation, and episodic narrative structure, making it a challenging testbed for evaluating whether LLMs can move beyond surface fluency toward deeper literary competence. In this paper, we present the first controlled evaluation study of maqama generation with LLMs, comparing five models under zero-shot, few-shot, and rule-based prompting, and evaluating outputs through both human annotation and an LLM-as-a-judge framework across dimensions such as rhetorical richness, saj density, structural coherence, and stylistic authenticity. Our results show that prompting strategy plays a strong role in stylistic quality: few-shot prompting most consistently improves saj density, while its effects on rhetoric and coherence vary by model, with the strongest models (GPT-4o and GPT-5.4-mini) benefiting most from rule-based prompting on these dimensions, though zero-shot prompting yields the highest aggregate scores across all five models. We further observe systematic differences between models in stylistic alignment with Arabic maqama conventions, and corroborate our findings with a second independent LLM judge, paired statistical significance testing, and non-LLM proxy measures of saj.

Chinese Translation

大型语言模型（LLMs）在创意文本生成方面表现出色，但它们在生成具有文化根基和风格约束的文学形式方面的能力仍然未得到充分探索。以往的研究主要集中在现代语言变体和诗歌上，而古典散文传统如马卡马（maqama）则基本未被研究。马卡马是一种古典文学体裁，以押韵散文（saj）、密集的修辞装饰和情节叙述结构为特征，使其成为评估LLMs是否能够超越表面流畅性，向更深层次的文学能力迈进的挑战性试验场。在本文中，我们首次进行了一项关于马卡马生成的控制评估研究，比较了五个模型在零样本、少样本和基于规则的提示下的表现，并通过人类注释和LLM作为评判者的框架，从修辞丰富性、saj密度、结构连贯性和风格真实性等维度评估输出结果。我们的结果表明，提示策略在风格质量中起着重要作用：少样本提示最一致地提高了saj密度，而其对修辞和连贯性的影响因模型而异，最强的模型（GPT-4o和GPT-5.4-mini）在这些维度上最能从基于规则的提示中获益，尽管零样本提示在所有五个模型中产生了最高的综合得分。我们进一步观察到模型在与阿拉伯马卡马传统的风格一致性方面存在系统性差异，并通过第二个独立的LLM评判者、配对统计显著性测试以及非LLM的saj代理测量来验证我们的发现。

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cs.CL / 65 / 2609.28250

Complementary Roles of Activation and Parametric Memory in Few-Shot Learning

激活记忆与参数记忆在少样本学习中的互补作用

Niu, Miaohe, Zhao, Runsong, Liu, Xinyu, Jin, Bo, Qiao, Yucheng, Zhang, Chunliang, Zhu, Jingbo, Xiao, Tong

Abstract

At test time, large language models (LLMs) can encode historical information in activation memory (i.e., KV caches) and parametric memory (i.e., updated parameters). While activation memory is generally considered effective for factual recall and parametric memory for learning new tasks, their interplay remains unclear. In this work, we systematically investigate the role of memory in few-shot learning through controlled experiments. We find that activation memory is superior for recalling facts, whereas parametric memory does not consistently outperform activation memory in task learning. Moreover, our experiments show that the composite task, Conditional Arithmetic, requires the synergy of both memory types. Through neuron-level analysis, we find that the model activates distinct sets of neurons when accessing the same historical information through activation versus parametric memory. When both memory types are combined, the model recruits neurons from both sets, which is crucial for solving Conditional Arithmetic. These findings suggest that neither memory mechanism alone is sufficient for this composite task, highlighting the importance of their collaboration.

Chinese Translation

在测试阶段，大型语言模型（LLMs）可以在激活记忆（即 KV 缓存）和参数记忆（即更新的参数）中编码历史信息。虽然激活记忆通常被认为在事实回忆方面有效，而参数记忆则在学习新任务方面更为有效，但它们之间的相互作用仍不清楚。在本研究中，我们通过控制实验系统地调查了记忆在少样本学习中的作用。我们发现，激活记忆在回忆事实方面表现优越，而参数记忆在任务学习中并不总是优于激活记忆。此外，我们的实验表明，复合任务条件算术（Conditional Arithmetic）需要两种记忆类型的协同作用。通过神经元级别的分析，我们发现模型在通过激活记忆与参数记忆访问相同历史信息时，会激活不同的神经元集合。当两种记忆类型结合时，模型会同时招募来自两组的神经元，这对于解决条件算术任务至关重要。这些发现表明，单一的记忆机制不足以完成这一复合任务，强调了它们协作的重要性。

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cs.CL / 66 / 2609.28270

Predicting Quantization Price for Selecting PTQ Configurations Before Deployment

在部署前预测量化价格以选择 PTQ 配置

Qiu, Junbin, Mu, Jian, Zhang, Weitong, Shu, Yao

Abstract

Weight-space post-training quantization (PTQ) must choose finite formats, granularities, quantizer families, transformations, and bits before the completed quantized model reveals its output-distribution drift. Existing PTQ methods predict important pieces of this degradation, including reconstruction error, Hessian sensitivity, transformation effects, and downstream loss, but these pieces are usually scored after fixing the quantization geometry or inside separate configuration families. We formulate weight-space PTQ as pre-deployment configuration selection using priced layer-output error. Each admissible layer configuration is treated as an error generator with a deployment cost, which induces a layer-output error covariance $\boldsymbol{\Sigma}_l(\alpha_l)$, and the full-precision model prices that covariance by downstream curvature, $\widehat{\rho}_l(\alpha_l)=\frac{1}{2}\operatorname{Tr}\left(\widehat{\mathbf{H}}_l\,\widehat{\boldsymbol{\Sigma}}_l(\alpha_l)\right)$. The price follows from full-precision-to-quantized forward KL, whose first-order term cancels at the reference model. It turns reconstruction and diagonal scores into reduced proxies that drop price factors, while finite formats, codebooks, granularities, and equivalent transformations become comparable candidates through the covariances they induce and the costs they pay. A trace reduction then yields a calibration-time price table and a budgeted price-guided selector, making fixed-geometry bit allocation a special case rather than the organizing problem.

Chinese Translation

权重空间后训练量化（PTQ）必须在完成量化模型揭示其输出分布漂移之前选择有限的格式、粒度、量化器家族、变换和位数。现有的 PTQ 方法预测了这一退化的重要组成部分，包括重建误差、海森敏感性、变换效应和下游损失，但这些组成部分通常是在固定量化几何或独立配置家族内进行评分的。我们将权重空间 PTQ 形式化为使用定价的层输出误差进行的部署前配置选择。每个可接受的层配置被视为一个具有部署成本的误差生成器，这会引入层输出误差协方差 $oldsymbol{ ext{Σ}}_l( ext{α}_l)$，而全精度模型通过下游曲率对该协方差进行定价，$ ext{ˆρ}_l( ext{α}_l)=rac{1}{2} ext{Tr}ig( ext{ˆH}_l ext{ˆΣ}_l( ext{α}_l)ig)$。该价格源于全精度到量化的前向 KL，其一阶项在参考模型处抵消。它将重建和对角评分转化为减少的代理，去掉价格因素，而有限格式、代码本、粒度和等效变换通过它们引入的协方差和支付的成本成为可比较的候选项。然后，迹的减少产生一个校准时的价格表和一个预算导向的选择器，使得固定几何位分配成为一个特例，而不是组织问题。

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cs.CL / 67 / 2609.28272

Towards Efficient Reasoning: Learning Causal Shortcuts for Diffusion Language Models

朝向高效推理：为扩散语言模型学习因果捷径

Jin, Dian, Han, Kairong, Li, Baohong, Dong, Xinpeng, Hu, Zijing, Shan, Nuanqiao, Wu, Fei, Kuang, Kun

Abstract

Diffusion Language Models (DLMs) have attracted significant attention for their strong reasoning ability. However, under a bidirectional attention mechanism, DLMs operate over an exponentially large exploration space compared to autoregressive models (ARMs), making it challenging to focus on reasoning-guiding tokens under random masking. We define causal shortcuts as token chains that cover the full sequence and provide explicit guidance towards correct reasoning trajectories. We analyze the effects of causal shortcuts on the reasoning accuracy and convergence speed of DLMs, and find that they largely improve answer convergence efficiency and generation accuracy. Motivated by this, we propose a Causal Shortcut Learning (CSL) Framework for DLMs. Specifically, we introduce a step-by-step token extraction procedure to extract causal shortcuts from data, and apply parallel prioritized masking on these tokens during training to enable efficient and accurate convergence to correct answers via causal shortcuts. Extensive experiments across multiple reasoning benchmarks and two base models demonstrate that CSL consistently outperforms existing SFT-variant baselines, achieving an average improvement of $1.92\%$ over SFT-only models, and up to $4.20\%$ on MATH-500. The code is available at the \href{https://github.com/ZJUDianJin/Causal-Shortcuts-Learning}{https://github.com/ZJUDianJin/Causal-Shortcuts-Learning

Chinese Translation

扩散语言模型（Diffusion Language Models, DLMs）因其强大的推理能力而受到广泛关注。然而，在双向注意机制下，DLMs 的操作探索空间相较于自回归模型（Autoregressive Models, ARMs）呈指数级增长，使得在随机掩蔽下聚焦于推理引导的标记变得具有挑战性。我们将因果捷径定义为覆盖整个序列的标记链，并提供明确的指导以指向正确的推理轨迹。我们分析了因果捷径对 DLMs 推理准确性和收敛速度的影响，发现它们在很大程度上提高了答案收敛效率和生成准确性。基于此，我们提出了一种用于 DLMs 的因果捷径学习框架（Causal Shortcut Learning, CSL）。具体而言，我们引入了一种逐步标记提取程序，从数据中提取因果捷径，并在训练过程中对这些标记应用并行优先掩蔽，以便通过因果捷径实现高效且准确的收敛到正确答案。在多个推理基准和两个基础模型上的大量实验表明，CSL 始终优于现有的 SFT 变体基线，平均提高了 $1.92\%$，在 MATH-500 上最高提高了 $4.20\\%$。代码可在 {https://github.com/ZJUDianJin/Causal-Shortcuts-Learning}{https://github.com/ZJUDianJin/Causal-Shortcuts-Learning} 获取。

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cs.CL / 68 / 2609.28290

Computation Over Geometry: Meaning Identity Is Computed, Not Shipped in the Embeddings

几何上的计算：意义身份是计算得出的，而不是通过嵌入传递的

Deng, Jiaqi

Abstract

Meaning identity (whether two sentences say the same thing after wording changes) is treated in retrieval and RAG as a geometric fact about independently encoded sentence vectors. We show that, for frozen off-the-shelf encoders and language models, it is not: identity is computed when both sentences share one forward pass, and is not a property of the embedding geometry those systems ship. On overlap-matched PAWS-X, purpose-built encoders (BGE, E5, GTE, MiniLM, E5-Mistral-7B) reach English confirm AUC only 0.55-0.65 (dense peak 0.70). Independently encoded last-token states of Llama 3, Mistral, and Qwen do no better; late fusion of the two vectors stays near chance. The same probe on a joint forward pass reaches 0.90-0.96 from 1.5B to 32B, collapses under partner shuffle, is mid-depth, saturates near 0.94 by 3B, and appears more weakly in GPT-2 XL (0.76). The gap holds beyond Llama-style models on other causal LMs, bidirectional encoders (DeBERTa, RoBERTa), and encoder-decoders (Flan-T5, T5, BART). Fixed or linear readers over frozen independent encodings never unlock identity; nonlinear pair readers recover part of it only on the full 49k-pair PAWS train split (0.68-0.87). Off-the-shelf rerankers split: BGE-reranker-large reaches 0.94, while MS-MARCO and Jina stay at 0.55-0.64. Independently trained families compute the same relation and a 1.5B joint reader can distill it from unlabelled teacher scores, while no linear function of the teachers own independent vectors can. Bi-encoders can be fine-tuned to fit PAWS (0.87-0.93), but transfer and STS-B suffer. Cosine compares wording neighbourhoods; identity is a cheap computed operator, not a property of either sentence vector.

Chinese Translation

意义身份（即两句话在措辞变化后是否表达相同的意思）在检索和RAG中被视为独立编码的句子向量的几何事实。我们展示了，对于固定的现成编码器和语言模型而言，意义身份并非如此：当两个句子共享一个前向传播时，身份是被计算出来的，而不是这些系统所提供的嵌入几何的属性。在重叠匹配的PAWS-X上，专门构建的编码器（BGE、E5、GTE、MiniLM、E5-Mistral-7B）在英语确认AUC上仅达到0.55-0.65（密集峰值为0.70）。独立编码的Llama 3、Mistral和Qwen的最后一个令牌状态表现并无改善；这两个向量的后期融合接近随机水平。同样的探测器在联合前向传播中达到0.90-0.96，从1.5B到32B，在合作方洗牌下崩溃，处于中等深度，在3B时饱和接近0.94，并在GPT-2 XL中表现较弱（0.76）。这一差距在其他因果语言模型、双向编码器（DeBERTa、RoBERTa）和编码-解码器（Flan-T5、T5、BART）中依然存在。对固定或线性读取器进行独立编码从未解锁身份；非线性对读取器仅在完整的49k对PAWS训练集上恢复部分身份（0.68-0.87）。现成的重排序器表现不一：BGE-reranker-large达到0.94，而MS-MARCO和Jina停留在0.55-0.64。独立训练的系列计算相同的关系，而1.5B的联合读取器能够从未标记的教师分数中提取该关系，而教师自身的独立向量的任何线性函数则无法做到。双编码器可以微调以适应PAWS（0.87-0.93），但迁移和STS-B表现不佳。余弦比较措辞邻域；身份是一个廉价的计算操作，而不是任一句子向量的属性。

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cs.CL / 69 / 2609.28352

Digital diglossia: Arabic between X and Facebook

数字双语现象：阿拉伯语在X与Facebook之间

Hussen, Fahad Al, Shormani, Mohammed Q.

Abstract

This study highlights the distribution of Standard Arabic (SA; H(igh) variety) and Colloquial Arabic (CA; L(ow) variety) across X and Facebook. 16754 public posts were collected via Python, with 10000 retained as the net dataset. Posts were classified into 7 discourse categories: *politics, technology, science, business, culture, fun,* and *sports*. Bivariate analyses, including Chi-square tests and Cramer's V (CV), examined associations among platform, discourse category, and diglossic choice, while binary logistic regression with Platform x Discourse Category interactions tested whether these associations varied across platforms. Findings reveal that there are significant associations between discourse category and diglossic choice on X, chi-square(6, *N* = 5000) = 600.35, p < .001, CV = .347, and Facebook, chi-square(6, N = 5000) = 1249.52, p < .001, CV = .500. Across platforms, platform was also associated with diglossic choice, chi-square(1, N = 10000) = 262.16, p < .001, CV = .162. Binary logistic regression further shows higher odds of SA use on X than Facebook in the political reference category (*OR* = 1.31, p = .0028), with significant platform-by-domain interactions for Culture (OR = 2.65), Fun (*OR* = 6.34), Sports (*OR* = 26.71), Science (OR = 0.41), and Technology (OR = 0.71). The study concludes that the diglossic use of SA and CA contributes to the growing body of research on digital discourse, unveiling that the digital age reshapes but does not erode diglossic boundaries, giving rise instead to a reconfigured digital diglossia.

Chinese Translation

本研究强调了标准阿拉伯语（SA；高语体）和口语阿拉伯语（CA；低语体）在X与Facebook上的分布。通过Python收集了16754条公共帖子，其中10000条被保留为网络数据集。帖子被分类为7个话语类别：*政治、技术、科学、商业、文化、娱乐*和*体育*。双变量分析，包括卡方检验和克拉默V（CV），考察了平台、话语类别和双语选择之间的关联，而带有平台与话语类别交互项的二元逻辑回归则测试了这些关联在不同平台上的变化。研究结果显示，在X上，话语类别与双语选择之间存在显著关联，卡方(6, *N* = 5000) = 600.35, p < .001, CV = .347；在Facebook上也存在显著关联，卡方(6, N = 5000) = 1249.52, p < .001, CV = .500。在不同平台之间，平台也与双语选择相关，卡方(1, N = 10000) = 262.16, p < .001, CV = .162。二元逻辑回归进一步显示，在政治参考类别中，X上使用SA的几率高于Facebook（*OR* = 1.31, p = .0028），并且在文化（OR = 2.65）、娱乐（*OR* = 6.34）、体育（*OR* = 26.71）、科学（OR = 0.41）和技术（OR = 0.71）领域存在显著的平台与领域交互作用。研究结论认为，SA和CA的双语使用为数字话语研究的不断增长的文献做出了贡献，揭示了数字时代重塑而非侵蚀双语边界，反而导致了一种重新配置的数字双语现象。

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cs.CL / 70 / 2609.28395

Fine-Tuning LLMs for Translation: General Forgetting Mitigation Does Not Preserve MT-Specific Instruction Following

针对翻译的细调大型语言模型：通用遗忘缓解并未保留机器翻译特定的指令遵循

Scholz, Niklas, Thulke, David, Nasir, Abdallah, Allred, Will, Matusov, Evgeny, Ney, Hermann

Abstract

Fine-tuning large language models on parallel data improves translation quality but can cause catastrophic forgetting. Mitigation methods are generally evaluated by retention on general benchmarks. We ask whether these findings transfer to machine translation (MT) fine-tuning and to MT-specific instruction following (MT-IF): instructions that modify a translation, such as formality, grammatical gender, and length control. We compare methods anchored to auxiliary data, to model outputs, and to the base model parameters, first in a screening study with Llama 3.2 1B Instruct, then on Llama 3.1 8B Instruct fine-tuned on bidirectional Arabic-English or Spanish-English data. Elastic Weight Consolidation preserves general capabilities best in both stages; on the 8B Spanish model the average score on general benchmarks drops 1.7 points versus 11.0 for standard fine-tuning, yet its scores for formality and grammatical gender control remain close to standard fine-tuning. Only data mixing with control-task examples preserves these controls, but its gains do not transfer to unseen prompts for the same task.

Chinese Translation

在平行数据上细调大型语言模型可以提高翻译质量，但可能导致灾难性遗忘。缓解方法通常通过在通用基准上的保留情况进行评估。我们探讨这些发现是否适用于机器翻译（MT）细调及机器翻译特定的指令遵循（MT-IF）：修改翻译的指令，例如正式程度、语法性别和长度控制。我们比较了基于辅助数据、模型输出和基础模型参数的方法，首先在使用 Llama 3.2 1B Instruct 的筛选研究中进行，然后在使用 Llama 3.1 8B Instruct 细调的双向阿拉伯语-英语或西班牙语-英语数据上进行。在两个阶段中，弹性权重整合（Elastic Weight Consolidation）在保留通用能力方面表现最佳；在 8B 西班牙语模型上，通用基准的平均得分下降了 1.7 分，而标准细调则下降了 11.0 分，但其在正式程度和语法性别控制方面的得分仍接近标准细调。只有与控制任务示例的数据混合能够保留这些控制，但其增益并未转移到同一任务的未见提示上。

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cs.CL / 71 / 2609.28416

Agent-Editing World Model: Rethinking World Modeling for LLM Agents

代理编辑世界模型：重新思考大语言模型代理的世界建模

Sun, Shuang, Chen, Guoxin, Meng, Fanzhe, Deng, Jia, Song, Huatong, Jiang, Jinhao, Zhao, Wayne Xin, Xu, Hongteng, Wen, Ji-Rong

Abstract

Recent advances in large language models (LLMs) have enabled agents to tackle long-horizon tasks across diverse environments. To further improve agent performance, existing language world models typically predict environment observations, yet reconstructing high-entropy, execution-dependent tool responses offers limited value when real feedback is available. Meanwhile, agents suffer from \emph{task-state contamination}, where unsupported assumptions and outdated plans persist in history and distort subsequent decisions. We propose the \textbf{Agent-Editing World Model (AEWM)}, which models how reasoning and actions shape future task progress rather than simulating tool responses. AEWM combines \textbf{Action Judge} to distinguish \textsc{Critical}, \textsc{Exploratory}, and \textsc{Noisy} decisions with \textbf{State Revision} to edit noisy reasoning--action continuations from the same observed history. \textbf{EditAct} integrates these capabilities with real execution, directly changing the state underlying subsequent decisions rather than merely providing critiques. We train AEWM across Search, Terminal, and Software Engineering through mid-training and supervised fine-tuning. AEWM achieves 70.5\% macro-F1 on our Action Judge benchmark, exceeding the strongest frontier baseline by 10.6 points. Across six benchmarks and three agent backbones, EditAct improves average scores by 3.2--6.7 points over the strongest baseline. Furthermore, rejection sampling fine-tuning on verified EditAct trajectories, termed \textbf{AEWM-RFT}, improves over Self-RFT by 2.2--2.6 points across three domains without online AEWM guidance.

Chinese Translation

近期大语言模型（LLMs）的进展使得代理能够在多样化环境中处理长期任务。为了进一步提升代理的性能，现有的语言世界模型通常预测环境观察，但在真实反馈可用时，重建高熵、依赖执行的工具响应的价值有限。同时，代理面临着 extit{任务状态污染}的问题，即不支持的假设和过时的计划在历史中持续存在，并扭曲后续决策。我们提出了 extbf{代理编辑世界模型（AEWM）}，该模型关注推理和行动如何塑造未来任务进展，而不是模拟工具响应。AEWM结合了 extbf{行动判断器}，以区分 extsc{关键}、 extsc{探索}和 extsc{噪声}决策，以及 extbf{状态修订}，以编辑来自相同观察历史的噪声推理-行动延续。 extbf{EditAct}将这些能力与真实执行相结合，直接改变后续决策所依赖的状态，而不仅仅是提供批评。我们通过中期训练和监督微调在搜索、终端和软件工程领域训练AEWM。AEWM在我们的行动判断基准上达到了70.5 ext{%}的宏观F1分数，超越了最强前沿基线10.6分。在六个基准和三个代理骨干网络中，EditAct在最强基线基础上平均提升了3.2--6.7分。此外，在经过验证的EditAct轨迹上进行拒绝采样微调，称为 extbf{AEWM-RFT}，在三个领域中相较于自我RFT提高了2.2--2.6分，而无需在线AEWM指导。

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cs.CL / 72 / 2609.28430

Cross-Scale Transfer Learning for Depression Severity Prediction: From PHQ-8 to HAMD-17 Across Languages and Clinical Paradigms

跨尺度迁移学习用于抑郁症严重程度预测：从PHQ-8到HAMD-17的语言和临床范式的转变

Feng, Wenjie, Zojaji, Sahba, Nakamura, Satoshi

Abstract

This work addresses continuous depression-severity score prediction from clinical interview transcripts under data scarcity. We propose a sequential low-rank adaptation (LoRA) protocol for cross-scale transfer: a Qwen3 backbone with a bounded regression head is first fine-tuned on the English DAIC-WOZ dataset (189 avatar-mediated sessions, PHQ-8), and the adapter then initializes fine-tuning on the Chinese PDCH dataset (100 real clinical consultations, HAMD-17), where a reinitialised, scale-specific head predicts the clinician-assigned score. All configurations use patient-level stratified 5-fold, 2-repeat cross-validation. On the data-scarce HAMD-17 target, the sequential protocol attains the best point-estimate MAE , RMSE, and macro-$F_1$ on both 0.6B and 1.7B backbones, outperforming target-only training and non-LLM baselines---4.96/6.59/0.36 with Qwen3-0.6B and 4.38/5.62/0.46 with Qwen3-1.7B. Ablations suggest that correctly aligned source supervision gives the best point estimates (unsupervised exposure and shuffled-label controls also show partial gains), that native-Chinese target input outperforms machine-translated English input, and that the reversed order yields no clear gain within run-to-run variance. The study is an exploratory, single-site internal evaluation: it does not establish screening or diagnostic utility, nor separately identify the contribution of the scale, language, or paradigm shifts. To our knowledge, no prior study evaluates this specific DAIC-WOZ-to-PDCH sequential transfer setting.

Chinese Translation

本研究针对在数据稀缺情况下，从临床访谈记录中预测连续抑郁症严重程度评分的问题。我们提出了一种顺序低秩适应（LoRA）协议用于跨尺度迁移：首先在英语DAIC-WOZ数据集（189个虚拟角色介导的会话，PHQ-8）上对Qwen3主干进行微调，然后适配器在中文PDCH数据集（100个真实临床咨询，HAMD-17）上初始化微调，其中重新初始化的特定尺度头预测临床医生分配的评分。所有配置均使用患者级分层5折、2次重复交叉验证。在数据稀缺的HAMD-17目标上，顺序协议在0.6B和1.7B主干上均获得了最佳点估计MAE、RMSE和宏观$F_1$，优于仅目标训练和非LLM基线——Qwen3-0.6B的结果为4.96/6.59/0.36，Qwen3-1.7B的结果为4.38/5.62/0.46。消融实验表明，正确对齐的源监督提供了最佳点估计（无监督暴露和标签洗牌控制也显示出部分增益），本土中文目标输入优于机器翻译的英文输入，且反向顺序在运行间方差内未显示出明显增益。本研究为探索性单中心内部评估：并未建立筛查或诊断效用，也未单独识别尺度、语言或范式转变的贡献。据我们所知，尚无先前研究评估这一特定的DAIC-WOZ到PDCH的顺序迁移设置。

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cs.CL / 73 / 2609.28471

Contrastive Learning for Authorship Verification

对比学习在作者验证中的应用

Kirby, Peter

Abstract

Our results show that contrastive learning outperforms a classification-based approach to authorship verification under the tested settings. We identify loss function, batch size, training duration, pre-trained model, input context length, and random text span data augmentation as important factors of model performance. Based on these considerations, we develop a ModernBERT Bi-Encoder model that achieves 98.4% accuracy on the PAN21 authorship verification task.

Chinese Translation

我们的研究结果表明，在测试的设置下，对比学习的表现优于基于分类的方法进行作者验证。我们确定了损失函数、批量大小、训练时长、预训练模型、输入上下文长度以及随机文本跨度数据增强等因素对模型性能的重要性。基于这些考虑，我们开发了一个ModernBERT双编码器模型，在PAN21作者验证任务中达到了98.4%的准确率。

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