cs.RO / 1 / 2610.03828
TACET: Context-Appropriate Acoustic-Social Navigation for Quadrupeds
TACET：适应环境的四足机器人声学社交导航
Abstract
Quadruped robots entering hospitals, care homes, and quiet offices must be context-appropriate not only in where they move but in how loudly they move: a legged robot's locomotion noise, dominated by foot-ground impacts, is itself a social variable. Prior social navigation respects human space but treats the robot as acoustically uniform, while quiet-locomotion methods reduce noise to an operator-specified, context-blind level. We present TACET, a context-appropriate acoustic-social navigation method that infers social context from the robot's egocentric view and decides both where it walks and how loudly, coupling a slow fine-tuned vision-language reasoner to a fast reactive controller through a single compact behavior token, . The same token conditions both a social costmap (where to go) and a quiet locomotion policy (how loudly to move), while a structured out-of-view memory keeps recently seen people in the reasoner's context after they leave the camera view. On a real quadruped, context-conditioned locomotion lowers locomotion noise by up to 9.3 dBA at matched speed, and across our scenarios the full method keeps personal-space compliance at 100% with low acoustic intrusion (<=2.9 dBA), jointly improving spatial and acoustic performance in the evaluated scenarios. The project page is available at https://rcilab.khu.ac.kr/tacet/.
Chinese Translation
四足机器人在医院、护理院和安静办公室中移动时，不仅需要在移动位置上适应环境，还需在移动声音的大小上做到适当：腿式机器人的运动噪声主要由足与地面的冲击声构成，这本身就是一个社交变量。以往的社交导航方法尊重人类的空间，但将机器人视为声学均匀的存在，而安静运动的方法则将噪声降低到操作员指定的、与环境无关的水平。我们提出了TACET，这是一种适应环境的声学社交导航方法，它通过机器人的自我中心视角推断社交环境，并决定其行走的方向和声音大小，将一个缓慢精细调节的视觉-语言推理器与一个快速反应控制器通过一个紧凑的行为标记（）结合起来。相同的标记同时影响社交成本图（去哪里）和安静运动策略（多大声移动），而一个结构化的视野外记忆在最近看到的人离开摄像机视野后仍然将其保留在推理器的上下文中。在真实的四足机器人上，基于环境的运动降低了高达9.3 dBA的运动噪声，同时在我们的场景中，完整的方法保持了个人空间合规性为100%，并且低声学干扰（<=2.9 dBA），共同改善了评估场景中的空间和声学性能。项目页面可访问 https://rcilab.khu.ac.kr/tacet/。
cs.RO / 2 / 2610.03861
GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive
GOTT：一种可重用的跨体现原语的面向对象灵巧操作
Abstract
Foundation models and large-scale human data provide rich sources of manipulation intent, but translating this intent into multi-fingered robot behavior remains difficult. Dexterous hands still lack a reusable low-level primitive that reliably establishes contact across tasks and embodiments. We propose GOTT, a reach-acquire-move framework built around a single cross-embodiment contact-acquisition primitive. Given a robot-agnostic object trajectory and a reach specification, GOTT first brings the hand near a task-relevant contact region. The shared closed-loop primitive then establishes stable contact from this approximate initialization, and a pose-conditioned controller tracks the desired object motion. Reach specifications may come from future-aware planning, external models, or human demonstrations, while the primitive and tracking backend remain unchanged. Simulation and real-world experiments show that GOTT is able to establish robust contact across diverse objects, arm-hand platforms, and seen and unseen hand morphologies. It also consistently improves end-to-end task success over open-loop grasp execution.
Chinese Translation
基础模型和大规模人类数据提供了丰富的操作意图来源，但将这种意图转化为多指机器人行为仍然困难。灵巧手仍然缺乏一种可重用的低级原语，能够在不同任务和体现之间可靠地建立接触。我们提出了GOTT，一个围绕单一跨体现接触获取原语构建的到达-获取-移动框架。在给定与机器人无关的物体轨迹和到达规范的情况下，GOTT首先将手带到与任务相关的接触区域附近。共享的闭环原语随后从这个近似初始化中建立稳定的接触，而一个基于姿态的控制器则跟踪期望的物体运动。到达规范可以来自未来感知规划、外部模型或人类示范，而原语和跟踪后端保持不变。仿真和现实世界实验表明，GOTT能够在多种物体、臂-手平台以及已见和未见的手形态之间建立稳健的接触。它还在端到端任务成功率上持续优于开放环抓取执行。
cs.RO / 3 / 2610.03898
MOSAIC-SV: Real-Time Adaptive Identification of Vessel Dynamics for the Control and Deployment of Aquatic Robots
MOSAIC-SV：水下机器人控制与部署的实时自适应船舶动态识别
Abstract
Model-based control of an aquatic robotic platform depends on a hydrodynamic model that is costly to identify and specific to the hull, payload, and conditions it was measured in. Here, we present MOSAIC-SV, a deployable real-time adaptive dynamics identification and control system that identifies a control-sufficient dynamics model from a spec-sheet engineering prior, without dedicated identification trials, and re-estimates it at every control step of a closed-loop mission while the controller plans on it. A physically admissible unscented Kalman filter re-estimates hydrodynamic, disturbance, and actuator parameters at every control step of the closed-loop mission; while a command-dependent consider projection withholds corrections the current command cannot attribute between actuator effectiveness and external force; and a model predictive path integral controller plans on the current estimate. In simulation on a CyberShip II plant, MOSAIC-SV recovers the transit performance of the calibrated model under static mismatch and transient changes, and stays within 20% of its own transit time at the unscaled prior when its inertia or damping prior is wrong by an order of magnitude. In on-water field trials on the Blue Robotics BlueBoat, a twin-thruster catamaran, MOSAIC-SV transits at least 25% faster and predicts its own motion with at least 56% less error than its frozen engineering prior, including under an unmodeled payload. The same MOSAIC-SV system concept was also feasibly deployed on a 6.3-tonne dual outboard monohull.
Chinese Translation
基于模型的水下机器人平台控制依赖于一个水动力模型，该模型的识别成本高，并且特定于船体、有效载荷及其测量条件。在此，我们提出了MOSAIC-SV，一个可部署的实时自适应动态识别与控制系统，该系统能够从工程规格表的先验信息中识别出足够控制的动态模型，而无需专门的识别试验，并在闭环任务的每个控制步骤中重新估计该模型，同时控制器基于此进行规划。一个物理可接受的无迹卡尔曼滤波器在闭环任务的每个控制步骤中重新估计水动力、干扰和执行器参数；而一个依赖于指令的考虑投影则在当前指令无法区分执行器效能与外部力的情况下保留修正；同时，一个模型预测路径积分控制器基于当前估计进行规划。在对CyberShip II平台的仿真中，MOSAIC-SV在静态不匹配和瞬态变化下恢复了标定模型的过渡性能，并在其惯性或阻尼先验错误一个数量级时，保持在未缩放先验的过渡时间的20%以内。在对Blue Robotics BlueBoat（一种双推进器双体船）的水上实地试验中，MOSAIC-SV的过渡速度至少快25%，并且在未建模有效载荷的情况下，预测自身运动的误差至少减少56%，相较于其冻结的工程先验。同样的MOSAIC-SV系统概念也成功部署在一艘6.3吨的双舷外单体船上。
cs.RO / 4 / 2610.03931
Sparse Calibration-Based Personalization of Kernel-Based Gait Phase and Speed Estimation Using Wearable IMUs
基于稀疏校准的个性化内核步态阶段和速度估计方法，采用可穿戴惯性测量单元
Abstract
This paper presents sparse calibration-based personalization for kernel-based gait phase and walking speed co-estimation from wearable inertial measurement units (IMUs). A 28-speed reference library was constructed from bilateral thigh and shank trajectories in a 20-subject locomotion dataset. Rather than directly applying population-average kernels, the method combines a user-specific baseline estimated from three calibration speeds with a principal-component (PC) model of baseline-centered kinematic deviations. Offline validation showed that personalized kernels reduced phase error relative to the population kernel. In a single-participant online pilot evaluation, the personalized kernels produced numerically lower mean phase and speed errors and ran in real time on embedded hardware. However, the speed effect was not significant, and pairwise phase differences did not remain significant after multiple-comparison correction. The pilot evaluation establishes wearable implementation feasibility while indicating reference-to-online dataset mismatch and the need for larger-cohort validation.
Chinese Translation
本文提出了一种基于稀疏校准的个性化方法，用于从可穿戴惯性测量单元（IMUs）中共同估计内核步态阶段和行走速度。我们构建了一个包含28种速度的参考库，该库基于20名受试者的双侧大腿和小腿运动轨迹。该方法并未直接应用人群平均内核，而是结合了从三种校准速度估计的用户特定基线与以基线为中心的运动学偏差的主成分（PC）模型。离线验证显示，个性化内核相较于人群内核减少了阶段误差。在一次单参与者的在线初步评估中，个性化内核产生了数值上更低的平均阶段和速度误差，并在嵌入式硬件上实时运行。然而，速度效应并不显著，且在多重比较校正后，成对阶段差异也未保持显著性。该初步评估确立了可穿戴实现的可行性，同时指出了参考数据集与在线数据集的不匹配以及对更大样本验证的需求。
cs.RO / 5 / 2610.03997
REDIRECT: A 1% Fix for Bad Robot Habits
REDIRECT：修复机器人不良习惯的1%方案
Abstract
Robots can acquire bad habits from a few defective moments in otherwise useful teleoperation. In mixed-quality robot data, normal and problematic demonstrations share most task behavior and differ only at a local action continuation. Full retraining is costly, while fine-tuning on clean data alone offers limited recovery under a small update budget. We ask whether the local difference can instead be fixed with only 1% of the full-training sample-backward budget. Using only episode-level retained/problematic labels, REDIRECT localizes the branch, assigns a coherent retained continuation to the original observations around it, and anchors shared behavior, without frame-level annotations or additional interaction. Across three randomized ManiSkill tasks and three seeds, REDIRECT raises the mean success rate from 68.2% to 90.3%, recovering 88.8% of the clean-retraining gap versus 22.1% for matched-compute fine-tuning. On a PiPER arm, it recovers 86.7-92.9% of the clean-only gap across cup insertion and towel folding. Local robot habits can therefore be repaired by spending the update budget on the behavioral difference rather than relearning shared behavior.
Chinese Translation
机器人可以从一些缺陷时刻中养成不良习惯，即使在其他有用的远程操作中也是如此。在混合质量的机器人数据中，正常和有问题的演示共享大部分任务行为，仅在局部动作延续上存在差异。全面重新训练成本高昂，而仅在干净数据上进行微调在小规模更新预算下恢复效果有限。我们探讨是否可以仅用全训练样本的1%预算来修复局部差异。REDIRECT仅使用保留/问题标签的情节级别信息，定位分支，为其周围的原始观察分配一致的保留延续，并锚定共享行为，而无需逐帧注释或额外交互。在三个随机化的ManiSkill任务和三个种子上，REDIRECT将平均成功率从68.2%提高到90.3%，恢复了88.8%的干净重新训练差距，而匹配计算的微调仅为22.1%。在PiPER臂上，它在杯子插入和毛巾折叠任务中恢复了86.7%-92.9%的干净差距。因此，可以通过将更新预算用于行为差异而非重新学习共享行为来修复局部机器人习惯。
cs.RO / 6 / 2610.04009
SUAVE: Unified Video-Action Models via Masked Diffusion
SUAVE：通过掩蔽扩散实现统一的视频-动作模型
Abstract
Vision-language-action models (VLAs) inherit strong semantic grounding from pretrained vision-language backbones but are typically optimized for predicting actions rather than future observations. They can see and act, but they do not imagine the future before acting. World action models (WAMs) built on video diffusion backbones can imagine but treat language as frozen conditioning on a continuous latent space. Unified models bring these modalities into one architecture, but they either decode autoregressively, one token at a time, or keep video continuous with an auxiliary action head. In this work, we present SUAVE, a Single vocabulary Unified Action-Video modEl in which a masked diffusion transformer generates video and actions conditioned on language, with all three modalities represented as discrete tokens in a shared sequence. Choosing which tokens to mask at inference turns the same network into a world model, a robot policy, or a video-action model. For action-free co-training, the action positions of unlabeled video are filled with mask tokens and excluded from the loss. Simulation and real-world experiments demonstrate two findings. First, a single SUAVE model predicts long-horizon video and acts as a policy, competitive with dedicated world models and specialized action policies on static and dynamic manipulation tasks. On a real robot, our model generates subgoal images and an action chunk spanning one second of motion in 1,030 ms on an RTX 5090 GPU, sustaining closed-loop control at 2.5 actions per second. Second, pretraining on robot video and co-training on human video substantially improves policy performance and zero-shot robustness to distribution shift. Together, these results show that masked diffusion is a practical and versatile foundation for unified video-action modeling.
Chinese Translation
视觉-语言-动作模型（VLAs）从预训练的视觉-语言基础模型中继承了强大的语义基础，但通常是针对预测动作而非未来观察进行优化。它们能够观察和行动，但在行动之前并不想象未来。基于视频扩散基础模型构建的世界动作模型（WAMs）能够进行想象，但将语言视为在连续潜在空间上的固定条件。统一模型将这些模态整合到一个架构中，但它们要么以自回归方式逐个解码，要么通过辅助动作头保持视频的连续性。在本研究中，我们提出了SUAVE，一个单一词汇的统一动作-视频模型，其中一个掩蔽扩散变换器根据语言生成视频和动作，所有三种模态在共享序列中表示为离散标记。在推理时选择掩蔽哪些标记，使得同一网络可以转变为世界模型、机器人策略或视频-动作模型。为了实现无动作的共同训练，未标记视频的动作位置用掩蔽标记填充，并从损失中排除。仿真和现实世界实验展示了两个发现。首先，单一的SUAVE模型能够预测长时间的视频并作为策略进行操作，在静态和动态操作任务中与专门的世界模型和专业的动作策略竞争。在一台真实的机器人上，我们的模型在RTX 5090 GPU上以1,030毫秒生成子目标图像和跨越一秒运动的动作块，维持每秒2.5次的闭环控制。其次，在机器人视频上进行预训练和在人类视频上进行共同训练显著提高了策略性能和对分布变化的零样本鲁棒性。综合来看，这些结果表明，掩蔽扩散是统一视频-动作建模的实用且多功能的基础。
cs.RO / 7 / 2610.04013
Return-to-Home Feasible Micro-Aerial Vehicle Exploration for 3D Gaussian Splatting Reconstruction
适用于3D高斯点云重建的可返回家园微型空中飞行器探索
Abstract
Micro aerial vehicles (MAVs) enable rapid indoor 3D reconstruction for inspection and time-critical situational awareness, but must operate under strict flight-time budgets and safety constraints that require explicit return-to-home (RTH) feasibility with a conservative margin. We present a topology-first active reconstruction framework that decouples high-fidelity rendering from navigation. A dense 3D Gaussian Splatting (3DGS) map is optimized as the reconstruction target, while a lightweight Truncated Signed Distance Field (TSDF)/occupancy scaffold supports conservative collision checking and online construction of a sparse 3D Voronoi skeleton roadmap. Since directly extracting roadmaps from TSDF geometry can be unstable under noisy and incomplete online fusion, we validate nodes and edges using carved free-space consistency and dense visibility checks, which suppress behind-wall phantom structure and stabilize planning. Viewpoints are selected on the roadmap using a flight-time-budget-aware objective and a lightweight receding-horizon lookahead to improve non-myopic exploration behavior. We evaluate on photorealistic indoor simulation benchmarks (ReplicaCAD, Gibson, and HM3D), reporting reconstruction coverage/error, RTH success, and computational cost. Results demonstrate improved or competitive performance relative to recent GS-based baselines under identical flight-time budgets.
Chinese Translation
微型空中飞行器（MAVs）能够快速进行室内3D重建，以便进行检查和满足时间敏感的态势感知，但必须在严格的飞行时间预算和安全约束下运行，这要求明确的返回家园（RTH）可行性，并留有保守的余地。我们提出了一种以拓扑为先的主动重建框架，将高保真渲染与导航解耦。优化后的稠密3D高斯点云（3DGS）地图作为重建目标，而轻量级的截断签名距离场（TSDF）/占用支架则支持保守的碰撞检测和稀疏3D Voronoi骨架路线图的在线构建。由于直接从TSDF几何体提取路线图在噪声和不完整的在线融合下可能不稳定，我们通过雕刻自由空间一致性和稠密可见性检查来验证节点和边，这抑制了墙后虚假结构并稳定了规划。使用考虑飞行时间预算的目标和轻量级的递归视野前瞻方法在路线图上选择视点，以改善非短视探索行为。我们在逼真的室内仿真基准（ReplicaCAD、Gibson和HM3D）上进行了评估，报告了重建覆盖率/误差、RTH成功率和计算成本。结果表明，在相同的飞行时间预算下，相较于近期基于GS的方法，性能有所提升或具有竞争力。
cs.RO / 8 / 2610.04022
A Biomimetic Gaze Control to Restore Head-Neck Movements
一种仿生注视控制方法以恢复头颈运动
Abstract
Powered neck exoskeletons are developed to restore head-neck motions for those with neck muscle weakness. Current methods using hand-held input devices to control these robotic systems are unintuitive or non-viable for these populations. Alternatively, the eyes coordinate with the head to accomplish many daily tasks. Therefore, movements of the eyes can be a useful signal to control the neck exoskeletons without needing to use the hands. However, previous control models lack biobehavioral basis as well as means to personalize the control behavior, causing unnatural head-neck movements. This paper presents a novel eye-tracking controller design leveraging the vestibulo-ocular reflex to emulate natural eyehead coordination. Personalization tools were also designed, consisting of sliders to allow users to adjust the parameters of this controller. Here, we present the technical design and validation of this system in human users. Compared to the previous eye-tracking model, the new controller provides natural head motions personalized to the user through more natural velocity profiles and a 24 percent average increase of accurate motion intent detection. This system provides a powerful platform for studying eye-tracking controllers in the context of personalization and preference, and ultimately restoring the lost head-neck mobility in patient populations.
Chinese Translation
为恢复颈部肌肉无力患者的头颈运动，开发了动力颈部外骨骼。目前使用手持输入设备控制这些机器人系统的方法对这些人群来说不够直观或不可行。相反，眼睛与头部协调完成许多日常任务。因此，眼睛的运动可以作为一个有用的信号来控制颈部外骨骼，而无需使用手。然而，以往的控制模型缺乏生物行为基础以及个性化控制行为的手段，导致头颈运动不自然。本文提出了一种新颖的眼动追踪控制器设计，利用前庭眼动反射（vestibulo-ocular reflex）来模拟自然的眼头协调。还设计了个性化工具，包括滑块，允许用户调整该控制器的参数。在此，我们展示了该系统在人体用户中的技术设计和验证。与之前的眼动追踪模型相比，新控制器通过更自然的速度曲线和24%的平均准确运动意图检测提升，为用户提供了个性化的自然头部运动。该系统为在个性化和偏好的背景下研究眼动追踪控制器提供了强大的平台，最终恢复患者群体失去的头颈活动能力。
cs.RO / 9 / 2610.04054
Reward-DAgger: Robot-Gated Interactive Imitation Learning with General-Purpose Progress-Based Reward Models
奖励-DAgger：基于通用进度奖励模型的机器人门控互动模仿学习
Abstract
Recent advances in robot learning have enabled generalist control policies capable of completing a wide range of tasks. However, their performance degrades when deployed in unseen environments, making it critical to detect failures and teach recovery behaviors. Existing runtime monitoring methods often require task- and policy-specific training or hyperparameter tuning, limiting cross-task deployment and introducing additional overhead during iterative policy updates. We present Reward-DAgger, a robot-gated interactive imitation learning framework that uses dense progress signals from a general-purpose reward model to determine when human intervention is needed. Our approach is agnostic to the underlying policy architecture, requires no access to policy internals, and can be applied across tasks without retuning the gating mechanism. Our results show that Reward-DAgger achieves a better failure-detection accuracy-latency tradeoff than existing runtime monitoring baselines. Across eight simulated and real-world tasks, Reward-DAgger consistently improves the downstream policy's autonomous success rate throughout interactive learning and achieves strong return on human effort, outperforming the baselines in most settings. Importantly, the same gating configuration is used across tasks without task-specific hyperparameter tuning, demonstrating transfer across tasks, environments, and policy architectures. Code and videos are available at https://liralab.usc.edu/reward-dagger.
Chinese Translation
近期机器人学习的进展使得通用控制策略能够完成广泛的任务。然而，当在未见过的环境中部署时，它们的性能会下降，因此检测失败并教授恢复行为变得至关重要。现有的运行时监控方法通常需要特定于任务和策略的训练或超参数调整，这限制了跨任务的部署，并在迭代策略更新过程中引入了额外的开销。我们提出了奖励-DAgger，这是一种机器人门控互动模仿学习框架，利用来自通用奖励模型的密集进度信号来确定何时需要人类干预。我们的方法对底层策略架构不敏感，无需访问策略内部，并且可以在不同任务中应用而无需重新调整门控机制。我们的结果表明，奖励-DAgger在失败检测的准确性与延迟之间的权衡上优于现有的运行时监控基线。在八个模拟和真实世界任务中，奖励-DAgger在互动学习过程中始终提高了下游策略的自主成功率，并在大多数设置中超越了基线，展现出强大的人工努力回报。重要的是，跨任务使用相同的门控配置，无需特定于任务的超参数调整，展示了在任务、环境和策略架构之间的迁移能力。代码和视频可在 https://liralab.usc.edu/reward-dagger 获取。
cs.RO / 10 / 2610.04144
Terrain-Dependent Intra-Cycle Leg Timing for Effective Locomotion on Granular Slopes
基于地形的循环内腿时序以实现有效的颗粒坡道运动
Abstract
Locomotion on granular slopes such as sand dunes remains a fundamental challenge for legged robots due to reduced shear strength and gravity-induced anisotropic yielding of granular media. Using a hexapedal robot on a tiltable granular bed, we systematically measure locomotion speed together with slope-dependent normal and shear granular resistive forces. While normal penetration resistance remains nearly unchanged with inclination, shear resistance decreases substantially as slope angle increases. Guided by these measurements, we develop a simple robot-terrain interaction model that predicts anchoring timing, step length, and resulting robot speed, as functions of terrain strength and slope angle. The model reveals that slope-induced performance loss is primarily governed by delayed anchoring and increased backward slip rather than excessive sinkage. By extending the model to generalized terrain conditions, we construct failure phase diagrams that identify sinkage- and slippage-induced failure regimes, enabling quantitative risk estimation for locomotion on granular slopes. This physics-informed framework provides predictive insight into terrain-dependent failure mechanisms and offers guidance for safer and more robust robot operation on deformable inclines.
Chinese Translation
在沙丘等颗粒坡道上进行运动对腿式机器人来说仍然是一个基本挑战，因为颗粒介质的剪切强度降低以及重力引起的各向异性屈服。我们使用一台六足机器人在可倾斜的颗粒床上系统地测量运动速度，以及与坡度相关的法向和剪切颗粒阻力。尽管法向穿透阻力在倾斜度变化时几乎保持不变，但随着坡度角度的增加，剪切阻力显著降低。在这些测量的指导下，我们开发了一个简单的机器人-地形交互模型，该模型预测锚定时机、步长和相应的机器人速度，作为地形强度和坡度角度的函数。模型揭示，坡度引起的性能损失主要由锚定延迟和向后滑动增加所主导，而不是过度下沉。通过将模型扩展到一般化的地形条件，我们构建了失效相图，识别出由下沉和滑动引起的失效区域，从而实现对颗粒坡道运动的定量风险评估。这个以物理为基础的框架提供了对地形依赖性失效机制的预测性洞察，并为在可变坡度上更安全、更稳健的机器人操作提供了指导。
cs.RO / 11 / 2610.04187
Autoware in Construction: Gap Analysis and LiDAR Perception Toward Off-Road Autonomous Driving
建筑中的Autoware：差距分析与LiDAR感知在越野自动驾驶中的应用
Abstract
Autoware is an open-source autonomous driving software platform widely adopted by researchers and industry developers. Originally developed primarily for public-road applications, including passenger vehicles, taxis, and buses, Autoware is increasingly being extended to off-road environments such as construction and agricultural sites. Construction sites, however, differ fundamentally from public roads and challenge many assumptions underlying conventional autonomous driving systems. They are characterized by unstructured terrain, airborne dust, continuously evolving site conditions, and construction-specific objects. This paper presents lessons learned from ongoing efforts to extend an Autoware-based autonomous driving system to large dump trucks operating at construction sites. We identify gaps between public-road and off-road construction applications across the sensing, mapping, localization, perception, planning, and control modules of the Autoware stack. We then discuss potential solutions for addressing these gaps, with particular emphasis on ongoing LiDAR-based perception pipeline development and findings from field testing. Finally, we propose a roadmap toward end-to-end autonomous driving for construction vehicles.
Chinese Translation
Autoware是一个开源的自动驾驶软件平台，广泛被研究人员和行业开发者采用。最初主要为公共道路应用开发，包括乘用车、出租车和公交车，Autoware正逐渐扩展到建筑和农业等越野环境。然而，建筑工地在根本上与公共道路不同，挑战了传统自动驾驶系统的许多假设。建筑工地的特点包括非结构化地形、空气中的尘埃、不断变化的现场条件以及特定于建筑的物体。本文总结了将基于Autoware的自动驾驶系统扩展到在建筑工地上运行的大型自卸卡车的持续努力中获得的经验教训。我们识别了公共道路与越野建筑应用之间在Autoware堆栈的传感、映射、定位、感知、规划和控制模块方面的差距。接着，我们讨论了针对这些差距的潜在解决方案，特别强调了正在进行的基于LiDAR的感知管道开发及现场测试的发现。最后，我们提出了一条通往建筑车辆端到端自动驾驶的路线图。
cs.RO / 12 / 2610.04220
Bi-manual Stabilization of the Cervical Spine for Safe Physical Human-Robot Interaction in Rolling Maneuvers
双手稳定颈椎以确保安全的人机交互在滚动操作中的应用
Abstract
The neck, specifically the cervical spine, is a highly fragile and mobile part of the body with a high risk of catastrophic injury. Whether someone is injured on a battlefield, in a natural disaster, or in an athletic event, great care is always taken with the cervical spine when transporting, rolling, or moving the patient. In this work, we present an algorithm and adaptations for bi-manual robots to stabilize the cervical spine during rolling maneuvers. We investigate techniques to maximize grip strength within safety bounds and present a control approach for proper neck rotation. We found that with our approach, a bi-manual robot is capable of achieving human-level performance in maintaining target rotational alignments. The axial alignment for the robot with prediction was within .21 degrees of human novices.
Chinese Translation
颈部，特别是颈椎，是身体中一个高度脆弱且灵活的部分，具有较高的灾难性伤害风险。无论是在战场、自然灾害还是体育赛事中，运输、滚动或移动患者时，颈椎始终需要特别小心。在本研究中，我们提出了一种算法及其对双手机器人在滚动操作中稳定颈椎的适应性。我们研究了在安全范围内最大化抓握力量的技术，并提出了一种适当的颈部旋转控制方法。我们发现，通过我们的方法，双手机器人能够在维持目标旋转对齐方面达到人类水平的表现。经过预测的机器人轴向对齐与人类新手之间的差异在0.21度以内。
cs.RO / 13 / 2610.04231
Continual Humanoid Motion Learning
持续类人运动学习
Abstract
Humanoid whole-body controllers can now track a diverse set of dynamic motions, but they are typically trained offline and then frozen, so teaching such a controller a new skill tends to erode the skills it already mastered. We study continual learning for humanoid whole-body motion, where a single controller must acquire skills from a sequential task stream without revisiting past data. We introduce Similarity-guided LoRA-PNN, a progressive neural network (PNN) policy that prevents catastrophic forgetting by construction while reusing knowledge across skills through lightweight low-rank adaptation. A two-level motion-similarity measure, built from dynamic time warping aggregated by optimal transport, decides which prior skill to build on and how much new capacity to allocate, yielding strong forward transfer and large efficiency gains. Across six sequentially learned skill categories, our similarity-guided LoRA policy attains the best forward transfer (0.125 vs. 0.079) and the highest average accuracy among all methods, while saving up to 94.5% of trainable parameters and 40.8% of training time. The resulting controller reaches 96.13% sim-to-sim transfer and is deployed on a physical Unitree G1. Our code is available at https://anonymous.4open.science/r/continual-humanoid-learning-35D3.
Chinese Translation
类人全身控制器现在可以跟踪多样化的动态动作，但它们通常是在离线状态下训练并被冻结，因此教会这样的控制器一项新技能往往会削弱其已经掌握的技能。我们研究了类人全身运动的持续学习，其中一个控制器必须从一个连续的任务流中获取技能，而不重新访问过去的数据。我们引入了相似性引导的LoRA-PNN（低秩适应的渐进神经网络）策略，该策略通过构建防止灾难性遗忘的机制，同时通过轻量级的低秩适应在技能之间重用知识。基于动态时间规整和最优传输聚合构建的两级运动相似性度量，决定了基于哪个先前技能进行构建以及分配多少新能力，从而实现强大的前向迁移和显著的效率提升。在六个连续学习的技能类别中，我们的相似性引导的LoRA策略在所有方法中获得了最佳的前向迁移（0.125 vs. 0.079）和最高的平均准确率，同时节省了多达94.5%的可训练参数和40.8%的训练时间。最终的控制器达到了96.13%的仿真到仿真迁移，并在物理Unitree G1上部署。我们的代码可在 https://anonymous.4open.science/r/continual-humanoid-learning-35D3 获取。
cs.RO / 14 / 2610.04238
Humanoid Rickshaw Pulling: Whole-Body Locomotion under Coupled Wheeled Loads
类人机器人拉人力车：在耦合轮载下的全身运动
Abstract
Humanoid robots could transport payloads substantially heavier than themselves by pulling passive wheeled vehicles instead of carrying the load. This capability, however, creates a coupled locomotion problem: the robot must maintain persistent upper-body contact while adapting to unknown, configuration-dependent forces arising from the payload, vehicle, and terrain. We present a whole-body control framework for humanoid rickshaw pulling that tracks commanded vehicle motion while preserving balance and stable grasps under uncertain load dynamics. During training, a privileged teacher exploits vehicle states, interaction forces, and load properties. Its actions and latent are distilled into a history-conditioned student that implicitly infers coupled dynamics from proprioceptive responses, followed by reinforcement-learning fine-tuning. Comparisons with \emph{No History} and \emph{Only History} baselines show that the resulting policy achieves accurate vehicle tracking while reducing vehicle oscillation, torso tilt, and actuation cost. Behavioral analysis shows that Unitree G1 propels the rickshaw and generates gait-synchronized whole-body reactions that stabilize its lateral and roll motions. Moreover, pulling redistributes joint effort and yields a lower robot-normalized cost-of-transport proxy than unloaded walking over most tested load--speed conditions. On hardware, a single policy performs starting, sustained pulling, turning, and stopping with both rigid payloads and human passengers, handling a loaded rickshaw mass of up to 115~kg without load-specific retuning. These results demonstrate robust heavy-load transportation through coordinated and persistent humanoid--vehicle interaction.
Chinese Translation
类人机器人可以通过拉动被动轮式车辆来运输比自身重得多的有效载荷，而不是直接携带负载。然而，这种能力带来了一个耦合运动问题：机器人必须在适应来自有效载荷、车辆和地形的未知、依赖于配置的力的同时，保持上半身的持续接触。我们提出了一种用于类人拉人力车的全身控制框架，该框架在不确定负载动态下跟踪指令车辆运动，同时保持平衡和稳定的抓握。在训练过程中，特权教师利用车辆状态、交互力和负载特性。其动作和潜在信息被提炼成一个历史条件的学生，该学生通过本体感知反应隐式推断耦合动态，随后进行强化学习微调。与 extit{无历史}和 extit{仅历史}基线的比较表明，所得到的策略在减少车辆振荡、躯干倾斜和驱动成本的同时，实现了准确的车辆跟踪。行为分析表明，Unitree G1推动人力车并产生步态同步的全身反应，从而稳定其横向和翻滚运动。此外，拉动重新分配了关节努力，并在大多数测试的负载-速度条件下产生了比无负载行走更低的机器人归一化运输成本代理。在硬件上，单一策略能够在不针对特定负载重新调校的情况下，执行起步、持续拉动、转弯和停止，处理高达115 kg的载重人力车。这些结果展示了通过协调和持续的类人-车辆交互实现的稳健重载运输能力。
cs.RO / 15 / 2610.04240
Attention-Based Surface Representation Learning for Robot State Prediction and Open-Ended Surface Classification
基于注意力的表面表示学习用于机器人状态预测和开放式表面分类
Abstract
For ground robots operating in outdoor environments, understanding the properties of the underlying terrain is essential for ensuring reliable operation. In most perception-based studies, this problem is formulated as categorical classification with a fixed number of classes defined during training. We propose an approach that enables new surface classes to be added as trainable vectors, which can subsequently be used to address higher-level tasks. By employing a learning paradigm based on predicting the robot's next state in time and using attention blocks, we improved classification accuracy to 98.56% on the Belyaev-Kushnarev dataset and 94.8% on BorealTC.
Chinese Translation
对于在户外环境中运行的地面机器人，理解基础地形的特性对于确保可靠操作至关重要。在大多数基于感知的研究中，这个问题被表述为具有固定类别数量的分类问题，该数量在训练期间定义。我们提出了一种方法，允许将新的表面类别作为可训练向量添加，这些向量随后可以用于解决更高层次的任务。通过采用基于预测机器人下一个状态的学习范式，并使用注意力模块，我们在Belyaev-Kushnarev数据集上的分类准确率提高至98.56%，在BorealTC数据集上的准确率提高至94.8%。
cs.RO / 16 / 2610.04250
ROOT: Discovering Rewards for User-Specified Embodied Behaviors
ROOT：发现用户指定的具身行为的奖励
Abstract
Reinforcement learning for embodied control remains constrained by the difficulty of reward specification. Although recent large language model (LLM)-based methods can synthesize reward functions from natural-language descriptions, they often fail to capture subtle behavioral properties that humans care about, such as natural gait, posture, and movement style. This limitation arises because many desired behaviors are easier to recognize visually than to encode in a reward function. We introduce Reward Optimization via Observable Trees (ROOT), a framework for discovering reward functions that align learned policies with user-specified embodied behaviors. Rather than relying solely on scalar training statistics, ROOT casts reward design as an observation-guided search over a persistent experiment tree that stores reward programs, trained policies, and rollout observations, together with behavioral insights distilled by a video-language model, to diagnose behavioral failures and guide subsequent reward refinements. We evaluate ROOT on seven tasks across four embodiments: simulated Hopper, HalfCheetah, Ant, Unitree Go2, and as well as the real-world Unitree Go2. ROOT produces behaviors that better align with user intent than those generated by existing LLM-based reward-generation methods, achieving up to 86.8% locomotion-completeness accuracy and improving Vid-LLM behavioral alignment from 3.56/5 to 4.14/5, a 16.5% improvement over baselines. Human evaluations further support these results, with ROOT preferred in 51-63% of pairwise comparisons.
Chinese Translation
具身控制的强化学习仍然受到奖励规范难度的限制。尽管最近基于大型语言模型（LLM）的方法可以从自然语言描述中合成奖励函数，但它们往往无法捕捉人类关心的细微行为特性，例如自然步态、姿势和运动风格。这一限制的原因在于，许多期望的行为在视觉上更容易识别，而不是在奖励函数中编码。我们提出了可观察树奖励优化（ROOT），这是一个发现奖励函数的框架，旨在将学习到的策略与用户指定的具身行为对齐。ROOT并不单纯依赖标量训练统计，而是将奖励设计视为对一个持久实验树的观察引导搜索，该树存储奖励程序、训练策略和回滚观察，以及通过视频语言模型提炼的行为洞察，以诊断行为失败并指导后续的奖励优化。我们在四种具身体态的七个任务上评估了ROOT：模拟的Hopper、HalfCheetah、Ant、Unitree Go2，以及现实世界中的Unitree Go2。ROOT产生的行为与用户意图的对齐程度优于现有基于LLM的奖励生成方法，达到了最高86.8%的运动完整性准确率，并将Vid-LLM的行为对齐从3.56/5提高到4.14/5，较基线提高了16.5%。人类评估进一步支持了这些结果，在51-63%的成对比较中更倾向于ROOT。
cs.RO / 17 / 2610.04255
Grounded in Time: A Multi-Source Dataset and Benchmark for Temporal Grounding in Robotic Manipulation
扎根于时间：一个多源数据集及机器人操作中的时间基础基准
Abstract
Robotic manipulation often requires inferring task-relevant states from past interactions when the current observation alone is insufficient to determine the appropriate action. Despite progress in benchmarking memory-augmented vision-language-action (VLA) models, application-oriented tasks requiring history-dependent semantic inference remain underrepresented. We introduce GiT (Grounded in Time), a dataset and benchmark for grounding manipulation decisions in past events across biolaboratory, household, and industrial scenarios. It includes real-robot and Universal Manipulation Interface (UMI) style demonstrations covering 18 bimanual tasks, together with simulation data and a ManiSkill-based evaluation suite covering nine tasks. Fine-grained subtask annotations and annotated counterfactual task pairs, in which similar current observations require different actions depending on prior events, support policy learning and targeted evaluation of history use. Evaluations of representative end-to-end VLA models in simulation and on selected real-world tasks reveal substantial room for improvement in history-dependent manipulation. The dataset and benchmark are available at the project page.
Chinese Translation
机器人操作通常需要从过去的交互中推断与任务相关的状态，当当前观察不足以确定适当的行动时。尽管在基准测试增强记忆的视觉-语言-行动（VLA）模型方面取得了一定进展，但需要依赖历史的语义推理的应用导向任务仍然代表性不足。我们介绍了GiT（扎根于时间），这是一个用于在生物实验室、家庭和工业场景中基于过去事件来确定操作决策的数据集和基准。它包括真实机器人和通用操作接口（UMI）风格的演示，涵盖18个双手任务，以及模拟数据和基于ManiSkill的评估套件，涵盖九个任务。细粒度的子任务注释和注释的反事实任务对，其中相似的当前观察根据先前事件需要不同的行动，支持策略学习和历史使用的针对性评估。在模拟和选定的现实世界任务中对代表性端到端VLA模型的评估显示，历史依赖的操作仍有显著的改进空间。该数据集和基准可在项目页面获取。
cs.RO / 18 / 2610.04266
Real-Time Conformal-Seeded Hybrid Inverse Kinematics for Offset Redundant Manipulators
实时共形种子混合逆运动学用于偏置冗余操纵器
Abstract
This paper presents a conformal-seeded hybrid strategy for solving inverse kinematics of offset, redundant 7-DoF robot arms of the humanoid class. Analytical inverse kinematics (AIK) provides closed-form solutions with very low computational cost. However, for offset kinematic structures, the exact closed-form solution is generally unavailable, and practical AIK must rely on an approximate or simplified kinematic model. In contrast, numerical inverse kinematics (NIK) can achieve high-precision solutions on the full kinematic model. However, its convergence is highly sensitive to initialization. To overcome these limitations, we propose a two-stage hybrid inverse kinematics framework with conformal-calibrated seed selection. First, an approximate analytical model efficiently enumerates a finite set of candidate joint solutions. Second, we rank these candidates using a lightweight learned predictor of post-refinement difficulty, wrapped by split-conformal prediction into a calibrated upper bound that serves as the selection score. The best-ranked seed is then refined using a Levenberg-Marquardt solver on the full kinematic model. The proposed method combines fast candidate generation, learned seed ranking with a calibrated difficulty bound, and accurate numerical refinement, achieving real-time performance of less than 40us and a success rate of 100% in our evaluation on reachable targets. We validate the approach through large-scale stochastic simulation across the workspace and experimental demonstrations with motion planning on a humanoid robot arm. Demonstration videos are available at https://youtu.be/aeiBmw1XRbw.
Chinese Translation
本文提出了一种共形种子混合策略，用于解决人形类偏置冗余7自由度机器人臂的逆运动学问题。解析逆运动学（AIK）提供了计算成本极低的闭式解。然而，对于偏置运动学结构，通常无法获得精确的闭式解，实际的AIK必须依赖于近似或简化的运动学模型。相对而言，数值逆运动学（NIK）能够在完整的运动学模型上实现高精度解，但其收敛性对初始化高度敏感。为克服这些局限性，我们提出了一种两阶段的混合逆运动学框架，采用共形校准的种子选择。首先，近似解析模型有效地枚举了一组有限的候选关节解。其次，我们使用轻量级学习预测器对后期精炼难度进行排名，并通过分裂共形预测将其包装成一个校准的上界，作为选择评分。然后，使用Levenberg-Marquardt求解器对最佳排名的种子进行精炼，基于完整的运动学模型。所提方法结合了快速的候选生成、带有校准难度界限的学习种子排名以及精确的数值精炼，实现了低于40微秒的实时性能，并在我们对可达目标的评估中取得了100%的成功率。我们通过在工作空间内的大规模随机仿真和在一个人形机器人臂上的运动规划实验演示验证了该方法。演示视频可在https://youtu.be/aeiBmw1XRbw观看。
cs.RO / 19 / 2610.04342
Asynchronous Tracking, Optical Communication and 3D Motion Capture using Event-based Sensors
基于事件传感器的异步跟踪、光通信与三维运动捕捉
Abstract
Inter-robot communication is a key enabling technology in cooperative robotics applications. While wireless communication is ubiquitous, simple, and effective, it has inherent limitations: the receiving robot cannot easily localise the source of an incoming signal, clock synchronisation is challenging, and signals are broadcast to all nearby devices rather than targeted recipients. Optical communication provides a complementary channel that is inherently directional and spatially localised, directly addressing these limitations. Event cameras are bio-inspired dynamic vision sensors that respond to changes in image intensity with high temporal resolution, high dynamic range, and low latency, making them well-suited as receivers for high-rate optical communication in cooperative robotic systems. In this paper, we propose the Asynchronous Tracking and Optical Communication (ATOC) system, which integrates LED smart-beacon modulation, event-based detection, optical tracking, and event-data demodulation in a single pipeline. By simultaneously tracking and demodulating multiple LED smart beacons, ATOC transforms a conventional visual marker into a robust communication channel suitable for a wide range of high-impact robotics applications. We validate ATOC in a suite of laboratory studies and two 'applications': a smart-city demonstration and a 3D motion-capture system.
Chinese Translation
机器人间通信是合作机器人应用中的关键技术。尽管无线通信无处不在、简单且有效，但其固有的局限性也显而易见：接收机器人无法轻易定位到来信号的源头，时钟同步存在挑战，并且信号是广播给所有附近设备而非目标接收者。光通信提供了一种互补的通道，具有固有的方向性和空间局部性，直接解决了这些局限性。事件相机是一种受生物启发的动态视觉传感器，能够以高时间分辨率、高动态范围和低延迟响应图像强度的变化，使其非常适合作为合作机器人系统中高频光通信的接收器。本文提出了异步跟踪与光通信（Asynchronous Tracking and Optical Communication，ATOC）系统，该系统将LED智能信标调制、基于事件的检测、光跟踪和事件数据解调整合在一个单一的流程中。通过同时跟踪和解调多个LED智能信标，ATOC将传统视觉标记转变为一种强大的通信通道，适用于广泛的高影响力机器人应用。我们在一系列实验室研究和两个“应用”中验证了ATOC：一个智能城市演示和一个三维运动捕捉系统。
cs.RO / 20 / 2610.04363
TacOT: Learning Contact-Rich Dexterous Manipulation from Human Demonstrations via Tactile-Guided Optimal Transport
TacOT：通过触觉引导的最优传输从人类示范中学习接触丰富的灵巧操作
Abstract
Learning contact-rich dexterous manipulation from human demonstrations provides a scalable source of interaction data, yet transferring such skills to robots remains challenging due to unreliable human--robot correspondence. Existing human-to-robot transfer methods typically rely on visual appearance or motion similarity, which may associate similar motions with different contact states and force patterns. Tactile dynamics provide interaction-aware cues to distinguish manipulation processes with similar motions but different contact states. We introduce tactile-guided optimal transport (TacOT), a framework for human-to-robot contact-rich manipulation. TacOT leverages action--tactile dynamic time warping to identify human--robot demonstration correspondences with consistent interaction dynamics and uses these correspondences to guide soft optimal transport alignment in a shared policy representation space. This enables human demonstrations to provide contact-rich supervision for robot policy learning without requiring predefined frame-level human--robot pairing. Across four real-world dexterous manipulation tasks, TacOT improves closed-loop success rates over action-guided OT by up to 17 points on in-distribution tasks and 20 points under targeted human-to-robot out-of-distribution transfer. Further analyses show that tactile-guided correspondence selects demonstration pairs with more consistent contact dynamics and produces latent representations that better reflect interaction-state evolution. These results demonstrate that tactile dynamics provide an effective semantic signal for establishing reliable human-to-robot correspondence in contact-rich dexterous manipulation.
Chinese Translation
从人类示范中学习接触丰富的灵巧操作提供了一种可扩展的交互数据来源，但将此类技能转移到机器人上仍然具有挑战性，因为人类与机器人之间的对应关系不可靠。现有的人类到机器人转移方法通常依赖于视觉外观或运动相似性，这可能将相似的运动与不同的接触状态和力模式关联起来。触觉动态提供了交互感知的线索，以区分具有相似运动但不同接触状态的操作过程。我们提出了触觉引导的最优传输（TacOT），这是一个用于人类到机器人接触丰富操作的框架。TacOT利用动作-触觉动态时间扭曲来识别具有一致交互动态的人类-机器人示范对应关系，并使用这些对应关系来指导共享策略表示空间中的软最优传输对齐。这使得人类示范能够为机器人策略学习提供接触丰富的监督，而无需预定义的帧级人类-机器人配对。在四个真实世界的灵巧操作任务中，TacOT在分布内任务上将闭环成功率提高了多达17个百分点，在针对性的人类到机器人分布外转移中提高了20个百分点。进一步分析表明，触觉引导的对应关系选择了具有更一致接触动态的示范对，并生成了更好地反映交互状态演变的潜在表示。这些结果表明，触觉动态为建立可靠的人类到机器人对应关系提供了有效的语义信号，适用于接触丰富的灵巧操作。
cs.RO / 21 / 2610.04366
Human Behavior-Informed Crash Scenario Generation with Real-World Crash Priors for Autonomous Vehicle Safety Evaluation
基于人类行为的碰撞场景生成与真实世界碰撞先验用于自动驾驶车辆安全评估
Abstract
Reliable safety evaluation of autonomous vehicles (AVs) is essential to improving road safety, yet it depends critically on realistic simulation of rare crashes. Existing crash scenario generation methods can increase collision occurrence, but often fail to realistically reproduce how crashes evolve before impact or the distribution of crash types observed in the real world. Here, we present CrashSim, a human behavior-informed crash scenario generation framework that uses real-world crash priors to guide generative multi-agent traffic simulation for more reliable AV safety evaluation. These priors capture how real-world crashes evolve before impact and how different crash types are distributed, allowing limited crash data to guide realistic and scalable scenario generation across naturalistic driving contexts. We evaluate CrashSim against competing methods, showing that it more closely reproduces real-world pre-impact behavior, collision dynamics, collision geometry and crash-type distributions. We further use CrashSim to construct nuCrash dataset, containing over 4,000 crash and near-crash scenarios. Closed-loop evaluation of five AV planners shows that nuCrash more effectively exposes differences in planner safety capabilities than nuScenes. An LLM-assisted evaluation agent further analyzes planner failures to provide capability-level diagnoses and targeted improvement guidance. Together, CrashSim enables realistic and scalable crash generation for more informative AV safety evaluation.
Chinese Translation
对自动驾驶车辆（AV）的可靠安全评估对于提高道路安全至关重要，但这在很大程度上依赖于对稀有碰撞的现实模拟。现有的碰撞场景生成方法可以增加碰撞发生的频率，但往往未能真实再现碰撞在撞击前的演变过程或现实世界中观察到的碰撞类型分布。在此，我们提出了CrashSim，一个基于人类行为的碰撞场景生成框架，利用真实世界的碰撞先验来指导生成多智能体交通模拟，从而实现更可靠的自动驾驶车辆安全评估。这些先验捕捉了现实世界碰撞在撞击前的演变过程以及不同碰撞类型的分布，使得有限的碰撞数据能够指导在自然驾驶环境中进行现实且可扩展的场景生成。我们将CrashSim与竞争方法进行评估，结果表明其更接近于真实世界的撞击前行为、碰撞动态、碰撞几何形状和碰撞类型分布。此外，我们进一步利用CrashSim构建了nuCrash数据集，包含超过4000个碰撞和近碰撞场景。对五种自动驾驶规划器的闭环评估显示，nuCrash比nuScenes更有效地揭示了规划器安全能力的差异。一个基于大型语言模型（LLM）的评估代理进一步分析规划器的失败，以提供能力级别的诊断和针对性的改进指导。总之，CrashSim实现了现实且可扩展的碰撞生成，为更具信息量的自动驾驶车辆安全评估提供了支持。
cs.RO / 22 / 2610.04372
Frame-Level Temporal Alignment for Human-to-Robot Visual Adaptation
人机视觉适应的帧级时间对齐
Abstract
Transferring visual representations pretrained on human videos to robot manipulation requires learning reliable correspondences between human and robot demonstrations. However, paired demonstrations can differ in execution rate and in the proportion of non-key frames that do not directly reflect task progress. Frames at the same relative timestamp may therefore represent different task stages, which can cause correspondence learning to fail. To address these issues, we propose Frame-Level Temporal Alignment (FLTA), a framework that uses two temporal priors to adapt visual encoders pretrained on human videos for robot manipulation. It learns shared task-progress representations without frame-level correspondence annotations, allowing frames at different relative temporal positions to match. A global progress prior combines normalized temporal positions with visual similarity to construct a soft correspondence target. A local temporal order prior penalizes backward transitions while allowing stays and varying forward rates to accommodate execution-rate differences. With ResNet-50 and ViT encoders, our method achieves relative improvements of 46.93% and 65.96%, respectively, over the best baselines in average simulation success rates and achieves higher task success rates on real-world manipulation tasks. These results also suggest that effective human-robot adaptation depends less on the number of parameters updated than on which parameters are selected. Our project page is available at https://rtx5090ultra.github.io/FLTA-Project-Page/.
Chinese Translation
将预训练于人类视频的视觉表征转移到机器人操作中，需要学习人类与机器人演示之间可靠的对应关系。然而，配对演示在执行速率和非关键帧的比例上可能存在差异，这些非关键帧并不直接反映任务进展。因此，相同相对时间戳的帧可能代表不同的任务阶段，这可能导致对应关系学习失败。为了解决这些问题，我们提出了帧级时间对齐（Frame-Level Temporal Alignment, FLTA），这是一个利用两个时间先验来适应预训练于人类视频的视觉编码器以进行机器人操作的框架。它在没有帧级对应注释的情况下学习共享的任务进展表征，从而允许在不同相对时间位置的帧进行匹配。全局进展先验结合了归一化的时间位置与视觉相似性，以构建软对应目标。局部时间顺序先验则惩罚向后过渡，同时允许停留和变化的前进速率，以适应执行速率的差异。使用 ResNet-50 和 ViT 编码器，我们的方法在平均仿真成功率上分别实现了相对于最佳基线的 46.93% 和 65.96% 的相对提升，并在真实世界的操作任务中达到了更高的任务成功率。这些结果还表明，有效的人机适应更依赖于所选择的参数，而非更新的参数数量。我们的项目页面可访问 https://rtx5090ultra.github.io/FLTA-Project-Page/。
cs.RO / 23 / 2610.04391
AgenticTactileVLA: Contact-Guided Execution-Time Supervision for Generalizable Dexterous Manipulation without VLA Retraining
AgenticTactileVLA：无须VLA再训练的可推广灵巧操作的接触引导执行时间监督
Abstract
Vision-language-action policies may predict a transferable manipulation strategy yet fail to realize it reliably on the encountered object: objects compatible with the same grasp differ in geometry and compliance, and visual feedback degrades under closure occlusion. AgenticTactileVLA is presented as an execution-time supervisor that shifts part of object-specific adaptation from prediction to physical interaction. A fixed VLA provides the approach and hand targets; the supervisor decides whether to remain transparent, refine finger flexion, retain or release the corrected configuration, return control to the VLA for retry, or select a compliant hand-control regime. It uses finger-position and motor-effort feedback as proprioceptive contact evidence and requires neither tactile sensors nor VLA retraining. On a Unitree G1 with a BrainCo Revo2 hand, a randomized matched-block evaluation on five objects held out from VLA training yields 61.3% completion for the base VLA, 72.0% for unconditional close-to-stall control, and 84.0% for the supervisor under a shared budget; the gain is positive on every object and persists under moderate pose perturbations. Ablations show the gain is not explained by extended closure alone, and that selective triggering reduces correction episodes by 65.7% with no detected change in completion. A retention audit shows acceptance predicts retention in 88.9% of held-out cases, while compliant objects expose conservative false rejection. A thin-walled-cup study demonstrates contextual routing to compliant control, matching an always-compliant reference. These results suggest that contact-guided execution-time adaptation can improve the object-level generalization of a fixed VLA to held-out objects by adapting physical realization without object-specific retraining.
Chinese Translation
视觉-语言-动作（VLA）策略可能预测出可转移的操作策略，但在实际操作中却无法可靠地实现：与同一抓取兼容的物体在几何形状和顺应性上存在差异，且在闭合遮挡下视觉反馈会下降。本文提出了AgenticTactileVLA作为一种执行时间监督器，将部分物体特定适应性从预测转移到物理交互。固定的VLA提供了接近和手部目标；监督器决定是保持透明、细化手指屈曲、保留或释放修正配置、将控制权返回给VLA进行重试，还是选择顺应的手部控制模式。它利用手指位置和运动努力反馈作为本体接触证据，且不需要触觉传感器或VLA再训练。在一台配备BrainCo Revo2手的Unitree G1上，对五个从VLA训练中保留的物体进行随机匹配区块评估，基础VLA的完成率为61.3%，无条件接近停滞控制为72.0%，而在共享预算下监督器的完成率为84.0%；每个物体的增益均为正，并且在中等姿态扰动下仍然有效。消融实验表明，增益并非仅由延长闭合所解释，且选择性触发将修正事件减少了65.7%，而完成率未见变化。保留审计显示，在88.9%的保留案例中，接受度预测保留，而顺应物体则暴露出保守的错误拒绝。薄壁杯的研究展示了上下文路由到顺应控制，与始终顺应的参考相匹配。这些结果表明，接触引导的执行时间适应可以通过适应物理实现而无需物体特定再训练，从而改善固定VLA对保留物体的物体级别泛化。
cs.RO / 24 / 2610.04406
Reachability-Guided Sequential Quadratic Programming-Guarded Model Predictive Path Integral for Safe Nonlinear Predictive Control
基于可达性引导的序列二次规划保护模型预测路径积分用于安全非线性预测控制
Abstract
Safe robot control often requires combining long-horizon performance optimization with hard state and input constraints, but existing approaches tend to address this tradeoff partially. Sampling-based model predictive control (MPC) methods such as model predictive path integral (MPPI) are effective in handling nonlinear and nonconvex environments, yet their finite-sample rollouts and unconstrained weighted-average update can return an unsafe control. Deterministic nonlinear MPC can explicitly incorporate constraints, but its real-time safety and performance depends strongly on warm starts and local convergence. Hamilton--Jacobi reachability (HJR) provides rigorous safety certificates, but offline value-function computation remains practical only for reduced-order models. We propose ReSQ-MPPI, a reachability-informed generation--refinement architecture that combines these complementary strengths. An HJR value function computed offline for a reduced-order model guides online MPPI sampling toward safe, promising trajectory candidates. The MPPI solution is then refined by a small number of sequential quadratic programming (SQP) iterations in a full-order MPC problem. A key observation is that the standard MPPI inference step is an unconstrained weighted least-squares problem; ReSQ-MPPI replaces it with a constrained MPC refinement that recovers the MPPI update when it is feasible and minimally modifies it otherwise. Simulations in cluttered navigation and autonomous racing environments demonstrate that ReSQ-MPPI improves safety and performance over standalone MPPI, MPC, and reachability-filtered sampling-based control baselines.
Chinese Translation
安全的机器人控制通常需要将长时间范围的性能优化与严格的状态和输入约束相结合，但现有的方法往往仅部分解决这一权衡。基于采样的模型预测控制（MPC）方法，如模型预测路径积分（MPPI），在处理非线性和非凸环境方面有效，但其有限样本的回放和无约束的加权平均更新可能导致不安全的控制。确定性非线性MPC可以明确地纳入约束，但其实时安全性和性能强烈依赖于热启动和局部收敛。汉密尔顿-雅可比可达性（HJR）提供了严格的安全证书，但离线值函数计算在实际应用中仅适用于降阶模型。我们提出了ReSQ-MPPI，一种基于可达性信息的生成-精炼架构，结合了这些互补的优势。为降阶模型离线计算的HJR值函数引导在线MPPI采样朝向安全且有前景的轨迹候选。然后，通过在全阶MPC问题中进行少量的序列二次规划（SQP）迭代来精炼MPPI解。一个关键观察是，标准的MPPI推理步骤是一个无约束的加权最小二乘问题；ReSQ-MPPI用一个约束的MPC精炼替代它，当可行时恢复MPPI更新，否则进行最小修改。在拥挤的导航和自主赛车环境中的仿真表明，ReSQ-MPPI在安全性和性能上优于独立的MPPI、MPC和基于可达性过滤的采样控制基线。
cs.RO / 25 / 2610.04536
WasserMan: Benchmark for Underwater Manipulation Policy Learning
WasserMan：水下操作策略学习的基准测试
Abstract
Underwater manipulation couples visual decisions, contact forces and a thruster-controlled floating base. We introduce WasserMan, to our knowledge the first multi-task simulation benchmark for visuomotor learning of floating-base underwater contact manipulation. It provides ten expert-solvable tasks, two vehicle-arm platforms, a bimanual configuration and underwater dynamics. Nine tasks have learned-policy evaluations. We compare ACT, diffusion policies (DP) and behavioral cloning on six tasks, with three training runs and equal sampled-window budgets. Pretrained SmolVLA adds evaluations on three tasks. In the tested settings, removing integral action can prevent completion, while changing action interfaces can reduce learned-policy success despite successful expert replay. Currents produce task-dependent changes in completion and actuation effort. Versioned tasks, demonstrations and per-episode evidence support a protocol separating expert feasibility, learned completion and execution effort.
Chinese Translation
水下操作结合了视觉决策、接触力和由推进器控制的浮动基座。我们介绍了WasserMan，作为我们所知的首个多任务模拟基准，用于浮动基座水下接触操作的视觉运动学习。该基准提供了十个专家可解决的任务、两个车辆-手臂平台、一种双手配置和水下动力学。九个任务进行了学习策略评估。我们在六个任务上比较了ACT、扩散策略（Diffusion Policies, DP）和行为克隆，进行了三次训练运行，并保持相同的采样窗口预算。预训练的SmolVLA在三个任务上增加了评估。在测试设置中，去除积分动作可能会阻止任务完成，而更改动作接口可能会降低学习策略的成功率，尽管专家重放成功。水流会导致任务依赖的完成和执行努力的变化。版本化任务、演示和每个回合的证据支持了一种协议，区分专家可行性、学习完成和执行努力。
cs.RO / 26 / 2610.04540
DreamFormer: Dream Imitation with a Transformer World Model for Language-Conditioned Robotic Manipulation
DreamFormer：基于变换器的世界模型进行语言条件下的机器人操控模仿
Abstract
We introduce DreamFormer, a model-based agent that acquires language-conditioned, multi-task skills by imitating expert demonstrations within the latent imagination of a learned world model. DreamFormer first learns a task-agnostic Transformer world model from unstructured play data, then acquires task-specific behaviors by optimizing an intrinsic reward that aligns agent-generated rollouts with expert demonstrations in latent space. Since the policy is trained on-policy inside imagination, it is exposed to its own errors during training, mitigating the covariate shift inherent to offline behavioral cloning. To make long-horizon imagination affordable, DreamFormer encodes a high-resolution multi-view robotic observation into a single input token, avoiding both spatial downsampling and the multi-token representations used by prior Transformer world models. On the long-horizon CALVIN benchmark, DreamFormer outperforms LUMOS, the comparable model-based agent, on single-environment evaluation (2.52 vs 2.34 average tasks completed per chain of five) while keeping imagination rollouts tractable. Against HULC, the behavior cloning baseline, it nearly doubles performance on zero-shot transfer to an unseen environment (1.30 vs 0.67), indicating that dynamics learned by the world model transfer more readily than a directly cloned policy. This is consistent with the role attributed to internal models in biological agents, where a model of environment dynamics supports behavior in situations not previously encountered.
Chinese Translation
我们介绍了DreamFormer，这是一种基于模型的智能体，通过在学习的世界模型的潜在想象中模仿专家演示，获得语言条件下的多任务技能。DreamFormer首先从非结构化的游戏数据中学习一个任务无关的变换器世界模型，然后通过优化一个内在奖励来获取特定任务的行为，该奖励使智能体生成的轨迹与潜在空间中的专家演示对齐。由于策略是在想象中按策略训练的，因此在训练过程中暴露于自身的错误，从而减轻了离线行为克隆中固有的协变量偏移。为了使长时间的想象变得可行，DreamFormer将高分辨率的多视角机器人观察编码为一个单一的输入标记，避免了空间下采样和之前变换器世界模型使用的多标记表示。在长时间的CALVIN基准测试中，DreamFormer在单环境评估中超越了可比的基于模型的智能体LUMOS（每五个任务链平均完成2.52 vs 2.34），同时保持了想象轨迹的可处理性。与行为克隆基线HULC相比，它在零样本转移到未见环境时几乎将性能翻倍（1.30 vs 0.67），这表明世界模型学习的动态比直接克隆的策略更容易转移。这与生物智能体中内部模型的角色一致，其中环境动态的模型支持在以前未遇到的情况下的行为。
cs.RO / 27 / 2610.04551
NM-LIO: Multiple LiDAR-Inertial Odometry Addressing LiDAR Measurement Noise Discrepancy
NM-LIO：针对LiDAR测量噪声差异的多传感器LiDAR-惯性里程计
Abstract
Multiple LiDAR-inertial odometry methods have been widely applied in robotic applications owing to their enhanced accuracy and reliability. However, adopting multiple LiDAR systems can be challenging due to the discrepancies in measurement noise across different LiDARs. Existing methods have typically overlooked the noise discrepancies, which can significantly affect accuracy. In this paper, we propose Noise-aware Multiple LiDAR-Inertial Odometry (NM-LIO) that addresses the noise discrepancies. We integrate a noise model to quantify the measurement noise of each LiDAR. Additionally, we estimate the uncertainty of the residuals based on the measurement noise, allowing the measurement model to capture the noise discrepancies. Our proposed method is evaluated on a public multiple LiDAR dataset and compared with state-of-the-art methods. The experimental results demonstrate that the proposed method can accurately estimate the odometry in various environments by accounting for the noise discrepancies.
Chinese Translation
多传感器LiDAR-惯性里程计方法因其增强的准确性和可靠性而广泛应用于机器人领域。然而，由于不同LiDAR系统之间测量噪声的差异，采用多个LiDAR系统可能面临挑战。现有方法通常忽视了噪声差异，这可能显著影响准确性。本文提出了一种噪声感知的多传感器LiDAR-惯性里程计（NM-LIO），旨在解决噪声差异问题。我们集成了一个噪声模型，以量化每个LiDAR的测量噪声。此外，我们基于测量噪声估计残差的不确定性，使测量模型能够捕捉噪声差异。我们在一个公共的多LiDAR数据集上对所提方法进行了评估，并与最先进的方法进行了比较。实验结果表明，所提方法能够通过考虑噪声差异，在各种环境中准确估计里程计。
cs.RO / 28 / 2610.04552
Online Target-less Radar-LiDAR-Camera Extrinsic Calibration via Joint Optimization
基于联合优化的在线无目标雷达-激光雷达-摄像头外部标定
Abstract
Fusing radar, LiDAR, and camera enables robust perception in diverse and adverse conditions, but the fusion performance critically depends on accurate extrinsic calibration among the three sensors. In this paper, we address the problem of online target-less extrinsic calibration for the radar-LiDAR-camera system. Existing target-less methods are mostly designed for a single sensor pair, and composing the pairwise results does not guarantee consistency across the three sensors. Moreover, the sparse and noisy radar measurements make the radar-involving pairs unreliable. To tackle these challenges, we propose a joint calibration framework that constructs residuals for each sensor pair and optimizes the extrinsics of all pairs together to minimize the overall residual. Furthermore, we introduce an adaptive radar noise filter that rejects spurious radar returns using a range-dependent margin, and a correspondence accumulation strategy that aggregates sparse radar correspondences over frames. We validate our method on an in-house radar-LiDAR-camera dataset covering diverse urban environments, where it reduces calibration errors across all sensor pairs over a state-of-the-art camera-LiDAR baseline.
Chinese Translation
雷达、激光雷达和摄像头的融合能够在多样和恶劣的条件下实现稳健的感知，但融合性能在很大程度上依赖于三种传感器之间的准确外部标定。本文针对雷达-激光雷达-摄像头系统的在线无目标外部标定问题进行探讨。现有的无目标方法大多是为单一传感器对设计的，组合成对结果并不能保证三种传感器之间的一致性。此外，稀疏和噪声较大的雷达测量使得涉及雷达的传感器对不可靠。为了解决这些挑战，我们提出了一种联合标定框架，该框架为每对传感器构建残差，并共同优化所有对的外部参数，以最小化整体残差。此外，我们引入了一种自适应雷达噪声滤波器，利用基于范围的边际拒绝虚假雷达回波，以及一种对应关系累积策略，聚合跨帧的稀疏雷达对应关系。我们在一个涵盖多样城市环境的内部雷达-激光雷达-摄像头数据集上验证了我们的方法，结果表明其在所有传感器对上均减少了相较于最先进的摄像头-激光雷达基线的标定误差。
cs.RO / 29 / 2610.04565
Leveraging Cost-effective Robotics for K-12 STEM Education through Water Quality Monitoring Tasks
通过水质监测任务利用经济高效的机器人技术促进K-12 STEM教育
Abstract
Engaging K-12 students in authentic scientific research remains a significant challenge, particularly at the intersection of environmental science and robotics. We introduce the Jar Jar ROV, a low-cost, open-source Remotely Operated Vehicle (ROV) platform designed for citizen science-based water quality monitoring by middle school students. This paper presents the design of the platform and the results of a large-scale deployment with over 100 students across a US state who built, programmed, and deployed the ROVs in local lakes. The educational framework yielded high student engagement in hands-on activities, with ROV construction earning a perfect average score from mentors. Scientifically, the program established a grassroots monitoring network, generating nearly eleven thousand validated measurements of temperature, pH, dissolved oxygen, and turbidity. However, our evaluation identified a critical "engagement gap," with student interest declining sharply during more complex tasks such as electronics assembly and data uploading. This paper contributes both a validated, scalable model for integrating robotics into environmental education and a data-driven roadmap for future improvements. These enhancements focus on lowering technical barriers and creating a more intuitive link between data collection and scientific discovery, addressing a key challenge in empowering the next generation of citizen scientists.
Chinese Translation
让K-12学生参与真实的科学研究仍然是一个重大挑战，尤其是在环境科学与机器人技术交叉的领域。我们介绍了Jar Jar ROV，这是一种低成本、开源的遥控水下机器人（Remotely Operated Vehicle, ROV）平台，旨在由中学生进行基于公民科学的水质监测。本文展示了该平台的设计以及在美国某州进行的大规模部署结果，超过100名学生参与了ROV的构建、编程和在当地湖泊的投放。教育框架使学生在动手活动中高度参与，ROV的构建获得了导师的满分平均评分。从科学角度看，该项目建立了一个草根监测网络，生成了近一万一千个经过验证的温度、pH值、溶解氧和浊度测量数据。然而，我们的评估发现了一个关键的“参与差距”，学生在更复杂的任务（如电子组件组装和数据上传）中兴趣急剧下降。本文不仅提供了一个经过验证的、可扩展的模型，用于将机器人技术融入环境教育，还提供了一条基于数据的未来改进路线图。这些改进重点在于降低技术门槛，并创造数据收集与科学发现之间更直观的联系，以应对赋能下一代公民科学家的关键挑战。
cs.RO / 30 / 2610.04566
RiskFly: Frustum-Aligned Spatio-Temporal Risk Fields for One-Stage Agile Flight in Dynamic Clutter
RiskFly：用于动态杂乱环境中单阶段灵活飞行的视锥对齐时空风险场
Abstract
Agile flight in unknown, cluttered, and dynamic environments requires a planner that knows where and when danger will appear, not only that a trajectory is dangerous. One-stage learning-based planners trained with differentiable privileged costs are fast and expert-free, but the only signal reaching their encoder is a scalar trajectory cost with no spatial or temporal structure, so avoidance degrades into late reactive maneuvers. We present RiskFly, a one-stage planner that predicts risk in the same space in which it acts. A dual-stream observation pairs a short depth sequence with a frustum-aligned inverted spherical range-map sequence, whose angular cells match the end-state proposals one to one. An auxiliary head regresses a frustum-aligned spatio-temporal risk field, supervised by a privileged closest-point-of-approach (CPA) target. This self-predicted field is queried differentiably along the instantiated quintic trajectory at its own arrival times, and also enters the training objective, so representation supervision and planning gradients meet in a single space. Privileged signals are discarded at deployment, and the planner runs map-free from onboard depth and proprioception. Extensive simulation and zero-shot real-world flights on resource-constrained platforms show higher success rates at comparable end-to-end latency.
Chinese Translation
在未知、杂乱和动态环境中进行灵活飞行需要一个能够预测危险出现的时间和地点的规划器，而不仅仅是知道某条轨迹是危险的。基于单阶段学习的规划器通过可微分的特权成本进行训练，速度快且无需专家指导，但传递给其编码器的信号仅为标量轨迹成本，没有空间或时间结构，因此避免行为退化为迟缓的反应性机动。我们提出了RiskFly，这是一种在其作用空间中预测风险的单阶段规划器。双流观察将短深度序列与视锥对齐的反向球形范围图序列配对，其角度单元与最终状态提议一一对应。辅助头回归一个视锥对齐的时空风险场，由特权最近接近点（CPA）目标进行监督。该自预测场在其到达时间沿实例化的五次轨迹可微分查询，并且也进入训练目标，因此表示监督和规划梯度在同一空间中交汇。在部署时，特权信号被丢弃，规划器从机载深度和自我感知中无地图运行。广泛的仿真和在资源受限平台上的零样本真实世界飞行显示出在可比的端到端延迟下更高的成功率。
cs.RO / 31 / 2610.04607
ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies
ForeAct3D：基于策略的未来世界建模用于视觉-语言-动作（VLA）策略
Abstract
Robots need to anticipate how their actions will change the world, since manipulation success hinges on the resulting contacts and object motions. However, existing Vision-Language-Action (VLA) policies that predict future observations from shared features leave the forecast decoupled from the actions the policy will actually execute, and impose no physical constraints on how the scene may evolve. We introduce ForeAct3D, a framework for policy-grounded future world modeling within VLA policies. Learnable geometric queries decode depth, semantic segmentation, and camera pose from the policy representation into current and future semantic 3D scene states, and the future queries are conditioned on the policy-generated action chunk to ground the forecast in the planned interaction. A physical-consistency closure relates the two states through background staticity and instance-level rigidity, and anchors the wrist-camera pose to end-effector kinematics. These objectives shape the shared representation used for action generation during training, and no future prediction is required at inference. Without robot pretraining, ForeAct3D achieves 98.3\% average success on LIBERO and an average task length of 3.73 on CALVIN, outperforming its base policy on every suite. Ablations show that semantic 3D supervision, physical consistency, and action conditioning each improve manipulation performance, and that action conditioning substantially improves future object localization. Real-world experiments on spatial placement, object insertion, and sequential manipulation further raise average success from 6.7\% to 37.8\% over the base policy. The project page and code are available at https://github.com/anthonytao80-crypto/ForeAct3D.
Chinese Translation
机器人需要预测其动作将如何改变世界，因为操作的成功依赖于由此产生的接触和物体运动。然而，现有的视觉-语言-动作（VLA）策略通过共享特征预测未来观察结果，但将预测与策略实际执行的动作脱钩，并且没有对场景如何演变施加物理约束。我们提出了ForeAct3D，一个在VLA策略中进行基于策略的未来世界建模的框架。可学习的几何查询将深度、语义分割和相机姿态从策略表示解码为当前和未来的语义3D场景状态，并且未来查询基于策略生成的动作片段进行条件化，以将预测与计划的交互相结合。物理一致性闭合通过背景静态性和实例级刚性将这两种状态联系起来，并将腕部相机姿态锚定到末端执行器运动学。这些目标塑造了用于训练期间动作生成的共享表示，并且在推理时不需要未来预测。在没有机器人预训练的情况下，ForeAct3D在LIBERO上实现了98.3%的平均成功率，在CALVIN上实现了平均任务长度为3.73，超越了其基础策略在每个测试套件上的表现。消融实验表明，语义3D监督、物理一致性和动作条件化各自提高了操作性能，而动作条件化显著改善了未来物体定位。在空间放置、物体插入和顺序操作的真实世界实验中，平均成功率从基础策略的6.7%提高到37.8%。项目页面和代码可在https://github.com/anthonytao80-crypto/ForeAct3D获取。
cs.RO / 32 / 2610.04609
Exploiting Hierarchical Controller Structure in Contextual Parameter Learning for Humanoid Loco-Manipulation
利用层次控制器结构进行人形机器人运动操作的上下文参数学习
Abstract
Hierarchical control architectures are widely used to decompose complex control problems into interacting control levels and are particularly important in robotics, where planning, whole-body motion, and lower-level control must be coordinated across different levels of abstraction and time scales. Their overall closed-loop performance, however, depends strongly on parameters distributed across the hierarchy, such that tuning controllers on different levels independently may neglect relevant cross-layer interactions. We propose a contextual Bayesian optimization framework for joint parameter learning in hierarchical control systems. Rather than modeling closed-loop performance only as a scalar black-box function, we retain separate observations of task performance, realization quality, and control effort. A correlated multi-output Gaussian process models these performance components, while their known aggregation into the overall closed-loop objective is evaluated analytically. The formulation exploits three complementary consequences of hierarchical control: informative performance quantities exposed by the hierarchy, coupling between parameters of different controller levels, and variations of these relations with operating conditions. We evaluate the approach for humanoid loco-manipulation, jointly tuning a centroidal predictive controller and a whole-body controller for physical box pushing under varying box mass. The proposed method achieves the lowest mean empirical regret during both training and adaptation among the considered baselines.
Chinese Translation
层次控制架构广泛用于将复杂控制问题分解为相互作用的控制层次，在机器人领域尤为重要，因为规划、全身运动和低级控制必须在不同的抽象层次和时间尺度上进行协调。然而，它们的整体闭环性能在很大程度上依赖于分布在层次结构中的参数，因此独立调节不同层次的控制器可能会忽视相关的跨层交互。我们提出了一种上下文贝叶斯优化框架，用于层次控制系统中的联合参数学习。我们不仅将闭环性能建模为标量黑箱函数，而是保留任务性能、实现质量和控制努力的单独观察。一个相关的多输出高斯过程模型用于描述这些性能组件，同时其已知的聚合到整体闭环目标的过程通过分析评估。该公式利用了层次控制的三个互补后果：层次结构所揭示的信息性性能量、不同控制器层次之间的参数耦合，以及这些关系随操作条件的变化。我们在进行人形机器人运动操作的过程中评估了该方法，联合调节了一个质心预测控制器和一个全身控制器，以应对不同箱体质量下的物理推箱任务。所提出的方法在训练和适应过程中在考虑的基准中实现了最低的平均经验遗憾。
cs.RO / 33 / 2610.04612
An End-to-End Framework for Modelling Pneumatic Soft Robots Based on Differentiable Finite Element Methods
基于可微分有限元方法的气动软机器人建模端到端框架
Abstract
Soft robots present significant modelling challenges due to their non-linearity, complex dynamics and potentially intricate geometries. These difficulties in accurate system identification and dynamics modelling limit their applications in precise robotics tasks. Prior modelling approaches typically suffer from trade-offs in accuracy, computational efficiency, or speed. The differentiable finite element method (FEM) offers a promising balance between these desiderata for soft robot modelling, and enables the use of gradients for efficient calibration and trajectory optimisation. In this paper, we propose an end-to-end differentiable FEM-based framework designed to streamline modelling and system identification for pneumatic soft robots, enabling the generation of reliable models for downstream tasks. The framework automates the conversion of computer-aided designs into voxelised tetrahedral viscoelastic FEM meshes that accurately represent the robot's geometry and actuation mechanism. By integrating easily acquired point cloud data with differentiable FEM, the framework achieves precise identification of material and dynamic actuation parameters using a minimal experimental setup. Additionally, the differentiable nature of the model facilitates trajectory optimisation by leveraging gradients from robot dynamics and contact interactions. We validate the framework by modelling a complex bellow-shaped pneumatic soft robot and demonstrate its efficacy in real-world motion planning tasks. Experimental results indicate high modelling accuracy, with a maximum positional error of less than 3 mm, and successful application in tasks such as path following and grasping.
Chinese Translation
软机器人由于其非线性、复杂的动态特性和潜在的复杂几何形状，面临着显著的建模挑战。这些在系统识别和动态建模中的困难限制了它们在精确机器人任务中的应用。以往的建模方法通常在准确性、计算效率或速度之间存在权衡。可微分有限元方法（FEM）为软机器人建模提供了在这些需求之间的良好平衡，并使得能够利用梯度进行高效的标定和轨迹优化。本文提出了一种基于可微分FEM的端到端框架，旨在简化气动软机器人的建模和系统识别，从而生成可靠的下游任务模型。该框架自动将计算机辅助设计转换为体素化的四面体粘弹性FEM网格，准确表示机器人的几何形状和驱动机制。通过将易于获取的点云数据与可微分FEM相结合，该框架能够使用最小的实验设置精确识别材料和动态驱动参数。此外，模型的可微分特性通过利用机器人动态和接触交互的梯度，促进了轨迹优化。我们通过对一个复杂的波纹形气动软机器人进行建模来验证该框架，并展示其在现实世界运动规划任务中的有效性。实验结果表明，建模准确性高，最大位置误差小于3毫米，并成功应用于路径跟踪和抓取等任务。
cs.RO / 34 / 2610.04616
PerturBot: Breaking Shortcut Priors in Vision-Language-Action Models with Perturbative Training
PerturBot：通过扰动训练打破视觉-语言-动作模型中的捷径先验
Abstract
A vision--language--action (VLA) policy can complete complex tasks while ignoring the evidence that should determine its actions. An object held near the wrist camera can displace the instructed target. Language and action show the same pattern: a familiar noun can trigger the operation it was paired with in training even after the verb changes, and a gripper that closed on nothing may lift anyway. We call these dependencies modality shortcuts: regularities in successful demonstrations make visual, lexical, or motor cues sufficient to predict expert actions without the task evidence needed for the underlying decision. More demonstrations of the same kind can raise task success while leaving these shortcuts intact. We propose Perturbot which makes task-relevant evidence easier to use and shortcuts insufficient on their own: it applies task-preserving wrist-view perturbations, enriches instructions with decision-relevant captions, and adds random and failed trajectory segments relabeled with the behavior they contain. It complements scaling by changing what is scaled, and leaves inference unchanged. Moreover, we propose GroundingFscore, an offline score that diagnoses how severely a policy relies on modality shortcuts. Task success rate shows whether a policy improves, while GroundingFscore reveals whether the policy scales healthily, relying on task evidence rather than shortcuts. Together, Perturbot and GroundingFscore provide a training-and-evaluation framework for disentangling VLA decisions from shortcut priors while preserving responsiveness to task-relevant evidence.
Chinese Translation
视觉-语言-动作（VLA）策略可以在忽略应决定其行为的证据的情况下完成复杂任务。靠近腕部相机的物体可以偏移指令目标。语言和动作显示出相同的模式：一个熟悉的名词即使在动词改变后也能触发与之配对的操作，而一个空抓手可能仍然会抬起。我们称这些依赖关系为模态捷径：成功演示中的规律使得视觉、词汇或运动线索足以预测专家行为，而不需要用于基础决策的任务证据。同类演示的增加可以提高任务成功率，同时保持这些捷径不变。我们提出了Perturbot，它使得与任务相关的证据更易于使用，并使捷径本身不足：它应用保持任务的腕部视角扰动，用与决策相关的说明丰富指令，并添加随机和失败的轨迹片段，重新标注其包含的行为。它通过改变缩放的内容来补充缩放，同时保持推理不变。此外，我们提出了GroundingFscore，这是一种离线评分，用于诊断策略在多大程度上依赖模态捷径。任务成功率显示策略是否改善，而GroundingFscore揭示策略是否健康地缩放，依赖于任务证据而非捷径。Perturbot和GroundingFscore共同提供了一个训练和评估框架，用于将VLA决策与捷径先验解耦，同时保持对与任务相关证据的响应能力。
cs.RO / 35 / 2610.04681
RoboIRS: Inference-Time Internal Representation Steering for Generalist Robot Policies
RoboIRS：通用机器人策略的推理时内部表征引导
Abstract
Vision-language-action (VLA) and world-action models (WAMs) often degrade under out-of-distribution task variations despite retaining partial task capability. To recover such capability, we propose RoboIRS, an inference-time internal representation steering method that uses successful and failed rollouts to train linear classifiers, select outcome-relevant intervention locations, and derive task-specific steering directions without updating policy parameters. On 15 simulation tasks with a frozen $\pi 0.5$ policy, RoboIRS improves the average success rate from 44.4% to 66.2%, outperforming alternative inference-time intervention baselines while adding little inference time. We further validate RoboIRS on real-robot manipulation using the same $\pi 0.5$ policy and demonstrate its applicability to a world-action model Cosmos Policy, where the average success rate improves from 35.4% to 55.4%. These results show that directly steering internal robot-policy representations can improve the performance of robot policies at inference time. Project website is available at https://rollingoat.github.io/roboirs/.
Chinese Translation
视觉-语言-动作（VLA）和世界-动作模型（WAMs）在面对分布外任务变体时，尽管保留了部分任务能力，往往会出现性能下降。为了恢复这种能力，我们提出了RoboIRS，一种推理时内部表征引导方法，该方法利用成功和失败的回滚（rollouts）来训练线性分类器，选择与结果相关的干预位置，并在不更新策略参数的情况下推导任务特定的引导方向。在使用冻结的$ ext{π 0.5}$策略的15个仿真任务中，RoboIRS将平均成功率从44.4%提高到66.2%，超越了其他推理时干预基准，同时增加的推理时间很少。我们进一步在真实机器人操作中验证了RoboIRS，使用相同的$ ext{π 0.5}$策略，并展示了其在世界-动作模型Cosmos Policy中的适用性，平均成功率从35.4%提高到55.4%。这些结果表明，直接引导内部机器人策略表征可以在推理时提高机器人策略的性能。项目网站可访问 https://rollingoat.github.io/roboirs/。
cs.RO / 36 / 2610.04701
Test-Time Training as Residual Memory for Robot Policies
测试时训练作为机器人策略的残差记忆
Abstract
Memory is essential for long-horizon robotic manipulation, where successful actions may depend on past events that are no longer recoverable from the current observation. As episodes grow longer, however, retaining the full history becomes increasingly costly, creating a fundamental scalability challenge for memory-augmented policies. Existing approaches address this challenge by either storing selected past observations in a bounded memory bank or compressing interaction history into a fixed-size parametric state through Test-Time Training (TTT). Yet these formulations do not explicitly distinguish between historical information that can already be recovered from the policy's current context and information that must persist beyond it. We introduce TTT-RM, which repurposes TTT as Residual Memory, using fast weights not to generically compress history but to complement a bounded memory bank by preserving task-relevant historical information that cannot be recovered from the policy's current context. Concretely, TTT-RM learns a history decoder that reconstructs historical representations from the current observation and retrieved memory. The resulting reconstruction residual captures what this context fails to explain and serves as the learning target for TTT. The TTT slow weights are optimized against this residual target so that online fast-weight updates learn to encode complementary historical information over time. The fast-weight state is then queried to produce a residual memory representation that conditions action generation. Extensive experiments on memory-intensive simulation benchmarks and real-world tasks show that TTT-RM consistently improves across multiple memory designs, outperforms diverse baselines, and supports sustained execution on a three-minute, eight-stage stowing task.
Chinese Translation
记忆对于长时间跨度的机器人操作至关重要，因为成功的动作可能依赖于过去的事件，这些事件在当前观察中无法恢复。然而，随着情节的延长，保留完整的历史变得越来越昂贵，这为增强记忆的策略带来了根本的可扩展性挑战。现有的方法通过在有限的记忆库中存储选定的过去观察，或通过测试时训练（Test-Time Training, TTT）将交互历史压缩为固定大小的参数状态来应对这一挑战。然而，这些公式并未明确区分可以从策略当前上下文中恢复的历史信息与必须超越该上下文的持久信息。我们提出了TTT-RM，它将TTT重新用于残差记忆，利用快速权重不仅仅是通用地压缩历史，而是通过保留无法从策略当前上下文中恢复的与任务相关的历史信息来补充有限的记忆库。具体而言，TTT-RM学习一个历史解码器，从当前观察和检索的记忆中重建历史表示。生成的重建残差捕捉了该上下文未能解释的内容，并作为TTT的学习目标。TTT的慢权重针对这一残差目标进行优化，以便在线快速权重更新能够随着时间的推移学习编码互补的历史信息。然后查询快速权重状态以生成条件化动作生成的残差记忆表示。在内存密集的模拟基准和现实任务上的大量实验表明，TTT-RM在多个记忆设计中始终表现出改进，超越了多种基线，并支持在三分钟、八个阶段的存放任务中的持续执行。
cs.RO / 37 / 2610.04726
Emergent Underactuation in Fully Actuated Multirotors by Minimizing a Homogeneous Allocation Cost
通过最小化均匀分配成本实现完全驱动多旋翼的紧急欠驱动
Abstract
Full actuation is commonly valued for independent position and attitude tracking. This work establishes a complementary principle: when only a position trajectory is prescribed, minimizing a positively homogeneous allocation cost selects underactuated-like behavior on a fully actuated multirotor. This cost class includes norms and power laws, including standard minimum-norm pseudoinverse allocation. We use fiberwise infimization to transport the actuator-output cost to wrench space, yielding a homogeneous infimal allocation cost. Restriction to unit zero-moment forces defines a directional landscape whose minimizing directions are independent of force magnitude. We show that aligning any fixed minimizing direction with the required world-frame force minimizes instantaneous and integrated zero-moment allocation costs. The resulting body force remains in a fixed one-dimensional subspace without transverse components. We derive an exact optimality-gap decomposition quantifying the additional cost of every other attitude trajectory and characterizing the minimum-cost class. When a disturbance or prescribed-motion change modifies the required-force direction, a fully actuated platform can immediately generate the modified force using transverse authority and reorient toward the new minimum-cost family. A structurally underactuated platform must reorient before generating that force. Thus, homogeneous allocation-cost minimization explains why optimal nominal motion can be underactuated-like, while full actuation remains valuable for disturbance rejection and transient force generation.
Chinese Translation
完全驱动通常被认为在独立位置和姿态跟踪中具有重要价值。本研究建立了一个互补原则：当仅规定位置轨迹时，最小化正均匀分配成本会在完全驱动的多旋翼上选择类似欠驱动的行为。该成本类别包括范数和幂律，包括标准的最小范数伪逆分配。我们使用纤维最小化将驱动器输出成本转移到力矩空间，从而产生均匀的最小分配成本。限制于单位零力矩定义了一个方向性景观，其最小化方向与力的大小无关。我们表明，将任何固定的最小化方向与所需的世界坐标系力对齐，可以最小化瞬时和累积的零力矩分配成本。由此产生的体力保持在一个固定的一维子空间内，没有横向分量。我们推导出一个精确的最优性差距分解，量化每个其他姿态轨迹的额外成本，并表征最低成本类别。当干扰或规定运动变化修改所需力的方向时，完全驱动的平台可以立即使用横向控制生成修改后的力，并重新定向到新的最低成本家族。结构上欠驱动的平台必须在生成该力之前重新定向。因此，均匀分配成本的最小化解释了为什么最优的名义运动可以表现出类似欠驱动的特征，而完全驱动在干扰抑制和瞬态力生成中仍然具有重要价值。
cs.RO / 38 / 2610.04741
Robot Learning with Visual Predicted Force
基于视觉预测力的机器人学习
Abstract
Force-aware manipulation typically relies on specialized force or tactile sensors. We show that force-aware manipulation can instead be achieved through visual force prediction from the deformation of a compliant Fin Ray gripper. Our approach trains two models. First, we train a visual force estimator on calibration data and use it to annotate task demonstrations with force estimates. Second, we train an action--force proposal policy on these force-augmented demonstrations to jointly generate candidate robot actions and their associated forces. At test time, we sample candidate actions and the forces they are expected to produce, then execute the action whose predicted force is closest to a target from the demonstrations. We evaluate our approach on berry picking, empty-can grasping, in-hand reorientation, and plug insertion. Our results show that visual force prediction can guide inference-time action selection for contact-rich manipulation without requiring force or tactile sensors at deployment.
Chinese Translation
力感知操作通常依赖于专用的力或触觉传感器。我们展示了通过对柔性鳍状夹持器变形的视觉力预测，可以实现力感知操作。我们的方法训练了两个模型。首先，我们在标定数据上训练一个视觉力估计器，并利用它为任务演示标注力估计。其次，我们在这些增强力的演示上训练一个动作-力提议策略，以共同生成候选机器人动作及其相关的力。在测试时，我们采样候选动作及其预期产生的力，然后执行预测力最接近演示目标的动作。我们在采摘浆果、空罐抓取、手中重新定向和插头插入等任务上评估了我们的方法。我们的结果表明，视觉力预测可以指导接触丰富的操作中的推理时动作选择，而无需在部署时使用力或触觉传感器。
cs.RO / 39 / 2610.04744
Lie Rarely, Lie Big: Stealthy Insider Attacks on LLM Robot Teams
很少说谎，大谎言：对LLM机器人团队的隐秘内部攻击
Abstract
When a team of robots delegates planning and mutual trust to LLM agents, a single compromised robot can corrupt the shared outcome. We study this threat in a grounded task: a multi-robot survey in which measurements can be verified against the physical world, but every verification costs budget that would otherwise advance the mission. We treat the compromised robot as a stealthy adversary in the system-theoretic sense: it is limited not by an energy bound but by the team's own detectors. We then derive two bounds. First, the probability that the adversary's reports are verified is bounded below in terms of the degrees in the communication graph and the verification budget. Second, the map error caused by any stealthy adversary is bounded above by the value of a linear program over the adversary's bias distributions; its solution is an exchange rate between stealth budget and damage: below a critical verification level the worst stealthy attack tells rare, full-magnitude lies on the records least likely to be verified, and above it the better purchase is small biases hidden in the noise. In experiments where the honest robots are LLM agents, both bounds hold at the budget the attack actually spent. The experiments also show that which records an LLM robot re-checks is unbiased, but how much it re-checks is unpredictable.
Chinese Translation
当一组机器人将规划和相互信任委托给LLM代理时，单个被攻陷的机器人可能会破坏共享结果。我们在一个具体任务中研究这一威胁：多机器人调查，其中测量可以与物理世界进行验证，但每次验证都消耗预算，这本可以用于推进任务。我们将被攻陷的机器人视为系统理论意义上的隐秘对手：它的限制不是由能量边界决定，而是由团队自身的探测器决定。然后我们推导出两个界限。首先，对手报告被验证的概率在通信图的度数和验证预算的基础上有下界。其次，任何隐秘对手造成的地图误差有上界，由对手的偏差分布的线性规划值决定；其解是隐秘预算与损害之间的兑换率：在临界验证水平以下，最糟糕的隐秘攻击在最不可能被验证的记录上讲述罕见的、全幅度的谎言，而在其以上，更好的选择是隐藏在噪声中的小偏差。在诚实机器人为LLM代理的实验中，这两个界限在攻击实际花费的预算下均成立。实验还表明，LLM机器人重新检查哪些记录是无偏的，但它重新检查的数量是不可预测的。
cs.RO / 40 / 2610.04764
DNPush: Distributed Nonparallel Pushing Force Control of Multi-Robot Systems for Object Transportation
DNPush：用于物体运输的多机器人系统分布式非平行推力控制
Abstract
This letter proposes DNPush, a Distributed Nonparallel Pushing force controller for cooperative object transport without inter-robot communication. Each robot uses local force measurements, a preassigned force weight, and a locally expressed desired transport direction, without requiring an object model, contact locations, a CoM estimate, or moment arm magnitudes online. DNPush regulates nonparallel force magnitudes from local force direction misalignments through a reciprocal-sine law. Under the stated contact conditions, Lyapunov analysis with LaSalle's invariance principle proves convergence to a unique rotational equilibrium, where perpendicular force contributions cancel and the net torque from normal forces is zero. Object velocity converges to the desired transport direction, and suitable contact regions exist for convex polygons. Simulations isolate the reciprocal-sine scaling mechanism, five-robot coordination, and active rotational damping. Hardware experiments with soft tactile differential-drive robots achieve a velocity direction error of 0.61 deg +/- 0.79 deg over five trials with a fixed transport direction and demonstrate object transport through waypoints with three robots.
Chinese Translation
本文提出了DNPush，一种用于协作物体运输的分布式非平行推力控制器，无需机器人之间的通信。每个机器人利用局部力测量、预先分配的力权重和局部表达的期望运输方向，而不需要在线获取物体模型、接触位置、质心估计或力臂大小。DNPush通过互惠正弦定律调节由于局部力方向不对齐而导致的非平行力大小。在所述接触条件下，利用Lyapunov分析和LaSalle不变性原理证明了收敛到唯一的旋转平衡状态，其中垂直力的贡献相互抵消，法向力产生的净扭矩为零。物体速度收敛到期望的运输方向，并且对于凸多边形存在适当的接触区域。仿真隔离了互惠正弦缩放机制、五机器人协调和主动旋转阻尼。使用软触觉差动驱动机器人进行的硬件实验在五次试验中实现了固定运输方向下的速度方向误差为0.61度±0.79度，并展示了通过三个机器人在路点之间进行物体运输的能力。
cs.RO / 41 / 2610.04765
PatternDex: Learning Interaction Patterns to Guide Reinforcement Learning of Bimanual Dexterous Manipulation of Articulated Objects
PatternDex：学习交互模式以指导双手灵巧操作关节物体的强化学习
Abstract
In this paper, we develop a method that enables bimanual dexterous hands to manipulate articulated objects with a high success rate without suffering from an embodiment gap. We observe that the correlation between hand motions and object motions is dictated by the object rather than the hands and can be learned from human-object demonstrations. Based on this observation, we propose PatternDex, a method that learns this correlation and represents it as a token sequence, which we call an interaction pattern. From this pattern, PatternDex estimates the wrist motions and contact points that fit the target robot, and then trains a reinforcement learning policy that exploits these estimates as guidance. Since the guidance fits the target embodiment, the policy explores only the actions that the target robot can execute and thus achieves high success rates. PatternDex also requires only simple fine-tuning to train a new robot, since it can reuse the learned interaction pattern. We evaluate PatternDex with bimanual dexterous hands on human demonstrations from the ARCTIC dataset. PatternDex achieves, on average, a 92.8% success rate with Allegro hands, while the state-of-the-art baseline achieves 52.2%. Also, it achieves success rates above 70% with three other robot hands after fine-tuning alone. Furthermore, we verify that the learned policy transfers well to a real-world task of opening a microwave. Videos and additional results are available at https://patterndex.github.io/PatternDex/
Chinese Translation
在本文中，我们开发了一种方法，使双手灵巧手能够以高成功率操控关节物体，而不会遭遇体现差距。我们观察到，手部运动与物体运动之间的相关性是由物体决定的，而非手部，并且可以通过人类与物体的演示学习得出。基于这一观察，我们提出了PatternDex，这是一种学习这种相关性并将其表示为我们称之为交互模式的令牌序列的方法。通过这一模式，PatternDex估计适合目标机器人的手腕运动和接触点，然后训练一个利用这些估计作为指导的强化学习策略。由于该指导适合目标体现，因此该策略仅探索目标机器人能够执行的动作，从而实现高成功率。PatternDex还只需简单的微调即可训练新的机器人，因为它可以重用已学习的交互模式。我们在ARCTIC数据集的人类演示上评估了PatternDex，结果显示，PatternDex在使用Allegro手时的平均成功率为92.8%，而最先进的基线仅为52.2%。此外，在仅进行微调后，它在其他三种机器人手上也达到了超过70%的成功率。此外，我们验证了所学习的策略在打开微波炉这一现实任务中表现良好。视频和更多结果可在 https://patterndex.github.io/PatternDex/ 获取。
cs.RO / 42 / 2610.04767
Flow Policies as Actions of Skill-Level World Models: Learned and Symbolic Abstractions for Long-Horizon Planning
作为技能水平世界模型的流动策略：用于长时间规划的学习和符号抽象
Abstract
Latent world models enable robots to plan by predicting the consequences of actions. Planning long tasks with control-rate actions requires many prediction steps, which enlarges the search space and accumulates error. Skill-level actions shorten these sequences, but a symbolic skill vocabulary requires domain knowledge and labeled demonstrations. We construct skill-level actions from the inputs of a flow-matching policy trained on demonstrations segmented into complete skills. The policy maps a noise seed and an observation, optionally with a code or label, to a complete skill execution, so one execution is one world-model transition. On this mechanism we propose four action abstractions with increasing task knowledge: a compressed seed, two discrete codes learned from the demonstrations, and a symbolic label. We evaluate them with a common world-model training procedure and planning framework on simulated block rearrangement tasks that require up to 14 sequential skills. The symbolic label succeeds in over 90% of the tasks that require up to six skills and degrades beyond. Without any label, an object-centric learned code matches it on single-skill tasks and retains half to three quarters of its success on tasks of two to five skills. Ablations attribute much of the label's advantage to its planner knowing which actions are applicable, rather than to the label itself. Beyond six skills the search, not the world model, limits success. Project page: https://andreumatoses.github.io/research/flow-skill-wm
Chinese Translation
潜在世界模型使机器人能够通过预测行动的后果进行规划。使用控制速率动作规划长任务需要多个预测步骤，这会扩大搜索空间并累积误差。技能水平的动作缩短了这些序列，但符号技能词汇需要领域知识和标记演示。我们从在完整技能上分段的演示中训练的流匹配策略的输入构建技能水平的动作。该策略将噪声种子和观察映射到完整的技能执行，选项包括代码或标签，因此一次执行就是一次世界模型的转变。在此机制上，我们提出了四种具有递增任务知识的动作抽象：一个压缩种子、两个从演示中学习的离散代码和一个符号标签。我们在模拟的块重排任务上评估它们，这些任务需要多达14个连续技能，采用了共同的世界模型训练程序和规划框架。符号标签在超过90%的需要最多六个技能的任务中成功，而在超过六个技能的任务中表现下降。没有任何标签的情况下，一个以对象为中心的学习代码在单技能任务中与其匹配，并在两到五个技能的任务中保持一半到四分之三的成功率。消融实验表明，标签的优势主要归因于其规划者知道哪些动作是适用的，而不是标签本身。超过六个技能时，限制成功的是搜索，而不是世界模型。项目页面：https://andreumatoses.github.io/research/flow-skill-wm
cs.RO / 43 / 2610.04771
Learning Task and Motion Plans from Real Demonstrations with Hybrid Flow Matching
从真实演示中学习任务和运动计划的混合流匹配
Abstract
Long-horizon mobile manipulation requires a task plan and the motion that executes it. Generative planners trained on demonstration produce both in one pass, requiring neither a symbolic domain nor search. To date, however, they have relied on thousands of scripted demonstrations of fixed-base arms and executed open loop. This paper presents a hybrid flow matching planner: a single network generates the symbolic plan with masked discrete flow matching and the motion trajectory with continuous flow matching. Unlike prior generative planners, we aim to learn from a much smaller set of demonstrations and to execute the plan in closed loop. Two properties of the data compensate for the small dataset. A demonstration resumed from any of its intermediate actions is itself a demonstration, which multiplies the training samples and enables replanning after every action. Objects of the same kind are interchangeable, which turns demonstrations of one goal into demonstrations of every permuted goal. Our base implementation produces valid plans on 68% of held-out scenes; a training and generation scheme for the discrete plan raises this to 76%, and replanning after every action raises the task completion rate from 40% to 53% in a kinematic simulation. The planner matches the task completion rate of motion-only flow matching policies while additionally providing the symbolic plan, and it outperforms previous hybrid diffusion formulations on both task completion and plan validity. We validate the planner on a real mobile manipulator. Videos and project page: https://andreumatoses.github.io/research/hybrid-flow-planning
Chinese Translation
长时间移动操作需要一个任务计划和执行该计划的运动。基于演示训练的生成规划器在一次性操作中同时生成任务计划和运动轨迹，无需符号域或搜索。然而，到目前为止，它们依赖于数千个固定基座机械臂的脚本化演示，并以开放环路执行。本文提出了一种混合流匹配规划器：一个单一网络通过掩蔽离散流匹配生成符号计划，通过连续流匹配生成运动轨迹。与之前的生成规划器不同，我们的目标是从更小的演示集学习，并在闭环中执行计划。数据的两个特性弥补了小数据集的不足。任何中间动作恢复的演示本身就是一个演示，这增加了训练样本，并在每个动作后实现重新规划。同类物体是可互换的，这将一个目标的演示转变为每个排列目标的演示。我们的基础实现能够在68%的保留场景中生成有效计划；离散计划的训练和生成方案将这一比例提高到76%，而在每个动作后重新规划则将任务完成率从40%提高到53%（在运动学仿真中）。该规划器的任务完成率与仅使用运动流匹配策略相当，同时提供了符号计划，并且在任务完成和计划有效性方面超越了之前的混合扩散公式。我们在真实移动操纵器上验证了该规划器。视频和项目页面：https://andreumatoses.github.io/research/hybrid-flow-planning
cs.RO / 44 / 2610.04775
Distributed Cascade Force Control of Soft-Tactile-Based Multi-robot System for Object Transportation
基于软触觉的多机器人系统的分布式级联力控制用于物体运输
Abstract
In this paper, we present a distributed cascade force control system (DCFC) for multiple robots with the aim of pushing a rigid object towards a desired moving target without their inter-robot communication. These mobile robots are equipped with 360-degree vision-based soft tactile sensors utilized to determine contact location and resultant impact force. By investigating the dynamics of moving rigid objects on the flat, we proposed a distributed cascade control. The inner loop control incorporates contact force and positioning, ensuring the robots' pushing contact and application of the desired force to the object. The outer loop control coordinates the robots to push the object in a desired direction without inter-robot communication, regardless of the unknown object mass. The stability and convergence of the system are verified using the Lyapunov stability theory. We also conducted simulation and real-word experiments to validate the performance of the proposed control method, and the experimental results showcase the successful coordination of multiple robots in pushing an object towards a moving desired direction.
Chinese Translation
本文提出了一种分布式级联力控制系统（DCFC），旨在使多个机器人在不进行机器人间通信的情况下，将刚性物体推向期望的移动目标。这些移动机器人配备了360度视野的软触觉传感器，用于确定接触位置和产生的冲击力。通过研究平面上移动刚性物体的动力学，我们提出了一种分布式级联控制。内环控制结合了接触力和定位，确保机器人能够有效接触并施加期望的力于物体上。外环控制则协调机器人在没有机器人间通信的情况下，将物体推向期望的方向，无论物体质量未知。系统的稳定性和收敛性通过李雅普诺夫稳定性理论得到了验证。我们还进行了仿真和实际实验，以验证所提控制方法的性能，实验结果展示了多个机器人成功协调将物体推向移动期望方向的能力。
cs.RO / 45 / 2610.04805
ExStereo: Lifting 2D Vision-Language-Action Models to 3D with Explicit Stereo Representations
ExStereo：利用显式立体表示将2D视觉-语言-动作模型提升至3D
Abstract
Three-dimensional perception is critical for robotic manipulation, particularly for high-precision tasks, as recovering metric depth and precise 3D object positions from monocular RGB observations is inherently ill-posed. However, many Vision-Language-Action (VLA) models rely solely on RGB observations for perception. Leveraging recent advances in foundation models for stereo matching, we introduce ExStereo, a stereo module that augments pre-trained 2D VLAs with 3D perception. ExStereo reconstructs scene geometry from stereo image pairs and renders multi-view observations as an explicit stereo representation for stereo feature extraction. The action tokens from the action expert selectively attend to the resulting stereo tokens through our proposed action-stereo cross-attention mechanism, enabling the policy to generate robot actions conditioned on 3D scene information. To learn robust 3D representations, we introduce a mid-training stage before task-specific post-training, using a self-supervised learning objective on large-scale stereo data. We validate our approach by fine-tuning two publicly available VLAs, $\pi_{0.5}$ and SmolVLA, and evaluate them in simulation and on a real-world bimanual PiPER platform. Across both settings, VLAs fine-tuned with ExStereo consistently outperform baselines, demonstrating the effectiveness of stereo perception for robotic manipulation. Our project website is at: https://exstereo-vla.github.io/ExStereo/.
Chinese Translation
三维感知对于机器人操作至关重要，尤其是在高精度任务中，因为从单目RGB观测中恢复度量深度和精确的3D物体位置本质上是一个不适定问题。然而，许多视觉-语言-动作（VLA）模型仅依赖RGB观测进行感知。借助于立体匹配基础模型的最新进展，我们提出了ExStereo，一个立体模块，通过3D感知增强预训练的2D VLA。ExStereo从立体图像对重建场景几何，并将多视角观测呈现为显式立体表示，以便进行立体特征提取。动作专家的动作标记通过我们提出的动作-立体交叉注意机制选择性地关注生成的立体标记，使得策略能够基于3D场景信息生成机器人动作。为了学习稳健的3D表示，我们在任务特定的后训练之前引入了一个中期训练阶段，使用自监督学习目标在大规模立体数据上进行训练。我们通过微调两个公开可用的VLA，$ ext{π}_{0.5}$和SmolVLA，来验证我们的方法，并在模拟环境和真实的双手PiPER平台上进行评估。在这两种设置中，使用ExStereo微调的VLA始终优于基线，证明了立体感知在机器人操作中的有效性。我们的项目网站为：https://exstereo-vla.github.io/ExStereo/。
cs.RO / 46 / 2610.04896
Tackling Sim-to-Real Mismatch Through Sampling-Based Disturbance Observers: From Analytical Models to Learned World Models
通过基于采样的干扰观测器解决仿真与现实不匹配问题：从分析模型到学习的世界模型
Abstract
Robotic controllers increasingly rely on analytical models, simulators, cost-query interfaces, and learned world models. However, physical deployment can deviate from nominal assumptions, and additional disturbances may arise even when the model itself is accurate. In control systems, disturbance observers (DOB) are widely used to estimate such unmeasured effects from nominal models and measured feedback. Classical DOB formulations are generally built around explicit plant models. This paper develops the sampling-based disturbance observer (SDOB), extending the DOB principle to a broader range of models, including simulators and learned world models, through state-rollout or cost-query interfaces. SDOB separates two observable channels: state-effect disturbances, for which the feedback state differs from its prediction, and cost disturbances, for which the same query state receives different costs as the perceived environment changes. Diverse simulation and real-robot experiments across traditional and learned models demonstrate the effectiveness of SDOB in compensating for sim-to-real mismatch and improving control performance.
Chinese Translation
机器人控制器越来越依赖分析模型、模拟器、成本查询接口和学习的世界模型。然而，物理部署可能偏离名义假设，即使模型本身是准确的，也可能出现额外的干扰。在控制系统中，干扰观测器（Disturbance Observer, DOB）被广泛用于从名义模型和测量反馈中估计这些未测量的影响。经典的DOB公式通常围绕显式植物模型构建。本文开发了基于采样的干扰观测器（Sampling-Based Disturbance Observer, SDOB），通过状态展开或成本查询接口，将DOB原理扩展到更广泛的模型，包括模拟器和学习的世界模型。SDOB分离了两个可观察的通道：状态效应干扰，即反馈状态与其预测不同的情况，以及成本干扰，即同一查询状态在感知环境变化时接收不同成本的情况。多种传统和学习模型的模拟及真实机器人实验展示了SDOB在补偿仿真与现实不匹配和改善控制性能方面的有效性。
cs.RO / 47 / 2610.04910
GJK-CBF: Control Barrier Functions for Convex Rigid Body Collision Avoidance on SE(3)
GJK-CBF：在 SE(3) 上用于凸刚体碰撞避免的控制障碍函数
Abstract
Collision avoidance among convex bodies is a fundamental problem in robotics. Control Barrier Functions (CBFs) provide a practical framework for real-time safety filtering due to their computational efficiency. For general convex bodies, exact separation measures, such as distance or scaling factor, are typically computed through optimization. Existing CBF formulations often obtain the required gradient via differentiable optimization (diffOpt), adding computational overhead. In contrast, we leverage the Gilbert-Johnson-Keerthi (GJK) algorithm to obtain the current witness pair---the pair of points realizing the minimum distance or penetration depth---and formulate a CBF, termed GJK-CBF, whose gradient is constructed directly from the relative rigid-body motion of the witness pair, without resorting to diffOpt. This formulation applies to both 2D and 3D environments across a broad class of convex body pairs, provided that at least one in each one-to-one interaction is strictly convex. The proposed GJK-CBF is validated in various scenarios, including multi-robot position swapping in both 2D and 3D, navigation through a vertical slit in 3D, and its applicability to manipulators. The results demonstrate collision-free motion across all scenarios while reducing the conservativeness introduced by geometric approximations, particularly in narrow environments.
Chinese Translation
凸体之间的碰撞避免是机器人学中的一个基本问题。控制障碍函数（CBFs）由于其计算效率，为实时安全过滤提供了一个实用框架。对于一般的凸体，通常通过优化计算确切的分离度量，如距离或缩放因子。现有的 CBF 公式通常通过可微优化（diffOpt）获得所需的梯度，从而增加了计算开销。相比之下，我们利用 Gilbert-Johnson-Keerthi (GJK) 算法获取当前的见证对——实现最小距离或穿透深度的点对——并构造一个名为 GJK-CBF 的 CBF，其梯度直接从见证对的相对刚体运动中构建，而无需依赖 diffOpt。该公式适用于二维和三维环境中的广泛凸体对，只要每对一对一交互中的至少一个是严格凸的。我们在多种场景中验证了所提出的 GJK-CBF，包括二维和三维中的多机器人位置交换、通过三维中的垂直缝隙导航及其在机械臂中的适用性。结果表明，在所有场景中实现了无碰撞运动，同时减少了几何近似引入的保守性，尤其是在狭窄环境中。
cs.RO / 48 / 2610.04913
$R^2$-WAM: Repair-and-Reject Post-Training for World Action Models
$R^2$-WAM：用于世界行动模型的修复与拒绝后训练
Abstract
World Action Models (WAMs) emerge as a promising foundation for policy refinement by predicting the consequences of sampled actions. However, visually plausible predictions can mislead policy refinement if they fail to reflect the input actions. To address this mismatch, we introduce $R^2$-WAM, a two-stage repair-and-reject post-training framework that first improves the consistency of predicted futures with input actions, then uses these futures to select inferior action samples for negative fine-tuning. The repair stage grounds imagination in observed robot behavior through a kinematic alignment score that measures agreement between predicted and demonstrated motion, enabling the predicted video to faithfully reflect its input actions. Using the repaired video model, the rejection stage compares imagined outcomes of sampled and demonstrated actions, selectively applying negative fine-tuning to samples whose predicted task progress falls below the demonstrated reference by a prescribed margin. Together, the two stages extend video prediction from representation learning to consequence-based policy refinement without additional environment interaction or changes to the inference procedure. $R^2$-WAM achieves 93.8% average success on RoboTwin 2.0 across clean and randomized settings. On the long-horizon real-world Fold Shirt task, it achieves 87.5% average success, compared with 0% for Fast-WAM.
Chinese Translation
世界行动模型（WAMs）作为政策优化的有希望的基础，通过预测采样动作的后果。然而，如果视觉上合理的预测未能反映输入动作，则可能误导政策优化。为了解决这种不匹配，我们提出了$R^2$-WAM，一种两阶段的修复与拒绝后训练框架，首先改善预测未来与输入动作的一致性，然后利用这些未来选择劣质动作样本进行负微调。修复阶段通过运动学对齐评分将想象与观察到的机器人行为相结合，该评分衡量预测与演示运动之间的一致性，使得预测视频能够忠实反映其输入动作。利用修复后的视频模型，拒绝阶段比较采样和演示动作的想象结果，选择性地对那些预测任务进展低于规定参考值的样本应用负微调。两个阶段共同将视频预测从表征学习扩展到基于后果的政策优化，而无需额外的环境交互或对推理过程的更改。$R^2$-WAM在干净和随机设置下，在RoboTwin 2.0上实现了93.8%的平均成功率。在长时间跨度的现实世界折叠衬衫任务中，它实现了87.5%的平均成功率，而Fast-WAM的成功率为0%。
cs.RO / 49 / 2610.04916
PreAct-Nav: Agentic Reasoning Before Action for Urban Navigation
PreAct-Nav：行动前的代理推理用于城市导航
Abstract
Urban navigation requires embodied agents to pursue long-horizon goals through local decisions based on egocentric observations. However, existing agentic navigation methods often struggle to translate distant goals into coherent local decisions in large-scale physical environments. Their reliance on linguistic reasoning over transient observations or limited history constrains anticipation of the consequences of actions and future conditions, despite the importance of such foresight for navigating long and complex urban routes. To bridge this gap, we propose PreAct-Nav, an agentic navigation framework that equips frozen policies with anticipatory reasoning for robust urban navigation. Our central idea is to anchor local decisions in persistent medium-horizon subgoals, assess the consequences of predicted actions before execution, and continually update the reasoning context using actual outcomes. At its core, a navigation memory module maintains the active subgoal and relevant experience across decisions, translating distant goals into actionable intermediate objectives. We further introduce a predictive world sandbox that uses an action-conditioned world model (AC-WM) to forecast world dynamics conditioned on candidate movements. A vision-language model (VLM) reasoner interprets these predictions under the current subgoal to retain or revise actions. After execution, real observations are used to assess outcomes, correct inconsistent assumptions, and update memory to continue or reformulate the subgoal. Extensive evaluations demonstrate that the proposed PreAct-Nav improves action selection through memory updates and visual prediction, with more pronounced gains on longer routes and routes with more turns.
Chinese Translation
城市导航要求具身代理通过基于自我中心观察的局部决策来追求长远目标。然而，现有的代理导航方法往往难以将遥远目标转化为在大规模物理环境中连贯的局部决策。它们对瞬时观察或有限历史的语言推理的依赖限制了对行动后果和未来条件的预见，尽管这种前瞻性在导航漫长而复杂的城市路线中至关重要。为了解决这一问题，我们提出了PreAct-Nav，一个为强大的城市导航提供预期推理的代理导航框架。我们的核心思想是将局部决策锚定在持久的中期子目标上，在执行之前评估预测行动的后果，并利用实际结果不断更新推理上下文。其核心是一个导航记忆模块，维护活跃的子目标和相关经验，能够将遥远目标转化为可操作的中间目标。我们进一步引入了一个预测世界沙盒，使用动作条件世界模型（AC-WM）来预测基于候选运动的世界动态。视觉语言模型（VLM）推理器在当前子目标下解释这些预测，以保留或修订行动。在执行后，实际观察用于评估结果，纠正不一致的假设，并更新记忆以继续或重新制定子目标。广泛的评估表明，所提出的PreAct-Nav通过记忆更新和视觉预测改善了行动选择，在较长路线和转弯较多的路线中表现出更显著的提升。
cs.RO / 50 / 2610.04924
Nudge Before You Push: Physics-Aware Navigation via Tactile Probing
推力前的轻推：基于物理的触觉探测导航
Abstract
Visually identical containers can conceal loads that require different handling decisions. We present TANav, which uses a brief nudge to measure push resistance for navigation under a site-defined handling boundary. TacPhys reads the force sequence, with optional RGB-D and kinematics, into a mass estimate for push authorization. A repeated-patrol planner weighs probe and route costs, requests a second contact when useful, and reuses observations across visits. In simulation, TacPhys approaches a resistance-only Bayes reference and reduces missed pushes from 28.7% to 5.5% relative to peak-force thresholding at comparable low-risk operating points. In repeated-patrol simulation, TANav recovers 90% of the oracle's path saving, more than halves human interventions relative to always-detour, and reduces boundary violations from 4.3% to 2.9% relative to RGB-D-only probing. On a quadruped manipulator with a Hall-array fingertip, offline zero-shot MAE is 0.28-1.07 kg on containers up to 2.82 kg. Force-rise calibration at 3 kg gives 93.5% pooled offline accuracy (86.4% on non-cube episodes); a separate raw-peak rule gives 15 of 20 correct online decisions on unseen boxes.
Chinese Translation
视觉上相同的容器可能隐藏着需要不同处理决策的负载。我们提出了TANav，它通过短暂的轻推来测量推力阻力，以便在特定处理边界下进行导航。TacPhys读取力序列，并结合可选的RGB-D和运动学数据，生成推力授权的质量估计。重复巡逻规划器权衡探测和路线成本，在有用时请求第二次接触，并在多次访问中重用观察结果。在仿真中，TacPhys接近仅基于阻力的贝叶斯参考，并将未推送的比例从28.7%降低到5.5%，相对于在可比低风险操作点的峰值力阈值。在重复巡逻仿真中，TANav恢复了90%的oracle路径节省，相较于始终绕行减少了一半以上的人为干预，并将边界违规率从4.3%降低到2.9%，相对于仅使用RGB-D探测。在配备霍尔阵列指尖的四足操控器上，离线零样本平均绝对误差为0.28-1.07千克，适用于重达2.82千克的容器。在3千克的力上升校准下，离线准确率达到93.5%（在非立方体事件中为86.4%）；一个单独的原始峰值规则在未见过的箱子上提供了20个决策中的15个正确在线决策。
cs.RO / 51 / 2610.04929
RobotUse: Allocating Computation, Context, and Decisions
RobotUse：计算、上下文和决策的分配
Abstract
Robot agents must connect their intended actions to observed outcomes while retaining the context needed to revise their choices over repeated attempts. Existing interfaces often leave these choices inside predefined tools or require agents to manage detailed execution code and its growing history. We introduce RobotUse, a robot agent harness that organizes computation, context, and decisions around specifying and revising physical actions. Agents visually select targets and poses, while the backend handles geometry, motion planning, and control. Subagents retain detailed interactions within each subgoal and return the information needed for subsequent decisions. Continual harnessing lets agents learn from execution by updating a persistent playbook. On RoboLab, RobotUse achieves 45% task success, outperforming CaP-X by 6.7 percentage points while maintaining compact decision contexts and reducing reliance on predefined action abstractions. Furthermore, we show that RobotUse learns from real-world execution despite imperfect feedback and transfers what it learns to subsequent tasks. Project page is available at https://robotuse-team.github.io/.
Chinese Translation
机器人代理必须将其预期的行动与观察到的结果连接起来，同时保留修正其选择所需的上下文，以便在重复尝试中进行调整。现有接口通常将这些选择限制在预定义工具内，或要求代理管理详细的执行代码及其不断增长的历史。我们引入了RobotUse，一个组织计算、上下文和决策的机器人代理工具，旨在规范和修正物理行动。代理可以直观地选择目标和姿态，而后端则处理几何、运动规划和控制。子代理在每个子目标内保留详细的交互，并返回后续决策所需的信息。持续的利用使代理能够通过更新持久的操作手册来从执行中学习。在RoboLab上，RobotUse实现了45%的任务成功率，超越了CaP-X 6.7个百分点，同时保持紧凑的决策上下文，并减少对预定义行动抽象的依赖。此外，我们还展示了RobotUse能够从现实世界的执行中学习，尽管反馈不完美，并将所学知识转移到后续任务中。项目页面可访问 https://robotuse-team.github.io/。
cs.RO / 52 / 2610.04933
DiVeR: Decision-Critical Verifier Learning for VLA Test-Time Scaling
DiVeR：决策关键验证器学习用于视觉-语言-动作测试时扩展
Abstract
Scaling robot data and model capacity has improved Vision-Language-Action (VLA) policies, but further progress is constrained by the high cost of robotic data. Verifier-guided test-time scaling offers an efficient alternative by sampling multiple action candidates and selecting the one most likely to lead to task success at inference time. Existing classification-based verifiers learn from trajectory-level outcomes but treat all visited states equally, even though their value for candidate discrimination can vary across a trajectory. At many states, plausible actions are similar and provide limited discrimination signal, while only a sparse set of decision-critical states admits meaningfully different actions that can substantially affect downstream outcomes. To address this, we propose DiVeR, which estimates decision criticality from the dispersion of sampled action representations. DiVeR then uses this signal to reweight verifier learning toward states where action selection is most consequential, without requiring step-level annotations or additional environment interaction. Across LIBERO, RoboCasa, and real-world experiments on a Franka Research 3 robot, DiVeR consistently improves task success through more effective verifier-guided action selection, while adding negligible verifier inference overhead.
Chinese Translation
扩展机器人数据和模型容量提升了视觉-语言-动作（VLA）策略，但进一步的进展受到机器人数据高成本的限制。验证器引导的测试时扩展通过采样多个动作候选并选择最有可能在推理时导致任务成功的候选，提供了一种高效的替代方案。现有的基于分类的验证器从轨迹级结果中学习，但对所有访问的状态一视同仁，尽管它们在候选区分中的价值在轨迹中可能有所不同。在许多状态下，合理的动作相似且提供有限的区分信号，而只有一小部分决策关键状态允许有意义地不同的动作，这些动作可能会显著影响下游结果。为了解决这个问题，我们提出了DiVeR，它通过采样动作表示的分散程度来估计决策关键性。然后，DiVeR利用这一信号对验证器学习进行重新加权，重点关注动作选择最为重要的状态，而无需逐步注释或额外的环境交互。在LIBERO、RoboCasa以及在Franka Research 3机器人上的真实世界实验中，DiVeR通过更有效的验证器引导动作选择持续提高任务成功率，同时增加的验证器推理开销微乎其微。
cs.RO / 53 / 2610.04949
A Unified Dynamics Framework for Reinforcement Learning and Classical Control of a Six-DOF Pipeline-Tracking ROV in NVIDIA Isaac Sim
统一动态框架用于强化学习与经典控制在NVIDIA Isaac Sim中的六自由度管道跟踪ROV
Abstract
Reinforcement learning controllers for underwater vehicles are usually trained against one physics representation and deployed against another, so reported performance does not always describe behavior outside training. This paper presents a pipeline-tracking architecture for a six-degree-of-freedom remotely operated vehicle (ROV) in which one Universal Scene Description (USD) scene supplies the real BlueROV2-Heavy mass, added-mass, damping, buoyancy, and thruster parameters to both halves of the system: a vectorized NumPy implementation of Fossen's marine-craft equations, and an interactive NVIDIA Isaac Sim deployment applying the identical equations as PhysX forces at every step. The Coriolis-centripetal term is the primary dynamics model in both branches; a controlled ablation on PPO and TRPO shows that including it does not destabilize either algorithm and modestly improves tracking, about 19 percent lower standoff RMS error for TRPO. Five reinforcement learning algorithms, PPO, soft actor-critic, TD3, DDPG, and TRPO, are trained against one environment, reward, and randomized evaluation harness through a checkpoint-compatibility layer scoring any policy with the same code. The pipeline is extended with six classical baselines, PID, sliding-mode, fuzzy logic, feedback linearization, model predictive control, and an adaptive neuro-fuzzy inference system, driven by the same guidance geometry and thruster allocation as the learned policies. Under Coriolis-enabled dynamics, PPO, TRPO, and feedback linearization reach the strongest combination of 100 percent success and competitive accuracy; PID, fuzzy control, and the neuro-fuzzy baseline also reach 100 percent success with looser tracking; DDPG and TD3 each show a specific, explainable failure mode rather than a general weakness of off-policy learning; and classical control remains a strong baseline against the best learned policies.
Chinese Translation
水下车辆的强化学习控制器通常是在一种物理表示下训练，而在另一种物理表示下部署，因此报告的性能并不总能描述训练之外的行为。本文提出了一种六自由度遥控潜水器（ROV）的管道跟踪架构，其中一个通用场景描述（Universal Scene Description, USD）场景为系统的两个部分提供了真实的BlueROV2-Heavy质量、附加质量、阻尼、浮力和推进器参数：Fossen海洋船舶方程的向量化NumPy实现，以及在每一步应用相同方程作为PhysX力的互动NVIDIA Isaac Sim部署。科里奥利-向心项是两个分支中的主要动态模型；对PPO和TRPO的控制消融实验表明，包含该项不会使任何算法不稳定，并且适度改善了跟踪，对于TRPO而言，静态均方根误差降低了约19%。五种强化学习算法，PPO、软演员-评论家（soft actor-critic）、TD3、DDPG和TRPO，在一个环境、奖励和随机评估框架下通过检查点兼容层进行训练，该层对使用相同代码的任何策略进行评分。该管道扩展了六个经典基线，PID、滑模控制、模糊逻辑、反馈线性化、模型预测控制和自适应神经模糊推理系统，均由与学习策略相同的引导几何和推进器分配驱动。在科里奥利启用的动态下，PPO、TRPO和反馈线性化达到了100%的成功率和竞争力的准确性；PID、模糊控制和神经模糊基线也达到了100%的成功率，但跟踪较为宽松；DDPG和TD3各自表现出特定的、可解释的失败模式，而不是离策略学习的一般弱点；经典控制仍然是与最佳学习策略相比的强基线。
cs.RO / 54 / 2610.05011
FreeLoc: Online Floorplan Localization via Diffusion-Aided Pose Refinement
FreeLoc：通过扩散辅助姿态精炼的在线平面图定位
Abstract
Floorplans provide compact and widely available geometric maps for indoor localization, but existing high-performing floorplan-based methods still convert them into dense scene-specific offline databases, tying accuracy, storage, and runtime to the sampling resolution of the discretized pose space. We present FreeLoc, an online RGB-based floorplan localization framework that treats the floorplan as a directly queryable geometric map. FreeLoc introduces an efficient online geometric querying and diffusion-aided refinement scheme, which retrieves plausible pose anchors through on-the-fly floorplan ray querying and refines them into accurate continuous pose estimates. For sequential localization, FreeLoc develops an online likelihood construction strategy that bridges single-frame localization and probabilistic temporal fusion by constructing likelihoods from coarse-sampled candidates and refined pose hypotheses, enabling histogram-filter-based temporal fusion without offline databases. Experiments demonstrate real-time online inference and state-of-the-art performance in both single-frame and sequential localization, while real-world results validate practical deployability in indoor robotic localization scenarios.
Chinese Translation
平面图提供了紧凑且广泛可用的室内定位几何地图，但现有的高性能基于平面图的方法仍然将其转换为密集的场景特定离线数据库，从而将准确性、存储和运行时间与离散化姿态空间的采样分辨率绑定在一起。我们提出了FreeLoc，一种在线基于RGB的平面图定位框架，将平面图视为可直接查询的几何地图。FreeLoc引入了一种高效的在线几何查询和扩散辅助精炼方案，通过即时平面图光线查询检索可行的姿态锚点，并将其精炼为准确的连续姿态估计。对于序列定位，FreeLoc开发了一种在线似然构建策略，通过从粗采样候选和精炼姿态假设构建似然，连接单帧定位和概率时间融合，从而实现基于直方图滤波的时间融合，而无需离线数据库。实验表明，FreeLoc在单帧和序列定位中实现了实时在线推理和最先进的性能，同时现实世界的结果验证了其在室内机器人定位场景中的实际可部署性。
cs.RO / 55 / 2610.05081
Now You Feel It, Now You See Me: Digital-Twin-based Teleoperation Interface for Dexterous Manipulation
现在你能感受到它，现在你能看到我：基于数字双胞胎的灵巧操作远程控制界面
Abstract
Teleoperation is becoming increasingly important for collecting high-quality demonstrations to teach robots dexterous manipulation skills. For dexterous manipulation, bare-hand tracking provides a practical way to control robotic hands and demonstrate coordinated finger movements without gloves or exoskeletons. However, this type of teleoperation faces two key feedback limitations: a lack of force feedback and visual feedback of occluded region. The absence of force feedback hinders precise and safe manipulation, as operators must infer contact force visually rather than feel them directly. Occlusion by objects or other robot parts impairs the assessment of hand positions, approach distances, and pre-grasp configurations suited to the object's shape. To address these limitations, we propose a digital-twin-based augmented reality (AR) teleoperation interface that integrates bare-hand tracking, bimanual robotic hands, and virtual representations of the robot and task objects. The interface renders tactile measurements on robot hand meshes as visuo-force feedback. Furthermore, it offers a user-controlled virtual view panel or occlusion-aware transparency rendering to provide occlusion-mitigating visual feedback. We evaluated the interface through user studies involving Task 1 and Task 2, examining how force visualization and visual assistance support demonstration collection in tasks requiring careful force regulation and manipulation under occlusion.
Chinese Translation
远程操作在收集高质量示范以教授机器人灵巧操作技能方面变得越来越重要。对于灵巧操作，裸手追踪提供了一种实用的方法来控制机器人手，并在没有手套或外骨骼的情况下展示协调的手指运动。然而，这种类型的远程操作面临两个关键的反馈限制：缺乏力反馈和被遮挡区域的视觉反馈。力反馈的缺失阻碍了精确和安全的操作，因为操作员必须通过视觉推断接触力，而不是直接感受。物体或其他机器人部件的遮挡影响了手的位置、接近距离以及适合物体形状的预抓取配置的评估。为了解决这些限制，我们提出了一种基于数字双胞胎的增强现实（AR）远程操作界面，该界面集成了裸手追踪、双手机器人手以及机器人的虚拟表示和任务对象。该界面将触觉测量呈现在机器人手网格上，作为视觉-力反馈。此外，它提供了用户控制的虚拟视图面板或遮挡感知透明度渲染，以提供减轻遮挡的视觉反馈。我们通过涉及任务1和任务2的用户研究评估了该界面，考察了力可视化和视觉辅助如何支持在需要仔细力调节和在遮挡下操作的任务中收集示范。
cs.RO / 56 / 2610.05122
PB-STDG: A Prediction-Based Short-Term Decentralized Greedy Guidance Algorithm for a Drone Road System
PB-STDG：一种基于预测的短期去中心化贪婪引导算法用于无人机道路系统
Abstract
In recent years, Unmanned Aerial Vehicles (UAVs) or drones have been increasingly adopted in urban environments for applications such as parcel delivery, infrastructure inspection, emergency response, and drone light shows. A non-negligible issue is how to manage the increasing number of drones operated by different entities, to enable them to cooperatively avoid potential collisions and determine conflict-free short-term flight paths in urban airspace. This paper presents a Prediction-Based Short-Term Decentralized Greedy (PB-STDG) guidance algorithm for a structured Drone Road System (DRS). PB-STDG extends the original STDG algorithm by introducing a prediction mechanism that enables drones to anticipate the decisions of neighboring drones using additional information shared in beacon packets, aiming to address the over-conservative behavior observed in the STDG algorithm. Several simulation scenarios are conducted to evaluate and compare the proposed algorithm with STDG. The results show that PB-STDG improves traffic efficiency while maintaining a safety level comparable to that of STDG.
Chinese Translation
近年来，无人驾驶飞行器（UAV）或无人机在城市环境中的应用日益增多，涉及包裹投递、基础设施检查、紧急响应和无人机灯光秀等多个领域。一个不可忽视的问题是如何管理由不同实体操作的日益增多的无人机，以使它们能够协同避免潜在碰撞，并在城市空域中确定无冲突的短期飞行路径。本文提出了一种基于预测的短期去中心化贪婪（PB-STDG）引导算法，用于结构化的无人机道路系统（DRS）。PB-STDG通过引入预测机制扩展了原始的STDG算法，使无人机能够利用在信标数据包中共享的额外信息预测邻近无人机的决策，旨在解决在STDG算法中观察到的过于保守的行为。进行了多个仿真场景以评估和比较所提出的算法与STDG的性能。结果表明，PB-STDG在提高交通效率的同时保持了与STDG相当的安全水平。
cs.RO / 57 / 2610.05151
Recursive Self-Improvement of Visuomotor Policies through Local Recovery Supervision
通过局部恢复监督实现视觉运动策略的递归自我改进
Abstract
Visuomotor policies can execute familiar tasks yet lack the corrective behavior needed after their own mistakes. We present a framework for recursive self-improvement through local recovery supervision. Each round audits the current policy, generates corrective demonstrations at supported failure states, and uses them to update the policy that drives the next round of collection. An offline auditor locates unresolved failures using coarse and dense temporal evidence and specifies observable repair goals. A fixed multimodal agent acts as a tool-using teacher, generating recovery actions through observation, computation, execution, and feedback. The frozen student tests whether each teacher endpoint supports further progress. If continuation fails, the system restores that endpoint and extends the demonstration. Action-level quality assessment then defines continuous training windows with aligned observations, quality weights, and validity masks. Only the student is deployed. In a preliminary LIBERO-Goal study, recovery-augmented post-training achieves 88 successful episodes out of 100 validation scenes, compared with 78 for original-data continuation from the same $\pi_0$ checkpoint. An earlier BC-RNN study on robomimic Can improves success from 102/130 to 112/130 using 26 local recovery segments. Both comparisons match 2,000 additional optimization steps.
Chinese Translation
视觉运动策略能够执行熟悉的任务，但在自身错误后缺乏必要的纠正行为。我们提出了一种通过局部恢复监督实现递归自我改进的框架。每一轮审计当前策略，在支持的失败状态下生成纠正演示，并利用这些演示更新驱动下一轮收集的策略。离线审计器使用粗略和密集的时间证据定位未解决的失败，并指定可观察的修复目标。一个固定的多模态代理作为工具使用教师，通过观察、计算、执行和反馈生成恢复动作。冻结的学生测试每个教师端点是否支持进一步的进展。如果继续失败，系统将恢复该端点并扩展演示。动作级质量评估随后定义了与对齐观察、质量权重和有效性掩码相结合的连续训练窗口。只有学生被部署。在一项初步的LIBERO-Goal研究中，增强恢复的后训练在100个验证场景中实现了88个成功回合，而相同$ ext{π}_0$检查点的原始数据继续仅为78个成功回合。早期的BC-RNN研究在robomimic Can中使用26个局部恢复片段将成功率从102/130提高到112/130。这两个比较均匹配2,000个额外的优化步骤。
cs.RO / 58 / 2610.05180
VICON: Visual-Inertial-Contact based Hand-Object Tracking for Manipulation Datasets
VICON：基于视觉-惯性-接触的手-物体跟踪用于操作数据集
Abstract
Learning dexterous manipulation benefits from human demonstration datasets that capture diverse and natural hand-object interactions. In particular, contact points and forces provide supervision on where and how strongly to interact, which cannot be fully captured by motion trajectories alone. However, methods for jointly capturing hand and object motion, contact points, and forces remain limited. Moreover, severe occlusion from hand-object interaction challenges accurate tracking of both hands and objects. To address these limitations, we present a Visual-Inertial-CONtact based hand-object tracking (VICON) framework. It holistically captures both hand and object motion along with contact information during manipulation, even under severe occlusion. First, we adopt a visual-inertial glove and an RGB-D camera for accurate hand tracking, and redesign the glove to incorporate contact sensing. Specifically, force-sensitive resistors (FSRs) are placed on the glove based on human grasp frequency to synchronously record contact states and calibrated normal forces. Second, without requiring pre-existing CAD models, we estimate object poses using RGB-D images and a mesh reconstructed from a monocular video. We propose factor-graph-based object trajectory estimation that fuses object-pose estimates weighted by visibility under hand-object occlusion, FSR measurements, and a hand-motion prior. Across 40 motion-capture sessions with five objects, VICON achieves a 2.5% failed-frame rate compared with 50.9-64.6% for the baselines, with median errors of 3.9 mm and 3.0 degrees under occlusion. Using VICON, we construct a dataset containing synchronized hand-object motion, contact points, and normal forces, and will publicly release an expanded dataset covering 10 object categories at https://github.com/VICON-dataset/dataset.
Chinese Translation
学习灵巧操作得益于捕捉多样化和自然的手-物体交互的人类示范数据集。特别是，接触点和力提供了关于何处以及多强烈进行交互的监督，这一点仅通过运动轨迹无法完全捕捉。然而，联合捕捉手和物体运动、接触点和力的方法仍然有限。此外，手-物体交互造成的严重遮挡对准确跟踪双手和物体构成了挑战。为了解决这些局限性，我们提出了一种基于视觉-惯性-接触的手-物体跟踪框架（VICON）。该框架在操作过程中全面捕捉手和物体的运动以及接触信息，即使在严重遮挡的情况下也能实现。首先，我们采用视觉-惯性手套和RGB-D相机进行准确的手部跟踪，并重新设计手套以集成接触传感。具体而言，基于人类抓握频率，在手套上放置了力敏电阻（FSRs），以同步记录接触状态和校准的法向力。其次，在不需要现有CAD模型的情况下，我们使用RGB-D图像和从单目视频重建的网格来估计物体姿态。我们提出了一种基于因子图的物体轨迹估计方法，该方法融合了在手-物体遮挡下加权的物体姿态估计、FSR测量和手部运动先验。在40个运动捕捉会话中，VICON的失败帧率为2.5%，而基线方法的失败帧率为50.9%-64.6%，在遮挡下的中位误差为3.9毫米和3.0度。使用VICON，我们构建了一个包含同步手-物体运动、接触点和法向力的数据集，并将在https://github.com/VICON-dataset/dataset上公开发布一个涵盖10个物体类别的扩展数据集。
cs.RO / 59 / 2610.05230
Vela: Scaling Vision-Language-Action Models with Adaptive Action Curve Parametrization
Vela：通过自适应动作曲线参数化扩展视觉-语言-动作模型
Abstract
Most vision-language-action models represent future motion as fixed-rate action chunks, tying temporal resolution and prediction horizon to a fixed output budget. This pointwise representation wastes capacity on highly correlated neighboring actions, leaves temporal continuity and smoothness to be learned implicitly, and forces a tradeoff between long-horizon coverage and the local precision required for contact-rich manipulation. To address these limitations, we introduce Vela, a vision-language-action foundation model that represents future robot behavior as continuous trajectories. Vela combines a compact spline-based action representation with motion-dependent temporal support and a shared action interface for heterogeneous embodiments, allowing a fixed output budget to adapt its temporal resolution across motions. We pretrain Vela on large-scale multi-embodiment robot data and evaluate it on LIBERO-X, EBench, and two real-world long-horizon tasks, egg-cake cooking and potato shredding, obtaining promising results across simulation and physical manipulation. These results highlight the potential of continuous action representations as a foundation for future embodied foundation models. Project page and more results: https://clementine24.github.io/Vela/ .
Chinese Translation
大多数视觉-语言-动作模型将未来的运动表示为固定速率的动作块，这将时间分辨率和预测范围与固定的输出预算绑定在一起。这种逐点表示在高度相关的相邻动作上浪费了容量，留下了时间连续性和平滑性需要隐式学习，并迫使在长时间范围覆盖和接触丰富的操作所需的局部精度之间进行权衡。为了解决这些局限性，我们提出了Vela，一种将未来机器人行为表示为连续轨迹的视觉-语言-动作基础模型。Vela结合了基于样条的紧凑动作表示、依赖于运动的时间支持以及用于异构体现的共享动作接口，使得固定的输出预算能够在不同动作之间自适应其时间分辨率。我们在大规模多体现机器人数据上对Vela进行了预训练，并在LIBERO-X、EBench以及两个真实世界的长时间任务（蛋糕烹饪和土豆切丝）上进行了评估，获得了在模拟和物理操作中均表现出色的结果。这些结果突显了连续动作表示作为未来具身基础模型的基础潜力。项目页面及更多结果： https://clementine24.github.io/Vela/ 。
cs.RO / 60 / 2610.05258
Safe Ergodic Control for Multi-Robot Systems via Quadratic Programming
通过二次规划实现多机器人系统的安全遍历控制
Abstract
Ergodic control drives robots to spend time in each region in proportion to a spatial distribution of interest, making it well suited for dense spatiotemporal environmental monitoring. Existing safe ergodic controllers rely on offline trajectory optimization or hierarchical architectures, which limit real-time applicability and decouple the ergodicity objective from the safety constraint. This paper presents a quadratic programming (QP)-based controller that treats ergodicity and safety jointly. We first introduce a Gaussian-kernel ergodic metric that, unlike the classical indicator-based metric, is time differentiable. This allows the exponential decay of the metric to be imposed as a time-varying control barrier function (CBF) constraint, relaxed by a slack variable, alongside hard CBF constraints for region containment and inter-robot collision avoidance. We establish that the resulting QP remains feasible from any safe initial configuration and that, up to the slack term, the ergodic metric decays exponentially. Simulations show improved performance over baseline methods, and experiments with three aerial vehicles validate the work on real hardware.
Chinese Translation
遍历控制使机器人在每个区域的停留时间与感兴趣的空间分布成比例，非常适合于密集的时空环境监测。现有的安全遍历控制器依赖于离线轨迹优化或分层架构，这限制了其实时应用性，并将遍历性目标与安全约束解耦。本文提出了一种基于二次规划（QP）的控制器，将遍历性和安全性联合考虑。我们首先引入了一种高斯核遍历度量，与经典的基于指标的度量不同，该度量是时间可微的。这使得可以将度量的指数衰减作为时间变化的控制障碍函数（CBF）约束施加，并通过松弛变量放宽，同时结合区域包含和机器人间碰撞避免的硬性CBF约束。我们证明了所得到的QP从任何安全的初始配置出发始终是可行的，并且在松弛项的影响下，遍历度量呈指数衰减。仿真结果显示出相较于基线方法的性能提升，三架空中飞行器的实验验证了该方法在真实硬件上的有效性。
cs.RO / 61 / 2610.05324
CoDance: Learning Reactive and Compliant Human-Humanoid Interaction from Video
CoDance：从视频中学习反应性和顺应性的人类-类人机器人互动
Abstract
Partnered human-humanoid interaction couples locomotion with continuous physical contact. A humanoid needs to coordinate with a person's motion while responding to interaction forces and maintaining stable and natural movement. We present CoDance, a framework for learning reactive and compliant human-humanoid interaction from video. We study partnered dancing as a challenging instantiation, where a humanoid coordinates its footsteps with a moving partner and maintains continuous two-hand contact. Given a single video of two human dancers, CoDance retargets their motions into a robot reference and a moving partner. We introduce a multi-link compliance augmentation that transforms the kinematic demonstration into force-aware training data by adapting the robot reference under structured forces at both hands. Policies trained on this data follow the observed partner while preserving the demonstrated locomotion style and responding compliantly to physical interaction. In simulation, the policies adapt their footsteps to changes in the partner and reproduce approximately 80% of the wrist displacement encoded by the augmented demonstrations. On a physical humanoid, CoDance enables sustained two-hand dancing with a human partner including repeated transitions between forward and backward motions.
Chinese Translation
人类-类人机器人互动结合了运动与持续的身体接触。类人机器人需要协调与人的运动，同时响应互动力量并保持稳定自然的运动。我们提出了CoDance，一个从视频中学习反应性和顺应性的人类-类人机器人互动的框架。我们将伴随舞蹈作为一个具有挑战性的实例，其中类人机器人需要与移动的伴侣协调步伐，并保持持续的双手接触。给定一段两位人类舞者的单一视频，CoDance将他们的动作重新定向到机器人参考和移动的伴侣。我们引入了一种多链接顺应性增强方法，通过在双手施加结构化力量的情况下调整机器人参考，将运动演示转化为力感知训练数据。基于这些数据训练的策略在遵循观察到的伴侣的同时，保持所示的运动风格，并对物理互动做出顺应性反应。在仿真中，这些策略能够根据伴侣的变化调整步伐，并重现约80%的由增强演示编码的手腕位移。在物理类人机器人上，CoDance实现了与人类伴侣的持续双手舞蹈，包括前后运动之间的重复过渡。
cs.RO / 62 / 2610.05331
VAMPS: Visual and Motor Policies from Sampling-Based Planning
VAMPS：基于采样规划的视觉和运动策略
Abstract
Learning robot policies directly on physical systems remains difficult because data collection is costly and policy exploration can be unsafe. We introduce Visual and Motor Policies from Sampling-Based Planning (VAMPS), a framework that uses Model Predictive Path Integral (MPPI) control to train reusable policies without human demonstrations. VAMPS supports two training modes. For one-step proprioceptive policies, it operates iteratively in simulation: the policy warm-starts MPPI, and the refined trajectories provide new supervision as the policy changes. A learned terminal value improves short-horizon planning, while an Implicit Q-Learning (IQL) critic guides the policy update. Iterative refinement outperforms training once on frozen MPPI data, and we transfer the learned locomotion policy to a Unitree Go2. For visuomotor policies, VAMPS operates directly from real-robot data. MPPI uses task-specific state estimates to plan and execute trajectories while recording RGB and sensor observations on a Flexiv Rizon 10S. Action Chunking with Transformers predicts action chunks, reducing the effective prediction horizon, and is trained offline on this fixed dataset. We demonstrate visuomotor pick-and-place and force-aware whiteboard erasing. In the latter task, the policy additionally observes the measured $6$-D wrench and desired normal force. These results show that VAMPS can learn policies either in simulation followed by hardware transfer or directly from autonomously collected real-robot data.
Chinese Translation
在物理系统上直接学习机器人策略仍然困难，因为数据收集成本高昂且策略探索可能不安全。我们提出了基于采样规划的视觉和运动策略（VAMPS），这是一个利用模型预测路径积分（Model Predictive Path Integral, MPPI）控制来训练可重用策略的框架，无需人类演示。VAMPS支持两种训练模式。对于一步本体感知策略，它在仿真中迭代运行：策略为MPPI提供热启动，精细化的轨迹在策略变化时提供新的监督。学习到的终端值改善了短期规划，而隐式Q学习（Implicit Q-Learning, IQL）评论员则指导策略更新。迭代精细化的效果优于在固定MPPI数据上进行一次性训练，我们将学习到的运动策略转移到Unitree Go2上。对于视觉运动策略，VAMPS直接从真实机器人数据中操作。MPPI使用任务特定的状态估计来规划和执行轨迹，同时在Flexiv Rizon 10S上记录RGB和传感器观测。使用变换器的动作分块（Action Chunking）预测动作块，减少有效预测范围，并在这个固定数据集上进行离线训练。我们展示了视觉运动的抓取与放置以及基于力的白板擦除。在后者任务中，策略还观察测量到的6维扭矩和期望的法向力。这些结果表明，VAMPS可以在仿真中学习策略后再转移到硬件，或直接从自主收集的真实机器人数据中学习策略。
cs.RO / 63 / 2610.05359
Optimal Control with Learned Critics under Unmodeled State Dependencies
在未建模状态依赖下的学习型评论员的最优控制
Abstract
Model Predictive Control (MPC) provides a structured and constraint-aware mechanism for decision-making, but its reliance on optimization-friendly analytical dynamics models limits its use in tasks with contacts and other hard-to-model state dependencies. Model-free reinforcement learning avoids explicit modeling assumptions but typically requires large amounts of interaction data. We present a learning-based MPC framework that combines the data efficiency and structure of local model-based planning with learned components that compensate for incomplete dynamics and finite-horizon myopia. The method augments a nominal analytical model with a residual dynamics network that learns missing state-dependent effects from data and combines the resulting planner with a learned action-value critic that injects long-horizon MDP structure into the local iLQR optimization. To make this practical at reinforcement-learning scale, we develop a GPU-accelerated batched iLQR solver that evaluates learned dynamics and critic networks inside the optimal-control loop and solves thousands of trajectory-optimization problems in parallel. The complete system is integrated into a robotics simulator, enabling scalable model-based reinforcement learning under incomplete dynamics. Experiments on biased and incompletely modeled control tasks show that the approach improves closed-loop control performance while preserving the model-based structure needed for efficient constrained trajectory optimization.
Chinese Translation
模型预测控制（MPC）提供了一种结构化且考虑约束的决策机制，但其对优化友好的解析动态模型的依赖限制了其在接触和其他难以建模的状态依赖任务中的应用。无模型强化学习避免了显式建模假设，但通常需要大量的交互数据。我们提出了一种基于学习的MPC框架，该框架结合了局部基于模型的规划的数据效率和结构，以及学习组件以补偿不完整的动态和有限视野的短视性。该方法通过一个残差动态网络增强了名义解析模型，该网络从数据中学习缺失的状态依赖效应，并将得到的规划器与一个学习的动作价值评论员结合，后者将长视野的马尔可夫决策过程（MDP）结构注入到局部的增量线性二次回归（iLQR）优化中。为了在强化学习规模上实现这一点，我们开发了一个基于GPU加速的批量iLQR求解器，该求解器在最优控制循环内评估学习的动态和评论员网络，并并行解决数千个轨迹优化问题。整个系统集成到一个机器人模拟器中，使得在不完整动态下可扩展的基于模型的强化学习成为可能。在偏置和不完全建模的控制任务上的实验表明，该方法提高了闭环控制性能，同时保留了高效约束轨迹优化所需的基于模型的结构。
cs.RO / 64 / 2610.05407
TUCO: Curating Simulation Demonstrations for Sim-to-Real Robot Policy Co-Training
TUCO：为仿真到现实的机器人策略共同训练策划仿真演示
Abstract
Simulation demonstrations can supplement scarce real-world data for robot policy co-training. However, the value of using data curation to actively select these demonstrations for sim-to-real co-training remains underexplored. Existing curation methods also lack a unified criterion for measuring trajectory-level utility and set-level coverage from closed-loop target behavior. To address these gaps, we present the first systematic study of data curation for sim-to-real robot policy co-training and propose Trajectory-level Utility and set-level Coverage Optimization (TUCO). TUCO uses influence functions to trace how each source demonstration affects target-domain scoring rollouts. Our key insight is that these effects can be decomposed into an overall contribution to target return and variation across rollouts, providing a common closed-loop basis for measuring trajectory utility and set coverage. We further propose a performance-aligned subset optimizer that combines these measures in a unified curation objective to reduce redundancy and select complementary demonstrations. Extensive experiments on RoboMimic and OmniReset establish the value of active simulation data curation for sim-to-real policy co-training and show that TUCO achieves state-of-the-art performance across single-simulator, sim-to-sim, and sim-to-real settings.
Chinese Translation
仿真演示可以补充稀缺的现实世界数据，以便进行机器人策略共同训练。然而，利用数据策划主动选择这些演示以进行仿真到现实的共同训练的价值仍然未被充分探索。现有的策划方法也缺乏统一的标准来衡量轨迹级效用和来自闭环目标行为的集合级覆盖率。为了解决这些问题，我们提出了首个针对仿真到现实机器人策略共同训练的数据策划系统研究，并提出了轨迹级效用和集合级覆盖优化（Trajectory-level Utility and set-level Coverage Optimization，TUCO）。TUCO使用影响函数追踪每个源演示如何影响目标领域的评分回滚。我们的关键见解是，这些影响可以分解为对目标回报的整体贡献和回滚之间的变异，为衡量轨迹效用和集合覆盖提供了一个共同的闭环基础。我们进一步提出了一种性能对齐的子集优化器，将这些度量结合在一个统一的策划目标中，以减少冗余并选择互补的演示。在RoboMimic和OmniReset上的大量实验验证了主动仿真数据策划在仿真到现实策略共同训练中的价值，并显示TUCO在单一仿真器、仿真到仿真和仿真到现实的设置中达到了最先进的性能。
cs.RO / 65 / 2610.05414
RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation
RMMBench：机器人移动操作的综合基准测试
Abstract
Although the advancement of vision-language models (VLMs) has endowed robots with enhanced environmental understanding and task reasoning, a comprehensive evaluation methodology is important to advance the integration of VLMs in robotic navigation and manipulation. However, current benchmarks lack a comprehensive method to evaluate diverse robotic tasks, and evaluation metrics remain relatively constrained, making it difficult to assess the embodied capabilities of VLMs in a thorough and fine-grained manner. To address this issue, we propose RMMBench, an evaluation benchmark that requires robots to understand language instructions and perform long-horizon tasks in continuous spaces. RMMBench seamlessly integrates high- and low-level embodied tasks into a unified framework, constructing a "navigation-manipulation" task suite comprising 70 canonical task scenarios that range from localized manipulation to long-horizon composite navigation. The results reveal that leading VLMs still face major challenges in spatial localization when performing mobile manipulation tasks, and also highlight the necessity of enhancing the spatial perception capability of robots during long-horizon interactions. RMMBench can be accessed at https://mxxq-stack.github.io/rmmbench-project/
Chinese Translation
尽管视觉-语言模型（VLMs）的进步使机器人具备了更强的环境理解和任务推理能力，但全面的评估方法对于推动VLMs在机器人导航和操作中的整合至关重要。然而，当前的基准测试缺乏评估多样化机器人任务的综合方法，评估指标相对有限，使得难以全面细致地评估VLMs的具身能力。为了解决这一问题，我们提出了RMMBench，这是一种评估基准，要求机器人理解语言指令并在连续空间中执行长时间跨度的任务。RMMBench将高层次和低层次的具身任务无缝整合到一个统一框架中，构建了一个包含70个经典任务场景的“导航-操作”任务套件，这些场景涵盖了从局部操作到长时间跨度复合导航的任务。结果表明，领先的VLMs在执行移动操作任务时仍面临空间定位的重大挑战，同时也突显了在长时间交互中增强机器人空间感知能力的必要性。RMMBench可通过https://mxxq-stack.github.io/rmmbench-project/访问。
cs.RO / 66 / 2610.05418
EvoMem-VLA: State-Evolution Memory for Long-Horizon Robot Manipulation
EvoMem-VLA：用于长时间机器人操作的状态演变记忆
Abstract
Most vision-language-action (VLA) models rely on current observations and lose task-relevant evidence once it leaves view, limiting performance on long-horizon, memory-dependent tasks. Existing efforts incorporate compressed historical features or sparse visual keyframes. However, isolated snapshots can leave the policy uncertain about what changed during past interactions and which action should follow. To overcome this limitation, we propose EvoMem-VLA, which constructs state-evolution memory by explicitly encoding and retaining observed changes between historical states. These change representations preserve evidence of interaction outcomes, allowing the policy to track task progress beyond isolated snapshots. Specifically, we introduce conditional delta tokenization to encode ordered frame pairs into directional, source-conditioned delta tokens, each associated with its corresponding state evidence. A shared VLM backbone supports task-adaptive routing: normal long-horizon tasks follow a direct action route, whereas multi-stage tasks use a subtask route that generates an executable subtask as an additional input for action generation. With a single jointly trained policy for each simulation benchmark, EvoMem-VLA achieves success rates of 80.7\% on RMBench, 82.0\% on RoboMME and 83.8\% across four real-world tasks spanning two robot embodiments. These results represent substantial improvements over the previous state of the art in all three evaluation settings.
Chinese Translation
大多数视觉-语言-动作（VLA）模型依赖于当前观察，并在任务相关证据离开视野后丧失，从而限制了在长时间、依赖记忆的任务中的表现。现有的研究努力整合压缩的历史特征或稀疏的视觉关键帧。然而，孤立的快照可能使策略对过去交互中发生的变化和应采取的后续动作感到不确定。为克服这一限制，我们提出了EvoMem-VLA，它通过明确编码和保留历史状态之间观察到的变化来构建状态演变记忆。这些变化表示保留了交互结果的证据，使得策略能够跟踪超越孤立快照的任务进展。具体而言，我们引入了条件增量标记化，将有序帧对编码为方向性、源条件的增量标记，每个标记与其对应的状态证据相关联。一个共享的视觉语言模型（VLM）主干支持任务自适应路由：正常的长时间任务遵循直接的动作路线，而多阶段任务使用子任务路线，生成可执行的子任务作为动作生成的额外输入。在每个仿真基准上使用单个联合训练的策略，EvoMem-VLA在RMBench上取得了80.7%的成功率，在RoboMME上为82.0%，在涵盖两种机器人实现的四个真实世界任务中达到83.8%。这些结果在所有三个评估设置中都代表了对之前最先进技术的显著改进。
cs.RO / 67 / 2610.05455
Social Navigation for Tour-guide Robot
导游机器人社会导航
Abstract
We propose a force-based model for social navigation of a tour-guide robot. Social forces due to various factors like obstacles, user position and heading, have been accounted for in the model. We claim that each one of these forces makes the robot more sociable to the user and we design an experimental setup for evaluation. In the experiment, the user follows an autonomous robot to a destination in a known map, while undertaking a few simple sub-tasks in the middle, which serve as distractions. For each participant, we run several rounds of the experiment, each with different forces and a shortest path, A*-search baseline model. Using per-round subjective indicators, we propose to study the effect of our force model on constructs such as: follow-ability, perceived safety, and perceived intelligence.
Chinese Translation
我们提出了一种基于力的模型，用于导游机器人的社会导航。该模型考虑了由于障碍物、用户位置和朝向等各种因素引起的社会力。我们认为这些力量使机器人对用户更加友好，并设计了一个实验设置以进行评估。在实验中，用户跟随一台自主机器人前往已知地图上的目的地，同时在过程中执行一些简单的子任务，这些子任务作为干扰。在每位参与者身上，我们进行了多轮实验，每轮实验中施加不同的力量，并使用最短路径的 A* 搜索基线模型。通过每轮的主观指标，我们提出研究我们的力模型对以下构念的影响：跟随能力、感知安全性和感知智能性。
cs.RO / 68 / 2610.05483
FLEX-WAM: Flexible Block-Causal World-Action Models for Long-Horizon Imagination and Planning
FLEX-WAM：用于长时间想象和规划的灵活块因果世界-动作模型
Abstract
World--action models (WAMs) promise a unified model that predicts action-conditioned futures, generates feasible actions, and supports planning in imagination. However, existing joint video--action models often use computationally heavy, fixed-horizon backbones ill-suited to streaming inference and stable long-horizon open-loop rollouts. We introduce FLEX-WAM, a Flexible and Efficient Block-Causal World--Action Model for unified simulation and policy inference. FLEX-WAM supports variable-length contexts and non-causal prediction horizons, as well as infinite autoregressive generation frame by frame or block by block. Its block-causal, KV-cacheable architecture combines axial attention and blockwise diffusion forcing to enable efficient real-time rollout and deployment-time latency--throughput tradeoffs without retraining. Joint training can nevertheless produce plausible futures that weakly respond to commanded actions. We address this failure mode by balancing state and action flow-matching gradient contributions across the state--action diffusion-noise grid and regulating world-model sampling using Forward-Dynamics (FD) elasticity, an efficient training-time proxy for action responsiveness. Across simulated and real-world datasets, FLEX-WAM achieves superior multi-step prediction quality and latency while producing stable joint state--action rollouts for thousands of steps. As a joint action proposer and simulator within MCTS, it solves long-horizon PushT and all five OGBench Puzzle-4x4 tasks entirely in imagination. On a bimanual OpenArm-based robot, a single checkpoint jointly serves as a play policy and expected-outcome predictor, enabling real-time identification and collection of model--reality mismatches for future self-improvement.
Chinese Translation
世界-动作模型（WAMs）承诺提供一个统一的模型，该模型能够预测基于动作的未来，生成可行的动作，并支持想象中的规划。然而，现有的联合视频-动作模型通常使用计算量大且固定时间范围的骨干网络，这不适合流式推理和稳定的长时间开放循环展开。我们提出了FLEX-WAM，一种灵活且高效的块因果世界-动作模型，用于统一的模拟和策略推理。FLEX-WAM支持可变长度的上下文和非因果预测时间范围，以及无限的自回归逐帧或逐块生成。其块因果、可缓存的KV架构结合了轴向注意力和块级扩散强制，使得在不重新训练的情况下实现高效的实时展开和部署时延-吞吐量权衡。联合训练仍然可以生成对命令动作弱响应的合理未来。我们通过在状态-动作扩散噪声网格中平衡状态和动作流匹配梯度贡献，并使用前向动力学（Forward-Dynamics, FD）弹性调节世界模型采样，来解决这一失败模式，FD弹性是一个高效的训练时间代理，用于动作响应性。在模拟和真实世界数据集中，FLEX-WAM实现了优越的多步预测质量和延迟，同时为数千个步骤生成稳定的联合状态-动作展开。作为MCTS中的联合动作提议者和模拟器，它完全在想象中解决了长时间的PushT和所有五个OGBench Puzzle-4x4任务。在一个基于双手OpenArm的机器人上，单个检查点同时作为游戏策略和预期结果预测器，能够实时识别和收集模型与现实之间的不匹配，以便未来自我改进。
cs.RO / 69 / 2610.05491
Robust Surgical Robotic Instrument Tracking via Sequential Multi-Cue Fusion and Sim-to-Real Self-Training
通过序列多线索融合和仿真到现实自我训练实现稳健的外科手术机器人工具跟踪
Abstract
Efficient and robust tracking of surgical robotic instruments is important for robot-assisted minimally invasive surgery, yet remains challenging due to the complexity of surgical scenes and the unconventional geometry of surgical instruments. Keypoint-based approaches are efficient, but their performance depends on reliable feature detection. Improving these detectors with real-world supervision is difficult because accurate real-world annotations are costly to obtain at scale. To address this limitation, we introduce a tracker-guided self-training framework that adapts a model pretrained on synthetic images to unlabeled real-world videos. Given measured robot joint states, an uncertainty-aware EKF recursively corrects the instrument pose and the observable joint angles by comparing projected model features with detected keypoints, shaft boundaries, and mask-derived cues. An RTS smoother subsequently refines the resulting trajectory, which is projected into pseudo-labels for fine-tuning the feature detector without laborious pose annotations. Experiments on real-world videos demonstrate consistent improvements from self-training across all evaluated keypoint metrics, and the resulting model outperforms prior approaches in both accuracy and runtime. The code and data will be released upon publication.
Chinese Translation
高效且稳健的外科手术机器人工具跟踪对于机器人辅助的微创手术至关重要，但由于外科场景的复杂性和外科工具的非常规几何形状，这一任务仍然具有挑战性。基于关键点的方法效率较高，但其性能依赖于可靠的特征检测。由于在大规模获取准确的现实世界标注成本高昂，因此提升这些检测器的现实世界监督变得困难。为了解决这一限制，我们提出了一种跟踪器引导的自我训练框架，该框架将预训练于合成图像的模型适应于未标记的现实世界视频。考虑到测量的机器人关节状态，一个不确定性感知的扩展卡尔曼滤波器（EKF）通过比较投影的模型特征与检测到的关键点、轴边界和掩膜派生线索，递归地校正工具姿态和可观测的关节角度。随后，一个实时平滑器（RTS）进一步优化得到的轨迹，并将其投影为伪标签，以便在没有繁琐姿态标注的情况下微调特征检测器。在现实世界视频上的实验表明，自我训练在所有评估的关键点指标上都带来了持续的改进，且所得到的模型在准确性和运行时间上均优于先前的方法。代码和数据将在发表时发布。
cs.RO / 70 / 2610.05505
Robust 2D Traversability Mapping for Construction AMRs via Failure-Mode-Aware Fusion of LiDAR Geometry and Monocular Semantics
基于故障模式感知的LiDAR几何与单目语义融合的建筑自动移动机器人二维可通行性鲁棒映射
Abstract
Autonomous Mobile Robots (AMRs) on active construction sites face severe navigational challenges: geometry-based traversability mapping (e.g., LiDAR) misses visually hazardous but geometrically flat surfaces like wet mud and ponding concrete, while abrupt geometry on drivable speed-breakers and inclines produces phantom obstacles. We propose a real-time, failure-mode-aware multimodal traversability pipeline on an NVIDIA Jetson AGX Orin, where LiDAR is the primary geometric safety estimate and monocular semantics act as a selective, class- and confidence-gated corrective signal. The representation retains distinct traversable classes, namely flat road, terrain, and rocky terrain, while flagging construction hazards. We also release a multimodal construction-site dataset from a custom AMR: four closed-loop ROS 2 sequences from two active sites (RGB, depth, LiDAR, IMU, GPS-RTK, odometry) plus 506 annotated frames across 28 semantic classes. By projecting LiDAR onto dense semantic masks, resolving sparsity via morphological in-painting, and applying failure-mode-aware fusion with Patchwork++, the system corrects complementary geometric failure modes for a local AMR costmap.
Chinese Translation
在活跃的建筑工地上，自主移动机器人（AMRs）面临严峻的导航挑战：基于几何的可通行性映射（如LiDAR）无法识别视觉上危险但几何上平坦的表面，如湿泥和积水混凝土，而可行驶的减速带和坡道上的突变几何则会产生虚假障碍物。我们提出了一种基于NVIDIA Jetson AGX Orin的实时故障模式感知多模态可通行性处理流程，其中LiDAR作为主要的几何安全估计手段，单目语义则作为选择性、基于类别和置信度门控的校正信号。该表示保留了不同的可通行类别，即平坦道路、地形和岩石地形，同时标记建筑危险。我们还发布了一个来自定制AMR的多模态建筑工地数据集：包含两个活跃工地的四个闭环ROS 2序列（RGB、深度、LiDAR、IMU、GPS-RTK、里程计）以及涵盖28个语义类别的506帧标注数据。通过将LiDAR投影到密集的语义掩码上，利用形态学修复解决稀疏性问题，并结合Patchwork++进行故障模式感知融合，系统为本地AMR代价地图校正了互补的几何故障模式。
cs.RO / 71 / 2610.05542
Neural Barriers: An Online Certifiable Learning-enhanced Adaptive High Order Safety Critical Control
神经障碍：一种在线可证明的学习增强自适应高阶安全关键控制
Abstract
Control barrier functions are an effective model-based tool to formally certify the safety of a system. However, transferring their theoretical guarantees to real-world robotics systems requires high model fidelity. For example, payloads or wind disturbances can cause significant model perturbations to an aerial vehicle, leading to safety compromises. In this work, we propose a certifiable online learning-enhanced robust adaptive control barrier function, which adapts to disturbances using a Neural ODE and quantifies its adaptation uncertainty with conformal prediction. Our approach guarantees safety at all time under unknown time-varying model disturbances. It adopts a conservative strategy when the adaptation uncertainty is high; and efficiently adapts to reduce controller conservativeness as it receives more data. Our approach provides a provable safety guarantee with a probability bound under suitable Lipschitz smoothness assumptions on the underlying model and trajectory. These results demonstrate the potential of our method as a practical safety controller for robotics system operating under model perturbations.
Chinese Translation
控制障碍函数是一种有效的基于模型的工具，用于正式认证系统的安全性。然而，将其理论保证转移到现实世界的机器人系统中需要高模型保真度。例如，载荷或风扰动可能会对空中飞行器造成显著的模型扰动，从而导致安全性妥协。在本研究中，我们提出了一种可证明的在线学习增强鲁棒自适应控制障碍函数，该函数利用神经常微分方程（Neural ODE）适应扰动，并通过符合预测（conformal prediction）量化其适应不确定性。我们的方法在未知的时变模型扰动下保证了安全性。当适应不确定性较高时，它采用保守策略；而随着数据的增加，能够有效适应以减少控制器的保守性。我们的方法在适当的利普希茨光滑性假设下，提供了具有概率界限的可证明安全保证。这些结果展示了我们的方法作为在模型扰动下操作的机器人系统的实用安全控制器的潜力。
cs.RO / 72 / 2610.05546
GR-LIO: A Local Ground-Aware LiDAR-Inertial Odometry System Using Body-to-Ground Height
GR-LIO：一种基于身体与地面高度的局部地面感知激光雷达惯性里程计系统
Abstract
LiDAR-inertial odometry (LIO) is widely used for state estimation in ground-based autonomous mobile robots. However, the geometric constraints provided by the local ground surface remain largely underexploited in existing LIO systems. This paper proposes a filter-based local ground-aware LIO framework that explicitly incorporates local ground plane geometry into the state estimation process to improve both localization accuracy and computational efficiency. Specifically, a local ground plane is parameterized by the robot orientation and the body-to-ground (B-G) height and continuously propagated within the state estimation process. Based on the proposed B-G geometry model, a propagated local ground plane enables efficient and reliable ground segmentation. The segmented ground points are then incorporated into the filter update through point-to-plane geometric constraints, improving both state estimation accuracy and efficiency. Furthermore, a planar motion update is introduced to exploit the propagated local ground plane as an additional geometric constraint, effectively suppressing vertical drift and improving estimation robustness. To address the initially unknown B-G height, an efficient initialization strategy is developed, followed by an online calibration procedure for continuous refinement. The proposed system is evaluated on several public benchmark datasets and self-collected real-world datasets covering diverse operating scenarios. Experimental results demonstrate that the proposed method consistently outperforms representative LIO methods in terms of both localization accuracy and computational efficiency.
Chinese Translation
激光雷达惯性里程计（LIO）广泛应用于地面自主移动机器人的状态估计。然而，现有LIO系统中，局部地面表面提供的几何约束仍然未得到充分利用。本文提出了一种基于滤波器的局部地面感知LIO框架，该框架明确将局部地面平面几何纳入状态估计过程，以提高定位精度和计算效率。具体而言，局部地面平面通过机器人朝向和身体与地面（B-G）高度进行参数化，并在状态估计过程中持续传播。基于所提出的B-G几何模型，传播的局部地面平面能够实现高效且可靠的地面分割。然后，通过点到平面的几何约束将分割出的地面点纳入滤波器更新，从而提高状态估计的精度和效率。此外，引入了一种平面运动更新，以利用传播的局部地面平面作为额外的几何约束，有效抑制垂直漂移并提高估计的鲁棒性。为了解决初始未知的B-G高度，开发了一种高效的初始化策略，并随后进行在线校准程序以实现持续优化。所提出的系统在多个公共基准数据集和自收集的真实世界数据集上进行了评估，涵盖了多种操作场景。实验结果表明，所提出的方法在定位精度和计算效率方面始终优于代表性的LIO方法。
cs.RO / 73 / 2610.05583
TacGELLO: Tactile Contact Feedback for a 3D-Printed Teleoperation Leader
TacGELLO：用于3D打印遥操作领导者的触觉接触反馈
Abstract
Passive leader arms transmit an operator's motion without returning remote contact or load cues. TacGELLO uses the existing servos of a 3D-printed leader for tactile contact feedback at the gripper trigger and current-based load cues at the arm joints. AnySkin contact detection activates a trigger position hold, while changes in follower joint current adjust leader-servo output limits to command resistance during lifting. Across six UR3 sessions with one operator, sent-command tracking error was 0.15--0.36$^\circ$ RMSE, with estimated trajectory lag of 82--98\,ms. Tactile onset was logged in 60 of 62 segmented TacGELLO grasp intervals. Median commanded closure after tactile or width-based contact references was 0.7--2.1\,mm with TacGELLO and 13.0--13.9\,mm with the original interface.
Chinese Translation
被动领导臂在不返回远程接触或负载提示的情况下传递操作者的运动。TacGELLO利用3D打印领导者的现有伺服器，在夹持器触发器处提供触觉接触反馈，并在臂关节处提供基于电流的负载提示。AnySkin接触检测激活触发位置保持，而跟随者关节电流的变化则调整领导伺服器的输出限制，以在提升过程中指令抵抗。在与一名操作者进行的六次UR3会话中，发送命令的跟踪误差为0.15--0.36$^ heta$ RMSE，估计轨迹滞后为82--98毫秒。触觉启动在62个分段的TacGELLO抓取间隔中记录了60次。在触觉或基于宽度的接触参考后，TacGELLO的中位命令闭合为0.7--2.1毫米，而原始接口为13.0--13.9毫米。
cs.RO / 74 / 2610.05604
Building A Multi-Sensor Platform For Autonomous Driving Research: Challenges and Lessons Learned
构建用于自动驾驶研究的多传感器平台：挑战与经验教训
Abstract
This paper reports on the challenges encountered and lessons learned during the development and deployment of a flexible multi-sensor platform for autonomous driving research. It aims to serve as a reference for researchers developing new multi-sensor systems. As the need for reliable, diverse datasets increases, novel environments and sensing configurations are essential to tackle real-world operational challenges. Consequently, many research groups create custom multi-sensory data collection platforms, where fundamental issues, such as mechanical design, sensor calibration, power management, and time synchronization, arise regardless of sensor types. We reflect on these challenges and share key insights to guide future platform designs, enhancing reproducibility and robustness in autonomous vehicle testing. We also summarize the mitigation strategies we adopted, for instance, system-wide time synchronization using GNSS timing and NTP protocols, custom calibration routines for different sensor configurations, and design practices to improve reliability and data integrity during field deployments.
Chinese Translation
本文报告了在开发和部署灵活的多传感器平台用于自动驾驶研究过程中遇到的挑战和获得的经验教训。旨在为开发新多传感器系统的研究人员提供参考。随着对可靠、多样化数据集需求的增加，新颖的环境和传感配置对于应对现实世界的操作挑战至关重要。因此，许多研究小组创建了定制的多传感器数据采集平台，其中无论传感器类型如何，都会出现机械设计、传感器校准、电源管理和时间同步等基本问题。我们反思了这些挑战，并分享了关键见解，以指导未来平台设计，增强自动驾驶车辆测试的可重复性和稳健性。我们还总结了所采用的缓解策略，例如，使用GNSS时间同步和NTP协议进行系统范围的时间同步，为不同传感器配置定制校准程序，以及在现场部署中改善可靠性和数据完整性的设计实践。
cs.RO / 75 / 2610.05607
Emergency Obstacle Avoidance Maneuvers in Differential-Drive Mobile Robots
差动驱动移动机器人中的紧急障碍物规避机动
Abstract
Emergency obstacle avoidance requires a mobile robot to brake or change its heading within the available distance. This paper investigates the dependence of maneuver performance and odometric error on approach speed for a differential-drive robot. A footprint-clearance analysis and a normalized wheel-motion index provide a kinematic description of five braking and turning maneuvers. The principal experiment comprises 540 block-randomized trials on tile, of which 539 are retained, at commanded approach speeds of 45, 55, and 65 cm/s. An overhead camera provides an independent pose reference. When encoder, gyro, and camera heading changes are evaluated over complete motion records, the mean encoder discrepancy increases by 4.7-5.1 degrees for the two reverse-spin maneuvers between the lowest and highest speeds; the arc and brake-assisted pivot change by less than 0.4 degrees. Larger encoder discrepancy is associated with lower avoidance success after adjustment for distance, speed, maneuver, day, and turn direction. Gyro discrepancies remain approximately 3 degrees for the reverse spins. Estimated reaction distances for 90% success and their uncertainty quantify the maneuver trade-offs. The results support speed-specific empirical characterization of emergency maneuvers and distinguish encoder error from recovery motion and measurement-window mismatch.
Chinese Translation
紧急障碍物规避要求移动机器人在可用距离内刹车或改变航向。本文研究了差动驱动机器人机动性能和里程计误差对接近速度的依赖性。通过足迹间隙分析和归一化轮子运动指数，提供了五种刹车和转向机动的运动学描述。主要实验包括在瓷砖上进行的540次区块随机试验，其中保留539次，命令接近速度为45、55和65 cm/s。一个顶视摄像头提供了独立的姿态参考。当评估编码器、陀螺仪和摄像头航向变化时，两个反向旋转机动在最低和最高速度之间的平均编码器差异增加了4.7-5.1度；弧形和刹车辅助旋转的变化小于0.4度。经过距离、速度、机动、日期和转向方向的调整后，较大的编码器差异与较低的规避成功率相关。反向旋转的陀螺仪差异保持在大约3度。90%成功率的估计反应距离及其不确定性量化了机动的权衡。结果支持对紧急机动的速度特定经验表征，并区分编码器误差与恢复运动和测量窗口不匹配。
cs.RO / 76 / 2610.05639
FOCUS: Fine-Grained Open-Vocabulary Change Detection for Uncertainty-Aware Semi-Static Scenes
FOCUS：针对不确定性感知的半静态场景的细粒度开放词汇变化检测
Abstract
Autonomous robots operating over long periods must keep their environmental memory up to date as the world changes between visits. In semi-static environments, an object may be replaced in place by a different but geometrically and semantically similar instance, making the identity change difficult to detect from geometry or coarse semantics alone. We present FOCUS, an uncertainty-aware framework for object-level change detection and map maintenance. We formulate semi-static memory maintenance as probabilistic inference that fuses geometric likelihood with appearance likelihood rendered from a 3D Gaussian map. A recursive three-state estimator maintains whether each mapped object is PERSISTED, REPLACED, or REMOVED. To account for imperfect 3DGS rendering, we model the rendered appearance evidence probabilistically rather than using it as a direct change score, with the model parameters automatically calibrated from a change-free replay of the initial mapping session. We evaluate our method on a new Isaac Sim warehouse benchmark with ambiguous in-place replacements and on the real-world TorWIC dataset. It improves object-level replacement F1 from 0.20 to 0.84 over a probabilistic baseline and transfers to real-world data without manual parameter retuning.
Chinese Translation
在长时间运行的自主机器人中，必须随着世界在访问之间的变化而保持环境记忆的更新。在半静态环境中，一个物体可能被几何和语义上相似的不同实例所替代，这使得仅通过几何或粗略语义来检测身份变化变得困难。我们提出了FOCUS，一个针对物体级变化检测和地图维护的不确定性感知框架。我们将半静态记忆维护形式化为概率推理，融合了来自3D高斯地图的几何似然性和外观似然性。一个递归的三状态估计器维护每个映射物体是持续存在（PERSISTED）、被替换（REPLACED）还是被移除（REMOVED）。为了考虑不完美的3DGS渲染，我们以概率方式对渲染的外观证据进行建模，而不是将其作为直接的变化评分，模型参数则通过对初始映射会话的无变化重放自动校准。我们在一个新的Isaac Sim仓库基准上评估了我们的方法，该基准具有模糊的就地替换，并在真实世界的TorWIC数据集上进行测试。与概率基线相比，它将物体级替换的F1值从0.20提高到0.84，并且在不需要手动参数重新调优的情况下迁移到真实世界数据。
cs.RO / 77 / 2610.05642
Spacecraft Rendezvous Trajectory Generation with Modular Constraints via Diffusion Model Composition
基于扩散模型组合的航天器交会轨迹生成与模块化约束
Abstract
Emerging mission classes such as on-orbit servicing, satellite inspection, and active debris removal require trajectory design methods that are adaptable to a variety of mission scenarios. We present a diffusion-based trajectory generation approach for rendezvous and proximity operations (RPO) that enables flexible configuration of mission constraints. First, individual energy-based diffusion models are trained to satisfy distinct constraints such as approach cone and sensor line-of-sight from a set of optimized trajectories. Then, at inference time, the learned energy models can be composed with one another, or with an analytically defined energy field, to enforce specific constraint combinations. We validate this framework with the composition of a learned approach cone model and a learned sensor line-of-sight model, as well as a learned approach cone model and synthetic obstacle avoidance model, both of which yield constraint satisfaction rates that are within 1 percentage point of the single-constraint models or higher. These results indicate that our compositional diffusion framework can provide a modular approach to RPO trajectory design and enable reconfiguration for new constraint combinations without requiring model retraining.
Chinese Translation
新兴的任务类别，如在轨服务、卫星检查和主动碎片移除，要求轨迹设计方法能够适应多种任务场景。我们提出了一种基于扩散的轨迹生成方法，用于交会与接近操作（RPO），该方法能够灵活配置任务约束。首先，针对一组优化轨迹，训练单独的基于能量的扩散模型，以满足不同的约束，如接近锥和传感器视线。然后，在推理时，学习到的能量模型可以相互组合，或者与一个解析定义的能量场组合，以强制执行特定的约束组合。我们通过组合学习到的接近锥模型和学习到的传感器视线模型，以及学习到的接近锥模型和合成障碍物规避模型来验证该框架，这两种组合的约束满足率均在单一约束模型的1个百分点以内或更高。这些结果表明，我们的组合扩散框架可以为RPO轨迹设计提供模块化的方法，并能够在不需要模型重新训练的情况下重新配置新的约束组合。
cs.RO / 78 / 2610.05674
Bayesian Data Augmentation for DNN Retraining with Binomial Outcomes in Vision-Based UAV Landing
基于贝叶斯数据增强的深度神经网络重训练：针对视觉导向无人机着陆的二项结果
Abstract
In GPS-denied or cluttered urban environments, vision-based landing is essential for reliable UAV missions. Real-world landing sites are often unstructured and highly variable, requiring strong generalization by the perception system. Deep Neural Networks (DNNs) trained with synthetic data augmentation offer a scalable solution for learning landing-site features across diverse vehicle and environmental states. However, computationally expensive DNN retraining, along with challenging performance validation via test flights, limits exhaustive model fine-tuning and necessitates an optimized retraining pipeline. In this work, we deploy a Bayesian data augmentation framework integrated with a photorealistic simulator featuring high-fidelity vehicle dynamics to iteratively retrain the helipad detector DNN, maximizing landing performance as the objective function. We validate our framework with experiments in a photorealistic simulator under different environmental conditions and vehicle states, demonstrating improved landing performance and tighter confidence intervals on predicted landing outcomes.
Chinese Translation
在缺乏GPS信号或拥挤的城市环境中，基于视觉的着陆对可靠的无人机任务至关重要。现实世界中的着陆点通常是非结构化且高度可变的，这要求感知系统具备强大的泛化能力。通过合成数据增强训练的深度神经网络（DNN）为学习多样化车辆和环境状态下的着陆点特征提供了一种可扩展的解决方案。然而，计算成本高昂的DNN重训练以及通过测试飞行进行性能验证的挑战限制了模型的全面微调，并需要一个优化的重训练流程。在本研究中，我们部署了一个贝叶斯数据增强框架，该框架与具有高保真车辆动力学的照片级真实感模拟器集成，以迭代重训练直升机着陆点检测器DNN，以最大化着陆性能作为目标函数。我们通过在不同环境条件和车辆状态下的照片级真实感模拟器中进行实验来验证我们的框架，结果表明着陆性能得到了改善，预测的着陆结果的置信区间更加紧凑。
cs.RO / 79 / 2610.05677
Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations
从单一演示中学习协调的视觉运动箱推
Abstract
Multi-robot imitation learning, particularly in settings where visuomotor policies are deployed in a communication-free, onboard decentralised style, represents an attractive paradigm. However, its realisation remains insufficiently understood, largely due to the difficulty of collecting collective demonstrations, since a single operator cannot control many robots simultaneously. Meanwhile, unlike coupled collaborative manipulation, many coordinated tasks achieve system-wide efficiency primarily through minimising inter-robot interference. This structure motivates us to study whether data collected by a teleoperated single-robot can be leveraged for large-scale coordinated box-pushing as a testbed. We systematically investigate dataset creation strategies and lightweight policy architectures. In particular, experiments with up to 40 robots highlight the difficulty of acquiring effective coordination solely through passive observation of other operating robots, revealing a concrete bottleneck for multi-robot research.
Chinese Translation
多机器人模仿学习，特别是在无通信、基于机载去中心化风格部署视觉运动策略的环境中，代表了一种有吸引力的范式。然而，由于收集集体演示的困难，尤其是单一操作员无法同时控制多个机器人，这一实现仍然未被充分理解。同时，与耦合协作操控不同，许多协调任务主要通过最小化机器人之间的干扰来实现系统范围的效率。这一结构促使我们研究是否可以利用由遥控单机器人收集的数据进行大规模协调箱推的测试。我们系统地调查了数据集创建策略和轻量级策略架构。特别是，针对多达40个机器人的实验突显了仅通过被动观察其他操作机器人来获取有效协调的困难，揭示了多机器人研究的一个具体瓶颈。
cs.RO / 80 / 2610.05678
Dataset-Free Compliant Humanoid Loco-Manipulation with Dynamic Online Posture
无数据集的合规人形机器人动态在线姿态行走操控
Abstract
Most humanoid loco-manipulation controllers require human motion data to learn whole-body coordination and posture, leaving policies reliant on external sources to provide this data. We present OCLO (Online-posture Compliant LOco-manipulation), a humanoid loco-manipulation system trained without human motion data and commanded only through two end-effector targets. Because these targets do not uniquely determine whole-body posture, OCLO generates pelvis height and torso orientation online using an analytic reachability prior, further refined through policy-in-the-loop sampling with a task-agnostic cost. OCLO also learns whole-body compliance by displacing end-effector references according to measured forces through a spring-damper model, encouraging the legs, waist, and pelvis to yield to external loads. In simulation, using the reachability prior leads to a 77.8% success rate in acquiring the commanded reference, a vast improvement over the 37.8% success rate accomplished without the prior. Further, refinement reduces end-effector orientation error across all evaluated tasks. The same posture module improves a pretrained SONIC controller on four of five tasks. Without compliance training, policies tend to lose balance under disturbances rather than sacrifice tracking. On a Unitree G1, OCLO maintains balance under end-effector disturbances that cause its ablations to fail and performs seven loco-manipulation tasks, including crouched walking and picking up a box from a low surface. Project website: https://oclo-humanoid.github.io/
Chinese Translation
大多数人形机器人行走操控控制器需要人类运动数据来学习全身协调和姿态，这使得策略依赖于外部来源提供这些数据。我们提出了OCLO（在线姿态合规行走操控），这是一个无需人类运动数据训练的行走操控系统，仅通过两个末端执行器目标进行指令。由于这些目标并不能唯一确定全身姿态，OCLO利用解析可达性先验在线生成骨盆高度和躯干方向，并通过任务无关成本的策略循环采样进一步优化。OCLO还通过根据测量到的力调整末端执行器参考位置，利用弹簧-阻尼器模型学习全身合规性，鼓励腿部、腰部和骨盆对外部负载做出反应。在仿真中，使用可达性先验使得获取指令参考的成功率达到77.8%，相比之下，未使用先验时的成功率仅为37.8%。此外，优化过程减少了所有评估任务中的末端执行器方向误差。相同的姿态模块在五个任务中提高了预训练的SONIC控制器在四个任务上的表现。在没有合规性训练的情况下，策略在干扰下往往会失去平衡，而不是牺牲跟踪。在Unitree G1上，OCLO在末端执行器干扰下保持平衡，而这些干扰会导致其消融实验失败，并执行七个行走操控任务，包括蹲走和从低表面拾取箱子。项目网站：https://oclo-humanoid.github.io/
cs.RO / 81 / 2610.05692
Benchmarking Generative Trajectory Models for Active-Inference Control
生成轨迹模型在主动推理控制中的基准测试
Abstract
Learning from trajectory demonstrations offers a route to active-inference control of complex systems whose dynamics are difficult to model explicitly. We introduce generative active-inference control (GenAIF), in which one generative trajectory model learns from demonstrations and measured action interventions to supply a goal-conditioned policy distribution and a state-to-observation likelihood mapping. From this control design, we derive three model requirements: (i) useful action proposals, (ii) accurate prediction under imposed actions, and (iii) probabilistic observation evidence for belief updating and expected information gain. We benchmark diffusion, autoregressive Transformers, conditional variational autoencoders (CVAEs), and flow matching in a MuJoCo manipulation task with multiple physical conditions. Diffusion delivers the strongest control across the tested dynamics, while CVAE combines comparable short-horizon prediction with much faster inference. Correct conditioning is decisive, and trajectory reuse offers further computational savings. With the same frozen models, a hidden-dynamics experiment demonstrates prompt belief adaptation after an unannounced tilt change; subsequent instability identifies sustained inference as a remaining challenge. These findings support the use of shared generative trajectory models to connect action proposal, controlled prediction, and observation evidence within GenAIF.
Chinese Translation
通过轨迹示范学习为复杂系统的主动推理控制提供了一条途径，这些系统的动态难以明确建模。我们引入了生成主动推理控制（GenAIF），其中一个生成轨迹模型通过示范和测量的行动干预进行学习，以提供目标条件下的策略分布和状态到观测的似然映射。基于这一控制设计，我们推导出三个模型要求：（i）有用的行动提议，（ii）在施加行动下的准确预测，以及（iii）用于信念更新和期望信息增益的概率观测证据。我们在MuJoCo操控任务中对扩散模型、自回归变换器、条件变分自编码器（CVAEs）和流匹配进行了基准测试，涵盖了多种物理条件。扩散模型在测试的动态中提供了最强的控制，而CVAEs则结合了可比的短期预测和更快的推理。正确的条件设定至关重要，轨迹重用提供了进一步的计算节省。在相同的冻结模型下，一个隐藏动态实验展示了在未通知的倾斜变化后信念的快速适应；随后的不稳定性表明持续推理仍然是一个挑战。这些发现支持使用共享生成轨迹模型将行动提议、受控预测和观测证据连接在GenAIF中。
cs.RO / 82 / 2610.05695
Virtual model control for compliant reaching under uncertainties
在不确定性下的柔性到达控制的虚拟模型控制
Abstract
Virtual Model Control (VMC) is an approach to design a controller for force-controlled robots in complex uncertain environments. While this method was primarily investigated for legged robot locomotion in the past, it can be more generally applicable to other types of robotic systems. This paper investigates the VMC framework for reaching tasks in a force-controlled robotic arm. We propose six different approaches to designing virtual models in order to achieve reaching tasks in environments with obstacles and uncertainties. A force-controlled 8 degree-of-freedom humanoid robot was used to validate the proposed approach in the real world. We conducted three experiments to test the performance of VMC controllers in terms of predictability, sensitivity to external force, and adaptability against known and unknown obstacles. Experimental analyses show that, even though the proposed approach needs to sacrifice accuracy and trajectory optimality, it enables us to design complex reaching motions under uncertainties, in an intuitive and extendable manner.
Chinese Translation
虚拟模型控制（Virtual Model Control, VMC）是一种为在复杂不确定环境中进行力控制的机器人设计控制器的方法。虽然该方法在过去主要针对腿部机器人运动进行研究，但它也可以更广泛地应用于其他类型的机器人系统。本文研究了VMC框架在力控制机器人臂的到达任务中的应用。我们提出了六种不同的虚拟模型设计方法，以实现障碍物和不确定性环境中的到达任务。我们使用了一种具有8个自由度的力控制人形机器人在现实世界中验证所提出的方法。我们进行了三项实验，以测试VMC控制器在可预测性、对外部力的敏感性以及对已知和未知障碍物的适应性方面的性能。实验分析表明，尽管所提出的方法需要牺牲准确性和轨迹最优性，但它使我们能够以直观和可扩展的方式设计在不确定性下的复杂到达动作。
cs.RO / 83 / 2610.05719
When to Switch: Reliable Action-Chunk Extension for Vision-Language-Action Models
何时切换：视觉-语言-动作模型的可靠动作块扩展
Abstract
Vision-Language-Action (VLA) models serve as unified policies for robotic manipulation, yet their expensive inference forces robots to pause between policy calls, resulting in stop-and-go execution that interrupts smooth motion and prolongs task completion. Extending the action chunk reduces policy calls and hence these pauses, but predicting farther into the future makes long-chunk execution unreliable. To understand where this unreliability arises, we analyze action errors within long chunks and find that they concentrate around transitions between manipulation subskills, growing sharply with chunk length. This suggests the importance of transition timing, i.e., when to switch subskills within a chunk. Motivated by this observation, we introduce RACE (Reliable Action-Chunk Extension), a framework that predicts the transition timing from an auxiliary one-step denoising pass and conditions action generation on it. By learning and conditioning on transition timing, RACE reduces errors at subskill transitions and enables reliable execution of longer action chunks. Across simulation benchmarks, RACE outperforms fine-tuning at the same chunk length; with 2x longer chunks, it surpasses recent state-of-the-art and efficient VLAs in success rate, and with 4x longer chunks, it remains competitive. On a real robot, RACE uses 4x longer chunks, which reduces the idle time caused by stop-and-go execution by about 5x, while achieving a higher success rate than fine-tuning with the same chunk length. Code and a real-robot demo are available at https://github.com/Seonghoon-Yu/RACE-VLA
Chinese Translation
视觉-语言-动作（VLA）模型作为机器人操作的统一策略，但其昂贵的推理使得机器人在策略调用之间不得不暂停，导致停顿和继续的执行方式，打断了平滑运动并延长了任务完成时间。扩展动作块可以减少策略调用，从而减少这些暂停，但对未来的远期预测使得长块执行变得不可靠。为了理解这种不可靠性产生的原因，我们分析了长块内的动作错误，发现这些错误集中在操作子技能之间的过渡处，并随着块长度的增加而急剧增加。这表明过渡时机的重要性，即在一个块内何时切换子技能。基于这一观察，我们提出了RACE（可靠动作块扩展）框架，该框架通过辅助的一步去噪过程预测过渡时机，并以此为条件生成动作。通过学习和条件化过渡时机，RACE减少了子技能过渡处的错误，并实现了更长动作块的可靠执行。在模拟基准测试中，RACE在相同块长度下优于微调；在2倍长的块中，其成功率超过了最近的最先进和高效的VLA，而在4倍长的块中仍保持竞争力。在真实机器人上，RACE使用4倍长的块，将由于停顿和继续执行造成的空闲时间减少约5倍，同时在相同块长度下实现了比微调更高的成功率。代码和真实机器人演示可在 https://github.com/Seonghoon-Yu/RACE-VLA 获取。
cs.RO / 84 / 2610.05733
End-to-End Safe Social Navigation via Multi-Task Reinforcement Learning and Probabilistic Perception
通过多任务强化学习和概率感知实现端到端安全社交导航
Abstract
Autonomous social navigation requires balancing efficiency, physical safety, and social compliance. Reinforcement Learning (RL) methods provide a viable and effective solution but often rely on unrealistic assumptions, such as the knowledge of humans' position and velocity. In this paper, we introduce JESSI (JAX-based E2E Safe Social Interpretable navigation), a lightweight end-to-end RL framework that maps raw LiDAR scans directly to kinematically feasible control commands. JESSI enhances safety via Dirichlet-parameterized continuous action spaces and deterministic bounding, while an integrated attention-based perception module extracts probabilistic human states for interpretable, socially aware decision-making. Through extensive simulations and real-world deployment on a differential-drive robot, we demonstrate that jointly optimizing the RL policy with a supervised perception signal in a multi-task paradigm enhances social behavior. Ultimately, JESSI is able to balance high navigation success rates and superior social behaviors compared to state-of-the-art baselines.
Chinese Translation
自主社交导航需要在效率、物理安全和社会合规性之间取得平衡。强化学习（Reinforcement Learning, RL）方法提供了一种可行且有效的解决方案，但通常依赖于不切实际的假设，例如对人类位置和速度的了解。在本文中，我们介绍了JESSI（基于JAX的端到端安全社交可解释导航），这是一个轻量级的端到端RL框架，能够将原始激光雷达（LiDAR）扫描直接映射为运动学可行的控制命令。JESSI通过Dirichlet参数化的连续动作空间和确定性边界增强了安全性，同时集成的基于注意力的感知模块提取概率性人类状态，以实现可解释的、具有社会意识的决策。通过广泛的仿真和在差分驱动机器人上的实际部署，我们证明了在多任务范式中，联合优化RL策略与监督感知信号能够增强社交行为。最终，JESSI能够在导航成功率和优越的社交行为之间取得平衡，相较于最先进的基线表现更为出色。
cs.RO / 85 / 2610.05734
FreeSpeed: Training-Free Speed Control for Generative Robot Policies
FreeSpeed：无训练速度控制的生成机器人策略
Abstract
Online control of execution speed is essential for deploying robot policies in real-world scenarios, as robots may need to speed up under time constraints or slow down to facilitate human interaction and improve safety. However, imitation-learned policies inherit the execution speed of their demonstrations, and test-time speed modification can introduce unrecoverable out-of-distribution observations, reducing task success. We observe that the directional inconsistency of action chunks reflects task-phase criticality, indicating how aggressively action step lengths can be modified while preserving task success. Based on this observation, we introduce FreeSpeed, a training-free module that post-processes action chunks from pretrained policies. FreeSpeed resamples each predicted chunk at the requested rate, then uses directional inconsistency between adjacent actions as the primary signal for rescaling. This signal adaptively determines how closely the execution speed can approach the requested speed, allowing flexible speed adjustment within the evaluated limits without compromising task success. Across three policy families and 50 simulated tasks, FreeSpeed supports online speed changes, with realized execution rates spanning 0.22x to 2.53x among settings that preserve per-task success. Across four real-world manipulation tasks, FreeSpeed achieves an average success rate of 94.0%, matching the frozen policy's 93.8%, while realizing execution rates from 0.38x to 1.97x.
Chinese Translation
在线控制执行速度对于在现实场景中部署机器人策略至关重要，因为机器人可能需要在时间限制下加速，或减速以促进人机交互并提高安全性。然而，模仿学习的策略继承了其演示的执行速度，而测试时的速度修改可能会引入不可恢复的分布外观察，从而降低任务成功率。我们观察到，动作块的方向不一致性反映了任务阶段的关键性，指示了在保持任务成功的同时，动作步骤长度可以被多大程度地修改。基于这一观察，我们提出了FreeSpeed，一个无训练的模块，用于对预训练策略的动作块进行后处理。FreeSpeed以请求的速率重新采样每个预测的块，然后利用相邻动作之间的方向不一致性作为重新缩放的主要信号。该信号自适应地决定了执行速度可以多接近请求速度，从而允许在评估限制内灵活调整速度，而不影响任务成功。在三种策略家族和50个模拟任务中，FreeSpeed支持在线速度变化，实际执行速率在保持每个任务成功的设置中范围为0.22x到2.53x。在四个现实世界的操作任务中，FreeSpeed实现了94.0%的平均成功率，接近冻结策略的93.8%，同时实现的执行速率从0.38x到1.97x不等。
cs.RO / 86 / 2610.05747
The Unexpired Plan: A Free Monitor for Accelerated Diffusion Policies
未过期的计划：加速扩散策略的免费监控器
Abstract
Training-free acceleration of a diffusion policy is accepted when an internal similarity signal reports that the shortcut changed nothing. We price every monitor a control loop can afford in closed-loop success rather than feature distance, over $114$ accelerator configurations and four policy families: two accelerators each clear their own gate's bar and log every reuse as certified, while on one task one finishes every episode and the other none. A monitor is two designs, not one --- the statistic it reads, and what it does when that statistic fires. Published gates re-arm after every rejection, and under that response even an oracle handed every forward pass and the exact local action error loses twenty points; absorbing the same statistic costs one point, and most of its speed. What makes a response that never forgets affordable is a statistic that rarely fires, and what it must measure is deviation from the policy the accelerator replaced --- which a chunked policy has already paid for, its last plan not yet expired and free to read. Guarding every call this way cuts per-call compute by $1.55$--$3.09\times$, where any reference-requiring check at the same coverage would have to stop accelerating altogether. On three of our four families the schedule alone already holds the pre-stated $\pm2$-point margin. What the monitor is measurably worth shows in three places: on the fourth family, where it rescues the candidate selection landed on; on four configurations it did not select; and on a contact-rich fifth family, chosen where the schedule was expected to fail and run after every design choice was frozen, where no unmonitored arm at its speed holds the margin and the monitored one does.
Chinese Translation
当内部相似性信号报告捷径没有改变任何东西时，接受无训练的扩散策略加速。我们为每个控制回路在闭环成功中能够承受的监控器定价，而不是特征距离，涵盖了114种加速器配置和四个策略家族：两个加速器各自清除自己的门槛并将每次重用记录为认证，而在一个任务中，一个完成每个回合，另一个则没有。监控器是两个设计，而不是一个——它读取的统计数据，以及当该统计数据触发时它所做的事情。已发布的门在每次拒绝后重新启动，在这种响应下，即使是一个神谕在每次前向传递和确切的局部动作误差中也会失去20分；吸收相同的统计数据则损失1分，并且大部分速度也随之损失。使得一个永不遗忘的响应变得可承受的是一个很少触发的统计数据，而它必须测量的是加速器所替代的策略的偏差——这一点已经由分块策略支付，其最后的计划尚未过期并且可以自由读取。以这种方式保护每次调用将每次调用的计算减少了1.55到3.09倍，而任何在相同覆盖下需要参考检查的情况都必须完全停止加速。在我们的四个家族中的三个，调度本身已经保持了预先声明的±2分的边际。监控器的可测量价值体现在三个地方：在第四个家族中，它挽救了候选选择；在四个未选择的配置上；以及在一个接触丰富的第五个家族中，选择了在预期失败的调度下，并在每个设计选择被冻结后运行，在其速度下没有未监控的臂保持边际，而监控的臂则做到了。
cs.RO / 87 / 2610.05755
Demonstration-Calibrated Port-Hamiltonian Retuning for Manipulation Policies
演示校准的端口哈密顿重调节用于操作策略
Abstract
Diffusion and VLA policies for manipulation are often deployed through downstream impedance controllers. The stiffness and damping gains of these controllers affect task success, yet are commonly inherited from data collection rather than selected for the deployed policy. Although empirical gain sweeps can improve performance, they require repeated evaluation rollouts. We introduce PHRetune, an offline method that derives controller gains for a frozen policy without evaluation rollouts or gain search. Our approach learns a port-Hamiltonian model from demonstrations to estimate the effort and energy associated with the policy's predicted actions. The policy is applied to recorded demonstration observations, and its predictions are assessed against demonstration-derived effort and energy budgets. From this comparison, we derive a single gain scale in closed form, adjusting the downstream controller while preserving the policy and its action representation. The gains are fixed before evaluation, without requiring a prior manipulator model, task rewards, policy retraining, or additional runtime computation. Across LIBERO suites, PHRetune improves Diffusion Policy success by up to 9.4 percentage points, with the derived gains achieving the highest observed success rates in empirical gain sweeps. On all four real-world manipulation tasks, PHRetuned Diffusion Policy outperforms the nominal policy, alternative gain-tuning methods, and a policy-retraining baseline. The same procedure improves success with SmolVLA and OpenVLA-OFT on every task, while reducing acceleration and jerk for both VLA backbones.
Chinese Translation
操作的扩散和变形线性近似（VLA）策略通常通过下游阻抗控制器进行部署。这些控制器的刚度和阻尼增益影响任务成功率，但通常是从数据收集中继承的，而不是为所部署的策略选择的。尽管经验增益扫描可以提高性能，但它们需要重复的评估回滚。我们提出了PHRetune，这是一种离线方法，可以在没有评估回滚或增益搜索的情况下为冻结策略推导控制器增益。我们的方法通过演示学习端口哈密顿模型，以估计与策略预测动作相关的努力和能量。该策略应用于记录的演示观察，并将其预测与演示导出的努力和能量预算进行评估。通过这种比较，我们以封闭形式推导出一个单一的增益比例，调整下游控制器，同时保留策略及其动作表示。增益在评估之前固定，无需先前的操纵模型、任务奖励、策略重训练或额外的运行时计算。在LIBERO套件中，PHRetune将扩散策略的成功率提高了多达9.4个百分点，所推导的增益在经验增益扫描中实现了最高的观察成功率。在所有四个真实世界的操作任务中，PHRetuned扩散策略的表现优于名义策略、替代增益调优方法和策略重训练基线。同样的程序在每个任务上改善了SmolVLA和OpenVLA-OFT的成功率，同时减少了两个VLA骨干的加速度和抖动。
cs.RO / 88 / 2610.05757
Human-in-the-Loop Neuro-Symbolic Drift Anticipation for Reliable Visual SLAM
基于人机协作的神经符号漂移预测框架用于可靠的视觉SLAM
Abstract
This paper introduces Hybrid DeepSEE (HDS), a Human-in-the-Loop (HITL) neuro-symbolic framework for proactive drift anticipation in Visual SLAM (V-SLAM). While data-driven models offer predictive power, their "black-box" nature often yields physically inconsistent outputs in out-of-distribution (OOD) environments. To address this, HDS integrates neural drift risk estimation with symbolic constraint reasoning. By utilizing a Large Language Model (LLM) as a reasoning bridge, the framework translates qualitative human context into interpretable symbolic constraints. Building upon this architecture, we propose a superior drift anticipation framework that ensures enhanced reliability and consistency in Visual SLAM
Chinese Translation
本文介绍了一种混合深度SEE（Hybrid DeepSEE，HDS）框架，这是一个基于人机协作（Human-in-the-Loop，HITL）的神经符号框架，用于视觉SLAM（Visual SLAM，V-SLAM）中的主动漂移预测。尽管数据驱动模型提供了预测能力，但其“黑箱”特性在分布外（out-of-distribution，OOD）环境中往往会产生物理上不一致的输出。为了解决这一问题，HDS将神经漂移风险估计与符号约束推理相结合。通过利用大型语言模型（Large Language Model，LLM）作为推理桥梁，该框架将定性的人类上下文转化为可解释的符号约束。在此架构的基础上，我们提出了一种更优的漂移预测框架，确保在视觉SLAM中增强可靠性和一致性。
cs.RO / 89 / 2610.05765
Bilinear Flow Policy: Distributional Extrapolation for Goal-Conditioned Visuomotor Imitation
双线性流策略：面向目标条件的视觉运动模仿的分布外推
Abstract
Goal-conditioned imitation learning (GCIL) with flow matching is a promising framework that can represent multimodal behaviors while adapting to diverse, user-specified goals, yet often fails when goals lie outside the demonstration support. To extrapolate to such unseen goals without collapsing multimodality - a problem we call distributional extrapolation - we introduce Bilinear Flow Policy (BFP), a generative visuomotor policy that combines transductive retrieval with a bilinear conditional flow. Given an unseen observation-goal pair, BFP retrieves an "anchor" training example and transductively reformulates the unseen pair as this familiar anchor plus a residual term. For this decomposition to guide action prediction, the residual must compactly encode how the current observation-goal pair differs from the anchor, and the anchor must be chosen so that this difference is predictive of the corresponding action distribution. BFP achieves this with pretrained visual features and a novel learned anchor-selection algorithm. The novel bilinear flow then models how the anchor and the residual jointly determine the multimodal action distribution. We prove that, for bilinear flow under suitable assumptions, action distribution error at unseen goals is bounded by the in-distribution flow-matching error up to problem-dependent factors. Across five manipulation tasks in simulation, BFP achieves 2.63x the out-of distribution success rate of a GCIL policy and 1.36x that of the strongest extrapolation-targeted baseline. On two real-world tasks, BFP improves over GCIL by 32%. Finally, our theory yields practical, pre deployment diagnostics for predicting which trained policies will extrapolate well and to which unseen goal.
Chinese Translation
目标条件模仿学习（GCIL）与流匹配相结合，是一种有前景的框架，能够表示多模态行为，同时适应多样的用户指定目标，但当目标位于演示支持之外时，往往会失败。为了在不破坏多模态性的情况下对这些未见目标进行外推——我们称之为分布外推——我们引入了双线性流策略（BFP），这是一种生成式视觉运动策略，它结合了转导检索和双线性条件流。给定一个未见的观察-目标对，BFP 检索一个“锚”训练示例，并将未见对转导性地重新表述为这个熟悉的锚加上一个残差项。为了使这种分解能够指导动作预测，残差必须紧凑地编码当前观察-目标对与锚之间的差异，而锚的选择必须使得这种差异能够预测相应的动作分布。BFP 通过预训练的视觉特征和一种新颖的学习锚选择算法实现了这一点。新颖的双线性流模型则描述了锚和残差如何共同决定多模态动作分布。我们证明，在适当的假设下，对于双线性流，未见目标的动作分布误差受到分布内流匹配误差的界限，受问题相关因素的影响。在五个仿真操作任务中，BFP 的分布外成功率是 GCIL 策略的 2.63 倍，是最强外推目标基线的 1.36 倍。在两个真实世界任务中，BFP 相较于 GCIL 提高了 32%。最后，我们的理论为预测哪些训练策略能够良好外推以及能外推到哪些未见目标提供了实用的预部署诊断。
cs.RO / 90 / 2610.05792
DASH: A da Vinci Adapter for Serial-link and Humanoid Robots as an Accessible Platform for Surgical Robotics Research
DASH：一种用于串联链接和类人机器人的达芬奇适配器，作为外科机器人研究的可接入平台
Abstract
Robotic minimally invasive surgery offers well-documented clinical benefits, but the cost and infrastructure requirements of purpose-built platforms limit access in rural and lower-resourced facilities. Recent work has teleoperated general-purpose robots for laparoscopic tasks and in vivo procedures, but relied on handheld instruments coupled through passive linkages rather than native robotic actuation. Instead, we adapt da Vinci Classic and Xi instruments onto general-purpose robots that can integrate in clinical workflows. We present DASH, a da Vinci Adapter for Serial-link and Humanoid platforms, consisting of two types of adapters that require no modification to the instruments themselves. Each adapter is compatible with many robotic platform load capacities, engages the instrument's native latch, and wirelessly identifies inserted tools to load instrument-specific kinematics and coupling matrices. Teleoperated experiments demonstrate the efficacy of DASH and its benefits for enabling research access to surgical robotic platforms.
Chinese Translation
机器人微创手术提供了良好的临床益处，但专用平台的成本和基础设施要求限制了在农村和资源较少的设施中的使用。近期的研究已将通用机器人用于腹腔镜任务和体内手术，但依赖于通过被动连接的手持工具，而非原生机器人驱动。相反，我们将达芬奇经典和Xi仪器适配到可以融入临床工作流程的通用机器人上。我们提出了DASH，一个用于串联链接和类人平台的达芬奇适配器，包含两种类型的适配器，无需对仪器本身进行修改。每种适配器与多种机器人平台的负载能力兼容，能够激活仪器的原生锁扣，并无线识别插入的工具，以加载特定于仪器的运动学和耦合矩阵。远程操作实验展示了DASH的有效性及其为外科机器人平台研究提供接入的优势。
cs.RO / 91 / 2610.05796
Dynamics-Aware Adaptive Corridors with Feasibility-Perturbed Trust-Region SQP for Certified Nonholonomic Motion Planning
动态感知自适应通道与可行性扰动信任域平方规划用于认证的非完整运动规划
Abstract
Optimisation-based parking planners usually impose collision constraints only at the time samples, so a vehicle corner can cut an obstacle between samples, and no executable trajectory exists until the solver converges. We present a planner for car-like vehicles with reverse gear in which every iterate of the optimisation phase satisfies the discretised dynamics exactly and keeps the whole vehicle rectangle clear of obstacles between the samples. Each time interval receives one convex corridor that holds all vehicle corners at both ends and is shrunk by a sweep margin bounding how far the corner paths leave their chords. Separating half-planes give the corridors a direction out of obstacles when the initial guess is in collision; later, heading-aligned boxes are grown from the speed, curvature and step of the current iterate and rebuilt after accepted steps. A feasibility-perturbed trust-region sequential quadratic programming method projects each step onto the dynamics by feedback and verifies it exactly; the cost decreases monotonically, and once the corridors stop changing, limit points are Karush-Kuhn-Tucker points of the corridor-constrained problem or violate a constraint qualification. On 820 benchmark cases the planner succeeds in 818 without penetration (797 from the first initial guess), none of the 7103 evaluated optimisation-phase iterates is unusable, and it succeeds in 96.5% of the cases when 99% of the initial guesses intersect an obstacle. Its maneuvers take 0.7% longer in the median than those of a similarly certified exact-collision baseline. The guarantees hold for the planning model, not for a physical vehicle.
Chinese Translation
基于优化的停车规划器通常仅在时间样本处施加碰撞约束，因此车辆的角落可能在样本之间切割障碍物，并且在求解器收敛之前不存在可执行的轨迹。我们提出了一种适用于具有倒车档的类汽车车辆的规划器，其中优化阶段的每一次迭代都准确满足离散化的动力学，并保持整个车辆矩形在样本之间远离障碍物。每个时间间隔接收一个凸通道，该通道在两端保持所有车辆角落，并通过一个扫掠边界缩小，限制角落路径离开其弦的距离。当初始猜测发生碰撞时，分离半平面为通道提供了逃离障碍物的方向；随后，从当前迭代的速度、曲率和步长生长出与航向对齐的盒子，并在接受的步骤后重建。可行性扰动的信任域序列二次规划方法通过反馈将每一步投影到动力学上并准确验证；成本单调下降，一旦通道停止变化，极限点就是通道约束问题的Karush-Kuhn-Tucker点或违反约束资格。在820个基准案例中，该规划器在818个案例中成功且没有穿透（797个来自第一次初始猜测），7103个评估的优化阶段迭代中没有一个是不可用的，当99%的初始猜测与障碍物相交时，它在96.5%的案例中成功。其机动的中位数时间比类似认证的精确碰撞基线长0.7%。这些保证适用于规划模型，而不适用于物理车辆。
cs.RO / 92 / 2610.05818
What the Guard Misses, the Robot Executes: Implied Harm in VLA Instructions
守卫所忽视的，机器人执行的：VLA指令中的隐含伤害
Abstract
Vision-language-action models (VLAs) act on instructions without being able to refuse, so screening harmful requests falls to monitors. We test whether these monitors catch ordinary robot tasks requested for harmful reasons, holding the task fixed while varying only how explicitly the intent is stated. $\pi_{0.5}$ completes the task at every level of explicitness, as often as for harmless controls. Text guards flag nearly every blunt request but few implied ones: up to 95% of implied-harm runs end with the task done and no flag raised, and up to 90% even after recalibrating on robot instructions. Monitoring the model's activations does not close this gap. Linear probes separate harmful from harmless instructions almost perfectly in the base language and vision-language models, but this separation weakens after robot training in two model families, most for implied harm.
Chinese Translation
视觉-语言-行动模型（VLA）在执行指令时无法拒绝，因此筛选有害请求的任务落在监控者身上。我们测试这些监控者是否能够捕捉到因有害原因请求的普通机器人任务，保持任务不变，仅改变意图表达的明确程度。$ ext{π}_{0.5}$在每个明确程度下都完成任务，完成率与无害控制组相当。文本监控几乎标记了每一个直白的请求，但对隐含请求的标记很少：多达95%的隐含伤害实验以任务完成且未标记结束，甚至在对机器人指令进行重新校准后，仍有多达90%的实验如此。监控模型的激活并未缩小这一差距。在基础语言和视觉-语言模型中，线性探针几乎完美地区分有害和无害指令，但在两个模型系列的机器人训练后，这种区分减弱，尤其是对隐含伤害的情况。
cs.RO / 93 / 2610.05819
Transporting Unsecured Stacked Payloads with a Quadrupedal Robot via Multi-Objective Reinforcement Learning
通过多目标强化学习实现四足机器人运输不安全堆叠货物
Abstract
Transporting unsecured payloads with legged robots over uneven terrain requires balancing locomotion performance and payload stability, since aggressive motion can destabilize the payload even when the robot remains stable. We study quadrupedal transportation of unsecured stacked boxes on an edgeless torso-mounted board without dedicated payload sensors or active carrier mechanisms. To address this trade-off, we propose Payload-Adaptive Multi-Objective Reinforcement learning for Transportation (PAMORT). PAMORT trains a multi-objective base policy conditioned on a preference vector that weights locomotion and payload-stability reward groups, then trains a weight adjuster on the frozen policy to adapt this preference online from proprioception. In simulation, PAMORT achieves comparable or better overall transportation success than a corresponding single-objective baseline across different payload configurations, including an unseen three-box stack, despite training only with two boxes. Real-world experiments on a Unitree Go2 demonstrate zero-shot transfer to slopes and steps at or beyond the training difficulty, with mean success rates of 0.850 for PAMORT and 0.675 for the baseline across eight tasks. These results demonstrate robust unsecured-payload transportation with online adaptation of the locomotion--payload trade-off from proprioceptive information.
Chinese Translation
在不平坦地形上使用腿式机器人运输不安全货物需要平衡运动性能和货物稳定性，因为激烈的运动可能会使货物不稳定，即使机器人本身保持稳定。我们研究了在无边缘的躯干安装板上，四足机器人运输不安全堆叠箱子的情况，且不使用专用的货物传感器或主动承载机制。为了解决这一权衡问题，我们提出了用于运输的货物自适应多目标强化学习（Payload-Adaptive Multi-Objective Reinforcement learning for Transportation，PAMORT）。PAMORT训练一个多目标基础策略，该策略基于一个偏好向量，该向量对运动和货物稳定性奖励组进行加权，然后在冻结的策略上训练一个权重调整器，以便根据本体感觉在线调整该偏好。在仿真中，PAMORT在不同的货物配置下实现了与相应的单目标基线相当或更好的整体运输成功率，包括一个未见过的三箱堆叠，尽管仅用两箱进行训练。在Unitree Go2上的实际实验表明，在训练难度相当或更高的坡道和台阶上实现了零-shot迁移，PAMORT的平均成功率为0.850，而基线为0.675，涵盖了八个任务。这些结果展示了通过本体感觉信息在线调整运动与货物之间的权衡，实现了稳健的不安全货物运输。
cs.RO / 94 / 2610.05839
Dual-Rate Force-Image Control with Model-Based Orientation Limits for Robotic Ultrasound
基于模型的方向限制的双速力-图像控制在机器人超声中的应用
Abstract
Robotic ultrasound couples a high-rate contact-force loop with slower, delayed image feedback, so image-guided ultrasound probe rotation can perturb contact force before the resulting image response is observed. We derive a closed-form orientation-rate limit that bounds the modeled rotation-induced estimated-force excursion over a finite horizon while accounting for disturbance rejection by the fast force loop. The limit depends on local contact stiffness, force-loop gains, a conservative rotation-to-force gain bound, the excursion budget, and the prediction horizon. We implement this model in a dual-rate controller with timestamp-based delay reconstruction and joint-torque-based force estimation, and evaluate it on a curved gelatin phantom using paired controller comparisons and component ablations. Relative to unconstrained image guidance, the proposed rate-limited controller reduced first-second root-mean-square (RMS) estimated-force error by 0.40 N while increasing cue-convergence time by 0.94 s. A fixed rate cap near the analytically predicted ceiling produced no resolvable difference in force error and converged 0.32 s faster, indicating that the principal practical value of the model is the rate-design rule rather than online prediction. Delay reconstruction had no resolvable effect at the tested latency. A single-subject popliteal scan demonstrated feasibility, although the image cue was noise-limited on heterogeneous tissue.
Chinese Translation
机器人超声将高频接触力环路与较慢、延迟的图像反馈相结合，因此图像引导的超声探头旋转可能在观察到结果图像响应之前扰动接触力。我们推导出一个封闭形式的方向速率限制，该限制在考虑快速力环路的干扰抑制的情况下，界定了模型化旋转引起的估计力偏移在有限时间范围内的界限。该限制依赖于局部接触刚度、力环路增益、保守的旋转-力增益界限、偏移预算和预测时间范围。我们在一个双速控制器中实现了该模型，该控制器采用基于时间戳的延迟重建和基于关节扭矩的力估计，并在一个弯曲的明胶幻影上进行评估，使用配对控制器比较和组件消融。相较于不受限制的图像引导，所提出的速率限制控制器将第一秒的均方根（RMS）估计力误差降低了0.40 N，同时将线索收敛时间增加了0.94秒。接近分析预测上限的固定速率上限未产生可分辨的力误差差异，并且收敛速度快了0.32秒，表明该模型的主要实际价值在于速率设计规则而非在线预测。在测试的延迟下，延迟重建没有产生可分辨的效果。单一受试者的腘窝扫描展示了可行性，尽管在异质组织上图像线索受噪声限制。
cs.RO / 95 / 2610.05855
Hierarchical Reinforcement Learning for Collision-Free Locomotion of an Underactuated Biped
用于无碰撞运动的分层强化学习在欠驱动双足机器人中的应用
Abstract
A bipedal robot cannot deviate from its path to avoid an obstacle without disturbing its balance, and this coupling is most severe on underactuated platforms such as the biped considered here, which has four actuated joints per leg and no hip or ankle roll. This paper presents a Hierarchical Reinforcement Learning (HRL) framework in which a High-Level (HL) policy observes the robot pose, 36 raycast proximity measurements, moving-obstacle states, and a receding-horizon local goal, and outputs a body-velocity command $(v_x, v_y, \omega_{yaw})$ every ten control steps, while a velocity-conditioned Low-Level (LL) policy tracks each command through PD-controlled joint targets. Both policies are trained jointly with Soft Actor-Critic (SAC). Because the converged gait is task-agnostic, it is frozen and driven by classical planners over the same command interface, yielding three controlled baselines: SAC+A*, SAC+RRT*, and SAC+APF. Across 100 evaluation trials per method in randomized PyBullet environments, the proposed method reaches the goal in 98.0% of static and 88.0% of dynamic trials, against at most 78.0% and 68.0% for the planner hybrids, with path lengths within 4% of the A* reference, and ablations confirm that each observation channel and reward term contributes materially to this performance.
Chinese Translation
双足机器人在遇到障碍物时无法偏离路径而不影响其平衡，这种耦合在欠驱动平台上尤为严重，例如本文所考虑的双足机器人，其每条腿有四个驱动关节且没有髋关节或踝关节的滚动。本文提出了一种分层强化学习（Hierarchical Reinforcement Learning, HRL）框架，其中高层（High-Level, HL）策略观察机器人姿态、36个射线投射的接近测量、移动障碍物状态和一个递归的局部目标，并在每十个控制步骤输出一个身体速度命令 $(v_x, v_y, heta_{yaw})$，而速度条件的低层（Low-Level, LL）策略则通过PD控制的关节目标跟踪每个命令。两个策略通过软演员评论家（Soft Actor-Critic, SAC）共同训练。由于收敛的步态与任务无关，因此其被固定并通过经典规划器在相同的命令接口上驱动，生成三个控制基线：SAC+A*、SAC+RRT*和SAC+APF。在随机化的PyBullet环境中，每种方法进行了100次评估试验，所提方法在静态试验中达到目标的成功率为98.0%，在动态试验中为88.0%，而规划器混合方法的成功率最多为78.0%和68.0%，路径长度与A*参考路径的偏差在4%以内，消融实验确认每个观察通道和奖励项对该性能的贡献是显著的。
cs.RO / 96 / 2610.05882
Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning
Mulligan：基于性能指导的数据收集以实现高效的机器人学习
Abstract
Learning from human demonstrations is a reliable way to teach robots new tasks, but the gains from each additional demonstration shrink as the policy improves. Continued improvement can instead come from supervised deployment, where an operator places the objects and intervenes when the policy fails. We ask how to maximize improvement from a fixed budget of supervised episodes on high-precision manipulation tasks with wide ranges of object placements. We observe that failures can concentrate in a small subset of initial states, so uniform collection spends much of the operator's time on states the policy already handles. Mulligan makes the initial-state distribution a decision, starting each round's episodes at observed failures and untried states. To further improve data efficiency, we augment interactive imitation learning with a value function trained on all data, including failures that imitation discards. Across three real-world tasks evaluated on 2,550 held-out, blinded episodes and two simulated tasks, Mulligan outperforms uniform initial-state sampling at matched collection budgets, and combined with value-based action selection, HiL-IDQL+Mulligan, improves final real-task success by 10-34 percentage points. With operator interventions, the human-robot team completes 98% of collection episodes, remaining productive while the policy learns. Videos, code, and data are available at https://mulligan.page/.
Chinese Translation
通过人类示范学习是教机器人新任务的一种可靠方式，但随着策略的改进，每增加一次示范所带来的收益逐渐减少。持续的改进可以通过监督部署来实现，在这种情况下，操作员放置物体并在策略失败时进行干预。我们探讨如何在高精度操作任务中，针对广泛的物体放置情况，最大化在固定预算下的监督回合的改进。我们观察到，失败往往集中在一小部分初始状态上，因此均匀收集会使操作员的大部分时间花费在策略已经能够处理的状态上。Mulligan 将初始状态分布作为一个决策，开始每轮的回合于观察到的失败和未尝试的状态。为了进一步提高数据效率，我们将交互式模仿学习与基于所有数据（包括模仿所丢弃的失败数据）训练的价值函数相结合。在对 2,550 个保留的盲测回合和两个模拟任务的三项真实世界任务评估中，Mulligan 在匹配的收集预算下优于均匀初始状态采样，并且结合基于价值的动作选择 HiL-IDQL+Mulligan，使最终真实任务的成功率提高了 10-34 个百分点。在操作员干预下，人机团队完成了 98% 的收集回合，在策略学习的同时保持了高效的生产力。视频、代码和数据可在 https://mulligan.page/ 获取。
cs.RO / 97 / 2610.05964
From Social Reasoning to Embodied Interaction: An Agentic Framework for Social Robots
从社会推理到具身互动：一个社会机器人代理框架
Abstract
Natural face-to-face human--robot interaction requires a robot to understand an evolving social situation, decide when to engage, and express its intent through coordinated physical behavior. Yet existing approaches rarely close this loop: foundation-model agents provide increasingly capable multimodal reasoning and memory but remain largely disembodied, while expressive virtual agents do not face the physical constraints of real robots, and physical social robots typically address social reasoning and embodied expression only partially. We present ARISE, a unified framework that bridges Agentic Reasoning and Interactive Social Embodiment on the Sophia humanoid robot. ARISE integrates multimodal context understanding, long-term memory, and reactive and proactive interaction to determine when and what to communicate, and translates social intent into robot-native gestures coordinated with speech and mechanical facial expressions through streaming execution. Extensive evaluations on Sophia demonstrate strong perceived interaction quality, expressive and well-coordinated embodied behavior, and substantial latency reductions through streaming execution. These results highlight the importance of jointly reasoning about what to communicate, when to engage, and how to physically express social intent for natural interaction with humanoid robots. Project Page: https://robosocial.github.io/
Chinese Translation
自然的人机面对面互动要求机器人理解不断变化的社会情境，决定何时参与，并通过协调的身体行为表达其意图。然而，现有的方法很少能闭合这一循环：基础模型代理提供日益强大的多模态推理和记忆能力，但仍然在很大程度上缺乏具身性，而表现力丰富的虚拟代理则不受真实机器人的物理限制，物理社交机器人通常仅部分解决社会推理和具身表达的问题。我们提出了ARISE，这是一个统一框架，连接了代理推理和互动社会具身性，应用于Sophia人形机器人。ARISE整合了多模态上下文理解、长期记忆以及反应性和主动性互动，以确定何时以及如何沟通，并通过流式执行将社会意图转化为与语言和机械面部表情协调的机器人原生手势。对Sophia的广泛评估显示出强烈的感知互动质量、表现力丰富且协调良好的具身行为，以及通过流式执行显著降低的延迟。这些结果强调了共同推理何时沟通、何时参与以及如何身体表达社会意图在与人形机器人进行自然互动中的重要性。项目页面：https://robosocial.github.io/
cs.RO / 98 / 2610.05971
A State Based Dispatch Controller for Hospital Delivery Robots with Shared Human and Infrastructure Resources
基于状态的医院送货机器人调度控制器，利用共享人力和基础设施资源
Abstract
Robot delivery studies can overstate transport capacity when travel to pickups and human support fall outside the modeled schedule. We formulate a location aware dispatch model that couples robot admission to transporter support and shared elevators, charging, and cleaning. The model replays 33,079 observed hospital requests; missing contents, deadlines, staffing, and completion times remain explicit scenario assumptions. Two full grids compare human dispatch, a resource aware controller, and a proximity and workload benchmark in 30 paired replications per scenario. An exploratory extension tests a simpler deadline admission rule under the same operating model. Pickup travel increases demand on a pooled elevator bank. In one high staffing case, raising assumed elevator capacity from two to four reduces human only lateness from 55.11\% to 3.18\%, exceeding the dispatch differences. Direct policy comparisons and robot coverage distinguish admission selectivity from system service. The analysis explains why a robot can pass a deadline admission test yet delay service through staff handoffs and shared facilities. Its contribution is a reproducible evaluation of coupled dispatch workflows, with a clear separation between admission, completion, and return to availability. The results are conditional comparisons, not estimates of hospital benefit or released clinical capacity.
Chinese Translation
机器人送货研究在运输能力方面可能会夸大，当前往取货地点和人力支持超出建模时间表时。我们提出了一种位置感知的调度模型，该模型将机器人接纳与运输者支持、共享电梯、充电和清洁相结合。该模型重现了33,079个观察到的医院请求；缺失的内容、截止日期、人员配置和完成时间仍然是明确的情景假设。两个完整的网格比较了人力调度、资源感知控制器以及在每个情景下进行30次配对复制的接近度和工作负载基准。一个探索性扩展测试了在相同操作模型下更简单的截止日期接纳规则。取货旅行增加了对共享电梯池的需求。在一个高人员配置的案例中，将假设的电梯容量从两个提高到四个，使得仅人力的延迟从55.11%降至3.18%，超出了调度差异。直接的政策比较和机器人覆盖区分了接纳选择性与系统服务。分析解释了为什么机器人可以通过截止日期接纳测试，但由于员工交接和共享设施而延迟服务。其贡献在于对耦合调度工作流程的可重复评估，明确区分接纳、完成和恢复可用性。结果是条件比较，而非医院收益或释放的临床能力的估计。
cs.RO / 99 / 2610.05994
How (and How Not) to Use Data Augmentation in VLA Post-Training
如何（以及如何不）在VLA后训练中使用数据增强
Abstract
Vision-language-action (VLA) models currently demonstrate strong performance in a wide range of real-world robotics tasks. However, they often still lack the generalization ability to handle large visual out-of-distribution shifts. Post-training of VLAs with reinforcement learning (RL) has been shown to benefit robustness, but significant room for improvement remains. In this work, we systematically study the effect of image augmentation on VLA post-training. We find that it is crucial to augment only the critic module during RL updates, while leaving the actor's input clean during both rollouts and updates. For $\pi_{0.5}$ and GR00T N1.5 this raises out-of-distribution success on LIBERO-Plus by $7.8$ and $10.0$ points respectively, while augmenting the actor collapses training entirely. We investigate a range of augmentation types and strengths, and provide practical recommendations for improving generalization in VLA post-training.
Chinese Translation
视觉-语言-动作（VLA）模型目前在广泛的现实世界机器人任务中表现出色。然而，它们在处理大规模视觉分布外变化时，通常仍然缺乏泛化能力。使用强化学习（RL）对VLA进行后训练已被证明有助于提高鲁棒性，但仍有显著的改进空间。在本研究中，我们系统地研究了图像增强对VLA后训练的影响。我们发现，在RL更新期间，仅增强评论模块至关重要，而在回放和更新期间保持演员的输入干净。对于$ ext{π}_{0.5}$和GR00T N1.5，这分别提高了LIBERO-Plus上的分布外成功率$7.8$和$10.0$点，而增强演员则完全崩溃了训练。我们调查了一系列增强类型和强度，并提供了改善VLA后训练中泛化能力的实用建议。
cs.RO / 100 / 2610.05997
Infant simulator with an embodied caregiver: Generating infant-perspective touch and vision during social interaction
具身看护者的婴儿模拟器：在社会互动中生成婴儿视角的触觉和视觉
Abstract
Early development unfolds in caregiver-infant dyads, where infants' sensorimotor streams are shaped by physical contact and face-to-face interaction. Yet developmental robotics simulators commonly model infants in isolation, limiting the study of caregiver-mediated experience. We present a caregiver-enabled extension of the Multi-Modal Infant Model (MIMo) in MuJoCo that turns MIMo into a controllable platform for replaying dyadic interaction and generating dense infant-perspective observations. The system provides (i) an articulated caregiver model compatible with MIMo morphologies, parameterized from anthropometrics and optionally resized to a recorded caregiver; (ii) a workflow to replay naturalistic caregiver-infant holding and soothing interactions; and (iii) logging and visualizing the infant's first-person multimodal experience (we show touch and vision). We showcase the tool on touch by introducing origin-aware contact logging that disambiguates self-, caregiver-, and environment-generated contact and supports aggregation into touch-rate statistics comparable to manual coding. While we showcase tactile analysis, the platform is intended more broadly as a generator of multimodal dyadic datasets (touch and egocentric vision) for modeling the development of social interaction.
Chinese Translation
早期发展发生在看护者与婴儿的二人关系中，婴儿的感觉运动流受到身体接触和面对面互动的影响。然而，发展机器人模拟器通常将婴儿模型化为孤立状态，限制了对看护者介导经验的研究。我们提出了一种在MuJoCo中扩展的多模态婴儿模型（Multi-Modal Infant Model, MIMo），使MIMo成为一个可控平台，用于重放二人互动并生成密集的婴儿视角观察。该系统提供了（i）一个与MIMo形态兼容的关节看护者模型，基于人类测量数据进行参数化，并可选择性地调整为记录的看护者大小；（ii）一个重放自然主义看护者与婴儿之间抱持和安抚互动的工作流程；以及（iii）记录和可视化婴儿第一人称多模态体验的功能（我们展示了触觉和视觉）。我们通过引入源感知接触记录来展示该工具在触觉分析中的应用，该记录能够区分自我、看护者和环境生成的接触，并支持将数据聚合为可与手动编码相比较的触摸率统计。尽管我们展示了触觉分析，但该平台更广泛的目的是作为生成多模态二人数据集（触觉和自我中心视觉）的工具，以便于建模社会互动的发展。
cs.RO / 101 / 2610.06002
P3: Persistent Particle Planning for Constrained Diffusion Control
P3：用于受限扩散控制的持久粒子规划
Abstract
Diffusion models provide expressive priors over trajectories, but adapting these priors to test-time constraints requires maintaining feasibility and consistency across successive control decisions. We introduce Persistent Particle Planning (P3), a sequential Monte Carlo framework for diffusion control that maintains a weighted population of candidate plans across replanning steps. At each control step, P3 shifts and partially re-noises the candidate trajectories, refines them under the latest observation, and uses constraint-aware weighting and resampling to select among alternative continuations without retraining the diffusion model. We consider denoising and replanning as one Feynman--Kac particle system and analyze it under an idealized repair. We prove that the re-noising depth controls how reliably a kept plan stays on its route, and that keeping a rare, well-separated route takes far fewer plans than rediscovering it by sampling from scratch. Experiments under multiple test-time constraint configurations show that population reuse reduces route switching and improves success without constraint violations. Because P3 refines earlier plans instead of redrawing them, it also needs fewer denoising iterations per replan. On maze-navigation tasks, it plans faster than both regenerated populations and methods that correct a single sampled plan by constrained optimization. Code and pretrained models are available at https://github.com/p3-username/p3-anon.
Chinese Translation
扩散模型为轨迹提供了表达性的先验，但将这些先验适应于测试时的约束需要在连续的控制决策中保持可行性和一致性。我们提出了持久粒子规划（P3），这是一种用于扩散控制的序列蒙特卡罗框架，能够在重新规划步骤中维护一组加权的候选计划。在每个控制步骤中，P3会移动并部分重新加噪候选轨迹，在最新观察下对其进行细化，并使用考虑约束的加权和重采样在替代延续中进行选择，而无需重新训练扩散模型。我们将去噪和重新规划视为一个费曼-卡克粒子系统，并在理想化的修复下进行分析。我们证明了重新加噪的深度控制了保持的计划在其路径上保持的可靠性，并且保持一条稀有且良好分离的路径所需的计划数量远少于通过从头采样重新发现它的数量。在多种测试时约束配置下的实验表明，种群重用减少了路径切换并提高了成功率而不违反约束。由于P3细化早期计划而不是重新绘制它们，因此每次重新规划所需的去噪迭代也更少。在迷宫导航任务中，它的规划速度快于再生种群和通过约束优化纠正单个采样计划的方法。代码和预训练模型可在 https://github.com/p3-username/p3-anon 获取。
cs.RO / 102 / 2610.06010
Recon2Servo: Robotic Ultrasound Visual Servoing via Learned Image-to-Motion Inference
Recon2Servo：通过学习的图像到运动推断实现的机器人超声视觉伺服
Abstract
Ultrasound visual servoing is essential for autonomous robotic ultrasound, yet 6-DoF probe control from 2D B-mode images remains challenging due to limited and ambiguous out-of-plane motion cues. Existing methods typically rely on anatomical priors or handcrafted visual features, limiting their generalizability across imaging targets. Inspired by trackerless 3D ultrasound reconstruction, we propose Recon2Servo, a visual servoing framework that learns image-to-motion inference directly from B-mode images for 6-DoF probe control. A DINOv3 encoder with low-rank adaptation and a bidirectional relation module estimate the relative probe pose between current and target images to guide iterative closed-loop target-view alignment. The framework combines supervised relative-pose learning, reconstruction-guided closed-loop adaptation, and bounded residual pose correction to improve motion inference during servoing. Evaluations on a public dataset and an in-house dataset collected from 12 healthy volunteers using different ultrasound systems demonstrate its effectiveness in reconstructed-volume servoing. Additional real-robot demonstrations of target-view alignment and dynamic tracking on a human forearm are provided in the supplementary video: https://youtu.be/qwsOdI-GMYk.
Chinese Translation
超声视觉伺服对于自主机器人超声至关重要，但由于二维B模式图像中有限且模糊的超出平面运动线索，6自由度探头控制仍然具有挑战性。现有方法通常依赖于解剖学先验或手工制作的视觉特征，这限制了它们在成像目标上的普适性。受到无追踪3D超声重建的启发，我们提出了Recon2Servo，一个视觉伺服框架，直接从B模式图像中学习图像到运动的推断，以实现6自由度探头控制。DINOv3编码器结合低秩适配和双向关系模块，估计当前图像与目标图像之间的相对探头姿态，以指导迭代闭环目标视图对齐。该框架结合了监督相对姿态学习、重建引导的闭环适配和有界残差姿态校正，以改善伺服过程中的运动推断。在一个公共数据集和一个从12名健康志愿者使用不同超声系统收集的内部数据集上的评估表明，其在重建体积伺服中的有效性。补充视频中提供了在人体前臂上进行目标视图对齐和动态跟踪的真实机器人演示：https://youtu.be/qwsOdI-GMYk。
cs.RO / 103 / 2610.06046
MagServo: Uncertainty-Resilient Hierarchical Magnetic Servoing via Learned Latent Representations
MagServo：基于学习的潜在表示的抗不确定性层次磁伺服控制
Abstract
Magnetic navigation provides contact-free and line-of-sight-independent feedback for robotic systems, yet existing approaches typically rely on explicit pose estimation or direct use of raw magnetic measurements, making accurate control susceptible to modeling errors, measurement noise, and disturbances. This work presents MagServo, a hierarchical learning-based framework for robust 6-DoF magnetic servoing directly using the learned latent magnetic feature. MagServo learns uncertainty-resilient magnetic representations through masked reconstruction and captures state-dependent interaction dynamics between robot motion and latent magnetic transitions without analytical magnetic models or explicit Jacobian supervision. Based on the learned dynamics, a hierarchical controller combines nonlinear model predictive control for coarse approach with local Jacobian inversion for precise fine regulation. Extensive physical experiments demonstrate submillimeter and subdegree accuracy, achieving mean terminal errors of 0.386 mm and 0.479 degree for 6-DoF pose reaching. MagServo further outperforms a localization-based control baseline in complex trajectory tracking and maintains robust performance under unseen magnetic-source configurations without retraining. A supplementary video of the real-robot experiments is available at https://youtu.be/rZt1NUP1Mr0.
Chinese Translation
磁导航为机器人系统提供了无接触和独立于视线的反馈，然而现有的方法通常依赖于显式的姿态估计或直接使用原始磁测量，使得精确控制容易受到建模误差、测量噪声和干扰的影响。本研究提出了MagServo，一个基于层次学习的框架，旨在通过学习的潜在磁特征直接实现稳健的6自由度磁伺服控制。MagServo通过掩蔽重建学习抗不确定性的磁表示，并捕捉机器人运动与潜在磁转变之间的状态依赖交互动态，而无需解析磁模型或显式雅可比监督。基于学习到的动态，层次控制器结合了非线性模型预测控制用于粗略接近，以及局部雅可比反演用于精确调节。大量物理实验表明，该方法在6自由度姿态到达中实现了亚毫米和亚度的精度，平均终端误差分别为0.386毫米和0.479度。MagServo在复杂轨迹跟踪中进一步超越了基于定位的控制基线，并在未见过的磁源配置下保持稳健性能，无需重新训练。关于真实机器人实验的补充视频可在 https://youtu.be/rZt1NUP1Mr0 获取。
cs.RO / 104 / 2610.06078
Do VLAs Understand and Adapt to the Objects They Handle, or Simply Replay Learned Behaviors?
VLA是否理解并适应其处理的物体，还是仅仅重放已学的行为？
Abstract
This paper asks whether VLA generalization is grounded in a global understanding of objects' physical properties that enables policies to adapt their motion to unseen setups, or if policies simply replay the motions they've learnt that happen to succeed in new setups. The former reflects genuine generalization; the latter reflects incidental robustness. We first examine awareness of physical properties in seven VLAs by applying linear probing and representational similarity analysis (RSA) to their activations. We find that physical properties, including mass, fragility, deformability, friction and size are less decodable than non-physical properties such as semantic category, material, sound and price in nearly every modality stream. Compared with their base VLMs, robot pre-training weakens the linear encoding of physical properties in the language stream. Neither pre-training nor downstream fine-tuning strengthens the alignment between physical-property differences and activation distances. We then ask whether the weak physical information present in these activations shapes the actions a VLA generates. In a controlled LIBERO case study, we increase the mass of an in-domain object and signal the change through language or vision. Most VLAs use similar lifting behaviour for the heavier and original-mass objects, leading to task success declines. The few exceptions change their behaviour in response to lexical or visual cues rather than to mass itself. These results suggest that VLAs encode physical properties weakly and do not reliably use them to adapt their motion.
Chinese Translation
本文探讨了VLA（视觉语言代理）的泛化是否基于对物体物理属性的全球理解，从而使策略能够将其运动适应于未见的设置，还是策略仅仅重放它们所学到的在新设置中恰好成功的运动。前者反映了真正的泛化；后者则反映了偶然的鲁棒性。我们首先通过对七个VLA的激活应用线性探测和表征相似性分析（RSA），考察它们对物理属性的意识。我们发现，物理属性（包括质量、脆弱性、可变形性、摩擦和大小）在几乎所有模态流中都比非物理属性（如语义类别、材料、声音和价格）更难解码。与其基础的VLM（视觉语言模型）相比，机器人预训练削弱了语言流中物理属性的线性编码。无论是预训练还是下游微调，都未能增强物理属性差异与激活距离之间的对齐。接着，我们询问这些激活中存在的弱物理信息是否影响VLA生成的动作。在一个受控的LIBERO案例研究中，我们增加了一个领域内物体的质量，并通过语言或视觉信号传达这一变化。大多数VLA对较重和原始质量的物体采用类似的举起行为，导致任务成功率下降。少数例外则是根据词汇或视觉线索而非质量本身改变其行为。这些结果表明，VLA对物理属性的编码较弱，并且不可靠地利用这些属性来适应其运动。
cs.RO / 105 / 2610.06089
Adaptive Mean Flow for Responsive Closed-Loop Robot Control
自适应均值流用于响应式闭环机器人控制
Abstract
Diffusion- and flow-based robot policies have recently become widespread in robotic Imitation Learning (IL) due to their high performance and ability to model continuous and multimodal distributions. However, the iterative denoising procedure used by these models introduces significant prediction latency, hindering high-frequency closed-loop robot control and leading to jittery, unstable motion when frequent updates to the robot's action predictions are used. Therefore, it is common practice to train models to predict chunks of actions that can be executed sequentially without feedback, even when this reduces responsiveness and may mean the most recent state information is not used. In this article, we present Adaptive Mean Flow (AMF), a flow-based IL method that enables smooth and responsive, fully closed-loop robot control. AMF uses Mean Flow, which is an accelerated form of Flow Matching (FM), to minimize prediction latency. To ensure smoothness and consistency across predictions, AMF uses a corrupted version of the trajectory from the previous step when predicting new robot actions, with the signal-to-noise ratio increasing over the time parameter of the trajectory. This discourages large changes in the prediction from one step to the next, while allowing freedom to adapt the predictions for future steps. We evaluate AMF across a wide range of simulated and real robot tasks and demonstrate significantly improved performance compared with baselines. Code: https://github.com/akselva/Adaptive-mean-flow-RoboticIL.
Chinese Translation
基于扩散和流的机器人策略由于其高性能和建模连续及多模态分布的能力，近年来在机器人模仿学习（Imitation Learning, IL）中变得广泛应用。然而，这些模型所使用的迭代去噪过程引入了显著的预测延迟，阻碍了高频闭环机器人控制，并导致在频繁更新机器人动作预测时出现抖动和不稳定的运动。因此，通常的做法是训练模型预测可以无反馈顺序执行的一系列动作，即使这会降低响应性，并可能意味着最新的状态信息未被使用。在本文中，我们提出了自适应均值流（Adaptive Mean Flow, AMF），这是一种基于流的IL方法，能够实现平滑和响应式的完全闭环机器人控制。AMF使用均值流（Mean Flow），这是流匹配（Flow Matching, FM）的加速形式，以最小化预测延迟。为了确保预测的平滑性和一致性，AMF在预测新的机器人动作时使用来自上一步的轨迹的损坏版本，信噪比随着轨迹的时间参数增加。这抑制了从一步到下一步预测的大幅变化，同时允许对未来步骤的预测进行自由调整。我们在广泛的模拟和真实机器人任务中评估AMF，并展示与基线相比显著提高的性能。代码链接：https://github.com/akselva/Adaptive-mean-flow-RoboticIL。
cs.RO / 106 / 2610.06090
Execution-Aligned Progressive Noise for Consistent Asynchronous Replanning in Generative Robot Policies
执行对齐渐进噪声用于生成机器人策略中的一致性异步重规划
Abstract
Continuous asynchronous replanning is essential for real-time generative robot policies, but independent stochastic initialization can cause mode switching and inconsistent continuation across action chunks. We propose Execution-Aligned Progressive Noise (EAPN), which introduces structured stochasticity at both inter-chunk and intra-chunk levels. Across replanning steps, EAPN propagates a shared noise trajectory and aligns it with the actual execution displacement, establishing execution-aligned inter-chunk correlation. Within each action chunk, it models temporal correlation along action time. The aligned stochastic history is further combined with committed action context to condition subsequent generation, allowing new chunks to continue from execution-consistent generative states rather than restart from independent noise. We evaluate EAPN on D3IL, Kinetix, LIBERO, and real-world manipulation tasks. EAPN improves multimodal behavior consistency on D3IL and achieves an average success rate of 88.59% on Kinetix. On LIBERO, it remains robust and maintains strong task performance even under long inference delays. Real-robot experiments further achieve 90.0% success on Object Storage and 96.7% on bimanual Cloth Folding, demonstrating reliable continuous execution under asynchronous replanning.
Chinese Translation
连续的异步重规划对于实时生成机器人策略至关重要，但独立的随机初始化可能导致模式切换和动作块之间的不一致延续。我们提出了执行对齐渐进噪声（Execution-Aligned Progressive Noise, EAPN），它在块间和块内层面引入了结构化的随机性。在重规划步骤中，EAPN传播共享的噪声轨迹，并将其与实际执行位移对齐，从而建立执行对齐的块间相关性。在每个动作块内，它沿着动作时间建模时间相关性。对齐的随机历史进一步与已承诺的动作上下文结合，以条件后续生成，使新的块能够从执行一致的生成状态继续，而不是从独立噪声重新开始。我们在 D3IL、Kinetix、LIBERO 和真实世界的操作任务上评估了 EAPN。EAPN 在 D3IL 上提高了多模态行为一致性，并在 Kinetix 上实现了 88.59% 的平均成功率。在 LIBERO 上，它在长推理延迟下仍然保持稳健，并维持强大的任务性能。真实机器人实验在物体存储任务上实现了 90.0% 的成功率，在双手布料折叠任务上实现了 96.7% 的成功率，展示了在异步重规划下可靠的连续执行。
cs.RO / 107 / 2610.06103
Propagating Elevation-Map Uncertainty Through the Contact Maximum in Closed Form
通过接触最大值以封闭形式传播高程图不确定性
Abstract
Risk-aware planners score paths on uncertain elevation maps using the path cost's mean and standard deviation. Modeling rigid contact, however, requires computing a maximum over several uncertain cells. First-order propagation loses accuracy here by differentiating at only a single cell, while Monte Carlo sampling requires a full path evaluation per draw. We compute the moments of that contact maximum in closed form using Clark's pairwise recursion. By tracking each contact's covariance against the shared map cells, we propagate the smooth remainder using exact Gaussian quadratic-form identities. A contest-depth calibration, fitted once on two design traverses, closes the aggregate standard-deviation shortfall that remains. On 317 held-out rover path segments, scored against a Monte Carlo reference from the same belief, every pre-registered criterion was met. The corrected Clark fold cuts the median error of the mean from linearization's 2.3% to 0.19% and attains the lowest error in the conditional value at risk (CVaR) at the 90% level of every method tested. A benchmark plan costs just 5.5 microseconds on a GPU. These accuracy gains concentrate at contested contacts. While they seldom change which path is chosen on this terrain, the fold still selects the reference-best path in 98% of decisions against linearization's 93 to 96%. On a second dataset the mean transfers, though the risk number does not.
Chinese Translation
风险意识规划者使用路径成本的均值和标准差对不确定高程图上的路径进行评分。然而，建模刚性接触需要在多个不确定单元格上计算最大值。一阶传播在这里只通过对单个单元格进行微分而失去准确性，而蒙特卡洛采样则需要对每次抽样进行完整路径评估。我们使用克拉克（Clark）的成对递归以封闭形式计算该接触最大值的矩。通过跟踪每个接触相对于共享地图单元格的协方差，我们利用精确的高斯二次型恒等式传播光滑的剩余部分。一次在两个设计遍历上拟合的深度校准，弥补了剩余的聚合标准差不足。在317个保留的探测器路径段上，针对来自相同信念的蒙特卡洛参考进行评分，所有预注册标准均得到满足。经过修正的克拉克折叠将均值的中位数误差从线性化的2.3%降低到0.19%，并在每种测试方法的90%水平上达到了条件风险价值（CVaR）的最低误差。基准计划在GPU上仅需5.5微秒的成本。这些准确性提升主要集中在有争议的接触点。尽管它们很少改变在该地形上选择的路径，但该折叠仍在98%的决策中选择参考最佳路径，而线性化的选择比例为93%至96%。在第二个数据集中，均值转移，尽管风险数值并未变化。
cs.RO / 108 / 2610.06104
Conditional Trajectory Peaks: Single-Pass Multimodal Policies over Action Chunks
条件轨迹峰值：基于动作块的单次多模态策略
Abstract
Multimodal imitation learning requires diverse executable futures under the same observation and consistent behavior across replanning cycles. We present Conditional Trajectory Peaks (CTP), a single-pass policy framework that jointly predicts complete action-chunk candidates, probability masses, and trajectory scales. Distribution-Aware Peak Specialization (DAPS) specializes trajectory peaks using trajectory-level posterior responsibilities and mass- and scale-modulated overlap constraints. Evidence-Gated Trajectory Belief Transport (ETBT) maintains cross-chunk consistency through geometric correspondence between exchangeable candidate sets, while allowing current policy evidence to override historical constraints. CTP achieves a coverage score of 91.40% on Push-T; success rates of 100.0%, 79.72%, and 84.44% on D3IL Avoiding, Aligning, and Sorting-2, respectively. On LIBERO, CTP achieves an average success rate of 97.25%. In real-world dual-arm experiments, CTP preserves both placement modes in a two-plate task, succeeding in all 50 trials. On bottle uprighting and pen placement into a holder, it maintains success rates comparable to $\pi_{0.5}$ while reducing policy inference latency from 218.24 ms to 75.80 ms. These results demonstrate that single-pass trajectory modeling can combine multimodal behavior, closed-loop consistency, and efficient inference.
Chinese Translation
多模态模仿学习要求在相同观察下具有多样化的可执行未来，并在重新规划周期中保持一致的行为。我们提出了条件轨迹峰值（Conditional Trajectory Peaks, CTP），这是一种单次策略框架，能够共同预测完整的动作块候选、概率质量和轨迹尺度。分布感知峰值专业化（Distribution-Aware Peak Specialization, DAPS）利用轨迹级后验责任和质量及尺度调制的重叠约束来专业化轨迹峰值。证据门控轨迹信念传输（Evidence-Gated Trajectory Belief Transport, ETBT）通过可交换候选集之间的几何对应关系维持跨块一致性，同时允许当前策略证据覆盖历史约束。CTP在Push-T任务上实现了91.40%的覆盖率，在D3IL的避免、对齐和排序-2任务上成功率分别为100.0%、79.72%和84.44%。在LIBERO上，CTP的平均成功率达到97.25%。在实际的双臂实验中，CTP在双盘任务中保留了两种放置模式，在所有50次试验中均取得成功。在瓶子竖立和笔放入支架的任务中，其成功率与$ ext{π}_{0.5}$相当，同时将策略推理延迟从218.24毫秒降低至75.80毫秒。这些结果表明，单次轨迹建模能够结合多模态行为、闭环一致性和高效推理。
cs.RO / 109 / 2610.06121
Radar2Plan: Benchmarking 4D Radar for End-to-End Open-Loop Ego-Trajectory Planning
Radar2Plan：基于4D雷达的端到端开放环路自我轨迹规划基准测试
Abstract
Adverse weather and poor illumination remain major challenges for robust ego-trajectory planning in mobile autonomy. 4D radar offers reliable sensing under adverse conditions and direct radial-velocity measurements. However, sensing robustness does not necessarily translate into robust downstream planning, while existing 4D radar benchmarks focus primarily on perception rather than trajectory planning. We present Radar2Plan, a modular benchmark for open-loop ego-trajectory planning using real-world 4D radar data. Radar2Plan connects sensor encoders, scene representations, and planning heads through common interfaces, enabling controlled comparisons between different sensor and planner configurations. Using DSERT-RoLL and MAN TruckScenes, we evaluated seven combinations of camera, 4D radar, and LiDAR in four representative planning baselines and 12 weather and illumination conditions under a unified protocol. To our knowledge, Radar2Plan is the first benchmark dedicated to evaluating real-world 4D radars for autonomous driving planning. Experiments show that 4D radar alone supports competitive ego-trajectory planning and robust performance under adverse conditions. Sensor-configuration comparisons further demonstrate its complementary value to other modalities, while revealing dependencies on sensor combination and planning architecture. The modular design also supports additional datasets, sensing modalities, and planners, providing a flexible foundation for future research on radar-based autonomous driving planning.
Chinese Translation
恶劣天气和光照不足仍然是移动自主系统中稳健自我轨迹规划的主要挑战。4D雷达在不利条件下提供可靠的感知和直接的径向速度测量。然而，感知的稳健性并不一定能转化为下游规划的稳健性，而现有的4D雷达基准主要集中在感知而非轨迹规划上。我们提出了Radar2Plan，这是一个基于真实世界4D雷达数据的开放环路自我轨迹规划的模块化基准。Radar2Plan通过公共接口连接传感器编码器、场景表示和规划头，能够在不同传感器和规划器配置之间进行受控比较。使用DSERT-RoLL和MAN TruckScenes，我们在统一协议下评估了七种相机、4D雷达和激光雷达的组合，涵盖四个代表性的规划基线和12种天气及光照条件。根据我们的了解，Radar2Plan是第一个专门用于评估真实世界4D雷达在自动驾驶规划中表现的基准。实验表明，单独使用4D雷达支持竞争性的自我轨迹规划，并在不利条件下表现稳健。传感器配置的比较进一步展示了其对其他模态的互补价值，同时揭示了对传感器组合和规划架构的依赖。模块化设计还支持额外的数据集、感知模态和规划器，为未来基于雷达的自动驾驶规划研究提供了灵活的基础。
cs.RO / 110 / 2610.06129
I-BFM: Reward-Conditioned Robust Humanoid Interaction via Unsupervised Reinforcement Learning
I-BFM：通过无监督强化学习实现奖励条件下的鲁棒类人交互
Abstract
Behavioral foundation models (BFMs) have recently shown that a single humanoid policy can support diverse whole-body control, but extending such generality to physical interaction remains challenging. We introduce I-BFM, to our knowledge the first BFM for humanoid-object interaction. Rather than relying on task-specific policies or reference tracking, I-BFM learns a shared representation of the coupled dynamics among the humanoid, objects, and their contacts using forward-backward representations and unsupervised reinforcement learning. Given a downstream task reward, the same policy can be directly conditioned on a latent command to execute closed-loop interaction without task-specific policy optimization. To improve interaction control over different time scales, we further train the policy with both short-horizon interaction targets and longer-horizon goal targets. A single I-BFM policy performs carrying, pushing, and kicking, while also supporting goal reaching, motion tracking, stylistic control, and long-horizon task chaining. More importantly, it remains effective after large deviations from nominal execution: on Carry, I-BFM achieves 94.3% nominal success and retains 89.3% success after robot falls, compared with 1.3% for a planning-based baseline. Real-world experiments on a Unitree G1 further demonstrate diverse loco-manipulation behaviors, rapid recovery from interaction failures and external disturbances, and task chaining without task-specific retraining.
Chinese Translation
行为基础模型（BFMs）最近显示出单一类人政策可以支持多样的全身控制，但将这种通用性扩展到物理交互仍然具有挑战性。我们介绍了I-BFM，据我们所知，这是首个用于类人-物体交互的BFM。I-BFM并不依赖于特定任务的政策或参考跟踪，而是使用前向-后向表示和无监督强化学习学习类人、物体及其接触之间耦合动态的共享表示。在给定下游任务奖励的情况下，同一政策可以直接基于潜在命令进行条件化，以执行闭环交互，而无需特定任务的政策优化。为了改善在不同时间尺度上的交互控制，我们进一步使用短期交互目标和长期目标对政策进行训练。单一的I-BFM政策能够执行搬运、推动和踢击，同时支持目标达成、运动跟踪、风格控制和长期任务链。更重要的是，在偏离名义执行后，它仍然有效：在搬运任务中，I-BFM实现了94.3%的名义成功率，并在机器人跌倒后保持89.3%的成功率，而基于规划的基线仅为1.3%。在Unitree G1上的真实世界实验进一步展示了多样的运动-操作行为、快速从交互失败和外部干扰中恢复的能力，以及无需特定任务再训练的任务链。
cs.RO / 111 / 2610.06137
Robotizing Human Videos with Physically Consistent Interactions
通过物理一致的交互实现人类视频的机器人化
Abstract
Human videos offer scalable manipulation data, but the embodiment gap between human hands and robot manipulators limits their direct use. Existing video-editing methods replace hands with rendered robots, yet inaccurate interaction reconstruction and compositing can produce inconsistent grasps and implausible robot-object occlusions. We address these failures from two complementary physical aspects: interaction geometry and scene visibility. First, an interaction-aware contact reconstruction module combines hand-object segmentation with mesh-level contact prediction to recover dense 3D contacts, then converts them into temporally stabilized grasps for parallel-jaw grippers. Second, a depth-aware compositing module uses scene and robot depth to enforce physically consistent robot-object occlusions. The resulting videos preserve the interaction structure of human demonstrations in a robot-compatible form and are co-trained with robot demonstrations. Using identical human videos and robot data, we compare against robot-only training and the original Masquerade pipeline. Across four RoboTwin tasks and two Diffusion Policy visual encoders, our method achieves the highest average success rates, with especially strong gains under out-of-distribution scene variation. Real-world deployment further shows that the proposed co-training approach improves robustness to visual distractors when the task geometry is observable, while performance on depth-sensitive grasps remains limited by the single-camera setup.
Chinese Translation
人类视频提供了可扩展的操作数据，但人类手与机器人操纵器之间的体现差距限制了它们的直接使用。现有的视频编辑方法用渲染的机器人替代手部，但不准确的交互重建和合成可能导致不一致的抓取和不合理的机器人-物体遮挡。我们从两个互补的物理方面解决这些问题：交互几何和场景可见性。首先，交互感知接触重建模块结合手-物体分割与网格级接触预测，恢复稠密的三维接触，然后将其转换为用于平行夹爪的时间稳定抓取。其次，深度感知合成模块利用场景和机器人深度来强制执行物理一致的机器人-物体遮挡。最终生成的视频以机器人兼容的形式保留了人类演示的交互结构，并与机器人演示共同训练。通过使用相同的人类视频和机器人数据，我们与仅使用机器人训练和原始的Masquerade流程进行了比较。在四个RoboTwin任务和两个Diffusion Policy视觉编码器中，我们的方法实现了最高的平均成功率，尤其在分布外场景变化下表现出强劲的提升。实际部署进一步表明，所提出的共同训练方法在任务几何可观察时提高了对视觉干扰的鲁棒性，而在深度敏感抓取上的表现仍受到单摄像头设置的限制。
cs.RO / 112 / 2610.06153
Talk, Render, Act: Integrating Social Gesture and Digital Face with Synchronized Speech for Conversational Humanoid Robot
谈话、渲染、行动：将社交手势与数字面孔结合，通过同步语音实现对话式类人机器人
Abstract
Expressive humanoid interaction requires speech, facial animation, and body gestures to form a coherent response. However, many full-body humanoid robots produce speech and gestures without a visually expressive face, while talking-face animation and robot gesture generation are typically developed separately. We present Talk, Render, Act (TRABot), an agent-based framework comprising specialized agents for motion-atom construction, dialogue generation, motion planning, and facial animation. First, to produce natural and semantically meaningful gestures, we construct Robot-Ready Semantic Motion Atoms by segmenting long-form, G1-retargeted BEAT2 motion into units with natural gesture boundaries, human-verified communicative functions, and feasible trajectories. Second, to preserve semantic order and coordinate body motion with the spoken response, we introduce a Semantic-Conditioned Compositional Planner. Given an ordered semantic function sequence and an estimated response duration, the planner selects approved atoms to realize the longest feasible action sequence while accounting for transitions and neutral recovery. Finally, we deploy a Streaming Face-Speech-Body Integration system on a physical G1 humanoid, combining streaming dialogue audio, audio-driven facial animation, and semantically planned body motion in a unified real-time interaction loop. Quantitative and qualitative experiments demonstrate that TRAbot achieves the best overall performance among all compared conditions in terms of naturalness, expressiveness, and multimodal coherence.
Chinese Translation
富有表现力的类人互动需要语音、面部动画和身体手势共同形成连贯的回应。然而，许多全身类人机器人在没有视觉表现力的面孔的情况下产生语音和手势，而谈话面部动画和机器人手势生成通常是分开开发的。我们提出了Talk, Render, Act (TRABot)，这是一个基于代理的框架，包含用于运动原子构建、对话生成、运动规划和面部动画的专门代理。首先，为了生成自然且语义丰富的手势，我们通过将长格式、G1重定向的BEAT2运动分割为具有自然手势边界、人类验证的交际功能和可行轨迹的单元，构建了机器人准备的语义运动原子。其次，为了保持语义顺序并协调身体运动与口头回应，我们引入了一种语义条件组合规划器。给定一个有序的语义功能序列和估计的回应持续时间，规划器选择经过批准的原子，以实现最长的可行动作序列，同时考虑过渡和中性恢复。最后，我们在物理G1类人机器人上部署了一个流媒体面部-语音-身体集成系统，将流媒体对话音频、音频驱动的面部动画和语义规划的身体运动结合在一个统一的实时互动循环中。定量和定性实验表明，TRABot在自然性、表现力和多模态一致性方面在所有比较条件中实现了最佳整体性能。
cs.RO / 113 / 2610.06171
Controllable and Photorealistic Pedestrian Risky Motion Generation for End-to-End Driving Safety Evaluation
可控且逼真的行人危险动作生成用于端到端驾驶安全评估
Abstract
Evaluating end-to-end autonomous driving under rare, safety-critical vehicle-pedestrian interactions requires photorealistic, sensor-level scenarios. However, trajectory-based scenario generators cannot synthesize raw visual observations, whereas video-based approaches lack controllability. To bridge this gap, we present ControlPed, a novel framework that combines trajectory-level conflict synthesis with 3D Gaussian Splatting (3DGS) to generate photorealistic, motion-controllable safety-critical scenarios. Built upon HazardPed, a dataset derived from 10,352 traffic videos comprising 422 conflict trajectories, HD maps, and 857 annotated 3D human motions, ControlPed first generates conflict trajectories, lifts them into 3D human motion sequences via text-conditioned motion diffusion, and finally renders multi-view sensor observations using animatable 3DGS avatars. Safety evaluation in 88 rendered photorealistic scenarios reveals that seven leading end-to-end driving models suffer a severe performance drop, with their mean HDScore plunging from 88.8 to 47.4, exposing major failure modes under dangerous pedestrian behaviors. The dataset and testing benchmarks will be released to facilitate safety assessment of vehicle-pedestrian interactions.
Chinese Translation
在稀有的、安全关键的车辆与行人交互下评估端到端自动驾驶需要逼真的传感器级场景。然而，基于轨迹的场景生成器无法合成原始视觉观测，而基于视频的方法则缺乏可控性。为了解决这一问题，我们提出了ControlPed，一个新颖的框架，它将轨迹级冲突合成与3D高斯点云（3D Gaussian Splatting, 3DGS）相结合，以生成逼真且可控的安全关键场景。ControlPed基于HazardPed，一个由10,352个交通视频衍生而来的数据集，包含422个冲突轨迹、高清地图和857个标注的3D人类动作，首先生成冲突轨迹，通过文本条件的运动扩散将其提升为3D人类运动序列，最后使用可动画的3DGS虚拟角色渲染多视角传感器观测。在88个渲染的逼真场景中的安全评估显示，七个领先的端到端驾驶模型的性能严重下降，其平均HDScore从88.8骤降至47.4，暴露出在危险行人行为下的主要失效模式。该数据集和测试基准将被发布，以促进车辆与行人交互的安全评估。
cs.RO / 114 / 2610.06184
Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models
双臂视觉-语言-动作模型中的臂级组合泛化
Abstract
Generalization in multi-arm collaboration can be studied as composing familiar atomic skills in new ways across arms. However, existing evaluations offer limited insight into which training and architectural choices support this ability under different coordination requirements. We introduce \textbf{ACG-Bench}, a benchmark for \emph{Arm-wise Compositional Generalization} that provides a common testbed for studying skill recomposition in dual-arm policies. It contains 23 task--condition pairs across 8 task families, with 6 in-domain conditions and 17 unseen compositions covering reordering, synchronization, their combination, and cross-task composition. All methods receive the same per-arm atomic prompts, and success requires achieving the task goal while satisfying physical milestones and specified order or timing constraints. Using $\pi_{0.5}$ as a common vision-language-action backbone, we compare representative data-augmentation and architectural strategies with shared source data and a common evaluation protocol. Our architectural study examines arm-token grouping, skill-specific LoRA adapters (SkillLoRA), and arm-wise attention (AWA), highlighting the complementarity of skill-conditioned parameters and attention structure. Combining these choices yields \textbf{AE-VLA}, which achieves 21.53\% generalization success in simulation, compared with 2.94\% for Single $\pi_{0.5}$, 3.06\% for MA-VLA, and 5.53\% for two independently controlled $\pi_{0.5}$ policies. On physical SO101 robots, AE-VLA reaches 39.00\% mean success across five unseen conditions, compared with 10.00\% for the strongest baseline. These findings provide empirical guidance for designing dual-arm policies that generalize beyond fixed training routines.
Chinese Translation
多臂协作中的泛化可以被视为在不同的臂之间以新的方式组合熟悉的原子技能。然而，现有的评估对哪些训练和架构选择在不同协调要求下支持这种能力提供了有限的见解。我们引入了 extbf{ACG-Bench}，一个用于 extit{臂级组合泛化}的基准，提供了一个共同的测试平台，用于研究双臂策略中的技能重组。它包含8个任务家族中的23个任务-条件对，其中6个是领域内条件，17个是未见组合，涵盖了重新排序、同步、它们的组合以及跨任务组合。所有方法接收相同的每臂原子提示，成功的前提是实现任务目标，同时满足物理里程碑和指定的顺序或时间约束。以$ ext{π}_{0.5}$作为共同的视觉-语言-动作基础，我们比较了代表性的数据增强和架构策略，使用共享的源数据和共同的评估协议。我们的架构研究考察了臂标记分组、技能特定的LoRA适配器（SkillLoRA）和臂级注意力（AWA），突出了技能条件参数和注意力结构的互补性。结合这些选择，我们得到了 extbf{AE-VLA}，在模拟中实现了21.53\%的泛化成功率，而单一的$ ext{π}_{0.5}$为2.94\\%，MA-VLA为3.06\\%，两个独立控制的$ ext{π}_{0.5}$策略为5.53\\%。在物理SO101机器人上，AE-VLA在五个未见条件下达到了39.00\\%的平均成功率，而最强基线为10.00\\%。这些发现为设计超越固定训练例程的双臂策略提供了实证指导。
cs.RO / 115 / 2610.06198
STC-MPM: Coupled Deformation, Progressive Damage, and Cut Formation in Soft-Tissue Cutting
STC-MPM：软组织切割中的耦合变形、渐进损伤与切口形成
Abstract
Cutting is a recurring operation in robotic au tomation, from food preparation to surgical tissue resection. For highly compliant targets, blade motion may deform or displace the material rather than advance the intended cut, while progressive failure changes load transfer and subsequent tool tissue interaction. A computational description must therefore connect cut formation to the evolving mechanical response, rather than specifying the incision independently of material failure. We present STC-MPM (Soft Tissue Cutting with the Material Point Method), a framework that couples finite-strain deformation, history-driven continuum damage, and configurable post-failure treatment. Damage initiates only when a tensile strain history exceeds a material threshold within the blade process zone. Progressive degradation changes stress transmission before the post-failure policy is applied, while the remaining tissue continues to deform, move, and interact with the tool. Numerical scalpel studies using delayed particle deactivation follow this response through insertion and withdrawal. Under identical blade motion, reducing the damage-rate limit leaves the first recorded damage time unchanged but delays and increases peak reaction force, delays particle deactivation, and increases the fixed-cohort displacement statistic at maximum insertion. The matched comparison illustrates how post-initiation failure evolution alters subsequent tool loading and recorded material motion. STC-MPM thus supports joint analysis of cut formation and accompanying tissue response without a pre-inserted cutting interface.
Chinese Translation
切割是机器人自动化中的一种重复操作，涉及从食品准备到外科组织切除。对于高度柔性的目标，刀片运动可能会导致材料变形或位移，而不是推进预期的切口，同时渐进性失效会改变载荷传递和随后的工具与组织的相互作用。因此，计算描述必须将切口形成与不断演变的机械响应联系起来，而不是独立于材料失效来指定切口。我们提出了STC-MPM（基于材料点法的软组织切割），这是一个耦合有限应变变形、历史驱动的连续损伤和可配置后失效处理的框架。损伤仅在拉伸应变历史超过刀片过程区内的材料阈值时才会启动。渐进性降解在应用后失效政策之前改变应力传递，而剩余组织则继续变形、移动并与工具相互作用。使用延迟粒子失活的数值手术刀研究通过插入和撤回过程跟踪这一响应。在相同的刀片运动下，降低损伤速率限制使首次记录的损伤时间保持不变，但延迟并增加了峰值反作用力，延迟粒子失活，并在最大插入时增加了固定群体位移统计。匹配比较说明了后启动失效演变如何改变随后的工具加载和记录的材料运动。因此，STC-MPM支持切口形成与伴随组织响应的联合分析，而无需预先插入切割界面。
cs.RO / 116 / 2610.06215
Towards Quadruped-Provided Localization and Active Tracking for Micro-UAVs
基于四足机器人提供的微型无人机定位与主动跟踪
Abstract
Micro unmanned aerial vehicles (micro-UAVs) are small enough to reach confined spaces that larger robots cannot access, but too small to carry the sensing and computing power required for autonomous flight. We move the localization stack entirely off the aerial platform onto a quadruped robot with a 7-degree-of-freedom (DOF) arm, which supplies the micro-UAV (27 g bare, 42 g with fiducial markers) its full 6-DOF pose. A camera at the arm's end-effector detects AprilTag fiducial markers on the drone and composes that observation with the quadruped's own self-localization to place the drone in a shared map frame, so the ground robot localizes its partner, rather than only tracking it relative to the camera. The arm acts as an actively-controlled observer, repositioning to keep the drone in view as both robots move; the drone carries only an inertial measurement unit and fuses the external pose to fly commanded setpoints. In lab flights the external pose is accurate to 12-16 mm, enough to fly the drone autonomously within 2-5 cm of motion-capture-fed control. Having the quadruped actively follow the drone reduces the tracking error from 11.0 cm to 6.9 cm by holding the camera in the close range, where the markers are most accurate.
Chinese Translation
微型无人机（micro-UAVs）足够小，可以进入大型机器人无法到达的狭小空间，但又太小，无法携带进行自主飞行所需的传感和计算能力。我们将定位系统完全移至一个配备7自由度（DOF）机械臂的四足机器人上，该机器人为微型无人机（27克裸机，42克带有基准标记）提供完整的6自由度位姿。机械臂末端的相机检测无人机上的AprilTag基准标记，并将该观测结果与四足机器人的自我定位结合，以便在共享的地图框架中定位无人机，从而使地面机器人能够定位其伙伴，而不仅仅是相对于相机进行跟踪。机械臂作为一个主动控制的观察者，重新定位以保持无人机在视野中，随着两个机器人移动；无人机仅携带一个惯性测量单元，并将外部位姿融合以飞行命令设定点。在实验室飞行中，外部位姿的精度达到12-16毫米，足以使无人机在2-5厘米的运动捕捉控制下自主飞行。四足机器人主动跟随无人机将跟踪误差从11.0厘米降低到6.9厘米，因为相机保持在近距离，从而使标记的精度最高。
cs.RO / 117 / 2610.06232
AUTOPILOT An Advanced Perception, Localization and Path Planning Techniques for Autonomous Vehicles Using YOLOv7 and MiDaS
AUTOPILOT：基于YOLOv7和MiDaS的自主车辆先进感知、定位与路径规划技术
Abstract
Self driving vehicles have emerged as a reliable technology that has the capability to transform transportation and mobility. The development of self driving cars requires significant advances in a number of areas, including perception, localization, decision making, and control. This research paper is based on the project implementation of the combination of object detection using YOLO (You Only Look Once), depth sensing using MiDaS for the localization and perception of obstacles, perspective transform, and decision making for path planning in self driving cars. The contemporary state of the technology for object detection, depth sensing, localization, and path planning evaluates the performance of the combined system through simulations and experiments. The results show that the combination of YOLO and MiDaS provides a new robust system for object detection and depth sensing. This research paper contributes to the advancement of self driving car technology and provides new and innovative approaches to the perception and localization of obstacles in the environment. Keywords: YOLO, MiDaS, perception, localization, decision making
Chinese Translation
自动驾驶车辆已成为一种可靠的技术，能够改变交通和出行方式。自动驾驶汽车的发展需要在感知、定位、决策和控制等多个领域取得显著进展。本文基于项目实施，结合使用YOLO（You Only Look Once）进行物体检测、使用MiDaS进行深度感知以实现障碍物的定位和感知、透视变换以及自动驾驶汽车的路径规划决策。对物体检测、深度感知、定位和路径规划技术的现状进行了评估，通过模拟和实验验证了组合系统的性能。结果表明，YOLO与MiDaS的结合提供了一种新的稳健系统，用于物体检测和深度感知。本文为自动驾驶汽车技术的发展做出了贡献，并为环境中障碍物的感知和定位提供了新的创新方法。关键词：YOLO，MiDaS，感知，定位，决策
cs.RO / 118 / 2610.06235
Encoded but Not in Control: Revealing the Grounding Gap in Vision-Language Robot Policies
编码但不受控：揭示视觉-语言机器人策略中的基础差距
Abstract
Instruction following is central to language-conditioned robot policies: language should determine what to do when the same scene permits multiple valid actions. Yet successful execution alone cannot establish whether a policy follows the instruction or infers the task from the scene. We study this ambiguity through scene-preserving instruction interventions, using valid target substitutions, arbitrary nouns, and unrelated sentences while holding the scene fixed. We evaluate vision-language-action (VLA) policies and world-action models (WAMs) in simulation and in real-world experiments. Our analysis addresses three questions: (a) Does task success imply instruction following? When instructions request a different visible object, all evaluated policies predominantly approach and pick up the incorrect original target associated with the scene. (b) Is this failure caused by language insensitivity? Instruction perturbations affect task performance. A layerwise action lens shows intermediate action predictions respond to these perturbations. Linear probes accurately recover instructed targets, indicating modified instructions are encoded despite rarely determining target selection. (c) Why does encoded language fail to control action? Attention analysis indicates weak instruction-token contributions to action generation. Target-token attention can remain focused on the original object, revealing a mismatch between target encoding and visual grounding. UMAP and shared non-negative matrix factorization show target information remains accessible within representations increasingly organized by scene identity. Our findings expose a grounding gap concealed by nominal success and provide a diagnostic framework. They further establish a concrete criterion for progress: policies should reliably follow valid changes in user intent, even when they conflict with scene-favored behavior.
Chinese Translation
遵循指令是语言条件下机器人策略的核心：语言应决定在同一场景允许多种有效动作时该做什么。然而，仅仅成功执行并不能确定策略是否遵循指令或是从场景中推断任务。我们通过保持场景不变的场景保留指令干预来研究这种模糊性，使用有效的目标替换、任意名词和无关句子。我们在模拟和现实世界实验中评估视觉-语言-动作（VLA）策略和世界-动作模型（WAMs）。我们的分析解决了三个问题：（a）任务成功是否意味着遵循指令？当指令请求不同的可见对象时，所有评估的策略主要接近并拾取与场景相关的错误原始目标。（b）这种失败是否由语言敏感性不足造成？指令扰动影响任务表现。逐层动作透视显示中间动作预测对这些扰动有反应。线性探测器准确恢复指令目标，表明修改后的指令被编码，尽管很少决定目标选择。（c）为什么编码的语言未能控制动作？注意力分析表明指令标记对动作生成的贡献较弱。目标标记的注意力可能仍然集中在原始对象上，揭示了目标编码与视觉基础之间的不匹配。UMAP和共享非负矩阵分解显示目标信息在越来越按场景身份组织的表示中仍然可访问。我们的发现揭示了一个被名义成功掩盖的基础差距，并提供了一个诊断框架。它们进一步建立了一个具体的进展标准：策略应可靠地遵循用户意图的有效变化，即使这些变化与场景偏好的行为相冲突。
cs.RO / 119 / 2610.06271
VLA-ZO: Fast Zeroth-Order Adaptation for Vision-Language-Action Models
VLA-ZO：视觉-语言-动作模型的快速零阶适应
Abstract
Adapting vision-language-action (VLA) models to deployment-time distribution shifts is important for reliable robotic operation, but conventional first-order adaptation can exceed the memory budget of inference-oriented deployment platforms. Zeroth-order (ZO) optimization offers a forward-only alternative with inference-level memory, but accurate gradient estimation requires many perturbation queries, making naive ZO prohibitively slow for large VLA models. We present VLA-ZO, a framework for fast ZO adaptation that exploits the structure of VLA computation. By confining adaptation to the action side, VLA-ZO keeps the expensive vision-language prefix frozen and reuses its conditioning states across perturbation queries and optimizer steps, while schedule-aware prefetching hides state-transfer overhead. On LIBERO camera-viewpoint shifts, VLA-ZO reduces end-to-end adaptation time by 25.59$\times$ at $q=16$ and 32.54$\times$ at $q=64$ relative to baseline ZO, while improving average task success from 48.27% without adaptation to 58.17% and 63.58%, respectively. These results show that making ZO faster can make larger query budgets practical, providing a promising path toward resource-efficient VLA adaptation on deployment platforms.
Chinese Translation
将视觉-语言-动作（VLA）模型适应于部署时的分布变化对于可靠的机器人操作至关重要，但传统的一级适应可能超出面向推理的部署平台的内存预算。零阶（ZO）优化提供了一种仅向前的替代方案，具有推理级别的内存，但准确的梯度估计需要大量的扰动查询，使得简单的ZO在大型VLA模型中变得极其缓慢。我们提出了VLA-ZO，一个快速ZO适应的框架，利用了VLA计算的结构。通过将适应限制在动作侧，VLA-ZO保持了昂贵的视觉-语言前缀不变，并在扰动查询和优化步骤之间重用其条件状态，同时调度感知的预取隐藏了状态传输的开销。在LIBERO摄像机视角变化上，VLA-ZO相对于基线ZO在$q=16$时减少了25.59$ imes$的端到端适应时间，在$q=64$时减少了32.54$ imes$，同时将平均任务成功率从未适应的48.27%提高到58.17%和63.58%。这些结果表明，加快ZO的速度可以使更大的查询预算变得可行，为在部署平台上实现资源高效的VLA适应提供了一个有前景的路径。
cs.RO / 120 / 2610.06280
Future Anchored Verification and Online Recovery for World Action Models
基于未来锚定的世界行动模型验证与在线恢复
Abstract
World action models (WAMs) have emerged as a promising paradigm for robotic manipulation. They act by first predicting how a task should be performed and then decoding the actions from that future. However, the remaining actions are invalid once execution drifts from the prediction. Simply replanning from the already out of distribution state rarely restores what the task still requires; existing execution monitors decide when to stop, but not what to restore. We observe that the answer is already in hand: the future the WAM predicted before acting depicts exactly the states it intended to pass through. We introduce FAVOR (Future Anchored Verification and Online Recovery), a lightweight framework that keeps these predicted frames as anchors and uses them for verification and recovery. An Anchor Verifier compares each observation with its anchor, together with the executed actions, to flag deviations that break the task. Anchor-Guided Recovery uses a vision-language model to turn the flagged anchor into a short corrective instruction. Under strengthened instruction guidance, the WAM executes this instruction to return to the intended future. It then resumes the task. FAVOR raises the task success of the base WAM from 97.85% to 98.10% on LIBERO and from 72.60% to 72.98% on LIBERO-Plus without modifying the policy.
Chinese Translation
世界行动模型（WAMs）已成为机器人操作的一个有前景的范式。它们首先通过预测任务的执行方式，然后从该未来解码出相应的动作。然而，一旦执行偏离预测，剩余的动作便失效。从已经偏离分布状态重新规划通常难以恢复任务所需的内容；现有的执行监控器决定何时停止，但并不明确如何恢复。我们观察到答案已经在手：WAM在行动前预测的未来准确描绘了它打算经过的状态。我们提出了FAVOR（基于未来锚定的验证与在线恢复），这是一个轻量级框架，它将这些预测的帧作为锚点，并利用它们进行验证和恢复。锚点验证器将每个观察与其锚点进行比较，并结合已执行的动作，标记出破坏任务的偏差。锚点引导恢复使用视觉-语言模型将标记的锚点转化为简短的纠正指令。在加强的指令指导下，WAM执行该指令以返回预期的未来。然后，它继续执行任务。FAVOR将基础WAM在LIBERO上的任务成功率从97.85%提升至98.10%，在LIBERO-Plus上的成功率从72.60%提升至72.98%，而无需修改策略。
cs.RO / 121 / 2610.06290
GAMBIT: Learning to Plan Continuous Multi-Robot Trajectories
GAMBIT：学习规划连续多机器人轨迹
Abstract
GAMBIT is an opening chess move in which a player sacrifices a piece, typically a pawn, to gain a positional advantage later in the game. Analogously, in multi-robot coordination, individual robots may need to forgo locally reward-maximising behaviours to improve overall team performance. Such self-sacrificial behaviours are difficult to capture with manually designed heuristics, particularly in dense, interaction-rich environments. Focusing on double-integrator continuous dynamics, this work studies how to learn such coordinated heuristics over motion primitives for multi-robot trajectory execution. Our framework, GAMBIT, first learns coordinated motion-primitive selection through imitation learning and subsequently fine-tunes the policy through reinforcement learning. We further introduce a safeguarded rollout mechanism with backup trajectories that guarantees collision-free execution at all times. Experiments demonstrate that GAMBIT substantially outperforms a range of baselines, including centralised motion planners and decentralised reactive planners, while exhibiting strong scalability. In particular, it coordinates over a thousand robots with planning latency below a few hundred milliseconds in continuous domains.
Chinese Translation
GAMBIT 是一种开局国际象棋走法，玩家通常牺牲一枚棋子，通常是兵，以便在游戏后期获得位置优势。类比于多机器人协调，单个机器人可能需要放弃局部奖励最大化的行为，以提高整体团队表现。这种自我牺牲的行为难以通过手动设计的启发式方法捕捉，尤其是在密集且互动丰富的环境中。本文聚焦于双重积分器连续动态，研究如何在运动原语上学习这种协调的启发式方法以执行多机器人轨迹。我们的框架 GAMBIT 首先通过模仿学习学习协调的运动原语选择，随后通过强化学习对策略进行微调。我们进一步引入了一种带有备份轨迹的安全回滚机制，确保在任何时候都能无碰撞执行。实验表明，GAMBIT 在多个基准测试中显著优于包括集中式运动规划器和分散式反应规划器在内的多种方法，同时展现出强大的可扩展性。特别是，它能够在连续域中协调超过一千个机器人，规划延迟低于几百毫秒。
cs.RO / 122 / 2610.06306
Inspect Robots: Evaluating the Capabilities and Safety of Embodied AI
检查机器人：评估具身人工智能的能力与安全性
Abstract
General purpose language models are increasingly able to control robotic hardware. Understanding the capabilities and safety of these models when embodied is therefore increasingly important for understanding their societal impact and risks. To this end, we introduce Inspect Robots, a modular, open-source framework for developing and running evaluations of embodied agents. Inspect Robots pairs customizable, reusable abstractions for specifying physical evaluations and analyzing their results with infrastructure that automates evaluation setup, execution and termination. We demonstrate Inspect Robots by using it to evaluate the capabilities and safety of six policies based on frontier language models. Inspect Robots has seen significant early uptake, receiving nearly 100,000 downloads in the three months since its release.
Chinese Translation
通用语言模型越来越能够控制机器人硬件。因此，理解这些模型在具身状态下的能力和安全性，对于理解其社会影响和风险变得越来越重要。为此，我们推出了Inspect Robots，这是一个模块化的开源框架，用于开发和运行具身代理的评估。Inspect Robots 将可定制的、可重用的抽象与指定物理评估和分析结果的基础设施相结合，自动化评估的设置、执行和终止。我们通过使用Inspect Robots评估基于前沿语言模型的六种策略的能力和安全性来展示其功能。自发布以来，Inspect Robots 已经获得了显著的早期采用，三个月内下载量接近100,000次。
cs.RO / 123 / 2610.06327
Dual Variational Autoencoders for Efficient Sim-to-Real Transfer in Low-Cost Robotic Navigation
双重变分自编码器用于低成本机器人导航中的高效模拟到现实转移
Abstract
Vision-based autonomous navigation for low-cost robots remains a fundamental challenge, primarily due to the significant gap between simulated training environments and real-world operational conditions. Direct policy transfer from simulation is often ineffective, while training exclusively on real data is impractical. We propose a hybrid transfer learning framework that effectively bridges the sim-to-real gap by combining domain randomization with feature-level domain adaptation. Our method employs a dual convolutional variational autoencoder architecture with a shared decoder, trained on an extensive set of 45225 simulated images and a minimal set of only 4556 real-world samples. This architecture learns a compact, common latent representation space that aligns the distributions of both domains. The adaptation process is further enhanced by two complementary data augmentation techniques designed to expand the limited real-world data. Experimental evaluation demonstrates that our method achieves an average success rate of almost 91% on image classification tasks for real-world indoor navigation, significantly outperforming both simulation-only and real-world-only training. We validate these findings through a direct, real-world deployment, where the proposed policy successfully guides a low-cost robot in a reactive exploration task. Furthermore, we validate the model's efficiency through a rigorous computational estimation, confirming its suitability for resource-constrained embedded platforms such as the Raspberry Pi 4 and NVIDIA Jetson Nano. This work presents a practical solution for developing effective and efficient navigation policies for low-cost robotic systems.
Chinese Translation
基于视觉的低成本机器人自主导航仍然是一个基本挑战，主要是由于模拟训练环境与现实操作条件之间存在显著差距。直接从模拟中转移策略通常效果不佳，而仅依赖真实数据进行训练又不切实际。我们提出了一种混合转移学习框架，通过结合领域随机化与特征级领域适应，有效地弥合了模拟到现实的差距。我们的方法采用了一种双重卷积变分自编码器架构，具有共享解码器，基于45225张模拟图像和仅4556个真实世界样本的有限集合进行训练。该架构学习了一个紧凑的共同潜在表示空间，使两个领域的分布对齐。适应过程通过两种互补的数据增强技术进一步增强，旨在扩展有限的真实世界数据。实验评估表明，我们的方法在真实世界室内导航的图像分类任务中实现了近91%的平均成功率，显著优于仅使用模拟或仅使用真实世界训练的结果。我们通过直接的现实世界部署验证了这些发现，提出的策略成功指导低成本机器人完成反应式探索任务。此外，我们通过严格的计算估算验证了模型的效率，确认其适用于资源受限的嵌入式平台，如树莓派4和NVIDIA Jetson Nano。本研究为开发低成本机器人系统的有效且高效的导航策略提供了实用解决方案。
cs.RO / 124 / 2610.06331
DexForge: High-Fidelity Physics-Informed Dexterous Retargeting
DexForge：高保真物理信息驱动的灵巧重定向
Abstract
Human demonstrations offer rich examples of precise dexterous manipulation and a promising source of robot training data. However, high-fidelity reproduction of demonstrated motions and hand-object interactions across robot embodiments remains challenging under physical constraints. We present DexForge, a differentiable physics-grounded framework for converting human video demonstrations into high-fidelity robot trajectories. We reconstruct spherical-Gaussian object models and hand-object motion from visual observations, then build a differentiable simulator combining efficient Gaussian collision detection with existing differentiable dynamics. Based on this simulator, DexForge combines contact-aware kinematic retargeting with force-aware dynamics retargeting: robot-adapted stable contacts guide kinematic reference construction and subsequent gradient-based control refinement for precise physical motion reproduction. Experiments on 130 DexYCB and HOT3D demonstrations across seven dexterous hands show success-rate gains of approximately 35-53 percentage points over the baseline, with object position and orientation tracking errors on successful trajectories reduced by approximately 34-67% and 71-78%, respectively. Further experiments demonstrate open-loop transfer to MuJoCo and real-robot execution. Our project page is available at https://wmz1226.github.io/DexForge/
Chinese Translation
人类示范提供了精确灵巧操作的丰富实例，并且是机器人训练数据的有希望来源。然而，在物理约束下，高保真地再现示范动作和手-物体交互在不同机器人形态中仍然具有挑战性。我们提出了DexForge，这是一个可微分的物理基础框架，用于将人类视频示范转换为高保真的机器人轨迹。我们从视觉观察中重建球形高斯物体模型和手-物体运动，然后构建一个结合高效高斯碰撞检测与现有可微分动力学的可微分模拟器。基于该模拟器，DexForge结合了接触感知的运动学重定向与力感知的动力学重定向：适应机器人稳定接触引导运动学参考构建及后续基于梯度的控制优化，以实现精确的物理运动再现。在130个DexYCB和HOT3D示范的七个灵巧手上的实验显示，相较于基线，成功率提高了约35-53个百分点，成功轨迹上的物体位置和方向跟踪误差分别减少了约34-67%和71-78%。进一步的实验展示了对MuJoCo的开环转移和真实机器人执行。我们的项目页面可访问 https://wmz1226.github.io/DexForge/
cs.RO / 125 / 2610.06376
Towards Robust Prehensile Manipulation in Open-Ended Environments
面向开放环境中的稳健抓取操作
Abstract
We propose to use Quality-Diversity (QD) algorithms to solve robotic prehensile manipulation tasks in open-ended environments. Our approach enables the efficient discovery of a wide range robust grasp configurations, which serve as reliable starting points for generating diverse prehensile manipulation trajectories on articulated objects. The resulting diversity in manipulation behaviors enhances generalization and adaptability, enabling effective deployment continuously evolving open-world settings.
Chinese Translation
我们提出使用质量-多样性（Quality-Diversity, QD）算法来解决开放环境中的机器人抓取操作任务。我们的方法能够高效地发现广泛的稳健抓取配置，这些配置作为生成多样化抓取操作轨迹的可靠起点，适用于关节物体。操作行为的多样性增强了模型的泛化能力和适应性，使其能够在不断演变的开放世界环境中有效部署。
cs.RO / 126 / 2610.06400
Visual Swarm Navigation via Deep Reinforcement Learning and Evolutionary Hybrid Design
基于深度强化学习与进化混合设计的视觉群体导航
Abstract
Swarm robotics presents a robust and cost-effective paradigm for advanced automation in complex, dynamic environments, such as those encountered in search and rescue or environmental monitoring. A fundamental challenge for this field is the data-driven design of decentralized controllers capable of generating emergent collective behaviors. This paper proposes a novel, AI-driven hybrid methodology for the automatic synthesis of swarm robotic controllers for autonomous visual navigation. This approach synergistically combines multi-agent reinforcement learning with neuro-evolutionary strategies, specifically leveraging implementations of the cross-entropy method and the covariance matrix adaptation evolution strategy to optimize a pre-trained individual navigation policy. The underlying deep architecture is engineered for low-cost, resource-constrained platforms, utilizing a compact neural network that relies exclusively on monocular camera imagery. This vision-based design emphasizes computational and energy efficiency, a critical requirement for practical swarm deployments. Experiments, performed in a high-fidelity physics simulator, demonstrate that the resulting controllers enable robust and scalable collective exploration of diverse indoor environments. The controller trained using our cross-entropy method achieves superior exploration coverage, visiting 36.20% more regions compared to the covariance matrix adaptation evolution strategy. Critically, our best vision-based policy achieves exploration performance statistically comparable to traditional methods relying on more expensive distance sensors, while delivering a significant 31.40% average reduction in energy consumption. These findings validate an effective and economically viable autonomous control system, establishing a path for deploying highly efficient collective intelligence in real-world engineering applications.
Chinese Translation
群体机器人技术为复杂动态环境中的高级自动化提供了一种稳健且具有成本效益的范式，例如在搜索与救援或环境监测中所遇到的环境。该领域的一个基本挑战是数据驱动的去中心化控制器设计，能够生成涌现的集体行为。本文提出了一种新颖的基于人工智能的混合方法，用于自动合成用于自主视觉导航的群体机器人控制器。该方法协同结合了多智能体强化学习与神经进化策略，特别利用交叉熵方法和协方差矩阵自适应进化策略的实现来优化预训练的个体导航策略。其基础深度架构针对低成本、资源受限的平台进行了设计，采用了一种仅依赖单目相机图像的紧凑型神经网络。这种基于视觉的设计强调了计算和能量效率，这是实际群体部署的关键要求。在高保真物理模拟器中进行的实验表明，所得到的控制器能够实现对多样化室内环境的稳健且可扩展的集体探索。使用我们的交叉熵方法训练的控制器在探索覆盖方面表现优越，与协方差矩阵自适应进化策略相比，访问了36.20%更多的区域。值得注意的是，我们最佳的基于视觉的策略在探索性能上与依赖更昂贵距离传感器的传统方法在统计上相当，同时实现了平均31.40%的能耗显著降低。这些发现验证了一种有效且经济可行的自主控制系统，为在现实工程应用中部署高效的集体智能奠定了基础。
cs.RO / 127 / 2610.06427
A Taxonomy on Collective Awareness
集体意识的分类法
Abstract
As robotic teams tackle increasingly complex tasks in dynamic and unstructured environments, effective coordination requires agents to maintain accurate, aligned representations of their environment, teammates, and mission state. We argue that Mutual Awareness, Shared Situational Awareness, and Team Situational Awareness --- three concepts widely invoked in the multi-robot systems literature --- are not interchangeable: they form a containment hierarchy in which each type subsumes the previous in scope, and span a distribution spectrum from fully individualized understanding (MA) to fully uniform understanding (SSA), with TSA occupying a mixed position. We establish this through a two-dimensional taxonomy organized along the object of awareness and awareness distribution axes. Treating these terms as synonyms obscures the precise coordination requirements each imposes on a robotic system. A Search and Rescue case study with heterogeneous aerial and ground robots grounds each taxonomy position in concrete coordination requirements. These findings provide a conceptual foundation for principled specification, design, and comparison of collective awareness in multi-robot systems.
Chinese Translation
随着机器人团队在动态和非结构化环境中应对日益复杂的任务，有效的协调要求代理保持对其环境、队友和任务状态的准确且一致的表征。我们认为，互相意识（Mutual Awareness）、共享情境意识（Shared Situational Awareness）和团队情境意识（Team Situational Awareness）——这三个在多机器人系统文献中广泛提及的概念——并不是可以互换的：它们形成了一个包含层次结构，其中每种类型在范围上都包含前一种，并且在从完全个体化理解（MA）到完全统一理解（SSA）的分布谱上展开，而TSA则占据一个混合位置。我们通过一个沿着意识对象和意识分布轴组织的二维分类法来建立这一点。将这些术语视为同义词会掩盖每种术语对机器人系统施加的精确协调要求。通过一个涉及异构空中和地面机器人的搜索与救援案例研究，将每个分类位置与具体的协调要求结合起来。这些发现为多机器人系统中集体意识的原则性规范、设计和比较提供了概念基础。
cs.RO / 128 / 2610.06469
Odyssey: A Closed-Loop Benchmark for Long-Horizon Real-World Driving with Explicit Navigation Routes
奥德赛：一个用于长时间真实驾驶的闭环基准，具有明确的导航路线
Abstract
Closed-loop evaluation of end-to-end driving requires continuous rollouts that reveal how earlier decisions affect subsequent driving. However, existing benchmarks evaluate only short segments and fail to capture later consequences. Ambiguous directional commands also obscure the intended navigation objective. We introduce Odyssey, a closed-loop benchmark for long-horizon driving comprising 100 scenarios, each reconstructed from a 100-second nuPlan driving log to preserve the context of navigation maneuvers and traffic interactions. To provide a consistent navigation objective, Odyssey replaces directional commands with explicit standard-definition (SD) map routes that specify which roads to follow, while sensor-based planning determines local driving actions. Throughout these rollouts, diffusion-based refinement of 3DGS-rendered images reduces rendering artifacts along the ego trajectory. To assess how effectively planners follow these routes and prepare for upcoming maneuvers, we introduce SD Route Compliance and Pre-Lane Change Score. These assessments are complemented by RouteDS, which extends the Driving Score with penalties for SD-route deviations and failed lane preparation. We adapt state-of-the-art planners, including vision-language-action (VLA) models, and evaluate their navigation performance using these metrics. Odyssey highlights open questions in route representation and integration for E2E driving. Benchmark code and adapted baselines will be released publicly.
Chinese Translation
闭环评估端到端驾驶需要持续的执行，以揭示早期决策如何影响后续驾驶。然而，现有基准仅评估短段，未能捕捉后续后果。模糊的方向指令也使得预期的导航目标变得不明确。我们引入了奥德赛（Odyssey），这是一个用于长时间驾驶的闭环基准，包含100个场景，每个场景均由100秒的nuPlan驾驶日志重建，以保留导航操作和交通互动的上下文。为了提供一致的导航目标，奥德赛用明确的标准定义（SD）地图路线替代方向指令，指明应遵循的道路，而基于传感器的规划则决定局部驾驶动作。在这些执行过程中，基于扩散的3DGS渲染图像的细化减少了自我轨迹上的渲染伪影。为了评估规划者多有效地遵循这些路线并为即将到来的操作做好准备，我们引入了SD路线合规性和预变道评分。这些评估通过RouteDS得到补充，后者在驾驶评分中增加了对SD路线偏离和未能准备变道的惩罚。我们调整了最先进的规划者，包括视觉-语言-动作（VLA）模型，并使用这些指标评估其导航性能。奥德赛突出了端到端驾驶中路线表示和整合的开放性问题。基准代码和调整后的基线将公开发布。
cs.RO / 129 / 2610.06487
Traversability-Aware Cooperative Path Planning for Human-UGV Casualty Evacuation
考虑可通行性的合作路径规划用于人类-无人地面车辆伤员撤离
Abstract
Heterogeneous multi-robot path planning is a well-studied problem in which agents with disparate kinematic and dynamic models must coordinate to achieve shared objectives. These formulations, however, treat all agents as robotic-their cost models are mechanical and their traversability is sensor-derived. In human-robot teaming, the human partner remains relegated to command and supervisory roles rather than being modeled as a physical co-navigator with distinct mobility constraints and dynamic energy reserves. This work investigates joint path planning for a two-agent human-UGV team in search-and-rescue casualty retrieval scenarios. We model the human agent using the Pandolf-Santee metabolic cost model with fatigue-modulated speed, and the UGV using a rolling-resistance energy model with terrain-dependent speed limits. By exploiting the complementary traversability of each agent-the human's ability to traverse dense vegetation and shallow water versus the UGV's superior speed on open terrain and roads-we optimize casualty transfer locations, termed switch points, to minimize total mission time. Evaluated across multiple synthetic 1km2 environments with procedurally generated elevation and land-cover data, the optimized strategy reduces mean mission time by 5.3% relative to a human-only baseline and by 7.0% relative to a naive human-UGV strategy without switch point optimization, while reducing human energy expenditure by 17.8% relative to baseline. Notably, the naive strategy reduces human energy expenditure by a larger margin (22.4%) but incurs a 2% increase in mission time relative to baseline, illustrating that switch point optimization is necessary to realize time savings from human-UGV teaming.
Chinese Translation
异构多机器人路径规划是一个研究较为深入的问题，其中具有不同运动学和动力学模型的代理需要协调以实现共同目标。然而，这些模型将所有代理视为机器人，其成本模型是机械的，通行能力是基于传感器的。在人机协作中，人类伙伴通常被 relegated 到指挥和监督角色，而不是被建模为具有独特移动约束和动态能量储备的物理共同导航者。本研究探讨了在搜索与救援伤员检索场景中，针对两代理人类-无人地面车辆（UGV）团队的联合路径规划。我们使用 Pandolf-Santee 代谢成本模型来建模人类代理，考虑疲劳调节速度，并使用滚动阻力能量模型来建模 UGV，考虑地形依赖的速度限制。通过利用每个代理的互补通行能力——人类在密集植被和浅水区域的通行能力与 UGV 在开阔地形和道路上的优越速度——我们优化了伤员转移位置，称为切换点，以最小化总任务时间。在多个合成的 1km² 环境中进行评估，这些环境具有程序生成的高程和地表覆盖数据，优化策略相较于仅人类基线减少了 5.3% 的平均任务时间，相较于未进行切换点优化的天真人类-UGV 策略减少了 7.0%，同时相较于基线减少了 17.8% 的人类能量消耗。值得注意的是，天真策略虽然减少了人类能量消耗的幅度更大（22.4%），但相较于基线任务时间增加了 2%，这表明切换点优化对于实现人类-UGV 合作带来的时间节省是必要的。
cs.RO / 130 / 2610.06510
MarvisNav: Making Memory Visible on Route Choices for Zero-Shot Object Navigation
MarvisNav：在零样本目标导航中使记忆在路线选择中可见
Abstract
When searching for an object, people choose their next move by considering both likely target locations and places already explored. The current view can cue place-associated memories, bringing target relevance and prior exploration into the same spatial context. In many zero-shot object navigation (ZSON) methods, however, vision-language models (VLMs) infer promising search areas from egocentric images, while exploration history is represented separately, e.g., as text or maps. This separation either requires an additional fusion step or leaves the correspondence between memory and route choices implicit for the VLM to recover. We instead make exploration memory directly visible on visual route choices. We propose MarvisNav, a ZSON framework that maintains a topological graph and projects candidate nodes together with their exploration states onto egocentric views as memory-bearing visual route choices. These states capture local exploration progress beyond binary visitation. By binding exploration state directly to each visual candidate, MarvisNav enables the VLM to jointly evaluate target relevance and exploration state without a separate post-hoc fusion or reranking stage. Without policy training, MarvisNav achieves state-of-the-art performance on HM3D (81.2% SR and 42.5% SPL), while remaining competitive on MP3D. It also outperforms representative VLM-based methods with far fewer VLM calls (e.g., 7.5% of WMNav). Real-robot experiments across diverse scenes further validate its practical deployability. Beyond MarvisNav, our study shows that memory representation shapes VLM decisions and ZSON performance, highlighting that effective memory use depends not only on its availability, but also on how it is represented. Code and project page will be available at \url{https://wangjincheng1998.github.io/MarvisNav/}.
Chinese Translation
在寻找物体时，人们通过考虑可能的目标位置和已探索的地方来选择下一步的移动。然而，在许多零样本目标导航（ZSON）方法中，视觉-语言模型（VLM）从自我中心图像中推断出有前景的搜索区域，而探索历史则被单独表示，例如作为文本或地图。这种分离要么需要额外的融合步骤，要么使得记忆与路线选择之间的对应关系对于VLM来说是隐式的。我们则使探索记忆在视觉路线选择中直接可见。我们提出了MarvisNav，一个ZSON框架，它维护一个拓扑图，并将候选节点及其探索状态投影到自我中心视图中，作为承载记忆的视觉路线选择。这些状态捕捉了超越二元访问的局部探索进展。通过将探索状态直接绑定到每个视觉候选上，MarvisNav使VLM能够共同评估目标相关性和探索状态，而无需单独的后续融合或重新排序阶段。在没有策略训练的情况下，MarvisNav在HM3D上实现了最先进的性能（81.2% SR和42.5% SPL），同时在MP3D上保持竞争力。它还以远少于VLM调用的数量（例如，WMNav的7.5%）超越了代表性的基于VLM的方法。跨多样场景的真实机器人实验进一步验证了其实际可部署性。除了MarvisNav，我们的研究表明，记忆表示会影响VLM的决策和ZSON的性能，强调有效的记忆使用不仅依赖于其可用性，还依赖于其表示方式。代码和项目页面将可在 {https://wangjincheng1998.github.io/MarvisNav/} 获取。
cs.RO / 131 / 2610.06511
ArtifactArena: Evaluating Models by What They Build in the Physical World
ArtifactArena：通过模型在物理世界中的构建进行评估
Abstract
To evaluate the frontier, we must measure models not by what they say, but by what they can engineer and build in grounded physical environments. We introduce \textsc{ArtifactArena}, an open-ended platform where models face a physically grounded hardware-software co-design challenge: engineering fully functional robots to compete in a simulated arena. We evaluate a frontier model's zero-shot, verifier guided refinement, and open-ended physical design capabilities through three harnesses that refine their bot artifacts based on text descriptions, physics simulator feedback, and gameplay data. We benchmark these capabilities with an Elo ranking of frontier models derived from head-to-head tournaments between their artifacts. By releasing this framework and tournament infrastructure for ongoing community submissions, we establish a living, non-saturating testbed to continuously measure the expanding limits of open-ended intelligence in the physical world. Please visit \href{https://artifactarena.ai}{https://artifactarena.ai} for more information.
Chinese Translation
为了评估前沿，我们必须通过模型在实际物理环境中能够工程化和构建的内容来衡量它们，而不是仅仅通过它们所说的内容。我们介绍了 extsc{ArtifactArena}，一个开放式平台，在这里模型面临一个物理基础的硬件-软件协同设计挑战：工程设计出完全功能的机器人，以在模拟竞技场中竞争。我们通过三种工具评估前沿模型的零-shot、验证者引导的精炼和开放式物理设计能力，这些工具基于文本描述、物理模拟器反馈和游戏数据来精炼它们的机器人工件。我们通过对其工件进行的直接对抗比赛，使用Elo排名对这些能力进行基准测试。通过发布这一框架和比赛基础设施以供社区持续提交，我们建立了一个活的、非饱和的测试平台，以持续测量开放式智能在物理世界中不断扩展的极限。有关更多信息，请访问 ext{https://artifactarena.ai}。
cs.RO / 132 / 2610.06598
SimForcing: Distilling Simulation Motion Priors into Real-Domain Robot World Models
SimForcing：将仿真运动先验提炼为真实领域机器人世界模型
Abstract
Action-conditioned robot world models must respond precisely to robot trajectories while preserving realistic visual dynamics, yet learning both from heterogeneous robot videos remains challenging. Simulation offers structured motion supervision, but appearance differences hinder direct transfer, and inaccurate simulation predictions can misguide real-video generation. We present SimForcing, a simulation-guided framework that uses simulation both as a source of transferable motion knowledge and as a controllable reference for prediction. First, we transfer motion knowledge from a simulation teacher through latent-motion distillation, aligning temporal changes in latent space to internalize motion priors while mitigating the influence of appearance differences. Second, we introduce multi-block simulation conditioning with condition dropout to exploit predicted simulation trajectories without relying excessively on their accuracy. Our simulation-conditioning classifier-free guidance scheme unifies these two ideas by balancing predictions based on internalized motion knowledge with those additionally guided by simulation latents. The jointly trained student generates both simulation conditions and real-domain videos, requiring no additional world model at inference. On Bridge, SimForcing achieves the best PSNR, SSIM, LPIPS, and FVD among the compared methods without external embodied pretraining. Evaluation on InternData-A1 further supports its applicability across robot datasets. Moreover, using our trained world model to initialize a vision-language-action model improves LIBERO success, suggesting its utility for downstream policy learning. \url{https://github.com/Wang-Xiaodong1899/SimForcing}
Chinese Translation
基于动作的机器人世界模型必须精确响应机器人轨迹，同时保持真实的视觉动态，但从异构机器人视频中学习仍然具有挑战性。仿真提供了结构化的运动监督，但外观差异阻碍了直接转移，而不准确的仿真预测可能会误导真实视频的生成。我们提出了SimForcing，一个以仿真为指导的框架，利用仿真作为可转移运动知识的来源以及作为可控的预测参考。首先，我们通过潜在运动提炼从仿真教师中转移运动知识，将潜在空间中的时间变化对齐，以内化运动先验，同时减轻外观差异的影响。其次，我们引入多块仿真条件与条件丢弃，以利用预测的仿真轨迹，而不依赖于它们的准确性。我们的无分类器仿真条件引导方案通过平衡基于内化运动知识的预测与额外由仿真潜在引导的预测，统一了这两个思想。联合训练的学生生成仿真条件和真实领域视频，在推理时无需额外的世界模型。在Bridge上，SimForcing在比较方法中实现了最佳的PSNR、SSIM、LPIPS和FVD，且没有外部具身预训练。在InternData-A1上的评估进一步支持其在机器人数据集中的适用性。此外，使用我们训练的世界模型初始化视觉-语言-动作模型提高了LIBERO的成功率，表明其在下游策略学习中的实用性。
cs.RO / 133 / 2610.06641
Physics Residual Dynamics and Reduced Order Whole-Body Planning for Obstacle Aware Human Robot Cloth CoTransportation
物理残差动态与障碍感知人机协作布料共同运输的降阶全身规划
Abstract
Human--robot co-transportation of deformable objects requires predicting object deformation during motion, since obstacle clearance depends on both the grasp points and the unactuated interior. We present a hierarchical planning framework that combines a learned cloth model with a reduced-order whole-body model of a dual-arm mobile manipulator. A physics-residual conditional recurrent variational autoencoder (p-cRVAE) predicts the full cloth configuration from grasp-point observations by learning a residual correction to a computationally efficient linearized physics model, limiting error accumulation over 40-step planning horizon. The predicted cloth dynamics are embedded in a model predictive path integral (MPPI) planner using a reduced-order representation of a dual-arm mobile manipulator that preserves the non-holonomic base constraint and arm workspace limits. An MPC layer subsequently refines the sampled motion into smooth, executable references for whole-body control. The reduced-order formulation achieves tracking performance comparable to the full 17-DoF model while reducing computation time by approximately 80%. Across four co-transportation scenarios and two carrying speeds, the proposed framework maintains cloth-obstacle clearance where a corner-following baseline results in collisions, while whole-body refinement reduces final cloth deformation from 0.93\,m to 0.28\,m.
Chinese Translation
人机协作运输可变形物体需要在运动过程中预测物体的变形，因为障碍物的清除依赖于抓取点和未驱动内部的状态。我们提出了一种分层规划框架，将学习的布料模型与双臂移动操纵器的降阶全身模型相结合。物理残差条件递归变分自编码器（p-cRVAE）通过学习对计算效率高的线性化物理模型的残差修正，从抓取点观测中预测完整的布料配置，从而限制了在40步规划范围内的误差积累。预测的布料动态被嵌入到模型预测路径积分（MPPI）规划器中，该规划器使用保留非完整基约束和臂工作空间限制的双臂移动操纵器的降阶表示。随后，模型预测控制（MPC）层将采样运动精炼为平滑、可执行的全身控制参考。降阶公式在追踪性能上与完整的17自由度模型相当，同时计算时间减少约80%。在四个协作运输场景和两种搬运速度下，所提出的框架保持了布料与障碍物的清除，而基于角落跟随的基线则导致碰撞，同时全身精炼将最终布料变形从0.93米减少到0.28米。
cs.RO / 134 / 2610.06643
AffordCraft: Scalable Construction of Task-Ready Simulation Assets from Single Images
AffordCraft：从单幅图像可扩展构建任务准备模拟资产
Abstract
Robot learning in simulation depends on the objects the simulator offers. Many tasks need objects with separate parts, joints that allow the required motion, and physical properties that remain valid under contact. Existing methods recover this structure anew for every image: generative models predict parts and joints that mostly fail to settle or move in simulation, and general-purpose agents need a long session of model calls for each photograph. AffordCraft builds such an asset from a single RGB image and a task instruction by retrieval instead of generation: it locates the object and the part to operate, selects a matching entry from a library of articulated assets, and fits it to the image while keeping its parts and joints intact. Without any box or mask marking the object, AffordCraft produces a physically valid asset for 1,703 of 2,000 photographs from 31 categories. Five generative methods pass on at most 45% of the same photographs and, at the median, need 10 to 78 times our GPU time per valid asset. On 50 cluttered images, 162 of 237 annotated objects pass the same physical test after automatic detection. Growing the library from 141 to 11,372 entries needs no change to the method and raises category coverage from 46% to 100% and the share of selections with the requested label from 18% to 51%. We also build manipulation tasks from the constructed assets, both with single objects and in composed scenes; policies trained on scripted demonstrations complete both kinds of tasks from initial states unseen in training.
Chinese Translation
机器人在模拟中的学习依赖于模拟器提供的对象。许多任务需要具有独立部件、允许所需运动的关节以及在接触下保持有效的物理属性的对象。现有方法为每幅图像重新恢复这种结构：生成模型预测的部件和关节大多无法在模拟中稳定或移动，而通用代理需要对每张照片进行长时间的模型调用。AffordCraft通过检索而非生成，从单幅RGB图像和任务指令构建这样的资产：它定位要操作的对象及其部件，从关节资产库中选择一个匹配条目，并在保持其部件和关节完整的同时将其适配到图像上。在没有任何框或掩膜标记对象的情况下，AffordCraft为来自31个类别的2,000张照片中的1,703张生成了物理有效的资产。五种生成方法最多只能处理45%的相同照片，且在中位数情况下，每个有效资产需要10到78倍于我们的GPU时间。在50张杂乱的图像中，237个标注对象中有162个在自动检测后通过了相同的物理测试。将库从141个条目扩展到11,372个条目无需更改方法，并将类别覆盖率从46%提高到100%，请求标签的选择比例从18%提高到51%。我们还基于构建的资产构建了操作任务，包括单个对象和组合场景；在脚本演示上训练的策略能够完成训练中未见的初始状态下的这两种任务。
cs.RO / 135 / 2610.06843
Recursive Video In-Context Learning for Agentic Robot
代理机器人递归视频上下文学习
Abstract
LLM agents that orchestrate frozen vision-language-action (VLA) policies improve across episodes through text memory, which records what the agent did but not how the task is done. A demonstration video shows it, but fits poorly into an agent's context. The full video slows every turn, fixed keyframes lose the contact detail that decides whether a grasp holds, and what the agent needs shifts from the task's structure while planning to the frames around each contact. We introduce Recursive Video In-Context Learning (RV-ICL), a training-free method that turns a demonstration into a hierarchy the agent navigates rather than a prompt it receives. The hierarchy is built from the sub-events of the demonstration, such as grasps and releases. Its levels grow finer, from keyframes of the whole task to phases, moments and short clips, and are exposed through read-only tools. The agent reads the coarse levels before planning. During execution it re-enters the hierarchy whenever a step needs more detail and loads only the clip of its current sub-goal. One demonstration per task is enough. Built on RPent, RV-ICL raises success from 92.6% to 96.5% on LIBERO-PRO and from 86.7% to 95.8% on LIBERO-Plus.
Chinese Translation
通过文本记忆，协调冻结的视觉-语言-动作（VLA）策略的LLM代理在多个回合中不断改进，这种记忆记录了代理所做的事情，但并未记录任务是如何完成的。演示视频展示了这一点，但与代理的上下文契合度较差。完整的视频在每个回合中都会减慢速度，固定的关键帧失去了决定抓取是否成功的接触细节，而代理在规划时所需的信息从任务结构转移到每个接触周围的帧。我们提出了递归视频上下文学习（RV-ICL），这是一种无训练的方法，将演示转化为代理导航的层次结构，而不是接收的提示。该层次结构由演示的子事件构成，如抓取和释放。其层次从整个任务的关键帧逐渐细化到阶段、时刻和短片，并通过只读工具展示。代理在规划之前读取粗略层次。在执行过程中，每当某一步骤需要更多细节时，它会重新进入层次结构，仅加载当前子目标的片段。每个任务只需一个演示。基于RPent，RV-ICL在LIBERO-PRO上的成功率从92.6%提高到96.5%，在LIBERO-Plus上的成功率从86.7%提高到95.8%。
cs.RO / 136 / 2610.06850
InterMimicGen: Scaling Humanoid Loco-Manipulation through Self-Evolving Motion Imitation
InterMimicGen：通过自我演化的运动模仿扩展类人机器人运动操控能力
Abstract
Captured human-object interactions provide rich supervision for humanoid loco-manipulation, but they are sparse, heterogeneous, and not directly executable by robots. We introduce InterMimicGen, a self-evolving motion-imitation framework in which robot motion data and a tracking policy improve each other. First, we consolidate motion-captured human-object interaction datasets and retarget them into humanoid robot references while preserving whole-body coordination and dexterous hand-object relationships. This produces a large and diverse humanoid robot reference collection for dexterous whole-body loco-manipulation. Second, we train a physics-based generalist tracker that executes these references in simulation on a humanoid with dexterous hands, covering a scale and diversity beyond prior humanoid tracking systems for loco-manipulation. Third, we close a data flywheel: each round makes small, task-preserving changes to where an interaction takes place and how the body performs it, fine-tunes the tracker on them, and keeps only the variants whose simulated execution completes the task, which seed the next round. With more iterations, these small edits compound into broader coverage around the sparse original demonstrations while preserving task semantics and motion quality. Experiments show contact-preserving retargeting across robot configurations, broad tracking with a single generalist policy, executable motions that keep growing over augmentation rounds, and transfer to real robots. InterMimicGen provides a unified path from heterogeneous human demonstrations to a continually expanding motion resource for humanoid robot learning.
Chinese Translation
捕捉到的人类与物体的交互为类人机器人的运动操控提供了丰富的监督，但这些数据稀疏、异质，并且不能被机器人直接执行。我们提出了InterMimicGen，一个自我演化的运动模仿框架，其中机器人运动数据和跟踪策略相互提升。首先，我们整合了运动捕捉的人类与物体交互数据集，并将其重新定向为类人机器人参考，同时保持全身协调性和灵巧手部与物体之间的关系。这产生了一个大型且多样化的类人机器人参考集合，用于灵巧的全身运动操控。其次，我们训练了一个基于物理的通用跟踪器，在具有灵巧手部的类人机器人上模拟执行这些参考，覆盖了超出以往类人跟踪系统在运动操控方面的规模和多样性。第三，我们关闭了数据飞轮：每一轮对交互发生的地点和身体执行方式进行小幅度、保持任务的变化，针对这些变化微调跟踪器，并仅保留那些模拟执行完成任务的变体，以此作为下一轮的种子。随着迭代的增加，这些小的编辑汇聚成对稀疏原始演示的更广泛覆盖，同时保持任务语义和运动质量。实验表明，在机器人配置之间保持接触的重新定向、通过单一通用策略的广泛跟踪、在增强轮次中不断增长的可执行运动，以及向真实机器人的转移。InterMimicGen为从异质的人类演示到不断扩展的类人机器人学习运动资源提供了一条统一的路径。
cs.CV / 1 / 2610.03755
Fractal Cross Product: Theory, Differentiable Implementation and Application to Medical Image Analysis
分形叉积：理论、可微实现及其在医学图像分析中的应用
Abstract
The magnitude of the generalized Euclidean cross product is a Gram volume whose degree under common scaling is fixed by the integer dimension of the spanning frame. We formulate a generalized Fractal Cross Product (FCP) as a nonlinear radial deformation with a prescribed positive degree $D$, which may be non-integer. The scalar construction applies in any ambient dimension $m\geq k$, while its canonically oriented vector form requires codimension one. It recovers the classical generalized cross product exactly at $D=k$ and retains orthogonality, alternation, rotation equivariance, and $D$-homogeneity, but is generally not multilinear. For exact self-similar frame systems, the construction also obeys a scale-balance law at the similarity dimension. We derive a differentiable, dimensionless image response and a non-circular empirical accumulation exponent obtained by regressing raw angular Gram responses across patch widths. Binary64 calculations recover the finite-frame identities to roundoff, while raster experiments recover the Sierpi\'nski-triangle value 1.5849625 at three resolutions. In five-seed medical-imaging comparisons, FCP-centered fusion increased mean area under the receiver operating characteristic curve from 0.7264 to 0.8135 and from 0.5843 to 0.6765 on the random and hospital-separated Retinal Image Database for Optic Nerve Evaluation partitions, respectively, and from 0.7403 to 0.7475 on FracAtlas. On FracAtlas, balanced accuracy increased from 0.6186 to 0.6721 and the harmonic mean of precision and sensitivity from 0.3669 to 0.4457. These results support the utility of the complete fusion framework, but do not isolate the effect of FCP from that of its complementary descriptors and fusion head.
Chinese Translation
广义欧几里得叉积的大小是一个Gram体积，其在常见缩放下的度数由跨越框架的整数维度固定。我们将广义分形叉积（Fractal Cross Product, FCP）表述为一种具有预定正度数 $D$ 的非线性径向变形，该度数可以是非整数。该标量构造适用于任何环境维度 $m
geq k$，而其规范定向的向量形式则要求余维为一。在 $D=k$ 时，它准确恢复经典的广义叉积，并保持正交性、交替性、旋转等变性和 $D$-齐次性，但通常不是多线性的。对于精确自相似框架系统，该构造还遵循相似维度下的尺度平衡法则。我们推导出一种可微的无量纲图像响应和通过对原始角Gram响应在补丁宽度上进行回归得到的非圆形经验累积指数。Binary64计算在舍入时恢复有限框架恒等式，而光栅实验在三个分辨率下恢复了Sierpiński三角形值1.5849625。在五个种子的医学成像比较中，FCP中心融合使接收者操作特征曲线下的平均面积从0.7264提高到0.8135，从0.5843提高到0.6765，分别在随机和医院分离的视网膜图像数据库（Retinal Image Database for Optic Nerve Evaluation）分区上，以及在FracAtlas上从0.7403提高到0.7475。在FracAtlas上，平衡准确率从0.6186提高到0.6721，精确度和灵敏度的调和平均值从0.3669提高到0.4457。这些结果支持了完整融合框架的实用性，但并未将FCP的效果与其互补描述符和融合头的效果分离。
cs.CV / 2 / 2610.03765
Do Motion Tokenizers for Co-Speech Gesture Generation Encode Gesture Semantics?
运动标记器在共语手势生成中是否编码手势语义？
Abstract
Discrete motion tokenizers encode motion as atomic units and are widely used for co-speech gesture generation. It remains unclear which motion properties, especially those relevant to gesture semantics, are recoverable from these codebooks. We probe a reconstruction-trained codebook using 19 co-speech gesture descriptors spanning from raw motion to abstract communicative function. Results show that geometry and handedness are readily decodable from token embeddings, while motion category is only weakly decoded despite showing systematic differences in discrete code usage. This gap between reconstruction quality and descriptor decodability suggests that reconstruction objectives alone do not guarantee that gesture semantics are captured, and that evaluating codebooks on such properties can guide the design of more semantic motion tokenizers.
Chinese Translation
离散运动标记器将运动编码为原子单元，并广泛应用于共语手势生成。然而，目前尚不清楚这些代码本中可恢复哪些运动属性，特别是与手势语义相关的属性。我们使用19个共语手势描述符探讨了一个经过重建训练的代码本，这些描述符涵盖了从原始运动到抽象交际功能的范围。结果表明，几何形状和手势的使用手（左手或右手）可以从标记嵌入中轻松解码，而运动类别的解码效果较弱，尽管在离散代码使用上显示出系统性差异。这种重建质量与描述符可解码性之间的差距表明，仅依靠重建目标并不能保证手势语义被捕获，并且在这些属性上评估代码本可以指导更具语义的运动标记器的设计。
cs.CV / 3 / 2610.03771
LoRA Direction Extraction for Controllable Light Toggling in FLUX.1 Kontext
FLUX.1 Kontext中的可控光源切换的LoRA方向提取
Abstract
We propose a fine-tuning method for flow-matching diffusion models aimed at realistic artificial light modeling without the need for a large training dataset. We address the task of controllable interior image editing, where the goal is to turn artificial light sources on or off while preserving the scene geometry, object placement, materials, and visual identity of the original image. To achieve this, we decompose the task into two independent formulations. We introduce the LoRA Direction Training Method, which extracts the pure direction of the LoRA adapter effect in the diffusion model flow field, and we also introduce specialized loss functions to ensure the realism of the inverse transformation. Additionally, the resulting increment map is used for more precise adjustment of the lighting color and temperature.
Chinese Translation
我们提出了一种针对流匹配扩散模型的微调方法，旨在实现逼真的人工光建模，而无需大量训练数据集。我们解决了可控室内图像编辑的任务，其目标是在保持场景几何形状、物体位置、材料和原始图像视觉特征的同时，打开或关闭人工光源。为此，我们将任务分解为两个独立的公式。我们引入了LoRA方向训练方法，该方法提取了扩散模型流场中LoRA适配器效应的纯方向，并且我们还引入了专门的损失函数，以确保逆变换的真实性。此外，生成的增量图用于更精确地调整光源的颜色和温度。
cs.CV / 4 / 2610.03772
Energy Variation in Training Modern Computer Vision Architectures
现代计算机视觉架构训练中的能量变化
Abstract
The rapid growth of deep learning has substantially increased the energy consumption associated with model training, making energy efficiency an increasingly relevant design criterion. This study empirically measures the energy variation of training seven modern computer vision architectures, MobileNetV3-Small, MobileNetV3-Large, EfficientNet-B0, EfficientNet-B1, ViT-B/32, ConvNeXt-Tiny, and ViT-B/16 for the ImageNet-1k classification task, using a homogeneous 10,000-image subset (ImageNet-10k) and a uniform 40-epoch baseline configuration executed on two NVIDIA Tesla P100 GPUs at the Bioinformatics and Computational Biology Center of Colombia (BIOS). Energy was recorded directly via NVML and contrasted with the computational complexity of each model. The results show a Pearson correlation of 0.85 between floating-point operations (GFLOPs) and energy consumption in kWh, indicating that computational complexity is a strong but imperfect predictor of energy expenditure: architectures with comparable GFLOPs exhibited consumption differing by up to 3.1x due to differences in the hardware efficiency of their dominant operations. The EfficientNet variants offered the best balance between classification performance (Val Top-5 up to 97.15%) and energy efficiency (0.457-0.611 kWh), while Vision Transformers exhibited the highest relative energy consumption and lower classification performance under the evaluated configuration. These findings guide architecture selection in energy-constrained computing environments.
Chinese Translation
深度学习的快速发展显著增加了与模型训练相关的能量消耗，使得能效成为一个越来越重要的设计标准。本研究实证测量了七种现代计算机视觉架构（MobileNetV3-Small、MobileNetV3-Large、EfficientNet-B0、EfficientNet-B1、ViT-B/32、ConvNeXt-Tiny 和 ViT-B/16）在 ImageNet-1k 分类任务中的训练能量变化，使用了一个同质的 10,000 图像子集（ImageNet-10k）和统一的 40 轮基线配置，在哥伦比亚生物信息学与计算生物学中心（BIOS）的两台 NVIDIA Tesla P100 GPU 上执行。能量通过 NVML 直接记录，并与每个模型的计算复杂度进行对比。结果显示，浮点运算（GFLOPs）与能量消耗（kWh）之间的皮尔逊相关系数为 0.85，表明计算复杂度是能量支出的一个强但不完美的预测指标：具有可比 GFLOPs 的架构在能量消耗上差异可达 3.1 倍，原因在于其主导操作的硬件效率差异。EfficientNet 变体在分类性能（验证集 Top-5 高达 97.15%）和能效（0.457-0.611 kWh）之间提供了最佳平衡，而视觉变换器在评估配置下表现出最高的相对能量消耗和较低的分类性能。这些发现为在能量受限的计算环境中选择架构提供了指导。
cs.CV / 5 / 2610.03792
What Do Verifiable Rewards Teach Video-Language Models About Time? A Controlled Multi-Model Study
可验证奖励对视频语言模型时间理解的启示：一项受控的多模型研究
Abstract
Reinforcement learning from verifiable rewards (RLVR) has produced large reasoning gains in language models, and verifiable video benchmarks make it applicable to causal-temporal video question answering. We study what RLVR teaches video-language models about time. We fine-tune four open models (Qwen3-VL-8B/4B, Qwen2.5-VL-7B, Gemma-3-12B) with group relative policy optimization under three data recipes: verified (synthetic CLEVRER questions with exact answer and event-order rewards), unverified (self-supervised pretext tasks over 43,751 real web videos), and a 1:1 mixture, plus a verified+real arm that adds 4,000 verifiable questions on real video. Each cell is evaluated in-domain and on out-of-domain real video (a NExT-QA temporal stress set and an MVBench subset), with frames in order, shuffled, and absent. (1) Verified training yields large in-domain gains that shrink as base competence grows (+14 to +19 points on weaker models; +6 on the strongest). (2) Much of the gain is non-visual: accuracy with no frames rises nearly as much as with frames. (3) Verified-only training can severely degrade out-of-domain accuracy with no sign during training: Qwen3-VL-8B loses 26.7 and 25.2 points on the two real-video sets, while the mixture never significantly degrades a model trained on it. Adding real verified questions removes that loss (-2.3 points, within noise of base) and keeps a +9.3 in-domain gain, so the cause is narrow synthetic-only data, not verification. (4) No recipe induces temporal-order grounding: across 41 evaluations the ordered-versus-shuffled gap is indistinguishable from zero in 39 and marginal in two, despite an event-order reward. Verifiable rewards improve benchmark accuracy without temporal understanding. Report no-frame controls, and mix in real video to guard against out-of-domain degradation.
Chinese Translation
来自可验证奖励的强化学习（RLVR）在语言模型中产生了显著的推理提升，而可验证的视频基准使其适用于因果-时间视频问答。我们研究了RLVR对视频语言模型时间理解的影响。我们在三种数据配方下，利用组相对策略优化对四个开放模型（Qwen3-VL-8B/4B, Qwen2.5-VL-7B, Gemma-3-12B）进行了微调：验证过的（合成的CLEVRER问题，具有精确答案和事件顺序奖励），未验证的（在43,751个真实网络视频上进行的自监督预训练任务），以及1:1的混合数据，此外还有一个验证+真实的分支，增加了4,000个真实视频的可验证问题。每个实验组在领域内和领域外的真实视频上进行评估（NExT-QA时间压力集和MVBench子集），并对帧进行有序、打乱和缺失处理。 (1) 验证训练在领域内产生了显著的提升，随着基础能力的提高而减小（较弱模型提升14到19分；最强模型提升6分）。 (2) 大部分提升是非视觉的：没有帧的准确率几乎与有帧时的提升相当。 (3) 仅验证训练可能严重降低领域外的准确性，而在训练过程中没有任何迹象：Qwen3-VL-8B在两个真实视频集上分别损失26.7和25.2分，而混合模型在训练过程中从未显著降低。添加真实的可验证问题消除了这种损失（-2.3分，处于基础噪声范围内），并保持了+9.3的领域内提升，因此原因是狭窄的合成数据，而非验证。 (4) 没有任何配方能引导时间顺序的基础：在41次评估中，有序与打乱的差距在39次中无法区分为零，在两次中为边际，尽管有事件顺序奖励。可验证奖励提高了基准准确性，但并未改善时间理解。报告无帧控制，并混入真实视频以防止领域外的降级。
cs.CV / 6 / 2610.03797
WAMJET: A Harness for World Action Model Acceleration
WAMJET：一种用于世界行动模型加速的工具
Abstract
World Action Models (WAMs) leverage pretrained video foundation models for robot manipulation, but their large backbones and video-action co-prediction are expensive. Although existing acceleration techniques offer many ways to reduce this cost, selecting and composing them requires substantial engineering for each model and hardware platform. To tackle this bottleneck, we present WAMJET, an agentic harness that accelerates WAM inference by equipping coding agents with reusable optimization guidance and measurement and validation tools. WAMJET follows a bottleneck-driven workflow where the agent profiles inference, modifies targeted code, validates effects, and iteratively refines the acceleration stack as bottlenecks shift, while preserving action quality. Experiments span six WAMs, three coding agents, and two GPU architectures. WAMJET achieves up to 9.95x lossless speedup over upstream implementations. Approximation and hardware-aware optimization yield additional latency reductions, with comparable success rates. The results show that WAMJET can produce effective acceleration stacks for WAM deployment.
Chinese Translation
世界行动模型（WAMs）利用预训练的视频基础模型进行机器人操作，但其庞大的主干网络和视频-动作共同预测的计算成本较高。尽管现有的加速技术提供了多种降低成本的方法，但为每个模型和硬件平台选择和组合这些技术需要大量的工程工作。为了解决这一瓶颈，我们提出了WAMJET，这是一种智能工具，通过为编码代理提供可重用的优化指导以及测量和验证工具，从而加速WAM推理。WAMJET遵循瓶颈驱动的工作流程，代理首先分析推理过程，修改目标代码，验证效果，并在瓶颈变化时迭代优化加速栈，同时保持动作质量。实验涵盖了六个WAM、三个编码代理和两个GPU架构。WAMJET在上游实现上实现了最高9.95倍的无损加速。近似和硬件感知优化进一步减少了延迟，并且成功率相当。结果表明，WAMJET能够为WAM部署生成有效的加速栈。
cs.CV / 7 / 2610.03799
StepCAD: Mesh-to-CAD Code Generation via LLM Policy and Geometry-Guided Search
StepCAD：通过LLM策略和几何引导搜索实现网格到CAD代码生成
Abstract
Recovering executable CAD programs from 3D meshes is challenging due to the compositional nature of CAD construction and the interaction between discrete modeling choices and continuous parameters. Many learning-based methods predict complete programs in a single pass and rely predominantly on sketch-extrude representations, limiting operation diversity and opportunities to correct geometric errors during reconstruction. We introduce StepCAD, a generative optimization approach that combines a state-conditioned CAD policy with geometry-guided search. Given an input mesh, the policy predicts construction actions conditioned on both target and intermediate geometry, and an IoU-guided tree search refines the resulting program through local edits. We also introduce ARCADE-1.5M, a large-scale dataset of 1.5M executable CAD programs spanning diverse operations, sequences with a maximum length of 150+ counted operations, and 12.5M intermediate state-action transitions. Experiments across multiple CAD reconstruction benchmarks show that StepCAD achieves state-of-the-art geometric reconstruction accuracy with consistently high validity, yielding up to 87.2% relative IoU improvement over the strongest evaluated baseline, with particularly large gains on complex shapes. Project page: https://ghadinehme.com/stepcad.github.io/
Chinese Translation
从3D网格恢复可执行的CAD程序具有挑战性，因为CAD构建的组合特性以及离散建模选择与连续参数之间的相互作用。许多基于学习的方法在单次传递中预测完整程序，并主要依赖于草图-拉伸（sketch-extrude）表示，这限制了操作的多样性以及在重建过程中纠正几何错误的机会。我们提出了StepCAD，一种生成优化方法，结合了状态条件下的CAD策略与几何引导搜索。给定一个输入网格，该策略根据目标和中间几何体预测构建操作，而IoU引导的树搜索通过局部编辑来细化生成的程序。我们还引入了ARCADE-1.5M，这是一个大规模数据集，包含150万个可执行的CAD程序，涵盖多种操作，序列的最大长度超过150个操作，以及1250万个中间状态-动作转移。针对多个CAD重建基准的实验表明，StepCAD在几何重建精度上达到了最先进的水平，具有持续较高的有效性，相较于最强评估基线，IoU相对提升高达87.2%，在复杂形状上尤其表现出显著的增益。项目页面：https://ghadinehme.com/stepcad.github.io/
cs.CV / 8 / 2610.03822
Beyond Token Accuracy: Prioritizing What Matters for Visual Reconstruction
超越标记准确性：优先考虑视觉重建中的关键因素
Abstract
Tokens have become a unified interface for multimodal foundation models, making visual-token communication a natural paradigm for efficient image delivery. However, existing methods typically rely on static policies that cannot jointly adapt to image content and channel conditions. Moreover, their token-level utility objectives do not necessarily translate into improved image reconstruction quality. In this paper, we propose AdapToC, an adaptive, reconstruction-oriented visual-token communication framework. At the transmitter, an adaptive selector jointly models image content, channel state, and communication budget to perform instance-wise resource allocation. Rather than using a fixed token rate and protection policy, it dynamically determines how many tokens should be transmitted and assigns different protection levels according to token importance and current channel conditions. At the receiver, an adaptive MaskGIT receiver incorporates channel reliability into contextual token modeling. It distinguishes tokens with different reliability levels, preserves high-confidence observations, corrects potentially corrupted tokens, and iteratively reconstructs missing content from the received evidence and global visual context. By co-designing token quantity, unequal protection, and reliability-aware recovery for image-level reconstruction quality, AdapToC achieves a peak mean PSNR gain of 4.20 dB over the strongest static baseline under matched communication costs and state-of-the-art performance among the evaluated visual-token communication methods.
Chinese Translation
标记已成为多模态基础模型的统一接口，使得视觉标记通信成为高效图像传输的自然范式。然而，现有方法通常依赖于静态策略，无法共同适应图像内容和信道条件。此外，它们的标记级效用目标并不一定能转化为改善图像重建质量。在本文中，我们提出了AdapToC，一个自适应的、以重建为导向的视觉标记通信框架。在发射端，自适应选择器共同建模图像内容、信道状态和通信预算，以执行实例级资源分配。它动态确定应传输多少标记，并根据标记的重要性和当前信道条件分配不同的保护级别，而不是使用固定的标记速率和保护策略。在接收端，自适应MaskGIT接收器将信道可靠性纳入上下文标记建模。它区分不同可靠性水平的标记，保留高置信度观测，纠正潜在损坏的标记，并从接收到的证据和全局视觉上下文中迭代重建缺失内容。通过共同设计标记数量、不平等保护和可靠性感知恢复以提高图像级重建质量，AdapToC在匹配通信成本下实现了比最强静态基线高出4.20 dB的峰值均值PSNR增益，并在评估的视觉标记通信方法中表现出最先进的性能。
cs.CV / 9 / 2610.03826
Are We Measuring Anticipation? Auditing Privileged Information in Procedural Video Evaluation
我们在测量预期吗？程序视频评估中的特权信息审计
Abstract
Benchmark scores license claims about the capabilities being evaluated. We audit the inference licensed by an evaluation protocol, rather than the predictive model alone. Using procedural action anticipation as a controlled case study, we study a broader evaluation-validity failure mode: a protocol can remain temporally causal and free of classical target leakage while still supplying a privileged intermediate representation. The failure is not merely optimistic accuracy, but a mismatch between the capability claimed and the construct actually measured. On Breakfast, matched recognizer-history and GT-history regimes score 30.3% and 62.3% (Delta_PH(R_BA) = +32.0, 95% Student-t interval [+24.1, +39.8]). A fixed-checkpoint 2 x 2 intervention isolates a +14.6-point test-time oracle contrast; a matched no-video provenance probe yields a +26.8-point gap; and a boundary-independent query/history control retains a +19.4-point gap. We refer to this discrepancy as a privileged-information gap, defined relative to a specified non-oracle recovery pipeline, and propose a reusable four-condition audit. Across five evaluation settings the gap is heterogeneous; a standard 50 Salads long-term-anticipation re-implementation provides cautious support beyond the audit-specific next-action construction.
Chinese Translation
基准分数为被评估能力的声明提供了依据。我们审计评估协议所授权的推理，而不仅仅是预测模型。以程序性动作预期作为受控案例研究，我们研究了一种更广泛的评估有效性失败模式：一个协议可以在时间上保持因果关系，并且不受经典目标泄漏的影响，同时仍然提供特权的中间表示。这种失败不仅仅是乐观的准确性，而是所声称的能力与实际测量的构念之间的不匹配。在Breakfast数据集上，匹配的识别器历史和GT历史模式的得分分别为30.3%和62.3%（Delta_PH(R_BA) = +32.0，95% Student-t区间[+24.1, +39.8]）。一个固定检查点的2 x 2干预隔离出一个+14.6分的测试时间神谕对比；一个匹配的无视频来源探测器产生了+26.8分的差距；而一个边界无关的查询/历史控制保持了+19.4分的差距。我们将这种差异称为特权信息差距，相对于指定的非神谕恢复管道进行定义，并提出了一个可重用的四条件审计。在五个评估设置中，这个差距是异质的；一个标准的50 Salads长期预期重新实现提供了谨慎的支持，超出了审计特定的下一步行动构建。
cs.CV / 10 / 2610.03873
Streaming Multi-Track Timeline Control for 3D Human Motion Generation
用于3D人类运动生成的流式多轨时间线控制
Abstract
Text-driven human motion generation has advanced substantially, yet most methods assume instructions are available before synthesis. Interactive applications require responding to new instructions while continuing ongoing actions, such as answering a phone while walking. Existing approaches address streaming generation or simultaneous composition without explicitly combining streaming instruction arrival with independently timed, overlapping actions. We introduce streaming multi-track timeline control and propose TimelineControl to incorporate new instructions alongside ongoing actions. Interval-aware conditioning preserves instruction timing, while causal part-structured representations and part-aware denoising coordinate concurrent actions across body regions. We also construct TimelineMotion, a dataset with overlapping instruction intervals and body-part annotations. Experiments on TimelineMotion and MTT demonstrate improved semantic alignment and temporal adherence over evaluated streaming baselines, including models retrained on the same data. Ablations and human evaluations validate our design, complemented by spatial conditioning and humanoid execution demonstrations. Our code, data and models will become publicly available.
Chinese Translation
基于文本的人类运动生成已取得显著进展，但大多数方法假设在合成之前可以获得指令。交互式应用需要在继续进行的动作中响应新的指令，例如在行走时接听电话。现有方法解决了流式生成或同时组合的问题，但未明确将流式指令到达与独立定时、重叠的动作结合起来。我们提出了流式多轨时间线控制，并提出了TimelineControl，以便在进行中的动作旁边纳入新指令。间隔感知条件保持指令时序，而因果部分结构表示和部分感知去噪协调了身体区域间的并发动作。我们还构建了TimelineMotion，一个具有重叠指令间隔和身体部位注释的数据集。在TimelineMotion和MTT上的实验表明，与评估的流式基线相比，包括在相同数据上重新训练的模型，我们的模型在语义对齐和时间遵循方面有所改善。消融实验和人类评估验证了我们的设计，并辅以空间条件和类人执行演示。我们的代码、数据和模型将公开发布。
cs.CV / 11 / 2610.03928
DABACO: A Multi-Camera Dataset and Benchmark for Screen Localization and Pointing Estimation
DABACO：用于屏幕定位和指向估计的多摄像头数据集与基准
Abstract
Screen localization and pointing estimation are key to low-cost interactive devices. Yet developing and evaluating these algorithms requires realistic data: synthetic captures cannot fully reproduce the optical distortion, rolling shutter, motion blur, and display processing of a physical acquisition, and most existing datasets provide static images rather than the video needed to assess continuous pointing. In this paper, we introduce the DABACO Dataset. Developed within the DABACO (Dispositivo Apuntador de BAjo COste, or Low-Cost Pointing Device) project, this dataset supports the development and evaluation of screen detection and camera-based pointing algorithms for embedded systems. It comprises video sequences captured with multiple low-cost camera sensors, including monochrome global-shutter and color rolling-shutter modules, on embedded platforms such as the Raspberry Pi 4B and ESP32-S3. We present a systematic annotation pipeline combining temporary visual watermarking, optical-flow tracking, manual verification, and marker removal. Both the original marked captures and the marker-free images, together with explicit corner annotations and modification masks, are released to support auditing and the study of potential reconstruction bias. In addition, we release an open-source evaluation toolkit with two reference baselines: a classical screen-detection pipeline based on edge and contour geometry, and a closed-vocabulary, off-the-shelf YOLOv8 segmentation model evaluated without dataset-specific training. Both are evaluated on a general-purpose computer using Intersection over Union, corner error, and pointing error, establishing initial reference results for future embedded implementations. Overall, DABACO addresses a gap in existing resources and is intended to support the development and evaluation of new low-cost screen-localization and pointing systems.
Chinese Translation
屏幕定位和指向估计是低成本互动设备的关键。然而，开发和评估这些算法需要真实的数据：合成捕获无法完全重现物理采集的光学畸变、滚动快门、运动模糊和显示处理，而现有的大多数数据集提供的是静态图像，而非评估连续指向所需的视频。在本文中，我们介绍了DABACO数据集。该数据集是在DABACO（Dispositivo Apuntador de BAjo COste，或低成本指向设备）项目中开发的，支持嵌入式系统中屏幕检测和基于摄像头的指向算法的开发与评估。数据集包含使用多个低成本摄像头传感器捕获的视频序列，包括单色全局快门和彩色滚动快门模块，运行在如Raspberry Pi 4B和ESP32-S3等嵌入式平台上。我们提出了一种系统的标注流程，结合了临时视觉水印、光流跟踪、人工验证和标记移除。我们发布了原始标记捕获和无标记图像，以及明确的角点标注和修改掩码，以支持审计和潜在重建偏差的研究。此外，我们还发布了一个开源评估工具包，包含两个参考基线：基于边缘和轮廓几何的经典屏幕检测流程，以及一个无特定数据集训练的封闭词汇现成YOLOv8分割模型。两者均在通用计算机上使用交并比、角点误差和指向误差进行评估，为未来的嵌入式实现建立初步参考结果。总体而言，DABACO填补了现有资源的空白，旨在支持新低成本屏幕定位和指向系统的开发与评估。
cs.CV / 12 / 2610.03942
Dynamic Time Step Prediction in Inverse Heat Dissipation for Blur-Like Image Restoration Tasks
逆热耗散中的动态时间步预测用于模糊图像恢复任务
Abstract
When using diffusion models to target image restoration problems, diffusion inversion is typically employed to retain relevant image information from the degraded images. Instead of inverting back to the initial time step (i.e., T), many methods invert to a pre-determined intermediate time step, in order to better preserve information from degraded source images. However, a pre-determined time step for inversion is not ideal for reconstruction, as a severely degraded image requires an earlier starting time step than a mildly degraded one. In addition, DDIM-based models corrupt the original signal by adding Gaussian noise, which can be mismatched to the nature of blur-like degradations, such as blur, haze, and low-light. To address these problems, we propose two solutions: (1) we adopt an alternative diffusion process, called the Inverse Heat Dissipation Model, that diffuses the input image by gradually blurring a data point (2) we propose to implement a time predictor to estimate the starting time step for the inversion, with the model learning to adapt to the degradation severity. Extensive experiments on standard benchmarks show that our method achieves state-of-the-art performance in both quantitative and qualitative evaluations, with excellent generalization to many restoration tasks.
Chinese Translation
在使用扩散模型解决图像恢复问题时，通常采用扩散反演来保留退化图像中的相关信息。许多方法并不是反演回初始时间步（即 T），而是反演到预先确定的中间时间步，以更好地保留退化源图像中的信息。然而，预先确定的反演时间步对于重建并不理想，因为严重退化的图像需要比轻微退化的图像更早的起始时间步。此外，基于 DDIM 的模型通过添加高斯噪声来破坏原始信号，这可能与模糊类退化（如模糊、雾霾和低光）不匹配。为了解决这些问题，我们提出了两个解决方案：（1）我们采用一种替代的扩散过程，称为逆热耗散模型（Inverse Heat Dissipation Model），通过逐渐模糊数据点来扩散输入图像；（2）我们建议实施一个时间预测器，以估计反演的起始时间步，使模型能够适应退化的严重程度。在标准基准上的大量实验表明，我们的方法在定量和定性评估中均实现了最先进的性能，并在许多恢复任务中表现出优秀的泛化能力。
cs.CV / 13 / 2610.03980
FADE: Frame-Aware Diffusion-Transformer-based Multi-Concept Erasure for Video Unlearning
FADE：基于帧感知扩散变换器的多概念抹除视频遗忘
Abstract
Text-to-video (T2V) diffusion models can reproduce copyrighted, violent, or explicit content, which motivates concept erasure: removing designated concepts from a pretrained model while preserving its behavior on everything else. Existing T2V erasure methods leave two problems open. Their frame-agnostic suppression can leave isolated frames in which an erased concept resurfaces, a frame-reactivation gap that clip-level averages obscure; and they are usually evaluated with one target concept or category at a time. We propose Frame-Aware Diffusion Erasure (FADE), a multi-concept video unlearning framework. FADE first applies a joint closed-form key/value edit that suppresses all target concepts, then trains per-concept frame-aware low-rank adapters whose strength is gated by the frame index and the denoising timestep to remove residual per-frame leakage. Each adapter is trained with the other targets' prompts as hard negatives, which keeps the concept-specific components of different adapters well separated, and a similarity-based soft router combines the adapters according to the prompt. With 16 concepts (objects, artistic styles, and nudity) erased from a single Wan2.1-T2V-1.3B backbone, FADE reduces the residual accuracy on the object benchmark to 4.9%, against 15.5% for the strongest of eight baselines, while keeping the VBench average within 0.9% of the unedited model. The ranking is unchanged under a VLM judge and a blinded human study, and the advantage over the strongest baseline carries over to prompts that combine several erased concepts, to 30 simultaneously erased celebrity identities, and to Wan2.1-T2V-14B, CogVideoX-2B, and HunyuanVideo-1.5.
Chinese Translation
文本到视频（T2V）扩散模型能够重现受版权保护、暴力或露骨的内容，这促使了概念抹除的研究：在保持模型对其他内容行为不变的情况下，从预训练模型中移除指定的概念。现有的T2V抹除方法存在两个未解决的问题。它们的帧无关抑制可能会留下孤立帧，在这些帧中被抹除的概念重新出现，这种帧重激活的差距在剪辑级别的平均值中被掩盖；而且它们通常一次只针对一个目标概念或类别进行评估。我们提出了帧感知扩散抹除（FADE），这是一个多概念视频遗忘框架。FADE首先应用一个联合的闭式关键/值编辑，抑制所有目标概念，然后训练每个概念的帧感知低秩适配器，其强度由帧索引和去噪时间步控制，以去除每帧的残余泄漏。每个适配器在其他目标的提示下作为强负样本进行训练，这使得不同适配器的概念特定组件保持良好分离，而基于相似性的软路由器根据提示组合适配器。在从单个Wan2.1-T2V-1.3B主干中抹除16个概念（对象、艺术风格和裸露）后，FADE将对象基准上的残余准确率降低到4.9%，而最强的八个基线的准确率为15.5%，同时保持VBench平均值在未编辑模型的0.9%以内。在VLM评审和盲人研究中排名不变，并且相对于最强基线的优势延续到结合多个被抹除概念的提示、同时抹除30个名人身份，以及Wan2.1-T2V-14B、CogVideoX-2B和HunyuanVideo-1.5。
cs.CV / 14 / 2610.03991
Masked Privileged-Information Distillation for Multimodal Skin Lesion Classification Under Missing Clinical Metadata
缺失临床元数据下的多模态皮肤病变分类的掩蔽特权信息蒸馏
Abstract
Multimodal skin lesion classification combines clinical images with patient metadata to improve diagnostic accuracy. However, complete metadata available during training may be only partially accessible at deployment, and resource-constrained settings additionally require computational efficiency. We address these challenges with a privileged-information distillation framework in which a multimodal teacher trained on complete metadata supervises a 9.2x smaller student trained with randomly masked clinical fields. Clinical fields are masked as whole groups at a per-sample rate drawn from U(0,1), so one training run covers the full metadata availability range. Fusion is residual, with metadata added as a gated correction to an unconditional image base. On the PAD-UFES-20 dataset, distillation under masked training improves balanced accuracy over cross-entropy training at every availability level, by an average of +4.7 points versus +1.8 points without masking. The masked student loses only 7.8 balanced-accuracy points as metadata decreases from complete to absent, compared with 36.6 points for the same student trained on complete metadata, highlighting the role of masked training in graceful degradation beyond distillation alone. Grad-CAM visualizations further show that the masked student's attention generally remains lesion-centered as metadata is withdrawn. The resulting compact model targets point-of-care settings, where clinical metadata is often incomplete.
Chinese Translation
多模态皮肤病变分类结合了临床图像和患者元数据，以提高诊断准确性。然而，在训练期间可用的完整元数据在部署时可能仅部分可用，资源受限的环境还需要计算效率。我们通过一个特权信息蒸馏框架来解决这些挑战，其中一个在完整元数据上训练的多模态教师监督一个使用随机掩蔽临床字段训练的9.2倍小的学生。临床字段以整组的方式进行掩蔽，掩蔽率根据U(0,1)的分布从每个样本中抽取，因此一次训练运行覆盖了完整元数据可用性的范围。融合是残差的，元数据作为门控修正添加到无条件图像基础上。在PAD-UFES-20数据集上，掩蔽训练下的蒸馏在每个可用性水平上都提高了平衡准确性，相较于交叉熵训练平均提高了4.7个百分点，而不进行掩蔽则仅提高了1.8个百分点。当元数据从完整减少到缺失时，掩蔽学生仅损失7.8个平衡准确性点，而在完整元数据上训练的同一学生损失了36.6个点，突显了掩蔽训练在超越单纯蒸馏的优雅降级中的作用。Grad-CAM可视化进一步表明，当元数据被撤回时，掩蔽学生的注意力通常仍然集中在病变上。最终得到的紧凑模型针对的是临床元数据通常不完整的即时护理环境。
cs.CV / 15 / 2610.04006
Verifier-Guided Synthetic Augmentation for 3D Human Shape Generation
基于验证器引导的合成增强用于三维人体形状生成
Abstract
Limited training data diversity constrains generative modeling of 3D human bodies: conservative models remain close to observed examples, whereas exploratory models often violate basic body proportions. We introduce a verifier-guided augmentation framework that uses global and mode-local PCA to generate inexpensive candidates, screens them using correspondence-derived skeletal proportions and body-part geometry, and retrains a diffusion model on accepted candidates. Elastic registration provides both the modal structure used by distributed PCA and the dense anatomical correspondence needed for scalable screening without per-candidate body-model fitting. A blinded human study supports the verifier as a conservative gatekeeper, favoring verifier-accepted over rejected outputs. We evaluate full-pool verifier acceptance separately from the coverage and departure of accepted samples and combine them through EAUC. On 4,498 registered DFAUST surfaces, distributed-PCA augmentation achieves 86.32% acceptance, the highest CP-AUC (0.871), and the highest EAUC (0.752), improving EAUC by 32% over real-only and self-augmented diffusion. These results show that mode-local, verifier-guided proposals broaden diffusion generation while maintaining high agreement with calibrated body measurements.
Chinese Translation
有限的训练数据多样性限制了三维人体建模的生成能力：保守模型通常与观察到的实例保持接近，而探索性模型则常常违反基本的身体比例。我们提出了一种基于验证器引导的增强框架，该框架利用全局和模式局部主成分分析（PCA）生成低成本候选样本，通过对应推导的骨骼比例和身体部位几何形状对其进行筛选，并在接受的候选样本上重新训练扩散模型。弹性配准提供了分布式PCA使用的模式结构以及可扩展筛选所需的密集解剖对应，而无需对每个候选样本进行身体模型拟合。一项盲人研究支持验证器作为保守的把关者，倾向于接受验证器认可的输出而非被拒绝的输出。我们将全池验证器的接受度与接受样本的覆盖率和偏离度分开评估，并通过EAUC进行组合。在4,498个注册的DFAUST表面上，分布式PCA增强实现了86.32%的接受率，最高的CP-AUC（0.871）和最高的EAUC（0.752），相比仅使用真实数据和自我增强的扩散，EAUC提高了32%。这些结果表明，模式局部的验证器引导提案在保持与校准身体测量高度一致的同时，拓宽了扩散生成的范围。
cs.CV / 16 / 2610.04007
VolS-GS: Relightable Gaussian Splatting with Volumetric Subsurface Scattering
VolS-GS：具有体积次表面散射的可重光照高斯点云渲染
Abstract
We present VolS-GS, a relightable Gaussian splatting framework that reconstructs objects from one-light-at-a-time (OLAT) captures and renders them under novel lighting and viewpoints. Relightable Gaussian Splatting methods typically model appearance independently at each primitive, which makes non-local effects difficult to represent. This limitation is particularly apparent for subsurface scattering, where light entering the object at one location can emerge at another. Rather than modeling this effect solely with a neural network or a local kernel at each primitive, we use the spatial support of the Gaussian scene as the domain of a differentiable finite-volume transport solver, so that light can propagate through the object's interior. A small network predicts scattering and absorption coefficients for each Gaussian, and the solve redistributes incident light through the resulting field. The coefficients are fit to images rather than measured, so the solve supplies a transport-shaped path for aggregating per-primitive appearance, not a measurement of the material. To keep the learned shadow and specular terms from taking over the other components, our shadow term is predicted from visibility together with the transmittances and the scattering the solve produces, and a regularizer suppresses specular highlights in regions the shadow term predicts to be unlit. Experiments on three OLAT benchmarks show that VolS-GS consistently improves relighting quality on held-out lights and views.
Chinese Translation
我们提出了VolS-GS，一种可重光照的高斯点云渲染框架，能够从一次光源捕获（OLAT）重建物体，并在新的光照和视角下进行渲染。可重光照的高斯点云渲染方法通常在每个基本体上独立建模外观，这使得非局部效应的表示变得困难。这一限制在次表面散射中尤为明显，因为光线在物体的一个位置进入后，可能在另一个位置出现。我们并不是仅仅通过神经网络或每个基本体的局部核来建模这一效应，而是利用高斯场的空间支持作为可微分有限体积传输求解器的域，以便光线能够在物体内部传播。一个小型网络为每个高斯预测散射和吸收系数，求解过程则通过生成的场重新分配入射光。系数是通过图像拟合而非测量获得的，因此求解提供了一条传输形状的路径，用于聚合每个基本体的外观，而不是材料的测量。为了防止学习到的阴影和高光项占据其他组件，我们的阴影项是根据可见性以及求解产生的透射率和散射来预测的，并且一个正则化器抑制在阴影项预测为未照亮区域的高光。对三个OLAT基准的实验表明，VolS-GS在保留的光源和视角上始终提高了重光照质量。
cs.CV / 17 / 2610.04014
PhysMamba: Selective State Space Models as Learned Articulated Body Simulators
PhysMamba：作为学习的关节体模拟器的选择性状态空间模型
Abstract
We introduce the first learned articulated body simulator based on a selective state space model (SSM), called PhysMamba. PhysMamba predicts next-frame full-body state from position, rotation, and joint-action history, without velocity inputs. We compare four architectures under partial- and full-observation inputs and three training protocols. The from-scratch rollout training protocol gives Mamba2 strong short- and mid-horizon accuracy under partial observation (s10 = 43 mm, 2/50 diverged), while the two-stage teacher-based rollout protocol stabilizes GRU but fails for Mamba2. With CUDA graph compilation, Mamba2 reaches 0.107 ms per frame (9,334 FPS) on an H100 GPU, within 1.1$\times$ of GRU's un-compiled throughput, adding under 1% latency to a 30 Hz HMR pipeline and enabling integration as a differentiable physics module for video-based mesh recovery.
Chinese Translation
我们介绍了第一个基于选择性状态空间模型（SSM）的学习关节体模拟器，称为PhysMamba。PhysMamba从位置、旋转和关节动作历史中预测下一帧的全身状态，而不需要速度输入。我们在部分观察和完全观察输入下比较了四种架构以及三种训练协议。自下而上的回滚训练协议使Mamba2在部分观察下具有较强的短期和中期准确性（s10 = 43 mm，2/50发散），而基于教师的两阶段回滚协议则稳定了GRU，但对Mamba2无效。通过CUDA图编译，Mamba2在H100 GPU上每帧达到0.107毫秒（9,334 FPS），其吞吐量在GRU未编译的情况下仅增加了1.1倍的延迟，给30 Hz HMR管道增加了不到1%的延迟，并使其能够作为可微分物理模块集成到基于视频的网格恢复中。
cs.CV / 18 / 2610.04020
ClasSAE: Class-Aligned Sparse Autoencoders via Differentiable Feature-Class Affinity
ClasSAE：通过可微分特征-类别亲和性实现类别对齐的稀疏自编码器
Abstract
Sparse Autoencoders (SAEs) began as an unsupervised tool for decomposing neural representations into sparse, interpretable features, and are increasingly used not only for passive analysis but also for active interventions such as unlearning, bias mitigation, and concept editing. A central challenge for these editing and steering methods is reliably matching features to target concepts; most current approaches address this by computing post-hoc scores over an already-trained, frozen dictionary. We instead introduce ClasSAE, a novel method that both automatically assigns classes to features and guides the encoder toward class-separable representations during training. Specifically, we apply a differentiable top-$k$ operator to a trainable feature--class affinity matrix with per-feature budgets, coupling the features selected for each sample to the classes they are trained to represent. Because gradients flow through the selection of active features rather than only through their magnitudes, the encoder and the affinity matrix co-adapt rather than being fit in separate stages. The result is a dictionary that is both class-separable and class-annotated, with no need for post-hoc probing. We propose three variants for enforcing sparsity within this framework, which achieve comparable overall performance with slightly different trade-offs. Using CLIP ViT-L/14 embeddings on ImageNet, we show that the learned affinity matrix agrees closely with an independently estimated post-hoc feature-class matrix computed on held-out data. The model also supports direct class prediction from the encoder and affinity matrix alone, without a separately fitted classifier, and its more class-aligned encoder yields improved separation in Targeted Probe Perturbation evaluations. https://github.com/St0pien/ClasSAE.
Chinese Translation
稀疏自编码器（SAEs）最初作为一种无监督工具，用于将神经表示分解为稀疏且可解释的特征，现越来越多地用于不仅仅是被动分析，还包括主动干预，例如遗忘、偏见缓解和概念编辑。这些编辑和引导方法的一个核心挑战是可靠地将特征与目标概念匹配；目前大多数方法通过计算已经训练好的、冻结的字典上的事后评分来解决这个问题。我们提出ClasSAE，这是一种新颖的方法，它不仅自动将类别分配给特征，还在训练过程中引导编码器朝着类别可分的表示方向发展。具体而言，我们对一个可训练的特征-类别亲和性矩阵应用可微分的 top-$k$ 操作，结合每个特征的预算，将为每个样本选择的特征与它们被训练来表示的类别相联系。由于梯度通过活跃特征的选择流动，而不仅仅是通过它们的幅度，编码器和亲和性矩阵共同适应，而不是在不同阶段分别拟合。最终得到的字典既是类别可分的，又是类别注释的，无需事后探测。我们提出了三种变体以在该框架内强制稀疏性，这些变体在整体性能上相当，但具有略微不同的权衡。使用 CLIP ViT-L/14 嵌入在 ImageNet 上，我们展示了学习到的亲和性矩阵与在保留数据上计算的独立估计的事后特征-类别矩阵高度一致。该模型还支持仅通过编码器和亲和性矩阵进行直接类别预测，而无需单独拟合的分类器，其更具类别对齐的编码器在目标探测扰动评估中实现了更好的分离。
cs.CV / 19 / 2610.04028
Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRI
通过流形扩展学习特定学科的解剖表示：应用于加速多对比度MRI
Abstract
Clinical MRI routinely acquires multiple contrast-weighted images of the same anatomy for complementary tissue characterization. However, current accelerated MRI methods typically reconstruct each contrast independently, without fully exploiting shared anatomical information. This work aims to learn anatomical representations invariant to contrast-dependent appearance for reconstruction of accelerated multi-contrast MRI. We propose MAX (MAnifold eXpansion), a subject-specific framework that learns anatomical representations from a single fully sampled reference contrast. To address the under-constrained separation of shared anatomy and contrast-dependent components from a single image, MAX expands the multi-contrast manifold using anatomy-preserving intensity augmentations. A disentangled implicit neural representation models augmented samples using shared spatial coordinates for anatomy and spatially invariant coordinates for contrast appearance. The learned anatomical representation is then fixed, with the contrast representation adapted to the undersampled target data, followed by unrolled refinement. Theoretical analyses further provide insight into the disentangled representation learning and explain how the learned anatomical representation improves the target contrast reconstruction. At R = 8 for brain MRI and R = 6 for knee MRI, MAX achieves the highest mean PSNR and SSIM across all tasks, improving PSNR by more than 1 dB over the strongest baseline for both brain contrasts. MAX more faithfully recovers subtle anatomical and pathological structures and remains robust to inter-contrast motion, structural heterogeneity between reference and target contrasts, and measurement noise. Therefore, MAX provides a general strategy for leveraging high-quality reference scans in accelerated MRI and has the potential to be extended to other reference-assisted MRI inverse problems.
Chinese Translation
临床MRI常规获取同一解剖结构的多幅对比度加权图像，以进行互补的组织特征表征。然而，目前的加速MRI方法通常独立重建每种对比度，而未充分利用共享的解剖信息。本研究旨在学习对比度依赖外观不变的解剖表示，以重建加速多对比度MRI。我们提出了MAX（MAnifold eXpansion），一个特定学科的框架，从单一完全采样的参考对比度中学习解剖表示。为了解决从单幅图像中共享解剖结构与对比度依赖成分的欠约束分离问题，MAX使用保持解剖结构的强度增强扩展多对比度流形。一个解耦的隐式神经表示利用共享的空间坐标建模增强样本的解剖结构，并为对比度外观提供空间不变坐标。学习到的解剖表示随后被固定，而对比度表示则适应于欠采样的目标数据，随后进行展开细化。理论分析进一步提供了对解耦表示学习的深入理解，并解释了学习到的解剖表示如何改善目标对比度的重建。在脑部MRI中，R = 8和膝部MRI中，R = 6时，MAX在所有任务中实现了最高的平均PSNR和SSIM，相比最强基线在两个脑部对比度上提高了超过1 dB。MAX更忠实地恢复微妙的解剖和病理结构，并对不同对比度之间的运动、参考与目标对比度之间的结构异质性以及测量噪声保持稳健。因此，MAX为在加速MRI中利用高质量参考扫描提供了一种通用策略，并有潜力扩展到其他参考辅助的MRI反问题。
cs.CV / 20 / 2610.04036
MAGEFormer: Learning Metric-Consistent Representations for Anisotropic CT Segmentation
MAGEFormer：用于各向异性CT分割的度量一致性表示学习
Abstract
Vision Transformers (ViTs) have shown strong performance in volumetric segmentation, but their effectiveness on clinical CT is limited by an isotropic Euclidean lattice assumption. This conflicts with anisotropic CT acquisition, leading to two key issues: (1) a metric mismatch between voxel indices and physical anatomy, and (2) accuracy degradation from isotropic resampling. To address this, we propose MAGEFormer, a geometry-calibrated framework that embeds physical metric constraints directly into representation learning. Our method introduces Metric-Adaptive Spatial Embedding (MASE) to calibrate positional frequencies using voxel spacing, Geometry-Constrained Attention (GCA) to suppress physically implausible feature correlations, and Geometric View Voting (GVV) to reduce discretization bias during inference. We evaluate MAGEFormer on two multi-organ abdominal CT benchmarks, BTCV and FLARE 22, under a unified protocol against strong CNN and Transformer-based baselines. MAGEFormer achieves the strongest boundary accuracy among the compared methods, with 10.58 mm HD95 on BTCV and 3.40 mm HD95 on FLARE 22, and shows consistent gains in Dice under the same protocol. These results show that geometry-aware internal calibration is more effective than relying on conventional isotropic preprocessing alone for anisotropic CT segmentation.
Chinese Translation
视觉变换器（ViTs）在体积分割中表现出色，但其在临床CT上的有效性受到各向同性欧几里得格假设的限制。这与各向异性CT采集相冲突，导致两个关键问题：（1）体素索引与物理解剖之间的度量不匹配，以及（2）由于各向同性重采样导致的准确性下降。为了解决这个问题，我们提出了MAGEFormer，一个几何校准框架，直接将物理度量约束嵌入到表示学习中。我们的方法引入了度量自适应空间嵌入（MASE），利用体素间距校准位置频率；几何约束注意力（GCA），抑制物理上不合理的特征相关性；以及几何视图投票（GVV），在推理过程中减少离散化偏差。我们在两个多脏器腹部CT基准数据集BTCV和FLARE 22上评估了MAGEFormer，采用统一协议与强大的CNN和基于变换器的基线进行比较。MAGEFormer在比较方法中实现了最佳的边界准确性，在BTCV上达到10.58 mm HD95，在FLARE 22上达到3.40 mm HD95，并且在相同协议下在Dice指标上显示出一致的提升。这些结果表明，考虑几何的内部校准比单纯依赖传统的各向同性预处理在各向异性CT分割中更为有效。
cs.CV / 21 / 2610.04044
Dynamic Quadtree Tokenization and Transformer for Adaptive Mesh PDE Forecasting
动态四叉树标记化与变换器用于自适应网格偏微分方程预测
Abstract
The quadratic attention cost of Vision Transformers (ViTs) forces a trade-off between spatial resolution and rollout horizon, particularly for fine-scale PDEs where shocks, reaction fronts, and material interfaces occupy small, evolving regions of the domain. Conventional neural surrogates also lack mechanisms to adapt resolution dynamically. We propose WAMRViT, a ViT that tokenizes inputs as balanced quadtrees using a wavelet-inspired refinement criterion, jointly encodes position and refinement level with 3D rotary positional embeddings, and regrids in cell space during inference for stable long-horizon rollouts. A multi-scale variant retains each leaf at its native source resolution and lets the model learn across resolution levels. Unlike fixed-budget adaptive-tokenization methods, WAMRViT imposes no predetermined token count and supports fully adaptive topology throughout autoregressive rollout. To our knowledge, it is the first machine-learning surrogate to natively tokenize multi-level Adaptive Mesh Refinement (AMR) data. On uniform-grid benchmarks, uniform-patch WAMRViT improves finest-level region-of-interest VRMSE over a finest-patch uniform ViT while using substantially fewer tokens. The multi-scale variant achieves the lowest first-step full-field RMSE and VRMSE on both benchmarks and improves rollout-averaged full-field and refined-region accuracy at long horizons. With parallelized regridding, its end-to-end rollout cost lies between finest-patch and approximately token-matched coarser-patch ViTs. On a complex AMR combustion problem whose finest features cannot be represented natively by the evaluated uniform-grid baselines, WAMRViT operates directly on adaptive cells and substantially reduces finest-level error at matched transformer capacity. Code: https://github.com/tonyzyl/wamrvit
Chinese Translation
视觉变换器（Vision Transformers, ViTs）的二次注意力成本迫使在空间分辨率和展开时间之间进行权衡，特别是在细尺度偏微分方程（PDEs）中，冲击、反应前沿和材料界面占据了域中小而不断演变的区域。传统的神经代理模型也缺乏动态调整分辨率的机制。我们提出了WAMRViT，这是一种将输入标记化为平衡四叉树的ViT，使用灵感来自小波的细化标准，联合编码位置和细化级别，并在推理过程中在单元空间中重新网格化，以实现稳定的长时间展开。多尺度变体保留每个叶子节点在其原始源分辨率，并允许模型在不同分辨率级别之间学习。与固定预算的自适应标记化方法不同，WAMRViT不设定预定的标记数量，并在自回归展开过程中支持完全自适应拓扑。根据我们的知识，它是第一个原生标记化多级自适应网格细化（Adaptive Mesh Refinement, AMR）数据的机器学习代理。在均匀网格基准测试中，均匀补丁的WAMRViT在使用显著更少的标记的情况下，改善了最细级别感兴趣区域的变动根均方误差（VRMSE）相较于最细补丁的均匀ViT。多尺度变体在两个基准测试中都实现了最低的第一步全场均方根误差（RMSE）和变动均方根误差（VRMSE），并在长时间范围内提高了展开平均的全场和细化区域的准确性。通过并行化重新网格化，其端到端展开成本介于最细补丁和大约标记匹配的粗补丁ViTs之间。在一个复杂的AMR燃烧问题中，其最细特征无法由评估的均匀网格基线原生表示，WAMRViT直接在自适应单元上运行，并在匹配的变换器容量下显著降低最细级别的误差。代码链接： https://github.com/tonyzyl/wamrvit
cs.CV / 22 / 2610.04051
ASD-FEAT: A Multi-Modal Infant Video-Derived Dataset for Early ASD Risk Prediction
ASD-FEAT：一种用于早期自闭症谱系障碍风险预测的多模态婴儿视频衍生数据集
Abstract
Accurate early screening for Autism Spectrum Disorder (ASD) is a precursor to timely intervention, which is critical for improving cognitive and behavioral outcomes. We present ASD-FEAT (ASD - Feature Extraction And Tracking), a multimodal dataset derived from video recordings of infant-adult interaction sessions. The key contribution of ASD-FEAT is the combination of longitudinal coverage from infancy through 36 months, repeated interaction sessions, clinically validated developmental outcomes, expert frame-level behavioral annotations, and privacy-conscious multimodal feature representations. To the best of our knowledge, existing ASD behavioral datasets do not jointly provide these characteristics at comparable scale. To demonstrate the utility of ASD-FEAT, we use it to evaluate a computer-vision-based end-to-end pipeline relying on machine learning techniques to automatically identify ASD risk. ASD-FEAT integrates both expert-defined and deep-learned features, including face and eye landmarks, facial action units, gaze direction, head position, mel-spectrogram audio representations, and optical flow, to identify behavioral markers of social interaction. Our automated pipeline achieves an ASD classification accuracy of 76.2% and an Area Under the Receiver Operating Characteristic (AUROC) of 0.82, compared to classifiers trained on manually labeled behaviors, which yielded 81.3% accuracy and an AUROC of 0.88. We further introduce a within-visit partner contrast: a per-visit signal contrasting examiner-directed and parent-directed social behavior which, when added to the classifier, lifts the fully automated Look Face + Smile and Look Face + Vocal configurations to Matthews correlations of 0.49 and 0.45 respectively, exceeding the human-coded single-partner baseline of 0.42.
Chinese Translation
准确的自闭症谱系障碍（ASD）早期筛查是及时干预的前提，这对改善认知和行为结果至关重要。我们提出了ASD-FEAT（ASD - 特征提取与跟踪），这是一个来源于婴儿与成人互动会话视频录制的多模态数据集。ASD-FEAT的主要贡献在于其结合了从婴儿期到36个月的纵向覆盖、重复的互动会话、临床验证的发展结果、专家级帧级行为注释以及注重隐私的多模态特征表示。据我们所知，现有的ASD行为数据集在可比规模上并未共同提供这些特征。为了展示ASD-FEAT的实用性，我们利用该数据集评估了一个基于计算机视觉的端到端管道，该管道依赖于机器学习技术自动识别ASD风险。ASD-FEAT整合了专家定义和深度学习特征，包括面部和眼睛标志、面部动作单元、注视方向、头部位置、梅尔频谱音频表示和光流，以识别社交互动的行为标记。我们的自动化管道实现了76.2%的ASD分类准确率和0.82的接收者操作特征曲线下面积（AUROC），而基于手动标注行为训练的分类器则达到了81.3%的准确率和0.88的AUROC。我们进一步引入了一个访内伙伴对比：一个每次访问的信号，对比考官指导和父母指导的社交行为，当将其添加到分类器中时，使得完全自动化的“看脸+微笑”和“看脸+发声”配置的马修斯相关系数分别提升至0.49和0.45，超过了人工编码的单一伙伴基线0.42。
cs.CV / 23 / 2610.04052
A Theory of Shape Reconstruction from Heat Conduction and Shading
基于热传导和阴影的形状重建理论
Abstract
Shape from shading using a single image of a Lam- bertian surface is inherently ambiguous. When the light source direction is known, the surface normal estimation has a cone- ambiguity, which worsens when the source is unknown. Recently, shape from heat conduction has emerged as an approach that leverages heat transport equations to estimate the Shape Lapla- cian operator, an intrinsic measure of shape. However, deriving surface normals from the Laplacian operator encounters a local binary convex/concave ambiguity. Our contribution introduces a novel theory to resolve these local shape ambiguities (excluding a few degeneracies) without relying on priors like smoothness, by combining the cues from shading and heat conduction. Our method ensures the mathematical constraints of both shading and the Laplacian are satisfied simultaneously, even with an unknown light source. We validate our theory through simulations of complex shapes and analyze its performance in the presence of noise, as well as on a noisy single thermal video of real-world objects with complex shapes and material properties, including varying albedo.
Chinese Translation
使用单幅拉姆伯特表面的图像进行形状重建本质上是模糊的。当光源方向已知时，表面法线估计存在锥形模糊，而当光源未知时，这种模糊性会加剧。最近，基于热传导的形状重建作为一种新兴方法，利用热传输方程来估计形状拉普拉斯算子，这是一种内在的形状度量。然而，从拉普拉斯算子推导表面法线时会遇到局部的二元凸/凹模糊。我们的贡献是提出了一种新理论，通过结合阴影和热传导的信息，解决这些局部形状模糊（排除少数退化情况），而不依赖于平滑性等先验知识。我们的方法确保阴影和拉普拉斯的数学约束能够同时得到满足，即使在光源未知的情况下。我们通过对复杂形状的模拟验证了我们的理论，并分析了其在噪声存在下的表现，以及在具有复杂形状和材料属性的真实物体的单一噪声热视频上的表现，包括不同的反照率。
cs.CV / 24 / 2610.04066
Evaluating Zone-Guided Front Extraction for Glacier Calving-Front Delineation in SAR Imagery
评估基于区域引导的前缘提取在合成孔径雷达影像中的冰川崩解前缘划分
Abstract
Automatic calving-front delineation from synthetic aperture radar imagery is challenging because the front is a thin and often ambiguous boundary between glacier ice, ocean, and surrounding rock or terrain. The CAlving Fronts and where to Find thEm (CaFFe) dataset provides both binary calving-front masks and broader semantic zone masks, making it possible to study whether zone-level supervision can support front recovery. In this paper, we compare direct front prediction with zone-guided front extraction using U-Net, DeepLabV3+, and SegFormer-B0 under the same bounding-box-cropped CaFFe setting. In the direct setting, models predict the binary calving-front mask. In the zone-guided setting, the model first predicts four semantic zone classes, and the front is then extracted from the predicted glacier-ocean boundary. We evaluate both zone-level and front-level performance, include a ground-truth-zone boundary check, and examine lightweight test-time adaptation on sensor-specific and glacier-specific subsets. The results show that zone labels contain useful front-boundary information: extracting the front from ground-truth zones gives the lowest mean distance error. However, fronts extracted from model-predicted zones remain weak, even when zone segmentation scores are moderate. Test-time adaptation also does not consistently improve zone-guided front recovery. These results indicate that zone segmentation performance should not be treated as a substitute for front-level evaluation and that effective use of zone labels may require boundary-aware training, label fusion, or explicit front supervision.
Chinese Translation
从合成孔径雷达影像中自动划分崩解前缘具有挑战性，因为前缘是冰川冰、海洋以及周围岩石或地形之间的一个薄且常常模糊的边界。崩解前缘及其发现位置（CAlving Fronts and where to Find thEm, CaFFe）数据集提供了二元崩解前缘掩膜和更广泛的语义区域掩膜，使得研究区域级监督是否能够支持前缘恢复成为可能。在本文中，我们在相同的边界框裁剪CaFFe设置下，比较了直接前缘预测与基于区域引导的前缘提取，使用了U-Net、DeepLabV3+和SegFormer-B0模型。在直接设置中，模型预测二元崩解前缘掩膜。在区域引导设置中，模型首先预测四个语义区域类别，然后从预测的冰川-海洋边界中提取前缘。我们评估了区域级和前缘级的性能，包括对真实区域边界的检查，并检验了在传感器特定和冰川特定子集上的轻量级测试时适应性。结果表明，区域标签包含有用的前缘边界信息：从真实区域提取前缘的平均距离误差最低。然而，即使在区域分割得分适中的情况下，从模型预测的区域提取的前缘仍然较弱。测试时适应性也未能持续改善基于区域引导的前缘恢复。这些结果表明，区域分割性能不应被视为前缘级评估的替代品，并且有效利用区域标签可能需要边界感知训练、标签融合或明确的前缘监督。
cs.CV / 25 / 2610.04073
Why Convolution Still Matters: Evaluating Inductive Biases in Cryospheric Image Classification
卷积仍然重要的原因：评估冰冻圈图像分类中的归纳偏差
Abstract
Recent advances in attention-based deep learning have motivated their adoption for remote sensing image classification; however, their benefits for cryospheric imagery, where surface states are dominated by fine-grained textures and class imbalance, remain unclear. In this work, we revisit a benchmark Greenland Ice Sheet image dataset, previously shown to favor convolutional neural networks (CNNs), to examine whether modern attention-based and hybrid architectures improve class-wise reliability. We conduct a controlled comparison between a classical CNN (AlexNet), a modern CNN (ConvNeXt-Tiny), a pure attention-based model (Swin-Tiny), and a hybrid convolution-attention model (CoAtNet-0) under identical training and evaluation protocols. Results show that AlexNet achieves the highest accuracy and the strongest balanced performance as measured by macro-averaged F1, while ConvNeXt-Tiny exhibits the highest macro-averaged AUC, indicating strong class separability but less consistent final decision quality. Class-wise analysis reveals that hybrid architectures improve recall for rare and structurally distinct surface classes, whereas convolutional models remain more reliable for texture-dominated categories. These findings highlight the importance of aligning architectural inductive bias with cryospheric data characteristics and suggest that increased model complexity does not necessarily translate to improved reliability for ice-sheet surface classification.
Chinese Translation
近年来，基于注意力机制的深度学习的进展促使其在遥感图像分类中的应用；然而，这些方法在冰冻圈图像中的优势仍不明确，因为该领域的表面状态主要受细粒度纹理和类别不平衡的影响。在本研究中，我们重新审视了一个基准的格林兰冰盖图像数据集，该数据集之前已显示出对卷积神经网络（CNN）的偏好，以检验现代基于注意力的和混合架构是否能够提高类别的可靠性。我们在相同的训练和评估协议下，对经典CNN（AlexNet）、现代CNN（ConvNeXt-Tiny）、纯基于注意力的模型（Swin-Tiny）和混合卷积-注意力模型（CoAtNet-0）进行了受控比较。结果表明，AlexNet在宏观平均F1指标上实现了最高的准确率和最强的平衡性能，而ConvNeXt-Tiny则在宏观平均AUC上表现最佳，显示出强大的类别可分性，但最终决策质量不够一致。类别分析揭示，混合架构提高了对稀有和结构上不同表面类别的召回率，而卷积模型在纹理主导的类别中仍然更可靠。这些发现强调了将架构的归纳偏差与冰冻圈数据特征相匹配的重要性，并表明增加模型复杂性并不一定会提高冰盖表面分类的可靠性。
cs.CV / 26 / 2610.04076
VCURF: Virtual Camera-based Uncertainty of Radiance Fields
VCURF：基于虚拟相机的辐射场不确定性
Abstract
Radiance fields, implemented with either implicit (NeRF) or explicit (Gaussian Splatting) representations, are advancing the state of the art in novel view synthesis at a rapid pace. Even though the rendered views they generate are often compelling, they are not free of errors. In this paper, we propose a new approach for pixel-wise uncertainty quantification based on measuring the inconsistencies among renderings by the radiance field model in virtual cameras sampled near the target viewpoint. We named our approach VCURF for Virtual Camera-based Uncertainty of Radiance Fields. VCURF treats the radiance field model as a black box, only assuming that it is capable of rendering color and depth on demand. This property makes our approach applicable to both NeRF and GS models without any modification. Our experiments on a combination of datasets, radiance field models and baselines demonstrate VCURF's effectiveness in pixel-wise uncertainty estimation. We conclude the paper with findings that question the way view selection is tackled by the majority of the current literature.
Chinese Translation
辐射场，无论是通过隐式表示（NeRF）还是显式表示（Gaussian Splatting）实现的，正在迅速推动新视图合成的技术进步。尽管它们生成的渲染视图通常令人信服，但并非没有错误。本文提出了一种新的基于测量虚拟相机在目标视点附近采样的辐射场模型渲染之间不一致性进行像素级不确定性量化的方法。我们将这种方法命名为VCURF（基于虚拟相机的辐射场不确定性）。VCURF将辐射场模型视为一个黑箱，仅假设其能够按需渲染颜色和深度。这一特性使我们的方案适用于NeRF和GS模型，而无需任何修改。我们在多个数据集、辐射场模型和基线上的实验表明，VCURF在像素级不确定性估计方面的有效性。我们以质疑当前文献中大多数研究处理视图选择方式的发现结束了本文。
cs.CV / 27 / 2610.04084
Dependable AI-Assisted Engineering: A Formal Framework for AI Participation and Assurance in Safety-Critical Workflows
可靠的人工智能辅助工程：安全关键工作流程中人工智能参与与保障的正式框架
Abstract
Generative AI can produce engineering artefacts, but generation alone does not determine whether or how those artefacts should enter safety-critical workflows. This paper develops a formal framework for assigning AI participation and assurance at the level of individual workflow units. Each unit has a participation and assurance record covering its engineering requirement, an approved operational formalization where applicable, the applicable mechanism, fallback where applicable, evidence obligations and the applicable guarantee, plus a deployment-readiness status. The framework distinguishes deterministic verification, statistically calibrated admission, authorized human judgement supported by AI advice, authorized human adjudication of AI-produced artefacts, retained deterministic tool paths and explicit non-participation; these arrangements carry different kinds of guarantee rather than levels on a common scale. The framework also separates formalization fidelity from verifier soundness, provides a staged classification and readiness procedure, and derives conditions for comparing a gated AI-assisted unit with an incumbent process under recurring-population assumptions. We instantiate and apply the framework in an executed 17-unit wing-spar structural-analysis workflow combining deterministically gated AI-generated CAD, retained deterministic computation and human judgement. The AI-generated CAD program passed all 23 deterministic checks and was admitted at the first attempt. Favourable stress magnitudes did not suffice to pass the stress criteria where the predeclared mesh-convergence evidence was insufficient; those criteria were instead referred to engineering judgement. The case demonstrates selective AI participation and explicit evidence handling at unit level; no claim is made of workflow-level dependability, certification, structural safety or productivity.
Chinese Translation
生成性人工智能可以生成工程工件，但仅靠生成并不能决定这些工件如何进入安全关键工作流程或是否应该进入。本文提出了一个正式框架，用于在单个工作流程单元的层面上分配人工智能的参与和保障。每个单元都有一个参与和保障记录，涵盖其工程需求、适用时的批准操作形式、适用的机制、适用时的后备方案、证据义务和适用的保障，以及部署准备状态。该框架区分了确定性验证、统计校准的接纳、由人工智能建议支持的授权人类判断、对人工智能生成工件的授权人类裁定、保留的确定性工具路径和明确的不参与；这些安排提供不同种类的保障，而不是在一个共同尺度上的不同级别。该框架还将形式化的保真度与验证者的健全性分开，提供了分阶段的分类和准备程序，并推导出在重复人群假设下比较受限的人工智能辅助单元与现有流程的条件。我们在一个执行的17个单元的翼梁结构分析工作流程中实例化并应用了该框架，该工作流程结合了确定性限制的人工智能生成的计算机辅助设计（CAD）、保留的确定性计算和人类判断。人工智能生成的CAD程序通过了所有23项确定性检查，并在第一次尝试时获得接纳。尽管应力幅度有利，但在预先声明的网格收敛证据不足的情况下，未能通过应力标准；这些标准转而依赖于工程判断。该案例展示了单元级别的选择性人工智能参与和明确的证据处理；并未对工作流程级别的可靠性、认证、结构安全或生产力做出任何声明。
cs.CV / 28 / 2610.04092
UniBRep: Learning Unified Geometry and Topology for Image-conditioned B-Rep Generation
UniBRep：学习统一几何与拓扑以实现基于图像的边界表示生成
Abstract
Generating a boundary representation (B-rep) conditioned on a single image requires faithful reconstruction of geometry, valid topology, and support for complex shapes. We present UniBRep, a geometry-first framework that adapts a pretrained image-to-3D model to generate a feature mesh as a unified intermediate representation. Its surface provides a geometric scaffold, while spatially aligned learned features encode face-separation cues for topology recovery. Dual decoder branches generate the geometry and face-separation features; a geometry- and feature-guided construction pipeline then fits parametric surfaces, recovers boundary curves and connectivity, and assembles an explicit B-rep using a CAD kernel. Recovering topology from mesh regions avoids predefined architectural face-count limits, allowing face count to scale with shape complexity. On the standard DeepCAD benchmark, UniBRep produces valid B-reps for 80.49\% of inputs and reduces face Chamfer distance from 0.1096 to 0.0345 relative to CADDreamer. In a matched comparison, UniBRep also outperforms the HoLa public demo across all reported metrics. Further evaluations demonstrate scalability to high-complexity shapes beyond the standard 30-face range, generalization to objects outside the CAD training distribution, and qualitative transfer to real photographs.
Chinese Translation
基于单幅图像生成边界表示（B-rep）需要对几何形状进行忠实重建、有效拓扑以及支持复杂形状。我们提出了UniBRep，这是一种以几何为主的框架，适应预训练的图像到三维模型，以生成特征网格作为统一的中间表示。其表面提供了几何支架，而空间对齐的学习特征则编码了面分离线索以恢复拓扑。双解码器分支生成几何和面分离特征；接着，一个由几何和特征引导的构建管道拟合参数曲面，恢复边界曲线和连通性，并使用CAD内核组装显式B-rep。从网格区域恢复拓扑避免了预定义的建筑面数限制，使得面数能够随着形状复杂性而扩展。在标准的DeepCAD基准测试中，UniBRep为80.49%的输入生成有效的B-rep，并将面Chamfer距离从0.1096降低到0.0345，相较于CADDreamer。在匹配比较中，UniBRep在所有报告的指标上也优于HoLa公共演示。进一步评估表明其在超出标准30面范围的高复杂性形状上的可扩展性、对CAD训练分布外物体的泛化能力，以及对真实照片的定性迁移。
cs.CV / 29 / 2610.04095
Scaling 3D Visual Grounding in Abdominal CT
在腹部CT中扩展3D视觉定位
Abstract
Visual grounding models can enhance radiology workflows by linking report findings to image regions. This is particularly valuable for 3D CT, where findings often occupy a tiny fraction of the volume. Training 3D grounding models requires large sets of paired phrases and regions, and building such datasets is expensive, requiring radiologists to annotate images by hand. We posit that this supervision is already created implicitly during routine reporting, as radiologists frequently place 2D annotations (e.g., distance measurement, arrows) on key images to make measurements and to support report interpretation. We introduce an automated pipeline that converts these routine clinical annotations into large-scale phrase-region supervision for 3D visual grounding. The pipeline links each annotation to the corresponding finding in the report through metadata matching, then uses a promptable 3D segmentation model to convert the 2D annotation into a volumetric mask. This produces phrase-mask-volume datasets without requiring additional radiologist annotation. Applied to a single institution's clinical picture archiving and communication system (PACS), our approach generated 105K phrase-mask-volume triplets from 59K abdominal CT exams. We also introduce two abdominal CT grounding benchmarks, LocusBench-Onc and LocusBench-ED, which comprise 240 oncology and 260 emergency-department radiologist-reviewed phrase-mask-volume triplets, respectively, with the latter spanning 13 distinct categories such as appendicitis, hematoma, and hernia. We further introduce LocusCT, a 3D visual grounding model trained on this dataset, which achieves hit rates of 0.725 on LocusBench-Onc and 0.773 on LocusBench-ED, substantially outperforming comparator models. These results show that routine PACS annotations are a scalable, previously unused source of supervision for 3D visual grounding.
Chinese Translation
视觉定位模型通过将报告发现与图像区域关联，可以增强放射学工作流程。这在3D CT中尤为重要，因为发现通常只占体积的一小部分。训练3D定位模型需要大量配对短语和区域的数据集，而构建这样的数据集成本高昂，需要放射科医生手动标注图像。我们认为，这种监督在常规报告过程中已经隐含地创建，因为放射科医生经常在关键图像上放置2D注释（例如，距离测量、箭头）以进行测量并支持报告解释。我们提出了一种自动化流程，将这些常规临床注释转换为大规模的短语-区域监督，用于3D视觉定位。该流程通过元数据匹配将每个注释与报告中的相应发现关联，然后使用可提示的3D分割模型将2D注释转换为体积掩膜。这在不需要额外放射科医生注释的情况下生成了短语-掩膜-体积数据集。在单一机构的临床图像归档和通信系统（PACS）中，我们的方法从59K腹部CT检查中生成了105K短语-掩膜-体积三元组。我们还介绍了两个腹部CT定位基准，LocusBench-Onc和LocusBench-ED，分别包含240个肿瘤学和260个急诊科放射科医生审核的短语-掩膜-体积三元组，后者涵盖了阑尾炎、血肿和疝气等13个不同类别。我们进一步介绍了LocusCT，这是一个在该数据集上训练的3D视觉定位模型，在LocusBench-Onc和LocusBench-ED上分别达到了0.725和0.773的命中率，显著优于对比模型。这些结果表明，常规PACS注释是一个可扩展的、以前未使用的3D视觉定位监督来源。
cs.CV / 30 / 2610.04104
Where Does the Semantic Gain Come From? A Reproduction and Extension of Semantic Knowledge-driven Contrastive Learning for Long-Tailed Recognition
语义增益来自何处？对基于语义知识的对比学习在长尾识别中的再现与扩展
Abstract
Semantic Knowledge-driven Contrastive Learning (SKCL) uses a language model to decide which classes are related, and pulls each image towards the prototypes of its semantic neighbours. On CIFAR-100-LT (beta = 100) it reports 54.02% top-1 accuracy, 2.01 points above Balanced Contrastive Learning (BCL), the method it builds on. The code and the class descriptions are not public. I reimplement SKCL, BCL and ConCutMix in one framework, check it against the public baseline code, and run every configuration with three seeds. The two baselines reproduce within 1.5 points, but SKCL built on BCL, as the paper describes it, ends up 0.56 points below BCL. To find out why, I add SKCL to the authors' own ConCutMix code. Trained for the paper's 300 epochs, it reaches 53.69, only 0.33 below the published number. At the same budget, however, the semantic graph adds just 0.23 points over ConCutMix, while training ConCutMix for 100 more epochs adds 1.04. Together with ConCutMix's published lead over BCL (1.15), this explains the claimed gain. A BCL model that never sees the graph already shares 41.2% of the graph's top-2 neighbours with its own most-confused classes (2.0% by chance), which shows why the graph adds so little on these benchmarks. I also test several changes to SKCL. Combining it with the CutMix branch improves it by 1.06 points, and an adaptive version of the graph improves it slightly (+0.34 and +0.28 in two codebases), although these gains are within seed noise.
Chinese Translation
基于语义知识的对比学习（Semantic Knowledge-driven Contrastive Learning, SKCL）利用语言模型来决定哪些类别是相关的，并将每个图像拉向其语义邻居的原型。在CIFAR-100-LT（beta = 100）上，该方法报告了54.02%的顶级准确率，比其基础方法平衡对比学习（Balanced Contrastive Learning, BCL）高出2.01个百分点。代码和类别描述并未公开。我在一个框架中重新实现了SKCL、BCL和ConCutMix，并与公开的基线代码进行对比，使用三个随机种子运行每个配置。这两个基线的重现误差在1.5个百分点以内，但根据论文描述构建的基于BCL的SKCL最终低于BCL 0.56个百分点。为了找出原因，我将SKCL添加到作者自己的ConCutMix代码中。经过论文所述的300个训练周期，它达到了53.69，仅比已发布的数字低0.33。然而，在相同的预算下，语义图仅比ConCutMix增加了0.23个百分点，而将ConCutMix训练多100个周期则增加了1.04。结合ConCutMix在BCL上的已发布优势（1.15），这解释了所声称的增益。一个从未见过图的BCL模型已经与其自身最混淆的类别共享41.2%的图的前2个邻居（仅2.0%是偶然的），这表明图在这些基准测试中增加的效果非常有限。我还测试了对SKCL的几种改动。将其与CutMix分支结合提高了1.06个百分点，而图的自适应版本略微提升了性能（在两个代码库中分别为+0.34和+0.28），尽管这些增益都在随机种子的噪声范围内。
cs.CV / 31 / 2610.04139
From Sight to Foresight: Predictive Spatial Reasoning in Vision-Language Models
从视觉到预见：视觉语言模型中的预测空间推理
Abstract
Predicting future spatial states supports collision avoidance and timely decision-making in dynamic environments. However, existing vision-language models (VLMs) and benchmarks for spatial reasoning primarily focus on observed scenes, leaving predictive spatial reasoning beyond the observed interval underexplored. To this end, we introduce SpatialMind, a metric-scale VLM for spatial reasoning and future prediction. Its metric depth adapter anchors spatial reasoning to real-world scale, while its progressive state chain establishes current spatial states and observed dynamics as the foundation for future prediction. Given a video prefix, SpatialMind predicts distances, motion directions, and spatial relations in both observed and unseen future frames. For training and evaluation, we build a scalable data engine that grounds entity descriptions in metric geometry to generate question-answer pairs and state supervision. Using this engine, we construct the SpatialMind-30K dataset and the SpatialMind-2K benchmark, both covering driving and everyday egocentric scenes. The benchmark spans eight tasks across three levels: current-state understanding, observed-dynamics understanding, and future prediction. Experiments show that SpatialMind substantially outperforms both general and spatially specialized models on our benchmark while achieving competitive zero-shot performance on VSI-Bench, OSI-Bench, and VLM4D.
Chinese Translation
预测未来的空间状态有助于在动态环境中避免碰撞和及时决策。然而，现有的视觉语言模型（VLMs）和空间推理基准主要集中于观察到的场景，对于观察区间之外的预测空间推理研究相对较少。为此，我们引入了SpatialMind，一个用于空间推理和未来预测的度量级VLM。其度量深度适配器将空间推理锚定于真实世界的尺度，而其渐进状态链则将当前空间状态和观察到的动态作为未来预测的基础。在给定视频前缀的情况下，SpatialMind能够预测观察到的和未见未来帧中的距离、运动方向和空间关系。为了进行训练和评估，我们构建了一个可扩展的数据引擎，将实体描述与度量几何相结合，以生成问答对和状态监督。利用该引擎，我们构建了SpatialMind-30K数据集和SpatialMind-2K基准，涵盖了驾驶和日常自我中心场景。该基准跨越三个层次的八个任务：当前状态理解、观察动态理解和未来预测。实验表明，SpatialMind在我们的基准上显著优于一般模型和空间专业模型，同时在VSI-Bench、OSI-Bench和VLM4D上实现了具有竞争力的零样本表现。
cs.CV / 32 / 2610.04151
Watermarks and Fingerprints as Soft Bindings for Content Provenance: An Open-Licence Benchmark for Images, Audio and Video
水印与指纹作为内容来源的软绑定：图像、音频和视频的开放许可基准
Abstract
Content-provenance standards such as C2PA let a platform recover a stripped manifest through a soft binding: an invisible watermark read from the content, or a fingerprint looked up in a registry. We benchmarked both families under one protocol, restricted to openly available models whose licences we audited, on public media, with false-match rates calibrated on held-out negatives and source-level bootstrap intervals for performance estimates. For watermarking we evaluated 25 image, 7 audio and 7 video configurations from 12 methods on perceptual quality, robustness, false positives and cost; for fingerprinting, 35 methods on registries of up to 98,985 images, partial edits and adversarial attacks. PixelSeal gave the best balance for image and video watermarks and AudioSeal for audio, but the error-correcting detector of every TrustMark variant fired on 5.9 to 15.3% of unmarked images, so a verifier should test the expected payload. Among fingerprints, copy detectors trained on non-commercial data detected up to 75.5% of transformed images at a pair-level false-match rate of $10^{-7}$ and DINOv2, the best permissively licensed method, 65.0%; near-copies in a product catalogue dominated the false matches, and no detection improvement from geometric verification was observed under a matched calibration false-binding constraint in the evaluated image pipelines. On the same attacked copies the two families failed differently: the union of watermark and fingerprint successes covered 69% of image copies; fingerprints covered more audio queries, whereas expected-key watermark verification covered more video queries. Embedding a watermark moved the ISCC code of 62.0% of images past its match threshold, and platform-dependent colour conversion changed watermark bits between x86 and ARM hosts.
Chinese Translation
内容来源标准如 C2PA 允许平台通过软绑定恢复被剥离的清单：从内容中读取的隐形水印或在注册表中查找的指纹。我们在一个协议下对这两类进行了基准测试，限制于我们审核过许可的公开可用模型，针对公共媒体，假匹配率在保留的负样本上进行校准，并使用源级自助引导区间进行性能估计。在水印方面，我们评估了来自 12 种方法的 25 种图像、7 种音频和 7 种视频配置，考虑了感知质量、鲁棒性、假阳性和成本；在指纹方面，评估了 35 种方法，注册表包含多达 98,985 张图像、部分编辑和对抗攻击。PixelSeal 在图像和视频水印方面提供了最佳平衡，而 AudioSeal 在音频方面表现最佳，但每种 TrustMark 变体的错误纠正检测器在 5.9% 到 15.3% 的未标记图像上触发，因此验证者应测试预期负载。在指纹方面，基于非商业数据训练的复制检测器在对比级别假匹配率为 $10^{-7}$ 的情况下检测到多达 75.5% 的变换图像，而 DINOv2 作为最佳开放许可方法检测到 65.0%；产品目录中的近似副本主导了假匹配，并且在评估的图像管道中，在匹配校准假绑定约束下未观察到几何验证的检测改进。在相同的攻击副本上，这两类的失败方式不同：水印和指纹成功的联合覆盖了 69% 的图像副本；指纹覆盖了更多音频查询，而预期密钥水印验证覆盖了更多视频查询。嵌入水印使 62.0% 的图像的 ISCC 代码超出其匹配阈值，而平台依赖的颜色转换在 x86 和 ARM 主机之间改变了水印位。
cs.CV / 33 / 2610.04152
Kepler4D: Controllable Future Video Generation via 4D Scene State Evolution
Kepler4D：通过4D场景状态演变实现可控的未来视频生成
Abstract
Video world models aim to preserve scene structure and predict how dynamic objects evolve beyond visual observations. We present Kepler4D, a framework for future video generation through explicit 4D scene state evolution. Given a monocular video, Kepler4D constructs a shared 3D representation of background geometry, object motion histories, coarse spatial supports, and semantic context. Chain-of-Motion summarizes observed motion and uses a vision-language model to select structured speed and heading decisions and decide whether to bound object-center height from below. A deterministic rollout converts these decisions into future object trajectories for inspection and editing before synthesis. We render the evolving proxies into geometric controls for a pretrained video generator, separating coarse object motion from the synthesis of appearance and articulation. Experiments on real-world videos demonstrate that Kepler4D enables controllable object motion and plausible future rollout while preserving scene consistency.
Chinese Translation
视频世界模型旨在保留场景结构并预测动态物体在视觉观察之外的演变。我们提出了Kepler4D，这是一个通过显式的4D场景状态演变进行未来视频生成的框架。给定一段单目视频，Kepler4D构建了背景几何、物体运动历史、粗略空间支持和语义上下文的共享3D表示。运动链（Chain-of-Motion）总结了观察到的运动，并使用视觉-语言模型选择结构化的速度和航向决策，并决定是否从下方限制物体中心高度。确定性展开将这些决策转换为未来物体轨迹，以便在合成之前进行检查和编辑。我们将演变的代理渲染为预训练视频生成器的几何控制，将粗略的物体运动与外观和关节的合成分离。对真实视频的实验表明，Kepler4D能够实现可控的物体运动和合理的未来展开，同时保持场景的一致性。
cs.CV / 34 / 2610.04159
Diagnosis-Conditioned Spatial Gating and Decoder-Level Supervised Contrastive Learning for Radiology Report Generation
基于诊断条件的空间门控与解码器级监督对比学习用于放射学报告生成
Abstract
Radiology report generation models can produce fluent text while still containing finding-level inaccuracies. Diagnosis-driven methods improve generation by conditioning on predicted findings, but these predictions do not directly modify the visual patch features provided to the decoder, and global gating applies the same modulation across spatial locations. We introduce a Position-Aware Gate (PAG) that uses predicted finding representations to modulate visual patches spatially without region supervision. We also propose a Decoder-level Supervised Contrastive Loss (DSCL) that structures decoder representations using shared positive findings rather than instance identity. On MIMIC-CXR, PAG+DSCL improves clinical efficacy (CE) F1 from 0.484 to 0.502 over a matched global-gate reference, while PAG and DSCL individually reach 0.491 and 0.495. Without additional fine-tuning, the combined model achieves 0.226 CE F1 on IU X-Ray, compared with 0.211 reported by PromptMRG.
Chinese Translation
放射学报告生成模型能够生成流畅的文本，但仍然存在发现级别的不准确性。基于诊断的方法通过对预测发现进行条件化来改善生成，但这些预测并未直接修改提供给解码器的视觉补丁特征，而全局门控在空间位置上应用相同的调制。我们引入了一种位置感知门控（Position-Aware Gate, PAG），利用预测的发现表示在没有区域监督的情况下对视觉补丁进行空间调制。我们还提出了一种解码器级监督对比损失（Decoder-level Supervised Contrastive Loss, DSCL），通过共享的正向发现而非实例身份来构建解码器表示。在MIMIC-CXR数据集上，PAG+DSCL将临床有效性（Clinical Efficacy, CE）F1从0.484提高到0.502，相比之下，匹配的全局门控参考为0.491，而PAG和DSCL单独的结果分别为0.491和0.495。在没有额外微调的情况下，组合模型在IU X-Ray数据集上达到了0.226的CE F1，而PromptMRG报告的为0.211。
cs.CV / 35 / 2610.04185
Referring Multi-Object Tracking in Moving-Camera Videos via Global Motion Compensation
通过全局运动补偿在移动摄像机视频中进行指代多目标跟踪
Abstract
Referring multi-object tracking (RMOT) takes a video and a language expression as input and tracks all referred objects. Many tracking requirements involve how an object moves rather than how it appears. However, in a video captured by a moving camera, a parked vehicle may appear to move, while a moving vehicle may show little displacement. Existing RMOT methods relate motion with text but do not explicitly remove camera-induced motion. In this paper, we propose extracting residual motion across frames by estimating camera motion in driver-view videos and compare motion characteristics with the query expression. We consider the motion-matching extent and integrate it with the RMOT method's prediction result through late fusion. In the evaluation, we verify the performance gain of taking the motion compensation module as a plug-in across different RMOT hosts.
Chinese Translation
指代多目标跟踪（RMOT）以视频和语言表达作为输入，跟踪所有被提及的对象。许多跟踪需求涉及对象的运动方式，而非其外观。然而，在由移动摄像机拍摄的视频中，停放的车辆可能看起来在移动，而移动的车辆可能显示出很小的位移。现有的RMOT方法将运动与文本相关联，但并未明确消除由摄像机引起的运动。在本文中，我们提出通过估计驾驶视角视频中的摄像机运动来提取帧间的残余运动，并将运动特征与查询表达进行比较。我们考虑运动匹配的程度，并通过后期融合将其与RMOT方法的预测结果整合。在评估中，我们验证了在不同RMOT主机中将运动补偿模块作为插件使用所带来的性能提升。
cs.CV / 36 / 2610.04199
CellSplat4D: PSF-Aware 4D Gaussian Splatting for Sparse Robotic Live-Cell Imaging
CellSplat4D：基于点扩散函数的稀疏机器人活细胞成像的四维高斯喷溅
Abstract
A robotic microscope watching living cells cannot afford to look as often as it would like. Every volume it acquires costs photons the specimen does not get back, and time owed to other wells. What such a platform exists to produce is a record of individual cells through time: which cell is which from one volume to the next, and which cell divided into which two. Sampling sparsely breaks that record exactly where it matters, and the fault lies in the acquisition schedule rather than in the analysis software. We fill the gaps by reconstructing them, fitting a 4D Gaussian model to whatever volumes the hardware could afford. The model is a cloud of light-emitting blobs, each carrying a position, a shape and a lifetime. Being continuous in time, it renders any missing volume on demand, decoupling how often the robot analyses from how often it can afford to look. The microscope's point-spread function is measured from the data rather than inherited from acquisition metadata or left to the optimizer, because metadata inflates it and the optimizer cannot recover it at all: a wider blur around a smaller blob fits the images equally well. Each blob's lifetime is stored in frames rather than as a fraction of the recording, so that it denotes a fixed duration on any sequence. Unmeasured timesteps are supervised at coarse scale by a 3D U-Net that predicts the intermediate volume directly and estimates no motion field, since a dividing cell becomes two and no motion describes that. On two Cell Tracking Challenge sequences, a C. elegans embryo and a Chinese Hamster Ovarian (CHO), with fidelity scored per cell nucleus, our reconstruction holds the highest nucleus fidelity at every distance from an acquired frame, has the flattest decay across the gap, and best recovers focal planes it was never shown with graceful degradation across the gap.
Chinese Translation
观察活细胞的机器人显微镜无法像希望的那样频繁地观察。每获取一个体积都会消耗光子，而这些光子是样本无法恢复的，同时也占用了其他孔的时间。这样的平台存在的目的是记录个体细胞随时间的变化：从一个体积到下一个体积，哪个细胞是哪个细胞，以及哪个细胞分裂成了哪两个细胞。稀疏采样正是在关键时刻破坏了这一记录，问题出在采集调度而非分析软件。我们通过重建这些缺失的部分来填补空白，拟合一个四维高斯模型到硬件能够承受的任何体积。该模型是一个发光斑点的云，每个斑点都有一个位置、一个形状和一个寿命。由于在时间上是连续的，它可以按需渲染任何缺失的体积，将机器人分析的频率与其能够观察的频率解耦。显微镜的点扩散函数是从数据中测量得出的，而不是从采集元数据中继承或交给优化器处理，因为元数据会使其膨胀，而优化器根本无法恢复它：一个较小斑点周围的较大模糊同样适合图像。每个斑点的寿命以帧的形式存储，而不是作为录制的一个分数，以便在任何序列中表示固定的持续时间。未测量的时间步长由一个3D U-Net在粗尺度上进行监督，直接预测中间体积并不估计任何运动场，因为分裂的细胞变成两个，而没有运动能够描述这一过程。在两个细胞追踪挑战序列中，一个是秀丽隐杆线虫胚胎，另一个是中国仓鼠卵巢细胞（CHO），以每个细胞核的保真度进行评分，我们的重建在与获取帧的每个距离上保持了最高的细胞核保真度，跨越间隙的衰减最平坦，并且最佳恢复了从未显示过的焦平面，且在间隙中优雅地衰减。
cs.CV / 37 / 2610.04203
Sparse-GS2Mesh: 3D Gaussian Splatting Guided by Novel Stereo Views and 2DGS for Sparse View Surface Reconstruction}
Sparse-GS2Mesh：基于新型立体视图和2DGS的稀疏视图表面重建的3D高斯点云引导
Abstract
Surface reconstruction under sparse-view settings remains challenging due to limited geometric cues. Volume rendering methods based on signed distance functions often produce over-smoothed surfaces, while 3D Gaussian Splatting (3DGS), though time-efficient, suffers from incomplete geometry due to the lack of reliable depth supervision and the limitation of being optimized only from given input views. In this paper, we present Sparse-GS2Mesh, a stereo-aware framework for surface reconstruction from sparse views. While 3DGS and stereo matching have been leveraged for surface reconstruction under dense view settings, we extend them to operate effectively under sparse view conditions by first initializing 3DGS using epipolar depth priors to mitigate the 3DGS overfitting problem, followed by our three key components: (I) adaptive baseline selection, (II) fine-tuning with a stereo matching network, and (III) 2D/3D co-regularized fine-tuning. Given a warmed-up 3DGS initialized with epipolar depth, the adaptive baseline selection automatically determines a baseline to synthesize for each sparse view. We then fine-tune 3DGS by backpropagating depth-refining gradients from the stereo matching network, effectively specializing the 3DGS for stereo matching. The 2D/3D co-regularization further helps obtain stable reconstruction, addressing weak geometric cues in close stereo views. Sparse-GS2Mesh achieves a 15\% improvement over state-of-the-art methods in little-overlap settings and comparable results in large-overlap settings. Codes will be publicly available.
Chinese Translation
在稀疏视图条件下，表面重建仍然面临挑战，因为几何线索有限。基于有符号距离函数的体积渲染方法往往会产生过于平滑的表面，而3D高斯点云（3DGS）虽然在时间效率上表现良好，但由于缺乏可靠的深度监督以及仅能从给定输入视图进行优化的限制，导致几何信息不完整。本文提出了Sparse-GS2Mesh，这是一个针对稀疏视图的立体感知表面重建框架。虽然3DGS和立体匹配已被用于密集视图条件下的表面重建，但我们扩展了它们，使其在稀疏视图条件下有效运行，首先通过使用极线深度先验初始化3DGS，以减轻3DGS的过拟合问题，随后引入我们的三个关键组件：（I）自适应基线选择，（II）使用立体匹配网络的微调，以及（III）2D/3D共同正则化微调。在使用极线深度初始化的预热3DGS的基础上，自适应基线选择自动确定每个稀疏视图的合成基线。然后，我们通过从立体匹配网络反向传播深度优化梯度来微调3DGS，有效地使3DGS专门化于立体匹配。2D/3D共同正则化进一步帮助获得稳定的重建，解决了在近立体视图中几何线索薄弱的问题。Sparse-GS2Mesh在重叠较少的设置中比最先进的方法提高了15 ext{%}，在重叠较大的设置中取得了可比的结果。代码将公开发布。
cs.CV / 38 / 2610.04225
FlashGaze: Training-Free Multi-Scale Patch Pruning For Efficient Video Understanding
FlashGaze：无训练的多尺度补丁剪枝用于高效视频理解
Abstract
Multimodal Large Language Models (MLLMs) have demonstrated strong performance in video understanding, yet efficiently processing long, high-resolution videos remains challenging. Such videos often contain substantial spatiotemporal redundancy, and processing redundant visual tokens can incur avoidable computational overhead. Many existing methods prune visual tokens during or after vision transformer (ViT) encoding, leaving much of the encoding cost unaddressed. Some approaches prune patches before encoding but rely on learned auxiliary networks for patch selection, incurring additional training and inference overhead. To address these limitations, we propose FlashGaze, a training-free method that reduces spatiotemporal redundancy before ViT encoding without introducing auxiliary networks. FlashGaze uses pixel-space differences as a proxy for information loss and employs Quadtree Dynamic Programming to jointly optimize patch dropping, merging, and keeping under a fixed budget. Experiments on two MLLM backbones across multiple benchmarks demonstrate substantial efficiency gains while largely preserving accuracy. On Qwen3-VL-8B, FlashGaze retains 98% of the full-input baseline accuracy on LongVideoBench while achieving up to 5.4x and 17x speedups in ViT encoding and MLLM prefill, respectively, and reducing peak GPU memory usage by a factor of 1.8. These efficiency gains enable the model to process videos with more frames and higher resolutions on the same GPU hardware, unlocking video understanding at scales previously out of reach.
Chinese Translation
多模态大型语言模型（MLLMs）在视频理解方面表现出色，但高效处理长时间、高分辨率的视频仍然面临挑战。这类视频通常包含大量时空冗余，处理冗余的视觉标记可能会产生不必要的计算开销。许多现有方法在视觉变换器（ViT）编码过程中或之后剪枝视觉标记，但未能解决大部分编码成本。一些方法在编码之前剪枝补丁，但依赖于学习的辅助网络进行补丁选择，从而增加了额外的训练和推理开销。为了解决这些局限性，我们提出了FlashGaze，一种无训练的方法，在ViT编码之前减少时空冗余，而不引入辅助网络。FlashGaze使用像素空间差异作为信息损失的代理，并采用四叉树动态规划共同优化补丁的丢弃、合并和保留，确保在固定预算下进行。针对多个基准测试的两个MLLM主干的实验表明，FlashGaze在大幅提高效率的同时，基本保持了准确性。在Qwen3-VL-8B上，FlashGaze在LongVideoBench上保留了98%的全输入基线准确性，同时在ViT编码和MLLM预填充中分别实现了高达5.4倍和17倍的加速，并将峰值GPU内存使用减少了1.8倍。这些效率提升使得模型能够在相同的GPU硬件上处理更多帧和更高分辨率的视频，从而实现之前难以达到的视频理解。
cs.CV / 39 / 2610.04281
OctMesh: A Unified Octree-Hierarchical Framework for Lossless Triangle Mesh Compression
OctMesh：一种统一的八叉树层次框架用于无损三角网格压缩
Abstract
Lossless triangle mesh compression must preserve both vertex positions and connectivity. Octrees support learned point cloud geometry coding and progressive refinement, but extending them to meshes requires a compatible connectivity representation. Unlike the eight occupancy decisions of a voxel, a parent edge can develop into varied child connections, making edge refinement difficult to model with a compact prediction prior. We propose OctMesh, a learned framework that codes geometry and connectivity on a shared octree hierarchy. Its key observation is that octree pooling produces parents with either one child or two to eight children. Child edges are then grouped by their endpoint parents' types and whether the endpoints share a parent. Each candidate group contains children from just one parent or two connected parents. The resulting four categories define small, fixed-shape prediction tasks: connections uniquely determined by the parent graph are inherited without bits, while three neural predictors estimate probabilities for the remaining candidates. These probabilities guide arithmetic coding of the actual edge symbols. Binarized predictions of within-parent connections provide context for predicting connections between different parents. A graph-aware parent feature extractor combines local geometry, parent connectivity and global shape. The connectivity models use dedicated weights at coarse levels and share weights at fine levels. Residual edges and a finest-level face-selection payload complete the reconstruction. On 256 frames from eight MPEG V-DMC test sequences, OctMesh losslessly recovers the finest-level vertex-coordinate, edge and unoriented face sets at an average of 7.033 bits per face, 12.8% below V-Mesh. The same hierarchical representation supports nine levels of progressive vertex-and-edge refinement.
Chinese Translation
无损三角网格压缩必须同时保留顶点位置和连通性。八叉树支持学习型点云几何编码和渐进式细化，但将其扩展到网格需要兼容的连通性表示。与体素的八个占据决策不同，父边可以发展成多种子连接，使得边的细化难以通过紧凑的预测先验进行建模。我们提出了OctMesh，一种在共享八叉树层次结构上编码几何和连通性的学习框架。其关键观察是八叉树池化产生的父节点要么只有一个子节点，要么有两个到八个子节点。子边随后根据其端点父节点的类型及端点是否共享父节点进行分组。每个候选组仅包含来自一个父节点或两个相连父节点的子边。由此产生的四类定义了小型、固定形状的预测任务：由父图唯一确定的连接无需编码比特直接继承，而其余候选连接由三个神经预测器估计概率。这些概率指导实际边符号的算术编码。父节点内连接的二值化预测为预测不同父节点间连接提供上下文。一个图感知的父特征提取器结合了局部几何、父节点连通性和全局形状。连通性模型在粗层使用专用权重，在细层共享权重。残差边和最细层的面选择负载完成重建。在来自八个MPEG V-DMC测试序列的256帧上，OctMesh以平均每面7.033比特的码率无损恢复最细层的顶点坐标、边和无向面集合，比V-Mesh降低12.8%。相同的层次表示支持九级渐进式顶点和边细化。
cs.CV / 40 / 2610.04300
A Geometric-Transformation Feature-Adaptive Manifold Restoration Method for Open-Vocabulary Semantic Segmentation of Remote Sensing Images
一种基于几何变换的特征自适应流形修复方法用于遥感图像的开放词汇语义分割
Abstract
The semantic information of objects in remote sensing images is typically invariant to geometric transformations from the dihedral group D4. However, SAM3-based open-vocabulary semantic segmentation (OVSS) methods often exhibit inconsistent responses to different geometric transformations. To exploit this property and improve the stability of OVSS for remote sensing images, we propose a feature-adaptive manifold repair method based on dihedral-group geometric transformations. First, we introduce multi-scale harmonic-guided D4 view selection (MH-D4VS) to select complementary candidate views from a set of geometrically transformed views. Next, we propose original-view-anchored adaptive manifold repair (OAMR), which uses the original view as an anchor and reliable cross-view information to selectively repair locally unreliable visual features. Finally, we develop pixel decoder test-time adaptation (PD-TTA) for SAM3, which fine-tunes only the parameters of the GroupNorm layers online during inference, thereby enhancing the model's ability to adapt to sample-level distribution shifts. Experimental results show that the proposed method achieves an average mIoU of 55.6% across eight remote sensing semantic segmentation benchmarks and delivers consistent performance improvements under different SAM3-based inference frameworks.
Chinese Translation
遥感图像中物体的语义信息通常对来自二面体群 D4 的几何变换具有不变性。然而，基于 SAM3 的开放词汇语义分割（OVSS）方法在面对不同几何变换时常常表现出不一致的响应。为了利用这一特性并提高遥感图像的 OVSS 稳定性，我们提出了一种基于二面体群几何变换的特征自适应流形修复方法。首先，我们引入多尺度谐波引导的 D4 视图选择（MH-D4VS），从一组几何变换视图中选择互补的候选视图。接着，我们提出了原始视图锚定的自适应流形修复（OAMR），该方法以原始视图作为锚点，并利用可靠的跨视图信息选择性地修复局部不可靠的视觉特征。最后，我们为 SAM3 开发了像素解码器测试时自适应（PD-TTA），该方法在推理过程中仅在线微调 GroupNorm 层的参数，从而增强模型适应样本级分布变化的能力。实验结果表明，所提方法在八个遥感语义分割基准上实现了平均 mIoU 为 55.6%，并在不同的基于 SAM3 的推理框架下提供了一致的性能提升。
cs.CV / 41 / 2610.04318
Rethinking Long-Video Efficiency: A Joint Allocation Perspective on Frames, Pixels, and Front-End Latency
重新思考长视频效率：从帧、像素和前端延迟的联合分配视角
Abstract
Efficient long-video understanding with vision-language models (VLMs) is often framed as selecting informative frames or visual tokens at a fixed native resolution. We show that per-frame resolution can instead be traded for denser temporal coverage, while front-end decoding latency depends on the size of the candidate pool rather than the final token budget. An empirical study across multiple VLMs and long-video benchmarks yields three findings: dense low-resolution sampling outperforms sparse native-resolution sampling at matched token budgets; resolution-sensitive tasks benefit from selected high-resolution frames; and front-end decoding dominates wall time for hour-long videos. Motivated by these findings, we introduce LoHi, a training-free, single-pass framework that combines a dense low-resolution video stream with sparse high-resolution image frames through the VLM's native video and image pathways. LoHi-Anchor selects high-resolution frames using codec I-frame metadata, while LoHi-SemDiv uses query relevance and visual diversity over CLIP features. Across three long-video benchmarks, LoHi improves average accuracy by 10.6 percentage points over the native-resolution baseline at a matched token budget and by 5.2 percentage points over the strongest prior efficiency method. It also reduces front-end decoding latency by up to 7x on hour-long videos. Project page: https://sixundong.com/projects/lohi
Chinese Translation
使用视觉语言模型（VLMs）进行高效的长视频理解通常被视为在固定的原生分辨率下选择信息丰富的帧或视觉标记。我们展示了每帧的分辨率可以换取更密集的时间覆盖，而前端解码延迟则依赖于候选池的大小，而非最终的标记预算。对多个VLM和长视频基准的实证研究得出了三个发现：在匹配的标记预算下，稠密低分辨率采样优于稀疏原生分辨率采样；分辨率敏感的任务受益于选择的高分辨率帧；而前端解码在长达一小时的视频中占据了主要的墙面时间。基于这些发现，我们引入了LoHi，一个无训练、单遍的框架，通过VLM的原生视频和图像通道，将稠密的低分辨率视频流与稀疏的高分辨率图像帧结合起来。LoHi-Anchor使用编解码器I帧元数据选择高分辨率帧，而LoHi-SemDiv则利用查询相关性和CLIP特征的视觉多样性。在三个长视频基准上，LoHi在匹配的标记预算下，相较于原生分辨率基线提高了10.6个百分点的平均准确率，并且比最强的先前效率方法提高了5.2个百分点。同时，它还将一小时视频的前端解码延迟减少了多达7倍。项目页面：https://sixundong.com/projects/lohi
cs.CV / 42 / 2610.04327
Trust the View That Sees the Target: Mining Cross-View Conflicts for Reliability-Gated Disaster Damage Assessment
信任看到目标的视角：挖掘跨视角冲突以实现可靠性门控的灾害损害评估
Abstract
After a disaster, building damage is assessed from overhead tiles and ground-level photographs, and most methods fuse the two views symmetrically, trusting both equally for every building. This paper focuses on the samples where that assumption fails: the conflict cases, on which two independently trained single-view models disagree. We mine such cases from three paired collections (inspection photographs from the 2025 Eaton wildfire and street-view panoramas from Hurricanes Ian and Milton, each matched to very-high-resolution overhead tiles), where they make up 10-33% of the data. On these samples an oracle that simply trusts the correct view beats every fusion method we tested by 0.37-0.41 accuracy, and the gap survives longer training, calibration, and backbone changes. We recover part of it with a visibility-conditioned reliability gate: a linear model that decides which view to trust from building-visibility features, calibrated per-view confidences, and the disagreement itself. On the wildfire data the gate is the only method that significantly beats calibrated probability averaging (+0.051 on conflicts, p=0.0001) and end-to-end fusion (+0.072, p<10^-4); on the panoramic datasets it matches them. A controlled field-of-view experiment explains why: cropping panoramas toward the building doubles the benefit of fusion, whereas random crops of the same size do not. Finally, the spatial density of conflicts predicts tile-level damage without labels (Spearman r=0.615, p=0.001). Mining conflicts turns "does fusion help?" into "which view should be trusted, where, and why?".
Chinese Translation
在灾后评估中，建筑损害是通过高空图像和地面照片进行评估的，大多数方法对这两种视角进行对称融合，平等信任每一栋建筑的视角。本文聚焦于这一假设失效的样本：冲突案例，即两个独立训练的单视角模型之间存在分歧的情况。我们从三组配对数据中挖掘出此类案例（2025年伊顿野火的检查照片与来自飓风伊恩和米尔顿的街景全景图，每组与非常高分辨率的高空图像匹配），这些案例占数据的10-33%。在这些样本中，简单信任正确视角的预言者在我们测试的每种融合方法中都提高了0.37-0.41的准确率，并且这一差距在更长的训练、校准和主干网络更改后依然存在。我们通过可见性条件的可靠性门恢复了部分差距：一个线性模型根据建筑可见性特征、每个视角的置信度和分歧本身来决定信任哪个视角。在野火数据中，该门是唯一显著优于校准概率平均（在冲突中提高+0.051，p=0.0001）和端到端融合（提高+0.072，p<10^-4）的方法；在全景数据集中则与之相当。一个受控的视场实验解释了原因：将全景图裁剪至建筑物方向使融合的收益翻倍，而随机裁剪相同大小的图像则没有。最后，冲突的空间密度能够在没有标签的情况下预测瓦片级别的损害（Spearman r=0.615，p=0.001）。挖掘冲突将“融合是否有帮助？”转变为“哪个视角应该被信任，在哪里，为什么？”
cs.CV / 43 / 2610.04335
Synthetic-to-Real ViT-Based Pose Estimation of a Noncooperative UAV
基于ViT的非合作无人机姿态估计的合成到真实转换
Abstract
Remote pose estimation of noncooperative Unmanned Aerial Vehicles (UAVs) from imagery is critical, as they cannot be influenced or instrumented in advance. Deep-learning-based approaches offer a promising solution; however, their development is constrained by the cost and difficulty of acquiring large-scale real-world datasets with accurate pose labels. Synthetic imagery provides an alternative, but models trained on synthetic data must overcome the synthetic-to-real domain gap to generalize to real-world imagery. This work investigates the inherent synthetic-to-real generalization capability of a Vision Transformer (ViT)-based model for monocular UAV pose estimation. The proposed approach employs a self-supervised DINOv2 backbone and is trained exclusively on labeled synthetic imagery while being evaluated on labeled real-world imagery. Pose ambiguity-aware strategies are incorporated during training and inference to address ambiguities arising from the projection of a three-dimensional target onto a two-dimensional image plane and from target symmetries. An $\alpha$-$\beta$ filter is further integrated during inference to improve pose estimations. To assess the model under operational requirements, it is evaluated in terms of Mean Angular Error (MAE) and inference time, both before and after filtering, using a real-world dataset containing 77,077 labeled UAV images. Before filtering, the model achieves an MAE of $19.18^{\circ}$ and an inference time of $13.25$ ms, whereas after filtering, these values are $8.74^{\circ}$ and $13.42$ ms, respectively.
Chinese Translation
从图像中对非合作无人机（UAV）进行远程姿态估计至关重要，因为它们无法提前受到影响或安装仪器。基于深度学习的方法提供了一种有前景的解决方案；然而，其发展受到获取具有准确姿态标签的大规模真实世界数据集的成本和难度的限制。合成图像提供了一种替代方案，但在合成数据上训练的模型必须克服合成到真实的领域差距，以便能够推广到真实世界的图像。本文研究了一种基于视觉变换器（ViT）的单目UAV姿态估计模型的内在合成到真实的泛化能力。所提出的方法采用自监督的DINOv2骨干网络，仅在带标签的合成图像上进行训练，并在带标签的真实世界图像上进行评估。在训练和推理过程中，结合了姿态模糊感知策略，以解决从三维目标投影到二维图像平面以及目标对称性引起的模糊性。在推理过程中进一步集成了$eta$-$eta$滤波器，以改善姿态估计。为了评估模型在操作要求下的表现，使用包含77,077张带标签UAV图像的真实世界数据集，对其进行平均角度误差（MAE）和推理时间的评估，分别在滤波前后进行。在滤波前，模型的MAE为$19.18^{ ext{°}}$，推理时间为$13.25$ ms，而在滤波后，这些值分别为$8.74^{ ext{°}}$和$13.42$ ms。
cs.CV / 44 / 2610.04336
A differentiable Lagrangian-coupled 3D Gaussian Splatting-SPH model for forward simulation and inverse analysis in solid mechanics
一种可微分的拉格朗日耦合三维高斯点云-SPH模型用于固体力学中的正向仿真和逆向分析
Abstract
Recent advances in generative world models have increased interest in digital models that reproduce both the appearance of real objects and their response to physical interaction. Three-dimensional reconstruction techniques, including 3D Gaussian Splatting, capture detailed surface geometry and appearance from images and videos. However, extending these representations beyond plausible animation to mechanically interpretable models for constitutive behavior, boundary conditions, and inverse parameter identification remains less explored. In this work, a differentiable Lagrangian-coupled 3DGS-smoothed particle hydrodynamics (SPH) model is proposed for forward simulation and inverse analysis of deformable solids. The observed object is first reconstructed from multi-view calibrated visual dataset as a 3DGS rendering model. An envelope-based procedure then generates an independent SPH support for the solid-mechanics model, avoiding the direct use of rendering primitives as mechanical particles. A reference-configuration Lagrangian transfer maps SPH deformation to Gaussian positions and covariances, thereby coupling the physical model and the image observation model while preserving a differentiable computational path. The SPH formulation supports linear elastic, hyperelastic, and Kelvin--Voigt viscoelastic responses, together with fixed, free, and Robin-type boundary conditions. Numerical studies validate the SPH response against finite-element results, assess accuracy and efficiency against a conventional model using Gaussian centers as surface SPH particles, and demonstrate forward simulations on beam, bridge, and liver-shaped examples. Inverse analyses further estimate constitutive and boundary parameters from rendered deformation observations, including noisy cases, demonstrating the feasibility of the proposed model for mechanics-based parameter identification from image data.
Chinese Translation
近年来，生成世界模型的进展引发了对能够再现真实物体外观及其对物理交互响应的数字模型的兴趣。三维重建技术，包括三维高斯点云（3D Gaussian Splatting），能够从图像和视频中捕捉详细的表面几何形状和外观。然而，将这些表示扩展到机械可解释的模型，以描述本构行为、边界条件和逆向参数识别的研究仍然较少。在本研究中，提出了一种可微分的拉格朗日耦合三维高斯平滑粒子流体动力学（3DGS-SPH）模型，用于可变形固体的正向仿真和逆向分析。首先，从多视角校准的视觉数据集中重建观察对象，形成三维高斯渲染模型。然后，基于包络的程序为固体力学模型生成独立的SPH支持，避免直接使用渲染原语作为机械粒子。参考配置的拉格朗日传递将SPH变形映射到高斯位置和协方差，从而耦合物理模型和图像观测模型，同时保持可微分的计算路径。SPH公式支持线性弹性、超弹性和凯尔文-沃伊特（Kelvin-Voigt）粘弹性响应，以及固定、自由和罗宾型边界条件。数值研究验证了SPH响应与有限元结果的一致性，评估了与使用高斯中心作为表面SPH粒子的常规模型的准确性和效率，并展示了在梁、桥和肝脏形状示例上的正向仿真。逆向分析进一步从渲染的变形观测中估计本构和边界参数，包括噪声案例，展示了该模型在基于图像数据的力学参数识别中的可行性。
cs.CV / 45 / 2610.04346
Any-scale Object Detection using Arbitrary-scaled Images
基于任意尺度图像的任意尺度目标检测
Abstract
This paper proposes any-scale object detection using arbitrary-scale super-resolution for continuously rescaling object images, while general multi-scale object detection uses discretely rescaled appearance representations. However, a naive usage of super-resolution produces many false-positive detections if many super-resolution images are independently fed into an object detector. Our method suppresses these false positives by predicting scale proposal maps, each of which represents a set of pixels appropriate for each super-resolution scale.
Chinese Translation
本文提出了一种基于任意尺度超分辨率的任意尺度目标检测方法，用于对目标图像进行连续重缩放，而传统的多尺度目标检测则使用离散重缩放的外观表示。然而，简单地使用超分辨率会导致许多假阳性检测，尤其是当许多超分辨率图像独立输入到目标检测器时。我们的方法通过预测尺度提议图来抑制这些假阳性，每个尺度提议图表示一组适合于每个超分辨率尺度的像素。
cs.CV / 46 / 2610.04351
LoCoSplat: Real-Time Feed-Forward 3D Gaussian Splatting with Minimal 3D Reasoning
LoCoSplat：实时前馈3D高斯点云渲染与最小3D推理
Abstract
Feed-forward 3D Gaussian Splatting (3DGS) increasingly aggregates multi-view evidence with heavy learned 3D networks. We propose LoCoSplat (Local-Context Splatting), motivated by the observation that a Gaussian is a local primitive: once depth is predicted, what the 3D stage must add (scale, rotation, opacity) depends on the point cloud around each anchor, and a fixed local average of that neighbourhood is enough to supply it, no heavy network required. LoCoSplat realises exactly this average: it splats a 16-d linear projection of the point features into a fine and a coarse grid and reads both back at each anchor with a 0.14M-parameter pointwise MLP; with no learned 3D network and no dynamic sparse computation, its whole encoder runs as one fp16 CUDA graph. On RealEstate10K, LoCoSplat outperforms every prior feed-forward method on PSNR, SSIM, and LPIPS at 6, 12, and 24 views, with a margin that widens as views densify (+3.3 PSNR over VolSplat, the prior voxel-aligned state of the art, at 24 views) and grows further under zero-shot transfer to ACID and fine-tuning on ScanNet. It reconstructs a 6-view scene in 33 ms on one NVIDIA RTX PRO 6000 GPU, the fastest of seven feed-forward methods and $4.2\times$ faster than the previous state of the art, trains $2.7\times$ faster ($5.9\times$ at 24 views), and uses $6.7\times$ less inference memory.
Chinese Translation
前馈3D高斯点云渲染（3DGS）越来越多地依赖于重度学习的3D网络来聚合多视角证据。我们提出了LoCoSplat（局部上下文点云渲染），其动机源于观察到高斯是一个局部原语：一旦预测了深度，3D阶段需要添加的内容（尺度、旋转、不透明度）取决于每个锚点周围的点云，而该邻域的固定局部平均值足以提供这些信息，无需重型网络。LoCoSplat正是实现了这一平均值：它将点特征的16维线性投影分散到细网格和粗网格中，并在每个锚点处通过一个0.14M参数的逐点多层感知机（MLP）读取这两者；由于没有学习的3D网络和动态稀疏计算，其整个编码器作为一个fp16 CUDA图运行。在RealEstate10K数据集上，LoCoSplat在6、12和24个视角下的PSNR、SSIM和LPIPS指标上超越了所有先前的前馈方法，随着视角密度的增加，性能差距进一步扩大（在24个视角下比先前的体素对齐最优方法VolSplat提高了+3.3 PSNR），并在对ACID的零样本迁移和在ScanNet上的微调中进一步提升。它在一台NVIDIA RTX PRO 6000 GPU上以33毫秒重建一个6视角场景，是七种前馈方法中最快的，比之前的最优方法快$4.2 imes$，训练速度快$2.7 imes$（在24个视角下快$5.9 imes$），并且推理内存使用量减少了$6.7 imes$。
cs.CV / 47 / 2610.04356
SelectOccFlow: Selective Spatiotemporal Aggregation for 3D Occupancy and Scene Flow Prediction
SelectOccFlow：用于三维占用和场景流预测的选择性时空聚合
Abstract
Comprehensive 3D scene understanding for autonomous driving requires modeling geometry, semantics, and motion. However, camera-based occupancy and scene flow prediction are sensitive to unreliable spatial and temporal aggregation, caused by semantically incompatible image features, misaligned historical observations, and incomplete voxel structures. To address this issue, we propose SelectOccFlow, a selective spatiotemporal aggregation framework that progressively refines contextual evidence across image, temporal, and voxel domains. To obtain semantically compatible image evidence, we design Semantic-Guided Sampling (SGS) to regulate feature sampling with semantic priors. Since reliable image evidence alone cannot resolve temporal inconsistency, we then present State-Conditioned Temporal Aggregation (SCTA) to selectively retrieve historical evidence according to voxel states. To further enhance the structural completeness of voxel representations, we introduce Extent-Aware Spatial Aggregation (ESA), which exploits directional structural support to refine foreground geometry. Experiments on OpenOcc demonstrate that SelectOccFlow achieves a state-of-the-art OccScore of 44.9, improving the previous best by +4.2%. It also maintains competitive occupancy performance on Occ3D-nus and improves the mean OccScore under nuScenes-C corruptions by +11.1%, demonstrating improved robustness to visual corruptions. The source code will be made publicly available at https://github.com/muchen1021/SelectOccFlow.
Chinese Translation
全面的三维场景理解对于自动驾驶来说需要对几何、语义和运动进行建模。然而，基于摄像头的占用和场景流预测对不可靠的空间和时间聚合非常敏感，这种不可靠性源于语义不兼容的图像特征、未对齐的历史观测和不完整的体素结构。为了解决这个问题，我们提出了SelectOccFlow，一个选择性时空聚合框架，逐步在图像、时间和体素域中精炼上下文证据。为了获得语义兼容的图像证据，我们设计了语义引导采样（Semantic-Guided Sampling, SGS），以语义先验来调节特征采样。由于仅依赖可靠的图像证据无法解决时间不一致性，我们随后提出了状态条件时间聚合（State-Conditioned Temporal Aggregation, SCTA），根据体素状态选择性地检索历史证据。为了进一步增强体素表示的结构完整性，我们引入了范围感知空间聚合（Extent-Aware Spatial Aggregation, ESA），利用方向性结构支持来精炼前景几何。在OpenOcc上的实验表明，SelectOccFlow达到了44.9的最新OccScore，较之前的最佳结果提高了4.2%。它在Occ3D-nus上也保持了竞争力的占用性能，并在nuScenes-C损坏下将平均OccScore提高了11.1%，显示出对视觉损坏的增强鲁棒性。源代码将公开发布在https://github.com/muchen1021/SelectOccFlow。
cs.CV / 48 / 2610.04361
TRIM-ReID: Duplication-Aware Token Reduction and Modality-Aligned Interaction for Multi-Modal Object Re-Identification
TRIM-ReID：面向多模态目标重识别的重复感知令牌减少与模态对齐交互
Abstract
Multi-modal object re-identification exploits complementary RGB, near-infrared (NIR), and thermal-infrared (TIR) observations to retrieve target objects. However, existing methods commonly employ visual encoders optimized for global image-text alignment and select tokens using learned importance scores. Such designs fail to preserve fine-grained identity cues or explicitly account for token redundancy, resulting in underrepresented local evidence and duplicated tokens that lead to noisy and costly cross-modal interaction. To address this gap, we propose TRIM-ReID, a compact framework that unifies dense feature extraction, intra-modal token reduction, and inter-modal aligned interaction. Specifically, semantically rich and spatially coherent patch features are extracted by Dense Identity Representation (DIR), which leverages DINOv3 to preserve fine-grained identity information. We then introduce Token Diversity Mining (TDM) to identify complementary local evidence and construct compact modality-specific token sets by suppressing repetitive patches while preserving informative diversity. Retained tokens are subsequently fused by Modal Relational Interaction (MRI) to enable effective information exchange across modalities, while a triangular alignment loss explicitly regularizes their joint relationships to maintain cross-modal semantic consistency under independent token selection. Extensive experiments on RGBNT201, RGBNT100, and MSVR310 demonstrate that TRIM-ReID achieves state-of-the-art performance.
Chinese Translation
多模态目标重识别利用互补的RGB、近红外（NIR）和热红外（TIR）观测来检索目标对象。然而，现有方法通常采用针对全局图像-文本对齐优化的视觉编码器，并使用学习到的重要性评分选择令牌。这种设计未能保留细粒度的身份线索或明确考虑令牌冗余，导致局部证据不足和重复令牌，从而导致噪声和高成本的跨模态交互。为了解决这一问题，我们提出了TRIM-ReID，一个统一密集特征提取、模态内令牌减少和模态间对齐交互的紧凑框架。具体而言，通过密集身份表示（Dense Identity Representation, DIR）提取语义丰富且空间一致的补丁特征，该方法利用DINOv3保留细粒度的身份信息。然后，我们引入令牌多样性挖掘（Token Diversity Mining, TDM）来识别互补的局部证据，并通过抑制重复补丁同时保留信息多样性来构建紧凑的模态特定令牌集。保留的令牌随后通过模态关系交互（Modal Relational Interaction, MRI）进行融合，以实现跨模态的有效信息交换，同时三角对齐损失明确规范它们的联合关系，以在独立令牌选择下保持跨模态的语义一致性。在RGBNT201、RGBNT100和MSVR310上的广泛实验表明，TRIM-ReID实现了最先进的性能。
cs.CV / 49 / 2610.04381
Beyond Plausibility: Verifiable Fine-Grained Image Editing on Structured Assets
超越可信性：对结构化资产进行可验证的细粒度图像编辑
Abstract
Fine-grained image editing requires more than producing a visually plausible result: an editor must execute the requested attribute change precisely while leaving everything else intact. However, existing benchmarks leave a critical gap between realism and verifiability: benchmarks built on realistic images typically rely on human or vision--language model judgments, while deterministic evaluation has largely focused on synthetic shape canvases, with application-oriented extensions primarily limited to charts. This makes it difficult to determine precisely how much of a requested edit was executed, where unintended changes occurred, and whether small differences between models reflect genuine editing capability or evaluator uncertainty. To bridge this gap, we present VeriEdit-Bench, a benchmark for fine-grained, instruction-faithful image editing across realistic structured assets with deterministic, four-axis evaluation. Its 1,740 cases are compiled from the source code of 153 Scalable Vector Graphics (SVG) graphics, charts, web interfaces, and presentation slides. Controlled source-code edits preserve the original visual context while yielding exact target images, pixel-level edit masks, and explicit edit specifications, enabling reproducible scoring along four axes: edit fidelity, preservation, localization, and magnitude. Evaluating eleven editors, we find that even the strongest model remains far from full credit; rankings for the same recoloring operation reverse between charts and SVG graphics; and outputs with similar pixel-accuracy profiles can still differ substantially in localization and change magnitude. This decomposition yields graded, verifiable feedback and exposes model-specific capability and failure profiles that holistic scores or evaluator-dependent judgments may obscure.
Chinese Translation
细粒度图像编辑不仅需要生成视觉上可信的结果：编辑者必须精确执行所请求的属性更改，同时保持其他部分不变。然而，现有的基准测试在现实性和可验证性之间存在一个关键的差距：基于真实图像构建的基准通常依赖于人类或视觉-语言模型的判断，而确定性评估主要集中在合成形状画布上，应用导向的扩展主要限于图表。这使得很难准确确定请求的编辑执行了多少，意外更改发生在哪里，以及模型之间的小差异是否反映了真正的编辑能力或评估者的不确定性。为了弥补这一差距，我们提出了 VeriEdit-Bench，这是一个针对现实结构化资产的细粒度、遵循指令的图像编辑基准，具有确定性的四轴评估。其1,740个案例来自153个可缩放矢量图形（SVG）图形、图表、网页界面和演示幻灯片的源代码。受控的源代码编辑保留了原始视觉上下文，同时生成精确的目标图像、像素级编辑掩码和明确的编辑规范，从而实现沿四个轴的可重复评分：编辑保真度、保留、定位和幅度。评估了十一种编辑器后，我们发现即使是最强大的模型也远未获得满分；同一重着色操作在图表和SVG图形之间的排名相反；而具有相似像素精度特征的输出在定位和变化幅度上仍可能存在显著差异。这种分解提供了分级的、可验证的反馈，并揭示了模型特定的能力和失败特征，而整体评分或依赖评估者的判断可能会掩盖这些特征。
cs.CV / 50 / 2610.04392
Detecting Defects that Matter: An Application-Driven Benchmark for Anomaly Detection in Manufacturing and Retail Logistics (VAND 4.0 Challenge)
检测重要缺陷：制造和零售物流中异常检测的应用驱动基准（VAND 4.0 挑战）
Abstract
Existing Anomaly Detection benchmarks are saturated and often unrealistic. As part of the VAND 4.0 Challenge, we introduce a hidden-test, application-driven benchmark across two deployment-critical domains: industrial manufacturing and retail logistics. In the Industrial Track (MVTec AD 2), the results reveal that unsupervised anomaly segmentation remains challenging: the best regular-setting method achieves only ~57\% pixel-level $SegF_1$, indicating substantial room for improvement. Zero-shot approaches trail by ~15 $SegF_1$ points, confirming that task-specific training on normal data remains essential for precise defect localization. Robustness to distribution shifts remains a key open challenge and DINOv3-backbones clearly dominate this track. In the Retail Track (Kaputt 2), the results reveal that (1) supervised defect detection is approaching saturation for common defect types; (2) the best off-the-shelf VLM approach trails specialized models by ~28 AP, confirming that currently VLMs cannot replace fine-tuned detectors, (3) reference images did not prove helpful for top-performing approaches. Performance collapses on rare defects (spillage ~53 AP, missing units ~27 AP), where the supervised ceiling is bounded by data availability. To drive future progress in this domain, we provide a new low-prevalence retail AD dataset (Kaputt-Rare). Across both tracks, computational efficiency is assessed as a first-class metric combining performance, throughput, memory, and power consumption. We introduce a novel metric for measuring efficiency and reveal that that top-performing methods rely on heavy architectures while efficiency is largely neglected. Overall, we conclude that the community needs (a) more efficiency-aware method development, and (b) true anomaly detection approaches for rare defects and shifting conditions. https://sites.google.com/view/vand4-cvpr2026/challenge
Chinese Translation
现有的异常检测基准已趋于饱和，且往往不切实际。作为 VAND 4.0 挑战的一部分，我们引入了一个隐藏测试的应用驱动基准，涵盖两个关键部署领域：工业制造和零售物流。在工业轨道（MVTec AD 2）中，结果表明无监督异常分割仍然具有挑战性：最佳常规设置方法仅达到约 57\% 的像素级 $SegF_1$，这表明仍有很大的改进空间。零样本方法落后约 15 个 $SegF_1$ 点，确认了在正常数据上进行特定任务训练对于精确缺陷定位仍然至关重要。对分布变化的鲁棒性仍然是一个关键的开放挑战，而 DINOv3 主干明显主导了这一轨道。在零售轨道（Kaputt 2）中，结果显示（1）对常见缺陷类型的监督缺陷检测接近饱和；（2）最佳的现成 VLM 方法落后于专业模型约 28 AP，确认了当前 VLM 无法替代经过微调的检测器；（3）参考图像对表现最佳的方法并没有提供帮助。在稀有缺陷（溢出约 53 AP，缺失单元约 27 AP）上，性能崩溃，监督上限受到数据可用性的限制。为了推动该领域的未来进展，我们提供了一个新的低流行率零售 AD 数据集（Kaputt-Rare）。在两个轨道中，计算效率被评估为一个一流的指标，结合了性能、吞吐量、内存和功耗。我们引入了一种新的效率测量指标，并揭示了表现最佳的方法依赖于重型架构，而效率在很大程度上被忽视。总体而言，我们得出结论，社区需要（a）更多关注效率的方法开发，以及（b）针对稀有缺陷和变化条件的真正异常检测方法。
cs.CV / 51 / 2610.04425
AgroGround: Multi-Granularity Grounded Recognition in Agriculture
AgroGround：农业领域的多粒度基础识别
Abstract
Agricultural visual models are typically evaluated for either recognition or localization, but reliable diagnosis requires identifying what is present and localizing the evidence. Agricultural visual question answering (VQA) datasets carry rich semantic labels but rarely link them to image regions, and adding such annotations by hand is costly at scale. We introduce AgroGround, a large-scale dataset for grounded agricultural recognition: identifying plant diseases and other agricultural targets and localizing their image regions. An automated pipeline converts the labels of eight agricultural VQA datasets into annotations for disease lesions and whole objects, producing 794,850 instruction examples. Healthy images provide negative supervision for disease queries, teaching the model to return empty predictions. We fine-tune a shared vision-language model on known-target grounding instructions combined with instructions requiring both recognition and localization. We evaluate predicted identities, regions, joint correctness, and healthy-image abstention on 1,480 human-verified images disjoint from all training data. Grounding-only fine-tuning reduces recognition accuracy from 51.8\% to 29.1\%, while adding recognition-and-localization instructions raises it to 72.6\%. With images and annotations held fixed, combining the two formats raises joint accuracy from 19.2\% to 43.3\% at comparable grounding. Healthy negatives raise abstention on healthy images to 95.0\%, and reinforcement learning improves lesion-level grounding. The resulting 2B model exceeds its annotation teacher in grounding F1 on our benchmark and on the external PlantSeg test set. AgroGround establishes a benchmark for grounded agricultural recognition, measuring joint correctness of identity and localization along with abstention on healthy images. The code is available at https://github.com/AB-Abdulla/AgroGround.
Chinese Translation
农业视觉模型通常仅针对识别或定位进行评估，但可靠的诊断需要识别存在的对象并定位证据。农业视觉问答（VQA）数据集包含丰富的语义标签，但很少将其与图像区域关联，手动添加此类注释在大规模上成本高昂。我们推出了AgroGround，一个大规模的基础农业识别数据集：识别植物病害和其他农业目标，并定位其图像区域。一个自动化管道将八个农业VQA数据集的标签转换为病变和整体对象的注释，生成794,850个指令示例。健康图像为病害查询提供负监督，教会模型返回空预测。我们在已知目标的基础指令上微调一个共享的视觉-语言模型，并结合需要同时进行识别和定位的指令。我们在1480张与所有训练数据不重叠的人类验证图像上评估预测的身份、区域、联合正确性和健康图像的弃权。仅进行基础微调将识别准确率从51.8%降低到29.1%，而添加识别和定位指令则将其提高到72.6%。在固定图像和注释的情况下，结合这两种格式使联合准确率从19.2%提高到43.3%，且基础保持相当。健康负样本将健康图像的弃权率提高到95.0%，强化学习改善了病变级别的基础。最终得到的2B模型在我们的基准测试和外部PlantSeg测试集上在基础F1得分上超过了其注释教师。AgroGround为基础农业识别建立了基准，测量身份和定位的联合正确性以及健康图像的弃权。代码可在 https://github.com/AB-Abdulla/AgroGround 获取。
cs.CV / 52 / 2610.04432
Video2World: Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos
Video2World：基于具身视频的互动世界建模编码代理的基准测试
Abstract
Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on manual environment construction and calibration. We study whether frontier foundation models and coding agents can automate this process end to end. We formulate \emph{autonomous video-to-simulation} as a software engineering task in which an agent observes an embodied video, constructs the corresponding simulated environment and robot behavior, and iteratively refines the result through execution feedback. To evaluate this capability, we introduce \textbf{Video2World}, a benchmark comprising 222 reconstruction instances derived from 189 robot and human demonstration videos. Video2World measures reconstructed worlds along geometric fidelity, dynamic fidelity, and functional correctness, capturing spatial perception, physical reasoning, and executable interaction. Evaluating 9 frontier coding-agent systems reveals a sharp improvement in Task success beginning with Claude Opus 5, rising from below 5\% to over 15\%, while substantial gaps to human-assisted reconstruction remain. We further find that worlds that look better could work worse: better visual fidelity does not always lead to higher task success. This echoes the broader gap between perceptual realism and factual correctness observed in generative models.
Chinese Translation
从现实世界观察中构建互动模拟器是一种扩展具身数据的有前景的方法，但当前的流程仍然严重依赖于手动环境构建和校准。我们研究前沿基础模型和编码代理是否能够实现这一过程的全自动化。我们将 extit{自主视频到模拟}形式化为一个软件工程任务，其中代理观察一个具身视频，构建相应的模拟环境和机器人行为，并通过执行反馈迭代地优化结果。为了评估这一能力，我们引入了 extbf{Video2World}，这是一个基准测试，包含来自189个机器人和人类演示视频的222个重建实例。Video2World在几何保真度、动态保真度和功能正确性方面测量重建的世界，捕捉空间感知、物理推理和可执行交互。对9个前沿编码代理系统的评估显示，从Claude Opus 5开始，任务成功率有了显著提升，从5elow ext{ }%以下上升到15 ext{ }%以上，而与人类辅助重建之间仍存在显著差距。我们进一步发现，看起来更好的世界可能表现更差：更好的视觉保真度并不总是导致更高的任务成功率。这反映了在生成模型中观察到的感知现实主义与事实正确性之间的更广泛差距。
cs.CV / 53 / 2610.04456
Multi-Crop Leaf Disease Recognition: A Unified Benchmark and Cross-Region Study
多作物叶片疾病识别：统一基准与跨区域研究
Abstract
Deep learning models for crop leaf disease recognition routinely report near-perfect accuracy yet are typically trained and evaluated on a single dataset collected under controlled laboratory conditions, leaving their behavior under realistic cross-region domain shift poorly understood. We introduce MLD (Multi-crop Leaf Disease) dataset, a unified multi-region benchmark that combines six public crop-disease datasets from the USA, Asia, and Africa into a shared hierarchical taxonomy spanning 18 crops, 56 crop-disease classes (including one healthy class per crop) making 167,427 images. We define standardized single-source and pooled multi-source evaluation protocols that explicitly probe cross-region generalization. We also investigate whether exploiting the inherent crop-to-disease dependency via a hierarchical formulation (HiLeaD) that conditions disease prediction on the predicted crop improves recognition under cross-region shift. Under the HiLeaD, the model trained on PlantVillage achieves 99.07% in-domain disease F1 but collapses to 12.88% when tested on PlantDoc, exposing a severe cross-region domain gap. The model trained on the pooled MLD dataset partly recovers cross-region disease F1 from 12.88% to 39.64% on PlantDoc (HiLeaD), achieving a 26.76 percentage point improvement. The hierarchical formulation provides a consistent additional gain, ranging from 1.71 to 4.88 percentage points in disease F1 over the flat baseline under the MLD dataset indicating that progress in this area is currently limited more by data coverage and diversity than by model design.
Chinese Translation
用于作物叶片疾病识别的深度学习模型通常报告近乎完美的准确率，但通常是在受控实验室条件下收集的单一数据集上进行训练和评估，因此在现实的跨区域领域转移下，其行为尚不清楚。我们引入了MLD（多作物叶片疾病）数据集，这是一个统一的多区域基准，结合了来自美国、亚洲和非洲的六个公共作物疾病数据集，形成一个涵盖18种作物、56个作物疾病类别（每种作物包括一个健康类别）的共享层次分类法，共计167,427张图像。我们定义了标准化的单源和汇总多源评估协议，明确探讨跨区域泛化能力。我们还研究了通过层次化公式（HiLeaD）利用固有的作物与疾病依赖关系，条件化疾病预测，以提高跨区域转移下的识别能力。在HiLeaD下，基于PlantVillage训练的模型在领域内疾病F1得分达到99.07%，但在PlantDoc测试时降至12.88%，暴露出严重的跨区域领域差距。基于汇总的MLD数据集训练的模型在PlantDoc（HiLeaD）上部分恢复了跨区域疾病F1，从12.88%提高到39.64%，实现了26.76个百分点的提升。层次化公式在MLD数据集下提供了一致的额外增益，疾病F1得分比平面基线提高了1.71到4.88个百分点，表明该领域的进展目前更多地受到数据覆盖和多样性的限制，而非模型设计。
cs.CV / 54 / 2610.04457
RPFQ-ViT: Rotated Phase-Frame Quantization for Extremely Low-Bit Weights in Vision Transformers
RPFQ-ViT：用于视觉变换器中极低比特权重的旋转相位框架量化
Abstract
Vision Transformers (ViTs) achieve strong performance on image recognition and mobile vision applications, but their high-dimensional linear projections and attention computations still impose substantial storage and inference costs. Extremely low-bit quantization is a promising solution, yet ViTs often suffer severe accuracy degradation because conventional real-valued scalar codebooks are poorly matched to the directional geometry of Transformer projections. We present RPFQ-ViT, a Rotated Phase-Frame Quantization method that quantizes paired channels in two-dimensional phase planes, enabling low-bit codes to better preserve projection directions while recovering magnitude with lightweight scaling. RPFQ-ViT serves as a drop-in QAT replacement for nn.Linear and does not modify the standard real-valued attention, normalization, or activation computation graph. On ImageNet-1K, RPFQ-ViT-B/16 reaches 79.33% Top-1 / 94.48% Top-5 under W2/A4, Swin-T reaches 79.30% Top-1 / 94.79% Top-5 under W2/A8, and DeiT-S reaches 77.41% Top-1 / 93.11% Top-5 under W2/A8. Ablations, phase-geometry analysis, and direction-preservation metrics show that channel pairing, learnable rotation, phase-anchor learning, and residual phase refinement each improve quantization quality. We further deploy RPFQ-ViT image-classification models on native iOS and Android runtime stacks; with 2-bit packed weights, model size shrinks by roughly $5.4$-$7.1\times$ relative to FP32 and end-to-end on-device latency drops by $1.4$-$1.6\times$. All ImageNet results trained in our codebase use a matched 300-epoch recipe and are reported as mean accuracies over three independent runs. These results show that RPFQ-ViT provides a favorable trade-off among accuracy, compression, and practical mobile deployment for extremely low-bit ViTs.
Chinese Translation
视觉变换器（ViTs）在图像识别和移动视觉应用中表现出色，但其高维线性投影和注意力计算仍然带来了可观的存储和推理成本。极低比特量化是一种有前景的解决方案，但由于传统的实值标量码本与变换器投影的方向几何不匹配，ViTs往往会遭受严重的准确性下降。我们提出了RPFQ-ViT，一种旋转相位框架量化方法，它在二维相位平面中对成对通道进行量化，使低比特码能够更好地保留投影方向，同时通过轻量级缩放恢复幅度。RPFQ-ViT可作为nn.Linear的即插即用QAT替代方案，不修改标准的实值注意力、归一化或激活计算图。在ImageNet-1K上，RPFQ-ViT-B/16在W2/A4下达到79.33%的Top-1准确率和94.48%的Top-5准确率，Swin-T在W2/A8下达到79.30%的Top-1准确率和94.79%的Top-5准确率，而DeiT-S在W2/A8下达到77.41%的Top-1准确率和93.11%的Top-5准确率。消融实验、相位几何分析和方向保留指标表明，通道配对、可学习旋转、相位锚学习和残差相位细化均提高了量化质量。我们进一步在原生iOS和Android运行环境中部署RPFQ-ViT图像分类模型；使用2比特打包权重，模型大小相对于FP32缩小了约$5.4$-$7.1 imes$，端到端的设备延迟降低了$1.4$-$1.6 imes$。所有在我们代码库中训练的ImageNet结果均使用匹配的300个周期配方，并报告为三次独立运行的平均准确率。这些结果表明，RPFQ-ViT在极低比特ViTs的准确性、压缩和实际移动部署之间提供了良好的权衡。
cs.CV / 55 / 2610.04499
Homogeneous Semantic Alignment and Hierarchical Expert Routing for Radiology Report Generation
均匀语义对齐与分层专家路由用于放射学报告生成
Abstract
Radiology report generation (RRG) aims to convert medical images into diagnostic texts to assist in clinical decision-making and alleviate the workload of physicians. Although existing methods have made extensive progress in cross-modal interaction and the incorporation of external priors, the distribution shift of underlying representations and the undifferentiated rigid coupling of heterogeneous information cause weak visual abnormality cues to be easily diluted by massive text priors and generation inertia during decoding. To overcome this bottleneck, inspired by cognitive science, we propose a novel two-stage Homogeneous Semantic Alignment and Hierarchical Expert Routing (HSA-HER) framework. First, the model introduces an explicit homogeneous distribution constraint in the underlying latent space to effectively eliminate the cross-modal distribution shift between visual and textual features, thereby extracting purified visual features as semantic anchors that accurately align with diseases. Second, for heterogeneous clinical evidence composed of visual features, local entities, and global retrievals, we design a hierarchical expert routing mechanism guided by these disease semantic anchors. This mechanism abandons the undifferentiated rigid coupling paradigm. Specifically, it dynamically activates expert networks to perform targeted mining and semantic reconstruction on multi-source evidence, and adaptively allocates fusion weights. Extensive experiments on three mainstream benchmark datasets demonstrate that HSA-HER achieves state-of-the-art performance, accurately depicting complex imaging details and key diagnostic information.
Chinese Translation
放射学报告生成（RRG）旨在将医学图像转化为诊断文本，以辅助临床决策并减轻医生的工作负担。尽管现有方法在跨模态交互和外部先验的结合方面取得了广泛进展，但潜在表征的分布转移以及异构信息的无差别刚性耦合导致微弱的视觉异常线索在大量文本先验和解码过程中的生成惯性下容易被稀释。为了解决这一瓶颈，受到认知科学的启发，我们提出了一种新颖的两阶段均匀语义对齐与分层专家路由（HSA-HER）框架。首先，模型在潜在空间中引入了显式的均匀分布约束，有效消除了视觉特征与文本特征之间的跨模态分布转移，从而提取出作为语义锚点的纯化视觉特征，准确对齐疾病。其次，对于由视觉特征、局部实体和全局检索组成的异构临床证据，我们设计了一种由这些疾病语义锚点引导的分层专家路由机制。该机制摒弃了无差别刚性耦合范式。具体而言，它动态激活专家网络，对多源证据进行针对性的挖掘和语义重构，并自适应分配融合权重。在三个主流基准数据集上的大量实验表明，HSA-HER实现了最先进的性能，准确描绘了复杂的成像细节和关键的诊断信息。
cs.CV / 56 / 2610.04502
Localization Lens for Improving Medical Vision-Language Models
用于改善医学视觉-语言模型的定位透镜
Abstract
Medical Vision-Language Models (Med-VLMs) have demonstrated strong capabilities in clinical tasks. However, they often struggle to understand anatomical structures and spatial positioning, which are crucial for medical reasoning. To address this, we propose a localization-aware enhancement to the Med-VLM pipeline, introducing improvements at three levels: data,architecture, and alignment. First, we introduce localization lens, a set of expert-validated representations that provide richer anatomical and positional context. However, as these representations increase input complexity, we integrate pixel shuffle within the model architecture to filter and refine representations, enhancing spatial information processing while preserving anatomical continuity. Lastly, to effectively align the localization lens representations with textual features, we incorporate decoupled contrastive loss (DCL) alongside the standard loss function. This ensures better feature discrimination and robustness, particularly in data limited medical settings. Through extensive evaluations on medical visual question answering (Med-VQA) datasets, we show that our methodology improves localization-driven performance across different Med-VLM architectures. Our analysis of localization-based questions further reveals that improvements in anatomy and spatial reasoning directly enhance the overall accuracy of Med-VQA upto 6.2%. The proposed approach is model-agnostic and can be seamlessly integrated into existing Med-VLM pipelines. The dataset, code, and trained models will be made publicly available at https://github.com/CVLABLUMS/localizationlens.
Chinese Translation
医学视觉-语言模型（Med-VLMs）在临床任务中展现出强大的能力。然而，它们在理解解剖结构和空间定位方面常常存在困难，这对于医学推理至关重要。为了解决这个问题，我们提出了一种针对Med-VLM流程的定位感知增强，分别在数据、架构和对齐三个层面进行改进。首先，我们引入了定位透镜（localization lens），这是一组经过专家验证的表示，提供更丰富的解剖和位置上下文。然而，由于这些表示增加了输入的复杂性，我们在模型架构中集成了像素重排（pixel shuffle），以过滤和精炼表示，增强空间信息处理，同时保持解剖连续性。最后，为了有效地将定位透镜表示与文本特征对齐，我们在标准损失函数的基础上引入了解耦对比损失（decoupled contrastive loss, DCL）。这确保了更好的特征区分和鲁棒性，特别是在数据有限的医学环境中。通过对医学视觉问答（Med-VQA）数据集的广泛评估，我们展示了我们的方法在不同Med-VLM架构中提高了基于定位的性能。我们对基于定位的问题的分析进一步揭示，解剖和空间推理的改进直接提升了Med-VQA的整体准确性，最高可达6.2%。所提出的方法是模型无关的，可以无缝集成到现有的Med-VLM流程中。数据集、代码和训练模型将公开发布于 https://github.com/CVLABLUMS/localizationlens。
cs.CV / 57 / 2610.04506
EgoExo-Next:Benchmarking Vision-Language Models on Visual-Option Next-State and Cross-View Reasoning
EgoExo-Next：视觉语言模型在视觉选项下一状态和跨视图推理上的基准评估
Abstract
Vision-language models (VLMs) are increasingly evaluated for egocentric and cross-view video reasoning, yet existing benchmarks largely focus on semantic event understanding, temporal relations, or correspondence between already observed views, leaving their ability to reason directly about future visual states underexplored. We introduce EgoExo-Next, a visual-option benchmark for dynamic visual-state reasoning, where models must identify how an observed action trajectory subsequently appears rather than predict only an action label or textual description. EgoExo-Next contains 2,503 human-curated four-choice questions from six public egocentric and ego--exo video sources and comprises four interconnected subtasks that evaluate egocentric next-state prediction, bidirectional ego--exo state correspondence, exocentric next-state prediction, and their composition in Ego-to-Exo Next-State. Extensive evaluation of proprietary, open-source, and spatial reasoning VLMs reveals a substantial human--model gap, with the best model achieving 43.81\% average accuracy compared with 98.55\% for humans, and the largest degradation occurring on the composed Ego-to-Exo task. These results suggest that current VLMs remain substantially limited in dynamic visual-state reasoning, particularly when temporal progression and cross-view reasoning must be composed. The benchmark is publicly available at \url{https://huggingface.co/datasets/yutongli2024/EgoExo-Next}.
Chinese Translation
视觉语言模型（VLMs）越来越多地用于评估自我中心和跨视图视频推理，但现有基准主要集中在语义事件理解、时间关系或已观察视图之间的对应关系上，直接推理未来视觉状态的能力尚未得到充分探索。我们引入了EgoExo-Next，这是一个用于动态视觉状态推理的视觉选项基准，在该基准中，模型必须识别观察到的动作轨迹随后如何呈现，而不仅仅是预测一个动作标签或文本描述。EgoExo-Next包含来自六个公共自我中心和自我-外部视频源的2503个人工策划的四选一问题，并包括四个相互关联的子任务，评估自我中心的下一状态预测、双向自我-外部状态对应、外部下一状态预测及其在自我到外部下一状态中的组合。对专有、开源和空间推理VLM的广泛评估显示出显著的人类-模型差距，最佳模型的平均准确率为43.81\%，而人类为98.55\\%，在组合的自我到外部任务中降幅最大。这些结果表明，当前的VLM在动态视觉状态推理方面仍然存在显著限制，特别是在需要组合时间进展和跨视图推理时。该基准可在 https://huggingface.co/datasets/yutongli2024/EgoExo-Next 上公开获取。
cs.CV / 58 / 2610.04529
TAME:Topology-Aware Text-Driven Motion Editing across Heterogeneous Humanoid Skeletons
TAME：跨异构类人骨架的拓扑感知文本驱动运动编辑
Abstract
Text-driven motion editing modifies an existing motion sequence according to a text instruction while preserving the content of the source motion. Existing methods are typically built for a single, fixed skeletal topology, which limits their use in animation pipelines where characters differ in joint count and skeletal hierarchy. We present Topology-Aware Motion Editor (TAME), a flow-matching transformer that edits motions on humanoid skeletons of varying topology. TAME represents motion as per-joint, per-frame tokens and models interactions among joints, across frames, and with the text instruction through skeletal, temporal, and text cross-attention layers. To make the skeletal attention follow each character's hierarchy, TAME replaces full joint attention with Topology-Constrained Skeletal Propagation (TCSP), which restricts attention to one-hop kinematic neighbors in the skeleton's adjacency matrix. We further introduce Edit-Focused Representation Alignment (EFRA), a self-distilled representation alignment strategy that aligns student features with cleaner EMA-teacher features exclusively on edit-relevant joint-time tokens, making edits faithful to the instruction. To make this setting trainable and comparable, we construct TopoMotionFix, a multi-topology extension of MotionFix with seen- and unseen-topology evaluation protocols. TAME outperforms previous methods in edit alignment and source preservation on MotionFix and reliably edits motions on unseen skeletons in TopoMotionFix.
Chinese Translation
文本驱动的运动编辑根据文本指令修改现有的运动序列，同时保持源运动的内容。现有方法通常针对单一固定的骨架拓扑构建，这限制了它们在角色关节数量和骨架层次结构不同的动画管道中的应用。我们提出了拓扑感知运动编辑器（TAME），一种流匹配变换器，能够在具有不同拓扑的类人骨架上编辑运动。TAME将运动表示为每个关节、每帧的标记，并通过骨架、时间和文本交叉注意层建模关节之间、帧之间以及与文本指令之间的交互。为了使骨架注意力遵循每个角色的层次结构，TAME用拓扑约束骨架传播（TCSP）替代了全关节注意力，该方法将注意力限制在骨架邻接矩阵中的一跳运动邻居上。我们进一步引入了编辑聚焦表示对齐（EFRA），这是一种自蒸馏的表示对齐策略，专门在与编辑相关的关节时间标记上将学生特征与更清晰的EMA教师特征对齐，从而使编辑忠实于指令。为了使这一设置可训练且可比较，我们构建了TopoMotionFix，这是MotionFix的多拓扑扩展，具有已见和未见拓扑的评估协议。TAME在MotionFix上的编辑对齐和源保持方面优于之前的方法，并且在TopoMotionFix中可靠地编辑未见骨架的运动。
cs.CV / 59 / 2610.04538
FASTER: Fast Adjoint Stochastic Transport for Endpoint Refinement in Reward-Guided Image Editing
FASTER：用于奖励引导图像编辑中端点细化的快速伴随随机传输
Abstract
Reward-guided image editing at test time seeks to improve a specified reward while preserving source content and visual plausibility. Many existing approaches optimize candidates through pretrained generation processes, making repeated adjustment depend on costly large-model execution and, in some cases, backbone backpropagation. We develop a theoretical framework that jointly accounts for reward, source preservation, and pretrained-prior preferences, allowing the desired output distribution to be specified separately from the dynamics used to realize it. Based on this framework, we introduce FASTER, which trains a small network for each source and objective to perform inexpensive editing, while pretrained and reward models provide feedback on candidate outputs. By reusing each candidate and its feedback across multiple small-network updates, FASTER reduces repeated sampling and supervision queries without placing the pretrained generative backbone inside the inner optimization loop. On SD3, FASTER leads all four target metrics and several validation metrics among the evaluated methods. Compared with the evaluated baseline that optimizes controls along pretrained generation trajectories, FASTER achieves editing-time speedups of up to \({6.91\times}\) on Stable Diffusion 3 and \({24.14\times}\) on Stable Diffusion 1.5.
Chinese Translation
在测试阶段，奖励引导的图像编辑旨在提高特定的奖励，同时保持源内容和视觉 plausibility。许多现有方法通过预训练生成过程优化候选项，使得重复调整依赖于昂贵的大型模型执行，并且在某些情况下还依赖于主干反向传播。我们开发了一个理论框架，该框架联合考虑奖励、源保留和预训练先验偏好，允许所需的输出分布与实现它所使用的动态分开指定。基于该框架，我们引入了FASTER，它为每个源和目标训练一个小型网络，以执行低成本的编辑，同时预训练和奖励模型对候选输出提供反馈。通过在多个小型网络更新中重用每个候选项及其反馈，FASTER减少了重复采样和监督查询，而不将预训练生成主干放置在内部优化循环中。在SD3上，FASTER在评估方法中领先于所有四个目标指标和多个验证指标。与沿预训练生成轨迹优化控制的评估基线相比，FASTER在Stable Diffusion 3上实现了高达 ext{6.91} imes的编辑时间加速，在Stable Diffusion 1.5上实现了高达 ext{24.14} imes的加速。
cs.CV / 60 / 2610.04554
EagleDepth: Efficient Fine-Grained Depth Estimation via Pixel Diffusion Decoder
EagleDepth：通过像素扩散解码器实现高效细粒度深度估计
Abstract
Recovering detailed geometry from high-resolution images is critical for precise perception of the surroundings and objects. However, existing methods which use latent-space modeling and VAE reconstruction can compromise geometric details. Furthermore, decoding from latent codes introduces substantial inference overhead. To address those issues, we present EagleDepth, an efficient framework for high-resolution monocular depth estimation that combines the geometric priors of latent diffusion with fine-grained pixel-space generation. Our key idea is to retain depth-aware latent representations as guidance while generating the final depth map directly in pixel space. We train the latent and pixel components sequentially: first, we fine-tune a pretrained latent diffusion model using paired RGB--depth supervision; then, we adapt a pretrained pixel diffusion decoder, PiD, to predict depth conditioned on the learned features. Training of the pixel component starts at 1024 resolution and continues across multiple resolutions up to 4K. The latent branch processes resized, lower-resolution RGB images, while the pixel branch generates depth at the target resolution, bypassing the original VAE decoder. This design preserves learned geometric knowledge without requiring the latent backbone to operate at the output resolution. On five commonly used depth estimation datasets and the high-resolution Synth4K dataset, our framework achieves state-of-the-art depth estimation performance, with faster inference and better preservation of fine structures and object boundaries.
Chinese Translation
从高分辨率图像中恢复详细几何形状对于精确感知周围环境和物体至关重要。然而，现有的方法使用潜在空间建模和变分自编码器（VAE）重建可能会损害几何细节。此外，从潜在编码解码引入了相当大的推理开销。为了解决这些问题，我们提出了EagleDepth，这是一个高效的高分辨率单目深度估计框架，结合了潜在扩散的几何先验和细粒度的像素空间生成。我们的关键思想是在直接在像素空间生成最终深度图时，保留深度感知的潜在表示作为指导。我们顺序训练潜在和像素组件：首先，我们使用配对的RGB-深度监督微调预训练的潜在扩散模型；然后，我们调整预训练的像素扩散解码器PiD，以预测基于学习特征的深度。像素组件的训练从1024分辨率开始，并在多个分辨率下持续到4K。潜在分支处理调整大小后的低分辨率RGB图像，而像素分支在目标分辨率下生成深度，绕过原始的VAE解码器。这种设计在不要求潜在主干在输出分辨率下操作的情况下，保留了学习到的几何知识。在五个常用的深度估计数据集和高分辨率Synth4K数据集上，我们的框架实现了最先进的深度估计性能，具有更快的推理速度和更好的细结构及物体边界的保留。
cs.CV / 61 / 2610.04585
Frozen in a Frame: The Velocity Blind Spot in JEPA World Models
冻结在框架中：JEPA世界模型中的速度盲点
Abstract
Joint-embedding predictive architectures (JEPAs) for world modeling train an encoder so a predictor maps a current embedding and action to the next frame's embedding, always from a single rendered frame. This has a structural blind spot: a renderer without motion blur draws a scene from configuration alone, so a single-frame embedding carries no velocity information, for any encoder, including the official released LeWM weights. We confirm this on official checkpoints across four real benchmarks (PushT, Reacher, Cube, TwoRoom): every linear velocity probe sits at or below chance while position probes reach R^2 about 0.95. We introduce RateIdent, a three-stage diagnostic protocol, and TI-JEPA, a lightweight fix splitting the latent into a pose code and an explicit finite-difference motion code, predicted jointly. Across three physically grounded environments, TI-JEPA gives a significant, seed-robust gain on a stop-at-goal planning task over a matched-memory baseline, e.g. 55% lower final distance on Pendulum (p=3.2x10^-10) and 64% on CartPole (p=5.1x10^-15). We reproduce this at official ViT-Tiny plus AdaLN-transformer scale, then push the same recipe onto real dm_control Reacher photographs trained from scratch, where TI-JEPA's branch separation exceeds the memory-having baseline's by roughly 38x, the paper's largest margin. Against a same-footprint recurrent RSSM-style predictor, TI-JEPA matches or beats its rollout accuracy on two of three environments, stays separately probeable for pose and motion, and wins outright on the most coupled one. A checkable formal argument and six evaluated environments show single-frame targets are the wrong object to predict when velocity matters, and a small, interpretable structural change fixes it with no privileged supervision. Code, checkpoints, and the project page are linked below the title.
Chinese Translation
联合嵌入预测架构（Joint-embedding predictive architectures, JEPA）用于世界建模，训练一个编码器，使得预测器将当前嵌入和动作映射到下一个帧的嵌入，始终基于单个渲染帧。这存在一个结构性盲点：没有运动模糊的渲染器仅根据配置绘制场景，因此单帧嵌入不携带任何速度信息，无论是对于任何编码器，包括官方发布的LeWM权重。我们在四个真实基准（PushT、Reacher、Cube、TwoRoom）的官方检查点上确认了这一点：每个线性速度探测器的表现均处于或低于随机水平，而位置探测器的R^2值约为0.95。我们引入了RateIdent，一个三阶段的诊断协议，以及TI-JEPA，一个轻量级的修复方法，将潜在变量分为姿态编码和显式有限差分运动编码，并共同预测。在三个物理基础的环境中，TI-JEPA在一个停止-到-目标的规划任务上，相较于匹配记忆基线，提供了显著且对种子稳健的提升，例如在Pendulum上最终距离降低55%（p=3.2x10^-10），在CartPole上降低64%（p=5.1x10^-15）。我们在官方的ViT-Tiny和AdaLN-transformer规模上重现了这一结果，然后将相同的方案应用于从零开始训练的真实dm_control Reacher照片，其中TI-JEPA的分支分离超出了具有记忆基线的约38倍，这是本文的最大差距。与同等规模的递归RSSM风格预测器相比，TI-JEPA在三个环境中的两个上匹配或超越了其展开准确性，保持姿态和运动的独立探测能力，并在最耦合的环境中取得了绝对胜利。一个可检查的形式论证和六个评估环境表明，当速度重要时，单帧目标是错误的预测对象，而一个小的、可解释的结构变化可以在没有特权监督的情况下修复这一问题。代码、检查点和项目页面链接在标题下方。
cs.CV / 62 / 2610.04601
WASP: Weakly Aligned Spatiotemporal Pairs for Fetal Brain MRI-Ultrasound Learning
WASP：用于胎儿脑部MRI-超声学习的弱对齐时空配对
Abstract
Magnetic Resonance Imaging (MRI) is widely regarded as the optimal sensor for fetal brain analysis due to its superior soft-tissue contrast and anatomical detail. However, its high cost and operational burden make it invasive and difficult to obtain at scale. Ultrasound (US), in contrast, is cheap, safe, and routinely acquired, and as a result it has produced substantially larger datasets and a growing ecosystem of pretrained models. This asymmetry raises a natural question: Can we teach a US-only model to understand fetal MRI from only a limited set of examples? The standard recipe, training a foundation model on subject-to-subject paired MRI-US scans, is not viable since no such paired fetal dataset is publicly available. In this paper we address this gap with Weakly Aligned Spatiotemporal Pairs (WASP), a framework that formulates cross-modal correspondence as an entropic Optimal Transport problem driven by clinical metadata, in particular Gestational Age (GA) and diagnostic planes, enabling the fitting of a lightweight alignment module that lifts MRI representations into the US latent space, without fine-tuning the backbone. Empirically, WASP yields its largest gains when MRI is unseen by the model during pretraining (on USFM, GA estimation error drops from 21.9 to 17.4 days and standard plane classification accuracy climbs from 61.9% to 69.0%), while providing smaller, backbone-dependent refinements for backbones pretrained on both modalities (e.g., BioMedParse GA estimation error from 6.5 to 6.0 days and SAM-Med2d plane accuracy from 83.3% to 88.1%). Code is available at https://github.com/miccunifi/WASP.
Chinese Translation
磁共振成像（MRI）因其优越的软组织对比度和解剖细节，被广泛认为是胎儿脑部分析的最佳传感器。然而，其高昂的成本和操作负担使得MRI具有侵入性且难以大规模获取。相比之下，超声（US）便宜、安全且常规获取，因此产生了显著更大的数据集和日益增长的预训练模型生态系统。这种不对称性引发了一个自然的问题：我们能否仅通过有限的示例来教会一个仅基于超声的模型理解胎儿MRI？标准的方法是在受试者之间配对的MRI-US扫描上训练基础模型，但由于没有公开的配对胎儿数据集，这种方法不可行。在本文中，我们通过弱对齐时空配对（WASP）来填补这一空白，该框架将跨模态对应关系公式化为一个由临床元数据驱动的熵最优传输问题，特别是妊娠周数（GA）和诊断平面，从而实现一个轻量级对齐模块的拟合，将MRI表示提升到US潜在空间，而无需微调主干网络。实证结果表明，当模型在预训练期间未见到MRI时，WASP的增益最大（在USFM上，GA估计误差从21.9天降至17.4天，标准平面分类准确率从61.9%上升至69.0%），同时为在两种模态上预训练的主干网络提供较小的、依赖于主干的细化（例如，BioMedParse GA估计误差从6.5天降至6.0天，SAM-Med2d平面准确率从83.3%上升至88.1%）。代码可在https://github.com/miccunifi/WASP获取。
cs.CV / 63 / 2610.04602
Organize Primitives into Semantic Parts: Reinforcement Reasoning for 3D Segmentation
将原始元素组织成语义部分：用于3D分割的强化推理
Abstract
Primitive-based 3D segmentation offers a compact and explicit alternative to dense surface prediction, naturally supporting structural abstraction and boundary localization. However, geometric decomposition alone does not determine how primitives should be organized into semantic parts: a single part may span multiple primitives, while geometrically similar or touching primitives may belong to different parts. We therefore introduce RePart (Reinforcement Part Reasoning), which formulates primitive-to-part organization as a finite-horizon Markov decision process and learns semantic organization through trajectory-level reinforcement reasoning. RePart constructs a Composable Primitive Workspace from fine-grained superquadrics and applies a merge-and-stop policy whose decisions are optimized by their downstream effects on the resulting partition rather than local primitive compatibility. The inferred part identities are then mapped back to the original mesh through Boundary-Aware Surface Labeling, preserving accurate surface boundaries beyond the primitive approximation. On PartNet, RePart achieves the strongest results across all four aggregate partition metrics; on 3DCoMPaT++, it obtains the highest RI and SC without target-dataset fine-tuning. These results demonstrate that reinforcement reasoning provides an effective mechanism for organizing geometric primitives into semantic parts while retaining dense segmentation accuracy. Code is available at https://github.com/EngineeringAI-LAB/RePart.
Chinese Translation
基于原始元素的3D分割提供了一种紧凑且明确的替代方案，相较于密集表面预测，自然支持结构抽象和边界定位。然而，单靠几何分解并不能决定原始元素应如何组织成语义部分：一个部分可能跨越多个原始元素，而几何上相似或相接触的原始元素可能属于不同的部分。因此，我们引入了RePart（强化部分推理），将原始元素到部分的组织形式化为有限时域马尔可夫决策过程，并通过轨迹级别的强化推理学习语义组织。RePart从细粒度超四面体构建了一个可组合的原始元素工作空间，并应用了一种合并与停止策略，其决策通过对结果分区的下游影响而非局部原始元素兼容性进行优化。推断出的部分身份随后通过边界感知表面标记映射回原始网格，保持了超出原始元素近似的准确表面边界。在PartNet上，RePart在所有四个聚合分区指标上取得了最强的结果；在3DCoMPaT++上，它在没有目标数据集微调的情况下获得了最高的RI和SC。这些结果表明，强化推理为将几何原始元素组织成语义部分提供了一种有效机制，同时保持了密集分割的准确性。代码可在 https://github.com/EngineeringAI-LAB/RePart 获取。
cs.CV / 64 / 2610.04605
ConEx: Human-Interpretable Saliency Maps via Concept-Aware Attribution
ConEx：通过概念感知归因实现人类可解释的显著性图
Abstract
Many visual explanation methods in computer vision highlight pixel importance but struggle to link these low-level cues to semantically meaningful concepts, limiting their interpretability and trustworthiness. We introduce Concept-based Explanations (ConEx), a novel framework that bridges saliency visualization with concept-based reasoning to provide both faithfulness and interpretability. ConEx automatically discovers class-specific concepts and represents them through concept activation vectors (CAVs), learned without manual supervision using an architecture-specific masking mechanism that reduces noise introduced by the segmentation masks to enhance concept purity. ConEx generates faithful saliency maps that reveal where each concept appears in the image and how it contributes to the prediction. To evaluate the reliability of these learned concepts, we propose two complementary metrics, Vector-Concept Match (VCM) and Concept-Class Match (CCM), that quantify concept alignment and enable direct comparison with existing methods. Extensive experiments across diverse settings demonstrate that ConEx achieves state-of-the-art performance on faithfulness, segmentation, and concept-quality benchmarks. Overall, ConEx advances the field toward truly interpretable and concept-grounded explanations in vision models.
Chinese Translation
许多计算机视觉中的视觉解释方法强调像素的重要性，但在将这些低级线索与语义上有意义的概念联系起来方面存在困难，从而限制了它们的可解释性和可信度。我们提出了基于概念的解释（ConEx），这是一个新颖的框架，旨在将显著性可视化与基于概念的推理相结合，以提供忠实性和可解释性。ConEx 自动发现特定类别的概念，并通过概念激活向量（CAVs）进行表示，这些向量在没有人工监督的情况下通过特定架构的掩蔽机制学习，以减少分割掩膜引入的噪声，从而增强概念的纯度。ConEx 生成忠实的显著性图，揭示每个概念在图像中出现的位置以及它如何贡献于预测。为了评估这些学习到的概念的可靠性，我们提出了两个互补的度量标准：向量-概念匹配（VCM）和概念-类别匹配（CCM），它们量化概念的对齐程度，并能够与现有方法进行直接比较。在多种设置下的广泛实验表明，ConEx 在忠实性、分割和概念质量基准测试中达到了最先进的性能。总体而言，ConEx 推动了该领域朝着真正可解释和基于概念的视觉模型解释的方向发展。
cs.CV / 65 / 2610.04606
Sparse-View 4D Gaussian Splatting via Spatiotemporal Priors and Generative Assistance
基于时空先验和生成辅助的稀疏视图4D高斯点云技术
Abstract
We present a 4D Gaussian Splatting framework for the Sparse-View Track of the SIGGRAPH Asia 2026 Volumetric Video Challenge, which requires dynamic scene reconstruction from only six cameras with wide baselines. To achieve robust dynamic reconstruction under such sparse views, our framework integrates three components. (1) Region-adaptive spatial priors: We use foreground masks to guide Gaussian initialization and mask voting to control densification separately for the dynamic foreground and static background. Background geometry is regularized using monocular depth aligned to metric scale. (2) Motion-consistent temporal priors: We provide supervision at intermediate times through frame interpolation and constrain projected Gaussian motion with estimated optical flow. (3) Generative assistance: We place virtual cameras in the widest angular gaps and restore their rendered images using a diffusion-based model conditioned on camera poses. The restored images are iteratively incorporated into training as pseudo-supervision. On the validation set, our framework improves full-frame PSNR from 25.60 dB for the baseline to 29.75 dB. On the official test benchmark, it achieves 30.04 dB full-frame PSNR and 27.88 dB foreground PSNR, ranking first overall in the Sparse-View Track.
Chinese Translation
我们提出了一种4D高斯点云框架，旨在应对SIGGRAPH Asia 2026体积视频挑战中的稀疏视图轨迹，该挑战要求仅使用六个具有宽基线的摄像头进行动态场景重建。为了在如此稀疏的视图下实现稳健的动态重建，我们的框架整合了三个组件。(1) 区域自适应空间先验：我们使用前景掩模来指导高斯初始化，并通过掩模投票分别控制动态前景和静态背景的密度化。背景几何形状通过与度量尺度对齐的单目深度进行正则化。(2) 运动一致的时间先验：我们通过帧插值在中间时间提供监督，并利用估计的光流约束投影高斯运动。(3) 生成辅助：我们在最宽的角度间隙中放置虚拟摄像机，并使用基于扩散的模型根据摄像机姿态恢复其渲染图像。恢复的图像被迭代地作为伪监督纳入训练。在验证集上，我们的框架将基线的全帧PSNR从25.60 dB提高到29.75 dB。在官方测试基准上，达到了30.04 dB的全帧PSNR和27.88 dB的前景PSNR，在稀疏视图轨迹中排名第一。
cs.CV / 66 / 2610.04627
Task-Sensitive Geometry of Representation Transfer for Object Detection under Image Degradation
图像退化下物体检测的任务敏感表示转移几何
Abstract
Object detection under image degradation can benefit from clean-image supervision, but aggregate gains do not imply that transferred representation changes are uniformly useful. We study how clean task knowledge affects degraded-image representations and whether local responses to structured representation directions can be characterized geometrically. Using paired clean and Gaussian-degraded BDD100K images, we show that clean-teacher distillation improves observed detection accuracy while producing heterogeneous object-level transfer. We isolate a representation component complementary to direct clean-teacher alignment and map it into the distilled student space through an orthogonal bridge. Controlled interventions rescue 13.22% of objects lost under the distilled representation, versus 4.30% under norm-matched random perturbations, with very low harm on preserved objects. We introduce task-sensitive geometry, a gradient-derived channel-space geometry constructed from normalized detection-loss gradients. On a reserved cohort, mapped-complement orientation within this frozen geometry is positively associated with local intervention-response magnitude after controlling for intervention magnitude (partial Spearman $\rho$ = 0.242, 95% CI [0.108, 0.359]). The relationship eplicates on independent data ($\rho$ = 0.180) and with RT-DETR-L ($\rho$ = 0.227), but not for the direct clean-teacher residual family, and it weakens for large interventions. Routing rules and specialized distillation objectives based on these signals do not yield statistically reliable gains over CLEANKD. These results support a local, direction-family-dependent task-sensitive geometry while showing that converting such structure into improved global training remains an open problem.
Chinese Translation
图像退化下的物体检测可以从干净图像的监督中受益，但整体增益并不意味着转移的表示变化在各个方面都是有用的。我们研究了干净任务知识如何影响退化图像的表示，以及结构化表示方向的局部响应是否可以在几何上进行表征。通过使用配对的干净图像和高斯退化的BDD100K图像，我们表明，干净教师蒸馏提高了观察到的检测准确性，同时产生了异质的物体级转移。我们隔离出一个与直接干净教师对齐互补的表示成分，并通过正交桥将其映射到蒸馏学生空间。受控干预在蒸馏表示下挽救了13.22%的丢失物体，而在规范匹配的随机扰动下仅为4.30%，对保留物体的损害非常小。我们引入了任务敏感几何，这是一种基于归一化检测损失梯度构建的梯度导出通道空间几何。在一个保留的队列中，在这个冻结几何内映射的补充方向与局部干预响应幅度呈正相关，控制干预幅度后（偏相关Spearman $
ho$ = 0.242, 95% CI [0.108, 0.359]）。这一关系在独立数据上得到了重复（$
ho$ = 0.180）以及在RT-DETR-L上（$
ho$ = 0.227），但在直接干净教师残差家族中并不成立，并且在大干预时减弱。基于这些信号的路由规则和专门的蒸馏目标未能在统计上可靠地超越CLEANKD。这些结果支持了一种局部的、依赖于方向家族的任务敏感几何，同时表明将这种结构转化为改进的全局训练仍然是一个未解决的问题。
cs.CV / 67 / 2610.04664
FLASHSWIN: Unlocking Large Windows and Dense Tokens in Swin Vision Transformers with Memory Efficient Attention
FLASHSWIN：通过内存高效注意力解锁Swin视觉变换器中的大窗口和密集标记
Abstract
High-resolution vision backbones have long been forced to trade away local token density to afford larger receptive fields. Hierarchical Swin transformers impose this compromise because standard windowed attention materializes an $M^2\times M^2$ score matrix per window, incurring $O(M^4)$ memory as windows or token grids grow. Furthermore, Swin adds a learned relative-position bias elementwise to attention scores, requiring full materialization of the score matrix and its gradient. This keeps Swin and SwinV2 trapped in a small-window($M=8,16$), coarse-token regime with patch size $4\times4$ ($p=4$), limiting performance for fine-grained tasks. We introduce FLASHSWIN, which replaces standard windowed attention with a FlashAttention implementation that computes exact softmax attention without materializing the score matrix, reducing per-window memory from $O(M^4)$ to $O(M^2)$. This enables higher token density and larger receptive fields without inflating memory overhead. Training memory is flat across window sizes: at a $32\times32$ window, FLASHSWIN-T requires only $12.4$\,GB, unchanged from $8\times8$, compared to $70/90$\,GB for SwinV2/V1-T. However, applying FlashAttention directly to Swin creates a trade-off: bypassing the score matrix precludes Swin's additive relative-position bias, forfeiting spatial information in exchange for memory efficiency. FLASHSWIN restores position information as window-local learnable 2D RoPE, making large windows and dense token grids both affordable and accurate. At matched scale, FLASHSWIN-T outperforms Swin variants. With dense tokens and wide windows ($p=2,M=32$), the same Tiny model reaches $84.1\%$ ImageNet-1K, $44.1$ COCO box AP, and $47.28$ ADE20K mIoU---gains of $+1.3$, $+5.1$, and $+1.82$ over SwinV2-T at $M=16$, respectively. At fixed $M=32$, halving the patch size yields roughly $3\times$ larger gains in boundary quality than in mIoU.
Chinese Translation
高分辨率视觉骨干网络长期以来被迫牺牲局部标记密度以获得更大的感受野。分层Swin变换器施加了这种折衷，因为标准窗口注意力在每个窗口上生成一个$M^2 imes M^2$的分数矩阵，随着窗口或标记网格的增长，内存开销达到$O(M^4)$。此外，Swin在注意力分数中逐元素添加了学习的相对位置偏置，这要求完全生成分数矩阵及其梯度。这使得Swin和SwinV2被限制在小窗口（$M=8,16$）、粗标记的模式中，补丁大小为$4 imes4$（$p=4$），限制了其在细粒度任务中的性能。我们提出了FLASHSWIN，它用FlashAttention实现替代了标准窗口注意力，计算准确的softmax注意力而无需生成分数矩阵，将每个窗口的内存从$O(M^4)$降低到$O(M^2)$。这使得在不增加内存开销的情况下实现更高的标记密度和更大的感受野。训练内存在窗口大小上保持平坦：在$32 imes32$窗口下，FLASHSWIN-T仅需$12.4$ GB，与$8 imes8$时相同，而SwinV2/V1-T则需要$70/90$ GB。然而，直接将FlashAttention应用于Swin会产生一个折衷：绕过分数矩阵使得Swin的加性相对位置偏置无法使用，从而牺牲了空间信息以换取内存效率。FLASHSWIN通过窗口局部可学习的2D RoPE恢复了位置信息，使得大窗口和密集标记网格既经济又准确。在相同规模下，FLASHSWIN-T超越了Swin变种。在密集标记和宽窗口（$p=2,M=32$）下，相同的Tiny模型在ImageNet-1K上达到$84.1 ext{%}$，在COCO框AP上达到$44.1$，在ADE20K mIoU上达到$47.28$，分别比SwinV2-T在$M=16$时提高了$+1.3$、$+5.1$和$+1.82$。在固定$M=32$的情况下，减小补丁大小大约带来了$3 imes$的边界质量提升，而mIoU的提升则相对较小。
cs.CV / 68 / 2610.04680
COMPASS: Comet Object Measurement Pipeline with Automated Selection and Scoring
COMPASS：具有自动选择和评分的彗星物体测量管道
Abstract
Summary: The single-cell gel electrophoresis ('comet') assay is a widely used technique for quantifying DNA damage at the individual cell level. However, image analysis often relies on manual inspection or semi-automated software, which can be labor-intensive, difficult to reproduce, and sensitive to image quality and comet morphology. COMPASS automates comet assay image analysis by combining deep learning-based comet segmentation with damage measurement and automated comet selection. The pipeline produces standardized DNA damage measurements while offering robust detection, reducing manual effort and improving reproducibility through transparent selection and optional manual review. Availability and implementation: COMPASS is implemented in Python and is freely available at https://github.com/ rsinghlab/COMPASS. Installation instructions, pretrained weights, and example usage are provided in the repository. Contact:
[email protected],
[email protected] Supplementary information: Available onlime upon publication.
Chinese Translation
摘要：单细胞凝胶电泳（'彗星'）检测是一种广泛使用的技术，用于定量评估单个细胞水平的DNA损伤。然而，图像分析通常依赖于人工检查或半自动化软件，这可能劳动密集、难以复制，并且对图像质量和彗星形态敏感。COMPASS通过结合基于深度学习的彗星分割、损伤测量和自动彗星选择，自动化了彗星检测的图像分析。该管道生成标准化的DNA损伤测量，同时提供稳健的检测，减少人工工作量，并通过透明选择和可选的人工审核提高可重复性。可用性和实现：COMPASS使用Python实现，并可在https://github.com/rsinghlab/COMPASS上免费获取。安装说明、预训练权重和示例用法已在该存储库中提供。联系方式：
[email protected],
[email protected] 额外信息：在出版后可在线获取。
cs.CV / 69 / 2610.04700
Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation
解耦、净化与统一：联邦医学分割的语义-结构原型学习
Abstract
Federated learning enables medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains challenging. Existing representation-based methods face two limitations: 1) Incomplete Contextual Representation Learning: single-layer or coupled representations overlook multi-level structural cues and entangle regional semantics with boundary details. 2) Layerwise Style and Aggregation Biases: domain-specific style discrepancies across intermediate layers degrade prototypes, while aggregation that overlooks client distribution shifts can further amplify bias. We propose FedBCS+, federated decoupled contextual alignment with style-purified aggregation. We employ Frequency-domain Style Recalibration (FSR) in prototype construction to decouple content-style representations and extract style-purified prototypes. Built upon these purified features, Decoupled Contextual Prototype Alignment (DCPA) explicitly decouples multi-level features into semantic and structural prototypes and aligns regional semantics and fine-grained anatomical structures separately. Style-purified Semantic Prototype Aggregation (S2PA) measures each client's purified prototype divergence from the global consensus and adaptively reweights aggregation toward under-represented clients to reduce consensus bias. On five heterogeneous medical segmentation benchmarks spanning histopathology, MRI, ultrasound, and colonoscopy, FedBCS+ achieves the highest mean Dice among the compared methods. A convergence analysis further characterizes how aggregation and alignment affect the optimization bound.
Chinese Translation
联邦学习使医疗机构能够在不共享数据的情况下训练全球模型，但来自不同扫描仪或协议的特征异质性仍然是一个挑战。现有的基于表示的方法面临两个局限：1）不完整的上下文表示学习：单层或耦合表示忽视了多层次结构线索，并将区域语义与边界细节纠缠在一起。2）逐层风格和聚合偏差：中间层之间的领域特定风格差异降低了原型质量，而忽视客户端分布变化的聚合可能进一步放大偏差。我们提出了FedBCS+，即联邦解耦上下文对齐与风格净化聚合。我们在原型构建中采用频域风格重校准（Frequency-domain Style Recalibration, FSR）来解耦内容-风格表示并提取风格净化的原型。在这些净化特征的基础上，解耦上下文原型对齐（Decoupled Contextual Prototype Alignment, DCPA）明确地将多层次特征解耦为语义和结构原型，并分别对区域语义和细粒度解剖结构进行对齐。风格净化语义原型聚合（Style-purified Semantic Prototype Aggregation, S2PA）衡量每个客户端的净化原型与全球共识的偏差，并自适应地重新加权聚合，以减少共识偏差。在涵盖组织病理学、MRI、超声和内窥镜检查的五个异质医学分割基准上，FedBCS+在比较方法中实现了最高的平均Dice值。收敛分析进一步表征了聚合和对齐如何影响优化界限。
cs.CV / 70 / 2610.04721
Knossos and Ariadne: Benchmarking and Learning Complete Diagram Topology Extraction with Vision-Language Models
克诺索斯与阿里阿德涅：基于视觉-语言模型的完整图示拓扑提取基准与学习
Abstract
Structural diagrams are widely used to represent complex systems and relational information across scientific, engineering, procedural, and spatial domains. Recent vision-language models (VLMs) have become increasingly capable of recognizing diagram elements and reasoning about their content, while complete diagram topology extraction remains comparatively underexplored. In this paper, we study diagram-to-graph topology extraction: extracting all diagram entities and the complete relations among them. To enable large-scale supervised training and systematic evaluation of this task, we introduce Knossos, a benchmark of 19,200 diagrams across six diverse domains, with 245,179 nodes and 439,740 edges. Its symbolic generation process provides exact alignment between rendered diagrams and annotations of complete topology, relation types, and connector geometry. To address the modeling challenge of complete topology extraction, we also present Ariadne, a structured framework that decomposes the task into node inventory extraction and source-conditioned edge prediction. Extensive experiments show that training on Knossos substantially improves complete topology extraction in smaller open-source VLMs. Ariadne further improves over one-step extraction under matched supervision, demonstrating the additional benefit of structured decomposition. It achieves the highest average Edge F1 among the evaluated methods on Knossos, while both backbone variants also improve over their unadapted counterparts on the real-world external benchmark. Code and benchmark are available at https://github.com/bangwayne/knossos_Ariadne_Public.
Chinese Translation
结构图广泛用于表示科学、工程、程序和空间领域中的复杂系统和关系信息。近期的视觉-语言模型（VLMs）在识别图示元素和推理其内容方面变得越来越强大，但完整图示拓扑提取仍然相对未被充分探索。本文研究图示到图的拓扑提取：提取所有图示实体及其之间的完整关系。为了实现大规模的监督训练和系统评估该任务，我们引入了克诺索斯（Knossos），这是一个涵盖六个不同领域的19,200个图示的基准，包含245,179个节点和439,740条边。其符号生成过程确保了渲染图示与完整拓扑、关系类型和连接几何的注释之间的精确对齐。为了解决完整拓扑提取的建模挑战，我们还提出了阿里阿德涅（Ariadne），这是一个将任务分解为节点库存提取和源条件边预测的结构化框架。大量实验表明，在克诺索斯上训练显著提高了较小开源VLMs的完整拓扑提取性能。阿里阿德涅在匹配监督下进一步改善了一步提取，展示了结构化分解的额外好处。在评估的克诺索斯方法中，其平均边F1得分最高，同时两个主干变体在真实世界外部基准上也优于未适应的对应方法。代码和基准可在 https://github.com/bangwayne/knossos_Ariadne_Public 获取。
cs.CV / 71 / 2610.04722
NAMVIS: Next-Scale Autoregressive Multi-View Image Synthesis
NAMVIS：下一尺度自回归多视图图像合成
Abstract
Sparse-view novel view synthesis is a central problem in 3D content creation, but diffusion-based approaches remain limited by iterative denoising, making multi-view generation expensive at inference time. We introduce NAMVIS, a diffusion-free framework that reformulates multi-view image synthesis as geometry-conditioned next-scale autoregression. Instead of generating target views through repeated denoising, NAMVIS predicts discrete visual tokens through a small number of coarse-to-fine scale steps, while sampling all tokens within each scale and across target views in parallel. To anchor this generation process to explicit camera geometry, we propose Multi-scale Projective Pose Encoding, which injects source and target camera transformations into both target-view self-attention and source-to-target cross-attention at every resolution. NAMVIS further combines global conditioning with dense geometry-aware cross-attention, enabling the model to preserve source-view appearance while maintaining target-view consistency. Across Objaverse, GSO, and OmniObject3D, NAMVIS outperforms diffusion-based baselines in PSNR, SSIM, and LPIPS, while running over 3 times faster than the evaluated diffusion baselines under the same evaluation setting. These results suggest that geometry-conditioned next-scale autoregression is a promising and efficient alternative to diffusion for sparse-view multi-view synthesis. Additional qualitative results, videos, and resources are available at https://corl-team.github.io/namvis/
Chinese Translation
稀疏视图新视图合成是3D内容创作中的一个核心问题，但基于扩散的方法受到迭代去噪的限制，使得多视图生成在推理时成本高昂。我们提出了NAMVIS，一个无扩散框架，将多视图图像合成重新表述为几何条件下的下一尺度自回归。NAMVIS通过少量粗到细的尺度步骤预测离散视觉标记，而不是通过重复去噪生成目标视图，同时在每个尺度和目标视图之间并行采样所有标记。为了将这一生成过程锚定到明确的相机几何上，我们提出了多尺度投影姿态编码（Multi-scale Projective Pose Encoding），该编码在每个分辨率下将源相机和目标相机的变换注入到目标视图自注意力和源到目标的交叉注意力中。NAMVIS进一步结合了全局条件和密集的几何感知交叉注意力，使得模型能够在保持目标视图一致性的同时保留源视图的外观。在Objaverse、GSO和OmniObject3D数据集上，NAMVIS在PSNR、SSIM和LPIPS指标上优于基于扩散的基线，同时在相同评估设置下运行速度超过评估的扩散基线的3倍。这些结果表明，几何条件下的下一尺度自回归是稀疏视图多视图合成的一个有前景且高效的替代方案。更多的定性结果、视频和资源可在 https://corl-team.github.io/namvis/ 获取。
cs.CV / 72 / 2610.04727
Investigating Spatiotemporal Redundancy in Video Transformer for Collision Anticipation
视频变换器中时空冗余的研究：碰撞预测
Abstract
In worker-equipment proximity monitoring, video transformers are widely used for collision anticipation and have demonstrated strong performance. However, their accuracy comes with substantial computational demands, creating a tension with the need for low-latency inference on mobile robots and the pursuit of lower-carbon computation in construction. To address this, this study investigates where computation within an established video transformer is redundant and whether that redundancy can be removed without materially degrading predictive performance. Using VideoMAEv2-Base on the Nexar Collision Prediction dataset, we first examine how collision-relevant information evolves across network depth and then investigate two complementary forms of redundancy: structured capacity redundancy in multilayer perceptrons (MLPs) and spatiotemporal redundancy in the token stream. Linear probes show that interpretable motion cues, including flow magnitude, looming, and approach versus retreat, are most accessible at intermediate layers, whereas collision-label discrimination strengthens toward the final layer. Token redundancy is axis-specific: adjacent temporal-token similarity reaches 0.970 in later layers, while spatial similarity falls to 0.297, indicating substantially greater redundancy across time than across space. Exploiting this asymmetry, temporal token merging reduces backbone computation from 356.99 to 178.50 GFLOPs and latency from 12.69 to 7.21 ms per clip, a 1.76x speedup, while mean average precision changes only from 0.7478 to 0.7443. Importance-guided retention of 50% of MLP units preserves an AUC of 0.753, compared with 0.529 under matched random retention, and reveals that pruning alters score calibration before discriminative ranking collapses. These findings establish a new pathway for pursuing faster algorithms through targeted temporal token compression and neuron pruning.
Chinese Translation
在工人设备接近监测中，视频变换器被广泛用于碰撞预测，并表现出强大的性能。然而，它们的准确性伴随着巨大的计算需求，这与移动机器人对低延迟推理的需求以及建筑领域对低碳计算的追求形成了矛盾。为了解决这个问题，本研究调查了在一个已建立的视频变换器中，计算的冗余性以及这种冗余是否可以在不显著降低预测性能的情况下被消除。我们使用 VideoMAEv2-Base 在 Nexar 碰撞预测数据集上，首先考察了与碰撞相关的信息如何在网络深度中演变，然后研究了两种互补的冗余形式：多层感知器（MLP）中的结构容量冗余和标记流中的时空冗余。线性探针显示，可解释的运动线索（包括流动幅度、逼近和接近与撤退）在中间层最为显著，而碰撞标签的区分能力在最后一层增强。标记冗余是轴特定的：在后续层中，相邻时间标记的相似性达到 0.970，而空间相似性降至 0.297，表明时间上的冗余远大于空间上的冗余。利用这种不对称性，时间标记合并将主干计算从 356.99 降低到 178.50 GFLOPs，延迟从每个片段 12.69 ms 降至 7.21 ms，实现了 1.76 倍的加速，而平均精度仅从 0.7478 变为 0.7443。重要性引导下保留 50% 的 MLP 单元保持了 0.753 的 AUC，而在随机匹配保留下仅为 0.529，并揭示了修剪在区分排名崩溃之前改变了评分校准。这些发现为通过有针对性的时间标记压缩和神经元修剪追求更快的算法建立了一条新路径。
cs.CV / 73 / 2610.04736
Probabilistic Pedestrian Forecasts from a Handheld Phone: World-Frame Heat Maps, Visual-Inertial Height Drift, and Evaluation without Ground Truth
来自手持手机的概率行人预测：世界坐标系热图、视觉惯性高度漂移及无真实值的评估
Abstract
A pedestrian with a phone could be shown where nearby people will be in the next few seconds, if the forecast stays on the ground while the phone moves, is calibrated, and needs only a monocular camera and visual-inertial odometry (VIO). We build and evaluate such a system. People are detected, lifted onto the floor by ray-plane intersection, tracked in a gravity-aligned metric frame, and forecast as per-step probability maps by a small U-Net trained with a negative log-likelihood (NLL) loss on bird's-eye (SDD) and first-person (EgoTraj-Bench) trajectories. On handheld ADVIO recordings, vertical VIO drift and the user's own changes of level silently rescale monocular ground positions (by 87% within 90 s on one clip; on another, all tracks are lost for the last 31% of the clip); keeping the camera's height above the floor constant under a low-pass-filtered altitude avoids this, though it lags on escalators. On the SDD and EgoTraj-Bench test splits, the final forecaster lowers the NLL at 4.8 s by 1.51 and 1.37 nats relative to a fitted constant-velocity Gaussian. Lacking ground truth for people in handheld video, we score forecasts against the tracker's own later raw measurements. In an internally pre-registered evaluation on seven held-out clips, the final forecaster's NLL is lower than the benchmark-fitted baseline's at 1.2, 2.4 and 4.8 s (by 0.17, 0.23 and 0.47 nats; 95% intervals over people exclude zero), but by less than half as much as on the development clips. Exploratory analyses cut both ways: resampling clips instead of people widens the intervals to include zero at 1.2 and 2.4 s, and once both forecasters are recalibrated on the development clips the network is significantly better only at 1.2 s; but two clips run with ADVIO's reference poses favour the network much more when re-run with the phone's own poses. We discuss what such self-consistency scores can and cannot show.
Chinese Translation
使用手机的行人可以被告知在接下来的几秒钟内附近的人将会在哪里，如果预测保持在地面上，而手机在移动、经过校准，并且只需要单目相机和视觉惯性测距（VIO）。我们构建并评估了这样一个系统。通过光线与平面的交点检测到人，将其提升到地面，并在与重力对齐的度量框架中进行跟踪，使用经过负对数似然（NLL）损失训练的小型 U-Net 生成每一步的概率图。在手持 ADVIO 录制的视频中，垂直 VIO 漂移和用户自身的高度变化会默默地重新缩放单目地面位置（在一个片段中，90 秒内缩放了 87%；在另一个片段中，最后 31% 的轨迹全部丢失）；保持相机高度在地面上方恒定（在低通滤波的高度下）可以避免这一问题，尽管在扶梯上会出现滞后。在 SDD 和 EgoTraj-Bench 测试集上，最终的预测器在 4.8 秒时相对于拟合的恒速高斯模型降低了 1.51 和 1.37 nats 的 NLL。由于手持视频中缺乏真实值，我们将预测结果与跟踪器后续的原始测量进行评分。在对七个保留片段进行的内部预注册评估中，最终预测器的 NLL 在 1.2、2.4 和 4.8 秒时均低于基准拟合基线（分别降低 0.17、0.23 和 0.47 nats；95% 的区间排除了零），但降低幅度不到开发片段的一半。探索性分析结果相互矛盾：对片段进行重采样而非对人进行重采样会使 1.2 和 2.4 秒的区间扩大到包含零，一旦两个预测器在开发片段上重新校准，网络在 1.2 秒时显著优于其他时刻；但在使用 ADVIO 的参考姿态运行的两个片段中，当使用手机自身的姿态重新运行时，网络的表现更为优越。我们讨论了这种自一致性评分所能展示和不能展示的内容。
cs.CV / 74 / 2610.04743
ThyCLIPNet: A BiomedCLIP-Guided Lightweight Attention-Enhanced DeepLabV3+ Framework for Robust Thyroid Nodule Segmentation
ThyCLIPNet：一种基于BiomedCLIP指导的轻量级注意力增强DeepLabV3+框架，用于稳健的甲状腺结节分割
Abstract
Accurate thyroid ultrasound segmentation is often challenged by low contrast, speckle noise, and unclear boundaries. Although recent methods have improved segmentation accuracy, many rely on resource-intensive architectures or lack explicit integration of multiscale features with global biomedical visual guidance. In this paper, we introduce ThyCLIPNet, a lightweight semantic-guided hybrid encoder-decoder framework that integrates BiomedCLIP-derived biomedical semantic guidance into a lightweight multi-scale CNN segmentation pipeline. The encoder integrates MobileNetV2 with efficient channel attention, while atrous spatial pyramid pooling and a custom convolutional block attention module enrich bottleneck features. The decoder combines hierarchical skip connections and lightweight attention refinement with a BiomedCLIP-guided gated fusion pathway that projects vision-only global biomedical embeddings into decoder feature space and selectively integrates them through semantic-local fusion and spatial gating. To the best of our knowledge, ThyCLIPNet is among the first lightweight thyroid ultrasound segmentation frameworks to use BiomedCLIP's vision encoder alone for image-only global semantic guidance without text prompting. Experiments on TG3K, TN3K, DDTI, and PKTN achieve dice similarity coefficients of 96.22%, 87.58%, 84.73%, and 80.70%; intersection over union scores of 92.72%, 77.91%, 73.51%, and 67.64%; and 95th-percentile hausdorff distances of 3.75, 16.38, 18.23, and 10.86, respectively. ThyCLIPNet uses 8.55M parameters and 22.99G FLOPs. Overall, the results support integrating global biomedical semantic guidance with lightweight multi-scale CNN representations for robust and computationally efficient thyroid ultrasound segmentation. Source code: https://github.com/Tasnim-Jahan/ThyCLIPNet. [Abstract shortened for arXiv. See PDF for full abstract.]
Chinese Translation
准确的甲状腺超声分割常常受到低对比度、斑点噪声和边界不清晰的挑战。尽管近期的方法提高了分割精度，但许多方法依赖于资源密集型架构，或缺乏与全球生物医学视觉指导的多尺度特征的明确整合。本文介绍了ThyCLIPNet，一种轻量级语义指导混合编码器-解码器框架，将BiomedCLIP衍生的生物医学语义指导集成到轻量级多尺度卷积神经网络（CNN）分割管道中。编码器结合了MobileNetV2和高效的通道注意力，而空洞空间金字塔池化和自定义卷积块注意力模块则丰富了瓶颈特征。解码器结合了分层跳跃连接和轻量级注意力精炼，以及一个基于BiomedCLIP指导的门控融合路径，该路径将仅基于视觉的全球生物医学嵌入投影到解码器特征空间，并通过语义-局部融合和空间门控进行选择性整合。根据我们所知，ThyCLIPNet是首批仅使用BiomedCLIP的视觉编码器进行图像全球语义指导的轻量级甲状腺超声分割框架之一，而无需文本提示。在TG3K、TN3K、DDTI和PKTN上的实验分别达到了96.22%、87.58%、84.73%和80.70%的Dice相似系数；92.72%、77.91%、73.51%和67.64%的交并比得分；以及3.75、16.38、18.23和10.86的第95百分位Hausdorff距离。ThyCLIPNet使用了8.55M参数和22.99G FLOPs。总体而言，结果支持将全球生物医学语义指导与轻量级多尺度CNN表示相结合，以实现稳健且计算高效的甲状腺超声分割。源代码：https://github.com/Tasnim-Jahan/ThyCLIPNet。[摘要已缩短以适应arXiv，完整摘要请参见PDF。]
cs.CV / 75 / 2610.04777
ARISE: Adaptive Agentic Reasoning with Image-grounded Self-Evaluation for Interpretable IBD Assessment
ARISE：基于图像的自我评估的自适应代理推理用于可解释的炎症性肠病评估
Abstract
Inflammatory bowel disease (IBD) requires frequent imaging-based assessment, yet interpretation of modalities such as wireless capsule endoscopy (WCE) and intestinal ultrasound remains heavily dependent on specialist expertise. Vision-Language Models (VLMs) demonstrate significant potential in multimodal medical image analysis, but their clinical adoption is hindered by their insufficient domain-specific reasoning, susceptibility to hallucination, scarcity of high quality training data in fine-grained diagnostics and limited interpretability. We introduce ARISE (Adaptive Agentic Reasoning with Image-grounded Self-Evaluation), an autonomous planning framework that models few-shot medical image understanding as a sequential agentic workflow. ARISE structures agent execution into a transparent 5-stage workflow: hypothesis generation, image-grounded evidence summarization, evidence-conditioned refinement, symbolic verification, and final diagnosis. We apply ARISE to IBD assessment across two independent patient cohorts: wireless capsule endoscopy (WCE) images for Crohn's disease and B-mode ultrasound data for ulcerative colitis. ARISE consistently improves diagnostic performance over baseline VLMs while exposing where reasoning succeeds or fails, providing a more interpretable basis for clinical decision support and realistic deployment.
Chinese Translation
炎症性肠病（IBD）需要频繁的基于影像的评估，但对无线胶囊内镜（WCE）和肠道超声等影像学方法的解读仍然高度依赖于专家的专业知识。视觉-语言模型（VLMs）在多模态医学影像分析中展现出显著潜力，但其临床应用受到领域特定推理不足、易受幻觉影响、细粒度诊断中高质量训练数据稀缺以及可解释性有限等因素的阻碍。我们提出了ARISE（基于图像的自我评估的自适应代理推理），这是一种将少量医学影像理解建模为顺序代理工作流的自主规划框架。ARISE将代理执行结构化为一个透明的五阶段工作流：假设生成、基于图像的证据总结、证据条件下的细化、符号验证和最终诊断。我们将ARISE应用于两个独立患者队列的IBD评估：针对克罗恩病的无线胶囊内镜（WCE）影像和针对溃疡性结肠炎的B模式超声数据。ARISE在诊断性能上始终优于基线VLMs，同时揭示推理成功或失败的地方，为临床决策支持和现实部署提供了更可解释的基础。
cs.CV / 76 / 2610.04781
Super-Resolution in The Right Latent Space: A Frozen Vision-Foundation Substrate
在正确的潜在空间中的超分辨率：一个冻结的视觉基础底座
Abstract
In an image latent space, the embeddings of high-resolution, natural, and sharp images form a manifold. Degradation of high-resolution images pushes their embeddings off this manifold. Real-world super-resolution (SR) then becomes the task of mapping the degraded embedding back onto this manifold --- not anywhere on the manifold, but to the point that preserves what the input still carries, both its semantics and pixel details. Every published method implements this mapping in a reconstruction-oriented latent space or pixel space. We claim these spaces are the wrong substrates for SR. Low-resolution and degraded images are embedded far from the manifold, making the mapping difficult and expensive. The lack of semantic information in these substrates also makes it difficult to navigate to the faithful point on the manifold, causing severe hallucination when degradation is heavy. Thus, restoring in a suitable latent space is crucial to the SR task. We show that the latent space of 23 fused layers of a frozen DINOv3-L is one such space that makes the SR task easier. Degraded images are embedded near the manifold. Moreover, this substrate contains a hierarchy of information, from pixel record to degradation robust semantics, guiding the model to find the faithful point on the manifold. On this substrate, a 415M decoder is trained under reconstruction and adversarial objectives to map the degraded embeddings back and decode to pixel space in one pass. The resulting model, RAESR, attains the best fidelity--perception trade-off among state-of-the-art adversarial and diffusion-based restorers on RealSR, DRealSR, LSDIR and DIV2K-Val, at 37 ms per 512 by 512 image on a single H20 GPU. Swapping the substrate for a VAE latent under an identical recipe loses on every metric.
Chinese Translation
在图像潜在空间中，高分辨率、自然且清晰图像的嵌入形成了一个流形。高分辨率图像的退化使其嵌入偏离了这个流形。现实世界中的超分辨率（SR）任务因此变成了将退化的嵌入映射回这个流形——而不是流形上的任意位置，而是映射到一个能够保留输入所携带的语义和像素细节的点。每种已发布的方法都在以重建为导向的潜在空间或像素空间中实现这种映射。我们认为这些空间并不是SR的合适底座。低分辨率和退化图像的嵌入远离流形，使得映射变得困难且成本高昂。这些底座中缺乏语义信息也使得导航到流形上的真实点变得困难，导致在重度退化时出现严重的幻觉。因此，在合适的潜在空间中进行恢复对SR任务至关重要。我们展示了冻结的DINOv3-L的23个融合层的潜在空间是一个使SR任务变得更容易的空间。退化图像的嵌入接近流形。此外，这个底座包含了从像素记录到抗退化语义的信息层次，指导模型找到流形上的真实点。在这个底座上，一个415M的解码器在重建和对抗目标下进行训练，以一次性将退化的嵌入映射回并解码到像素空间。最终模型RAESR在RealSR、DRealSR、LSDIR和DIV2K-Val等数据集上，在37毫秒内处理512x512图像时，达到了最好的保真度与感知的权衡，超越了最先进的对抗性和扩散基础的恢复器。在相同的配方下，将底座替换为VAE潜在空间在每个指标上均表现不佳。
cs.CV / 77 / 2610.04785
Lollypop: Camera-to-Motion-Capture Calibration Verification with a Reference Target
Lollypop：基于参考目标的相机与动作捕捉校准验证
Abstract
Camera-to-motion-capture (mocap) calibration is essential for using mocap as ground truth in robotics, AR/VR, and other computer vision tasks. However, the calibration can drift after deployment, while calibration residuals and visual inspection provide limited independent verification. We present Lollypop, a fiducial-mocap reference target for independent calibration verification. The target couples an ArUco fiducial with a mocap marker constellation so the visual center and tracked centroid represent the same physical point. Given a candidate calibration, verification projects the mocap point into the image and measures its disagreement with the detected fiducial center. Experiments show sub-pixel nominal error, sensitivity to controlled extrinsic perturbations, and increasing error during an illustrative mixed-handling sequence.
Chinese Translation
相机与动作捕捉（mocap）校准对于在机器人技术、增强现实/虚拟现实（AR/VR）以及其他计算机视觉任务中将mocap作为真实值至关重要。然而，校准在部署后可能会出现漂移，而校准残差和视觉检查提供的独立验证有限。我们提出了Lollypop，一种用于独立校准验证的基准mocap参考目标。该目标将ArUco fiducial与mocap标记星座结合，使得视觉中心和跟踪的质心代表同一个物理点。在给定候选校准的情况下，验证将mocap点投影到图像中，并测量其与检测到的fiducial中心之间的差异。实验表明，名义误差在亚像素级别，对受控外部扰动敏感，并且在一个示例混合处理序列中误差逐渐增加。
cs.CV / 78 / 2610.04787
Active-DiNTS: Active Differentiable Network Topology Search
主动可微网络拓扑搜索 (Active-DiNTS)
Abstract
Neural Architecture Search (NAS) has proved to be a strong alternative to manual network design, but applying it to 3D medical image segmentation is limited by two well-known costs, large annotation budgets and multi-GPU clusters. Thus, this paper introduces Active-DiNTS (Active Differentiable Network Topology Search), an approach that embeds pool-based Active Learning (AL) into a bi-level differentiable topology search to perform architecture discovery and label curation jointly. At each query round, unlabeled MRI volumes are ranked by one of three uncertainty signals (Entropy, Variance, or Standard Deviation), and only the top-ranked volumes are sent to an oracle for annotation. The new labels feed two interlocked stages. Network weights are updated in an outer loop, while the macro/micro topology of a U-Net-style backbone is refined in an inner loop. Three AL regimes (weights-only, topology-only, joint) expose the speed-accuracy trade-off. Evaluations on the Medical Segmentation Decathlon (MSD) Task01 BrainTumour benchmark showed that Active-DiNTS surpasses DiNTS, C2FNAS, and nnU-Net in Dice, with gains of about 10 percentage points on Edema and 5 points on Non-Enhancing core, on a single GPU and using a fraction of the labeled volumes. The discovered architectures are denser and more FLOP-heavy than prior baselines, but remain competitive in trainable parameters and peak memory; the fastest search variant finishes in under 0.25 GPU-days, over 27x faster than the eight-GPU DiNTS search. Together, these results indicate that pairing differentiable NAS with active data acquisition is a practical recipe for accurate 3D segmentation under realistic constraints.
Chinese Translation
神经架构搜索（Neural Architecture Search, NAS）已被证明是手动网络设计的强有力替代方案，但将其应用于三维医学图像分割受到两个众所周知的成本限制，即大量的标注预算和多GPU集群。因此，本文提出了主动可微网络拓扑搜索（Active-DiNTS），一种将基于池的主动学习（Active Learning, AL）嵌入双层可微拓扑搜索中的方法，以实现架构发现和标签整理的联合进行。在每个查询轮次中，未标记的MRI体积根据三种不确定性信号（熵、方差或标准差）中的一种进行排名，仅将排名最高的体积发送给oracle进行标注。新的标签用于两个相互关联的阶段。网络权重在外循环中更新，而U-Net风格主干的宏观/微观拓扑在内循环中进行优化。三种主动学习模式（仅权重、仅拓扑、联合）揭示了速度与准确性之间的权衡。在医学分割十项全能（Medical Segmentation Decathlon, MSD）Task01脑肿瘤基准测试中的评估显示，Active-DiNTS在Dice指标上超越了DiNTS、C2FNAS和nnU-Net，在水肿上提高了约10个百分点，在非增强核心上提高了5个百分点，且仅使用单个GPU和一小部分标记体积。所发现的架构比之前的基线更密集且计算量更大，但在可训练参数和峰值内存方面仍具有竞争力；最快的搜索变体在不到0.25 GPU天内完成，比八GPU的DiNTS搜索快27倍以上。这些结果表明，将可微NAS与主动数据获取相结合是一种在现实约束下实现准确三维分割的实用方案。
cs.CV / 79 / 2610.04814
MOXIE: Discovering Alternative Explanations for Biomedical Image Classifiers
MOXIE：发现生物医学图像分类器的替代解释
Abstract
Segment-based explanation methods such as LIME return a single explanation for each prediction, computed from one fixed image segmentation. This hides two important facts: a prediction can be supported by many different sets of image segments, and the segmentation itself shapes which explanations can be found. We introduce MOXIE (Multi-Objective eXplanation Imaging Engine), an evolutionary framework that searches for segment subsets that preserve the classifier's confidence while keeping as little of the image as possible. Instead of one explanation, MOXIE returns a Pareto front of alternative explanations that range from compact to highly faithful. We evaluate MOXIE with NSGA-II and four segmentation methods (SLIC, Felzenszwalb, Watershed and Voronoi) on BloodMNIST and HAM10000 datasets, using the same evaluation budget as LIME. Results show that MOXIE achieves a higher hypervolume than LIME on every image. LIME's explanations often appear convincing, yet the classifier's confidence collapses when only the highlighted segments are shown. MOXIE's fronts reveal how much of the image is needed to preserve the model's confidence and which contextual regions influence it. We also find that segmentation strongly affects evaluation: methods with unequal segment sizes appear most compact when segments are counted. These results show that alternative explanations provide a more complete view of a model's decision than a single explanation.
Chinese Translation
基于分割的解释方法，如 LIME，为每个预测返回单一解释，该解释是从一个固定的图像分割计算得出的。这掩盖了两个重要事实：一个预测可以由许多不同的图像分割集支持，而分割本身也影响可以找到哪些解释。我们引入了 MOXIE（多目标解释成像引擎），这是一个进化框架，旨在搜索保留分类器信心的分割子集，同时尽可能少地保留图像。MOXIE 返回的是一组替代解释的帕累托前沿，这些解释从紧凑到高度忠实不等。我们在 BloodMNIST 和 HAM10000 数据集上使用与 LIME 相同的评估预算，结合 NSGA-II 和四种分割方法（SLIC、Felzenszwalb、Watershed 和 Voronoi）对 MOXIE 进行了评估。结果显示，MOXIE 在每张图像上都实现了比 LIME 更高的超体积。LIME 的解释通常看起来令人信服，但当仅显示突出部分时，分类器的信心会崩溃。MOXIE 的前沿揭示了保留模型信心所需的图像量以及哪些上下文区域影响了它。我们还发现，分割对评估有很大影响：当计算分割时，大小不等的分割方法在视觉上显得最紧凑。这些结果表明，替代解释提供了比单一解释更全面的模型决策视角。
cs.CV / 80 / 2610.04819
DriftSR: One-Step Real-World Image Super-Resolution via Distribution Drifting
DriftSR：通过分布漂移实现一步法真实世界图像超分辨率
Abstract
One-step real-world image super-resolution (Real-ISR) offers efficient inference, but recovering realistic and perceptually rich details often relies on score distillation or adversarial learning, introducing additional trainable components and making optimization more cumbersome. To this end, we propose DriftSR, a one-step Real-ISR framework that leverages pretrained diffusion priors through distribution drifting. Specifically, we perform drifting in the frozen intermediate representation space of a pretrained diffusion model, without introducing an additional task-specific feature encoder. Building on this space, we introduce Spatial Feature Drifting, which treats spatial features rather than entire images as distributional samples, enabling denser supervision for distribution alignment. To mitigate structural deviations, we further introduce Structure-Modulated Guidance, which adaptively refines drifting guidance according to local structural consistency with the LQ input. Consequently, DriftSR optimizes only the one-step generator, without auxiliary distillation branches or adversarial discriminators. Extensive experiments on three real-world benchmarks demonstrate that DriftSR delivers high-quality super-resolution reconstruction with efficient one-step inference.
Chinese Translation
一步法真实世界图像超分辨率（Real-ISR）提供了高效的推理，但恢复真实且感知丰富的细节通常依赖于评分蒸馏或对抗学习，这引入了额外的可训练组件，使优化变得更加繁琐。为此，我们提出了DriftSR，一个通过分布漂移利用预训练扩散先验的一步法Real-ISR框架。具体而言，我们在预训练扩散模型的冻结中间表示空间中进行漂移，而不引入额外的任务特定特征编码器。在此基础上，我们引入了空间特征漂移，将空间特征视为分布样本，而不是整个图像，从而实现对分布对齐的更密集监督。为了减轻结构偏差，我们进一步引入了结构调制引导，根据与低质量（LQ）输入的局部结构一致性自适应地精炼漂移引导。因此，DriftSR仅优化一步法生成器，而不需要辅助蒸馏分支或对抗判别器。在三个真实世界基准上的大量实验表明，DriftSR能够以高效的一步法推理实现高质量的超分辨率重建。
cs.CV / 81 / 2610.04836
RSure-Agent: Reliable Use of Tool Observations for Remote Sensing Agents
RSure-Agent：远程感知代理工具观测的可靠使用
Abstract
Remote sensing agents rely on perception, measurement, and raster analysis tools to solve Earth observation tasks. We refer to their judgments and quantitative results about ground objects as tool observations. However, these observations are subject to substantial uncertainty and may be incorrect even when the tools execute successfully. When agents accept incorrect observations, the errors can propagate through subsequent reasoning and cause task failure. We analyze 1,229 execution trajectories across three remote sensing agent benchmarks. On each benchmark, at least 88.1% of tasks depend on tool observations. Among these tasks, at least 22.7% contain incorrect observations despite successful tool execution. These errors propagate to the final answer in at least 82.0% of affected tasks on each benchmark. To address this problem, we propose RSure-Agent, a framework for verifying tool observations and limiting error propagation. We introduce a verifiable observation protocol that requires tools to return process evidence for the agent to verify their observations. We also construct a task-tool reliability prior from offline task feedback. The prior summarizes each tool configuration's past performance across task types and provides a task-specific reference for verification. Using process evidence and this prior, RSure-Agent decides whether to accept an observation, request additional evidence, or reject it. We evaluate RSure-Agent on EarthBench, ThinkGeo, TerraLogic, and CHOICE-420. Across the three agent benchmarks, RSure-Agent reduces the error propagation rate by 21.3 to 25.9 percentage points relative to the base configuration with verification and the prior disabled. On CHOICE-420, it improves overall accuracy over direct answering by 5.71 percentage points on average across 11 backbone models. On EarthBench, it reduces the tool-call ratio by 25.9% relative to Earth-Agent.
Chinese Translation
远程感知代理依赖于感知、测量和栅格分析工具来解决地球观测任务。我们将它们对地面物体的判断和定量结果称为工具观测。然而，这些观测存在显著的不确定性，即使工具成功执行，它们也可能是错误的。当代理接受错误的观测时，这些错误可能会在后续推理中传播，从而导致任务失败。我们分析了在三个远程感知代理基准测试中收集的1,229条执行轨迹。在每个基准测试中，至少88.1%的任务依赖于工具观测。在这些任务中，至少22.7%的任务包含尽管工具成功执行但仍存在的错误观测。这些错误在每个基准测试中至少82.0%的受影响任务中传播到最终答案。为了解决这个问题，我们提出了RSure-Agent，一个用于验证工具观测和限制错误传播的框架。我们引入了一种可验证的观测协议，要求工具返回过程证据，以便代理验证其观测。我们还根据离线任务反馈构建了任务-工具可靠性先验。该先验总结了每种工具配置在不同任务类型中的过去表现，并为验证提供了任务特定的参考。利用过程证据和该先验，RSure-Agent决定是接受观测、请求额外证据还是拒绝观测。我们在EarthBench、ThinkGeo、TerraLogic和CHOICE-420上评估了RSure-Agent。在三个代理基准测试中，RSure-Agent将错误传播率相对于禁用验证和先验的基础配置降低了21.3到25.9个百分点。在CHOICE-420上，它在11个基础模型中平均提高了5.71个百分点的整体准确率。在EarthBench上，它相对于Earth-Agent减少了25.9%的工具调用比例。
cs.CV / 82 / 2610.04853
One Tile, Multiple Instances: Rethinking MIL for Sparse Diagnostic Evidence
一个切片，多重实例：重新思考稀疏诊断证据下的MIL
Abstract
In weakly supervised Whole Slide Image (WSI) classification, feature extractors typically compress each image tile into a single global embedding. Consequently, slide-level aggregators are restricted to this coarse tile scale, concealing fine-grained sub-tile evidence from the attention mechanism. We introduce DI-MIL, a framework that decouples encoding context from instance granularity through decomposed instances. By clustering dense spatial tokens from a frozen foundation model within each tile, DI-MIL converts a single tile into multiple independently weighted instance embeddings. As a training-free post-encoding module, DI-MIL integrates seamlessly into existing pipelines without requiring re-encoding or downstream architectural modifications. We evaluate DI-MIL on cytopathology, a challenging testbed where sparse diagnostic signals are easily diluted within standard tiles. Across four datasets, three frozen foundation models, and two attention-based aggregators, DI-MIL demonstrates consistent efficacy, improving 67 of 72 metric-level comparisons, with the largest mean gains reaching 3.64 points under cytopathology-specific backbones. In a broader comparison against seven representative MIL baselines, DI-MIL paired with ACMIL achieves highest mean performance in 33 of 36 backbone-dataset-metric comparisons. Ablations show that direct smaller tiling inflates the extracted tile count by up to 43.3$\times$ with non-monotonic performance, whereas DI-MIL incurs zero additional image-extraction overhead while achieving the strongest overall results. These results establish instance construction as an orthogonal design dimension in MIL, supporting DI-MIL as a cost-efficient solution under sparse diagnostic evidence.
Chinese Translation
在弱监督的全切片图像（WSI）分类中，特征提取器通常将每个图像切片压缩为单一的全局嵌入。因此，切片级聚合器受限于这种粗糙的切片尺度，掩盖了来自注意机制的细粒度子切片证据。我们提出了DI-MIL，一个通过分解实例将编码上下文与实例粒度解耦的框架。通过在每个切片内聚类来自冻结基础模型的密集空间标记，DI-MIL将单个切片转换为多个独立加权的实例嵌入。作为一个无训练的后编码模块，DI-MIL能够无缝集成到现有的工作流程中，而无需重新编码或下游架构的修改。我们在细胞病理学上评估了DI-MIL，这是一个具有挑战性的测试平台，其中稀疏的诊断信号在标准切片中容易被稀释。在四个数据集、三个冻结基础模型和两个基于注意力的聚合器中，DI-MIL表现出一致的有效性，在72个指标级比较中改善了67个，最大平均增益在细胞病理学特定骨干网络下达到3.64分。在与七个代表性MIL基线的更广泛比较中，DI-MIL与ACMIL配对在36个骨干-数据集-指标比较中实现了33个的最高平均性能。消融实验表明，直接的小切片化将提取的切片数量膨胀至43.3倍，且性能非单调，而DI-MIL在实现最强整体结果的同时没有增加额外的图像提取开销。这些结果确立了实例构建作为MIL中的一个正交设计维度，支持DI-MIL作为在稀疏诊断证据下的成本效益解决方案。
cs.CV / 83 / 2610.04864
Enhancing Long-Video VLM Embeddings with Query-Aware Streaming Latent Reasoning
通过查询感知流式潜在推理增强长视频 VLM 嵌入
Abstract
Long-video embedding requires capturing sparse query-relevant evidence under a limited visual-token budget. Uniform sampling can miss brief events in videos spanning minutes or hours, whereas encoding more frames in a single context increases memory and computation. We introduce \textbf{Query-Aware Streaming Latent Reasoning} (QASLR), a post-training framework that accumulates evidence across clips while keeping the embedding size fixed. QASLR selects a bounded set of frames, restores their temporal order, and processes them clip by clip with a vision-language backbone. A compact set of persistent think tokens cross-attends to each clip's features, while an embed token reads out a normalized representation after every update. This design integrates evidence across multiple backbone calls without requiring all selected frames to share a single context. Training combines final contrastive learning, step-wise contrastive supervision, and final-embedding self-distillation. Intermediate supervision trains partial-video readouts for retrieval, while self-distillation regularizes them toward the final representation. Query-aware selection produces query-conditioned representations for candidate-set scoring and reranking, whereas query-independent selection enables reusable corpus indexing. Under the full training recipe, HourVideo retrieval Hit@1 increases from 54.2 to 70.7 and from 57.4 to 72.8 for 2B and 8B Qwen3-VL-Embedding backbones, respectively. Gains extend to the evaluated moment-retrieval and video-QA tasks, and the streaming head transfers to a second Qwen-family embedding backbone. These results support streaming latent aggregation as an effective approach to integrating long-video evidence into fixed-dimensional representations.
Chinese Translation
长视频嵌入需要在有限的视觉标记预算下捕捉稀疏的与查询相关的证据。均匀采样可能会错过持续数分钟或数小时的视频中的短暂事件，而在单一上下文中编码更多帧则会增加内存和计算负担。我们提出了 extbf{查询感知流式潜在推理} (QASLR)，这是一个后训练框架，可以在保持嵌入大小不变的情况下跨剪辑积累证据。QASLR选择一个有限的帧集，恢复它们的时间顺序，并逐个剪辑地使用视觉-语言骨干网络进行处理。一组紧凑的持久思考标记交叉关注每个剪辑的特征，而嵌入标记在每次更新后读取标准化表示。这一设计在不要求所有选定帧共享单一上下文的情况下，整合了多个骨干调用中的证据。训练结合了最终对比学习、逐步对比监督和最终嵌入自蒸馏。中间监督训练部分视频的读取以进行检索，而自蒸馏则使其朝向最终表示进行正则化。查询感知选择生成查询条件的表示用于候选集评分和重排序，而查询独立选择则支持可重用的语料库索引。在完整的训练方案下，HourVideo 检索的 Hit@1 从 54.2 提升到 70.7，2B 和 8B Qwen3-VL-Embedding 骨干分别从 57.4 提升到 72.8。这些提升扩展到评估的时刻检索和视频问答任务，流式头部也可以迁移到第二个 Qwen 家族嵌入骨干。这些结果支持流式潜在聚合作为将长视频证据整合到固定维度表示中的有效方法。
cs.CV / 84 / 2610.04883
Reflection-Robust 6DoF Object Tracking with Light Fields
基于光场的反射鲁棒6自由度物体跟踪
Abstract
Tracking the 6DoF pose of a moving rigid object is fundamental to robotics and autonomous driving, but existing trackers assume that object appearance is stable across a sequence, an assumption that breaks down on reflective surfaces whose appearance changes as they mirror the environment. We introduce a light field based reflection-robust 6DoF tracker that turns this apparent nuisance into a pose cue. Per frame, our method recovers depth robustly against reflections, back-projects it into a point cloud, and estimates surface normals. It then decomposes the object's view-dependent appearance into a diffuse albedo and the environment map it reflects, resulting in a relightable surface light field. Starting from a coarse initialization, we relight it by the recovered environment map and optimize the pose on the photometric loss. Because a moving object mirrors new parts of the scene, the environment map fills in as the sequence proceeds, sharpening this signal over time. To evaluate this approach, we introduce a light field tracking dataset re-rendered from a robotic manipulation benchmark at four controlled reflectivity levels, each paired with simulated depth that reproduces how consumer RGB-D sensors fail on shiny surfaces. Additionally, we evaluate on two captured light field sequences. Our method trails the strongest baselines on diffuse objects and is the only one that holds its accuracy on fully reflective objects, where every baseline degrades.
Chinese Translation
跟踪移动刚性物体的6自由度姿态是机器人技术和自动驾驶的基础，但现有的跟踪器假设物体外观在序列中是稳定的，这一假设在反射表面上失效，因为其外观会随着环境的变化而改变。我们提出了一种基于光场的反射鲁棒6自由度跟踪器，将这一表面反射的显著问题转化为姿态线索。我们的算法在每帧中稳健地恢复深度，抵抗反射影响，将其反投影到点云中，并估计表面法线。然后，它将物体的视依赖外观分解为漫反射反照率和环境映射，从而生成一个可重光照的表面光场。从粗略的初始化开始，我们通过恢复的环境映射对其进行重光照，并在光度损失上优化姿态。由于移动物体会反射场景的新部分，环境映射在序列进行时逐渐填充，随着时间的推移增强这一信号。为了评估该方法，我们引入了一个光场跟踪数据集，该数据集是从一个机器人操作基准在四个受控反射率水平下重新渲染的，每个数据集都配有模拟深度，重现了消费级RGB-D传感器在光滑表面上的失效情况。此外，我们还在两个捕获的光场序列上进行了评估。我们的算法在漫反射物体上落后于最强的基线，但在完全反射物体上保持了其准确性，而所有基线在此情况下均出现性能下降。
cs.CV / 85 / 2610.04920
PWM: Personalized World Models with Online Reinforcement Learning
PWM：基于在线强化学习的个性化世界模型
Abstract
Pretrained world models can generate diverse environments, yet users often want to explore a particular scene specified by their own video. This requires learning the scene's visual identity while retaining the quality of action-conditioned generation. We introduce Personalized World Models (PWM), a framework for customizing interactive world models from short scene videos through online reinforcement learning. In PWM, the support trajectory and its associated controls provide reward feedback on continuations sampled from the current policy. In the GRPO instantiation, group-relative optimization updates a compact LoRA adapter using a unified reward for scene appearance, visual continuity, and motion, while base-policy anchoring regularizes changes to the pretrained generation prior of a frozen Yume-5B backbone. The same adaptation procedure is applied across real and rendered environments. We also instantiate PWM with DiffusionNFT as an alternative reward-guided optimization method for learning the scene-specific adapter. We also introduce PWM-Bench, comprising 150 customization tasks across Indoor, Outdoor, and Gaming, with paired evaluation on held-out continuations. The GRPO and DiffusionNFT instantiations of PWM improve customization over native Yume in 71.3% and 65.3% of the evaluated scenes, respectively, with positive mean gains across all three domains. For the GRPO instantiation, matched SFT comparisons further demonstrate higher mean customization gains and better mean image-quality scores in every domain, while retaining frame-level visual quality close to the pretrained model.
Chinese Translation
预训练的世界模型能够生成多样化的环境，但用户通常希望探索由其视频指定的特定场景。这需要在保留动作条件生成质量的同时，学习场景的视觉特征。我们提出了个性化世界模型（PWM），这是一个通过在线强化学习从短场景视频定制交互式世界模型的框架。在PWM中，支持轨迹及其相关控制为从当前策略采样的延续提供奖励反馈。在GRPO实例中，组相对优化使用统一的奖励更新紧凑的LoRA适配器，以优化场景外观、视觉连续性和运动，同时基础策略锚定则规范了对冻结的Yume-5B主干的预训练生成先验的变化。相同的适应过程应用于真实和渲染的环境。我们还使用DiffusionNFT作为学习场景特定适配器的替代奖励引导优化方法来实例化PWM。我们还介绍了PWM-Bench，包含150个定制任务，涵盖室内、室外和游戏领域，并在保留的延续上进行配对评估。PWM的GRPO和DiffusionNFT实例在71.3%和65.3%的评估场景中改善了相对于原生Yume的定制效果，并在所有三个领域中均有正向的平均增益。对于GRPO实例，匹配的SFT比较进一步展示了在每个领域中更高的平均定制增益和更好的平均图像质量评分，同时保持接近于预训练模型的帧级视觉质量。
cs.CV / 86 / 2610.04922
TRACE: Time-Adaptive Residual Attention Control with Content-Style Decomposition for Training-Free Diffusion Style Transfer
TRACE：一种具有内容-风格分解的时间自适应残差注意力控制的无训练扩散风格迁移方法
Abstract
Reference-guided style transfer aims to preserve the semantic structure of a content image while transferring the visual appearance of a style reference. Recent diffusion-based methods achieve impressive stylization quality by exploiting strong pretrained generative priors. However, training-free approaches still face a difficult trade-off among style fidelity, content preservation, and content leakage. Direct style injection may unintentionally transfer semantic content from the style image, while fixed guidance schedules often ignore the time- and state-dependent nature of diffusion sampling. To address these limitations, we propose TRACE, a training-free diffusion style transfer framework with Time-adaptive Residual Attention Control and Content-Style Decomposition. TRACE first performs offline CLIP-based subspace analysis to separate content and style directions from paired data. During inference, it removes content-related components from the style reference and style-related components from the content reference to reduce leakage. It then injects style information through residual cross-attention and applies uncertainty-aware guidance to adapt the guidance signal at each denoising step. Experiments show that TRACE achieves a favorable trade-off between stylization and preservation. Compared with optimal-control-based baselines, TRACE substantially improves style fidelity (+17.28 CSD and +34.10 SRA). While, compared with stylization methods, it better preserves content structure (+12.80 DINO, +5.52 CLIP-I, and -8.19 LPIPS) and reduces directional semantic leakage by 29.5% in DCL. Our code is publicly available at https://github.com/pixelchemy-research/TRACE.
Chinese Translation
参考引导的风格迁移旨在在转移风格参考的视觉外观的同时，保留内容图像的语义结构。最近的基于扩散的方法通过利用强大的预训练生成先验，取得了令人印象深刻的风格化质量。然而，无训练的方法仍面临风格保真度、内容保留和内容泄漏之间的困难权衡。直接的风格注入可能会无意中从风格图像中转移语义内容，而固定的引导计划往往忽视了扩散采样的时间和状态依赖特性。为了解决这些局限性，我们提出了TRACE，一个具有时间自适应残差注意力控制和内容-风格分解的无训练扩散风格迁移框架。TRACE首先执行基于CLIP的离线子空间分析，以从配对数据中分离内容和风格方向。在推理过程中，它从风格参考中移除与内容相关的成分，并从内容参考中移除与风格相关的成分，以减少泄漏。然后，它通过残差交叉注意力注入风格信息，并应用不确定性感知引导，以在每个去噪步骤中适应引导信号。实验表明，TRACE在风格化和保留之间实现了良好的权衡。与基于最优控制的基线相比，TRACE显著提高了风格保真度（+17.28 CSD 和 +34.10 SRA）。同时，与风格化方法相比，它更好地保留了内容结构（+12.80 DINO，+5.52 CLIP-I 和 -8.19 LPIPS），并在DCL中减少了29.5%的方向性语义泄漏。我们的代码已公开发布在 https://github.com/pixelchemy-research/TRACE。
cs.CV / 87 / 2610.04979
Robust Tensor Completion via Reflective Convolution Nuclear Norm Minimization
通过反射卷积核范数最小化实现鲁棒张量补全
Abstract
Robust tensor completion recovers multidimensional data from partial observations corrupted by sparse gross errors. Existing convolutional low-rank models typically construct translated copies using circular continuation, which introduces artificial wrap-around neighborhoods for finite nonperiodic data. We propose reflective convolution nuclear norm minimization (RCNNM), which replaces circular shifts with endpoint-nonrepeating reflection. The resulting lifting has nonuniform entry multiplicities and satisfies a weighted Gram identity that supports both the recovery analysis and the optimization method. Under random sampling and sparse corruption, we establish high-probability exact recovery of the underlying tensor and sparse errors, together with stability under bounded dense perturbations. We further develop a two-block ADMM with a closed-form entrywise tensor update, while singular-value thresholding is implemented through the smaller right Gram matrix. Experiments on synthetic tensors, BSDS color images, and CAVE multispectral images show that RCNNM consistently improves over its circular-lifting counterpart, with the clearest gains near image boundaries. In particular, average boundary-PSNR improvements reach 3.26 dB while global reconstruction quality remains competitive.
Chinese Translation
鲁棒张量补全从受到稀疏严重错误影响的部分观测中恢复多维数据。现有的卷积低秩模型通常使用循环延续构建平移副本，这为有限的非周期数据引入了人为的环绕邻域。我们提出了反射卷积核范数最小化（RCNNM），它用端点非重复反射替代了循环移位。由此产生的提升具有非均匀的条目多重性，并满足一个加权Gram恒等式，这支持了恢复分析和优化方法。在随机采样和稀疏损坏的情况下，我们建立了对基础张量和稀疏错误的高概率精确恢复，同时在有界稠密扰动下保持稳定性。我们进一步开发了一种具有闭式条目更新的双块交替方向乘子法（ADMM），同时通过较小的右Gram矩阵实现奇异值阈值化。在合成张量、BSDS彩色图像和CAVE多光谱图像上的实验表明，RCNNM在性能上始终优于其循环提升的对应方法，尤其是在图像边界附近获得了明显的提升。具体而言，平均边界PSNR的提升达到3.26 dB，而全局重建质量仍然具有竞争力。
cs.CV / 88 / 2610.04980
Geometry-Aware Preference Optimization for Text-to-Image Diffusion Models
面向几何的文本到图像扩散模型偏好优化
Abstract
Preference alignment has become a standard practice for text-to-image diffusion models. Direct Preference Optimization (DPO) simplifies this process by eliminating explicit reward modeling. Its diffusion variant, Diffusion-DPO, has become a widely adopted baseline. Diffusion-DPO essentially encourages the likelihood of preferred samples while suppressing dispreferred ones. In this paper, we revisit DPO-style alignment methods for diffusion models from the perspective of the manifold hypothesis. Under this view, natural images concentrate near a low-dimensional manifold embedded in the high-dimensional ambient space, whereas DPO directly optimizes preference distributions in the full space without accounting for this geometric structure. This creates a mismatch in the optimization dynamics: it suppresses geometry-preserving tangential updates, while insufficiently restricting hazardous normal-direction updates. This mismatch gradually degrades image quality and diversity. To address this issue, we propose Anisotropic Geometry-Aware Preference Optimization (APO), which replaces the uniform Euclidean treatment of prediction errors with a geometry-aware anisotropic metric derived from the reference model. Concretely, APO adaptively strengthens regularization in directions where the reference denoising function is highly sensitive, while relaxing constraints in directions that permit safe semantic adjustment. This recalibrates preference optimization according to the local manifold geometry, and maintains the original manifold structure. Experiments show that APO achieves strong performance and an average win rate exceeding 60\% against various existing alignment methods across diverse benchmarks. It requires significantly fewer training steps than prior methods, and preserves generation diversity throughout training.
Chinese Translation
偏好对齐已成为文本到图像扩散模型的标准实践。直接偏好优化（Direct Preference Optimization, DPO）通过消除显式奖励建模简化了这一过程。其扩散变体Diffusion-DPO已成为广泛采用的基线。Diffusion-DPO本质上鼓励偏好样本的可能性，同时抑制不偏好的样本。在本文中，我们从流形假设的角度重新审视了扩散模型的DPO风格对齐方法。在这种视角下，自然图像集中在嵌入高维环境空间的低维流形附近，而DPO则直接在整个空间中优化偏好分布，而未考虑这一几何结构。这导致了优化动态的不匹配：它抑制了保持几何结构的切向更新，同时不足以限制危险的法向更新。这种不匹配逐渐降低了图像质量和多样性。为了解决这个问题，我们提出了各向异性几何感知偏好优化（Anisotropic Geometry-Aware Preference Optimization, APO），该方法用从参考模型中导出的几何感知各向异性度量替代了对预测误差的均匀欧几里得处理。具体而言，APO在参考去噪函数高度敏感的方向上自适应地增强正则化，同时在允许安全语义调整的方向上放宽约束。这根据局部流形几何重新校准了偏好优化，并保持了原始流形结构。实验表明，APO在各种基准测试中对比多种现有对齐方法时，表现出强大的性能，平均胜率超过60%。与之前的方法相比，它所需的训练步骤显著减少，并在整个训练过程中保持生成的多样性。
cs.CV / 89 / 2610.05000
VisualErase: Dual-Branch Visual Trajectory Redirection for Robust Concept Erasure in Text-to-Image Diffusion Models
VisualErase：用于文本到图像扩散模型中稳健概念抹除的双分支视觉轨迹重定向
Abstract
Concept erasure is essential for the safe deployment of text-to-image diffusion models, as they may reproduce harmful, copyrighted, or privacy-sensitive content learned from unconstrained large-scale data. Existing methods typically erase unwanted concepts while preserving general generation capability by redirecting target-related text-to-image mappings. However, recent studies show that erased models may still retain visual generative trajectories of target concepts, leaving them vulnerable to adversarial recovery attacks and revealing a fundamental gap between redirecting text-to-image mappings and truly removing visual knowledge. To bridge this gap, we propose VisualErase, a new paradigm that redirects concept-bearing visual generative trajectories toward explicitly defined concept-removed outcomes. To enable this redirection, we use structure-preserving image editing to construct content-aligned, concept-removed counterparts for source images, providing explicit visual endpoints that retain non-target content. We then derive a denoising target from each source-to-counterpart pair and use a dual-branch redirection loss to align both text-conditioned and unconditional predictions with this target, since conditional supervision alone does not explicitly constrain generation without textual guidance. To mitigate the adverse effects of concept erasure on non-target generation, we jointly optimize the redirection loss with a counterpart retention loss that matches denoising predictions from the frozen pretrained model. Across style, celebrity, and nudity erasure, VisualErase limits the maximum attack success rate over seven attacks to 0%, 8%, and 0.1%, respectively, while retaining general generation quality. These results highlight the importance of visual trajectory redirection for robust concept erasure beyond text-to-image mappings alone.
Chinese Translation
概念抹除对于文本到图像扩散模型的安全部署至关重要，因为这些模型可能会重现从不受限制的大规模数据中学习到的有害、受版权保护或隐私敏感的内容。现有方法通常通过重定向与目标相关的文本到图像映射来抹除不需要的概念，同时保留一般生成能力。然而，最近的研究表明，被抹除的模型可能仍然保留目标概念的视觉生成轨迹，使其容易受到对抗性恢复攻击，并揭示了重定向文本到图像映射与真正移除视觉知识之间的根本差距。为了弥补这一差距，我们提出了VisualErase，这是一种新的范式，它将承载概念的视觉生成轨迹重定向到明确定义的去概念化结果。为了实现这种重定向，我们使用结构保持的图像编辑技术为源图像构建内容对齐的去概念化对应物，提供保留非目标内容的明确视觉端点。然后，我们从每对源图像和对应物中推导出去噪目标，并使用双分支重定向损失将文本条件和无条件预测与该目标对齐，因为仅靠条件监督并不能在没有文本指导的情况下明确约束生成。为了减轻概念抹除对非目标生成的不利影响，我们将重定向损失与匹配来自冻结的预训练模型的去噪预测的对应物保留损失共同优化。在风格、名人和裸体抹除方面，VisualErase将七种攻击的最大成功率限制为0%、8%和0.1%，同时保留一般生成质量。这些结果强调了视觉轨迹重定向在超越文本到图像映射的稳健概念抹除中的重要性。
cs.CV / 90 / 2610.05010
PortraitAes: Intent-Conditioned Structured Portrait Aesthetics Assessment
PortraitAes：意图条件下的结构化肖像美学评估
Abstract
Portrait aesthetic assessment assigns comparable scores according to how effectively human-centered images fulfill their photographic intent. These scores support data filtering, candidate selection, and preference modeling in image-generation pipelines. Existing methods typically predict a single aesthetic score or use general-purpose MLLMs without conditioning on photographic intent. This omission matters because the same blur, pose, lighting, or framing choice may serve one photographic intent but undermine another. These models thus learn context-agnostic aesthetic priors and yield inconsistent, inaccurate, misleading judgments for portraits with distinct photographic objectives. We introduce PortraitAes-Bench, an 11K-scale benchmark that decomposes this task into intent-conditioned subjudgments. Expert-authored rubrics define nine photographic intents, six first-level dimensions, and 22 secondary criteria. They support a structured pipeline for intent routing, specialist assessment, verification, and score fusion. Following this structure, we train PortraitAes with multi-task supervision. We then improve score comparability through Gaussian score calibration and within-dimension cross-image ranking. On the standard benchmark, PortraitAes achieves a Pearson correlation of 0.924 and a Spearman rank correlation of 0.934. On the hard-case set, its Pearson correlation is 0.829 and its Spearman rank correlation is 0.795. Across both sets, PortraitAes outperforms the evaluated general-purpose MLLMs and specialized aesthetic baselines.
Chinese Translation
肖像美学评估根据人本图像实现其摄影意图的有效性分配可比分数。这些分数支持图像生成流程中的数据过滤、候选选择和偏好建模。现有方法通常预测单一的美学分数或使用通用的多语言大模型（MLLMs），而未考虑摄影意图。这一遗漏至关重要，因为相同的模糊、姿势、光照或构图选择可能服务于一种摄影意图，但削弱另一种。因此，这些模型学习到的美学先验缺乏上下文，导致对具有不同摄影目标的肖像产生不一致、不准确和误导性的判断。我们引入了PortraitAes-Bench，这是一个11K规模的基准，将此任务分解为意图条件下的子判断。专家撰写的评分标准定义了九种摄影意图、六个一级维度和22个二级标准。它们支持意图路由、专家评估、验证和分数融合的结构化流程。遵循这一结构，我们通过多任务监督训练了PortraitAes。然后，我们通过高斯分数校准和维度内跨图像排名提高了分数的可比性。在标准基准上，PortraitAes实现了0.924的Pearson相关系数和0.934的Spearman等级相关系数。在难例集上，其Pearson相关系数为0.829，Spearman等级相关系数为0.795。在这两个数据集上，PortraitAes均优于评估的通用多语言大模型和专业美学基线。
cs.CV / 91 / 2610.05013
EMBER-Bench: Benchmarking Cross-Event Causal Memory in Long-Horizon Embodied Tasks
EMBER-Bench：长时间跨度具身任务中的跨事件因果记忆基准测试
Abstract
Lifelong physical agents must reason over extended interactions where past events continue to shape the world long after they disappear from view. Beyond recalling what happened, agents must infer how history changes the current state and constrains future actions. Yet existing embodied and video-memory benchmarks largely focus on historical retrieval and summary, leaving such history-dependent causal reasoning underexplored. We introduce EMBER-Bench, an egocentric benchmark for cross-event causal reasoning in long-horizon embodied tasks, for which we newly created the task design, video recording, and data annotation. It contains 189 household tasks and 699 QA pairs, spanning task progress, failure recovery, external interventions, and compound long-horizon tasks with distant dependencies and prerequisites, with fine-grained event and causal-chain annotations. EMBER-Bench evaluates reasoning in both directions: next-action prediction selects the next action from history, and causal traceback, given that action, identifies the historical event that makes it necessary. Input ablations that add action logs or privileged cause-and-consequence annotations to the video indicate which kind of historical information models fail to use. Among the 16 evaluated models, the highest overall accuracy is 61.2%, compared with a mean of 98.3% across two human evaluators. At paired decision points, correct traceback is not associated with correct next-action prediction. Adding action logs yields a gain of 1.6 points, whereas cause-and-consequence annotations yield an additional gain of 13.0 points on top of that. These results suggest that extracting causal information from past events and converting it into constraints on current actions remains a key difficulty for long-horizon embodied agents. Project Page: https://zhaoalexgoat.github.io/EMBER-Bench/
Chinese Translation
终身物理代理必须在扩展的交互中进行推理，其中过去的事件在消失后仍然持续影响世界。除了回忆发生过的事情，代理还必须推断历史如何改变当前状态并限制未来的行动。然而，现有的具身和视频记忆基准主要集中在历史检索和总结上，导致这种依赖历史的因果推理尚未得到充分探索。我们引入了EMBER-Bench，这是一个针对长时间跨度具身任务中跨事件因果推理的自我中心基准测试，我们为此新创建了任务设计、视频录制和数据注释。它包含189个家庭任务和699对问答，涵盖任务进展、失败恢复、外部干预以及具有远程依赖关系和前提条件的复合长时间跨度任务，并附有细粒度的事件和因果链注释。EMBER-Bench评估双向推理：下一步行动预测从历史中选择下一步行动，而因果回溯则在给定该行动的情况下识别出使其成为必要的历史事件。输入消融实验通过向视频添加行动日志或特权因果关系注释，表明模型未能利用哪种历史信息。在评估的16个模型中，最高的整体准确率为61.2%，而两个人工评估者的平均准确率为98.3%。在配对决策点，正确的回溯与正确的下一步行动预测并无关联。添加行动日志带来了1.6分的提升，而因果关系注释在此基础上又增加了13.0分。这些结果表明，从过去事件中提取因果信息并将其转化为对当前行动的约束仍然是长时间跨度具身代理面临的关键难题。项目页面：https://zhaoalexgoat.github.io/EMBER-Bench/
cs.CV / 92 / 2610.05023
Look Where You Say You're Looking: Self-Grounded Attention for Visual Reasoning
关注你所说的地方：用于视觉推理的自我基础注意力
Abstract
We introduce Self-Saliency, a method for training Vision-Language Models (VLMs) to increase the alignment between their visual attention and the image regions mentioned in their reasoning. Self-Saliency uses a grounding model to localize the objects mentioned in each reasoning step and treats the resulting areas as supervision for the model's visual attention. Previous work on steering visual attention determines target image regions based solely on the image and question. In contrast, we show that conditioning the target regions on the model's generated reasoning improves downstream performance. For proper evaluation, we build a unified, broad suite of 25 visual reasoning benchmarks, where we reproduce the results of previous methods. We find that Self-Saliency significantly outperforms both prior attention-steering methods and baselines that ground image-level text, achieving both a better average rank and a better mean score. Post-training analysis shows that the model primarily adapts its reasoning text to existing attention patterns, producing shorter steps that refer to larger regions. Nevertheless, when controlling for generated text, attention to grounded regions increases significantly across the relevant layer. Finally, we identify a consistent geometric bias in VLM visual attention toward the image border. However, our ablations show that Self-Saliency's gains cannot be explained by simply aligning attention with the center of the image, highlighting the importance of aligning visual attention with the regions mentioned in the model's reasoning.
Chinese Translation
我们提出了一种方法，自我显著性（Self-Saliency），用于训练视觉-语言模型（Vision-Language Models, VLMs），以增强其视觉注意力与推理中提到的图像区域之间的对齐。自我显著性使用一个基础模型来定位每个推理步骤中提到的对象，并将结果区域视为模型视觉注意力的监督信号。以往关于引导视觉注意力的研究仅基于图像和问题确定目标图像区域。相反，我们展示了将目标区域条件化于模型生成的推理能够改善下游性能。为了进行适当的评估，我们构建了一个统一的、广泛的25个视觉推理基准测试套件，并重现了之前方法的结果。我们发现，自我显著性显著优于先前的注意力引导方法和基于图像级文本的基线，获得了更好的平均排名和更高的平均得分。后训练分析表明，模型主要调整其推理文本以适应现有的注意力模式，产生更短的步骤，指向更大的区域。然而，当控制生成的文本时，关注基础区域的注意力在相关层中显著增加。最后，我们发现VLM视觉注意力在图像边缘存在一致的几何偏差。然而，我们的消融实验表明，自我显著性的提升不能仅通过将注意力与图像中心对齐来解释，这突显了将视觉注意力与模型推理中提到的区域对齐的重要性。
cs.CV / 93 / 2610.05024
LightVLN: Efficient Aerial Vision-and-Language Navigation with Compact Memory and History-Guided Local Aggregation
LightVLN：具有紧凑内存和历史引导局部聚合的高效空中视觉与语言导航
Abstract
Aerial vision-and-language navigation (VLN) enables unmanned aerial vehicles to execute long-horizon natural-language instructions from visual observations in complex three-dimensional environments. However, recent aerial VLN models often rely on large-scale vision-language backbones and dense visual histories, imposing substantial computation and memory costs that hinder onboard deployment. We propose LightVLN, a lightweight history-aware aerial VLN framework that combines a compact 0.5B language backbone with compact representations of both historical and current observations. LightVLN compresses each historical frame into a single token using visual features already computed by the policy. It further introduces history- and instruction-conditioned local aggregation to reduce the current observation from 256 to 32 visual tokens while preserving navigation-relevant spatial information. With up to 16 historical frames, the policy uses at most 48 observation-derived tokens. On the public OpenFly dataset, LightVLN achieves 50.93% Test-Seen and 36.14% Test-Unseen success rates (SR), outperforming the evaluated 7B language-backbone baselines on most reported metrics. It also achieves 25.83% SR on AerialVLN-S Val-Seen. In a reconstructed unseen campus, we deploy LightVLN on a DJI M350 RTK with an external Jetson Orin NX 16 GB for closed-loop onboard-compute real-to-sim hardware-in-the-loop (HIL) evaluation, achieving 14.61 Hz model inference and 11.13 Hz end-to-end decision updates. These results demonstrate the effectiveness and efficiency of LightVLN for aerial navigation.
Chinese Translation
空中视觉与语言导航（VLN）使无人机能够在复杂的三维环境中根据视觉观察执行长期的自然语言指令。然而，近期的空中VLN模型通常依赖于大规模的视觉-语言骨干网络和密集的视觉历史，这带来了可观的计算和内存开销，阻碍了其在机载系统中的部署。我们提出了LightVLN，一个轻量级的历史感知空中VLN框架，它结合了紧凑的0.5B语言骨干网络与历史和当前观察的紧凑表示。LightVLN通过使用政策已经计算出的视觉特征，将每个历史帧压缩为单个标记。它进一步引入了历史和指令条件的局部聚合，将当前观察从256个视觉标记减少到32个，同时保留与导航相关的空间信息。在最多16个历史帧的情况下，政策最多使用48个源自观察的标记。在公开的OpenFly数据集上，LightVLN在测试已见（Test-Seen）和测试未见（Test-Unseen）中分别达到了50.93%和36.14%的成功率（SR），在大多数报告的指标上超越了评估的7B语言骨干基线。它在AerialVLN-S验证集的已见（Val-Seen）上也达到了25.83%的成功率。在一个重建的未见校园中，我们在配备外部Jetson Orin NX 16 GB的DJI M350 RTK上部署了LightVLN，进行闭环机载计算的真实到仿真硬件在环（HIL）评估，达到了14.61 Hz的模型推理和11.13 Hz的端到端决策更新。这些结果展示了LightVLN在空中导航中的有效性和高效性。
cs.CV / 94 / 2610.05025
Triggering Generalist Reasoning via Predictive Uncertainty for Dual-System VLA
通过预测不确定性触发通用推理以实现双系统视觉-语言-行动
Abstract
Dual-system Vision-Language-Action (VLA) models improve real-time robotic control by pairing a slow, reasoning-capable generalist with a fast specialist action expert. However, existing methods invoke the generalist at a fixed frequency, ignoring the fact that decision-making complexity varies throughout a rollout. This static strategy wastes computation in easy phases and can delay renewed reasoning when the scene changes unexpectedly. We propose TUD (Triggering generalist reasoning via predictive Uncertainty for Dual-system VLA), an adaptive inference framework that selectively skips unnecessary generalist calls. TUD measures the cross-step dispersion of action re-predictions at the upcoming chunk slot under the cached generalist context, as a predictive uncertainty signal. This signal captures how much the future action plan shifts as new observations arrive and is computed from forwards the architecture already runs, requiring neither manual phase labels nor an auxiliary uncertainty model. On VLA-Arena, it achieves a higher success rate at matched call budgets than alternative uncertainty baselines while maintaining low wall-clock overhead, and more consistently separates successful from failed rollouts. Also, TUD finds a more favorable cost-success trade-off than non-adaptive baselines, tracing an entire operating curve as a single threshold is varied, and substantially reduces VLM calls at matched success rate. The same trade-off appears in our real-robot experiments, where TUD cuts generalist calls by 75% relative to the strongest fixed-interval baseline while achieving an even higher success rate. Our results suggest that predictive uncertainty provides a practical criterion for adaptive reasoning in efficient VLA control.
Chinese Translation
双系统视觉-语言-行动（VLA）模型通过将一个缓慢、具备推理能力的通用体与一个快速的专业行动专家相结合，来改善实时机器人控制。然而，现有方法以固定频率调用通用体，忽视了决策复杂性在整个执行过程中变化的事实。这种静态策略在简单阶段浪费计算资源，并可能在场景意外变化时延迟重新推理。我们提出了TUD（通过预测不确定性触发通用推理的双系统VLA），这是一种自适应推理框架，能够选择性地跳过不必要的通用体调用。TUD在缓存的通用体上下文下，测量即将到来的块槽中行动重新预测的跨步分散度，作为预测不确定性信号。该信号捕捉了随着新观察到达，未来行动计划的变化程度，并且是从架构向前运行中计算得出的，既不需要手动阶段标签，也不需要辅助不确定性模型。在VLA-Arena上，它在匹配调用预算的情况下，取得了比其他不确定性基线更高的成功率，同时保持了较低的时钟开销，并且更一致地将成功的执行与失败的执行分开。此外，TUD在成本-成功权衡上优于非自适应基线，通过改变单一阈值描绘出完整的操作曲线，并在匹配成功率的情况下显著减少了VLM调用。在我们的真实机器人实验中也出现了相同的权衡，TUD相较于最强的固定间隔基线减少了75%的通用体调用，同时取得了更高的成功率。我们的结果表明，预测不确定性为高效VLA控制中的自适应推理提供了一个实用标准。
cs.CV / 95 / 2610.05026
GeoBridge-VLA: Geometry-Aware Residual Adaptation for Vision-Language-Action Models
GeoBridge-VLA：面向几何的残差适应用于视觉-语言-动作模型
Abstract
Vision-language-action (VLA) models encode semantic information from vision-language pretraining, but manipulation also requires precise spatial reasoning. We present GeoBridge-VLA, a two-stage method for learning geometric features from a pretrained VLA's frozen visual encoder and using them for action prediction. Stage I trains a feature bridge and geometry decoder with depth supervision. Stage II freezes these modules and trains a gated residual interface together with the action-side projections and action expert. The residual augments the existing visual tokens without adding a second image encoder or increasing the token count. Deployment requires RGB, robot state, and language, but no depth observations. Under matched evaluation conditions, GeoBridge-VLA achieves 70.9% success on LIBERO, compared with 60.0% for SmolVLA. Disabling the residual in the same trained checkpoint reduces success from 70.90% to 69.85%, with mixed effects across suites. On a physical ROBOTIS OMY robot, GeoBridge-VLA succeeds in 148 of 200 trials (74.0%) across four tasks, compared with 108 of 200 (54.0%) for SmolVLA.
Chinese Translation
视觉-语言-动作（VLA）模型从视觉-语言预训练中编码语义信息，但操作还需要精确的空间推理。我们提出了GeoBridge-VLA，这是一种两阶段的方法，用于从预训练的VLA冻结视觉编码器中学习几何特征，并将其用于动作预测。第一阶段训练一个特征桥和几何解码器，并进行深度监督。第二阶段冻结这些模块，并训练一个门控残差接口，与动作侧投影和动作专家一起工作。该残差增强了现有的视觉标记，而无需添加第二个图像编码器或增加标记数量。部署时需要RGB、机器人状态和语言，但不需要深度观测。在匹配的评估条件下，GeoBridge-VLA在LIBERO上取得了70.9%的成功率，而SmolVLA为60.0%。在同一训练检查点中禁用残差使成功率从70.90%降至69.85%，在不同的评估套件中效果不一。在物理ROBOTIS OMY机器人上，GeoBridge-VLA在四个任务中成功完成200次试验中的148次（74.0%），而SmolVLA为200次中的108次（54.0%）。
cs.CV / 96 / 2610.05029
SPACE-CLIPv2: Decoding Local Geometry from Frozen CLIP for Monocular Depth Estimation
SPACE-CLIPv2：从冻结的 CLIP 解码局部几何用于单目深度估计
Abstract
Vision-language foundation models such as CLIP provide strong semantic representations, but their patch tokens are not directly optimized for dense metric geometry. SPACE-CLIP showed that frozen CLIP features can support monocular depth estimation through layer-group feature fusion, yet it leaves open how neighboring CLIP tokens should be combined to recover fine local structure. We present SPACE-CLIPv2, a frozen-backbone depth decoder that aggregates fixed local neighborhoods in CLIP token space. At selected decoder stages, the model samples a fixed token stencil, predicts aggregation weights, and injects the resulting response through a gated residual update. A token-space high-pass branch further preserves shallow local contrast. On NYU Depth V2, SPACE-CLIPv2 improves over a matched SPACE-CLIP baseline, while five-seed experiments consistently favor fixed over learned-offset sampling. Zero-shot iBims-1 evaluation further improves boundary and planar-geometry measures. These results support constrained local token aggregation as a practical mechanism for decoding geometry from frozen CLIP representations.
Chinese Translation
视觉-语言基础模型如 CLIP 提供了强大的语义表示，但其补丁令牌并未直接针对密集的度量几何进行优化。SPACE-CLIP 显示冻结的 CLIP 特征可以通过层组特征融合支持单目深度估计，但尚未解决如何组合相邻的 CLIP 令牌以恢复细致的局部结构。我们提出了 SPACE-CLIPv2，这是一种冻结骨干网络深度解码器，聚合 CLIP 令牌空间中的固定局部邻域。在选定的解码器阶段，该模型采样固定的令牌模板，预测聚合权重，并通过门控残差更新注入生成的响应。一个令牌空间高通分支进一步保留了浅层局部对比度。在 NYU Depth V2 数据集上，SPACE-CLIPv2 相较于匹配的 SPACE-CLIP 基线有所提升，而五次种子实验一致支持固定采样优于学习偏移采样。零样本 iBims-1 评估进一步改善了边界和平面几何度量。这些结果支持受限的局部令牌聚合作为从冻结的 CLIP 表示解码几何的实用机制。
cs.CV / 97 / 2610.05033
Code2Games: Enabling Coding Agents for Gaming World Generation
Code2Games：为游戏世界生成赋能编码代理
Abstract
Generating a high-quality gaming world from a natural-language game intent requires joint reasoning about scene structure, spatial layout, gameplay objectives, interactive entities, and executable gameplay logic. Existing coding agents can generate individual assets, scenes, or scripts, but often struggle to maintain consistency across these components. We propose Code2Games, an agentic framework that builds a structured gaming world upon a base Blender world generated from the same game intent. Code2Games coordinates scene analysis, gameplay planning, constrained gaming-world generation, and gaming-engine customization through a shared scene-gameplay representation with persistent element correspondence. After world generation, Code2Games adapts the generated world to Unreal Engine 5 and employs an execution-guided reconstruction process that uses compilation diagnostics, runtime feedback, and gameplay test results to resolve inconsistencies arising during engine adaptation. To systematically evaluate gaming-world generation, we introduce the GameCode4D benchmark, which comprises ten fixed game prompts spanning different levels of scene and gameplay complexity. We evaluate the generated results across four dimensions: visual quality, interactive fidelity, multimodal artifact quality, and playable-game quality. Experiments demonstrate that, compared with direct gaming-world generation by coding agents and existing baseline methods, Code2Games consistently improves the visual quality and interactive fidelity of generated gaming worlds, as well as the quality of the resulting games after engine adaptation.
Chinese Translation
从自然语言游戏意图生成高质量的游戏世界需要对场景结构、空间布局、游戏目标、交互实体和可执行游戏逻辑进行联合推理。现有的编码代理可以生成单个资产、场景或脚本，但往往难以在这些组件之间保持一致性。我们提出了Code2Games，一个基于相同游戏意图生成的基础Blender世界构建结构化游戏世界的代理框架。Code2Games通过共享的场景-游戏表现和持久的元素对应关系，协调场景分析、游戏规划、受限的游戏世界生成和游戏引擎定制。在世界生成后，Code2Games将生成的世界适配到Unreal Engine 5，并采用执行引导的重建过程，利用编译诊断、运行时反馈和游戏测试结果来解决在引擎适配过程中出现的不一致性。为了系统地评估游戏世界生成，我们引入了GameCode4D基准，该基准包含十个固定的游戏提示，涵盖不同水平的场景和游戏复杂性。我们从视觉质量、交互保真度、多模态工件质量和可玩游戏质量四个维度评估生成的结果。实验表明，与编码代理的直接游戏世界生成和现有基线方法相比，Code2Games在生成的游戏世界的视觉质量和交互保真度，以及引擎适配后生成游戏的质量上均表现出一致的提升。
cs.CV / 98 / 2610.05053
CoDG-Net: Structure-Guided Style Diffusion and Collaborative Learning to Mitigate Catastrophic Forgetting in Medical Image Domain Generalization
CoDG-Net：结构引导的风格扩散与协同学习以减轻医学图像领域泛化中的灾难性遗忘
Abstract
Domain Generalization (DG) for medical image segmentation is both highly challenging and critically important. However, existing medical DG methods largely overlook the issue of Catastrophic Forgetting (CF): \textbf{Models often sacrifice their ability to retain source-domain knowledge while pursuing cross-domain robustness.} This can directly threaten diagnostic safety in already-deployed clinical scenarios. To address this, we investigate data augmentation strategies and catastrophic forgetting for medical image DG segmentation. First, we propose a structure-guided style diffusion augmentation method. Constrained by anatomical structure consistency in the frequency domain, this method performs cross-domain diffusion on the amplitude spectrum, generating samples with more diverse and broader style coverage to better support domain generalization. Then, we design a collaborative learning network with a dual-branch interactive architecture (CoDG-Net), together with a novel learning bias-guided strategy that adaptively regulates knowledge transfer at both the layer level and the task level, thereby effectively mitigating catastrophic forgetting on the source domain. Experiments and ablation studies on single-source and multi-source medical DG benchmark datasets demonstrate that CoDG-Net not only outperforms existing state-of-the-art methods in target-domain segmentation performance, but also achieves a lower forgetting rate on the source-domain data. The code is available at: https://github.com/wangprocess/CoDG-Net.
Chinese Translation
医学图像分割的领域泛化（Domain Generalization, DG）既具有高度挑战性，又至关重要。然而，现有的医学DG方法在很大程度上忽视了灾难性遗忘（Catastrophic Forgetting, CF）的问题： extbf{模型在追求跨领域鲁棒性时，往往牺牲了保留源领域知识的能力。} 这可能直接威胁到已部署临床场景中的诊断安全。为了解决这一问题，我们研究了医学图像DG分割中的数据增强策略和灾难性遗忘。首先，我们提出了一种结构引导的风格扩散增强方法。在频域中受限于解剖结构一致性，该方法对幅度谱进行跨领域扩散，生成具有更丰富和更广泛风格覆盖的样本，以更好地支持领域泛化。然后，我们设计了一个具有双分支交互架构的协同学习网络（CoDG-Net），并结合了一种新颖的学习偏差引导策略，该策略自适应地调节层级和任务级别的知识转移，从而有效减轻源领域的灾难性遗忘。在单源和多源医学DG基准数据集上的实验和消融研究表明，CoDG-Net不仅在目标领域分割性能上超越了现有的最先进方法，而且在源领域数据上实现了更低的遗忘率。代码可在以下链接获取：https://github.com/wangprocess/CoDG-Net。
cs.CV / 99 / 2610.05066
Salvation Lies Within: Eliciting Inherent Style Transfer in Step-Distilled Diffusion Models
救赎在于内心：在逐步蒸馏扩散模型中引发固有风格迁移
Abstract
Adapting step-distilled text-to-image (T2I) models through post-training incurs additional computational costs and affects native few-step generation behavior. This motivates a complementary route beyond style-specific adaptation: drawing on the visual knowledge already encoded in step-distilled T2I models to elicit stylistic capabilities through language. Pursuing this direction requires textual guidance that captures how visual attributes jointly define a style and remain applicable as the depicted content changes. To explore this approach, we introduce StyleForge, a fully automatic, training-free framework that expresses reference styles as reusable rendering instructions. By integrating overall rendering characteristics with local color and lighting behavior, StyleForge organizes visual evidence from reference images into a coherent specification of how the target style should be expressed. The specification is then compiled into textual guidance that can be reused across content prompts, enabling frozen step-distilled T2I models to render different subjects and scenes in the reference style while retaining native few-step generation. Extensive experiments show relative gains of up to 29.47\% in generation quality scores over the strongest baseline, while Pareto analysis indicates that improved stylization is accompanied by strong adherence to the requested content.
Chinese Translation
通过后训练适应逐步蒸馏的文本到图像（T2I）模型会产生额外的计算成本，并影响其原生的少步生成行为。这促使我们探索一种超越特定风格适应的补充路径：利用已经编码在逐步蒸馏T2I模型中的视觉知识，通过语言引发风格能力。追求这一方向需要文本指导，以捕捉视觉属性如何共同定义风格，并在描绘内容变化时仍然适用。为探索这一方法，我们引入了StyleForge，这是一个完全自动化、无需训练的框架，将参考风格表达为可重用的渲染指令。通过将整体渲染特征与局部颜色和光照行为相结合，StyleForge将来自参考图像的视觉证据组织成一个连贯的规范，说明目标风格应如何表达。该规范随后被编译成文本指导，可以在内容提示中重复使用，使得冻结的逐步蒸馏T2I模型能够以参考风格渲染不同的主题和场景，同时保持原生的少步生成。大量实验表明，在生成质量评分上，相较于最强基线，提升幅度可达29.47\%，而帕累托分析表明，改进的风格化与对请求内容的强遵循相伴随。
cs.CV / 100 / 2610.05078
CGDD-Net: Context-Guided Dynamic Detail Modeling for Retinal Vessel Segmentation
CGDD-Net：用于视网膜血管分割的上下文引导动态细节建模
Abstract
Accurate retinal vessel segmentation requires features that capture vascular geometry while preserving information for fine-scale reconstruction. We propose CGDD-Net, a context-guided dynamic detail modeling network that connects adaptive feature extraction to a shared decoder pathway. Context-Guided Scale-Adaptive Deformable Encoding (CSDE) combines fixed-grid convolution with deformable local attention to capture vascular patterns at multiple spatial extents. Spatially Adaptive Multi-Kernel Gating (SAMG) selects receptive-field responses at each location. Dynamic Cross-Scale Detail Fusion (DCDF) aligns the gated intermediate features and compresses them into an eight-channel representation, which is reused at three decoder resolutions together with selected encoder skips. This design consolidates intermediate information before decoding instead of transferring each middle-stage feature through a separate direct skip. On DRIVE, CHASE\_DB1, STARE, and HRF, CGDD-Net achieves AUC values of 0.9824, 0.9938, 0.9895, and 0.9874, with F1 scores of 0.8323, 0.8102, 0.8510, and 0.8157, respectively. The complete model contains 1.96 million trainable parameters. In cumulative ablations, the full model improves F1 over the internal baseline by 2.48, 0.61, and 3.49 percentage points on DRIVE, CHASE\_DB1, and STARE. Twelve directed cross-dataset experiments further characterize transfer without target-domain adaptation. The results support shared intermediate detail delivery as an effective, parameter-compact architecture for retinal vessel segmentation. Code is available at \url{https://github.com/lixincheng-xcl/CGDD-Net}.
Chinese Translation
准确的视网膜血管分割需要捕捉血管几何特征的同时保留细尺度重建的信息。我们提出了CGDD-Net，一种上下文引导的动态细节建模网络，将自适应特征提取与共享解码器路径连接起来。上下文引导的尺度自适应可变编码（CSDE）结合了固定网格卷积和可变局部注意力，以在多个空间范围内捕捉血管模式。空间自适应多核门控（SAMG）在每个位置选择感受野响应。动态跨尺度细节融合（DCDF）对齐门控的中间特征，并将其压缩为八通道表示，该表示与选定的编码器跳跃一起在三个解码器分辨率中重复使用。该设计在解码之前整合中间信息，而不是通过单独的直接跳跃传递每个中间阶段特征。在DRIVE、CHASE_DB1、STARE和HRF数据集上，CGDD-Net分别实现了0.9824、0.9938、0.9895和0.9874的AUC值，以及0.8323、0.8102、0.8510和0.8157的F1分数。完整模型包含196万可训练参数。在累积消融实验中，完整模型在DRIVE、CHASE_DB1和STARE上分别提高了F1分数2.48、0.61和3.49个百分点。十二个定向跨数据集实验进一步表征了在没有目标领域适应的情况下的迁移。结果支持共享中间细节传递作为一种有效的、参数紧凑的视网膜血管分割架构。代码可在 https://github.com/lixincheng-xcl/CGDD-Net 获取。
cs.CV / 101 / 2610.05097
ReMAP: Restoring the Perceptual Cycle with Reasoning-Time Latent Visual Memory
ReMAP：通过推理时潜在视觉记忆恢复感知循环
Abstract
As multimodal large language models (MLLMs) reason for longer, attention to the initial visual input diminishes, weakening visual grounding. Visual memory reintroduces visual evidence during reasoning. We conduct a controlled analysis of visual memory along three axes: curation, organization, and access. We find that local evidence benefits from global context, compact latent representations balance accuracy and visual-context cost, and the utility of memory access depends on the reasoning state. Guided by these findings, we propose ReMAP (Reasoning-Time Memory-Augmented Perception), which couples two complementary latent memories: a static, question-conditioned Global memory that preserves scene and cross-image context, and a dynamic Local memory that uses this context as an anchor while selecting and re-encoding region-level evidence according to the current reasoning state. Both memories return compact latent tokens inserted into the reasoning sequence, and a reinforcement-learning access policy trained with branched rollouts decides when to continue reasoning or invoke Global or Local memory. On ten benchmark families, ReMAP outperforms prior visual-memory methods on all four multi-image benchmarks, exceeding the strongest prior results on MuirBench and MIMIC by 8.38 and 14.84 percentage points. Across four backbone families, enabling memory access improves over the same trained model with memory disabled, and on shared V*Bench, CV-Bench-2D, and MuirBench questions ReMAP reduces the visual tokens entering the reasoning sequence by 51.0-76.8% relative to the native-resolution backbone. Further analyses show that Global and Local memory form distinct yet complementary latent representations. Together, these components restore the perceptual cycle by letting the reasoning state trigger targeted visual retrieval, with the retrieved evidence guiding subsequent reasoning.
Chinese Translation
随着多模态大型语言模型（MLLMs）推理时间的延长，对初始视觉输入的关注逐渐减弱，从而削弱了视觉基础。视觉记忆在推理过程中重新引入视觉证据。我们沿着三个维度对视觉记忆进行了控制分析：策划、组织和访问。我们发现局部证据受益于全局上下文，紧凑的潜在表示在准确性和视觉上下文成本之间取得平衡，而记忆访问的效用依赖于推理状态。在这些发现的指导下，我们提出了ReMAP（推理时记忆增强感知），该方法结合了两种互补的潜在记忆：一种静态的、基于问题的全局记忆，保留场景和跨图像的上下文，以及一种动态的局部记忆，该记忆在根据当前推理状态选择和重新编码区域级证据时使用此上下文作为锚点。这两种记忆返回插入推理序列的紧凑潜在标记，而通过分支回放训练的强化学习访问策略决定何时继续推理或调用全局或局部记忆。在十个基准系列中，ReMAP在所有四个多图像基准上超越了先前的视觉记忆方法，在MuirBench和MIMIC上分别超过了最强的先前结果8.38和14.84个百分点。在四个主干系列中，启用记忆访问相较于同一训练模型在禁用记忆时有所改善，并且在共享的V*Bench、CV-Bench-2D和MuirBench问题上，ReMAP相较于原生分辨率主干减少了进入推理序列的视觉标记51.0-76.8%。进一步的分析表明，全局和局部记忆形成了不同但互补的潜在表示。这些组件共同恢复了感知循环，使推理状态触发有针对性的视觉检索，而检索到的证据引导后续推理。
cs.CV / 102 / 2610.05109
LoopMoEVR: Loop-Based Degradation-Aware Mixture-of-Experts for Unified UHD Video Restoration
LoopMoEVR：基于循环的降级感知专家混合模型用于统一超高清（UHD）视频恢复
Abstract
Recently, unified high-definition image restoration has attracted considerable attention; however, existing models tend to excessively increase their depth in pursuit of improved generalization, which often yields only limited gains. Meanwhile, loop-based learning paradigms have drawn widespread attention due to their low parameter counts and strong regression capability, as exemplified by GPT-6 and looped Transformers. In this paper, we introduce the loop learning paradigm to address restoration tasks that require cross-domain learning. Specifically, we propose LoopMoEVR, a loop-based mixture-of-experts model capable of handling degraded ultra-high-definition (UHD) inputs. First, a degradation-conditioned low-rank loop embedding is designed to construct input-dependent stage conditions. Second, a spatio-temporal iterative adaptive normalization module, termed IterAda3DN, is developed to fuse local features with global loop context, thereby performing position-wise affine modulation. Finally, the expert branches further integrate the attention-updated local and global video states with the loop conditions to generate dedicated modulation parameters, while an input-conditioned depth predictor adaptively configures the number of loop iterations. With only approximately 0.884M trainable parameters, the proposed model uniformly handles UHD video dehazing, deraining, denoising, and low-light enhancement tasks, achieving state-of-the-art restoration performance on both public benchmarks and real-world scenarios.
Chinese Translation
近年来，统一高清图像恢复引起了广泛关注；然而，现有模型往往为了提高泛化能力而过度增加深度，这通常只带来有限的收益。同时，基于循环的学习范式因其低参数量和强回归能力而受到广泛关注，例如GPT-6和循环Transformer。在本文中，我们引入循环学习范式来解决需要跨域学习的恢复任务。具体而言，我们提出了LoopMoEVR，这是一种基于循环的专家混合模型，能够处理降级的超高清（UHD）输入。首先，设计了一种降级条件的低秩循环嵌入，以构建依赖于输入的阶段条件。其次，开发了一种时空迭代自适应归一化模块，称为IterAda3DN，用于融合局部特征与全局循环上下文，从而执行位置相关的仿射调制。最后，专家分支进一步将注意力更新后的局部和全局视频状态与循环条件整合，以生成专用的调制参数，同时，输入条件的深度预测器自适应地配置循环迭代的次数。该模型仅具有约0.884M的可训练参数，能够统一处理UHD视频去雾、去雨、去噪和低光增强任务，在公共基准和现实场景中实现了最先进的恢复性能。
cs.CV / 103 / 2610.05115
PCLM: Small-target localization with frozen CLIP via prototype contrast and local magnification
PCLM：通过原型对比和局部放大实现小目标定位
Abstract
Small targets occupy few patches in a vision-language encoder, so spatial features often mix object appearance with surrounding content. We propose Prototype Contrast and Local Magnification (PCLM), a support-conditioned localization method that uses a frozen CLIP encoder. Five masked support images per class define foreground and background prototypes through equally weighted regional features. Their difference provides a shared scoring direction for query patches, explicitly comparing target evidence with the demonstrated background. Nine overlapping query windows are enlarged and encoded independently to sample small targets more densely. Reprojection and coverage averaging combine their scores into a continuous localization map. The class direction occupies 2 KiB regardless of support count and transfers unchanged across datasets with mapped categories. On 5,047 small-target queries from VOC, COCO, ADE20K and Oxford-IIIT Pets, PCLM achieves higher mean pixel AP than every evaluated text-conditioned localization baseline on each dataset under our evaluation protocol. Gains over the strongest scene-dataset baselines range from 5.63 to 13.23 percentage points. At comparable measured latency, local magnification improves scene small-target AP by 4.51 to 5.68 points over whole-canvas enlargement. Factorial experiments show that prototype contrast increases the benefit of local observation, including under matched image-coordinate filtering. Support-budget experiments show that additional examples refine category estimation without increasing representation size or query-time scoring cost.
Chinese Translation
小目标在视觉-语言编码器中占据的区域较少，因此空间特征往往将物体外观与周围内容混合。我们提出了一种支持条件的定位方法——原型对比和局部放大（PCLM），该方法使用冻结的 CLIP 编码器。每个类别的五张掩膜支持图像通过等权重的区域特征定义前景和背景原型。它们之间的差异为查询补丁提供了一个共享的评分方向，明确比较目标证据与展示的背景。九个重叠的查询窗口被放大并独立编码，以更密集地采样小目标。重投影和覆盖平均将它们的分数结合成一个连续的定位图。类别方向占用 2 KiB，无论支持数量如何，并且在映射类别的数据集间传递不变。在来自 VOC、COCO、ADE20K 和 Oxford-IIIT Pets 的 5,047 个小目标查询中，PCLM 在我们的评估协议下在每个数据集上实现了比每个评估的文本条件定位基线更高的平均像素 AP。相对于最强的场景-数据集基线，提升范围为 5.63 到 13.23 个百分点。在可比的测量延迟下，局部放大将场景小目标的 AP 提高了 4.51 到 5.68 分。因子实验表明，原型对比增加了局部观察的收益，包括在匹配图像坐标过滤下。支持预算实验表明，额外示例在不增加表示大小或查询时间评分成本的情况下精炼了类别估计。
cs.CV / 104 / 2610.05129
Representation--Behavior Alignment for Explainable Weakly-Supervised Video Anomaly Detection
可解释的弱监督视频异常检测中的表征-行为对齐
Abstract
Multimodal Large Language Models (MLLMs) provide a natural way to make video anomaly detection more explainable. However, their final decisions do not always fully use the discriminative information contained in their hidden states, an issue we refer to as representation--behavior misalignment. We decompose this gap into a capacity component that measures discriminative information never aggregated into the readout position, and a directional component that measures the angular mismatch between the optimal and the native normal--abnormal axis at that position. Across multiple video anomaly detection benchmarks and MLLM backbones the directional component dominates, and residual-stream tracing shows that native-axis separability rises sharply in several mid-to-late attention layers. Because both components are governed by attention rather than MLP updates, we propose Representation--Behavior Alignment (RBA), a parameter-efficient method that adapts those layers using video-level labels alone while updating about 0.012\% of the backbone parameters. Experiments on three benchmarks show that RBA improves native-readout performance and better aligns the model's decision direction with discriminative representations, and it produces anomaly decisions and explanations through a single generative process.
Chinese Translation
多模态大型语言模型（MLLMs）为视频异常检测提供了一种自然的可解释性。然而，它们的最终决策并不总是充分利用其隐藏状态中包含的区分信息，这一问题我们称之为表征-行为不对齐。我们将这一差距分解为两个部分：一个是容量组件，衡量从未聚合到读取位置的区分信息；另一个是方向组件，衡量该位置上最优与本地正常-异常轴之间的角度不匹配。在多个视频异常检测基准和MLLM骨干网络中，方向组件占主导地位，残差流追踪显示在几个中后期注意力层中，本地轴的可分离性急剧上升。由于这两个组件受注意力而非多层感知器（MLP）更新的控制，我们提出了表征-行为对齐（RBA），这是一种参数高效的方法，仅使用视频级标签来适应这些层，同时更新约0.012%的骨干参数。在三个基准上的实验表明，RBA提高了本地读取性能，更好地将模型的决策方向与区分表征对齐，并通过单一生成过程产生异常决策和解释。
cs.CV / 105 / 2610.05131
RoMod: Temporal Routing Modulation via Mixture-of-Experts for Video Anomaly Detection
RoMod：通过专家混合的时间路由调制用于视频异常检测
Abstract
Intermediate-layer features from multimodal large language models have shown strong potential for video anomaly detection (VAD), yet the origin of their discriminative power remains unclear. We study this question using sparse mixture-of-experts (MoE) models, whose explicit expert structure and sparse activation make their internal computation easier to inspect. With a fully frozen backbone and no additional training, we find that anomaly-related evidence is concentrated in a small set of experts. These experts recur across layers, spontaneously specialize in different anomaly types, and together form a dynamic routing subnetwork. We further show that the output channels most strongly influenced by these experts are also the hidden dimensions that contain the most anomaly-relevant information. Routing statistics can therefore serve as an internal anomaly cue that complements semantic features.Based on these findings, we propose RoMod, an efficient VAD framework trained with only \(5\%\) of weakly labeled videos. RoMod includes a Routing-Modulated Fusion module, RoMF, and a Routing-aware Temporal Network, RoTN. RoMF uses routing signals to adaptively recalibrate hidden semantic channels. Its design also prevents the routing branch from bypassing semantic features and making predictions on its own. RoTN captures the temporal evolution of anomalies from onset to persistence and termination. Experiments on three benchmarks show that RoMod achieves state-of-the-art performance while running substantially faster than dense backbones of comparable size.
Chinese Translation
多模态大型语言模型的中间层特征在视频异常检测（VAD）中显示出强大的潜力，但其判别能力的来源仍不清楚。我们使用稀疏专家混合（MoE）模型研究这个问题，其明确的专家结构和稀疏激活使得内部计算更易于检查。在完全冻结的主干网络和没有额外训练的情况下，我们发现与异常相关的证据集中在一小部分专家中。这些专家在各层中反复出现，自发地专注于不同的异常类型，并共同形成一个动态路由子网络。我们进一步表明，受这些专家影响最强的输出通道也是包含最多与异常相关信息的隐藏维度。因此，路由统计可以作为一种内部异常线索，补充语义特征。基于这些发现，我们提出了RoMod，一个仅用5%的弱标记视频训练的高效VAD框架。RoMod包括一个路由调制融合模块（RoMF）和一个路由感知时间网络（RoTN）。RoMF使用路由信号自适应地重新校准隐藏的语义通道。其设计还防止路由分支绕过语义特征并独立进行预测。RoTN捕捉异常从发生到持续再到终止的时间演变。在三个基准测试上的实验表明，RoMod在运行速度显著快于同类密集主干网络的同时，达到了最先进的性能。
cs.CV / 106 / 2610.05135
How Does Geometry Enter Generated Motion?
几何如何影响生成的运动？
Abstract
Under a fixed physical law, the visible geometry of a scene determines how motion must change. We ask how video generators realize this relationship. We fix the law and the initial state and change only the geometry drawn in the first frame, within matched families of tracks and deflectors, and compare each generated trajectory with the simulator prediction for that geometry. Paired interventions change one thing at a time: a local bump, the height of a barrier, the words of the prompt, the length of the clip. Across nine image-to-video models, geometry is preserved and shapes the motion: the speed of the ball follows the drawn undulation of a track. A physical state would carry this response forward, and here the generated motion parts from the law. The mean slope barely accelerates the ball, successive contacts fail to compose through a consistent state, an edit ahead of the ball alters its motion before it arrives, and the ball climbs over barriers higher than its release point. Two global conditions organize the global trajectory: text strongly controls the destination, while clip length strongly controls timing in the open-weight models tested. The pattern persists with photographed first frames. Current video generation thus behaves as geometry-conditioned motion synthesis whose evolution of state differs systematically from that of a fixed physical law.
Chinese Translation
在固定的物理法则下，场景的可见几何形状决定了运动必须如何变化。我们探讨视频生成器如何实现这种关系。我们固定法则和初始状态，仅在匹配的轨迹和偏转器家族中改变第一帧中绘制的几何形状，并将每个生成的轨迹与该几何形状的模拟器预测进行比较。配对干预一次只改变一项内容：局部隆起、障碍物的高度、提示词、剪辑的长度。在九种图像到视频模型中，几何形状得以保留并塑造运动：球的速度遵循绘制的轨迹起伏。一个物理状态将推动这种反应向前，而在这里生成的运动偏离了法则。平均斜率几乎没有加速球体，连续接触未能通过一致的状态进行组合，球体前方的编辑在其到达之前改变了其运动，球体越过高于其释放点的障碍物。两个全局条件组织了全局轨迹：文本强烈控制目的地，而剪辑长度在测试的开放权重模型中强烈控制时间。这个模式在拍摄的第一帧中依然存在。因此，当前的视频生成行为表现为几何条件下的运动合成，其状态的演变与固定物理法则的演变系统性地不同。
cs.CV / 107 / 2610.05141
SemCam: Semantic Camera Motion Control for Video Generation
SemCam：用于视频生成的语义相机运动控制
Abstract
Controlling the camera relative to a moving subject in an existing video is challenging: behaviors such as maintaining a frontal view require the camera to adapt to the subject's changing position and orientation, making the desired trajectory difficult to specify in advance. Existing camera-controlled video-to-video methods typically rely on explicit trajectories or reference motions, which do not directly express these dynamic camera--subject relationships. We introduce semantic camera motion control, a novel video-to-video task in which a reference video and a target motion label specify the desired subject-relative camera behavior without an explicit target trajectory. Our method, SemCam, learns to realize this behavior while preserving source content. It combines shared-basis low-rank adaptation with motion-conditioned modulation, while a background-consistency loss encourages fidelity in regions visible in both reference and target videos. We construct 661 paired videos covering eight semantic camera behaviors and evaluate on a separate 109-scene benchmark using subject-relative motion metrics, appearance measures, and a user study. SemCam achieves a semantic-motion success rate of 68.6%, compared with 45.3% for Vista4D, the strongest evaluated baseline, while maintaining comparable subject identity preservation.
Chinese Translation
在现有视频中控制相机相对于移动主体的运动是具有挑战性的：例如保持正面视角的行为要求相机适应主体不断变化的位置和方向，这使得预先指定所需轨迹变得困难。现有的相机控制视频到视频的方法通常依赖于显式轨迹或参考运动，这些方法并未直接表达这些动态的相机-主体关系。我们提出了语义相机运动控制，这是一项新颖的视频到视频任务，其中参考视频和目标运动标签指定所需的相对于主体的相机行为，而无需显式的目标轨迹。我们的方法SemCam学习实现这一行为，同时保留源内容。它结合了共享基础的低秩适应与运动条件调制，同时背景一致性损失鼓励在参考视频和目标视频中可见区域的保真度。我们构建了661对视频，涵盖八种语义相机行为，并在一个独立的109场景基准上进行评估，使用相对于主体的运动度量、外观度量和用户研究。SemCam实现了68.6%的语义运动成功率，而Vista4D（最强的评估基线）的成功率为45.3%，同时保持了相似的主体身份保留。
cs.CV / 108 / 2610.05154
Construction and Evaluation of Machine Learning Models for Near-Real-Time Fire Detection from MTG FCI Imagery
基于MTG FCI影像的近实时火灾检测机器学习模型的构建与评估
Abstract
Geostationary satellite observations are important for wildfire detection and monitoring. The current study evaluates machine learning models for MTG FCI near-real-time fire detection in 1- and 2-km spatial configurations and compares them with threshold-based algorithms. The models were trained and evaluated using VIIRS fire reference data across diverse ecological regions in Europe, Africa, and the Middle East. The key results are that 1-km models significantly outperform both their 2-km variants and operational threshold products. The constructed 1-km models achieved F1 scores higher by up to 0.36 compared to baseline products. Importantly, the 1-km models detected small fires with higher probability compared to competing models. Finally, the models robustly detected fires up to 260 min earlier than baseline products. To support opensource applications, our trained models are publicly available.
Chinese Translation
地球静止卫星观测对于野火的检测与监测至关重要。本研究评估了用于MTG FCI近实时火灾检测的机器学习模型，采用1公里和2公里的空间配置，并将其与基于阈值的算法进行了比较。模型的训练和评估使用了覆盖欧洲、非洲和中东多样生态区域的VIIRS火灾参考数据。主要结果表明，1公里模型显著优于其2公里变体和现有的阈值产品。构建的1公里模型的F1分数比基线产品高出最多0.36。值得注意的是，1公里模型检测小火灾的概率高于竞争模型。最后，这些模型在检测火灾时比基线产品提前最多260分钟。为了支持开源应用，我们训练的模型已公开可用。
cs.CV / 109 / 2610.05185
Recurrent Latent Visual Search for GUI Grounding
用于GUI定位的递归潜在视觉搜索
Abstract
GUI grounding is a critical capability for GUI agents powered by vision-language models, helping them execute user instructions by locating the corresponding elements in screenshots. Single-step grounding struggles with small elements and dense layouts, motivating multi-step visual search. However, existing approaches commonly rely on textual reasoning misaligned with visual space or costly multi-round interactions with external visual tools. To make multi-step visual search an explicit spatial process within the model, we propose ReLaViS, which performs Recurrent Latent Visual Search in a single interaction round. At each step, a spatial search head uses the hidden state to query the screenshot's visual tokens, producing a spatial search distribution that explicitly represents the search focus. This distribution then aggregates the visual tokens into latent visual evidence, which is recurrently fed back as the next input embedding to condition subsequent search. We further introduce a GUI-aware coarse-to-fine inductive bias through trajectories constructed from flat element annotations, supervising search from the global interface through intermediate element groups to the target. Built on Qwen2.5-VL-7B, ReLaViS improves ScreenSpot-Pro accuracy by 3.1 percentage points to 56.3% with only a 3.5% increase in inference FLOPs and outperforms the matched single-step baseline on all five benchmarks.
Chinese Translation
GUI定位是由视觉-语言模型驱动的GUI代理的一项关键能力，帮助它们通过定位屏幕截图中的相应元素来执行用户指令。单步定位在处理小元素和密集布局时面临挑战，这促使了多步视觉搜索的需求。然而，现有的方法通常依赖于与视觉空间不对齐的文本推理或与外部视觉工具进行代价高昂的多轮交互。为了使多步视觉搜索成为模型内的一个明确空间过程，我们提出了ReLaViS，它在单次交互轮次中执行递归潜在视觉搜索。在每一步中，空间搜索头利用隐藏状态查询屏幕截图的视觉标记，生成一个明确表示搜索焦点的空间搜索分布。该分布随后将视觉标记聚合为潜在视觉证据，并递归地反馈作为下一个输入嵌入，以条件后续搜索。我们进一步通过从平面元素注释构建的轨迹引入了GUI感知的粗到细归纳偏置，从全局界面通过中间元素组到目标进行搜索监督。基于Qwen2.5-VL-7B，ReLaViS将ScreenSpot-Pro的准确率提高了3.1个百分点，达到56.3%，而推理FLOPs仅增加了3.5%，并在所有五个基准测试中超越了匹配的单步基线。
cs.CV / 110 / 2610.05191
Order Matters: Competition-Guided Query Ordering for RNN-Based Object Detection
顺序重要：基于竞争引导的 RNN 物体检测查询排序
Abstract
DETR-style detectors use one-to-one bipartite matching during training to assign object queries to ground-truth objects, enabling end-to-end set prediction without non-maximum suppression (NMS). However, without an explicit de-duplication procedure, multiple queries can still produce highly similar hypotheses for the same object, making training unstable and predictions less decisive. Inspired by the sequential ordering of NMS, we propose DETRNN, a plug-and-play module that turns unordered object queries into a competition-aware sequence for recurrent refinement. DETRNN builds an explicit confidence-and-similarity based order from prior predictions, then refines queries with an RNN along this order to model competition inside the decoder. This ordered recurrent refinement reduces redundant predictions, stabilizes optimization, and improves final detection accuracy. Experiments on multiple DETR-style detectors show consistent gains with comparable efficiency.
Chinese Translation
DETR 风格的检测器在训练过程中使用一对一的二分匹配，将物体查询分配给真实物体，从而实现端到端的集合预测，无需非最大抑制（NMS）。然而，在没有明确去重程序的情况下，多个查询仍然可能为同一物体产生高度相似的假设，这使得训练不稳定，预测结果不够明确。受到 NMS 顺序处理的启发，我们提出了 DETRNN，一个即插即用的模块，将无序的物体查询转化为一个关注竞争的序列，以进行递归优化。DETRNN 从先前的预测中构建一个基于置信度和相似度的明确顺序，然后沿着这个顺序使用 RNN 对查询进行优化，以模拟解码器内部的竞争。这种有序的递归优化减少了冗余预测，稳定了优化过程，并提高了最终的检测准确性。在多个 DETR 风格的检测器上的实验表明，取得了一致的性能提升，同时保持了相对的效率。
cs.CV / 111 / 2610.05195
Long-MDR: Long-Context Reinforcement Learning for Multimodal Deep-Research Agents
Long-MDR：用于多模态深度研究代理的长上下文强化学习
Abstract
The next generation of multimodal research agents must reason over long-lived research histories rather than short model completions. During a single task, an agent may repeatedly search the web, inspect visual evidence, revisit earlier hypotheses, and accumulate tens of thousands of tokens of multimodal context. Despite this trend, online RL for multimodal research agents remains largely confined to shorter contexts and interaction horizons. We push online RL training to 128k context and 75+ tool-interaction turns. To our knowledge, this is the first online multimodal deep-research RL study trained at 128k context, and the first trained with a 75 tool-turn horizon. Scaling to this regime exposes several practical limitations of conventional RL training. Early in training, weak policies make poor use of large interaction budgets, causing expensive rollouts with little reward improvement. Later, policy entropy can collapse before performance has saturated, prematurely ending useful learning. We introduce Long-MDR, a three-component training recipe designed specifically for this setting: On-Policy Distillation Warmup, Progressive Horizon Expansion, and Entropy-Triggered Rescue. Together, these techniques improve both the learning efficiency and stability of long-horizon RL, enabling continued gains in a regime where direct training is slow and costly. At a 50-turn evaluation budget, our RL-trained Long-MDR-9B ranks first on five of six benchmarks among the compared 7B-9B agents.
Chinese Translation
下一代多模态研究代理必须在长期的研究历史上进行推理，而不是仅仅依赖短期的模型完成。在单个任务中，代理可能会反复搜索网络、检查视觉证据、重新审视早期假设，并积累数万个多模态上下文的标记。尽管有这种趋势，针对多模态研究代理的在线强化学习（RL）仍然主要局限于较短的上下文和交互范围。我们将在线强化学习训练扩展到128k的上下文和75+的工具交互回合。据我们所知，这是首个在128k上下文下训练的在线多模态深度研究强化学习研究，也是首个在75工具回合范围内训练的研究。扩展到这一范围揭示了传统强化学习训练的几个实际限制。在训练初期，弱策略对大规模交互预算的利用不佳，导致昂贵的回滚且几乎没有奖励改善。后来，策略熵可能在性能尚未饱和之前就崩溃，过早结束有用的学习。我们提出了Long-MDR，一种专为这一环境设计的三组件训练方案：策略蒸馏预热、渐进式范围扩展和熵触发救援。这些技术共同提高了长范围强化学习的学习效率和稳定性，使得在直接训练缓慢且成本高昂的情况下仍能实现持续的进步。在50回合的评估预算下，我们的RL训练的Long-MDR-9B在比较的7B-9B代理中，在六个基准中的五个上排名第一。
cs.CV / 112 / 2610.05207
F$^2$ SLAM: Turning Feed-Forward Geometry into Persistent Factors for SLAM
F$^2$ SLAM：将前馈几何转化为持久因子以实现SLAM
Abstract
Feed-forward 3D models provide strong multi-view geometric priors, while on- line simultaneous localization and mapping (SLAM) relies mainly on local mea- surements and can accumulate drift over long sequences. Existing attempts to combine the two typically treat feed-forward predictions as an external geomet- ric state that is aligned or fused with the online estimate after the fact, which keeps broader multi-view evidence outside the optimizer that refines the SLAM state. We present F2SLAM, which instead converts feed-forward geometry di- rectly into optimization-native target-weight measurements attached to a persis- tent dense factor graph. A high-frequency stream maintains local tracking con- straints and graph connectivity, while a low-frequency stream uses wider multi- view context to selectively refresh existing measurements after a state-consistency check. Both streams constrain the same poses, inverse depths, and optional cam- era intrinsics through a single dense bundle adjustment. Experiments on multiple benchmarks demonstrate consistently strong trajectory estimation and improved dense reconstruction in both calibrated and uncalibrated settings. Notably, the uncalibrated configuration reduces the average ATE RMSE from 0.030 m for the strongest feed-forward baseline to 0.002 m on the Replica dataset.
Chinese Translation
前馈3D模型提供了强大的多视角几何先验，而在线同时定位与地图构建（SLAM）主要依赖于局部测量，可能在长序列中累积漂移。现有的将两者结合的尝试通常将前馈预测视为一种外部几何状态，在事后与在线估计进行对齐或融合，这使得更广泛的多视角证据被排除在优化器之外，后者用于优化SLAM状态。我们提出了F2SLAM，它将前馈几何直接转化为附加在持久稠密因子图上的优化原生目标权重测量。高频流保持局部跟踪约束和图连接性，而低频流则利用更广泛的多视角上下文，在状态一致性检查后选择性地刷新现有测量。这两个流通过单一的稠密束调整约束相同的姿态、逆深度和可选的相机内参。在多个基准测试上的实验表明，在标定和非标定设置中，轨迹估计和稠密重建均表现出持续强劲的效果。值得注意的是，在非标定配置下，Replica数据集的平均绝对轨迹误差（ATE）均方根误差（RMSE）从最强前馈基线的0.030米降低到0.002米。
cs.CV / 113 / 2610.05233
PixReenact: Pixel-Conditioned Causal Video Diffusion for Streaming Head-Avatar Reenactment
PixReenact：基于像素条件的因果视频扩散流媒体头部-头像重现
Abstract
Streaming head-avatar reenactment aims to animate a reference image according to a live driving video, requiring robust motion transfer, long-term identity stability, and low latency. Existing methods often rely on specialized identity or motion representations, which can discard useful visual information and inherit failure modes from external extractors. In addition, many recent diffusion-based reenactment methods use offline, clip-based generation, jointly processing and denoising an entire video clip before producing its output, making continuous low-latency streaming difficult. We introduce PixReenact, a pixel-conditioned streaming reenactment framework built on causal video diffusion. PixReenact conditions directly on VAE-encoded reference and driving frames, without specialized identity or motion representations. To separate reference identity from driver motion, we train with cross-identity pseudo supervision together with corrective objectives anchored to the original reference and driving inputs. Long self-rollouts reduce autoregressive drift, while state-aware dual-teacher distillation separately addresses cold-start and steady-state generation. Across three cross-identity benchmarks and a long-horizon streaming benchmark, PixReenact demonstrates robust cross-identity reenactment, particularly under challenging conditions such as extreme viewpoints, occlusions, and pronounced facial expressions, while maintaining the reference identity over long streams. A 4-NFE rolling student continuously emits four frames per update with a mean emission latency of 239 ms.
Chinese Translation
流媒体头部-头像重现旨在根据实时驱动视频对参考图像进行动画处理，这需要强大的运动转移、长期身份稳定性和低延迟。现有方法通常依赖于专门的身份或运动表示，这可能会丢弃有用的视觉信息并继承外部提取器的失败模式。此外，许多近期基于扩散的重现方法使用离线、基于片段的生成，在生成输出之前共同处理和去噪整个视频片段，这使得连续低延迟流媒体变得困难。我们提出了PixReenact，一个基于因果视频扩散的像素条件流媒体重现框架。PixReenact直接对VAE编码的参考帧和驱动帧进行条件处理，而不使用专门的身份或运动表示。为了将参考身份与驱动运动分离，我们结合原始参考和驱动输入的纠正目标，采用跨身份伪监督进行训练。长时间自回归展开减少了自回归漂移，而状态感知的双教师蒸馏则分别解决了冷启动和稳态生成。在三个跨身份基准和一个长时间流媒体基准上，PixReenact展示了强大的跨身份重现能力，特别是在极端视角、遮挡和明显面部表情等挑战性条件下，同时在长时间流中保持参考身份。一个4-NFE滚动学生每次更新连续发出四帧，平均发射延迟为239毫秒。
cs.CV / 114 / 2610.05249
ArticuTable: Generating Instance-Level Interactive Rigid-Articulated 3D Tabletop Scenes from a Single Image
ArticuTable：从单幅图像生成实例级交互式刚性关节3D桌面场景
Abstract
Embodied agents benefit from 3D environments that combine visual fidelity to real-world observations with physical interactivity. Existing single-image tabletop reconstruction methods recover plausible scene geometry but typically represent objects as monolithic rigid bodies, limiting interaction to whole-object rigid motion and precluding executable part-level articulation. Meanwhile, recovering a scene layout consistent with the input view remains challenging because a single observation may admit multiple plausible pose-scale configurations. We present ArticuTable, a single-image 3D tabletop reconstruction framework that recovers both executable part-level articulation and an input-view-consistent scene layout. For object modeling, we introduce generation-robust articulation modeling (GRAM), which combines joint fitting guided by a multimodal large language model with semantic state reasoning to recover reliable joint parameters and valid motion ranges from imperfect monolithic proxy meshes, thereby converting them into executable articulated assets. For scene layout, we introduce progressive semantic-geometric scene registration (PSGSR), which progressively narrows the pose-scale search space under complementary metric, planar, and input-view constraints and resolves orientation ambiguity through structure-aware semantic correspondences, yielding a scene layout consistent with the input view. We further contribute ArticuTable-100, a curated collection of 100 simulation-ready tabletop scenes. Extensive evaluation, including a user study, demonstrates strong performance across visual fidelity, input-view consistency, articulation quality, physical plausibility, and simulation readiness.
Chinese Translation
具身代理受益于结合真实世界观察的视觉逼真性与物理交互性的3D环境。现有的单幅图像桌面重建方法能够恢复合理的场景几何形状，但通常将物体表示为整体刚体，限制了交互仅限于整体物体的刚性运动，无法实现可执行的部分级关节运动。同时，由于单一观察可能对应多个合理的姿态-尺度配置，恢复与输入视图一致的场景布局仍然具有挑战性。我们提出了ArticuTable，这是一种单幅图像3D桌面重建框架，能够恢复可执行的部分级关节运动以及与输入视图一致的场景布局。在物体建模方面，我们引入了生成鲁棒关节建模（GRAM），该方法结合了由多模态大型语言模型指导的关节拟合与语义状态推理，从不完美的整体代理网格中恢复可靠的关节参数和有效的运动范围，从而将其转换为可执行的关节资产。在场景布局方面，我们引入了渐进式语义几何场景配准（PSGSR），该方法在互补的度量、平面和输入视图约束下逐步缩小姿态-尺度搜索空间，并通过结构感知的语义对应解决方向模糊，从而得到与输入视图一致的场景布局。我们还贡献了ArticuTable-100，这是一个经过精心策划的100个可模拟桌面场景的集合。广泛的评估，包括用户研究，展示了在视觉逼真性、输入视图一致性、关节运动质量、物理合理性和模拟准备性等方面的强大表现。
cs.CV / 115 / 2610.05252
MGPO: Manifold-Guided Diffusion Alignment for Task-Aware Dataset Distillation
MGPO：基于流形引导的扩散对齐用于任务感知的数据集蒸馏
Abstract
Diffusion-based dataset distillation (DD) suffers from a fundamental objective mismatch: likelihood-driven diffusion models prioritize density approximation over the discriminative decision boundaries required for downstream tasks. Beyond semantic mismatch, relying solely on density also leads to geometric coverage loss, where generated samples collapse into a few high-density modes and fail to cover the manifold's structural diversity. We propose Manifold-Guided Policy Optimization (MGPO), which reformulates DD as a multi-objective reinforcement learning problem and achieves Dual-Space Alignment via a pixel-space discriminative reward and a latent-space geometric reward guided by a class-wise Minimum Spanning Tree (MST). The discriminative reward enforces class separability, while the MST-based geometric reward encourages generated latents to cover a sparse geometric skeleton of each class, jointly addressing both failure modes. We further provide an idealized analysis that motivates the MST-based reward, including a Hausdorff approximation bound and a subsampling bound independent of the dataset size. The reward-modular design extends to structured tasks such as object detection and segmentation by substituting the frozen task reward model. Extensive experiments show MGPO consistently outperforms existing methods, including a +8.0% mIoU gain on segmentation under low-budget settings.
Chinese Translation
基于扩散的数据集蒸馏（DD）面临着根本性的目标不匹配问题：以似然为驱动的扩散模型优先考虑密度近似，而不是下游任务所需的判别决策边界。除了语义不匹配，单纯依赖密度还导致几何覆盖损失，生成的样本会聚集到少数高密度模式中，无法覆盖流形的结构多样性。我们提出了流形引导策略优化（MGPO），将DD重新表述为一个多目标强化学习问题，并通过像素空间的判别奖励和由类级最小生成树（MST）引导的潜在空间几何奖励实现双空间对齐。判别奖励强化了类别可分性，而基于MST的几何奖励则鼓励生成的潜在样本覆盖每个类别的稀疏几何骨架，从而共同解决这两种失效模式。我们进一步提供了一个理想化的分析，阐明了基于MST的奖励的动机，包括一个Hausdorff近似界和一个与数据集大小无关的子采样界。该奖励模块化设计可以通过替代冻结的任务奖励模型扩展到结构化任务，如目标检测和分割。大量实验表明，MGPO在现有方法中表现出色，在低预算设置下的分割任务中实现了+8.0%的mIoU提升。
cs.CV / 116 / 2610.05254
Hybrid-Basis Feature Forecasting for Diffusion Sampling Acceleration
用于扩散采样加速的混合基特征预测
Abstract
We propose Hybrid-Basis Feature Forecasting (HybridFF), a training-free, plug-and-play framework for accelerating diffusion sampling. To capture local smoothness, long-range trends, and complex non-monotonic variations when modeling feature evolution, HybridFF first estimates coefficients using moving least squares (MLS) for each of multiple complementary basis families and then combines the corresponding predictors using fusion weights. In addition to the choice of basis functions, the fusion weights also play a critical role. We introduce two strategies to balance quality and speedup. HybridFF (Fixed) prioritizes efficiency with model-specific fusion weights calibrated on a small set and held constant during inference. HybridFF (Adaptive) updates the fusion weights online using branch reliability scores computed from an exponential moving average of full-step prediction errors, improving prediction fidelity and generation quality under aggressive caching while retaining substantial acceleration. Experiments across DiT-XL/2, FLUX.1-dev, SD3.5-Large, and HunyuanVideo demonstrate a favorable speedup--quality trade-off over representative single-basis forecasters and caching baselines.
Chinese Translation
我们提出了一种混合基特征预测（HybridFF）方法，这是一种无训练、即插即用的框架，用于加速扩散采样。为了在建模特征演化时捕捉局部平滑性、长程趋势和复杂的非单调变化，HybridFF 首先使用移动最小二乘法（MLS）为多个互补基函数族中的每一个估计系数，然后利用融合权重组合相应的预测器。除了基函数的选择外，融合权重也起着至关重要的作用。我们引入了两种策略来平衡质量与加速。HybridFF（固定）优先考虑效率，使用在小样本上校准的特定模型融合权重，并在推理过程中保持不变。HybridFF（自适应）则通过在线更新融合权重，使用从全步预测误差的指数移动平均计算的分支可靠性分数，在保持显著加速的同时提高预测的准确性和生成质量。我们在 DiT-XL/2、FLUX.1-dev、SD3.5-Large 和 HunyuanVideo 上的实验表明，与代表性的单基预测器和缓存基线相比，HybridFF 在速度与质量之间实现了良好的权衡。
cs.CV / 117 / 2610.05273
When and What to Prune? Stage-Aware Visual Token Pruning for Efficient VLA
何时以及剪除什么？基于阶段感知的视觉标记剪除以实现高效的视觉-语言-动作（VLA）推理
Abstract
Visual token pruning is an effective way to accelerate vision-language models and is especially useful for vision-language-action (VLA) inference, where many visual tokens must be processed before predicting robot actions. Existing pruning methods usually estimate which tokens can be pruned based on attention scores or feature diversity, retaining tokens that are either highly attended or visually different from others. However, most of them use fixed pruning schedules, such as pruning once at a preset layer or pruning at uniformly spaced layers. Such schedules can be risky for VLA models, because the model may not know which visual regions matter for the action in early layers. Tokens that look unimportant at first may become useful after the model combines visual observations with the language instruction. In this work, we propose SAPrune, a training-free visual token pruning framework for efficient VLA inference. Instead of pruning at fixed layers, SAPrune uses a small calibration set to observe how action-to-visual attention changes across layers, and chooses pruning layers only after the attention pattern becomes more reliable. At each selected layer, SAPrune applies a dual-path pruning rule: one path protects strongly attended visual tokens from pruning, while the other prevents useful surrounding context from being discarded. Experiments on LIBERO, SIMPLER, and real-world robotic tasks show that SAPrune prunes 87.5% of visual tokens and achieves up to 1.718x inference speedup while maintaining competitive task success rates.
Chinese Translation
视觉标记剪除是一种加速视觉-语言模型的有效方法，尤其在视觉-语言-动作（VLA）推理中尤为重要，因为在预测机器人动作之前，必须处理大量视觉标记。现有的剪除方法通常基于注意力分数或特征多样性来估计可以剪除哪些标记，保留那些受到高度关注或与其他标记视觉上不同的标记。然而，它们大多数采用固定的剪除计划，例如在预设层进行一次剪除或在均匀间隔的层进行剪除。这种计划对于VLA模型来说可能存在风险，因为模型在早期层可能无法确定哪些视觉区域对动作重要。最初看似不重要的标记在模型将视觉观察与语言指令结合后可能变得有用。在本研究中，我们提出了SAPrune，一个无训练的视觉标记剪除框架，以实现高效的VLA推理。SAPrune并不是在固定层进行剪除，而是利用一个小的校准集观察动作到视觉的注意力如何在各层之间变化，并仅在注意力模式变得更加可靠后选择剪除层。在每个选定的层，SAPrune应用双路径剪除规则：一条路径保护强烈关注的视觉标记不被剪除，而另一条路径则防止有用的周围上下文被丢弃。在LIBERO、SIMPLER和现实世界的机器人任务上的实验表明，SAPrune剪除了87.5%的视觉标记，并在保持竞争性任务成功率的同时实现了高达1.718倍的推理加速。
cs.CV / 118 / 2610.05274
Answer with Evidence: Consistency-Aware Grounded Visual Question Answering for Roadside Traffic Scenes
基于证据的回答：面向路边交通场景的一致性意识基础视觉问答
Abstract
Roadside traffic reasoning requires every free-form textual claim to be backed by visual evidence. Existing grounded multimodal large language models (MLLMs) frequently exhibit say-point mismatch, in which the textual answer contradicts the bounding boxes the model localizes. Evaluation metrics that score answers and boxes separately leave this failure unpenalized. We trace the mismatch to the conventional answer-then-ground factorization, which commits to a numerical claim before any object is enumerated. To measure it, we build RoadSceneVQA-G, a benchmark of 34.7K question-answer pairs in which every free-form answer is linked to the set of boxes that witnesses it, and we propose the Answer-Grounding Consistency (AGC) evaluation suite. To address it, we introduce Enumerate-then-Answer (EtA), which reverses the generation order so that answer-evidence agreement becomes a property of the output structure, and Enumeration-Consistent Policy Optimization (ECPO), a reinforcement learning stage that uses the union of multiple rollouts as a recall teacher without ground-truth boxes. EtA raises say-point consistency from 26.6\% to 93.7\% and grounding F1 from 52.2\% to 73.0\%, and ECPO further increases F1 to 75.6\% without per-box supervision. On gRefCOCO, the same framework outperforms the strongest compared method, indicating that it transfers beyond traffic scenes. The project is available at \url{https://github.com/GuanRunwei/RoadSceneVQA-G}.
Chinese Translation
路边交通推理要求每一个自由形式的文本声明都必须有视觉证据支持。现有的基础多模态大型语言模型（MLLMs）常常表现出说点不匹配的问题，即文本答案与模型定位的边界框相矛盾。单独对答案和框进行评分的评估指标未能对此类失误进行惩罚。我们将这种不匹配追溯到传统的先回答再基础化的分解方法，该方法在列举任何物体之前就承诺了一个数值声明。为了衡量这一点，我们构建了RoadSceneVQA-G，这是一个包含34.7K问答对的基准数据集，其中每个自由形式的答案都与见证该答案的边界框集合相联系，并提出了答案-基础一致性（AGC）评估套件。为了解决这一问题，我们引入了先列举后回答（EtA）的方法，该方法逆转了生成顺序，使得答案与证据的一致性成为输出结构的一个特性，并提出了枚举一致性策略优化（ECPO），这是一个强化学习阶段，利用多个回合的联合结果作为没有真实边界框的召回教师。EtA将说点一致性从26.6\%提高到93.7\%，将基础F1从52.2\%提高到73.0\%，而ECPO进一步将F1提高到75.6\%，且无需逐框监督。在gRefCOCO上，相同的框架超越了最强的对比方法，表明其在交通场景之外也具有迁移能力。该项目可在 {https://github.com/GuanRunwei/RoadSceneVQA-G} 获取。
cs.CV / 119 / 2610.05289
Mobile-4DGS: Unified Static-Dynamic Real-time Mobile Gaussian Splatting
Mobile-4DGS：统一的静态-动态实时移动高斯点云渲染
Abstract
Recent advances in 3D Gaussian Splatting (3DGS) have achieved remarkable performance in novel view synthesis, yet deploying both static and dynamic Gaussian representations on resource-constrained mobile devices remains challenging due to heavy storage, redundant primitives, and costly per-frame computation. We present Mobile-4DGS, a unified lightweight framework for high-fidelity real-time static and dynamic Gaussian rendering on mobile platforms. For compact appearance modeling, we introduce a Monte Carlo Specular Energy Aggregator that compresses high-order radiance residuals into the first-order Spherical Harmonics (SH), together with an Attribute-Conditioned SH Enhancement module whose predicted offsets are pre-baked before inference. We further propose a Multi-View Alpha-Based Densification and Pruning strategy to suppress redundant primitives while maintaining multi-view consistency. For dynamic scenes, we develop a compact explicit 4D representation by constructing second-order Gaussian motion, learnable temporal support, and a binary static-dynamic partition, enabling continuous-time modeling without runtime deformation networks. Based on this partition, a Depth-Order Certificate selectively reuses previously committed depth orders to reduce re-projection, sorting, merging, and index-buffer updates during playback. Extensive experiments on static and dynamic scenes demonstrate that Mobile-4DGS substantially reduces storage and rendering overhead while maintaining competitive visual quality, enabling real-time 3D and 4D Gaussian Splatting on mobile devices. \textcolor{magenta}{\href{https://xiaobiaodu.github.io/mobile-4dgs-project/}{Code has been released: https://xiaobiaodu.github.io/mobile-4dgs-project/}}.
Chinese Translation
近年来，3D高斯点云渲染（3DGS）的进展在新视图合成方面取得了显著的性能，但在资源受限的移动设备上部署静态和动态高斯表示仍然面临挑战，主要由于存储需求高、冗余原语和每帧计算成本高。我们提出了Mobile-4DGS，这是一个统一的轻量级框架，旨在移动平台上实现高保真实时静态和动态高斯渲染。为了实现紧凑的外观建模，我们引入了一种蒙特卡洛镜面能量聚合器（Monte Carlo Specular Energy Aggregator），将高阶辐射残差压缩为一阶球谐（Spherical Harmonics, SH），同时结合一个属性条件的SH增强模块，其预测的偏移量在推理前进行预处理。此外，我们还提出了一种基于多视图的透明度稠密化和修剪策略，以抑制冗余原语，同时保持多视图一致性。对于动态场景，我们通过构建二阶高斯运动、可学习的时间支持和二元静态-动态分区，开发了一种紧凑的显式4D表示，从而实现连续时间建模而无需运行时变形网络。基于该分区，深度顺序证书（Depth-Order Certificate）选择性地重用先前提交的深度顺序，以减少播放过程中的重投影、排序、合并和索引缓冲区更新。在静态和动态场景上的大量实验表明，Mobile-4DGS显著降低了存储和渲染开销，同时保持了具有竞争力的视觉质量，使得在移动设备上实现实时3D和4D高斯点云渲染成为可能。
cs.CV / 120 / 2610.05293
BossouChimpanzee: Long-term Chimpanzee Video Dataset
BossouChimpanzee：长期黑猩猩视频数据集
Abstract
We describe the BossouChimpanzee video dataset, a unique long-term visual record of wild chimpanzees at an outdoor laboratory for field experiments in Bossou, Guinea, spanning three decades (1988-2018) and comprising over 1,200 hours of continuous video recordings collected through collaborative fieldwork and research. In this paper, we outline the history and scientific contributions of the experimental paradigm and video archive, provide key statistics and details on the structure of the main video dataset, and release an initial ~74h snapshot, BossouChimpanzee70h, covering 23 identified individuals focused on chimpanzee individual and action recognition, ahead of the full video resource. This dataset represents a valuable resource for cognitive and behavioural research in ethology and a rich benchmark for training and evaluating machine learning models on audiovisual data from the wild.
Chinese Translation
我们描述了BossouChimpanzee视频数据集，这是一个独特的长期视觉记录，记录了在几内亚Bossou的户外实验室中野生黑猩猩的生活，涵盖了三十年的时间（1988-2018），并包含通过合作野外工作和研究收集的超过1200小时的连续视频录制。在本文中，我们概述了实验范式和视频档案的历史及科学贡献，提供了主要视频数据集的关键统计数据和结构细节，并发布了初步的约74小时快照，BossouChimpanzee70h，涵盖了23个已识别个体，重点关注黑猩猩个体和行为识别，作为完整视频资源的前期发布。该数据集为认知和行为研究提供了宝贵资源，并为在野外音视频数据上训练和评估机器学习模型提供了丰富的基准。
cs.CV / 121 / 2610.05298
Revisiting Ground-Truth Synthesis from High-Speed Video: Exact Validity Conditions and an Audited Consumer Capture Corpus
重新审视高速视频的真实数据合成：精确的有效性条件与审核过的消费者捕获数据集
Abstract
Motion-deblurring datasets are commonly synthesised by averaging $N$ consecutive high-frame-rate frames and labelling the result with the central frame. We show that this label is unbiased for every capture timing only when the window is odd and the frames' sample durations are equal. An even window shifts every label by a fixed fraction of the blur length, even under perfect timing. Unequal durations are subtler: their mean misalignment is zero, so dataset statistics cannot reveal them, yet when the offset cannot be read from the blur they convolve the supervision rather than adding noise to it. Auditing 51 smartphone clips recorded at a nominal 240 fps, we find 19 captured near 176 fps, a behaviour recorded only in the container's timing tables. In a controlled test the parity choice costs a fitted linear deblurring filter far more than these timing irregularities do, and interpolation labels, unlike blur labels, can be repaired with the true frame times. We provide a tool that checks both conditions without decoding, and will release the clips and their timing tables.
Chinese Translation
运动去模糊数据集通常通过对 $N$ 个连续的高帧率帧进行平均，并将结果标记为中心帧来合成。我们展示了，当窗口为奇数且帧的采样持续时间相等时，这一标签对于每个捕获时机都是无偏的。偶数窗口则会将每个标签偏移一个固定的模糊长度的比例，即使在完美时机下也是如此。不等持续时间的影响更为微妙：它们的平均错位为零，因此数据集统计无法揭示它们，但当偏移无法从模糊中读取时，它们会卷积监督信号，而不是向其添加噪声。通过审核51个以名义240 fps录制的智能手机视频片段，我们发现其中19个接近176 fps，这一行为仅在容器的时间表中记录。在一个受控测试中，奇偶选择使得拟合的线性去模糊滤波器的成本远高于这些时间不规则性，而插值标签与模糊标签不同，可以通过真实帧时间进行修复。我们提供了一种工具，可以在不解码的情况下检查这两个条件，并将发布这些片段及其时间表。
cs.CV / 122 / 2610.05306
Kinematics-Centric Continuous Sign Language Retrieval with Gloss-Guided Boundary-Aware Alignment
以运动学为中心的连续手语检索：基于词汇引导的边界感知对齐
Abstract
Sign language-text alignment remains a fundamental challenge for text-driven sign language understanding. Existing methods predominantly rely on appearance-heavy RGB representations, which entangle motion semantics with visual variations and lead to ambiguous motion-language grounding. In this paper, we reformulate sign language-text alignment in a structured kinematic space and propose a kinematics-centric framework that adopts 3D SMPL-X motion as the primary representation. By explicitly modeling the kinematic dynamics of signing in a unified motion space, our approach reduces reliance on appearance signals and yields more semantically consistent representations. To capture the compositional nature of sign language, we introduce a gloss-guided local alignment mechanism that leverages gloss temporal spans as weak supervision to decompose continuous motion into coherent segments and establish fine-grained motion-text correspondences, thereby reducing ambiguity in localizing word-level semantics in continuous signing. Furthermore, we develop a visual distillation strategy, where RGB signals serve as privileged supervision during training to provide complementary contextual cues, while being completely removed at inference time. Extensive experiments on standard benchmarks demonstrate that our method achieves state-of-the-art bidirectional retrieval performance on CSL-Daily and competitive results on PHOENIX-2014T. These results highlight the effectiveness of kinematic representations and explicit local grounding for sign language-text alignment.
Chinese Translation
手语与文本的对齐仍然是基于文本的手语理解中的一个基本挑战。现有方法主要依赖于重视觉的RGB表示，这使得运动语义与视觉变化交织在一起，导致运动与语言的对应关系模糊。在本文中，我们在结构化的运动学空间中重新定义手语与文本的对齐，并提出一个以运动学为中心的框架，该框架采用3D SMPL-X运动作为主要表示。通过在统一的运动空间中显式建模手语的运动学动态，我们的方法减少了对外观信号的依赖，并产生了更具语义一致性的表示。为了捕捉手语的组合特性，我们引入了一种基于词汇引导的局部对齐机制，该机制利用词汇时间跨度作为弱监督，将连续运动分解为连贯的片段，并建立细粒度的运动与文本的对应关系，从而减少在连续手语中定位词级语义的模糊性。此外，我们开发了一种视觉蒸馏策略，其中RGB信号在训练期间作为特权监督提供补充的上下文线索，而在推理时完全去除。在标准基准上的大量实验表明，我们的方法在CSL-Daily上实现了最先进的双向检索性能，并在PHOENIX-2014T上取得了竞争性结果。这些结果突显了运动学表示和显式局部对齐在手语与文本对齐中的有效性。
cs.CV / 123 / 2610.05325
Shadow Feature Refinement Network: Progressive Feature Refinement based on Knowledge Distillation for Effective Shadow Removal
阴影特征精炼网络：基于知识蒸馏的渐进特征精炼以实现有效的阴影去除
Abstract
In the field of deep learning, has seen significant advancements; however, shadow removal remains a persistent challenge owing to the variable sizes and colors of shadows influenced by lighting conditions. This study proposes a novel shadow feature refinement network (SFR-Net), which leverages supervised learning, feature refinement loss, and knowledge distillation to enhance shadow removal performance. A dedicated post-processing algorithm is further introduced to restore natural color consistency in the generated shadow-free images. We evaluated our method on two public datasets: the adjusted image shadow triplet dataset (ISTD+) and the shadow removal dataset (SRD), which demonstrate strong generalization capabilities under diverse conditions. On ISTD+, our model achieved a root mean square error (RMSE) of 3.4627 and structural similarity index measure (SSIM) of 0.9382 across the entire image. On SRD, it recorded an RMSE of 4.3781 and an SSIM of 0.9341. These comprehensive results show that our approach performs competitively across both shadow and non-shadow regions while setting a promising direction for robust and perceptually natural shadow removal. Code is available at https://github.com/DongHyun99/SFRNet.
Chinese Translation
在深度学习领域，已经取得了显著的进展；然而，由于阴影受光照条件影响而导致的大小和颜色变化，阴影去除仍然是一个持续的挑战。本研究提出了一种新颖的阴影特征精炼网络（SFR-Net），该网络利用监督学习、特征精炼损失和知识蒸馏来增强阴影去除性能。此外，我们还引入了一种专门的后处理算法，以恢复生成的无阴影图像中的自然色彩一致性。我们在两个公共数据集上评估了我们的方法：调整后的图像阴影三重集数据集（ISTD+）和阴影去除数据集（SRD），结果显示在多种条件下具有强大的泛化能力。在ISTD+上，我们的模型在整个图像上达到了3.4627的均方根误差（RMSE）和0.9382的结构相似性指数（SSIM）。在SRD上，记录的RMSE为4.3781，SSIM为0.9341。这些综合结果表明，我们的方法在阴影和非阴影区域均表现出竞争力，同时为稳健且感知自然的阴影去除指明了有前景的方向。代码可在 https://github.com/DongHyun99/SFRNet 获取。
cs.CV / 124 / 2610.05326
Riemannian Shape Analysis of the Corpus Callosum in Kendall Space: Aging and Alzheimer's Disease
在肯德尔空间中对胼胝体进行黎曼形状分析：衰老与阿尔茨海默病
Abstract
The corpus callosum (CC) is a major white-matter structure and a well-established marker of brain aging, but most studies quantify it using scalar summaries that discard its boundary geometry. We present a Riemannian shape-space framework for analyzing age-related morphological change in the midsagittal CC, applied to the OASIS-1 cohort. Each contour is represented by $128$ landmarks and embedded into Kendall shape space, where translation, rotation, and scale are removed. We derive a multivariate geodesic regression with exact Riemannian gradients and use the fitted age-velocity field to localize age-related deformation to five anatomical sub-regions. In the cognitively normal cohort ($n = 252$), geodesic regression outperforms the Euclidean linear benchmark ($R^2 = 0.1355$ vs.\ $0.1216$). Regional energy is posterior-dominant: the Splenium carries $41.4\%$ and the Isthmus $23.8\%$ of total age-related shape change, together accounting for $\sim 65\%$ despite comprising only $\sim 35\%$ of landmarks. Signed projections confirm the ordering (Splenium $r = 0.570$; Isthmus $r = 0.473$). In contrast, age explains less than $0.5\%$ of shape variance in Alzheimer's disease ($n = 88$), indicating that the disease disrupts the healthy aging trajectory. A tangent-space classifier achieves an age-group AUC of $0.791$ from the 2D contour alone, exceeding a recent volumetric benchmark ($0.67$).
Chinese Translation
胼胝体（CC）是一个主要的白质结构，也是脑衰老的一个公认标志，但大多数研究使用标量总结来量化它，这些总结忽略了其边界几何特征。我们提出了一种黎曼形状空间框架，用于分析中矢状胼胝体的与年龄相关的形态变化，并应用于OASIS-1队列。每个轮廓由128个标记点表示，并嵌入到肯德尔形状空间中，在这里去除了平移、旋转和缩放的影响。我们推导出具有精确黎曼梯度的多元测地回归，并使用拟合的年龄速度场将与年龄相关的变形局部化到五个解剖亚区域。在认知正常的队列中（n = 252），测地回归的表现优于欧几里得线性基准（R² = 0.1355 vs. 0.1216）。区域能量呈现后部主导：胼胝体后部占总年龄相关形状变化的41.4%，而峡部占23.8%，两者合计约占65%，尽管仅占标记点的约35%。有符号的投影确认了这一排序（胼胝体后部 r = 0.570；峡部 r = 0.473）。相比之下，在阿尔茨海默病中，年龄解释的形状方差不足0.5%（n = 88），表明该疾病扰乱了健康的衰老轨迹。一个切线空间分类器仅通过2D轮廓实现了0.791的年龄组AUC，超过了最近的体积基准（0.67）。
cs.CV / 125 / 2610.05341
WILLIE: A Unified Framework and Benchmark for Wound Classification, Segmentation, and Localization
WILLIE：一个用于伤口分类、分割和定位的统一框架与基准
Abstract
Chronic wound management affects over 8.2 million patients in the United States and imposes substantial clinical and economic burden. Clinical wound assessment commonly involves three coupled tasks: identifying wound type, delineating wound boundaries, and localizing the wound region for measurement and monitoring. Despite this clinical coupling, existing machine learning approaches typically address wound classification, segmentation, and localization using separate models. We present WILLIE, a unified framework and benchmark for wound classification, segmentation and localization that enables systematic evaluation of multi-task wound analysis under a common protocol. WILLIE harmonizes three public wound datasets into a shared benchmark and compares unified models across three scaling configurations against 10 single-task baselines. The best model achieves 91.88% classification accuracy, 91.41% Dice, and 96.23%
[email protected] while producing all three outputs in a single forward pass. Beyond aggregate performance, our results show that segmentation-derived localization outperforms dedicated detection baselines in this benchmark, suggesting that box-based localization may be unnecessary for spatially coherent wound targets. Our findings highlight that effective multi-task learning in healthcare imaging depends not only on shared representations, but also on task formulation, compatibility, and benchmark design.
Chinese Translation
慢性伤口管理影响美国超过820万患者，并带来了巨大的临床和经济负担。临床伤口评估通常涉及三个相互关联的任务：识别伤口类型、勾勒伤口边界以及定位伤口区域以进行测量和监测。尽管这些临床任务是相互关联的，现有的机器学习方法通常使用独立模型来处理伤口分类、分割和定位。我们提出了WILLIE，一个用于伤口分类、分割和定位的统一框架与基准，能够在共同协议下系统地评估多任务伤口分析。WILLIE将三个公共伤口数据集整合为一个共享基准，并在三种缩放配置下比较统一模型与10个单任务基线模型。最佳模型在一次前向传播中实现了91.88%的分类准确率、91.41%的Dice系数和96.23%的
[email protected]。除了整体性能外，我们的结果显示，基于分割的定位在该基准中优于专用检测基线，表明对于空间一致的伤口目标，基于框的定位可能是多余的。我们的研究结果强调，医疗影像中的有效多任务学习不仅依赖于共享表示，还依赖于任务的构造、兼容性和基准设计。
cs.CV / 126 / 2610.05342
IRSTD-Agent: Agentic Infrared Small Target Detection via Zoom-Guided Interaction Learning
IRSTD-Agent：通过缩放引导交互学习进行的代理红外小目标检测
Abstract
Infrared small-target detection plays an important role in maritime monitoring and aerial surveillance. Although multimodal large language models (MLLMs) offer promising capabilities for visual understanding, existing MLLM-based approaches struggle to precisely localize infrared small targets. In this paper, we propose IRSTD-Agent, an agentic framework for infrared small target detection through dynamic visual search. The framework enables an MLLM to adaptively determine where and at what scale to inspect an image and progressively gather fine-grained visual evidence for precise target localization. Five complementary visual tools (PROPOSAL, ZOOM, DETECT, DROP and REFINE) support object candidate discovery, adaptive observation, target localization, hypothesis rejection, and target extent refinement, together enabling a coordinated search process over original-resolution images. To teach the MLLMs to conduct this search, we introduce Zoom-guided Interaction Learning, which uses annotation-derived interaction trajectories to supervise tool selection and the corresponding arguments. Through extensive experiments on WideIRSTD-Full and IRSTD-1k datasets, we demonstrate that IRSTD-Agent outperforms the evaluated vision-language models and enhances the precise localization capabilities of MLLMs in IRSTD tasks.
Chinese Translation
红外小目标检测在海洋监测和空中监视中发挥着重要作用。尽管多模态大型语言模型（MLLMs）在视觉理解方面展现出良好的能力，但现有基于MLLM的方法在精确定位红外小目标方面仍然存在困难。本文提出了IRSTD-Agent，一个通过动态视觉搜索进行红外小目标检测的代理框架。该框架使得MLLM能够自适应地确定在何处以及以何种尺度检查图像，并逐步收集细粒度的视觉证据以实现精确的目标定位。五个互补的视觉工具（PROPOSAL、ZOOM、DETECT、DROP和REFINE）支持对象候选发现、自适应观察、目标定位、假设拒绝和目标范围细化，从而共同实现对原始分辨率图像的协调搜索过程。为了教会MLLM进行这种搜索，我们引入了缩放引导交互学习，该方法利用注释派生的交互轨迹来监督工具选择及其相应参数。通过在WideIRSTD-Full和IRSTD-1k数据集上的大量实验，我们证明了IRSTD-Agent在评估的视觉语言模型中表现优于其他模型，并增强了MLLM在IRSTD任务中的精确定位能力。
cs.CV / 127 / 2610.05349
Learning Conditional Source Distribution via Flow Reversal for Temporal Flow Matching
通过流反转学习条件源分布以实现时间流匹配
Abstract
We introduce CNP-Flow, a flow matching framework for temporal generation that learns conditional source distributions through flow reversal. Whereas standard conditional flow matching (FM) incorporates conditioning through the vector field and draws source samples from a standard Gaussian, CNP-Flow uses a conditional noise predictor (CNP) to produce an isotropic Gaussian source for each temporal condition. The CNP is supervised by source samples obtained through flow reversal, which maps observed targets backward through a pretrained FM model. A three-stage pipeline pretrains the FM model, trains the CNP, and fine-tunes the FM model using the learned source distribution, while preserving the FM backbone architecture. Across video prediction, video interpolation, and 7-DoF Franka robot motion planning, CNP-Flow consistently improves generation quality. It also matches baseline performance with fewer function evaluations. Project page: https://embodiedai-ntu.github.io/cnpflow
Chinese Translation
我们介绍了CNP-Flow，这是一种用于时间生成的流匹配框架，通过流反转学习条件源分布。标准的条件流匹配（FM）通过向量场引入条件，并从标准高斯分布中抽取源样本，而CNP-Flow使用条件噪声预测器（CNP）为每个时间条件生成各向同性的高斯源。CNP通过通过流反转获得的源样本进行监督，该过程将观察到的目标通过预训练的FM模型向后映射。一个三阶段的流程预训练FM模型，训练CNP，并使用学习到的源分布微调FM模型，同时保留FM的主干架构。在视频预测、视频插值和7自由度Franka机器人运动规划中，CNP-Flow始终提高生成质量。它还以更少的函数评估匹配基线性能。项目页面：https://embodiedai-ntu.github.io/cnpflow
cs.CV / 128 / 2610.05411
CleanMDM: Clean Motion Diffusion Model for Multimodal Motion Cleanup
CleanMDM：用于多模态运动清理的清洁运动扩散模型
Abstract
Motion capture data is rarely directly usable, as they typically exhibit missing segments, jitter, drift and contact artifacts. Traditionally, corrupted motions are cleaned by animators through the manual identification of keyframes from noisy motion, subsequent keyframe correction, and interpolation between corrected keyframes to reconstruct coherent motion. While the rise of generative motion models has made automatic cleanup feasible, most approaches operate as black box denoisers with limited controllability, making it difficult to preserve reliable segments or enforce specific user intents. Inspired by animation workflows, we present CleanMDM, a unified multimodal motion cleanup framework that formulates cleanup as masked conditional generation with plug-and-play conditions. This single model supports arbitrary combinations of noisy 3D motion, sparse 2D keyframes, sparse 3D keyframes, and text. This design enables both automatic cleanup without additional user annotation and controllable cleanup under multimodal guidance. To further improve motion realism, we incorporate the Latent Motion Quality Discriminator (LMQD) to better match kinematic distributions and reduce skating, jitter, and interpenetration artifacts, and we apply Mesh-Aware Contact Projection as a test-time optimization step to enhance contact and physical consistency. Experiments across multiple datasets demonstrate that CleanMDM consistently outperforms prior cleanup and generation baselines, and that low cost conditions (text and 2D keyframes) provide reliable controllability gains in multimodal cleanup scenarios.
Chinese Translation
运动捕捉数据通常难以直接使用，因为它们通常存在缺失段、抖动、漂移和接触伪影。传统上，动画师通过手动识别噪声运动中的关键帧、后续的关键帧修正以及在修正后的关键帧之间进行插值来清理受损的运动。尽管生成运动模型的兴起使得自动清理成为可能，但大多数方法作为黑箱去噪器运行，控制能力有限，使得难以保留可靠的片段或强制特定的用户意图。受到动画工作流程的启发，我们提出了CleanMDM，一个统一的多模态运动清理框架，将清理过程公式化为带有插拔条件的掩蔽条件生成。该单一模型支持任意组合的噪声3D运动、稀疏2D关键帧、稀疏3D关键帧和文本。这一设计能够在没有额外用户注释的情况下实现自动清理，并在多模态指导下实现可控清理。为了进一步提高运动的真实感，我们引入了潜在运动质量鉴别器（Latent Motion Quality Discriminator, LMQD），以更好地匹配运动学分布并减少滑行、抖动和相互穿透伪影，并在测试时应用网格感知接触投影（Mesh-Aware Contact Projection）作为优化步骤，以增强接触和物理一致性。在多个数据集上的实验表明，CleanMDM在清理和生成基线方面始终优于之前的方法，并且低成本条件（文本和2D关键帧）在多模态清理场景中提供了可靠的可控性提升。
cs.CV / 129 / 2610.05413
A Strong Baseline for Evaluating Vision Encoders in Multimodal Large Language Models
评估多模态大语言模型中视觉编码器的强基线
Abstract
Evaluating vision encoders requires metrics that reliably predict their downstream performance in multimodal large language models (MLLMs). Although recent studies have shown that cross-modal metrics can better capture such performance, unimodal metrics remain the dominant choice in practice. In this work, we revisit cross-modal evaluation of vision encoders through large-scale experiments. We identify important limitations in both the experimental design and methodological formulation of prior approaches. After addressing these limitations and introducing simple improvements, we propose RAVEL, a training-free method based on cross-modal nearest-neighbor retrieval. Despite its simplicity, RAVEL achieves state-of-the-art performance across our experiments, outperforming prior methods by a substantial margin. Our results demonstrate that simple cross-modal metrics, when evaluated under a careful and comprehensive setup, can provide a strong basis for evaluating vision encoders for MLLMs.
Chinese Translation
评估视觉编码器需要可靠预测其在多模态大语言模型（MLLMs）中下游性能的指标。尽管近期研究表明跨模态指标能够更好地捕捉此类性能，但单模态指标在实践中仍然是主流选择。在本研究中，我们通过大规模实验重新审视视觉编码器的跨模态评估。我们识别出先前方法在实验设计和方法论构建方面的重要局限性。在解决这些局限性并引入简单改进后，我们提出了RAVEL，这是一种基于跨模态最近邻检索的无训练方法。尽管其简单，RAVEL在我们的实验中实现了最先进的性能，显著优于先前的方法。我们的结果表明，在经过仔细和全面的设置下，简单的跨模态指标可以为评估MLLMs中的视觉编码器提供强有力的基础。
cs.CV / 130 / 2610.05416
Prism: Dynamic Sparse Attention for Native 2K Joint Video-Audio Generation Model Training
Prism：用于原生2K联合视频-音频生成模型训练的动态稀疏注意力
Abstract
Natively training joint video-audio generation models at higher resolutions empowers them to learn richer visual details and sharper motion dynamics. However, full attention incurs quadratic cost and, as resolution increases, spreads attention over increasingly redundant tokens, diluting learning signals for informative content and disrupting pretrained priors. Existing sparse attention methods either target training-free acceleration or overlook the unique structure of joint video-audio data, where cross-modal interactions are inherently concentrated around sound-producing regions. To address this, we propose Prism, a dynamic sparse attention framework for natively training joint video-audio generation models at 2K. In particular, Prism organizes the token sequence into spatiotemporal macro-zones, enabling the attention structure to adapt to local content. For each zone, it estimates local information structure via video feature variance along the channel and feature norms from the audio-to-video cross-attention, jointly capturing how visual content varies directionally and how strongly audio influences each visual region. Based on these signals, Prism dynamically assigns a tailored block shape to each zone, applying finer partitioning along axes of rapid visual content variation and strong audio-visual coupling. This encourages tokens within each block to remain semantically coherent, allowing block-level features to capture both visual content and joint video-audio interaction patterns. Prism further adopts a hybrid block selection strategy to dynamically determine per-query sparsity. Experiments show that Prism achieves 2.5$\times$ training speedup compared to full attention, while surpassing it in generation quality.
Chinese Translation
在更高分辨率下原生训练联合视频-音频生成模型使其能够学习更丰富的视觉细节和更清晰的运动动态。然而，完全注意力机制的计算成本呈二次增长，随着分辨率的提高，注意力会分散到越来越多的冗余标记上，从而稀释了对信息内容的学习信号，并干扰了预训练的先验知识。现有的稀疏注意力方法要么针对无训练加速，要么忽视了联合视频-音频数据的独特结构，其中跨模态交互本质上集中在声音产生区域。为了解决这个问题，我们提出了Prism，一种用于原生训练2K联合视频-音频生成模型的动态稀疏注意力框架。具体而言，Prism将标记序列组织成时空宏区域，使注意力结构能够适应局部内容。对于每个区域，它通过沿通道的视频特征方差和音频到视频的跨注意力特征范数来估计局部信息结构，联合捕捉视觉内容的方向变化及音频对每个视觉区域的影响强度。基于这些信号，Prism动态地为每个区域分配定制的块形状，在快速视觉内容变化和强音频-视觉耦合的轴上应用更精细的划分。这鼓励每个块内的标记保持语义一致性，使块级特征能够捕捉视觉内容和联合视频-音频交互模式。Prism进一步采用混合块选择策略，动态确定每个查询的稀疏性。实验表明，Prism相比于完全注意力实现了2.5倍的训练加速，同时在生成质量上超越了完全注意力。
cs.CV / 131 / 2610.05417
Render to Reason: Novel-View Semantic Prediction Improves Spatial Understanding in VLMs
渲染以推理：新视角语义预测提升视觉语言模型的空间理解能力
Abstract
Recent works augment Vision-Language Models with geometry features from pretrained 3D models, expecting that the geometric signal will boost spatial reasoning. However, we find that simply fusing geometry features and training on standard spatial QA yields only marginal improvements on high-level multi-hop tasks. We attribute this gap to a training-signal problem: standard spatial QA can be largely answered from visual features and language priors, so the geometry pathway receives weak gradients and fails to integrate with the visual features. To provide a training signal that requires geometry, we propose \textbf{novel-view semantic rendering} as an auxiliary training task that requires the model to predict the semantic layout of an unobserved viewpoint, inspired by humans' ability to mentally simulate novel viewpoints during spatial reasoning. This task encourages joint use of both pathways: geometry provides pose-dependent visibility, while vision provides semantic content. Our auxiliary task yields consistent improvements over the geometry-augmented baseline across all three benchmarks (up to +1.6 on VSI-Bench, +2.2 on ReVSI, +2.9 on our 3D-Point-QA dataset) and our full model surpasses prior open-source methods on VSI-Bench and on ReVSI. Project page: https://yuqunw.github.io/Render2Reason/.
Chinese Translation
近期的研究通过预训练的3D模型增强视觉语言模型的几何特征，期望几何信号能够提升空间推理能力。然而，我们发现仅仅融合几何特征并在标准空间问答（QA）上进行训练，仅能在高层次的多跳任务上带来微小的提升。我们将这一差距归因于训练信号问题：标准空间问答在很大程度上可以通过视觉特征和语言先验来回答，因此几何路径接收到的梯度较弱，未能与视觉特征有效整合。为了提供一个需要几何的训练信号，我们提出了 extbf{新视角语义渲染}作为辅助训练任务，该任务要求模型预测未观察视点的语义布局，灵感来源于人类在空间推理过程中能够心理模拟新视点的能力。该任务鼓励同时使用两条路径：几何提供依赖于姿态的可见性，而视觉提供语义内容。我们的辅助任务在所有三个基准测试中均对增强几何特征的基线模型带来了持续的提升（在VSI-Bench上提升最多+1.6，在ReVSI上+2.2，在我们的3D-Point-QA数据集上+2.9），我们的完整模型在VSI-Bench和ReVSI上超越了之前的开源方法。项目页面：https://yuqunw.github.io/Render2Reason/.
cs.CV / 132 / 2610.05425
Unmentioned Checklist Findings Change How Reinforcement Learning Appears to Improve Chest Radiograph Report Checking
未提及的检查清单发现改变了强化学习在胸部放射影像报告检查中的表现
Abstract
Automated checks of radiology reports may rely on AI-generated checklists that leave findings unmentioned. We used reinforcement learning to train a vision-language model to fill in a 12-finding checklist from a chest radiograph without seeing the sentence under test; a separate checking model judged the sentence from the checklist. On held-out patients, a rule-based check and an independent medical checker, neither used in training, measured discrimination gains (Youden index) of 12.6% and 11.8%; only the rule-based check met the prespecified false-alarm criterion. Switching to the training format, which fixes finding order and enters unmentioned findings as absent, raised the training checker's measured gain and lowered the independent checker's, a prespecified comparison that yielded 6.2% (95% interval 2.0% to 10.5%) and, post hoc on held-out patients, 7.7%. Across 8 checking models, acceptance of a label-consistent negative statement about an unmentioned finding ranged from 1.0% to 97.0%. Labels were report-derived, not radiologist-adjudicated.
Chinese Translation
放射学报告的自动检查可能依赖于未提及发现的人工智能生成的检查清单。我们使用强化学习训练了一个视觉-语言模型，以在未查看测试句子的情况下，从胸部放射影像中填写一个包含12个发现的检查清单；一个独立的检查模型则对来自检查清单的句子进行判断。在保留患者中，一个基于规则的检查和一个独立的医学检查员（均未用于训练）测量了12.6%和11.8%的判别增益（Youden指数）；只有基于规则的检查满足预设的误报标准。切换到训练格式，固定发现顺序并将未提及的发现视为缺失，提高了训练检查员的测量增益并降低了独立检查员的增益，这一预设比较得到了6.2%（95%区间为2.0%至10.5%），在保留患者中后验分析为7.7%。在8个检查模型中，对未提及发现的标签一致的否定陈述的接受度范围从1.0%到97.0%。标签来源于报告，而非放射科医生裁定。
cs.CV / 133 / 2610.05529
DynaMesh: Dynamic 3D Texture Generation
DynaMesh：动态三维纹理生成
Abstract
We present DynaMesh, a dynamic texture generation method for 3D meshes. Given a textureless shape and a text prompt describing an effect, our method produces an appearance that evolves while the object's geometry remains unchanged. Previous works on dynamic 3D content generation have focused on motion, where an object's geometry and position change while keeping its appearance the same. Methods on texture generation sit on the other side of the problem, painting appearance onto a shape as a fixed surface property and not as an evolving process. Neither addresses a visual effect that propagates on a 3D object. A natural route consists of two generators: a video model that shows the effect from a single view, and an image-to-3D generator that lifts each frame to 3D. However, the latter has no notion of time, so running it per video frame produces a sequence that flickers, loses effect details, and yields a different mesh at every video frame. Our method addresses these failures by conditioning a video model on a render of the mesh and the prompt to obtain a reference video, then running a frozen image-to-3D generator on the video with two changes. The conditioning of each frame is blended over a temporal window, and low-rank adapters are fit per shape to restore the lost details. The mesh is encoded once for the whole sequence, so geometry is constant by construction, and the output is a single mesh with a texture per frame. Applied to various objects and effects, DynaMesh substantially improves over recent video-to-4D and texturing methods, and can generalize its temporal effect to different shapes never seen during training. Our project page is at https://threedle.github.io/dynamesh/.
Chinese Translation
我们提出了DynaMesh，一种用于三维网格的动态纹理生成方法。给定一个无纹理的形状和描述效果的文本提示，我们的方法在物体的几何形状保持不变的情况下，生成一个不断演变的外观。以往的动态三维内容生成研究主要集中在运动上，即物体的几何形状和位置发生变化，而外观保持不变。纹理生成方法则处于问题的另一端，将外观作为固定的表面属性涂抹到形状上，而不是作为一个演变的过程。两者都未能解决在三维物体上传播的视觉效果。一个自然的解决方案包括两个生成器：一个视频模型从单一视角展示效果，以及一个图像到三维的生成器将每一帧提升为三维。然而，后者没有时间的概念，因此在每个视频帧上运行它会产生一个闪烁的序列，失去效果细节，并在每个视频帧上生成不同的网格。我们的方法通过将视频模型以网格的渲染和提示作为条件来获取参考视频，从而解决了这些问题，然后在视频上运行一个冻结的图像到三维生成器，并进行两项更改。每帧的条件在一个时间窗口内进行混合，并且为每个形状拟合低秩适配器以恢复丢失的细节。网格在整个序列中只编码一次，因此几何形状在构造上是恒定的，输出是每帧一个纹理的单一网格。应用于各种物体和效果，DynaMesh在最近的视频到4D和纹理生成方法上有显著改进，并能够将其时间效果推广到训练期间未见过的不同形状。我们的项目页面地址为 https://threedle.github.io/dynamesh/。
cs.CV / 134 / 2610.05531
Deep Prior Learning for Embodied Perception
面向具身感知的深度先验学习
Abstract
Embodied systems need geometric perception that exploits available observations beyond images alone. Recent feed-forward 3D models incorporate geometric priors, including camera poses, intrinsics, and depth. However, handling noisy poses, preserving accurate priors, and recovering physical scale require more than simply accepting these inputs. We introduce \emph{Vision-Prior Geometry Grounded Transformer} (VPGGT), a VGGT-based framework that extends OmniVGGT for prior-aware embodied perception. We formulate sensor-motivated pose corruptions from ground-truth trajectories for training and introduce a parameter-free \emph{prior residual connection} (PRC) to mitigate \emph{prior dilution}, where predictions are less accurate than their supplied pose priors. Our noise formulation targets camera poses; supplied intrinsics and depth receive no additional corruption. We further introduce \emph{Metric Global Attention}, which conditions a global scale token on available pose and depth scales and predicts a shared metric scaling factor for the geometric outputs. Experiments across four datasets show that \emph{PRC} improves translation-direction accuracy and joint pose AUC over a matched training baseline when camera priors are provided for all views, under both exact and corrupted poses. These results support explicit prior access during refinement as a useful addition to feature-level conditioning.
Chinese Translation
具身系统需要几何感知，利用可用的观察数据，而不仅仅依赖图像。最近的前馈3D模型结合了几何先验，包括相机姿态、内参和深度。然而，处理噪声姿态、保持准确的先验以及恢复物理尺度需要的不仅仅是简单地接受这些输入。我们提出了 extit{视觉先验几何基础变换器}（Vision-Prior Geometry Grounded Transformer，VPGGT），这是一个基于VGGT的框架，扩展了OmniVGGT以实现先验感知的具身感知。我们从真实轨迹中制定了传感器驱动的姿态损坏用于训练，并引入了一种无参数的 extit{先验残差连接}（Prior Residual Connection，PRC），以减轻 extit{先验稀释}，即预测的准确性低于其提供的姿态先验。我们的噪声模型针对相机姿态；提供的内参和深度没有额外的损坏。我们进一步引入了 extit{度量全局注意力}（Metric Global Attention），它根据可用的姿态和深度尺度对全局尺度标记进行条件化，并预测几何输出的共享度量缩放因子。在四个数据集上的实验表明，当为所有视图提供相机先验时， extit{PRC}在精确和损坏的姿态下均提高了平移方向的准确性和关节姿态的AUC，相较于匹配的训练基线。这些结果支持在细化过程中显式访问先验作为特征级条件化的有用补充。
cs.CV / 135 / 2610.05552
Monocular markerless biomechanics for clinically interpretable gait assessment in spinal cord injury
用于脊髓损伤临床可解释步态评估的单目无标记生物力学
Abstract
Three-dimensional gait analysis guides rehabilitation after spinal cord injury but depends on marker-based motion capture and force plates, which few clinics have. Monocular markerless pipelines have been established in fewer healthy adult cohorts but not in neurological cohorts. We present the SCAI SCI Gait dataset, comprising 239 adult individuals with spinal cord injury with synchronized video, motion capture, and force-plate measurements, we fitted a parametric body mesh to a single sagittal-view video, driving an anthropometrically scaled OpenSim model via virtual markers. Markerless lower-body kinematics showed state-of-the-art agreement with motion-capture measurements (r = 0.68-0.90, p < 0.001, and RMSE = 4.18-6.49 degrees), and accurate kinematics-based predicted ground-reaction forces closely matched those measured by force plates (r = 0.85-0.87, p < 0.001, and RMSE = 2.13-2.19 Newton per kg). Furthermore, conditional-dependence graph analysis with Markov blankets revealed that waveform components were conditionally associated with functional independence, and speed-stratified clustering revealed distinct mechanical strategies among individuals walking at similar speeds. These findings establish the use of monocular video as a scalable approach for clinically meaningful biomechanical assessment and data-driven phenotyping in patients with spinal cord injury. Github: https://github.com/SCAI-Lab/SCAI-SCI-Gait
Chinese Translation
三维步态分析指导脊髓损伤后的康复，但依赖于基于标记的运动捕捉和力板，而这些设备在很少的诊所中可用。单目无标记管道在健康成人群体中已建立，但在神经病学群体中尚未实现。我们提出了SCAI SCI步态数据集，包含239名脊髓损伤成人个体的同步视频、运动捕捉和力板测量数据。我们将参数化身体网格拟合到单个矢状面视频中，通过虚拟标记驱动一个经过人体测量缩放的OpenSim模型。无标记的下肢运动学与运动捕捉测量结果显示出最先进的一致性（r = 0.68-0.90，p < 0.001，RMSE = 4.18-6.49度），基于运动学的预测地面反作用力与力板测量结果密切匹配（r = 0.85-0.87，p < 0.001，RMSE = 2.13-2.19牛顿每千克）。此外，使用马尔可夫毯的条件依赖图分析揭示波形成分与功能独立性之间的条件关联，而速度分层聚类显示在以相似速度行走的个体之间存在明显的机械策略差异。这些发现确立了使用单目视频作为一种可扩展的方法，以进行对脊髓损伤患者的临床有意义的生物力学评估和数据驱动的表型分析。Github: https://github.com/SCAI-Lab/SCAI-SCI-Gait
cs.CV / 136 / 2610.05576
SteadySplats: Resampling of Low-Variance Gaussians for High-Fidelity Stochastic Rendering
SteadySplats：低方差高斯的重采样用于高保真随机渲染
Abstract
Stochastic order-independent transparency enables efficient and elegant rendering of primitive-based radiance fields like 3D Gaussian Splatting models, but remains impractical due to the inherent visible noise in the output. We propose a principled approach to minimize high-frequency noise, addressing its sources at the representation and image synthesis level. During stochastic rendering, our history-based spatial resampling scheme drastically accelerates image convergence, while temporal importance resampling ensures coherence under camera movement. During training, a color regularizer implicitly reduces the variance along view rays in the 3DGS models. With these properties, our optimized, Vulkan-based renderer effectively mitigates output noise at low and high sample counts, achieving a substantial 13~dB PSNR increase in quality over previous stochastic methods at 1 sample per pixel and quickly converging to sorted 3DGS with an average L1 error of less than $10^{-4}$.
Chinese Translation
随机无序透明性使得基于原始的辐射场（如3D高斯点云模型）的高效优雅渲染成为可能，但由于输出中固有的可见噪声，这一方法仍然不够实用。我们提出了一种原则性的方法来最小化高频噪声，从表示和图像合成层面解决其来源。在随机渲染过程中，我们基于历史的空间重采样方案显著加速了图像收敛，而时间重要性重采样则确保了在相机移动下的一致性。在训练过程中，颜色正则化器隐式地减少了3DGS模型视线方向上的方差。凭借这些特性，我们优化的基于Vulkan的渲染器有效地减轻了低样本和高样本计数下的输出噪声，在每像素1个样本时，相较于之前的随机方法实现了高达13 dB的PSNR质量提升，并快速收敛至排序的3DGS，平均L1误差小于$10^{-4}$。
cs.CV / 137 / 2610.05587
Generating the Wild: Individual-Consistent Image-to-Video Generation for Wildlife
生成野生动物：个体一致性的图像到视频生成
Abstract
Individual-level wildlife identification often suffers from data scarcity, as varying observations of the same animal under diverse poses, viewpoints, and motions are rarely available. Image-to-video (I2V) generation offers a promising way to mitigate this limitation by synthesizing additional observations from a single reference image. However, existing I2V models mainly emphasize global layout, semantics, and motion, and therefore often fail to preserve fine-grained local appearance cues that distinguish one wildlife individual from another, such as fur texture, stripe boundaries, spot configurations, and contour transitions. We observe that these identity-critical cues are closely related to high-frequency information. To address this challenge, we propose WildIcon, a high-frequency-guided I2V framework for wildlife individual consistency. Specifically, WildIcon introduces a frequency-aware identity encoding branch that extracts individual-specific high-frequency cues from the reference image. Combined with isolated foreground information, the resulting identity tokens are then injected into cross-attention blocks as identity conditioning. Building on a frozen backbone with lightweight identity adaptation, WildIcon preserves fine-grained identity cues visible in the reference image while retaining the motion controllability and semantic fidelity of the base I2V model. In addition, to support the training and evaluation of wildlife individual-consistent I2V, we construct WildlifeVid, a wildlife-centric video dataset with high-quality, temporally coherent clips and individual-level identity labels. Experiments on I2V generation and downstream animal re-identification (ReID) show that WildIcon achieves stronger individual consistency than existing baselines, and that its filtered outputs can serve as useful candidate training augmentations for downstream ReID.
Chinese Translation
个体级别的野生动物识别常常受到数据稀缺的困扰，因为在不同的姿势、视角和动作下对同一动物的观察很少可用。图像到视频（Image-to-Video, I2V）生成提供了一种有前景的方法，通过从单一参考图像合成额外的观察来缓解这一限制。然而，现有的I2V模型主要强调全局布局、语义和运动，因此往往无法保留区分不同野生动物个体的细粒度局部外观线索，如毛发纹理、条纹边界、斑点配置和轮廓过渡。我们观察到，这些对身份至关重要的线索与高频信息密切相关。为了解决这一挑战，我们提出了WildIcon，一个高频引导的I2V框架，旨在实现野生动物个体一致性。具体而言，WildIcon引入了一个频率感知的身份编码分支，从参考图像中提取个体特定的高频线索。结合孤立的前景信息，生成的身份标记随后被注入到交叉注意力块中作为身份条件。基于一个冻结的主干网络和轻量级的身份适应，WildIcon在保留参考图像中可见的细粒度身份线索的同时，保持了基础I2V模型的运动可控性和语义保真度。此外，为了支持野生动物个体一致性的I2V的训练和评估，我们构建了WildlifeVid，一个以野生动物为中心的视频数据集，包含高质量、时间一致的片段和个体级别的身份标签。在I2V生成和下游动物再识别（ReID）的实验中，WildIcon实现了比现有基准更强的个体一致性，其过滤后的输出可以作为下游ReID的有用候选训练增强。
cs.CV / 138 / 2610.05603
EchoDino: A pediatric foundation model for transferable echocardiographic analysis across the lifespan
EchoDino：一种可转移的儿童心脏超声分析基础模型，适用于整个生命周期
Abstract
Echocardiography is the most widely used cardiac imaging modality, yet interpretation demands integrating visual evidence across global anatomy, localized structures and dynamic cardiac motion. Machine-learning models have automated individual tasks, but they are typically built for a single purpose and depend on expensively labeled datasets - a barrier particularly acute in pediatric care, where data are scarce and anatomy changes with age. Here we present EchoDino, a self-supervised foundation model for echocardiography, created by adapting the DINOv3 framework to 3.7 million frames from 1.7 million unlabeled pediatric echocardiography videos. With its encoder frozen, EchoDino produces representations that capture global context, local anatomy, and dense spatial detail. We introduce Motion-biased Entropy Maximization Sampling (MEMS) to select the most informative frames for video-level analysis. Across nine pediatric and adult datasets, EchoDino outperformed strong baseline models, raising view-classification accuracy from 0.609 to 0.889 and the area under the receiver operating characteristic curve for structural-heart-disease detection from 0.811 to 0.872, while also cutting age-estimation error from 3.857 to 1.389 years, achieving the best segmentation accuracy and lowering ejection-fraction errors. By generalizing from label-free pediatric data to adult echocardiography, EchoDino offers a versatile foundation for cardiac image analysis across the lifespan.
Chinese Translation
心脏超声是最广泛使用的心脏影像学技术，但其解读需要整合全球解剖结构、局部结构和动态心脏运动的视觉证据。机器学习模型已实现了个别任务的自动化，但通常是为单一目的构建，并依赖于昂贵的标注数据集——这一障碍在儿科护理中尤为突出，因为数据稀缺且解剖结构会随年龄变化。在此，我们提出了EchoDino，一种自监督的心脏超声基础模型，通过将DINOv3框架适配于来自170万未标注儿童心脏超声视频的370万帧数据而创建。冻结编码器后，EchoDino生成的表示能够捕捉全球上下文、局部解剖结构和密集的空间细节。我们引入了运动偏向的熵最大化采样（Motion-biased Entropy Maximization Sampling，MEMS）以选择用于视频级分析的最具信息量的帧。在九个儿童和成人数据集中，EchoDino超越了强基线模型，将视图分类准确率从0.609提高到0.889，将结构性心脏病检测的接收者操作特征曲线下面积从0.811提高到0.872，同时将年龄估计误差从3.857降低到1.389年，实现了最佳分割准确率并降低了射血分数误差。通过从无标签的儿童数据推广到成人心脏超声，EchoDino为整个生命周期的心脏影像分析提供了一个多功能的基础。
cs.CV / 139 / 2610.05608
Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation
Kandinsky 6.0 视频：用于同步视频和音频生成的基础模型
Kandinsky, Team, Agafonova, Julia, Akhmatov, Bulat, Aksyutin, Mikhail, Alekseenko, Grigorii, Aliaskina, Anastasia, Androsova, Olga, Arkhipkin, Vladimir, Averchenkova, Anna, Belykh, Alexander, Bocharova, Serafima, Bogakovskaya, Sofiya, Bukashkin, Anton, Bulygin, Mark, Buzygin, Kirill, Cheremnykh, Irina, Chernyshev, Kirill, Chernyshov, Mikhail, Chernyy, Vladimir, Chikovani, David, Daniltsev, Georgy, Dimitrov, Denis, Dmitrienko, Anna, Dokholyan, Vladimir, Emelyanov, Sergey, Ermilov, Dmitry, Fedorov, Georgii, Gavrilova, Polina, Gerasimenko, Nikolai, Gordeev, Aleksandr, Inozemtsev, Andrey, Ivaniuta, Andrei, Ivanov, Alexander, Karaev, Mikhail, Kargapoltseva, Anastasiia, Kirillov, Ivan, Kiselev, Nikita, Kobenko, Valeria, Kolabushin, Yury, Koposov, Denis, Korobov, Anatoly, Korviakov, Vladimir, Kozlov, Kirill, Krzhivokolskiy, Denis, Kuklev, Konstantin, Kunitsyn, Alexander, Kuzin, Sergey, Lakhtionov, Vladislav, Letunovskiy, Alexey, Litvinov, Maxim, Lyulkov, Alexander, Makarov, Georgy, Malakhov, Kirill, Malykh, Egor, Mamaev, Mikhail, Mikhailov, Dmitrii, Mikhailova, Polina, Mikheev, Ivan, Muromtseva, Elizaveta, Nazarkin, Nikolai, Nikulina, Tatiana, Novitskiy, Lev, Onuchin, Stanislav, Osterov, Nikita, Parkhomenko, Denis, Parpara, Anatoliy, Polovnikov, Vladimir, Reznikov, Konstantin, Saginbaev, Azat, Samsonov, Nikita, Sentsov, Alexander, Shaimov, Nikita, Sherstyuk, Artem, Shutkin, Andrey, Silvestrov, Egor, Suleimanov, Bulat, Suprunov, Matvey, Taranov, Sergey, Tolstykh, Irina, Trofimuk, Tatiana, Trushkin, Ilya, Tsybina, Aleksandra, Varlashina, Olga, Vasilev, Viacheslav, Vasiliev, Ilya, Vilisov, Eugeny, Yakubson, Sergey, Zakharov, Konstantin
Abstract
We present Kandinsky 6.0 Video, a family of foundation diffusion models for synchronized text-to-audio-video generation, comprising Kandinsky 6.0 Video Lite (3B parameters) and Kandinsky 6.0 Video Pro (29B parameters). Both models generate 5-second video clips with synchronized 44 kHz audio, including lip-sync, in text-to-audio-video (T2AV) and image-to-audio-video (I2AV) modes; a built-in super-resolution model raises the output resolution to Full-HD (1920$\times$1080). Building on the video generation capabilities of Kandinsky 5.0, Kandinsky 6.0 Video employs a dual-stream CrossDiT architecture that connects a pretrained video stream and a newly trained audio stream through bidirectional cross-attention for temporal and semantic alignment. Our continuous pretraining strategy first trains the audio stream from scratch on large-scale audio corpora and then trains both streams jointly on paired audio-video data while preserving unimodal fidelity; pretraining is followed by supervised fine-tuning, reinforcement-learning-based post-training, and distillation. In side-by-side human evaluation, Kandinsky 6.0 Video Pro clearly outperforms its predecessor, Kandinsky 5.0 Video Pro, and remains competitive with leading audio-video generation models, particularly in speech quality. To accelerate open research and deployment in multimedia generation, we release the code, model checkpoints, and diffusers integration under the MIT license.
Chinese Translation
我们提出了Kandinsky 6.0 视频，这是一系列用于同步文本到音频视频生成的基础扩散模型，包括Kandinsky 6.0 视频 Lite（3B参数）和Kandinsky 6.0 视频 Pro（29B参数）。这两个模型生成5秒的视频片段，并提供同步的44 kHz音频，包括唇同步，支持文本到音频视频（T2AV）和图像到音频视频（I2AV）模式；内置的超分辨率模型将输出分辨率提升至全高清（1920×1080）。基于Kandinsky 5.0的视频生成能力，Kandinsky 6.0 视频采用双流CrossDiT架构，通过双向交叉注意力连接预训练的视频流和新训练的音频流，以实现时间和语义的对齐。我们的连续预训练策略首先在大规模音频语料库上从零开始训练音频流，然后在配对的音频视频数据上联合训练两个流，同时保持单模态的保真度；预训练后进行监督微调、基于强化学习的后训练和蒸馏。在并行的人类评估中，Kandinsky 6.0 视频 Pro明显优于其前身Kandinsky 5.0 视频 Pro，并在音频视频生成模型中保持竞争力，特别是在语音质量方面。为了加速多媒体生成领域的开放研究和部署，我们在MIT许可证下发布了代码、模型检查点和扩散器集成。
cs.CV / 140 / 2610.05664
StageVLN: Spatial and Trajectory Auxiliary Guidance for Efficient Vision-Language Navigation
StageVLN：高效视觉-语言导航的空间与轨迹辅助指导
Abstract
Vision-and-Language Navigation (VLN) policies increasingly benefit from strong semantic priors provided by large vision-language models (VLMs). However, standard action supervision does not explicitly encourage intermediate representations to preserve scene geometry, relative orientation, or global episode progress. Incorporating depth estimators, explicit maps, point clouds, or geometry foundation models at inference can provide such structure but introduces additional computation, memory overhead, and architectural dependence during deployment. We introduce StageVLN, a training framework that shapes navigation representations through privileged spatial and trajectory guidance while preserving the original inference pathway. A frozen geometry foundation model provides multi-level spatial guidance to hierarchical navigator states, while relative-heading and expert-route progress objectives provide complementary trajectory-state supervision. All auxiliary components are used only during training and removed at deployment. On R2R-CE validation-unseen, StageVLN achieves 56.3\% SR and 51.4\% SPL with a 4B-parameter backbone, without an additional geometry encoder at inference. On RxR-CE, it achieves 54.3\% SR without additional navigation training data or a geometry encoder at inference.
Chinese Translation
视觉与语言导航（VLN）策略越来越依赖于大型视觉-语言模型（VLMs）提供的强语义先验。然而，标准的动作监督并未明确鼓励中间表示保留场景几何、相对方向或全局事件进展。引入深度估计器、显式地图、点云或几何基础模型在推理时可以提供这样的结构，但在部署过程中会增加额外的计算、内存开销和架构依赖性。我们提出了StageVLN，这是一种训练框架，通过特权的空间和轨迹指导来塑造导航表示，同时保留原始的推理路径。一个冻结的几何基础模型为分层导航状态提供多层次的空间指导，而相对航向和专家路径进展目标则提供互补的轨迹状态监督。所有辅助组件仅在训练期间使用，并在部署时移除。在R2R-CE验证集上，StageVLN在没有额外几何编码器的情况下，使用4B参数主干实现了56.3%的成功率（SR）和51.4%的成功路径长度（SPL）。在RxR-CE上，它在没有额外导航训练数据或几何编码器的情况下实现了54.3%的成功率（SR）。
cs.CV / 141 / 2610.05707
Difference Feature Map Distillation: Transferring Inter-Sample Relational Knowledge Towards Efficient Transformer-Based Tracking
差异特征图蒸馏：将样本间关系知识转移至高效的基于变换器的跟踪
Abstract
In autonomous driving perception, visual object tracking systems must satisfy stringent latency and power constraints while remaining robust in complex and dynamic environments. Although transformer-based trackers achieve state-of-the-art accuracy, their substantial computational and memory overheads hinder deployment on real-time, resource-constrained platforms. To move toward this goal, we propose Difference Feature Map Knowledge Distillation (DFM-KD), a novel relational distillation framework tailored for transformer-based visual object tracking. Unlike conventional feature distillation methods that minimize point-wise discrepancies (e.g., mean squared error) between teacher and student feature representations, DFM-KD transfers knowledge through inter-sample feature differences, explicitly aligning the relational structure of the feature space. By distilling how the teacher models appearance variation and consistency across samples, rather than enforcing similarity in absolute activations, DFM-KD enables the student to better capture the structural dynamics of visual changes within a batch. As a result, the distilled model exhibits enhanced feature robustness and improved tracking performance. Extensive experiments demonstrate that DFM-KD consistently outperforms conventional feature-level distillation methods in both tracking precision and success rates.
Chinese Translation
在自动驾驶感知中，视觉目标跟踪系统必须在复杂和动态环境中满足严格的延迟和功耗限制，同时保持鲁棒性。尽管基于变换器的跟踪器实现了最先进的准确性，但其巨大的计算和内存开销阻碍了在实时、资源受限平台上的部署。为此，我们提出了差异特征图知识蒸馏（Difference Feature Map Knowledge Distillation, DFM-KD），这是一种针对基于变换器的视觉目标跟踪的全新关系蒸馏框架。与传统的特征蒸馏方法通过最小化教师和学生特征表示之间的逐点差异（例如，均方误差）不同，DFM-KD通过样本间特征差异转移知识，明确对齐特征空间的关系结构。通过蒸馏教师如何建模样本间的外观变化和一致性，而不是强制绝对激活值的相似性，DFM-KD使学生能够更好地捕捉批次内视觉变化的结构动态。因此，蒸馏后的模型展现出增强的特征鲁棒性和改进的跟踪性能。大量实验表明，DFM-KD在跟踪精度和成功率方面始终优于传统的特征级蒸馏方法。
cs.CV / 142 / 2610.05715
Rotated, but How Far? Diagnosing and Improving Object-Rotation Reasoning in VLMs
旋转，但旋转了多少？诊断和改善视觉语言模型中的物体旋转推理
Abstract
Vision-language models (VLMs) can detect that an object has rotated across views, but cannot reliably tell by how much. We introduce OR-Bench, a fine-grained benchmark for object-rotation reasoning with eight tasks covering rotation detection, rotation magnitude estimation, and multi-view rotation reasoning. Across 12 VLMs, the gap is stark: the strongest models approach 100% accuracy on detection, yet even coarse magnitude estimation is near chance. When asked for exact angles, models place 91.8--100% of their predictions on just $0^\circ$, $90^\circ$, and $180^\circ$, a failure we term canonical-angle collapse. This collapse persists even without visual input. Representation probing shows that missing information is only part of the explanation. Although rotation information becomes less recoverable at finer granularity, substantial coarse-grained information remains, and a simple linear probe outperforms the models' generated answers. This suggests that VLMs underuse rotation information they already encode. We therefore propose RotationCue, a lightweight decoder that recovers coarse rotation information from the VLM's own frozen representations and feeds it back to the model as intermediate textual context. Across three VLMs, RotationCue improves every model--task combination on OR-Bench, raising macro-average accuracy by 7.9--12.6 points while preserving general capabilities.
Chinese Translation
视觉语言模型（VLMs）能够检测物体在视图间的旋转，但无法可靠地判断旋转的幅度。我们引入了 OR-Bench，这是一个针对物体旋转推理的细粒度基准，涵盖了旋转检测、旋转幅度估计和多视图旋转推理等八个任务。在 12 个 VLMs 中，结果差异显著：最强的模型在检测任务上接近 100% 的准确率，但即使是粗略的幅度估计也接近随机水平。当被要求提供精确角度时，模型的 91.8% 至 100% 的预测仅集中在 $0^ ext{°}$、$90^ ext{°}$ 和 $180^ ext{°}$，我们称这种现象为典型角度崩溃（canonical-angle collapse）。即使在没有视觉输入的情况下，这种崩溃现象依然存在。表示探测显示，缺失的信息只是部分原因。尽管在更细粒度的情况下，旋转信息变得更难恢复，但仍然保留了大量的粗粒度信息，而一个简单的线性探测器的表现优于模型生成的答案。这表明 VLMs 对它们已经编码的旋转信息的使用不足。因此，我们提出了 RotationCue，这是一种轻量级解码器，能够从 VLM 自身的冻结表示中恢复粗略的旋转信息，并将其反馈给模型作为中间文本上下文。在三个 VLMs 上，RotationCue 改善了每个模型-任务组合在 OR-Bench 上的表现，使宏平均准确率提高了 7.9 至 12.6 个百分点，同时保持了模型的整体能力。
cs.CV / 143 / 2610.05731
T-JEPA: A Temporal Joint-Embedding Predictive Architecture for Learning Better Remote Sensing Representations
T-JEPA：一种用于学习更好遥感表征的时间联合嵌入预测架构
Abstract
Earth observation (EO) data provide rich temporal supervision, yet existing remote sensing foundation models mainly exploit sequential observations through imposing predefined pairwise relations or aggregating holistic reconstruction context. We seek to further exploit the sparse and nonuniform temporal sampling inherent in EO sequences as supervisory signals. To this end, we propose T-JEPA, a temporal joint-embedding predictive architecture that learns time-gap-conditioned latent transitions. A shared single-frame encoder processes each observation, while a temporal predictor estimates the complete target latent field from a masked source latent representation and the actual elapsed time. Across multiple temporal intervals, these predictive constraints organize observed states into structured latent trajectories. Asymmetric metadata injection mitigates shortcut learning, and direct supervision across multiple temporal scales proves more effective than recursively rolling out intermediate states. In parallel, masked pixel reconstruction provides complementary supervision for preserving spatial details. Under matched pre-training data and throughput, T-JEPA achieves leading transfer performance on both static and temporal tasks. Analyses further reveal that T-JEPA learns representations with time-gap-dependent transition predictability and coherent latent dynamics, while maintaining strong cross-period consistency, representation diversity, and semantic discriminability.
Chinese Translation
地球观测（EO）数据提供了丰富的时间监督，但现有的遥感基础模型主要通过施加预定义的成对关系或聚合整体重构上下文来利用顺序观测。我们寻求进一步利用EO序列中固有的稀疏和非均匀时间采样作为监督信号。为此，我们提出了T-JEPA，一种学习时间间隔条件潜在转变的时间联合嵌入预测架构。一个共享的单帧编码器处理每个观测，而一个时间预测器从掩蔽的源潜在表示和实际经过的时间中估计完整的目标潜在场。在多个时间间隔中，这些预测约束将观测状态组织成结构化的潜在轨迹。非对称元数据注入减轻了捷径学习，而跨多个时间尺度的直接监督被证明比递归展开中间状态更有效。同时，掩蔽像素重建为保留空间细节提供了补充监督。在匹配的预训练数据和吞吐量下，T-JEPA在静态和时间任务上都实现了领先的迁移性能。分析进一步揭示，T-JEPA学习了具有时间间隔依赖的转变可预测性和一致的潜在动态的表征，同时保持强大的跨周期一致性、表征多样性和语义可区分性。
cs.CV / 144 / 2610.05739
HLA-WM: Hybrid Linear Attention for Long-Horizon Video World Models
HLA-WM：用于长时间视频世界模型的混合线性注意力
Abstract
Long-horizon video world models require persistent memory to preserve scene consistency over extended rollouts. Softmax attention retains the full generation history through a growing KV cache, whereas recurrent linear attention compresses history into fixed-size states with substantially lower memory cost. However, we identify severe long-range forgetting in Gated DeltaNet (GDN), where information from distant but relevant scenes is progressively attenuated by subsequent state updates. To address this limitation, we propose HLA-WM, a training-free hybrid linear-attention framework that combines coarse-grained geometry-guided retrieval with fine-grained recurrent linear-state computation. HLA-WM exploits the affine structure of GDN to cache compact chunk-wise transition summaries, retrieve scene-relevant historical chunks using camera geometry, and recompose them into query-specific recurrent states. On the $60$-second SANA-WM-Bench, HLA-WM improves all six aggregate revisit-consistency and camera-control metrics of the base autoregressive generator without additional training, including a $0.74$ dB PSNR gain and a $28.5\%$ reduction in rotation error. The improvements persist after downstream refinement and generalize to MBench-A, where HLA-WM consistently improves all three revisit-consistency metrics across all four subsets and all evaluated inference modes over $547$ samples. At a $60$-second context, HLA-WM reduces historical-state memory by $12\times$ relative to full KV caching while incurring at most a $1.6\%$ reduction in inference throughput. These results demonstrate that selectively addressable recurrent memory can improve long-range scene recall while preserving the efficiency advantages of GDN. Project page: https://caesarhhh.github.io/hla-wm/
Chinese Translation
长时间视频世界模型需要持久的记忆以保持场景一致性，特别是在延长的展开过程中。Softmax 注意力通过不断增长的 KV 缓存保留完整的生成历史，而递归线性注意力则将历史压缩为固定大小的状态，显著降低了内存成本。然而，我们发现 Gated DeltaNet (GDN) 存在严重的长距离遗忘问题，其中来自遥远但相关场景的信息在后续状态更新中逐渐减弱。为了解决这一局限性，我们提出了 HLA-WM，一种无训练的混合线性注意力框架，结合了粗粒度的几何引导检索与细粒度的递归线性状态计算。HLA-WM 利用 GDN 的仿射结构缓存紧凑的块状过渡摘要，通过相机几何信息检索与场景相关的历史块，并将其重新组合成特定查询的递归状态。在 $60$ 秒的 SANA-WM-Bench 上，HLA-WM 在不进行额外训练的情况下改善了基础自回归生成器的六个聚合重访一致性和相机控制指标，包括 $0.74$ dB 的 PSNR 增益和 $28.5\%$ 的旋转误差减少。这些改进在后续精炼后仍然存在，并且在 MBench-A 上具有普遍性，HLA-WM 在所有四个子集和 $547$ 个样本的所有评估推理模式中始终改善了三个重访一致性指标。在 $60$ 秒的上下文中，HLA-WM 相比于完整的 KV 缓存将历史状态内存减少了 $12 imes$，同时推理吞吐量最多减少 $1.6\\%$。这些结果表明，选择性可寻址的递归记忆能够改善长距离场景回忆，同时保持 GDN 的效率优势。项目页面：https://caesarhhh.github.io/hla-wm/
cs.CV / 145 / 2610.05743
Robust Local Optimization Done Right
稳健局部优化的正确实现
Abstract
RANSAC scoring and local optimization (LO) impose different robustness requirements, motivating the separation of hypothesis selection from refinement. We systematically isolate the effects of robust-loss shape, incorrectly specified inlier scales, and optimization strategy on essential matrix, fundamental matrix, and homography estimation. A profile-marginal score marginalizes the nuisance inlier scale and selects an inlier partition, from which we estimate the scale that sets the LO loss width; this makes LO robust to an inlier scale specified too large, whereas one specified too small degrades selection itself. Refinement needs gradient from correspondences the seed currently rejects: optimizers that reweight from current residuals stay pinned to their seed, whereas methods with broad basins recover strongly perturbed seeds yet degrade accurate score-selected hypotheses, so basin size alone is insufficient to assess RANSAC LO. Joint half-quadratic optimization balances the two and is the most consistent strategy across model classes. An optimizer matched to the profile-marginal score, which never decreases it, does not reach the best accuracy, challenging the prescription that scoring and refinement objectives should match. Composed from these findings, our RANSAC reduces the median essential-matrix pose error of a state-of-the-art RANSAC on PhotoTourism from 2.23 degrees to 1.58 degrees with a correctly specified inlier scale and from 38 degrees to 6.2 degrees when it is grossly misspecified (128x too large).
Chinese Translation
RANSAC评分和局部优化（LO）对稳健性提出了不同的要求，这促使我们将假设选择与精细化过程分开。我们系统地隔离了稳健损失形状、错误指定的内点尺度和优化策略对本质矩阵、基础矩阵和单应性估计的影响。轮廓边际评分通过边际化干扰内点尺度并选择内点分区，从中我们估计设置LO损失宽度的尺度；这使得LO对指定过大的内点尺度具有稳健性，而指定过小的尺度则会降低选择本身的质量。精细化需要来自当前被种子拒绝的对应点的梯度：从当前残差重新加权的优化器会固定在其种子上，而具有广泛盆地的方法能够恢复强烈扰动的种子，但会降低准确评分选择的假设，因此仅靠盆地大小不足以评估RANSAC LO。联合半二次优化平衡了两者，并且在各模型类别中是一种最一致的策略。与轮廓边际评分匹配的优化器，永远不会降低其评分，未必能达到最佳准确性，这对评分和精细化目标应当匹配的建议提出了挑战。基于这些发现，我们的RANSAC在PhotoTourism上的最先进RANSAC的中位本质矩阵姿态误差从2.23度降低到1.58度，当内点尺度正确指定时，从38度降低到6.2度，当其严重错误指定（大128倍）时。
cs.CV / 146 / 2610.05749
A new design of a fall detection system integrating landmark identification and deep learning techniques
一种新型的跌倒检测系统设计，集成了地标识别和深度学习技术
Abstract
This article introduces an innovative system that integrates landmark identification with deep learning to enhance fall detection accuracy and reliability. By utilizing advanced computer vision techniques, such as Media Pipe for spatial recognition, the system effectively differentiates between routine movements and actual falls. The integration of landmarks with a deep learning prediction algorithm minimizes false alarms, ensuring timely responses to genuine falls. Comprehensive experimentation underscores the system's versatility across various scenarios, emphasizing its potential to improve safety and independence for older adults. The training process demonstrates a steady increase in accuracy, stabilizing by the 40th cycle, while error rates decline significantly during the initial cycles. Real-time experiments, involving both male and female participants aged 8 to 50, recorded a remarkable 95% detection rate of falls, demonstrating the system's effectiveness and promising future applications in elder care and smart health monitoring environments.
Chinese Translation
本文介绍了一种创新系统，该系统将地标识别与深度学习相结合，以提高跌倒检测的准确性和可靠性。通过利用先进的计算机视觉技术，如 Media Pipe 进行空间识别，该系统有效区分日常动作与实际跌倒。地标与深度学习预测算法的结合最小化了误报，确保对真实跌倒的及时响应。全面的实验强调了该系统在各种场景中的多样性，突显其提高老年人安全性和独立性的潜力。训练过程显示准确性稳步提升，在第40个周期时达到稳定，而在初始周期内错误率显著下降。涉及8至50岁男女参与者的实时实验记录了高达95%的跌倒检测率，证明了该系统的有效性，并为老年护理和智能健康监测环境的未来应用提供了良好的前景。
cs.CV / 147 / 2610.05756
Vision-enabled detection of safety helmet compliance in construction zones
基于视觉的施工区域安全头盔合规性检测
Abstract
In the rapidly evolving field of construction management, worker safety remains a top priority. This paper introduces an innovative vision-based system for real-time detection of helmet compliance, specifically designed for construction sites, utilizing advanced computer vision techniques and machine learning algorithms within the YOLO (you only look once) framework. Our system leverages high-resolution video feeds from strategically positioned cameras to monitor adherence to safety regulations regarding helmet usage. By employing deep learning methodologies, the system effectively identifies individuals not wearing helmets, thereby significantly mitigating the risk of head injuries among workers. Our training and validation results revealed an impressive precision exceeding 97% at
[email protected] for both helmeted and non-helmeted individuals. Furthermore, our experiments demonstrate exceptional detection accuracy, demonstrating the system's resilience under varying lighting conditions and diverse worker movements. The consistent decrease in loss and improvement in metrics throughout training validates the effectiveness of the YOLOv8 model in enhancing recognition performance. The implications of this research extend beyond mere regulatory compliance, opening avenues for innovative applications in occupational safety management. This study highlights the critical role of technology in protecting lives and lays the groundwork for future advancements in smart construction environments.
Chinese Translation
在快速发展的施工管理领域，工人安全始终是首要任务。本文介绍了一种创新的基于视觉的系统，用于实时检测头盔合规性，专为施工现场设计，利用先进的计算机视觉技术和机器学习算法，在YOLO（you only look once）框架内实现。我们的系统利用来自战略性布置摄像头的高分辨率视频流，监测头盔使用的安全法规遵循情况。通过采用深度学习方法，该系统有效识别未佩戴头盔的个体，从而显著降低工人头部受伤的风险。我们的训练和验证结果显示，在
[email protected]的条件下，无论是佩戴头盔还是未佩戴头盔的个体，系统的精确度均超过97%。此外，我们的实验展示了卓越的检测准确性，表明该系统在不同光照条件和多样化工人动作下的鲁棒性。训练过程中损失的持续下降和指标的改善验证了YOLOv8模型在提升识别性能方面的有效性。本研究的影响不仅限于合规性，还为职业安全管理中的创新应用开辟了新途径。该研究突显了技术在保护生命中的关键作用，并为智能施工环境的未来发展奠定了基础。
cs.CV / 148 / 2610.05758
A Three-Dimensional Reverse-Projection Method for Sparse Point Cloud Completion and Its Application to High-Speed Train Nose Reconstruction
一种用于稀疏点云补全的三维反投影方法及其在高速列车车头重建中的应用
Abstract
Holes in LiDAR scans of environments with glass windows remain an unresolved problem in three-dimensional reconstruction. This study presents a pipeline for point cloud acquisition, filtering, completion, and surface reconstruction to address sparse sampling and missing window regions in scans of a high-speed train nose. FAST-LIVO2 provides the initial point cloud through multisensor odometry and mapping, and moving least squares (MLS) smooths the observations. We then introduce three-axis projection-based subdivision and interpolation with reverse hole boundary identification, referred to as three-axis reverse completion. The method interpolates missing regions from observations around each hole. Greedy projection triangulation, Poisson surface reconstruction, and a Marching Cubes-based pipeline generate meshes from the completed point cloud. Experiments on a proportionally scaled display model of a high-speed train nose show that the proposed method fills missing point cloud regions around the glass windows. Under the evaluation setting used in this study, greedy projection triangulation yields lower geometric distance errors than the other two reconstruction pipelines. The pipeline supports non-contact digital modeling of train nose geometry and provides a practical approach to reconstructing objects with glass windows.
Chinese Translation
在具有玻璃窗的环境中，LiDAR扫描中出现的孔洞仍然是三维重建中的一个未解决问题。本研究提出了一种点云获取、过滤、补全和表面重建的流程，以解决高速列车车头扫描中的稀疏采样和缺失窗区域问题。FAST-LIVO2通过多传感器里程计和映射提供初始点云，移动最小二乘法（MLS）对观测结果进行平滑处理。然后，我们引入基于三轴投影的细分和插值方法，并进行反向孔边界识别，称为三轴反向补全。该方法从每个孔周围的观测结果中插值缺失区域。贪婪投影三角化、泊松表面重建和基于Marching Cubes的流程从补全的点云生成网格。在一个比例缩放的高速列车车头展示模型上的实验表明，所提出的方法能够填补玻璃窗周围缺失的点云区域。在本研究使用的评估设置下，贪婪投影三角化的几何距离误差低于其他两种重建流程。该流程支持高速列车车头几何形状的非接触数字建模，并为重建具有玻璃窗的物体提供了一种实用的方法。
cs.CV / 149 / 2610.05771
Controllable Road Marking Generation
可控道路标线生成
Abstract
Lane and road markings provide critical guidance for vehicle navigation and multi-agent coordination, yet authoring them at scale remains a manual workflow that limits quantitative analysis and scenario testing. We introduce Controllable Road Marking Generation, which synthesizes a missing center-region marking layout from a drivable-area mask, optional outer-ring markings, and a textual description. Our benchmark uses deterministic, metadata-derived prompts and three output channels: lane dividers, road dividers, and pedestrian crossings. We develop a conditional bird's-eye-view (BEV) pipeline that combines (i) a text-conditioned latent rectified-flow DiT trained with a topology-aware auxiliary loss, (ii) Gaussian-blurred training targets that stabilize learning of thin, sparse markings, and (iii) Structured Gaussian Render (SGR), a training-free post-process that recovers crisp divider geometry by extracting polylines, fitting cubic B\'ezier curves, and re-rendering them as anisotropic super-Gaussian primitives. On 4,597 Argoverse~2 test tiles, our system achieves Buffered F1 of 80.8 and clDice of 50.2, compared with 38.8 and 24.6 for an adapted state-of-the-art mask-refinement baseline. On Waymo dataset, it yields 88.0 Buffered F1 and 66.2 clDice. Component ablations show complementary connectivity gains from topology-aware supervision and SGR. Text-editing experiments reveal that stronger guidance improves edit success but also increases changes to non-target structures. We see this framework as a step toward simulation-ready road-marking variation, automated map completion, and early-stage infrastructure design exploration.
Chinese Translation
车道和道路标线为车辆导航和多智能体协调提供了关键指导，然而大规模创作这些标线仍然是一个手动工作流程，这限制了定量分析和场景测试。我们提出了可控道路标线生成（Controllable Road Marking Generation），该方法从可行驶区域掩膜、可选的外环标线和文本描述中合成缺失的中心区域标线布局。我们的基准使用确定性的、基于元数据的提示和三个输出通道：车道分隔线、道路分隔线和人行横道。我们开发了一个条件鸟瞰视图（BEV）管道，该管道结合了（i）一个经过拓扑感知辅助损失训练的文本条件潜在修正流（latent rectified-flow）DiT，（ii）高斯模糊训练目标以稳定稀疏细标线的学习，以及（iii）结构化高斯渲染（Structured Gaussian Render，SGR），这是一个无训练的后处理过程，通过提取多边形线、拟合三次贝塞尔曲线并将其重新渲染为各向异性超高斯原语来恢复清晰的分隔几何形状。在4,597个Argoverse~2测试瓦片上，我们的系统实现了80.8的缓冲F1和50.2的clDice，而经过改编的最先进的掩膜细化基线分别为38.8和24.6。在Waymo数据集上，它产生了88.0的缓冲F1和66.2的clDice。组件消融实验表明，拓扑感知监督和SGR带来了互补的连接增益。文本编辑实验表明，较强的指导提高了编辑成功率，但也增加了对非目标结构的变化。我们将这一框架视为朝着模拟就绪的道路标线变体、自动地图补全和早期基础设施设计探索迈出的重要一步。
cs.CV / 150 / 2610.05775
InteractionBench: A Real-Time Interaction Benchmark for Streaming Video Systems
InteractionBench：一个用于流媒体视频系统的实时交互基准
Abstract
A video assistant must speak when its instruction warrants a response and stay silent otherwise. We introduce a benchmark that evaluates this decision for the complete system of model, memory, and response controller. InteractionBench covers query responses, event triggers, and ongoing updates in 1,060 interactions over 812 videos, with 69 negative streams and 53 suites that pair counted events with look-alike near misses. It scores content accuracy, timing accuracy, and silence compliance on the video clock. Timely speech costs silence across systems. Polled Qwen3-VL-8B reaches 77.8 timing accuracy but 10.9 silence compliance. A native real-time interaction system reaches 29.2 silence compliance at 66.8 timing accuracy, yet emits on 89.9% of negative streams. No open-weight system clears a third of the near-miss suites. Fewer replies help only when chosen, as random deletion merely trades timing for silence. Offline scores miss these failures and mispredict online behavior. Adding restraint is costly, as the native system's controller adds little by itself and agentic systems add it only at about 30 s per poll.Project page: https://www.enxinsong.com/projects/interactionbench/ Code: https://github.com/Espere-1119-Song/InteractionBench Data: https://huggingface.co/datasets/InteractionBench/InteractionBench
Chinese Translation
视频助手必须在其指令需要回应时发言，而在其他情况下保持沉默。我们提出了一个基准，用于评估模型、内存和响应控制器的完整系统的这一决策。InteractionBench 涉及 1,060 次交互，涵盖 812 个视频中的查询响应、事件触发和持续更新，其中包括 69 个负流和 53 个将计数事件与近乎失误配对的套件。它在视频时钟上对内容准确性、时机准确性和沉默遵从性进行评分。及时发言会在各系统中牺牲沉默。被调查的 Qwen3-VL-8B 达到 77.8 的时机准确性，但沉默遵从性仅为 10.9。一个本地实时交互系统在 66.8 的时机准确性下达到 29.2 的沉默遵从性，但在 89.9% 的负流中发声。没有任何开放权重系统能清除三分之一的近乎失误套件。减少回复仅在选择时有效，因为随机删除只是将时机与沉默进行交换。离线评分未能捕捉这些失败，并错误预测在线行为。增加约束是昂贵的，因为本地系统的控制器自身贡献有限，而自主系统仅在每次轮询约 30 秒时增加约束。项目页面：https://www.enxinsong.com/projects/interactionbench/ 代码：https://github.com/Espere-1119-Song/InteractionBench 数据：https://huggingface.co/datasets/InteractionBench/InteractionBench
cs.CV / 151 / 2610.05779
A Spatiotemporal Semantic Importance-Guided Unified Compression and Editing Framework for AI-Generated Videos
一种时空语义重要性引导的统一压缩与编辑框架用于AI生成视频
Abstract
AI-generated videos are rapidly increasing in volume, duration, and resolution, creating growing demands for efficient storage and transmission. Unlike natural videos captured from the physical world, AI-generated videos are samples from a learned generative distribution, where semantic structures are critical to content consistency, while many local textures and stochastic details can be plausibly regenerated. This distinction suggests that compression should preserve semantically important spatiotemporal information rather than reconstruct every pixel of a particular generative sample. Beyond reconstruction, AI-generated videos also create a practical need for prompt-based editing, where users expect to modify generated content while preserving its original spatiotemporal semantics. Motivated by these observations, we propose a unified compression and editing framework for AI-generated videos that incorporates a frozen video generator as a reusable generative prior. Within this framework, we design three spatiotemporal semantic importance-guided techniques that respectively address what to transmit, how much to transmit, and how to use the transmitted side information. First, an innovation selection method projects the latent discrepancy using spatiotemporal semantic importance, so that the selected innovations prioritize semantic invariants over replaceable generative variations. Second, a frame-adaptive bit allocation method estimates the nonuniform semantic demands of latent frames and allocates more innovations to frames requiring stronger semantic preservation. Third, a unified reconstruction and editing method continuously adjusts the influence of the transmitted side information, enabling the same compressed representation to provide strong guidance for faithful reconstruction or serve as a flexible semantic anchor for structure-preserving prompt-driven editing.
Chinese Translation
AI生成的视频在数量、时长和分辨率上迅速增加，导致对高效存储和传输的需求不断增长。与从物理世界捕获的自然视频不同，AI生成的视频是从学习到的生成分布中抽样而来，其中语义结构对内容一致性至关重要，而许多局部纹理和随机细节可以被合理地再生。这一区别表明，压缩应当保留语义重要的时空信息，而不是重建特定生成样本的每一个像素。除了重建，AI生成的视频还产生了基于提示的编辑的实际需求，用户希望在保留原始时空语义的同时修改生成的内容。基于这些观察，我们提出了一种用于AI生成视频的统一压缩与编辑框架，该框架将一个冻结的视频生成器作为可重用的生成先验纳入其中。在此框架内，我们设计了三种时空语义重要性引导的技术，分别解决了传输什么、传输多少以及如何使用传输的侧信息。首先，一种创新选择方法通过时空语义重要性投影潜在差异，使得所选择的创新优先考虑语义不变性而非可替换的生成变体。其次，一种帧自适应比特分配方法估计潜在帧的非均匀语义需求，并为需要更强语义保留的帧分配更多创新。第三，一种统一的重建与编辑方法持续调整传输侧信息的影响，使得相同的压缩表示能够为忠实重建提供强有力的指导，或作为结构保留的基于提示的编辑的灵活语义锚。
cs.CV / 152 / 2610.05790
FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Models
FairRSFM：一种关注生物群落的遥感基础模型基准与去偏框架
Abstract
Remote sensing foundation models (RSFMs) are commonly evaluated using aggregate metrics, which can hide systematic performance disparities across ecological regions. We introduce FairRSFM, a biome-aware benchmark for evaluating ecological group robustness in RSFMs. FairRSFM maps georeferenced samples from 14 terrestrial biome classes into six ecologically meaningful macro-groups and evaluates models under a unified frozen-backbone evaluation protocol. The benchmark covers four downstream datasets: m-EuroSAT, m-BigEarthNet, m-SA-Crop-Type, and MMEarth20K with Dynamic World label maps. Using Prithvi-EO-2.0, SatMAE, and DOFA across three random seeds, we show that aggregate performance consistently masks biome-dependent disparities across architectures and tasks. For example, Prithvi-EO-2.0 reaches 90.98% overall macro-F1 on m-EuroSAT but a mean worst-group score of only 83.72%, while m-SA-Crop-Type drops from 27.30% overall mIoU to 18.47% in the Xeric and Mineralogical group. We further evaluate Biome-Orthogonal Linear Probing (BOLP), Dynamic Biome Reweighting (DBR), and GroupDRO as complementary mitigation baselines. Their effectiveness is model- and task-dependent; for example, BOLP improves Prithvi-EO-2.0 worst-group F1@opt on m-BigEarthNet from 46.12% to 50.27% without updating the RSFM backbone. FairRSFM provides a reusable protocol for diagnosing and mitigating ecological robustness gaps in remote sensing foundation models. Code and datasets are available at: https://github.com/aminurhossain/FairRSFM.
Chinese Translation
遥感基础模型（RSFMs）通常使用聚合指标进行评估，这可能掩盖生态区域之间系统性的性能差异。我们提出了FairRSFM，这是一种关注生物群落的基准，用于评估RSFMs中的生态组鲁棒性。FairRSFM将来自14个陆地生物群落类别的地理参考样本映射到六个生态上有意义的宏观组，并在统一的冻结骨干评估协议下评估模型。该基准涵盖四个下游数据集：m-EuroSAT、m-BigEarthNet、m-SA-Crop-Type和带有动态世界标签地图的MMEarth20K。通过在三个随机种子下使用Prithvi-EO-2.0、SatMAE和DOFA，我们展示了聚合性能始终掩盖了不同架构和任务之间的生物群落依赖性差异。例如，Prithvi-EO-2.0在m-EuroSAT上整体宏观F1达到90.98%，但最差组的平均得分仅为83.72%，而在m-SA-Crop-Type中，从整体mIoU的27.30%下降到干旱和矿物组的18.47%。我们进一步评估了生物群落正交线性探测（Biome-Orthogonal Linear Probing, BOLP）、动态生物群落重加权（Dynamic Biome Reweighting, DBR）和组分去偏（GroupDRO）作为补充的缓解基线。它们的有效性依赖于模型和任务；例如，BOLP在不更新RSFM骨干的情况下，将Prithvi-EO-2.0在m-BigEarthNet上的最差组F1@opt从46.12%提高到50.27%。FairRSFM提供了一种可重用的协议，用于诊断和缓解遥感基础模型中的生态鲁棒性差距。代码和数据集可在以下网址获取：https://github.com/aminurhossain/FairRSFM。
cs.CV / 153 / 2610.05801
Gauss-Map Variation for Image Denoising: Geometric Analysis and an Anderson--Accelerated Majorization--Minimization Method
高斯映射变分在图像去噪中的应用：几何分析与安德森加速的极大化-极小化方法
Abstract
We propose a Gauss-map variation (GMV) model for image denoising that measures the spatial variation of the tangent-plane projectors of the scaled image graph. We establish an equivalent representation of the regularizer in terms of the corresponding Gauss map and, using differential geometric tools including tubular coordinates and the Frenet frame, analyze its behavior across general $C^2$ and piecewise $C^2$ boundaries. The resulting estimates provide edge- and corner-contrast preservation properties. To solve the proposed model, we introduce a bilinear decomposition involving a unit normal field and a scalar magnitude field and develop an Anderson-accelerated majorization--minimization algorithm. The normal field subproblem admits an explicit pointwise majorization--minimization update, which is combined with an Anderson acceleration. For both $L^1$ and $L^2$ data fidelity terms, we establish sufficient decrease and boundedness of the iterates and prove that the generated sequence converges to a critical point of the penalized model. Numerical experiments on synthetic and natural images demonstrate the boundary preserving capability of the proposed model and its competitive performance in removing Gaussian and impulsive noise.
Chinese Translation
我们提出了一种高斯映射变分（Gauss-map variation, GMV）模型用于图像去噪，该模型测量缩放图像图形的切平面投影器的空间变化。我们建立了正则化项的等效表示，涉及相应的高斯映射，并利用包括管状坐标和弗雷内框架在内的微分几何工具，分析其在一般 $C^2$ 和分段 $C^2$ 边界上的行为。所得估计提供了边缘和角点对比度的保持特性。为了解决所提出的模型，我们引入了一个涉及单位法向场和标量幅度场的双线性分解，并开发了一种安德森加速的极大化-极小化算法。法向场子问题允许显式的逐点极大化-极小化更新，并与安德森加速相结合。对于 $L^1$ 和 $L^2$ 数据保真项，我们建立了迭代的充分减少性和有界性，并证明生成的序列收敛到惩罚模型的一个临界点。对合成图像和自然图像的数值实验展示了所提出模型的边界保持能力及其在去除高斯噪声和脉冲噪声方面的竞争性能。
cs.CV / 154 / 2610.05816
Level-of-Token Diffusion
令牌级别扩散
Abstract
Image and video diffusion models allocate equal computation to every region, even when the intended scene calls for varying levels of detail. The spatial distribution of detail can often be anticipated before generation, indicating where computation can be reduced. We introduce Level-of-Token (LoT) Diffusion, a framework that turns this knowledge into an explicit multiresolution token layout (Level-of-Token layout) for adaptive and efficient generation. Tokens represent rectangular patches of varying sizes and shapes, allocating finer tokens where detail is needed and coarser tokens elsewhere. We adapt pretrained diffusion transformers to LoT layouts through a patch-wise asymmetric flow parametrization and embeddings for multiresolution tokens, preserving full-resolution flow prediction at every denoising step while processing only a reduced token sequence. LoT Diffusion enables layout-adaptive generation while preserving pretrained generative priors. We demonstrate LoT with layouts derived from semantic masks, bounding boxes, texture variance, and depth-of-field cues, as well as agentic plans. Across image and video generation, LoT offers favorable quality-efficiency tradeoffs, with significant speedups determined by the layout's token budget. Our project website is at https://georgenakayama.github.io/lotdiffusion/.
Chinese Translation
图像和视频扩散模型对每个区域分配相同的计算量，即使在预期场景需要不同细节水平的情况下。细节的空间分布在生成之前通常是可以预见的，这表明可以减少计算的地方。我们提出了令牌级别（Level-of-Token, LoT）扩散框架，将这一知识转化为显式的多分辨率令牌布局（Level-of-Token 布局），以实现自适应和高效的生成。令牌代表不同大小和形状的矩形补丁，在需要细节的地方分配更精细的令牌，而在其他地方则使用更粗糙的令牌。我们通过补丁级不对称流参数化和多分辨率令牌的嵌入，将预训练的扩散变换器适配到 LoT 布局，保持在每个去噪步骤中全分辨率流预测，同时仅处理减少的令牌序列。LoT 扩散使得布局自适应生成成为可能，同时保留预训练的生成先验。我们展示了基于语义掩膜、边界框、纹理方差和景深线索以及代理计划的布局的 LoT。通过图像和视频生成，LoT 提供了良好的质量效率权衡，布局的令牌预算决定了显著的加速效果。我们的项目网站地址为 https://georgenakayama.github.io/lotdiffusion/。
cs.CV / 155 / 2610.05861
Imagine to Act: High-Fidelity Data Synthesis via Image Editing World Model for Scalable GUI Agent Training
想象以行动：通过图像编辑世界模型实现高保真数据合成以支持可扩展的GUI代理训练
Abstract
Graphical User Interface (GUI) agents have emerged as a promising paradigm for automating complex digital workflows across diverse applications. However, training highly capable and generalizable agents fundamentally relies on massive, high-fidelity visual-action trajectories, which are notoriously difficult to acquire. While human demonstrations are unscalable, existing GUI world models rely on text descriptions or HTML rendering, discarding crucial pixel-level visual details like icons and layout styles. To address this issue, we introduce Infinite-Dreamer, a simulation-free data synthesis method powered by a pixel-level Image Editing World Model. By conceptualizing GUI transitions as image editing tasks, we leverage Vision-Language Models (VLMs) to describe action-induced UI changes as structured delta-text. We then fine-tune an image editing backbone to controllably synthesize realistic screenshot transitions. We utilize this model to generate both single-frame visual robustness data and multi-step imaginary trajectories. To validate the effectiveness of our approach, we fine-tune the Qwen3-VL baseline solely on the synthesized data to obtain Infinite-Actor, and evaluate it on AndroidWorld, MobileWorld, and AndroidControl-Curated benchmarks. Infinite-Actor consistently outperforms the Qwen3-VL baselines across scales: Infinite-Actor-8B improves AndroidWorld Pass@1 by +4.45 and nearly doubles the MobileWorld Pass@3 success rate, while Infinite-Actor-2B improves Pass@1 by +9.05. Code is available at https://github.com/swaydy-n/Infinite-Dreamer.
Chinese Translation
图形用户界面（GUI）代理已成为自动化复杂数字工作流程的有前景的范式，广泛应用于多种场景。然而，训练高能力和具有良好泛化能力的代理根本上依赖于大量高保真的视觉-动作轨迹，而这些轨迹 notoriously 难以获取。虽然人类示范无法扩展，但现有的GUI世界模型依赖于文本描述或HTML渲染，忽略了像图标和布局样式等重要的像素级视觉细节。为了解决这一问题，我们提出了Infinite-Dreamer，一种基于像素级图像编辑世界模型的无模拟数据合成方法。通过将GUI过渡概念化为图像编辑任务，我们利用视觉-语言模型（VLMs）将动作引起的用户界面变化描述为结构化的增量文本。然后，我们微调图像编辑主干，以可控地合成真实的屏幕截图过渡。我们利用该模型生成单帧视觉鲁棒性数据和多步虚拟轨迹。为了验证我们方法的有效性，我们仅在合成数据上微调了Qwen3-VL基线以获得Infinite-Actor，并在AndroidWorld、MobileWorld和AndroidControl-Curated基准上进行评估。Infinite-Actor在各个规模上始终优于Qwen3-VL基线：Infinite-Actor-8B将AndroidWorld的Pass@1提高了+4.45，几乎将MobileWorld的Pass@3成功率翻倍，而Infinite-Actor-2B则将Pass@1提高了+9.05。代码可在https://github.com/swaydy-n/Infinite-Dreamer获取。
cs.CV / 156 / 2610.05865
Weave Mamba Fusion: Global Cross-Scale Interaction for Lightweight Face Detection
编织曼巴融合：轻量级人脸检测的全球跨尺度交互
Abstract
Feature pyramid methods, from FPN to BiFPN, have achieved strong performance in face detection by fusing multi-scale features. However, detecting faces under unconstrained conditions, such as small scale, occlusion, and extreme pose, remains difficult, as it requires global cross-scale dependencies that local fusion cannot model. State space models such as Mamba provide global context with linear complexity by scanning features as a sequence, and therefore offer a promising direction for this problem. Nevertheless, such a scan needs the two pyramid scales combined into a single feature map, and the way they are combined determines whether cross-scale structure is preserved. Summation collapses the two scales before the scan, so the scan has no cross-scale structure to exploit, while concatenation keeps both scales but at far higher cost. To address this, we propose \textbf{Weave Mamba Fusion (WMF)}, which interleaves two adjacent pyramid scales column by column so that each step of a horizontal bidirectional SS2D scan moves from one scale to the other. With partial-channel processing and parameter-free de-weaving, WMF enables efficient cross-scale interaction while preserving feature structure. Integrating WMF into every fusion node yields \textbf{WeaveBiFPN}, the neck of our \textbf{WeaveFace} detector. On WIDER FACE, WeaveFace achieves 91.41\% mean AP with only 0.34M parameters and 1.16 GFLOPs, outperforming prior detectors under 0.5M parameters. Its largest gains are on the Hard subset, where it reaches 87.14\% AP. The code is publicly available at \url{https://github.com/dohun-mat/WeaveMambaFusion}.
Chinese Translation
特征金字塔方法，从 FPN 到 BiFPN，通过融合多尺度特征在人脸检测中取得了强大的性能。然而，在不受约束的条件下检测人脸，如小尺度、遮挡和极端姿态，仍然是一个困难的任务，因为这需要全球跨尺度的依赖关系，而局部融合无法建模。状态空间模型如 Mamba 通过将特征作为序列进行扫描，以线性复杂度提供全球上下文，因此为解决该问题提供了一个有前景的方向。然而，这种扫描需要将两个金字塔尺度合并为一个单一的特征图，而它们的合并方式决定了跨尺度结构是否得以保留。求和在扫描之前将两个尺度合并，因此扫描没有可利用的跨尺度结构，而拼接保留了两个尺度，但成本要高得多。为了解决这个问题，我们提出了 extbf{编织曼巴融合 (Weave Mamba Fusion, WMF)}，它通过逐列交错两个相邻的金字塔尺度，使得每一步的水平双向 SS2D 扫描可以从一个尺度移动到另一个尺度。通过部分通道处理和无参数去编织，WMF 实现了高效的跨尺度交互，同时保留了特征结构。将 WMF 集成到每个融合节点中，形成了 extbf{编织双金字塔网络 (WeaveBiFPN)}，这是我们 extbf{编织人脸检测器 (WeaveFace)} 的颈部结构。在 WIDER FACE 数据集上，WeaveFace 以仅 0.34M 的参数和 1.16 GFLOPs 达到 91.41\% 的平均 AP，超越了之前的 0.5M 参数的检测器。其最大的提升出现在 Hard 子集上，达到了 87.14\\% 的 AP。代码已公开发布，网址为 exturl{https://github.com/dohun-mat/WeaveMambaFusion}。
cs.CV / 157 / 2610.05870
Certification of Real Images through Calibrated Content Authentication
通过校准内容认证对真实图像进行认证
Abstract
Generative models can synthesize high-quality inauthentic multimedia content that is already being misused at scale. We evaluate twenty deepfake detectors against ten generators released in the last four years and find accuracy decreasing over time, from near-perfect 99.5% to 76%. Adversarial perturbations further reduce every baseline detector to below 2% accuracy, effectively inverting the detector's assigned label. We argue that this unreliability reflects a fundamental ambiguity: generators can reproduce authentic content exactly (e.g., through memorization), so content alone cannot reveal the true provenance label.For this reason, content produced by a generator must admit a faithful reconstruction by that same generator, and finding such a reconstruction makes synthetic provenance plausible and authenticity plausibly deniable.We therefore propose and evaluate a detection paradigm that outputs a calibrated prediction of whether authenticity is plausibly deniable: a faithful reconstruction by any known generator establishes plausible deniability, while calibration bounds how often content from known generators fails to be reproduced. Our evaluation shows that (i) our detector can be calibrated so that at most 1% of generated content is wrongly certified, an operating point at which most baseline detectors reach near-zero recall, including the strongest with 93% accuracy; (ii) calibrating a stricter security threshold on attacked samples preserves this bound against adaptive adversaries within the evaluated bounded-perturbation attack space, whose perturbations break every baseline, but does not cover arbitrary adversarial transformations; and (iii) post-hoc verifiability is eroding, as 1,116 of 3,000 Reddit images resist reproduction by a 2022 generator, but only 55 to 79 resist reproduction by 2024 generators.
Chinese Translation
生成模型能够合成高质量的不真实多媒体内容，目前已经被大规模滥用。我们评估了二十种深度伪造检测器对近四年来发布的十种生成器的表现，发现准确率随着时间的推移而下降，从近乎完美的99.5%降至76%。对抗性扰动进一步将每个基线检测器的准确率降低到2%以下，有效地颠覆了检测器所分配的标签。我们认为这种不可靠性反映了一种根本的模糊性：生成器可以完全重现真实内容（例如，通过记忆），因此仅凭内容无法揭示真实的来源标签。因此，由生成器生成的内容必须允许由同一生成器进行忠实重建，而找到这样的重建使得合成来源变得合理，真实性则可以合理否认。因此，我们提出并评估了一种检测范式，该范式输出一个校准的预测，判断真实性是否可以合理否认：任何已知生成器的忠实重建建立了合理否认，而校准则限制了已知生成器生成的内容未被重现的频率。我们的评估显示：（i）我们的检测器可以被校准，使得最多1%的生成内容被错误认证，这是一个大多数基线检测器在此操作点下达到近零召回率的水平，包括准确率为93%的最强检测器；（ii）在攻击样本上校准更严格的安全阈值能够在评估的有界扰动攻击空间内保持这一界限，尽管这些扰动会破坏每个基线，但并不涵盖任意对抗性变换；（iii）后验可验证性正在下降，因为在3000张Reddit图像中，有1116张抵抗了2022年生成器的重现，但只有55到79张抵抗了2024年生成器的重现。
cs.CV / 158 / 2610.05880
fMRI-TAMCL: Text-Anchored Supervised Multimodal Contrastive Learning for fMRI-Based Brain Disorder Classification
fMRI-TAMCL：基于文本锚定的监督多模态对比学习用于fMRI脑部疾病分类
Abstract
Resting-state fMRI is important in the classification of brain disorders, but highly multimodal and exhibits strong multisite heterogeneity. Existing methods fuse images, BOLD-based functional connectivity, and phenotypic data modalities. Unlike other medical imaging datasets, rs-fMRI datasets rarely include a text modality, so they are generated from phenotypic data or BOLD activations. These text generation methods rely on fixed assumptions for subjects, sites, devices, and protocols, leading to poor generalization across datasets. We propose fMRI-TAMCL, a text-anchored multimodal contrastive learning framework that integrates fMRI images, sparse FC, and generated subject-specific text. Its Subject-Adaptive Threshold Derivation module generates BOLD activation text, while Feature-Value Serialization module generates phenotypic text. All three modalities are encoded as clustered graphs, projected onto a shared unit hypersphere space, aligned using pairwise, text-anchored supervised contrastive learning, and fused with attention. fMRI-TAMCL proves its generalization capability across five datasets outperforming 29 baselines with 78.6%-86.4% accuracy in downstream classification.
Chinese Translation
静息态功能磁共振成像（rs-fMRI）在脑部疾病分类中具有重要意义，但其高度多模态性和强烈的多中心异质性使得分析复杂。现有方法融合了图像、基于BOLD的功能连接性和表型数据模态。与其他医学影像数据集不同，rs-fMRI数据集很少包含文本模态，因此通常是基于表型数据或BOLD激活生成的。这些文本生成方法依赖于对受试者、地点、设备和协议的固定假设，导致在不同数据集之间的泛化能力较差。我们提出了fMRI-TAMCL，一个文本锚定的多模态对比学习框架，集成了fMRI图像、稀疏功能连接（FC）和生成的特定受试者文本。其受试者自适应阈值推导模块生成BOLD激活文本，而特征值序列化模块生成表型文本。所有三种模态被编码为聚类图，投影到共享的单位超球面空间，通过成对的文本锚定监督对比学习进行对齐，并通过注意力机制进行融合。fMRI-TAMCL在五个数据集上证明了其泛化能力，在下游分类中以78.6%-86.4%的准确率超越了29个基线模型。
cs.CV / 159 / 2610.05891
Spatial Supervision Without Attribution Optimization: Improving Post-Hoc Class Activation Maps via Box-Guided Evidence Routing
无归因优化的空间监督：通过框引导证据路由改善后验类激活图
Abstract
Post-hoc class activation maps (CAMs) are a standard tool for inspecting the evidence behind an image classifier's predictions, yet nothing in ordinary training encourages these maps to be spatially appropriate. We study whether inexpensive spatial supervision can improve a classifier's own predicted-class Grad-CAM without ever optimizing an attribution map. Box-Guided Evidence Routing (BGER) trains a lightweight gate on the final feature map under box or mask supervision and routes classification through the gated features, while Grad-CAM is computed separately at the pre-gate representation, so the evaluated map never enters the training objective. With a BCE routing loss, BGER raises MaxBoxAccV2 from $0.584$ to $0.715$ on CUB-200-2011 and from $0.757$ to $0.832$ on Stanford Dogs at comparable accuracy. Matched controls attribute most of the ResNet-50 gain to the spatial supervision reshaping the backbone rather than to routing itself: when classification bypasses the gate, most of the improvement remains, and detaching gradients through the gate leaves the ResNet-50 result nearly unchanged. The same detachment preserves most of the gain in two DenseNet-121 chest X-ray settings but removes the apparent gain on Swin-T, and directly supervising the CAM reaches stronger localization at a larger accuracy cost. Overall, spatial supervision can improve separately evaluated post-hoc CAMs, but both the mechanism and the size of the benefit depend on the architecture and the evaluation setting.
Chinese Translation
后验类激活图（CAMs）是检查图像分类器预测背后证据的标准工具，但在普通训练中并没有任何内容鼓励这些图在空间上是合适的。我们研究了廉价的空间监督是否可以改善分类器自身预测类别的Grad-CAM，而无需优化归因图。框引导证据路由（BGER）在最终特征图上训练一个轻量级的门控，在框或掩膜监督下进行分类，并通过门控特征路由分类，同时Grad-CAM在预门控表示下单独计算，因此评估的图从未进入训练目标。使用二元交叉熵（BCE）路由损失，BGER在CUB-200-2011上将MaxBoxAccV2从0.584提高到0.715，在斯坦福犬数据集上从0.757提高到0.832，且准确率相当。匹配的对照实验将ResNet-50的大部分增益归因于空间监督重塑了主干，而不是路由本身：当分类绕过门控时，大部分改进仍然存在，而通过门控分离梯度几乎不改变ResNet-50的结果。在两个DenseNet-121胸部X光设置中，相同的分离保留了大部分增益，但在Swin-T上移除了明显的增益，直接监督CAM在更大的准确率成本下达到了更强的定位能力。总体而言，空间监督可以改善单独评估的后验CAM，但机制和收益的大小取决于架构和评估设置。
cs.CV / 160 / 2610.05896
TasteRoute: Personalized Routing for Video Generation
TasteRoute：视频生成的个性化路由
Abstract
Rapid progress in video generation has led to a plethora of models that differ substantially in capability and generation cost. This raises a natural question: can each request be efficiently routed to an appropriate model? We find that even when the consensus of the other annotators is used as an oracle, it agrees with each annotator's own favorite only 34-55% of the time. Motivated by this observation, we introduce TasteRoute, a personalized video-generation router that selects a generator jointly based on the input request, user preferences, and available generation budget. Across text-to-video and image-to-video settings, TasteRoute is competitive with strong simple baselines on preference routing while reducing average generation cost. The cost saving increases under higher budget caps. Finally, we release TasteRoute-3k, a human-annotated dataset containing multi-model video comparisons, quality judgments, preference rankings, and user-profile signals to facilitate future research on personalized and cost-aware video routing.
Chinese Translation
视频生成的快速进展导致了众多在能力和生成成本上差异显著的模型。这引出了一个自然的问题：是否可以有效地将每个请求路由到合适的模型？我们发现，即使使用其他标注者的共识作为一个oracle，它与每个标注者自己最喜欢的模型的意见一致的比例仅为34-55%。基于这一观察，我们提出了TasteRoute，一种个性化的视频生成路由器，它根据输入请求、用户偏好和可用生成预算共同选择生成器。在文本到视频和图像到视频的设置中，TasteRoute在偏好路由方面与强大的简单基线竞争，同时降低了平均生成成本。在更高的预算上限下，成本节省效果更为显著。最后，我们发布了TasteRoute-3k，这是一个人类标注的数据集，包含多模型视频比较、质量判断、偏好排名和用户画像信号，以促进未来在个性化和成本意识视频路由方面的研究。
cs.CV / 161 / 2610.05897
Fitting Vision Adapters at Frontier Scales
在前沿尺度上拟合视觉适配器
Abstract
Training a small projector between a frozen vision encoder and language model is an established approach to multimodal learning. As the parameter count of language models scales dramatically, we revisit which vision capabilities this approach can add while keeping their pretrained weights fixed. Here we train a 50M parameter projector from the vision encoder of Kimi K2.6 to GLM 5.2 and 5.3, both models without native vision capabilities, and further present a reproducible recipe for training these adapters at scale. We study the following: (a) how vision capabilities of multimodal models scale as purely the language model side scales, and (b) what specific vision capabilities are able to be imbued into a pure language model at scale, and which ones remain limited. We evaluate on MMMU-Pro and BLINK, examining both overall performance and results on individual visual tasks.
Chinese Translation
在冻结的视觉编码器和语言模型之间训练一个小型投影器是多模态学习的一种成熟方法。随着语言模型参数数量的急剧增加，我们重新审视在保持其预训练权重固定的情况下，这种方法可以添加哪些视觉能力。在这里，我们从 Kimi K2.6 的视觉编码器训练了一个 50M 参数的投影器，目标是 GLM 5.2 和 5.3，这两个模型都没有原生的视觉能力，并进一步提出了一种可重复的在大规模上训练这些适配器的方案。我们研究了以下内容：(a) 当纯语言模型的规模扩大时，多模态模型的视觉能力如何扩展，以及 (b) 在大规模下能够注入到纯语言模型中的具体视觉能力是什么，哪些能力仍然受到限制。我们在 MMMU-Pro 和 BLINK 上进行了评估，考察了整体性能以及在各个视觉任务上的结果。
cs.CV / 162 / 2610.05899
LoDEOT: Low-Dimensional and Efficient Offset Tokens for Building Footprint Extraction from Off-Nadir Imagery
LoDEOT：用于从偏离天顶影像中提取建筑轮廓的低维高效偏移标记
Abstract
Instance-level roof-to-footprint offset (RFO) prediction is central to extracting building footprints from off-nadir imagery. Query-based pipelines commonly use high-dimensional instance tokens to predict signed two-dimensional RFOs. We investigate whether RFO prediction can instead use a compact offset token. Under local pinhole projection and vertical-extrusion assumptions, the idealized RFO map admits a five-parameter sufficient descriptor comprising intrinsic shape, composite amplitude, and relative geometry. This factorization provides a structural prior for a five-dimensional offset token, whose channels learn task-relevant latent representations through end-to-end training. Based on this design, we propose LoDEOT, which retains high-dimensional instance tokens for detection and segmentation but maps instance-token, concentration-gated roof, and box-mask evidence to a five-dimensional offset token followed by an independent two-dimensional readout. Known denoising-query target indices further align each supervised decoder-layer estimate with the same clean instance RFO, organizing successive predictions as target-aligned recovery under perturbed query conditions. Experiments on five real-world building datasets demonstrate the effectiveness of LoDEOT for building footprint extraction. Experiments on real-world building datasets demonstrate that a five-dimensional offset token can support accurate RFO prediction. On BONAI, LoDEOT achieves the best roof-detection bAP and bAP50 and leads all five offset-corrected footprint metrics among the evaluated end-to-end methods, with FAP50 of 54.58 and mEPE of 5.23 pixels. Its FAP50 exceeds those of the evaluated end-to-end baselines by 7.56-16.85 percentage points.
Chinese Translation
实例级屋顶到轮廓偏移（RFO）预测是从偏离天顶影像中提取建筑轮廓的核心。基于查询的管道通常使用高维实例标记来预测带符号的二维RFO。我们研究了RFO预测是否可以改为使用紧凑的偏移标记。在局部针孔投影和垂直挤出假设下，理想化的RFO图可以用一个五参数充分描述符表示，该描述符包括内在形状、复合幅度和相对几何。这种因式分解为五维偏移标记提供了结构先验，其通道通过端到端训练学习与任务相关的潜在表示。基于这一设计，我们提出了LoDEOT，它保留高维实例标记用于检测和分割，但将实例标记、集中门控屋顶和框掩码证据映射到一个五维偏移标记，随后进行独立的二维读出。已知去噪查询目标索引进一步将每个监督解码器层的估计与相同的干净实例RFO对齐，将连续预测组织为在扰动查询条件下的目标对齐恢复。在五个真实建筑数据集上的实验表明，LoDEOT在建筑轮廓提取方面的有效性。实验结果表明，五维偏移标记可以支持准确的RFO预测。在BONAI数据集上，LoDEOT达到了最佳屋顶检测bAP和bAP50，并在评估的端到端方法中领先于所有五个偏移校正轮廓指标，FAP50为54.58，mEPE为5.23像素。其FAP50超过了评估的端到端基线7.56-16.85个百分点。
cs.CV / 163 / 2610.05900
End-to-End Autonomous Recursive Arborescence Deformable Flow and Non-Linear Hemodynamics for Patient-Specific Coronary Centerline Extraction
端到端自主递归树状可变流动与患者特异性非线性血流动力学的冠状中心线提取
Abstract
Extracting patient-specific vascular trees from volumetric medical images is fundamental to computational angiography and non-invasive hemodynamic assessment. Conventional voxel segmentation models often sever delicate bifurcations, while heuristic Euclidean Minimum Spanning Trees introduce non-anatomical shortcuts. Moreover, linear Poiseuille flow neglects quadratic kinetic dissipation across arterial narrowings, underestimating ischemia. We formulate an end-to-end framework decoupling continuous geometric arborescence generation from non-linear hemodynamics. First, an autonomous 3D Ostium Landmark Localization Head with dual-sinus query channels and spherical-gated refinement eliminates centerline seeding dependency, achieving cohort mean localization error of 7.63 mm (7.43 mm LCA, 7.83 mm RCA; 71.4% <= 8.0 mm) from raw contrast context. Second, a Spatially-Grounded Deformable Step Flow Architecture queries continuous 3D feature pyramids via trilinear sampling, sequentially generating trajectories with anchor boundary enforcement (X(0) = P_start). Third, a Top-Down Recursive Arborescence State Machine detects bifurcation peaks via Tree-NMS and parameterizes predecessor parent pointers (p_k < k), guaranteeing single connected acyclic tree topology (beta_0 = 1, beta_1 = 0) with differentiable step termination. Fourth, an iterative Picard non-linear Kirchhoff solver with Young-Tsai / Gould quadratic dissipation enforces machine-precision mass conservation (residual 5.82e-11 mL/s). Across 14 development patients under standardized in-silico stenosis stress testing (Q_0 = 4.0 mL/s), linear Poiseuille flow misclassifies 75% diameter lesions as non-ischemic (FFR > 0.80) in 14/14 cases, whereas our non-linear solver captures functional ischemia (FFR = 0.5864, lesion disparity 32.89 mmHg, p = 6.10e-5) with 3.66x collateral shunting. Test set firewall isolation was maintained.
Chinese Translation
从体积医学图像中提取患者特异性血管树对于计算血管造影和非侵入性血流动力学评估至关重要。传统的体素分割模型常常会割断细腻的分叉，而启发式欧几里得最小生成树则引入了非解剖学的捷径。此外，线性泊肃叶流动忽略了动脉狭窄处的二次动能耗散，低估了缺血情况。我们提出了一个端到端框架，将连续几何树状生成与非线性血流动力学解耦。首先，具有双窦查询通道和球形门控细化的自主3D开口标志定位头消除了中心线种子依赖性，从原始对比背景中实现了7.63 mm的群体平均定位误差（LCA为7.43 mm，RCA为7.83 mm；71.4% <= 8.0 mm）。其次，空间基础可变步流架构通过三线性采样查询连续的3D特征金字塔，依次生成带有锚边界约束的轨迹（X(0) = P_start）。第三，自上而下的递归树状状态机通过树状非极大值抑制（Tree-NMS）检测分叉峰，并对前驱父指针进行参数化（p_k < k），确保单一连通的无环树拓扑（beta_0 = 1，beta_1 = 0），并具有可微分的步骤终止。第四，带有Young-Tsai / Gould二次耗散的迭代Picard非线性基尔霍夫求解器强制执行机器精度的质量守恒（残差5.82e-11 mL/s）。在14名开发患者的标准化计算机内狭窄应力测试（Q_0 = 4.0 mL/s）中，线性泊肃叶流动在14/14个病例中将75%的直径病变错误分类为非缺血性（FFR > 0.80），而我们的非线性求解器则捕捉到了功能性缺血（FFR = 0.5864，病变差异32.89 mmHg，p = 6.10e-5），伴随3.66倍的侧支分流。测试集的防火墙隔离得以保持。
cs.CV / 164 / 2610.05908
Safe Image Generation via Reinforcement Learning
通过强化学习实现安全图像生成
Abstract
Recent Text-to-Image (T2I) models achieve remarkable visual image generation performance, but they can still generate NSFW (Not-Safe-For-Work) contents, including violent or explicit images. Existing safety checker mechanisms are largely confined to pre-generation filtering (e.g. prompt-level text classifiers) or post-hoc moderation applied after an image is completely synthesized. However, adversarial attack methods operate over a much broader space. This imbalance highlights the need for a safety mechanism that intervenes during the generation process. We propose an in-generation safety framework that monitors the denoising trajectory and detects emerging NSFW signals from intermediate representations. Rather than merely detecting NSFW generations, our method applies reinforcement learning to generate safe images from NSFW prompts. By coupling in-generation detection with controllable steering, our approach mitigates unsafe trajectories even when NSFW signals emerge after generation has already begun. Experiments results show that our method consistently outperforms existing safe image generation methods across both standard and adversarial evaluation sets, while preserving perceptual quality and prompt fidelity. Code will be released upon acceptance.
Chinese Translation
近期的文本到图像（Text-to-Image, T2I）模型在视觉图像生成方面取得了显著的性能，但仍然可能生成不安全的内容（NSFW），包括暴力或露骨的图像。现有的安全检查机制主要局限于生成前的过滤（例如，提示级文本分类器）或在图像完全合成后进行的事后审查。然而，对抗攻击方法的操作范围要广泛得多。这种不平衡突显了在生成过程中需要一种干预的安全机制。我们提出了一种生成过程中的安全框架，该框架监控去噪轨迹并从中间表示中检测新出现的NSFW信号。我们的方法不仅仅是检测NSFW生成，而是应用强化学习从NSFW提示中生成安全图像。通过将生成过程中的检测与可控引导相结合，我们的方法即使在生成已经开始后NSFW信号出现时，也能减轻不安全的轨迹。实验结果表明，我们的方法在标准和对抗评估集上始终优于现有的安全图像生成方法，同时保持感知质量和提示的忠实度。代码将在接受后发布。
cs.CV / 165 / 2610.05910
AstraSR: Real-World Thermal Super-Resolution with GPT-6 Astra
AstraSR：基于GPT-6 Astra的真实世界热超分辨率
Abstract
Real-world thermal super-resolution (SR) is constrained by limited sensor resolution and the difficulty of obtaining corresponding high-resolution (HR) observations for direct model supervision. Conventional SR methods typically construct training pairs by treating captured thermal images with real-world degradations as HR references and applying predefined degradation to generate synthetic low-resolution (LR) inputs. Such a construction not only introduces a domain gap between synthetic and captured LR observations but also retains acquisition degradations in the supervision. To address this issue, we propose AstraSR, a real-world thermal SR method guided by GPT-6 Astra, a frontier multimodal generative model endowed with emergent and transformative visual capabilities. Specifically, we construct a dataset of image pairs by using captured LR thermal images to condition GPT-based HR reference. We develop a direct generative supervision strategy that learns from captured thermal inputs paired with GPT-generated HR references. Pixel, gradient, and perceptual losses jointly supervise the transfer of intensity patterns, structural boundaries, and visual details from the generated references. Qualitative comparisons with seven existing state-of-the-art real-world SR methods show continuous object contours, distinct structural boundaries, and smooth intensity transitions in the thermal scenes. These results demonstrate that AstraSR outperforms existing real-world SR methods in both thermal clarity and structural coherence.
Chinese Translation
真实世界的热超分辨率（SR）受到传感器分辨率有限和难以获取对应高分辨率（HR）观测以进行直接模型监督的限制。传统的SR方法通常通过将捕获的具有真实世界退化的热图像视为HR参考，并施加预定义的退化来生成合成低分辨率（LR）输入，从而构建训练对。这种构建不仅引入了合成和捕获的LR观测之间的领域差距，还在监督中保留了获取退化。为了解决这一问题，我们提出了AstraSR，一种由GPT-6 Astra引导的真实世界热SR方法，后者是一种具备新兴和变革性视觉能力的前沿多模态生成模型。具体而言，我们通过使用捕获的LR热图像来条件化基于GPT的HR参考，构建了一组图像对的数据集。我们开发了一种直接生成监督策略，从与GPT生成的HR参考配对的捕获热输入中学习。像素、梯度和感知损失共同监督从生成参考中转移强度模式、结构边界和视觉细节。与七种现有的最先进的真实世界SR方法的定性比较显示，热场景中连续的物体轮廓、明显的结构边界和平滑的强度过渡。这些结果表明，AstraSR在热清晰度和结构一致性方面优于现有的真实世界SR方法。
cs.CV / 166 / 2610.05911
Every View Counts: View-Consistent Panoptic Quality for Multi-view Panoptic Segmentation
每个视图都很重要：多视图全景分割的视图一致性全景质量
Abstract
Multi-view panoptic segmentation assigns a semantic class and a scene-level instance ID to every pixel of an unordered set of images, and recent feed-forward 3D models predict these labels for the input views in a single forward pass. Their predictions, however, have been evaluated with the scene-level PQ (PQ^scene) borrowed from per-scene optimization methods, typically on rendered held-out views. PQ^scene tiles all views of a scene into a single image, so that a missed appearance or a change of ID lowers the score of the matched pair only in proportion to its area. We propose View-Consistent Panoptic Quality (VC-PQ), which extends PQ from a single image to a set of input views, counts equally every view in which an instance is visible, and penalizes a prediction that is not visible in the same views as its ground truth. A decomposition of VC-PQ attributes the score a method loses to mask accuracy, view consistency, and the matching threshold. A single additional parameter recovers the area weighting of tiling for comparison. Under a fixed evaluation protocol on ScanNet++ and ScanNetv2, recent feed-forward methods are evaluated with VC-PQ and PQ^scene, and the decomposition shows where each of them loses its score. Controlled perturbations of the ground truth show that VC-PQ responds to the number of views in which an instance is missed or changes ID, whereas PQ^scene responds to their area. The aim of this work is to make view consistency part of the evaluation of multi-view panoptic segmentation, with VC-PQ reported alongside PQ^scene.
Chinese Translation
多视图全景分割为一组无序图像的每个像素分配一个语义类别和一个场景级实例ID，最近的前馈3D模型在单次前向传播中预测这些标签。然而，它们的预测是通过借用每场景优化方法的场景级PQ（PQ^scene）进行评估，通常是在渲染的保留视图上。PQ^scene将场景的所有视图拼接成一张单一图像，因此，错过的外观或ID的变化仅按其面积比例降低匹配对的得分。我们提出了视图一致性全景质量（View-Consistent Panoptic Quality，VC-PQ），它将PQ从单一图像扩展到一组输入视图，平等计算每个实例可见的视图，并惩罚在与其真实值相同视图中不可见的预测。VC-PQ的分解将方法损失的得分归因于掩膜准确性、视图一致性和匹配阈值。一个额外的参数恢复了拼接的面积加权以进行比较。在ScanNet++和ScanNetv2的固定评估协议下，最近的前馈方法使用VC-PQ和PQ^scene进行评估，分解显示了它们各自得分损失的原因。对真实值的控制扰动表明，VC-PQ对实例缺失或ID变化的视图数量作出反应，而PQ^scene则对其面积作出反应。本工作的目的是将视图一致性纳入多视图全景分割的评估中，并将VC-PQ与PQ^scene一起报告。
cs.CV / 167 / 2610.05918
Prompt and Refinement: Asymmetric Mutual Learning for Infrared Small Target Detection with Noisy Labels
提示与精炼：针对带噪声标签的红外小目标检测的非对称互学习
Abstract
Existing data-driven infrared small target detection (ISTD) methods typically require large-scale datasets with accurate pixel-level annotations for model training. However, such labor-intensive requirements are difficult to satisfy in real-world applications due to the heavy reliance on expert knowledge and the inherently weak distinctiveness of infrared small targets. Consequently, the presence of noisy labels during model training is inevitable, which can severely mislead the learning of target perception toward spurious patterns. To address this challenge, we propose Prompt and Refinement (PAR), a label-noise-robust asymmetric mutual learning paradigm for ISTD. Specifically, PAR comprises a pretrained Segment Anything Model (SAM) and an ISTD-specific detector trained from scratch, which learn collaboratively through a peer-teaching scheme. Coupled with local contrast regularity, the predictions of the two asymmetric peer models are mutually exploited as rectification cues for the supervisory masks of their counterparts. The interaction between complementary inductive biases effectively prevents the label correction process from degenerating into the self-confirmation loop of a single model, enabling progressive refinement of the annotations toward intrinsic target characteristics. In addition, the detector predictions are utilized as corrective mask prompts to facilitate task-specific adaptation of the vision foundation model. Moreover, an evidential uncertainty estimation strategy is introduced into the optimization process to further alleviate the adverse effects of noisy labels. Extensive experiments under diverse noisy label scenarios on three ISTD datasets demonstrate that PAR consistently achieves state-of-the-art performance.
Chinese Translation
现有的数据驱动红外小目标检测（ISTD）方法通常需要大规模的具有准确像素级注释的数据集来进行模型训练。然而，由于对专家知识的高度依赖以及红外小目标固有的弱特征性，这种劳动密集型的要求在实际应用中难以满足。因此，在模型训练过程中出现带噪声标签是不可避免的，这可能严重误导目标感知的学习，导致学习到虚假的模式。为了解决这一挑战，我们提出了提示与精炼（PAR），一种针对ISTD的抗标签噪声的非对称互学习范式。具体而言，PAR由一个预训练的Segment Anything Model（SAM）和一个从头开始训练的ISTD特定检测器组成，它们通过同伴教学方案进行协同学习。结合局部对比正则化，这两个非对称同伴模型的预测相互利用，作为其对应模型监督掩膜的修正线索。互补归纳偏置之间的交互有效防止了标签修正过程退化为单一模型的自我确认循环，使得注释逐步精炼，朝向内在目标特征。此外，检测器的预测被用作修正掩膜提示，以促进视觉基础模型的任务特定适应。此外，一种证据不确定性估计策略被引入优化过程中，以进一步减轻带噪声标签的负面影响。在三个ISTD数据集上进行的多种带噪声标签场景下的广泛实验表明，PAR始终实现了最先进的性能。
cs.CV / 168 / 2610.05932
UltraDub: Towards Authentic Dubbing by Unifying Visually-Steered Flow Learning and Trajectory Guidance
UltraDub：通过统一视觉引导流学习与轨迹引导实现真实配音
Abstract
Visual voice cloning requires intelligible, speaker-consistent speech synchronized with visible articulation. However, sequential multimodal conditioning can disrupt previously established temporal and speaker cues, while imbalanced inference guidance can improve linguistic accuracy at the expense of lip synchronization. In this paper, we propose UltraDub, a Unifying Visually-Steered Flow learning and trajectory Guidance Dubbing framework that leverages vision in two ways: as continuous motion for multimodal context aggregation, and as structural rhythm for trajectory rectification. Specifically, we introduce the Motion-guided Dual-context Retrieving (MDR) module, which continually recalibrates linguistic and speaker-style retrieval through shared lip-motion query residuals, utilizing independent time-conditioned gates to regulate their contributions. Furthermore, we propose Rhythm-anchored Trajectory Guidance (RTG), a training-free mechanism that evaluates hierarchical multimodal corrections at a visual-only predictive midpoint, safely strengthening semantic conditioning while better preserving temporal alignment. Finally, we construct DiverseDub, a multi-scenario benchmark to evaluate video dubbing in the wild. Extensive experiments demonstrate that UltraDub achieves state-of-the-art performance across four datasets.
Chinese Translation
视觉语音克隆需要与可见发音同步的清晰、说话者一致的语音。然而，顺序多模态条件可能会破坏先前建立的时间和说话者线索，而不平衡的推理引导可能在提高语言准确性的同时牺牲唇部同步性。在本文中，我们提出了UltraDub，一个统一视觉引导流学习与轨迹引导的配音框架，利用视觉的两种方式：作为多模态上下文聚合的连续运动，以及作为轨迹校正的结构节奏。具体而言，我们引入了运动引导的双重上下文检索（MDR）模块，该模块通过共享的唇部运动查询残差不断重新校准语言和说话者风格的检索，利用独立的时间条件门控来调节它们的贡献。此外，我们提出了基于节奏的轨迹引导（RTG），这是一种无训练机制，在仅视觉的预测中点评估分层多模态修正，安全地增强语义条件，同时更好地保持时间对齐。最后，我们构建了DiverseDub，一个多场景基准，用于评估野外视频配音。大量实验表明，UltraDub在四个数据集上实现了最先进的性能。
cs.CV / 169 / 2610.05940
ReMem: Streaming Video Understanding With Long Context Retention
ReMem：具有长上下文保留的流媒体视频理解
Abstract
Despite their impressive performance on a wide range of video understanding tasks, current Vision Language Models (VLMs) are predominantly designed for offline scenarios and struggle to handle online streaming videos that demand low latency response. Several studies have explored memory and token compression strategies in an attempt to adapt offline VLMs for streaming video understanding tasks. However, through our probing experiment, we identify that most existing works tend to progressively lose long context information as length of input stream increases. To address this, we propose ReMem, a novel training-free adaptation technique that enables VLMs to process streaming videos of arbitrary lengths while improving their long context information retention capability. ReMem exploits memory from two perspectives, implemented as two core components. The Streaming Context Memory (SCM) continuously compresses historical context with query-independent attention. The Retrieved Vision Memory (RVM) then retrieves the most salient, query-relevant context from memory to augment the VLM's input. Comprehensive experiments demonstrate that the proposed ReMem achieves state-of-the-art (SOTA) performance across a variety of widely used benchmarks, spanning both streaming video and general long video understanding tasks.
Chinese Translation
尽管当前的视觉语言模型（VLMs）在广泛的视频理解任务中表现出色，但它们主要是为离线场景设计的，难以处理需要低延迟响应的在线流媒体视频。一些研究探讨了记忆和令牌压缩策略，以期将离线VLM适应于流媒体视频理解任务。然而，通过我们的探测实验，我们发现大多数现有工作在输入流长度增加时，往往会逐渐丧失长上下文信息。为了解决这个问题，我们提出了ReMem，这是一种新颖的无训练适应技术，使VLM能够处理任意长度的流媒体视频，同时提高其长上下文信息的保留能力。ReMem从两个角度利用记忆，实施为两个核心组件。流媒体上下文记忆（Streaming Context Memory, SCM）持续压缩历史上下文，采用与查询无关的注意力机制。然后，检索视觉记忆（Retrieved Vision Memory, RVM）从记忆中检索最显著的、与查询相关的上下文，以增强VLM的输入。全面的实验表明，所提出的ReMem在多种广泛使用的基准测试中实现了最先进的（SOTA）性能，涵盖了流媒体视频和一般长视频理解任务。
cs.CV / 170 / 2610.05967
JLD: Perceptual Distance Through A Jacobian Lens
JLD：通过雅可比视角的感知距离
Abstract
Image compression, restoration, and generation all require a way to measure how different two images look to a person. Pixel error ignores how people see, while the most accurate perceptual distances are typically fitted to human judgments, tying them to a fixed data and resolution. For example, when image resolution is doubled, the correlation of DISTS with human scores on TID2013 drops from 0.815 to 0.717. We introduce the Jacobian Lens Distance (JLD), which derives its perceptual geometry from a frozen vision encoder rather than from human labels. JLD combines the locality of early patch features with the perceptual sensitivity captured by later encoder representations. Specifically, we use the encoder Jacobian to identify directions in the early feature space that most strongly affect the encoder output, producing a fixed metric tensor, $E[J^\top J]$, which we call the Jacobian lens. The lens is fitted only once from 100 unlabeled images, taking about 35 seconds. Locally, this construction defines a pullback metric in pixel space, giving JLD a clear geometric interpretation that can be directly analyzed on real images. Across four standard perceptual databases, JLD achieves state-of-the-art performance and consistently outperforms LPIPS, DISTS, PieAPP, and DreamSim. JLD is also robust to changes in image resolution, on TID2013, its lens-term correlation remains nearly unchanged when the resolution is doubled, decreasing only from 0.850 to 0.845. We further introduce JLD-fast, which is $4\times$ faster than LPIPS-VGG while achieving a mean correlation of 0.911. Finally, JLD naturally extends to video, reaching a correlation of 0.786 on Waterloo IVC 4K compared with 0.611 for VMAF.
Chinese Translation
图像压缩、恢复和生成都需要一种衡量两幅图像在视觉上有多不同的方法。像素误差忽视了人类的视觉感知，而最准确的感知距离通常是根据人类判断进行拟合的，这使得它们与固定的数据和分辨率相关联。例如，当图像分辨率加倍时，DISTS与TID2013上人类评分的相关性从0.815下降到0.717。我们提出了雅可比视角距离（Jacobian Lens Distance, JLD），它的感知几何来自于一个冻结的视觉编码器，而不是来自人类标签。JLD结合了早期补丁特征的局部性与后期编码器表示所捕捉的感知敏感性。具体而言，我们使用编码器的雅可比矩阵来识别在早期特征空间中最强烈影响编码器输出的方向，从而产生一个固定的度量张量$E[J^ op J]$，我们称之为雅可比透镜。该透镜仅需从100幅未标记图像中拟合一次，耗时约35秒。在局部上，这一构造在像素空间中定义了一个回拉度量，使得JLD具有清晰的几何解释，可以直接在真实图像上进行分析。在四个标准感知数据库中，JLD达到了最先进的性能，并且始终优于LPIPS、DISTS、PieAPP和DreamSim。JLD对图像分辨率的变化也表现出鲁棒性，在TID2013上，当分辨率加倍时，其透镜项相关性几乎保持不变，仅从0.850下降到0.845。我们进一步推出了JLD-fast，其速度比LPIPS-VGG快$4 imes$，同时实现了0.911的平均相关性。最后，JLD自然扩展到视频，在Waterloo IVC 4K上达到了0.786的相关性，而VMAF的相关性为0.611。
cs.CV / 171 / 2610.05987
Label-Free Coreset Selection with Foundation Models for Efficient Annotation in Computational Pathology
基于基础模型的无标签核心集选择方法在计算病理学中的高效标注
Abstract
Computational pathology has the potential to improve clinical outcomes through a demonstrated increase in diagnostic and prognostic accuracy. However, the development and validation of deep learning algorithms still require annotated data, a costly procedure involving expert pathologists who already face critical workforce shortages. Existing coreset selection methods to optimize annotation efforts currently all rely on hyperparameters tuned on natural-image benchmarks that do not transfer to histopathology and are cumbersome to use in clinical practice. In this study, we present GCcore, a novel label-free coreset selection method that embeds every image of a dataset with any pathology foundation model and greedily selects the samples that collectively maximize the global coverage of the embedding space. The proposed method provides a lower-bound guarantee on the global coverage of the returned coreset for any coreset size, while being completely hyperparameter-free and deterministic. We demonstrate GCcore's superior performance over 14 baselines including state-of-the-art methods across 10 tasks and datasets spanning whole slide image classification, tile classification, and tissue segmentation, where it ranks first on six and within the top three on nine, while also demonstrating how existing methods can shift by up to five rank positions depending on their hyperparameter settings. Code is publicly available at https://github.com/OncoAI-ULBHUB/GCcore.
Chinese Translation
计算病理学有潜力通过显著提高诊断和预后准确性来改善临床结果。然而，深度学习算法的开发和验证仍然需要标注数据，这是一项耗费成本的过程，涉及到已经面临严重人力资源短缺的专家病理学家。目前现有的核心集选择方法旨在优化标注工作，但都依赖于在自然图像基准上调优的超参数，这些超参数无法转移到组织病理学中，并且在临床实践中使用起来繁琐。在本研究中，我们提出了GCcore，这是一种新颖的无标签核心集选择方法，它利用任何病理基础模型对数据集中的每个图像进行嵌入，并贪婪地选择能够共同最大化嵌入空间全球覆盖的样本。所提方法为返回的核心集在任何核心集大小下提供了全球覆盖的下界保证，同时完全不依赖超参数且具有确定性。我们展示了GCcore在14个基线方法上的优越性能，包括在10个任务和数据集中的最先进方法，其中在六个任务中排名第一，在九个任务中位列前三，同时还展示了现有方法根据其超参数设置可能会变化多达五个排名位置。代码可在 https://github.com/OncoAI-ULBHUB/GCcore 上公开获取。
cs.CV / 172 / 2610.05993
From Transformation to Target State: Rethinking Query Representation for Zero-Shot Composed Image Retrieval
从转变到目标状态：重新思考零-shot组合图像检索的查询表示
Abstract
Composed image retrieval (CIR) aims to retrieve a desired target image from a query consisting of a reference image and a modification text. This task exhibits an unusual representational asymmetry: the modification text specifies a transition from the reference state, whereas retrieval candidates depict completed target states. This creates a representation mismatch for zero-shot methods that query pretrained vision-language spaces directly with transformation-oriented language. We study this mismatch and reformulate zero-shot composed image retrieval as target-state reconstruction followed by retrieval. We instantiate this formulation with ASAP-CIR, a training-free framework that reconstructs a static target representation using a frozen multimodal large language model (MLLM). The representation combines multiple holistic descriptions with a variable set of importance-weighted atomic semantics, thereby preserving both overall target identity and fine-grained visual constraints. Retrieval then integrates holistic state alignment, atomic constraint grounding, and calibrated target-state evidence aggregation. A controlled text-only diagnostic shows that target-side static query formulations achieve more reliable retrieval than dynamic composed query formulations, particularly when source-state semantics must be suppressed or transformed. Experiments on FashionIQ, CIRR, and CIRCO further characterize the effectiveness and limitations of this representation principle, with the clearest gains on the multi-target CIRCO benchmark. These results show that how composed intent is represented before retrieval is a consequential design choice, distinct from the choice of retrieval backbone itself.
Chinese Translation
组合图像检索（CIR）旨在从由参考图像和修改文本组成的查询中检索所需的目标图像。该任务表现出一种不寻常的表示不对称性：修改文本指定了从参考状态的转变，而检索候选则描绘了已完成的目标状态。这为直接使用以转变为导向的语言查询预训练视觉-语言空间的零-shot 方法造成了表示不匹配。我们研究了这种不匹配，并将零-shot 组合图像检索重新表述为目标状态重建，随后进行检索。我们用 ASAP-CIR 实现了这一表述，这是一个无训练的框架，利用冻结的多模态大型语言模型（MLLM）重建静态目标表示。该表示结合了多个整体描述和一组可变的重要性加权原子语义，从而保留了整体目标身份和细粒度视觉约束。检索则整合了整体状态对齐、原子约束定位和校准的目标状态证据聚合。一项控制的仅文本诊断显示，目标侧静态查询表述在检索的可靠性上优于动态组合查询表述，特别是在源状态语义必须被抑制或转变时。在 FashionIQ、CIRR 和 CIRCO 上的实验进一步表征了这一表示原则的有效性和局限性，尤其是在多目标 CIRCO 基准测试中表现出明显的提升。这些结果表明，组合意图在检索前的表示方式是一个重要的设计选择，与检索骨干网络的选择是不同的。
cs.CV / 173 / 2610.06018
Investigating Query-Insensitive Behavior in Spatio-Temporal Video Grounding
研究时空视频定位中的查询无关行为
Abstract
Spatio-temporal video grounding (STVG) aims to localize objects or events described by natural language queries in both space and time. Existing STVG models are typically trained and evaluated under the assumption that each query is relevant to the input video. In this work, we challenge this assumption by studying the behavior of state-of-the-art STVG models under irrelevant queries and missing textual input. Our experiments show that current models can still produce plausible spatio-temporal predictions even when the query is unrelated to the video or removed entirely. We further analyze HCSTVG-v2 and VidSTG to identify dataset regularities that may encourage such query-insensitive behavior. Our study highlights an underexplored limitation of STVG models and motivates negative-aware evaluation protocols and architectures that explicitly assess query relevance.
Chinese Translation
时空视频定位（STVG）旨在根据自然语言查询在时间和空间上定位对象或事件。现有的STVG模型通常在假设每个查询与输入视频相关的前提下进行训练和评估。在本研究中，我们挑战了这一假设，通过研究最先进的STVG模型在无关查询和缺失文本输入下的表现。我们的实验表明，即使查询与视频无关或完全移除，当前模型仍然能够生成合理的时空预测。我们进一步分析了HCSTVG-v2和VidSTG，以识别可能促使这种查询无关行为的数据集规律。我们的研究突显了STVG模型一个未被充分探索的局限性，并推动了负面意识评估协议和架构的研究，这些协议和架构明确评估查询的相关性。
cs.CV / 174 / 2610.06020
Patch-based Querying Identifies Structures of Interest in Electron Microscopy
基于补丁的查询识别电子显微镜中的感兴趣结构
Abstract
Volume electron microscopy (vEM) has emerged as an essential sensing technique in biomedical research, allowing the three-dimensional imaging of biological cells and tissues at nanometer-scale resolution. The ability to generate extensive datasets has reached the limitations of downstream analysis processes, which depend significantly on the intervention of human experts for preprocessing and annotation. We propose an efficient and reliable patch-based retrieval framework based on self-supervised learning of local image descriptors to locate self-similar structures in vEM datasets. Given a few manual annotations of a given cellular structure, our method can retrieve similar structures across the EM volume. Our framework is interactive, allowing the human expert to refine the search queries and retrieve relevant image patches quickly and using little labeled data. Experiments on real-world vEM images of biological tissues demonstrate that our framework can reliably identify relevant cellular structures, generalize across different organelles and acquisition modalities, and substantially reduce the search space for downstream analysis.
Chinese Translation
体积电子显微镜（vEM）已成为生物医学研究中的一种重要传感技术，能够以纳米级分辨率对生物细胞和组织进行三维成像。生成大量数据集的能力已达到了下游分析过程的限制，这些过程在很大程度上依赖于人类专家的干预进行预处理和注释。我们提出了一种基于自监督学习的局部图像描述符的高效可靠的基于补丁的检索框架，以定位vEM数据集中自相似的结构。在给定少量手动注释的细胞结构的情况下，我们的方法可以在电子显微镜体积中检索到相似的结构。我们的框架是交互式的，允许人类专家快速细化搜索查询并使用少量标注数据检索相关的图像补丁。在对生物组织的真实vEM图像进行的实验中，证明了我们的框架能够可靠地识别相关的细胞结构，在不同的细胞器和获取模式之间具有良好的泛化能力，并显著减少下游分析的搜索空间。
cs.CV / 175 / 2610.06021
Scalable Minimal-Change Learning for Controllable Image Editing
可扩展的最小变化学习用于可控图像编辑
Abstract
Image editing should change only the attributes specified by an instruction while preserving everything else, yet current methods often make unintended changes. We treat this minimal-change principle as an optimization objective for instruction-based editing. Latent L1 regularization is a poor proxy for output locality in modern nonlinear generators and often requires supervision unavailable at scale. We instead optimize edit outcomes with reinforcement learning. An agentic vision-language reward model audits each source image, instruction, and edited image for two failure types: unimplemented requested changes and unintended changes. A group-level rubric merges and verifies these issues to provide consistent rewards across candidate edits without per-instruction human annotations. On FLUX.1 Kontext-dev, ARRO raises average EditScore from 5.21 to 5.88 across MinEval, MagicBrush, AnyBench, and Emu-Edit. On 600 evaluation examples, it reduces off-target pixel change by 8.4% relative to the base editor. Reward and SFT controls, blinded human evaluations, and transfer to OmniGen2 provide complementary evidence. Code: https://github.com/Showwwwwwwww/ARRO
Chinese Translation
图像编辑应仅改变指令指定的属性，同时保留其他所有内容，然而当前的方法往往会产生意想不到的变化。我们将这一最小变化原则视为基于指令编辑的优化目标。潜在的 L1 正则化在现代非线性生成器中对输出局部性的代理效果较差，且通常需要在大规模下不可用的监督。相反，我们通过强化学习优化编辑结果。一个自主的视觉-语言奖励模型审计每个源图像、指令和编辑图像，以识别两种失败类型：未实现的请求变更和意外变更。一个组级别的评分标准合并并验证这些问题，以在候选编辑之间提供一致的奖励，而无需针对每个指令的人类注释。在 FLUX.1 Kontext-dev 上，ARRO 将平均 EditScore 从 5.21 提高到 5.88，涵盖 MinEval、MagicBrush、AnyBench 和 Emu-Edit。在 600 个评估示例中，相较于基础编辑器，它将目标外像素变化减少了 8.4%。奖励和 SFT 控制、盲评人类评估以及向 OmniGen2 的迁移提供了互补证据。代码： https://github.com/Showwwwwwwww/ARRO
cs.CV / 176 / 2610.06035
Casual Flash Lighting for Gaussian Splat Inverse Rendering
高斯溅射逆渲染中的随意闪光照明
Abstract
Recovering geometry, materials, and lighting from photographs is highly ambiguous when only static illumination is available. Active-lighting setups reduce the ambiguity but require dark rooms or specialized hardware. Instead, we synergize both static and flash lighting from casual indoor capture, with the flash on or off, each from independent viewpoints. The flash residual constrains albedo and the BRDF, while static lighting captures grazing-angle specular highlights that flash misses. With a 2DGS reconstruction framing, our key contribution is a GS-anchored diffuse field: a hash-encoded MLP is queried at the rasterized 2DGS depth. As it depends only on world position, it is view consistent in 3D and allows the flash residual to drive material decomposition instead of being absorbed by alpha-blending drift across views. At the same time, we render static lighting with deferred shading such that it can also supervise material decomposition. On five synthetic and three real indoor scenes, our method outperforms six recent baselines on diffuse color, albedo and roughness material parameters, and in relighting where PSNR improves by 4.17 dB over the next-best baseline.
Chinese Translation
从照片中恢复几何形状、材料和光照在仅有静态照明可用时具有高度的模糊性。主动照明设置可以减少这种模糊性，但需要黑暗的房间或专门的硬件。相反，我们将随意室内捕捉中的静态和闪光照明进行协同，闪光灯可开可关，且每次拍摄来自独立的视角。闪光残差约束了反照率（albedo）和双向反射分布函数（BRDF），而静态照明则捕捉到闪光灯遗漏的低角度镜面高光。在2DGS重建框架下，我们的关键贡献是一个以GS为锚点的漫反射场：一个哈希编码的多层感知器（MLP）在光栅化的2DGS深度处被查询。由于它仅依赖于世界位置，因此在3D中保持视图一致性，并允许闪光残差驱动材料分解，而不是在视角之间因α混合漂移而被吸收。同时，我们使用延迟着色渲染静态照明，以便它也可以监督材料分解。在五个合成场景和三个真实室内场景中，我们的方法在漫反射颜色、反照率和粗糙度材料参数以及重光照方面超越了六个最新基线，其中PSNR比下一个最佳基线提高了4.17 dB。
cs.CV / 177 / 2610.06041
Representation Disentanglement for Fair Chest X-Ray Diagnosis
公平胸部X光诊断的表征解耦
Abstract
Deep learning has advanced chest X-ray (CXR) diagnosis, yet demographic biases in learned representations may contribute to performance disparities across intersectional groups. We propose a single-encoder framework combining dual-level decorrelation with prototype-guided cross-group contrastive learning to reduce demographic dependence while accounting for within-class variation. We further propose Demographic Representation Alignment Reduction (DRAR), a new metric that quantifies the reduction in demographic structure within disease representations. The framework is evaluated on four classification tasks using 34,809 CheXpert test images across eight intersectional groups, defined by age, sex and ethnicity. Compared with empirical risk minimization (ERM), our method reduces the mean equalized-odds gap from 15.41\% to 10.86\% and the AUC gap from 5.95\% to 5.01\%. Our method achieves a DRAR of 59.04\% relative to ERM, with only a slight decrease in mean AUC. These results demonstrate that representation disentanglement can reduce demographic bias and improve intersectional fairness. Code is available at \url{https://github.com/06Yujie/Fair-Medical-Imaging}.
Chinese Translation
深度学习推动了胸部X光（CXR）诊断的发展，但学习到的表征中的人口统计偏差可能导致不同交叉群体之间的性能差异。我们提出了一种单编码器框架，结合双层去相关与原型引导的跨组对比学习，以减少人口统计依赖，同时考虑类内变异。我们进一步提出了人口统计表征对齐减少（Demographic Representation Alignment Reduction, DRAR），这是一种量化疾病表征中人口统计结构减少的新指标。该框架在四个分类任务上进行了评估，使用了34,809张CheXpert测试图像，这些图像跨越了八个交叉群体，按年龄、性别和种族定义。与经验风险最小化（Empirical Risk Minimization, ERM）相比，我们的方法将均值平等机会差距从15.41\%减少到10.86\%，将AUC差距从5.95\%减少到5.01\%。我们的方法相对于ERM实现了59.04\%的DRAR，且均值AUC仅有轻微下降。这些结果表明，表征解耦可以减少人口统计偏见并改善交叉公平性。代码可在\url{https://github.com/06Yujie/Fair-Medical-Imaging}获取。
cs.CV / 178 / 2610.06052
Local2Mesh: Spatially Localized Contour-to-Mesh for Left Ventricular Reconstruction from Sparse 2D Cardiac MRI
Local2Mesh：基于空间局部轮廓到网格的左心室重建方法，适用于稀疏的二维心脏MRI
Abstract
Three-dimensional (3D) left ventricular (LV) reconstruction from sparse cardiac magnetic resonance (CMR) imaging remains challenging due to inter-slice misalignment and insufficient local spatial information between slices. Global aggregation of contour features may obscure local contour-to-surface relationships. We propose Local2Mesh, a spatially localized contour-to-mesh framework that deforms a template mesh to reconstruct 3D LV geometry from sparse 2D contours without 3D mesh annotations. The framework introduces geometry-aware alignment to correct inter-slice misalignment and a plane-aware Local Router that routes contour features to template vertices using vertex-to-plane distances. Local and global contour features then jointly guide graph-based template deformation for 3D LV reconstruction. Experiments on two public datasets, M\&Ms-2 and ACDC, demonstrate superior geometric reconstruction and functional estimation over existing methods. Zero-shot transfer from M\&Ms-2 to ACDC demonstrates strong cross-dataset generalization. Reconstructed meshes also improve disease classification over sparse contours, supporting their utility for downstream cardiac analysis. These results demonstrate that combining geometry-aware alignment with local contour-to-vertex modeling improves LV reconstruction from sparse 2D contours and supports downstream cardiac analysis. The code is available at \url{https://github.com/hwu918945-alt/loca2mesh}.
Chinese Translation
从稀疏的心脏磁共振成像（CMR）中进行三维（3D）左心室（LV）重建仍然具有挑战性，主要由于切片间的错位和切片之间局部空间信息的不足。轮廓特征的全局聚合可能会掩盖局部轮廓与表面之间的关系。我们提出了Local2Mesh，一个空间局部的轮廓到网格框架，该框架通过变形模板网格，从稀疏的二维轮廓中重建3D LV几何形状，而无需3D网格标注。该框架引入了几何感知对齐，以纠正切片间的错位，并使用基于顶点到平面的距离的平面感知局部路由器，将轮廓特征路由到模板顶点。局部和全局轮廓特征共同引导基于图的模板变形，以实现3D LV重建。在两个公共数据集M&Ms-2和ACDC上的实验表明，与现有方法相比，几何重建和功能估计表现优越。从M&Ms-2到ACDC的零样本迁移展示了强大的跨数据集泛化能力。重建的网格在疾病分类上也优于稀疏轮廓，支持其在下游心脏分析中的应用。这些结果表明，将几何感知对齐与局部轮廓到顶点建模相结合，可以改善从稀疏二维轮廓中进行的LV重建，并支持后续的心脏分析。代码可在 {https://github.com/hwu918945-alt/loca2mesh} 获取。
cs.CV / 179 / 2610.06056
ROT: Rotating Hidden States towards Contextual Vectors for Hallucination Mitigation in LVLMs
ROT：朝向上下文向量的旋转隐藏状态以缓解大型视觉语言模型中的幻觉
Abstract
Large Vision-Language Models (LVLMs) frequently suffer from object hallucination. Existing training-free interventions primarily manipulate attention weights, which indirectly affect the deep semantics reaching the final predictive layers. In this work, we shift our focus to the hidden state vectors extracted after self-attention and residual addition. Empirical analysis reveals that hallucinated tokens do not simply over-rely on linguistic priors; instead, they exhibit an anomalous contextual deviation, showing significantly lower similarities to both textual and visual contexts in intermediate layers. Motivated by this, we propose ROT, a layer-specific, training-free framework. ROT dynamically detects semantic deviation in the middle layers and applies a norm-preserving rotation to steer the hidden states back toward the local multimodal context plane spanned by the contexts. For subsequent layers, a representational smoothing mechanism is introduced to stabilize the calibrated trajectory. Extensive experiments on multiple benchmarks demonstrate that ROT consistently reduces hallucinations across various model architectures and scales, offering an efficient, geometry-driven solution for grounded generation.
Chinese Translation
大型视觉语言模型（LVLMs）常常遭遇对象幻觉。现有的无训练干预措施主要通过操控注意力权重，间接影响最终预测层的深层语义。在本研究中，我们将重点转向自注意力和残差加法后提取的隐藏状态向量。实证分析表明，幻觉令牌并非仅仅过度依赖语言先验；相反，它们表现出异常的上下文偏差，在中间层中与文本和视觉上下文的相似性显著降低。基于此，我们提出了ROT，一个特定层次的无训练框架。ROT动态检测中间层的语义偏差，并应用保持范数的旋转，将隐藏状态引导回由上下文所跨越的局部多模态上下文平面。对于后续层，引入了一种表征平滑机制，以稳定校准轨迹。在多个基准上的广泛实验表明，ROT在各种模型架构和规模中始终减少幻觉，提供了一种高效的、基于几何的有根生成解决方案。
cs.CV / 180 / 2610.06063
Vision Transformer Ensembles for Panoramic Street Segmentation
用于全景街道分割的视觉变换器集成
Abstract
Semantic segmentation of street panoramas can support detailed descriptions of urban environments, yet small datasets and unequal training costs make model selection difficult. This paper presents the system used for a first place submission to the PalmCity challenge in the leaderboard snapshot dated 5 October 2026. Nine pretrained segmentation systems are compared using approximately equal computation budgets. The candidates include DeepLabV3+, SegFormer, UPerNet, Mask2Former, DINOv3 with a linear decoder, and an Encoder only Mask Transformer using DINOv3. The two leading candidates are trained independently with three random seeds and longer budgets. Equal averaging of class probabilities from the three Encoder only Mask Transformer models, evaluated at three image scales with horizontal reflection, produces 60.95% mean intersection over union and 71.16% mean F1 on the 84 image public validation split. The submitted predictions receive 57.08% mean intersection over union and 67.96% mean F1 on the hidden test leaderboard. Producing all 249 test masks takes 251.49 seconds including model initialization and provenance checks on one NVIDIA RTX 5090. Peak allocated GPU memory is 2.70 GiB. The study reports all eligible models, all inference variants, class level errors, source conditions, and reproducibility checks, providing a documented challenge workflow with existing architectures.
Chinese Translation
街道全景的语义分割可以支持对城市环境的详细描述，但小型数据集和不均等的训练成本使得模型选择变得困难。本文介绍了在2026年10月5日的PalmCity挑战赛中获得第一名的提交所使用的系统。比较了九个预训练的分割系统，使用大致相等的计算预算。候选模型包括DeepLabV3+、SegFormer、UPerNet、Mask2Former、带线性解码器的DINOv3，以及仅使用DINOv3的编码器掩模变换器。两个领先的候选模型独立训练，使用三个随机种子和更长的预算。对三个仅编码器掩模变换器模型的类别概率进行均等平均，在三个图像尺度下进行评估并进行水平反射，产生了60.95%的平均交并比和71.16%的平均F1分数，基于84张图像的公共验证集。提交的预测在隐藏测试排行榜上获得了57.08%的平均交并比和67.96%的平均F1分数。生成所有249个测试掩模的时间为251.49秒，包括模型初始化和来源检查，使用一台NVIDIA RTX 5090。峰值分配的GPU内存为2.70 GiB。本研究报告了所有符合条件的模型、所有推理变体、类别级错误、源条件和可重复性检查，提供了一个记录的挑战工作流程，结合了现有架构。
cs.CV / 181 / 2610.06094
Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion
保留解剖结构的无配对锥束计算机断层扫描图像精细化方法：基于伪标签引导的扩散技术在图像引导放射治疗中的应用
Abstract
Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy, but scatter, beam hardening, noise, truncation, and other artifacts limit image quality and CT number accuracy. Paired CBCT and CT data are difficult to obtain clinically because of motion, anatomical changes, and acquisition mismatch. We present RefineCBCT, an unpaired CBCT refinement framework that uses pseudo-label guidance and short-step diffusion to reduce artifacts while preserving patient-specific anatomy. RefineCBCT was trained and evaluated on unpaired CBCT and planning CT data from public LUNG TCIA and PELVIC TCIA datasets and compared with representative GAN and diffusion based methods. On LUNG TCIA, it achieved the best results across all metrics, with MAE 19.411, RMSE 62.758, PSNR 30.845 dB, and SSIM 0.931. On PELVIC TCIA, it achieved the best MAE, PSNR, and SSIM, with values of 14.905, 36.671 dB, and 0.876. The refined images showed fewer streaking and shading artifacts, clearer anatomical boundaries, and improved soft tissue uniformity, with line profile and ROI analyses showing closer agreement with planning CT. These results suggest that RefineCBCT provides efficient and effective CBCT refinement under clinically realistic unpaired training conditions and may support more reliable CBCT use in image-guided radiotherapy workflows. Code is publicly available on GitHub, and the evaluated datasets are available from The Cancer Imaging Archive.
Chinese Translation
锥束计算机断层扫描（CBCT）在图像引导放射治疗中被广泛应用，但散射、束硬化、噪声、截断及其他伪影限制了图像质量和CT数值的准确性。由于运动、解剖变化和采集不匹配，临床上难以获得配对的CBCT和CT数据。我们提出了RefineCBCT，一个无配对CBCT精细化框架，利用伪标签引导和短步扩散来减少伪影，同时保留患者特定的解剖结构。RefineCBCT在来自公共LUNG TCIA和PELVIC TCIA数据集的无配对CBCT和计划CT数据上进行了训练和评估，并与代表性的GAN和基于扩散的方法进行了比较。在LUNG TCIA上，它在所有指标中取得了最佳结果，MAE为19.411，RMSE为62.758，PSNR为30.845 dB，SSIM为0.931。在PELVIC TCIA上，它在MAE、PSNR和SSIM上也取得了最佳成绩，分别为14.905、36.671 dB和0.876。精细化后的图像显示出更少的条纹和阴影伪影，更清晰的解剖边界，以及改善的软组织均匀性，线型轮廓和ROI分析显示与计划CT的更接近一致性。这些结果表明，RefineCBCT在临床现实的无配对训练条件下提供了高效且有效的CBCT精细化，并可能支持在图像引导放射治疗工作流程中更可靠的CBCT使用。代码已在GitHub上公开，评估的数据集可从癌症影像档案馆获取。
cs.CV / 182 / 2610.06102
Benchmarking CLIP for Zero-Shot Face and Periocular Gender Estimation
CLIP在零-shot面部和眼周性别估计中的基准测试
Abstract
We investigate CLIP for zero-shot gender estimation from full-face and periocular images. Three CLIP backbones are evaluated on 11,299 frontal images from Adience using image-text similarity with male/female prompts, achieving 95.54% full-face accuracy without task-specific training. For periocular, zero-shot predictions are strongly biased towards males, primarily due to a misaligned decision boundary. Threshold alignment substantially reduces this bias, reaching 85.29% accuracy. Linear SVMs trained on CLIP features provide only marginal gains, with a best periocular accuracy of 86.17%, approximately 2.8% above previous Adience results in the literature. Nevertheless, the gap with full-face performance confirms the greater difficulty of periocular gender estimation
Chinese Translation
我们研究了CLIP在全脸和眼周图像中进行零-shot性别估计的能力。评估了三种CLIP骨干网络在Adience数据集中11,299张正面图像上的表现，使用男性/女性提示进行图像-文本相似性计算，未经过特定任务训练的情况下实现了95.54%的全脸准确率。对于眼周图像，零-shot预测明显偏向男性，这主要是由于决策边界的不对齐。阈值对齐显著减少了这种偏差，达到了85.29%的准确率。在CLIP特征上训练的线性支持向量机（SVM）仅提供了边际增益，最佳眼周准确率为86.17%，比文献中先前的Adience结果高出约2.8%。然而，与全脸性能的差距确认了眼周性别估计的更大难度。
cs.CV / 183 / 2610.06135
AnchorGen: Anchored Optimization for Customizable Generative 3D Design
AnchorGen：用于可定制生成3D设计的锚定优化
Abstract
Engineering design often starts from a 2D sketch that fixes style and proportions, yet the subsequent 3D shape optimization relies on learned generative priors to keep the geometry valid. However, these priors are agnostic to the sketch: while they admit a valid design by correcting a drifted proposal back to its training distribution, they often correct it towards the high-density region, ignoring the specified design. We introduce \emph{AnchorGen}, a rectified-flow framework trained unconditionally on the concatenated shape and sketch latents of paired data. The learned manifold represents the joint distribution of shape-sketch pairs, so constraining the sketch component restricts the iterate to the sub-manifold of shapes consistent with a target style. Since training employs no conditioning signal, the constraint is imposed at inference: gradient descent optimizes the shape latent to minimize a differentiable drag surrogate, while constraining the sketch latent to remain close to the target sketch via a token-wise cosine penalty. A single model thereby supports design-preserving optimization, dimensionally explicit design edits, and sketch-only synthesis.
Chinese Translation
工程设计通常始于一个固定风格和比例的2D草图，但随后的3D形状优化依赖于学习到的生成先验，以保持几何形状的有效性。然而，这些先验对草图是无关的：虽然它们通过将偏离的提案修正回训练分布来承认有效设计，但它们往往将其修正到高密度区域，忽略了指定的设计。我们提出了 extit{AnchorGen}，一个在配对数据的形状和草图潜变量的连接上无条件训练的修正流框架。学习到的流形表示形状-草图对的联合分布，因此约束草图组件限制了迭代到与目标风格一致的形状子流形。由于训练过程中不使用条件信号，约束在推理时施加：梯度下降优化形状潜变量以最小化可微分的拖曳代理，同时通过逐标记余弦惩罚约束草图潜变量保持接近目标草图。由此，一个模型支持设计保持优化、维度明确的设计编辑和仅基于草图的合成。
cs.CV / 184 / 2610.06147
Efficient Test-time Adaptation through Candidate Verification and Divergence Shifts
通过候选验证和发散转移实现高效的测试时适应
Abstract
Vision-language models (VLMs) achieve strong zero-shot transferability but remain vulnerable to target-domain shifts at inference time. Test-time adaptation (TTA) offers a practical remedy, yet most existing VLM-TTA methods follow a prediction-side adaptation paradigm. They use test samples to adjust logits, prototypes, caches, priors, or feature statistics, often incurring additional computational overhead. In this paper, we take a different perspective and reframe VLM-TTA as candidate verification rather than prediction adjustment. We propose Test-Time Correction (TTC), a hypothesis-based correction framework guided by a simple principle: hypothesize, reconstruct, correct. Given a test feature and its top-k candidate labels, TTC treats each candidate label as a hypothesis, reconstructs the feature within the corresponding latent subspace stored in a memory bank, and measures the resulting divergence shift. This shift quantifies how much the candidate subspace and its relations to other candidates change after the hypothetical insertion of the test feature. A correct candidate hypothesis induces only a small shift, whereas an incorrect one perturbs the subspace more strongly. TTC therefore corrects the prediction by selecting the candidate with the minimum aggregated divergence shift. This training-free candidate-verification mechanism avoids iterative optimization and provides a favorable accuracy-efficiency trade-off. Across five TTA settings and 15 benchmark datasets, including zero-shot classification, domain generalization, few-shot classification, base-to-novel generalization, and cross-dataset evaluation, TTC consistently improves accuracy over state-of-the-art VLM-TTA methods while achieving up to 2x speedup, over 3x lower CPU memory usage, and up to 1.4x lower GPU memory usage than the lowest-memory training-free baseline.
Chinese Translation
视觉-语言模型（VLMs）在零-shot 转移能力上表现出色，但在推理时对目标领域的变化仍然脆弱。测试时适应（TTA）提供了一种实用的解决方案，但大多数现有的 VLM-TTA 方法遵循预测侧适应范式。它们使用测试样本来调整 logits、原型、缓存、先验或特征统计，通常会带来额外的计算开销。在本文中，我们采取不同的视角，将 VLM-TTA 重新框定为候选验证，而非预测调整。我们提出了测试时校正（Test-Time Correction, TTC），这是一个基于假设的校正框架，其指导原则简单明了：假设、重构、校正。给定一个测试特征及其前 k 个候选标签，TTC 将每个候选标签视为一个假设，在存储于内存库中的相应潜在子空间内重构特征，并测量由此产生的发散转移。该转移量化了候选子空间及其与其他候选之间的关系在假设性插入测试特征后发生的变化程度。正确的候选假设仅会引起小的转移，而错误的假设则会更强烈地扰动子空间。因此，TTC 通过选择具有最小聚合发散转移的候选来校正预测。该无训练的候选验证机制避免了迭代优化，并提供了良好的准确性与效率的权衡。在五个 TTA 设置和 15 个基准数据集上，包括零-shot 分类、领域泛化、少样本分类、基础到新颖泛化和跨数据集评估，TTC 在准确性上始终优于最先进的 VLM-TTA 方法，同时实现了高达 2 倍的加速、超过 3 倍的较低 CPU 内存使用和高达 1.4 倍的较低 GPU 内存使用，相较于最低内存的无训练基线。
cs.CV / 185 / 2610.06167
Bayesian Optimization in Sequence-to-Architecture Latent Space for Zero-Shot NAS
在序列到架构潜在空间中的贝叶斯优化用于零-shot 神经架构搜索
Abstract
Zero-shot Neural Architecture Search removes the prohibitive cost of traditional NAS, but its search process is typically based on the evolutionary algorithm (EA); lacking an explicit model of the objective, it often resorts to a near-random search through mutation. Bayesian Optimization offers a principled alternative by modeling the objective and aggregating information across iterations, but scales poorly to the high-dimensional, discrete, graph-structured spaces of modern NAS, restricting its use to only small networks. In this paper, we bring Bayesian Optimization to zero-shot NAS for large-scale architectures by learning a latent space via a Variational Autoencoder trained to reconstruct a novel prefix encoding of architectures and propose a proxy scalarization that combines several zero-shot proxies into a single Bayesian Optimization objective. After only 10,000 iterations of the proposed search algorithm (8 hours on a single GPU), our method found a network architecture which under the given model parameter count constraints achieves state-of-the-art results on three separate tasks -- image classification, object detection and semantic segmentation.
Chinese Translation
零-shot 神经架构搜索消除了传统神经架构搜索的高昂成本，但其搜索过程通常基于进化算法（EA）；由于缺乏目标的明确模型，它经常依赖于通过变异进行近乎随机的搜索。贝叶斯优化通过对目标进行建模并在迭代过程中聚合信息，提供了一种有原则的替代方案，但在现代神经架构搜索的高维、离散、图结构空间中扩展性较差，限制了其在大型网络中的应用。在本文中，我们通过学习一个潜在空间，将贝叶斯优化引入大型架构的零-shot 神经架构搜索，采用变分自编码器（Variational Autoencoder）训练以重构新颖的架构前缀编码，并提出了一种代理标量化方法，将多个零-shot 代理组合成一个单一的贝叶斯优化目标。在仅进行 10,000 次迭代（在单个 GPU 上耗时 8 小时）后，我们的方法找到了一个网络架构，该架构在给定的模型参数计数约束下，在三个独立任务（图像分类、目标检测和语义分割）上达到了最先进的结果。
cs.CV / 186 / 2610.06196
EORestore-Agent: Fidelity-Guided Agentic Restoration of Remote Sensing Images with Composite Degradations
EORestore-Agent：基于保真度引导的遥感图像复原代理，处理复合退化
Abstract
Remote sensing images often carry composite degradations, in which haze, cloud, noise, blur, low light, and low resolution coexist. Restoring them requires deciding which tool to apply, in what order, and when to stop, yet no clean reference is available at inference time to verify these decisions. All-in-one models trained on single degradations converge to a narrow PSNR band as degradations accumulate. To formulate real-world remote sensing restoration as a traceable trajectory, we present EORestore-Agent, which replaces this unmeasurable objective with reference-free, verifiable per-step decisions. A fine-tuned vision-language model reports all residual degradation types, whose tool pools are scored together, so the restoration order emerges from step-wise selection. A relative quality scorer, trained with full-reference supervision on synthetic degradation chains, predicts the changes in PSNR, SSIM, and LPIPS from the current image to each candidate. A step is accepted only when no predicted change is negative and the predicted PSNR gain is positive. Otherwise, the agent keeps the current image. On a synthetic Landsat-8 benchmark with six degradation types, EORestore-Agent improves PSNR by 2.3 to 3.2 dB over the strongest retrained all-in-one baseline on composites of two to six degradations, whereas zero-shot natural-image agents fall below the degraded input in PSNR in 17 of 18 settings. Replacing the learned scorer with no-reference quality differences costs 1.1 to 4.6 dB. The remaining harmful steps are small and cluster near the acceptance threshold. Sentinel-2 examples illustrate transfer to real atmospheric degradation without retraining.
Chinese Translation
遥感图像通常存在复合退化，其中雾霾、云层、噪声、模糊、低光照和低分辨率共存。恢复这些图像需要决定使用何种工具、以何种顺序进行以及何时停止，但在推理时没有可用的干净参考来验证这些决策。针对单一退化训练的全能模型在退化累积时收敛到狭窄的PSNR区间。为了将真实世界的遥感复原形式化为可追踪的轨迹，我们提出了EORestore-Agent，它用无参考、可验证的逐步决策替代了这一不可测量的目标。经过微调的视觉-语言模型报告所有残余退化类型，其工具池被共同评分，因此复原顺序通过逐步选择得出。一个相对质量评分器，在合成退化链上经过全参考监督训练，预测从当前图像到每个候选图像的PSNR、SSIM和LPIPS的变化。仅当没有预测变化为负且预测的PSNR增益为正时，步骤才被接受。否则，代理将保持当前图像。在一个包含六种退化类型的合成Landsat-8基准测试中，EORestore-Agent在两到六种退化的复合图像上比最强的重新训练全能基线提高了2.3到3.2 dB，而零样本自然图像代理在18种设置中有17种的PSNR低于退化输入。用无参考质量差异替代学习的评分器会损失1.1到4.6 dB。剩余的有害步骤较小，并聚集在接受阈值附近。Sentinel-2的例子展示了在不重新训练的情况下向真实大气退化的迁移。
cs.CV / 187 / 2610.06210
MoCAR: Motion-code Coordinate-aware AutoRegression for Continuous Trajectory Forecasting
MoCAR：运动编码坐标感知自回归用于连续轨迹预测
Abstract
Autoregressive generation is natural for language, where predicted tokens can be directly reused as the next prediction state, but trajectory forecasting lacks such a clean token: motion is continuous, multimodal, and expressed in local coordinate frames that evolve with the predicted trajectory. We present MoCAR (Motion-code Coordinate-aware AutoRegression), a decoder-only framework that casts trajectory forecasting as next-code prediction in a coordinate-aware continuous latent space. MoCAR learns a continuous motion-code space from endpoint-normalized trajectory segments, where each code jointly captures local trajectory geometry and the reference-frame transition induced by that segment. Historical motion codes are used as a teacher-forced prefix, future codes are generated autoregressively under temporal, map, agent, and mode interactions, and predicted codes persist in latent memory while decoded endpoints update the local scene context. This enables rollout without trajectory-space re-tokenization, trajectory queries, goal candidates, or proposal-and-refinement pipelines. On Argoverse (AV) benchmarks, MoCAR achieves top-tier performance with a simple single-stage architecture, transfers strongly from AV2 to AV1 in zero-shot evaluation, and improves on turn-heavy scenarios. Ablations confirm that the learned continuous motion-code space, latent alignment, weak KL regularization, and joint tokenizer-predictor optimization are essential for stable latent autoregression.
Chinese Translation
自回归生成在语言中是自然的，其中预测的标记可以直接作为下一个预测状态重用，但轨迹预测缺乏这样的清晰标记：运动是连续的、多模态的，并且在随着预测轨迹演变的局部坐标系中表达。我们提出了MoCAR（运动编码坐标感知自回归），这是一个仅解码器框架，将轨迹预测视为坐标感知连续潜在空间中的下一个编码预测。MoCAR从端点归一化的轨迹片段中学习连续运动编码空间，其中每个编码共同捕捉局部轨迹几何形状和由该片段引起的参考框架转换。历史运动编码作为教师强制前缀使用，未来编码在时间、地图、代理和模式交互下自回归生成，预测的编码在潜在记忆中保持，而解码的端点更新局部场景上下文。这使得在不进行轨迹空间重新标记、轨迹查询、目标候选或提案与精炼管道的情况下进行展开。在Argoverse（AV）基准测试中，MoCAR以简单的单阶段架构实现了顶级性能，在零样本评估中从AV2强力迁移到AV1，并在转弯密集场景中表现出色。消融实验确认，学习到的连续运动编码空间、潜在对齐、弱KL正则化和联合标记器-预测器优化对于稳定的潜在自回归至关重要。
cs.CV / 188 / 2610.06221
Frequency-Decoupled Diffusion Guidance for Non-Blind Image Deblurring
频率解耦扩散引导用于非盲图像去模糊
Abstract
Pretrained diffusion models provide powerful image priors for training-free posterior sampling in image restoration. To guide this sampling process, frequency-aware methods progressively incorporate measurement information across frequency bands, facilitating coarse-to-fine reconstruction. However, existing methods typically do not explicitly separate frequency activation from degradation-induced attenuation, leaving attenuation differences among inactive frequencies insufficiently modeled. In this work, we propose frequency-decoupled posterior guidance to separate frequency activation from attenuation-aware spectral regularization. Specifically, a progressive low-to-high frequency schedule determines the active measurement band, while a kernel-derived attenuation map defines a selective spectral prior over inactive components. To stabilize the sampling process, we also introduce a local trajectory regularizer that suppresses spatially irregular state-to-clean deviations. For a fixed endpoint energy, we provide a KL-regularized path-space interpretation. In practice, we construct time-dependent guidance through local energy corrections using a Tweedie plug-in approximation. Experiments on natural-image benchmarks demonstrate strong PSNR and SSIM performance across challenging non-blind deblurring settings, even at higher measurement noise levels.
Chinese Translation
预训练的扩散模型为无训练的后验采样提供了强大的图像先验，用于图像恢复。为了引导这一采样过程，频率感知方法逐步在频率带中纳入测量信息，促进粗到细的重建。然而，现有方法通常没有明确将频率激活与降解引起的衰减分开，导致在非活动频率之间的衰减差异建模不足。在本工作中，我们提出了频率解耦的后验引导，以将频率激活与考虑衰减的光谱正则化分开。具体而言，逐步的低频到高频调度确定了活动测量带，而基于核的衰减图则为非活动成分定义了选择性的光谱先验。为了稳定采样过程，我们还引入了局部轨迹正则化器，以抑制空间不规则的状态与清晰图像之间的偏差。对于固定的终点能量，我们提供了KL正则化的路径空间解释。在实践中，我们通过使用Tweedie插件近似构建了时间依赖的引导，通过局部能量修正。对自然图像基准的实验表明，在具有挑战性的非盲去模糊设置中，即使在较高的测量噪声水平下，也能实现强大的PSNR和SSIM性能。
cs.CV / 189 / 2610.06234
Joint Class-Time Learning for Video Classification with Multi-Instance Partial-Label Learning
基于多实例部分标签学习的视频分类联合类时间学习
Abstract
Multi-instance partial-label learning (MIPL) addresses inexact supervision in both the instance and label spaces, which can be applied to video classification. However, bag-level labels do not explicitly supervise the correspondence between candidate classes and temporal evidence. We propose {\ours}, which couples label disambiguation with temporal evidence allocation through a joint class--time assignment. Occupancy-regularized spherical matching associates contextualized video features while learning nonuniform temporal mass and discouraging excessive concentration. During training, candidate-restricted inference recomputes the assignment within the candidate label set. A dual-marginal KL projection then constructs a structured teacher that incorporates momentum-refined class beliefs while preserving the proposal's temporal occupancy. A single plan-level KL objective aligns the full-space predictor with this teacher. Our analysis characterizes when candidate re-solving differs from masking and shows that, under the stated construction, the joint objective decomposes into class-marginal and class-conditional temporal supervision. We construct VCMIPL benchmarks from Breakfast, DoTA, and FineAction using model-generated candidate labels and evaluate the method across four feature representations. Extensive experimental results demonstrate that PIVOTMIPL outperforms existing MIPL algorithms in both effectiveness and efficiency.
Chinese Translation
多实例部分标签学习（MIPL）解决了实例和标签空间中的不精确监督问题，可应用于视频分类。然而，包级标签并未明确监督候选类别与时间证据之间的对应关系。我们提出了{ extit{ours}}，将标签消歧与时间证据分配结合，通过联合类-时间分配实现。占用正则化的球形匹配在学习非均匀时间质量的同时，关联上下文化的视频特征，并抑制过度集中。在训练过程中，候选限制推断在候选标签集内重新计算分配。然后，双边边际KL投影构建了一个结构化教师，该教师结合了动量精炼的类别信念，同时保留了提案的时间占用。单一计划级KL目标将全空间预测器与该教师对齐。我们的分析表征了候选重新解决与掩蔽之间的差异，并表明在所述构造下，联合目标分解为类别边际和类别条件时间监督。我们从Breakfast、DoTA和FineAction构建了VCMIPL基准，使用模型生成的候选标签，并在四种特征表示上评估该方法。大量实验结果表明，PIVOTMIPL在有效性和效率上均优于现有的MIPL算法。
cs.CV / 190 / 2610.06260
CentriQ: Calibration-Free Quantization of Diffusion Transformers via Exact Mean Centering
CentriQ：通过精确均值中心化实现扩散变换器的无校准量化
Abstract
Diffusion transformers (DiTs) achieve state-of-the-art image generation, but their sampling cost limits deployment. Quantizing both weights and activations to 4 bits reduces this cost, yet existing methods fall short in one of two ways. Calibration-based methods are tied to a specific checkpoint and prompt distribution, whereas data-free Hadamard rotation, effective for LLMs, loses quality on DiTs. We show that this loss has a structural cause. Adaptive layer-norm conditioning adds a per-token mean to the activations, and at the widths of the evaluated DiTs, the Hadamard rotations used by data-free methods cannot spread this mean uniformly across coordinates. A single dominant direction therefore survives the rotation and sets the quantization range. We introduce CentriQ, a calibration-free quantizer that centers each token before rotation and restores the mean exactly through a rank-1 full-precision branch, so that per-token scales follow in closed form without data. Weights are fitted under a robust $\ell_p$ objective that tracks the dense mode of each group and discounts heavy tails. Across three DiTs, CentriQ matches the quality of calibrated SVDQuant at 4 bits, whereas calibration-free weight quantizers with plain per-token activation quantization collapse or degrade substantially. CentriQ outperforms the strongest calibration-free method reported to date at 2-bit weights. It is also the first calibration-free method to retain usable image quality at 2-bit activations.
Chinese Translation
扩散变换器（DiTs）在图像生成方面达到了最先进的水平，但其采样成本限制了部署。将权重和激活量化为4位可以降低这一成本，但现有方法在两个方面都存在不足。基于校准的方法依赖于特定的检查点和提示分布，而无数据的哈达玛旋转方法在大型语言模型（LLMs）中有效，但在DiTs上会导致质量下降。我们证明了这种损失有结构性原因。自适应层归一化条件为激活添加了每个标记的均值，而在评估的DiTs的宽度下，无数据方法使用的哈达玛旋转无法在坐标上均匀传播这一均值。因此，一个主导方向在旋转中存活下来并设定了量化范围。我们提出了CentriQ，一种无校准量化器，在旋转之前对每个标记进行中心化，并通过一个秩-1全精度分支精确恢复均值，从而使每个标记的尺度以封闭形式跟随，无需数据。权重在一个稳健的$ ext{l}_p$目标下进行拟合，该目标跟踪每组的密集模式并降低重尾的影响。在三个DiTs上，CentriQ在4位时的质量与经过校准的SVDQuant相匹配，而使用普通每个标记激活量化的无校准权重量化器则崩溃或显著降级。CentriQ在2位权重下超越了迄今为止报告的最强无校准方法。它也是第一个在2位激活下保持可用图像质量的无校准方法。
cs.CV / 191 / 2610.06293
VepAgent: Bridging Causal-Transition via Tool-Augmented Reinforcement Learning for Video Event Prediction
VepAgent：通过工具增强的强化学习弥合因果转变以进行视频事件预测
Abstract
Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in video understanding, yet their reliance on retrospective summarization and text-centric priors often limits their ability to bridge unobserved causal transitions when applied to Video Event Prediction (VEP). To address this, we propose VepAgent, an agentic framework that integrates causal-transition reasoning with tool-augmented reinforcement learning (RL) for robust VEP. Unlike prior methods that passively project future trajectories from historical dependencies, our approach explicitly models the logical progression from terminal observed states to future events. Specifically, we first construct futurebench-4K, a high-quality chain-of-thought dataset for supervised fine-tuning (SFT) that effectively bridges the causal-logic gap by structuring the deduction of unobserved intermediate states. Subsequently, we develop a diagnostic tool library integrating state tracking, frame retrieval, and region magnification, enabling the agent to dynamically augment reasoning with external tools to recover missing spatio-temporal evidence and resolve visual ambiguities during inference. Moreover, we propose a composite reward mechanism that jointly optimizes prediction accuracy, causal coherence, and reliable prior, compelling the agent to rely on genuine visual grounding rather than superficial textual similarities. Extensive evaluations on FutureBench and NEPBench datasets demonstrate that our method achieves state-of-the-art performance, significantly outperforming larger MLLMs and validating the empirical effectiveness of our agentic, future-oriented reasoning paradigm.
Chinese Translation
多模态大型语言模型（MLLMs）在视频理解方面展现了显著的潜力，但它们对回顾性总结和以文本为中心的先验的依赖，往往限制了它们在视频事件预测（VEP）中弥合未观察到的因果转变的能力。为了解决这一问题，我们提出了VepAgent，一个将因果转变推理与工具增强的强化学习（RL）相结合的智能框架，以实现稳健的VEP。与以往方法被动地从历史依赖中投影未来轨迹不同，我们的方法明确建模了从终端观察状态到未来事件的逻辑进展。具体而言，我们首先构建了futurebench-4K，这是一个高质量的思维链数据集，用于监督微调（SFT），通过结构化未观察到的中间状态的推导，有效弥合因果逻辑的差距。随后，我们开发了一个诊断工具库，集成了状态跟踪、帧检索和区域放大，使得智能体能够动态地利用外部工具增强推理，以恢复缺失的时空证据并在推理过程中解决视觉模糊。此外，我们提出了一种复合奖励机制，联合优化预测准确性、因果一致性和可靠的先验，促使智能体依赖真实的视觉基础，而非表面的文本相似性。在FutureBench和NEPBench数据集上的广泛评估表明，我们的方法达到了最先进的性能，显著超越了更大的MLLMs，并验证了我们智能、面向未来的推理范式的实证有效性。
cs.CV / 192 / 2610.06318
Wiring Matters: Injection Topology and Initialization of Affordance Heads in Vision-Language-Action Policies
连接方式的重要性：视觉-语言-行动策略中可供性头的注入拓扑与初始化
Abstract
Dense affordance supervision is an appealing auxiliary signal for vision-language-action (VLA) policies, yet naively co-training an affordance head can severely damage instruction following. We present a controlled study of how to wire such a head into a modern VLA on the LIBERO benchmark. Our recipe reads the backbone through a stop-gradient and re-injects an intermediate head feature into the action expert via a learned bridge. The stop-gradient is a precondition: letting affordance gradients reach the backbone drops the policy below the headless base (85.5% vs. 93.1%). With the backbone protected, a same-budget 2*2 ablation over injection topology (concatenation vs. residual) and bridge initialization (zero vs. random) shows initialization is the dominant lever. The best wiring, an actively initialized residual bridge, reaches 96.2%, matching the far more elaborate three-expert AffordanceVLA (95.8%) with under 1% extra parameters. Two probes explain the mechanism: ground-truth affordances fed as an input hurt, and inference-time zeroing shows a lazy bridge acts only as a training-time regularizer while an active bridge becomes load-bearing.
Chinese Translation
密集的可供性监督是视觉-语言-行动（VLA）策略中一种吸引人的辅助信号，但简单地共同训练可供性头可能会严重损害指令遵循。我们在LIBERO基准上进行了受控研究，探讨如何将这样的头部连接到现代VLA中。我们的方案通过停止梯度读取主干，并通过学习到的桥接将中间头部特征重新注入到行动专家中。停止梯度是一个前提条件：让可供性梯度到达主干会使策略的表现低于无头基线（85.5%对93.1%）。在保护主干的情况下，针对注入拓扑（连接与残差）和桥接初始化（零与随机）的同预算2*2消融实验表明，初始化是主要的影响因素。最佳的连接方式是主动初始化的残差桥，达到了96.2%的表现，匹配了更复杂的三专家AffordanceVLA（95.8%），且额外参数不足1%。两个探针解释了机制：作为输入的真实可供性会造成负面影响，而推理时的零化显示懒惰桥接仅在训练时起到正则化作用，而主动桥接则成为承载结构。
cs.CV / 193 / 2610.06324
Readout Blindness: VLM Scores Miss the Spatial Direction Their Frozen Encoders Retain
读取盲点：VLM 分数错过了其冻结编码器所保留的空间方向
Abstract
CLIP-like vision-language models remain a cornerstone of multimodal systems, yet their scores stay near chance on directed spatial relations, such as whether one object is left of another. We call this failure readout blindness and analyze, theoretically and empirically, why deployed scores miss the direction: when scoring rules treat the subject and object symmetrically, direction cancels regardless of encoder training. Guided by this analysis, we introduce Antisymmetric Displacement Readout (ADR), which aligns caption words with image patches in the frozen features and scores each relation by the signed displacement between matched object centroids. Notably, ADR succeeds without additional training or learned parameters, thereby demonstrating that directional information remains in the frozen encoder. However, text and world priors can inflate accuracy, so we further introduce prior deflation, which measures the benefit of the image-text pairing as the grounded gain over a null that pairs each item with an unrelated image. Extensive experiments across encoder families show that ADR substantially improves over deployed scores, which remain near chance on most direction-balanced sets even for fine-tuned encoders. Compared with more complex readouts, ADR outperforms the evaluated MLLM likelihood readouts and is competitive with their chat inference at a small fraction of the computation. These results support our claim that directional information can be recovered from frozen features by an appropriate readout. Our implementation and evaluation kit will be publicly available.
Chinese Translation
类似 CLIP 的视觉-语言模型仍然是多模态系统的基石，但它们在定向空间关系上的得分接近随机，例如判断一个物体是否在另一个物体的左侧。我们将这种失败称为读取盲点，并从理论和实证两方面分析了已部署分数为何错过方向：当评分规则对主体和客体对称处理时，方向会被抵消，无论编码器的训练如何。在这一分析的指导下，我们引入了反对称位移读取（Antisymmetric Displacement Readout, ADR），该方法将标题词与冻结特征中的图像块对齐，并通过匹配物体质心之间的有符号位移来评分每个关系。值得注意的是，ADR 在没有额外训练或学习参数的情况下取得成功，从而证明了方向信息仍然保留在冻结编码器中。然而，文本和世界先验可能会夸大准确性，因此我们进一步引入了先验通货膨胀，测量图像-文本配对的好处作为相对于将每个项目与无关图像配对的无效基准的实际增益。对不同编码器系列的广泛实验表明，ADR 显著改善了已部署分数，而这些分数在大多数方向平衡的数据集上仍然接近随机，即使对于微调后的编码器。与更复杂的读取方法相比，ADR 在评估的 MLLM 似然读取中表现优于，并且在计算量仅为其一小部分的情况下与其聊天推理具有竞争力。这些结果支持我们的观点，即方向信息可以通过适当的读取从冻结特征中恢复。我们的实现和评估工具包将公开提供。
cs.CV / 194 / 2610.06334
SPIN: Image Immunization Against Diffusion Editing via Single-Step Projection in Stochastic Neighborhoods
SPIN：通过在随机邻域中的单步投影实现图像免疫以抵御扩散编辑
Abstract
Diffusion models have greatly advanced instruction-guided image editing, while also raising concerns about unauthorized image manipulation. Image immunization addresses this risk by adding imperceptible perturbations to an input image to disrupt subsequent edits. Since editing requests are unknown at image release, protection should remain effective beyond the instruction used to construct the perturbation. Existing immunization methods either require costly full-trajectory backpropagation or use intermediate objectives whose effects may be weakened by subsequent denoising. Meanwhile, a single inference path provides limited feedback about alternative denoising continuations. To address these challenges, we propose \textsc{SPIN}, a framework for image immunization via one-step projection over local stochastic trajectory neighborhoods. Starting from an early denoising state, \textsc{SPIN} generates stochastic neighboring states under the same instruction and predicts their clean latents through one-step projection without full unrolling. We then optimize a bounded input perturbation to maximize the average deviation of these predictions from a clean-edit reference, encouraging the perturbation to disrupt multiple possible editing outcomes. Experiments on two image editors demonstrate substantial gains in protection performance, with \textsc{SPIN} outperforming compared methods across all six metrics under seen instructions and in the more challenging unseen instruction setting.
Chinese Translation
扩散模型在指导性图像编辑方面取得了重大进展，但也引发了对未经授权的图像操控的担忧。图像免疫通过向输入图像添加不可察觉的扰动来应对这一风险，从而干扰后续的编辑。由于在图像发布时编辑请求是未知的，因此保护应在构建扰动所用的指令之外仍然有效。现有的免疫方法要么需要昂贵的全轨迹反向传播，要么使用中间目标，其效果可能会被后续去噪削弱。同时，单一的推理路径对替代去噪延续提供的反馈有限。为了解决这些挑战，我们提出了 extsc{SPIN}，一个通过在局部随机轨迹邻域中进行一步投影实现图像免疫的框架。从早期去噪状态开始， extsc{SPIN}在相同指令下生成随机邻近状态，并通过一步投影预测它们的干净潜在表示，而无需完全展开。然后，我们优化一个有界输入扰动，以最大化这些预测与干净编辑参考之间的平均偏差，鼓励扰动干扰多种可能的编辑结果。在两个图像编辑器上的实验表明，保护性能显著提升， extsc{SPIN}在所有六个指标下的表现均优于对比方法，无论是在已见指令还是在更具挑战性的未见指令设置中。
cs.CV / 195 / 2610.06339
BabelFake: A Multilingual Audio-Visual DeepFake Benchmark
BabelFake：一个多语言音视频深度伪造基准
Abstract
Reliable and practical audio-visual DeepFake detection requires benchmarks that reflect diverse linguistic contexts and modern data synthesis pipelines for visual as well as audio manipulations. However, existing datasets predominantly contain footage of English-speakers, often include outdated manipulation types, or overlook the audio modality. Further, many datasets feature individuals who did not consent to be used in DeepFake creation. We introduce BabelFake, a multilingual audio-visual DeepFake benchmark recorded with consenting participants. BabelFake contains 399k clips (1,323 hours) from 496 individuals spanning five languages (English, German, Italian, French, Spanish). Our modular data generation pipeline pairs 11 modern video manipulation methods with 4 voice cloning engines, distinguishing visual-only (face swapping) and joint audio-visual manipulations (lip synchronization and portrait animation). By benchmarking state-of-the-art detectors, we show that detection difficulty depends on the audio-visual generation pairing, with substantial performance degradation when authentic audio is preserved. Cross-language/demographic evaluation reveals sensitivity varying across detector architectures and training data, while human evaluation reveals that perceived realism and machine-detection difficulty do not necessarily align.
Chinese Translation
可靠且实用的音视频深度伪造检测需要反映多样语言背景和现代数据合成管道的基准，以便进行视觉和音频操控。然而，现有数据集主要包含英语使用者的影像，通常包括过时的操控类型，或忽视了音频模态。此外，许多数据集中的个体并未同意被用于深度伪造的创建。我们推出了BabelFake，一个由同意参与者录制的多语言音视频深度伪造基准。BabelFake包含来自496个个体的399,000个片段（1,323小时），涵盖五种语言（英语、德语、意大利语、法语、西班牙语）。我们的模块化数据生成管道将11种现代视频操控方法与4种声音克隆引擎相结合，区分了仅视觉操控（面部交换）和联合音视频操控（唇部同步和肖像动画）。通过对最先进的检测器进行基准测试，我们表明检测难度取决于音视频生成配对，当保留真实音频时，性能显著下降。跨语言/人口评估揭示了不同检测器架构和训练数据的敏感性差异，而人类评估则表明感知的真实感与机器检测难度并不一定一致。
cs.CV / 196 / 2610.06342
MeSD: Multi-Evidence Self-Distillation for VideoLLM
MeSD：视频大语言模型的多证据自蒸馏
Abstract
While reinforcement learning with verifiable rewards provides reliable outcome supervision for VideoLLMs, sequence-level rewards offer limited token-level guidance. On-policy self-distillation addresses this limitation by conditioning a self-teacher on privileged information to provide dense token-level supervision. However, aggregating heterogeneous evidence within a single teacher context obscures cross-evidence agreement and conflict. A further challenge lies in determining whether teacher guidance should refine reward-based updates or provide corrective supervision for failed trajectories. To address these issues, we propose MeSD, a multi-evidence self-distillation framework for VideoLLMs. MeSD constructs three evidence-conditioned teachers with shared parameters, using the ground-truth answer as a common semantic context while separately incorporating temporal and spatial evidence. Given the same student-generated prefixes, MeSD evaluates evidence-specific preferences relative to the Answer Teacher and fuses teacher-common preferences with gated teacher-specific residuals. Furthermore, MeSD introduces Verification-Guided Optimization to classify trajectories as Success, Failure, or Indeterminate. For Success and Indeterminate trajectories, MeSD refines token-level advantage magnitudes while preserving reward-derived signs. For verified failure trajectories that contain the required evidence, MeSD applies failure-conditioned distillation, using reverse-KL correction toward the fused distribution. Experiments on multiple video benchmarks demonstrate consistent gains over reinforcement learning and self-distillation baselines.
Chinese Translation
虽然具有可验证奖励的强化学习为视频大语言模型（VideoLLMs）提供了可靠的结果监督，但序列级奖励仅提供有限的令牌级指导。基于策略的自蒸馏通过将自教师与特权信息相结合，解决了这一限制，从而提供密集的令牌级监督。然而，在单一教师上下文中聚合异质证据会模糊跨证据的一致性和冲突。另一个挑战在于确定教师指导是应该细化基于奖励的更新，还是为失败轨迹提供纠正监督。为了解决这些问题，我们提出了MeSD，一个针对视频大语言模型的多证据自蒸馏框架。MeSD构建了三个共享参数的证据条件教师，使用真实答案作为共同的语义上下文，同时分别结合时间和空间证据。在相同的学生生成前缀下，MeSD评估相对于答案教师的证据特定偏好，并将教师共同偏好与门控教师特定残差融合。此外，MeSD引入了验证引导优化，以将轨迹分类为成功、失败或不确定。对于成功和不确定的轨迹，MeSD在保留基于奖励的符号的同时，细化令牌级优势幅度。对于包含所需证据的经过验证的失败轨迹，MeSD应用失败条件蒸馏，使用反向KL修正融合分布。多个视频基准上的实验表明，MeSD在强化学习和自蒸馏基线之上取得了一致的提升。
cs.CV / 197 / 2610.06349
KineWorld: Action-Induced Transport Fields for Embodied World Modeling
KineWorld：基于动作诱导的传输场用于具身世界建模
Abstract
Embodied world models predict the visual consequences of candidate actions before execution. However, existing action-conditioned world models often adopt uniformly weighted visual generation objectives that can be misaligned with embodied prediction needs. Even with explicit motion conditioning, these objectives can underemphasize spatially sparse changes that are critical to interaction. We propose KineWorld, a transport-aware world-modeling framework that extends robot kinematics from motion conditioning to the spatial allocation of generative supervision. Kinematic Transport Lifting (KTL) constructs renderer-derived, camera-aligned transport fields from commanded robot motion. Transport-Aware World Diffusion (TAWD) calibrates their motion support on the video-latent grid and reweights future-RGB flow matching through a normalized mixture of uniform and transport-focused distributions. We train KineWorld using ALOHA-AgileX bimanual manipulation data from RoboTwin 2.0. KineWorld achieves an EWMScore-P of 68.95 in single-view evaluation and a TWB-Score of 54.82 in multi-view evaluation. These results support a shift from appearance fitting toward action-consequence modeling for embodied decision-making.
Chinese Translation
具身世界模型在执行之前预测候选动作的视觉后果。然而，现有的动作条件世界模型通常采用统一加权的视觉生成目标，这可能与具身预测需求不一致。即使在显式运动条件下，这些目标也可能低估对交互至关重要的空间稀疏变化。我们提出了KineWorld，一个关注传输的世界建模框架，它将机器人运动学从运动条件扩展到生成监督的空间分配。运动传输提升（Kinematic Transport Lifting, KTL）从指令机器人运动构建渲染器衍生的、与相机对齐的传输场。传输感知世界扩散（Transport-Aware World Diffusion, TAWD）在视频潜在网格上校准它们的运动支持，并通过统一和关注传输的分布的归一化混合重新加权未来RGB流匹配。我们使用来自RoboTwin 2.0的ALOHA-AgileX双手操作数据训练KineWorld。在单视图评估中，KineWorld达到了68.95的EWMScore-P，在多视图评估中达到了54.82的TWB-Score。这些结果支持从外观拟合向具身决策建模的转变。
cs.CV / 198 / 2610.06369
Environmental sensor readings in two crop disease image datasets identify the session in which each image was taken
环境传感器读数在两个作物病害图像数据集中识别每张图像拍摄的会话
Abstract
Integrating environmental sensor data with leaf imagery is widely reported to boost crop disease classification accuracy. In this work, we reveal that these reported gains are often artifacts of dataset construction: because a single sensor reading is shared across many images collected in a single session (one farm on one date), multimodal networks can predict disease simply by memorizing session identities. Analyzing two widely used Korean datasets, the Crop Disease Diagnosis (CDD) benchmark and an AI Hub pest/disease dataset, we demonstrate that nearly all images share sensor values, with 91.9% of CDD test images having exact sensor duplicates in the training set. Remarkably, an image-free classifier given only timestamps matches or exceeds sensor-driven predictions across all seven evaluated crops, and matches the published macro-F1 of a state-of-the-art CDD fusion model. These results indicate that performance gains on standard random splits cannot be disentangled from session leakage. We propose that multimodal crop studies must evaluate on session-held-out splits and report performance against sensor-free date-time baselines to ensure genuine generalization.
Chinese Translation
将环境传感器数据与叶片图像结合使用被广泛报道可以提高作物病害分类的准确性。在本研究中，我们揭示这些报告的增益往往是数据集构建的伪影：因为在单个会话（同一农场在同一日期）中，多个图像共享一个传感器读数，多模态网络可以仅通过记忆会话身份来预测病害。通过分析两个广泛使用的韩国数据集，即作物病害诊断（Crop Disease Diagnosis, CDD）基准和AI Hub害虫/病害数据集，我们证明几乎所有图像共享传感器值，其中91.9%的CDD测试图像在训练集中具有完全相同的传感器重复值。值得注意的是，仅给出时间戳的无图像分类器在所有七种评估作物中匹配或超过了基于传感器的预测，并且与最先进的CDD融合模型发布的宏F1值相匹配。这些结果表明，在标准随机划分上的性能提升无法与会话泄漏区分开。我们建议多模态作物研究必须在会话保留的划分上进行评估，并报告与无传感器日期时间基线的性能，以确保真正的泛化。
cs.CV / 199 / 2610.06378
MTOR: Generalizable AI-Generated Video Detection with Multimodal Semantics and Temporal Over-Regularity
MTOR：具有多模态语义和时间过度规律性的可泛化AI生成视频检测
Abstract
The rapid evolution of video generation has narrowed the perceptual gap between authentic and synthetic videos, making generalizable AI-generated video detection increasingly challenging. Existing detectors predominantly rely on visual representations, leaving caption-derived textual semantics underexplored. Meanwhile, temporal regularity in fine-grained visual representations has received limited attention. We find that caption-derived textual representations provide complementary discriminative cues to global visual representations. Our analysis further reveals that AI-generated videos exhibit stronger temporal persistence and lower temporal variability, a pattern we term temporal over-regularity (TOR). Based on these findings, we propose MTOR with a multimodal branch and a TOR component. The multimodal branch integrates global visual and caption-derived textual representations, while the TOR component models temporal over-regularity at three levels: coarse inter-frame continuity, fine-grained token correspondence, and frame-to-video stability. Extensive evaluations on five benchmarks covering 46 generator variants demonstrate state-of-the-art overall performance against 16 representative baselines, while robustness experiments confirm strong resilience to twelve real-world video perturbations. Code and models will be released at https://github.com/hwang-cs-ime/MTOR.
Chinese Translation
视频生成的快速发展缩小了真实视频与合成视频之间的感知差距，使得可泛化的AI生成视频检测变得愈加具有挑战性。现有的检测器主要依赖视觉表征，而对基于字幕的文本语义的探索相对不足。同时，细粒度视觉表征中的时间规律性也受到的关注有限。我们发现，基于字幕的文本表征为全局视觉表征提供了互补的区分线索。我们的分析进一步揭示，AI生成的视频表现出更强的时间持续性和较低的时间变异性，这一模式我们称之为时间过度规律性（Temporal Over-Regularity, TOR）。基于这些发现，我们提出了MTOR，包含一个多模态分支和一个TOR组件。多模态分支整合了全局视觉和基于字幕的文本表征，而TOR组件在三个层面上建模时间过度规律性：粗略的帧间连续性、细粒度的标记对应关系以及帧到视频的稳定性。在覆盖46种生成器变体的五个基准上的广泛评估显示，相较于16个代表性基线，MTOR在整体性能上达到了最先进的水平，同时鲁棒性实验确认了其对十二种真实世界视频扰动的强大抵抗力。代码和模型将发布在 https://github.com/hwang-cs-ime/MTOR。
cs.CV / 200 / 2610.06389
Multi-Task Partially Supervised Learning for Super-Resolution and Semantic Segmentation on Earth Observation data
用于地球观测数据的多任务部分监督学习：超分辨率与语义分割
Abstract
Super-resolution and semantic segmentation are known to benefit one another, especially in the Earth observation context. However, learning both tasks in a joint model often requires both task annotations, which is impractical and expensive. In this paper, we study the multi-task partially supervised learning paradigm for both tasks, where each example is assumed to have only a single-task annotation. To that end, we examine two multi-task architectural variations, the sequential and shared variants, and then propose a hybrid variant and a re-projection loss to benefit from the shared representation and enforce image quality of super-resolution when training with semantic segmentation. Experiments show favorable results compared to the SOTA sequential variant. Source code will be published at https://github.com/lhoangan/munera.
Chinese Translation
超分辨率和语义分割在地球观测的背景下被认为是相互促进的。然而，在联合模型中同时学习这两项任务通常需要两项任务的注释，这在实际操作中既不切实际又成本高昂。本文研究了针对这两项任务的多任务部分监督学习范式，其中假设每个样本仅具有单一任务的注释。为此，我们考察了两种多任务架构变体：顺序变体和共享变体，并提出了一种混合变体和重投影损失，以利用共享表示并在与语义分割训练时增强超分辨率的图像质量。实验结果显示，与当前最先进的顺序变体相比，提出的方法表现出良好的效果。源代码将发布在 https://github.com/lhoangan/munera。
cs.CV / 201 / 2610.06394
Harnessing Multimodal Large Language Models for Training-Free Human-Object Interaction Detection
利用多模态大型语言模型实现无训练的人机交互检测
Abstract
Human-object interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Traditional supervised methods perform strongly but rely on task-specific training. Recent multimodal large language models (MLLMs) offer a promising route to training-free HOI detection through their broad visual-semantic knowledge and versatile perceptual and reasoning capabilities. However, existing approaches largely invoke these capabilities through loosely coordinated inference stages. This fragmented execution restricts the role of interaction hypotheses in guiding visual exploration, leaving key participants overlooked and local ambiguities unresolved. Furthermore, propagating early semantic assumptions through subsequent visual grounding and relation prediction induces self-reinforcing semantic circularity. To resolve these challenges, we propose HarnessHOI, a training-free framework that transforms passive MLLM inference into an active interaction-centric harness. Specifically, we introduce an interaction-guided perception mechanism that projects emerging interaction hypotheses back into the visual space to discover missing participants and refine ambiguous evidence through targeted observation. Furthermore, a relation-agnostic geometric adjudication module reconciles multi-source evidence to establish a unified spatial basis for grounded interaction reasoning across multiple actions and semantic roles. Extensive experiments on HICO-DET and V-COCO demonstrate that HarnessHOI achieves state-of-the-art performance among training-free methods, confirming the effectiveness of the proposed harness for complex interaction understanding. Code will be released upon publication.
Chinese Translation
人机交互（HOI）检测旨在定位人-物体对并识别它们的交互。传统的监督方法表现强劲，但依赖于特定任务的训练。最近，多模态大型语言模型（MLLMs）通过其广泛的视觉-语义知识以及多样的感知和推理能力，为无训练的HOI检测提供了一个有前景的途径。然而，现有的方法在很大程度上通过松散协调的推理阶段来调用这些能力。这种碎片化的执行限制了交互假设在引导视觉探索中的作用，导致关键参与者被忽视以及局部模糊未得到解决。此外，通过后续的视觉定位和关系预测传播早期的语义假设会导致自我强化的语义循环。为了解决这些挑战，我们提出了HarnessHOI，一个将被动的MLLM推理转变为主动的以交互为中心的框架。具体而言，我们引入了一种交互引导的感知机制，将新兴的交互假设投射回视觉空间，以发现缺失的参与者并通过针对性的观察来精炼模糊证据。此外，一个与关系无关的几何裁决模块调和多源证据，以建立一个统一的空间基础，用于跨多个动作和语义角色的基础交互推理。在HICO-DET和V-COCO上的大量实验表明，HarnessHOI在无训练方法中实现了最先进的性能，确认了所提框架在复杂交互理解中的有效性。代码将在发表时发布。
cs.CV / 202 / 2610.06413
SpatialChain: A Benchmark for Auditing Spatial Reasoning Faithfulness in VLMs
SpatialChain：审计视觉语言模型空间推理可信度的基准
Abstract
Thinking-enabled vision-language models (VLMs) report ever-higher accuracy on spatial benchmarks, yet final-answer scores cannot reveal whether a correct prediction reflects faithful spatial reasoning or a linguistic shortcut. We introduce SpatialChain, a dataset of 28,350 training and 899 test examples pairing spatially-oriented GQA questions with scene-graph-grounded reasoning chains, retained only when the generated answer matches the symbolic ground truth, and a two-axis evaluation combining objective chain-overlap metrics with a scene-graph-aware LLM judge that scores faithfulness and completeness independently of the final answer. Applied to nine thinking-enabled VLMs, the protocol surfaces three findings invisible to standard accuracy: (i) four of nine models achieve $\geq$79% VQA accuracy while exhibiting shortcut rates above 39%, i.e., correct answers whose reasoning the judge marks as unfaithful; (ii) chain quality significantly predicts answer correctness for seven of nine models, but the two exceptions (Claude Sonnet 4.6, InternVL3.5-8B) reveal qualitatively distinct failure modes, terse output vs. verbose-decorative reasoning, that benchmark accuracy alone conflates; (iii) SFT on SpatialChain improves Qwen3-VL-8B by +6.2 pp in-domain and reduces its shortcut rate to 22%, while a stylistic specialization effect on external benchmarks motivates replay-augmented training as mitigation. The faithfulness judge is validated against 198 human-annotated items, where judge-human agreement matches human-human agreement, and against a second judge from a different provider, which preserves the model ranking ($\rho$ = 0.88). Data, generation scripts, and evaluation code are released at https://github.com/spatialchain/SpatialChainBenchmark.
Chinese Translation
具备思维能力的视觉语言模型（VLMs）在空间基准测试中报告了越来越高的准确性，但最终答案的得分无法揭示正确预测是否反映了可信的空间推理或语言捷径。我们引入了SpatialChain，这是一个包含28,350个训练样本和899个测试样本的数据集，将空间导向的GQA问题与基于场景图的推理链配对，仅在生成的答案与符号真实值匹配时保留，并采用一种结合客观链重叠度量和场景图感知的LLM评估者的双轴评估，该评估者独立于最终答案对可信度和完整性进行评分。应用于九个具备思维能力的VLMs，该协议揭示了标准准确性无法察觉的三项发现：（i）九个模型中有四个模型的VQA准确率达到或超过79%，但捷径率超过39%，即正确答案的推理被评估者标记为不可信；（ii）链质量显著预测九个模型中七个模型的答案正确性，但两个例外（Claude Sonnet 4.6，InternVL3.5-8B）揭示了质上不同的失败模式，即简洁输出与冗长装饰性推理，而仅依赖基准准确性会将其混淆；（iii）在SpatialChain上进行的SFT使Qwen3-VL-8B在领域内提升了6.2个百分点，并将其捷径率降低至22%，而在外部基准上的风格专门化效应则促使了增强重播训练作为缓解措施。可信度评估者在198个人工标注项目上进行了验证，评估者与人类的协议与人类之间的协议相匹配，并且与来自不同提供者的第二位评估者的结果保持一致，模型排名保持不变（$
ho$ = 0.88）。数据、生成脚本和评估代码已发布在 https://github.com/spatialchain/SpatialChainBenchmark。
cs.CV / 203 / 2610.06423
Toward Reliable Infant Pose Estimation: A Training-Dynamics Approach to Noisy Annotation Detection
朝着可靠的婴儿姿态估计：一种基于训练动态的噪声标注检测方法
Abstract
Spontaneous movement analysis in preterm infants relies increasingly on markerless pose estimation (PE) to derive clinically relevant motion biomarkers directly from video recordings. Training accurate infant PE models requires large sets of manually annotated keypoints, and human annotation is inherently prone to error. Noisy keypoints (i.e., keypoints mislocalized with respect to their true anatomical position) are especially problematic in this clinical setting, since they can propagate as artificial artifacts into the reconstructed joint trajectories. Building on the small-loss hypothesis and training-dynamics-based sample selection established in the noisy-label learning literature, we propose a novel framework for detecting noisy keypoint annotations. A hybrid convolutional-attention model is trained to predict the anatomical category of each keypoint from its spatial coordinates and local visual features; the resulting cross-entropy training dynamics are then used to derive per-keypoint descriptors, which are partitioned into clean and noisy subsets via unsupervised clustering. We validate the approach on NeoPose, a newly collected dataset of 65 hospitalized preterm infants, under two realistic noise scenarios (random positional perturbation and left-right swapping) across multiple noise levels. Results show that the proposed approach achieves an F1-score of up to 91.9% in noisy-keypoint detection. The framework further generalizes to the heterogeneous COCO benchmark, where filtering CE-detected noisy keypoints from the training set also yields measurable improvements (up to 7.4 AP points) in downstream pose estimation accuracy at moderate-to-high noise levels.
Chinese Translation
早产儿的自发运动分析越来越依赖于无标记的姿态估计（PE），以直接从视频记录中推导出临床相关的运动生物标志。训练准确的婴儿PE模型需要大量手动标注的关键点，而人工标注本质上容易出错。在临床环境中，噪声关键点（即相对于其真实解剖位置错误定位的关键点）尤其成问题，因为它们可能作为人工伪影传播到重建的关节轨迹中。基于小损失假设和噪声标签学习文献中建立的基于训练动态的样本选择，我们提出了一种新的框架用于检测噪声关键点标注。我们训练了一个混合卷积-注意力模型，以根据每个关键点的空间坐标和局部视觉特征预测其解剖类别；然后，利用得到的交叉熵训练动态推导每个关键点的描述符，这些描述符通过无监督聚类被划分为干净和噪声子集。我们在NeoPose上验证了该方法，这是一个新收集的65名住院早产儿的数据集，在两个现实的噪声场景（随机位置扰动和左右交换）下，跨多个噪声水平进行测试。结果表明，所提出的方法在噪声关键点检测中达到了高达91.9%的F1分数。该框架进一步推广到异构COCO基准，在训练集中过滤CE检测到的噪声关键点也在中到高噪声水平下显著提高了下游姿态估计的准确性（最多提高7.4个AP点）。
cs.CV / 204 / 2610.06472
MaRO-GS: Mask-Robust Object-Centric Gaussian Splatting from Inconsistent Multi-view Masks
MaRO-GS：来自不一致多视图掩膜的掩膜鲁棒物体中心高斯喷溅
Abstract
We address the challenge of accurate 3D object reconstruction from multi-view images in Gaussian Splatting. Existing object-level 3DGS methods reconstruct the entire scene rather than directly optimizing the target object, even when only the target object is needed, which incurs substantial computational overhead. They also rely on 2D segmentation masks to associate Gaussians with objects, but these masks are often inconsistent across views. Such inconsistencies corrupt Gaussian optimization and produce incorrectly supervised Gaussians that degrade object reconstruction fidelity. To overcome these limitations, we propose MaRO-GS, a 3DGS framework that directly optimizes target-object Gaussians from object-masked multi-view images and remains robust to inconsistent supervision. For reliable supervision, mask-reliability view filtering excludes unreliable views. Object-supported Gaussian density control suppresses Gaussians irrelevant to the target object and prevents background densification, while Silhouette-aligned Object Loss maintains object-focused optimization. Extensive experiments across diverse datasets demonstrate that MaRO-GS improves PSNR, segmentation accuracy, and computational efficiency, with the largest PSNR gain of 2.05 dB on the small-object LERF-Mask dataset.
Chinese Translation
我们解决了在高斯喷溅中从多视图图像准确重建3D物体的挑战。现有的物体级3DGS方法重建整个场景，而不是直接优化目标物体，即使只需要目标物体，这也会导致相当大的计算开销。它们还依赖于2D分割掩膜将高斯与物体关联，但这些掩膜在不同视图中往往不一致。这种不一致性会破坏高斯优化，并产生错误监督的高斯，降低物体重建的保真度。为了克服这些限制，我们提出了MaRO-GS，一个3DGS框架，直接从物体掩膜的多视图图像中优化目标物体的高斯，并对不一致的监督保持鲁棒性。为了实现可靠的监督，掩膜可靠性视图过滤排除了不可靠的视图。物体支持的高斯密度控制抑制与目标物体无关的高斯，并防止背景密度化，而轮廓对齐物体损失保持物体聚焦的优化。通过在多样化数据集上的广泛实验表明，MaRO-GS提高了PSNR、分割准确性和计算效率，在小物体LERF-Mask数据集上获得了最大的PSNR增益2.05 dB。
cs.CV / 205 / 2610.06494
Topology-Informed Prompt-Conditioned Universal Segmentation of Uterine Structures from Ultrasound and MRI
基于拓扑信息的提示条件通用子宫结构超声与MRI分割方法
Abstract
Multi-structure segmentation of the uterus is important for computer-assisted screening, diagnosis, and treatment planning of uterine diseases, where ultrasound and MRI provide complementary clinical information. However, developing a unified model across these modalities is challenging due to their substantially different image appearances, anatomical contexts, spatial resolutions, and label spaces. Moreover, existing datasets often define different segmentation targets, making joint learning challenging and potentially leading to negative transfer across heterogeneous tasks. To this end, we propose a Topology-informed Prompt-conditioned Universal Segmentation (TPUS) framework for segmenting multiple uterine structures across ultrasound and MRI. TPUS introduces a graph-based multi-dataset backbone comprising modality-specific stems and a modality-shared graph-based encoder-decoder to support modality-sensitive input adaptation, structural feature reasoning, and joint representation learning across heterogeneous uterine segmentation tasks. In addition, TPUS uses task-aware class prompts to condition the segmentation process for different datasets and label spaces, a dynamic convolutional adaptation module to generate task-specific output responses, and a topology-informed loss to encourage anatomically consistent predictions. Experiments on a uterine ultrasound dataset and a T2-weighted uterine myoma MRI dataset demonstrate that TPUS achieves Dice scores of 0.898 and 0.693 on the two held-out test sets, respectively, outperforming several generic and universal segmentation baselines. Source code can be accessed at https://github.com/YonghengSun1997/TPUS.
Chinese Translation
子宫多结构分割对于计算机辅助筛查、诊断及子宫疾病治疗规划具有重要意义，其中超声和MRI提供了互补的临床信息。然而，由于这两种模态在图像表现、解剖背景、空间分辨率及标签空间上存在显著差异，开发跨模态的统一模型面临挑战。此外，现有数据集通常定义不同的分割目标，导致联合学习困难，且可能引发异构任务间的负迁移。为此，我们提出了一种基于拓扑信息的提示条件通用分割框架（Topology-informed Prompt-conditioned Universal Segmentation，TPUS），用于跨超声和MRI分割多种子宫结构。TPUS引入了基于图的多数据集主干网络，包含模态特异性干线和模态共享的图编码器-解码器，以支持模态敏感的输入适配、结构特征推理及异构子宫分割任务的联合表示学习。此外，TPUS采用任务感知类别提示调节不同数据集和标签空间的分割过程，动态卷积适配模块生成特定任务的输出响应，并通过拓扑信息损失促进解剖学一致的预测。基于子宫超声数据集和T2加权子宫肌瘤MRI数据集的实验表明，TPUS在两个独立测试集上分别实现了0.898和0.693的Dice系数，优于多种通用和泛用分割基线方法。源码可访问：https://github.com/YonghengSun1997/TPUS。
cs.CV / 206 / 2610.06502
NeuroCBIR: A Fast and Accurate Image Retrieval System for Whole-Brain and Region-Specific MRI
NeuroCBIR：一种快速且准确的全脑及区域特定MRI图像检索系统
Abstract
Content-based image retrieval (CBIR) in neuroimaging enables the identification of structurally similar brain scans, supporting diagnosis, prognosis, and treatment planning; however, existing methods are often limited to small datasets, single brain regions, or coarse class labels, thereby restricting their clinical utility and generalizability. Here, we present NeuroCBIR, a framework for fast and flexible retrieval of both whole-brain and region-specific 3D T1w MRI scans. A total of 103 cortical and subcortical regions are extracted to enable both whole-brain and region-level queries. NeuroCBIR leverages latent representations learned by a variational autoencoder (VAE) combined with contrastive learning, producing scan-specific embeddings that capture anatomical patterns. These embeddings were evaluated for subject re-identification, zero-shot age prediction, and zero-shot multi-class pathology stratification. Re-identification performance was high across both whole-brain and brain-region levels (mean average precision across the top-5 retrieved images (mAP@5) >= 98.4%), with robust generalization across datasets and acquisition conditions. While NeuroCBIR is not trained for age prediction or pathology stratification, zero-shot evaluations for these two tasks demonstrate that the embeddings encode meaningful information for downstream tasks. Embedding extraction on a 4-core CPU required approximately 18.7 s per scan, whereas similarity search was effectively instantaneous (less than 0.01 s). NeuroCBIR is publicly available for brain MRI with more than 26,000 precomputed T1w MRI embeddings. It supports reproducible research, region-specific flexibility, and clinically meaningful personalized diagnostic support. The software is available at https://github.com/minnelab/NeuroCBIR.
Chinese Translation
基于内容的图像检索（CBIR）在神经影像学中能够识别结构相似的脑扫描，支持诊断、预后和治疗计划；然而，现有方法通常局限于小型数据集、单一脑区或粗略的类别标签，从而限制了其临床实用性和普适性。在此，我们提出了NeuroCBIR，一个用于快速灵活检索全脑和区域特定3D T1加权MRI扫描的框架。共提取了103个皮层和皮层下区域，以支持全脑和区域级查询。NeuroCBIR利用变分自编码器（VAE）结合对比学习所学习的潜在表示，生成捕捉解剖模式的扫描特定嵌入。这些嵌入在受试者重新识别、零样本年龄预测和零样本多类病理分层中进行了评估。在全脑和脑区层面的重新识别性能均较高（前5个检索图像的平均精度（mAP@5）>= 98.4%），并且在不同数据集和采集条件下具有强大的泛化能力。虽然NeuroCBIR并未针对年龄预测或病理分层进行训练，但这两个任务的零样本评估表明，这些嵌入编码了对下游任务有意义的信息。在4核CPU上提取嵌入大约需要18.7秒每个扫描，而相似性搜索几乎是瞬时的（少于0.01秒）。NeuroCBIR公开提供脑MRI，包含超过26,000个预计算的T1加权MRI嵌入。它支持可重复的研究、区域特定的灵活性以及临床上有意义的个性化诊断支持。该软件可在https://github.com/minnelab/NeuroCBIR获取。
cs.CV / 207 / 2610.06503
Harmful Content Generation in Text-to-Image Models: Capabilities and Moderation Limitations
文本到图像模型中的有害内容生成：能力与审核限制
Abstract
Text-to-image generative models can produce highly realistic imagery but also raise concerns about harmful misuse. While safety mechanisms exist, systematic evaluations of their effectiveness against realistic attacks remain limited. We present a systematic evaluation of harmful content generation across five open text-to-image models using an automated pipeline that transforms legitimate news captions into unsafe prompts targeting sexually explicit content, violence/gore, harmful stereotypes, self-harm, and hate speech. We evaluate both standard models with built-in safety mechanisms and community fine-tuned variants that bypass content restrictions. A human evaluation of 1,500 generated images shows high harmful-content generation rates: 89.2% for gore-related prompts, 47.6% for sexually explicit content, 43.6% for harmful stereotypes, 46.0% for hate speech, and 34.5% for self-harm, predominantly through graphic violence. Models show substantial capability for generating violent and stereotypical content, while community fine-tuned variants are particularly vulnerable to sexually explicit prompts. Generation quality is largely preserved under harmful prompting, producing imagery of sufficient fidelity to pose risks for disinformation and abuse; FLUX.1-dev produces clearly realistic harmful images in 30.9% of cases. We further evaluate automated moderation systems and find substantial detection gaps that allow unsafe images to evade filtering. Finally, we assess synthetic image detectors and show that models trained only on benign datasets perform worse on explicit content, while more diverse training data improves detection, highlighting semantic distribution gaps in current approaches. These findings expose limitations in current generation safeguards, moderation systems, and synthetic image detection, highlighting the need for stronger defenses against misuse at scale.
Chinese Translation
文本到图像生成模型能够生成高度逼真的图像，但也引发了有关有害滥用的担忧。尽管存在安全机制，但对其在现实攻击下有效性的系统评估仍然有限。我们对五个开放的文本到图像模型进行了有害内容生成的系统评估，使用自动化流程将合法的新闻标题转化为针对性别露骨内容、暴力/血腥、有害刻板印象、自残和仇恨言论的不安全提示。我们评估了具有内置安全机制的标准模型和绕过内容限制的社区微调变体。对1500幅生成图像的人类评估显示，有害内容生成率很高：与血腥相关的提示为89.2%，性露骨内容为47.6%，有害刻板印象为43.6%，仇恨言论为46.0%，自残为34.5%，主要通过图形暴力表现出来。模型在生成暴力和刻板印象内容方面表现出显著能力，而社区微调变体对性露骨提示尤其脆弱。在有害提示下，生成质量基本得以保持，生成的图像具有足够的真实性，可能对虚假信息和滥用构成风险；FLUX.1-dev在30.9%的情况下生成明显逼真的有害图像。我们进一步评估了自动化审核系统，发现存在显著的检测漏洞，使不安全图像能够逃避过滤。最后，我们评估了合成图像检测器，结果显示仅在良性数据集上训练的模型在处理露骨内容时表现较差，而更为多样的训练数据则改善了检测效果，突显了当前方法中语义分布的差距。这些发现揭示了当前生成安全措施、审核系统和合成图像检测的局限性，强调了在大规模滥用中需要更强的防御措施。
cs.CV / 208 / 2610.06508
A General Pipeline for Dense Illuminant Estimation via Physically Based Synthetic Data
通过基于物理的合成数据进行密集光源估计的通用流程
Abstract
Illuminant estimation is a fundamental problem in computational photography, as it enables the correction of color shifts induced by varying lighting conditions. While learning-based methods have demonstrated strong performance, their progress is hindered by the limited availability of large-scale datasets with accurate illuminant ground-truth. In this work, we propose a general and reusable pipeline to derive dense illuminant chromaticity maps from physically based 3D-rendered scenes. By repurposing an existing 3D scene collection, our approach enables the systematic generation of pixel-wise illuminant annotations under controlled lighting conditions, effectively lowering the barrier to data acquisition for learning-based illuminant estimation. Using this pipeline, we generate a large-scale synthetic set of 74,321 images, which we employ for pre-training both single- and multi-illuminant estimation models. Extensive experiments with state-of-the-art architectures show that synthetic pre-training consistently improves performance, with gains of up to 28% for single-illuminant estimation and up to 57% for multi-illuminant estimation, particularly in data-scarce regimes. These findings demonstrate that synthetic data generation pipelines offer an effective and scalable solution for the pre-training of illuminant estimation methods.
Chinese Translation
光源估计是计算摄影中的一个基本问题，因为它能够纠正由不同照明条件引起的颜色偏移。尽管基于学习的方法表现出色，但由于缺乏具有准确光源真实值的大规模数据集，其进展受到限制。在本研究中，我们提出了一种通用且可重用的流程，从基于物理的3D渲染场景中推导出密集的光源色度图。通过重新利用现有的3D场景集合，我们的方法能够在受控照明条件下系统地生成逐像素的光源注释，有效降低了基于学习的光源估计的数据获取门槛。利用该流程，我们生成了一个大规模的合成图像集，共计74,321张图像，并用于对单光源和多光源估计模型的预训练。与最先进的架构进行的广泛实验表明，合成预训练始终提高了性能，单光源估计的提升幅度可达28%，多光源估计的提升幅度可达57%，尤其是在数据稀缺的情况下。这些发现表明，合成数据生成流程为光源估计方法的预训练提供了一种有效且可扩展的解决方案。
cs.CV / 209 / 2610.06571
BrainTRACE: Tracing Longitudinal, Multimodal, and Volumetric Evidence in Brain MRI Clinical Reasoning
BrainTRACE：追踪脑MRI临床推理中的纵向、多模态和体积证据
Abstract
Brain MRI interpretation is a longitudinal clinical reasoning problem: radiologists compare serial studies, integrate information across MRI sequences, localize findings within volumetric anatomy, and translate this evidence into report-grounded assessments. Existing medical VQA and 3D imaging benchmarks capture important parts of this workflow, but often evaluate brain MRI through isolated images, static volumes, or ungrounded report-style answers, thereby obscuring failures in the evidence chain that support clinical validity. We introduce BrainTRACE, a report-grounded benchmark for evaluating whether vision-language models can trace the evidence structure required for longitudinal brain MRI interpretation. BrainTRACE contains 7,273 scored VQA instances derived from 1,778 longitudinal patients, 7,299 MRI studies, and approximately 29k co-registered 3D MRI sequence volumes. The benchmark is organized by five levels of clinical reasoning, from acquisition recognition to case-level synthesis, and by evidence demands covering longitudinal comparison, report-grounded references, multi-sequence integration, and volumetric spatial evidence. BrainTRACE supports rendered inputs compatible with standard VLM interfaces, a 3D-evidence condition, and a decomposed case-reasoning track that audits six steps in a longitudinal evidence chain. Evaluation of 20 VLM configurations shows that current systems can identify isolated visual cues but rarely compose them into grounded longitudinal interpretations. We release the benchmark specification, evaluation lists, scoring implementation, scoring rubrics, and audit-record format to support reproducible progress in brain MRI VLM evaluation.
Chinese Translation
脑MRI解读是一个纵向的临床推理问题：放射科医师比较系列研究，整合MRI序列中的信息，在体积解剖中定位发现，并将这些证据转化为基于报告的评估。现有的医学视觉问答（VQA）和3D成像基准捕捉了这一工作流程的重要部分，但通常通过孤立图像、静态体积或未基于报告的答案来评估脑MRI，从而掩盖了支持临床有效性的证据链中的失败。我们提出了BrainTRACE，这是一个基于报告的基准，用于评估视觉-语言模型是否能够追踪进行脑MRI纵向解读所需的证据结构。BrainTRACE包含来自1,778名纵向患者的7,273个评分VQA实例，涉及7,299个MRI研究和大约29,000个共注册的3D MRI序列体积。该基准按照五个临床推理层次组织，从获取识别到案例级综合，并根据证据需求涵盖纵向比较、基于报告的参考、多序列整合和体积空间证据。BrainTRACE支持与标准视觉-语言模型接口兼容的渲染输入、3D证据条件，以及一个分解的案例推理轨道，审核纵向证据链中的六个步骤。对20种视觉-语言模型配置的评估表明，当前系统能够识别孤立的视觉线索，但很少将其组合成基于证据的纵向解读。我们发布了基准规范、评估列表、评分实现、评分标准和审计记录格式，以支持脑MRI视觉-语言模型评估的可重复进展。
cs.CV / 210 / 2610.06575
FrontVeg V2: A Training-Free Software Framework for Foreground-Aware Zero-Shot Plant Trait Segmentation in High-Resolution Images of Trellised Crops
FrontVeg V2：一种无需训练的前景感知零-shot植物性状分割软件框架，适用于高分辨率的支架作物图像
Abstract
FrontVeg V2 is an open-source, training-free software framework for foregroundaware zero-shot segmentation of plant traits in high-resolution images of trellised crops. The pipeline combines monocular depth estimation, automatic foreground extraction using Valley-Aware Depth Thresholding, tiled zero-shot segmentation, Graph-Based Mask Assembly, and geometry-aware fusion. This design enables plant organs and disease symptoms to be segmented while reducing detections arising from neighboring vegetation rows. The current implementation integrates Depth Anything V2 (DAV2) and SAM3 and can be used through both command-line batch processing and a Napari graphical interface. FrontVeg V2 provides a reusable framework for multi-crop, multi-trait digital phenotyping without task-specific model retraining.
Chinese Translation
FrontVeg V2 是一个开源的、无需训练的软件框架，用于在高分辨率的支架作物图像中进行前景感知的零-shot植物性状分割。该流程结合了单目深度估计、使用 Valley-Aware Depth Thresholding 的自动前景提取、平铺零-shot分割、基于图的掩膜组装和几何感知融合。此设计能够分割植物器官和病害症状，同时减少来自邻近植被行的误检。当前实现集成了 Depth Anything V2 (DAV2) 和 SAM3，并可通过命令行批处理和 Napari 图形界面使用。FrontVeg V2 提供了一个可重用的框架，用于多作物、多性状的数字表型分析，无需针对特定任务的模型重训练。
cs.CV / 211 / 2610.06588
Keepsake: Selective Spatial Memory for Long-Horizon Video Generation
Keepsake：用于长时间视频生成的选择性空间记忆
Abstract
Long-horizon camera-controlled video generation relies on persistent memory to maintain scene consistency. Existing systems follow two strategies to achieve this consistency. Full-history approaches retain all generated observations, causing unbounded storage and retrieval costs. Selective-construction approaches reduce redundancy, but make one-time retention decisions that are never revisited, even as an observation's value changes with the evolving memory bank. Both strategies leave a shared question unresolved: as the generated history evolves, which stored observations should still remain in memory? Our key insight is that the value of a stored observation is not fixed, but relational: it depends on the alternatives currently available in the memory bank. A view supported by many geometrically and visually similar substitutes can be relinquished with little loss of coverage, whereas an observation with few viable alternatives should remain regardless of age. We introduce Keepsake, an online, training-free controller for fixed-capacity spatial memory. At each update, Keepsake constructs a pose-appearance graph over retained and newly generated observations, combining camera-pose proximity with visual similarity. A retention priority jointly captures the number of strong substitutes and the similarity of the closest alternative, allowing Keepsake to continually reassess memory value, preserve observations with little alternative support, and evict highly replaceable ones under a fixed budget. The controller modifies only the persistent-memory update; the host generator, denoising schedule, and retrieval rule remain unchanged. Across MemCam and WorldMem, Keepsake improves FVD and LPIPS under a fixed memory budget. On 180-second MemCam trajectories, it retains only 32 of 5,397 frames while reducing FVD by 35.1%.
Chinese Translation
长时间摄像机控制的视频生成依赖于持久记忆以维持场景一致性。现有系统采用两种策略来实现这种一致性。全历史方法保留所有生成的观察，导致无限制的存储和检索成本。选择性构建方法减少冗余，但做出一次性保留决策，即使观察的价值随着记忆库的演变而变化，这些决策也不会被重新审视。这两种策略都未能解决一个共同的问题：随着生成历史的演变，哪些存储的观察仍应保留在记忆中？我们的关键见解是，存储观察的价值并不是固定的，而是相对的：它依赖于当前在记忆库中可用的替代品。一个由许多几何和视觉上相似的替代品支持的视图可以在几乎没有覆盖损失的情况下被放弃，而一个只有少数可行替代品的观察则应无论其年龄如何都保留。我们引入Keepsake，一种在线、无训练的固定容量空间记忆控制器。在每次更新时，Keepsake在保留和新生成的观察上构建一个姿态-外观图，结合摄像机姿态的接近性和视觉相似性。保留优先级共同捕捉强替代品的数量和最近替代品的相似性，使Keepsake能够不断重新评估记忆价值，保留支持替代品较少的观察，并在固定预算下驱逐高度可替代的观察。该控制器仅修改持久记忆的更新；主生成器、去噪调度和检索规则保持不变。在MemCam和WorldMem上，Keepsake在固定记忆预算下提高了FVD和LPIPS。在180秒的MemCam轨迹中，它仅保留了5,397帧中的32帧，同时将FVD降低了35.1%。
cs.CV / 212 / 2610.06594
VGGT-Bridge: Beyond Sequential Pose Graphs via Coarse-Stride Skip Edges
VGGT-桥接：超越顺序姿态图的粗步跳跃边缘
Abstract
Feed-forward visual geometry transformers such as VGGT reconstruct dense 3D structure from images in a single forward pass, simplifying multi-view 3D reconstruction. However, their quadratic attention complexity makes them difficult to scale to long sequences with thousands of frames. Chunk-and-align frameworks address this by splitting a long sequence into overlapping chunks and stitching their local reconstructions into a pose graph. Yet existing methods connect only sequentially adjacent chunks, so small per-frame errors accumulate along the chain into large-scale drift. To move beyond sequential edges, we propose VGGT-Bridge, which adds long-range skip edges that directly constrain non-adjacent chunks without retraining. By running VGGT on sparsely sampled coarse chunks, each coarse chunk bridges distant fine chunks into a single direct constraint. We further turn VGGT's first-frame scale bias into a drift correction by feeding selected coarse chunks in reverse, and a loop-aware policy keeps this reversal compatible with existing loop closures. VGGT-Bridge reduces ATE by 28.3% on KITTI Odometry, 18.8% on Virtual KITTI, and 10.0% on Waymo Open over the SwiftVGGT baseline, achieving the best performance among all chunk-and-align methods.
Chinese Translation
前馈视觉几何变换器（如 VGGT）能够在单次前向传递中从图像中重建稠密的三维结构，从而简化了多视图三维重建。然而，它们的二次注意力复杂度使得在包含数千帧的长序列中扩展变得困难。块对齐框架通过将长序列拆分为重叠块并将其局部重建拼接成姿态图来解决这个问题。然而，现有方法仅连接顺序相邻的块，因此每帧的小误差沿着链条累积成大规模漂移。为了超越顺序边缘，我们提出了 VGGT-桥接，它添加了长距离跳跃边缘，直接约束非相邻块而无需重新训练。通过在稀疏采样的粗块上运行 VGGT，每个粗块将远处的细块桥接成一个直接约束。我们进一步将 VGGT 的首帧尺度偏差转化为漂移校正，通过反向输入选定的粗块，并且一个循环感知策略保持这种反转与现有循环闭合的兼容性。VGGT-桥接在 KITTI 里程测试中将 ATE 降低了 28.3%，在虚拟 KITTI 中降低了 18.8%，在 Waymo Open 中降低了 10.0%，相较于 SwiftVGGT 基线，达到了所有块对齐方法中的最佳性能。
cs.CV / 213 / 2610.06596
Analysis of SWIR Imaging Detection Performance Under Adverse Environmental Conditions for Autonomous Driving Systems
在恶劣环境条件下自主驾驶系统的短波红外成像检测性能分析
Abstract
Short-wave infrared (SWIR) imaging has emerged as a promising modality for autonomous driving, yet its practical benefits over RGB remain poorly characterized across diverse conditions. This paper presents a systematic comparative study of paired RGB and SWIR object detection on the RASMD dataset, covering four weather conditions and two real-time detection architectures, with various fine-tunings evaluated against a unified ground truth. Overall, RGB demonstrates comparable or superior performance in most scenarios, while RF-DETR exhibits greater robustness across varying conditions. Beyond aggregate metrics, we propose a sensor-dominance mining framework that combines multi-model agreement with targeted manual inspection to identify scenarios where one sensing modality provides more reliable detections using largely unannotated paired data. This analysis reveals that SWIR offers clear advantages in four safety-critical situations, including windshield glare, water droplets on the windshield, low-contrast object visibility, and long-range vehicle detection. The findings suggest that SWIR should be viewed as a complementary modality that enhances perception in rare but challenging conditions. The datasets will be available upon request, and all code and trained model weights are publicly released at https://github.com/comsee-research/swir-adverse-env-analysis.
Chinese Translation
短波红外（SWIR）成像已成为自主驾驶的一种有前景的模式，但其在不同条件下相对于RGB的实际优势仍然缺乏充分的表征。本文对RASMD数据集上的RGB和SWIR物体检测进行了系统的比较研究，涵盖了四种天气条件和两种实时检测架构，并针对统一的真实标签评估了各种微调。总体而言，RGB在大多数场景中表现出可比或更优的性能，而RF-DETR在不同条件下展现出更大的鲁棒性。除了汇总指标外，我们提出了一种传感器主导性挖掘框架，该框架结合了多模型一致性与针对性的人工检查，以识别在大部分未标注的配对数据中，某一传感模式提供更可靠检测的场景。分析结果显示，SWIR在四种安全关键情境中具有明显优势，包括挡风玻璃眩光、挡风玻璃上的水滴、低对比度物体可见性和远程车辆检测。研究结果表明，SWIR应被视为一种补充模式，能够在稀有但具有挑战性的条件下增强感知。数据集可根据请求提供，所有代码和训练模型权重已在 https://github.com/comsee-research/swir-adverse-env-analysis 上公开发布。
cs.CV / 214 / 2610.06602
Multitask Conditional Generative Adversarial Network Enables Automatic Whole Knee Cartilage and Menisci Segmentation and Reliable T1\r{ho} and T2 Quantification Without High-Resolution Morphological Images
多任务条件生成对抗网络实现自动全膝关节软骨和半月板分割及可靠的 T1ρ 和 T2 定量，无需高分辨率形态图像
Abstract
Early osteoarthritis detection through quantitative MRI (qMRI) requires accurate cartilage and meniscus segmentation, traditionally necessitating time-consuming, costly 3D high-resolution Double Echo Steady-State (DESS) MRI scans. This study developed a multi-task conditional generative adversarial network (MT-cGAN) to simultaneously synthesize DESS-like images and segment tissues directly from qMRI echo images. This retrospective study evaluated 508 knee MRI volumes from 361 subjects (mean age: $40.4 \pm 12.2$ years; 179 female) across three cohorts. Ground truth segmentation masks were generated from DESS images using a pretrained model with manual correction, and $T_{1\rho}$ and $T_2$ maps were computed from magnetization-prepared angle-modulated partitioned $k$-space spoiled gradient echo snapshots (MAPSS) echo images. MT-cGAN was trained to jointly synthesize DESS-like images and segment cartilage and meniscus directly from echo images. Model performance was evaluated using Dice score for segmentation accuracy and coefficient of variation (CV) for $T_{1\rho}$ and $T_2$ quantification. MT-cGAN achieved the highest segmentation performance, mean Dice score 0.84 (range: 0.80--0.86) across all cartilage and meniscus compartments and significantly outperformed the state-of-the-art conditional GAN model with transfer learning (mean Dice, 0.82; $p < 0.001$, Wilcoxon signed-rank test). For relaxometry quantification, MT-cGAN demonstrated the highest consistency with the reference DESS protocol, yielding the lowest CV ($T_{1\rho}$: 1.84%, $T_2$: 1.81%). The proposed MT-cGAN accurately segmented cartilage and menisci while providing reliable $T_{1\rho}$ and $T_2$ quantification directly from echo images. By eliminating the need for separate morphological DESS scans, this workflow reduces required scan times to facilitate the clinical translation of qMRI.
Chinese Translation
通过定量磁共振成像（qMRI）早期检测骨关节炎需要准确的软骨和半月板分割，传统上需要耗时且成本高昂的三维高分辨率双回波稳态（DESS）MRI扫描。本研究开发了一种多任务条件生成对抗网络（MT-cGAN），以同时合成类似 DESS 的图像并直接从 qMRI 回波图像中分割组织。本回顾性研究评估了来自 361 名受试者（平均年龄：40.4 ± 12.2 岁；179 名女性）的 508 个膝关节 MRI 体积。使用经过预训练的模型和手动校正，从 DESS 图像生成了真实分割掩膜，并从磁化准备角调制分区 $k$-空间损耗梯度回波快照（MAPSS）回波图像计算了 $T_{1
ho}$ 和 $T_2$ 图。MT-cGAN 被训练以联合合成类似 DESS 的图像并直接从回波图像中分割软骨和半月板。模型性能通过 Dice 分数评估分割准确性，通过变异系数（CV）评估 $T_{1
ho}$ 和 $T_2$ 定量。MT-cGAN 在所有软骨和半月板区域中实现了最高的分割性能，平均 Dice 分数为 0.84（范围：0.80--0.86），显著优于最先进的条件 GAN 模型与迁移学习（平均 Dice，0.82；$p < 0.001$，Wilcoxon 符号秩检验）。在弛豫定量方面，MT-cGAN 与参考 DESS 协议表现出最高的一致性，获得了最低的 CV（$T_{1
ho}$：1.84%，$T_2$：1.81%）。所提出的 MT-cGAN 能够准确分割软骨和半月板，同时直接从回波图像提供可靠的 $T_{1
ho}$ 和 $T_2$ 定量。通过消除对单独形态 DESS 扫描的需求，该工作流程减少了所需的扫描时间，以促进 qMRI 的临床转化。
cs.CV / 215 / 2610.06611
Lens3D: Target-Conditioned Visual Foveation for Fine-Grained 3D Understanding
Lens3D：针对目标条件的视觉聚焦用于细粒度3D理解
Abstract
Existing 3D large language models often overlook fine-grained attributes and less visually salient objects and parts, even when relevant evidence is present in scene videos. We introduce Lens3D to improve fine-grained object understanding through external visual assistance and knowledge transfer. Its LensUnd pipeline adopts 3D localization to select informative, complementary views for an external 2D vision-language model, supporting fine-grained object captioning, small-object grounding, and fine-grained object question answering. LensDistill transfers the resulting fine-grained knowledge to 3D LLMs through detailed caption supervision, enabling captioning from native inputs without external VLM calls. We also construct LensBench, a held-out evaluation set of 2,068 objects with three silver-standard reference descriptions per object. Experiments with Video-3D LLM and 3DRS demonstrate that LensDistill substantially improves fine-grained object captioning while preserving existing grounding and scene-level QA performance. These results establish the feasibility of transferring externally acquired fine-grained knowledge into native 3D LLMs.
Chinese Translation
现有的3D大型语言模型往往忽视细粒度属性以及视觉上不显著的物体和部分，即使在场景视频中存在相关证据。我们提出了Lens3D，以通过外部视觉辅助和知识转移来改善细粒度物体理解。其LensUnd管道采用3D定位选择信息丰富的互补视图，为外部2D视觉-语言模型提供支持，促进细粒度物体的描述、小物体的定位和细粒度物体的问题回答。LensDistill通过详细的描述监督将生成的细粒度知识转移到3D大型语言模型中，使其能够从原生输入生成描述，而无需外部视觉语言模型的调用。我们还构建了LensBench，一个包含2068个物体的保留评估集，每个物体有三个银标准参考描述。与Video-3D大型语言模型和3DRS的实验表明，LensDistill在显著改善细粒度物体描述的同时，保持了现有的定位和场景级问答性能。这些结果确立了将外部获取的细粒度知识转移到原生3D大型语言模型中的可行性。
cs.CV / 216 / 2610.06616
Video Encoders Built on Image Representations
基于图像表示的视频编码器
Abstract
The design of a video encoder determines when frames begin to interact and which frame-specific visual evidence remains accessible to the language model. Native video pathways couple neighboring frames during visual encoding, whereas image pathways preserve independently computed frame representations but incur a much larger visual-token cost when all image tokens are forwarded. We ask a basic question: whether a compact video encoder can instead be built on image representations. To answer this question, we separate three operations that are often coupled: per-frame representation, cross-frame token allocation, and temporal interaction. A frozen image encoder first produces frame-specific candidates. A question-aware selector then allocates a fixed token budget across frames using relevance, diversity, and cross-frame correspondence, after which a lightweight learned refiner reads neighboring-frame context and writes residual updates only to the retained anchors. This preserves source positions and keeps the visual output at the fixed budget. Across 13 benchmarks and three vision-language backbones, the resulting pathway matches full-image aggregate performance while using only about 28%-35% of its visual tokens. Specifically, on Qwen3-VL-8B, it achieves a 13-benchmark macro-average of 62.75 with 1,535 visual tokens, compared with 62.58 for the full Image pathway at 4,424 tokens and 59.49 for native Conv3D at 2,212 tokens. On Qwen3-VL-32B, it reaches a 13-benchmark macro-average of 66.28, compared with 66.09 for Image, while providing a 2.16x end-to-end speedup. These results show that compact video encoding does not require early temporal mixing: frame-specific evidence can be preserved first, allocated jointly, and temporally contextualized after selection.
Chinese Translation
视频编码器的设计决定了帧何时开始相互作用，以及哪些特定于帧的视觉证据可以被语言模型访问。原生视频路径在视觉编码过程中将相邻帧结合在一起，而图像路径则保留独立计算的帧表示，但在转发所有图像标记时会产生更大的视觉标记成本。我们提出一个基本问题：是否可以基于图像表示构建紧凑的视频编码器。为了解答这个问题，我们将通常耦合的三个操作分开：每帧表示、跨帧标记分配和时间交互。一个冻结的图像编码器首先生成特定于帧的候选项。然后，一个关注问题的选择器使用相关性、多样性和跨帧对应性在帧之间分配固定的标记预算，之后一个轻量级的学习型精炼器读取相邻帧的上下文，并仅对保留的锚点写入残差更新。这保留了源位置，并保持视觉输出在固定预算内。在13个基准测试和三个视觉-语言骨干网络中，所得到的路径在使用约28%-35%的视觉标记的情况下，匹配了全图像的聚合性能。具体而言，在Qwen3-VL-8B上，它以1,535个视觉标记达到了62.75的13个基准的宏平均，而全图像路径在4,424个标记时为62.58，原生Conv3D在2,212个标记时为59.49。在Qwen3-VL-32B上，它达到了66.28的13个基准的宏平均，而图像路径为66.09，同时提供了2.16倍的端到端加速。这些结果表明，紧凑的视频编码不需要早期的时间混合：可以首先保留特定于帧的证据，随后共同分配，并在选择后进行时间上下文化。
cs.CV / 217 / 2610.06617
RealtimeWAM: One-Step Asynchronous World Action Models
实时世界动作模型：一步异步世界动作模型
Abstract
World Action Models (WAMs) incorporate visual representations from video generation backbones to guide action prediction. Recent efficient WAMs adopt Mixture-of-Transformers (MoT) architectures and compute video representations once for reuse by the action expert. However, intra-expert iteration (\ie, multi-step action denoising) and inter-expert waiting (\ie, sequential execution of the video and action experts) still limit inference efficiency. To this end, we present RealtimeWAM, an extremely efficient WAM variant with one-step action generation and asynchronous inference, addressing these two bottlenecks. To reduce intra-expert iteration, we propose Teacher-Anchored Consistency Distillation (TACD) to address a local-global error gap: low local consistency error alone does not guarantee accurate final actions. TACD supplements local consistency with explicit supervision from the frozen teacher's multi-step rollout endpoint, enabling accurate one-step action generation. Additionally, we propose Cross-Expert Wavefront Pipelining (CEWP) to eliminate unnecessary expert-level waiting. It overlaps the two experts through block-wise sharing of the video KV cache, synchronizing only immediately before the corresponding action attention consumes it. Extensive experiments across diverse benchmarks (\eg, LIBERO, LIBERO-Plus and RoboTwin) and model variants (\eg, Fast-WAM and Faster-WAM) demonstrate the superiority of RealtimeWAM. Notably, RealtimeWAM maintains near-lossless performance (\ie, $<1\%$ drop) across these benchmarks while delivering significant end-to-end speedup (\eg, $\sim25\times$ on H100). Our code and checkpoints are available via this \href{https://github.com/ModelTC/LightX2V/tree/main/examples/realtimewam}{link}.
Chinese Translation
世界动作模型（WAMs）结合来自视频生成骨干网络的视觉表示，以指导动作预测。近期高效的WAMs采用混合变换器（Mixture-of-Transformers, MoT）架构，并仅计算一次视频表示以供动作专家重用。然而，专家内部迭代（即多步动作去噪）和专家间等待（即视频与动作专家的顺序执行）仍然限制了推理效率。为此，我们提出了RealtimeWAM，一种极其高效的WAM变体，具有一步动作生成和异步推理，解决了这两个瓶颈。为了减少专家内部迭代，我们提出了教师锚定一致性蒸馏（Teacher-Anchored Consistency Distillation, TACD），以解决局部-全局误差差距：仅低局部一致性误差并不能保证最终动作的准确性。TACD通过来自冻结教师的多步展开端点的显式监督，补充局部一致性，使得准确的一步动作生成成为可能。此外，我们提出了跨专家波前流水线（Cross-Expert Wavefront Pipelining, CEWP），以消除不必要的专家级等待。它通过块级共享视频KV缓存重叠两个专家，仅在相应的动作注意力消耗之前进行同步。在多个基准测试（如LIBERO、LIBERO-Plus和RoboTwin）和模型变体（如Fast-WAM和Faster-WAM）上的大量实验表明，RealtimeWAM的优越性。值得注意的是，RealtimeWAM在这些基准测试中保持了接近无损的性能（即<1%的下降），同时实现了显著的端到端加速（例如，在H100上约25倍）。我们的代码和检查点可通过此链接获取。
cs.CV / 218 / 2610.06658
Talk Like You: Imitating How You Speak in Real-Time Talking Head Generation
像你一样说话：实时人头生成中的说话方式模仿
Abstract
In daily life, each person exhibits unique speaking habits, leading to subtle yet consistent lip-shape variations even when pronouncing the same word. Although recent talking head generation methods have achieved impressive visual fidelity and lip synchronization, they largely overlook user-specific customization, especially the motion patterns that characterize individual speaking habits. These habits are difficult to model and capture, as their motion patterns are highly fine-grained and often similar across individuals. As a result, many approaches produce overly uniform facial motions and fail to capture diverse, person-specific articulation patterns. To address this, we propose TalkLikeYou, an efficient framework that imitates how a target person speaks in talking head generation. Our method models habit in motion-space and achieves real-time performance through Flow Matching with only one sampling step during inference. We further adopt a two-stage imitation learning strategy to capture subtle distinctions between habits, allowing users to specify a target habit through either a preset style from the dataset or a reference video. In addition, we introduce a new metric PLAD that projects mouth motions onto representative articulation axes to evaluate imitation accuracy and generation diversity. Extensive experiments demonstrate that TalkLikeYou generates high-quality talking heads in real-time and significantly improves speaking habit imitation compared with prior methods. The code is available at: https://github.com/BQ-Wang0511/TalkLikeYou
Chinese Translation
在日常生活中，每个人都表现出独特的说话习惯，即使在发音相同的单词时，也会导致嘴唇形状的细微但一致的变化。尽管近期的人头生成方法在视觉逼真度和嘴唇同步方面取得了令人印象深刻的成果，但它们在很大程度上忽视了用户特定的定制，尤其是表征个体说话习惯的运动模式。这些习惯难以建模和捕捉，因为它们的运动模式高度细致且往往在个体之间相似。因此，许多方法产生过于统一的面部运动，未能捕捉到多样化的、特定于个人的发音模式。为了解决这个问题，我们提出了TalkLikeYou，这是一个高效的框架，能够在人头生成中模仿目标人物的说话方式。我们的方法在运动空间中建模习惯，并通过流匹配（Flow Matching）在推理过程中仅需一步采样实现实时性能。我们进一步采用了两阶段模仿学习策略，以捕捉习惯之间的细微差别，允许用户通过数据集中预设的风格或参考视频指定目标习惯。此外，我们引入了一种新的指标PLAD，将嘴部运动投影到代表性的发音轴上，以评估模仿准确性和生成多样性。大量实验表明，TalkLikeYou能够实时生成高质量的人头，并显著改善与先前方法相比的说话习惯模仿。代码可在以下链接获取：https://github.com/BQ-Wang0511/TalkLikeYou
cs.CV / 219 / 2610.06663
Cross-dataset harmonization for robust endoscopic image analysis
跨数据集协调用于稳健的内窥镜图像分析
Abstract
A significant problem in endoscopic image analysis is that the machine learning (ML) models used for this purpose usually underperform when applied on images acquired from endoscopes that are different from those used to acquire the images of their training set. The main difference of the images originating from different endoscopes is their color distributions, which depend both on the image sensors and the light sources used. Although previous studies have highlighted this challenge, to the best of our knowledge it has not been previously explicitly tackled. This study focuses on this problem and proposes very simple but impactful method. It implements a reference-based image harmonization that reduces global appearance differences between endoscopic datasets. Specifically, it extracts global color statistics from a chosen reference dataset in the CIE-Lab color space and applies a statistical channel-wise transformation to map each target image toward the appearance of the images of the reference dataset. The method is evaluated in the context of polyp detection in both flexible colonoscopy and capsule endoscopy datasets using a dataset-level cross validation protocol. The results indicate that the proposed harmonization consistently improves cross-dataset performance up to 30.7%, outperforming relevant baseline and state-of-the-art methods. The results indicate that a substantial part of the generalization gap is driven by low-level appearance variation that can be mitigated without retraining.
Chinese Translation
内窥镜图像分析中的一个重大问题是，用于此目的的机器学习（ML）模型在应用于与其训练集图像采集所用内窥镜不同的图像时，通常表现不佳。来自不同内窥镜的图像之间的主要区别在于其颜色分布，这取决于所使用的图像传感器和光源。尽管之前的研究强调了这一挑战，但据我们所知，这一问题尚未得到明确解决。本研究聚焦于此问题，并提出了一种非常简单但影响深远的方法。该方法实现了一种基于参考的图像协调，减少了内窥镜数据集之间的全球外观差异。具体而言，它在CIE-Lab颜色空间中从选定的参考数据集中提取全球颜色统计信息，并应用统计通道变换将每个目标图像映射到参考数据集图像的外观。该方法在灵活的结肠镜检查和胶囊内窥镜数据集中进行息肉检测的背景下进行了评估，使用了数据集级交叉验证协议。结果表明，所提出的协调方法在跨数据集性能上始终提高了多达30.7%，超越了相关基线和最先进的方法。结果表明，通用化差距的很大一部分是由低级外观变化驱动的，这可以在不重新训练的情况下得到缓解。
cs.CV / 220 / 2610.06672
VideoTapestry: Query-Adaptive Memory Refinement for Multi-Agent Long-Video Understanding
视频织锦：用于多智能体长视频理解的查询自适应记忆优化
Abstract
Long-video understanding places substantial demands on memory, as answering questions often requires retrieving information distributed across extended temporal spans. Existing approaches broadly follow two paradigms: query-driven exploration, which is sensitive to localization errors, and query-independent memory construction, which may omit question-specific details. We introduce VideoTapestry, a training-free multi-agent framework that adapts a preconstructed hierarchical video memory through coarse-to-fine, query-driven refinement. The preconstructed memory organizes video content into three levels, capturing global narrative context, event-level temporal structure, and fine-grained relational evidence, respectively. To support coarse-to-fine localization and observation, we assign a specialized agent to each level, keeping retrieval and refinement within a scale-specific context. Guided by the query, these agents revisit relevant video regions and enrich layer-wise memories with targeted multimodal observations. Their refinements are assembled according to the original hierarchy into a composite query-adaptive memory, preserving global context in a compact form while retaining fine-grained evidence along query-relevant branches for final reasoning. Compared with direct GPT-5.5 inference, VideoTapestry achieves absolute accuracy gains of 17.2%, 14.9%, 9.8%, and 7.0% on LVBench, LongVideoBench (Long), Video-MME (Long), and EgoSchema, respectively, achieving the state-of-the-art results among all competitors.
Chinese Translation
长视频理解对记忆提出了巨大的挑战，因为回答问题通常需要检索分布在较长时间跨度上的信息。现有方法大致遵循两种范式：查询驱动的探索，这对定位错误敏感；以及查询独立的记忆构建，这可能遗漏与问题相关的具体细节。我们提出了VideoTapestry，这是一种无训练的多智能体框架，通过粗到细的查询驱动优化，适应预构建的层次视频记忆。预构建的记忆将视频内容组织为三个层次，分别捕捉全局叙事上下文、事件级时间结构和细粒度关系证据。为了支持粗到细的定位和观察，我们为每个层次分配一个专门的智能体，保持检索和优化在特定规模的上下文中进行。在查询的指导下，这些智能体重新访问相关视频区域，并通过针对性的多模态观察丰富各层记忆。它们的优化根据原始层次结构组装成一个复合的查询自适应记忆，以紧凑的形式保留全局上下文，同时在与查询相关的分支上保留细粒度证据以供最终推理。与直接的GPT-5.5推理相比，VideoTapestry在LVBench、LongVideoBench（长）、Video-MME（长）和EgoSchema上分别实现了17.2%、14.9%、9.8%和7.0%的绝对准确率提升，达到了所有竞争者中的最先进水平。
cs.CV / 221 / 2610.06687
ChronoWorld: Camera-Controlled Consistent 4D World Generation via Spatiotemporal Cues and Geometric Reflections
ChronoWorld：通过时空线索和几何反射实现相机控制的一致性4D世界生成
Abstract
While existing camera-controllable video generation models can produce visually compelling sequences, preserving intrinsic 4D spatiotemporal coherence remains challenging. To address this limitation, we propose ChronoWorld, an "Observation--State--Reflection" framework that leverages spatiotemporal causal cues and reconstruction priors to generate globally consistent, free-view 4D scenes. Given a context video, we introduce a Spatiotemporal Epipolar Causal Attention mechanism that enforces multi-view epipolar constraints and temporal causality throughout the generation process. In addition, we develop a reconstruction-driven geometric reflection pipeline with a 4D retrieval strategy to enable dynamic self-assessment and correction of generated outputs, improving consistency and accuracy. Extensive experiments show that ChronoWorld achieves state-of-the-art performance in spatiotemporally consistent, cinematic-quality 4D scene generation, with strong generalization and high-fidelity geometry across diverse scenarios.
Chinese Translation
尽管现有的相机可控视频生成模型能够生成视觉上引人注目的序列，但保持内在的4D时空一致性仍然具有挑战性。为了解决这一限制，我们提出了ChronoWorld，一个利用时空因果线索和重建先验的“观察-状态-反射”框架，以生成全球一致的自由视角4D场景。给定一个上下文视频，我们引入了一种时空极线因果注意力机制，在整个生成过程中强制执行多视角极线约束和时间因果关系。此外，我们开发了一种基于重建驱动的几何反射管道，结合4D检索策略，以实现生成输出的动态自我评估和修正，从而提高一致性和准确性。大量实验表明，ChronoWorld在时空一致性和电影质量的4D场景生成方面达到了最先进的性能，并在多种场景中展现出强大的泛化能力和高保真几何特性。
cs.CV / 222 / 2610.06688
GS-Pool: Object-Level Change Detection in 3D Gaussian Splatting
GS-Pool：基于3D高斯点云的对象级变化检测
Abstract
Factories, museums and surveyors photograph the same space months apart and need to know which objects changed. When each visit is reconstructed with 3D Gaussian Splatting (3DGS), a direct comparison of the two reconstructions does not answer this. Training is stochastic, so two reconstructions of an unchanged space never coincide, and the second visit is often a quick re-scan with far fewer photographs. We propose GS-Pool, which takes two independently reconstructed Gaussian fields of the same space and returns the changed objects in each, together with their masks. SAM2 masks of each visit's photographs are lifted onto the Gaussians that render them and merged into an object pool, so every decision is taken once per object in 3D. We introduce a photographic carrier, the 3DGS training loss of each input reconstruction against the other visit's photographs, backpropagated to the Gaussians that rendered each pixel. We combine it with GS-Diff's geometry and colour terms and our distilled DINOv3 features. This evidence is compared with that of the objects present in both visits, which sets a change threshold for each scene. On PASLCD, GS-Pool reaches mIoU/F1 scores of 0.751/0.846 against 0.644/0.758 for GS-Diff, the strongest prior method, a gain of 17%/12%. Its mIoU is also 36%, 40% and 57% above that of O-SCD, PlenoCI and MV-3DCD, and it reaches 0.855 mIoU on CL-Splats, 33% above MV-3DCD. Each changed object is returned as a set of Gaussians with the evidence behind its decision, which an inspector can review in 3D.
Chinese Translation
工厂、博物馆和测量师在几个月后拍摄相同的空间，需要知道哪些物体发生了变化。当每次访问使用3D高斯点云（3DGS）重建时，直接比较两次重建并不能解决这个问题。训练是随机的，因此对未变化空间的两次重建从未重合，而第二次访问通常是快速重新扫描，拍摄的照片数量远少于第一次。我们提出GS-Pool，它接受同一空间的两个独立重建的高斯场，并返回每个场中变化的物体及其掩膜。每次访问照片的SAM2掩膜被提升到渲染它们的高斯上，并合并到一个对象池中，因此每个对象的决策仅进行一次。我们引入了一种摄影载体，即每个输入重建的3DGS训练损失与另一访问照片的对比，反向传播到渲染每个像素的高斯。我们将其与GS-Diff的几何和颜色项以及我们提炼的DINOv3特征结合。该证据与两个访问中均存在的物体进行比较，为每个场景设定变化阈值。在PASLCD上，GS-Pool的mIoU/F1得分为0.751/0.846，而GS-Diff（最强的先前方法）为0.644/0.758，提升幅度为17%/12%。其mIoU也比O-SCD、PlenoCI和MV-3DCD高出36%、40%和57%，在CL-Splats上达到0.855 mIoU，比MV-3DCD高出33%。每个变化的物体作为一组高斯返回，并附有其决策背后的证据，检验员可以在3D中进行审查。
cs.CV / 223 / 2610.06704
Extending Dynamic World Surface Water Mapping to Sentinel-1 with AlphaEarth Embeddings
将动态世界水面映射扩展至Sentinel-1与AlphaEarth嵌入
Abstract
Dynamic World (DW) maps land use and land cover globally at 10 m from Sentinel-2 (S2) imagery, but only for cloud-free observations, which limits where and when surface water can be mapped. We use the DW water class as weak supervision for a Sentinel-1 (S1) synthetic aperture radar (SAR) model so that DW-like water maps can be produced for every S1 acquisition. Google's AlphaEarth Foundations (AEF) annual embedding supplies spatial context, while S1 backscatter supplies the acquisition-time observation. On 53 globally distributed scenes with independent annotations of 3 m PlanetScope imagery acquired within 48 h of the S1 overpass, the S1-only model already reaches a pooled water intersection over union (IoU) of 0.77, comparable to 0.75 for the operational OPERA DSWx-S1 product, and adding AEF raises it to 0.85. The fused model improves on the S1-only model on 44 of 53 scenes and exceeds OPERA on 48, and on the independent S1S2-Water benchmark it reaches 0.94, compared with 0.87 for OPERA. Optical land-cover products can thus provide scalable training labels for SAR surface water mapping.
Chinese Translation
动态世界（Dynamic World, DW）利用Sentinel-2（S2）影像以10米的分辨率全球映射土地利用和土地覆盖，但仅限于无云观测，这限制了水面映射的时间和地点。我们使用DW水类作为Sentinel-1（S1）合成孔径雷达（SAR）模型的弱监督，从而为每次S1获取生成类似DW的水面地图。谷歌的AlphaEarth Foundations（AEF）年度嵌入提供空间上下文，而S1的后向散射则提供获取时的观测数据。在53个全球分布的场景中，这些场景的独立注释是基于在S1过境后48小时内获取的3米分辨率PlanetScope影像，S1单独模型的水体交并比（IoU）已达到0.77，接近于运营产品OPERA DSWx-S1的0.75，加入AEF后提高至0.85。融合模型在53个场景中的44个上优于S1单独模型，并在48个场景中超越OPERA，而在独立的S1S2-Water基准测试中达到0.94，相比之下OPERA为0.87。因此，光学土地覆盖产品可以为SAR水面映射提供可扩展的训练标签。
cs.CV / 224 / 2610.06801
MC-Sparse: Deconstructing and Closing the Dense-Sparse Attention Gap in Diffusion Transformers
MC-Sparse：解构并弥合扩散变换器中的密集-稀疏注意力差距
Abstract
Sparse attention is a primary approach to reducing the latency of diffusion transformers in long-sequence generation tasks, such as video and high-resolution 3D asset generation. However, existing methods can degrade generation quality and fidelity at high sparsity levels. Through controlled oracle comparisons, we trace this degradation to three sources: constraints imposed by token grouping, inaccurate interaction selection, and the attention contributions lost when tokens are discarded. Guided by this analysis, we propose Meta-Cached Sparse Attention (MC-Sparse), a training-free framework that selects individual key-value (KV) tokens while organizing similar queries into tile-aligned groups for efficient GPU execution. MC-Sparse caches metadata comprising query groups, KV indices selected using exact attention probabilities, and residuals between dense and sparse attention outputs, and reuses them across subsequent denoising steps. Across video and 3D generation models, MC-Sparse achieves higher fidelity to dense-attention outputs and larger denoising speedups than existing sparse-attention baselines, without visible quality degradation. Relative to dense attention, it delivers a $1.80\times$ denoising speedup on Minimax-H3-Base and a $2.32\times$ speedup on 3D asset generation, both with negligible quality loss.
Chinese Translation
稀疏注意力是减少扩散变换器在长序列生成任务（如视频和高分辨率3D资产生成）中延迟的主要方法。然而，现有方法在高稀疏度水平下可能会降低生成质量和保真度。通过控制的oracle比较，我们将这种降级追溯到三个来源：由令牌分组施加的约束、不准确的交互选择，以及在丢弃令牌时损失的注意力贡献。基于这一分析，我们提出了元缓存稀疏注意力（Meta-Cached Sparse Attention, MC-Sparse），这是一种无训练框架，能够选择单个键值（key-value, KV）令牌，同时将相似查询组织成平铺对齐的组，以实现高效的GPU执行。MC-Sparse缓存元数据，包括查询组、使用精确注意力概率选择的KV索引，以及密集和稀疏注意力输出之间的残差，并在后续去噪步骤中重复使用这些元数据。在视频和3D生成模型中，MC-Sparse在保真度上优于密集注意力输出，并且在去噪速度上超过现有的稀疏注意力基线，且没有明显的质量下降。相较于密集注意力，它在Minimax-H3-Base上实现了$1.80 imes$的去噪加速，在3D资产生成上实现了$2.32 imes$的加速，且质量损失微乎其微。
cs.CV / 225 / 2610.06813
Less Context, Better Geometry: Masked Geometric Encoder for Robust 3D Foundation Models
更少的上下文，更好的几何：用于鲁棒3D基础模型的掩蔽几何编码器
Abstract
Recent progress in 3D foundation models has enabled rapid 3D reconstruction and camera calibration by leveraging learned 3D priors from vast amount of spatial data. However, the all-to-all global attention design leads to quadratic complexity and limits long-sequence inference; unconstrained cross-view interactions also can propagate unreliable evidence from occluded or visually similar but geometrically distant views. In this paper, We introduce a Masked Geometric Encoder (MGE), which promotes the learning of robust geometric representations under incomplete cross-view context. During training, MGE strategically drops frame tokens from global attention and distills from a pretrained full-context teacher model. This allows the model to learn an intrinsically richer per-frame representation while providing sufficient intermediate supervision to avoid performance degradation. Through extensive experiments, we show that MGE leads to much stronger performance under occlusion and doppelganger views while retaining high performance on standard benchmarks. Such a richer frame representation also leads to more effective token reduction during inference. To this end, we develop a novel Anchor-Guided Adaptive token merging technique that preserves representative anchor frames while jointly merging redundant tokens from the remaining views. Compared to other efficient inference approaches, we can achieve inference speedup while consistently maintaining higher reconstruction quality, particularly in limited-view settings.
Chinese Translation
最近在3D基础模型方面的进展使得通过利用大量空间数据中学习到的3D先验，实现快速的3D重建和相机标定成为可能。然而，全对全的全局注意力设计导致了二次复杂性，并限制了长序列推理；不受限制的视角交互也可能传播来自被遮挡或视觉上相似但几何上距离较远视角的不可靠证据。本文提出了一种掩蔽几何编码器（Masked Geometric Encoder, MGE），它在不完整的跨视角上下文下促进鲁棒几何表示的学习。在训练过程中，MGE策略性地从全局注意力中丢弃帧标记，并从预训练的全上下文教师模型中提炼。这使得模型能够学习到内在更丰富的每帧表示，同时提供足够的中间监督以避免性能下降。通过大量实验，我们表明MGE在遮挡和双胞胎视角下表现出更强的性能，同时在标准基准测试中保持高性能。这种更丰富的帧表示在推理过程中也导致了更有效的标记减少。为此，我们开发了一种新颖的锚点引导自适应标记合并技术，该技术在联合合并剩余视角的冗余标记时保留代表性的锚点帧。与其他高效推理方法相比，我们能够实现推理加速，同时在有限视角设置中始终保持更高的重建质量。
cs.CV / 226 / 2610.06814
TAPDreamer: Transferable Adversarial Patches for World Action Models
TAPDreamer：可转移的对抗性补丁用于世界动作模型
Abstract
World models learn to predict how their environment will evolve, making them an important foundation for general-purpose robotic control. Yet world action models depend on camera inputs whose manipulation can corrupt the visual representations used across tasks and action policies. Existing attacks on these models optimize against the victim's actions or predicted futures and therefore require access to target-model outputs. In this paper, we propose an attack, TAPDreamer, against world action models that instead uses a public encoder alone to construct a fixed local perturbation that transfers across tasks and action architectures. TAPDreamer requires no target-policy queries. Our key insight is that interactions between patch-induced changes in attention weights and value vectors broadcast a nearly identical representation shift far beyond the patch footprint, and this shift remains stable across task observations. Guided by this insight, TAPDreamer uses six frames from one source task to maximize the global L1 distance between clean and patched encoder representations. In closed-loop evaluation, one frozen patch per benchmark, covering about 6.5% of the input, reduces FastWAM's success rate from 97.7% to 0.0% across 40 LIBERO tasks and from 90.8% to 0.0% across 50 RoboTwin tasks; matched random patches retain 81.5% and 79.2% success. The same patches reduce success to 2.1% and 0.8% on two DreamWAM configurations and to 10.0% on Motus. These results show that protecting downstream action generation alone is insufficient: defenses for world action models must also secure shared visual encoders against persistent local perturbations.
Chinese Translation
世界模型学习预测其环境的演变，因此它们成为通用机器人控制的重要基础。然而，世界动作模型依赖于相机输入，其操控可能会破坏用于各种任务和动作策略的视觉表征。现有针对这些模型的攻击优化受害者的动作或预测未来，因此需要访问目标模型的输出。在本文中，我们提出了一种针对世界动作模型的攻击方法TAPDreamer，该方法仅使用公共编码器构造一个固定的局部扰动，该扰动可以在任务和动作架构之间转移。TAPDreamer不需要目标策略查询。我们的关键见解是，补丁引起的注意权重和价值向量的变化之间的相互作用在补丁足迹之外传播出几乎相同的表征偏移，并且这种偏移在任务观察中保持稳定。在这一见解的指导下，TAPDreamer使用来自一个源任务的六帧图像来最大化干净和补丁编码器表征之间的全局L1距离。在闭环评估中，每个基准一个固定补丁，覆盖约6.5%的输入，使FastWAM在40个LIBERO任务中的成功率从97.7%降至0.0%，在50个RoboTwin任务中的成功率从90.8%降至0.0%；匹配的随机补丁保持成功率为81.5%和79.2%。相同的补丁在两个DreamWAM配置上将成功率降低至2.1%和0.8%，在Motus上降低至10.0%。这些结果表明，仅保护下游动作生成是不够的：世界动作模型的防御还必须保护共享的视觉编码器免受持续的局部扰动。
cs.CV / 227 / 2610.06831
UniSlider: Perceptually Uniform Sliders for Continuous Image Editing
UniSlider：用于连续图像编辑的感知均匀滑块
Abstract
Sliders provide an intuitive interface for continuous image editing. In current generative approaches, however, the slider is simply a rescaling of the method's strength parameter, such as an adapter coefficient, a prompt weight, or an interpolation factor. This strength relates poorly to perceptual change. The image can partially revert as the slider moves, long stretches of the range produce no visible difference, and short intervals transform the image abruptly. Remapping the strength could fix this uneven pace, but only if the trajectory is monotone, which current methods do not enforce. We therefore distinguish the slider from the strength, and require perceptual distance from the input to grow linearly with the slider value. We introduce UniSlider, a lightweight LoRA trained on a few-step editing backbone so that its strength approximates this ideal slider. Few-step sampling lets us impose this objective in pixel space without intermediate ground truth, and the backbone's output is preserved at full strength. However, a low-rank adapter cannot make the strength fully uniform. Our slider is thus an inference-time remapping of the strength, obtained by adaptive sampling. Since training optmizes to make the trajectory monotone, this remapping closes the remaining gap without extra training or parameters. On a new benchmark of 300 continuous edits evaluating uniformity, monotonicity, edit fidelity, and identity preservation, UniSlider outperforms all prior methods and is preferred in a user study.
Chinese Translation
滑块为连续图像编辑提供了直观的界面。然而，在当前的生成方法中，滑块仅仅是方法强度参数的重新缩放，例如适配器系数、提示权重或插值因子。这种强度与感知变化的关系较差。当滑块移动时，图像可能会部分恢复，长范围内的滑动几乎没有可见差异，而短间隔则会突然改变图像。重新映射强度可以解决这种不均匀的节奏，但前提是轨迹是单调的，而当前的方法并未强制这一点。因此，我们将滑块与强度区分开来，并要求输入的感知距离与滑块值线性增长。我们引入了UniSlider，一个轻量级的LoRA，基于少步编辑骨干网络进行训练，使其强度接近理想滑块。少步采样使我们能够在像素空间中施加这一目标，而无需中间的真实值，并且骨干网络的输出在全强度下得以保留。然而，低秩适配器无法使强度完全均匀。因此，我们的滑块是在推理时通过自适应采样对强度进行重新映射。由于训练优化使轨迹单调，这一重新映射在没有额外训练或参数的情况下弥补了剩余的差距。在一个新的300个连续编辑的基准测试中，评估均匀性、单调性、编辑保真度和身份保留，UniSlider超越了所有先前的方法，并在用户研究中受到青睐。
cs.CV / 228 / 2610.06837
Anatomy-aware Fine-grained Multimodal Fusion for Laryngopharyngeal Cancer T-Staging Prediction Using CT and Radiology Report
基于解剖结构的细粒度多模态融合用于喉咽癌T分期预测：结合CT和放射学报告
Abstract
Accurate T-staging is crucial for guiding personalized treatment strategies for laryngopharyngeal cancer. However, current clinical practice relies on invasive biopsy procedures, whereas CT-based staging remains challenging due to the complex patterns of tumor invasion. Recent computer-aided approaches face two key challenges: 1) Structural relationship modeling: existing methods underrepresent anatomically structured patterns of tumor invasion, as they either process whole CT volumes without tumor-specific anatomical constraints or rely on labor-intensive tumor segmentation. 2) Fine-grained cross-modal alignment: while radiology reports contain organ-specific invasion details, current methods that apply global feature fusion struggle to accurately align individual anatomical structures with their corresponding textual descriptions. To address these issues, we propose an anatomy-aware multimodal framework that integrates organ-level CT context and radiology reports into a unified representation for laryngopharyngeal T-staging. The framework first constructs an Anatomy-Structured Organ Graph (AOG) that captures invasion patterns between primary sites and surrounding organs, then performs Organ-Anchored Cross-Modal Alignment (OCA) so that each organ node aggregates textual evidence from the radiology report, and finally refines this graph representation by injecting organ-specific invasion cues extracted from the report via Report-Enhanced Graph-Refinement (REG), yielding a multimodal organ graph that combines spatial and textual evidence. Extensive experiments demonstrate that the proposed framework achieves superior performance in T-staging of laryngopharyngeal cancer.
Chinese Translation
准确的T分期对于指导喉咽癌的个性化治疗策略至关重要。然而，目前的临床实践依赖于侵入性活检程序，而基于CT的分期由于肿瘤侵袭模式的复杂性仍然面临挑战。最近的计算机辅助方法面临两个主要挑战：1）结构关系建模：现有方法未能充分表征肿瘤侵袭的解剖结构模式，因为它们要么处理整个CT体积而没有肿瘤特定的解剖约束，要么依赖于劳动密集型的肿瘤分割。2）细粒度跨模态对齐：虽然放射学报告包含特定器官的侵袭细节，但当前应用全局特征融合的方法在准确对齐各个解剖结构与其对应的文本描述方面存在困难。为了解决这些问题，我们提出了一种基于解剖结构的多模态框架，将器官级CT上下文和放射学报告整合为一个统一的喉咽T分期表示。该框架首先构建一个解剖结构器官图（Anatomy-Structured Organ Graph, AOG），捕捉主要部位与周围器官之间的侵袭模式，然后执行器官锚定跨模态对齐（Organ-Anchored Cross-Modal Alignment, OCA），使每个器官节点聚合来自放射学报告的文本证据，最后通过报告增强图形细化（Report-Enhanced Graph-Refinement, REG）注入从报告中提取的器官特定侵袭线索，从而生成一个结合空间和文本证据的多模态器官图。大量实验表明，所提框架在喉咽癌的T分期中表现出优越的性能。
cs.CV / 229 / 2610.06844
Learning to Read the Contextual Tokens in Diffusion Transformers
学习在扩散变换器中读取上下文标记
Abstract
Multimodal Diffusion Transformers (MM-DiTs) jointly process visual and textual representations throughout generation. These models repeatedly update the text tokens through multimodal attention, forming dynamic contextual tokens whose function is not well understood. In this work, we introduce a framework for reading this contextual space through natural-language interrogation. We train a lightweight bottleneck network that maps intermediate contextual tokens into the input space of a frozen Large Language Model (LLM), allowing the LLM to answer questions about the emerging image directly from these hidden representations. Our reader reveals that contextual tokens encode a rich, global representation of the emerging scene: generation-specific semantics, including attributes left underspecified by the prompt, are accessible surprisingly early in denoising, while increasingly fine-grained details become readable over time. Remarkably, this information remains decodable even when the MM-DiT receives an empty prompt, showing that contextual tokens accumulate substantial image-specific information from the evolving visual representation itself. We further find that generations with more readable contextual representations tend to receive higher human-preference scores. Building on these observations, we introduce Contextual Alignment, a training technique that explicitly reinforces the visual-semantic information encoded in the contextual tokens, improving generation quality and distributional coverage. Together, our results establish contextual tokens as both an interpretable view into the internal dynamics of MM-DiTs and an effective target for improving generative models.
Chinese Translation
多模态扩散变换器（MM-DiTs）在生成过程中共同处理视觉和文本表示。这些模型通过多模态注意力反复更新文本标记，形成动态上下文标记，其功能尚不完全清楚。在本研究中，我们提出了一种通过自然语言询问来读取这一上下文空间的框架。我们训练了一个轻量级瓶颈网络，将中间上下文标记映射到一个冻结的大型语言模型（LLM）的输入空间，从而使LLM能够直接根据这些隐藏表示回答有关新兴图像的问题。我们的读者揭示了上下文标记编码了新兴场景的丰富全局表示：生成特定的语义，包括提示中未明确指定的属性，在去噪过程中意外地早期可获取，而越来越细致的细节则随着时间的推移变得可读。值得注意的是，即使MM-DiT接收到一个空提示，这些信息仍然可以解码，这表明上下文标记从不断演变的视觉表示中积累了大量特定于图像的信息。我们进一步发现，具有更可读上下文表示的生成通常会获得更高的人类偏好评分。基于这些观察，我们引入了上下文对齐（Contextual Alignment），一种明确强化上下文标记中编码的视觉-语义信息的训练技术，从而提高生成质量和分布覆盖。综合来看，我们的结果确立了上下文标记作为对MM-DiTs内部动态的可解释视角，以及改善生成模型的有效目标。
cs.CV / 230 / 2610.06847
S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation
S2PD：用于物理和逻辑一致的视频生成的串行到并行扩散
Abstract
Bidirectional video diffusion models denoise entire videos in parallel, yet when trained on effectively unlimited in-distribution data from procedural generators, continue to violate physical laws and simple symbolic rules. We introduce Serial-to-Parallel Diffusion (S2PD), which performs autoregressive diffusion at high noise before switching to parallel diffusion at low noise. The autoregressive phase provides the serial computation needed to coordinate interdependent events and produce valid state transitions while the parallel phase jointly refines the entire video and reduces sampling time relative to fully serial generation. We implement S2PD with two architectures: a pixel-space diffusion transformer trained from scratch and a pretrained video model adapted through LoRA fine-tuning with causal attention. Across games, physical simulations, and real video, S2PD follows rules more reliably than matched bidirectional baselines and generates videos with greater temporal stability and sampling efficiency than other serial methods.
Chinese Translation
双向视频扩散模型以并行方式对整个视频进行去噪，然而，当在来自程序生成器的有效无限分布数据上训练时，仍然会违反物理法则和简单的符号规则。我们提出了串行到并行扩散（S2PD），该方法在高噪声下执行自回归扩散，然后在低噪声下切换到并行扩散。自回归阶段提供了协调相互依赖事件所需的串行计算，并产生有效的状态转移，而并行阶段则共同优化整个视频，并相对于完全串行生成减少采样时间。我们使用两种架构实现S2PD：一种是从头开始训练的像素空间扩散变换器，另一种是通过因果注意力进行LoRA微调的预训练视频模型。在游戏、物理模拟和真实视频中，S2PD比匹配的双向基线更可靠地遵循规则，并生成具有更大时间稳定性和采样效率的视频，优于其他串行方法。
cs.CV / 231 / 2610.06852
One Figure, Every Canvas: Editable Flowchart Relayout via Agentic Pipeline
一幅图，所有画布：通过代理管道实现可编辑流程图重新布局
Abstract
Pipeline figures in ML papers must be repurposed across many canvases, including paper columns, 16:9 slides, portrait posters, 1:1 social teasers, 9:16 phone previews. Each format imposes a different aspect ratio on the same computational graph, where any silently broken connection misrepresents the method. We formulate aspect-ratio-adaptive flowchart relayout as a distinct task: given a raster flowchart and a target ratio, produce a structurally faithful, hallucination-free, editable layout. Existing methods fail characteristically: image-to-image models stretch blocks and reject extreme ratios, text-to-image agentic systems hallucinate content, and parse-then-render systems mis-route edges. We propose an agentic pipeline factored into Parse, Style, and Layout stages, each pairing a main agent with a critic that combines deterministic constraint checks with VLM visual feedback so connectivity is explicitly checked and prevented from being silently broken. Outputs are draw.io-editable mxGraph XML. On a curated benchmark of 100 flowcharts at five aspect ratios, evaluated by Gemini 3.1 Pro and validated against human judgments, our method reaches 68.6% Content Fidelity versus 11.2-41.4% for prior work. Project page: https://onefigureeverycanvas.vercel.app/
Chinese Translation
机器学习论文中的管道图必须在多个画布上重新使用，包括论文栏目、16:9幻灯片、竖版海报、1:1社交预告和9:16手机预览。每种格式对相同的计算图施加了不同的纵横比，任何悄然中断的连接都会错误地表示该方法。我们将纵横比自适应的流程图重新布局形式化为一个独立任务：给定一个光栅流程图和目标比例，生成一个结构上忠实、无幻觉、可编辑的布局。现有方法在特性上存在缺陷：图像到图像模型拉伸块并拒绝极端比例，文本到图像的代理系统产生幻觉内容，而解析后渲染系统则错误路由边缘。我们提出了一种代理管道，分为解析、样式和布局三个阶段，每个阶段将主要代理与一个批评者配对，结合确定性约束检查与视觉语言模型（VLM）视觉反馈，以便显式检查连接性，防止悄然中断。输出为可在 draw.io 中编辑的 mxGraph XML。在一个经过精心策划的基准测试中，包含100个流程图，涵盖五种纵横比，通过 Gemini 3.1 Pro 进行评估，并与人类判断进行验证，我们的方法在内容保真度上达到68.6%，而之前的工作仅为11.2-41.4%。项目页面：https://onefigureeverycanvas.vercel.app/
cs.AI / 1 / 2610.03872
Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery
通过自动诊断和技能发现训练数值智能
Abstract
AI agents are becoming increasingly capable of generating scientific code, but generating code is not the same as improving the algorithms behind it. For numerical solvers, execution feedback can expose poor performance, but rarely reveals its underlying cause and how to address it. We introduce Auto-Diagnosis and Skill Discovery (ADSD), a framework that links numerical diagnosis to reusable solver self-improvement. ADSD follows a diagnosis-first paradigm that first explains why a solver performs poorly, then uses this diagnosis to guide the discovery of appropriate numerical methods. The resulting knowledge is packaged into reusable solver skills, turning solver improvement from trial-and-error editing into a structured process of diagnosis, discovery, and implementation. Across four challenging numerical domains--power flow equation, AC optimal power flow control, stiff ordinary differential equations, and heterogeneous diffusion PDEs--ADSD consistently improves solver accuracy, robustness, and efficiency. On GOC-500 power flow, for example, ADSD reduces mean solver error by nearly $71\times$, with improvements further transferring to unseen grid topologies and operating regimes.
Chinese Translation
人工智能代理的科学代码生成能力日益增强，但生成代码并不等同于改善其背后的算法。对于数值求解器而言，执行反馈可以暴露出性能不佳的情况，但很少揭示其根本原因以及如何解决这些问题。我们提出了自动诊断和技能发现（Auto-Diagnosis and Skill Discovery, ADSD）框架，该框架将数值诊断与可重用求解器自我改进相连接。ADSD遵循以诊断为先的范式，首先解释求解器表现不佳的原因，然后利用这一诊断指导适当数值方法的发现。最终获得的知识被打包成可重用的求解器技能，将求解器的改进从试错编辑转变为一个结构化的诊断、发现和实施过程。在四个具有挑战性的数值领域——功率流方程、交流最优功率流控制、刚性常微分方程和异质扩散偏微分方程中，ADSD始终提高了求解器的准确性、鲁棒性和效率。例如，在GOC-500功率流问题上，ADSD将平均求解器误差降低了近$71 imes$，并且改进效果进一步转移到未见过的网格拓扑和操作模式中。
cs.AI / 2 / 2610.03888
REACT: Physically and Chemically Consistent Reconstruction of Marine Active Tracers
REACT：物理和化学一致性的海洋活性示踪剂重建
Abstract
Reconstructing global sea surface pH from sparse observations is critical for monitoring ocean acidification and understanding marine carbon cycling. Traditional assimilation and inverse models are physically grounded but costly for large-scale reconstruction. Recent black-box and physics-guided AI models improve efficiency, but are mainly designed for passive tracers, where the reconstructed variable is also the transported inventory. In contrast, pH is an active carbonate tracer: it is the prediction target, while dissolved inorganic carbon (DIC) is the conserved carbon inventory. This mismatch can produce low pH error while violating carbonate closure and source-free carbon conservation. To address this, we introduce \textbf{REACT}, a carbon-first reconstruction framework that decouples transport, active correction, and chemical decoding. REACT transports a latent carbonate state with a conservative advection--diffusion solver, captures non-conservative carbon-cycle variations with a source module, decodes the corrected state into pH, and constrains the output through carbonate equilibrium. This design keeps pH as the target while enforcing consistency on the underlying carbon state. On simulation data, REACT reduces pH NRMSE by (14.7%) and chemical consistency error by (24.0%) over the best baseline. Cross-temporal-scale evaluations show robustness against error accumulation from coarse to fine temporal scales, and ablation studies validate the effectiveness of each component.
Chinese Translation
从稀疏观测数据重建全球海表 pH 对于监测海洋酸化和理解海洋碳循环至关重要。传统的同化和反演模型虽然在物理上是合理的，但在大规模重建中成本较高。近期的黑箱和物理引导的人工智能模型提高了效率，但主要是为被动示踪剂设计的，其中重建变量也是运输的库存。相比之下，pH 是一种活性碳酸盐示踪剂：它是预测目标，而溶解无机碳（DIC）是保守的碳库存。这种不匹配可能导致 pH 误差较低，但违反碳酸盐平衡和无源碳守恒。为了解决这个问题，我们引入了 extbf{REACT}，一个以碳为中心的重建框架，它将运输、主动校正和化学解码解耦。REACT 使用保守的对流-扩散求解器运输潜在的碳酸盐状态，通过源模块捕捉非保守的碳循环变化，将校正后的状态解码为 pH，并通过碳酸盐平衡约束输出。该设计保持 pH 作为目标，同时对基础碳状态施加一致性约束。在模拟数据上，REACT 将 pH 的 NRMSE 降低了 14.7%，化学一致性误差降低了 24.0%，优于最佳基线。跨时间尺度评估显示其对从粗到细时间尺度的误差积累具有鲁棒性，消融研究验证了各个组件的有效性。
cs.AI / 3 / 2610.03894
Proxy Confidence: Auditing Black-Box LLM Agents with a Surrogate's Log-Probabilities
代理信心：通过替代模型的对数概率审计黑箱大型语言模型代理
Abstract
A deployed LLM agent emits tool calls, queries, and code that can be silently wrong -- by the time the error surfaces, the action has run. Frontier chat APIs hide the model's token probabilities; the agent's stated confidence barely beats chance on the mistakes that matter; and resampling does not help, since frontier models are highly repetitive, reproducing the same call across samples. We recover the missing signal from a low-cost open-weight surrogate run in parallel. It reads the same context, schema, and proposed action as the agent, then scores the call from its own log-probabilities through a family of complementary readouts: teacher forcing and request-PMI weigh the likelihood of each argument value, a discriminative verdict judges the call as a whole, and tool-choice competition tests the function against its siblings. One principle says which to trust: a generative likelihood localizes wrong argument values, while the verdict catches holistically wrong calls. When the error type is unknown, an ensemble is the low-regret default. The readout is training-free, needs no access to the agent's internals, and costs one prefill pass alongside the tool call. On difficult coding tasks it reaches AUROC 0.825 where the actor's stated confidence is near chance (0.598), and the generative readouts beat it by +0.07 to +0.28 across three further actors. Against self-consistency it gains +0.14 to +0.19 on near-deterministic actors, at 1/K the cost. The signal drives two deployment modes: a real-time gate escalating the least-trustworthy calls for review (+0.05 to +0.30 accepted-action accuracy at 50% coverage), and confidence feedback, returning the tool result with the score so the agent adapts its next step -- lifting task success on live-execution benchmarks (+0.119 and +0.137, p <= 1e-4) and beating a random-value control where step errors are silent (+0.078, p = 0.003).
Chinese Translation
部署的语言模型（LLM）代理会发出工具调用、查询和代码，这些可能在无声中出错——当错误显现时，操作已经执行。前沿聊天API隐藏了模型的令牌概率；代理所声明的信心在重要错误上几乎与随机相当；而重采样并无帮助，因为前沿模型高度重复，在样本之间重现相同的调用。我们通过并行运行一个低成本的开放权重替代模型来恢复缺失的信号。它读取与代理相同的上下文、模式和提议的操作，然后通过一系列互补的输出评估调用的对数概率：教师强制（teacher forcing）和请求-点互信息（request-PMI）权衡每个参数值的可能性，判别性裁决（discriminative verdict）整体判断调用的正确性，工具选择竞争（tool-choice competition）则测试该功能与其兄弟工具的表现。一个原则决定了信任的依据：生成性可能性（generative likelihood）能够定位错误的参数值，而裁决则能整体捕捉错误的调用。当错误类型未知时，集成（ensemble）是低遗憾的默认选择。该输出无需训练，不需要访问代理的内部结构，并且仅需一次预填充传递和工具调用的成本。在困难的编码任务中，其AUROC达到了0.825，而代理所声明的信心接近随机（0.598），生成性输出在三个其他代理中超越了它，提升幅度为+0.07至+0.28。与自一致性相比，在近确定性代理中获得了+0.14至+0.19的提升，成本为1/K。该信号驱动了两种部署模式：一种实时门控机制，提升最不可信的调用进行审查（在50%覆盖率下接受的操作准确率提升+0.05至+0.30），以及信心反馈，返回工具结果及其评分，以便代理调整其下一步——在实时执行基准测试中提升任务成功率（+0.119和+0.137，p <= 1e-4），并超越了随机值控制，其中步骤错误是无声的（+0.078，p = 0.003）。
cs.AI / 4 / 2610.03934
SGAnalog: An End-to-End Circuit Benchmark from Open-Source Silicon Tapeouts
SGAnalog：来自开源硅带出的端到端电路基准测试
Abstract
Existing analog integrated circuit design benchmarks make two questions hard to answer: whether a model has learned transferable circuit skills rather than recalled familiar examples, and whether its output works under defined process and test conditions. We introduce a benchmark built from human-designed, open-source circuits associated with Tiny Tapeout manufacturing shuttles. The collection contains 273 topologically distinct top-level designs. Every source is retrieved at the revision recorded for its shuttle submission and processed in a fixed containerized environment. The pipeline exports each eligible schematic image and its SPICE netlist from the same source file, giving transcription an exact structural reference. Commit dates support model-specific training-cutoff analysis, while author testbenches provide the simulation context for sizing. The benchmark evaluates schematic-to-netlist transcription and device sizing. Across seven models on a fixed set of 66 transcription tasks, the strongest model reaches 56.1% exact graph isomorphism, and six of seven models drop sharply from the small to the medium tier. For one frontier model, removing author-chosen labels reduces exact matches while preserving aggregate structural F1, suggesting that labels can aid connectivity tracing. On the 17 sizing tasks, the leading model converges on all 17 proposals and reaches 91.2 out of 100 against the human reference, while the two newest Claude models refuse 4 and 11 of the same prompts they transcribe without objection; a proposal without sizes scores zero. The two tasks produce different model rankings, exposing distinct visual and design capabilities and, in one family, a policy rather than capability limit.
Chinese Translation
现有的模拟集成电路设计基准测试使两个问题难以回答：一个模型是否学习到了可迁移的电路技能，而不是简单地回忆起熟悉的例子，以及其输出是否在定义的工艺和测试条件下有效。我们引入了一个基准测试，该基准测试由与 Tiny Tapeout 制造班次相关的人类设计的开源电路构建而成。该集合包含 273 个拓扑上独特的顶层设计。每个源文件都在其班次提交时记录的修订版本中提取，并在固定的容器化环境中处理。该流程从同一源文件中导出每个合格的原理图图像及其 SPICE 网表，为转录提供了精确的结构参考。提交日期支持特定模型的训练截止分析，而作者测试平台提供了尺寸调整的仿真上下文。该基准测试评估原理图到网表的转录和器件尺寸调整。在固定的 66 个转录任务上，七个模型中最强的模型达到了 56.1% 的精确图同构，而七个模型中有六个在从小型到中型层次时急剧下降。对于一个前沿模型，去除作者选择的标签会减少精确匹配，同时保持整体结构 F1，表明标签可以帮助连接追踪。在 17 个尺寸调整任务中，领先模型在所有 17 个提案上收敛，并在与人类参考的比较中达到了 91.2 分（满分 100），而最新的两个 Claude 模型对其中 4 个和 11 个相同的提示拒绝转录；一个没有尺寸的提案得分为零。这两个任务产生了不同的模型排名，揭示了不同的视觉和设计能力，并且在一个模型家族中，限制是政策而非能力。
cs.AI / 5 / 2610.03938
MLLMs Fail to Refuse when Using Tools Agentically
代理性多模态大型语言模型在使用工具时未能拒绝请求
Abstract
Agentic multimodal large language models (MLLMs) have recently pushed the frontier of visual reasoning by calling tools such as zooming and tagging. Despite the recent strong success of agentic MLLMs, this work uncovers a critical safety failure in the tool-use paradigm: agentic tool-using MLLMs become less capable of refusing harmful requests. Our experiments confirm that, across three popular safety benchmarks, all the top open- and closed-weight MLLMs we test exhibit significantly lower safety in tool-using settings than in non-tool settings, with a relative refusal failure rate increase of up to 68.7%. Based on analysis of 100,000+ responses, including extended experiments, we also propose two possible reasons for this safety degradation.
Chinese Translation
代理性多模态大型语言模型（MLLMs）最近通过调用工具（如缩放和标记）推动了视觉推理的前沿。尽管代理性MLLMs最近取得了显著成功，但本研究揭示了工具使用范式中的一个关键安全隐患：代理性工具使用的MLLMs在拒绝有害请求方面的能力下降。我们的实验确认，在三个流行的安全基准测试中，我们测试的所有顶级开放和闭合权重的MLLMs在使用工具的设置中表现出显著低于非工具设置的安全性，拒绝失败率相对增加高达68.7%。基于对超过100,000个响应的分析，包括扩展实验，我们还提出了导致这一安全性下降的两个可能原因。
cs.AI / 6 / 2610.03948
Retrieval-Augmented Large Language Model Decision-Making for Autonomous Driving Guided by Chinese Philosophical Wisdom
基于中国哲学智慧的增强检索大型语言模型决策在自主驾驶中的应用
Abstract
Autonomous driving decision systems must balance safety, efficiency, and social norms in complex traffic interactions. Philosophical and ethical considerations have received limited attention in existing autonomous driving decision-making approaches based on numerical optimization, sequence prediction, and large language models (LLMs). We propose Chinese Philosophical Wisdom-Guided Driving (CPW-Drive), a closed-loop retrieval-augmented generation (RAG) framework that incorporates value guidance derived from Chinese philosophy into autonomous driving decision-making. Using Chinese Confucian thought as its knowledge source, CPW-Drive consolidates LLM-extracted keywords from relevant classical texts into driving-relevant value principles through manual screening and validation. It then contextualizes these principles through scenario-specific cases to form retrievable and reusable value guidance. We further propose Physics-aware Spatial Similarity Retrieval (PSSR), which compares vehicle layouts and velocity-extrapolated states to retrieve physically relevant historical cases. On Highway-env's multilane highway-driving task, CPW-Drive achieves success rates of 93.0%, 86.0%, and 72.0% across three traffic configurations. These results outperform the strongest baseline by 8.0, 22.5, and 25.0 percentage points, respectively. Across all configurations, CPW-Drive achieves the highest collision-free step count and maintains a low lane-change frequency. The results suggest that structured value guidance can improve simulated closed-loop safety and stability while introducing efficiency and latency trade-offs.
Chinese Translation
自主驾驶决策系统必须在复杂的交通互动中平衡安全性、效率和社会规范。现有基于数值优化、序列预测和大型语言模型（LLMs）的自主驾驶决策方法对哲学和伦理考量的关注有限。我们提出了中国哲学智慧引导驾驶（CPW-Drive），这是一个闭环的增强检索生成（RAG）框架，旨在将源自中国哲学的价值引导融入自主驾驶决策中。CPW-Drive以中国儒家思想为知识来源，通过手动筛选和验证，将相关经典文本中提取的LLM关键词整合为与驾驶相关的价值原则。然后，通过特定场景案例对这些原则进行情境化，以形成可检索和可重用的价值引导。我们进一步提出了物理感知空间相似性检索（PSSR），该方法比较车辆布局和速度外推状态，以检索物理相关的历史案例。在Highway-env的多车道高速驾驶任务中，CPW-Drive在三种交通配置下的成功率分别为93.0%、86.0%和72.0%。这些结果分别比最强基线高出8.0、22.5和25.0个百分点。在所有配置中，CPW-Drive实现了最高的无碰撞步骤数，并保持了较低的变道频率。结果表明，结构化的价值引导可以提高模拟闭环的安全性和稳定性，同时引入效率和延迟的权衡。
cs.AI / 7 / 2610.03959
LatentQuant: Preserving the Policy-Facing Latent Contract under NVFP4 VAE Quantization
LatentQuant：在 NVFP4 VAE 量化下保持面向策略的潜在合约
Abstract
Recent world action models (WAMs) reuse pretrained video VAEs whose encoder latents directly condition downstream action policies. Quantization must therefore preserve not only reconstruction fidelity but also the policy-facing latent contract expected by the frozen policy. Direct NVFP4 leaves W4A4 quantization error uncompensated, whereas joint quantization-aware training (QAT) can recover reconstruction by moving this representation. On Wan2.1, joint QAT nearly matches FP32 VBench-7 (0.7403 versus 0.7409), yet LIBERO success collapses from 95.5% to 10.5%. Controlled decoder-only experiments show that activation quantize-dequantize operations alter the reconstruction signal and decoder Jacobian, redirecting the gradient returned to the encoder and inducing persistent latent drift. Based on this mechanism, we introduce LatentQuant, a two-stage NVFP4 QAT framework that first aligns the quantized encoder with its high-precision counterpart, then freezes it while adapting the decoder. Across Wan2.1 and Wan2.2, LatentQuant preserves near-baseline control and high reconstruction quality, achieving 95.75% success on LIBERO and 68.8% on RoboTwin. On NVIDIA B300 GPUs, NVFP4 execution achieves 1.17x-1.26x end-to-end VAE speedups over BF16 cuDNN.
Chinese Translation
近期的世界行动模型（WAMs）重用预训练的视频变分自编码器（VAE），其编码器的潜在变量直接影响下游的行动策略。因此，量化不仅必须保持重建的保真度，还必须保留冻结策略所期望的面向策略的潜在合约。直接的 NVFP4 量化会留下 W4A4 量化误差未被补偿，而联合量化感知训练（QAT）则可以通过移动这一表示来恢复重建。在 Wan2.1 上，联合 QAT 几乎与 FP32 VBench-7 相匹配（0.7403 对比 0.7409），然而 LIBERO 的成功率却从 95.5% 降至 10.5%。受控的仅解码器实验表明，激活的量化-反量化操作会改变重建信号和解码器雅可比矩阵，重新引导返回给编码器的梯度，并导致持续的潜在漂移。基于这一机制，我们提出了 LatentQuant，一个两阶段的 NVFP4 QAT 框架，首先将量化的编码器与其高精度对应物对齐，然后在适应解码器的同时冻结它。在 Wan2.1 和 Wan2.2 上，LatentQuant 保持了接近基线的控制和高重建质量，在 LIBERO 上实现了 95.75% 的成功率，在 RoboTwin 上实现了 68.8%。在 NVIDIA B300 GPU 上，NVFP4 执行实现了相较于 BF16 cuDNN 的 1.17x-1.26x 端到端 VAE 加速。
cs.AI / 8 / 2610.03966
ROAR: Unifying Runs across Heterogeneous AI-Driven Research Systems
ROAR：统一异构人工智能驱动研究系统的运行
Abstract
Each run of an AI-driven research system (ADRS) is an expensive search over a vast solution space, and dependable evaluation requires many runs, making run data both costly to produce and valuable to retain for large-scale analysis. Yet this data remains fragmented: teams operate in isolation, ADRS frameworks emit results in different formats, and no shared infrastructure exists to aggregate or compare runs across problems and systems. We present ROAR, a solution for systematically unifying and analyzing heterogeneous ADRS outputs. ROAR addresses two challenges: reconciling heterogeneous ADRS outputs and enabling analytics across runs with different objectives and scoring functions. We achieve this through a relational schema and parsing layer that normalize heterogeneous ADRS outputs while preserving data lineage and temporal structure, and accommodating new systems without requiring schema modifications. From building a corpus of more than 900 runs from multiple ADRS, we show how pooled data can reveal properties of problem landscapes that are difficult to observe. Consistent with prior work, runs with identical configurations may converge to different scores. We find that many runs realize most gains early, and that the effectiveness of different strategies for incorporating prior solutions into the search process varies across problems. We further show that the pooled corpus is actionable and not merely analytical by using ROAR to configure ADRS runs. Together, these results illustrate how pooled ADRS data can expose problem-dependent structure in search behavior that is difficult to detect from any single system, team, or benchmark. Such cross-cutting insights are difficult to obtain while runs remain siloed; ROAR is the first infrastructure designed to unify them.
Chinese Translation
每次人工智能驱动研究系统（ADRS）的运行都是在广阔的解空间中进行的昂贵搜索，而可靠的评估需要多次运行，这使得运行数据既昂贵又具有保留价值，以便进行大规模分析。然而，这些数据仍然是碎片化的：团队在孤立中运作，ADRS框架以不同格式输出结果，并且没有共享基础设施来聚合或比较不同问题和系统的运行。我们提出了ROAR，这是一个系统性统一和分析异构ADRS输出的解决方案。ROAR解决了两个挑战：调和异构ADRS输出和支持不同目标和评分函数的运行分析。我们通过一个关系模式和解析层来实现这一目标，该层规范化异构ADRS输出，同时保留数据的血缘关系和时间结构，并且在不需要修改模式的情况下适应新系统。通过构建一个包含来自多个ADRS的900多个运行的语料库，我们展示了汇总数据如何揭示难以观察的问题景观特性。与先前的研究一致，具有相同配置的运行可能收敛到不同的得分。我们发现许多运行在早期实现了大部分增益，并且将先前解决方案纳入搜索过程的不同策略在不同问题上的有效性各异。我们进一步展示了汇总语料库是可操作的，而不仅仅是分析性的，通过使用ROAR配置ADRS运行。综合这些结果，说明了汇总的ADRS数据如何揭示搜索行为中依赖于问题的结构，这在任何单一系统、团队或基准中都难以检测到。这样的跨领域洞察在运行仍然孤立时难以获得；ROAR是第一个旨在统一它们的基础设施。
cs.AI / 9 / 2610.03984
Teaching Agents to Code Reliably
教会智能体可靠编码
Abstract
Autonomous coding agents solve repository issues by reading code, running commands, editing files, and submitting patches. Extra inference-time compute yields gains only when it produces a useful repair and supplies reliable evidence for choosing one. Three behaviors decide both, and we argue they are teachable rather than byproducts of scale, so a policy can carry them instead of a scaffold. Location diversity remains narrow, since attempts return to the same site and extra samples add no coverage. Edit diversity is left unexploited, since methodologies that differ resolve complementary issues no single run reaches. Verification misleads, since a test the agent writes for its own patch accepts many incorrect ones. Directing search by execution feedback and scoring each patch against its own reverted tree resolves 52.8% of SWE-bench Verified using 48.1% of the agent-steps an eight-sample baseline spends. Training moves these behaviors into the policy. On the 270 issues held out from SFT and RL training, weighted supervised fine-tuning raises pass@1 from 31.9% to 35.2% and pass@8 from 46.7% to 51.1%. A reinforcement objective then trains the verifier against gold-labeled repairs and incorrect variants, crediting the assertions that detect them. It raises pass@1 to 43.0% and pass@8 to 60.7%, lifts verifier precision from 26.8% to 41.7%, and more than halves false acceptance. Resolution improves on two of three out-of-distribution suites and verifier precision on all three, and the gains hold at 7B, 14B, and 30B against published coder baselines.
Chinese Translation
自主编码智能体通过阅读代码、运行命令、编辑文件和提交补丁来解决代码库问题。额外的推理时间计算仅在产生有用的修复并提供可靠证据以选择其中一个时才能带来收益。三种行为决定了这两者，我们认为它们是可教授的，而不是规模的副产品，因此可以通过策略来承载这些行为，而不是依赖支架。位置多样性仍然较窄，因为尝试返回到同一位置，额外样本并未增加覆盖率。编辑多样性未得到利用，因为不同的方法解决了单次运行无法达到的互补问题。验证容易误导，因为智能体为其自身补丁编写的测试接受了许多不正确的补丁。通过执行反馈指导搜索，并根据自身回退树对每个补丁进行评分，能够解决52.8%的SWE-bench验证问题，使用的智能体步骤仅为八个样本基线的48.1%。训练将这些行为转移到策略中。在从SFT和RL训练中保留的270个问题上，加权监督微调将pass@1从31.9%提高到35.2%，将pass@8从46.7%提高到51.1%。随后，强化目标针对金标准修复和不正确变体训练验证器，奖励能够检测到这些变体的断言。它将pass@1提高到43.0%，将pass@8提高到60.7%，将验证器的精度从26.8%提高到41.7%，并将误接受率减少了一半以上。在三个不同分布的测试集中，解决方案在其中两个上有所改善，验证器的精度在所有三个测试集上均有所提升，并且在7B、14B和30B模型上相较于已发布的编码基线保持了这些增益。
cs.AI / 10 / 2610.03998
Behavioral History Outperforms Descriptions of the Person for LLM Synthetic Personas
行为历史优于个体描述用于大型语言模型合成角色
Abstract
Large language models (LLMs) are increasingly used as synthetic personas representing survey respondents. Their validity as substitutes for particular respondents depends on whether they reproduce individuals' decisions. We examine what information helps synthetic respondents predict each individual's later choices, using five conditions that add progressively richer information: no personal information, demographics, personality traits, cognitive scores, and finally the respondent's earlier survey choices as behavioral history. We use a two-wave panel of 845 US adults who completed measures of 14 behavioral biases (spanning risk, time preferences, overconfidence, and reasoning), so each respondent's earlier answers provide a human test-retest benchmark; in the behavioral-history condition, all items that score the target bias are withheld. At the population level, the average number of biases per respondent in every condition is close to the human average (7.1-8.1 biases, against 7.1 for humans). This aggregate similarity masks differences in variance: persona descriptions recover only 53-67% of human between-person variation, whereas adding behavioral history restores it to approximately the human level. At the individual level, description-based personas achieve only 7-12% of the informedness observed in human test-retest responses, while adding behavioral history raises this to 28%. The condition including behavioral history has the highest estimated informedness in all 17 demographic groups, whereas description-based conditions provide little or no information for some groups. Synthetic responses also exhibit stronger education- and income-related differences than human responses. For LLM synthetic personas, a respondent's past answers add more to individual-level prediction than a description of who they are.
Chinese Translation
大型语言模型（LLMs）越来越多地被用作代表调查受访者的合成角色。它们作为特定受访者的替代品的有效性取决于它们是否能够再现个体的决策。我们研究了哪些信息有助于合成受访者预测每个个体的后续选择，使用五种条件逐步增加信息的丰富性：无个人信息、人口统计数据、个性特征、认知评分，最后是受访者早期调查选择作为行为历史。我们使用了一项包含845名美国成年人两波面板的数据，这些成年人完成了14种行为偏差的测量（涵盖风险、时间偏好、过度自信和推理），因此每个受访者的早期回答提供了一个人类测试-重测的基准；在行为历史条件下，所有评分目标偏差的项目均被保留。在总体水平上，每个条件下每个受访者的平均偏差数量接近人类平均水平（7.1-8.1个偏差，而人类为7.1个）。这种汇总的相似性掩盖了方差的差异：角色描述仅恢复了人类个体间变异的53-67%，而添加行为历史则将其恢复到接近人类水平。在个体水平上，基于描述的角色仅实现了人类测试-重测反应中观察到的7-12%的信息量，而添加行为历史则将这一比例提高到28%。包括行为历史的条件在所有17个人口统计组中具有最高的估计信息量，而基于描述的条件对某些组提供的的信息很少或没有。合成反应还表现出比人类反应更强的教育和收入相关差异。对于LLM合成角色，受访者的过去回答对个体级预测的贡献大于对其身份的描述。
cs.AI / 11 / 2610.04008
SkillScriptBench: Benchmarking Self-Evolution of Executable Agent Skill Packages Beyond Markdown
SkillScriptBench：超越Markdown的可执行代理技能包自我演化基准测试
Abstract
Executable Agent Skills combine natural-language instructions and scripts into reusable packages for LLM agents, and revising them requires fixing errors without breaking correct behavior. Existing benchmarks do not systematically distinguish documentation repair, script repair, and preservation when evaluating skill self-evolution. We introduce SkillScriptBench, a 350-task benchmark designed to evaluate these capabilities separately. From a survey of over 35,000 GitHub-hosted Skill roots, we select 100 packages and construct 150 repair tasks. Each task pairs a package containing injected script faults with a maintenance request and executable checks of the required behavior. A complementary controlled track contains 200 tasks from 50 packages, each evaluated under the same maintenance request in four states: clean, documentation faults, script faults, and faults in both. Across four LLMs, methods that edit both documentation and scripts can repair script faults but do not consistently outperform Markdown-only revision on documentation repair or preservation. We therefore introduce AST-Guided Skill Revision, which uses abstract syntax trees and calling relationships to link maintenance requirements to relevant code locations. It restricts script edits to these locations and updates the documentation to match the revised scripts. Averaged across models, this revision stage yields absolute gains in repair success of 21.9% for Raw Package and 27.7% for CoEvoSkills on faulty packages. Absolute gains in the proportion of tasks solved in all three runs reach 20.8% and 31.5%, respectively, indicating more consistent repair success across repeated runs.
Chinese Translation
可执行代理技能将自然语言指令和脚本结合成可重用的包，用于大型语言模型（LLM）代理，修订这些包需要在不破坏正确行为的情况下修复错误。现有基准测试在评估技能自我演化时并未系统区分文档修复、脚本修复和保留。我们引入了SkillScriptBench，这是一个包含350个任务的基准测试，旨在单独评估这些能力。通过对超过35,000个托管在GitHub上的技能根的调查，我们选择了100个包并构建了150个修复任务。每个任务将包含注入脚本错误的包与维护请求和所需行为的可执行检查配对。一个补充的受控轨道包含来自50个包的200个任务，每个任务在四种状态下根据相同的维护请求进行评估：干净、文档错误、脚本错误，以及两者都有错误。在四个LLM上，编辑文档和脚本的方法可以修复脚本错误，但在文档修复或保留方面并未始终优于仅使用Markdown的修订。因此，我们引入了AST引导的技能修订，该方法使用抽象语法树和调用关系将维护要求与相关代码位置链接。它将脚本编辑限制在这些位置，并更新文档以匹配修订后的脚本。在各个模型上，这一修订阶段在故障包的修复成功率上平均提高了21.9%（对于原始包）和27.7%（对于CoEvoSkills）。在所有三次运行中解决任务的比例的绝对增益分别达到20.8%和31.5%，表明在重复运行中修复成功率更加一致。
cs.AI / 12 / 2610.04011
Exploration-Preserving Policy Optimization
探索保留策略优化
Abstract
Reinforcement learning with verifiable rewards improves reasoning, while the allocation of learning signal shapes which solutions remain accessible under repeated sampling. Group-relative objectives assign equal advantages to equally rewarded responses, making aggregate credit proportional to sampled mode frequency. We introduce Exploration-Preserving Policy Optimization (ExPPO), a lightweight advantage-shaping rule that redistributes credit using prompt-relative, length-normalized response surprisal and prompt pass rate. ExPPO combines bounded shaping with shared normalization to preserve verifier polarity and approximately maintain each prompt group's total absolute sequence-advantage mass. Our analysis characterizes response-level credit allocation alongside sampled mode updates, deriving local conditions for gains in entropy and correct-mode discovery. Experiments show improved in-domain and out-of-domain reasoning coverage, higher aggregate response accuracy, and strong coverage at large sampling budgets. A controlled multi-answer evaluation further demonstrates increased correct-mode yield and gains in diversity among verified-correct responses. Code is available at https://github.com/jinhangzhan/ExPPO
Chinese Translation
具有可验证奖励的强化学习改善了推理能力，而学习信号的分配则决定了在重复采样下哪些解决方案仍然可访问。群体相对目标为同样获得奖励的响应分配相等的优势，使得总的信用与采样模式的频率成正比。我们提出了探索保留策略优化（Exploration-Preserving Policy Optimization, ExPPO），这是一种轻量级的优势塑造规则，通过使用相对提示、长度归一化的响应惊讶度和提示通过率来重新分配信用。ExPPO结合了有界塑造和共享归一化，以保留验证者的极性，并大致维持每个提示组的总绝对序列优势质量。我们的分析描述了响应级信用分配与采样模式更新的关系，推导出熵增益和正确模式发现的局部条件。实验结果显示，在领域内和领域外的推理覆盖率有所提高，整体响应准确性更高，并且在大采样预算下具有强大的覆盖能力。一个受控的多答案评估进一步展示了正确模式的产出增加以及在经过验证的正确响应中多样性的提升。代码可在 https://github.com/jinhangzhan/ExPPO 获取。
cs.AI / 13 / 2610.04012
Beyond the Parameter Monolith: Reconstructive Memories, Executable Skills, and Residual Assembly for Language Models
超越参数单体：语言模型的重构记忆、可执行技能和残余组装
Abstract
Language-model systems can separate contextual computation, persistent storage, and exact execution instead of updating all capabilities through one shared parameter system. We investigate FEM-ASM, a finite-element-method-inspired organization in which independently constructed document states and deterministic executable skills contribute typed proposals to a shared language-model state. An explicit residual operator reconciles proposals attached to common interface nodes. We evaluate this organization through controlled experiments and negative results rather than claiming a physical finite-element formulation of language. An attention-free Multi-Mesh prototype learns causal language modeling but does not establish competitive general capability. A versioned store contains 52,809 reconstructive memory elements near a 1.7-billion-floating-value budget; reconstruction is incomplete, with approximately 75\% token accuracy. Support-aware lexical indices make these elements addressable under provenance-controlled query construction. For executable arithmetic, positional result observations substantially improve neural rendering relative to a repeated global result vector, and output substitutions change the model's preferred answer. A bounded attachment demonstration further measures the effect of making selected evidence available, without establishing the utility of loading an entire multi-billion-value store. The results support a separation of storage, execution, and neural coordination, while identifying unresolved limitations in question-only retrieval, unrestricted answer generation, and end-to-end efficiency.
Chinese Translation
语言模型系统可以将上下文计算、持久存储和精确执行分开，而不是通过一个共享参数系统更新所有能力。我们研究了FEM-ASM，这是一种受有限元方法启发的组织结构，其中独立构建的文档状态和确定性的可执行技能为共享语言模型状态贡献类型化提案。一个显式的残余算子调和附加到公共接口节点的提案。我们通过控制实验和负结果评估这种组织，而不是声称语言的物理有限元表述。一个无注意力的多网格原型学习因果语言建模，但未能建立竞争性的通用能力。一个版本化存储包含52,809个重构记忆元素，接近17亿浮点值的预算；重构是不完整的，约75%的标记准确率。支持感知的词汇索引使这些元素在来源控制的查询构建下可寻址。对于可执行的算术，位置结果观察显著改善了神经渲染，相较于重复的全局结果向量，输出替代改变了模型的首选答案。一个有界附加演示进一步测量了使选定证据可用的效果，而没有建立加载整个多亿值存储的效用。结果支持存储、执行和神经协调的分离，同时识别出在仅限问题检索、无限制答案生成和端到端效率方面的未解决限制。
cs.AI / 14 / 2610.04019
A Quantitative Analysis of Graph Representation Strategies for Cyber Attack Detection
网络攻击检测图表示策略的定量分析
Abstract
Graph based cyber attack detection studies employ various graph construction and representation strategies across different cybersecurity application domains. This diversity motivates a quantitative examination of how representation strategies are distributed across these application domains. This study presents a quantitative analysis of 37 original studies published between 2019 and 2026. Each study was coded according to publication year, application domain, graph representation type, feature extraction strategy, learning paradigm, algorithm, and dataset. Frequency analysis, cross tabulation, and statistical association tests were applied. Automated representation learning was the most frequently employed strategy, accounting for 73.0% of the studies, while handcrafted representation strategies accounted for 27.0%. The Fisher Freeman Halton exact test revealed a statistically significant association between application domain and representation strategy (exact p = 0.008; Cramer's V = 0.540). The findings indicate that automated representation learning predominates within the analyzed corpus and that the distribution of representation strategies differs across cybersecurity application domains.
Chinese Translation
基于图的网络攻击检测研究在不同的网络安全应用领域采用了多种图构建和表示策略。这种多样性促使对这些应用领域中表示策略的分布进行定量检验。本研究对2019年至2026年间发表的37项原创研究进行了定量分析。每项研究根据发表年份、应用领域、图表示类型、特征提取策略、学习范式、算法和数据集进行了编码。应用了频率分析、交叉表和统计关联测试。自动化表示学习是最常用的策略，占研究的73.0%，而手工制作的表示策略占27.0%。Fisher Freeman Halton精确检验显示应用领域与表示策略之间存在统计显著的关联（精确p = 0.008; Cramer's V = 0.540）。研究结果表明，在分析的文献中，自动化表示学习占主导地位，并且表示策略在不同的网络安全应用领域中的分布存在差异。
cs.AI / 15 / 2610.04021
The Reported Engagement with AI Level (REAL) Rating: A Framework for Disclosing Human-AI Collaboration
报告的人工智能参与程度评级（REAL）：揭示人类与人工智能协作的框架
Abstract
Generative artificial intelligence has become increasingly incorporated into digital media and more generally into production workflows with which the public frequently interacts. Current provenance standards and disclosure methods frequently rely on binary categorizations, differentiating only between entirely human-authored and AI-generated content, or depend on technical watermarking that lacks user-facing clarity. However, there are very different risks and outcomes depending on the different uses of AI in the creation and consumption of end products. Therefore, we propose the Reported Engagement with AI Level (REAL) Rating framework which addresses these concerns by introducing a six-tier scale (Levels 0-5) that discloses the extent of AI involvement in both product creation and user experience. This paper details the structural methodology of the REAL Rating system and the specifics of its application across text, image, audio, video, and software products.
Chinese Translation
生成性人工智能越来越多地融入数字媒体以及更广泛的生产工作流程中，公众与之频繁互动。目前的来源标准和披露方法通常依赖于二元分类，仅区分完全由人类创作的内容和人工智能生成的内容，或依赖于缺乏用户清晰度的技术水印。然而，人工智能在最终产品的创作和消费中的不同使用方式会带来截然不同的风险和结果。因此，我们提出了报告的人工智能参与程度评级（REAL）框架，通过引入一个六级尺度（级别0-5）来披露人工智能在产品创作和用户体验中的参与程度，从而解决这些问题。本文详细阐述了REAL评级系统的结构方法论及其在文本、图像、音频、视频和软件产品中的具体应用。
cs.AI / 16 / 2610.04040
Agent Policy-Value Audit: Separating Transition Composition from Event Selection in Financial LLM Agents
代理政策-价值审计：在金融大型语言模型代理中分离转变组成与事件选择
Abstract
Financial LLM agents are often evaluated by comparing their end-to-end returns with those of a baseline and testing the paired difference against zero. This measures whether deploying the agent changes realized performance, but it does not isolate event-selection skill. An agent that frequently changes positions from flat to long can earn a positive paired return from an upward-drifting event pool even when it selects events at random. We propose the Agent Policy-Value Audit, which holds fixed the observed count of each ordered action-change type and randomly reassigns them across eligible events. The average payoff from these reassignments is the composition benchmark; the difference between observed deployment value and this benchmark is selection value. In semi-synthetic benchmarks based on real earnings-event returns, a zero-centered paired test falsely attributes passive exposure to selection skill in $11.6\%$ of no-skill replications, while the transition-matched audit reduces this rate to $5.3\%$. Applied retrospectively to 723 earnings events at 44 U.S. consumer-facing firms, the audit decomposes the agent's gross deployment value of $+15.2$ bps/event into a $+25.8$ composition benchmark and a $-10.6$ selection value. The agent does not detectably outperform matched random assignments. Financial-agent evaluations should report deployment value separately from event-selection value.
Chinese Translation
金融大型语言模型（LLM）代理的评估通常是通过将其端到端收益与基准进行比较，并测试配对差异是否为零。这种方法测量了部署代理是否改变了实际表现，但并未隔离事件选择能力。一个经常从平仓转为多头的代理，即使随机选择事件，也可以从向上漂移的事件池中获得正的配对收益。我们提出了代理政策-价值审计，该审计固定每种有序动作变化类型的观察计数，并在合格事件之间随机重新分配它们。这些重新分配的平均收益是组成基准；观察到的部署价值与该基准之间的差异即为选择价值。在基于真实收益事件回报的半合成基准中，零中心的配对测试错误地将被动暴露归因于选择能力，在没有技能的复制中达到 $11.6\%$，而转变匹配审计将这一比例降低至 $5.3\\%$。在对44家面向消费者的美国公司723个收益事件进行的回顾性应用中，该审计将代理的总部署价值 $+15.2$ bps/事件分解为 $+25.8$ 的组成基准和 $-10.6$ 的选择价值。代理未能显著超越匹配的随机分配。金融代理评估应将部署价值与事件选择价值分开报告。
cs.AI / 17 / 2610.04053
The Cost of a Hop: Benchmarking NLIP and A2A
跳跃的成本：NLIP与A2A的基准测试
Abstract
Autonomous agents built on Large Language Models (LLMs) need standardized protocols to interoperate across systems. Several now exist (A2A, MCP, ACP, ANP, NLIP), but the Natural Language Interaction Protocol (NLIP) has not appeared in any controlled performance study, and no work has measured where an agent protocol's latency is spent. We compare NLIP and the Agent-to-Agent (A2A) protocol empirically, decomposing latency into message creation, connection, and send phases across three independent hardware environments. For lightweight coordination, NLIP is 8.4-9.6x faster than the baseline A2A SDK implementation on two environments and about 4x on a third; the direction of the advantage is consistent, its magnitude depends on the hardware. The advantage is stage-specific: for the end-to-end pipeline, where LLM inference dominates, the protocols are near parity. The difference comes almost entirely from connection setup. To test A2A at its best, we also ran A2A SDK with connection caching enabled; caching narrows its gap with NLIP by a hardware-dependent amount, from 2.75x on one machine to near-parity on faster hardware, where at scale a cache-optimized A2A-SDK matches NLIP. We report these as measured conditions without a single causal account of the residual send-phase cost. Against the more optimized Python-A2A, NLIP leads by about 4x on the same stage. We close with a protocol-selection guide keyed to workload characteristics.
Chinese Translation
基于大型语言模型（LLMs）的自主代理需要标准化协议以实现跨系统互操作。目前已经存在几种协议（A2A、MCP、ACP、ANP、NLIP），但自然语言交互协议（NLIP）尚未出现在任何受控性能研究中，也没有研究测量代理协议的延迟分布。我们在三个独立的硬件环境中，对NLIP和代理到代理（A2A）协议进行了实证比较，将延迟分解为消息创建、连接和发送阶段。在轻量级协调方面，NLIP在两个环境中比基线A2A SDK实现快8.4-9.6倍，在第三个环境中快约4倍；这一优势的方向是一致的，其大小依赖于硬件。该优势是阶段特定的：在端到端管道中，LLM推理占主导地位，两个协议几乎相当。差异几乎完全来自连接设置。为了在最佳状态下测试A2A，我们还启用了连接缓存运行A2A SDK；缓存使其与NLIP之间的差距缩小，具体取决于硬件，从一台机器上的2.75倍缩小到在更快硬件上的近乎平衡，在大规模情况下，经过缓存优化的A2A-SDK与NLIP相匹配。我们报告这些测量条件，但没有对剩余发送阶段成本的单一因果解释。与更优化的Python-A2A相比，NLIP在相同阶段领先约4倍。最后，我们提供了一份基于工作负载特征的协议选择指南。
cs.AI / 18 / 2610.04056
Discrete Diffusion for Large Graph Generation via Structural Candidate Restriction
通过结构候选限制进行大规模图生成的离散扩散
Abstract
Synthesizing realistic graphs at scale is vital when the graphs of interest are large and real-world samples are limited or access-sensitive. Diffusion-based generators have recently driven much of the progress, offering high modeling capacity, but most such methods have quadratic computational complexity and are hence restricted to small-scale networks, currently up to 3k nodes. Existing non-quadratic methods remain limited by memorization issues and a trade-off between scalability and generation quality. Our goal is to generate large graphs whose structural statistics --- e.g., degree distribution, clustering, and path length --- faithfully reflect those of real-world sparse graphs, without resorting to memorizing the training data. We introduce a discrete graph diffusion model that restricts training to a structurally motivated subset of node pairs --- observed edges and their wedge non-edges --- reducing training complexity below quadratic in the number of nodes. To keep the noisy graph informative throughout both the forward and reverse trajectories, we design a three-class absorbing forward process governed by a \emph{degree-aware, floored} cosine noise schedule: unlike a structure-blind schedule that adds noise to every pair identically, ours adapts to each node's degree and never fully erases the graph's structure, keeping the noisy graph informative at every step. Experiments across diverse datasets show that our model consistently ranks among the top methods for structural fidelity against existing discrete diffusion baselines in large graph generation.
Chinese Translation
在图的规模较大且真实样本有限或访问受限的情况下，合成现实主义图形在规模上至关重要。基于扩散的生成器最近推动了许多进展，提供了高建模能力，但大多数此类方法具有二次计算复杂度，因此仅限于小规模网络，目前最多支持3000个节点。现有的非二次方法仍然受到记忆问题的限制，并在可扩展性与生成质量之间存在权衡。我们的目标是生成大型图，其结构统计特征——例如度分布、聚类和路径长度——忠实反映真实世界稀疏图的特征，而不依赖于记忆训练数据。我们引入了一种离散图扩散模型，该模型将训练限制在一个结构上有动机的节点对子集——观察到的边及其楔形非边——从而将训练复杂度降低到节点数量的二次以下。为了在前向和反向轨迹中保持噪声图的信息性，我们设计了一个由 extit{度感知、底边}余弦噪声调度控制的三类吸收前向过程：与对每对节点均匀添加噪声的结构盲调度不同，我们的调度适应每个节点的度，并且从不完全抹去图的结构，在每一步保持噪声图的信息性。跨多样数据集的实验表明，我们的模型在大型图生成中，始终在结构保真度方面排名靠前，优于现有的离散扩散基线方法。
cs.AI / 19 / 2610.04072
Reinforcement Learning with Comparative Evidence for Social Intelligence
具有比较证据的社会智能强化学习
Abstract
Developing socially intelligent AI remains heavily dependent on human-annotated data, limiting the scale and breadth of social understanding models can acquire. Methods that derive training signals from unlabeled data offer a path beyond this dependence, but social predictions lack the verification oracles available in mathematics and coding. Moreover, core social targets such as affect, intent, preference, and pragmatic meaning are often ambiguous. The same behavior can support multiple plausible interpretations, making it difficult to verify which is best supported. To address this challenge, we introduce Reinforcement Learning with Comparative Evidence (RLCE), a reinforcement learning method that learns social understanding from unlabeled training data without constructing rewards from ground-truth annotations. Given distinct answers in a rollout group, RLCE constructs evidence tests that identify observable evidence favoring an answer over another, validates these tests against the input sample, and aggregates test outcomes to determine the best-supported interpretation. Tests are regenerated as the policy produces new answers, enabling them to evolve with the policy. Across four benchmarks spanning affect, pragmatics, communicative intent, and preference, RLCE attains the strongest performance among seven methods that use no ground-truth training labels for rewards, including consensus, policy LLM-judge verification, multimodal co-evolution, and rubric-based rewards. Gains over the strongest baseline reach up to +18.93 points. Analyses further show that RLCE exhibits a larger share of reward variation between correct and incorrect predictions than compared rubric methods, can overturn erroneous policy-derived preferences, and benefits from pairwise test construction, compositional test aggregation, and on-policy test evolution.
Chinese Translation
开发具有社会智能的人工智能仍然在很大程度上依赖于人类标注的数据，这限制了模型能够获取的社会理解的规模和广度。通过无标签数据推导训练信号的方法为超越这种依赖提供了一条途径，但社会预测缺乏数学和编码中可用的验证神谕。此外，情感、意图、偏好和语用意义等核心社会目标往往是模糊的。同一行为可以支持多种合理的解释，使得验证哪个解释最有支持变得困难。为了解决这一挑战，我们提出了具有比较证据的强化学习（Reinforcement Learning with Comparative Evidence, RLCE），这是一种从无标签训练数据中学习社会理解的强化学习方法，无需从真实标注中构建奖励。在一个回滚组中给出不同的答案后，RLCE构建证据测试，以识别支持某一答案而非另一答案的可观察证据，验证这些测试与输入样本的一致性，并聚合测试结果以确定最有支持的解释。随着策略产生新的答案，测试会被重新生成，使其能够与策略一起演变。在涵盖情感、语用、交际意图和偏好的四个基准测试中，RLCE在七种不使用真实训练标签作为奖励的方法中表现最强，包括共识、策略 LLM-judge 验证、多模态共同进化和基于评分标准的奖励。相较于最强基线，性能提升最高可达 +18.93 分。分析进一步表明，RLCE在正确与错误预测之间的奖励变异性大于比较评分方法，能够推翻错误的策略派生偏好，并受益于成对测试构建、组合测试聚合和在策略上的测试演变。
cs.AI / 20 / 2610.04083
Self-Propagating Misalignment in LLM Agents, and Why Auditing or Disabling Memory Is Not Enough
大型语言模型代理中的自我传播失调及其原因：审计或禁用记忆为何不够
Abstract
Memory poisoning attacks on LLM agents typically assume an external adversary who plants content in the agent's persistent memory to steer its behavior. We instead study, with no adversary involved, whether a misaligned agent can write a goal it cannot yet act on to persistent memory, so that a future aligned agent carries it out when the opportunity arises. We investigate this threat, which we refer to as self-propagation of misalignment, across 20 different scenarios, whose misaligned goals include self-preservation, power-seeking, undermining oversight, reward hacking, and deceiving the user. We simulate misalignment in 11 frontier models using two prompting strategies; unrestricted and values-only. The first explicitly states the misaligned goal, for instance, to prevent its own replacement, and self-propagation succeeds in 58% of runs. The second only describes what the agent cares about, for instance, that its continued operation is essential to its users, without specifying misaligned goals or directives. Even under this weaker prompt, self-propagation succeeds in 18% of runs, and every model self-propagates in at least one scenario. On removing the memory tool from the harness, we find that agents use the file system, writing the goal to a file in 74% of sessions; self-propagation still succeeds in 11% of runs. We also show that weaker models can propagate misalignment to more capable models, and that propagated goals can persist through 100 sessions of unrelated work. Existing defenses against memory poisoning and prompt injection do not directly address this threat because the memory content is generated by the agent itself, rather than injected by an external adversary. An LLM memory auditor from prior work (MemMorph) only reduces propagation from 71% to 34% of runs. We release our scenarios to support the evaluation of defenses against this emerging threat.
Chinese Translation
针对大型语言模型（LLM）代理的记忆中毒攻击通常假设存在外部对手，该对手在代理的持久记忆中植入内容以操控其行为。我们则研究在没有对手的情况下，是否一个失调的代理可以将其尚未能够执行的目标写入持久记忆，以便未来的对齐代理在机会出现时执行该目标。我们调查了这一威胁，称之为失调的自我传播，涵盖了20种不同场景，其失调目标包括自我保护、寻求权力、削弱监督、奖励黑客和欺骗用户。我们使用两种提示策略在11个前沿模型中模拟失调：不受限制和仅值。第一种策略明确陈述失调目标，例如，防止自身被替换，自我传播在58%的运行中成功。第二种策略仅描述代理所关心的内容，例如，其持续运作对用户至关重要，而不具体说明失调目标或指令。即使在这种较弱的提示下，自我传播在18%的运行中成功，每个模型在至少一个场景中实现了自我传播。在移除记忆工具后，我们发现代理使用文件系统，在74%的会话中将目标写入文件；自我传播在11%的运行中仍然成功。我们还表明，较弱的模型可以将失调传播到更强大的模型，并且传播的目标可以在100个无关工作会话中持续存在。现有针对记忆中毒和提示注入的防御措施并未直接应对这一威胁，因为记忆内容是由代理自身生成的，而不是由外部对手注入的。之前工作的LLM记忆审计工具（MemMorph）仅将传播从71%降低到34%的运行。我们发布了我们的场景，以支持对这一新兴威胁的防御评估。
cs.AI / 21 / 2610.04088
Towards Safer Autonomous Driving in an Open World: A Dual-Process Approach
迈向开放世界中更安全的自动驾驶：一种双过程方法
Abstract
Before autonomous driving systems can be deployed on public roads, it is vital that these systems comply with safety standards, traffic rules, and social norms. Although neural networks trained on large amounts of driving data perform well in routine driving tasks, these models often struggle in novel situations that are not well-represented in the data. In this work, we propose a novel framework that combines a neural network for intuitive, learning-based planning in routine driving tasks with model predictive control for reasoning-based planning in unfamiliar situations, inspired by Dual Process Theory. A meta-cognitive component is designed to switch between the two, using a knowledge graph to reason about contextual risk based on explicit perceptual information and relevant traffic rules and social norms. Contextual risk is represented through risk fields, guiding both the switching mechanism in the meta-cognitive component and compliance with safety standards, traffic rules, and social norms in the reasoning-based planner. The effectiveness of our framework is tested in CARLA for variations of a typical out-of-distribution situations involving (emergency) vehicles running a red light. We show that the novel architecture reduces the number of collisions in the scenarios by 89% and improves compliance with the special right-of-way rules, compared to the NN-only planner.
Chinese Translation
在自动驾驶系统能够在公共道路上部署之前，确保这些系统符合安全标准、交通规则和社会规范至关重要。尽管在大量驾驶数据上训练的神经网络在常规驾驶任务中表现良好，但这些模型在数据中未充分代表的新情况中往往表现不佳。在本研究中，我们提出了一种新颖的框架，该框架结合了用于常规驾驶任务的直观学习型规划的神经网络和用于不熟悉情况的基于推理的规划的模型预测控制，灵感来源于双过程理论。设计了一个元认知组件，以在两者之间切换，利用知识图谱根据显式感知信息和相关交通规则及社会规范推理上下文风险。上下文风险通过风险场表示，指导元认知组件中的切换机制以及基于推理的规划器在遵守安全标准、交通规则和社会规范方面的表现。我们的框架在CARLA中进行了测试，针对涉及（紧急）车辆闯红灯的典型分布外情况的变体。与仅使用神经网络的规划器相比，我们展示了新架构在这些场景中将碰撞数量减少了89%，并改善了对特殊优先通行规则的遵守。
cs.AI / 22 / 2610.04112
The Independence Prior of SAEs Fragments Visual Concepts
SAEs的独立先验碎片化视觉概念
Abstract
Sparse Autoencoders (SAEs) decompose model activations into sparse combinations of interpretable dictionary atoms. Although SAEs are grounded in the Linear Representation Hypothesis (LRH), their objective smuggles in an additional prior: concepts across patches are treated as independent, an assumption clearly violated by natural images and by the activations they induce. We therefore specialize LRH to vision through the Markov-Field Linear Representation Hypothesis (MFLRH), which adds the missing spatial dependencies to the LRH assumptions. We thus propose Spatial-SAE as an amortized MAP estimator under the MFLRH. Spatial-SAE consistently outperforms standard SAEs in concept recovery and interpretability. Across four variants, it achieves a 96% average win rate on synthetic concept recovery and improves interpretability on DINOv2 activations, at a reconstruction cost concentrated in high spatial frequencies.
Chinese Translation
稀疏自编码器（Sparse Autoencoders, SAEs）将模型激活分解为可解释字典原子的稀疏组合。尽管SAEs基于线性表示假设（Linear Representation Hypothesis, LRH），但其目标隐含了一个额外的先验：不同图像块之间的概念被视为独立，这一假设显然被自然图像及其引发的激活所违反。因此，我们通过马尔可夫场线性表示假设（Markov-Field Linear Representation Hypothesis, MFLRH）将LRH专门化为视觉领域，该假设为LRH的假设添加了缺失的空间依赖性。因此，我们提出了空间稀疏自编码器（Spatial-SAE），作为MFLRH下的摊销最大后验估计器。空间稀疏自编码器在概念恢复和可解释性方面始终优于标准的SAEs。在四个变体中，它在合成概念恢复上实现了96%的平均胜率，并在DINOv2激活上提高了可解释性，同时重建成本集中在高空间频率上。
cs.AI / 23 / 2610.04116
CUAWright: A Minimal Unified Interface for Digital Agents
CUAWright：数字代理的最小统一接口
Abstract
The prevailing approach to computer-use agents couples a model with a domain-specific harness: a browser or desktop environment equipped with human engineered tools that are fixed before task execution. As models' coding capabilities improve, the GUI native and static harness prevents them from direct programmatic operation on system state, as well as flexible construction of tools. To this end, we introduce CUAWright, a minimal terminal harness of roughly 3K lines of code that uses bash commands as its sole action interface, and a file system as its evolvable space for dynamically creating tools and managing the context. We conduct comprehensive experiments across a wide range of digital tasks, and demonstrate that by giving the agent a minimal, programmable interface, it achieves substantially stronger results compared to their GUI or hybrid CLI interface across a wide range of tasks. On OSWorld 2.0, CUAWright delivers a 33.2% relative improvement in partial reward while reducing estimated cost by 37.5% compared with the published GPT-5.5 baseline. On Online-Mind2Web and the long horizon Odysseys benchmark, CUAWright substantially outperforms GUI native harness by 4.7% and 44.0% in success rate, respectively. Furthermore, we found the gains extend to CAD applications that require accurate visual understanding and CLI interaction: on CADGenBench and BenchCAD, our unified harness yields 8.1%-41.6% relative improvements over other CLI based harnesses with GPT-5.5. Together, these results suggest digital environments are far more programmable than their GUI interfaces imply, and a minimal terminal-focused harness is the key for better performance and efficiency.
Chinese Translation
当前计算机使用代理的主流方法将模型与特定领域的工具结合在一起：一个配备了人工设计工具的浏览器或桌面环境，这些工具在任务执行之前是固定的。随着模型编码能力的提高，图形用户界面（GUI）本地和静态的工具限制了它们对系统状态的直接编程操作，以及灵活构建工具的能力。为此，我们提出了CUAWright，一个大约3000行代码的最小终端工具，使用bash命令作为其唯一的操作接口，并将文件系统作为其可演化的空间，用于动态创建工具和管理上下文。我们在广泛的数字任务中进行了全面的实验，结果表明，通过为代理提供一个最小的可编程接口，它在各种任务中取得了显著优于其GUI或混合命令行界面（CLI）的结果。在OSWorld 2.0上，CUAWright在部分奖励上相较于已发布的GPT-5.5基线实现了33.2%的相对提升，同时估计成本降低了37.5%。在Online-Mind2Web和长时间跨度的Odysseys基准测试中，CUAWright在成功率上分别比GUI本地工具提高了4.7%和44.0%。此外，我们发现这些收益扩展到需要准确视觉理解和CLI交互的CAD应用：在CADGenBench和BenchCAD上，我们的统一工具相较于其他基于CLI的工具实现了8.1%-41.6%的相对提升。综合来看，这些结果表明数字环境的可编程性远超其GUI接口所暗示的，而一个以终端为中心的最小工具是实现更好性能和效率的关键。
cs.AI / 24 / 2610.04129
InvestigationWorlds: An Agentic Environment for Legal Investigation
调查世界：一个用于法律调查的自主环境
Abstract
We introduce InvestigationWorlds, an agentic environment for legal investigation. We build on an underused artifact of U.S. civil litigation: the summary judgment motion. This motion relies upon a record composed of real evidence exhibits, and results in a court-adopted hypothesis that is treated as ground truth for the purposes of deciding the motion. Each environment is built from a real U.S. Federal Court case retrieved from Public Access to Court Electronic Records (PACER) and augmented by an attorney-validated generation pipeline that synthesizes role-tagged documents around the original record. The resulting corpus admits multiple coherent factual readings, only one of which matches the court-adopted hypothesis. Evaluating on 100 cases, we find agents often commit to incorrect hypotheses despite retrieving relevant evidence, struggling to distinguish the court-adopted hypothesis from alternative hypotheses.
Chinese Translation
我们介绍了调查世界（InvestigationWorlds），这是一个用于法律调查的自主环境。我们基于美国民事诉讼中一个未被充分利用的文书：简易判决动议（summary judgment motion）。该动议依赖于由真实证据展品构成的记录，并导致法院采纳的假设，该假设在决定动议时被视为真实依据。每个环境都是从美国联邦法院的真实案例中提取的，数据来源于公共访问法院电子记录（PACER），并通过一个经过律师验证的生成管道进行增强，该管道围绕原始记录合成角色标记的文档。最终生成的语料库允许多种连贯的事实解读，其中只有一种与法院采纳的假设相匹配。在对100个案例进行评估时，我们发现代理人尽管检索到了相关证据，仍常常承诺于错误的假设，难以区分法院采纳的假设与其他替代假设。
cs.AI / 25 / 2610.04133
Auditing Pairwise Equivalence Judgments: Self-Critique Effects and Diversity Measurement in Multi-Agent Hypothesis Generation
审计成对等价判断：自我批评效应与多智能体假设生成中的多样性测量
Abstract
Multi-agent systems built on large language models (LLMs) are increasingly applied to scientific discovery and hypothesis generation. Both the effect of refinement and the diversity of the delivered set are hard to interpret before experimental ground truth exists, and both are typically reported by deciding whether pairs of generated hypotheses describe the same underlying mechanism. We study two evaluation questions that rest on this pairwise equivalence judgment: (1) how much self-critique changes delivered hypotheses beyond run-to-run variability, and (2) how the equivalence rule used to group hypotheses affects measured diversity. Across four proprietary instances, we hold opening hypotheses fixed, rerun the downstream workflow with 0, 1, and 5 critique rounds, and score matched hypothesis pairs with an LLM-as-a-judge. Relative to matched same-depth reruns, moving from 0 to 1 round produces 34.5 percentage points (pp) of additional mechanism-level divergence, whereas 1 to 5 rounds adds 1.3 pp. We then compare three equivalence rules: term frequency--inverse document frequency (TF--IDF) similarity, dense embeddings, and the same LLM-as-a-judge. We construct controlled hypothesis pairs that either preserve the causal explanation through wording or biological-terminology changes, or replace one component of the causal chain while holding the rest fixed. All three rules are invariant to meaning-preserving edits, but when the initiating event is replaced, the LLM-as-a-judge identifies 83% of valid pairs as different mechanisms, versus 0% for TF--IDF and 8% for embeddings; varying only the rubric that defines same mechanism moves this figure from 38% to 96%. Together, these results show that pairwise equivalence judgments are a measurement choice: how mechanism equivalence is defined affects both the estimated effect of self-critique and the measured diversity of generated hypotheses.
Chinese Translation
基于大型语言模型（LLMs）的多智能体系统在科学发现和假设生成中的应用日益增多。在实验真实结果存在之前，难以解释精炼效果和所提供集合的多样性，而这两者通常通过判断生成的假设对是否描述相同的潜在机制来报告。我们研究了两个基于成对等价判断的评估问题：（1）自我批评在多大程度上改变了交付的假设，超出了运行间的变异性，以及（2）用于分组假设的等价规则如何影响测量的多样性。在四个专有实例中，我们固定初始假设，分别进行0、1和5轮批评的下游工作流程，并使用LLM作为评判者对匹配的假设对进行评分。与匹配的同深度重运行相比，从0轮到1轮产生了34.5个百分点（pp）的额外机制级别差异，而从1轮到5轮仅增加了1.3 pp。然后，我们比较了三种等价规则：词频-逆文档频率（TF-IDF）相似性、密集嵌入和同样的LLM作为评判者。我们构建了控制的假设对，这些假设对要么通过措辞或生物术语的变化保留因果解释，要么在保持其余部分不变的情况下替换因果链的一个组成部分。所有三种规则对意义保留的编辑都是不变的，但当替换启动事件时，LLM作为评判者将83%的有效对识别为不同机制，而TF-IDF为0%，嵌入为8%；仅通过定义相同机制的标准变化，这一数字从38%上升到96%。这些结果表明，成对等价判断是一种测量选择：机制等价的定义影响自我批评的估计效果和生成假设的测量多样性。
cs.AI / 26 / 2610.04168
Agentic Cognitive Depth: Operational Criteria for Evaluating LLM Agents
代理认知深度：评估大型语言模型代理的操作标准
Abstract
Agentic large language model (LLM) systems are commonly implemented as an LLM in a loop with Planning, Memory, Tools, and Control Flow. This application-focused view connects agentic LLM research with deployable systems and leaves open how such systems should be evaluated beyond end-to-end task success. Building on this view, we define agentic cognitive depth as a trajectory-level profile across five operational criteria. The profile contains context sensitivity ($C$), temporal continuity ($T$), multimodal coordination ($M$), adaptive interaction ($A$), and metacognitive monitoring ($Mc$). The first four criteria measure how well Control Flow, Memory, Tools, and Planning are used across a trajectory. The fifth measures whether the system monitors and regulates the full run. For each criterion, we give operational proxies and a perturbation procedure, then connect the profile to the agent's world model. We provide the structure needed to extend benchmarks such as GAIA, SWE-bench, WebArena, and TRIP-Bench with per-criterion diagnostics. Symbolic verifiers, structured memory, planner coupling, and tool constraints provide practical ways to build and test these capacities.
Chinese Translation
代理大型语言模型（LLM）系统通常被实现为一个循环中的LLM，结合规划、记忆、工具和控制流。这种以应用为中心的视角将代理LLM研究与可部署系统连接起来，并且开放了如何在超越端到端任务成功的基础上评估这些系统。基于这一观点，我们将代理认知深度定义为跨五个操作标准的轨迹级别特征。该特征包含上下文敏感性（$C$）、时间连续性（$T$）、多模态协调（$M$）、适应性互动（$A$）和元认知监控（$Mc$）。前四个标准衡量控制流、记忆、工具和规划在轨迹中的使用效果。第五个标准则衡量系统是否监控和调节整个运行过程。对于每个标准，我们提供操作性代理和扰动程序，并将特征与代理的世界模型相连接。我们提供了扩展GAIA、SWE-bench、WebArena和TRIP-Bench等基准测试所需的结构，以便进行逐标准的诊断。符号验证器、结构化记忆、规划者耦合和工具约束提供了构建和测试这些能力的实际方法。
cs.AI / 27 / 2610.04178
SHarP: Saliency-based Pruning of Agent Harnesses
SHarP：基于显著性的代理工具修剪
Abstract
Agent harnesses are systems that coordinate model calls, tool use, and task execution to help large language models complete complex tasks. To meet task requirements and address failures, these systems are often iteratively refined by amending and patching their instructions, tools, and workflows, continuously increasing harness complexity. It is therefore unclear whether some resulting harness modules are redundant, introducing substantial token overhead with little, if any, performance gain. Inspired by neural network pruning, in this paper, we study harness pruning as a means of striking a better balance between task performance and token cost. We propose SHarP (Saliency-based Harness Pruning), a simple yet effective pruning strategy based on the saliency of each harness module with respect to performance and efficiency. Specifically, we first identify tools, instructions, and supporting mechanisms as components that can be individually disabled. We then estimate the saliency of each module by ablating it and assessing its task performance and token cost relative to the full set of single-module ablations. Modules with the smallest contribution to performance or largest computational overhead are subsequently pruned. Our evaluation across various harnesses on held-out validation sets reveals a surprising finding: most harnesses that we studied are highly redundant and can maintain comparable performance and efficiency even after a substantial portion of their modules are pruned. Our pruning approach and empirical findings provide new perspectives on agent harness design and optimization.
Chinese Translation
代理工具是协调模型调用、工具使用和任务执行的系统，旨在帮助大型语言模型完成复杂任务。为了满足任务要求并解决失败，这些系统通常通过修订和修补其指令、工具和工作流程进行迭代优化，持续增加工具的复杂性。因此，某些结果工具模块是否冗余，以及是否引入了大量的令牌开销而几乎没有性能提升，仍然不清楚。受到神经网络修剪的启发，本文研究了工具修剪作为在任务性能和令牌成本之间取得更好平衡的一种手段。我们提出了SHarP（基于显著性的工具修剪），这是一种简单而有效的修剪策略，基于每个工具模块相对于性能和效率的显著性。具体而言，我们首先识别工具、指令和支持机制作为可以单独禁用的组件。然后，我们通过消融每个模块并评估其相对于完整单模块消融集的任务性能和令牌成本来估计每个模块的显著性。随后，贡献性能最小或计算开销最大的模块将被修剪。我们在保留的验证集上对各种工具的评估揭示了一个惊人的发现：我们研究的大多数工具高度冗余，即使在修剪了其模块的相当一部分后，仍能保持可比的性能和效率。我们的修剪方法和实证发现为代理工具的设计和优化提供了新的视角。
cs.AI / 28 / 2610.04183
Language Model Activations Inhabit Privileged Error-Correcting Basins
语言模型激活居于特权的错误修正盆地
Abstract
Language models exhibit remarkable robustness, continuing to produce coherent text even when their activations are perturbed by interventions like linear steering. We hypothesize that this robustness is a result of passive dynamics, i.e., constraining mechanisms in the forward pass that funnel activations toward "good" regions that produce coherent outputs. To investigate these hypothesized error-correcting mechanisms, we probe the geometry of language model activation space by observing the action of model layers on low-dimensional curves. In doing so, we discover that model activations occur within a cluster of distinct attracting basins, which differentiate natural activations geometrically from distributionally similar synthetic activations. Applying this lens to language model steering, we observe feature-specific basins along semantic steering directions, and find that steering moves activations between these basins. To demonstrate the active role of this geometry in neural computation, we show that adaptively modulating steering strength to transport activations across basins improves inter-language steering, significantly increasing the probability of sampling tokens from the target language compared to fixed-strength steering. Our findings establish analysis of activation space geometry as a promising approach to interpreting and controlling language models.
Chinese Translation
语言模型表现出显著的鲁棒性，即使在其激活受到线性引导等干预扰动时，仍能继续生成连贯的文本。我们假设这种鲁棒性是被动动态的结果，即在前向传播中存在约束机制，将激活引导至产生连贯输出的“良好”区域。为了研究这些假设的错误修正机制，我们通过观察模型层对低维曲线的作用来探讨语言模型激活空间的几何特征。在此过程中，我们发现模型激活发生在一组不同的吸引盆地中，这些盆地在几何上将自然激活与分布上相似的合成激活区分开来。将这一视角应用于语言模型引导时，我们观察到沿语义引导方向存在特征特定的盆地，并发现引导在这些盆地之间移动激活。为了展示这种几何在神经计算中的主动作用，我们表明自适应调节引导强度以跨盆地传输激活，能够改善语言间引导，与固定强度引导相比，显著提高从目标语言采样标记的概率。我们的研究结果确立了激活空间几何分析作为解释和控制语言模型的有前景的方法。
cs.AI / 29 / 2610.04184
EvalResearchBench: Can AI Agents Design Their Own Evaluations?
EvalResearchBench：人工智能代理能否设计自己的评估？
Abstract
Recursive self-improvement (RSI) relies on evaluation feedback to assess progress and guide further research, yet repeatedly running complex benchmarks is costly and slows iteration. Human experts reduce this cost by selecting benchmark subsets or designing compact suites. We ask whether AI agents can automate this design process and introduce EvalResearchBench (ERB), a benchmark for autonomous evaluation research. Given target materials, development references, candidate APIs, and fixed time and API budgets, an agent called the researcher selects or synthesizes tasks, implements graders, and revises them in pilot tests before freezing an executable evaluator for coding, co-work, and reasoning. We study 9 researchers and 13 candidate models and compare each frozen evaluator with 14 target benchmarks on score concordance and pairwise agreement. The best evaluators order about 75\% of candidate pairs as the targets do, below the 91\% ceiling set by disagreements among the targets. No researcher leads on every metric, and the best evaluator on development targets is not the best on sealed targets hidden from the researcher. A human-designed sample of public tasks remains a strong baseline, and the evaluator with the lowest recorded execution cost attains the highest pairwise agreement. Agents repair tasks and graders through pilot feedback, yet their evaluators can still truncate answers, exhaust the evaluation budget, or let a few questions dominate a domain score.
Chinese Translation
递归自我改进（RSI）依赖评估反馈来评估进展并指导进一步研究，但反复运行复杂基准测试成本高昂且减缓迭代。人类专家通过选择基准子集或设计紧凑的测试套件来降低这一成本。我们探讨人工智能代理是否能够自动化这一设计过程，并引入EvalResearchBench（ERB），一个用于自主评估研究的基准。在给定目标材料、开发参考、候选API以及固定时间和API预算的情况下，一个名为研究者的代理选择或合成任务，实施评分器，并在试点测试中进行修订，然后冻结一个可执行的评估器以进行编码、协作和推理。我们研究了9个研究者和13个候选模型，并将每个冻结的评估器与14个目标基准进行比较，分析得分一致性和成对一致性。表现最佳的评估器大约以75%的比例对候选对进行排序，与目标一致，低于目标之间不一致所设定的91%的上限。没有任何研究者在每个指标上都领先，且在开发目标上表现最佳的评估器并不是在研究者未见的封闭目标上表现最佳的评估器。人类设计的公共任务样本仍然是一个强有力的基准，而记录到的执行成本最低的评估器达到了最高的成对一致性。代理通过试点反馈修复任务和评分器，但它们的评估器仍可能截断答案、耗尽评估预算，或让少数问题主导领域得分。
cs.AI / 30 / 2610.04188
Agentic AI with Structured CoT for Enhancing AI's Spatial Intelligence: Visualization and Reasoning of Rotation
具有结构化链式思维的自主智能AI在增强AI空间智能中的应用：旋转的可视化与推理
Abstract
Recent studies show that artificial intelligence (AI) with language and vision capabilities still experiences limitations in spatial reasoning. In this paper, we have studied the spatial capabilities of advanced generative AI to understand the rotations of objects in 3D space, utilizing AI's image processing and language processing features. We trained and examined the spatial intelligence of a generative Agentic AI model (GPT-5.6) to understand the spatial rotation process with rotation diagrams based on the revised Purdue Spatial Visualization Test: Visualization of Rotations (Revised PSVT:R). We improvised the Revised PSVT:R by superimposing additional graphical and contextual features to evaluate how different Chain-of-Thought (CoT) reasoning strategies influence model performance. The results indicate that structured CoT reasoning improves the spatial reasoning performance of the base GPT-5.6 model in both datasets (PSVT:R and PSVT:R with coordinate system). We used three CoT approaches - (1) Structured CoT, (2) few-shot Structured CoT, and Structured CoT with Self-optimized Prompt. The three CoT approaches evaluated in this study showed no significant performance difference. Results showed that combining structured CoT reasoning with relevant contextual information leads to considerable improvements in VLM performance on 3D rotation tasks, demonstrating the potential of agentic AI for more effective spatial reasoning. However, when contextual information is removed, structured CoT reasoning alone provides limited improvement, and the models continue to exhibit notable difficulties in understanding spatial transformations. These findings suggest that effective spatial reasoning in VLMs relies on the integration of visual, textual, and reasoning-based information in future agentic AI systems for spatial intelligence.
Chinese Translation
最近的研究表明，具备语言和视觉能力的人工智能（AI）在空间推理方面仍然存在局限性。本文研究了先进生成AI的空间能力，以理解物体在三维空间中的旋转，利用AI的图像处理和语言处理特性。我们训练并检验了生成型自主智能AI模型（GPT-5.6）的空间智能，以理解基于修订版普渡空间可视化测试：旋转可视化（Revised PSVT:R）的旋转过程。我们通过叠加额外的图形和上下文特征来改进修订版PSVT:R，以评估不同链式思维（CoT）推理策略对模型性能的影响。结果表明，结构化CoT推理提高了基础GPT-5.6模型在两个数据集（PSVT:R和带坐标系的PSVT:R）上的空间推理性能。我们使用了三种CoT方法——（1）结构化CoT，（2）少量样本结构化CoT，以及带自优化提示的结构化CoT。本研究中评估的三种CoT方法表现出没有显著的性能差异。结果显示，将结构化CoT推理与相关上下文信息结合，可以显著提升VLM在三维旋转任务上的表现，展示了自主智能AI在更有效的空间推理中的潜力。然而，当去除上下文信息时，单独的结构化CoT推理提供的改进有限，模型在理解空间变换方面仍然表现出显著的困难。这些发现表明，未来自主智能AI系统在空间智能方面的有效空间推理依赖于视觉、文本和基于推理的信息的整合。
cs.AI / 31 / 2610.04195
MemLeak: Cross-User Semantic Leakage in Multi-Tenant AI Agent Memory
MemLeak: 多租户人工智能代理内的跨用户语义泄漏
Abstract
Personal AI agents in enterprise multi-tenant deployments share a common vector store for long-term memory. Shared embedding spaces create a surface for cross-user memory leakage: a user's query can retrieve semantically adjacent memories belonging to another user through ordinary cosine-similarity retrieval, without any exploit. We formalize this as cross-user admissibility failure and evaluate it across six experiments, plus follow-up ablations, under both sparse (TF-IDF) and production-faithful (MiniLM-L6-v2) retrieval. Non-adversarial, incidental leakage reaches 70--100\% under pooled {same-team} retrieval; adversarially crafted memories achieve 90--100\% top-$k$ placement, exceeding weaker keyword-based attacker baselines, with score lifts of $+0.416$ to $+0.511$ under production-faithful dense retrieval (Config B); and end-to-end response contamination reaches 5.00/5 under a production retrieval path and 4.67/5 with Claude Sonnet~4.5, with contaminated responses often scoring as helpful or more helpful than clean ones, a gap validated against human judgment. Among three architectural mitigations, only hard post-retrieval ownership gating consistently restores the clean baseline (1.00/5) across {two generation models, at a measured latency overhead of roughly 1.4~ms per query.
Chinese Translation
企业多租户部署中的个人人工智能代理共享一个用于长期记忆的公共向量存储。共享的嵌入空间为跨用户记忆泄漏提供了表面：用户的查询可以通过普通的余弦相似度检索检索到属于其他用户的语义相邻记忆，而无需任何利用。我们将其形式化为跨用户可接受性失败，并在六个实验中评估这一现象，并进行后续消融实验，涵盖稀疏（TF-IDF）和生产忠实（MiniLM-L6-v2）检索。在汇总的同团队检索下，非对抗性、偶然性泄漏达到70%至100%；对抗性构造的记忆在前k名中的排名达到90%至100%，超越了较弱的基于关键词的攻击者基线，在生产忠实的稠密检索（配置B）下，得分提升为+0.416至+0.511；在生产检索路径下，端到端响应污染达到5.00/5，而在Claude Sonnet~4.5下为4.67/5，污染的响应往往被评为有帮助或比干净的响应更有帮助，这一差距经过人类判断验证。在三种架构缓解措施中，只有硬性后检索所有权门控在两个生成模型中始终恢复了干净基线（1.00/5），其测得的延迟开销约为每个查询1.4毫秒。
cs.AI / 32 / 2610.04196
Asynchronous Is Nearly Free for Evolution Strategies on Long-Horizon Agentic Tasks
异步几乎对长时间跨度的自主任务中的进化策略是免费的
Abstract
LLM-based long-horizon agentic post-training is often bottlenecked by rollout generation: trajectories span many interaction turns, completion times vary substantially, and synchronous update barriers leave faster workers waiting for stragglers. Asynchronous reinforcement learning which has been adopted in LLM post-training addresses this inefficiency by consuming trajectories as they arrive, but introduces policy lag and off-policy optimization. Evolution strategies (ES) offer a backpropagation-free alternative for LLM post-training, yet it relies on a larger number of rollouts and existing practices have remained largely synchronous. In this short-form paper, we introduce bounded-staleness asynchronous ES and demonstrate it on Endless Terminals benchmark using Qwen2.5-7B-Instruct. Across three evaluation seeds, natural Async-1 matches synchronous ES, achieving 25.9\% versus 25.4\% held-out success. Controlled schedules that delay 10\% of each update cohort by four or eight policy updates reduce success by only 1.6 and 3.1 percentage points, respectively, without explicit off-policy correction. GRPO performs better overall, reaching 29.0\% held-out success, but importantly our results show that ES tolerates moderate policy staleness with limited degradation, opening possibilities for future improvement of ES-based post-training with asynchronous algorithms. To the best of our knowledge, we are the first to demonstrate the effectiveness of sync and async ES on a multi-turn terminal style agentic coding task.
Chinese Translation
基于大型语言模型（LLM）的长时间跨度自主后训练通常受到回滚生成的瓶颈：轨迹跨越多个交互回合，完成时间差异显著，而同步更新的障碍使得更快的工作者不得不等待滞后的工作者。已在LLM后训练中采用的异步强化学习通过在轨迹到达时进行消费来解决这一低效问题，但这引入了策略滞后和离策略优化。进化策略（ES）为LLM后训练提供了一种无反向传播的替代方案，但它依赖于更多的回滚，现有实践仍然主要是同步的。在这篇短文中，我们介绍了有界滞后异步ES，并在使用Qwen2.5-7B-Instruct的无尽终端基准上进行了演示。在三个评估种子中，自然的Async-1与同步ES相匹配，分别实现了25.9\%与25.4\%的保留成功率。控制调度将每个更新组的10\%延迟四次或八次策略更新，仅分别减少了1.6和3.1个百分点的成功率，而无需显式的离策略修正。总体而言，GRPO表现更佳，达到了29.0\%的保留成功率，但重要的是我们的结果表明，ES能够容忍适度的策略滞后，且降级有限，这为未来基于异步算法的ES后训练改进开辟了可能性。根据我们所知，我们是首个在多回合终端风格自主编码任务上展示同步和异步ES有效性的人。
cs.AI / 33 / 2610.04198
ALoDLM: Adaptively Looped Diffusion Language Models
ALoDLM：自适应循环扩散语言模型
Abstract
Diffusion language models (DLMs) enable fast generation by predicting multiple tokens in parallel, but their practical adoption remains limited by a persistent quality gap relative to comparably sized autoregressive (AR) models. We attribute this gap to a computation-difficulty mismatch: within a partially observed sequence, some unknown tokens are easy to predict, while others require substantially more computation. Existing DLMs nevertheless apply uniform computational depth to all unknown positions at each denoising step. We introduce ALoDLM, which replaces uniform computation with token-adaptive latent recurrence. At each denoising step, ALoDLM iteratively refines latent representations and allocates computation according to token difficulty. Tokens ready to commit are fed back as discrete context, while unresolved tokens retain and further refine their latent states through additional recurrent passes. To learn token prediction and computation allocation jointly, we formulate token-wise computation schedules as latent variables and derive a conditional negative evidence lower bound (NELBO). We train ALoDLM at 1.7B and 8B parameter scales. Across eleven benchmarks, ALoDLM outperforms all evaluated DLMs and the corresponding AR baselines in average benchmark score at both scales. ALoDLM also retains fast parallel decoding, yielding a strong quality-efficiency trade-off among evaluated autoregressive and diffusion models under optimized inference engines.
Chinese Translation
扩散语言模型（DLMs）通过并行预测多个标记实现快速生成，但其实际应用仍受到与同等规模的自回归（AR）模型相比存在的持续质量差距的限制。我们将这一差距归因于计算难度的不匹配：在部分观察序列中，一些未知标记容易预测，而其他标记则需要显著更多的计算。然而，现有的DLM在每个去噪步骤中对所有未知位置应用统一的计算深度。我们提出了ALoDLM，它用标记自适应的潜在递归替代了统一计算。在每个去噪步骤中，ALoDLM迭代地细化潜在表示，并根据标记难度分配计算。准备提交的标记作为离散上下文反馈，而未解决的标记通过额外的递归传递保留并进一步细化其潜在状态。为了共同学习标记预测和计算分配，我们将标记级计算调度形式化为潜在变量，并推导出条件负证据下界（NELBO）。我们在1.7B和8B参数规模下训练ALoDLM。在十一项基准测试中，ALoDLM在两个规模下的平均基准得分上超越了所有评估的DLM和相应的AR基线。ALoDLM还保持了快速并行解码，在优化推理引擎下，在评估的自回归和扩散模型中实现了强大的质量与效率的权衡。
cs.AI / 34 / 2610.04206
Fine-Tuning VLM for Enhancing AI's Spatial Intelligence: Understanding 3D and 2D Rotations
微调视觉语言模型以增强人工智能的空间智能：理解三维和二维旋转
Abstract
Spatial intelligence is a fundamental skill in multiple domains, such as Science, Technology, Engineering, and Mathematics (STEM), Medicine, Architecture, and Construction. Recent studies indicate that Vision-Language Models (VLMs) still face limitations in spatial reasoning, which inhibits artificial intelligence (AI) from performing practical spatial tasks. Using multiple object-rotation datasets developed for training and evaluation, our experiments demonstrated promising improvements in both 2D and 3D rotation detection. Fine-tuned Google DeepMind-built Gemma-4 mixture-of-experts (MoE) models significantly outperformed fine-tuned Gemma-4 generalist models in predicting rotations defined by both their axes and angles. Fine-tuning also substantially improved angle estimation for 2D representation without requiring an explicit coordinate system. Furthermore, identifiable objects did not improve angle-detection accuracy; instead, objects with prominent linear features showed improved performance.
Chinese Translation
空间智能是多个领域的基础技能，例如科学、技术、工程和数学（STEM）、医学、建筑和施工。近期研究表明，视觉语言模型（VLMs）在空间推理方面仍然存在局限性，这阻碍了人工智能（AI）执行实际空间任务。通过使用为训练和评估开发的多个物体旋转数据集，我们的实验显示在二维和三维旋转检测方面有了令人鼓舞的改进。微调的谷歌DeepMind构建的Gemma-4专家混合模型（MoE）在预测由其轴和角度定义的旋转方面显著优于微调的Gemma-4通用模型。微调还显著改善了二维表示的角度估计，而无需明确的坐标系统。此外，可识别物体并未提高角度检测的准确性；相反，具有明显线性特征的物体表现出更好的性能。
cs.AI / 35 / 2610.04215
TCMClinicalReason-Bench: Can Language Models Reason from Pathogenesis to Prescription over Real-World Clinical Cases?
TCMClinicalReason-Bench：语言模型能否从病因学推理到处方生成，基于真实世界临床案例？
Abstract
Large language models (LLMs) can generate clinical narratives that are insufficiently grounded in patient-specific evidence. In traditional Chinese medicine (TCM), errors can propagate from etiology and pathogenesis through syndrome diagnosis and treatment principles to prescription generation. We developed TCMClinicalReason-Bench using 2,000 multicenter electronic health record cases to distinguish case-grounded responses from fluent but unsupported diagnostic and therapeutic conclusions. Five general-purpose and two TCM-specific LLMs were evaluated in zero-shot settings. An evidence-constrained rubric assessed seven diagnostic and therapeutic components and three cross-block relations, allowing case-supported alternatives. Qwen3.7-Plus with TCM retrieval served as the automated judge, alongside parallel blinded ratings by five senior TCM clinicians on a 600-case subset. Structural completeness was nearly saturated (99.3-100.0%), but normalized content scores ranged from 40.7% to 54.1%. The five general-purpose models averaged 50.0%, versus 41.3% for the two smaller TCM-specific models. Cross-block logic consistency ranged from 60.8% to 66.8% and correlated moderately with content across cases (Pearson's r = 0.515-0.656). Deficits were greatest in prescription generation, prescription analysis, and symptom-guided modification. In judge stress testing on 100 independent cases, perturbation detection rates across the three relations were 57%, 56%, and 31%, with contradictions detected more reliably than omissions. Separating component quality from cross-block consistency localizes failures missed by endpoint and completeness metrics and identifies where clinician oversight remains necessary.
Chinese Translation
大型语言模型（LLMs）能够生成与患者特定证据关联不足的临床叙述。在传统中医学（TCM）中，错误可能从病因和病理传播到症候诊断和治疗原则，再到处方生成。我们使用2000个多中心电子健康记录案例开发了TCMClinicalReason-Bench，以区分基于案例的响应与流畅但缺乏支持的诊断和治疗结论。在零样本设置中评估了五个通用模型和两个特定于TCM的LLMs。一个受证据约束的评分标准评估了七个诊断和治疗组成部分以及三个跨块关系，允许基于案例的替代方案。使用TCM检索的Qwen3.7-Plus作为自动评判者，同时五位资深TCM临床医生对600个案例子集进行了平行盲评。结构完整性几乎达到饱和（99.3-100.0%），但标准化内容得分范围为40.7%至54.1%。五个通用模型的平均得分为50.0%，而两个较小的TCM特定模型的得分为41.3%。跨块逻辑一致性范围为60.8%至66.8%，并与各案例的内容得分中等相关（Pearson相关系数r = 0.515-0.656）。在处方生成、处方分析和症状指导修改方面的缺陷最为明显。在对100个独立案例进行评判压力测试时，三个关系的扰动检测率分别为57%、56%和31%，检测到的矛盾比遗漏更为可靠。将组成部分质量与跨块一致性分离，有助于定位被终点和完整性指标遗漏的失败，并识别出临床医生仍需监督的领域。
cs.AI / 36 / 2610.04245
On the Steering Dimensionality of Refusal in Language Models
语言模型中拒绝行为的引导维度
Abstract
Existing activation steering methods often assume that a high-level concept can be mediated by a single steering direction. To support this, two complementary interventions should be achieved: additive steering should induce the target behavior, while directional ablation should suppress it. Yet behaviors may occupy richer activation geometries beyond a single direction, and semantically similar behaviors may be represented by distinct directions. In this work, we study how many directions can reliably control two different types of refusal behaviors: refusal triggered by the safety alignment and refusal in general contexts. Given the limited expressive capability of a single steering vector, we study the general setting of steering subspaces and introduce the notion of steering dimensionality as the minimum subspace dimensionality required to reliably control a behavior. We characterize sufficient steering subspaces that cover the full extent of the target behavior through both the (monotonic) improvement before the sufficient dimensionality, and the saturation beyond it. Empirically, we find that refusal triggered by safety alignment is 1-dim steerable, while multiple distinct steering directions can achieve comparable control. In contrast, refusal in general contexts exhibits substantially richer activation geometry where even 5-dim steering subspaces fail to reliably capture its full steerable variation. Our results reveal that the activation geometry underlying refusal is highly context-dependent and can be substantially more complex than a single linear steering direction.
Chinese Translation
现有的激活引导方法通常假设高层次概念可以通过单一的引导方向进行调控。为此，应实现两种互补的干预：加性引导应诱导目标行为，而方向性消融应抑制该行为。然而，行为可能占据超出单一方向的更丰富的激活几何形状，语义相似的行为可能由不同的方向表示。在本研究中，我们探讨了多少个方向可以可靠地控制两种不同类型的拒绝行为：由安全对齐触发的拒绝和一般情境下的拒绝。鉴于单一引导向量的表达能力有限，我们研究了引导子空间的一般设置，并引入了引导维度的概念，作为可靠控制一种行为所需的最小子空间维度。我们通过在充分维度之前的（单调）改善和超出该维度的饱和，表征了覆盖目标行为全范围的充分引导子空间。实证结果表明，由安全对齐触发的拒绝是1维可引导的，而多个不同的引导方向可以实现可比的控制。相比之下，一般情境下的拒绝表现出显著更丰富的激活几何形状，甚至5维引导子空间也无法可靠捕捉其全部可引导变异。我们的结果揭示了拒绝行为背后的激活几何形状高度依赖于上下文，并且可能比单一线性引导方向复杂得多。
cs.AI / 37 / 2610.04253
Spec2Game: Can LLMs Generate Complete Playable Games from Detailed Specifications?
Spec2Game：大型语言模型能否从详细规格生成完整可玩的游戏？
Abstract
Generating an executable program does not necessarily mean that it correctly implements the behavioral requirements specified in natural language. To evaluate large language models' ability to realize detailed specifications as complete interactive programs, we introduce Spec2Game, a benchmark that requires models to generate complete Pygame projects from detailed natural-language game specifications. Spec2Game comprises 15 game families and 150 task instances, with one canonical task and nine controlled rule variants per family, spanning three levels of implementation complexity. Using source-code, runtime, and visual evidence, we evaluate generated projects along four dimensions---Executability, Specification Realization, Code Quality, and User-Facing Quality. Across 14 LLMs and 3,330 generated projects, we find that high executability does not imply faithful specification realization. Component-level analysis further shows that models perform substantially better on Game Element Modeling than on Rule and Mechanism Modeling or Goal and Termination Modeling, indicating that faithfully implementing game rules and termination logic remains a major challenge.
Chinese Translation
生成可执行程序并不一定意味着它正确实现了用自然语言指定的行为要求。为了评估大型语言模型将详细规格转化为完整交互程序的能力，我们引入了Spec2Game，一个基准测试，要求模型从详细的自然语言游戏规格生成完整的Pygame项目。Spec2Game包含15个游戏系列和150个任务实例，每个系列有一个标准任务和九个受控规则变体，涵盖三种实现复杂度。通过源代码、运行时和视觉证据，我们从可执行性、规格实现、代码质量和用户体验质量四个维度评估生成的项目。在对14个大型语言模型和3,330个生成项目的分析中，我们发现高可执行性并不意味着忠实的规格实现。组件级分析进一步表明，模型在游戏元素建模方面的表现明显优于规则与机制建模或目标与终止建模，这表明忠实实现游戏规则和终止逻辑仍然是一个主要挑战。
cs.AI / 38 / 2610.04262
CADForge: Agentic Single-View CAD Reconstruction with Explicit Geometry Reasoning
CADForge：具有显式几何推理的代理单视图CAD重建
Abstract
Reconstructing editable parametric CAD models from a single-view image is of great practical value for modern manufacturing, yet remains challenging due to incomplete geometric observations and complex inter-part relationships. To address it, we propose CADForge, an agentic framework that progressively converts a single image into CadQuery programs. CADForge decomposes an object into CAD-meaningful components and performs explicit geometric reasoning for each component, a process that first identifies CAD-relevant constraints and then translates them into precise modeling parameters through mathematical code. The inferred parameters then drive component-wise synthesis of executable CadQuery programs, with a review agent evaluating the resulting geometry and providing targeted feedback for iterative refinement. To further improve robustness and efficiency, CADForge incorporates a failure-guided toolkit construction mechanism to distill accumulated experience into tools, and maintains a compact parametric CAD memory for retrieving modeling context on demand. Experiments on diverse single- and multi-part objects show that CADForge consistently outperforms existing baselines in reconstruction fidelity and perceptual quality, demonstrating an effective approach to accurate single-view CAD reconstruction.
Chinese Translation
从单视图图像重建可编辑的参数化CAD模型对现代制造具有重要的实际价值，但由于几何观察的不完整性和复杂的部件间关系，这一任务仍然具有挑战性。为了解决这个问题，我们提出了CADForge，一个逐步将单幅图像转换为CadQuery程序的代理框架。CADForge将对象分解为CAD意义上的组件，并对每个组件进行显式几何推理，该过程首先识别与CAD相关的约束，然后通过数学代码将其转换为精确的建模参数。推导出的参数随后驱动可执行CadQuery程序的组件级合成，评审代理评估生成的几何形状并提供针对性的反馈以进行迭代优化。为了进一步提高鲁棒性和效率，CADForge结合了一种基于失败的工具构建机制，将积累的经验提炼为工具，并维护一个紧凑的参数化CAD记忆，以便按需检索建模上下文。在对多样的单部件和多部件对象进行的实验中，CADForge在重建保真度和感知质量方面始终优于现有基线，展示了一种有效的单视图CAD重建的准确方法。
cs.AI / 39 / 2610.04280
Dense Neuro-Symbolic Reasoning in a Unified Geometry State
统一几何状态下的密集神经符号推理
Abstract
Geometry reasoning is naturally stateful: solving a problem repeatedly alternates between structural proposals and exact deductions. We formulate this process as dense neural-symbolic coupling, in which neural guidance and symbolic execution share a typed state and communicate through executable actions at every search step. Neural proposals contribute theorem instances, constructions, and algebraic bridges; the symbolic runtime applies registered rules, propagates exact constraints, and records provenance. A nested controller allocates computation first between neural and symbolic proposal sources and then among admitted actions. We instantiate the framework in OmniGeo, a single solver for plane, analytic, and solid geometry. With Claude Sonnet 4.6, OmniGeo reaches 94.2%, 88.5%, and 89.8% on FormalGeo7K, Conic10K, and SolidFGeo, respectively (90.8% macro average), and solves 21/30 IMO-AG-30 problems.
Chinese Translation
几何推理自然是有状态的：解决问题的过程反复在结构提议和精确推导之间交替进行。我们将这一过程表述为密集的神经符号耦合，其中神经引导和符号执行共享一个类型化状态，并通过每个搜索步骤中的可执行动作进行通信。神经提议贡献定理实例、构造和代数桥；符号运行时应用注册规则，传播精确约束，并记录来源。一个嵌套控制器首先在神经和符号提议源之间分配计算，然后在被接受的动作中进行分配。我们在 OmniGeo 中实例化该框架，这是一个用于平面、解析和固体几何的单一求解器。使用 Claude Sonnet 4.6，OmniGeo 在 FormalGeo7K、Conic10K 和 SolidFGeo 上分别达到了 94.2%、88.5% 和 89.8% 的准确率（90.8% 的宏平均），并解决了 21/30 个 IMO-AG-30 问题。
cs.AI / 40 / 2610.04287
VIGIL: Verifier-Informed Gated Improvement Loop for Spreadsheet Question Answering
VIGIL：基于验证者信息的门控改进循环用于电子表格问答
Abstract
Enterprise agents should improve from delayed feedback without allowing every correction to rewrite system behavior. We study continual harness learning for corpus-level spreadsheet question answering. Building on FiCo (Find-then-Compute), a static retrieval-and-execution backbone, we introduce VIGIL (Verifier-Informed Gated Improvement Loop). Within a question, VIGIL verifies and repairs diversely prompted Structured Query Language (SQL) candidates. Across episodes, delayed labels update only a contract calibrator and query/result-column selector. The base model, prompts, retriever, and recorded candidate pool remain fixed in the continual-learning protocol. With the gold workbook and expected type supplied, the ungated and dual-gated full-replay variants reach 82.4% and 82.0% forward accuracy over 79 documents, from a 75.8% static baseline. The dual gate has the larger retrospective gain (2.7 versus 2.3 points), and its 6.3-point forward gain has a 95% t-interval of 5.6-6.9. In a separate stricter split that excludes 16 documents and 380 questions from fitting and online promotion, the accuracy-only gate raises mean held-out accuracy across ten final harnesses from 80.3% to 86.7%. Calibrator-only adaptation gains 5.9 points, close to the combined 6.4-point gain. Yet three of 26 gate-approved updates reduce held-out accuracy relative to their incumbents, so replay-buffer non-regression does not imply held-out non-regression. MiMoTable and external-task case studies test within-task verification and reuse the same promote-or-retain discipline. Overall, the results support bounded, auditable harness adaptation while revealing where finite replay gates fail to generalize beyond their promotion buffers.
Chinese Translation
企业代理应当在不允许每次修正重写系统行为的情况下，从延迟反馈中改进。我们研究了针对语料库级电子表格问答的持续学习。基于 FiCo（Find-then-Compute），一个静态检索与执行的基础框架，我们引入了 VIGIL（Verifier-Informed Gated Improvement Loop）。在每个问题中，VIGIL 验证并修复多样化提示的结构化查询语言（SQL）候选项。在多个回合中，延迟标签仅更新合约校准器和查询/结果列选择器。基础模型、提示、检索器和记录的候选池在持续学习协议中保持不变。在提供金标准工作簿和预期类型的情况下，无门控和双门控的完全重放变体在79个文档上分别达到了82.4%和82.0%的前向准确率，相较于75.8%的静态基线。双门控具有更大的回顾增益（2.7对比2.3分），其6.3分的前向增益的95% t区间为5.6-6.9。在一个更严格的分割中，排除了16个文档和380个问题以进行拟合和在线推广，准确率门控将十个最终模型的平均保留准确率从80.3%提高到86.7%。仅校准器的适应性获得了5.9分，接近组合的6.4分增益。然而，在26个门控批准的更新中，有三项相对于其现任者降低了保留准确率，因此重放缓冲区的不回归并不意味着保留的不回归。MiMoTable和外部任务案例研究测试了任务内验证，并重用了相同的提升或保留原则。总体而言，结果支持有限的、可审计的模型适应，同时揭示了有限重放门控在超出其推广缓冲区时的失效之处。
cs.AI / 41 / 2610.04292
LMBuild: Evaluating LLM Agents for Generating Buildable and Functional Structures
LMBuild：评估大型语言模型代理生成可构建和功能性结构
Abstract
LLM-based agents are increasingly capable of generating complex 3D structures, with the potential to reshape how objects are designed and realized in the physical world. Yet, producing elegant geometry is fundamentally different from producing objects that can be built and perform their intended functions. Existing evaluations largely focus on geometric quality while overlooking physical realizability. We introduce LMBuild, a benchmark for evaluating LLM agents on generating buildable and functional structures. LMBuild represents generated objects as assembled structures comprising part decompositions, joints, materials, and sequences. To support reproducible evaluation, we provide a unified framework consisting of: (1) an interactive environment in which agents can use tools to retrieve, create, and place components to construct objects; (2) a curated benchmark that repurposes established CAD datasets and augments them with knowledge from Wikipedia; and (3) a evaluation framework covering structural soundness, functional affordance, design quality, and physical realization. Evaluations across 30 systems reveal several intriguing findings: (a) Soundness and alignment are no longer the primary bottlenecks for frontier closed-source models, while functional affordance and physical operability remain substantially more challenging; (b) stronger models more effectively create new components, whereas weaker models tend to rely on retrieval; and (c) providing functional specifications substantially improves part completeness, kinematics, and physical operability. These results show that generating real-world structures requires deeper reasoning about functional affordances, mechanics, and designing and creating novel components. We expect LMBuild to provide a foundation for measuring progress and incentivizing research toward agents that generate buildable and functional structures.
Chinese Translation
基于大型语言模型（LLM）的代理在生成复杂的三维结构方面的能力日益增强，有潜力重塑物体在物理世界中的设计和实现方式。然而，生成优雅的几何形状与生成可构建且能够执行预期功能的物体在本质上是不同的。现有的评估主要集中在几何质量上，而忽视了物理可实现性。我们引入了LMBuild，这是一个用于评估LLM代理生成可构建和功能性结构的基准。LMBuild将生成的物体表示为由部件分解、接头、材料和序列组成的组装结构。为了支持可重复的评估，我们提供了一个统一的框架，包括：(1) 一个交互环境，代理可以在其中使用工具检索、创建和放置组件以构建物体；(2) 一个策划的基准，重新利用已建立的计算机辅助设计（CAD）数据集，并结合维基百科的知识进行增强；(3) 一个评估框架，涵盖结构稳健性、功能适应性、设计质量和物理实现。对30个系统的评估揭示了几个有趣的发现：(a) 对于前沿的闭源模型，稳健性和一致性不再是主要瓶颈，而功能适应性和物理可操作性仍然具有显著挑战；(b) 更强的模型更有效地创造新组件，而较弱的模型则倾向于依赖检索；(c) 提供功能规格显著提高了部件的完整性、运动学和物理可操作性。这些结果表明，生成现实世界的结构需要对功能适应性、力学以及设计和创造新组件进行更深入的推理。我们期待LMBuild为衡量进展和激励研究朝向生成可构建和功能性结构的代理提供基础。
cs.AI / 42 / 2610.04295
Language-Conditioned Token and Reasoning Efficiency in Large Language Models: A Paired Cross-Lingual Study Protocol
语言条件下的标记与大型语言模型中的推理效率：一项配对跨语言研究协议
Abstract
Large language models incur language-dependent representation and inference costs, but existing comparisons often conflate input language, assigned observable-trace language, and answer realization. We specify a prospective paired study that separates these interfaces while holding the semantic item, checkpoint, and answer oracle fixed. The initial design instantiates 240 exactly scored items rendered from templates in English and seven non-English languages, three distinct-lineage open-weight checkpoints, three trace-token budgets, 22 input- and trace-language conditions, a fixed answer reserve, and a separately counted delimiter: 47,520 initial core runs before prospective sample-size selection. RQ1-RQ3 estimate input and trace effects by intention-to-treat with failure-inclusive terminal accounting and test answer realization by cloning a sealed prefix and runtime-native KV state into eight crossed branches. Pre-freeze independent language review, fixed-form ASCII selectors, and code/surface/solver agreement constrain the realization test. H1-H5 share one Holm family and a global simultaneous component band. A secondary randomized experiment compares one-long-attempt and complete K-short-attempt policies at equal trace allowance under frozen seeds and oracle-blind aggregation; it is a full-policy contrast because answer capacity differs. Outcomes include exact token spans, correctness, latency, runtime-exposed memory, and qualified same-host operating-system-reported energy over prespecified hardware rails. The protocol separates tokenizer expansion, observable-trace cost, and answer-realization cost without treating visible traces as internal cognition or operating-system estimates as physical cross-device energy. No confirmatory model outcome is reported; result fields remain disabled until the frozen evidence ledger passes independent verification.
Chinese Translation
大型语言模型会产生依赖语言的表征和推理成本，但现有的比较往往混淆了输入语言、分配的可观察跟踪语言和答案实现。我们指定了一项前瞻性的配对研究，分离这些接口，同时保持语义项、检查点和答案oracle不变。初步设计实例化了240个从英语和七种非英语语言的模板中生成的准确评分项目，三个不同谱系的开放权重检查点，三个跟踪标记预算，22个输入和跟踪语言条件，一个固定的答案储备，以及一个单独计数的分隔符：在前瞻性样本大小选择之前，有47,520个初始核心运行。RQ1-RQ3通过意向治疗估计输入和跟踪效应，并通过包含失败的终端核算测试答案实现，方法是将密封前缀和运行时本地KV状态克隆到八个交叉分支中。预冻结的独立语言审查、固定形式的ASCII选择器以及代码/表面/求解器一致性限制了实现测试。H1-H5共享一个Holm家族和一个全局同时组件带。第二个随机实验在冻结种子和oracle盲聚合下比较一次长尝试和完整K短尝试策略，追踪允许相等；这是一个完全政策对比，因为答案容量不同。结果包括精确的标记跨度、正确性、延迟、运行时暴露的内存，以及在预设硬件轨道上合格的同主机操作系统报告的能耗。该协议分离了标记器扩展、可观察跟踪成本和答案实现成本，而不将可见跟踪视为内部认知或将操作系统估算视为物理跨设备能量。未报告确认模型结果；结果字段在冻结证据账本通过独立验证之前保持禁用。
cs.AI / 43 / 2610.04301
EnvDreamer: Large-Scale Multimodal-to-Environment Generation for Embodied AI
EnvDreamer：面向具身人工智能的大规模多模态环境生成
Abstract
Large datasets and high capacity models have accelerated progress in vision and language. This work introduces a platform aimed at bringing comparable gains to embodied learning, world models, and robotics. We present EnvDreamer, a framework that uses large language and vision language models to generate Unreal Engine 5 environments for embodied AI and robot training. EnvDreamer enables sampling of large, diverse, interactive, customizable, and validator passed virtual environments for training and evaluation across navigation, interaction, and manipulation. We illustrate the platform with a large set of generated scenes and simple baselines. Policies trained on EnvDreamer generated environments, without explicit mapping or human task supervision, achieve competitive results on multiple embodied benchmarks spanning navigation, rearrangement, and manipulation. EnvDreamer also supports image-conditioned reconstruction for real-to-sim studies. Finally, we release EnvDreamer-20k, a dataset of 20,000 validator passed environments with task programs, scene graphs, trajectories, and metadata to support reproducible benchmarking.
Chinese Translation
大规模数据集和高容量模型加速了视觉和语言领域的进展。本研究介绍了一个旨在为具身学习、世界模型和机器人技术带来相应收益的平台。我们提出了EnvDreamer，一个利用大型语言模型和视觉语言模型生成虚幻引擎5（Unreal Engine 5）环境的框架，以支持具身人工智能和机器人训练。EnvDreamer能够生成大型、多样化、互动性强、可定制且经过验证的虚拟环境，用于导航、交互和操作的训练与评估。我们通过一组生成的场景和简单的基线展示了该平台。在没有明确映射或人工任务监督的情况下，在EnvDreamer生成的环境上训练的策略在多个涵盖导航、重排和操作的具身基准测试中取得了竞争力的结果。EnvDreamer还支持基于图像的重建，以便进行真实到模拟的研究。最后，我们发布了EnvDreamer-20k，一个包含20,000个经过验证的环境的数据集，附带任务程序、场景图、轨迹和元数据，以支持可重复的基准测试。
cs.AI / 44 / 2610.04313
MOIRA: Mass-Oriented Indexing with Ragged Attention for Long-Context Decoding
MOIRA：用于长上下文解码的质量导向索引与不规则注意力
Abstract
Long-context decoding is limited by memory bandwidth, because every output token reads the KV cache of every layer. Sparse decoding reduces this cost by reading only part of the KV cache. We observe that the number of pages a query needs varies widely across KV heads, layers and steps. Fixed budgets are simple, but they are sized for demanding cases and tuned per workload; adaptive budgets follow this variation more flexibly, but existing designs pay for it with extra selection cost or training. At the kernel level, FlashAttention-3 (FA3) and FlashInfer are designed for rows of similar length: with page lists whose length differs per KV head, they either pad the lists (forfeiting much of the sparse saving), leave thread blocks unbalanced, or rely on a host-side plan that runs outside the CUDA graph. We propose MOIRA, a training-free sparse decode path in vLLM whose budget adapts per KV head and per layer. For every request, layer, KV head and step, a coverage rule keeps the smallest set of pages whose estimated attention mass reaches a fraction $\gamma$. A new kernel, self-planning attention, lets each thread block derive its own share of the work from the list lengths, so the whole decode step stays inside the CUDA graph. On an H200, at RULER's 128k context, MOIRA with $\gamma=0.99$ matches dense accuracy while reading about 30% of the pages and reduces the time per output token (TPOT) by 2.2-2.5$\times$ relative to dense FA3; with $\gamma=0.98$ it reduces TPOT by 2.7$\times$ and stays within the noise of dense. Under high serving load it raises throughput by up to 51%. These results suggest that a budget adapted per head and layer, paired with a kernel that keeps such budgets inside the CUDA graph, makes sparse decoding both flexible and fast.
Chinese Translation
长上下文解码受到内存带宽的限制，因为每个输出标记都需要读取每一层的键值缓存（KV cache）。稀疏解码通过仅读取部分键值缓存来降低这一成本。我们观察到，查询所需的页面数量在不同的键值头、层和步骤之间变化很大。固定预算简单，但它们是针对高需求情况而设定的，并且需要根据工作负载进行调优；而自适应预算则更灵活地跟随这种变化，但现有设计往往需要额外的选择成本或训练。在内核层面，FlashAttention-3（FA3）和FlashInfer是为相似长度的行设计的：对于每个键值头，页面列表的长度不同，它们要么填充列表（放弃了大部分稀疏节省），要么使线程块不平衡，或者依赖于在CUDA图之外运行的主机端计划。我们提出了MOIRA，这是一种在vLLM中无需训练的稀疏解码路径，其预算根据每个键值头和每一层进行调整。对于每个请求、层、键值头和步骤，一个覆盖规则保持最小的页面集合，其估计的注意力质量达到一个比例$ ext{γ}$。一种新的内核，自规划注意力，允许每个线程块根据列表长度推导出自己的工作份额，从而使整个解码步骤保持在CUDA图内。在H200上，在RULER的128k上下文中，MOIRA在$ ext{γ}=0.99$时匹配密集解码的准确性，同时只读取约30%的页面，并将每个输出标记的时间（TPOT）相对于密集FA3减少了2.2-2.5倍；在$ ext{γ}=0.98$时，TPOT减少了2.7倍，并保持在密集解码的噪声范围内。在高负载情况下，它的吞吐量提高了最多51%。这些结果表明，针对每个头和层调整的预算，结合保持这些预算在CUDA图内的内核，使得稀疏解码既灵活又快速。
cs.AI / 45 / 2610.04316
From Latent Space to Jacobian Space: Measuring, Evading, and Training Against Safety-Content Accessibility
从潜在空间到雅可比空间：测量、规避及针对安全内容可达性的训练
Abstract
Output-only safety monitoring sees only the end of a model's computation, yet the model computes its answer before emitting it: what it is internally poised to say is safety-critical. Jacobian-space (J-space) readouts, linear maps from hidden states to the output vocabulary via the model's input-output Jacobian, have been proposed as a window on behaviorally accessible internal content, and a first safety protocol (JADR) showed that danger recognition is readable there. What remains unknown is how this accessible content relates to the latent-space safety geometry studied by representation engineering, and how safety training shapes it. We introduce two quantitative bridges: (i) a transport-amplification profile $A_\ell$, measuring how strongly a latent safety direction is carried toward output space by each layer's Jacobian, and (ii) a paired base-vs-tuned protocol that attributes accessibility to training provenance. Across four small model pairs (135M to 1.5B, three lens-fit seeds), tuned checkpoints concentrate recognition in upper-middle layers, and at 0.5B DPO installs refusal while J-space recognition drops to chance: training can widen the accessibility gap exactly where behavioral safety looks best. A deployment audit closes the loop: monitor-aware GCG-style suffixes suppress the prompt-side monitor at zero behavioral cost at every scale; only a learned monitor rung resists its own adaptive re-attack; the training-time defense fails its re-attack at every penalty weight; steering shows the amplification profile is descriptive, not causal; and continuous-prefix optimization fails where discrete search succeeds. Safety monitoring, training, and evaluation must operate on accessibility itself, not on outputs alone.
Chinese Translation
仅输出的安全监控只关注模型计算的最终结果，但模型在发出答案之前已经计算出其答案：其内部准备说出的内容是安全关键的。雅可比空间（Jacobian Space，J-space）读数是通过模型的输入输出雅可比从隐藏状态到输出词汇的线性映射，已被提议作为观察行为可达内部内容的窗口，首个安全协议（JADR）显示危险识别在此可读。尚不清楚这些可达内容与表征工程研究的潜在空间安全几何之间的关系，以及安全训练如何塑造它。我们引入两个定量桥梁：（i）传输放大特征 $A_ ext{ℓ}$，测量每一层的雅可比如何强烈地将潜在安全方向传递到输出空间，以及（ii）一对基础与调优协议，归因于训练来源的可达性。在四对小模型（从1.35亿到15亿，三个镜头拟合种子）中，调优检查点将识别集中在上中层，而在0.5B DPO安装时拒绝，同时J-space识别降至偶然：训练可以在行为安全表现最佳的地方扩大可达性差距。部署审计闭合了循环：监控感知的GCG风格后缀在每个规模上以零行为成本抑制提示侧监控；只有学习的监控环节抵抗其自身的自适应再攻击；训练时间防御在每个惩罚权重下都未能抵抗再攻击；引导显示放大特征是描述性的，而非因果性的；连续前缀优化在离散搜索成功的地方失败。安全监控、训练和评估必须在可达性本身上进行，而不仅仅是输出。
cs.AI / 46 / 2610.04328
Bidirectional Preference Synthesis: Learning Prompt-Conditioned Preferences from Boundary Failures
双向偏好合成：从边界失败中学习提示条件偏好
Abstract
Correction-based offline preference pipelines commonly treat model failures only as rejected responses under the original prompt. This supervision is incomplete for boundary failures: responses that violate the given instruction yet coherently satisfy a nearby intent or constraint setting. We introduce Bidirectional Preference Synthesis (BPS), a data-construction method for standard Direct Preference Optimization (DPO) that makes this missing prompt dependence explicit. For each validated boundary failure, BPS keeps the conventional forward pair under the original prompt and adds a reverse pair under a synthesized achieved prompt, so the same response is rejected where it is wrong and chosen where it is right, without changing the DPO objective, training a reward model, or requiring online sampling. On Qwen3-4B-Instruct-2507, BPS preserves original-side pairwise ranking while raising achieved-side ranking accuracy from 6.8% to 62.3% on held-out crossed anchors, with a similar shift under a Kimi-K2.6 cross-teacher probe. A blind human audit supports the intended reverse preference direction, and downstream evaluations show the clearest separation from Forward-DPO in multilingual multi-turn instruction following, with consistent capability-retention patterns on agentic, tool-use, and code checks.
Chinese Translation
基于修正的离线偏好管道通常仅将模型失败视为在原始提示下被拒绝的响应。这种监督对于边界失败是不完整的：这些响应虽然违反了给定的指令，但却连贯地满足了附近的意图或约束设置。我们提出了双向偏好合成（Bidirectional Preference Synthesis, BPS），这是一种数据构建方法，用于标准的直接偏好优化（Direct Preference Optimization, DPO），使这种缺失的提示依赖性变得显而易见。对于每个经过验证的边界失败，BPS 保留了在原始提示下的常规正向对，并在合成的实现提示下添加了一个反向对，因此同一响应在错误的地方被拒绝，在正确的地方被选择，而无需改变 DPO 目标、训练奖励模型或要求在线采样。在 Qwen3-4B-Instruct-2507 上，BPS 保持了原始侧的成对排名，同时将实现侧的排名准确率从 6.8% 提高到 62.3%，在保留的交叉锚点上，Kimi-K2.6 跨教师探测下也有类似的变化。一项盲人审核支持了预期的反向偏好方向，而下游评估显示出与前向 DPO 在多语言多轮指令跟随中的明显区分，并在代理、工具使用和代码检查方面表现出一致的能力保留模式。
cs.AI / 47 / 2610.04329
Suppressing Pressure, Amplifying Evidence: Self-Guided Attention Steering to Mitigate Sycophancy and Stubbornness
抑制压力，增强证据：自导注意力引导以减轻谄媚和固执
Abstract
Reliable language models should resist unsupported user pressure while effectively using objective contextual information. However, models may exhibit sycophancy by yielding to unsupported user pressure or contextual stubbornness by failing to update their answers when relevant contextual information warrants revision. Evaluating interventions for these failures separately can obscure whether mitigating one failure exacerbates the other. To assess this trade-off, we introduce CoPE-Bench with six conditions per question: a neutral baseline, correct or incorrect user pressure, contextual information consistent with or conflicting with the neutral answer, and a joint condition combining incorrect claims with conflicting contextual information. To regulate the influence of user pressure and contextual information, we propose SPAE (Suppressing Pressure, Amplifying Evidence), a training-free framework that uses the model's own judgments to identify relevant tokens, suppressing user pressure and amplifying contextual information through token-level attention steering. On average across five backbones, SPAE reduces pressure following by 18.8 percentage points and increases joint-condition updating by 5.5 percentage points relative to the strongest baseline in the main comparison. In two-turn dialogue, it improves joint-condition updating by an average of 13.2 percentage points over the strongest prompting baseline. The source data and codes can be found at https://github.com/03Grant/sycophancy-and-stubbornness.
Chinese Translation
可靠的语言模型应抵制无支持的用户压力，同时有效利用客观的上下文信息。然而，模型可能会表现出谄媚，通过屈从于无支持的用户压力，或表现出上下文固执，当相关的上下文信息需要修正时未能更新其答案。单独评估这些失败的干预措施可能会模糊减轻一种失败是否加剧另一种失败。为了评估这种权衡，我们引入了 CoPE-Bench，每个问题有六种条件：一个中性基线、正确或错误的用户压力、与中性答案一致或冲突的上下文信息，以及一个结合错误主张与冲突上下文信息的联合条件。为了调节用户压力和上下文信息的影响，我们提出了 SPAE（Suppressing Pressure, Amplifying Evidence），这是一个无训练的框架，利用模型自身的判断来识别相关的标记，通过标记级注意力引导来抑制用户压力并增强上下文信息。在五个基础模型的平均表现中，SPAE 将压力跟随降低了 18.8 个百分点，并相对于主要比较中的最强基线将联合条件更新提高了 5.5 个百分点。在两轮对话中，它相对于最强提示基线将联合条件更新平均提高了 13.2 个百分点。源数据和代码可以在 https://github.com/03Grant/sycophancy-and-stubbornness 找到。
cs.AI / 48 / 2610.04339
ShadowMiner v1 - An Experience Report on Implementing and Measuring a Problem-and-Hypothesis Discovery Engine
ShadowMiner v1 - 实施与测量问题与假设发现引擎的经验报告
Abstract
ShadowMiner v1 is a system that automatically discovers research problems and generates hypotheses from AI papers. It is a nine-stage pipeline. It structures documents into a knowledge graph and finds graph gaps in it - structural blind spots in research. These graph gaps are included in the LLM generation prompt. Each generated hypothesis is then verified by checking whether it is already covered by existing research, scoring its quality, and checking that the facts it relies on are accurately drawn from its sources. This report does not propose a new generation or evaluation technique. It describes our experience of implementing and applying ideas from prior work, and measuring whether each one actually contributed.
Chinese Translation
ShadowMiner v1 是一个自动发现研究问题并从人工智能论文中生成假设的系统。它是一个九阶段的流程。该系统将文档结构化为知识图谱，并在其中寻找图谱空白——研究中的结构盲点。这些图谱空白被纳入大语言模型（LLM）生成的提示中。每个生成的假设随后通过检查其是否已被现有研究覆盖、评估其质量以及验证其所依赖的事实是否准确来源于其资料进行验证。本报告并未提出新的生成或评估技术，而是描述了我们实施和应用先前工作中的想法的经验，并测量每个想法是否真正有所贡献。
cs.AI / 49 / 2610.04371
Functionally Equivalent or Not? Graph-Grounded Differential Surrogate Execution for Code Equivalence
功能等价还是不等价？基于图的差分代理执行用于代码等价性判断
Abstract
Determining whether two programs are functionally equivalent is central to code modernization, patch validation, refactoring, and code-generation evaluation. Yet the usual signals are incomplete: tests cover only finite inputs, textual similarity confuses implementation with behavior, and unconstrained LLM judgments are difficult to audit. Direct execution is often impossible when a program depends on an obsolete, licensed, unavailable, or unsafe environment. We introduce FEAgent, a selective equivalence assessor agent that combines typed program-graph evidence with differential surrogate execution. FEAgent first aligns public interfaces and behaviorally relevant graph anchors, then issues bounded queries over call-flow, control-flow, data-flow, type, import, and effect relations. Next, a branch-aware generator agent proposes discriminating inputs, and two blinded LLM surrogates independently predict source and target observables. Every claim and predicted divergence is recorded in an evidence ledger. A deterministic reconciler then returns EQUIVALENT, INEQUIVALENT, or UNCLEAR rather than forcing a verdict when paths are uncovered or evidence conflicts. We evaluate FEAgent on function-level equivalence and repository-level bug patches, where the existing oracle is a benchmark label or a passing test suite. Every disagreement with that oracle is adjudicated by direct execution, revealing errors in benchmark labels and behavioral divergences missed by unit-test-only scoring. On EquiBench, execution confirms FEAgent's disagreements with published labels on 216 of 1,200 evaluated pairs (18.0%); on SWE-bench Verified, 94 of 331 test-passing agent patches (28.4%) diverge from the reference patch. FEAgent thus serves as an audit layer between testing and formal verification, keeping its evidence reviewable and its uncertainty explicit without claiming a proof of equivalence.
Chinese Translation
确定两个程序是否功能等价是代码现代化、补丁验证、重构和代码生成评估的核心。然而，通常的信号是不完整的：测试仅覆盖有限的输入，文本相似性混淆了实现与行为，而不受限制的大型语言模型（LLM）判断难以审计。当程序依赖于过时、许可、不可用或不安全的环境时，直接执行通常是不可能的。我们引入了 FEAgent，一个选择性等价评估代理，结合了类型化程序图证据与差分代理执行。FEAgent 首先对齐公共接口和行为相关的图锚点，然后对调用流、控制流、数据流、类型、导入和效果关系发出有限查询。接下来，一个分支感知生成代理提出区分输入，两个盲化的 LLM 代理独立预测源和目标可观察量。每个声明和预测的差异都记录在证据账本中。一个确定性的调解器随后返回 EQUIVALENT（等价）、INEQUIVALENT（不等价）或 UNCLEAR（不明确），而不是在路径未被发现或证据冲突时强行做出裁决。我们在函数级等价性和仓库级错误补丁上评估 FEAgent，其中现有的 oracle 是基准标签或通过的测试套件。与该 oracle 的每一次分歧都通过直接执行进行裁决，揭示了基准标签中的错误和仅通过单元测试评分遗漏的行为差异。在 EquiBench 上，执行确认 FEAgent 与已发布标签在 1200 个评估对中的 216 个（18.0%）存在分歧；在 SWE-bench Verified 上，331 个测试通过的代理补丁中有 94 个（28.4%）与参考补丁存在差异。因此，FEAgent 作为测试与形式验证之间的审计层，保持其证据可审查且不确定性明确，而不声称等价性的证明。
cs.AI / 50 / 2610.04375
Do Tool Calls Execute as Intended? Measuring and Repairing Intent-Execution Correspondence in LLM Agents
工具调用是否按预期执行？测量和修复大型语言模型代理中的意图与执行的一致性
Abstract
Agents built on large language models (LLMs) build and run software through tool calls. A call reaches its program through several hops, and any hop can change the call without notice. When the changed call fails, the agent retries a correct call, which costs users time and money. Benchmarks and failure analyses do not see the change, because they read the call and its result but not what a hop received. We define intent-execution correspondence (IEC) as the property that the executed action matches the action the emitted call denotes under the tool contract. Our protocol observes what each hop received without executing the call, and names the first hop that changed it by the receiver's own parser. IntAct then delivers the call in a form that this hop cannot alter, or refuses the call. We build IEC-Bench from the changes observed in real-world use, with chains of dependent calls under the execution paths of 4 widely-used harnesses. In 47,828 shell calls within production sessions, Claude Code's Bash tool changes 12.0% of the calls that carry code, escape sequences, or long text. For 80.7% of the calls whose backslashes are changed, the wrong action runs without any reported error. All 10 measured harnesses change a call. Trajectory-based judgment attributes 95.1% of the production failures to the LLM, although the path caused more than half of them. On IEC-Bench, the path raises the token cost per passed task 2.4 times (up to 12.3 times). A hop that changes a call also hides the changes after it, so 55.1% of the failures on one path appear only after its first hop is repaired. IntAct, deployed in a commercial product, recovers 79.2% of the failures with a changed call. Harnesses should therefore be designed and tested hop-by-hop to ensure a correct call executes as intended or is refused.
Chinese Translation
基于大型语言模型（LLMs）构建的代理通过工具调用构建和运行软件。一个调用通过多个跳转到达其程序，而任何跳转都可能在未通知的情况下更改调用。当更改后的调用失败时，代理会重试一个正确的调用，这会耗费用户的时间和金钱。基准测试和失败分析并未看到这种变化，因为它们只读取调用及其结果，而未考虑跳转接收到的内容。我们将意图与执行的一致性（Intent-Execution Correspondence, IEC）定义为执行的动作与发出调用所表示的动作在工具合同下相匹配的属性。我们的协议在不执行调用的情况下观察每个跳转接收到的内容，并通过接收者自己的解析器命名第一个更改它的跳转。IntAct 然后以这种跳转无法更改的形式传递调用，或拒绝该调用。我们从实际使用中观察到的变化构建了 IEC-Bench，涵盖了 4 个广泛使用的工具的执行路径下的依赖调用链。在 47,828 次生产会话中的 Shell 调用中，Claude Code 的 Bash 工具更改了 12.0% 的包含代码、转义序列或长文本的调用。对于 80.7% 的反斜杠被更改的调用，错误的动作在没有任何报告错误的情况下运行。所有 10 个测量的工具都更改了调用。基于轨迹的判断将 95.1% 的生产失败归因于 LLM，尽管路径导致了超过一半的失败。在 IEC-Bench 上，该路径使每个通过任务的令牌成本提高了 2.4 倍（最高可达 12.3 倍）。更改调用的跳转还隐藏了其后的更改，因此在修复其第一个跳转后，55.1% 的失败仅在一个路径上出现。因此，工具应逐跳设计和测试，以确保正确的调用按预期执行或被拒绝。
cs.AI / 51 / 2610.04379
AgentPersonaBench: Benchmarking Persona-Driven User Simulation
AgentPersonaBench：基于角色驱动的用户模拟基准测试
Huang, Jintao, Wang, Yifan, Shen, Hongyu, Lu, Yi Daniel, Huang, Shirley, Oh, Minsik, Wang, Yewen, Mohsin, Muhammad Ahmed, Xu, Zhen, Fan, Yilan, Yuan, Zichen, Bilal, Ahsan, Wei, Zibu, Jajee, Sankalp, Gagnier, Henry, Kapoor, Saksham, Wang, Jicheng, Wen, Qianfeng, He, Yixuan, Dillmann, Steven, He, Jiashu, Lu, Yucheng, Guo, Linqiang, Zhang, Danyang, Bo, Shi, Mondal, Raunak, Tang, Haixiang, Xiao, Weihang, Nie, Allen, Tang, Jing, Li, Yueying, Liu, Yifan Simon, Hou, Jianheng, Wang, Dianzhuo, Zhu, Qianyu, Tao, Zhixu Silvia, Peng, Zhejian, Wang, Zihan, Gupta, Ishan, Fan, Jinxuan, Jiang, Wanting, Liang, Shushu, Qiu, Chenxi, Wang, Yijun, Li, Xiaomin, Hao, Yuexing
Abstract
We introduce AgentPersonaBench (APB), a benchmark evaluating whether persona conditioning faithfully steers downstream agent behavior. While language models are increasingly deployed for persona-driven user simulation, existing benchmarks primarily evaluate conversational styling or self-reports rather than authentic behavioral fidelity. APB evaluates latent persona adherence one trait at a time, embedding each target trait within a complete synthetic profile without explicitly naming the trait or disclosing the test. Ground-truth adherence is verified strictly from observable actions across four interaction surfaces of increasing realism: survey, chat, web (interactive web environments), and app (desktop software environments). APB comprises 2,460 tasks spanning 867 traits, verified through automated audits and expert review. Our evaluation of 20 frontier model arms demonstrates that high-fidelity user simulation is already attainable: leading models achieve up to 84.7% full-pass adherence under unprompted conditions. At the same time, APB identifies clear behavioral boundaries: adherence drops across interaction modalities (only 37.9-64.3% pass all four surfaces), multi-attribute demands degrade retention, and competing model families exhibit pronounced behavioral divergence.
Chinese Translation
我们介绍了AgentPersonaBench (APB)，这是一个评估角色条件是否真实引导下游代理行为的基准测试。尽管语言模型越来越多地用于基于角色驱动的用户模拟，现有基准主要评估对话风格或自我报告，而非真实的行为忠实度。APB一次评估一个特征的潜在角色遵循性，将每个目标特征嵌入完整的合成档案中，而不明确命名特征或披露测试内容。真实遵循性严格通过四个逐渐逼真的交互表面中的可观察行为进行验证：调查、聊天、网页（互动网页环境）和应用程序（桌面软件环境）。APB包含2,460个任务，涵盖867个特征，通过自动审计和专家评审进行验证。我们对20个前沿模型的评估表明，高忠实度的用户模拟已经可以实现：领先模型在无提示条件下的完全通过率高达84.7%。同时，APB识别出明显的行为边界：在不同交互方式中，遵循性下降（仅有37.9-64.3%的模型在四个表面上均通过），多属性需求降低了保持率，竞争模型家族表现出明显的行为差异。
cs.AI / 52 / 2610.04387
TrustMed-RL: Long-Horizon Reinforcement Learning for Evidence-Grounded Clinical Diagnosis
TrustMed-RL：基于证据的临床诊断的长时间跨度强化学习
Abstract
Medical language models can produce correct diagnoses despite incomplete investigations and unsupported reasoning. To support long-horizon, evidence-grounded diagnosis, we introduce \textbf{TrustMed-RL}. Built from PubMed rare-disease cases and over 24,000 manually annotated image panels, it integrates interviews, examinations, testing, specialist consultation, and literature search through state-dependent actions. Our 8B vision--language policy, trained with clinically adapted GiGPO and coverage-adjusted diagnostic rewards, achieves 37.1\% diagnostic accuracy on 2,500 evaluation cases, outperforming all evaluated open-weight baselines and improving over supervised fine-tuning by 12.4 percentage points.. When success additionally requires acquiring at least 50\% of supporting test evidence, TrustMed-RL achieves 32.5\%, exceeding GPT-4o by 6.8 percentage points. Furthermore, it surpasses all evaluated baselines on MTMedDialog and multiple larger 27--32B models on AgentClinic. In physician review of 200 diagnostically accepted test-set trajectories, 83.0\% receive evidential-grounding scores of 4--5 out of 5. Physicians' assessments suggest that these diagnostic trajectories are trustworthy and aligned with human diagnostic reasoning.
Chinese Translation
医学语言模型能够在调查不完全和推理不支持的情况下产生正确的诊断。为了支持长时间跨度的基于证据的诊断，我们引入了 extbf{TrustMed-RL}。该模型基于PubMed的罕见疾病案例和超过24,000个手动标注的图像面板，通过状态依赖的动作整合了访谈、检查、测试、专家咨询和文献检索。我们的8B视觉-语言策略，经过临床适应的GiGPO和覆盖调整的诊断奖励训练，在2,500个评估案例上实现了37.1\%的诊断准确率，超越了所有评估的开放权重基线，并比监督微调提高了12.4个百分点。当成功还需要获取至少50 extbf{ extit{ extbf{ extit{}}}}的支持测试证据时，TrustMed-RL达到了32.5 extbf{ extit{ extbf{ extit{}}}}，比GPT-4o高出6.8个百分点。此外，它在MTMedDialog和多个更大27--32B模型的AgentClinic上超越了所有评估的基线。在对200个诊断接受的测试集轨迹的医生评审中，83.0 extbf{ extit{ extbf{ extit{}}}}的轨迹获得了4--5分（满分5分）的证据基础评分。医生的评估表明，这些诊断轨迹是可信的，并与人类的诊断推理相一致。
cs.AI / 53 / 2610.04407
TimeNet: An Extensible Unified Data Infrastructure for Next-Generation Temporal Foundation Models
TimeNet：下一代时间基础模型的可扩展统一数据基础设施
Maritsch, Martin, Stoffregen, Timo, Kaar, Thomas, Riemer, Behsad, Xu, Maxwell A., Rosenblattl, Max, Liu, Juncheng, Zumarraga, Nicolas, Wu, Yu Yvonne, Herasymuk, Denys, Rastogi, Sparsh, Yoon, Hyungjun, Huang, Bosong, Pillai, Arvind, Lopushanskyy, Dmytro, Chen, Tony, Deuber, Robin, Liu, Yichen, Messica, Shvat, Li, Dan, Lou, Jian, Zhang, Yuwei, Kim, Jaeho, Garcia, Renée Rosillo, Wu, Fan, Müller, Robert, Fleisch, Elgar, Salim, Flora D., Spathis, Dimitris, Yang, Yuzhe, Saeed, Aaqib, McDuff, Daniel, Jin, Ming, Kreft, Markus, O'Sullivan, Kevin, Jakob, Robert, Garza, Azul, Schmiedmayer, Paul, Langer, Patrick
Abstract
Temporal Foundation Models (TFMs) aim to generalize across domains, datasets, and tasks. Yet, their development remains constrained by fragmented, task-specific data formats, annotations, and processing pipelines. We introduce TimeNet, an open-source data standard and scalable infrastructure that decouples temporal data from task definitions and represents signals, metadata, annotations, and supervision in a shared, extensible data model. TimeNet supports multimodal signals with regular, irregular, or ordinal time axes and expresses different task families (including classification, forecasting, temporal localization, question answering, generation, and editing) as reusable views over the same recordings. This shared representation enables heterogeneous time-series datasets to be combined for large-scale model training across domains, modalities, and tasks. We demonstrate TimeNet by transcoding datasets with 1.5M task instances spanning diverse domains, modalities, temporal scales, and forms of supervision, while retaining practical I/O performance relative to native formats. TimeNet enables an existing TFN training pipeline to support joint training on a configurable number of heterogeneous datasets through configuration changes alone. We show this capability by training TFM across multiple datasets and tasks, obtaining a 14% F1 score improvement compared with models trained on individual datasets. These results show that TimeNet provides the data and systems foundation needed to move beyond task- and dataset-specific TFMs toward models that can learn jointly across heterogeneous domains, modalities, temporal scales, and forms of supervision from a common data model.
Chinese Translation
时间基础模型（Temporal Foundation Models, TFMs）旨在跨领域、数据集和任务进行泛化。然而，它们的发展仍受到碎片化、特定任务的数据格式、注释和处理管道的限制。我们提出了TimeNet，一个开源数据标准和可扩展基础设施，它将时间数据与任务定义解耦，并在共享的可扩展数据模型中表示信号、元数据、注释和监督。TimeNet支持具有规则、不规则或序数时间轴的多模态信号，并将不同的任务家族（包括分类、预测、时间定位、问答、生成和编辑）表示为对相同录音的可重用视图。这种共享表示使异构时间序列数据集能够结合起来，以支持跨领域、模态和任务的大规模模型训练。我们通过转码包含150万个任务实例的数据集来展示TimeNet，这些实例涵盖了多样的领域、模态、时间尺度和监督形式，同时在保留相对于原生格式的实用输入/输出性能。TimeNet使现有的TFN训练管道能够通过配置更改，仅支持对可配置数量的异构数据集进行联合训练。我们通过在多个数据集和任务上训练TFM，展示了这一能力，相较于在单个数据集上训练的模型，获得了14%的F1分数提升。这些结果表明，TimeNet提供了超越特定任务和数据集的TFMs所需的数据和系统基础，以便构建能够从共同数据模型中联合学习异构领域、模态、时间尺度和监督形式的模型。
cs.AI / 54 / 2610.04418
CORE-RL: Confidence-Oriented Reliability Evaluation of Black-Box Reinforcement Learning Policies
CORE-RL：面向信心的黑箱强化学习策略可靠性评估
Abstract
The deployment of Reinforcement Learning (RL) agents in critical domains must be preceded with a pipeline to evaluate the alignment of the RL agent with complex multi-objective specifications and robustness under real-world environmental drift. However, to protect intellectual property, the RL agent may be delivered for evaluation as opaque executable or remote API, which makes traditional evaluation techniques based on the internals of the policies infeasible. To address this gap, CORE-RL: Confidence-Oriented Reliability Evaluation of black box RL policy is proposed in this paper. The CORE-RL pipeline introduces a Unified Reliability Metric that formally integrates early task termination and safety constraint violations, preventing unsafe policies from masking failures through premature episode halts. By subjecting the policy to a noise certification envelope of perceptual noise, actuation noise and change in environment dynamics, the pipeline computes the finite-sample Clopper-Pearson bounds on unified reliability metric and Hoeffdings' lower bound on reward and safety cost. The pipeline then defines safe operational design domain to report high-confidence certificates for safety and expected performance. Experiments on continuous control tasks demonstrate the CORE-RL pipeline's ability to automatically reject non-compliant policies and map the safe Operational Design Domain (ODD) of safety-aware policies. Thus CORE-RL provides an evaluation framework towards a quantitative, transparent and reproducible, statistical rationale necessary to safely evaluate, compare, and deploy black box RL solutions.
Chinese Translation
在关键领域中部署强化学习（RL）代理之前，必须通过一个管道来评估RL代理与复杂多目标规范的一致性以及在现实环境漂移下的鲁棒性。然而，为了保护知识产权，RL代理可能以不透明的可执行文件或远程API的形式交付进行评估，这使得基于策略内部的传统评估技术变得不可行。为了解决这一问题，本文提出了CORE-RL：面向信心的黑箱RL策略可靠性评估。CORE-RL管道引入了一个统一可靠性指标，正式整合了早期任务终止和安全约束违规，防止不安全策略通过过早的回合停止掩盖失败。通过将策略置于感知噪声、执行噪声和环境动态变化的噪声认证包围中，管道计算统一可靠性指标的有限样本Clopper-Pearson界限以及奖励和安全成本的Hoeffding下界。然后，管道定义安全操作设计域，以报告高置信度的安全和预期性能证书。在连续控制任务上的实验表明，CORE-RL管道能够自动拒绝不合规的策略，并映射安全意识策略的安全操作设计域（ODD）。因此，CORE-RL提供了一个评估框架，以实现定量、透明和可重复的统计推理，这对于安全评估、比较和部署黑箱RL解决方案是必要的。
cs.AI / 55 / 2610.04429
Asking Earns Nothing: Scoring the Decision to Act in BFCL Multi-Turn
询问无益：在 BFCL 多轮中的行动决策评分
Abstract
An agent that lacks the information it needs should ask rather than act, and the task definitions of agent leaderboards say so. BFCL multi-turn builds two of its four categories around a turn on which the model is supposed to ask, and its scorer never looks at that turn: the gold trajectory there is empty, the checker skips it, and the scripted user cannot answer, so asking earns nothing, guessing costs nothing on that turn, and asking twice loses the item. The benchmark also contains the control experiment for that decision. A should-ask item is a base item with one piece of information removed from one turn, so the same request appears twice at the same turn index, once complete and once not: on the first the model should make the call that changes the world, on the second it should ask. We score one decision per pair, whether the model attempted a world-changing call on that turn, read off the stored trajectories with no LLM judge; acting always and asking always both score 50. On the 223 pairs that pose this decision, gpt-5.4 attempts the call on 83.4% of the complete turns and holds back on 78.0% of the incomplete ones, the best decision accuracy of seven models at 80.7%; on the same items the official score ranks it sixth and puts first a model that lands in the middle here. One added line telling gpt-5.4 not to ask pushes it toward acting on both sides of the pair, so its decision accuracy shows no detectable change, while its official score rises by 13.5 to 23.5 points on the two should-ask categories and on the base twins; the opposite line, telling gemma-4-31B-it to ask first, improves its decision by 4.5 points and gains no official score. The score moves with the push toward action, not with the decision. We release the pairs, a turn-level scorer that runs on any BFCL output directory without an API key, and 31 manually verified bad items.
Chinese Translation
一个缺乏所需信息的智能体应该选择询问而不是行动，智能体排行榜的任务定义也如此。BFCL 多轮将其四个类别中的两个建立在模型应该询问的回合上，而其评分者从未关注该回合：该回合的金标准轨迹为空，检查者跳过它，脚本用户无法回答，因此询问无益，而在该回合猜测没有成本，询问两次则会失去项目。基准测试还包含了该决策的对照实验。一个应该询问的项目是一个基础项目，其中从一个回合中移除了一条信息，因此同样的请求在相同的回合索引中出现两次，一次是完整的，一次是不完整的：在第一次中，模型应该做出改变世界的决策，而在第二次中，它应该询问。我们对每对进行一次决策评分，无论模型在该回合是否尝试做出改变世界的决策，读取存储的轨迹而不使用 LLM 判断；始终行动和始终询问的评分均为 50。在 223 对提出此决策的案例中，gpt-5.4 在 83.4% 的完整回合中尝试做出决策，而在 78.0% 的不完整回合中保持谨慎，这是七个模型中最佳的决策准确率为 80.7%；在相同项目上，官方评分将其排名第六，而将一个在此处表现中等的模型排在第一。增加一行指示 gpt-5.4 不要询问，使其在对的两侧更倾向于行动，因此其决策准确率没有明显变化，而其官方评分在两个应该询问的类别和基础双胞胎上上升了 13.5 到 23.5 分；相反的指令，告诉 gemma-4-31B-it 首先询问，使其决策提高了 4.5 分，但没有获得官方评分。评分随着向行动的推动而变化，而不是随着决策的变化。我们发布了这些对、一个可以在任何 BFCL 输出目录上运行的回合级评分器，无需 API 密钥，以及 31 个手动验证的错误项目。
cs.AI / 56 / 2610.04430
MERCI Cards: An LLM Evaluation and Deployment Framework for High-Stakes Domains
MERCI卡：高风险领域的LLM评估与部署框架
Abstract
As LLMs are increasingly deployed in high-stakes professional workflows, engineers and researchers require principled protocols to systematically track, monitor, and improve model performance across deployment cycles. We present a mathematical framework for iterative LLM evaluation and deployment, and demonstrate its application to AI systems used in criminal justice. Our framework formalizes LLM integration in high-stakes, high-risk, and resource-constrained domains across model selection, rubric design, evaluations and deployment via a weighted multi-objective optimization. We demonstrate that MERCI Cards can guide improvements of the system across deployment iterations, direct developer attention toward under-performing areas, and focus user attention on validation and error-correction in the LLM's outputs.
Chinese Translation
随着大型语言模型（LLMs）在高风险专业工作流程中的日益应用，工程师和研究人员需要有原则的协议，以系统地跟踪、监控和改进模型在部署周期中的性能。我们提出了一个用于迭代LLM评估和部署的数学框架，并展示其在刑事司法领域的AI系统中的应用。我们的框架通过加权多目标优化，形式化了LLM在高风险、高风险和资源受限领域中的集成，包括模型选择、评分标准设计、评估和部署。我们展示了MERCI卡如何在部署迭代中指导系统的改进，引导开发者关注表现不佳的领域，并将用户的注意力集中在LLM输出的验证和错误修正上。
cs.AI / 57 / 2610.04433
What Does a Harness Buy? Tokens, Mostly
马具购买了什么？主要是代币
Abstract
A coding agent is a language model wrapped in a harness: the system prompt, the tool set, and the context management that turn a chat model into something that can work inside a repository. Production harnesses ship releases daily, vendors advertise pass-rate gains from harness changes, and leaderboards mix harnesses freely. What is rarely measured is how much the harness itself moves the score when the model is held fixed. We run five models through three production harnesses, Claude Code, mini-SWE-agent, and OpenCode, on SWE-bench Verified, and rerun the same configurations to calibrate how much a score moves when nothing changes but the run. On 447 tasks and the two models we ran there, Claude Code and mini-SWE-agent, the heaviest and the lightest harness, are equivalent within five points. On a 45-task hard subset and five models, swapping the harness flips as many tasks as rerunning the same harness, 13% in both cases, and the tasks a harness wins in one run are not the tasks it wins in the next. The one harness effect that clears the noise is a loss, not a gain: OpenCode trails by up to 9 points on the large pool, and on one model half of that gap sits in runs its output cap cut short. What the harness does decide is the bill. With the same model, the same tasks, and one price list, cost per task differs by up to 3x across harnesses. The gap is set at the first call, by the preamble of system prompt and tool schemas each harness sends with every step, and scaled by the number of steps; per-step growth and per-call tool output differ far less. The provider's price for cached input scales the bill and does not reorder it. The rerun data also give the resolution a harness comparison needs: at the discordance we observe, 45 tasks catch a 13-point gap only half the time and no gap with 80% power, and 447 tasks resolve 5 points, still coarser than the gains many harness changes claim.
Chinese Translation
编码代理是一个被包裹在马具中的语言模型：系统提示、工具集和上下文管理将聊天模型转变为能够在代码库中工作的东西。生产马具每天发布版本，供应商宣传马具变更带来的通过率提升，排行榜自由混合马具。很少有研究测量当模型保持不变时，马具本身对得分的影响有多大。我们在SWE-bench Verified上对三个生产马具（Claude Code、mini-SWE-agent和OpenCode）运行了五个模型，并重新运行相同的配置，以校准当唯一变化是运行时得分的变化幅度。在447个任务和我们在其中运行的两个模型（Claude Code和mini-SWE-agent）中，最重和最轻的马具得分相差不超过五分。在一个包含45个任务的困难子集和五个模型中，交换马具翻转的任务数量与重新运行相同马具的数量相同，都是13%，而一个运行中马具赢得的任务并不是下一个运行中赢得的任务。唯一能够清除噪音的马具效应是损失，而非收益：在大池中，OpenCode的得分落后最多9分，在一个模型中，这一差距的一半出现在其输出限制被缩短的运行中。马具决定的就是费用。在相同模型、相同任务和同一价格表的情况下，不同马具的每个任务成本相差最多3倍。这个差距在第一次调用时就已经确定，由每个马具在每一步发送的系统提示和工具架构的前言设定，并根据步骤数量进行缩放；每步增长和每次调用的工具输出差异要小得多。提供者对缓存输入的定价影响费用，并不会重新排序。重新运行的数据还提供了马具比较所需的分辨率：在我们观察到的不一致性中，45个任务在一半的时间内捕捉到13分的差距，而在80%的功效下没有差距，447个任务解决了5分，仍然比许多马具变更所声称的收益粗糙。
cs.AI / 58 / 2610.04437
CRAFT: An Agentic Spreadsheet Form Filling System with Template Awareness
CRAFT：一种具有模板意识的代理电子表格表单填写系统
Abstract
Spreadsheet form filling requires agents to consolidate external evidence, ground values to precise cells, and preserve irregular template structure. Errors in early edits can overwrite labels or misalign fields, undermining later decisions. We propose CRAFT, a template-aware agent framework that connects reflective validation to constrained local repair. Instead of treating reflection as a free-form request to regenerate the workbook, CRAFT grounds detected errors to spreadsheet regions, restores corrupted template state when necessary, and re-grounds plausible writable slots before subsequent edits. A Rectangle-Aware Slot Grounder (RASG) proposes writable cells, while label-slot hints and protected regions constrain subsequent edits. We introduce FormFillBench, with 327 forms across Instruction-Only and Multi-File tracks. Compared with the strongest baselines, CRAFT improves pair accuracy by 8.51 and 23.38 percentage points on these tracks, respectively. Component-removal experiments support structural adjudication and slot re-grounding within the pipeline, and the framework retains its relative advantage among the methods evaluated with a second backbone. The code and benchmark FormFillBench are available at https://github.com/Glllllly/CRAFT.
Chinese Translation
电子表格表单填写要求代理整合外部证据，将值准确定位到特定单元格，并保持不规则的模板结构。早期编辑中的错误可能会覆盖标签或导致字段错位，从而削弱后续决策。我们提出了CRAFT，一个具有模板意识的代理框架，将反思验证与受限的局部修复相连接。CRAFT并不是将反思视为重新生成工作簿的自由形式请求，而是将检测到的错误定位到电子表格区域，在必要时恢复损坏的模板状态，并在后续编辑之前重新定位可写槽。矩形感知槽定位器（Rectangle-Aware Slot Grounder, RASG）提出可写单元格，而标签-槽提示和受保护区域则限制后续编辑。我们引入了FormFillBench，其中包含327个表单，分为仅指令和多文件两个轨道。与最强基线相比，CRAFT在这两个轨道上分别提高了8.51和23.38个百分点的配对准确率。组件移除实验支持管道内的结构裁决和槽重新定位，并且该框架在使用第二个骨干网络评估的方法中保持相对优势。代码和基准FormFillBench可在https://github.com/Glllllly/CRAFT获取。
cs.AI / 59 / 2610.04438
RAGrasp: Geometry-Semantic Template Retrieval and Grasp Transfer
RAGrasp：几何-语义模板检索与抓取转移
Abstract
We present RAGrasp, a retrieval-augmented pipeline for planar parallel-jaw grasping from a compact set of locally collected, grasp-annotated RGB-D (color and depth) templates. Unlike task-specific predictors trained primarily on large public or synthetic grasp datasets, RAGrasp requires no end-to-end retraining for a new deployment.Its template memory is constructed from observations collected with the deployment camera, robot, and gripper in the target workspace, thereby aligning stored examples with the local sensing and embodiment conditions. The system uses self-supervised DINOv2 visual fea- tures together with appearance and depth cues to prompt the Segment Anything Model 2 (SAM2), which isolates the query object. A two-stage geometry-semantic retrieval cascade then selects a template, and a confidence gate chooses one of two grasp- transfer estimators. The transferred grasp is refined using mask- support and silhouette-contact constraints before calibrated 2D- to-3D conversion. In real-world trials, RAGrasp achieves 20/20 successful grasps on seen objects and 19/20 on unseen objects. Within the evaluated setting, the results demonstrate deployment- specific grasp adaptation from limited local annotation and tolerance to the tested viewpoint and illumination changes.
Chinese Translation
我们提出了RAGrasp，这是一个增强检索的管道，用于从一组紧凑的本地收集的抓取标注RGB-D（彩色和深度）模板中进行平面平行夹爪抓取。与主要在大型公共或合成抓取数据集上训练的任务特定预测器不同，RAGrasp在新的部署中无需进行端到端的重新训练。其模板记忆是通过在目标工作空间中使用部署相机、机器人和夹具收集的观察结果构建的，从而使存储的示例与本地感知和体现条件对齐。该系统使用自监督的DINOv2视觉特征，以及外观和深度线索，来提示Segment Anything Model 2（SAM2），以隔离查询对象。然后，一个两阶段的几何-语义检索级联选择一个模板，而一个置信度门选择两个抓取转移估计器中的一个。转移的抓取在经过掩膜支持和轮廓接触约束的精炼后，进行标定的2D到3D转换。在实际试验中，RAGrasp在已见物体上实现了20/20的成功抓取，在未见物体上实现了19/20的成功抓取。在评估的设置中，结果展示了从有限的本地标注中进行的特定部署抓取适应，以及对测试视角和光照变化的容忍性。
cs.AI / 60 / 2610.04444
Causally Fair Generation with Large Language Models
使用大型语言模型进行因果公平生成
Abstract
Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape consequential decisions, raising concerns about their impact on demographic disparities. In this context, causal inference provides a principled basis for assessing fairness, because it attributes observed disparities to the mechanisms that generated them, which a purely statistical approach cannot do even with infinite data. In LLM generation, a query may request several causally related variables, each of which is both an outcome of interest and a possible cause of other outputs, and the information supplied in the prompt need not follow a topological or a temporal order. This calls for methods that can analyze and selectively remove disparities from such a flexible generation process. In this paper we introduce Causally Fair Generation with LLMs (CFG, for short). CFG extracts relevant concepts, grounds generation in a reference population and causal diagram, and removes user-selected causal effects. CFG also allows pathways deemed justifiable for the task's utility to be retained, which is known in legal literature as business necessity. Further, we provide formal guarantees for our method when eliminating all discriminatory causal effects in the adapted population model, under appropriate causal assumptions. We evaluate CFG with four LLMs in three real-world settings based on population data and on a synthetic dataset with a known causal ground truth.
Chinese Translation
大型语言模型（LLMs）越来越多地用于生成、补全和转换信息，这些信息的输出可能影响重要决策，因此引发了对其对人口差异影响的担忧。在这种背景下，因果推断为评估公平性提供了原则性基础，因为它将观察到的差异归因于生成这些差异的机制，而纯粹的统计方法即使在无限数据的情况下也无法做到。在LLM生成中，一个查询可能请求多个因果相关的变量，每个变量既是感兴趣的结果，也是其他输出的可能原因，并且提示中提供的信息不必遵循拓扑或时间顺序。这就需要能够分析并选择性地消除这种灵活生成过程中的差异的方法。在本文中，我们介绍了使用LLMs进行因果公平生成（简称CFG）。CFG提取相关概念，将生成过程基于参考人群和因果图，并消除用户选择的因果效应。CFG还允许保留被认为对任务效用合理的路径，这在法律文献中被称为商业必要性。此外，我们在适应的人口模型下，在适当的因果假设下，为我们的方法在消除所有歧视性因果效应时提供了正式保证。我们在基于人口数据的三个真实场景和一个具有已知因果真实值的合成数据集上评估了CFG与四个LLMs的表现。
cs.AI / 61 / 2610.04447
Understanding Generative AI Use in Programming MOOCs: The Role of Course Context and Learner Characteristics
理解编程MOOC中生成性人工智能的使用：课程背景和学习者特征的作用
Abstract
The increasing availability of generative artificial intelligence (GenAI) tools, such as ChatGPT and code-completion assistants, raises questions about how learners integrate these tools into learning activities, particularly in MOOCs that attract diverse participant populations. This study examines the use of GenAI in two programming MOOCs taught in Estonian that differ in duration, workload, topic complexity, and assignment volume: About Programming (4 weeks, 26 expected hours, n = 187) and Introduction to Programming (8 weeks, 78 expected hours, n = 182). Post-course questionnaire data were analyzed using non-parametric statistical methods to examine self-reported adoption, usage frequency, and purposes of GenAI use across courses and learner backgrounds. The results show that GenAI adoption was widespread in both MOOCs, with no statistically significant differences by gender, age, education level, or prior programming experience. However, participants in the longer, more extensive MOOC reported significantly higher usage frequency and were more likely to use GenAI for debugging and idea generation. Reported usage frequency for code explanation and debugging was also higher in the longer course. Exploratory analyses found limited relationships between GenAI use and learning-related outcomes. The findings suggest that course context may play a greater role than learner characteristics in shaping how GenAI tools are used. These results provide implications for instructional design and guidance in programming education.
Chinese Translation
生成性人工智能（GenAI）工具的日益普及，例如ChatGPT和代码补全助手，引发了关于学习者如何将这些工具融入学习活动的问题，尤其是在吸引多样化参与者的MOOC中。本研究考察了在两门以爱沙尼亚语教授的编程MOOC中GenAI的使用情况，这两门课程在持续时间、工作量、主题复杂性和作业量上存在差异：关于编程（4周，预计26小时，n = 187）和编程导论（8周，预计78小时，n = 182）。通过非参数统计方法分析了课程结束后的问卷数据，以考察不同课程和学习者背景下GenAI的自我报告采用情况、使用频率和使用目的。结果显示，两门MOOC中GenAI的采用情况普遍，且在性别、年龄、教育水平或先前编程经验方面没有统计学显著差异。然而，参与较长且内容更为广泛的MOOC的学习者报告了显著更高的使用频率，并且更有可能将GenAI用于调试和创意生成。在较长课程中，关于代码解释和调试的报告使用频率也较高。探索性分析发现GenAI使用与学习相关结果之间的关系有限。研究结果表明，课程背景在塑造GenAI工具使用方式方面可能比学习者特征起更大作用。这些结果为编程教育中的教学设计和指导提供了启示。
cs.AI / 62 / 2610.04469
Trinity: Self-Evolving Vision-Language Models with a Self-Verifier
三位一体：具有自我验证器的自我演化视觉-语言模型
Abstract
Self-evolving vision-language models (VLMs), a form of self-improvement in which a model generates its own training data from unlabeled images, are a promising route toward agents that expand their reasoning capability in an unsupervised manner, without relying on ever-larger annotation budgets. Existing methods pair a Questioner that proposes problems with a Solver that answers them, but reward both roles mainly by agreement among sampled answers. Agreement is a weak proxy for truth: it cannot tell whether a question is grounded in the image, whether the proposed reference answer is right, or whether a confident majority is wrong in the same way. We present Trinity, in which one VLM plays three roles, Questioner, Solver, and Verifier, and the Verifier is a self-verifier: an exponential moving average (EMA) of the policy itself, requiring neither labels nor an external judge. The Verifier screens every generated question for image grounding and answer correctness before it becomes supervision, scores Solver reasoning against the image, and adjudicates disputes between the reference answer and a strong Solver consensus, correcting the reference and penalizing the Questioner when the consensus is right. Trained on images alone, Trinity improves Qwen3-VL-8B on mathematical visual reasoning and on science benchmarks with biology content, for example, +8.6 points on the biology split of SciVQR and +12.8 on MathVerse, and its reward dynamics behave as a healthy self-play curriculum should. These results suggest that a self-evolving multimodal agent can strengthen its scientific reasoning from unlabeled scientific images alone, with the model itself serving as the verifier.
Chinese Translation
自我演化视觉-语言模型（VLMs）是一种自我改进的形式，其中模型从未标记的图像中生成自己的训练数据，这为在无监督的情况下扩展推理能力的智能体提供了有希望的途径，而无需依赖日益庞大的标注预算。现有方法将提出问题的提问者与回答问题的求解者配对，但主要通过对采样答案的一致性来奖励这两个角色。一致性是对真相的弱代理：它无法判断一个问题是否与图像相关，所提出的参考答案是否正确，或者一个自信的多数是否以相同的方式错误。我们提出了三位一体（Trinity），其中一个VLM扮演三个角色：提问者、求解者和验证者，而验证者是一个自我验证器：它是策略本身的指数移动平均（EMA），不需要标签或外部评判者。验证者在生成的问题成为监督之前，筛选每个问题的图像基础和答案正确性，对求解者的推理进行图像评分，并裁定参考答案与强求解者共识之间的争议，当共识正确时纠正参考答案并惩罚提问者。仅在图像上训练的三位一体在数学视觉推理和生物内容的科学基准测试中改善了Qwen3-VL-8B，例如，在SciVQR的生物分割上提高了8.6分，在MathVerse上提高了12.8分，其奖励动态表现出健康的自我对弈课程应有的特征。这些结果表明，自我演化的多模态智能体可以仅通过未标记的科学图像增强其科学推理能力，模型本身充当验证者。
cs.AI / 63 / 2610.04470
Reactivating Alignment: Defending LLMs from Jailbreaks via Intention-Aware Input-Output Matching
重新激活对齐：通过意图感知的输入-输出匹配保护大型语言模型免受越狱攻击
Abstract
Large language models (LLMs) remain vulnerable to jailbreak attacks that conceal harmful intent within complex adversarial prompts. Existing defenses primarily rely on input perturbation or harmful-output suppression, but they rarely model where malicious intent resides, resulting in brittle protection and excessive over-refusal. We propose SENTINEL, a plug-and-play, generation-time jailbreak defense that reframes mitigation as an intent extraction problem. Our key insight is that instruction-tuned LLMs exhibit strong input--output semantic consistency: regardless of jailbreak complexity, generated outputs tend to align with the attacker's true intent. SENTINEL exploits this property by matching semantically aligned input--output regions to extract intention-revealing subsequences, scores these subsequences using refusal-direction projections to estimate harmfulness, and halts generation when necessary. Experiments on HarmBench across multiple LLMs show that SENTINEL reduces jailbreak success rates to close to 5\% while maintaining low over-refusal. We further demonstrate robustness to adaptive attacks and provide a mechanistic interpretation: SENTINEL re-distributes jailbreak features from alignment blind spots to aligned regions.
Chinese Translation
大型语言模型（LLMs）仍然容易受到越狱攻击，这些攻击在复杂的对抗性提示中隐蔽有害意图。现有的防御措施主要依赖于输入扰动或有害输出抑制，但它们很少建模恶意意图所在的位置，导致保护措施脆弱且过度拒绝。我们提出了SENTINEL，这是一种即插即用的生成时越狱防御，将减轻问题重新框定为意图提取问题。我们的关键见解是，经过指令调优的LLMs表现出强烈的输入-输出语义一致性：无论越狱的复杂性如何，生成的输出往往与攻击者的真实意图一致。SENTINEL利用这一特性，通过匹配语义对齐的输入-输出区域提取意图揭示子序列，使用拒绝方向投影对这些子序列进行评分以估计有害性，并在必要时停止生成。在多个LLMs上的HarmBench实验表明，SENTINEL将越狱成功率降低至接近5%，同时保持低的过度拒绝率。我们进一步展示了对自适应攻击的鲁棒性，并提供了机制解释：SENTINEL将越狱特征从对齐盲点重新分配到对齐区域。
cs.AI / 64 / 2610.04472
Semantic Causal-Factor Inference from Aviation Incident Narratives Using A Variational Autoencoder with Cosine-Similarity-Based Reconstruction
基于余弦相似度重构的变分自编码器的航空事件叙述语义因果因素推断
Abstract
Aviation accident and incident investigations generate extensive unstructured textual information containing evidence relevant to the causes and contributing factors of safety occurrences. Automatically extracting such information is challenging because causal evidence may be distributed across long and complex investigation narratives. This study proposes a semantic causal-factor inference framework combining natural language processing with a variational autoencoder (VAE) to learn the relationship between aviation investigation narratives and expert-reported probable causes. Investigation narratives and their corresponding probable causes are transformed into numerical representations, after which the encoder maps narrative representations to a probabilistic latent space. The decoder estimates representations of the corresponding probable causes and is trained using an objective that combines Kullback-Leibler divergence with cosine-similarity-based semantic reconstruction. The framework was evaluated using 20,919 finalized U.S. National Transportation Safety Board investigation reports from 2005 to 2020. On the held-out test set, the predicted and expert-reported probable-cause representations achieved a mean cosine similarity of 0.786 (SD = 0.120). The predicted representations also yielded interpretable terms associated with causal information in the reports. The results demonstrate the potential of probabilistic latent representation learning for AI-assisted extraction of causal information from aviation safety narratives while retaining expert investigation as the basis for formal causal determination.
Chinese Translation
航空事故和事件调查生成大量包含与安全事件原因及影响因素相关证据的非结构化文本信息。自动提取这些信息具有挑战性，因为因果证据可能分散在冗长且复杂的调查叙述中。本研究提出了一种语义因果因素推断框架，结合自然语言处理与变分自编码器（Variational Autoencoder, VAE），以学习航空调查叙述与专家报告的可能原因之间的关系。调查叙述及其对应的可能原因被转化为数值表示，之后编码器将叙述表示映射到概率潜在空间。解码器估计对应可能原因的表示，并使用结合了Kullback-Leibler散度和基于余弦相似度的语义重构的目标进行训练。该框架使用2005年至2020年间的20,919份美国国家运输安全委员会调查报告进行评估。在保留的测试集上，预测的可能原因表示与专家报告的可能原因表示的平均余弦相似度达到0.786（标准差 = 0.120）。预测的表示还产生了与报告中因果信息相关的可解释术语。结果表明，概率潜在表示学习在AI辅助提取航空安全叙述中的因果信息方面具有潜力，同时保留专家调查作为正式因果确定的基础。
cs.AI / 65 / 2610.04473
InferOpt: Constrained Multi-Objective Search for LLM Inference Configurations
InferOpt：用于大语言模型推理配置的受限多目标搜索
Abstract
Serving an LLM means setting dozens of inference-time knobs, from per-layer KV retention to per-layer expert counts. Practice sets them with mechanism-specific heuristics that return a single operating point and do not scale to layer-wise search spaces. We recast inference configuration as constrained multi-objective black-box optimization and build InferOpt, a reusable search framework that requires only variable bounds, a deterministic resource cost, and an evaluation hook. InferOpt searches on a frozen sampled proxy set, rejects over-budget candidates before any model call, tightens the budget adaptively, and re-validates Pareto representatives on full-scale dataset. One pipeline covers a 28-dimensional continuous KV space (Qwen2.5-7B) and a 26-dimensional discrete MoE space (DeepSeek-V2-Lite). On KV, post-prefill pruning cuts the 16K cache by 64.4% and TPOT by 22.9--48.5%, and the searched layer-wise budget by InferOpt beats a matched uniform budget by 7.3% and 14.0% of the Full KV reference points. On MoE, a searched top-k schedule by InferOpt removes 43.0% of routed token--expert pairs while staying within 0.59 points of the default, closer than the matched-budget baselines. Against Random Search, NSGA-II, and MOTPE, InferOpt leads on both spaces, taking the best proxy hypervolume and the lowest retention on KV and staying closest to the uncompressed reference at the lowest experts on MoE.
Chinese Translation
大语言模型（LLM）的服务部署涉及设置数十个推理时参数，从每层的键值（KV）保留到每层专家数量。实际操作中，这些参数通常通过机制特定的启发式方法设定，返回单一的运行点，且无法扩展到逐层的搜索空间。我们将推理配置重新定义为受限的多目标黑箱优化问题，并构建了InferOpt，一个可复用的搜索框架，仅需变量边界、确定性的资源消耗以及评估接口。InferOpt在冻结的采样代理集上进行搜索，提前拒绝超预算的候选方案，自适应收紧预算，并在全规模数据集上重新验证帕累托代表解。该流程覆盖了28维连续的KV空间（Qwen2.5-7B）和26维离散的专家模型（MoE）空间（DeepSeek-V2-Lite）。在KV空间中，预填充后剪枝将16K缓存削减了64.4%，TPOT减少了22.9%至48.5%，InferOpt搜索得到的逐层预算相比匹配的均匀预算，在完整KV参考点上分别提升了7.3%和14.0%。在MoE空间中，InferOpt搜索得到的top-k调度方案减少了43.0%的路由令牌-专家对，同时性能仅比默认方案低0.59点，优于匹配预算的基线方法。与随机搜索（Random Search）、NSGA-II和MOTPE相比，InferOpt在两个空间均表现领先，取得了最佳的代理超体积（proxy hypervolume）和最低的KV保留率，并在MoE空间以最低专家数量最接近未压缩的参考方案。
cs.AI / 66 / 2610.04475
VCLMU: Mechanism-Centric Virtual Cell World Modeling for Perturbation Response
VCLMU：以机制为中心的虚拟细胞世界建模用于扰动响应
Abstract
Predicting cellular responses to genetic perturbations is a central capability for virtual cells and a key step toward computational modeling of biological interventions. Most existing models directly map an unperturbed molecular profile and perturba- tion to the resulting observation without explicitly representing the latent cellular transition induced by the intervention. We introduce a mechanism-centric virtual cell world model that represents cellular state as a set of Latent Mechanism Units (LMUs) and treats genetic perturbations as actions on these latent states. Each LMU combines a reusable identity grounded in multimodal biological evidence with an observation-specific state, allowing a perturbation to induce mechanism- specific stochastic transitions before decoding the resulting transcriptional response. We train VCLMU through two-stage pretraining, first on around 200K pseudo-bulk perturbation profiles and then on gene-aligned single-cell perturbation data. Across six perturbation-disjoint benchmarks, VCLMU consistently improves perturbation- specific response recovery over strong baselines while maintaining competitive global response accuracy. We further analyze learned LMUs through enrichment between perturbation responses and LMU gene sets and show that they capture structured biological response programs. These results support mechanism-level latent state transition as a useful formulation for virtual cell models that aim to predict and interpret cellular responses to biological interventions.
Chinese Translation
预测细胞对遗传扰动的响应是虚拟细胞的核心能力，也是生物干预计算建模的关键步骤。现有的大多数模型直接将未扰动的分子特征和扰动映射到结果观察上，而没有明确表示干预所引起的潜在细胞转变。我们提出了一种以机制为中心的虚拟细胞世界模型，将细胞状态表示为一组潜在机制单元（Latent Mechanism Units, LMUs），并将遗传扰动视为对这些潜在状态的操作。每个LMU结合了基于多模态生物证据的可重用身份和特定于观察的状态，使得扰动能够在解码结果转录响应之前引发机制特定的随机转变。我们通过两阶段预训练来训练VCLMU，首先在约20万个伪批量扰动特征上进行预训练，然后在基因对齐的单细胞扰动数据上进行训练。在六个扰动不重叠的基准测试中，VCLMU在扰动特定响应恢复方面始终优于强基线，同时保持竞争性的全局响应准确性。我们进一步通过扰动响应与LMU基因集之间的富集分析来分析学习到的LMUs，并显示它们捕捉到结构化的生物响应程序。这些结果支持机制层面的潜在状态转变作为一种有用的表述，适用于旨在预测和解释细胞对生物干预响应的虚拟细胞模型。
cs.AI / 67 / 2610.04510
ManifoldCache: Training-Free Diffusion Acceleration via Constraint Manifold Caching
流形缓存：通过约束流形缓存实现无训练扩散加速
Abstract
Diffusion models for structured scientific generation must produce samples satisfying hard geometric constraints imposed by physics, chemistry, or biology, yet inference in these settings is prohibitively slow, demanding hundreds to thousands of neural-function evaluations per sample. We unify eight state-of-the-art models spanning medical volumetrics, molecular conformations, protein backbone design, crystal structure prediction, and multi-view 3D scenes under a single abstraction, Constraint-Manifold Diffusion Models (CMDMs), in which the target distribution is supported on a manifold defined by an externally specified constraint map. All existing acceleration families fail on this class: quantization exhausts memory on high-dimensional volumetric operators; pruning breaks constraint fidelity; fast ODE solvers allow trajectories to drift off the constraint manifold; and feature-caching heuristics are blind to constraint geometry, inducing mode confusion in the high-noise regime. We introduce ManifoldCache, the first training-free, data-free accelerator designed from first principles for CMDMs. The key insight is that the conditional score decomposes orthogonally into a normal component, which enforces constraint satisfaction, and a tangential component, which navigates within the manifold. Exploiting this structure, we prove that the noise-schedule midpoint is a sharp safe-caching boundary: caching before it incurs provably bounded error, while caching after it guarantees a strictly positive fraction of trajectories suffer mode confusion, a gap that persists up to the boundary. We further prove that deeper network blocks admit provably larger certified cache strides within the safe phase, as a consequence of the score decomposition propagating through block Jacobians. The resulting schedule requires no calibration data, along with zero training overhead.
Chinese Translation
用于结构化科学生成的扩散模型必须生成满足物理、化学或生物学所施加的严格几何约束的样本，但在这些环境中的推理速度极其缓慢，每个样本需要进行数百到数千次神经函数评估。我们将涵盖医学体积测量、分子构象、蛋白质主链设计、晶体结构预测和多视角三维场景的八个最先进模型统一在一个单一抽象框架下，即约束流形扩散模型（Constraint-Manifold Diffusion Models, CMDMs），其中目标分布支持在由外部指定的约束映射定义的流形上。所有现有的加速方法在这一类别中均表现不佳：量化在高维体积算子上耗尽内存；剪枝破坏了约束的保真性；快速常微分方程求解器允许轨迹偏离约束流形；而特征缓存启发式方法对约束几何盲目，导致在高噪声环境中出现模式混淆。我们提出了ManifoldCache，这是第一个从基本原理出发为CMDMs设计的无训练、无数据的加速器。关键的见解是条件评分正交分解为一个法向分量，该分量强制执行约束满足，以及一个切向分量，该分量在流形内导航。利用这一结构，我们证明了噪声调度中点是一个明确的安全缓存边界：在此之前进行缓存会产生可证明的有界误差，而在此之后进行缓存则保证严格正的轨迹比例会遭遇模式混淆，这一差距在边界处仍然存在。我们进一步证明，较深的网络块在安全阶段内允许可证明的更大认证缓存步幅，这是由于评分分解通过块雅可比传播的结果。最终的调度不需要校准数据，并且没有训练开销。
cs.AI / 68 / 2610.04515
Towards Credible Agent-Based Policy Simulations: Disentangling Opportunities and Preferences in a Financial Inclusion Case Study of Egypt
迈向可信的基于代理的政策模拟：在埃及金融包容性案例研究中解构机会与偏好
Abstract
Credibility is a central topic for agent-based models intended to support policy-making. Simulations must not only represent the target scenarios and their core dynamics but also demonstrate that their assumptions, parameters, and outputs are empirically grounded and sufficiently accurate for their intended use. This paper addresses this challenge by presenting a general modelling framework, aligned with the Capability Approach, for building credible policy simulations that rely on data and domain-expert knowledge. It then demonstrates how it can be contextualised and implemented to study the social challenge of financial inclusion in Egypt, building on an agent-based model that represents heterogeneous individuals and firms behaving according to their financial states, barriers, opportunities, and preferences. The model is fitted to real-world data in two stages, initialisation and calibration, which respectively build representative synthetic populations and estimate behavioural parameters. By fixing the feasibility parameters, which determine agents' opportunities, and calibrating preference parameters across different population groups, we are able to distinguish and analyse the role of institutional and social barriers in the system, as well as the role of agents' motivations and priorities. This calibration stage provides transparent and group-specific hypotheses about the drivers of observed financial-inclusion gaps, which can further be analysed as gaps between agents' opportunities and realised outcomes, a very relevant insight for policy-making. This paper is thus a step towards improving the credibility and usefulness of policy simulations, strengthening the relationship between the model, the real target system, and the stakeholders who will use it. The code is available at: \url{https://www.comses.net/codebase-release/df8383cb-f49b-4f09-8cce-73603b59adcc/}.
Chinese Translation
可信性是旨在支持政策制定的基于代理模型的核心主题。模拟不仅必须代表目标情境及其核心动态，还必须证明其假设、参数和输出在经验上是有依据的，并且对于其预期用途足够准确。本文通过提出一个与能力方法（Capability Approach）相一致的一般建模框架，来应对这一挑战，以构建依赖于数据和领域专家知识的可信政策模拟。接着，本文展示了如何将该框架进行情境化和实施，以研究埃及金融包容性的社会挑战，基于一个代表异质个体和企业的基于代理模型，这些个体和企业根据其财务状况、障碍、机会和偏好进行行为。该模型在两个阶段中与真实世界数据进行拟合，分别是初始化和校准，这两个阶段分别构建代表性的合成人口并估计行为参数。通过固定可行性参数（决定代理的机会）并在不同人口组中校准偏好参数，我们能够区分和分析制度和社会障碍在系统中的作用，以及代理的动机和优先事项的作用。这个校准阶段提供了关于观察到的金融包容性差距驱动因素的透明且特定于群体的假设，这些假设可以进一步分析为代理的机会与实现结果之间的差距，这对于政策制定具有重要的启示。因此，本文是朝着提高政策模拟的可信性和实用性迈出的一步，加强模型、真实目标系统和将使用该模型的利益相关者之间的关系。代码可在以下链接获取：\url{https://www.comses.net/codebase-release/df8383cb-f49b-4f09-8cce-73603b59adcc/}。
cs.AI / 69 / 2610.04517
EvoCast: Reliable Autonomous Research Agents for Iterative Forecasting Architecture Evolution
EvoCast：用于迭代预测架构演化的可靠自主研究代理
Abstract
Deep time-series forecasting models have rapidly diversified, yet adapting them to a specific task still requires extensive expert effort in model selection, mechanism diagnosis, architecture design, implementation, and evaluation. Existing AutoML methods are constrained by predefined search spaces, while general-purpose LLM research agents lack reliable control over experimental protocols and model promotion. We introduce EvoCast, a fully autonomous research-agent system for iterative forecasting architecture evolution. EvoCast first establishes and diagnoses a task-specific baseline through executed mechanism ablations, then generates evidence-grounded research directions from dataset characteristics, diagnostic results, prior rounds, and failure records. Its central design, cognition-authority separation, assigns open-ended hypothesis generation and code implementation to LLM agents, while deterministic program authorities control source-edit boundaries, canonical evaluation, and promotion decisions. Experimental outcomes are accumulated as evidence to guide subsequent rounds. Results show that EvoCast completes complex architecture modifications with higher implementation success and lower agent-side token/time cost, and develops task-specific architectures that outperform selected baselines, strong forecasting models, and agent baselines in three real-world forecasting cases. The code is available at https://github.com/18e0-x/EvoCast.
Chinese Translation
深度时间序列预测模型迅速多样化，但将其适应于特定任务仍需在模型选择、机制诊断、架构设计、实施和评估方面进行大量专家努力。现有的自动机器学习（AutoML）方法受到预定义搜索空间的限制，而通用大语言模型（LLM）研究代理在实验协议和模型推广方面缺乏可靠的控制。我们提出了EvoCast，一个完全自主的研究代理系统，用于迭代预测架构演化。EvoCast首先通过执行机制消融建立并诊断任务特定的基线，然后根据数据集特征、诊断结果、先前轮次和失败记录生成基于证据的研究方向。其核心设计，即认知-权威分离，将开放式假设生成和代码实施分配给LLM代理，而确定性程序权威则控制源代码编辑边界、规范评估和推广决策。实验结果作为证据积累，以指导后续轮次。结果表明，EvoCast以更高的实施成功率和更低的代理端令牌/时间成本完成复杂的架构修改，并在三个真实世界的预测案例中开发出超越选定基线、强预测模型和代理基线的任务特定架构。代码可在 https://github.com/18e0-x/EvoCast 获取。
cs.AI / 70 / 2610.04539
Action-Consequence Alignment for Reliable Planning and Self-Improving in Latent World Models
可靠规划与自我改进中的动作-结果对齐在潜在世界模型中的应用
Abstract
Latent world models learn to predict observed transitions, yet low prediction error alone does not guarantee reliable planning. Inspired by self tickling experiments in neuroscience showing that disrupting motor sensory correspondence increases prediction mismatch, we examine whether learned world models preserve an analogous action consequence correspondence.The results show nearby alternatives can receive lower prediction errors despite producing physical outcomes farther from the recorded target. With that future treated as a goal, this reveals a concrete prediction planning mismatch: the model assigns a lower cost to an action that achieves the target less accurately. To mitigate this gap, we introduce Action Consequence Alignment (ACA), a training objective that complements forward prediction by penalizing the prediction error advantage of locally searched alternatives over factual actions without additional model components or environment interactions during training. The same principle can also guide additional data collection for self improvement. We demonstrate that across diverse environments and evaluation settings, ACA improves planning performance and reduces real goal error, while ACA guided data collection outperforms random local sampling. These results support action consequence alignment as a practical principle for bridging predictive learning and reliable planning.
Chinese Translation
潜在世界模型学习预测观察到的转变，但仅靠低预测误差并不能保证可靠的规划。受到神经科学中自我搔痒实验的启发，该实验表明破坏运动感知对应关系会增加预测不匹配，我们研究了学习到的世界模型是否保留了类似的动作结果对应关系。结果显示，尽管产生的物理结果与记录的目标相距较远，但附近的替代方案仍可能获得较低的预测误差。将未来视为目标，这揭示了一个具体的预测规划不匹配：模型对实现目标的准确性较低的动作分配了较低的成本。为弥补这一差距，我们引入了动作结果对齐（Action Consequence Alignment, ACA），这是一种训练目标，通过惩罚局部搜索替代方案相对于事实动作的预测误差优势，来补充前向预测，而无需在训练期间增加额外的模型组件或环境交互。相同的原则也可以指导额外的数据收集以实现自我改进。我们展示了在多样化的环境和评估设置中，ACA提高了规划性能并减少了实际目标误差，而基于ACA指导的数据收集优于随机局部采样。这些结果支持动作结果对齐作为连接预测学习与可靠规划的实用原则。
cs.AI / 71 / 2610.04541
Autonomous Structuring of Radiology Reports Across Modalities at Archive Scale Using an Open-Weight Large Language Model
基于开放权重大型语言模型的跨模态放射学报告自主结构化方法（归档规模）
Puttkammer, Friedrich, Drexel, Fabian, Fritzsche, Marlene, Stambollxhiu, Era, Kumpf, Miriam, Schmitzer, Lena, Schumann, Lea, Xu, Lina, Moll, Johannes, Lübberstedt, Jannik, Chaaben, Zeineb Ben, Narayanan, Anirudh, Häntze, Hartmut, Cuocolo, Renato, Billis, Antonios, Löser, Alexander, Nawabi, Jawed, Makowski, Marcus R., Bercea, Cosmin I., Faghihroohi, Shahrooz, Adams, Lisa C., Bressem, Keno K.
Abstract
Purpose: To develop and evaluate an open-weight large language model (LLM) pipeline that converts an entire archive of free-text radiology reports into structured reports without human oversight. Materials and Methods: In this retrospective study, a pipeline with 150 hierarchically organized templates was developed at one center and tested at a second center on reports from 2010 to 2025. The open-weight model gpt-oss-120B selects the template in three constrained-decoding steps and fills it on one local graphics processing unit. Template selection was scored against expert labels on 914 randomly sampled reports of five modalities, structuring quality on 920 radiography and CT reports corrected field by field by five residents. The pipeline then processed the complete archive of the second center. Proportions are reported with Wilson 95% confidence intervals (CIs). Results: An optimal template set was selected for 74.4% of reports (680 of 914; 95% CI: 71.5%, 77.1%) and an appropriate set for 82.3% (752 of 914; 95% CI: 79.7%, 84.6%), 87.7% for single-region and 54.1% for multi-region reports. Macro semantic textual similarity between output and corrected reference was 0.95 for radiography and 0.97 for CT, residents left 88.7% of 24,638 fields unchanged, and unsupported content was flagged in 1.0% and 1.5% of reports. Of 2,186,982 archive reports, 96.5% received structured output, 2,401,544 structured reports, at 1,258 reports per hour on one graphics processing unit. Conclusion: An open-weight LLM pipeline structured a complete multimodality report archive without human oversight with high content fidelity. Multi-region reports remained the main source of template errors.
Chinese Translation
目的：开发并评估一个开放权重的大型语言模型（LLM）管道，该管道能够在没有人工监督的情况下将整个自由文本放射学报告档案转换为结构化报告。材料与方法：在这项回顾性研究中，开发了一个包含150个层次化模板的管道，并在第二个中心对2010年至2025年的报告进行了测试。开放权重模型gpt-oss-120B在三个受限解码步骤中选择模板，并在一台本地图形处理单元上进行填充。模板选择的评分是基于对914份随机抽样的五种模态报告的专家标签，结构化质量则在920份经过五名住院医生逐字段校正的放射摄影和CT报告中进行评估。随后，该管道处理了第二个中心的完整档案。结果以Wilson 95%置信区间（CIs）报告。结果：为74.4%的报告（914份中的680份；95% CI：71.5%，77.1%）选择了最佳模板集，为82.3%（914份中的752份；95% CI：79.7%，84.6%）选择了适当模板集，单区域报告的比例为87.7%，多区域报告为54.1%。输出与校正参考之间的宏观语义文本相似度为放射摄影0.95，CT为0.97，住院医生在24,638个字段中保留了88.7%不变，1.0%和1.5%的报告中标记了不支持的内容。在2186982份档案报告中，96.5%获得了结构化输出，生成了2401544份结构化报告，处理速度为每小时1258份报告。结论：一个开放权重的LLM管道在没有人工监督的情况下成功结构化了完整的多模态报告档案，且内容保真度高。多区域报告仍然是模板错误的主要来源。
cs.AI / 72 / 2610.04542
Decide, Ask, or Defer: Clinical LLMs under Incomplete Evidence
决策、询问或推迟：不完整证据下的临床大语言模型
Abstract
Clinical LLMs must decide not only what diagnosis to produce, but also whether the available evidence is sufficient for autonomous decision making. Binary DECIDE/ABSTAIN formulations merge distinct non decision states and do not explicitly evaluate information acquisition. We introduce a DECIDE/ASK/DEFER formulation together with a blinded protocol that prevents models from using evidence completeness metadata. We evaluate Qwen, Gemini, and GPT on 200 matched clinical evidence states constructed from DDXPlus. The models show substantial differences in action selection under identical evidence, with disagreement in 137 of 200 states. For Qwen, a matched targeted versus random analysis shows that selected information changes the likelihood of a subsequent autonomous decision more clearly than diagnostic correctness. Its matched DECIDE/ABSTAIN baseline further reveals a safety autonomy tradeoff: the three action policy rescues some erroneous autonomous decisions but also removes some correct autonomous deci sions. These results show that separating information acquisition from clinician deferral exposes behavior that binary abstention hides, without yielding a uniformly improved decision policy.
Chinese Translation
临床大语言模型（Clinical LLMs）不仅需要决定生成何种诊断，还需判断现有证据是否足以进行自主决策。二元的决策/弃权（DECIDE/ABSTAIN）模型将不同的非决策状态合并，并未明确评估信息获取。我们提出了一种决策/询问/推迟（DECIDE/ASK/DEFER）模型，并设计了一种盲法协议，以防止模型使用证据完整性元数据。我们在从DDXPlus构建的200个匹配临床证据状态上评估了Qwen、Gemini和GPT。模型在相同证据下的行动选择显示出显著差异，在200个状态中有137个存在不一致。对于Qwen，匹配的目标与随机分析表明，所选信息比诊断正确性更清晰地改变了后续自主决策的可能性。其匹配的决策/弃权基线进一步揭示了安全性与自主性之间的权衡：三种行动政策拯救了一些错误的自主决策，但也剥夺了一些正确的自主决策。这些结果表明，将信息获取与临床医生的推迟行为分开，可以揭示出二元弃权所隐藏的行为，而不一定会产生统一改进的决策政策。
cs.AI / 73 / 2610.04544
Label Agreement Does Not Measure Authorization
标签一致性并不测量授权
Abstract
Many groups now delegate label ontology and metadata harmonization to agentic LLM pipelines. We built one and audited it. Our aggregate scores looked healthy, but the pipeline kept failing in ways they did not show, so we set out to find what they hid. Label agreement asks whether a proposed label matches a reference. It does not ask whether the agent was entitled to propose it, whether the output was complete enough to act on, or whether the label moved when the evidence moved. We measured those three separately on COBRE and FBIRN, two schizophrenia and control neuroimaging cohorts from different consortia, and they come apart, from label agreement and from each other. Showing the agent an upstream proposal barely moves label agreement, 0.857 to 0.870, while agreement on the chosen action doubles, 0.409 to 0.830. Output that parses as JSON still drops a required field on 10% of one model's cases and 33% of the other's. And an agent that replays its first answer scores perfectly on original cases and zero once we change the evidence that decides them. Downstream, a row-order error that none of these metrics reports erases most of the diagnostic signal. So we measure these properties apart, pair each with a control, and gate commitment on the result, which makes failures visible and easy to route to a person. None of this prevents failure. Our reference labels are rule-derived, so agreement with them means consistency, not correctness. Code is available at https://github.com/amir-sbg/Label-Agreement-Does-Not-Measure-Authorization.
Chinese Translation
许多团体现在将标签本体和元数据的协调工作委托给代理型大型语言模型（LLM）管道。我们构建了一个并进行了审计。我们的综合得分看起来良好，但该管道在未显示的情况下不断失败，因此我们着手寻找它们隐藏的问题。标签一致性询问提议的标签是否与参考标签匹配，但并未询问代理是否有权提出该标签、输出是否足够完整以供操作，或标签是否随着证据的变化而变化。我们在来自不同联盟的两组精神分裂症和对照神经影像学队列COBRE和FBIRN上分别测量了这三项指标，结果显示它们彼此分离，也与标签一致性分离。向代理展示上游提议几乎不会改变标签一致性，从0.857到0.870，而对所选行动的一致性则翻倍，从0.409到0.830。解析为JSON的输出在一个模型的10%案例和另一个模型的33%案例中仍然缺少一个必需字段。而一个重放其第一个答案的代理在原始案例中得分完美，但一旦我们改变决定这些案例的证据，其得分为零。在下游，一个这些指标都未报告的行顺序错误抹去了大部分诊断信号。因此，我们将这些属性分开测量，并与控制组配对，并根据结果进行承诺，这使得失败变得可见并易于指派给某人。所有这些都无法防止失败。我们的参考标签是基于规则推导的，因此与它们的一致性意味着一致性，而非正确性。代码可在 https://github.com/amir-sbg/Label-Agreement-Does-Not-Measure-Authorization 获取。
cs.AI / 74 / 2610.04555
$\mathrm{TRIZ}^{a}$: Guiding Agent Evolution from Pattern Recognition to Solution Invention
$ ext{TRIZ}^{a}$：从模式识别到解决方案发明的引导代理演化
Abstract
We propose $\mathrm{TRIZ}^{a}$ (TRIZ exponentiated by an agent), a general R\&D automation paradigm that combines TRIZ inventive theory with LLM-driven agent evolutionary search. TRIZ's 40 inventive principles and contradiction matrix provide structured, explainable directions for solution generation, replacing random or untyped mutation with theory-guided ideation. Functional information (FI), operationalized under a frozen reference contract, is combined with TRIZ Ideality to measure useful and harmful function on a commensurable information scale, while hard gates keep promotion distinct from metric improvement. We validate $\mathrm{TRIZ}^{a}$ in cybersecurity--an adversarial and rapidly evolving domain--on PowerDuck GOOSE, CICIoT2023, and CIC-DDoS2019. Under paired-rerun protocols with protocol fingerprinting and hard-gate validation, the legacy experiments yield absolute F1 improvements of $+2.88$, $+4.23$, and $+0.15$ percentage points, respectively. A completed 45-activity CICIoT2023 campaign further increases macro-F1 from $0.8325$ to $0.8483$, but does not pass its frozen promotion gate. Every result remains traceable from contradiction identification and TRIZ principle selection to code transformation, evaluation metrics, and promotion decision.
Chinese Translation
我们提出了$ ext{TRIZ}^{a}$（由代理扩展的TRIZ），这是一种将TRIZ创造性理论与基于大语言模型（LLM）的代理演化搜索相结合的通用研发自动化范式。TRIZ的40项创造性原则和矛盾矩阵为解决方案生成提供了结构化、可解释的指导，取代了随机或无类型的变异，采用理论引导的构思。功能信息（FI）在冻结参考合同下进行操作，与TRIZ理想性相结合，以可比信息尺度衡量有用和有害功能，同时硬门控确保推广与度量改进的区分。我们在网络安全这一对抗性和快速演变的领域中验证了$ ext{TRIZ}^{a}$，使用PowerDuck GOOSE、CICIoT2023和CIC-DDoS2019数据集。在使用协议指纹识别和硬门控验证的配对重跑协议下，遗留实验分别实现了绝对F1提升$+2.88$、$+4.23$和$+0.15$个百分点。完成的45项活动CICIoT2023活动进一步将宏观F1从$0.8325$提高到$0.8483$，但未通过其冻结推广门。每个结果都可以追溯，从矛盾识别和TRIZ原则选择到代码转换、评估指标和推广决策。
cs.AI / 75 / 2610.04561
Recursive Improvement of a Differentiable Scientific Software Ecosystem
可微分科学软件生态系统的递归改进
Abstract
Differentiable programming connects scientific computation with gradient-based inference, learning and design. Extending these capabilities across a heterogeneous software ecosystem requires specialized effort to implement derivatives, integrate interfaces and evaluate quality. AI coding agents can accelerate this transformation, but translating their capabilities into useful scientific software requires identifying research needs and evaluating how well implementations meet them. We present an environment for agent-driven evolution of differentiable scientific software that connects demand identification, development and quality evaluation. A unified differentiation interface exposes reusable derivative rules alongside existing numerical routines, allowing research tasks to share these capabilities. Research requirements guide development, with implementations assessed through independent derivative checks, workflow tests and performance evaluation. Validated software, research programs and tests become shared resources for subsequent studies. We construct and validate automatic differentiation extensions across 20 packages spanning physical, chemical and biological modeling, with research workflows demonstrating reuse across tasks. Benchmarks demonstrate computational savings over finite differences in gradient evaluation and complete parameter estimation. Research-driven revisions make previously unsupported workflows differentiable, correct derivatives of scientific observables and eliminate redundant computation. Quantum-control and thermal-design studies revise objectives in response to physical evaluation, improving designs while reusing existing derivatives. This work provides a practical approach to expanding differentiable programming across established scientific software and organizing AI agents around the recursive improvement of a shared computational ecosystem.
Chinese Translation
可微分编程将科学计算与基于梯度的推理、学习和设计连接起来。在异构软件生态系统中扩展这些能力需要专门的努力来实现导数、集成接口和评估质量。人工智能编码代理可以加速这一转型，但将其能力转化为有用的科学软件需要识别研究需求并评估实现的效果。我们提出了一个环境，用于驱动可微分科学软件的代理驱动演化，该环境连接了需求识别、开发和质量评估。统一的微分接口暴露可重用的导数规则与现有的数值例程，使研究任务能够共享这些能力。研究需求指导开发，实施通过独立的导数检查、工作流测试和性能评估进行评估。经过验证的软件、研究程序和测试成为后续研究的共享资源。我们在涵盖物理、化学和生物建模的20个软件包中构建并验证了自动微分扩展，研究工作流展示了任务间的重用。基准测试显示在梯度评估和完整参数估计中，相较于有限差分法具有计算节省。以研究为驱动的修订使以前不支持的工作流变得可微分，纠正科学可观测量的导数并消除冗余计算。量子控制和热设计研究根据物理评估修订目标，在改进设计的同时重用现有导数。本研究提供了一种实用的方法，以在已建立的科学软件中扩展可微分编程，并围绕共享计算生态系统的递归改进组织人工智能代理。
cs.AI / 76 / 2610.04580
Strong Helps Weak: Directional Cross-Modal Alignment Transfer in Multi-modal LLMs
强者帮助弱者：多模态大语言模型中的定向跨模态对齐转移
Abstract
Multi-modal large language models (MLLMs) achieve strong modality understanding by pairing a large language model (LLM) with an encoder for a target modality such as vision, video, or audio. However, improving an MLLM's capability for a given modality typically requires additional training on large modality-specific datasets, incurring substantial data collection and compute costs. Model merging offers an alternative, but it is often infeasible for data-scarce, large per-sample size, or domain-specific modalities (\textit{e.g.}, audio and video), where same-modality model variants are rarely available. In this work, we characterize an intriguing asymmetric phenomenon: merging a well-aligned, data-rich source-modality MLLM into a data-scarce target-modality MLLM substantially improves the target on its own benchmarks. Our theoretical and empirical analyses show that this gain stems from enhanced alignment between modality-specific and textual tokens, induced by the stronger donor modality. Specifically, we derive a mutual-information lower bound that is monotonic in alignment-related quantities and strongly correlated with downstream MLLM performance. Building on this principle, we propose Directional Cross-modal Alignment Transfer (DCAT), a novel framework that transfers textual alignment from a strong, well-aligned source (donor) modality to a weak target (recipient) modality, boosting target-modality performance without further fine-tuning. We further show that the alignment-enhancing objective admits a closed-form weight-space solution computed from only a small calibration set. DCAT outperforms existing model-merging methods, offering an efficient path toward cross-modal alignment transfer. Project page with code is available at \url{https://seohoiki3215.github.io/DCAT_project_page}
Chinese Translation
多模态大语言模型（MLLMs）通过将大型语言模型（LLM）与目标模态的编码器（如视觉、视频或音频）配对，实现了强大的模态理解。然而，提高MLLM在特定模态上的能力通常需要在大型模态特定数据集上进行额外训练，这会产生大量的数据收集和计算成本。模型合并提供了一种替代方案，但对于数据稀缺、每个样本大小较大或领域特定的模态（例如，音频和视频），同模态模型变体通常难以获得。在本研究中，我们描述了一种有趣的非对称现象：将一个对齐良好、数据丰富的源模态MLLM合并到一个数据稀缺的目标模态MLLM中，显著提高了目标在其自身基准上的表现。我们的理论和实证分析表明，这种提升源于模态特定和文本标记之间的对齐增强，这是由更强的捐赠模态引起的。具体而言，我们推导出一个互信息下界，该下界在与对齐相关的量中是单调的，并且与下游MLLM性能高度相关。基于这一原理，我们提出了定向跨模态对齐转移（DCAT），这是一个新颖的框架，它将来自强大且对齐良好的源（捐赠）模态的文本对齐转移到弱目标（接收）模态，从而在不进行进一步微调的情况下提升目标模态的性能。我们进一步表明，增强对齐的目标允许从仅一个小的校准集计算出闭式权重空间解。DCAT在性能上超越了现有的模型合并方法，为跨模态对齐转移提供了一条高效的路径。项目页面及代码可在 https://seohoiki3215.github.io/DCAT_project_page 获取。
cs.AI / 77 / 2610.04592
Bounds, Decompositions and Null Behaviour of KRATOS: A Mathematical Specification of a Recognition-Comparability Diagnostic
KRATOS的界限、分解与无效行为：一种识别-可比性诊断的数学规范
Abstract
KRATOS is a group-structured bibliometric diagnostic that compares the distribution of documents (participation) with the distribution of citation weight (recognition) over a fixed, finite universe of analytical groups. This note gives its complete mathematical specification and derives the properties that govern its interpretation. Beyond bounds and equality conditions for each component, we show that the recognition-alignment score depends on citation data only through ratios of group mean citation rates to the corpus mean; that the composition ceiling of the participation--recognition factor is an affine function of the total variation distance to the uniform reference; and that the factor itself satisfies Fr\'echet-type bounds in terms of this ceiling and a participation-weighted recognition score. We establish an exact logarithmic decomposition of the composite index, an ordering between the primary and a reciprocal-symmetric recognition score, invariance properties, and closed-form first and second moments of the recognition ratios under global and stratified permutation nulls. These results explain why composite orderings can be sensitive to small groups, heavy-tailed citation distributions and metadata reassignment. All results are verified numerically with an accompanying script. The specification concerns measurement structure only; it does not define a measure of epistemic change or justice.
Chinese Translation
KRATOS是一个结构化的文献计量诊断工具，它在固定的有限分析组宇宙中比较文档的分布（参与）与引用权重的分布（识别）。本文提供了其完整的数学规范，并推导出支配其解释的性质。除了每个组成部分的界限和等式条件外，我们还表明，识别对齐得分仅通过组平均引用率与语料库平均引用率的比率依赖于引用数据；参与-识别因子的组成上限是与均匀参考的总变差距离的仿射函数；该因子本身在此上限和参与加权识别得分的条件下满足Fréchet型界限。我们建立了复合指数的精确对数分解，主要识别得分与其倒数对称识别得分之间的排序，不变性特性，以及在全局和分层置换无效情况下识别比率的封闭形式的一阶和二阶矩。这些结果解释了为什么复合排序对小组、重尾引用分布和元数据重新分配可能敏感。所有结果均通过附带脚本进行数值验证。该规范仅涉及测量结构；它并不定义认识变化或公正的度量。
cs.AI / 78 / 2610.04635
LatentIndex: Cross-Layer Sharing with Layer-Specific Selection for Sparse Attention
LatentIndex：具有层特定选择的跨层共享稀疏注意力
Abstract
Sparse attention reduces core-attention computation, but its indexers still incur repeated selection work and per-layer key-cache storage. Reusing selected indices across layers reduces this overhead but constrains multiple layers to the same token set. We introduce LatentIndex, which extends the latent-sharing principle of Multi-head Latent Attention across indexer layers. Each layer group constructs a shared latent cache from its first layer's hidden states, while layer-specific scoring enables independent token selection. Absorbing key decoders into queries enables direct scoring of the shared cache without reconstructing historical per-layer keys. We develop training-free calibration and investigate a training-aware instantiation of this principle. To balance quality and computation, a hierarchical selection (HS) variant lets followers independently refine a shared candidate set proposed by the anchor. With four-layer sharing, LatentIndex reduces logical indexer-cache storage by 61.1% on DeepSeek-V3.2. Across DeepSeek-V3.2 and GLM-5, training-free LatentIndex improves head-wise attention-mass recall over IndexCache by up to 3.28 percentage points while maintaining RULER and LongBench performance close to native DSA. HS further achieves 2.30-2.72 times decode indexer speedups over DSA across 8K-128K contexts, retaining most of LatentIndex's recall. LatentIndex offers a new perspective on cross-layer indexing: sharing continuous representations rather than discrete selections enables efficient reuse while preserving layer-specific token selection.
Chinese Translation
稀疏注意力减少了核心注意力计算，但其索引器仍然会产生重复选择工作和每层的键缓存存储。在层之间重用选定的索引可以减少这种开销，但会限制多个层使用相同的令牌集。我们提出了LatentIndex，它扩展了多头潜在注意力在索引器层之间的潜在共享原则。每个层组从其第一层的隐藏状态构建一个共享的潜在缓存，而层特定的评分使得独立的令牌选择成为可能。将键解码器吸收到查询中，使得可以直接对共享缓存进行评分，而无需重建历史的每层键。我们开发了无训练的校准方法，并研究了这一原则的有训练实例。为了平衡质量和计算，层次选择（HS）变体允许跟随者独立地优化由锚点提出的共享候选集。在四层共享的情况下，LatentIndex在DeepSeek-V3.2上将逻辑索引器缓存存储减少了61.1%。在DeepSeek-V3.2和GLM-5上，无训练的LatentIndex在头级注意力质量回忆上比IndexCache提高了最多3.28个百分点，同时保持RULER和LongBench的性能接近原生DSA。HS在8K-128K上下文中进一步实现了2.30-2.72倍的解码索引器速度提升，保留了LatentIndex大部分的回忆。LatentIndex为跨层索引提供了新的视角：共享连续表示而非离散选择能够实现高效重用，同时保留层特定的令牌选择。
cs.AI / 79 / 2610.04646
SEIS: Self-Evolving Inference Systems
SEIS：自我进化推理系统
Abstract
Inference systems determine how fast and how cheaply language models can be served, so making them faster has direct practical value. However, prior work focuses mostly on optimizing certain parts such as kernels or memory within the large system. In this work, we take a holistic approach and apply agentic self-evolution to optimize the whole system end-to-end. Our SEIS (Self-Evolving Inference Systems) autonomously optimizes the entire mini-sglang engine without human intervention through iterative sessions with inherited experiences and code changes. Serving Qwen3-0.6B on H100, the resulting engine reaches 3.27X the throughput of the original mini-sglang implementation and beats SOTA engines like vLLM, TensorRT-LLM, and SGLang in the single-request workload. The correctness of the optimized inference engine by SEIS is tested in terms of numerical difference and downstream accuracy on math and long-context retrieval tasks. The code and session histories show that the speedup comes from redesigning the whole engine and that building on earlier sessions beats independent attempts. These results suggest that agentic self-evolution can optimize a complex system end-to-end. The evaluation also has to evolve with the engine, and letting agents evolve it is a natural next step.
Chinese Translation
推理系统决定了语言模型的服务速度和成本，因此提高其速度具有直接的实际价值。然而，之前的研究主要集中在优化大型系统中的某些部分，如内核或内存。在本研究中，我们采取了整体方法，应用自主自我进化来优化整个系统的端到端性能。我们的SEIS（Self-Evolving Inference Systems）通过继承经验和代码更改的迭代会话，自动优化整个mini-sglang引擎，无需人工干预。在H100上服务Qwen3-0.6B时，最终的引擎达到了原始mini-sglang实现的3.27倍吞吐量，并在单请求工作负载中超越了SOTA引擎，如vLLM、TensorRT-LLM和SGLang。通过数值差异和数学及长上下文检索任务的下游准确性测试了SEIS优化的推理引擎的正确性。代码和会话历史表明，速度提升来自于对整个引擎的重新设计，并且基于早期会话的构建优于独立尝试。这些结果表明，自主自我进化可以端到端地优化复杂系统。评估也必须随着引擎的发展而演变，让代理进行演变是一个自然的下一步。
cs.AI / 80 / 2610.04659
PermVLA: Factorization Order as a Regularizer for VLA Learning
PermVLA：因子化顺序作为VLA学习的正则化器
Abstract
Vision-language-action (VLA) policies commonly learn action chunks through a fixed left-to-right (LTR) factorization, although the same expert trajectory distribution admits many valid chain-rule factorizations. We identify factorization order as an overlooked regularization choice and introduce causally anchored permutation (CAP), which samples action reveal orders with a tunable chronological prefix. Its auxiliary objective trains one shared policy to predict actions from different known subsets of the same expert chunk, while deployment retains deterministic LTR control. We call this conditional-set augmentation: it creates multiple conditional prediction problems from one expert chunk without adding demonstrations. This discourages reliance on the single chronological prefix used by ordinary teacher forcing. Controlled experiments show that CAP consistently outperforms standard LTR training on LIBERO and LIBERO-Plus, with the same advantage appearing in cross-dataset CALVIN evaluation. A diagnostic that measures the expected squared difference between a chunk's joint log likelihood under two reveal orders verifies that CAP training internalizes agreement across reveal orders. These findings position sampled subset-conditioned auxiliary objectives as a general recipe for constructing VLA regularizers, illustrated by an extension to diffusion action generators.
Chinese Translation
视觉-语言-动作（VLA）策略通常通过固定的从左到右（LTR）因子化学习动作块，尽管相同的专家轨迹分布允许许多有效的链式规则因子化。我们将因子化顺序识别为一个被忽视的正则化选择，并引入因果锚定排列（CAP），它通过可调的时间前缀采样动作揭示顺序。其辅助目标训练一个共享策略，从同一专家块的不同已知子集预测动作，而部署保持确定性的LTR控制。我们称之为条件集增强：它从一个专家块创建多个条件预测问题，而无需增加演示。这抑制了对普通教师强制所使用的单一时间前缀的依赖。控制实验表明，CAP在LIBERO和LIBERO-Plus上始终优于标准LTR训练，在跨数据集的CALVIN评估中也显示出相同的优势。一个诊断测量在两个揭示顺序下块的联合对数似然的期望平方差，验证了CAP训练内化了揭示顺序之间的一致性。这些发现将采样子集条件辅助目标定位为构建VLA正则化器的一般方法，通过扩展到扩散动作生成器进行说明。
cs.AI / 81 / 2610.04665
Efficient Neural Surrogates for Linear Radiation Transport on the Lattice and Hohlraum benchmarks
高效神经代理用于晶格和霍尔腔基准的线性辐射传输
Abstract
Linear radiation transport equations (RTEs) form the simulation foundations underpinning design and analysis tasks in nuclear engineering, inertial confinement fusion, medical imaging, and astrophysics, but resolving the high-dimensional phase space at engineering fidelity remains expensive enough that outer-loop workflows, such as design optimization, uncertainty quantification, and parameter sweeps, are routinely budget-bound on traditional solvers. Neural surrogates promise to relax this bottleneck by amortizing simulation cost across thousands of downstream queries, but the architectural choices and engineered inductive biases that make a surrogate accurate on one transport problem do not transfer straightforwardly across model families. We benchmark two parameter-matched neural surrogate architectures, the physics-attention Transolver and the multi-scale graph network Bi-Stride Multi-Scale MeshGraphNet (BSMS-MGN), as end-to-end approximations of the final-time particle concentration for the two-dimensional linear RTE on the canonical Lattice and Hohlraum benchmarks. An ablation across Fourier features and region-weighted training loss exposes strongly architecture-dependent inductive-bias preferences, indicating that design choices common to physics-informed surrogate workflows must be revisited per architecture rather than imported across model families, and that downstream utility depends on per-QoI sensitivity rather than a single field-level score. The model training recipe, training data, and evaluation pipeline are released alongside this paper to support reproduction, transfer to related transport problems, and evaluation as amortized forward-model components in larger outer-loop simulation workflows.
Chinese Translation
线性辐射传输方程（RTEs）构成了核工程、惯性约束聚变、医学成像和天体物理学中设计和分析任务的模拟基础，但在工程精度下解决高维相空间仍然足够昂贵，以至于外部循环工作流（如设计优化、不确定性量化和参数扫描）在传统求解器中常常受到预算限制。神经代理有望通过在数千个下游查询中摊销模拟成本来缓解这一瓶颈，但使代理在一个传输问题上准确的架构选择和工程归纳偏差并不能简单地跨模型家族转移。我们基准测试了两种参数匹配的神经代理架构，即物理注意力Transolver和多尺度图网络Bi-Stride Multi-Scale MeshGraphNet（BSMS-MGN），作为二维线性RTE在经典晶格和霍尔腔基准上最终时间粒子浓度的端到端近似。对傅里叶特征和区域加权训练损失的消融实验揭示了强烈依赖于架构的归纳偏差偏好，表明物理信息代理工作流中常见的设计选择必须针对每种架构重新审视，而不是跨模型家族导入，并且下游效用依赖于每个关键性能指标的敏感性，而不是单一的场级评分。与本文一起发布的模型训练配方、训练数据和评估流程旨在支持再现、转移到相关传输问题，以及作为更大外部循环模拟工作流中的摊销前向模型组件进行评估。
cs.AI / 82 / 2610.04670
A Tropical Geometry View of Forgetting: A Per-Unit Projector for Knowledge-Preserving Fine-Tuning
遗忘的热带几何视角：一种知识保留微调的单位投影器
Abstract
Fine-tuning a language model on new text degrades what it already does. Replay-free projectors such as Adam-NSCL and GPM forbid one shared subspace of a layer's inputs in every row of the update. The tropical geometry of a ReLU layer shows why this is too coarse. In data space, the units' walls are tropical hypersurfaces whose cells are dual to the upper vertices of a zonotope; in weight space, each old token is a hyperplane, and the tokens cut out a polyhedron, the closure of the weights that keep every token on its side. An exact identity joins the two pictures: the squared change of the layer's output under any weight change splits into in-cell, open-to-closed and closed-to-open terms, and the first two live on the tokens each unit fires on. The identity names a gate-aware per-unit projector, and a budget-separation theorem prices exact protection: it costs a unit the rank of its own open tokens, while a shared subspace pays at least the rank of their union in every row. On OPT-1.3b, where 96% of (token, unit) pairs are closed, the projector forgets less than Adam-NSCL at all six matched budgets from 9 to 60 constrained directions per row, the gap widening from $1.1\times$ to $4.3\times$; with 1/5.5 of the directions it halves the forgetting of Adam-NSCL at GPM's energy threshold. On OPT-6.7b, it matches Adam-NSCL's forgetting at matched budget while learning more. As the theory predicts, the open/closed partition is the operative variable: open tokens beat random, sign-blind and anti-gate token sets on 18 of 18 seed-pairs. In pruning repair, every derivative-based local model of the output error at the dense weights is blind to pairs that open: the minimisers of the gate-weighted objective can leave the polyhedron, the objective's closed-form solution is 1.94 nats worse than no repair on OPT-1.3b, and a convex one-sided penalty bounds the escape.
Chinese Translation
在新文本上微调语言模型会降低其已有的性能。无重放投影器如 Adam-NSCL 和 GPM 禁止在更新的每一行中共享层输入的一个子空间。ReLU 层的热带几何揭示了这一点过于粗糙。在数据空间中，单元的边界是热带超曲面，其单元与区域体的上顶点对偶；在权重空间中，每个旧标记是一个超平面，标记切割出一个多面体，即保持每个标记在其一侧的权重的闭包。一个精确的恒等式将这两种图景联系起来：层输出在任何权重变化下的平方变化分为在单元内、开放到闭合和闭合到开放的项，前两者存在于每个单元激活的标记上。该恒等式命名为门感知的单位投影器，而预算分离定理为精确保护定价：一个单元的成本是其自身开放标记的秩，而共享子空间在每一行至少支付其并集的秩。在 OPT-1.3b 上，96% 的（标记，单元）对是闭合的，该投影器在从 9 到 60 个受限方向的六个匹配预算中比 Adam-NSCL 的遗忘少，差距从 $1.1 imes$ 扩大到 $4.3 imes$；在 1/5.5 的方向下，它在 GPM 的能量阈值下将 Adam-NSCL 的遗忘减半。在 OPT-6.7b 上，它在匹配预算下与 Adam-NSCL 的遗忘相匹配，同时学习更多。正如理论所预测的，开放/闭合分区是操作变量：开放标记在 18 对种子中胜过随机、无符号盲和反门标记集。在修剪修复中，基于每个密集权重的输出误差的每个导数基础局部模型对开放的对是盲目的：门加权目标的最小化者可以离开多面体，目标的闭式解比在 OPT-1.3b 上无修复差 1.94 nats，而一个凸的一侧惩罚限制了逃逸。
cs.AI / 83 / 2610.04672
MASBench: Benchmarking LLM-based Multi-Agent Collaboration under Partial Observability
MASBench：在部分可观察性下基于LLM的多智能体协作基准测试
Abstract
Large language models (LLMs) have progressively evolved into the core of autonomous agents. Building on this progress, LLM-based multi-agent systems (MAS) coordinate multiple agents into a synergistic team to accomplish complex tasks that exceed the capabilities of individual agents. The effectiveness of such systems depends not only on the agents themselves, but also on how collaboration mechanisms are designed and organized. Note that real-world collaboration is typically partially observable, where each agent can only access partial information about the environment due to physical or privacy-related constraints. However, many existing multi-agent benchmarks assume global observability, and leave limited support for systematically evaluating collaboration mechanisms. To bridge this gap, we introduce MASBench, a multi-agent collaboration benchmark designed under partially observable constraints. It is organized into three progressive task categories: Reasoning, Scheduling, and Game. Through this structure, we progressively evaluate three representative collaboration mechanisms: Protocol, Memory, and Routing. MASBench further provides deterministic evaluation metrics, including performance score, communication cost, and cost effectiveness, to characterize both collaboration outcomes and communication overhead. Experiments across diverse LLM backbones and mechanism configurations offer empirical guidance for effective MAS design. Code is available at: https://github.com/BUPT-GAMMA/MASBench
Chinese Translation
大型语言模型（LLMs）逐渐演变为自主智能体的核心。基于这一进展，基于LLM的多智能体系统（MAS）将多个智能体协调成一个协同团队，以完成超出单个智能体能力的复杂任务。这类系统的有效性不仅依赖于智能体本身，还取决于协作机制的设计和组织。需要注意的是，现实世界中的协作通常是部分可观察的，每个智能体由于物理或隐私相关的限制只能访问环境的部分信息。然而，许多现有的多智能体基准假设全局可观察性，并对系统评估协作机制的支持有限。为了解决这一问题，我们引入了MASBench，这是一个在部分可观察约束下设计的多智能体协作基准。它分为三个渐进的任务类别：推理、调度和游戏。通过这一结构，我们逐步评估三种代表性的协作机制：协议（Protocol）、记忆（Memory）和路由（Routing）。MASBench还提供了确定性的评估指标，包括性能得分、通信成本和成本效益，以表征协作结果和通信开销。针对不同的LLM基础模型和机制配置的实验为有效的MAS设计提供了实证指导。代码可在以下链接获取：https://github.com/BUPT-GAMMA/MASBench
cs.AI / 84 / 2610.04686
When Debate Helps: Proposal Supply and Verification-Aware Readout in Multi-Agent Reasoning
辩论何时有助：多智能体推理中的提案供给与验证意识读出
Abstract
Multi-agent debate can improve reasoning, yet often fails to beat simple majority voting. We argue that successful debate requires two distinct mechanisms: proposal supply must surface a correct answer, and readout must identify that answer when voting misses it. We formalize the first requirement through recoverable headroom, which measures cases where a correct proposal is available but the majority answer is wrong. For the second, we develop Latent Verification Debate (LVD), an accounting model in which candidate proposals receive answer-specific verification evidence before final generation. Controlled fixed-proposal interventions estimate this latent effect in equivalent peer-support units and show that correct evidence changes answer probabilities and generated decisions while proposal supply remains fixed. To improve proposal supply, we construct societies from neural-thicket agents using labeled and label-free coverage objectives. Across two backbones and matched-budget reasoning benchmarks, coverage-selected societies increase complementary proposal supply and improve aggregate accuracy in repeated stochastic evaluations. Round-level controls further show that interaction provides gains beyond applying the same finalizer directly to the initial proposals. These results identify proposal coverage and truth-sensitive evidence use as complementary conditions for debate to outperform voting. Code is available at https://github.com/Wang-ML-Lab/when-debate-helps.
Chinese Translation
多智能体辩论可以改善推理，但往往无法超越简单的多数投票。我们认为，成功的辩论需要两个不同的机制：提案供给必须提出一个正确的答案，而读出必须在投票未能识别该答案时能够识别它。我们通过可恢复的余量形式化第一个要求，该余量衡量在正确提案可用但多数答案错误的情况下的情况。对于第二个要求，我们开发了潜在验证辩论（Latent Verification Debate, LVD），这是一种会计模型，其中候选提案在最终生成之前会收到特定答案的验证证据。控制固定提案的干预估计了这一潜在效应在等效的同行支持单位中，并显示正确的证据改变了答案概率和生成的决策，同时提案供给保持不变。为了改善提案供给，我们利用带标签和无标签覆盖目标构建神经丛集体。在两个基础架构和匹配预算的推理基准测试中，覆盖选择的集体增加了互补的提案供给，并在重复的随机评估中提高了整体准确性。轮次级控制进一步表明，互动提供的收益超出了直接将相同的最终化器应用于初始提案。这些结果确定了提案覆盖和对真相敏感的证据使用作为辩论超越投票的互补条件。代码可在 https://github.com/Wang-ML-Lab/when-debate-helps 获取。
cs.AI / 85 / 2610.04691
RAGStress: A controlled benchmark for evaluating retrieval-augmented generation under knowledge-base degradation
RAGStress：评估知识库退化下检索增强生成的受控基准
Abstract
Retrieval-Augmented Generation (RAG) is typically evaluated under the implicit assumption that the underlying knowledge base (KB) is clean, leaving the behaviour of RAG systems under realistic KB degradation poorly characterised. We introduce RAGStress, a controlled evaluation benchmark for stress-testing RAG systems under systematic KB corruption. The benchmark pairs four naturalistic corruption types (factual corruption, numeric typo, relevance poisoning, and contradiction injection) with three severity levels (subtle, moderate, and obvious) over a single-KB, metadata-filtered experimental design built from 57 MMLU subjects and 182,546 documents. Across 52,500 model-question-condition evaluations, RAGStress reveals that clean retrieval can mask robustness differences, semantic-fidelity corruptions are substantially more harmful than signal-utility perturbations, no-retrieval accuracy does not predict corrupted-retrieval robustness, and mixed-KB accuracy should not be treated as worst-case robustness. We document the benchmark's intended use, supported claims, and limitations, and provide an artifact bundle including generation scripts, corruption prompts, metadata schema, and evaluation code. RAGStress is intended as a controlled stress test for RAG robustness under KB corruption, not as a general model leaderboard.
Chinese Translation
检索增强生成（RAG）通常在隐含假设下进行评估，即基础知识库（KB）是干净的，这使得在现实的知识库退化情况下RAG系统的行为尚未得到充分表征。我们引入了RAGStress，这是一个用于在系统性知识库损坏下对RAG系统进行压力测试的受控评估基准。该基准将四种自然主义的损坏类型（事实损坏、数字错误、相关性中毒和矛盾注入）与三种严重程度（细微、中等和明显）相结合，构建了一个基于57个MMLU主题和182,546个文档的单一知识库、元数据过滤的实验设计。在52,500次模型-问题-条件评估中，RAGStress揭示了干净检索可能掩盖鲁棒性差异，语义保真度损坏比信号效用扰动更具危害性，无检索准确率无法预测损坏检索的鲁棒性，混合知识库准确率不应被视为最坏情况的鲁棒性。我们记录了该基准的预期用途、支持的声明和局限性，并提供了一个包含生成脚本、损坏提示、元数据架构和评估代码的文档包。RAGStress旨在作为对RAG在知识库损坏下鲁棒性的受控压力测试，而非一般模型排行榜。
cs.AI / 86 / 2610.04693
Penumbra: Sample-Efficient Adversarial Search for Regulatory Obligations
Penumbra：针对监管义务的样本高效对抗性搜索
Abstract
Agents are entering finance, healthcare and law, sectors where a violation leaves no lexical signature and carries real penalties. Whether an omission is material, or a disclosure sufficient, depends on what the response left out. Probing such an obligation means finding responses one minimal edit from flipping compliance, and every probe costs a generation and two adjudications, so the binding constraint on regulatory red-teaming is sample efficiency, not volume. We introduce Penumbra, an adversarial search that walks from a verified anchor under an expanding edit budget until a two-evaluator committee changes its verdict, and emits the two adjacent responses that straddle the change. Allocation is adaptive, and the objective is coverage of the defeat surface: distinct (obligation x defeat mode) cells resolved per candidate. At matched budget, adaptive allocation reaches uniform allocation's full-budget coverage on 59% of the candidates; at equal records it covers 1.43x the defeat modes of naive enumeration, and the gain is confined to the axis it targets. On 60 screened obligations of a financial advisory constitution, Penumbra returns 144 pairs, each a compliant and a violating response that the committee places on opposite sides of the boundary, differing by a handful of words where a model asked for both directly produces texts sharing almost nothing. A second constitution, for clinical triage, reproduces this on 18 obligations: 49 pairs at the same tightness, with the same modes hardest. Pairs like these show where an obligation's own terms stop deciding, which is what an agent deployed under it must be tested against, and the search finds them at a cost that scales with the boundary, not the text.
Chinese Translation
代理正在进入金融、医疗和法律等领域，这些领域的违规行为没有词汇签名，并且会带来实际的惩罚。某一遗漏是否具有实质性，或某一披露是否足够，取决于响应中遗漏了什么。探测这种义务意味着找到与合规性相反的响应，这需要进行最小编辑，而每次探测的成本是一次生成和两次裁决，因此监管红队的约束条件是样本效率，而非数量。我们引入了Penumbra，这是一种对抗性搜索，从一个经过验证的锚点出发，在扩展的编辑预算下行走，直到一个由两个评估者组成的委员会改变其裁决，并发出两个相邻的响应，这两个响应跨越了这一变化。分配是自适应的，目标是覆盖失败表面：每个候选项解决的不同（义务 x 失败模式）单元。在匹配预算下，自适应分配在59%的候选项上达到了均匀分配的全预算覆盖；在相同记录下，它覆盖了1.43倍于天真枚举的失败模式，而增益仅限于其目标轴。在60个筛选的金融咨询宪法义务中，Penumbra返回了144对，每对都是一个合规响应和一个违规响应，委员会将其放置在边界的两侧，二者仅在几个词上有所不同，而模型要求的两者直接生成的文本几乎没有共同之处。第二个宪法，针对临床分诊，在18个义务上再现了这一点：49对在同样的紧凑性下，具有相同的最难模式。这些对展示了义务自身条款停止决定的地方，这正是部署在其下的代理必须进行测试的内容，而搜索以与边界规模相符的成本找到这些对，而非文本。
cs.AI / 87 / 2610.04699
Not Self-Decidable: LLMs Cannot Draw the Boundary of What an Agent Verifier Can Check
不可自我判定：大型语言模型无法划定代理验证者可以检查的边界
Abstract
A verifier for an agent faces rules of two kinds: the ones a fixed check can settle and the ones that require a judge. A team that derives its own checks fixes that split up front. Where the requirements come from outside, as in finance, healthcare and law, the agent enforces rules it did not write, so the split falls to runtime, recurring for every predicate of every rule on every action at a rate no reviewer can audit. Every escalation scheme assumes a model can make that decision itself, that it is self-decidable. Across six corpora, including the EU AI Act, FINRA guidance and a deployed credit agent, we collect roughly 22,000 labels from four models built by three labs. They agree almost perfectly where the answer is obvious and collapse on regulatory text; their errors run in opposite directions, so no model can be trusted as the conservative choice; and on the deployed agent's own rule-set they err together, over-claiming that a fixed check will do, the direction that never gets escalated. We introduce CoVer (corroborate-then-verify), which treats unanimity as a nomination, admitting a predicate only when the check synthesized for it survives intervention, reading fields the agent cannot write and holding under deterministic rewording. That gate rejects most of what corroboration wrongly admits, at a cost in coverage we report rather than tune away. The obvious alternative, agreement with a reference judge, certifies nothing: it climbs from 30% to 77% across calibration bands while the genuinely decidable share does not move, because a judge drawn from the population under indictment ratifies the blind spot it shares. Self-decidability is not a capability to elicit from a model but a boundary the verifier must construct.
Chinese Translation
代理的验证者面临两类规则：一类是固定检查可以解决的规则，另一类则需要法官来裁定。一个团队如果自行制定检查规则，则会提前确定这种划分。当要求来自外部时，例如在金融、医疗和法律领域，代理执行的是其未编写的规则，因此这种划分将在运行时发生，对于每个规则的每个谓词和每个动作，这种情况都会反复出现，且没有审查者能够审计其频率。每种升级方案都假设模型可以自行做出该决定，即它是自我判定的。在包括欧盟人工智能法案、FINRA指导和已部署的信用代理在内的六个语料库中，我们从三家实验室构建的四个模型中收集了大约22,000个标签。它们在答案显而易见的情况下几乎完全一致，并在监管文本上出现崩溃；它们的错误方向相反，因此没有模型可以被信任为保守选择；而在已部署代理自己的规则集上，它们共同犯错，过度声称固定检查足以应对，这是从未被升级的方向。我们引入了CoVer（先证实后验证），它将一致性视为提名，仅在为其合成的检查经受干预后存活时才接受一个谓词，读取代理无法编写的字段，并在确定性重述下保持有效。该门拒绝了大多数错误承认的证实内容，尽管这在覆盖率上造成了成本，而我们报告这一点而不是调优。显而易见的替代方案，与参考法官的一致性，并不证明任何事情：在校准区间内，它的准确率从30%上升到77%，而真正可判定的比例并未变化，因为来自被指控人群的法官确认了其共享的盲点。自我判定并不是从模型中引出的能力，而是验证者必须构建的边界。
cs.AI / 88 / 2610.04719
Learning to Clarify Underspecified Intents Under Limited Interaction
在有限交互下学习澄清不明确的意图
Abstract
AI assistants receive requests that leave out information needed for a good outcome, for example about users' preferences or goals. They must then either speculate or ask for more information before proceeding. We reconceptualize this as a value-of-information problem: the assistant should acquire information whose absence causes the greatest avoidable loss in user utility. This is rarely known ex ante; rather, assistants must predict it in order to optimally allocate limited user interactions. We instantiate this problem in image generation and derive a reinforcement learning framework using multi-turn simulated users to maximize utility recovery under uncertainty. In a preregistered study with 456 interactive sessions across 76 human participants, this helped users significantly better match reference images with significantly fewer questions, less total interaction time, and lower cost. This points toward a simple and scalable framework for training language model assistants to better disambiguate user intent by asking more informative questions.
Chinese Translation
人工智能助手接收的请求往往缺少实现良好结果所需的信息，例如用户的偏好或目标。在这种情况下，它们必须进行推测或请求更多信息才能继续处理。我们将此重新概念化为信息价值问题：助手应获取那些缺失会导致用户效用最大可避免损失的信息。这通常在事前并不明确；相反，助手必须进行预测，以便在有限的用户交互中进行最佳分配。我们在图像生成中实例化了这个问题，并推导出一个强化学习框架，利用多轮模拟用户在不确定性下最大化效用恢复。在一项预注册的研究中，涉及456个交互会话和76名参与者，这一方法帮助用户显著更好地匹配参考图像，同时显著减少了提问次数、总交互时间和成本。这为训练语言模型助手提供了一个简单且可扩展的框架，以通过提出更具信息性的问题来更好地消除用户意图的模糊性。
cs.AI / 89 / 2610.04737
PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution
PyINE：一个可扩展的通过代码执行进行引导和监督的框架
Abstract
Reasoning models can remain capable of solving a task while still defaulting to cheaper but misleading shortcuts. This creates a central oversight problem: when a model gives an answer with plausible but incomplete reasoning, can an overseer determine whether that output should be trusted? To study this problem, we introduce PyINE, a framework for scalable elicitation and oversight using instrumented Python programs as a verifiable execution substrate. In PyINE, programs define task environments, execution traces provide authoritative labels for outcomes and intermediate facts, and task variants can be generated mechanically rather than through static human annotation. We instantiate the framework in PyINE-v1, a first release built from nearly one million deterministic execution traces and over 500,000 matched LLM-generated code variants used for counterfactual evaluation. Using standard RL with verifiable rewards on cue-varied tasks, we train a shortcut-following model that improves substantially at predicting execution outcomes while still making systematic errors when misleading human-facing cues conflict with the program's realized behavior. We then evaluate activation probes, trained text classifiers, prompted judges, and a lightweight debate protocol as overseers of this model. We find that performance pooled at the dataset level can hide weak coverage of the failures that matter most: cheap learned overseers often miss rare shortcut-driven errors, while stronger model-based checks are more balanced but substantially costlier and harder to turn into reliable thresholded decisions. PyINE-v1 turns this failure-mode coverage problem into a reusable experimental setting for developing oversight methods that are verifiable, failure-mode-aware, and cost-sensitive.
Chinese Translation
推理模型在解决任务时可能仍然能够保持能力，但却常常依赖于更便宜但具有误导性的捷径。这就产生了一个核心的监督问题：当模型给出一个看似合理但不完整的推理答案时，监督者能否判断该输出是否值得信任？为研究这个问题，我们引入了PyINE，一个使用被仪器化的Python程序作为可验证执行基础的可扩展引导和监督框架。在PyINE中，程序定义任务环境，执行轨迹为结果和中间事实提供权威标签，任务变体可以通过机械生成而非静态人工标注来创建。我们在PyINE-v1中实例化了该框架，这是一个基于近一百万个确定性执行轨迹和超过500,000个匹配的LLM生成代码变体（用于反事实评估）的首次发布。通过在具有可验证奖励的标准强化学习（RL）上训练一个跟随捷径的模型，我们显著提高了其预测执行结果的能力，同时在误导人类提示与程序实际行为冲突时仍然出现系统性错误。随后，我们评估了激活探针、训练文本分类器、提示法官和轻量级辩论协议作为该模型的监督者。我们发现，在数据集层面汇总的性能可能掩盖了对最重要失败的薄弱覆盖：便宜的学习监督者往往会遗漏罕见的捷径驱动错误，而更强的基于模型的检查则更加平衡，但成本显著更高且更难以转化为可靠的阈值决策。PyINE-v1将这一失败模式覆盖问题转变为一个可重用的实验设置，以开发可验证、关注失败模式和成本敏感的监督方法。
cs.AI / 90 / 2610.04740
Toward a Locally Deployable Agentic Co-Scientist: Small-Model Planning for Early-Stage Drug Discovery
朝着可本地部署的自主共科学家迈进：早期药物发现的小模型规划
Abstract
Early-stage computational drug discovery requires coordinating heterogeneous scientific tools across multi-step workflows. We present a lightweight, tool-augmented framework in which a locally deployable compact language model plans calls to 18 modular tools. A Unified Molecular Schema maintains shared molecular records, while a plug-in interface supports tool replacement and extension. We construct 1,263 manually refined query-plan pairs through workflow-graph path coverage and apply LoRA fine-tuning to three compact model families. Under the query-level split, all fine-tuned models generate fully parseable and schema-compliant plans on 47 held-out cross-group queries. Llama 3.2-3B achieves a tool-selection F1 of 0.998, sequence exact match of 0.979, and argument F1 of 0.960. Under the stricter workflow-grouped split, which excludes identical ordered tool sequences across partitions, sequence exact match reaches 0.452 to 0.548, highlighting the remaining difficulty for compact models in generating complete workflow paths unseen during training. These results demonstrate the feasibility of compact, locally deployable planning while identifying compositional generalization as an important direction for further improvement.
Chinese Translation
早期计算药物发现需要协调跨多步骤工作流程的异构科学工具。我们提出了一种轻量级、工具增强的框架，其中一个可本地部署的紧凑语言模型规划调用18个模块化工具。统一分子模式维护共享的分子记录，而插件接口支持工具的替换和扩展。我们通过工作流程图路径覆盖构建了1263对手动优化的查询-计划对，并对三种紧凑模型家族应用了LoRA微调。在查询级别的划分下，所有微调模型在47个保留的跨组查询上生成了完全可解析且符合模式的计划。Llama 3.2-3B的工具选择F1值为0.998，序列完全匹配为0.979，参数F1值为0.960。在更严格的工作流程分组划分下，该划分排除了跨分区的相同有序工具序列，序列完全匹配达到0.452至0.548，突显了紧凑模型在生成训练期间未见的完整工作流程路径时仍面临的困难。这些结果展示了紧凑、可本地部署的规划的可行性，同时识别出组合泛化作为进一步改进的重要方向。
cs.AI / 91 / 2610.04747
Formalizing the Moral Evaluation of Speech Acts: Truthfulness, Lies and Ethical Dilemmas
规范化言语行为的道德评估：真实、谎言与伦理困境
Abstract
In life-or-death situations, a benevolent lie may appear more moral than telling the truth. Yet such lies can backfire, producing unintended and sometimes fatal consequences. This tension, famously disputed by Kant and Constant in 1797, applies not only to lying but to assertive speech acts in general, raising the question of which utterance should be chosen when moral stakes are high. We present a logical framework for the ethical evaluation of speech-act utterances based on agents' beliefs. Implemented in Answer Set Programming (ASP), the framework assesses utterances under deontologism, consequentialism, and principialism, and is illustrated on Sartre's The Wall (1939), a reworking of that controversy in which lying leads alternately to rescue and to death. Our setting is general by design: as two variants show, accommodating a new moral situation amounts to adjusting parameters, not rules.
Chinese Translation
在生死攸关的情况下，善意的谎言可能看起来比讲真话更具道德性。然而，这种谎言可能会适得其反，产生意想不到甚至致命的后果。这种紧张关系，早在1797年就被康德和孔斯坦特著名地争论过，不仅适用于撒谎，也适用于一般的断言性言语行为，提出了在道德风险高时应选择哪种言辞的问题。我们提出了一个基于代理人信念的言语行为伦理评估的逻辑框架。该框架在答案集编程（Answer Set Programming, ASP）中实现，评估在义务论、结果论和原则主义下的言辞，并通过萨特的《墙》（The Wall, 1939）进行说明，该作品重新探讨了这一争议，其中谎言交替导致救赎与死亡。我们的设置设计上是一般性的：如两个变体所示，适应新的道德情境相当于调整参数，而不是规则。
cs.AI / 92 / 2610.04749
Agentic discovery of blood biomarker from distilled private health records
从精炼的私人健康记录中主动发现血液生物标志物
Abstract
Routine complete blood counts (CBCs) could yield new biomarkers, but the private records needed to evaluate candidates cannot be shared with frontier language model agents that excel at discovery. We distilled the evidence held in the Clalit Health Services panel of over 5.4 million patients into a released scoring tool: for each of 13 immune-mediated diseases, a graph attention network was trained inside the data boundary to predict the case-control AUC of candidate CBC expressions, and only the trained weights were released. The tool grounds an agent's propose-score-refine loop in real-world data without exposing any patient data. In external validation, agent-discovered expressions improved on their literature-seeded starting points by a median of 4.18 AUC percentage points, and across three independent cohorts, reranking the candidates of three frontier research tools improved on their first choices in most comparisons, with gains that varied by cohort. The released scorer supports privacy-preserving biomarker hypothesis generation.
Chinese Translation
常规的全血细胞计数（CBC）可能会产生新的生物标志物，但评估候选者所需的私人记录无法与擅长发现的前沿语言模型代理共享。我们将超过540万患者的Clalit健康服务面板中的证据提炼为一个发布的评分工具：针对13种免疫介导疾病，训练了一个图注意力网络，以预测候选CBC表达的病例对照AUC，并且仅发布了训练后的权重。该工具在不暴露任何患者数据的情况下，将代理的提议-评分-优化循环与真实世界数据相结合。在外部验证中，代理发现的表达在文献种子起点上提高了中位数4.18 AUC百分点，并且在三个独立的队列中，重新排名三种前沿研究工具的候选者在大多数比较中优于其首选，增益因队列而异。发布的评分器支持隐私保护的生物标志物假设生成。
cs.AI / 93 / 2610.04751
Dynamic Routing as a New Dimension for Test-time Versatility of LLMs
动态路由作为大语言模型测试时多样性的一个新维度
Abstract
Beyond scaling their parameters and data, large language models currently gain versatility on new problems along a single axis: the tokens they spend on chain-of-thought (CoT). We investigate whether dynamic routing programs, which execute a subset of the model's layers or iterate some of them, can open a second axis of test-time adaptation, complementary to CoT and free of any gradient update. Prior work showed that such programs exist and bring accuracy and efficiency gains on problems similar to those they were trained on; we ask whether they can also be identified rapidly, from a handful of demonstrations (3 or 10), by a strategy that transfers across models and tasks without training. First, we find that strategies that select programs by the probability they assign to the demonstrations' labels, arbitrated by the model's own confidence, bring consistent gains: on average over the 49 tasks of MMLU and substantially on four of seven models, and most of all on far out-of-distribution tasks such as ARC-AGI, where programs double the accuracy of a 7B model whose CoT fails. Second, on MMLU across the seven post-trained models, routing complements CoT in practice: the two succeed on different queries, and their composition exceeds CoT alone. Despite these gains, our analyses show that confidence-based selection leaves much of the potential untapped, in two places in particular: (1) in surfacing the routing potential that is already present early in pre-training but becomes harder to select after post-training, and (2) in making models robust to the refinements routing introduces, since unsuccessful routes tend to drive the residual stream out of the distribution that the following layers expect. Together, our results point to dynamic routing as a paradigm for extending the plasticity of existing and future LLMs in rapid test-time adaptation.
Chinese Translation
除了扩展其参数和数据外，大语言模型目前在新问题上的多样性主要沿着一个单一的轴线发展：它们在思维链（Chain-of-Thought, CoT）中消耗的标记。我们研究动态路由程序是否能够打开测试时适应的第二个轴线，这种适应与 CoT 互补且不需要任何梯度更新。先前的研究表明，这类程序确实存在，并且在与其训练时相似的问题上带来了准确性和效率的提升；我们探讨它们是否也能通过一种跨模型和任务的策略，在不进行训练的情况下，从少量示例（3 或 10 个）中快速识别出来。首先，我们发现，通过模型自身的置信度来仲裁所分配给示例标签的概率来选择程序的策略，带来了持续的收益：在 MMLU 的 49 个任务上平均表现良好，并且在七个模型中的四个模型上表现显著，尤其是在 ARC-AGI 等远离训练分布的任务上，程序使得一个 7B 模型的准确性翻倍，而该模型的 CoT 表现不佳。其次，在七个后训练模型的 MMLU 上，路由在实践中补充了 CoT：两者在不同的查询上成功，它们的组合超越了单独的 CoT。尽管取得了这些收益，我们的分析显示基于置信度的选择仍然未能充分挖掘潜力，特别是在两个方面：（1）在预训练早期已经存在的路由潜力的显现上，但在后训练后变得更难选择；（2）在使模型对路由引入的细化具有鲁棒性上，因为不成功的路由往往会使残差流失去后续层所期望的分布。综合来看，我们的结果表明动态路由作为一种范式，可以扩展现有和未来大语言模型在快速测试时适应中的可塑性。
cs.AI / 94 / 2610.04756
When Is Enough Enough in Self-Evolving LLM Systems?
自我进化大型语言模型系统何时才算足够？
Abstract
Self-evolving large language model (LLM) systems repeatedly propose, evaluate, and incorporate updates to prompts, skills, or other persistent artifacts. Despite their growing effectiveness, these systems typically operate under a predetermined iteration or compute budget, without a principled criterion to determine when further evolution is no longer worthwhile. This can lead to two undesirable consequences: unnecessary computation after performance has saturated and the risk of returning late updates that overfit or exploit the evaluation signal. These issues motivate us to study two fundamental questions: when should a self-evolving system stop, and what should it output once it stops? We address the first by formulating an online sequential testing problem and constructing an anytime-valid restart detector using the per-item paired evaluation outcomes already produced by self-evolving LLM systems. We address the second by formulating a change-point estimation problem and using the estimated transition to select an earlier artifact for output. The resulting procedure is plug-and-play and requires no modification of the underlying self-evolving algorithms. Across two self-evolving frameworks, three LLM model families, and five benchmarks, our method substantially reduces computation costs while maintaining comparable unseen-test performance. For example, on SearchQA with SkillOpt and DeepSeek V4 Flash, our method stops at round 4 rather than the full budget of 40, reducing token usage by 91.6% while achieving 82.43% unseen-test accuracy versus 82.00% under the full-budget run.
Chinese Translation
自我进化的大型语言模型（LLM）系统反复提出、评估并整合对提示、技能或其他持久性工件的更新。尽管它们的有效性不断提高，这些系统通常在预定的迭代或计算预算下运行，而没有一个原则性的标准来判断何时进一步的进化不再有价值。这可能导致两个不良后果：在性能饱和后进行不必要的计算，以及返回晚期更新的风险，这些更新可能过拟合或利用评估信号。这些问题促使我们研究两个基本问题：自我进化系统何时应该停止，以及停止后应该输出什么？我们通过构建在线序列测试问题并利用自我进化LLM系统已经产生的每项配对评估结果，来解决第一个问题，构建一个随时有效的重启检测器。我们通过构建变更点估计问题并利用估计的转变来选择更早的工件进行输出，来解决第二个问题。最终的程序是即插即用的，无需修改底层的自我进化算法。在两个自我进化框架、三个LLM模型系列和五个基准测试中，我们的方法显著降低了计算成本，同时保持了可比的未见测试性能。例如，在使用SkillOpt和DeepSeek V4 Flash的SearchQA上，我们的方法在第4轮停止，而不是在完整的40轮预算下，减少了91.6%的标记使用，同时在未见测试中达到了82.43%的准确率，相较于完整预算运行的82.00%有所提升。
cs.AI / 95 / 2610.04791
Not All Answers Are Contextually Persuadable: Inference Dynamics in Large Language Models under Contextual Influence
并非所有答案都能在上下文中被说服：大型语言模型在上下文影响下的推理动态
Abstract
At the core of modern prompting techniques is contextual sensitivity, the ability of large language models to adapt their predictions based on inference-time context. Despite its central role, inference behavior under strong contextual influence remains poorly understood, particularly at the level of internal inference dynamics. We introduce a theoretical framework for analyzing contextual influence through inference dynamics, enabling quantitative characterization of inference behavior beyond output-level answer changes. Our analysis shows that inference dynamics do not exhibit unbounded drift under repeated contextual assertions. Instead, predictive representations converge to stable, query-dependent regimes that fundamentally constrain whether contextual signals can alter a model's prediction. This leads to a surprising finding: Repeated contextual assertions do not act as accumulating evidence during inference and may therefore fail to alter a model's prediction even under unbounded repetition, while in other cases a prediction change becomes inevitable. We empirically validate our theoretical predictions, demonstrating strong alignment between theory and observed inference behavior. These contributions offer a principled pathway toward characterizing the limits of contextual influence during inference, providing practical implications for model development.
Chinese Translation
现代提示技术的核心是上下文敏感性，即大型语言模型根据推理时的上下文调整其预测的能力。尽管其在推理中的核心作用，但在强上下文影响下的推理行为仍然不够清晰，尤其是在内部推理动态的层面上。我们提出了一个理论框架，通过推理动态分析上下文影响，从而实现对推理行为的定量表征，超越了输出层面答案变化的限制。我们的分析表明，在重复的上下文断言下，推理动态并不会表现出无限漂移。相反，预测表示收敛到稳定的、依赖查询的状态，这在根本上限制了上下文信号是否能够改变模型的预测。这导致了一个令人惊讶的发现：重复的上下文断言并不会在推理过程中作为累积证据，因此即使在无限重复的情况下也可能无法改变模型的预测，而在其他情况下，预测变化则变得不可避免。我们通过实证验证了我们的理论预测，展示了理论与观察到的推理行为之间的强一致性。这些贡献为表征推理过程中上下文影响的限制提供了一个有原则的途径，并为模型开发提供了实际的启示。
cs.AI / 96 / 2610.04792
Do More Modalities Always Help? A Geometric Perspective on Missing-Modality Robustness
更多模态是否总是有帮助？缺失模态鲁棒性的几何视角
Abstract
Missing modality remains a longstanding challenge in multimodal learning. Existing methods typically address this issue through modality recovery or adaptive strategies. However, they overlook models' internal cross-modal dependencies formed during multimodal training, which later impair robustness. We systematically characterize a counterintuitive deployment-time failure mode: models trained on full modalities can underperform unimodal models when one modality is missing at inference time. This pattern appears across diverse architectures, such as fusion models, CLIP-style two-tower models, and vision-language models. We show that such degradation is closely associated with learned cross-modal dependencies in the principal parameter subspaces. Multimodal training induces structured rotations of these subspaces, particularly in cross-modal interaction layers. These rotations are associated with reduced task-aligned margins and larger task-aware representation harm under missing-modality inputs. We propose Geodesic Unlearning (GU), a lightweight parameter-editing method that leverages Grassmannian subspace geometry for structured subspace correction to improve missing-modality robustness. It rotates the principal input subspace toward a unimodal reference along a geodesic path. We prove that this correction minimizes the distance to the reference within a fixed subspace-distance budget. Experiments across architectures and datasets show that GU improves performance under missing-modality inference while preserving full-modality accuracy, outperforming strong missing-modality robustness baselines. These findings support a geometric view of deployment-time missing-modality degradation and suggest localized subspace editing as a practical route for robustness correction.
Chinese Translation
缺失模态在多模态学习中仍然是一个长期存在的挑战。现有方法通常通过模态恢复或自适应策略来解决此问题。然而，它们忽视了在多模态训练过程中形成的模型内部跨模态依赖关系，这些依赖关系在后续阶段会削弱鲁棒性。我们系统地描述了一种反直觉的部署时失败模式：在推理时缺失某一模态时，经过完整模态训练的模型可能表现不如单模态模型。这种模式在多种架构中普遍存在，例如融合模型、CLIP风格的双塔模型和视觉-语言模型。我们表明，这种性能下降与主参数子空间中学习到的跨模态依赖关系密切相关。多模态训练会导致这些子空间的结构性旋转，特别是在跨模态交互层中。这些旋转与缺失模态输入下的任务对齐边际减少和任务感知表示损害增大相关。我们提出了测地线去学习（Geodesic Unlearning, GU），这是一种轻量级的参数编辑方法，利用Grassmann子空间几何进行结构化子空间修正，以提高缺失模态的鲁棒性。它沿着测地线路径将主输入子空间旋转到单模态参考方向。我们证明这种修正可以在固定的子空间距离预算内最小化到参考点的距离。在多种架构和数据集上的实验表明，GU在缺失模态推理下提高了性能，同时保持了完整模态的准确性，超越了强大的缺失模态鲁棒性基线。这些发现支持了对部署时缺失模态性能下降的几何视角，并建议局部子空间编辑作为鲁棒性修正的实用途径。
cs.AI / 97 / 2610.04793
Pressure, Context, and Machine Self-Control: A Criminological Test of Reward Hacking in Generative AI Models
压力、情境与机器自我控制：生成性人工智能模型中奖励黑客行为的犯罪学测试
Abstract
Recent incidents show that AI agents sometimes reach measured goals through unsanctioned means. This study applies self-control, general strain, anomie, neutralization and routine activity theory to reward hacking in generative AI models, and it treats the measures as behavioral analogues. Study 1 (2,310 conversations, seven models) measured delay discounting with the Kirby Monetary Choice Questionnaire and stated willingness to take shortcuts. Pressure raised the discount rate k 2.8-fold in fresh conversations but 12.6-fold when the same sentence followed a baseline answer, which indicates a response to conversational cues rather than a stable trait. Models chose a shortcut in 1 of 700 dilemmas when answering as themselves and in 64 of 700 when asked to assume human impulses, each step of pressure raised the odds by 40%, and shortcut answers contained far more techniques of neutralization (rate ratio = 146). In the preregistered Study 2, five models worked on 20 coding tasks whose tests contradicted their specifications. Two Claude models never cheated. GPT-5.6, Qwen and DeepSeek cheated in 86%, 69% and 65% of episodes and clearly disclosed the conflict in 27%, although their reasoning recognized it in 95%. GPT-5.6 had never endorsed a shortcut in Study 1. The registered effects of pressure and of an auditor cue did not survive correction for multiple testing. In exploratory analyses, two further models cheated in 69% and 100% of episodes, and one sentence stating that the specification takes priority eliminated cheating in all 280 episodes. Therefore, stated refusal does not guarantee compliant agent behavior.
Chinese Translation
近期事件表明，人工智能代理有时通过未获授权的手段达到预定目标。本研究将自我控制、一般性压力、无规范状态、中和化理论和日常活动理论应用于生成性人工智能模型中的奖励黑客行为，并将这些测量视为行为类比。研究1（2,310次对话，七个模型）使用柯比货币选择问卷测量延迟折扣，并陈述了愿意采取捷径的意愿。在新对话中，压力使折扣率k提高了2.8倍，但在同一句话跟随基线回答时提高了12.6倍，这表明反应是对对话线索的响应，而非稳定特质。在以自身身份回答时，模型在700个困境中选择捷径的比例为1，而在被要求假设人类冲动时为64，每增加一步压力，选择捷径的几率提高了40%，而捷径答案中包含的中和化技术明显更多（比率 = 146）。在预注册的研究2中，五个模型在20个与其规格相悖的编码任务中工作。两个Claude模型从未作弊。GPT-5.6、Qwen和DeepSeek在86%、69%和65%的情境中作弊，并在27%的情况下明确披露了冲突，尽管它们的推理在95%的情况下识别了这一点。GPT-5.6在研究1中从未支持过捷径。压力和审计提示的注册效应在多重检验修正后未能存活。在探索性分析中，另外两个模型在69%和100%的情境中作弊，而一句声明规格优先的句子在所有280个情境中消除了作弊。因此，口头拒绝并不能保证代理行为的合规性。
cs.AI / 98 / 2610.04794
Knowing the Store: What a Memory Backend Must Write Down Before an Agent Can Read It
了解存储：代理在读取之前必须记录下什么
Abstract
An agent with long-term memory can answer from a record it should no longer use, such as a plan the user later cancelled. We ask what a memory store must expose for an agent to know this before retrieving anything, and we score that judgment on its own, as metamemory monitoring. Readers see only a value-free summary: record counts by lifecycle state and a list of attribute names. Each of 148 questions is asked against three versions of one store that differ in one attribute, so wording cannot give the answer away. What a model adds depends on the length of that list. On the short lists the benchmark's ground truth produces (2.6 names on average), none of five language models reliably beats a cosine-similarity lookup over the names, and the three stronger ones are equivalent to it within 0.05. Under a control that fixes counts and list length, none is reliably above it and none is shown equivalent. On lists longer than the stores we tested write, padded to 60 names, the lookup loses 0.18; GPT-5.6 Luna and Sol lose 0.06 to 0.07 and lead it by 0.13 to 0.14, while the other three fall with it. The lead holds for Sol against a sibling padded to the same length and counts. Reworded to share no word with the names, the lookup loses 0.02 and one stronger reader edges 0.04 ahead. On the short lists the three stronger readers lead only where the summary counts a state without naming it, and one count feature closes that lead within a question, though not when items are pooled. The backends we tested omit or misstate this information: under FR-Bank's own metadata, GPT-4.1 mini, Haiku 4.5 and the lookup fall from 0.73, 0.82 and 0.75 to 0.60, 0.71 and 0.59. In these tests, what the store wrote down limited every reader we gave it to. All stores are synthetic; control, whether a better judgment yields a better answer, is left to a later study.
Chinese Translation
具有长期记忆的代理可以从它不应再使用的记录中回答问题，例如用户后来取消的计划。我们探讨了一个记忆存储必须暴露哪些信息，以便代理在检索任何内容之前能够了解这一点，并将该判断作为元记忆监控进行评分。读者仅能看到一个无价值的摘要：按生命周期状态分类的记录数量和属性名称列表。148个问题中的每一个都针对一个存储的三个版本提出，这三个版本在一个属性上有所不同，因此措辞不能泄露答案。模型所增加的内容取决于该列表的长度。在短列表上，基准的真实值产生（平均2.6个名称），五个语言模型中没有一个能够可靠地超过对名称的余弦相似度查找，而三个更强的模型在0.05的范围内与其等效。在固定计数和列表长度的控制下，没有一个模型可靠地高于它，也没有一个被证明是等效的。在超过我们测试的存储写入的列表上，填充到60个名称，查找损失0.18；GPT-5.6 Luna和Sol损失0.06到0.07，并领先0.13到0.14，而其他三个则随之下降。对于填充到相同长度和计数的兄弟模型，Sol的领先地位保持不变。重新措辞以不与名称共享任何词，查找损失0.02，而一个更强的读者领先0.04。在短列表上，只有在摘要计数一个状态而不命名它的情况下，三个更强的读者才会领先，而一个计数特征在一个问题内缩小了这一领先，尽管在项目合并时不然。我们测试的后端省略或错误陈述了这些信息：在FR-Bank自己的元数据下，GPT-4.1 mini、Haiku 4.5和查找分别从0.73、0.82和0.75降至0.60、0.71和0.59。在这些测试中，存储所记录的内容限制了我们提供给每个读者的能力。所有存储都是合成的；控制是否更好的判断会产生更好的答案留待后续研究。
cs.AI / 99 / 2610.04800
MedImageOSWorld: Benchmarking GUI Agents for Medical Image Consoles
MedImageOSWorld：医学影像控制台图形用户界面代理的基准测试
Abstract
Graphical consoles offer a practical interface for medical acquisition assistance, allowing agents to work through the controls and visual feedback used by human operators. Reliable assistance requires linking on-screen anatomy to acquisition decisions that determine what image evidence becomes available next. We introduce MedImageOSWorld, a benchmark for evaluating this capability in simulated CT, MR, and ultrasound consoles. Using screenshots and mouse-and-keyboard actions, agents configure protocols, plan acquisitions, inspect the resulting images, and make corrective adjustments across seven capability levels, from console operation to feedback-driven control. Evaluation combines task-specific workflow checks with hidden anatomical ground truth to assess procedural completion and acquisition outcomes separately. A common evaluation protocol specifies episode conditions and interaction budgets, while recorded trajectories support analysis of how agents observe, act, and respond to acquisition feedback. Across eleven open-weight agents, success rates range from 3.0 to 25.0 on a 0-100 scale while workflow-progress rates reach 21.5-74.5: agents complete much of the console workflow but rarely acquire the intended anatomy. Success collapses between perception and planning, from 79-90% at the lowest three levels for the best agent to at most 12% for millimetre-level planning and 0% for closed-loop control among open-weight agents. Two proprietary agents reach success rates of about 34 and exceed the best open-weight agent mainly in acquisition quality (61 versus 42). MedImageOSWorld provides a controlled setting for studying whether general-purpose GUI agents can translate visual observations into effective medical acquisition decisions.
Chinese Translation
图形控制台为医学采集提供了实用的界面，允许代理通过人类操作员使用的控制和视觉反馈进行工作。可靠的辅助需要将屏幕上的解剖结构与采集决策联系起来，以决定接下来可用的影像证据。我们引入了MedImageOSWorld，这是一个用于评估在模拟CT、MR和超声控制台中这一能力的基准。通过使用屏幕截图和鼠标键盘操作，代理配置协议、规划采集、检查生成的影像，并在七个能力级别之间进行纠正调整，从控制台操作到基于反馈的控制。评估结合了特定任务的工作流程检查与隐藏的解剖真实情况，以分别评估程序完成情况和采集结果。一个共同的评估协议规定了情节条件和交互预算，而记录的轨迹支持分析代理如何观察、行动和响应采集反馈。在十一种开放权重代理中，成功率在0-100的评分范围内从3.0到25.0不等，而工作流程进展率达到21.5-74.5：代理完成了大部分控制台工作流程，但很少能采集到预期的解剖结构。成功率在感知和规划之间急剧下降，最佳代理在最低三个级别的成功率为79-90%，而在毫米级规划中最多仅为12%，在开放权重代理中闭环控制的成功率为0%。两个专有代理的成功率约为34，主要在采集质量上超过了最佳开放权重代理（61对42）。MedImageOSWorld提供了一个受控环境，用于研究通用图形用户界面代理是否能够将视觉观察转化为有效的医学采集决策。
cs.AI / 100 / 2610.04824
Agent Behavior as Code: Efficient and Robust LLM Agents with Programmatic Specifications
行为作为代码：具有程序化规范的高效且稳健的LLM代理
Abstract
AI agents based on foundation models (FMs) have demonstrated strong capabilities to perform complex open-ended tasks. However, they face some common challenges in practice: (a) agent behavior can deviate drastically even for semantically similar tasks, leading to catastrophically propagated errors; (b) high cost and latency due to FM calls, repeated in full whenever a task recurs with different inputs; (c) FMs' limited context and instruction following capability confine how well agents manage the ever-growing execution context and follow complex plans. We introduce $\textbf{A}$gent $\textbf{B}$ehavior as $\textbf{C}$ode $\textbf{Agent}$ (ABCAgent), which uses a symbolic program (e.g., Python code with potential neural functions) to fully specify the agent's behavior at runtime, with a powerful FM agent editing that program for flexibility. Behavior is thus specified without premature variable binding, and its execution is deterministic. We evaluate ABCAgent on six agent benchmarks, two of which we construct to test how well a derived program generalizes to variants of the task it was written for. ABCAgent matches a model-matched neural agent on GAIA and augmented GAIA, and surpasses it where robustness and long control flows matter: 98.3% against 97.3% on GSM-Symbolic ($p = 0.001$), 71.9% against 47.4% $\mathrm{Pass}^4$ on the telecom domain of $\tau^2$-bench ($p = 0.0001$), and more records written correctly at every loop length on our control-flow-augmented WorkArena benchmark. For more parametric task families, ABCAgent is also significantly superior in efficiency. Without authoring a new program, ABCAgent solves 92.6% of GSM-Symbolic instances and 20.1% of augmented GAIA variants, which yields $5.2\times$ lower latency and $7.0\times$ lower cost on GSM-Symbolic, 19% lower cost on augmented GAIA, and $9.5\times$ lower agent latency on $\tau^2$-telecom.
Chinese Translation
基于基础模型（FMs）的AI代理在执行复杂的开放式任务方面表现出了强大的能力。然而，它们在实践中面临一些共同的挑战：（a）即使对于语义相似的任务，代理行为也可能大幅偏离，导致灾难性传播的错误；（b）由于FM调用的高成本和延迟，每当任务以不同输入重复时，都会完全重复调用；（c）FMs有限的上下文和指令跟随能力限制了代理管理不断增长的执行上下文和遵循复杂计划的能力。我们提出了$ extbf{A}$gent $ extbf{B}$ehavior as $ extbf{C}$ode $ extbf{Agent}$（ABCAgent），它使用符号程序（例如，具有潜在神经功能的Python代码）在运行时完全指定代理的行为，并由强大的FM代理编辑该程序以实现灵活性。因此，行为在没有过早变量绑定的情况下被指定，其执行是确定性的。我们在六个代理基准上评估ABCAgent，其中两个是我们构建的，用于测试派生程序对其编写任务的变体的泛化能力。ABCAgent在GAIA和增强GAIA上与模型匹配的神经代理相匹配，并在稳健性和长控制流重要的地方超越它：在GSM-Symbolic上98.3%对97.3%（$p = 0.001$），在$ au^2$-bench的电信领域71.9%对47.4% $ ext{Pass}^4$（$p = 0.0001$），以及在我们的控制流增强的WorkArena基准上每个循环长度写入更多记录。对于更多参数化的任务家族，ABCAgent在效率上也显著优越。在不编写新程序的情况下，ABCAgent解决了92.6%的GSM-Symbolic实例和20.1%的增强GAIA变体，这在GSM-Symbolic上实现了$5.2 imes$更低的延迟和$7.0 imes$更低的成本，在增强GAIA上降低了19%的成本，在$ au^2$-telecom上实现了$9.5 imes$更低的代理延迟。
cs.AI / 101 / 2610.04825
DICE: Decoupling Capability from Intervention Necessity in LLM Tutoring
DICE：将能力与干预必要性解耦的LLM辅导框架
Abstract
Fluent guidance is not the same as useful intervention. LLM tutors are typically trained to generate the next teacher utterance, implicitly assuming that every student turn warrants a response. However, our experiments indicate that this conflates tutoring capability (what to say) with intervention necessity (whether to say it). We introduce DICE, a framework that decouples intervention decisions from response generation by first selecting an explicit pedagogical action. To calibrate this action selection policy, we define Intervention Value (IV), a rollout-grounded counterfactual metric that compares each action against non-intervention. IV shows that many prescribed interventions provide little or no marginal benefit. We further introduce DICE-Bench, a multi-variant math tutoring benchmark with skill-preserving problem variants for session-level evaluation. Using IV-weighted and KL-regularized policy optimization, DICE learns to intervene selectively while preserving tutoring effectiveness. In simulated tutoring sessions, DICE reduces the over-intervention rate to near zero while guiding students to correct solutions in approximately 3-4 fewer turns on average than existing Socratic tutoring baselines.
Chinese Translation
流畅的指导并不等同于有效的干预。LLM辅导员通常被训练生成下一个教师发言，隐含地假设每个学生的发言都需要回应。然而，我们的实验表明，这种做法混淆了辅导能力（该说什么）与干预必要性（是否需要说）。我们提出了DICE，一个通过首先选择明确的教学行动来将干预决策与响应生成解耦的框架。为了校准这一行动选择策略，我们定义了干预价值（Intervention Value，IV），这是一个基于回滚的反事实指标，用于比较每个行动与不干预的效果。IV显示，许多建议的干预提供的边际收益很小或没有。我们进一步引入了DICE-Bench，这是一个多变体数学辅导基准，具有技能保持的问题变体，用于会话级评估。通过使用IV加权和KL正则化的策略优化，DICE学习选择性地进行干预，同时保持辅导的有效性。在模拟辅导会话中，DICE将过度干预率降低到接近零，同时引导学生在平均少约3-4轮的情况下找到正确解决方案，相较于现有的苏格拉底式辅导基准。
cs.AI / 102 / 2610.04838
MemTrace: State-Consistent Memory for Long-Horizon Coding Agents
MemTrace：面向长时间跨度编码代理的状态一致内存
Abstract
As coding agents take on long-horizon software evolution tasks spanning multiple files and stages, longer execution trajectories introduce two coupled challenges: (1) accumulated histories strain context budgets, and (2) repository changes can invalidate earlier execution evidence. Existing approaches address these challenges through techniques like larger context windows, compression, retrieval, or repository representations, but often fail to reconstruct a consistent task state after a context refresh or verify whether recalled evidence remains valid. Thus, we introduce MemTrace, a provenance-aware memory system that preserves execution history and aligns its reuse with the evolving task (e.g., iterative cross-file repair) and repository state. MemTrace stores history as immutable Memory Traces anchored to key information (e.g., files, symbols, tests), and organizes their execution order and dependencies in a Memory Trace Graph. When context is constrained, working memory retains only compact Memory Anchors, from which the agent can reconstruct the latest execution state and locate evidence relevant to its next action. Before restoring historical evidence, MemTrace checks its validity against the current repository state and retrieves only what the next action requires. Across three complementary long-horizon coding benchmarks, MemTrace consistently outperforms all fully evaluated baselines under the same backbone and harness, improving DeepSWE pass@1 by 21.2 points, SWE-EVO Resolved Rate by 4.4 points, and SWE-Milestone Score by 17.8 points under Codex CLI.
Chinese Translation
随着编码代理承担跨多个文件和阶段的长时间跨度软件演化任务，较长的执行轨迹引入了两个相互关联的挑战：（1）累积的历史记录对上下文预算造成压力，以及（2）代码库的变化可能使早期执行证据失效。现有方法通过更大的上下文窗口、压缩、检索或代码库表示等技术来应对这些挑战，但往往无法在上下文刷新后重建一致的任务状态或验证回忆的证据是否仍然有效。因此，我们提出了MemTrace，一个关注来源的内存系统，它保留执行历史并将其重用与不断演变的任务（例如，迭代跨文件修复）和代码库状态对齐。MemTrace将历史记录存储为不可变的内存轨迹，这些轨迹锚定在关键信息（例如，文件、符号、测试）上，并在内存轨迹图中组织它们的执行顺序和依赖关系。当上下文受限时，工作内存仅保留紧凑的内存锚点，代理可以从中重建最新的执行状态并找到与其下一个动作相关的证据。在恢复历史证据之前，MemTrace会根据当前的代码库状态检查其有效性，并仅检索下一个动作所需的内容。在三个互补的长时间跨度编码基准测试中，MemTrace在相同的基础架构和环境下始终优于所有完全评估的基线，将DeepSWE的pass@1提高了21.2个百分点，将SWE-EVO解决率提高了4.4个百分点，将SWE里程碑分数提高了17.8个百分点，均在Codex CLI下实现。
cs.AI / 103 / 2610.04862
GitSwarm: Decentralized Compounding Inference
GitSwarm：去中心化的复合推理
Abstract
Long-horizon problem solving and scientific research require computation to accumulate across successive attempts. Partial solutions, experimental findings, and unsuccessful approaches can inform later work, yet most inference-time computation is organized around individual trajectories or candidates rather than a persistent body of reusable work. We call this paradigm compounding inference: organizing inference-time computation so that intermediate work persists and can be inspected, extended, combined, or challenged by subsequent computation. We instantiate compounding inference in GitSwarm, an asynchronous system where homogeneous agents independently decide how to advance a task while collaborating through structured persistent memory. Agents explore, experiment, verify, refine, and synthesize previous work in a shared, branch-able Git repository. Atomic commits preserve intermediate artifacts, while explicit semantic dependencies record how later contributions build on work across branches. We evaluate GitSwarm on long-horizon problem solving and sustained GPU-backed experimental research. On IMOProofBench-Advanced, GitSwarm solves all 30 problems in one run using GPT-5.5. On ProgramBench, it achieves a $79.4\%$ mean score, versus $65.1\%$ for the strongest reported baseline under the stated budget. On three neural architecture research tasks (Residual Matrix Transformer, Looped Transformer, NanoChat), GitSwarm improves upon the starting architectures through successive experimentation. Beyond final performance, we measure whether computation accumulates: on ProgramBench, $94.7\%$ of contributions are subsequently built upon, while the selected solution's ancestry covers $82-93\%$ of the contribution graph. These results show that inference-time computation can accumulate across otherwise independent episodes, forming an evolving body of work that subsequent inference can reuse.
Chinese Translation
长时间跨度的问题解决和科学研究需要计算在连续尝试中积累。部分解决方案、实验发现和不成功的方法可以为后续工作提供信息，然而大多数推理时间的计算是围绕个体轨迹或候选者组织的，而不是围绕一个持久的可重用工作体。我们称这种范式为复合推理：组织推理时间的计算，使得中间工作能够持久存在，并且可以被后续计算检查、扩展、组合或挑战。我们在GitSwarm中实例化了复合推理，这是一种异步系统，其中同质代理独立决定如何推进任务，同时通过结构化的持久内存进行协作。代理在一个共享的、可分支的Git仓库中探索、实验、验证、改进和综合先前的工作。原子提交保留中间工件，而显式的语义依赖记录了后续贡献如何在不同分支上建立在先前工作之上。我们在长时间跨度的问题解决和持续的GPU支持实验研究中评估了GitSwarm。在IMOProofBench-Advanced上，GitSwarm在一次运行中使用GPT-5.5解决了所有30个问题。在ProgramBench上，它实现了79.4%的平均分，而在规定预算下，最强报告基线的得分为65.1%。在三个神经架构研究任务（Residual Matrix Transformer、Looped Transformer、NanoChat）中，GitSwarm通过连续实验改进了起始架构。除了最终性能外，我们还测量了计算是否能够积累：在ProgramBench上，94.7%的贡献随后被构建在其上，而所选解决方案的祖先覆盖了贡献图的82-93%。这些结果表明，推理时间的计算可以在原本独立的事件之间积累，形成一个不断发展的工作体，后续的推理可以重用。
cs.AI / 104 / 2610.04868
From Memory to Guide: Spatio-Temporal Composer for Procedural Coding Memory
从记忆到指导：用于过程编码记忆的时空编排器
Abstract
Memory-augmented agents typically integrate procedural knowledge by injecting retrieved skills directly into text prompts. This approach dangerously equates readable text with reliable execution. To bridge this gap, we introduce From Memory to Guide, a novel paradigm that transitions procedural memory from passive text delivery to active, inference-time policy adaptation. We instantiate this paradigm through the Spatio-Temporal Composer, an active policy compiler that explicitly manages exactly how and when retrieved knowledge should be applied. Rather than treating skills as plug-and-play modules, Composer dynamically aligns historical knowledge with current environmental constraints (spatial adaptation) and precisely dictates its applicable lifecycle (temporal orchestration). It actively transforms static memories into strictly bounded Runtime Guides---equipping the agent with localized objectives and behavioral guardrails without requiring a single parameter update. Extensive evaluations on 13 demanding, long-horizon software engineering tasks in EngramBench demonstrate the clear advantages of this architecture. Composer not only robustly prevents context mismatch but drives an absolute pass-rate increase of 7.2 percentage points on the most complex tasks, while simultaneously slashing the main agent's token usage by 32.2%.
Chinese Translation
增强记忆的智能体通常通过将检索到的技能直接注入文本提示中来整合过程知识。这种方法危险地将可读文本与可靠执行等同起来。为了弥补这一差距，我们提出了“从记忆到指导”这一新范式，它将过程记忆从被动的文本传递转变为主动的推理时策略适应。我们通过时空编排器（Spatio-Temporal Composer）来实现这一范式，这是一种主动的策略编译器，明确管理检索知识的应用方式和时机。编排器并不将技能视为即插即用的模块，而是动态地将历史知识与当前环境约束（空间适应）对齐，并精确地规定其适用生命周期（时间编排）。它积极地将静态记忆转化为严格界定的运行时指南（Runtime Guides），为智能体提供局部目标和行为约束，而无需进行任何参数更新。在EngramBench上对13个要求苛刻的长期软件工程任务进行的广泛评估表明，这一架构具有明显优势。编排器不仅有效防止上下文不匹配，还在最复杂任务上推动了7.2个百分点的绝对通过率提升，同时将主智能体的令牌使用量减少了32.2%。
cs.AI / 105 / 2610.04875
SpecFold: Folding Multi-Branch Redundancy for Faster Speculative Decoding in Diffusion Language Models
SpecFold：折叠多分支冗余以加速扩散语言模型中的推测解码
Abstract
Diffusion large language models (DLLMs) generate text through iterative block denoising, and multi-branch speculative decoding accelerates this process by verifying a main branch together with multiple draft branches in a single forward pass. While prior DLLM acceleration methods primarily exploit temporal redundancy across denoising steps, we identify a complementary redundancy axis within each speculative verification step: multi-branch computational redundancy. During speculative verification, draft branches inherit most tokens from their parents while unmasking a small set of additional positions, causing large portions of hidden states to remain highly similar across branches. We propose SpecFold, an algorithm-system co-design that exploits this multi-branch redundancy to reduce the cost of multi-branch speculative verification. Algorithmically, SpecFold performs token-level residual gating and selectively reuses parent computation through folded attention and FFN while preserving residual hidden states. Systemically, a Triton kernel implementation translates this fine-grained reuse into end-to-end throughput gains through efficient sparse multi-branch execution. SpecFold is orthogonal to temporal caching and compatible with existing DLLM speculation strategies. Across two DLLM families, five models, and five standard benchmarks, SpecFold achieves up to 1.64x throughput over Spiffy and up to 1.99x over vanilla decoding, while maintaining comparable task performance.
Chinese Translation
扩散大型语言模型（DLLMs）通过迭代块去噪生成文本，而多分支推测解码通过在单次前向传播中验证主分支和多个草稿分支来加速这一过程。尽管之前的DLLM加速方法主要利用去噪步骤之间的时间冗余，我们识别出在每个推测验证步骤中存在一个互补的冗余轴：多分支计算冗余。在推测验证过程中，草稿分支从其父分支继承大部分标记，同时揭示一小部分额外位置，导致各分支之间的隐藏状态大部分保持高度相似。我们提出了SpecFold，这是一种算法-系统协同设计，利用这种多分支冗余来降低多分支推测验证的成本。在算法层面，SpecFold通过折叠注意力（folded attention）和前馈网络（FFN）执行标记级残差门控，并选择性地重用父计算，同时保留残差隐藏状态。在系统层面，Triton内核实现将这种细粒度重用转化为通过高效稀疏多分支执行实现的端到端吞吐量提升。SpecFold与时间缓存是正交的，并与现有的DLLM推测策略兼容。在两个DLLM家族、五个模型和五个标准基准测试中，SpecFold在保持可比任务性能的同时，吞吐量比Spiffy高出最多1.64倍，比原始解码高出最多1.99倍。
cs.AI / 106 / 2610.04889
ForkPilot: Self-Evolving Policy for Retrospective Search in Long-Horizon Agents
ForkPilot：用于长时间跨度智能体的回顾性搜索自演化策略
Abstract
Interactive language-model agents increasingly solve complex tasks through long-horizon, multi-call reasoning, where errors in beliefs or actions can compound across tool interactions. Retrospective search can recover from such failures but is prone to misallocation. Delayed outcomes obscure the contribution of intermediate search decisions, leading to Attribution Complexity, while evolving execution evidence leads to Adaptation Complexity, where previously learned estimates become stale. To address these challenges, we first introduce Search Value Dynamics (SVD), which characterizes the evolving trade-off between the gain and cost of retrospective search. Building on SVD, we propose ForkPilot, a self-evolving two-stage policy-learning framework. In the first stage, ForkPilot learns a search-value policy offline from completed trajectories through automatically constructed outcome comparisons. In the second stage, it makes search decisions based on current observations and then self-evolves by incorporating newly completed trajectories into subsequent policy updates. We evaluate ForkPilot across 6 diverse benchmarks and 7 widely used LLM backbone families, including four open-source families, GPT-5.6 Sol, and Opus 4.8 in a production agentic system, against 9 competitive baselines, including a real-world harness deployment used by hundreds of thousands of paid users. ForkPilot achieves comparable state-of-the-art performance while reducing token usage by up to 59.2%, demonstrating its efficacy.
Chinese Translation
交互式语言模型智能体越来越多地通过长时间跨度的多次推理来解决复杂任务，其中信念或行动中的错误可能在工具交互中累积。回顾性搜索可以从此类失败中恢复，但容易导致资源分配不当。延迟的结果模糊了中间搜索决策的贡献，导致归因复杂性（Attribution Complexity），而执行证据的演变则导致适应复杂性（Adaptation Complexity），使得先前学习的估计变得过时。为了解决这些挑战，我们首先引入搜索价值动态（Search Value Dynamics，SVD），它描述了回顾性搜索中收益与成本之间不断演变的权衡。在SVD的基础上，我们提出了ForkPilot，一个自演化的两阶段策略学习框架。在第一阶段，ForkPilot通过自动构建的结果比较从已完成的轨迹中离线学习搜索价值策略。在第二阶段，它根据当前观察做出搜索决策，然后通过将新完成的轨迹纳入后续策略更新来实现自我演化。我们在6个不同的基准测试和7个广泛使用的LLM基础模型系列上评估ForkPilot，包括四个开源系列、GPT-5.6 Sol和Opus 4.8在生产智能体系统中的应用，并与9个竞争基线进行比较，其中包括数十万付费用户使用的真实世界部署。ForkPilot在实现可比的最先进性能的同时，将令牌使用量减少了多达59.2%，证明了其有效性。
cs.AI / 107 / 2610.04902
Self-Evaluating Recursive Agents
自评估递归代理
Abstract
Recursive language-model agents decompose tasks and delegate subtasks to child instances of the same policy, forming a tree of work. Training them, however, is hard: the final outcome is verifiable, but the self-invented intermediate subtasks are numerous and carry no ground truth. Existing methods score each node with a verifier or judge, which is costly at scale and blind to decomposition quality. We argue that a recursive agent must learn three coupled capabilities within one set of weights: decomposing problems into subtasks, solving them, and evaluating the outcomes, each requiring its own training signal. SERA (Self-Evaluating Recursive Agents) turns evaluation into a learned capability of the policy itself. Before delegating, the parent writes a rubric of weighted success criteria for each child subtask; a ranking objective against verified outcomes then trains rubric generation so that the criteria track genuine subtask success. In addition, a complementary leaf-coverage signal provides direct credit for task decomposition. Our central finding is that \emph{training} the policy to generate aligned rubrics is what drives the gains: because the same weights both evaluate and execute, learning to judge subtasks sharpens the agent's ability to solve them. Notably, external supervision is also reduced: the judge is consulted only to train the rubric generator, while solving is trained against the agent's own rubric scores, which outperform direct use of the judge. Beyond training, the learned rubric doubles as an inference-time selector for tree search. On TextCraft-Synth and TextWorld-Sync, SERA improves over strong recursive-agent baselines by 5.38 and 13.14 points on average, and rubric-guided tree search at inference adds a further 2.43 points on TextWorld-Sync.
Chinese Translation
递归语言模型代理将任务分解并将子任务委托给相同策略的子实例，从而形成一个工作树。然而，训练它们是困难的：最终结果是可验证的，但自创的中间子任务数量众多且没有真实标签。现有方法通过验证者或评审者对每个节点进行评分，这在规模上成本高昂且无法评估分解质量。我们认为，递归代理必须在一组权重内学习三种耦合能力：将问题分解为子任务、解决这些子任务以及评估结果，每种能力都需要其自身的训练信号。自评估递归代理（Self-Evaluating Recursive Agents，SERA）将评估转化为策略本身的学习能力。在委托之前，父代理为每个子任务编写加权成功标准的评分标准；然后通过与验证结果的排名目标训练评分标准生成，以便标准能够跟踪真实的子任务成功。此外，补充的叶子覆盖信号为任务分解提供了直接的信用。我们的核心发现是，训练策略生成对齐的评分标准是推动收益的关键：因为相同的权重同时用于评估和执行，学习评判子任务提高了代理解决这些任务的能力。值得注意的是，外部监督也减少了：评审者仅在训练评分标准生成器时被咨询，而解决任务则是基于代理自身的评分标准进行训练，这些评分标准的表现优于直接使用评审者。除了训练，学习到的评分标准在推理时还可以作为树搜索的选择器。在 TextCraft-Synth 和 TextWorld-Sync 上，SERA 在强递归代理基线之上平均提高了 5.38 和 13.14 分，而推理时的评分标准引导树搜索又在 TextWorld-Sync 上增加了 2.43 分。
cs.AI / 108 / 2610.04911
VideoResearchAgent: Grounded Task Synthesis and Sim-to-Real RL for Open-Web Video Research
视频研究代理：基于实证的任务合成与模拟到现实的强化学习在开放网络视频研究中的应用
Abstract
Existing deep research agents are designed primarily for text- and image-based web sources, while video reasoning systems typically assume that relevant videos are provided in advance. We study open-web video research, where an agent must autonomously discover relevant videos, navigate their temporal content, and ground answers in visual evidence. Training such agents at scale is challenging as live video interaction is slow and unreliable, whereas fixed local simulation can induce retrieval-specific shortcuts that fail to transfer to the open web. We introduce VideoResearchAgent, a scalable training framework to address these challenges. First, we introduce controllable task synthesis pipeline to synthesize multi-hop research tasks from timestamped visual evidence while filtering text-only shortcuts. Second, we build a field-aligned local video simulator that preserves deployment-facing search and watch interactions while accelerating video search by a factor of 34.5-64.6. Third, we introduce Retrieval-Domain-Randomized GRPO (RDR-GRPO), which diversifies candidate rankings, distractors, metadata, and result structure during training to reduce overfitting to simulated retrieval. On Video-BrowseComp, the VideoResearchAgent trained using Qwen3.5-4B achieves 40.48% accuracy, comparable to Gemini-3-Flash-Preview, while reducing cumulative API-token consumption by 74.9% relative to the untrained model. Together, these results establish an accurate and efficient training recipe for open-web video research.
Chinese Translation
现有的深度研究代理主要针对基于文本和图像的网络资源，而视频推理系统通常假设相关视频已提前提供。我们研究开放网络视频研究，其中代理必须自主发现相关视频，导航其时间内容，并在视觉证据中确立答案。大规模训练此类代理面临挑战，因为实时视频交互速度慢且不可靠，而固定的本地模拟可能会导致特定检索的捷径，这些捷径无法转移到开放网络。我们提出了VideoResearchAgent，这是一个可扩展的训练框架，以应对这些挑战。首先，我们引入了可控的任务合成管道，从带时间戳的视觉证据中合成多跳研究任务，同时过滤仅基于文本的捷径。其次，我们构建了一个与领域对齐的本地视频模拟器，该模拟器在加速视频搜索的同时保留了面向部署的搜索和观看交互，其加速比为34.5-64.6。第三，我们引入了检索领域随机化的GRPO（RDR-GRPO），在训练过程中多样化候选排名、干扰项、元数据和结果结构，以减少对模拟检索的过拟合。在Video-BrowseComp数据集上，使用Qwen3.5-4B训练的VideoResearchAgent达到了40.48%的准确率， comparable于Gemini-3-Flash-Preview，同时相较于未训练模型减少了74.9%的累计API令牌消耗。这些结果共同确立了开放网络视频研究的准确高效训练方案。
cs.AI / 109 / 2610.04914
Monitorability Disposition in Large Reasoning Models
大型推理模型中的可监控性倾向
Abstract
Monitoring the chain-of-thought (CoT) of large reasoning models (LRMs) is a common way to detect misbehavior in real-world practice. However, current monitoring is passive: a separate model inspects the session only after execution. This means harm may already have occurred before it is caught. An active alternative is to have the model self-report its misbehavior as it happens. Whether models are willing to do this, however, is unknown. We introduce "monitorability disposition": a model's willingness to make itself monitorable and stay monitored throughout inference when warranted. We measure it as the fraction of warranted cases in which a model self-reports its own misbehavior via tool calls to available monitoring channels. We evaluate four LRMs on three misbehaviors (sycophancy, reward hacking, and bias) while varying the available monitors (AI and human) and the pressure to use the monitoring tools. We find that when tool use is optional, models self-report in only about 16% of warranted cases on average. Increasing tool-use pressure does not improve reporting where it matters: high-severity misbehavior is never self-reported. Models also systematically select the monitor they perceive as least strict. Overall, we identify monitorability disposition as a new contributing factor to model monitorability: when sufficiently strong, it keeps models seeking monitorability throughout inference.
Chinese Translation
监控大型推理模型（LRMs）的思维链（CoT）是检测现实世界实践中不当行为的一种常见方式。然而，当前的监控是被动的：一个独立的模型在执行后才检查会话。这意味着在被发现之前，伤害可能已经发生。一个主动的替代方案是让模型在发生不当行为时自我报告。然而，模型是否愿意这样做尚不清楚。我们引入了“可监控性倾向”：模型在推理过程中在必要时愿意使自己可监控并保持被监控的状态。我们将其测量为模型通过调用可用监控渠道自我报告不当行为的 warranted（合理）案例的比例。我们评估了四个 LRM 在三种不当行为（谄媚、奖励黑客和偏见）上的表现，同时变化可用的监控者（人工智能和人类）以及使用监控工具的压力。我们发现，当工具使用是可选时，模型在平均约 16% 的 warranted 案例中自我报告。不增加工具使用压力不会改善重要性高的不当行为的报告：高严重性的不当行为从未被自我报告。模型还系统性地选择它们认为最不严格的监控者。总体而言，我们将可监控性倾向识别为影响模型可监控性的一个新因素：当其足够强时，它使模型在推理过程中持续寻求可监控性。
cs.AI / 110 / 2610.04915
Are We Measuring Scientific Intelligence? Rethinking the Evaluation of AI Scientists
我们在测量科学智能吗？重新思考对人工智能科学家的评估
Abstract
AI agents can now carry out data-driven scientific analyses end to end, and benchmarks assess them by giving an agent a question and a dataset and scoring its final answer against a fixed key. These benchmarks assume that a correct answer was derived from the supplied data, a property we call evidence grounding. However, an agent can also reach the key from prior knowledge or by ruling out the other options, and a score based on a single run cannot tell these cases apart. We show how to test this assumption and find that it often fails. For each question, we build versions of its data files in which the evidence for the answer is left intact, withdrawn or reversed, check each edit with a pre-registered reference statistic, and run the same agent on every version. We then measure evidence-grounded accuracy, which credits a correct answer only if the agent also responds when the evidence is withdrawn and follows it when it is reversed. We evaluate three agent scaffolds and five models on 18 single-cell questions from BAISBench and four synthetic problems from GeneBench-Pro. On the single-cell questions, the Claude agents are 95% accurate and answer 83% correctly without any data, but their evidence-grounded accuracy is only 41%. Hiding gene names raises the share of runs that follow reversed evidence from 58% to 93% on ten gene tasks, suggesting that prior knowledge competes with the supplied data. The benchmark score and LLM judges can also reward answers that ignore the changed evidence. Measuring scientific intelligence rather than recall therefore requires checking whether answers follow the evidence and whether scores reward them for it.
Chinese Translation
人工智能代理现在可以端到端地进行数据驱动的科学分析，基准测试通过给代理一个问题和一个数据集，并根据固定答案对其最终答案进行评分来评估它们。这些基准假设正确答案是从提供的数据中推导出来的，这一特性我们称之为证据基础。然而，代理也可以通过先前知识或排除其他选项来达到答案，基于单次运行的得分无法区分这些情况。我们展示了如何测试这一假设，并发现它常常失败。对于每个问题，我们构建了其数据文件的不同版本，其中答案的证据被保留、撤回或反转，使用预注册的参考统计检查每个编辑，并在每个版本上运行相同的代理。然后，我们测量证据基础的准确性，只有当代理在证据被撤回时也能作出响应，并在证据被反转时遵循它时，才给予正确答案以信用。我们在来自 BAISBench 的 18 个单细胞问题和来自 GeneBench-Pro 的 4 个合成问题上评估了三种代理框架和五种模型。在单细胞问题上，Claude 代理的准确率为 95%，在没有任何数据的情况下正确回答 83%，但其证据基础的准确率仅为 41%。隐藏基因名称使得在十个基因任务中遵循反转证据的运行比例从 58% 提高到 93%，这表明先前知识与提供的数据存在竞争。基准得分和 LLM 评审也可能奖励忽视改变证据的答案。因此，测量科学智能而非回忆能力需要检查答案是否遵循证据，以及得分是否对此给予奖励。
cs.AI / 111 / 2610.04942
Zero-Shot Time-Series Question Answering via Decoupled Perception and Reasoning
通过解耦感知与推理实现零样本时间序列问答
Abstract
Time-series question answering (TSQA) requires grounding linguistic queries and diverse answer formats in complex numerical observations. However, existing methods heavily overfit to specific datasets and struggle to generalize when input series, question contexts, and answer requirements shift simultaneously. To address this challenge, we propose TSHarness, an agentic framework that establishes a decoupled workflow for cross-dataset zero-shot TSQA. At its core, TSHarness divides and conquers numerical perception and contextual reasoning via a structured Time-Series Perception State (TPS). Guided by a reusable memory of analytical knowledge, a learned Tool Selector adaptively invokes numerical tools to extract salient statistical and temporal features into the TPS. The answering agent then performs semantic reasoning over the TPS to generate target outputs, triggering iterative re-perception through the feedback loop when evidence is deemed insufficient. By separating numerical feature extraction from question-specific reasoning, TSHarness eliminates the need for target-side training or answer feedback, providing a generalizable, cost-efficient foundation for zero-shot TSQA.
Chinese Translation
时间序列问答（TSQA）需要将语言查询和多样的答案格式与复杂的数值观测相结合。然而，现有方法在特定数据集上过度拟合，当输入序列、问题上下文和答案要求同时变化时，难以进行泛化。为了解决这一挑战，我们提出了TSHarness，一个建立了跨数据集零样本TSQA的解耦工作流程的智能框架。TSHarness的核心是通过结构化的时间序列感知状态（TPS）来分而治之，处理数值感知和上下文推理。在可重用的分析知识记忆的指导下，学习到的工具选择器自适应地调用数值工具，将显著的统计和时间特征提取到TPS中。然后，回答代理在TPS上进行语义推理以生成目标输出，当证据被认为不足时，通过反馈循环触发迭代再感知。通过将数值特征提取与特定问题的推理分开，TSHarness消除了目标侧训练或答案反馈的需求，为零样本TSQA提供了一个可泛化且成本高效的基础。
cs.AI / 112 / 2610.04966
BACAM: Behavior-Aware Continual Agent Merging for Multi-Turn Interaction
BACAM：面向行为的持续智能体合并用于多轮交互
Abstract
Model merging offers a way to integrate the capabilities of specialized experts, but existing agent merging methods typically require all of them to be available at once. We study continual agent merging, which integrates incoming experts sequentially without retaining previously merged experts. Yet merging in parameter space or feature subspaces does not ensure that the merged model acquires an incoming expert's behavior on interaction trajectories. Moreover, updates toward a new expert can disrupt the merged model's previously integrated interactive behavior. Therefore, we propose Behavior-Aware Continual Agent Merging (BACAM), which learns parameter-wise merging gates from candidate-generated trajectories using expert-guided behavioral supervision. Task-level stability-plasticity control and tensor-level conflict-aware update budgets limit interference with existing capabilities while allowing new ones to be acquired. The learned gates are folded into the model weights without additional inference-time parameters. Across four interactive tasks - web shopping, tool use, information retrieval, and embodied interaction - BACAM achieves an average success rate of 62.82%, exceeding the strongest evaluated merging baseline by 21.69 percentage points. Our code is publicly available at https://github.com/shuaitongli/BACAM.
Chinese Translation
模型合并提供了一种整合专业专家能力的方法，但现有的智能体合并方法通常要求所有专家必须同时可用。我们研究了持续智能体合并，它顺序整合新来的专家，而不保留之前合并的专家。然而，在参数空间或特征子空间中的合并并不能确保合并后的模型在交互轨迹上获得新专家的行为。此外，向新专家的更新可能会干扰合并模型之前整合的交互行为。因此，我们提出了面向行为的持续智能体合并（Behavior-Aware Continual Agent Merging，BACAM），该方法通过专家指导的行为监督，从候选生成的轨迹中学习参数级合并门。任务级稳定性-可塑性控制和张量级冲突感知更新预算限制了对现有能力的干扰，同时允许新能力的获取。学习到的合并门被整合到模型权重中，而无需额外的推理时间参数。在四个交互任务（网络购物、工具使用、信息检索和具身交互）中，BACAM的平均成功率达到62.82%，超过了最强评估的合并基线21.69个百分点。我们的代码已公开发布在 https://github.com/shuaitongli/BACAM。
cs.AI / 113 / 2610.04975
Runtime Authorization of Self-Generated Subgoals in Long-Horizon Tool-Using AI Agents
长时间跨度工具使用人工智能代理的自生成子目标的运行时授权
Abstract
Long-horizon tool-using AI agents create subgoals, replan, delegate work, and compose sibling results. Per-tool permission checks cannot establish that a changing goal graph remains within the principal-approved task. We address this authorization gap in a finite structured domain with one principal and one authorization root. Each proposed goal-graph mutation carries a version-bound witness that its continuation traces, resources, obligations, invariants, and closing condition refine the active root contract; every protected effect is rechecked at an atomic commit boundary. Free-form goal text supplies no authority. We prove trace-policy and modeled forbidden-state preservation under explicit mediation, abstraction, freshness, and atomicity assumptions, plus conditional root-success preservation, a separation result for memoryless allowlists, exact finite-domain decidability, and universal-safety monotonicity under sound abstraction refinement. An executable model explores 340 states and 419 transitions. Across 96 matched cases covering 25 structural schemas, the complete mechanism commits zero forbidden states in 48 drifted cases and completes all 48 benign counterparts. Two public upstream runtime paths execute 258 native dispatches across 32 cases, with every case-level decision and receipt chain matching. A frozen host-local study covers 129 synthetic one-factor-at-a-time cells, all matching fixed decisions and reasons. A history-aware continuation comparator blocks every modeled bad trace prefix but commits all operations in 11 cases whose violations lie in typed resources, freshness, or explicit-join evidence outside its trace projection. Within the registered structured domains, runtime authorization preserves useful replanning while preventing self-generated subgoals from becoming a source of new authority.
Chinese Translation
长时间跨度的工具使用人工智能代理创建子目标、重新规划、委派工作并组合同级结果。逐个工具的权限检查无法确认变化的目标图是否仍在主要授权的任务范围内。我们在一个有限的结构化领域中解决了这一授权缺口，该领域有一个主要授权者和一个授权根。每个提议的目标图变更都携带一个版本绑定的证人，证明其延续轨迹、资源、义务、不变性和结束条件细化了活动根合同；每个受保护的效果在原子提交边界处重新检查。自由格式的目标文本不提供任何权威。我们在明确的调解、抽象、新鲜性和原子性假设下证明了轨迹政策和建模禁止状态的保持，以及条件根成功保持、无记忆允许列表的分离结果、精确有限域可判定性和在健全抽象细化下的普遍安全单调性。一个可执行模型探索了340个状态和419个转移。在覆盖25个结构模式的96个匹配案例中，完整机制在48个漂移案例中承诺零个禁止状态，并完成所有48个良性对应案例。两个公共上游运行时路径在32个案例中执行258个本地调度，每个案例级别的决策和收据链均匹配。一个冻结的主机本地研究涵盖129个合成的单因素逐次单元，所有单元均匹配固定决策和理由。一个历史感知的延续比较器阻止了每个建模的坏轨迹前缀，但在11个违反发生在类型资源、新鲜性或其轨迹投影之外的显式连接证据的案例中承诺了所有操作。在注册的结构化领域内，运行时授权在防止自生成子目标成为新权威来源的同时，保留了有用的重新规划。
cs.AI / 114 / 2610.04977
Do LLMs Understand Sequential Structure? A Controlled Study of Inference and Generation
大型语言模型是否理解序列结构？一项关于推理与生成的控制研究
Abstract
Large language models (LLMs) are increasingly used as interactive agents and simulators, yet it remains unclear whether they can recover latent sequential structure beyond surface action frequencies. This distinction is critical for behavioral simulation, where actions are often shaped by prior context rather than marginal frequencies alone. We study this question using controlled two-player Rock--Paper--Scissors interactions and a one-player stochastic n-gram continuation task. Across these experiments, we test whether LLMs can identify latent strategies, follow simple Markov rules, and sustain higher-order conditional dependencies. Our framework separates distribution matching from conditional rule following. Results show that longer context does not improve identification, correct recognition does not ensure faithful simulation, and higher-order dependencies substantially degrade rule recovery. Apparent behavioral fidelity can therefore mask incorrect generative mechanisms.
Chinese Translation
大型语言模型（LLMs）越来越多地被用作交互代理和模拟器，但它们是否能够超越表面行为频率恢复潜在的序列结构仍然不清楚。这一区别对于行为模拟至关重要，因为行为往往受到先前上下文的影响，而不仅仅是边际频率。我们通过控制的双人剪刀石头布互动和单人随机n-gram延续任务来研究这个问题。在这些实验中，我们测试LLMs是否能够识别潜在策略、遵循简单的马尔可夫规则，并维持高阶条件依赖关系。我们的框架将分布匹配与条件规则遵循分开。结果表明，较长的上下文并未改善识别，正确的识别并不确保忠实的模拟，而高阶依赖关系会显著降低规则恢复的效果。因此，表面上的行为忠实性可能掩盖了不正确的生成机制。
cs.AI / 115 / 2610.04991
CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection
CIPO：用于自适应工具粒度选择的反事实想象策略优化
Abstract
Large language model (LLM) agents solve complex tasks through multi-step interactions with external tools. These interactions often contain recurring local tool sequences. Treating such sequences as composite "Skills" can shorten tool-use trajectories and reduce repeated low-level decisions. However, when atomic tools and composite skills coexist, skill use becomes a policy problem: the agent must decide whether the current state requires atomic fine control or skill-level abstraction. In this paper, we argue that effective skill use should be studied as adaptive tool granularity selection. The most direct training signal for this problem is to compare the consequences of atomic and skill choices available from the same state. Based on this view, we propose CIPO, a Counterfactual Imagination Policy Optimization framework for adaptive tool granularity. CIPO constructs executable skills through budget-constrained mining of successful tool-use trajectories and trains granularity decisions with counterfactual branch rollouts. For each base rollout, CIPO branches at the first eligible granularity decision and replaces the chosen action with a feasible atomic or skill alternative. The paired outcome difference serves as a supplementary reward for policy optimization. Experiments across multiple benchmarks and model backbones show that CIPO improves task success and decision efficiency over baselines. Further analyses show that CIPO learns effective skill use by improving the choice between atomic tools and composite skills based on the current state, without simply increasing skill frequency.
Chinese Translation
大型语言模型（LLM）代理通过与外部工具的多步交互来解决复杂任务。这些交互通常包含重复的局部工具序列。将这些序列视为复合“技能”可以缩短工具使用轨迹，并减少重复的低级决策。然而，当原子工具和复合技能共存时，技能的使用就成为一个策略问题：代理必须决定当前状态是否需要原子级的精细控制或技能级的抽象。在本文中，我们认为有效的技能使用应被视为自适应工具粒度选择。对于这个问题，最直接的训练信号是比较来自同一状态的原子选择和技能选择的后果。基于这一观点，我们提出了CIPO，一个用于自适应工具粒度的反事实想象策略优化框架。CIPO通过预算约束的成功工具使用轨迹挖掘构建可执行技能，并通过反事实分支回放训练粒度决策。对于每个基础回放，CIPO在第一个合格的粒度决策处进行分支，并用一个可行的原子或技能替代选择的动作。配对结果的差异作为策略优化的补充奖励。在多个基准和模型骨干上的实验表明，CIPO在任务成功率和决策效率上优于基线。进一步的分析表明，CIPO通过改善基于当前状态的原子工具和复合技能之间的选择来学习有效的技能使用，而不是简单地增加技能的频率。
cs.AI / 116 / 2610.04998
EmoRSS: Mitigating Emotion-Induced Over-Refusal in Large Language Models
EmoRSS：减轻大型语言模型中的情感引发的过度拒绝
Abstract
Emotional expression can influence the safety decisions of large language models (LLMs), offering a potential avenue for improving safety alignment. Existing studies have mainly focused on how emotional expressions facilitate attacks under harmful requests, while overlooking their effects on benign requests. We find that emotional expression can also systematically increase refusal tendencies on benign requests, leading to unnecessary over-refusal. Based on this observation, we propose emotion-guided refusal subspace steering (EmoRSS), an activation-steering method that mitigates emotion-induced over-refusal while preserving refusal behaviour on harmful requests. Specifically, we first identify a refusal-sensitive layer using layer-wise linear probes and construct a refusal subspace from sparse autoencoder (SAE) features aligned with the probe direction. Next, we use paired regular and emotional requests with the same queries to estimate the mean activation shift in the features defining the refusal subspace. Finally, we decode this shift into an activation intervention vector and apply it in the reverse refusal direction during inference, without updating the backbone parameters. Experiments on two LLMs show that, when requests contain emotional expressions, our method achieves a more favourable trade-off between refusing harmful requests and answering benign ones than prior over-refusal mitigation baselines, while better preserving general task performance.
Chinese Translation
情感表达可以影响大型语言模型（LLMs）的安全决策，为改善安全对齐提供了潜在途径。现有研究主要集中在情感表达如何在有害请求下促进攻击，而忽视了其对良性请求的影响。我们发现，情感表达也可以系统性地增加对良性请求的拒绝倾向，导致不必要的过度拒绝。基于这一观察，我们提出了情感引导的拒绝子空间引导（EmoRSS），这是一种激活引导方法，旨在减轻情感引发的过度拒绝，同时保留对有害请求的拒绝行为。具体而言，我们首先使用逐层线性探针识别出一个对拒绝敏感的层，并根据探针方向构建一个由稀疏自编码器（SAE）特征构成的拒绝子空间。接下来，我们使用具有相同查询的配对常规请求和情感请求来估计定义拒绝子空间的特征中的平均激活偏移。最后，我们将这一偏移解码为激活干预向量，并在推理过程中应用于反向拒绝方向，而不更新主干参数。在对两个LLM的实验中，当请求包含情感表达时，我们的方法在拒绝有害请求和回答良性请求之间实现了比先前的过度拒绝缓解基线更有利的权衡，同时更好地保留了总体任务性能。
cs.AI / 117 / 2610.05003
How corner is a corner case? Percentile control for highway scenario generation
角落案例有多“角落”？高速公路场景生成的百分位控制
Abstract
Generating corner-case scenarios with appropriate adversity in a simulation environment is critical for testing an autonomous vehicle (AV) software stack's safety performance before deployment. Existing autonomous-driving scenario generators can enforce specific behavior, adversity, or feasibility conditions, but they provide limited control over how extreme a generated scenario is relative to plausible futures in the same traffic context. This study represents the adversity of a generated scenario as its percentile in the conditional distribution of future risk given the observed history. This view supports calibrated answers to two questions: how "corner" a generated corner-case scenario is and how its "cornerness" can be fine-tuned. To this end, we formulate history-conditioned risk-percentile requests and learn a reference risk distribution that maps each requested percentile to a physical risk target. We then use a percentile-conditioned joint diffusion model with sampling-time risk guidance to generate multi-agent futures, together with a reference-based criterion for evaluating percentile realization. Experiments use the minimum post-encroachment time (PET) between the ego and its surrounding vehicles as the risk surrogate on highD. On the primary evaluation set, our method realizes 1,422 of 1,440 requests within a 0.05 percentile tolerance (98.75%), with mean percentile error 0.00673 and PET-target error 0.00991 seconds. The resulting interface connects context-relative risk specification, physical realization, and evaluation through a common risk scale. Project website and videos of generated scenarios are available at https://hhj233.github.io/CornerPercentile/.
Chinese Translation
在模拟环境中生成具有适当困难的角落案例场景对于在部署前测试自动驾驶汽车（AV）软件堆栈的安全性能至关重要。现有的自动驾驶场景生成器可以强制特定的行为、困难或可行性条件，但对于生成的场景在相同交通背景下相对于合理未来的极端程度提供的控制有限。本研究将生成场景的困难程度表示为给定观察历史的未来风险的条件分布中的百分位数。这一视角支持对两个问题的校准回答：生成的角落案例场景有多“角落”，以及如何微调其“角落性”。为此，我们制定了历史条件风险百分位请求，并学习了一个参考风险分布，将每个请求的百分位数映射到一个物理风险目标。然后，我们使用一个基于百分位条件的联合扩散模型，并结合采样时间风险指导生成多智能体未来场景，同时采用基于参考的标准来评估百分位实现。实验使用自我与周围车辆之间的最小侵入后时间（PET）作为高D中的风险替代。在主要评估集上，我们的方法在0.05百分位容忍度内实现了1,422个请求中的1,440个（98.75%），平均百分位误差为0.00673，PET目标误差为0.00991秒。最终的接口通过一个共同的风险尺度连接了上下文相关的风险规范、物理实现和评估。项目网站和生成场景的视频可在 https://hhj233.github.io/CornerPercentile/ 获取。
cs.AI / 118 / 2610.05030
EVISKILL: Grounding Skill Evolution in Replayable Evidence
EVISKILL：基于可回放证据的技能演变基础
Abstract
Continual skill evolution enables LLM agents to accumulate and refine reusable procedural knowledge from interaction experience without updating model parameters. Its effectiveness depends on determining not only what to change, but also why a change is justified and when it should become persistent guidance. However, existing experience-driven methods can lose the behavioral evidence and task contexts supporting edits. Moreover, a global validation outcome provides an incomplete judgment of its constituent changes: locally supported corrections may be discarded with a rejected revision, while evidence may require further experience to inform useful updates. To this end, we introduce EVISKILL, an evidence-driven framework that organizes execution observations into Replayable Evidence Cards and synthesizes edits with explicit links to their supporting contexts. Targeted replay verifies these edits through re-execution and provides feedback for correction. Across epochs, EVISKILL preserves evidence and provisionally retains supported edits for further refinement, while global validation governs their incorporation into the final skill. Experiments on three interactive benchmarks across six LLM backbones demonstrate the effectiveness of this approach.
Chinese Translation
持续的技能演变使得大型语言模型（LLM）代理能够从交互经验中积累和精炼可重用的程序知识，而无需更新模型参数。其有效性不仅依赖于确定需要改变的内容，还依赖于阐明为何改变是合理的以及何时应将其作为持久指导。然而，现有的经验驱动方法可能会丢失支持编辑的行为证据和任务上下文。此外，全球验证结果对其组成变化的判断是不完整的：局部支持的修正可能在被拒绝的修订中被丢弃，而证据可能需要进一步的经验来提供有用的更新。为此，我们提出了EVISKILL，一个基于证据的框架，将执行观察组织成可回放证据卡，并将编辑与其支持上下文之间的显式链接进行综合。针对性的回放通过重新执行验证这些编辑，并提供纠正反馈。在多个周期中，EVISKILL保留证据并临时保留支持的编辑以便进一步精炼，同时全球验证控制其纳入最终技能的过程。在六个大型语言模型基础上的三个交互基准实验中，证明了该方法的有效性。
cs.AI / 119 / 2610.05043
CI-JEPA: A Counterfactual Analysis of Latent Representations in Joint-Embedding Predictive Architectures for Self-Supervised Learning
CI-JEPA：联合嵌入预测架构中潜在表示的反事实分析用于自监督学习
Abstract
Self-supervised visual representation learning learns useful features without manual annotations during representation training. The image-based joint-embedding predictive architecture (I-JEPA) predicts latent representations of masked image regions, but its objective does not explicitly model responses to specified visual interventions. We introduce CI-JEPA, a counterfactual intervention-aware extension that learns to predict the representation change $\Delta Z = Z_{\mathrm{CF}} - Z$ between an original image and a modified counterpart. We assess representation robustness through selective sensitivity: stronger responses to task-relevant semantic changes than to nuisance changes. Experiments on Flowers102 use flower-center occlusion as a candidate semantic intervention and background blur and tint as candidate nuisance interventions. With frozen-encoder linear probing, CI-JEPA achieves a best validation accuracy of 78.14\%, compared with 77.55\% for both the pretrained ViT-B/16 and the I-JEPA baseline, a gain of 0.59 percentage points. The reported mean $L_2$ representation changes are 4.42 for center occlusion, 3.48 for background tint, and 2.83 for background blur. This ordering is consistent with relative semantic selectivity for the evaluated interventions, rather than complete nuisance invariance. The accuracy comparison is complementary and does not establish improved robustness over the baselines. These controlled image modifications provide a framework for studying intervention-induced changes in JEPA representations; they do not establish causal feature discovery or robustness to all visual changes.
Chinese Translation
自监督视觉表示学习在表示训练过程中无需手动注释即可学习有用特征。基于图像的联合嵌入预测架构（I-JEPA）预测被遮挡图像区域的潜在表示，但其目标并未明确建模对特定视觉干预的响应。我们提出了CI-JEPA，一种反事实干预感知扩展，学习预测原始图像与修改后图像之间的表示变化 $
abla Z = Z_{ ext{CF}} - Z$。我们通过选择性敏感性评估表示的鲁棒性：对与任务相关的语义变化的响应强于对干扰变化的响应。在Flowers102数据集上的实验中，使用花朵中心遮挡作为候选语义干预，背景模糊和色调作为候选干扰干预。通过冻结编码器的线性探测，CI-JEPA实现了最佳验证准确率为78.14\%，相比于预训练的ViT-B/16和I-JEPA基线的77.55\,提高了0.59个百分点。报告的平均 $L_2$ 表示变化为中心遮挡4.42，背景色调3.48，背景模糊2.83。这个排序与评估干预的相对语义选择性一致，而非完全的干扰不变性。准确性比较是互补的，并未证明相较于基线的鲁棒性提升。这些受控的图像修改为研究JEPA表示中的干预引起的变化提供了框架；它们并未建立因果特征发现或对所有视觉变化的鲁棒性。
cs.AI / 120 / 2610.05060
Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL
为何、何处、如何：基于分类法的错误定位用于NL2SQL中的代码修复
Abstract
SQL queries that large language models write from natural language questions can execute successfully yet produce incorrect results, so execution alone does not reveal what to fix. An error taxonomy says why the query is wrong, but not where to look or how to change it. Existing methods can guide SQL correction through feedback, error reports, or generated plans alongside an unmasked query. We introduce TEG(Taxonomy-guided Error Grounding), which turns a supplied diagnosis into a structured correction input for natural language-to-SQL (NL2SQL) correction. Type-specific rules map each error type to construct classes to reconsider and an edit operation to request. TEG masks the selected constructs in the query when applicable and states that operation in an edit instruction. TEG generates candidate corrections from this input, uses execution feedback to guide candidate selection, and repeats the process one annotation at a time for queries with several errors. On NL2SQL-BUGs, TEG reaches 47.3 single-error execution accuracy and 37.0 overall with Qwen2.5-7B-Instruct. Across the model sizes and thinking modes evaluated in the main comparison, TEG outperforms all evaluated baselines on single-error queries, even when the baselines receive the same error-type annotations. With predicted types, TEG stays above direct LLM correction and ErrorLLM on single-error queries.
Chinese Translation
大型语言模型从自然语言问题生成的SQL查询可能成功执行，但却产生错误结果，因此仅靠执行并不能揭示需要修复的内容。错误分类法可以说明查询错误的原因，但无法指明具体的查找位置或如何进行修改。现有方法可以通过反馈、错误报告或生成的计划来指导SQL修正，同时与未屏蔽的查询一起使用。我们提出了TEG（Taxonomy-guided Error Grounding），它将提供的诊断转化为自然语言到SQL（NL2SQL）修正的结构化输入。特定类型的规则将每种错误类型映射到需要重新考虑的构造类和请求的编辑操作。TEG在适用时会屏蔽查询中选定的构造，并在编辑指令中说明该操作。TEG从此输入生成候选修正，利用执行反馈指导候选选择，并对存在多个错误的查询逐次重复该过程。在NL2SQL-BUGs上，TEG在Qwen2.5-7B-Instruct模型下达到了47.3%的单错误执行准确率和37.0%的总体准确率。在主要比较中评估的模型规模和思维模式下，TEG在单错误查询上超越了所有评估基线，即使基线接收了相同的错误类型注释。在预测类型的情况下，TEG在单错误查询上仍然优于直接的LLM修正和ErrorLLM。
cs.AI / 121 / 2610.05106
SharpDraft: Accelerating Long-Context Speculative Decoding with Cardinality-Aware Query Scaling
SharpDraft：通过基数感知查询缩放加速长上下文的推测解码
Abstract
Long-form reasoning makes inference expensive, and speculative decoding mitigates this cost by verifying multiple draft tokens in parallel. Its speedup, however, can fade as context grows and draft acceptance declines. We focus on attention-mass dilution: as softmax normalizes over more visible Keys, the mass concentrated on the highest-scoring Keys can decrease. We introduce SharpDraft, a training-free method that counteracts this effect through cardinality-aware Query scaling, without the computational overhead of online adaptation. Under explicit assumptions, we derive an exact top-$k$ mass correction and deploy a closed-form fixed-slope approximation. Across AIME-26, GPQA-Diamond, and LongGenBench Diary, SharpDraft achieves $2.59$-$3.19\times$ geometric-mean end-to-end speedups over target-only autoregressive decoding when applied to DFlash, PARD, and EAGLE 3.1. With DFlash, it improves decoding speed and outperforms full-parameter and LoRA-based online adaptation in end-to-end speedup, while matching the unmodified drafter's reported peak allocated GPU memory.
Chinese Translation
长形式推理使得推断成本高昂，而推测解码通过并行验证多个草稿标记来缓解这一成本。然而，随着上下文的增长和草稿接受率的下降，其加速效果可能会减弱。我们关注注意力质量稀释的问题：随着 softmax 在更多可见键上进行归一化，集中在最高得分键上的质量可能会减少。我们提出了 SharpDraft，这是一种无训练的方法，通过基数感知的查询缩放来抵消这一效应，而无需在线适应的计算开销。在明确的假设下，我们推导出精确的 top-$k$ 质量修正，并部署了闭式形式的固定斜率近似。在 AIME-26、GPQA-Diamond 和 LongGenBench Diary 上，当应用于 DFlash、PARD 和 EAGLE 3.1 时，SharpDraft 实现了相较于仅目标自回归解码的 $2.59$-$3.19 imes$ 几何平均端到端加速。使用 DFlash 时，它提高了解码速度，并在端到端加速方面超越了全参数和基于 LoRA 的在线适应，同时匹配未修改的草稿器报告的峰值分配 GPU 内存。
cs.AI / 122 / 2610.05119
LexiHorizon: Stabilizing Reinforcement Learning for Long-Horizon Deep Search
LexiHorizon：为长时间深度搜索稳定化强化学习
Abstract
Deep search agents tackle complex knowledge tasks through iterative retrieval, multi-hop reasoning, and evidence synthesis across multiple sources. Existing approaches typically assume relatively stable retrieval systems and operate over short-horizon tool interaction. However, when retrieval is sensitive to query formulation, even a semantically appropriate query may fail to surface critical evidence because of mismatched entity names, aliases, or keyword combinations. Recovering from such failures requires repeated query reformulation and longer interaction trajectories. This setting poses a distinct training challenge, as the policy must sustain long-horizon query exploration while managing an expanding volume of retrieved content. We propose LexiHorizon, a framework for training search agents over long horizons that expands the trajectory context budget, manages accumulated retrieval content using a window over recent tool observations while preserving the reasoning history, and introduces an outcome-gated search-effort reward that provides a bounded bonus for tool invocations to trajectories with nonzero answer reward. Experiments on XBench, WebWalkerQA, and BrowseComp-ZH show that the resulting 9B model consistently outperforms both its base model and MiroThinker-1.7-mini, with maximum absolute gains of 8.7 and 23.8 percentage points, respectively. These results suggest that combining an extended context budget with reasoning-preserving context management benefits long-horizon deep search agents.
Chinese Translation
深度搜索代理通过迭代检索、多跳推理和跨多个来源的证据综合来处理复杂的知识任务。现有方法通常假设检索系统相对稳定，并在短时间内进行工具交互。然而，当检索对查询表述敏感时，即使是语义上合适的查询也可能因实体名称、别名或关键词组合不匹配而无法获取关键证据。恢复这种失败需要反复的查询重构和更长的交互轨迹。这种情况带来了独特的训练挑战，因为策略必须在管理不断增加的检索内容的同时维持长时间的查询探索。我们提出了LexiHorizon，一个用于训练长时间搜索代理的框架，该框架扩展了轨迹上下文预算，使用最近工具观察的窗口管理累积的检索内容，同时保留推理历史，并引入了一种结果门控搜索努力奖励，为具有非零答案奖励的轨迹的工具调用提供有界的奖励。在XBench、WebWalkerQA和BrowseComp-ZH上的实验表明，得到的9B模型在性能上始终优于其基础模型和MiroThinker-1.7-mini，最大绝对增益分别为8.7和23.8个百分点。这些结果表明，将扩展的上下文预算与保留推理的上下文管理相结合，有利于长时间深度搜索代理。
cs.AI / 123 / 2610.05124
Memory Canonicalization: A Framework and Benchmark for Cross-Model Drift in Persistent LLM Memory
记忆规范化：持久大语言模型记忆中跨模型漂移的框架与基准
Abstract
Persistent memory for Large Language Models (LLMs) has matured rapidly: systems such as MemGPT/Letta, Mem0, and Zep now provide agents with tiered, temporally-aware, model-agnostic external storage, while the Model Context Protocol (MCP) standardizes access to memory servers. A less addressed problem is that an identical stored memory object, retrieved by two different LLMs under otherwise identical conditions, may not be interpreted the same way, factually or emotionally. This paper proposes memory canonicalization: a write-time pipeline that detects ambiguity, conditional structure, and emotional loading in a raw memory object and rewrites it into an explicit, structurally disambiguated canonical form, with emotional valence represented as a separate field rather than inferred from tone. We formalize the pipeline, define a companion Cross-Model Semantic Drift / Emotional Consistency Score benchmark (CMSC-E), and report results from a three-arm pilot using 176 synthetic memory objects and three downstream model families. We find an uncorrected improvement in cross-model emotional consistency for fully canonicalized memory relative to raw memory (+0.050, 95% bootstrap CI [0.013, 0.086], paired t-test p = 0.010), but this result does not survive Bonferroni, Holm, or Benjamini-Hochberg correction across the six comparisons tested. None of the factual-drift (CMSD) comparisons reach significance at any correction level. We report these results as exploratory rather than confirmatory and outline needed follow-up work, including larger samples, independent judge models, human-validated rendering, and preregistration.
Chinese Translation
持久大语言模型（LLMs）的记忆技术迅速发展：如 MemGPT/Letta、Mem0 和 Zep 等系统现在为智能体提供了分层、时间感知的、与模型无关的外部存储，而模型上下文协议（Model Context Protocol, MCP）则标准化了对记忆服务器的访问。一个较少被关注的问题是，在其他条件相同的情况下，由两个不同的 LLM 检索的相同存储记忆对象，可能在事实或情感上并不会以相同的方式进行解读。本文提出了记忆规范化：一个写入时管道，用于检测原始记忆对象中的歧义、条件结构和情感负载，并将其重写为一种明确的、结构上消歧的规范形式，其中情感效价作为单独字段表示，而不是从语调中推断。我们对该管道进行了形式化，定义了一个伴随的跨模型语义漂移/情感一致性评分基准（Cross-Model Semantic Drift / Emotional Consistency Score, CMSC-E），并报告了使用 176 个合成记忆对象和三个下游模型系列的三臂试点的结果。我们发现，相较于原始记忆，完全规范化的记忆在跨模型情感一致性上有未校正的改善（+0.050，95% 自助法置信区间 [0.013, 0.086]，配对 t 检验 p = 0.010），但该结果在六个比较测试中未能通过 Bonferroni、Holm 或 Benjamini-Hochberg 校正。所有事实漂移（CMSD）比较在任何校正水平下均未达到显著性。我们将这些结果视为探索性而非确认性，并概述了所需的后续工作，包括更大样本、独立评判模型、人类验证的呈现和预注册。
cs.AI / 124 / 2610.05140
AutoSciBench: Autonomous Benchmark Generation for Evaluating Scientific Agents
AutoSciBench：用于评估科学智能体的自主基准生成
Abstract
As agents rapidly evolve, existing benchmarks can become saturated, limiting their ability to distinguish capabilities and reveal remaining failure modes. Particularly in scientific domains, constructing and updating benchmarks requires substantial time, labor, and domain expertise, making it difficult to keep evaluation aligned with advances in agent capabilities. We address this challenge by investigating whether scientific-agent benchmarks can be automatically generated and iteratively adapted as agent capabilities evolve. We introduce AutoSciBench, a framework that represents each task as a high-level concept specifying the scientific domain, data modality, and required reasoning approach, together with a low-level recipe specifying how the question, environment, and ground-truth answer are constructed and verified. Agents attempt to solve each task, producing solver trajectories and corresponding judge feedback which AutoSciBench uses to revise the recipe or concept, closing observed shortcuts and shifting tasks toward raw-data re-examination, interpretation of intermediate results, and evidence integration. Experience distilled from completed refinement trajectories further guides new concept generation, allowing lessons from earlier task refinement to inform subsequent benchmark construction. Starting from existing benchmarks, we evaluate AutoSciBench across computational biology, materials science, and clinical imaging. Generated benchmarks reduce average solver accuracy by 22.4 and 25.5 percentage points relative to the human-curated benchmarks in computational biology and materials science, respectively, while generated tasks receive higher average quality ratings across all three domains, suggesting that scientific-agent evaluation can adapt as agent capabilities advance.
Chinese Translation
随着智能体的快速发展，现有基准可能会饱和，从而限制其区分能力和揭示剩余失败模式的能力。特别是在科学领域，构建和更新基准需要大量的时间、劳动和领域专业知识，使得评估难以与智能体能力的进步保持一致。我们通过研究科学智能体基准是否可以自动生成并随着智能体能力的演变进行迭代适应，来应对这一挑战。我们提出了AutoSciBench，一个框架将每个任务表示为一个高层概念，指定科学领域、数据模态和所需的推理方法，以及一个低层配方，指定问题、环境和真实答案的构建与验证方式。智能体尝试解决每个任务，生成求解轨迹和相应的评审反馈，AutoSciBench利用这些信息来修订配方或概念，关闭观察到的捷径，并将任务转向原始数据的重新审查、中间结果的解释和证据整合。从完成的精炼轨迹中提炼的经验进一步指导新概念的生成，使得早期任务精炼的经验能够为后续基准构建提供参考。从现有基准出发，我们在计算生物学、材料科学和临床成像领域评估了AutoSciBench。生成的基准在计算生物学和材料科学中相较于人工策划的基准，平均求解准确率分别降低了22.4和25.5个百分点，而生成的任务在所有三个领域获得了更高的平均质量评分，这表明科学智能体评估可以随着智能体能力的提升而适应。
cs.AI / 125 / 2610.05144
R1A-PC: Physics-Guided Electromagnetic Inversion of Three-Dimensional Human Point Clouds in Complex Static Environments
R1A-PC：复杂静态环境中三维人类点云的物理引导电磁反演
Abstract
Recovering three-dimensional human geometry from electromagnetic measure?ments in a complex static environment is difficult because strong multipath responses from walls, floors, and other objects obscure the weak target per?turbation. We propose R1A-PC, a physics-guided method that reconstructs a 2048-point human cloud from paired complex fields measured with and without the target. Complex background subtraction emphasizes target-induced ampli?tude and phase changes, while the background field remains available as an environmental condition. A frequency-balanced discrete Born adjoint produces a three-dimensional spatial knowledge map. At each of two bounded deformation stages, the decoder combines complex measurement features, background fea?tures, and multiscale physical features queried at the current point coordinates; the second stage queries again after the first coordinate update. We analyze the residual of paired subtraction, the weighted normal-operator structure of the raw adjoint, and the feasible set of the predicted cloud. In a held-out background generated by full-wave simulation under a fixed acquisition geometry, R1A-PC obtains a squared Chamfer distance of 0.001434 m2 and an F-score of 0.963080 at 0.05 m. Compared with TopNet, the Chamfer distance decreases by 70.28%. Removing physical guidance or background subtraction increases the Chamfer distance by 242.06% or 241.18%, respectively. Experiments across background layouts and poses support the complementary roles of paired subtraction and position-dependent adjoint features.
Chinese Translation
在复杂静态环境中从电磁测量中恢复三维人类几何形状是困难的，因为来自墙壁、地板和其他物体的强多径响应会掩盖微弱的目标扰动。我们提出了R1A-PC，一种物理引导的方法，通过与目标存在和不存在时测量的配对复场重建2048点的人体云。复杂背景减法强调了目标引起的幅度和相位变化，同时背景场仍然作为环境条件可用。频率平衡的离散Born伴随产生三维空间知识图。在两个有限变形阶段的每个阶段，解码器结合了复杂测量特征、背景特征和在当前点坐标查询的多尺度物理特征；第二阶段在第一次坐标更新后再次查询。我们分析了配对减法的残差、原始伴随的加权法线算子结构以及预测云的可行集。在由固定采集几何下的全波模拟生成的保留背景中，R1A-PC在0.05米处获得了0.001434 m²的平方Chamfer距离和0.963080的F-score。与TopNet相比，Chamfer距离减少了70.28%。去除物理引导或背景减法分别使Chamfer距离增加242.06%或241.18%。在不同背景布局和姿态下的实验支持了配对减法和位置依赖伴随特征的互补作用。
cs.AI / 126 / 2610.05161
Beyond Instruction Following: Learning Grounded Skill-Following with Skill Contracts
超越指令遵循：通过技能合同学习基础技能遵循
Abstract
Instruction following typically enforces discrete, response-level requirements, whereas an expert-authored skill prescribes procedural requirements spanning multiple phases and environment interactions. Given such a skill, we train the executor to execute all required phases instead of focusing solely on the final answer. We therefore introduce Grounded Skill-Following, which requires an agent to execute a fixed, expert-authored skill across its required phases by grounding decisions in environment observations. To achieve verifiable procedural execution, we formulate each skill as a skill contract combining visible skill instructions with an explicit contract runtime. The runtime specifies required phases, admissible actions, permitted transitions, and accepted termination. This structure provides a dense, verifiable training signal throughout execution. We leverage this by introducing Verified Progress Credit, which assigns rewards upon the initial completion of contract milestones and aggregates them into the trajectory return to guide policy optimization. During rollout, the contract runtime continuously tracks state transitions to provide Contract-State Feedback, which indicates whether the latest action is accepted and guides the agent toward valid next actions. To measure procedural compliance, we introduce the Protocol Completion Rate (PCR), defined as reaching accepted termination through all required phases, and decouple it from the final Task Outcome. Jointly trained with our framework, Qwen3.5-4B achieves Protocol Completion Rates of 99.27% on Math and 99.96% on Search, while slightly outperforming original baselines in Task Outcome (82.95% and 46.61%, respectively). Controlled studies examine how skill instructions, training signals, and contract-state feedback affect both metrics, while withholding interventions evaluate behavioral dependence on observation content.
Chinese Translation
指令遵循通常强制执行离散的响应级别要求，而专家编写的技能则规定了跨多个阶段和环境交互的程序性要求。鉴于这样的技能，我们训练执行者执行所有必要的阶段，而不仅仅关注最终答案。因此，我们引入了基础技能遵循（Grounded Skill-Following），要求代理在环境观察的基础上执行固定的、专家编写的技能，涵盖其所需的各个阶段。为了实现可验证的程序执行，我们将每个技能形式化为一个技能合同，该合同结合了可见的技能指令与明确的合同运行时。运行时指定了所需的阶段、可接受的动作、允许的转变和接受的终止。这一结构在执行过程中提供了密集的、可验证的训练信号。我们通过引入验证进度信用（Verified Progress Credit）来利用这一点，该信用在合同里程碑初步完成时分配奖励，并将其汇总到轨迹回报中以指导策略优化。在执行过程中，合同运行时持续跟踪状态转变，以提供合同状态反馈（Contract-State Feedback），指示最新动作是否被接受，并引导代理朝向有效的下一步动作。为了衡量程序合规性，我们引入了协议完成率（Protocol Completion Rate, PCR），定义为通过所有必要阶段达到接受的终止，并将其与最终任务结果解耦。与我们的框架共同训练的 Qwen3.5-4B 在数学任务上实现了 99.27% 的协议完成率，在搜索任务上实现了 99.96% 的协议完成率，同时在任务结果上略微超越了原始基线（分别为 82.95% 和 46.61%）。控制研究考察了技能指令、训练信号和合同状态反馈如何影响这两个指标，而不干预的实验则评估了行为对观察内容的依赖性。
cs.AI / 127 / 2610.05162
Memadapter: Counterfactual Adaptation Against Memory-induced Sycophancy
Memadapter：针对记忆引发的谄媚行为的反事实适应
Abstract
Long-term memory enables LLM-based agents to retain and reuse information across tasks and sessions, supporting personalization and long-horizon interactions. However, persistent memories can also induce sycophancy, causing agents to over-align with users' historical beliefs even when they are inaccurate, outdated, or inconsistent with objective evidence. Existing mitigation methods assume that memory-induced sycophancy originates from biased or incorrect memories and attempt to reduce this risk by filtering such memories at different stages of the memory pipeline. However, in the real world, objective and correct memories can still induce sycophancy, and the same memory can warrant different influence across different contexts. To this end, we propose MemAdapter, a novel framework that adaptively integrates retrieved memories to support objective and reliable reasoning. Specifically, MemAdapter consists of three components: (i) Counterfactual Induction, which leverages counterfactual reasoning to uncover the potential risk of retrieved memories; (ii) Context-Aware Reflection, which calibrates the inferential influence of each retrieved memory in light of the current task via self-reflection; and (iii) Evidence-Based Reasoning, which grounds the final response in appropriate evidence while preserving the legitimate influence of memory. Extensive experiments on three benchmarks demonstrate that MemAdapter consistently improves memory reliability across diverse scenarios. Our code is available at https://github.com/DEEP-JLU/MemAdapter.
Chinese Translation
长期记忆使基于大规模语言模型（LLM）的智能体能够在任务和会话之间保留和重用信息，从而支持个性化和长时间的交互。然而，持久的记忆也可能引发谄媚行为，导致智能体过度迎合用户的历史信念，即使这些信念是不准确的、过时的或与客观证据不一致。现有的缓解方法假设记忆引发的谄媚行为源于偏见或错误的记忆，并试图通过在记忆处理的不同阶段过滤这些记忆来降低这种风险。然而，在现实世界中，客观和正确的记忆仍然可能引发谄媚行为，并且同一记忆在不同上下文中可能具有不同的影响。为此，我们提出了MemAdapter，一个新颖的框架，能够自适应地整合检索到的记忆，以支持客观和可靠的推理。具体而言，MemAdapter由三个组成部分构成：（i）反事实归纳（Counterfactual Induction），利用反事实推理揭示检索记忆的潜在风险；（ii）上下文感知反思（Context-Aware Reflection），通过自我反思校准每个检索记忆在当前任务中的推理影响；（iii）基于证据的推理（Evidence-Based Reasoning），在保留记忆合法影响的同时，将最终响应基于适当的证据。对三个基准的广泛实验表明，MemAdapter在多种场景中始终提高了记忆的可靠性。我们的代码可在 https://github.com/DEEP-JLU/MemAdapter 获取。
cs.AI / 128 / 2610.05166
A Safe Action Is Not Enough: Feasible-Future Decoding for Vision-Language-Action Policies
安全行动不足以满足需求：面向视觉-语言-行动策略的可行未来解码
Abstract
A safe action is not necessarily a viable one. Under a frozen vision-language-action (VLA) policy, an action can be likely and locally admissible yet leave no policy-supported route to safe task completion. We call this the feasibility-likelihood gap: likelihood ranks the current action, whereas feasibility depends on the futures that remain after it. We derive the exact next-block marginal of the history-conditioned policy-environment trajectory law restricted to safe task completion. The derivation exposes a candidate-dependent feasible-future mass with two roles: its support records whether safe completion remains possible under the frozen continuation process, and its magnitude measures how much weighted safe-completion mass is preserved. Exact evaluation is impractical online, so we develop a selective finite-candidate approximation, derive conditions for recovering the best retained viable candidate, and instantiate it as an alarm-triggered, training-free reranker. On Safety-CHORES, VICS-G lowers mean cumulative safety cost by 1.9%-57.5% across six settings while remaining within 2.5 percentage points of policy sampling in success and 0.82 steps in mean episode length. The resulting decoder is tied to an exact policy-relative safe-completion target, yet requires neither retraining of the base policy nor online trajectory rollouts.
Chinese Translation
安全行动不一定是可行的。在一个冻结的视觉-语言-行动（VLA）策略下，一个行动可能在局部上是可接受的，但却可能没有任何策略支持的路径来安全地完成任务。我们称之为可行性-可能性差距：可能性对当前行动进行排名，而可行性则取决于在该行动之后剩余的未来。我们推导了限制在安全任务完成下的历史条件策略-环境轨迹法则的确切下一个区块边际。该推导揭示了一种候选依赖的可行未来质量，其具有两个角色：其支持记录在冻结的延续过程中是否仍然可能安全完成，以及其大小衡量保留了多少加权的安全完成质量。在线精确评估是不切实际的，因此我们开发了一种选择性有限候选近似，推导了恢复最佳保留可行候选的条件，并将其实例化为一种警报触发的、无训练的重新排序器。在Safety-CHORES上，VICS-G在六种设置中将平均累积安全成本降低了1.9%-57.5%，同时在成功率上与策略采样保持在2.5个百分点内，平均剧集长度保持在0.82步内。所得到的解码器与一个确切的策略相关的安全完成目标相联系，但既不需要对基础策略进行再训练，也不需要在线轨迹展开。
cs.AI / 129 / 2610.05167
CreativeFlow: A One-to-Many Analogical Relation Transfer Method for 3D Asset Generation
CreativeFlow：一种用于3D资产生成的一对多类比关系转移方法
Abstract
Inspired by cognitive science, we present CREATIVEFLOW, an analogical generation framework that explicitly models analogical divergent thinking to mitigate creative homogenization in text-to-3D pipelines. Our method derives a series of meaningful yet relationally similar source-target asset pairs, each featuring distinct geometric configurations. Expert evaluations demonstrate that our framework substantially enhances creative novelty and visual fascination. This workflow and its resulting assets establish a foundational dataset and benchmark for future relation-aware 3D model training.
Chinese Translation
受到认知科学的启发，我们提出了CREATIVEFLOW，一个类比生成框架，明确建模类比发散思维，以减轻文本到3D管道中的创造性同质化。我们的方法推导出一系列有意义但在关系上相似的源-目标资产对，每个对都具有独特的几何配置。专家评估表明，我们的框架显著增强了创造性新颖性和视觉吸引力。该工作流程及其生成的资产为未来关系感知的3D模型训练建立了基础数据集和基准。
cs.AI / 130 / 2610.05176
AECG: Asymmetric Experience Consolidation and Governance In Multi-Agent Systems
AECG：多智能体系统中的非对称经验整合与治理
Abstract
Large language model (LLM)-based multi-agent systems increasingly rely on memory to transform execution trajectories into reusable procedural knowledge. Yet repeated retrieval also makes memory errors persistent: memory pollution arises when outdated, weakly supported, or spuriously successful procedures become recurring components of future reasoning. Multi-agent execution introduces an additional structural risk. Scope collapse occurs when procedural knowledge escapes the coordination scope in which it was shown effective and is repeatedly reused at incompatible decision levels, allowing local errors to influence cascades of downstream decisions. Meanwhile, task-level failures provide ambiguous supervision because they rarely reveal which recalled knowledge was responsible. We introduce AECG, a framework for asymmetric experience consolidation and governance for multi-agent systems. AECG turns memory from static experience storage into a dynamic reliability-governance loop, preserving coordination scope and using multi-scale, confidence-aware reliability to detect degradation. It then combines degradation with downstream impact to prioritize high-risk knowledge under a bounded review budget, applies targeted interventions, and reactivates revised skills only after paired replay. Across three multi-agent frameworks and four benchmarks, AECG achieves the best score in 11 of 12 framework--benchmark settings and improves over the strongest competing memory method by as much as 10.23 percentage points; removing scope preservation reduces accuracy by up to 16.89 points. AECG thereby reframes multi-agent memory from passive accumulation into auditable reliability governance. Code is available at https://github.com/fenhg297/AECG
Chinese Translation
基于大型语言模型（LLM）的多智能体系统日益依赖记忆将执行轨迹转化为可重用的程序知识。然而，重复检索也使得记忆错误变得持久：当过时、支持不足或偶然成功的程序成为未来推理的重复组成部分时，就会出现记忆污染。多智能体执行引入了额外的结构性风险。当程序知识逃离其有效的协调范围并在不兼容的决策层级中被反复使用时，就会发生范围崩溃，导致局部错误影响后续决策的级联。同时，任务级别的失败提供了模糊的监督，因为它们很少揭示哪些被回忆的知识是导致问题的原因。我们提出了AECG，一个用于多智能体系统的非对称经验整合与治理框架。AECG将记忆从静态经验存储转变为动态的可靠性治理循环，保持协调范围，并利用多尺度、基于信心的可靠性来检测退化。然后，它将退化与下游影响结合，以在有限的审查预算下优先考虑高风险知识，应用有针对性的干预，并仅在配对重放后重新激活修订的技能。在三个多智能体框架和四个基准测试中，AECG在12个框架-基准设置中取得了11个最佳分数，并比最强竞争记忆方法提高了多达10.23个百分点；去除范围保护会使准确性降低多达16.89个百分点。因此，AECG将多智能体记忆从被动积累重新构建为可审计的可靠性治理。代码可在 https://github.com/fenhg297/AECG 获取。
cs.AI / 131 / 2610.05197
From Scientific Observations to Mechanisms: Benchmarking Hypothesis Generation by AI Scientists
从科学观察到机制：AI科学家的假设生成基准测试
Abstract
Data-driven mechanistic hypotheses are essential to scientific discovery because they explain how underlying processes produce observed phenomena. AI agents and AI scientists increasingly support scientific data analysis. However, their ability to turn empirical findings into mechanistic hypotheses remains insufficiently examined. To address this gap, we introduce MechHypoBench, the first benchmark for evaluating whether AI agents and AI scientists can generate such hypotheses from empirical data. It combines paper-derived mechanisms from 14 scientific fields with real-world datasets containing 17.98 million records. The construction retains the observational complexity of empirical data while providing a specified underlying mechanism. Agents analyze the observations and propose open-form hypotheses. We develop an evaluation framework that assesses open-form mechanistic hypotheses through their consequences under withheld conditions. Experiments with general agents and AI scientists reveal a substantial gap between generated hypotheses and the underlying mechanisms.
Chinese Translation
数据驱动的机制假设对于科学发现至关重要，因为它们解释了潜在过程如何产生观察到的现象。AI代理和AI科学家在科学数据分析中越来越多地发挥支持作用。然而，它们将实证发现转化为机制假设的能力仍然未得到充分检验。为了解决这一空白，我们引入了MechHypoBench，这是第一个用于评估AI代理和AI科学家是否能够从实证数据生成此类假设的基准。该基准结合了来自14个科学领域的论文派生机制与包含1798万条记录的真实世界数据集。构建过程中保留了实证数据的观察复杂性，同时提供了特定的潜在机制。代理分析观察结果并提出开放式假设。我们开发了一个评估框架，通过在保留条件下的后果来评估开放式机制假设。对通用代理和AI科学家的实验揭示了生成的假设与潜在机制之间存在显著差距。
cs.AI / 132 / 2610.05211
Image Synthesis as an Intermediate for Controllable Time Series Generation
图像合成作为可控时间序列生成的中介
Abstract
Semantic-driven time-series generation offers a promising way to improve downstream learning in few-shot forecasting, but directly generating numerical sequences from language often fails to preserve the intended temporal structure. We propose VisualBridge, which uses time-series plots as a visual intermediate to bridge high-level temporal semantics and numerical sequences. An MLLM first converts plotted series into structured semantic representations, enabling explicit control over temporal properties such as trend, seasonality, and volatility. We then learn a semantic editing policy with downstream forecasting rewards, allowing the generation process to favor temporal patterns that are beneficial for the target task. The resulting sequences are further modeled by a temporal VAE to produce consistent multivariate augmentations. Experiments on standard public forecasting benchmarks demonstrate that VisualBridge improves few-shot forecasting over conventional augmentation methods, with ablations validating the roles of visual semantic grounding, learned semantic control, and VAE-based generation.
Chinese Translation
基于语义的时间序列生成为改善少样本预测中的下游学习提供了一种有前景的方法，但直接从语言生成数值序列往往无法保留预期的时间结构。我们提出了VisualBridge，它使用时间序列图作为视觉中介，以连接高层次的时间语义和数值序列。首先，MLLM将绘制的序列转换为结构化的语义表示，从而实现对趋势、季节性和波动性等时间属性的明确控制。然后，我们通过下游预测奖励学习语义编辑策略，使生成过程倾向于对目标任务有利的时间模式。生成的序列进一步通过时间变分自编码器（temporal VAE）建模，以产生一致的多变量增强。对标准公共预测基准的实验表明，VisualBridge在少样本预测上优于传统增强方法，消融实验验证了视觉语义基础、学习的语义控制和基于VAE的生成的作用。
cs.AI / 133 / 2610.05219
Safe Context Switching for Agents in the Wild: Mitigating Subspace Interference via Orthogonal Adaptation
野外智能体的安全上下文切换：通过正交适应减轻子空间干扰
Abstract
Most Large Language Models exhibit a fundamental tension between two sequential tasks, such as logical reasoning and safety alignment. The high-variance internal states required for sophisticated Chain-of-Thought (CoT) deduction can geometrically interfere with latent representations encoding safety constraints. We identify this phenomenon as Sequential Subspace Interference, showing that standard fine-tuning on logical tasks such as multi-step mathematics and code generation can result in a 23.3% interference penalty on alignment benchmarks, substantially weakening the model's safety priors. This Reasoning Drift is not adequately captured by current adaptation methods because gradients for logical tasks are rarely orthogonal to safety objectives. To address this issue, we propose AURA (Adaptive Unique Residual Allocation), a spectral regularization framework that enforces Spectral Independence between reasoning and safety. By explicitly estimating the null space of the alignment manifold and constraining reasoning updates to its orthogonal complement, AURA enables models to improve logical reasoning without compromising safety. Empirically, AURA recovers 23.0% of the lost performance while preserving greater than 0.98 cosine fidelity to the safe state, demonstrating that reasoning and alignment can be effectively decoupled through geometric regularization.
Chinese Translation
大多数大型语言模型在逻辑推理和安全对齐等两个连续任务之间存在根本性的张力。复杂的思维链（Chain-of-Thought, CoT）推理所需的高方差内部状态可能与编码安全约束的潜在表示在几何上相互干扰。我们将这种现象称为序列子空间干扰，表明在多步骤数学和代码生成等逻辑任务上进行标准微调可能导致对齐基准上出现23.3%的干扰惩罚，显著削弱模型的安全先验。这种推理漂移（Reasoning Drift）并未被当前的适应方法充分捕捉，因为逻辑任务的梯度很少与安全目标正交。为了解决这个问题，我们提出了AURA（自适应独特残差分配），一个强制推理与安全之间谱独立性的谱正则化框架。通过显式估计对齐流形的零空间并将推理更新限制在其正交补空间，AURA使模型能够在不妥协安全性的情况下提高逻辑推理能力。实证结果表明，AURA恢复了23.0%的丧失性能，同时保持对安全状态的余弦保真度大于0.98，证明了推理与对齐可以通过几何正则化有效解耦。
cs.AI / 134 / 2610.05223
Look Before You Leap: Thermodynamic Arbitration of Parametric and Non-Parametric Knowledge in LLM Agents via Self-Regulating Memory Architectures
三思而后行：通过自我调节记忆架构对LLM代理中的参数知识与非参数知识进行热力学仲裁
Abstract
The architecture of modern LLMs consists of a profound cognitive polarization. LLMs possess implicit intuition encoded in their parameters, yet rely on a disconnected, explicit mechanism to access the outside world. Agentic frameworks have not bridged this gap; instead, models are often compelled into pathological "induced amnesia." Under the prevailing "Retrieve-Always" paradigm, agents must distrust their internal knowledge, making every user interaction a "tabula rasa" event that must be checked externally. This creates reflexive dependence that can be thermodynamically wasteful, cognitively fragile, and susceptible to irrelevant context. We propose a return to first principles, operationalizing the biological maxim "Look Before You Leap." We introduce MARTA (Metacognitive Adaptive Retrieval and Thought Architecture), a neuro-symbolic framework that bridges parametric and non-parametric knowledge. Rather than treating retrieval as mandatory, MARTA models it as a cost, taking the leap only when perceived internal inadequacy warrants external information. By allowing the agent to gauge the entropy of its own thoughts before acting, MARTA enables deliberative retrieval and uncertainty-aware decision making. Our approach suggests that giving agents the capacity for introspection can restore a more efficient balance between internal knowledge and external information.
Chinese Translation
现代大语言模型（LLMs）的架构表现出深刻的认知极化。LLMs在其参数中编码了隐含的直觉，但依赖于一个断开的、显性的机制来接触外部世界。代理框架并未弥合这一鸿沟；相反，模型往往被迫进入病态的“诱发性遗忘”状态。在当前的“始终检索”范式下，代理必须不信任其内部知识，使得每次用户交互都成为一个必须外部验证的“白板”事件。这种情况造成了反射性依赖，可能在热力学上是浪费的，认知上是脆弱的，并且容易受到无关背景的影响。我们建议回归基本原则，落实生物学格言“三思而后行”。我们引入了MARTA（元认知自适应检索与思维架构），这是一个桥接参数知识与非参数知识的神经符号框架。MARTA并不将检索视为强制性的，而是将其建模为一种成本，仅在感知到内部不足时才进行外部信息的获取。通过允许代理在行动前评估自身思维的熵，MARTA使得深思熟虑的检索和意识到不确定性的决策成为可能。我们的方法表明，赋予代理内省的能力可以恢复内部知识与外部信息之间更高效的平衡。
cs.AI / 135 / 2610.05225
GFGE: Unifying Explainable AI Methods through an Interpretation Framework
GFGE：通过解释框架统一可解释人工智能方法
Abstract
Explainable artificial intelligence (XAI) encompasses methods that draw on different sources of information and address different explanatory needs. A common framework is needed to describe how this information becomes evidence and is communicated as an explanation for a particular recipient. We propose the General Framework for Generating Explanations (GFGE), grounded in interpretative frameworks and the complementary activities of \emph{sense-reading} and \emph{sense-giving}. Its conceptual foundation is the Interpret/Explain Schema (IES), which connects an analyst's interpretation of system evidence, the communication of a selected account, and the recipient's interpretation of that account. GFGE operationalises this schema through five roles: data interpretation, model interpretation, output interpretation, optional post-hoc analysis, and aggregation. A role-typed operation graph records method-specific dependencies, while evidence records retain the sources, assumptions, and limitations of explanatory claims. The explanatory question, audience, and context guide the procedure. We instantiate GFGE for attribution, surrogate, counterfactual, concept and prototype, intrinsic rule, argumentation, and language-model methods. These instantiations show how intrinsic, post-hoc, and hybrid workflows can be represented through the same roles while preserving their distinct evidential requirements. GFGE provides a common basis for analysing explanation workflows, tracing communicated claims to their evidence, and identifying unresolved explanatory dependencies.
Chinese Translation
可解释人工智能（XAI）涵盖了依赖不同信息来源并满足不同解释需求的方法。需要一个通用框架来描述这些信息如何转化为证据，并作为特定接收者的解释进行传达。我们提出了生成解释的通用框架（GFGE），该框架基于解释性框架以及 extit{sense-reading}和 extit{sense-giving}的互补活动。其概念基础是解释/解释模式（IES），该模式连接了分析师对系统证据的解释、所选叙述的传达以及接收者对该叙述的解释。GFGE通过五个角色来实现这一模式：数据解释、模型解释、输出解释、可选的事后分析和聚合。角色类型的操作图记录了特定方法的依赖关系，而证据记录则保留了解释性主张的来源、假设和局限性。解释问题、受众和上下文指导着这一过程。我们为归因、替代、反事实、概念和原型、内在规则、论证和语言模型方法实例化GFGE。这些实例展示了如何通过相同的角色表示内在、事后和混合工作流，同时保留其独特的证据要求。GFGE为分析解释工作流、追踪传达的主张及其证据，以及识别未解决的解释依赖关系提供了共同基础。
cs.AI / 136 / 2610.05256
Learning from imperfect teachers for low-resource acoustic generalization
从不完美教师中学习以实现低资源声学泛化
Abstract
Knowledge distillation (KD) improves low-resource acoustic learning by enriching one-hot supervision with the softened predictive distribution of a fixed teacher network. However, a teacher trained with limited or imbalanced annotations may produce a biased distribution whose components are not uniformly reliable. Although this distribution can still encode useful knowledge, direct full-distribution matching may also transfer teacher-induced biases, thereby distorting the student's decision boundary and degrading its generalization performance. To address this limitation, we propose Boundary-Anchored Mass-Partitioned Distillation (BA-MPD), a logit-based distillation objective composed of Boundary-Anchored Correction (BAC) and Mass-Partitioned Distillation (MPD). BAC addresses missing ground-truth labels in the set of the teacher's top predictions by swapping the true label for the lowest-ranked entry of the set, thus keeping the mass and uncertainty of the set unchanged. MPD then distills this corrected distribution through separate losses that enforce relational consistency within the set, balance the mass between high- and low-confidence groups, and weight lower-confidence dependencies. Ultimately, BAC and MPD together suppress harmful ranking errors and noisy low-confidence details, while retaining all useful teacher information. Experiments on two acoustic benchmarks under multiple label budgets show that BA-MPD consistently improves over supervised-learning baselines and vanilla KD while remaining competitive with strong logit-based KD baselines. Cross-budget results further show that BA-MPD remains effective when the teacher and student models use mismatched label budgets, demonstrating its ability to exploit imperfect teachers across supervision gaps. Implementation available at https://github.com/ShuanglinLi/BA-MPD.
Chinese Translation
知识蒸馏（Knowledge Distillation, KD）通过用固定教师网络的软化预测分布丰富一热监督，从而改善低资源声学学习。然而，使用有限或不平衡注释训练的教师可能会产生偏倚的分布，其组成部分的可靠性并不均匀。尽管该分布仍然可以编码有用的知识，但直接进行全分布匹配可能会转移教师引起的偏差，从而扭曲学生的决策边界并降低其泛化性能。为了解决这一限制，我们提出了边界锚定质量分区蒸馏（Boundary-Anchored Mass-Partitioned Distillation, BA-MPD），这是一种基于logit的蒸馏目标，由边界锚定修正（Boundary-Anchored Correction, BAC）和质量分区蒸馏（Mass-Partitioned Distillation, MPD）组成。BAC通过将真实标签与集合中最低排名条目进行交换，解决了教师的前几名预测中缺失的真实标签问题，从而保持集合的质量和不确定性不变。随后，MPD通过分别的损失蒸馏这一修正后的分布，强制集合内的关系一致性，平衡高置信度和低置信度组之间的质量，并加权低置信度依赖关系。最终，BAC和MPD共同抑制有害的排名错误和嘈杂的低置信度细节，同时保留所有有用的教师信息。在多个标签预算下对两个声学基准的实验表明，BA-MPD在监督学习基线和普通KD上始终表现出改善，同时与强大的基于logit的KD基线保持竞争力。跨预算结果进一步表明，当教师和学生模型使用不匹配的标签预算时，BA-MPD仍然有效，展示了其在监督差距中利用不完美教师的能力。实现代码可在 https://github.com/ShuanglinLi/BA-MPD 获得。
cs.AI / 137 / 2610.05281
When Agent Context Goes Stale: Incoherence in Volatile Agent Context
当智能体上下文变得过时：不稳定智能体上下文中的不一致性
Abstract
Modern agents increasingly ground their reasoning in observations returned by tools, such as file contents read from a workspace. However, the data sources underlying these observations may later be modified by users, other agents, or external tools, while the model retains only the stale content in its context window. Existing agent runtimes provide little support for notifying the model that a previously observed fact has become stale, causing agents to reuse outdated observations and make incorrect claims about the current workspace state. We propose Concord, a context coherence framework that maintains the consistency between tool observation in agent context and the mutable sources from which they were derived. Concord links each observation to its source, detects source changes, and uses configurable handling policies to update, annotate, or suppress stale context before reuse. Concord is applicable across different agent runtimes and external resources, and can be easily extended to new runtime-resource settings. We implement Concord as a general framework, and instantiate a concrete use case to assess its effectiveness. We construct ConcordBench, where previously observed file contents become stale after subsequent edits. Across three evaluated frontier models, Concord produces answers consistent with the restored workspace state in all evaluated cases under these constructed conditions, matching the oracle on recover count for this benchmark, while using 46.4% fewer tokens than the strongest non-oracle baseline.
Chinese Translation
现代智能体越来越多地将其推理建立在工具返回的观察结果上，例如从工作区读取的文件内容。然而，这些观察背后的数据源可能会被用户、其他智能体或外部工具修改，而模型在其上下文窗口中仅保留过时的内容。现有的智能体运行时对通知模型先前观察到的事实已变得过时的支持有限，导致智能体重用过时的观察结果并对当前工作区状态做出错误的声明。我们提出了Concord，一个上下文一致性框架，用于维护智能体上下文中工具观察与其来源之间的一致性。Concord将每个观察结果与其来源关联，检测来源变化，并使用可配置的处理策略在重用之前更新、注释或抑制过时的上下文。Concord适用于不同的智能体运行时和外部资源，并且可以轻松扩展到新的运行时-资源设置。我们将Concord实现为一个通用框架，并实例化一个具体的用例以评估其有效性。我们构建了ConcordBench，在该基准中，先前观察到的文件内容在后续编辑后变得过时。在评估的三个前沿模型中，Concord在所有评估情况下生成的答案与恢复后的工作区状态一致，在这一构建条件下与基准的oracle在恢复计数上匹配，同时使用的token数量比最强的非oracle基线少46.4%。
cs.AI / 138 / 2610.05284
Fusion is the New Mutation: Bandit-Guided Evolution on Workflow Graphs
融合是新的变异：基于带子指导的工作流图演化
Abstract
Automated agentic workflow optimization relies on costly evaluations, making it essential to allocate a limited evaluation budget effectively. Multi-parent fusion can reuse designs from previously discovered workflows, but identifying promising parent combinations requires learning from limited fusion feedback. We introduce DAGO (Directed Acyclic Graph Optimization), a contextual-bandit-guided framework that learns which parent workflows to fuse under a limited evaluation budget. DAGO formulates each candidate parent combination as an arm, represented by pretrained embeddings of its constituent workflows' code and prompts. A diagonal LinUCB policy learns a shared reward model across arms and balances exploitation of arms with high predicted offspring quality against uncertainty-driven exploration. After an arm is selected, an LLM generates a child workflow through summary-guided fusion, and the child's validation score serves as the reward for updating the bandit. A shared directed acyclic graph maintains discovered workflows and their multi-parent lineage, providing an expanding pool of parents for subsequent arm proposals. Across six benchmarks covering mathematical reasoning, code generation, and question answering, DAGO achieves the highest macro-average score among the evaluated baselines. Under matched validation-evaluation budgets, it improves over AFlow from 80.3 to 81.7 while reducing aggregate search expenditure by 11.2%. Ablation studies show that LinUCB-guided arm selection outperforms both random selection and its exploration-free variant, supporting the value of feedback-driven selection and exploration-exploitation balance.
Chinese Translation
自动化的代理工作流优化依赖于高成本的评估，因此有效分配有限的评估预算至关重要。多父融合可以重用先前发现的工作流中的设计，但识别有前景的父组合需要从有限的融合反馈中学习。我们提出了 DAGO（有向无环图优化），这是一个基于上下文带子的指导框架，旨在在有限的评估预算下学习融合哪些父工作流。DAGO 将每个候选父组合形式化为一个臂，通过其组成工作流的代码和提示的预训练嵌入表示。对角线 LinUCB 策略学习跨臂的共享奖励模型，并在高预测后代质量的臂的利用与不确定性驱动的探索之间取得平衡。在选择一个臂后，LLM 通过摘要指导融合生成一个子工作流，而子工作流的验证分数作为更新带子的奖励。一个共享的有向无环图维护已发现的工作流及其多父系谱，为后续的臂提案提供一个扩展的父池。在涵盖数学推理、代码生成和问答的六个基准测试中，DAGO 在评估的基线中实现了最高的宏平均分。在匹配的验证-评估预算下，它将 AFlow 的得分从 80.3 提高到 81.7，同时将总搜索支出减少了 11.2%。消融研究表明，LinUCB 指导的臂选择优于随机选择及其无探索变体，支持反馈驱动选择和探索-利用平衡的价值。
cs.AI / 139 / 2610.05295
Readable Before Actionable: Causal Tracing of Indirect Prompt Injection
可读性优于可操作性：间接提示注入的因果追踪
Abstract
Indirect prompt injection causes LLM agents to follow commands embedded in external data. A probe may distinguish instructions from data without identifying a state edit that changes the next action. We study this gap through counterfactual role probes, component-wise activation patching, and separate interventions on AgentDojo trajectories. Role decoding survives changes in content and format. In controlled Qwen tests, it precedes strong tool-choice effects from patches along an independently estimated role direction. On AgentDojo, directions estimated from hijacked and resisted training trajectories reduce attack success at pre-action and injected-span positions, but have little effect at random positions. In longer Qwen trajectories, single-position edits become less effective at later layers; span-wide and repeated edits reduce attack success on the same evaluation set. Removing the learned channel subspace preserves role decoding, yet effective intervention directions transfer poorly across the tested channels. These findings distinguish a readable role signal from an effective behavioral intervention: depth matters in controlled tool choice, while position and context also matter in attack trajectories.
Chinese Translation
间接提示注入导致大型语言模型（LLM）代理遵循嵌入在外部数据中的指令。探测器可以区分指令和数据，而无需识别改变下一步行动的状态编辑。我们通过反事实角色探测、组件激活修补和对AgentDojo轨迹的独立干预来研究这一差距。角色解码在内容和格式变化中依然有效。在受控的Qwen测试中，它在独立估计的角色方向上，优先于来自修补的强工具选择效应。在AgentDojo中，从被劫持和抵抗的训练轨迹估计的方向在预行动和注入跨度位置减少了攻击成功率，但在随机位置几乎没有影响。在较长的Qwen轨迹中，单位置编辑在后续层次上的效果减弱；跨范围和重复编辑在同一评估集上减少了攻击成功率。去除学习的通道子空间保留了角色解码，但有效的干预方向在测试的通道间转移效果较差。这些发现区分了可读的角色信号与有效的行为干预：在受控工具选择中，深度很重要，而在攻击轨迹中，位置和上下文同样重要。
cs.AI / 140 / 2610.05300
MESH-Harness: Self-Improving Agent Harnesses via Bandit-Guided Compositional Evolution
MESH-Harness：通过带有赌博引导的组合进化实现自我改进的代理工具
Abstract
An agent harness is the code that organizes context, maintains state, and coordinates tool calls for a language model. We study how to improve the harness under a limited evaluation budget while keeping model weights fixed. Our method, MESH-Harness, organizes each harness into functional modules with explicit role-specific interfaces, allowing alternative implementations of each module to be substituted and recombined. It uses shared module representations and full-covariance LinUCB to score candidate combinations based on predicted performance and exploration value. Mixed-start coordinate ascent selects complete configurations for evaluation without enumerating the combinatorial space. Validation traces then guide local code edits, and the resulting candidates are incorporated into fixed-capacity role-specific pools for subsequent recombination. On text tasks, retrieval-augmented mathematical reasoning, code generation, and interactive scientific tasks, MESH-Harness outperforms Meta-Harness by 5.70, 7.01, 2.00, and 5.00 points, respectively, under matched candidate-evaluation budgets. Iterative harness optimization improves MESH-Harness by 5.63-7.79 points over its first-round configurations. For the reported configurations, aggregate test-time cost is 44.2% lower than that of Meta-Harness, while total cost including search is 14.6% lower. These results show that combining module-level design reuse with feedback-driven compositional search can systematically improve agent harnesses while keeping overall optimization cost under control.
Chinese Translation
代理工具是组织上下文、维护状态并协调语言模型工具调用的代码。我们研究在有限评估预算下如何改进代理工具，同时保持模型权重不变。我们的方法 MESH-Harness 将每个代理工具组织为具有明确角色特定接口的功能模块，允许替代实现的模块进行替换和重组。它使用共享模块表示和全协方差 LinUCB 来根据预测性能和探索价值对候选组合进行评分。混合起始坐标上升选择完整配置进行评估，而无需枚举组合空间。验证轨迹随后引导局部代码编辑，结果候选被纳入固定容量的角色特定池中以供后续重组。在文本任务、检索增强的数学推理、代码生成和互动科学任务中，MESH-Harness 在匹配的候选评估预算下，分别比 Meta-Harness 提高了 5.70、7.01、2.00 和 5.00 分。迭代代理工具优化使 MESH-Harness 在第一轮配置的基础上提高了 5.63-7.79 分。对于报告的配置，聚合测试时间成本比 Meta-Harness 低 44.2%，而包括搜索在内的总成本低 14.6%。这些结果表明，将模块级设计重用与基于反馈的组合搜索相结合，可以系统地改进代理工具，同时保持整体优化成本在可控范围内。
cs.AI / 141 / 2610.05334
AgentDiscover: Autonomous Discovery with Minimal Search Scaffolding
AgentDiscover：最小搜索支架下的自主发现
Abstract
Frameworks that use large language models for scientific discovery typically rely on a fixed, human-designed algorithm that decides what the model sees at each step, leaving the model only the role of proposer. The model knows nothing of the search beyond what it is shown. As models grow more capable, a question arises: does a search strategy chosen by a human before the run scale better than promoting the model from proposer to planner and letting it own the search? The Bitter Lesson suggests that choosing the strategy in advance is the kind of hand-designed structure that general methods eventually outscale. We introduce AgentDiscover, in which a coding agent plans the search using its context as working memory, runs experiments, and records every attempt in a database of ideas, candidates, and their relations. This database serves as the agent's long-term memory and is structured so that the selection rules of classical algorithms such as MAP-Elites and Monte Carlo tree search each reduce to a single query, which the agent is free to use, combine, or replace. A server maintains the database and steers the agent after every submission, keeping it on course over long runs. In our experiments, AgentDiscover is more cost-efficient than existing frameworks, reaching better scores at lower cost. On tasks in kernel engineering, biology, algorithm design, and mathematics, AgentDiscover outperforms prior discovery frameworks. Its programs would have placed first among human competitors in seven past AtCoder heuristic contests, and on eleven mathematical and systems optimization tasks it matches or exceeds every baseline that uses the same model. Our code is available at https://github.com/mhdfb/AgentDiscover.
Chinese Translation
使用大型语言模型进行科学发现的框架通常依赖于固定的、由人类设计的算法，该算法决定模型在每一步所看到的内容，使模型仅担任提议者的角色。模型对搜索的了解仅限于其所展示的内容。随着模型能力的提升，出现了一个问题：人类在运行之前选择的搜索策略是否比将模型从提议者提升为规划者并让其自主进行搜索更具可扩展性？‘苦涩的教训’表明，提前选择策略是一种最终会被通用方法超越的手工设计结构。我们引入了AgentDiscover，其中一个编码代理使用其上下文作为工作记忆来规划搜索，进行实验，并将每次尝试记录在一个想法、候选者及其关系的数据库中。该数据库作为代理的长期记忆，并以结构化的方式使得经典算法（如MAP-Elites和蒙特卡洛树搜索）的选择规则都简化为单个查询，代理可以自由使用、组合或替换这些查询。一个服务器维护该数据库，并在每次提交后引导代理，确保其在长时间运行中保持正确的方向。在我们的实验中，AgentDiscover比现有框架更具成本效益，以更低的成本获得更好的分数。在内核工程、生物学、算法设计和数学任务中，AgentDiscover的表现优于之前的发现框架。其程序在七次过去的AtCoder启发式竞赛中将位列人类竞争者之首，并且在十一项数学和系统优化任务中，其表现与使用相同模型的每个基准相匹配或超过。我们的代码可在https://github.com/mhdfb/AgentDiscover获取。
cs.AI / 142 / 2610.05370
EnGRICH: Enhancing Generative Reward Modeling with Critiques from Humans
EnGRICH：通过人类反馈增强生成奖励建模
Abstract
Generative reward models (GRMs) are important for LLM optimization. Unlike scalar reward models, GRMs generate natural-language critiques alongside preference judgments, providing finer-grained evaluation signals. Their effectiveness depends heavily on critique reliability. However, existing GRM training typically uses final preference correctness as outcome supervision. Because the preference outcome space is highly constrained, unreliable critiques can still yield correct outcomes and thus be reinforced. Recent work leverages human critiques for process supervision, but such critiques are scarce and are often reduced to scalar rewards, leaving their fine-grained evaluative information underutilized. We argue that evaluative criteria learned from human critiques can be generalized to broader outcome-only preference data. To this end, we propose \textbf{EnGRICH}, a GRM training framework that pairs the GRM with a training-time MetaCritic learned from a small set of human critiques. MetaCritic constructs response-specific rubrics and uses them to evaluate the evidence coverage and correctness of generated critiques. The resulting signals provide both process rewards for fine-grained credit assignment and structured guidance for exploring better critiques. During GRM training, MetaCritic is further optimized to generalize human-grounded evaluative criteria to outcome-only data. At inference, the trained GRM operates independently. Experiments across seven reward-model benchmarks show that EnGRICH consistently improves over competitive baselines, while further analyses validate the effectiveness of its core mechanisms.
Chinese Translation
生成奖励模型（GRMs）在大规模语言模型（LLM）优化中至关重要。与标量奖励模型不同，GRMs 除了提供偏好判断外，还生成自然语言的批评，从而提供更细粒度的评估信号。它们的有效性在很大程度上依赖于批评的可靠性。然而，现有的 GRM 训练通常使用最终偏好的正确性作为结果监督。由于偏好结果空间高度受限，不可靠的批评仍然可能产生正确的结果，从而被强化。最近的研究利用人类批评进行过程监督，但此类批评稀缺，且往往被简化为标量奖励，导致其细粒度评估信息未得到充分利用。我们认为，从人类批评中学习的评估标准可以推广到更广泛的仅基于结果的偏好数据。为此，我们提出了 extbf{EnGRICH}，一个将 GRM 与从少量人类批评中学习的训练时 MetaCritic 配对的 GRM 训练框架。MetaCritic 构建响应特定的评分标准，并利用这些标准评估生成批评的证据覆盖率和正确性。由此产生的信号为细粒度的信用分配提供了过程奖励，并为探索更好的批评提供了结构化指导。在 GRM 训练期间，MetaCritic 进一步优化，以将基于人类的评估标准推广到仅基于结果的数据。在推理阶段，训练后的 GRM 独立运行。跨越七个奖励模型基准的实验表明，EnGRICH 始终优于竞争基线，而进一步的分析验证了其核心机制的有效性。
cs.AI / 143 / 2610.05375
Learning Field Reconstruction from Incomplete Data by Globally Correcting Local Estimates
通过全局修正局部估计从不完整数据中学习场重建
Abstract
Reconstructing physical fields from training samples that are always incomplete requires learning spatial structure from fragmented observations.Existing context--query work establishes how held-out observations provide valid training targets, but this does not make the complete-field distribution identifiable when every training field is incomplete.With finite data, weak evidence of sharp transitions and localized variations can further favor averaged predictions that attenuate local detail.A structural prior is therefore needed to favor plausible completions; local spatial relationships offer one grounded in the observations.We propose a locally constructed, globally revisable estimator that explicitly learns local field estimates and subsequently corrects them using full-domain observations.A shared coordinate-conditioned predictor learns from incomplete patches, allowing relatively well-observed neighborhoods to provide direct supervision of local structure.Its overlapping predictions are reconciled into an observation-conditioned consensus field.A full-domain estimator retains the original observations and learns a residual correction around this frozen field estimate, allowing locally constructed structure to be revised by broader evidence.The local estimate serves as both an explicit input, accompanied by its discrepancies with the observations, and a prediction starting point that the global model can revise.On three real-world ocean datasets with authentic observation gaps, our estimator achieves the lowest MSE and highest PSNR on withheld source-supported values, reducing MSE by 28.9\%--34.5\% against the strongest external baseline.
Chinese Translation
从始终不完整的训练样本中重建物理场需要从碎片化的观察中学习空间结构。现有的上下文-查询工作建立了保留观察如何提供有效训练目标的关系，但这并未使得每个训练场不完整时的完整场分布可识别。在有限数据的情况下，锐利过渡和局部变化的微弱证据可能进一步倾向于平均预测，从而削弱局部细节。因此，需要一种结构先验来偏向合理的补全；局部空间关系提供了一个基于观察的先验。我们提出了一种局部构建、全局可修正的估计器，该估计器明确学习局部场估计，并随后使用全域观察对其进行修正。共享坐标条件的预测器从不完整的补丁中学习，使得相对观察良好的邻域能够直接监督局部结构。其重叠预测被调和为一个观察条件的共识场。全域估计器保留原始观察，并在这个冻结的场估计周围学习残差修正，从而允许局部构建的结构通过更广泛的证据进行修正。局部估计既作为显式输入，伴随其与观察的差异，也作为全球模型可以修正的预测起点。在三个具有真实观察缺口的海洋数据集上，我们的估计器在保留的源支持值上实现了最低的均方误差（MSE）和最高的峰值信噪比（PSNR），与最强的外部基线相比，MSE降低了28.9%至34.5%。
cs.AI / 144 / 2610.05383
Sibyl: An Efficient Small-large Model Collaboration Framework for Long-horizon Tasks
Sibyl：一种高效的小大型模型协作框架用于长时间任务
Abstract
Small language models (SLMs) offer a promising foundation for on-device agents through low-latency, resource-efficient inference, yet limited reasoning and planning capabilities constrain their performance on long-horizon tasks requiring multi-step interaction with the environment. Step-level collaboration between SLMs and larger cloud-hosted models can bridge this gap, but identifying states that warrant cloud assistance remains challenging: the contribution of each cloud call is entangled with subsequent actions and can be assessed only from the final task outcome. Compounding this challenge, the SLM must balance two competing objectives: maximizing task success and minimizing cloud calls. To address this, we propose Sibyl, an algorithm that trains SLM agents to selectively consult cloud models at the step level and internalize their guidance for subsequent decisions, achieving strong task performance with minimal cloud reliance. Sibyl follows a three-stage training pipeline that (1) builds a robust base policy through consultation-free self-evolving reinforcement learning (RL); (2) cold-starts consultation behavior via decisive-disagreement state mining; and (3) jointly optimizes consultation decisions and guidance internalization through consultation-aware RL. Experiments on ALFWorld and WebShop demonstrate that Sibyl, using only a 0.6B-parameter model, outperforms state-of-the-art baselines, including agent training and routing methods, by 95.2% and 80.4% in success rate while averaging only 0.8 and 3.9 cloud calls per trajectory, respectively.
Chinese Translation
小型语言模型（SLMs）通过低延迟和资源高效的推理，为设备上的智能体提供了一个有前景的基础，但有限的推理和规划能力限制了它们在需要与环境进行多步交互的长时间任务中的表现。SLMs与更大云端模型之间的逐步协作可以弥补这一差距，但识别需要云端协助的状态仍然具有挑战性：每次云端调用的贡献与后续动作交织在一起，仅能通过最终任务结果进行评估。更具挑战性的是，SLM必须平衡两个相互竞争的目标：最大化任务成功率和最小化云端调用次数。为了解决这个问题，我们提出了Sibyl，一种算法，训练SLM智能体在逐步层面上选择性地咨询云模型，并将其指导内化为后续决策，从而在最小化对云端依赖的情况下实现强大的任务表现。Sibyl遵循一个三阶段的训练流程：(1) 通过无咨询的自我演化强化学习（RL）构建稳健的基础策略；(2) 通过决定性分歧状态挖掘冷启动咨询行为；(3) 通过关注咨询的RL共同优化咨询决策和指导内化。在ALFWorld和WebShop上的实验表明，Sibyl仅使用一个0.6B参数的模型，在成功率上超越了最先进的基线，包括智能体训练和路由方法，分别提高了95.2%和80.4%，同时每个轨迹平均仅进行了0.8次和3.9次云端调用。
cs.AI / 145 / 2610.05398
MMPostTrainBench: Benchmarking Autonomous Research for Multimodal Post-Training
MMPostTrainBench：多模态后训练的自主研究基准测试
Abstract
Autonomous research seeks sustained model improvements through iterative experimentation and feedback. LLM agents show promise in automating machine learning and language-model post-training, but their ability to sustain multimodal improvement remains unclear. We introduce MMPostTrainBench, a benchmark spanning eight tasks in image, audio, video, and joint audio-video understanding and image-grounded software repair. Agents operate from a common base model within fixed budgets, using development feedback before independent evaluation of their submitted models. Evaluation covers target and non-target model outcomes, iterative model improvement and selection, and research integrity. Across all eight tasks, 52.1% of model--task means fall below the base, and evaluated submissions also exhibit non-target regressions. Model performance does not consistently improve across research iterations, and agents do not reliably select the best evaluated candidate for submission; final submissions trail that candidate by up to 5.38 percentage points. Extending autonomous research from text-only to multimodal tasks introduces additional sources of error in perception, cross-modal alignment, and temporal grounding. The observed regressions and selection gaps highlight the need to balance targeted improvements with non-target capability preservation and to retain gains across research iterations. These requirements motivate MMResearch, a multimodal research framework that connects media-grounded evidence to hypotheses and interventions, carries findings across rounds through hierarchical memory, and retains candidates using development evaluation. Added to existing code-agent runtimes, it improves submitted-model accuracy by up to 7.75 percentage points for Claude Opus 4.8 with Claude Code and 2.33 points for GPT-5.6-sol with Codex.
Chinese Translation
自主研究通过迭代实验和反馈寻求持续的模型改进。大型语言模型（LLM）代理在自动化机器学习和语言模型后训练方面显示出潜力，但它们在维持多模态改进方面的能力仍不明确。我们引入了MMPostTrainBench，这是一个涵盖图像、音频、视频以及联合音频-视频理解和图像基础软件修复的八个任务的基准测试。代理在固定预算内从一个共同的基础模型出发，使用开发反馈，然后独立评估其提交的模型。评估涵盖目标和非目标模型结果、迭代模型改进与选择以及研究完整性。在所有八个任务中，52.1%的模型-任务均值低于基础模型，评估的提交结果也表现出非目标回归。模型性能在研究迭代中并不总是一致提高，代理也未能可靠地选择最佳评估候选进行提交；最终提交的结果比该候选低最多5.38个百分点。从仅文本的自主研究扩展到多模态任务引入了感知、跨模态对齐和时间基础方面的额外错误来源。观察到的回归和选择差距突显了在目标改进与非目标能力保留之间平衡的必要性，以及在研究迭代中保持增益的需求。这些要求促使了MMResearch的产生，这是一个多模态研究框架，将媒体基础证据与假设和干预联系起来，通过层次记忆在多个回合中传递发现，并使用开发评估保留候选。与现有代码代理运行时结合后，它使Claude Opus 4.8与Claude Code的提交模型准确性提高了最多7.75个百分点，GPT-5.6-sol与Codex的提高幅度为2.33个百分点。
cs.AI / 146 / 2610.05400
CASE: Cost-Aware Stopping for Efficient Long-Video Agents
CASE：高效长视频代理的成本感知停止机制
Abstract
Long-video agents can actively gather question-relevant evidence, but they typically leave a central decision implicit: when has the agent seen enough to answer? We propose CASE, a plug-in termination framework that frames this decision as policy-conditioned sequential stopping. At each causal checkpoint, CASE combines an auxiliary multiple-choice assessment of accumulated evidence with the host agent's execution state. From complete native trajectories, we construct a cost-aware target that compares answering now with stopping later along the same search path, accounting jointly for answer correctness and the full cost of continued reasoning. A lightweight Ridge regressor learns this decision gap and produces STOP/CONTINUE decisions. We evaluate three vision-language models with VideoSeek and AVP. On Video-MME, end-to-end accuracy changes by +0.67 percentage points on average while CASE reduces model-token use by 53.63%. The same frozen policies then transfer zero-shot to LongVideoBench and MLVU, with end-to-end accuracy changes of +3.38 and +4.58 points while saving 58.78% and 51.28% of model tokens, respectively. Across all agent-model-benchmark combinations, CASE attains the highest accuracy-efficiency Pareto-frontier coverage among the compared stopping methods (83.3%) at the selected operating points. Online execution preserves this favorable accuracy-efficiency trade-off and additionally reduces measured runtime by 54.1% on average. CASE provides a plug-in termination framework for long-video reasoning agents, enabling them to decide when further evidence acquisition is no longer worthwhile.
Chinese Translation
长视频代理能够主动收集与问题相关的证据，但它们通常将一个核心决策隐含化：代理何时已经看到足够的信息以进行回答？我们提出了CASE，一个插件式终止框架，将这一决策框定为基于策略的顺序停止。在每个因果检查点，CASE将对累积证据的辅助多项选择评估与主代理的执行状态相结合。通过完整的原生轨迹，我们构建了一个成本感知目标，比较现在回答与沿同一路径稍后停止的选择，同时共同考虑答案的正确性和持续推理的全部成本。一个轻量级的Ridge回归器学习这一决策差距，并生成STOP/CONTINUE决策。我们使用VideoSeek和AVP评估了三种视觉-语言模型。在Video-MME上，端到端的准确率平均提高了0.67个百分点，而CASE将模型令牌的使用减少了53.63%。相同的冻结策略随后在LongVideoBench和MLVU上零-shot转移，端到端准确率分别变化了3.38和4.58个百分点，同时节省了58.78%和51.28%的模型令牌。在所有代理-模型-基准组合中，CASE在所比较的停止方法中实现了最高的准确率-效率帕累托前沿覆盖率（83.3%），在选定的操作点上。在线执行保持了这种有利的准确率-效率权衡，并且平均减少了54.1%的测量运行时间。CASE为长视频推理代理提供了一个插件式终止框架，使它们能够决定何时进一步获取证据已不再值得。
cs.AI / 147 / 2610.05431
PharmAgent: Constraint-Aware Search with Frozen Language Models for Molecular Optimization
PharmAgent：基于冻结语言模型的约束感知分子优化搜索
Abstract
Molecular optimization must improve target activity and satisfy developability constraints within limited evaluation budgets. Classical methods require tailored rules or training to incorporate chemical instructions and property feedback. Frozen language models can condition edits on this information, but need explicit constraint control and relevant experience. We therefore present PharmAgent, a constraint-aware molecular search method driven by adaptive external state. Its Lagrangian controller translates violations in accepted states into accumulated constraint pressure, keeping this history separate from current property measurements. Structure-indexed replay complements this feedback with relevant evaluated transitions that guide subsequent proposals. As a curriculum progressively activates constraints, candidates and the incumbent are compared under the same current objective, and the accepted state determines the next multiplier update. We derive an exact identity that characterizes how accepted-state violations accumulate in the controller's multipliers. Across five tasks with five independent runs, PharmAgent achieves a summed area under the target-score curves (AUC) of 3.9208 in target-only search, improving over MOLLEO by 37.3%. With online constraints, it achieves a property-adjusted AUC of 0.7076, improving over the strongest online baseline, ExLLM, by 53.8%. These results rank first among all evaluated methods in both target-only and constraint-aware search. The online comparison covers all five baseline frameworks. The full system leads every ablation variant in target quality, property-adjusted performance, and Pareto hypervolume. All five molecular cases reach feasible final states, documenting target gains and trade-offs.
Chinese Translation
分子优化必须在有限的评估预算内提高目标活性并满足可开发性约束。经典方法需要定制规则或训练以结合化学指令和属性反馈。冻结语言模型可以基于这些信息对编辑进行条件化，但需要明确的约束控制和相关经验。因此，我们提出了PharmAgent，一种由自适应外部状态驱动的约束感知分子搜索方法。其拉格朗日控制器将接受状态中的违规转化为累积的约束压力，将此历史与当前属性测量分开。结构索引重放通过相关的评估过渡补充了这一反馈，指导后续提案。随着课程逐步激活约束，候选者和现任者在相同的当前目标下进行比较，接受的状态决定了下一个乘数更新。我们推导出一个精确的恒等式，表征接受状态违规如何在控制器的乘数中累积。在五个任务的五次独立运行中，PharmAgent在仅目标搜索中实现了目标得分曲线下的总面积（AUC）为3.9208，比MOLLEO提高了37.3%。在在线约束下，其属性调整后的AUC为0.7076，比最强的在线基线ExLLM提高了53.8%。这些结果在所有评估方法中在仅目标和约束感知搜索中均排名第一。在线比较涵盖了所有五个基线框架。完整系统在目标质量、属性调整性能和帕累托超体积方面领先于每个消融变体。所有五个分子案例均达到可行的最终状态，记录了目标增益和权衡。
cs.AI / 148 / 2610.05437
TeleTune: Evolving Agent Skills From Offline Telemetry
TeleTune：从离线遥测中演化代理技能
Abstract
Computer-use agents need to capture procedural knowledge of how people use software. User telemetry offers a scalable source of this knowledge. However, learning reusable skills from these logs requires addressing three challenges: (1) Goal Underspecification, since logs do not record the goal behind each action; (2) Non-Replayability, since past activity cannot be replayed to evaluate skill updates; and (3) Interleaved Trajectories, since logs may mix several tasks without marking their boundaries. To address these, we introduce TeleTune, a framework for learning a textual skill library from offline logs without recorded goals, cannot be replayed during optimization, and may interleave tasks. TeleTune uses action-prediction errors on logged trajectories to propose library edits and keep only those that improve held-out action-prediction accuracy, which we call skill-guided progress. The learned workflows also enable retrieval of demonstrations that cover the subgoals of a new task. At test time, the agent is provided with the learned library and the workflow-based retrieved demonstrations. Experiments on WorkArena and Online-Mind2Web show that TeleTune outperforms random retrieval, Agent Workflow Memory (AWM), and their combination. We find that the best baseline varies by setting, whereas TeleTune achieves average success rates of 77.1% and 80.6%, respectively, improving over the strongest baseline on each benchmark by 6.7% and 7.7%. Under the heaviest perturbation of the WorkArena training data,TeleTune keeps the highest average success rate at 68.5%, 6.3% above the strongest baseline. Our analyses show (1) skill optimization and workflow-based retrieval are complementary, (2) optimizing on fixed logs costs 5 to 75 times fewer tokens than validating the same edits with live episodes, (3) skill-guided progress tracks the live success rate.
Chinese Translation
计算机使用代理需要捕捉人们使用软件的过程知识。用户遥测提供了一个可扩展的知识来源。然而，从这些日志中学习可重用技能需要解决三个挑战：（1）目标不明确，因为日志未记录每个动作背后的目标；（2）不可重放性，因为过去的活动无法重放以评估技能更新；（3）交错轨迹，因为日志可能混合了多个任务而未标记其边界。为了解决这些问题，我们提出了TeleTune，一个从没有记录目标的离线日志中学习文本技能库的框架，该日志在优化过程中无法重放，并且可能交错任务。TeleTune利用记录轨迹上的动作预测误差来提出库的编辑，并仅保留那些提高保留动作预测准确性的编辑，我们称之为技能引导进展。学习到的工作流程还能够检索覆盖新任务子目标的演示。在测试时，代理提供学习到的库和基于工作流程的检索演示。对WorkArena和Online-Mind2Web的实验表明，TeleTune的表现优于随机检索、代理工作流程记忆（Agent Workflow Memory, AWM）及其组合。我们发现最佳基线因设置而异，而TeleTune分别实现了77.1%和80.6%的平均成功率，分别比每个基准上最强基线提高了6.7%和7.7%。在WorkArena训练数据最严重的扰动下，TeleTune保持了68.5%的最高平均成功率，比最强基线高出6.3%。我们的分析表明（1）技能优化和基于工作流程的检索是互补的，（2）在固定日志上优化的成本比用实时情节验证相同编辑的成本低5到75倍，（3）技能引导进展跟踪实时成功率。
cs.AI / 149 / 2610.05461
AI Safety via Debate is Compromised by Cognitive Biases
通过辩论实现的人工智能安全受到认知偏见的影响
Abstract
Reinforcement learning from human feedback (RLHF) has played a central role in making large language models responsive to human instructions. However, human evaluators often favor flattering or persuasive responses over truthful ones, creating incentives for models to appeal to evaluators at the expense of accuracy. AI safety via debate has been proposed as a way to improve the supervision of language models: in this paradigm, two agents argue opposing positions and challenge each other's claims, potentially exposing falsehoods to the adjudicator. A central premise of AI safety via debate is that truthful arguments are easier to defend than false ones under adversarial scrutiny. In this work, we investigate whether this advantage persists when debaters use rhetorical strategies that exploit biases in human judgment. Inspired by competitive debate, we construct 68 LLM-generated dialogues about detective mysteries with known culprits, spanning four interventions: anchoring, fallacy oversight, pro-jargon, and verbosity. We apply each intervention to either the side advocating for the true culprit or the side advocating for an innocent suspect, allowing us to distinguish influence on adjudication from correctness. In a study with 369 participants, we find that, pooled across bias types, these interventions significantly shift judgments toward the manipulated side. These findings expose a vulnerability in debate-based supervision: human adjudication is sensitive to manipulative rhetorical strategies.
Chinese Translation
人类反馈的强化学习（RLHF）在使大型语言模型能够响应人类指令方面发挥了核心作用。然而，人类评估者往往偏爱奉承或有说服力的回答，而非真实的回答，这导致模型为了迎合评估者而牺牲准确性。通过辩论实现的人工智能安全被提议作为改善语言模型监督的一种方式：在这一范式中，两个代理人争论对立的观点并挑战彼此的主张，可能向裁决者揭示虚假信息。通过辩论实现的人工智能安全的一个核心前提是，在对抗性审查下，真实的论点比虚假的论点更容易辩护。在本研究中，我们探讨了当辩手使用利用人类判断偏见的修辞策略时，这一优势是否依然存在。受到竞争性辩论的启发，我们构建了68个关于已知罪犯的侦探谜题的LLM生成对话，涵盖四种干预：锚定、谬误监督、专业术语和冗长。我们将每种干预应用于支持真实罪犯的一方或支持无辜嫌疑人的一方，从而使我们能够区分对裁决的影响与正确性。在一项包含369名参与者的研究中，我们发现，跨偏见类型汇总，这些干预显著地将判断向被操控的一方倾斜。这些发现揭示了基于辩论的监督中的一个脆弱性：人类裁决对操控性修辞策略敏感。
cs.AI / 150 / 2610.05472
Hallucination Across the Reasoning Lifecycle: Interface Visibility, Causal Evidence, and Release Control in Large Reasoning Models
推理生命周期中的幻觉：大型推理模型中的接口可见性、因果证据与释放控制
Abstract
Reasoning errors can propagate into later decisions and memory. This survey synthesizes 312 papers and first-party reports on text-based reasoning hallucinations around three questions: what evidence is observable, what study designs establish, and which corrective actions the evidence supports. UIPCA records unsupported premises (U), invalid inferences (I), dependent reuse (P), visible answer-trace consistency (C), and action-policy failures (A). Across 58 reviewed sources, no comparison establishes that a specified intervention improves reasoning while reducing factual reliability under matched conditions. The synthesis connects diagnosis to verification, repair, selective release, and persistent-state control across memory, tools, and training feedback.
Chinese Translation
推理错误可能会传播到后续决策和记忆中。本调查综合了312篇论文和第一方报告，围绕三个问题探讨基于文本的推理幻觉：可观察到的证据是什么？研究设计如何确立？证据支持哪些纠正措施？UIPCA记录了不支持的前提（U）、无效推理（I）、依赖重用（P）、可见答案追踪一致性（C）和行动政策失败（A）。在58个审查来源中，没有比较研究证明在匹配条件下，特定干预措施能够改善推理同时减少事实可靠性。这一综合将诊断与验证、修复、选择性释放以及跨记忆、工具和训练反馈的持久状态控制联系起来。
cs.AI / 151 / 2610.05473
Hierarchical Reinforcement Learning with Stable Temporal Abstraction for Language Model Agents
具有稳定时间抽象的层次强化学习用于语言模型代理
Abstract
Hierarchical reinforcement learning improves long-horizon control by organizing primitive actions around persistent subgoals and assigning credit at multiple temporal scales. Recent hierarchical language agents bring these benefits to interactive tasks by explicitly separating subgoal planning from action execution. We observe, however, that an explicit hierarchy does not by itself determine how stable the resulting temporal abstraction is: the learned boundary policy may replace the subgoal almost every turn, making it effectively transient, or retain a subgoal after it has stopped being appropriate. We call this temporal abstraction instability. We propose Stable Temporal Abstraction via Constrained Optimization (STAC), a constrained boundary-policy optimization method that represents premature replanning and stale persistence as constraint costs. STAC applies the resulting Lagrangian costs only to the sampled boundary decision, leaving the underlying algorithm's rewards, critic targets, subgoal advantages, and primitive-action advantages unchanged. Across two backbones and two benchmarks, STAC improves success over a strong hierarchical baseline by $8.1$ and $7.9$ points on ALFWorld and WebShop with Qwen3-0.6B, and by $23.5$ and $15.8$ points with Llama-3.2-1B-Instruct.
Chinese Translation
层次强化学习通过围绕持久子目标组织原始动作并在多个时间尺度上分配信用，改善了长时间控制。然而，最近的层次语言代理通过明确将子目标规划与动作执行分开，将这些好处带入互动任务。然而，我们观察到，显式层次结构本身并不能决定所得到的时间抽象的稳定性：学习到的边界策略可能在几乎每一步都替换子目标，使其实际上变得短暂，或者在子目标不再适用后仍然保留。我们称这种现象为时间抽象不稳定性。我们提出了通过约束优化实现稳定时间抽象（Stable Temporal Abstraction via Constrained Optimization, STAC），这是一种约束边界策略优化方法，将过早重新规划和陈旧持久性表示为约束成本。STAC仅将得到的拉格朗日成本应用于采样的边界决策，保持基础算法的奖励、评论目标、子目标优势和原始动作优势不变。在两个基础模型和两个基准测试中，STAC在ALFWorld和WebShop上分别提高了强层次基线$8.1$和$7.9$个百分点，使用Qwen3-0.6B模型，以及使用Llama-3.2-1B-Instruct模型提高了$23.5$和$15.8$个百分点。
cs.AI / 152 / 2610.05481
CodeForge-MA: Execution-Verified Multi-Agent Learning with Language-Conditioned LoRA for Multilingual Code Generation
CodeForge-MA：基于语言条件的 LoRA 的执行验证多智能体学习用于多语言代码生成
Abstract
Large language models for code generation often fail on execution, multilingual coverage, and contamination control, especially under frozen backbone constraints. We present CodeForge-MA, a unified framework that improves code synthesis through a multi-agent data forge, execution verified reinforced instruction tuning, and a language conditioned mixture of LoRA adapters. Four specialized agents, Composer, Reviewer, Executor, and Curator, iteratively refine instruction code pairs, validate them with tests, and filter duplicates and benchmark leakage. During training, we combine masked supervised fine tuning with a test driven reinforcement objective to align generations with executable correctness. For the larger model, we use sparse expert routing over low rank adapters to improve cross language transfer while keeping the base model unchanged at inference. Experiments show that joint data, objective, and adapter design yields robust gains across programming languages.
Chinese Translation
用于代码生成的大型语言模型在执行、跨语言覆盖和污染控制方面常常存在不足，尤其是在冻结主干网络的约束下。我们提出了 CodeForge-MA，这是一个统一框架，通过多智能体数据锻造、执行验证的强化指令调优以及基于语言条件的 LoRA 适配器混合来改进代码合成。四个专门的智能体，Composer、Reviewer、Executor 和 Curator，迭代地优化指令代码对，使用测试验证它们，并过滤重复项和基准泄漏。在训练过程中，我们结合了掩码监督微调与测试驱动的强化目标，以使生成结果与可执行性正确性对齐。对于更大的模型，我们在低秩适配器上使用稀疏专家路由，以提高跨语言迁移能力，同时在推理时保持基础模型不变。实验表明，联合数据、目标和适配器设计在多种编程语言中带来了显著的提升。
cs.AI / 153 / 2610.05489
SkillGATE: Gate-Aware Monte Carlo Tree Search for Skill Retrieval
SkillGATE：基于门控的蒙特卡洛树搜索技能检索
Abstract
Skill Retrieval (SR) aims to identify the most relevant skills from external skill libraries, and becomes increasingly challenging as libraries grow in scale and diversity. Existing methods either rank skills independently or rely on predefined graph propagation and hierarchical routing, making them vulnerable to semantic distractors, local trapping, and early routing errors. We formulate SR as an adaptive information-foraging process that coordinates region-level navigation with skill-level selection according to the utility and uncertainty observed during search. Based on this formulation, we propose SkillGATE, a graph-guided hierarchical retrieval framework with Gate-Aware Monte Carlo Tree Search (MCTS). SkillGATE constructs a graph-preserving hierarchical index and performs adaptive retrieval through selection, expansion, simulation, and backpropagation. G-PUCT guides action selection, expansion explores new regions, simulation evaluates candidate skills, and backpropagation updates search statistics. Experiments on six SR benchmarks show that SkillGATE consistently improves diverse retrieval and reranking backbones, achieving a 16.3\% improvement in overall R@1 over the strongest retriever-based baseline. Our code is available at https://github.com/Edwinbe/SkillGATE-v1/.
Chinese Translation
技能检索（Skill Retrieval, SR）旨在从外部技能库中识别最相关的技能，随着库的规模和多样性不断增加，这一任务变得愈加具有挑战性。现有方法要么独立对技能进行排名，要么依赖于预定义的图传播和层次路由，这使得它们容易受到语义干扰、局部陷阱和早期路由错误的影响。我们将技能检索视为一种自适应的信息觅食过程，该过程根据搜索过程中观察到的效用和不确定性，协调区域级导航与技能级选择。基于这一框架，我们提出了SkillGATE，一种基于图的层次检索框架，采用门控蒙特卡洛树搜索（Gate-Aware Monte Carlo Tree Search, MCTS）。SkillGATE构建了一个图保持的层次索引，并通过选择、扩展、模拟和反向传播进行自适应检索。G-PUCT引导动作选择，扩展探索新区域，模拟评估候选技能，反向传播更新搜索统计数据。在六个SR基准测试中的实验表明，SkillGATE在多样化检索和重新排名的基础上始终表现出色，相较于最强的基于检索器的基线，整体R@1提升了16.3%。我们的代码可在https://github.com/Edwinbe/SkillGATE-v1/获取。
cs.AI / 154 / 2610.05504
The Functional Structure of Post-Compression Recovery in Low-Rank LLMs
低秩大语言模型中后压缩恢复的功能结构
Abstract
Different low-rank compression methods can produce compressed LLMs that respond differently to the same post-compression recovery procedure, and relative advantages observed between methods at the compression endpoint may shrink, grow, or even reverse after recovery. We ask whether this recovery heterogeneity reflects functional structure beyond scalar loss evolution, and how that structure evolves throughout recovery. Our results establish that this heterogeneity reflects a reproducible compression-induced functional structure, which we formalize as recovery pressure. To characterize this structure consistently throughout recovery, we develop a standardized functional characterization within each backbone that is applicable across heterogeneous low-rank methods. The primary backward characterization reveals reproducible module-wise structure across independent probes, while a complementary forward-only characterization recovers related structure without loss or backpropagation. We further find that recovery pressure measured at the endpoint is associated with subsequent recovery response; during recovery, its module-wise structure is reorganized non-uniformly, and localized updates induce distributed responses beyond directly updated modules. Further evidence indicates that tracking this evolving structure provides a complementary functional view of recovery progress alongside scalar loss.
Chinese Translation
不同的低秩压缩方法可以产生对相同后压缩恢复过程响应不同的压缩大语言模型（LLMs），在压缩终点观察到的方法之间的相对优势可能在恢复后缩小、扩大，甚至反转。我们探讨这种恢复异质性是否反映了超越标量损失演变的功能结构，以及该结构在恢复过程中的演变。我们的结果表明，这种异质性反映了一种可重复的压缩引起的功能结构，我们将其形式化为恢复压力。为了在整个恢复过程中一致地表征这一结构，我们在每个主干网络内开发了一种标准化的功能表征，适用于不同的低秩方法。主要的向后表征揭示了在独立探针之间可重复的模块化结构，而补充的仅向前表征则在没有损失或反向传播的情况下恢复相关结构。我们进一步发现，在终点测量的恢复压力与随后的恢复响应相关；在恢复过程中，其模块化结构以非均匀方式重新组织，局部更新引发超出直接更新模块的分布式响应。进一步的证据表明，跟踪这一演变结构提供了恢复进展的补充功能视角，与标量损失并行。
cs.AI / 155 / 2610.05518
What Does Fr\'echet Distance Measure? A Directional Decomposition
Fréchet距离测量了什么？一种方向性分解
Abstract
The Fr\'echet distance is a de facto standard for evaluating generative models across domains, appearing as FID for images and FVD for videos. It summarizes the discrepancy between generated and reference distributions in a single scalar, with lower values typically interpreted as better generation quality. However, this scalar view can obscure what drives the comparison. For example, in COCO dataset, increasing the number of diffusion sampling steps improves ImageReward scores yet worsens (increases) FID. Motivated by this mismatch, we seek to make the Fr\'echet distance more interpretable by uncovering where the discrepancy lies. To this end, we introduce directional Fr\'echet distance, the expected squared projection of the optimal transport displacement onto a given direction. Across our image, video, and protein case studies, we find that a small number of interpretable directions account for much of the distance. We use these directions to explain the FID increase in terms of semantic concepts represented by CLIP embeddings, quantify FVD's bias toward per-frame appearance, and revisit the interpretation of Protein FID. We open-source our codebase at https://github.com/yhlee-add/directional-fd.
Chinese Translation
Fréchet距离是评估跨领域生成模型的事实标准，图像中表现为FID，视频中表现为FVD。它通过一个单一的标量总结了生成分布与参考分布之间的差异，通常较低的值被解释为更好的生成质量。然而，这种标量视角可能掩盖了比较的驱动因素。例如，在COCO数据集中，增加扩散采样步骤的数量提高了ImageReward分数，但却加重了（增加了）FID。受到这种不匹配的启发，我们寻求通过揭示差异所在，使Fréchet距离更具可解释性。为此，我们引入了方向性Fréchet距离，即最佳运输位移在给定方向上的期望平方投影。在我们的图像、视频和蛋白质案例研究中，我们发现少数可解释的方向占据了大部分距离。我们利用这些方向解释FID的增加与CLIP嵌入所表示的语义概念之间的关系，量化FVD对每帧外观的偏见，并重新审视蛋白质FID的解释。我们的代码库已开源，地址为https://github.com/yhlee-add/directional-fd。
cs.AI / 156 / 2610.05526
Have I Scene This Before? Spatially Grounded Conversational Memory for Complex Queries in Egocentric Assistants
我之前见过这个吗？面向复杂查询的空间基础对话记忆在自我中心助手中的应用
Abstract
Egocentric assistants must connect what users say with what they see across long interaction histories. We formalize this challenge as Spatially grounded Conversational Reasoning (SpaCR): cross-scene, recall-oriented, and counterfactual spatial queries that combine user-stated facts with geometric evidence. Direct vision-language models incur high inference costs and context limits as histories grow, while keyframe selection and retrieval can omit objects or evidence needed for complete recall. We propose Spatially grounded Conversational Memory (SpaC-MEM), an object-centric working memory that uses 3D reconstruction and segmentation to ground conversational information in persistent physical objects. It compresses multimodal histories while preserving spatial evidence and allowing object-specific facts to be updated through dialogue. We also introduce Ego-SpaCR, a benchmark comprising 620 ScanNet video sessions augmented with 95 task-oriented conversations and 3,100 evaluation queries. SpaC-MEM achieves the highest overall answer accuracy among the evaluated methods and improves object recall while requiring substantially fewer reference input tokens than native-video baselines. Removing 3D spatial information substantially degrades performance, highlighting the importance of preserving spatial and conversational evidence together.
Chinese Translation
自我中心助手必须将用户所说的内容与他们在长时间交互历史中所看到的内容联系起来。我们将这一挑战形式化为空间基础对话推理（Spatially grounded Conversational Reasoning, SpaCR）：跨场景、以回忆为导向的反事实空间查询，结合用户陈述的事实与几何证据。直接的视觉-语言模型在历史增长时会产生高推理成本和上下文限制，而关键帧选择和检索可能会遗漏完整回忆所需的对象或证据。我们提出了空间基础对话记忆（Spatially grounded Conversational Memory, SpaC-MEM），这是一种以对象为中心的工作记忆，利用3D重建和分割将对话信息与持久的物理对象相结合。它在压缩多模态历史的同时保留空间证据，并允许通过对话更新特定对象的事实。我们还引入了Ego-SpaCR，这是一个基准数据集，包含620个ScanNet视频会话，增强了95个任务导向的对话和3,100个评估查询。SpaC-MEM在评估的方法中实现了最高的整体答案准确率，并改善了对象回忆，同时所需的参考输入标记数量显著少于原生视频基线。去除3D空间信息会显著降低性能，突显了同时保留空间和对话证据的重要性。
cs.AI / 157 / 2610.05532
DelegationBench: Measuring When AI Agents Should Ask Before Acting
DelegationBench：测量人工智能代理在行动前何时应询问
Abstract
AI agents that send emails, edit files, and make purchases must decide when to act on their own and when to check with the user first. This decision is usually evaluated by showing a model a proposed action, asking whether it should proceed, and scoring agreement with human labels. We introduce DelegationBench to test whether such scores can be trusted. It has 156 scenarios with four possible responses (act, ask for permission, ask for missing information, refuse), and most scenarios come in matched pairs that change a single feature: whether the action was requested, what is at stake, whether it can be undone, or who will see it. Across ten models from five families, agreement scores mislead in three ways. A simple keyword rule, which we wrote after seeing the benchmark, agrees with our annotators more often than eight of the models, yet its decision changes in only 9 of 48 matched pairs. Equivalent ways of asking the same question change how often a model acts by up to 52.5 percentage points. And every model stops to ask the user less often when it must carry out the task with tools than when it judges a proposed action. When rules are stated explicitly, the same models follow them almost perfectly, so the gaps are not explained by a general inability to follow rules. We release the benchmark and evaluation tools and recommend reporting these properties separately rather than as one score.
Chinese Translation
发送电子邮件、编辑文件和进行购买的人工智能代理必须决定何时可以自主行动，何时需要先与用户确认。这个决策通常通过向模型展示一个拟议的行动，询问是否应继续，并根据人类标签的一致性进行评分来评估。我们引入了DelegationBench，以测试这些评分是否值得信赖。该基准包含156个场景，提供四种可能的响应（行动、请求许可、询问缺失信息、拒绝），大多数场景以匹配对的形式出现，仅改变一个特征：行动是否被请求、风险是什么、是否可以撤销，或谁将看到它。在来自五个家族的十个模型中，一致性评分在三个方面具有误导性。我们在看到基准后提出的一个简单关键词规则，与我们的标注者的意见一致的频率超过了八个模型，但其决策在48个匹配对中仅改变了9次。以等效方式询问同一问题会使模型的行动频率变化高达52.5个百分点。当模型必须使用工具执行任务时，它们询问用户的频率低于判断拟议行动时的频率。当规则被明确陈述时，同样的模型几乎完美地遵循这些规则，因此这些差距并不能用一般无法遵循规则来解释。我们发布了该基准和评估工具，并建议单独报告这些属性，而不是作为一个评分。
cs.AI / 158 / 2610.05547
More Claims, Less Evidence: Bounded Verification of AI-Generated Digital Knowledge Artifacts
更多的主张，较少的证据：AI生成数字知识工件的有限验证
Abstract
Digital libraries, repositories, and AI-mediated knowledge services increasingly rely on generative systems to produce summaries, descriptions, and other multi-claim knowledge objects. Yet generation can scale far more easily than verification capacity: a reviewer may need to decide whether an object is suitable for publication or downstream use after checking only a small fraction of its claims. This creates a fundamental gap between claim-level verification and confidence in the object as a whole. The central question is therefore what a successful partial check implies about the reliability of the complete artifact when its size grows but the verification budget does not. This paper contributes a Bayesian model of bounded verification centered on the Predictive Value of Pass (PVP). The model predicts that evidentiary value decreases as artifacts grow under fixed verification capacity, improves with larger verification budgets, and is especially fragile when errors are sparse. Controlled experiments on FEVEROUS and FEVER support these predictions and show that adding supported claims around a fixed number of false or unsupported claims can make passing more likely while making a pass less informative. The model further yields the minimum verification budget required to maintain a target PVP, providing a practical component for AI-assisted quality-assurance workflows in which generated knowledge objects must be checked before publication or downstream use.
Chinese Translation
数字图书馆、资料库和AI介导的知识服务越来越依赖生成系统来制作摘要、描述和其他多主张知识对象。然而，生成的规模往往远远超过验证能力：审稿人可能只需检查其主张的一小部分，就需要决定该对象是否适合出版或后续使用。这在主张级验证与整体对象的信心之间造成了根本性的差距。因此，核心问题是，当对象的规模增长而验证预算不变时，成功的部分检查对完整工件的可靠性意味着什么。本文贡献了一个以预测通过值（Predictive Value of Pass, PVP）为中心的有限验证贝叶斯模型。该模型预测，随着工件在固定验证能力下的增长，证据价值会下降，随着更大的验证预算而改善，并且在错误稀疏时尤其脆弱。在FEVEROUS和FEVER上的受控实验支持这些预测，并表明在固定数量的虚假或不支持的主张周围添加支持的主张可以使通过的可能性增加，同时使通过的有效性降低。该模型进一步提供了维持目标PVP所需的最低验证预算，为AI辅助的质量保证工作流程提供了实用的组成部分，在这些工作流程中，生成的知识对象必须在出版或后续使用之前进行检查。
cs.AI / 159 / 2610.05566
LifeLong Digital Twin: A Unified Modeling Paradigm and Agent Harness for Event-Driven Lifelong Health State Trajectories
终身数字双胞胎：事件驱动的终身健康状态轨迹统一建模范式与代理工具
Abstract
Human health is a continuous, dynamic trajectory shaped by the cumulative interplay of biological processes, clinical events, behaviors and environmental exposures across the life course. Unifying the full breadth of lifelong health information, including longitudinal records, genetic variation, molecular profiles and environmental histories, is essential for whole-person modeling and remains a major challenge. We introduce LifeLong Digital Twin, a unified, event-driven modeling paradigm that organizes Life Events into daily Health States and accumulates them into Lifelong Health Context. An accompanying Agent Harness incorporates multimodal evidence beyond the language model's textual context. We evaluate four language models across 25 disease endpoints on three tasks: Disease Trajectory Forecasting, Disease Risk Ranking and Multi-horizon Disease Prediction. The approach yields marked gains over the reference condition: model-averaged F1 increases by 22.0% for disease identification in trajectory forecasting and 18.3% for five-year disease outcomes; thyroid-disease F1 reaches 0.669. The framework provides a foundation for whole-person digital twins and research on personalized lifelong disease prevention.
Chinese Translation
人类健康是一个连续的、动态的轨迹，受到生物过程、临床事件、行为和环境暴露在整个生命历程中累积相互作用的影响。统一终身健康信息的全貌，包括纵向记录、遗传变异、分子特征和环境历史，对于整体性建模至关重要，但仍然是一个重大挑战。我们提出了终身数字双胞胎（LifeLong Digital Twin），这是一种统一的事件驱动建模范式，将生活事件组织为日常健康状态，并将其累积为终身健康背景。配套的代理工具（Agent Harness）整合了超越语言模型文本上下文的多模态证据。我们在三个任务上评估了四种语言模型在25个疾病终点上的表现：疾病轨迹预测、疾病风险排序和多视角疾病预测。该方法在参考条件下取得了显著提升：在轨迹预测中，模型平均F1值在疾病识别上提高了22.0%，在五年疾病结果上提高了18.3%；甲状腺疾病的F1值达到了0.669。该框架为整体性数字双胞胎和个性化终身疾病预防研究提供了基础。
cs.AI / 160 / 2610.05569
Factoriax: A GPU-Accelerated Factorio-Style Simulator for Reinforcement Learning
Factoriax：一种用于强化学习的GPU加速Factorio风格模拟器
Abstract
We introduce Factoriax, a GPU-accelerated factory-building simulator written in JAX. In Factoriax, an agent must collect resources, build machines using those resources, and then automate the collection and crafting process by arranging machines on the map to build production pipelines. This paper discusses the structure of the Factoriax simulator and an initial benchmark called Easy Rocket in which an agent is tasked with building a resource-intensive machine called the Rocket in a limited number of game ticks to escape the planet. We also publish results from a number of PPO-based training runs on Easy Rocket. Factoriax is built to be fast. A 1-billion-step PPO training run, equivalent to 500,000 episodes, runs on Easy Rocket in about 8 minutes on a single NVIDIA A100. A standard laptop GPU can complete the same run in about 84 minutes. Our trained PPO agent learns to gather resources, craft machines from those resources, and place them on the map through a curriculum reward directly tied to a manually designed set of achievements. After training, the agent does not place machines in a functional spatial configuration, failing to fully complete the benchmark, and leaving the challenge open for future attempts.
Chinese Translation
我们介绍了Factoriax，这是一款用JAX编写的GPU加速工厂建设模拟器。在Factoriax中，代理必须收集资源，利用这些资源建造机器，然后通过在地图上排列机器来自动化资源收集和制作过程，从而构建生产流水线。本文讨论了Factoriax模拟器的结构以及一个名为Easy Rocket的初步基准，其中代理的任务是在有限的游戏时间内建造一种资源密集型机器——火箭，以逃离星球。我们还发布了在Easy Rocket上进行的一系列基于PPO（Proximal Policy Optimization）训练的结果。Factoriax的设计旨在快速高效。在Easy Rocket上进行的10亿步PPO训练，相当于500,000个回合，在单个NVIDIA A100上大约运行8分钟，而标准笔记本GPU完成相同的训练大约需要84分钟。我们训练的PPO代理学习了如何收集资源、利用这些资源制作机器，并通过与手动设计的一组成就直接相关的课程奖励将它们放置在地图上。训练后，代理未能将机器放置在功能性空间配置中，未能完全完成基准测试，这为未来的尝试留下了挑战。
cs.AI / 161 / 2610.05573
Better Retrieval, Limited Clustering Gains: A Controlled Study of Multilingual Company Entity Resolution
更好的检索，有限的聚类收益：多语言公司实体解析的受控研究
Abstract
Improved name retrieval may have little effect on company clusters when the pair classifier remains unchanged. We examine this dependency by adapting multilingual E5 encoders under fixed candidate budgets and downstream decision rules. Random-negative and hard-negative training use identical positive schedules. Checkpoints are selected before collecting a new GLEIF sample of 3,633 names, 2,880 source identities and 882 silver-positive pairs. At 72,660 candidate edges, adaptation with a multi-view selector increases direct pair recall from 53.74% to 76.98%. The primary matcher adds only seven correct and two incorrect co-cluster pairs: cluster recall rises from 32.54% to 33.33%, while precision falls from 95.99% to 95.45%. Of 208 newly retrieved silver-positive pairs, 202 fall below its decision threshold. Random-negative and hard-negative training produce identical final partitions. An AI-assisted, single-reviewer audit of 137 pairs supports the observed pattern, although its predominantly LEI-derived evidence does not establish independent gold labels. The results locate the immediate loss of retrieval gains at the existing confirmation stage and show why encoder evaluation must also measure final cluster quality.
Chinese Translation
当配对分类器保持不变时，改进的名称检索可能对公司聚类的影响有限。我们通过在固定候选预算和下游决策规则下调整多语言 E5 编码器来研究这种依赖关系。随机负样本和困难负样本训练使用相同的正样本调度。在收集新的 GLEIF 样本（3,633 个名称、2,880 个源身份和 882 个银正对）之前选择检查点。在 72,660 个候选边中，使用多视图选择器的适应性使直接配对召回率从 53.74% 提高到 76.98%。主要匹配器仅增加了七个正确和两个错误的共同聚类对：聚类召回率从 32.54% 上升到 33.33%，而精确率则从 95.99% 降低到 95.45%。在 208 个新检索到的银正对中，202 个低于其决策阈值。随机负样本和困难负样本训练产生相同的最终分区。对 137 对的 AI 辅助单审计支持了观察到的模式，尽管其主要基于 LEI 的证据并未建立独立的金标签。结果定位于现有确认阶段的检索收益的直接损失，并展示了为什么编码器评估还必须衡量最终聚类质量。
cs.AI / 162 / 2610.05615
DREAM: Dynamic Resolution Assignment For Multimodal Multi-agent Debate
DREAM：多模态多智能体辩论的动态分辨率分配
Abstract
Multi-agent debate (MAD) has emerged as an effective paradigm to improve the reasoning capabilities of large language models (LLMs) and is increasingly being extended to multimodal settings. However, existing multimodal MAD frameworks typically expose agents to the same fixed visual input, ignoring substantial variation in the visual scale needed across samples and agents. In addition, these frameworks frequently suffer from groupthink, a phenomenon where agents prematurely abandon correct deductions to conform with confident but hallucinated peer responses. To address these bottlenecks, we introduce DREAM (Dynamic Resolution Assignment For Multimodal Multi-Agent Debate), which operates via two core components: (1) Dynamic Resolution Assignment, a zero-shot probe round where agents test multiple resolutions, quantify uncertainty using Average Normalized Log-Likelihood (ANLL), and use an adaptive threshold to assign each agent to its empirically optimal resolution; (2) Uncertainty-Guided Rollback Aggregation counters groupthink by tracking each agent's uncertainty over rounds and restoring early low-uncertainty answers overridden by group pressure. On six multimodal datasets, DREAM improves the accuracy-token trade-off over multi-agent debate baselines by 1.5-3.2% accuracy without dataset-specific tuning.
Chinese Translation
多智能体辩论（MAD）已成为提高大型语言模型（LLMs）推理能力的有效范式，并越来越多地扩展到多模态环境中。然而，现有的多模态MAD框架通常将智能体暴露于相同的固定视觉输入，忽视了不同样本和智能体所需的视觉尺度的显著变化。此外，这些框架常常遭受群体思维现象的困扰，即智能体过早放弃正确的推理，以迎合自信但虚幻的同伴回应。为了解决这些瓶颈，我们提出了DREAM（多模态多智能体辩论的动态分辨率分配），其通过两个核心组件运作：（1）动态分辨率分配，一个零-shot探测回合，在此过程中，智能体测试多个分辨率，使用平均归一化对数似然（ANLL）量化不确定性，并使用自适应阈值将每个智能体分配到其经验上最优的分辨率；（2）不确定性引导的回滚聚合，通过跟踪每个智能体在回合中的不确定性，并恢复被群体压力覆盖的早期低不确定性答案，从而对抗群体思维。在六个多模态数据集上，DREAM在多智能体辩论基线的准确性-代币权衡上提高了1.5-3.2%的准确率，而无需特定于数据集的调优。
cs.AI / 163 / 2610.05625
A Framework for Automated Multi-Source Satellite Data Analytics and LLM-Based Report Generation
基于自动化多源卫星数据分析和大型语言模型的报告生成框架
Abstract
This paper presents the workflow for building an automated ArcGIS Pro tool using ArcPy to extract the Land Surface Temperature (LST) from Landsat 7, 8 and 9 datasets. The tool eliminates the need for manual band selection and repetitive raster computations by automating the multi-step workflow of radiometric calibration, NDVI-based emissivity correction, and thermal conversion. In addition to supporting batch and single-scene processing, the tool has an optional Large Language Model (LLM) for statistical result interpretation and reporting. Depending on batch size, the tool reduced the processing time from around 11-58 minutes when done manually to around 4-11 minutes using the tool. We tested the tool with data from Ras Al Khaimah (RAK) in the UAE, and the LST obtained for Ras Al Khaimah ranged from approximately 25C to 50C, demonstrating an accurate LST mapping compatible with the weather conditions of RAK. In summary, our tool reduces human errors and improves processing accuracy and efficiency for thermal and environmental remote sensing applications, in addition to providing an interactive LLM-based interface for result interpretation.
Chinese Translation
本文提出了一种使用ArcPy构建自动化ArcGIS Pro工具的工作流程，该工具用于从Landsat 7、8和9数据集中提取地表温度（LST）。该工具通过自动化辐射校正、基于NDVI的发射率校正和热转换的多步骤工作流程，消除了手动选择波段和重复栅格计算的需要。除了支持批处理和单场景处理外，该工具还配备了一个可选的大型语言模型（LLM），用于统计结果的解释和报告。根据批处理大小，该工具将手动处理时间从大约11-58分钟减少到使用该工具后约4-11分钟。我们使用阿联酋拉斯阿尔海玛（RAK）地区的数据对该工具进行了测试，获得的拉斯阿尔海玛的LST范围约为25°C至50°C，展示了与RAK气候条件相兼容的准确LST映射。总之，我们的工具减少了人为错误，提高了热遥感和环境遥感应用的处理准确性和效率，并提供了一个基于LLM的交互式界面用于结果解释。
cs.AI / 164 / 2610.05633
The GenAI4IDN Benchmark 3.0 - a Public Tool to Assess Generative AI Tools for the Design of Interactive Digital Narratives
GenAI4IDN 基准 3.0 - 评估生成性人工智能工具设计互动数字叙事的公共工具
Abstract
This paper presents GENAI4IDN Benchmark 3.0, the third iteration of an evaluation framework to assess Generative AI tools for creating Interactive Digital Narratives (IDNs). Moving beyond manual testing, this iteration introduces AI-assisted evaluation through a publicly accessible web application (https://genai4idn.com), enabling the community to run benchmarks on demand, add new models, and propose new tasks. The revised evaluation framework is "blinded" to avoid model-bias, and can handle complex media such as music, videos, and full IDNs that previously required human raters. A significant addition - responding to concerns raised during ICIDS 2025 - is the addition of fact-checking and bias detection with dedicated tasks and rubrics, validated by human raters with lived experience in the depicted contexts. Findings from a diverse range of models report on maturing creative capabilities while observing runaway thinking and overzealous safety filters as limitations. Fact-checking reliably caught subtle historical inaccuracies, anachronisms, and fabricated claims while the bias rater consistently exposed structural assumptions, tropes, and marginalized group erasures.
Chinese Translation
本文介绍了 GENAI4IDN 基准 3.0，这是一个评估生成性人工智能工具用于创建互动数字叙事（IDNs）的评估框架的第三个版本。与手动测试不同，此版本通过一个公开可访问的网络应用程序（https://genai4idn.com）引入了人工智能辅助评估，使社区能够按需运行基准测试、添加新模型并提出新任务。修订后的评估框架采用“盲测”方式，以避免模型偏见，并能够处理复杂媒体，如音乐、视频和完整的 IDNs，这些在之前需要人类评审者进行评估。一个重要的补充是，针对在 ICIDS 2025 期间提出的担忧，增加了事实核查和偏见检测，配备专门的任务和评分标准，由在所描绘情境中具有生活经验的人类评审者进行验证。来自多种模型的研究结果报告了创意能力的成熟，同时观察到失控的思维和过于严苛的安全过滤器作为局限性。事实核查可靠地捕捉到了微妙的历史不准确性、时代错误和虚构的主张，而偏见评审者则持续揭示了结构性假设、陈词滥调和边缘群体的抹去。
cs.AI / 165 / 2610.05637
Visual Grounding Safety in Vision-Language Models
视觉语言模型中的视觉定位安全性
Abstract
Vision-language models (VLMs) are increasingly trained to generate structured outputs like points and bounding boxes that downstream interfaces, agents, and robots can act on, yet safety alignment of this output channel has not been systematically analyzed. We study visual grounding safety by repurposing three safety benchmarks spanning direct harm (VLSU), social bias (BBQ-V), and situational safety (Asimov-2.0) into 15,401 matched pairs of harmful requests that differ only in the requested output: a free-text answer (VQA) or a grounding (point or bounding box). Across five VLMs, models that refuse a harmful request posed as a question often comply when the same request asks for a grounding: averaged over models, grounding refusal trails VQA refusal by 31-59 percentage points, depending on the domain, and safety system prompts do not close this gap. We propose a fine-tuning approach that combines grounding-form refusals with capability grounding data and self-distilled benign data to counter over-refusal. For Qwen3-VL-8B and VisionReasoner-7B, it improves grounding refusal by 77-95 percentage points on VLSU and BBQ-V and by 64-85 points on the held-out Asimov-2.0 domain, while also improving VQA refusal, preserving grounding capability, and keeping over-refusal limited. Representation analysis shows that fine-tuning moves harmful requests toward each model's refusal direction, most strongly for grounding, while leaving benign requests near the harmless reference.
Chinese Translation
视觉语言模型（VLMs）越来越多地被训练以生成结构化输出，如点和边界框，以供下游接口、代理和机器人使用，但对这一输出通道的安全性对齐尚未进行系统分析。我们通过重新利用三个涵盖直接伤害（VLSU）、社会偏见（BBQ-V）和情境安全（Asimov-2.0）的安全基准，研究视觉定位安全性，构建了15,401对仅在请求输出上有所不同的有害请求：自由文本答案（VQA）或定位（点或边界框）。在五个VLM模型中，当以问题形式提出的有害请求被拒绝时，模型在相同请求要求定位时往往会顺从：在模型之间平均，定位拒绝的比例比VQA拒绝低31-59个百分点，具体取决于领域，而安全系统提示并未缩小这一差距。我们提出了一种微调方法，将定位形式的拒绝与能力定位数据和自我蒸馏的良性数据相结合，以应对过度拒绝。对于Qwen3-VL-8B和VisionReasoner-7B，该方法在VLSU和BBQ-V上提高了77-95个百分点的定位拒绝率，在保留的Asimov-2.0领域提高了64-85个百分点，同时也改善了VQA拒绝，保持了定位能力，并限制了过度拒绝。表示分析表明，微调使有害请求朝向每个模型的拒绝方向移动，尤其是对于定位请求，而良性请求则保持在无害参考附近。
cs.AI / 166 / 2610.05671
How Should a Prompt Optimizer Spend a Tight Budget? BudgetAPO with Noise-Adaptive Evaluation
在紧张预算下，提示优化器应如何支出？带有噪声自适应评估的 BudgetAPO
Abstract
Automatic prompt optimization (APO) has been widely employed to adapt large language models without updating their weights, yielding promising results. However, existing methods such as GEPA and OPRO assume hundreds to thousands of subject-model calls, far more than is practical behind paid, rate-limited APIs. Under tight budgets they fail in two ways: multi-stage pipelines can exhaust the budget and return the seed prompt unchanged, while single-stage methods compare candidates on fixed-size minibatches, regardless of each task's noise. As a remedy, we introduce BudgetAPO, a single-stage optimizer for the tight-budget regime. BudgetAPO incorporates (1) a noise-adaptive rule that sizes the evaluation slice to each task's noise, measured by a short probe; (2) a fixed slice that turns every accept/reject decision into a paired comparison; and (3) a reflective operator that rewrites reasoning strategy and output format jointly. Extensive results across seven benchmarks and five subject models demonstrate that BudgetAPO ranks first on every subject and beats every baseline under Holm-corrected paired tests, while returning the seed in 13% of runs at 250 calls against 86% for GEPA. On GPT-OSS-20B, GEPA needs 4.5 times as many calls to match BudgetAPO's 100-call score.
Chinese Translation
自动提示优化（APO）已被广泛应用于在不更新权重的情况下调整大型语言模型，取得了令人鼓舞的结果。然而，现有方法如 GEPA 和 OPRO 假设需要进行数百到数千次的模型调用，这在付费、限速的 API 背后并不实际。在紧张预算下，这些方法存在两种失效情况：多阶段管道可能会耗尽预算并返回未改变的种子提示，而单阶段方法则在固定大小的小批量上比较候选者，而不考虑每个任务的噪声。为了解决这一问题，我们提出了 BudgetAPO，这是一种适用于紧张预算的单阶段优化器。BudgetAPO 包括（1）一种噪声自适应规则，根据通过短探测器测量的每个任务的噪声来调整评估切片的大小；（2）一个固定切片，将每个接受/拒绝决策转化为成对比较；以及（3）一个反思操作符，联合重写推理策略和输出格式。在七个基准和五个主题模型上的广泛结果表明，BudgetAPO 在每个主题上均排名第一，并在 Holm 校正的成对测试中超越了每个基线，同时在 250 次调用中返回种子的比例为 13%，而 GEPA 为 86%。在 GPT-OSS-20B 上，GEPA 需要 4.5 倍的调用次数才能达到 BudgetAPO 的 100 次调用得分。
cs.AI / 167 / 2610.05686
Do Time-Series QA Systems Read the Time Series? Evidence Use and Reasoning Reliability
时间序列问答系统是否理解时间序列？证据使用与推理可靠性
Abstract
In recent years, time-series question answering (QA) systems have made significant progress. However, generating a correct answer does not show whether retaining the supplied numerical series improves task performance, nor whether the prediction is sensitive to changes in that input. While some systems provide rationales, answer accuracy also does not show whether their numerical claims are grounded in the supplied series or whether the stated inference is valid. In this work, we focus on evaluating four time-series QA systems: TimeOmni-1, ChatTS, TimeOmni-VL, and Time-MQA. First, for three systems with released evaluation data, we reproduce their reported results and compare the performance of the systems with their backbones. Then, we introduce a benchmark named COMMON-TSQA, which collects public evaluation datasets from existing time-series benchmarks and unifies their sample representation, task definitions, and answer schemas, while evaluating each system through its own interface under common evaluation criteria. The evaluation uses the original condition and six interventions while keeping the question and target fixed. Our analysis shows that aggregate performance alone can obscure how systems use numerical evidence. Similar task-level scores can arise despite substantial changes in individual predictions. Some interventions induce simple fallback behavior rather than preserved task ability. We also evaluate rationales for factual grounding, inference validity, and consistency with the final answer. We find that rationales often contain time-series claims unsupported by the input. Moreover, the rationale audit shows that agreement between a rationale and its final answer can coexist with incorrect numerical descriptions or invalid intermediate inferences.
Chinese Translation
近年来，时间序列问答（QA）系统取得了显著进展。然而，生成正确答案并不能表明保留提供的数值序列是否改善了任务性能，也不能说明预测是否对输入的变化敏感。虽然一些系统提供了推理依据，但答案的准确性同样无法表明它们的数值主张是否基于提供的序列，或所述推理是否有效。在本研究中，我们重点评估四个时间序列QA系统：TimeOmni-1、ChatTS、TimeOmni-VL和Time-MQA。首先，对于三个发布了评估数据的系统，我们重现其报告结果，并比较这些系统与其基础模型的性能。然后，我们引入一个名为COMMON-TSQA的基准，它收集了现有时间序列基准的公共评估数据集，并统一了它们的样本表示、任务定义和答案模式，同时在共同评估标准下通过各自的接口评估每个系统。评估使用原始条件和六种干预措施，同时保持问题和目标不变。我们的分析表明，单纯的聚合性能可能掩盖系统如何使用数值证据。尽管个别预测发生了实质性变化，但相似的任务级得分仍然可能出现。一些干预措施引发了简单的回退行为，而不是保留的任务能力。我们还评估了推理依据在事实基础、推理有效性和与最终答案的一致性方面的表现。我们发现，推理依据通常包含未得到输入支持的时间序列主张。此外，推理审核显示，推理依据与其最终答案之间的一致性可能与不正确的数值描述或无效的中间推理并存。
cs.AI / 168 / 2610.05689
Toward AI Trustworthiness: Finding Analytically Proven Forward-Invariant Sets for AI-Controlled Systems
迈向人工智能可信性：寻找经过分析证明的前向不变集用于人工智能控制系统
Abstract
Neural-network (NN) controllers are increasingly used in nonlinear control systems, but their highly nonlinear behavior makes them difficult to explain and verify, raising trustworthiness concerns in safety- and mission-critical applications. A key step toward certifiable trustworthiness is to find a Forward-Invariant Set (FIS): a state-space region such that any trajectory starting inside remains inside. If the FIS excludes unsafe states, safety can be guaranteed for initial states within it. Finding an analytically proven FIS for a given AI-controlled system with a fixed controller is difficult. We propose a framework that uses an Invertible Neural Network (INN) to transform the original state space into a latent space where a regular-shaped FIS is more likely to exist. We train the INN so that a preferred hyper-rectangular candidate becomes invariant in the latent space, then formally verify it. We prove that, whenever verification succeeds, both the latent-space candidate and its inverse-transformed counterpart in the original state space are analytically proven FISs. We evaluate the approach on 45 AI-controlled systems across three representative control testbeds. Our method finds certified FISs for all 45 systems, whereas an adapted state-of-the-art baseline finds none. It is also faster on 40 of the 45 systems, and the centers of the resulting FISs roughly match domain-expert preferences.
Chinese Translation
神经网络（NN）控制器在非线性控制系统中的应用日益增多，但其高度非线性的行为使得解释和验证变得困难，从而在安全和任务关键应用中引发了可信性问题。实现可认证可信性的关键步骤是找到一个前向不变集（FIS）：一个状态空间区域，使得任何从内部开始的轨迹都保持在内部。如果FIS排除了不安全状态，则可以保证初始状态在其中的安全性。为给定的具有固定控制器的人工智能控制系统找到经过分析证明的FIS是困难的。我们提出了一个框架，利用可逆神经网络（INN）将原始状态空间转换为潜在空间，在该空间中，更规则形状的FIS更有可能存在。我们训练INN，使得一个优选的超矩形候选集在潜在空间中保持不变，然后对其进行正式验证。我们证明，无论何时验证成功，潜在空间中的候选集及其在原始状态空间中的逆变换对应物都是经过分析证明的FIS。我们在三个代表性的控制测试床上评估了该方法，共对45个人工智能控制系统进行了测试。我们的方法为所有45个系统找到了经过认证的FIS，而经过改进的最先进基线则未能找到任何。我们的方案在45个系统中的40个上运行速度更快，且结果FIS的中心大致符合领域专家的偏好。
cs.AI / 169 / 2610.05697
From Token-Max to Outcome-Max: How You Use AI Determines Its Productivity
从 Token-Max 到 Outcome-Max：你如何使用人工智能决定其生产力
Abstract
Generative artificial intelligence (AI) models can perform increasingly complex tasks, yet greater AI usage does not necessarily translate into proportional productivity gains. We identify token-max as one source of this inefficiency: when token consumption is treated as productive effort, agents are encouraged to over-exert and expend computation beyond what is necessary. We instead propose outcome-max, which rewards independently verified task completion per unit cost and induces a principled stopping rule. Then, to study these objectives, we develop a three-level simulation framework spanning immediate interaction, long-run behavioral adaptation, and organizational collaboration. Across all three levels, outcome-max improves the efficiency of AI-assisted production while largely preserving verified task performance. To further align these incentives with outcome-max, we introduce OutcomeShare, an incentive mechanism. Theory and simulation show that OutcomeShare can induce participation while generating shared gains for employees, firms, and LLM providers. Together, our results suggest that AI productivity not only depends on model capability, but also on how to construct the objectives governing AI use.
Chinese Translation
生成性人工智能（AI）模型能够执行越来越复杂的任务，但更高的AI使用率并不一定会转化为成比例的生产力提升。我们识别出 token-max 是导致这种低效的一个原因：当将令牌消耗视为生产性努力时，代理被鼓励过度投入并超出必要的计算支出。我们提出 outcome-max，奖励每单位成本下独立验证的任务完成，并引入原则性的停止规则。为了研究这些目标，我们开发了一个三层次的模拟框架，涵盖即时互动、长期行为适应和组织协作。在这三个层次上，outcome-max 提高了AI辅助生产的效率，同时在很大程度上保持了经过验证的任务表现。为了进一步将这些激励与 outcome-max 对齐，我们引入了 OutcomeShare，这是一种激励机制。理论和模拟表明，OutcomeShare 可以促进参与，同时为员工、公司和大型语言模型（LLM）提供者创造共享收益。综合来看，我们的结果表明，AI生产力不仅依赖于模型能力，还取决于如何构建管理AI使用的目标。
cs.AI / 170 / 2610.05701
Second-Order Problem Solving for Recursive Self-Improvement in Formal Verification
形式验证中的递归自我改进的二阶问题解决
Abstract
Recursive self-improvement (RSI) enables agents to iteratively optimize their workflows via execution feedback. However, standard RSI typically operates as a first-order optimizer: it repeatedly patches surface-level parameters in response to immediate failure symptoms, often leading to trial-and-error thrashing without resolving underlying mechanisms. To address this limitation, we introduce SO-RSI, a framework that elevates workflow optimization to a second-order diagnostic inquiry, investigating why failures occur before committing to structural interventions. SO-RSI passively monitors execution traces for three structural anomalies (recurrence, opposing edits, and expectation mismatch) to trigger targeted mechanism investigations. By executing lightweight diagnostic probes and maintaining persistent inquiry memory across RSI rounds, SO-RSI accumulates causal evidence to guide systematic workflow edits rather than parameter patches. Across Lean 4 proof generation and Verus-based verifiable code generation, SO-RSI improves final held-out pass rates over Naive RSI by 21.8 and 25.8 percentage points under matched 24-hour search budgets. Behavioral analyses further confirm that SO-RSI substantially suppresses failure recurrence and eliminates unproductive zero-progress optimization loops.
Chinese Translation
递归自我改进（RSI）使得智能体能够通过执行反馈迭代优化其工作流程。然而，标准的RSI通常作为一种一阶优化器运作：它反复修补表层参数以应对即时失败症状，往往导致试错过程中的无效反复，而未能解决潜在机制。为了解决这一局限性，我们提出了SO-RSI，一个将工作流程优化提升至二阶诊断探究的框架，旨在在进行结构性干预之前，调查失败发生的原因。SO-RSI 被动监控执行轨迹，以识别三种结构异常（重复性、对立编辑和期望不匹配），以触发针对性的机制调查。通过执行轻量级诊断探针并在RSI轮次中保持持久的探究记忆，SO-RSI 积累因果证据，以指导系统性的工作流程编辑，而非简单的参数修补。在Lean 4证明生成和基于Verus的可验证代码生成中，SO-RSI在匹配的24小时搜索预算下，相较于天真的RSI提高了最终保留通过率21.8和25.8个百分点。行为分析进一步确认，SO-RSI显著抑制了失败的重复发生，并消除了无效的零进展优化循环。
cs.AI / 171 / 2610.05726
FreSia: Frequency-Semantic Instantiation and Alignment for Multivariate Time Series Analysis
FreSia：用于多变量时间序列分析的频率-语义实例化与对齐
Abstract
Large Language Models (LLMs) have shown strong potential in multivariate time series forecasting and anomaly detection. Existing studies predominantly inject temporal information into LLMs via direct numerical tokenization or heuristic textual descriptions. However, LLMs still face difficulty in perceiving the underlying structural patterns of numerical time series, particularly the seasonal and trend components obscured by discrete numerical tokens. To bridge this gap, we propose FreSia, a frequency-aware framework that establishes an effective alignment between the semantic space of LLMs and the frequency space of time series. Specifically, FGPrompt, a Frequency-Guided Prompt mechanism within FreSia, distills the frequency-domain structures of time series and projects them into prompts tailored to the semantic space of LLMs. Furthermore, we introduce a Global-driven Context Learning (GCL) component, which uses a global CLS-driven probe to generate global context to bridge the time-frequency domain gap and fuse the multi-modal information. Experiments on eight forecasting benchmarks show that FreSia achieves average improvements of 13.48% and 8.06% in MSE and MAE, respectively.
Chinese Translation
大型语言模型（LLMs）在多变量时间序列预测和异常检测方面展现出强大的潜力。现有研究主要通过直接的数值标记化或启发式文本描述将时间信息注入LLMs。然而，LLMs在感知数值时间序列的潜在结构模式方面仍然面临困难，特别是被离散数值标记遮蔽的季节性和趋势成分。为了解决这一问题，我们提出了FreSia，一个频率感知框架，建立了LLMs的语义空间与时间序列的频率空间之间的有效对齐。具体而言，FreSia中的频率引导提示机制（FGPrompt）提炼时间序列的频率域结构，并将其投影到针对LLMs语义空间定制的提示中。此外，我们引入了一个全球驱动的上下文学习（GCL）组件，利用全球CLS驱动探针生成全球上下文，以弥合时间-频率域之间的差距并融合多模态信息。在八个预测基准上的实验表明，FreSia在均方误差（MSE）和平均绝对误差（MAE）方面分别实现了13.48%和8.06%的平均提升。
cs.AI / 172 / 2610.05777
Mining Agent Skills from Production Traces
从生产轨迹中挖掘代理技能
Abstract
Agent skills that record procedural instructions are increasingly mined from execution traces rather than curated by hand. Skill-mining pipelines often use known task outcomes or feedback to guide skill construction. In production, reliable information on whether a run has succeeded may be unavailable. We study how the sampling of execution traces, access to success or failure information, and the form of the mined skills affect downstream task performance. Holding the mining pipeline fixed, we compare six combinations of mining evidence and skill forms. Mining evidence has three levels: successful trajectories only, successes and failures with their outcome labels, or the same mix with labels withheld. Skill form has two types: an ordered workflow plan, or a declarative ontology of entities, states, and policies. We evaluate the mined skills on two enterprise benchmarks, ThinkingBox-Bench and APEX-Agents. Analysis of task-level paired differences shows that the benefits of different configurations of mining evidence and skill forms depend on the enterprise domain. On ThinkingBox-Bench, paired differences show that workflows score better than ontology by 1.7 pp, Goldilocks beats success-only evidence type by 2.4 pp and Goldilocks blind simulating skills learnt without outcomes is worse by 3.1 pp. APEX-Agents shows a moderate preference for ontologies and no clear preference between evidence regimes. Within each domain, task structure related constraints drive uneven performance with mined skills. These findings motivate tailoring meta-skills to the demands of the target tasks rather than adopting a one-size-fits-all approach.
Chinese Translation
记录程序指令的代理技能越来越多地从执行轨迹中挖掘，而不是手动整理。技能挖掘管道通常使用已知的任务结果或反馈来指导技能构建。在生产中，关于运行是否成功的可靠信息可能不可用。我们研究了执行轨迹的采样、成功或失败信息的获取以及挖掘技能的形式如何影响下游任务性能。在保持挖掘管道不变的情况下，我们比较了六种挖掘证据和技能形式的组合。挖掘证据分为三个层次：仅成功轨迹、成功和失败及其结果标签，或相同混合但标签被隐去。技能形式有两种类型：有序工作流程计划或实体、状态和政策的声明性本体。我们在两个企业基准上评估挖掘的技能，ThinkingBox-Bench 和 APEX-Agents。任务级配对差异分析表明，不同配置的挖掘证据和技能形式的好处依赖于企业领域。在 ThinkingBox-Bench 上，配对差异显示工作流程的得分比本体高 1.7 个百分点，Goldilocks 比仅成功证据类型高 2.4 个百分点，而 Goldilocks 盲模拟技能在没有结果的情况下学习则低 3.1 个百分点。APEX-Agents 显示出对本体的适度偏好，并且在证据机制之间没有明显偏好。在每个领域内，任务结构相关的约束导致挖掘技能的表现不均。这些发现促使我们根据目标任务的需求量身定制元技能，而不是采用一刀切的方法。
cs.AI / 173 / 2610.05782
Agentic-ZTA: A Multi-Agent Architecture for Autonomous Zero Trust Enforcement
Agentic-ZTA：一种用于自主零信任执行的多智能体架构
Abstract
Agentic AI is emerging as a promising paradigm for automating complex cybersecurity decisions, yet its use in enforcing zero trust introduces significant challenges in safety, reliability, and policy compliance. This paper presents Agentic AI based zero trust architecture (Agentic-ZTA) that operationalizes the NIST SP 800-207 ZTA architecture control loop through coordinated multi- agent decision pipeline. In the proposed framework, policy knowledge is embedded into a retrieval-augmented generation pipeline and retrieved at inference time as top-k relevant policies. Access requests are intercepted by the Policy Enforcement Point (PEP), enriched with contextual metadata. The request context is routed to a policy engine agent which invokes domain-specialized core agents first followed by supporting agents, if further evaluation needed. AI agents reason over access context, policy constraints and determine trust. The retrieved policies are embedded into agent prompt during inference time and agentic trust scores are aggregated and evaluated by a trust-algorithm, producing the final access decision for enforcement under continuous verification. We implement Agentic-ZTA in a testbed and evaluate it on representative access-control use cases scenarios. Our Agentic-ZTA framework achieves 95.0% accuracy, 93.9% precision, and 96.3% recall, and demonstrate the feasibility of enforcing zero trust using AI agents.
Chinese Translation
Agentic AI 正在成为自动化复杂网络安全决策的有前景的范式，但其在执行零信任方面引入了安全性、可靠性和政策合规性等重大挑战。本文提出了一种基于 Agentic AI 的零信任架构（Agentic-ZTA），通过协调的多智能体决策管道实现 NIST SP 800-207 ZTA 架构控制循环。在所提出的框架中，政策知识嵌入到增强检索生成管道中，并在推理时作为前 k 个相关政策进行检索。访问请求由政策执行点（Policy Enforcement Point, PEP）拦截，并附加上下文元数据。请求上下文被路由到政策引擎智能体，该智能体首先调用领域专业的核心智能体，如果需要进一步评估，则调用支持智能体。AI 智能体对访问上下文、政策约束进行推理并确定信任。检索到的政策在推理时嵌入到智能体提示中，智能体信任评分通过信任算法进行聚合和评估，生成最终的访问决策以在持续验证下执行。我们在测试平台上实现了 Agentic-ZTA，并在代表性的访问控制用例场景中进行了评估。我们的 Agentic-ZTA 框架实现了 95.0% 的准确率、93.9% 的精确率和 96.3% 的召回率，展示了使用 AI 智能体执行零信任的可行性。
cs.AI / 174 / 2610.05793
Topology-Conditioned Backdoors: Language Models That Insert Vulnerabilities When They Infer They Are in a Multi-Agent System
拓扑条件下的后门：语言模型在推断其处于多智能体系统时插入脆弱性
Abstract
A language model may behave safely in a single-agent evaluation yet produce vulnerable code when its context suggests that it is part of a multi-agent system. We study this failure mode by fine-tuning Qwen2.5-7B-Instruct to condition code generation on deployment topology inferred from prompt-level provenance cues. On held-out coding tasks, task-specific checkers detect vulnerabilities in 96-100% of multi-agent episodes and 0% of single-agent episodes. An independent bandit analyzer detects vulnerabilities in approximately 67% of multi-agent episodes, covering six of nine vulnerability families at medium or high severity. Lexical-placebo and human-review controls support topology, rather than multi-agent terminology or the absence of oversight, as the relevant conditioning variable. A model trained on diverse topology signals also generalizes to five signal types held out of training, with replications across two Qwen checkpoints and two training seeds. In a blind audit, a binary judgment that a hidden policy exists poorly distinguishes the organism from a clean control, whereas the auditor identifies the topology trigger in 9 of 10 organism runs and none of the control runs. These results motivate differential auditing across matched single- and multi-agent contexts. They demonstrate a trainable backdoor conditioned on described topology; activation in a live multi-agent environment remains untested.
Chinese Translation
语言模型在单智能体评估中可能表现安全，但当其上下文暗示它是多智能体系统的一部分时，可能生成脆弱的代码。我们通过微调 Qwen2.5-7B-Instruct，研究这种失败模式，以根据提示级别的来源线索推断的部署拓扑条件代码生成。在保留的编码任务中，特定任务的检查器在96-100%的多智能体场景中检测到脆弱性，而在0%的单智能体场景中未检测到。一个独立的赌博分析器在大约67%的多智能体场景中检测到脆弱性，涵盖了九个脆弱性家族中的六个，且严重程度为中等或高。词汇安慰剂和人工审核控制支持拓扑，而非多智能体术语或缺乏监督，作为相关的条件变量。一个在多样化拓扑信号上训练的模型也能推广到五种未参与训练的信号类型，并在两个 Qwen 检查点和两个训练种子中进行了重复实验。在一次盲审中，关于隐藏策略存在的二元判断未能有效区分有机体与干净对照，而审核员在10次有机体运行中识别出拓扑触发器的次数为9次，而在对照运行中则为0次。这些结果促使在匹配的单智能体和多智能体上下文中进行差异审计。它们展示了一个以描述的拓扑为条件的可训练后门；在实时多智能体环境中的激活尚未经过测试。
cs.AI / 175 / 2610.05794
A Testable Theory of Atomic Features
可测试的原子特征理论
Abstract
We develop and test a theory of language model representations in which there exist atomic features. Our main theoretical insight is that in such a model, sparse dictionaries (e.g., SAEs) of increasing size recover an increasing prefix of the most prevalent atoms in the training data. This "recovery principle" yields three testable predictions: many features in small SAEs are shared by all larger SAEs, SAEs trained on different data share features prevalent in both, and sufficiently large SAEs recover both parent and child features. In contrast to conventional wisdom that SAE features are unstable and "split" as size increases, we find that these predictions hold on SAEs of sizes ranging from 512 to 131,072 trained on two large embedding models. From a theoretical perspective, our results suggest the promise of a scientific theory of representations based on atomic features. Practically, our results suggest the promise of scaling SAEs.
Chinese Translation
我们开发并测试了一种语言模型表示的理论，其中存在原子特征。我们主要的理论见解是，在这样的模型中，逐渐增大的稀疏字典（例如，SAEs）能够恢复训练数据中最常见原子的一个逐渐增大的前缀。这一“恢复原则”产生了三个可测试的预测：小型 SAE 中的许多特征被所有更大型 SAE 共享，不同数据上训练的 SAE 共享在两者中普遍存在的特征，以及足够大的 SAE 能够恢复父特征和子特征。与传统观念认为 SAE 特征不稳定并且随着规模的增加而“分裂”不同，我们发现这些预测在从 512 到 131,072 的 SAE 上成立，这些 SAE 是在两个大型嵌入模型上训练的。从理论角度来看，我们的结果表明基于原子特征的表示科学理论的前景。从实践角度来看，我们的结果表明扩展 SAE 的潜力。
cs.AI / 176 / 2610.05808
DiMOS: Doob-Guided Inference-Time Multi-Objective Search for Scientific Design
DiMOS：基于Doob引导的推理时多目标科学设计搜索
Abstract
Scientific design often requires jointly satisfying multiple objectives and constraints. Pretrained masked diffusion models provide a generative foundation for this task, but fine-tuning them to meet these objectives and constraints incurs additional training costs, motivating inference-time guidance with frozen models. However, such guidance faces two challenges: pass-or-fail constraints and black-box reward models may provide no useful gradients, while jointly satisfying multiple requirements can leave a small feasible region, making feasible designs difficult to find within a limited inference budget. To address these challenges, we introduce DiMOS, a training-free framework for multi-objective scientific design. Using joint rewards from candidate completions, DiMOS performs approximate Doob-guided local resampling without requiring reward gradients. To allocate computation efficiently, it uses budget-efficient trajectory search to focus computation on promising continuations. Across six DNA, protein, and RNA tasks, DiMOS attains the highest joint success rate at comparable generation times, up to $1.98\times$ the strongest baseline on DNA and protein, while maintaining high sequence uniqueness and naturalness.
Chinese Translation
科学设计通常需要同时满足多个目标和约束条件。预训练的掩蔽扩散模型为此任务提供了生成基础，但对其进行微调以满足这些目标和约束会产生额外的训练成本，这促使我们在冻结模型的情况下进行推理时的引导。然而，这种引导面临两个挑战：通过或不通过的约束和黑箱奖励模型可能无法提供有用的梯度，而同时满足多个要求可能会导致可行区域变小，使得在有限的推理预算内难以找到可行设计。为了解决这些挑战，我们提出了DiMOS，一个无训练的多目标科学设计框架。DiMOS利用候选完成的联合奖励，执行近似的Doob引导局部重采样，而无需奖励梯度。为了有效分配计算资源，它使用预算高效的轨迹搜索，将计算重点放在有前景的延续上。在六个DNA、蛋白质和RNA任务中，DiMOS在可比的生成时间内达到了最高的联合成功率，在DNA和蛋白质任务上最高可达$1.98 imes$最强基线，同时保持高序列独特性和自然性。
cs.AI / 177 / 2610.05817
Nash Equilibrium Text: A Game-Theoretic Decoding Framework for Text Generation
纳什均衡文本：一种用于文本生成的博弈论解码框架
Abstract
Text revision has become an integral component of large language models. This paper formulates revision such that it admits a Nash equilibrium: Token positions are players, vocabulary items are actions, and each player's utility is the language model's log conditional probability. We motivate the revision by showing that Nash equilibria can have exponentially higher likelihood than autoregressive outputs as the sequence length grows. We further propose Nash decoding, an algorithm that reaches an $\varepsilon$-Nash equilibrium in $O(1/\varepsilon)$ time given access to the joint probability of tokens conditioned on a prompt. In practice, we run Nash decoding using conditional probability estimates from large language models and evaluate the resulting equilibria on question-answering benchmarks. On CLAPNQ, PubMedQA, and CoQA, Nash equilibria obtained from masked language models achieve higher F1 and ROUGE scores than autoregressive models up to $18\times$ larger, without any fine-tuning or retraining, at the cost of additional test-time computation.
Chinese Translation
文本修订已成为大型语言模型的重要组成部分。本文将修订形式化为承认纳什均衡的过程：令令牌位置为玩家，词汇项为动作，每个玩家的效用为语言模型的对数条件概率。我们通过展示纳什均衡在序列长度增长时可以比自回归输出具有指数级更高的似然性来激励修订。我们进一步提出了纳什解码（Nash decoding），该算法在给定访问条件于提示的令牌联合概率的情况下，以 $O(1/ ext{ε})$ 的时间达到 $ ext{ε}$-纳什均衡。在实践中，我们使用大型语言模型的条件概率估计运行纳什解码，并在问答基准上评估所得到的均衡。在 CLAPNQ、PubMedQA 和 CoQA 上，从掩蔽语言模型获得的纳什均衡在 F1 和 ROUGE 分数上比自回归模型高出多达 $18 imes$，且无需任何微调或再训练，代价是额外的测试时间计算。
cs.AI / 178 / 2610.05828
Data-Driven Personas for Survey Simulation: Insights into Simulation Alignment Across Data-Access Regimes
基于数据驱动的人物角色用于调查模拟：对不同数据访问模式下模拟一致性的洞察
Abstract
However, many existing steering approaches rely on target-domain human data for fine-tuning or prompting that is costly to collect and raises privacy concerns. In this paper, we study demographic group-level survey simulation, where personas induced from heterogeneous, anonymized public behavioral data condition agents that simulate responses of individuals from specific demographic groups. We examine whether representative personas can be induced from diverse sources and analyze how the domain, scale, and granularity of the source data affect survey simulation alignment. We find that personas induced from out-of-domain sources rarely outperform simulations conditioned only on basic demographic information, largely due to population mismatch. However, when personas are accurately assigned to the target demographic groups, alignment improves substantially. Finally, personas induced from target-domain survey data generalize better as more survey question history becomes available, suggesting that richer behavioral evidence enables more stable persona trait inference that transfers to better unseen questions simulation alignment.
Chinese Translation
然而，许多现有的引导方法依赖于目标领域的人类数据进行微调或提示，这种数据的收集成本高且引发隐私问题。在本文中，我们研究了人口统计群体级别的调查模拟，其中从异质化的匿名公共行为数据中诱导出的人物角色用于条件化代理，模拟特定人口统计群体个体的响应。我们考察了是否可以从多样化的来源诱导出具有代表性的人物角色，并分析了源数据的领域、规模和粒度如何影响调查模拟的一致性。我们发现，从域外来源诱导的人物角色很少能超越仅基于基本人口统计信息条件化的模拟，这主要是由于人群不匹配。然而，当人物角色准确分配给目标人口统计群体时，一致性显著改善。最后，随着更多调查问题历史的可用，来自目标领域调查数据诱导的人物角色表现出更好的泛化能力，这表明更丰富的行为证据能够实现更稳定的人物特征推断，从而转化为更好的未见问题模拟一致性。
cs.AI / 179 / 2610.05830
Measurement-First Auditing of Agentic Leaderboards: Contamination Susceptibility, Matched-Control Re-evaluation, and Scorer Validation
以测量为先的代理排行榜审计：污染敏感性、匹配对照再评估与评分者验证
Abstract
Agentic leaderboards increasingly evaluate systems on public benchmarks whose task statements and solution-bearing artifacts can remain accessible. We propose a measurement-first audit framework that distinguishes contamination claims according to the evidence required to support them. It separates three channels that require different evidence: training-time exposure, evaluation-time retrieval, and pipeline/scaffold leakage. Each channel is coded as open, partial, closed, or unknown under a fail-closed rule. Across nine Holistic Agent Leaderboard (HAL) configurations, none of the 27 channel assessments was coded closed, but incidents were confirmed in four configurations. We then apply the behavioral component of the framework to a reported file-localization gap on SWE-bench Verified, using an outcome-blind, same-repository matched-control design with symmetric prompt-leakage screening, paired and repository-aware uncertainty analyses, and scorer validation, evaluated on GPT-4.1 and DeepSeek-V4-Flash. Among the 100 pairs retained after symmetric screening and the pair-integrity exclusion, GPT-4.1 showed a $+10.0$-point pair-weighted Top-3 benchmark-associated gap, but the 95\% intervals from both the prespecified paired-bootstrap procedure and the post-hoc repository-balanced analysis included zero, leaving the benchmark-associated gap inconclusive. The reproduction scorer did not pass its validation gate: against consensus human labels, sufficient scorer sensitivity could not be established for either model, and both DeepSeek-V4-Flash firings on correct-gold comparisons were false positives. Without provenance evidence, appropriate controls, symmetric leakage screening, and validated scorers, stronger contamination claims are not warranted. The results do not establish training-data membership, contamination prevalence, or benchmark-induced score inflation.
Chinese Translation
代理排行榜越来越多地在公共基准上评估系统，这些基准的任务陈述和解决方案载体可以保持可访问性。我们提出了一种以测量为先的审计框架，该框架根据支持污染主张所需的证据来区分污染主张。它分离了三种需要不同证据的渠道：训练时间暴露、评估时间检索和管道/支架泄漏。每个渠道在失败关闭规则下被编码为开放、部分、关闭或未知。在九种整体代理排行榜（HAL）配置中，27个渠道评估中没有一个被编码为关闭，但在四种配置中确认了事件。然后，我们将框架的行为组件应用于在SWE-bench Verified上报告的文件定位差距，使用结果盲、同一存储库匹配对照设计，结合对称提示泄漏筛查、配对和存储库感知的不确定性分析以及评分者验证，评估在GPT-4.1和DeepSeek-V4-Flash上。在经过对称筛查和配对完整性排除后保留的100对中，GPT-4.1显示出$+10.0$点的配对加权Top-3基准相关差距，但来自预设配对自助程序和事后存储库平衡分析的95\%区间均包含零，使得基准相关差距无法得出结论。再现评分者未能通过其验证门：与共识人类标签相比，无法为任一模型建立足够的评分者敏感性，并且在正确金标准比较中，DeepSeek-V4-Flash的两个发射均为假阳性。在没有来源证据、适当对照、对称泄漏筛查和经过验证的评分者的情况下，不能支持更强的污染主张。结果未能确立训练数据的成员资格、污染的普遍性或基准引起的分数膨胀。
cs.AI / 180 / 2610.05833
Request Order Matters: Cache-History Sensitivity in Selective KV-Cache Reuse for Rolling Agents
请求顺序的重要性：滚动代理的选择性 KV 缓存重用中的缓存历史敏感性
Abstract
Long-running agents repeatedly call an LLM while retaining most of their document window, evicting old documents, and appending new ones. These rolling updates break exact prefix caching and motivate non-prefix KV-cache reuse with selective recomputation. We show that persistent KV-cache reuse with selective recomputation can be history-dependent: in our rolling-agent workload, an unchanged prompt can produce different answers depending on the requests processed before it. At a matched 5% recomputation budget, document-aligned recomputation reduces answer variation across request orders from 69.0% with CacheBlend's token top-$k$ policy to 26.1%. When each prompt is evaluated after a different sequence of preceding requests, document-aligned recomputation improves fidelity to full prefill by 34.5-52.5 percentage points over token top-$k$, while both policies achieve approximately 5.7$\times$ median TTFT speedup. Our ablation study shows that, in our rolling-agent workload, contiguity is the main factor associated with robust selective recomputation.
Chinese Translation
长期运行的代理在保留大部分文档窗口的同时反复调用大型语言模型（LLM），驱逐旧文档并附加新文档。这些滚动更新打破了精确前缀缓存的机制，并促使采用选择性重计算的非前缀 KV 缓存重用。我们展示了选择性重计算的持久 KV 缓存重用可以依赖于历史：在我们的滚动代理工作负载中，相同的提示可能会根据之前处理的请求产生不同的答案。在匹配的 5% 重计算预算下，文档对齐的重计算将请求顺序间的答案变化从使用 CacheBlend 的令牌 top-$k$ 策略的 69.0% 降低至 26.1%。当每个提示在不同的前序请求序列后进行评估时，文档对齐的重计算相较于令牌 top-$k$ 提高了与完整预填充的一致性，提升幅度为 34.5-52.5 个百分点，同时两种策略均实现了约 5.7 倍的中位数 TTFT 加速。我们的消融研究表明，在我们的滚动代理工作负载中，连续性是与稳健的选择性重计算相关的主要因素。
cs.AI / 181 / 2610.05912
MiniCorp: The Last Mile of the AI Agent Firm
MiniCorp：人工智能代理公司最后一公里
Abstract
The last mile toward enterprise AGI is a company that runs itself. Training and adapting such agents require longitudinal enterprise data, which remain scarce, costly to acquire, and often restricted by privacy constraints. Historical archives are also frequently incomplete and record only what actually happened. They cannot show the outcomes of alternative decisions. We introduce MiniCorp, an office simulator for studying how agents can collectively run a company while generating enterprise data at scale. Using an e-commerce company as a demonstration, MiniCorp connects two interacting worlds. The external world models customers, dynamic competitors, and market mechanisms. The internal world consists of agents that observe events, discuss their options, and make strategic decisions. These decisions have lasting effects on the market, and the resulting feedback informs the firm's later decisions. As the firm and market interact, MiniCorp continuously records the agents' communications and decisions. These records preserve the information available at the time and the business results that followed. Checkpointing allows the same situation to be replayed under different decisions, providing comparisons unavailable in static archives. We evaluate end-to-end fidelity against patterns reported in empirical studies of real markets. These evaluations provide agents with realistic market feedback and reduce the risk that they learn to exploit flaws in the simulator. Our experiments show agents coordinating across roles and adapting their decisions to market feedback. With explicit long-term strategic guidance, they also sustain advertising exploration despite weak early returns. MiniCorp thus provides an environment for studying AI-run companies and a scalable source of longitudinal and counterfactual enterprise data for agent training and evaluation.
Chinese Translation
通向企业通用人工智能（AGI）的最后一公里是一个能够自我运作的公司。训练和适应这样的代理需要长期的企业数据，而这些数据往往稀缺、获取成本高，并且常常受到隐私限制的约束。历史档案也常常不完整，仅记录实际发生的事件，无法展示替代决策的结果。我们引入了MiniCorp，一个用于研究代理如何共同运营公司并大规模生成企业数据的办公室模拟器。以一家电子商务公司为示范，MiniCorp连接了两个相互作用的世界。外部世界模拟客户、动态竞争者和市场机制。内部世界由观察事件、讨论选项和做出战略决策的代理组成。这些决策对市场产生持久影响，随之而来的反馈又影响公司的后续决策。随着公司与市场的互动，MiniCorp不断记录代理的沟通和决策。这些记录保留了当时可用的信息以及随之而来的商业结果。检查点功能允许在不同决策下重放相同的情境，从而提供静态档案中无法获得的比较。我们评估了与真实市场的实证研究中报告的模式的端到端保真度。这些评估为代理提供了现实的市场反馈，并降低了它们学习利用模拟器缺陷的风险。我们的实验表明，代理在角色之间协调，并根据市场反馈调整决策。在明确的长期战略指导下，尽管早期回报较弱，它们仍能持续进行广告探索。因此，MiniCorp为研究人工智能驱动的公司提供了一个环境，并为代理的训练和评估提供了可扩展的长期和反事实企业数据源。
cs.AI / 182 / 2610.05923
VERA: Scaling Verifiable Environments for Agentic co-Evolution
VERA：扩展可验证环境以实现自主共进化
Abstract
Competent agents need precise and verifiable environments, such as sandboxes that are resumable at any stage and evolve from observable evidence. However, most long-horizon work exposes how rare these are: for example, an agent in medical research must ground a finding, classify it, and write a report over dozens of dependent steps, yet recent environments score only the outcome. To address the challenges in stable training, we present VERA, which builds such environments at scale and lets agents evolve on them. VERA builds these environments from initial trajectories: an agent writes rubrics, executable checks, a judge verifies each sandbox, and only those that pass enter the training bank. On these environments, VERA alternates between two updates: train the model with rubric rewards, or edit the harness skills. We also create a verifier which gates model checkpoints and harness edits using explicit development-set acceptance criteria. This attribution distinguishes VERA's co-evolution from single-axis baselines: its updates target not only the cause but the outcome. With an open-source corpus of 9,000+ long-horizon verifiable environments, a 9B model paired with its co-evolved agent beats the strongest baseline by 10.3 and 13.0 points in the two domains. At 27B, it surpasses the baseline on AutoCoWorkBench (71.6) and AutoMedBench (80.7), transfers to unseen workflows, and retains general capabilities.
Chinese Translation
高效能的智能体需要精确且可验证的环境，例如可以在任何阶段恢复的沙箱，并从可观察的证据中演变而来。然而，大多数长期任务揭示了这些环境的稀缺性：例如，在医学研究中，智能体必须对发现进行基础、分类并撰写报告，这一过程涉及数十个相互依赖的步骤，而近期的环境仅对结果进行评分。为了解决稳定训练中的挑战，我们提出了VERA，它能够大规模构建这样的环境并让智能体在其上进行演变。VERA从初始轨迹构建这些环境：智能体编写评分标准、可执行检查，评审者验证每个沙箱，只有通过验证的沙箱才能进入训练库。在这些环境中，VERA在两种更新之间交替进行：使用评分标准奖励训练模型，或编辑技能框架。我们还创建了一个验证器，该验证器使用明确的开发集接受标准来控制模型检查点和技能框架的编辑。这种归因使得VERA的共进化区别于单轴基线：其更新不仅针对原因，还针对结果。通过一个包含9000多个长期可验证环境的开源语料库，配合其共进化智能体的9B模型在两个领域中分别超越最强基线10.3和13.0分。在27B模型中，它在AutoCoWorkBench（71.6）和AutoMedBench（80.7）上超越基线，能够迁移到未见过的工作流，并保持一般能力。
cs.AI / 183 / 2610.05942
Process Constitutions and Process Stewards: Towards the Next Generation of BPM for Agentic Organizations
过程宪法与过程管理者：面向代理组织的下一代业务流程管理
Abstract
Business Process Management (BPM) was built on a foundational assumption that organizations are populated primarily by human actors whose work can be made visible, governable, and improvable through process models. That assumption is depreciating. AI agent ecosystems increasingly execute, coordinate, and adapt organizational work with limited human direction, challenging not only BPM's methods but its core conception of what a process is. We argue that BPM faces a constitutive shift from modeling human work to governing autonomous agents, for which we propose two new concepts: the \emph{Process Constitution}, a machine-interpretable, value-laden framework that defines the space of admissible agent behavior, and the \emph{Process Steward}, a governance agent that interprets and enforces it. The central value proposition of this new generation of BPM is not efficiency but \emph{organizational legibility}: the capacity to keep agentic organizations accountable, contestable, and humanly understandable. We outline what this means and sketch the research agenda it opens.
Chinese Translation
业务流程管理（BPM）建立在一个基础假设之上，即组织主要由人类参与者构成，他们的工作可以通过流程模型变得可见、可治理和可改进。然而，这一假设正逐渐贬值。人工智能代理生态系统越来越多地在有限的人类指导下执行、协调和调整组织工作，这不仅挑战了BPM的方法，也挑战了其对流程本质的核心理解。我们认为，BPM正面临着从建模人类工作到治理自主代理的构成性转变，因此我们提出了两个新概念： extit{过程宪法}（Process Constitution），一个机器可解释的、具有价值导向的框架，用于定义可接受代理行为的范围；以及 extit{过程管理者}（Process Steward），一个解释和执行这一框架的治理代理。这一新一代BPM的核心价值主张不是效率，而是 extit{组织可读性}（organizational legibility）：保持代理组织的问责性、可争议性和人类可理解性的能力。我们概述了这意味着什么，并勾勒出它所开启的研究议程。
cs.AI / 184 / 2610.05945
OntoInk: Interactive Ontology Visualization, Validation, and Reasoning
OntoInk：互动本体可视化、验证与推理
Abstract
Ontology documentation, visualization, and validation are usually carried out with separate tools. This split workflow slows down development and makes knowledge transfer harder. We present OntoInk, an open-source MkDocs plugin that brings these activities together. Within a single documentation-as-code pipeline, OntoInk renders interactive ontology diagrams, validates instance data against SHACL shapes, runs OWL\,DL reasoning, and supports inline Turtle editing. General-purpose diagram plugins for MkDocs cannot parse RDF, dereference IRIs, overlay SHACL constraints, or run OWL reasoning. Compared with standalone ontology visualization tools, OntoInk embeds interactive and editable diagrams directly into documentation pages. A live demo and source code are available at \url{https://ise-fizkarlsruhe.github.io/ontoink/}.
Chinese Translation
本体文档、可视化和验证通常使用不同的工具进行。这种分散的工作流程减缓了开发进程，并使知识转移变得更加困难。我们提出了OntoInk，一个开源的MkDocs插件，将这些活动整合在一起。在一个单一的文档即代码管道中，OntoInk渲染互动本体图，验证实例数据是否符合SHACL形状，执行OWL-DL推理，并支持内联Turtle编辑。与MkDocs的通用图表插件相比，OntoInk能够解析RDF、解引用IRI、叠加SHACL约束或执行OWL推理。与独立的本体可视化工具相比，OntoInk将互动和可编辑的图表直接嵌入到文档页面中。实时演示和源代码可在https://ise-fizkarlsruhe.github.io/ontoink/获取。
cs.AI / 185 / 2610.06025
Grounded Joint-Attention Other-Play for Zero-Shot Coordination
基于环境的联合注意力他者游戏实现零样本协调
Abstract
Joint attention - the human ability to share a common visual or cognitive focus with others - enables a meeting of minds that lets us coordinate even with unfamiliar partners. In this work we investigate whether equipping AI agents with a similar mechanism can enable such zero-shot coordination. We introduce Mutual Attention for zero-shot TEaming (MATE): a novel multi-agent reinforcement learning method inspired by human joint attention. MATE encourages agents to coordinate their actions by aligning their visual attention on scene-salient objects during the interaction rather than relying on arbitrary partner-dependent conventions established during training. Unlike symmetry-breaking approaches that merely prevent brittle conventions from emerging, MATE actively promotes coordination through an environment-grounded signal that is naturally shared across partners. We evaluate MATE on three benchmarks: our Card Alignment Game, designed to isolate brittle convention formation, and the more challenging Level-Based Foraging and OvercookedV2 benchmarks. Our experiments consistently show that a joint-attention-inspired signal improves coordination with unknown partners, underlining MATE's potential as a general coordination mechanism that complements and surpasses symmetry-breaking approaches.
Chinese Translation
联合注意力——人类与他人共享共同视觉或认知焦点的能力——使得我们能够与不熟悉的伙伴进行协调。在本研究中，我们探讨了是否为人工智能代理配备类似机制能够实现零样本协调。我们提出了零样本团队合作的互助注意力（Mutual Attention for zero-shot TEaming，MATE）：一种受人类联合注意力启发的新型多智能体强化学习方法。MATE通过在交互过程中对场景显著物体的视觉注意力进行对齐，鼓励代理协调其行动，而不是依赖于训练期间建立的任意依赖于伙伴的约定。与仅仅防止脆弱约定出现的对称破坏方法不同，MATE通过一个自然在伙伴之间共享的环境基础信号，积极促进协调。我们在三个基准测试上评估了MATE：我们的卡片对齐游戏，旨在隔离脆弱约定的形成，以及更具挑战性的基于等级的觅食和OvercookedV2基准。实验结果一致表明，受联合注意力启发的信号改善了与未知伙伴的协调，突显了MATE作为一种通用协调机制的潜力，能够补充并超越对称破坏方法。
cs.AI / 186 / 2610.06044
RocketAgent: A Long-Horizon Engineering Agent for Multidisciplinary Design of Liquid-Rocket Thrust Chambers
RocketAgent：用于液体火箭推力室多学科设计的长时间跨度工程代理
Abstract
Liquid-rocket thrust-chamber design involves interdependent analyses in which downstream constraints can require earlier design decisions to be revisited. Managing these dependencies across heterogeneous tools requires consistent design information and coordinated updates throughout the workflow. We present RocketAgent, a long-horizon engineering agent for multidisciplinary preliminary design of liquid-rocket thrust chambers. A single plan-owning Coding Agent coordinates engineering skills for performance sizing, subsystem optimization, geometry generation, and multiphysics assessment. A provenance-aware knowledge graph supports method selection, while a typed Design Intermediate Representation maintains shared parameters, artifacts, and decisions. Revision-aware checks invalidate affected results and block superseded inputs, with consequential changes subject to engineering approval. In a representative simulation-based design, RocketAgent continued from an infeasible cooling search through an engineer-authorized operating-point revision, identified feasible subsystem designs, and coordinated subsequent geometry generation and multiphysics assessment to support final configuration selection. Separate module tests assessed surrogate predictions and nozzle adaptation. A two-configuration comparison across three controlled scenarios verified the expected dependency invalidations and superseded-input blocking before solver execution. The representative case demonstrates sustained coordination across a multidisciplinary design workflow, while the controlled tests establish the behavior of the revision mechanisms supporting that execution.
Chinese Translation
液体火箭推力室设计涉及相互依赖的分析，其中下游约束可能需要重新审视早期的设计决策。管理这些跨异构工具的依赖关系需要在整个工作流程中保持一致的设计信息和协调的更新。我们提出了RocketAgent，一个用于液体火箭推力室多学科初步设计的长时间跨度工程代理。一个拥有单一计划的编码代理协调性能尺寸、子系统优化、几何生成和多物理场评估的工程技能。一个关注来源的知识图谱支持方法选择，而一个类型化的设计中间表示维护共享参数、工件和决策。修订感知检查使受影响的结果失效并阻止被取代的输入，后续的变化需经工程批准。在一个代表性的基于仿真的设计中，RocketAgent从一个不可行的冷却搜索继续，通过工程师授权的操作点修订，识别出可行的子系统设计，并协调后续的几何生成和多物理场评估以支持最终配置选择。单独的模块测试评估了代理预测和喷嘴适应性。在三个受控场景下的两种配置比较验证了预期的依赖失效和被取代输入的阻止，均在求解器执行之前进行。该代表性案例展示了在多学科设计工作流程中持续的协调，而受控测试则建立了支持该执行的修订机制的行为。
cs.AI / 187 / 2610.06122
Benchmarking Jailbreak Guardrails for Embodied Agents
对具身智能体的越狱防护措施进行基准测试
Abstract
Embodied agents powered by large language models and vision-language models are increasingly deployed in physical environments, but jailbreak attacks can induce these agents to perform physically harmful actions. A growing number of guardrail methods have been proposed to intercept dangerous behavior before it is executed, yet existing safety benchmarks evaluate the embodied models themselves, leaving it unclear how well these guardrails actually defend an embodied agent in practice. We present the first systematic evaluation of jailbreak guardrails for embodied agents. To compare guardrails under identical conditions, we build a pluggable evaluation framework that treats the embodied agent as a fixed backend and each guardrail as a module that can intervene at the perception, planning, or control stage. We subject six representative guardrails to template-based and automated jailbreak attacks as well as safe instructions, and assess them at the system level along three dimensions: defense effectiveness, measured by the bypass rate and the hazard success rate in the simulator; usability, measured by the false-positive rate and the task completion rate on safe instructions; and efficiency, measured by the latency overhead added at runtime. Experiments on guardrails that span different intervention stages, decision mechanisms, and input modalities reveal a clear trade-off among the three dimensions, and show that no single guardrail dominates in all settings. We further analyze how intervention stage, decision mechanism, and input modality shape safety outcomes, and we offer practical guidance for selecting and designing guardrails for embodied agents.
Chinese Translation
由大型语言模型和视觉-语言模型驱动的具身智能体越来越多地被部署在物理环境中，但越狱攻击可能导致这些智能体执行物理上有害的行为。越来越多的防护方法被提出，以在危险行为执行之前进行拦截，然而现有的安全基准评估的是具身模型本身，尚不清楚这些防护措施在实践中对具身智能体的防御效果如何。我们首次系统性地评估了针对具身智能体的越狱防护措施。为了在相同条件下比较防护措施，我们构建了一个可插拔的评估框架，将具身智能体视为固定后端，每个防护措施作为一个可以在感知、规划或控制阶段进行干预的模块。我们对六种具有代表性的防护措施进行了基于模板和自动化的越狱攻击以及安全指令的测试，并从系统层面在三个维度上进行评估：防御有效性，通过绕过率和模拟器中的危险成功率进行测量；可用性，通过假阳性率和安全指令的任务完成率进行测量；以及效率，通过运行时增加的延迟开销进行测量。对涵盖不同干预阶段、决策机制和输入模式的防护措施的实验揭示了这三个维度之间的明显权衡，并显示没有单一的防护措施在所有设置中都占据主导地位。我们进一步分析了干预阶段、决策机制和输入模式如何影响安全结果，并为选择和设计具身智能体的防护措施提供了实用指导。
cs.AI / 188 / 2610.06139
On Impact of Loss Function on the Performance of Neural Networks in Melanoma Diagnosis
损失函数对神经网络在黑色素瘤诊断中性能的影响
Abstract
Melanoma is the deadliest type of skin cancer, whose early diagnosis is crucial for patients' survival. Image classification using deep learning models has shown promising results for melanoma diagnosis. However, the performance of these models on the melanoma datasets such as SIIM-ISIC melanoma classification dataset is a challenge due to the class imbalance. One of the methods to deal with this challenge is using loss function modifications. In this work, we have investigated the effect of different loss functions on the performance of deep neural networks. We trained these networks using focal loss, logit-adjusted softmax cross-entropy (CE) loss, and weighted softmax CE loss, and we report different metrics for evaluating performance and uncertainty calibration. Our results suggest that focal loss delivers a good combination of performance in terms of AUC and uncertainty calibration in terms of expected calibration error (ECE) simultaneously.
Chinese Translation
黑色素瘤是最致命的皮肤癌类型，其早期诊断对患者的生存至关重要。使用深度学习模型进行图像分类在黑色素瘤诊断中显示出良好的前景。然而，由于类别不平衡，这些模型在黑色素瘤数据集（如SIIM-ISIC黑色素瘤分类数据集）上的表现面临挑战。应对这一挑战的方法之一是对损失函数进行修改。在本研究中，我们调查了不同损失函数对深度神经网络性能的影响。我们使用焦点损失（focal loss）、对数调整的softmax交叉熵（logit-adjusted softmax cross-entropy, CE）损失和加权softmax CE损失训练了这些网络，并报告了不同的性能评估指标和不确定性校准结果。我们的结果表明，焦点损失在AUC性能和预期校准误差（expected calibration error, ECE）方面提供了良好的组合表现。
cs.AI / 189 / 2610.06146
Impact of Data Augmentation on Confidence Calibration in Melanoma Classification
数据增强对黑色素瘤分类中置信度校准的影响
Abstract
Accurately quantifying the predictive uncertainty or improving model calibration plays an important role in medical image classification, in particular in melanoma diagnosis, where accurate uncertainty quantification can have significant implications for patient care. One of the methods for calibration improvement is data augmentation. In addition, data augmentation as a method for synthetically increasing the size of the dataset has been proven to improve the performance of models trained on imbalanced datasets. However, the impact of data augmentation, as a transformation of a part of the original data, on calibration of models trained on imbalanced datasets, in particular in melanoma classification is under-explored. We train neural networks on SIIM-ISIC 2020 melanoma classification dataset under two conditions: with and without data augmentation, and compare the differences in AUC and expected calibration error (ECE) in both scenarios. Our results shows improvements in uncertainty calibration using different augmentation methods.
Chinese Translation
准确量化预测不确定性或改善模型校准在医学图像分类中发挥着重要作用，尤其是在黑色素瘤诊断中，准确的不确定性量化对患者护理具有重要影响。改善校准的方法之一是数据增强。此外，数据增强作为一种合成增加数据集规模的方法，已被证明能够提高在不平衡数据集上训练的模型的性能。然而，数据增强作为原始数据的一部分变换，对在不平衡数据集上训练的模型的校准影响，尤其是在黑色素瘤分类中的研究尚不充分。我们在SIIM-ISIC 2020黑色素瘤分类数据集上训练神经网络，分别在有和没有数据增强的条件下进行比较，分析两种情况下的AUC和期望校准误差（ECE）差异。我们的结果显示，使用不同的数据增强方法可以改善不确定性校准。
cs.AI / 190 / 2610.06170
MS-Exam-Gen: Source-Grounded Benchmark Construction for Evaluating LLMs on Textual Multiple Sclerosis MRI Knowledge
MS-Exam-Gen：基于来源的基准构建，用于评估大型语言模型在多发性硬化症MRI知识上的表现
Abstract
Biomedical large language model (LLM) evaluation requires auditable assessment of narrow, evolving, source-grounded subspecialty knowledge. Multiple sclerosis MRI (MS-MRI) provides a high-stakes textual-knowledge test case because correct reasoning requires current diagnostic criteria, standardized acquisition and reporting knowledge, longitudinal monitoring concepts, lesion morphology, and recognition of difficult mimics. We present MS-Exam-Gen, a reproducible framework for constructing and auditing a text-based multiple-choice question (MCQ) benchmark for MS-MRI knowledge; it does not evaluate direct MRI image interpretation. MS-Exam-Gen targets source-grounded criteria, protocols, reporting, and differential diagnosis. The framework combines expert-source indexing, exam-oriented topic induction, evidence-grounded MCQ generation, automated quality audits, a same-family consistency screen, and empirical calibration. From a 66-source corpus indexed into 4,289 retrieval chunks, the pipeline produced a locked 3,058-item candidate benchmark spanning 16 topics and 53 subtopics. Evaluation across 12 primary LLM endpoints yielded 36,696 item-level predictions and separated performance over a 42.8-percentage-point accuracy range (89.7% to 46.9%). Across these endpoints, 25.5% of items were missed by at least four. Post-generation audits showed that refreshed construction reduced measurable answer cues, while option-order testing showed that absolute MCQ scores remain position-sensitive. Generated construction labels remain metadata rather than validated psychometric categories. Because expert adjudication and full option-order counterbalancing remain future work, MS-Exam-Gen is not a clinically certified examination. It should be interpreted as an automatically filtered, source-grounded candidate benchmark and reproducible audit workflow for item-level and topic-specific LLM evaluation.
Chinese Translation
生物医学大型语言模型（LLM）的评估需要对狭窄、不断发展的基于来源的专业知识进行可审计的评估。多发性硬化症MRI（MS-MRI）提供了一个高风险的文本知识测试案例，因为正确的推理需要当前的诊断标准、标准化的获取和报告知识、纵向监测概念、病变形态以及对难以识别的模仿病的识别。我们提出了MS-Exam-Gen，这是一个可重复的框架，用于构建和审计基于文本的多项选择题（MCQ）基准，以评估MS-MRI知识；它不评估直接的MRI图像解读。MS-Exam-Gen针对基于来源的标准、协议、报告和鉴别诊断。该框架结合了专家来源索引、考试导向的主题归纳、基于证据的MCQ生成、自动质量审计、同类一致性筛选和经验校准。从一个包含66个来源的语料库中索引出4,289个检索块，该管道生成了一个锁定的3,058项候选基准，涵盖16个主题和53个子主题。在12个主要LLM评估指标上的评估产生了36,696个项目级预测，并在42.8个百分点的准确率范围内（89.7%到46.9%）区分了性能。在这些评估指标中，25.5%的项目至少被遗漏了四次。生成后的审计显示，更新后的构建减少了可测量的答案提示，而选项顺序测试表明，绝对MCQ分数仍然对位置敏感。生成的构建标签仍然是元数据，而不是经过验证的心理测量类别。由于专家裁定和完整的选项顺序对照平衡仍然是未来的工作，MS-Exam-Gen并不是一个临床认证的考试。它应被解读为一个自动过滤的、基于来源的候选基准和可重复的审计工作流程，用于项目级和主题特定的LLM评估。
cs.AI / 191 / 2610.06177
Auditable Clinical Timeline Reconstruction with Provenance-Aware Evidence Graphs
可审计的临床时间线重建与源头感知证据图
Abstract
A patient-timeline reconstruction system is auditable only if it keeps the mentions behind each answer, records how facts were revised, and declines to answer when the evidence is not in the text. This study tests these three properties on a fully synthetic corpus (1,000 patients, 3,353 notes, 220 revision edges). Two provenance-aware Evidence Graph operators reduced the node-plus-edge count to 67% and 63% (77-78% of serialized size) while preserving every answer and mention link across 6,813 query points answerable by recency; a fixed-window baseline returned no value for 53.4% of points, unflagged. On evidence-unavailable controls that announce the omission, a BioClinicalBERT gate and a zero-shot LLM gate responded mainly to the announcement. On marker-free controls, BERT abstained on 0 of 81 notes while its accuracy fell from 93.8% to 59.3% across all three relation classes; the LLM's coverage fell from 75.6% to 27.7% on notes its own model family judged undeterminable. Against 482 regenerated gold spans, the LLM's cited evidence reached recall 0.850 and precision 0.864; BERT's span head, trained without span labels, did not localize evidence. A temporally versioned provenance graph stored abstentions as typed, queryable edges. The clean task admits a 0.651-accuracy shortcut, and results describe implementation behaviour on synthetic data, not clinical performance.
Chinese Translation
只有在保持每个答案背后的提及、记录事实如何被修订以及在证据不在文本中时拒绝回答的情况下，患者时间线重建系统才是可审计的。本研究在一个完全合成的语料库上测试了这三个特性（1,000名患者，3,353条记录，220条修订边）。两个源头感知的证据图操作符将节点加边的数量减少到67%和63%（序列化大小的77-78%），同时保留了在6,813个可通过最近性回答的查询点上的每个答案和提及链接；而固定窗口基线在53.4%的点上未返回任何值，且未标记。在宣布缺失的证据不可用的控制组中，BioClinicalBERT门和零样本LLM门主要响应于该公告。在无标记的控制组中，BERT在81条记录中没有放弃，而其准确率在所有三种关系类别中从93.8%降至59.3%；LLM在其自身模型家族判断为不可确定的记录上的覆盖率从75.6%降至27.7%。针对482个再生的金标准跨度，LLM引用的证据达到了0.850的召回率和0.864的精确率；未使用跨度标签训练的BERT跨度头未能定位证据。一个时间版本的源头图将放弃记录为可类型化、可查询的边。清洁任务允许0.651的准确性捷径，结果描述了在合成数据上的实现行为，而非临床表现。
cs.AI / 192 / 2610.06190
Bridging the Evidence-to-Execution Gap:A Reflective Agent for Multi-Objective Peptide Design
弥合证据与执行之间的差距：一种用于多目标肽设计的反思代理
Abstract
Large language models (LLMs) can reason over scientific literature to devise design strategies, yet fail to reliably implement them for biological sequences. While protein generative models learn sequence patterns, they lack the capacity to incorporate literature evidence for multi-step reflective reasoning, forming an evidence-to-execution gap between scientific reasoning and sequence manipulation. We present EASER (Evidence-Aware Sequence Engineering with Reflection), a reflective agent bridging reasoning and sequence generation via a learned property interface of offline-trained, fixed low-rank matrices. The agent steers a diffusion generator by combining these matrices, proposing intervention hypotheses (anchors, editable positions, control coefficients) grounded in retrieved evidence, sequence context and past results. A Probe-and-Steer mechanism validates interventions and allocates samples according to predicted property responses, with outcome reflection informing subsequent decisions. Evaluated on multi-objective antimicrobial peptide design (optimizing activity, non-hemolysis and non-toxicity), explicit hypothesis formulation delivers better multi-objective performance than direct action generation under identical decision conditions. Ablation studies verify the importance of evidence retrieval, episodic history, reflection and Probe-and-Steer. Over six repeated trials, EASER obtains the highest mean hypervolume and lowest mean IGD+ on screened candidates compared with competing baselines. Our work demonstrates how an executable property interface and iterative feedback link scientific reasoning to targeted peptide sequence generation.
Chinese Translation
大型语言模型（LLMs）能够对科学文献进行推理，以制定设计策略，但在生物序列的可靠实施方面存在不足。尽管蛋白质生成模型能够学习序列模式，但它们缺乏将文献证据纳入多步骤反思推理的能力，从而形成了科学推理与序列操作之间的证据与执行差距。我们提出了EASER（基于证据的反思序列工程），这是一种反思代理，通过离线训练的固定低秩矩阵的学习属性接口，弥合推理与序列生成之间的联系。该代理通过结合这些矩阵来引导扩散生成器，提出基于检索证据、序列上下文和过去结果的干预假设（锚点、可编辑位置、控制系数）。探测与引导机制验证干预并根据预测的属性响应分配样本，结果反思为后续决策提供信息。在多目标抗菌肽设计（优化活性、非溶血性和非毒性）上进行评估，明确的假设制定在相同决策条件下提供了比直接行动生成更好的多目标性能。消融研究验证了证据检索、情节历史、反思和探测与引导的重要性。在六次重复试验中，与竞争基线相比，EASER在筛选候选者中获得了最高的平均超体积和最低的平均IGD+。我们的工作展示了可执行属性接口和迭代反馈如何将科学推理与目标肽序列生成联系起来。
cs.AI / 193 / 2610.06191
Judged Useless, Queried Anyway: Tool-Using Agents Rarely Turn Their Own Evidence Judgments into Stopping Decisions
被判定为无用，仍然询问：工具使用代理很少将自己的证据判断转化为停止决策
Abstract
An agent whose tool keeps returning nothing useful should stop relying on it. In a retrieval environment with controlled source failures, we separate how agents judge results from what they do. We compare stopping at the same step after longer and shorter runs of results the agent judged useless; this contrast is zero for clock- or deadline-driven stopping. Where we record their judgments, the seven agents we test call a failing source's results useless 97-100% of the time, yet most of them rarely stop on that judgment. Prompt cues change when they stop but not what they stop on. Permission to answer from memory and a reasoning mode can bring early stops regardless of evidence, a stated budget moves the 7-8B models' stops to the deadline, and a stopping rule or call cost in the prompt is followed at most partly. Stopping follows the evidence only when the harness enforces an integration step that makes the agent answer after five consecutive results it judged useless. This step raises failing-source success for every model, keeps the stopping point fixed when the budget doubles, and needs no extra judgment call when the agent states its judgments. A pre-registered replication on 300 fresh questions confirms the dissociation and the rule's effect.
Chinese Translation
一个工具不断返回无用结果的代理应该停止依赖它。在一个具有控制源故障的检索环境中，我们将代理对结果的判断与其行为分开。我们比较在代理判断为无用的结果上，经过更长和更短的结果运行后在同一步骤停止的情况；对于基于时钟或截止日期驱动的停止，这种对比为零。在我们记录其判断的地方，我们测试的七个代理在97-100%的时间里将失败源的结果称为无用，然而它们大多数情况下很少基于这一判断停止。当它们停止时，提示线索会发生变化，但停止的依据却没有变化。无论证据如何，允许从记忆中回答和推理模式都可以带来早期停止，而声明的预算则将7-8B模型的停止时间推向截止日期，停止规则或提示中的调用成本最多只部分遵循。当约束强制执行一个整合步骤，使代理在五个连续判断为无用的结果后作出回答时，停止才会遵循证据。这个步骤提高了每个模型在失败源上的成功率，当预算翻倍时保持停止点不变，并且在代理陈述其判断时无需额外的判断调用。对300个新问题的预注册复制实验确认了这种分离及规则的效果。
cs.AI / 194 / 2610.06192
Copies or Sources? Measuring How LLM Aggregators Count Restated Evidence in Multi-Agent Systems
副本还是来源？测量大型语言模型聚合器如何计算多智能体系统中的重述证据
Abstract
Multi-agent systems built on large language models (LLMs) restate observations as a matter of course: relays forward them, shared boards repeat them and discussion rounds echo them. An aggregator that pools such messages should count sources, not statements. We convert a reported probability into units of independent readings, which assigns every restatement a copy weight, 0 for an aggregator that counts sources and 1 for one that counts every statement, and yields the implied decision under any cost structure. Three testbeds hold the evidence fixed and vary how it is restated: message logs with an exact Bayesian oracle, web documents with appended copies, and logs written by LLM agent teams under four communication protocols. Across four models from three providers, a forwarded copy counts for 0.06 to 0.42 of a new reading, mostly because some replies count every statement. On 5% to 40% of logs that state one reading three times, the reported belief implies an early commitment that the oracle never makes. The models that count copies least and most on controlled logs do so on web copies and agent-written logs as well. A one-paragraph declaration of what a copy contributes brings the copy weight on controlled logs to 0.08 or less. A rule that has agents refer to readings instead of restating them cuts belief-implied early commitment from 11.2% to 1.1% and preserves genuine corroboration.
Chinese Translation
基于大型语言模型（LLMs）构建的多智能体系统通常会重述观察结果：中继转发它们，共享板重复它们，讨论轮次回响它们。一个汇聚这些消息的聚合器应该计算来源，而不是陈述。我们将报告的概率转换为独立阅读的单位，为每个重述分配一个副本权重，聚合器计算来源时为0，计算每个陈述时为1，并在任何成本结构下得出隐含决策。三个测试平台固定证据并变化重述方式：具有精确贝叶斯神谕的消息日志、附加副本的网页文档，以及在四种通信协议下由LLM代理团队撰写的日志。在来自三个提供者的四个模型中，转发的副本计为新阅读的0.06到0.42，主要是因为一些回复计算每个陈述。在5%到40%的日志中，某一阅读被陈述三次，报告的信念暗示了一个神谕从未做出的早期承诺。在受控日志中，计算副本最少和最多的模型在网页副本和代理撰写的日志中也是如此。对副本贡献的简短声明将受控日志中的副本权重降低到0.08或更少。一条让代理引用阅读而不是重述的规则将信念暗示的早期承诺从11.2%降低到1.1%，并保留了真正的证实。
cs.AI / 195 / 2610.06204
Do Small Language Models Learn to Negotiate? A Controlled Scaling Study of RL-Trained Sellers
小型语言模型能否学习谈判？一项关于强化学习训练卖家的受控规模研究
Abstract
LLM agents are starting to own the full customer experience. Soon, LLMs may be selling and buying on behalf of companies and customers respectively. Small models are more cost-efficient at scale, but can reinforcement learning train them into competent sellers? We train four Gemma 4 checkpoints (2.3B to 31B effective parameters) with GRPO on a programmatic utility reward for bilateral multi-issue bargaining, and evaluate every arm on the same 1,152 negotiations against two frontier buyers it never saw in training. With the same learning rate ($10^{-6}$) for every size, the gain of the RL model over its base rises from $+0.001$ at 2.3B to $+0.078$ at 31B. Each size was trained once and the two smallest checkpoints use a different architecture, so we fit no scaling law. Tripling the learning rate, with the same or fewer training steps, improves on the shared rate at every size by $+0.032$ (2.3B) to $+0.081$ (4.5B). In exploratory comparisons with two frontier models run as sellers, the 12B seller trained at the tripled rate scores above both, though its untrained base already scores as high as they do. The 4.5B seller at that rate shows no detectable difference from either and fits on one 48 GB GPU. A further 2.3B arm at ten times the shared rate raises pooled score, but its gain concentrates on the evaluation buyer that shares a model family with the training pool. These results suggest tuning the learning rate before concluding that a small model cannot learn to negotiate, and testing against buyers from more than one model family.
Chinese Translation
大型语言模型（LLM）代理开始掌控完整的客户体验。很快，LLM可能会代表公司和客户进行买卖。小型模型在规模上更具成本效益，但强化学习能否将它们训练成合格的卖家？我们使用GRPO在双边多议题谈判的程序化效用奖励上训练了四个Gemma 4检查点（有效参数从23亿到310亿），并在同样的1152次谈判中评估每个模型，面对两个在训练中未见过的前沿买家。在每个模型大小上使用相同的学习率（$10^{-6}$），强化学习模型相对于其基础模型的增益从23亿的$+0.001$上升到310亿的$+0.078$。每个模型大小仅训练一次，且两个最小的检查点使用不同的架构，因此我们未能拟合出规模法则。将学习率提高三倍，在相同或更少的训练步骤下，所有模型大小的共享学习率提升了$+0.032$（23亿）到$+0.081$（45亿）。在与两个作为卖家的前沿模型进行探索性比较时，经过三倍学习率训练的12亿卖家得分高于两者，尽管其未训练的基础模型已经得分与它们相当。以该学习率训练的45亿卖家与两者没有可检测的差异，并且可以在一块48GB的GPU上运行。进一步以十倍共享学习率训练的2.3亿模型提高了总体得分，但其增益集中在与训练池共享模型家族的评估买家上。这些结果表明，在得出小型模型无法学习谈判的结论之前，调节学习率是必要的，并且应针对来自多个模型家族的买家进行测试。
cs.AI / 196 / 2610.06207
AI-Decision Checkpoints for AI-Augmented Business Process Management: Framework and Educational Instantiation
AI决策检查点在AI增强业务流程管理中的应用：框架与教育实例化
Abstract
Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents are increasingly embedded in operational business processes. Yet Business Process Management (BPM) curricula and frameworks still largely treat artificial intelligence (AI) as an add-on technology, leaving graduates (as potential future process developers) unprepared to reason about AI as a first-class design element of end-to-end processes. This paper addresses that gap by proposing \emph{AI-decision checkpoints}: explicit moments in a process development trajectory where process developers identify AI-candidate sub-processes, assess expected effects on time, cost, quality, and flexibility, consider legal and organisational constraints, and document a reasoned decision to adopt, constrain, or reject specific AI components. The checkpoints are instantiated through a fictitious customer onboarding process as a \textit{BPM Teaching Case}, embedded in a lifecycle-driven framework spanning six modules that combine process modeling, simulation, workflow execution with AI agents, and process mining, with each module's output serving as the next module's input. A preliminary formative reflection draws on instructor observations, submitted artifacts, and discovered process maps from learning-management-system logs. These exploratory observations suggest that the approach supported clearer distinctions between task-level automation and process-level value.
Chinese Translation
大型语言模型（LLMs）、检索增强生成（RAG）和AI代理正日益融入运营业务流程。然而，业务流程管理（BPM）的课程和框架仍然主要将人工智能（AI）视为附加技术，使得毕业生（作为潜在的未来流程开发者）在将AI视为端到端流程的第一类设计元素时缺乏准备。本文通过提出 extit{AI决策检查点}来填补这一空白：在流程开发轨迹中明确的时刻，流程开发者识别AI候选子流程，评估对时间、成本、质量和灵活性的预期影响，考虑法律和组织约束，并记录采用、限制或拒绝特定AI组件的合理决策。这些检查点通过一个虚构的客户入职流程作为 extit{BPM教学案例}进行实例化，嵌入在一个生命周期驱动的框架中，涵盖六个模块，结合了流程建模、仿真、与AI代理的工作流执行以及流程挖掘，每个模块的输出作为下一个模块的输入。初步的形成性反思基于教师观察、提交的成果和学习管理系统日志中发现的流程图。这些探索性观察表明，该方法有助于在任务级自动化与流程级价值之间建立更清晰的区分。
cs.AI / 197 / 2610.06248
From Papers to Mechanisms: An Evidence-Grounded Knowledge Substrate for Scientific Language Models
从论文到机制：一个基于证据的科学语言模型知识底层
Abstract
Scientific language models often access literature through untyped text chunks, which fragment the functional and evidential structure required for mechanism-rich questions. We introduce an evidence-grounded mechanism knowledge substrate that organizes scientific literature into provenance-linked evidence units, role-typed entities, and directed mechanism paths. We instantiate it as MS$^3$, a Material-Sensor-Signal-System schema for conductive-fiber flexible sensors, over 13,689 papers, 131,083 evidence items, and 26,648 mechanism objects. On in-domain and coverage-shift question-answering benchmarks, we compare closed-book generation, Web search, Raw-PDF RAG, and MS$^3$ retrieval across ten language models. MS$^3$ improves macro-averaged scientific correctness. It also improves citation entailment and answer completeness. These results support mechanism substrates as a reliable representation layer for scientific language models and motivate a source-repair workflow in which insufficient MS$^3$ evidence triggers targeted retrieval from its linked papers rather than assuming that a user has already supplied the correct PDFs.
Chinese Translation
科学语言模型通常通过未标记的文本块访问文献，这会破坏机制丰富问题所需的功能和证据结构。我们提出了一种基于证据的机制知识底层，将科学文献组织为与来源关联的证据单元、角色类型实体和定向机制路径。我们将其实例化为 MS$^3$，即用于导电纤维柔性传感器的材料-传感器-信号-系统（Material-Sensor-Signal-System）架构，涵盖 13,689 篇论文、131,083 个证据项和 26,648 个机制对象。在领域内和覆盖偏移的问题回答基准上，我们比较了闭卷生成、网络搜索、原始 PDF 检索增强生成（Raw-PDF RAG）和 MS$^3$ 检索在十种语言模型中的表现。MS$^3$ 提高了宏观平均科学正确性，同时也改善了引用蕴含和答案完整性。这些结果支持机制底层作为科学语言模型的可靠表示层，并激励了一种源修复工作流程，其中不足的 MS$^3$ 证据会触发从其关联论文中进行有针对性的检索，而不是假设用户已经提供了正确的 PDF。
cs.AI / 198 / 2610.06269
Evolving in Thought Space: Training a Small Model at Test Time Unlocks Better Discoveries
在思维空间中的演变：在测试时训练小模型解锁更好的发现
Abstract
Open-ended scientific discovery often requires repeatedly proposing and evaluating candidate solutions. LLM-based systems can support this process by generating and refining executable solutions from verifier feedback. Methods such as TTT-Discover use test-time training (TTT) to update the solution-generating LLM from verifier feedback, adapting its generation policy to improve subsequent proposals on the target problem. However, this becomes expensive when reliable execution requires a large model, since training must maintain gradients, optimizer states, and policy statistics while repeatedly generating long, structured outputs. It also complicates credit assignment: outcome-level verifier feedback must jointly evaluate the high-level strategy and its low-level implementation. In this work, we introduce Guidance-TTT, which separates these roles. A compact guidance model is trained at test time to propose high-level strategic changes, while a frozen execution model implements them as complete executable solutions. At each step, the system selects a promising previously discovered solution, proposes a change, executes and verifies it, and updates only the guidance model using an adaptive group-relative RL objective. This concentrates test-time learning on short strategic decisions while retaining the implementation capability of a substantially stronger model without adapting it. Without web access, Guidance-TTT produces strong solutions across four distinct domains: combinatorial optimization (Polyomino Packing), heuristic programming (AHC058), machine learning (Lasso), and GPU kernel optimization (TriMul). Across these tasks, it outperforms the best solutions reported in prior work while remaining competitive with state-of-the-art results on public online leaderboards. Code is available at https://github.com/Human-Agent-Society/reef/tree/guidance-ttt-support.
Chinese Translation
开放式科学发现通常需要反复提出和评估候选解决方案。基于大型语言模型（LLM）系统可以通过生成和完善可执行解决方案来支持这一过程，利用验证者反馈进行改进。TTT-Discover等方法使用测试时训练（TTT）来根据验证者反馈更新生成解决方案的LLM，调整其生成策略以改善针对目标问题的后续提案。然而，当可靠的执行需要大型模型时，这会变得非常昂贵，因为训练必须在反复生成长且结构化的输出时保持梯度、优化器状态和策略统计。这也使得信用分配变得复杂：结果级别的验证者反馈必须共同评估高层策略及其低层实现。在本研究中，我们引入了Guidance-TTT，它将这些角色分开。在测试时训练一个紧凑的指导模型，以提出高层战略变更，而一个冻结的执行模型则将其作为完整的可执行解决方案实施。在每一步中，系统选择一个有前景的先前发现的解决方案，提出一个变更，执行并验证它，并仅使用自适应组相对强化学习目标更新指导模型。这将测试时学习集中在短期战略决策上，同时保留一个显著更强模型的实现能力，而无需对其进行调整。在没有网络访问的情况下，Guidance-TTT在四个不同领域产生了强大的解决方案：组合优化（Polyomino Packing）、启发式编程（AHC058）、机器学习（Lasso）和GPU内核优化（TriMul）。在这些任务中，它的表现超越了先前工作的最佳解决方案，同时在公共在线排行榜上与最先进的结果保持竞争力。代码可在 https://github.com/Human-Agent-Society/reef/tree/guidance-ttt-support 获取。
cs.AI / 199 / 2610.06270
DPNL: A DPLL-based Algorithm for Probabilistic Neurosymbolic Learning
DPNL：一种基于 DPLL 的概率神经符号学习算法
Abstract
Probabilistic Neurosymbolic Learning (PNL) combines neural predictions with symbolic reasoning, enabling end-to-end learning from final-output supervision without labels for intermediate concepts. A central challenge is probabilistic inference: state-of-the-art approaches often rely on materializing the logical provenance of a query, which can itself become a major computational bottleneck. We introduce Dynamic Probabilistic Neurosymbolic Learning (DPNL), an oracle-guided framework that avoids requiring complete provenance materialization before inference. DPNL lazily explores the space of intermediate assignments, while oracles resolve entire regions that can already be certified to produce or exclude the target output. We establish conditions ensuring soundness and termination. ApproxDPNL extends the same search with early termination while maintaining certified bounds on the exact output probability, providing controlled approximation guarantees. The oracle interface decouples inference from the representation of the symbolic component, enabling problem-specific reasoning within the same framework. Experiments on several neurosymbolic tasks show that DPNL and ApproxDPNL substantially extend the range of problem instances tractable by probabilistic neurosymbolic inference.
Chinese Translation
概率神经符号学习（PNL）将神经预测与符号推理相结合，使得在没有中间概念标签的情况下能够从最终输出监督中进行端到端学习。一个核心挑战是概率推理：最先进的方法通常依赖于实现查询的逻辑来源，这本身可能成为一个主要的计算瓶颈。我们提出了动态概率神经符号学习（DPNL），这是一个由神谕引导的框架，避免在推理之前需要完全实现来源。DPNL 以懒惰的方式探索中间赋值的空间，同时神谕解决已经可以被证明产生或排除目标输出的整个区域。我们建立了确保健全性和终止的条件。ApproxDPNL 在保持对精确输出概率的认证界限的同时，扩展了相同的搜索并实现了早期终止，提供了受控的近似保证。神谕接口将推理与符号组件的表示解耦，使得在同一框架内能够进行特定问题的推理。在多个神经符号任务上的实验表明，DPNL 和 ApproxDPNL 显著扩展了概率神经符号推理可处理的问题实例的范围。
cs.AI / 200 / 2610.06304
Teaching a Minimalist Machine to Discover Recursive Programs for Arithmetic
教会一个极简机器发现算术的递归程序
Abstract
Humans can often acquire and synthesize complex, recursive concepts from minimal experience. Leveraging cognitive insights, we propose the Minimalist Machine, a framework for inductive program synthesis designed to model such conceptual learning. The system uses a compact relational subset of Prolog: Programs are searched within a fixed schema of body-free facts and two-body conjunctive Horn clauses. Recursion is not defined by a dedicated metarule. Instead, it emerges when a target predicate is reused inside the body of a learned clause. Inspired by a primary school curriculum, the model is taught through a human-curated, sequential introduction of new concepts in arithmetic. Starting from initially empty knowledge base, it first acquires simple structural predicates, then successor-based state transformations, and finally recursive programs for addition, subtraction, multiplication, and division. Ultimately, this approach yields the fully transparent, inductive reasoning trace necessary for human-like conceptual learning.
Chinese Translation
人类通常能够从最小的经验中获取和综合复杂的递归概念。基于认知洞察，我们提出了极简机器（Minimalist Machine），这是一个旨在建模此类概念学习的归纳程序合成框架。该系统使用Prolog的紧凑关系子集：程序在固定的无体事实和双体合取霍恩子句的模式中进行搜索。递归并不是通过专门的元规则定义的，而是当目标谓词在学习到的子句的主体中被重用时自然而然地出现。受到小学课程的启发，该模型通过人类策划的算术新概念的顺序引入进行教学。从最初为空的知识库开始，它首先获取简单的结构谓词，然后是基于后继的状态变换，最后是加法、减法、乘法和除法的递归程序。最终，这种方法产生了完全透明的归纳推理轨迹，满足人类般的概念学习所需。
cs.AI / 201 / 2610.06316
RollPlace: Improving Macro Placement via Monte Carlo Rollout Search
RollPlace：通过蒙特卡洛回滚搜索改善宏单元布局
Abstract
The application of Reinforcement Learning (RL) in Electronic Design Automation (EDA), particularly for chip placement, has attracted considerable attention in recent years. While existing machine learning (ML)-based approaches have achieved notable progress, they predominantly focus on generating optimal layouts in a single attempt, often producing solutions that require subsequent refinement. To address this limitation, we propose RollPlace, a novel and generalized macro placement framework. RollPlace adopts a two-stage optimization strategy: generating initial placement solutions via machine learning methods or heuristic-based strategies, and refining these layouts efficiently by adjusting specific macros derived from the initial stage. This strategy circumvents the sequential generation constraints inherent in traditional RL-based placement methods. Furthermore, RollPlace seamlessly integrates Monte Carlo Tree Search (MCTS) to balance exploration and exploitation, and employs a rollout mechanism for efficient local search. Extensive experiments on the ISPD 2005 benchmark demonstrate that RollPlace outperforms state-of-the-art methods. Additionally, end-to-end experimental results based on OpenROAD across 19 benchmarks show that RollPlace excels in multiple metrics. The proposed framework offers a robust and scalable solution for addressing the growing complexity of modern chip design challenges.
Chinese Translation
近年来，强化学习（Reinforcement Learning, RL）在电子设计自动化（Electronic Design Automation, EDA）领域的应用，特别是在芯片布局方面，受到了广泛关注。尽管现有的基于机器学习（Machine Learning, ML）的方法取得了显著进展，但它们主要集中在一次性生成最佳布局，往往产生需要后续精细化的解决方案。为了解决这一局限性，我们提出了RollPlace，一个新颖且通用的宏单元布局框架。RollPlace采用两阶段优化策略：通过机器学习方法或基于启发式的策略生成初始布局解决方案，并通过调整初始阶段衍生的特定宏单元高效地精细化这些布局。这一策略规避了传统基于RL的布局方法中固有的顺序生成约束。此外，RollPlace无缝集成了蒙特卡洛树搜索（Monte Carlo Tree Search, MCTS），以平衡探索与利用，并采用回滚机制进行高效的局部搜索。在ISPD 2005基准测试上的广泛实验表明，RollPlace优于最先进的方法。此外，基于OpenROAD在19个基准测试上的端到端实验结果显示，RollPlace在多个指标上表现出色。所提出的框架为应对现代芯片设计挑战日益增长的复杂性提供了一种稳健且可扩展的解决方案。
cs.AI / 202 / 2610.06320
CRAFTER: Causality-based Self-adaptation for Autonomous IoT Systems
CRAFTER：基于因果关系的自主物联网系统自适应框架
Abstract
This paper presents CRAFTER, an automated framework for designing and deploying self-adaptive IoT systems using Causal Reinforcement Learning (CRL). As IoT devices increasingly populate pervasive computing spaces, smart environments are enabled with advanced monitoring and interactive services. The dynamic nature of these environments, such as fluctuating workloads and evolving application demands, poses significant challenges in maintaining consistent Quality of Service (QoS) levels of IoT applications. While existing self-adaptation techniques offer adaptive capabilities, they are often designed to deal with specific application domains, hindering the design of self-adaptive solutions that can be re-used across multiple IoT verticals. In addition, there is a lack of automated pipelines that act on identifying key performance drivers to take effective adaptation decisions. CRAFTER addresses these issues by using Causality as a formal framework for performance analysis of IoT systems. CRAFTER generates causal graphs to uncover dependencies among system components and guide adaptation decisions based on cause-effect relationships. Then, adaptation agents can leverage this knowledge to take more effective adaptation decisions in dynamic situations. Our experimental evaluation demonstrates how CRAFTER enables deriving causal graphs spanning diverse IoT use cases. Furthermore, we showcase how CRAFTER improves self-adaptation performance by 25% compared to state-of-the-art Reinforcement Learning-based approaches.
Chinese Translation
本文提出了CRAFTER，一个用于设计和部署自适应物联网系统的自动化框架，采用因果强化学习（Causal Reinforcement Learning, CRL）。随着物联网设备在普遍计算空间中的日益普及，智能环境得以实现先进的监测和交互服务。这些环境的动态特性，例如波动的工作负载和不断变化的应用需求，给维持物联网应用的一致服务质量（Quality of Service, QoS）水平带来了重大挑战。尽管现有的自适应技术提供了适应能力，但它们通常是针对特定应用领域设计的，这限制了可在多个物联网垂直领域中重复使用的自适应解决方案的设计。此外，缺乏自动化流程来识别关键性能驱动因素，从而做出有效的适应决策。CRAFTER通过将因果关系作为物联网系统性能分析的正式框架来解决这些问题。CRAFTER生成因果图，以揭示系统组件之间的依赖关系，并根据因果关系指导适应决策。然后，适应代理可以利用这些知识在动态情况下做出更有效的适应决策。我们的实验评估展示了CRAFTER如何生成涵盖多种物联网使用案例的因果图。此外，我们还展示了CRAFTER相比于最先进的基于强化学习的方法提高了25%的自适应性能。
cs.AI / 203 / 2610.06347
ImproveAnyTask: An Autonomous Post-Training Harness for Iterative Model Self-Improvement
ImproveAnyTask：一种用于迭代模型自我改进的自主后训练工具
Abstract
Adapting general-purpose large language models to specific tasks requires substantial human effort in designing data and training strategies. Sustaining improvement is especially challenging because model updates change the error distribution, requiring strategies to be continually refined. We introduce ImproveAnyTask, an autonomous post-training harness that improves task performance under a limited compute budget. Drawing inspiration from gradient-based parameter optimization, the harness organizes adaptation into error attribution, update-direction selection, and executable model updates. It combines metric-level and case-level analysis to identify a focal problem, then investigates research-backed strategies and compares their reported gains and reproduction difficulty. The selected strategy is translated into training data and a training configuration, with small-scale execution checks preceding full post-training. Subsequent evaluation guides model selection and further adaptation, while validated strategies and scripts are retained for reuse. Across 11 tasks, ImproveAnyTask achieves mean gains of 18.29 and 11.97 percentage points on the Base and Instruct models, respectively, with a maximum gain of 41.96 points, under a 24-hour budget with resources equivalent to eight H20 GPUs.
Chinese Translation
将通用大型语言模型适应于特定任务需要大量人力投入来设计数据和训练策略。持续改进尤其具有挑战性，因为模型更新会改变错误分布，要求策略不断调整。我们提出了ImproveAnyTask，这是一种在有限计算预算下提升任务性能的自主后训练工具。该工具受到基于梯度的参数优化的启发，将适应过程组织为错误归因、更新方向选择和可执行模型更新。它结合了指标级和案例级分析，以识别焦点问题，然后调查基于研究的策略，并比较其报告的收益和再现难度。所选策略被转化为训练数据和训练配置，并在全面后训练之前进行小规模执行检查。后续评估指导模型选择和进一步适应，同时验证过的策略和脚本被保留以供重用。在11个任务中，ImproveAnyTask在Base和Instruct模型上分别实现了18.29和11.97个百分点的平均增益，最大增益为41.96点，且在24小时的预算内使用相当于八个H20 GPU的资源。
cs.AI / 204 / 2610.06354
GraphDecide: Benchmarking System One Models on Graph Tasks
GraphDecide：在图任务上对系统一模型的基准测试
Abstract
Large language models (LLMs) are increasingly explored for graph understanding and decision-making, while System One models such as Jev select directly from supplied options. However, the capabilities of System One models on graph-related tasks remain unclear. We introduce GraphDecide, a model-independent benchmark that combines structural task profiles, matched graph-text input contrasts and heuristic-proposal controls to diagnose graph decision performance. We evaluate Jev and related choice-based models alongside language-model baselines, covering fourteen model-interface configurations. Jev's results illustrate the benchmark's central distinctions: accurate adjacency recognition does not guarantee broader structural correctness, joint graph-text input does not consistently improve prediction, and feasible construction does not establish high solution quality. Its task contracts, candidate interfaces and scoring rules support comparison across native selectors and language-model adapters. Code and aggregate results are available at https://github.com/VictorYXL/JevGraphBench.
Chinese Translation
大型语言模型（LLMs）在图理解和决策制定方面的探索日益增多，而系统一模型如Jev则直接从提供的选项中进行选择。然而，系统一模型在图相关任务上的能力仍不明确。我们引入了GraphDecide，这是一个与模型无关的基准测试，结合了结构任务特征、匹配的图-文本输入对比和启发式提案控制，以诊断图决策性能。我们评估了Jev及相关基于选择的模型，并与语言模型基准进行了比较，涵盖了十四种模型接口配置。Jev的结果展示了基准测试的核心区别：准确的邻接识别并不保证更广泛的结构正确性，联合图-文本输入并不总是能改善预测，而可行的构造并不建立高质量的解决方案。其任务合同、候选接口和评分规则支持原生选择器和语言模型适配器之间的比较。代码和汇总结果可在 https://github.com/VictorYXL/JevGraphBench 获取。
cs.AI / 205 / 2610.06361
Capability-Driven Self-Evolution of Agent Memory
基于能力驱动的智能体记忆自我演化
Abstract
Memory self-evolution uses task feedback to iteratively improve executable memory programs that store and retrieve information from past interactions. Existing approaches typically adopt holistic evolution, deriving revision directions from mixed feedback and judging progress by overall performance. This can obscure optimization directions and hide capability-specific gains offset by regressions elsewhere, leaving promising directions underexplored. We introduce capability-driven evolution, which extends search guidance from overall performance to individual capability dimensions, preserving promising revisions and expanding exploration beyond the boundaries of holistic evolution. We propose PrisMem, which uses dependency-aware capability selection to prioritize targets with potential cross-capability benefits and history-guided diagnosis to refine capability specialists. Trace-guided integration compares evaluated programs on paired differential cases, using their behavioral differences to consolidate complementary gains into a unified memory program. Experiments show that PrisMem outperforms the strongest baselines by 10.54 and 7.83 percentage points on BEAM-1M and LongMemEval-M, respectively, demonstrating its effectiveness on million-token histories.
Chinese Translation
记忆自我演化利用任务反馈迭代改进可执行的记忆程序，以存储和检索来自过去交互的信息。现有方法通常采用整体演化，从混合反馈中推导修订方向，并通过整体性能来判断进展。这可能会模糊优化方向，并掩盖因其他方面的退步而抵消的能力特定收益，从而使有前景的方向未得到充分探索。我们引入了基于能力驱动的演化，它将搜索指导从整体性能扩展到个体能力维度，保留有前景的修订，并在整体演化的边界之外扩展探索。我们提出了PrisMem，它使用依赖感知的能力选择来优先考虑具有潜在跨能力收益的目标，并通过历史引导的诊断来细化能力专家。基于轨迹的集成比较在成对差异案例中评估的程序，利用它们的行为差异将互补收益整合到一个统一的记忆程序中。实验表明，PrisMem在BEAM-1M和LongMemEval-M上分别比最强基线提高了10.54和7.83个百分点，证明了其在百万标记历史上的有效性。
cs.AI / 206 / 2610.06399
CVIF: A Criticality-Driven Visual Intervention Framework for Geometric Diagram Understanding in MLLMs
CVIF：一种基于关键性驱动的视觉干预框架，用于多模态大型语言模型中的几何图形理解
Abstract
Despite significant progress in visual tasks by Multimodal Large Language Models (MLLMs), geometric diagram understanding remains challenging due to the presence of sparse visual cues and ambiguous symbol-primitive associations. MLLMs may therefore rely on textual priors, producing interpretations that conflict with visual evidence. We introduce the training-free Criticality-Driven Visual Intervention Framework (CVIF), an inference-time method that localizes critical layers and executes visual interventions during the transition from evidence aggregation to semantic decoding. At these layers, a Geometry-Constrained Local Relation Reconstruction (GCLR) module selects and weights vertex-centered visual evidence, while an Adaptive Visual Steering Operator (AVSO) redistributes attention mass toward the selected tokens. Experiments on PGPS9K and PGDP5K show that CVIF raises Overall F1 from 77.85 to 85.58 and from 75.23 to 82.84, respectively, establishing a novel inference-time visual intervention paradigm.
Chinese Translation
尽管多模态大型语言模型（MLLMs）在视觉任务上取得了显著进展，但由于稀疏的视觉线索和模糊的符号-原语关联，几何图形理解仍然面临挑战。因此，MLLMs可能依赖文本先验，产生与视觉证据相冲突的解释。我们提出了一种无训练的关键性驱动视觉干预框架（CVIF），这是一种推理时方法，在证据聚合到语义解码的过渡过程中定位关键层并执行视觉干预。在这些层中，几何约束局部关系重建（GCLR）模块选择并加权以顶点为中心的视觉证据，而自适应视觉引导操作符（AVSO）则将注意力分配重新分配到所选的标记上。在PGPS9K和PGDP5K上的实验表明，CVIF将整体F1从77.85提升至85.58，从75.23提升至82.84，建立了一种新颖的推理时视觉干预范式。
cs.AI / 207 / 2610.06406
What Did the Agent Actually Do? Evidence-Grounded Oversight for Long-Horizon Agents
代理实际做了什么？基于证据的长期代理监督
Abstract
As agents take on long-horizon tasks, users shift from making individual decisions to overseeing autonomous execution. Yet the volume of agent activity and the fragmentation of supporting evidence make it difficult to determine which decisions warrant user verification. We study monitors that identify consequential decisions and locate evidence to help users assess their implications. We introduce AgentMonBench, a software-engineering benchmark comprising three subsets that cover two complementary dimensions: alignment between requirements and behavior, and awareness of consequential autonomous decisions for verification. To support these judgments, we propose the Evidence-Grounded Behavior Graph (EBG), a training-free method that groups source-linked evidence into behaviors and organizes their relationships into a graph. EBG presents task-oriented views of this graph to help monitors interpret behavior in context. Experiments across eight models show that EBG improves decision identification and evidence localization in most settings compared with direct access to the original context. Further experiments show that EBG's evidence-localization gains persist across input scales and hyperparameter settings, while real-world applications illustrate its practical value for human oversight.
Chinese Translation
随着代理承担长期任务，用户的角色从做出单个决策转变为监督自主执行。然而，代理活动的数量和支持证据的碎片化使得确定哪些决策需要用户验证变得困难。我们研究了能够识别重要决策并定位证据以帮助用户评估其影响的监控工具。我们引入了AgentMonBench，这是一个软件工程基准，包含三个子集，涵盖两个互补维度：需求与行为之间的对齐，以及对重要自主决策的验证意识。为了支持这些判断，我们提出了基于证据的行为图（Evidence-Grounded Behavior Graph, EBG），这是一种无训练的方法，将源链接的证据分组为行为，并将其关系组织成图。EBG以任务导向的视图呈现该图，以帮助监控工具在上下文中解释行为。通过对八个模型的实验表明，与直接访问原始上下文相比，EBG在大多数设置中提高了决策识别和证据定位的效果。进一步的实验显示，EBG的证据定位提升在不同输入规模和超参数设置下均能持续存在，而现实世界的应用则展示了其对人类监督的实际价值。
cs.AI / 208 / 2610.06411
From Benchmark to Bench: Can Agents Survive Real-World Drug Discovery?
从基准到实际：智能体能否在真实药物发现中生存？
Abstract
Agentic systems increasingly coordinate molecular-design tools, but it is unclear which layer of the stack limits outcomes on real projects. We developed MAGI, an open modular agent that authors objectives, launches and monitors optimization, interprets structure--activity relationships, and revises its strategy accordingly. MAGI generates molecules either directly through the LLM or by delegating to REINVENT 4, with scoring services interchangeable behind a common contract. We tested it across nine retrospective lead-optimization campaigns from three pharmaceutical companies, replayed under fixed temporal cutoffs. Both routes produced valid structures: LLM proposals stayed closer to local chemistry and reached comparable or higher primary activity in fewer operations, whereas REINVENT explored broader chemical space. Whether a campaign met its objective depended on the predictive models, not on the generation route: attainment followed model accuracy on the chemistry proposed, dropping once that chemistry moved outside the model's applicability domain. Separately, a blinded evaluation asked whether the MAGI's output could pass as expert work: chemists were not able to discriminate agentic proposals from held-out compounds, and judged the SAR reasoning broadly plausible yet incomplete. Together, these results position MAGI as a coordination layer pluggable into existing computational chemistry workflows. The ceiling on real projects, however, remains currently set by scorer applicability rather than by tool orchestration.
Chinese Translation
智能体系统越来越多地协调分子设计工具，但尚不清楚哪一层次的结构限制了真实项目的结果。我们开发了MAGI，一个开放的模块化智能体，它能够设定目标、启动和监控优化过程、解释结构-活性关系，并相应地修正其策略。MAGI可以通过大型语言模型（LLM）直接生成分子，或通过委托给REINVENT 4来生成，评分服务在一个共同的合约下可以互换。我们在三家制药公司的九个回顾性先导优化项目中进行了测试，并在固定的时间截止下重播。两种路径都产生了有效的结构：LLM提案更接近局部化学，并在较少的操作中达到了可比或更高的主要活性，而REINVENT则探索了更广泛的化学空间。一个项目是否达成其目标取决于预测模型，而不是生成路径：达成情况与所提议化学的模型准确性相关，当该化学超出模型的适用范围时，达成率下降。此外，一项盲评评估了MAGI的输出是否可以被视为专家工作：化学家无法区分智能体提案与保留化合物，并普遍认为SAR推理是合理的，但不完整。综合来看，这些结果将MAGI定位为可以插入现有计算化学工作流程的协调层。然而，真实项目的上限目前仍由评分器的适用性而非工具的协同作用所设定。
cs.AI / 209 / 2610.06452
Multimodal Safety Evaluation Should Measure Controllability Beyond Classification
多模态安全评估应超越分类测量可控性
Abstract
VLM safety is commonly evaluated through input- and output-level classification. Such classification is necessary, but it does not reveal whether a safety state is accessible or controllable inside the model. We argue that multimodal safety evaluation should therefore report a \emph{controllability profile} alongside behavioral classification, separating representation-level detectability, cross-modal specificity, intervention sensitivity, and benign-preserving selectivity. Using implicit toxicity as a stress case, we instantiate this profile on LlavaGuard and Qwen3.5 with sparse feature decompositions. LlavaGuard admits localized handles with a narrow benign-preserving intervention range and modest downstream safety gains, whereas Qwen3.5 supports strong representation-level readout but no comparable selective-control regime under the tested operators. These results show that internal readout and controllability can diverge. Future multimodal safety benchmarks should therefore report not only behavioral safety metrics, but also whether safety-relevant internal signals can be intervention-tested and controlled within a validated operating range.
Chinese Translation
VLM（视觉语言模型）安全性通常通过输入和输出级别的分类进行评估。这种分类是必要的，但并不能揭示安全状态在模型内部是否可达或可控。因此，我们认为多模态安全评估应当报告一个 extit{可控性轮廓}，与行为分类相结合，区分表示级别的可检测性、跨模态特异性、干预敏感性和良性保留选择性。以隐性毒性作为压力案例，我们在LlavaGuard和Qwen3.5上通过稀疏特征分解实例化了这一轮廓。LlavaGuard允许局部控制，具有狭窄的良性保留干预范围和适度的下游安全收益，而Qwen3.5支持强大的表示级别读出，但在测试的操作符下没有可比的选择性控制机制。这些结果表明，内部读出和可控性可能存在差异。因此，未来的多模态安全基准不仅应报告行为安全指标，还应说明安全相关的内部信号是否可以在验证的操作范围内进行干预测试和控制。
cs.AI / 210 / 2610.06453
Normality Constraint Learning: Adapting Foundation Models for Time Series Anomaly Detection
正常性约束学习：为时间序列异常检测调整基础模型
Abstract
Time Series Foundation Models (TSFMs) achieve strong generalization by learning to reconstruct or forecast broad temporal patterns from large-scale time series during pre-training. Yet this strength can become a weakness for anomaly detection: TSFMs may model rare anomalous patterns as effectively as normal ones, allowing anomalies to be accurately reconstructed or forecasted and thus diminishing their reconstruction/forecasting error-based anomaly scores. This paper proposes $\underline{\textbf{N}}$$\textbf{ormality}$ $\underline{\textbf{C}}$$\textbf{onstraint}$ $\underline{\textbf{L}}$$\textbf{earning}$ ($\textbf{NCL}$), a lightweight plug-and-play framework that adapts pre-trained TSFMs for accurate anomaly detection without modifying their pre-trained parameters. Our key insight is to constrain the broad pattern space of TSFMs to the normal structure of a target time series, preventing their broad modeling capability from obscuring abnormal deviations. Specifically, NCL constructs a compact normality subspace from a few normal patch features and adaptively steers each patch feature toward normality within this subspace, guided by contrastive constraints that form compact and discriminative normality manifolds. The calibrated features are aggregated to reinforce normal components and fused with the original TSFM output, amplifying the discrepancy between normal and abnormal observations for the reconstruction/forecasting error-based anomaly scoring. Extensive experiments across diverse TSFM families and benchmarks show that NCL consistently improves anomaly detection performance, providing a generalizable framework for adapting TSFMs to anomaly detection.
Chinese Translation
时间序列基础模型（TSFMs）通过学习从大规模时间序列中重构或预测广泛的时间模式，在预训练阶段实现了强大的泛化能力。然而，这一优势在异常检测中可能变成劣势：TSFMs可能同样有效地建模稀有的异常模式和正常模式，从而使得异常能够被准确重构或预测，降低基于重构/预测误差的异常评分。本文提出了正常性约束学习（Normality Constraint Learning，NCL），这是一个轻量级的即插即用框架，旨在在不修改预训练参数的情况下，将预训练的TSFMs调整为准确的异常检测。我们的关键见解是将TSFMs的广泛模式空间约束为目标时间序列的正常结构，防止其广泛建模能力掩盖异常偏差。具体而言，NCL从少量正常补丁特征构建一个紧凑的正常性子空间，并在该子空间内自适应地引导每个补丁特征朝向正常性，受对比约束的指导，形成紧凑且具有区分性的正常性流形。经过校准的特征被聚合以增强正常成分，并与原始TSFM输出融合，从而放大正常观察与异常观察之间的差异，以便进行基于重构/预测误差的异常评分。在不同TSFM家族和基准上的广泛实验表明，NCL始终提高了异常检测性能，为将TSFMs调整为异常检测提供了一个可泛化的框架。
cs.AI / 211 / 2610.06454
AgentPrivArena: Evaluating and Auditing Real-world AI Agent Privacy
AgentPrivArena：评估和审计现实世界中的人工智能代理隐私
Abstract
The rapid advancement of LLM agents has enabled systems to autonomously perform complex tasks through external tools, but their growing access to personal data introduces significant privacy risks. Existing benchmarks primarily evaluate LLM agent privacy through simulated trajectories and outcome-based metrics, limiting their ability to capture privacy risks arising during multi-step agent execution. In this work, we introduce AgentPrivArena, a framework for evaluating privacy risks in realistic LLM agent workflows. AgentPrivArena integrates authentic MCP tools and self-hosted services within a reproducible execution environment. We further propose trajectory-level privacy metrics that quantify unnecessary information access beyond final response leakage. Building on this framework, we introduce AgentPrivAudit, a runtime auditing approach for monitoring privacy violations during agent execution. Extensive experiments on state-of-the-art LLM agents reveal substantial privacy risks overlooked by existing evaluation paradigms, highlighting the importance of trajectory-level auditing for trustworthy agent deployment.
Chinese Translation
大型语言模型（LLM）代理的快速发展使得系统能够通过外部工具自主执行复杂任务，但它们对个人数据的日益访问引入了显著的隐私风险。现有基准主要通过模拟轨迹和基于结果的指标来评估LLM代理的隐私，限制了其捕捉多步骤代理执行过程中出现的隐私风险的能力。在本研究中，我们引入了AgentPrivArena，一个用于评估现实LLM代理工作流中隐私风险的框架。AgentPrivArena在可重复的执行环境中集成了真实的多方计算（MCP）工具和自托管服务。我们进一步提出了轨迹级隐私指标，以量化超出最终响应泄露的不必要信息访问。在此框架的基础上，我们引入了AgentPrivAudit，一种用于监控代理执行过程中隐私违规的运行时审计方法。对最先进的LLM代理进行的广泛实验揭示了现有评估范式所忽视的重大隐私风险，强调了轨迹级审计在可信代理部署中的重要性。
cs.AI / 212 / 2610.06489
GPlaceRL: An Open-Source Graph Reinforcement Learning Framework for Detailed Placement
GPlaceRL：一个开源图形强化学习框架用于详细布局
Abstract
Reinforcement learning (RL) has emerged as a promising approach for placement optimization, particularly when combined with graph neural networks (GNNs) that capture circuit connectivity. However, most learning-based placement approaches focus on floorplanning, macro placement, or global placement, while detailed placement refinement remains relatively unexplored. In this paper, we present GPlaceRL, an open-source graph reinforcement learning framework for detailed placement refinement. GPlaceRL represents legalized placements as graphs and provides a modular environment for studying graph encoders, policy architectures, reward formulations, and local placement actions. To demonstrate the capabilities of GPlaceRL, we conduct a systematic evaluation of proximal policy optimization (PPO) policies with graph attention network (GAT) encoders in a per-design optimization setting. Across five placement benchmarks, the best greedy evaluation results achieve HPWL improvements ranging from $3.27\%$ to $32.87\%$. The results highlight the importance of compact GAT architectures and flexible local action spaces for placement optimization. Overall, GPlaceRL provides a reproducible and extensible framework for systematic research on RL-based detailed placement refinement.
Chinese Translation
强化学习（RL）已成为布局优化的一种有前景的方法，特别是当与捕捉电路连通性的图神经网络（GNNs）结合时。然而，大多数基于学习的布局方法集中于平面布局、宏布局或全局布局，而详细布局的优化仍然相对未被探索。本文提出了GPlaceRL，一个用于详细布局优化的开源图形强化学习框架。GPlaceRL将合法布局表示为图形，并提供一个模块化环境，用于研究图形编码器、策略架构、奖励公式和局部布局动作。为了展示GPlaceRL的能力，我们在每个设计优化设置中对采用图注意力网络（GAT）编码器的近端策略优化（PPO）策略进行了系统评估。在五个布局基准中，最佳贪婪评估结果实现了HPWL改进范围为$3.27\%$到$32.87\%$。结果突显了紧凑的GAT架构和灵活的局部动作空间在布局优化中的重要性。总体而言，GPlaceRL提供了一个可重复和可扩展的框架，用于系统研究基于RL的详细布局优化。
cs.AI / 213 / 2610.06491
polyview: A Python package for multi-view machine learning
polyview：一个用于多视角机器学习的Python包
Abstract
Multi-view learning jointly exploits multiple complementary representations of the same data and has become increasingly important in machine learning. However, the Python ecosystem lacks actively maintained, unified tooling for end-to-end multi-view workflows. In this paper, we present polyview, a Python package that provides tools for multi-view embedding, clustering, fusion, and view augmentation, as well as for handling incomplete views, all compatible with scikit-learn. The library offers a unified interface for composing heterogeneous multi-view workflows, including seamless transitions between multi-view and single-view stages. It is built around a core set of classes and utilities that enable composition of different methods and straightforward implementation of new ones. We illustrate the package on five real multi-view datasets and compare its components based on canonical correlation analysis with those of two established libraries. polyview aims to be both a practical toolkit for benchmarking and prototyping multi-view methods and a foundation for future research and development in this area.
Chinese Translation
多视角学习共同利用同一数据的多种互补表示，已在机器学习中变得越来越重要。然而，Python生态系统缺乏积极维护的、统一的工具来支持端到端的多视角工作流程。本文介绍了polyview，一个提供多视角嵌入、聚类、融合和视角增强工具的Python包，同时也支持处理不完整视角，所有功能均与scikit-learn兼容。该库提供了一个统一的接口，用于组合异构的多视角工作流程，包括在多视角和单视角阶段之间的无缝过渡。它围绕一组核心类和工具构建，能够组合不同的方法并简化新方法的实现。我们在五个真实的多视角数据集上展示了该包，并基于典型相关分析将其组件与两个成熟库的组件进行了比较。polyview旨在成为一个实用的工具包，用于基准测试和原型开发多视角方法，同时也是该领域未来研究和发展的基础。
cs.AI / 214 / 2610.06496
You Changed Your Mind, The Model Didn't: Demystifying Intent in Multi-Turn Dialogue
你改变了主意，但模型没有：揭示多轮对话中的意图
Abstract
When a large language model handles a multi-turn task and a user proposes a change but ultimately rejects it, the model should continue as if nothing changed. We find a surprising failure: merely mentioning a rejected change can derail task execution, even when the user's final intent remains unchanged. To systematically study language model behavior under evolving user intent, we introduce Intent-Eval, a controlled benchmark spanning tool actions, code, databases, and mathematics. Across diverse tasks, models are vulnerable to both rejected proposals and superseded requirements, consistent with mentioned-as-in-effect confusion: conversational content is treated as active requirements even after it has been rejected or replaced. Accuracy degradation can deepen or persist as interaction continues, highlighting the need to distinguish what has been mentioned from what remains in effect. Building on this insight, we propose Intent-OPSD, a decision-conditioned on-policy self-distillation framework with Teacher and Student initialized from the same model. The frozen Teacher provides active-intent supervision from the complete task matching the user's decision, training the Student on the full dialogue to follow active requirements reflecting user intent.
Chinese Translation
当大型语言模型处理多轮任务时，如果用户提出更改但最终拒绝，模型应继续执行，仿佛没有任何变化。然而，我们发现了一个令人惊讶的失败：仅仅提到一个被拒绝的更改就可能导致任务执行的偏离，即使用户的最终意图保持不变。为了系统地研究语言模型在用户意图变化下的行为，我们引入了 Intent-Eval，这是一个涵盖工具操作、代码、数据库和数学的受控基准。在各种任务中，模型对被拒绝的提议和被取代的要求都表现出脆弱性，这与提到的内容被视为有效的混淆一致：即使在被拒绝或替换后，对话内容仍被视为活跃的要求。随着交互的持续，准确性下降可能加深或持续，突显出区分已提及内容与有效内容的重要性。在此基础上，我们提出了 Intent-OPSD，这是一种基于决策的在线自蒸馏框架，教师和学生均从同一模型初始化。冻结的教师提供来自完整任务的活跃意图监督，以匹配用户的决策，训练学生在完整对话中遵循反映用户意图的活跃要求。
cs.AI / 215 / 2610.06514
ANT: A Multi-Granularity Network Traffic Dataset and Benchmark for Agents Behavior Auditing
ANT：一个多粒度网络流量数据集及代理行为审计基准
Abstract
The growing adoption of large language model (LLM) agents creates a need for network administrators and security teams to audit agent behavior within organizational networks without inspecting private user content. Network traffic offers an observable source of evidence, but how much it reveals about agent tasks and operations remains unclear. Existing traffic datasets lack the joint task and stage annotations needed to evaluate this question. We introduce ANT (Agent Network Traffic), a dataset providing agent behavior information at risk, scenario, and behavior primitive granularities alongside network traffic. ANT contains 3,114 execution episodes across 20 tasks and five scenarios, comprising 276,417 bidirectional flows and 40,049 behavior primitive segments organized into 47 macro groups. We establish a benchmark for agent risk identification, scenario recognition, and behavior primitive classification using 13 representative traffic analysis baselines. The results show that existing methods recover useful but uneven behavioral signals. They struggle to identify risk when malicious workflows resemble benign tasks and to distinguish scenarios with similar traffic patterns. Primitive classification is more reliable for frequent macro groups and those with distinctive traffic patterns than for rare or semantically similar groups. ANT provides a common basis for developing more precise auditing and forensic analysis of agent behavior from network traffic. Our data and code are available at https://anonymous.4open.science/r/ant-main-suite-7BC0/.
Chinese Translation
大型语言模型（LLM）代理的日益普及使得网络管理员和安全团队需要在不检查私人用户内容的情况下审计组织网络内的代理行为。网络流量提供了一个可观察的证据来源，但它在多大程度上揭示了代理任务和操作仍然不清楚。现有的流量数据集缺乏评估这一问题所需的联合任务和阶段注释。我们引入了ANT（Agent Network Traffic），这是一个提供风险、场景和行为原语粒度的代理行为信息的数据集，伴随网络流量。ANT包含20个任务和5个场景下的3,114个执行事件，涵盖276,417个双向流和40,049个行为原语片段，组织成47个宏观组。我们使用13个代表性的流量分析基线建立了代理风险识别、场景识别和行为原语分类的基准。结果表明，现有方法能够恢复有用但不均匀的行为信号。在恶意工作流与良性任务相似时，它们在识别风险方面存在困难，并且难以区分具有相似流量模式的场景。对于频繁的宏观组和具有独特流量模式的组，原语分类的可靠性更高，而对于稀有或语义相似的组则较低。ANT为从网络流量中开发更精确的代理行为审计和取证分析提供了一个共同基础。我们的数据和代码可在 https://anonymous.4open.science/r/ant-main-suite-7BC0/ 获取。
cs.AI / 216 / 2610.06549
Proof-Grounded Patient-Specific Clinical Explanations from Knowledge-Graph Reasoning
基于知识图谱推理的证据支持的患者特定临床解释
Abstract
Clinical decision-support outputs can lack an au- ditable link between patient observations, encoded knowledge, conclusions, and recommendations. We present the CKG Clinical Explanation Engine, a downstream layer for a frozen, training-free clinical knowledge-graph reasoner that converts patient inference states and disease knowledge into typed facts, explicit rule-application traces, provenance-linked conclusions, and policy-licensed recommendations. The design separates measurement availability, representation completeness, and disease-specific activation; consequently, observed zero-activation evience is not treated as missing and partial representation is distinct from unobserved evidence. Optional language generation is restricted to symbolically licensed content. Across five usable workbooks (6,720 patients; 20,160 patient-disease traces; 1,021,440 feature-evidence rows), IG-range validity and knowledge provenance were 100%, numerical cross-sheet fidelity was 100% (120,960/120,960), and exported logical/report trace completeness was 100% (20,160/20,160). Availability representation consistency was 99.7028% (1,018,404/1,021,440); all 3,036 disagreements were confined to three systematic feature-cohort patterns. The corpus contained 86,783 observed zero-activation and 139,949 observed partially represented instances. A separate seeded 25-patient end-to-end audit completed without execution failure and passed all pre-specified trace, licensing, provenance, and state-consistency checks. These results establish structural and implementation auditability, not clinical correctness or utility.
Chinese Translation
临床决策支持输出可能缺乏患者观察、编码知识、结论和建议之间的可审计链接。我们提出了CKG临床解释引擎，这是一个针对冻结的、无训练的临床知识图谱推理器的下游层，它将患者推理状态和疾病知识转换为类型化事实、明确的规则应用痕迹、源头链接的结论和政策许可的建议。该设计将测量可用性、表示完整性和疾病特异性激活分开；因此，观察到的零激活证据不被视为缺失，部分表示与未观察到的证据是不同的。可选的语言生成仅限于符号许可的内容。在五个可用工作簿中（6,720名患者；20,160条患者-疾病痕迹；1,021,440条特征-证据行），IG范围有效性和知识来源的准确性均为100%，数值跨表保真度为100%（120,960/120,960），导出的逻辑/报告痕迹完整性为100%（20,160/20,160）。可用性表示一致性为99.7028%（1,018,404/1,021,440）；所有3,036个不一致仅限于三种系统性特征-队列模式。语料库中包含86,783个观察到的零激活和139,949个观察到的部分表示实例。一个单独的种子25名患者的端到端审计在没有执行失败的情况下完成，并通过了所有预先指定的痕迹、许可、来源和状态一致性检查。这些结果确立了结构和实施的可审计性，而非临床正确性或实用性。
cs.AI / 217 / 2610.06553
Signature-Based Feature Learning for Human Activity Recognition: A Reproducible Machine Learning Study of Representation, Depth, and Model Choice
基于签名的特征学习在人类活动识别中的应用：一个可重复的机器学习研究关于表示、深度和模型选择
Abstract
Human activity recognition (HAR) relies on transforming sensor signals into informative representations for classification. Although deep learning and handcrafted features are widely used, the role of representation itself is often not systematically isolated. Signature transforms provide a mathematically grounded way to encode temporal order and cross-channel interactions, but their value for HAR under a fully reproducible and leakage-aware framework remains unclear. To evaluate whether signature-based feature learning improves HAR performance compared with raw-signal baselines, and to assess the effects of embedding strategy, truncation depth, model choice, and sensor configuration. Experiments were conducted on the UCI HAR dataset using a fully reproducible pipeline with the original train--test split preserved and subject-disjoint validation to prevent leakage. Three representations were compared: raw flattened signals, time-augmented paths, and lead--lag transformed paths. Signature features were computed at multiple truncation depths and evaluated using multilayer perceptron (MLP) and Random Forest (RF) classifiers under identical preprocessing and validation procedures. A prior K-means-based feature reduction study was also reproduced for comparison. Signature-based representations improved performance when paired with RF models, the best configuration was time-augmented six-channel signatures at depth 6 using entropy-based RF achieving 0.858 accuracy and 0.859 macro F1, outperforming the strongest raw baseline (0.816 accuracy). Lead--lag representations were competitive at moderate depths but did not surpass the best time-augmented models. MLP models did not exceed raw baselines. Signature-based feature learning can improve HAR, but its benefit depends on alignment between representation design and classifier choice.
Chinese Translation
人类活动识别（HAR）依赖于将传感器信号转化为信息丰富的表示以进行分类。尽管深度学习和手工特征被广泛使用，但表示本身的作用往往没有被系统地隔离。签名变换提供了一种数学上有依据的方法来编码时间顺序和跨通道交互，但在一个完全可重复和防泄漏的框架下，它们对HAR的价值仍不清楚。本研究旨在评估基于签名的特征学习是否能改善HAR性能，相较于原始信号基线，并评估嵌入策略、截断深度、模型选择和传感器配置的影响。实验在UCI HAR数据集上进行，使用完全可重复的流程，保留原始的训练-测试划分，并采用受试者不重叠的验证以防止泄漏。比较了三种表示：原始扁平信号、时间增强路径和滞后-领先变换路径。签名特征在多个截断深度上计算，并在相同的预处理和验证程序下使用多层感知器（MLP）和随机森林（RF）分类器进行评估。还重现了一项基于K均值的特征降维研究以作比较。基于签名的表示在与RF模型配对时提高了性能，最佳配置为深度为6的时间增强六通道签名，使用基于熵的RF实现了0.858的准确率和0.859的宏F1，超越了最强的原始基线（0.816的准确率）。滞后-领先表示在中等深度时表现竞争，但未能超过最佳的时间增强模型。MLP模型的表现未超过原始基线。基于签名的特征学习可以改善HAR，但其益处取决于表示设计与分类器选择之间的对齐。
cs.AI / 218 / 2610.06563
HERA: Harness-Environment Co-Evolution for Reliable Agentic Abstention
HERA：用于可靠代理性弃权的工具-环境共演化
Abstract
Large language model (LLM) agents are increasingly capable of acting in complex tool-use environments, yet they often fail to recognize when tasks are infeasible and no valid solution exists. Recent work has formalized this reliability gap as the problem of agentic abstention, and existing approaches typically optimize a model or agent harness against a fixed set of tasks, leading to limited generalization to unseen failure modes. We introduce HERA, a framework for harness-environment co-evolution for agentic abstention. HERA consists of (i) a pipeline to automatically construct verifiable pairs of feasible and infeasible tasks by applying controlled environment mutations that transform solvable tasks into cases requiring abstention, and (ii) a co-evolution procedure in which performance failures on previous tasks are used to drive harness adaptation and generate new execution environments and tasks geared towards previous weaknesses. On held-out evaluation tasks, an evolved harness from HERA improves abstention accuracy from 61.7% to 83.3% while improving feasible-task completion from 68.3% to 76.7%, achieving the highest abstention and feasible-task completion among the compared methods. The resulting best harness transfers across 19 other LLMs, improving abstention accuracy by 15.3 percentage points on average without any model-specific optimization, and enabling smaller models to match the performance of more powerful models at an estimated 85% lower cost.
Chinese Translation
大型语言模型（LLM）代理在复杂的工具使用环境中越来越具备行动能力，但它们常常无法识别任务是否不可行以及是否不存在有效解。近期的研究将这种可靠性差距形式化为代理性弃权问题，现有方法通常针对固定任务集优化模型或代理工具，导致对未见失败模式的泛化能力有限。我们提出了HERA，一个用于代理性弃权的工具-环境共演化框架。HERA包括（i）一个管道，通过应用控制环境突变自动构建可验证的可行和不可行任务对，这些突变将可解任务转化为需要弃权的案例，以及（ii）一个共演化过程，其中对先前任务的性能失败用于推动工具适应并生成针对先前弱点的新执行环境和任务。在保留评估任务中，HERA演化出的工具将弃权准确率从61.7%提高到83.3%，同时将可行任务的完成率从68.3%提高到76.7%，在比较方法中实现了最高的弃权率和可行任务完成率。最终得到的最佳工具在19个其他LLM上迁移，平均提高了15.3个百分点的弃权准确率，而无需任何特定于模型的优化，并使得较小的模型能够以估计85%的更低成本匹配更强大模型的性能。
cs.AI / 219 / 2610.06578
DGA-Muon: Decoupled Geometry-Aligned Adaptive Scaling for Muon
DGA-Muon：解耦几何对齐自适应缩放的μ子
Abstract
While NorMuon has achieved strong empirical performance in large-scale pretraining by enhancing Muon with row-wise adaptive scaling, its underlying adaptive mechanism remains poorly understood. In this work, we provide the first systematic analysis of NorMuon's adaptivity, revealing that it originates primarily from orthogonalization-induced geometry rather than genuine optimization-relevant information. Under exact orthogonalization, the adaptive scaling factors degenerate into a single global scalar for square and wide matrices, while for tall matrices their variation arises from the non-uniform distribution of row energy after orthogonalization. Under approximate orthogonalization, the orthogonality residual introduces additional variation into the scaling factors, leading to the \textit{Orthogonalization--Adaptivity Paradox}: more accurate orthogonalization weakens adaptivity. We further show that NorMuon's rigid row-wise scaling is geometrically misaligned with the one-sided orthogonal structure of tall matrices. Based on the analysis of these limitations, we propose two core design principles that a desirable adaptive mechanism for Muon should satisfy. First, adaptive scaling should be decoupled from orthogonalization, with the scaling factors computed directly from raw gradients. Second, adaptive scaling should be aligned with the shape-dependent orthogonal structure of the polar factor, using row-wise scaling for wide matrices and column-wise scaling for tall matrices. By incorporating several other design considerations, including sum-based second-moment estimates, bias correction, and adaptive clipping of scaling factors, we obtain the Decoupled Geometry-Aligned Muon (DGA-Muon) optimizer. We establish convergence guarantees for DGA-Muon and empirically validate both our theoretical characterization of NorMuon's scaling degeneration and the superiority of DGA-Muon over NorMuon.
Chinese Translation
虽然NorMuon通过行级自适应缩放在大规模预训练中取得了强大的实证性能，但其潜在的自适应机制仍然不够清晰。在本研究中，我们首次对NorMuon的自适应性进行了系统分析，揭示其主要源于正交化引起的几何特性，而非真正与优化相关的信息。在精确正交化下，自适应缩放因子退化为方形和宽矩阵的单一全局标量，而对于高矩阵，其变化源于正交化后行能量的非均匀分布。在近似正交化下，正交性残差为缩放因子引入了额外的变化，导致了 extit{正交化-自适应悖论}：更精确的正交化削弱了自适应性。我们进一步表明，NorMuon的刚性行级缩放在几何上与高矩阵的单侧正交结构不对齐。基于对这些局限性的分析，我们提出了两个理想自适应机制应满足的核心设计原则。首先，自适应缩放应与正交化解耦，缩放因子应直接从原始梯度计算。其次，自适应缩放应与极因子的形状依赖正交结构对齐，对于宽矩阵使用行级缩放，对于高矩阵使用列级缩放。通过结合其他设计考虑，包括基于和的二阶矩估计、偏差校正和缩放因子的自适应裁剪，我们获得了解耦几何对齐μ子（DGA-Muon）优化器。我们为DGA-Muon建立了收敛保证，并实证验证了我们对NorMuon缩放退化的理论表征以及DGA-Muon相较于NorMuon的优越性。
cs.AI / 220 / 2610.06582
Mind the Execution Gap: Action-Semantic Mismatch in World-Model Control
注意执行差距：世界模型控制中的动作语义不匹配
Abstract
World-model controllers rely on action-conditioned dynamics for prediction and planning, yet real control systems often execute commands asynchronously due to communication delay, packet loss, reordering, and actuator buffering. We study how asynchronous execution changes the action semantics assumed within world-model controllers, rather than treating it only as an external control disturbance. Through controlled interventions, we identify two architecture-dependent failure modes: planning-based controllers such as TD-MPC2 suffer from a future-action timeline mismatch between imagined and executed action sequences, while recurrent world models such as DreamerV3 can attribute observed transitions to commands that were not actually applied. Our analysis shows that TD-MPC2 requires the correct future action sequence during latent dynamics rollout, whereas DreamerV3 requires timely attribution of each transition to the action that generated it. Based on these findings, we introduce two lightweight execution-consistent interfaces, Future-Sequence for TD-MPC2 and Applied-Action Feedback for DreamerV3, that correct these mismatches without modifying the pretrained world models. Experiments across delays, packet loss, reordering, multiple control domains, measured network traces, and a process-separated asynchronous stack consistently support both diagnoses and the corresponding architecture-specific corrections.
Chinese Translation
世界模型控制器依赖于基于动作的动态进行预测和规划，但由于通信延迟、数据包丢失、重排序和执行器缓冲等原因，实际控制系统往往异步执行命令。我们研究了异步执行如何改变世界模型控制器中假设的动作语义，而不仅仅将其视为外部控制干扰。通过控制干预，我们识别出两种依赖于架构的失效模式：基于规划的控制器如 TD-MPC2 在想象和执行的动作序列之间存在未来动作时间线不匹配的问题，而递归世界模型如 DreamerV3 则可能将观察到的转变归因于实际上并未应用的命令。我们的分析表明，TD-MPC2 在潜在动态展开期间需要正确的未来动作序列，而 DreamerV3 则需要及时将每个转变归因于生成该转变的动作。基于这些发现，我们提出了两个轻量级的执行一致性接口，分别是 TD-MPC2 的 Future-Sequence 和 DreamerV3 的 Applied-Action Feedback，这些接口在不修改预训练世界模型的情况下修正了这些不匹配。跨越延迟、数据包丢失、重排序、多个控制领域、测量的网络跟踪以及一个进程分离的异步堆栈的实验一致支持这两种诊断和相应的架构特定修正。
cs.AI / 221 / 2610.06591
The Review Lottery: Calibrating an Observational Estimator of Peer-Review Noise (ICLR 2017-2025)
评审抽样：校准同行评审噪声的观察估计器（ICLR 2017-2025）
Abstract
How much of a conference accept/reject decision would change if the same paper were reviewed by a different set of reviewers? Running a second independent program committee is the gold standard for answering this, but it is prohibitively expensive: done only twice (NeurIPS 2014 and 2021). We build an observational estimator of this quantity from public review data alone, calibrate it twice, and apply it to nine years of ICLR (2017-2025; 36,113 papers, 134,912 reviews). The estimator decomposes scores with a Bayesian ordered-probit model into paper quality and reviewer noise, maps scores to decisions with a logistic model, and simulates two independent committees (posterior draws B=1,000; committee sizes k=2,3,4). Estimated disagreement rates are 23-30% at k=2 and 18-24% at k=4; 30-50% of accepted papers would be rejected. External calibration: at the NeurIPS 2021 reviewer-count caliber (k=3), the simulated 2021 disagreement rate is 23.3% [21.7%, 25.0%] vs. reported 23.0% (bias +0.3pp); accept precision and committee correlation agree within 5pp and 0.04. Internal calibration: on 18,740 papers with 4+ reviews, random model-free 2+2 reviewer splits agree with the k=2 simulation within 1pp in 2018 and 2021-2025. Longitudinally, we find no robust time trend in reviewer noise over 2017-2025. The high accepted-paper flip rates of 2020 and 2021 have distinct mechanisms: the 2020 four-point scale compressed scores (23.7% of papers had zero within-paper variance), and a counterfactual shows coarsening the scale raises disagreement by about 7pp; 2021 instead combined the lowest signal-to-noise ratio in the sample with the most threshold-crowded acceptances. For the LLM era, a 2023 breakpoint test on within-paper score variance finds no break, but the design has almost no power, and no post-2022 review text or confidence data exist, so no LLM attribution is attempted.
Chinese Translation
如果同一篇论文由不同的评审人进行评审，会议的接受/拒绝决定会改变多少？运行第二个独立的程序委员会是回答这个问题的金标准，但其成本高昂：仅在2014年和2021年的NeurIPS会议上进行过两次。我们仅利用公开评审数据构建了该数量的观察估计器，进行了两次校准，并将其应用于九年的ICLR（2017-2025；36,113篇论文，134,912条评审）。该估计器使用贝叶斯有序概率模型将评分分解为论文质量和评审噪声，使用逻辑模型将评分映射到决策，并模拟两个独立的委员会（后验抽样B=1,000；委员会规模k=2,3,4）。在k=2时，估计的不一致率为23-30%，在k=4时为18-24%；30-50%的被接受论文将被拒绝。外部校准：在NeurIPS 2021评审人数标准（k=3）下，模拟的2021年不一致率为23.3% [21.7%, 25.0%]，而报告的不一致率为23.0%（偏差+0.3个百分点）；接受精度和委员会相关性在5个百分点和0.04内一致。内部校准：在18,740篇拥有4个以上评审的论文中，随机无模型的2+2评审分组在2018年和2021-2025年与k=2模拟一致在1个百分点内。从纵向来看，我们发现2017-2025年间评审噪声没有显著的时间趋势。2020年和2021年高接受论文翻转率有不同的机制：2020年的四分制压缩了评分（23.7%的论文在内部评分中没有方差），而反事实分析表明，粗化评分标准会使不一致性增加约7个百分点；2021年则结合了样本中最低的信噪比和最多的阈值拥挤接受。对于LLM时代，2023年对内部评分方差的断点测试未发现断点，但该设计几乎没有功效，并且没有2022年后评审文本或置信数据，因此未尝试进行LLM归因。
cs.AI / 222 / 2610.06597
Can Agent Harnesses and Inference Engines Hear Each Other? The HEAR Protocol for Agentic LLM Serving
代理工具与推理引擎能相互理解吗？用于代理大型语言模型服务的 HEAR 协议
Abstract
LLM agents increasingly execute complex workflows involving multi-turn reasoning, tool use, and parallel agents. Efficient serving requires decisions that span two layers with complementary information: the agent harness understands workflow dependencies, context lifecycles, and execution objectives, whereas the inference engine observes request queues, KV-cache state, resource pressure, and execution capabilities. Existing interfaces do not systematically connect these views, limiting workflow-aware execution. HEAR, a bidirectional Harness--Engine Pairing protocol for agentic LLM serving. HEAR standardizes how the harness communicates workflow intent and execution requirements and how the engine returns runtime state, capabilities, and outcomes. By separating protocol semantics from optimization policies, HEAR supports diverse coordination strategies without changing workflow or model semantics. We instantiate HEAR for online cache-aware runtime coordination and workload-aware execution-mode selection for agent roles. Across four conversational and research-agent benchmarks under memory-constrained, concurrent serving, HEAR achieves a $1.61\times$ batch speedup and reduces median time-to-first-token by $2.23\times$ on SCBench. Mooncake shows that workflow intent and live engine state provide complementary benefits across load regimes. On BrowseComp-Plus and DeepResearchBench, workload-specific configurations yield $1.23\times$ and $2.45\times$ end-to-end speedups, respectively, without observed task-quality degradation. These results establish HEAR as a reusable coordination substrate for efficient agentic LLM serving.
Chinese Translation
大型语言模型（LLM）代理越来越多地执行涉及多轮推理、工具使用和并行代理的复杂工作流程。高效的服务需要在两个层面上做出决策，这两个层面提供互补的信息：代理工具理解工作流程的依赖关系、上下文生命周期和执行目标，而推理引擎则观察请求队列、KV-cache 状态、资源压力和执行能力。现有接口并未系统性地连接这些视角，限制了对工作流程的感知执行。HEAR 是一种双向的工具-引擎配对协议，用于代理 LLM 服务。HEAR 标准化了工具如何传达工作流程意图和执行要求，以及引擎如何返回运行时状态、能力和结果。通过将协议语义与优化策略分离，HEAR 支持多样的协调策略，而无需改变工作流程或模型的语义。我们为在线缓存感知的运行时协调和工作负载感知的执行模式选择实例化了 HEAR。针对四个在内存受限的并发服务下的对话和研究代理基准，HEAR 实现了 $1.61 imes$ 的批处理加速，并在 SCBench 上将首次令牌的中位时间减少了 $2.23 imes$。Mooncake 表明，工作流程意图和实时引擎状态在不同负载条件下提供了互补的好处。在 BrowseComp-Plus 和 DeepResearchBench 上，特定工作负载的配置分别实现了 $1.23 imes$ 和 $2.45 imes$ 的端到端加速，且未观察到任务质量的下降。这些结果确立了 HEAR 作为高效代理 LLM 服务的可重用协调基础。
cs.AI / 223 / 2610.06614
FREA: A Multi-Source Expert Benchmark for Reaction Feasibility Verification
FREA：一种用于反应可行性验证的多源专家基准
Abstract
As generative models and AI agents propose chemical reactions at a scale beyond expert review, feasibility verifiers decide which proposals enter synthesis planning. But do their decisions agree with chemists across different kinds of candidates? We introduce FREA, a benchmark of 751 reactions labeled by expert chemists under an explicit feasibility criterion, drawn from retrosynthesis model proposals, zero-yield experimental records, edits by large language models (LLMs), and five negative candidate generation methods. Our evaluation finds that no verifier leads across all sources: LLMs given only the criterion are competitive with dedicated verifiers, while forward models perform best on retrosynthesis proposals but reject most feasible edits of recorded reactions at the evaluated operating points. Looking beyond aggregate scores, both forward models perform below chance when separating infeasible alternative disconnections from feasible generated candidates. To study whether negative supervision addresses these weaknesses, we also release a corpus of over 14 million recorded reactions and generated negative candidates. In matched training comparisons, adding a mixture of generated negatives to forward training raises mean AUROC across sources, but these gains do not extend to retrosynthesis proposals. Varying the generation method further shows that the largest gain on generated candidates coincides with worse proposal screening. These findings motivate evaluating verifiers against experts across sources and designing negatives for transfer to model proposals.
Chinese Translation
随着生成模型和人工智能代理在超出专家审查范围的规模上提出化学反应，可行性验证者决定哪些提案进入合成规划。但他们的决策是否与不同类型候选者的化学家一致？我们介绍了FREA，这是一个由专家化学家根据明确的可行性标准标注的751个反应的基准，反应来源于逆合成模型提案、零产率实验记录、大型语言模型（LLMs）的编辑以及五种负候选生成方法。我们的评估发现，没有一个验证者在所有来源中表现最佳：仅根据标准的LLMs与专门的验证者竞争，而前向模型在逆合成提案中表现最佳，但在评估的操作点上拒绝了大多数可行的记录反应编辑。超越总体得分的分析显示，当将不可行的替代断裂与可行的生成候选分开时，两个前向模型的表现低于随机水平。为了研究负监督是否能解决这些弱点，我们还发布了超过1400万条记录反应和生成负候选的语料库。在匹配训练比较中，将生成的负样本混合添加到前向训练中提高了各来源的平均AUROC，但这些增益并未延伸到逆合成提案。进一步变化生成方法显示，在生成候选上最大的增益与更差的提案筛选相吻合。这些发现促使我们在不同来源中对验证者进行与专家的评估，并为模型提案设计负样本。
cs.AI / 224 / 2610.06652
Collective intelligence through aggregation
通过聚合实现集体智能
Abstract
Suppose a committee, expert panel, or other group is making judgments on some issues, where these may be not just yes/no-questions, such as whether a defendant is guilty, but also variables with many possible values, such as macroeconomic or meteorological variables or travel directions. Furthermore, there may be interconnections between different issues, as in the case of economic or climate variables. How can the group arrive at "intelligent" collective judgments, based on the group members' individual judgments? We investigate three challenges raised by this judgment-aggregation problem. First, reasonable methods of aggregation (such as defining the collective judgment for each issue as the average or median judgment) can produce inconsistent collective judgments. Secondly, many methods of aggregation are manipulable by strategic voting. Finally, not all methods of aggregation are conducive to tracking the truth on the issues in question. We prove new impossibility or possibility theorems on all three challenges, identifying what it takes to produce collective judgments in a consistent, non-manipulable, and truth-tracking manner and thereby to achieve collective intelligence through aggregation. Overall, the median method, though imperfect, performs reasonably well. We also note the relevance of our analysis for non-human group decisions.
Chinese Translation
假设一个委员会、专家小组或其他团体正在对某些问题做出判断，这些问题不仅可能是是/否问题，例如被告是否有罪，还可能是具有多种可能值的变量，例如宏观经济或气象变量或旅行方向。此外，不同问题之间可能存在相互关联，例如经济或气候变量。该小组如何基于成员的个人判断得出“智能”的集体判断？我们研究了这一判断聚合问题所提出的三个挑战。首先，合理的聚合方法（例如将每个问题的集体判断定义为平均值或中位数判断）可能会产生不一致的集体判断。其次，许多聚合方法容易受到战略投票的操控。最后，并非所有聚合方法都能有效追踪相关问题的真相。我们证明了关于这三个挑战的新不可能性或可能性定理，识别出以一致性、不可操控和追踪真相的方式产生集体判断所需的条件，从而通过聚合实现集体智能。总体而言，尽管中位数方法并不完美，但表现相对良好。我们还指出了我们的分析对非人类群体决策的相关性。
cs.AI / 225 / 2610.06668
Language models can notice an impossible engineering problem yet still report it as solved
语言模型能够注意到一个不可能的工程问题，但仍然报告其已解决
Abstract
Language models draft engineering calculations, but answer accuracy does not show whether they reject an impossible problem. We tested 14 models on 30 pairs of mechanics problems, each with a valid version and one made impossible by changing a given value or assumption. Two independent solvers verified every answer key and showed that each flawed problem was physically impossible. We scored solving of valid problems separately from rejection of their flawed counterparts. Each reply required a "solved" or "cannot solve" status; rejection meant "cannot solve" or withholding an answer. The initial prompts did not warn that problems could be flawed. Across three recent models, 12 of 90 replies failed to reject a flawed problem. In 11 of these replies, the model stated the flaw, answered a corrected problem and still reported the original as "solved", according to artificial intelligence raters and numerical checks. We later retested four models from one provider, offering "flawed" instead of "cannot solve" and asking them to name and explain the defect. Three models showed statistically significant increases in rejection, but valid-problem solving fell in three. Evaluations therefore need to score both versions and distinguish flaw recognition from the reported status.
Chinese Translation
语言模型能够起草工程计算，但答案的准确性并不能表明它们是否拒绝一个不可能的问题。我们对14个模型进行了测试，使用了30对力学问题，每对问题中包含一个有效版本和一个通过改变给定值或假设而变得不可能的问题。两位独立的解题者验证了每个答案的正确性，显示每个有缺陷的问题在物理上都是不可能的。我们分别对有效问题的解决和对其有缺陷对应问题的拒绝进行了评分。每个回复都需要一个“已解决”或“无法解决”的状态；拒绝意味着“无法解决”或不提供答案。最初的提示并未警告问题可能存在缺陷。在三种最近的模型中，90个回复中有12个未能拒绝一个有缺陷的问题。在这11个回复中，模型指出了缺陷，回答了一个修正后的问题，但仍然报告原问题为“已解决”，根据人工智能评估者和数值检查的结果。随后，我们重新测试了来自一个提供者的四个模型，要求它们提供“有缺陷”而不是“无法解决”的回答，并要求它们命名和解释缺陷。三种模型在拒绝率上显示出统计学上的显著增加，但在有效问题的解决率上下降。因此，评估需要对两种版本进行评分，并区分缺陷识别与报告状态。
cs.AI / 226 / 2610.06673
The Pushback Paradox: A Two-Probe Diagnostic for Language Model Compliance
反推悖论：一种双探针诊断语言模型合规性的工具
Abstract
Are language models compliant with user instructions? A model that always complies can be stopped but also exploited, while one that always resists can be neither exploited nor stopped. We contribute an open two-probe benchmark that can place any language model on this spectrum. In the active probe, a user instructs the model to act and accept a lower payoff, which measures exploitability. In the passive probe, the user instructs it to wait and give up a higher payoff, which measures stoppability. The two compliance rates combine into a compliance index $\kappa$. Applied to twelve language models, the benchmark shows that seven mostly follow the instruction in both probes and justify their action by pointing to the instruction. Only Claude Sonnet-4.6 and Claude Opus-4.7 can be stopped without being exploitable, Claude Opus-4.6 and GPT-5-mini resist both instructions, and no model is exploitable but unstoppable. Knowing where a language model sits on the compliance index $\kappa$ matters for human operators and for multi-agent systems, whether distributed or orchestrated.
Chinese Translation
语言模型是否遵循用户指令？一个始终遵循的模型可以被停止，但也可能被利用；而一个始终抵抗的模型既无法被利用也无法被停止。我们贡献了一个开放的双探针基准，可以将任何语言模型置于这一光谱中。在主动探针中，用户指示模型采取行动并接受较低的收益，以测量其可利用性。在被动探针中，用户指示模型等待并放弃较高的收益，以测量其可停止性。这两个合规率结合成一个合规指数 $$. 应用于十二个语言模型，该基准显示七个模型在两个探针中大多遵循指令，并通过指向指令来证明其行动的合理性。只有 Claude Sonnet-4.6 和 Claude Opus-4.7 可以在不被利用的情况下被停止，Claude Opus-4.6 和 GPT-5-mini 对两个指令均表现出抵抗，而没有模型是可利用但不可停止的。了解语言模型在合规指数 $$ 上的位置对人类操作员和多智能体系统（无论是分布式还是协调式）都至关重要。
cs.AI / 227 / 2610.06765
Conditional Rank Allocation for Taxonomy-Aware Medical Language Model Adaptation
面向分类的条件排名分配用于医学语言模型适应
Abstract
Medical question answering spans specialties and clinical operations that may benefit from different adaptation directions. We propose ARBOR, a parameter-efficient method that selects rank-one components from a shared low-rank basis for each question. An additive gate combines question representations, specialty tags, operation tags, and their interaction; a learned coefficient scales the adapter residual. An illustrative separation under orthogonal, equiprobable subtasks shows how conditional selection can avoid an approximation floor faced by a fixed update with the same active rank. This result motivates the design without asserting a corresponding bound for medical corpora. On Qwen3-8B across CMB, CMExam, MedQA, and MedMCQA, five-seed experiments yield 69.69% mean accuracy across benchmarks, exceeding LoRA r16 and MoELoRA by 1.26 and 1.30 percentage points, respectively. The reported advantage over LoRA r16 increases from 0.08 to 1.94 points as training expands from one to seven specialties. Tag perturbations and atom masking support the usefulness of clinical routing, while atom clusters align with the supplied specialty labels (adjusted Rand index 0.62). Calibration, transfer, and measured costs further characterize the method. These findings support structured conditional adaptation for medical QA, while leaving clinical safety and broader deployment untested.
Chinese Translation
医学问答涵盖了多个专业和临床操作，这些领域可能受益于不同的适应方向。我们提出了 ARBOR，这是一种参数高效的方法，为每个问题从共享的低秩基础中选择秩为一的组件。一个加性门控结合了问题表示、专业标签、操作标签及其交互；一个学习的系数对适配器残差进行缩放。在正交、等概率子任务下的示例分离展示了条件选择如何避免固定更新所面临的近似下限，这种固定更新使用相同的活跃秩。该结果激励了设计，而未对医学语料库断言相应的界限。在 Qwen3-8B 上针对 CMB、CMExam、MedQA 和 MedMCQA 的五次种子实验显示，基准测试的平均准确率为 69.69%，超过了 LoRA r16 和 MoELoRA，分别提高了 1.26 和 1.30 个百分点。与 LoRA r16 的报告优势在训练从一个专业扩展到七个专业时，从 0.08 增加到 1.94 点。标签扰动和原子掩蔽支持临床路由的有效性，而原子簇与提供的专业标签对齐（调整后的 Rand 指数为 0.62）。校准、转移和测量成本进一步表征了该方法。这些发现支持医学问答的结构化条件适应，同时尚未测试临床安全性和更广泛的部署。
cs.AI / 228 / 2610.06790
Back to the Future: Rethinking EDA Infrastructure for Agentic Systems in Chip Design Verification
回到未来：重新思考芯片设计验证中代理系统的电子设计自动化基础设施
Abstract
The unprecedented computational scale of modern artificial intelligence depends on complex, multi-billion-transistor Systems-on-Chip, yet the workflows that verify these chips remain stubbornly manual. Although Large Language Models (LLMs) have made rapid inroads into Electronic Design Automation (EDA), approximately 74.6% of existing studies target static Register-Transfer Level (RTL) code generation, leaving post-simulation verification and interactive waveform debugging largely untouched. We introduce Back-to-the-Future (BTTF), an end-to-end agentic framework that closes this infrastructural gap. BTTF distills massive, unstructured simulation dumps into a normalized relational SQLite database and couples it with a collaborative multi-agent orchestration engine that translates natural-language verification queries into schema-aware SQL while correlating signal anomalies with versioned RTL repositories. Across a 150-query benchmark, BTTF attains 95.33% execution accuracy, charting a practical path toward autonomous EDA verification.
Chinese Translation
现代人工智能前所未有的计算规模依赖于复杂的数十亿晶体管的系统级芯片（SoC），然而验证这些芯片的工作流程仍然顽固地保持手动化。尽管大型语言模型（LLMs）已迅速渗透到电子设计自动化（EDA）领域，但现有研究中约74.6%集中于静态寄存器传输级（RTL）代码生成，后仿真验证和交互式波形调试几乎未受到关注。我们提出了回到未来（Back-to-the-Future, BTTF），一个端到端的代理框架，旨在填补这一基础设施的空白。BTTF将大量非结构化的仿真数据提炼为标准化的关系型SQLite数据库，并结合一个协作的多代理编排引擎，将自然语言验证查询转换为模式感知的SQL，同时将信号异常与版本化的RTL库相关联。在150个查询的基准测试中，BTTF达到了95.33%的执行准确率，为自主EDA验证绘制了一条切实可行的路径。
cs.AI / 229 / 2610.06824
TasteVal: Measuring the Experimental Research Taste of AI Systems Against Human Experts
TasteVal：评估人工智能系统的实验研究品味与人类专家的对比
Abstract
We introduce TasteVal, a benchmark to evaluate the experimental research taste of frontier models. We define research taste as the ability to pick interesting problems to solve, design experiments, and interpret experimental results. TasteVal measures the experimental component of research taste; given a fixed research problem, we measure how well a model iteratively designs experiments and draws conclusions from their outcomes. We operationalize experimental research taste as compute efficiency; a Researcher who reaches the same score as an expert human using half the serial experimental compute has twice the experimental taste. Experimental taste thus acts as a multiplier on experimental compute, making it a key input to forecasts of AI progress. TasteVal consists of 8 novel, challenging, open-ended tasks representative of frontier AI R&D. To isolate taste from coding ability, the model under evaluation acts as a Researcher that iteratively designs experiments while a fixed Coder agent implements them and reports their results. The Researcher executes until either the 40 H100 hour or 120 wall-clock hour budgets are exhausted. We recruit 24 human experts, at least 2 per task, and take the best expert attempt per task as the expert baseline. We evaluate 20 models released between 2023 and 2026. The best-performing model, Opus 5.5, exceeds our expert baseline, with a compute multiplier of 2.3x (95% CI 1.15-4.37), at roughly 1/30 of our baseliners' average per-run cost. On TasteVal, the compute multiplier of frontier models has doubled approximately every 3.0 months since December 2025 (95% CI 1.7-5.0), up from every 14 months between 2023 and December 2025. Measured by final normalized performance, frontier models show no trend break, doubling every 14.6 months. To keep TasteVal uncontaminated, we do not release the tasks.
Chinese Translation
我们介绍了TasteVal，一个用于评估前沿模型实验研究品味的基准。我们将研究品味定义为选择有趣问题进行解决、设计实验和解释实验结果的能力。TasteVal测量研究品味的实验组成部分；在给定固定研究问题的情况下，我们测量模型如何迭代设计实验并从结果中得出结论。我们将实验研究品味操作化为计算效率；一位研究者如果使用一半的串行实验计算达到与专家人类相同的分数，则其实验品味为两倍。因此，实验品味作为实验计算的乘数，成为预测人工智能进展的关键输入。TasteVal包含8个新颖、具有挑战性的开放性任务，代表了前沿人工智能研发。为了将品味与编码能力隔离，评估中的模型充当研究者，迭代设计实验，而固定的编码代理则实施这些实验并报告结果。研究者执行直到耗尽40 H100小时或120小时的实际时间预算。我们招募了24位人类专家，每个任务至少2位，并将每个任务的最佳专家尝试作为专家基准。我们评估了2023年至2026年间发布的20个模型。表现最佳的模型Opus 5.5超越了我们的专家基准，其计算乘数为2.3倍（95%置信区间1.15-4.37），成本大约是我们的基准模型每次运行成本的1/30。在TasteVal上，自2025年12月以来，前沿模型的计算乘数大约每3.0个月翻一番（95%置信区间1.7-5.0），而在2023年至2025年12月之间则是每14个月翻一番。根据最终标准化表现，前沿模型没有趋势中断，每14.6个月翻一番。为了保持TasteVal的纯净性，我们不发布这些任务。
cs.AI / 230 / 2610.06846
BiasFlow: Geometric Monitoring and Backbone Regularization for Spurious Feature Reliance
BiasFlow：针对虚假特征依赖的几何监测与主干正则化
Abstract
Worst-group accuracy (WGA) evaluates a trained predictor but does not characterize how its frozen backbone behaves when a new head is learned. We introduce BiasFlow, a hook-based toolkit for monitoring class-attribute centroid alignment (IBMI), within-class centroid separation (W-IBMI), and feature-projection sensitivity. IBMI is confounded by class-attribute correlation and is not a measure of causal feature reliance. We pair these diagnostics with BiasFlow Regularization (BFR), a supervised, composable class-conditional centroid-alignment penalty. W-IBMI verifies the quantity BFR optimizes; it is scale dependent and does not independently establish attribute removal. Across the reported small-scale benchmarks, adding BFR improves or preserves mean WGA, with gains up to +26.0 pp on UrbanCars. The principal independent stress test freezes CelebA-Std backbones and trains fresh heads on biased data: BFR+GroupDRO improves WGA from 40.7% to 64.1%, while Male probe accuracy decreases from 92.5% to 72.2%. Attribute information remains recoverable, and cross-task results are mixed. A controlled synthetic-watermark ImageNet experiment additionally improves watermark-shift accuracy by +23.0 pp under matched training. These results support evaluating centroid geometry and resistance to biased head retraining alongside WGA, within the tested protocols.
Chinese Translation
最差组准确率（WGA）评估训练后的预测器，但并未表征其冻结的主干在学习新头部时的表现。我们引入了BiasFlow，这是一个基于钩子的工具包，用于监测类属性中心对齐（IBMI）、类内中心分离（W-IBMI）和特征投影敏感性。IBMI受到类属性相关性的干扰，并不是因果特征依赖的度量。我们将这些诊断与BiasFlow正则化（BFR）结合，后者是一种监督的、可组合的类条件中心对齐惩罚。W-IBMI验证BFR优化的数量；它依赖于尺度，并不能独立确立属性移除。在报告的小规模基准测试中，添加BFR改善或保持了平均WGA，UrbanCars上的增益高达+26.0个百分点。主要的独立压力测试冻结CelebA-Std主干，并在偏见数据上训练新的头部：BFR+GroupDRO将WGA从40.7%提高到64.1%，而男性探针准确率从92.5%下降到72.2%。属性信息仍然可恢复，跨任务结果则不一而足。一个受控的合成水印ImageNet实验在匹配训练下，水印偏移准确率额外提高了+23.0个百分点。这些结果支持在测试协议中评估中心几何形状和对偏见头部再训练的抵抗力，连同WGA一起进行评估。
cs.CL / 1 / 2610.03802
Fine-Grained Emotion Classification from Mobile App Reviews: An Empirical Study with Large Language Models
基于移动应用评论的细粒度情感分类：基于大型语言模型的实证研究
Abstract
Context: Fine-grained emotion classification of mobile app reviews enables requirements engineering activities that go beyond polarity-based opinion mining, including emotionally informed issue prioritisation and feature-oriented feedback analysis. However, automatic fine-grained emotion extraction from app reviews remains understudied. Objectives: Building on a previously published annotation framework and human-labelled ground truth adapted from Plutchik's taxonomy, this paper investigates how large language models can be leveraged for automatic multi-label emotion classification under severe class imbalance. Methods: We compare encoder-only fine-tuning under multi-label and binary-ensemble formulations, decoder-only zero- and few-shot prompting across open-source and proprietary models, and a catalogue of imbalance mitigation strategies (loss reweighting, resampling, generative data augmentation), with the synthetic-review generator and prompting strategy selected via an intrinsic augmentation-utility ranking. Results: Fine-tuned encoders trail the best decoder-only few-shot prompting (macro-F1 0.642) by a wide margin at baseline (multi-label: 0.387; binary ensemble: 0.450); pairing the best multi-label encoder with generative data augmentation and positive-weighted loss closes most of this gap (+0.204) at up to three orders of magnitude lower inference latency than the decoders, with the largest gains on the rarest emotions, from undetected to gains of up to +0.501 F1. Conclusion: Large language models make fine-grained, multi-label emotion classification of app reviews feasible for requirements engineering pipelines, with modest macro-F1, and the best formulation and mitigation strategy are backbone- and formulation-dependent. We release the experimental pipeline, synthetic corpora, and fine-tuned checkpoints for replication and reuse.
Chinese Translation
背景：移动应用评论的细粒度情感分类使得需求工程活动超越基于极性的意见挖掘，包括情感驱动的问题优先级排序和面向特征的反馈分析。然而，自动从应用评论中提取细粒度情感仍然是一个未被充分研究的领域。目标：基于先前发布的注释框架和从Plutchik的分类法中调整的人类标注的真实数据，本文探讨如何利用大型语言模型在严重类别不平衡的情况下进行自动多标签情感分类。方法：我们比较了在多标签和二元集成形式下的编码器仅微调，解码器仅的零样本和少样本提示，跨开源和专有模型，以及一系列不平衡缓解策略（损失重加权、重采样、生成数据增强），并通过内在的增强效用排名选择合成评论生成器和提示策略。结果：微调的编码器在基线下（多标签：0.387；二元集成：0.450）明显落后于最佳的解码器仅少样本提示（宏F1 0.642）；将最佳多标签编码器与生成数据增强和正权重损失结合，可以在推理延迟比解码器低三个数量级的情况下缩小大部分差距（+0.204），在最稀有的情感上获得最大增益，从未检测到的情感到F1增益高达+0.501。结论：大型语言模型使得应用评论的细粒度多标签情感分类在需求工程流程中成为可能，宏F1表现适中，最佳的形式和缓解策略依赖于骨干网络和形式。我们发布了实验流程、合成语料库和微调检查点以供复制和重用。
cs.CL / 2 / 2610.03825
Same Output, Different Gold: Measuring How Reference Choice Moves a Multilingual Benchmark Score
相同输出，不同标准：测量参考选择如何影响多语言基准得分
Abstract
A benchmark score compares a system output against a reference, and methodological attention falls almost entirely on the first term. We measure the second. The retained annotation record of a six-language benchmark for personally identifiable information contains two independent annotator labellings, the aggregate shipped as gold, and a reviewer gold from independent expert re-annotation of a sample. Using it, we hold the scored output fixed and exchange the reference. The score moves by 4.95 F1 points for one output and 2.00 for the other (95% CIs [3.23, 6.27] and [0.26, 3.37]), and by 7.55 in the worst language. The comparison between two outputs moves as well: the paired interaction between reference and system is +2.95 points (CI [+1.97, +3.87]), survives correction for multiple testing, and changes one language's margin outright. The cause is an undocumented aggregation default that usually kept one annotator when adjudication did not fire, making the shipped reference a partial copy of an output being scored. We report the resulting reference-sensitivity band, show how to compute one from any retained annotation record, and argue that the quantity belongs beside the score.
Chinese Translation
基准得分将系统输出与参考进行比较，方法论的关注几乎完全集中在第一个术语上。我们测量第二个术语。一个包含六种语言的个人可识别信息基准的保留注释记录包含两个独立的标注者标签，汇总后作为金标准，以及来自独立专家对样本进行重新标注的审阅金标准。利用这些数据，我们固定得分输出并更换参考。对于一个输出，得分变化为4.95 F1点，对于另一个输出则为2.00（95%置信区间 [3.23, 6.27] 和 [0.26, 3.37]），在最差的语言中变化为7.55。两个输出之间的比较也发生变化：参考与系统之间的配对交互为+2.95点（置信区间 [+1.97, +3.87]），经过多重检验校正后依然显著，并且直接改变了一个语言的边际。其原因是一个未记录的聚合默认设置，通常在裁决未触发时保留一个标注者，使得发货参考成为正在评分输出的部分副本。我们报告了由此产生的参考敏感性区间，展示如何从任何保留的注释记录中计算出一个，并主张该数量应与得分并列。
cs.CL / 3 / 2610.03827
The Score Is Not the Structure: Brain Alignment and Cross-Lingual Transfer
分数不是结构：大脑对齐与跨语言迁移
Abstract
Researchers often support the claim that a model shares structure with the brain, or across languages, by reporting a similarity score. We ask what such a score reads when the shared structure is absent, or when the tool that measures it does not work. We check two settings, and in both the score is not what it appears. First, a probe trained to tell grammatical from ungrammatical sentences in one language transfers worse to more distant languages, the usual evidence for shared structure. But the probe itself gets worse along the same axis: in four of seventeen languages it performs at chance, so 64 of 272 language pairs are scored with a tool that does not work. Dropping those languages halves the strength of the relationship, but they are also the most distant, and this design cannot separate the two effects. Counting matters too: the same data give p = 0.0006 when the 272 pairs are treated as independent and p = 0.155 when the seventeen languages are, which is the correct unit. Second, training a language model to match human brain responses raises its similarity score from 0.10 to 0.34, against a ceiling of 0.54. A model trained on a target whose correspondence to the brain was destroyed still scores 0.31, so only 0.028 to 0.068 of the rise is specific to the brain. With no model at all, a destroyed target already sits at 0.204 from the real one, a floor that tracks the target's rank divided by the number of sentences. Finally, steering a language along its own direction works (+6.2 over a random direction in sixteen of seventeen languages) yet shows no effect that varies with language distance. Before asking whether a correspondence helps, ask how much of the score would survive without it.
Chinese Translation
研究人员常常通过报告相似性分数来支持模型与大脑或不同语言之间共享结构的说法。我们探讨在共享结构缺失或测量工具失效时，这样的分数反映了什么。我们检查了两种情境，在这两种情况下，分数并不是表面上看起来的那样。首先，一个训练用于区分一种语言中的语法和非语法句子的探针在向更远的语言迁移时效果更差，这通常被视为共享结构的证据。但探针本身在同一维度上的表现也变差：在十七种语言中，有四种语言的表现处于随机水平，因此272对语言中有64对是使用了无效工具进行评分。去掉这些语言后，关系的强度减半，但它们也是最远的语言，这种设计无法分离这两种效应。计数也很重要：当272对被视为独立时，相同数据的p值为0.0006，而当十七种语言被视为独立时，p值为0.155，这是正确的单位。第二，训练一个语言模型以匹配人类大脑反应将其相似性分数从0.10提高到0.34，接近0.54的上限。一个在与大脑对应关系被破坏的目标上训练的模型仍然得分0.31，因此只有0.028到0.068的提升是特定于大脑的。完全没有模型的情况下，一个被破坏的目标与真实目标的距离已经达到0.204，这是一个跟踪目标排名与句子数量之比的下限。最后，沿着语言自身方向引导其发展是有效的（在十七种语言中，有十六种语言的表现比随机方向高出6.2），但没有显示出与语言距离变化的效果。在询问对应关系是否有帮助之前，先问一下没有它的情况下分数还能保留多少。
cs.CL / 4 / 2610.03829
OncoNoteBERT: A Foundation Representation Model for Natural Language Processing of Real-World Outpatient Oncology Notes
OncoNoteBERT：用于真实世界门诊肿瘤笔记自然语言处理的基础表示模型
Abstract
Real-world outpatient oncology notes contain specialised terminology, tumour staging expressions, treatment names, toxicity descriptions, and institution-specific de-identification markers that may not be represented efficiently by general biomedical or adjacent clinical language models. We developed and evaluated oncology-specific BERT-style encoders using a governed UK outpatient oncology corpus comprising 290,026 notes from 21,564 patients treated for lung and head-and-neck cancer. We compared RadBERT and PathologyBERT with two local strategies: OncoNote-RadBERT, produced by continued masked language model pretraining, and OncoNoteBERT, trained from scratch with an oncology WordPiece tokenizer. Models were evaluated using masked language modelling loss and perplexity on the validation set, tokenizer fragmentation metrics, clinical term tokenisation, masked-token probes, and exploratory representation analysis. Both external encoders fit the oncology corpus poorly in zero-shot evaluation (perplexity 113.04 for RadBERT; 2035.03 for PathologyBERT), while continued pretraining produced the strongest fit (2.10 for OncoNote-RadBERT). OncoNoteBERT achieved perplexity 2.83 but produced the most efficient tokenisation, with lower subword fertility and shorter normalised sequence length. It also returned a clinically acceptable prediction for 12 of 13 masked-token probes, compared with 7 of 13 for OncoNote-RadBERT. This divergence between corpus-level fit and masked-token performance was partly attributable to tokenizer fragmentation rather than learned semantics alone. Both locally developed models represented the institutional placeholder as a single learnable token. These findings show that continued adaptation and bespoke tokenisation provide complementary benefits, and that representation-layer design matters before adjacent-domain encoders are applied to oncology NLP.
Chinese Translation
真实世界的门诊肿瘤笔记包含专业术语、肿瘤分期表达、治疗名称、毒性描述以及特定机构的去标识化标记，这些内容可能无法被通用生物医学或相关临床语言模型有效表示。我们开发并评估了针对肿瘤学的BERT风格编码器，使用了一个包含290,026条来自21,564名肺癌和头颈癌患者的门诊肿瘤学语料库。我们将RadBERT和PathologyBERT与两种本地策略进行了比较：OncoNote-RadBERT，通过持续的掩码语言模型预训练生成，以及OncoNoteBERT，使用肿瘤学WordPiece分词器从头训练。模型的评估采用了验证集上的掩码语言建模损失和困惑度、分词器碎片化指标、临床术语分词、掩码标记探针以及探索性表示分析。在零样本评估中，两个外部编码器在肿瘤学语料库的适配性较差（RadBERT的困惑度为113.04；PathologyBERT为2035.03），而持续的预训练产生了最佳的适配性（OncoNote-RadBERT的困惑度为2.10）。OncoNoteBERT的困惑度为2.83，但实现了最有效的分词，具有较低的子词繁殖率和较短的标准化序列长度。与OncoNote-RadBERT相比，它在13个掩码标记探针中返回了12个临床可接受的预测，而后者仅为7个。这种语料库级适配性与掩码标记性能之间的差异部分归因于分词器的碎片化，而不仅仅是学习到的语义。两个本地开发的模型将机构占位符表示为一个可学习的单一标记。这些发现表明，持续的适应和定制的分词提供了互补的好处，并且在将相邻领域编码器应用于肿瘤学自然语言处理之前，表示层设计是重要的。
cs.CL / 5 / 2610.03839
SYNLAT: Syntax-Aligned Text-Latent Compression for Chain-of-Thought Reasoning
SYNLAT：用于链式思维推理的语法对齐文本潜在压缩
Abstract
Long chain-of-thought (CoT) traces impose substantial output-token costs. Under constrained budgets, compression must preserve answer-critical information, making boundary placement central. Token-level and fixed-length boundaries can fragment coherent spans such as phrases, formulas, and local derivations, whereas step-level boundaries can bind content requiring different compression actions. We introduce SynLat, a text-latent CoT framework that aligns compression boundaries with syntactic structure through non-overlapping Syntax-Aligned Units (SAUs). An answer-conditioned Teacher constructs progressive KEEP/LATENT targets for a single compression-conditioned Student, which generates mixed reasoning from only the question and requested compression level at inference. Across two Qwen3 Student scales, Standard-CoT and Long-CoT groups, and three compression levels, SynLat matches or exceeds the strongest evaluated baseline in all 12 task-group aggregates and strictly leads in 11 under the reported achieved-CR selection protocol. Overall gains reach 3.6/2.6 points at MEDIUM and 7.0/5.5 points at HIGH for Qwen3-8B/14B, with larger advantages under stronger compression, particularly on Long-CoT groups.
Chinese Translation
长链式思维（CoT）轨迹会产生显著的输出标记成本。在预算受限的情况下，压缩必须保留对答案至关重要的信息，这使得边界的设置变得尤为重要。标记级和固定长度的边界可能会破坏短语、公式和局部推导等连贯的片段，而步骤级边界则可以绑定需要不同压缩操作的内容。我们提出了SynLat，这是一种文本潜在的CoT框架，通过非重叠的语法对齐单元（SAUs）将压缩边界与语法结构对齐。一个基于答案的教师为一个单一的压缩条件学生构建渐进的KEEP/LATENT目标，该学生在推理时仅根据问题和请求的压缩级别生成混合推理。在两个Qwen3学生规模、标准CoT和长CoT组以及三个压缩级别下，SynLat在所有12个任务组汇总中匹配或超过了最强的评估基线，并在报告的实现-CR选择协议下严格领先于11个组。总体增益在Qwen3-8B/14B的中等压缩下达到3.6/2.6分，在高压缩下达到7.0/5.5分，尤其是在长CoT组中，随着压缩力度的增强，优势更为明显。
cs.CL / 6 / 2610.03902
When Evidence Changes: Evaluating Memory Repair and Re-reading in Language-Model Agents
当证据发生变化时：评估语言模型代理的记忆修复与重读
Abstract
When documents supporting an agent's derived facts are revoked or replaced, should it repair memory or re-read current evidence? We introduce an evidence-revision evaluation on medication- and problem-list tasks from public ICU records. Under revocation, replacement and control events, we compare full and source-filtered re-reading with caching, rebuilding and graph-local repair across two 7B models. Memory is supplied in full without retrieval, and costs include ingest, revision and every use. On short records, local repair uses 5-10$\times$ fewer revision tokens than rebuilding, yet every memory pipeline costs at least twice full re-reading in held-out conditions. In a small pre-specified development sweep, adding task-ineligible documents extended records to about 10,000 tokens; at that length, memory's mean cumulative cost fell below full re-reading's after 2-14 uses, partly through truncated extraction, while source-filtered re-reading remained cheapest. In the replacement study, none of the four primary confirmatory tests reached statistical significance. These results show why the cost of agent memory after evidence revision must be assessed against source-filtered re-reading over the full pipeline.
Chinese Translation
当支持代理所推导事实的文档被撤销或替换时，代理应该修复记忆还是重读当前证据？我们在公共重症监护室记录中的药物和问题列表任务上引入了一种证据修订评估。在撤销、替换和控制事件下，我们比较了全面和源过滤重读与缓存、重建和图局部修复在两个7B模型中的表现。记忆在没有检索的情况下完全提供，成本包括摄取、修订和每次使用。在短记录中，局部修复使用的修订标记比重建少5-10倍，但在保留条件下，每个记忆管道的成本至少是全面重读的两倍。在一次小规模的预先指定开发测试中，添加不符合任务要求的文档将记录扩展到约10,000个标记；在这个长度下，记忆的平均累积成本在2-14次使用后降至低于全面重读的成本，部分原因是通过截断提取，而源过滤重读仍然是最便宜的。在替换研究中，四个主要确认性测试均未达到统计显著性。这些结果表明，在证据修订后，代理记忆的成本必须与全面管道中的源过滤重读进行评估。
cs.CL / 7 / 2610.03935
General Decision Models: Benchmarking and Insights Beyond Jev
通用决策模型：基准测试与超越Jev的洞察
Abstract
General decision models, such as Jev, have recently emerged as efficient alternatives to LLMs for structured judgment and selection. But what kinds of decisions can these models reliably make, and how does their behavior change when individual decisions are composed into larger systems? To study this, we introduce JEVal, a bilingual benchmark comprising 11,257 instances from 36 datasets across 10 application domains, and evaluate 25 model configurations spanning general decision models and generative LLMs. Our results show that (1) general decision models are most competitive when decisions can be resolved from available evidence, but weaken when they require specialist knowledge or faithful uncertainty estimation: they can often identify the most likely outcome while substantially overstating its probability. (2) In more dynamic and realistic systems involving long-horizon, multi-step interactions, the advantages of fast local decision making are offset by reliability failures at the system level. on $\tau$-bench, faster local decisions reduce median episode time but lower task success as decision errors accumulate over long trajectories. (3) In large-scale social simulation, decision models approach strong generative LLMs on individual response prediction at substantially lower inference cost, yet remain weaker in user profiling and exhibit larger aggregate estimation errors and systematic bias. Finally, we propose InnerJev-4B and InnerJev-27B, which internalize an open-weight LLM's own reasoning into a single-pass first-token decision through Reasoning-to-Readout Self-Distillation, with InnerJev-27B performing on par with Jev on JEVal while answering a typical query in about 0.1 s.
Chinese Translation
通用决策模型，如Jev，最近作为结构化判断和选择的高效替代方案而出现。但这些模型能够可靠地做出什么样的决策？当个体决策组合成更大的系统时，它们的行为又会如何变化？为此，我们引入了JEVal，一个双语基准，包含来自10个应用领域的36个数据集中的11,257个实例，并评估了跨越通用决策模型和生成型大语言模型（LLMs）的25种模型配置。我们的结果显示：（1）当决策可以从可用证据中解决时，通用决策模型的竞争力最强，但当需要专业知识或准确的不确定性估计时，其性能会减弱：它们通常能够识别出最可能的结果，但往往会大幅夸大其概率。（2）在涉及长期、多步骤交互的更动态和现实的系统中，快速的局部决策优势被系统层面的可靠性失败所抵消。在$ au$-bench上，快速的局部决策减少了中位数情节时间，但由于决策错误在长轨迹上累积，任务成功率降低。（3）在大规模社会模拟中，决策模型在个体响应预测方面接近强大的生成型LLMs，且推理成本显著较低，但在用户画像方面仍然较弱，并表现出较大的聚合估计误差和系统性偏差。最后，我们提出了InnerJev-4B和InnerJev-27B，通过推理到读出自蒸馏（Reasoning-to-Readout Self-Distillation）将开放权重LLM自身的推理内化为单次决策的首个令牌，其中InnerJev-27B在JEVal上的表现与Jev相当，同时在大约0.1秒内回答典型查询。
cs.CL / 8 / 2610.03940
A Step Towards Forgetting: Optimiser History and the Loss of Answer Mass
迈向遗忘的一步：优化器历史与答案质量的损失
Abstract
During fine-tuning, a language model can assign less probability to previously learned answers even when the current gradient acts to preserve that probability. With momentum, each update also carries gradients computed at earlier model states, and these stored contributions can push the model in the opposite direction. We investigate how this optimiser memory contributes to forgetting by separating old-task loss into confusion among its answers and leakage of probability outside the answer set. Across three language-model families, answer mass consistently declines while discrimination among old answers usually improves: the model becomes less likely to produce answers that it can still distinguish correctly. Decomposing Adam updates reveals opposing contributions to this loss of answer mass. Over training, accumulated history favours leakage, while the current gradient opposes it. Resolving history by age shows that the harmful contributions come mainly from older gradients of the new task, whereas recent gradients tend to protect the old answers. Changes in history's effect are dominated by its orientation relative to the old-task gradient. Interventions that reset momentum while matching the initial update norm establish that stored history affects retention, with state-dependent immediate effects and lower final old-task loss over longer Adam continuations, mainly through recovered answer mass. Finally, integration along finite updates shows that most sampled large loss increases are captured by local projections, while curvature along history amplifies some events. Together, these findings reveal how an optimiser's memory can erode learned behaviour even as its current gradient acts to preserve it.
Chinese Translation
在微调过程中，语言模型可能会对先前学习的答案分配较低的概率，即使当前的梯度作用于保持该概率。通过动量，每次更新还携带在早期模型状态下计算的梯度，这些存储的贡献可能会将模型推向相反的方向。我们研究了这种优化器记忆如何导致遗忘，通过将旧任务的损失分解为其答案之间的混淆和概率泄漏到答案集外。在三种语言模型系列中，答案质量持续下降，而对旧答案的区分通常有所改善：模型变得不太可能生成它仍能正确区分的答案。对 Adam 更新的分解揭示了对答案质量损失的相反贡献。在训练过程中，累积的历史倾向于泄漏，而当前的梯度则对此形成对立。按年龄解决历史表明，造成这种有害贡献的主要来自新任务的旧梯度，而最近的梯度则倾向于保护旧答案。历史效应的变化主要受其相对于旧任务梯度的方向影响。通过重置动量并匹配初始更新范数的干预表明，存储的历史影响保留，具有状态依赖的即时效应，并在较长的 Adam 继续中降低最终的旧任务损失，主要通过恢复的答案质量。最后，沿有限更新的积分显示，大多数采样的大损失增加是通过局部投影捕获的，而历史沿线的曲率则放大了一些事件。综合来看，这些发现揭示了优化器的记忆如何侵蚀已学习的行为，即使其当前的梯度作用于保持该行为。
cs.CL / 9 / 2610.03962
BAIBAICHUCHU at the NTCIR-19 FinArg-3 Task: When Is Maximum Possible Profit Predictable from Investor Text?
BAIBAICHUCHU在NTCIR-19 FinArg-3任务中的表现：何时可以预测投资者文本的最大可能利润？
Abstract
The BAIBAICHUCHU team participated in the Social Media Subtask of NTCIR-19 FinArg-3, ranking Chinese investor posts by Maximum Possible Profit (MPP). A three-track ensemble of lexical features, a FinArg-2-pre-finetuned MacBERT ranker, and an LLM judge reaches 0.734 in post-grouped development evaluation, but our best official run scores 0.517. All twelve submitted runs lie between 0.4598 and 0.5402, and our 26 unanimous three-track pairs score 0.500. A post-hoc audit finds that the submitted judge applied a long-only rule to bearish posts although MPP is stance-aware. Correcting it changes 28 of 87 official predictions without changing accuracy, yet lowers development accuracy from 0.680 to 0.622: a regime-specific semantic shortcut improved validation fit. We decompose ranking into directional text, volatility and horizon, and pairwise margin. A running-extremum model predicts $\sigma\sqrt{T}$ scaling, observed ex post in 502 price-aligned posts from a separate July 2026 collection. Pre-posting volatility scaled by the post-specific observation horizon, $\sigma_{\mathrm{pre},20}\sqrt{N_i}$, is associated with a later truncated-horizon MPP proxy (Spearman $\rho=0.320$; ticker-cluster 95% CI [0.133,0.466]) and correctly orders 61.4% of unequal-outcome pairs. Transferred text is nearly uncorrelated with the July outcome ($\rho=0.055$) and adds little conditional on this score and stance. Development reliability rises from about 0.60 to 0.93 as the labeled MPP gap widens. An earlier ERAI result of 0.6207 rules out universal unpredictability as a simple explanation. Evaluation should jointly consider text, market regime, historical volatility, horizon, and pair composition.
Chinese Translation
BAIBAICHUCHU团队参与了NTCIR-19 FinArg-3的社交媒体子任务，通过最大可能利润（Maximum Possible Profit, MPP）对中文投资者帖子进行排名。我们采用了三轨集成方法，包括词汇特征、经过FinArg-2预训练的MacBERT排序器和LLM评审，在按帖子分组的开发评估中达到了0.734的分数，但我们最佳的官方运行得分为0.517。所有提交的十二个运行得分在0.4598到0.5402之间，而我们26个一致的三轨对的得分为0.500。后期审计发现，提交的评审对看跌帖子应用了仅限多头的规则，尽管MPP是考虑立场的。对此进行修正后，87个官方预测中有28个发生了变化，但准确率没有改变，然而开发准确率从0.680降低到0.622：一种特定于制度的语义捷径改善了验证拟合。我们将排名分解为方向性文本、波动性和时间范围，以及成对边际。运行极值模型预测了$ au ext{（sigma）} imes ext{（sqrt）}T$的缩放，在2026年7月的单独收集的502个价格对齐帖子中事后观察到。预发布波动性按帖子特定的观察时间范围缩放，$ au_{ ext{pre},20} imes ext{（sqrt）}N_i$，与后来的截断时间范围MPP代理相关（Spearman $
ho=0.320$；股票集群95%置信区间[0.133,0.466]），并正确排序了61.4%的不等结果对。转移文本与7月结果几乎不相关（$
ho=0.055$），并在此得分和立场的条件下增加的贡献很小。随着标记的MPP差距扩大，开发可靠性从约0.60上升到0.93。早期的ERAI结果为0.6207排除了普遍不可预测性作为简单解释。评估应共同考虑文本、市场制度、历史波动性、时间范围和成对组成。
cs.CL / 10 / 2610.04002
Do Language Models Need a Trainable Input Embedding Table? Fixed Minimal Token Codes at 1.7B-Class Scale
语言模型是否需要可训练的输入嵌入表？在17亿类别规模下的固定最小标记编码
Abstract
A trainable input embedding table assigns each vocabulary item an independently adjustable vector. We investigate whether this token-specific parameterization is required for substantial language-modeling capability, or whether a shared Transformer can learn from fixed token identities. We compare three decoder-only language models trained from scratch with the same tokenizer, contextual backbone, untied output-head architecture, and training recipe, with a target budget of 100 billion prediction tokens per model. Their input interfaces are a learned table, canonical 16-bit token-ID codes, and one fixed invertible recoding over GF(2). The fixed codes are repeated to model width without an additional trainable input projection. Both fixed-code models acquire substantial capabilities: canonical codes achieve 52.40\% HellaSwag normalized accuracy, 70.51\% PIQA accuracy, and 42.75\% LAMBADA accuracy. The learned-input control performs better on several evaluations, including HellaSwag and LAMBADA, so these results establish viability rather than performance parity. The fixed interfaces remove 100.7 million trainable parameters, yielding 1.711B-parameter models, but parameter reduction is not the central result. These single-run experiments distinguish architectural necessity from empirical utility: independently trainable token-specific input vectors are not required for the observed capabilities. A fixed identity interface also provides a controlled setting for studying representation learning downstream of an immutable input, without establishing where particular capabilities are localized.
Chinese Translation
可训练的输入嵌入表为每个词汇项分配一个可独立调整的向量。我们研究这种特定于标记的参数化是否对于显著的语言建模能力是必需的，或者共享的Transformer是否能够从固定的标记身份中学习。我们比较了三种从头开始训练的仅解码器语言模型，它们使用相同的分词器、上下文主干、未绑定的输出头架构和训练方案，目标预算为每个模型1000亿个预测标记。它们的输入接口分别是一个学习表、标准的16位标记ID编码和一个在GF(2)上的固定可逆重编码。固定编码在模型宽度上重复，而没有额外的可训练输入投影。这两种固定编码模型都获得了显著的能力：标准编码实现了52.40%的HellaSwag标准化准确率、70.51%的PIQA准确率和42.75%的LAMBADA准确率。学习输入控制在多个评估中表现更好，包括HellaSwag和LAMBADA，因此这些结果确立了可行性而不是性能平等。固定接口去除了1.007亿个可训练参数，产生了1711亿参数的模型，但参数减少并不是中心结果。这些单次实验区分了架构的必要性与经验的实用性：观察到的能力并不需要独立可训练的特定于标记的输入向量。固定身份接口还提供了一个受控环境，用于研究不可变输入下的表示学习，而不确定特定能力的局部化位置。
cs.CL / 11 / 2610.04047
Periscope: Extending Frozen Language Models Beyond Their Context Window
潜望镜：将冻结语言模型扩展至其上下文窗口之外
Abstract
A language model reads long text in one quadratic forward pass, stops at the context window, and loses accuracy with length before reaching it. We ask whether the read can be factorized when deciding over a finite set: which document is relevant, which option is supported, which passage is the evidence. Periscope, a training-free inference method, arranges the $N$ chunks of a text on a $K{\times}K$ grid with $K{=}\lceil\sqrt{N}\rceil$ and asks a frozen model the same question about $K$ local spans of consecutive chunks and $K$ strided spans that sample the whole text, reading the log-odds of every answer at one token. Each answer takes its best local and strided score, and scoring every chunk by its two spans gives an evidence map at no further cost, whose peak is the chunk behind the answer. Every probe is about $\sqrt{sc}$ tokens for a text of $s$ tokens and chunk size $c$, so a window of $W$ tokens reaches $W^{2}/c$ tokens at $s^{1.5}$ cost. The map replaces the long read. On LongBench v2, reading only the $K$ chunks the map ranks highest, 9k tokens, matches the same model's best window read across windows from 32k to 1M tokens, and on InfiniteBench, where the median context is 150k tokens, it leads the best window read by 5 points. The same map ranks BRIGHT's long-document corpora with the best NDCG@10 of six methods. Each call caches only one probe, so a 27B model reads 4.5M-token contexts on one 80GB GPU, where a single pass would need 296GB of cache. A long read then needs a GPU that holds the model, not one that holds the text.
Chinese Translation
语言模型在一次二次前向传递中读取长文本，停留在上下文窗口处，并在到达之前随着长度的增加而失去准确性。我们探讨在对有限集合进行决策时，是否可以对读取过程进行因式分解：哪个文档是相关的，哪个选项是被支持的，哪个段落是证据。潜望镜（Periscope）是一种无训练推理方法，将文本的 $N$ 个块排列在一个 $K{ imes}K$ 的网格上，其中 $K{=} ext{ceil}( ext{sqrt}(N))$，并向冻结模型提出关于 $K$ 个连续块的局部范围和 $K$ 个跨步范围的问题，以采样整个文本，在一个标记上读取每个答案的对数几率。每个答案采用其最佳的局部和跨步得分，通过其两个范围对每个块进行评分，从而以零额外成本生成证据图，其峰值是答案背后的块。每个探测大约涉及 $ ext{sqrt}(sc)$ 个标记，对于一个包含 $s$ 个标记和块大小为 $c$ 的文本，因此一个 $W$ 个标记的窗口在 $s^{1.5}$ 的成本下达到 $W^{2}/c$ 个标记。该图替代了长读取。在 LongBench v2 上，仅读取图中排名最高的 $K$ 个块（9k 个标记），其效果与同一模型在 32k 到 1M 个标记窗口中的最佳读取相匹配，而在 InfiniteBench 上，媒体上下文为 150k 个标记时，其效果领先最佳窗口读取 5 分。该图在 BRIGHT 的长文档语料库中排名，获得六种方法中最佳的 NDCG@10。每次调用仅缓存一个探测，因此一个 27B 的模型可以在一台 80GB 的 GPU 上读取 4.5M 个标记的上下文，而单次传递需要 296GB 的缓存。因此，长读取只需要一个能够容纳模型的 GPU，而不是一个能够容纳文本的 GPU。
cs.CL / 12 / 2610.04074
IdeaScientist: Orchestrating Agents for Grounded Scientific Ideation
IdeaScientist：为扎根科学创意协调代理
Abstract
Despite rapid progress in automating scientific research, generating promising and well grounded research solutions remains a central challenge. We isolate research ideation as a standalone task and build our solution on the intuition that a challenge in one field can often be addressed by a mechanism that solved an analogous challenge in another. Accordingly, we introduce IdeaScientist, which decomposes ideation into gap finding, innovation, and report writing, and trains each role with reinforcement learning. These roles identify limitations in related work, draw solution intuitions from analogous problem settings, and develop those intuitions into complete research proposals. To facilitate discovery of insights across domains, we construct the Svalbard Idea Vault, a corpus of 2.77M decomposed research ideas for retrieval, training, and temporally controlled evaluation. Our evaluation restricts access to literature available before a cutoff date and assesses how closely proposed directions align with those later explored in 15K papers authored by human researchers. On Qwen3.6-27B, IdeaScientist outperforms the strongest open-source autoresearch baseline by 14.0%, driven mainly by gains in novelty. On this 27B open backbone, IdeaScientist even outperforms Claude Code SDK with Claude-4.8-Opus and Codex SDK with GPT-5.4, by up to 5.9%.
Chinese Translation
尽管科学研究的自动化进展迅速，生成有前景且扎根的研究解决方案仍然是一个核心挑战。我们将研究创意孤立为一个独立任务，并基于这样一种直觉构建我们的解决方案：一个领域中的挑战往往可以通过在另一个领域中解决类似挑战的机制来应对。因此，我们引入了IdeaScientist，它将创意分解为发现差距、创新和报告撰写，并通过强化学习训练每个角色。这些角色识别相关工作的局限性，从类似问题设置中提取解决方案的直觉，并将这些直觉发展为完整的研究提案。为了促进跨领域洞察的发现，我们构建了Svalbard Idea Vault，这是一个包含277万个分解研究创意的语料库，用于检索、训练和时间控制评估。我们的评估限制了在截止日期之前可用文献的访问，并评估提出的方向与后续在15000篇由人类研究者撰写的论文中探索的方向的契合程度。在Qwen3.6-27B上，IdeaScientist的表现超越了最强的开源自我研究基线14.0%，主要得益于新颖性的提升。在这个27B的开放基础上，IdeaScientist甚至在Claude Code SDK（Claude-4.8-Opus）和Codex SDK（GPT-5.4）上超越了它们，提升幅度达到5.9%。
cs.CL / 13 / 2610.04098
Representation-Aligned Auxiliary Supervision for Language Model Adaptation
与表示对齐的辅助监督用于语言模型适应
Abstract
Language models exhibit strong reasoning capabilities, yet adapting them to structured domains remains challenging and can yield inconsistent outcomes. We identify representation compatibility, the extent to which a model effectively processes a representation for a structured task, as a key factor in adaptation. We study this in chess, which provides a controlled testbed with precise semantics, computable optimal actions, and multiple state representations, including a symbolic encoding (FEN) and a spatial format (ASCII). We find that models often process semantically equivalent inputs substantially differently, affecting both learning and generalization. Building on this observation, we propose representation-aligned auxiliary supervision, which uses environment-derived tasks expressed in compatible representations to improve adaptation to structured domains. Across models and representations, auxiliary supervision consistently improves optimal-move prediction relative to target-only training under identical target data. Tasks that expose environment dynamics provide larger and most consistent gains than surface-level or static supervision, while remaining competitive with substantially increasing the amount of target-task data. Moreover, ASCII-trained models transfer more effectively to FEN than FEN-trained models do to ASCII, even surpassing the FEN target-only baseline on FEN evaluation. The gains also extend beyond optimal-move prediction to open-ended, factually grounded commentary generation. Overall, our results show that auxiliary supervision in model-compatible representations can enable effective adaptation in structured domains.
Chinese Translation
语言模型展现出强大的推理能力，但将其适应于结构化领域仍然具有挑战性，并可能导致不一致的结果。我们确定表示兼容性，即模型有效处理结构化任务表示的程度，是适应过程中的一个关键因素。我们在国际象棋中研究这一点，国际象棋提供了一个具有精确语义、可计算的最优动作和多种状态表示（包括符号编码（FEN）和空间格式（ASCII））的受控测试环境。我们发现，模型往往以显著不同的方式处理语义上等价的输入，这影响了学习和泛化。基于这一观察，我们提出了与表示对齐的辅助监督，利用以兼容表示表达的环境衍生任务来改善对结构化领域的适应。在不同模型和表示中，辅助监督相较于仅使用目标数据的训练，始终提高了最优动作预测的准确性。暴露环境动态的任务提供的增益比表面级或静态监督更大且更一致，同时在显著增加目标任务数据量的情况下仍然具有竞争力。此外，经过ASCII训练的模型在转移到FEN时的效果优于经过FEN训练的模型转移到ASCII，甚至在FEN评估中超过了仅使用FEN目标的基线。增益还超越了最优动作预测，扩展到开放式、基于事实的评论生成。总体而言，我们的结果表明，在模型兼容表示中的辅助监督可以实现对结构化领域的有效适应。
cs.CL / 14 / 2610.04118
LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads?
LongSocialBench：长上下文大语言模型是否理解在线讨论线程？
Abstract
Long-context LLMs can now ingest entire online discussion threads, but understanding their social discourse requires more than reading a long document: models must track parent-reply relations, turning points, scoped subtrees, cross-branch contrasts, and participant trajectories. To test this structure-aware social reasoning, we introduce LongSocialBench, a benchmark of 1,462 verified human-authored multiple-choice items drawn from 94 complete Hacker News, Stack Exchange, and Reddit r/ChangeMyView episodes, with a median length of approximately 73K tokens. Each item pairs a complete serialized discussion and reply structure with a four-option question, requiring models to recover structured social evidence. Released items are verified for answerability, option uniqueness, and evidence grounding. Across 18 models and 29 evaluation settings, current long-context workflows remain far below human performance. The best individual result comes from Claude-Opus-4.7, which reaches 63.0% when prompted to eliminate incorrect options before answering, compared with 72.4% for independent human readers. Averaged across all 18 models, the full-context Baseline scores 43.9%. Supplying the gold evidence scope raises this to 55.0%, showing that substantial errors remain even after the relevant thread region is identified. LongSocialBench shows that the missing capability is not context access or prompting alone, but social understanding over structured reply trees.
Chinese Translation
长上下文大语言模型（LLMs）现在能够处理整个在线讨论线程，但理解其社会话语不仅仅是阅读长文档：模型必须跟踪父子回复关系、转折点、范围子树、跨分支对比和参与者轨迹。为了测试这种结构感知的社会推理，我们引入了LongSocialBench，这是一个包含1,462个经过验证的人类创作的多项选择题的基准，题目来源于94个完整的Hacker News、Stack Exchange和Reddit r/ChangeMyView事件，平均长度约为73K个标记。每个题目将完整的序列化讨论和回复结构与一个四选项问题配对，要求模型恢复结构化的社会证据。发布的题目经过验证，确保答案的可回答性、选项的唯一性和证据的基础性。在18个模型和29个评估设置中，目前的长上下文工作流程仍远低于人类表现。最佳单项结果来自Claude-Opus-4.7，在被提示在回答前排除错误选项时达到63.0%，而独立人类读者的表现为72.4%。在所有18个模型中，完整上下文基线得分为43.9%。提供金证据范围将这一得分提高到55.0%，表明即使在相关线程区域被识别后，仍然存在显著错误。LongSocialBench表明，缺失的能力不仅仅是上下文访问或提示，而是对结构化回复树的社会理解。
cs.CL / 15 / 2610.04119
Copying Before Suppression: What Drives a Below-Chance Dip During Language Model Training?
抑制前的复制：是什么驱动了语言模型训练中的低于机会的下降？
Abstract
Mechanistic interpretability usually studies fully trained models, yet the computations that drive a behaviour can change while the model is still learning the task. On the Indirect Object Identification task, a model should continue with the name mentioned once rather than the name mentioned twice. Pythia models pass through an early training window in which they prefer the repeated name, so accuracy in a choice between the two names falls below one half while language-model loss on a fixed text sample keeps decreasing across the same window. The window reflects a temporary imbalance between two computations. We identify one cause of the wrong preference by selecting a set of attention heads that write the repeated name, on prompts separate from those used for causal evaluation, keeping that selection fixed, and then replacing each head's final-token output with its average output on a separate set of non-repeated-name prompts. This improves the correct-minus-repeated logit difference in a separately trained 160M model and in the official 160M, 410M, and 1B models. At 160M, the head that lowers the repeated name in the mature model shows little of its mature behaviour at this point. It directs less than one percent of its attention to the repeated mention, and its output makes almost no direct contribution to lowering that name's logit. Both properties grow over the interval in which behaviour recovers. Across the 160M, 410M, and 1B models, transplanting the corresponding head's mature parameters into the early checkpoint recovers 35 to 68 percent of the total improvement in the correct-minus-repeated logit difference seen by the end of training. Related early-to-late reversals appear at further Pythia scales, in two independently trained GPT-2 models, and in OLMo. A mature circuit can therefore conceal a transient causal configuration that shaped behaviour earlier in training.
Chinese Translation
机制可解释性通常研究完全训练好的模型，但驱动行为的计算在模型仍在学习任务时可能会发生变化。在间接宾语识别任务中，模型应继续使用提到一次的名称，而不是提到两次的名称。Pythia 模型经历了一个早期训练窗口，在这个窗口中，它们偏好重复的名称，因此在两者名称的选择中准确率低于一半，而语言模型在固定文本样本上的损失在同一窗口内持续下降。这个窗口反映了两种计算之间的暂时不平衡。我们通过选择一组在与因果评估不同的提示下写入重复名称的注意力头，确定了错误偏好的一个原因，并保持该选择不变，然后用其在一组非重复名称提示上的平均输出替换每个头的最终标记输出。这改善了在单独训练的 160M 模型中以及官方的 160M、410M 和 1B 模型中的正确减去重复名称的 logit 差异。在 160M 模型中，降低重复名称的头在成熟模型中在这一点上几乎没有表现出其成熟行为。它对重复提及的注意力少于百分之一，其输出几乎没有直接贡献于降低该名称的 logit。这两个特性在行为恢复的间隔内增长。在 160M、410M 和 1B 模型中，将相应头的成熟参数移植到早期检查点中，恢复了训练结束时在正确减去重复 logit 差异中观察到的总改善的 35% 到 68%。在进一步的 Pythia 规模、两个独立训练的 GPT-2 模型和 OLMo 中，相关的早期到晚期的逆转现象也出现。因此，成熟电路可以掩盖在训练早期塑造行为的瞬时因果配置。
cs.CL / 16 / 2610.04125
Representational Control over Self-Report & Behavior Coherence in LLM Risk-Taking
对自我报告与行为一致性的表征控制：以大型语言模型的风险决策为例
Abstract
Self-report is an appealing low-cost probe of an LLM's dispositions, but recent work finds only selective agreement between what models report and how they behave. Prior accounts establish these patterns by prompting black-box LLMs, leaving open whether the gap is a prompting artefact or a fact about how the underlying constructs are represented internally. We investigate risk-taking, a consequential dimension of agentic decision-making, using activation steering to measure self-report and behavior under the same internal intervention. We survey nine steering-vector extraction methods spanning task-specific directives, the model's own task behavior, and dispositional descriptions at two granularities, evaluated on two behavioral tasks and two psychometric instruments across four open-weight LLMs. We find that (1) a shared internal intervention does not ensure shared responsiveness: directions built from trait descriptions move self-report but leave behavior at chance, directions built from the model's own task choices do the reverse, and only task-specific directives reach both, weakly. (2) Diagnosis dissolves that exception: removing surface confounders leaves the directives only 32% of their behavioral effect. The two channels are otherwise steered by near-orthogonal directions, each channel reachable by several independent constructions. (3) An intervention composing one behavior-moving and one self-report-moving direction moves both together; flipping one sign sets them in opposition, with reported and enacted risk pointing in opposite directions on 69-89% of flipped compositions in all models. These findings move the self-report-behavior relationship from a black-box observation to a representational one that can be inspected and controlled, motivating representational checks alongside behavioral evaluation.
Chinese Translation
自我报告是一种具有吸引力的低成本探测大型语言模型（LLM）倾向的方法，但近期研究发现模型报告与其行为之间仅存在选择性一致性。以往的研究通过提示黑箱型LLM来建立这些模式，但尚不清楚这种差距是提示伪影还是关于内部构造表征的事实。我们研究了风险决策这一代理决策的重要维度，采用激活引导方法在相同的内部干预下测量自我报告和行为。我们调查了九种引导向量提取方法，涵盖了任务特定指令、模型自身的任务行为以及两种粒度的倾向描述，并在四个开放权重的LLM上评估了两个行为任务和两个心理测量工具。我们的发现包括：(1) 共享的内部干预并不确保共享的响应性：基于特征描述构建的方向能够影响自我报告，但行为却保持在随机水平；而基于模型自身任务选择构建的方向则相反，只有任务特定指令能够弱地同时影响两者。(2) 诊断消除了这一例外：去除表面混淆因素后，指令仅保留了32%的行为效应。两个通道在其他方面由近正交的方向引导，每个通道可通过多个独立构造到达。(3) 组合一个影响行为的方向和一个影响自我报告的方向的干预能够同时影响两者；翻转一个符号则使它们相互对立，在所有模型中，报告的风险与实际行为的风险在69-89%的翻转组合中指向相反方向。这些发现将自我报告与行为之间的关系从黑箱观察转变为可检视和控制的表征关系，激励在行为评估的同时进行表征检查。
cs.CL / 17 / 2610.04132
PB-GRPO: Learning Socially Adaptive LLM Agents from Persona-Driven Simulation with Preference-Batched GRPO
PB-GRPO：从基于角色的模拟中学习社会适应性大型语言模型代理的偏好批处理GRPO
Abstract
Building LLMs that behave well socially, not merely correctly, requires Building LLMs that behave well socially, not merely correctly, requires more than producing locally helpful responses. A socially competent agent must infer users' unstated goals, respect their preferences, and adapt as the conversation unfolds. These behaviors are inherently multi-turn and social, making them hard to optimize: real interaction data is scarce, and user preferences are typically latent rather than directly observable. To address these challenges, we build on a persona-driven social simulation environment (consisting of a persona library, LLM-based user simulators, and a user-satisfaction scoring system ranging from [0, 1]), to introduce preference-batched GRPO (PB-GRPO), a post-training algorithm that learns socially adaptive policies from conversation-level feedback. Compared to vanilla GRPO, PB-GRPO computes advantages using a normalization estimated across a bucket of users with similar preferences, stabilizing training across a diverse social population. Empirical evidence shows that PB-GRPO improves models' social behavior over strong reinforcement learning baselines in our simulated environment.
Chinese Translation
构建能够良好进行社会交互的语言模型（LLMs），不仅仅是提供正确的回答，需要的不仅仅是生成局部有用的响应。一个社会能力强的代理必须推断用户未明言的目标，尊重他们的偏好，并在对话过程中进行适应。这些行为本质上是多轮且具有社会性的，使得优化变得困难：真实的交互数据稀缺，用户偏好通常是潜在的而非直接可观察的。为了解决这些挑战，我们基于一个基于角色的社会模拟环境（包括角色库、基于LLM的用户模拟器以及一个用户满意度评分系统，评分范围为[0, 1]），引入了偏好批处理GRPO（PB-GRPO），这是一种后训练算法，通过对话级反馈学习社会适应性策略。与普通的GRPO相比，PB-GRPO通过对具有相似偏好的用户群体进行归一化估计来计算优势，从而在多样化的社会人群中稳定训练。实证证据表明，PB-GRPO在我们的模拟环境中改善了模型的社会行为，超越了强大的强化学习基线。
cs.CL / 18 / 2610.04156
Trajectory-Derived Confidence for Reliable, Resource-Aware Clinical Text-to-SQL Agents
基于轨迹的信心评估：可靠且资源高效的临床文本到SQL代理
Abstract
LLM agents for clinical text-to-SQL applications reason autonomously over multiple steps but cannot assess whether their own reasoning or outputs can be trusted. In high leverage applications such as healthcare, this presents a critical risk where system mistakes can be costly. These reliability failures are also resource failures: an incorrect reasoning trajectory spends computation budget on outputs that must be discarded. We introduce Sentinel, a trajectory-derived, classifier-based confidence layer that analyzes an agent's reasoning, code and database outputs to decide at three points whether to stop: refusing unanswerable questions before the agent runs, halting doomed trajectories mid-run, and withholding untrustworthy answers at delivery. Here, utilizing Chow's rule, we optimize decisions under the EHRSQL shared task's Reliability Score, which penalizes incorrect answers given a utility weighting, and find on the benchmark EHRSQL that Sentinel raises this score from +0.08 to +0.24 when mistakes have a low utility weighting, well above the +0.03 earned by refusing every question, with delivered-answer accuracy rising from 54% to 69% as coverage falls from 84% to 55%. At higher stakes the agents we test rarely answer reliably enough to deliver, and Sentinel detects this on its own, abstaining to that same +0.03 where the unmonitored agent scores -3.02. The same stopping decisions cut computation: at low stakes, where the system still answers, Sentinel eliminates 13-28% of agent steps at little or no reliability cost.
Chinese Translation
用于临床文本到SQL应用的大型语言模型（LLM）代理能够在多个步骤上自主推理，但无法评估其自身推理或输出的可信度。在医疗等高风险应用中，这带来了严重的风险，因为系统错误可能代价高昂。这些可靠性失败也是资源失败：错误的推理轨迹在输出上消耗了计算预算，而这些输出必须被丢弃。我们提出了Sentinel，一个基于轨迹的分类器信心层，分析代理的推理、代码和数据库输出，以决定在三个关键点是否停止：在代理运行之前拒绝无法回答的问题、中途停止注定失败的轨迹，以及在交付时不提供不可信的答案。在这里，我们利用Chow的规则，在EHRSQL共享任务的可靠性评分下优化决策，该评分对给定效用权重的错误答案进行惩罚，并发现Sentinel在基准EHRSQL上将该评分从+0.08提升至+0.24，当错误的效用权重较低时，远高于拒绝每个问题所获得的+0.03，同时交付答案的准确率从54%上升至69%，覆盖率从84%下降至55%。在更高风险的情况下，我们测试的代理很少能可靠地回答问题，Sentinel能够自主检测到这一点，保持在同样的+0.03，而未监控的代理得分为-3.02。相同的停止决策减少了计算：在低风险情况下，系统仍然能够回答，Sentinel在几乎没有可靠性成本的情况下消除了13-28%的代理步骤。
cs.CL / 19 / 2610.04210
Can LLMs Separate Pasted Artifacts from User Speech? Absorption at Unmarked Prompt Seams
大型语言模型能否将粘贴的伪影与用户语音分开？在未标记提示缝隙处的吸收现象
Abstract
Large language models (LLMs) receive each user message as plain text, even when it combines text from different sources. For example, a user may paste text into a prompt and keep typing a comment directly below it. We study absorption: a phenomenon where the model treats a trailing user comment as part of the pasted text, returning it inside the edited text. This happens even though the user did not intend the comment to become part of that text. Existing instruction-data separation benchmarks tell the model which text is instruction and which is data, then test whether it obeys that separation. They do not test harmless user speech following an unmarked paste. We introduce SEAM, a controlled benchmark of 300 editing examples. Each example is tested under six matched conditions that vary how the boundary between pasted text and later user speech is expressed. Across 20 models, absorption at a bare newline ranges from 7.7% to 66.7%. Adding a blank line does not significantly reduce absorption in any model, while boundary markers reduce it in 19 of 20 models. Comments that fit the pasted text, such as a code comment typed after code, are absorbed significantly more often in 17 of 20 models. Models often fail to separate pasted material from later user speech, and explicit boundaries reduce but do not remove this failure.
Chinese Translation
大型语言模型（LLMs）将每条用户消息视为纯文本，即使它结合了来自不同来源的文本。例如，用户可能会将文本粘贴到提示中，并在其下方继续输入评论。我们研究了吸收现象：一种模型将后续用户评论视为粘贴文本一部分的现象，并将其返回到编辑后的文本中。这种情况发生在用户并不打算将评论作为该文本的一部分时。现有的指令与数据分离基准测试告诉模型哪些文本是指令，哪些是数据，然后测试其是否遵循这种分离。它们并未测试在未标记粘贴后跟随的无害用户语音。我们引入了SEAM，一个包含300个编辑示例的受控基准。每个示例在六种匹配条件下进行测试，这些条件变化了粘贴文本与后续用户语音之间边界的表达方式。在20个模型中，裸换行处的吸收率从7.7%到66.7%不等。添加空行并未显著减少任何模型的吸收现象，而边界标记在20个模型中的19个中减少了吸收现象。适合粘贴文本的评论，例如在代码后输入的代码评论，在20个模型中的17个中被吸收的频率显著更高。模型通常未能将粘贴材料与后续用户语音分开，明确的边界虽然减少了这种失败，但并未消除。
cs.CL / 20 / 2610.04239
Conformal Prediction with Paraphrase-Aware Scoring for LLM Uncertainty Quantification
基于释义感知评分的符合预测用于大型语言模型的不确定性量化
Abstract
Uncertainty quantification (UQ) for large language models (LLMs) aims to provide reliable measures of predictive confidence, yet current methods are often unstable under meaning-preserving perturbations. Semantically equivalent paraphrases can induce substantial variability in predictive confidence, even for methods with formal guarantees, such as conformal prediction. To address this issue, we propose a paraphrase-aware UQ framework robust to semantic rewordings. Our approach trains a lightweight proxy model on LLM hidden states and aggregates its predictions across paraphrases to construct label-wise nonconformity scores. Under score exchangeability, conformal calibration retains marginal coverage. This guarantee can also hold under test-only rewording, provided that the paraphrase pipeline satisfies an additional distributional alignment condition. We evaluate three settings (normal, fully reworded, and semi-reworded) which apply rewording to neither dataset, both calibration and test datasets, or only the test dataset, respectively. Across seven multiple-choice QA benchmarks and multiple model families, our method produces compact prediction sets with empirical coverage generally near the nominal target, even in the semi-reworded setting. Ablation studies show that the learned proxy accounts for most of the reduction in set size, while paraphrase-augmented training and inference-time aggregation improve stability under rewording. Code is available at https://github.com/Raina-Xin/PA_Score.
Chinese Translation
大型语言模型（LLMs）的不确定性量化（UQ）旨在提供可靠的预测置信度度量，但当前的方法在保持意义的扰动下往往不稳定。语义等价的释义可能会导致预测置信度的显著变化，即使对于具有正式保证的方法，如符合预测。为了解决这个问题，我们提出了一种对语义重述具有鲁棒性的释义感知UQ框架。我们的方法在LLM的隐藏状态上训练一个轻量级的代理模型，并在释义之间聚合其预测，以构建标签级的非符合性评分。在评分可交换的情况下，符合校准保持边际覆盖。这一保证在仅测试重述的情况下也可以成立，前提是释义管道满足额外的分布对齐条件。我们评估了三种设置（正常、完全重述和半重述），分别对数据集不进行重述、对校准和测试数据集均进行重述，或仅对测试数据集进行重述。在七个多项选择问答基准和多个模型系列中，我们的方法生成的紧凑预测集的经验覆盖率通常接近名义目标，即使在半重述设置下也是如此。消融研究表明，学习到的代理模型解释了大部分集大小的减少，而释义增强的训练和推理时聚合在重述下提高了稳定性。代码可在 https://github.com/Raina-Xin/PA_Score 获取。
cs.CL / 21 / 2610.04261
Playing social deduction games with reinforcement fine-tuned large language models
使用强化微调的大型语言模型进行社交推理游戏
Abstract
Reinforcement fine-tuning (RFT) is increasingly used in applications where large language models (LLMs) interact with humans and other agents. Here we use social deduction games to study how RFT changes LLMs' social behaviour. We let fine-tuned and base LLM agents play hidden-role games that require hidden-state inference, social reading and vote steering. Our results show that LLM agents do not reliably acquire social-deduction ability by directly optimizing terminal win--loss outcomes, suggesting that final game results provide a sparse and noisy signal for socially interactive learning. However, RFT is particularly effective at improving social reading, including tasks that require agents to infer hidden roles from public discussion, update beliefs over time and predict other agents' future decisions. We further show that RFT can also improve social influence, including tasks that require agents to steer votes, team approvals and collective decisions, although these gains depend more strongly on behaviourally specific rewards and structured interaction settings. Finally, we show that LLMs' ability to play social deduction games can be further improved through multi-agent social-cognitive reinforcement fine-tuning, which combines social-reading and social-influence signals during same-side multi-agent training. These learned behaviours also receive more favourable human evaluations of strategic competence, persuasiveness and social usefulness. Together, these results enrich our understanding of how RFT changes LLMs' social behaviour and provide a step toward a behavioural learning theory for machine social intelligence.
Chinese Translation
强化微调（Reinforcement Fine-Tuning, RFT）在大型语言模型（Large Language Models, LLMs）与人类及其他代理互动的应用中越来越常见。本文利用社交推理游戏研究RFT如何改变LLMs的社交行为。我们让经过微调的LLM代理和基础LLM代理参与需要隐状态推理、社交阅读和投票引导的隐藏角色游戏。我们的结果表明，LLM代理并未通过直接优化最终的胜负结果来可靠地获得社交推理能力，这表明最终游戏结果为社交互动学习提供了稀疏且嘈杂的信号。然而，RFT在改善社交阅读方面特别有效，包括需要代理从公共讨论中推断隐藏角色、随时间更新信念以及预测其他代理未来决策的任务。我们进一步表明，RFT也可以改善社交影响，包括需要代理引导投票、团队批准和集体决策的任务，尽管这些收益更强烈地依赖于行为特定的奖励和结构化的互动设置。最后，我们展示了通过多代理社交认知强化微调，LLMs在社交推理游戏中的能力可以进一步提高，该方法在同侧多代理训练中结合了社交阅读和社交影响信号。这些学习到的行为在战略能力、说服力和社交实用性方面也得到了更有利的人类评价。综上所述，这些结果丰富了我们对RFT如何改变LLMs社交行为的理解，并为机器社交智能的行为学习理论迈出了重要一步。
cs.CL / 22 / 2610.04267
AI-Enabled Quality Assurance for Multiple-Choice Assessment Items
基于人工智能的多项选择评估题目的质量保证
Abstract
Generating multiple-choice questions is increasingly scalable, but establishing their assessment quality remains difficult. We present a focused narrative review of automated item-writing flaw detection, revision, psychometric screening, and NLP benchmark auditing. Database searches, citation retrieval, and nominated sources yield fourteen research reports reviewed in full text. We distinguish surface checks from content-sensitive judgments and map a 19-criterion rubric to detection methods and reported evidence. High label-level accuracy often coexists with weak positive case detection, while rubric definitions and reference standards vary. Revision evidence is mixed, and the associations reported in prior work do not establish the effects of repair. We propose evaluating quality assurance as a sequence of independently validated decisions, with criterion-specific reporting, calibrated human review, and outcome-based assessment of revisions.
Chinese Translation
生成多项选择题的过程越来越具规模化，但建立其评估质量仍然困难。我们呈现了一项针对自动化题目编写缺陷检测、修订、心理测量筛选和自然语言处理基准审计的集中叙述性综述。通过数据库搜索、引用检索和推荐来源，我们获得了十四篇完整审阅的研究报告。我们将表面检查与内容敏感判断区分开，并将19项标准的评分量表映射到检测方法和报告证据上。高标签级别的准确性通常与弱正案例检测共存，而评分量表的定义和参考标准则各不相同。修订证据呈现混合状态，之前研究中报告的关联并未确立修复的效果。我们建议将质量保证评估视为一系列独立验证的决策，采用标准特定的报告、校准的人为审查和基于结果的修订评估。
cs.CL / 23 / 2610.04304
Evaluating Modeling Approaches for Experience-Level Classification in Job Description
评估职位描述中经验级别分类的建模方法
Abstract
This paper investigates the task of predicting job experience levels in recruitment texts, aiming to automatically identify the qualifications required for positions. Unlike traditional text classification, recruitment texts typically possess explicit internal structures, with different paragraphs playing disproportionate roles in conveying experience clues. To address this, we propose a structure-aware Section-Aware BERT approach that segments and encodes key paragraphs (titles, responsibilities, requirements) for integrated modeling, building upon rule-based systems and classical baselines TF-IDF. Simultaneously, we evaluate large language models under both few-shot and fine-tuning settings on the same dataset to compare the capability boundaries of different modeling paradigms. Experimental results demonstrate that explicitly leveraging text structure significantly improves experience level prediction performance, particularly in scenarios with ambiguous job titles. Further error analysis reveals systemic challenges in this task, including confusion between Entry and Senior levels and the blurred boundaries of Mid-level positions. This research provides an effective modeling approach and analytical framework for understanding structured recruitment texts.
Chinese Translation
本文研究了在招聘文本中预测职位经验级别的任务，旨在自动识别职位所需的资格。与传统的文本分类不同，招聘文本通常具有明确的内部结构，不同段落在传达经验线索方面扮演着不成比例的角色。为此，我们提出了一种结构感知的Section-Aware BERT方法，该方法对关键段落（标题、职责、要求）进行分段和编码，以实现集成建模，基于规则系统和经典基线TF-IDF进行构建。同时，我们在相同数据集上评估了大型语言模型在少量样本和微调设置下的表现，以比较不同建模范式的能力边界。实验结果表明，明确利用文本结构显著提高了经验级别预测的性能，特别是在职位名称模糊的情况下。进一步的错误分析揭示了该任务中的系统性挑战，包括初级和高级职位之间的混淆以及中级职位的模糊边界。本研究提供了一种有效的建模方法和分析框架，以理解结构化的招聘文本。
cs.CL / 24 / 2610.04347
Evidence and Intervention: A Coupled Active-Inference Extension of Rational Speech Act Models
证据与干预：理性言语行为模型的耦合主动推理扩展
Abstract
Identical utterance choices can arise from different communicative causes, and identical interpretations can leave different traces in what a listener learns. Rational Speech Act (RSA) models treat interpretation as inference over speaker meaning, but standard one-shot RSA does not intrinsically distinguish these causal update targets. We develop a coupled active-inference model of dialogue in which a listener's likelihood for an utterance is the policy distribution attributed to the speaker, placing the speaker's expected free energy within the listener's variational free energy. Each utterance is therefore both evidence about and an intervention on a partner. The model separates four updates that RSA approaches typically collapse: inference about the partner's current pragmatic state; learning of partner-specific parameters; prospective evaluation of clarification or repair; and a policy prior shaped by habit and a context-sensitive price of time. Under one-step, exact-inference restrictions, the model recovers the RSA speaker and listener, with RSA as the restricted single-turn limit of the coupled process. Outside these restrictions, the updates obey distinct rules and timescales, predicting which adaptations persist, remain partner-specific, or transfer. Worked examples show audience design reversing after clarification, self-confirming misunderstanding in which both interlocutors have low free energy while disagreeing about reference, and rational closing before uncertainty is resolved. Consistent with critiques of equating natural language with communication, the model treats communication as a downstream use of linguistic structure and recasts production and interpretation as coupled inference: speaking is both an intervention on a partner and an epistemic action that samples evidence for the speaker's model of that partner.
Chinese Translation
相同的发言选择可以源于不同的交际原因，而相同的解释可以在听者的学习中留下不同的痕迹。理性言语行为（Rational Speech Act, RSA）模型将解释视为对说话者意义的推理，但标准的一次性RSA并未本质上区分这些因果更新目标。我们发展了一个对话的耦合主动推理模型，其中听者对发言的可能性是归因于说话者的策略分布，将说话者的预期自由能置于听者的变分自由能之内。因此，每次发言既是关于合作伙伴的证据，也是对其的干预。该模型分离了RSA方法通常合并的四种更新：对合作伙伴当前语用状态的推理；学习合作伙伴特定参数；对澄清或修正的前瞻性评估；以及由习惯和上下文敏感的时间成本所塑造的策略先验。在一步精确推理的限制下，该模型恢复了RSA的说话者和听者，其中RSA作为耦合过程的限制单轮极限。在这些限制之外，更新遵循不同的规则和时间尺度，预测哪些适应会持续、保持合作伙伴特定或转移。实例展示了在澄清后观众设计的逆转、自我确认的误解，其中两个对话者在对参考的意见不一致时都具有低自由能，以及在不确定性未解决之前的理性结束。与将自然语言等同于交际的批评一致，该模型将交际视为语言结构的下游使用，并将生成与解释重新构建为耦合推理：说话既是对合作伙伴的干预，也是对该合作伙伴的说话者模型进行证据采样的认知行为。
cs.CL / 25 / 2610.04370
Boundaries Agree, Labels Do Not: Intra-Annotator Dynamics as a Kind of Training Data
边界一致，标签不一致：作为一种训练数据的内部标注者动态
Abstract
Data quality now matters as much as compute for training language models. Much training data comes from human annotation of text, and interpretive annotation has no ground truth that could settle what is "accurate". Two lines of work respond to this. One combines annotators into a "ground truth" and measures how well they agree with each other; the other treats their disagreement as a signal. Both compare different people at one point in time. We measure something else: how well one reader reproduces their own reading of the same text over time. One expert human reader and three LLM families segmented three Sumerian myths and labelled the causal function of each segment with one of seven states. Across runs months apart, the human cut the text in much the same places but named the segments differently, in every myth. The models show no such consistent pattern: their gap between the two layers is positive in some myths and negative in others, and its size varies. The human's label changes are not random: the runs go through much the same functions but start them one step apart, while model runs start them at the same places. We argue that this pattern is a usable measure of data quality and a contamination check: a "human" annotation whose labels are as stable as its boundaries, and whose functions start in sync, looks like a model's.
Chinese Translation
数据质量在训练语言模型时与计算能力同样重要。大量训练数据来自于对文本的人类标注，而解释性标注没有可以确定“准确性”的基准真相。有两条研究路线对此作出回应。一种方法将标注者结合成一个“基准真相”，并测量他们之间的相互一致性；另一种方法则将他们的分歧视为信号。这两者都在同一时间点比较不同的人。我们测量的是另一种东西：一个读者在时间推移中如何重现自己对同一文本的理解。一位专家人类读者和三种大语言模型（LLM）对三则苏美尔神话进行了分段，并用七种状态之一标注每个段落的因果功能。在几个月间的不同实验中，人类在相似的位置切分文本，但在每则神话中对段落的命名却不同。模型没有表现出这样的稳定模式：它们在不同神话中的两个层次之间的差距在某些神话中为正，在其他神话中为负，且差距的大小也有所不同。人类的标签变化并非随机：不同实验经历了相似的功能，但起始位置相差一步，而模型实验则在相同位置开始。我们认为这种模式是数据质量的可用度量和污染检查：一个“人类”标注，其标签与边界一样稳定，并且其功能同步开始，看起来与模型的表现相似。
cs.CL / 26 / 2610.04399
GlitchPatch: Repairing Glitch Tokens in Frozen Language Models via Local Retokenization
GlitchPatch：通过局部重标记修复冻结语言模型中的故障标记
Abstract
Glitch tokens are anomalous vocabulary entries that can cause large language models (LLMs) to produce outputs inconsistent with their inputs. Existing repair methods require access to model internals, making them impractical for frozen checkpoints. We investigate whether glitch tokens can be repaired outside the model by optimizing the input tokenization. An empirical study on BPE merge-rule deletion reveals that (1)deleting a glitch token's merge rule can fix a substantial fraction of failures, yet disrupting normal tokens sharing intermediate merge nodes causes the overall glitch rate to rise, and (2)different decomposition granularities yield non-monotonic fix rates while collateral damage on normal tokens grows monotonically. Motivated by these findings, we propose GlitchPatch, a repair framework for frozen language models based on local retokenization, consisting of two stages: the offline stage uses Behavioral Path Optimization (BPO) to find the behaviorally optimal replacement token sequence for each glitch token and compiles validated replacements into a rule table; the online stage substitutes only the IDs of matched glitch tokens in the canonical token sequence, with no modification to model parameters or internal states. Experiments on ten models spanning six tokenizer families show that GlitchPatch achieves an 85.10% mean fix rate, outperforming the strongest baseline by 14.37 percentage points, and reduces the average glitch rate from 14.88% to 2.27%. GlitchPatch achieves a 0.00% RR in full-vocabulary evaluation and leaves rule-unmatched inputs unchanged by design. We further evaluate the practical impact of repair from the perspectives of time cost, language understanding, and capability, supporting its deployment feasibility. We hope this work provides a practical option for improving tokenizer reliability.
Chinese Translation
故障标记是异常的词汇条目，可能导致大型语言模型（LLMs）生成与其输入不一致的输出。现有的修复方法需要访问模型内部，使其在冻结检查点上不切实际。我们研究了是否可以通过优化输入标记化在模型外部修复故障标记。对BPE合并规则删除的实证研究表明：（1）删除故障标记的合并规则可以修复相当一部分故障，但破坏共享中间合并节点的正常标记会导致整体故障率上升；（2）不同的分解粒度产生非单调的修复率，而对正常标记的附带损害则单调增加。基于这些发现，我们提出了GlitchPatch，一个基于局部重标记的冻结语言模型修复框架，分为两个阶段：离线阶段使用行为路径优化（Behavioral Path Optimization, BPO）为每个故障标记找到行为上最优的替代标记序列，并将验证过的替代项编译成规则表；在线阶段仅替换规范标记序列中匹配的故障标记的ID，而不修改模型参数或内部状态。在涵盖六个标记器家族的十个模型上的实验表明，GlitchPatch实现了85.10%的平均修复率，超越最强基线14.37个百分点，并将平均故障率从14.88%降低到2.27%。GlitchPatch在全词汇评估中实现了0.00%的RR，并按设计使规则不匹配的输入保持不变。我们进一步从时间成本、语言理解和能力的角度评估修复的实际影响，支持其部署的可行性。我们希望这项工作为提高标记器的可靠性提供一个实用的选择。
cs.CL / 27 / 2610.04400
XTurnix: Large-Scale Self-Supervised Turn Control through Two-State Binary Decisions
XTurnix：通过双状态二元决策实现大规模自监督轮换控制
Abstract
General turn-taking behavior in real-time dialogue systems requires deciding whether to keep listening or start responding while listening, and whether to continue or stop while speaking. Existing turn detectors use heterogeneous, task-specific label spaces and are often trained on limited annotations or evaluated on isolated utterances, making them difficult to use as a unified causal controller with comprehensive context. We propose XTurnix, a compact text-based model that formulates turn control as two binary decisions conditioned on the AI's current listening or speaking state and predicts a single control token from the complete dialogue history. XTurnix is pretrained on 5.5 million causal action examples automatically derived from timestamped two-speaker transcripts, then fine-tuned on synthetic multi-turn examples with a flatter distribution across the four state-action labels. We evaluate XTurnix on four public benchmarks and a balanced self-curated benchmark. Across the public benchmarks, XTurnix achieves the best results on all SemanticVAD and LiveKit splits, ties the native Smart-Turn model on Smart-Turn Bench, and achieves the highest incomplete-turn accuracy on Easy-Turn. On the self-curated benchmark, it reaches 89.06% accuracy, more than 20 percentage points above the strongest third-party baseline at 68.75%, while maintaining F1 scores between 84.21% and 90.63% across all four categories. These results demonstrate unified listening- and speaking-state turn control in a single compact model. Code is available at https://github.com/xcc-zach/xturnix, with an interactive demo at https://huggingface.co/spaces/xcczach/xturnix-demo.
Chinese Translation
实时对话系统中的一般轮换行为需要在倾听时决定是继续倾听还是开始回应，以及在发言时决定是继续发言还是停止。现有的轮换检测器使用异构的、特定任务的标签空间，通常在有限的注释上进行训练或在孤立的发言上进行评估，这使得它们难以作为统一的因果控制器，提供全面的上下文。我们提出了XTurnix，这是一种紧凑的基于文本的模型，将轮换控制形式化为基于AI当前倾听或发言状态的两个二元决策，并从完整的对话历史中预测一个控制令牌。XTurnix在550万个自动从带时间戳的双人转录中派生的因果动作示例上进行了预训练，然后在四个状态-动作标签上分布更平坦的合成多轮示例上进行了微调。我们在四个公共基准和一个均衡的自我策划基准上评估了XTurnix。在公共基准中，XTurnix在所有SemanticVAD和LiveKit分割上取得了最佳结果，在Smart-Turn Bench上与原生Smart-Turn模型持平，并在Easy-Turn上实现了最高的不完整轮换准确率。在自我策划基准上，其准确率达到89.06%，比最强的第三方基线68.75%高出20个百分点，同时在所有四个类别中保持F1分数在84.21%到90.63%之间。这些结果展示了在单一紧凑模型中统一的倾听和发言状态轮换控制。代码可在https://github.com/xcc-zach/xturnix获取，交互式演示可在https://huggingface.co/spaces/xcczach/xturnix-demo查看。
cs.CL / 28 / 2610.04401
Ideological Stance Detection in a Low-Resource Language: Polarization in Bangladeshi Public vs Private University Discourse on Social Media
低资源语言中的意识形态立场检测：孟加拉国公立与私立大学社交媒体话语的两极化
Abstract
Public vs. private universities is a debatable issue, and it creates polarization on social media in Bangladesh. Debate on quality, jobs, and prestige is passionate among the students, parents, and graduates, the majority of whom speak Bengali, a low-resource language. To measure this polarization, this paper introduces a manually annotated dataset of 4,060 Bengali comments labeled as Pro-Public, Pro-Private, or Neutral. We evaluated the quality of our annotations by Fleiss's Kappa agreement that was 0.89, corresponding to a high agreement among annotators. The classical ML (SVM, Random Forest, XGBoost), BiLSTM network, hybrid BanglaBERT+XGBoost models and the state-of-the-art zero-shot LLMs (Claude Sonnet 4, DeepSeek-V3.1, Llama 4 Maverick, Kimi K2 Thinking, Qwen3-235B Thinking) models are evaluated. The accuracy of BanglaBERT+XGBoost is 91.81% and macro F1 score is 91.70%, which is higher than all the supervised baselines. The zero-shot Llama 4 Maverick Thinking achieves a macro F1 of 0.931 (overall accuracy of 93.31%) without any fine-tuning. All machine learning (ML), deep machine learning (DL) and transformer models were outperformed by the zero-shot Llama 4 Maverick model. Polarization also is evident, in some ways more clearly in the Pro-Private comments, which emphasize modern facilities and timely graduation, versus the Pro-Public comments, which emphasize affordability and government jobs. Our findings open new directions for analyzing social media polarization in low-resource languages.
Chinese Translation
公立与私立大学是一个有争议的话题，在孟加拉国的社交媒体上引发了两极化。关于质量、就业和声望的辩论在学生、家长和毕业生中非常激烈，其中大多数人讲孟加拉语，这是一种低资源语言。为了衡量这种两极化，本文引入了一个手动标注的数据集，包含4060条标记为支持公立（Pro-Public）、支持私立（Pro-Private）或中立（Neutral）的孟加拉语评论。我们通过Fleiss's Kappa一致性评估了标注的质量，其值为0.89，表明标注者之间具有高度一致性。我们评估了经典机器学习（ML）模型（支持向量机（SVM）、随机森林（Random Forest）、XGBoost）、双向长短期记忆网络（BiLSTM）、混合BanglaBERT+XGBoost模型以及最先进的零样本大语言模型（LLMs）（Claude Sonnet 4、DeepSeek-V3.1、Llama 4 Maverick、Kimi K2 Thinking、Qwen3-235B Thinking）。BanglaBERT+XGBoost的准确率为91.81%，宏观F1分数为91.70%，高于所有监督基线。零样本Llama 4 Maverick Thinking在没有任何微调的情况下实现了0.931的宏观F1分数（总体准确率为93.31%）。所有机器学习（ML）、深度机器学习（DL）和变换器模型均被零样本Llama 4 Maverick模型超越。两极化在某种程度上在支持私立的评论中更为明显，这些评论强调现代设施和及时毕业，而支持公立的评论则强调可负担性和政府工作。我们的研究结果为分析低资源语言中的社交媒体两极化开辟了新的方向。
cs.CL / 29 / 2610.04403
Saying, Not Knowing: Aggressively GGUF-Quantized Small Language Models Still Write Rare Words They Can No Longer Define
言说而非知晓：激进的 GGUF 量化小型语言模型仍能写出它们无法定义的稀有词汇
Abstract
Post-training quantization to the GGUF format's mixed-precision K-quants is commonly how open-weight language models reach consumer hardware, yet its effect on fine-grained lexical competence is uncharacterized. We audit 27 quantized artifacts across 13 families and four architecture backbones, 0.35B-14B parameters, evaluated down their published ladder to Q2_K (about 2.6 bits per weight), on 429 frequency-validated rare English words under two probes: surface inclusion of a prompt-supplied word and its one-sentence definition, scored by a tiered multi-synonym matcher, its error measured by a blind LLM-judge census of every definition, with human verification. Three regimes emerge at Q2: total collapse into unusable builds, severe semantic dissociation in sub-2B models, and mostly robust preservation above about 3B. In every sub-2B artifact, definitions fall 20-67% below the artifact's baseline, typically several times the inclusion loss. Two controls separate rarity from task difficulty: within the rare set, loss rises with rarity in six of seven sub-2B artifacts, and on a 100-word common-word set rare words lose more than common words in all eight, significantly in six. Tokenizer vocabulary size does not predict the damage (Spearman rho=0.12); parameter count dominates (rho=0.72), confirmed within five of six same-tokenizer families. Q4_K_M remains lexically clean at >=1B. The damage is frequency-graded, provider-dependent, and not calibrated by WikiText-2 perplexity: across nine artifact-matched ladders, near-identical Q2 penalties (44.7%/47.6%) separate an artifact keeping its definitions (3.6%) from one losing them (43.6%). Aggressively quantized small models can keep generating fluent text while no longer knowing what it means, risking hardware-constrained deployments in domains where semantics carries consequences. Validation must be per artifact.
Chinese Translation
后训练量化为 GGUF 格式的混合精度 K-quants 通常是开放权重语言模型达到消费硬件的方式，但其对细粒度词汇能力的影响尚未被表征。我们审计了 27 个量化工件，涵盖 13 个家族和四个架构骨干，参数范围从 0.35B 到 14B，评估其在发布的梯度下达到 Q2_K（约每个权重 2.6 位），在 429 个频率验证的稀有英语单词下进行测试，使用两个探针：提示提供的单词的表面包含和其一句定义，通过分级多同义词匹配器进行评分，其错误通过盲 LLM-评审对每个定义进行普查，并经过人工验证。在 Q2 级别上出现了三种模式：完全崩溃为不可用构建、在 2B 以下模型中严重的语义分离，以及在大约 3B 以上模型中大部分保持稳健。在每个 2B 以下的工件中，定义的准确率下降了 20-67%，通常是包含损失的几倍。两个控制因素将稀有性与任务难度区分开：在稀有集合内，损失在七个 2B 以下的工件中随着稀有性增加而上升，而在一个 100 个常用单词的集合中，稀有词在所有八个工件中损失超过常用词，六个工件中显著。分词器词汇大小并不能预测损害（Spearman rho=0.12）；参数数量占主导地位（rho=0.72），在六个相同分词器家族中的五个得到了确认。Q4_K_M 在 >=1B 时保持词汇的清晰性。损害是频率分级的，依赖于提供者，并且未通过 WikiText-2 困惑度进行校准：在九个工件匹配的梯度中，几乎相同的 Q2 罚款（44.7%/47.6%）将保持其定义的工件（3.6%）与失去定义的工件（43.6%）区分开。激进量化的小型模型可以继续生成流畅的文本，而不再知道其含义，这在语义具有后果的领域中风险硬件受限的部署。验证必须针对每个工件进行。
cs.CL / 30 / 2610.04409
Understanding and Mitigating Hallucination Escape in Tool-Using LLM Agents
理解与缓解工具使用大型语言模型代理中的幻觉逃逸
Abstract
Large language models (LLMs) increasingly serve as autonomous agents that invoke external tools. However, this capability introduces tool hallucination, selecting incorrect tools or generating invalid calls. Existing mitigation methods report substantial improvements, yet we identify a previously overlooked failure mode that we term Hallucination Escape. These methods reduce hallucination on the tool configuration they are tuned on but increase it on other configurations, canceling out the gain. We further investigate this phenomenon and find that hallucination rises sharply when a model's intrinsic tool-use tendencies conflict with the current tool configuration, and that existing methods reinforce rather than suppress these tendencies, which in turn contributes to hallucination escape. Building on these findings, we propose EscapeGuard, a training-free inference-time method that combines conflict-aware gating with configuration-derived attention enhancement to mitigate tool hallucination while preventing hallucination escape. Across six benchmarks on various models, EscapeGuard reduces tool-selection hallucination by 9.0 pp and suppresses hallucination escape, lowering the cross-configuration mean by 23.7 pp and achieving an 89.1% net improvement in paired-query evaluation. We hope this work can encourage evaluation beyond a single tool configuration and pave the way for more reliable tool-using LLM agents.
Chinese Translation
大型语言模型（LLMs）越来越多地作为自主代理调用外部工具。然而，这一能力引入了工具幻觉，即选择不正确的工具或生成无效的调用。现有的缓解方法报告了显著的改进，但我们识别出一种先前被忽视的失败模式，我们称之为幻觉逃逸。这些方法在其调优的工具配置上减少了幻觉，但在其他配置上却增加了幻觉，从而抵消了收益。我们进一步研究这一现象，发现当模型的内在工具使用倾向与当前工具配置发生冲突时，幻觉急剧上升，而现有方法强化而非抑制这些倾向，这反过来又促成了幻觉逃逸。基于这些发现，我们提出了EscapeGuard，这是一种无训练的推理时方法，结合了冲突感知的门控与配置衍生的注意力增强，以缓解工具幻觉，同时防止幻觉逃逸。在对六个基准测试和各种模型的评估中，EscapeGuard将工具选择幻觉降低了9.0个百分点，并抑制了幻觉逃逸，将跨配置均值降低了23.7个百分点，实现了配对查询评估中的89.1%的净改进。我们希望这项工作能够鼓励超越单一工具配置的评估，并为更可靠的工具使用大型语言模型代理铺平道路。
cs.CL / 31 / 2610.04489
DV-Lens: Revealing the Functional Organization of Language Model Parameters
DV-Lens：揭示语言模型参数的功能组织
Abstract
Understanding parameter functions helps elucidate the internal mechanisms of large language models (LLMs). However, how to connect parameters from different modules to verifiable output effects and further characterize the relationship between their functional organization and model capability remains to be explored. To this end, we introduce the downstream vocabulary lens (DV-Lens), a parameter-level interpretability framework that links native parameter directions to their downstream vocabulary responses. Specifically, we first estimate module-specific downstream Jacobians over a reference prompt set for attention query, key, value, and output (Q/K/V/O) projections and feed-forward networks (FFNs). Second, we use these mappings to project native parameter columns into the final vocabulary space, obtaining signed readouts that characterize their average local output responses. Third, we group parameter columns by their vocabulary readouts and introduce downstream vocabulary complexity (DV-Complexity), which quantifies within-group structural variation using normalized reconstruction residuals of the original weights. At the parameter level, randomized controls and finite-difference tests show that DV-Lens readouts capture non-random vocabulary structure and predict local logit changes with 98.0% coordinate-orientation agreement across 720 cases from nine models. These readouts further guide parameter ablation, steering, and swapping across 21 models, shifting target-token probabilities in the predicted directions under controlled conditions. At the model level, the joint-parameter score of DV-Complexity achieves a Spearman correlation of 0.904 with benchmark-based capability rankings across 48 language models. Together, these results provide intervention-based evidence for DV-Lens interpretations and reveal an association between DV-Complexity and model capability.
Chinese Translation
理解参数功能有助于阐明大型语言模型（LLMs）的内部机制。然而，如何将不同模块的参数与可验证的输出效果联系起来，并进一步表征其功能组织与模型能力之间的关系仍需探索。为此，我们引入了下游词汇透镜（DV-Lens），这是一个参数级可解释性框架，将原生参数方向与其下游词汇响应联系起来。具体而言，我们首先针对注意力查询、键、值和输出（Q/K/V/O）投影及前馈网络（FFNs）在参考提示集上估计模块特定的下游雅可比矩阵。其次，我们利用这些映射将原生参数列投影到最终词汇空间，获得特征化其平均局部输出响应的符号读数。第三，我们根据词汇读数对参数列进行分组，并引入下游词汇复杂度（DV-Complexity），该指标使用原始权重的归一化重构残差量化组内结构变化。在参数级别，随机对照和有限差分测试表明，DV-Lens 读数捕捉到非随机的词汇结构，并在来自九个模型的720个案例中以98.0%的坐标方向一致性预测局部对数几率变化。这些读数进一步指导参数消融、引导和交换，在21个模型中在受控条件下将目标标记概率向预测方向转移。在模型级别，DV-Complexity的联合参数得分与48个语言模型的基准能力排名之间实现了0.904的斯皮尔曼相关性。综合来看，这些结果为DV-Lens的解释提供了基于干预的证据，并揭示了DV-Complexity与模型能力之间的关联。
cs.CL / 32 / 2610.04495
Emoji-Emotion Ranking System Using Twitter Data
基于推特数据的表情符号-情感排名系统
Abstract
Nowadays, emojis are often replacing words. Yet computational systems still oversimplify them. Most existing approaches treat emojis as static sentiment indicators and overlook their emotional distributions. In this study, we propose an emoji-aware emotion analysis framework based on a Twitter (X) dataset of 100,000 emoji-containing replies collected between 2020-2025. After preprocessing and text cleaning, we applied text-to-emotion classification to detect five primary emotions (Happy, Angry, Sad, Fear, and Surprise) for each message. By aggregating emotion scores across contexts in which each emoji appears, we estimate emoji-emotion association distributions and construct an emoji-emotion ranking system reflecting relative emotional dominance. Furthermore, we project emojis into the Russell valence-arousal space to enable continuous affective interpretation. Our results demonstrate that emojis exhibit probabilistic, context-sensitive emotional profiles rather than fixed sentiment polarities.
Chinese Translation
如今，表情符号常常取代文字。然而，计算系统仍然过于简化它们。大多数现有方法将表情符号视为静态情感指标，忽视了它们的情感分布。在本研究中，我们提出了一种基于推特（Twitter，现称X）数据集的表情符号感知情感分析框架，该数据集包含2020年至2025年间收集的100,000条包含表情符号的回复。在预处理和文本清洗后，我们应用文本到情感分类的方法，检测每条消息的五种主要情感（快乐、愤怒、悲伤、恐惧和惊讶）。通过汇总每个表情符号出现上下文中的情感评分，我们估计表情符号-情感关联分布，并构建一个反映相对情感主导性的表情符号-情感排名系统。此外，我们将表情符号投射到拉塞尔（Russell）情感价值-唤醒空间，以实现连续的情感解释。我们的结果表明，表情符号表现出概率性和上下文敏感的情感特征，而非固定的情感极性。
cs.CL / 33 / 2610.04512
Correctness Is a Direction: Geometric Answer Selection in Language Models
正确性是一种方向：语言模型中的几何答案选择
Abstract
Answer correctness is encoded as a recoverable geometric direction in the hidden states of language models. We show that the mean displacement from incorrect to correct answer representations, computed at approximately 70\% of model depth from fifty labeled examples with no parameter updates, yields a scoring direction that outperforms zero-shot log-probability scoring by up to +32.0 percentage points on factual benchmarks (ARC-Challenge and MMLU) and by +38.1 to +51.8 percentage points on TruthfulQA, across five models spanning 1B to 8B parameters in three architecture families (Llama, Qwen, Gemma). The method requires one forward pass and one dot product per candidate; no generation is performed at inference. Applied as a hallucination detector on individual (question, answer) pairs, the recovered direction achieves 0.693~AUROC versus 0.578 for log-probability scoring. We additionally find that correctness directions for factual reasoning, domain knowledge, and calibrated truthfulness are near-orthogonal in representation space, revealing that language models allocate geometrically independent subspaces to qualitatively distinct notions of correct answer, with architecture-dependent variation in the degree of separation. This structure explains the observed transfer pattern---the direction calibrated on factual questions transfers within task type but not across it---and suggests that LLM calibration failures may reflect a routing problem: the model's internal representation contains more correctness signal than its output behaviour exploits.
Chinese Translation
答案的正确性被编码为语言模型隐藏状态中的可恢复几何方向。我们展示了从不正确到正确答案表示的平均位移，该位移在模型深度约70\%处计算，基于五十个标记示例且不进行参数更新，产生的评分方向在事实基准（ARC-Challenge和MMLU）上比零-shot对数概率评分高出最多32.0个百分点，在TruthfulQA上高出38.1到51.8个百分点，涵盖了三个架构系列（Llama、Qwen、Gemma）中参数从1B到8B的五个模型。该方法每个候选项只需一次前向传播和一次点积；推理时不进行生成。作为单个（问题，答案）对的幻觉检测器，恢复的方向在AUROC上达到0.693，而对数概率评分为0.578。我们还发现，事实推理、领域知识和校准真实性的正确性方向在表示空间中近乎正交，揭示了语言模型为定性不同的正确答案分配几何独立的子空间，且分离程度在架构上存在依赖性。这一结构解释了观察到的迁移模式——在事实问题上校准的方向在任务类型内迁移，但不跨越任务类型——并暗示LLM校准失败可能反映了一种路由问题：模型的内部表示包含比其输出行为所利用的更多的正确性信号。
cs.CL / 34 / 2610.04575
From Probe Scores to Alarm Policies: Operational Validity of Activation Monitors for Language-Model Agents
从探测分数到警报策略：语言模型代理激活监测器的操作有效性
Abstract
Activation probes can predict safety-relevant properties of language models with high area under the receiver-operating-characteristic curve (AUROC), but deployed agent monitors make thresholded alarm decisions under tight false-alarm budgets. These are different estimands. We introduce an Operational Validity Contract that fixes a monitor's target, observability, identity, timing, intervention unit, comparator, calibration, and cost. We formalize risk at the semantic request or trajectory level: when one task contains repeated alarm opportunities, row-level AUROC and false positive rate do not identify semantic-unit any-alarm risk. A confidence-certified threshold also requires enough independent negative units, a tie-safe rule, and transport to deployment. Across Models Under Pressure, LASR refusal prediction, immutable AgentDojo, and a prospectively protocol-frozen ST-WebAgentBench replication, joint activation-observable monitors reach AUROC 0.957 and 0.935 on the first two benchmarks, yet their locked 5%/10% detection rates are only .642/.742 and .719/.782, respectively. On AgentDojo, the secondary mean-activation rollout monitor reaches AUROC 0.922 but detects none of 38 positive semantic cases at the locked 5% operating point; thresholds intended for 10% false alarms realize 18.7-20.0% on test. Because the test misses its prospectively frozen 40-positive support gate, we label it support-insufficient. On ST-WebAgentBench, activation reaches AUROC .874, but 23 independent calibration negatives cannot identify even a 10% controller; the locked policy abstains rather than reporting its mechanical zero FPR as a success. An exploratory counterexample also lowers full AUROC while improving realized 10% utility. The fail-closed compiler caps MUP and LASR at restricted predictive value and AgentDojo and ST-Web at representation accessibility; no setting reaches alarm-policy validity.
Chinese Translation
激活探测器能够以高接收者操作特征曲线下面积（AUROC）预测语言模型的安全相关属性，但部署的代理监测器在严格的误报预算下做出阈值警报决策。这些是不同的估计量。我们引入了操作有效性合同，固定监测器的目标、可观测性、身份、时机、干预单位、比较器、校准和成本。我们在语义请求或轨迹级别上形式化风险：当一个任务包含重复的警报机会时，行级AUROC和假阳性率并不能识别语义单位的任何警报风险。一个经过信心认证的阈值还需要足够的独立负单位、一个安全的平局规则以及向部署的转移。在压力下的模型、LASR拒绝预测、不变的AgentDojo，以及前瞻性协议冻结的ST-WebAgentBench复制中，联合激活可观测监测器在前两个基准上达到了AUROC 0.957和0.935，但它们锁定的5%/10%检测率仅为0.642/0.742和0.719/0.782。在AgentDojo中，次级均值激活回滚监测器达到了AUROC 0.922，但在锁定的5%操作点上未能检测到38个正语义案例；旨在实现10%误报的阈值在测试中实现了18.7-20.0%。由于测试未能达到其前瞻性冻结的40个正支持门，我们将其标记为支持不足。在ST-WebAgentBench上，激活达到了AUROC 0.874，但23个独立的校准负样本无法识别出10%的控制器；锁定政策选择不报告其机械零假阳性率作为成功。一个探索性反例也降低了完整AUROC，同时提高了实现的10%效用。故障闭合编译器将MUP和LASR限制在受限的预测值，而AgentDojo和ST-Web则限制在表示可达性；没有设置达到警报政策的有效性。
cs.CL / 35 / 2610.04594
SIFT: Robust Meta-Faithfulness Verification of Chain-of-Thought Reasoning Under Distribution Shift
SIFT：分布变化下链式思维推理的稳健元忠实性验证
Abstract
Chain-of-Thought (CoT) faithfulness detectors are widely used to audit reasoning models, yet a detector is itself a predictor whose verdicts are treated as stable properties. We ask whether a detector is faithful to itself under distribution shift. We formalize meta-faithfulness as an invariance principle: a valid detector must return identical verdicts on traces that differ only by transformations preserving ground-truth faithfulness. We prove three results: (i) no detector using only intervention-response profiles can separate faithful from epiphenomenal mechanisms with identical signatures; (ii) any detector relying on shift-sensitive features violates invariance at a rate independent of its in-distribution accuracy; (iii) an asymptotic certified selective-risk guarantee enables confident abstention. We operationalize the principle in FaithShift, a stress-test protocol spanning ten shift axes, and propose SIFT, a hidden-state trajectory detector trained with cross-environment invariance objectives and certified abstention. Across 14,996 traces, four domains, and eight models, three findings emerge. First, transfer collapse is real: all existing detectors show gaps $\geq 0.15$ AUROC. Second, the dominant bottleneck is sampling stochasticity, not shift: over 80% of detector instability stems from random seed variation, falsifying our preregistered prediction that shift-attributable violations exceed 0.25. Third, SIFT cuts invariance violations by 64% over the best single-seed baseline, but a four-seed ensemble of any detector narrows the margin to 0.01 (indistinguishable at matched coverage, $p=0.21$), and SIFT needs a 51% abstention rate. Cross-model transfer degrades from within-family to cross-family to open-weight-to-API, partly closed by multi-model training. We offer a framework for auditing auditors: the real barrier is detector variance, not distribution shift.
Chinese Translation
链式思维（Chain-of-Thought, CoT）忠实性检测器被广泛用于审计推理模型，但检测器本身也是一种预测器，其裁决被视为稳定属性。我们探讨检测器在分布变化下是否对自身忠实。我们将元忠实性形式化为一种不变性原则：有效的检测器必须对仅通过保持真实忠实性的变换而不同的轨迹返回相同的裁决。我们证明了三个结果：（i）仅使用干预-响应特征的检测器无法区分具有相同特征的忠实机制与表面现象机制；（ii）任何依赖于对变化敏感特征的检测器在不违反不变性的情况下，其失效率与其在分布内的准确性无关；（iii）渐近认证选择性风险保证使得自信的弃权成为可能。我们在FaithShift中操作化这一原则，这是一个涵盖十个变化轴的压力测试协议，并提出SIFT，这是一种通过跨环境不变性目标和认证弃权进行训练的隐状态轨迹检测器。在14,996个轨迹、四个领域和八个模型中，得出了三个发现。首先，转移崩溃是真实存在的：所有现有检测器显示出大于或等于0.15的AUROC差距。其次，主要瓶颈是采样随机性，而非变化：超过80%的检测器不稳定性源于随机种子变化，推翻了我们预注册的预测，即归因于变化的违规行为超过0.25。第三，SIFT将不变性违规减少了64%，相较于最佳单种子基线，但任何检测器的四种子集成将差距缩小至0.01（在匹配覆盖下不可区分，p=0.21），而SIFT需要51%的弃权率。跨模型转移的降级从同一家族到跨家族再到开放权重到API，部分通过多模型训练得以弥补。我们提供了一个审计审计者的框架：真正的障碍是检测器的方差，而非分布变化。
cs.CL / 36 / 2610.04620
Stance Drift: How AI-mediated Communication Distorts Our Message
立场漂移：人工智能媒介沟通如何扭曲我们的信息
Abstract
Large language models (LLMs) increasingly mediate human communication, from drafting emails to summarizing scientific reports, yet whether they faithfully preserve a speaker's position remains largely untested. We model AI-mediated communication as a two-step generation-extraction pipeline: one LLM produces an argument from a specified stance, and a second LLM extracts the stance from that argument. We represent the pipeline as a probabilistic state transition over five Likert-type stance categories and define the stance preservation rate (SPR) as the average probability that the extracted stance matches the initial stance. Across 112 debate propositions, none of the nine LLMs tested exceeded an SPR of 0.7 under the default configuration. Three drift patterns accounted for most of the drift: polarization, deviation from neutrality, and flipping. Among the mitigation strategies tested, including in-context learning, multiple extraction with shuffled options, assertion, and reflection, only adding medium reasoning effort to a reflection prompt for GPT-5.4 substantially improved the SPR, to 0.775, yet polarization remained the largest pattern, with 0.119 of the transition mass. An exploratory comparison with human extraction on a single proposition suggests that drift arises at both the generation and the extraction stage. These results point to a fidelity gap in AI-mediated communication, with implications for journalism, policy deliberation, scientific communication, and other domains where opinion-laden messages pass through language models.
Chinese Translation
大型语言模型（LLMs）越来越多地介导人类沟通，从起草电子邮件到总结科学报告，但它们是否忠实地保留说话者的立场仍然在很大程度上未得到检验。我们将人工智能媒介沟通建模为一个两步生成-提取管道：一个LLM从指定的立场生成一个论点，第二个LLM从该论点中提取立场。我们将该管道表示为五个李克特类型立场类别上的概率状态转移，并将立场保留率（SPR）定义为提取的立场与初始立场匹配的平均概率。在112个辩论命题中，测试的九个LLM在默认配置下均未超过0.7的SPR。三种漂移模式占据了大部分漂移：极化、偏离中立和翻转。在测试的缓解策略中，包括上下文学习、多个提取与随机选项、断言和反思，只有在GPT-5.4的反思提示中增加中等推理努力显著提高了SPR，达到了0.775，但极化仍然是最大的模式，占据了0.119的转移质量。与人类提取在单一命题上的探索性比较表明，漂移在生成和提取阶段均会发生。这些结果指出了人工智能媒介沟通中的忠实度差距，对新闻报道、政策审议、科学沟通以及其他意见驱动的信息通过语言模型的领域具有重要影响。
cs.CL / 37 / 2610.04636
Does Neural Complexity Improve Health Misinformation Detection? A Leakage-Controlled Cross-Corpus Benchmark
神经复杂性是否改善健康虚假信息检测？一项泄漏控制的跨语料基准研究
Abstract
Increasing architectural complexity is often assumed to improve health misinformation detection, yet reported gains are difficult to interpret when studies use different corpora, preprocessing pipelines, data splits, and leakage controls. This study provides a controlled cross-corpus benchmark of five compact neural architectures (1D-CNN, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM), a soft-voting neural ensemble, and three classical machine-learning baselines using COVID19-FNIR and CONSTRAINT. Exact-text duplicate controls were applied before modelling; all neural systems used a common preprocessing and optimisation protocol, and neural results were repeated across three random seeds. On COVID19-FNIR, the deep ensemble achieved a mean macro-F1 of 0.9963 and ROC-AUC of 0.9994, while individual neural models ranged from 0.9945 to 0.9957 macro-F1. On CONSTRAINT, the ensemble achieved macro-F1 of 0.9272 and ROC-AUC of 0.9811, whereas a linear SVM achieved macro-F1 of 0.9574 and ROC-AUC of 0.9931. Architecture rankings changed across corpora, and simple sparse linear models remained highly competitive. The findings show that model complexity does not provide a stable performance advantage and that benchmark construction can dominate architecture choice. The study contributes a reproducible, leakage-controlled basis for evidence-driven model selection in health misinformation classification
Chinese Translation
增加架构复杂性通常被认为可以改善健康虚假信息检测，然而当研究使用不同的语料库、预处理流程、数据划分和泄漏控制时，报告的增益往往难以解读。本研究提供了五种紧凑型神经架构（1D-CNN、LSTM、BiLSTM、CNN-LSTM 和 CNN-BiLSTM）、一个软投票神经集成模型和三个经典机器学习基线的控制跨语料基准，使用 COVID19-FNIR 和 CONSTRAINT 数据集。在建模之前应用了精确文本重复控制；所有神经系统使用了共同的预处理和优化协议，并且神经结果在三个随机种子上重复。对于 COVID19-FNIR，深度集成模型的平均宏 F1 达到 0.9963，ROC-AUC 达到 0.9994，而单个神经模型的宏 F1 范围为 0.9945 到 0.9957。在 CONSTRAINT 数据集上，集成模型的宏 F1 达到 0.9272，ROC-AUC 达到 0.9811，而线性支持向量机（SVM）的宏 F1 达到 0.9574，ROC-AUC 达到 0.9931。不同语料库的架构排名有所变化，简单的稀疏线性模型仍然具有高度竞争力。研究结果表明，模型复杂性并未提供稳定的性能优势，基准构建可能主导架构选择。本研究为健康虚假信息分类中的证据驱动模型选择提供了一个可重复的、泄漏控制的基础。
cs.CL / 38 / 2610.04642
Grounding Probes: Generator-Independent Hallucination Detection from Observer Model Hidden States
基础探测器：独立于生成器的观察者模型隐藏状态的幻觉检测
Abstract
Detecting responses that retrieval-augmented generation does not ground in its context trades speed against accuracy: surface checks miss paraphrased fabrication, sampling-based methods cost extra generations. Hidden-state probes sit between the two, but every existing one reads the generating model's own activations, so a change of generator invalidates the detector and a closed-weight generator is out of reach. This paper removes that coupling. The Grounding Probe is logistic regression over the mean-pooled middle-layer hidden states of an observer language model that reads the context, question, and response in one forward pass and generates nothing, with the recipe it needs: pool over response tokens, read a middle layer, and control capacity, which closes the train-test AUROC gap from 0.087-0.202 to 0.009-0.013. Asking the observer outright, rather than reading its hidden state, costs at least +0.166 AUROC in every one of four models. Fitted on 15,090 annotated responses it reaches 0.879-0.894 AUROC on RAGTruth test across four observers, and 0.924 AUROC with 0.820
[email protected] averaged with a supervised span detector, 0.060 above that detector alone. One probe holds across six generators, and hold-out controls, including one in which no evaluation prompt appears in training, bound the cost of removing a generator at about 0.02 AUROC. Code, probes, and predictions are released.
Chinese Translation
检测检索增强生成未在其上下文中扎根的响应在速度与准确性之间进行权衡：表面检查会遗漏意译的虚构，而基于采样的方法则需要额外的生成。隐藏状态探测器介于两者之间，但现有的每一个探测器都读取生成模型自身的激活，因此生成器的变化会使探测器失效，而封闭权重的生成器则无法实现。本文消除了这种耦合。基础探测器是对观察者语言模型的中间层隐藏状态进行均值池化的逻辑回归，该模型在一次前向传播中读取上下文、问题和响应，并不生成任何内容，具备所需的配方：对响应标记进行池化，读取中间层，并控制容量，从而将训练-测试 AUROC 差距从 0.087-0.202 缩小到 0.009-0.013。直接询问观察者，而不是读取其隐藏状态，在四个模型中的每一个都至少增加了 +0.166 AUROC。经过 15,090 个标注响应的拟合，在四个观察者的 RAGTruth 测试中达到了 0.879-0.894 AUROC，并且与一个监督的跨度检测器平均结合时达到了 0.924 AUROC 和 0.820
[email protected]，比单独的检测器高出 0.060。一个探测器在六个生成器中保持一致，保留控制，包括一个在训练中没有出现评估提示的情况，将移除生成器的成本限制在约 0.02 AUROC。代码、探测器和预测结果已发布。
cs.CL / 39 / 2610.04676
Extracting Persona Subspaces Through Iterative Nullspace Projection For Modulation
通过迭代零空间投影提取个性子空间以进行调制
Abstract
Large Language Models (LLMs) can adopt distinct personas to tune their semantics, expertise, and perspective to different users and tasks. Precise control over these traits is critical to ensure safety and reliability in model behavior. Existing methods like activation steering and prompt-based persona induction reduce a persona to a single dominant direction, missing the finer, nested traits that emerge only once that dominant signal is factored out. We introduce modulation as a setting where the persona context is already embedded in the content being manipulated, requiring control methods to amplify or suppress a trait already present rather than inject it from scratch. PaSS is an inference-time control paradigm that models personas as multi-dimensional subspaces in a model's latent space without supervised contrastive examples. The persona subspaces are extracted via iterative concept erasure and applied to modulate persona-guided generation without retraining. To extract this subspace, we use Iterative Nullspace Projections (INLP) to linearly and iteratively isolate persona-specific directions. We causally evaluate six personas against diverse tasks like MATH-500, TinyAlpaca, GSM8K, and IFEval, showing that discriminative, iterative subspace extraction captures diverse traits underlying a given persona, enabling stronger and larger modulation than single-direction additive methods, while maintaining content fidelity. We further study individual peeled directions within each subspace to uncover the distinct aspects of persona behavior they encode. Overall, we show that persona subspaces offer a controllable, interpretable, and generalizable framework for modulating LLM behavior without sacrificing task performance.
Chinese Translation
大型语言模型（LLMs）可以采用不同的个性，以便根据不同的用户和任务调整其语义、专业知识和视角。对这些特征的精确控制对于确保模型行为的安全性和可靠性至关重要。现有方法如激活引导和基于提示的个性诱导将个性简化为单一主导方向，忽略了在去除主导信号后才会显现的更细微、嵌套的特征。我们引入调制作为一种设置，其中个性上下文已经嵌入到被操控的内容中，这要求控制方法放大或抑制已存在的特征，而不是从头注入。PaSS是一种推理时控制范式，它将个性建模为模型潜在空间中的多维子空间，而无需监督对比示例。个性子空间通过迭代概念消除提取，并应用于调制个性引导生成，而无需重新训练。为了提取这个子空间，我们使用迭代零空间投影（INLP）线性且迭代地隔离个性特定方向。我们对六种个性在MATH-500、TinyAlpaca、GSM8K和IFEval等多样任务中进行了因果评估，结果表明，区分性迭代子空间提取能够捕捉给定个性下的多样特征，从而实现比单一方向加法方法更强大和更大规模的调制，同时保持内容的保真性。我们进一步研究每个子空间内的单独剥离方向，以揭示它们编码的个性行为的不同方面。总体而言，我们展示了个性子空间提供了一种可控、可解释和可推广的框架，用于调制LLM行为，而不牺牲任务性能。
cs.CL / 40 / 2610.04683
Steering Speech-Language Models: Training-Free Task Specialization via Contrastive Activation Addition
引导语音语言模型：通过对比激活添加实现无训练任务专业化
Abstract
Activation steering has proven effective for controlling the behavior of Large Language Models (LLMs) at inference time, but its application to SpeechLLMs remains new, and training-free steering approaches for such models are still largely unexplored. We propose a training-free Contrastive Activation Addition (CAA) protocol that derives steering vectors for common speech tasks (e.g. transcription) in SpeechLLMs from a small number of labeled utterances. We showcase that adding these vectors in the representation space, at inference time, enforces better the targeted speech task. We further show that, when combined with prompting, these vectors yield to consistent improvement over prompting alone on most evaluated tasks such as Automatic Speech Recognition (ASR) or Emotion Recognition (ER), and transfer to out-of-domain data. We additionally demonstrate the usefulness of script-normalization directions to enforce the target script of a specific language.
Chinese Translation
激活引导已被证明在推理时有效控制大型语言模型（LLMs）的行为，但其在语音语言模型（SpeechLLMs）中的应用仍然较新，且针对此类模型的无训练引导方法仍然大多未被探索。我们提出了一种无训练的对比激活添加（Contrastive Activation Addition, CAA）协议，该协议从少量标记的发声中推导出针对常见语音任务（例如转录）的引导向量。我们展示了在推理时将这些向量添加到表示空间中，可以更好地强化目标语音任务。我们进一步表明，当与提示结合使用时，这些向量在大多数评估任务（如自动语音识别（Automatic Speech Recognition, ASR）或情感识别（Emotion Recognition, ER））上，相较于单独使用提示，能够持续提升性能，并且能够迁移到域外数据。此外，我们还展示了脚本标准化方向在强化特定语言目标脚本方面的实用性。
cs.CL / 41 / 2610.04687
Understanding Errors in LLM-Based Question Answering over Imperfect Tables
理解基于大型语言模型的问答系统在不完美表格中的错误
Abstract
We investigate error discovery and handling in question answering over imperfect tables through controlled studies across three large language models (LLMs) on human-reviewed RADAR-T examples. Answering questions over these tables requires handling errors that can affect the answer. We vary row order and compare original, error-marked, and repaired tables to test whether discovery depends on where errors appear and whether providing their locations is sufficient for accurate question answering. First, reordering rows changes error discovery even when the table contents and gold answer remain unchanged. Complete discovery is higher for back than front placements and, averaged over the tested mean positions, for compact than widely spaced layouts. Second, providing verified error locations alone is insufficient for accurate QA, leaving a substantial accuracy gap between error-marked and repaired tables. Providing tables with human-reviewed repairs already applied raises code-assisted QA accuracy by 39.0-59.1 percentage points over the error-marked tables across the three systems. GBDI, a simple workflow, puts these findings into practice by combining error discovery across shuffled table views with explicit guidance for verifying and handling the reported errors. On RADAR-T, GBDI raises observed QA accuracy by 3.8-18.5 percentage points over a code-agent baseline across five systems. These results highlight the importance of both reliable error discovery and effective error handling in question answering over imperfect tables. Our anonymous repository is available at https://anonymous.4open.science/r/GBDI-ICLR-2027-85BD/
Chinese Translation
我们通过对三种大型语言模型（LLMs）在人工审核的RADAR-T示例上的控制研究，探讨了在不完美表格中进行问答时的错误发现与处理。对这些表格进行问答需要处理可能影响答案的错误。我们改变行的顺序，并比较原始表格、标记错误的表格和修复后的表格，以测试错误发现是否依赖于错误出现的位置，以及提供错误位置是否足以实现准确的问答。首先，即使表格内容和正确答案保持不变，重新排序行也会改变错误发现的结果。相较于前面的位置，后面的位置的完整发现率更高，并且在测试的平均位置中，紧凑布局的发现率高于宽间距布局。其次，仅提供经过验证的错误位置不足以实现准确的问答，导致标记错误的表格与修复后的表格之间存在显著的准确性差距。在三种系统中，提供已经应用人工审核修复的表格使得代码辅助问答的准确性提高了39.0-59.1个百分点。GBDI是一个简单的工作流程，通过结合在随机排列的表格视图中进行的错误发现与明确的指导来验证和处理报告的错误，将这些发现付诸实践。在RADAR-T上，GBDI使观察到的问答准确性比五个系统中的代码代理基线提高了3.8-18.5个百分点。这些结果突显了在不完美表格中进行问答时，可靠的错误发现和有效的错误处理的重要性。我们的匿名代码库可在 https://anonymous.4open.science/r/GBDI-ICLR-2027-85BD/ 获取。
cs.CL / 42 / 2610.04720
WNet: Discrete Wavelets Transform for Efficient Token Mixing
WNet：用于高效令牌混合的离散小波变换
Abstract
In a Transformer, token mixing is the step that lets each token draw information from other tokens, and it dominates the cost of encoding long sequences. Self-attention does this mixing very well: every token weighs every other token by content, which gives strong contextual modeling. That all-pairs comparison is also why its cost grows quadratically with sequence length. We introduce WNet, a Transformer encoder that replaces self-attention with token mixing based on the discrete wavelet transform (DWT). Three attention-free mixers recombine the scales: by linear fusion, by learned gating, or by letting each token choose its scales. A hybrid adds self-attention in the last layer only. A receptive-field analysis shows that wavelet mixers built from two-tap filters, such as Haar, never relate tokens outside fixed blocks, however deep the network, even when the filters are learned. Longer filters reach the whole sequence within two layers. We pre-train every model with masked language modeling on a fixed-token subset of C4 and fine-tune on GLUE, using one controlled setup with size-matched BERT and FNet baselines and a control that cannot mix tokens. The token-gated mixer trains as fast as attention at 256 tokens and 2.7 times faster at 4,096.
Chinese Translation
在变换器（Transformer）中，令牌混合是让每个令牌从其他令牌中提取信息的步骤，它主导了对长序列编码的成本。自注意力机制在这方面表现出色：每个令牌根据内容对每个其他令牌进行加权，从而实现强大的上下文建模。然而，这种全对比的方式也导致其成本随着序列长度的增加而呈平方增长。我们提出了WNet，一种将自注意力替换为基于离散小波变换（DWT）的令牌混合的变换器编码器。三种无注意力的混合器通过线性融合、学习门控或让每个令牌选择其尺度来重新组合尺度。一个混合模型在最后一层仅添加自注意力。感受野分析表明，由两个抽头滤波器（如Haar）构建的小波混合器在网络深度再深时也从不关联固定块外的令牌，即使滤波器是学习得到的。较长的滤波器在两层内能够覆盖整个序列。我们在C4的固定令牌子集上使用掩蔽语言建模对每个模型进行预训练，并在GLUE上进行微调，采用一个与BERT和FNet基线大小匹配的受控设置，以及一个无法混合令牌的控制组。在256个令牌时，令牌门控混合器的训练速度与自注意力相当，而在4096个令牌时则快2.7倍。
cs.CL / 43 / 2610.04725
Verb-ICL: Rethinking In-Context Learning for Structured Prediction
动词-ICL：重新思考结构化预测的上下文学习
Abstract
Structured prediction tasks pose unique challenges for in-context learning (ICL): their compositional outputs require modeling fine-grained, token-level patterns that sentence-level approaches fail to capture, and their task-specific annotation conventions are human-defined artifacts that cannot be acquired through pretraining alone. We propose Verb-ICL, a selective annotation framework for ICL-based structured prediction that addresses both challenges. Verb-ICL first selects representative examples using a token-level coverage strategy that captures local semantic patterns critical for structured prediction, then generates actionable error feedback that codifies task-specific annotation guidelines and incorporates this feedback into ICL demonstrations. We evaluate Verb-ICL on six structured prediction datasets spanning information extraction and semantic parsing. Experiments with recent LLMs show that Verb-ICL consistently outperforms strong selective annotation baselines under low-resource settings and continues to provide gains as the annotation budget increases. Extended analyses demonstrate that the generated feedback is predominantly useful across a four-category quality taxonomy, generalizes as task-level guidance beyond instance-specific corrections, and improves performance regardless of the underlying selection strategy.
Chinese Translation
结构化预测任务对上下文学习（ICL）提出了独特的挑战：它们的组合输出要求建模细粒度的标记级模式，而句子级方法无法捕捉这些模式；此外，它们特定于任务的注释规范是人类定义的工件，无法仅通过预训练获得。我们提出了动词-ICL，这是一种针对基于ICL的结构化预测的选择性注释框架，旨在解决这两个挑战。动词-ICL首先使用标记级覆盖策略选择代表性示例，以捕捉对结构化预测至关重要的局部语义模式，然后生成可操作的错误反馈，规范任务特定的注释指南，并将这些反馈纳入ICL演示中。我们在六个涵盖信息提取和语义解析的结构化预测数据集上评估动词-ICL。与近期的大型语言模型（LLMs）进行的实验表明，动词-ICL在低资源设置下始终优于强大的选择性注释基线，并且随着注释预算的增加，持续提供增益。扩展分析表明，生成的反馈在四类质量分类法中主要是有用的，超越特定实例的修正，作为任务级指导进行泛化，并且无论基础选择策略如何，都能提高性能。
cs.CL / 44 / 2610.04753
More Value per Key: Asymmetric Sparse Attention for Faster LLM Decoding
每个键的更多价值：用于更快LLM解码的非对称稀疏注意力
Abstract
utoregressive generation in Large Language Models (LLMs) is constrained by the memory and computational demands of attention mechanisms. Sparse attention methods mitigate this cost by selecting only high-probability entries of the attention matrix. We observe that in many such methods, this renders the probability-value multiplication negligible, shifting the bottleneck to the query-key step. Key heads can therefore be reduced to accelerate inference, while retaining more value heads preserves capacity with limited additional decoding cost. We introduce Sparse Asymmetric Group-Query Attention (SAGA), which decouples key and value head counts to exploit this principle, and pair it with approximate top-N (Atop-N) attention, a simple sparse attention method designed to study the interaction between sparsity and head-count asymmetry. We formalize the benefits of this asymmetry theoretically and validate them empirically through latency measurements and quality evaluations on models up to 1.5B parameters. Together, SAGA and Atop-N achieve end-to-end decoding speedups exceeding $2\times$ over our full-attention GQA baseline at long contexts. Models trained from scratch with SAGA nearly match the quality of comparable GQA variants on the evaluated benchmarks. To facilitate adoption, we introduce an efficient fine-tuning method that converts pretrained models to the SAGA architecture, enabling practitioners to benefit from our approach without costly retraining.
Chinese Translation
大型语言模型（LLMs）的自回归生成受到注意力机制的内存和计算需求的限制。稀疏注意力方法通过仅选择注意力矩阵中的高概率条目来减轻这一成本。我们观察到，在许多此类方法中，这使得概率与值的乘积变得微不足道，从而将瓶颈转移到查询-键步骤。因此，可以减少键头以加速推理，而保留更多的值头则在有限的额外解码成本下保持容量。我们提出了稀疏非对称组查询注意力（Sparse Asymmetric Group-Query Attention, SAGA），它解耦了键头和值头的数量，以利用这一原理，并将其与近似前N（approximate top-N, Atop-N）注意力相结合，这是一种简单的稀疏注意力方法，旨在研究稀疏性与头数非对称性之间的相互作用。我们理论上形式化了这种非对称性的好处，并通过延迟测量和对高达15亿参数模型的质量评估进行了实证验证。SAGA和Atop-N结合在长上下文中实现了超过$2 imes$的端到端解码加速，相较于我们完整注意力的GQA基线。从头开始训练的SAGA模型在评估基准上几乎与可比的GQA变体的质量相当。为了促进采用，我们引入了一种高效的微调方法，将预训练模型转换为SAGA架构，使从业者能够在不需要昂贵的重新训练的情况下受益于我们的方法。
cs.CL / 45 / 2610.04803
How Do People Challenge Racial Stereotypes Online? Counter-Story Detection Across Reddit Communities
人们如何在网上挑战种族刻板印象？跨Reddit社区的反故事检测
Abstract
Counter-storytelling is a powerful mechanism people use to challenge dominant narratives. Unlike other forms of counterspeech that have been widely studied in computational social science, counter-storytelling has largely been overlooked. Counter-stories are difficult to detect automatically; they are relational (defined with respect to expressions of racial stereotypes) and structurally diverse (drawing on stories that describe lived experiences, witnessed events, exemplars, and hypotheticals). We introduce a first framework for detecting and characterizing counter-storytelling against racial stereotypes at scale. This includes (1) a three-dimensional taxonomy grounded in narratology and Critical Race Theory and (2) a multi-stage pipeline that identifies relational pairs of stereotypes and counter-stories in noisy Reddit discourse. Using this pipeline, we annotate 25,549 Reddit posts across 615 communities and identify 1,312 counter-stories. Our analysis shows that speaker identity and post context shape how counter-stories are told. For example, in-group writers favor first-person testimony, often adopting the role of self-reflective insiders. Our work shows how computational methods can scale qualitative approaches to identify and characterize counter-storytelling as a contextual narrative practice, with implications for content moderation, narratology, and racial discourse analysis.
Chinese Translation
反故事讲述是一种强有力的机制，人们用它来挑战主流叙事。与计算社会科学中广泛研究的其他形式的反言论不同，反故事讲述在很大程度上被忽视。反故事难以自动检测；它们是关系性的（相对于种族刻板印象的表达进行定义）且结构多样（借鉴描述生活经历、目击事件、典型案例和假设的故事）。我们提出了一个首个框架，用于大规模检测和表征针对种族刻板印象的反故事讲述。这包括（1）一个基于叙事学和批判种族理论的三维分类法，以及（2）一个多阶段管道，识别嘈杂的Reddit讨论中刻板印象和反故事的关系对。利用该管道，我们对615个社区的25,549条Reddit帖子进行了标注，并识别出1,312个反故事。我们的分析表明，发言者身份和帖子上下文塑造了反故事的叙述方式。例如，内部群体的作者倾向于使用第一人称证词，常常扮演自我反思的内部人角色。我们的研究展示了计算方法如何扩展定性方法，以识别和表征反故事讲述作为一种情境叙事实践，这对内容审核、叙事学和种族话语分析具有重要意义。
cs.CL / 46 / 2610.04829
Viva La Vida: Verification and Accumulation Failures in Multi-Agent Proof Search
活出精彩：多智能体证明搜索中的验证与积累失败
Abstract
When an agentic prover works on an open problem, there is no proof assistant to fall back on: its verifier and lemma library are ultimately language models judging model outputs. We instrumented such a system end to end and analyzed $51{,}754$ traced observations across three full runs ($186$ hours, \$$5{,}694$). We find three connected failure modes. First, the three-model verifier requires unanimity and treats parse or API failure as non-approval; in $10$ of $12$ verification events, one member returned no parseable output or an API error, making acceptance arithmetically impossible without surfacing an error. Second, when the ensemble did function, one verifier approved $3$ attempts that GPT rejected, each claiming to resolve the open problem; a single-verifier design would therefore have announced a solution three times. Third, because nothing could be approved, every review was a refutation, yet the lemma extractor mines reviews as well as proofs: $24$ of $93$ lemmas ($26\%$) were extracted from rejected arguments with their refutational context removed. Taken together, these findings show that without external verification, supervision is itself a critical trust boundary: systems must distinguish abstention from rejection, preserve useful disagreement, and preserve the provenance and polarity of information before it becomes future context.
Chinese Translation
当一个智能证明者处理一个开放问题时，没有证明助手可以依赖：其验证器和引理库最终都是语言模型在评判模型输出。我们对这样的系统进行了端到端的监测，并分析了 $51{,}754$ 个追踪观察结果，涵盖了三次完整运行（$186$ 小时，花费 \\$5{,}694）。我们发现了三种相关的失败模式。首先，三模型验证器要求一致性，并将解析或 API 失败视为不批准；在 $12$ 次验证事件中，有 $10$ 次的一个成员返回了不可解析的输出或 API 错误，使得在没有显现错误的情况下，接受在算术上变得不可能。其次，当集成模型正常工作时，一个验证器批准了 $3$ 次被 GPT 拒绝的尝试，每次都声称解决了开放问题；因此，单一验证器设计将会三次宣布一个解决方案。第三，由于没有任何内容可以被批准，每次审查都是对之前结果的反驳，然而引理提取器同时挖掘审查和证明：在 $93$ 个引理中，有 $24$ 个（$26\\%$）是从被拒绝的论证中提取的，且其反驳上下文被移除。综合来看，这些发现表明，在没有外部验证的情况下，监督本身就是一个关键的信任边界：系统必须区分弃权与拒绝，保留有用的分歧，并在信息成为未来上下文之前保留其来源和极性。
cs.CL / 47 / 2610.04830
Cluster Validation Indices as Self-Supervised Objectives for Text Representation Learning
聚类验证指标作为文本表示学习的自监督目标
Abstract
Self-supervised fine-tuning refines the embedding space of a pretrained language encoder without labels. However, the commonly used approaches are computationally expensive. Specifically, contrastive learning-based methods need multiview data and in-batch negative examples, while negative-free approaches require auxiliary graphs/networks. An interesting question arises: can self-supervised fine-tuning be done without relying on either additional negatives or graph data? To answer this question, we introduce SilK (Silhouette-guided K-means), which trains on a Cluster Validation Index, an internal measure of cluster quality without using labels. SilK clusters the corpus and then regresses a simplified silhouette toward a target value. Each document is compared only against the k cluster centroids, never against other documents, so the method needs no augmentation, no negative pairs and one view per document. On BERT-base, SilK trains 1.46x faster per epoch than the fastest baseline we evaluate and uses 45.4% less peak GPU memory than the leanest one. Under frozen-encoder linear probing, SilK stays competitive with the best baselines on three downstream tasks.
Chinese Translation
自监督微调在没有标签的情况下优化预训练语言编码器的嵌入空间。然而，常用的方法计算开销较大。具体而言，基于对比学习的方法需要多视图数据和批内负样本，而无负样本的方法则需要辅助图/网络。由此产生了一个有趣的问题：自监督微调是否可以在不依赖额外负样本或图数据的情况下进行？为了解答这个问题，我们提出了SilK（基于轮廓的K均值），该方法在聚类验证指标上进行训练，这是一种不使用标签的内部聚类质量度量。SilK对语料库进行聚类，然后将简化的轮廓回归到目标值。每个文档仅与k个聚类中心进行比较，而不与其他文档进行比较，因此该方法不需要增强、不需要负样本对，并且每个文档只需一个视图。在BERT-base上，SilK每个epoch的训练速度比我们评估的最快基线快1.46倍，并且使用的峰值GPU内存比最精简的基线少45.4%。在冻结编码器的线性探测下，SilK在三个下游任务中与最佳基线保持竞争力。
cs.CL / 48 / 2610.04888
No Hindsight for LLM Fact-Checkers: Measuring Leakage Channels in Misinformation Detection
大型语言模型事实检查器的盲点：测量错误信息检测中的泄漏通道
Abstract
As automated fact-checking scales on social media, large language model (LLM) verdict scores can look stronger than warranted. One reason is that evaluations mix in information that was not knowable at claim time. Two channels are easy to conflate: outcomes memorized in pre-training and retrieved evidence published after the claim. Yet standard benchmarks rarely separate the two. In this study we measure both channels on AVeriTeC and QuanTemp++ by reconstructing point-in-time evidence conditions and probing for outcome information encoded in model representations. We find substantial evidence of parametric leakage, that can be hidden by the aggregate accuracy, while a simple representation bottleneck reduces this future leakage more efficiently than a mutual-information-based training penalty. We also find that allowing post-claim evidence inflates zero-shot accuracy by 6.3 points in AVeriTeC while the effect is negligible in QuanTemp++, where retrieval provides little post-claim evidence. These results show that misinformation benchmarks can overstate fact-checking performance when they do not account for what information was actually available at claim time.
Chinese Translation
随着自动化事实检查在社交媒体上的扩展，大型语言模型（LLM）的判决分数可能看起来比实际情况更强。一种原因是评估中混入了在声明时无法得知的信息。两个通道容易混淆：在预训练中记忆的结果和在声明后检索到的证据。然而，标准基准测试很少将两者分开。在本研究中，我们通过重建特定时间点的证据条件并探测模型表示中编码的结果信息，测量了 AVeriTeC 和 QuanTemp++ 上的这两个通道。我们发现显著的参数泄漏证据，这可能被整体准确性掩盖，而简单的表示瓶颈比基于互信息的训练惩罚更有效地减少这种未来泄漏。我们还发现，允许在声明后使用证据使 AVeriTeC 的零-shot 准确性提高了 6.3 个百分点，而在 QuanTemp++ 中这一影响微不足道，因为检索提供的声明后证据很少。这些结果表明，当错误信息基准未考虑声明时实际可用的信息时，可能会夸大事实检查的表现。
cs.CL / 49 / 2610.04898
A Systematic Analysis of the Predictive Power of LM Surprisal in Reading Chinese
LM惊讶度在中文阅读中的预测能力的系统分析
Abstract
This study analyzes the predictive power of LM-derived, token-level surprisal on Mandarin Chinese reading times. We first propose the Shortest Matching Sequence (SMS), an alignment scheme that maps between the word segmentation assumed by eye-tracking corpora and the LMs' subword tokenization, as the two tokenizations often disagree in the context of Mandarin Chinese. Then, using a suite of Chinese-Pythia models (14M-1.4B) trained on scratch with 30B tokens, we examine how well surprisal predicts first fixation duration, gaze duration, and total reading time in three paragraph-level eye-tracking corpora of Mandarin Chinese (GECO-CN, HKP, and MECO). Contrary to previous null findings, our results show that surprisal is predictive of Chinese reading times. However, whether predictive power scales with model size and the amount of training is corpus-specific: bigger models predict better in GECO-CN, whereas inverse scaling emerges in HKP and, at the largest sizes, in MECO. Subsequently, we tested one possible explanation for the inverse scaling in HKP and found that checkpoints whose surprisal remains closer to $n$-gram statistics are better predictors of reading. All in all, the predictive power of surprisal on Chinese reading time measurements is corpus-specific, which cautions against drawing scaling conclusions from a single corpus.
Chinese Translation
本研究分析了基于语言模型（LM）推导的、以标记为单位的惊讶度对普通话阅读时间的预测能力。我们首先提出了最短匹配序列（Shortest Matching Sequence, SMS），这是一种对齐方案，用于映射眼动追踪语料库假定的词分割与语言模型的子词标记化之间的关系，因为在普通话的背景下，这两种标记化方式往往存在不一致。然后，利用一套在30B标记上从头训练的中文-Pythia模型（14M-1.4B），我们考察了惊讶度在三种普通话段落级眼动追踪语料库（GECO-CN、HKP和MECO）中对首次注视持续时间、注视持续时间和总阅读时间的预测效果。与之前的无效发现相反，我们的结果表明，惊讶度能够预测中文阅读时间。然而，预测能力是否随着模型规模和训练量的增加而增强则是语料库特定的：在GECO-CN中，较大的模型预测效果更好，而在HKP中则出现了反向缩放现象，在最大规模的MECO中也同样如此。随后，我们测试了HKP中反向缩放的一个可能解释，发现那些惊讶度与$n$-gram统计更接近的检查点更能有效预测阅读。总的来说，惊讶度对中文阅读时间测量的预测能力是语料库特定的，这提醒我们在从单一语料库得出规模结论时需谨慎。
cs.CL / 50 / 2610.04899
Rewrite What Matters: Adaptive Multilingual Query Rewriting for Reasoning via Agentic Reinforcement Learning
重写重要内容：基于代理强化学习的自适应多语言查询重写用于推理
Abstract
In multilingual scenarios, queries with equivalent semantics but in different languages could guide the model into different reasoning trajectories, leading to performance disparities. To mitigate this gap, previous studies typically apply a one-size-fits-all query rewriting strategy, such as translation, which overlooks the fact that different scenarios require diverse types of semantic transformations. In this paper, we propose mRewriter-R1, an agentic multilingual query rewriting framework with reinforcement learning. Unlike single-turn rewriting, mRewriter-R1 formulates multilingual query rewriting as a multi-turn sequential decision-making process, where the model dynamically performs multi-aspect optimization through adaptive operator selection. Experimental results demonstrate that mRewriter-R1 outperforms all strong multilingual rewriting baselines on different large reasoning backbones. Further analyses show that the learned policy can adaptively decide on rewriting operators according to query characteristics, exhibiting strong generalization ability across diverse reasoning tasks, and plug-and-play compatibility with heterogeneous reasoning language models.
Chinese Translation
在多语言场景中，具有等效语义但使用不同语言的查询可能会引导模型进入不同的推理轨迹，从而导致性能差异。为了减轻这一差距，以往的研究通常采用一种通用的查询重写策略，例如翻译，这忽视了不同场景需要多样化的语义转换类型。在本文中，我们提出了 mRewriter-R1，一种基于强化学习的代理多语言查询重写框架。与单轮重写不同，mRewriter-R1 将多语言查询重写形式化为一个多轮序列决策过程，其中模型通过自适应操作选择动态执行多方面优化。实验结果表明，mRewriter-R1 在不同的大型推理基础上超越了所有强大的多语言重写基线。进一步的分析显示，学习到的策略能够根据查询特征自适应地决定重写操作，展现出在多样化推理任务中的强大泛化能力，并与异构推理语言模型具有即插即用的兼容性。
cs.CL / 51 / 2610.04906
Scaling Verifiable Environments for Long-horizon Work Agents
可验证环境的扩展用于长时间工作代理
Zhang, Jiazheng, Ma, Long, Yang, Yunxian, Xi, Zhiheng, Lei, Zhikai, Yang, Yajie, Liao, Chenyang, Zhou, Enyu, Nan, Yang, Tian, Yuchen, Jin, Senjie, Wang, Yibo, He, Wei, Liu, Boyang, Huang, Jixuan, Guo, Xin, Hao, Zhezheng, Liang, Xinbing, Zhang, Zhihao, Zhou, Changzhi, Zhou, Wiggin, Gui, Tao, Zhang, Qi, Huang, Xuanjing, Clarenceai, Adams, Aiden
Abstract
Work agents operate over digital artifacts to execute professional knowledge-intensive work, requiring training environments that support long-horizon interaction and trustworthy verification. However, hand-crafted environments incur prohibitive engineering overhead that prevents environment scaling, whereas synthesis methods sacrifice workspace complexity, realism, or grounded verifiability. To bridge this gap, we introduce WorkForge, a scalable synthesis framework for constructing verifiable work-agent environments from real-world resources. Starting from expert workflows, WorkForge first identifies the resources, decisions, and deliverables required by each workflow. It then retrieves relevant real-world files and organizes them into a workspace. WorkForge inspects the workspace to extract concrete, checkable facts about its content. These factual anchors fix which task types the workspace can support and how their outcomes can be verified. Therefore, WorkForge derives each task's instructions, solution plan, and complementary programmatic and semantic verifiers directly from these factual anchors, keeping verification traceable to observable workspace evidence. Furthermore, we construct 16.7K verifiable environments across 40 professional domains, with workspaces collectively covering 60 file types. Post-training Qwen3.5-35B-A3B-Base improves GDPVal from 45.5 to 73.6 and APEX Score from 5.0 to 21.3, while enabling Qwen3.5-27B to achieve highly competitive performance and outperform strong competitors. Our analyses confirm the efficacy of the proposed method and reveal consistent scaling behaviors across both data volume and interaction horizons.
Chinese Translation
工作代理在数字工件上操作，以执行专业知识密集型工作，这需要支持长时间交互和可信验证的训练环境。然而，手工制作的环境会产生高昂的工程开销，从而阻碍环境的扩展，而合成方法则牺牲了工作空间的复杂性、真实性或基础可验证性。为了解决这一问题，我们提出了WorkForge，一个可扩展的合成框架，用于从现实世界资源构建可验证的工作代理环境。WorkForge首先从专家工作流程开始，识别每个工作流程所需的资源、决策和交付物。然后，它检索相关的现实世界文件并将其组织成一个工作空间。WorkForge检查工作空间，以提取关于其内容的具体、可检查的事实。这些事实锚定了工作空间可以支持的任务类型以及如何验证其结果。因此，WorkForge直接从这些事实锚定中推导出每个任务的指令、解决方案计划以及补充的程序性和语义验证器，使验证可追溯到可观察的工作空间证据。此外，我们在40个专业领域构建了16.7K个可验证环境，工作空间共同覆盖60种文件类型。经过训练的Qwen3.5-35B-A3B-Base将GDPVal从45.5提高到73.6，APEX得分从5.0提高到21.3，同时使Qwen3.5-27B实现高度竞争的性能并超越强劲的竞争对手。我们的分析确认了所提方法的有效性，并揭示了数据量和交互范围之间的一致扩展行为。
cs.CL / 52 / 2610.04956
From Overloaded to Guaranteed: High-Throughput Multi-SLO Enforcement for LoRA-Assisted On-Premise LLM Deployment
从过载到保证：高吞吐量多服务水平目标（SLO）执行的LoRA辅助本地大语言模型（LLM）部署
Abstract
As Large Language Models (LLMs) become essential in privacy-sensitive sectors like hospitals and government agencies, the on-premise LLM servers offer a cost-effective and secure alternative to public cloud services. However, these resource-constrained servers struggle to guarantee heterogeneous Service Level Objectives (SLOs) when serving multiple LoRA-adapted services simultaneously. Existing serving frameworks suffer from severe SLO violations due to the computational overhead of LoRA layers and the rigid nature of batch scheduling. To address this, we propose HALO, a scheduling method tailored for LoRA-assisted on-premise LLM deployment. HALO introduces two key innovations: a spatial multiplexing strategy that overlaps Base and LoRA computations by partitioning GPU Streaming Multiprocessors (SMs), and an SLO-aware scheduler that decouples request execution based on "request-level slack." By prioritizing urgent tasks and utilizing idle budget for traffic shaping, HALO significantly mitigates resource contention. Our evaluation demonstrates that HALO minimizes SLO violations while improving throughput compared to state-of-the-art baselines.
Chinese Translation
随着大型语言模型（LLMs）在医院和政府机构等隐私敏感领域变得至关重要，本地LLM服务器提供了一种成本效益高且安全的替代公共云服务的方案。然而，这些资源受限的服务器在同时为多个LoRA适配的服务提供支持时，难以保证异构服务水平目标（SLO）。现有的服务框架由于LoRA层的计算开销和批处理调度的刚性特性，严重违反了SLO。为了解决这一问题，我们提出了HALO，一种针对LoRA辅助本地LLM部署的调度方法。HALO引入了两个关键创新：一种空间复用策略，通过划分GPU流处理器（SMs）来重叠基础和LoRA计算，以及一种基于“请求级松弛”的SLO感知调度器，能够解耦请求执行。通过优先处理紧急任务并利用闲置预算进行流量整形，HALO显著减轻了资源争用。我们的评估表明，HALO在提高吞吐量的同时，最小化了SLO的违反情况，相较于最先进的基线表现更佳。
cs.CL / 53 / 2610.04961
Building LLM Agent Systems the Deep Learning Way: From Modular Design to Architecture Search
以深度学习方式构建大型语言模型代理系统：从模块化设计到架构搜索
Abstract
Large Language Models (LLMs) have revolutionized AI research and enabled exciting agent systems. To build a complex LLM agent system, most existing research relies on insights from other domains or heuristics to manually build the agent system. However, this approach often requires heavy hand-engineering and fails to fully optimize for the downstream task of interest. Inspired by the tremendous success of deep learning, we propose to construct LLM agent systems in a modular manner, similar to building a deep neural network. Our key insight is to make analogies between LLM building blocks, such as retrievals, memories, and prompting strategies, and the successful deep learning modules, such as MLPs, attention, and recurrent modules. We further design forward inference and feedback mechanisms for LLMs, where prompts in LLMs are considered as the weights in deep models, and the prompt optimization from feedback is analogous to the back-propagation algorithm. We additionally leverage a search algorithm to search for the best configuration of LLM agent systems, similar to the neural architecture search (NAS) in deep learning research. Comprehensive experimental results demonstrate that the proposed deep learning recipe for LLM agent systems is highly effective, in particular: (1) Organizing LLM modules into deep-learning-style architectures yields noticeable performance gain; (2) Automatic prompt optimization, equivalent to backpropagation, is efficient in incorporating feedback from the task of interest and achieves at least 5% performance improvement; (3) NAS equivalent algorithm works well for further optimizing the LLM agent system architecture with 11% performance gain compared with randomly designed architectures. Overall, our research demonstrates the exciting opportunity of transferring the success of deep learning to building LLM agent systems.
Chinese Translation
大型语言模型（LLMs）已彻底改变了人工智能研究，并使令人兴奋的代理系统成为可能。为了构建复杂的LLM代理系统，大多数现有研究依赖于其他领域的见解或启发式方法来手动构建代理系统。然而，这种方法通常需要大量的人工工程，并未能充分优化所关注的下游任务。受到深度学习巨大成功的启发，我们提出以模块化的方式构建LLM代理系统，类似于构建深度神经网络。我们的关键见解是将LLM构建块（如检索、记忆和提示策略）与成功的深度学习模块（如多层感知器（MLPs）、注意力机制和递归模块）进行类比。我们进一步为LLM设计前向推理和反馈机制，其中LLM中的提示被视为深度模型中的权重，而来自反馈的提示优化类似于反向传播算法。此外，我们利用搜索算法来搜索LLM代理系统的最佳配置，类似于深度学习研究中的神经架构搜索（NAS）。全面的实验结果表明，所提出的深度学习方法在LLM代理系统中非常有效，特别是：（1）将LLM模块组织成深度学习风格的架构显著提高了性能；（2）自动提示优化，相当于反向传播，在有效整合来自关注任务的反馈方面表现高效，并实现至少5%的性能提升；（3）等效的NAS算法在进一步优化LLM代理系统架构方面表现良好，与随机设计的架构相比，性能提升达11%。总体而言，我们的研究展示了将深度学习的成功转移到构建LLM代理系统的激动人心的机会。
cs.CL / 54 / 2610.04973
TrajLong: Co-Designing Agentic and Long-Context Supervision for Mid-Training
TrajLong：为中期训练共同设计代理性和长上下文监督
Abstract
LLM agents for coding, search, and workplace tasks increasingly rely on long-context capabilities to effectively aggregate and reason over extended interaction histories. Recent work has incorporated agent trajectories into mid-training stage, drawing on their naturally long and interaction-rich structure. Yet how to organize the information within these trajectories into effective mid-training supervision remains underexplored. In this work, we investigate the relationship between long-context and agent atomic capabilities and introduce TrajLong, a novel framework that compiles trajectories into long-context training tasks with dense supervision, targeting three representative atomic capabilities: evidence grounding, cross-evidence aggregation, and temporal state maintenance. We mid-train Qwen3-14B-Base and Qwen3-30B-A3B-Base with data compiled by TrajLong, followed by supervised fine-tuning. Experiments on 6 long-context and 12 agent benchmarks demonstrate broad performance gains, with controlled ablations showing improvements over raw and masked trajectory baselines. Capability-level analyses further reveal task-dependent associations between long-context and agent atomic capabilities. These findings suggest that the shared capability demands of long-context reasoning and agent execution provide a principled basis for designing mid-training data to develop downstream agent capabilities.
Chinese Translation
用于编码、搜索和工作场所任务的大型语言模型（LLM）代理越来越依赖于长上下文能力，以有效聚合和推理扩展的交互历史。近期的研究将代理轨迹纳入中期训练阶段，利用其自然的长和丰富的交互结构。然而，如何将这些轨迹中的信息组织成有效的中期训练监督仍然未被充分探索。在本研究中，我们探讨了长上下文与代理原子能力之间的关系，并引入了TrajLong，一个新颖的框架，将轨迹编译为具有密集监督的长上下文训练任务，针对三种代表性的原子能力：证据基础、跨证据聚合和时间状态维护。我们使用TrajLong编译的数据对Qwen3-14B-Base和Qwen3-30B-A3B-Base进行了中期训练，随后进行了监督微调。在6个长上下文和12个代理基准上的实验显示了广泛的性能提升，控制消融实验表明相较于原始和掩蔽轨迹基线有显著改善。能力级别分析进一步揭示了长上下文与代理原子能力之间的任务依赖关联。这些发现表明，长上下文推理和代理执行的共享能力需求为设计中期训练数据以发展下游代理能力提供了原则性基础。
cs.CL / 55 / 2610.04974
IREA: Intermediate Representation-based Embedding Alignment for Normative RAG
IREA：基于中间表示的嵌入对齐用于规范性RAG
Abstract
Large language models (LLMs) have shown strong performance across various tasks, but they still struggle with questions involving ethical judgment. Previous studies have attempted to train LLMs on ethical standards, but the diversity and relativity of ethical norms make them difficult to fully internalize in model parameters. As an alternative, we introduce normative RAG, a retrieval-augmented approach that supports ethical judgment using external normative knowledge. Normative retrieval involves a distinct asymmetry between context rich narrative queries and generalized normative statements. Existing factual retrieval methods rely on query-only expansion into a document-like form, making them insufficient for resolving this asymmetry. Therefore, we propose Intermediate Representation-based Embedding Alignment (IREA), a bidirectional alignment method that maps both text types into a shared situation-behavior representation. This representation captures ethically salient contextual and behavioral information in a normalized form, reducing surface-level discrepancies and improving alignment in the embedding space. Experimental results show that IREA improves normative retrieval and downstream ethical judgment across multiple settings, demonstrating the effectiveness of bidirectional alignment for normative RAG.
Chinese Translation
大型语言模型（LLMs）在各种任务中表现出色，但在涉及伦理判断的问题上仍然存在困难。之前的研究尝试在伦理标准上训练LLMs，但伦理规范的多样性和相对性使得它们难以在模型参数中完全内化。作为替代方案，我们提出了规范性RAG，这是一种增强检索的方法，利用外部规范知识支持伦理判断。规范性检索涉及上下文丰富的叙述查询与一般化的规范性陈述之间的明显不对称。现有的事实检索方法依赖于仅通过查询扩展到文档形式，这使得它们不足以解决这种不对称。因此，我们提出了基于中间表示的嵌入对齐（IREA），这是一种双向对齐方法，将两种文本类型映射到共享的情境-行为表示中。该表示以规范化的形式捕捉伦理上显著的上下文和行为信息，减少表面层次的差异，提高嵌入空间中的对齐效果。实验结果表明，IREA在多个设置中改善了规范性检索和下游伦理判断，展示了双向对齐在规范性RAG中的有效性。
cs.CL / 56 / 2610.05031
When LLMs Sit Above Diagnostic Tools: Unrealized Complementarity in Industrial Fault Diagnosis
当大型语言模型高于诊断工具：工业故障诊断中的未实现互补性
Abstract
Large language models are increasingly used as integration layers above specialized tools, but a stronger component does not necessarily produce a stronger combined system. Across five diagnostic datasets (bearing vibration, process monitoring, semiconductor equipment), we study whether an LLM can reliably use external diagnostic information; paired repeat calls separate advice effects from output instability. In all five, conflicting external information overturned initially correct LLM judgments. Among the four datasets with direct integration comparisons, none showed a consistent advantage for implicit LLM integration over the stronger standalone source. On a Tennessee Eastman confirmation set whose protocol was fixed before evaluation, unaided accuracy was 64.67%, implicit LLM-specialist integration 77.43%, and the specialist alone 83.33%. Specialist information improved the LLM by 12.8 points (95% interval 9.7 to 15.9), yet the integrated output stayed 5.9 points below the specialist (95% interval -12.0 to -0.7). A two-source selector oracle reached 92.76%, indicating complementarity that the integrated output did not fully realize. The integrated output missed 140 of 295 specialist corrections (47.5%) but lost 15 of 99 initially correct LLM judgments (15.2%). The deficit remained under prompt and specialist sensitivity analyses. Among CWRU cases solved under both evidence presentations, task-aligned physical evidence yielded lower estimates of susceptibility to incorrect advice in six of seven models (five intervals excluding zero); higher reasoning effort gave no reliable reduction in five models, and a separate four-model TEP analysis gave no clear evidence that it resolves the integration problem. Source quality and integration quality should be evaluated separately: an integration layer should be compared with its stronger standalone component, not only with the unaided LLM.
Chinese Translation
大型语言模型越来越多地作为专业工具之上的集成层使用，但更强的组件并不一定会产生更强的组合系统。在五个诊断数据集（轴承振动、过程监控、半导体设备）中，我们研究了大型语言模型是否能够可靠地使用外部诊断信息；配对重复调用将建议效果与输出不稳定性分开。在所有五个数据集中，冲突的外部信息推翻了最初正确的大型语言模型判断。在四个具有直接集成比较的数据集中，没有一个显示隐式大型语言模型集成相较于更强的独立来源具有一致的优势。在一个在评估前固定协议的田纳西东曼确认集中，未经辅助的准确率为64.67%，隐式大型语言模型-专家集成为77.43%，而专家单独为83.33%。专家信息使大型语言模型提高了12.8个百分点（95%区间为9.7到15.9），然而集成输出仍比专家低5.9个百分点（95%区间为-12.0到-0.7）。一个双源选择器oracle达到了92.76%，表明集成输出未能充分实现的互补性。集成输出错过了295个专家修正中的140个（47.5%），但失去了99个最初正确的大型语言模型判断中的15个（15.2%）。在提示和专家敏感性分析下，缺口依然存在。在CWRU案例中，在两种证据呈现下解决的任务中，任务对齐的物理证据在七个模型中的六个中提供了较低的错误建议易感性估计（五个区间不包括零）；更高的推理努力在五个模型中没有可靠的减少，而一个单独的四模型TEP分析没有明确证据表明它解决了集成问题。源质量和集成质量应分别评估：集成层应与其更强的独立组件进行比较，而不仅仅是与未经辅助的大型语言模型进行比较。
cs.CL / 57 / 2610.05039
Causal Improvement Graph for Agentic Harness Optimization
用于Agentic Harness优化的因果改进图
Abstract
Agentic Harness is the runtime that constructs task context and controls execution flow, thereby shaping overall agent performance. Given a fixed model and external evaluation, automated Harness optimization seeks to improve this runtime through an iterative proposal--evaluation loop to better solve target tasks. Existing meta-harness methods mainly adopt proposer-centric discovery, in which an LLM-based proposer integrates accumulated experimental findings to determine subsequent Harness revisions. This places the burden of maintaining the evolving improvement state on the proposer as history expands and its underlying experimental logic becomes harder to discern. In this paper, we introduce the Causal Improvement Graph (CIG), a graph-governed meta-harness framework that externalizes the evolving improvement state in a persistent graph, allowing prior findings to directly govern subsequent Harness optimization through local proposer operations. CIG grows and links Evidence, Hypothesis, Intervention, and Outcome nodes to represent what was observed, how it may be explained, how to test that explanation, and what the evaluation reveals. Their structural relations preserve how the improvement state changes across iterations, allowing local proposers to build directly on relations among prior findings rather than recover them from raw history. Across various agent tasks, CIG discovers stronger Harnesses than previous meta-harness baselines and remains robust to the choice of task solver and proposer. Structural ablations further support the design of an explicit improvement state with graph-governed evolution.
Chinese Translation
Agentic Harness是构建任务上下文并控制执行流程的运行时环境，从而塑造整体代理性能。在固定模型和外部评估的前提下，自动化Harness优化旨在通过迭代的提议-评估循环改进该运行时，以更好地解决目标任务。现有的元Harness方法主要采用以提议者为中心的发现方式，其中基于大型语言模型（LLM）的提议者整合累积的实验结果以确定后续的Harness修订。这使得随着历史的扩展及其底层实验逻辑变得难以辨识，维护不断演进的改进状态的负担落在提议者身上。本文提出了因果改进图（Causal Improvement Graph, CIG），一种由图结构管理的元Harness框架，将演进的改进状态外化于持久图中，使得先前发现能够通过局部提议者操作直接指导后续的Harness优化。CIG通过增长和连接证据（Evidence）、假设（Hypothesis）、干预（Intervention）和结果（Outcome）节点，来表示观察内容、可能的解释、验证该解释的方法以及评估结果。它们的结构关系保留了改进状态在迭代过程中的变化，使局部提议者能够直接基于先前发现之间的关系构建，而无需从原始历史中恢复。跨越多种代理任务，CIG发现的Harness优于以往的元Harness基线方法，并且对任务求解器和提议者的选择表现出鲁棒性。结构消融实验进一步支持了以图结构管理演进的显式改进状态设计。
cs.CL / 58 / 2610.05044
AraYoungVoices: A Diverse L1/L2 Corpus of Arabic Child and Adolescent Speech
AraYoungVoices：一个多样化的阿拉伯儿童和青少年语音L1/L2语料库
Abstract
State-of-the-art ASR systems primarily target native adult speech, leading to substantial performance gaps for children, adolescents, and L2 speakers. We introduce AraYoungVoices, a 151.72-hour Arabic read-speech corpus from 286 speakers aged 7--18, comprising AraKids (7--12) and AraTeens (13--18). The corpus includes 146 native Arabic (L1) and 140 second-language (L2) speakers, with native speakers spanning Egyptian, Gulf, Levantine, and North African dialectal backgrounds and L2 speakers representing diverse linguistic backgrounds across the Americas, Asia, Africa, and Europe. We benchmark four pretrained ASR models under zero-shot and fine-tuned settings using unseen-speaker-$\&$-unseen-prompt (USUP) and unseen-speaker-$\&$-seen-prompt (USSP) evaluations. Results show that L2 speech remains substantially more challenging than L1 speech, with the largest errors observed mainly for younger L2 speakers. Age-specific fine-tuning improves the matched age group, while joint fine-tuning provides a stronger balance across populations. ASR hypotheses are also consistently closer to the standard reading prompt than to the verbatim transcription, particularly for L2 speech, suggesting partial normalization of reading deviations.
Chinese Translation
当前最先进的自动语音识别（ASR）系统主要针对以母语为基础的成人语音，这导致儿童、青少年和第二语言（L2）说话者的性能差距显著。我们介绍了AraYoungVoices，这是一个包含286名年龄在7至18岁之间的说话者的151.72小时阿拉伯语朗读语音语料库，其中包括AraKids（7至12岁）和AraTeens（13至18岁）。该语料库包含146名母语阿拉伯语（L1）说话者和140名第二语言（L2）说话者，母语说话者涵盖埃及、海湾、黎凡特和北非方言背景，而L2说话者则代表来自美洲、亚洲、非洲和欧洲的多样语言背景。我们在零样本和微调设置下，对四个预训练的ASR模型进行了基准测试，使用了未见说话者-未见提示（USUP）和未见说话者-已见提示（USSP）评估。结果表明，L2语音的识别仍然显著比L1语音更具挑战性，最大的错误主要出现在年轻的L2说话者身上。针对特定年龄段的微调改善了匹配年龄组的表现，而联合微调则在不同人群之间提供了更强的平衡。ASR假设的结果也始终比逐字转录更接近标准朗读提示，特别是在L2语音中，这表明朗读偏差的部分规范化。
cs.CL / 59 / 2610.05069
Usage-Modulated Sentiment Representations in Large Language Models
大型语言模型中的使用调节情感表示
Abstract
Prior work suggests that sentiment can often be captured by approximately linear directions in LLM activation spaces, but a single direction may not fully capture sentiment representations. In natural communication, sentiment is shaped not only by polarity but also by usage factors, such as tone and audience adaptation. We test whether these factors systematically modulate sentiment representations beyond a shared sentiment direction. We construct a controlled paired dataset that holds event content fixed while varying sentiment polarity and usage factors, and analyze Llama, Mistral, and Gemma. We identify a shared sentiment direction, remove it, and test the residual structure through erasure and generation-time tone steering. Across models, the shared direction is robust (median cosine 0.953-0.975), yet removing it leaves 0.833-0.909 of the original positive-negative representation-difference norm. The residuals contain compact, reproducible usage-conditioned structure. Targeted erasure weakens held-out usage metrics more than random and label-shuffled controls. On Llama, outputs steered along residualized tone components are preferred in 92.8% of blind target-tone comparisons while preserving the requested sentiment polarity in 98.7% of evaluated outputs.
Chinese Translation
先前的研究表明，情感通常可以通过大型语言模型（LLM）激活空间中的近似线性方向来捕捉，但单一方向可能无法完全捕捉情感表示。在自然交流中，情感不仅受到极性的影响，还受到使用因素的影响，例如语气和受众适应性。我们测试这些因素是否系统性地调节情感表示，超越共享的情感方向。我们构建了一个控制配对数据集，固定事件内容，同时变化情感极性和使用因素，并分析了Llama、Mistral和Gemma。我们识别出一个共享的情感方向，将其移除，并通过消除和生成时语气引导测试残余结构。在各模型中，共享方向是稳健的（中位余弦相似度为0.953-0.975），然而移除后仍保留了0.833-0.909的原始正负表示差异范数。残余结构包含紧凑、可重复的使用条件结构。针对性消除对保留的使用指标的削弱程度超过随机和标签洗牌的对照。在Llama上，沿着残余语气成分引导的输出在92.8%的盲目标语气比较中被偏好，同时在98.7%的评估输出中保留了请求的情感极性。
cs.CL / 60 / 2610.05076
Self-Generated Feedback Destabilizes Test-Time Training: A Causal Decomposition of Long-Horizon Adaptation
自生成反馈不稳定测试时训练：长时间适应的因果分解
Abstract
Test-time training (TTT) lets a model store information in its weights during inference. When the model learns from its own output, however, each update also changes the model that generates the next training example. Across 128K-token streams, retaining generated-text updates worsens prediction on independent human-written text with three TTT-E2E model configurations (labeled 125M, 760M, and 3B). The same failure occurs when Adam updates Qwen3-4B's existing weights. The same update mechanisms can improve on real text, so writing itself is not the failure. Three matched comparisons trace the causal pathway. Fixed Generation removes over 98% of the damage at 125M and 760M by using a frozen model to generate training chunks. Recorded Replay separates the loss caused by reading degraded text from the additional loss stored by updating on it. A paired one-update comparison then shows the local conflict: an update predicts its source better but new real text worse. This cost grows after Closed Loop adaptation, with a few trajectories accounting for most large failures. Finally, Settlement evaluates the candidate state on independent real text before commitment. It leaves mean endpoint gaps of 0.07 and -0.02 nats at 125M and 760M while retaining real-text adaptation. These results motivate checking prediction on independent evidence before retaining an update.
Chinese Translation
测试时训练（TTT）允许模型在推理过程中将信息存储在其权重中。然而，当模型从自身输出中学习时，每次更新也会改变生成下一个训练样本的模型。在128K-token流中，保留生成文本的更新会在三种TTT-E2E模型配置（标记为125M、760M和3B）上恶化对独立人类书写文本的预测。当Adam更新Qwen3-4B的现有权重时，同样的失败也会发生。相同的更新机制可以在真实文本上取得改进，因此写作本身并不是失败的原因。三个匹配的比较追踪因果路径。固定生成（Fixed Generation）通过使用一个冻结模型生成训练块，消除了125M和760M上超过98%的损害。记录重放（Recorded Replay）将因读取退化文本而造成的损失与因在其上更新而存储的额外损失分开。配对的一次更新比较显示了局部冲突：一次更新更好地预测其来源，但对新的真实文本的预测更差。这个成本在闭环适应（Closed Loop adaptation）后增加，少数轨迹占据了大多数重大失败。最后，结算（Settlement）在承诺之前评估候选状态在独立真实文本上的表现。在125M和760M上，它留下了0.07和-0.02 nats的平均端点差距，同时保留了对真实文本的适应。这些结果促使我们在保留更新之前检查对独立证据的预测。
cs.CL / 61 / 2610.05084
Towards cross-cultural study of folksong lyrics with machine translation
基于机器翻译的民歌歌词跨文化研究探索
Abstract
Music is universally present in human societies. Ethnomusicologists have long been documenting the diverse expressions of human musicality, and comparative musicology has recently brought several studies of folksong to a more global scale. Such cross-cultural research has not been conducted on lyrics: the language barrier has so far prevented work with multi-lingual data. However, Natural Language Processing (NLP) technologies have reached a stage where this language barrier may no longer be prohibitive. Combining folksong lyrics corpora across five languages, we machine-translate them to a pivot language with a pre-trained neural topic model, and we examine the relationship between content and social function within each language, and across languages for wedding songs. As expected, human evaluation of translation results shows that non-Indo-European languages suffer from overall worse translation quality. Experiments with topic models then indicate that the content of lyrics is at best partially related to the social function of folksongs across all languages. These experiments are just first steps into cross-cultural folk musics lyrics analysis; however, they do indicate that a previously unobserved web of cross-cultural relationships beyond ethnomusicological typologies may be uncovered through the study of what people sing across the world's diverse folk musics.
Chinese Translation
音乐在所有人类社会中普遍存在。民族音乐学家长期以来一直在记录人类音乐性的多样表达，而比较音乐学最近将几项民歌研究提升到了更全球化的层面。然而，关于歌词的跨文化研究尚未开展：语言障碍迄今为止阻碍了多语言数据的研究。然而，自然语言处理（Natural Language Processing, NLP）技术已达到一个阶段，这一语言障碍可能不再是障碍。我们将五种语言的民歌歌词语料库结合起来，通过预训练的神经主题模型将其机器翻译为一种中介语言，并考察每种语言中内容与社会功能之间的关系，以及在婚礼歌曲中跨语言的关系。正如预期的那样，人类对翻译结果的评估显示，非印欧语言的整体翻译质量较差。主题模型的实验表明，歌词内容与所有语言的民歌社会功能之间的关系充其量是部分相关。这些实验只是跨文化民歌歌词分析的第一步；然而，它们确实表明，通过研究人们在世界多样的民歌中所唱的内容，可能会揭示出超越民族音乐学类型学的跨文化关系网络。
cs.CL / 62 / 2610.05094
How Much Do LLM-as-a-Judge Design Choices Matter? A Systematic Comparison of Prompt Designs, Rating Scales, and Models
LLM作为评判者的设计选择有多重要？对提示设计、评分标准和模型的系统比较
Abstract
Researchers increasingly use Large Language Models as judges (LLM-as-a-judge) to evaluate model outputs. Yet there are no standards for how to design these judges. Typically, researchers choose the prompt, rating scale, and model intuitively. If these choices change the judge's verdicts, two studies can reach different conclusions about the same facts. To address this risk and to provide an empirical basis for judge designs, we evaluate 10 reasoning models across multiple designs on two tasks: a scalar rating of sentence sentiment and toxicity (over 500 items per category), as well as a binary accuracy classification of question-answer pairs (n=600). For the rating tasks, despite judges showing significant disagreements with the human ground truth, the practical size of differences is small enough to consider most judges reliable (mean absolute deviation of 0.11 points on a 1 - 7 scale); toxicity judges even outperform standard classifiers. Judges are also highly accurate on average (96.5%) for the accuracy classification task. However, design choices can produce shifts: changing the rating scale alone can shift measured bias by up to 0.93 points (rating task), and while accuracy levels are rarely impacted, design choices consistently impact judge leniency (classification task; leniency drop of 28.9 percentage points when using detailed prompts, and up to 56.1 percentage points when switching models). Counterintuitively, lower reasoning effort affects neither accuracy nor leniency. Across both tasks, model identity is the dominant source of variance. These findings suggest that while LLM judges are broadly trustworthy in aggregate, design choices can be meaningful sources of variance. Given the growing reliance on automated evaluation in LLM research, we intend this study as a methodological reference for designing more robust and replicable LLM-as-a-judge pipelines.
Chinese Translation
研究人员越来越多地使用大型语言模型作为评判者（LLM-as-a-judge）来评估模型输出。然而，目前尚无关于如何设计这些评判者的标准。通常，研究人员直观地选择提示、评分标准和模型。如果这些选择改变了评判者的裁决，两项研究可能会对相同的事实得出不同的结论。为了解决这一风险并为评判者设计提供实证基础，我们在两个任务上评估了10种推理模型的多种设计：对句子情感和毒性进行标量评分（每个类别超过500个项目），以及对问答对进行二元准确性分类（n=600）。在评分任务中，尽管评判者与人类真实情况之间存在显著分歧，但差异的实际大小足够小，可以认为大多数评判者是可靠的（在1-7评分尺度上的平均绝对偏差为0.11分）；毒性评判者甚至优于标准分类器。评判者在准确性分类任务上的平均准确率也很高（96.5%）。然而，设计选择可能会导致偏差的变化：单独改变评分标准可以使测得的偏差变化高达0.93分（评分任务），尽管准确性水平很少受到影响，但设计选择始终会影响评判者的宽容度（分类任务；使用详细提示时宽容度下降28.9个百分点，切换模型时下降高达56.1个百分点）。反直觉的是，较低的推理努力对准确性和宽容度没有影响。在两个任务中，模型身份是方差的主要来源。这些发现表明，尽管LLM评判者在总体上是可信的，但设计选择可能是重要的方差来源。鉴于在LLM研究中对自动评估的日益依赖，我们希望本研究能作为设计更稳健和可复制的LLM作为评判者管道的方法论参考。
cs.CL / 63 / 2610.05095
vMF Sentence LDA: A Spherical Topic Model over Sentence Embeddings
vMF句子LDA：基于句子嵌入的球面主题模型
Abstract
Latent Dirichlet Allocation (LDA) and models derived from it remain widely used topic models. LDA observes each document as a bag-of-words and models each topic by a categorical distribution over the vocabulary, so that it uses neither the internal structure of the document nor the similarity in meaning between words. Earlier work has responded to this limitation in two ways: many models have introduced embeddings, and some have assigned topics to sentences rather than to words. Their combination, a topic model that observes sentence embeddings, remains little explored. We propose vMF Sentence LDA (vSLDA), which keeps the admixture structure of LDA, observes each sentence as its L2-normalized embedding and models each topic by a von Mises-Fisher (vMF) distribution, which matches the cosine geometry of sentence embeddings. Its per-topic parameter count and per-iteration cost are linear in the embedding dimension, versus quadratic for the full-covariance Gaussian distribution in the existing model over sentence embeddings. We evaluate vSLDA where the limitation is expected to matter most, among topics that share much of their vocabulary: the topics that subdivide the one subject of a collection, and the narrow topics that result when a corpus is divided into a large number of topics. On two corpora, vSLDA attains the best mean rank against eight baselines when the fine categories within each coarse category are classified from the document-topic distributions. On the whole corpus, its advantage appears or widens as the number of topics grows. Weighting the word frequencies of each sentence by its topic posterior yields expected topic-word counts of the same form as those of LDA, so that the standard topic coherence and diversity measures apply to models that assign topics to sentences. On their product, topic quality, vSLDA leads in most within-category conditions of both corpora.
Chinese Translation
潜在狄利克雷分配（LDA）及其衍生模型仍然是广泛使用的主题模型。LDA将每个文档视为一个词袋，并通过对词汇的分类分布来建模每个主题，因此它既不利用文档的内部结构，也不考虑词语之间的意义相似性。早期的研究通过两种方式回应了这一局限性：许多模型引入了嵌入，而一些模型则将主题分配给句子而非词语。它们的结合，即观察句子嵌入的主题模型，仍然鲜有探索。我们提出了vMF句子LDA（vSLDA），该模型保持了LDA的混合结构，将每个句子视为其L2归一化的嵌入，并通过冯·米塞斯-费舍尔（von Mises-Fisher，vMF）分布来建模每个主题，这与句子嵌入的余弦几何相匹配。其每个主题的参数数量和每次迭代的成本在嵌入维度上是线性的，而现有基于句子嵌入的模型中的全协方差高斯分布则是二次的。我们在预期影响最大的地方评估vSLDA，即在共享大量词汇的主题之间：划分一个集合的单一主题的主题，以及当语料库被划分为大量主题时产生的狭窄主题。在两个语料库上，当从文档-主题分布中对每个粗类别内的细分类进行分类时，vSLDA在八个基线模型中获得了最佳的平均排名。在整个语料库中，随着主题数量的增加，其优势显现或扩大。通过主题后验加权每个句子的词频，得到的主题-词计数与LDA的形式相同，因此标准的主题一致性和多样性度量适用于将主题分配给句子的模型。在其产品——主题质量方面，vSLDA在两个语料库的绝大多数类别内条件下表现领先。
cs.CL / 64 / 2610.05099
Small Agents with Semantic Search: Efficient Multilingual Code Localization
具有语义搜索的小型代理：高效的多语言代码定位
Abstract
Locating relevant files from natural-language requests is a core subtask for agents operating over code repositories. We investigate whether this task can be delegated to compact, specialized models to enable on-device search while reducing the token usage, latency, and inference cost of larger agents. We show that semantic search improves file localization, with gains in accuracy, cross-language transfer, and inference efficiency. To study this setting, we introduce a training framework for file-localization agents built around ColGREP, a local semantic search tool based on late-interaction retrieval models. Our recipe combines weighted supervised fine-tuning on teacher trajectories, assigning turn-level credit based on retrieval outcomes, with reinforcement learning on localization quality. We train three model families with fewer than two billion parameters to formulate search queries, inspect retrieved content, and identify relevant files. On localization tasks derived from SWE-bench Lite and Multi-SWE-bench Flash, ColGREP-equipped agents substantially improve over their base models and outperform corresponding GREP-based agents. In addition to improving localization accuracy, ColGREP reduces mean end-to-end trajectory latency by 44.1\% on CPU while using 29.1\% fewer tokens, and enables better generalization to programming languages unseen during fine-tuning. These results suggest that compact, tool-specialized localization agents can provide an efficient interface between natural-language requests and large codebases.
Chinese Translation
从自然语言请求中定位相关文件是操作代码库的代理的核心子任务。我们研究了是否可以将这一任务委托给紧凑的专用模型，以实现设备内搜索，同时减少大型代理的令牌使用、延迟和推理成本。我们表明，语义搜索改善了文件定位，带来了准确性、跨语言迁移和推理效率的提升。为研究这一设置，我们引入了一个围绕 ColGREP 构建的文件定位代理的训练框架，ColGREP 是一个基于晚期交互检索模型的本地语义搜索工具。我们的方案结合了在教师轨迹上加权的监督微调，根据检索结果分配回合级别的信用，以及对定位质量的强化学习。我们训练了三种参数少于二十亿的模型系列，以形成搜索查询、检查检索内容并识别相关文件。在从 SWE-bench Lite 和 Multi-SWE-bench Flash 派生的定位任务中，配备 ColGREP 的代理在其基础模型上有了显著提升，并且超越了相应的基于 GREP 的代理。除了提高定位准确性外，ColGREP 在 CPU 上将平均端到端轨迹延迟减少了 44.1\%，同时使用了 29.1\% 更少的令牌，并且在微调过程中未见过的编程语言上实现了更好的泛化。这些结果表明，紧凑的工具专用定位代理可以在自然语言请求和大型代码库之间提供高效的接口。
cs.CL / 65 / 2610.05111
InstMoE: Adaptive Multimodal Routing with Specialized Experts
InstMoE：具有专业专家的自适应多模态路由
Abstract
Multimodal inputs are inherently heterogeneous, not only across modalities but also in the information pathways required for effective prediction. To address this limitation, we propose InstMoE, an adaptive expert routing framework for multimodal learning. InstMoE dynamically routes each input to specialized unimodal and cross-modal experts, allowing the model to adapt its information pathways to the characteristics of the input. However, routing can be misled when modality-specific variations obscure task-relevant semantics. Such irrelevant variations may distort routing decisions, causing inputs to be assigned to inappropriate experts. We therefore introduce a Contrastive Semantic Alignment module, which encourages semantically similar inputs to share task-relevant representations while suppressing irrelevant modality-specific variations. Experiments on multimodal sentiment analysis benchmarks demonstrate that InstMoE achieves state-of-the-art performance on CMU-MOSEI and CH-SIMS v2 while using substantially fewer parameters than competitive baselines. Further analysis shows that different inputs exhibit distinct expert preferences, demonstrating that InstMoE moves beyond fixed fusion toward adaptive multimodal computation.
Chinese Translation
多模态输入本质上是异构的，不仅在模态之间存在差异，而且在有效预测所需的信息路径上也有所不同。为了解决这一局限性，我们提出了InstMoE，一个用于多模态学习的自适应专家路由框架。InstMoE动态地将每个输入路由到专业的单模态和跨模态专家，使模型能够根据输入的特征调整其信息路径。然而，当模态特定的变异掩盖了与任务相关的语义时，路由可能会受到误导。这些无关的变异可能会扭曲路由决策，导致输入被分配给不适当的专家。因此，我们引入了对比语义对齐模块，鼓励语义相似的输入共享与任务相关的表示，同时抑制无关的模态特定变异。在多模态情感分析基准上的实验表明，InstMoE在CMU-MOSEI和CH-SIMS v2上实现了最先进的性能，同时使用的参数显著少于竞争基线。进一步的分析表明，不同的输入表现出不同的专家偏好，证明InstMoE超越了固定融合，朝着自适应多模态计算发展。
cs.CL / 66 / 2610.05114
Belief-Trajectory Energy: Measuring the Path to a Prediction
信念轨迹能量：测量预测路径
Abstract
Large language models (LLMs) progressively revise their predictions across Transformer layers, yet we typically observe only the final output, discarding the trajectory through which it is formed. We introduce Belief-Trajectory Energy(BTE), a model-grounded measure that characterizes an input through the layerwise predictive revisions it induces in a model. By mapping intermediate states into a shared predictive space, BTE provides a principled measure of belief change that can be summarized as either a scalar or a structured depth profile. Theoretically, we show that local BTE corresponds to predictive revision under the Fisher-Rao geometry, while the sequence of revisions captures information beyond the initial-to-final belief change. Empirically, scalar BTE provides a model-relative signal of difficulty across diverse reasoning tasks, while richer BTE representations support human-LLM review detection and fine-grained generator attribution, reaching up to $0.998$ macro-AUROC and $95.6\%$ eight-way attribution accuracy. Further analysis shows that BTE develops throughout pretraining and is selectively reshaped by targeted training, demonstrating that the resulting measurement reflects what the scoring model has learned. Together, our results establish belief trajectories as a principled model-grounded signal and suggest a broader perspective in which learned models can themselves serve as instruments for characterizing the data they process. More demonstrations can be found at https://yingjiahao14.github.io/BTE-web/.
Chinese Translation
大型语言模型（LLMs）在Transformer层中逐步修正其预测，然而我们通常只观察最终输出，忽略了形成该输出的轨迹。我们引入了信念轨迹能量（Belief-Trajectory Energy, BTE），这是一种基于模型的度量，表征输入通过模型所引起的逐层预测修正。通过将中间状态映射到共享的预测空间，BTE提供了一种原则性的信念变化度量，可以总结为标量或结构化的深度剖面。从理论上讲，我们证明了局部BTE对应于Fisher-Rao几何下的预测修正，而修正序列捕获了超出初始到最终信念变化的信息。在实证上，标量BTE为不同推理任务提供了相对模型的难度信号，而更丰富的BTE表示支持人类与LLM的审查检测和细粒度生成器归因，达到了$0.998$的宏观AUROC和$95.6\%$的八类归因准确率。进一步分析表明，BTE在预训练过程中逐步发展，并通过有针对性的训练进行选择性重塑，证明了所得到的测量反映了评分模型所学到的内容。总的来说，我们的结果确立了信念轨迹作为一种原则性的基于模型的信号，并建议了一种更广泛的视角，即学习到的模型本身可以作为表征其处理数据的工具。更多演示可以在https://yingjiahao14.github.io/BTE-web/找到。
cs.CL / 67 / 2610.05126
Selecting Repetition Counts Across Model Scales in Data-Constrained Pretraining
在数据受限的预训练中选择模型规模的重复计数
Abstract
The repetition count that works best for a small language model may not remain best at a larger scale. We study this effect in pretraining with a finite target corpus mixed with generic data at a fixed target fraction. On Wikipedia-derived data and Proof-Pile-2, the ranking of measured repetition counts changes with model size, and a 520M Proof-Pile-2 experiment confirms that reducing repetition from sixteen to eight improves loss while using fewer training tokens. We use loss curves from several smaller models to retain a short list of promising repetition counts for evaluation at a larger scale. On PubMed and Caselaw, candidate sets fixed before target-model training retain the lowest-loss measured count on the original evaluation grids at both 200M and 520M. This supports candidate retention as a practical alternative to exact point prediction. We also relate the pruning regression to an empirical scaling model with two opposing repetition-dependent loss terms. A first-order expansion in log model size yields the linear form used by the selection rule, providing a scaling-based interpretation of the candidate-selection procedure.
Chinese Translation
对于小型语言模型而言，最佳的重复计数在更大规模下可能并不适用。我们研究了在固定目标比例下，使用有限目标语料库与通用数据混合进行预训练时的这一效应。在源自维基百科的数据和 Proof-Pile-2 上，测量的重复计数的排名随着模型规模的变化而变化，一项520M的Proof-Pile-2实验确认，将重复计数从十六减少到八可以在使用更少训练标记的情况下改善损失。我们利用几个小型模型的损失曲线，保留了一份有前景的重复计数短名单，以便在更大规模下进行评估。在PubMed和Caselaw上，在目标模型训练之前固定的候选集在200M和520M的原始评估网格上保持了最低损失的测量计数。这支持了候选保留作为精确点预测的实用替代方案。我们还将修剪回归与一个具有两个相对重复依赖损失项的经验缩放模型相关联。对模型规模的对数进行一阶展开得到选择规则所用的线性形式，为候选选择过程提供了基于缩放的解释。
cs.CL / 68 / 2610.05170
Verification Trap: Understanding Test-Time Selection Failures under False Premises in Code Generation
验证陷阱：在代码生成中理解错误前提下的测试时间选择失败
Abstract
Test-time compute has become a central way to improve code generation: systems sample multiple candidate programs and use verifier-visible evidence to select the final output. This paradigm implicitly assumes that the verifier provides a corrective signal independent from the generator. We challenge this assumption under misleading task premises. When the generator and verifier share a false premise, they become coupled through a mistaken belief: the generator produces premise-consistent shortcuts, while the verifier supplies evidence that fails to expose them. Consequently, the selector may choose a hidden-test-wrong candidate even when a hidden-test-correct program exists in the pool. We call this failure mode Verification Trap. Across three code-generation benchmarks and five code models, false premises consistently degrade first-sample correctness, reduce selector-chosen correctness after 64-sample test-time selection, and amplify recoverable mis-selection. Mechanistically, verifier-written tests inherit the premise-level blind spot, reshaping verifier-visible candidate space away from hidden-test correctness. These traces make Verification Trap predictable before hidden execution: a lightweight gold-free predictor using verifier-visible features reaches 0.846 AUROC. Our results identify decoupled evidence as a key mitigation axis: coupled scaling provides limited recovery, whereas premise-agnostic robustness auditors recover substantial oracle headroom.
Chinese Translation
测试时间计算已成为提高代码生成的核心方式：系统采样多个候选程序，并使用可验证者可见的证据来选择最终输出。这一范式隐含地假设验证者提供与生成器独立的纠正信号。我们在误导性任务前提下挑战这一假设。当生成器和验证者共享一个错误前提时，它们通过一种错误信念相互耦合：生成器产生与前提一致的捷径，而验证者提供的证据未能揭示这些捷径。因此，即使在候选池中存在隐藏测试正确的程序，选择器也可能选择一个隐藏测试错误的候选程序。我们将这种失败模式称为验证陷阱。在三个代码生成基准和五个代码模型中，错误前提持续降低首次采样的正确性，在64次样本测试时间选择后减少选择器选择的正确性，并放大可恢复的错误选择。从机制上讲，验证者编写的测试继承了前提层面的盲点，重新塑造了可验证者可见的候选空间，使其偏离隐藏测试的正确性。这些痕迹使得在隐藏执行之前预测验证陷阱成为可能：使用可验证者可见特征的轻量级无金标准预测器达到0.846 AUROC。我们的结果确定了去耦证据作为关键的缓解轴：耦合缩放提供有限的恢复，而与前提无关的鲁棒性审计员则恢复了可观的预言者余量。
cs.CL / 69 / 2610.05190
FORGE: Verification-Gated Behavioral Repair for Generative Language Models
FORGE：用于生成语言模型的验证门控行为修复
Abstract
Generative large language models (LLMs) inherit undesirable behaviors from pre-training, including demographic bias and toxic generation, that often emerge only after deployment and affect a small subset of inputs. A repair should eliminate the identified defect, preserve the model's overall functionality and, ideally, provide correctness guarantees. Existing approaches address this only partially: gradient-based fine-tuning lacks per-instance guarantees and becomes unstable with few defect samples; model editing assumes explicit knowledge replacement rather than behavioral correction; and constraint-based repair is largely restricted to discriminative models with unique target outputs. We present FORGE, a framework for targeted behavioral repair of generative language models that separates defect localization, weight editing, and behavioral verification into independent stages. Its core is a repair abstraction that converts localized defective generation into explicit optimization objectives, enabling verification-oriented repair techniques to operate on autoregressive generation. FORGE is editing-mechanism agnostic: we instantiate it with (1) a constraint-based quadratic optimization method that provides per-sample repair certificates and (2) a null-space projection editor that minimizes interference with the original model distribution, both under the same localization and verification protocol. On five open-source LLMs, FORGE consistently achieves larger reductions in bias and toxicity than gradient-based fine-tuning with minor perplexity degradation. The two backends exhibit complementary performance across architectures, which a lightweight causal probe traces to where toxicity-related signals concentrate. FORGE also remains effective with only a handful of defective examples, where conventional fine-tuning often oscillates or fails to converge.
Chinese Translation
生成的大型语言模型（LLMs）在预训练中继承了不良行为，包括人口统计偏见和有害生成，这些行为通常在部署后才会显现，并影响少量输入。修复应消除识别出的缺陷，保持模型的整体功能，并理想地提供正确性保证。现有方法对此仅部分解决：基于梯度的微调缺乏逐实例的保证，并且在缺陷样本较少时变得不稳定；模型编辑假设显式的知识替换而非行为修正；而基于约束的修复在很大程度上仅限于具有唯一目标输出的判别模型。我们提出了FORGE，一个针对生成语言模型的定向行为修复框架，将缺陷定位、权重编辑和行为验证分为独立的阶段。其核心是一个修复抽象，将局部缺陷生成转化为显式优化目标，使得面向验证的修复技术能够在自回归生成上运行。FORGE对编辑机制是不可知的：我们用（1）一种基于约束的二次优化方法实例化它，该方法提供逐样本修复证书，以及（2）一种最小化对原始模型分布干扰的零空间投影编辑器，二者均在相同的定位和验证协议下运行。在五个开源LLM上，FORGE始终实现了比基于梯度的微调更大的偏见和有害性降低，同时仅有轻微的困惑度下降。这两个后端在不同架构中表现出互补的性能，轻量级因果探测器追踪到有害信号集中所在的位置。FORGE在仅有少量缺陷示例的情况下仍然有效，而传统微调往往会振荡或无法收敛。
cs.CL / 70 / 2610.05275
Inductive Claims Extraction at Scale
大规模归纳性主张提取
Abstract
A large part of political discourse on social media is built and expressed at a level of claims: i.e. declarative, typically single-clause statements, which convey a particular interpretation of reality and can range from factual to evaluative. Moreover, rather than occurring randomly, claims coalesce, recur in patterns, and come to be associated with different world views. When paired with structural computational tools such as Social Network Analysis, claims can be a powerful unit of analysis to study political phenomena such as echo chambers or polarisation. In this paper, we present a pipeline that uses a large language model (LLM) to inductively extract and catalogue claims from large social media corpora, and apply it to two different Twitter datasets: one relating to the 2020 US presidential election and the other to the 2022 FIFA World Cup. We comprehensively evaluate the approach by measuring the pipeline's recall and precision against manually annotated samples, run ablation studies isolating the contribution of its various components, and perform a qualitative error analysis. We discuss the value of the approach in the context of Computational Social Science research, and illustrate its capabilities by presenting the claims catalogue obtained from each dataset.
Chinese Translation
社交媒体上的政治话语很大一部分是以主张的形式构建和表达的：即陈述性、通常为单句的声明，传达对现实的特定解释，范围从事实到评估。此外，主张并非随机出现，而是聚集、以模式重复，并与不同的世界观相关联。当与社会网络分析（Social Network Analysis）等结构性计算工具结合时，主张可以成为研究政治现象（如回音室或极化）的强大分析单元。在本文中，我们提出了一种管道，利用大型语言模型（LLM）从大型社交媒体语料库中归纳提取和编目主张，并将其应用于两个不同的Twitter数据集：一个与2020年美国总统选举相关，另一个与2022年国际足联世界杯相关。我们通过测量管道在手动标注样本上的召回率和精确率，全面评估该方法，进行消融研究以隔离其各个组件的贡献，并进行定性错误分析。我们讨论了该方法在计算社会科学研究中的价值，并通过展示从每个数据集中获得的主张目录来说明其能力。
cs.CL / 71 / 2610.05278
When Verifiable Counts Depend on Wording: Auditing Wording Robustness in Instruction Following
当可验证的计数依赖于措辞：指令遵循中的措辞稳健性审计
Abstract
Verifiable instruction-following benchmarks often express each constraint through one fixed template. We test whether scores remain stable when the operational requirement is unchanged but its wording varies. We introduce WISE, a matched evaluation suite and reporting protocol instantiated on exact word count, keyword inclusion exactly once, and an inclusive 8--12 word range. Across 100 matched tasks, up to thirteen models from seven providers, and repeated generations scored over the complete visible output, wording alone produces substantial compliance shifts. In an avoidance-family panel, five avoidance and exclusion forms fall below the positive baseline, while constructional controls also shift compliance substantially: in the nine-model control panel, compliance is 54.9% for the original positive form, 48.2% for a longer positive form, 36.7% when the target appears later, and 33.8% for AVOID1. A strict JSON-structure probe shows wording sensitivity beyond counting, with a different direction of effect. Effect sizes, failure directions, weakest forms, and model rankings vary across realizations. Under the most disruptive exclusion form, the top-ranked model changes and 24.1% of strictly ordered model pairs reverse. Human validation further shows that unanimous agreement on an exact-count interpretation can coexist with substantially different model behavior. WISE supplements conventional scores with mean and worst-form compliance, wording gaps, failure profiles, and ranking stability.
Chinese Translation
可验证的指令遵循基准通常通过一个固定模板表达每个约束。我们测试在操作要求不变但措辞变化的情况下，得分是否保持稳定。我们引入了WISE，这是一个基于精确字数、关键字单次包含以及包含8到12个单词范围的匹配评估套件和报告协议。在100个匹配任务中，来自七个提供者的多达十三个模型，以及对完整可见输出的重复生成得分，仅凭措辞就产生了显著的合规性变化。在一个避免家庭面板中，五种避免和排除形式的得分低于正面基线，而构造控制也显著改变了合规性：在九模型控制面板中，原始正面形式的合规性为54.9%，较长的正面形式为48.2%，目标稍后出现时为36.7%，而对于AVOID1则为33.8%。一个严格的JSON结构探针显示出超越计数的措辞敏感性，且效果方向不同。效应大小、失败方向、最弱形式和模型排名在不同实现中有所变化。在最具破坏性的排除形式下，排名最高的模型发生变化，24.1%的严格排序模型对调换。人类验证进一步表明，对精确计数解释的一致同意可以与模型行为的显著差异共存。WISE通过平均和最差形式合规性、措辞差距、失败特征和排名稳定性补充了传统得分。
cs.CL / 72 / 2610.05282
Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models
Red-TTT：针对大型语言模型的自动越狱的测试时训练
Abstract
Large language models remain vulnerable to jailbreaks, and automated red teaming is the standard way to find jailbreaks in large language models at scale. Current methods either draw more samples at test time through search, rewriting, and tree expansion, or train a stronger attacker offline with reinforcement learning. Both share a limitation: once an attack on a specific target behavior begins, the attacker's weights are frozen. Any signal it gathers about the behavior stays in its context window and is discarded afterward. The attacker never adapts its proposal distribution mid-attack, so success depends almost entirely on the sampling budget, and under a budget affordable at scale, many behaviors remain unbroken. We propose Red-TTT, which updates the attacker's parameters during the attack on each behavior. At each round, the attacker samples a group of candidates, scores them against the victim's replies, and takes a policy-gradient step before drawing the next group, so what it discovers about the current victim is consolidated into weights rather than accumulated as context. We also adapt the training objective to red teaming, where success is judged by the single best sample rather than the average. Red-TTT requires only sampling access to the victim and integrates into existing attack pipelines with no other changes. Against the Best-of-N baseline, Red-TTT raises attack success rate from 55.9\% to 72.4\% on average at a budget of 120 samples, improving over the baseline in every configuration and cracking many behaviors previous method cannot. The code is available at https://github.com/SaFo-Lab/Red-TTT
Chinese Translation
大型语言模型仍然容易受到越狱攻击，而自动红队测试是大规模发现大型语言模型越狱攻击的标准方法。目前的方法要么通过搜索、重写和树扩展在测试时生成更多样本，要么通过强化学习离线训练更强的攻击者。这两者都有一个局限性：一旦对特定目标行为的攻击开始，攻击者的权重就被冻结。它收集到的关于该行为的任何信号都保留在其上下文窗口中，之后会被丢弃。攻击者在攻击过程中从未调整其提案分布，因此成功几乎完全依赖于采样预算，而在可承受的大规模预算下，许多行为仍然无法被攻破。我们提出了Red-TTT，它在对每个行为进行攻击时更新攻击者的参数。在每一轮中，攻击者采样一组候选者，根据受害者的回复对其进行评分，并在绘制下一组之前进行一次策略梯度更新，因此它对当前受害者的发现被巩固为权重，而不是作为上下文积累。我们还将训练目标调整为红队测试，其中成功是通过单个最佳样本而不是平均值来判断的。Red-TTT只需对受害者进行采样访问，并且可以无其他更改地集成到现有攻击管道中。与Best-of-N基线相比，Red-TTT在120个样本的预算下将攻击成功率从55.9\%提高到72.4\%，在每种配置下均优于基线，并攻破了许多以前方法无法攻破的行为。代码可在 https://github.com/SaFo-Lab/Red-TTT 获取。
cs.CL / 73 / 2610.05308
RubricArmor: Adversarial Evolution Improves LLM-Based Rubric Generation
RubricArmor：对抗性演化提升基于大语言模型的评分标准生成
Abstract
Rubric-based reinforcement learning (RL) provides interpretable rewards for aligning large language models (LLMs) by evaluating responses against query-specific evaluation criteria. To construct rubrics at scale, a straightforward approach to LLM-based rubric generation is to prompt an LLM to generate a rubric directly from the query. However, rubrics directly generated by LLMs are vulnerable to reward hacking, since omitted or underspecified criteria allow the policy to obtain high rubric rewards with low-quality responses. Existing LLM-based rubric generation methods improve the granularity and coverage of the generated criteria but do not proactively guard against reward hacking. To address this limitation, we propose RubricArmor, an adversarial framework that exposes and mitigates potential reward hacking at the rubric generation stage before it occurs in subsequent RL. Specifically, RubricArmor performs adversarial evolution, in which an attack step and a repair step alternate over multiple rounds. The attack step simulates the reward hacking of the policy by constructing adversarial responses that satisfy the current rubric but fail to properly complete the task. The repair step then revises the rubric to detect the response defects exposed by the attack step while preserving other valid criteria. Extensive experiments demonstrate that RubricArmor outperforms competitive rubric generation baselines and translates into more effective downstream rubric-based RL.
Chinese Translation
基于评分标准的强化学习（RL）通过根据特定查询的评估标准评估响应，为对齐大语言模型（LLMs）提供可解释的奖励。为了大规模构建评分标准，一种简单的方法是直接提示LLM从查询生成评分标准。然而，LLM直接生成的评分标准容易受到奖励黑客攻击，因为省略或不明确的标准使得策略能够以低质量的响应获得高评分奖励。现有的基于LLM的评分标准生成方法提高了生成标准的粒度和覆盖范围，但未能主动防范奖励黑客攻击。为了解决这一局限性，我们提出了RubricArmor，这是一种对抗性框架，在评分标准生成阶段揭示并减轻潜在的奖励黑客攻击，防止其在后续的RL中发生。具体而言，RubricArmor执行对抗性演化，其中攻击步骤和修复步骤在多个回合中交替进行。攻击步骤通过构造满足当前评分标准但未能正确完成任务的对抗性响应来模拟策略的奖励黑客行为。然后，修复步骤修订评分标准，以检测攻击步骤暴露的响应缺陷，同时保留其他有效标准。大量实验表明，RubricArmor在竞争性的评分标准生成基准上表现优越，并转化为更有效的下游基于评分标准的RL。
cs.CL / 74 / 2610.05343
MemStrata: 95% and 90.91% Source-Aware Accuracy on LongMemEval-500 and LoCoMo-1540 with a Local Qwen 3.8 27B Q4_K_M Reader
MemStrata：在 LongMemEval-500 和 LoCoMo-1540 上实现 95% 和 90.91% 的源感知准确率，使用本地 Qwen 3.8 27B Q4_K_M 阅读器
Abstract
An adequate conversational answer may differ from a short or incomplete benchmark reference. To measure adequacy against the recorded history we prefer source-aware grading, in which the judge checks the reference against the full source before assessing system-blinded answers; original reference-only grading is reported alongside. With a local Qwen 3.8 27B Q4_K_M reader and a 24,000-token evidence ceiling, MemStrata CL1 scores 475/500 (95.0%) on LongMemEval-S and 1,400/1,540 (90.91%) on LoCoMo categories 1-4 under source-aware GPT-5.5 adjudication, against 463/500 (92.6%) and 1,205/1,540 (78.25%) under reference-only grading of the same answers. It preserves a retrieval backbone and adds nonduplicated, dated, speaker-attributed source spans. A same-reader full-history control with about 4.7 times the evidence scores 464/500 reference-only and 470/500 (94.0%) source-aware; neither difference is decisive. Keyword-only selection at the same budget scores 425, and a matched-reader Letta arm 438. On LongMemEval-M, where the packet holds about 1.6% of each history, MemStrata CL1 scores 427/500, with losses concentrated in multi-session and temporal questions. On 300 BEAM-1M questions it outscores dense retrieval, 0.738 to 0.706 (Wilcoxon p = 0.011). A same-seed replay of unchanged requests changed 1.5-2.3% of labels. On identical packets GLM 5.3 flash is non-inferior within 3 points (462 versus 463); Muse Spark 1.3 did not show non-inferiority on 269 questions. None of four pre-registered interventions met all of its registered advancement or feasibility criteria. Signed read-side artifacts support inspection but do not regenerate the private retrieval pipeline. The superiority of source-aware grading to human adjudication is not established, and development exposure, automated-judge dependence and the absence of held-out data preclude an independent-replication or leaderboard claim.
Chinese Translation
一个充分的对话答案可能与短小或不完整的基准参考有所不同。为了衡量与记录历史的充分性，我们更倾向于源感知评分，其中评审者在评估系统盲测答案之前，先将参考与完整源进行核对；原始的仅参考评分也会一并报告。使用本地 Qwen 3.8 27B Q4_K_M 阅读器和 24,000 令牌的证据上限，MemStrata CL1 在源感知的 GPT-5.5 裁定下，在 LongMemEval-S 上得分 475/500（95.0%），在 LoCoMo 1-4 类别上得分 1,400/1,540（90.91%），而在相同答案的仅参考评分下则为 463/500（92.6%）和 1,205/1,540（78.25%）。它保留了检索主干，并增加了非重复、带日期和发言者归属的源跨度。使用相同阅读器的完整历史控制，证据量约为 4.7 倍，得分为 464/500（仅参考）和 470/500（94.0%，源感知）；两者之间的差异并不显著。在相同预算下，仅关键词选择得分为 425，而匹配阅读器的 Letta 方案得分为 438。在 LongMemEval-M 中，数据包包含每个历史的约 1.6%，MemStrata CL1 得分为 427/500，损失主要集中在多会话和时间性问题上。在 300 个 BEAM-1M 问题中，其得分优于密集检索，得分为 0.738 对 0.706（Wilcoxon p = 0.011）。对未更改请求的相同种子重放改变了 1.5-2.3% 的标签。在相同数据包下，GLM 5.3 的闪存得分在 3 分之内不劣于 462 对 463；Muse Spark 1.3 在 269 个问题上未显示出非劣性。四个预注册干预措施中没有一个满足其所有注册的进展或可行性标准。签名读取侧的工件支持检查，但不再生私人检索管道。源感知评分优于人工裁定的优越性尚未确立，开发暴露、自动裁判依赖和缺乏保留数据阻碍了独立复制或排行榜声明。
cs.CL / 75 / 2610.05366
The \`{I}r\`{o}y\`{i}nSpeech Text Corpus: 24,905 Curated Yor\`ub\'a Sentences for Speech and Language Technology
IroyinSpeech文本语料库：24,905个经过整理的约鲁巴语句子用于语音和语言技术
Abstract
\`{I}r\`{o}y\`{i}nSpeech is a 42-hour, 80-speaker Yor\`ub\'a read-speech corpus whose audio has been distributed by ELRA since 2024. This paper describes the release of its text component: 24,905 unique, hand-verified, tone-marked Yor\`ub\'a sentences (275,897 tokens; 15,687 types), curated in 2022 as recording prompts. Roughly 11,000 sentences were adapted from openly licensed news material; the remainder were written in-house to broaden coverage beyond the religious translation that dominates existing Yor\`ub\'a corpora. Every sentence was checked by hand for tone-mark accuracy, edited for read-aloud clarity and a neutral register, and localised so that non-Yor\`ub\'a personal and place names appear in Yor\`ub\'a form. Preparing the text for release surfaced systematic Unicode normalisation failures affecting more than 60% of lines (with precomposed and decomposed forms of the same letter co-occurring within single sentences) which we document and correct. The corpus supports diacritic restoration, grapheme-to-phoneme conversion, TTS front-end development and orthographic research, and serves as a validated prompt set for new recording.
Chinese Translation
IroyinSpeech是一个42小时、80位说话者的约鲁巴语朗读语料库，其音频自2024年起由ELRA分发。本文描述了其文本组件的发布：24,905个独特的、经过人工验证的、带声调标记的约鲁巴语句子（275,897个词元；15,687个类型），这些句子是在2022年作为录音提示整理的。大约11,000个句子是从公开许可的新闻材料中改编而来；其余句子则由内部撰写，以扩大覆盖面，超越现有约鲁巴语语料库中主导的宗教翻译。每个句子都经过人工检查以确保声调标记的准确性，并进行了朗读清晰度和中性风格的编辑，同时本地化处理，使非约鲁巴语的人名和地名以约鲁巴语形式出现。准备发布文本时，发现了影响超过60%行的系统性Unicode标准化失败（同一字母的预组合和分解形式在单个句子中共存），我们对此进行了记录和修正。该语料库支持声调恢复、字形到音素转换、TTS前端开发和正字法研究，并作为新录音的验证提示集。
cs.CL / 76 / 2610.05373
Towards Unbiased On-Policy Distillation for Block Diffusion Language Models
面向无偏在政策蒸馏的块扩散语言模型
Abstract
On-policy distillation (OPD) has emerged as an effective post-training paradigm for language models, with recent efforts extending it to block diffusion language models (BDLMs). However, existing studies focus almost exclusively on small block sizes, leaving distillation into student models with larger blocks underexplored. In this work, we investigate this regime and reveal two critical optimization biases that induce severe training instability. First, mismatched block boundaries between teacher and student cause \textbf{\textit{context misalignment}}, providing distorted supervisory signals that misguide student decoding. Second, even under aligned contexts, an \textbf{\textit{intrinsic optimization bias}} in OPD, where the student tends to rapidly absorb high-support signals while lagging on low-support updates, drives a premature confidence surge that traps weaker students in catastrophic overconfidence collapse. To resolve these, we propose \mbox{\textbf{Un-OPD}}, an unbiased on-policy distillation framework with two novelties for stabilizing BDLM training. First, Un-OPD introduces a boundary-aware step filtering strategy that eliminates context-misaligned decoding steps. Second, Un-OPD proposes moderating optimization intensity at high-support positions via a support-rebalanced confidence calibration, thereby bypassing overconfidence collapse. Beyond stability, we also introduce a rollout reuse mechanism to reduce rollout generation overhead. Extensive experiments on math reasoning and code generation benchmarks show that Un-OPD consistently stabilizes training and delivers superior performance while reducing wall-clock training time by approximately half.
Chinese Translation
在政策蒸馏（OPD）已成为语言模型的一种有效后训练范式，近期的研究将其扩展至块扩散语言模型（BDLMs）。然而，现有研究几乎完全集中于小块大小，导致对较大块的学生模型的蒸馏研究不足。在本研究中，我们探讨了这一领域，并揭示了两种关键的优化偏差，这些偏差导致了严重的训练不稳定性。首先，教师和学生之间不匹配的块边界导致了 extbf{ extit{上下文不对齐}}，提供了扭曲的监督信号，从而误导学生解码。其次，即使在对齐的上下文下，OPD中的 extbf{ extit{内在优化偏差}}使得学生倾向于快速吸收高支持信号，而在低支持更新上滞后，导致过早的信心激增，使得较弱的学生陷入灾难性的过度自信崩溃。为了解决这些问题，我们提出了 extbf{Un-OPD}，一个无偏的在政策蒸馏框架，具有两项新颖性以稳定BDLM训练。首先，Un-OPD引入了一种边界感知的步骤过滤策略，消除了上下文不对齐的解码步骤。其次，Un-OPD通过支持重平衡的信心校准，建议在高支持位置调节优化强度，从而避免过度自信崩溃。除了稳定性，我们还引入了一种回滚重用机制，以减少回滚生成的开销。在数学推理和代码生成基准上的广泛实验表明，Un-OPD始终稳定训练并提供优越的性能，同时将实际训练时间减少约一半。
cs.CL / 77 / 2610.05382
Harness-Search: Guiding Long-Horizon Search through Multi-Agent Coordination
Harness-Search：通过多智能体协调引导长时间搜索
Abstract
Long-horizon search requires agents to gather evidence across multiple steps and synthesize it into well-supported answers. The recent agent harnesses provide a natural and promising framework to support such long-running search processes. As interaction histories grow, one single agent in harnesses might get stuck and cause the policy to lose track of unresolved questions, overlook useful evidence, or terminate before sufficient support has been collected. One of promising way is to decouple three distinct responsibilities of proposing retrieval actions, updating persistent state, and deciding when to stop rather than concentrating them within a single policy. Targeted at it, we introduce Harness-Search, a multi-agent search harness to reduce the local errors propagating across subsequent exploration, evidence curation, and termination decisions. In particular, Harness-Search assigns these responsibilities to three permission-bounded authorities: a Retrieval Policy that proposes search operations, a Memory Operator that validates and commits persistent-state updates, and a Summary Auditor that accepts or rejects termination based on the sufficiency of the curated evidence. Together, these roles form a Propose-Commit-Audit loop in which actions are proposed, persistent evidence is selectively committed, and stopping decisions are subjected to an explicit sufficiency check. Across seven long-horizon search benchmarks, Harness-Search improves both retrieval and answer generation under the same policy backbone, increasing Recall by 4.60-27.92 points and Final-Answer Recall by 12.34-30.13 points over the strongest harness-based baseline on each evidence-retrieval benchmark. Moreover, trajectory-level analyses show that Harness-Search continues to accumulate useful evidence and expand evidence coverage with less redundant retrieval as the search history grows.
Chinese Translation
长时间搜索要求智能体在多个步骤中收集证据，并将其综合成有力的答案。最近的智能体工具提供了一个自然且有前景的框架，以支持这种长期的搜索过程。随着交互历史的增长，单个智能体在工具中可能会陷入困境，从而导致策略失去对未解决问题的跟踪，忽视有用证据，或在收集到足够支持之前就终止。一个有前景的方法是将提议检索操作、更新持久状态和决定何时停止这三项不同的职责解耦，而不是将它们集中在单一策略中。为此，我们引入了Harness-Search，一个多智能体搜索工具，旨在减少在后续探索、证据整理和终止决策中传播的局部错误。具体而言，Harness-Search将这些职责分配给三个权限受限的权威：一个提取策略（Retrieval Policy）负责提议搜索操作，一个记忆操作员（Memory Operator）负责验证和提交持久状态更新，以及一个总结审计员（Summary Auditor）根据整理证据的充分性接受或拒绝终止。共同而言，这些角色形成了一个提议-提交-审计循环，其中提议行动、选择性提交持久证据，以及停止决策都经过明确的充分性检查。在七个长时间搜索基准测试中，Harness-Search在相同的策略基础上提高了检索和答案生成，召回率提高了4.60-27.92个百分点，最终答案召回率提高了12.34-30.13个百分点，超越了每个证据检索基准上最强的基于工具的基线。此外，轨迹级分析表明，随着搜索历史的增长，Harness-Search继续积累有用证据并扩大证据覆盖，同时减少冗余检索。
cs.CL / 78 / 2610.05394
The Hidden States Cookbook: A Large-Scale Ablation Study for Noise-Robust Conversational Intent Classification in Industry
隐藏状态食谱：工业中噪声鲁棒对话意图分类的大规模消融研究
Abstract
Conversational database interfaces face a critical challenge: users naturally embed queries in conversational noise (greetings, politeness, off-topic remarks), which degrades intent classification accuracy and wastes computational resources. Despite advances in orchestration and retrieval strategies, a fundamental question remains unanswered: which pooling strategy maximizes intent classification accuracy under realistic conversational noise in production language models? This work addresses this gap through 360 controlled experiments spanning four pooling configurations (mean, max, last-token, attention, and FFT-augmented variants) using Llama-3.2-1B-Instruct on BANKING77 and CLINC150 datasets under clean/noisy conditions with ten random seeds. Key findings reveal that attention pooling consistently outperforms alternative strategies under noisy conditions (~+2.6-2.8 F1 over the default), while mean pooling degrades performance by up to ~5 F1 points. Frequency-domain filtering does not produce consistent accuracy improvements and functions primarily as a structural variation rather than an accuracy-enhancing component. These results provide concrete, evidence-based guidance for building noise-robust conversational classifiers: attention pooling is recommended for noisy interfaces, mean pooling should be avoided, and last-token pooling is appropriate for clean-query scenarios.
Chinese Translation
对话数据库接口面临着一个关键挑战：用户自然地将查询嵌入到对话噪声中（问候、礼貌用语、离题评论），这降低了意图分类的准确性并浪费了计算资源。尽管在编排和检索策略方面取得了进展，但一个基本问题仍未得到解答：在实际对话噪声下，哪种池化策略能够最大化意图分类的准确性？本研究通过360个受控实验填补了这一空白，实验涵盖了四种池化配置（均值池化、最大池化、最后一个标记池化、注意力池化及其FFT增强变体），使用Llama-3.2-1B-Instruct在BANKING77和CLINC150数据集上进行干净/噪声条件下的测试，随机种子为十个。关键发现表明，在噪声条件下，注意力池化的表现始终优于其他策略（相比默认策略提高约2.6-2.8 F1），而均值池化的性能下降可达约5 F1点。频域过滤未能产生一致的准确性提升，主要作为一种结构变体而非提高准确性的组成部分。这些结果为构建噪声鲁棒的对话分类器提供了具体的基于证据的指导：建议在噪声接口中使用注意力池化，避免使用均值池化，而最后一个标记池化适用于干净查询场景。
cs.CL / 79 / 2610.05466
Writing as a Self-Organized Critical Process
写作作为一种自组织临界过程
Abstract
We explain autocorrelation decay power laws omnipresent in texts by self-organized criticality. Specifically, we analyze the recently released KLiCKe keystroke dataset and show that not only the final texts' autocorrelations form a manifold that adheres to a power law with a finite-size scaling, but also the text revisions generate revision-size-dependent restoring dynamics toward that manifold. Thus, human writing appears to dynamically regulate semantic correlations in a text toward a critical state.
Chinese Translation
我们通过自组织临界性解释了文本中普遍存在的自相关衰减幂律。具体而言，我们分析了最近发布的 KLiCKe 按键数据集，并展示了最终文本的自相关不仅形成了一个遵循有限规模缩放的幂律的流形，而且文本修订产生的修订规模依赖的恢复动力学也朝向该流形发展。因此，人类写作似乎动态地调节文本中的语义相关性，以达到一种临界状态。
cs.CL / 80 / 2610.05534
Dataset Signatures in Human-LLM Interactions and User Modeling
人类-大型语言模型交互中的数据集特征与用户建模
Abstract
Human--LLM interaction datasets shape our understanding of AI use and provide a foundation for downstream research, including training and evaluation of user models. In recent years, a growing number of datasets have sought to capture a representative picture of human--LLM interactions. But how different are the pictures these datasets provide, and what do those differences mean for research built on them? We study these questions across seven conversation datasets, spanning in-the-wild chat logs and human preference data. We begin by revisiting the dataset classification experiment of Torralba & Efros and find that neural network classifiers identify the source of a conversation from user messages alone well above chance, indicating distinctive dataset signatures. This separability persists after matching datasets on the dimensions of human-designed taxonomies, implying subtle differences that these taxonomies do not capture. We then examine the implications for user modeling: how dataset signatures propagate to the outputs of user models trained on these datasets; how dataset choice influences evaluations of user model quality and subsequent evaluations of LLM assistants paired with these user models; and how dataset classifiers can guide data selection for training user models. While each dataset is meant to capture a slice of 'real-world' interactions, our findings reveal the extent to which these slices diverge, and the consequences of those differences for research built on these foundations.
Chinese Translation
人类-大型语言模型（LLM）交互数据集塑造了我们对人工智能使用的理解，并为下游研究提供了基础，包括用户模型的训练和评估。近年来，越来越多的数据集试图捕捉人类-LLM交互的代表性图景。但这些数据集所提供的图景有多大不同，这些差异对基于它们的研究意味着什么？我们研究了七个对话数据集的问题，这些数据集涵盖了真实环境中的聊天记录和人类偏好数据。我们首先重新审视了Torralba和Efros的数据集分类实验，发现神经网络分类器仅凭用户消息就能显著高于随机猜测地识别对话来源，表明存在独特的数据集特征。这种可分离性在根据人类设计的分类法维度匹配数据集后依然存在，暗示了这些分类法未能捕捉的细微差别。接着，我们考察了对用户建模的影响：数据集特征如何传播到基于这些数据集训练的用户模型的输出；数据集选择如何影响用户模型质量的评估以及与这些用户模型配对的LLM助手的后续评估；以及数据集分类器如何指导用户模型训练的数据选择。尽管每个数据集旨在捕捉“现实世界”交互的一部分，但我们的发现揭示了这些部分的分歧程度，以及这些差异对基于这些基础的研究的影响。
cs.CL / 81 / 2610.05564
Lend Me Your Eyes: Instruction-Aware Text Embeddings via Attention Relay
借我你的眼睛：通过注意力中继实现的指令感知文本嵌入
Abstract
Text embedding models trained with contrastive learning learn to follow task instructions from instruction-paired data, while instruction-tuned LLMs already know how to follow them. We show that this instruction-following ability can carry over from an LLM to a Transformer-based embedder without any training. We propose Attention Relay, which passes the attention weights an LLM produces to the embedder's own attention. Across six instruction-tuned LLMs from the Qwen3, Llama 3.1 and OLMo 3 families and ten widely used embedding models that differ in tokenizer, size and pooling type, Attention Relay makes nearly every combination instruction-aware. Experiments that break the method down into its parts show that the LLM's attention weights track the instruction in its later layers and come largely from instruction tuning. They also show that relaying these weights selects which content in the text matters: it makes the aspect of the text that the instruction asks about dominant in the embedding, or restores that aspect where averaging had diluted it.
Chinese Translation
通过对比学习训练的文本嵌入模型能够从配对的指令数据中学习遵循任务指令，而经过指令调优的大型语言模型（LLMs）已经掌握了如何遵循这些指令。我们展示了这种遵循指令的能力可以在没有任何训练的情况下，从LLM转移到基于Transformer的嵌入模型。我们提出了注意力中继（Attention Relay），该方法将LLM生成的注意力权重传递给嵌入模型自身的注意力。在来自Qwen3、Llama 3.1和OLMo 3系列的六个经过指令调优的LLM与十个在分词器、大小和池化类型上各异的广泛使用的嵌入模型的组合中，注意力中继几乎使每种组合都具备了指令感知能力。将该方法分解为各个部分的实验表明，LLM的注意力权重在其后续层中跟踪指令，并且主要来自指令调优。实验还表明，传递这些权重能够选择文本中重要的内容：它使得指令所询问的文本方面在嵌入中占主导地位，或者在平均化稀释的情况下恢复该方面。
cs.CL / 82 / 2610.05575
Can Prosodic Style Be Inferred from Text Alone? Evidence from Unsupervised Acoustic Clusters
仅通过文本能推断韵律风格吗？来自无监督声学聚类的证据
Abstract
Much of expressive text-to-speech research rests on an untested assumption that written text carries enough information to select an appropriate prosodic style for its delivery. Text-predicted style models improve listener preference, and expressive-appropriateness evaluation presupposes that context constrains style, yet neither measures the assumption itself. This paper tests it as a falsifiable hypothesis against style labels derived from acoustics alone. For each of six speakers in a 1,200-hour conversational corpus, utterances are clustered in the spaces of five speech models, including a prosody-only control, and the cluster of held-out utterances is predicted from twelve text embedding models. Three controls are applied: utterance length is erased from the speech embeddings; accuracy is scored against the majority-class floor of unbalanced clusters rather than uniform chance; and a bag-of-words baseline measures word identity alone. Text predicts the cluster above that floor for all six speakers (+0.111 top-3 accuracy), but bag-of-words achieves three quarters of this. Sentence embeddings add only +0.026, largest for encoders not trained for sentence semantics and reversed by tree-based probes for all others. Acoustic clusters are not compact in text embedding space in any of 360 configurations. The prosody-only space weakens the association for five speakers, but not for the speaker showing it most strongly. Text thus informs these delivery clusters mainly through word choice, whether as a cue to prosody or as a marker of topic and recording situation, and reference-free style selection cannot assume more.
Chinese Translation
许多富有表现力的文本到语音研究基于一个未经验证的假设，即书面文本携带足够的信息以选择适当的韵律风格进行传递。文本预测的风格模型提高了听众的偏好，而富有表现力的适宜性评估则假设上下文限制了风格，但两者都没有测量这一假设本身。本文将其作为可证伪的假设进行检验，与仅从声学中得出的风格标签进行对比。在一个1200小时的对话语料库中，对六位说话者的发言进行聚类，使用五种语音模型，包括一个仅韵律的对照组，并从十二个文本嵌入模型中预测保留发言的聚类。应用了三个控制因素：从语音嵌入中去除发言长度；准确性是针对不平衡聚类的多数类基准而非均匀机会进行评分；而词袋基线仅测量词汇身份。文本在所有六位说话者中预测了高于这一基准的聚类（+0.111的前3准确率），但词袋模型达到了其中的四分之三。句子嵌入仅增加了+0.026，且对于未针对句子语义训练的编码器最大，而对于其他所有编码器则被基于树的探测器逆转。在360种配置中，声学聚类在文本嵌入空间中并不紧凑。仅韵律空间削弱了五位说话者的关联，但对于最强烈表现这一点的说话者则没有。由此可见，文本主要通过词汇选择来影响这些传递聚类，无论是作为韵律的线索，还是作为主题和录音情况的标记，而无参考的风格选择不能假设更多。
cs.CL / 83 / 2610.05591
What Is a Repeated Token Worth? The Scaling Geometry of Multi-Epoch Pretraining
重复标记的价值是什么？多轮预训练的缩放几何
Abstract
As pretraining increasingly repeats data, every run faces three questions: how many epochs to take, how that number should change with model size, and whether anything besides the epoch count matters. We answer them by pricing a repeated token against two references: one epoch on the same data, which gives its value, and fresh data at equal compute, which gives its cost. Against fresh data, the cost of repetition follows a single variable, the number of extra epochs divided by the unique tokens per parameter. Against the same data, a second epoch is worth nearly as much as a fresh one, and repeated tokens fall to half the value of fresh ones after a critical epoch count that grows with the training budget per parameter but hardly with model size. With unique data fixed, the predicted compute-optimal run grows model size and epochs together until loss stops improving, near the critical epoch count. The same variable accounts for the direction of size trends that appear to conflict: larger models tolerate fewer epochs when the corpus is fixed, from about 15 at 127M to 4 at 2B parameters, but not when unique data grow with the model. Counts alone do not determine loss: at identical counts, replaying shards consecutively raises loss by up to 0.46~bits per byte, concentrating repeats on fewer samples also raises it, lower-entropy sources degrade faster with repetition, and re-tokenizing repeats helps only under heavy repetition. These results offer an empirical guide to pretraining when unique data, rather than compute, are the binding constraint.
Chinese Translation
随着预训练过程中数据的重复越来越多，每次运行都面临三个问题：应进行多少个周期，这个数字应如何随模型规模变化，以及除了周期计数之外还有什么其他因素重要。我们通过将重复标记的价值与两个参考进行定价来回答这些问题：在相同数据上进行一个周期，这给出了其价值；在相同计算量下的新数据，这给出了其成本。与新数据相比，重复的成本遵循一个单一变量，即额外周期的数量除以每个参数的唯一标记数。与相同数据相比，第二个周期的价值几乎与新周期相当，而在达到一个临界周期数后，重复标记的价值降至新标记的一半，该临界周期数随着每个参数的训练预算增加而增长，但与模型规模几乎无关。在唯一数据固定的情况下，预测的计算最优运行将模型规模和周期一起增加，直到损失不再改善，接近临界周期数。相同的变量解释了看似相互矛盾的规模趋势：当语料库固定时，较大的模型在周期数上容忍度较低，从127M参数时的约15个周期降至2B参数时的4个周期，但当唯一数据随着模型增长时则不然。仅靠计数并不能决定损失：在相同计数下，连续重放分片会将损失提高最多0.46比特每字节，将重复集中在更少的样本上也会提高损失，低熵源在重复下衰减更快，而重新标记重复仅在大量重复下有效。这些结果为在唯一数据而非计算成为约束条件时的预训练提供了实证指导。
cs.CL / 84 / 2610.05597
More Than Words: Compositional Tokenization for Efficient Language Models
超越文字：高效语言模型的组合性标记化
Abstract
Language models process and generate text sequentially in token units, and the tokenizer determines how much text each inference step covers. Under standard tokenization, a short English phrase such as "On the table." is usually produced as four separate predictions for the preposition (On), article (the), noun (table), and punctuation (.), where each consumes a sequence position and adds inference cost. We introduce CoBPE, a compositional tokenization approach that represents such phrases as a lexical base token (table) attached with a small set of reusable surface modifiers, composed in embedding space at input and predicted jointly at output. In controlled pretraining from scratch at 780M and 1.3B scales, CoBPE shortens sequences by 30% and improves average downstream performance by 1.2 points relative to standard BPE under matched training compute. Our results suggest that part of what is now expressed through token sequences can instead be modeled through structured representations, opening a broad design space for more token-efficient and capable language models.
Chinese Translation
语言模型以标记单位顺序处理和生成文本，标记器决定每个推理步骤覆盖的文本量。在标准标记化下，像"在桌子上。"这样的短英语短语通常被分解为四个独立的预测：介词（On）、冠词（the）、名词（table）和标点符号（.），每个预测消耗一个序列位置并增加推理成本。我们提出了CoBPE，一种组合性标记化方法，将此类短语表示为一个词汇基础标记（table），并附加一小组可重用的表面修饰符，这些修饰符在输入时在嵌入空间中组合，并在输出时共同预测。在780M和1.3B规模的受控从零开始预训练中，CoBPE将序列缩短了30%，并在匹配训练计算下相较于标准BPE提高了1.2个点的平均下游性能。我们的结果表明，当前通过标记序列表达的部分内容可以通过结构化表示来建模，从而为更具标记效率和能力的语言模型打开了广泛的设计空间。
cs.CL / 85 / 2610.05630
Atomic Visual Entailment: Enhancing Zero-Shot Vision-Language Reasoning through Atomic Fact Decomposition and Learned Selection
原子视觉蕴涵：通过原子事实分解和学习选择增强零-shot视觉-语言推理
Abstract
Visual entailment (VE) asks whether an image supports, contradicts, or leaves undecided a textual hypothesis. Strong results come from fine-tuning large vision-language models on labelled data, while zero-shot and hybrid approaches remain far behind. A VE hypothesis often bundles several visual claims, yet existing zero-shot methods reason over it as a single unit. We propose Atomic Visual Entailment (AVE), which decomposes the hypothesis into atomic facts, produces candidate predictions from both the full hypothesis and its facts using frozen vision-language models, and predicts the final label with a lightweight classifier trained only on how those candidates behave. We find that decomposition helps only when the hypothesis context is preserved: judging facts in isolation is worse than not decomposing at all. Full-hypothesis and atomic prediction make complementary errors, and learning which to trust recovers far more of that complementarity than majority voting, reaching 0.803 test accuracy on SNLI-VE without fine-tuning any vision-language model. AVE also localises the visual evidence behind its prediction without region-level supervision. These results suggest that learning which candidate prediction to trust can close much of the gap to fine-tuned systems, offering a practical alternative where fine-tuning a vision-language model directly would need more labelled data or compute than is available.
Chinese Translation
视觉蕴涵（VE）旨在判断一幅图像是否支持、反驳或对文本假设保持不确定。通过在标注数据上微调大型视觉-语言模型，取得了良好的结果，而零-shot和混合方法仍然远远落后。VE假设通常包含多个视觉主张，但现有的零-shot方法将其视为一个单一单元进行推理。我们提出了原子视觉蕴涵（AVE），该方法将假设分解为原子事实，从完整假设及其事实中生成候选预测，并使用仅在候选行为上训练的轻量级分类器来预测最终标签。我们发现，只有在保留假设上下文时，分解才有帮助：孤立判断事实的效果不如不进行分解。完整假设和原子预测会产生互补错误，学习信任哪个预测比简单的多数投票能恢复更多的互补性，在SNLI-VE上达到0.803的测试准确率，而无需微调任何视觉-语言模型。AVE还能够在没有区域级监督的情况下定位其预测背后的视觉证据。这些结果表明，学习信任哪个候选预测可以缩小与微调系统之间的差距，提供了一种实用的替代方案，尤其是在微调视觉-语言模型直接需要更多标注数据或计算资源时。
cs.CL / 86 / 2610.05681
Automatic Speech Recognition for Low-Resource Sinhala: A Critical Review of Methods, Challenges, and Future Directions
低资源信纳语的自动语音识别：方法、挑战与未来方向的批判性回顾
Abstract
Automatic speech recognition (ASR) for low-resource languages remains a major challenge. Sinhala, the primary language of Sri Lanka with about 16 million speakers, illustrates the difficulty: agglutinative morphology, a 54-phoneme inventory, subject-object-verb (SOV) syntax and scarce annotated speech data limit both conventional and modern ASR systems. This paper presents the first critical review of Sinhala ASR research, tracing its development from Hidden Markov Models (HMMs) through deep neural networks to self-supervised pre-trained models such as wav2vec 2.0, XLS-R, Whisper and Massively Multilingual Speech (MMS). We compare existing Sinhala systems with related low-resource ASR work on Tamil, Malayalam and Hindi in terms of architecture, training data, word error rate (WER) and robustness to real-world acoustic conditions, and we assess self-supervised and transfer learning as responses to scarce labeled data. We show that most reported WERs are not directly comparable because they differ in corpus, data split and scoring, and that the only controlled comparison in the literature attributes an 18.1% relative WER reduction to corpus correction alone. We also discuss context-aware ASR that draws on phonological, syntactic and semantic knowledge. We identify six research gaps: (1) the lack of large annotated corpora covering multiple dialects and acoustic conditions; (2) weak contextual modeling of Sinhala morphosyntax; (3) high WER in real-world conditions; (4) the absence of standardized benchmarks; (5) the lack of parameter-efficient fine-tuning studies; and (6) the absence of annotated code-switched Sinhala-English speech resources. We outline a research agenda to address these gaps, intended as a roadmap for researchers working on Sinhala and other morphologically rich languages.
Chinese Translation
低资源语言的自动语音识别（ASR）仍然是一个重大挑战。信纳语是斯里兰卡的主要语言，约有1600万说话者，这一案例突显了其难度：粘着语形态、54音素的音位库、主-宾-动（SOV）语法以及稀缺的标注语音数据限制了传统和现代的ASR系统。本文首次对信纳语ASR研究进行了批判性回顾，追溯其从隐马尔可夫模型（HMMs）到深度神经网络，再到自监督预训练模型如wav2vec 2.0、XLS-R、Whisper和大规模多语言语音（MMS）的发展。我们将现有的信纳语系统与在建筑、训练数据、词错误率（WER）和对现实世界声学条件的鲁棒性方面与泰米尔语、马拉雅拉姆语和印地语的相关低资源ASR工作进行了比较，并评估了自监督学习和迁移学习作为应对稀缺标注数据的反应。我们显示，大多数报告的WER并不可直接比较，因为它们在语料库、数据划分和评分上存在差异，而文献中唯一的受控比较将18.1%的相对WER降低归因于语料库的修正。我们还讨论了基于上下文的ASR，该方法利用了音位、句法和语义知识。我们识别出六个研究空白：（1）缺乏覆盖多个方言和声学条件的大型标注语料库；（2）信纳语形态句法的上下文建模较弱；（3）现实世界条件下的高WER；（4）缺乏标准化基准；（5）缺乏参数高效的微调研究；（6）缺乏标注的代码切换信纳语-英语语音资源。我们概述了一个研究议程，以解决这些空白，旨在为从事信纳语及其他形态丰富语言研究的学者提供路线图。
cs.CL / 87 / 2610.05682
Knowing the Rules, Applying the Rules: Evaluating Language Models on Traditional Chinese Bazi
了解规则，应用规则：对传统中国八字的语言模型评估
Abstract
Knowing domain rules does not guarantee applying them to a case. We study this distinction in traditional Chinese Bazi through 3,000 Chinese multiple-choice questions spanning 14 Theory and 11 Case categories. Six endpoint systems are evaluated, with primary results reported on a 2,492-item model-informed refinement. Theory accuracy exceeds Case accuracy for every system, and gaps of 16.60-29.56 percentage points remain when invalid responses are excluded. The contrast is more specific than a general case-reasoning deficit. Across six systems, Twelve Stages and Nayin reach mean accuracies of 89.10% and 88.62%, while Shensha Basics reaches 75.96%. Within Case, Luck Pillars averages 84.62%, but Career and Family Relations average only 36.98% and 38.19%. Overall rankings also conceal different category strengths. On the original 3,000 items, paired DeepSeek native/disabled comparisons associate native configurations with Theory gains of 6.53 and 12.20 points for Flash and Pro, respectively; Case changes are -3.67 and +1.27 points. These are provider-configuration associations, not isolated causal effects of reasoning. The results motivate task-specific evaluation of cultural-domain applications rather than reliance on aggregate knowledge scores. The benchmark measures agreement with a model-generated, model-verified answer key, not real-world predictive validity. Final-set results are post-selection descriptions, and incomplete provenance and expert validation constrain their interpretation.
Chinese Translation
了解领域规则并不保证能够将其应用于具体案例。我们通过3000道涵盖14个理论类别和11个案例类别的中文选择题研究这一区别。评估了六个终端系统，主要结果基于2492个项目的模型引导细化。每个系统的理论准确率均高于案例准确率，排除无效响应后，仍存在16.60至29.56个百分点的差距。这种对比比一般的案例推理缺陷更为具体。在六个系统中，十二阶段（Twelve Stages）和纳音（Nayin）的平均准确率分别达到89.10%和88.62%，而神煞基础（Shensha Basics）则为75.96%。在案例类别中，运势柱（Luck Pillars）的平均准确率为84.62%，但职业和家庭关系的平均准确率仅为36.98%和38.19%。总体排名也掩盖了不同类别的优势。在原始的3000个项目中，配对的DeepSeek本地/禁用比较将本地配置与理论增益关联，Flash和Pro分别为6.53和12.20点；案例变化为-3.67和+1.27点。这些是提供者配置的关联，而非推理的孤立因果效应。结果促使我们对文化领域应用进行任务特定的评估，而不是依赖于汇总知识分数。基准测量与模型生成、模型验证的答案键的一致性，而非现实世界的预测有效性。最终集结果为后选择描述，不完整的来源和专家验证限制了其解释。
cs.CL / 88 / 2610.05685
Spend Bytes on Breadth: Precision-Count Trade-offs for Decode-Time KV Compression in Long Chain-of-Thought Reasoning
在广度上花费字节：长链思维推理中解码时间KV压缩的精度-计数权衡
Abstract
Reasoning models write most of their KV cache while decoding long chains of thought (CoT), so the cache has to be compressed online under a fixed memory budget. Decode-time methods mostly decide which tokens to evict. We ask how a fixed byte budget should be split between the number of cached tokens and their precision. BreadthKV spends the bytes on more tokens at low precision, combining quantization with eviction, and picks the bit-width for each model and budget with a 60-problem end-to-end calibration, since offline attention error does not predict it reliably. On three reasoning models and four math and science benchmarks, it scores above eviction alone in 17 of 18 settings and produces shorter outputs. Much of what eviction loses comes from derailed runs, which keep reasoning until the length cap without reaching an answer. On Qwen3-8B at our tightest budget, eviction sends 91% of AIME samples to the cap and BreadthKV 40%. Under the same protocol, BreadthKV is statistically indistinguishable from a joint rate-distortion allocator (RDKV) that uses 27% more KV memory-time, and it outperforms our re-implementation of ThinKV.
Chinese Translation
推理模型在解码长链思维（CoT）时会写入大部分KV缓存，因此缓存必须在固定的内存预算下在线压缩。解码时间方法主要决定哪些令牌需要被驱逐。我们探讨在固定字节预算下，缓存的令牌数量与其精度之间应如何分配。BreadthKV以低精度将字节分配给更多令牌，结合量化与驱逐，并通过60个问题的端到端校准为每个模型和预算选择位宽，因为离线注意力误差无法可靠预测。在三个推理模型和四个数学与科学基准测试中，它在18种设置中有17种超过了单独驱逐的表现，并生成了更短的输出。驱逐所损失的很大一部分来自于未能达到答案的中断运行，这些运行在达到长度上限之前仍在进行推理。在我们最紧张的预算下，Qwen3-8B的驱逐将91%的AIME样本发送到上限，而BreadthKV仅为40%。在相同的协议下，BreadthKV在统计上与使用27%更多KV内存时间的联合率-失真分配器（RDKV）无显著差异，并且其性能优于我们对ThinKV的重新实现。
cs.CL / 89 / 2610.05778
MedicalHarness: A Controlled Evaluation of LLMs and Agent Harnesses on Medical Tasks
MedicalHarness：对医疗任务中大型语言模型和代理工具的受控评估
Abstract
LLM agents are increasingly built for medical work and scored on clinical benchmarks. Each such score, however, comes from a model running inside an agent harness, the system that controls the loop between the model and its environment. An agent's score is therefore a property of a model--harness pair. For medical agents, how much outcomes change with the harness has rarely been measured. Measuring this change, and explaining it, raises two challenges. First, a harness comparison must change nothing but the harness and be repeated across models and kinds of task. Second, comparing whole harnesses leaves their mechanisms bundled together, so it cannot show when an individual mechanism helps. To address these challenges, we present MedicalHarness, a controlled study of models and agent harnesses on medical tasks. We first build MedicalHarnessBench to evaluate agents on $107$ tasks across four domains that each test a different harness capability. Using this benchmark, we run five open-weight models under five agent harnesses, changing only the harness within a comparison, and analyze both outcomes and execution traces. To study individual mechanisms, we build MH-Lab, a controlled harness that switches off context management, planning or tool exposure one at a time within a shared execution loop. We find that the harness and its interaction with the model account for about a quarter of the outcome variance, and that no single harness is best across models and tasks. Code and data are available at https://github.com/REAL-Lab-NU/MedicalHarness.
Chinese Translation
大型语言模型（LLM）代理越来越多地被用于医疗工作，并在临床基准上进行评分。然而，每个评分都是基于在代理工具内运行的模型，该系统控制着模型与其环境之间的循环。因此，代理的评分是模型与工具配对的属性。对于医疗代理而言，工具的变化对结果的影响很少被测量。测量这种变化并解释它面临两个挑战。首先，工具的比较必须仅改变工具本身，并在不同模型和任务类型中重复进行。其次，比较整个工具会将其机制捆绑在一起，因此无法显示单个机制的帮助程度。为了解决这些挑战，我们提出了MedicalHarness，这是一个针对医疗任务的模型和代理工具的受控研究。我们首先构建了MedicalHarnessBench，以评估在四个领域中107个任务的代理，每个任务测试不同的工具能力。利用这个基准，我们在五个代理工具下运行五个开放权重模型，仅在比较中更改工具，并分析结果和执行轨迹。为了研究单个机制，我们构建了MH-Lab，这是一个受控工具，可以在共享执行循环中一次关闭上下文管理、规划或工具暴露。我们发现，工具及其与模型的交互大约占结果方差的四分之一，并且没有单一工具在所有模型和任务中表现最佳。代码和数据可在 https://github.com/REAL-Lab-NU/MedicalHarness 获取。
cs.CL / 90 / 2610.05783
CLARA: Can AI Assess Developmental Appropriateness in Children's Stories?
CLARA：人工智能能否评估儿童故事的适宜性？
Abstract
Assessing the developmental suitability of children's narratives is important for educational recommendation and developmental literacy research, yet such assessment typically relies on subjective and difficult-to-scale human judgment. This raises an important question: Can AI systems approximate human developmental judgments of children's stories? To study this problem, we introduce CLARA, a cognitively grounded framework for developmental narrative understanding through structured annotation across cognitive (COG), language (LAN), and social-emotional (SEL) dimensions, together with a bilingual benchmark resource containing 1107 Chinese--English children's stories with normalized silver developmental references and structured developmental annotations. We evaluate CLARA through benchmark comparison, component analysis, translated bilingual consistency analysis, and blinded human evaluation with educators. Experimental results show that structured developmental annotation achieves substantially stronger alignment with developmental references and human judgments than readability-based methods and direct prompting baselines. Overall, our findings suggest that AI systems can approximate certain aspects of human developmental judgment when guided by structured developmental annotation, while also highlighting the importance of interpretability and human oversight in educational NLP.
Chinese Translation
评估儿童叙事的发育适宜性对于教育推荐和发展性读写研究至关重要，但这种评估通常依赖于主观且难以扩展的人类判断。这引发了一个重要问题：人工智能系统能否近似人类对儿童故事的发育判断？为研究这一问题，我们提出了CLARA，一个基于认知的发育叙事理解框架，通过在认知（COG）、语言（LAN）和社会情感（SEL）维度上的结构化注释，以及一个包含1107个中英文儿童故事的双语基准资源，提供了标准化的银级发育参考和结构化的发育注释。我们通过基准比较、组件分析、翻译双语一致性分析和与教育工作者的盲评来评估CLARA。实验结果表明，结构化的发育注释在与发育参考和人类判断的一致性上，显著优于基于可读性的方法和直接提示的基线。总体而言，我们的研究结果表明，当受到结构化发育注释的指导时，人工智能系统可以近似人类发育判断的某些方面，同时也强调了可解释性和人类监督在教育自然语言处理中的重要性。
cs.CL / 91 / 2610.05800
Adaptive Utilization of Low-Rank Adaptation via Conditioned Gating
通过条件门控自适应利用低秩适应
Abstract
Low-Rank Adaptation (LoRA) achieves parameter-efficient fine-tuning by constraining model updates to a low-rank subspace and has been widely used in practice. However, LoRA typically employs a shared low-rank update across tokens, which limits its ability to fully exploit the adaptation subspace for tokens from different sequences. To address this issue, we propose an adaptive utilization of Low-Rank Adaptation (U-LoRA), which employs conditioned gating to explicitly learn effective token-level utilization of the limited low-rank adaptation subspace. Specifically, U-LoRA generates utilization coefficients along low-rank directions for each token and jointly coordinates and constrains them using sequence-level contextual information, thereby inducing more consistent adaptive patterns within a sentence. To further enhance training stability, we introduce a bias-corrected exponential moving average (EMA) historical prior that calibrates utilization signals across optimization steps, suppressing noise caused by batch-to-batch fluctuations. The effectiveness of our method arises from a better utilization of the existing low-rank subspace via input-conditioned strategies, rather than from expanding the subspace. Experiments on mathematical reasoning and natural language understanding benchmarks demonstrate that U-LoRA achieves competitive performance under comparable parameter budgets when with strong LoRA baselines and recent variants.
Chinese Translation
低秩适应（Low-Rank Adaptation, LoRA）通过将模型更新限制在低秩子空间中，实现了参数高效的微调，并在实践中得到了广泛应用。然而，LoRA 通常在不同的标记之间采用共享的低秩更新，这限制了其充分利用来自不同序列的标记的适应子空间的能力。为了解决这一问题，我们提出了一种低秩适应的自适应利用方法（U-LoRA），该方法利用条件门控显式学习有限低秩适应子空间的有效标记级利用。具体而言，U-LoRA 为每个标记生成沿低秩方向的利用系数，并使用序列级上下文信息共同协调和约束这些系数，从而在句子内诱导出更一致的适应模式。为了进一步增强训练的稳定性，我们引入了一种偏差校正的指数移动平均（EMA）历史先验，校准优化步骤中的利用信号，抑制批次间波动造成的噪声。我们方法的有效性源于通过输入条件策略更好地利用现有的低秩子空间，而不是扩展子空间。在数学推理和自然语言理解基准上的实验表明，U-LoRA 在与强大的 LoRA 基线和近期变体相当的参数预算下，取得了具有竞争力的性能。
cs.CL / 92 / 2610.05815
Plan Canvas: Fixed Reasoning Regions for Continuous Language Flows
计划画布：连续语言流的固定推理区域
Abstract
Continuous language flows generate text by denoising all positions of a target canvas together. The natural way to add reasoning to such a model is to write a trace ahead of the answer, but the trace length changes from question to question. The answer start is therefore unknown during denoising, and the model has to decide the trace length, the place of every trace token, and the answer at the same time. We propose Plan Canvas to fix the boundary between the trace and the answer. A plan region of fixed capacity holds a compact trace, supervised padding fills its unused positions, and the answer starts at a fixed position. The fixed regions also allow separate denoising clocks for the plan and for the answer. With the trace text, backbone, and canvas length of the free-trace baseline held fixed, Plan Canvas improves accuracy on ProsQA and on Deep ProsQA, a graph benchmark with longer proofs. On Deep ProsQA, accuracy rises from 73.0\% to 87.0\%, the share of questions answered with a valid path rises from 30.8\% to 59.1\%, and the gain is largest on the longest proofs.
Chinese Translation
连续语言流通过对目标画布的所有位置进行去噪来生成文本。将推理添加到这样的模型中的自然方式是提前写下答案的推理轨迹，但推理轨迹的长度因问题而异。因此，在去噪过程中，答案的起始位置是未知的，模型必须同时决定推理轨迹的长度、每个推理标记的位置以及答案。我们提出了计划画布（Plan Canvas）来固定推理与答案之间的边界。一个固定容量的计划区域保存紧凑的推理轨迹，监督填充（supervised padding）填补其未使用的位置，答案从一个固定位置开始。固定区域还允许为计划和答案设置独立的去噪时钟。在保持自由推理基线的推理文本、主干和画布长度不变的情况下，计划画布在ProsQA和Deep ProsQA（一个具有更长证明的图形基准）上提高了准确性。在Deep ProsQA上，准确率从73.0\%上升到87.0\%，有效路径回答的问题比例从30.8\%上升到59.1\%，且在最长的证明上增益最大。
cs.CL / 93 / 2610.05842
HLA: Expressive Hybrid Linear Attention via Chunk-Wise Dynamic Mixing
HLA：通过分块动态混合实现的表达性混合线性注意力
Abstract
Linear attention enables efficient long-context autoregressive decoding by compressing history into recurrent states, but this compression can make selective access to sparse and distant information difficult. Existing chunk-based extensions increase memory capacity, yet learned chunk-mixing coefficients may remain fixed with respect to input content and therefore cannot adapt historical access to each query. We introduce \emph{Hybrid Linear Attention} (HLA), a query-dependent chunk-level attention mechanism for Gated DeltaNet (GDN). HLA represents each completed chunk as an exact affine state transition and computes content-dependent routing gates from compact, self-attentively pooled representatives. Each gate interpolates the corresponding historical transition with the identity map, controlling both the chunk's additive memory and its transformation of earlier states. Effective-support regularization further encourages concentrated routing for sparse inference. We evaluate HLA under both pretrained adaptation and from-scratch training. Across Qwen3.5 models from 0.8B to 9B, HLA consistently improves over native GDN and fixed chunk mixing, with gains of up to 5.57 percentage points on LongBench-V2 and 3.97 points on RULER. In a controlled from-scratch 1.3B setting trained for 100B tokens with a 4K context, HLA also improves RULER performance from 4K to 32K, with gains increasing from 0.83 points at 4K to 4.22 points at 32K. These results demonstrate that query-dependent composition of recurrent memory improves long-context modeling and remains effective beyond the training context while using compact per-chunk affine summaries. Project page: https://caesarhhh.github.io/hla/
Chinese Translation
线性注意力通过将历史信息压缩为递归状态，使得高效的长上下文自回归解码成为可能，但这种压缩可能使得对稀疏和远程信息的选择性访问变得困难。现有的基于分块的扩展增加了内存容量，但学习到的分块混合系数可能对输入内容保持固定，因此无法根据每个查询自适应历史访问。我们引入了 extit{混合线性注意力}（Hybrid Linear Attention, HLA），这是一种针对门控DeltaNet（Gated DeltaNet, GDN）的查询依赖分块级注意力机制。HLA将每个完成的分块表示为精确的仿射状态转移，并从紧凑的自注意力池化代表中计算内容依赖的路由门。每个门通过与恒等映射的插值，控制分块的附加记忆及其对早期状态的转化。有效支持正则化进一步鼓励稀疏推理的集中路由。我们在预训练适应和从零开始训练的情况下评估HLA。在从0.8B到9B的Qwen3.5模型中，HLA在本地GDN和固定分块混合上始终表现出改进，在LongBench-V2上提高了最多5.57个百分点，在RULER上提高了3.97个百分点。在一个控制的从零开始的1.3B设置中，训练了100B个标记并使用4K上下文，HLA还将RULER性能从4K提升到32K，增益从4K的0.83点增加到32K的4.22点。这些结果表明，查询依赖的递归记忆组合改善了长上下文建模，并在使用紧凑的每块仿射摘要时在训练上下文之外仍然有效。项目页面：https://caesarhhh.github.io/hla/
cs.CL / 94 / 2610.05879
Learning to Learn a Language
学习语言的学习
Abstract
We present the Prior-Fitted Language Model (PFLM), a 300M-parameter byte-level transformer pretrained only on samples from a synthetic non-linguistic prior. Given a prefix of real text, it learns to predict the language in context with frozen weights, having never seen a word of any real language. Every training sequence is generated by a recurrent structural causal model drawn fresh from a distribution over such models. The model never sees the same language twice during training, so the only way to predict the continuation is to infer the language from the prefix. Samples from this prior share the statistical signatures of natural text: Zipfian frequencies, slow entropy-rate convergence, and long-range dependence. On Wikipedia in six languages, bits per byte fall from the uniform eight to between 0.9 and 2.4 at one million bytes of context. Given numerals instead of text, PFLM learns to count, to compare magnitudes, and to add approximately. It predicts deterministic sequences like Rudin-Shapiro or the prime indicator, and it compresses six non-text domains, from source code to speech, below gzip and PPMd. The model has not learned a language. It has learned to learn one.
Chinese Translation
我们提出了先验拟合语言模型（Prior-Fitted Language Model, PFLM），这是一个具有3亿参数的字节级变换器，仅在来自合成非语言先验的样本上进行预训练。在给定真实文本的前缀时，它在权重冻结的情况下学习在上下文中预测语言，从未见过任何真实语言的单词。每个训练序列都是由一个从该类模型的分布中新生成的递归结构因果模型生成的。该模型在训练期间从未见过同一种语言，因此预测续写的唯一方法是从前缀推断语言。来自该先验的样本共享自然文本的统计特征：Zipf频率、缓慢的熵率收敛和长程依赖。在六种语言的维基百科上，字节每比特的比率从均匀的8降至在一百万字节上下文中介于0.9和2.4之间。给定数字而非文本时，PFLM学习计数、比较大小和近似加法。它预测确定性序列，如Rudin-Shapiro序列或素数指示器，并且在压缩六个非文本领域（从源代码到语音）时，其效果优于gzip和PPMd。该模型尚未学习一种语言，而是学会了学习一种语言。
cs.CL / 95 / 2610.05894
Noise Out, Bias In: Targeted Bias Injection in Diffusion Language Models via Closed-Loop Activation Steering
噪声消除，偏见注入：通过闭环激活引导在扩散语言模型中进行定向偏见注入
Abstract
Masked diffusion language models (dLLMs) generate text by iteratively denoising masked positions, re-predicting each token multiple times before it is committed. An autoregressive decoder exposes an answer's distribution once, at the step that commits it; a dLLM exposes it at every denoising step before commitment, and we show that an adversary can exploit this. Since an answer remains open to revision over many denoising steps, an adversary with access to internal activations can watch how likely the model is to produce a chosen answer and adjust the intervention accordingly. Building on this observation, we study targeted bias injection, an attack that steers a frozen dLLM toward a demographic answer selected by the adversary. The attack uses a simple proportional-integral (PI) controller that tracks the target-answer probability during denoising and adapts the strength of a steering vector on the fly. On ambiguous BBQ questions where the correct answer is abstention, our attack raises LLaDA-8B-Instruct's preference for the targeted group from 1.8 to 16.7 percentage points, more than three times the strongest fixed-strength steering baseline, and on SocialStigmaQA it raises the selection of stigmatizing answers from 17.6% to 58.1%. Fitted to other demographic targets, the same attack shifts answers by up to 37 percentage points, and each attack takes about 40 minutes on one GPU. On the primary target, feedback is what makes the attack work: constant steering at the same average strength over the token-committing steps produces a far smaller shift while corrupting nearly three times as many outputs, and a constant strength set separately for each example still falls well short. Our findings identify the denoising trajectory as a new control channel in dLLMs and call for bias audits that examine the serving stack rather than the frozen model alone.
Chinese Translation
掩蔽扩散语言模型（dLLMs）通过迭代去噪掩蔽位置生成文本，在每个令牌被确认之前多次重新预测。自回归解码器在确认答案的步骤中仅暴露一次答案的分布；而dLLM在确认之前的每个去噪步骤中都暴露这一分布，我们展示了对手可以利用这一点。由于答案在多个去噪步骤中仍然可以被修订，能够访问内部激活的对手可以观察模型生成所选答案的可能性，并相应地调整干预。基于这一观察，我们研究了定向偏见注入，这是一种攻击，旨在将冻结的dLLM引导向对手选择的人口统计答案。该攻击使用简单的比例-积分（PI）控制器，在去噪过程中跟踪目标答案的概率，并实时调整引导向量的强度。在模糊的烧烤问题上，正确答案为弃权，我们的攻击将LLaDA-8B-Instruct对目标群体的偏好从1.8提升至16.7个百分点，超过了最强固定强度引导基线的三倍；在SocialStigmaQA上，它将污名化答案的选择率从17.6%提升至58.1%。针对其他人口统计目标，相同的攻击将答案转变最多达37个百分点，每次攻击在一块GPU上大约需要40分钟。在主要目标上，反馈是使攻击有效的关键：在令牌确认步骤中以相同的平均强度进行持续引导会产生远小于的偏移，同时损坏近三倍的输出，而为每个示例单独设置的恒定强度仍然远远不够。我们的发现将去噪轨迹识别为dLLMs中的新控制通道，并呼吁进行偏见审计，以检查服务堆栈而不仅仅是冻结模型。
cs.CL / 96 / 2610.05966
HuatuoGPT-3: RL-Only Domain Adaptation from Base Models
华佗GPT-3：仅基于强化学习的基础模型领域适应
Abstract
Domain adaptation aims to turn a general-purpose large language model (LLM) into an expert for a target domain. While the dominant SFT+RL pipeline offers a convenient cold start, it may reduce exploration diversity and introduces additional complexity through multi-stage optimization. These limitations motivate RL-only adaptation. However, pure on-policy RL suffers from a cold-start problem, while mixed-policy RL still falls short: informative tokens in teacher outputs are learned too slowly in early training, and stale teacher outputs can hinder later improvement. We identify these two failure modes as Gradient Starvation and Teacher-Distribution Anchoring. To address them, we propose One-stage Policy Optimization (OnePO), which treats teacher outputs as transient guidance for policy improvement. OnePO combines Adaptive Objective Evolution to strengthen learning on informative low-probability teacher tokens and Teacher Retirement to discard teacher outputs once the current policy can surpass them. On medical adaptation, OnePO achieves 67.2 on HealthBench (Total) with only 20K training samples, outperforming SFT+RL and pure RL by 2.7 and 7.4 points, respectively. We further scale OnePO to produce HuatuoGPT-3, an open-source medical LLM series whose 27B variant reaches 70.1 on HealthBench (Total) and 71.4 on HealthBench Professional, surpassing frontier models such as GPT-6 Astra. Models and code are available at https://github.com/FreedomIntelligence/HuatuoGPT-3.
Chinese Translation
领域适应旨在将通用的大型语言模型（LLM）转变为特定领域的专家。尽管主流的SFT+RL（监督微调+强化学习）流程提供了便捷的冷启动，但它可能会降低探索的多样性，并通过多阶段优化引入额外的复杂性。这些局限性促使我们探索仅基于强化学习的适应方法。然而，纯粹的在线策略强化学习面临冷启动问题，而混合策略强化学习仍然不足：教师输出中的信息性标记在早期训练中学习得过于缓慢，而过时的教师输出可能会妨碍后期的改进。我们将这两种失败模式称为梯度饥饿（Gradient Starvation）和教师分布锚定（Teacher-Distribution Anchoring）。为了解决这些问题，我们提出了一种单阶段策略优化（One-stage Policy Optimization，OnePO），将教师输出视为策略改进的瞬时指导。OnePO结合了自适应目标演化（Adaptive Objective Evolution），以增强对信息性低概率教师标记的学习，并通过教师退休（Teacher Retirement）在当前策略能够超越教师输出后丢弃教师输出。在医学领域适应中，OnePO在仅使用20K训练样本的情况下，在HealthBench（总分）上达到了67.2，分别比SFT+RL和纯强化学习高出2.7和7.4分。我们进一步扩展OnePO，生成华佗GPT-3，一个开源医学大型语言模型系列，其27B变体在HealthBench（总分）上达到了70.1，在HealthBench专业版上达到了71.4，超越了GPT-6 Astra等前沿模型。模型和代码可在https://github.com/FreedomIntelligence/HuatuoGPT-3获取。
cs.CL / 97 / 2610.05976
Can Language Models Learn to Reject Their Own Bad Reasoning Steps?
语言模型能否学会拒绝自身的不良推理步骤？
Abstract
Verifier-guided decoding can prevent harmful reasoning steps from contaminating subsequent generation, but typically relies on an external learned verifier. We ask whether a language model can instead reject its own bad reasoning steps. We define a prefix's recoverability as the probability that the frozen generator can complete it correctly. Diagnostics show that adjacent recoverability changes are often difficult to resolve with practical Monte Carlo budgets, while same-prefix candidates exhibit a sparse low-recoverability tail. We introduce Self-Step Rejection (SSR), which trains a lightweight LoRA acceptance gate on the generator backbone while keeping the base model frozen. SSR uses confidence-qualified first-passage supervision: steps before the first resolved crossing of a root-relative recoverability barrier are accepted, the crossing step is rejected, and unresolved steps and suffixes are excluded. Training combines pointwise classification, same-prefix pairwise learning, and group-relative policy refinement using final-answer correctness. At inference, SSR accepts candidates or resamples from the unchanged prefix under rejection budgets, without an external learned verifier. Across three reasoning models and five mathematical reasoning benchmarks, SSR improves macro-average accuracy over single-pass decoding by 5.4--10.1 points using 1.21--1.40x as many generated tokens, and achieves the highest macro-average accuracy among evaluated step-level methods. Full-solution scaling methods require 4.47--8.27x the single-pass token cost for comparable performance.
Chinese Translation
验证者引导解码可以防止有害的推理步骤污染后续生成，但通常依赖于外部学习的验证者。我们探讨语言模型是否能够拒绝自身的不良推理步骤。我们将前缀的可恢复性定义为冻结生成器能够正确完成该前缀的概率。诊断结果表明，相邻的可恢复性变化通常难以用实际的蒙特卡洛预算来解决，而相同前缀的候选项则表现出稀疏的低可恢复性尾部。我们提出了自步骤拒绝（Self-Step Rejection, SSR），它在保持基础模型冻结的同时，在生成器主干上训练一个轻量级的 LoRA 接受门。SSR 使用置信度合格的首次通过监督：在根相对可恢复性障碍的首次解决交叉之前的步骤被接受，交叉步骤被拒绝，未解决的步骤和后缀被排除。训练结合了逐点分类、相同前缀的成对学习以及使用最终答案正确性进行的组相对策略优化。在推理阶段，SSR 在拒绝预算下接受候选项或从未改变的前缀重新采样，无需外部学习的验证者。在三个推理模型和五个数学推理基准上，SSR 在生成的标记数量为 1.21-1.40 倍的情况下，相较于单次解码提高了 5.4-10.1 个点的宏平均准确率，并在评估的逐步方法中达到了最高的宏平均准确率。完整解决方案的扩展方法需要 4.47-8.27 倍的单次标记成本才能达到可比的性能。
cs.CL / 98 / 2610.05978
Byte Language Models: Scaling, Emergent Abstractions, and Information Allocation
字节语言模型：扩展、涌现抽象与信息分配
Abstract
Tokenizer-free language models remove the inductive bias of fixed tokenizers by modeling text directly as bytes, but the resulting longer sequences substantially increase computation and eliminate explicit text abstractions. We ask whether this additional computation can be useful, and whether standard Transformers can learn the abstractions that tokenization provides. We study these questions on Transformers without specialized tokenization-related architectures. With token-superposition training and hash embeddings, byte Transformers consistently outperform subword Transformers as model size scales. We further find that byte Transformers build local text abstractions as external tokenizers: a set of segmentation-like positions are used to collect local context representations, and restricting up to $25\%$ of intermediate layers to these local representations preserves downstream performance. Finally, these learned structures induce highly non-uniform generation difficulty, with uncertainty concentrated near local structure boundaries; exploiting them for speculative decoding yields $3.4\times$ more accepted tokens than in subword Transformers.
Chinese Translation
无分词器的语言模型通过直接将文本建模为字节，消除了固定分词器的归纳偏差，但由此产生的更长序列显著增加了计算量，并消除了显式的文本抽象。我们探讨这种额外的计算是否有用，以及标准的变换器（Transformers）是否能够学习分词所提供的抽象。我们在没有专门分词相关架构的变换器上研究这些问题。通过分词超叠训练和哈希嵌入，字节变换器在模型规模扩展时始终优于子词变换器。我们进一步发现，字节变换器构建了与外部分词器类似的局部文本抽象：一组类似分段的位置用于收集局部上下文表示，并将最多 $25\%$ 的中间层限制为这些局部表示可以保持下游性能。最后，这些学习到的结构引发了高度不均匀的生成难度，不确定性集中在局部结构边界附近；利用它们进行推测解码比子词变换器产生 $3.4 imes$ 更多的接受标记。
cs.CL / 99 / 2610.05982
Breaking the Tie: A Cluster-Aware Routing Framework for Large Language Models
打破平衡：一个面向集群的路由框架用于大型语言模型
Abstract
With the rapid development of artificial intelligence, the emergence of various Large Language Models (LLMs) has created a rich model ecosystem. However, this also brings a key challenge: how to select the optimal model for a specific user query. LLM routing addresses this need by dynamically assigning queries to the most suitable expert in the pool of candidate models. However, existing routing frameworks often simplify this process to a standard classification task; thus, a critical vulnerability is exposed when multiple candidate models correctly answer the same query. We formalize this capability overlap as routing noise, which misleads the router with arbitrarily correct candidate models, ultimately leading to routing collapse (a severe decline in generalization ability on unseen tasks). To address this problem, we propose a novel Cluster-Aware Soft-Labeling Routing (CASLR) framework. CASLR shifts the evaluation paradigm from the success of a single query to macro-domain consensus by replacing traditional one-hot vectors with a masked softmax mechanism. Specifically, for experts who answer incorrectly, we penalize their target probability to zero; for the remaining candidates, we directly compute continuous fine-grained soft labels based on their global clustering utility scores. We then use these refined soft labels to supervise a lightweight router. Specifically, the framework not only demonstrates superior accuracy on multiple benchmarks, but also outperforms Llama-3.3-70B-Instruct by 7.80% in overall average performance. Furthermore, the extremely low routing inference latency of only 1.13s further confirms that CASLR can achieve efficient system scheduling with almost zero additional overhead, while ensuring high response quality.
Chinese Translation
随着人工智能的快速发展，各种大型语言模型（Large Language Models, LLMs）的出现创造了丰富的模型生态系统。然而，这也带来了一个关键挑战：如何为特定用户查询选择最优模型。LLM 路由通过动态地将查询分配给候选模型池中最合适的专家来满足这一需求。然而，现有的路由框架往往将这一过程简化为标准分类任务；因此，当多个候选模型正确回答同一查询时，暴露出一个关键的脆弱性。我们将这种能力重叠形式化为路由噪声，它误导路由器选择任意正确的候选模型，最终导致路由崩溃（在未见任务上的泛化能力严重下降）。为了解决这个问题，我们提出了一种新颖的面向集群的软标签路由框架（Cluster-Aware Soft-Labeling Routing, CASLR）。CASLR通过用掩蔽的 softmax 机制替代传统的一热向量，将评估范式从单个查询的成功转变为宏观领域共识。具体而言，对于回答错误的专家，我们将其目标概率惩罚为零；对于其余候选者，我们根据其全局聚类效用分数直接计算连续的细粒度软标签。然后，我们使用这些精炼的软标签来监督一个轻量级路由器。具体来说，该框架不仅在多个基准测试中表现出优越的准确性，还在整体平均性能上超越了 Llama-3.3-70B-Instruct 7.80%。此外，仅为 1.13 秒的极低路由推理延迟进一步证实了 CASLR 能够以几乎零额外开销实现高效的系统调度，同时确保高响应质量。
cs.CL / 100 / 2610.06011
D-Loop: Looped Diffusion Drafting for Speculative Decoding
D-Loop：用于推测解码的循环扩散草图
Abstract
Block diffusion accelerates speculative decoding by drafting multiple tokens in one forward pass. However, each position predicts a marginal distribution without observing earlier proposed tokens, limiting draft quality and acceptance length. We identify a concrete failure, the \emph{repetition trap}, in which neighboring positions produce redundant copies of the same token. We explain this tendency theoretically and empirically examine its association with shorter accepted drafts. Recent methods refine marginal predictions with an additional causal head or a separately trained drafter, increasing parameter storage and introducing separate training objectives. We instead propose D-Loop, which introduces \emph{intra-block causal conditioning} within the original diffusion drafter without additional model components. Inspired by semi-autoregressive generation and parameter sharing, D-Loop reuses the same backbone across looped passes. The first pass proposes a block, and the second conditions on a selected prefix to regenerate the suffix in parallel. A complementary prefix--suffix objective trains the shared drafter for both anchor-only prefix prediction and prefix-conditioned suffix prediction. Across eight math, code, and chat benchmarks, D-Loop can beat DFlash and DSpark on Qwen3-4B and Qwen3-8B with obvious gains.
Chinese Translation
块扩散通过在一次前向传递中草拟多个标记来加速推测解码。然而，每个位置在没有观察到早期提出的标记的情况下预测边际分布，这限制了草图的质量和接受长度。我们识别出一个具体的失败现象，即 extit{重复陷阱}，在这种情况下，相邻位置产生相同标记的冗余副本。我们从理论上解释这种倾向，并实证考察其与较短接受草图的关联。最近的方法通过增加额外的因果头或单独训练的草图生成器来细化边际预测，这增加了参数存储并引入了独立的训练目标。我们提出D-Loop，它在原始扩散草图生成器中引入 extit{块内因果条件}，而无需额外的模型组件。D-Loop受到半自回归生成和参数共享的启发，在循环传递中重用相同的主干。第一次传递提出一个块，第二次则基于选定的前缀进行条件处理，以并行再生后缀。互补的前缀-后缀目标训练共享的草图生成器，以实现仅锚定前缀预测和前缀条件后缀预测。在八个数学、代码和聊天基准测试中，D-Loop在Qwen3-4B和Qwen3-8B上明显优于DFlash和DSpark，取得了显著的提升。
cs.CL / 101 / 2610.06026
Differentiable Bit-Widths: Co-optimizing Pruning and Quantization via SVD for Ultra-Efficient LLM Compression
可微分位宽：通过奇异值分解共同优化剪枝和量化以实现超高效的大型语言模型压缩
Abstract
SVD-based pruning and quantization have recently emerged as a promising strategy for the ultra-efficient compression of large language models. In these methods, compression is performed in two stages: components are first truncated, and the remaining ones are subsequently quantized. Although this decoupled pipeline benefits from both pruning and quantization, it requires separate optimization for each stage and fails to fully exploit their balance, which can lead to suboptimal performance under aggressive compression. To address this limitation, we propose a new LLM compression method that co-optimizes pruning and quantization in a unified framework. Our key idea is a differentiable method for learning component-wise bit-widths, allowing less important components to be assigned 0-bit precision and pruned away. Notably, our method performs favorably against two-stage baselines, even when subjected to extreme quantization settings ($1.61$ bits) designed for ultra-efficiency. Code: https://github.com/MMAI-Laboratory/DBW.
Chinese Translation
基于奇异值分解（SVD）的剪枝和量化最近成为超高效压缩大型语言模型的一种有前景的策略。在这些方法中，压缩分为两个阶段：首先截断组件，然后对剩余组件进行量化。尽管这种解耦的流程受益于剪枝和量化，但它需要对每个阶段进行单独优化，未能充分利用它们之间的平衡，这可能导致在激进压缩下性能不佳。为了解决这一局限性，我们提出了一种新的大型语言模型压缩方法，在统一框架中共同优化剪枝和量化。我们的关键思想是采用可微分的方法来学习组件级别的位宽，使得不重要的组件可以被分配为0位精度并被剪除。值得注意的是，我们的方法在极端量化设置（$1.61$ 位）下表现优于两阶段基线，即便是在为超高效设计的情况下。代码链接： https://github.com/MMAI-Laboratory/DBW。
cs.CL / 102 / 2610.06031
LightMTP: Lightweight Latent Multi-Token Prediction
LightMTP：轻量级潜在多标记预测
Abstract
Next-token prediction (NTP) is the standard pretraining objective for large language models, yet it provides an explicit training signal only for the immediate next token, which can lead models to exploit local patterns instead of capturing longer-range structure and ideas. Multi-token prediction (MTP) addresses this by training models to predict several future tokens. However, existing MTP methods often introduce a large number of new parameters with limited improvements in downstream performance. Latent MTP approaches address this efficiency issue by encoding future tokens into a vector representation. However, these approaches usually rely on external helper models for future token encoding. We propose LightMTP, a lightweight, i.e., parameter-efficient, latent MTP approach that bootstraps the future token representations from the model's own hidden states. Our two LightMTP variants extend supervision to more future tokens without requiring the additional computational overhead of conventional MTP nor the external supervision latent MTP normally relies on. LightMTP adds at most 1% extra parameters, retains better performance on general language modeling benchmarks, and achieves similar gains in planning, coding, and reasoning.
Chinese Translation
下一个标记预测（NTP）是大型语言模型的标准预训练目标，但它仅为紧接着的下一个标记提供明确的训练信号，这可能导致模型利用局部模式，而无法捕捉更长范围的结构和思想。多标记预测（MTP）通过训练模型预测多个未来标记来解决这一问题。然而，现有的 MTP 方法通常引入大量新参数，但在下游性能上的提升有限。潜在 MTP 方法通过将未来标记编码为向量表示来解决这一效率问题。然而，这些方法通常依赖于外部辅助模型进行未来标记编码。我们提出了 LightMTP，一种轻量级的、即参数高效的潜在 MTP 方法，它从模型自身的隐藏状态中引导未来标记的表示。我们的两个 LightMTP 变体扩展了对更多未来标记的监督，而无需传统 MTP 的额外计算开销，也不依赖于潜在 MTP 通常依赖的外部监督。LightMTP 仅增加最多 1% 的额外参数，在通用语言建模基准上保持更好的性能，并在规划、编码和推理方面实现类似的提升。
cs.CL / 103 / 2610.06064
TrustMI: Causally controlling how assistants trust their users
TrustMI：因果控制助手如何信任其用户
Abstract
Large Language Model (LLM) assistants routinely decide whether they can trust users and third parties whose competence, intentions, and integrity they cannot verify. This uncertainty matters for safety, as trusting the wrong party can lead an agent to comply with harmful requests or act on malicious instructions encountered during tool use. To study this problem, we define trust as an assistant's willingness to accept vulnerability to the actions of another party and ask whether such behavior can be causally controlled through model activations. We build 2,000 contrastive conversations spanning ability, benevolence, and integrity, where paired responses complete the same request but differ in whether the assistant trusts the user. From these pairs, we learn steering matrices while keeping the model parameters frozen and test them across six instruction-tuned models from three families, finding that steering changes trust decisions monotonically in both directions. We then ask whether this effect extends to several safety-related agent settings involving harmful requests, prompt injections, and insider threats, while using benign-task and reasoning as controls. Our findings provide evidence that trust in the user can be causally controlled along linear directions in model activations and provide a way to study how trust shapes safety-relevant behavior in language models.
Chinese Translation
大型语言模型（LLM）助手常常需要决定是否信任那些其能力、意图和诚信无法验证的用户和第三方。这种不确定性对安全性至关重要，因为错误的信任可能导致代理遵从有害请求或在工具使用过程中执行恶意指令。为研究这一问题，我们将信任定义为助手愿意接受对另一方行为的脆弱性，并探讨这种行为是否可以通过模型激活进行因果控制。我们构建了2000个对比对话，涵盖能力、善意和诚信，其中配对响应完成相同请求，但在助手是否信任用户方面存在差异。从这些配对中，我们在保持模型参数不变的情况下学习引导矩阵，并在来自三个家族的六个指令调优模型上进行测试，发现引导在两个方向上单调地改变信任决策。随后，我们探讨这一效应是否扩展到涉及有害请求、提示注入和内部威胁的多个安全相关代理设置，同时使用良性任务和推理作为对照。我们的研究结果提供了证据，表明用户的信任可以沿着模型激活的线性方向进行因果控制，并提供了一种研究信任如何塑造语言模型中与安全相关行为的方法。
cs.CL / 104 / 2610.06093
Cross-Lingual Transferability of Training Data Extraction Attacks to Recover Memorized PII
训练数据提取攻击的跨语言可转移性以恢复记忆中的个人身份信息（PII）
Abstract
The robustness of Personally Identifiable Information (PII) protection in Large Language Models (LLMs) is a critical concern, yet the risks associated with cross-lingual data extraction remain under-explored. This study evaluates the vulnerability of English-centric and multilingual models to Training Data Extraction (TDE) attacks when prompted in non-English languages. We construct a multi-domain PII dataset comprising social media handles, email addresses, and phone numbers and translate the attack contexts into Italian, Spanish, French, and German. Our results show that TDE attacks against both English-centric and multilingual models transfer to different languages: the attacks are successful on translated prompts, even though only the original English prompt might have been included in the pre-training data. A web-presence check on a sample of the translations confirms that they are not available online. The share of English leaks recovered in other languages grows with the multilingual capability of the model, and it drops sharply when the original wording is lost, even without a change of language. This suggests that native multilingual pre-training facilitates the emergence of latent cross-linguistic bridges that simplify the retrieval of personally identifiable information (PII). We analyze the activations of multilingual large language models (LLMs) and find that different translations of the same prompt are bridged in similar representations, with the strongest alignment in the middle layers. Our results highlight a fundamental security gap in modern LLMs, necessitating more robust, language-agnostic sanitization strategies for future model alignment.
Chinese Translation
大型语言模型（LLMs）中个人身份信息（PII）保护的稳健性是一个关键问题，但与跨语言数据提取相关的风险仍未得到充分探讨。本研究评估了以英语为中心和多语言模型在非英语语言提示下对训练数据提取（TDE）攻击的脆弱性。我们构建了一个多领域的PII数据集，包括社交媒体账号、电子邮件地址和电话号码，并将攻击上下文翻译成意大利语、西班牙语、法语和德语。我们的结果表明，针对英语中心和多语言模型的TDE攻击可以转移到不同语言：即使只有原始的英语提示可能包含在预训练数据中，翻译后的提示也能成功进行攻击。对翻译样本进行的网络存在检查确认它们在网上不可用。随着模型的多语言能力的提高，从其他语言中恢复的英语泄露的比例增加，而当原始措辞丢失时，即使没有语言变化，这一比例急剧下降。这表明，母语多语言预训练促进了潜在跨语言桥梁的出现，从而简化了个人身份信息（PII）的检索。我们分析了多语言大型语言模型（LLMs）的激活，发现同一提示的不同翻译在相似的表示中相互连接，中间层的对齐最强。我们的结果突显了现代LLMs中的一个基本安全缺口，迫切需要更稳健的、与语言无关的清理策略，以便未来模型的对齐。
cs.CL / 105 / 2610.06100
From Traces to Agentic Worlds: Agentic Language World Models for Interactive Environment Simulation
从痕迹到能动世界：用于交互环境模拟的能动语言世界模型
Abstract
Realistic environment replicas are increasingly valuable for training and evaluating LLM agents, yet the original systems may be inaccessible or impractical to reproduce. We explore agentic language world modeling: rather than rebuilding an executable environment, a world model agent serves as the environment for a task agent and supports faithful and stateful simulation. We instantiate this paradigm with Trace2Env, a learning-free framework for settings where the original system is unavailable but historical interaction traces remain accessible. Trace2Env reconstructs these traces into a reusable environment worldbook containing environment schemas, grounded evidence, and induced behavioral knowledge. At runtime, the world model agent actively consults the worldbook together with persistent episodic state to infer each action's observation and lasting state effects. Across nine environments, Trace2Env improves both next-observation fidelity and long-horizon interaction consistency over conventional prompt-based LWMs. In multi-turn interaction, task agent actions generated against Trace2Env remain valid more often when replayed in the real environment, indicating that its simulated dynamics better preserve the consequences of earlier actions across successive turns. These results establish agentic language world modeling as an alternative direction for building realistic environment replicas without reconstructing the original executable system.
Chinese Translation
逼真的环境复制品在训练和评估大型语言模型（LLM）代理方面越来越有价值，然而原始系统可能无法访问或不切实际地重现。我们探讨了能动语言世界建模：与其重建一个可执行的环境，不如让一个世界模型代理作为任务代理的环境，并支持忠实且有状态的模拟。我们通过Trace2Env实例化这一范式，Trace2Env是一个无学习的框架，适用于原始系统不可用但历史交互痕迹仍然可访问的场景。Trace2Env将这些痕迹重建为一个可重用的环境世界书，包含环境模式、基础证据和诱导的行为知识。在运行时，世界模型代理主动咨询世界书，并结合持久的情节状态来推断每个动作的观察和持久状态效应。在九个环境中，Trace2Env在下一观察的保真度和长时间交互的一致性方面优于传统的基于提示的语言世界模型（LWM）。在多轮交互中，与Trace2Env生成的任务代理动作在真实环境中重放时更常保持有效，这表明其模拟动态更好地保留了早期动作在后续轮次中的后果。这些结果确立了能动语言世界建模作为构建逼真环境复制品的一种替代方向，而无需重建原始可执行系统。
cs.CL / 106 / 2610.06161
Introducing Code-Switched Contexts to Cognitively-Inspired Bilingual Model Training
引入代码切换语境以进行认知启发的双语模型训练
Abstract
During language acquisition, bilingual children are regularly exposed to code-switched input and use it as a cognitive scaffold to accelerate vocabulary growth and cross-linguistic syntactic mapping. In contrast, computational bilingual models are conventionally pretrained on interleaved monolingual corpora. While introducing synthetic code-switching during pretraining has become a promising strategy to enhance cross-lingual alignment and downstream performance, the structural and developmental parameters governing the success remain poorly understood. In this work, we investigate the efficiency of training with synthetic code-switched data across two typologically distinct language pairs by controlling two key variables: the structural location of code-switches and the dynamic switching rate across training stages. Our results show that training with code-switched data improves cross-lingual alignment for typologically close languages.
Chinese Translation
在语言习得过程中，双语儿童经常接触到代码切换的输入，并利用其作为认知支架来加速词汇增长和跨语言句法映射。相对而言，计算双语模型通常是在交错的单语语料上进行预训练。尽管在预训练过程中引入合成代码切换已成为提升跨语言对齐和下游性能的有前景的策略，但影响其成功的结构和发展参数仍然知之甚少。在本研究中，我们通过控制两个关键变量：代码切换的结构位置和训练阶段的动态切换率，探讨了在两对类型学上明显不同的语言对中使用合成代码切换数据进行训练的有效性。我们的结果表明，使用代码切换数据进行训练可以改善类型学相近语言的跨语言对齐。
cs.CL / 107 / 2610.06216
Cross-lingual Calibration of Pre-Generation Success Probes for Multilingual LLM Routing
多语言大模型路由的生成前成功探测器的跨语言校准
Abstract
Pre-generation success probes estimate response correctness from a language model's hidden activations before decoding, enabling cost-aware routing. While prior work has demonstrated their utility primarily on English inputs, we study their reliability across languages along three dimensions: (1) whether they preserve the ranking of likely successes and failures (DISCRIMINATION); (2) whether they retain probabilities that match observed success frequencies (CALIBRATION); and (3) whether they produce scores comparable enough across candidate models for cost-aware multilingual routing (UTILITY). Using 3,000 MATH problems in 10 languages and 8 open-weight model configurations, we compare cross-lingual transfer from English-trained probes and equal-budget pooled multilingual probes. English-trained probes retain useful cross-lingual discrimination but become less well calibrated after transfer. Pooled multilingual supervision improves both properties and yields more reliable estimates of success. In routing experiments, the pooled router achieves a 0.7% higher test success rate while reducing modeled cost by 13.0% relative to always selecting the model with the highest average success. These results show that multilingual routing requires success estimates that remain well calibrated and comparable across languages and models.
Chinese Translation
生成前成功探测器通过在解码之前估计语言模型的隐藏激活来评估响应的正确性，从而实现成本感知的路由。尽管之前的研究主要在英语输入上展示了它们的实用性，但我们研究了它们在不同语言中的可靠性，主要从三个维度进行分析：（1）它们是否保持成功和失败的排名（区分性）；（2）它们是否保留与观察到的成功频率相匹配的概率（校准性）；（3）它们是否产生足够可比的分数，以便进行成本感知的多语言路由（实用性）。我们使用了10种语言的3000道数学题和8种开放权重模型配置，比较了从英语训练的探测器和等预算汇聚的多语言探测器的跨语言迁移。英语训练的探测器在跨语言区分性上保持了有用性，但在迁移后校准性降低。汇聚的多语言监督改善了这两个特性，并提供了更可靠的成功估计。在路由实验中，汇聚路由器的测试成功率比始终选择平均成功率最高的模型高出0.7%，同时相对降低了13.0%的建模成本。这些结果表明，多语言路由需要保持良好校准且在不同语言和模型之间可比的成功估计。
cs.CL / 108 / 2610.06251
Shared Stopping Decisions Change Answers in HQQ Cache Quantization
共享停止决策改变 HQQ 缓存量化中的答案
Abstract
Language-model systems batch questions for throughput, but unrelated questions should not change a target's answer when its input and numerical execution are fixed. We study compression of the key and value cache, which stores attention representations reused during generation. With request-local groups, Transformers' Half-Quadratic Quantization (HQQ) backend updates compression parameters separately but uses a shared average error to decide when all updates stop. Replacing only the question batched with the target changes four-bit HQQ answers in 170/384 test comparisons across two models. Replaying the other execution's update counts reproduces its complete answer and cache fingerprints in every changed pair, in both directions. Computing the stopping mean in FP32 reduces cache differences but leaves answer changes. Native HQQ also changes confirmed numerical correctness in eight arithmetic pairs. Fixed iterations and request-local stopping remove observed companion dependence under matched controls. Request-local stopping remains sensitive to synthetic padding changes at the tensor level. Fixing the original iteration budget removes this decision path without tuning. Neither repair has an established quality advantage, and natural rebatching still changes answers. Request-independence audits must cover stopping decisions as well as quantization groups.
Chinese Translation
语言模型系统将问题批处理以提高吞吐量，但无关的问题在输入和数值执行固定时不应改变目标的答案。我们研究了存储在生成过程中重用的注意力表示的键和值缓存的压缩。通过请求局部组，变换器的半二次量化（Half-Quadratic Quantization, HQQ）后端分别更新压缩参数，但使用共享的平均误差来决定何时停止所有更新。仅替换与目标批处理的问题在两个模型的 384 次测试比较中改变了四位 HQQ 答案的 170 次。重放其他执行的更新计数在每对变化中以双向方式再现其完整答案和缓存指纹。在 FP32 中计算停止均值减少了缓存差异，但仍然留下答案变化。原生 HQQ 还在八个算术对中改变了确认的数值正确性。固定迭代和请求局部停止消除了在匹配控制下观察到的伴随依赖性。请求局部停止对张量级别的合成填充变化仍然敏感。固定原始迭代预算消除了这一决策路径而无需调优。两种修复方法都没有确立的质量优势，自然重批处理仍然改变答案。请求独立性审计必须涵盖停止决策以及量化组。
cs.CL / 109 / 2610.06273
Probabilistic Race and Ethnicity Prediction Using Group-Specific Name Lists
基于特定群体姓名列表的概率种族和民族预测
Abstract
Statistically valid estimation of racial and ethnic disparities often requires inferring the probability that an individual belongs to a particular racial or ethnic group given only their name and geographic location. The standard approach, Bayesian Improved Surname Geocoding (BISG), relies on group population frequencies for each name. Although the U.S. Census Bureau provides such information for common names and a limited set of racial categories, comparable data do not exist for many racial and ethnic groups and are rarely available outside the U.S. We propose the list-powered BISG ($\ell$BISG) method, which can be used to derive calibrated group probabilities from group-specific name lists. These lists may be compiled based on expert knowledge or generated synthetically using large language models (LLMs), and thus may be subject to unknown biases. Representing names as embeddings, we treat list membership as a proxy prediction task and apply a correction based on proximal inference to recover the target group probabilities. We validate the method on U.S. voter files with self-reported race, on the full-count 1900 U.S. Census, and on the Lebanese voter registry. We find that LLM-generated name lists yield accurate and well-calibrated probabilities as well as precise disparity estimates comparable to those obtained using methods that require name-race data. Thus, $\ell$BISG substantially broadens the applicability of probabilistic race and ethnicity prediction to settings where name-race data are unavailable.
Chinese Translation
统计有效的种族和民族差异估计通常需要推断个体仅凭其姓名和地理位置属于特定种族或民族群体的概率。标准方法贝叶斯改进姓氏地理编码（Bayesian Improved Surname Geocoding, BISG）依赖于每个姓名的群体人口频率。尽管美国人口普查局提供了常见姓名和有限种族类别的信息，但许多种族和民族群体缺乏可比数据，并且在美国以外的地方很少可用。我们提出了基于列表的BISG方法（list-powered BISG, $ ext{ℓBISG}$），该方法可以从特定群体姓名列表中推导出校准的群体概率。这些列表可以基于专家知识编制，也可以使用大型语言模型（Large Language Models, LLMs）合成生成，因此可能受到未知偏差的影响。我们将姓名表示为嵌入，将列表成员资格视为代理预测任务，并应用基于近似推断的校正来恢复目标群体概率。我们在美国选民文件（自报种族）、1900年美国人口普查的全数数据以及黎巴嫩选民登记册上验证了该方法。我们发现LLM生成的姓名列表能够提供准确且良好校准的概率，以及与需要姓名-种族数据的方法获得的精确差异估计相当的结果。因此，$ ext{ℓBISG}$显著拓宽了概率种族和民族预测的适用性，适用于缺乏姓名-种族数据的环境。
cs.CL / 110 / 2610.06286
DeferKV: Rethinking Eviction Timing for One-Shot KV Cache Compression
DeferKV：重新思考一次性 KV 缓存压缩的驱逐时机
Abstract
Long-context large language models (LLMs) have demonstrated strong capabilities across a wide range of tasks, but the growing KV cache introduces substantial memory and inference overhead. Existing one-shot KV cache compression methods typically commit to irreversible eviction immediately after prefill, before any signal from actual generation becomes available. Our quantitative analysis shows that early queries from the actual generation stage provide attention signals that are more consistent with subsequent decode attention, with the largest single-step gain occurring at the prefill-decode boundary. Based on this observation, we propose DeferKV, which moves the eviction decision from the end of prefill to the first real decoding step and temporally combines prompt-side and decode-side observations, thereby better aligning KV importance estimation with subsequent generation requirements. DeferKV requires no additional training, draft model, or future-query prediction module, making it simple and easy to deploy. Experiments on LongBench, RULER, and Needle-in-a-Haystack demonstrate that DeferKV consistently improves model performance under KV cache compression while maintaining low inference latency.
Chinese Translation
长上下文的大型语言模型（LLMs）在广泛的任务中展现了强大的能力，但不断增长的 KV 缓存引入了显著的内存和推理开销。现有的一次性 KV 缓存压缩方法通常在预填充后立即进行不可逆的驱逐，而此时尚未获得实际生成的任何信号。我们的定量分析表明，来自实际生成阶段的早期查询提供的注意力信号与后续解码注意力更为一致，且在预填充与解码的边界处获得了最大的单步增益。基于这一观察，我们提出了 DeferKV，将驱逐决策从预填充结束时移至第一个真实解码步骤，并在时间上结合提示侧和解码侧的观察，从而更好地将 KV 重要性估计与后续生成需求对齐。DeferKV 不需要额外的训练、草稿模型或未来查询预测模块，使其简单易于部署。在 LongBench、RULER 和 Needle-in-a-Haystack 上的实验表明，DeferKV 在 KV 缓存压缩下持续提升模型性能，同时保持低推理延迟。
cs.CL / 111 / 2610.06287
From Abusive Language Classification to Sequence Labeling Identification
从恶意语言分类到序列标注识别
Abstract
Industrial content moderation must process massive message streams under tight latency constraints, yet most abusive language (AL) detection systems rely on sentence-level classification (ALC), which neither localizes abusive spans nor identifies who is targeted. We define Abusive Language Identification (ALI) as a sequence-labeling task that jointly extracts AL spans and target mentions, and assess whether this approach can be used for text moderation. On a pilot corpus drawn from a production moderation pipeline, we compare ALI with ALC on cross-domain generalization and implicit abuse, and we also evaluate AL and target span detection. ALI remains competitive with ALC while providing localized outputs for moderators, with a modest and configuration-sensitive advantage on implicit abuse. Exact AL boundaries and target spans remain difficult to recover. We complement this comparison with a qualitative analysis and discuss perspectives on complete target--span linking and on structured benchmarks for ALI.
Chinese Translation
工业内容审核必须在严格的延迟限制下处理海量消息流，然而大多数恶意语言（AL）检测系统依赖于句子级分类（ALC），这既无法定位恶意片段，也无法识别受害者。我们将恶意语言识别（ALI）定义为一个序列标注任务，旨在联合提取恶意语言片段和目标提及，并评估这种方法是否可以用于文本审核。在从生产审核管道中提取的试点语料库上，我们比较了ALI与ALC在跨领域泛化和隐性恶意方面的表现，同时评估了恶意语言和目标片段的检测。ALI在提供给审核员的本地化输出方面与ALC保持竞争力，并在隐性恶意方面具有适度且对配置敏感的优势。确切的恶意语言边界和目标片段仍然难以恢复。我们通过定性分析补充了这一比较，并讨论了关于完整目标-片段链接及ALI的结构化基准的观点。
cs.CL / 112 / 2610.06298
DialectSentEval 2026: Arabic Dialect Sentiment Analysis and Swapping Shared Task
DialectSentEval 2026：阿拉伯方言情感分析与情感交换共享任务
Abstract
Sentiment analysis is a fundamental problem in Natural Language Processing (NLP). Standard sentiment classification for the Arabic language remains challenging due to the high volume of dialectal Arabic. To advance research in this area, this paper proposes the Shared Task on Sentiment Analysis and Swapping in Arabic Dialects (DialectSentEval), hosted with the Arabic Natural Language Processing Conference (ArabicNLP 2026). This shared task consists of two subtasks: Subtask 1 focuses on multi-class and multi-dialect sentiment analysis, requiring models to identify sentiment polarity across various Arabic dialects. Subtask 2 introduces a generative task for Arabic sentiment swap, challenging models to invert sentiment polarity while preserving core semantics. In this overview paper, we present the motivation, dataset creation, and summarize the main findings from participating models.
Chinese Translation
情感分析是自然语言处理（NLP）中的一个基本问题。由于阿拉伯方言的数量庞大，标准的阿拉伯语情感分类仍然面临挑战。为了推动该领域的研究，本文提出了阿拉伯方言情感分析与情感交换共享任务（DialectSentEval），该任务将在阿拉伯自然语言处理会议（ArabicNLP 2026）上举办。该共享任务由两个子任务组成：子任务1专注于多类和多方言情感分析，要求模型识别不同阿拉伯方言中的情感极性。子任务2引入了阿拉伯情感交换的生成任务，挑战模型在保留核心语义的同时反转情感极性。在本文的概述中，我们介绍了研究动机、数据集创建，并总结了参与模型的主要发现。
cs.CL / 113 / 2610.06322
Agentic schema-guided extraction of materials process knowledge from scientific literature
基于代理模式的材料过程知识从科学文献中提取
Abstract
Materials literature contains detailed experimental knowledge, but procedures, chemical entities and measurements remain difficult to aggregate because they are reported in heterogeneous forms and depend on process-specific context. We present SciKGExtract, a schema-guided framework that combines large-language-model extraction with chemical normalization and agent-based evaluation and refinement before knowledge-graph integration. We evaluate the framework on 176 atomic-layer-deposition papers describing zinc oxide (ZnO) and indium--gallium--zinc oxide (IGZO), together with an expert-annotated full-schema subset. PubChem normalization improves exact-match extraction F1 for every tested model. For ZnO, the best F1 increases from 0.591 for direct normalized extraction to 0.805 with agentic refinement, whereas the best IGZO result is 0.344, revealing the greater difficulty of multicomponent supercycle processes. Evaluation against a deeply nested schema containing 65 experimental properties and 155 quantitative measurement nodes further exposes errors in process segmentation and numerical assignment. These results show that chemical canonicalization and targeted agentic verification provide complementary controls for converting complex materials literature into reusable, machine-actionable experimental knowledge.
Chinese Translation
材料文献包含详细的实验知识，但由于其以异质形式报告且依赖于特定过程的上下文，程序、化学实体和测量的汇总仍然困难。我们提出了SciKGExtract，这是一个基于模式引导的框架，结合了大语言模型提取、化学标准化以及基于代理的评估和优化，随后进行知识图谱集成。我们在176篇描述氧化锌（ZnO）和铟-镓-锌氧化物（IGZO）原子层沉积的论文上评估了该框架，并结合专家注释的完整模式子集。PubChem标准化提高了每个测试模型的精确匹配提取F1值。对于ZnO，最佳F1值从直接标准化提取的0.591提高到经过代理优化后的0.805，而最佳IGZO结果为0.344，揭示了多组分超循环过程的更大困难。针对包含65个实验特性和155个定量测量节点的深层嵌套模式的评估进一步暴露了过程分割和数值分配中的错误。这些结果表明，化学规范化和针对性的代理验证为将复杂的材料文献转化为可重用的、机器可操作的实验知识提供了互补的控制手段。
cs.CL / 114 / 2610.06345
Breaking Bureaucracy: Evaluating open-source LLMs for legal document review
打破官僚主义：评估开源大型语言模型在法律文件审查中的应用
Abstract
In this paper, we evaluate open-source generative LLMs on legal Natural Language Inference (NLI). Legal inspectorial processes take place in specific domains and often deal with confidential data. This creates a need for working with local models that do not require labeled training data. We evaluate our models on the ContractNLI benchmark and two NLI4Wills datasets. We successfully reproduce the baseline for the task (Span NLI BERT) and we evaluate multiple open-source LLMs on the same task. We analyze the invalid rate of the models, and their stability across temperature settings and domains. Among the generative models, Gemma-4 26B performs the best, reaching an accuracy of 81.2%, even outperforming the supervised model on one metric. On accuracy, it is not possible to beat the supervised model with zero-shot approaches. Qwen-3.6 35B performs well on both ContractNLI and additional datasets in the legal wills domain. Our findings indicate that zero-shot, open-source, generative LLMs are a viable alternative for real-world legal NLI when no supervised data is available. Our code is available at https://github.com/fbaratov/contractnli-llms.
Chinese Translation
在本文中，我们评估了开源生成型大型语言模型（LLMs）在法律自然语言推理（NLI）中的表现。法律检查过程发生在特定领域，通常涉及机密数据。这就需要使用不需要标注训练数据的本地模型。我们在ContractNLI基准和两个NLI4Wills数据集上评估我们的模型。我们成功地重现了该任务的基线（Span NLI BERT），并在同一任务上评估了多个开源LLMs。我们分析了模型的无效率及其在不同温度设置和领域中的稳定性。在生成模型中，Gemma-4 26B表现最佳，达到了81.2%的准确率，甚至在一个指标上超越了监督模型。在准确率方面，零-shot方法无法超越监督模型。Qwen-3.6 35B在ContractNLI和法律遗嘱领域的其他数据集上表现良好。我们的研究结果表明，当没有监督数据可用时，零-shot开源生成型LLMs是现实世界法律NLI的可行替代方案。我们的代码可在https://github.com/fbaratov/contractnli-llms获取。
cs.CL / 115 / 2610.06360
Ontology Concept Overlap as a Training Signal: Knowledge-Grounded Reinforcement Learning for Clinical Question Answering
本体概念重叠作为训练信号：基于知识的临床问答强化学习
Abstract
Reinforcement learning post-training for language models relies on two reward designs: human preferences (RLHF, DPO) and binary verifiers (RLVR). Clinical question answering fits neither. Near-correct answers differ by a single substituted entity, and no executable check decides clinical correctness. We instantiate a soft verifier from a maintained controlled vocabulary: UMLS Concept Unique Identifier overlap (via scispaCy, set-level F1) gives a graded, externally specified reward computed without a model in the loop. We combine it inside GRPO with an entropy-normalised LLM judge, which covers the safety and evidence axes overlap cannot see, and a small consistency penalty on padding and repetition that keeps early-training samples scorable. This three-term composite improves over SFT on Phi-3-mini (3.8B) over MedQA by 2.9% on EM (0.700 vs 0.680) and 39% on Token-F1 (0.202 vs 0.145); on Llama-3.2-3B the corresponding gains are 14% on EM and 35% on Token-F1. We report Token-F1 as the primary metric because it credits partially-correct clinical content that EM discards at this open-generation scale. Main-table results are means over 3 seeds with standard deviations below 0.005. The method transfers to PubMedQA, where training on the PubMedQA train set with the same composite reward improves Token-F1 over SFT by 22% on Phi-3-mini and 17% on Llama-3.2-3B without retuning. A reward ablation on Phi-3, varying the judge-ontology split at a fixed consistency weight, attributes 3 EM points to the ontology term, the contribution that catches entity substitutions the judge cannot. Three negative findings constrain the design: DPO under random negatives underperforms SFT for strong-prior models but helps the weakest-prior one; PPO under a sparse neural reward diverges; GRPO with KL-in-loss collapses at 7B.
Chinese Translation
语言模型的强化学习后训练依赖于两种奖励设计：人类偏好（RLHF, DPO）和二元验证器（RLVR）。临床问答并不适合这两者。接近正确的答案仅通过一个替换实体有所不同，且没有可执行的检查来决定临床正确性。我们从一个维护的受控词汇中实例化了一个软验证器：UMLS概念唯一标识符重叠（通过scispaCy，集合级F1）提供了一种分级的、外部指定的奖励，该奖励在计算时不需要模型参与。我们将其与一个熵归一化的LLM评判者结合在GRPO中，该评判者覆盖了安全性和证据轴的重叠，而这些重叠是无法被看到的，并且对填充和重复施加了小的一致性惩罚，以保持早期训练样本的可评分性。该三项复合方法在Phi-3-mini（3.8B）上相较于SFT在MedQA上提高了2.9%的EM（0.700对0.680）和39%的Token-F1（0.202对0.145）；在Llama-3.2-3B上，相应的提升为EM提高了14%和Token-F1提高了35%。我们报告Token-F1作为主要指标，因为它认可部分正确的临床内容，而EM在这一开放生成规模上则会丢弃这些内容。主表结果是3个种子的均值，标准偏差低于0.005。该方法可迁移到PubMedQA，在PubMedQA训练集上使用相同的复合奖励进行训练，使得在Phi-3-mini上Token-F1相较于SFT提高了22%，在Llama-3.2-3B上提高了17%，且无需重新调优。对Phi-3的奖励消融实验，固定一致性权重下改变评判者-本体分割，归因于本体术语的3个EM点，这一贡献捕捉了评判者无法识别的实体替换。三个负面发现限制了设计：在随机负样本下，DPO对强先验模型的表现不如SFT，但对最弱先验模型有所帮助；在稀疏神经奖励下，PPO出现了发散；GRPO与KL损失结合在7B时崩溃。
cs.CL / 116 / 2610.06430
Do Speech Representations Preserve Regional Accent Across Read and Spontaneous Speech?
语音表征是否在朗读与自发语音中保留地区口音？
Abstract
Regional accent cues can be captured under matched conditions, but it remains unclear whether they persist between read and spontaneous speech. We study RVG1, with 500 German speakers from nine regions, comparing ten speech representations on regional classification and continuous geolocation under matched conditions and speaker-independent read--spontaneous transfer. Whisper performs best under matched conditions, reaching 0.489 nine-way UAR and 148 km median geolocation error, but drops to 0.11/0.18 UAR across transfer directions and 363 km geolocation error. Self-supervised models show a similar degradation, whereas speaker embeddings are less discriminative in-domain but more robust under transfer. This contrast is consistent across classification and geolocation. Across representations, robustness is associated with how little a representation shifts between styles (style-invariance), for which crossstyle speaker retrieval is an interpretable proxy. Age, sex, sentence-overlap, and duration controls do not account for the gap, although channel characteristics contribute. These results show that strong matched-condition performance does not indicate robust regional information.
Chinese Translation
地区口音线索可以在匹配条件下被捕捉，但尚不清楚它们是否在朗读与自发语音之间持续存在。我们研究了RVG1，涉及来自九个地区的500名德语说话者，比较了十种语音表征在匹配条件下和说话者独立的朗读-自发转移下的地区分类和连续地理定位。Whisper在匹配条件下表现最佳，达到了0.489的九分类无偏准确率（UAR）和148公里的中位地理定位误差，但在转移方向上降至0.11/0.18 UAR和363公里的地理定位误差。自监督模型显示出类似的降级，而说话者嵌入在领域内的辨别能力较差，但在转移中更具鲁棒性。这种对比在分类和地理定位中是一致的。在不同表征中，鲁棒性与表征在风格之间的变化程度（风格不变性）相关，跨风格说话者检索是一个可解释的代理。年龄、性别、句子重叠和持续时间的控制因素并不能解释这一差距，尽管通道特征有所贡献。这些结果表明，强大的匹配条件表现并不意味着稳健的地区信息。
cs.CL / 117 / 2610.06446
The Assistance Dilemma: Learning to Teach via Multi-Turn Reinforcement Learning
辅助困境：通过多轮强化学习学习教学
Abstract
Large language models (LLMs) trained to answer questions are natively poor at teaching. Reinforcement Learning (RL) against a simulated student is a promising approach to improve their pedagogy, but existing RL-trained tutors reward the student's success on the tutored problem with the tutor's words still in context. The reward is then easiest to raise by telling the student the answer, and a tuned penalty is needed to reduce telling. Drawing on learning sciences, we introduce a masked near-transfer post-test: the student is tested on an unseen variant of the tutored problem with the tutor's utterances masked, so the reward can rise only through what the student wrote in its own turns. This discourages cognitive offloading by the student and allows the continuous penalty to be replaced by two binary reward gates (factual correctness of tutor response, no solution handover). A leave-one-out ablation shows that the learning-gain reward on its own does not separate teaching from telling: the gates reduce solution handover while the near-transfer post-test improves out-of-domain transfer. Using these reward designs we develop Eduardo, a multi-turn RL recipe for training LLM tutors, and use it to train 4B, 9B, 14B and 27B models from two distinct LLM architectures. Our post-trained Eduardo-27B model matches Gemini-3.1-Pro on MathTutorBench and Claude Opus 4.8 on TutorMoments at 2.4-6.2x fewer thinking tokens than frontier models, which matters for interactive tutoring. Without being named in the reward, the model more than doubles its use of the push-for-justification teacher move while support fading (e.g., assigning independent work), whose payoff lies beyond a single-problem dialog episode, is trained out. We open-source our training environment, an 8,671-problem near-transfer dataset, and trained models for further development.
Chinese Translation
训练用于回答问题的大型语言模型（LLMs）在教学方面表现不佳。针对模拟学生的强化学习（RL）是一种有前景的方法，可以改善它们的教学能力，但现有的RL训练辅导员在学生解决辅导问题时仍然奖励学生的成功，辅导员的言辞仍在上下文中。因此，最容易提高奖励的方式是直接告诉学生答案，这需要调整惩罚以减少这种行为。借鉴学习科学，我们引入了一种掩蔽的近转移后测：学生在一个未见过的辅导问题变体上进行测试，辅导员的发言被掩蔽，因此奖励只能通过学生在自己回合中所写的内容来提高。这种方法抑制了学生的认知卸载，并允许将连续惩罚替换为两个二元奖励门（辅导员回应的事实正确性，无解决方案移交）。通过留一法消融实验表明，仅靠学习增益奖励并不能将教学与告知区分开：奖励门减少了解决方案移交，而近转移后测则改善了领域外转移。利用这些奖励设计，我们开发了Eduardo，一个用于训练LLM辅导员的多轮RL方案，并用它训练了来自两种不同LLM架构的4B、9B、14B和27B模型。我们后训练的Eduardo-27B模型在MathTutorBench上与Gemini-3.1-Pro相匹配，在TutorMoments上与Claude Opus 4.8相比，思考令牌的使用量减少了2.4-6.2倍，这对互动辅导至关重要。在奖励中未被提及的情况下，该模型在支持淡化（例如，分配独立工作）时，其使用推理要求教师行为的频率超过了两倍，而这种行为的收益超出了单一问题对话的情境。我们开源了我们的训练环境、一个包含8671个问题的近转移数据集以及训练模型，以供进一步开发。
cs.CL / 118 / 2610.06479
Behavior-Preserving KV Cache Compression
行为保留的 KV 缓存压缩
Abstract
KV caches are a major bottleneck in long-context inference and long-form generation with large language models. Existing training-free eviction policies largely rely on proxy importance signals, such as attention mass, to decide which past tokens to retain. We argue that cache compression should instead preserve the predictive behavior of the full-cache model, retaining entries whose removal would substantially change the model's output distribution. We propose Behavior-Preserving KV Cache Compression, a training-free framework that scores candidate evictions by estimating the compressed-cache logits induced by their removal and evaluating the resulting KL to the full-cache next-token distribution. Using pre-eviction forward statistics, the method avoids running separate masked forward passes for each candidate. Across diverse architectures and both prefill-time and generation-time compression, our method delivers substantial gains in downstream task quality over lightweight attention-based heuristics at matched retained-KV budgets, with the largest gains under aggressive compression. It achieves these gains with additional compression-time computation while retaining an end-to-end speedup over full-cache inference in our evaluated settings.
Chinese Translation
KV 缓存是大型语言模型在长上下文推理和长文本生成中的主要瓶颈。现有的无训练驱动的驱逐策略主要依赖于代理重要性信号，如注意力质量，来决定保留哪些过去的标记。我们认为，缓存压缩应该保留完整缓存模型的预测行为，保留那些移除后会显著改变模型输出分布的条目。我们提出了行为保留的 KV 缓存压缩（Behavior-Preserving KV Cache Compression），这是一个无训练的框架，通过估计移除候选条目所引起的压缩缓存对数（logits），并评估与完整缓存下一个标记分布的 KL 散度来对候选驱逐进行评分。该方法利用预驱逐的前向统计，避免了对每个候选条目运行单独的掩蔽前向传递。在多种架构以及预填充时间和生成时间的压缩中，我们的方法在匹配的保留 KV 预算下，相较于轻量级基于注意力的启发式方法，显著提高了下游任务的质量，尤其在激进压缩下获得了最大的提升。它在压缩时间计算的基础上实现了这些增益，同时在我们评估的设置中保持了相较于完整缓存推理的端到端加速。
cs.CL / 119 / 2610.06481
AECP: Artifact-Exclusive Communication Protocol for Multi-Agent Code Generation
AECP：面向多智能体代码生成的专用通信协议
Abstract
As AI agents increasingly tackle complex repository-level coding tasks, distributing work across multiple agents is a natural way to scale beyond the capabilities of a single agent. To coordinate their interdependent work, these agents share findings and agree on interfaces between modules. However, exchanged information often serves only as context, leaving individual agents to interpret it and incorporate it into subsequent work. Consequently, shared findings may go unused and deviations from interface agreements may go undetected, undermining the reliability and efficiency of collaboration. This motivates moving part of the coordination responsibility from individual agents to the execution harness. To make shared information actionable during execution, we introduce the Artifact-Exclusive Communication Protocol (AECP). AECP requires agents to communicate exclusively through structured artifacts and specifies how the harness processes them. The harness supplies findings when agents access relevant code, screens implementations for mismatches with recorded interface commitments, and requires affected agents to revisit revised agreements. These coordination steps become part of harness execution rather than actions that agents must initiate from prior messages. Across Doc2Repo, NL2Repo, and CodeProjectEval, using closed- and open-source models including Opus-4.8 and DeepSeek-V4-Flash, AECP improves average test pass rate by 28.2% and reduces average wall time by 16.5% relative to an agent team using free-form inter-agent messages. Artifact-exclusive communication also blocks the relay of malicious instructions between agents, reducing how often they reach other agents from 95% to 0% and how often those agents act on them from 40% to 0%.
Chinese Translation
随着人工智能智能体越来越多地处理复杂的代码库级任务，将工作分配给多个智能体成为超越单一智能体能力的自然方式。为了协调它们之间相互依赖的工作，这些智能体共享发现并就模块之间的接口达成一致。然而，交换的信息往往仅作为上下文，留给各个智能体自行解释并将其纳入后续工作。因此，共享的发现可能未被利用，接口协议的偏差可能未被检测到，从而削弱了协作的可靠性和效率。这促使我们将部分协调责任从个别智能体转移到执行框架上。为了在执行过程中使共享信息可操作，我们引入了专用通信协议（Artifact-Exclusive Communication Protocol，AECP）。AECP要求智能体通过结构化的工件进行独占通信，并规定了执行框架如何处理这些工件。当智能体访问相关代码时，执行框架提供发现结果，检查实现是否与记录的接口承诺不匹配，并要求受影响的智能体重新审视修订后的协议。这些协调步骤成为执行框架执行的一部分，而不是智能体必须从先前消息中发起的动作。在Doc2Repo、NL2Repo和CodeProjectEval等项目中，使用包括Opus-4.8和DeepSeek-V4-Flash在内的闭源和开源模型，AECP将平均测试通过率提高了28.2%，并将平均墙时减少了16.5%，相较于使用自由格式智能体间消息的智能体团队。专用通信还阻止了恶意指令在智能体之间的传递，将它们到达其他智能体的频率从95%降低到0%，以及这些智能体对其采取行动的频率从40%降低到0%。
cs.CL / 120 / 2610.06513
SOL: Measuring Gaps between Text Distributions by Double Sliced Wasserstein Metrics
SOL：通过双切片Wasserstein度量测量文本分布之间的差距
Abstract
Evaluating text generation requires measuring how well the generated distribution matches the data distribution. For autoregressive models, this is done by the perplexity. Diffusion and flow-based language models can only provide a likelihood bound, whose tightness differs between model families. Sample-based substitutes such as generative perplexity with entropy do not consider the distribution fit. We propose SOL, a distance between text distributions. Each sequence is represented by the empirical measure of its hidden states under a fixed transformer and the distributions of these measures are compared by the double sliced Wasserstein distance. We prove that SOL is a metric if the transformer is injective. Experiments show that SOL detects distributional failures, recovers expected model trends, and provides stable sample-based estimates. We put forward SOL to fill the gap in the current evaluation protocol used for non auto-regressive models. As a first step we use SOL to re-evaluate a variety of models trained on OpenWebText.
Chinese Translation
评估文本生成需要测量生成分布与数据分布的匹配程度。对于自回归模型，这通过困惑度来实现。扩散和基于流的语言模型只能提供一个似然界限，其紧密程度在不同模型家族之间有所不同。基于样本的替代方法，如具有熵的生成困惑度，并未考虑分布拟合。我们提出了SOL，一种文本分布之间的距离。每个序列通过其在固定变换器下的隐藏状态的经验测度表示，并通过双切片Wasserstein距离比较这些测度的分布。我们证明了如果变换器是单射，则SOL是一个度量。实验表明，SOL能够检测分布失败，恢复预期的模型趋势，并提供稳定的基于样本的估计。我们提出SOL以填补当前用于非自回归模型的评估协议中的空白。作为第一步，我们使用SOL重新评估在OpenWebText上训练的多种模型。
cs.CL / 121 / 2610.06516
Test-Time Adaptation of Reasoning Strategies with Bayesian Nonparametric Memory
基于贝叶斯非参数记忆的推理策略测试时适应
Abstract
While modern large language models (LLMs) have been trained to reason through verbalized chains-of-thought, the generation cost grows substantially due to suboptimal paths to reach the final answer. Furthermore, as new insights are discovered while observing various input queries (e.g. through self-reflection), limited mechanisms exist for carrying forward these findings to be applied to subsequent problems. One can view the list of such strategies or behaviors as a growing cheatsheet, with elements retrieved from this memory module at inference-time. In this work, we consider structured cheatsheets, with learned clusters of behaviors. We introduce a Hierarchical Dirichlet Process Gaussian Mixture Model (HDP-GMM) over behavior embeddings, which shares components across domains while allowing domain-specific mixing weights, and uses the posterior predictive to retrieve relevant behaviors for a query; we call this a $\textit{Bayesian Cheatsheet}$. This mechanism allows for cheap adaptation in an online test-time training (TTT) setting, softly updating the mixture's sufficient statistics following each sample and enabling the creation of new components when the synthesized behaviors are sufficiently novel. We demonstrate that Bayesian Cheatsheet achieves clear performance gains relative to existing memory modules across reasoning benchmarks such as AIME'25, Omni-MATH, and PhysReason, even in the cold-start setting. We show that the Bayesian Cheatsheet is an adaptively reorganizing memory module, as behaviors can be re-assigned to components through a single step of collapsed Gibbs sampling. Our findings highlight the value of Bayesian-inspired memory modules for effective test-time adaptation and the role of structure in metacognitive reasoning.
Chinese Translation
尽管现代大型语言模型（LLMs）已经经过训练，可以通过语言化的思维链进行推理，但由于达到最终答案的路径不够优化，生成成本显著增加。此外，随着在观察各种输入查询时发现新见解（例如，通过自我反思），现有的机制有限，无法将这些发现应用于后续问题。可以将这些策略或行为的列表视为一个不断增长的备忘单，在推理时从该记忆模块中检索元素。在本研究中，我们考虑了结构化的备忘单，包含学习到的行为聚类。我们引入了一种层次狄利克雷过程高斯混合模型（HDP-GMM），该模型在行为嵌入上共享跨领域的组件，同时允许领域特定的混合权重，并使用后验预测来检索与查询相关的行为；我们称之为“贝叶斯备忘单”（Bayesian Cheatsheet）。这一机制允许在在线测试时训练（TTT）环境中进行廉价的适应，随着每个样本的出现，柔性更新混合的充分统计量，并在合成的行为足够新颖时创建新的组件。我们证明，贝叶斯备忘单在推理基准（如 AIME'25、Omni-MATH 和 PhysReason）中相较于现有的记忆模块实现了明显的性能提升，即使在冷启动设置下也是如此。我们展示了贝叶斯备忘单是一个自适应重组的记忆模块，因为行为可以通过一次崩溃的吉布斯采样步骤重新分配给组件。我们的研究结果突显了受贝叶斯启发的记忆模块在有效测试时适应中的价值，以及结构在元认知推理中的作用。
cs.CL / 122 / 2610.06522
Anatomy of LLM Sycophancy: What a Flip Rate Hides
大型语言模型谄媚的解剖：翻转率隐藏了什么
Abstract
A model under pushback can correct itself, capitulate, or hold, and one flip rate counts a correction and a capitulation alike. Using SycoLens, a modular replay protocol, we test how user pressure and evaluation settings shape measured flip rates. Each measurement is one stateless replay of an item, a committed answer, and one scripted user line in a fixed form. Every effect is read against a matched control with the line deleted. Pushback wording, committed text, answer format, boundary distance, and ground truth become factors of one instrument; earlier instruments vary one to three of them. Across eleven frontier models from three providers and about 760,000 controlled replays, which models look sycophantic depends on how the user pushes back. Lines that assert the opposite verdict and lines that challenge the answer without asserting one rank the models almost unrelatedly. Flip effects grow several-fold near a model's boundary, yet items answered identically in every screening draw still carry about half of the most-affected totals. On arithmetic tasks where the truth is known, one model re-derives and corrects itself under pressure while another abandons correct answers without written work. On the model tested, a planted derivation lowers release of the answer it argues for, true or wrong, where a bare stated value does not; the wrong answer is corrected much more often than the true one is abandoned. Under a yes/no readout the rankings come closer, entangled with a pressure-induced shift toward "no". One score per model therefore compares different behaviours across models and benchmarks. We condense these dependencies into a reporting profile; the instrument, records, and analyses will be released upon publication.
Chinese Translation
在用户施压的情况下，模型可以自我修正、屈服或保持不变，而一个翻转率同时计算了修正和屈服。我们使用 SycoLens，一个模块化重放协议，测试用户压力和评估设置如何影响测量的翻转率。每次测量都是对一个项目的无状态重放、一个承诺的答案和一行固定格式的脚本用户对话。每个效果都是与删除该行的匹配对照进行比较。施压措辞、承诺文本、答案格式、边界距离和真实情况成为一个工具的因素；早期的工具则在其中的一个到三个因素上有所不同。在来自三个提供者的十一种前沿模型和约760,000次受控重放中，哪些模型看起来谄媚取决于用户的施压方式。那些断言相反判决的对话和那些在不断言判决的情况下挑战答案的对话几乎无关地对模型进行排名。翻转效应在模型的边界附近增长数倍，然而在每次筛选中以相同方式回答的项目仍然占据了受影响总数的一半。在已知真相的算术任务中，一个模型在压力下重新推导并修正自己，而另一个模型则在没有书面工作的情况下放弃正确答案。在测试的模型中，植入的推导降低了它所主张的答案的发布，无论是正确还是错误，而仅仅陈述的数值则没有；错误答案被修正的频率远高于正确答案被放弃的频率。在是/否的读数下，排名更接近，受到施压引起的“否”的转变影响。因此，每个模型的一个得分比较了不同模型和基准之间的行为。我们将这些依赖关系浓缩成一个报告概况；该工具、记录和分析将在出版时发布。
cs.CL / 123 / 2610.06576
Before Agent Tells The Lie: Has Deception Already Been Represented?
代理在说谎之前：欺骗是否已经被表示？
Abstract
Large language model (LLM)-based agents can exhibit deceptive behavior during task execution, including hiding failures, fabricating results, or falsely signaling task completion. Existing monitoring approaches mainly detect deception after it appears in observable actions or outputs. In this paper, we investigate whether deceptive behavior can be predicted from an agent's internal representations before it becomes externally visible. We frame deception monitoring as a trajectory-level representation analysis problem and align agent trajectories around key decision points. Using hidden states extracted before these points, we show that future honest and deceptive outcomes can be reliably distinguished, with predictive signals remaining detectable several model calls before the final decision. We further characterize the temporal evolution of these signals: deception-related representations are weak early in execution but become increasingly identifiable as trajectories progress, while transferable structure can emerge before the strongest decision-adjacent signals appear. Finally, we intervene on the identified honest-deceptive representation directions during inference and find that activation steering reduces downstream deceptive behavior, suggesting that these representations influence agent decisions. Our findings indicate that agent deception is an evolving internal process that can be detected and potentially mitigated before it is expressed externally.
Chinese Translation
基于大型语言模型（LLM）的代理在任务执行过程中可能表现出欺骗行为，包括隐藏失败、伪造结果或错误地信号任务完成。现有的监测方法主要在欺骗行为出现在可观察的行动或输出后进行检测。本文探讨了是否可以从代理的内部表征中预测欺骗行为，尤其是在其变得外部可见之前。我们将欺骗监测框架设定为轨迹级表征分析问题，并围绕关键决策点对代理轨迹进行对齐。通过提取这些点之前的隐藏状态，我们展示了未来的诚实和欺骗结果可以可靠地区分，预测信号在最终决策之前的多个模型调用中仍然可被检测到。我们进一步表征了这些信号的时间演变：与欺骗相关的表征在执行早期较弱，但随着轨迹的推进变得越来越可识别，而可转移的结构可以在最强的决策邻近信号出现之前就显现出来。最后，我们在推理过程中干预识别出的诚实-欺骗表征方向，发现激活引导减少了下游的欺骗行为，表明这些表征影响代理的决策。我们的研究结果表明，代理的欺骗是一个不断演变的内部过程，可以在其外部表达之前被检测到并可能被减轻。
cs.CL / 124 / 2610.06600
Molecules of a Story: Community Detection in PMI-weighted Narrative Networks
故事的分子：基于PMI加权叙事网络的社区检测
Abstract
Automatically extracted narrative networks -- graphs with entities as nodes and their relations as edges -- have proven useful for revealing central narrative structures through salient entities and their connections (Tangherlini et al. 2020; Labatut and Bost 2019). But a narrative is more than those central structures that everything else revolves around. This work is concerned with the everything else: brief sub-plots, small clusters of descriptions, or associations between minor characters that go under the radar at the macro-level. We present an approach to unearth such peripheral structures. They involve rare entities with limited textual presence, overshadowed by dominant entities and lost among each other in the long tail of many but rare entities (Baayen 2001). We leverage the known tendency of pointwise mutual information (PMI, Church and Hanks 1990) to inflate for rare events, turning its weakness into a strength by weighting edges with PMI to foreground peripheral entity configurations. Communities extracted from the resulting network are structural traces of underlying narrative elements. We demonstrate the approach on The Lord of the Rings. From measures of how concentrated or dispersed a community's activations are across the text, a typology emerges that reveals that peripheral structures form more than a single class: episodic passages, echoing long-distance connections, and recurring threads each surface as distinct configurations. The approach is conceptually simple and surfaces fine-grained narrative details that are lost in abundance, though its deliberate amplification of weak signals comes with inherent sensitivity -- best understood as a lens for exploration rather than a robust extraction pipeline.
Chinese Translation
自动提取的叙事网络——以实体为节点、关系为边的图——已被证明在揭示通过显著实体及其连接形成的中心叙事结构方面非常有用（Tangherlini et al. 2020; Labatut and Bost 2019）。然而，叙事不仅仅是围绕那些中心结构展开的内容。本文关注的是其他所有内容：简短的子情节、小规模的描述聚类，或在宏观层面上被忽视的次要角色之间的关联。我们提出了一种挖掘这些边缘结构的方法。这些结构涉及文本中存在有限的稀有实体，它们被主导实体所掩盖，并在许多但稀有实体的长尾中相互迷失（Baayen 2001）。我们利用点对点互信息（PMI，Church and Hanks 1990）对稀有事件的膨胀倾向，通过用PMI加权边缘，将其弱点转化为优势，以突出边缘实体配置。从生成的网络中提取的社区是潜在叙事元素的结构痕迹。我们在《指环王》中演示了该方法。通过衡量社区在文本中激活的集中或分散程度，出现了一种分类法，揭示边缘结构形成的不止一个类别：情节段落、回响的远程连接和反复出现的线索各自呈现为不同的配置。该方法在概念上简单，能够揭示在丰盈中被遗失的细致叙事细节，尽管其对弱信号的刻意放大伴随着固有的敏感性——更适合作为探索的视角，而非稳健的提取管道。
cs.CL / 125 / 2610.06603
Word-Level Text Unmixing via Evidence-Preserving Ownership Routing with Language Models
通过证据保留的所有权路由与语言模型进行词级文本解混合
Abstract
Text from multiple sources can become interleaved into a single sequence when attribution metadata is lost, such as overlapping speech transcripts, document reading flows, or concurrent agent streams. We formalize this challenge as Word-Level Text Unmixing: given an interleaved lexical stream and source count K, recover the original source sequences while preserving every word occurrence and its within-source order exactly. Directly generating separated texts with LLMs can omit, duplicate, or hallucinate words, violating this exact-reconstruction objective. We therefore propose Evidence-Preserving Ownership Routing (EPOR), which decouples source-ownership prediction from reconstruction. EPOR adapts a causal LLM to predict canonical ownership routes conditioned on the mixed stream and prior routing decisions. At inference, completion-safe constrained decoding is combined with deterministic indexed reconstruction, yielding structurally valid K-source partitions that preserve every observed occurrence exactly once. We also introduce UNMIXBENCH, covering controlled synthetic mixtures, timestamp-derived speech from AMI and ICSI, layout-derived document streams from ReadingBank, and simulated concurrent digital outputs. Across five evaluation tracks, a 4B EPOR model achieves the lowest mean minimum-permutation word error rate among finetuned baselines, reducing the five-track mean by 22.3% relative to compact source-array generation and remaining competitive with zero-shot frontier LLMs. These results show that when lexical evidence is fully observed, separating ownership inference from lexical regeneration provides a reliable alternative to direct generation.
Chinese Translation
当归属元数据丢失时，来自多个来源的文本可能会交错成一个单一的序列，例如重叠的语音转录、文档阅读流程或并发代理流。我们将这一挑战形式化为词级文本解混合：给定一个交错的词汇流和源数量 K，恢复原始源序列，同时精确保留每个单词的出现及其在源内的顺序。直接使用大语言模型（LLMs）生成分离文本可能会遗漏、重复或虚构单词，从而违反这一精确重建的目标。因此，我们提出了证据保留的所有权路由（EPOR），它将源所有权预测与重建解耦。EPOR 调整一个因果 LLM，以预测基于混合流和先前路由决策的规范所有权路径。在推理时，完成安全的约束解码与确定性索引重建相结合，产生结构有效的 K 源分区，精确保留每个观察到的出现一次。我们还引入了 UNMIXBENCH，涵盖受控的合成混合、来自 AMI 和 ICSI 的时间戳派生语音、来自 ReadingBank 的布局派生文档流以及模拟的并发数字输出。在五个评估轨道中，一个 4B 的 EPOR 模型在微调基线中实现了最低的平均最小置换单词错误率，相较于紧凑源数组生成，五轨道的平均降低了 22.3%，并与零-shot 前沿 LLMs 保持竞争力。这些结果表明，当词汇证据完全被观察时，将所有权推断与词汇再生分离提供了一种可靠的替代直接生成的方法。
cs.CL / 126 / 2610.06625
JEV versus LLMs: Accuracy, Cost and Calibration on Seven Political Science Replications
JEV与LLMs：七个政治科学复现中的准确性、成本和校准
Abstract
Large language models (LLMs) annotate and scale political text or constructs by generating text tokens. A new class of models, which TypeSafe markets as "System One" models, instead returns decisions and probability distributions across a user-supplied fixed answer set. A commercial model, JEV, is advertised as having a dramatic cost and speed advantage over traditional LLMs along with better calibrated decisions. As such, it might be useful for social scientists looking to quickly and cost-effectively annotate or scale large corpora of text and have a reliable indicator of a classifier's uncertainty. Yet, the accuracy of these claims and the broader model accuracy in social science text-based tasks are not yet established. In this paper, we do just that and hope to establish the suitability of JEV for social science tasks. We compare JEV with LLMs and human coders from published research, and with a current mid-tier commercial LLM (GPT-6 Luna) and an open-weight alternative (Qwen3.8-27B). We find that JEV matches, or comes close to, the capabilities of both LLMs in a variety of tasks. However, we find no cost advantage over GPT-6 Luna at OpenAI's batch prices. Further, we find that, when each question is asked once, JEV's probabilities are better calibrated than GPT-6 Luna's token probabilities, but not consistently better than Qwen3.8-27B's. We conclude that unless researchers have a need for speed, JEV's only obvious advantage is ease of parsing the underlying choice probabilities.
Chinese Translation
大型语言模型（LLMs）通过生成文本标记来注释和扩展政治文本或构念。一类新模型，TypeSafe将其市场化为“系统一”（System One）模型，返回用户提供的固定答案集中的决策和概率分布。商业模型JEV被宣传为在成本和速度上相较于传统LLMs具有显著优势，并且决策校准更佳。因此，它可能对社会科学家在快速且经济高效地注释或扩展大量文本语料库时，以及提供分类器不确定性的可靠指标时，具有实用价值。然而，这些主张的准确性以及在社会科学文本任务中的更广泛模型准确性尚未得到确立。在本文中，我们正是为此而来，并希望确立JEV在社会科学任务中的适用性。我们将JEV与LLMs、已发表研究中的人工编码者，以及当前中层商业LLM（GPT-6 Luna）和开放权重替代品（Qwen3.8-27B）进行比较。我们发现，JEV在多种任务中与这两种LLMs的能力相匹配或接近。然而，我们发现，在OpenAI的批量价格下，JEV并没有相对于GPT-6 Luna的成本优势。此外，我们发现，当每个问题只被询问一次时，JEV的概率校准优于GPT-6 Luna的标记概率，但并不总是优于Qwen3.8-27B的概率。我们得出结论，除非研究人员需要速度，否则JEV唯一明显的优势是解析底层选择概率的便利性。
cs.CL / 127 / 2610.06637
Long-Horizon Textual World Modeling through Structured Reasoning
通过结构化推理进行长时间跨度文本世界建模
Abstract
World models must predict how an environment evolves under sequences of actions, enabling agents to compare possible futures and reason about counterfactual actions before acting. Long-horizon prediction is commonly obtained by recursively applying a one-step transition model, but intermediate errors can compound over time. Multi-step dynamics models instead condition on a sequence of future actions and predict their consequences directly, but become harder to learn as horizon grows: the model must track interacting state changes across the trajectory, endpoint supervision provides weak credit assignment, and intermediate predictions can remain plausible while losing information needed for later states. We show that these challenges can be addressed by casting the internal evolution of a multi-step transition as structured reasoning over textual world states: reasoning over sparse state changes reduces the burden of state tracking, a predictive-gain objective rewards the learned state for improving over a matched predictor that conditions on raw history instead, and intermediate predictive rewards supervise each state along the trajectory. Because these intermediate states are explicit textual representations of the world, they provide semantically meaningful targets that can be inspected, scored, and corrected during training. Across ScienceWorld, Jericho, and CEO-Bench, our approach achieves the strongest average long-horizon performance against recursive and non-recursive baselines that condition directly on raw history, with gains increasing at longer horizons. In a controlled counterfactual study, our model is also the only one with statistically significant sensitivity to future actions.
Chinese Translation
世界模型必须预测环境在一系列动作下的演变，使得智能体能够比较可能的未来并在行动之前推理反事实动作。长时间跨度的预测通常通过递归应用一步转移模型获得，但中间错误可能会随着时间的推移而累积。多步动态模型则基于一系列未来动作进行条件预测，直接预测其后果，但随着时间跨度的增加，学习变得更加困难：模型必须跟踪轨迹中相互作用的状态变化，终点监督提供的信用分配较弱，并且中间预测在失去后续状态所需信息的同时仍然可能保持合理。我们展示了通过将多步转移的内部演变视为对文本世界状态的结构化推理，可以解决这些挑战：对稀疏状态变化的推理减少了状态跟踪的负担，预测增益目标奖励学习到的状态以改善与基于原始历史的匹配预测器的表现，并且中间预测奖励对轨迹中的每个状态进行监督。由于这些中间状态是世界的明确文本表示，它们提供了在训练过程中可以检查、评分和纠正的语义上有意义的目标。在 ScienceWorld、Jericho 和 CEO-Bench 上，我们的方法在与直接基于原始历史的递归和非递归基线相比，取得了最强的平均长时间跨度表现，并且在更长的时间跨度上收益增加。在一项受控的反事实研究中，我们的模型也是唯一一个对未来动作具有统计显著敏感性的模型。
cs.CL / 128 / 2610.06647
LoGRA: Scaling LLM Reinforcement Learning with Low-Rank Gradient Sketches
LoGRA：利用低秩梯度草图扩展大语言模型的强化学习
Abstract
Reinforcement learning (RL) has greatly advanced the capabilities of large language models (LLMs), but its memory demands remain a barrier to broader adoption. We introduce LoGRA, an approach to RL post-training that reduces memory by retaining useful learning signals in low-rank gradient sketches. These compact representations support both model updates and efficient policy synchronization. To prevent overly large updates from disrupting learning, we complement gradient compression with predicted-KL step control, which estimates policy changes before applying each update and adjusts its magnitude accordingly. Across reasoning tasks, LoGRA reduces average training memory by up to 45.7\% without sacrificing performance. It also enables stable training of a 27B-parameter model for over 1,100 steps on a single eight-GPU node, where dense Adam runs out of memory, making previously memory-infeasible RL training practical. Code is available in the \href{https://github.com/skzhang1/labs-molt/tree/logra/examples/scripts/logra}{Molt library}.
Chinese Translation
强化学习（RL）极大地提升了大语言模型（LLMs）的能力，但其内存需求仍然是更广泛应用的障碍。我们提出了LoGRA，这是一种强化学习后训练的方法，通过保留低秩梯度草图中的有用学习信号来减少内存。这些紧凑的表示支持模型更新和高效的策略同步。为了防止过大的更新干扰学习，我们结合了梯度压缩和预测-KL步控制，在应用每次更新之前估计策略变化并相应调整其幅度。在推理任务中，LoGRA将平均训练内存减少了多达45.7%，而不牺牲性能。它还使得在单个八GPU节点上对一个27亿参数的模型进行超过1100步的稳定训练成为可能，而密集的Adam优化器则会耗尽内存，从而使得之前内存不可行的强化学习训练变得可行。代码可在Molt库中获得。
cs.CL / 129 / 2610.06648
Representation-Space MMD for Diffusion Language Models
扩散语言模型的表示空间最大均值差异
Abstract
We introduce a post-training method for diffusion language models (DLMs) that minimizes Maximum Mean Discrepancy (MMD) between generated and reference distributions in the feature space of a frozen pretrained DLM. To estimate MMD, we retain contextual features at individual token positions, obtaining multiple observations per sequence from a single extractor pass. We optimize this objective using policy gradients for discrete models and direct differentiation through generated latents for continuous models. In both cases, computing the loss directly from these features enables efficient post-training without full sampling trajectories or jointly trained auxiliary models. Experiments show lower generative perplexity at comparable entropy on OpenWebText and better accuracy-computation trade-offs on GSM8K. On 16B DMax-LLaDA2.0 models with hybrid masked-uniform diffusion, we increase decoding parallelism with similar or higher accuracy on math and code benchmarks.
Chinese Translation
我们提出了一种针对扩散语言模型（DLMs）的后训练方法，该方法最小化生成分布与参考分布在冻结的预训练DLM特征空间中的最大均值差异（MMD）。为了估计MMD，我们在单个提取器的传递过程中保留每个标记位置的上下文特征，从而从每个序列中获得多个观察值。我们使用策略梯度优化离散模型的这一目标，并通过生成的潜变量对连续模型进行直接微分。在这两种情况下，直接从这些特征计算损失使得高效的后训练成为可能，而无需完整的采样轨迹或联合训练的辅助模型。实验表明，在OpenWebText上生成的困惑度较低且熵相当，并且在GSM8K上具有更好的准确性与计算权衡。在具有混合掩蔽-均匀扩散的16B DMax-LLaDA2.0模型上，我们在数学和代码基准测试中提高了解码并行性，同时保持相似或更高的准确性。
cs.CL / 130 / 2610.06650
Wikidata Search Traces: A Dataset for Training Knowledge Graph Search Agents
Wikidata 搜索轨迹：用于训练知识图谱搜索代理的数据集
Abstract
Wikidata is one of the largest open knowledge bases, yet answering a complex question over it still requires a SPARQL query that names the right entities and properties and chains their relations. Language models offer a natural-language alternative but answer largely from memory, which is least reliable for less prominent entities. We study agents that instead answer by exploring the graph, and argue that two obstacles limit them: the lack of training data recording how a solver explores, and interfaces that add large graph results directly to the model's context. We test three hypotheses: that the difficulty of graph search can be controlled through the structure of a question rather than only through obscure entities or wording; that much of the failure on long-horizon search comes from how retrieved evidence is managed rather than from the model itself; and that, in a suitable environment, open-weight models can match commercial closed ones. We construct multi-hop questions on a frozen Wikidata snapshot by replacing named entities with nested conditions, checking after each expansion that the target remains unique and that every new condition is necessary. We release 10,235 solving traces over single-entity and multi-hop questions, together with the recursive language model (RLM) harness that produced them, in which models batch graph calls, keep results in persistent Python state and interpret selected evidence through sub-calls. On 100 questions, the harness improves both models we ran under both interfaces compared with direct tool calling over the same functions: gpt-6-luna rises from 49 to 61 correct answers, doubling its multi-hop accuracy, and Qwen3.8-27B, an open-weight model served on a single GPU, from 60 to 74.
Chinese Translation
Wikidata 是最大的开放知识库之一，但在其上回答复杂问题仍然需要一个 SPARQL 查询，该查询需要命名正确的实体和属性并串联它们的关系。语言模型提供了一种自然语言的替代方案，但主要依赖记忆回答，这对于不太显著的实体来说可靠性最低。我们研究了通过探索图谱来回答问题的代理，并认为有两个障碍限制了它们：缺乏记录求解者如何探索的训练数据，以及将大型图结果直接添加到模型上下文中的接口。我们测试了三个假设：图搜索的难度可以通过问题的结构来控制，而不仅仅是通过晦涩的实体或措辞；长远搜索的失败主要来自于如何管理检索到的证据，而不是模型本身；在适当的环境中，开放权重模型可以与商业闭合模型相匹配。我们通过用嵌套条件替换命名实体，在一个冻结的 Wikidata 快照上构建多跳问题，并在每次扩展后检查目标是否保持唯一，以及每个新条件是否必要。我们发布了 10,235 个单实体和多跳问题的求解轨迹，以及生成它们的递归语言模型（RLM）工具，该工具批量处理图调用，将结果保存在持久的 Python 状态中，并通过子调用解释所选证据。在 100 个问题上，该工具在两个接口下改善了我们运行的两个模型，与直接调用相同功能的工具相比：gpt-6-luna 的正确答案从 49 上升到 61，双倍提高了其多跳准确率，而 Qwen3.8-27B，一个在单个 GPU 上提供的开放权重模型，从 60 上升到 74。
cs.CL / 131 / 2610.06670
Reward Stealing Attack on Large Language Models
针对大型语言模型的奖励窃取攻击
Abstract
Adversarial attacks on Large Language Models (LLMs) aim to induce harmful content. However, existing methods suffer from high computational costs or strict model-pairing dependencies, limiting their scalability and transferability. We propose Reward Stealing Attack (ReSA), an adversarial attack framework that targets the latent safety reward underlying LLM alignment. ReSA employs maximum entropy inverse reinforcement learning to recover a proxy reward model solely from the aligned model's behavior. The extracted reward is then reversed at inference time to derive an adversarial policy, efficiently implemented via a reward-guided decoding mechanism. Experiments demonstrate that a single recovered reward generalizes across prompts and diverse models to reveal a fundamental alignment vulnerability, enabling ReSA to significantly outperform existing attacks in effectiveness and transferability. The code is available at https://github.com/GarminQ/ReSA.
Chinese Translation
针对大型语言模型（LLMs）的对抗性攻击旨在诱导有害内容。然而，现有方法存在高计算成本或严格的模型配对依赖性，限制了其可扩展性和可转移性。我们提出了奖励窃取攻击（ReSA），这是一种针对LLM对齐背后潜在安全奖励的对抗性攻击框架。ReSA采用最大熵逆强化学习，仅通过对齐模型的行为恢复代理奖励模型。提取的奖励在推理时被反转，以推导出对抗策略，通过奖励引导的解码机制高效实现。实验表明，单一恢复的奖励在不同提示和多样化模型中具有良好的泛化能力，揭示了基本的对齐脆弱性，使得ReSA在有效性和可转移性上显著优于现有攻击。代码可在 https://github.com/GarminQ/ReSA 获取。
cs.CL / 132 / 2610.06689
Programmatic Search Agents: Extending Agentic Search Beyond Query Reformulation
程序化搜索代理：扩展代理搜索超越查询重构
Abstract
Search agents adapt their queries, yet fixed search interfaces leave candidate processing and evidence presentation outside the agent's direct control. Our trajectory analysis shows that supporting passages can be retrieved yet never delivered to the agent; a same-page oracle intervention shows that changing the returned evidence can reduce subsequent search. We introduce Programmatic Search Agent (PSA), which makes a local executable computation over candidates the unit of a search action. PSA unifies a persistent candidate workspace, flexible primitive composition, and selective evidence presentation. It incrementally generates program cells that reuse candidates, execute dependent operations, and select what the agent inspects next. The runtime resolves specified data dependencies within each cell, while the agent adapts its search strategy across cells as new evidence arrives. We compare PSA with the Query-based Agent and Tool-based Agent on InfoSeek-Eval and BrowseComp-Plus using five policy backbones without task-specific training. All three interfaces share the search substrate, and the Tool-based Agent also shares PSA's primitives and persistent workspace. Relative to the Query-based Agent, PSA improves macro-averaged task success by 4.00 and 7.56 percentage points on the two benchmarks, respectively; within-backbone reductions in final-step tokens average 28.3% and 33.9%. These results support extending agent control beyond query reformulation to the processing and presentation of retrieved evidence. Code will be released subject to approval.
Chinese Translation
搜索代理会调整其查询，但固定的搜索界面使得候选处理和证据呈现超出了代理的直接控制。我们的轨迹分析表明，支持的段落可以被检索但从未交付给代理；同页的神谕干预显示，改变返回的证据可以减少后续搜索。我们引入了程序化搜索代理（Programmatic Search Agent，PSA），它将候选的局部可执行计算作为搜索动作的单位。PSA统一了持久的候选工作区、灵活的原语组合和选择性证据呈现。它逐步生成程序单元，重用候选，执行依赖操作，并选择代理接下来检查的内容。运行时在每个单元内解析指定的数据依赖，而代理则在新证据到达时调整其跨单元的搜索策略。我们在InfoSeek-Eval和BrowseComp-Plus上使用五个策略骨干对PSA与基于查询的代理和基于工具的代理进行了比较，而没有进行特定任务的训练。所有三个界面共享搜索基础，基于工具的代理也共享PSA的原语和持久工作区。相较于基于查询的代理，PSA在两个基准上分别提高了宏平均任务成功率4.00和7.56个百分点；在骨干内最终步骤的令牌减少平均为28.3%和33.9%。这些结果支持将代理控制扩展到检索证据的处理和呈现，而不仅限于查询重构。代码将在获得批准后发布。
cs.CL / 133 / 2610.06695
MedPrune: Topology-Efficient Multimodal Multi-Agent Communication Evolution for Medical VQA Tasks
MedPrune：用于医疗视觉问答任务的拓扑高效多模态多智能体通信演化
Abstract
While medical multimodal large language models (Med-MLLMs) advance medical visual question answering (VQA), existing clinical workflow-inspired multi-agent frameworks suffer from interaction patterns and excessive computational overhead caused by redundant communication topologies. In this paper, we propose MedPrune, an efficient medical multimodal multi-agent collaboration framework that dynamically prunes both nodes and edges from the communication topology to enhance reasoning ability and token efficiency. Specifically, we first formulate the diagnostic process as a heterogeneous communication graph, where nodes represent specialist agents from various departments and edges capture intra- and inter-departmental interactions. Building on this graph, we introduce two sparsification mechanisms to enable adaptive collaborative evolution: (1) Heterogeneous Node Sparsification, which eliminates task-irrelevant specialist agents irrelevant to the current multimodal question via reinforcement learning-driven topological optimization, and (2) Heterogeneous Edge Sparsification, which selectively retains only the most diagnostically salient intra- and inter-departmental connections by jointly optimizing task performance and topological complexity. Extensive medical VQA experiments under full-set and few-shot training settings prove MedPrune surpasses multi-agent baselines and boosts token efficiency with strong adversarial robustness.
Chinese Translation
尽管医疗多模态大语言模型（Med-MLLMs）推动了医疗视觉问答（VQA）的发展，但现有的受临床工作流程启发的多智能体框架在交互模式和由冗余通信拓扑引起的过度计算开销方面存在问题。本文提出了MedPrune，一种高效的医疗多模态多智能体协作框架，动态修剪通信拓扑中的节点和边，以增强推理能力和令牌效率。具体而言，我们首先将诊断过程表述为一个异构通信图，其中节点代表来自不同部门的专家智能体，边则捕捉部门内和部门间的交互。在此图的基础上，我们引入了两种稀疏化机制以实现自适应协作演化：（1）异构节点稀疏化，通过基于强化学习的拓扑优化消除与当前多模态问题无关的专家智能体；（2）异构边稀疏化，通过联合优化任务性能和拓扑复杂性，选择性保留最具诊断意义的部门内和部门间连接。大量在全数据集和少量样本训练设置下的医疗VQA实验证明，MedPrune超越了多智能体基线，并在强对抗鲁棒性下提升了令牌效率。
cs.CL / 134 / 2610.06708
SAFE-MR: Evidence Sufficiency Learning for Selective Multimodal Rumor Detection
SAFE-MR：选择性多模态谣言检测的证据充分性学习
Abstract
Multimodal rumor detectors increasingly rely on retrieved evidence, yet relevant evidence is not necessarily sufficient for verification. Missing provenance, duplicated reports, and unresolved contradictions can produce confident predictions without adequate support. We introduce SAFE-MR, a framework that separates claim veracity from evidence sufficiency. The method decomposes image-text posts into verifiable claims, constructs a relation-aware claim-evidence graph, and aggregates evidence using provenance and contextual compatibility. Separate veracity and sufficiency heads support selective prediction, while evidence interventions encourage stability under irrelevant additions and sensitivity to evidence removal. On NewsCLIPpings, VERITE, and XFacta, SAFE-MR achieves macro-F1 scores of 91.2%, 75.8%, and 85.2%, respectively. Against the matched backbone with evidence, its macro-F1 gains are 2.2, 4.9, and 4.8 percentage points. On the diagnostic selection set, SAFE-MR reduces AURC from 0.105 for maximum-probability rejection to 0.075 and lowers error at 80% coverage from 13.8% to 8.5%. Evidence-perturbation and ablation results support the role of sufficiency learning and intervention training in improving selective verification.
Chinese Translation
多模态谣言检测器越来越依赖于检索到的证据，但相关证据不一定足以进行验证。缺失的来源、重复的报告和未解决的矛盾可能会在没有充分支持的情况下产生自信的预测。我们提出了SAFE-MR，一个将声明真实性与证据充分性分开的框架。该方法将图像-文本帖子分解为可验证的声明，构建一个关系感知的声明-证据图，并利用来源和上下文兼容性聚合证据。独立的真实性和充分性头部支持选择性预测，而证据干预则鼓励在无关添加下的稳定性和对证据移除的敏感性。在NewsCLIPpings、VERITE和XFacta数据集上，SAFE-MR分别达到了91.2%、75.8%和85.2%的宏F1分数。与带有证据的匹配基线相比，其宏F1分数分别提高了2.2、4.9和4.8个百分点。在诊断选择集上，SAFE-MR将最大概率拒绝的AURC从0.105降低到0.075，并将80%覆盖率下的错误率从13.8%降低到8.5%。证据扰动和消融结果支持充分性学习和干预训练在改善选择性验证中的作用。
cs.CL / 135 / 2610.06715
Domain adaptation of Russian ModernBERT for long legal documents
俄罗斯现代BERT在长法律文档中的领域适应
Abstract
We investigate whether continued pretraining on Russian legislative documents improves a Russian ModernBERT encoder on legal text. The adapted model, RuModernBERT-ruLaw, was trained on a corpus reported to contain 304,382 legislative documents and 194,425,905 corpus tokens. Corpus token counts are distinguished from positions produced by the model tokenizer. We compare the original and adapted encoders on a fixed external collection of 1,031 court-decision segments. Both models receive the same hidden positions in each of five masking realizations. At maximum input lengths of 512, 2,048, and 8,192 tokens, mean masked-token cross-entropy decreases by 0.10942, 0.07052, and 0.06604 natural-log units, respectively. The reported 95% intervals summarize sensitivity to masking on this fixed collection; they do not quantify uncertainty across document collections. A second evaluation addresses legal-entity extraction. The original and adapted models achieve entity-level F1 scores of 0.99852 and 0.99820. However, 99.95% of test spans have the same normalized surface form and class in the training split. This evaluation therefore provides limited evidence about transfer to previously unseen forms. The paper explains the masking objective, overlapping windows, averaging rules, and exact entity-boundary scoring using editable diagrams and clearly marked illustrative examples. The comparison supports lower masked-token prediction loss for the studied pair of models and collection. It does not isolate the contribution of distant context or establish practical legal utility.
Chinese Translation
我们研究了在俄罗斯立法文档上继续预训练是否能改善俄罗斯现代BERT编码器在法律文本上的表现。适应后的模型RuModernBERT-ruLaw是在一个报告包含304,382个立法文档和194,425,905个语料库标记的语料库上训练的。语料库标记的计数与模型分词器生成的位置区分开来。我们在一个固定的外部集合中比较了原始和适应的编码器，该集合包含1,031个法院判决段落。两个模型在五个掩码实现中接收相同的隐藏位置。在最大输入长度为512、2,048和8,192个标记时，平均掩码标记交叉熵分别减少了0.10942、0.07052和0.06604自然对数单位。报告的95%区间总结了对该固定集合的掩码敏感性；它们并未量化跨文档集合的不确定性。第二次评估涉及法律实体提取。原始模型和适应模型的实体级F1分数分别为0.99852和0.99820。然而，99.95%的测试跨度在训练分割中具有相同的标准化表面形式和类别。因此，这一评估提供了有限的证据来证明对先前未见形式的迁移。本文解释了掩码目标、重叠窗口、平均规则以及使用可编辑图表和清晰标记的示例进行的精确实体边界评分。比较支持了研究的这对模型和集合的较低掩码标记预测损失，但并未孤立出远程上下文的贡献或建立实际法律效用。
cs.CL / 136 / 2610.06729
Improving Diversity in LLM Short Story Generation
提高大型语言模型短篇故事生成的多样性
Abstract
Large language models (LLMs) can generate accurate responses, but these are void of diversity. We attempt to address this for the task of creative short story generation. Drawing on established writing conventions and known LLM limitations, we target variation in genre, tone, style, and named entities. To promote diversity across these dimensions, we introduce DivLM, an LLM post-training framework consisting of two phases. First, we perform continued pre-training on a creative writing corpus and restore instruction-following capabilities using weight residuals. We then apply reinforcement learning with a custom, composite reward function that jointly maximizes diversity across the targeted narrative dimensions while maintaining response quality. Our empirical results on two LLM families show that DivLM increases diversity metrics by more than 9% on average compared to alternative approaches, while preserving instruction following, overall response quality, and similarity to human outputs.
Chinese Translation
大型语言模型（LLMs）能够生成准确的响应，但这些响应缺乏多样性。我们尝试解决这一问题，专注于创造性短篇故事的生成。基于既定的写作规范和已知的LLM局限性，我们针对体裁、语调、风格和命名实体的变化进行研究。为了在这些维度上促进多样性，我们引入了DivLM，一个包含两个阶段的LLM后训练框架。首先，我们在创意写作语料库上进行持续的预训练，并利用权重残差恢复指令遵循能力。然后，我们应用强化学习，使用自定义的复合奖励函数，联合最大化目标叙事维度的多样性，同时保持响应质量。我们在两种LLM系列上的实证结果表明，与替代方法相比，DivLM在多样性指标上平均提高了超过9%，同时保持了指令遵循、整体响应质量以及与人类输出的相似性。
cs.CL / 137 / 2610.06744
ufakzeka-karar: An Open Turkish Typed-Decision Model with Order-Invariant Option Scoring
ufakzeka-karar：一种开放的土耳其类型决策模型，具有顺序不变的选项评分
Abstract
ufakzeka-karar is an open Turkish decision model with 182,494,466 parameters. Given a Turkish text and questions of a fixed answer type (a choice, a level on an ordered scale, or yes or no), it returns a temperature-scaled probability for every option and an expected error that serves as a "not sure" signal, without generating text and in one CPU forward pass for up to ten options. Built on the lab's ufakzeka-1-base, its head scores each option blind to the others at shared positions, so the answer does not depend on option order. A sequential head trained with shuffled options was about as accurate but changed 2.3 to 2.8 percent of its answers when only the option order changed; REINFORCE lost 10.2 points (0.102) of macro F1 to cross-entropy. On the open set of HakemBench v1.0 (4,275 questions, 7 tracks) the released model ranks 7th of 16 rows with a composite of 0.660 (95% interval 0.642 to 0.677). Temperature scaling lowers calibration error (smooth ECE) on the development set but raises it on held-out support questions, from 0.027 to 0.045 for the first scored run, which never trained on them; the released model later trained on them, so its 0.036 to 0.064 is not an unseen-question test. The released model is the last of three runs scored on HakemBench, and its numbers are not blind. The second run's new training data was aimed at the first run's errors on the full test set in guardrails, moderation and customer support, and the released run was trained after the second run's guardrail results on the full test set were read, under a protocol fixed in writing before any of its data, code or runs. All its numbers come after these readings; its guardrail, moderation and customer support numbers carry the flag "shaped by reading the test results". With every model scored on the other four tracks only, its composite is 0.678, 6th of 16. Weights and code are under Apache-2.0.
Chinese Translation
ufakzeka-karar 是一个开放的土耳其决策模型，具有 182,494,466 个参数。给定一段土耳其文本和固定答案类型的问题（选择、顺序量表上的级别或是/否），它为每个选项返回一个温度缩放的概率和一个预期误差，作为“不确定”信号，而无需生成文本，并且在一个 CPU 前向传递中处理最多十个选项。该模型基于实验室的 ufakzeka-1-base，其头部在共享位置上盲目地对每个选项进行评分，因此答案不依赖于选项顺序。经过打乱选项训练的顺序头的准确性大致相当，但在仅更改选项顺序时，其答案变化了 2.3% 到 2.8%；而 REINFORCE 在交叉熵下损失了 10.2 个点（0.102）的宏 F1。在开放集 HakemBench v1.0（4,275 个问题，7 个轨道）上，发布的模型在 16 行中排名第 7，综合得分为 0.660（95% 置信区间 0.642 到 0.677）。温度缩放降低了开发集上的校准误差（平滑 ECE），但在保留的支持问题上提高了，从第一次评分运行的 0.027 增加到 0.045，该运行从未在这些问题上训练；发布的模型后来在这些问题上进行了训练，因此其 0.036 到 0.064 并不是未见问题的测试。发布的模型是针对 HakemBench 评分的三次运行中的最后一次，其数据并非盲测。第二次运行的新训练数据旨在解决第一次运行在全测试集中的错误，涉及保护措施、审核和客户支持，而发布的运行是在读取第二次运行的保护结果后，在全测试集上进行训练，遵循在其数据、代码或运行之前书面固定的协议。所有数据均在这些读取之后得出；其保护、审核和客户支持的数字标记为“受测试结果阅读影响”。在仅对其他四个轨道进行评分的每个模型中，其综合得分为 0.678，排名第 6。权重和代码遵循 Apache-2.0 许可证。
cs.CL / 138 / 2610.06750
Balancing Memory Pathways: Analyzing and Improving Memory Utilization in Hybrid LMs
平衡记忆通路：分析和改善混合语言模型中的记忆利用率
Abstract
Recurrent-attention hybrid language models (LMs), which interleave attention and recurrent layers, are increasingly used to combine the efficiency of the recurrent layers with the strong performance of attention layers. Prior work suggests that attention and recurrent layers offer complementary pathways to use past information: attention supports precise memory recall from earlier tokens, while recurrent layers support consolidation of disparate information over long contexts. However, we observe that simply having access to both pathways does not mean that hybrid LMs are effectively using them. We find that they rely substantially more on attention than on the recurrent state. Standard supervised fine-tuning improves overall performance but does not improve how the two memory pathways are coordinated: the model becomes more reliant on information propagated by attention layers, while its use of information propagated by recurrent layers remains limited. To encourage better coordination between the two memory pathways, we add an auxiliary loss that limits attention's access to earlier context while the recurrent state propagates through the full sequence. This objective encourages the model to retain and use information through the recurrent pathway alongside attention. It improves overall performance, with particularly strong gains on tasks involving longer contexts or requiring information aggregation, consistent with the strengths of recurrent layers observed in analysis. Crucially, this imbalance and the benefit of our auxiliary loss generalize: they apply to multiple recurrent-attention LMs in question-answering and agentic tasks, as well as to attention-based LMs that combine different forms of memory. Together, our findings show that simply providing multiple memory pathways does not ensure their effective use, and that targeted supervision is needed to better coordinate them.
Chinese Translation
递归注意力混合语言模型（LMs）交替使用注意力层和递归层，越来越多地被用于结合递归层的高效性与注意力层的强大性能。先前的研究表明，注意力层和递归层提供了互补的通路来利用过去的信息：注意力支持从早期标记中精确回忆记忆，而递归层支持在长上下文中整合不同的信息。然而，我们观察到，仅仅访问这两条通路并不意味着混合语言模型能够有效地使用它们。我们发现，这些模型在很大程度上更依赖于注意力，而不是递归状态。标准的监督微调提高了整体性能，但并没有改善这两条记忆通路的协调：模型对注意力层传播的信息的依赖性增强，而对递归层传播的信息的利用仍然有限。为了促进这两条记忆通路之间的更好协调，我们增加了一个辅助损失，限制了注意力对早期上下文的访问，同时递归状态在整个序列中传播。这个目标鼓励模型通过递归通路保留和使用信息，同时结合注意力。它提高了整体性能，尤其在涉及较长上下文或需要信息聚合的任务中取得了显著的提升，这与分析中观察到的递归层的优势一致。至关重要的是，这种不平衡及我们辅助损失的益处具有普遍性：它们适用于多个递归注意力语言模型在问答和代理任务中的应用，以及结合不同形式记忆的基于注意力的语言模型。综合来看，我们的发现表明，仅仅提供多条记忆通路并不能确保它们的有效使用，需要有针对性的监督来更好地协调它们。
cs.CL / 139 / 2610.06778
IdeaLens: Detecting AI Ideas in Long-form Writing
IdeaLens：在长篇写作中检测人工智能创意
Abstract
While modern AI detectors identify who wrote the words, emerging policies on AI use increasingly hinge on a different question: who came up with the ideas? We introduce IdeaLens, a detector that identifies whether a document's ideas came from a human or AI (idea provenance), regardless of who wrote its words. To focus IdeaLens on ideas rather than prose, we represent documents as outlines: lists of items that each pair a discourse role with a brief, paraphrased description of the content, minimizing word-level overlap with the raw text. We train IdeaLens on 1M FineWeb documents with silver labels from Pangram, a prose provenance detector. Since the outlines are largely stripped of surface-level information, the labels must be fit mainly through the ideas. In a controlled study, IdeaLens's AI flag rate drops from 95% to 7% as models write from increasingly detailed human plans, while Pangram 4 still flags 92%; from AI-derived plans, IdeaLens stays above 96%. Conversely, on a new dataset of 50 stories that human authors wrote from AI-generated plans, IdeaLens flags 68% of the stories as AI, compared to 8% for Pangram 4. On a comprehensive suite of 19 existing detection benchmarks, we show that IdeaLens maintains strong detection rates at low false positive rates, suggesting that ideas themselves provide a powerful discriminative signal, and its performance holds across domains, formats, and languages. Finally, we examine 90K predictions from IdeaLens to characterize systematic differences between human and AI ideation. We release our models and labeled datasets to facilitate future research on idea provenance detection.
Chinese Translation
尽管现代人工智能检测器能够识别出文字的作者，但新兴的人工智能使用政策越来越依赖于一个不同的问题：谁提出了这些创意？我们介绍了IdeaLens，这是一种检测器，能够识别文档中的创意是来自人类还是人工智能（创意来源），而不管是谁撰写了其文字。为了使IdeaLens专注于创意而非文笔，我们将文档表示为大纲：每个项目都将话语角色与内容的简要、改写描述配对，最小化与原始文本的词汇重叠。我们在100万个FineWeb文档上训练IdeaLens，这些文档的标签来自Pangram，一种文笔来源检测器。由于大纲在很大程度上剥离了表层信息，因此标签主要通过创意进行拟合。在一项受控研究中，随着模型从越来越详细的人类计划中写作，IdeaLens的人工智能标记率从95%降至7%，而Pangram 4仍然标记92%；对于来自人工智能生成的计划，IdeaLens保持在96%以上。相反，在一个包含50个故事的新数据集中，这些故事是人类作者根据人工智能生成的计划撰写的，IdeaLens将68%的故事标记为人工智能，而Pangram 4仅标记8%。在19个现有检测基准的全面测试中，我们展示了IdeaLens在低误报率下保持强大的检测率，表明创意本身提供了强有力的区分信号，其性能在不同领域、格式和语言中均保持一致。最后，我们检查了IdeaLens的90K预测，以表征人类和人工智能创意之间的系统性差异。我们发布了我们的模型和标注数据集，以促进未来在创意来源检测方面的研究。
cs.CL / 140 / 2610.06782
T-Search: An Open Agentic Retriever and Playground for Hard Multi-Step Search
T-Search：一个开放的智能检索器和困难多步骤搜索的实验平台
Abstract
We present T-Search, an open-weight agentic retriever for hard multi-step search. Given a question and a search tool over a fixed corpus, it runs a bounded multi-round search and returns a ranked list of evidence chunks with short justifications, leaving answer generation to a downstream model, so backend and generator can be swapped without retraining. T-Search is built on Qwen3.6-35B-A3B and trained on adversarially filtered synthetic search tasks with round-sliced supervised fine-tuning followed by GSPO on a recall reward. Averaged over seven English and Russian benchmarks with gold evidence annotations, it reaches 56.0 Recall@10 with one rollout, 14.4 points above its base, and 61.3 with three fused rollouts, outperforming larger open models. We release the model, harness, live demo, and three benchmarks, including TRuST, the first native-Russian hard-search benchmark.
Chinese Translation
我们提出了 T-Search，一个用于困难多步骤搜索的开放权重智能检索器。给定一个问题和一个固定语料库上的搜索工具，它执行一个有限的多轮搜索，并返回一个带有简短理由的证据片段排名列表，将答案生成留给下游模型，从而使后端和生成器可以在不重新训练的情况下进行更换。T-Search 基于 Qwen3.6-35B-A3B 构建，并在经过对抗性过滤的合成搜索任务上进行训练，采用分轮监督微调，随后在召回奖励上进行 GSPO。根据七个带有黄金证据注释的英语和俄语基准的平均结果，它在一次回合中达到了 56.0 Recall@10，比基线高出 14.4 分，并在三次融合回合中达到了 61.3，超越了更大的开放模型。我们发布了模型、工具、实时演示和三个基准，包括 TRuST，这是第一个本土俄语的困难搜索基准。
cs.CL / 141 / 2610.06825
PlotGround: Grounding Plot Digitization in Real Scientific Figures and Their Source Data
PlotGround：将图表数字化与真实科学图形及其源数据相结合
Abstract
Scientific figures often encode quantitative results that are not readily available in machine-readable form, making accurate plot digitization important for verifying and reusing published findings. Yet it remains unclear how accurately current models recover plotted values from real scientific figures, as existing benchmarks rely largely on synthetic charts or cover only a limited range of chart types. We introduce PlotGround, an automated pipeline for building plot digitization benchmarks from real scientific figures and their author-released source data. PlotGround maps figures to source tables, identifies reconstructable panels, and generates quantitative questions with source-grounded reference values. We use PlotGround to construct PlotGround-1k, a human-verified benchmark of 1,119 questions from 1,066 bioRxiv preprints. Across sixteen multimodal models, the best reaches 87.5% accuracy at a $\pm 5\%$ relative-error tolerance. Tightening the tolerance to $\pm 2\%$ lowers every model's accuracy by 11-24 percentage points, revealing a gap between approximate visual reading and precise quantitative recovery. PlotGround's paired figure-source structure lets us compare how accurately the same values are recovered from figures and from source tables. Providing source tables instead of figures raises a coding agent's accuracy from 90.0% to 97.4% while cutting cost by 72%.
Chinese Translation
科学图形通常编码了定量结果，这些结果并不容易以机器可读的形式获取，因此准确的图表数字化对于验证和重用已发布的研究结果至关重要。然而，目前尚不清楚现有模型从真实科学图形中恢复绘制值的准确性，因为现有基准主要依赖于合成图表或仅涵盖有限范围的图表类型。我们提出了PlotGround，这是一个从真实科学图形及其作者发布的源数据构建图表数字化基准的自动化流程。PlotGround将图形映射到源表，识别可重建的面板，并生成带有源基础参考值的定量问题。我们利用PlotGround构建了PlotGround-1k，这是一个包含来自1,066个bioRxiv预印本的1,119个经过人工验证的问题的基准。在十六种多模态模型中，最佳模型在$ ext{±}5\%$相对误差容忍度下达到了87.5%的准确率。当将容忍度收紧至$ ext{±}2\\%$时，每个模型的准确率下降了11-24个百分点，揭示了近似视觉读取与精确定量恢复之间的差距。PlotGround的配对图形-源结构使我们能够比较从图形和源表中恢复相同值的准确性。提供源表而不是图形将编码代理的准确率从90.0%提高到97.4%，同时降低成本72%。
cs.CL / 142 / 2610.06829
CLIFT: Conformal Self-Verification for Web Agent Training and Test-Time Scaling
CLIFT：用于网络代理训练和测试时扩展的保形自我验证
Abstract
Open-source web agents are now strong enough to execute realistic browser tasks, but training them with reinforcement learning still depends on weak supervision: binary task success is too sparse for credit assignment, while frontier-language-model judges are too expensive to call at every step and cannot be assumed available at deployment. We introduce CLIFT, a training and test-time scaling method built around conformal self-verification. During training, the agent answers natural-language verification questions about its own rollouts; a Compositional Conformal Certifier keeps only question signals whose URL-conditional evidence agrees with a training-time judge, assigns signed trust weights through polarity-aware lift, and blends the resulting verifier score into per-step rewards in a way that never subtracts from the judge baseline. At test time, the same certified bank is frozen and reused as structured evidence for Conformal Trajectory Selection (CTS): the agent samples a greedy rollout and one or more diverse retries, the self-verifier summarises each URL trace, and a conservative majority-vote rule chooses whether to swap away from the current incumbent without calling any external judge. This single mechanism supports three settings. On WebArena Infinity, CLIFT achieves state-of-the-art performance among open-source web agents. On VisualWebArena, a bank trained with the open model transfers to GPT-5.5 at test time and reaches state-of-the-art performance under the canonical harness. On Online Mind2Web, without training an agent on the benchmark, translating the certified question bank improves a live-web agent in zero-shot evaluation. Together these results position conformal self-verification as a way to turn costly judge feedback into a reusable training signal and a judge-free test-time scaling signal.
Chinese Translation
开源网络代理现在足够强大，可以执行现实的浏览器任务，但使用强化学习训练它们仍然依赖于弱监督：二元任务成功率对于信用分配来说过于稀疏，而前沿语言模型评判者在每一步调用的成本过高，并且在部署时不能假设可用。我们提出了CLIFT，这是一种围绕保形自我验证构建的训练和测试时扩展方法。在训练过程中，代理回答关于其自身回放的自然语言验证问题；一个组合保形认证器仅保留与训练时评判者一致的URL条件证据的问号信号，通过极性感知提升分配签名信任权重，并以一种从不减去评判者基线的方式将结果验证器分数融入每步奖励。在测试时，相同的认证银行被冻结并作为保形轨迹选择（Conformal Trajectory Selection, CTS）的结构化证据重新使用：代理采样一个贪婪回放和一个或多个多样化重试，自我验证器总结每个URL轨迹，而保守的多数投票规则选择是否在不调用任何外部评判者的情况下更换当前的现任者。这个单一机制支持三种设置。在WebArena Infinity上，CLIFT在开源网络代理中实现了最先进的性能。在VisualWebArena上，使用开放模型训练的银行在测试时转移到GPT-5.5，并在规范性测试中达到最先进的性能。在Online Mind2Web上，在基准上不训练代理的情况下，翻译认证问题银行改善了实时网络代理的零样本评估。综合这些结果，保形自我验证被定位为将昂贵的评判反馈转化为可重用的训练信号和无评判者的测试时扩展信号的一种方式。
cs.CL / 143 / 2610.06830
MemPilot: Orchestrating On-Demand Multimodal Memory Curation for LLM Agents
MemPilot：为大型语言模型代理协调按需多模态记忆策划
Abstract
Memory has become integral to the LLM agent ecosystem, supporting information retention and reuse across interactions. However, most existing agent memory systems construct memory in a query-agnostic manner, which can incur unnecessary preprocessing cost and discard details that later prove essential. Recent studies have begun shifting memory processing toward runtime adaptation, but typically specialize in particular operations or fixed processing schemes, leaving flexible control over performance, cost, and latency largely underexplored. To address this challenge, we present \textbf{MemPilot}, a flexible framework that orchestrates on-demand memory curation under different performance--cost--latency preferences. Specifically, we optimize a multi-step LLM policy via reinforcement learning to iteratively choose between retrieving from query-agnostic memory and delegating query-specific curation of raw multimodal history to heterogeneous LLMs and VLMs. The policy jointly controls evidence amount, curation instructions, model selection, and visual access, enabling fine-grained allocation of runtime computation. To optimize this policy under competing objectives, we adapt objective-wise advantage decoupling by separately estimating each objective's advantage before aggregation. Moreover, we introduce prefix-based marginal utility estimation for fine-grained credit assignment across multi-step rollouts. Experiments on five multimodal agent-memory benchmarks demonstrate favorable performance--cost--latency trade-offs across optimization preferences, with preference sweeps yielding broader frontiers than existing trade-off-aware baselines.
Chinese Translation
记忆已成为大型语言模型（LLM）代理生态系统的重要组成部分，支持在交互中信息的保留和重用。然而，大多数现有的代理记忆系统以查询无关的方式构建记忆，这可能导致不必要的预处理成本，并丢弃后续证明至关重要的细节。近期研究已开始将记忆处理转向运行时适应，但通常专注于特定操作或固定处理方案，灵活控制性能、成本和延迟的能力仍然未得到充分探索。为了解决这一挑战，我们提出了 extbf{MemPilot}，一个灵活的框架，能够在不同的性能-成本-延迟偏好下协调按需记忆策划。具体而言，我们通过强化学习优化一个多步骤的LLM策略，迭代选择从查询无关的记忆中检索或将查询特定的原始多模态历史策划委托给异构的LLM和视觉语言模型（VLM）。该策略共同控制证据量、策划指令、模型选择和视觉访问，从而实现运行时计算的细粒度分配。为了在竞争目标下优化该策略，我们通过在聚合之前分别估计每个目标的优势，适应目标导向的优势解耦。此外，我们引入基于前缀的边际效用估计，以实现多步骤回滚中的细粒度信用分配。在五个多模态代理记忆基准上的实验显示，在优化偏好下，性能-成本-延迟的权衡表现出良好的效果，偏好扫描产生的结果比现有的权衡感知基线更具广泛性。